As an efficient signal acquisition and reconstruction technology, compressed sensing (CS) has key application value in single-pixel imaging, magnetic resonance imaging (MRI) and other fields. The core challenge is to achieve high image reconstruction through a small amount of measurement data while solving the problem of low fixed sampling efficiency. It is difficult for a single network to balance local details and global dependence. In this paper, Multi-Stage Adaptive Sampling Coupled with Residual Dense-Transformer (MASRDT-Net) is proposed, which combines a multi-stage adaptive sampling module and a hybrid reconstruction architecture. Its core design includes: 1) multi-stage adaptive sampling module, which captures the image infrastructure at the initial sampling rate and distributes the remaining sampling rate to the iteration stage in proportion. Combined with a trainable floating-point perception matrix, it realizes “priority sampling in important areas” and improves measurement data utilization at low sampling rates and 2) residual dense-Transformer hybrid reconstruction module strengthens local feature extraction through residual dense blocks (RDB), Swin Transformer controls complexity and captures global dependencies, and corrects errors with the “PreReconBlock-Transformer-PostReconBlock” iterative process. Experiments show that the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of MASRDT-Net are better than ReconNet, ISTA-Net, and other methods at different sampling rates, realizing the balance of “high-efficiency sampling-high-precision reconstruction”, providing practical solutions for practical CS applications.

Background:While antidepressant side effects are often expected to diminish with continued treatment, empirical evidence remains limited. This study investigates co-trajectories of depressive severity with side-effect burden (SEB) and associated factors in patients with major depressive disorder (MDD). Methods:We analyzed longitudinal data from 1377 MDD outpatients across three Chinese multicenter studies (2015-2022), with assessments at baseline, weeks 2, 4, 8, 12, and 24. Depression severity was measured using the Quick Inventory of Depressive Symptomatology-Self Report (QIDS-SR16). At the same time, SEB was quantified using the frequency, intensity, and burden of side effects rating (FIBSER). Dual-trajectory modeling was used to identify distinct SEB and depression severity trajectories, with linear mixed-effects models comparing depression changes across SEB groups. Results:Four SEB trajectories were identified: no SEB (41.5%), early-onset SEB (29.1%), late-onset SEB (14.7%), and persistent SEB (13.9%). Depressive severity followed three trajectories: mild-responsive (66.6%), moderate-progressive (18.7%), and chronic-severe (14.7%). Persistent SEB was associated with higher baseline depression severity (OR = 1.13, 95% CI: 1.08-1.17), antidepressant combination (OR = 3.7, 95% CI: 1.52- 9.02), and poorer treatment outcomes. 7.3% exhibited concurrent chronic-severe depressive severity and persistent SEB. Female (OR = 1.95, 95% CI: 1.11-3.42), younger age (OR = 5.02, 95% CI: 2.63-9.55), higher education (high school: OR = 2.5, 95% CI: 1.12-5.36; bachelor and above: OR = 2.68, 95% CI: 1.24-5.82), and combination antidepressant (OR = 8.27, 95% CI: 2.69-25.45) were significant risk factors for concurrent severe symptoms and persistent SEB. Conclusion:Persistent antidepressant side effects coevolve with unfavorable depression trajectories over 6 months. Clinicians should prioritize early monitoring and tailored interventions for high-risk subgroups, particularly those with severe baseline symptoms or on combination therapy. These findings underscore the importance of continuous monitoring and personalized interventions to manage antidepressant side effects effectively.

BACKGROUND:Current treatment algorithms for major depressive disorder (MDD) lack dynamic prediction capabilities, leading to delayed therapeutic adjustments. This study sought to develop escitalopram-specific decision tree models to identify critical treatment adjustment time points and optimize personalized treatment strategies for MDD. METHODS:Using longitudinal data from two multicenter studies in China (2015-2020), we analyzed 800 patients with MDD receiving escitalopram monotherapy. Decision tree models incorporated baseline characteristics (age, BMI, disease duration, depressive symptoms) and dynamic treatment parameters (dose, 2-/4-week improvement) to predict full response (>50% symptom reduction) or non-full response (≤50% reduction) at weeks 2 and 4, and remission status (QIDS-SR16≤5 vs. >5) at week 8. Model performance was assessed by accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). RESULTS:The week 2 model (n = 800) identified BMI, age, disease duration, course and baseline symptom severity as primary predictors (accuracy = 61.88%, NPV = 84.04%). By week 4 (n = 650), early response status (week 2) merged as a key predictor (accuracy = 69.23%, NPV = 71.62%). The week 8 model (n = 456) demonstrated enhanced predictive power, driven by life quality score, week 2/4 response status, and week 4 dosage (accuracy = 78.02%, PPV = 81.48%, NPV = 72.97%). Logistic regression confirmed week 4 response status as a significant predictor of week 8 outcome (p < 0.005). CONCLUSIONS:Week 4 emerges as a key decision point for escitalopram-treated MDD patients, where integration of baseline profiles, early response patterns, and dose parameters allows timely intervention. Our decision tree framework offers a methodological approach for dynamic decision points that warrant prospective validation and extension to other antidepressants.

