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This study aims to examine the relationship between government spending and economic development in Kenya, given the lack of consensus on the topic globally. While there is an agreement that some level of government expenditure is necessary for economic development, the specific areas of spending that are most beneficial still need to be clarified. This study uses the auto regressive distributed lag(ARDL) model and a Support Vector Machine to explore the effect of public spending on the Kenyan economy from 1980 to 2021. The variables used in this study include GDP, infrastructure, education, health, and defense expenditures. This study hypothesizes that increased investment in infrastructure, health, education, and defense spending leads to accelerated economic development growth in Kenya. The results indicate that education has a negative effect on economic development in the short run but a positive effect in the long run, while health and defense spending have a positive impact on economic development Infrastructure spending has adverse impact on economic development in both the short and long run, but its correlation is low. This study suggests that Kenya should increase funding for education and health, maintain defense spending, and improve infrastructure spending while addressing corruption and bureaucracy.

Objectives: Aim of this study was to explore the immune-related lncRNAs having prognostic role and establishing risk score model for better prognosis and immunotherapeutic coherence for esophageal cancer (EC) patients. Methods: To determine the role of immune-related lncRNAs in EC, we analyzed the RNA-seq expression data of 162 EC patients and 11 non-cancerous individuals and their clinically relevant information from the cancer genome atlas (TCGA) database. Bioinformatic and statistical analysis such as Differential expression analysis, co-expression analysis, Kaplan Meier survival analysis, Cox proportional hazards model, ROC analysis of risk model was employed. Results: Utilizing a cutoff criterion (log2FC > 1 + log2FC

IntroductionRising fluoride levels in groundwater resources have become a worldwide concern, presenting a significant challenge to the safe utilization of water resources and posing potential risks to human well-being. Elevated fluoride and its vast spatial variability have been documented across different districts of Punjab, India, and it is, therefore, imperative to predict the fluoride levels for efficient groundwater resources planning and management.MethodsIn this study, five different models, Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (Xgboost), Extreme Learning Machine (ELM), and Multilayer Perceptron (MLP), are proposed to predict groundwater fluoride using the physicochemical parameters and sampling depth as predictor variables. The performance of these five models was evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE).Results and discussionELM outperformed the remaining four models, thus exhibiting a strong predictive power. The R2, MAE, and RMSE values for ELM at the training and testing stages were 0.85, 0.46, 0.36 and, 0.95, 0.31, and 0.33, respectively, while other models yielded inferior results. Based on the relative importance scores, total dissolved solids (TDS), electrical conductivity (EC), sodium (Na+), chloride (Cl−), and calcium (Ca2+) contributed significantly to model performance. High variability in the target (fluoride) and predictor variables might have led to the poor performance of the models, implying the need for better data pre-processing techniques to improve data quality. Although ELM showed satisfactory results, it can be considered a promising model for predicting groundwater quality.

Emerging infectious diseases (EIDs) pose an increasingly significant global burden, driven by urbanization, population explosion, global travel, changes in human behavior, and inadequate public health systems. The recent SARS-CoV-2 pandemic highlights the urgent need for innovative and robust technologies to effectively monitor newly emerging pathogens. Rapid identification, epidemiological surveillance, and transmission mitigation are crucial challenges for ensuring public health safety. Genomics has emerged as a pivotal tool in public health during pandemics, enabling the diagnosis, management, and prediction of infections, as well as the analysis and identification of cross-species interactions and the categorization of infectious agents. Recent advancements in high-throughput DNA sequencing tools have facilitated rapid and precise identification and characterization of emerging pathogens. This review article provides insights into the latest advances in various genomic techniques for pathogen detection and tracking and their applications in global outbreak surveillance. We assess methods that leverage pathogen sequences and explore the role of genomic analysis in understanding the epidemiology of newly emerged infectious diseases. Additionally, we address technical challenges and limitations, ethical and legal considerations, and highlight opportunities for integrating genomics with other surveillance approaches. By delving into the prospects and obstacles of genomics, we can gain valuable insights into its role in mitigating the threats posed by emerging pathogens and improving global preparedness in the face of future outbreaks.

A glioma is a solid brain tumor which originates in the brain or brain stem area. The diagnosis of gliomas based on standard-of-care (SOC) techniques includes karyotyping, fluorescence in situ hybridization (FISH), and chromosomal microarray (CMA), for detecting the pathogenic variants and chromosomal abnormalities. But these techniques do not reveal the complete picture of genetic complexity, thus requiring an alternative technology for better characterization of these tumors. The present study aimed to evaluate the clinical performance and feasibility of using optical genome mapping (OGM) for chromosomal characterization of gliomas. Herein, we evaluated 10 cases of gliomas that were previously characterized by CMA. OGM analysis showed concordance with the results of CMA in identifying the characterized Structural Variants (SVs) in these cases. More notably, it also revealed additional clinically relevant aberrations, demonstrating a higher resolution and sensitivity. These clinically relevant SVs included cryptic translocation, and SVs which are beyond the detection capabilities of CMA. Our analysis highlights the unique capability of OGM to detect all classes of SVs within a single assay, thereby unveiling clinically significant data with a shorter turnaround time. Adopting this diagnostic tool as a standard of care for solid tumors like gliomas shows potential for improving therapeutic management, potentially leading to more personalized and timely interventions for patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease (COVID-19), induced a global pandemic with a diverse array of clinical manifestations. While the acute phase of the pandemic may be waning, the intricacies of COVID-19′s impact on neurological health remain a crucial area of investigation. Early recognition of the spectrum of COVID-19 symptoms, ranging from mild fever and cough to life-threatening respiratory distress and multi-organ failure, underscored the significance of neurological complications, including anosmia, seizures, stroke, disorientation, encephalopathy, and paralysis. Notably, patients requiring intensive care unit (ICU) admission due to neurological challenges or due to them exhibiting neurological abnormalities in the ICU have shown increased mortality rates. COVID-19 can lead to a range of neurological complications such as anosmia, stroke, paralysis, cranial nerve deficits, encephalopathy, delirium, meningitis, seizures, etc., in affected patients. This review elucidates the burgeoning landscape of neurological sequelae associated with SARS-CoV-2 infection and explores the underlying neurobiological mechanisms driving these diverse manifestations. A meticulous examination of potential neuroinvasion routes by SARS-CoV-2 underscores the intricate interplay between the virus and the nervous system. Moreover, we dissect the diverse neurological manifestations emphasizing the necessity of a multifaceted approach to understanding the disease’s neurological footprint. In addition to elucidating the pathophysiological underpinnings, this review surveys current therapeutic modalities and delineates prospective avenues for neuro-COVID research. By integrating epidemiological, clinical, and diagnostic parameters, we endeavor to foster a comprehensive analysis of the nexus between COVID-19 and neurological health, thereby laying the groundwork for targeted therapeutic interventions and long-term management strategies.

The novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as one of the most significant global health crises in recent history. The clinical characteristics of COVID-19 patients have revealed the possibility of immune activity changes contributing to disease severity. Nevertheless, limited information is available regarding the immune response in human lung tissue, which is the primary site of infection. In this study, we conducted an extensive analysis of lung tissue to screen for differentially expressed miRNAs and mRNAs in five individuals who died due to COVID-19 and underwent a rapid autopsy, as well as seven control individuals who died of other causes unrelated to COVID-19. To analyze the host response gene expression, miRNA microarray and Nanostring’s nCounter XT gene expression assay were performed. Our study identified 37 downregulated and 77 upregulated miRNAs in COVID-19 lung biopsy samples compared to the controls. A total of 653 mRNA transcripts were differentially expressed between the two sample types, with most transcripts (472) being downregulated in COVID-19-positive specimens. Hierarchical and PCA K-means clustering analysis showed distinct clustering between COVID-19 and control samples. Enrichment and network analyses revealed differentially expressed genes important for innate immunity and inflammatory response in COVID-19 lung biopsies. The interferon-signaling pathway was highly upregulated in COVID-19 specimens while genes involved in interleukin-17 signaling were downregulated. These findings shed light on the mechanisms of host cellular responses to COVID-19 infection in lung tissues and could help identify new targets for the prevention and treatment of COVID-19 infection.

