Public Health Protection Research Articles

Waste management from dental clinics: A case study in Belo Horizonte, Brazil

Healthcare waste management is a critical aspect of public health and environmental protection, particularly in establishments such as dental clinics. This study examined the dental clinic waste (DCW) management processes in clinics within the city of Belo Horizonte, Brazil. Utilizing data from Healthcare Waste Management Plans (HCWMP) provided by the Urban Cleaning Superintendence, the study investigated waste generation, segregation, storage, collection, treatment, and final disposal practices. The results revealed that hazardous DCW represented a significant portion (26.5 %) of waste generated in dental clinics, exceeding the World Health Organization’s recommended threshold. Biological waste (22.9 %), mainly consisting of cotton, gauze, and gloves contaminated with blood or body fluids, was the most generated hazardous waste group, followed by chemical (2.2 %) and sharps waste (1.3 %). Incineration was the predominant treatment method for hazardous DCW, raising concerns about environmental impacts and greenhouse gas emissions. Non-hazardous waste, primarily destined for landfills, had limited recycling rates (2.4 %), emphasizing the need for improved waste management strategies to minimize environmental impacts and increase circular economy. Challenges in DCW management included inadequate segregation practices, limited recycling initiatives, and incomplete HCWMPs lacking descriptions of waste management beyond establishment boundaries. Addressing these challenges requires comprehensive training programs, strengthened regulations, and increased environmental awareness among healthcare professionals. In conclusion, improving DCW management in dental clinics is crucial for mitigating occupational and environmental risks. Collective efforts are needed to enhance waste segregation, promote recycling, and ensure compliance with regulations, ultimately safeguarding public health and the environment.

Machine learning driven image segmentation and shape clustering of algal microscopic images obtained from various water types

Algae and cyanobacteria are microorganisms found in almost all fresh and marine waters, where they can pose environmental and public health risks when they grow excessively and produce blooms. Accurate identification and quantification of these microorganisms are vital for ecological research, water quality monitoring, and public health safety. However, traditional methods of manually counting and morphologically identifying these microorganisms are time-consuming and prone to human error. Application of the machine learning-driven Fast Segment Anything Model (FastSAM), an image segmentation model, automates and potentially enhances the accuracy and efficiency of cell identification and enumeration from microscopic images. We assessed FastSAM for algal cell image segmentation, and three clustering evaluation metrics. Segmentation of microscopic images of algal and cyanobacterial cells in water and treated wastewater samples using the FastSAM algorithm, which is a Vision Transformer (ViT) based algorithm that utilizes a compact ViT encoder, demonstrated benefits and challenges of this machine learning driven image processing. Notably, the pre-trained algorithm segmented entire elements in all microscopic images used in this study. Depending on the shape, 50–100 % similarity was observed between machine-based segmentation and manual validation of all segmented elements, with 100 % of single cells being correctly segmented by FastSAM. The performance of clustering metrics varied between 57–94 % with the Spectral Angle Mapper achieving the most accurate performance, 84–94 %, compared to the manually chosen clustering benchmarks. Cyanobacterial and algal communities are biologically diverse and have ecological significance. The application of image clustering techniques in studying their cell shapes marks an important advancement in microbial ecology and environmental monitoring. As technology progresses, these methods will become increasingly utilised to decipher the complex roles that algae and cyanobacteria play in our ecosystems supporting mitigation and public health protection measures.

Advancing Analytical Techniques in PET and rPET: Development of an ICP-MS Method for the Analysis of Trace Metals and Rare Earth Elements.

