ObjectiveTo establish the specific chromatogram and quantitative analysis of multi-components by single-marker（QAMS） based on linear calibration using two reference substances（LCTRS）， explore the consistency between Hedyotis Herba dispensing granules and standard decoction， and evaluate the quality of the dispensing granules. MethodsHigh performance liquid chromatography（HPLC） specific chromatogram was established based on 15 batches of Hedyotis Herba standard decoction and 10 batches of the dispensing granules， and LCTRS was used to locate chromatographic peaks. The actual retention times of 7 characteristic peaks in the specific chromatogram was measured on 24 different types of C₁₈ columns， taking deacetyl asperulosidic acid and asperulosidic acid as the dual standard compounds， the retention times of the other 5 characteristic peaks were predicted and validated. Based on this， QAMS was developed to determine the contents of four components（deacetyl asperulosidic acid， deacetyl asperulosidic acid methyl ester， asperulosidic acid， and p-coumaric acid）. Then， the relative correction factors of deacetyl asperulosidic acid， deacetyl asperulosidic acid methyl ester and p-coumaric acid were calculated using the reference peak of asperulosidic acid in the dual standard compounds， and each component was quantified accordingly. Finally， the consistency between the dispensing granules and standard decoction was assessed by taking extract rate of the standard decoction， consistency of the specific chromatograms， contents and transfer rates of the indicator components as indexes， and the quality of the dispensing granules was evaluated. ResultsThere were 7 common peaks in the characteristic chromatogram of samples of Hedyotis Herba standard decoction and the dispensing granules， and four of them were identified by reference standards， namely deacetyl asperulosidic acid（peak 1）， deacetyl asperulosidic acid methyl ester（peak 3）， asperulosidic acid（peak 6） and p-coumaric acid（peak 7）. The similarity between the dispensing granules and the standard decoction was >0.9. The absolute deviation in the predicted retention time for each component by LCTRS was lower than that of the relative retention time method. The extract rate of the 15 batches of Hedyotis Herba standard decoction ranged from 7.89% to 14.60%， the contents of deacetyl asperulosidic acid， deacetyl asperulosidic acid methyl ester， asperulosidic acid and p-coumaric acid were 6.62-19.70， 3.83-17.99， 1.57-6.69， 1.62-4.52 mg·g^-1， and the transfer rates of these components from decoction pieces to the standard decoction were 22.89%-39.60%， 34.03%-62.24%， 24.25%-43.70%， and 40.58%-73.71%， respectively. The extract rate， index component contents and transfer rates from decoction pieces to the three batches of Hedyotis Herba dispensing granules（P1-P3）， produced by manufacturer A， were similar to those of the standard decoction prepared from the same batch of decoction pieces， and all fell within the specified range. The contents of the 4 indicator components in 7 batches of the dispensing granules（P4-P10） from manufacturers B-E were all within the range of the content converted from the standard decoction based on the quantity of the dispensing granules. ConclusionThe established specific chromatogram and QAMS based on LCTRS are reasonable and reliable. Based on the evaluation indicators of standard decoction yield， consistency of specific chromatograms， contents and transfer rates of the four index components， the 10 batches of Hedyotis Herba dispensing granules from various manufacturers have exhibited good consistency with the standard decoction， indicating that the current production process is relatively reasonable.

Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

ObjectiveTo construct a family-centered care model for children with tuberculosis based on the double ABC-X model, and to evaluate its clinical effects. MethodsFrom December 2022 to October 2023, 64 newly admitted children with tuberculosis who met the criteria and their caregivers were recruited from the tuberculosis department of Shanghai Public Health Clinical Center were randomly divided into an experimental group (32 cases) and a control group (32 cases).The control group was given a conventional health care, while the experimental group was given a family-centered health care intervention based on the double ABC-X model, in which a multidisciplinary care team provided personalized information and emotional support for the caregivers and their children. Medication adherence of the children, caregiver’s teading burden, and disease management competence were compared between the 2 groups. ResultsA total of 29 cases in the experimental group and 27 cases in the control group completed the intervention. At 12 weeks of intervention, the medication adherence score (7.72±0.45 vs 7.41±0.50, P<0.05) and disease management competence score (36.97±7.85 vs 31.56±7.30, P<0.05) were higher in the experimental group than that in the control group while the caregiving burden score (31.79±13.40 vs 40.04±9.01, P<0.05) and difficulty of disease management score (30.41±12.41 vs 38.56±9.48, P<0.05) were lower than that in the control group. At 24 weeks of intervention, the medication adherence score (7.34±0.97 vs 6.70±1.14, P<0.05) and disease management competence score (42.07±6.93 vs 35.63±7.32, P<0.05) were higher in the experimental group than that in the control group as well, but the caregiving burden score (31.62±11.72 vs 39.63±10.17, P<0.05) and difficulty of disease management score (30.59±10.87 vs 37.81±9.32, P<0.05) were lower than that in the control group. ConclusionFamily-centered care based on the double ABC-X model can effectively promote medication adherence among children with tuberculosis, reduce caregivers’ care burden and disease management difficulties, and improve caregiver’s disease management competence.

