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DFU_DIALNet: Towards reliable and trustworthy diabetic foot ulcer detection with synergistic confluence of Grad-CAM and LIME

Diabetic Foot Ulcer (DFU) is a major complication of diabetes which needs early detection to help in timely treatment for preventing future serious consequences. Due to peripheral neuropathy, high blood glucose levels, and untreated wounds, DFUs can cause the disintegration of the skin and exposing the tissue below it, if not adequately treated. Recently deep learning (DL) has advanced and has shown its ability to automate DFU detection and classification by analysing medical images. The use of DL has been proven to be very useful for healthcare professionals, enabling earlier diagnosis and effective treatment of DFU. However, most of the studies predominantly rely on a single dataset (e.g., DFUC2021 or DFUC2020) without external validation or cross-dataset testing, raising concerns about generalizability and trustworthiness. The aim of this study is to develop a robust, reliable, and transparent DFU detection framework which is not only good performing but also can effectively give attention to the proper region of the images which are crucial for DFU detection. So, to make DFU detection robust, reliable in a single study, we proposed a custom approach, DFU_DIALNet and to enhance transparency and interpret the model decisions in this study, we integrated Grad-CAM and LIME heatmaps to precisely localize ulcer regions. This allows visual verification of the model’s focus and clarifies the decision-making process, thereby increasing the model’s reliability. DFU_DIALNet outperforms all other traditional models with 99.33% accuracy, 99% F1 score, and 100% AUC score, and compared it to other DL models—DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0, ResNet50V2 and VGG16—in the merged dataset of DFUC2021 with our collected 500 images. We have checked our model’s reliability with 2 other popular datasets—-the KDFU and DFUC2020 datasets, where our proposed approach gives the highest accuracy of 95.61% and 99.54%, respectively, compared to other deep learning approaches. Lastly, we have developed a web app using Streamlit to detect DFU efficiently. This study fills the gap between reliable and interpretable systems with a proposed approach to the efficient detection of DFU.

The role of physical acgtivity in life satisfaction and psychological resilience among college students: Mediating effects of self-efficacy and perceived stress

Background With the increasing global attention on mental health issues, especially the psychological stress and life satisfaction problems faced by college students, it has become particularly important to explore how physical activity is associated with college students’ psychological resilience and quality of life through psychological mechanisms. This study aims to examine the association between physical activity on college students’ life satisfaction and psychological resilience, and to investigate the mediating roles of self-efficacy and perceived stress. Methods This study collected data from college students in several universities in China through online questionnaires, using the Body Self-Concept Questionnaire, Life Satisfaction Scale, the Chinese Revised Version of the Connor–Davidson Resilience Scale, General Self-Efficacy Scale, and Perceived Stress Scale to measure each variable. A total of 560 undergraduate students from three universities participated in the survey, reporting on their physical activity, life satisfaction, psychological resilience, self-efficacy, and perceived stress. Results The findings show that physical activity was significantly associated with higher life satisfaction (r = 0.439, p < 0.001) and psychological resilience (r = 0.521, p < 0.001). Both self-efficacy (95% CI = [0.138, 0.255] and [0.245, 0.399]) and perceived stress (95% CI = [0.013, 0.070] and [0.040, 0.134]) played significant mediating roles in these processes. Specifically, physical activity was linked to better mental health and quality of life through its association with higher self-efficacy and lower perceived stress. Discussion and implications This study validates the association between physical activity and improved college students’ life satisfaction and psychological resilience and reveals the mediating mechanisms of self-efficacy and perceived stress. These findings provide a scientific basis for designing exercise and psychological intervention programs for college students, with important practical implications.

Factors associated with unrealized fertility among women approaching the end of reproductive age in sub-Saharan Africa