Despite the extensive use of recycled polyethylene terephthalate (rPET) in food contact materials (FCMs), research on the presence of heavy metals (HMs) and rare earth elements (REEs) during various recycling stages (e.g., flakes, granules, and preforms) remains limited. This study aimed to address these gaps by validating a rapid and sensitive analytical method to quantify 26 HMs and 4 REEs in PET and rPET matrices. An ICP-MS method was validated per EURACHEM guidelines, assessing linearity, limits of detection (LOD), limits of quantification (LOQ), accuracy, and repeatability. The method was employed for initial screening of HMs and REEs classified as non-intentionally added substances (NIASs) in PET and rPET samples. The findings showed high accuracy and reliability, with recovery rates between 80% and 120%. Analysis revealed varying concentrations of HMs and REEs, with the highest levels in 100% rPET preforms, notably Zn, Cu, and Al among HMs, and La among REEs. The study identified critical contamination points during the recycling process, highlighting the need for targeted interventions. This research provides a crucial analytical framework for assessing HMs and REEs in PET and rPET, ensuring FCM safety compliance and supporting efforts to enhance rPET product safety, promoting public health protection and advancing the circular economy.

The dark side of beauty: an in-depth analysis of the health hazards and toxicological impact of synthetic cosmetics and personal care products.

Over the past three decades, the popularity of cosmetic and personal care products has skyrocketed, largely driven by social media influence and the propagation of unrealistic beauty standards, especially among younger demographics. These products, promising enhanced appearance and self-esteem, have become integral to contemporary society. However, users of synthetic, chemical-based cosmetics are exposed to significantly higher risks than those opting for natural alternatives. The use of synthetic products has been associated with a variety of chronic diseases, including cancer, respiratory conditions, neurological disorders, and endocrine disruption. This review explores the toxicological impact of beauty and personal care products on human health, highlighting the dangers posed by various chemicals, the rise of natural ingredients, the intricate effects of chemical mixtures, the advent of nanotechnology in cosmetics, and the urgent need for robust regulatory measures to ensure safety. The paper emphasizes the necessity for thorough safety assessments, ethical ingredient sourcing, consumer education, and collaboration between governments, regulatory bodies, manufacturers, and consumers. As we delve into the latest discoveries and emerging trends in beauty product regulation and safety, it is clear that the protection of public health and well-being is a critical concern in this ever-evolving field.

Assessment of Air Pollution Levels from a Building Construction Site on Lagos Island

The introduction highlights the challenges of air pollution from construction activities on a site in Lagos Island, Nigeria, emphasizing the need for comprehensive studies to assess air pollution levels and evaluate its implications for public health and environmental quality. The methodology outlines the monthly data collection process, using the Earth Sense Zephyr (equipped with electrochemical detectors for gases) to measure CO, NO, NO₂, O₃, and Optical light scattering for particles) to measure PM₂.₅, and PM₁₀, and the ARA n-FRM Sampler for additional data collection on PM₂.₅, and PM₁₀. The study found that CO, NO, and NO₂ levels were influenced by construction activities, vehicle emissions and industrial sources, with notable peaks in CO and NO concentrations during specific months. Ozone levels remained consistently low, likely due to the "titration effect," while particulate matter (PM₂.₅ and PM₁₀) showed significant seasonal variation, peaking during the dry season due to construction dust and dry weather conditions. The findings underscore the need for stringent regulatory measures and effective dust control practices, particularly during periods of increased construction activity and dry weather, to mitigate air pollution and protect public health. In conclusion, the study provides valuable insights into the dynamics of air pollution from a typical construction site in Lagos Island, emphasizing the urgency of sustainable interventions to safeguard public health and environmental integrity. The study proposes enhanced monitoring and surveillance, stringent regulatory measures, promotion of sustainable construction practices, and public awareness and education, to address the challenges associated with construction-related air pollution on Lagos Island.