OBJECTIVES:To evaluate correlations and score equivalence among four widely used clinician-rated and self-report depression scales-the 17-item Hamilton Rating Scale for Depression (HAMD-17), 6-item Hamilton Rating Scale for Depression (HAMD-6), 16-item Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR16), and Patient Health Questionnaire-9 (PHQ-9)-to support cross-walk-based interpretation and comparison across studies and research settings. STUDY DESIGN AND SETTING:This national, multicenter prospective cohort study included 1418 adults with major depressive disorder recruited from 12 sites across 11 regions in China. Depression severity was assessed at baseline and at months 1, 2, 3, 4, and 6, yielding 5597 assessments. All four scales were administered concurrently at each visit. Agreement was evaluated using Spearman correlation coefficients and Bland-Altman plots. Equipercentile linking with log-linear smoothing was applied to derive score conversions across scales. RESULTS:All pairwise baseline correlations between scales were statistically significant, with the strongest correlation observed between HAMD-6 and HAMD-17 (r = 0.73). Clinician-rated scales consistently yielded higher response and remission rates than self-report scales. Bland-Altman analyses revealed systematic differences across measures, with the widest limits of agreement observed between HAMD-6 and HAMD-17 (-3.47 to 14.81), followed by PHQ-9 (-6.14 to 10.61) and QIDS-SR16 (-5.56 to 11.02). Equipercentile linking curves and conversion table were generated to translate HAMD-6, QIDS-SR16, and PHQ-9 scores into HAMD-17 equivalents. CONCLUSION:Despite significant correlations, commonly used depression scales exhibit systematic differences in measurement and outcome classification across instruments. Equipercentile linking provides a practical framework for score translation, facilitating cross-study comparisons, secondary data analyses, and harmonization of depression outcomes. PLAIN LANGUAGE SUMMARY:Different scales are commonly used to measure depression severity, but their scores are not directly interchangeable. In this large multicenter study from China, we examined how four widely used depression scales relate to one another when completed at the same time. Although the scales showed moderate to strong correlations, they differed systematically in how treatment response and remission were classified. By developing conversion tables between these instruments, our findings help clinicians and researchers compare results across studies and better interpret depression scores when different scales are used.

BACKGROUND:Physical activity reduces the risk of mortality and age-related chronic diseases, yet its association with biological age measured by DNA methylation (DNAm) clocks remains unclear. This systematic review and meta-analysis aims to evaluate the association between physical activity and biological age measured by DNAm clocks. METHODS:In this systematic review and meta-analysis, we conducted a systematic search of Embase, Cochrane Central Register of Controlled Trials, PubMed, Ovid, Scopus, and Web of Science from Jan 1, 2011, to June 6, 2025, to identify articles on the associations of physical activity and DNAm age, epigenetic age acceleration (EAA), or epigenetic age deviation in humans. Studies were included if they were peer-reviewed, published in English, included a study population with a mean or median age of 18 years or older, and investigated the association between DNAm clocks and physical activity in humans. Studies were excluded if the study population was a disease-specific population without controls. We evaluated risk of bias using an adapted Newcastle-Ottawa Scale and Cochrane Risk of Bias scale. We then performed a random-effects meta-analysis using reported or estimated standardised β coefficients and SEs. We also conducted a publication bias analysis and influence analysis. The study was registered with PROSPERO, CRD42024499021. FINDINGS:We identified 34 437 articles and, after removal of duplicates and screening, 44 studies were included in the systematic review comprising 145 465 participants: 62 887 (43·2%) females and 82 578 (56·8%) males, with mean ages ranging from 24·1 years to 78·5 years. Across studies, higher levels of physical activity were generally associated with lower DNAm age, although many individual associations did not reach statistical significance. Seven cross-sectional studies contributed to the meta-analysis. Each one SD higher in metabolic equivalent of tasks-min per week was associated with 0·03 SD lower Horvath EAA (β=-0·03 [95% CI -0·05 to -0·01]) and 0·09 SD lower GrimAge EAA (-0·09 [-0·12 to -0·05]). No statistically significant association was observed for Hannum EAA or PhenoAge EAA. INTERPRETATION:Higher physical activity is significantly associated with lower biological age as measured by Horvath EAA and GrimAge EAA. However, evidence is predominantly from cross-sectional studies, limiting causal inference. Future longitudinal studies and clinical trials using standardised, objectively measured physical activity are warranted to clarify dose-response relationships, and to determine whether physical activity can causally modify ageing trajectories, thereby informing precision strategies for healthy longevity. FUNDING:The National University of Singapore and the National Medical Research Council of Singapore.

