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Molecular profiling of breast cancer methylation pattern in triple negative versus non- triple negative breast cancer
Abstract Epigenetic alterations, especially promotor methylation, have a significant impact on gene expression, molecular subtyping, prognosis, and outcome of breast cancer (BC). The methylation profile was assessed for 22 genes of the BC tissue using the EpiTect Methyl II PCR System in 40 triple-negative BC (TNBC) patients compared to 50 non-TNBC group. The data were corelated with the disease-free (DFS) and overall survival (OS) of the patients. Genes that were differentially hypermethylated in TNBC patients compared to those with non-TNBC included CCND2, CDKN2A, ESR1, CDH1, BRCA1, GSTP, RASSF1, SLIT2, MGMT, PTEN, TP73, and PRDM2. These panel achieved 95% sensitivity, 98% specificity, 97.44% positive predictive value (PPV), 94.23% negative predictive value (NPV), and AUC of 0.993. Hypermethylation of BRCA1, CDH1, CDKN2A, ESR1, GSTP, HIC1, MGMT, PRDM2, PTEN, PYCARDM, RASSF1M, THBS1, and TP73 associated significantly with worse OS and DFS in TNBC cohort. Meanwhile, CCNA1 and CDH1 hypermethylation demonstrated significant associations with poor DFS but did not show significant relationships with OS in TNBC patients. PTGS2 and TNFRSF10C methylation were associated with better DFS and OS rates in TNBC patients. On multivariate Cox regression, CCND2 and PTEN hypermethylation were independent predictors of DFS in the overall BC patients. The hypermethylation of BRCA1 and GSTP were independent predictors of DFS, while PTEN hypermethylation was an independent predictor of OS in the TNBC cohort. The identification of hypermethylated genes, such as BRCA1, CCND2, CDH1, ESR1, GSTP, RASSF1, SLIT2, MGMT, and PTEN may serve as potential biomarkers or therapeutic targets for TNBC.
Risk factors and predictive modeling in a US population with sarcopenia: a propensity score cohort study
Topic modeling analysis of the Allen Human Brain Atlas
Uterine and body temperament in women with and without polycystic ovary syndrome: a case-control study
N-benzyl-N-methyldecane-1-amine derived from garlic ameliorates UVB-induced photoaging in HaCaT cells and SKH-1 hairless mice
miR-16-5p, miR-21-5p, and miR-155-5p in circulating vesicles as psoriasis biomarkers
Abstract Psoriasis is a chronic skin disorder marked by fast skin cell growth, leading to thick, red, scaly patches. MicroRNAs are small, non-coding RNA molecules that play a crucial role in post-transcriptional gene regulation. This study investigates miR-16-5p, miR-21-5p, and miR-155-5p expression in psoriasis EVs and assesses their biomarker potential, exploring associated target genes and pathways via bioinformatics. A cross-sectional and case-control study included 40 psoriasis patients, with blood samples collected in EDTA tubes. RNA from extracellular vesicles was isolated using Qiagen kits, and miRNAs were quantified via RT-qPCR. Bioinformatic analysis predicted target genes using databases like miRDB and TargetScan. Gene expression data from GEO was processed, and differentially expressed genes were identified. This study assessed miR-16-5p, miR-21-5p, and miR-155-5p expression in psoriasis patients’ circulating vesicles versus controls, finding significantly lower levels in patients. ROC analysis confirmed their diagnostic potential. A positive correlation of miR-16-5p with the Psoriasis Area Severity Index (PASI) suggests severity marker potential. Bioinformatics identified 378 common dysregulated genes, revealing key pathways and gene interactions in psoriasis. A heat map confirmed miRNA-mediated gene suppression in the disease. This study identifies miR-16-5p, miR-21-5p, and miR-155-5p as potential psoriasis biomarkers, in addition to finding significant gene interactions and pathways involved in psoriasis pathophysiology.
Effects of startle on cognitive performance and physiological activity revealed by fNIRS and thermal imaging
Abstract Sudden and threatening stimuli can trigger a startle reflex, a stereotyped physiological response that may lead to a brief cognitive incapacitation. Better understanding this reaction would be beneficial to safety-critical occupational domains. We characterized some physiological correlates of the startle response while participants completed a difficult task (Toulouse N-back task) tapping executive functions. During the task, loud and threatening sounds were presented unpredictably to trigger a startle reflex. Brain activity and facial skin temperature were measured in 34 participants using functional near-infrared spectroscopy (fNIRS) and functional infrared thermal imaging (fITI), respectively. In the high difficulty condition, participants were generally less efficient, but their performance improved slightly following startle in the high difficulty condition. Brain activity in the right prefrontal cortex was also higher following startle, potentially reflecting a compensatory overactivation to sustain performance. Interestingly, higher trait-anxiety was associated with lower task performance, still following startle in the high difficulty condition. Finally, we found a decrease in temperature of the right eye and right cheek as well as an increase in the nose temperature following startle. These results underscore the complexity of startle-induced cognitive and physiological dynamics, which may have implications for occupational settings where managing sudden stressors is crucial.
