Identification of key signaling pathways and novel computational drug target for oral cancer, metabolic disorders and periodontal disease.

Aim: Due to conventional endocrinological methods, there is presently no shared work available, and no therapeutic options have been demonstrated in oral cancer (OC) and periodontal disease (PD), type 2 diabetes (T2D), and obese patients. The aim of this study is to determine the similar molecular pathways and potential therapeutic targets in PD, OC, T2D, and obesity that may be used to anticipate the progression of the disease.

Methods: Four Gene Expression Omnibus (GEO) microarray datasets (GSE29221, GSE15773, GSE16134, and GSE13601) are used for finding differentially expressed genes (DEGs) for T2D, obese, and PD patients with OC in order to explore comparable pathways and therapeutic medications.

A robust deep learning approach for identification of RNA 5-methyluridine sites.

RNA 5-methyluridine (m5U) sites play a significant role in understanding RNA modifications, which influence numerous biological processes such as gene expression and cellular functioning. Consequently, the identification of m5U sites can play a vital role in the integrity, structure, and function of RNA molecules. Therefore, this study introduces GRUpred-m5U, a novel deep learning-based framework based on a gated recurrent unit in mature RNA and full transcript RNA datasets.

Unraveling the potential effects of non-synonymous single nucleotide polymorphisms (nsSNPs) on the Protein structure and function of the human gene on type 2 diabetes and colorectal cancer: An approach.

Background And Aims: The single nucleotide polymorphisms (SNPs) in gene have been recognized as contributing to type 2 diabetes (T2D) susceptibility and colorectal cancer. This study aims to predict the structural stability, and functional impacts on variations in non-synonymous SNPs (nsSNPs) in the human gene using various computational techniques.

Materials And Methods: Several tools, including SIFT, Predict-SNP, SNPs&GO, MAPP, SNAP2, PhD-SNP, PANTHER, PolyPhen-1,PolyPhen-2, I-Mutant 2.

Exploring the therapeutic targets of stevioside in management of type 2 diabetes by network pharmacology and in-silico approach.

Aims: The main objective of the current study is to investigate the pathways and therapeutic targets linked to stevioside in the management of T2D using computational approaches.

Methods: We collected RNA-seq datasets from NCBI, then employed GREIN to retrieve differentially expressed genes (DEGs). Computer-assisted techniques DAVID, STRING and NetworkAnalyst were used to explore common significant pathways and therapeutic targets associated with T2D and stevioside.

AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features.

Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs.

Au-TiO-Coated Spectroscopy-Based Human Teeth Disorder Detection Sensor: Design and Quantitative Analysis.

Human tooth functionality is the most important for the human body to become fit and healthy. Due to the disease attacks in human teeth, parts may lead to different fatal diseases. A spectroscopy-based photonic crystal fiber (PCF) sensor was simulated and numerically analyzed for the detection of dental disorders in the human body.

PreOBP_ML: Machine Learning Algorithms for Prediction of Optical Biosensor Parameters.

To develop standard optical biosensors, the simulation procedure takes a lot of time. For reducing that enormous amount of time and effort, machine learning might be a better solution. Effective indices, core power, total power, and effective area are the most crucial parameters for evaluating optical sensors.

A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron.

Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset.

Design and Analysis of Multi-Analyte Detection Based Biosensor in the Visible to Near-Infrared (VNIR) Region.

This manuscript introduces a highly sensitive dual-core photonic crystal fiber (PCF) based multi-analyte surface plasmon resonance (SPR) sensor, possessing the ability to detect multiple analytes at once. A chemically stable thin plasmonic substance of gold (Au) layer, holding a thickness of 30 nm, is employed to the outer portion of the stated design that manifests a negative real permittivity. Moreover, an ultra-thin film of aluminum oxide (AlO) , having a thickness of 10 nm, is inserted into the exterior of the gold film to calibrate the resonance wavelength as well as magnify the coupling strength.

[Pacemaker infection in fragile patients].

Complications associated with cardiac implantable electric devices (CIED) are manifold. They include lead dislocation, twiddler's syndrome, device malfunction, haematoma formation and infection. Infections can be divided into acute, subacute and late infections.

Semantically preserving adversarial unsupervised domain adaptation network for improving disease recognition from chest x-rays.

Supervised deep learning methods have been successfully applied in medical imaging. However, training deep learning systems often requires ample annotated data. Due to cost and time restrictions, not all collected medical images, e.

High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images.

