Realm Of Public Health Research Articles

Uncovering the Complexities of Drug Bans in India: Are Key Considerations Being Overlooked?

The use of fixed dose combinations (FDCs) in healthcare has significant public health implications, both positive and negative. Positive aspects include simplifying treatment regimens, improving patient adherence, and cost-effectiveness, particularly in resource-constrained settings. However, negative implications arise when FDCs are used without careful consideration, leading to irrational combinations, reduced treatment options, and potential overuse. Moreover, substandard FDCs and aggressive marketing can pose risks, such as antibiotic resistance and treatment failures. Mitigating the negative implications of FDCs requires a comprehensive strategy. Strengthening regulatory oversight, rigorous approval processes, and clear scientific evidence for FDCs’ benefits over single-drug formulations are essential. Transparency in the approval process, regular reviews, and postmarketing surveillance hold regulatory agencies accountable. Training and education for healthcare professionals, emphasizing evidence-based decision-making, and encouraging adverse event reporting can help ensure rational use. Pharmacovigilance programs, clinical practice guidelines, and national formularies aid in monitoring and guiding FDC use. Public awareness campaigns and patient engagement foster informed discussions and reporting. Research and development efforts should focus on optimizing FDC formulations and assessing cost-effectiveness. International collaboration to harmonize standards and prevent substandard FDCs’ global spread is crucial. Legal measures should be in place to deter misleading marketing and distribution of unsafe FDCs by pharmaceutical companies. In conclusion, addressing the negative implications of FDCs involves a multifaceted approach that spans healthcare professionals, regulators, pharmaceutical companies, and the public. By ensuring rational and safe use, FDCs can maximize their benefits while minimizing associated risks in the realm of public health.

Strengthening Indonesia’s health system through community and technological engagements in public health interventions

The 7th International Symposium on Public Health (ISoPH 2023), was held on October 2-3, 2023, in Surabaya, Indonesia, which brought together a diverse array of public health professionals, researchers, and policymakers from around the globe. The purpose of the symposium was to facilitate the exchange of the latest information and ideas within the realm of public health. The symposium aspired to foster discussions and the exchange of insights regarding scientific issues in the field of public health globally and within the context of Indonesia. Organized by the Faculty of Public Health at Airlangga University in Indonesia, the symposium addressed the urgent need for robust, resilient health systems capable of navigating the complex challenges of the modern era, particularly in the context of the Society 5.0 paradigm.

Challenges to implementing AI in health care

Abstract Germany, renowned for its robust healthcare system, finds itself at the early stages of AI integration. This is a reality for many European countries, emphasizing that, despite general technological prowess, AI adoption remains in its absolute infancy. However, in Germany there are areas where AI shows promise, notably in fields like radiology. Beyond such niche applications though, Germany grapples with a significant challenge - the scarcity of robust data essential for meaningful AI utilization, particularly in the realm of public health. While these insights are specific to the German context, understanding the intricate hurdles impeding widespread AI implementation is a meaningful takeaway for countries around the world. In order to effectively leverage new technologies, there is an acute need for advancements in data infrastructure and governance to unlock the full potential of AI in healthcare. Overcoming barriers and charting a path towards a future where AI serves as a transformative force in German healthcare, could one day lead to enhanced system efficiencies and patient experiences, but for now we have to be thoughtful about the realities of implementing AI within current existing health system infrastructure.

Scientific policy advice during the SARS-CoV-2 pandemic – a comparison of 25 democratic countries

Abstract Background The SARS-CoV-2 pandemic required rapid and effective policy responses from governments worldwide, especially in the realm of public health. Due to the global impact of the pandemic, it is important to compare country-specific approaches. A systematic analysis of the role and institutionalization of different bodies of scientific policy advice during the pandemic was conducted to gain insights into scientific policy advice structures and to inform future solutions. Methods We contacted the governments of 35 democratic countries about the role and institutionalization of bodies of scientific advisory advice during the pandemic. Data collection took place between September 2023 and March 2024. We analyzed primary data provided by the governments by qualitative systematic document analysis. Results We received feedback from 25 countries within the research period (including 19 European countries). The results were divided into three main categories: (1) temporality, (2) structure, function, & composition, and (3) transparency. Further sub-categories emerged that helped to compare and classify the countries’ approaches, such as initiation, financing, selection, expertise, and diversity of members, systematics of documentation. For example, some used existing structures from ministries, research institutions or the private sector while others were entirely newly established. National bodies of scientific policy advice mostly developed ad hoc, were temporary, and showed several differences between countries. Conclusions Different types of bodies of scientific policy advice were identified in terms of e.g. initiation, function, expertise, and institutionalization. The results demonstrate a significant variation regarding the role and impact of scientific policy advice during the SARS-CoV-2 pandemic. Our study can help to identify essential aspects of scientific policy advice and to formulate recommendations for future health crises. Key messages • The innovative methodological approach and the data which was collected from 25 countries give new insights into an important field of research. • National bodies of scientific policy advice mostly developed ad hoc, were temporary, and showed several structural differences between countries.

