Environmental Assessment of Microplastic Pollution Induced by Solid Waste Landfills in the Akmola Region (North Kazakhstan)

[EN] This paper presents the outcomes derived from an environmental assessment of microplastic pollution resulting from solid waste landfills in the Akmola Region, situated in North Kazakhstan. This research represents a pioneering investigation conducted on microplastics within this specific region. This study encompasses a comprehensive examination of plastic waste disposal sites across the Akmola region, with a particular emphasis on evaluating the status of the municipal solid waste management system. To characterize the plastic content within the waste present at the landfill sites, quantitative techniques were employed. Through experimental means, the composition and fractionation of plastics within the municipal solid waste (MSW) at the landfills were determined. These data were subjected to a comparative analysis, aligning them with official statistics and previously published scientific data from both Kazakhstan and other regions globally. The methodologies employed focused on the ¿soft¿ removal of organic substances through the use of oxidants which do not damage plastics, and were tested using a water-bath therapeutic treatment. Furthermore, an analysis of soil samples taken from the landfills unveiled the ultimate retention of microplastic particles, attributed to leachate and rainwater runoff. Extracts were obtained from the subsoil samples using a density-based separation process, involving a three-step extraction followed by subsequent filtration of the resulting supernatants. In addition, the soil samples underwent examination through dry-phase particle fractional separation. The particles were meticulously enumerated and classified, and their dimensions were measured employing microscopic techniques coupled with photographic documentation. The outcomes stemming from these diverse tests will serve as fundamental input for the forthcoming numerical modeling endeavor, which aims to simulate the behavior of microplastics within both soil and water. This endeavor represents a continuation of the research project, the preliminary findings of which are expounded upon in this paper.This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan through the research project entitled ¿Health Risk Modelling Based on the Identification of Microplastics in Water Systems and the Reasoning About Actions to Manage the Water Resources Quality¿ (Grant No. AP14869081).Salikova, NS.; Rodrigo-Ilarri, J.; Rodrigo-Clavero, M.; Urazbayeva, SE.; Askarova, AZ.; Magzhanov, KM. (2023). Environmental Assessment of Microplastic Pollution Induced by Solid Waste Landfills in the Akmola Region (North Kazakhstan). Water. 15(16). https://doi.org/10.3390/w15162889151

Environmental Assessment of Microplastic Pollution Induced by Solid Waste Landfills in the Akmola Region (North Kazakhstan)

This paper presents the outcomes derived from an environmental assessment of microplastic pollution resulting from solid waste landfills in the Akmola Region, situated in North Kazakhstan. This research represents a pioneering investigation conducted on microplastics within this specific region. This study encompasses a comprehensive examination of plastic waste disposal sites across the Akmola region, with a particular emphasis on evaluating the status of the municipal solid waste management system. To characterize the plastic content within the waste present at the landfill sites, quantitative techniques were employed. Through experimental means, the composition and fractionation of plastics within the municipal solid waste (MSW) at the landfills were determined. These data were subjected to a comparative analysis, aligning them with official statistics and previously published scientific data from both Kazakhstan and other regions globally. The methodologies employed focused on the “soft” removal of organic substances through the use of oxidants which do not damage plastics, and were tested using a water-bath therapeutic treatment. Furthermore, an analysis of soil samples taken from the landfills unveiled the ultimate retention of microplastic particles, attributed to leachate and rainwater runoff. Extracts were obtained from the subsoil samples using a density-based separation process, involving a three-step extraction followed by subsequent filtration of the resulting supernatants. In addition, the soil samples underwent examination through dry-phase particle fractional separation. The particles were meticulously enumerated and classified, and their dimensions were measured employing microscopic techniques coupled with photographic documentation. The outcomes stemming from these diverse tests will serve as fundamental input for the forthcoming numerical modeling endeavor, which aims to simulate the behavior of microplastics within both soil and water. This endeavor represents a continuation of the research project, the preliminary findings of which are expounded upon in this paper

