Critical Health Research Articles

Dysregulation of the 3β-hydroxysteroid dehydrogenase type 2 enzyme and steroid hormone biosynthesis in chronic kidney disease.

Chronic kidney disease (CKD) presents a critical global health challenge, marked by the progressive decline of renal function. This study explores the role of the 3β-hydroxysteroid dehydrogenase type 2 enzyme (HSD3B2) and the steroid hormone biosynthesis pathway in CKD pathogenesis and progression. Using an adenine-induced CKD mouse model, we conducted an untargeted metabolomic analysis of plasma samples to identify key metabolite alterations associated with CKD. Immunohistochemistry, Western blotting, and qPCR analyses were performed to confirm HSD3B2 expression in both human and mouse tissues. Additionally, Nephroseq and Human Protein Atlas data were utilized to assess the correlation between HSD3B2 and kidney function. Functional studies were conducted on HK2 cells with HSD3B2 knockdown to evaluate the impact on cell proliferation and apoptosis. Metabolic characteristics revealed significant shifts in CKD, with 61 metabolites increased and 65 metabolites decreased, highlighting the disruption in steroid hormone biosynthesis pathways influenced by HSD3B2. A detailed examination of seven key metabolites underscored the enzyme's central role. HSD3B2 exhibited a strong correlation with kidney function, supported by data from Nephroseq and the Human Protein Atlas. Immunohistochemistry, Western blotting, and qPCR analyses confirmed a drastic reduction in HSD3B2 expression in CKD-affected kidneys. Suppressed proliferation and increased apoptosis rates in HSD3B2 knocked down HK2 cells further demonstrated the enzyme's significance in regulating renal pathophysiology. These findings underscore the potential of HSD3B2 as a clinical diagnostic and therapeutic target in CKD. While further studies are warranted to fully elucidate the mechanisms, our results provide valuable insights into the intricate interplay between steroid hormone biosynthesis and CKD. This offers a promising avenue for precision medicine approaches and personalized treatment strategies.

Recovery rate and predictors among children aged 6-59 months with severe acute malnutrition in Addis Ababa, Ethiopia: a retrospective follow-up study.

Severe acute malnutrition (SAM) is a critical public health concern in Ethiopia, contributing to high morbidity and mortality rates among children. Despite the improvement in hospital coverage and the development of standardized WHO treatment guidelines, recent reviews indicated a wide range in recovery rates (34%-88%) due to several context-specific factors. Understanding the factors influencing the recovery time can help inform targeted interventions and improve the overall management of SAM cases. Therefore, this study aimed to assess the time to recovery and predictors of children aged 6-59 months with severe acute malnutrition in Addis Ababa, Ethiopia, in 2023. An institutional-based retrospective follow-up study was conducted among children aged 6-59 months who were admitted to Tirunesh Beijing Hospital (TBH), Addis Ababa, Ethiopia, from July 2019 to June 2023. The Kaplan-Meir estimate and survival curve were used to compare the time to recovery using a log-rank test among different characteristics. A Cox proportional hazard regression analysis model was used to identify significant predictors of time to recovery. Finally, a p-value of <0.05 was used to declare a significant association. The median survival time to recovery was 17 (95% CI: 16.39-17.60) days, and the incidence rate of recovery from SAM was 37.8 per 1,000 child days. Moreover, exclusive breastfeeding [adjusted hazard ratio (aHR): 1.97, 95% CI: 1.45-2.68], amoxicillin provision (aHR = 1.62, 95% CI: 1.11-2.35), and deworming (aHR = 2.14, 95% CI: 1.48-3.09) were protective factors. However, complications at admission (aHR = 0.41, 95% CI: 0.23-0.73) and diarrhea during admission (aHR = 0.64, 95% CI: 0.45-0.91) were identified as risk factors of recovery from SAM. The time to recovery among the current study participants was low compared with the sphere standard. Besides, exclusive breastfeeding, complications at admission, diarrhea, amoxicillin provision, and deworming were independent predictors. Therefore, appropriate provision of routine medication and early management of medical comorbidity as per the national SAM management protocol can reduce the mortality of children with severe acute malnutrition significantly.

