Objective To explore the mediating effect of sleep quality between somatic symptoms and severity of depression in patients with depression. Methods A total of 384 drug-naive patients diagnosed with depression were recruited from the Department of Psychological Medicine of Zhongshan Hospital, Fudan University, during the period from February to August 2024. The severity of depression, somatic symptoms, and sleep quality were assessed using Patient Health Qusetionaire (PHQ)-9, PHQ-15, and Pittsburgh sleep quality index (PSQI), respectively. Based on the PHQ-15 scores, all participants were stratified into two groups: a mild somatic symptoms group（＜10 points, n＝136）and a moderate-to-severe somatic symptoms group（≥10 points, n＝248）. Comparisons of sleep quality between the two groups were conducted, and partial correlation analysis was performed to examine the correlation between sleep quality and somatic symptoms. Additionally, linear regression and mediation analyses were conducted to investigate the mediating effect of sleep quality between somatic symptoms and severity of depression. Results The PSQI scores in moderate-to-severe somatic symptoms group were significantly higher than those in mild somatic symptoms group (P＜0.001). Partial correlation analysis indicated that, after controlling for depression severity, the positive correlation between PSQI and PHQ-15 scores remained significant in both groups (P＜0.01). Regression analysis identified both sleep quality and somatic symptoms as predictors of severity of depression (P＜0.001). Additionally, mediation analysis demonstrated that sleep quality partially mediated the relationship between somatic symptoms and severity of depression, accounting for 26.63% (0.090/0.338) of the total effect. Conclusions In patients with depression, sleep quality is associated with somatic symptoms, and both contribute to an increased risk of the severity of depression. Moreover, sleep quality plays a partial mediating effect between somatic symptoms and severity of depression, highlighting the importance of addressing sleep-related issues in the management of depression.

The complement component 4 binding protein alpha(C4BPA)gene is the alpha chain of complement binding protein 4.As a plasma protein involved in the complement and coagulation systems,it can influence immune responses and lipid metabolism.In order to study the polymor-phism of C4BPA gene and its correlation with milk quality traits in Chinese Holstein cows,genom-ic DNA was extracted from blood samples of 92 Chinese Holstein cows,and the target fragment of C4BPA gene was amplified by PCR,and the association analysis was performed by using direct se-quencing to obtain the SNP loci and milk quality traits.The results showed that among the four SNPs found at the third intron of the C4BPA gene,I3-11 G＞A was highly significantly correlated with milk protein and urea nitrogen(P＜0.05),I3-291 T＞G was significantly correlated with lac-tose(P＜0.05),I3-374 C＞T was highly significantly correlated with lactose and urea nitrogen(P＜0.05),and I3-375 T＞G was highly significantly correlated with lactose(P＜0.05),milk pro-tein and urea nitrogen.The chi-square test values for each point indicated that the population was in genetic equilibrium.Individuals of haplotype combination H1 H1 had the highest lactose content,and haplotype combination H1H2 can be used as the best haplotype combination in the molecular selection work of dairy cows.

OBJECTIVE: To develop a quality consistency evaluation strategy for traditional Chinese medicine (TCM) granules using sugar free Yangwei Granules as a model drug, and demonstrate the effectiveness of the developed method. METHODS: The strategy integrates several methods including, HPLC fingerprint and physical fingerprint methods analyze the similarity in chemical and physical properties of the TCM granule samples. Near-infrared (NIR) spectroscopy with principal components cluster analysis method is used to monitor normal operating conditions (NOC) samples accurately and to identify different types of abnormal operating conditions (AOC) samples, particularly those that deviate from the normal range. RESULTS: The combined use of HPLC fingerprint and physical fingerprint provides insights into the chemical and physical properties of the samples. NIR spectroscopy, combined with principal components cluster analysis, achieves high accuracy in monitoring NOC samples and identifying AOC samples without misjudgment. The approach proves useful as a complementary method in cases where HPLC fingerprint and physical fingerprint alone lack sufficient resolution. CONCLUSION This study establishes the feasibility and utility of the integrated approach for assessing the quality consistency of TCM granules. The strategy shows a high degree of generalization and holds significant importance for enhancing the quality control processes of TCM granules.

