Prostate cancer is the second leading cause of death in males in America, with advanced prostate cancers exhibiting a 5-year survival rate of only 32%. Castration-resistance is often developed during the course of treatment, though its influences require further understanding. This study explores the human microbiome for its implications in castration-resistance and metastasis in prostate cancer. RNA sequencing data was downloaded for bone and soft tissue biopsies of patients with metastatic castration-resistant prostate cancer. These sequences were mapped to bacterial sequences to yield species-level abundance approximations. Numerous species were found to correlate to the expression of known markers of castration-resistance, including AR, PI3K, and AKT. Castration-resistance-associated signaling pathways were also enriched with these species, including PI3K-AKT signaling and endocrine resistance. For their implications in cancer aggression and metastasis, cancer stem cell markers were further explored for relation to these species. EGFR and SLC3A2 were widely downregulated with greater abundance of most species. Our results suggest that the microbiome is heavily associated with castration-resistance and stemness in prostate cancer. By considering the microbiome’s importance in these factors, we may better understand the highly aggressive and highly invasive nature of prostate cancer, allowing for needed improvement in the treatment of this disease.

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.

Lynch syndrome (LS) is an inherited cancer predisposition disorder associated with an elevated risk of various epithelial cancers. Despite sharing the same pathogenic variant (PV), Lynch syn-drome variant heterozygotes (LSVH) exhibit considerable phenotypic variability in cancer risk. The role of Human Leukocyte Antigen (HLA) in modifying cancer risk prompted our investiga-tion into whether HLA variations act as genetic modifiers influencing age at cancer diagnosis in a unique cohort of LSVH carrying a PV in the hMLH1 gene in South Africa. Within our extensive LS cohort, 426 individuals carried the same hMLH1 PV (MLH1:c.1528C>T). We selected 100 indi-viduals with the greatest diversity in age at cancer diagnosis and the oldest unaffected individu-als for high-throughput HLA genotyping of 12 HLA class I and II loci using next-generation se-quencing. Statistical analyses employed Kaplan-Meier survival analyses with Logrank tests and Cox proportional hazards. Following the robust application of statistical correction methods, six HLA alleles (3.2%) were significantly associated with a young age at cancer diagnosis. Notably, HLA-B*15:17 and HLA-DPB1*55:01 correlated significantly with very young colorectal cancer (CRC) diagnosis (Mean age: 21y [17-25]; HR = 71.59; q

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R², MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection which detects the patient's exhaled VOCs combined with sensor arrays of Convolutional Neural Network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, due to low accuracy, high complexity computation and large number of parameters make the CNN algorithms difficult transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models such as EfficientNet, LTNet has fewer parameters (32K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than that of the five existing models’, and it also offers the shortest training (844.38s and 584.67s) and inference time (23s and 14s) in the same validation set. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.

Biliary tract cancers, including intrahepatic, perihilar, and distal cholangiocarcinomas, as well as gallbladder cancer, are a heterogeneous group of cancers with distinct molecular characteristics in each anatomic and pathological subtype. The pathological classification of BTC includes grossly classifiable growth patterns, such as mass-forming, periductal infiltrating, and intraductal growing. The small duct and large duct types of intrahepatic cholangiocarcinoma have been recently introduced in the WHO classification. Typical clinical presentations and various radiological, endoscopic, and molecular modalities for diagnosis are described in detail. To overcome the limitations of traditional tissue acquisition, new diagnostic modalities are being investigated. In fact, the treatment landscape is evolving rapidly owing to the emergence of distinct subgroups harboring unique molecular alterations with corresponding targeted therapies. In addition, we highlight the key points in the diagnostic process in real-world clinical practice for biliary tract cancer.

Blood cancers, including leukemia, lymphoma, and myeloma, pose significant diagnostic challenges due to their diverse and complex nature. Detecting these cancers early and accurately is crucial for timely intervention and improved patient outcomes. This review explores current and emerging detection methods for human blood cancers, focusing on their principles, advantages, limitations, and potential clinical applications.

In the era of nanotechnology, researchers are implementing point to care service to cancer patients to detect malignancy beforehand and to reduce the mortality rate of cancers. Cancer is known to be the most fatal disease among all other diseases and the survivability from cancer is quite impossible if the stage of the cancer is an advanced level. Though the early detection of cancer can increase the chances of survival with a double fold. Biosensor is a part of nanotechnology which is capable to provide point to care service in the field of medicine. With the rising number of cancer occurrences being identified around the world and the increasing number of deaths because of the identification of advanced cancer, biosensors can play a significant part in the early detection of cancer. New molecular methods, including as genomic and proteomic approaches, are increasingly being used to study patient molecular profiles. When such diagnosis method is paired with bioinformatics tools, they generate new data that can be used to discover new disease biomarkers. Finding precise and sensitive indicators that are corelated to a specific disease, as with many other diseases, can be challenging. Furthermore, the concentration of biomarkers in biological fluids varies according to illness states and phases. Peptides, proteins, up or down regulated expression of gene markers, and gene alternation are all examples of molecular markers that are commonly used to diagnose cancer. In this article, we have highlighted six different deadliest cancers such as Ovarian, Breast, Prostrate, Lung, Colorectal and Liver cancer. The article contains distinct types of biomarkers which are normally found in these kinds of cancer and generally used as a potential diagnostic target in the medicine field. The article mainly summarized the application of different types of biosensors devices in the detection of the mostly found biomarkers in the above cancer types.

Cancer risk is known to increase tremendously when the immune system is suppressed, e.g., as observed in young organ-transplant recipients and AIDS patients. Based on such data, it may be hypothesized that the main reason for the development of clinical cancer is the weakening or suppression of the immune system, and that uncontrolled multiplication of cancer cells occurs when some aspects of the immune system fall below certain critical levels. Therefore, cancer may be prevented and treated by boosting these critical aspects of the immune system so that they are maintained above the critical levels. If multiple immune system boosting interventions are utilized, more aspects of the immune system would be boosted, increasing the likelihood of enhancing the critical aspects of the immune system and generating a cancer preventive and/or therapeutic effect. Clinical trials are needed to validate this approach for cancer prevention and treatment. If validated, the proposed approach could result in a major reduction of the death and suffering caused by cancer in the world.

We developed a novel machine learning algorithm to augment clinical diagnosis of prostate cancer utilizing first and second-order texture analysis metrics and novel application of machine learning radiomics analysis. We successfully discriminated between significant prostate cancers versus non-tumor regions and provided accurate prediction between Gleason score cohorts with statistical sensitivity of greater than 0.91 sensitivity across three separate cohorts. Tumor heterogeneity and prediction between GS cohorts were quantified using two feature selection approaches and two classifiers with tuned hyperparameters (including grid search and randomized search). There was a total of 71 patients analyzed in this study. Multiparametric MRI, incorporating T2WI and the ADC maps, were used to derive radiomics features. Recursive feature elimination (RFE), the least absolute shrinkage and selection operator (LASSO), and two classification approaches, including a support vector machine (SVM) (with grid search) and random forest (RF) (with randomized search), were utilized to differentiate between non-tumor regions versus significant cancer and prediction among Gleason score groups. In T2WI images, the RFE feature selection approach combined with RF and SVM classifiers outperformed LASSO with SVM and RF classifiers regarding performance. The detection precision of the radiomics framework depending on multiple imaging modalities was lower than the diagnostic accuracy of single imaging modality model with every machine learning approach assessed. The best performance was achieved by combining LASSO and SVM into a model that used both T2WI and ADC. This model had an area under the curve (AUC) of 0.91. Radiomic features computed from ADC and T2WI images were used to predict three groups of Gleason score using two kinds of feature selection methods (RFE and LASSO), RF and SVM classifier models with tuned hyperparameters. Using combined sequences (T2WI and ADC map images) and combined radiomics (1st and GLCM features), LASSO, with a feature selection method with RF, was able to predict G3 with the highest sensitivity at a level AUC of 0.92. To predict G3 for single sequence (T2WI images) using GLCM features, LASSO with SVM achieved the highest sensitivity with an AUC of 0.92.

When the skin is afflicted by disease such as cancer, these essential functions are disrupted, putting the entire person at risk. While the American Cancer Society lists 6 major cancer types, the National Institute of Arthritis and Musculoskeletal and Skin Diseases identifies 13 significant benign skin disorders, reflecting the diversity of skin conditions in dermatology. This topical review aims to provide an overview of the pathophysiology of these major skin cancers and disorders and summarize conventional diagnostic methods and current treatment approaches.

Elevated resting heart rate is frequently observed in cancer patients, and is associated with increased mortality. Although specific chemotherapeutic agents can induce cardiotoxicity, the presence of sinus tachycardia in chemotherapy-naive patients suggests other factors likely contribute to this clinical presentation. Despite its prevalence, cancer-associated resting sinus tachycardia has not been fully recognized and comprehensively described as a separate clinical entity. Secondary effects of cancer, especially structural cardiac changes, secretory factors (inflammatory cytokines), and thromboembolic disease can cause resting tachycardia. Alternatively, rapid heart rate may reflect compensatory mechanisms responding to increased metabolic demands, raised cardiac output states, and even pain. Hence, cancer-associated tachycardia presents a clinical dilemma; acute life-threatening conditions (such as sepsis, pulmonary embolism, etc.) must be ruled out, but cancer itself can explain resting sinus tachycardia and more conservative management can avoid unnecessary testing, cost and patient stress. Furthermore, identification and management of cardiac conditions associated with cancer may improve survival and the quality of life of cancer patients.

: Image guided superficial radiation therapy (IGSRT) combines superficial radiation therapy (SRT) with full dermal visualization (FDV) via high resolution dermal ultrasound (HRDUS) for the treatment of non-melanoma skin cancer (NMSC). The gold standard for IGSRT delivery includes a comprehensive cancer care model with support for the dermatologists by a multidisciplinary team. When delivered in this model, IGSRT can achieve cure rates of 99% for treatment of NMSC. This paper focuses on the benefits of IGSRT during therapy. Medical records from 7 dermatology clinics of 883 patients with 1,507 cases of NMSC treated with IGSRT be-tween 2017 and 2018 were retrospectively reviewed. 92% of the NMSC lesions showed daily depth fluctuations, 60.32% of lesions did not require changes during therapy, and nearly 40% of lesions required at least one compensatory change during therapy. 83% of NMSC lesions were labeled high-risk based on the 2024 NCCN guidelines. Increasing and decreasing tumor depth measurements during IGSRT inform dermatologists when adaptive changes in energy (kV), TDF and dose will result in more efficacy and less toxicity, respectively.

Photoacoustic imaging (PAI) is a cutting-edge biomedical imaging modality, providing detailed anatomical and functional information about the area beneath the skin surface. Its light energy deposition is such that PAI typically provides clear images of the skin with high signal-to-noise ratios. Specifically, the rich optical contrast of PAI allows biological information related to lesion growth, malignancy, treatment response, and prognosis to be seen. Given its significant advantages and emerging role in imaging skin lesions, we summarize and comment on representative studies of skin PAI, such as the guidance of skin cancer biopsies and surgical excisions, and the accurate diagnosis of psoriasis. We conclude with our insights about the clinical significance of skin PAI, showing how its use to identify biological characteristics in lesion microenvironments allows early diagnosis and prognosis of disease.

Molecular imaging modalities are used for different type of cancers detection and diagnosis. In recent few years, there has been an increased focus on developing novel nanoparticles as new imaging contrast agents for early detection of cancer. The aim of this review article is to summarize molecular imaging technologies accompanying with using nanoparticles to improve potential imaging for cancer detection and hence valuable therapy in the future. Nanoprobes are rapidly becoming potentially transformative tools on cancer diagnostics for a wide range of imaging modalities such as CT, MRI, SPECT, PET, Ultrasound and Optical imaging. The study results seen in the recent literature are provided and discussed the diagnostic performance of imaging modalities for cancer diagnosis and their future directions. With knowledge of the correlation between the application of nanoparticles and molecular imaging modalities and with the development of targeted contrast agents or nanoprobes, they may provide better cancer diagnosis in the future.

Alternative splicing can produce transcripts that affect cancer development and shows potential for cancer diagnosis and treatment. However, intron retention (IR), a type of alternative splicing, has been less systematically studied in cancer biology research. Here, we generated a pan-cancer IR landscape for more than 10,000 samples across 33 cancer types from The Cancer Genome Atlas (TCGA). We characterized differentially retained introns between tumor and normal samples and identified retained introns associated with survival. We discovered 988 differentially retained introns in 14 cancers, some of which demonstrated diagnostic potential in multiple cancer types. We also inferred a large number of prognosis-related introns in 33 cancer types, and the associated genes included well-known cancer hallmarks such as angiogenesis, metastasis, and DNA mutations. Notably, we discovered a novel intron retention event inside 5′UTR of STN1 that is associated with the survival of lung cancer patients. The retained intron reduces translation efficiency by producing upstream open reading frames (uORFs) and thereby inhibits colony formation and cell migration of lung cancer cells. Besides, the IR-based prognostic model achieved good stratification on certain cancers, as illustrated in acute myeloid leukemia. Taken together, we performed a comprehensive IR survey at a pan-cancer level, and the results implied that IR has the potential to be diagnostic and prognostic cancer biomarkers, as well as new drug targets.

