Tag: statistics

Int Rev Psychiatry. 2023 Nov-Dec;35(7-8):591-604. doi: 10.1080/09540261.2023.2268720. Epub 2023 Oct 20.

ABSTRACT

There is clear evidence that university students are experiencing significant mental health difficulties, further exacerbated by the temporary closure of university campuses during the height of the COVID-19 pandemic. Against this backdrop, our study – Student Wellbeing and Experiential Learning Spaces (SWELS) – explored the role of experiential learning spaces in supporting student wellbeing. We adopted a mixed-methods approach, consisting of an online survey and interviews with students from three research intensive UK Universities. The survey results revealed that compared to the national average of 16-25-year-olds from the UK Office for National Statistics’ (ONS) wellbeing questionnaire, the sampled students exhibited significantly lower levels of life satisfaction, happiness, perceived worthwhileness and higher levels of anxiety. The qualitative results further confirmed that students perceived their wellbeing to be affected by their university experience and the COVID pandemic. However, the results also suggest that experiential learning spaces (such as museums, collections, libraries, and gardens) hold strong potential to support student mental health. Accordingly, the study indicates that diversifying module content and conscientiously considering both physical and digital learning spaces can positively impact students. In short, curricula that are cognisant of the physical learning environment and embed a focus on wellbeing into their content might help to bolster student wellbeing.

PMID:38461379 | DOI:10.1080/09540261.2023.2268720

Knee Surg Sports Traumatol Arthrosc. 2024 Mar 9. doi: 10.1002/ksa.12135. Online ahead of print.

ABSTRACT

PURPOSE: To investigate the landing strategies used after discontinuing and continuing the use of a functional knee brace (FKB) while performing a drop jump.

METHODS: Following published methodology and power analysis, 23 uninjured male athletes, mean age of 19.4 ± 3.0 years, performed seven tests, during three test conditions (nonbraced, braced and removed brace or continued brace use), over 6 days of 12 testing sessions (S) for a total of 38.5 h. Each subject was provided with a custom-fitted FKB. This study focuses on the single leg drop jump kinetics during S12 when subjects were randomly selected to remove the FKB after 17.5 h or continued use of FKB. The time to peak vertical ground reaction forces (PVGRF) and PVGRF were recorded on landing in eight trials.

RESULTS: After brace removal, a significantly shorter mean time to PVGRF was recorded (9.4 ± 22.9 msec (3.9%), p = 0.005, 95% confidence interval (95% CI): -168.1, 36.1), while continued brace use required a nonsignificant (n.s.) longer mean duration to achieve PVGRF (19.4 ± 53.6 msec (8.9%), n.s., 95% CI: -49.7, 73.4). No significant mean PVGRF difference was found in brace removal (25.3 ± 65.8 N) and continued brace use (25.1 ± 23.0 N).

CONCLUSION: Removal of FKB after 17.5 h of use led to a significantly shorter time to achieve PVGRF, while continued brace use for 21 h required a longer duration to achieve PVGRF, suggesting faster and slower knee joint loading, respectively. Understanding the concerns associated with the use of FKB and the kinetics of the knee joint will assist clinicians in counselling athletes about the risks and benefits of using an FKB.

LEVEL OF EVIDENCE: Level II.

PMID:38461373 | DOI:10.1002/ksa.12135

Int J Environ Health Res. 2024 Mar 9:1-13. doi: 10.1080/09603123.2024.2325629. Online ahead of print.

ABSTRACT

Satellite-based exposure of fine particulate matters has been seldom used as a predictor of mortality. PM_2.5 was predicted using Aerosol Optical Depths (AOD) through a two-stage regression model. The predicted PM_2.5 was corrected for the bias using two approaches. We estimated the impact by two different scenarios of PM_2.5 in the model. We statistically found different distributions of the predicted PM_2.5 over the region. Compared to the reference value (5 µg/m³), 90th and 95th percentiles had significant adverse effect on total mortality (RR _{90th percentile}:1.45; CI 95%: 1.08-1.95 and RR _{95th percentile}:1.53; CI 95%: 1.11-2.1). Nearly 1050 deaths were attributed to any range of the air pollution (unhealthy range), of which more than half were attributed to high concentration range. Given the adverse effect of extreme values compared to the both scenarios, more efforts are suggested to define local-specific reference values and preventive strategies.

PMID:38461371 | DOI:10.1080/09603123.2024.2325629

Sci Rep. 2024 Mar 9;14(1):5790. doi: 10.1038/s41598-024-56397-4.

