Suicide is a leading cause of maternal mortality, yet there are currently no evidence-based perinatal suicide prevention programs. Given the risk of serious outcomes if undetected or inadequately treated, the goal of this study was to further understand the screening and treatment experiences of individuals with perinatal suicidal thoughts and behaviors (STBs). Qualitative data were generated from in-depth interviews with 13 individuals primarily from the United States who experienced perinatal suicidality at least 6 months prior to participation. Thematic analysis was used to examine the experiences of participants with respect to screening and treatment of perinatal STBs. Regarding screening, three major themes were identified: (1) gaps in comprehensive/routine screening for STBs (e.g., infrequent screenings or non-specific to suicide), (2) attitudes toward disclosure of STBs (resulting in omission of symptoms or downplaying of severity), and (3) importance of follow-up after screening. Three themes influenced participants' treatment experiences: (1) providers' engagement in care, (2) shared decision-making between provider and patient, and (3) impact of perinatal-specific treatment programs. Findings from this study highlight critical gaps in screening for and treatment of perinatal STBs. Implementing routine screening and comprehensive follow-up and improving treatment experiences are essential for improving the care of individuals with perinatal STBs and reducing maternal mortality.
Neuroimaging studies have revealed altered functional connectome dynamics in autism spectrum disorder (ASD) and linked these alterations to clinical symptoms. However, most studies have emphasized population-level contrasts, leaving interindividual variability in connectome dynamics and its structural underpinnings poorly understood. To address this gap, we analyzed resting-state functional and structural MRI data from 939 male participants (440 with ASD, 499 typically developing controls) across 18 sites in the Autism Brain Imaging Data Exchange (ABIDE). Whole-brain functional state dynamics was characterized using five leading activity modes and their expressions via eigen-microstate analysis. Age-related trajectories of mode expressions were constructed for typically developing controls using normative modeling, enabling quantification of individual-level deviations in functional dynamics. Compared with controls, ASD individuals showed greater interindividual variability in functional deviation profiles. Unsupervised clustering of these profiles identified two robust ASD subtypes with distinct mode-specific dysfunctions. One subtype primarily involved the visual, default-mode, frontoparietal, and dorsal attention networks, whereas the other subtype primarily involved the somatomotor, visual, frontoparietal, and ventral attention networks. These subtypes were clinically dissociable, differing in restricted and repetitive behaviors and social impairments, and exhibited mode-specific brain-symptom associations. Furthermore, the subtypes exhibited distinct cortical thickness alterations, and individual subtype membership was predicted with high accuracy (83%) using a random forest classifier based on cortical thickness. The main findings were replicated in an independent cohort outside ABIDE. This study delineates two reproducible and clinically dissociable ASD subtypes and links functional connectome dynamics to structural substrates, offering novel insights into the neurobiological basis behind ASD heterogeneity.
Periodontitis, a chronic inflammatory disease, is increasingly prevalent among young people and impairs their quality of life. Adverse childhood experiences (ACE), depressive symptoms, and suboptimal health status (SHS) are linked to health risks and chronic diseases, but their interrelationships with periodontitis in Chinese young adults remain unclear. This study aimed to explore associations among these factors. From December 2024 to May 2025, 2,888 participants (aged 18-35) from Tongji Hospital completed surveys on demographics, ACE, depressive symptoms, and SHS. Periodontitis was diagnosed according to the 2018 criteria. Simple, parallel, and chain mediation models were used, controlling for age, sex, marital status, and smoking. Periodontitis prevalence was 25.00% and higher in married individuals (P < 0.001) and smokers (P = 0.004). ACE correlated positively with depressive symptoms (r = 0.28, P < 0.001), SHS (r = 0.19, P < 0.001), and periodontitis (r = 0.16, P < 0.001). Mediation analyses showed: Simple model: Depressive symptoms and SHS partially mediated the effect of ACE on periodontitis (indirect effect = 0.011 for both). Parallel model: Only SHS significantly mediated the effect (indirect effect = 0.011). Chain model: ACE was related to periodontitis via "depressive symptoms → SHS" (indirect effect = 0.010), with significant direct and indirect effects. ACE associated with higher periodontitis risk in young people. This association included both a direct link between ACE and periodontitis, and an indirect link through the chain pathway of "depressive symptoms → SHS"; among these pathways, SHS was a key mediator. The study was registered in the Chinese Clinical Trial Registry (ChiCTR) with the registration number ChiCTR2500103464. Childhood trauma can exert long‐term impacts on health, including oral health. This study involving 2,888 Chinese young adults aged 18‐35 found that 25% of the participants had periodontitis. Those who experienced childhood abuse, neglect, or family issues showed a higher association with the disease. The research revealed two pathways linking early trauma to periodontitis: a direct association and an indirect chain of “depressive symptoms → suboptimal health status (e.g., persistent fatigue).” While depressive symptoms played a role, suboptimal health status was the critical mediator. Higher periodontitis rates in married individuals and smokers may relate to stress or lifestyle factors. The findings suggested that early identification of childhood trauma, combined with interventions targeting mental health or overall well‐being (e.g., counseling, health management), could be more effective than oral care alone in prevention. This underscored the association between early‐life experiences and long‐term health and the need for integrated interventions.
