Critical Health Research Articles

Development of Nanomaterials-Based Agents for Selective Antibacterial Activity.

Bacterial infections continue to threaten public health due to limitations in rapid and accurate diagnostic techniques. While broad-spectrum antibiotics offer empirical treatment, their overuse has fuelled the emergence of antimicrobial resistance (AMR) pathogens, posing a critical global public health challenge. In this critical scenario, nanomaterial-based antibacterial agents emerge as a promising solution to combat bacteria and inhibit their proliferation. However, selective elimination of pathogenic bacteria is paramount. This review highlights recent advancements in developing nanomaterials for selective antibacterial activity. We categorize these agents based on their mode of action, exploring how they selectively interact with bacteria and their potential antibacterial mechanisms. This review offers crucial insights for researchers exploring the potential of nanotechnology to address the growing threat of AMR.

Predictive nomogram for ischemic stroke risk in clear cell renal cell carcinoma patients

Clear cell renal cell carcinoma (ccRCC) and ischemic stroke are critical global health challenges with a notable association. This study explores the correlation between tumor-related factors and ischemic stroke risk, aiming to construct a predictive nomogram model for ischemic stroke in ccRCC patients. We retrospectively analyzed data from ccRCC patients who underwent nephrectomy at the First Hospital of Shanxi Medical University between January 1, 2013, and May 31, 2022. The data were randomly divided into a training cohort (70%) and a validation cohort (30%). Predictive factors were identified using univariate logistic regression, least absolute shrinkage and selection operator regression, and multivariate logistic regression. A nomogram and a Shiny local calculator were developed using these predictors. We identified six predictors for the nomogram: WHO/ISUP grade, diabetes, hypertension, LDL-C, age, and D-dimer. The nomogram showed good discrimination, with an area under the ROC curve of 0.816 in the training cohort and 0.775 in the validation cohort. The optimal cutoff value was 53.7%. The model demonstrated excellent calibration and clinical applicability. WHO/ISUP grade correlates with ischemic stroke risk, offering insights into cancer-related ischemic stroke mechanisms. This nomogram aids in identifying high-risk individuals among ccRCC patients, facilitating early management and improved outcomes.

Study protocol for a randomized controlled trial to adapt a posttraumatic stress disorder intervention of patients with opioid-stimulant polysubstance use receiving methadone maintenance treatment

BackgroundThe purpose of the Treatment for Harnessing Resiliency, Improving emotional regulation, and empowering indiViduals for a brighter future (THRIVE) study is to adapt an evidence-based posttraumatic stress disorder (PTSD) treatment for use among a polysubstance population receiving methadone maintenance treatment (MMT) at an opioid treatment program. Polysubstance use of high-risk combinations, such as illicit opioids and stimulants, is a critical public health issue. Individuals who engage in these high-risk combinations are more likely to have histories of childhood trauma, multiple traumas, PTSD, and greater PTSD severity as compared to mono-substance using individuals. Trauma, co-morbid mental health disorders such as PTSD, and polysubstance use complicate treatment outcomes. This study will use eight study phases to adapt an existing evidence-based PTSD intervention, Skills Training in Affective and Interpersonal Regulation with Narrative Therapy (STAIR-NT), via a massed treatment model (i.e., condensed treatment schedule) for patients in MMT who are engaged in sustained opioid-stimulant polysubstance use.Methods and analysisThe intervention is an adapted version of the STAIR-NT protocol. The massed version created includes four 60-min sessions of skill building and two weeks of four 60-min sessions of narrative therapy. A preliminary randomized controlled trial (RCT) with 80 participants, randomized 1:1, will be conducted to assess the intervention's implementation and impact on primary short-term outcomes of polysubstance use and PTSD symptoms.Ethics and disseminationThe results of this study will inform a fully-powered effectiveness trial for individuals with PTSD and polysubstance use receiving MMT. The findings are expected to provide valuable insights into improving both PTSD and substance use outcomes, and real-world implementation insights to integrating trauma-informed care in treatment settings for vulnerable populations.RegistrationThis study is registered at ClinicalTrials.Gov as NCT06307340. Registration date 03/2024.