Breast cancer is the foremost common in women, and its early diagnosis will help treat and increase patients' survival. This review article aims to studies on recent findings of standard imaging techniques and their characteristics for breast cancer diagnosis and recent nanoparticles (NPs) which are used for breast cancer detection. Herein, The search was performed in the literature through scientific citation websites, including Google Scholar, PubMed, Scopus, and Web of Science until May 2023. A comprehensive study on different imaging modalities and NPs for breast cancer diagnosis is reviewed, and their successes, challenges, and limitations are discussed.

Lung cancer is the leading cause of cancer death worldwide therefore studies about the etiological factors and carcinogenesis are important.The aim of the study is to detect the presence and types of Human papilloma virus (HPV) in lung cancer and non-tumoral lung and Epidermal Growth Factor Receptor (EGFR) mutation prevalance in lung cancer with Polymerase Chain Reaction (PCR) method.Fifty patients who were histopathologically diagnosed lung cancer and 7 patients with non-neoplastic lung disease were enrolled in the study. HPV positivity was detected in 75.4% of the patients. The rate of high-risk HPV positivity was 100% in adenocarcinomas, 64.5% in squamous cell carcinomas, and 57% in non-neoplastic lung. HPV 51 was the most frequent type followed by HPV 16 (43.9%). There was no statistically significant relationship between HPV infection and age, gender and smoking history and EGFR mutation. The prevalence of HPV positivity is variable in lung cancer in different studies and this may be due to many factors including HPV detection methods, number of patients, geographic regions. New studies about the role of HPV infection need to be performed in order to obtain information about the use of prophylactic and theurepatic agents in lung cancer.

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.

Iron (Fe) is a trace element that plays essential roles in various biological processes such as DNA synthesis and repair, as well as cellular energy production, or oxygen transport, and it is currently widely recognized that iron homeostasis is dysregulated in many cancers. Indeed, several iron homeostasis proteins may be responsible for malignant tumor initiation, proliferation, and for metastatic spread of tumors. A large number of studies demonstrated the potential clinical value of turning these deregulated proteins as prognostic and/or predictive biomarkers of malignancy and /or response to anticancer treatments. Additionally, the iron addiction of cancer cells and the importance of iron in ferroptosis cell death signaling pathways prompted the development of therapeutic strategies against advanced stage or resistant cancers. In this review, we selected relevant and promising studies in the field of iron metabolism in cancer research and clinical oncology. Besides, we discuss some co-existing discrepant findings. We will also present and discuss the latest lines of research related to targeting iron, or its regulatory pathways, as potential promising anti-cancer strategies for human therapy. Iron chelators, such as deferoxamine or iron-oxide based nanoparticles, which are already tested in clinical trials, alone or in combination with chemotherapy will also be reported.

There are numerous challenges involved in the diagnosis and treatment of lung cancer. Globally, majority of people suffers from cancerous disease involving throat cancer, lung cancer, stomach cancer, cancerous brain tumor. Among these the most common ones is the lung cancer or lung carcinoma. The leading cause of the lung cancer is the smoking. Around 80% to 90% of deaths are caused due to Non-small cell lung cancer (NSCLC). The inadequate diagnostic techniques and low chances for the survival of lung cancer patients results from the lack of an early prognosis and incompetency in traditional therapies. However, such challenges involved in the prognosis and treatment of lung cancer are on decline with the progression in magnetic nanoparticle (MNP) technology. Many break-through discoveries and inventions have been made in the field of cancer therapy by using magnetic nanoparticles. The implication of nanotechnology has led to the recent advancement in nanomedicine field. This has encouraged the improvement in different therapeutic and diagnosis strategies employing nanotechnology. The generation of immense technological benefits for nanoparticles systems has been accredited to its remarkable nanoscale physico-chemical properties. This in turns provides the early detection of lung cancer and active drugs delivery for an improved theranostics strategy. The present review provides a general idea of the current progression in the therapeutic and prognosis purpose of magnetic nanoparticles. Further, we disclose the development in the lung cancer theranostics by functionalization of magnetic nanoparticles. The established importance of magnetic nanoparticles in the theranostics centers for lung cancer has been revealed in this paper. The challenges existing in the theranostics of lung cancer are addressed through the functioning of magnetic nanoparticles in the process.

Background: Esophageal cancer was hardly diagnosed in early stage, and more potential biomarkers should be found. Methods: 252 patients and normal controls which recruited in Renmin Hospital of Wuhan University, were divided into esophageal carcinoma group (105 cases), disease control group (75 cases) and the control group of healthy people (72 cases). Moreover, TISIDB and GEPIA databases were used to investigate the different expression of EC and normal tissues, and explore the roles of C1q in tumor-immune system interactions in EC. Results: The concentration of serum C1q in EC group is 196.8(180~219.4) mg/L, which is higher than the level of DC [178.10(153.70~200.85) mg/L]and HC [183.00(167.75~201.00) mg/L] (P<0.05). A higher expression level of C1q was observed in Ⅲ and Ⅳ grades [214(192~237.3) mg/ml] than grades Ⅰ and Ⅱ [180.95(172.03~193.85) mg/L] (P<0.05). C1q was positively correlated with eosinophils, active CD8 T cells, myeloid derived suppressor cells, natural killer cells, monocytes and macrophages (r = 0.373; r = 0.659; r = 0.846; r = 0.760; r = 0.499; r = 0.757; P<0.05). Conclusion: The concentrations of C1q increased in EC and related to the severity of EC, which had potential value of diagnosis of EC. There were correlations in C1q and tumor-infiltrating lymphocytes.

Endometrial cancer (EC) is the most frequent gynaecological malignancy. The ESGO/ESTRO/ESP 2020 guidelines identify prognostic groups based on morpho-molecular characteristics. This study aims to evaluate the clinical applicability of NGS analysis to define an appropriate risk class and application for a better diagnostic and prognostic stratification of ECs. Cases of serous carcinoma (OHEC), high (HGEC) and low (LGEC) grade endometrioid carcinoma diagnosed with the morphological and immunohistochemical (IHC) protocols were considered. After a standardized pre-analytical phase, the tumor DNA was semi-automatically extracted and analyzed by NGS with a panel of 14 genes. A total of 63 cases were considered. NGS analysis was successful in 60 cases; all of these were classified according to the new diagnostic algorithm. The molecular risk classification showed a good correlation with the morphological (k=0.8). The study showed that the protocols of the pre-analytical and analytical phases used are robust and can lead to molecular results that fall within the standards required for use in clinical practice for a more precise diagnostic-therapeutic management of patients. The implementation of the classification is particularly relevant for better prognostic stratification of HGECs. In addition, the identification of a suspicious VUS in POLE questions the classification of truncating variants.

Improvements in the treatment of childhood cancer have considerably enhanced survival rates over the last decades, to over 80% as per today. However, this great achievement has been accompanied by the occurrence of several early and long-term treatment-related complications major of which is the cardiotoxicity. The chemotherapeutic agents that have been implicated as a cause of cardiotoxicity, are mostly anthracyclines and mitoxantrone, alkylating agents, proteasome inhibitors and to a lesser extent anti-microtubule agents, cisplatin, monoclonal antibodies, small molecular tyrosine kinase inhibitors, and nucleotide synthesis inhibitors. Routine diagnosis and monitoring of cardiotoxicity rely on electrocardiography and echocardiography. For the early detection of cardiotoxicity, in recent years, major studies have been conducted using biomarkers such as troponin, N-terminal pro b-natriuretic peptide, etc. Despite refinements in diagnostics still severe limitations exist, due to the increase of the above-mentioned biomarkers, only after significant cardiac damage has occurred. Lately, research has been expanded by introducing new technologies and finding new markers by omics approach. These new markers could be used not only for early detection, but also for early prevention of cardiotoxicity. The omics science, which includes genomics, transcriptomics, proteomics, and metabolomics, offers new opportunities for biomarker discovery in cardiotoxicity and may provide understanding of mechanisms of cardiotoxicity, beyond traditional technologies. This article reviews the contemporary definition of cardiotoxicity, older and newer chemotherapeutic agents mainly involved in cardiotoxicity and the methods of early and preventive diagnosis, using omics technology.

Breast cancer is the most common cause of mortality due to cancer for woman both in Lithuania and worldwide. Chances of survival after diagnosis differ significantly depending on the stage of disease at the time of diagnosis and other factors. One way to estimate survival is to construct a Kaplan-Meier estimate for each factor value separately. However, in cases when it is impossible to observe a large number of patients (for example, in case of countries with lower numbers of inhabitants) dividing data into subsets, say, by stage at diagnosis may lead to results where some subsets contain too little data so that results of Kaplan Meier (or any other) method will become statistically incredible. The problem may become even more acute if the researcher would like to use more risk factors, such as stage at diagnosis, sex, place of living, treatment method, etc. Alternatively, Cox models are used to analyse survival data with covariates, and they don’t require dividing the data into subsets according to chosen risks factors (hazards).

Polymeric based nanoparticles are garnering significant interest for their potential in drug delivery. Specially, nanopolymers that have been functionalized with molecules, such as proteins or peptides, are a versatile vehicle for drug delivery. Curcumin (Cur) is one of the most commonly studied anticancer compounds and is widely acknowledged as having positive effects on human health. However, its insolubility and low bio-distribution greatly hinder the exploitation of its beneficial traits. In this study, our team created a nano delivery system which utilized a nanopolymer called PCL-PEG (poly(ε-caprolactone)-poly (ethylene glycol)). This system was functionalized with A6 peptide to enable targeted delivery of Cur. The system was designed with advantageous nanoparticles (NPs), including a stable zeta potential (–32.7), small size (54.3), uniform surface morphology, and high hydrophilicity (13.72°). In addition, targeted delivery systems exhibited high encapsulation efficacy (93 ± 0.89 %), and drug loading (16.7 ± 0.9 %), and were characterized by a slow and gradual release profile with a release of approximately 83.13 ± 0.12%. The MTT assay results showed that the formulation known as Cur-NPs-A6 caused a significant increase in cell death in MDA-MB-231 cancer cells (IC50 = 23.31 ± 0.85 µM). The designed delivery system did not cause any harm to the noncancerous MCF-10A cells (normal group). Furthermore, the results of both the RT-qPCR and invasion assays demonstrated that the designed system was able to effectively activate the apoptosis pathway and inhibit cell invasion. Our findings suggest that the use of the designed smart delivery system, which is functionalized with A6 peptide that has a high affinity for CD44 receptor - known to be upregulated in numerous malignant conditions, could be a highly efficient approach to treating cancer.

Differential diagnosis of solid pancreatic lesions (SPLs) using B-mode endoscopic ultrasonography (EUS) is challenging. Detective flow imaging (DFI) offers the potential for detecting low-flow vessels in the pancreas, thus enhancing diagnostic accuracy. This study aimed to investigate DFI-EUS findings of SPLs and analyze their differential diagnostic accuracy for pancreatic cancer. This retrospective study included patients with pathologically confirmed SPLs who underwent EUS between April 2021 and June 2023. Expert endosonographers, blinded to the patients’ clinical data, evaluated images obtained through B-mode, eFLOW, and DFI-EUS. Frame rate and vessel detection sensitivity between eFLOW and DFI were compared, and diagnostic criteria for pancreatic cancer were established. This study included 104 patients, most diagnosed with pancreatic cancer. DFI-EUS demonstrated a significantly higher vessel visualization rate (96%) in SPLs compared to eFLOW (27%). Additionally, DFI showed a superior frame rate, sensitivity (99%), and accuracy (88%) in detecting pancreatic cancer, although with a modest specificity (43%). On DFI-EUS, characteristics such as hypovascular, peritumoral, or spotty vessel findings were suggestive of pancreatic cancer. DFI-EUS significantly improved the visualization of vascular structures within the SPLs, highlighting its efficacy as a diagnostic modality for pancreatic cancer.

Gastric cancer is the 5th most common disease in the world and the 4th most common cause of death. It is diagnosed through esophagogastroduodenoscopy with biopsy, however there are limitations in finding lesions in the early stages. Recently, research has been actively conducted to use liquid biopsy to diagnose various cancers, including gastric cancer. Various substances derived from cancer are reflected in the blood. By analyzing these substances, it was expected that not only the presence or absence of cancer but also the type of cancer can be diagnosed. However, the amount of these substances is extremely small, and even these have various variables depending on the characteristics of the individual or the characteristics of the cancer. To overcome these, we collected methylated DNA fragments using MeDIP and compared them with normal plasma to characterize gastric cancer tissue or patient’s plasma. And we attempted to diagnose gastric cancer using the characteristics of cancer reflected in the blood through the cancer tissue and patient’s plasma. As a result, we confirmed that the consistency of common methylated fragments between tissue and plasma was approximately 41.2%, and found the possibility of diagnosing and characterizing cancer using the characteristics of the fragments through SFR and 5'end-motif analysis.

