Middle East region experiences a high prevalence of hypovitaminosis D, yet most genetic studies on Vitamin D have focused on European populations. Furthermore, there is a lack of research on the genomic risk factors affecting the elderly population, who are more susceptible to health burden. We investigated the genetic determinants of 25-hydroxyvitamin D levels in elderly Lebanese individuals (n=199) through a whole exome-based genome-wide association study. We identified new loci with suggestive evidence of an association with Vitamin D levels, including rs141064014 in the MGAM gene (P-value of 4.40 × 10−06) and rs7036592 in PHF2 (P-value of 8.43 × 10−06). A meta-analysis of the Lebanese data and the largest European genome-wide association study confirmed consistence replication of numerous variants, including rs2725405 in SLC38A10 (P-value of 3.73 x 10-08). Despite the lower performance of European-derived polygenic risk scores compared to the European estimations, it still effectively predicted Vitamin D deficiency among elderly Lebanese individuals. Our findings provide novel insights into the genetic mechanisms of Vitamin D deficiency in Middle Eastern elderly populations, facilitating the development of personalized approaches for more effective management of hypovitaminosis D. Additionally, we demonstrated that whole exome-based genome-wide association study is an effective method for identifying genetic components associated with phenotypes.

Oestrogen receptor expression in breast cancer (BC) cells is a marker of high cellular differentiation and allows the identification of two BC groups (ER-positive and ER-negative) that, although not completely homogeneous, differ in biological characteristics, clinical behaviour and therapeutic options. The study, based on three publicly available EWAS datasets, focuses on the comparison between these two groups of breast cancer using an epimutation score. The score is calculated not only based on the presence of the epimutation but also on the deviation amplitude of the methylation outlier value. For each dataset, we performed a functional analysis based first on the functional gene region of each annotated gene (we aggregated the data per gene region TSS1500, TSS200, first-exon, body-gene identified by the information from the Illumina Data Sheet) and then we performed a pathway enrichment analysis through the REACTOME database based on the genes with the highest epimutation score. Thus, we blended our results and found common pathways for all three data sets. We found that a higher and significant epimutation score due to hypermethylation in ER-positive BC is present in the promoter region of the genes belonging to the SUMOylation pathway, the NOTCH pathway, the IFN-$\gamma$ signalling pathway and the deubiquitination protease pathway; while a higher and significant level of epimutation due to hypomethylation in ER-positive BC is present in the promoter region of the genes belonging to the ESR-mediated pathway. The presence of this state of promoter hypomethylation in the ESR-mediated signalling genes is consistent and coherent with an active signalling pathway mediated by oestrogen function in the group of ER-positive BC. SUMOylation and NOTCH pathways are associated with BC pathogenesis and have been found to play distinct roles in the two BC subgroups. We speculated that the altered methylation profile may play a role in regulating signalling pathways with specific functions in the two subgroups of ER-BC.

Intelligent Tutoring Systems (ITS) are increasingly popular for online learning. These systems use adaptive algorithms to recommend relevant content based on students' profiles. However, instructors need to periodically assess students' performance to ensure learning outcomes and adjust strategies accordingly. Our objective is to predict students' progress in advance, enabling teachers to make quicker decisions and facilitating the iterative process of adaptive algorithms. For this study, we collected a dataset from ALIN, an online learning platform, consisting of over 5,000 students' learning records and test results. Using this dataset, we conducted experiments employing various machine learning algorithms. The results indicate that learning behavior contributes to improving forecast performance, while students' progress strongly correlates with their previous test results. Additionally, we discovered that students' progress can be indirectly predicted by forecasting their scores. Furthermore, by breaking down overall scores into several distinct components and predicting individual scores for each component, the accuracy of the forecasts can be improved.

Data mining techniques have garnered significant attention within the realm of education. However, the procurement of ample student data poses a formidable challenge. In response to this challenge, we present a student dataset characterized by its size and distinctive attributes. This dataset encompasses various task-related topics interconnected through a learning pathway, thereby enabling researchers to delve into the data from novel perspectives. Moreover, it encompasses extensive longitudinal student behavioral data, a rarity that adds substantial value. Spanning the years from 2010 to 2021, our dataset comprises a cohort of 7,933 students, 64,344 test scores, and 183,390 behavior records, solidifying its status as a valuable resource for educational research. In our experiments, we achieved successful predictions of students' test outcomes based on behavioral learning data. The strengths of our dataset render it apt for analyzing the nexus between student conduct and academic performance, crafting personalized learning recommendations, and pursuing various other research pursuits.

The airport ground handling service (AGHS) equipment vendor selection (AGHSEVS) problem is critical for ramp work safety management, because AGHS equipment malfunctions affect airport ramp work safety. Appropriate vendor selection can prevent aircraft damage and delays in airlines schedules, and ensure reliable and high-quality ground handling service. The AGHSEVS problem is a time-consuming and complex process that requires professional knowledge and experience to make judgments. Specifically, AGHSEVS is a multi-criteria decision-making (MCDM) problem. Previous research has seldom integrated MCDM methods with linear and goal programming to solve the AGHSEVS problem. The objective of this study was to develop a new system evaluation model for AGHSEVS by considering both qualitative and quantitative methods. We test the proposed approach on an AGHS company in Taiwan.

A key step in providing management/treatment options to men with suspected prostate cancer (PCa) is categorizing the risk for the presence of benign, low risk, intermediate risk, or high-risk disease. Our novel modality brings new evidence, based on the long-known hallmark characteristic of PCa – decreased Zinc (Zn), which is the most direct metabolic sign of malignancy and its aggressiveness. To date, this approach has not been adopted for clinical use for a number of reasons that are described in this article and which have been addressed by our approach: Zn has to be measured on fresh samples, prior to fixating in formalin, therefore samples have to be scanned during the biopsy session; as Zn depletion occurs in the glands, where the tumors develop, estimation of the glands’ levels in the scanned tissue along with their compactness, are essential for accurate diagnosis. Combined with the Zn depletion, this facilitates a reliable assessment of the disease aggressiveness. Data gathered in the clinical study described here indicate that in addition to improving the biopsy quality by real-time interactive guidance, a malignancy score can now be established for the entire prostate, allowing higher granularity personalized risk stratification and more decisive treatment decisions for all PCa patients.

