Browse Articles

X-ray-Induced Photodegradation of Hydrogels by the Incorporation of X-ray-Activated Long Persistent Luminescent Nanoparticles

USP37 prevents premature disassembly of stressed replisomes by TRAIP

Abstract The eukaryotic replisome, which consists of the CDC45-MCM2-7-GINS (CMG) helicase, replicative polymerases, and several accessory factors, sometimes encounters proteinaceous obstacles that threaten genome integrity. These obstacles are targeted for removal or proteolysis by the E3 ubiquitin ligase TRAIP, which associates with the replisome. However, TRAIP must be carefully regulated to avoid inappropriate ubiquitylation and disassembly of the replisome. Here, we demonstrate that human cells lacking the de-ubiquitylating enzyme USP37 are hypersensitive to topoisomerase poisons and other replication stress-inducing agents. Furthermore, TRAIP loss rescues the hypersensitivity of USP37 knockout cells to topoisomerase inhibitors. In Xenopus egg extracts depleted of USP37, TRAIP promotes premature CMG ubiquitylation and disassembly when converging replisomes stall. Finally, guided by AlphaFold-Multimer, we discovered that binding to CDC45 mediates USP37’s response to topological stress. We propose that USP37 protects genome stability by preventing TRAIP-dependent CMG unloading when replication stress impedes timely termination.

Study on the quantitative analysis of Tilianin based on Raman spectroscopy combined with deep learning

Tilianin is a commonly used pharmaceutical ingredient with various biological activities such as antioxidant, anti-inflammatory, and anticancer, which is able to exert antitumor effects by inhibiting tumor cell proliferation, inducing apoptosis and inhibiting angiogenesis. Studies have demonstrated to be particularly useful in a variety of cancers such as liver, lung and gastric cancers. Quantitative analysis of Tilianin can improve the quality control of related drugs and assist in guiding clinical application and disease treatment. However, there are limited studies on the quantitative analysis of Tilianin. High performance liquid chromatography (HPLC) and mass spectrometry (MS) are commonly used methods for the quantitative analysis of the components, but they often require complex pretreatment steps and specialized analytical capabilities, and are sample-destructive. The method based on Raman spectroscopy and deep learning is a widely used non-destructive analysis method. For this reason, this paper proposes a residual self-attention mechanism model based on Raman spectroscopy and deep learning for quantitative analysis of 6 concentrations of Tilianin. Six different concentrations of Tilianin-methanol solutions were prepared, and a total of 120 spectral samples were collected, which were pre-processed and inputted into our Raman Spectrum with Self-Attention Quantification Net (RSAQN) for analyzing and predicting. The structure of this model not only focuses on the deep and shallow features of the spectrum, but also the information between different channels, and the self-attention mechanism further extracts the features and outputs the predicted values of Tilianin concentration through the fully connected layer. In this paper, five sets of comparison models are set up, including two machine learning models (Random Forest, K-Nearest Neighbors, Artificial Neural Network) and two deep learning models (Convolutional Neural Network and Variational Autoencoder), and the results show that the model in this paper fits the best, obtaining an R2 of 0.9144, as well as a small error.

Cryo-EM structure of a blue-shifted channelrhodopsin from Klebsormidium nitens

Circular saw blade wear status prediction based on generative adversarial network and CNN-LSTM model

