2dSpAn-Auto: an automated tool for analysis of two-dimensional dendritic spine images.

Background: Quantitative analysis of dendritic spine morphology and density is crucial for understanding synaptic plasticity and its role in neuropsychiatric disorders, including Alzheimer's disease and schizophrenia. While both 3D and 2D approaches exist for spine analysis, 2D methods offer advantages in computational efficiency, rapid assessment, and more reasonable to use in case of limited z-resolution images acquired through confocal and previous generation super-resolution microscopy. In this work, we developed a modality-agnostic spine segmentation approach based on 2D skeletonization.

Oyster shell powder reinforced chitosan-poly(vinyl alcohol) freeze-dried composite sponge for on-site hemorrhage control.

A composite hemostatic sponge consisting of chitosan (CS) with oyster shell powder (OSP) has been developed as a potentially sustainable composite material for controlling hemorrhage at the injury site. The system is designed assuming that Ca released by OSP will accelerate the effect of chitosan at damage sites, enhancing the overall hemostatic efficacy. The sponge was thoroughly characterized using FTIR, SEM, and EDX analysis.

Dual drug-loaded self-wearable electrospun nanofibers for synergistic pharmacological intervention through tertiary hemostasis in prehospital trauma care.

Uncontrolled bleeding is a critical concern in both wartime and civilian trauma emergencies. Current mechanical hemostatic patches do not always suffice to control bleeding while also not addressing rebleeding, which is often observed during patient transportation. The unmet clinical need has led to exploration of drug-loaded hemostat dressings, providing mechanical hemostasis as well as drug delivery at the bleeding site to stabilize the clots.

EPI-SF: essential protein identification in protein interaction networks using sequence features.

Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins.

Computational drug repurposing for viral infectious diseases: a case study on monkeypox.

The traditional method of drug reuse or repurposing has significantly contributed to the identification of new antiviral compounds and therapeutic targets, enabling rapid response to developing infectious illnesses. This article presents an overview of how modern computational methods are used in drug repurposing for the treatment of viral infectious diseases. These methods utilize data sets that include reviewed information on the host's response to pathogens and drugs, as well as various connections such as gene expression patterns and protein-protein interaction networks.

MEM-FET: Essential protein prediction using membership feature and machine learning approach.

Proteins are played key roles in different functionalities in our daily life. All functional roles of a protein are a bit enhanced in interaction compared to individuals. Identification of essential proteins of an organism is a time consume and costly task during observation in the wet lab.

In Vitro and In Vivo Bone Regeneration Assessment of Titanium-Doped Waste Eggshell-Derived Hydroxyapatite in the Animal Model.

In this work, a titanium-doped hydroxyapatite (HAp) scaffold was produced from two different sources (natural eggshell and laboratory-grade reagents) to compare the efficacy of natural and synthetic resources of HAp materials on new bone regeneration. This comparative study also reports the effect of Ti doping on the physical, mechanical, and in vitro as well as in vivo biological properties of the HAp scaffold. Pellets were prepared in the conventional powder metallurgy route, compacted, and sintered at 900 °C, showing sufficient porosity for bony ingrowth.

Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human-Coronavirus Family Interactome.

SARS-CoV-2 is a novel coronavirus that replicates itself via interacting with the host proteins. As a result, identifying virus and host protein-protein interactions could help researchers better understand the virus disease transmission behavior and identify possible COVID-19 drugs. The International Committee on Virus Taxonomy has determined that nCoV is genetically 89% compared to the SARS-CoV epidemic in 2003.

MM-CCNB: Essential protein prediction using MAX-MIN strategies and compartment of common neighboring approach.

Background And Objective: Proteins are indispensable for the flow of the life of living organisms. Protein pairs in interaction exhibit more functional activities than individuals. These activities have been considered an essential measure in predicting their essentiality.

ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19.

Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues.

PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms.

Protein function prediction is gradually emerging as an essential field in biological and computational studies. Though the latter has clinched a significant footprint, it has been observed that the application of computational information gathered from multiple sources has more significant influence than the one derived from a single source. Considering this fact, a methodology, PFP-GO, is proposed where heterogeneous sources like Protein Sequence, Protein Domain, and Protein-Protein Interaction Network have been processed separately for ranking each individual functional GO term.

Rule-Based Pruning and In Silico Identification of Essential Proteins in Yeast PPIN.

Proteins are vital for the significant cellular activities of living organisms. However, not all of them are essential. Identifying essential proteins through different biological experiments is relatively more laborious and time-consuming than the computational approaches used in recent times.

Supplier Satisfaction as a Mediating Effect on Delivery and Service: A PLS-SEM Approach in India During COVID-19 Pandemic.

