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Epidemics Jun 2024Antibiotic-resistant Enterobacterales (ARE) are a public health threat worldwide. Dissemination of these opportunistic pathogens has been largely studied in hospitals....
BACKGROUND
Antibiotic-resistant Enterobacterales (ARE) are a public health threat worldwide. Dissemination of these opportunistic pathogens has been largely studied in hospitals. Despite high prevalence of asymptomatic colonization in the community in some regions of the world, less is known about ARE acquisition and spread in this setting. As explaining the community ARE dynamics has not been straightforward, mathematical models can be key to explore underlying phenomena and further evaluate the impact of interventions to curb ARE circulation outside of hospitals.
METHODS
We conducted a systematic review of mathematical modeling studies focusing on the transmission of AR-E in the community, excluding models only specific to hospitals. We extracted model features (population, setting), formalism (compartmental, individual-based), biological hypotheses (transmission, infection, antibiotic impact, resistant strain specificities) and main findings. We discussed additional mechanisms to be considered, open scientific questions, and most pressing data needs.
RESULTS
We identified 18 modeling studies focusing on the human transmission of ARE in the community (n=11) or in both community and hospital (n=7). Models aimed at (i) understanding mechanisms driving resistance dynamics; (ii) identifying and quantifying transmission routes; or (iii) evaluating public health interventions to reduce resistance. To overcome the difficulty of reproducing observed ARE dynamics in the community using the classical two-strains competition model, studies proposed to include mechanisms such as within-host strain competition or a strong host population structure. Studies inferring model parameters from longitudinal carriage data were mostly based on models considering the ARE strain only. They showed differences in ARE carriage duration depending on the acquisition mode: returning travelers have a significantly shorter carriage duration than discharged hospitalized patient or healthy individuals. Interestingly, predictions across models regarding the success of public health interventions to reduce ARE rates depended on pathogens, settings, and antibiotic resistance mechanisms. For E. coli, reducing person-to-person transmission in the community had a stronger effect than reducing antibiotic use in the community. For Klebsiella pneumoniae, reducing antibiotic use in hospitals was more efficient than reducing community use.
CONCLUSIONS
This study raises the limited number of modeling studies specifically addressing the transmission of ARE in the community. It highlights the need for model development and community-based data collection especially in low- and middle-income countries to better understand acquisition routes and their relative contribution to observed ARE levels. Such modeling will be critical to correctly design and evaluate public health interventions to control ARE transmission in the community and further reduce the associated infection burden.
PubMed: 38944024
DOI: 10.1016/j.epidem.2024.100783 -
JMIR Mental Health Jun 2024Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of... (Review)
Review
BACKGROUND
Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of mental health and suicide prevention.
OBJECTIVE
This systematic review aimed to determine how machine learning and data analysis can be applied to text-based digital media data to understand mental health and aid suicide prevention.
METHODS
A systematic review of research papers from the following major electronic databases was conducted: Web of Science, MEDLINE, Embase (via MEDLINE), and PsycINFO (via MEDLINE). The database search was supplemented by a hand search using Google Scholar.
RESULTS
Overall, 19 studies were included, with five major themes as to how data analysis and machine learning techniques could be applied: (1) as predictors of personal mental health, (2) to understand how personal mental health and suicidal behavior are communicated, (3) to detect mental disorders and suicidal risk, (4) to identify help seeking for mental health difficulties, and (5) to determine the efficacy of interventions to support mental well-being.
CONCLUSIONS
Our findings show that data analysis and machine learning can be used to gain valuable insights, such as the following: web-based conversations relating to depression vary among different ethnic groups, teenagers engage in a web-based conversation about suicide more often than adults, and people seeking support in web-based mental health communities feel better after receiving online support. Digital tools and mental health apps are being used successfully to manage mental health, particularly through the COVID-19 epidemic, during which analysis has revealed that there was increased anxiety and depression, and web-based communities played a part in reducing isolation during the pandemic. Predictive analytics were also shown to have potential, and virtual reality shows promising results in the delivery of preventive or curative care. Future research efforts could center on optimizing algorithms to enhance the potential of text-based digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and using machine learning to forecast these sources of happiness. This could extend to understanding how various activities result in improved happiness across different socioeconomic groups. Using insights gathered from such data analysis and machine learning, there is an opportunity to craft digital interventions, such as chatbots, designed to provide support and address mental health challenges and suicide prevention.
