Browse Articles

Association of chronic stress during studies with depressive symptoms 10 years later

Abstract The long-tern implications of stress during university for individuals’ mental health are not well understood so far. Hence, we aimed to examine the potential effect of stress while studying at university on depression in later life. We analysed data from two waves of the longitudinal Study on Occupational Allergy Risks. Using the ‘work overload’ and ‘proving oneself’ scales of the Trier Inventory for Chronic Stress and the Patient Health Questionnaire-2 (PHQ-2), participants reported chronic stress during university (2007–2009, mean age 22.2 years, T1) and depressive symptoms ten years later (2017–2018, mean age 31.6 years, T2). We performed linear regression analyses to explore the association between stress during university (T1) and later depressive symptoms (T2). Participants (N = 548, 59% female) indicated rather low levels of stress and depression (PHQ-2 mean score: 1.14 (range: 0–6)). We observed evidence for a linear association between overload at T1 and depression at T2 (regression coefficient (B) = 0.270; 95% confidence interval (CI) = 0.131 to 0.409; standardised regression coefficient (β) = 0.170). Our analyses yielded evidence for an association between chronic stress while studying and risk of depressive symptoms later in life. This finding underlines the importance of implementing sustainable preventive measures against stress among students.

A machine learning interpretation of the correlation between poverty and air pollution in the contiguous United States

Second-generation BRAF inhibitor Encorafenib resistance is regulated by NCOA4-mediated iron trafficking in the drug-resistant malignant melanoma cells

Sex differences in the time trends of sepsis biomarkers following polytrauma

Ecological risk assessment and response prediction caused by LUCC in the central Yunnan Province

Impact of the COVID-19 pandemic on utilization of mental health services among children and adolescents using an interrupted time series analysis

Optimization and DFT study for boosted electooxidation of formic acid at NiOx modified Pt using urea derivatives as blending fuels

Abstract This paper addresses the enhancement of formic acid electrooxidation (FAO) at Pt and Pt-NiOx nanoparticles based-catalysts assisted with urea derivatives as blending fuels. Blending formic acid with various ratios of urea derivatives showed noticeable enhancements of FAO as demonstrated by a favorable negative shift of its onset potential ( E onset ) and increase of its peak current density concurrently with suppression of the amount of CO poisoning reaction intermediate. Among all the used derivatives, phenyl urea (PU) showed superior enhancing effect towards the direct FAO with a minimal CO formation together with a favorable negative shift of E onset by 150 mV. The superb enhancing effect of PU over U and/or other urea derivatives (investigated herein) is attributed mainly to the withdrawing inductive effect of the phenyl group attached to urea. That is the formation of 8 membered ring via hydrogen bonding between PU and formate anion is thought to enrich the electrode/electrolyte interface by FA in such a favorable orientation facilitating the C-H scissoring resulting in the direct oxidation of FA (to CO 2 ) with almost no possibility for CO formation. DFT calculations are used to support this assumption in line with experimental results.

Novel geological framework to understand the origin and diversity of orthopyroxene, olivine, spinel (OOS) lithologies on the Moon

Association between wide-ranging food intake and Parkinson’s disease: a comprehensive mendelian randomization study

Abstract Parkinson’s disease (PD) is a complex neurodegenerative disorder influenced by both genetic and environmental factors, including dietary habits. Despite considerable research, the relationship between food intake and PD risk remains poorly understood. Here, we conducted a comprehensive Mendelian randomization analysis to investigate the association between a wide spectrum of food intake and PD risk. Utilizing data from large-scale genome-wide association studies (GWAS) and dietary databases, we constructed genetic instruments for various dietary factors, including fruit, vegetable, meat, fish, dairy, and grain intake, among others, totaling 170 different food items. Using multivariable inverse variance weighted methods, we found a causal relationship between Mozzarella intake and Parkinson’s disease (odds ratio [OR] = 9.83, 95% confidence interval [CI] = 2.52–38.34, P-value < 0.05). Additionally, a causal relationship was observed between Pancake intake and Parkinson’s disease (odds ratio [OR] = 0.20, 95% confidence interval [CI] = 0.07–0.59, P-value < 0.05). Furthermore, our reverse Mendelian randomization analysis and multivariable Mendelian randomization analysis provided further support for these findings. To our knowledge, we are the first to investigate the causal relationship between the broad intake of 170 different food items and Parkinson’s disease. Our study reveals the causal relationships between Pancake intake, and Mozzarella intake with Parkinson’s disease.