1-Benzhydryl-piperazine: Isolation, structure determination, and in silico studies for a novel potential narcotic agent detected in sports supplements

Adulteration of sports supplements poses significant health risks, necessitating stringent regulatory oversight. This study investigates deceptive practices by manufacturers, including concealment of formulations and manipulation of label information, impacting a wide demographic beyond elite athletes. Cinnarizine (CIN), and its degradation product, 1-benzhydryl-piperazine (BHP), which has amphetamine-like effects, are detected for the first time in 8 out of 41 sports supplements. An HPTLC method was developed to quantify CIN, and validated with specificity, linearity, the limit of detection (LOD), the limit of quantification (LOQ), accuracy, and precision studies. The linear range was between 50–800 ng spot−1. LOD and LOQ were 15 and 50 ng spot−1, respectively. The average recovery was calculated as 88.32 %, and %RSD values were less than 4 %. CIN’s degradation products were detected using GC-MS. CIN's decomposition into BHP also was confirmed via NMR analysis. Molecular docking studies revealed BHP's superior binding affinity and stability compared to 1-benzyl-piperazine (BZP), a prohibited narcotic agent, for dopamine and serotonin receptors, indicating potent psychoactive effects. Molecular dynamics simulations further elucidated interaction patterns, highlighting BHP's robust binding and potential for abuse, necessitating regulatory scrutiny and potential reevaluation of its legal status. It is also the first in silico study to compare BHP with BZP in terms of receptor interactions, which makes it pioneering. This study emphasizes the critical need for regulatory vigilance in the sports supplement industry to ensure consumer safety. Methodologies developed here for detecting adulterants provide foundational tools for future research aimed at enhancing public health protections against adulterated supplements. Regulatory collaboration and enhanced quality controls are essential to mitigate risks associated with deceptive practices and ensure the well-being of consumers.

Assessment of the Demographic Burden of Epidemics and Diseases within the African Sub-Region: A Review of Recent Epidemiological Data, Responses, and Resultant Psychosocial Risk Tendencies

Background: Over the past few decades, epidemic outbreaks and disease occurrences have become more frequent and widespread in Africa, posing challenges for poor countries in the region and impacting vulnerable populations. Limited resources, inadequate healthcare infrastructure, socioeconomic inequalities, malnutrition, mass gatherings, and lack of proper sanitation and hygiene contribute to Africa’s high burden of epidemics and infectious diseases such as cholera, measles, monkeypox, Ebola viral disease, and COVID-19. This paper reviewed epidemic outbreaks and diseases transmitted through intimate contact in Africa and their impacts on vulnerable populations. Methods: A narrative review approach was adopted to gather and analyze the relevant literature on epidemic outbreaks in African sub-regions. The review encompassed causes, transmission patterns, demographic impact, community susceptibility, preventive measures, and psychosocial risk-taking behaviors. Results: This study emphasizes the importance of addressing the causes of response lapses and the resulting human, material, and economic losses in the region. Effective preventive measures include disease surveillance, early mitigation strategies, contact tracing, personal protective measures, improved political and public health leadership, and socioeconomic equity. However, the review highlights challenges in implementing these measures effectively due to limited resources, delayed detection, and difficulties in scaling up response solutions. Conclusions: There is a need for a comprehensive approach involving health departments, infrastructure development, and addressing socioeconomic factors.

High-performance NO2 gas sensor based on reduced graphene oxide/ZrO2 hybrids

The increasing concern over environmental pollution, particularly from nitrogen dioxide (NO2) emissions, necessitates the development of efficient NO2 detection sensors. This study introduces reduced graphene oxide (rGO)/ZrO2 hybrids for enhanced NO2 gas sensing. Utilizing a modified Hummer’s method, graphene oxide (GO) flakes were synthesized and subsequently sputtered with 10 nm ZrO2 film, followed by thermal annealing to produce rGO/ZrO2 hybrids. The hybrids were characterized using various techniques including SEM, TEM, AFM, Raman spectroscopy, and XRD, confirming successful synthesis and reduction of GO, as well as the formation of ZrO2 nanoparticles. Gas sensing tests revealed superior sensitivity to NO2 in the hybrids due to efficient electron transfer between rGO and ZrO2, resulting in increased hole concentration in rGO and enhanced conductivity. The cyclic performance of the hybrids showed stable response and recovery to NO2, while selectivity tests demonstrated high sensitivity to NO2 over other gases including NH3, ethanol, and oxygen. This study highlights the potential of rGO/ZrO2 hybrids as high-performance NO2 gas sensors, offering promising prospects for environmental monitoring and public health protection.

