OBJECTIVE: To investigate chronic hepatitis C virus (HCV) infection's effect on gestational liver function, pregnancy and delivery complications, and neonatal development. METHODS: A total of 157 HCV antibody-positive (anti-HCV[+]) and HCV RNA(+) patients (Group C) and 121 anti-HCV(+) and HCV RNA(-) patients (Group B) were included as study participants, while 142 anti-HCV(-) and HCV RNA(-) patients (Group A) were the control group. Data on biochemical indices during pregnancy, pregnancy complications, delivery-related information, and neonatal complications were also collected. RESULTS: Elevated alanine aminotransferase (ALT) rates in Group C during early, middle, and late pregnancy were 59.87%, 43.95%, and 42.04%, respectively-significantly higher than Groups B (26.45%, 15.70%, 10.74%) and A (23.94%, 19.01%, 6.34%) ( P < 0.05). Median ALT levels in Group C were significantly higher than in Groups A and B at all pregnancy stages ( P < 0.05). No significant differences were found in neonatal malformation rates across groups ( P > 0.05). However, neonatal jaundice incidence was significantly greater in Group C (75.16%) compared to Groups A (42.25%) and B (57.02%) ( χ ² = 33.552, P < 0.001). HCV RNA positivity during pregnancy was an independent risk factor for neonatal jaundice ( OR = 2.111, 95% CI 1.242-3.588, P = 0.006). CONCLUSIONS Chronic HCV infection can affect the liver function of pregnant women, but does not increase the pregnancy or delivery complication risks. HCV RNA(+) is an independent risk factor for neonatal jaundice.
Humans ; Female ; Pregnancy ; Adult ; Pregnancy Complications, Infectious/epidemiology* ; Retrospective Studies ; Pregnancy Outcome ; Infant, Newborn ; Viremia/virology* ; Hepatitis C ; Hepacivirus/physiology* ; Hepatitis C, Chronic/virology* ; Young Adult ; Alanine Transaminase/blood*

Objective:To examine the effect of cumulative hemoglobin A 1c (HbA 1c) levels and insulin dosage on insulin resistance (IR)-related metabolic disturbances in newly diagnosed patients with type 1 diabetes (T1D). Methods:This retrospective cohort study included T1D patients admitted to the Second Xiangya Hospital of Central South University from November 2015 to March 2023. Clinical data collected comprised age, sex, disease duration, insulin dosage, body mass index, waist circumference, blood pressure, HbA 1c levels, islet autoantibodies, and fasting blood lipid profiles. IR-related metabolic disturbances assessed were overweight, obesity, central obesity, hypertension, and dyslipidemia. Cox regression and cluster analyses were applied to assess the influence of cumulative HbA 1c and insulin dosage on these metabolic disturbances. Results:A total of 235 patients were included, with 97 males (41.3%) and 138 females (58.7%). The median age was 19.8 (13.3, 31.1) years, and the median follow-up duration was 30.8 (20.8, 45.6) months. During follow-up, 41.6% (72/173) of patients developed IR-related metabolic disturbances. Multivariate Cox regression analysis revealed that a cumulative HbA 1c ≥60 mmol/mol was an independent risk factor for any IR-related metabolic disturbance [ HR (95% CI): 1.739 (1.067-2.835) ] and for triglyceride abnormalities [ HR (95% CI): 3.277 (1.176-9.127)]. Additionally, a cumulative insulin dosage ≥0.5 U·kg -1·d -1 was identified as an independent risk factor for overweight, obesity, or central obesity [ HR (95% CI): 2.374 (1.059-5.323)]. Cluster analysis further identified that patients with higher levels of cumulative HbA 1c and insulin dosage, particularly those with adolescent-onset diabetes, had the highest likelihood of developing hypertension ( HR=2.460, 95% CI 1.008-6.005), overweight/obesity/central obesity ( HR=2.707, 95% CI 1.062-6.900), triglyceride abnormalities ( HR=5.495, 95% CI 1.842-16.391), high-density lipoprotein cholesterol abnormalities ( HR=11.054, 95% CI 4.107-29.751), and any IR-related metabolic disturbance ( HR=5.833, 95% CI 2.602-13.077). Conclusions:Elevated cumulative HbA 1c and insulin dosage levels in T1D patients are associated with an increased risk of developing IR-related metabolic disturbances. These findings underscore the urgent need for novel therapeutic strategies tailored to this population.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To examine the effect of cumulative hemoglobin A 1c (HbA 1c) levels and insulin dosage on insulin resistance (IR)-related metabolic disturbances in newly diagnosed patients with type 1 diabetes (T1D). Methods:This retrospective cohort study included T1D patients admitted to the Second Xiangya Hospital of Central South University from November 2015 to March 2023. Clinical data collected comprised age, sex, disease duration, insulin dosage, body mass index, waist circumference, blood pressure, HbA 1c levels, islet autoantibodies, and fasting blood lipid profiles. IR-related metabolic disturbances assessed were overweight, obesity, central obesity, hypertension, and dyslipidemia. Cox regression and cluster analyses were applied to assess the influence of cumulative HbA 1c and insulin dosage on these metabolic disturbances. Results:A total of 235 patients were included, with 97 males (41.3%) and 138 females (58.7%). The median age was 19.8 (13.3, 31.1) years, and the median follow-up duration was 30.8 (20.8, 45.6) months. During follow-up, 41.6% (72/173) of patients developed IR-related metabolic disturbances. Multivariate Cox regression analysis revealed that a cumulative HbA 1c ≥60 mmol/mol was an independent risk factor for any IR-related metabolic disturbance [ HR (95% CI): 1.739 (1.067-2.835) ] and for triglyceride abnormalities [ HR (95% CI): 3.277 (1.176-9.127)]. Additionally, a cumulative insulin dosage ≥0.5 U·kg -1·d -1 was identified as an independent risk factor for overweight, obesity, or central obesity [ HR (95% CI): 2.374 (1.059-5.323)]. Cluster analysis further identified that patients with higher levels of cumulative HbA 1c and insulin dosage, particularly those with adolescent-onset diabetes, had the highest likelihood of developing hypertension ( HR=2.460, 95% CI 1.008-6.005), overweight/obesity/central obesity ( HR=2.707, 95% CI 1.062-6.900), triglyceride abnormalities ( HR=5.495, 95% CI 1.842-16.391), high-density lipoprotein cholesterol abnormalities ( HR=11.054, 95% CI 4.107-29.751), and any IR-related metabolic disturbance ( HR=5.833, 95% CI 2.602-13.077). Conclusions:Elevated cumulative HbA 1c and insulin dosage levels in T1D patients are associated with an increased risk of developing IR-related metabolic disturbances. These findings underscore the urgent need for novel therapeutic strategies tailored to this population.

