Machine learning driven optimization of compressive strength of 3D printed bio polymer composite material.

3D printing has brought significant changes to manufacturing sectors, making it possible to produce intricate, multi-layered designs with greater ease. This study focuses on optimizing the compressive strength (CS) of functionally graded multi-material (PLA/Almond Shell Reinforced PLA) which is fabricated with the aid of the FFF process, a widely used additive manufacturing technique. Six different machine learning models (ML) were utilized to estimate CS using key process parameters, namely print speed (PS), layer height (LH), and printing temperature (PT).

Neuroprotective Potential of Jimson Weed in Methotrexate-Induced Neurotoxicity: Insights into Anti-Oxidative, Anti-Inflammatory, and Anti-Apoptotic Mechanisms via Modulation of Caspase-3, Interleukin-6, and Tumor Necrosis Factor-Alpha: In Silico.

Objective: Methotrexate (MTX) is a drug of choice for the treatment of different types of cancers and autoimmune disorders. Despite its effectiveness, its toxicity is the major drawback of its use. It is a chemotherapy drug known to cause neurotoxicity, leading to oxidative stress, inflammation, and apoptosis in the brain.

Hybrid molecule SA-10 and its PLGA nanosuspension protect human and rodent retinal ganglion cells against neuronal injury.

Background: Glaucoma is a leading cause of blindness characterized by retinal ganglion cell (RGC) degeneration. SA-10, a dual-acting compound with ROS scavenging and NO-donating properties, was evaluated to enhance RGC survival and function in models of oxidative stress, ischemia/reperfusion (I/R) injury, and neurotrophic factor (NF) deprivation.

Methods: SA-10-loaded nanoparticles (SA-10-NP) with a size of 279.

Fabrication and comprehensive experimental evaluation of surfactant-activated PEDOT:PSS/SnO thin films deposited via spin coating for advanced sensing applications.

This research investigates the fabrication of surfactant-mixed tin oxide (SnO) nanostructured thin films on a fluorine-doped tin oxide (FTO) substrate via hydrothermal synthesis, focusing on their structural, morphological, optical, and electrical properties for sensor applications. To examine the effect of surfactant concentration, cetyltrimethylammonium bromide (CTAB) was incorporated at varying weight percentages (0%, 6%, 11%, 16%, and 20%), resulting in five distinct sensor samples, labelled SnO-1, SnO-2, SnO-3, SnO-4, and SnO-5, respectively. X-Ray Diffraction (XRD) analysis confirms a tunable crystallite size from 12.

CTAB modified SnO PEDOT PSS heterojunction humidity sensor with enhanced sensitivity stability and machine learning evaluation.

This study presents the development of a high-performance resistive humidity sensor based on a cetyltrimethylammonium bromide (CTAB)-assisted tin oxide (SnO₂) nanostructured thin film integrated with a Poly(3,4-ethylenedioxythiophene): Poly(styrenesulfonate) (PEDOT: PSS)/SnO₂ heterojunction. The sensor design incorporates CTAB at varying weight percentages (0%, 6%, 11%, 16%, and 20%) during the hydrothermal synthesis of SnO₂ to regulate crystal growth, morphology, and surface area. The sample with 20 wt% CTAB (SnO-5) exhibited a flower-like stacked nanostructure, confirmed via field emission scanning electron microscopy (FESEM), which significantly enhanced water molecule adsorption and charge transport pathways.

Robust Brain Tumor Detection and Classification From Multichannel MRI Using Deep Learning.

Brain tumor detection and classification from multichannel magnetic resonance imaging (MRI) using deep learning techniques for an accurate detection and classification of brain tumors from multichannel MRI are essential for guiding effective treatment strategies and improving patient outcomes. Traditional methods often struggle with handling large volumes of MRI data, leading to limitations in both efficiency and reliability. This study aims to develop a robust approach for brain tumor detection and classification by leveraging computer vision and deep learning techniques, addressing the limitations of conventional methods.

ABCE1 facilitates tumour progression via aerobic glycolysis and inhibits cell death in human colorectal cancer cells through the p53 signalling pathway.

Colorectal cancer (CRC) poses a formidable challenge to global health, necessitating the quest for novel biomarkers to improve therapeutic strategies. This study explores ABCE1 (ATP-binding cassette subfamily E member 1) as a potential biomarker for CRC and delves into its intricate molecular mechanisms. Through integrated bioinformatic analyses, this study underscores the significant oncogenic role of ABCE1 in CRC, opening new avenues for promising therapeutic interventions.

Prostate cancer prediction through a hybrid deep learning method applied to histopathological image.