Psoriasis is a chronic inflammatory skin disease with keratinocyte hyperproliferation and T cells as key mediators of lesional and systemic inflammatory changes. To date, no suitable differential biomarkers are available for the disease diagnosis. More recently, microRNAs have been identified as critical regulators of lesional and systemic immune changes in psoriasis with diagnostic potential. We have performed expression profiling of T cell-specific miRNAs in 38 plasma samples from psoriasis vulgaris patients and an equal number of age- and gender-matched healthy subjects. Our findings have identified a panel of five blood-based circulatory miRNAs with a significant change in their expression levels, comprising miR-215, miR-148a, miR-125b-5p, miR-223, and miR-142-3p, which can differentiate psoriasis vulgaris patients from healthy individuals. The receiver operating characteristic (ROC) curves for all five miRNAs individually and in combination exhibited a significant disease discriminatory area under the curve with an AUC of 0.762 and a p < 0.0001 for all the miRNAs together. Statistically, all five miRNAs in combination depicted the best-fit model in relation to disease severity (PASI) compared with individual miRNAs, with the highest R2 value of 0.94 and the lowest AIC score of 131.8. Each of the miRNAs also exhibited a significant association with at least one of the other miRNAs in the panel. Importantly, the five miRNAs in the panel regulate one or more immune-inflammation pathways based on target prediction, pathway network analysis, and validated roles in the literature. The miRNA panel provides a rationalized combination of biomarkers that can be tested further on an expanded cohort of patients for their diagnostic value.

Myopia is a widespread and complex refractive error in which a person's ability to see distant objects clearly is impaired. Its prevalence rate is increasing worldwide, and as per WHO, it is projected to increase from 22% in 2000 to 52% by 2050. It is more prevalent in developed, industrial areas and affects individuals of all ages. There are a number of treatments available for the control of myopia, such as glasses, contact lenses, laser surgery, and pharmaceuticals agents. However, these treatments are less beneficial and have significant side effects. A novel molecule, 7-methylxanthine (7-MX), has been found to be a highly beneficial alternate in the treatment of myopia and excessive eye elongation. Many preclinical and clinical studies showed that 7-MX is effective for the treatment of myopia and is presently under phase II of clinical investigation. We have also investigated preclinical toxicity studies such as acute, sub-acute, sub-chronic, and chronic on rats. In these studies, 7-MX was found to be non-toxic as compared to other reported anti-myopic agents. Moreover, as an ideal drug, 7-MX is observed to have no or low toxicity, brain permeability, non-allergic, higher oral administration efficacy, and low treatment costs and thus qualifies for the long-term treatment of myopia. This review article on 7-MX as an alternative to myopia treatment will highlight recent findings from well-designed preclinical and clinical trials and propose a potential future therapy.

Plain English Summary Immigrants often find accessing and navigating the healthcare system difficult upon arriving in Canada. Difficulties are often due to cultural differences, language barriers, limited health literacy, and health system complexity. This article shares the experiences of our research team as we engaged in patient-oriented research (POR) to better understand the unique barriers faced by newcomers trying to access and navigate the health system after immigrating to Canada. POR teams include researchers, patient partners, health care providers, and policymakers working together throughout each stage of the research process. This method of research embraces patient experience as expertise and provides patients an opportunity to influence decisions about their care. This research approach is aimed to improve patient outcomes by focusing on patient priorities. Through sharing our experiences, this article defines POR, describes the creation of our research team, and shares how impactful co-creating research with patients was for our team. Lastly, we provide examples of how taking a POR approach can increase research capacity and personal growth for both patient partners and researchers.