Topics: Humans; Machine Learning; Suicide Prevention; Mental Health; Social Media; Data Analysis
PubMed: 38935419
DOI: 10.2196/55747 -
Environmental Monitoring and Assessment Jun 2024Microplastics in the environment are considered complex pollutants as they are chemical and corrosive-resistant, non-biodegradable and ubiquitous. These microplastics...
Microplastics in the environment are considered complex pollutants as they are chemical and corrosive-resistant, non-biodegradable and ubiquitous. These microplastics may act as vectors for the dissemination of other pollutants and the transmission of microorganisms into the water environment. The currently available literature reviews focus on analysing the occurrence, environmental effects and methods of microplastic detection, however lacking a wide-scale systematic review and classification of the mathematical microplastic modelling applications. Thus, the current review provides a global overview of the modelling methodologies used for microplastic transport and fate in water environments. This review consolidates, classifies and analyses the methods, model inputs and results of 61 microplastic modelling studies in the last decade (2012-2022). It thoroughly discusses their strengths, weaknesses and common gaps in their modelling framework. Five main modelling types were classified as follows: hydrodynamic, process-based, statistical, mass-balance and machine learning models. Further, categorisations based on the water environments, location and published year of these applications were also adopted. It is concluded that addressed modelling types resulted in relatively reliable outcomes, yet each modelling framework has its strengths and weaknesses. However, common issues were found such as inputs being unrealistically assumed, especially biological processes, and the lack of sufficient field data for model calibration and validation. For future research, it is recommended to incorporate macroplastics' degradation rates, particles of different shapes and sizes and vertical mixing due to biofouling and turbulent conditions and also more experimental data to obtain precise model inputs and standardised sampling methods for surface and column waters.
Topics: Environmental Monitoring; Microplastics; Models, Chemical; Models, Theoretical; Water Pollutants, Chemical
PubMed: 38935176
DOI: 10.1007/s10661-024-12731-x -
Journal of Diabetes and Metabolic... Jun 2024Although several randomized clinical trials have tested the effect of prenatal dietary supplements on plasma glucose and lipid levels in non-pharmacologically managed...
The dietary supplements effect on metabolic markers in non-pharmacologically managed gestational diabetes mellitus patients: a systematic review and meta-analysis and meta-regression of randomized controlled trials.
BACKGROUND
Although several randomized clinical trials have tested the effect of prenatal dietary supplements on plasma glucose and lipid levels in non-pharmacologically managed gestational diabetes mellitus patients (GDM), a rigorous meta-analytic compendium lacks in the context. Therefore, this study aims to address this evidence gap.
METHOD
Eligible trials retrieved from searches in the PubMed, Embase, and Scopus databases were appraised using the Revised Cochrane risk-of-bias tool for randomized trials (RoB 2). The weighted mean differences (WMD) between dietary supplements and placebo were estimated using random-effect meta-analysis models for plasma glycemic and lipid markers. Meta-regression analysis ensued for effect modifier identification. The statistical significance estimation happened at < 0.05 (95% confidence interval).
RESULTS
This review included 19 trials (mostly Iranian and of low risk of bias primarily) of > 8000 GDM patients. Meta-analysis showed favorable effects of dietary supplementation on fasting plasma glucose (WMD: -5.42 mg/dL, p < 0.001), homeostasis model assessment indexes- insulin resistance (HOMA-IR; WMD: -1.02, p < 0.001), quantitative insulin sensitivity check index (WMD: 0.01, p < 0.001), total cholesterol (TC; WMD: -7.70 mg/dL, = 0.006), triglycerides (WMD: -10.23 mg/dL, = 0.0083), TC/high-density lipoprotein (WMD: -0.31 mg/dL, < 0.001), low-density lipoprotein (WMD: -5.79 mg/dL; < 0.001) and very-low-density lipoprotein (WMD: -5.67 mg/dL, < 0.001) levels. However, the HOMA- ß-cell function didn't increase (WMD: -17.91, < 0.001). Baseline maternal age ( = 0.28, = 0.014) and GDM diagnostic criteria ( = 0.90, = 0.012) were effect moderators of HOMA-IR and body mass index (BMI) ( = 6.07, = 0.022) and supplement type (solo versus combined) ( = 14.99, = 0.006) were effect moderators of triglyceride levels.