Aging is a complex biological process, and biological aging can be quantified by epigenetic clocks. Depression and anxiety are highly prevalent among older adults and are established risk factors for adverse health outcomes. However, their relationships with epigenetic age acceleration (EAA) remain unclear, particularly in Asian populations. Using data from the Diet and Healthy Aging cohort, the present study examined the associations between depressive and anxiety symptoms and EAA within community-dwelling older adults (aged ≥ 60 years, n = 672). Depressive symptoms were assessed using the Geriatric Depression Scale (GDS), and anxiety symptoms were measured using the Geriatric Anxiety Inventory (GAI). Linear mixed-effects models were fitted to all available observations to account for within-person clustering, with additional within-person change analysis conducted among participants with repeated DNA methylation profiles (n = 116). These were followed by rigorous sensitivity analyses to inspect robustness. We found that depressive symptoms, but not anxiety, were robustly associated with higher EAA, primarily indexed by PCPhenoEAA. In fully adjusted models, each standard deviation (SD) increase in depressive symptoms corresponded to a 0.087 SD increase in PCPhenoEAA (β = 0.087, 95% CI [0.023, 0.151], p = 0.008). Participants screening positive for depression (GDS ≥ 5) exhibited, on average, 0.244 SD higher PCPhenoEAA compared with those without depression (β = 0.244, 95% CI [0.027, 0.461], p = 0.030). Despite relatively stable EAA across the follow-up period, within-person change in depressive symptoms was associated with a concomitant increase in PCPhenoEAA. Our findings highlight depression as an important and potentially modifiable factor in delaying biological aging among older Asian adults, highlighting the need for timely screening and interventions to promote healthy aging.

Objective:To investigate the association between pain intensity and cognitive function, and to explore the influence of demographic, clinical, and mood factors on this relationship. Design:Cross-sectional study. Setting:A tertiary comprehensive teaching hospital. Subjects:2661 inpatients admitted for pain management who completed cognitive function screening. Methods:Pain intensity was assessed using the Numeric Rating Scale (NRS). Cognitive function was evaluated with the Montreal Cognitive Assessment (MoCA). Anxiety and depressive symptoms were assessed using the Hamilton Anxiety Scale (HAMA) and the Hamilton Depression Scale (HAMD), respectively. Univariable and multivariable analyses were performed to examine the association between pain intensity and cognitive function, and mediation analysis was conducted to explore potential mediating effects. Results:The median pain duration was 3.0 months (IQR 0.6-12.0), and the median admission NRS score was 6 (IQR 4-6). Among the 2661 patients, 1502 (56.4%) had a MoCA score below 26, indicating mild cognitive impairment. Multiple linear regression model showed that higher pain intensity was significantly associated with higher MoCA (β = 0.22, 95% CI [0.12, 0.33], p < 0.001) after controlling for age, sex, education, pain duration, anxiety, and depression. Mediation analysis showed that baseline pain significantly predicted higher depressive symptoms (β = 0.41, p < 0.001), and depressive symptoms were negatively associated with cognitive performance (β = -0.16, p < 0.001). Conclusion:This large-scale cross-sectional study demonstrated a complex association between pain intensity and cognitive performance. While pain was directly associated with cognitive scores, depressive symptoms exerted a significant negative indirect effect, indicating partially opposing pathways. These findings suggest that mood-related mechanisms play a critical role in the pain-cognition relationship, highlighting the importance of integrating mood assessment into pain management.

