Tag: nevin manimala

Sci Rep. 2023 Nov 28;13(1):20917. doi: 10.1038/s41598-023-47899-8.

ABSTRACT

Kazakhstan is experiencing a high burden of cardiovascular disease (CVD), and the country has implemented a range of strategies aimed at controlling CVD. The study aims to conduct a content analysis of the policies implemented in the country and augment it with an analysis of official statistics over a 15-year period, from 2006 to 2020. The study also includes comparisons of incidence rates between urban and rural areas. A comprehensive search was conducted to identify policy documents that regulate the provision of primary, secondary, and tertiary prevention of cardiovascular diseases. Additionally, official data on the incidence of arterial hypertension, ischemic heart disease, acute myocardial infarction, and cerebrovascular disease were extracted from official statistics, disaggregated by urban and rural areas. Forecast modeling was utilized to project disease incidences up to 2030. The study reveals that Kazakhstan primarily focuses on tertiary prevention of cardiovascular diseases, with less attention given to secondary prevention, and primary prevention is virtually non-existent. In general, screening for arterial hypertension appears to be more successful than for ischemic heart disease. The incidence of arterial hypertension has increased threefold for urban residents and 1.7-fold for rural residents. In urban areas, residents saw a twofold increase in ischemic heart disease incidence, while it remained the same in rural areas. The findings of this study have practical implications for decision-makers, who can use the results to enhance the effectiveness of existing CVD prevention strategies.

PMID:38017260 | DOI:10.1038/s41598-023-47899-8

Forensic Sci Med Pathol. 2023 Nov 29. doi: 10.1007/s12024-023-00754-8. Online ahead of print.

ABSTRACT

To research the effect of the COVID-19 pandemic on mental health, the prevalence and characteristics of all completed suicides in the city of Frankfurt am Main were compared for a 10-month period before the pandemic (March 2019-December 2019) with one during the early pandemic (March 2020-December 2020). Medicolegal data collected in the context of the FraPPE suicide prevention project were evaluated using descriptive statistical methods. In total, there were 81 suicides during the early pandemic period, as opposed to 86 in the pre-pandemic period. Though statistically not significant, the proportion of male suicides (73%) was higher during the early pandemic period than before (63%). The age-at-death was comparable in the pre-pandemic and pandemic periods (average, 54.8 vs. 53.1 years). Between these two periods, there was no difference in respect to the three most commonly used suicide methods by men: fall from a height (26% vs. 22%), intoxication, and strangulation (each 24% vs. 19%). For women, there was, however, a shift in methods from strangulation (38%), intoxication (28%), and fall from a height (19%) to fall from a height (50%), strangulation (18%), intoxication, and collision with a rail vehicle (14% each). There was a trend towards more suicides among non-German nationals during the early pandemic (suicide rate/100,000 inhabitants: German, 14.3 vs. 11.5; non-German, 4.4 vs. 8.8). Before the pandemic, 54% of the suicides were known to have a mental illness in contrast to 44% during the early pandemic. Overall, no increase in completed suicides could be observed in Frankfurt am Main during the early pandemic.

PMID:38017259 | DOI:10.1007/s12024-023-00754-8

J Genet Eng Biotechnol. 2023 Nov 29;21(1):144. doi: 10.1186/s43141-023-00597-4.

ABSTRACT

BACKGROUND: Factorial design is a simple, yet elegant method to investigate the effect of multiple factors and their interaction on a specific response simultaneously. Hence, this type of study design reaches the best optimization conditions of a process. Although the interaction between the variables is widely prevalent in cell culture procedures, factorial design per se is infrequently utilized in improving cell culture output. Therefore, we aim to optimize the experimental conditions for generating mature bone marrow-derived dendritic cells (BMDCs). Two different variables were investigated, including the concentrations of the inducing factors and the starting density of the bone marrow mononuclear cells. In the current study, we utilized the design of experiments (DoE), a statistical approach, to systematically assess the impact of factors with varying levels on cell culture outcomes. Herein, we apply a two-factor, two-level (2²) factorial experiment resulting in four conditions that are run in triplicate. The two variables investigated here are cytokines combinations with two levels, granulocyte-macrophage colony-stimulating factor (GM-CSF) alone or with interleukin-4 (IL4). The other parameter is cell density with two different concentrations, 2 × 10⁶ and 4 × 10⁶ cells/mL. Then, we measured cell viability using the trypan blue exclusion method, and a flow cytometer was used to detect the BMDCs expressing the markers FITC-CD80, CD86, CD83, and CD14. BMDC marker expression levels were calculated using arbitrary units (AU) of the mean fluorescence intensity (MFI).

RESULTS: The current study showed that the highest total viable cells and cells yield obtained were in cell group seeded at 2 × 10⁶ cells/mL and treated with GM-CSF and IL-4. Importantly, the expression of the co-stimulatory molecules CD83 and CD80/CD86 were statistically significant for cell density of 2 × 10⁶ cells/mL (P < 0.01, two-way ANOVA). Bone marrow mononuclear cells seeded at 4 × 10⁶ in the presence of the cytokine mix less efficiently differentiated and matured into BMDCs. Statistical analysis via two-way ANOVA revealed an interaction between cell density and cytokine combinations.

