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ObjectiveThis study aimed to investigate the microscopic structure and characteristics of nevi on the conjunctiva of the lacrimal caruncle by in vivo confocal microscopy.MethodsIn total, four patients with nevi growing on the lacrimal caruncle conjunctiva were recruited. The morphological characteristics of the nevi were evaluated by in vivo confocal microscopy before excision surgery; the results were compared with histopathological analyses of the surgical specimens.ResultsThe nevi of the four patients were all located at the conjunctiva of the lacrimal caruncle, with a slightly nodular surface, mixed black and brown color, and clear boundary. The nevi were round and highly protruded on the surface of the lacrimal caruncle, with an average diameter of 4.5 ± 1.29 mm. Under in vivo confocal microscopy, the pigmented nevus cells on the conjunctiva of the lacrimal caruncle were observed to be clustered in nests with irregular boundaries. The cells were round or irregular, with clear cell boundaries, hyper-reflective at the periphery, with low reflectivity in the center. Vascular crawling was observed in some regions. Histopathological analysis showed that nevus cells were roughly equal in size and distributed in a nodular pattern. Melanin granules were observed in the cytoplasm. No atypia or mitotic figures of the cells were found.ConclusionThis study revealed that the microstructure of nevi growing on the conjunctiva of the lacrimal caruncle can be identified by in vivo confocal microscopy.

BackgroundIntracerebral hemorrhage (ICH) is one of the most serious complications in patients with chronic kidney disease undergoing long-term hemodialysis. It has high mortality and disability rates and imposes a serious economic burden on the patient's family and society. An early prediction of ICH is essential for timely intervention and improving prognosis. This study aims to build an interpretable machine learning-based model to predict the risk of ICH in patients undergoing hemodialysis.MethodsThe clinical data of 393 patients with end-stage kidney disease undergoing hemodialysis at three different centers between August 2014 and August 2022 were retrospectively analyzed. A total of 70% of the samples were randomly selected as the training set, and the remaining 30% were used as the validation set. Five machine learning (ML) algorithms, namely, support vector machine (SVM), extreme gradient boosting (XGB), complement Naïve Bayes (CNB), K-nearest neighbor (KNN), and logistic regression (LR), were used to develop a model to predict the risk of ICH in patients with uremia undergoing long-term hemodialysis. In addition, the area under the curve (AUC) values were evaluated to compare the performance of each algorithmic model. Global and individual interpretive analyses of the model were performed using importance ranking and Shapley additive explanations (SHAP) in the training set.ResultsA total of 73 patients undergoing hemodialysis developed spontaneous ICH among the 393 patients included in the study. The AUC of SVM, CNB, KNN, LR, and XGB models in the validation dataset were 0.725 (95% CI: 0.610 ~ 0.841), 0.797 (95% CI: 0.690 ~ 0.905), 0.675 (95% CI: 0.560 ~ 0.789), 0.922 (95% CI: 0.862 ~ 0.981), and 0.979 (95% CI: 0.953 ~ 1.000), respectively. Therefore, the XGBoost model had the best performance among the five algorithms. SHAP analysis revealed that the levels of LDL, HDL, CRP, and HGB and pre-hemodialysis blood pressure were the most important factors.ConclusionThe XGB model developed in this study can efficiently predict the risk of a cerebral hemorrhage in patients with uremia undergoing long-term hemodialysis and can help clinicians to make more individualized and rational clinical decisions. ICH events in patients undergoing maintenance hemodialysis (MHD) are associated with serum LDL, HDL, CRP, HGB, and pre-hemodialysis SBP levels.

Aims: To evaluate whether low level left vagus nerve stimulation (LLVNS) in early stage of myocardial infarction (MI) could effectively prevent ventricular arrhythmias (VAs) and protect cardiac function, and explore the underlying mechanisms.Methods and Results: After undergoing implantable cardioverter defibrillators (ICD) and left cervical vagal stimulators implantation and MI creation, 16 dogs were randomly divided into three groups: the MI (n = 6), MI+LLVNS (n = 5), and sham operation (n = 5) groups. LLVNS was performed for 3 weeks. VAs, the left ventricular function, the density of the nerve fibers in the infarction area and gene expression profiles were analyzed. Compared with the MI group, dogs in the MI+LLVNS group had a lower VAs incidence (p < 0.05) and better left ventricular function. LLVNS significantly inhibited excessive sympathetic nerve sprouting with the evidences of decreased density of TH, GAP43 and NF positive nerves (p < 0.05). The gene expression profiling found a total of 206 genes differentially expressed between MI+LLVNS and MI dogs, mainly involved in cardiac tissue remodeling, cardiac neural remodeling, immune response and apoptosis. These genes, including 55 up-regulated genes and 151 down-regulated genes, showed more protective expressions under LLVNS.Conclusions: This study suggests that LLVNS was delivered without altering heart rate, contributing to reduced incidences of VAs and improved left ventricular function. The potential mechanisms included suppressing cardiac neuronal sprouting, inhibiting excessive sympathetic nerve sprouting and subduing pro-inflammatory responses by regulating gene expressions from a canine experimental study.