Claims Millions Research Articles

A novel C-4-modified isotetrone acts as a potent bio-enhancer to augment the activities of anti-tuberculosis drugs against Mycobacterium tuberculosis

Mycobacterium tuberculosis is a deadly pathogen that claims millions of lives every year. Current research focuses on finding new anti-tuberculosis drugs that are safe and effective, with lesser side effects and toxicity. One important approach is to identify bio-enhancers that can improve the effectiveness of anti-tuberculosis drugs, resulting in reduced doses and shortened treatment times. The present study investigates the use of C-4 modified isotetrones as bio-enhancers. A series of studies suggest an isotetrone, labeled as C11, inhibits growth, improves MIC, MBC and enhances the killing of M. tuberculosis H37Rv strain when used in combination with the first line and injectable anti-TB drugs in a dose-dependent manner. The combination of C11 and rifampicin also reduces the generation of spontaneous mutants against rifampicin and reaches a mutation prevention concentration (MPC) with moderate rifampicin concentrations. The identified compounds are effective against the MDR strain of M. tuberculosis and non-cytotoxic in HepG2 cells. We find that C11 induces the generation of reactive oxygen species (ROS) inside macrophages and within bacteria, resulting in better efficacy.

Mechanisms of ferroptosis and glucagon-like peptide-1 receptor agonist in post-percutaneous coronary intervention restenosis.

Cardiovascular disease (CVD) claims millions of lives every year, with atherosclerotic cardiovascular disease (ASCVD) being the main cause. ASCVD treatment includes drug therapy, lifestyle intervention, and Percutaneous Coronary Intervention (PCI) all of which significantly enhance cardiovascular function and reduce mortality. However, hyperplasia can lead to vascular obstruction, worsen angina symptoms, or even cause heart disease, affecting patients' long-term prognosis. Therefore, finding effective ways to combat hyperplasia is crucial for cardiovascular therapy. In recent years, ferroptosis has gained attention as a new form of cell death closely associated with several diseases, including cardiovascular diseases. It involves complex metabolic processes critical for cellular homeostasis and normal function. Abnormal proliferation and phenotypic transformation of vascular smooth muscle cells (VSMC) are crucial mechanisms underlying cardiovascular disease development. Inhibiting ferroptosis in VSMC has the potential to significantly reduce neointima proliferation. Glucagon-like peptide-1 receptor agonist (GLP-1RA) constitutes a widely employed class of hypoglycemic agents with direct implications for the cardiovascular system, mitigating adverse cardiovascular events. Research indicates that the stimulation of GLP-1 holds promise as a therapeutic strategy in mitigating cardiovascular events such as restenosis. Hence, investigating the potential of GLP-1RA as a treatment option for cardiovascular ailments carries immense clinical significance.

Targeting mycobacterial efflux system to enhance tuberculosis therapy

The worldwide health epidemic of tuberculosis (TB) persists. It still claims millions of lives each year despite medical breakthroughs. Long-term therapies, drug-resistant TB strains, and co-infections with conditions like HIV necessitate novel approaches. The use of Mycobacterium tuberculosis (Mtb) efflux pumps, which actively expel drugs and decrease their effectiveness, is one such approach. EPIs (Efflux Pump Inhibitors) have the potential to treat tuberculosis. This review offers insights into Mtb's immune evasion mechanisms, including its strong cell wall, granuloma formation, immunological modulation, and dormancy, as well as TB epidemiology, worldwide eradication efforts, and those processes. Dissecting Efflux Pumps highlights their significance in drug resistance, particularly against first-line TB medications. Efflux pumps are notorious for causing antibiotic resistance. EPIs may have benefits such as increased drug efficacy, resistance reversal, shortened treatment durations, less toxicity, improved adherence, and targeting of latent TB. The effectiveness of various EPI classes, including phenylalkylamines, protonophores, phenothiazines, and plant-derived derivatives, against Mtb growth and efflux pumps is evaluated. The creation of effective EPIs, a clearer understanding of efflux pump control, and essential clinical trials for validation remain obstacles.