Bladder cancer (BC) possesses distinct molecular profiles that influence progression depending on its biological nature and delivered treatment intensity. Muscle-invasive BC (MIBC) and non-MIBC (NMIBC) demonstrate great intrinsic heterogeneity regarding different prognoses, survival, progression, and treatment outcomes. Transurethral resection of bladder tumor (TURBT) is the standard of care in treating NMIBC and serves both diagnostic and therapeutic purposes, despite the prevalent recurrence and progression among many patients. In particular, flat urothelial car-cinoma in situ and urothelial carcinoma with lamina propria invasion are the major precursors of MIBC. A new‐generation photosensitizer, 5‐Aminolevulinic acid (5-ALA), demonstrates high tumor specificity by illuminating the tumor lesion with a specific wavelength of light to produce fluorescence, and has been studied for photodynamic diagnosis to detect precise tumor areas by TURBT. Additionally, it has been applied for treatment by producing its cytotoxic reactive oxygen species, as well as screening for urological carcinomas by excreting porphyrin in the blood and urine. Moreover, 5-ALA may contribute to screening before and after TURBT in NMIBC. Here, we summarize the updated evidence and ongoing research on photodynamic technology for NMIBC, providing insight into the potential for improving patient outcomes.

High hopes were associated with the new anti-angiogenic medicament, bevacizumab, which due to the possibility of binding to isoform A of vascular endothelial growth factor (VEGF), inhibited the creation of new blood vessels. However, not only blood vessels are responsible for the tumor cells' spread. During the process of tumor growth, lymphangiogenesis is mediated by other isoforms of the VEGF family, specifically VEGF-C and VEGF-D, which are independent of the bevacizumab action. Therefore, based on the mechanism of bevacizumab action and the processes of angio- and lymphangiogenesis, we formed three hypotheses: 1) if the lymph nodes in the primary ovarian cancers are metastatic, the outcome of bevacizumab treatment is worsened; 2) concerning the second-line treatment, bevacizumab will act in a weakened manner if the recurrence occurs in lymph nodes compared to local recurrences; 3) patients treated by bevacizumab are more likely to have recurrences in lymph nodes. These hypotheses raise the issue of the existing gap of knowledge, which concerns the effect of bevacizumab on the metastatic lymph nodes.

The innate immune system is the first line of defense against pathogens. It’s composition includes barriers, mucus and other substances as well as phagocytic and other cells. The purpose of the paper is to analyze in general grounds the immune system and the body immunity to cancer. Simple ideas and the qualitative theory of differential equations are used along with general principles such as the minimization of the pathogen load and economy of resources. In the simplest linear model, the annihilation rate of pathogens in any tissue should be greater than the pathogen’s average rate of growth. When nonlinearities are added, a reference value for the number of pathogens is set, and a stability condition emerges, which relates strength of regular threats, barrier height and annihilation rate. On the other hand, in cancer immunity, the linear model leads to an expression for the lifetime risk, which accounts for both the effects of carcinogens (endogenous or external) and the immune response. The stability condition allows a comparison of immunity in different tissues. The way the tissue responds to an infection shows a correlation with the way it responds to cancer. These statements are formulated at the qualitative level, but may and deserve to be quantitatively checked.

Abstract: The ability to detect several types of cancer using a non-invasive, blood-based test holds the potential to revolutionize oncology screening. We mined tumor methylation array data from the Cancer Genome Atlas (TCGA) covering 14 cancer types and identified two novel, broadly oc-curring methylation markers at TLX1 and GALR1. To evaluate their performance as a general-ized blood-based screening approach, along with our previously reported methylation bi-omarker, ZNF154, we rigorously assessed each marker individually or combined. Utilizing the TCGA methylation data and applying logistic regression models within each individual cancer type, we found that the three-marker combination significantly increased the average area under the ROC curve (AUC) across the 14 tumor types compared to single markers (p= 1.158e-10; Friedman test). Furthermore, we simulated dilutions of tumor DNA into healthy blood cell DNA and demonstrated increased AUC of combined markers across all dilution levels. Finally, we evaluated assay performance in bisulfite sequenced DNA from patient tumors and plasma, in-cluding early-stage samples. When combining all three markers the assay achieved 100% sensi-tivity and specificity, as demonstrated in lung cancer plasma samples. In patient plasma from hepatocellular carcinoma, ZNF154 alone yielded the highest combined sensitivity and specificity values averaging 68% and 72%, whereas multiple markers could achieve higher sensitivity or specificity, but not both. Altogether this study presents a comprehensive pipeline for the identifi-cation, testing and validation of multi-cancer methylation biomarkers with a considerable poten-tial for detecting a broad range of cancer types in patient blood samples.

Background: The endocannabinoid system (ECS) is a very heterogeneous array consisting of many proteins like ligands, enzymes and receptors synthetized in various tissues and immunity cells. The main endogenous ligands are unsaturated fatty acid derivatives like anandamide(AEA), 2-arachidonoylglycerol(2-AG), but many others are under study. Endocannabinoids are involved in both physiological and pathological conditions. ECS plays an important role in the regulation of main processes which lead to cancer and also in sex steroid hormone-related cancers. Methods: With focus on gynaecological cancers, main papers and review articles, up to April 2018, on the role of the ECS, were acquired by PubMed searches using the search terms: ‘cancer’, ‘cannabinoid’, ‘endocannabinoid’, ‘gynaecology’ and ‘malignancy’. Results: The review of recent literature data showed the involvement of the endocannabinoid system in numerous physiological and pathological conditions of the female genital tract up to the development of gynaecological malignancy as cervical, endometrial and ovarian cancer. Conclusions: The endocannabinoid system has an important role in antitumor actions involving different signalling receptor and receptor-independent pathways. It represents an exciting challenge to researchers for its potential use in diagnosis and treatment of all gynaecological malignancies

MicroRNAs are used as biomarkers for classification of cancer subtypes since certain miRNAs are differentially expressed in normal and patient samples. Moreover, miRNAs target mRNAs and can heavily influence Gene Expressions. Thus, deregulation of miRNAs is linked to various disorders. Thus, miRNAs can be used for prognosis and developing personalized health solutions for patients. Given the importance of miRNAs, there has been substantial work done in the field. In this paper, recent works in the field of using miRNAs expressions of patients were considered. A total of 20 papers were surveyed which utilized feature selection ensembles, fuzzy logic as well as deep learning. 10 papers have been reported which offer insight into how miRNAs can be utilized for subtype-specific or generalized cancer diagnosis.

The standard care for metastatic breast cancer (MBC) is systemic therapies with imbrication of focal treatment for symptoms. Recently, thanks to implementation of radiological and metabolic exams and development of new target therapies, oligometastatic and oligoprogressive settings are even more common, paving the way to a paradigm change of focal treatments role. In fact, according to immunophenotype, radiotherapy can be considered with radical intent in these settings of patients. The aim of this literature review is to analyse available clinical data on prognosis of bone metastases from breast cancer and benefits of available treatments for developing a practical guide for clinicians.

Multi-cancer early detection (MCED) tests are being developed, but little is known about patient receptivity to their use for cancer screening. The current study assessed patient interest in such testing. Our team conducted a prospective, observational study among primary care patients in a large, urban health system. They were asked to complete a telephone survey that briefly described a new blood test in development to identify multiple types of cancer, but was not currently recommended or covered by insurance. The survey included items to assess respondent background characteristics, perceptions about MCED testing, and interest in having such an MCED test. We also used multivariable analyses to identify factors associated with patient interest in test use. In 2023, we surveyed 159 (32%) of 500 identified patients. Among respondents, 125 (79%) reported a high level of interest in having an MCED test. Interest was not associated with personal background characteristics, but was positively associated with the following expectations: testing would be recommended for cancer screening, be convenient, and be effective in finding early-stage disease (OR=11.70, 95% CI: 4.02, 34.04, p < 0.001). Research is needed to assess patient interest and actual uptake when detailed information on testing is presented in routine care.

Early detection of pancreatic ductal adenocarcinoma (PDAC) requires further examination after selecting cases with risk factors for the condition, such as family history, hereditary pancreatic carcinoma syndrome, intraductal papillary mucinous neoplasms, or chronic pancreatitis. The Japan Study Group on the Early Detection of Pancreatic Cancer has investigated and clarified the clinicopathological features for the early diagnosis of PDAC. Further approaches for the early diagnosis of PDAC are warranted.

Lung cancer remains the leading cause of cancer-related mortality worldwide. The main issue is the absence of a screening test available in clinical practice; the identification of non-invasive biomarkers is thus an urgent clinical necessity. Currently, low-dose computed tomography (LD-CT) demonstrates a 20% reduction of lung cancer mortality. However, it is not particularly suitable for clinical practice because of the costs, radiation, and the false positive rate. Several studies have therefore focused on research into biomarkers in body fluids. Despite the power of certain molecules to distinguish lung cancer patients from healthy subjects, no biomarker has yet been shown to significantly and reliably influence clinical decisions or to be translated from the laboratory to clinical practice. We performed an overview of the peer-reviewed biomedical literature published in the last 10 years on the research regarding biomarkers for the early diagnosis of lung cancer via a comprehensive analysis of the reviews published this past year. Our main aim was to bring to light the limitations and strengths of the research already conducted. Furthermore, future prospectives have been explored, bearing in mind the management of clinical trials and their integration into clinical practice.

One of the most dangerous diseases that threaten people is Cancer. Cancer if diagnosed in earlier stages can be eradicated with its life threatening consequences. In addition, accuracy in prediction plays a major role. Hence, developing a reliable model that contributes much towards the medical community in early diagnosis of Biopsy images with perfect accuracy come to the scenario. The article aims towards development of better predictive models using multi-variate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimization (SSO) algorithm tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the NN classifier by the SSO algorithm. The performance of the proposed strategy is analysed with the performance metrics, such as accuracy, sensitivity, specificity, and MCC measures, and are attained to be 95.9181%, 94.2515%, 97.125%, and 97.68% respectively, which shows the effectiveness of the proposed method in effective cancer disease diagnosis.

Background: The interruption of the activity of the population-based organized colorectal cancer (CRC) screening programs due to the COVID pandemic may have affected their results in terms of detection of preneoplastic lesions and CRC. We evaluated the impact of the COVID pandemic on the delays, participation, adherence to colonoscopies, lesions detected, and CRC stage at di-agnosis in a CRC screening program. Methods: We analyzed all the invitations between January 1, 2019, and December 31, 2021. We defined the pandemic period as the period after March 12, 2020. We calculated the delay intervals (successive and all rounds), the rates of participation, adherence to colonoscopy after a positive fecal immunochemical test (FIT), and the diagnostic yield of colonoscopy, specifically CRC and colorectal neoplasia (CRC and/or adenoma), as well as CRC stage at diagnosis. Results: In the period analyzed, 976,187 invitations were sent (61.0% pandemic), 439,687 FIT were returned (62.4% pandemic) and 23,092 colonoscopies were performed (59.1% pandemic). Colon-oscopies were normal in 7,378 subjects (32.4%) and a CRC was detected in 916 subjects (4.0%). In successive rounds, the delay increased significantly seven months during the pandemic period (p<0.001). In all the invitations, the delay from the invitation to the colonoscopy increased sig-nificantly by 8 days (p<0.001). Once adjusted for the confounding variables, participation in the screening program increased significantly (OR=1.1; 95% CI=1.09-1.11), with no changes in the adherence to colonoscopy (OR=0.9; 95% CI=0.8-1.0). We found no differences in the diagnostic yield of colonoscopy in terms of CRC (OR=0.90; 95% CI=0.78-1.02) or colorectal neoplasia (OR=0.98; 95% CI=0.92-1.03) detection. Finally, we found no differences in CRC stage at diagnosis (p=0.2). Conclusions: Although the interruption of the CRC screening program due to the COVID pan-demic increased the delays, it did not reduce participation, adherence to colonoscopy, or the di-agnostic yield of colonoscopy.

The article focuses on breast cancer, mammography, and artificial intelligence. First, breast cancer is a widespread health problem that affects millions of people worldwide, and mammography is a widely adopted screening method. Then it introduced the advantages of mammography after AI participation, and introduced the importance of early detection of breast cancer and the application of artificial intelligence in breast cancer detection and treatment. From these aspects extend to the entire medical field of artificial intelligence issues. Several times throughout the paper, ethical issues that could arise from applying AI to healthcare are highlighted. At the end of the article, the paper describes the continued development of AI-based mammography analysis over the next period of time.

This research paper presents a deep learning approach to early detection of skin cancer using image augmentation techniques. The authors propose a two-stage image augmentation technique that involves the use of geometric augmentation and generative adversarial network (GAN) to classify skin lesions as either benign or malignant. This research utilized the public HAM10000 dataset to test the proposed model. Several pre-trained models of CNN were employed, namely Xception, Inceptionv3, Resnet152v2, EfficientnetB7, InceptionresnetV2, and VGG19. Our approach achieved accuracy, precision, recall, and F1-score of 96.90%, 97.07%, 96.87%, 96.97%, respectively, which is higher than the performance achieved by other state-of-the-art methods. The paper also discusses the use of SHapley Additive exPlanations (SHAP), an interpretable technique for skin cancer diagnosis, which can help clinicians understand the reasoning behind the diagnosis and improve trust in the system. Overall, the proposed method presents a promising approach to automated skin cancer detection that could improve patient outcomes and reduce healthcare costs.