Parabiotics (also termed paraprobiotics) are defined as non-viable microbial cells or their components, including peptidoglycans, teichoic acids, surface proteins, that confer health benefits without requiring viability which distinguishes them from traditional probiotics. Their non-viable nature eliminates risks such as microbial translocation, bacteremia, and sepsis, making them suitable for vulnerable populations including immunocompromised, critically ill, paediatric and elderly individuals. In addition, parabiotic exhibit improved thermal stability, extended shelf life, and easier incorporation into functional foods, nutraceuticals, and pharmaceutical formulations without cold-chain requirements. Mechanistically, parabiotics retain immunomodulatory, anti-inflammatory and have barrier-enhancing activities through interactions with host pattern recognition receptors, including Toll-like receptors, modulation of cytokine responses, and reinforcement of gut epithelial integrity. Preclinical and clinical studies support their therapeutic potential such as in case of heat-killed Lactobacillus acidophilus LB (L. acidophilus) has shown efficiency in managing acute paediatric diarrhoea, while heat-inactivated Lacticaseibacillus paracasei PS23 (Lcb. paracasei) has demonstrated improvements in muscle strength and inflammatory markers, including reduced C-reactive protein and interleukin-6 and increased interlukin-10 in elderly individuals. Similarly, inactivated Lactiplantibacillus plantarum (Lpb. plantarum) and Bifidobacterium strains have been associated with benefits in irritable bowel syndrome, atopic dermatitis, respiratory infections, visceral fat reduction, and antibiotic-associated dysbiosis. Synergistic combinations with prebiotics, postbiotics and related bioactives further enhance therapeutic outcomes in inflammatory, metabolic and infectious conditions. Advances in metagenomics, next-generation sequencing, proteomics, metabolomics, CRISPR-Cas systems, and synthetic biology are accelerating strain characterization, functional evaluation, and scalable production. Despite ongoing challenges in standardization and regulated harmonization, parabiotics represent a safe and effective approach for microbiome-targeted interventions. This review synthesizes current evidence on their therapeutic applications, technological advancements, and translational potential, highlighting their role in precision health and next-generation functional nutrition.
Tanzania has adopted artificial intelligence (AI)-assisted chest X-ray screening for tuberculosis (TB), including the use of CAD4TB version 6, which is registered by the Tanzania Medicines and Medical Devices Authority (TMDA). While GeneXpert, practical reference standard used in routine practice, remains the primary bacteriological confirmatory test in routine practice, there is currently no established national threshold for CAD4TB use in either active case finding (ACF) or passive case finding (PCF) settings. This study evaluates the implementation and operational use of CAD4TB version 6 within mobile TB screening units in Tanzania and highlights challenges affecting its effective use. We conducted a retrospective analysis of screening data from 11,923 individuals collected from mobile clinics equipped with digital X-ray, CAD4TB version 6, and GeneXpert systems. Comparisons were made between manual chest X-ray interpretation, CAD4TB scores, and GeneXpert results within the subset of individuals who underwent confirmatory testing. The findings reveal substantial inconsistencies in screening workflows, including non-uniform use of CAD4TB prior to GeneXpert testing, missing radiological records, and deviations from intended protocols across sites. Descriptive analysis showed that CAD4TB scores generally aligned with GeneXpert-positive cases within the tested subset; however, due to selective application of GeneXpert and incomplete data, these observations cannot be interpreted as measures of diagnostic accuracy. This study should be interpreted as an implementation and operational assessment of AI-assisted TB screening rather than a diagnostic accuracy or threshold-setting study. The findings highlight important gaps in protocol adherence, data completeness, and workflow standardization, underscoring the need for prospective, protocol-driven studies to establish validated national thresholds for CAD4TB use in Tanzania.