Exploring the Antidiabetic and Antihypertensive Potential of Peptides Derived from Bitter Melon Seed Hydrolysate

Background/Objectives: Type 2 diabetes (T2D) has become a critical global health issue, with an increasing prevalence that contributes to significant morbidity and mortality. Inhibiting dipeptidyl peptidase-IV (DPP4) is a promising strategy for managing T2D. This study aimed to explore the DPP4 inhibitory peptide derived from bitter melon seed protein (BMSP) hydrolysate. Methods: Reversed-phase high-performance liquid chromatography (RP-HPLC) was utilized to fractionate the hydrolysate. Peptide in the highest activity fraction was analyzed using liquid chromatography-mass spectrometry (LC-MS/MS). Peptide synthetic was used for further characterizations, such as bioactivity exploration, inhibition mechanism, molecular docking, and peptide stability against in vitro simulated gastrointestinal (SGI) digestion. Results: The BMSP hydrolysate was digested with gastrointestinal proteases (GP) and assessed for DPP4 inhibitory activity, yielding an IC50 of 1448 ± 105 μg/mL. Following RP-HPLC fractionation, MPHW (MW4) and VPSGAPF (VF7) were identified from fraction F8 with DPP4 IC50 values of 128.0 ± 1.3 µM and 150.6 ± 3.4 µM, respectively. Additionally, MW4 exhibited potential antihypertensive effects through ACE inhibition with an IC50 of 172.2 ± 10.6 µM. The inhibitory kinetics and molecular docking simulations indicated that both MW4 and VF7 were competitive inhibitors of DPP4, while MW4 was also a competitive inhibitor of ACE. Importantly, both peptides remained stable during simulated gastrointestinal digestion, suggesting their resistance to human digestive processes and their capacity to maintain biological activity. Conclusions: The findings suggest that BMSP-GP hydrolysate may have potential in terms of the development of health foods or therapeutic agents. However, in vivo studies are also essential for further confirmation of efficacy.

Exploring factors associated with domestic violence among pregnant women in Pakistan and its implications for maternal and perinatal health

ABSTRACT Domestic violence during pregnancy is a critical health issue due to its significant implications for both maternal and fetal well-being. Women in South Asian countries are particularly at a higher risk of experiencing domestic violence. The objective of this study was to identify factors associated with domestic violence among pregnant women attending tertiary care antenatal services in Pakistan. The secondary aim was to evaluate the impact of domestic violence on maternal and perinatal outcomes. The study participants were 681 pregnant women registered for antenatal care and delivery at a tertiary care hospital in Islamabad, Pakistan. Data were collected through in-person surveys and medical chart reviews. Associations between domestic violence and study variables were examined by logistic regression analysis. Among the study participants, 31.4%experienced some form of domestic violence during pregnancy, with psychological abuse (16.4%) being the most prevalent followed by sexual abuse (9.5%) and physical violence (1.8%). Women whose husbands were unemployed were three times more likely to experience domestic violence (aOR; 3.3, 95%CI: 1.6, 7.0). Similarly, husband’s substance abuse was significantly associated with domestic violence during pregnancy (aOR: 120.8, 95%CI: 57.9, 252.2). Domestic violence was a significant predictor of adverse maternal outcomes, including anemia, preterm labor, and recurrent abdominal pain. Similarly, domestic violence was a significant predictor of NICU admission (aOR: 20.6, 95%CI: 12.6, 33.8) and low birthweight (aOR: 24.4, 95%CI: 10.2, 58.4). The study findings emphasize the urgent need for comprehensive interventions targeting domestic violence among women and preventing its cascading effects on pregnancies.

Power, data and social accountability: defining a community-led monitoring model for strengthened health service delivery.

