Background::Clinical opportunistic screening is a cost-effective cancer screening modality. This study aimed to establish an easy-to-use diagnostic model serving as a risk stratification tool for identification of individuals with malignant gastric lesions for opportunistic screening.Methods::We developed a questionnaire-based diagnostic model using a joint dataset including two clinical cohorts from northern and southern China. The cohorts consisted of 17,360 outpatients who had undergone upper gastrointestinal endoscopic examination in endoscopic clinics. The final model was derived based on unconditional logistic regression, and predictors were selected according to the Akaike information criterion. External validation was carried out with 32,614 participants from a community-based randomized controlled trial.Results::This questionnaire-based diagnostic model for malignant gastric lesions had eight predictors, including advanced age, male gender, family history of gastric cancer, low body mass index, unexplained weight loss, consumption of leftover food, consumption of preserved food, and epigastric pain. This model showed high discriminative power in the development set with an area under the receiver operating characteristic curve (AUC) of 0.791 (95% confidence interval [CI]: 0.750–0.831). External validation of the model in the general population generated an AUC of 0.696 (95% CI: 0.570–0.822). This model showed an ideal ability for enriching prevalent malignant gastric lesions when applied to various scenarios.Conclusion::This easy-to-use questionnaire-based model for diagnosis of prevalent malignant gastric lesions may serve as an effective prescreening tool in clinical opportunistic screening for gastric cancer.

Objective With the widespread of transcatheter aortic valve replacement(TAVR)in patients with severe symptomatic aortic stenosis(AS),the life-cycle management has become a major determinant of prognosis.Methods A total of 408 AS patients who underwent successfully TAVR from June 2021 to August 2023 were consecutively enrolled in Hospital Valve Intervention Center.Patients were assigned to the Usual Care(UC)group between June 2021 and October 2022,while patients were assigned to the Heart Multi-parameter Monitoring(HMM)group between November 2022 and August 2023.The primary endpoint was defined as composite endpoint within 6 months post-TAVR,including all-cause death,cardiovascular death,stroke/transient ischemic attack,conduction block,myocardial infarction,heart failure rehospitalization,and major bleeding events.Secondary endpoints were the time interval(in hours)from event occurrence to medical consultation or advice and patient satisfaction.Statistical analysis was performed using Kaplan-Meier and multivariable Cox proportional hazards models.Results The incidence of primary endpoint in HMM group was significantly lower than that in UC group(8.9％vs.17.7％,P=0.016),the driving event was the rate of diagnosis and recognition of conduction block.The average time intervals from event occurrence to receiving medical advice were 3.02 h in HHM group vs.97.09 h in UC group(P＜0.001).Using cardiac monitoring devices and smart healthcare platforms provided significant improving in patients long-term management(HR 0.439,95％CI 0.244-0.790,P=0.006).Conclusions The utilization of cardiac monitoring devices and smart healthcare platforms effectively alerted clinical events and improved postoperative quality of life during long-term management post TAVR.

This study investigated the characteristics of gut microbiota imbalance in patients with infectious diarrhea caused by various pathogenic infections,and the role of Bacteroides in maintaining homeostasis in the intestinal environment.The gut microbiota in patients with diarrhea caused by pathogenic infections,such as viral and bacterial infections,was determined through full-length 16S rRNA amplicon sequencing.Patients with diarrhea were grouped and analyzed according to the presence of single bacterial infection,single viral infection,mixed infection,or Clostridioides difficile infection.Bacteroides had the highest absolute number and relative abundance in the gut microbiota in healthy people,whereas patients with infectious diar-rhea showed lower relative abundance of Bacteroides at each phylum/order/family/genus taxonomic level.Alpha diversity anal-ysis indicated no significant differences among groups.NMDS and PCoA indicated formation of distinct clusters in the control group compared with the different infectious diarrhea groups.The diversity of the gut microbiota was higher in the control group than the infectious diarrhea groups.Patients with infec-tious diarrhea caused by different pathogens showed differing predominant gut microbiota.Bifidobacterium predominated in the single viral infection group,Streptococcus predominated in the single bacterial infection group,and Lachnoclostridium predominated in the mixed infection group.Escherichia and Klebsiella were the major gut microbiota in the C.difficile infection group.Meanwhile,the dominant gut microbiota in the healthy population was Bacteroides.COG function prediction revealed that the healthy control group formed a distinct cluster from the different infection groups.The functions of defense mechanisms,cell wall synthesis,protein modification,cellular differentiation,and replication and recombination were signifi-cantly diminished in all infectious diarrhea groups.In general,patients with infectious diarrhea caused by different pathogens showed dysbiosis,with diminished gut microbiota diversity and the emergence of related biomarkers.Our findings indicated that Bacteroides has a key role in maintaining the homeostasis of the human intestinal environment,thus providing new ideas for the subsequent treatment of infectious diarrhea and research in other fields.