Background: Human papillomavirus (HPV) is diagnosed in most cervical cancers and is the fourth cause of cancer worldwide. Currently, fourteen HPV types are considered carcinogenic (HPV 16/18/31/33/35/39/45/51/52/56/58/59/66/68) with different oncogenicity. Cervical lesions might be due to HR (high risk)-HPV for what is important to identify new methods to genotype cervical lesions. The main goal was to identify, for the first time, HPV-HR in cervical lesions using Anyplex™ Detection kit in paraffin-embedded tissue. (2) Methods: It was performed a retrospective study in 45 women from Academic Hospital of Cova da Beira (CHUCB). DNA extraction was performed and through Anyplex™ II HPV-HR Detection kit HPV-HR were genotyped. (3) Results: HPV-HR were identified in 38 women; HPV-16 (55,26%), HPV-18/39/56/58/59 (5,26%), HPV-31 (21,05%), HPV-35 (7,89%), HPV-51/6 (2,63%) and HPV-52 (10,53%). (4) Conclusions: Our results reflect that Anyplex™ II HPV-HR Detection kit, designed for HR-HPV genotyping of cervical cancer screening, might be properly used to HR-HPV genotyping in histology. The proposed methodology allows an easy and cheaper technique that should be used as a future cervical risk stratification. It is a strong tool to be implemented in clinical practice in order to detect HR-HPV detection in cervical lesions, contributing to a more accurate diagnosis.

OBJECTIVE: Cervical cancer is a major cause of cancer-related mortality, necessitating effective screening and diagnostic methods. This study aimed to assess the performance of cervicovaginal smear (CVS) and Human papilloma virus (HPV)-DNA cotest. STUDY DESIGN: The pathology results of 225 female patients who underwent HPV-DNA testing with CVS between 2014 and 2022 and were subsequently diagnosed by colposcopic cervical biopsy or second CVS were retrospectively analyzed. RESULTS: CVS samples showed Atypical Squamous Cells of Undetermined Significance (ASCUS), Low-grade squamous intraepithelial lesion (LSIL), High-grade squamous intraepithelial lesion (HSIL) and cervical cancer. Concordance between the first and second diagnoses demonstrated moderate agreement for LSIL. ASCUS cases exhibited a significant correlation with HPV-DNA positivity and higher-grade cervical lesions. In biopsy, sensitivity and specificity for CIN 2 were 67.8% and 67.9%, respectively, while for CIN 3, they were 81.8% and 58.5%. HPV testing showed significant correlation with histopathologic results. In women over 40 years, more intraepithelial lesions were diagnosed compared to younger women (p<0.005). The conventional smear technique proved reliable in detecting high-grade lesions. CONCLUSION: Despite the limitations of our study, our results emphasize the value of HPV-DNA testing to avoid unnecessary interventions and to establish appropriate follow-up strategies.

Background Due to the COVID-19 pandemic, teledermoscopy has been increasingly used in the remote diagnosis of skin cancers. In a study conducted in 2020, we demonstrated a potential role of an inexpensive device (NurugoTM Derma) as a first triage to select the skin lesions that require a face-to-face consultation with dermatologists. Herein, we report the results of a novel study that aims to better investigate the performance of NurugoTM. Objectives i) verify whether the NurugoTM can be a communication tool between the General Practitioner (GP) and dermatologist in the first assessment of skin lesions; ii) analyze the degree of diagnostic-therapeutic agreement between dermatologists; iii) estimate the number of potentially serious diagnostic errors. Methods One hundred forty-four images of skin lesions were collected at the Dermatology Outpatient Clinic in Novara using a conventional dermatoscope (instrument F), the NurugoTM (instrument N), and the latter with the interposition of a laboratory slide (instrument V). The images were evaluated in-blind by four dermatologists, and each was asked to make a diagnosis and to specify a possible treatment. Results: Our data show that F gave higher agreement values for all dermatologists, concerning the real clinical diagnosis. Nevertheless, a medium/moderate agreement value was obtained also for N and V instruments and that can be considered encouraging and indicate that all examined tools can potentially be used for the first screening of skin lesions. The total amount of misclassified lesions was limited (especially with the V tool), with up to nine malignant lesions wrongly classified as benign. Conclusions NurugoTM, with adequate training, can be used to build a specific support network between GP and dermatologist or between dermatologists. Furthermore, its use could be extended to the diagnosis and follow-up of other skin diseases, especially for frail patients in emergencies such as the current pandemic context.

Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, more specifically with higher prevalence and mortality in developing countries. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. The recent advances in technology and algorithms gave rise to the computer-aided diagnosis (CAD) of PCa. With the availability of medical image datasets and emerging trends in state-of-the-art machine and deep learning techniques, there is a growth in recent related publications. Materials and Methods: In this study, we present a systematic review of PCa diagnosis with medical images using machine learning and deep learning techniques. We conducted a thorough review through relevant studies indexed in four databases (IEEE, PubMed, Springer and ScienceDirect) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. With well-defined search terms, a total of 608 articles were identified and 77 met the final inclusion criteria. Key elements in the included papers were presented and conclusions were drawn from them. Results: Findings showed that the United States has the most research in PCa diagnosis with machine learning, Magnetic Resonance Images are the most used datasets and transfer learning is the most used method of diagnosing PCa in recent times. In addition, some available PCa datasets and some key considerations for choice of loss function in the deep learning models were presented. The limitations and lessons learnt were discussed and some key recommendations were made. Conclusion: The discoveries and the conclusions of this work have been organized so as to enable researchers in the same domain to use this work and make crucial implementation decisions.

As a relatively high percentage of adenoma polyps are missed, a computer-aided diagnosis (CAD) tool based on deep learning can aid the endoscopist in diagnosing colorectal polyps or colorectal cancer in order to decrease polyps missing rate and prevent colorectal cancer mortality. Convolutional Neural Network (CNN) is a deep learning method and has achieved better results in detecting and segmenting specific objects in images in the last decade than conventional models such as regression, support vector machines or artificial neural networks. In recent years, based on the studies in medical imaging criteria, CNN models have acquired promising results in detecting masses and lesions in various body organs, including colorectal polyps. In this review, the structure and architecture of CNN models and how colonoscopy images are processed as input and converted to the output are explained in detail. In most primary studies conducted in the colorectal polyp detection and classification field, the CNN model has been regarded as a black box since the calculations performed at different layers in the model training process have not been clarified precisely. Furthermore, I discuss the differences between the CNN and conventional models, inspect how to train the CNN model for diagnosing colorectal polyps or cancer, and evaluate model performance after the training process.

Gallbladder diseases are very common and their diagnosis is based on clinical-laboratory evaluation and imaging techniques. Considering the different imaging diagnostic tools, ultrasound (US) has the advantage of high accuracy combined with easy availability. Therefore, when a gallbladder disease is suspected, US can readily assist the clinician in the medical office or in the emergency department. The high performance of US in the diagnosis of gallbladder diseases is mainly related to its anatomic location. The most frequent gallbladder pathological condition is gallstones disease, easily diagnosed by US examination. Acute cholecystitis (AC), a possible complication of gallstone disease, can be readily recognized, due to its specific sonographic features. Additionally, a number of benign, borderline or malignant gallbladder lesions may be detected by US evaluation. The combined use of standard B-mode US and additional sonographic techniques, such as contrast-enhanced ultrasonography (CEUS), may provide a more detailed study of gallbladder lesions. Multiparametric US (combination of multiple sonographic tools) can improve the diagnostic yield during gallbladder examination.

Mutation analysis of circulating tumor DNA (ctDNA) has applications in monitoring of colorectal cancer (CRC) patients for recurrence. Considering the low tumor fraction of ctDNA in cell-free DNA (cfDNA) isolated from blood plasma, the sensitivity of the detection method is important. Here, plasma DNA collected at diagnosis and follow-up from 25 CRC patients was analyzed using a multiplex superRCA mutation detection assay. The assay was also performed on genomic DNA (gDNA) from tumor and normal tissue from 20 of these patients. The lower limit of detection for most sequence variants was in the range of 10^-5, while when analyzing cfDNA from plasma with a typical input of 33 ng, the practical detection limit was ~10^-4 or 0.01% mutant allele frequency (MAF). In 17 of 19 patients with identified hotspot mutations in tumor gDNA, at least one hotspot mutation could be detected in plasma DNA at the time of diagnosis. The MAF increased at subsequent time points in four of the patients who experienced a clinical relapse. Multiplex superRCA analysis of the remaining six patients did not reveal any hotspot mutations. In conclusion, multiplex superRCA assays proved suitable for monitoring CRC patients by analyzing hotspot mutations in cfDNA, and dynamic changes in MAF were observed in patients with clinical relapse.

Recent advancements in molecular diagnostics have highlighted the potential of circulating cell-free DNA (ccfDNA) as a biomarker in various diseases. This study aims to explore the role of ccfDNA in the early diagnosis, prognosis, and therapeutic monitoring of diseases, with a focus on oncology. We conducted a comprehensive review and analysis of current literature, combined with experimental approaches involving advanced genomic sequencing and bioinformatics techniques. Our research systematically characterizes the molecular composition of ccfDNA, including its origins, size distribution, and methylation patterns, across different disease states. Furthermore, we investigate the dynamics of ccfDNA release and clearance in the bloodstream, elucidating mechanisms that could influence its diagnostic accuracy. In the context of cancer, we analyze the correlation between tumor-derived ccfDNA and disease progression, response to therapy, and overall survival. Our findings indicate that ccfDNA holds promise as a non-invasive biomarker for early disease detection, real-time monitoring of treatment efficacy, and potentially, predicting relapse. This study contributes to the growing field of liquid biopsies and opens new avenues for personalized medicine, emphasizing the need for standardized protocols in ccfDNA analysis to maximize its clinical utility. The implications of our findings extend beyond oncology, suggesting potential applications of ccfDNA in other pathological conditions.

Background: The patients with cancer, especially pancreas cancer, are frequently associated with thrombosis which is one of causes for poor outcome, and hypercoagulability exists in cancer patients. Hypercoagulability is considered to be caused by thrombin burst. Methods: Activated partial thromboplastin time (APTT), small amount of tissue factor induced FIX activation (sTF/FIXa) assays and thrombin time (TT) using clot waveform analysis (CWA) were performed in 138 patients with malignant neoplasm including pancreas cancer and 66 non-cancer patients. Results: Thrombosis was frequently associated in pancreas cancer and was observed in stage I. CWA-APTT showed that the peak times and heights were markedly long and heigh, respectively, in cancer patients, and the peak times were significantly longer in pancreas cancer patients than benign pancreas diseases, and the 1st DPH was significantly higher in pancreas cancers than other cancers or benign pancreas diseases. CWA-sTF/FIXa showed that the peak times and heights were longer and higher, respectively, in patients with cancer than those without cancers, and that the 1st DPH was significantly higher in pancreas cancers than in other cancers. CWA-TT showed that the peak times were significantly shorter in cancer patients than in healthy volunteers and that the peak heights were significantly higher in cancer than in benign pancreas diseases. Conclusions: Cancer patients including pancreas cancer were frequently associated with thrombosis due to hypercoagulability with thrombin burst detected by CWA.

Fanconi anemia (FA) patients display an exacerbated risk of oral squamous cell carcinoma (OSCC) and precursor lesions at young ages, mainly at the oral cavity. As patients have defects in DNA repair mechanisms, standard-of-care treatments to OSCC such as radiotherapy and chemotherapy give rise to severe toxicities. New methods for early diagnosis are urgently necessary to allow treatments in early disease stages and achieve better clinical outcomes. We have conducted a prospective, longitudinal study whereby liquid biopsies from sixteen lesion/tumor-free patients were analyzed for the presence of mutations in cancer genes. DNA from saliva and plasma were sequentially collected and deep-sequenced, and the clinical evolution followed during a median time of around 2 years. In 9/16 FA patients we detected mutations in cancer genes (mainly TP53) with molecular allele frequencies (MAF) down to 0.07 %. Importantly, all patients having mutations and clinical follow-up data after mutation detection (n=6) developed oral precursor lesions or OSCC. Lead-time between mutation detection and tumor diagnosis ranged from 23 to 630 days. Strikingly, FA patients without mutations display significantly lower risk of developing precursor lesions or OSCC. Therefore, our diagnostic approach could help to stratify FA patients into risk groups, which would allow closer surveillance for OSCC or precursor lesions.

Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operation, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying breast cancer from unregistered multi-view mammograms. This motivates us to leverage the architecture of Vision Transformers to capture long-range relationships of multiple mammograms from the same patient. For this purpose, we employ local Transformer blocks to separately learn patch relationships within a specific view (CC/MLO) of one side (right/left) of mammogram. The outputs from different views and sides are concatenated and fed into global Transformer blocks, to jointly learn patch relationships between two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of CC and MLO view mammograms, which include 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our two-view Transformer-based model achieves lesion classification accuracy of 77.0% and area under ROC curve (AUC = 0.814), outperforming state-of-the-art multi-view CNNs by 3.1% and 3%, respectively. Meanwhile, the new two-view model improves mammographic case classification accuracies of two single-view models by 7.4% (CC) and 4.5% (MLO), respectively. The promising results unveil the great potential of using Transformers to develop high-performing computer-aided diagnosis (CADx) schemes of mammography.

