Safety management assessment systems for national level units’ in South Korea focus on responding capacity to cope with impending accident occurrence and danger occurrence. Since the four stage systems for prevention-preparation-response-recovery, which are core elements of national disaster management, assess the capacities by item such as those of individuals, disaster management departments, institutions, and management networks, there is no assessment function for the organic operation states of the entire systems. Therefore, for efficient disaster management, systematic evaluation indices that will enable active pre-checks in departments in organizations should be developed in place of the existing simply checking methods. In this study, an assessment model that will enable active disaster management centered on practice was developed using resilience engineering techniques. This model consists of disaster management items from the viewpoint of proactive responses instead of prevention. A total of 56 items that constitute four capacities; which are prediction (13 items), monitoring (14 items), proactive response (15 items), and safety learning (14 items) capacities were adopted in this model through Delphi analysis. Institutional capacities for infectious disease disaster management were evaluated based on this model and the resultant scores were prediction 4.41, monitoring 4.63, proactive response 4.69, safety learning 4.56 out of the full score of 5.0 points with an overall average of 4.51. This is an excellent capacity management score comparable to the score 4.57 of diagnosis of similar capacities by the WHO\_JEE (The Joint External Evaluation) in 2017. In fact, in 2015, when infectious disease capacity management was poor, in case of MERS (Middle East Respiratory Syndrome) infectious disease spread in South Korea, 36 patients died and 6,729 patients were isolated. However, through capacity reinforcement, in the case of MERS occurrence in South Korea in September 2018, a management capacity that prevented spread was shown as one confirmed case was completely cured in 10 days and 21 contacts were isolated and tested negative. Therefore, this capacity management assessment model is judged to be usable in enhancing disaster response and management capacities.

An energy diagnosis is a tool used to seek the improvement of energy saving measures, environmental conservation and energy efficiency, making relevant its implementation in any kind of buildings. For this article, an energy diagnosis of third level was carried out in buildings of the Instituto de Energías Renovables (IER) from Universidad Nacional Autónoma de México (UNAM) through survey and census of the 36 buildings in the IER, in order to characterize current patterns of energy consumption and demand, and generating specific strategies towards savings and energy efficiency, such as indicators and corrective proposals within and non-financial investment.

The vibration signal of heavy gearbox presents non-stationary and nonlinear characteristics, which increases the difficulty to extract the fault feature. When the gear has a subtle fault, it may cause a perceptible change of local fluctuation rather than the large scale fluctuation. Therefore, the feature parameters extracted from local fluctuation can effectively improve the recognition performance of the gear fault. In this paper, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to identify the gear fault of heavy gearbox. Firstly, the raw vibration signal is decomposed several mode components by VMD, which is an adaptive and non-recursive signal decomposition method. Next, the sensitive mode component is selected by a maximal indicator, which is composed of kurtosis and correlation coefficient of relative higher frequency mode components corresponding to local fluctuation of raw vibration signal. Finally, the characteristics of the double-scales feature parameters of selected sensitive mode are extracted by DFA. In addition, the position of turning point of double scales is estimated by sliding windowing algorithm. The proposed method is evaluated through its application to gear fault classification using vibration signal. The results demonstrates that the recognization rate of gear faults condition have marked improvement by proposed method than the DFA of Small Time Scale (STS-DFA) method.

Pinus patula is one of the most planted wood conifer species worldwide; however, no foliar nutrient standards exist for this species up to date. The objective of the present study was to generate and verify two sets of foliar nutrient standards for nearly ten-year-old P. patula trees: critical nutrient concentrations and DRIS norms. Nutrients studied were N, P, K, Ca, Mg, Fe, Cu, Zn, Mn, and B. The reference standards were verified experimentally by installing two fertilization trials; one of them located in Huayacocotla, state of Veracruz and the other one in Aquixtla, state of Puebla, Mexico. Nutrient status of each fertilization trial was correctly predicted by critical nutrient values and DRIS as well. Both standards were able to detect the secondary growth-limiting nutrient deficiency in the Huayacocotla trial, where the primary limitation for growth was scarcity of solar radiation within tree crowns. The limiting nutrient in both experimental trials was K.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

The platelet count, a component of the full blood count, has been identified as a useful diagnostic marker for cancer in primary care. The reference range for the platelet count is 150 to 400 or 450 × 109/l; this range does not account for natural variation in platelet count by age and sex. This study used three primary care cohorts from England Canada, and Australia. Patients aged 40 years and over with a full blood count were included and stratified by age (in 10-year bands), sex, (male/female), and platelet count group. Cancer incidence within one year of test date was estimated from linked registry data. In all three countries there was a clear upwards trend in cancer incidence with increasing platelet count, for both sexes and at all age groups. Lung and colorectal were the most common sites. These results have important implications for the international application of this work; analysis of local health datasets will be crucial to determine appropriate thresholds. Appropriate upper thresholds will depend on local populations, healthcare needs, and priorities. Further research is needed to assess the likely impact of new recommendations on the healthcare system, on cancer outcomes, and patient benefit.

The article discusses early diagnosis and intervention for developmental delays in children, emphasizing the role of specialized services and personnel in supporting recovery and development. It presents findings from a pilot project initiated in Bucharest, aiming to assess the incidence of developmental delays in children aged 1-3 years in selected kindergartens, including ASD. The study involved children, parents, and specialists, utilizing systematic behavior observation (SBO) and semi-structured interviews. Results indicate that out of 613 evaluated children, 81 received specialized control recommendations, with 4 cases showing potential risk of ASD. Additionally, 8 children were diagnosed with autism. The incidence of autism in children aged 1-3 years was 1 in 51. Although exploratory, these findings suggest a model of good practice for future early diagnosis campaigns nationally. Conclusively, the study underscores the importance of early diagnosis and intervention for developmental delays, advocating for a responsive community and emphasizing the significance of national-level efforts to address these issues effectively.

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalance and shaft bow in these systems can lead to decreased efficiency and increased vibrations. Early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry for years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) has become a trend, and in the foreseeable future, AI combined with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as the availability of fault data is often insufficient. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach, that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalance and shaft-bow demonstrated) in a Jeffcott rotor. To overcome the lack of real-world faulted, labeled datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose the imbalance and shaft-bow faults using the simulated data and the real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-fault, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

Several criteria exist to diagnose pulmonary aspergillosis with varying degrees of certainty in specific populations, including oncohaematological patients (EORTC/MSG), ICU patients (AspICU) and Covid-19 patients (ECMM). At the beginning of the pandemic, however, the diagnosis of Covid-19 Associated Pulmonary Aspergillosis (CAPA) could not be performed easily, and the decision to treat (DTT) was empirical. In this cross-sectional retrospective study including patients with SARS-CoV-2 infection and a suspicion of CAPA, we studied the concordance between the DTT and the three diagnostic criteria using Cohen’s coefficient, then we identified the factors associated with the DTT and we corrected by treatment to study the influence of the diagnostic criteria on survival. We showed good concordance of DTT and AspICU and ECMM criteria, with “compatible signs”, “positive culture” and “positive galattomannan” influencing the DTT. Treatment also showed a positive effect on survival once corrected for a putative, possible or probable diagnosis of CAPA using AspICU and ECMM criteria. We conclude that EORTC/MSGERC are not considered applicable in clinical practice due to lack of inclusion of signs and symptoms and do not lead to improved survival. AspICU and ECMM criteria showed a good degree of agreement with the DTT and a positive correlation with patient recovery.

In this study, we introduce a neural-learning based approach for the early detection of Parkinson Disease using DaT SPECT imaging, a promising technique in neurodegenerative disease diagnosis. Leveraging the advanced capabilities of deep learning, we developed a convolutional neural network model based on the Inception V3 architecture, which has been enhanced by modulating custom layers to fine-tune the model specifically for the analysis of DaT SPECT images. The imaging dataset was obtained through the Parkinson's Progression Markers Initiative (PPMI), and the model was trained to classify whether a patient had Parkinson Disease through a binary classification of their DaT SPECT Scan using the fine-tuned InceptionV3 model. This specialized adaptation allows for more accurate and nuanced interpretation of the scans, a crucial factor in early and reliable diagnosis of Parkinsonian syndromes. Our model underwent rigorous hyperparameter tuning, focusing on learning rates, the number of neurons in the dense layer, and dropout rates, to ensure optimal performance. Remarkably, the proposed model demonstrated a significant improvement in accuracy, achieving 74.65%, compared to a standard Sequential Model, which only reached an accuracy of 51.41%. Diagnosis of Parkinson Disease through DaT SPECT Scans is a promising approach not only as a more effective diagnostic tool but also as a beacon for future research in the application of deep learning to medical imaging for neurodegenerative diseases. By building layers on the InceptionV3 Architecture, we achieve a higher accuracy on DaT SPECT Scan diagnosis and pave the way for automated diagnosis of patients with Parkinson Disease, allowing for easier medical accessibility and paving the way for advancements in early diagnosis and personalized medicine strategies for Parkinson's disease and related disorders.

Modern gastroenterology, which occupies the pages of scientific journals without alternative, began as an initiative of manufacturers of diagnostic equipment. For widespread advertising, they selected practical doctors who were not familiar with the methodology of science and superficial knowledge of physiology. This is how a group led by DeMeester arose, which in 1976 published the DeMeester score, i.e., the normal limit for esophageal pH monitoring. pH <4, less than 4% of the time per day was considered normal, i.e., it was unreasonably claimed that such reflux is physiological. This article proves that this study was carried out with numerous methodological errors, which led to an erroneous conclusion. For a long time, pH monitoring was considered the gold standard and with its help hypotheses were proposed that form the basis of modern gastroenterology. The article proves the fallacy of 13 hypotheses, which are presented as theories. Numerous articles using pH monitoring have publicized this method. Practitioners became "great scientists", equipment manufacturers became successful businessmen. Over time, they found other groups of practitioners to promote devices for impedance ph monitoring, high-resolution manometry, and many others. At the same time, a network of reviewers who believe in the infallibility of the luminaries do not allow studies that contradict their faith to be published. Thus, fundamental science was destroyed, which alone could assess the harm of false research both for science and for patients. Conclusion This study provides a list of hypotheses, that were previously considered theories, but since scientific evidence has proven these assumptions to be false, they should be excluded from subsequent use and citation.

Prenatal diagnosis of clubfoot traditionally relied on two-dimensional ultrasonography. To enhance diagnosis and predict postnatal outcomes, we examined the parameters that differentiate pathological clubfoot using three-dimensional ultrasonography. In our retrospective study, we examined the findings of prenatal ultrasound and the postnatal outcomes of pregnancies with suspected congenital clubfoot between 2018 and 2021. Based on the three-dimensional perspective, we measured the angles of varus, equinus, calcaneopedal block and forefoot adduction and compared the sonographic variables between the postnatal treated and non-treated groups. We evaluated 31 pregnancies (47 feet) with suspected clubfoot using three-dimensional ultrasonography. After delivery, a total of 37 feet (78.7%) underwent treatment involving serial casting only or additional Achilles tenotomy. The treated group showed significantly greater hindfoot varus deviation (60.5° vs. 46.6°, p=0.026) and calcaneopedal block deviation (65.6° vs. 26.6°, p &lt;0.05) compared to the non-treated group. The calcaneopedal block had an area under the curve of 0.98 with a diagnostic threshold of 46.2 degrees (sensitivity of 97%, specificity of 90%, positive predictive value of 97%, and negative predictive value of 90%). During prenatal evaluation of clubfoot using three-dimensional ultrasonography, the calcaneopedal block deviation has the potential to predict postnatal treatment.

Availability and continuous operation under critical conditions are very important in electric machine drive systems. However, such systems, are suffering from several type of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnosis multiple open-circuit fault in three phase two level voltage source inverter for induction motor drive applications. The proposed approach uses fault diagnosis variables obtained from sigmoid transformation of the motor stator currents. The open-circuit fault diagnosis variables are then introduced to a Bidirectional Long Short-Term Memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault diagnosis algorithm effectiveness and robustness.

This study is to explore the feasibility using neural network (NN) and deep learning to diagnose three common respiratory diseases with only few symptom words. These three diseases are nasopharyngitis, upper respiratory infection, and bronchitis/bronchiolitis. Through natural language processing, the symptom word vectors are encoded by GPT-2 and classified by the last linear layer of the NN. The experimental results are promising, showing that this model achieves a high performance in predicting all these three diseases. They reach 90% in accuracy, which suggests the implications of the developed model, highlighting its potential use in assisting patients understanding their conditions via a remote diagnosis. Unlike previous studies that focus on extracting various categories of information from medical records, this study directly extracts sequential features from unstructured text data, reducing the effort required for data pre-process.