BACKGROUND:Cognitive impairment is a significant health concern among older adults, highlighting the need for non-pharmacological interventions, such as mind-body exercises. However, a comprehensive synthesis of the effects of various traditional Chinese exercises (TCEs) on cognitive function in older adults is lacking. METHODS:A systematic review and meta-analysis of randomised controlled trials (RCTs) was conducted. Six databases were searched from inception to 2 May 2024 for studies examining the effects of TCEs on cognitive outcomes in adults aged 60 years and older. Studies were included if they were RCTs involving TCEs and reported outcome measures for global cognition, or individual cognitive domains. RESULTS:Twenty-eight RCTs with a total of 2297 participants were included. Meta-analysis revealed that TCEs led to significant improvements in global cognition: Montreal Cognitive Assessment [mean differences (MD) = 1.67; 95% confidence interval (CI): (1.20, 2.14)], Mini-Mental State Examination [MD = 0.76; 95% CI: (0.04, 1.48)]; executive function: Trail Making Test (B-A) [MD = -7.96; 95% CI: (-15.34, -0.59)], Category Fluency for Animals [MD = 2.96, 95% CI (2.08, 3.85)]; working memory: Digit Span-Backwards [MD = 0.48; 95% CI: (0.07, 0.90)]; processing speed: Digit Symbol Coding [MD = 4.16; 95% CI: (1.82, 6.50)]; memory function: Memory Quotient [MD = 13.13; 95% CI: (4.06, 22.20)], Auditory Verbal Learning Test: immediate recall [MD = 1.13; 95% CI: (0.07, 2.20)], short-term delayed recognition [MD = 0.80; 95% CI: (0.28, 1.32)] and long-term delayed recognition [MD = 1.38; 95% CI: (0.68, 2.09)]. CONCLUSIONS:TCEs are effective in improving cognitive function in older adults, particularly in domains such as global cognition, executive function, working memory, processing speed and memory function. However, given the methodological limitations and heterogeneity of the included studies, these findings require confirmation in further large-scale, high-quality RCTs.

BackgroundContinuous follow-up for patients with major depressive disorder (MDD) is essential for treatment decisions and a better prognosis. There remains limited evidence regarding the critical issue of depression variation trajectory prediction using mobile health (mHealth) measures. Moreover, the temporal dynamics of mHealth measures have not been fully modeled in previous studies, and the poor patient adherence to mHealth records poses great challenges to the dynamic feature modeling. ObjectiveThis study aimed to examine the contribution of mHealth measures in predicting depression variation trajectory for patients with MDD, with full consideration of the temporal dynamics of mHealth measures. MethodsA total of 229 patients with MDD from a multiple-center, prospective cohort were included. A 12-week follow-up was conducted involving the collection of the Hamilton Depression Rating Scale (HAMD-17), along with patient-reported outcomes (Immediate Mood Scaler and Altman Self-Rating Mania Scale) via mobile devices and sleep duration through wearable wristbands. We used functional data analysis to extract dynamic features from the sparse mHealth records, rather than aggregating the data to a single scalar summary measure through collapsing over time. Subsequently, 3 machine learning models were applied to predict the depression variation trajectory classes based on the baseline characteristics and these extracted dynamic features. ResultsBased on the variation of HAMD-17 scores within 12 weeks, the participants were labeled into 4 classes through the k-means algorithm. The classes included stable decline (n=93), fluctuate decline (n=44), fast decline (n=60), and delayed and fluctuate (n=32), in light of the shape of depression trajectories. With both baseline features and dynamic features of the mHealth measures, accuracy rates for the overall data were 54.35%, 60.87%, and 56.52%, for the stable decline patients were 78.95%, 84.21%, and 73.68%, for the nonstable decline patients were 59.26%, 62.96%, and 70.37% based on the 3 machine learning models, respectively. The results were significantly superior to the prediction obtained without mHealth measures (with an overall accuracy below 50%) and only showed a marginal reduction in accuracy relative to the ideal prediction with assessment obtained from clinical visits. Moreover, in the construction of the most accurate prediction model, dynamic features of the Immediate Mood Scaler, the Altman Self-Rating Mania Scale, and sleep duration emerged as the most influential predictors, ranking first, third, and fourth, respectively, in terms of their relative importance. ConclusionsLongitudinal mHealth measures show potential in depression variation trajectory monitoring for patients with MDD even under poor patient adherence. Our work provides practical help in alleviating the follow-up burden for patients with MDD and validates the effectiveness of mHealth measures in clinical applications.

