Objective: To analyze the trajectory of dietary behaviors among middle school students in Jiading District, Shanghai, from 2021 to 2023, and longitudinally verify their association with anxiety and depression symptoms, aiming to provide scientific evidence for promoting the mental health of adolescents. Methods: The data were sourced from the National Monitoring and Intervention Project on Common Diseases and Health Impact Factors of students in Jiading District, Shanghai. A total of 1 217 middle school students who participated in at least two surveys from 2021 to 2023 were selected as the research objects, and group-based trajectory model was constructed to identify their dietary behavior scores trajectories. Modified Poisson regression was used to investigate the impact of dietary behavior scores trajectories on anxiety and depression, while Logistic regression was employed to explore the association between trajectories and changes in depression score levels. Results: The dietary behavior scores trajectories of middle school students were divided into three groups: Persistent Healthy Dietary Behavior (9.5%), Persistent Relatively Unhealthy Dietary Behavior (85.0%), and Persistent Very Unhealthy Dietary Behavior (5.5%). Students who perceived their academic performance as poor and whose parents had a cultural level of high school or below had a significantly lower proportion in the Persistent Healthy Dietary Behavior group compared to students with other characteristics ( χ 2=12.87, 8.69, 6.50, P <0.05). Compared with the Persistent Healthy Dietary Behavior group, the risk of anxiety symptoms in middle school students in the Persistent Very Unhealthy Dietary Behavior group was significantly increased ( aRR=3.04, 95%CI =1.15-8.02); Persistent Relatively Unhealthy Dietary Behavior and Persistent Very Unhealthy Dietary Behavior increased the risk of depressive symptoms ( aRR = 1.80 , 2.45, respectively), and were positively correlated with the increase in depression scores ( aOR =1.70, 2.24) ( P <0.05). Conclusions The dietary behavior of middle school students have not changed significantly in the past three years, with persistent unhealthy dietary behavior being the most common. Unhealthy dietary behaviors are positively correlated with the risk of anxiety and depressive symptoms and an increase in depression scores.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

BACKGROUND: Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic inflammation and joint destruction. Iguratimod (IGU) is a novel conventional synthetic disease-modifying antirheumatic drugs (csDMARD) with good efficacy and safety for the treatment of active RA in China and Japan. However, the long-term effects of IGU on the progression of bone destruction or radiographic progression in patients with active RA remain unknown. We aimed to investigate the efficacy and safety of iguratimod (IGU), a combination of methotrexate (MTX) and IGU, and IGU in patients with active rheumatoid arthritis (RA) who were naïve to MTX. METHODS: This multicenter, double-blind, randomized, non-inferiority clinical trial was conducted at 28 centers for over 52 weeks in China. In total, 911 patients were randomized (1:1:1) to receive MTX monotherapy (10-15 mg weekly, n = 293), IGU monotherapy (25 mg twice daily, n = 297), or IGU + MTX (10-15 mg weekly for MTX and 25 mg twice daily for IGU, n = 305) for 52 weeks. The patients' clinical characteristics, Simplified Disease Activity Index (SDAI), Clinical Disease Activity Index (CDAI), disease activity score in 28 joints-C-reactive protein (DAS28-CRP) level, and disease activity score in 28 joints-erythrocyte sedimentation rate (DAS28-ESR) were assessed at baseline. The primary endpoints were the proportion of patients with ≥20% improvement according to the American College of Rheumatology (ACR20) response and changes in the van der Heijde-modified total Sharp score (vdH-mTSS) at week 52. RESULTS: The proportions of patients achieving an ACR20 response at week 52 were 77.44%, 77.05 %, and 65.87% for IGU monotherapy, IGU + MTX, and MTX monotherapy, respectively. The non-inferiority of IGU monotherapy to MTX monotherapy was established with the ACR20 (11.57%; 95% confidence interval [CI], 4.35-18.79%; P <0.001) and vdH-mTSS (-0.37; 95% CI, -1.22-0.47; P = 0.022). IGU monotherapy was also superior to MTX monotherapy in terms of ACR20 ( P = 0.002) but not the vdH-mTSS. The superiority of IGU + MTX over MTX monotherapy was confirmed in terms of the ACR20 (11.18%; 95% CI, 3.99-18.37%; P = 0.003), but not in the vdH-mTSS (-0.68; 95% CI, -1.46-0.11; P = 0.091). However, the difference in the incidence rates of adverse events was not statistically significant. CONCLUSIONS: IGU monotherapy/IGU + MTX showed a more favorable clinical response than did MTX monotherapy. IGU may have some clinical benefits over MTX in terms of radiographic progression, implying that IGU may be considered as an initial therapeutic option for patients with active RA. TRIAL REGISTRATION https://classic.clinicaltrials.gov/ , NCT01548001.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Antirheumatic Agents/therapeutic use* ; Arthritis, Rheumatoid/drug therapy* ; Chromones/adverse effects* ; Double-Blind Method ; Drug Therapy, Combination ; Methotrexate/adverse effects* ; Treatment Outcome ; Sulfonamides