In this paper we introduce the concept of Nano Beta Closure, Nano Beta Interior and study the relationship between them also study some of their properties. Based on these notions we introduced the topological Approach to Decision Making and Its Application in the Medical Field" which describes the use of topological methods in decision-making, specifically through the utilization of nano beta closure and nano beta interior. These mathematical tools are applied in the medical field to improve diagnostic accuracy and aid in treatment planning. The authors demonstrate the effectiveness of this approach through a case study on the diagnosis of Chronic Kidney. The results show that the topological approach using nano beta closure and nano beta interior provides a reliable and accurate method for medical decision-making. Finally, we introduce a medical application for disease groups patients of with chronic kidney. To identify the main causative agent of kidney disease. where using this approach, the medical team can identify the specific areas of the kidney that are affected by the infection, as well as the extent of damage and any potential complications. This information is used to create an individualized treatment strategy that is customized to meet the specific requirements of the patient. and medical history. We write an algorithm that can be a helpful tool for doctors to utilize in diagnosing infection disease Chronic Kidney infections. By utilizing this approach, clinicians can provide accurate diagnoses, develop effective treatment plans, and ultimately improve patient outcomes. finally, we show that fractals hold significant importance in future nano topological generation of the human factor. Their unique properties and self-similar patterns offer insights into the complex organization of biological systems and provide a foundation for designing advanced Nanosystems.

Migratory flows and international travel are triggering an increase in imported cases of schistosomiasis in non-endemic countries, with serology being the recommended method for screening migrants. The present study aims to evaluate, for the first time, the LAMP technique on patients’ urine samples for the diagnosis of imported schistosomiasis in a non-endemic area in comparison to a commercial immunochromatographic test and microscopic examination of feces and urine. A prospective observational study was conducted in sub-Saharan migrants attending at the Tropical Medicine Unit, Almería, Spain. For schistosomiasis diagnosis, serum samples were tested using an immunochromatographic test (Schistosoma ICT IgG-IgM). Stool and urine samples were examined by microcopy. Urine samples were evaluated by combining three LAMP assays for the specific detection of Schistosoma mansoni and S. haematobium, and an assay for the genus Schistosoma. To evaluate the diagnostic accuracy of Schistosoma-LAMP in comparison to microscopy and Schistosoma ICT IgG-IgM test, a Latent Class Analysis (LCA) was performed. A total of 115 patients were included; 21 patients (18.3%) were diagnosed with schistosomiasis confirmed by microscopy, with S. haematobium being the most frequent species identified (18/115; 15.7%). The Schistosoma ICT IgG-IgM test resulted 100% positive and Schistosoma-LAMP was 61.9% positive, reaching as high as 72.2% for S. haematobium. The sensitivity and specificity estimated by LCA, respectively, were: 92% and 76% for Schistosoma ICT IgG-IgM, 68% and 44% for Schistosoma-LAMP and, 46% and 97% for microscopy. The Schistosoma-LAMP technique presented a higher sensitivity than microscopy for the diagnosis of imported urinary schistosomiasis, which could improve the diagnosis both in referral centers and in centers with limited experience or scarce resources and infrastructure.

The identification of an emerging pathogen in humans can remain difficult by conventional methods such as enrichment culture assays that remain highly selective, require appropriate medium and cannot avoid misidentifications, or serological tests that use surrogate antigens and are often hampered by the level of detectable antibodies. Although not originally designed for this purpose, the implementation of polymerase-chain-reaction (PCR) has resulted in an increasing number of diagnostic tests for many diseases. However, the design of specific molecular assays relies on the availability and reliability of published genetic sequences for the target pathogen as well as enough knowledge on the genetic diversity of species and/or variants giving rise to the same disease symptoms. Usually designed for clinical isolates, molecular tests are often not suitable for environmental samples in which the target DNA is mixed with a mixture of environmental DNA. A key challenge of such molecular assays is thus to ensure high specificity of the target genetic markers when focusing on clinical and environmental samples in order to follow the dynamics of disease transmission and emergence in humans. Here we focus on Buruli ulcer (BU), a human necrotizing skin disease mainly affecting tropical and subtropical areas, commonly admitted to be caused by Mycobacterium ulcerans worldwide although other mycolactone-producing mycobacteria and even mycobacterium species were found associated with BU or BU-like cases. By revisiting the literature, we show that many studies have used non-specific molecular markers (IS2404, IS2606, KR) to identify M. ulcerans from clinical and environmental samples and propose that all mycolactone-producing mycobacteria should be definitively considered as variants from the same group rather than different species. Importantly, we provide evidence that the diversity of mycolactone-producing mycobacteria variants as well as mycobacterium species potentially involved in BU or BU-like skin ulcerations might have been underestimated. We also suggest that the specific variants/species involved in each BU or BU-like case should be carefully identified during the diagnosis phase, either via the key of genetic identification proposed here or by broader metabarcoding approaches, in order to guide the medical community in the choice for the most appropriate antibiotic therapy.

Evidence-based data for endometriosis management are limited. Experiments are excluded without adequate animal models. Data are limited to symptomatic women and occasional observations. Hormonal medical therapy cannot be blinded if recognised by the patient. Randomised controlled trials are not realistic for surgery since a variable disease with low numbers. Each diagnosis and treatment is an experiment with an outcome and experience is Bayesian updating from the past. If the experience of many is similar, this has more value than an opinion. The combined experience of a group of endometriosis surgeons was used to discuss problems in the management of endometriosis. Considering endometriosis as several genetically/epigenetically different diseases is important for medical therapy. Imaging cannot exclude endometriosis and diagnostic accuracy is limited for superficial lesions, deep lesions, and cystic corpora lutea. Surgery should not be avoided for emotional reasons. Shifting infertility treatment to IVF without considering fertility surgery is questionable. The concept of complete excision should be reconsidered. Surgeons should introduce quality control and teaching should move to explain why it is done. The perception of information has a personal bias. These are the major problems in the management of endometriosis identified by the combined experience of endometriosis surgeons.

Blood-based circulating cell free DNA(cfDNA) detection offers a non-invasive and easily accessible way for early cancer detection. Despite the extensive utility of cfDNA, there are still many challenges to develop the clinical biomarkers. For example, cfDNA with genetic alterations often compose a small portion of the DNA circulating in plasma, which can be confounded by cfDNA contributed by normal cells. Therefore, filtering out the potential false-positive cfDNA mutations from health population will be important for the cancer-based biomarkers. Additionally, many low-frequency genetic alterations are easily overlooked in small amount of cfDNA-based cancer test. We hypothesize that, the combination of diverse types of cancer studies on cfDNA can provide us a new insight to identify low-frequency genetic variant across cancer types for early clinical detection of cancers. By building a standardized computational pipeline for 1358 cfDNA samples across seven cancer types, we prioritize 129 shard genetic variants in the major cancer types. Further functional analysis of the 129 variants found that they are mainly enriched in ribosome pathways such as cotranslational protein targeting to membrane, some of which are tumor suppressor, oncogene and related to cancer initiation. In summary, our integrative analysis revealed the important roles of ribosome proteins as the common biomarkers in early cancer diagnosis.

Early detection of breast cancer, one of the most common cancers in women worldwide, raises survival rates and lowers the cost of treatment. Although breast cancer detection and classification (CAD) tools have improved, some issues and restrictions still require further research. The creation of breast cancer CAD systems, particularly with deep learning models, was significantly impacted by recent advancements in machine learning and image processing techniques. The current deep learning-based CAD system to identify and categorize masses in mammography is presented in a structured manner in this survey. The survey provides the most popular breast cancer CAD system evaluation metrics, publicly available mammographic datasets, and current image modalities. The survey examines the available literature, emphasizing its strengths and limitations. The survey also emphasizes the difficulties and drawbacks of the current methods for classifying and detecting breast cancer. We point out research gaps and make suggestions for further study. This systematic review may be beneficial for clinicians using CAD systems to diagnose breast cancer early on and for researchers looking for knowledge gaps and making more contributions to the field.

Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited its routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance: primarily, small sample sizes which compound upon sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.

The combination of statistical learning technologies with large databases of psychophysiological data has appropriately generated enthusiastic interest in future clinical applicability. It is argued here that this enthusiasm should be tempered with the understanding that significant obstacles must be overcome before the systematic introduction of psychophysiological measures into neuropsychiatric practice becomes possible. The nonspecificity of psychophysiological measures complicates their use in diagnosis. Low test-retest reliability complicates use in longitudinal assessment, and quantitative psychophysiological measures can normalize in response to placebo intervention. Ten cautionary observations are introduced and, in some instances, possible directions for remediation are suggested.

Hadhramout Initiative Against Corona (HIAC ) formed a working group of clinicians relevant to the management of COVID-19 to formulate clinical practice guidelines (CPG) for clinicians caring for the patients infected with COVID-19 in Yemen. The regional guidelines on the management of COVID-19 were thoroughly reviewed and its applicability was assessed for Yemen. HIAC’s recommendations covered (2) sections: the first one is the diagnosis, including case definition, risk stratification of the affected cases, investigations (prioritization of reverse transcription-polymerase chain reaction (RT-PCR) testing, D-dimer, chest x-ray, and chest computed tomography, while the second part covers the treatments (mainly Favipiravir, Remdesivir, Hydroxychloroquine, and Glucocorticoid, Anticoagulants, and Supportive measures). In conclusion, the adoption of cost-effective and evidence-based guidelines in Yemen will standardize the management of the patients infected with COVID-19 and protect both the patients and the health care workers from malpractice.

In order to overcome this problem our group designed a novel machine learning technique, multivariate linear method (MLM) which can capture convergent data from voxel-based morphometry, functional resting state and task-related neuroimaging and the relevant clinical measures. In this paper we report results from convergent cross-validation of biological signatures of disease in a sample of patients with schizophrenia as compared to depression. Our model provides evidence that the combination of the neuroimaging and clinical data in MLM analysis can inform the differential diagnosis in terms of incremental validity to reach 90 % accuracy of the prediction.

Diagnosis and prognosis of failures for aircrafts’ integrity are some of the most important regular functionalities in complex and safety-critical aircraft structures. Further, development of failure diagnostic tools such as Non-Destructive Testing (NDT) techniques, in particular, for aircraft composite materials, has been seen as a subject of intensive research over the last decades. The need for diagnostic and prognostic tools for composite materials in aircraft applications rises and draws increasing attention. Yet, there is still an ongoing need for developing new failure diagnostic tools to respond to the rapid industrial development and complex machine design. Such tools will ease the early detection and isolation of developing defects and the prediction of damages propagation; thus allowing for early implementation of preventive maintenance and serve as a countermeasure to the potential of catastrophic failure. In this paper, following a short introductory summary and definitions, this paper provides a brief literature review of recent research on failure diagnosis of composite materials with an emphasis on the use of NDT techniques in aerospace industry. In addition to this, within a some of significant NDT application extents, prognosis of composites is also briefly discussed.

Background: A new coronavirus (SARS-CoV-2) that emerged from Wuhan, Hubei Province, China, has spread throughout the world and is declared a pandemic by the World Health Organization (WHO). A lot remains to be understood of SARS-CoV-2 and the disease (COVID-19). SARS-CoV-2 has until recently been identified as responsible for both asymptomatic and serious life-threatening infections. The unavailability of specific therapeutic agents is a major hurdle in the treatment and management of COVID-19 patients. The present review attempts to evaluate the immunobiochemical aspects of the pathogenesis, diagnosis, and management of SARS-CoV-2 infection. Main Body: This review is a comprehensive evaluation of the data collected through various sources, including Google Scholar, PubMed, and Scopus. The articles were searched and selected using key words such as “Coronavirus disease (COVID-19)”, “Diagnosis of COVID-19”, Pathogenesis of Covid-19”, “management of COVID-19”, “Immunology of COVID-19”, and “Complications of COVID-19”. The study noted that the novel Coronavirus infection could result in an exaggerated immune response, causing a cytokine storm and damaging several organs of the body. The infected patients develop several complications, including immunological, hematological, and biochemical alterations. Consequently, COVID-19 patients may develop cardiovascular, liver, renal, and neurological complications, among others. Conclusion: An increased understanding of the immunobiochemical aspects of the disease may contribute to better management of SARS-CoV-2-infected persons, as evidenced from the available literature. A holistic approach to the management of COVID-19 patients taking into consideration the effect of COVID-19 infection on various organs of the body assumes increased significance in patient management.

In recent years, intelligent fault diagnosis algorithms using deep learning method have achieved much success. However, the signals collected by sensors contain a lot of noise, which will have a great impact on the accuracy of the diagnostic model. To address this problem, we propose a one-dimensional convolutional neural network with multi-scale kernels (MSK-1DCNN) and apply this method to bearing fault diagnosis. We use a multi-scale convolution structure to extract different fault features in the original signal, and use the ELU activation function instead of the ReLU function in the multi-scale convolution structure to improve the anti-noise ability of MSK-1DCNN; then we use the training set with pepper noise to train the network to suppress overfitting. We use the Western Reserve University bearing data to verify the effectiveness of the algorithm and compare it with other fault diagnosis algorithms. Experimental results show that the improvements we proposed have effectively improved the diagnosis performers of MSK-1DCNN under strong noise and the diagnosis accuracy is higher than other comparison algorithms.