Reward brain circuitry dysfunction is a hypothesized mechanism of bipolar disorder and alcohol use disorder co-occurrence (BD + AUD) that remains largely untested. This neuroimaging study represents the first investigation of functional connectivity in BD + AUD. Following a two-by-two factorial design (N = 90), individuals with BD + AUD (n = 22), AUD alone (n = 20), BD alone (n = 23), and healthy control participants (n = 25) were administered a fMRI alcohol-cue reactivity paradigm. Generalized psychophysiological interaction (PPI) modeling (p < 0.001; p-FDR < 0.05) was performed for regions of interest, including the right dorsal anterior insula, inferior frontal gyrus, and bilateral amygdala and dorsal striatum (i.e., caudate body). Extracted beta weights were explored for bivariate associations with key behavioral correlates (AUD age of onset, alcohol craving and dependence severity, abstinence duration, and impulsivity) (p < 0.05). BD + AUD individuals exhibited cue-modulated hyperconnectivity between the left dorsal striatum and right posterior cingulate cortex (p-FDR = 0.045) versus the AUD and BD groups, who both exhibited hypoconnectivity between these regions versus healthy participants. Additionally, there were main effects of AUD and BD (p-FDR ≤ 0.040) on cue-modulated functional connectivity of the right dAI (↓ middle frontal gyrus [MFG]) and left amygdala (↑ right superior temporal gyrus, anterior cingulate cortex, and MFG), respectively. Select functional connectivity data were associated with trait characteristics of AUD in BD + AUD (r ≥±0.50, p ≤ 0.026) but not AUD. A distinct pattern of cortico-striato-limbic functional connectivity and brain-behavior relationships was found to characterize BD + AUD with implications for treatment development. Namely, leveraging neuromodulation techniques that can effectively normalize the identified circuitry disruptions could represent a novel path for treatment advances in BD + AUD.
With waning vaccine-induced immunity and the continued emergence of immune-evasive SARS-CoV-2 variants, booster vaccination has become essential for sustaining population-level protection. However, scalable and reliable serological tools for monitoring post-booster humoral immunity across different vaccine platforms remain insufficiently evaluated. We conducted a cross-sectional study and a booster cohort study to systematically compare the performance and concordance of surrogate virus neutralization test (sVNT), pseudovirus neutralization test (pVNT), and binding IgG antibody assays. In the cross-sectional analysis, serum samples from 259 individuals collected 1-2 months after a third dose of inactivated vaccine were used to compare sVNT and pVNT. In the booster cohort, paired sera from 288 participants receiving a fourth dose with different vaccine platforms were analyzed to evaluate the relationship between sVNT and IgG responses. Correlation and agreement were assessed using Spearman correlation and Bland-Altman analyses. In the cross-sectional study, sVNT showed strong concordance with pVNT, demonstrated by a very high correlation (r = 0.97) and good agreement in Bland-Altman analysis. In the booster cohort study, IgG antibody levels correlated strongly with sVNT overall (r = 0.91). The correlation strength remained highly consistent across different vaccine platforms, with no statistically significant differences observed. Collectively, these findings demonstrate that sVNT closely reflects functional neutralizing activity, supporting its utility for large-scale immune monitoring. sVNT offers an optimal balance of analytical performance and scalability for high-throughput monitoring without requiring cell-based assays. Consequently, we propose a tiered immune monitoring strategy: utilizing sVNT for broad population-level surveillance and reserving pVNT for targeted, precise functional assessment.
