OBJECTIVE:In recent years,the number of clinical research reports on acupuncture and manipulation for the treatment of greater occipital neuralgia has gradually increased,but the quality is uneven.There is currently no literature evaluating the quality of published reports,which is not conducive to the promotion of clinical use of these therapies.Therefore,this article assessed the reporting quality of randomized controlled trials on acupuncture and manipulation for greater occipital neuralgia.METHODS:Cochrane Library,PubMed,Web of Science,Embase,China National Knowledge Infrastructure(CNKI),VIP,WanFang Data,and Chinese BioMedical Literature Database(CBM)from inception to May 20,2024 were searched.The reporting quality of included randomized controlled trials was independently evaluated by two investigators using the CONSORT statement,STRICTA checklist,and Cochrane bias of risk assessment tool.A third investigator resolved any disagreement.RESULTS:A total of 62 articles were included.Based on the CONSORT statement,59.46％(22/37)of all entries had a reporting rate of less than 50％,mainly including"Identification as a randomized trial in the title(1/62,1.61％),""How sample size was determined(7/62,11.29％),""Implementation(1/62,1.61％),""Blinding(1/62,1.61％),"and"Reports of Funding(4/62,6.45％)."According to the STRICTA checklist,29.41％(5/17)of all entries had a reporting rate of less than 50％,mainly including"Details of other interventions(7/58,12.07％),""Setting and context of treatment(0/58,0％),"and"Description of participating acupuncturists(0/58,0％)."CONCLUSION:The reporting quality of randomized controlled trials on acupuncture and manipulation therapy for greater occipital neuralgia remains low.Future researchers need to make greater efforts to strictly adhere to the CONSORT statement and STRICTA checklist during trial design,implementation,and reporting.This will facilitate the standardization of research in this field and enhance the reliability and reproducibility of the research results.

Objective To explore practical pathways for the connotation quality control of Zhuang medical records by le-veraging the reform of DRG-based medical insurance payment systems as an opportunity,thereby enhancing record quality and fostering the coordinated development of ethnic medicine with modern medical insurance payment systems.Methods By analy-zing the requirements imposed by the DRG-based payment reform on the quality management of Zhuang medical records,a quality control management system focused on the connotation quality control of these records was established.A scoring standard for the connotation quality of Zhuang medical records,supplemented by DRG,was developed.Dynamic quality control was implemented through a combination of information-based intelligent review and expert manual review to ensure rectification.Results Key in-dicators,such as the qualification rate of the first page of medical records,the A-level rate of inpatient records,and the DRG en-rollment rate,showed significant improvements.The documentations of surgical and procedural records were standardized,and the records of ward rounds and case discussions by senior physicians were comprehensive and detailed,accurately reflecting the diagnostic and therapeutic methods and outcomes of Zhuang medicine.Conclusion The DRG-based quality control model for Zhuang medical records effectively aligns medical insurance payment with the unique characteristics of ethnic medicine,offering a replicable model for the quality management of medical records in ethnic medicine institutions under DRG-based reforms.

Objective To explore practical pathways for the connotation quality control of Zhuang medical records by le-veraging the reform of DRG-based medical insurance payment systems as an opportunity,thereby enhancing record quality and fostering the coordinated development of ethnic medicine with modern medical insurance payment systems.Methods By analy-zing the requirements imposed by the DRG-based payment reform on the quality management of Zhuang medical records,a quality control management system focused on the connotation quality control of these records was established.A scoring standard for the connotation quality of Zhuang medical records,supplemented by DRG,was developed.Dynamic quality control was implemented through a combination of information-based intelligent review and expert manual review to ensure rectification.Results Key in-dicators,such as the qualification rate of the first page of medical records,the A-level rate of inpatient records,and the DRG en-rollment rate,showed significant improvements.The documentations of surgical and procedural records were standardized,and the records of ward rounds and case discussions by senior physicians were comprehensive and detailed,accurately reflecting the diagnostic and therapeutic methods and outcomes of Zhuang medicine.Conclusion The DRG-based quality control model for Zhuang medical records effectively aligns medical insurance payment with the unique characteristics of ethnic medicine,offering a replicable model for the quality management of medical records in ethnic medicine institutions under DRG-based reforms.