Monitoring the status of circular saw blades is an effective measure to ensure the production efficiency and safety of spent fuel assembly cutting. However, the prediction of wear during the cutting of stainless steel shells of spent fuel assemblies by circular saw blades is not entirely accurate in complicated working conditions. The main challenges include deficiency of data acquisition, signal features extracting complex process, and insufficient robustness of models. To enhance the precision of forecasting consequence, a circular saw blade wear prediction method combining generative adversarial network (GAN) and CNN-LSTM models is proposed. The main fault types during the cutting of stainless steel shells by circular saw blades should be identified in advance, and vibration signals of each fault state is able to be collected then. The collected data is supposed to be preprocessed through sampling overlapping, single-layer wavelet transform denoising, and normalization. GAN optimized by the Pearson correlation coefficient (PCC) has been utilized aiming to expand the data volume of each fault state to 300 samples and resulting in a total data volume of 2100 samples; A CNN-LSTM model based on dual feature fusion has been established to identify the wear status of circular saw blades, achieving an accuracy rate of 100%, higher than Long Short-Term Memory (LSTM) neural networks (86.2%) and Radial Basis Function Neural Networks (RBFNNs) (94.9%). This study effectively solves the problem of small sample sizes for circular saw blade wear data, and provides an efficient and accurate method for circular saw blade wear identification under complex working conditions, which has important practical significance for improving the safety and efficiency of spent fuel assembly cutting.

Enzymatic twists evolved stereo-divergent alkaloids in the Solanaceae family

Comparison of longitudinal PSQI and actigraphy-assessed sleep from pregnancy to postpartum

Despite the importance of sleep for perinatal health, there is limited research examining whether different measurement modalities may yield inconsistent data from pregnancy through postpartum. We aimed to: 1) describe how sleep patterns change across pregnancy and postpartum using self-report PSQI and actigraphy measures and 2) determine the level of correspondence between these two measurement modalities. Pregnant women from the Stress and Health in Pregnancy and Postpartum (SHIPP) study completed visits during the 3rd trimester, 4–6 weeks postpartum, and 4 months, 8 months, and 12 months postpartum. At each study visit, participants completed questionnaires and wore wrist-actigraphy (Actiwatch 2) for one week prior to each visit. Self-reported global sleep quality was measured with the Pittsburgh Sleep Quality Index (PSQI). Actigraphy and self-reported PSQI sleep characteristics were summarized at each of the five study visits. Using generalized linear mixed modeling, we examined if there were differences by sleep measurement (actigraphy vs. PSQI) for the overlapping sleep outcomes: total sleep time, time in bed, sleep latency, and sleep efficiency. Participants (n = 74; 28.9 years ± 4.6) were mostly white (73%), non-Hispanic (96%), married (78.5%) and over 60% had at least one child previously. Average PSQI global score was > 5 (cutoff for poor sleep) at each study visit. Total sleep time, sleep efficiency, and sleep latency measurements were significantly different between self-report and actigraphy throughout pregnancy and postpartum. Actigraphy-assessed sleep may reflect longer total sleep times, shorter sleep latency, and greater sleep efficiency compared to self-reported sleep among pregnant and postpartum women. This may be due to measurement error in actigraphy or recall bias when completing self-reported sleep measures. These factors should be taken into consideration both at the time of study design and when comparing results from different studies to facilitate the highest quality research and clinical decision-making in this population.

Carbon-fibre composites can be broken down into reusable components

Switch-like gene expression modulates disease risk

Constraints on the source of ions in the Jianhe hot springs in Guizhou Province, China by water-rock interaction experiments

Due to the lack of experimental studies, the effect of water-rock interactions on the hydrochemical characteristics of hot springs within belted reservoir remains poorly understood. To solve this issue, we analyzed the hydrochemical characteristics of the hot springs and the geochemical features of the reservoir rocks in the Jianhe hot springs in Guizhou province, SW China. All water sample analyses adhered to the China analytical procedures (GB 8538−2022), then carried out water-rock interacting experiments with representative reservoir rocks (e.g., metamorphosed tuff, metamorphosed quartz sandstone, and slate) under varying reaction time, temperature, and pH conditions. The results indicate that the concentration of dissolved ions in the solution increased with time, then gradually stabilized, reaching dynamic equilibrium around 35 days. Higher temperatures facilitated the leaching of K+, Na+, and H2SiO3, meanwhile reduced the leaching of Ca2+ and Mg2+. However, both Ca2+ and Mg2+ in the solution showed a pronounced response to pH changes from 4 to 10, whereas the K+, Na+, and H2SiO3 concentrations were less sensitive to pH changes. In particular, under experimental conditions corresponding to the reservoir (90°C), the Ca2+, concentrations as leached from metamorphosed tuff agreed well with the hydrochemical data in Jianhe hot springs, which are significantly lower than those in the solutions interacted with quartz sandstone or slate, and indicate that metamorphosed tuff should be the primary sources for K+, Na+, Ca2+, Mg2+ and H2SiO3 in the hot springs.