The genetic architecture of cardiovascular traits is poorly characterised in non-European populations, limiting our understanding of disease aetiology and contributing to health disparities. Here, we analyse the genetic architecture of four cardiovascular traits (systolic and diastolic blood pressure, pulse rate, and maximum heart rate) using multi-trait analysis of genome-wide association studies and local genetic correlation analysis in 459,327 European (EUR) and 6654 African (AFR) ancestry individuals from the UK Biobank. Our analysis identifies 957 and 45 novel variants in the EUR and AFR cohorts, respectively, but reveals a profound divergence in the pleiotropic architecture of blood pressure. We identify 181 genomic loci with significant local genetic correlation between systolic and diastolic blood pressure (SBP-DBP) in the European sample, whereas such signals are completely absent in the African ancestry cohort. This marked disparity in local genetic correlation structure highlights that pleiotropic mechanisms can be highly ancestry-specific, underscoring the limitations of transferring genetic risk models across populations and the critical need for inclusive genomic research.
Alzheimer's disease (AD) is a growing public health concern, with neuroinflammation implicated in its pathogenesis. Allergic rhinitis (AR), a prevalent chronic inflammatory disorder, may contribute to systemic inflammation and potentially influence AD risk. This study sought to critically assess the association between a history of AR and subsequent AD development in a large, representative Taiwanese cohort. Leveraging Taiwan's National Health Insurance Research Database (LHID2010), this nationwide case-control study identified 4,681 individuals aged ≥ 65 years with a first-time AD diagnosis (cases) and 14,043 propensity-score-matched controls. A rigorous definition of prior AR required at least two clinical diagnoses, including one by an otolaryngology specialist. Multivariable logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for potential confounders. The prevalence of prior AR was significantly higher in AD patients than in controls (25.29% vs. 21.01%, p < 0.001). Following meticulous adjustment for demographic variables, socioeconomic status, geographic factors, and medical comorbidities (including hyperlipidemia, diabetes, coronary heart disease, hearing loss, and hypertension), prior AR was robustly associated with elevated odds of AD (adjusted OR = 1.279, 95% CI = 1.182 ~ 1.384). This association remained significant for both males (adjusted OR = 1.196, 95% CI = 1.053 ~ 1.358) and females (adjusted OR = 1.339, 95% CI = 1.210 ~ 1.482). This study suggests a significant association between prior AR and an increased odds of developing AD in an elderly Taiwanese population. These findings highlight chronic peripheral inflammation as a factor potentially associated with neurodegeneration.
With the popularity of short video platforms, Short Video Addiction has become a growing mental health concern. From a cognitive-emotional perspective, this study examined the association between Parental Neglect and Short Video Addiction among college students, focusing on the mediating role of Thought Suppression and the moderating effect of Thought Control Ability. A cross-sectional survey was conducted among 1,337 college students using the Parental Neglect Scale, Short Video Addiction Scale, White Bear Suppression Inventory, and Thought Control Ability Questionnaire. The results showed that Parental Neglect, Thought Suppression, and Short Video Addiction were all significantly and positively correlated with one another, while Thought Control Ability was significantly and negatively correlated with these three variables. Thought Suppression partially mediated the relationship between Parental Neglect and Short Video Addiction, and Thought Control Ability moderated the path from Thought Suppression to Short Video Addiction: for individuals with lower Thought Control Ability, Thought Suppression was a stronger positive predictor of Short Video Addiction; whereas for those with higher Thought Control Ability, this positive association was significantly attenuated. These findings suggest that enhancing individual Thought Control Ability may be associated with reduced negative influence of Thought Suppression on Short Video Addiction. This provides a theoretical and practical basis for cognitive-level interventions to help prevent excessive use of short video platforms among young adults.
The aim of this study was to examine triceps surae neuromechanical function during cycling at a range of exercise intensities in endurance populations with different loading regimes, and to quantify differences in morphological properties. Kinematic and neuromuscular data were collected from 29 participants (10 cyclists, nine triathletes, and 10 controls) during cycling at four intensities (150, 200, 250, 300 W). Resting muscle and tendon morphology was assessed with ultrasound. During cycling, there were no differences between groups, despite triathletes possessing thicker Achilles' tendons. At higher intensities, ankle dorsiflexion increased (p < 0.001), leading to higher gastrocnemius medialis muscle-tendon unit range (p < 0.001), but no change in fascicle range or shortening velocity (p ≥ 0.919). Therefore, although there is evidence of some stretch-shortening cycle-like mechanism in the triceps surae during cycling, this does not happen at the muscle level, suggesting that energy storage and return could occur predominantly in the non-contractile series-elastic elements. Despite this, there were no differences detected between trained and untrained individuals in gastrocnemius medialis neuromechanical behavior at the exercise intensities tested, even though triathletes possessed a higher Achilles' tendon thickness.