BACKGROUND:Lifestyle factors play a critical role in healthy aging, yet their relationships with aging biomarkers remain insufficiently characterized, particularly in Asian populations. This study aimed to examine the cross-sectional and longitudinal associations between 15 modifiable lifestyle factors and two DNA methylation (DNAm) clocks (GrimAge acceleration [AgeDev] and DunedinPACE) in a cohort of older Asian adults. METHODS:We conducted a cross-sectional analysis of 631 participants (median age 70.0 years; 72.6% female) and a longitudinal analysis of 114 participants (mean follow-up 3.96 years) from the Singapore Diet and Healthy Aging (DaHA) cohort. Lifestyle exposures were assessed using validated self-administered questionnaires. Peripheral blood DNAm profiles were generated using the Illumina MethylationEPIC array. Multivariable linear regression models were applied to evaluate associations between lifestyle factors and DNAm clocks, adjusting for sociodemographic covariates, health status, and immune cell-type proportions. RESULTS:In cross-sectional analyses, smoking history showed robust positive associations with accelerated epigenetic aging (GrimAge AgeDev: β = 1.45, 95% CI 1.13-1.77, p < 0.0001; DunedinPACE: β = 0.63, 95% CI 0.22-1.05, p = 0.003). Conversely, weekly physical activity was associated with slower aging (GrimAge AgeDev: β = -0.22, 95% CI -0.40 to -0.04, p = 0.02), as was daily engagement in cognitively stimulating activities (GrimAge AgeDev: β = -0.16, 95% CI -0.31 to -0.01, p = 0.04). Weekly feelings of stress were initially associated with greater GrimAge AgeDev, but this relationship was attenuated after full adjustment. No significant longitudinal associations were detected, which may reflect limited statistical power and the stability of long-standing lifestyle behaviors over the follow-up period. CONCLUSIONS:These findings highlight significant cross-sectional associations between key modifiable lifestyle factors, particularly smoking, physical activity, and cognitive engagement, and epigenetic aging in an older Asian cohort. The results suggest that interventions targeting these behaviors may modulate the pace of biological aging. The absence of significant longitudinal associations underscores the need for larger prospective studies with longer follow-up and continued validation of epigenetic clocks in diverse populations to confirm these relationships over time.

China's dietary transition presents opportunities to advance planetary health while addressing diet-related non-communicable disease (NCD) burdens. Using an integrated framework combining life cycle assessment (LCA) with disability-adjusted life year (DALY)-based health impact modeling, nutrient screening, dietary cost estimation, and Chinese Food Pagoda adherence, we evaluated the environmental, health, and economic impacts of ten progressive meat-to-legume substitution scenarios across three substitution strategies. China Health and Nutrition Survey data from 12,236 adults were used to characterize observed food-group intake and compare it with the model reference diet. Relative to the reference diet, M3S9 reduced median greenhouse-gas emissions by 30.6%, water use by 19.7%, land occupation by 14.4%, and estimated dietary cost by 16.6%, while HENI increased by 157.0 min/day under the central dietary-risk coefficients. Its estimated energy supply was 2624.1 kcal/day, above the prespecified range. M3S9 and S10 formed the Pareto set: M3S9 had lower greenhouse-gas emissions and land occupation, whereas S10 had higher HENI and lower water use and estimated cost. Across the 30 deterministic strategy-scenario combinations, the continuous Chinese Healthy Food Pagoda sensitivity score was inversely correlated with HENI (r = −0.713) and positively correlated with environmental burdens. Environmental trajectories also showed reversals at the initial and final transition steps, illustrating composition-dependent trade-offs. These findings demonstrate the value of integrating health, environmental, nutritional, guideline-adherence, and economic indicators when assessing dietary transitions in China.