BACKGROUND: In the interim analysis of MONARCH plus, adding abemaciclib to endocrine therapy (ET) improved progression-free survival (PFS) and objective response rate (ORR) in predominantly Chinese postmenopausal women with HR+/HER2- advanced breast cancer (ABC). This study presents the final pre-planned PFS analysis. METHODS: In the phase III MONARCH plus study, postmenopausal women in China, India, Brazil, and South Africa with HR+/HER2- ABC without prior systemic therapy in an advanced setting (cohort A) or progression on prior ET (cohort B) were randomized (2:1) to abemaciclib (150 mg twice daily [BID]) or placebo plus: anastrozole (1.0 mg/day) or letrozole (2.5 mg/day) (cohort A) or fulvestrant (500 mg on days 1 and 15 of cycle 1 and then on day 1 of each subsequent cycle) (cohort B). The primary endpoint was PFS of cohort A. Secondary endpoints included cohort B PFS (key secondary endpoint), ORR, overall survival (OS), safety, and health-related quality of life (HRQoL). RESULTS: In cohort A (abemaciclib: n  = 207; placebo: n  = 99), abemaciclib plus a non-steroidal aromatase inhibitor improved median PFS vs . placebo (28.27 months vs . 14.73 months, hazard ratio [HR]: 0.476; 95% confidence interval [95% CI]: 0.348-0.649). In cohort B (abemaciclib: n  = 104; placebo: n  = 53), abemaciclib plus fulvestrant improved median PFS vs . placebo (11.41 months vs . 5.59 months, HR: 0.480; 95% CI: 0.322-0.715). Abemaciclib numerically improved ORR. Although immature, a trend toward OS benefit with abemaciclib was observed (cohort A: HR: 0.893, 95% CI: 0.553-1.443; cohort B: HR: 0.512, 95% CI: 0.281-0.931). The most frequent grade ≥3 adverse events in the abemaciclib arms were neutropenia, leukopenia, anemia (both cohorts), and lymphocytopenia (cohort B). Abemaciclib did not cause clinically meaningful changes in patient-reported global health, functioning, or most symptoms vs . placebo. CONCLUSIONS: Abemaciclib plus ET led to improvements in PFS and ORR, a manageable safety profile, and sustained HRQoL, providing clinical benefit without a high toxicity burden or reduced quality of life. TRIAL REGISTRATION ClinicalTrials.gov (NCT02763566).
Humans ; Female ; Fulvestrant/therapeutic use* ; Breast Neoplasms/metabolism* ; Aminopyridines/therapeutic use* ; Benzimidazoles/therapeutic use* ; Middle Aged ; Aromatase Inhibitors/therapeutic use* ; Aged ; Receptor, ErbB-2/metabolism* ; Adult ; Letrozole/therapeutic use* ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Anastrozole/therapeutic use*

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Background: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/ hyperactivity disorder (ADHD). However, these reports can have several problems because they are diverse, unstructured, subjective, and involve human errors. Additionally, physicians often do not read the entire report, and the number of reports is lower than that of diagnoses. Methods: We developed explainable predictive models for classifying IDs and ADHDs based on written reports to address these issues. The reports of 1,475 patients with IDs and ADHDs who underwent intelligence tests were used for the models. These models were developed by analyzing reports using natural language processing (NLP) and incorporating the physician’s diagnosis for each report. We selected n-gram features from the models’ results by extracting important features using SHapley Additive exPlanations and permutation importance to make the models explainable. Developing the n-gram feature-based original text search system compensated for the lack of human readability caused by NLP and enabled the reconstruction of human-readable texts from the selected n-gram features. Results: The maximum model accuracy was 0.92, and the 80 human-readable texts were restored from four models. Conclusion The results showed that the models could accurately classify IDs and ADHDs, even with a few reports. The models were also able to explain their predictions. The explainability-enhanced model can help physicians understand the classification process of IDs and ADHDs and provide evidence-based insights.