ObjectiveBased on the TCM theory of "Yang transforms materials to Qi while Yin constitutes material form"， this paper explored the effects of Zuogui Wan and Yougui Wan on the molecular mechanism of mitochondrial biogenesis during the adipogenic differentiation process of rat bone marrow mesenchymal stem cells （BMSCs） by mediating peroxisome proliferator-activated receptor γ coactivator-1α （PGC-1α） and peroxisome proliferators-activated receptor γ （PPARγ）， providing theoretical support for the prevention and treatment of postmenopausal osteoporosis （PMOP） using Zuogui Wan and Yougui Wan. MethodsBMSCs were divided into a blank group， Zuogui Wan （ZGW） group， Yougui Wan （YGW） group， and Progynova group. Cell identification was performed using flow cytometry. The growth curves of BMSCs were plotted using the methylthiazolyldiphenyl-tetrazolium bromide （MTT） method， and the effects of Zuogui Wan and Yougui Wan on the proliferation of BMSCs were detected. The Oil red O staining method was used to detect lipid droplet formation. The Western blot method was used to detect the expression of adipogenesis-related factors PPARγ， CCAAT/enharcer-binding protein （C/EBP）α， C/EBPβ， lipoprotein lipase （LPL） protein， brown adipose tissue-related （BAT） proteins PGC-1α， uncoupcing protein 1 （UCP1）， PR domdin-containing protein 16 （PRDM16）， mitochondrial biogenesis-related PGC-1α， nuclear respiratory factor 1 （Nrf1）， nuclear factor E₂-related factor 2 （Nrf2）， and mitochondrial transcription factor A （TFAM）. The expression of adipogenesis-related factors PPARγ， C/EBPα， C/EBPβ， LPL genes， and the copy number of cytochrome B （CytoB mtDNA） gene was detected using real-time polymerase chain reaction （Real-time PCR）. Mitochondrial ultrastructure was detected using transmission electron microscopy. ResultsCompared with that in the blank group， the proliferation ability of BMSCs in each treatment group increased continuously as the intervention progressed， and lipid droplets significantly decreased after the drug intervention. The mRNA and protein expression levels of adipogenesis-related factors PPARγ， C/EBPα， C/EBPβ， and LPL were significantly downregulated （P<0.01）， while those of the BAT-related factors PGC-1α， UCP1， PRDM16 were significantly upregulated （P<0.01）. The number of mitochondria increased， accompanied by reduced swelling. The double membrane and cristae structure were clear， and the internal cristae rupture was reduced. The copy number of CytoB mtDNA in each treatment group was significantly increased （P<0.01）. The protein expression levels of mitochondrial biogenesis-related PGC-1α， Nrf1， Nrf2， and TFAM in each treatment group were significantly increased （P<0.01）. ConclusionBoth Zuogui Wan and Yougui Wan can prevent and treat PMOP by intervening in mitochondrial biogenesis in BMSCs through PGC-1α/PPARγ.