Background Understanding women’s fertility preferences is essential for addressing reproductive behaviors and family planning needs. In sub-Saharan Africa, fertility rates remain high, yet many women experience unrealized fertility, which is having fewer children than desired. However, the factors influencing unrealized fertility remain underexplored. This study assessed the determinants of unrealized fertility among women approaching the end of their reproductive years in sub-Saharan Africa. Methods A secondary data analysis was conducted using phase eight Demographic and Health Surveys data from 19 sub-Saharan African countries. The weighted sample included 46,408 women aged 40–49 years. A multilevel Poisson regression model with robust variance was used to identify factors associated with unrealized fertility. Adjusted prevalence ratios with 95% confidence intervals (CI) were reported, and variables with a p value <0.05 were considered statistically significant. Results The pooled prevalence of unrealized fertility among women aged 40–49 years was 61.43% (95% CI: 57.63%, 65.24%). Rwanda (37.40; 95% CI: 27.92%, 46.88%) and Sierra Leone (69.34; 95% CI: 60.30%, 78.38%) had the lowest and highest prevalence, respectively. Older maternal age at first birth, being employed, having no children or only children of one sex, and experiencing child death were associated with higher prevalence of unrealized fertility. Conversely, higher maternal education, the use of contraceptives, having both male and female children, and residing in rural areas were associated with lower prevalence of unrealized fertility. Conclusions A large proportion of women nearing the end of their reproductive careers in sub-Saharan Africa have experienced unrealized fertility. Therefore, addressing cultural norms surrounding sex preference and number of children, alongside empowering women through improved access to education, healthcare, and comprehensive sexual and reproductive health services, is critical.

Deciphering the role of the lncRNA TRIBAL in hepatocyte models

We recently reported that the long non-coding RNA TRIBAL /TRIB1AL was required to sustain key hepatocyte functions. Here, we identify HepaRG cells as a model for studying TRIBAL and provide additional validation and functional insights. In contrast to HepG2 and HuH-7 cells, differentiated HepaRG cells showed similarities to primary hepatocytes in response to TRIBAL suppression. TRIBAL suppression was associated with reduced HNF4A and MLXIPL abundance in hepatocytes and HepaRG cells. TRIBAL targeting using a panel of cognate antisense oligonucleotides confirmed specificity. A comparison of TRIBAL -suppressed hepatocyte and HepaRG transcriptomics identified extensive functional overlap. Biological ontologies associated with key hepatic metabolic functions were predicted to be inhibited in both models. Comparative analyses with TRIB1 -suppressed HepaRG cells, a central metabolic regulator vicinal to TRIBAL , also revealed extensive functional congruence with TRIBAL . Interestingly, TRIBAL transduction failed to restore function in TRIBAL -suppressed cells, which may be linked to structural differences, as supported by contrasting RNAse R sensitivities between the endogenous and transduced forms. In summary, these findings support the use of HepaRG cells as an experimental model to study TRIBAL and underscore its importance in regulating key hepatocyte genes essential for metabolic function.

Predicting 30-day hospital readmissions using ClinicalT5 with structured and unstructured electronic health records

Hospital readmission prediction is a crucial area of research due to its impact on healthcare expenditure, patient care quality, and policy formulation. Accurate prediction of patient readmissions within 30 days post-discharge remains a considerable challenging, given the complexity of healthcare data, which includes both structured (e.g., demographic, clinical) and unstructured (e.g., clinical notes, medical images) data. Consequently, there is an increasing need for hybrid approaches that effectively integrate these two data types to enhance all-cause readmission prediction performance. Despite notable advancements in machine learning, existing predictive models often struggle to achieve both high precision and balanced predictions, mainly due to the variability in patients’ outcome and the complex factors influencing readmissions. This study seeks to address these challenges by developing a hybrid predictive model that combines structured data with unstructured text representations derived from ClinicalT5, a transformer-based large language model. The performance of these hybrid models is evaluated against text-only models, such as PubMedBERT, using multiple metrics including accuracy, precision, recall, and AUROC score. The results demonstrate that the hybrid models, which integrate both structured and unstructured data, outperform text-only models trained on the same dataset. Specifically, hybrid models achieve higher precision and balanced recall, reducing false positives and providing more reliable predictions. This research underscores the potential of hybrid data integration, using ClinicalT5, to improve hospital readmission prediction, thereby improving healthcare outcomes through more accurate predictions that can support better clinical decision making and reduce unnecessary readmissions.

