ObjectiveTo observe the clinical efficacy of Sanren Runchang formula in treating constipation-predominant irritable bowel syndrome （IBS-C） by regulating the brain-gut axis and the effects of the formula on serum levels of 5-hydroxytryptamine （5-HT）， vasoactive intestinal peptide （VIP）， and substance P （SP）. MethodsA randomized controlled design was adopted， and 72 IBS-C patients meeting Rome Ⅳ criteria were randomized into observation and control groups （36 cases）.The observation group received Sanren Runchang formula granules twice daily， and the control group received lactulose oral solution daily for 4 weeks. IBS Symptom Severity Scale （IBS-SSS）， IBS Quality of Life Scale （IBS-QOL）， and Bristol Stool Form Scale （BSFS） were used to assess clinical symptoms， and bowel movement frequency was recorded. The Self-Rating Anxiety Scale （SAS） and Self-Rating Depression Scale （SDS） were employed to evaluate psychological status. ELISA was employed to measure the serum levels of 5-HT， VIP， and SP. ResultsThe total response rate in the observation group was 91.67% （33/36）， which was higher than that （77.78%， 28/36） in the control group （χ²=4.50， P<0.05）. After treatment， both groups showed increased defecation frequency and BSFS scores， decreased IBS-SSS total score， abdominal pain and bloating scores， IBS-QOL health anxiety， anxiety， food avoidance， and behavioral disorders scores， SAS and SDS scores， serum 5-HT and VIP levels， and increased SP levels （P<0.05， P<0.01）. Moreover， the observation group showed more significant changes in the indicators above than the control group （P<0.05， P<0.01）. The SP level showed no significant difference between the two groups. During the 4-week follow-up， the recurrence rate was 5.88% in the observation group and 31.25% in the control group. No adverse events occurred in observation group， and 2 cases of mild diarrhea occurred in the control group. ConclusionSanren Runchang formula demonstrated definitive efficacy in alleviating gastrointestinal symptoms and improving the psychological status and quality of life in IBS-C patients， with a low recurrence rate. The formula can regulate serum levels of neurotransmitters such as 5-HT and VIP， suggesting its potential regulatory effect on the brain-gut axis through modulating neurotransmitters and neuropeptides. However， its complete mechanism of action requires further investigation through detection of additional brain-gut axis-related biomarkers.

Background and Aims:Triple-negative breast cancer(TNBC)is a clinically aggressive subtype of breast cancer with limited treatment options.Immune checkpoint blockade(ICB)combined with chemotherapy has emerged as a key neoadjuvant therapeutic strategy for TNBC.However,significant variability in ICB efficacy exists among patients,and the underlying mechanisms related to the tumor immune microenvironment(TME)remain unclear.Cancer-associated fibroblasts(CAFs),as major stromal components,regulate TME and influence immunotherapy responses.Fibroblast activation protein(FAP),a key marker of CAFs,has been associated with poor prognosis in multiple solid tumors,yet its immunological role in TNBC has not been systematically investigated.This study aims to elucidate the expression pattern of FAP in TNBC,its impact on the immune microenvironment and ICB efficacy,and to explore its potential immunosuppressive mechanisms and clinical implications.Methods:The data from TCGA and the I-SPY2 clinical trial were integrated to assess the association of FAP expression with prognosis,immune cell infiltration,and immune checkpoint molecule expression in TNBC.Immune landscape profiling was conducted using CIBERSORT,GSEA enrichment analysis,and differential gene expression analysis(DESeq2)to characterize the immune features associated with FAP expression and to identify downstream genes at the transcriptomic level.CAF models with FAP overexpression or knockdown were constructed and co-cultured with CD8? T cells to evaluate FAP's regulatory effects on CD8? T cell activity and apoptosis.The expression of COL1A1,a potential FAP-regulated gene identified from transcriptomic analysis,was validated using qPCR and Western blot.Finally,clinical data and multiplex immunofluorescence pathology samples from TNBC patients at Xiangya Hospital were analyzed alongside I-SPY2 data to determine the predictive value of FAP expression for pathological complete response(pCR)following neoadjuvant immunotherapy.Results:FAP was significantly upregulated in TNBC tumor tissues compared to normal tissues and associated with shorter overall survival.Multivariate Cox regression analysis identified FAP as an independent adverse prognostic factor.High FAP expression was correlated with reduced infiltration of CD8? T cells,NK cells,and Tfh cells,as well as upregulation of immune checkpoints including CD276,TIM-3,and PD-L2.In CAF models,FAP overexpression suppressed CD8? T cell activity and promoted apoptosis,while FAP knockdown had the opposite effect.Transcriptomic analysis showed that COL1A1 and other collagen-related genes were significantly upregulated in the FAP-high group and positively correlated with FAP expression;qPCR and Western blot confirmed that FAP positively regulates COL1A1 expression.Analysis of I-SPY2 data revealed that FAP-low patients receiving pembrolizumab plus chemotherapy had significantly higher pCR rates compared to FAP-high patients.Consistently,clinical data from the Xiangya cohort showed reduced CD8? T cell infiltration and lower pCR rates in FAP-high patients,with a ROC AUC of 0.857 for predicting treatment response.Conclusion:FAP-high CAFs contribute to the formation of an immunosuppressive TME in TNBC by promoting COL1A1 secretion,inhibiting CD8? T cell function,and upregulating immune checkpoint molecules.High FAP expression is associated with poor prognosis and reduced response to immunotherapy,highlighting FAP as both a prognostic biomarker and a potential therapeutic target for stratified and combination treatment strategies in TNBC.

