OBJECTIVE To establish a high-risk medication list and preventive and therapeutic measures for drug-induced hypofibrinogenemia， and to provide a reference for the prevention and treatment of this condition. METHODS By integrating domestic and international case reports， retrospective case-control studies， and spontaneous adverse drug reaction reporting databases， 19 domestically marketed high-risk drugs for drug-induced hypofibrinogenemia were identified. Based on the clinical characteristics and mechanisms of these drugs， relevant risk factors were systematically reviewed， and existing treatment options were summarized， leading to the preliminary development of recommended preventive and therapeutic measures. A two-round Delphi consultation was conducted to evaluate， revise， and ultimately reach consensus on the preliminary findings， using a mean importance score of ≥3.5 points for indicators and a coefficient of variation ＜0.3 as screening criteria. RESULTS The coefficient of expert authority for both rounds of expert consultation was 0.904. In the first round， the Kendall coordination coefficients （Kendall’s W ） for the high-risk medication list and the proposed preventive and therapeutic measures were 0.390 and 0.223 （ P ＜0.05）， respectively. In the second round， the Kendall’s W were 0.227 and 0.200 （ P ＜0.05）， respectively. After two rounds of expert consultation and discussion， 11 high-risk drugs for drug-induced hypofibrinogenemia， represented by hemocoagulase and certain anti-infective agents， were ultimately identified， along with 5 preventive and therapeutic measures spanning the entire process of “pre-medication assessment， intra-medication monitoring， and bleeding event management”. CONCLUSIONS This study has established a scientific and reliable high-risk medication list， and corresponding preventive and therapeutic measures for drug-induced hypofibrinogenemia， providing a theoretical basis and practical support for the early identification， stratified management， and precise intervention of this condition.

Objective To explore the relationship between axillary artery and axillary vein by combining computed tomography angiography computed tomography angiography(CTA)and computed tomography venography(CTV),and to provide imaging data for establishing an effective blood circulation pathway for vascular injury in clinical transaxillary surgery.Methods The image data of 30 patients who underwent left upper limb and axillary CTA and CTV at the same time were collected.After three-dimensional reconstruction of the images by GE AW4.6 workstation,the course,branch type and variation of axillary artery,the course of axillary vein,the reflux of subordinate branches and the relationship between axillary artery and axillary vein were observed.The length of the whole segment,the length of segment and the inner diameter of the starting point of each segment were measured and statistically analyzed.Results According to the number of branches from the main trunk,the axillary artery was divided into 7 types.According to the variation of the number of blood vessels,the axillary vein was divided into 5 types;The unknown vein branches converge into 32 branches,of which the first segment accounted for 37.5％,the second segment accounted for 46.9％,and the third segment accounted for 15.6％.According to the absence of arterial branches,the relationship between axillary artery and axillary vein was divided into 2 types.Conclusion There are many types of branches of axillary arteries and branches of axillary veins,and the variation types are complex.The changes of branches of axillary arteries affect the distribution of branches of axillary veins.Combined with CTA and CTV images,the relationship between axillary vessels can be reflected clearly and intuitively,which can provide imaging reference for the establishment of new ways of blood supply and reflux in clinical axillary treatment.

Objective : To explore the expression profile of core 1 β1,3-galactosyltransferase 1(C1GALT1) in glioblastoma(GBM) and to elucidate its impact on the initiation and progression of GBM. Methods : The expression levels and prognostic significance of C1GALT1 in GBM were analyzed using the GEPIA and CGGA databases. Two representative glioblastoma cells(U251 and LN18) were selected to construct C1GALT1-knockdown cell lines and performed in vitro experiments. The Cell Counting Kit-8(CCK-8) and Transwell assays were employed to evaluate the impact of C1GALT1 on proliferation, migration and invasion of GBM cells. Transcriptome data were analyzed to identify potential signaling pathways. Senescence β-Galactosidase Staining Kit was used to detect β-galactosidase activity. Results : nalysis of GEPIA and CGGA databases revealed that C1GALT1 was significantly upregulated in GBM tissues compared to adjacent non-cancerous tissues (P < 0. 05) , and its high expression was associated with poor prognosis of patients (P < 0. 000 1) . The CCK-8 experiment demonstrated a significant reduction in prolifera- tion rate following C1GALT1 knockdown (P < 0. 05) . Transwell assay showed that cell migration and invasion de- creased after C1GALT1 was knocked down ( P < 0. 001) . Transcriptome sequencing and senescence β-galactosi- dase staining showed that C1GALT1 was involved in the cellular senescence signaling pathway , and the activity of β-galactosidase associated with cellular senescence significantly increased after C1GALT1 was knocked down(P < 0. 05) . Conclusion C1GALT1 is overexpressed in GBM tissues and may promote the proliferation , migration and invasion of GBM cells by inhibiting cellular senescence .