Backgroundː Previous studies have shown that both serum creatinine and D-dimer levels were associated with atherosclerotic coronary artery disease (CAD). We aimed to determine whether DCR is associated with coronary Gensini score in patients with ST-elevation myocardial infarction (STEMI). Methodsː 337 STEMI patients with complete D-dimer and creatinine and other necessary information were included in the analysis. According to the values of the DCR, patients were divided into the lower DCR group (DCR ≤ 1.42, n = 173) and the higher DCR group (DFR > 1.42, n = 174), and the differences between the two groups were compared. Multivariate linear and multivariate logistic regression analyses were performed to determine independent predictors of Gensini score. Resultsː High DCR group had higher Gensini score compared with low DCR group (P < 0.05). DCR was positively correlated with Gensini score (r=0.493, P < 0.001). Multiple linear regression analysis showed that Previous MI (r=11.312, P=0.035) and DCR (r=5.129, P<0.001) were independent risk factors associated with Gensini score. Multivariate logistic regression analysis showed that, compared to the group 1, DCR is independent risk factor for Group 2, Group 3, Group 4 (P <0.001). DCR is positively correlated with coronary Gensini score in STEMI patients and can be used as an independent predictor of higher Gensini score. Conclusionsː As a new and useful clinical marker, DCR is positively correlated with coronary Gensini score in STEMI patients and can be used as an independent predictor of higher Gensini score.

The Australian dairy sheep industry is small and mostly based on a natural grass grazing system which can limit productivity. The current study tested different plant oil-infused and rumen protected polyunsaturated fats and their interactions with sire breeds to improve lactation traits and body condition score (BCS) of ewes grazing low quality pastures. It was hypothesised that supplementing lactating ewe diets plant-derived polyunsaturated oils will improve milk production and composition without compromising BCS. Sixty ewes (n=10/treatment) in mid-lactation, balanced by sire breed, parity, milk yield, body condition score, and liveweight were supplemented with: 1) control: wheat-based pellets without oil inclusion; wheat-based pellets including 2) canola oil (CO); 3) rice bran oil (RBO); 4) flaxseed oil (FSO), 5); safflower oil (SFO) and 6) rumen protected fat containing eicosapentaenoic acid and docosahexaenoic acid (RPO). Except for the control group, all supplementary diets included the same level of 50 ml/kg DM of oil and all diets were isocaloric and isonitrogenous. Experimental animals were grazed in the same paddock with ad libitum access to pasture, hay and water during the 10-week study. RPO was the most effective diet that enhanced milk, fat and protein yields by approximately 30, 13, and 31% respectively (P<0.0001). Significant increase in milk production was also observed in CO, RBO, and SFO (P<0.0001). Breed significantly influenced animal performance with higher milk yield recorded for crossbred Awassi x East Friesian (AW x EF) (578 g/day) vs purebred Awassi (452 g/day) (P<0.0001). This study provides empirical evidence for the use of rumen-protected and plant-derived oil-infused pellets as supplements under low quality pasture grazing conditions, to improve production performance of purebred Awassi and crossbred AW x EF ewes.

Posttraumatic stress disorder (PTSD) and/or specific PTSD symptoms may evoke disordered eating and unhealthy lifestyles, resulting in adverse cardiometabolic events (e.g., hypertension and obesity) in certain groups, which may implicate the treatment of this complex condition. The diagnostic criteria of PTSD have lately expanded beyond the three common symptoms (intrusion, avoidance, and hyperarousal). Simultaneously, four-, five-, and six-dimensional structures of the Impact of Event Scale-Revised (IES-R), a popular PTSD measure, seem to be more robust than the original three-dimension structure. Within the context of COVID-19, this instrumental study used a convenience sample of 58 dental healthcare workers (HCWs) from Russia (mean age = 44.1±12.2 years, 82.8% females) to examine the criterion and predictive validity of two IES-R structures: the IES-R3 and the IES-R6 (with the added symptoms of numbing, sleep disturbance, and irritability). The subscales of the two IES-R structures expressed good internal consistency, strong correlations with the PTSD Symptom Scale (PSS), hypertension diagnosis, and high body mass index (BMI). In receiver-operating characteristic (ROC) curve analysis, all IES-R models perfectly predicted the PSS (all area under the curve (AUC) >0.9, p values <0.001). The IES-R, both hyperarousal subscales, and the IES-R3 intrusion subscale significantly predicted high BMI. Both avoidance subscales and the IES-R3 intrusion subscale, not the IES-R, significantly predicted hypertension. In conclusion, both IES-R structures can reliably measure PTSD symptoms. The IES-R, hyperarousal, and intrusion may be credible criterion variables for predicting high BMI within PTSD while the intrusion and avoidance subscales may reflect the cardiovascular consequences associated with PTSD more reliably than the IES-R itself. The IES-R subscales may predict cardiometabolic adversities in PTSD, signifying a need for proper assessment of lifestyle and the application of dietary and exercise interventions in order to lower physical morbidity in PTSD. Replicating the study in larger samples, which comprise different physical and mental conditions from heterogenous cultural contexts is pivotal to validate the results (e.g., in specific groups such as those with confirmed traumatic exposure and comorbid mood dysfunction).