Background Epigenetic modification is a hallmark of aging that encloses physiological information relevant to health and longevity and has been used to construct epigenetic clocks that estimate epigenetic age acceleration (EAA) to monitor population health across the life span. Psychological adversities (PA) are recognized contributors to poor health; however, their potential role in EAA remains insufficiently understood in older adults. Objective This study systematically reviews and meta-analyzes the association between PA and EAA from midlife onward. Methods Eligible literatures were indexed in five databases up to July 2025. Study quality was assessed using an adapted Newcastle-Ottawa scale. Meta-analyses were performed using random-effects models, followed by post hoc and sensitivity analyses to assess robustness. Results Twenty-two studies were included, of which fifteen were classified as high quality. Irrespective of the type of psychological adversity, positive associations were consistently observed for second-generation epigenetic clocks (PhenoAge and GrimAge). Meta-analyses revealed that greater loneliness (β = 0.07, 95 % CI [0.06, 0.08], I2 = 0 %, p = 0.002), depression (β = 0.08, 95 % CI [0.04, 0.13], I2 = 55.2 %, p = 0.003), and stress (β = 0.10, 95 % CI [0.03, 0.16], I2 = 68.4 %, p = 0.009) were each associated with higher EAA. Conclusions Psychosocial stress, depression, and loneliness are each associated with accelerated aging from midlife onward. Notable gaps include the lack of studies examining anxiety and underrepresentation of non-Western population. Whether alleviating psychological adversities translates into decelerated aging trajectories requests future intervention studies.

Background: As the global population ages, the prevalence of mild cognitive impairment (MCI) and dementia is increasing substantially. Studies conducted predominantly in Western populations suggest that adherence to healthy dietary patterns may help preserve cognitive health. However, evidence in Asian populations remains limited and inconsistent. Objective: To investigate the cross-sectional and longitudinal associations between adherence to the alternate Mediterranean diet (aMED), Dietary Approaches to Stop Hypertension (DASH), and Healthy Diet Indicator (HDI) dietary patterns and odds of MCI among older Singaporean adults. Design: Cross-sectional and longitudinal analyses of data from the Diet and Healthy Aging cohort. Setting: Community-dwelling older adults in Singapore. Participants: A total of 620 older adults (mean age 67.7 ± 6.0 years; 71.8% women) were included in the cross-sectional analysis, while 313 cognitively normal participants were included in the longitudinal analysis. Measurements: Dietary adherence scores were derived from a validated food frequency questionnaire. MCI was diagnosed using the Singapore-modified Mini-Mental State Examination (SM-MMSE), Clinical Dementia Rating (CDR), and a battery of standardized neurocognitive tests. Logistic regression models adjusted for potential confounders were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Results: In cross-sectional analyses, higher adherence to aMED, but not DASH or HDI, was associated with lower odds of MCI (OR = 0.35, 95% CI: 0.17−0.73, P-trend < 0.005). In longitudinal analysis, none of the dietary patterns were significantly associated with incident MCI. Conclusion: Higher adherence to aMED was associated with lower odds of MCI cross-sectionally, although no significant longitudinal associations were observed. Further prospective studies with larger sample sizes and repeated dietary assessments are needed to clarify the role of predefined dietary patterns in cognitive health among Asian older adults.

To evaluate the effectiveness of a digital medication system in improving adherence among patients with serious mental disorders (SMD), we conducted a cluster-randomized controlled trial across 30 communities in Beijing. Participants, aged 18-65 years, were diagnosed with schizophrenia or bipolar disorder and either received intermittent medication or refused treatment. Recruitment occurred from September 2, 2022, to January 12, 2023. The intervention group received a digital medication system. The control group used an online medication diary. The primary outcome was poor adherence, defined as missing 20% or more of prescribed doses at 12 months. Among 216 recruited patients, 206 completed the study. The intervention group showed significantly higher adherence (84/108 vs. 23/108), with an adjusted risk difference of 52.34% (95% CI: 34.65%-70.03%; P < 0.0001). This trial provides the first robust evidence that the digital medication system can significantly improve medication adherence in patients with SMD. Trial Registration: The trial was registered on the Chinese Clinical Trial Registry (chictr.org.cn) on May 29, 2022 (ChiCTR-ICR-2200060359).