Study of heavy metals adsorption using a silicate-based material: Experiments and theoretical insights

Heavy metal toxicity in water is a serious problem with harmful effects on human health and the ecosystem. This research studied a silicate-based material as an adsorbent for removing four heavy metals from aqueous solutions. The target metal pollutants selected include manganese (Mn2+), copper (Cu2+), cobalt (Co2+), and zinc (Zn2+). First, theoretical tools including potential energy surface analysis, Natural Population Analysis, AIM, Wiberg Bond Index, QTAIM, and topological methods offer profound insights into the nature of interactions present in the Mg2O8Si3M (M = Mn2+, Cu2+, Co2+, Zn2+) clusters. Second, the synthesis and characterization of eco-friendly hydrated amorphous magnesium silicate (MgOSiO2nH2O) was developed. Last, a simple kinetic adsorption test was applied to assess the material selectivity towards heavy metals and support theoretical results. The kinetic adsorption study was analyzed through the pseudo-first and second-order kinetics, Elovich, and the intraparticle diffusion models. The theoretical analysis of the adsorption energies indicates that the adsorption of four metal ions on the Mg2O8Si3 surface is energetically favorable in all cases. The material displayed the following adsorption sequence: Cu2+ (59 mg g-1) > Zn2+(25 mg g-1) ≈ Co2+ (23 mg g-1) > Mn2+ (15 mg g-1). This knowledge can then be used to design and optimize low-cost silicate-based materials for effective heavy metal removal, contributing to efforts to address environmental pollution and protect public health.

Determination of Soil Contamination Due to the Influence of Cemeteries for the Surrounding Land and People in Central Ecuador—Worldwide Implications

Human decomposition processes generate pulses of nutrients, such as carbon (C) and nitrogen (N) in the form of ammonium and nitrate (NO3−), which are released into the surrounding environment. The little exploration related to the potential of cadaveric leachate to influence the physical chemistry and biology of the soil makes it difficult to obtain data and scientific evidence, and subsequently the predominant objective of the current study was to determine soil contamination through the analysis of parameters of physical chemistry that included organic matter (OM), NO3−, texture, humidity, and pH. Soil samples were taken at different depths in two temporary trials (the dry and rainy seasons) in central Ecuador. The Kruskal–Wallace and ANOVA statistical analyses determined significant differences in relation to the sampling sections and by categories, while there were no significant differences in the inter-season analysis; therefore, the study was based on the data obtained in the dry season. The results indicate a tendency towards contamination in cemeteries categorized as critical, that is, moderate, light, and not suitable due to the high values of OM and humidity measured. On the contrary, the soils that corresponded to the cemeteries classified as suitable yielded low values of the analyzed parameters, which corroborates their capacity for the present and future location of cemeteries. Monitoring and managing soil health is crucial to ensure sustainable environmental practices and protect public health; nonetheless, additional research is suggested to confirm the findings of the current study.

Co-occurrence of polycyclic aromatic hydrocarbons and heavy metals in various environmental matrices of a chronic petroleum polluted region in Iran; Pollution characterization, and assessment of ecological and human health risks