Background: Prostate Cancer (PCa) is a severe disease that affects males globally. The Gleason grading system is a widely recognized method for diagnosing the aggressiveness of PCa using histopathological images. This system evaluates prostate tissue to determine the severity of the disease and guide treatment decisions.

Development and prospective implementation of a large language model based system for early sepsis prediction.

Sepsis is a dysregulated host response to infection with high mortality and morbidity. Early detection and intervention have been shown to improve patient outcomes, but existing computational models relying on structured electronic health record data often miss contextual information from unstructured clinical notes. This study introduces COMPOSER-LLM, an open-source large language model (LLM) integrated with the COMPOSER model to enhance early sepsis prediction.

Carpal tunnel release surgery restores force control but not force coordination in CTS patients.

Efficient manipulation of objects is crucial for daily interactions with the world. Compression neuropathies like carpal tunnel syndrome (CTS) can have detrimental effects on hand function, which can be improved by surgical intervention. Twelve patients with CTS grasped and lifted an instrumented handle while maintaining static equilibrium under different torque conditions.

Health risk assessment of Po and Pb due to consumption of dried fish from natural high background radiation areas of Kanyakumari coast, Tamil Nadu, India.

This study investigated the concentrations of naturally occurring radionuclides, specifically polonium-210 (Po) and Lead-210 (Pb), and their associated health risks in dried fish samples collected from Natural High Background Radiation Areas (NHBRAs) along the Kanyakumari coast, India. Samples of water, sediment and twelve dried fish species were collected from seven coastal locations known for monazite-rich beach sands. Following rigorous electrochemical separation, low background Alpha and Beta counting system was employed to determine the activity concentrations of the radionuclides.

Quadratic discriminant feature selected broken stick regressive deep convolution neural learning classification for turmeric crop yield prediction.

In this study, a novel technique termed Quadratic Discriminant Feature Selected Broken Stick Regressive Deep Convolution Neural Learning Classification (QDFSBSRDCNLC) Technique is proposed for disease classification and hence yields prediction of turmeric crop. Initially, we gathered the images of turmeric crops with and without diseases. The images are collected from the turmeric research field at Bhavanisagar.

Biodistribution of Reconstituted High-Density Lipoprotein Nanoparticles for Targeted Delivery to Retinal Ganglion Cells.

Nanoparticle-based drug delivery systems offer a promising approach for overcoming the challenges of ocular drug delivery. Our study evaluated the biodistribution and potential targeting of reconstituted high-density lipoprotein nanoparticles (rHDL NPs) loaded with near-infrared dye IR780 to retinal ganglion cells (RGCs) and optic nerve head astrocytes (ONHAs) as a model for neuroprotective drug delivery in glaucoma. A stable rHDL-payload complex was formulated using IR780, phosphatidylcholine, and apolipoprotein A-I (Apo A-I) by using a novel preparation method.

Fluorescence Resonance Energy Transfer for Drug Loading Assessment in Reconstituted High-Density Lipoprotein Nanoparticles.

Reconstituted high-density lipoprotein nanoparticles (NPs), which mimic the structure and function of endogenous human plasma HDL, hold promise as a robust drug delivery system. These nanoparticles, when loaded with appropriate agents, serve as powerful tools for targeted drug delivery. The fundamental challenge lies in controlling and estimating the actual drug load and the efficiency of drug release at the target.

Development and Prospective Implementation of a Large Language Model based System for Early Sepsis Prediction.

Sepsis is a dysregulated host response to infection with high mortality and morbidity. Early detection and intervention have been shown to improve patient outcomes, but existing computational models relying on structured electronic health record data often miss contextual information from unstructured clinical notes. This study introduces COMPOSER-LLM, an open-source large language model (LLM) integrated with the COMPOSER model to enhance early sepsis prediction.

Health risk assessment of natural radionuclides through the consumption of fish species in Pazhaverkadu Lagoon, Southeast Coast of India.

Radionuclides can bioaccumulate in the bodies of aquatic creatures, albeit to trace amounts in the surrounding water. Due of its importance as a fishing destination in South India, the current study is being conducted in the Pazhaverkadu lagoon. For 20 distinct edible fish species, the average fish bone gross alpha and beta activity were 13.

A Neuroprotective Peptide Modulates Retinal cAMP Response Element-Binding Protein (CREB), Synapsin I (SYN1), and Growth-Associated Protein 43 (GAP43) in Rats with Silicone Oil-Induced Ocular Hypertension.