The ongoing pandemic of COVID-19 has not only commenced a global health emergency but agitated various aspects of humanity. During this period of crisis researchers over the world have ramped their efforts to constrain the disease in all possible ways whether it is vaccination, therapy, or diagnosis. Since the spread of the disease has not yet elapsed sharing the ongoing research findings could be the key to disease control and management. An early and efficient diagnosis could leverage the outcome until a successful vaccine is developed. Molecular tests both in-house and commercial kits are preferably being used worldwide in the COVID-19 diagnosis. However, the limitation of high prices and lengthy procedures impede their use for mass testing. Keeping the constant rise of infection in mind search for an alternative test that should be cost-effective, simple, and suitable for large scale testing and surveillance is a need of an hour. One such alternative could be the immunological tests. Therefore, in the last few months deluge of immunological rapid tests has been developed and validated across the globe. The objective of the present review is to share the diagnostic performance of various immunological assays reported so far in SARS-CoV-2 case detection. The article consolidated the studies (published and preprints) related to the serological tests such as chemiluminescence, enzyme-linked and lateral flow-based point-of-care tests in COVID-19 diagnosis and updated the current scenario. This review will hopefully be an add-on in COVID-19 research and will contribute to congregate the evidence for decision-making.

Introduction: Coronaviruses are zoonotic viruses that include human epidemic pathogens such as the Middle East Respiratory Syndrome virus (MERS-CoV), and the Severe Acute Respiratory Syndrome virus (SARS-CoV), among others (e.g., COVID-19, the recently emerging coronavirus disease). The role of animals as potential reservoirs for such pathogens remains an unanswered question. No systematic reviews have been published on this topic to date. Methods: We performed a systematic literature review with meta-analysis, using three databases to assess MERS-CoV and SARS-CoV infection in animals and its diagnosis by serological and molecular tests. We performed a random-effects model meta-analysis to calculate the pooled prevalence and 95% confidence interval (95%CI). Results: 6,493articles were retrieved (1960-2019). After screening by abstract/title, 50 articles were selected for full-text assessment. Of them, 42 were finally included for qualitative and quantitative analyses. From a total of 34 studies (n=20,896 animals), the pool prevalence by RT-PCR for MERS-CoV was 7.2% (95%CI 5.6-8.7%), with 97.3% occurring in camels, in which pool prevalence was 10.3% (95%CI 8.3-12.3). Qatar was the country with the highest MERS-CoV RT-PCR pool prevalence, 32.6% (95%CI 4.8-60.4%). From 5 studies and 2,618 animals, for SARS-CoV, the RT-PCR pool prevalence was 2.3% (95%CI 1.3-3.3). Of those, 38.35% were reported on bats, in which the pool prevalence was 14.1% (95%CI0.0-44.6%). Discussion: A considerable proportion of infected animals tested positive, particularly by nucleic acid amplification tests (NAAT). This essential condition highlights the relevance of individual animals as reservoirs of MERS-CoV and SARS-CoV. In this meta-analysis, camels and bats were found to be positive by RT-PCR in over 10% of the cases for both; thus, suggesting their relevance in the maintenance of wild zoonotic transmission.

Background: Cancer has been one of the most critical health problems during the past decades which require serious consideration, especially in developing countries. Methods: 80 parents of the children with different types of cancers were studied via both questionnaire and person-to-person interview after announcing their informed consent. Results: The highest delay in patients' initial referral was due to the following factors: lack of attention or ignoring the first symptoms, delay in referring to the physician, low economic status and even lack of family support for the patient. In addition, visiting several doctors after the initial diagnosis, uncertainty about the first proposed method, and high cost of treatment can be mentioned as the main causes of delays in the start of treatment. Conclusion: Education plays an important role in identifying the signs of cancer. In addition, proper relationship and cooperation between the health system and physicians as well as provision of adequate information to patients could lead to the long-term cooperation of these patients in continuing their treatment.

Covid-19 infections are rapidly spreading worldwide with more than 100000 death and thus understanding the molecular mechanism of tropism of human cells is an urgent need for drug design. We have described here a bioinformatics approach to predict the functional aspects of non-structural nsp16 protein of Corona virus. The covid-19 7098 AA large polyprotein was degraded into sixteen proteins and last nsp16 protein was found an RlmE type rRNA methyltransferase. Nsp16 has no similarity to bacterial RlmABCD but has 25 percent similarity to the bacterial RlmE protein which methylates the U2551 2-hydroxy group of Ribose. The nsp16 proteins of different corona viruses like covid-19, bat-coronavirus, SARS and MERS have strong homology. Mrm2 and Dim1 like yeast and mammalian rRNA methyltransferases have 26-33 percent homologies but not with 2-O-capping MTase as reported previously. Rrp8 MTases also has no similarity to nsp16. We postulated that mitochondrial rRNA methylation of bronchial cells were mediated by the nsp16 protein causing inhibition of protein synthesis due to poor assembly of aminoacyl-tRNA or mRNA and peptidyl transferase at the PTC. This is one of the new molecular mechanism of corona virus cellular tropism and different than ACE-2 mediated blockage of cellular signalling to inhibit aldesterone biosynthesis with abnormal Na⁺ ions in cells. We also designed primers based on nsp16 cDNA sequence (nt 20659-21552, accession no MT121215) specific for Covid-19 diagnosis by RT-PCR.

A novel strategy is presented for reliable diagnosis and progression prediction of diseases with special attention to COVID-19 pandemy. A plan is presented for how the model can be implemented worldwide in healthcare and how novel treatments and targets can be detected. The idea is based on poikilosis, pervasive heterogeneity and variation at all levels, systems and mechanisms. Poikilosis in diseases can be taken account in pathogenicity model, which is based on distribution of three independent condition measures – extent, modulation and severity. Pathogenicity model is a population or cohort-based description of disease components. Evidence-based thresholds can be applied to the pathogenicity model and used for diagnosis as well as for early detection of patients in risk of developing the most severe forms of the disease. Analysis of patients with differential course of disease can help in detecting biomarkers of diagnostic and prognostic significance. A practical and feasible plan is presented how the concepts can be implemented in practice. Collaboration of many actors, including the World Health Organization and national health authorities, will be essential for success.

Diesel engines, as power equipment, are widely used in the fields of automobile industry, ship and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in time domain and angular domain, on which the current diagnosis methods based, are easily affected by working conditions or hard to extract accurate enough, as the diesel engine keeps running in transient and non-stationary process. This work arms at diagnosing this fault mainly based on frequency band features which would change when the valve clearance fault occurs. For the purpose of extracting a series of frequency band features adaptively，a decomposition technique based on improved variational mode decomposition is investigated in this work. As the connection between the features and the fault is fuzzy, the random forest algorithm is used to analyze the correspondence between features and faults. In addition, the feature dimension is reduced to improve the operation efficiency according to importance score. The experimental results under variable speed condition show that the method based on variational mode decomposition and random forest is capable to detect valve clearance fault effectively.

For the diagnosis of disease, the ability to quantitatively detect trace amounts of the causal proteins from bacteria/viruses as biomarkers in patient specimens is highly desirable. Here we introduce a simple, rapid, and colorimetric assay as a de novo, ultrasensitive detection method. This ultrasensitive assay consists of sandwich enzyme-linked immunosorbent assay (ELISA) and thionicotinamide-adenine dinucleotide (thio-NAD) cycling, forming an ultrasensitive ELISA, in which the signal substrate (i.e., thio-NADH) accumulates in a triangular manner, and the accumulated thio-NADH is measured at its maximum absorption wavelength of 400 nm. We have successfully achieved a limit of detection of ca. 10–18 moles/assay for a target protein. As an example of infectious disease detection, HIV-1 p24 could be measured at 0.0065 IU/assay (i.e., 10⁻¹⁸ moles/assay), and as a marker for a lifestyle-related disease, adiponectin could be detected at 2.3 × 10⁻¹⁹ moles/assay. In particular, despite the long-held belief that the trace amounts of adiponectin in urine can only be detected using a radioisotope, our ultrasensitive ELISA was able to detect urinary adiponectin. This method is highly versatile, because simply changing the antibody enables the detection of various proteins. This assay system requires only the measurement of absorbance, thus it requires equipment that is easily obtained by medical facilities, which facilitates diagnosis in hospitals and clinics. Moreover, we describe an expansion of our ultrasensitive ELISA to a non-amplification nucleic acid detection method for nucleic acids using hybridization. These de novo methods will enable simple, rapid, and accurate diagnosis.

Pheochromocytoma and sympathetic paraganglioma (PPGL) are rare neuroendocrine tumors characterized by catecholamine production in the adrenal medulla and extra-adrenal paraganglia. PPGL with metastasis was termed malignant PPGL. However, the distinction between “benign” and “malignant” PPGLs has been debated. Currently, all PPGLs are believed to have some metastatic potential and are assigned malignant tumors (ICD-O/3) by the WHO Classification of Endocrine Organs (2017, 4th edition). Therefore, the previous categories benign and malignant PPGL have been eliminated in favor of a risk stratification approach. The Grading of Adrenal Pheochromocytoma and Paraganglioma (GAPP) is a tool for risk stratification for predicting metastasis and the prognosis of patients. At least 30% of PPGLs are hereditary, with 20 genes identified and genotype-phenotype correlations clarified. Of these, VHL, RET, and NF1 have been well investigated and are the primary cause of bilateral PCC. In addition, succinate dehydrogenase gene subunits SDHB and SDHD are strongly correlated with extra-adrenal location, younger age, multiple tumors, metastasis, and poor prognosis. Disease stratification by catecholamine phenotype and molecular profiling correlates with histological grading by GAPP. PPGLs should be understood comprehensively based on clinical, biochemical, molecular, and pathological data for patient care. A flow chart for pathological diagnosis is included.

Image processing is a field of which its popularity increases and continues and, that grows dynamically with new technologies. Nowadays, image processing finds itself in use in almost every field. One of these uses is undoubtedly in the field of medicine, where diagnosis and treatment planning are made from images and, which is constantly changing with newly developed techniques. Of course, the most important factor in using this so widely in the medical field is the acquisition of images on every medical field. With the help of these images, the complaints can be seen more easily and the doctor can follow a path in the treatment of the disease. In our study, we used the Region Growing segmentation method to detect the intestinal mass. This study compares the area determined by the specialist with the area obtained with the segmentation process and, it is seen that the created software system can be used as an auxiliary system to specialist doctors.

We attempted to apply the optoelectronic sensor entitled 'OPT101' in intensive care unit clinics, based on its optoelectronic response characteristics in near-infrared wavelength range and near-infrared spectroscopy principle. The successful novel applications in our lab include early-diagnosis and therapeutic effect tracking of thrombus, noninvasive monitoring of patients' shock severity, and fatigue evaluation. This study also expects further improvements of the detector in noninvasive clinical applications.

The right heart catheterisation constitutes the gold standard for pulmonary hypertension (PH) diagnosis. However, echocardiography remains a reliable, non-invasive, inexpensive, convenient, and easily reproducible modality not only for the preliminary screening of PH but also for PH prognosis. The aim of this review is to describe a cluster of echocardiographic parameters for the detection and prognosis of PH and analyse the challenges of echocardiography implementation in patients with suspected or established PH. The most important echocardiographic index is the calculation of pulmonary arterial systolic pressure (PASP) through the tricuspid regurgitation (TR). It has shown high correlation with invasive measurement of pulmonary pressure, but several drawbacks have questioned its accuracy. Besides this, the right ventricular outflow track acceleration time (RVOT-AT) has been proposed for PH diagnosis. A plethora of echocardiographic indices: right atrial area, pericardial effusion, the tricuspid annular plane systolic excursion (TAPSE), the TAPSE/PASP ratio, tricuspid annular systolic velocity (s′) and recently, can reflect the severity and prognosis of PH. Recent advances in echocardiography with 3-dimensional right ventricular (RV) ejection fraction, RV free wall strain and right atrial strain may further assist the prognostication of PH.

Cancer diagnosis stands at the cusp of a profound transformation, driven by the burgeoning capabilities of Artificial Intelligence (AI) and Machine Learning (ML). This comprehensive review illuminates the extraordinary impact of AI and ML in the field, unraveling their multifaceted roles in the realm of oncology. In the arena of cancer diagnosis, AI and ML serve as invaluable allies, empowering healthcare professionals with unparalleled tools for precision and efficiency. Notably, AI's prowess in analyzing medical images, including radiological scans and pathology slides, elevates the early detection of malignancies to new heights. Coupled with its ability to dissect genomic data, AI tailors therapeutic strategies to the individual, promising optimized treatment outcomes. However, the incorporation of AI and ML into clinical practice necessitates a careful navigation of ethical considerations, data privacy, and regulatory landscapes. Safeguarding patient data, ensuring transparency, and addressing algorithmic biases emerge as pivotal challenges that require vigilant attention. Yet, the future of AI and ML in cancer diagnosis is brimming with promise. The integration of multi-modal data, real-time monitoring, and Explainable AI (XAI) methods holds the potential to enrich diagnostic capabilities and engender patient trust. Global collaboration and data sharing initiatives are fostering the development of robust AI models. Furthermore, AI's role in cancer prevention, identifying high-risk individuals and enabling targeted preventive strategies, is poised to revolutionize healthcare. AI and ML are forging a path toward an era of unparalleled accuracy, efficiency, and personalization in cancer diagnosis. Challenges notwithstanding, these technologies bear the promise of fundamentally reshaping patient care, elevating outcomes, and advancing the fight against cancer. The ongoing exploration and responsible implementation of AI and ML in oncology will be pivotal in harnessing their full potential and paving the way for a brighter future for cancer patients.