Lessons Learned From Developing Dashboards to Support Decision-Making for Community Opioid Response by Community Stakeholders: Mixed Methods and Multisite Study.

Data dashboards are published tools that present visualizations; they are increasingly used to display data about behavioral health, social determinants of health, and chronic and infectious disease risks to inform or support public health endeavors. Dashboards can be an evidence-based approach used by communities to influence decision-making in health care for specific populations. Despite widespread use, evidence on how to best design and use dashboards in the public health realm is limited. There is also a notable dearth of studies that examine and document the complexity and heterogeneity of dashboards in community settings. Community stakeholders engaged in the community response to the opioid overdose crisis could benefit from the use of data dashboards for decision-making. As part of the Communities That HEAL (CTH) intervention, community data dashboards were created for stakeholders to support decision-making. We assessed stakeholders' perceptions of the usability and use of the CTH dashboards for decision-making. We conducted a mixed methods assessment between June and July 2021 on the use of CTH dashboards. We administered the System Usability Scale (SUS) and conducted semistructured group interviews with users in 33 communities across 4 states of the United States. The SUS comprises 10 five-point Likert-scale questions measuring usability, each scored from 0 to 4. The interview guides were informed by the technology adoption model (TAM) and focused on perceived usefulness, perceived ease of use, intention to use, and contextual factors. Overall, 62 users of the CTH dashboards completed the SUS and interviews. SUS scores (grand mean 73, SD 4.6) indicated that CTH dashboards were within the acceptable range for usability. From the qualitative interview data, we inductively created subthemes within the 4 dimensions of the TAM to contextualize stakeholders' perceptions of the dashboard's usefulness and ease of use, their intention to use, and contextual factors. These data also highlighted gaps in knowledge, design, and use, which could help focus efforts to improve the use and comprehension of dashboards by stakeholders. We present a set of prioritized gaps identified by our national group and list a set of lessons learned for improved data dashboard design and use for community stakeholders. Findings from our novel application of both the SUS and TAM provide insights and highlight important gaps and lessons learned to inform the design of data dashboards for use by decision-making community stakeholders. ClinicalTrials.gov NCT04111939; https://clinicaltrials.gov/study/NCT04111939.

The power of interactive maps for communicating spatio-temporal data to health professionals.

While more and more health-related data is being produced and published every day, few of it is being prepared in a way that would be beneficial for daily use outside the scientific realm. Interactive visualizations that can slice and condense enormous amounts of multi-dimensional data into easy-to-digest portions are a promising tool that has been under-utilized for health-related topics. Here we present two case studies for how interactive maps can be utilized to make raw health data accessible to different target audiences: i) the European Notifiable Diseases Interactive Geovisualization (ENDIG) which aims to communicate the implementation status of disease surveillance systems across the European Union to public health experts and decision makers, and ii) the Zoonotic Infection Risk in Twente-Achterhoek Map (ZIRTA map), which aims to communicate information about zoonotic diseases and their regional occurrence to general practitioners and other healthcare providers tasked with diagnosing infectious diseases on a daily basis. With these two examples, we demonstrate that relatively straight-forward interactive visualization approaches that are already widely used elsewhere can be of benefit for the realm of public health.

