ObjectiveTo investigate the triglyceride-glucose (TyG) index and the level of serum homocysteine (Hcy) in association with the incidence of stroke in type 2 diabetes mellitus (T2DM) patients. MethodsBased on the chronic disease risk factor surveillance cohort in Pudong New Area, Shanghai, excluding those with stroke in baseline survey, T2DM patients who joined the cohort from January 2016 to October 2020 were selected as the research subjects. During the follow-up period, a total of 318 new-onset ischemic stroke patients were selected as the case group, and a total of 318 individuals matched by gender without stroke were selected as the control group. The Cox proportional hazards regression model was used to adjust for confounding factors and explore the serum TyG index and the Hcy biochemical indicator in association with the risk of stroke. ResultsThe Cox proportional hazards regression results showed that after adjusting for confounding factors, the risk of stroke in T2DM patients with 10 μmol·L⁻¹-¹ and Hcy>15 μmol·L⁻¹ was 1.532 times (95%CI: 1.017‒2.309 times, P=0.041) and 1.738 times (95%CI: 1.119‒2.699 times, P=0.014) higher respectively, compared to T2DM patients with Hcy≤10 μmol·L⁻¹. T2DM patients with TyG>9.074 had 1.396 times higher risk of stroke (95%CI: 1.091‒1.787, P=0.008) compared to those with TyG≤9.074. The study found that urea and α1-antitrypsin (α1-AT) were independent risk factors for the incidence of stroke in T2DM patients. For every unit increase in urea andα1-AT, the risk of stroke in T2DM patients increased by 233.6% (HR=3.336, 95%CI: 1.588‒7.009, P=0.001) and 11.3% (HR=1.113, 95%CI: 1.028‒1.205, P=0.008), respectively. Cumulative risk curves showed that Hcy>10 μmol·L⁻¹ and TyG>9.074 accelerated the time to stroke occurrence in T2DM patients. ConclusionElevated levels of Hcy>10 μmol·L⁻¹ and a higher TyG index are risk factors for stroke in T2DM patients. Effective interventions for Hcy and TyG should be conducted for diabetic patients to reduce the incidence of stroke.

Background The serious air pollution of highway toll booths poses a high occupational exposure risk to toll collectors. It is urgent to develop purification methods suitable for airborne particles and microbial pathogens in highway toll booths. Objective To verify the purification effect of low temperature plasma air purifiers on airborne particles and microbes in highway toll booths. Methods Based on controlled-intervention design, we selected three toll booths in an expressway toll station as on-site experimental locations for 6 d (no-intervention period: the low-temperature plasma purifier was turned off in the first three days; intervention period: the purifier was turned on from 9:00 to 17:00 in the following three days). The indoor and outdoor PM_2.5 and PM₁₀ concentrations were continuously monitored during the study. At 9:00, 12:00, and 17:00 of every day during the experiment, indoor and outdoor air samples were collected to analyze the concentration of airborne culturable colonies with a plankton sampler. Airborne particle samples were collected in the outermost exit continuously from 9:00 to 17:00 every day during the experiment using a medium flow particulate sampler, and the species and relative abundance of fungi and bacteria contained in the samples were analyzed by gene sequencing. Independent-sample t test was used to compare the concentration of indoor PM_2.5, PM₁₀, and culturable colonies between the intervention period and the non-intervention period. α diversity analysis, β diversity analysis, and t test were used to compare the diversity and relative abundance of specific species of bacteria and fungi, as well as typical pathogenic bacteria and fungi in the samples between the non-intervention period and the intervention period to reflect the purification effect of low temperature plasma air purifier on airborne PM_2.5, PM₁₀, and microorganisms. Results During the intervention period, the mean indoor concentrations of PM_2.5, PM₁₀, and culturable colonies were lower than those of the no-intervention period (P<0.01 or P<0.001). The ratios of indoor to outdoor concentration (I/O) of PM_2.5 and PM₁₀ during the intervention period were significantly lower than those of the no-intervention period (P<0.001), except the I/O of culturable colonies. Compared with the average concentration at 9:00, the average cleaning rates at 12:00 and 17:00 for PM_2.5 were 49.0% and 46.1%, for PM₁₀ were 49.7% and 45.4%, for airborne culturable colonies were 50.8% and 49.9%, respectively. The β diversity analysis showed that there were significant differences in composition at the level of species of bacteria, and at the levels of genus and species of fungi between the intervention and the no-intervention periods. The relative abundances of 10 species of bacteria such as Lactobacillus and 7 species of fungi such as Torula in the intervention period were significantly lower than those in the non-intervention period, but the relative abundances of fungi such as unclassified_f_cladosporiaceae, trichomerium, and cercospora were higher (P<0.05). For typical pathogenic bacteria, the relative abundances of Lactobacillus and Clostridium_sensu_stricto_1 during the intervention period were 73.5% and 86.9% lower than those in the no-intervention period, and the relative abundance of Talaromyces was 53.5% lower (P<0.05). Conclusion Low temperature plasma air purifier has a good purification effect on indoor PM_2.5, PM₁₀, and culturable colonies in highway toll booths, and likely a limited effect on some fungi.