Aims: To study clinico-social, biochemical and angiographic profile of patients presenting with premature coronary artery disease (PCAD) who are drivers by profession. To highlight the impact of occupational hazard and air pollution in them. Subjects and Methods: Of 3450 patients registered in the PCAD registry till date, 755 (21.88%) were found to satisfy the inclusion criteria. The data was analyzed by statistical software R version 3.5.0. Further analysis of smokers versus nonsmokers was done. Results: The average age of the group was 33.10 years. Almost all 754 (99.8%) were males, of which 323 (42.78%) smoked. The group had 83 (10.95%) diabetics and 71 (9.4%) hypertensives. Around 99 (13.11%) had a family history of coronary artery disease (CAD). Majority of them, 440 (58.27%), were urban drivers with average driving of 10 h/day. In the group, 482 (63.8%) had abnormal body mass index (BMI) and 539 (71.41%) had abdominal obesity. Low HDL was seen in 508 (67%) patients. ST-elevation myocardial infarction was the most common presentation in 415 (54.96%). Obstructive atherosclerotic CAD was seen in 217 (34.22%). Further analysis of smokers versus nonsmokers showed that hypertension, diabetes, and abnormal BMI were less common among nonsmokers, indicating the probable role of air pollution in them. Conclusions: Occupational hazard due to the stress related to prolonged driving hours, obesity, smoking and the possible role of air pollution are the important cardiovascular (CV) risks which come to light in this group of patients. Furthermore, the majority of them presented with predominant thrombotic lesions, however, smokers, who also had other CV risk factors, presented more often with obstructive atherosclerotic CAD.

The emergence of COVID-19 has led to significant morbidity and mortality, with around seven million deaths worldwide as of February 2023. There are several risk factors such as age and sex that are associated with the development of severe symptoms due to COVID-19. There have been limited studies that have explored the role of sex differences in SARS-CoV-2 infection. As a result, there is an urgent need to identify molecular features associated with sex and COVID-19 pathogenesis to develop more effective interventions to combat the ongoing pandemic. To address this gap, we explored sex-specific molecular factors in both mouse and human datasets. The host immune targets such as TLR7, IRF7, IRF5, and IL6, which are involved in the immune response against viral infections, and the sex-specific targets such as AR and ESSR were taken to investigate any possible link with the SARS-CoV-2 host receptors ACE2 and TMPRSS2. For the mouse analysis, a single-cell RNA sequencing dataset was used, while bulk RNA-Seq datasets were used to analyze the human clinical data. Additional databases such as the Database of Transcription Start Sites (DBTS), STRING-DB, and the Swiss Regulon Portal were used for further analysis. We identified a 6-gene signature that showed differential expression in males and females. Additionally, this gene signature showed potential prognostic utility by differentiating ICU patients from non-ICU patients due to COVID-19. Our study highlights the importance of assessing sex differences in SARS-CoV-2 infection, which can assist in the optimal treatment and better vaccination strategies.

Malware is a growing threat to all mobile platforms and hundreds of new malicious applications are being detected every day. At the same time, the development of automated software obfuscation techniques allows for the easy production of new malware variants even by attackers with entry-level programming skills. Such obfuscation techniques can evade the signature-based mechanism implemented in current antimalware technology. This paper presents the results of a study that examined how automated obfuscation techniques affect malicious and benign applications by two widely used malware detection approaches, namely static and dynamic analyses. The research explored 5000 samples of malware and benign programs and evaluated the impact of automated obfuscation on Android applications. The experimental results indicated that (1) up to 73% of the reviewed applications “survived” the automated obfuscation; (2) automated obfuscation reduced the detection ratio to 65–85% depending on the obfuscation method used. These findings call for a more active use of advanced malware detection methods in commonly used antivirus platforms.

Patients, especially children, are the most difficult to treat in all groups of population mainly because they can not swallow the solid dosage form. Due to this reason they are often prescribed liquid dosage forms. But these formulations have their own disadvantages (lack of dose accuracy during administration, spitting by children, spillage, lack of stability, difficulty in transportation, etc.). Oral strip technology is one such technology to surpass these disadvantages. Desloratadine, a descarboethoxy derivative of loratadine, is a second generation antihistaminic drug approved for usage in allergic rhinitis among paediatric population and is available in markets as suspension. An attempt has been made to design and optimize the oral strip containing desloratadine as an active ingredient. Oral strip was optimized with the help of optimal experimental design using polymer concentration, plasticizer type, and plasticizer concentration as independent variables. Prepared oral strips were evaluated for physicochemical parameter, mechanical strength parameters, disintegration time, dissolution, surface pH, and moisture sorption tendency. Optimized formulation was further evaluated by scanning electron microscopy, moisture content, and histological alteration in oral mucosa. Accelerated stability studies were also carried out for optimized formulations. Results were analysed with the help of various statistical tools at and .