Background and aim: To explore the discriminant accuracy of Cytokeratin 18 (CK18, including M65 and M30 forms) for an elevated fatty liver index (FLI) as a validated proxy of Non-Alcoholic Fatty Liver Disease (NAFLD), and cardiovascular disease (CVD) risk in the general population. Methods: Both serum CK18 forms were measured using a commercial immunoassay in randomly selected samples from 312 participants of the PREVEND general population cohort. FLI ≥60 was used to indicate NAFLD. Framingham Risk Score (FRS) and the SCORE2 were used to estimate the 10-year risk of CVD. Receiver Operating Characteristic (ROC) curve, linear/logistic regression models and Spearman's correlations were used. Results: Intricate associations were found between CK18, FLI and CVD risk scores. While M30 was the only independent predictor of FLI≥60, M65 discriminated best NAFLD individuals at very-high 10-years CVD risk according to SCORE2 (AUC:0.71; p=0.001). Values above the predefined manufacturer cut-off (400 U/l), were associated with an independent 5-fold increased risk (adjusted odds ratio: 5.44, p=0.01), with negative predictive value of 93%. Conclusions: Confirming that NAFLD is associated with an increased CVD risk, our results point to CK18 M65 as a candidate biomarker to identify NAFLD individuals at low CVD risk in European general population.

Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers’ relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real-time. Based on Weight Agnostic Neural Networks (WANNs) methodology, we propose an automated searching neural net architectures strategy that can perform various tasks such as identify zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets.

Endometrial Cancer (EC) is the sixth most commonly occurring cancer in women with 380 000 cases in 2018. Sadly, EC morbidity and mortality are continuously increasing, therefore the medical society have a substantial need for an accurate and inexpensive diagnostic test for EC early detection and a prognostic tool for treatment planning and evaluation. Considering experience with different types of cancers C-reactive protein (CRP) appears to be a promising diagnostic and prognostic factor. Aiming to investigate its potential and in view of EC authors, this paper reviewed the following databases for metanalysis, randomized controlled trials and review articles published up to June 2020: Pubmed, Scopus, Google scholar and ClinicalKey. Studies indicate CRP >3.33 mg/l correlate with EC incidence with HR = 2.29 (p<0.05). Moreover, High-sensitivity CRP assay allows to detect CRP in very low concentrations and distinguish patients with endometriosis, soft tissue sarcomas and possibly EC. Preoperational and postoperational CRP, as well as its dynamic change are independent prognostic factors for EC and are more reliable if analyzed together. However, CRP-to-albumin ratio as well as Glasgow Prognostic Scale have greater prognostic value that CRP alone. Additionally, CRP is possibly a mediator of carcinogenesis and cancer progression through activation of inter alia FcgRs/MAPK/ERK, FcgRs/IL-6/AKT/STAT3 and FcgRs/NF-κB/NLRP3 pathways.

Patients with Peripheral-artery-disease (PAD) are prone to risks of major-adverse-limb-events (MALE), and major-adverse-cardiovascular-events (MACE). CHA2DS2-VASc is a prognostic score for Atrial fibrillation stroke risk, however, no study exists that evaluated its predictability for MALE and MACE in PAD patients. In this study, patients with PAD were stratified into four risk groups based on CHA2DS2-VASc score. Cox-proportional hazards models,10-fold cross-validation and Receiver operating characteristic (ROC) analyses were utilized to evaluate the predictability of CHA2DS2-VASc for predicting MALE, MACE, and MALE+MACE. Kaplan-Meier analysis estimated the survival-probability of risk-groups. CHA2DS2-VASc was found to be a significant predictor of MACE (hazard ratio (95% confidence interval; P) = 3.47 (0.99 - 12.18; 0.052), 4.12 (1.19 - 14.14; 0.024), and 5.06 (1.48 – 17.28; 0.009), for moderate-, high- and very-high-risk groups, respectively, while for MALE and MALE+MACE significance was achieved only for high-risk group. MACE demonstrated AUC > 0.8, good-discrimination (c-index > 0.8) and good-calibration. However, MALE and MALE+MACE models failed to achieve good performances. Based on all findings, CHA2DS2-VASc could potentially serve as a risk stratification score for predicting MACE in patients with PAD but failed to qualify as a good predictor for MALE.

(1) Background: Although multiple studies have reported on the therapeutic effects of Atezo+Bev for the treatment of NASH-related liver cancer, few studies have focused on its effect on liver reserve. Here, we aimed to compare the changes in hepatic reserve during the Atezo+Bev treatment of NAFLD/NASH-related HCC and of non-NAFLD/non-NASH-related HCC. (2) Methods: We retrospectively compared the changes in ALBI score in 109 patients with NAFLD/NASH or non-NAFLD/non-NASH during 12 weeks of treatment with Atezo+Bev for advanced HCC between September 2019 and January 2022. PSM was performed, and the OS, PFS, and PPS of the two groups were compared. (3)Results: The ALBI after 12 weeks of Atezo+Bev treatment was significantly worse in the NAFLD/NASH group than in the non-NAFLD/non-NASH group. Analysis of the 24 propensity score-matched pairs of cases with NAFLD/NASH or non-NAFLD non-NASH yielded similar results. The prognoses of the two groups were similar with respect to OS and PFS, but the NAFLD/NASH group had a significantly shorter PPS. The 24 matched pairs had similar OS and PFS, but the PPS of the NAFLD/NASH group was shorter. (4)Conclusion: Atezo+Bev treatment may worsen hepatic reserve, and there-fore life expectancy, in patients with NAFLD/NASH-related liver cancer.

Farmers are continually looking for new reliable, objective and non-invasive methods for evaluation of ewe condition. Live weight (LW) and body condition score (BCS) are used by farmers as a basis to determine the condition of the animal. Body composition is an important aspect of monitoring animal condition. The body composition is the amount of fat, lean and bone; knowing the amount of each is important because the information can be used for better strategic management interventions. Experiments were conducted to establish the relationship between body composition and body parameters, at key life’s stages (weaning and pre-mating), using measurements automatically determined by an image processing ap-plication at Lincoln University sheep farm for 88 Coopworth ewes. Computerized Tomography technology was used to develop relationship with body parameters and a subset was used to validate the predicted model. Multivariate linear regression (MLR), artificial neural network (ANNs) and regression tree (RT) statistical analysis methods were evaluated to determine their efficacy to predict body fat, lean and bone. The results showed a correlation between fat, lean and bone determined by CT and the fat, lean, bone weight estimated by live weight and body parameters calculated using the image processing application with R2 values of 0.90 for fat, 0.72 for lean and 0.50 for bone using ANNs statistical model. From these results, farmers can utilize accurate measures of fat which will enhance nutritional and management practices.