Objective:To evaluate the effect of superficial temporal artery to middle cerebral artery (STA-MCA) bypass on cognitive function, cerebral perfusion, and integrity of white matter tracts by comparing cognitive function scores, fractional anisotropy (FA), time to maximum (T max), and cerebral blood flow (CBF) at different time points before and after STA-MCA bypass, and analyze the relations of cognitive function with cerebral perfusion and white matter tract integrity so as to provide evidences for treatment of moyamoya disease (MMD) patients with mild cognitive impairment. Methods:A retrospective analysis was performed; 30 MMD patients with mild cognitive impairment received STA-MCA bypass at Department of Neurosurgery, Lianyungang Hospital Affiliated to Xuzhou Medical University (Lianyungang First People's Hospital) from January 2023 to August 2024 were enrolled. Before and 1, 3, and 6 months after STA-MCA bypass, all patients accepted Montreal cognitive assessment (MoCA), CT perfusion imaging, and diffusion tensor imaging (DTI). Differences in MoCA score, CBF, T max, and FA at different time points before and after surgery were compared. Spearman rank correlation was used to analyze the correlation of MoCA score with cerebral perfusion parameters and FA. Results:(1) In these MMD patients with mild cognitive impairment, CBF 3 and 6 months after STA-MCA bypass was significantly increased compared with that before STA-MCA bypass, and CBF 6 months after STA-MCA bypass was significantly higher than that 1 and 3 months after STA-MCA bypass ( P<0.05); T max 1, 3 and 6 months after STA-MCA bypass was significantly shortened compared with that before STA-MCA bypass, and T max 6 months after STA-MCA bypass was significantly shortened than that 1 and 3 months after STA-MCA bypass ( P<0.05); FA 6 months after STA-MCA bypass was significantly increased compared with that before, and 1 and 3 months after STA-MCA bypass ( P<0.05); MoCA score 6 months after STA-MCA bypass was significantly increased compared with that before and 1 month after STA-MCA bypass ( P<0.05). (2) In MMD patients with mild cognitive impairment, the preoperative MoCA score was positively correlated with preoperative CBF and FA ( r s=0.428, P=0.018; r s=0.438, P=0.015) and negatively correlated with preoperative T max ( r s=-0.380, P=0.039); 6 months after STA-MCA bypass, the MoCA score was positively correlated with CBF and FA ( r s=0.365, P=0.047; r s=0.400, P=0.028) and negatively correlated with T max ( r s=-0.371, P=0.043). Conclusion:STA-MCA bypass can improve cerebral perfusion, white matter fiber tract repair and cognitive function in MMD patients with mild cognitive impairment, and improvement of cognitive function is related to cerebral perfusion and white matter fiber tract repair.

