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AIMS: Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes. However, most HF patients are not hospitalized, and manual review of big electronic health record (EHR) data is not practical. The US Department of Veterans Affairs (VA) has the largest integrated healthcare system in the nation, and an estimated 1.5 million patients have ICD codes for HF (HF ICD-code universe) in their VA EHR. The objective of our study was to develop artificial intelligence (AI) models to phenotype HF in these patients. METHODS AND RESULTS: The model development cohort (n = 20 000: training, 16 000; validation 2000; testing, 2000) included 10 000 patients with HF and 10 000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60 days). HF status was ascertained by manual chart reviews in VAs External Peer Review Program for HF (EPRP-HF) and non-HF status was ascertained by the absence of ICD codes for HF in VA EHR. Two clinicians annotated 1000 random snippets with HF-related keywords and labelled 436 as HF, which was then used to train and test a natural language processing (NLP) model to classify HF (positive predictive value or PPV, 0.81; sensitivity, 0.77). A machine learning (ML) model using linear support vector machine architecture was trained and tested to classify HF using EPRP-HF as cases (PPV, 0.86; sensitivity, 0.86). From the HF ICD-code universe, we randomly selected 200 patients (gold standard cohort) and two clinicians manually adjudicated HF (gold standard HF) in 145 of those patients by chart reviews. We calculated NLP, ML, and NLP + ML scores and used weighted F scores to derive their optimal threshold values for HF classification, which resulted in PPVs of 0.83, 0.77, and 0.85 and sensitivities of 0.86, 0.88, and 0.83, respectively. HF patients classified by the NLP + ML model were characteristically and prognostically similar to those with gold standard HF. All three models performed better than ICD code approaches: one principal hospital discharge diagnosis code for HF (PPV, 0.97; sensitivity, 0.21) or two primary outpatient encounter diagnosis codes for HF (PPV, 0.88; sensitivity, 0.54). CONCLUSIONS: These findings suggest that NLP and ML models are efficient AI tools to phenotype HF in big EHR data to create contemporary HF registries for clinical studies of effectiveness, quality improvement, and hypothesis generation.

Mobile technology growth in Nepal offers promising opportunities for using mobile health (mHealth) interventions to facilitate HIV prevention efforts. However, little is known about access and utilization of communication technology and their willingness to use mHealth for HIV prevention services in Nepal. We conducted a cross-sectional respondent-driven sampling survey of 250 MSM in Kathmandu Valley of Nepal from October to December 2022. We collected information on participant characteristics, HIV risk-related behaviors, ownership, or access to and frequency of use of communication technology (phones, tablets, laptops, and computers), and willingness to use mHealth to access HIV prevention services. Descriptive, bivariate, and multivariate linear regression analyses were performed. Almost all participants had smartphones with the internet (231/250, 92.4%) and accessed the internet daily (219/250, 87.6%) on the smartphone (236/250, 94.4%). The median score for willingness to use mHealth for HIV prevention was 10 (IQR: 3 to 17). Willingness to use mHealth was higher among those participants with a high school or above education (β = 0.223, p = < 0.001), had experienced violence (β = 0.231, p = 0.006), and had moderate to severe depressive symptoms (β = 0.223, p = < 0.001). However, monthly income above NPR 20,000 (USD 150) (β= -0.153, p = 0.008), disclosure of their sexual orientation to anyone (β= -0.159, p = < 0.007), and worry about being negatively judged by health care workers (β= -0.136, p = 0.023) were less willing to use mHealth strategies. The findings from this study suggest that there is a high willingness for utilizing mHealth interventions for HIV prevention in MSM population who are at higher risk of HIV acquisition.