Textile and clothing is one of the most important industrial sectors, not only due to the significant number of jobs generated, but also because it addresses one of the people’s fundamental needs (clothing). It is, however, a sector with a huge environmental impact, all over the world, and also an important social impact, especially in developing countries. Sustainability in the Textile &amp; Clothing value chain is a known issue, concerning both environmental, and economical-social facets of sustainability. One way to improve sustainability in this sector is by measuring the environmental, economic and social impacts of value chain activities and, ultimately, computing an Environmental and Circular score for each batch of textile and clothing product, and an Economical and Social score for each involved firm, reflected in their products. The consumer will then have the opportunity and responsibility for selecting products with the least negative environmental, economical and social impact. This article presents the results of STVgoDigital project - PPS1, developed in Portugal with the aim of implementing the traceability of textile and clothing products, boosting sustainability in this sector, and preparing the ground for the implementation of the circular economy. More exactly, a blockchain-based textile and clothing value-chain traceability platform is presented, for tracing textile product lots and activities, along the value chain, classifying them with a score, which measures their environmental and social impact. The environmental, economic and social impact scores are based on a set of proposed indicators. The results are assessed through the presentation of a case study of a face towel (home textile) and a case study of a T-shirt (clothing).

This paper proposes a non-extensive entropy econometric technique to predict energy efficiency at province (NUT-2) level based on imperfect knowledge of the national overall efficiency in the sectors of industry, transport, households and services. The model is applied to the polish case. As acknowledged in recent literature, non-extensive entropy model should remain a valuable device for econometric modelling even in the case of low frequency series since outputs provided by the Gibbs-Shannon entropy approach correspond to the Tsallis entropy limiting case of the Gaussian law when the Tsallis q-parameter converges to unity. Therefore, we set up a q-Tsallis-Kullback-Leibler entropy criterion function with a priori consistency moment and model data constraints, including province energy intensity (known with uncertainty), regional climate differentiation and regular conditions. The model outputs continue to conform to empirical expectations. In spite of the close to unity q-Tsallis parameter, this Tsallis related approach reflects higher stability for parameter computation in comparison with the Shannon-Gibbs entropy econometrics technique. The proposed technique can be applied in different EU countries and elsewhere for example in the context of experimental official statistics.

Given that the outcome of hepatic arterial infusion chemotherapy (HAIC) with cisplatin for intrahepatic advanced hepatocellular carcinoma (HCC) is unclear, we aimed to compare prognostic factors for overall survival (OS) following HAIC with cisplatin versus sorafenib for intrahepatic advanced HCC using propensity score-matched analysis. We enrolled 348 patients with intrahepatic advanced HCC who received HAIC with cisplatin (n = 97) or sorafenib (n = 251) between June 2006 and March 2020. No significant difference was observed in OS between HAIC with cisplatin and sorafenib cohorts (median survival time [MST]: 13.9 vs. 12.7 months; p = 0.0989). To reduce confounding effects, 176 patients were selected using propensity score-matched analysis (n = 88 for each treatment). HAIC with cisplatin significantly prolonged OS compared with sorafenib (MST: 16.2 vs. 12.2 months; p = 0.0060). Following stratification according to the Child–Pugh classification, for both patients with class A (MST: 24.0 vs. 15.6 months; p = 0.0097) and class B (MST: 8.5 vs. 6.9 months; p = 0.0391), HAIC with cisplatin rather than sorafenib significantly prolonged OS. Our findings suggest that HAIC with cisplatin demonstrates longer prognostic effects than sorafenib in intrahepatic advanced HCC, regardless of the hepatic reserve.

In the face of escalating cybercriminal sophistication, an innovative approach to network anomaly detection has been pursued in this longitudinal study, integrating computational data analytics in a geographic, organizational, and behavioral context. A data-driven scoring mechanism was employed to systematically analyze and correlate source countries of IP addresses and organization-associated Autonomous System (AS) Numbers (ASN) with network anomalies. Significant correlations between certain countries, specific organizations, and high behavior scores were identified through the data analytics. An increase in connection requests was also found to be linked with elevated behavior scores. Validated by cross-validation techniques, these findings emphasize the necessity for continuous model recalibration. The transformative role of integrative data analytics in cybersecurity is underscored, paving the way for the development of more sophisticated, context-aware anomaly detection systems. Specifically, the analysis underscores the need for organizations to adopt a proactive and adaptive approach to cybersecurity that can keep pace with the evolving threat landscape.

Background: Current prostate cancer evaluation can be inaccurate and burdensome. Quantitative evaluation of MRI sequences non-invasively helps prostate tumor assessment. However, including Dynamic Contrast Enhancement in the examined MRI sequence set can add complications, inducing possible side effects from the IV placement or injected contrast material, and prolongs scanning time. Purpose: Predict the risk of developing Clinically Significant (Insignificant) prostate cancer CsPCa (CiPCa) and International Society of Urologic Pathology (ISUP) grade using processed Signal to Clutter Ratio (SCR) derived from spatially registered bi-parametric MRI (SRBP-MRI) and thereby enhance non-invasively management of prostate cancer. Methods: This pilot study retrospectively analyzed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRI (Apparent Diffusion Coefficient, High B-value, T2) were resized, translated, cropped, and stitched to form spatially registered SRBP-MRI. Efficacy of noise reduction was tested by regularizing, eliminating principal components (PC), and minimizing elliptical volume from the covariance matrix to optimize the SCR. MRI guided biopsy (MRBx), Systematic Biopsy (SysBx), combination (MRBx+SysBx), or radical prostatectomy determined the ISUP grade for each patient. ISUP grade >=2 (<2) was judged as CsPCa (CiPCa). Linear and Logistic regression were fitted to ISUP grade and CsPCa/CiPCa SCR. Correlation Coefficients (R) and Area Under the Curves (AUC) for Receiver Operator Curves (ROC) evaluated the performance. Results: High correlation coefficients (R) (>0.55) and high AUC (=1.0) for linear and/or logistic fit from processed SCR and z-score for SRBP-MRI greatly exceed fits using prostate serum antigen, prostate volume, and patient age (R~0.17). Patients assessed with combined MRBx+SysBx and from individual MRI scanners achieved higher R (R=0.207+/-0.118) than using all patients in the fits. Conclusions: In the first study to date to apply and test hyperspectral approaches for assessing tumor aggressiveness on SRBP-MRI, high values of R and exceptional AUC to fit the ISUP grade and CsPCA/CiPCA were comparable or better than those from artificial intelligence in other studies.