Oil spills from pipeline accidents can result in long-lasting health effect in the people living in a ‎polluted region‎. In this study, the level of the 16 US EPA priority polycyclic aromatic hydrocarbons (PAHs‎) and heavy metals (HMs) have been ‎analyzed in environmental matrices of a region with frequent oil ‎pipeline accidents in Iran‎.‎ The results showed that the mean ‎concentration of ΣPAHs and ΣHMs decreased from the upstream to the downstream and also the levels were ‎higher in the wet season than those in the dry season. The average concentration of HMs in sediments was higher than that in other ‎environments. The 3-ring and 4-ring PAHs were dominant in all of the studied matrices with the average values of 32.61 % and 45.85 %, respectively. The ecological risks of PAHs and HMs were medium and high in all matrices, respectively. In wet season, the total cancer risk (TCR) related to PAHs in agricultural soil was greater than 10−4, whereas it’s very close to the threshold for HMs in water. This study offers a reference for assessing the long-term impact of oil spills in contaminated environmental matrices. The results are crucial for developing effective strategies to mitigate oil pollution ‎impacts and protect environmental and public health.

Water Contaminant Detection and Water Purification of Household Drinking Water Using a New Stacking Ensemble Model

ABSTRACTContaminated drinking water sources pose a significant health risk worldwide. Monitoring programs for drinking water quality aims to ensure safe water supply by informing management practices. Improved online monitoring of water systems is necessary as current lab‐based methods are slow and do not offer real‐time public health protection. Rapid detection and response to potential contamination events are crucial to mitigate health risks. Mark of‐purpose water treatment strategies offer a reasonable method for upgrading drinking water quality at the family level and forestalling waterborne illnesses. This study focuses on collecting household drinking water and utilizing various sensors to measure parameters such as pH, turbidity, water level, temperature, and humidity. A consistent water quality noticing system using the stacking outfit model, which solidifies Bayesian association and decision tree techniques, is proposed in this article. Bayesian network analyzes the input data attained from sensors collecting real‐time data and concludes whether the data represents the contamination event. DTs are utilized to demonstrate the connections between multivariate water boundaries utilized in the review. Afterward, a multiobjective, such as a biobjective optimization model and a nondominated genetic algorithm (NGA) are used in this work of optimization to minimize the volume of contaminated water. After the pollution in the water is identified, water decontamination processes are done given point of purpose medicines like ceramic channels and solar water disinfection (SODIS). The method outlined is executed through Python software. The findings indicate that the estimated values for PH, temperature, and turbidity are 7.3, 31.8, and 0.77, respectively. However, the proposed method is compared with the existing C‐NSGA‐II, while compared to this method, the proposed system produces improved cost functions. Consequently, suitable water treatment and supply should be considered to reduce the effects on people's health as well as to improve living conditions, respectively.

Development of a reusable polymeric fluorescence sensor based on acryloyl β-cyclodextrin for the determination of aflatoxin B1 in grain products

AFB1 is a harmful substance that can be found in agricultural products and can seriously affect human health, even in trace amounts. Therefore, monitoring AFB1 levels to ensure food safety and protect public health is crucial. New, highly reliable, selective, and rapid detection methods are needed to achieve this goal. Our work involves the development of a polymeric membrane sensor using radical polymerization that can accurately detect AFB1. Various spectroscopic techniques (Fourier transform infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM)) were used to obtain information about the structural and morphological properties of the prepared sensor. The sensor displayed fluorescence selectively responsive to AFB1 at the excitation wavelength of 376 nm and emission wavelength of 423 nm. The polymeric fluorescence sensor showed good sensitivity and a wide linear range from 9.61 × 10−10 and 9.61 × 10−9 mol/L for AFB1quantification. The limit of detection (LOD) is as low as 3.84 × 10−10 mol/L for AFB1. Other mycotoxins, such as aflatoxin B2 and aflatoxin G1, did not interfere with the sensor’s high selectivity towards AFB1. To test the sensor’s effectiveness in detecting AFB1 in real samples, three different grain samples – peanuts, hazelnut butter, and peanuts with a sauce known to contain AFB1 – were utilized. The results were satisfactory and demonstrated that the sensor can be successfully employed in real samples, with an error range of 0.43 % to 12.10 %.