Combined transcriptome and metabolome analysis revealed the antimicrobial mechanism of Griseorhodin C against Methicillin-resistant Staphylococcus aureus

The global rise of multidrug-resistant pathogens, particularly Methicillin-resistant Staphylococcus aureus (MRSA), has become a critical public health concern, necessitating the urgent discovery of new antimicrobial agents. Griseorhodin C, a hydroxyquinone compound isolated from Streptomyces, has demonstrated significant inhibitory effects against MRSA. In this study, we employed a comprehensive approach combining transcriptome and metabolome analyses to investigate the underlying antimicrobial mechanism of Griseorhodin C. Our findings reveal that Griseorhodin C interferes with multiple bacterial metabolic pathways, including those essential for the biosynthesis and metabolism of amino acids, purine metabolism and energy metabolism, ultimately leading to bacterial growth inhibition and cell death. Notably, Griseorhodin C showed superior inhibitory effects compared to the clinical standard, vancomycin, both in vivo and vitro. These results highlight the potential of Griseorhodin C as a promising candidate for the development of new therapeutic strategies aimed at combating MRSA infections. The study underscores the importance of exploring natural products as sources of novel antibiotics in the ongoing fight against antimicrobial resistance.

Making a Difference from Day One: The Urgent Need for Universal Neonatal Hearing Screening

Neonatal hearing screening (NHS) is a critical public health measure for early identification of hearing loss, ensuring timely access to interventions that can dramatically improve a child’s language development, cognitive abilities, and social inclusion. Beyond clinical benefits, NHS provides long-term advantages in education and quality of life. Given that congenital hearing loss affects approximately 1–2 in every 1000 newborns worldwide, the case for universal screening is clear. Countries like the United States and Australia have successfully implemented NHS, leading to earlier diagnoses, improved language development, and better educational outcomes. In Europe, while many nations have adopted NHS, consistency remains a challenge. Countries such as Norway and the United Kingdom stand out for their well-established systems, whereas others, like Greece, have made notable progress but have yet to mandate NHS nationwide. This highlights the need for cohesive national policies across Europe to ensure universal coverage. Screening methods such as Transient Evoked Otoacoustic Emissions (TEOAE) and Automated Auditory Brainstem Response (AABR) are established tools for detecting hearing impairments in neonates. Despite their demonstrated efficacy, NHS remains inconsistent globally, particularly in low- and middle-income regions that lack mandatory policies or access to reliable screening technologies. This perspective advocates for the urgent need to make NHS mandatory in all countries, emphasizing its societal benefits and cost-effectiveness. Early diagnosis supports prompt intervention, like hearing aids or cochlear implants, which are most effective when implemented before six months. It also empowers families to make informed decisions, fosters educational inclusion, and mitigates the social and emotional challenges of undiagnosed hearing loss. Policymakers, healthcare providers, and international organizations must prioritize universal NHS to ensure no child is left behind due to unaddressed hearing loss.

Res2U++: Deep learning model for segmentation of ischemic stroke lesions

Stroke is a critical global health issue characterized by the interruption of blood flow to the brain, resulting in a depletion of oxygen and nutrients and causing severe damage or death to brain cells. Early detection and intervention are pivotal in preventing adverse outcomes. Diagnostic imaging modalities like CT and MRI are essential for accurate diagnosis and treatment planning. However, precise segmentation of stroke lesions remains challenging due to variations in appearance, size, location, and intensity within the brain, exacerbated by noise and variability in manual segmentation. To address these challenges, we propose a novel deep neural network model for automatic stroke segmentation. Our model features an encoder–decoder architecture, combining the robust deep and multi-scale feature extraction capabilities of the ResUnet++ encoder with a modified UNet decoder that enhances segmentation accuracy and preserves spatial information effectively. Incorporating the Atrous Spatial Pyramidal Pooling (ASPP) block in the bottleneck strengthens the model, enabling efficient capture of multi-scale contextual information and improving its capability to handle diverse object sizes and shapes in semantic segmentation tasks. We evaluate the model on the Ischemic Stroke Lesion Segmentation Challenge 2015 (SISS 2015) and ISLES 2022 databases. During training, our model achieves a Dice coefficient of 0.85 ± 0.18 on SISS 2015 and 0.85 ± 0.11 on ISLES 2022. For testing on SISS 2015, it achieves 0.80 ± 0.33, surpassing current state-of-the-art results for these datasets.