Threonine phosphorylation of STAT1 restricts interferon signaling and promotes innate inflammatory responses

Since its discovery over three decades ago, signal transducer and activator of transcription 1 (STAT1) has been extensively studied as a central mediator for interferons (IFNs) signaling and antiviral defense. Here, using genetic and biochemical assays, we unveil Thr748 as a conserved IFN-independent phosphorylation switch in Stat1, which restricts IFN signaling and promotes innate inflammatory responses following the recognition of the bacterial-derived toxin lipopolysaccharide (LPS). Genetically engineered mice expressing phospho-deficient threonine748-to-alanine (T748A) mutant Stat1 are resistant to LPS-induced lethality. Of note, T748A mice exhibited undisturbed IFN signaling, as well as total expression of Stat1. Further, the T748A point mutation of Stat1 recapitulates the safeguard effect of the genetic ablation of Stat1 following LPS-induced lethality, indicating that the Thr748 phosphorylation contributes inflammatory functionalities of Stat1. Mechanistically, LPS-induced Toll-like receptor 4 endocytosis activates a cell-intrinsic IκB kinase-mediated Thr748 phosphorylation of Stat1, which promotes macrophage inflammatory response while restricting the IFN and anti-inflammatory responses. Depletion of macrophages restores the sensitivity of the T748A mice to LPS-induced lethality. Together, our study indicates a phosphorylation-dependent modular functionality of Stat1 in innate immune responses: IFN phospho-tyrosine dependent and inflammatory phospho-threonine dependent. Better understanding of the Thr748 phosphorylation of Stat1 may uncover advanced pharmacologically targetable molecules and offer better treatment modalities for sepsis, a disease that claims millions of lives annually.

Molecular regulation of Mycobacterium tuberculosis Sigma factor H with Anti-sigma factor RshA under stress condition

Mycobacterium tuberculosis is the causative agent of tuberculosis, the leading fatal infectious disease that claims millions of lives every year. M. tuberculosis regulates its stress condition response using its regulatory protein, Sigma Factor H, which binds with its cognate anti-sigma factor RshA in normal conditions, forming a complex inhibiting transcription. During oxidative stress, SigH is released from the complex and binds to RNA Polymerase (RNAP) to initiate transcription. Thus, it is important to understand the molecular conformational state of SigH in complex with different protein partners under different cellular or environmental contexts. This work intends to analyze the SigH-RshA complex, which revealed the variation in SigH shown during complex formation with RNAP and RshA, respectively. Previously, Hydrogen Deuterium Exchange-Mass Spectrometry (HDX-MS) analysis of SigH-RshA interaction provided a detailed insight into the critical residues participating in the interaction. The HDX-MS data were used to dock RshA on the open conformation of SigH from the SigH-RNAP complex structure (PDB: 5ZX2), and closed conformation was obtained from protein modelling. The docking revealed that closed conformation of SigH complexing with RshA in terms of HDX-MS data revealed a major structural shift in SigH while interacting with two different binding partners, RshA and RNAP, under variable environmental conditions. This structural shift of SigH with RshA and RNAP has significance in understanding the stress response of M. tuberculosis, and SigH could prove to be a potential drug target.

Nickel oxide nanoparticles from Sargassumwightii: Synthesis,Characterization, and biomedical applications