Background: The aim was to diagnose Candida in the oral cavity of subjects with type 2 diabetes mellitus (T2DM) using a genotyping technique and to compare the results with those from con-ventional diagnosis by Papanicolau (Pap) staining. Methods: Palatal mucosa smears were per-formed in 18 dental care patients diagnosed with T2DM and grade I, II, and III prosthetic stomatitis who met the inclusion criteria; 18 healthy control subjects were also included in the study. He-moglobin A1c (HbA1c) levels were determined from total blood. Using exfoliative cytology, the Pap staining technique was used to diagnose candidiasis. Exfoliative cytology was also used for molecular diagnosis; DNA was obtained for Candida genotyping and RNA was used for gene ex-pression studies. Results: Clinical patterns indicated that all subjects were positive for Candida; however, Pap analysis revealed only three positive subjects, whereas end-point polymerase chain reaction (PCR) analysis revealed fifteen subjects with some type of Candida. The most common Candida species found were Candida guilliermondii (38.8%), Candida krusei (33.3%), Candida tropicalis, and Candida lusitaniae (22.2%). Interestingly, the coexpression of different species of Candida was found in various patients. In all patients, HbA1c levels were increased. Gene expression analysis showed a significant decrease (p ≤ 0.05) in TLR2 expression in positive subjects, whereas TLR4 expression did not differ significantly among patients. Conclusions: The end-point PCR technique showed better sensitivity for diagnosis of Candida when compared with diagnosis by Pap staining. T2DM subjects showed an increased presence of C. guilliermondii that was correlated with decreased TLR2 expression

Diagnosis of canine leishmaniosis (CanL) still represents a major challenge due to the variable clinical manifestations and the large number of asymptomatic dogs. Serological tests are most commonly used to detect infected animals, revealing anti-Leishmania antibodies, mainly of IgG isotype. Recently, a new diagnostic antigen, rKLi8.3, containing 8.3 kinesin tandem repeats (TR) from an Leishmania infantum strain from Sudan has been shown to provide excellent specificity and sensitivity to Leishmania infected humans and dogs. However, asymptomatic animals with very low antibody titers are often difficult to detect by serodiagnosis. Thus, we wondered whether addition of an anti-IgG-enhancing step in the protein A/G-based rKLi8.3-ELISA will improve the diagnostic performance without decreasing the specificity. For this, parasitologically confirmed CanL cases with low or high clinical score, uninfected healthy controls and dogs with other infections were tested by rKLi8.3-ELISA as well as two different immunochromatographic rapid tests, rKLi8.3-lateral flow test (LFT) and Dual Path Platform (DPP®) based on the rK28 antigen. Our results show that the diagnostic accuracy of the rKLi8.3-ELISA and LFT was similar to that of DPP, missing several asymptomatic animals. However, addition of a secondary, amplifying anti-dog IgG antibody in the protein A/G-based rKLi8.3-ELISA enabled the detection of nearly all asymptomatic dogs without affecting its specificity.

Static and functional occlusal relationships are the well-defined objectives of orthodontic therapy. To accomplish it, the jaws must be positioned in centric relation (CR)/ Central occlusion (CO) and appropriately proportioned in three space planes. Orthodontists have well-established techniques when analyzing the skeletal connection between the maxilla and mandible in both sagittal and vertical planes. Although there exist multiple analyses for the skeletal transverse dimension (STD), their inclusion in conventional orthodontic diagnosis is not widely acknowledged. Transverse tooth compensations have been visually represented in prosthodontic literature using a crosshatch arch that passes through the buccal and palatal cusps of maxillary molars, known as the Wilson curve. An inflated Wilson curve has long been seen as a prosthetic maxim, increasing the possibility of working and non-working side interferences. Research has indicated a connection between higher masticatory muscle activity and posterior occlusal contacts or interferences. When skeletal transverse issues are concealed by dental expansion, there is an elevated susceptibility to gingival recession in orthodontic patients who have a small Maxilla or constricted upper jaw. The temporomandibular joints, muscles, periodontal tissue, and airway might all suffer in a susceptible patient with a transverse insufficiency brought on by a narrow maxilla. As for orthodontists, the objective is to create skeletal connections and functional occlusion that are as near ideal as feasible. This will reduce the likelihood of occlusion-related inconsistencies exacerbating the negative consequences on the dentition, periodontium, or joints. An accurate skeletal and dental diagnostic in all three space planes is required. Our goal is to explain all approaches to enable readers to use a transverse skeletal diagnostic in their practice regardless of the technological level at their disposal. The initial step is to ascertain the kind and presence of deficiencies in the maxilla. Since fewer soft tissue alterations are caused by maxilla hypoplasia in the transverse direction, skeletal transverse dimension (STD) assessment is a more significant challenge than vertical or sagittal discrepancy assessment. Model analysis, clinical assessments, radiographic measurements, and occlusograms have all proven successful in producing accurate assessments. Most of the time, surgically assisted maxillary expansion (SARME) is reported to have higher stability than orthopedic maxillary expansion (OME). We examine and discourse on possible research opportunities using the Collaborative Cross (CC) mouse model as a substitute research model.

Abstract: Female pelvic floor dysfunction (FPFD) is a series of conditions caused by the displacement or dysfunction of the patient's pelvic organs due to defects, injuries, and deterioration of the pelvic floor supportive structures, with stress urinary incontinence and pelvic organ prolapse being the most common. The mechanism of pelvic floor dysfunction is complex and not yet fully understood, and is often related to one or more functional abnormalities of muscles, connective tissues, fascia, nerves and blood vessels. Specialized clinicians are often unable to cover the comprehensive medical professional information of various specialties such as gynecology and obstetrics, urology, anus and intestines, imaging, etc., and the systematic and comprehensive diagnosis and treatment of the disease will be limited. The application of artificial intelligence concepts in the medical field not only improves the diagnostic efficiency of physicians, but also provides reference and basis for the development of comprehensive treatment programs. This article follows and draws on the diagnosis and treatment process of pelvic floor dysfunction recommended in various guidelines, incorporates physical therapy techniques into the conventional non-surgical treatment of pelvic floor dysfunction, provides guidance for the diagnosis and treatment of pelvic floor dysfunction, and briefly introduces the clinical application and value of pelvic floor intelligent diagnosis and treatment in pelvic floor dysfunction.

Lung cancer (LC) stands as the foremost cause of cancer-related fatality rates worldwide. Early diagnosis significantly enhances patient survival rate. Nowadays, low dose computed tomography (LDCT) is widely employed on chest as a tool for large-scale lung cancer screening. Nonetheless, a large amount of chest radiographs causes an onerous burden on radiologists. Some computer-aided diagnostic (CAD) tools can provide insights to medical images for diagnosis and augmenting diagnostic speed. However, due to variation in parameter settings across different patients, substantial discrepancies in image voxels persist. We find different voxel-size causing a compromise between model generalization and diagnostic efficacy. This study investigates the performance disparities of diagnostic models trained on original images and LDCT images reconstructed to different voxel-sizes but isotropic. The application is to differentiate a nodule to be benign or malignant. Using 11 features, a support vector machine (SVM) is trained on LDCT images with an isotropic voxel having side-length of 1.5 mm for 225 patients in house. The result yields a favorable model performance with an accuracy of 0.9596 and an area under the receiver operating characteristic curve (ROC/AUC) of 0.9855. In addition to furnish CAD tools for clinical applications, future research is encouraged to include LDCT images from multi-centers.

The abnormal accumulation of cells in the human brain, if left untreated, may cause brain damage. Management and treatment of these tumours require an early and accurate diagnosis, while their prognostic characterisation can also be beneficial in the choice of care planning for the patient. CDSSs are being continuously developed and integrated into routine clinical practice as they assist clinicians and radiologists to deal with an enormous amount of medical data, reduce clinical errors, and improve diagnostic capabilities. They assist detection, classification, and grading of brain tumours as well as alerting physicians of requirement of change in treatment plans. The aim of this systematic review is to identify various CDSSs used in brain tumour diagnosis and prognosis, that rely on data captured by any imaging modality. Based on the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, the literature search was conducted in PubMed and Engineering Village Compendex databases. This review examines various CDSS tool types, system features, techniques used, accuracy, and outcome, to provide the latest evidence available in the field of neuro-oncology. An overview of different types of CDSSs used to support clinical decision-making in the management and treatment of brain tumours, along with highlighting their benefits, challenges, and future perspectives has been provided.

Alzheimer's disease (AD) is an incurable neurodegenerative disorder. Early screening, particularly in blood has been regarded as an effective approach for AD diagnosis and prevention. In addition, metabolic dysfunction has been demonstrated to be closely related to AD, which might be reflected in whole blood transcriptome. Hence, we hypothesized that establishment of diagnostic model based on metabolic signatures of blood is a workable strategy. To the end, we initially constructed metabolic pathway pairwise (MPP) signatures to characterize the interplay among metabolic pathways. Then, a series of bioinformatic methodologies, e.g., differential expression analysis, functional enrichment analysis, and network analysis, etc. were used to investigate the molecular mechanism behind AD. Moreover, an unsupervised clustering analysis based on the MPP signature profile via Non-Negative Matrix Factorization (NMF) algorithm was utilized to stratify AD patients. Finally, aimed at distinguishing AD patients from non-AD group, a metabolic pathway-pairwise scoring system (MPPSS) was established using multi-machine learning methods. As a result, many metabolic pathways correlated to AD were disclosed, including oxidative phosphorylation and fatty acid biosynthesis, etc. NMF clustering analysis divided AD patients into two subgroups (S1 and S2), which exhibit distinct activities of metabolism and immunity. Typically, oxidative phosphorylation in S2 exhibits a lower activity than that in S1 and non-AD group, suggesting the patients in S2 might possess a more compromised brain metabolism. Additionally, Immune infiltration analysis showed that the patients in S2 might have phenomena of immune suppression, compared with S1 and non-AD group. These findings indicated that S2 probably have a more severe progression of AD. Finally, MPPSS could achieve an AUC of 0.73 in training dataset, 0.71 in testing dataset and an AUC of 0.82 on weighted average in five external validation datasets. Overall, our study successfully established a novel metabolism-based scoring system for AD diagnosis using blood transcriptome, and provided new insight into the molecular mechanism of metabolic dysfunction implicated in AD.

This study was performed to synthesize multimodal radiopharmaceutical designed for the diagnosis and treatment of prostate cancer. To achieve this goal, Super Paramagnetic Iron Oxide (SPIO) nanoparticles were used as a platform for targeting molecule (PSMA-617) and for complexation of two scandium radionuclides, 44Sc for PET imaging and 47Sc for radionuclide therapy. TEM and XPS images showed that the Fe3O4 NPs have a uniform cubic shape and a diameter from 38 to 50 nm. The Fe3O4 core are surrounded by SiO2 and an organic layer. The saturation magnetization of the SPION core was 60 emu/g. However, coating the SPIONs with silica and polyglycerol reduces the magnetization significantly. The obtained bioconjugates were labeled with 44Sc and 47Sc, with a yield higher than 97%. The radiobioconjugate exhibited high affinity and cytotoxicity towards the human prostate cancer LNCaP (PSMA+) cell line, much higher than for PC-3 (PSMA-) cells. High cytotoxicity of the radiobioconjugate was confirmed by radiotoxicity studies on LNCaP 3D-spheroids. In addition, the magnetic properties of the radiobioconjugate should allow for its use in guide drug delivery driven by magnetic field gradient.

Tuberculosis (TB) disease still remain a major global threat due to the growing number of drug-resistant species and global warming. Despite the fact that there are new molecular diagnostic approaches, however, majority of developing countries and remote clinics depends on conventional approaches such as Tuberculin test, microscopic examinations and radiographic imaging (Chest X-ray). These techniques are hindered by several challenges which can lead to miss-diagnosis especially when interpreting large number of sample cases. Thus, in order to reduce workload and prevent miss-diagnosis, scientists incorporated computer-aided technology for detection of medical images known as Computer aided Detection (CADe) or Diagnosis (CADx). The use of AI-powered techniques has shown to improve accuracy, sensitivity, specificity. In this review, we discussed about the epidemiology, pathology, diagnosis and treatment of tuberculosis. The review also provides background information on Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Transfer Learning (TL) and their applications in detection of tuberculosis from both microscopic slide images and X-ray images. The review also proposed an IoT/AI powered system which allows transfer of results obtained from DL models with end users through internet networks. The concept of futuristic diagnosis, limitations of current techniques and open research issues are also discussed.

The article is devoted to solving the problem of searching for universal explainable features that can remain explainable for a wide class of objects and phenomena and become an integral part of Explainable AI (XAI). The study is implemented on the example of an applied problem of early diagnostics of plant stress, using Thermal IR (TIR) and HSI, presented by 8 vegetation indices/channels. Each such index was presented by 5 statistical values. A Single-Layer-Perceptron classifier was used as the main instrument. TIR turned out to be the best of the indices in terms of efficiency in the field and sufficient to detect all 7 key days with 100% accuracy. Our study shows also that there are a number of indices, inluding NDVI, and usual color channels Red, Green, Blue, which are close to TIR possibilities in early plant stress detection with 100% accurasy or near, and can be used for wide class of plants and in different conditions their treatment. The stability of the stress classification in our study was maintained when the training set was reduced up to 10% of the dataset volume. The entropy-like feature of (max-min) for any indices/channels have determined as a leadersheep universal high-level explainable feature for the plant stress detection, which used in interaction with some of other statistical features.