Abstract Background Chronic hepatitis C virus (CHC) infection represents a crucial health problem, especially among children and adolescents. The ledipasvir (LDV)/sofosbuvir (SOF) regimen has been approved to treat adolescents (aged 12 to 17 years old) infected with hepatitis C virus (HCV) genotypes 1, 4, 5, and 6 and then extended to include children above or equal to 3 years old. The current study aims to evaluate the safety and efficacy of SOF/LDV combination in treating CHC-infected 12- to 17-year-old adolescents. Patients and methods This retrospective cohort study was performed on 147 Egyptian adolescents with CHC. The patients were treated with SOF 400 mg/LDV 90 mg combination once daily for 12 weeks. Possible side effects and laboratory data including HCV ribonucleic acid polymerase chain reaction (RNA PCR), complete blood count (CBC), and liver tests were recorded at baseline and week 12 after the end of treatment (EOT). Results Sustained virological response 12 weeks (SVR12) after end of treatment (EOT) was observed in 146 out of 147 patients (99.3%). The treatment regimen was efficiently tolerated with no reported cases of discontinuation caused by intolerability. Moreover, the side effects were minimal; 71.4% of the patients did not report any side effects related to the treatment. However, the rest mentioned fatigue, headache, or both of them. Fatigue was the main side effect reported in 16.3% of the patients. Furthermore, ALT and AST levels were normalized after treatment. FIB-4 and APRI scores were statistically significantly decreased 2 years post-SVR, in comparison to their levels before treatment, from 0.34 and 0.36 to 0.25 and 0.17, respectively. Conclusion The LDV/SOF regimen is one of the safe regimens used to treat adolescent patients with CHC infection.

Abstract Optimization algorithms are essential tools used to address real-world problems through minimization or maximization processes. In the context of photovoltaic (PV) systems, various optimization algorithms have been employed to extract solar cell parameters using minimization techniques, and to identify the maximum power point (MPP) using maximization approaches. However, under partial shading conditions or during rapidly changing irradiance, many of these algorithms tend to get trapped in local minima, failing to locate the global maximum power point (GMPPT). This shortcoming leads to significant energy losses from PV cells. Similarly, the extraction of solar cell parameters is often compromised by the random search behavior and inadequate exploration and exploitation capabilities of many existing algorithms. The Jellyfish Search Optimization (JSO) algorithm is a recent, parameter-free method developed to tackle a wide range of optimization problems. Despite its innovative design, JSO is prone to premature convergence and exhibits a longer convergence time. To address these limitations, this paper proposes a modified version of the JSO algorithm, which integrates the state-of-the-art features of JSO with the Lévy flight characteristic from the cuckoo search algorithm. This combination aims to achieve a better balance between exploration and exploitation. To validate the effectiveness of the proposed Improved Jellyfish Search Optimization (IJSO) algorithm in PV systems, extensive experiments were conducted. These experiments included benchmarking with complex test functions, extracting cell parameters using various solar cell models, and maximizing energy output from PV systems under different environmental conditions. The results demonstrate that the proposed IJSO algorithm effectively enhances parameter extraction and global maximum power point tracking, making it a promising solution for optimizing energy extraction from PV cells in dynamic conditions.

Abstract Monoclonal antibodies (mAbs) are used to prevent, detect, and treat a broad spectrum of non-communicable and communicable diseases. Over the past few years, the market for mAbs has grown exponentially with an expected compound annual growth rate (CAGR) of 11.07% from 2024 (237.64 billion USD estimated at the end of 2023) to 2033 (679.03 billion USD expected by the end of 2033). Ever since the advent of hybridoma technology introduced in 1975, antibody-based therapeutics were realized using murine antibodies which further progressed into humanized and fully human antibodies, reducing the risk of immunogenicity. Some benefits of using mAbs over conventional drugs include a drastic reduction in the chances of adverse reactions, interactions between drugs, and targeting specific proteins. While antibodies are very efficient, their higher production costs impede the process of commercialization. However, their cost factor has been improved by developing biosimilar antibodies as affordable versions of therapeutic antibodies. Along with the recent advancements and innovations in antibody engineering have helped and will furtherly help to design bio-better antibodies with improved efficacy than the conventional ones. These novel mAb-based therapeutics are set to revolutionize existing drug therapies targeting a wide spectrum of diseases, thereby meeting several unmet medical needs. This review provides comprehensive insights into the current fundamental landscape of mAbs development and applications and the key factors influencing the future projections, advancement, and incorporation of such promising immunotherapeutic candidates as a confrontation approach against a wide list of diseases, with a rationalistic mentioning of any limitations facing this field.

