Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective:To compare the efficacy and safety of transjugular intrahepatic portosystemic shunt (TIPS) and endoscopic treatment for liver cirrhosis with esophageal gastric variceal bleeding (EGVB) and portal vein thrombosis (PVT).Methods:A total of 183 liver cirrhosis patients with EGVB and PVT in Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School and the First Affiliated Hospital of Wenzhou Medical University were selected from January 2017 to December 2019, and 66 patients were assigned to the endoscopic group (received endoscopi treatment) and 117 the TIPS group (received TIPS treatment). Propensity score matching was performed according to the 1∶1 ratio, yielding 58 patients in each group for analysis, with a median follow-up time of 36 months in both groups. Postoperative survival, postoperative rebleeding rate, and postoperative hepatic encephalopathy incidence were assessed using Kaplan-Meier survival curves.Results:During the follow-up, the rebleeding rate in the endoscopic group was significantly higher [39.7% (23/58) VS 17.2% (10/58), P=0.005], and the TIPS group had a higher incidence of postoperative hepatic encephalopathy [29.3% (17/58) VS 13.8% (8/58), P=0.048], and no significant difference in survival rate was observed between the two groups [74.1% (43/58) VS 75.9% (44/58), P=0.769]. In the subgroup analysis of mild degree of PVT, there was no significant difference in survival [88.0% (22/25) VS 72.0% (18/25), P=0.164], rebleeding rate [28.0% (7/25) VS 12.0% (3/25), P=0.164], or incidence of hepatic encephalopathy [8.0% (2/25) VS 20.0% (5/25), P=0.202] between the endoscopic group and the TIPS group. In the subgroup analysis of severe degree of PVT, the rebleeding rate in the endoscopic group was significantly higher than that in the TIPS group [48.5% (16/33) VS 21.2% (7/33), P=0.010], while there was no significant difference in the incidence of hepatic encephalopathy [18.2% (6/33) VS 36.4% (12/33), P=0.133], or the survival rate [63.6% (21/33) VS 78.8% (26/33), P=0.154]. Conclusion:For liver cirrhosis patients with mild PVT and EGVB, combining endoscopic therapy with anticoagulation may be superior to TIPS. Conversely, in patients with severe PVT, TIPS may be a more suitable option due to a significantly reduced rebleeding risk without a notable increase in hepatic encephalopathy incidence.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective To understand the change in distribution and antimicrobial resistance of bacteria isolated from blood specimens of Hunan Province,and provide for the initial diagnosis and treatment of clinical bloodstream infection(BSI).Methods Data reported from member units of Hunan Province Antimicrobial Resistance Survei-llance System from 2012 to 2021 were collected.Bacterial antimicrobial resistance surveillance method was imple-mented according to the technical scheme of China Antimicrobial Resistance Surveillance System(CARSS).Bacteria from blood specimens and bacterial antimicrobial susceptibility testing results were analyzed by WHONET 5.6 soft-ware and SPSS 27.0 software.Results A total of 207 054 bacterial strains were isolated from blood specimens from member units in Hunan Province Antimicrobial Resistance Surveillance System from 2012 to 2021,including 107 135(51.7％)Gram-positive bacteria and 99 919(48.3％)Gram-negative bacteria.There was no change in the top 6 pathogenic bacteria from 2012 to 2021,with Escherichia coli(n=51 537,24.9％)ranking first,followed by Staphylococcus epidermidis(n=29 115,14.1％),Staphylococcus aureus(n=17 402,8.4％),Klebsiella pneu-moniae(17 325,8.4％),Pseudomonas aeruginosa(n=4 010,1.9％)and Acinetobacter baumannii(n=3 598,1.7％).The detection rate of methicillin-resistant Staphylococcus aureus(MRSA)decreased from 30.3％in 2015 to 20.7％in 2021,while the detection rate of methicillin-resistant coagulase-negative Staphylococcus(MRCNS)showed an upward trend year by year(57.9％-66.8％).No Staphylococcus was found to be resistant to vancomy-cin,linezolid,and teicoplanin.Among Gram-negative bacteria,constituent ratios of Escherichia coli and Klebsiella pneumoniae were 43.9％-53.9％and 14.2％-19.5％,respectively,both showing an upward trend(both P＜0.001).Constituent ratios of Pseudomonas aeruginosa and Acinetobacter baumannii were 3.6％-5.1％and 3.0％-4.5％,respectively,both showing a downward trend year by year(both P＜0.001).From 2012 to 2021,resistance rates of Escherichia coli to imipenem and ertapenem were 1.0％-2.0％and 0.6％-1.1％,respectively;presenting a downward trend(P＜0.001).The resistant rates of Klebsiella pneumoniae to meropenem and ertapenem were 7.4％-13.7％and 4.8％-6.4％,respectively,presenting a downward trend(both P＜0.001).The resistance rates of Pseudomonas aeruginosa and Acinetobacter baumannii to carbapenem antibiotics were 7.1％-15.6％and 34.7％-45.7％,respectively.The trend of resistance to carbapenem antibiotics was relatively stable,but has de-creased compared with 2012-2016.The resistance rates of Escherichia coli to the third-generation cephalosporins from 2012 to 2021 were 41.0％-65.4％,showing a downward trend year by year.Conclusion The constituent ra-tio of Gram-negative bacillus from blood specimens in Hunan Province has been increasing year by year,while the detection rate of carbapenem-resistant Gram-negative bacillus remained relatively stable in the past 5 years,and the detection rate of coagulase-negative Staphylococcus has shown a downward trend.