Optical Chemical Structure Recognition (OCSR) aims to convert two-dimensional molecular images into machine-readable formats such as SMILES strings. Deep learning has substantially improved OCSR performance, yet most methods rely on synthetic training data and struggle to generalize to real-world inputs, especially hand-drawn diagrams, where stroke width, geometry, and drawing conventions vary widely across individuals. In this work, we propose an image-to-graph model AdaptMol that enables effective transfer from synthetic to real-world data without requiring manual graph annotations in the target domains. AdaptMol is an integrated pipeline that starts with training a base model on synthetic data, and then refines model representations through unsupervised domain adaptation and self-training. Our key insight is that bond features are domain-invariant in nature; they encode structural relationships between atoms that are independent of visual variations across domains. Thus, during domain adaptation, we align bond-level feature distributions via class-conditional Maximum Mean Discrepancy (MMD) to enforce cross-domain consistency. We also design a comprehensive data augmentation strategy to enhance the robustness of the base model, facilitating stable self-training on unlabeled target samples. On hand-drawn molecular images, our model achieves 82.6% accuracy and outperforms the best prior method by 10.7 points, while maintaining competitive performance across four benchmarks comprising molecular images from scientific literature and patent documents.Scientific contributionWe propose AdaptMol, an image-to-graph model that predicts molecular structures as graphs of atoms and bonds, achieving effective transfer from synthetic to hand-drawn molecular images without requiring target domain graph annotations. We combine class-conditional Maximum Mean Discrepancy to align bond features across domains with comprehensive data augmentation to increase training data variation, jointly improving base model accuracy sufficiently for self-training and addressing the critical failure mode of prior approaches that begin with insufficient accuracy. We further introduce a dual position representation that supervises atom positions through both discrete coordinate tokens and continuous spatial heatmaps to reduce false positives in atom localization.
The goal of this study was to identify symptoms that occur in children post-SARS-CoV-2 infection, their trajectory over the first-year post-enrollment, and relationship to age. Longitudinal comparison of infected and uninfected cohorts. Participants (0-21 years) with laboratory-confirmed SARS-CoV-2 infection were enrolled as infected. The uninfected cohort was individuals without laboratory evidence of SARS-CoV-2 infection. Primary outcome was presence or absence of symptoms. 852 participants (705 infected, 147 uninfected) completed baseline visits. Of those, 558 infected subjects completed a 12-month post-enrollment visit. Twenty symptoms were identified as more common in infected participants compared to uninfected, at either baseline or 12-months, with symptoms varying by age. Some symptoms in the infected were more frequent at baseline (e.g. fever, weight loss), whereas many symptoms persisted through 12-months. Several symptoms were more frequent at 12-months (e.g. dysmenorrhea, persistent headache). Presence of symptoms at 12-months was not significantly associated with the wave of circulating virus at original infection. Interim analysis at one-year post-enrollment identifies 20 symptoms that infected participants were more likely to report post SARS-CoV-2 infection compared to uninfected, at either visit. Type of symptoms varies by age. Ongoing longitudinal data up to 3-years post-enrollment will increase understanding of long-term symptoms of SARS-CoV-2 infection in children and their trajectory. NCT04830852. Although most children recover fully from SARS-CoV-2 infection, some children experience a variety of prolonged symptoms following infection. Many studies attempting to characterize these symptoms and trajectory are not prospective nor longitudinal and lack comparison to uninfected controls. This longitudinal analysis identifies and characterizes post-COVID symptoms in children and adolescents and their trajectory through the first-year post enrollment compared to an uninfected cohort. 20 post-infection symptoms were identified as occurring more frequently in the infected as compared to uninfected cohort. Age played a critical role in the type and frequency of symptoms after SARS-CoV-2 infection. Gastrointestinal symptoms were prominent.