Antimicrobial resistance and cancer claim millions of lives each year, making it important to find effective medications. The current study focuses on the rapid and sustainable manufacture of nickel oxide nanoparticles using Sargassum wightii aqueous extract (SW-NiO NPs) as a novel antibacterial and anticancer agent. The presence of phytochemicals in the Sargassumwightii in all extract was qualitatively screened and water extract, which is found to contain abundant phytochemical was used for the biosynthesis. Thus synthsized SW-NiO nanoparticles was characterised by UV–Visible, FT-IR, XRD, SEM, and TEM spectral studies, and then screend for its invitro anti microbial activity against bacterial strains (E. coli, K. pneumoniae, S. aureus, and B. subtilis) and fungal activity with the fungus strains (C. albicans, C. tropicalis),invitro anticancer and larvicidal activity. The SW-NiO NPs possessed cubic and hexagonal structures with an average crytallite size of 15–20 nm, according to the results of the analytical characterization. SEM and TEM analysis confirms the spherical like morphology and their respective elemental composition were identified by EDAX studies. The antimicrobial activity was carried by well diffusion method.Compared to gram-negative organisms, gram-positive species were more vulnerable to the SW-NiO NPs with the zone of inhibition 17 mm. The fungal activity results showed the zone of inhibition 16 and 14 mm. It was also found that SW-NiO NPs inhibit the growth of tested fungus at higher doses. The cytotoxicity studies with liver cancer cell line through MTT assay revealed the inhibition value at 52.25 ± 0.05 µM, indicating their sensitivity to the HepG2 cell line. SW-NiO NPs made with water extract showed the highest larvicidal activity and 88.33 % death of Aedes agypti(AE). The LC50 and LC90 values for SW-NiO NPs against Aedes agypti(AE) larvae are found to be 31.420 and 134.261 ppm, respectively. The findings of the antimicrobial, larvicidal and invitro anticancer screening of this work provide compelling evidence that SW-NiO NPs made from an aqueous extract of Sargassum wightii have considerable biological applications which can be explored in detail on identifying the potential phyto constituents which are responsible for the activity.

Mirror therapy as a method of rehabilitation for stroke patients

Mirror therapy as a rehabilitation method for stroke patients. Stroke claims millions of lives every year worldwide. Its severity is associated with the damage to important brain structures and severe disruptions of various physiological functions in patients, making the investigation of methods to alleviate the consequences of stroke relevant. The aim of this study is to highlight the methods of mirror therapy, their effectiveness, and to examine some possible mechanisms underlying the positive effects of mirror therapy on the recovery of limb functions. Materials and Methods. A review and analysis of literature data were conducted. Literature search was carried out in Scopus and Pubmed databases. Technical implementation of mirror therapy. Various three-dimensional structures are used to implement the method of mirror therapy, allowing the patient to perceive the work of the working limb as the work of an immobilized leg or arm, which has a positive effect on the rehabilitation process. In many studies, the effectiveness of mirror therapy has been demonstrated both in the chronic and acute phases after stroke. Several hypotheses have been put forward by scientists about the mechanisms of the effectiveness of mirror therapy, suggesting the activation of motor neuron centers and zones in the brain cortex. Conclusion. According to the results of numerous studies, mirror therapy shows a sufficiently high effectiveness in the rehabilitation of stroke patients and improves their quality of life.

Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods

BackgroundFraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed procedures may not be relevant to a given diagnosis and patient profile, resulting in unnecessary and unwarranted treatments and medical payments. This study aims to identify such unwarranted procedures from millions of healthcare claims. In the absence of labeled examples of unwarranted procedures, the study focused on the application of unsupervised machine learning techniques.MethodsExperiments were conducted with deep autoencoders to find claims containing anomalous procedure codes indicative of FWA, and were compared against a baseline density-based clustering model. Diagnoses, procedures, and demographic data associated with healthcare claims were used as features for the models. A dataset of one hundred thousand claims sampled from a larger claims database is used to initially train and tune the models, followed by experimentations on a dataset with thirty-three million claims. Experimental results show that the autoencoder model, when trained with a novel feature-weighted loss function, outperforms the density-based clustering approach in finding potential outlier procedure codes.ResultsGiven the unsupervised nature of our experiments, model performance was evaluated using a synthetic outlier test dataset, and a manually annotated outlier test dataset. Precision, recall and F1-scores on the synthetic outlier test dataset for the autoencoder model trained on one hundred thousand claims were 0.87, 1.0 and 0.93, respectively, while the results for these metrics on the manually annotated outlier test dataset were 0.36, 0.86 and 0.51, respectively. The model performance on the manually annotated outlier test dataset improved further when trained on the larger thirty-three million claims dataset with precision, recall and F1-scores of 0.48, 0.90 and 0.63, respectively.ConclusionsThis study demonstrates the feasibility of leveraging unsupervised, deep-learning methods to identify potential procedure overutilization from healthcare claims.

