Objective:To explore the current implementation status and challenges of family doctor contract services (FDCS) from the perspective of contracted residents.Methods:This qualitative study used purposive sampling to select contracted residents from 11 primary healthcare institutions across five cities in China. Semi-structured interviews were conducted from March to December 2024, covering topics such as awareness of contracting, service experience, health needs, service continuity, and policy recommendations. Thematic framework analysis was applied to organize, code, and summarize the data.Results:A total of 25 contracted residents were interviewed (6 men, 19 women; 11 from central urban areas, 14 from suburban or rural towns; 8 with chronic diseases). Three main themes and ten sub-themes emerged: Theme Ⅰ: Pathways to improved service accessibility (optimized chronic disease management, more efficient referrals, and improved health education). Theme Ⅱ: Structural misalignment between supply and demand (limited specialty services despite patient needs, insufficient coverage and public awareness of home-based medical care, imbalanced human resources, and service disruption due to clinician turnover). Theme Ⅲ: Challenges in service awareness and communication mechanisms (information asymmetry and public misperception regarding FDCS, perverse incentives in administrative performance evaluation, and communication barriers in building patient-doctor trust).Conclusions:While FDCS has shown progress in chronic disease management, referral coordination, and health education, structural supply-demand gaps and communication challenges continue to hinder service quality. Improvements in resource allocation and service models are needed to support high-quality development.

Objective:Interpret the key differences between the China Health-care Security Diagnosis Related Groups(CHS-DRG)2.0 and CHS-DRG 1.1,and provide reference for optimizing management strategies in medical institutions.Methods:Text analysis was used to import the CHS-DRG 2.0 and 1.1 grouping scheme dictionary data into the SQL database in a structured table format using SQL Server 2014.The key differences between the two schemes in grouping structure,grouping rules,grouping results,and other aspects were identified.Results:CHS-DRG 2.0 version added 26 groups,deleted 3 groups,and refined 10 groups into 20 groups for 14 clinical specialties at the ADRG level compared to CHS-DRG 1.1.Some group codes,names,and grouping rules were adjusted;Adjusted some grouping conditions and grouping results at the DRG level.Conclusion:CHS-DRG 2.0 version has improved grouping efficiency compared to CHS-DRG 1.1,solved some clinical bottleneck problems,and standardized the role of clinical diagnosis in grouping from the perspective of resource consumption.However,it has not completely solved the grouping problems of multi disease co treatment,multi disease treatment,and combined surgery.The adjustment of DRG weights and rates,the follow-up of related supporting policy reforms,and the negative effects of DRG will still pose challenges for medical institutions.

Natural killer cells(NK)are important innate immune cells that do not require prior antigen exposure and can directly recognize and attack virus-infected cells and tumor cells.The activation and effector functions of NK cells are regulated by a balance of signals delivered through their surface activating receptors and inhibitory re-ceptors,which bind to ligands on target cells to achieve cytotoxicity via"induced self"and"missing self"recogni-tion models.The killing mechanisms of NK cells primarily include release of cytotoxic granules such as perforin and granzymes to induce target cell lysis,death receptor-mediated apoptosis,secretion of various cytokines,chemokines and growth factors to coordinate with other immune cells in killing tumor cells,thereby generating secondary im-mune responses and antibody-dependent cellular cytotoxicity(ADCC).