Objective To investigate changes in the distribution and antimicrobial resistance of bacteria isolated from cerebrospinal fluid(CSF)specimens in Hunan Province,and provide reference for correct clinical diagnosis and rational antimicrobial use.Methods Data reported by member units of Hunan Province Antimicrobial Resistance Surveillance System from 2012 to 2021 were collected according to China Antimicrobial Resistance Surveillance Sys-tem(CARSS)technical scheme.Data of bacteria isolated from CSF specimens and antimicrobial susceptibility tes-ting results were analyzed with WHONET 5.6 and SPSS 20.0 software.Results A total of 11 837 bacterial strains were isolated from CSF specimens from member units of Hunan Province Antimicrobial Resistance Surveillance Sys-tem from 2012 to 2021.The top 5 strains were coagulase-negative Staphylococcus(n=6 397,54.0％),Acineto-bacter baumannii(n=764,6.5％),Staphylococcus aureus(n=606,5.1％),Enterococcus faecium(n=465,3.9％),and Escherichia coli(n=447,3.8％).The detection rates of methicillin-resistant coagulase-negative Staphyloco-ccus(MRCNS)and methicillin-resistant Staphylococcus aureus(MRSA)were 58.9％-66.3％and 34.4％-62.1％,respectively.No Staphylococcus spp.were found to be resistant to vancomycin,linezolid,and teicoplanin.The de-tection rate of Enterococcus faecium was higher than that of Enterococcus faecalis,and the resistance rates of En-terococcus f aecium to penicillin,ampicillin,high concentration streptomycin and levofloxacin were all higher than those of Enterococcus faecalis(all P=0.001).Resistance rate of Streptococcus pneumoniae to penicillin was 85.0％,at a high level.Resistance rate of Escherichia coli to ceftriaxone was＞60％,while resistance rates to enzyme inhibitors and carbapenem antibiotics were low.Resistance rate of Klebsiella pneumoniae to ceftriaxone was＞60％,to en-zyme inhibitors piperacillin/tazobactam and cefoperazone/sulbactam was＞30％,to carbapenem imipenem and me-ropenem was about 30％.Resistance rates of Acinetobacter baumannii to most tested antimicrobial agents were＞60％,to imipenem and meropenem were 59.0％-79.4％,to polymyxin B was low.Conclusion Among the bac-teria isolated from CSF specimens,coagulase-negative Staphylococcus accounts for the largest proportion,and the overall resistance of pathogenic bacteria is relatively serious.Bacterial antimicrobial resistance surveillance is very important for the effective treatment of central nerve system infection.