OBJECTIVE To provide suggestions for improving the path and system construction of patient engagement in drug regulation in China. METHODS By reviewing initiatives and experiences from the United States （U. S.）， European Union （EU）， and Japan in promoting patient engagement， this study summarizes the roles and contributions of patients in the entire drug regulatory process internationally. Combining China’s current progress and challenges in patient engagement， specific proposals are formulated to refine regulatory pathways and institutional systems. RESULTS & CONCLUSIONS With growing global emphasis on patient engagement as a regulatory strategy， countries or regions such as the U.S.， EU， and Japan have established clear policies， designated oversight agencies， and developed diversified pathways for patient engagement. Patients contribute to regulatory processes through advisory meetings， direct decision-making roles， and leveraging lived experiences and expertise to optimize drug evaluation and monitoring. In contrast， China’s patient engagement remains primarily limited to clinical value- oriented drug development， lacking formal policy guidance. It is recommended that China， based on its existing policy system， further strengthen the construction of a safeguard system for patient engagement， improve the capacity building and pathway models for patient participation in pharmaceutical regulation， and promote the continuous development of patient engagement in pharmaceutical regulation in our country.

OBJECTIVE To analyze and identify the metabolites of pirtobrutinib （PTN） in rats， and clarify the possible metabolic pathways of PTN in rats. METHODS Six rats were intragastrically administered with 10 mg/kg PTN suspension. Blood samples were collected from the rats 30 minutes before administration and at 0.25， 0.5， 1， 2， 4， 6， 8， 12， 24 hours after administration. Urine and feces samples were collected 12 hours before administration and 24 hours after administration. UHPLC- Orbitrap Exploris 240 system combined with Compound Discoverer 3.0 and Xcalibur 2.0 software were adopted for structural identification and metabolic pathway analysis of PTN metabolites in rat plasma， urine， and feces. RESULTS A total of 29 PTN metabolites were identified， including 17， 19 and 22 metabolites in plasma， urine and feces， respectively. The metabolic pathways of PTN mainly included oxidation， sulfation， glucuronidation， etc.， and its metabolites were mostly combination products of two or more different metabolic forms. In detail， a total of 26 metabolites were associated with phase Ⅰ metabolic reactions （14 oxidation metabolites， 9 reduction/dehydrogenation metabolites， 8 demethylation metabolites， and 5 hydrolysis metabolites）. Meanwhile， a total of 20 products were involved in phase Ⅱ metabolites （14 sulfation metabolites and 8 glucuronic acid binding metabolites）. CONCLUSIONS PTN exhibits a diverse range of metabolites in rat fecal samples， with the primary metabolic pathways being oxidation， sulfation， glucuronidation， and others.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Miniscrews are commonly utilized as temporary anchorage devices (TADs) in cases of maxillary protrusion and premolar extraction. This study aimed to investigate the effects and potential side effects of two conventional miniscrew configurations on the maxillary incisors. Methods: Eighty-two adult patients with maxillary dentoalveolar protrusion who had undergone bilateral first premolar extraction were retrospectively divided into three groups: non-TAD, two posterior miniscrews only (P-TADs), and two anterior and two posterior miniscrews combined (AP-TADs). Cone-beam computed tomography was used to evaluate the maxillary central incisors (U1). Results: The APTADs group had significantly greater U1 intrusion (1.99 ± 2.37 mm, n = 50) and less retroclination (1.70° ± 8.80°) compared to the P-TADs (–0.07 ± 1.65 mm and 9.45° ± 10.68°, n = 60) and non-TAD group (0.30 ± 1.61 mm and 1.91° ± 9.39°, n = 54).However, the AP-TADs group suffered from significantly greater apical root resorption (ARR) of U1 (2.69 ± 1.38 mm) than the P-TADs (1.63 ± 1.46 mm) and non-TAD group (0.89 ± 0.97 mm). Notably, the incidence of grade IV ARR was 16.6% in the AP-TADs group, significantly higher than the rates observed in the P-TADs (6.7%) and non-TAD (1.9%) groups. Multiple regression analysis revealed that after excluding tooth movement factors, the AP-TADs configuration resulted in an additional 0.5 mm of ARR compared with the P-TADs group. Conclusions In cases of maxillary protrusion and premolar extraction, the use of combined anterior and posterior miniscrews enhances incisor intrusion and minimizes torque loss of the maxillary incisors. However, this approach results in more severe ARR, likely due to the increased apical movement and composite force exerted.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective: Huntington’s disease (HD) is characterized by motor, cognitive, and neuropsychiatric symptoms. Oculomotor impairments and gait variability have been independently considered as potential markers in HD. However, an integrated analysis of eye movement and gait is lacking. We performed multiple examinations of eye movement and gait variability in HTT mutation carriers, analyzed the consistency between these parameters and clinical severity, and then examined the associations between oculomotor impairments and gait deficits. Methods: We included 7 patients with pre-HD, 30 patients with HD and 30 age-matched controls. We collected demographic data and assessed the Unified Huntington’s Disease Rating Scale (UHDRS) score. Examinations, including saccades, smooth pursuit tests, and optokinetic (OPK) tests, were performed to evaluate eye movement function. The parameters of gait include stride length, walking velocity, step deviation, step length, and gait phase. Results: HD patients have significant impairments in the latency and velocity of saccades, the gain of smooth pursuit, and the gain and slow phase velocities of OPK tests. Only the speed of saccades significantly differed between pre-HD patients and controls. There are significant impairments in stride length, walking velocity, step length, and gait phase in HD patients. The parameters of eye movement and gait variability in HD patients were consistent with the UHDRS scores. There were significant correlations between eye movement and gait parameters. Conclusion Our results show that eye movement and gait are impaired in HD patients and that the speed of saccades is impaired early in pre-HD. Eye movement and gait abnormalities in HD patients are significantly correlated with clinical disease severity.