ITO-Coated Vertical Nanowires-Based Biosensors for the Label-Free Detection of Escherichia coli

A major concern in public health is the direct in-situ detection of pathogenic bacteria in food, beverages, and the air [1]. This study utilizes ITO-coated vertical nanowires (ITO-VNWs) as sensing substrates for the detection of Escherichia coli (E. coli). The innovation in this research lies in the direct detection of various concentrations of E. coli using boronic acid (BA)-modified ITO-VNWs connected to a benchtop E4980A precision LCR meter. Impedance spectra were expressed in terms of real and imaginary parts, corresponding to resistance (R) and reactance (X). Serial dilutions of E. coli were prepared in saline for bacterial enumeration and tested using the standard plate count method. Subsequently, 100 μl of each bacterial dilutions (107, 106, 105, 104, 103, 102, and 101 CFU/ml) were applied to BA-modified ITO-VNWs. In summary, BA-modified ITO-VNWs demonstrated accurate and distinct detection of varying concentrations of E. coli. This accomplishment highlights the promising potential of our BA-modified ITO-VNW biosensor for precise bacterial detection across diverse applications. Further exploration and optimization of this platform could lead to advancements in the rapid and specific identification of pathogenic bacteria, making it an invaluable tool in the realm of public health and environmental safety. REFERENCES [1] A. Ahmed, J.V. Rushworth, N.A. Hirst, P.A. Millner, Biosensors for whole-cell bacterial detection, Clin Microbiol Rev 27(3) (2014) 631-46. Figure 1

Development and Evaluation of a Noninvasive Microfluidic-Based Paper Analytical Device for Leptospirosis Diagnosis.

Leptospirosis is a re-emerging infectious disease that presents a diagnostic enigma for clinicians with frequent misdiagnosis due to lack of rapid and accurate diagnostic tests, as the current methods are encumbered by inherent limitations. The development of a diagnostic sensor with a sample-in-result-out capability is pivotal for prompt diagnosis. Herein, we developed a microfluidic paper-based analytical device (spin-μPAD) featuring a sample-in-result-out fashion for the detection of Leptospira specific urinary biomarker, sph2 sphingomyelinase, crucial for noninvasive point-of-care testing. Fabrication of paper devices involved precise photolithography techniques, ensuring a high degree of reproducibility and replicability. By optimizing the device's configuration and protein components, a remarkable sensitivity and specificity was achieved for detecting leptospiral sph2 in urine, even at low concentrations down to 1.5 fg/mL, with an assay time of 15 min. Further, the spin-μPAD was validated with 20 clinical samples, suspected of leptospirosis including other febrile illnesses, and compared with gold standard microscopic agglutination test, culture, Lepto IgM ELISA, darkfield microscopy, and Leptocheck WB spot test. In contrast to commercial diagnostic tools, the spin-μPAD was noninvasive, rapid, easy to use, specific, sensitive, and cost-effective. The results highlight the potential of this innovative spin-μPAD for an efficient and dependable approach to noninvasive leptospirosis diagnosis, addressing critical needs in the realms of public health and clinical settings.

Editorial from Editor-in-Chief

In the realm of public health, the integration of academic research with practical application is crucial for driving meaningful change and improving health outcomes. In the field of health management, this synergy is especially vital when it comes to enhancing the quality of service, ensuring patient safety, and optimizing the utilization of resources. By bridging the gap between theory and practice, we can pave the way for more effective strategies that address the multifaceted challenges facing our healthcare system today. The integration of academic research with practical application is essential for driving positive changes in health management and improving health outcomes. By bridging the gap between theory and practice, we can create a more sustainable, efficient, and patient-centered healthcare system. It is imperative that researchers, practitioners, policymakers, and stakeholders work together to ensure that evidence-based practices are translated into real-world solutions that benefit individuals, communities, and society as a whole. Only through collaboration and innovation can we truly transform the landscape of public health and pave the way for a healthier future for all.

