Utilizing P-Scores to Quantify Excess Mortality During the COVID-19 Pandemic

Death numbers have been reported since the beginning of the COVID-19 pandemic, but they may not represent the true impact of the pandemic. Excess mortality is a robust metric and a critical indicator of the impact of the COVID-19 pandemic. P-scores provide estimates of excess mortality and have been widely utilized in many COVID-19 studies around the world. P-score analyses have revealed more pandemic-associated deaths than official COVID-19 statistics alone. While a substantial proportion of excess mortality during the COVID-19 pandemic can be directly attributed to the virus infection itself, mortality from major non-infectious chronic diseases substantially contributed to an increase in excess mortality P-scores. Thoughtful considerations of approaches to defining counterfactual mortality are important for ensuring the validity of calculated P-scores.

 Read More About This Article: 10.31031/OABB.2024.03.000569

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The First Experience of Tomographic Representation of an ECG-Signal

At present, spectacular progress is achieved in the development of probabilistic approach to the basic principles of quantum mechanics (for instance, see [1]). This approach is based on tomographic representation of density matrix and the Wigner function [2,3], yielded by the Radon transform [4]. The probabilistic approach made it possible to apply the methods of quantum mechanics in description of various signals. The most comprehensive description of this method is given in [5]. Especially interesting are the attempts to use the tomographic approach to transform and analyze the medical signals [6,7]. When processing such signals, the Fourier transform is widely used, representing the signal as a function of the frequency ω. This transform is currently a common tool in the analysis of Electrocardiograms (ECGs). The tomographic transformation makes it possible to represent the signal as a positively defined function that simultaneously depends on both time and frequency. Recently, the tomographic representation was applied for processing some biological signals [8,9]. These studies were carried out to evaluate new perspectives that open up when analyzing a human ECG using tomographic representation.

Tomographic Representation

The tomographic representation can be introduced as a generalization of the Wigner function to a positive definite distribution [10]. A rigorous description of this mathematical transformation is given in [5], and a detailed description of its application to the analysis of some biological signals is given in [8,9]. Here we will give a brief description of the essence of the tomographic representation.

Read More About This Article: 10.31031/OABB.2024.03.000568

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Managing Pandemic Disasters: A Resource-Based Analysis

Healthcare crises are well-known phenomena that occur and are highly disruptive. Consider, for example, Covid19 and other pandemics, wars, multi-casualty natural disasters, industrial disasters, etc. Healthcare systems in Brazil, Spain, Italy, and more around the world collapsed due to the lack of a clear methodology for handling such crises. Following a largescale value creation project with a large medical center we developed a resource-based methodology to cope with three healthcare crisis scenarios: “Peace”, “War” and “Tsunami”.

We identified three load/capacity scenarios for hospitals: “Peace”, “War” and “Tsunami”. The Peacetime scenario is the normal overload situation in a hospital. Figure 1 during “Peace” times, hospitals are usually 10%-20% short in resources. To overcome bottlenecks, managers apply evolutionary methods such as constraint management [1], the complete kit concept, etc. This is the normal condition in hospitals where there are fluctuations in supply and demand. Bottlenecks in this situation are typically medical crews: senior physicians and experienced nurses.

Read More About This Article: 10.31031/OABB.2024.03.000567

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Artificial Cell Membranes as Bioinformation Hubs: Unraveling Therapeutic Networks through Nano-Informatics

In this brief the innovative realm of artificial cell membranes as bioinformation hubs, specifically focusing on their role in creating therapeutic networks, is proposed. The integration of advanced nanotechnologies, bioinformatics, cheminformatics, and medical informatics has paved the way for the development of in silico tools that facilitate the simulation of interactions and mechanisms of toxicity in therapeutic products, particularly in drug delivery nanosystems. Various specialized cloud platforms incorporate libraries of diverse nanomaterials with comprehensive morphological and biological data, enabling correlation with potential adverse effects. Such tools prove invaluable in guiding the pharmaceutical industry in the development of innovative therapeutical formulations and aiding regulatory agencies in evaluating decision-making nanoplatforms. Finally, in this opinion-article we support the idea the cell membranes could be considered as bioinformation hubs and artificial pharmaceutical nanoplatforms.

 Read More About This Article: https://crimsonpublishers.com/oabb/fulltext/OABB.000566.php

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Breast Cancer Prediction Using Bayesian Logistic Regression_Crimson Publishers

 Breast Cancer Prediction Using Bayesian Logistic Regression by Ashok K Singh in Open Access Biostatistics & Bioinformatics

Prediction of breast cancer based upon several features computed for each subject is a binary classification problem. Several discriminant methods exist for this problem, some of the commonly used methods are: Decision Trees, Random Forest, Neural Network, Support Vector Machine (SVM), and Logistic Regression (LR). Except for Logistic Regression, the other listed methods are predictive in nature; LR yields an explanatory model that can also be used for prediction, and for this reason it is commonly used in many disciplines including clinical research. In this article, we demonstrate the method of Bayesian LR to predict breast cancer using the Wisconsin Diagnosis Breast Cancer (WDBC) data set available at the UCI Machine Learning Repository.

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Lindemann’s Conjecture_Crimson Publishers

Lindemann’s Conjecture by Kostas Psaromiligkos in Open Access Biostatistics & Bio information


X. Sun’s description of co-pair wise right-reducible groups was a milestone in convex K-theory. Now it is well known that every ideal is irreducible. A useful survey of the subject can be found in [4]. Here, injectivity is trivially a concern. Every student is aware that there exists arithmetic and injective Riemannian, minimal isometry. We wish to extend the results of [5] to ordered primes. In, the main result was the derivation of Minkowski [6], unconditionally contra-complex measure spaces. This reduces the results of [7] to standard techniques of analysis.

https://crimsonpublishers.com/abb/fulltext/OABB.000524.php

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