Objectives: Studies evaluating machine learning (ML) algorithms on cross-lingual RadLex® mappings for developing context-sensitive radiological reporting tools are lacking. Therefore, we investigated whether ML-based approaches can be utilized to assist radiologists in providing key imaging biomarkers – such as The Alberta stroke programme early CT score (APECTS). Material and Methods: A stratified random sample (age, gender, year) of CT reports (n=206) with suspected ischemic stroke was generated out of 3997 reports signed off between 2015-2019. Three independent, blinded readers assessed these reports and manually annotated clinico-radiologically relevant key features. The primary outcome was whether ASPECTS should have been provided (yes/no: 154/52). For all reports, both the findings and impressions underwent cross-lingual (German to English) RadLex®-mappings using natural language processing. Well-established ML-algorithms including classification trees, random forests, elastic net, support vector machines (SVMs) and boosted trees were evaluated in a 5 x 5-fold nested cross-validation framework. Further, a linear classifier (fastText) was directly fitted on the German reports. Ensemble learning was used to provide robust importance rankings of these ML-algorithms. Performance was evaluated using derivates of the confusion matrix and metrics of calibration including AUC, brier score and log loss as well as visually by calibration plots. Results: On this imbalanced classification task SVMs showed the highest accuracies both on human-extracted- (87%) and fully automated RadLex® features (findings: 82.5%; impressions: 85.4%). FastText without pre-trained language model showed the highest accuracy (89.3%) and AUC (92%) on the impressions. Ensemble learner revealed that boosted trees, fastText and SVMs are the most important ML-classifiers. Boosted trees fitted on the findings showed the best overall calibration curve. Conclusions: Contextual ML-based assistance suggesting ASPECTS while reporting neuroradiological emergencies is feasible, even if ML-models are restricted to be developed on limited and highly imbalanced data sets.

Published in July 2018, TAILORx aimed to establish non-inferiority of endocrine therapy (ET) compared to addition of chemotherapy (CHT-ET) in hormone receptor positive, human epidermal receptor 2 negative (HR+/HER2-) breast cancer (BC) patients with a 21-gene intermediate (11-25) recurrence score (RS). While this hypothesis proved correct, the study did show benefit of addition of chemotherapy (CHT) in a subgroup of women under 50 years of age, and particularly in the RS 16-25. The aim of this present study was to look at how TAILORx findings, including changes in RS categories, impacted CHT implementation at one oncologic center in Basel, Switzerland, and to identify main factors leading to these changes. Methods: We conducted a retrospective study on HR+/HER2-, BC patients who underwent 21-gene genomic testing between 2010-2021, at our center. Patients with metastatic disease were not included. We identified 326 eligible patients, of which 165 had a BC diagnosis before TAILORx (cohort A) and 161 after TAILORx publication (cohort B). Results: Demographic and tumor characteristics were similar in the two cohorts, although cohort B included significantly more women under the age of 50 when compared to A (34% vs. 24%, p<0.001). Median age and mean RS results were comparable 59 (IQR 16) in A and 58 (IQR 19) years in B and 17.72 (SD9.59) in A and 17.89 (SD9.53) in B, respectively. Patients in cohort A were slightly more overweight when compared to B (55% vs. 40% respectively, p<0.001). Most patients had stage II tumors of NST histologic type. Tumors in cohort B had higher Ki-67 than cohort A (39% vs. 32%, p=0.010). When compared based on manufacturer’s and based on TAILORx thresholds there were no significant differences in RS distribution. However, changes in score category led to shifts in patient population distribution, leading to a 40% drop in the low RS (from 60% to 20%), a doubling in the intermediate RS (from 30% to 60%) and an increase of 5% in the high RS (from 8-10% to 15%). Most patients had conservative surgery, adjuvant radiotherapy (RT) and ET. Overall CHT recommendation and application did not differ significantly in B vs. A. There was a reduction of 1% in the intermediate RS (11-25) and an increase of 13% CHT-ET application in the high RS (>26) category. In cohort B we noticed an increase in CHT-ET application among women <50 years old (by 12.5%), in lobular carcinomas (by 10%), grade 3 tumors (by 2%), node positive BC (by 3%) and node negative (by 2%). Tumor board recommendation for CHT dropped by 1% in the post TAILORx era, with a notable reduction of 5% in the intermediate RS (11-25) category. However, overall CHT administration rate was 19% in cohort A and 22% in B (p=0.763). Tumor Board recommended CHT for 90% of the BC patients that would have otherwise been assigned to CHT according to new RS guidelines in A and for 85% in B, showing a trend for undertreatment. Logistic regression analysis showed significance of age in both cohort A (OR 1.05, 95% CI 1.01-1.11, p=0.03) and cohort B (OR 0.89, 95% CI 0.84-0.94), p<0.001), and of nodal status in both cohort A (=R 3.32 95% CI 1.09-10-06, p=0.034) and cohort B (OR 3.31, 95% CI 1.29-8.53, p=0.013), while intermediate and high RS seem to be more relevant in cohort A (OR 0.12, 95% CI 0.03-0.43, p=0.01 and OR 0.04, 95% CI 0.01-0.21, p<0.01). Overall logistic regression analysis showed relevance of age (OR 0.93, 95% CI 0.08-0.97, p=0.001), pN (OR 4.77, 95% CI 2.03-11.22, p<0.001) and RS categories (RS 11-25: OR 0.02, 95% CI 0.01-0.07, p<0.001; RS>26: OR 617.93, 95% CI 57.97-6587.16, p<0.001). Conclusion: Our findings are similar to those reported across several studies: while tumor board recommendation for CHT decreases in the intermediate RS category, there is an increase being reported in the high RS category, leading to overall minor changes in CHT application. Administration of CHT-ET seems to be increasing among younger women, with unfavorable histo-pathological factors, such as lobular carcinoma and G3 histologic grade. Before TAILORx there is a tendency for undertreatment (-10%), especially among older BC patients, which seems to be maintained and even deepened (-15%) in the post TAILORx era, pointing to a personalized decision-making approach among Swiss oncologists.