Objective:To explore the levels of regulatory T cells(Tregs)and cytokines IL-35,TGF-β and IL-10 in peripheral blood of hemophilia A(HA)patients with F Ⅷ inhibitor and their clinical significance.Methods:43 HA patients admitted to the Hematology Department of the Affiliated Hospital of North China University of Science and Technology from October 2019 to December 2020 were selected,including 6 cases with F Ⅷ inhibitor and 37 cases without FⅧ inhibitor.In addition,20 healthy males who underwent physical examinations were selected as healthy controls.Flow cytometry was used to detect the levels of CD4+CD25+CD127-Tregs in peripheral blood of the HA patients and healthy controls,and ELISA assay was used to detect the expression levels of IL-35,TGF-β and IL-10 in serum,and their differences between different groups were compared.Results:Compared with the healthy control group,the level of Tregs in HA patients was decreased,and the level of Tregs in the FⅧ inhibitor positive group was the lowest,the difference was statistically significant(P＜0.05).There was no significant difference in the expression level of Tregs in HA patients of different severity levels.The serum IL-35,TGF-β,and IL-10 levels in both FⅧ inhibitor negative and positive groups were significantly lower than those in healthy control group,and those in FⅧ inhibitor positive group were significantly lower than those in FⅧ inhibitor negative group(all P＜0.05).Conclusion:The decrease of Tregs,IL-35,TGF-β,and IL-10 levels in HA patients may be related to the formation of FⅧ inhibitors.

Objective To assess the predictive value of computed tomography pulmonary angiography(CTPA)cardiovascular parame-ters for chronic thromboembolic pulmonary hypertension(CTEPH)under the 2022 European Society of Cardiology/European Respiratory Society(ESC/ERS)guidelines,and to compare with the 2021 Chinese guidelines.Methods A total of 201 suspected CTEPH patients were retrospectively selected.All patients underwent right heart catheterization(RHC)and CTPA evaluation.According to the Euro-pean guidelines,they were divided into three groups:mean pulmonary artery pressure(mPAP)≤20 mmHg control group(63 cases)(1 mmHg=0.133 kPa),mPAP＞20 mmHg CTEPH group(138 cases),and mPAP≥25 mmHg CTEPH group(123 cases).Inter-group comparison of CTPA cardiovascular parameters was performed,and receiver operating characteristic(ROC)curve analysis was performed for each parameter.Results Under the 2022 European guidelines,the diagnostic efficacy of the diameter of the main pulmonary artery trunk(MPAd)was the highest[area under the curne(AUC)was 0.933].The binary logistic regression analysis revealed that the MP Ad,right ventricular free wall thickness(RVWT),and interventricular septal angle(IVSA)were inde-pendent risk factors for the diagnosis of CTEPH(P＜0.05).Under both the Chinese and European guidelines,the MPAd,the transverse diameter and area of the right atrium,the transverse diameter and area of the bi-ventricle,the RVWT,and the IVSA showed significant statistical differences in CTEPH and control groups(P＜0.05).Conclusion Under both the Chinese and European guidelines,the MP Ad measured by CTPA has the highest diagnostic efficacy for CTEPH,while the IVSA has the strongest correlation with clinical prognostic indicators.The right atrium structure also has evaluation value.