Despite international commitment to achieving the end of HIV as a public health threat, progress is off-track and existing gaps have been exacerbated by COVID-19's collision with existing pandemics. Born out of models of political accountability and historical healthcare advocacy led by people living with HIV, community-led monitoring (CLM) of health service delivery holds potential as a social accountability model to increase the accessibility and quality of health systems. However, the effectiveness of the CLM model in strengthening accountability and improving service delivery relies on its alignment with evidence-based principles for social accountability mechanisms. We propose a set of unifying principles for CLM to support the impact on the quality and availability of health services. Building on the social accountability literature, core CLM implementation principles are defined. CLM programmes include a community-led and independent data collection effort, in which the data tools and methodology are designed by service users and communities most vulnerable to, and most impacted by, service quality. Data are collected routinely, with an emphasis on prioritizing and protecting respondents, and are then be used to conduct routine and community-led advocacy, with the aim of increasing duty-bearer accountability to service users. CLM efforts should represent a broad and collective community response, led independently by impacted communities, incorporating both data collection and advocacy, and should be understood as a long-term approach to building meaningful engagement in systems-wide improvements rather than discrete interventions. The CLM model is an important social accountability mechanism for improving the responsiveness of critical health services and systems to communities. By establishing a collective understanding of CLM principles, this model paves the way for improved proliferation of CLM with fidelity of implementation approaches to core principles, rigorous examinations of CLM implementation approaches, impact assessments and evaluations of CLM's influence on service quality improvement.

Assessing the Antimicrobial Efficacy of Graphene Oxide and Its PEGylated Derivative Against Staphylococcus aureus

The rise of antimicrobial resistance (AMR) has become a critical health challenge. This, plus the antimicrobial discovery void, had led scientists to search for an effective alternative to antimicrobials. In this context, nanomaterials, such as graphene oxide (GO), a two-dimensional (2D) carbon molecule with oxidized functional groups, have been shown to interact physically and chemically with bacteria. Moreover, the addition of polyethylene glycol (PEG) to its surface enhances GO’s biocompatibility and water solubility, making it a promising candidate for biomedical applications. This study evaluates the antimicrobial efficacy of GO and its polyethylene glycol-modified form (GO-PEG) against Staphylococcus aureus, a bacterium responsible for numerous hospital-acquired and multidrug-resistant infections. After their production, both nanomaterials were characterized using various techniques to provide insight into their morphology, stability, and functional group composition. Then, the antimicrobial activity of GO and GO-PEG was assessed using the Müeller–Hinton broth microdilution method, determining the minimum inhibitory concentration (MIC) for S. aureus among ten different concentrations of both nanomaterials (from 0.0625 to 32 mg/mL). The results demonstrate the potential of GO as an effective antimicrobial agent at 16 and 32 mg/mL, offering new strategies in the fight against AMR. Further research could establish its role in future therapeutic applications.

Optimising ovarian tumor classification using a novel CT sequence selection algorithm

Gynaecological cancers, especially ovarian cancer, remain a critical public health issue, particularly in regions like India, where there are challenges related to cancer awareness, variable pathology, and limited access to screening facilities. These challenges often lead to the diagnosis of cancer at advanced stages, resulting in poorer outcomes for patients. The goal of this study is to enhance the accuracy of classifying ovarian tumours, with a focus on distinguishing between malignant and early-stage cases, by applying advanced deep learning methods. In our approach, we utilized three pre-trained deep learning models—Xception, ResNet50V2, and ResNet50V2FPN—to classify ovarian tumors using publicly available Computed Tomography (CT) scan data. To further improve the model’s performance, we developed a novel CT Sequence Selection Algorithm, which optimises the use of CT images for a more precise classification of ovarian tumours. The models were trained and evaluated on selected TIFF images, comparing the performance of the ResNet50V2FPN model with and without the CT Sequence Selection Algorithm. Our experimental results show the Comparative evaluation against the ResNet50V2 FPN model, both with and without the CT Sequence Selection Algorithm, demonstrates the superiority of the proposed algorithm over existing state-of-the-art methods. This research presents a promising approach for improving the early detection and management of gynecological cancers, with potential benefits for patient outcomes, especially in areas with limited healthcare resources.