Public Health: Policy, Law and Right

Public health, as delineated in legal parlance, emanates as the discipline devoted to safeguarding the collective well-being, enhancing community health through educational initiatives, policy formulation, and diligent research aimed at injury prevention. Characterizations of public health may vary contingent upon individual perspectives. Practitioners within the realm of public health facilitate global engagement, addressing communal health challenges comprehensively, and wielding influence over policies shaping societal health. Public health law, in turn, constitutes the systematic study of governmental authority and obligations pertaining to the maintenance of conditions conducive to public health, while also delineating constraints on state power vis-à-vis fundamental rights such as privacy, liberty, and other legally safeguarded interests of individuals. This legal framework regulates the operational ambit of public health endeavors. Integral to the multifaceted domain of public health are an array of disciplines including biology, education, sociology, business, and others, each contributing distinctively to its overarching objectives. The imperative for bolstering public health infrastructure becomes conspicuous amidst a workforce deficit juxtaposed against a burgeoning populace within the United States. Notably, one-third of the requisite workforce remains unfilled, jeopardizing the capacity to identify, mitigate, and forestall emergent public health exigencies. The raison d'être of public health lies in its mission to foster and safeguard the populace's well-being. Whereas medical practitioners focus on remedial interventions for the ailing, public health practitioners’ endeavour to preclude afflictions and injuries proactively, thereby espousing a paradigm of wellness promotion through advocacy for healthy behaviours.

Data analytics for predicting disease outbreaks: A review of models and tools

The burgeoning field of data analytics has emerged as a pivotal force in the realm of public health, particularly in the context of predicting and mitigating disease outbreaks. This comprehensive review delves into the diverse landscape of models and tools employed in data analytics for disease outbreak prediction. With a focus on synthesizing existing knowledge, the paper aims to provide a nuanced understanding of the strengths, limitations, and future directions within this dynamic field. The review begins with an exploration of various models utilized for disease outbreak prediction, ranging from statistical approaches to machine learning models and epidemiological frameworks. Each model category is scrutinized for its efficacy in capturing the complexities inherent in infectious disease dynamics. Simultaneously, the paper investigates the array of tools and technologies leveraged in disease outbreak prediction, encompassing Geographic Information Systems (GIS), data visualization tools, and big data analytics platforms. A critical aspect of the review lies in the examination of diverse data sources contributing to predictive analytics. Epidemiological data, environmental factors, and the burgeoning influence of social media and web data are dissected for their roles in enhancing the accuracy and timeliness of outbreak predictions. Amidst the promises of data analytics, the paper navigates the challenges inherent in predicting disease outbreaks. Issues of data quality and availability, model complexity, interpretability, and ethical considerations are dissected, providing a holistic view of the hurdles that practitioners encounter. Drawing upon case studies and real-world applications, the review showcases instances where data analytics has proven successful in predicting disease outbreaks, shedding light on both triumphs and setbacks. The implications for public health, lessons learned from challenges, and the evolving role of data analytics in shaping global health preparedness are thoroughly discussed. As the paper unfolds, it illuminates future trends and innovations in the field, foreseeing the integration of advanced technologies, global collaboration for information sharing, and the adaptation of predictive analytics for emerging diseases. The review culminates in a comprehensive conclusion, summarizing key findings and emphasizing the potential transformative impact of data analytics on the landscape of disease outbreak prediction.

Green synthesis of ZnO nanoparticles using the mangosteen (Garcinia mangostana L.) leaf extract: Comparative preliminary in vitro antibacterial study

Abstract In the realm of public health, the rising threat caused by bacteria resistant to many drugs is a critical concern. In this work, we used the aqueous extract of mangosteen leaves to create zinc oxide (ZnO) nanoparticles (NPs) in an environmentally friendly manner. Through various analytical methods, we thoroughly characterized these biogenic ZnO NPs, including UV−visible, Fourier transform infrared, X-ray photoelectron spectroscopy, Raman spectroscopy, X-ray powder diffraction, field emission-scanning electron microscopy with energy dispersive X-ray and high resolution-transmission electron microscopy. ZnO NPs showed distinctive properties among different characterization techniques, including a small energy bandgap of 2.80 eV, a porous, a minimum crystalline size of 16.99 nm, an average particle size of 14.21 nm, and a spherical nanostructure. Additionally, we performed preliminary antibacterial experiments to assess ZnO NPs, copper oxide (CuO) NPs, and ZnO–CuO nanocomposites for antibacterial activity. Interestingly, ZnO NPs showed significant potential in suppressing the growth of Staphylococcus aureus ATCC BAA-1026, Bacillus subtilis ATCC 6633, Escherichia coli ATCC 25922, and Klebsiella pneumoniae ATCC 13883, with decreasing order of minimum inhibitory concentrations: S. aureus = B. subtilis (15.63 μg·mL−1) > E. coli (62.50 μg·mL−1) > K. pneumoniae (125.00 μg·mL−1). These results highlight the potential of biogenic NPs, particularly ZnO NPs, as effective agents against multi-drug-resistant bacteria.