The Role of Data Visualization in Enhancing Patient Health Literacy

This paper examines how data visualization can serve as a powerful tool to improve patient health literacy by translating complex health information into accessible visual formats. Health literacy, a vital factor in effective healthcare, is influenced by diverse factors such as education, socioeconomic status, and culture. Data visualizations provide a bridge to understanding, allowing patients to more easily comprehend critical health information and, consequently, make informed decisions about their care. The paper investigated the various types of data visualizations used in patient education, discussing design principles for creating effective health-related visual content that can engage patients across health literacy levels. Furthermore, the study examined case studies illustrating successful applications of data visualizations in real-world healthcare settings. In conclusion, the paper addresses the ethical considerations and challenges in employing data visualization as a communication tool, emphasizing the need for culturally competent and inclusive design approaches to maximize comprehension and patient safety. Keywords: Data Visualization, Health Literacy, Patient Education, Healthcare Communication, Ethical Design.

Cytokine profiles as predictors of HIV incidence using machine learning survival models and statistical interpretable techniques

HIV remains a critical global health issue, with an estimated 39.9 million people living with the virus worldwide by the end of 2023 (according to WHO). Although the epidemic’s impact varies significantly across regions, Africa remains the most affected. In the past decade, considerable efforts have focused on developing preventive measures, such as vaccines and pre-exposure prophylaxis, to combat sexually transmitted HIV. Recently, cytokine profiles have gained attention as potential predictors of HIV incidence due to their involvement in immune regulation and inflammation, presenting new opportunities to enhance preventative strategies. However, the high-dimensional, time-varying nature of cytokine data collected in clinical research, presents challenges for traditional statistical methods like the Cox proportional hazards (PH) model to effectively analyze survival data related to HIV. Machine learning (ML) survival models offer a robust alternative, especially for addressing the limitations of the PH model’s assumptions. In this study, we applied survival support vector machine (SSVM) and random survival forest (RSF) models using changes or means in cytokine levels as predictors to assess their association with HIV incidence, evaluate variable importance, measure predictive accuracy using the concordance index (C-index) and integrated Brier score (IBS) and interpret the model’s predictions using Shapley additive explanations (SHAP) values. Our results indicated that RSFs models outperformed SSVMs models, with the difference covariate model performing better than the mean covariate model. The highest C-index for SSVM was 0.7180 under the difference covariate model, while for RSF, it reached 0.8801 under the difference covariate model using the log-rank split rule. Key cytokines identified as positive predictors of HIV incidence included TNF-A, BASIC-FGF, IL-5, MCP-3, and EOTAXIN, while 29 cytokines were negative predictors. Baseline factors such as condom use frequency, treatment status, number of partners, and sexual activity also emerged as significant predictors. This study underscored the potential of cytokine profiles for predicting HIV incidence and highlighted the advantages of RSFs models in analyzing high-dimensional, time-varying data over SSVMs. It further through ablation studies emphasized the importance of selecting key features within mean and difference based covariate models to achieve an optimal balance between model complexity and predictive accuracy.

Proactive cervical cancer risk assessment using data-driven analytics

This study introduces a sophisticated predictive model integrating clinical and lifestyle data addressing the critical public health challenge of cervical cancer, particularly in regions lacking routine screenings. Leveraging data driven analytics, the proposed model undergoes comprehensive preprocessing, including exploratory data analysis, missing value imputation, and feature extraction. Feature selection is carried out using the XGBoost classifier to ensure model efficacy. Data normalization and class balance via oversampling techniques are applied, with model validation conducted through stratified cross-validation. The optimized feature vector is then employed to train a LightGBM model. Utilizing a retrospective dataset of 858 patients from the Hospital Universitario de Caracas, Venezuela, comprising demographic, lifestyle, and medical history data, the LightGBM model achieves an impressive accuracy of 98%, outperforming similar existing approaches. The study outcome demonstrates the effectiveness of the proposed data modelling framework and feature selection, along with the choice of LightGBM as a suitable classifier. The proposed predictive framework can efficiently aid healthcare professionals in prioritizing high-risk patients for further evaluation and intervention.

Real-time monitoring of cattle (Bos taurus) tissue using a novel point-of-care (POC) polymerase spiral reaction (PSR) colorimetric assay

Adulteration of cattle meat with other species raises critical public health concerns, as existing detection methods lack sensitivity, specificity and practicality. We address this gap by introducing a novel Point-of-Care (POC) polymerase spiral reaction (PSR) assay. This rapid, on-site tool enables highly specific and sensitive identification of cattle tissue, even in resource-limited settings. This innovation paves the way for improved food safety and consumer protection. The cattle-specific PSR assay employs COX3 gene-targeting primers and a chromogenic indicator triad (HNB, Malachite Green and Phenol Red) for rapid visual detection. Cross-amplification absence with other meats (buffalo, sheep, goat and pig) and concordance with SYBR Green I and agarose gel electrophoresis confirm specificity. PSR at 62 °C for 60minutes exhibits a 100 fg detection limit and identifies 0.1% meat admixing. Efficacy is demonstrated across a diverse range of cattle meat samples, blind samples and processed cattle muscle samples. Compatibility with both conventional (snap chill) and commercial DNA extraction kits enables on-site analysis within 90minutes, facilitating rapid field sample analysis.

