Prediction of Hospital Intesive Patients Using Neural Network Algorithm

This study aims to predict whether the patient deserves to be inpatient or outpatient by comparing several machine learning techniques, namely, logistic regression, decision tree, neural network, random forest, gradient boosting. The research method uses three stages of research, namely data collection, data preprocessing, and data modeling. Implementation of program code using google colab and python programming language. The dataset used as the research sample is Electronic Health Record Predicting data. Based on the accuracy results generated in this study, the use of the Neural Network machine learning algorithm to predict hospitalization decisions for patients has proven to be a machine learning algorithm that has the highest accuracy rate reaching 74, 47% compared to other comparison machine learning algorithms, namely logistic regression, decision tree, neural network, random forest, gradient boosting

Development of immunohistochemical triple staining method for the evaluation of different types of cell death in coronary thrombus

Immunohistochemistry is a powerful technique to identify the presence, distribution and extent of certain antigens at protein level in tissue specimen. Multiple immunostainings enable the detection, colocalization and comparison of several markers within one specimen. However, an optimization study is firstly needed to ensure the expression of multiple markers is clear and distinguishable. We formulated an optimized sequential triple immunostaining protocol which could identify the simultaneous presence of three types of cell death namely necrosis, apoptosis and etosis. These following antibodies were used: C-reactive protein (CRP, necrosis), (cleaved) Caspase-3 (Casp3, apoptosis) and citrullinated-Histone3 (CitH3, etosis). Several antigen retrieval methods, various concentration and order combinations of those antibodies as well as different combination color of chromogens were tested on coronary thrombus materials obtained from patients with acute myocardial infarction (AMI). The results showed that CRP (1:4000) visualized with 3,3'-diaminobenzidine (DAB) in brown, is better performed as the first staining, followed by CitH3 (1:8000) visualized with Perma Blue as the second staining and Casp3 (1:500) visualized with Perma Red as the last staining. In conclusion, we presented an immunohistochemical triple staining protocol to identify the comparative presence of different types of cell deaths: Necrosis, apoptosis and etosis in coronary thrombus specimens

Immunophenotypic analysis of the chronological events of tissue repair in aortic medial dissections

Acute medial dissection of aorta can occur in the context of a sudden and unexpected death. For medico-legal reasons it is important to estimate as accurately the histological age of dissections. We evaluated the additional value of a systematic application of immunohistochemistry, compared with conventional histology only, in determining chronological steps of injury and repair. Thirty two paraffin embedded specimens of aortic dissection were retrospectively allocated to one of four defined stages: acute (I), subacute (II), early organizing (III) and scarring (IV) using Hematoxylin and Eosin and Elastica van Gieson stained sections. Subsequent immunohistochemically staining was performed with the following markers: (myeloperoxidase (neutrophils), citrullinated-Histone 3 (neutrophil extracellular traps), CD68 (macrophages), CD3 (T-cells), CD31 and CD34 (endothelial cells), and smooth muscle actin. Immune stained sections were scored semi-quantitatively. Histologically, five cases were identified as stage I, 16 as II, 7 as III and 4 as IV. Additional immunostaining for smooth muscle cells and endothelial cells altered the classification in 25% of cases (all in groups II and III). Immunostaining and semi-quantitative grading of involvement of neutrophils, macrophages and NETs also provided specific distribution patterns over the 4 age categories, including unexpected involvement of the peri adventitial fat tissue. In conclusion, it appears that semi-quantitative immunohistochemistry of resident vascular wall cells, inflammatory cells and NETS represents a useful adjunct in detailed histopathological grading of the chronological age of aortic dissections