CONCLUSION
Altogether, antenatal dietary supplements achieved control over plasma glycemic and lipid profiles in non-pharmacologically treated GDM patients. Maternal age and GDM diagnostic criteria moderated HOMA-IR levels. BMI and supplement-type moderated triglyceride levels.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s40200-023-01369-0.
PubMed: 38932907
DOI: 10.1007/s40200-023-01369-0 -
Antioxidants (Basel, Switzerland) Jun 2024Bertoni, a no-calorie natural sweetener, contains a plethora of polyphenols that exert antioxidant properties with potential medicinal significance. Due to the variety... (Review)
Review
Bertoni, a no-calorie natural sweetener, contains a plethora of polyphenols that exert antioxidant properties with potential medicinal significance. Due to the variety of functional groups, polyphenols exhibit varying solubility depending on the nature of the extraction solvents (water, organic, or their mixtures, defined further on as hydroalcoholic extracts). In the present study, we performed a systematic review, following PRISMA guidelines, and meta-analysis, synthesizing all available data from 45 articles encompassing 250 different studies. Our results showed that the total phenolic content (TPC) of hydroalcoholic and aqueous extracts presents higher values (64.77 and 63.73 mg GAE/g) compared to organic extracts (33.39). Total flavonoid content (TFC) was also higher in aqueous and hydroalcoholic extracts; meta-regression analysis revealed that outcomes in different measuring units (mg QE/g, mg CE/g, and mg RUE/g) do not present statistically significant differences and can be synthesized in meta-analysis. Using meta-regression analysis, we showed that outcomes from the chemical-based ABTS, FRAP, and ORAC antioxidant assays for the same extract type can be combined in meta-analysis because they do not differ statistically significantly. Meta-analysis of ABTS, FRAP, and ORAC assays outcomes revealed that the antioxidant activity profile of various extract types follows that of their phenolic and flavonoid content. Using regression meta-analysis, we also presented that outcomes from SOD, CAT, and POX enzymatic antioxidant assays are independent of the assay type (-value = 0.905) and can be combined. Our study constitutes the first effort to quantitatively and statistically synthesize the research results of individual studies using all methods measuring the antioxidant activity of stevia leaf extracts. Our results, in light of evidence-based practice, uncover the need for a broadly accepted, unified, methodological strategy to perform antioxidant tests, and offer documentation that the use of ethanol:water 1:1 mixtures or pure water can more efficiently extract stevia antioxidant compounds.
PubMed: 38929131
DOI: 10.3390/antiox13060692 -
Frontiers in Bioengineering and... 2024Musculoskeletal simulations can be used to estimate biomechanical variables like muscle forces and joint torques from non-invasive experimental data using inverse and...
Musculoskeletal simulations can be used to estimate biomechanical variables like muscle forces and joint torques from non-invasive experimental data using inverse and forward methods. Inverse kinematics followed by inverse dynamics (ID) uses body motion and external force measurements to compute joint movements and the corresponding joint loads, respectively. ID leads to residual forces and torques (residuals) that are not physically realistic, because of measurement noise and modeling assumptions. Forward dynamic simulations (FD) are found by tracking experimental data. They do not generate residuals but will move away from experimental data to achieve this. Therefore, there is a gap between reality (the experimental measurements) and simulations in both approaches, the sim2real gap. To answer (patho-) physiological research questions, simulation results have to be accurate and reliable; the sim2real gap needs to be handled. Therefore, we reviewed methods to handle the sim2real gap in such musculoskeletal simulations. The review identifies, classifies and analyses existing methods that bridge the sim2real gap, including their strengths and limitations. Using a systematic approach, we conducted an electronic search in the databases Scopus, PubMed and Web of Science. We selected and included 85 relevant papers that were sorted into eight different solution clusters based on three aspects: how the sim2real gap is handled, the mathematical method used, and the parameters/variables of the simulations which were adjusted. Each cluster has a distinctive way of handling the sim2real gap with accompanying strengths and limitations. Ultimately, the method choice largely depends on various factors: available model, input parameters/variables, investigated movement and of course the underlying research aim. Researchers should be aware that the sim2real gap remains for both ID and FD approaches. However, we conclude that multimodal approaches tracking kinematic and dynamic measurements may be one possible solution to handle the sim2real gap as methods tracking multimodal measurements (some combination of sensor position/orientation or EMG measurements), consistently lead to better tracking performances. Initial analyses show that motion analysis performance can be enhanced by using multimodal measurements as different sensor technologies can compensate each other's weaknesses.