Comparative dimensions of COVID-19 visual health literacy: social media news imagery in Germany and China

The COVID-19 pandemic prompted governments worldwide to utilise social media news platforms for disseminating critical public health information and safety protocols. Visual aids, including photographs, videos, charts, and infographics, played a key role in conveying health-related messages. However, there are variations in visual communication practices across countries, influenced by local health communication habits and cultural contexts. This study examines these variations in the visual health discourse that emerged in China and Germany during the pandemic, focusing on metaphorical mapping as a central analytical tool. We introduce the concept of “visual dataset fingerprints,” outlining the distinct composition of public health visuals on eight social news media platforms in Germany and China. Our descriptive analysis encompasses over 3700 digital visual media posts related to COVID-19, shared by leading national news media accounts in German, English, and Mandarin on microblogs in 2022. As a preliminary outcome of our study, we observe differences in image use based on the analysis of 1678 media posts by four Chinese outlets and 1329 media posts by four German outlets. These differences may reflect variations in how public opinion, perceptions, and attitudes toward COVID-19 policies are shaped across languages and cultures. Through its comparative visual analysis, our pilot study offers the potential for a new research approach to global healthcare communication in social news media and digital visual cultures.

Machine Learning-Based Stroke Prediction: A Critical Analysis

Stroke is a critical public health issue that frequently has long-term impairment and negative effects. Devising innovative methods that enable timely and accurate identification and intervention is crucial. In this regard, machine learning (ML) and deep learning (DL) approaches of artificial intelligence (AI) play a crucial role in reducing the incidence of strokes. This study systematically analyzed articles from 2012 to 2022 using the PRISMA Method. PRISMA is a tool that facilitates researchers' access to an online platform for self-directed learning. The cumulative quantity of articles gathered for ten years reached 1405 from five databases. However, only 79 relevant articles were used for identification. The main objective was to provide a thorough taxonomy that classifies using and implementing machine learning approaches for stroke prediction. The results of this experiment confirm that machine-learning techniques have a great deal of potential for accurate stroke prediction. Nevertheless, challenges such as biased data and algorithms and the need for models that can be adjusted to accommodate various demographics and healthcare systems continue to exist. It is essential to recognize the need for additional research projects that thoroughly explore potential data biases, algorithmic biases, and the generalizability of models across various demographics and healthcare systems. More research is necessary to further the literature on the complete assessment of machine learning models in precisely forecasting stroke occurrences.

Women's self-reported symptoms of reproductive tract infection, medical consultations, and factors influencing them in less developed regions: perimenopausal and older women in need of urgent attention.

Reproductive tract infections (RTIs) represent a critical public health concern impacting women's health, particularly pronounced in developing countries. We aim to investigates the prevalence of self-reported RTI symptoms in women aged 18 to 65 and the factors influencing gynecological examination behavior and associated risks are explored. From May 6, 2022, to September 20, 2022, a comprehensive survey engaged 20,864 women aged 18 to 65 in 19 townships in Lueyang, Shaanxi. Each survey team, comprising a gynecologist, two female health center staff, and a master's student, conducted face-to-face questionnaire surveys in villages and households. The questionnaire featured three sections: prevalence of self-reported RTI symptoms, gynecological examination history, and factors influencing examination behavior. The high response rate of 98.69% yielded 20,590 valid questionnaires. Among surveyed women, 42.04% reported RTI symptoms, with a higher prevalence in those aged 30 and above, peaking at 44.88% in the 50-59 age group. Vulvar itching (24.73%), abnormal vaginal discharge (17.41%), and urinary tract irritation signs (11.73%) were most common. Older adult women (≥60) reported higher frequency and longer duration of symptoms. Only 9.88% of symptomatic women underwent gynecological examinations in 2022. Examination likelihood decreased with age, presenting a critical gap in healthcare utilization among older women. Reasons for checkups included routine checkups (47.02%), symptom concerns (41.29%), and the availability of free medical checkup programs (9.82%). Barriers included perceived lack of serious symptoms (23.7%), lack of necessity (4.41%) and lack of time (3.98%). Among those examined, 40.58% had a diagnosis of gynecological disorders, including pelvic inflammatory disease, human papilloma virus infection and cervical cancer. Widespread RTI symptoms, particularly in perimenopausal and older women, underscore the need for enhanced healthcare. Barriers to gynecological examinations include awareness, education, and accessibility issues. Strategies must prioritize health education, routine examinations, and improved healthcare infrastructure in underdeveloped regions.