Background::Acute pulmonary embolism (APE) is a fatal cardiovascular disease, yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs. A simple, objective technique will help clinicians make a quick and precise diagnosis. In population studies, machine learning (ML) plays a critical role in characterizing cardiovascular risks, predicting outcomes, and identifying biomarkers. This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.Methods::This is a single-center retrospective study. Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets. A total of 8 ML models, including random forest (RF), Na?ve Bayes, decision tree, K-nearest neighbors, logistic regression, multi-layer perceptron, support vector machine, and gradient boosting decision tree were developed based on the training set to diagnose APE. Thereafter, the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies, including the Wells score, revised Geneva score, and Years algorithm. Eventually, the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic (ROC) analysis.Results::The ML models were constructed using eight clinical features, including D-dimer, cardiac troponin T (cTNT), arterial oxygen saturation, heart rate, chest pain, lower limb pain, hemoptysis, and chronic heart failure. Among eight ML models, the RF model achieved the best performance with the highest area under the curve (AUC) (AUC = 0.774). Compared to the current clinical assessment strategies, the RF model outperformed the Wells score ( P = 0.030) and was not inferior to any other clinical probability assessment strategy. The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726. Conclusions::Based on RF algorithm, a novel prediction model was finally constructed for APE diagnosis. When compared to the current clinical assessment strategies, the RF model achieved better diagnostic efficacy and accuracy. Therefore, the ML algorithm can be a useful tool in assisting with the diagnosis of APE.

Objective To evaluate the feasibility and accuracy of virtual preoperative planning and 3D-printed templates for pre-contoured plates for the treatment of posterior wall fractures of the acetabulum.Methods A retrospective analysis of 29 patients with posterior acetabular wall fractures treated between August 2017 and March 2021 were divided into 2 groups based on whether to use preoperative virtual planning and 3D printed template.In 3D-printing group,there were 14 patients,includ-ing 10 males and 4 females;aged from 21 to 53 years old;CT-based virtual surgical planning was done using Mimics and 3-Matic software and 3D-printed templates for pre-contoured plates were adopted.In conventional group,there were 15 patients,including 10 males and 5 females;aged from 19 to 55 years old;conventional method of intra-operative contouring to adapt the plate to the fracture region was adopted.Blood loss,surgical time,radiographic quality of reduction,and hip function were compared between groups.Results The difference in operation time and intraoperative blood loss was significant(P＜0.05).Twenty-three patients were followed up from 12 to 30 months,and the fractures in both groups healed with a healing time of 3 to 6 months.At the last follow-up,the Merle d'Aubign-Postel score of the 3D printed group was lower than that of the conven-tional group(P＜0.05),with no significant differences in walking ability,hip mobility and total score(P＞0.05).In 3D printing group,6 cases were excellent,5 cases were good,3 cases were fair;in conventional group,5 cases were excellent,5 cases were good,4 cases were fair,1 case was worse;no significant difference between two groups(P＞0.05).Conclusion Virtual preopera-tive planning and 3D-printed templates for pre-contoured plates can reduce operative time and the blood loss of surgery,im-prove the quality of reduction.This method is efficient,accurate and reliable to treat acetabular posterior wall fractures.

OBJECTIVE：To provide reference for the identification and proces sing end-point determination of raw Morus alba and its processed products （honey-processed M. alba ）. METHODS ：UPLC method was adopted. The determination was performed on Waters BEH Shield RP C 18 column with mobile phase consisted of acetonitrile- 0.1％ phosphoric acid solution （gradient elution ） at the flow rate of 0.30 mL/min. The column temperature was set at 30 ℃. The program wavelengths were set at 280 nm（0-4 min） and 320 nm（4-35 min）. Similarity Evaluation System for Chromatogram Fingerprint of TCM （2012 edition）was used to establish UPLC fingerprint and carry out similarity evaluation of 13 batches of M. alba and honey-processed M. alba . The chromatographic peaks were identified with reference substance fingerprint. The colorimetric value （L，a，b） of 13 batches of M. alba and honey-processed M. alba powder were determined ，and average total colorimetric value （E）was calculated. OPLS-DA and cluster analysis were adopted to analyze the differences in fingerprints and colorimetric values of M. alba before and after processing. At the same time ，the dynamic change rule of fingerprint and colorimetric value of honey-processed M. alba at different processing time points were analyzed to determine the processing end-point. RESULTS ：There were obvious differences in fingerprints before and after processing ，and the similarity of 13 batches of M. alba and honey-processed M. alba were all higher than 0.9. Totally 21 common peaks were calibrated for M. alba ，and 23 common peaks for honey-processed M. alba ；peak 1 and peak 2 were newly produced compounds of honey-processed M. alba . Peak 2，peak 7，peak 14 and peak 19 were identified as 5-hydroxymethylfurfural， mulberry glucoside A ，oxidized resveratrol ，mulberry flavonoids G. Results of OPLS-DA showed that the peak area-sample quantity ratio of peak 1，peak 2，peak 18，peak 20 and the chromaticity values （L，a，b）were the most important factors affecting the difference of raw and processed products of M. alba . When the E ranged 75.84-80.88 as the processing end-point of honey-processed M. alba ，the processing time was determined as 22-34 min. CONCLUSIONS ： The established UPLC fingerprint and colorimetric value determination method can be used to identify the raw and processed products of M. alba as well as determine the processing end-point of honey-processed M. alba .