Objective To compare the incidence of complications after removal of chest drainage tube in the early and late stages after sublobectomy for non-small cell lung cancer (NSCLC), and to analyze the factors affecting postoperative pleural drainage volume (PDV), so as to explore the countermeasures and achieve rapid postoperative rehabilitation. Methods The patients with NSCLC who underwent minimally invasive sublobectomy in our hospital from January to October 2021 were enrolled. According to the median time of extubation, the patients were divided into an early extubation group (time with tube≤3 days) and a late extubation group (time with tube>3 days). The patients were matched via propensity score matching with a ratio of 1:1 and a caliper value of 0.02. The incidence of complications and perioperative parameters after removal of the thoracic drainage tube were analyzed and compared between the two groups, and univariate and multiple linear regression analyses were performed. Results A total of 157 patients were enrolled, including 79 males and 78 females, with an average age of (58.22±11.06) years. There were 76 patients in the early extubation group, 81 patients in the late extubation group, and 56 patients were in each group after propensity score matching. Compared with late extubation group, there was no significant difference in the incidence of infection after extubation (10.7% vs. 16.1%, P=0.405) or pleural effusion after extubation (5.4% vs. 3.6%, P=0.647) in early extubation group, and there was no second operation in both groups. Univariate analysis showed that smoking history (P=0.001), postoperative serum albumin reduction value (P=0.017), surgical approach (P=0.014), lesion location (P=0.027), differentiation degree (P=0.041), TNM stage (P=0.043), number of dissected lymph nodes (P=0.016), and intraoperative blood loss (P=0.016) were infuencing factors for increased postoperative PDV. Multiple linear regression analysis showed that smoking history (P=0.002), postoperative serum albumin reduction value (P=0.041), and the number of dissected lymph nodes (P=0.023) were independent risk factors for increased postoperative PDV. Conclusion There is no significant difference in the incidence of complications after extubation between early and late extubations. Preoperative smoking history, excessive postoperative serum albumin decreases, and excessive number of dissected lymph nodes during the surgery are independent risk factors for increased postoperative PDV.

The objective of this study was to determine the frequency and resistance patterns of ESBL-producing E.coli in lambs with diarrhea in the Kashi area,Xinjiang.The findings may provide guidance for the prevention and control of clinical E.coli disease.We collected 385 samples of perianal feces from lambs with diarrhea in the Kashgar area.From these samples,we isolated 371 strains of E.coli.We then used the double-paper-sheet synergistic method to screen for ESBL-producing E.coli.Additionally,we conducted analyses to identify drug-resistance genes,analyze drug resistance,and study the phylo-typing of the screened strains.Of 371 E.coli strains,204 were identified as ESBL-producing strains.The prevalence rates of blaCTX-M,blaCTX-M-1G,blaCTX-M-9G,and bla TEM resistance genes was 67.65％,69.12％,30.39％,and 63.73％,respectively.All ESBL-pro-ducing strains were resistant to multiple drugs,with resistance rates ranging from 90.69％to 100％for eight specific drugs:ampicillin,cefotaxime,gentamicin,enrofloxacin,azithromy-cin,tetracycline,chloramphenicol,methotrexate,and amitrazine.The phylogenetic subgroups of the strains were distributed primarily in groups A and D.Among group A strains,41.11％exhibited resistance to ten drugs,whereas among group D strains,40％exhibited resistance to 11 drugs.ESBL-pro-ducing strains of Escherichia coli are the main pathogens cau-sing diarrhea in lambs in the Kashgar region;group A is the main group,and all groups are multi-drug resistant.

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 investigate the causal correlation between depression and stress urinary incontinence(SUI)using Mendelian randomization(MR)analysis.Methods:We searched the FinnGen Consortium database for genome-wide association studies(GWAS)on depression and obtained 23 424 case samples and 192 220 control samples,with the GWAS data on SUI provided by the UK Biobank,including 4 340 case samples and 458 670 control samples.We investigated the correlation between depression and SUI based on the depression data collected from the Psychiatric Genomics Consortium(PGC).We employed inverse-variance weighting as the main method for the MR study,and performed sensitivity analysis to verify the accuracy and stability of the findings.Results:Analysis of the data from the UK Biobank and FinnGen Consortium showed that depression was significantly correlated with an increased risk of SUI(P=0.005),but not SUI with the risk of depression(P=0.927).And analysis of the PGC data verified the correlation of depression with the increased risk of SUI(P=0.043).Conclusion:Depression is associated with an increased risk of SUI,while SUI does not increase the risk of depression.

Objective To design a novel oxygenator to solve the existing problems of extracorporeal membrane oxygenation(ECMO)machine in high transmembrane pressure difference,low efficiency of blood oxygen exchange and susceptibility to thrombosis.Methods The main body of the oxygenator vascular access flow field was gifted with a flat cylindrical shape.The topology of the vascular access was modeled in three dimensions,and the whole flow field was cut into a blood inlet section,an inlet buffer,a heat exchange zone,a blood oxygen exchange zone,an outlet buffer and a blood outlet section.The oxygenator was compared with Quadrox oxygenator by means of ANSYS FLUENT-based simulation and prototype experiments.Results Simulation calculations showed the oxygenator designed was comparable to the clinically used ones in general,and gained advantages in transmembrane pressure difference,blood oxygen exchange and flow uniformity.Experimental results indicated that the oxygenator behaved better than Quadrox oxygenator in transmembrane pressure difference and blood oxygen exchange.Conclusion The oxygenator has advantages in transmem-brane pressure difference,temperature change,blood oxygen ex-change and low probability of thrombosis.[Chinese Medical Equipment Journal,2024,45(3):23-28]

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.