Purpose: The role of suppliers is emerging to be of considerable importance given the current state of competition in times of globalization and technological advancements. Past studies have depicted considerable importance in determining supplier satisfaction for generating client satisfaction. The purpose of this article is to investigate the role that Supplier Satisfaction (SS) plays in partnering with manufacturers and traders in connection with the SME in India during the difficult COVID-19 era of business and to propose and demonstrate these collaborations.

Computational modeling of human-nCoV protein-protein interaction network.

Novel coronavirus(SARS-CoV2) replicates the host cell's genome by interacting with the host proteins. Due to this fact, the identification of virus and host protein-protein interactions could be beneficial in understanding the disease transmission behavior of the virus as well as in potential COVID-19 drug identification. International Committee on Taxonomy of Viruses (ICTV) has declared that nCoV is highly genetically similar to the SARS-CoV epidemic in 2003 (∼89% similarity).

ENResNet: A novel residual neural network for chest X-ray enhancement based COVID-19 detection.

Recently, people around the world are being vulnerable to the pandemic effect of the novel Corona Virus. It is very difficult to detect the virus infected chest X-ray (CXR) image during early stages due to constant gene mutation of the virus. It is also strenuous to differentiate between the usual pneumonia from the COVID-19 positive case as both show similar symptoms.

An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples.

Background: Machine learning is one kind of machine intelligence technique that learns from data and detects inherent patterns from large, complex datasets. Due to this capability, machine learning techniques are widely used in medical applications, especially where large-scale genomic and proteomic data are used. Cancer classification based on bio-molecular profiling data is a very important topic for medical applications since it improves the diagnostic accuracy of cancer and enables a successful culmination of cancer treatments.

RFCM-PALM: In-Silico Prediction of S-Palmitoylation Sites in the Synaptic Proteins for Male/Female Mouse Data.

S-palmitoylation is a reversible covalent post-translational modification of cysteine thiol side chain by palmitic acid. S-palmitoylation plays a critical role in a variety of biological processes and is engaged in several human diseases. Therefore, identifying specific sites of this modification is crucial for understanding their functional consequences in physiology and pathology.

Detection of spreader nodes in human-SARS-CoV protein-protein interaction network.

The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putting an end to human life. International Committee on Taxonomy of Viruses (ICTV) has reached a consensus that SARS-CoV-2 is highly genetically similar (up to 89%) to the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), which had an outbreak in 2003.

Drug repurposing for COVID-19 using computational screening: Is Fostamatinib/R406 a potential candidate?

With the gradual increase in the COVID-19 mortality rate, there is an urgent need for an effective drug/vaccine. Several drugs like Remdesivir, Azithromycin, Favirapir, Ritonavir, Darunavir, etc., are put under evaluation in more than 300 clinical trials to treat COVID-19.

Ortho_Sim_Loc: Essential protein prediction using orthology and priority-based similarity approach.

Proteins are the essential macro-molecules of living organism. But all proteins cannot be considered as essential in different relevant studies. Essentiality of a protein is thus computed by computation methods rather than biological experiments which in turn save both time and effort.

Identifying the degree of genetic interactions using Restricted Boltzmann Machine-A study on colorectal cancer.

The phenomenon of two or more genes affecting the expression of each other in various ways in the development of a single character of an organism is known as gene interaction. Gene interaction not only applies to normal human traits but to the diseased samples as well. Thus, an analysis of gene interaction could help us to differentiate between the normal and the diseased samples or between the two/more phases any diseased samples.

A Novel Human Diabetes Biomarker Recognition Approach Using Fuzzy Rough Multigranulation Nearest Neighbour Classifier Model.

The selection of gene identifier from microarray databases is a challenging task since microarray contains large number of gene attributes for a few samples. This article proposes a novel fuzzy-rough set-based gene expression features selection using fuzzy-rough reduct under multi-granular space for human diabetes patient. Firstly, fuzzy multi-granular gain has been computed from the expression datasets via fuzzy entropy which reduces the dimension of the database.

Melanoma Diagnosis Using Deep Learning and Fuzzy Logic.

Melanoma or malignant melanoma is a type of skin cancer that develops when melanocyte cells, damaged by excessive exposure to harmful UV radiations, start to grow out of control. Though less common than some other kinds of skin cancers, it is more dangerous because it rapidly metastasizes if not diagnosed and treated at an early stage. The distinction between benign and melanocytic lesions could at times be perplexing, but the manifestations of the disease could fairly be distinguished by a skilled study of its histopathological and clinical features.

Identifying essential proteins using modified-monkey algorithm (MMA).

Proteins are the most essential macromolecules needed for the normal flow of life. Essential proteins play a key role to control other proteins in an interaction network for the growth and understanding of the molecular mechanism of cellular life. Though there are many computational algorithms for essential drug discovery depending on nature of essential proteins, but still lots of improvements and optimizations are required.