MOBILE HEALTH (MHEALTH) INNOVATIONS FOR PUBLIC HEALTH FEEDBACK: A GLOBAL PERSPECTIVE

In recent years, Mobile Health (mHealth) innovations have emerged as a transformative force in the realm of public health, revolutionizing the way healthcare is delivered and monitored worldwide. This review presents a comprehensive overview of the latest advancements in mHealth technologies, focusing particularly on their role in soliciting and utilizing public health feedback on a global scale. The proliferation of smartphones and mobile applications has paved the way for novel approaches to healthcare delivery, monitoring, and data collection. Through leveraging the ubiquity and connectivity of mobile devices, mHealth initiatives have facilitated enhanced communication between healthcare providers and patients, enabling real-time monitoring of health metrics, adherence to treatment regimens, and timely interventions. One of the key aspects of mHealth innovations is their capacity to engage diverse populations and solicit feedback regarding various aspects of public health, ranging from disease outbreaks to healthcare service quality. Through interactive platforms and user-friendly interfaces, individuals can provide valuable insights, report symptoms, and participate in surveys, thereby contributing to the generation of actionable data for public health interventions. Furthermore, mHealth solutions have demonstrated significant promise in overcoming barriers to healthcare access, particularly in underserved and remote communities. By delivering health information, diagnostic tools, and remote consultations via mobile platforms, these innovations have expanded the reach of essential healthcare services, thereby bridging gaps in healthcare delivery and improving health outcomes on a global scale. However, challenges such as privacy concerns, technological literacy, and disparities in digital access persist, underscoring the importance of equitable deployment and user-centered design in mHealth initiatives. Moreover, the integration of mHealth solutions into existing healthcare infrastructure requires careful coordination among stakeholders, including policymakers, healthcare providers, and technology developers. Mobile Health (mHealth) innovations hold immense potential to revolutionize public health feedback mechanisms on a global scale. By harnessing the power of mobile technologies, these initiatives can empower individuals, enhance healthcare delivery, and inform evidence-based public health policies for a healthier future.
 Keywords: Mobile Health, mHealth, Public Health, Healthcare, Innovation, Review.

MENTAL HEALTH AND DIGITAL TECHNOLOGY: A PUBLIC HEALTH REVIEW OF CURRENT TRENDS AND RESPONSES

The intersection of mental health and digital technology has become a critical focus in the realm of public health. This paper provides a concise overview of the current trends and responses within this evolving landscape. The widespread integration of digital technology into daily life has both positive and negative implications for mental health. On one hand, digital platforms offer innovative tools for mental health support, including virtual therapy, mental health apps, and online communities. These resources enhance accessibility and reduce barriers to seeking help. Conversely, concerns have arisen regarding the adverse impact of excessive digital screen time, social media use, and cyberbullying on mental well-being. The paper explores the prevalence of digital mental health interventions, emphasizing the need for evidence-based practices and regulatory frameworks to ensure their efficacy and user safety. Additionally, it addresses the growing recognition of the digital divide and disparities in access to mental health resources, emphasizing the importance of inclusivity in digital mental health initiatives. Responses to these challenges involve collaborative efforts among mental health professionals, technology developers, policymakers, and public health practitioners. The paper highlights the importance of research in understanding the nuanced relationship between digital technology and mental health, informing the development of targeted interventions. Furthermore, it emphasizes the need for comprehensive public health strategies that prioritize digital literacy, mental health education, and the promotion of positive online behaviors. The paper underscores the multifaceted nature of the relationship between mental health and digital technology, calling for a holistic and collaborative approach to address the evolving challenges and opportunities in this dynamic landscape.
 Keywords: Mental health, Digital technologies, Public health, Trends, Review.