Profiling of Potential Anti-Diabetic Active Compounds in White Tea: An Integrated Study of Polyphenol-Targeted Metabolomics, Network Pharmacology, and Computer Simulation.

Diabetes remains a critical global public health challenge, posing a growing threat to human health and well-being. White tea is a lightly fermented tea and one of the six traditional tea categories in China. Owing to its rich content of bioactive compounds such as catechins and alkaloids, it has demonstrated potential anti-diabetic properties. However, its precise bioactive components, mechanisms of action, and relevant molecular targets require further investigation. In this study, an integrated approach combining polyphenol-targeted metabolomics, in vitro antioxidant assays, α-glucosidase inhibition tests, network pharmacology analysis, GEO database exploration, molecular docking, and molecular dynamics simulations was employed to identify the potential anti-diabetic compounds, targets, and mechanisms of white tea. The findings revealed that white tea is particularly abundant in 10 bioactive compounds, including epigallocatechin gallate, epicatechin gallate, and catechin, all of which exhibit significant anti-diabetic potential. These compounds were found to exert their effects by interacting with core molecular targets, namely cathepsin V (CTSV) and nucleotide-binding oligomerization domain-containing protein 1 (NOD1), and engaging in pathways related to signal transduction, apoptosis, and immune responses. This study establishes a strong theoretical basis for advancing white tea research and underscores new opportunities for applying natural products in diabetes therapy.

Non-Conventional Data for Farming-Related Air Pollution: Contributions to Modelling and Risk Assessment in the Lombardy Region, Italy

Air pollution is one of the most critical global health concerns today. While emissions from industrial activities and combustion processes are the primary threats to air quality, intensive farming activities also contribute significantly, especially through ammonia emissions that promote the formation of secondary pollutants, such as particulate matter. Advancements in air quality research have been achieved by enhancements in emissions characterisation, modelling techniques, and sensor technology, expanding the availability of air pollution data beyond traditional ground sensor observations, which are often lacking in rural agricultural areas. Accordingly, this paper demonstrates the advantages of integrating traditional and non-conventional data to investigate farming-related air pollution through a case study in the Lombardy Region, Northern Italy. The study incorporates an array of data sources, including ground sensors and atmospheric composition model estimates. The concurrent utilisation of these diverse datasets is explored through machine learning modelling, focusing on assessing the influence of agricultural activities on particulate matter distribution patterns. Finally, the reliability of non-conventional air pollution data for health risk assessment applications is also investigated. The paper critically discusses the main findings based on empirical results, highlighting the significance of integrating multiple data sources to complement traditional air quality monitoring while outlining the main limitations in terms of the accuracy and usability of such non-conventional data.

Mitigating household air pollution exposure through kitchen renovation

Globally, over three billion people rely on traditional solid fuels for cooking and heating, leading to significant household air pollution and critical public health concerns. While transitioning to clean energy carriers faces challenges of accessibility and affordability—especially among low-income, rural populations—alternative strategies like kitchen layout modifications and the use of ventilation fans may effectively reduce exposure to pollutants. Here, we analyze factors influencing the adoption of separated kitchens and mechanical ventilation and evaluate changes in human exposure to PM2.5 under different kitchen renovation scenarios by conducting a nationwide survey of household kitchen characteristics in rural China. We found that although 82% of rural households have kitchens separated from other rooms, only 34% use mechanical ventilation. The adoption of ventilation fans is significantly influenced by income and education levels. We estimate that widespread implementation of ventilation fans and separated kitchen designs could prevent approximately 67400 premature deaths annually, resulting in a health benefit of about USD 19 billion per year—substantially exceeding the costs involved. These findings suggest that cost-effective kitchen renovations offer enormous potential for substantial health benefits and present a practical solution compared to the challenges of clean energy transitions in rural areas.