Prevalence and factors contributing to missed opportunities for vaccination in Mogadishu, Somalia

BackgroundVaccination is a critical public health intervention that significantly reduces morbidity and mortality from vaccine preventable diseases. Despite the proven benefits of vaccines, missed opportunities for vaccination (MOV) remain a significant challenge in many low-income countries, including Somalia. This study aimed to determine the prevalence and identify the factors contributing to MOV in Mogadishu, Somalia. MethodsA cross-sectional study was conducted at various healthcare facilities in Mogadishu, Somalia, from January to March 2024. A total of 234 caregivers of children under five years of age were interviewed using a structured questionnaire. Data on sociodemographic characteristics, health service utilization, and reasons for missed vaccinations were collected. Logistic regression analysis was used to identify factors associated with MOV. Results: The prevalence of MOV in Mogadishu was 26%. Key factors contributing to MOV included low socioeconomic status, lack of caregiver awareness about vaccination schedules, logistical challenges within the healthcare system, and misconceptions about vaccine safety and efficacy. Knowledge and beliefs of mothers about vaccination significantly affected the MOVs in children. Not knowing if all children should be vaccinated is associated with higher probability of (MOVs), (AOR =13.006, 95% CI = 1.175-143.924, p = 0.003). ConclusionThe study highlighted a significant prevalence of missed opportunities for vaccination in Mogadishu, Somalia. Addressing these missed opportunities requires a multifaceted approach, including enhancing public awareness about the importance of vaccination, improving healthcare infrastructure, and ensuring consistent vaccine supply and proper storage.

Green-Synthesis of CuO and Ce-Doped CuO Nanoparticles Using Aqueous Extract of Yam Peel and their Antimicrobial Properties

The rise of drug-resistant microbes presents a critical global health challenge in the 21st century, necessitating the development of potent antimicrobial agents with broad-spectrum activity. Therefore, in this study, we explored the antimicrobial activity of copper oxide (CuO) and cerium-doped CuO (Ce-doped CuO) obtained through a facile green synthesis approach. The nanoparticles were synthesized using aqueous extract of Dioscorea spp (yam peel) and characterized for their physicochemical properties using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), electron microscopy, and UV-Vis spectroscopy. Their antimicrobial efficacy was tested against three bacterial strains—Enterococcus faecalis, Escherichia coli, and Listeria monocytogenes—and three fungal strains—Aspergillus niger, Mucor mucedo, and Penicillium chrysogenum. The results demonstrated that cerium doping significantly altered the physicochemical properties of CuO, leading to enhanced antimicrobial activity. The antibacterial zone of inhibition ranged from 7.50 to 16.55 mm for CuO and 11.80 to 19.50 mm for Ce-doped CuO. For antifungal activity, the minimum inhibitory concentration (MIC) ranged from 0.25 to 0.30 mg/mL for CuO and 0.025 to 0.03 mg/mL for Ce-doped CuO. Notably, the Ce-doped CuO nanoparticles exhibited antimicrobial effects comparable to standard antibiotics. These findings highlight the potential of green-synthesized Ce-doped CuO nanoparticles as effective antimicrobial agents. By leveraging green synthesis and elemental doping, this study offers a promising solution to combat a wide spectrum of infectious pathogens.

Epidemiology and Pathogenesis of Aspiration Pneumonia.