Learning from Highly Imbalanced Big Data with Label Noise

This study explores the effects of class label noise on detecting fraud within three highly imbalanced healthcare fraud data sets containing millions of claims and minority class sizes as small as 0.1%. For each data set, 29 noise distributions are simulated by varying the level of class noise and the distribution of noise between the fraudulent and non-fraudulent classes. Four popular machine learning algorithms are evaluated on each noise distribution using six rounds of five-fold cross-validation. Performance is measured using the area under the precision-recall curve (AUPRC), true positive rate (TPR), and true negative rate (TNR) in order to understand the effect of the noise level, noise distribution, and their interactions. AUPRC results show that negative class noise, i.e. fraudulent samples incorrectly labeled as non-fraudulent, is the most detrimental to model performance. TPR and TNR results show that there are significant trade-offs in class-wise performance as noise transitions between the positive and the negative class. Finally, results reveal how overfitting negatively impacts the classification performance of some learners, and how simple regularization can be used to combat this overfitting and improve classification performance across all noise distributions.

Ukraine's tenurial tangle: Housing, land and property restitution in the Russian war

AbstractThe severity of the population dislocation and destruction of housing, land and property (HLP) in the Ukraine war has driven efforts for starting reconstruction planning prior to the war's end. This comes with the realization that recovery will entail considerable preparation, including efforts at using seized Russian assets to finance it. Engaging in HLP restitution and compensation will be a primary recovery challenge, with the Ukrainian government moving forward with legislation for facilitating this. However, the government's current approach to processing what will be millions of HLP claims for restitution and compensation faces a daunting challenge. Housing, land and property rights prior to the war comprised a dense tangle of confusion, corruption, and inadequate documentation; such that attempting to untangle each claim on a case‐by‐case basis as currently planned is highly problematic and risks instability. This article describes this tangle as five categories of problems: (1) the post‐Soviet transition, (2) rule of law problems, (3) administrative tangles, (4) corruption, and (5) war‐related issues. The article then recommends that the government and international community pursue a ‘mass claims and transitional justice’ approach to large‐scale HLP restitution which is aligned with international best practice and able to supersede the tenurial tangle.

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends.

Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.

Hybrid Classifier-Based Federated Learning in Health Service Providers for Cardiovascular Disease Prediction

One of the deadliest diseases, heart disease, claims millions of lives every year worldwide. The biomedical data collected by health service providers (HSPs) contain private information about the patient and are subject to general privacy concerns, and the sharing of the data is restricted under global privacy laws. Furthermore, the sharing and collection of biomedical data have a significant network communication cost and lead to delayed heart disease prediction. To address the training latency, communication cost, and single point of failure, we propose a hybrid framework at the client end of HSP consisting of modified artificial bee colony optimization with support vector machine (MABC-SVM) for optimal feature selection and classification of heart disease. For the HSP server, we proposed federated matched averaging to overcome privacy issues in this paper. We tested and evaluated our proposed technique and compared it with the standard federated learning techniques on the combined cardiovascular disease dataset. Our experimental results show that the proposed hybrid technique improves the prediction accuracy by 1.5%, achieves 1.6% lesser classification error, and utilizes 17.7% lesser rounds to reach the maximum accuracy.

Antiviral Peptide-Based Conjugates: State of the Art and Future Perspectives

Infectious diseases caused by microbial pathogens (bacteria, virus, fungi, parasites) claim millions of deaths per year worldwide and have become a serious challenge to global human health in our century. Viral infections are particularly notable in this regard, not only because humankind is facing some of the deadliest viral pandemics in recent history, but also because the arsenal of drugs to combat the high levels of mutation, and hence the antigenic variability of (mostly RNA) viruses, is disturbingly scarce. Therefore, the search for new antivirals able to successfully fight infection with minimal or no adverse effects on the host is a pressing task. Traditionally, antiviral therapies have relied on relatively small-sized drugs acting as proteases, polymerases, integrase inhibitors, etc. In recent decades, novel approaches involving targeted delivery such as that achieved by peptide-drug conjugates (PDCs) have gained attention as alternative (pro)drugs for tackling viral diseases. Antiviral PDC therapeutics typically involve one or more small drug molecules conjugated to a cell-penetrating peptide (CPP) carrier either directly or through a linker. Such integration of two bioactive elements into a single molecular entity is primarily aimed at achieving improved bioavailability in conditions where conventional drugs are challenged, but may also turn up novel unexpected functionalities and applications. Advances in peptide medicinal chemistry have eased the way to antiviral PDCs, but challenges remain on the way to therapeutic success. In this paper, we review current antiviral CPP-drug conjugates (antiviral PDCs), with emphasis on the types of CPP and antiviral cargo. We integrate the conjugate and the chemical approaches most often applied to combine both entities. Additionally, we comment on various obstacles faced in the design of antiviral PDCs and on the future outlooks for this class of antiviral therapeutics.

