Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes.

Introduction: Artificial intelligence (AI) is changing the field of nanomedicine by exploring novel nanomaterials for developing therapies of high efficacy. AI works on larger datasets, finding sought-after nano-properties for different therapeutic aims and eventually enhancing nanomaterials' safety and effectiveness. AI leverages patient clinical and genetic data to predict outcomes, guide treatments, and optimize drug dosages and forms, enhancing benefits while minimizing side effects.

Model Organoids: Integrated Frameworks for the Next Frontier of Healthcare Advancements.

The morphogenetic events leading to tissue formation can be recapitulated using organoids, which allows studying new diseases and modelling personalized medicines. In this review, culture systems comparable to human organs are presented, these organoids are created from pluripotent stem cells or adult stem cells. The efficient and reproducible models of human tissues are discussed for biobanking, precision medicine and basic research.

Light-Controlled Interconvertible Self-Assembly of Non-Photoresponsive Suprastructures.

Achieving light-induced manipulation of controlled self-assembly in nanosized structures is essential for developing artificially dynamic smart materials. Herein, we demonstrate an approach using a non-photoresponsive hydrogen-bonded (H-bonded) macrocycle to control the self-assembly and disassembly of nanostructures in response to light. The present system comprises a photoacid (merocyanine, 1-MEH), a pseudorotaxane formed by two H-bonded macrocycles, dipyridinyl acetylene, and zinc ions.

Enhanced biocatalytic laccase production using agricultural waste in solid-state fermentation by Aspergillus oryzae for p-chlorophenol degradation.

Agricultural residues are one of the most cost-effective and readily accessible carbon resources for producing commercially significant enzymes. Several enzymes have been used in different industries like pharmaceuticals, foods, textiles, and dyes that can be generated by various species of microbes found in waste from agriculture. The current research investigated laccase production by Aspergillus oryzae utilizing agricultural wastes.

Unveiling Functional Biomarkers in Schizophrenia: Insights from Region of Interest Analysis Using Machine Learning.

Background: Schizophrenia is a complex and disabling mental disorder that represents one of the most important challenges for neuroimaging research. There were many attempts to understand these basic mechanisms behind the disorder, yet we know very little. By employing machine learning techniques with age-matched samples from the auditory oddball task using multi-site functional magnetic resonance imaging (fMRI) data, this study aims to address these challenges.

LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population.

Parkinson's disease (PD) is a prevalent neurological disorder characterized by progressive dopaminergic neuron loss, leading to both motor and non-motor symptoms. Early and accurate diagnosis is challenging due to the subtle and variable nature of early symptoms. This study aims to address these diagnostic challenges by proposing a novel method, Localized Region Extraction and Multi-Modal Fusion (LRE-MMF), designed to enhance diagnostic accuracy through the integration of structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data.

Hybrid raven roosting intelligence framework for enhancing efficiency in data clustering.

The field of data exploration relies heavily on clustering techniques to organize vast datasets into meaningful subgroups, offering valuable insights across various domains. Traditional clustering algorithms face limitations in terms of performance, often getting stuck in local minima and struggling with complex datasets of varying shapes and densities. They also require prior knowledge of the number of clusters, which can be a drawback in real-world scenarios.

Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter.

The COVID-19 pandemic has disrupted people's lives and caused significant economic damage around the world, but its impact on people's mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic has raised serious concerns related to mental health among the masses. However, no systematic investigations have been conducted previously on mental health monitoring and, in particular, detection of post-traumatic stress disorder (PTSD).

Investigating the association between symptoms and functional activity in brain regions in schizophrenia: A cross-sectional fmri-based neuroimaging study.

Schizophrenia is a persistent neurological disorder profoundly affecting cognitive, emotional, and behavioral functions, prominently characterized by delusions, hallucinations, disordered speech, and abnormal motor activity. These symptoms often present diagnostic challenges due to their overlap with other forms of psychosis. Therefore, the implementation of automated diagnostic methodologies is imperative.

Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques.

Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn.

Pyrimidine-based sulfonamides and acetamides as potent antimicrobial Agents: Synthesis, Computational Studies, and biological assessment.

A series of novel sulfonamide and acetamide derivatives of pyrimidine were synthesized and their antimicrobial activities were assessed. Based on the Microbroth dilution method, the minimum inhibitory concentration (MIC) of the synthesized compounds demonstrated moderate to good levels of antifungal and antibacterial activity. Structure-activity relationship analysis suggested that the presence of electron-withdrawing groups, such as halogens, nitrile, and nitro groups, on the pyrimidine ring contributed to the enhanced antimicrobial potency, while electron-donating substituents led to a decrease in activity.