Robust fuzzy dynamic integrated environmental-economic-social scheduling considering demand response and user’s satisfaction with electricity under multiple uncertainties
An intelligent network framework for driver distraction monitoring based on RES-SE-CNN
Abstract As the quantity of motor vehicles and drivers experiences a continuous upsurge, the road driving environment has grown progressively more complex. This complexity has led to a concomitant increase in the probability of traffic accidents. Ample research has demonstrated that distracted driving constitutes a primary human - related factor precipitating these accidents. Therefore, the real - time monitoring and issuance of warnings regarding distracted driving behaviors are of paramount significance. In this research, an intelligent driver state monitoring methodology founded on the RES - SE - CNN model architecture is proposed. When compared with three classical models, namely VGG19, DenseNet121, and ResNet50, the experimental outcomes indicate that the RES - SE - CNN model exhibits remarkable performance in the detection of driver distraction. Specifically, it attains a correct recognition rate of 97.28%. The RES - SE - CNN network architecture model is characterized by lower memory occupancy, rendering it more amenable to deployment on vehicle mobile terminals. This study validates the potential application of the intelligent driver distraction monitoring model, which is based on transfer learning, within the actual driving environment.
Genetic variation and heritability of agronomic traits in a native perennial forage species from drylands: breeding potential of Festuca pallescens
Immune dysregulation in COVID-19 induced ARDS in kidney transplant recipients revealed by single-cell RNA sequencing
Impact of high-sensitivity flow cytometry on peri-transplant minimal residual disease kinetics in acute leukemia
Predicting creep failure life in adhesive-bonded single-lap joints using machine learning
Temporal distribution shifts of Chum salmon (Oncorhynchus keta) with sea surface temperature changes at their southern limit in the North Pacific
Understanding the responses of marine organisms to environmental changes at their distribution limits is crucial for predicting climate-change associated habitat changes. This study analyzed the effect of sea surface temperature (SST) on the temporal distribution of Chum salmon (Oncorhynchus keta) in the eastern and southern coastal waters of Korea (ESCK) and on the southern limit of their distribution in the North Pacific. The temporal distribution of Chum in the ESCK and adjacent rivers was statistically compared based on three SST types (T1–T3). Chum were first caught in the northern and then in the southern area, with riverine migration occurring faster in the south than in the north. These migration patterns did not change with SST type. There was no significant difference in the coastal arrival timing of the Chum between T1 and T3, which respectively represented the entire region cooling either rapidly or slowly compared to an average year. In T2, in which the north cooled rapidly and the south cooled slowly, the coastal arrival timing was approximately 4 days earlier compared to T1 and T3. Moreover, as the SST type shifted from T1 to T3, the coastal residence time in the north became shorter, while in the south became longer. These findings help us to understand the adaptation strategies of Chum, and to predict changes in their distribution and resources in the North Pacific under climate change.
Machine learning-based prediction of distant metastasis risk in invasive ductal carcinoma of the breast
More than 90% of deaths due to breast cancer (BC) are due to metastasis-related complications, with invasive ductal carcinoma (IDC) of the breast being the most common pathologic type of breast cancer and highly susceptible to metastasis to distant organs. BC patients who develop cancer metastases are more likely to have a poor prognosis and poor quality of life, so it is extremely important to recognize and diagnose whether distant metastases have occurred in IDC as early as possible. In this study, we develop a non-invasive breast cancer classification system for detecting cancer metastasis. We used Anaconda-Jupyter notebooks to develop various Python programming modules for text mining, data processing, and machine learning (ML) methods. A risk prediction model was constructed based on four algorithms: Random Forest, XGBoost, Logistic Regression, and SVM. Additionally, we developed a hybrid model based on a voting mechanism using these four algorithms as the base models. The models were compared and evaluated by the following metrics: accuracy, precision, recall, F1-score, and area under the ROC curve (AUC) values. The experimental results show that the hybrid model based on the voting mechanism exhibits the best prediction performance (accuracy: 0.867, precision: 0.929, recall: 0.805, F1-score: 0.856, AUC: 0.94). This stable risk prediction model provides a valuable reference support for doctors in assessing and diagnosing the risk of IDC hematogenous metastasis. It also improves the work efficiency of doctors and strives to provide patients with increased chances of survival.