OBJECTIVE To explore the possible molecular mechanism through which amygdala cell adhesion molecule-1(CADM1)is involved in acute sleep deprivation-induced anxiety.METHODS Sixteen 8-week-old C57BL/6J mice were randomly divided into the control group and para-chlorophe-nylalanine(PCPA)-induced acute sleep deprivation experimental group.The PCPA mice were intraperi-toneally injected with PCPA suspension(at a dose of 300 mg?kg-1)between 8∶00 and 9∶00 am for 2 consecutive days while the control mice were injected with the same dose of normal saline.The sleep latency and sleep duration of mice were detected via the righting reflex test.Anxiety-like behaviors were detected by the open field test and elevated plus maze test.The expression level of CADM1 in the mouse amygdala was detected by Western Blot and Immunofluorescence staining.The GeneNet-work database was used to analyze the association between Cadm1 genes and other genes in the mouse amygdala.The key candidate regulatory genes were screened,and the Cadm1-anxiety behavior phenotype network was constructed.The mRNA expression levels of the key candidate regulatory genes were analyzed via qPCR analysis.RESULTS Compared with the control mice,the sleep latency of PCPA mice was significantly prolonged(P<0.01)while the sleep duration was significantly shortened(P<0.01).The activity time and distance of PCPA mice in the open field center were significantly shorter than those of the control group(P<0.05).The elevated plus maze experiment showed that the percentage of the number of times PCPA mice entered the open arm and the percentage of residence time in the open arm were significantly lower than those of the control group(P<0.05).Western Blot and immuno-fluorescence staining showed that the expression of CADM1 protein in the amygdala from PCPA mice was down-regulated compared with the control mice(P<0.05).Based on gene-behavioral association network analysis,Cadm1 was significantly associated with 25 anxiety-like behavior-related genes.The enrichment analysis of Cadm1 co-expression genes showed that Cadm1 was associated with γ-amino-butyric acid GABAergic synaptic pathway(P=4.31e-09),and that the key genes were huntingtin associ-ated protein 1(Hap1)(r=0.705,P=1.09e-08)、inositol 1,4,5-triphosphate receptor type 1(Itpr1)(r=-0.751,P=3.34e-10)、gamma-aminobutyric acid type A receptor subunit delta(Gabrd)(r=-0.836,P=3.93e-14)、γ-aminobutyric acid A receptor β1 subunit gene(Gabrb1)(r=0.732,P=1.50e-09)and adrenoceptor alpha 2A(Adra2a)(r=0.759,P=1.73e-10).The results of qPCR analysis showed that the mRNA levels of Hap1(P<0.05)、Gabrb1 and Adra2a were significantly up-regulated(P<0.01)while those of Itpr1 and Gabrd were significantly down-regulated(P<0.01).CONCLUSION Acute sleep deprivation leads to down-regulation of Cadm1 expression in the amygdala,and induces anxiety-like behaviors by affecting the expression of GABAergic synaptic signaling pathways Hap1,Gabrb1,Adra2a,Itpr1 and Gabrd.

Purpose To explore the value of machine learning models based on synthetic MRI,dynamic contrast-enhanced MRI(DCE-MRI)and diffusion weighted imaging(DWI)parameters in identifying molecular subtypes of breast cancer.Materials and Methods A retrospective analysis was conducted on the data of 292 patients who underwent synthetic MRI,DCE-MRI and DWI examinations from September 2020 to September 2024 in Ningxia Medical University General Hospital before surgery and were pathologically confirmed to have breast cancer postoperatively.Patients were randomly divided into training and test sets using a ratio of 7:3.Multiple parameters were obtained from the synthetic MRI,DCE-MRI and DWI images.Variance analysis were used to screen the characteristic parameters among molecular subtype groups.Five machine learning models were established based on the selected characteristic parameters,and receiver operating characteristic curves were plotted to calculate the area under the curve among the molecular subtype groups.Results The support vector machine model exhibited the highest overall performance,with an area under the curve of 0.972,accuracy of 82.5％,specificity of 94.76％and sensitivity of 82.14％in the test set.This model's area under the curve values for differentiating luminal A,luminal B,human epidermal growth factor receptor-2 overexpression,and triple-negative groups in the training set were 0.979,0.925,0.971 and 0.982,respectively;in the test set,the area under the curve values were 0.973,0.873,0.956 and 0.955,respectively.Conclusion Machine learning models based on multimodal MRI parameters can assist clinicians in preoperatively determining the molecular subtypes of breast cancer and the support vector machine model shows relatively high comprehensive performance.