Abstract Background This prospective bicentric study aims to evaluate and compare the efficacy of intradiscal radiofrequency biacuplasty (IDB) versus conservative management (CM) in treating chronic discogenic low back pain (CDLBP). Methods Forty-four patients with CDLBP were randomly assigned to either the biacuplasty group or the conservative management group. The biacuplasty group received treatment with two radiofrequency (RF) needles, positioned bilaterally on the annulus fibrosus under fluoroscopic guidance and local anaesthesia, to administer bipolar RF. Patients were monitored for 6 months, with the allowance of pain medication as needed. In the conservative management group, patients who continued to experience low back pain after three months were permitted to switch to the biacuplasty group. The study was conducted over two years, starting on July 1, 2020. Results The studied groups had comparable age and gender distributions, with no significant difference in BMI. The Oswestry Disability Index (ODI) was assessed at baseline, 1 month, 3 months, and 6 months. At baseline, no significant difference was observed between the groups (p = 0.263). However, at the 1-month, 3-month, and 6-month follow-up, the biacuplasty group exhibited significantly lower ODI scores compared to the conservative management group (all p

Abstract Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and human epidermal growth factor receptors and has a poor prognosis as it is resistant to chemotherapy. A new treatment option for this type of cancer may be by putting these malignant cells into dormancy. The oocyte’s embryonic milieu presents a unique tumor reversion microenvironment by inducing growth arrest and changing cells’ phenotypes. We conducted an in-silico study to determine the most likely oocyte extract (OE) proteins involved in inducing dormancy using HDock, CluPro, and molecular dynamic (MD) simulation. Results showed low energy scores for complexes between OE proteins and four surface markers: K1C14, CLD3, CLD4, and ITA6. Apolipoprotein A1 (APOA1) and Apolipoprotein C3 (APOC3) showed the highest stability and affinity with these four surface markers: K1C14, CLD3, CLD4, and ITA6. These proteins are involved in key tumor-related pathways such as angiogenesis, proliferation, apoptosis, and migration. This will pave the way for exploring novel therapeutic options to induce dormancy in TNBC cells.

Abstract Background Certain gastrointestinal and extra-intestinal diseases are caused by the gram-negative bacterium Helicobacter pylori (H. pylori). We aimed to determine the potential H. pylori role in chronic rhinosinusitis (CRS) with or without nasal polyposis. This cross-sectional study was conducted on 80 subjects of any age and sex diagnosed with CRS (CRSWNP and CRSSNP) who had failed medical treatment and scheduled for surgery. Patients were further categorised into two equal groups: study group (n = 40) were diagnosed with CRS with or without nasal polyposis (CRSWNP and CRSSNP) who had failed medical treatment and scheduled for surgery, and control group (40 patients) were admitted for septoplasty. All patients had clinical assessment, physical assessment, diagnosis of rhinosinusitis (according to EPOS), based on symptoms, rhinoscopic/endoscopic findings, CT scan findings, questionnaire for gastroesophageal reflux disease (GERD), and questionnaire for laryngopharyngeal reflux (LPR). Samples were collected in the operating room during surgery from both groups and PCR tissue was done. Results The study group patients demonstrated significantly higher history of GERD, LPR, and H. pylori. H. pylori patients had significantly lower ages than those without (P = 0.03). Patients with H. pylori revealed significantly higher GERD history (P

Abstract This work initiates a concept of reduced reverse degree based $$\mathcal {RR_D\,M}$$ RR D M -Polynomial for a graph, and differential and integral operators by using this $$\mathcal {RR_D\,M}$$ RR D M -Polynomial. In this study twelve reduced reverse degree-based topological descriptors are formulated using the $$\mathcal {RR_D\,M}$$ RR D M -Polynomial. The topological descriptors, denoted as $$\mathbb{T}\mathbb{D}$$ T D ’s, are numerical invariants that offer significant insights into the molecular topology of a molecular graph. These descriptors are essential for conducting QSPR investigations and accurately estimating physicochemical attributes. The structural and algebraic characteristics of the graphene and graphdiyne are studied to apply this methodology. The study involves the analysis and estimation of Reduced reverse degree-based topological descriptors and physicochemical features of graphene derivatives using best-fit quadratic regression models. This work opens up new directions for scientists and researchers to pursue, taking them into new fields of study.