Blood levels of 21 metals and metalloids in riverside villagers of the Brazilian Amazon: A human biomonitoring study with associations with sociodemographic, dietary, and lifestyle factors

Human biomonitoring of toxic and essential trace elements is critically important for public health protection. Amazonian riverine communities exhibit distinctive dietary patterns, heavily reliant on locally sourced fish, fruits, and vegetables. These habits may result in unique exposure profiles compared to urban populations. However, comprehensive assessments of their exposure to toxic and essential metals are lacking, representing a critical gap in understanding the health risks faced by these communities. This study aimed to establish baseline levels of 21 metals and metalloids in human blood and explore the influence of sociodemographic factors, dietary habits, and lifestyle choices as potential sources of exposure to these elements. A cross-sectional biomonitoring investigation was conducted with 1,024 individuals from 13 communities in the Tapajós and Amazon Basins (Pará, Brazil). Most of the elements in study was determined for the first time in the region. Blood samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). The levels of all elements were summarized by quantiles and compared with cutoff values from other Brazilian populations. Multiple linear regression was used to assess possible associations between element concentrations and sociodemographic characteristics, dietary habits, and lifestyle choices. High detection rates (64%–100%) were observed, indicating the widespread presence of these elements. Elevated blood concentrations were found for mercury (median 21.1 μg.L−1, interquartile range: 12–34 μg.L−1), selenium (median 166 μg.L−1, interquartile range: 137–208 μg.L−1), and lead (median 34 μg.L−1, interquartile range: 20.8–64 μg.L−1). Regression analysis revealed a positive association between mercury levels and fish consumption, while manioc flour intake showed no relationship to lead levels. In conclusion, our findings emphasize the need for continued monitoring and public policy development for these vulnerable populations. Further studies should assess long-term trends and investigate the health implications of prolonged exposure to diverse chemicals in Amazonian riverside communities.

Facilitating inorganic arsenic speciation and quantification in waters: Polymer inclusion membrane preconcentration and X-ray fluorescence detection

BackgroundArsenic, classified as a priority pollutant and human carcinogen by the IARC, is subject to stringent regulatory limits in food and water. Among various arsenic species found in water samples, arsenite (As(III)) is identified as the most toxic form. Given the limitations of conventional spectroscopic techniques in speciation analysis, there is a crucial need for innovative and sustainable methodologies that enable arsenic speciation. Simplifying these methodologies is essential for widespread applicability and effective environmental monitoring. ResultsThis study proposes a simple and cost-effective analytical methodology for speciating inorganic arsenic in water samples. The method involves extracting As(III) into a polymer inclusion membrane (PIM) containing the extractant Cyanex 301 (bis(2,4,4-trimethylpentyl) dithiophosphinic acid), followed by analysis using energy dispersive X-ray fluorescence (EDXRF) spectrometry. The concentration of arsenate was measured after a reduction step using a thiosulfate/iodide mixture. This simple methodology allows a limit of quantification for trivalent arsenic (2 μg L−1), which is well below the World Health Organization's recommended maximum permissible level of As in drinking water (10 μg L−1). The method that is developed allows the determination of As at trace levels in waters with naturally occurring arsenic. Significance and noveltyThis study represents a significant advance in the field, providing a novel and efficient methodology for arsenic speciation analysis in water samples. By combining the advantages of polymer inclusion membrane (PIM) extraction with energy dispersive X-ray fluorescence (EDXRF) spectrometry, this study offers a cost-effective and environmentally friendly approach to address the critical issue of arsenic contamination in water sources, thereby contributing to enhanced environmental monitoring and public health protection.