Disease Discourse through Sentiment and Network Analysis

In the realm of public health, the discourse surrounding diseases significantly impacts awareness, funding, and stigma. Traditional methods of analyzing disease mentions often overlook the intricate relationships and sentiments expressed in text, which can provide deeper insights into public perception and information dissemination. Existing literature on disease discourse primarily focuses on quantitative metrics such as disease frequency counts or associations based on semantic similarity. However, such analyses rarely account for the sentiment of the discourse, which can play a critical role in shaping public perception and response to health information. Moreover, the complexity of textual data and the subtleties of language usage present significant challenges for extracting meaningful patterns, especially when considering the emotional context of disease-related discussions. This study introduces a novel approach that integrates sentiment analysis and network visualization to examine the discourse on diseases within a comprehensive textual dataset. By applying the TextBlob library, we analyze sentence-level sentiment and categorize it as positive or negative. Utilizing these sentiment scores, we construct two distinct network graphs to depict the relationships between diseases based on their co-occurrence within sentiment-laden sentences. The analysis reveals contrasting landscapes of disease discourse: the positive sentiment network highlights diseases frequently mentioned in the context of successful treatment or optimistic outcomes, forming distinct clusters around well-managed conditions. In contrast, the negative sentiment network elucidates diseases that coalesce around shared public concerns, fears, or complications, marking out critical areas for public health intervention. The findings underscore the importance of sentiment in understanding disease discourse, offering a novel perspective that can assist public health officials in tailoring communication strategies. By recognizing patterns in disease associations within negative contexts, interventions can be more effectively targeted to address misconceptions and alleviate public health anxieties. Conversely, identifying diseases with strong positive connections can guide campaigns to bolster preventive measures and highlight medical advancements. This methodology serves as a blueprint for leveraging sentiment analysis in the exploration of health-related textual data, paving the way for more nuanced and actionable insights into public health narratives.

Scope and Challenges of Artificial Intelligence in Public Health

Artificial Intelligence (AI) may be defined as the imitation of human cognition by a machine or software systems which compile integrate and analyse the data through different modes of learning and problem-solving mechanisms for achieving predefined objectives. Its capacity holds assurance for enhancing the effectiveness of public health initiatives aimed at advancing the well-being of diverse populations. This review article delineates the implementation of AI in the realm of public health, exploring its current applications and discussing potential areas for further development and prospects in future. Artificial Intelligence (AI) may be defined as the imitation of human cognition by a machine or software systems which compile integrate and analyse the data through different modes of learning and problem-solving mechanisms for achieving predefined objectives. Its capacity holds assurance for enhancing the effectiveness of public health initiatives aimed at advancing the well-being of diverse populations. This review article delineates the implementation of AI in the realm of public health, exploring its current applications and discussing potential areas for further development and prospects in future.

Analysis of the correlation between immune cell characteristics and insomnia: a Mendelian randomization study.

Insomnia, recognized as a prevalent sleep disorder, has garnered extensive attention within the realm of public health. Recent studies indicate a close interaction between the immune system and sleep; however, the specific mechanism remains not yet fully understood. Based on the publicly available Genome-Wide Association Study (GWAS) data, we used two-sample Mendelian randomization (MR) analyses to investigate the associations between 731 immune cell traits and insomnia risk. Five MR analysis methods and a comprehensive sensitivity analysis were used to evaluate the reliability of the results. In this study, we identified that 14 immune characteristics among four immune profiles [median fluorescence intensity (MFI), relative cell count (RC), absolute cell count (AC), and morphological parameters (MP)] demonstrated a significant causal association with insomnia. Specifically, eight immune cell characteristics were associated with an increased risk of insomnia, including CD11c+ monocyte% (P < 0.001), CD11c+ HLA DR++ monocyte% (P = 0.004), CD86+ plasmoid dendritic cell (DC) AC (P < 0.001), CD33br HLA DR+ CD14dim AC (P < 0.001), CD8dim AC (P = 0.002), CCR2 on CD14+ CD16- monocyte (P < 0.001), CD39 on monocyte (P < 0.001), and SSC-A on myeloid DC (P < 0.001). Six immune cell characteristics demonstrated protective effects against insomnia, including PB/PC %B cell (P < 0.001), CM CD4+% CD4+ (P < 0.001), T-cell AC (P < 0.001), BAFF-R on IgD- CD38br (P < 0.001), CD16-CD56 on HLA DR+ NK cells (P < 0.001), and CD14 on CD33br HLA DR+ CD14dim (P < 0.001). Our study established the correlation between immune cell characteristics and insomnia, offering a novel theoretical foundation for the concept of sleep-immune cross talk.NEW & NOTEWORTHY This study investigated the association between 731 immune cell characteristics and insomnia using Mendelian randomization, revealing that 14 immune cell characteristics across four groups of immune traits (MFI, RC, AC, and MP) have a significant and causal association with insomnia risk. Our results contribute to the understanding of the sleep-immune cross talk doctrine and offer a new theoretical basis for immune modulation in treating insomnia.

