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Stroke is one of the most common neurological disorder and third most common cause of death in the world. Low vitamin D concentrations have been shown to predict risk of cardiovascular disease and all-cause of mortality. The aim of this study was to estimate serum vitamin D level in acute ischemic stroke patients. This comparative cross-sectional type of study was conducted in the Department of Neurology and Department of Medicine at Mymensingh Medical College and Hospital, Bangladesh from November 2017 to June 2019 with a total number of 100 study subjects. Total fifty patients with acute ischemic stroke were enrolled in Group A and another fifty age and sex matched volunteer subjects were enrolled in Group B with no prior history of stroke or transient ischemic attacks. Serum vitamin D levels, fasting plasma glucose and lipid profile were assessed in both groups and compared with each other. P value <0.05 was considered as significant in the study. Mean fasting blood sugar, serum fasting total cholesterol (TC), serum fasting triglycerides, serum fasting Low density lipoprotein (LDL) were significantly higher in Group A than Group B (p<0.05). Serum vitamin D level in Group A was 25.28±8.47ng/ml and in Group B was 30.90±5.80, (p=0.001). Insufficient vitamin D level was found in 52.0% of ischemic stroke patients and in 30% of healthy controls (p=0.0002). Vitamin D deficiency was found in 20.0% ischemic stroke patients and 10.0% in healthy controls. This study demonstrates a positive association between low serum vitamin D level and acute ischemic stroke. Further studies are required to determine whether vitamin D supplementation could improve functional outcome in patients with ischemic stroke.

Pancreatic cancer is one of the most adverse diseases and it is very difficult to treat because the cancer cells formed in the pancreas intertwine themselves with nearby blood vessels and connective tissue. Hence, the surgical procedure of treatment becomes complicated and it does not always lead to a cure. Histopathological diagnosis is the usual approach for cancer diagnosis. However, the pancreas remains so deep inside the body that experts sometimes struggle to detect cancer in it. Computer-aided diagnosis can come to the aid of pathologists in this scenario. It assists experts by supporting their diagnostic decisions. In this research, we carried out a deep learning-based approach to analyze histopathology images. We collected whole-slide images of KPC mice to implement this work. The pancreatic abnormalities observed in KPC mice develop similar histological features to human beings. We created random patches from whole-slide images. Then, a convolutional autoencoder framework was used to embed these patches into an integrated latent space. We applied 'information maximization', a deep learning clustering technique to cluster the identical patches in an unsupervised manner since our dataset does not have annotation. Moreover, Uniform manifold approximation and projection, a nonlinear dimension reduction technique was utilized to visualize the embedded patches in a 2-dimensional space. Finally, we calculated a few internal cluster validation metrics to determine the optimal cluster set. Our work concentrated on patch-based anomaly detection in the whole slide histopathology images of KPC mice., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Rumman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)