In this study, multiple lung diseases are diagnosed with the help of the Neural Network algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia, Pneumothorax, Atelectasis, Edema, Effusion, Hernia, Cardiomegaly, Pulmonary Fibrosis, Nodule, and Consolidation, are studied from the ChestX-ray14 dataset. A proposed fine-tuned MobileLungNetV2 model is employed for analysis.

Drug Repurposing and Systems Biology approaches of Enzastaurin can target potential biomarkers and critical pathways in Colorectal Cancer.

Colorectal cancer (CRC) is a severe health concern that results from a cocktail of genetic, epigenetic, and environmental abnormalities. Because it is the second most lethal malignancy in the world and the third-most common malignant tumor, but the treatment is unavailable. The goal of the current study was to use bioinformatics and systems biology techniques to determine the pharmacological mechanism underlying putative important genes and linked pathways in early-onset CRC.

A flexible two-photon fiberscope for fast activity imaging and precise optogenetic photostimulation of neurons in freely moving mice.

We developed a flexible two-photon microendoscope (2P-FENDO) capable of all-optical brain investigation at near cellular resolution in freely moving mice. The system performs fast two-photon (2P) functional imaging and 2P holographic photostimulation of single and multiple cells using axially confined extended spots. Proof-of-principle experiments were performed in freely moving mice co-expressing jGCaMP7s and the opsin ChRmine in the visual or barrel cortex.

Terahertz Metasurface-Based Refractive Index Sensor for Amino Acid Detection: A Numerical Approach.

A graphene disk metasurface-inspired refractive index sensor (RIS) with a subwavelength structure is numerically investigated to enhance the functionality of flexible metasurface in the biosensor sector. The main aim behind the sensor development is to detect amino acids with high sensitivity. The results in form of transmittance and the electric field intensity are carried out to verify the sensor's performance.

Recent Advances in Biosensors for Detection of COVID-19 and Other Viruses.

This century has introduced very deadly, dangerous, and infectious diseases to humankind such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2 commonly known as COVID-19 and have caused epidemics and pandemics across the globe. For some of these diseases, proper medications, and vaccinations are missing and the early detection of these viruses will be critical to saving the patients. And even the vaccines are available for COVID-19, the new variants of COVID-19 such as Delta, and Omicron are spreading at large.

Evidence for charge-based mimicry in anti dsDNA antibody generation.

Mechanisms for the generation of anti-dsDNA autoantibodies are still not completely elucidated. One theory states that dsDNA interacts for mimicry with antibodies raised versus other antigens but molecular features for mimicry are unknown. Here we show that, at physiological acid-base balance, anti-Annexin A1 binds IgG2 dsDNA in a competitive and dose-dependent way with Annexin A1 and that the competition between the two molecules is null at pH 9.

MLBioIGE: integration and interplay of machine learning and bioinformatics approach to identify the genetic effect of SARS-COV-2 on idiopathic pulmonary fibrosis patients.

SARS-CoV-2, the virus that causes COVID-19, is a current concern for people worldwide. The virus has recently spread worldwide and is out of control in several countries, putting the outbreak into a terrifying phase. Machine learning with transcriptome analysis has advanced in recent years.

Discovering Common Pathophysiological Processes between COVID-19 and Cystic Fibrosis by Differential Gene Expression Pattern Analysis.

Coronaviruses are a family of viruses that infect mammals and birds. Coronaviruses cause infections of the respiratory system in humans, which can be minor or fatal. A comparative transcriptomic analysis has been performed to establish essential profiles of the gene expression of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) linked to cystic fibrosis (CF).

Mechanistic Basis for the Role of Phytochemicals in Inflammation-Associated Chronic Diseases.

Chronic inflammatory diseases occur in a large portion of the population and are associated with a poor diet. Key natural products found in fruits and vegetables may assist in lowering inflammation associated with chronic diseases such as obesity, diabetes, cardiovascular diseases, and cancer. This review seeks to examine the roles of several natural products, resveratrol (RES), quercetin (QUE), curcumin (CUR), piperine (PIP), epigallocatechin gallate (EGCG), and gingerol (GIN), in their ability to attenuate inflammatory markers in specific diseases states.

Federated Machine Learning for Detection of Skin Diseases and Enhancement of Internet of Medical Things (IoMT) Security.

Human skin disease, the most infectious dermatological ailment globally, is initially diagnosed by sight. Some clinical screening and dermoscopic analysis of skin biopsies and scrapings for accurate classification are medically compulsory. Classification of skin diseases using medical images is more challenging because of the complex formation and variant colors of the disease and data security concerns.

Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach.

Background: Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB.