Objective To analyze the clinical characteristics associated with prenatal diagnosis of FBN 1 gene mutations in a family.This study explores the correlation between gene mutations and their corresponding clini-cal phenotypes,emphasizing the significance of prenatal diagnosis in providing a foundation for subsequent follow-up and intervention.Methods Genomic DNA was extracted from the amniotic fluid of the fetus and the peripheral blood of the parents for trio-whole exome sequencing.The candidate variant identified was subsequently validated using Sanger sequencing.Results The pedigree comprised four generations and nine family members,with four individuals exhibiting slender limbs and toes.Among these,three showed congenital lens dislocation or subluxation.No abnormalities in the cardiovascular system were observed.Genetic testing of symptomatic individuals revealed a heterozygous mutation(c.6158G>T)in the FBN 1 gene.Conclusions The FBN 1 c.6158G>T(p.C2053F)muta-tion was identified as the pathogenic variant responsible for the condition in this family,exhibiting autosomal domi-nant inheritance.To our knowledge,this is the first reported case of the FBN 1 c.6158G>T(p.C2053F)mutation in China.Prenatal diagnosis can facilitate early confirmation of the condition and provide a foundation for subsequent in-terventions and follow-up care.

Objective:To investigate the risk factors and their diagnostic efficacy of perioperative lower limb deep vein thrombosis (DVT) in polytrauma patients with predominant severe limb trauma.Methods:A retrospective cohort study was conducted to analyze the clinical data of 155 polytrauma patients with predominant severe trauma who were admitted to Wuxi Ninth People′s Hospital from January 2021 to December 2024, including 64 males and 91 females, aged 13-95 years [(52.1±16.9)years]. Abbreviated injury scale (AIS) was 5-15 points [(7.4±2.1)points] and injury severity score (ISS) was 17-59 points [(21.3±6.5)points]. Based on the occurrence of DVT in the perioperative period, the patients were divided into preoperative DVT group with 17 patients (11.0%) and non-preoperative DVT group with 138 patients (89.0%) as well as postoperative DVT group with 24 patients (15.5%) and non-postoperative DVT group with 131 patients (84.5%). Basic clinical data were collected, including gender, age, body mass index (BMI), underlying diseases (hypertension, diabetes mellitus), hemoglobin level (Hb), platelet count (PLT), D-dimer, ISS, trauma site [cranial and brain trauma, thoracic and abdominal trauma, upper limb trauma, lower limb trauma (femoral fracture, patellar fracture, tibial or fibular fracture, foot fracture, vascular injury), and pelvic fracture], preoperative waiting time for surgery, surgical site (pelvis and lower limb, other areas), surgical protocols (pelvic and lower limb internal fixation, external fixation of lower limb, lower limb amputation), operation duration less or more than 2 hours, amount of intraoperative blood loss, intraoperative blood transfusion requirement, venous thromboembolism (VTE) prophylaxis (pharmacological and mechanical modalities) and length of hospital stay. Univariate analysis and multivariate binary Logistic regression analysis were conducted to investigate the correlation between the aforementioned indicators and incidence of perioperative lower limb DVT in polytrauma patients with predominant severe limb trauma and determine the independent risk factors. Receiver operating characteristic (ROC) curve and area under the curve (AUC) of the relevant risk factors were analyzed to evaluate and compare the diagnostic efficacy of the risk factors for perioperative lower limb DVT in polytrauma patients with predominant severe limb trauma.Results:Univariate analysis results showed that age, history of hypertension, D-dimer, thoracic and abdominal trauma, pelvic fracture, preoperative waiting time for surgery, and length of hospital stay were significantly correlated with preoperative of DVT of the lower limbs in the patients ( P<0.05). The results of multivariate binary Logistic regression analysis showed that age ( OR=1.05, 95% CI 1.00, 1.10, P<0.05), pelvic fracture ( OR=5.03, 95% CI 1.09, 23.20, P<0.05), preoperative waiting time for surgery ( OR=1.10, 95% CI 1.00, 1.22, P<0.05) and length of hospital stay ( OR=0.89,95% CI 0.81,0.98, P<0.05) were highly correlated with preoperative DVT of the lower limbs in the patients ( P<0.05). Univariate analysis results showed that age, D-dimer, ISS, foot fracture, and length of hospital stay were significantly correlated with postoperative DVT of the lower limbs in the patients ( P<0.05). The results of multivariate binary Logistic regression analysis showed that age ( OR=1.05, 95% CI 1.01, 1.08, P<0.01), D-dimer ( OR=1.05, 95% CI 1.00, 1.10, P<0.05), ISS ( OR=1.09, 95% CI 1.01, 1.17, P<0.05), and foot fracture ( OR=3.51 , 95% CI 1.25 , 9.87 , P<0.05) were significantly correlated with postoperative DVT of the lower limbs in the patients ( P<0.05). The results of the ROC curve analysis indicated that preoperative waiting time for surgery (AUC=0.83, 95% CI 0.75, 0.91) had the highest diagnostic efficacy for preoperative DVT of the lower limbs in the patients, with the diagnostic efficacies of pelvic fracture (AUC=0.75, 95% CI 0.65, 0.85) and age (AUC=0.70, 95% CI 0.59, 0.82) decreasing successively. For postoperative DVT of the lower limbs in the patients, D-dimer (AUC=0.71, 95% CI 0.61, 0.81) exhibited the highest diagnostic efficacy, followed by age (AUC=0.70, 95% CI 0.59, 0.81), ISS (AUC=0.64, 95% CI 0.51, 0.76) and foot fracture (AUC=0.62, 95% CI 0.49, 0.74), with diagnostic efficacy decreased successively. Conclusions:For polytrauma patients with predominant severe limb trauma, age, pelvic fracture and preoperative waiting time for surgery are independent risk factors for preoperative DVT, while age, D-dimer, ISS and foot fracture are independent risk factors for postoperative DVT. Additionally, preoperative waiting time for surgery has the best diagnostic efficacy for preoperative DVT, followed by pelvic fracture and age. D-dimer has the best diagnostic efficacy for postoperative DVT, followed by age, ISS and foot fracture.