Neolithic matrilineal society in ancient China

Psychological richness as a distinct dimension of well-being: Links to mental, social, and physical health

In recent years, well-being research has expanded beyond traditional dimensions, recognizing that a fulfilling life may encompass more than happiness and meaning. We examined the unique contributions of a newly proposed dimension of well-being—psychological richness—to mental, social, and physical health outcomes alongside the established dimensions of hedonic and eudaimonic well-being. We assessed well-being using validated scales that measure life satisfaction, meaning in life, and psychological richness and analyzed data from 11,041 participants. We evaluated health outcomes across mental, social, and physical dimensions using the Subjective Well-being Inventory. Our findings revealed that life satisfaction and meaning in life consistently enhanced health outcomes across most domains. However, psychological richness exhibited a more nuanced profile. Specifically, psychological richness was strongly correlated with positive mental and social health indicators, such as confidence in coping and perceived social support, but also uniquely linked to social isolation and perceived physical symptoms. These results suggest that psychological richness fosters cognitive resilience and social engagement despite potential physical health and social connectedness trade-offs. Notably, individuals high in psychological richness did not report heightened negative emotions, even when experiencing social isolation or physical discomfort, aligning this dimension with other forms of well-being. This study identified psychological richness as an essential addition to well-being models, offering fresh perspectives for tailored well-being interventions.

Cancer disrupts sex hormone-inflammation relationships: Analysis of ALI in males from NHANES 2007–2018

Background To investigate the correlation between sex hormone levels and Advanced Lung Cancer Inflammation Index (ALI) in male cancer populations, and compare with non-cancer male populations, in order to elucidate the potential regulatory role of sex hormones in cancer-related inflammation. Methods We analyzed data from NHANES 2007–2018, including 11,280 males (1,135 cancer patients, 10,145 non-cancer controls). Serum testosterone and estradiol were measured using ID-LC-MS/MS. ALI was calculated as BMI(kg/m²) × serum albumin(g/dL)/ NLR. We employed weighted multivariate regression models with progressive adjustment for confounders and conducted stratified analyses by cancer status, age, and race/ethnicity. Restricted cubic splines explored non-linear relationships. Results Cancer patients had higher mean age (68.70 ± 11.85 vs. 48.36 ± 17.00 years), lower testosterone levels (377.35 ± 204.26 vs. 415.02 ± 183.92 ng/dL, P < 0.001), and lower ALI values (64.86 ± 118.61 vs. 72.68 ± 185.25, P = 0.053) than non-cancer controls. In fully adjusted models, each 10 ng/dL increase in testosterone was associated with a 3.0% decrease in ALI (OR=0.970, 95%CI: 0.962–0.978, P < 0.001), while each 1 pg/mL increase in estradiol was associated with a 60.3% increase in ALI (OR=1.603, 95%CI: 1.318–1.949, P < 0.001). Notably, these associations were significant only in non-cancer populations (testosterone: OR=0.97, P < 0.001; estradiol: OR=1.64, P < 0.001), and generally absent in cancer patients except for older cancer patients (≥60 years) where testosterone maintained a significant negative correlation with ALI (OR=0.96, P = 0.020). The testosterone-ALI negative correlation was strongest in younger individuals (20–39 years), while the estradiol-ALI positive correlation weakened with age. Estradiol exhibited significant non-linear relationship with ALI (P = 0.027), with multiple inflection points suggesting concentration-dependent effects. Conclusion This study systematically reveals for the first time the association between male sex hormone levels and ALI and the moderating effect of cancer status. The negative correlation between testosterone and ALI and the positive correlation between estradiol and ALI are significant in non-cancer populations but generally absent in cancer patients, suggesting that cancer may profoundly alter the relationship between sex hormones and inflammation. These findings suggest cancer may fundamentally alter sex hormone-inflammation relationships, providing new insights into hormone-mediated inflammatory regulation in health and disease.