Neurodevelopmental Problems Not Linked to Antidepressant Exposure In Utero

Neurodevelopmental Problems Not Linked to Antidepressant Exposure In Utero

Novel thiadiazole-thiazole hybrids: synthesis, molecular docking, and cytotoxicity evaluation against liver cancer cell lines

One of the worst diseases, cancer claims millions of lives each year throughout the world, necessitating the creation of novel treatments. In this study, we designed a novel series of 1,3,4-thiadiazoles through the reaction of 2-(4-methyl-2-(2-(1-phenylethylidene)hydrazineyl)thiazole-5-carbonyl)-N-phenylhydrazine-1-carbothioamide (3) with the proper hydrazonoyl halides. Using the MTT assay, the newly synthesized thiadiazoles' growth-inhibitory potential against the liver cancer cell line HepG2-1 was assessed. In comparison to the standard drug doxorubicin (IC50 = 0.72 ± 0.52 µM), the results showed that two compounds, 16b and 21 (IC50 = 0.69 ± 0.41 and 1.82 ± 0.94 µM, respectively) had promising anticancer activity. The structural activity relationship (SAR) was investigated. In addition, molecular docking analysis onto quinone oxidoreductase2 (NQO2) receptor (PDB: 4ZVM) was investigated against the potent compounds to examine the reliability of the in vitro results. The newly prepared thiadiazole-thiazole hybrids are therefore regarded as potent anticancer drugs.

Emergence of infectious diseases and role of advanced nanomaterials in point-of-care diagnostics: a review

ABSTRACT Infectious outbreaks are the foremost global public health concern, challenging the current healthcare system, which claims millions of lives annually. The most crucial way to control an infectious outbreak is by early detection through point-of-care (POC) diagnostics. POC diagnostics are highly advantageous owing to the prompt diagnosis, which is economical, simple and highly efficient with remote access capabilities. In particular, utilization of nanomaterials to architect POC devices has enabled highly integrated and portable (compact) devices with enhanced efficiency. As such, this review will detail the factors influencing the emergence of infectious diseases and methods for fast and accurate detection, thus elucidating the underlying factors of these infections. Furthermore, it comprehensively highlights the importance of different nanomaterials in POCs to detect nucleic acid, whole pathogens, proteins and antibody detection systems. Finally, we summarize findings reported on nanomaterials based on advanced POCs such as lab-on-chip, lab-on-disc-devices, point-of-action and hospital-on-chip. To this end, we discuss the challenges, potential solutions, prospects of integrating internet-of-things, artificial intelligence, 5G communications and data clouding to achieve intelligent POCs.

A Survey Paper on Computer Aided Detection of Tuberculosis

Tuberculosis (TB) is a disease that claims millions of lives each year, primarily in impoverished places. Traditional TB screening procedures may not only take longer time, but may also be infeasible in areas with inadequate healthcare infrastructure. Artificial intelligence developments have propelled computer-aided diagnostic systems to new heights. The identification of TB using computer-aided approaches offers various benefits and has the potential to be superior to traditional diagnostic procedures, particularly in poor and middle-income countries where medical specialists and machinery are few. This survey article intends to describe the scientific effort done in computer assisted detection of TB and provides light on future research opportunities.