Objective To compare the long-term survival outcomes of patients with T_1-2N₀M₀ duodenal neuroendocrine tumor (DNET) after endoscopic resection (ER) or surgical resection (SR). Methods Patients diagnosed with T_1-2N₀M₀ DNET between January 1, 2004, and December 31, 2015, were extracted from the SEER database. Kaplan-Meier survival curve and log-rank test were used to compare overall survival (OS) rate and cancer-specific survival (CSS) rate between patients undergoing ER or SR. Propensity score matching (PSM) was used to reduce grouping differences, and multivariate Cox regression was used to analyze factors affecting OS and CSS before and after PSM. Results A total of 656 patients were included, with 457 in ER group and 199 in SR group. Before PSM, there was no significant difference in the 5-year OS rate between the ER and SR groups (88.9% vs 89.6%), but there was a significant difference in the 5-year CSS rate (99.3% vs 96.9%, P＝0.017). Before PSM, multivariate Cox regression analysis showed advanced age was an independent risk factor for decreased OS (P＜0.001). After PSM, there was no significant difference between the ER group (n＝187) and SR group (n＝187) in 5-year OS rate (90.2% vs 88.9%) or CSS rate (98.9% vs 96.7%). After PSM, multivariate Cox regression also showed advanced age was an independent risk factor for decreased OS, while resection method was not an independent factor for OS or CSS. Conclusions There is no significant difference in OS or CSS after endoscopic treatment and surgical treatments for patients with T_1-2N₀M₀ DNET, and advanced age is an independent factor for OS.

The major components of Olaflur raw material were characterized using ultra performance liquid chromatography-quadrupole time-of-flight-mass spectrometry(UPLC-Q-TOF/MS).The results revealed that cetyl amine fluoride(C16-AmF),octadecene amine fluoride(C18:1-AmF),and octadecyl amine fluoride(Olaflur)were the main components.The contents of C16-AmF,C18:1-AmF,and Olaflur in oral care products were determined via ultra performance liquid chromatography-charged aerosol detector coupled with solid-phase extraction(SPE-UPLC-CAD).The oral care sample was dispersed evenly with a 50%ethanol aqueous solution,and then vortexed with ethanol.The supernatant was collected by centrifugation,concentrated to near dryness,and redissolved with ultrapure water.The re-dissolved sample was loaded onto a Poly-Sery HLB Pro SPE column for purification and elution.The acetonitrile eluate was collected and concentrated to 1.0 mL.Finally,a prepared test solution was separated on a Thermo Acclaim Surfactant Plus chromatographic column(2.1 mm×150 mm,3 μm).Acetonitrile and 100 mmol/L acetic acid-ammonium acetate aqueous solution(pH=4.8)were used as the mobile phases for gradient elution.The flow rate was 0.3 mL/min and cloumn temperature was maintained at 40℃.The sample was detected using a charged aerosol detector,and quantified using an external standard method.The experimental results indicated that the three organic fluorinated amines showed good linear relationship in their respective concentration ranges.The correlation coefficients(r)were greater than 0.99.The limit of detection(LOD)and the limit of quantification(LOQ)of C16-AmF were 2.0 and 8.0 μg/mL,respectively.The LOD and LOQ of C18:1-AmF were 2.0 and 8.0 μg/mL,respectively.The LOD and LOQ of Olaflur were 3.0 μg/mL and 10.0 μg/mL,respectively.The spiked recoveries of the three organic fluorinated amines were 84.3%-104.2%,with relative standard deviations(RSDs)of 4.93%-5.82%.The 28 batches of commercial oral care samples were detected by this method and the results indicated that three organic fluorinated amines were detected in 18 samples and the total content were 22.2-11477.8 μg/g.This method had high sensitivity and good reproducibility.It was suitable for verifying the authenticity of the claims of oral care products promoted with Olaflur as the main efficacy ingredient and selling point,and provided a valuable reference for establishing and improving the standard analytical method for Olaflur.