This study evaluated the neuroprotective potential of peptain-1 conjugated to a cell-penetrating peptide (CPP-P1) in an ocular hypertension model of glaucoma. Brown Norway (BN) rats were subjected to intraocular pressure (IOP) elevation via intracameral injection of silicone oil (SO), with concurrent intravitreal injections of either CPP-P1 or a vehicle. Retinal cross-sections were analyzed for markers of neuroprotection, including cAMP response element-binding protein (CREB), phosphorylated CREB (p-CREB), growth-associated protein-43 (GAP43), synapsin-1 (SYN1), and superoxide dismutase 2 (SOD2).

Molecular mechanisms underlying the effects of beta-sitosterol on TGF-β1/Nrf2/SIRT1/p53-mediated signaling in the kidney of a high-fat diet and sucrose-induced type-2 diabetic rat.

Diabetic nephropathy, a severe problem of diabetes mellitus, is exacerbated by high-fat diets, prompting a need for interventions. Previous study from our laboratory has shown that β-sitosterol, a potent plant sterol has anti-inflammatory and glucose-lowering efficacy by involving insulin metabolic signalling pathway but its role on anti-oxidant signaling pathways, play a crucial role in mitigating oxidative stress and inflammation associated diabetic nephropathy, highlighting its importance as a potential therapeutic target for managing this debilitating complication of diabetes is unknown. This study was aimed to intricate the molecular mechanisms involved in the potential of β-sitosterol (BSIT) on TGF-β1/Nrf2/SIRT1/p53 signaling in high fat diet (HFD) and sucrose induced diabetic nephropathy (DN) in the rat kidney by employing various comprehensive bioinformatic analysis.

Harnessing the Potential of Natural Composites in Biomedical 3D Printing.

Natural composites are emerging as promising alternative materials for 3D printing in biomedical applications due to their biocompatibility, sustainability, and unique mechanical properties. The use of natural composites offers several advantages, including reduced environmental impact, enhanced biodegradability, and improved tissue compatibility. These materials can be processed into filaments or resins suitable for various 3D printing techniques, such as fused deposition modeling (FDM).

Large Language Models for More Efficient Reporting of Hospital Quality Measures.

Hospital quality measures are a vital component of a learning health system, yet they can be costly to report, statistically underpowered, and inconsistent due to poor interrater reliability. Large language models (LLMs) have recently demonstrated impressive performance on health care-related tasks and offer a promising way to provide accurate abstraction of complete charts at scale. To evaluate this approach, we deployed an LLM-based system that ingests Fast Healthcare Interoperability Resources data and outputs a completed Severe Sepsis and Septic Shock Management Bundle (SEP-1) abstraction.

A Reduction in Mitophagy Is Associated with Glaucomatous Neurodegeneration in Rodent Models of Glaucoma.

Glaucoma is a heterogenous group of optic neuropathies characterized by the degeneration of optic nerve axons and the progressive loss of retinal ganglion cells (RGCs), which could ultimately lead to vision loss. Elevated intraocular pressure (IOP) is a major risk factor in the development of glaucoma, and reducing IOP remains the main therapeutic strategy. Endothelin-1 (ET-1), a potent vasoactive peptide, has been shown to produce neurodegenerative effects in animal models of glaucoma.

The role of exercise in the prevention and treatment of Alzheimer's disease and mild cognitive impairments.

Large retrospective cohort studies have consistently shown that people who exercise regularly are at a markedly reduced risk of dementias such as Alzheimer's Disease (AD). Animal studies have also found that exercise can prevent cognitive decline, and recent studies have identified possible mechanisms. However, randomized controlled trials of exercise interventions in AD and mild cognitive impairment have not reached a consensus regarding the efficacy of this treatment, hampering clinical adoption of this technique.

Identification of FOXM1 as a novel protein biomarker and therapeutic target for colorectal cancer progression: Evidence from immune infiltration and bioinformatic analyses.

Colorectal cancer (CRC) remains a major global health challenge, with its underlying molecular mechanisms, particularly the role of FOXM1, not yet fully understood. This study employed an integrated approach combining bioinformatics along with experimental validation to explore the role of FOXM1 in CRC. Using advanced computational tools and experimental techniques, we aimed to clarify the biological significance of FOXM1 and its potential impact on CRC progression and treatment.

Ultrabroadband Optical Properties of 2D Titanium Carbide MXene.

MXenes have rapidly ascended as a prominent class of two-dimensional (2D) materials, renowned for their distinctive optical and electrical properties. Despite extensive exploration of MXenes' optical properties, existing studies predominantly focus on the near-infrared (NIR) to the ultraviolet spectral range, leaving the mid-infrared (mid-IR) range relatively uncharted. In this study, we conducted a comprehensive characterization of the intrinsic optical properties of TiCT MXene across an ultrabroadband spectral range, spanning from mid-IR (28 meV) to vacuum ultraviolet (VUV, 6.