Objective:To investigate the effect of pre-treatment plasma Epstein-Barr virus(EBV)DNA copy number on the clinical features and prognosis of patients with adult secondary hemophagocytic lymphohistiocytosis(sHLH).Methods:The clinical characteristics,survival rate,and prognostic factors of 171 patients with adult sHLH treated at Jiangsu Province Hospital from June 2017 to January 2022 were retrospectively analyzed in this study.Patients were divided into three groups,including the EBV DNA-negative group(＜5.0 × 102 copies/ml),lower EBV-DNA loads group(5.0 × 102-8.51 × 104 copies/ml),and higher EBV-DNA loads group(＞8.51 × 104 copies/ml),according to pre-treatment plasma EBV-DNA copy number.Cox regression model was established for screening prognostic factors.Adult sHLH survival prediction model was constructed and realized through the nomogram based on EBV-DNA load after adjusted the factors affecting survival of etiology and treatment strategy.Concordance index(C-index)and calibration curves were calculated to verify model predictive and discriminatory capacity.Results:Among 171 adult sHLH patients,84 patients were not infected with EBV(EBV DNA-negative group),and 87 with EBV(EBV DNA-positive group,48 lower EBV-DNA loads group and 39 higher EBV-DNA loads group).Consistent elevations in the levels of liver enzymes(ALT and AST),LDH,TG,β2-microglobulin and ferritin across the increasing of EBV-DNA load(all P＜0.05),while the levels of fibrinogen decrease(P＜0.001).The median follow-up time was 52 days(range 20-230 days),and 123 patients died.The overall survival(OS)rate of patients in EBV DNA-positive group was lower than that in EBV DNA-negative group(median OS:40 days vs 118 days,P＜0.001).Higher EBV-DNA loads had worse OS(median OS:24 days vs 45 days vs 118 days,P＜0.0001 for trend)compared to lower EBV-DNA loads and EBV DNA-negative group.Multivariate Cox analysis revealed that higher EBV-DNA loads(P=0.005),fibrinogen≤ 1.5 g/L(P=0.012),ferritin(P=0.041),associated lymphoma(P=0.002),and anti-tumor based strategy(P=0.001)were independent prognostic factors for OS.The C-indexes of 30 day,90 days,365 days survival rate were all greater than 0.8 of the nomogram model and calibration curves provided credibility to their predictive capability.Subgroup analysis showed that patients with higher EBV-DNA loads had a significantly worse prognosis in adult sHLH who were women,ferritin＞5 000 μg/L,β2-microglobulin＞7.4 mmol/L and regardless of age,etiologies,HScore points.Conclusion:The EBV-DNA load is a strong and independent predictor for survival in patients with sHLH.The prognostic nomogram based on EBV-DNA loads was dependable and provides a visual tool for evaluating the survival of adult sHLH.

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.

AIM To investigate the clinical effects of Buyang Huanwu Decoction combined with Erlong Xizhu Acupuncture Method on elderly patients with stroke.METHODS Ninety-six patients were randomly assigned into control group(48 cases)for 1-month intervention of Erlong Xizhu Acupuncture Method,and observation group(48 cases)for 1-month intervention of both Buyang Huanwu Decoction and Erlong Xizhu Acupuncture Method.The changes in neurological function indices(NIHSS score,NDS score),Hcy,CysC,vascular endothelial function indices(ET-1,NO,vWF),intracranial hemodynamic indices(Vmean,R,DR),hemorheological indices(WBLSV,WBHSV,FIB,HCT,EAI)and prognosis assessment indices(mRS score,BI score)were detected.RESULTS After the treatment,the two groups displayed decreased neurological function indices,Hcy,CysC,ET-1,vWF,R,DR,hemorheological indices,mRS score(P＜0.05),and increased NO,Vmean,BI score(P＜0.05),especially for the observation group(P＜0.05).CONCLUSION For the elderly patients with stroke,Buyang Huanwu Decoction combined with Erlong Xizhu Acupuncture Method can reduce serum Hcy,CysC levels,and improve vascular endothelial functions,intracranial hemodynamics,hemorheological indices,thus improve neurological functions and promote prognosis.

Objective:To summarize the distributional characteristics of postoperative occurrence of multi-drug resistant organism (MDRO) infections and their risk factors in simultaneous pancreas-kidney transplantation (SPK) recipients and examine the impact of MDRO infections on the survival of SPK recipients.Method:From January 2016 to December 2022, the relevant clinical data were retrospectively reviewed for 218 SPK recipients. The source of donor-recipient specimens and the composition percentage of MDRO pathogens were examined. According to whether or not MDRO infection occurred post-transplantation, they were assigned into two groups of MDRO (98 cases) and non-MDRO (120 cases). The clinical data of two groups of donors and recipients were analyzed. And the risk factors for an onset of MDRO infection were examined by binary Logistic regression. The survival rate of two recipient groups was compared by Kaplan-Meier method.Result:A total of 98/218 recipients (45%) developed MDRO infections. And 46 (46.9%) of sputum and 34 (34.7%) of urine were cultured positively and 49 (50%) pathogens expressed extended spectrum beta-lactamase. There were pneumonia (46 cases, 46.9%), urinary tract infections (34 cases, 34.7%), abdominal infections (16 cases, 16.3%) and bloodstream infections (2 cases, 2.0%). Univariate regression analysis revealed that length of renal failure ( P=0.037), length of hospitalization ( P<0.001), length of antibiotic use ( P<0.001), novel antibiotics ( P=0.014), albumin ( P<0.001) and leukocyte count ( P<0.001) were risk factors for an onset of MDRO infections. The results of multifactorial regression indicated that low albumin ( OR=0.855, 95% CI: 0.790~0.925, P<0.001) and leukopenia ( OR=0.656, 95% CI: 0.550~0.783, P<0.001) were independent risk factors for an onset of MDRO infections. The survival rates of recipients in MDRO group at Year 1/3 post-operation were 92.9% (91/98) and 89.8% (88/98). And the survival rate of recipients in non-MDRO group was 96.7% (116/120) at Year 1/3 post-operation. Inter-group difference was not statistically significant in 1-year survival rate of two recipient groups ( P=0.201); statistically significant inter-group difference in 3-year survival rate between two recipient groups ( P=0.041) . Conclusion:Low albumin and leukopenia are risk factors for MDRO infection. Infection with MDRO has some impact on the survival of recipients.

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 investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.