Tailoring treatment in patients with Hodgkin lymphoma (HL) is paramount to maximize outcomes while avoiding unnecessary toxicity. We aimed to compare the performance of SUVmax reduction (ΔSUVmax%) versus Deauville score (DS) in assessing chemotherapy response in pediatric HL patients undergoing 18F-FDG PET-CT. Fifty-two patients with biopsy-proven HL (age 8-16 years) were enrolled at baseline, interim (after the 2nd or 3rd chemotherapy round), and post-therapy (upon completion of first-line chemotherapy) 18F-FDG PET-CT. Interim and post-therapy DS and ΔSUVmax% were compared as response predictors. Patients were classified as responders and non-responders based on 24-month clinical follow-up. Interim DS showed a sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy of 100%, 80.4%, 100%, 40% and 82.7%, respectively in predicting response. Post-therapy DS showed a sensitivity, specificity, PPV, NPV and accuracy of 66.7%, 97.8%, 95.7%, 80% and 94.2%. Interim ΔSUVmax% showed a sensitivity, specificity, PPV, NPV and accuracy of 83.3%, 82.6%, 97.4%, 38.5% and 82.7%, with a 56.3% cutoff. Post-therapy ΔSUVmax% showed a sensitivity, specificity, PPV, NPV and accuracy of 83.3%, 84.8%, 97.5%, 41.7% and 84.6%, with a 76.8% cutoff. Sensitivity, specificity (p<0.05) and NPV (p<0.01) were significantly higher using DS than ΔSUVmax%. In conclusion, DS can predict chemotherapy response better than ΔSUVmax% in pediatric HL patients.

A number of epidemiological studies have implicated elevated iron levels with higher risks of cardiometabolic diseases, including the metabolic syndrome (MetS). MetS is a pathologic clustering of metabolic derangements characterized by central obesity, insulin resistance, hypertension, and dyslipidemia, which can all lead to increased cardiovascular disease morbidity and mortality. However, it remains unclear whether a causal association exists between higher iron status and MetS and its associated risk components. Using the data from the Taiwan Biobank (TWB) (19,012 males; 42,340 females) and of the European Whites from the UK Biobank (UKB) (128,549 males; 142,817 females), we first constructed ethnic- and sex-specific hemoglobin-polygenic risk scores (Hb-PRS) as instrumental variable (IV) for iron status with the Hb-associated SNPs we previously identified from genome-wide association studies (GWAS). We then conducted Mendelian randomization (MR) analyses to elucidate the causal role of elevated iron (either Hb-PRS quintiles or continuous Hb-PRS) on various metabolic components. MR-Egger regressions determined the potential pleiotropic effects of the constructed Hb-PRSs, while the causal relationship of Hb-PRS with anemia served as a control in the analyses. The mean Hb concentration range corresponding to the Hb-PRS quintiles is relatively narrow in both UKB (14.91-15.21 g/dL for males and 13.39-13.71 g/dL for females) and TWB (14.79-15.28 g/dL for males and 12.78-13.21 g/dL for females). In these windows of Hb-PRSs, we found differential associations of Hb-PRS with diabetes (T2D) or hyperglycemia (HGY) as compared to other MetS components. The highest quintiles of Hb-PRSs were causally associated with greater risks of hypertension (HTN) and hypertriglyceridemia (DLP) (i.e., [Taiwanese HC males: Q5 vs. Q1 ORHTN = 1.10 (1.00–1.21), p = 0.0473, Q4 vs. Q1 ORDLP = 1.10 (1.00–1.22), p = 0.0485; European White males: ORHTN = 1.05 (1.01–1.09), p = 0.0194, ORDLP = 1.04 (1.00–1.08), p = 0.0468]). Interestingly, we obtained a consistent negative causal association between increasing Hb-PRS quintiles and T2D or HGY risk across all sex subgroups (i.e., [Taiwanese HC females: ORHGY = 0.89 (0.84–0.95), 0.88 (0.83–0.94), 0.87 (0.82–0.93), 0.86 (0.81–0.92) from Q2 to Q5; European White females: ORHGY = 0.92 (0.87–0.98), 0.91 (0.86–0.97), 0.93 (0.87–0.98), 0.87 (0.82–0.93) from Q2 to Q5; all p &lt; 0.05]. The positive causal relationships between Hb-PRSs and diastolic blood pressure (DBP) and triglycerides (TG) but inverse causal association with glucose (GLU) and glycated hemoglobin (HbA1c) further corroborated such findings. There is, however, a lack of causal association with central obesity (CO), while a U-shaped type of causality was observed for gout (GT) or hyperuricemia (HUA). Sensitivity analyses, which excluded subjects based on self-reports of health conditions or medications and hospital episode statistics data, revealed similar results. Our MR studies confirm a modest causal relationship between propensity of elevated iron status and increased risk of HTN and DLP. We have further observed that modestly lower iron levels causally contribute to the development of T2D. Further validation and elucidation of the underlying biological mechanisms are highly warranted.