PubMed: 38919383
DOI: 10.3389/fbioe.2024.1386874 -
Annals of Surgical Oncology Jun 2024Cytoreductive surgery (CRS) is a widely acknowledged treatment approach for peritoneal metastasis, showing favorable prognosis and long-term survival. Intraoperative... (Review)
Review
BACKGROUND
Cytoreductive surgery (CRS) is a widely acknowledged treatment approach for peritoneal metastasis, showing favorable prognosis and long-term survival. Intraoperative scoring systems quantify tumoral burden before CRS and may predict complete cytoreduction (CC). This study reviews the intraoperative scoring systems for predicting CC and optimal cytoreduction (OC) and evaluates the predictive performance of the Peritoneal Cancer Index (PCI) and Predictive Index Value (PIV).
METHODS
Systematic searches were conducted in Embase, MEDLINE, and Web of Science. Meta-analyses of extracted data were performed to compare the absolute predictive performances of PCI and PIV.
RESULTS
Thirty-eight studies (5834 patients) focusing on gynecological (n = 34; 89.5%), gastrointestinal (n = 2; 5.3%) malignancies, and on tumors of various origins (n = 2; 5.3%) were identified. Seventy-seven models assessing the predictive performance of scoring systems (54 for CC and 23 for OC) were identified with PCI (n = 39/77) and PIV (n = 16/77) being the most common. Twenty models (26.0%) reinterpreted previous scoring systems of which ten (13%) used a modified version of PIV (reclassification). Meta-analyses of models predicting CC based on PCI (n = 21) and PIV (n = 8) provided an AUC estimate of 0.83 (95% confidence interval [CI] 0.79-0.86; Q = 119.6, p = 0.0001; I = 74.1%) and 0.74 (95% CI 0.68-0.81; Q = 7.2, p = 0.41; I = 11.0%), respectively.
CONCLUSIONS
Peritoneal Cancer Index models demonstrate an excellent estimate of CC, while PIV shows an acceptable performance. There is a need for high-quality studies to address management differences, establish standardized cutoff values, and focus on non-gynecological malignancies.
PubMed: 38918326
DOI: 10.1245/s10434-024-15629-7 -
Frontiers in Medicine 2024The rapid spread of COVID-19 pandemic across the world has not only disturbed the global economy but also raised the demand for accurate disease detection models....
The rapid spread of COVID-19 pandemic across the world has not only disturbed the global economy but also raised the demand for accurate disease detection models. Although many studies have proposed effective solutions for the early detection and prediction of COVID-19 with Machine Learning (ML) and Deep learning (DL) based techniques, but these models remain vulnerable to data privacy and security breaches. To overcome the challenges of existing systems, we introduced Adaptive Differential Privacy-based Federated Learning (DPFL) model for predicting COVID-19 disease from chest X-ray images which introduces an innovative adaptive mechanism that dynamically adjusts privacy levels based on real-time data sensitivity analysis, improving the practical applicability of Federated Learning (FL) in diverse healthcare environments. We compared and analyzed the performance of this distributed learning model with a traditional centralized model. Moreover, we enhance the model by integrating a FL approach with an early stopping mechanism to achieve efficient COVID-19 prediction with minimal communication overhead. To ensure privacy without compromising model utility and accuracy, we evaluated the proposed model under various noise scales. Finally, we discussed strategies for increasing the model's accuracy while maintaining robustness as well as privacy.