Autophagy is a highly conserved catabolic mechanism that mediates the degradation of damaged cellular components by inducing their fusion with lysosomes. This process provides cells with an alternative source of energy for the synthesis of new proteins and the maintenance of metabolic homeostasis in stressful environments. Numerous studies have demonstrated beneficial roles for the induction as well as the suppression of autophagy in cancer cells. Autophagy may induce either survival or death depending on the cell/tissue type. Radiation therapy is widely used therapeutic option to treat cancer, and it induces autophagy in human cancer cell line. Also, melatonin seems to affect cancer cell death via regulation of programmed cell death. In this review, we summarize the current understanding of autophagy and its regulation in cancer.

MicroRNAs (miRNAs) within the exosomes are short non-coding RNAs that are associated with many diseases including cancers and are crucial players in regulating gene expression and regula-tion. Owing to their potential role as emerging biomarkers, efficient isolation, and quantification methods of miRNA from the exosomes in the cell’s supernatant are complicated and challenging. The quantitative PCR method is the gold standard for miRNA identification and estimation. Nucleic acid-based assays are expensive, labour-intensive, and require expertise. In this work, we demon-strated an aptamer-based magnetic separation of the exosomes, and quantification of the miRNA using a fluorescence switching assay. The fluorescence assay relies on the fluorescence of the fluorophore labelled cDNA quenched by carbon nanomaterials coated on magnetic beads (OFF state). When the target miRNA210 is introduced to the system, the cDNA detaches from the quenching surface. As a result, the fluorescence intensity increases (ON state) with increasing miRNA concentration within the dynamic range of 0-100 nM. This method can detect miR210 as low as 5 pM. This method does not exhibit any cross-reactivity with other closely related miRNAs. The method was validated by spiking miRNA with the standard RT-PCR method using SYBR green as a fluorescent probe. This method can be used for the minimally invasive detection of cancer biomarkers in breast cancer patients.

Gut microbiota composition can affect tumor microenvironment and its interaction with the immune system, thereby having implications on treatment predictions. This article reviews the studies available to better understand how gut microbiomes help the immune system to fight cancer. To describe this fact, different mechanisms, and approaches of utilizing probiotics to improve advancements in cancer treatment will be discussed. Moreover, not only the calorie intake but also the variety and quality of diet can influence cancer patient’s immunotherapy treatment because dietary patterns could impair the immunological activities either by stimulating or suppressing the innate and adaptive immunity. Therefore, it is interesting and critical to understand the gut microbiome composition as a biomarker to decide cancer immunotherapy outcomes and responses. Here, more emphasis would be given to the recent development in immunotherapies utilizing microbiota to improve cancer therapies which is beneficial for cancer patients.

Seemingly normal tissues progressively become populated by mutant clones over time. Most of these clones bear mutations in well-known cancer genes but only rarely do they transform into cancer. This poses questions on what triggers cancer initiation and what implications somatic variation has for cancer early detection. We analysed recent mutational screens of healthy and cancer-free diseased tissues to compare somatic drivers and the causes of somatic variation across tissues. We then reviewed the mechanisms of clonal expansion and their relationships with age and diseases other than cancer. We finally discussed the relevance of somatic variation for cancer initiation and how it can help or hinder cancer detection and prevention. The extent of somatic variation is highly variable across tissues and depends on intrinsic features, such as tissue architecture and turnover, as well as the exposure to endogenous and exogenous insults. Most somatic mutations driving clone expansion are tissue-specific and inactivate tumour suppressor genes involved in chromatin modification and cell growth signalling. Some of these genes are more frequently mutated in normal tissues than cancer, indicating a context-dependent cancer promoting or protective role. Mutant clones can persist over a long time or disappear rapidly, suggesting that their fitness depends on the dynamic equilibrium with the environment. The disruption of this equilibrium is likely responsible for their transformation into malignant clones and knowing what triggers this process is key for cancer prevention and early detection. Somatic variation should be considered in liquid biopsy, where it may contribute cancer-independent mutations, and in the identification of cancer drivers, since not all mutated genes favouring clonal expansion also drive tumourigenesis. Somatic variation and the factors governing homeostasis of normal tissues should be taken into account when devising strategies for cancer prevention and early detection.

Transfer RNA fragments (tRFs) have gene silencing effects similarly to miRNAs, can be sorted into extracellular vesicles (EVs) and are emerging as potential circulating biomarkers for cancer diagnosis. We aimed at analyzing the expression of tRFs in gastric cancer (GC) and understanding their potential as biomarkers. We explored miRNA datasets from gastric tumors and normal adjacent tissues (NAT) from TCGA repository, and proprietary 3D-cultured GC cell lines and corresponding EVs, in order to identify differentially represented tRFs using MINTmap and R/Bioconductor packages. Selected tRFs were validated in patient-derived EVs. We found 613 Differentially Expressed (DE)-tRFs in the TCGA dataset, of which 19 were concomitantly upregulated in TCGA gastric tumors and present in 3D-cells and EVs, but barely expressed in NAT. Moreover, 20 tRFs were expressed in 3D-cells and EVs and downregulated in TCGA gastric tumors. Of these 39 DE-tRFs, 9 tRFs were also detected in patient-derived EVs. Interestingly, the targets of these 9 tRFs affect neutrophil activation and degranulation, cadherin binding, focal adhesion and cell-substrate junction, highlighting these pathways as major targets of EV-mediated crosstalk with the tumor microenvironment. Furthermore, as they are present in four distinct GC datasets and can be detected even in low quality patient-EV derived samples, they hold promise as GC biomarkers. By repurposing already available NGS data, we could identify and cross-validate a set of tRFs holding potential as GC diagnosis biomarkers.

Studies at the molecular, systemic, and epidemiological levels have shown that chronic metal exposure is linked to significant health consequences, including cancer, affecting hundreds of millions worldwide. Subtle and convoluted mechanisms underline metals' toxicity and carcinogenicity. The use of sensors for carcinogenic metals' trace detection is on the rise due to their selectivity, simplicity, and affordability. Biotechnology and microelectronics in the development of sensors have grown complementarily in recent years. This study offers a comprehensive overview of current research and advancements in developing sensors for detecting carcinogenic metals. Here, we have focussed on the developed biosensor platforms for group 1 carcinogens, i.e., arsenic, nickel, cadmium, chromium, and beryllium, along with their brief roles in human carcinogenesis. This review also looks at the importance of sensing such metal exposure in humans from a larger perspective, hoping to influence future research toward early prevention and treatment of illnesses like cancer.

Silencing of tumor suppressor genes (TSGs) by DNA promoter hypermethylation is an earlyevent in carcinogenesis; Hence TSGs may serve as early tumor biomarkers. We determinedthe promoter methylation levels of p16INK4a, RASSF1A, TIMP3 and PCQAP/MED15 TSGs in salivary DNA from oral cancer (OC), and oropharyngeal cancer (OPC) patients, using methylation specific PCR coupled with densitometry analysis. We assessed the association between DNA methylation of individual TSGs with OC and OPC risk factors. We evaluated the performance and the clinical validity of this quadruple methylation marker panel in discriminating OC and OPC patients from healthy controls using CombiROC web tool. Our study reported that RASSF1A, TIMP3 and PCQAP/MED15 TSGs were significantly hypermethylated in OC and OPC cases compared to healthy controls. We found that DNA methylation levels of TSGs were significantly augmented by smoking, alcohol use and betel quid chewing by indicating that the fact that frequent exposure to risk factors may drive oral and oropharyngeal carcinogenesis through TSG promoter hypermethylation. Also, this quadruple-methylation marker panel of p16INK4a, RASSF1A, TIMP3 and PCQAP/MED15 TSGs demonstrated excellent diagnostic accuracy in the early detection of OC at 91.7% sensitivity and 92.3% specificity, and OPC at 99.8% sensitivity and 92.1% specificity, from healthy controls.

Abstract On the background of new research results in screening of breast cancer, together with the expectation of the participants in a screening for breast cancer, the conventional mammography requires supplementation by means of tomosynthesis or additive ultrasound. Alternatively, ultrasound now seems to be an independent method of early detection of breast cancer because of its superior sensitivity, especially in the case of aggressive mammary carcinoma types. The MRI remains at present still a preferred method in high-risk cases and as an additive examination in case of insufficient presentation of the glandular tissue by conventional methods. MRI is also preoperatively valuable for more accurately measuring the extent of multifocal carcinomas.

Skin cancer is a global health concern with increasing prevalence, necessitating effective early detection and classification systems. Transfer learning has emerged as a powerful tool in this domain, enhancing diagnostic accuracy. Attention mechanisms, which selectively focus on pertinent image features, play a pivotal role in transfer learning. While previous studies have highlighted their effectiveness, their applicability across different models and datasets remains understudied. This empirical study explores the impact of various attention mechanisms on the performance of transfer learning models for skin cancer detection and classification. Using five transfer learning models (DenseNet121, InceptionV3, MobileNet, VGG16, and Xception) and six attention mechanisms (Channel Attention, Global Context Attention, Guided Attention, Nonlocal Attention, Positional Attention, and Spatial Attention), we conducted 105 experiments across three datasets. Traditional metrics (accuracy, precision, recall, and f1-score) were employed for empirical validation. The results reveal a nuanced relationship between attention mechanisms, transfer learning models, and datasets. Overall, attention mechanisms exhibit the potential to enhance skin cancer classification. Spatial and channel attention mechanisms consistently outperform others, offering simplicity and effectiveness. Model-specific selection of attention mechanisms is crucial, with a trade-off between model complexity and performance evident. This study provides insights into developing efficient skin cancer classification models utilizing attention mechanisms.

Pancreatic cancer represents one of the difficult problems of contemporary medicine. The development illness evolves very slowly, takes place in special sake (stroma) and manifests clinically close to a final stage. Another feature of this pathology is a coexistence (symbiotic) effect between cancer cells and normal cells inside stroma. All these aspects make it difficult to understand the pathogenesis of pancreatic cancer and develop a proper therapy. The emergence of pancreatic pre-cancer and cancer cells represents a branching stochastic process engaging populations of 64 cells differing in the number of acquired mutations. In this study we formulate and calibrate the mathematical model of pancreatic cancer using the quasispecies framework. The mathematical model incorporates the mutation matrix, fineness landscape matrix and the death rates. Each element of the mutation matrix presents the probability of appearing a specific mutation in the branching sequence of cells representing the accumulation of mutations. The model incorporates the cancer cell elimination by effect CD8 T cells (CTL). The down-regulation of the effector function of CTLs and exhaustion are parameterized. The symbiotic effect of coexistence of normal and cancer cells is considered. The computational predictions obtained with the model are consistent with empirical data. The modelling approach can be used to investigate other types of cancers and examine various treatment procedures.

The aim of the present study was to investigate the incidence of breast (females), prostate, testicular and thyroid cancer in the Italian National Priority Contaminated Sites (NPCSs), served by cancer registries, where the presence of endocrine disruptors (EDs), reported to be linked to these tumors, was documented. Evidence of carcinogenicity of EDs present in NPCs was assessed based on evaluation by international scientific institutions/committees. Standardized Incidence Ratios (SIRs) were computed for each NPCS and cancer site with reference to the time window 1996-2005. Excess incidence of one or more cancer site at study was found in twelve out of fourteen NPCSs. Significantly increased SIRs were found for breast cancer in eight NPCSs, for prostate cancer in six, for thyroid cancer (both gender) in four and for testicular cancer in two. Non significantly increased SIRs were found in five NPCSs for testicular cancer and in two for thyroid cancer (males). Even if increased incidence of one or more cancer sites at study were found in several NPCSs, the ecological study design and the multifactorial etiology of the considered tumors do not allow reaching conclusions in terms of causal links with environmental contamination. In light of the observation of some excesses in SIRs, continuing epidemiological surveillance is warranted.

Abstract: Cancer survivors are at an increased risk for cardiovascular disease (CVD). However, the association between particulate matter (PM) and CVD risk among cancer survivors (alive >5 years since diagnosis) is unclear. We investigated the risk of CVD among 40,899 cancer survivors within the Korean National Health Insurance Service database. Exposure to PM was determined by assessing yearly average PM levels obtained from the Air Korea database from 2008 to 2011. PMs with sizes <2.5 (PM2.5), <10 (PM10), or 2.5-10 (PM2.5-10) μm in diameter were compared, with each PM level exposure further divided into quintiles. Patients were followed up from January 2012 to date of CVD event, death, or December 2017, whichever came earliest. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for CVD were calculated using Cox proportional hazards regression by PM exposure levels. Compared with cancer survivors in the lowest quintile of PM2.5 exposure, those within the highest quintile had a greater risk for CVD (aHR 1.31, 95% CI 1.07-1.59). Conversely, increasing PM10 and PM2.5-10 levels were not associated with increased CVD risk (p for trend 0.078 and 0.361, respectively). Cancer survivors who reduce PM2.5 exposure may reduce their risk of developing CVD.