BURDEN OF SUBSTANCE USE AMONG U.S. POPULATION WITH CHILDHOOD AND ADULTONSET ASTHMA

INTRODUCTION: Asthma is the most common chronic childhood disease. Substance use is associated with increasing severity of asthma symptoms and a major risk factor for status asthmaticus or asthma-related death. In the last decade, there is no nationwide literature showing prevalence of various substance use among asthmatics. Aim of this study is to identify the burden of substance use in US population with childhood onset asthma (COA) and adult-onset asthma (AOA) METHODS: We conducted a retrospective cross-sectional study using NHANES data from 2013 to 2018. AOA and COA assessed using questionnaire MCQ010 and MCQ025. Drug use variables were summarized and univariate analysis was performed using Mann Whitney test and Chi-square test to determine association between asthma and drug use. Mix-effect multivariate survey logistic regression analysis was performed to identify predictors of AOA and COA. RESULTS: Out of a total of 402,167 participants, prevalence of COA was 10.51% (42,275) and AOA was 4.79% (19,245). Prevalence of methamphetamine use (42.03% vs 41.99% vs 39.34%, p\u3c.0001), was higher in AOA, than COA and no asthma. Smoking (21.41% vs 18.12% vs 16.09%, p\u3c.0001), high alcohol intake (50.98% vs 44.02% vs 47.33%, p\u3c.0001), cocaine use (97.14% vs 93.53% vs 95.30%, p\u3c.0001), marijuana use (62.22% vs 55.42% vs 51.67%, p\u3c.0001) and heroin use (18.89% vs 18.74% vs 14.50%, p\u3c.0001) were more prevalent in COA than AOA and no asthma. In regression analysis, AOA was associated with higher prevalence odds of marijuana use (aOR 2.53, 95%CI 2.53-2.54, p\u3c.0001) and heroin use (1.82, 1.82-1.83, p\u3c.0001) whereas COA was associated with higher prevalence odds of smoking (1.19, 1.19-1.19, p\u3c.0001), alcohol abuse (1.06, 1.06-1.06, p\u3c.0001), cocaine use (67.99, 67.67-68.31, p\u3c.0001) and methamphetamine use (1.67, 1.67-1.67, p\u3c.0001). CONCLUSIONS: Substance use is common in childhood and adult asthmatics. Given the high risk of association between asthma and substance use, policy makers and community programs should be built in a way to help improve the compliance in asthma care and decrease rates of substance use disorders in this population. Our study was limited with recall bias, absence of causality, absence of severity and follow up of asthma, and quantification of substance use

Utility and precision evidence of technology in the treatment of type 1 diabetes: a systematic review.

BACKGROUND: The greatest change in the treatment of people living with type 1 diabetes in the last decade has been the explosion of technology assisting in all aspects of diabetes therapy, from glucose monitoring to insulin delivery and decision making. As such, the aim of our systematic review was to assess the utility of these technologies as well as identify any precision medicine-directed findings to personalize care. METHODS: Screening of 835 peer-reviewed articles was followed by systematic review of 70 of them (focusing on randomized trials and extension studies with ≥50 participants from the past 10 years). RESULTS: We find that novel technologies, ranging from continuous glucose monitoring systems, insulin pumps and decision support tools to the most advanced hybrid closed loop systems, improve important measures like HbA1c, time in range, and glycemic variability, while reducing hypoglycemia risk. Several studies included person-reported outcomes, allowing assessment of the burden or benefit of the technology in the lives of those with type 1 diabetes, demonstrating positive results or, at a minimum, no increase in self-care burden compared with standard care. Important limitations of the trials to date are their small size, the scarcity of pre-planned or powered analyses in sub-populations such as children, racial/ethnic minorities, people with advanced complications, and variations in baseline glycemic levels. In addition, confounders including education with device initiation, concomitant behavioral modifications, and frequent contact with the healthcare team are rarely described in enough detail to assess their impact. CONCLUSIONS: Our review highlights the potential of technology in the treatment of people living with type 1 diabetes and provides suggestions for optimization of outcomes and areas of further study for precision medicine-directed technology use in type 1 diabetes

Disease-modifying therapies and features linked to treatment response in type 1 diabetes prevention: a systematic review