Objective To develop a multi-modal deep learning method for automatic classification of immune-mediated glomerular diseases based on images of optical microscopy(OM),immunofluorescence microscopy(IM),and transmission electron microscopy(TEM).Methods We retrospectively collected the pathological images from 273 patients and constructed a multi-modal multi-instance model for classification of 3 immune-mediated glomerular diseases,namely immunoglobulin A nephropathy(IgAN),membranous nephropathy(MN),and lupus nephritis(LN).This model adopts an instance-level multi-instance learning(I-MIL)method to select the TEM images for multi-modal feature fusion with the OM images and IM images of the same patient.By comparing this model with unimodal and bimodal models,we explored different combinations of the 3 modalities and the optimal methods for modal feature fusion.Results The multi-modal multi-instance model combining OM,IM,and TEM images had a disease classification accuracy of(88.34±2.12)%,superior to that of the optimal unimodal model[(87.08±4.25)%]and that of the optimal bimodal model[(87.92±3.06)%].Conclusion This multi-modal multi-instance model based on OM,IM,and TEM images can achieve automatic classification of immune-mediated glomerular diseases with a good classification accuracy.

Objective To explore the clinical diagnostic value of peripheral blood procalcitonin(PCT)and neutrophil lymphocyte ratio(NLR)levels in elderly patients with strangulated inguinal hernia.Methods The clinical data of 112 elderly patients with acute incarcerated inguinal hernia from January 2019 to December 2022 were retrospectively analyzed,and were divided into strangulated group and non-strangulated group according to the intraoperative exploration.Multivariate Logistic regression analysis of risk factors of strangulated inguinal hernia in the elderly.Evaluation of PCT and NLR levels in peripheral blood by ROC curve in the clinical diagnosis of strangulated inguinal hernia in the elderly.Results The NLR and PCT in the strangulation group[(4.54±1.67)and(6.74±2.42)ng/ml]were higher than those in the non-strangulation group[(3.78±1.48)and(4.97±2.53)ng/ml](P＜0.05).Multivariate Logistic regression analysis showed that preoperative NLR and PCT levels were risk factors for strangulated inguinal hernia in the elderly(P＜0.05).Both NLR and PCT have high clinical diagnostic value for strangulated inguinal hernia in the elderly(AUC＞0.7),and the combined detection of NLR and PCT can improve the diagnostic efficiency(AUC=0.792).Conclusion PCT and NLR levels in peripheral blood have high clinical diagnostic value for strangulated inguinal hernia in the elderly,and combined detection of NLR and PCT can improve the diagnostic efficiency.

Objective To develop a multi-modal deep learning method for automatic classification of immune-mediated glomerular diseases based on images of optical microscopy(OM),immunofluorescence microscopy(IM),and transmission electron microscopy(TEM).Methods We retrospectively collected the pathological images from 273 patients and constructed a multi-modal multi-instance model for classification of 3 immune-mediated glomerular diseases,namely immunoglobulin A nephropathy(IgAN),membranous nephropathy(MN),and lupus nephritis(LN).This model adopts an instance-level multi-instance learning(I-MIL)method to select the TEM images for multi-modal feature fusion with the OM images and IM images of the same patient.By comparing this model with unimodal and bimodal models,we explored different combinations of the 3 modalities and the optimal methods for modal feature fusion.Results The multi-modal multi-instance model combining OM,IM,and TEM images had a disease classification accuracy of(88.34±2.12)%,superior to that of the optimal unimodal model[(87.08±4.25)%]and that of the optimal bimodal model[(87.92±3.06)%].Conclusion This multi-modal multi-instance model based on OM,IM,and TEM images can achieve automatic classification of immune-mediated glomerular diseases with a good classification accuracy.