Malaria rapid diagnosis test (RDT) is crucial for managing the disease, and the effectiveness of detection depends on parameters such as sensitivity and specificity of the RDT. Several factors can affect the performance of RDT. In this study, we focus on pfhrp2 sequence variation and its impact on RDTs targeted by antigens encoded by pfhrp2. Field samples collected during cross-sectional surveys in Tanzania were sequenced to investigate pfhrp2 sequence diversity and evaluate the impact on HRP2-based RDT performance. We observed significant mean differences in amino acid repeats between current and previous studies. Several new amino acid repeats were found to occur at different frequencies, including types AAY, AHHAHHAAN and AHHAA. Based on the abundance of types 2 and 7 amino acid repeats, the binary predictive model was able to predict RDT insensitivity by about 69 % in the study area. About 85% of the major epitopes targeted by Monoclonal antibodies (MAbs) in RDT were identified. Our study suggests that the extensive sequence variation in the pfhrp2 could contribute to reduced RDT sensitivity. The correlation between the different combinations of amino acid repeats and the performance of RDT in different malaria transmission settings should be investigated further.

As of now, anthropogenic habitat injury via dissemination of manufactured chemicals is injuring the world’s ecosystems, but doctors have yet to recognize its consequences to humans. Recognition of overt toxicity due to chronic low-dose exposure will open the door to diagnosis, sequential elimination studies, and prescriptions for preventive cure that represent a first step in preventing escalating chronic diseases. This paper offers a diagnostic construct for such use: chronic ambient poisoning. Incorporating it into clinical practice can reduce misdiagnosis of gastro- and near-toxicity that doctors and patients presently suppress rather than remove, and reduce patient co-dependence with proton-pump inhibitors, anxiolytics, and antidepressants—and the thriving “alternative” health industry. This diagnostic elephant-in-the-room is presently missed for reasons of habit (when an ailment is not recognized, it cannot be included in the differential diagnosis) and of poor conduct and interpretation of non-clinical research studies that are taken to mean an absence of evidence is evidence of absence. Moreover, modern methods fail when: the exposure comprises a “daily cocktail” of poisons acting additively, interactively, and cumulatively over a lifetime; when there is no normal, unexposed comparison group; and when absence of a robust etiopathogenetic model disallows inapt statistics. Only patient-led, consultant-informed sequential elimination studies can “peel the onion” of toxicity and its recognition and “preventive cure”. Clinical use of the chronic ambient poisoning diagnosis can thus open the door to stemming and reversing anthropogenic epidemics.

Volatile compounds, abundant in breath, can be used to accurately diagnose and monitor a range of medical conditions. This offers a non-invasive, low-cost approach with screening applications; however, uptake of this diagnostic approach has been limited by conflicting published outcomes. Most published reports rely on large scale screening of the public, at single time points and without reference to ambient air. Here, we present a novel approach to volatile sampling from cellular headspace and mouse breath that incorporates multi-time point analysis and ambient air subtraction revealing compound flux as an effective proxy of active metabolism. This approach to investigating breath volatiles offers a new avenue for disease biomarker discovery and diagnosis. Using gas chromatography mass spectrometry (GC/MS), we focus on low molecular weight, metabolic substrate/by-product compounds and demonstrate that this non-invasive technique is sensitive (reproducible at ~1 µg cellular protein, or ~500,000 cells) and capable of precisely determining cell type, status and treatment. Isolated cellular models represent components of larger mammalian systems and we show that stress- and pathology-indicative compounds are detectable in mice, supporting further investigation using this methodology as a tool to identify volatile targets in human patients.

An electrocardiograph (ECG) reflects the health of the human heart and is used to help diagnose arrhythmia and myocardial infarction(MI) in clinical practice. Early diagnosis of arrhythmia helps implement preventive measures and plays a crucial role in saving a patient's life. With the increasing demand of clinicians for ECG analysis technology, intelligent detection and diagnosis of ECG signals has become a more efficient means to assist physicians in diagnosing cardiovascular diseases. This paper introduces an ECG diagnosis approach based on an ensemble deep learning combination of CNN(convolutional neural network) and SLAP(stacked-long short term memory architecture for prediction) architecture. ECG data is denoised and further divided into single heartbeats to achieve data standardization and sample diversity. Adam optimizer and BCEwithlogitsloss multi-classification loss function were used to enhance the model effect, and the system achieved the classification effect of 99.3% average accuracy, 99.0% F1-value, and 99.2% sensitivity in MIT-BIH standard database classification. It also shows good generalization ability on the Tianchi data set.

Disease-related data and information collected by physicians, patients, and researchers seem insignificant at first glance. Still, the same unorganized data contain valuable information that is often hidden. The task of data mining techniques is to extract patterns to classify the data accurately. One of the various Data mining and its methods have been used often to diagnose various diseases. In this study, a machine learning (ML) technique based on distributed computing in the Apache Spark computing space is used to diagnose diabetics or hidden pattern of the illness to detect the disease using a large dataset in real-time. Implementation results of three ML techniques of Decision Tree (DT) technique or Random Forest (RF) or Support Vector Machine (SVM) in the Apache Spark computing environment using the Scala programming language and WEKA show that RF is more efficient and faster to diagnose diabetes in big data.

Nowadays, colon cancer prognosis still difficult to predict, especially in the early stages. Recurrences remain elevated, even in the early stages after curative surgery. Carcidiag Biotechnologies has developed an immunohistochemistry (IHC) kit called ColoSTEM Dx, based on a MIX of biotinylated plant lectins that specifically detects colon cancer stem cells (CSCs) through glycan patterns that they specifically (over)express. A retrospective clinical study was carried out on tumor tissues from 208 non-treated and 21 treated patients with colon cancer, that were stained by IHC with the MIX. Clinical performances of the kit were determined, and prognostic and predictive values were evaluated. With 78.3% and 70.6% of diagnostic sensitivity and specificity respectively, our kit shows great clinical performances. Moreover, patient prognosis is significantly poorer when the MIX staining is “High” compared to “Low”, especially at 5-years of overall survival and for early stages. The ColoSTEM Dx kit allows an earlier and a more precise determination of patients’ outcome. Thus, it affords an innovating clinical tool for predicting tumor aggressiveness earlier and determining prognosis value regarding therapeutic response in colon cancer patients.

Epilepsy is one of the most common disorders of the central nervous system, impacting nearly 50 million people around the world. Heterogeneous in nature, epilepsy presents in children and adults alike. Currently, surgery is the only treatment that can cure epilepsy. However, not all individuals are eligible or have successful outcomes. Difficulty in accessing samples of human brain tissue along with advances in sequencing technology have driven researchers to investigate sampling liquid biopsies in blood, serum, plasma, and cerebrospinal fluid within the context of epilepsy. Liquid biopsies provide minimal or non-invasive sample collection approaches and can be assayed relatively easily across multiple time points, unlike tissue-based sampling. Various efforts have investigated circulating nucleic acids from these samples including microRNAs, cell-free DNA, transfer RNAs, and long non-coding RNAs. Here, we review nucleic acid-based liquid biopsies in epilepsy to improve understanding of etiology, diagnosis, prediction, and therapeutic monitoring.

The diagnosis of a rolling bearing for monitoring its status is critical for maintaining industrial equipment using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction to enhance the accuracy. 1D-CNN method not only can diagnose bearing faults accurately but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. Thus, it enhances the processing speed and improves the network structure to have a reasonable design for small sample data sets. This study proposes and tests a 1D-CNN method for diagnosing rolling bearings. By introducing the dropout operation, the method obtains high accuracy and improves the generalizing ability. The experimental results show 99.52% of the average accuracy under a single load and 98.26% under different loads.

(1) Background: It is unclear whether the reported presenting clinical features of coronavirus disease 2019 (COVID-19) are useful in identifying high-risk patients for early testing and isolation in the emergency department (ED). We aimed to compare the exposure history, clinical, laboratory, and radiographic features of ED patients who tested positive and negative for COVID-19; (2) Methods: We conducted a case-control study in seven EDs during the first five weeks of the COVID-19 outbreak in Hong Kong. Thirty-seven laboratory-confirmed COVID-19 patients were compared with 111 age- and gender-matched controls; (3) Results: There were no significant differences in patient characteristics and reported symptoms between the groups, except patient-reported fever. A positive travel history or contact history was the most significant predictor for COVID-19 infection. After adjustment for age and presumed location of acquiring the infection in Wuhan/Hubei, patient-reported fever (OR 2.6, 95% CI 1.1 to 6.3), delayed presentation (OR 5.0, 95% CI 2.0 to 12.5), having medical consultation before ED presentation (OR 7.4, 95% 2.9 to19.1), thrombocytopenia (OR 4.0, 95% CI 1.6 to 9.7), raised lactate dehydrogenase (OR 5.9, 95% CI 1.9 to 18.5), haziness, consolidation or ground-glass opacity on chest radiography (OR 5.6, 95% CI 2.0 to 16.0), and bilateral changes on chest radiography (OR 13.2, 95% CI 4.7 to 37.4) were associated with a higher odds of COVID-19 separately while neutrophilia was associated with a lower odds (OR 0.3, 95% CI 0.1-0.8); and (4) Conclusions: This study highlights several features that may be useful in identifying high-risk patients for early testing and isolation while waiting for test result. Further studies are warranted to verify the findings.

The power converter is the significant device in a wind power system. Wind turbine will be shut down and off grid immediately with the occurrence of the IGBT module open-circuit fault of power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies of power converter, there are few single and double IGBT modules open-circuit fault diagnosis methods producing negative results including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed. Above all, collecting the three-phase current varying with wind speed of 22 failure states including a normal state of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are used as the input of deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.

Background: Accurate diagnosis of the differentially aggressive fungus Leptosphaeria maculans and Leptosphaeria biglobosa causing Blackleg in crucifers is crucial. Available markers were designed decades ago which may become ineffective due to the ever evolving nature of the fungus, requiring the development of more precise molecular markers. Methods: The whole genomes of available isolates belonging to this two species were aligned using progressive MAUVE tool, species specific genomic regions were extracted and species specific primers were designed from the sequences that encode for effector proteins. Results: Three (Lm1, Lm2 and Lm5) and two (Lb3 and Lb3’) primer sets specifically detected the isolates of target species in PCR based assay, of which the primers Lm5 and Lb3’ were multiplexed for detection of Leptosphaeria maculans and Leptosphaeria biglobosa, generating PCR amplicons of 230 and 834 bp, respectively from a single PCR reaction. The markers were highly sensitive and were able to amplify target species from crude ‘pseudothecia and ascospores suspension’ without requiring DNA extraction. Conclusions: These markers, solitarily or in combination, designed from species specific genomic segments will serve as precise, sensitive and rapid detection of Leptosphaeria maculans and Leptosphaeria biglobosa species and will be helpful for surveillance, management and transboundary quarantine of the devastating disease.

Admission and discharge diagnoses of in-hospital patients are often in discord. Incorrect admission diagnoses are related to increased cost of care and patient safety. Additionally, due to the seasonality of many conditions, this discord may vary across the year. In this paper, we used medical claims data to develop a methodological framework that examines these differences, for Medicare beneficiaries. We provide examples for pneumonia, a condition with seasonal implications, and aneurysm, where early detection can be life-saving. Following a Bayesian approach, our work quantifies and visualizes with time series plots the degree that any clinical condition is correctly diagnosed upon admission. We examined differences in weekly intervals, during a calendar year. The mean length of stay and hospital charges were furthermore compared between matching and non-matching {admission, discharge Dx} pairs, and 95% confidence intervals of the difference of means were estimated. We applied Statistical Process Control methods and then visualized differences, for the hospital charges and the length of stay, per week, with time series plots. Our methodology and the visualizations underline the importance of a rigorous and non-delayed diagnostic process upon admission since there are significant implications in terms of hospital outcomes and cost of care.

Preliminaries Convolutional Neural Network (CNN) applications have recently emerged in Structural Health Monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT (Lead Zirconate Titanate) based method and CNN. Likewise, applications using CNN along with the Electromechanical Impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of 4 types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

Purpose: Expanded carrier screening (ECS) informs couples of their risk of having offspring affected by certain genetic conditions. Limited data exists assessing the actions and reproductive outcomes of at-risk couples (ARCs). We describe the impact of ECS on planned and actual pregnancy management in the largest sample of ARCs studied to date. Methods: Couples who elected ECS and were found to be at high risk of having a pregnancy affected by at least one of 176 genetic conditions were invited to complete a survey about their actions and pregnancy management. Results: Three hundred ninety-one ARCs completed the survey. Among those screened before becoming pregnant, 77% planned or pursued actions to avoid having affected offspring. Among those screened during pregnancy, 37% elected prenatal diagnostic testing (PNDx) for that pregnancy. In subsequent pregnancies that occurred in both the preconception and prenatal screening groups, PNDx was pursued in 29%. The decision to decline PNDx was most frequently based on the fear of procedure-related miscarriage, as well as the belief that termination would not be pursued in the event of a positive diagnosis. Conclusions: ECS results impacted couples’ reproductive decision-making and led to altered pregnancy management that effectively eliminates the risk of having affected offspring.