Kynurenine-AhR reduces T-cell infiltration and induces a delayed T-cell immune response by suppressing the STAT1-CXCL9/CXCL10 axis in tuberculosis.

Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), is a critical global health issue that is complicated by the ability of the pathogen to delay the host's T-cell immune response. This delay in T-cell recruitment to the site of infection is a pivotal survival strategy for Mtb, allowing it to establish a persistent chronic infection. To investigate the underlying mechanisms, this study focused on Mtb's exploitation of host tryptophan metabolism. Mtb upregulates indoleamine 2,3-dioxygenase 1 (IDO1) in inflammatory macrophages, thereby increasing kynurenine (Kyn) production. Kyn then activates the aryl hydrocarbon receptor (AhR), leading to the upregulation of suppressor of cytokine signaling 3 and subsequent inhibition of the JAK-STAT1 signaling pathway. This results in reduced secretion of the chemokines CXCL9 and CXCL10, which are crucial for T-cell recruitment to the lungs. Supported by in vivo mouse models, our findings reveal that disrupting this pathway through AhR knockout significantly enhances T-cell infiltration and activity, thereby undermining Mtb-induced immunosuppression. In contrast, additional Kyn injection obviously inhibited T-cell infiltration and activity. These results highlight potential therapeutic targets of AhR and IDO1, offering new avenues for enhancing the host immune response against tuberculosis and guiding future vaccine development efforts.

Adolescent Health in Lebanon: Exploring Alcohol Use, Dietary Patterns, Mental Health, Physical Activity, and Smoking Using the Global School-Based Student Health Survey Approach.

This study aims to assess alcohol consumption, dietary habits, physical activity, mental health, and smoking behaviors among Lebanese adolescents aged 13-17 years, with a focus on gender and school-type differences. A cross-sectional analysis was conducted in Lebanon between March and July 2022 using the Global School-based Student Health Survey (GSHS) questionnaire. A representative sample of students from public and private schools participated in the survey, with key variables analyzed to identify significant patterns and disparities. Our findings reveal that 6.3% of adolescents consumed alcohol, with males reporting a higher prevalence and earlier initiation (p = 0.003). Gender differences were evident in dietary habits, where males were more likely to consume sugary drinks (p = 0.04) and have consistent breakfast habits (p = 0.003). Adolescents from private schools exhibited distinct dietary behaviors, including lower milk consumption (p < 0.001) and higher fatty food intake (p = 0.008). Males were also more physically active and reported better mental health outcomes compared to females (p = 0.004). Smoking behaviors showed that males smoked more frequently, while private school students reported smoking less. No significant difference was observed in bullying experiences between genders or school types. The study highlights critical health behaviors among Lebanese adolescents, with significant variations by gender and school type. These findings underscore the need for targeted interventions to address the identified risk factors and promote healthier behaviors in this population.

Principal Component Analysis (PCA) of MALDI-TOF for the Identification of Waterborne Pathogenic Bacteria