Objective:To investigate the degree of satisfaction with education of undergraduates majoring in medical technology in medical universities in China and associated problems, and to put forward countermeasures and suggestions for student training.Methods:A questionnaire was distributed to undergraduates majoring in medical technology selected by stratified sampling from Shanghai Jiao Tong University, Tianjin Medical University, and Shanghai University of Medicine & Health Sciences. The questionnaire covered demographic characteristics, major choice motivation, education satisfaction, and various aspects, including a total of 54 variables (21 nominal variables and 33 continuous variables). Statistical analysis was performed by using SPSS 27.0 One-way analysis of variance was used for group comparison.Results:The mean degree of satisfaction with education of the students of medical technology was 4.02 points, with the highest score for curricula and teaching and the lowest score for academic atmosphere. Cultivation and management showed the strongest correlation with the degree of satisfaction with education. The degree of satisfaction with education differed significantly for different institutions and different major choice motivations ( P<0.05). Conclusions:Undergraduates of medical technology are generally satisfied with their education, and the degree of satisfaction is lower in double first-class universities than in application-oriented ordinary universities. Attention should be paid to student training and management, creating a positive academic atmosphere, and enhancing the attractiveness of colleges/universities and majors. Compared with application-oriented undergraduate colleges/universities, double first-class universities should well coordinate training goals with students' expectations, industry needs, and even national strategic needs. Colleges and universities can make appropriate adjustments in terms of curricula, teaching, and teaching resources, to promote the diverse and orderly development of medical technology talents based on their personal strengths.

An improved U-shaped multi-lesion segmentation model,namely dense dilated attention pyramid UNet(DDAPNet),is proposed to overcome the difficulty in learning multi-scale features and address the issue of blurry boundaries in diabetic retinopathy(DR)segmentation task.DR images are treated with Patch processing to enhance the model's ability to capture local lesion features.After backbone feature extraction,a redesigned dense dilated attention pyramid module is introduced to expand the receptive field and address the issue of blurry lesion boundaries;and simultaneously,pyramid split attention module is used for feature enhancement;and then,the features output by the two modules are fused.Additionally,an improved residual attention module is embedded within skip connections to reduce interference from shallow redundant information.The joint validation on DDR dataset and real dataset from a specific hospital shows that compared with the original model,DDAPNet model improves the Dice similarity coefficient for segmentations of microaneurysms,hemorrhages,soft exudates and hard exudates by 4.31%,2.52%,3.39%and 4.29%,respectively,and increases mean intersection over union by 1.80%,2.24%,4.28%and 1.98%,respectively.The proposed model makes the segmentation of lesion edges smoother and more continuous,notably enhancing the segmentation performance for conditions like soft exudates in retinal lesions.

Methods:Cultured human alveolar epithelial A549 cells were assigned into LPS group and blank control group. LPS group was stimulated with LPS and adenosine triphosphate to induce pyroptosis and inflammation. A549 cells were divided into 4 groups: miR-20a mimics group, mimics-negative control (NC) group, inhibitor group and inhibitor-NC group. MiRNA-20a mimics, mimics-NC, inhibitor, and inhibitor-NC were transfected respectively into A549 cells, and after 24 h, the cells were collected to verify transfection efficiency by qPCR. MiRNA-20a mimics and the constructed TLR4-3'UTR double luciferase reporter plasmid were co-transfected into A549 cells, and luciferase activity was analyzed. MiRNA-20a mimics/inhibitors were transfected into A549 cells, and then the cells were stimulated by LPS for 8 h followed by adenosine triphosphate for 30 min. QPCR, Western Blot and ELISA were used to detect the expression of GSDMD, inflammatory factors (ASC, NLRP3, Caspase-1, IL-1β) and Signaling molecules (TLR4、NF-κB) in A549 cells at mRNA level and protein level. Immunofluorescence was used to detect the expression of TLR4 in the A549 cells and NF-κB in the nucleus of A549 cells after transfecting with miRNA-20a mimics/inhibitor.Results:The mRNA and protein expression of pyroptosis marker molecule (GSDMD) and inflammatory factors (ASC, NLRP3, Caspase-1, IL-1β) in A549 cells stimulated with LPS were significantly higher than those in the blank control group, and the differences were statistically significant ( P<0.05). The expression of miRNA-20 in the mimics group was significantly higher than that in the mimic-NC group ( P<0.05), while the expression of miRNA-20a in the inhibitor group was lower than that in the inhibitor-NC group ( P<0.01). The double luciferase reporter gene experiment showed that the relative fluorescence value of the co-transfection group for TLR4-3'UTR-WT and miRNA-20a mimics was significantly lower than the co-transfection group for TLR4-3'UTR-WT and miRNA-20a mimics-NC ( P<0.05). The mRNA and protein levels of pyroptosis marker molecule (GSDMD) , inflammatory factors (ASC, NLRP3, Caspase-1, IL-1β) and signaling molecules (TLR4, NF-κB) were decreased in the mimics group compared to the mimics-NC group, and increased in inhibitor group compared to inhibitor-NC group. Conclusions:miRNA-20a may inhibit LPS-induced pyroptosis and inflammation of A549 cells via TLR4/NF-κB signal pathway.Objetive:To explore the potential role of miRNA-20a in lipopolysaccharide (LPS) induced pyroptosis and inflamation of human alveolar epithelial A549 cells and its regulation mechanisim.