CONCLUSION: The analysis of this study indicates a substantial interaction between cytokines combinations and cell densities on BMDC maturation. However, higher cell density is not associated with optimizing DC maturation. Notably, applying DoE in bioprocess designs increases experimental efficacy and reliability while minimizing experiments, time, and process costs.

PMID:38017248 | DOI:10.1186/s43141-023-00597-4

Sci Rep. 2023 Nov 28;13(1):20957. doi: 10.1038/s41598-023-48286-z.

ABSTRACT

Chronic anal fissure is one of the most common benign anorectal health conditions, causing significant morbidity, quality of life, and economic loss. Eight randomized controlled trials with a total population size of 1035 were eligible for analysis. Seven studies included both males and female, while one only included females. The majority of randomized controlled trials involved female dominance [54.9% (43.5-66.3)] and posterior midline location [86.1% (95% CI 81.5-90.8%)]. This meta-analysis of randomised control trials found that overall postoperative healing was 90.2%, recurrent anal fissure was 3.7%, and postoperative incontinence was 8.9% after LIS. Even though there was no statistically significant difference, closed lateral internal sphincterotomy (LIS) had higher rates of recurrent anal fissure (RR = 1.73 (95% CI 0.86-3.47, p = 0.90, I2 = 0%) and lower rates of postoperative incontinence rate (RR = 0.60 (95% CI 0.37-0.96, p = 0.76, I2-0) as compared with open LIS. We recommended that closed lateral internal sphincterotomy (LIS) is a safe and effective surgical treatment option for chronic anal fissures.

PMID:38017243 | DOI:10.1038/s41598-023-48286-z

Environ Sci Pollut Res Int. 2023 Nov 29. doi: 10.1007/s11356-023-31110-6. Online ahead of print.

ABSTRACT

Country risk, encompassing political, economic, and financial dimensions, represents a burgeoning area of research in contemporary academia. However, its relation with energy technology remains relatively unexplored. Unlike previous studies, the current study enhances the extant literature by investigating the influence of political, economic, and financial risk factors, in addition to GDP, on energy technology advancements within the context of China from 1990 to 2021. The authors employ time series data and select the most suitable econometric techniques for analyzing long-term relationships, such as quantile regression. This approach allows them to track the evolution of these variables, thereby offering valuable empirical insights. The study’s main findings are as follows: The Johansen cointegration tests confirm the existence of a long-run relationship among the variables under consideration. Furthermore, the quantile regression shows that political and economic risks reduce energy technology. In contrast, other variables, such as financial risk and GDP contribute positively to developing energy technology within the Chinese economy. These findings offer valuable insights for policymakers emphasizing the need to mitigate political and economic risks to facilitate future investment in energy technology.

PMID:38017219 | DOI:10.1007/s11356-023-31110-6

Behav Res Methods. 2023 Nov 28. doi: 10.3758/s13428-023-02287-y. Online ahead of print.

ABSTRACT

With mixed-effects regression models becoming a mainstream tool for every psycholinguist, there has become an increasing need to understand them more fully. In the last decade, most work on mixed-effects models in psycholinguistics has focused on properly specifying the random-effects structure to minimize error in evaluating the statistical significance of fixed-effects predictors. The present study examines a potential misspecification of random effects that has not been discussed in psycholinguistics: violation of the single-subject-population assumption, in the context of logistic regression. Estimated random-effects distributions in real studies often appear to be bi- or multimodal. However, there is no established way to estimate whether a random-effects distribution corresponds to more than one underlying population, especially in the more common case of a multivariate distribution of random effects. We show that violations of the single-subject-population assumption can usually be detected by assessing the (multivariate) normality of the inferred random-effects structure, unless the data show quasi-separability, i.e., many subjects or items show near-categorical behavior. In the absence of quasi-separability, several clustering methods are successful in determining which group each participant belongs to. The BIC difference between a two-cluster and a one-cluster solution can be used to determine that subjects (or items) do not come from a single population. This then allows the researcher to define and justify a new post hoc variable specifying the groups to which participants or items belong, which can be incorporated into regression analysis.

PMID:38017204 | DOI:10.3758/s13428-023-02287-y

Behav Res Methods. 2023 Nov 28. doi: 10.3758/s13428-023-02273-4. Online ahead of print.