BACKGROUND:Hydroxy safflower yellow A has anti-ischemia,anti-oxidation,anti-thrombotic and anti-inflammatory effects.Whether it affects neuronal pyroptosis after glucose-oxygen deprivation/reglucose-reoxygenation is still unclear. OBJECTIVE:To investigate the protective effect of hydroxy safflower yellow A on neuronal pyroptosis and its mechanism. METHODS:HT22 cells in logarithmic growth phase were randomly divided into five groups:normal group,model group,hydroxy safflower yellow A group,colivelin group,and colivelin+hydroxy safflower yellow A group.HT22 cells were treated with glucose-oxygen deprivation/reglucose-reoxygenation to establish neuronal pyroptosis model,and then treated with STAT3 agonist Colivelin and hydroxy safflower yellow A.JC-1 probe was employed to assess changes in mitochondrial membrane potential.Reactive oxygen species kit was used to determine the content of reactive oxygen species in cells.GSDMD/TUNEL staining was conducted to observe cell pyroptosis.Immunofluorescence analysis was performed to detect STAT3 and GSDMD protein expression.RT-PCR was utilized for assessing mRNA expression levels of STAT3,NLRP3,and Caspase-1.Western blot assay was utilized to measure the protein expression levels of p-STAT3,NLRP3,GSDMD,Cleaved-caspase-1,and interleukin-1β. RESULTS AND CONCLUSION:(1)Compared with the normal group,the number of pyroptotic cells increased in HT22 cells in the model group along with a significant increase in protein expression levels of p-STAT3,NLRP3,Cleaved-caspase-1,GSDMD,and interleukin-1β.Compared with the model group,the number of pyroptotic cells reduced,and the expression of pyroptosis-related proteins significantly decreased in the hydroxy safflower yellow A group.(2)In comparison with the model group,pyroptosis worsened in the colivelin group where mitochondrial membrane potential decreased along with elevated reactive oxygen species content and increased mRNA expression levels of STAT3,NLRP3,and Caspase-1,as well as increased protein expression levels of p-STAT3,NLRP3,GSDMD,Cleaved-caspase-1,and interleukin-1β.Compared with the Colivelin group,above indexes were improved in the colivelin+hydroxy safflower yellow A group.These results suggest that hydroxy safflower yellow A plays a neuroprotective role through STAT3 signaling pathway to inhibit HT22 pyroptosis after glucose-oxygen deprivation/reglucose-reoxygenation treatment.