KFERQ-selective protein autophagy in Caenorhabditis elegans depends on LMP-1

Mammalian cells exhibit three autophagy mechanisms: macroautophagy, microautophagy (MIA), and chaperone-mediated autophagy (CMA), each employing unique mechanisms for transporting cellular material to the lysosome for degradation. MIA involves the engulfment of proteins via lysosomes/late endosomes through membrane invagination, while CMA directly imports cytosolic proteins into lysosomes, selectively targeting those harboring the KFERQ pentapeptide motif, helped by the chaperone HSC70. Despite the identification of several genetic markers of these pathways, our understanding of the underlying mechanisms, particularly in MIA and CMA, remains limited. To study CMA in vivo we designed a photoactivatable CMA reporter consisting of a plasmid encoding the KFERQ consensus signal for CMA targeting. We generated transgenic C. elegans strains with diverse genetic backgrounds to analyze the role of known molecular components of CMA in mammals. Additionally, we conducted an in-silico analysis of the structural interaction between C. elegans LMP-1 or LMP-2 proteins with the HSP-1 chaperone. Results: Our study shows a significant alteration in the distribution pattern of the KFERQ reporter in muscle cells upon induction of selective autophagy (CMA or MIA). We found that the reporter localized into lysosomes only during starvation, which abrogated in the absence of LMP-1. This study validates CMA in C. elegans and provides the development of a new tool for understanding selective autophagy mechanisms and their potential implications in various organisms.

Observational suspected adverse drug reaction profiles of fluoro-pharmaceuticals and potential mimicry of per- and polyfluoroalkyl substances (PFAS) in the United Kingdom

Aims The aim of this research is to explore the suspected adverse drug reactions (ADRs) of perfluorinated medicines to determine whether side effects commonly associated with per- and poly-fluoroalkyl substances (PFAS) exposure were correlated to the type or number of fluorine atoms in these medications. Methods Thirteen fluorinated drugs and six non-fluorinated (or low fluorinated) comparators were selected after systematic triage. The reported ADR data from the Medicines and Healthcare Products Regulatory Agency’s (MHRA) Yellow Card, and prescribing data from the OpenPrescribing database and the National Health Service Business Service Authority (NHSBSA) over a 5-year period were curated. Prescribing data was used to standardise the ADRs by calculating ADRs/1,000,000 items dispensed for selected system organ classes (SOCs), associated with PFAS exposure, for all 19 drugs. The physiochemical and pharmacological properties of the selected drugs were determined from ChemDraw version 23.1.1, Drug Bank, electronic medicines compendium (EMC) and the chemical database of bioactive molecules with drug-like properties, European Molecular Biology Laboratory (ChEMBL). Results Excluding congenital, familial, and genetic disorders, and endocrine disorders, all other SOCs (n = 5) showed statistical significance (P < .05) for ADRs/1,000,000 items identified across the 13 fluorinated drugs. It was identified that leflunomide was suspected of more ADRs than other comparator medications, which had the highest suspected ADRs/1,000,000 items dispensed (n = 343) and lansoprazole had the lowest (n = 14). Both drugs contain same number of fluorine atoms (n = 3) and similar type of fluorine moiety (trifluoromethyl, -CF3). Conclusion No correlation between the fluorination status of the drugs and the ADRs were found.

Combining market surveys and participative approaches to map small ruminant mobility in three selected states in northern Nigeria

In Nigeria, a huge gap in knowledge on livestock mobility and its role on transboundary disease spread exists. As animals move, so do diseases. Therefore, there is a need to understand how livestock movements can contribute to the circulation and maintenance of infectious livestock diseases which can impede the design of particular surveillance and control tactics in the event of outbreaks. Our study aim was to reconstruct small ruminants’ mobility patterns in three selected states in Northern Nigeria for better surveillance and control of small ruminant’s transboundary animal diseases (TADs). To this end, a mixed approach was used to collect data. A market survey, employing structured questionnaires, was administered to 1,065 market traders. Additionally, 20 focus group discussions were conducted with traders and transhumance actors across 10 Local Government Areas (LGAs) spanning three northern Nigerian states: Plateau, Bauchi, and Kano. The respondent movements by type, animal movement, reason for movement was described and summarized. Data collected were used to reconstruct small ruminant mobility networks, whose nodes were LGAs, in the three states of the survey area and with other states in Nigeria and movement mapped. Characteristics of both networks were studied using a complex network approach either separately or combined. Using the two approaches provided a complementary view of small ruminant mobility. The reconstructed networks were connected, highly heterogeneous and had very low density. The networks included LGAs belonging up to 31 states. The presence of hubs increased the risk of disease spread. Gwarzo, Wudil (Kano) and Alkaleri (Bauchi) LGAs received the most sheep and goats, while Jos North (Plateau) and Gwarzo supplied more small ruminants. Bukuru and Alkaleri markets were classified as super-spreaders with a higher probability of detecting virus circulation. Four to six multistate communities were identified. Our findings could support policy choices to identify priority areas for surveillance and disease control in small ruminants.