Flight simulator for moths reveals they navigate by starlight

Infection prevention knowledge and perceptions: a nationwide survey among nurses and physicians in adult intensive care units in Finland

Background Healthcare-associated infections are a major complication of care for patients in intensive care, causing costs and additional mortality. Infection prevention practices, such as hand hygiene, have been suboptimal globally. This study aimed to explore the level of knowledge and perceptions of critical care staff regarding healthcare-associated infections as insufficient knowledge contributes to an increased burden of these infections. Methods A nationwide survey of physicians and nurses working in intensive care units of Finnish tertiary care hospitals was conducted to gain knowledge and explore perceptions regarding the prevention of healthcare-associated infections in intensive care units. Descriptive statistics were used to describe the study data, and a mainly nonparametric method was used to compare the groups. Results The respondents demonstrated moderately good knowledge of hand hygiene and infection prevention, with a median of 36 correct responses (Q1, Q3: 34, 37). However, there were notable gaps in their knowledge in infection prevention regarding the routes of infection transmission, with a median score of 4 (Q1, Q3: 4, 6). Conversely, perceptions of infection prevention were generally positive. The median score for perceptions was 51 (Q1, Q3: 47, 55), but no significant association was found between perceptions and knowledge levels. Conclusions The level of knowledge about healthcare-associated infections is not satisfactory. In particular, there is a lack of in-depth understanding of the mechanisms of infection transmission and prevention. Providing unit-tailored feedback on performance, along with education on the transmission mechanisms and infection prevention for healthcare workers is essential.

Text speaks louder: Insights into personality from natural language processing

In recent years, advancements in natural language processing (NLP) have enabled new approaches to personality assessment. This article presents an interdisciplinary investigation that leverages explainable AI techniques, particularly Integrated Gradients, to scrutinize NLP models’ decision-making processes in personality assessment and verify their alignment with established personality theories. We compare the effectiveness of typological (MBTI) and dimensional (Big Five) models, utilizing the Essays and MBTI datasets. Our methodology applies log-odds ratio with Informative Dirichlet Prior (IDP) and fine-tuned transformer-based models (BERT and RoBERTa) to classify personality traits from textual data. Our results demonstrate moderate to high accuracy in personality prediction, with NLP models effectively identifying personality signals in text in line with previous studies. Our findings reveal theory-coherent patterns in language use associated with different personality traits, while highlighting important biases in the MBTI dataset that yielded less robust results. The study underscores the potential of NLP in enhancing personality psychology and emphasizes the need for further interdisciplinary research to fully realize the capabilities of these transparent technologies.

How dopamine neurons devalue delayed rewards

Improved drug-screening tests of candidate anti-cancer drugs in patient-derived xenografts through use of numerous measures of tumor growth determined in multiple independent laboratories

Background Researchers screen candidate anti-cancer drugs for their ability to inhibit tumor growth in patient-derived xenografts (PDXs). Typically, a single laboratory will use a single measure of tumor growth. Purpose An effective drug-screening test as one that correctly identifies whether a drug treatment inhibits or does not inhibit tumor growth. We document improvements in the experimental design and statistical analysis of drug-screening tests based on the criteria of sensitivity and specificity. Methods We analyzed two published datasets. The response of each PDX model was known in advance. This information provided for statistical ground-truth classification. One dataset analyzed growth inhibition in the presence of one specific drug treatment for two PDX tumor models for numerous labs. A second dataset reported tumor growth of many PDX models in the presence of many drugs. A PDX model for which the treatment showed no tumor growth inhibition is referred to as Progressive Disease (PD). A PDX model for which the treatment showed complete tumor growth inhibition is referred to as Completely Responsive (CR). We created and analyzed four drug-screening tests, based on p-values for either a single-measure and single-lab, or p-values from meta-analysis and multiple-test correction. The outcome of each screening test was that either the drug treatment was effective or it was not. For both datasets, we computed median sensitivities and specificities by applying bootstrap resampling, and specification of a significance level. Results Our results showed that drug screening tests utilizing p-values from meta-analysis of numerous labs, or multiple test correction, produced median sensitivities and specificities that were always at least as high as those for the Single-Measure, Single-Lab test. This result was true for all significance levels. The 95% confidence intervals were usually greater in length for the Single-Measure, Single-Lab screening test.

