Pelvic organ prolapse (POP), whose etiology is influenced by genetic and clinical risk factors, considerably impacts women's quality of life. However, the genetic underpinnings in non-European populations and comprehensive risk models integrating genetic and clinical factors remain underexplored. This study constructed the first polygenic risk score (PRS) for POP in the Chinese population by utilizing 20 disease-associated variants from the largest existing genome-wide association study. We analyzed a discovery cohort of 576 cases and 623 controls and a validation cohort of 264 cases and 200 controls. Results showed that the case group exhibited a significantly higher PRS than the control group. Moreover, the odds ratio of the top 10% risk group was 2.6 times higher than that of the bottom 10%. A high PRS was significantly correlated with POP occurrence in women older than 50 years old and in those with one or no childbirths. As far as we know, the integrated prediction model, which combined PRS and clinical risk factors, demonstrated better predictive accuracy than other existing PRS models. This combined risk assessment model serves as a robust tool for POP risk prediction and stratification, thereby offering insights into individualized preventive measures and treatment strategies in future clinical practice.
Humans ; Female ; Pelvic Organ Prolapse/epidemiology* ; Middle Aged ; Risk Assessment/methods* ; China/epidemiology* ; Multifactorial Inheritance ; Aged ; Risk Factors ; Genome-Wide Association Study ; Genetic Predisposition to Disease ; Case-Control Studies ; Adult ; Polymorphism, Single Nucleotide ; Genetic Risk Score ; East Asian People

This study identified six novel azaphilones, isochromophilones G-L (1-6), and three novel biosynthetically related congeners (7-9) from Diaporthe sp. SCSIO 41011. The structures and absolute configurations were elucidated through comprehensive spectroscopic analyses combined with experimental and calculated electronic circular dichroism (ECD) spectra. Significantly, three highly oxygenated azaphilones contain an acetyl group at the terminal chain (4) or linear conjugated polyenoid moieties (5 and 6), which occur infrequently in the azaphilone family. Additionally, several compounds demonstrated inhibition of lipopolysaccharide (LPS)-induced nuclear factor kappa-B (NF-κB) activation in RAW 264.7 macrophages at 20 μmol·L^-1. The novel compound (1) effectively inhibited receptor activator of NF-κB ligand (RANKL)-induced osteoclast differentiation without exhibiting cytotoxicity in bone marrow and RAW 264.7 macrophages, indicating its potential as a promising lead compound for osteolytic disease treatment. This research presents the first documented evidence of azaphilone derivatives as inhibitors of RANKL-induced osteoclastogenesis.
Animals ; Mice ; RANK Ligand/genetics* ; RAW 264.7 Cells ; Osteoclasts/metabolism* ; Benzopyrans/isolation & purification* ; Osteogenesis/drug effects* ; Macrophages/metabolism* ; Molecular Structure ; Pigments, Biological/isolation & purification* ; Ascomycota/chemistry* ; NF-kappa B/genetics* ; Cell Differentiation/drug effects*

As the foremost among the four examinations in traditional Chinese medicine （TCM）， inspection and related equipment research face challenges in landing and transformation due to variations in evidence quality， lack of standardization， insufficient algorithm transparency， and poor reliability and stability of decision-making. Against the backdrop of rapid development of emerging technologies such as big data， the internet of things， and artificial intelligence， coupled with macro policy support from the government， digital and intelligent generalized inspection in TCM has emerged， with the aim of utilizing digital technologies to overcome the limitations of naked-eye inspection and comprehensively perceive and analyze facial and bodily expressions. The research in this field intelligently correlates Zang-fu organ functions with health conditions and disease progression and establishes a technical system for digital and intelligent inspection， multi-dimensional and multimodal perception， fusion analysis， and decision-making. This system aims to enhance the accuracy of disease risk warning and diagnosis， bridging the gap between inspection equipment and assistance in clinical decision-making. From an evidence-based perspective， this paper systematically examines the research ideas of digital and intelligent inspection and the development of related equipment， deeply explores how to propose clinical practice-oriented key scientific issues， comprehensively acquire and co-apply multi-dimensional data， establish precise inspection models driven by digital intelligence， optimize standards to enhance equipment interoperability and reliability， construct post-effect evaluation mechanisms to promote improvement， and actively address potential risks such as the black box nature and information security in the application of intelligent technology. This paper not only demonstrates the tremendous potential of digital technologies in improving the accuracy and clinical application efficiency of inspection but also provides new perspectives and ideas for the modernization of inspection in TCM， paving the way for the application of inspection in the global medical and health field.

Objective:To study the position of computed tomography(CT)slice of marker points of radiotherapy,which was determined by control scan(CS)method,on the application effect of patients with head and neck tumors who received radiotherapy.Methods:Based on Control Scan(CS)method,a calculator program of mark-point slice position was made,which was an enterprise WeChat program that could be used in calculating position and CT position scan of patients with head and neck tumor(slice thickness was 3mm).A total of 60 patients with head and neck tumor were selected,and the patients who underwent CT positioning scan by using CS method were divided into observation group,and patients who underwent CT position scan by using conventional method were divided into control group,with 30 cases in each group.The number of cases with three metal marker points displayed at the same slice,and the number of slices containing the CT images of marker point between the located CT scan were compared.Results:The number of patients in the observation group and the control group who showed three markers at the same level at the same time were respectively 26 cases and 13 cases,and observation group increased by 13 cases(43.4%)than control group,and the difference was significant(x2=12.382,P<0.05).The number of CT images with only 1 slice of observation group and control group were respectively 4 cases and 0 cases,which increased by 4 cases(13.3%)than the control group,and the difference was significant(x2=2.411,P<0.05).Conclusions:The CT localization scan of radiotherapy,which uses CS to assist patients with head and neck tumor,can precisely calculate and obtain the primary position of target of CT localization scan.It can take the images of 3 mental marker points of patient who receives radiotherapy to occur at the same CT slice as soon as possible,which has better application effect.It can effectively improve the convenience and work effectiveness of radiotherapy.