Evaluation of ISSA proactive leading indicators for safety, health and well-being: Application of multi-criteria decision-making methods based on hesitant fuzzy

Background The Vision Zero (VZ) strategy, adopted by the International Social Security Association (ISSA), is based on seven golden rules and 14 safety, health, and well-being (SHW) indicator called VZ Proactive Leading Indicators (VZPLI). Methods The study evaluates the status of SHW VZPLI in Iran’s Petrochemical Industries by IAM-VZPLI (ISSA Assessment Method for VZPLI) and proposes a new model for assessing these indicators, called EMA-VZPLI (Extended Method for Assessment of VZPLI). EMA-VZPLI uses an integrated method consisting of multi-criteria decision-making(MCDM), the best-worst method(BWM), Decision-Making Trial and Evaluation Laboratory (DEMATEL), and axiomatic design(AD) based on hesitant fuzzy(HF) sets. Results Weight of indicators and their relationships was determined using hesitant fuzzy best-worst method (HFBWM) and Interval-Valued Hesitant Fuzzy DEMATEL(IVHF-DEMATEL) method respectively. The results showed that the C2 indicator (competent leadership) was the most important, while the C10 indicator (Procurement) was the least important. Additionally, the C2 (competent leadership) and C1 (Visible leadership commitment) indicators were dependent, showing the highest degree of dependence. The indicators of the ‘well-being’ aspect in all studied petrochemical companies were worse than those of the safety’ and ‘health’ aspects. Conclusion The results showed a significant difference in ranking of petrochemical companies for VZPLIs in EMA-VZPLI compared to IAM-VZPLI. Therefore, this method could be applied in more accurate assessment of VZPLIs of VZ indicators.

Geoarchaeological research on site formation process, paleoenvironment, and human behaviors in the early Holocene of the Gobi Desert, Mongolia

This paper presents a rare example of the multi-proxy investigation results on the prehistoric settlement from vast areas of the Mongolian Gobi Desert, where, during favorable climatic conditions, postglacial hunter-gatherer groups occupied a seasonal lake district. The geoarchaeological research conducted at site FV92, located at the Luulityn Toirom Paleolake, provides insight into the problem of human relations with the changing environment of the Early Holocene, as well as the problem of the site formation process in the Gobi area. Sedimentological studies and luminescence dating of the Luulityn Toirom Lake sediments indicate the presence of the lake and favorable environmental conditions for human settlement in the Early Holocene in the period before 8130 ± 83 BP. Spatial analyses of the artifact distribution, as well as refitting studies of the discovered lithic assemblage, enabled the determination of the site’s formation process. Initially, the site was influenced by fluvial processes, but as the climate dried, it was subsequently affected by aeolian processes. The techno-typological analysis, refitting studies, and microscopic analyses carried out provide the first such detailed insight into the technological behavior and identification of the chaîne opératoire used by the Early Holocene hunter-gatherer communities of the Gobi area. The results confirmed that the lithic technology was mainly based on microblade technology. Microscopic analyses of traces created during tool use indicate butchery activity and the use of plant resources. The studies indicate a high degree of mobility of hunter-gatherer communities living by the lakes, as evidenced by the medium-range transport of raw material brought to the campsite from the surrounding mountainous Altai area.

Resilience enhancement of an urban road network during traffic accidents by optimally dispatching rescue teams

The efficient dispatch of rescue teams (RTs) during traffic accidents is crucial for the rapid restoration of normal operations in the affected urban road network (URN), thereby enhancing the network’s resilience during such events. However, previous studies focusing on optimizing RT dispatch strategies to enhance URN resilience remain limited. To address this gap, this paper develops a mixed-integer linear programming model aimed at optimizing RT dispatch during traffic accidents. The formulated model is solved using the commercial solver (i.e., CPLEX). Numerical experiments conducted on a hypothetical URN demonstrate that the model generates an optimal dispatch scheme. Compared to baseline strategies, the optimized scheme reduces the total objective function values by 27.36% in small-scale cases and 16.28% in large-scale case, respectively. Furthermore, sensitivity analysis reveal that accident severity and destination locations significantly influence the dispatch scheme design. Finally, the paper discusses the impact of several parameters on the model’s solution, showing that its performance is highly sensitive to several critical factors like RT dispatch costs, the maximum allowable delay time, passenger value of time, and vehicle travel speeds.