The Impact of Event Scale-Revised (IES-R) is the most popular measure of post-traumatic stress disorder (PTSD), which has been recently validated in Arabic. This instrumental study aimed to determine optimal cutoff scores of the IES-R and its subscales in Arab samples of psychiatric patients (N = 168, 70.8% females) and healthy adults (N = 992, 62.7% females) from Saudi Arabia during the COVID-19 pandemic as an ongoing collective traumatic event. Based on a cutoff score of 14 of the Depression Anxiety Stress Scale 8-items (DASS-8), receiver operator curve (ROC) analysis revealed two optimal points of 39.5 and 30.5 for the IES-R in the samples (area under the curve (AUC) = 0.86 &amp; 0.91, p values = 0.001, 95% CI: 0.80-0.92 &amp; 0.87 to 0.94, sensitivity = 0.85 &amp; 0.87, specificity = 0.73 &amp; 0.83, Youden index = 0.58 &amp; 0.70, respectively). Different cutoffs were detected for the six subscales of the IES-R, with numbing and avoidance expressing the lowest predictivity for distress. Meanwhile, hyperarousal followed by the irritability expressed stronger predictive capacity for distress than all subscales in both samples. In path analysis, pandemic-related irritability resulted from direct and indirect effects of key PTSD symptoms (intrusion, hyperarousal, and numbing). Irritability contributed to traumatic symptoms of sleep disturbance in both samples while the opposite was not true. The findings suggest usefulness of the IES-R at a score of 30.5 for detecting adults prone to trauma related distress, with higher scores needed for screening in psychiatric patients. Various PTSD symptoms may induce dysphoric mood, which represents a considerable burden that may induce circadian misalignment and more noxious psychiatric problems/ co-morbidities (sleep disturbance) in both healthy and diseased groups.

Gastric cancer is a heterogeneous group of diseases with only a fraction of patients responds to immunotherapy. The relationships between tumor DNA damage response, the immune system and immunotherapy have recently attracted attention. Accumulating evidence indicate that DNA repair landscape is a significant factor in driving response to immune checkpoint blockade (ICB) therapy. In this study, to explore new prognostic and predictive biomarkers for gastric cancer patients who are sensitive and responsible to immunotherapy, we developed a novel 15-DNA repair gene signature (DRGS) and its related scoring system and evaluated the efficiency of DRGS in discriminating different molecular and immune characteristics and therapeutic outcomes of gastric adenocarcinoma. The results showed that DRGS high score patients showed significantly better therapeutic outcomes compared to DRGS low score patients (P < 0.001). Integrated analysis of multi-omics data demonstrated that the patients with high DRGS score were characteristic of high levels of anti-tumor lymphocytes infiltration, tumor mutation burden (TMB) and PD-L1 expression, and these patients exhibited a longer overall survival and may benefit more from ICB therapy, as compared to the low-score patients. Therefore, the DRGS and its scoring system may have implications in tailoring immunotherapy in gastric cancers.

Chemotherapy for lung cancer has made remarkable progress, and its selection has been subdivided by genetic analysis. Although response evaluation criteria in solid tumors (RECIST) is a simple and highly objective evaluation method, it has been pointed out that it has the disadvantage of not being able to accurately evaluate the therapeutic effect of molecularly targeted drugs and immune checkpoint inhibitors. The purpose of this study is to determine whether quantitative evaluation of DWIBS is useful in determining the effectiveness of treatment for lung cancer. There were 31 patients with lung cancer and 56 patterns were obtained. First, the determination of treatment effect (PD, SD, PR) based on RECIST was performed on CT. Second, tDV and ADC (median) were measured using BD score from DWI images taken at the same time, and the rate of change was calculated. Then, we compared and examined the correlation between RECIST and the rate of change in tDV. There were correlations between RECIST and the rate of change in tDV in PD and PR cases respectively. Compared RECIST on CT, DWIBS using BD score would be more accurate for response evaluation of treatment. There was a tendency that tDV decreased with increasing ADC values, but some of the cases had a dissociated response with increasing ADC values and increasing tDV. Thus, since the color display of ADC values allows us to infer their contents, it would be useful for the evaluation of cases such as dissociated response and pseudoprogression, which are suspected to be atypical responses that are difficult to evaluate with RECIST and other methods.

The concept of neutrosophic number is a significant mathematical tool to deal with real scientific problems because it can tackle indeterminate and incomplete information which exists generally in real problems. In this article, we use neutrosophic numbers (a + bI), where a and bI denote determinate component and indeterminate component respectively. We explore the situations in which the input information is needed to express in terms of neutrosophic numbers. We define score functions and accuracy functions for ranking neutrosophic numbers. We then define a cosine function to determine unknown criteria weights. We define neutrosophic number harmonic mean operators and proved their basic properties. Then, we develop two novel MCGDM strategies using the proposed aggregation operators. We solve a numerical example to demonstrate the feasibility and effectiveness of the proposed two strategies. Sensitivity analysis with variation of “I” on neutrosophic numbers is performed to demonstrate how the preference ranking order of alternatives is sensitive to the change of “I”. The efficiency of the developed strategies is ascertained by comparing the obtained results from the proposed strategies with the existing strategies in the literature.

Polycystic ovary syndrome (PCOS) is a complex hormonal disorder which impair ovarian function. The adherence to healthy dietary patterns and physical exercise are the first line of recommended treatment for PCOS patients, but it is no clear what type of diet is more adequate. In this case-control study, we explore the association between the adherence to five dietary quality indices widely used and PCOS. We enrolled 126 cases of PCOS and 159 controls (Murcia, Spain). Diagnostic of PCOS and its phenotypes were established following the Rotterdam criteria [hyperandrogenism (H), oligoanovulation (O), polycystic ovaries morphology (POM)]. We used a validated food frequency questionnaires to calculate the scores for five dietary indices: alternate Healthy Eating index (AHEI), AHEI-2010, relative Mediterranean Dietary Score (rMED), alternate Mediterranean Dietary Score (aMED) and Dietary Approaches to Stop Hypertension (DASH). We used multivariable logistic regression to estimate adjusted odds ratios and confidence intervals. In the multivariable analysis, AHEI-2010 index was inversely associated with “H+O” PCOS’ phenotype (ORQ3 VS Q1= 0.1; 95% CI :(0.0; 0.9); P for trend= 0.02). In conclusion, we did not find any statistical significative association between dietary indices and total, anovulatory and ovulatory PCOS, but it seems interesting explore these association among the diverse phenotypes of PCOS in studies with higher sample size.