Aspiration pneumonia (AP) remains a critical health issue, especially among older and hospitalized patients. This review focuses on understanding the epidemiology and pathogenesis of AP, exploring key risk factors, and proposing diagnostic strategies. Aspiration pneumonia is commonly associated with hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP), where pathogens introduced into the lungs from gastric aspiration cause infection. Several factors, such as oropharyngeal dysphagia, silent aspiration, and poor oral health, contribute to increased risk, especially in frail elderly populations with chronic comorbidities. Diagnostic criteria for AP are not well-defined, complicating accurate diagnosis. Radiographic evidence of pulmonary infiltrates, history of aspiration, and clinical symptoms, such as cough and fever, guide diagnosis, but the absence of specific biomarkers remains a challenge. Furthermore, the microbiome of the lung, which shares characteristics with the oropharyngeal flora, plays a pivotal role in the development of infection. The incidence of AP is likely to increase given the growing elderly population with factors predisposing them to aspiration. It is one of the most common types of pneumonia in older adults, with its prevalence estimated to range from 5% to 24% in cases of CAP admissions. This revision highlights the growing need for comprehensive diagnostic tools and treatment protocols for AP, especially in high-risk populations such as the elderly and those in long-term care. Understanding the underlying mechanisms and the role of silent aspiration can improve prevention strategies and reduce morbidity and mortality in these vulnerable groups.

An adaptive enhanced human memory algorithm for multi-level image segmentation for pathological lung cancer images

Lung cancer is a critical health issue that demands swift and accurate diagnosis for effective treatment. In medical imaging, segmentation is crucial for identifying and isolating regions of interest, which is essential for precise diagnosis and treatment planning. Traditional metaheuristic-based segmentation methods often struggle with slow convergence speed, poor optimized thresholds results, balancing exploration and exploitation, leading to suboptimal performance in the multi-thresholding segmenting of lung cancer images. This study presents ASG-HMO, an enhanced variant of the Human Memory Optimization (HMO) algorithm, selected for its simplicity, versatility, and minimal parameters. Although HMO has never been applied to multi-thresholding image segmentation, its characteristics make it ideal to improve pathology lung cancer image segmentation.The ASG-HMO incorporating four innovative strategies that address key challenges in the segmentation process. Firstly, the enhanced adaptive mutualism phase is proposed to balance exploration and exploitation to accurately delineate tumor boundaries without getting trapped in suboptimal solutions. Second, the spiral motion strategy is utilized to adaptively refines segmentation solutions by focusing on both the overall lung structure and the intricate tumor details. Third, the gaussian mutation strategy introduces diversity in the search process, enabling the exploration of a broader range of segmentation thresholds to enhance the accuracy of segmented regions. Finally, the adaptive t-distribution disturbance strategy is proposed to help the algorithm avoid local optima and refine segmentation in later stages.The effectiveness of ASG-HMO is validated through rigorous testing on the IEEE CEC'17 and CEC'20 benchmark suites, followed by its application to multilevel thresholding segmentation in nine histopathology lung cancer images. In these experiments, six different segmentation thresholds were tested, and the algorithm was compared to several classical, recent, and advanced segmentation algorithms. In addition, the proposed ASG-HMO leverages 2D Renyi entropy and 2D histograms to enhance the precision of the segmentation process.Quantitative result analysis in pathological lung cancer segmentation showed that ASG-HMO achieved superior maximum Peak Signal-to-Noise Ratio (PSNR) of 31.924, Structural Similarity Index Measure (SSIM) of 0.919, Feature Similarity Index Measure (FSIM) of 0.990, and Probability Rand Index (PRI) of 0.924. These results indicate that ASG-HMO significantly outperforms existing algorithms in both convergence speed and segmentation accuracy. This demonstrates the robustness of ASG-HMO as a framework for precise segmentation of pathological lung cancer images, offering substantial potential for improving clinical diagnostic processes.

Mpox as an emerging health threat for survivors of sex trafficking

AbstractThe resurgence of mpox, declared a Public Health Emergency of International Concern by the World Health Organization in 2024, highlights gaps in global preparedness, particularly for marginalised populations. While much of the focus has been on men who have sex with men, survivors of sex trafficking remain overlooked, despite being highly vulnerable to mpox transmission. These individuals face significant health disparities, including limited access to healthcare, exposure to violence and forced sexual encounters in unsafe conditions, which amplify their risk of infection. The clandestine nature of trafficking further complicates public health interventions like contact tracing and vaccination efforts, leaving survivors at heightened risk of infection and limited access to critical health resources. Addressing these inequities requires targeted interventions, such as confidential testing, outreach, culturally sensitive healthcare and integrating mpox awareness into existing survivor support programmes. Additionally, public health systems must ensure equitable vaccine distribution, enhance surveillance and collaborate with anti‐trafficking organisations to improve prevention and care. A holistic approach that includes mental health support is crucial to address the unique challenges faced by trafficking survivors, ensuring they receive the protection and care needed during this global outbreak.