Congestive Heart Failure Category Classification Using Neural Networks in Short-Term Series

Congestive heart failure carries immense importance in the realm of public health. This significance arises from its substantial influence on the number of lives lost, economic burdens, the potential for prevention, and the opportunity to enhance the well-being of both individuals and the broader community through decision-making in healthcare. Several researchers have proposed neural networks for classification of different congestive heart failure categories. However, there is little information about the confidence of the prediction on short-term series. Therefore, evaluating classification models is required for effective decision-making in healthcare. This paper explores the use of three classical variants of neural networks to classify three groups of patients with congestive heart failure. The study considered the iterative method Multilayer Perceptron neural network (MLP), two non-iterative models (Extreme Learning Machine (ELM) and Random Vector Functional Link Network (RVFL)), and the CNN approach. The results showed that the deep feature learning system obtained better classification rates than MLP, ELM, and RVFL. Several scenarios designed by coupling some deep feature maps with the RVFL and MLP models showed very high simulation accuracy. The overall accuracy rate of CNN–MLP and CNN–RVFL varies between 98% and 99%.

Measuring radon concentration and investigation of it’s effects on lung cancer

The second most significant etiological factor contributing to the development of lung cancer, following tobacco smoking, is the presence of radon gas. Hence, the assessment of indoor radon levels within residential structures assumes paramount importance within the realm of public health. The primary objective of this investigation is to quantify the concentration of radon gas within the premises of Balikesir University Health Education and Research Hospital as well as the Faculty of Medicine Education building situated in the city of Balikesir. Furthermore, this research endeavors to ascertain the potential risk of lung cancer associated with these measured radon concentrations. To this end, an extensive, long-term (2–3 months) radon monitoring campaign was executed by strategically positioning Columbia Resin-39 (CR-39) solid-state nuclear track detectors at 28 distinct locations. The overarching aim of this effort is to comprehensively assess the health hazards posed to both staff and students and subsequently institute any requisite mitigating measures. The lowest measured value of the measured radon gas concentration is 6 Bq/m3, the highest measured value is 60 ± 8 Bq/m3. The average measurement value is 21.96 ± 3.16 Bq/m3. The measurement results were determined to be lower than the allowed radon activity concentration value in the “Radiation Safety Directive.

The Role of Insects in Ecosystems, an in-depth Review of Entomological Research

Insects, integral to Earth's ecosystems, play multifaceted roles that underpin environmental balance and human survival. Spanning roles from pollination to decomposition, these organisms also intersect with socio-economic, cultural, and public health sectors. This review delves into the diverse spheres of insect interactions within ecosystems, from their evolutionary histories to their roles as both predators and prey. The paper sheds light on the intricate predator-prey dynamics, emphasizing insects' roles in pest control and as pivotal food sources for various taxa. The significance of insects in soil ecosystems is elaborated upon, highlighting their contribution to soil health, nutrient cycling, and plant growth. With the looming threats of climate change, habitat destruction, and pollution, insects face unprecedented challenges, which in turn can have cascading effects on ecosystems. In the realm of public health, the review underscores the role of insects as disease vectors, necessitating a balanced approach to ecosystem health and disease management. As vectors, they also catalyze the spread of diseases, creating an intricate balance between maintaining biodiversity and safeguarding human health. The review also touches upon the cultural and economic contributions of insects, from traditional medicine to their utilization in contemporary diets, demonstrating their deep-rooted ties with human societies. With burgeoning technological advancements, the research landscape in entomology is undergoing a seismic shift. Embracing tools such as molecular studies, drones, and AI, the field is poised for groundbreaking insights. As the review suggests, the path forward demands an interdisciplinary approach, amalgamating knowledge from varied scientific domains to grasp the complexities of insect behaviors and interactions fully. In conclusion, insects, though diminutive in size, cast a vast shadow on our planet's functioning. Understanding their roles, challenges, and potential can pave the way for sustainable futures, balancing ecological health with human progress.