Depression, a prevalent mental disorder with significant socioeconomic burdens, underscores the urgent need for safe and effective non-pharmacological interventions. Recent advances in microbiome research have revealed the pivotal role of gut microbiota dysbiosis in the pathogenesis of depression. Concurrently, exercise, as a cost-effective and accessible intervention, has demonstrated remarkable efficacy in alleviating depressive symptoms. This comprehensive review synthesizes current evidence on the interplay among exercise, gut microbiota modulation, and depression, elucidating the mechanistic pathways through which exercise ameliorates depressive symptoms via the microbiota-gut-brain (MGB) axis. Depression is characterized by gut microbiota alterations, including reduced alpha and beta diversity, depletion of beneficial taxa (e.g., Bifidobacterium, Lactobacillus, and Coprococcus), and overgrowth of pro-inflammatory and pathogenic bacteria (e.g., Morganella, Klebsiella, and Enterobacteriaceae). Metagenomic analyses reveal disrupted metabolic functions in depressive patients, such as diminished synthesis of short-chain fatty acids (SCFAs), impaired tryptophan metabolism, and dysregulated bile acid conversion. For instance, Bifidobacterium longum deficiency correlates with reduced synthesis of neuroactive metabolites like homovanillic acid, while decreased Coprococcus abundance limits butyrate production, exacerbating neuroinflammation. Furthermore, elevated levels of indole derivatives from Clostridium species inhibit serotonin (5-HT) synthesis, contributing to depressive phenotypes. These dysbiotic profiles disrupt the MGB axis, triggering systemic inflammation, neurotransmitter imbalances, and hypothalamic-pituitary-adrenal (HPA) axis hyperactivity. Exercise exerts profound effects on gut microbiota composition, diversity, and metabolic activity. Longitudinal studies demonstrate that sustained aerobic exercise increases alpha diversity, enriches SCFA-producing genera (e.g., Faecalibacterium prausnitzii, Roseburia, and Akkermansia), and suppresses pathobionts (e.g., Desulfovibrio and Streptococcus). For example, a meta-analysis of 25 trials involving 1 044 participants confirmed that exercise enhances microbial richness and restores the Firmicutes/Bacteroidetes ratio, a biomarker of metabolic health. Notably, endurance training promotes Veillonella proliferation, which converts lactate into propionate, enhancing energy metabolism and delaying fatigue. Exercise also strengthens intestinal barrier integrity by upregulating tight junction proteins (e.g., ZO-1, occludin), thereby reducing lipopolysaccharide (LPS) translocation and systemic inflammation. However, excessive exercise may paradoxically diminish microbial diversity and exacerbate intestinal permeability, highlighting the importance of moderate intensity and duration. Exercise ameliorates depressive symptoms through multifaceted interactions with the gut microbiota, primarily via 4 interconnected pathways. First, exercise mitigates neuroinflammation by elevating anti-inflammatory SCFAs such as butyrate, which suppresses NF-κB signaling to attenuate microglial activation and oxidative stress in the hippocampus. Animal studies demonstrate that voluntary wheel running reduces hippocampal TNF‑α and IL-17 levels in stress-induced depression models, while fecal microbiota transplantation (FMT) from exercised mice reverses depressive behaviors by modulating the TLR4/NF‑κB pathway. Second, exercise regulates neurotransmitter dynamics by enriching GABA-producing Lactobacillus and Bifidobacterium, thereby counteracting neuronal hyperexcitability. Aerobic exercise also enhances the abundance of Lactobacillus plantarum and Streptococcus thermophilus, which facilitate 5-HT and dopamine synthesis. Clinical trials reveal that 12 weeks of moderate exercise increases fecal Coprococcus and Blautia abundance, correlating with improved 5-HT bioavailability and reduced depression scores. Third, exercise normalizes HPA axis hyperactivity by reducing cortisol levels and restoring glucocorticoid receptor sensitivity. In rodent models, chronic stress-induced corticosterone elevation is reversed by probiotic supplementation (e.g., Lactobacillus), which enhances endocannabinoid signaling and hippocampal neurogenesis. Furthermore, exercise upregulates brain-derived neurotrophic factor (BDNF) via microbial metabolites like butyrate, promoting histone acetylation and synaptic plasticity. FMT experiments confirm that exercise-induced