The study focuses on the application of Principal Component Analysis (PCA) combined with MALDI-TOF MS (Matrix-Assisted Laser Desorption/Ionization-Time of Flight Mass Spectrometry) for the identification of waterborne pathogenic bacteria in urban water networks. In this comprehensive research, 168 bacterial strains were meticulously isolated from the water networks of Abidjan, Côte d'Ivoire, a region known for its inadequate wastewater treatment infrastructure. The analysis aimed to rapidly and precisely identify these bacterial pathogens, leveraging the power of MALDI-TOF MS and the sophisticated data reduction capabilities of PCA. This approach not only accelerates the identification process but also enhances the accuracy of detecting various pathogens. The study identified a diverse range of pathogens, including Pseudomonas aeruginosa, Escherichia coli, Salmonella, Shigella, Vibrio cholerae, Morganella morganii, Proteus mirabilis, Acinetobacter baumannii, Klebsiella pneumoniae, Providencia rettgeri, Providencia stuartii, Aeromonas hydrophila, Bacillus cereus, Lysinibacillus fusiformis, Lysinibacillus sphaericus, and Staphylococcus aureus. These findings underscore the critical public health risks posed by microbial contamination in water networks, particularly in areas with deficient waste management systems. This study highlights the necessity for improved wastewater management practices, robust public health strategies, and regular monitoring to mitigate the risks associated with waterborne pathogens. Moreover, the integration of PCA with MALDI-TOF MS proves to be a powerful tool for enhancing the efficiency and accuracy of pathogen identification in environmental water samples, offering a promising solution for better public health protection and water quality management in urban settings.

Interdisciplinary Brain-based Learning Strategies in Addressing Liver Fluke Disease and Cultural Dietary Practices among Early Childhood Students

Liver fluke disease remains a critical public health issue in Southeast Asia, driven by cultural dietary practices involving raw or undercooked freshwater fish. This study evaluates the impact of an interdisciplinary brain-based learning intervention aimed at increasing awareness and modifying dietary behaviors among early childhood students in Central Northeast Thailand. A quasi-experimental design was employed, involving 122 students from four provinces. The intervention incorporated brain-based learning activities such as storytelling, songs, and visual aids to enhance engagement. Pre- and post-tests, behavioral observations, and attitudinal surveys were conducted to assess the program’s effectiveness. Post-intervention results indicated significant improvements in knowledge about liver fluke transmission, symptoms, and prevention, alongside positive changes in behavior, such as increased avoidance of raw fish consumption and improved hygiene practices. Attitudinal surveys showed a shift in preferences toward safer eating habits. These findings suggest integrating brain-based learning with culturally relevant health education can foster sustainable behavior change in early childhood populations. Further research is recommended to explore this approach’s long-term impacts and scalability.

Inhibitory Effect of Luteolin on Spike S1 Glycoprotein-Induced Inflammation in THP-1 Cells via the ER Stress-Inducing Calcium/CHOP/MAPK Pathway.

The global SARS-CoV-2 outbreak has escalated into a critical public health emergency, with the spike glycoprotein S1 subunit of SARS-CoV-2 (spike-S1) linked to inflammation in lung tissue and immune cells. Luteolin, a flavone with anti-inflammatory properties, shows promise, but research on its effectiveness against long-COVID-related inflammation and spike protein-induced responses remains limited. This study aims to elucidate the underlying mechanisms of inflammation in THP-1 cells induced by the spike-S1. Additionally, it seeks to assess the potential of luteolin in mitigating inflammatory responses induced by the spike-S1 in a THP-1 macrophage model. The gene expression profiles of spike-S1 in THP-1 cells were analyzed by transcriptome sequencing. The inhibitory effect of luteolin on ER stress and inflammation in spike-S1-induced THP-1 cells was investigated using Western blotting, RT-PCR, and ELISA. The candidate genes (CAMK2A, SIGLEC7, PPARGC1B, SEC22B, USP28, IER2, and TIRAP) were upregulated in the spike-S1-induced THP-1 group compared to the control group. Among these, calcium/calmodulin-dependent protein kinase II alpha (CAMK2A) was identified as the most promising molecule in spike-S1-induced THP-1 cells. Our results indicate that the spike S1 significantly increased the expression of ER-stress markers at both gene and protein levels. Luteolin significantly reduced ER stress by decreasing the expression of ER-stress marker genes and ER-stress marker proteins (p < 0.01). Additionally, luteolin exhibited anti-inflammatory properties upon spike S1-induction in THP-1 cells by significantly suppressing IL-6, IL-8, and IL-1β cytokine secretion in a dose-dependent manner (p < 0.05). Furthermore, our results revealed that luteolin exhibited the downregulation of the MAPK pathway, as evidenced by modulating the phosphorylation of p-ERK1/2, p-JNK and p-p38 proteins (p < 0.05). The results from this study elucidate the mechanisms by which the spike S1 induces inflammation in THP-1 cells and supports the use of naturally occurring bioactive compounds, like luteolin, against inflammation-related SARS-CoV-2 infection.