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

Objective:To evaluate the effect of superficial temporal artery to middle cerebral artery (STA-MCA) bypass on cognitive function, cerebral perfusion, and integrity of white matter tracts by comparing cognitive function scores, fractional anisotropy (FA), time to maximum (T max), and cerebral blood flow (CBF) at different time points before and after STA-MCA bypass, and analyze the relations of cognitive function with cerebral perfusion and white matter tract integrity so as to provide evidences for treatment of moyamoya disease (MMD) patients with mild cognitive impairment. Methods:A retrospective analysis was performed; 30 MMD patients with mild cognitive impairment received STA-MCA bypass at Department of Neurosurgery, Lianyungang Hospital Affiliated to Xuzhou Medical University (Lianyungang First People's Hospital) from January 2023 to August 2024 were enrolled. Before and 1, 3, and 6 months after STA-MCA bypass, all patients accepted Montreal cognitive assessment (MoCA), CT perfusion imaging, and diffusion tensor imaging (DTI). Differences in MoCA score, CBF, T max, and FA at different time points before and after surgery were compared. Spearman rank correlation was used to analyze the correlation of MoCA score with cerebral perfusion parameters and FA. Results:(1) In these MMD patients with mild cognitive impairment, CBF 3 and 6 months after STA-MCA bypass was significantly increased compared with that before STA-MCA bypass, and CBF 6 months after STA-MCA bypass was significantly higher than that 1 and 3 months after STA-MCA bypass ( P<0.05); T max 1, 3 and 6 months after STA-MCA bypass was significantly shortened compared with that before STA-MCA bypass, and T max 6 months after STA-MCA bypass was significantly shortened than that 1 and 3 months after STA-MCA bypass ( P<0.05); FA 6 months after STA-MCA bypass was significantly increased compared with that before, and 1 and 3 months after STA-MCA bypass ( P<0.05); MoCA score 6 months after STA-MCA bypass was significantly increased compared with that before and 1 month after STA-MCA bypass ( P<0.05). (2) In MMD patients with mild cognitive impairment, the preoperative MoCA score was positively correlated with preoperative CBF and FA ( r s=0.428, P=0.018; r s=0.438, P=0.015) and negatively correlated with preoperative T max ( r s=-0.380, P=0.039); 6 months after STA-MCA bypass, the MoCA score was positively correlated with CBF and FA ( r s=0.365, P=0.047; r s=0.400, P=0.028) and negatively correlated with T max ( r s=-0.371, P=0.043). Conclusion:STA-MCA bypass can improve cerebral perfusion, white matter fiber tract repair and cognitive function in MMD patients with mild cognitive impairment, and improvement of cognitive function is related to cerebral perfusion and white matter fiber tract repair.

OBJECTIVE To explore clinical characteristics and risk factors of drug-induced hypofibrinogenemia,providing a reference for rational clinical drug use.METHODS Retrospective case analyses literature on drug-induced hypofibrinogenemia were collected from domestic and international databases from their inception to December 31,2024.The patients'gender,age,fibrinogen(FIB)levels before and after treatment,drug types,the incidence of drug-induced hypofibrinogenemia,time of occurrence,bleeding rates,clinical manifestations,risk factors,and protective factors were all analyzed.RESULTS A total of 40 retrospective case analysis studies were included,involving 17 313 patients.Patient age ranged from 0.83 to 78.40 years,with males accounting for 16.90%-81.00%.The involved drugs comprised 5 categories and 13 specific agents,including tigecycline,snake venom hemocoagulase,tocilizumab,and alteplase,etc.The incidence of drug-induced hypofibrinogenemia ranged from 0 to 100%,occurring between 2 hours and 9 months after drug administration,and FIB levels rebounded in most patients after drug discontinuation.The bleeding rate varied from 0%to 91.30%,including epistaxis,airway bleeding,gastrointestinal bleeding,and cerebral hemorrhage.Risk factors included high drug dosage,prolonged treatment duration,abdominal infection,advanced age,and low baseline FIB levels.Protective factors were only mentioned in studies on tigecycline,including skin and soft tissue infections and high baseline FIB levels.CONCLUSIONS Drug-induced hypofibrinogenemia is commonly associated with tigecycline,hemocoagulase,and tocilizumab.Its clinical features vary depending on the drug,and risk factors include high drug dosage,prolonged treatment,low baseline FIB levels,and advanced age.For high-risk medications,individualized medication management and monitoring of FIB levels are recommended.