Assessment of heavy metal(oid) pollution and related health risks in agricultural soils surrounding a coal gangue dump from an abandoned coal mine in Chongqing, Southwest China

The coal gangue dump may introduce heavy metal(oid)s (HMs) into surrounding agricultural soils, posing potential health risks to nearby communities. This study evaluated heavy metal(oid) pollution in agricultural soils adjacent to a gangue dump at an abandoned coal mine in Chongqing, Southwest China. The concentrations of HMs (As, Cd, Cr, Cu, Ni, Pb, and Zn) were quantified using ICP-MS, and the contamination status was assessed using the Geoaccumulation Index (Igeo), Contamination Factor (CF), Pollution Load Index (PLI), and Potential Ecological Risk Index (RI). Heavy metal(oid) contamination was detected in soils across a depth of 0–30 cm, particularly pronounced in the topsoil layer (0–10 cm and 10–20 cm depths). Cu emerged as the predominant contaminant across all examined depths, with average Igeo values of 1.20, 1.21, and 1.16 for the 0–10 cm, 10–20 cm, and 20–30 cm depths, respectively, indicating moderate contamination. The CF for Cu was 3.55, 3.55, and 3.50 for these respective depths, classifying it as considerable contamination. The PLI values ranged from 1.61 to 2.50, with a mean value of 2.12, indicating overall contamination. The ecological risk assessment indicated that the soil's ecological risk was low at all depths. Cd was the major contributor to the RI, accounting for 48%, 47%, and 42% at 0–10 cm, 10–20 cm, and 20–30 cm depths, respectively. Health risk assessments revealed significant non-carcinogenic risks to children (mean HI = 1.30) and unacceptable carcinogenic risks to both adults and children (mean TCR = 3.26 × 10–4 and 1.53 × 10–3, respectively). This study underscores the critical need for comprehensive risk assessments using multiple indicators to prioritize remediation efforts for HMs, providing a scientific basis for effective environmental management and public health protection in the Three Gorges Reservoir Area.

Youth mental health care use during the COVID-19 pandemic in Alberta, Canada: an interrupted time series, population-based study

BackgroundDuring the COVID-19 pandemic, youth had rising mental health needs and changes in service accessibility. Our study investigated changes in use of mental health care services for Canadian youth in Alberta before and during the COVID-19 pandemic. We also investigated how youth utilization patterns differed for subgroups based on social factors (i.e., age, gender, socioeconomic status, and geography) previously associated with health care access.MethodsWe used cross-sectional population-based data from Alberta, Canada to understand youth (15–24 year) mental health care use from 2018/19 to 2021/22. We performed interrupted time series design, segmented regression modeling on type of mental health care use (i.e., general physician, psychiatrist, emergency room, and hospitalization) and diagnosis-related use. We also investigated the characteristics of youth who utilized mental health care services and stratified diagnosis-related use patterns by youth subgroups.ResultsThe proportion of youth using mental health care significantly increased from 15.6% in 2018/19 to 18.8% in 2021/22. Mental health care use showed an immediate drop in April 2020 when the COVID-19 pandemic was declared and public health protections were instituted, followed by a steady rise during the next 2 years. An increase was significant for general physician and psychiatrist visits. Most individual diagnoses included in this study showed significant increasing trends during the pandemic (i.e., anxiety, adjustment, ADHD, schizophrenia, and self-harm), with substance use showing an overall decrease. Mortality rates greatly increased for youth being seen for mental health reasons from 71 per 100,000 youth in 2018/19 to 163 per 100,000 in 2021/22. In addition, there were clear shifts over time in the characteristics of youth using mental health care services. Specifically, there was increased utilization for women/girls compared to men/boys and for youth from wealthier neighborhoods. Increases over time in the utilization of services for self-harm were limited to younger youth (15–16 year).ConclusionsThe study provides evidence of shifts in mental health care use during the COVID-19 pandemic. Findings can be used to plan for ongoing mental health needs of youth, future pandemic responses, and other public health emergencies.