OBJECTIVE: We propose a novel region- level self-supervised contrastive learning method USRegCon (ultrastructural region contrast) based on the semantic similarity of ultrastructures to improve the performance of the model for glomerular ultrastructure segmentation on electron microscope images. METHODS: USRegCon used a large amount of unlabeled data for pre- training of the model in 3 steps: (1) The model encoded and decoded the ultrastructural information in the image and adaptively divided the image into multiple regions based on the semantic similarity of the ultrastructures; (2) Based on the divided regions, the first-order grayscale region representations and deep semantic region representations of each region were extracted by region pooling operation; (3) For the first-order grayscale region representations, a grayscale loss function was proposed to minimize the grayscale difference within regions and maximize the difference between regions. For deep semantic region representations, a semantic loss function was introduced to maximize the similarity of positive region pairs and the difference of negative region pairs in the representation space. These two loss functions were jointly used for pre-training of the model. RESULTS: In the segmentation task for 3 ultrastructures of the glomerular filtration barrier based on the private dataset GlomEM, USRegCon achieved promising segmentation results for basement membrane, endothelial cells, and podocytes, with Dice coefficients of (85.69 ± 0.13)%, (74.59 ± 0.13)%, and (78.57 ± 0.16)%, respectively, demonstrating a good performance of the model superior to many existing image-level, pixel-level, and region-level self-supervised contrastive learning methods and close to the fully- supervised pre-training method based on the large- scale labeled dataset ImageNet. CONCLUSION USRegCon facilitates the model to learn beneficial region representations from large amounts of unlabeled data to overcome the scarcity of labeled data and improves the deep model performance for glomerular ultrastructure recognition and boundary segmentation.
Humans ; Electrons ; Endothelial Cells ; Learning ; Podocytes ; Kidney Diseases

Objective:To examine the clinical value of rapid detection of drug-resistant bacteria by immunochromatography and the effects of rapid detection on the prognosis of patients with severe intra-abdominal infection complicated by carbapenem-resistant Enterobacteriaceae (CRE) bloodstream infection.Methods:This was a retrospective cohort study. We analyzed clinical data of 73 patients with severe abdominal infections with sepsis or septic shock complicated by CRE bloodstream infection admitted to the general surgery department of Jinling Hospital between February 2022 and February 2023. Patients were divided into a colloidal gold immunochromatographic assay (GICA) group (17 patients) and conventional testing group (56 patients) based on whether a GICA for CRE had been performed on the patients' first blood culture sample during the diagnosis and treatment process. There were no statistically significant differences between the GICA and conventional testing groups in age ([55.9±17.3] vs. [47.6±16.4] years), sex ([16 men vs. one woman ] vs. [41 men vs. 15 women]), median Charlson comorbidity index (3.0[2.0,4.0] vs. 3.0[2.0, 4.8]), septic shock (10 vs. 39), or acute kidney injury (8 vs. 40) (all P>0.05). Both groups routinely underwent traditional bacterial identification and drug susceptibility testing. Additionally, patients in the GICA group were tested directly for positive blood cultures using a GICA carbapenemase test kit. The main outcomes were mortality rates on Days 28 and 90 after the first identification of CRE bloodstream infection in both groups. We also compared the microbial clearance rate, duration of hospitalization and intensive care unit stay, and time from onset of CRE bloodstream infection to initiation of targeted and appropriate antibiotics between the two groups. Results:The rate of microbial clearance of bloodstream infection was significantly greater in the GICA group than in the conventional testing group (15/17 vs. 34/56 [60.7%], χ 2=4.476, P=0.034), whereas the 28-day mortality tended to be lower in the GICA than conventional testing group [5/17 vs. 44.6% [25/56], χ 2=1.250, P=0.264). The 90-day mortality (8/17 vs. 53.6% [30/56], χ 2=0.222, P=0.638), median duration of hospitalization (37.0 [18.0, 46.5] days vs. 45.5 [32.2, 64.8] days, Z=-1.867, P=0.062), and median duration of intensive care unit stay (18.0 [6.5, 35.0] days vs. 32.0 [5.0, 51.8] days, Z=-1.251, P=0.209). The median time between the onset of bloodstream infection and administration of antibiotics was 49.0 (38.0, 69.0) hours in the GICA group, which is significantly shorter than the 163.0 (111.8, 190.0) hours in the conventional testing group ( Z=-5.731, P<0.001). The median time between the onset of bloodstream infection and administration of appropriate antibiotics was 40.0 (34.0, 80.0) hours in the GICA group, which is shorter than in the conventional testing group (68.0 [38.2, 118.8]) hours; however, this difference is not statistically significant ( Z=-1.686, P=0.093). Conclusions:GICA can provide information on carbapenemase- producing pathogens faster than traditional drug sensitivity testing, enabling early administration of the optimal antibiotics. The strategy of 'carbapenemase detection first' for managing bacterial infection has the potential to improve prognosis of patients and reduce mortality rate.