A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors.

In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy.

A Rare Case of a Primary Non-parasitic Splenic Cyst Removed via Laparoscopic Intervention.

Splenic cysts are differentiated into primary and secondary cysts based on epithelial lining. Primary non-parasitic epithelial splenic cysts are extremely rare. We report a case of a 24-year-old male with left hypochondrial swelling with no history of abdominal trauma.

On analysis of phthalocyanine network through statistical method.

Phthalocyanine derivative nanostructures are highly organized organometallic structures that exhibit two-dimensional polymeric phthalocyanine frameworks. We analyze phthalocyanine using the Zagreb-type indices, which offer important insights into the topological characteristics of the molecular structure. Furthermore, we use Pearson correlation analysis to examine the degree of relationship between various structural features and qualities.

A novel EWMA-based adaptive control chart for industrial application by using hastings approximation.

Control charts are commonly used for process monitoring under the assumption that the variable of interest follows a normal distribution. However, this assumption is frequently violated in real-world applications. In this study, we develop an adaptive control chart based on the exponentially weighted moving average (EWMA) statistic to monitor irregular variations in the mean of the Truncated Transmuted Burr-II (TTB-II) distribution, employing Hastings approximation for normalization.

A proficient approach for the classification of Alzheimer's disease using a hybridization of machine learning and deep learning.

Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI) scans has been increasingly used in the study of brain pathology related to the birth and growth of AD.

Cardiorenal outcomes associated with sodium-glucose co-transporter-2 inhibitors in chronic kidney disease stage 5 (CKD V): A propensity score-matched analysis.

Background: There remains a paucity of data regarding the cardio-renal benefits of sodium-glucose co-transporter-2 inhibitors (SGLT2i) in patients with chronic kidney disease stage 5 (CKD V) based on major clinical trials.

Objective: This retrospective study aimed to identify potential cardiovascular and renal outcomes associated with SGLT2i use in CKD V patients.

Methods: We queried the TriNetX Global collaborative network from Jan 2014 - Aug 2023 for patients ≥18 years diagnosed with CKD V but not on dialysis.

Balancing Activity and Selectivity in Two-Electron Oxygen Reduction through First Coordination Shell Engineering in Cobalt Single Atom Catalysts.

The electrochemical two-electron oxygen reduction reaction (2e ORR) offers a potentially cost-effective and eco-friendly route for the production of hydrogen peroxide (HO). However, the competing 4e ORR that converts oxygen to water limits the selectivity towards hydrogen peroxide. Accordingly, achieving highly selective HO production under low voltage conditions remains challenging.

Exploiting the features of deep residual network with SVM classifier for human posture recognition.

Over the last decade, there have been a lot of advances in the area of human posture recognition. Among multiple approaches proposed to solve this problem, those based on deep learning have shown promising results. Taking another step in this direction, this paper analyzes the performance of deep learning-based hybrid architecture for fall detection, In this regard, the fusion of the residual network (ResNet-50) deep features with support vector machine (SVM) at the classification layer has been considered.

Plant microbiome technology for sustainable agriculture.

Plants establish specific interactions with microorganisms, which are vital for promoting growth and resilience. Although advancements in microbiome modulation technologies show great potential for sustainable agriculture, several challenges have hindered the wider application of plant microbiomes in the field. These challenges may include inconsistent microbial colonization, competition with native microbiota, and environmental variability.

Treatment of Inflammatory Bowel Disease by Using Curcumin-Containing Self-Microemulsifying Delivery System: Macroscopic and Microscopic Analysis.

The lack of local availability for drugs in the colon can be addressed by preparing a self-microemulsifying drug delivery system (SMEDDS) of curcumin (Cur) which is ultimately used for the treatment of inflammatory bowel disease (IBD). From preformulation studies, Lauroglycol FCC (oil), Tween 80 (surfactant), Transcutol HP (co-surfactant), and Avicel (solid carrier) were selected for the preparation of blank liquid and solid Cur-loaded SMEDDSs (S-Cur-SMEDDSs). Z-average size (12.

Cardiovascular outcomes among giant cell myocarditis compared with cardiac sarcoidosis: A propensity score-matched analysis.

Background: Giant cell myocarditis (GCM) and cardiac sarcoidosis (CS) are rare inflammatory diseases of the myocardium with poor prognosis. Cardiovascular disease outcomes among both diseases have not been well studied with limited literature.

Objective: This study aims to investigate the cardiovascular outcomes among patients with GCM and CS.

Variance estimation using memory type estimators based on EWMA statistic for time scaled surveys in stratified sampling.

In this article, we have proposed memory-type exponential and non-exponential estimators for population variance based on exponentially weighted moving average (EWMA) statistic in stratified sampling. We drive mathematical expressions for mean square errors of the proposed estimators using Taylor and exponential expansions. Our analysis demonstrates that the proposed memory-type estimators outperform the conventional estimators under stratification, particularly, when the information of previous sample is utilized.

Design of a highly stable and conductive electrolyte by suppressing barium copper oxide formation at the grain interfaces in Cu-doped BaCeZrDyO sintered at a low temperature (1200 °C) for SOFCs.

Proton-conducting electrolytes with high conductivity and long-term stability, achievable at low sintering temperatures, are of paramount importance. In this study, we investigate the impact of Cu doping on the sintering mechanism, electrical performance, and stability of BaCeZrDyO (BCZD) electrolyte. The morphology, composition, structure, and chemical state of BCZD electrolytes were investigated using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS).

Analyzing boron oxide networks through Shannon entropy and Pearson correlation coefficient.

In the current age of chemical science, chemical graph theory has significantly advanced our understanding of the characteristics of chemical compounds. To simulate the mathematical, chemical, and physical aspects of networks, a topological index, a numerical measure obtained from the graph of a chemical network, employed. Recent work has explored the topological properties of boron oxide using chemical graph theory.

Forecasting mortality and DALYs from air pollution in SAARC nations.

This study investigates the projected impact of air pollution on mortality and Disability-Adjusted Life Years (DALYs) across SAARC countries. Utilizing Time Series and Machine Learning methodologies such as Autoregressive Integrated Moving Average, Exponential Smoothing, and Neural Network, the research aims to accurately forecast the mortality and DALYs attributed to air pollution from 2020 to 2030. Statistical analyses reveal a consistent upward trend in deaths and DALYs during the forecasting period, primarily driven by Ambient Particulate Matter Pollution (APM) and Ambient Ozone Pollution (AOP).

Synthesis, Urease Inhibition, Molecular Docking, and Optical Analysis of a Symmetrical Schiff Base and Its Selected Metal Complexes.

Designing and developing small organic molecules for use as urease inhibitors is challenging due to the need for ecosystem sustainability and the requirement to prevent health risks related to the human stomach and urinary tract. Moreover, imaging analysis is widely utilized for tracking infections in intracellular and in vivo systems, which requires drug molecules with emissive potential, specifically in the low-energy region. This study comprises the synthesis of a Schiff base ligand and its selected transition metals to evaluate their UV/fluorescence properties, inhibitory activity against urease, and molecular docking.

Turn-On Fluorescence Probe for Cancer-Related -Glutamyltranspeptidase Detection.

The design and development of fluorescent materials for detecting cancer-related enzymes are crucial for cancer diagnosis and treatment. Herein, we present a substituted rhodamine derivative for the chromogenic and fluorogenic detection of the cancer-relevant enzyme -glutamyltranspeptidase (GGT). Initially, the probe is non-chromic and non-emissive due to its spirolactam form, which hinders extensive electronic delocalization over broader pathway.