In this study we used a non-autonomous Chua’s Circuit, and the fractional Lorenz chaos system together with a detection method from Extension theory to analyze the voltage signals. The measured bearing signals by acceleration sensor were introduced into the master and slave systems through a Chua’s Circuit. In a chaotic system minor differences can cause significant changes that generate dynamic errors, and extension matter-element models can be used to judge the bearing conditions. Extension theory can be used to establish classical and sectional domains using the dynamic errors of the fault conditions. The results obtained were compared with those from Discrete Fourier Transform analysis, Wavelet analysis and an integer order chaos system. The diagnostic ratio showed the fractional order master and slave chaos system calculations. The results show that the method presented in this paper is very suitable for monitoring the operational state of ball bearing system to be superior to the other methods. The diagnosis ratio was better and there were other significant advantages such as low cost and few.

Owing to the large ratio of consumption in the building sector, energy saving strategies are required. Energy feedback is an energy-saving strategy that consumers to change their energy-consumption behaviors. The strategy has been principally focused on providing energy-consumption information. However, realization of energy savings using only consumption information remains limited. In this paper, a building-energy three-dimensional (3D) visualization solution is thus proposed. This solution includes the process of diagnosing a building and providing prediction of energy requirements if a building improvement is undertaken. Accurate diagnostic information is provided by real-time measurement data from sensors and building models using a close-range photogrammetry (CRP) method without depending on blueprints. The information is provided by employing visualization effects to increase the energy-feedback efficiency. The proposed strategy is implemented on two testbeds, and building diagnostics are performed accordingly. For the first testbed, the predicted energy improvement amount resulting from the facility upgrade is provided. The second testbed is provided with a 3D visualization of the energy information. The aim is to determine if the building manager will replace the facility after our recommendation is given to improve the building energy efficiency driven from the energy information. Unlike existing systems, which provide only ambiguous data that lack quantitative information, this study is meaningful because it provides energy information with the aid of visualization effects before and after building improvements.

Computers and artificial intelligence affect every field of life nowadays. In medical image interpretation automatic decision making using algorithms are used increasingly in every sub-field and computer aided diagnosis (CAD) is one of the main tools available to medical science today. CAD systems are used as an augmented option for both the medical practitioner and the patients, with image analysis and interpretation being of primary importance. In particular, spatial relations are used in knowledge representation, and these relations can be used for effective medical image interpretation. In this paper, we put forth an algorithm for defining non-small cells lungs cancer (NSCLC) stages in lungs images interpretation using topological spatial relations. We show an application case study in event motion predictions for lung cancer staging scoring - tumor, nodes and metastasis (TNM) - with combined topological and directional relations.

Most of α-thalassemia cases are caused by deletions of the structural α-globin genes. The degree of microcytosis and hypochromia has been correlated with the number of affected α-globin genes, suggesting a promising role of hematologic parameters as predictive diagnostic tools. However, specific cut-off points for these parameters to discriminate between the different subtypes of α-thalassemia remain to be clearly defined. Six hematologic parameters (total number of erythrocytes, hemoglobin concentration, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration and red cell distribution width) were evaluated in 174 cases of deletional α-thalassemia (92 heterozygous α+ thalassemia, 39 homozygous α+ thalassemia, 34 heterozygous α0 thalassemia and 9 cases of Hb H disease). A good correlation between the number of deleted alpha genes and MCV (r = -0.672, p<0.001), MCH (r = -0.788, p<0.001) and RDW (r = 0.633, p<0.001) was observed. The deletion of at least two alpha genes in adult individuals with microcytosis without iron deficiency and normal values of Hb A2 and Hb F should be discarded when MCH levels are lower than 23.15 pg. Furthermore, MCH <21.90 pg and/or MCV <70.80 fL are strongly suggestive of the presence of one α0 allele. Finally, an accurate presumptive diagnosis of Hb H disease can be made if both RDW ≥20% and MCH <18.45 pg are seen.

Purpose: To simplify the application of optical coherence tomography (OCT) for examination of subsurface morphology in the oral cavity and reduce barriers towards adoption of OCT as a biopsy guidance device. The aim of this work was to develop automated software tools for simplified analysis of the large volume of data collected during OCT. Methods: Imaging and corresponding histopathology were acquired in-clinic using a wide-field endoscopic OCT system. An annotated dataset (n = 294 images) from 60 patients (34 male, 26 female) was assembled to train four unique neural networks. A deep learning pipeline was built using convolutional and modified u-net models to detect the imaging field of view (network 1), detect artifacts (network 2), identify the tissue surface (network 3), and identify the presence and location of the basement membrane (network 4). Results: The area under the curve of the image and artifact detection networks was 1.00 and 0.94 respectively. The Dice similarity score for the surface and basement membrane segmentation networks was 0.98 and 0.83 respectively. Conclusion: Deep Learning (DL) techniques can identify the location and variations in the epithelial surface and basement membrane in OCT images of the oral mucosa. Segmentation results can be synthesized into accessible en face maps to allow easier visualization of changes.

Objective: The aim of our study was to perform a retrospective analysis of the volume of cervical screening tests, the number of patients treated with an excision method, and the incidence of invasive and non-invasive cervical during a pandemic and pre-pandemic period of 24 months. Results: There was a statistically significant age difference between the two study periods, both by the average age of patients and by age group. The mean difference was 32 years before the pan-demic and 35 years during the pandemic (p-value >0.05). The majority of patients presenting for investigations before and during the pandemic were in the 30-39-year-old age group (31.95%, respectively; 34.2% (p-value = 0.003). The biggest patient loss ratio identified by age group was in the 50–59 years: 14,53% in the pre-pandemic period and 9,1% in the pandemic period. In the pan-demic period, patients from rural areas presented in the clinical trial with a lower rate of 39.52% (83 patients) vs. 60.47% (127 patients) in urban areas. A higher percentage of patients experiencing cervicorrhagia as a clinical manifestation in the pandemic period vs. the prepandemic period, with an increase in more severe lesions in the pandemic period, has a statistical significance of 8% more newly diagnosed compared to the pre-pandemic period. Conclusion: The addressability of the patients during the COVID period was not affected in a drastic way in our study. We encountered a decrease in appointments in the age group 50–59 years and a decrease in patients with rural residence. In our study, we found an increase in cervicorrhagia as a reason for consultation in the pandemic period with a higher lesion degree, both on a pap smear and on a cervical biopsy.

Predominantly antibody deficiencies (PAD) represent the most common type of primary immu-nodeficiencies in humans, characterized by a wide variation in disease onset, clinical manifesta-tions, complications and outcome. Considering that the prevalence of PAD in Greece is unknown and there is limited knowledge on clinical and laboratory characteristics of affected patients, we conducted a nationwide study, including 153 patients (male/female: 66/87; median age: 43.0 years; range: 7.0-77.0) diagnosed and/or followed-up between April 2007 to September 2023. Furthermore, we classified our cohort into five Groups according to their medical history, im-munoglobulin levels, and CTLA4-mutational status: 123 had common variable immunodeficien-cy (CVID), 12 with CVID-like disease (with a history of a previous B-cell depletion immunother-apy for autoimmune or malignant disease), seven with combined IgA and IgG subclass deficien-cies, five with immune dysregulation syndrome and CTLA4 defects, and six with unclassified hypogammaglobulinemia. We demonstrated a remarkable delay of PAD diagnosis, several years after the onset of related symptoms (median: 9.0 years, range: 0-43.0). Family history of PAD was only 11.8%, with the majority of patients considered sporadic cases. Most patients were diag-nosed in the context of a diagnostic work-up for recurrent infections, or recurrent/resistant auto-immune cytopenias. Interestingly, ten patients (5.6%) had no history of infection, diagnosed due to either recurrent/resistant autoimmunity, or during a work-up of their medical/family history. Remarkable findings included an increased prevalence of lymphoproliferation (60.1%) and atopy (24.2%), while 39 patients (25.5%) developed bronchiectasis and 16 (10.5%) granulomatous dis-ease. Patients with CTLA4 defects displayed a variable clinical phenotype at diagnosis, with four patients diagnosed during a work-up of re-current infections, while one also displayed a massive splenomegaly. Cancer was a common complication in our cohort (25 patients, 16.3%), with B-cell malignancies representing the most common neoplasms (56.7%). Our findings indicate the necessity of awareness about PAD and their complications, aiming for early diagnosis and appropriate management of affected patients

Atherosclerosis is associated with vascular diseases, diabetes, and cancers. The Jumonji C-domain-containing 6 (JMJD6) gene plays an important role in diverse biological processes. In addition, autoantibodies are reportedly useful in the early diagnosis and treatment of many diseases. Here, JMJD6 was identified by serological identification of antigens by recombinant cDNA expression cloning as an antigen recognized by serum IgG from patients with unstable angina pectoris. Then, we performed amplified luminescent proximity homogeneous assay-linked immunosorbent assay using purified recombinant antigens and found elevated levels of serum anti-JMJD6 antibodies (s-JMJD6-Abs) in patients with atherosclerosis-related diseases including ischemic stroke, acute myocardial infarction (AMI), diabetes mellitus (DM), and cancers including esophageal cancer (EC), gastric cancer, lung cancer, and mammary cancer, compared with those in healthy donors. The s-JMJD6-Ab levels were closely associated with hypertension, habitual smoking, and the intima-media thickness, an atherosclerosis index. The better postoperative survival status was shown in patients with EC from the JMJD6-Ab-positive group than the negative group, especially from JMJD6-Ab-positive/PD-L1-negative group and JMJD6-Ab-positive/p53-Ab-negative group than JMJD6-Ab-negative/PD-L1-positive group and JMJD6-Ab-negative/p53-Ab-positive group, respectively. Hence, JMJD6 autoantibodies may reflect atherosclerosis severity, thereby a potential biomarker for atherosclerosis-related diseases including ischemic stroke, AMI, DM, and cancers and for predicting EC prognosis.

Circulating miRNA-451a has recently emerged as an important biomolecule with potential clinical values as a diagnostic marker for several diseases. However, to be used as such, it is critical to accurately assay miRNA-451a in the clinic. Yet, pre-analytical factors that can affect such an error-free clinical assay of miRNA-451a have not been explored. This study aimed at investigating several of these pre-analytical factors that may affect the accurate quantification of miRNA-451a in human blood samples. We initially evaluated miRNA-451a levels in red blood cells (RBCs), white blood cells (WBCs), platelets, and plasma by droplet digital PCR (ddPCR). Next, we quantified miRNA-451a levels in whole blood or plasma stored at different temperatures for different time periods by ddPCR and used hemolyzed plasma samples to study the effects of hemolysis on plasma miRNA-451a concentration. We found that WBCs and platelets contain trace amounts of miRNA-451a and that about 99.9% of miRNA-451a in the blood are localized in RBCs. Hemolyzed plasma showed 58.5-fold increase in miRNA-451 concentration as compared to non-hemolyzed plasma. Importantly, plasma stored at room temperature (RT) or 4ºC showed a steady statistically significant decrease in miRNA-451a levels over a 7-day period whereas plasma samples stored at -20 or -80ºC for up to 7 days showed stable miRNA-451a levels. Together, our data indicate that hemolysis and blood storage at RT or 4ºC may have significant negative effects on the levels of circulating miRNA-451a.

Tuberculosis (TB) caused by Mycobacterium tuberculosis remains a predominant cause of mortality, especially in low- and middle-income nations. Recently antimicrobial peptides were discovered that at low concentrations could stimulate the growth of M. tuberculosis (hormetic response). In this study, such a peptide was used to investigate the effects on the time to positivity (TTP). A systematic substitution analysis of peptide 14D was synthesized using Spot synthesis technology, resulting in 171 novel peptides. Our findings revealed a spectrum of interactions, with some peptides accelerating M. tuberculosis growth, potentially aiding in faster diagnostics, while others exhibited inhibitory effects. Notably, peptide NH2-wkivfiwrr-CONH2 significantly reduced the TTP by 25 hours compared to the wild-type peptide 14D, highlighting its potential in improving TB diagnostics by culture. Several peptides demonstrated potent antimycobacterial activity, with a minimum inhibitory concentration (MIC) of 20 µg/mL against H37Rv and a multidrug-resistant M. tuberculosis strain. Additionally, a reduction in the formation of cord-like structures was observed, which is indicative of reduced virulence and transmission potential. This study underscores the multifaceted roles of antimicrobial peptides in TB management, from enhancing diagnostic efficiency to offering therapeutic avenues against M. tuberculosis.

