Pulmonary tuberculosis presenting as a hilar mass.

Tuberculosis (TB) is a great mimicker due to its various unusual and atypical presentations. Mass-like lesions in thoracic radiology may raise the suspicion of lung malignancy. A man in his early 50s complained of cough, low-grade fever and dyspnoea.

A robust Parkinson's disease detection model based on time-varying synaptic efficacy function in spiking neural network.

Parkinson's disease (PD) is a neurodegenerative disease affecting millions of people around the world. Conventional PD detection algorithms are generally based on first and second-generation artificial neural network (ANN) models which consume high energy and have complex architecture. Considering these limitations, a time-varying synaptic efficacy function based leaky-integrate and fire neuron model, called SEFRON is used for the detection of PD.

Structural determinants of co-translational protein complex assembly.

Protein assembly into functional complexes is critical to life's processes. While complex assembly is classically described as occurring between fully synthesized proteins, recent work showed that co-translational assembly is prevalent in human cells. However, the biological basis for the existence of this process and the identity of protein pairs that assemble co-translationally remain unknown.

The positive implication of natural antioxidants on oxidative stress-mediated diabetes mellitus complications.

The complementary intervention to modulate diabetes mellitus (DM) metabolism has recently brought the global attention, since DM has become among the global burden diseases. Where, several related pathways elevate the production of superoxide in consequences. For example, the flux of glycation-derived end products (AGEs) could lead to the deactivation of insulin signaling pathways.

Ultra-wideband microstrip folded antenna for wireless LAN, 5G and Internet of Things applications.

This paper presents an ultra-wideband (UWB) microstrip antenna with a simple structure operating at 2.2-6 GHz bandwidth. The proposed antenna has been demonstrated for its suitability in UWB and multi-band applications, such as wireless local area network (WLAN), Wi-Fi, 5G, Internet of Things (IoT), etc.

Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models.

Relapse and metastasis occur in 30-40% of breast cancer patients, even after targeted treatments like trastuzumab for HER2-positive breast cancer. Accurate individual prognosis is essential for determining appropriate adjuvant treatment and early intervention. This study aims to enhance relapse and metastasis prediction using an innovative framework with machine learning (ML) and ensemble learning (EL) techniques.

EfficientNet-resDDSC: A Hybrid Deep Learning Model Integrating Residual Blocks and Dilated Convolutions for Inferring Gene Causality in Single-Cell Data.

Gene Regulatory Networks (GRNs) reveal complex interactions between genes in organisms, crucial for understanding the life system's operation. The rapid development of biotechnology, especially single-cell RNA sequencing (scRNA-seq), has generated a large amount of scRNA-seq data, which can be analyzed to explore the regulatory relationships between genes at the single-cell level. Previous models used to construct GRNs mainly aim at constructing associative relationships between genes, but usually fail to accurately reveal the causality between genes.

A Case Report of Larsen's Syndrome, Antiphospholipid Syndrome, Diaphragmatic Hernia, and a Colon Polyp: A Hidden Association or a Mere Coincidence.

Larsen's syndrome is an exceedingly rare and debilitating skeletal disorder characterized by multiple bony deformities and joint contractures. A 47-year-old female grappled with its severe manifestations, including functional quadriplegia, limb atrophy, and various orthopedic surgeries. This is the only reported case of the concurrent presence of antiphospholipid antibody syndrome (APLA) and Larsen's syndrome.

Active Learning with Particle Swarm Optimization for Enhanced Skin Cancer Classification Utilizing Deep CNN Models.

Skin cancer is a critical global health issue, with millions of non-melanoma and melanoma cases diagnosed annually. Early detection is essential to improving patient outcomes, yet traditional deep learning models for skin cancer classification are often limited by the need for large, annotated datasets and extensive computational resources. The aim of this study is to address these limitations by proposing an efficient skin cancer classification framework that integrates active learning (AL) with particle swarm optimization (PSO).

Prostate cancer prognosis using machine learning: A critical review of survival analysis methods.