Parkinson's Disease (PD) is a progressive neurodegenerative disorder that causes motor and cognitive impairments, affecting approximately 1% of individuals over 60 years of age. Speech impairments are among the earliest and most accessible biomarkers, making voice-based assessment a promising avenue for remote PD monitoring. However, existing speech-based PD prediction methods suffer from feature redundancy that degrades model performance, non-Gaussian data distributions that violate model assumptions, and limited systematic feature grouping strategies. This study introduces an adaptive approach to improve PD diagnostic precision by predicting the Motor Unified PD Rating Scale (UPDRS) and Total-UPDRS scores from biomedical voice measurements. The proposed framework addresses these challenges through three integrated components: (1) Box-Cox transformation to stabilize variance, reduce skewness, and normalize features; (2) a clustering-based feature selection method that groups correlated features via K-Means and selects the most informative representative per cluster using mutual information, thereby eliminating redundancy without losing discriminative power; and (3) an Extra Trees Regressor (ETR) whose extreme randomization in node splitting provides computational efficiency and reduced variance. To ensure rigorous evaluation, a subject-independent data splitting strategy is adopted to prevent data leakage, and k-fold cross-validation is employed to assess model stability. The proposed method is compared against multiple feature selection techniques-mutual information, recursive feature elimination, Lasso regression, and autoencoders-paired with nine regression models including Ridge, Lasso, Linear, Decision Tree, k-Nearest Neighbors, Random Forest, Gradient Boosting, AdaBoost, and Extra Trees Regressors. The clustering-based feature selection combined with ETR yielded the best performance, achieving [Formula: see text] scores of 0.999 for Motor-UPDRS and 0.997 for Total-UPDRS on the test set. These results are further supported by cross-validation analysis and feature importance evaluation, demonstrating the effectiveness and robustness of the proposed framework for speech-based PD telemonitoring.
Soft skills correspond to intrapersonal and interpersonal abilities related to how individuals interact, make decisions, and manage their activities. In the context of undergraduate nursing education, their development is fundamental to the preparation of professionals capable of acting in an ethical, critical, and relational manner, making it relevant to understand how these competencies are incorporated into the teaching and learning process. In this context, the objective of this study is to understand how faculty members in undergraduate nursing programs incorporate soft skills into their pedagogical approaches and practices, identifying the competencies considered essential and the challenges to their implementation. A qualitative study was conducted with 26 nursing faculty members from four federal public universities in southern Brazil. Data were collected between June and September 2025 through semi-structured interviews, following the criteria of the Consolidated Criteria for Reporting Qualitative Research checklist. The interviews were processed using IRaMuTeQ software and analyzed in light of Discursive Textual Analysis. Three analytical categories emerged: faculty understanding of soft skills in nursing education; pedagogical approaches and strategies for the development of these competencies; and perceived difficulties in their promotion within teaching. The faculty members recognize the relevance of soft skills and report the use of active methodologies and reflective strategies for their development. However, most had not received specific training, and the teaching of these competencies occurs predominantly in an implicit manner. The findings demonstrate that, although soft skills are widely valued in nursing education, their promotion still lacks pedagogical systematization and institutional support. Challenges such as the subjectivity of these competencies, the prioritization of technical skills by students, and distractions associated with the use of technologies limit their intentional development. These results contribute to the international literature in nursing education by highlighting the need for structured institutional strategies for faculty development and for the explicit integration of soft skills into nursing curricula.