Limited preventive care among Medicaid enrollees with HIV in the US South

Quantifying the pyroelectric and photovoltaic coupling series of ferroelectric films

Barriers facilitators and needs of female sex workers in Arak to access sexual health services a qualitative study

High transmission in 120-degree sharp bends of inversion-symmetric and inversion-asymmetric photonic crystal waveguides

Investigating the role of the metabolic score for visceral Fat in assessing the prevalence of chronic kidney disease from the NHANES 1999–2018

Abstract This study investigates the association between the Metabolic Score for Visceral Fat (METS-VF) and chronic kidney disease (CKD), assessing METS-VF as a potential predictor of CKD risk. Utilizing data from the 1999–2018 National Health and Nutrition Examination Survey (NHANES), this cross-sectional study included 24,387 adult participants. Multivariable logistic regression, restricted cubic spline models, and threshold effect analyses were employed to explore the relationship. The results revealed a significant positive association, with multivariable logistic regression showing that each unit increase in METS-VF was associated with an 86% higher risk of CKD (OR: 1.86, 95% CI: 1.48–2.34). Critical METS-VF thresholds (6.10 and 7.55) were identified, at which CKD risk increased substantially. Subgroup analyses indicated that the association was particularly pronounced among older adults and males. These findings suggest that METS-VF is a reliable predictor for assessing CKD risk and that lifestyle interventions, including dietary modifications and exercise programs, may mitigate this risk.

Multiplexed transcriptomic analyzes of the plant embryonic hourglass

Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8

Quantification and prediction of human fetal (-)-Δ9-tetrahydrocannabinol/(±)-11-OH-Δ9-tetrahydrocannabinol exposure during pregnancy to inform fetal cannabis toxicity

Impact of stain variation and color normalization for prognostic predictions in pathology

Abstract In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. One complication in preparing digital pathology datasets for DNN tasks is the variation in tinctorial qualities. A common way to address this is to perform stain normalization on the images. In this study, we show that a well-trained DNN model trained on one batch of histological slides failed to generalize to another batch prepared at a different time from the same tissue blocks, even when stain normalization methods were applied. This study used sample data from a previously reported DNN that was able to identify patients with early-stage non-small cell lung cancer (NSCLC) whose tumors did and did not metastasize, with high accuracy, based on training and then testing of digital images from H&E stained primary tumor tissue sections processed at the same time. In this study, we obtained a new series of histologic slides from the adjacent recuts of the same tissue blocks processed in the same lab but at a different time. We found that the DNN trained on either batch of slides/images was unable to generalize and failed to predict progression in the other batch of slides/images (AUCcross-batch = 0.52 - 0.53 compared to AUCsame-batch = 0.74 - 0.81). The failure to generalize did not improve even when the tinctorial difference corrections were made through either traditional color-tuning or stain normalization with the help of a Cycle Generative Adversarial Network (CycleGAN) process. This highlights the need to develop an entirely new way to process and collect consistent microscopy images from histologic slides that can be used to both train and allow for the general application of predictive DNN algorithms.

Single-step retrosynthesis prediction via multitask graph representation learning

Benchmarking protein language models for protein crystallization

Abstract The problem of protein structure determination is usually solved by X-ray crystallography. Several in silico deep learning methods have been developed to overcome the high attrition rate, cost of experiments and extensive trial-and-error settings, for predicting the crystallization propensities of proteins based on their sequences. In this work, we benchmark the power of open protein language models (PLMs) through the TRILL platform, a be-spoke framework democratizing the usage of PLMs for the task of predicting crystallization propensities of proteins. By comparing LightGBM / XGBoost classifiers built on the average embedding representations of proteins learned by different PLMs, such as ESM2, Ankh, ProtT5-XL, ProstT5, xTrimoPGLM, SaProt with the performance of state-of-the-art sequence-based methods like DeepCrystal, ATTCrys and CLPred, we identify the most effective methods for predicting crystallization outcomes. The LightGBM classifiers utilizing embeddings from ESM2 model with 30 and 36 transformer layers and 150 and 3000 million parameters respectively have performance gains by 3- $$5\%$$ than all compared models for various evaluation metrics, including AUPR (Area Under Precision-Recall Curve), AUC (Area Under the Receiver Operating Characteristic Curve), and F1 on independent test sets. Furthermore, we fine-tune the ProtGPT2 model available via TRILL to generate crystallizable proteins. Starting with 3000 generated proteins and through a step of filtration processes including consensus of all open PLM-based classifiers, sequence identity through CD-HIT, secondary structure compatibility, aggregation screening, homology search and foldability evaluation, we identified a set of 5 novel proteins as potentially crystallizable.