In the context of aging population, the issue of polypharmacy among elderly patients with mental disorders has become increasingly prominent.Cognitive decline and depressive symptoms render these patients more vulnerable to medication-related risks, while poorly managed physical illnesses further complicate their treatment.To address these challenges, this paper proposes a series of management strategies that emphasize the critical role of pharmacists in conducting medication reviews.A comprehensive assessment of drug risks, benefits, and patient adherence is essential.The proposed strategies not only require careful consideration of patients' clinical needs and individual preferences but also highlight the importance of multidisciplinary team collaboration to reach a consensus on medication therapy.The use of clinical decision support systems as an auxiliary tool is recommended to enhance the scientific rigor of medication decision-making.Furthermore, pharmacists can optimize medication regimens through scientifically validated methods and promote patient or family involvement in self-management to improve acceptance and adherence to treatment adjustments.

OBJECTIVE To explore comprehensive management and potential issues associated with long-term prescriptions medications of psychiatry， in order to provide a reference for the comprehensive management of long-term prescriptions of psychiatry in psychiatric hospitals and other medical institutions’ pharmacies. METHODS Starting from the applicable principles for long-term prescriptions of psychiatry， this study introduced the standardized assessment and precautions before issuing long-term prescriptions， the formulation and adjustment of the drug list， as well as the rational management of the long-term prescriptions. It also analyzed potential issues that may arise in the comprehensive management of long-term prescription medications and proposed corresponding countermeasures and suggestions. RESULTS & CONCLUSIONS Prior to initiating long-term prescriptions， a standardized assessment should be conducted on patients from the aspects of their psychiatric condition and long-term potential risk factors， pharmacological treatment plans and other non-pharmacological therapies， physical illnesses. Additionally， healthcare providers should fulfill their obligation to inform patients or their family members. The comprehensive management of long-term prescription medications should be jointly established and improved by multiple departments， and the formulation of drug catalogs should avoid including drugs with potential social harm or medication risks while complying with policy requirements. Furthermore， measures such as adding special identifiers to long-term prescriptions， providing patients with reminders about （No.YGLX202537） prescription expiration， or offering online consultations can also effectively enhance the rationality of medication use under long-term prescriptions. Currently， the implementation of long-term prescriptions in psychiatry remains challenged by inconsistencies in prescription duration， incomplete coverage of diagnostic categories， poor patient adherence， and the risk of deviation in clinical assessments. In this regard， measures such as collaborating with multiple departments to strengthen long-term prescription information management， providing matching pharmaceutical services， ensuring the quality and rationality of long-term prescription implementation， and using modern methods to screen high-risk patients can be taken to improve patient medication compliance and safety.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.