There are several well-described molecular mechanisms that influence cell growth and are relat-ed to the development of cancer. Chemokines constitute a fundamental element which are not only involved in local growth but also affect angiogenesis, tumor spread and metastatic disease. Among them, the C-X-C motif chemokine ligand 12 (CXCL12) and its specific receptor the chemo-kine C-X-C motif receptor 4 (CXCR4) have been widely studied. The overexpression in cell mem-branes of CXCR4 has been shown to be associated with the development of different kinds of his-tological malignancies, such as adenocarcinomas, epidermoid carcinomas, mesenchymal tumors or neuroendocrine neoplasms (NENs). The molecular synapsis between CXCL12 and CXCR4 leads to the interaction of G proteins and activation of different intracellular signaling pathways in both gastroenteropancreatic (GEP) and bronchopulmonary (BP) NENs conferring greater ca-pacity for locoregional aggressiveness, epithelial-mesenchymal transition (EMT) and the ap-pearance of metastases. Therefore, it has been hypothesized how to design tools that target this receptor. The aim of this review is to focus on current knowledge of the relationship between CXCR4 and NENs, with a special emphasis on diagnostic and therapeutic molecular targets.

Patients diagnosed with cancer are less frequently covered by preventive measures for cardiovascular diseases. The frequent co-occurrence of these diseases makes it necessary to apply parallel diagnostics and cardiological treatment with anti-cancer therapy. Case report: We present a case of a 73-year-old former smoker with hyperlipidemia, type 2 diabetes, and arterial hypertension, after a partial right nephrectomy in 2005 due to kidney cancer, diagnosed with SARS-COV-2 infection in April 2022. Follow-up chest imaging showed a 20 mm focal lesion in the left lung further classified as a small cell neuroendocrine carcinoma. Unexpectedly the patient was hospitalized for ST-segment elevation inferior left ventricular (LV) myocardial infarction treated successfully with coronary angioplasty, however heart failure (HF) with reduced left ventricle ejection fraction was diagnosed. One month later patient required another hospitalization due to the HF decompensation and cardiological treatment was optimized with flozin addition to the standard HF therapy. After cardiological approval chemotherapy was initiated with the cisplatinum-etoposide regimen and continued for 6 months without HF decompensation and significant deterioration of renal function. After that, the patient underwent radical radiotherapy. Follow-up chest computed tomography scans showed regression of the neoplastic lesion. Conclusions: Coincidence of newly recognized cancer and infection might contribute and provoke serious cardiological events . To reduce the risk of cardiovascular complications, early periodic cardiological surveillance and optimal pharmacotherapy are required.

Non-coding RNAs have been described as crucial regulators of gene expression and guards for cellular homeostasis. Some recent papers are focused on vault RNAs, one of the classes of non-coding RNA, and their role in cell proliferation, tumorigenesis, apoptosis, cancer response to therapy, and autophagy, which made them potential therapy targets in oncology. In the human genome, four vault RNA paralogues can be distinguished. They are associated with vault complexes, considered the largest ribonucleoprotein complex. The protein part of these complexes consists of a major vault protein (MVP) and two minor vault proteins (vPARP and TEP1). The name of the complex, as well as vault RNA, comes from the hollow barrel-shaped structure that reminds a vault. Their sequence and structure are highly evolutionary conserved and show many similarities in comparison with different species, but vault RNAs show various roles. Vaults were discovered in 1986, and their functions remained unclear for many years. Although not much is known about their contribution to cell metabolism, it has become clear that vault RNAs are involved in various processes and pathways associated with cancer progression and modulating cell functioning in normal and pathological stages. In this review, we discuss known functions of the human vault RNAs in the context of cellular metabolism, emphasizing processes related to cancer and cancer therapy efficacy.

Background: The Liver Imaging Reporting and Data System (LI-RADS) combines standardized terminology with a classification system for imaging findings in patients with HCC, therefore rendering diagnostic biopsy unnecessary in many cases. This retrospective study included 23 patients with a biopsy diagnosis of HCC, performed either before or after local interventional procedures, in order to evaluate the histopathologic changes induced by previous procedures and their potential influence on the response to immune therapy. Material and Methods: The study encompassed a cohort of patients diagnosed with Hepatocellular Carcinoma (HCC). Diagnosis was established through contrast-enhanced computer tomography or magnetic resonance imaging that identified LI-RADS-5 nodules in conjunction with historical liver disease and elevated alpha-fetoprotein (AFP) levels, or through histological examination confirming positivity for glypican3, heat shock protein70, and glutamine synthetase. The study detailed the liver disease etiology, LI-RADS scores, characteristics and dimensions of HCC nodules, serum AFP concentrations, Edmondson-Steiner grading, and the expression of programmed cell death ligand 1 (PD-L1) in the tumor cells. Results: Among the study's cohort of Hepatocellular Carcinoma (HCC) patients, a portion had not received any prior treatments, while the remainder experienced local HCC recurrence following trans-arterial chemoembolization or radiofrequency ablation. Observations indicated elevated alpha-fetoprotein (AFP) levels in those who had not undergone any previous interventions, showing statistical significance. The Edmondson-Steiner classification predominantly identified grade III differentiation across patients, irrespective of their treatment history. Furthermore, an increase in intra-tumoral programmed cell death ligand 1 (PD-L1) expression was noted in patients who had not been subjected to previous therapies. Conclusion: Liver biopsy offers valuable insights for patients with Hepatocellular Carcinoma (HCC), assisting in the tailoring of immune therapy strategies, particularly in cases of recurrence following prior local interventions.

Pancreatic cancer is the fourth most common cause of cancer mortality worldwide. Furthermore, patients with pancreatic cancer experience limited benefit from current chemotherapeutic approaches because of drug resistance. Therefore, an effective therapeutic strategy for patients with pancreatic cancer is urgently required. Deguelin is a natural chemopreventive drug that exerts potent antiproliferative activity in solid tumors by inducing cell death. However, the molecular mechanisms underlying this activity have not been fully elucidated. Here we show that deguelin blocks autophagy and induces apoptosis in pancreatic cancer cells in vitro. Autophagy induced by doxorubicin plays a protective role in pancreatic cancer cells, and suppressing autophagy by chloroquine or silencing autophagy protein 5 enhanced doxorubicin-induced cell death. Similarly, inhibition of autophagy by deguelin also chemosensitized pancreatic cancer cell lines to doxorubicin. These findings suggest that deguelin has potent anticancer effects against pancreatic cancer and potentiates the anti-cancer effects of doxorubicin. These findings provide evidence that combined treatment with deguelin and doxorubicin represents an effective strategy for treating pancreatic cancer.

Autoantibodies against specific lung cancer-associated antigens have been suggested for lung cancer diagnosis. This study aimed to evaluate the diagnostic performance of anti-gen-autoantibody immune complex (AIC) against its free antigens for CYFRA21-1, ProGRP, NGAL, and NSE in non-small cell lung cancer (NSCLC). In total, 85 patients with NSCLC and 120 healthy controls (HCs) were examined using a 9G DNA chip method. The ratios of AICs to their antigens and combinations of ratios consisting of two to four markers were calculated. The levels of AICs for CYFRA21-1, ProGRP, NGAL, and NSE were higher than their free antigens in all participants. The levels of each free antigen distinguished patients with NSCLC from HCs. The ratios of AIC to its antigen and seven combinations consisting of two to four ratios were significantly higher in patients with NSCLC than those in HCs. Excellent diagnostic performance was observed for all combination ratios (C4-1), with 85.9% sensitivity and 86.7% specificity at cutoff 3.51. Higher sensitivity was observed in the very early stages (0–I) and adenocarcinoma than in stages II–IV and other pathological types. The combination of all ratios of AICs and their antigens for all four markers is useful in the diagnosis of NSCLC.

Skin cancer is an exquisite disease globally nowadays. Because of the poor contrast and apparent resemblance between skin and lesions, automatic identification of skin cancer is complicated. The rate of human death can be massively reduced if melanoma skin cancer can be detected quickly using dermoscopy images. In this research, an anisotropic diffusion filtering method is used on dermoscopy images to remove multiplicative speckle noise and the fast-bounding box (FBB) method is applied to segment the skin cancer region. Furthermore, the paper consists of two feature extractor parts. One of the two features extractor parts is the hybrid feature extractor (HFE) part and another is the convolutional neural network VGG19 based CNN feature extractor part. The HFE portion combines three feature extraction approaches into a single fused feature vector: Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF). The CNN method also is used to extract additional features from test and training datasets. This two-feature vector is fused to design the classification model. This classifier performs the classification of dermoscopy images whether it is melanoma or non-melanoma skin cancer. The proposed methodology is performed on two ordinary datasets and achieved the accuracy 99.85%, sensitivity 91.65%, and specificity 95.70%, which makes it more successful than previous machine learning algorithms.

Cancer is a common illness with a high mortality. Compared to traditional technologies, biomarkers detection, with low cost and simple operation, has higher sensitivity and faster speed in early screening and prognosis of cancer. Therefore, extensive research has been focused on the development of biosensors and the construction of sensing interfaces. Molybdenum disulfide (MoS2) is a promising two-dimensional (2D) nanomaterial, whose unique adjustable bandgap shows excellent electronic and optical properties in the construction of biosensor interfaces. It not only has the advantages of high catalytic activity and low manufacturing costs, but also can further expand the application of hybrid structures by different functionalization, and is widely used in various biosensors fields. Herein, the application of electrochemical and optical sensing platforms based on MoS2 in the detection of cancer biomarkers was comprehensively reviewed. Firstly, the structure and preparation method of MoS2 were introduced, and its applicable characteristics in the field of biosensors were explored. Secondly, we comprehensively reviewed the recent construction and application of sensing platform based on MoS2 in the field of cancer biomarkers detection in both electrochemical and optical aspects. The prime characteristics and application performances of MoS2 and its composites were emphasized. Additionally, we also involved some other types of biosensors based on MoS2. Finally, we summarized the challenges and development prospects of MoS2 in the application of cancer biomarkers detection, and provided some insights for the application potential of this kind of emerging nano-materials in a broader field.

Cancer cells require substantial amounts of energy and substrates for their metabolic hyperactivity, enabling the synthesis of new cells at the expense of healthy ones. Preliminary in vitro data suggest that a mix of free essential amino acids (EAA-mix) can promote cancer cell apoptosis by enhancing autophagy. This study aimed to confirm, both in vitro and in vivo, whether EAA intake could influence the development of colon cancer in mice. We investigated changes in cancer proliferation in CT26 cells treated with EAA-mix, and in mice fed with EAA rich modified diets (EAARD) as compared to those on a standard laboratory diet (StD). CT26 cells were injected subcutaneously (s.c.) or intraperitoneally (i.p.). After 21 days, tumors were removed and measured. In vitro data corroborated that EAA-mix impairs cancer growth by inducing autophagy and apoptosis. In vivo data revealed that mice on StD developed significantly larger (s.c.) and more numerous (i.p.) cancers than those on EAARD. EAA administration appears to influence cancer cell survival with notable anti-proliferative properties.

We have been encouraging practicing gynecologists to adopt molecular diagnostics tests, PCR, and cancer biomarkers, as an alternative enabled by these platforms to traditional Papanicolaou and colposcopy tests, respectively. An aliquot of liquid-based cytology was used for the molecular test [high-risk HPV types, (HR HPV)], another for the PAP test and still one more for the p16/Ki67 dual stain cytology. A total of 4,499 laboratory samples were evaluated, and we found that 25.1% of low-grade samples and 47.9% of high-grade samples after PAP testing had a negative HR HPV-PCR result. In those cases, reported as Pap-negative, 22.1% got a positive HR HPV-PCR result. Dual staining with the p16/Ki67 biomarkers in samples was positive for HR HPV, and 31.7% were also positive for these markers. Of the PCR results positive for any of these high-risk HPV subtypes, n 68.3% we did not to find evidence for the presence of cancerous cells, highlighting the importance of performing dual staining with p16/Ki67 after PCR, to avoid unnecessary colposcopies. Encountered challenges are a deep-rooted social reluctance in Mexico to abandon traditional Pap smears and the opinion of many specialists, we still believe that colposcopy continues to be a preferred procedure over dual staining protocol.

Metabolic-associated fatty liver disease (MASLD) is not only associated with traditional cardiovascular risk factors such as T2DM and obesity, but it is also an independent risk factor for the development of cardiovascular disease. MASLD has been shown to be independently related to endothelial dysfunction and atherosclerosis. MASLD is characterized by a chronic proinflammatory response that, in turn, may induce a prothrombotic state. Several mechanisms such as endothelial and platelet dysfunction, changes in the coagulation cascade, decreased fibrinolytic activity can contribute to induce the prothrombotic state. Platelets are players and addresses of metabolic dysregulation; obesity and insulin resistance are related to platelet hyperactivation. Furthermore, platelets can exert a direct effect on liver cells, particularly through the release of mediators from granules. Growing data in literature support the use of antiplatelet agent as a treatment for MASLD. The use of antiplatelets drugs seems to have potential clinical implication for the prevention of HCC patients with MASLD, since platelets contribute to fibrosis progression and cancer development. This review aims to summarize the main data on the role of platelets in the pathogenesis of MAFLD and its main complications such as cardiovascular events and the development of liver fibrosis. Furthermore, we will examine the role of antiplatelet therapy not only in the prevention and treatment of cardiovascular events but also as a possible anti-fibrotic and anti-tumor agent.