Multiple sclerosis is a chronic, autoimmune disease that mainly affects the central nervous system, including the brain, spinal cord, and optic nerve. This disease can cause clinical symptoms such as cognitive decline, muscle weakness, spasms, and fatigue in patients, and the onset tends to be younger. Current medication can only prevent or alleviate symptoms, so early diagnosis of this disease can increase patients' chances of treatment. Although the use of nuclear magnetic resonance detection can improve the efficiency of early auxiliary diagnosis, it still requires experienced doctors to spend too much time and energy on comprehensive judgment. To reduce the time cost and improve the efficiency of multiple sclerosis diagnosis, this article proposes a detection and recognition algorithm for multiple sclerosis based on wavelet entropy and self-adaptive particle swarm optimization. Firstly, a triple discrete wavelet transform is performed on the brain image of multiple sclerosis, and then 10 wavelet entropies are extracted from the decomposed wavelet subbands, which can reduce the feature dimensions of the image; Then, the algorithm of self-adaptive particle swarm optimization is used to optimize the feedforward neural network, in order to obtain the optimal connection weights and thresholds during the training process. The result of the model is with an average sensitivity of 92.29±1.89, specificity of 92.54±0.67, precision of 92.48±0.59, accuracy of 92.42±0.88, and F1 score of 84.85±1.74, Matthews correlation coefficient of 92.37±0.96, and Fowlkes223 Mallows Index of 92.38±0.96. The experimental results indicate that this algorithm has a very important data support role in detecting multiple sclerosis.

This paper first introduces the main classification of brain diseases and the main causes of these diseases. It is followed by wavelet analysis, which is a mathematical function or wave-like pattern used to transform data, which can decompose the signal into different wavelet functions, each of which is related to a specific scale or frequency. Wavelet analysis has been applied in many fields such as image processing, data analysis and engineering. In addition, wavelet is also used in the analysis of biological signals. Wavelet analysis breaks down brain signals, such as EEG or fMRI data, into various frequency components at different scales. Wavelet analysis provides time-frequency localization. Pattern recognition can be enhanced by isolating salient features in the data. Wavelet denoising can effectively separate noise from underlying brain signals. Wavelet denoising usually adopts threshold method. Wavelet analysis can enhance pattern recognition by isolating salient features in the data. Researchers can train machine learning models based on these wavelet-derived features to recognize specific patterns associated with different neurological disorders. Wavelet analysis can track these changes by continuously evaluating the frequency content of the neuroimage data over time. Through continuous efforts, wavelet theory and technology have become valuable tools in the field of neuroscience and brain disease research.

This article reviews the introduction of Alzheimer's Disease (AD), neural networks, training and learning of neural networks, applications of neural networks in early diagnosis of AD, applications of neural networks in AD drug discovery, other brain diseases, and challenges faced by AD. First, the paper introduces the background and characteristics of AD. AD is a degenerative neurological disorder characterized by impaired memory, decreased cognitive function, and loss of neurons. These characteristics place a huge burden on the lives and families of patients. Next, the basic principle and structure of neural network are discussed. A neural network is a computational model made up of multiple neurons that can perform tasks by learning and adapting to input data. In particular, the key concepts of neural network hierarchy, activation function and weight adjustment are discussed. Then, the training and learning methods of neural networks are discussed. Common techniques such as backpropagation algorithm and gradient descent optimizer are introduced in detail, as well as the importance of data preprocessing and model evaluation. Next, the paper focuses on the application of neural network in early diagnosis of AD. By extracting features from brain image data, neural networks can automatically identify differences between AD patients and healthy subjects, enabling early diagnosis and intervention. In addition, the application of neural networks in AD drug discovery is also discussed. By analyzing and predicting a database of known drugs, neural networks can help discover potential treatments for AD and speed up the drug discovery process. The paper further explores the application of neural networks in other brain diseases and highlights the challenges faced by AD, such as the lack of reliable biomarkers, complex pathological mechanisms, etc. In summary, this paper presents a systematic overview of AD, neural networks, training and learning of neural networks, applications of neural networks in early diagnosis of AD and drug discovery, and other brain diseases and challenges associated with AD.

Cardiovascular and cerebrovascular disease incidence has risen mainly due to poor control of preventable risk factors and still constitutes a significant financial and health burden worldwide. ChatGPT is an artificial intelligence language-based model developed by OpenAI. Due to the model’s unique cognitive capabilities beyond data processing and the production of high-quality text, there has been a surge of research interest concerning its role in the scientific community and contemporary clinical practice. To fully exploit ChatGPT's potential benefits and reduce its possi-ble misuse, extreme caution must be taken to ensure its implications ethically and equitably. In this article, we explore the language model's possible applications and limitations while empha-sizing its potential value for diagnosing, managing, and prognosis cardiovascular and cerebro-vascular disease.

A teratoma is a neoplasm composed of cell populations or tissues that remind, in their appearance, normal elements derived from at least two embryonic layers. Fetal mature teratomas are normally benign, cystic, and typically occur along the midline, while they are rare in the posterior mediastinum. Teratomas are frequently solitary, however they may sometimes be associated with other congenital anomalies and/or with chromosomal abnormalities. Clinically, they are often asymptomatic but can occasionally cause compression symptoms. Prenatal diagnoses are uncommon and made with ultrasonography; differential diagnosis with other congenital conditions is mandatory. We report the case of a 21 weeks of gestational age-old fetus with a Mature Triphyllic Fetal Cystic Teratoma, grade 0, located in the right posterior mediastinum.

Background: Liquid biopsies are revolutionary tools to detect tumor-specific genetic alterations in body fluids, and cell-free DNA (cfDNA) can be used for molecular diagnosis in cancer patients. In brain tumors cerebrospinal fluid (CSF) cfDNA might be more informative than plasma cfDNA. Here, we assess the use of CSF cfDNA in pediatric embryonal brain tumors (EBT) for molecular diagnosis. Methods: CSF cfDNA of pediatric patients with medulloblastoma (n=18), ATRT (n=3), ETMR (n=1), CNS NB FOXR2 (n=2) and pediatric EBT NOS (n=1) (mean cfDNA concentration 48 ng/mL; range 4-442 ng/mL) and matched tumor genomic DNA were sequenced by WES, and/or a targeted sequencing approach, to determine single nucleotide variations (SNVs)/copy number alterations (CNA). A specific capture covering transcription start sites (TSS) of genes of interest was used for nucleosome footprinting in CSF cfDNA. Results: 15/25 CSF cfDNA samples yielded informative results, with informative CNA and SNVs in 11 and 15 cases, respectively. For cases with paired tumor and CSF cfDNA WES (n=15), a mean of 83 (range 1-160) shared SNVs was observed, including SNVs in classical medulloblastoma genes such as SMO and KMT2D. Interestingly, tumor-specific SNVs (mean 18; range 1-62) or CSF-specific SNVs (mean 5; range 0-25) were observed suggesting clonal heterogeneity. The TSS panel resulted in differential coverage profiles across all 112 studied genes in 7 cases, indicating distinct promoter accessibility. Conclusion: CSF cfDNA sequencing yielded informative results in 60% of cases (15/25), with WES feasible in 83% (15/18). These results pave the way for implementation of these novel approaches for molecular diagnosis and minimal residual disease monitoring.

When an aircraft malfunctions, quickly and accurately identifying the faulty unit is essential for ensuring normal operation. Unfortunately, maintenance engineers often struggle to acquire the necessary fault-related knowledge due to poor management and utilization of aircraft fault documents. To address this issue, we introduce knowledge graph technology into the field of aircraft fault diagnosis, exploring its construction and application for effective knowledge management. Our work starts by analyzing the critical knowledge elements required for aircraft fault diagnosis and designing a schema layer for fault knowledge graphs. We then we then combine deep learning and heuristic rules to extract fault knowledge from both structured and unstructured data, enabling the construction of aircraft fault knowledge graphs. Finally, we develop a fault question-answering system based on fault knowledge graphs that can accurately give solutions to questions posed by maintenance engineers. Our practice demonstrates that knowledge graphs provide an effective means of managing aircraft fault knowledge, assisting engineers in locating fault reasons accurately and quickly.

The development of the automotive industry leads to the improvement of the designs of automatic transmissions and drives and to the development of methods for monitoring and diagnosing their condition. The trend of improving transmissions comes down mainly to the endless improvement of existing designs of CVTs and variators, which inevitably leads to the complication of designs and methods for their control and diagnostics.There is a need to create a fundamentally new simplified gearbox based on the scientific achievements of mechanics. However, the created structures remained inoperable due to the lack of theoretical justification.The adaptive gear variator developed by the author, which has CVT functions, is a mechanism with two degrees of freedom and with an additional constraint of a fundamentally new type, called a force - speed constraint. The force - speed constraint imposes a force restriction on the movement of links, while maintaining the number of degrees of freedom in the kinematics. Monitoring the state of the developed gearbox in the form of a non-switchable gear variator is greatly simplified, since the largest number of faults occur in the control system, and there is no control system in the gear variator. It seems possible to apply the digital twin method to diagnose the developed simplified CVT.The proposed article is devoted to a theoretical description of a fundamentally new adaptive gearbox, created on the basis of the latest achievements in mechanics, with the prospect of developing a simplified monitoring and diagnostic system for it

Background and Aims. Most research on Parkinson's disease (PD) focuses on describing symp-toms and movement characteristics. Studies rarely focus on the early detection of PD and the search for suitable markers of a prodromal stage. Early detection is important, so treatments that may potentially change the course of the disease can be attempted early on. While gait disturb-ances are less pronounced in the early stages of the disease, the prevalence, and severity increase with disease progression. Therefore, postural instability and gait difficulties could be identified as sensitive biomarkers. The aim was to evaluate the discriminatory power of the Trail-Walking Test (Schott, 2015) as a potential diagnostic instrument to improve the predictive power of the clinical evaluation concerning the severity of the disease and record the different aspects of walking. Methods. 20 older healthy (M = 72.4 years, SD = 5.53) adults and 46 older adults with PD and the motor phenotypes postural instability/gait difficulty (PIGD; M = 69.7 years, SD = 8.68) and tremor dominant (TD; M = 68.2 years, SD = 8.94) participated in the study. The participants performed a motor-cognitive dual task (DT) of increasing cognitive difficulty in which they had to walk a given path (condition 1), walk to numbers in ascending order (condition 2), and walk to numbers and letters alternately and in ascending order (condition 3). Results. With an increase in the cognitive load, the time to complete the tasks (seconds) become longer in all groups, F(1.23, 73.5) = 121, p < .001, ɳ2p = .670. PD-PIGD shows the longest times in all conditions of the TWT, F(2, 60) = 8.15, p < .001, ɳ2p = .214. Mutual interferences in the cognitive and motor domain can be observed. How-ever, clear group-specific patterns cannot be identified. A differentiation between the motor phenotypes of PD is especially feasible with the purely motor condition (TWT-M; AUC = . 685, p = 0.44). Conclusions. PD patients with PIGD must be identified by valid, well-evaluated clinical tests that allow a precise assessment of the disease's individual fall risk, the severity of the dis-ease, and the prognosis of progression. The TWT covers various aspects of mobility, examines the relationship between cognitive functions and walking, and enables differentiation of the motor phenotypes of PD.

African swine fever (ASF) is one of the major threats to pig production, and real-time PCR (qPCR) protocols are integral part of ASF laboratory diagnosis. With the pandemic spread of ASF, commercial kits have risen on the market. In Germany, the kits have to go through an approval process and thus, general validation can be assumed. However, they were never compared to each other. In this study, 12 commercial PCR kits were compared to an OIE recommended method. Samples representing different matrices, genome loads, and genotypes were included in a panel that was tested under diagnostic conditions. The comparison included user-friendliness, internal controls, and the time required. All qPCRs were able to detect ASFV genome in different matrices across all genotypes and disease courses. With one exception, there were no significant differences when comparing the overall mean. The overall specificity was 100 % [95 % CI 87.66 - 100], and the sensitivity was between 95 % and 100 % [95 % CI 91.11 - 100]. As can be expected, variability concerned samples with low genome load. Concluding, all tests were fit for purpose. The test system can therefore be chosen based on compatibility and prioritization of the internal control system.

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on the technologies for gait characteristic assessment, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigen-analysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

Liquid biopsy, based on the circulating tumor cells (CTCs) and cell-free nucleic acids has potential applications at multiple points throughout the natural course of cancer, from diagnosis to follow-up. The advantages of doing ctDNA assessment vs. tissue-based genomic profile are the minimal procedural risk, the possibility to serial testing in order to monitor disease-relapse and response to therapy over time and to reduce hospitalization costs during the entire process. However some critical issues related to ctDNA assays should be taken in consideration. The sensitivity of ctDNA assays depends on the assessment technique and genetic platforms used, on tumor-organ, stage, tumor heterogeneity, tumor clonality. The specificity is usually very high, whereas the concordance with tumor-based biopsy is generally low. In patients with renal cell carcinoma (RCC) qualitative analyses of ctDNA have been performed with interesting results regarding selective pressure from therapy, therapeutic resistance, exceptional treatment response to everolimus and mutations associated with aggressive behavior. Quantitative analyses showed variations of cfDNA levels at different tumor stage. Compared to CTC assay, ctDNA is more stable than cells and easier to isolate. Splice variants, information at single-cell level and functional assays along with proteomics, transcriptomics and metabolomics studies can be performed only in CTCs.