Objective:To investigate the risk factors of pleural effusion after spinal separation surgery for patients with spinal metastatic tumors.Methods:A total of 427 patients with spinal metastatic tumors from January 2014 to January 2022 in the Second Affiliated Hospital of Zhejiang University School of Medicine were retrospectively analyzed. There were 252 males and 175 females, with an average age of 59±12 years (range, 15-87 years). All patients underwent separation surgery. Based on the chest CT within 1 month after surgery, the volume of pleural effusion was measured individually by reconstruction software. Pleural effusion was defined as small volume (0-500 ml), moderate volume (500-1 000 ml), and large volume (above 1 000 ml). Baseline data and perioperative clinical outcomes were compared between the groups, and indicators with statistically significant differences were included in a binary logistic regression analysis to determine the independent risk factors for the development of pleural effusion after isolation of spinal metastatic cancer. Receiver operating characteristic (ROC) curves were conducted to calculate the area under the curve (AUC) for each independent risk factor.Results:All patients successfully completed the operation. Among the 427 patients, there were 35 cases of large pleural effusion, 42 cases of moderate pleural effusion, and 350 cases of small pleural effusion. There were significant differences in tumor size (χ 2=9.485, P=0.013), intraoperative blood loss ( Z=-2.503, P=0.011), blood transfusion ( Z=-2.983, P=0.003), preoperative total protein ( Z=2.681, P=0.007), preoperative albumin ( Z=1.720, P= 0.085), postoperative hemoglobin ( t=2.950, P=0.008), postoperative total protein ( Z=4.192, P<0.001), and postoperative albumin ( t=2.268, P=0.032) in the large pleural effusion group versus the small and moderate pleural effusion group. Logistic regression analysis showed that decreased preoperative albumin ( OR=0.89, P=0.045) and metastases located in the thoracic spine ( OR=4.01, P=0.039) were independent risk factors for the occurrence of large pleural effusion after separation surgery. The ROC curve showed that the AUC and 95% CI for preoperative albumin, lesion location, and the combined model were 0.637 (0.54, 0.74), 0.421 (0.36, 0.48), and 0.883 (0.81, 0.92). The combined predictive model showed good predictive value. Conclusion:The volume of pleural effusion can be measured individually and quantitatively based on chest CT. Decreased preoperative albumin and metastases located in the thoracic spine are independent risk factors for the occurrence of large pleural effusion after separation surgery. The combined prediction of the two factors has better predictive efficacy.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.

This study included 116 professional postgraduate students majoring in clinical surgery who rotated in the Department of Breast and Thyroid Surgery of The First Affiliated Hospital of Army Medical University from 2019 to 2022. The students were provided with open online courses on precision medicine to build a strong theoretical foundation for evidence-based medicine; subsequently, precision medicine courses focusing on thyroid surgery were offered; and multidisciplinary team rounds for typical and difficult-to-diagnose cases were organized. Taking thyroid cancer as an example, questionnaire surveys and typical clinical case assessment were conducted to compare the scientific research and professional competencies of the students before and after evidence-based medicine education. The results showed that the students had significantly improved ability to use academic databases to acquire professional knowledge and solve problems, and showed increased enthusiasm in class, believing that the teaching content was easy to absorb and moderate in difficulty and the teaching effect was good.

Objective:To explore the risk factors of short-term prognosis of severe Budd-Chiari syndrome (BCS) patients,established and verified the nomogram prediction model for these BCS patients and evaluated its clinical application value.Methods:This study is a retrospective cohort study. The clinical data of 171 patients with severe BCS diagnosed were retrospectively analyzed in the Department of Hepatopancreatobiliary Surgery First Affiliated Hospital of Zhengzhou University from January 2018 to December 2023. There were 105 males and 66 females, aged (52.1±12.8) years (range: 18 to 79 years). The patients were divided into two groups based on whether they died within 28 days: the death group ( n=38) and the survival group ( n=133). The risk factors for short-term death of patients were analyzed,and independent risk factors were screened by univariate and multivariate analysis. Furthermore,these factors were used to establish the nomogram prediction model. The area under the curve(AUC),the Bootstrap Resampling,the Hosmer-Lemeshow test and the Decision Curve Analysis(DCA) were used to verify the model′s differentiation,internal verification,calibration degree and clinical effectiveness,respectively. Results:Univariate and multivariate Logistics regression analysis showed that the history of hepatic encephalopathy,white blood cell,glomerular filtration rate and prothrombin time were independent risk factors ( P<0.05). The above factors were used to successfully establish the prediction model with 0.908 of AUC and 0.895 of the internal verification of AUC,indicating that the predictive model was valuable. The 0.663 P-values in the Hosmer-Lemeshow test indicated the high calibration degree of the model. The clinical effectiveness of the model was proved by the 18% clinical benefit population using the DCA curve with the 17% probability threshold. Conclusions:The independent risk factors are the history of hepatic encephalopathy,white blood cell,glomerular filtration rate and prothrombin time. An adequate basis was acquired by establishing a nomogram prediction model of the short-term prognosis of severe BCS,which was helpful for early clinical screening and identification of high-risk patients with severe BCS who could die in the short term and timely providing timely intervention measures for improving the prognosis.