OBJECTIVE: To explore and quantify the association of hot night exposure during the sperm development period (0-90 lag days) with semen quality. METHODS: A total of 6,640 male sperm donors from 6 human sperm banks in China during 2014-2020 were recruited in this multicenter study. Two indices (i.e., hot night excess [HNE] and hot night duration [HND]) were used to estimate the heat intensity and duration during nighttime. Linear mixed models were used to examine the association between hot nights and semen quality parameters. RESULTS: The exposure-response relationship revealed that HNE and HND during 0-90 days before semen collection had a significantly inverse association with sperm motility. Specifically, a 1 °C increase in HNE was associated with decreased sperm progressive motility of 0.0090 (95% confidence interval [ CI]: -0.0147, -0.0033) and decreased total motility of 0.0094 (95% CI: -0.0160, -0.0029). HND was significantly associated with reduced sperm progressive motility and total motility of 0.0021 (95% CI: -0.0040, -0.0003) and 0.0023 (95% CI: -0.0043, -0.0002), respectively. Consistent results were observed at different temperature thresholds on hot nights. CONCLUSION Our findings highlight the need to mitigate nocturnal heat exposure during spermatogenesis to maintain optimal semen quality.
Humans ; Male ; Semen Analysis ; Adult ; Sperm Motility ; Hot Temperature/adverse effects* ; China ; Middle Aged ; Spermatozoa/physiology* ; Young Adult

Objective To analyze the clinical characteristics associated with prenatal diagnosis of FBN 1 gene mutations in a family.This study explores the correlation between gene mutations and their corresponding clini-cal phenotypes,emphasizing the significance of prenatal diagnosis in providing a foundation for subsequent follow-up and intervention.Methods Genomic DNA was extracted from the amniotic fluid of the fetus and the peripheral blood of the parents for trio-whole exome sequencing.The candidate variant identified was subsequently validated using Sanger sequencing.Results The pedigree comprised four generations and nine family members,with four individuals exhibiting slender limbs and toes.Among these,three showed congenital lens dislocation or subluxation.No abnormalities in the cardiovascular system were observed.Genetic testing of symptomatic individuals revealed a heterozygous mutation(c.6158G>T)in the FBN 1 gene.Conclusions The FBN 1 c.6158G>T(p.C2053F)muta-tion was identified as the pathogenic variant responsible for the condition in this family,exhibiting autosomal domi-nant inheritance.To our knowledge,this is the first reported case of the FBN 1 c.6158G>T(p.C2053F)mutation in China.Prenatal diagnosis can facilitate early confirmation of the condition and provide a foundation for subsequent in-terventions and follow-up care.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

