RATIONALE The capsular warning syndrome (CWS) is a rare and special type of transient ischemic attacks (TIAs) syndrome. The pathophysiology of CWS is very complicate, and intracranial atherosclerotic stenosis (ICAS) is rare cause. Moreover, the effective and standard therapy has not yet been established. PATIENT CONCERNS A 47-year-old man experienced repeated and exacerbated TIAs of right hemiparesis and dysarthria. Fourteen hours after the first episode of TIAs, he developed more severe right hemiparesis and dysarthria, the National Institute of Health Stroke Scale (NIHSS) score was 12 points, and did not recover in a long time. DIAGNOSIS The computed tomography (CT) angiography displayed high stenosis in the M1 segment of the left middle cerebral artery. The patient was diagnosed as CWS with ICAS. INTERVENTIONS Loading dose of clopidogrel and aspirin were started but were ineffective, then we used recombinant tissue plasminogen (r-tPA) for thrombolysis therapy after repeat CT scan that showed small acute infarcts in the right putamen and no bleeding. OUTCOMES The patient was successfully treated by r-tPA intravenous thrombolysis after loading dose of dual-anti-platelet. He recovered rapidly, and the NIHSS score was 0 point, modified Rankin Scale score was 0 point, and Barthel Index score was 100 points at 3-month follow-up. LESSONS r-tPA combined with loading dose of dual antiplatelet appears safe and effective in carefully selected CWS patients with ICAS. The collection of similar cases and further randomized controlled trial research would be desirable.This study aimed to investigate whether trunk fat mass measured using dual-energy X-ray absorptiometry (DEXA) correlates with balance and physical performance.This study utilized 2-year baseline data pertaining to 3014 participants from the database of the Korean frailty and aging cohort study. The trunk lean mass and fat mass were measured by DEXA. Trunk fat mass index (tFMI) was established using the following standard equation Trunk fat mass (Kg)/height (m). The clinical balance tests were performed using the timed up and go test (TUG), total balance score in short physical performance battery (SPPB). We performed SPPB and evaluated independence of daily living using activities of daily living, instrumental activities of daily living (IADL), sarcopenia screening tool (SARC-F) and both hand grip power. In our study, we tried to check the correlation of tFMI with balance and physical performance and to determine the factors associated with tFMI.The tFMI was positively correlated with mean values of 4 m gait speed, repeat chair stand time in SPPB, TUG, and SARC-F and negatively correlated with hand grip, IADL, total balance test score in SPPB, total SPPB score, and age. The results of the multiple generalized linear model analysis that assessed the factors associated with balance and physical performance indicated that tFMI had a significant correlation with repeat chair stand time in SPPB (seconds) (Beta estimate [B] 0.252), TUG (seconds) (B 0.25), 4 m gait speed (seconds) (B 0.055), and total balance score in SPPB (B -0.035).Higher tFMI using DEXA was correlated with low physical performance and balance, indicating that trunk fat mass was associated with balance and physical performance in community-dwelling older people.Despite the availability of a series of tests, detection of chronic traumatic osteomyelitis is still exhausting in clinical practice. We hypothesized that machine learning based on computed-tomography (CT) images would provide better diagnostic performance for extremity traumatic chronic osteomyelitis than the serological biomarker alone. A retrospective study was carried out to collect medical data from patients with extremity traumatic osteomyelitis according to the criteria of musculoskeletal infection society. In each patient, serum levels of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and D-dimer were measured and CT scan of the extremity was conducted 7 days after admission preoperatively. A deep residual network (ResNet) machine learning model was established for recognition of bone lesion on the CT image. A total of 28,718 CT images from 163 adult patients were included. Then, we randomly extracted 80% of all CT images from each patient for training, 10% for validation, and 10% for testing. Our results showed that machine learning (83.4%) outperformed CRP (53.2%), ESR (68.8%), and D-dimer (68.1%) separately in accuracy. Meanwhile, machine learning (88.0%) demonstrated highest sensitivity when compared with CRP (50.6%), ESR (73.0%), and D-dimer (51.7%). Considering the specificity, machine learning (77.0%) is better than CRP (59.4%) and ESR (62.2%), but not D-dimer (83.8%). Our findings indicated that machine learning based on CT images is an effective and promising avenue for detection of chronic traumatic osteomyelitis in the extremity.In China, there is a significant lack of awareness of diabetes and its complications. Screening of diabetic retinopathy has important for early detection, prevention, and treatment. This large, cross-sectional study aimed to characterize the demographic, physical, serological, and ocular characteristics of subjects with diabetes mellitus in Shijiazhuang, China. It also aimed to associate these characteristics with the presence of diabetic retinopathy.From May 2, 2018 to August 25, 2019, under diabetes care program, the diabetic patients (n = 1008) were subjected to standardized questionnaires to collect demographical characteristics. Also, telescreens and laboratory tests were performed for the enrolled patients. Vorinostat in vitro Multivariate logistic regression analysis was used to evaluate factors associated with diabetic retinopathy.Forty percent of diabetics in its population had some form of diabetic retinopathy. Diabetic retinopathic patients were likely to be elder (P = .0003), men (P = .018), hypertensive (P  less theetinopathy.Level of Evidence III.The stromal interaction molecule 1 (STIM1) gene contributes essentially to Ca transport, thus it is functionally related to neurodegenerative disorders. The objective of this study was to investigate the correlation between single nucleotide polymorphisms (SNP) in the non-coding region of STIM1 gene and the risk for Parkinson disease (PD) in a Chinese Han population.In a cohort composed of 300 PD patients and 300 healthy individuals from a Chinese Han population, we analyzed genotypes for five novel SNPs, rs7934581, rs3794050, rs1561876, rs3750994 and rs3750996 in the non-coding region of STIM1 gene. The levels of STIM1 protein in plasma of these subjects were also assessed by enzyme-linked immunosorbent assay (ELISA).We found that the SNPs of STIM1 gene rs7934581, rs3794050, rs1561876, and rs3750996 were associated with increased PD risk, while rs3750994 SNP was not. An increased risk of PD was observed in subjects with the TAAG and TGAG haplotypes of rs7934581, rs3794050, rs1561876, rs3750996. Moreover, PD risk was significantly elevated only in subjects with age ≥60 years or females who carry the STIM1 rs3794050 minor allele.