Diabetes is associated with a number of significant long-term effects. In this study we consider that purslane which possesses numerous of pharmacological properties, and metformin, an antidiabetic drug, may have a therapeutic effects on diabetes-induced memory impairments in rats. Forty male albino rats were randomly divided into five groups. Group I served as control group. The other four groups were first fed on HFD followed by a single interpretonial (i.p.) dose of STZ at a dose of (35) mg/kg then the groups were divided as following Group II diabetic group Group III, PEE group administered with oral dose of purslane ethanolic extract (100 mg/kg) for another four weeks. Group IV, MET group administered with oral dose of metformin (100 mg/kg) for another four weeks. Group V (PEE + MET) administered with oral dose of combination of both purslane ethanolic extract (50 mg/kg) and MET (50 mg/kg) for another four weeks. During the treatment rats were tested for memory and learning abilities (Morri's water maze test). Hippocampal samples were collected for biochemical, and histological measurements. Biochemical evaluation included (NO and TBARS) as an oxidative stress marker, (GSH, GPX, SOD, Catalase) as antioxidant, and inflammatory cytokines (tumor necrosis factor-α and interleukin-1β, interleukin-IL-6). Also, P-tau protein, (dopamine and GABA) as neurotransmitters, and for cholinergic system (acetylcholinesterase) were assessed, in addition to histological examinations of hippocampus. Diabetic rats showed a marked cognitive impairment in the Morris water maze test and alteration in the other biochemical and histological features. Intrestingly, PEE and MET treatments partially dramatically enhanced antioxidant levels. Also, reduced oxidative stress, pro-inflammatory mediators, and, phosphorylated tau levels. In addition, PEE and MET treatments partially modulated neurochemical profiles associated with memory function. The combined PEE + MET treatment showed the most pronounced improvement, reflecting synergistic effects. Individual data points highlighted consistent trends across animals. Also, it exhibited a significant restoration of normal hippocampal architecture, as confirmed by hematoxylin and eosin staining. The data obtained indicated that PEE, either alone or in combination with MET, has strong neuroprotective potential against STZ/HFD-induced diabetes. These safeguarding effects are probably because of its strong anti-inflammatory and antioxidant properties.
In the digital age, university students' sustained academic engagement and strong learning resilience in the face of increasing academic pressure and complex campus challenges are essential to the attainment of substantial academic achievement. At present, how to enhance students' academic engagement and foster learning resilience has become a pressing issue for educational administrators. Although previous studies have examined multiple factors influencing academic engagement and resilience, they have largely emphasized the isolated effects of psychological traits on individual learning performance while overlooking the complex possibility that perceived external contexts, such as the learning environment, learning climate, and social relationships, may jointly shape learning resilience through psychological and emotional regulatory mechanisms. Therefore, this study focuses on the interaction among external contexts, internal affective drivers (academic self-efficacy and perceived campus belonging), and learning resilience. Using questionnaire survey data and structural equation modeling, this study examines the extent to which external contexts are associated with academic self-efficacy and perceived campus belonging, explores whether these internal affective drivers are statistically associated with learning resilience through mediating pathways, and constructs an "external context-affective drivers-learning resilience" model to identify potential explanatory pathways and provide evidence-based implications for educational management.
Breast cancer patients often experience significant psychological distress. This study examined distress trajectories from diagnosis to 6 months post-treatment and explored differences across demographic, medical, and psychosocial subgroups. In this prospective cohort study, 528 patients with breast cancer were recruited between 1 December 2023 and 31 December 2024. Assessments were conducted at baseline (at diagnosis, T0), after the first treatment (T1), mid-treatment (T2), at treatment completion (T3), and at three (T4) and six months (T5) post-treatment. Growth mixture modeling (GMM) was used to identify distinct trajectories of psychological distress. Multinomial logistic regression analysis was performed to examine associations between patient-related factors and trajectory membership. Three psychological distress trajectories were identified: a high-distress remission group (17.05%), a moderate-stable distress group (11.93%), and a low-fluctuating distress group (71.02%). Multivariable analyses showed that higher educational attainment, breast-conserving surgery, early disease stage, partial self-management ability, and strong social support were associated with membership in the moderate-stable or low-fluctuating groups (p < 0.05). Employment, health insurance coverage, avoidant medical coping style, and higher baseline anxiety and depression scores were concurrently associated with membership in the high-distress remission group (p < 0.05). Although psychological distress generally decreased over time, 71.02% of patients followed a low-fluctuating trajectory, 11.93% maintained moderate distress with potential risk of persistence, and 17.05% showed high initial distress that remitted substantially within 6 months. Continuous monitoring and early psychosocial support are recommended, particularly for patients with moderate- or high-risk trajectories.
