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Background The diagnosis of Parkinson disease (PD) is mainly based on clinical symptoms, and there is a lack of objective methods for correct diagnosis. At present, there have been studies on retinal structural changes as a biomark for early diagnosis of PD, but machine learning based on retinal structural changes for predicting early PD has not yet been studied. Objective To construct a machine learning model based on the characteristics of retinal structural changes, explore its value in early PD diagnosis, and the accuracy of different machine learning algorithms for early PD diagnosis. Methods From October 2021 to September 2022, 49 PD patients aged 40 to 70 years old (PD group) who attended outpatient clinics and were hospitalized in the department of neurology of Henan Provincial People's Hospital (PD group) and 39 healthy people with matching age and sex (healthy control group) who came to the hospital for physical examination were collected. All study subjects underwent swept-source optical coherence tomography and swept-source optical coherence tomography angiography, the thickness and vessel density of the macular retina were also quantitatively analyzed. The 88 subjects were randomly divided into the 62 training sets and 26 validation set according to the ratio of 7∶3. Variables with significant differences between the PD group and healthy control group were selected as the characteristic variables for inclusion in the machine learning model, and Logistic regression (LR) , K-nearest neighbor algorithm (KNN) , decision tree (DT) , random forest (RF) and extreme gradient boosting (XGboost) models were constructed in the training set. The area under the curve (AUC) , accuracy, sensitivity and specificity of the receiver operating characteristic (ROC) curve were used to evaluate the predictive value of the machine learning model based on retinal structural changes for early PD. Results Compared with the healthy control group, the density of the upper outer ring (A6) , the outer temporal outer ring (A7) , the lower outer ring (A8) and the outer nasal ring (A9) of the superficial capillaries in the PD group were reduced, the thickness of the upper inner ring (A2) , the inner temporal inner ring (A3) , the inferior inner ring (A4) , the inner ring of the nasal side (A5) of the retinal layer, A6, A7, A8 and A9, the thickness of A6 of the ganglion cell complex layer, the thickness of A7 of the nerve fiber layer, A2 and A4, A5, A6, A7, A8, A9 became thinner (P

Background Parkinson's disease (PD) is frequently accompanied with anxiety, depression, sleep disorders, autonomic dysfunction, cognitive impairment, and other non-motor symptoms, among which autonomic dysfunction and cognitive impairment severely impair the quality of life. The relationship between autonomic dysfunction and cognitive impairment in PD is still controversial. Objective To analyze the characteristic of autonomic dysfunction and its correlation with cognitive impairment in PD patients. Methods A total of 156 patients with idiopathic PD admitted to Henan Provincial People's Hospital between September 2018 and November 2020 were enrolled. General data of patients, including age, sex, duration of disease, age of onset and years of education, were collected. According to the prevalence and duration of autonomic dysfunction, they were divided into autonomic dysfunction group (AutD group, n=107) and non-autonomic dysfunction group (NAutD group, n=49) . The part Ⅲ of the Unified Parkinson's Disease Rating Scale (UPDRS-Ⅲ) was used to assess motor symptoms. The Hoehn-Yahr (H-Y) scale was used to assess the severity of PD. The non-motor symptoms were assessed by the Non-motor Symptoms Scale (NMSS) . The Hamilton Anxiety Scale (HAMA) and Hamilton Rating Scale for Depression 24-item (HAMD-24) were used to evaluate anxiety and depression symptoms, respectively. The 39-item Parkinson's Disease Questionnaire (PDQ-39) was used to evaluate PD-specific health related quality of life. Autonomic function was evaluated by Scales for Outcomes in Parkinson's disease-Autonomic (SCOPA-AUT) . Cognitive function was evaluated by Mini-mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) . Spearman correlation analysis was used to explore the correlation between autonomic function (assessed using the SCOPA-AUT score) and cognitive function (assessed using the MMSE score and MoCA score) . Results Among the 156 patients, 83 were male and 73 were female, with a mean age of (63.9±0.7) years. AutD group had greater mean age (t=-4.391, P

Abstract: This paper obtains some sufficient conditions for the relative ϕ 0-stability of the two impulsive hybrid systems by using the method of perturbing Lyapunov functions. [Copyright &y& Elsevier]

Gastric cancer is one of the common gastrointestinal tumors. Tumor recurrence leads to a high death rate of gastric cancer. It is very important to find markers to effectively predict gastric cancer recurrence. We constructed a gastric cancer tissue microarray containing 89 tumors and corresponding normal tissues to explore the relationship between some proteins' expression and gastric cancer recurrence. The expression of JWA, Cullin1, p53, XRCC1, CHIP, FAK, MMP-2, MDM2 and p21 was determined on the microarray by immunohistochemistry. The relationship between the expression of these proteins and gastric cancer recurrence was analyzed. Tumor diameter, lymph node metastasis, and TNM stage were closely related with gastric cancer recurrence by Fisher's exact test (P<0.05). We used the univariate Cox regression analysis to find that JWA, XRCC1 were related to gastric cancer recurrence (P<0.05); Lymph node metastasis and TNM stage were closely related to gastric cancer recurrence (P<0.05). Multivariate Cox regression analysis revealed that XRCC1 or lymph node metastasis were independent risk factors of gastric cancer recurrence (P<0.05). Kaplan-Meier survival curve assay indicated that patients with low JWA or XRCC1 expression in gastric cancer had significantly shorter DFS than those with high-expressed proteins (P<0.05). JWA or XRCC1 may be effective markers to predict gastric cancer recurrence., Competing Interests: None., (IJCEP Copyright © 2020.)

The farnesoid X receptor (FXR) is a member of the nuclear receptor superfamily that is expressed in the vasculature; our previous work showed that FXR regulated vascular reactivity through NO mechanism. The underlying mechanism for the regulation of vascular tension by FXR remains unclear. The present work was designed to investigate whether FXR regulates calcium homeostasis in aortic vascular smooth muscle cells (VSMCs). Protein abundances of angiotensin II type 1 and 2 receptors (AT₁R, AT₂R), bradykinin type 1 and 2 receptors (B₁R, B₂R), and the inositol 1,4,5-trisphosphate receptor (IP₃R) were analyzed by western blotting. Kallikrein activity and bradykinin content were assayed by using spectrophotometry and a bradykinin assay kit, respectively. Aortic contraction, intracellular Ca²⁺ concentrations ([Ca²⁺]i), sarco/endoplasmic reticulum Ca²⁺ ATPase (SERCA) activities, and IP₃-evoked Ca²⁺ release were investigated, following FXR activation in the presence or absence of AT₂R and B₂R blockade. We found that the FXR agonists GW4064 and INT-747 increased the protein abundance of AT₂R and B₂R in rat aortic VSMCs. AT₂R blockade with PD123319 reversed the effects of FXR agonists on kallikrein activity, B₂R, and bradykinin levels. Moreover, we found that GW4064 and INT-747 decreased intracellular [Ca²⁺], increased SERCA activity, downregulated IP₃R₁ expression, and attenuated IP₃-induced Ca²⁺ release. These effects were partially reversed by AT₂R and B₂R blockade with PD123319 and HOE140, respectively. Our data suggest that FXR regulates vascular tension by modulating extracellular Ca²⁺ influx and intracellular Ca²⁺ release from the sarcoplasmic reticulum via activation of an AT₂R-B₂R pathway in rat aortic VSMCs., Competing Interests: The authors declare that they have no conflicts of interest.