Risk-based critical concentrations of enteric pathogens for recreational water criteria and recommended minimum sample volumes for routine water monitoring

Concerns are rising about the contamination of recreational waters from human and animal waste, along with associated risks to public health. However, existing guidelines for managing pathogens in these environments have not yet fully integrated risk-based pathogen-specific criteria, which, along with recent advancements in indicators and markers, are essential to improve the protection of public health. This study aimed to establish risk-based critical concentration benchmarks for significant enteric pathogens, i.e., norovirus, rotavirus, adenovirus, Cryptosporidium spp., Giardia lamblia, Campylobacter jejuni, Salmonella spp., and Escherichia coli O157:H7. Applying a 0.036 risk benchmark to both marine and freshwater environments, the study identified the lowest critical concentrations for children, who are the most susceptible group. Norovirus, C. jejuni, and Cryptosporidium presented lowest median critical concentrations for virus, bacteria, and protozoa, respectively: 0.74 GC, 1.73 CFU, and 0.39 viable oocysts per 100 mL in freshwater for children. These values were then used to determine minimum sample volumes corresponding to different recovery rates for culture method, digital polymerase chain reaction and quantitative PCR methods. The results indicate that for children, norovirus required the largest sample volumes of freshwater and marine water (52.08 to 178.57 L, based on the 5th percentile with a 10 % recovery rate), reflecting its low critical concentration and high potential for causing illness. In contrast, adenovirus and rotavirus required significantly smaller volumes (approximately 0.24 to 1.33 L). C. jejuni and Cryptosporidium, which required the highest sampling volumes for bacteria and protozoa, needed 1.72 to 11.09 L and 4.17 to 25.51 L, respectively. Additionally, the presented risk-based framework could provide a model for establishing pathogen thresholds, potentially guiding the creation of extensive risk-based criteria for various pathogens in recreational waters, thus aiding public health authorities in decision-making, strengthening pathogen monitoring, and improving water quality testing accuracy for enhanced health protection.

Therapeutic Potential of Psychedelic Drugs: Navigating High Hopes, Strong Claims, Weak Evidence, and Big Money.

Therapeutic claims about many psychedelic drugs have not been evaluated in any studies of even modest rigor. The science of psychedelic drugs is strengthening however, making it easier to differentiate some promising findings amid the hype that suffuses this research area. Ketamine has risks of adverse side effects (e.g., addiction and cystitis), but multiple studies suggest it can benefit individuals with treatment-resistant depression. Other therapeutic signals from psychedelic drug research that merit rigorous replication studies include 3,4-Methylenedioxymethamphetamine (MDMA) for post-traumatic stress disorder (PTSD) and psilocybin for depression, end of life dysphoria, and alcohol use disorder. The precise mechanisms through which psychedelic drugs can produce benefit and harm are not fully understood. Rigorous research is the best path forward for evaluating the therapeutic potential and mechanisms of psychedelic drugs. Policies governing the clinical use of these drugs should be informed by evidence and prioritize the protection of public health over the profit motive.

A new attention-based CNN_GRU model for spatial-temporal PM2.5 prediction.

Accurately predicting thespatial-temporal distribution of PM2.5 is challenging due to missing data and selecting an appropriate modeling method. Effective imputation of missing data must consider the relationships between variables while preserving their inherent variability and uncertainty. In this study, we employed machine learning techniques to impute missing data by analyzing the relationships between meteorological variables and other pollutants. Subsequently, we introduced an innovative spatiotemporal hybrid model, AC_GRU, which integrates a one-dimensional convolutional neural network (CNN), GRU, and an attention-based network to predict PM2.5 concentrations in urban areas. The AC_GRU model utilizes meteorological variables, PM2.5 concentrations from nearby air quality monitoring stations, and concentrations of other pollutants as inputs. This approach allows the model to learn spatiotemporal correlations within the time-series data, enhancing the accuracy of PM2.5 predictions. Additionally, the attention mechanism improves prediction accuracy by automatically weighting the past input variables based on their importance for future PM2.5 predictions. The experimental results demonstrate that our AC_GRU model outperforms state-of-the-art methods, making it a valuable tool for urban air quality management and public health protection.