Objective:To investigate the status of psychological distress, self-compassion, and self-management behaviors among patients with type 2 diabetes mellitus (T2DM), and explore the mediating role of self-compassion in the relationship between diabetes distress and self-management behaviors, and to provide a theoretical basis for the psychological intervention of patients with T2DM.Methods:A convenience sampling method was adopted to select 221 patients with T2DM hospitalized in the Department of Endocrinology and Metabolism, the First Hospital of China Medical University from November 2023 to April 2024. The General Information Form, the Diabetes Distress Scale-17, the Self-Compassion Scale and the Summary of Diabetes Self-Care Activity were utilized for investigation. A structural equation model was constructed with AMOS 24.0 to analyze the relationships among psychological distress, self-compassion, and self-management behaviors, and the mediating effect of self-compassion was tested using the Bootstrap method.Results:A total of 221 patients with T2DM were included, including 138 males and 83 females, aged (54.52 ±10.42) years old. The total score of psychological distress of patients with T2DM was (34.55 ± 18.82) points, the total score of self-compassion was (94.22 ± 26.04) points, and the total score of self-management behavior was (40.81 ± 22.64) points. Psychological distress in T2DM patients was significantly negatively correlated with both self-compassion ( r=-0.850, P<0.01) and self-management behaviors ( r=-0.736, P<0.01). In contrast, self-compassion was significantly positively correlated with self-management behaviors ( r=0.743, P<0.01). Self-compassion partially mediated the relationship between diabetes-related psychological distress and self-management behaviors, with a mediation effect size of -0.456. Conclusions:Psychological distress among T2DM patients is at a moderate level, and their self-management behaviors require urgent improvement. Psychological distress not only directly impacts self-management behaviors but also indirectly influences them through self-compassion. Healthcare professionals should prioritize enhancing patients′ self-compassion levels to mitigate the negative effects of psychological distress on their health behaviors.

In the context of aging population, the issue of polypharmacy among elderly patients with mental disorders has become increasingly prominent.Cognitive decline and depressive symptoms render these patients more vulnerable to medication-related risks, while poorly managed physical illnesses further complicate their treatment.To address these challenges, this paper proposes a series of management strategies that emphasize the critical role of pharmacists in conducting medication reviews.A comprehensive assessment of drug risks, benefits, and patient adherence is essential.The proposed strategies not only require careful consideration of patients' clinical needs and individual preferences but also highlight the importance of multidisciplinary team collaboration to reach a consensus on medication therapy.The use of clinical decision support systems as an auxiliary tool is recommended to enhance the scientific rigor of medication decision-making.Furthermore, pharmacists can optimize medication regimens through scientifically validated methods and promote patient or family involvement in self-management to improve acceptance and adherence to treatment adjustments.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.

Objective:To investigate the status of psychological distress, self-compassion, and self-management behaviors among patients with type 2 diabetes mellitus (T2DM), and explore the mediating role of self-compassion in the relationship between diabetes distress and self-management behaviors, and to provide a theoretical basis for the psychological intervention of patients with T2DM.Methods:A convenience sampling method was adopted to select 221 patients with T2DM hospitalized in the Department of Endocrinology and Metabolism, the First Hospital of China Medical University from November 2023 to April 2024. The General Information Form, the Diabetes Distress Scale-17, the Self-Compassion Scale and the Summary of Diabetes Self-Care Activity were utilized for investigation. A structural equation model was constructed with AMOS 24.0 to analyze the relationships among psychological distress, self-compassion, and self-management behaviors, and the mediating effect of self-compassion was tested using the Bootstrap method.Results:A total of 221 patients with T2DM were included, including 138 males and 83 females, aged (54.52 ±10.42) years old. The total score of psychological distress of patients with T2DM was (34.55 ± 18.82) points, the total score of self-compassion was (94.22 ± 26.04) points, and the total score of self-management behavior was (40.81 ± 22.64) points. Psychological distress in T2DM patients was significantly negatively correlated with both self-compassion ( r=-0.850, P<0.01) and self-management behaviors ( r=-0.736, P<0.01). In contrast, self-compassion was significantly positively correlated with self-management behaviors ( r=0.743, P<0.01). Self-compassion partially mediated the relationship between diabetes-related psychological distress and self-management behaviors, with a mediation effect size of -0.456. Conclusions:Psychological distress among T2DM patients is at a moderate level, and their self-management behaviors require urgent improvement. Psychological distress not only directly impacts self-management behaviors but also indirectly influences them through self-compassion. Healthcare professionals should prioritize enhancing patients′ self-compassion levels to mitigate the negative effects of psychological distress on their health behaviors.