Issues presented by the application of monoclonal antibodies in diagnostic assays and as curative agents can make the use of such molecules cost-prohibitive and sometimes even unsafe. This has warranted the development of short single-stranded oligonucleotides known as Aptamers. The structural malleability of these short DNA or RNA nucleotide segments allows them to exist in distinct conformations. SELEX (Systematic Evolution of Ligands by Exponential Enrichment) is a multi-step process for synthesis of aptamers. Each step of this procedure is governed by a diverse set of factors that influence production efficiency, binding affinity, and specificity of the oligonucleotides. Headway in aptamer research has been made in recent years by the introduction of newer iterations of the SELEX process. A greater number of studies are now being carried out to incorporate aptamers into existing disease detection tools and therapies. An overview has been given first on the key aptamer properties and the process of their production (with its newer iterations), contrasting each of them with that of monoclonal antibodies. Possible manifold applications afforded due to unique aptamer characteristics are also discussed. A keen review is further provided on the design, development and use of fluorescent aptamers in bioimaging, sequencing or profiling, and treatment of pathogenic bacteria and tumor cells.

Pancreatic cancer (PC) has a poor prognosis and displays resistance to immunotherapy. A better understanding of tumor-derived extracellular vesicles (EVs) effects on immune responses might contribute to improved immunotherapy. EVs derived from Capan-2 and BxPC-3 PC cells isolated by ultracentrifugation were characterized by atomic force microscopy, western blot (WB), nanoparticle tracking analysis and label-free proteomics. Fresh PBMCs from healthy donors were treated with PC- or control-derived heterologous EVs followed by flow cytometry analysis of CD8+ and CD4+ lymphocytes. Proteomics of lymphocytes sorted from EV-treated, or -untreated PBMCs was performed and IFN-γ concentration was measured by ELISA. Notably, most of the proteins identified in Capan-2 and BxPC-3 EVs by proteomic analysis were connected in a single functional network (p=1x10-16) and were involved in “Immune System” (FDR: 1.10x10-24 and 3.69x10-19, respectively). Interestingly, treatment of healthy donor-derived PBMCs with Capan-2 EVs, but not with BxPC-3 EVs or heterologous control EVs induced early activation of CD8+ and CD4+ lymphocytes. Proteomics of lymphocytes sorted from EV-treated PBMCs was consistent with their activation by Capan-2 EVs, indicating IFN-γ among major upstream regulators, as confirmed by ELISA. Proteomic and functional analyses indicate that PC-EVs have pleiotropic effects, and some may activate early immune response, which might be relevant for the development of highly needed immunotherapeutic strategies in this immune-cold tumor.

Cancer, characterized by abnormal cell proliferation driven by genetic mutations, remains a significant health challenge. Tailoring effective therapies depends on factors like tumor location, grade, and stage, alongside patient condition. Leveraging Nano-carrier encapsulated drugs offers a promising avenue, as they accumulate preferentially at tumor sites, enhancing therapeutic efficacy while mitigating side effects. Polymeric Nano-carriers, with their customizable properties, further optimize drug delivery efficiency and specificity. Incorporating active targeting ligands such as antibodies, carbohydrates, peptides, folates, and aptamers-onto polymeric nanoparticle surfaces through techniques like functionalization, adsorption, and conjugation plays a pivotal role in directing therapeutic agents to tumor cells. Consequently, polymeric nanoparticles emerge as the carrier of choice for delivering drugs to various tumor types, including breast, colon, lung, prostate, liver, and spleen tumors.

Microarray gene expression analysis is a powerful technique used in cancer classification and research to identify and understand gene expression patterns that can differentiate between different cancer types, subtypes, and stages. However, microarray databases are highly redundant, inherently nonlinear, and noisy. Therefore, extracting meaningful information from such a huge database is a challenging one. The paper adopts the Fast Fourier Transform (FFT) and Mixture Model (MM) for dimensionality reduction and utilizes the Dragonfly optimization algorithm as the feature selection technique. The classifiers employed in this research are Nonlinear Regression, Naïve Bayes, Decision Tree, Random Forest and SVM (RBF). The classifiers' performances are analyzed with and without feature selection methods. Finally, Adaptive Moment Estimation (Adam) and Random Adaptive Moment Estimation (RanAdam) hyper-parameter tuning techniques are used as improvisation techniques for classifiers. The SVM (RBF) classifier with the Fast Fourier Transform Dimensionality Reduction method and Dragonfly feature selection achieved the highest accuracy of 98.343% with RanAdam hyperparameter tuning compared to other classifiers.

Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, we present a comprehensive approach utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets are based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH Dataset, we deployed an ensemble strategy incorporating VGG16 and ResNet50 architec-tures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique, to preprocess a high-resolution image, which facilitates focused analysis of localized regions of interest. The annotated BACH dataset encompasses 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, we employed the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify mi-croscopic images into eight distinct categories (four benign and four malignant). For both models we leveraged a five-fold cross-validation approach for rigorous training and testing. Preliminary ex-perimental results indicate a Patch classification accuracy of 95.31% (on BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to on-going endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens.

In this multicenter, retrospective study we evaluated the added value of magnetic resonance dispersion imaging (MRDI) to standard multiparametric MRI (mpMRI) for PCa detection. The study included 76 patients - 51 with clinically significant prostate cancer (csPCa), who underwent radical prostatectomy and had an mpMRI including dynamic contrast enhanced-MRI. Two radiologists performed three separate randomized scorings based on mpMRI, MRDI and mpMRI+MRDI. Radical prostatectomy histopathology was used as the reference standard. Imaging and histopathology were both scored according to the Prostate Imaging-Reporting and Data System V2.0 sector map. Sensitivity and specificity for PCa detection were evaluated for mpMRI, MRDI and mpMRI+MRDI. Inter- and intra-observer variability for both radiologists were evaluated using Cohen’s Kappa. On a per patient level, sensitivity for csPCa for radiologist 1 (R1) for mpMRI, MRDI and mpMRI+MRDI were 0.94, 0.82 and 0.94, respectively. For the second radiologist (R2) these were 0.78, 0.94 and 0.96. R1 detected 4% additional csPCa cases using MRDI compared to mpMRI and R2 detected 20% extra csPCa cases using MRDI. Inter-observer agreement was significant only for MRDI (Cohen’s kappa = 0.4250, p=0.004). The results of this study show the potential of MRDI to improve interobserver variability and the detection of csPCa.

Lung cancer remains the leading cancer killer worldwide. Early diagnosis can effectively increase the patient cure rate but existing diagnostic methods limit early lung cancer diagnosis. Therefore, development of a simple but efficient lung cancer screening method is important to improvement of both the diagnosis rate and the survival rate of lung cancer patients. In this study, ten photosensitive materials with high sensitivity and high specificity were screened accurately to construct a microarray sensor that can rapidly identify six types of lung cancer biomarkers in exhaled breath. Results from hierarchical cluster analysis (HCA), principal component analysis (PCA) and difference maps showed that the classification of the analytes agreed with structure similarity laws. The detection results from parallel experiments and structurally similar analytes, in turn, cluster into a group; the fingerprints of the different analytes have specific response regions. The well-screened sensor chip fabrication workload and cost were both reduced by approximately two thirds, while the microfluidic device sensitivity and stability increased by approximately 1.3 times their corresponding values before optimization. The dual-channel device also offers real-time contrast detection and synchronous parallel detection functions and has potential application prospects for use in extensive screening of high-risk populations for lung cancer.

Oncological changes in the prostate are a common cause of death among elderly men. Malignant cells are difficult to identify at early stages, which are asymptomatic. The patient often seeks medical assistance when the disease has already progressed, and the treatment is less effective. Early diagnosis of prostate cancer (PC) is thus one of the priority tasks of modern urology and healthcare in general. The aim of this study is the detection of microRNAs (miRNAs), which are known to be associated with PC. The detection of the target miRNAs in human plasma samples has been performed with a nanoribbon biosensor system, which was based on "silicon-on-insulator" structures (SOI-NR biosensor). In order to provide biospecific detection of the target miRNAs, the surface of individual nanoribbons has been modified with DNA oligonucleotide probes (oDNA probes), whose nucleotide sequences were complementary to those of the target miRNAs. The high detection sensitivity has been demonstrated with the use of model oDNAs, which are complementary to nanoribbon-immobilized oDNA probes, in buffer solutions. The sensitivity threshold was 1.1 × 10^-17 M. The successful detection of target miRNAs, isolated from real plasma samples of PC patients, has also been demonstrated. We believe that the development of highly sensitive nanotechnology-based biosensors for PC markers is a step towards personalized medicine.

Melanoma is considered to be the most aggressive form of skin cancer. Due to the similar shape of malignant and benign cancerous lesions, doctors spend considerably more time when diagnosing these findings. At present, the evaluation of malignancy is performed primarily by invasive histological examination of the suspicious lesion. Developing an accurate classifier for early and efficient detection can minimize and monitor the harmful effects of skin cancer and increase patient survival rates. This paper proposes a multi-class classification task using the CoAtNet architecture, a hybrid model that combines the depthwise convolution matrix operation of traditional convolutional neural networks with the strengths of Transformer models and self-attention mechanics to achieve better generalization and capacity. The proposed multi-class classifier achieves an overall precision of 0.901, recall 0.895, and AP 0.923, indicating high performance compared to other state-of-the-art networks.

Cancer is a condition in which the body's cells proliferate unchecked. Skin cancer is one of the deadliest diseases that impacts the skin on many levels. There are several different types of the disease, including melanoma, basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevus. Due to its increased prevalence, skin cancer, in particular melanoma, is be-coming a serious health problem. Early identification of skin lesions is crucial for successful treatment. Due to their resemblance, many skin lesions are misclassified, which is a severe issue. Researchers seek computer-aided diagnostic tools for early malignant tumor detection. First, a new model based on the combination of "you only look once" (YOLOv5) and "ResNet50" is proposed for melanoma detection with its degree using humans against a machine with 10,000 training images (HAM10000). Second, feature maps integrate gradient change, which allows rapid inference, boosts precision, and reduces the number of hyperparameters in the model, making it smaller. Finally, to get the desired outcomes, the current YOLOv5 model is changed by adding new classes for dermatoscopic images of typical lesions with pigmented skin. The pro-posed approach improves melanoma detection with a real-time speed of 0.4 ms non-maximum suppression (NMS) per image. The performance metrics average is 99.0%, 98.6%, 98.8%, 99.5, 98.3%, and 98.7% for precision, recall, dice similarity coefficient (DSC), accuracy, mean average precision (MAP) from 0.0 to 0.5, and MAP from 0.5 to 0.95, respectively. Compared to cur-rent melanoma detection approaches, the provided approach is more efficient using deep fea-tures.

Bladder cancer (BC) diagnosis is reliant on cystoscopy, an invasive procedure associated with urinary tract infections. This has sparked interest in identifying non-invasive biomarkers in body fluids such as blood and urine. A source of biomarkers in these biofluids are extracellular vesicles (EVs), nanosized vesicles that contain a wide array of molecular cargo, including small non-coding RNA such as microRNA and transfer RNA-derived fragments (tRF). Here we performed small-RNA next-generation sequencing for urine EVs, serum EVs and serum supernatant from 41 patients with non-muscle invasive BC (27 stage Ta, 14 stage T1) and 15 non-cancer patients (NCP) with benign cystoscopy findings. Serum and urine samples were collected before transurethral resection of bladder tumors (TUR-b) and at routine postsurgery check-ups. Expression levels of tRFs in presurgery samples were compared to those at postsurgery check-ups and to NCPs. To strengthen the reliability of our results, samples from 10 patients with stage T1 disease were resequenced. When comparing tRF expression in urine EVs between BC patients with stage T1 disease and NCPs, 14 differentially expressed tRFs (DEtRFs) were identified. In serum supernatant, six DEtRFs were identified among stage T1 patients when comparing presurgery to postsurgery samples, and four DEtRFs were found when comparing presurgery samples to NCPs. By performing a blast search, we found that sequences of DEtRFs aligned with genomic sequences pertaining to processes relevant to cancer development, such as enhancers, regulatory elements and CpG islands. Our findings display a number of tRFs that may hold potential as biomarkers for the diagnosis and recurrence-free survival of BC.