Objective:To develop an exercise rehabilitation program based on the Temporal Self-regulation Theory and evaluate its effectiveness in elderly patients with hip fractures.Methods:A total of 84 elderly patients with hip fractures admitted to the Department of Trauma Orthopedics at the First Affiliated Hospital of University of South China from May to August 2024 were enrolled using convenience sampling. Participants were randomly assigned to an experimental group ( n=42) and a control group ( n=42) using a random number table. The control group received routine exercise rehabilitation, while the experimental group received an exercise rehabilitation program constructed based on the Temporal Self-regulation Theory. The program included components such as establishing a unified concept of "exercise-rehabilitation", enhancing time-limited efficacy, achieving behavioral advantages, and improving self-regulation of active movement. Both groups received interventions for 12 weeks. Functional exercise adherence, activities of daily living, and hip joint function were assessed using the Orthopedic Exercise Adherence Scale, Modified Barthel Index, and Harris Hip Score at four time points: before intervention, on the day of discharge, and at 4 and 12 weeks postoperatively. Results:During the study, two patients from each group dropped out. Results of the generalized estimating equations showed group effects, time effects, and interaction effects in adherence scores with statistically significant differences ( P<0.01). On the day of discharge, and at 4 and 12 weeks postoperatively, the adherence scores in the experimental group were significantly higher than those in the control group, and the differences were statistically significant ( P<0.05). Repeated measures ANOVA indicated that group effects, time effects, and interaction effects were also statistically significant in scores for daily living ability and hip joint function ( P<0.05). The experimental group scored higher than the control group at each time point with statistically significant differences ( P<0.05) . Conclusions:The exercise rehabilitation program based on the Temporal Self-regulation Theory is scientifically sound and practical. It can effectively improve functional exercise adherence, enhance daily living ability, and promote postoperative recovery of hip joint function in elderly patients with hip fractures.

Purpose To explore the value of machine learning models based on synthetic MRI,dynamic contrast-enhanced MRI(DCE-MRI)and diffusion weighted imaging(DWI)parameters in identifying molecular subtypes of breast cancer.Materials and Methods A retrospective analysis was conducted on the data of 292 patients who underwent synthetic MRI,DCE-MRI and DWI examinations from September 2020 to September 2024 in Ningxia Medical University General Hospital before surgery and were pathologically confirmed to have breast cancer postoperatively.Patients were randomly divided into training and test sets using a ratio of 7:3.Multiple parameters were obtained from the synthetic MRI,DCE-MRI and DWI images.Variance analysis were used to screen the characteristic parameters among molecular subtype groups.Five machine learning models were established based on the selected characteristic parameters,and receiver operating characteristic curves were plotted to calculate the area under the curve among the molecular subtype groups.Results The support vector machine model exhibited the highest overall performance,with an area under the curve of 0.972,accuracy of 82.5％,specificity of 94.76％and sensitivity of 82.14％in the test set.This model's area under the curve values for differentiating luminal A,luminal B,human epidermal growth factor receptor-2 overexpression,and triple-negative groups in the training set were 0.979,0.925,0.971 and 0.982,respectively;in the test set,the area under the curve values were 0.973,0.873,0.956 and 0.955,respectively.Conclusion Machine learning models based on multimodal MRI parameters can assist clinicians in preoperatively determining the molecular subtypes of breast cancer and the support vector machine model shows relatively high comprehensive performance.

Objective:Interpret the key differences between the China Health-care Security Diagnosis Related Groups(CHS-DRG)2.0 and CHS-DRG 1.1,and provide reference for optimizing management strategies in medical institutions.Methods:Text analysis was used to import the CHS-DRG 2.0 and 1.1 grouping scheme dictionary data into the SQL database in a structured table format using SQL Server 2014.The key differences between the two schemes in grouping structure,grouping rules,grouping results,and other aspects were identified.Results:CHS-DRG 2.0 version added 26 groups,deleted 3 groups,and refined 10 groups into 20 groups for 14 clinical specialties at the ADRG level compared to CHS-DRG 1.1.Some group codes,names,and grouping rules were adjusted;Adjusted some grouping conditions and grouping results at the DRG level.Conclusion:CHS-DRG 2.0 version has improved grouping efficiency compared to CHS-DRG 1.1,solved some clinical bottleneck problems,and standardized the role of clinical diagnosis in grouping from the perspective of resource consumption.However,it has not completely solved the grouping problems of multi disease co treatment,multi disease treatment,and combined surgery.The adjustment of DRG weights and rates,the follow-up of related supporting policy reforms,and the negative effects of DRG will still pose challenges for medical institutions.