Background and Aims:Triple-negative breast cancer(TNBC)is a clinically aggressive subtype of breast cancer with limited treatment options.Immune checkpoint blockade(ICB)combined with chemotherapy has emerged as a key neoadjuvant therapeutic strategy for TNBC.However,significant variability in ICB efficacy exists among patients,and the underlying mechanisms related to the tumor immune microenvironment(TME)remain unclear.Cancer-associated fibroblasts(CAFs),as major stromal components,regulate TME and influence immunotherapy responses.Fibroblast activation protein(FAP),a key marker of CAFs,has been associated with poor prognosis in multiple solid tumors,yet its immunological role in TNBC has not been systematically investigated.This study aims to elucidate the expression pattern of FAP in TNBC,its impact on the immune microenvironment and ICB efficacy,and to explore its potential immunosuppressive mechanisms and clinical implications.Methods:The data from TCGA and the I-SPY2 clinical trial were integrated to assess the association of FAP expression with prognosis,immune cell infiltration,and immune checkpoint molecule expression in TNBC.Immune landscape profiling was conducted using CIBERSORT,GSEA enrichment analysis,and differential gene expression analysis(DESeq2)to characterize the immune features associated with FAP expression and to identify downstream genes at the transcriptomic level.CAF models with FAP overexpression or knockdown were constructed and co-cultured with CD8? T cells to evaluate FAP's regulatory effects on CD8? T cell activity and apoptosis.The expression of COL1A1,a potential FAP-regulated gene identified from transcriptomic analysis,was validated using qPCR and Western blot.Finally,clinical data and multiplex immunofluorescence pathology samples from TNBC patients at Xiangya Hospital were analyzed alongside I-SPY2 data to determine the predictive value of FAP expression for pathological complete response(pCR)following neoadjuvant immunotherapy.Results:FAP was significantly upregulated in TNBC tumor tissues compared to normal tissues and associated with shorter overall survival.Multivariate Cox regression analysis identified FAP as an independent adverse prognostic factor.High FAP expression was correlated with reduced infiltration of CD8? T cells,NK cells,and Tfh cells,as well as upregulation of immune checkpoints including CD276,TIM-3,and PD-L2.In CAF models,FAP overexpression suppressed CD8? T cell activity and promoted apoptosis,while FAP knockdown had the opposite effect.Transcriptomic analysis showed that COL1A1 and other collagen-related genes were significantly upregulated in the FAP-high group and positively correlated with FAP expression;qPCR and Western blot confirmed that FAP positively regulates COL1A1 expression.Analysis of I-SPY2 data revealed that FAP-low patients receiving pembrolizumab plus chemotherapy had significantly higher pCR rates compared to FAP-high patients.Consistently,clinical data from the Xiangya cohort showed reduced CD8? T cell infiltration and lower pCR rates in FAP-high patients,with a ROC AUC of 0.857 for predicting treatment response.Conclusion:FAP-high CAFs contribute to the formation of an immunosuppressive TME in TNBC by promoting COL1A1 secretion,inhibiting CD8? T cell function,and upregulating immune checkpoint molecules.High FAP expression is associated with poor prognosis and reduced response to immunotherapy,highlighting FAP as both a prognostic biomarker and a potential therapeutic target for stratified and combination treatment strategies in TNBC.