Prostate Cancer is a disease that affects the male reproductive system. The irregularity of the symptoms makes it hard for the clinicians to pinpoint the disease in the earlier stages. Techniques such as Machine Learning, Data Science, Deep Learning, etc.

Heuristic machine learning approaches for identifying phishing threats across web and email platforms.

Life has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat. Without a doubt, phishing is one of the rising concerns that leads to stealing vital information such as passwords, security codes, and personal data from any target node through communication hijacking techniques.

The arginine/ornithine binding protein ArgT plays an essential role in / to prevent intracellular killing and contribute to chronic persistence in the host.

species are facultative intracellular bacterial pathogens that cause the contagious zoonotic disease, brucellosis. spp. infect a wide range of animals, including livestock, wild animals, and marine mammals.

Epigenetic frontiers: miRNAs, long non-coding RNAs and nanomaterials are pioneering to cancer therapy.

Cancer has arisen from both genetic mutations and epigenetic changes, making epigenetics a crucial area of research for innovative cancer prevention and treatment strategies. This dual perspective has propelled epigenetics into the forefront of cancer research. This review highlights the important roles of DNA methylation, histone modifications and non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long non-coding RNAs, which are key regulators of cancer-related gene expression.

Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing.

Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. We systematically varied PCA components and implemented a stacking model comprising random forest, decision tree, and K-nearest neighbors (KNN).

Ribosomal computing: implementation of the computational method.

Background: Several computational and mathematical models of protein synthesis have been explored to accomplish the quantitative analysis of protein synthesis components and polysome structure. The effect of gene sequence (coding and non-coding region) in protein synthesis, mutation in gene sequence, and functional model of ribosome needs to be explored to investigate the relationship among protein synthesis components further. Ribosomal computing is implemented by imitating the functional property of protein synthesis.

Assessing the effect of therapeutic level of oxytetracycline dihydrate on pharmacokinetics and biosafety in Oncorhynchus mykiss (Walbaum, 1792).

The aim of the experiment was to investigate the pharmacokinetics of oxytetracycline dihydrate after a single oral administration of 80 mg kg day in rainbow trout and assess its biosafety at concentration of 80, 240, 400, and 800 mg kg day over 30 days, focusing on various aspects such as effective feed consumption, physiological responses, drug tolerance, and detection of low drug concentrations in rainbow trout. The pharmacokinetics study spanned a duration of 5 days, while the assessment of biosafety extended for a 30-day safety margin, followed by a subsequent 10-day residual analysis. Pharmacokinetic analysis revealed slow absorption with low-rate constant in tissues.

Syntheses of 3-(2-Nitrovinyl)-indoles, Benzo[]carbazoles, Naphtho[2,1-]carbazoles, and 1-Hydroxy-β-carbolines Lead to Identification of Antiproliferative Compounds Active under Hypoxia.

Herein, we describe a novel reaction between C-2-substituted indoles and 2-nitroacetophenones leading to a variety of indole-containing heterocyclic scaffolds. At 60 °C in AcOH with HSO as catalyst, C-2 aryl indoles give 3-(2-nitrovinyl)-indoles with high or geometric selectivity depending on the type of substrate utilized. These compounds undergo an electrocyclization process in a sealed vial in a microwave apparatus in DMF at 250 °C to give benzo[]carbazoles and naphtho[2,1-]carbazoles depending on whether the C-2 aromatic moiety is phenyl or naphthyl.

Emergence of Aeromonas salmonicida subsp. masoucida MHJM250: unveiling pathological characteristics and antimicrobial susceptibility in golden mahseer, Tor putitora (Hamilton, 1822) in India.

Aeromonas salmonicida subsp. masoucida, designated as laboratory strain MHJM250, was characterized from a naturally infected farmed golden mahseer, Tor putitora. The infected fish exhibited clinical signs of erosion at the caudal fin and hemorrhage onx the ventral body surface.

A review on advancements in feature selection and feature extraction for high-dimensional NGS data analysis.

Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results.