OBJECTIVE To explore the possible molecular mechanism through which amygdala cell adhesion molecule-1(CADM1)is involved in acute sleep deprivation-induced anxiety.METHODS Sixteen 8-week-old C57BL/6J mice were randomly divided into the control group and para-chlorophe-nylalanine(PCPA)-induced acute sleep deprivation experimental group.The PCPA mice were intraperi-toneally injected with PCPA suspension(at a dose of 300 mg?kg-1)between 8∶00 and 9∶00 am for 2 consecutive days while the control mice were injected with the same dose of normal saline.The sleep latency and sleep duration of mice were detected via the righting reflex test.Anxiety-like behaviors were detected by the open field test and elevated plus maze test.The expression level of CADM1 in the mouse amygdala was detected by Western Blot and Immunofluorescence staining.The GeneNet-work database was used to analyze the association between Cadm1 genes and other genes in the mouse amygdala.The key candidate regulatory genes were screened,and the Cadm1-anxiety behavior phenotype network was constructed.The mRNA expression levels of the key candidate regulatory genes were analyzed via qPCR analysis.RESULTS Compared with the control mice,the sleep latency of PCPA mice was significantly prolonged(P<0.01)while the sleep duration was significantly shortened(P<0.01).The activity time and distance of PCPA mice in the open field center were significantly shorter than those of the control group(P<0.05).The elevated plus maze experiment showed that the percentage of the number of times PCPA mice entered the open arm and the percentage of residence time in the open arm were significantly lower than those of the control group(P<0.05).Western Blot and immuno-fluorescence staining showed that the expression of CADM1 protein in the amygdala from PCPA mice was down-regulated compared with the control mice(P<0.05).Based on gene-behavioral association network analysis,Cadm1 was significantly associated with 25 anxiety-like behavior-related genes.The enrichment analysis of Cadm1 co-expression genes showed that Cadm1 was associated with γ-amino-butyric acid GABAergic synaptic pathway(P=4.31e-09),and that the key genes were huntingtin associ-ated protein 1(Hap1)(r=0.705,P=1.09e-08)、inositol 1,4,5-triphosphate receptor type 1(Itpr1)(r=-0.751,P=3.34e-10)、gamma-aminobutyric acid type A receptor subunit delta(Gabrd)(r=-0.836,P=3.93e-14)、γ-aminobutyric acid A receptor β1 subunit gene(Gabrb1)(r=0.732,P=1.50e-09)and adrenoceptor alpha 2A(Adra2a)(r=0.759,P=1.73e-10).The results of qPCR analysis showed that the mRNA levels of Hap1(P<0.05)、Gabrb1 and Adra2a were significantly up-regulated(P<0.01)while those of Itpr1 and Gabrd were significantly down-regulated(P<0.01).CONCLUSION Acute sleep deprivation leads to down-regulation of Cadm1 expression in the amygdala,and induces anxiety-like behaviors by affecting the expression of GABAergic synaptic signaling pathways Hap1,Gabrb1,Adra2a,Itpr1 and Gabrd.

Objective:To develop an exercise rehabilitation program based on the Temporal Self-regulation Theory and evaluate its effectiveness in elderly patients with hip fractures.Methods:A total of 84 elderly patients with hip fractures admitted to the Department of Trauma Orthopedics at the First Affiliated Hospital of University of South China from May to August 2024 were enrolled using convenience sampling. Participants were randomly assigned to an experimental group ( n=42) and a control group ( n=42) using a random number table. The control group received routine exercise rehabilitation, while the experimental group received an exercise rehabilitation program constructed based on the Temporal Self-regulation Theory. The program included components such as establishing a unified concept of "exercise-rehabilitation", enhancing time-limited efficacy, achieving behavioral advantages, and improving self-regulation of active movement. Both groups received interventions for 12 weeks. Functional exercise adherence, activities of daily living, and hip joint function were assessed using the Orthopedic Exercise Adherence Scale, Modified Barthel Index, and Harris Hip Score at four time points: before intervention, on the day of discharge, and at 4 and 12 weeks postoperatively. Results:During the study, two patients from each group dropped out. Results of the generalized estimating equations showed group effects, time effects, and interaction effects in adherence scores with statistically significant differences ( P<0.01). On the day of discharge, and at 4 and 12 weeks postoperatively, the adherence scores in the experimental group were significantly higher than those in the control group, and the differences were statistically significant ( P<0.05). Repeated measures ANOVA indicated that group effects, time effects, and interaction effects were also statistically significant in scores for daily living ability and hip joint function ( P<0.05). The experimental group scored higher than the control group at each time point with statistically significant differences ( P<0.05) . Conclusions:The exercise rehabilitation program based on the Temporal Self-regulation Theory is scientifically sound and practical. It can effectively improve functional exercise adherence, enhance daily living ability, and promote postoperative recovery of hip joint function in elderly patients with hip fractures.