Study protocol for a randomized controlled clinical trial of a multifaceted cognitive training program using video games in childhood cancer survivors

This randomized controlled trial aims to evaluate the efficacy of a cognitive training program using video games in improving neuropsychological, neurological, immunological, and inflammatory parameters in childhood cancer survivors. This study will recruit 56 patients aged 8–17 years who have completed cancer treatment 1–8 years prior to enrollment. Participants will be randomized to either the video game intervention or waiting group. The primary objectives are analyzing potential changes in neuropsychological tests covering all neurocognitive domains, neuroimaging tests (structural, diffusion, and functional imaging), and immune and inflammatory biomarker levels after video game intervention. The secondary objectives are to define the prevalence of neurocognitive deficits in the study population, analyze psychological and emotional self-perception and parental perception after the intervention, and assess the feasibility of implementing this new intervention methodology. The inclusion criteria comprise specific diagnoses (central nervous system [CNS] cancer, hematologic malignancies, extracranial solid tumors, and nonmalignant hematological diseases requiring allogeneic hematopoietic progenitor transplantation) and treatments (CNS surgery, radiotherapy, intrathecal/intraventricular chemotherapy, neurotoxic systemic chemotherapy, and hematopoietic stem cell transplantation). Patients with active disease, relapse, or prior neurological or psychiatric pathology will be excluded. This study will improve the understanding and management of neurocognitive sequelae in childhood cancer survivors and ultimately enhance their quality of life. Trial identifier: NCT06312969

Decellularized rat brain extracellular matrix effectively induces the dopaminergic differentiation of human adipose-derived stem cells

The extracellular matrix (ECM) plays essential roles in regulating various aspects of nervous system development. The ECM can be obtained through decellularization techniques, which preserve the native structure of tissue while removing cells and genetic material. Despite recent advancements in decellularization methods, removing cells from brain tissue remains challenging due to its delicate mechanical structure. Moreover, previous studies have not specifically evaluated the impacts of decellularized brain ECM on dopaminergic specification of stem cells. Here, we decellularized rat brain sections using a combination of chemical and enzymatic factors. Successful decellularization of sections was confirmed by DAPI, Haematoxylin and Eosin and Masson’s trichrome staining, laminin immunostaining, DNA content analysis, and scanning electron microscopy. The sections were then recellularized with human adipose tissue-derived stem cells (hADSCs) and subjected to dopaminergic differentiation using a combination of growth factors. Some ADSCs were also differentiated in gelatin-coated tissue culture plates, employing a conventional two-dimensional culture method. After 12 days, the differentiated cells in both conditions expressed certain neuronal markers, especially those related to dopaminergic differentiation. However, GLI1, VMAT2, GIRK2, and TH genes, as well as NEFL, FOXA2, LMX1A, and TH proteins were upregulated in the ADSCs differentiated on decellularized sections. Furthermore, DDC and CALB were exclusively expressed by the ADSCs on decellularized brain sections. Overall, our findings indicated the significance of decellularized brain ECM to serve as an effective bioscaffold for dopaminergic differentiation of hADSCs. This highlights the importance of decellularization techniques for the advancement of midbrain tissue engineering and regenerative medicine for Parkinson’s disease in the future.

The role of kindling mechanism: A validation study of the Hungarian version of the Prediction of Alcohol Withdrawal Severity Scale

Background Early recognition of the complicated form of alcohol withdrawal syndrome (c-AWS) is critical. The Prediction of Alcohol Withdrawal Severity Scale (PAWSS) was developed for the risk analysis of the development of c-AWS. According to the kindling mechanism, the history of previous c-AWS has a pivotal role in the development of current c-AWS. The aims of this study were to reveal (1) the psychometric characteristics of the PAWSS among patients with alcohol withdrawal syndrome (AWS) and alcohol dependence syndrome (ADS) and (2) the role of kindling mechanism in the development of c-AWS by using the PAWSS. Methods This study enrolled 70 inpatients with ADS and AWS. The severity of dependence was measured using the Alcohol Use Disorder Identification Test and the Severity of Alcohol Dependence Questionnaire. Statistical analyses were performed using receiver operating characteristic (ROC) analysis, binary logistic regressions, and for the inter-rater reliability analysis Cohen’s Kappa coefficient was calculated. Results ROC analysis showed that > 6 is the optimal cutoff point for the Hungarian version of the PAWSS. In the case of predictive validity, higher PAWSS score (p < 0.001) predicted current c-AWS. Furthermore, the history of c-AWS (p < 0.001) was a significant variable for current c-AWS. The Cohen’s Kappa coefficient resulted in being 1. Conclusions The probability of current c-AWS was 12 times higher among patients with PAWSS scores of 6 or higher. The chance of current c-AWS was almost 7 times higher in the case of history of c-AWS. These findings suggest that the Hungarian version of PAWSS is a valid and reliable clinical tool for assessing the risk of c-AWS, and highlight the importance of the kindling mechanism in the background of c-AWS.