Evaluation of Moore and grab sampling method for Salmonella Typhi detection in environmental samples in Ghana
Background Typhoid fever causes substantial mortality and morbidity in low and middle-income countries (LMICs) as a result of inadequate water, hygiene, and sanitation facilities. The gold standard for typhoid diagnosis is blood culture, however this method is expensive and mostly unavailable in LMICs. Environmental surveillance (ES) could offer a low cost alternative to identify circulation of Salmonella enterica serovar Typhi (S.Typhi) and help inform public health interventions including vaccination. Methods We implemented standardized protocols for ES at 40 validated sites in peri-urban communities in Ghana from July 2022 to August 2023. Grab samples (GS) and Moore swabs (MS) were collected monthly for the initial 6 months and subsequently monthly MS were maintained for the rest of the study period. Wastewater samples were tested for S . Typhi target genes ( ttr, staG, tviB ) and a biomarker of human faecal contamination (Bacteroides HF183) using multiplex quantitative PCR (qPCR). Clinical surveillance for typhoid fever was performed by blood culture of febrile cases presenting to the local hospital who lived in the study area. Results For the first 6 months of wastewater ES, we observed a higher prevalence of S . Typhi in MS compared to GS [100/240 (42%; 95% Confidence Interval [CI]: 34-50% vs. 24/240 (10%; 95% CI: 6-16%)]; p-value < 0.001]. Overall, the detection of S. Typhi throughout the study period based on MS was 42.1% (202/480; 95% CI: 35-50%). The prevalence of S. Typhi in blood culture surveillance was 0.21% [12/5,576; 95% CI: 0.12-0.38%]. Precipitation (1.1 (95% CI: 1.02-1.10) and number of wet days (2.0 (95%CI: 1.40-2.88) were positively associated with an increased odds of S. Typhi detection in MS and GS. Conclusion Generally the proportion of S.Typhi detections in wastewater samples was less than blood culture-based detections. Limited detection of confirmed typhoid fever cases at the local hospital may reflect healthcare seeking behaviours, access as well as early treatment with over-the-counter antibiotics. Further work is required to confirm these qPCR detections with amplicon sequencing methods. Strategies also needs to be developed for integration of ES into public health decision making for the prevention of typhoid fever.
Comparison of two methods of measuring physical activity and its related components among middle age women: Pedometer versus self-report
Introduction Physical activity is essential for health maintenance across various dimensions; however, sedentary behavior remains widespread in numerous societies. Notably, women often exhibit higher levels of sedentary behavior than men, potentially exacerbating complications related to motherhood. This study aims to assess and compare physical activity levels in middle-aged women (30–45) using self-reported measures and pedometers while exploring associated factors in the context of reproductive health. Methods This study analyzed secondary data from a randomized controlled trial involving 91 women aged 30–45 from Semnan University of Medical Sciences. Participants completed a validated 12-item questionnaire and wore Omron Style Pro-2 pedometers for one week to assess physical activity levels. Women were categorized as active or inactive based on self-reports and pedometer data. Statistical analyses were conducted using SPSS version 27. Participants provided informed consent, ensuring confidentiality throughout the study. Results The study involved 91 women with a mean age of 27.4 years, of whom 82.6% were married. Only 11.7% self-reported as “low active,” while pedometer data revealed that 94.5% were inactive or had insufficient physical activity. A significant relationship was found between the number of children and physical activity levels, with women who had children being more active (p = 0.035). No correlation was observed between self-reported activity and pedometer measurements, indicating women often overestimate their physical activity levels. Discussion This study highlights the inaccuracies in self-reported physical activity assessments. Most women were found to be physically inactive, with the number of children influencing their activity levels. These findings underscore the need for interventions to increase women’s physical activity. Conclusion These findings bear important implications for health policy within communities. Further research involving larger sample sizes across various age and gender demographics is warranted to enhance our understanding of physical activity patterns and inform targeted interventions.
Performance evaluation of Cu2SrSnS4 based solar cell: effect of transition metal dichalcogenides buffer layer
Abstract The urgent demand for efficient renewable energy technologies has driven extensive research into quaternary chalcogenide materials, owing to their outstanding photovoltaic properties and potential for high performance. This study focuses on the design, performance optimization, and comparative analysis of Cu2SrSnS4-based solar cells, with particular emphasis on employing different transition metal dichalcogenide (TMD) buffer layers, specifically MoS2 and WS2. By utilizing SCAPS 1-D simulation software, the research systematically examines the impact of critical parameters such as buffer layer thickness, doping concentrations, and operating temperatures on the solar cell’s efficiency and stability. The simulation results demonstrate that the ZnO/MoS2/Cu2SrSnS4 configuration attained the highest efficiency, reaching an impressive 35.6%, significantly surpassing its counterpart with WS2 as the buffer layer, which achieved an efficiency of 29.1%. The findings demonstrate the significance of buffer layer selection and parameter optimization in maximizing the potential of Cu2SrSnS4 solar cells. Ultimately, this research offers valuable insights into the development of high-efficiency, stable photovoltaic technologies, advancing the future of next-generation quaternary chalcogenide solar cells.