Objective To investigate the distribution and antimicrobial susceptibility of clinically isolated bacteria from intensive care units(ICUs)in hospitals of Hunan Province Antimicrobial Resistance Surveillance System from 2012 to 2021.Methods According to China Antimicrobial Resistance Surveillance System,data of clinically isolated bacterial strains and antimicrobial susceptibility testing results of bacteria from ICUs reported by all member units of Hunan Province Antimicrobial Resistance Surveillance System were analyzed with WHONET 2022 software.Results From 2012 to 2021,the total number of bacteria isolated from ICUs of member units of the Hunan Province Antimi-crobial Resistance Surveillance System was 5 777-22 369,with Gram-negative bacteria accounting for 76.1％-78.0％annually.Staphylococcus aureus ranked first among isolated Gram-positive bacteria each year.The top 5 bacteria among Gram-negative bacteria were Acinetobacter baumannii,Klebsiella pneumoniae,Escherichia coli,Pseudo-monas aeruginosa,and Stenotrophomonas maltophilia.Detection rate of methicillin-resistant Staphylococcus aureus showed a downward trend year by year.No Staphylococcus spp.were found to be resistant to vancomycin,teico-planin and linezolid.Detection rates of vancomycin-resistant Enterococcus faecalis and vancomycin-resistant Entero-coccus faecium were 0.6-1.1％and 0.6％-2.2％,respectively.Resistance rates of Escherichia coli and Kleb-siella pneumoniae to imipenem were 3.1％-5.7％and 7.7％-20.9％,respectively.Resistance rates of Pseudo-monasaeruginosa and Acinetobacter baumannii to imipenem were 24.6％-40.1％and 76.1％-80.9％,respective-ly.Detection rates of carbapenem-resistant Pseudomonas aeruginosa declined year by year.Acinetobacter baumannii maintained high susceptibility to polymyxin B,with resistance rate＜10％.Conclusion Antimicrobial resistance of bacteria from ICUs is serious.Carbapenem-resistant Enterobacteriales has an upward trend after 2019.It is nece-ssary to strengthen the surveillance of bacterial resistance and carry out multidisciplinary collaboration.

Objective To understand the antimicrobial resistance of clinically isolated Enterococcus spp.in Hunan Province.Methods Surveillance data of Enterococcus spp.resistance from member units of Hunan Provincial Anti-microbial Resistance Surveillance System from 2012 to 2021 were collected.Data were cleaned according to a unified method,and WHONET 5.6 software was adopted for statistical analysis.Results From 2012 to 2021,a total of 110 652 non-repetitive Enterococcus spp.strains were included in the analysis,mainly Enterococcus faecalis and Enterococcus faecium,accounting for 46.9％(n=37 774)and 45.9％(n=36 968),respectively,followed by En-terococcus avium(2.5％,n=1 982),Enterococcus gallinarum(1.8％,n=1 428),and Enterococcus casseliflavus(1.5％,n=1 185).The main specimen sources of Enterococcus spp.was urine(51.8％,n=57 350),followed by secretions(9.6％,n=10 660)and bile(8.5％,n=9 377).From 2012 to 2021,the resistance rates of Enteroco-ccus faecalis to ampicillin,teicoplanin,and vancomycin were 5.5％-12.0％,1.3％-2.0％,and 0.6％-1.4％,respectively.The resistance rates of Enterococcus faecium to ampicillin,teicoplanin,and vancomycin were 69.2％-85.0％,1.5％-2.8％,and 0.7％-2.5％,respectively.Except for linezolid and minocycline,the resistance rates of Enterococcus faecium to tested antimicrobial agents were all higher than those of Enterococcus faecalis.The re-sistance rates of Enterococcus faecalis and Enterococcus faecium to vancomycin decreased from 1.4％and 2.1％in 2012 to 0.6％and 0.7％in 2021,respectively,presenting a decreased trend.Conclusion Clinically isolated En-terococcus spp.maintain high antimicrobial susceptibility to vancomycin and teicoplanin.Resistance rates of Entero-coccus faecalis and Enterococcus faecium to vancomycin present decreased trends.