Evaluating the effectiveness of prenatal exercise promotion strategies on the Xiaohongshu platform: Health beliefs, information quality, and source credibility

Although exercising during pregnancy offers numerous advantages, its prevalence in China remains relatively low. This is primarily attributed to the traditional Chinese belief that pregnancy is a period for rest and recuperation. To alter this perception, numerous individuals have promoted the benefits of prenatal exercise on Xiaohongshu, one of China’s most popular social media platforms. This study utilized the frameworks of the Health Belief Model (HBM) and the Heuristic - Systematic Model (HSM) to explore which strategies are effective in these promotional efforts. A total of 5,016 posts promoting prenatal exercise were identified. From these, 500 samples were randomly selected for coding. Negative binomial regression analysis was conducted to assess the influence of the constructs of HBM and HSM on public engagement. The Kruskal-Wallis test was used to compare various information sources’ differential effects. The results indicated that emphasizing the benefits, self-efficacy, and barriers to exercise significantly impact audience engagement in the context of social media information regarding exercise during pregnancy. Healthcare professionals and pregnant and postpartum women are the most influential information sources in attracting audience engagement. Moreover, source credibility significantly impacts public engagement, and information completeness positively increases the likelihood of favorites. These findings are valuable for optimizing the design of pregnancy exercise promotion information on social media, obtaining social support for prenatal exercise, and contributing to women’s health and well-being.

Risk factor analysis and establishment of a predictive model for epilepsy comorbid with depression

Objective This study aims to utilize our hospital’s existing Stereo Electroencephalography (SEEG) examination results combined with other clinical data to systematically analyze the risk factors for epilepsy comorbid with depression, and to establish a model for predicting the risk of developing depression in epilepsy patients. Clinically, this model can be used to predict the risk of comorbid depression in epilepsy patients, thereby enhancing the identification of this condition and providing a theoretical basis for proactive intervention in depressive symptoms among epilepsy patients. Methods A retrospective analysis was conducted on the clinical data of patients diagnosed with epilepsy in the Department of Neurosurgery at Tongde Hospital Of Zhejiang Province from 01/01/2020–31/12/2024, all of whom underwent Electroencephalography (EEG) examinations. According to the C-NDDI-E scores and clinical manifestations, the epilepsy patients were divided into an epilepsy with comorbid depression group (study group) and epilepsy without depression group (control group). Univariate analysis was performed using SPSS 26.0 software to screen for potential factors contributing to depression comorbid with epilepsy. Variables with a univariate P ≤ 0.05 were entered into a linear Lasso regression analysis. Those with statistical significance were then used to construct a nomogram model for predicting the risk of depression comorbid with epilepsy using R software. Results A total of 152 epilepsy patients were enrolled, including 43 in the study group and 109 in the control group. Univariate analysis showed statistically significant (P < 0.05) differences between the groups in terms of age, employment status, marital status, age of onset, frequency of epileptic seizures, type of drug treatment, scalp EEG-determined epileptogenic zone, SEEG-determined epileptogenic zone, and Activities of Daily Living (ADL) score. Lasso regression analysis revealed that marital status (p = 0.0008), Enrollment age (OR = 0.9152, P = 0.0003, 95% CI: 0.8673–0.9562), frequency of epileptic seizures (OR =5.9946, P = 0.0030, 95% CI: 1.8952–20.6541), type of drug treatment (OR = 44.4062, P = 0.0157, 95% CI: 1.3629–15.6702), SEEG results indicating the epileptogenic zone (hippocampal onset: OR = 12.3489, P = 0.0026, 95% CI: 2.5902–70.9811), and ADL score (OR = 0.9358, P = 0.0314, 95% CI: 0.8785–0.9930) were independent risk factors for depression comorbid with epilepsy. The area under the ROC curve (AUC) was 0.895, indicating strong discriminative ability and high predictive accuracy. Conclusion Independent risk factors for depression comorbid with epilepsy include: hippocampal origin of epilepsy as identified by SEEG, unstable marital status, younger age at the time of enrollment, higher frequency of epileptic seizures (>4 times/month), use of specific anti-seizure medications (such as topiramate, phenobarbital, levetiracetam, and perampanel), and lower activities of daily living (ADL) scores. The nomogram model established based on these factors performs well in relatively accurately predicting the risk of depression comorbid with epilepsy. This facilitates early identification of high-risk patients in clinical practice, enabling timely interventions to prevent the severe consequences of depressive episodes, improving patient adherence to epilepsy treatment, and emphasizing the link between psychological and neuroscientific aspects in epilepsy management to foster interdisciplinary collaboration for more comprehensive patient care.

