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Background: Due to confounders like hyperglycemia, patients with type 2 diabetes mellitus (T2DM) have an increased susceptibility to venous thromboembolism (VTE). However, formal risk assessment models, such as using the Padua score, do not include all T2DM-associated risk factors for VTE. Therefore, this study aims to develop and validate a predictive nomogram for VTE in non-surgical inpatients with T2DM. Methods: We retrospectively analyzed the clinical and biochemical data of 420 non-surgical inpatients with T2DM between 2017 and 2021 from three centers (the PLA 474(th) hospital, the Second Affiliated Hospital of Xinjiang Medical University and the Fifth Affiliated Hospital of Xinjiang Medical University). A multivariate analysis based on logistic regression model was performed to identify independent risk factors and construct a nomogram. The predictive values were compared by calculating the integrated discrimination improvement (IDI) and net reclassification improvement (NRI), and by decision curve analysis (DCA). Results: Old age, BMI, D-dimer, hypoproteinemia, acute infection, acute myocardial infarction, cerebral ischemic stroke, reduced mobility, and heart/respiratory failure were independent risk factors for VTE in non-surgical inpatients with T2DM, as indicated by the multivariate analysis. The nomogram demonstrated superior discriminative ability compared to the Padua score (area under the curve: 0.923 vs. 0.849). NRI and IDI were also observed, and the DCA identified the greater net benefit and clinical utilization of the new nomogram. Conclusions: A predictive nomogram for VTE in non-surgical inpatients with T2DM was developed and validated in this study. The nomogram is highly predictive and easy to operate, but external data verification is required before it can be further used.

Objective. To investigate the role of aberrant Dyrk1a expression in phosphorylation modification at the α-synuclein serine 129 (Ser129) site to analyze its molecular mechanism in mediating apoptosis of PD. Methods. The protein level of P-α-synuclein (Ser129), α-synuclein, Bcl-2, Bax, active caspase 3, GSK3β, PI3K, AKT, and cyclinD1 were detected. The mRNA transcript levels of Dyrk1a and DAT and protein levels of IL-1β, IL-6, COX-2, and TNF-α were detected. Results. P-α-synuclein (Ser129), α-synuclein, Bax, active caspase 3, GSK3β, and cyclinD1 expressions were decreased in Dyrk1a-AAV-ShRNA (P

Background. Transcranial sonography (TCS) is a noninvasive test that can reveal structural changes in the substantia nigra (SN) in Parkinson’s disease (PD). The purpose of this study was to investigate the relationship between SN signatures and clinical features in PD patients in a multiethnic region of China. Methods. A total of 147 patients with PD were included in the study, and all of whom had underwent a TCS examination. Clinical information was collected from PD patients, and motor and nonmotor symptoms were assessed using assessment scales. Results. There were differences in the substantia nigra hyperechogenicity (SNH) area between age of onset, visual hallucinations (VH), and UPDRS3.0 II scores (P

Background/Aims: The study aims to determine the effects of thermal preconditioning on tendon adhesion by regulating the expression of heat shock protein 72 (HSP72) in rat models. Methods: Sixty male Wistar rats were collected and randomly assigned into the thermal preconditioning and control groups. During the 4th and 8th weeks following surgery, 15 rats were sacrificed in each period respectively, and their tendon adhesion was observed and evaluated. Biomechanical testing was performed to measure the tensile strength and gliding distance of tendons. Hematoxylin-eosin (HE) was used to observe the morphological structure of the tendons. Immunohistochemical staining, quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting were used to detect the HSP72, fibroblast growth factor-2 (FGF-2), fibroblast growth factor receptor-1 (FGFR-1), β-catenin, epithelial cell adhesion molecule (EPCAM), Tenomodulin and scleraxis protein expressions. Pearson correlation analysis was applied to analyze the correlation between HSP72 expression and tendon adhesion. Results: At the 4th week after surgery, we found no differences in the tendon adhesion scores or mRNA and protein expressions of HSP72 between the thermal preconditioning and control groups. However, after the 8th week after surgery, the thermal preconditioning group had a lower tendon adhesion score and higher mRNA and protein expressions of HSP72 than the control group. During the same period, we found longer gliding distance and higher expression levels of FGF-2, FGFR-1, β-catenin, Tenomodulin and scleraxis, but lower EPCAM expression in the thermal preconditioning group. Pearson correlation analysis indicated that HSP72 mRNA and protein expression levels were negatively correlated with tendon adhesion. Conclusions: These findings provide evidence that thermal preconditioning may alleviate tendon adhesions via upregulation of HSP72 expression.

Although it is practical and significant to analyze the financial risk of the small and medium enterprise (SMEs), most scholars do research on unlisted small and medium enterprisespsila financial situation only with qualitative analysis methods, because it is difficult to get the financial data of unlisted small and medium enterprises. In this paper, a new data acquisition idea is put forward. Through this idea, quantitative analysis method could be taken to study on the financial risk early warning of unlisted SMEs. Data sets of financial risk analysis of unlisted SMEs are selected from listed SMEspsila data sets. Those data sets are disclosed to public and easily obtained. Then listed indicators are omit and decision tree model is made for financial risk early warning of unlisted SMEspsila. 221 listed SMEspsila financial data sets are chosen and models are built in the empirical analysis part of this paper. 30 unlisted SMEspsila financial status (whether in crisis in 2007) have been analyzed by a financial company and the results are generally in line with what are predicted with 30 SMEspsila financial data in 2006 through the models created in this paper. Thus feasibility of the financial risk early warning model is confirmed.