Gender in the Markets for Expertise

Stratification in professional careers arises in part from interpersonal dynamics in client-expert dyads. To reduce perceived uncertainty in judgments of the quality of experts, clients may rely on ascriptive characteristics of experts and on pairwise, relational factors to assess the advice they receive. Two such characteristics, expert gender and client-expert gender concordance, may lead to differences in clients’ trust in expert advice. To explore these issues, we investigate the incidence of patient-initiated second opinions (SOs) in medicine. In an examination of millions of medical claims in Massachusetts, we find that male patients are much more likely than female patients to obtain an SO if the first specialist they consult is female. Moreover, when the first specialist a patient consults is gender non-concordant and the patient seeks an SO, male patients are substantially more likely to switch to a same-gender specialist in the SO visit. Because patients who lack confidence in the advice of the first-seen specialist infrequently return to this specialist for medical services, female specialists generate lower billings. Analyses of medical spending in follow-up visits suggest that gendered patterns in questioning the advice of medical experts have the potential to contribute substantially to the gender pay gap in medicine.

The Implications of Covid-19 Pandemic on Consumer Burden in Malaysia: An Analysis

The effects of Covid-19 are continuing to increase around the world as the pandemic claims millions of lives. Malaysia is no exception and has been remarkably affected by the pandemic. Apart from the number of people who are or have been infected with this virus, millions of people are directly or indirectly facing many challenges to their living. The challenges include increased cost of living, income reduction or losses, decreased purchasing power, and unemployment, along with other societal issues created by the pandemic. This study contextualised the implications of the pandemic on consumers’ burden in Malaysia, focusing on the cost of living and unemployment. Observations on prices, inflation, and unemployment rates were conducted to provide meaningful evidence. Major findings revealed that the cost of living has risen, contributed by the increase in prices, especially for food items, healthcare, and education. The unemployment rate also expanded due to the lockdown measures, which led to income losses and reduction. The Covid-19 pandemic has brought challenges to the consumers as measures taken to curb the pandemic have altered many aspects of life and increased the consumers’ burden. Although the inflation rate remains manageable, a major concern lies in many people’s income reduction or income losses. This study implies that the government, together with private institutions and NGOs, needs to work hand in hand to minimise the consumers’ financial burden, especially during this pandemic. It is hoped that the discussion presented in this paper would shed light on the impact of the pandemic on consumers’ burden in Malaysia.

Combined assessment of tuberculosis case notification rate and infection control at health facilities of Dale districts, Sidama Zone, Southern Ethiopia.

BackgroundMycobacterium tuberculosis (TB) is the deadliest disease that claims millions of deaths globally. Ethiopia is among the countries heavily hit by the disaster. Despite the effective directly observed treatment and TB infection control (TBIC) measures provided by the world health organization (WHO), the rate of new cases increased daily throughout the country. Healthcare workers (HCWs) are at highest risk serving without having the necessary facility in place while overcrowding of patients exacerbated TB transmission. The study aimed to assess TBIC implementation and analyze case notification rate (CNR) of smear-positive pulmonary TB in the selected health facilities at Dale district, Sidama Zone, Southern Ethiopia.MethodsSeven health care facilities have been visited in the study area and smear-positive pulmonary TB notification rate was determined retrospectively during the years 2012 to 2014. Data on smear positive test results and demographic characteristics were collected from the TB unit registries. A structured questionnaire, facility survey, and observation checklists were used to assess the presence of TBIC plans at the health care facilities.ResultsThe overall case notification rate of smear-positive pulmonary tuberculosis was 5.3% among all 7696 TB suspected patients. The odds of being diagnosed with smear-positive TB were 24% more in males than in females (adj OR = 1.24, 95% CI: (1.22, 1.55). Moreover, in the study area, only 28% of the facilities have been practiced TB infection control and 71% of the facilities assigned a focal person for the TBIC plan. The implementation of environmental control measures in the facilities was ranged between 16–83%. N95 particulate respirators were found only in 14% of the facilities.ConclusionTB CNR in Dale district was low. Moreover, implementations of TBIC in Dale district health facilities were poor when the survey was done. Hence, urgent measures should be taken to reverse the burden of TB.