Assessment of luminescent copper nanomaterials as anti-germs, anti-proliferation efficiencies using green nano-strategy.

For the first time, we suggest using leaf extract from Ocimum americanum as the economically viable bio-fabrication of copper nanomaterials. The residuals of leaf extract bio-capping provide the stability of the nanomaterials in-situ. UV-Vis and XRD confirmed the formation, with the UV-Vis spectrum of Cu-NMs revealing a surface plasmon resonance characteristic peak at 350 nm.

Alzheimer's Disease Prediction Using Fly-Optimized Densely Connected Convolution Neural Networks Based on MRI Images.

Alzheimer's is a degenerative brain cell disease that affects around 5.8 million people globally. The progressive neurodegenerative disease known as Alzheimer's Disease (AD), affects the frontal cortex, the part of the brain in charge of memory, language, and cognition.

HDF-Net: Capturing Homogeny Difference Features to Localize the Tampered Image.

Modern image editing software enables anyone to alter the content of an image to deceive the public, which can pose a security hazard to personal privacy and public safety. The detection and localization of image tampering is becoming an urgent issue to be addressed. We have revealed that the tampered region exhibits homogenous differences (the changes in metadata organization form and organization structure of the image) from the real region after manipulations such as splicing, copy-move, and removal.

Multiplexed quantitative proteomics in prostate cancer biomarker development.

Prostate cancer (PCa) is the most common non-skin cancer among men in the United States. However, the widely used protein biomarker in PCa, prostate-specific antigen (PSA), while useful for initial detection, its use alone cannot detect aggressive PCa and can lead to overtreatment. This chapter provides an overview of PCa protein biomarker development.

Poly-vinyl-pyrrolidone capped luminescent zinc oxide nanocomposites as excellent tools for germicidal and photo-catalytic performances.

Alternate antibiotics developed through the involvement of nanomaterials are gaining interest due to their economical and lower toxicity concerns. A newly developed biopolymer-based polyvinylpyrrolidone/zinc oxide (PVP/ZnO) nanocomposite (NCs) was efficiently synthesized by an environment-friendly approach, utilizing onion and garlic peel extract as a bio-surfactant, zinc acetate as the source, PVP as the stabilizing agent, and sodium hydroxide as the precipitant. Fourier transform infrared spectroscopy (FT-IR) and X-ray diffraction (XRD) investigations verified the crystalline properties of ZnO, PVP, and PVP/ZnO-based NCs.

SSM-Net: Semi-supervised multi-task network for joint lesion segmentation and classification from pancreatic EUS images.

Pancreatic cancer does not show specific symptoms, which makes the diagnosis of early stages difficult with established image-based screening methods and therefore has the worst prognosis among all cancers. Although endoscopic ultrasonography (EUS) has a key role in diagnostic algorithms for pancreatic diseases, B-mode imaging of the pancreas can be affected by confounders such as chronic pancreatitis, which can make both pancreatic lesion segmentation and classification laborious and highly specialized. To address these challenges, this work proposes a semi-supervised multi-task network (SSM-Net) to leverage unlabeled and labeled EUS images for joint pancreatic lesion classification and segmentation.

State-of-art technologies, challenges, and emerging trends of computer vision in dental images.

Computer vision falls under the broad umbrella of artificial intelligence that mimics human vision and plays a vital role in dental imaging. Dental practitioners visualize and interpret teeth, and the structure surrounding the teeth and detect abnormalities by manually examining various dental imaging modalities. Due to the complexity and cognitive difficulty of comprehending medical data, human error makes correct diagnosis difficult.

Exploring the diverse performance of nickel and cobalt spinel ferrite nanoparticles in hazardous pollutant removal and gas sensing performance.

A simple sol-gel combustion process was employed for the creation of MFeO (M=Ni, Co) nanoparticles. The synthesized nanoparticles, acting as both photocatalysts and gas sensors, were analyzed using various analytical techniques. MFeO (M=Ni, Co) material improved the degradation of methylene blue (MB) under UV-light irradiation, serving as an enhanced electron transport medium.

Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques.

The study aimed to achieve the following objectives: (1) to perform the fusion of thermal and visible tongue images with various fusion rules of discrete wavelet transform (DWT) to classify diabetes and normal subjects; (2) to obtain the statistical features in the required region of interest from the tongue image before and after fusion; (3) to distinguish the healthy and diabetes using fused tongue images based on deep and machine learning algorithms. The study participants comprised of 80 normal subjects and age- and sex-matched 80 diabetes patients. The biochemical tests such as fasting glucose, postprandial, Hba1c are taken for all the participants.

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques.

Presently, close to two million patients globally succumb to gastrointestinal reflux diseases (GERD). Video endoscopy represents cutting-edge technology in medical imaging, facilitating the diagnosis of various gastrointestinal ailments including stomach ulcers, bleeding, and polyps. However, the abundance of images produced by medical video endoscopy necessitates significant time for doctors to analyze them thoroughly, posing a challenge for manual diagnosis.

Metal-organic frameworks for biomedical applications: A review.

Metal-organic frameworks (MOFs) are emergent materials in diverse prospective biomedical uses, owing to their inherent features such as adjustable pore dimension and volume, well-defined active sites, high surface area, and hybrid structures. The multifunctionality and unique chemical and biological characteristics of MOFs allow them as ideal platforms for sensing numerous emergent biomolecules with real-time monitoring towards the point-of-care applications. This review objects to deliver key insights on the topical developments of MOFs for biomedical applications.