An overview of biochar production techniques and application in iron and steel industries.

Integrating innovation and environmental responsibility has become important in pursuing sustainable industrial practices in the contemporary world. These twin imperatives have stimulated research into developing methods that optimize industrial processes, enhancing efficiency and effectiveness while mitigating undesirable ecological impacts. This objective is exemplified by the emergence of biochar derived from the thermo-chemical transformation of biomass.

Steel ribbed dome structural performance with different node connections and bracing system.

The conventional design of steel structure objects relies on a first-order elastic analysis, where the entire object is treated as a set of individual structural elements requiring time-consuming, semi-empirical design calculations. Such an approach leads to inefficient design time and excessive material consumption and may additionally result in designing on the verge of structural safety. The AEC sector's technological and digitization advancement process forces designers to use advanced design methods.

Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches.

Purpose: Liver disease causes two million deaths annually, accounting for 4% of all deaths globally. Prediction or early detection of the disease via machine learning algorithms on large clinical data have become promising and potentially powerful, but such methods often have some limitations due to the complexity of the data. In this regard, ensemble learning has shown promising results.

VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images.

Background: Lung cancer is the second most common cancer worldwide, with over two million new cases per year. Early identification would allow healthcare practitioners to handle it more effectively. The advancement of computer-aided detection systems significantly impacted clinical analysis and decision-making on human disease.

Exploring Curcumin-Loaded Lipid-Based Nanomedicine as Efficient Targeted Therapy for Alzheimer's Diseases.

Alzheimer's disease (AD) is a neurological condition currently with 47 million people suffering from it globally. AD might have many reasons such as genetic issues, environmental factors, and Aβ accumulation, which is the biomarker of the disease. Since the primary reason is unknown, there is no targeted treatment at the moment, but ongoing research aims to slow its progression by managing amyloid-beta peptide production rather than symptomatic improvement.

Electron Beam-Supported Fabrication of Biocompatible Silver/iota-Carrageenan for Wound Healing Application.

Silver nanoparticles (AgNPs) are a potent antibacterial agent, especially when used to treat bacteria that are multidrug resistant. However, it is challenging to eliminate the hazardous reducing agents that remain in AgNPs produced by the conventional chemical reduction process. To overcome these challenges, the presented research demonstrates the fabrication of AgNPs using iota-carrageenan (ι-carra) as a carbohydrate polymer using electron beam (EB) irradiation.

Enhancing biomethanation performance through co-digestion of diverse organic wastes: a comprehensive study on substrate optimization, inoculum selection, and microbial community analysis.

A blend of organic municipal solid waste, slaughterhouse waste, fecal sludge, and landfill leachate was selected in different mixing ratios to formulate the best substrate mixture for biomethanation. Individual substrates were characterized, and the mixing ratio was optimized with the help of a response surface methodology tool to a value of 1:1:1:1 (with a C/N ratio of 28±0.769 and total volatile fatty acid (VFA) concentration of 2500±10.

Residual phosphorus availability after soil application of different organic waste in varying soil P status soils.

Transformations of applied phosphorus (P) fertilizer to inaccessible residual soil P is the main cause of inadequate P availability to plants in the majority of the cultivated soils. This study investigated the effect of organic wastes (rice-residue biochar, farmyard manure (FYM), poultry manure (PM), green manure (GM), and wheat straw (WS) on residual-P mobilization and its bioavailability in maize crops under different P status soils. Surface soil samples of 'medium-P' (12.

Analysis and dynamical structure of glucose insulin glucagon system with Mittage-Leffler kernel for type I diabetes mellitus.

In this paper, we propose a fractional-order mathematical model to explain the role of glucagon in maintaining the glucose level in the human body by using a generalised form of a fractal fractional operator. The existence, boundedness, and positivity of the results are constructed by fixed point theory and the Lipschitz condition for the biological feasibility of the system. Also, global stability analysis with Lyapunov's first derivative functions is treated.

Hyphenated Fenton-column packed nMnO-modified wood biochar for tannery effluent treatment: Adsorption mechanism and reusability study.

Industrial wastewater contains a wide range of pollutants that, if released directly into natural ecosystems, have the potential to pose serious risks to the environment.This study aims to investigate sustainable and efficient approaches for treating tannery wastewater, employing a combination of hyphenated Fenton oxidation and adsorption processes. Rigorous analyses were conducted on wastewater samples, evaluating parameters like COD, sulphide, NH-N, PO, NO, and Cr(VI).