Changes in risk habits and influencing factors in the Taiwan oral cancer screening program

This study examines changes in oral risk habits and identifies factors influencing these changes among participants in a population-based oral cancer screening program to support effective public health interventions. The study included 2,569,920 individuals aged 30 and older who participated in Taiwan’s Oral Cancer Screening Program at least twice between 2010 and 2021. Changes in cigarette smoking and betel quid chewing were assessed between the first and last screenings and categorized as improved, unchanged, or worsened. A logistic regression model evaluated factors associated with habit improvement, including baseline oral habits, sex, age, education, screening adherence, and oral potentially malignant disorder (OPMD) findings. Among participants, 25.3% improved their oral habits. Baseline habits influenced how OPMD screening results affected behavior change. Among smokers, a positive screening result increased the likelihood of quitting or reducing smoking (adjusted odds ratio [aOR] = 1.18, 95% CI 1.16–1.20). However, among betel quid chewers, whether or not they smoked, a positive screening result was negatively associated with improved habits (aOR 0.79–0.88). Being female, older, college-educated, and regularly attending screenings were positively linked to behavior improvement. The program led to habit improvements in about one-quarter of participants, particularly older individuals, those with higher education, and frequent attendees. However, a diagnosis of OPMD motivated change only among smokers, not those engaging in both smoking and betel quid chewing, highlighting a lack of awareness in high-risk groups. Strengthening collaboration between health organizations and the screening program could enhance public awareness, improve program effectiveness, reduce oral cancer incidence, and lower long-term healthcare costs.

Development and validation of context-specific components of obstetric violence: Experiences from the central zone of Tanzania

Background Despite the known consequences of obstetric violence, studies have encountered challenges in defining and fully understanding obstetric violence. This difficulty arises from a relative scarcity of research addressing the definition of obstetric violence across various cultures and contexts. As a result, there is a lack of consensus regarding the operational definitions of the components of obstetric violence and variations that may be influenced by geographical and cultural factors. Objective This study describes the process of developing and validating the context specific components of obstetric violence in the Central Zone of Tanzania. Methods An iterative mixed-methods design was used, using the following stages; 1. collecting and analysing qualitative data on context specific components of obstetric violence along with a literature review 2. assessing the content validity with 24 maternal health experts and face validity with 27 postnatal mothers and nine health care providers. Descriptive analysis was employed to analyse participants’ characteristics and Likert scale responses from experts, postnatal mothers and health care providers. Item-level Content Validity Index (I-CVI) and Item-face Validity Index (I-FVI) was computed for each component. Results Seven categories of obstetric violence components were identified through this process.These included: physical violence, lack of supportive care and treatment, subjugation care, an unfavourable care environment, sexual violence, verbal violence, emotional and psychological violence. In addition, 24 subcategories of obstetric violence were identified. The Item-Level Content Validity Index (I-CVI) ranged from 0.791 to 0.958, while the Item-Face Validity Index (I-FVI) ranged from 0.777 to 0.925. Conclusion The validated components of obstetric violence in Tanzania will contribute to a better understanding of the issue within the Tanzanian context.This in turn, may facilitate a more accurate assessment of the magnitude and impact of obstetric violence while helping to identify key areas for intervention and policy development to promote respectful maternity care.