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Advancements in artificial intelligence(AI)and emerging technologies are rapidly expanding the exploration of chemical space,facilitating innovative drug discovery.However,the transformation of novel compounds into safe and effective drugs remains a lengthy,high-risk,and costly process.Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development.Despite this need,no comprehensive tool currently supports systematic evaluation and efficient screening.Here,we present druglikeFilter,a deep learning-based framework designed to assess drug-likeness across four critical dimensions:1)physicochemical rule evaluated by systematic determination,2)toxicity alert investigated from multiple perspectives,3)binding affinity measured by dual-path analysis,and 4)compound synthesizability assessed by retro-route prediction.By enabling automated,multidimensional filtering of compound libraries,druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates,which can be freely accessed at https://idrblab.org/drugfilter/.

Objective Based on the clinical data and imaging manifestations of patients with ischemic stroke to establish a simple clinical prediction model that is used for identifying intracranial atherosclerotic stenosis-acute large vessel occlusion(ICAS-LVO in posterior circulation before surgery.Methods The clinical data of patients with acute large vessel occlusion(LVO in the posterior circulation,who received endovascular intervention at the First Affiliated Hospital of Nanjing Medical University of China from January 2019 to September 2022,were retrospectively analyzed.According to the intraoperative angiographic findings,the patients were divided into ICAS-LVO group and non-ICAS-LVO group.Univariate analysis and multivariate logistic regression analysis were used to analyze the patient's demographic characteristics,clinical history,imaging findings,and laboratory results,based on which a clinical prediction model for ICAS-LVO was established,and according to the relevant parameters a nomogram prediction model was plotted.Results A total of 110 patients with LVO in the posterior circulation who received endovascular treatment were included in the final analysis.In 51 patients(49.6％)the cause of vascular occlusion was the atherosclerotic stenosis of the intracranial arteries.Compared with non-ICAS-LVO group,in ICAS-LVO group the patients were younger,the incidence of atrial fibrillation was lower,and the level of plasma D-dimer was lower.Three factors,including atrial fibrillation,occlusion site and collateral circulation status,were finally screened out to establish the prediction model for ICAS-LVO.This model demonstrated acceptable calibration(Hosmer-Lemeshow test,P=0.562)and good discrimination ability(AUC=0.956;95％CI:0.906-0.986).Conclusion The clinical prediction model for ICAS-LVO,which is established on the three predictive factors(absence of atrial fibrillation,occlusion located at the V4 segment of the vertebral artery or at the proximal to mid segment of the basilar artery,and a favorable collateral circulation),carries high sensitivity and accuracy.This model can help neurointervention physicians to make early identification of ICAS-LVO and to promptly formulate vascular recanalization treatment strategies.

The complement component 4 binding protein alpha(C4BPA)gene is the alpha chain of complement binding protein 4.As a plasma protein involved in the complement and coagulation systems,it can influence immune responses and lipid metabolism.In order to study the polymor-phism of C4BPA gene and its correlation with milk quality traits in Chinese Holstein cows,genom-ic DNA was extracted from blood samples of 92 Chinese Holstein cows,and the target fragment of C4BPA gene was amplified by PCR,and the association analysis was performed by using direct se-quencing to obtain the SNP loci and milk quality traits.The results showed that among the four SNPs found at the third intron of the C4BPA gene,I3-11 G＞A was highly significantly correlated with milk protein and urea nitrogen(P＜0.05),I3-291 T＞G was significantly correlated with lac-tose(P＜0.05),I3-374 C＞T was highly significantly correlated with lactose and urea nitrogen(P＜0.05),and I3-375 T＞G was highly significantly correlated with lactose(P＜0.05),milk pro-tein and urea nitrogen.The chi-square test values for each point indicated that the population was in genetic equilibrium.Individuals of haplotype combination H1 H1 had the highest lactose content,and haplotype combination H1H2 can be used as the best haplotype combination in the molecular selection work of dairy cows.