Objective:To explore the clinical features and prognostic factors of lymphoma-associated hemophagocytic lymphohistiocytosis (LAHS).Methods:A retrospective cohort study was conducted. The clinical data of 44 LAHS patients who diagnosed at the Second Hospital of Shanxi Medical University from September 2016 to September 2022 were retrospectively analyzed. All patients were divided into B cell LAHS (B-LAHS) group (19 cases) and NK/T cell LAHS (NK/T-LAHS) group (19 cases) according to the lymphoma types, except for 4 cases of classic Hodgkin lymphomas and 2 cases of splenic lymphoma lymphomas. The clinical features and the factors influencing the prognosis of both groups were compared. According to whether lymphoma treatment were included in the initial treatment regimen, the patients were divided into the group with lymphoma treatment and the group without lymphoma treatment, and the objective response rate (ORR) of both groups was compared. Kaplan-Meier method was used to analyze the overall survival (OS), the log-rank method was used for univariate analysis, and the Cox proportional hazards model was used for multivariate analysis.Results:Among 44 LAHS patients, 26 cases were male and 18 cases were female, with the age of (69±7) years. Most of them were secondary to diffuse large B-cell lymphoma (DLBCL), and 17 out of 44 patients were secondary to DLBCL. All patients had fever, lymphadenopathy, and ferritinemia. The differences in red blood cells (RBC), hemoglobin (Hb), activated partial thromboplastin time (APTT), peripheral blood Epstein-Barr virus DNA (EBV DNA) load, positive rate of Epstein-Barr virus-encoded small RNA (EBER) in tissues, triglycerides (TG), aspartate aminotransferase (AST) and fibrinogen between NK/T-LAHS patients and B-LAHS patients were statistically significant (all P<0.05). The ORR of 44 patients with LAHS was 52.3% (23/44); the median OS time of LAHS patients was 157 d, and that of NK/T-LAHS patients was 110 d; the median OS time of B-LAHS was 135 d; and the difference was statistically significant ( χ2 = 7.47, P = 0.024). The median OS was not reached in patients receiving initial treatment regimen containing lymphoma, while the median OS time was 65 d in patients receiving treatment without containing lymphoma, and the difference was statistically significant ( χ2 = 3.97, P = 0.046). The results of multivariate Cox proportional hazards model showed that age ≥ 70 years ( HR = 2.502, 95% CI: 1.047-5.978, P = 0.039), serum ferritin (SF) ≥ 1 500 μg/L( HR=0.521,95% CI:0.282~0.960, P=0.037), soluble CD25 ≥ 7 800 U/ml ( HR = 0.536, 95% CI: 0.348-0.828, P = 0.005), interleukin (IL)-10≥100 ng/L ( HR = 0.532, 95% CI: 0.33-0.85, P = 0.009), interferon (IFN)-γ≥5.0 ng/ L ( HR = 0.554, 95% CI: 0.32-0.95, P = 0.033), Th (CD3 + CD4 + T cells)/ Ts (CD3 + CD8 + T cells) < 2 ( HR = 1.731, 95% CI: 1.005-2.982, P = 0.048), and hepatomegaly ( HR = 2.581, 95% CI: 1.059-6.289, P = 0.037) were independent factors influencing the poor outcome of LAHS patients. Conclusions:LAHS is mostly secondary to DLBCL. The early clinical manifestations lack specificity and the prognosis is generally poor, and the worst prognosis occurs in patient with NK/T-LAHS. The initial treatment with lymphoma can prolong OS time in patients with LAHS.

Computational analysis can accurately detect drug-gene interactions(DGIs)cost-effectively.However,transductive learning models are the hotspot to reveal the promising performance for unknown DGIs(both drugs and genes are present in the training model),without special attention to the unseen DGIs(both drugs and genes are absent in the training model).In view of this,this study,for the first time,proposed an inductive learning-based model for the precise identification of unseen DGIs.In our study,by integrating disease nodes to avoid data sparsity,a multi-relational drug-disease-gene(DDG)graph was constructed to achieve effective fusion of data on DDG intro-relationships and inter-actions.Following the extraction of graph features by utilizing graph embedding algorithms,our next step was the retrieval of the attributes of individual gene and drug nodes.In this way,a hybrid feature charac-terization was represented by integrating graph features and node attributes.Machine learning(ML)models were built,enabling the fulfillment of transductive predictions of unknown DGIs.To realize inductive learning,this study generated an innovative idea of transforming known node vectors derived from the DDG graph into representations of unseen nodes using node similarities as weights,enabling inductive predictions for the unseen DGIs.Consequently,the final model was superior to existing models,with significant improvement in predicting both external unknown and unseen DGIs.The practical feasibility of our model was further confirmed through case study and molecular docking.In summary,this study establishes an efficient data-driven approach through the proposed modeling,suggesting its value as a promising tool for accelerating drug discovery and repurposing.