Recent advancements in non-invasive wearable electrochemical biosensors for biomarker analysis - A review.

A biomarker is a molecular indicator that can be used to identify the presence or severity of a disease. It may be produced due to biochemical or molecular changes in normal biological processes. In some cases, the presence of a biomarker itself is an indication of the disease, while in other cases, the elevated or depleted level of a particular protein or chemical substance aids in identifying a disease.

CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection.

Cataracts, known for lens clouding and being a common cause of visual impairment, persist as a primary contributor to vision loss and blindness, presenting notable diagnostic and prognostic challenges. This work presents a novel framework called the Cataract States Detection Network (CSDNet), which utilizes deep learning methods to improve the detection of cataract states. The aim is to create a framework that is more lightweight and adaptable for use in environments or devices with limited memory or storage capacity.

The effects of kettlebell training versus resistance training using the own body mass on physical fitness and physiological adaptations in obese adults: a randomized controlled trial.

Aim Of Study: This study aimed to explore the effects of different types of resistance training using kettlebells versus the own body mass, in comparison to a passive control, on key physical fitness and physiological parameters in young, obese adults.

Methods: Data from 60 sedentary, obese male college students, aged 17-26, were used for final analyses. Participants were randomly assigned to one of three groups: a control group (CG, n = 20, no training), a kettlebell resistance training group (KRTG, n = 20), or a bodyweight resistance training group (BWRTG, n = 20).

Control strategy for current limitation and maximum capacity utilization of grid connected PV inverter under unbalanced grid conditions.

Under grid voltage sags, over current protection and exploiting the maximum capacity of the inverter are the two main goals of grid-connected PV inverters. To facilitate low-voltage ride-through (LVRT), it is imperative to ensure that inverter currents are sinusoidal and remain within permissible limits throughout the inverter operation. An improved LVRT control strategy for a two-stage three-phase grid-connected PV system is presented here to address these challenges.

Endocrine disrupting chemicals: gestational diabetes and beyond.

Gestational Diabetes Mellitus (GDM) has been on the rise for the last two decades along with the growing incidence of obesity. The ubiquitous use of Endocrine-Disrupting Chemicals (EDCs) worldwide has been associated with this increase in GDM incidence. Epigenetic modifications such as DNA methylation, histone acetylation, and methylation have been associated with prenatal exposure to EDCs.

Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression.

Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring.

Crystal growth, Hirshfeld analysis, optical, thermal, mechanical, and third-order non-linear optical properties of Cyclohexylammonium picrate (CHAP) single crystal.

The organic single crystals of Cyclohexylammonium picrate (CHAP) had been grown using the method of slow evaporation solution growth. A determination was made regarding the solubility of the substance. The crystal's lattice cell parameters and morphology were characterized using single-crystal X-ray diffraction and powder X-ray diffraction techniques.

Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis.

Semantic segmentation of cityscapes via deep learning is an essential and game-changing research topic that offers a more nuanced comprehension of urban landscapes. Deep learning techniques tackle urban complexity and diversity, which unlocks a broad range of applications. These include urban planning, transportation management, autonomous driving, and smart city efforts.

Decoding the ubiquitin landscape by cutting-edge ubiquitinomic approaches.

Functional consequences of protein ubiquitination have gone far beyond the degradation regulation as was initially imagined during its discovery 40 years back. The state-of-the-art has revealed the plethora of signaling pathways that are largely regulated by ubiquitination process in eukaryotes. To no surprise, ubiquitination is often dysregulated in many human diseases, including cancer, neurodegeneration and infection.

Microplastics, their abundance, and distribution in water and sediments in North Chennai, India: An assessment of pollution risk and human health impacts.

Plastic particles, measuring <5 mm in size, mainly originate from larger plastic debris undergoing degradation, fragmenting into even smaller fragments. The goal was to analyze the spatial diversity and polymer composition of microplastics (MPs) in North Chennai, South India, aiming to evaluate their prevalence and features like composition, dimensions, color, and shape. In 60 sediment samples, a combined count of 1589 particles were detected, averaging 26 particles per 5 g of dry sediment.

Gonorrhea caused due to antimicrobial-resistant bacteria treated using probiotic peptide.

is a sexually transmitted disease from gonorrhea that lacks treatment; despite the urgency, the absence of adequate drugs, lack of human correlates of protection, and inadequate animal models of infection have delayed progress toward the prevention of gonococcal infection. is a lactic acid bacterium typically found in the human vaginal microbiota. Peptides from have shown a potential therapeutic option for targetting .

Ubiquitin E3 ligases assisted technologies in protein degradation: Sharing pathways in neurodegenerative disorders and cancer.

E3 ligases, essential components of the ubiquitin-proteasome-mediated protein degradation system, play a critical role in cellular regulation. By covalently attaching ubiquitin (Ub) molecules to target proteins, these ligases mark them for degradation, influencing various bioprocesses. With over 600 E3 ligases identified, there is a growing realization of their potential as therapeutic candidates for addressing proteinopathies in cancer and neurodegenerative disorders (NDDs).

A novel deep learning approach for early detection of cardiovascular diseases from ECG signals.

Cardiovascular diseases, often asymptomatic until severe, pose a significant challenge in medical diagnosis. Despite individuals' normal outward appearance and routine activities, subtle indications of these diseases can manifest in the electrocardiogram (ECG) signals, often overlooked by standard interpretation. Current machine learning models have been ineffective in discerning these minor variations due to the irregular and subtle nature of changes in the ECG patterns.