ObjectiveThis study aims to explore risk factors for the development of major adverse cardiovascular and cerebrovascular events （MACCEs） in middle-aged and elderly patients with type 2 diabetes mellitus complicated with stable angina pectoris （T2DM-SAP） based on real-world clinical data in traditional Chinese medicine （TCM）， so as to develop a COX proportional risk prediction model and visualize the predicted results using a nomogram. MethodBased on the clinical scientific research information sharing system， the medical records of 586 T2DM-SAP patients （45-94 years old） were collected from January 2012 to December 2019， including age， gender， course of disease， major medical history， laboratory examination， tongue image， pulse image， TCM syndrome， and major treatment drugs. MACCE outcome indicators of patients were obtained by telephone follow-up and re-hospitalization records. The data was divided into a training set and a validation set according to 7∶3. In the training set， COX univariate analysis was used to determine the risk factors for MACCE in T2DM-SAP patients， and then variables were screened by forward-backward stepwise regression method， so as to establish a MACCE risk prediction model and construct a nomogram. The predictive efficacy of the model was reflected by the C-index， receiver operating characteristic （ROC） curve， calibration map， and clinical decision curve. ResultThe history of cerebrovascular disease ［Hazard ratio （HR）=1.983， 95% confidence interval （CI，1.314-2.993）］， low-density lipoprotein （LDL-C/mmol·L^-1）≥4.1［HR=2.683， 95%CI（1.461-4.925）］， dull red tongue ［HR=1.955， 95%CI（1.273-3.002）］， dull purple tongue ［HR=4.214， 95%CI（2.017-8.803）］， white thick coating ［HR=3.030， 95%CI（1.634-9.293）］， thin and weak pulse ［HR=2.233， 95%CI（1.283-3.888）］， and syndrome of wind-phlegm blocking collaterals ［HR=2.007， 95%CI（1.179-3.418）］ were found to be risk factors in middle-aged and elderly T2DM-SAP patients. Insulin ［HR=0.604， 95%CI（0.399-0.914）］， glycosidase inhibitor ［HR=0.627， 95%CI（0.409-0.962）］， and TCM treatment ［HR=0.328， 95%CI（0.214-0.503）］ were protective factors in middle-aged and elderly T2DM-SAP patients. The prediction model was constructed based on the above risk factors. The C-index of the model was 0.818 （95% CI 0.777 -0.859） in the training set and 0.814 （95% CI 0.773-0.855） in the validation set， and the change of C-index over time was plotted. The AUC of patients for 5， 10， 15 years in the training set was 0.71， 0.67， and 0.61. The AUC of patients for 5， 10， and 15 years in the validation set was 0.60， 0.68， and 0.63， respectively. The calibration map and clinical decision curves of 5， 10， 15 years were drawn in the training set and the validation set， respectively. The model was well calibrated and clinically effective. ConclusionThe history of cerebrovascular disease， LDL， dull red tongue， dull purple tongue， white thick coating， thin and weak pulse， and syndrome of wind-phlegm blocking collaterals are risk factors for MACCE in middle-aged and elderly T2DM-SAP patients， and insulin， glycosidase inhibitors， TCM treatment are protective factors for MACCE in middle-aged and elderly T2DM-SAP patients. A clinical prediction model is established accordingly. This model has good discrimination， calibration degree， and clinical effectiveness and provides a scientific basis for the prevention and treatment of MACCE in middle-aged and elderly T2DM-SAP patients.

Objective To evaluate the clinical value of optic nerve sheath diameter(ONSD)measured on thin-slice CT scan in the diagnosis and prognostic assessment of cerebral venous sinus thrombosis(CVST).Methods The clinical data of patients with CVST,who were admitted to the First Affiliated Hospital of Nanjing Medical University of China to receive treatment from January 1,2016 to December 31,2022,were retrospectively analyzed.The difference in ONSD was compared between CVST patients and normal population,the postoperative changes in ONSD was analyzed.Results A total of 49 patients with CVST(CVST group)and 49 normal persons having no brain disorders(control group)were enrolled in this study.In CVST group,the preoperative ONSD was(5.33±0.50)mm,which was significantly higher than(4.40±0.40)mm in control group(P＜0.001),the postoperative ONSD remarkably decreased to(4.98±0.59)mm(P＜0.01).The difference value between postoperative ONSD and preoperative ONSD in the patients receiving pure anticoagulation treatment was not statistically significant different from that in the patients receiving endovascular treatment[(-0.43±0.22)mm vs.(-0.40±0.42)mm,P=0.84].The preoperative ONSD in the patients having intracranial hemorrhage and in the patients having no intracranial hemorrhage was(5.26±0.51)mm and(5.41±0.49)mm respectively(P=0.31),and the difference value between postoperative ONSD and preoperative ONSD was(-0.39±0.40)mm and(-0.45±0.25)mm respectively(P=0.66).At the three-month follow-up visit,the difference in ONSD between the patients having a good prognosis(mRS score being 0-2 points)and the patients having a poor prognosis was not statistically significant(P＞0.05).Conclusion ONSD that is measured on plain head CT scan can be used as a response indicator of elevated intracranial pressure in CVST patients,which can be used to monitor the changes in intracranial pressure before and after treatment,but its value in assessing the curative efficacy of different therapeutic methods needs to be further explored.