Abstract In India, the spatial coverage of air pollution data is not homogeneous due to the regionally restricted number of monitoring stations. In a such situation, utilising satellite data might greatly influence choices aimed at enhancing the environment. It is essential to estimate significant air contaminants, comprehend their health impacts, and anticipate air quality to safeguard public health from dangerous pollutants. The current study intends to investigate the spatial and temporal heterogeneity of important air pollutants, such as sulphur dioxide, nitrogen dioxide, carbon monoxide, and ozone, utilising Sentinel-5P TROPOMI satellite images. A comprehensive spatiotemporal analysis of air quality was conducted for the entire country with a special focus on five metro cities from 2019 to 2022, encompassing the pre-COVID-19, during-COVID-19, and current scenarios. Seasonal research revealed that air pollutant concentrations are highest in the winter, followed by the summer and monsoon, with the exception of ozone. Ozone had the greatest concentrations throughout the summer season. The analysis has revealed that NO2 hotspots are predominantly located in megacities, while SO2 hotspots are associated with industrial clusters. Delhi exhibits high levels of NO2 pollution, while Kolkata is highly affected by SO2 pollution compared to other major cities. Notably, there was an 11% increase in SO2 concentrations in Kolkata and a 20% increase in NO2 concentrations in Delhi from 2019 to 2022. The COVID-19 lockdown saw significant drops in NO2 concentrations in 2020; specifically, − 20% in Mumbai, − 18% in Delhi, − 14% in Kolkata, − 12% in Chennai, and − 15% in Hyderabad. This study provides valuable insights into the seasonal, monthly, and yearly behaviour of pollutants and offers a novel approach for hotspot analysis, aiding in the identification of major air pollution sources. The results offer valuable insights for developing effective strategies to tackle air pollution, safeguard public health, and improve the overall environmental quality in India. The study underscores the importance of satellite data analysis and presents a comprehensive assessment of the impact of the shutdown on air quality, laying the groundwork for evidence-based decision-making and long-term pollution mitigation efforts.

Abstract Background The variational method, a quantum mechanical approach, estimates effective charge distributions and ground-state energy by minimizing the Hamiltonian's expectation value using trial wave functions with adjustable parameters. This method provides valuable insights into system behavior and is widely used in theoretical chemistry and physics. This paper aims to investigate ground-state energies and isoelectronic sequences using the variational method, introducing a novel approach for analyzing multi-electron systems. This technique allows for determining effective charge values and ground-state energies for 2–5 electrons sequence up to Z ≤ 20. Hydrogenic wave functions are used as a trial wave function to calculate effective charge in 1 s, 2 s, and 2p states. Two varying parameters were used to calculate an approximate wave function for the system. These values are then used in non-relativistic Hamiltonian with electron–electron interaction terms to calculate the ground-state energy of an atom. Result The results align with the reported experimental values, showing a marginal 1% error. Conclusion A Python algorithm is established based on the variational principle. It was found that, based on a few selected parameters in scripting the program, a very promising result was obtained. Furthermore, adding more variational parameters can minimize the difference between experimental and theoretical values, and this technique can be extended to elements with higher atomic numbers.

Abstract Electric vehicles are considered as an emerging solution to mitigate the environmental footprint of transportation sector. Therefore, researchers and automotive developers devote significant efforts to enhance the performance of electric vehicles to promote broader adoption of such technology. One of the critical challenges of the electric vehicle is limited battery lifetime and entailed range anxiety. In his context, development of counter-aging control strategies based on precise battery modeling is regarded as an emerging approach that has a significant potential to address battery degradation challenges. This paper presents a combined trade-off strategy to minimize battery degradation while maintaining acceptable driving performance and charge retention in electric vehicles. A battery aging model has been developed and integrated into a full vehicle model. An optimal control problem has been formulated to tackle the afore-mentioned challenges. Non-dominant sorting genetic algorithms have been implemented to yield the optimal solution through the Pareto-front of three contending objectives, based upon which an online simulation has been conducted considering three standard driving cycles. The results reveal the ability of the proposed strategy to prolong the life cycle of the battery and extend the driving range by 25 % and 8 % respectively with minimal influence of 0.6 % on the driveability.