Objective:To investigate the diagnostic efficacy of time-dependent diffusion MRI (t d-dMRI)-derived microstructural parameters for clinically significant prostate cancer (csPCa) and their associations with the pathological grade of prostate cancer(PCa) based on the International Society of Urological Pathology (ISUP) grades. Methods:This cross-sectional study prospectively enrolled 196 patients suspected of PCa from March 2023 to March 2024 at the First Affiliated Hospital, Sun Yat-Sen University. All patients underwent multiparametric MRI and t d-dMRI to obtain microstructural parameters, including cell diameter (d), intracellular volume fraction (f in), extracellular diffusion coefficient (D ex), cellularity, and apparent diffusion coefficient (ADC) value at oscillation frequencies of 33 Hz, 17 Hz, 0 Hz (ADC 33, ADC 17, and ADC 0). Pathologically, 95 cases were classified as csPCa (ISUP 2-5), and the rest 101 cases were classified as non-csPCa (benign or ISUP 1). Comparison of these microstructural metrics was made between csPCa and non-csPCa groups by independent t-tests or Mann-Whitney U tests, and multivariable logistic regression was used to identify independent predictors. A combined diagnostic model was then constructed based on the independent predictors. The receiver operating characteristic curve analysis was used to evaluate the diagnostic performance. Finally, in PCa, the correlation between microstructural parameters and ISUP grades was investigated by Spearman correlation. Results:The t d-dMRI measurements, including d, f in, cellularity, ADC 33,ADC 17 and ADC 0, were significantly different between csPCa and non-csPCa groups (All P<0.05). But D ex was not significantly different between the two groups ( Z=-1.27, P=0.204). The area under the receiver operating characteristic curve (AUC) for diagnosing csPCa were 0.701 (95% CI 0.628-0.775) for d, 0.869 (95% CI 0.819-0.920) for f in, 0.884 (95% CI 0.835-0.932) for cellularity, 0.777 (95% CI 0.712-0.842) for ADC 33, 0.852 (95% CI 0.799-0.905) for ADC 17, and 0.840 (95% CI 0.786-0.894) for ADC 0. Cellularity ( OR=6.142, 95% CI 2.920-12.929, P<0.001) and ADC 17 ( OR=0.108, 95% CI 0.027-0.429, P=0.002) were identified as the independent predictors, and their combined model achieved an AUC of 0.896 (95% CI 0.852-0.941). In PCa f in and cellularity were positively correlated with ISUP grades ( r=0.490 and 0.397, P<0.001), while ADC 33, ADC 17, and ADC 0 were negatively correlated with ISUP grades ( r=-0.198, -0.345, -0.360; P=0.041,<0.001,<0.001). d and D ex were not correlated with ISUP grades ( P>0.05). Conclusion:t d-dMRI based microstructural mapping correlates with ISUP grades of PCa and may be useful for the differential diagnosis of csPCa.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Computational analysis can accurately detect drug-gene interactions (DGIs) cost-effectively. However, transductive learning models are the hotspot to reveal the promising performance for unknown DGIs (both drugs and genes are present in the training model), without special attention to the unseen DGIs (both drugs and genes are absent in the training model). In view of this, this study, for the first time, proposed an inductive learning-based model for the precise identification of unseen DGIs. In our study, by integrating disease nodes to avoid data sparsity, a multi-relational drug-disease-gene (DDG) graph was constructed to achieve effective fusion of data on DDG intro-relationships and inter-actions. Following the extraction of graph features by utilizing graph embedding algorithms, our next step was the retrieval of the attributes of individual gene and drug nodes. In this way, a hybrid feature characterization was represented by integrating graph features and node attributes. Machine learning (ML) models were built, enabling the fulfillment of transductive predictions of unknown DGIs. To realize inductive learning, this study generated an innovative idea of transforming known node vectors derived from the DDG graph into representations of unseen nodes using node similarities as weights, enabling inductive predictions for the unseen DGIs. Consequently, the final model was superior to existing models, with significant improvement in predicting both external unknown and unseen DGIs. The practical feasibility of our model was further confirmed through case study and molecular docking. In summary, this study establishes an efficient data-driven approach through the proposed modeling, suggesting its value as a promising tool for accelerating drug discovery and repurposing.