ABSTRACT

Attentional engagement is known to vary on a moment-to-moment basis. However, few self-report methods can effectively capture dynamic fluctuations in attentional engagement over time. In the current paper, we evaluated the utility of stimulated recall, a method wherein individuals are asked to remember their subjective states while using a mnemonic cue, for the measurement of temporal changes in attentional engagement. Participants were asked to watch a video lecture, during which we assessed their in-the-moment levels of attentional engagement using intermittent thought probes. Then, we used stimulated recall by cueing participants with short video clips from the lecture to retrospectively assess the levels of attentional engagement they had experienced when they first watched those clips within the lecture. Experiment 1 assessed the statistical overlap between in-the-moment and video-stimulated ratings. Experiment 2 assessed the generalizability of video-stimulated recall across different types of lectures. Experiment 3 assessed the impact of presenting video-stimulated probe clips in non-chronological order. Experiment 4 assessed the effect of video-stimulated recall on its own. Across all experiments, we found statistically robust correspondence between in-the-moment and video-stimulated ratings of attentional engagement, illustrating a strong convergence between these two methods of assessment. Taken together, our findings indicate that stimulated recall provides a new and practical methodological approach that can accurately capture dynamic fluctuations in subjective attentional states over time.

PMID:38017200 | DOI:10.3758/s13428-023-02273-4

NPJ Digit Med. 2023 Nov 28;6(1):223. doi: 10.1038/s41746-023-00967-9.

ABSTRACT

It is uncommon for risk groups defined by statistical or artificial intelligence (AI) models to be chosen by jointly considering model performance and potential interventions available. We develop a framework to rapidly guide choice of risk groups in this manner, and apply it to guide breast cancer screening intervals using an AI model. Linear programming is used to define risk groups that minimize expected advanced cancer incidence subject to resource constraints. In the application risk stratification performance is estimated from a case-control study (2044 cases, 1:1 matching), and other parameters are taken from screening trials and the screening programme in England. Under the model, re-screening in 1 year for the highest 4% AI model risk, in 3 years for the middle 64%, and in 4 years for 32% of the population at lowest risk, was expected to reduce the number of advanced cancers diagnosed by approximately 18 advanced cancers per 1000 diagnosed with triennial screening, for the same average number of screens in the population as triennial screening for all. Sensitivity analyses found the choice of thresholds was robust to model parameters, but the estimated reduction in advanced cancers was not precise and requires further evaluation. Our framework helps define thresholds with the greatest chance of success for reducing the population health burden of cancer when used in risk-adapted screening, which should be further evaluated such as in health-economic modelling based on computer simulation models, and real-world evaluations.

PMID:38017184 | DOI:10.1038/s41746-023-00967-9

Metabolomics. 2023 Nov 28;20(1):1. doi: 10.1007/s11306-023-02066-y.

ABSTRACT

AIMS: To identify metabolite and lipid biomarkers of diabetes in the Indian subpopulation in newly diagnosed diabetic and long-term diabetic individuals. To utilize the global polar metabolomic and lipidomic profiles to predict the susceptibility of an individual to diabetes using machine learning algorithms.

MATERIALS AND METHODS: 87 individuals, including healthy, newly diabetic, and long-term diabetics on medication, were included in the study. Post consent, their serum was used to isolate polar metabolome and lipidome. NMR and LCMS were used to identify the polar metabolites and lipids, respectively. Statistical analysis was done to determine significantly altered molecules. NMR and LCMS comprehensive data were utilized to generate diabetic models using machine learning algorithms. 10 more individuals (pre-diabetic) were recruited, and their polar metabolomic and lipidomic profiles were generated. Pre-diabetic metabolic profiles were then utilized to predict the diabetic status of the metabolome and lipidome beyond glucose levels.

RESULTS: Mannose, Betaine, Xanthine, Triglyceride (38:1), Sphingomyelin (d63:7), and Phosphatidic acid (37:2) are some of the top key biomarkers of diabetes. The predictive model generated showed the receiver operating characteristic area under the curve (ROC-AUC) as 1 on both test and validation data indicating excellent accuracy. This model then predicted the diabetic closeness of the metabolism of pre-diabetic individuals based on probability scores.

CONCLUSION: Polar metabolic and lipid profile of diabetic individuals is very different from that of healthy individuals. Lipid profile alters before the polar metabolic profile in diabetes-susceptible individuals. Without regard to glucose, the diabetic closeness of the metabolism of any individual can be determined.

PMID:38017183 | DOI:10.1007/s11306-023-02066-y

Sci Rep. 2023 Nov 28;13(1):20991. doi: 10.1038/s41598-023-48378-w.

ABSTRACT

The aim of this research was to determine the impact of the architecture of digital technologies in university teacher training as a catalyst for change and educational innovation. Methodologically, it is framed in a quantitative approach with a pre-experimental design and an explanatory level, with a sample of 269 teachers out of a total population of 450 at the university. The results show a significant impact of the architecture of digital technologies as a catalyst for change in university teacher training, with an increase of 22.88% from pre-test to post-test. The statistical significance of the results is supported by a P-value of 0.000, which means that it is less than the established significance level (α = 0.05), leading to the acceptance of hypothesis H1: The architecture of digital technologies as a catalyst for change in university teacher education has an impact. In conclusion, the architecture of digital technologies, consisting of the two academic pillar systems: the academic management system and the virtual learning system, contribute to the development of digital skills in key areas such as digital literacy, communication and collaboration, digital content creation and problem solving in virtual environments.

PMID:38017148 | DOI:10.1038/s41598-023-48378-w