This study aimed to explore the therapeutic mechanisms of Colquhounia Root Tablets(CRT) in treating diabetic kidney disease(DKD) by integrating biomolecular network mining with animal model verification. By analyzing clinical transcriptomics data, an interaction network was constructed between candidate targets of CRT and DKD-related genes. Based on the topological eigenvalues of network nodes, 101 core network targets of CRT against DKD were identified. These targets were found to be closely related to multiple pathways associated with type 2 diabetes, immune response, and metabolic reprogramming. Given that immune-inflammatory imbalance driven by metabolic reprogramming is one of the key pathogenic mechanisms of DKD, and that many core network targets of CRT are involved in this pathological process, receptor for advanced glycation end products(RAGE)-reactive oxygen species(ROS)-phosphatidylinositol 3-kinase(PI3K)-protein kinase B(AKT)-nuclear factor-κB(NF-κB)-NOD-like receptor family pyrin domain containing 3(NLRP3) signaling axis was selected as a candidate target for in-depth research. Further, a rat model of DKD induced by a high-sugar, high-fat diet and streptozotocin was established to evaluate the pharmacological effects of CRT and verify the expression of related targets. The experimental results showed that CRT could effectively correct metabolic disturbances in DKD, restore immune-inflammatory balance, and improve renal function and its pathological changes by inhibiting the activation of the RAGE-ROS-PI3K-AKT-NF-κB-NLRP3 signaling axis. In conclusion, this study reveals that CRT alleviates the progression of DKD through dual regulation of metabolic reprogramming and immune-inflammatory responses, providing strong experimental evidence for its clinical application in DKD.
Animals ; Diabetic Nephropathies/metabolism* ; Receptor for Advanced Glycation End Products/genetics* ; NF-kappa B/genetics* ; Signal Transduction/drug effects* ; Rats ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Proto-Oncogene Proteins c-akt/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Phosphatidylinositol 3-Kinases/genetics* ; Reactive Oxygen Species/metabolism* ; Humans ; Plant Roots/chemistry* ; Rats, Sprague-Dawley ; Tablets/administration & dosage*

The zebrafish model has attracted much attention due to its strong reproductive ability, short research cycle, and ease of maintenance. It has always been an important vertebrate model system, often used to carry out human disease research. Its motor behavior features have the advantages of being simpler, more intuitive, and quantifiable. In recent years, it has received widespread attention in the study of traditional Chinese medicine(TCM)for the treatment of sleep disorders, neurodegenerative diseases, fatigue, epilepsy, and other diseases. This paper reviews the characteristics of zebrafish motor behavior and its applications in the pharmacodynamic verification and mechanism research of TCM extracts, active ingredients, and TCM compounds, as well as in active ingredient screening and safety evaluation. The paper also analyzes its advantages and disadvantages, with the aim of improving the breadth and depth of zebrafish and its motor behavior applications in the field of TCM research.
Zebrafish/physiology* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/therapeutic use* ; Disease Models, Animal ; Drug Evaluation, Preclinical/methods* ; Animals ; Sleep Wake Disorders/physiopathology* ; Epilepsy/physiopathology* ; Neurodegenerative Diseases/physiopathology* ; Fatigue/physiopathology* ; Behavior, Animal/physiology* ; Motor Activity/physiology*

BACKGROUND: Early control of low-density lipoprotein cholesterol (LDL-C) is crucial for reducing the progress of cardiovascular disease. However, its additional role to the risk of primary osteoporosis in men with coronary heart disease was inconclusive. Our study aims to determine the association of LDL-C and its trajectories for osteoporosis risk in the middle-aged and aged men of China. METHODS: The retrospective cohort study of 1546 men aged 69.74 ± 11.30 years conducted in Beijing, China from 2015 to 2022. And the incidence of primary osteoporosis was annually recorded. LDL-C trajectories were further identified by latent class growth model using repeated measurements of LDL-C. The association of baseline LDL-C for osteoporosis was estimated using hazard ratio (HR) with 95% CI in Cox proportional hazard model, while mean level and trajectories of LDL-C for osteoporosis were evaluated using odds ratio (OR) with 95% CI in logistic regression model. RESULTS: During the median 6.2-year follow-up period, 70 men developed primary osteoporosis. The higher level of baseline LDL-C (HR = 1.539, 95% CI: 1.012-2.342) and mean LDL-C (OR = 2.190, 95% CI: 1.443-3.324) were associated with higher risk of osteoporosis in men with coronary heart disease after adjusted for covariates. Compared with those in the LDL-C trajectory of low-stable decrease, participants with medium-fluctuant trajectory, whose longitudinal LDL-C started with a medium LDL-C level and appeared an increase and then decrease, were negatively associated with osteoporosis risk (OR = 2.451, 95% CI: 1.152-5.216). And participants with initially high LDL-C level and then a rapid decrease demonstrated a tendency towards reduced risk (OR = 0.718, 95% CI: 0.212-2.437). CONCLUSIONS Elevated LDL-C level and its long-term fluctuation may increase the risk of primary osteoporosis in men. Early controlling a stable level of LDL-C is also essential for bone health.