Objective To analyze the distribution and changing trend of antimicrobial resistance of bacteria isolated from bile from Hunan Province Antimicrobial Resistance Surveillance System.Methods Data of pathogens isolated from bile from Hunan Province Antimicrobial Resistance Surveillance System from 2012 to 2021 were collected.The constituent of bacteria and antimicrobial susceptibility testing results were analyzed by WHONET 5.6 software.Changes in antimicrobial resistance was analyze by trend chi-square test.Results The major pathogenic bacteria isolated from bile were Gram-negative bacteria,accounting for 70.84％.The top three isolated Gram-negative pathogens were Escherichia coli(30.14％),Klebsiella pneumoniae(12.15％),and Pseudomonas aeruginosa(5.18％),and the top two Gram-positive bacteria were Enterococcus faecium(10.34％)and Enterococcus faecalis(9.52％).The resistance rates of Klebsiella pneumoniae and Escherichia coli to imipenem were highest in 2012-2013,being 15.7％and 14.9％,respectively,presenting an downward trend(P＜0.05);resistance rates to piperacillin/tazobactam and cefoperazone/sulbactam were＜24％,presenting an upward trend year by year(P＜0.05);the sus-ceptibility rate to amikacin was＞94％,to levofloxacin and ciprofloxacin was 15.5％-65.2％.The highest resis-tance rate of Pseudomonas aeruginosa to imipenem(32.0％)was higher than that of meropenem(22.9％),resis-tance rates to piperacillin/tazobactam and cefoperazone/sulbactam were＜19％.The highest resistance rates of Acinetobacter baumannii to imipenem and meropenem were 59.4％and 62.6％,respectively,resistance rate to cefo-perazone/sulbactam was＜48％,presenting an upward trend(P＜0.05);the highest resistance rate to ciprofloxa-cin(60.8％)was higher than levofloxacin(48.7％);resistance rate to tigecycline was＜8％.The resistance rates of Enterococcus faecium to penicillin and ampicillin were both higher than those of Enterococcus faecalis,presenting an upward trend(P＜0.05).Resistance rate of Enterococcus faecium to vancomycin was lower than that of Entero-coccus faecalis.The resistance rates of Enterococcus faecium to vancomycin and linezolid were 0.5％-4.5％and 0.5％-3.4％,respectively;resistance rates of Enterococcus faecalis to vancomycin and linezolid were 0.2％-1.7％and 0.5％-3.5％,respectively(both P＜0.05),all presenting a downward trend(all P＜0.05).Conclusion Pathogenic bacteria isolated from bile are mainly related to the intestinal flora.The resistance rates of Enterococcus faecalis and Enterococcus faecium to vancomycin and linezolid as well as resistance rate of Enterobacterales to car-bapenem antibiotics all present a downward trend.

Objective To understand the antimicrobial resistance of Escherichia spp.from member units of Hu-nan Province Antimicrobial Resistance Surveillance System from 2012 to 2021.Methods According to the technical scheme of China Antimicrobial Resistance Surveillance System(CARSS),data about Escherichia spp.and the anti-microbial susceptibility testing results reported from member units of Hunan Province Antimicrobial Resistance Sur-veillance System were analyzed by WHONET 5.6 software.Results From 2012 to 2021,a total of 476 351 clini-cally isolated Escherichia spp.were collected,475 520 of which were Escherichia coli,accounting for 99.8％;92.6％were isolated from inpatients;39.3％were isolated from urine specimens.Over the past 10 years,the proportion of Escherichia spp.in total detected pathogens remained relatively stable,ranging 20％-23％,the lowest rate was 18.7％in 2012,and the highest rate was 22.9％in 2015.In the past 10 years,the resistance rates of Escherichia spp.to ampicillin,ceftriaxone,cefotaxime and ampicillin/sulbactam were＞80％,＞47％,＞45％,and＞39％,respectively;resistance rates to piperacillin/tazobactam,cefoperazone/sulbactam,and nitrofurantoin were all＜8％,to tigecycline,amikacin,imipenem,and meropenem(except in 2012)were all＜5％.Resistance of Escherichia spp.to 22 commonly clinically used antimicrobial agents fluctuated,but overall trend decreased year by year.The resistance rates of Escherichia spp.from patients in the intensive care unit(ICU),non-ICU patients,outpatients,and emergency patients to 22 clinically commonly used antimicrobial agents were compared among different depart-ments,and the differences were statistically significant(all P＜0.05).The resistance rates of Escherichia spp.iso-lated from ICU and non-ICU patients were compared,and except for tigecycline,the resistance rates to the other 21 antimicrobial agents were statistically different(all P＜0.05).The resistance rates of Escherichia spp.isolated from patients to commonly clinically used antimicrobial agents were statistically different among patients of different age groups(all P＜0.05).Conclusion Escherichia spp.isolated from patients in different years,departments,specimens,and ages have different resistance to commonly used antimicrobial agents.It is necessary to continue to strengthen the surveillance on bacterial resistance,so as to guide the rational choice of antimicrobial agents.