A retrospective cohort study was performed to determine the likelihood of malignant stroke after mechanical thrombectomy via ASPECTS-PASS score(A-P score), a novel scoring system using ASPECT score and number of arterial-passes. This prediction score is designed to help clinicians take early measures. Malignant stroke is defined as a large MCA infarct with mass effect that develops during the first 5 days following presentation and is associated with approximately 80% mortality. Previous studies have shown an association of low ASPECTS and high number of passes with poor outcome.We performed a retrospective cohort analysis on 78 patients with anterior circulation stroke who underwent mechanical thrombectomy at our hospital. We analyzed the likelihood of developing malignant stroke post thrombectomy in the first 5 days following successful recanalization (TICI ≽ 2b). Malignant stroke is defined as severe symptoms secondary to edema as evidenced by midline shift, cistern engorgement, and hemorrhagic conversion with edema. Patients with ASPECTS of 0-5 were excluded. We used two variables to predict the outcome: ASPECT score and number-of-passes. We used the equation ASPECT score minus number-of-passes (A-P) to arrive at a single number to predict the risk of malignant stroke. A-P scoring scale ranged from 0-9. Our study showed a statistically significant inverse relationship of A-P score and risk of malignant stroke. Approximately 90% of patients with A-P score < 5 developed malignant stroke in our study. A-P score can help predict malignant stroke post thrombectomy even after successful recanalization. Thus, A-P score of ≤ 5 suggests very high risk. (A-P) Score can assist clinicians to take early measures, and thereby prevent worse outcomes.

INTRODUCTION: The purpose of the study was to determine (a) the overall preclinical character; (b) the cumulative cutoff values and the risk ratio, and (c) the factors associated with severity by a unidimensional and multidimensional analysis on 2173 Sars-Cov2 patients. METHODS: The machine learning study population consisted of 2173 patients (1587 mild and non symptoms patients, 377 moderate patients, 209 severe patients). The status of the patients was recorded from September 2021 to March 2022. RESULTS: The Covid19 Severity directly links with a significant correlation to Age, Score index of the chest X-ray, percentage and quantity of neutrophils, Albumin, C reactive protein, and ratio of Lymphocytes. Their important cut off values (from regression analysis) respectively are: 77.56 years old (the mild-moderate group), 5.53 (the mild-moderate group) and 10.51 (the moderate-severe group), 84.80% (the mild-moderate group) and 87.74%(the moderate-severe group), 11.77G/L (the moderate-severe group), 29.73g/L (the moderate-severe group), 7.46mg/dL (the mild-moderate group), 6.32% (the moderate-severe group). Their significant (p<0.0001) R score correlation with the severity of Covid19, are: 0.44, 0.52 and 0.52, 0.33 and 0.44, 0.42, -0.43, 0.40, -0.41. Their significant risk ratio (p<0.00001) from the meta-analysis, respectively are: 4.19 [3.58-4.95], 3.29 [2.76-3.92] and 3.03 [2.4023;3.8314], 3.18 [2.73-3.70] and 3.32 [2.6480;4.1529], 3.15 [2.6153;3.8025], 3.4[2.91-3.97], 0.46 [0.3650;0.5752] (p<0.00001), 0.34 [0.2743;0.4210]. The pair ALT – Leucocytes and Transferrin – Anion Chloride get the most important correlation shift. ALT – Leucocytes show the important negative link (R=-1, p<0.00001) in the mild group to the significant positive correlation in the moderate group (R=1, p<0.00001). Transferrin–anion Chloride has an important positive association (R=1, p<0.00001) in the mild group with a significant negative correlation in the moderate group (R=-0.59, p<0.00001). The network map and HCA show that in the mild-moderate group, the closest neighbors with the Covid19 severity are ferritins, Age. Then there is C-reactive protein, SI of X-ray, Albumin, and Lactate dehydrogenase, which are the next close neighbors of these three factors. In the moderate-severe group, the closest neighbors with the Covid19 severity are Ferritin, Fibrinogen, Albumin, the quantity of Lymphocytes, SI of X-ray, white blood cells count, Lactate dehydrogenase, and quantity of neutrophils. CONCLUSIONS: Complete multidimensional study in 2173 Covid19 patients in Vietnam shows the whole picture of all the preclinical factors, which may become the clinical reference marker for surveillance and diagnostic management

Spinal Cord Stimulation (SCS) is an effective and validated treatment to address chronic refractory neuropathic pain in Persistent Spinal Pain Syndrome-Type 2 (PSPS-T2) patients. Surgical SCS lead placement is traditionally performed under general anesthesia due to its invasiveness. In parallel, recent works have suggested that Awake Anesthesia (AA), consisting in Target Controlled Intra-Veinous Anesthesia (TCIVA), could be an interesting tool to optimize lead anatomical placement using patient intra-operative feedback. We hypothesized that combining AA with Minimal Invasive Surgery (MIS) could improve SCS outcomes. The goal of this study was to evaluate SCS lead performance (defined by the area of pain adequately covered by paraesthesia generated via SCS), using an intraoperative objective quantitative mapping tool, and secondarily to assess pain relief, functional improvement and change in quality of life with a composite score. We analyzed data from a prospective multicenter study (ESTIMET) to compare the outcomes of 115 patients implanted with MIS under AA (MISAA group) or General Anesthesia (MISGA group), or by Laminectomy under General Anesthesia (LGA group). All in all, MISAA appears to show significantly better performance in terms of patient pain coverage, as well as improved secondary outcomes. One step further, our results suggest that MISAA combined with intra-operative hypnosis could potentialize patient intraoperative cooperation and could be proposed as a personalized package offered to PSPS-T2 patients eligible for SCS implantation in highly dedicated neuromodulation centers.

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.