BACKGROUND: Type 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Prevention efforts have focused on immune modulation and supporting beta cell health before or around diagnosis; however, heterogeneity in disease progression and therapy response has limited translation to clinical practice, highlighting the need for precision medicine approaches to T1D disease modification. METHODS: To understand the state of knowledge in this area, we performed a systematic review of randomized-controlled trials with ≥50 participants cataloged in PubMed or Embase from the past 25 years testing T1D disease-modifying therapies and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument. RESULTS: We identify and summarize 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss at disease onset. Seventeen interventions, mostly immunotherapies, show benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employ precision analyses to assess features linked to treatment response. Age, beta cell function measures, and immune phenotypes are most frequently tested. However, analyses are typically not prespecified, with inconsistent methods of reporting, and tend to report positive findings. CONCLUSIONS: While the quality of prevention and intervention trials is overall high, the low quality of precision analyses makes it difficult to draw meaningful conclusions that inform clinical practice. To facilitate precision medicine approaches to T1D prevention, considerations for future precision studies include the incorporation of uniform outcome measures, reproducible biomarkers, and prespecified, fully powered precision analyses into future trial design. Type 1 diabetes (T1D) is a condition that results from the destruction of a type of cell in the pancreas that produces the hormone insulin, leading to lifelong dependence on insulin injections. T1D prevention remains a challenging goal, largely due to the immense variability in disease processes and progression. Therapies tested to date in medical research settings (clinical trials) work only in a subset of individuals, highlighting the need for more tailored prevention approaches. We reviewed clinical trials of therapies targeting the disease process in T1D. While the overall quality of trials was high, studies testing individual features affecting responses to treatments were low. This review reveals an important need to carefully plan high-quality analyses of features that affect treatment response in T1D, to ensure that tailored approaches may one day be applied to clinical practice. eng MJH Life Sciences and as a consultant for DRI Healthcare. C.E.M. reported serving on advisory boards for Provention Bio, Isla Technologies, MaiCell Technologies, Avotres, DiogenyX, and Neurodon; receiving in-kind research support from Bristol Myers Squibb and Nimbus Pharmaceuticals; and receiving investigator-initiated grants from Lilly Pharmaceuticals and Astellas Pharmaceuticals. L.A.D. reports research support to institutions from Dompe, Lilly, Mannkind, Provention, Zealand, and consulting relationships with Abata and Vertex. R.A.O. had a UK MRC Confidence in concept grant to develop a T1D GRS biochip with Randox Ltd and has ongoing research funding from Randox R & D. No other authors report any relevant conflicts of interest.Published version, submitted versionRDUH can access the full-text of this article by clicking on the 'Additional Link' above and logging in with NHS OpenAthens if prompted

Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review.

This is the final version. Available from Nature Research via the DOI in this record. Data availability: All studies reviewed were identified and can be accessed via publicly available databases (PubMed and Embase). Source data can be found in Supplementary Data 3. A full list of included studies is available in Supplementary Data 6. Article review data supporting the findings of this study are available upon reasonable request from the corresponding author.BACKGROUND: Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies. METHODS: We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment. RESULTS: Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation. CONCLUSIONS: Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.DiabDocs K12 programLeona M. & Harry B. Helmsley Charitable TrustNIH NIDDKDiabetes UK Harry Keen FellowshipNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute of HealthNational Institute of HealthNational Institute of Healt

Disease-modifying therapies and features linked to treatment response in type 1 diabetes prevention: a systematic review.

BACKGROUND: Type 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Prevention efforts have focused on immune modulation and supporting beta cell health before or around diagnosis; however, heterogeneity in disease progression and therapy response has limited translation to clinical practice, highlighting the need for precision medicine approaches to T1D disease modification. METHODS: To understand the state of knowledge in this area, we performed a systematic review of randomized-controlled trials with ≥50 participants cataloged in PubMed or Embase from the past 25 years testing T1D disease-modifying therapies and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument. RESULTS: We identify and summarize 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss at disease onset. Seventeen interventions, mostly immunotherapies, show benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employ precision analyses to assess features linked to treatment response. Age, beta cell function measures, and immune phenotypes are most frequently tested. However, analyses are typically not prespecified, with inconsistent methods of reporting, and tend to report positive findings. CONCLUSIONS: While the quality of prevention and intervention trials is overall high, the low quality of precision analyses makes it difficult to draw meaningful conclusions that inform clinical practice. To facilitate precision medicine approaches to T1D prevention, considerations for future precision studies include the incorporation of uniform outcome measures, reproducible biomarkers, and prespecified, fully powered precision analyses into future trial design