Background: Scabies is an unfortunately neglected tropical skin disease. Scabies occurs in Australia, however it is drastically more common amongst select socioeconomically disadvantaged groups. Amongst these, Aboriginal and Torres Strait Islander communities are affected particularly severely, likely secondary to rampant overcrowding. Methods: This literature review has comprehensively explored the relevant evidence available since the year 2000. Objectives: The objective of this article is to provide updates on the identification, management, and control of scabies in Aboriginal and Torres Strait Islander communities. The article also aims to reveal issues with these factors that may be contributing to the inflated prevalence of scabies amongst these groups. Conclusions: Diagnosis currently depends upon clinical examination, dermatoscopy, and skin scraping microscopy; all of which are prone to error. In Australia, topical permethrin remains first-line treatment, however resistance is developing. Other management aspects to consider are environmental measures and treating secondary infections. Scabies prevention is far more cost-effective than managing advanced individual cases, typically ensured through mass drug administration of permethrin or ivermectin. Sustained prevention can prove troublesome for some communities. In order to address the status of scabies as a neglected tropical disease, these issues must be managed first.

Volunteering has many social, emotional and health benefits and could be an option for people with younger/early onset dementia to engage in stage/age-appropriate, personally meaningful activity within their local community. Audio data of people with young onset dementia discussing their volunteering experiences were taken from the Dementia Diaries and co-analysed. This study explored how people with dementia volunteer in the UK, the benefits and barriers, and how we can improve access to volunteering opportunities. The findings suggest that stigma is the overarching concept (or net) which influences people’s experiences of volunteering with dementia. Within the stigma net, three themes were developed that employed a lifespan approach to challenging said stigma: 1) Valuing the past, 2) Valuing the present and 3) Valuing the future, each with additional subthemes. This research contributes to the limited pool of literature that highlights the clear benefits of volunteering for people with young onset dementia, while also considering how dementia stigma can create practical and institutional barriers to accessing volunteering. Guidance is provided for how to deliver meaningful and accessible volunteering opportunities.

This is a single tertiary center in southwest Romania, population-based, study. We retrospectively compared data obtained in two periods: January 2008 - December 2013 and January 2018 - De-cember 2023, respectively. The global incidence of transposition of great arteries (TGA) - the ter-minated cases added to the ones resulting in live-born pregnancies, remained almost constant. The live-born incidence of TGA decreased (0.34% to 0.013%). The prenatal detection rate increased slightly (71.42% to 87.5%). The median gestational age at the diagnosis decreased, from 29.3 ges-tational weeks (mean 25.4) to 13.4 weeks (mean 17.2). The proportion of terminated pregnancies significantly increased (14.28% to 75%, p = 0.019).

Advances in gastric cancer screening and endoscopic technology have made it possible to detect and treat gastric cancer at an early stage. As a result, about 40% of all gastric cancers have now been resected by endoscopic treatment in Japan. In endoscopic diagnosis, magnifying endoscopic observation using band-limited light such as narrow band imaging (NBI) has significantly improved the accuracy of gastric cancer diagnosis. At the same time, in endoscopic treatment, endoscopic submucosal dissection (ESD) has contributed to increase explosively the number of gastric cancers which could be resected endoscopically. In recent years, the construction of algorism for endoscopic diagnosis and treatment with a view to personalized medicine has been explored, and endoscopic use for gastric cancer managements is expected to develop further in the future. Keywords (5 words):

To enhance the overall reliability of the power system, engineers have redirected their focus toward health monitoring and early detection of faults in transformers. Among these faults, transformer core defects demand particular attention. While fault simulation using software has traditionally been the preferred approach, these methods suffer from data inaccuracies in real-world conditions. Consequently, conducting actual experimental setups with induced faults is imperative to investigate core issues. This study uses Hilbert Transform (HT) as a signal processing technique to extract crucial data characteristics, thereby enhancing the performance of the classifier model. The research involves analyzing electric current signals from a single-phase 1kVA transformer. A comparative assessment of our proposed model was conducted using raw data and Fast Fourier Transform (FFT), evaluating accuracy, precision, recall, F1-score, and computational time. The results demonstrate a significant improvement in all metrics for the classifier models, particularly the k-nearest neighbor (KNN) algorithm, which exhibited values of 83.89%, 84.39%, 83.89%, 83.79%, and 0.0156 seconds, respectively. Future work aims to extend this analysis under different loading conditions.

Glaucoma poses a serious public health concern as it is the second leading cause of blindness after cataracts, while also being the leading cause of irreversible blindness worldwide. Glaucoma is more prevalent among Black, Latino, and Asian populations and it is essential to examine both racial and ethnic disparities that exist in treatment and prevention. As the number of people with glaucoma continues to grow, it is important to review aspects of this condition in order to better care for those affected. This article will review significant forms of glaucoma and what differentiates them in a diagnosis, treatment options, and recommendations on prevention and improving awareness

Malaria is rare in Europe, but it is a medical emergency and programs for its control should ensure access to early diagnosis and prompt treatment within 24-48 h of the symptom’s onset. The consistent number of imported malaria cases and the risk of reappearance of autochthonous cases stimulated laboratories in non-endemic countries to evaluate diagnostic methods/algorithms: microscopy remains the gold standard, but with limitations. RDTs have greatly expanded the ability to diagnose malaria for rapid results due to simplicity and low cost, but they lack sensitivity and specificity. PCR-based assays provide more relevant information but need well-trained technicians. According to WHO global Technical Strategy for Malaria 2016-2030, the future direction for a rapid diagnosis is the development of Point-Of-Care Testing, which would be helpful for prompt/accurate treatment and for preventing the diseases spread. Despite the limitations of current diagnostic methods, they play important roles in dealing with the global malaria situation, including decreasing its incidence. Recently evidence suggested that Artificial Intelligence could be utilized for assisting pathologists in malaria diagnosis. Future studies need to be conducted to see whether this system can be used for routine malaria diagnostic procedures.

Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop, assessing seed viability of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible-Near Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500-1030 nm as a non-destructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50°C over 24-hour intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky-Golay (SG) and Multiplicative Scatter Correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of meta-heuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), Genetics (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 seconds) and LCA (44.9389 seconds) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected by the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), Multilayer Perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and NaiveBayes (NB) classifiers. The DSOS-DT and FOA-MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS-LR and DSOS-KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and non-destructive approach for detecting seed viability.

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice, affecting millions of individuals worldwide. The management of AF presents significant challenges due to its complex nature and diverse patient profiles. In recent years, the integration of artificial intelligence (AI) and machine learning technologies has emerged as a promising approach to enhance various aspects of AF management. This comprehensive review aims to explore the evolving role of AI in AF management, with a focus on its potential applications in diagnosis, risk prediction, treatment selection, and patient monitoring. Real academic references were employed to provide evidence-based insights into the impact of AI on AF management.

Cancer is the second major cause of disease-related dead worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient’s life. Cancer, as a complex and heterogeneous disorder, results from disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, histopathology of biopsy, and imaging (MRI, CT, PET, US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient’s response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient’s big data and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical-clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research.

Leukemia cutis (LC) is defined as leukemic infiltration of the epidermis, the dermis, and the subcutaneous tissue. Leukemia cutis may follow or occur simultaneously with the diagnosis of systemic leukemia. However, cutaneous lesions are occasionally diagnosed as the primary manifestation of leukemia. Leukemic skin infiltrations demonstrate considerable variation regarding number of changes, distribution, and morphology. The highest incidence of LC is observed in chronic lymphocytic leukemia, monocytic and myelomonocytic acute myeloid leukemia, and in T-cell lineage leukemia. Although the pathogenic mechanism of the invasion of leukemic cells into the skin is not well understood, chemokine receptors and adhesion molecules as well as genetic characteristics of leukemia are thought to play a role. Leukemic skin lesions may be localized or disseminated and may occur alone or in combination on any site of the skin, most frequently in the trunk and extremities. The most common clinical presentations of leukemia cutis are papules, nodules, macules, plaques, and ulcers. In most patients, complete or partial resolution of cutaneous infiltrations occurs simultaneously with hematologic remission. However, in patients with resistant disease or recurrent skin infiltration, local radiotherapy can be used. This review presents recent data on the pathogenesis, diagnosis and treatment of leukemic skin involvement in different types of leukemia

Scrub typhus (ST) is one of the most neglected tropical diseases, a leading cause of acute undifferentiated febrile illness in areas of the ‘tsutsugamushi triangle’, diagnosed frequently in South Asian countries. The bacteria Orientia tsutsugamushi is the causative agent of the disease, which enters to human body through the bite of trombiculid mite (Chigger) of the genus Leptotrombidium deliense.The diagnosis of the disease becomes challenging as its early symptoms mimic other febrile illnesses like dengue, influenza, and corona viruses. Lack of rapid, reliable, and cost-effective diagnostic methods further complicates the identification process. Northeast India, a mountainous region with a predominantly rural tribal population, has witnessed a resurgence of scrub typhus cases in recent years. Various ecological factors, including rodent population, habitat characteristics, and climatic conditions, influence its prevalence. Entomological investigations have confirmed the abundance of vector mites, highlighting the importance of understanding their distribution and the probability of transmission of scrub typhus in the region. Proper diagnosis, awareness campaigns, and behavioral interventions are essential for controlling scrub typhus outbreaks and reducing its impact on public health in Northeast India. Further research and community-based studies are necessary to accurately assess the disease burden and implement effective prevention strategies.

Background: Malnutrition in oesophageal and pharyngeal cancer patients constitutes a common and serious concern, which significantly reduces patients’ prognosis. Cancers of the oesophagus and the pharynx can considerably impair feeding in patients, resulting in severe undernutrition. This is a scoping review which aims to critically summarize and scrutinize the current clinical evidence on the use and prognostic role of diverse nutritional assessment tools on the prognosis of patients with oesophageal and pharyngeal cancers. Methods: PubMed, Scopus, Web of Science and Google Scholar databases were comprehensively explored utilizing relative keywords to detect clinical studies that investigate whether nutritional status may affect disease prognosis. Results: Several assessment tools have evaluated and highlighted the prognostic role of nutritional status on patients’ survival and disease complications in both oesophageal and pharyngeal cancers. Regarding oesophageal cancer, CONUT, PNI, PG-SGA and NRS-2002 are more commonly used, while albumin is also frequently evaluated. Regarding pharyngeal cancers, fewer studies are currently available. PNI has been evaluated and its significance as a factor for shorter overall survival, distant metastasis-free survival, progression-free survival, and locoregional recurrence-free has been highlighted. Comprehensive Nutritional Index has also been evaluated with positive results, as well as NRS 2002, GPS, and body weight status. However, there is currently a lack of studies with adequate female patients. There is also a significant lack of well-designed prospective studies with well-organized methodology. Conclusion: Nutritional status may significantly affect disease progression and patients’ survival, highlighting the importance of a good nutritional status in patients with oesophageal and pharyngeal cancers. Further large-scaled and well-designed prospective clinical studies are strongly recommended to verify the potential beneficial effects of nutritional status in patients with oesophageal and pharyngeal cancers.

Mild TBI (mTBI) accounts for most TBI cases, the leading cause of morbidity and mortality worldwide. Despite their high incidence, mTBI pathophysiology remains largely unknown. Recent studies have shown that the inflammatory response is activated early after mTBI and can persist for several weeks or months. However, limited evidence on the utility of inflammatory biomarkers as predictors of clinical outcomes in mTBI was previously provided. Thus, this study aims to provide an overview of the current knowledge on the role of inflammation in the pathogenesis of mTBI and the potential of some inflammatory biomolecules as biomarkers of mTBI. Thus, it was shown that the increase of IL-6, TNF-α, and IL-1β plasma levels could be implicated in the development of the early post-concussion symptoms. On the other hand, the persistence of the increased plasmatic concentrations of IL-10 and IL-8 for as long as six months following the brain injury event could suggest chronic inflammation leading to neuroinflammation and late or persistent symptoms. In this context, our findings showed that the inflammatory biomarkers could be relevant in diagnosing or predicting recovery or long-term outcomes of mTBI.

Background: Sensor usage in the classical scientific process has allowed new experimentation in medicine. We report on the design process of a MEMS sensor being used for muscle trauma evaluation in diagnosing acute compartment syndrome (ACS), a medical ailment costing society billions of dollars per year. Objectives: Modeling the disease with scientific process allows a more complete under-standing of the disease. The goal was to formulate a hypothesis that could be tested to aid in making the diagnosis of ACS accurately. Methods: Scientific process was observed throughout the disease modelling process. Background information was improved and clarified, new pre-clinical models were de-signed and verified, a hypothesis built on pressure measurement with MEMS sensors was carried out, and the testing of the model as verified against previous clinical data was accomplished. Results: Scientific process resulted in hypothesis generation around the relationship of intracompartmental pressure measurement and the disease process and therapy. This resulted in new understanding of ACS, accurate modelling, and sensor design resulting in a MEMS device that has an extremely high sensitivity and specificity (over 99%) in treating and diagnosing the disease. Conclusion: MEMS sensor technology defines the new gold standard of implanting a sensor in a muscle compartment that allows accurate diagnosis of ACS with continuous trends in pressure.