Objective:To explore the correlation between time in range (TIR) after short-term treatment and glycated hemoglobin after 3 months (HbA lc-3m) in patients with newly-diagnosed type 2 diabetes mellitus (T2DM). Methods:In this cross-sectional study, a total of 94 patients with newly-diagnosed T2DM who received treatment in the Department of Endocrinology of Inner Mongolia Autonomous Region People′s Hospital were enrolled from January 2018 to September 2022. The patients were followed-up for 3 months and had complete medical record. TIR was divided into three groups according to different target ranges of blood glucose (TIR1: TIR with blood glucose between 3.9 and 10.0 mmol/L, TIR2: TIR with blood glucose between 3.9 and 7.8 mmol/L, TIR3: TIR with fasting, premeal or bedtime blood glucose <6.1 mmol/L and 2 h postprandial blood glucose <8.0 mmol/L). The patients were divided into two groups based on whether their HbA 1c-3m level was less than 6.5%, and the baseline data and variations in TIR for distinct target glucose levels were compared between the two groups. Spearman′s correlation analysis and binary logistic regression analysis were used to analyze the relationship between baseline indicators, TIR after short-term treatment and HbA 1c-3m. Receiver operating characteristic curve (ROC) was drawn to evaluate the predictive ability of different TIR after short-term therapy for HbA 1c-3m. Results:There were statistically significant differences in TIR1 [81.0 (67.5, 94.6)% vs 71.4 (51.7, 85.7)%], TIR2 [57.7 (29.7, 70.8)% vs 40.9 (22.4, 52.3)%] and TIR3 [23.8 (10.2, 39.5)% vs 13.0 (4.8, 25.0)%] between patients with a HbA 1c-3m<6.5% and patients with a HbA 1c-3m≥6.5% (all P<0.05). Spearman correlation analysis showed that among all the patients with newly-diagnosed T2DM, TIR1, TIR2 and TIR3 were all negatively correlated with HbA 1c-3m [6.4 (6.1, 6.9)%] ( r=-0.322, -0.348, -0.303, respectively, all P<0.01). Logistic regression analysis showed that after adjusting for the confounding factors, TIR1 ( OR=1.021, 95% CI: 1.002-1.041; P=0.034), TIR2 ( OR=1.024, 95% CI: 1.006-1.043; P=0.011), TIR3 ( OR=1.037, 95% CI: 1.010-1.065; P=0.008) were all independently related to HbA 1c-3m. When HbA lc-3m<6.5% was taken as the target value, the area under the ROC curve: TIR1 was 0.639 (95% CI: 0.528-0.751), TIR2 was 0.671 (95% CI: 0.560-0.782), TIR3 was 0.659 (95% CI: 0.549-0.770), respectively. When HbA lc-3m<7.0% was taken as the target value, the area under the ROC curve: TIR1 was 0. 730 (95% CI: 0.619-0.841), TIR2 was 0.744 (95% CI: 0.642-0.846), TIR3 was 0.701 (95% CI: 0.588-0.814). There was no significant difference in the area among the three statistics ( P>0.05). Conclusions:For newly-diagnosed T2DM patients, TIR after short-term treatment is negatively correlated with HbA 1c after 3 months and has good predictive value for it.

Physiologically based pharmacokinetic (PBPK) models have been widely used to predict various stages of drug absorption, distribution, metabolism and excretion. Models based on machine learning (ML) and artificial intelligence (AI) can provide better ideas for the construction of PBPK models, which can accelerate the prediction speed and improve the prediction quality of PBPK. ML and AL can complement the advantages of PBPK model to accelerate the progress of drug research and development. This review introduces the application of machine learning and artificial intelligence in pharmacokinetics, summarizes the research progress of physiological pharmacokinetic models based on machine learning and artificial intelligence, and analyzes the limitations of machine learning and artificial intelligence applications and their application prospects and prospects.

OBJECTIVE: To explore the genotyping characteristics of human fecal Escherichia coli( E. coli) and the relationships between antibiotic resistance genes (ARGs) and multidrug resistance (MDR) of E. coli in Miyun District, Beijing, an area with high incidence of infectious diarrheal cases but no related data. METHODS: Over a period of 3 years, 94 E. coli strains were isolated from fecal samples collected from Miyun District Hospital, a surveillance hospital of the National Pathogen Identification Network. The antibiotic susceptibility of the isolates was determined by the broth microdilution method. ARGs, multilocus sequence typing (MLST), and polymorphism trees were analyzed using whole-genome sequencing data (WGS). RESULTS: This study revealed that 68.09% of the isolates had MDR, prevalent and distributed in different clades, with a relatively high rate and low pathogenicity. There was no difference in MDR between the diarrheal (49/70) and healthy groups (15/24). CONCLUSION We developed a random forest (RF) prediction model of TEM.1 + baeR + mphA + mphB + QnrS1 + AAC.3-IId to identify MDR status, highlighting its potential for early resistance identification. The causes of MDR are likely mobile units transmitting the ARGs. In the future, we will continue to strengthen the monitoring of ARGs and MDR, and increase the number of strains to further verify the accuracy of the MDR markers.
Humans ; Escherichia coli/genetics* ; Escherichia coli Infections/epidemiology* ; Multilocus Sequence Typing ; Genotype ; Beijing ; Drug Resistance, Multiple, Bacterial/genetics* ; Anti-Bacterial Agents/pharmacology* ; Diarrhea ; Microbial Sensitivity Tests