An information-theoretic foreshadowing of mathematicians’ sudden insights

The “eureka” insights that drive progress in science and mathematics remain shrouded in mystery. Sudden, unexpected, appearing like “flashes of lightning”, these insights have the hallmarks of critical transitions in complex systems. Here, zooming in on mathematicians working on proofs in their own departments, we show that sudden insights are anticipated by a system-agnostic, information-theoretic early warning signal. Using dense behavioral recordings of mathematicians’ moment-to-moment activity, we find that their blackboard interactions (e.g., writing, gesturing; N = 4 , 653 ) became increasingly unpredictable before an insight, analogous to the critical fluctuations that anticipate transitions in physical and ecological systems. We explore analytically when this early warning signal applies to varied systems with discrete, symbolic dynamics. While bibliometric analyses offer a zoomed-out perspective on innovation, publications are a coarse-grained record of individuals’ insights. Explaining the sudden insights of innovators, from scientists to sculptors, requires attending to the local, distributed systems of their intellectual activity.

Experiences of Stalking and Obtaining a Restraining Order Are Associated With Onset of Cardiovascular Events in Women: A Prospective Analysis in the Nurses’ Health Study II

BACKGROUND: Stalking is one of the most common forms of interpersonal violence, with nearly 1 in 3 women experiencing it in their lifetime. Restraining orders are a legal remedy aimed at protecting women who experience the most severe forms of stalking and interpersonal violence. Previous work has shown associations between experiences of violence and cardiovascular health among women, but little is known about the link between being stalked or obtaining a restraining order and risk of cardiovascular disease (CVD). METHODS: We examined whether being stalked or obtaining a restraining order (considered an indicator of severe violence) were associated with a greater risk of incident CVD over 20 years in the Nurses’ Health Study II cohort. We conducted Cox proportional hazards regression models among women free of CVD in 2001 when their exposure status was ascertained (n=66 270). CVD events included self-reported myocardial infarction or stroke through 2021. Analyses were adjusted for a range of covariates. Secondary analyses included examining stroke and myocardial infarction separately and confirmed (through medical records) or corroborated cases of CVD as the outcomes. RESULTS: The mean (SD) age in our sample was 46.3 (4.7) years in 2001. Overall, 7721 women (11.7%) reported experiencing stalking, and 3686 women (5.6%) reported obtaining a restraining order. Over follow-up (median [interquartile range] 19.9 [19.5, 20.0] years), a total of 1879 women (2.8%) reported new-onset CVD. Compared with women who had not been stalked, women who reported experiencing stalking had an increased risk of self-reported incident CVD (hazard ratio, 1.41 [95% CI, 1.24–1.60]), adjusting for sociodemographic and childhood or family factors. Women who had, versus had not, obtained a restraining order also had an increased risk of self-reported incident CVD (adjusted hazard ratio, 1.70 [95% CI, 1.44–1.98]). Results were similar when considering self-reported myocardial infarction and stroke as separate outcomes and confirmed or corroborated CVD. Results were slightly attenuated but remained statistically significant when adjusted for health behaviors, medications, health conditions, childhood abuse, and depressive symptoms. CONCLUSIONS: Experiences of stalking and obtaining a restraining order are both associated with an increased risk of developing CVD in women. Common experiences of violence among women may affect cardiovascular health and warrant consideration alongside more traditional risk factors.

Letter by Huang Regarding Article, “Associations of Circulating ANGPTL3, C-Terminal Domain-Containing ANGPTL4, and ANGPTL3/8 and ANGPTL4/8 Complexes with LPL Activity, Diabetes, Inflammation, and Cardiovascular Mortality”

Disguised Dip: An Unusual Atrial Signature on ECG