Objectives: Aim of this study was to explore the immune-related lncRNAs having prognostic role and establishing risk score model for better prognosis and immunotherapeutic coherence for esophageal cancer (EC) patients. Methods: To determine the role of immune-related lncRNAs in EC, we analyzed the RNA-seq expression data of 162 EC patients and 11 non-cancerous individuals and their clinically relevant information from the cancer genome atlas (TCGA) database. Bioinformatic and statistical analysis such as Differential expression analysis, co-expression analysis, Kaplan Meier survival analysis, Cox proportional hazards model, ROC analysis of risk model was employed. Results: Utilizing a cutoff criterion (log2FC > 1 + log2FC < −1 and FDR < 0.01), we identified 3737 RNAs were significantly differentially expressed in EC patients. Among these, 2222 genes were classified as significantly differentially expressed mRNAs (demRNAs), and 966 were significantly differentially expressed lncRNAs (delncRNA). Through Pearson correlation analysis between differentially expressed lncRNAs and immune related-mRNAs, we identified 12 immune-related lncRNAs as prognostic signatures for EC. Notably, through Kaplan-Meier analysis on these lncRNAs, we found the low-risk group patients showed significantly improved survival compared to the high-risk group. Moreover, this prognostic signature has consistent performance across training, testing and entire validation cohort sets. Using ESTIMATE and CIBERSORT algorithm we further observed significant enriched infiltration of naive B cells, regulatory T cells resting CD4+ memory T cells, and, plasma cells in the low-risk group compared to high-risk EC patients group. On the contrary, tumor-associated M2 macrophages were highly enriched in high-risk patients. Additionally, we confirmed immune-related biological functions and pathways such as inflammatory, cytokines, chemokines response and natural killer cell-mediated cytotoxicity, toll-like receptor signaling pathways, JAK-STAT signaling pathways, chemokine signaling pathways significantly associated with identified IRlncRNA signature and their co-expressed immune genes. Furthermore, we assessed the predictive potential of the lncRNA signature in immune checkpoint inhibitors; we found that programed cell death ligand 1 (PD-L1; P-value =.048), programed cell death ligand 2 (PD-L2; P-value =.002), and T cell immunoglobulin and mucin-domain containing-3 (TIM-3; P-value =.045) expression levels were significantly higher in low-risk patients compared to high-risk patients. Conclusion: We believe this study will contribute to better prognosis prediction and targeted treatment of EC in the future. [ABSTRACT FROM AUTHOR]

Introduction: Rising fluoride levels in groundwater resources have become a worldwide concern, presenting a significant challenge to the safe utilization of water resources and posing potential risks to human well-being. Elevated fluoride and its vast spatial variability have been documented across different districts of Punjab, India, and it is, therefore, imperative to predict the fluoride levels for efficient groundwater resources planning and management. Methods: In this study, five different models, Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (Xgboost), Extreme Learning Machine (ELM), and Multilayer Perceptron (MLP), are proposed to predict groundwater fluoride using the physicochemical parameters and sampling depth as predictor variables. The performance of these five models was evaluated using the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). Results and discussion: ELM outperformed the remaining four models, thus exhibiting a strong predictive power. The R², MAE, and RMSE values for ELM at the training and testing stages were 0.85, 0.46, 0.36 and, 0.95, 0.31, and 0.33, respectively, while other models yielded inferior results. Based on the relative importance scores, total dissolved solids (TDS), electrical conductivity (EC), sodium (Na+), chloride (Cl-), and calcium (Ca2+) contributed significantly to model performance. High variability in the target (fluoride) and predictor variables might have led to the poor performance of the models, implying the need for better data pre-processing techniques to improve data quality. Although ELM showed satisfactory results, it can be considered a promising model for predicting groundwater quality. [ABSTRACT FROM AUTHOR]