PubMed: 38912338
DOI: 10.3389/fmed.2024.1409314 -
International Journal of Nanomedicine 2024Due to their ability to replicate the in vivo microenvironment through cell interaction and induce cells to stimulate cell function, three-dimensional cell culture... (Review)
Review
Due to their ability to replicate the in vivo microenvironment through cell interaction and induce cells to stimulate cell function, three-dimensional cell culture models can overcome the limitations of two-dimensional models. Organoids are 3D models that demonstrate the ability to replicate the natural structure of an organ. In most organoid tissue cultures, matrigel made of a mouse tumor extracellular matrix protein mixture is an essential ingredient. However, its tumor-derived origin, batch-to-batch variation, high cost, and safety concerns have limited the usefulness of organoid drug development and regenerative medicine. Its clinical application has also been hindered by the fact that organoid generation is dependent on the use of poorly defined matrices. Therefore, matrix optimization is a crucial step in developing organoid culture that introduces alternatives as different materials. Recently, a variety of substitute materials has reportedly replaced matrigel. The purpose of this study is to review the significance of the latest advances in materials for cell culture applications and how they enhance build network systems by generating proper cell behavior. Excellence in cell behavior is evaluated from their cell characteristics, cell proliferation, cell differentiation, and even gene expression. As a result, graphene oxide as a matrix optimization demonstrated high potency in developing organoid models. Graphene oxide can promote good cell behavior and is well known for having good biocompatibility. Hence, advances in matrix optimization of graphene oxide provide opportunities for the future development of advanced organoid models.
Topics: Organoids; Animals; Graphite; Humans; Cell Proliferation; Cell Differentiation; Drug Combinations; Cell Culture Techniques; Cell Culture Techniques, Three Dimensional; Mice; Laminin; Collagen; Proteoglycans
PubMed: 38911499
DOI: 10.2147/IJN.S455940 -
Nutrition Reviews Jun 2024Hyperglycemia and hyperlipidemia increase the risk for diabetes and its complications, atherosclerosis, heart failure, and stroke. Identification of safe and...
CONTEXT
Hyperglycemia and hyperlipidemia increase the risk for diabetes and its complications, atherosclerosis, heart failure, and stroke. Identification of safe and cost-effective means to reduce risk factors is needed. Herbal teas may be a vehicle to deliver antioxidants and polyphenols for prevention of complications.
OBJECTIVE
This systematic review and meta-analysis were conducted to evaluate and summarize the impact of herbal tea (non-Camellia sinensis) on glucose homeostasis and serum lipids in individuals with type 2 diabetes (T2D).
DATA SOURCES
PubMed, FSTA, Web of Science, CINAHL, MEDLINE, and Cochrane Library databases were searched from inception through February 2023 using relevant keyword proxy terms for diabetes, serum lipids, and "non-Camellia sinensis" or "tea."
DATA EXTRACTION
Data from 14 randomized controlled trials, totaling 551 participants, were included in the meta-analysis of glycemic and serum lipid profile end points.
RESULTS
Meta-analysis suggested a significant association between drinking herbal tea (prepared with 2-20 g d-1 plant ingredients) and reduction in fasting blood glucose (FBG) (P = .0034) and glycated hemoglobin (HbA1c; P = .045). In subgroup analysis based on studies using water or placebo as the control, significant reductions were found in serum total cholesterol (TC; P = .024), low-density lipoprotein cholesterol (LDL-C; P = .037), and triglyceride (TG; P = .043) levels with a medium effect size. Meta-regression analysis suggested that study characteristics, including the ratio of male participants, trial duration, and region, were significant sources of FBG and HbA1c effect size heterogeneity; type of control intervention was a significant source of TC and LDL-C effect size heterogeneity.
CONCLUSIONS
Herbal tea consumption significantly affected glycemic profiles in individuals with T2D, lowering FBG levels and HbA1c. Significance was seen in improved lipid profiles (TC, TG, and LDL-C levels) through herbal tea treatments when water or placebo was the control. This suggests water or placebo may be a more suitable control when examining antidiabetic properties of beverages. Additional research is needed to corroborate these findings, given the limited number of studies.
PubMed: 38894639
DOI: 10.1093/nutrit/nuae068