Design of flexible polyimide-based serpentine EMG sensor for AI-enabled fatigue detection in construction

Physical fatigue and musculoskeletal disorders are critical health issues for construction workers, stemming from repetitive motions, heavy lifting, and awkward postures. These factors compromise worker well-being, productivity, and safety while increasing the risk of accidents and long-term health problems. Recent advancements in wearable health monitoring technology offer potential solutions, but current sensors encounter significant challenges in the dynamic construction environment. These include inadequate skin contact, increased contact impedance, and vulnerability to motion artifacts all of which degrade signal quality and reduce the accuracy of fatigue detection. This paper develops a fractal-based, flexible sensor for enhanced adaptability and accurate fatigue estimation. Finite element analysis compared five space-filling designs, with the serpentine curve exhibiting the highest contact area and lowest strain, making it the preferred choice for fabrication. Evaluations demonstrated significant improvements in signal-to-noise ratio (SNR) and motion artifact reduction, with the newly developed sensor achieving a 37 % to 59 % SNR improvement over commercial electrodes across different muscle groups. The developed flexible sensor was integrated with a fatigue-detecting framework based on a vision transformer model which provided an average accuracy of 87 % for fatigue detection. The developed sensor significantly enhances EMG signal quality and reliability, promising improved health monitoring and safety for construction workers.

Efficacy of lifestyle interventions in preventing cardiovascular disease in Nigeria: A review

Cardiovascular diseases (CVD) represent a critical public health issue worldwide, with Nigeria experiencing a marked increase in prevalence due to rapid urbanization, lifestyle changes, and socioeconomic factors. Lack of sufficient exercise and increased consumption of fatty foods, salty, and processed foods are causal factors of the risk factors associated with hypertension, hypercholesterolemia and obesity. Consequently, this review assesses the effectiveness of lifestyle intervention in dietary modifications, exercising, smoking cessation, and stress reduction on these factors among the Nigerian populace. Clinical studies support that communicating specific dietary measures together with the DASH modified for Nigerian foods and moderate aerobic exercise duration decreases blood pressure and cholesterol levels by dropping 12-15mm Hg and LDL cholesterol by 20mg/dL-Anticipated empirical analysis results show the significance of smoking cessation and stress management on CVD intervention. For instance, nicotine replacement therapy accompanied by behavioural counselling produced a 30% abstinence rate among the participants, and MBSR stress reduction programmes reduced the participants' blood pressure by 10 %. It also reflects the fact that cultural orientation to preventive measures needs to be culturally appropriate for the Nigerian setting, given that socioeconomic factors perpetuate risks. The findings of statistical studies in this review are presented regarding the changes in CVD indicators, which are valuable for forming subsequent public health policies. Because the burden of CVD is increasing in Nigeria and most low and middle-income countries, the use of lifestyle approaches to address risk factors is a good strategy. However, success is determined by countering systemic barriers such as poorly preventing or treating diseases and having a favourable perception of traditional diets. Within these developments, this article seeks to advance the implementation of these efficacious interventions into the Nigerian context, but with ideas for improving its accessibility, cost and receptiveness to accept these innovative practises in the country's health care system.

KNOWLEDGE AND SKILLS IN MANAGEMENT OF COMMON PAEDIATRIC EMERGENCIES AMONG HEALTH WORKERS IN PRIMARY HEALTH CARE CENTRES IN ZARIA