A Novel Cloud-Enabled Cyber Threat Hunting Platform for Evaluating the Cyber Risks Associated with Smart Health Ecosystems

The fast proliferation of Internet of Things (IoT) devices has dramatically altered healthcare, increasing the efficiency and efficacy of smart health ecosystems. However, this expansion has created substantial security risks, as cybercriminals increasingly target IoT devices in order to exploit their weaknesses and relay critical health information. The rising threat landscape poses serious concerns across various domains within healthcare, where the protection of patient information and the integrity of medical devices are paramount. Smart health systems, while offering numerous benefits, are particularly vulnerable to cyber-attacks due to the integration of IoT devices and the vast amounts of data they generate. Healthcare providers, although unable to control the actions of cyber adversaries, can take proactive steps to secure their systems by adopting robust cybersecurity measures, such as strong user authentication, regular system updates, and the implementation of advanced security technologies. This research introduces a groundbreaking approach to addressing the cybersecurity challenges in smart health ecosystems through the deployment of a novel cloud-enabled cyber threat-hunting platform. This platform leverages deception technology, which involves creating decoys, traps, and false information to divert cybercriminals away from legitimate health data and systems. By using this innovative approach, the platform assesses the cyber risks associated with smart health systems, offering actionable recommendations to healthcare stakeholders on how to minimize cyber risks and enhance the security posture of IoT-enabled healthcare solutions. Overall, this pioneering research represents a significant advancement in safeguarding the increasingly interconnected world of smart health ecosystems, providing a promising strategy for defending against the escalating cyber threats faced by the healthcare industry.

Identifying Clusters of Curatively-Treated Esophageal Cancer Patients Using Patient-Reported Outcome Measures at Three Months from Discharge: A Secondary Analysis from a Longitudinal Single Center Study.

This study aims to identify meaningful clusters based on Patient-Reported Outcome Measures (PROMs) in curatively-treated esophageal cancer patients at three months post-discharge. This secondary analysis of a longitudinal single-center study included 46 esophageal cancer patients who underwent curative surgery. Patients were selected based on their completion of PROMs surveys at three months post-discharge, were aged 18 years or older, and had undergone surgical resection (esophagectomy) with or without neoadjuvant chemotherapy and/or radiotherapy. The analysis utilized t-distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction and hierarchical clustering to analyze PROMs data collected three months post-discharge. Clustering was performed on physical, emotional, cognitive, and social functioning variables, symptom burden, and health literacy. Three distinct clusters were identified: Cluster 1 (n = 24) with higher functioning and moderate symptoms, Cluster 2 (n = 14) with moderate functioning, higher symptoms, and lower health literacy, and Cluster 3 (n = 8) with the highest functioning, lowest symptoms, and highest health literacy. Significant differences between squamous cell carcinoma and adenocarcinoma subtypes were observed across several PROMs domains, including critical health literacy, general health status/quality of life, nausea and vomiting, and insomnia. These clusters provide an exploratory framework for tailoring post-operative interventions to enhance patient recovery, which necessitates further confirmatory investigations, including outcomes such as complications and mortality, in the analysis. This study fills a research gap by demonstrating the utility of PROMs in identifying distinct recovery patterns in esophageal cancer patients post-surgery. The findings support the use of PROMs to guide personalized post-operative care, potentially improving patient outcomes and quality of life. Further research is needed to validate these findings in larger, diverse populations.