Human biomonitoring (HBM) is crucial for evaluating exposure to diet-related contaminants, whose effects may pose substantial health risks. Saliva is recognized as a promising non-invasive biological matrix due to its ease of collection and potential to reflect external and systemic exposure. However, suitability for monitoring dietary hazardous compounds remains uncertain. To assess the potential of saliva as a biomonitoring matrix for diet-related contaminants, identify compounds with robust diet-related associations, and highlight knowledge gaps. A systematic literature review was conducted to screen over 500 diet-related contaminants analyzed in saliva. Detailed information was extracted only for contaminants quantitatively measured in saliva, including concentration ranges, sample sizes, and analytical methods. Evidence of correlations with systemic concentrations, exposure pathways, and individual or lifestyle factors was compiled into a FAIR database to provide an integrated evaluation of saliva's biomonitoring potential. Only a limited subset of contaminant groups, including nitrite/nitrate, heavy metals, bisphenols, polycyclic aromatic hydrocarbons (PAHs), biogenic amines, pesticides, advanced glycation end products (AGEs), perchlorate, microplastics (MPs), parabens and phthalates, have been quantitatively measured in saliva. Compounds such as nitrate, arsenic, AGEs, pesticides and perchlorate demonstrate moderate to strong correlations between salivary and systemic levels, supporting saliva's potential to estimate exposure. Conversely, substances like PAHs, MPs, phthalates and parabens generally show weak or no correlation, reflecting recent or localised exposures rather than cumulative burden. Salivary composition is influenced by intrinsic and extrinsic factors, including diet, oral microbiota, physiology, and sampling conditions, resulting in high interindividual variability. Despite challenges, low salivary concentrations and lack of standardized collection protocols, saliva offers advantages for biomonitoring vulnerable populations, such as children and pregnant women. Harmonized collection procedures, validated sensitive methods, predictive models accounting for variability and exposure context, could establish saliva as a reliable complementary or alternative matrix for assessing human exposure to dietary and environmental contaminants. This systematic review synthesizes findings from 104 studies, covering over 500 diet-related contaminants measured in saliva, and compiles them into a FAIR database, providing the most comprehensive resource to date for saliva-based biomonitoring. Compounds such as nitrate, arsenic, advanced glycation end-products (AGEs), pesticides, and perchlorate show meaningful correlations with systemic levels, supporting saliva's potential as a non-invasive matrix for assessing human exposure. To fully realize saliva's potential, standardized collection protocols, validated analytical methods, and predictive models that account for interindividual variability and exposure context are urgently needed, enabling more accurate and ethical monitoring of vulnerable populations.
Therapeutic plasma exchange (TPE) is being increasingly utilized in the clinical management of severe rheumatic immune diseases, providing an effective means for rapidly removing pathogenic autoantibodies and inflammatory mediators. However, the non-selective nature of this technique can also lead to the unintended clearance of concomitantly administered antirheumatic drugs, potentially compromising therapeutic efficacy and disease control. Therefore, effective management of potential drug removal process during TPE and the implementation of individualized risk assessment are crucial for optimizing treatment outcomes in patients undergoing TPE. The variability in the extent of drug removal during TPE is primarily determined by their distinct pharmacokinetic characteristics, necessitating the establishment of a systematic, evidence-based strategy for adjusting drug administration regimens in patients receiving TPE treatment. This review synthesizes current evidence from 65 studies on the removal of antirheumatic drugs during TPE, identifying key determinants influencing clearance rates, including volume of distribution, protein binding, molecular size, and elimination half-life. Our analysis reveals that the risk of drug removal exists as a continuous spectrum: large monoclonal antibodies (e.g., rituximab, natalizumab), characterized by a large molecule size, low volume of distribution, with which mostly confined to the vascular space, are cleared with high efficiency. This finding supports the clinical recommendation of administering such drugs after TPE. For drugs with limited direct evidence, we propose a predictive model based on fundamental pharmacokinetic parameters to estimate their removal risk and guide clinical decision-making. Based on this evidence, we have constructed a stratified clinical management framework. It aims to maintain effective therapeutic drug exposure levels during chronic TPE therapy and to provide a rationale for the judicious application of TPE in overdose scenarios. Implementing this pharmacokinetic-informed, risk-adapted individualized strategy is important for ensuring treatment continuity, enhancing patient safety, and advancing empiricism-based therapy towards precision medicine.