Objective:To analyze the clinical characteristics of the Chikungunya fever outbreak in Shunde District, Foshan City, Guangdong Province in July 2025 and the risk factors associated with delayed viral RNA clearance.Methods:A total of 562 patients with Chikungunya fever admitted to three designated hospitals in Shunde District from July 10 to 30, 2025 were enrolled. Demographic data, clinical manifestations, and laboratory findings were collected. Patients were categorized into four age groups including minors (<18 years), young adults (18 to 39 years), middle-aged adults (40 to 64 years) and elderly adults (≥65 years). The differences of clinical characteristics among these age groups were analyzed. Intergroup comparisons were performed using chi-square test, one-way analysis of variance, or Kruskal-Wallis H test. Pairwise comparisons between groups were conducted using the Bonferroni or Games-Howell or Dunn method. Binary logistic regression was employed to analyze risk factors associated with delayed viral RNA clearance (>7 days). Results:The mean age of the 562 enrolled Chikungunya fever patients was (44.8±21.3) years. Fever, arthralgia and rash were the three core symptoms, with incidence rates of 87.5% (492/562), 88.4%(497/562) and 69.6%(391/562), respectively. At discharge, only 54.1%(304/562) of patients achieved complete symptom resolution, while 26.5%(149/562) still had arthralgia and 36.1%(203/562) had residual rash. Significant differences were observed among age groups in the incidence of fever ( χ2=9.43, P=0.024), peak body temperature ( F=6.54, P<0.001), incidence of arthralgia ( χ2=26.89, P<0.001), duration of arthralgia ( F=12.68, P=0.001), incidence of rash ( χ2=68.99, P<0.001), rate of residual rash at discharge ( χ2=32.37, P<0.001), lymphocyte count ( F=12.94, P<0.001), platelet count ( F=14.95, P<0.001), and C-reactive protein levels (CRP) ( H=94.18, P<0.001). Further pairwise comparisons revealed that compared to the middle-aged and elderly groups, the minor group had a higher incidence of fever and a lower incidence of arthralgia, and the duration of arthralgia was shorter than the elderly group (all P<0.008 3). Compared with the other three groups, the elderly group had lower incidence and residual rate of rash, and lower platelet counts (all P<0.008 3), and higher levels of CRP (all P<0.05). The elderly group had lower lymphocyte counts compared to the minor and young adult groups (both P<0.05). Significant differences were found among age groups in the time to viral RNA clearance ( F=5.77, P=0.003) and length of hospital stay ( F=11.64, P<0.001), with the elderly group having significantly longer duration for both compared to the other three groups (all P<0.05). Multivariate analysis showed that advanced age (odds ratio ( OR)=1.049, 95% confidence interval ( CI) 1.015 to 1.083), longer duration of fever ( OR=1.529, 95% CI 1.086 to 2.155) and longer duration of arthralgia ( OR=1.927, 95% CI 1.318 to 2.817) were independent risk factors for delayed viral RNA clearance (all P<0.05). Conclusions:Patients with Chikungunya fever in Shunde District primarily present with fever, arthralgia and rash. The incidence and characteristics of these three core symptoms show age-related variations. Elderly patients and those with longer durations of fever or arthralgia are more likely to experience delayed viral clearance.