Although a growing number of evidence emphasizes superior of VATS over conventional thoracotomy, still is little known about early postoperative diaphragm muscle function after lobectomy via these two approahces. To fill the gap in existing literature, we conducted comparative study between VATS and conventional thoracotomy in terms of postoperative diaphragm muscle function, assessing its contractility, strength, magnitude of effort and potential risk of dysfunction such as: atrophy and paralysis. A total of 59 patients (30 after VATS), who underwent anatomical pulmonary resection at our institution, were enrolled into this study. The control group was consisted of 28 health subjects without medical condition could contribute to diaphragm dysfunction. Diaphragm muscle was assessed before and after surgery using ultrasonography. We found that both surgical approaches were associated with postoperative impairment of diaphragm muscle function - compared to baseline data. Postoperative reduction of diaphragm contraction was demonstrated in all of 59 patients. In case of control group, the differences between measurements were no observed. We noted that lobectomy via thoracotomy was linked with greater percentage of patients with diaphragm paralysis or/and atrophy that VATS. Similar findings were observed in refer to diaphragm magnitude effort, as well as, diaphragm contraction strength, where minimal invasive surgery was associated with better diaphragm function parameters - in comparison to thoracotomy. Disturbance of diaphragm work was reported both at operated and non-operated side. Upper right and left lobectomy was connected with greater diaphragm function impairment than other segments. In conclusion, VATS technique seems to be less invasive than conventional thoracotomy providing a better postoperative function of a main respiratory muscle.

Objectives: We aimed to clarify the endoscopic and clinicopathological features of raspberry-shaped gastric lesions (RSGLs) and to establish an endoscopic diagnostic algorithm for RSGLs. Methods: We collected RSGLs from an endoscopic database at our hospital between May 2009 and August 2021. All RSGLs were histopathologically classified and compared based on their endoscopic and clinicopathological characteristics. Results: Sixty-five RSGLs in 54 patients were classified into five histopathological types: gastric adenocarcinoma of foveolar type (GA-FV, n = 43), gastric adenocarcinoma of fundic-gland type (GA-FG, n = 2), gastric adenocarcinoma of fundic-gland mucosa type (GA-FGM, n = 4), hyperplastic polyp (HP, n = 12), and proton pump inhibitor-related lesion (PPI-L, n = 4). All RSGLs exhibited polygonal or curved marginal crypt epithelium (MCE). GA-FV lesions had homogenously reddish (95%) and irregular microvascular (MV) pattern (91%). GA-FG lesions were heterogeneously reddish with a submucosal tumor shape (100%) and had a regular MV pattern (50%). GA-FGM lesions were homogenously reddish (75%) and occasionally had a submucosal tumor shape (50%) with an irregular MV pattern (75%). HP and PPI-L were homogeneously reddish (93%), with linear or dotted MCE (81%) and a regular MV pattern (100%). Conclusion: Our diagnostic algorithm for RSGLs constructed using endoscopic features might be useful for the endoscopic differential diagnosis of RSGLs.

Breast cancer is the leading cause of cancer deaths worldwide in women. This aggressive tumour can be categorized into two main groups: in situ and infiltrative, with the latter being the most common malignant lesions. The current use of Magnetic Resonance Imaging (MRI) was shown to provide highest sensitivity in the detection and discrimination between benign vs. malignant lesions, when interpreted by expert radiologists. In this article, we present the prototype of a Computer-Aided Detection/Diagnosis (CAD) system that could provide valuable assistance to the radiologist for the discrimination between in situ and infiltrating tumours. The system consists of two main processing levels: 1) Localization of possibly tumoral regions of interest (ROIs) through an iterative procedure based on intensity values (ROI Hunter) followed by a deep-feature extraction and classification method for false-positive rejection; 2) Characterization of the selected ROIs and discrimination between in situ and invasive tumour, consisting of Radiomics feature extraction and classification through a machine-learning algorithm. The CAD system was developed and evaluated using a DCE-MRI image database, containing at least one confirmed mass per image, as diagnosed by an expert radiologist. When evaluating the accuracy of the ROI Hunter procedure with respect to the radiologist-drawn boundaries, sensitivity to mass detection was found to be 75%. The AUC of the ROC curve for discrimination between in situ and infiltrative tumors was 0.70.

Because it can make exact double-strand breaks in DNA, CRISPR-Cas9 can help almost every species and type of cell. It is a powerful way to change the DNA code. CRISPR-Cas9 can be used for a lot more than just changing genes. We are close to being able to do high-throughput gene screening, epigenome editing, in-vivo cell tagging, and RNA change. With the help of CRISPR-Cas9, the function of genes can be studied better, and more realistic disease models can be made. The revolutionary new discipline of gene editing has the potential to profoundly impact healthcare and the life sciences. By simplifying the process of creating double-strand breaks in the DNA of almost any species or kind of cell, CRISPR-Cas9 has revolutionized gene editing. Numerous applications have been found for the CRISPR-Cas9 system. High-throughput gene screening, RNA modification, live-cell chromosome marking, and epigenome editing are all examples. CRISPR-Cas9 facilitates gene research, leading to the development of CRISPR-based disease models. CRISPR-Cas9-based methods of altering the genome will aid researchers in learning more about sickness and discovering better ways to cure it, despite the fact that there are still many questions and large difficulties to answer.

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

A detailed review of the scientific literature was undertaken to examine the most recent developments in plasma processing in the field of medicine. The first part of the review includes a detailed breakdown of the different types of coatings that can be applied onto medical devices using plasma, with a specific focus on antimicrobial surfaces. The developments during 2023 in plasma deposited biocompatible, drug delivery and adhesive coatings are described and specific applications in additive manufacturing are highlighted. The use of plasma and plasma activated liquids as standalone therapeutics continues to evolve and pertinent advances in this field are described. In addition, the combination of plasma medicine with conventional pharmaceutical interventions was reviewed and key emerging trends were highlighted, including the use of plasma to enhance drug delivery directly into tissue. The potential synergies between plasma medicine and chemotherapeutics for oncology and infection treatment is a growing area and recent advancements are noted. Finally, the use of plasma to control excess antibiotics and to intentionally degrade such materials in waste streams is described.

Cancer treatment is a significant focus in medicine owing to the increasing global incidence. Patients with advanced cancers that do not respond to conventional therapies have limited options and an unfavorable prognosis. Consequently, researchers are investigating complementary approaches to conventional treatments. One such approach is alkalization therapy, which aims to neutralize the acidic tumor microenvironment (TME) by increasing its pH level. The acidic TME promotes inflammation, tumor progression, and drug resistance. Alkalization therapy has been demonstrated to be effective for various cancers. In addition, natural products, such as triterpenoids, parthenolides, fulvic acid, Taxus yunnanensis, and apple pectin have the potential to alleviate symptoms, maintain physical fitness, and improve treatment outcomes of cancer patients through their anti-inflammatory, antioxidant, and anticancer properties. In this review, we focus on the effects of alkalization therapy and natural products on cancer. Furthermore, we present a case series of advanced cancer patients who received alkalization therapy and natural products alongside standard treatments, resulting in long-term survival. We posit that alkalization therapy together with the supplementation of natural products may confer benefits to cancer patients by mitigating the side effects of chemotherapy and complementing standard treatments. However, further research is warranted to validate these clinical findings.

We developed an ovarian cancer-targeted drug delivery system based on a follicle-stimulating hormone receptor (FSHR) peptide. The lipophilic chemotherapeutic drug SN38 and the photosensitizer IR820 were loaded into the phospholipid bilayer of liposomes. Near-infrared laser irradiation stimulated the generation of reactive oxygen species, causing short-term reactive molecular species and spatial-temporal restricted photodamage to the biological targets. The reactive molecular species and spatial-temporal restricted photodamage enhanced the release and uptake of chemotherapy drugs and increased the sensitivity to chemotherapy drugs in ovarian cancer cells. FSH liposomes loaded with SN38 and IR820 (SN38/IR820-Lipo@FSH) were prepared using thin-film hydration-sonication. FSH peptide binding was analyzed using 1H NMR spectrum and Maldi-Tof. The average size and zeta potential of SN38/IR820-Lipo@FSH were 105.1 ± 1.15 nm (PDI: 0.204 ± 0.03) and −27.8 ± 0.42 mV, respectively. SN38/IR820-Lipo@FSH exhibited good drug-loading properties and the in vitro release was slow. FSH significantly increased the uptake of liposomes, inhibited cell proliferation, and induced apoptosis in A2780 cells. Moreover, SN38/IR820-Lipo@FSH exhibited good tumor-targeting ability and anti-ovarian cancer activity in vivo. The SN38/IR820-Lipo@FSH exhibited specific targeting to A2780 ovarian cancer cells. The combination of chemotherapy and photodynamic treatment based on an FSH peptide-targeted delivery system may be an effective approach to treating ovarian cancer.

Abstract – Objective: Ovarian cancer, although not possessing a high incidence, is still the most common cancer-related deaths among women diagnosed with a gynecologic malignancy. The present study aims to highlight the epidemiology, risk factors of this disease and the significance of development of improved early detection strategies. Materials and Methods: This study was conducted using current published English studies by searching PubMed and Google Scholar. The search strategy included the keywords “ovarian cancer”, “diagnosis”, “risk factors”, “screening”, “epidemiology”. Studies on incidence and mortality were also considered. Case reports were excluded.Results: The highest incidence and mortality rates are observed in Central and Eastern Europe, while rates are relatively low in Asia and Africa. These rates are highest among the white population (14.3 per 100,000) and lowest among blacks (10 per 100,000) and Asians (9.7 per 100,000). The risk factors for this disease includes a family history, hormonal factors, nutrition and diet and physical activity, with some of them playing protective roles in reducing risk of ovarian cancers. There are no reliable screening methods for ovarian cancers. The most common diagnosis methods include a transvaginal ultrasound and a blood test to detect CA125 markers.Conclusions: The mortality rate of ovarian cancer is gradually increasing; thus, preventative measures are required to reduce lifetime risk of ovarian cancers and improve mortality rate.

Chronic myelogenous leukemia (CML) was the first malignancy for which clinical outcome was drastically improved by kinase inhibitor therapy. Kinase inhibitors targeting other well-known oncogenes have been introduced into clinical practice, but none have shown the same magnitude of clinical benefit as ABL1 inhibition in CML. We argue that early detection is an underappreciated, but critically important factor in success of ABL1 inhibitors in treatment of CML. We show that CML provides a window into how many types of cancer may look and behave at an early stage, prior to diagnosis and the development of additional genomic alterations. The remarkable clinical benefits of ABL1 inhibition is likely due to early detection of CML at a stage in which the tumor is driven by single oncogenic alteration which can be successfully controlled by the inhibitor. Thinking of CML as a prototype for effective systemic treatment based on early cancer detection may help to develop strategies for improving treatment for other types of cancer.

Background and Objectives: Cancer as the second leading cause of death in the United States poses a huge healthcare burden. Barriers to access to advanced therapies influence the outcome of cancer treatment. In this study, we examined whether insurance types affect the quality of cancer clinical care regarding access to an important monoclonal antibody, ramucirumab. Materials and Methods: Data for 13,340 cancer patients with Purchased or Medicaid insurance from the All of Us Database were collected for this study. The Chi-square test of proportions was employed to determine the significance of patient cohort characteristics and ramucirumab usage between Purchased and Medicaid insurance groups. The independent t-test was utilized to assess the influences of other determinants on insurance types. Results: Cancer patients who are African American, with lower socioeconomic status, or with lower educational attainment are more likely to be insured by Medicaid. Analysis of survey questions demonstrated the relationship between income and education level with insurance type as Medicaid cancer patients were less likely to receive primary care and specialist physician access and more likely to request lower cost medications. In addition, those with Medicaid insurance were identified to have poorer access to expensive therapeutics like ramucirumab compared to those with Purchased insurance.  Conclusions: The inequities of the US healthcare system are observed for cancer patient care; access to physicians and therapeutics are highly varied and dependent on insurance types. Socioeconomic status determines insurance type, which unfortunately generates a significant impact on cancer treatment and disease outcome.

Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography re-view paper provides a systematic review of computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The systematic review is meticulously carried out, adhering closely to the preferred reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are compre-hensively reviewed. The evaluation of a deep learning architecture based on self-extracted features for classification tasks demonstrated outclass performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations' sustainability de-velopment goals.

PIK3CA is a gene usually mutated in breast cancer and has an important role in tumor progression and treatment. Therefore, there is required a technique to detect low-rate PIK3CA mutations improving the clinical conduct. This study aimed to compare chip-based dPCR and Sanger sequencing to detect PIK3CA mutations in breast cancer patients. Fifty-seven tumor samples from breast cancer patients were collected and analyzed by Sanger sequencing and dPCR for PIK3CA mutations (E545K, H1047R, and H1047L). Digital PCR sensitivity, specificity, and overall performance were estimated by contingency tables, receptor operator characteristic (ROC), and area under the curve (AUC). Sanger sequencing identified PIK3CA mutations in six patients (10.5%), two with H1047R, and four with E545K. Digital PCR confirmed those mutations and identified 19 additional patients with at least one mutation. Comparison between dPCR and Sanger sequencing showed a sensitivity of 100% (95% CI 53-100%), and a specificity of 84.2% (95% CI 83 - 84.2%). Besides, H1047R mutation showed a significant association with breast cancer phenotype (p =0.019) and lymphatic node infiltration (p =0.046). Digital PCR showed a high sensitivity to detect mutations in tumor samples and it might be capable to detect low-rate mutations and tumor subpopulations not detected by Sanger sequencing.