microbiota elevates prefrontal BDNF expression, reversing stress-induced neuronal atrophy. Fourth, exercise reshapes microbial metabolic crosstalk, diverting tryptophan metabolism toward 5-HT synthesis instead of neurotoxic kynurenine derivatives. Butyrate inhibits indoleamine 2,3-dioxygenase (IDO), a key enzyme in the kynurenine pathway linked to depression. Concurrently, exercise-induced Akkermansia enrichment enhances mucin production, fortifies the gut barrier, and reduces LPS-driven neuroinflammation. Collectively, these mechanisms underscore exercise as a potent modulator of the microbiota-gut-brain axis, offering a holistic approach to alleviating depression through microbial and neurophysiological synergy. Current evidence supports exercise as a potent adjunct therapy for depression, with personalized regimens (e.g., aerobic, resistance, or yoga) tailored to individual microbiota profiles. However, challenges remain in optimizing exercise prescriptions (intensity, duration, and type) and integrating them with probiotics, prebiotics, or FMT for synergistic effects. Future research should prioritize large-scale randomized controlled trials to validate causality, multi-omics approaches to decipher MGB axis dynamics, and mechanistic studies exploring microbial metabolites as therapeutic targets. The authors advocate for a paradigm shift toward microbiota-centric interventions, emphasizing the bidirectional relationship between physical activity and gut ecosystem resilience in mental health management. In conclusion, this review underscores exercise as a multifaceted modulator of the gut-brain axis, offering novel insights into non-pharmacological strategies for depression. By bridging microbial ecology, neuroimmunology, and exercise physiology, this work lays a foundation for precision medicine approaches targeting the gut microbiota to alleviate depressive disorders.

OBJECTIVE To evaluate the quality of diagnosis and treatment guidelines and expert consensuses on childhood immune thrombocytopenic purpura （ITP） published domestically and internationally， in order to provide reference for clinical practice and future guideline/expert consensus development and improvement. METHODS A systematic search was conducted across multiple databases， including PubMed， Cochrane Library， Embase， CNKI， Wanfang data， VIP， CBM； additionally， supplementary searches were carried out on websites such as Medlive， the Chinese Medical Association’s official website， and National Institute for Health and Clinical Excellence in the UK. The retrieval time ranged from the inception to September 2， 2024. Researchers who had undergone systematic training independently evaluated the methodology and report quality included in the guideline/consensus using the Appraisal of Guidelines Research and Evaluation Ⅱ （AGREE Ⅱ） and the Reporting Items for Practice Guidelines in Healthcare （RIGHT）. RESULTS A total of 11 guidelines/consensuses were included. The average scores for the six domains of AGREE Ⅱ tool respectively were “range and purpose” （[ 66.67±17.98）% ]， “participants” [58.33% （13.89%，73.61%）]， “rigor” （[ 41.81±23.85）% ]， “clarity”（[ 69.57±19.35）%]， “applicability” （[ 35.98±17.83）%]， and “independence” [27.08% （0，75.00%）]； out of 11 articles， 9 had a recommendation level of B， 2 had a recommendation level of C， and there were no A-level articles. The average reporting rates of the 7 areas in the RIGHT tool were “basic information” （[ 72.35±12.95）% ]， “background” （[ 54.55±15.40）%]，“ evidence” （[ 36.36±24.81）%]，“ recommended opinions” （[ 53.25±19.20）%]，“ review and quality assurance” [0 （0， 25.00%）]， “funding and conflict of interest statement and management” [12.50%（0，25.00%）]， and other aspects [8.33%（0， 50.00%）]. In addition， there was no statistically significant difference in the AGREE Ⅱ and RIGHT scores between the guidelines and consensuses （P＞0.05）. CONCLUSIONS The overall quality of the guidelines and consensuses included in this study is not high， with a recommended level of B or C. It is recommended that clinical decision-making prioritize referring to the relatively high-quality guideline/consensus among them. The quality of evidence in the existing traditional Chinese medicine guidelines for children with ITP needs to be improved， and there is no integrated guideline/consensus for traditional Chinese and Western medicine. It is recommended to revise or write relevant guideline/consensus according to the requirements of AGREE Ⅱ and RIGHT in various fields to guide clinical practice.