Diet is a well-known determinant of mental health outcomes. However, epidemiologic evidence on salt consumption with the risk of developing depression and anxiety is still very limited. This study aimed to examine the association between adding salt to foods and incident depression and anxiety longitudinally. This study used data from 444,787 adults who had never been diagnosed with depression or anxiety at baseline from the UK Biobank, a national community-based cohort from 2006 to 2010. Adding salt to foods was measured using a four-point Likert scale at baseline from a touch-screen questionnaire. The outcomes were incidents of diagnosed depression (F32-F33) and anxiety (F40-F48), defined by the International Statistical Classification of Diseases and Related Health Problems, 10th Revision codes. Cox proportional hazards models were used to investigate the association between the frequency of adding salt to foods and incident depression and anxiety. During a mean follow-up period of 14.5 years, 16,319 incidents of depression and 18,959 incidents of anxiety were documented. A higher frequency of adding salt to foods was associated with elevated risk for depression and anxiety. Compared with the group of never/rarely adding salt to foods, the adjusted HRs of incident depression were 1.07 (95

OBJECTIVES:To characterize response trajectories in patients with major depressive disorder (MDD) and identify clinical and demographic predictors of symptom alleviation. METHODS:This study was a secondary analysis of longitudinal data collected from 10 governances of China from November 9, 2016, to December 30, 2020. Depressive symptoms were mainly measured using the 17-item Hamilton Depression Rating Scale (HAMD-17). Group-based trajectory modeling was used to identify symptom trajectories from baseline to week 24. RESULTS:A total of 1,438 participants were included. Two trajectories were identified: 74.27% of participants belonged to trajectory 1, labeled as the ''rapid symptom reduction trajectory,'' characterized by moderate depression followed by rapid symptom reduction after treatment; and 25.73% of participants belonged to trajectory 2, labeled as the ''delayed symptom reduction trajectory,'' characterized by severe depression and a gradual decline in symptoms over 24 weeks. Compared with those in the rapid symptom reduction trajectory, values (including the HAMD-17 total score at baseline, the proportion of female participants, and the duration of untreated episodes) were significantly higher in the delayed symptom reduction trajectory. CONCLUSIONS:Distinct response trajectories were identified in patients with MDD. Milder baseline severity, shorter untreated episodes, and male sex predicted rapid responses, aiding in treatment guidance.

Herein, a novel pyrrolo[2,3-b]pyridine-based glycogen synthase kinase 3 beta (GSK-3 beta) inhibitor, S01, was rationally designed and synthesised to target Alzheimer's disease (AD). S01 inhibited GSK-3 beta, with an IC50 of 0.35 +/- 0.06 nM, and had an acceptable kinase selectivity for 24 structurally similar kinases. Western blotting assays indicated that S01 efficiently increased the expression of p-GSK-3 beta-Ser9 and decreased p-tau-Ser396 levels in a dose-dependent manner. In vitro cell experiments, S01 showed low cytotoxicity to SH-SY5Y cells, significantly upregulated the expression of beta-catenin and neurogenesis-related biomarkers, and effectively promoted the outgrowth of differentiated neuronal neurites. Moreover, S01 substantially ameliorated dyskinesia in AlCl3-induced zebrafish AD models at a concentration of 0.12 mu M, which was more potent than Donepezil (8 mu M) under identical conditions. Acute toxicity experiments further confirmed the safety of S01 in vivo. Our findings suggested that S01 is a prospective GSK-3 beta inhibitor and can be tested as a candidate for treating AD.

Cognitive decline is one of the key challenges with aging, with significant implications for independence, quality of life, and healthcare burden. As pharmacological treatments remain limited in efficacy and often carry adverse effects, there is growing interest in safe, accessible, non-pharmacological strategies to preserve cognitive function. This Short Review explores the potential of combining two approaches that, individually, have shown beneficial effects on cognitive function: ergothioneine, a naturally occurring amino acid with antioxidant and anti-inflammatory properties, and physical exercise. Ergothioneine accumulates in the brain and other high-stress organs, where it modulates the redox balance, dampens chronic inflammation and supports mitochondrial function. Meanwhile, physical exercise has well-documented benefits for neuroplasticity, cerebral perfusion, and cognitive performance. Preclinical studies suggest ergothioneine supports exercise performance and muscle recovery without attenuating adaptive responses. Despite their distinct and complementary mechanisms, currently there are no available studies exploring the combined effects of ergothioneine and exercise on cognition. We propose a future research agenda that includes mechanistic animal studies, dose-response trials, and clinical interventions in at-risk populations. Together, the combination of ergothioneine and exercise may offer a low-risk, multifaceted approach to enhancing cognitive resilience in aging.