Every day, Nigeria witnesses the loss of approximately 2,300 children under the age of five, primarily due to preventable and treatable conditions such as pneumonia, malaria, and diarrhoea. These high mortality rates are disproportionately higher in rural areas compared to urban regions, where primary health care (PHC) centres often represent the only accessible medical services. Given that PHC centres serve as the first point of contact with the healthcare system for most Nigerians, it is imperative to assess the capabilities of health workers in these facilities, particularly in the management of paediatric emergencies. This study aimed to evaluate the knowledge and skills of health workers in PHC centres in Zaria concerning the management of common paediatric emergencies. A cross-sectional descriptive study was conducted among 139 health workers in PHC centres, randomly selected through a multi-stage sampling technique from both Sabon Gari and Zaria Local Government Areas. Data collection was performed using a pre-tested, semi-structured, self-administered questionnaire. The collected data were analysed using SPSS version 21, with the chi-square test applied to assess associations, and results presented in tables and charts. The findings revealed a generally low level of knowledge and skills in managing common paediatric emergencies among the health workers surveyed. Specifically, 66 (48.5%) of the respondents exhibited very poor knowledge of the causes of common paediatric emergencies, 32 (23.5%) had poor knowledge, 34 (25.0%) demonstrated good knowledge, and only 4 (2.9%) had excellent knowledge. In terms of treatment knowledge, 46.7% of respondents had very poor knowledge, 34.1% had poor knowledge, 16.3% had good knowledge, and only 3.0% had excellent knowledge. Diagnostic skills were similarly deficient, with 41 (37.3%) of respondents displaying very poor skills, 39 (35.5%) poor skills, 26 (23.6%) good skills, and only 4 (3.6%) exhibiting excellent skills. Additionally, 24.8% of respondents had very poor skills in the treatment of paediatric emergencies, 42.3% had poor skills, and 32.8% had good skills, with none achieving excellent skills. A statistically significant relationship was observed between academic qualification and knowledge of causes (p=0.004) and treatment (p=0.000), as well as between academic qualification and diagnostic skills (p=0.006). The pervasive low level of knowledge and skills among PHC health workers is alarming, particularly given Nigeria's already dire childhood mortality statistics. These findings underscore the urgent need for a strategic partnership between the Department of Paediatrics at Ahmadu Bello University Teaching Hospital and the Department of Primary Health Care in Zaria and Sabon Gari Local Governments. Such collaborations could facilitate quarterly training sessions and workshops aimed at enhancing the management of paediatric emergencies in these critical frontline health centers.

Characterization and performance of novel carbon-doped aluminum-modified bentonite for cost-effective fluoride removal

Elevated fluoride levels in water pose a critical global health issue, necessitating advanced remediation strategies. This study introduces a novel, cost-effective approach using clay minerals, specifically bentonite, enhanced with carbon-doped aluminum. The resulting composite, carbon-doped aluminum-bentonite (CCDAB), exhibits exceptional fluoride adsorption performance. Detailed structural analyses by SEM, FTIR, BET, and X-ray diffraction reveal successful carbon-doped aluminum integration, which markedly improves fluoride uptake. Adsorption kinetics adhere to pseudo-second-order models, signifying predominant chemical interactions, while the Langmuir isotherm model indicates monolayer coverage. CCDAB achieves a fluoride adsorption capacity of 85.51 mg/g and a removal efficiency of 95.3 %, demonstrating robust performance even in the presence of competing ions. Its efficacy over a broad pH range (3−7) is attributed to its high point of zero charge (pHpzc), which enhances electrostatic interactions. Carbon doping introduces additional active sites, synergistically enhancing fluoride removal alongside aluminum oxide. Thermodynamic analysis confirms that the process is both spontaneous and endothermic. The mechanisms of fluoride removal encompass electrostatic attraction, ion exchange, and surface complexation. This study highlights CCDAB’s promise as a highly effective and economical solution for fluoride contamination in water.

Developing a data-driven predictive model for substance abuse prevention among youth using behavioral analytics

Substance abuse among youth presents a critical public health challenge, necessitating innovative approaches for early intervention and prevention. This study proposes the development of a data-driven predictive model that leverages behavioral analytics to identify youth at risk of substance abuse. The model utilizes data from multiple sources, including social media activity, school performance records, and mental health screenings, to analyze behavioral patterns that may indicate a predisposition toward substance misuse. The core methodology integrates machine learning algorithms to process and analyze large datasets, uncovering correlations between specific behaviors and the likelihood of substance abuse. Predictive features such as changes in social engagement, academic performance fluctuations, and indicators of emotional distress are identified and incorporated into the model to enhance its accuracy. By applying supervised learning techniques, the model is trained to recognize patterns in historical data, allowing it to make predictions about future substance use risks. Furthermore, the model's design emphasizes real-time monitoring and adaptability, enabling health professionals and educators to receive timely alerts and intervene early when behavioral warning signs are detected. The application of behavioral analytics in this context offers a more proactive, personalized approach to prevention, targeting at-risk individuals before they develop harmful substance use habits. In addition to its predictive capabilities, the model also provides actionable insights into effective intervention strategies. By identifying the most influential behavioral factors, it informs tailored prevention programs that address specific risk behaviors among youth. These findings can support policymakers and healthcare providers in developing data-driven, evidence-based prevention initiatives that better allocate resources to high-risk populations.