ABSTRACT

Resolving the role of galactic processes in Solar System/Earth events necessitates a robust temporal model. However, astrophysical theory diverges with models varying from long-lasting spiral density waves with uniform pattern speeds and arm structures to others with fleeting and unpredictable features. Here, we address those issues with (1) an analysis of patterns of impact periodicity over periods of 10 to 250 million years (Myr) using circular statistics and (2), an independent logarithmic spiral arm model fitted to arm tangents of 870 micron dust. Comparison of the impact periodicity results with the best-fit spiral arm model suggests a galactic period of 660 Myr, i.e. 165 Myr to pass from one arm to the next in a four spiral arm model, with the most recent arm passage around 52 million years ago (Ma). The oldest impact ages imply that the emerging galactic chronology model is robust for at least the last 2 Gyr. The arm-passing time is consistent with spectral analyses of zircons across 3 Gyrs. Overall, the model provides a temporal framework against which to test hypotheses of galactic mechanisms for global events such as mass extinctions and superchrons.

PMID:38461319 | DOI:10.1038/s41598-024-56397-4

J Transl Med. 2024 Mar 9;22(1):258. doi: 10.1186/s12967-024-05053-6.

ABSTRACT

BACKGROUND: The term eGene has been applied to define a gene whose expression level is affected by at least one independent expression quantitative trait locus (eQTL). It is both theoretically and empirically important to identify eQTLs and eGenes in genomic studies. However, standard eGene detection methods generally focus on individual cis-variants and cannot efficiently leverage useful knowledge acquired from auxiliary samples into target studies.

METHODS: We propose a multilocus-based eGene identification method called TLegene by integrating shared genetic similarity information available from auxiliary studies under the statistical framework of transfer learning. We apply TLegene to eGene identification in ten TCGA cancers which have an explicit relevant tissue in the GTEx project, and learn genetic effect of variant in TCGA from GTEx. We also adopt TLegene to the Geuvadis project to evaluate its usefulness in non-cancer studies.

RESULTS: We observed substantial genetic effect correlation of cis-variants between TCGA and GTEx for a larger number of genes. Furthermore, consistent with the results of our simulations, we found that TLegene was more powerful than existing methods and thus identified 169 distinct candidate eGenes, which was much larger than the approach that did not consider knowledge transfer across target and auxiliary studies. Previous studies and functional enrichment analyses provided empirical evidence supporting the associations of discovered eGenes, and it also showed evidence of allelic heterogeneity of gene expression. Furthermore, TLegene identified more eGenes in Geuvadis and revealed that these eGenes were mainly enriched in cells EBV transformed lymphocytes tissue.

CONCLUSION: Overall, TLegene represents a flexible and powerful statistical method for eGene identification through transfer learning of genetic similarity shared across auxiliary and target studies.

PMID:38461317 | DOI:10.1186/s12967-024-05053-6

BMC Bioinformatics. 2024 Mar 9;25(1):105. doi: 10.1186/s12859-024-05723-8.

ABSTRACT

MOTIVATION: The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the characteristics of the drug itself and the genomic characteristics of the patient can greatly influence the results of cancer drug response. Therefore, accurate, efficient, and comprehensive methods for drug feature extraction and genomics integration are crucial to improve the prediction accuracy.

RESULTS: Accurate prediction of cancer drug response is vital for guiding the design of anticancer drugs. In this study, we propose an end-to-end deep learning model named DeepAEG which is based on a complete-graph update mode to predict IC50. Specifically, we integrate an edge update mechanism on the basis of a hybrid graph convolutional network to comprehensively learn the potential high-dimensional representation of topological structures in drugs, including atomic characteristics and chemical bond information. Additionally, we present a novel approach for enhancing simplified molecular input line entry specification data by employing sequence recombination to eliminate the defect of single sequence representation of drug molecules. Our extensive experiments show that DeepAEG outperforms other existing methods across multiple evaluation parameters in multiple test sets. Furthermore, we identify several potential anticancer agents, including bortezomib, which has proven to be an effective clinical treatment option. Our results highlight the potential value of DeepAEG in guiding the design of specific cancer treatment regimens.

PMID:38461284 | DOI:10.1186/s12859-024-05723-8

BMC Oral Health. 2024 Mar 9;24(1):319. doi: 10.1186/s12903-024-04064-5.

ABSTRACT

BACKGROUND: In the regenerative endodontic procedures, scaffolds could influence the prognosis of affected teeth. Currently, there is controversy regarding the postoperative evaluation of various scaffolds for pulp regeneration. The objective of this study was to access whether other scaffolds, used alone or in combination with blood clot (BC), are more effective than BC in regenerative endodontic procedures.

METHODS: We systematically search the PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), Embase, and Google Scholar databases. Randomized controlled trials examining the use of BC and other scaffold materials in the regenerative endodontic procedures were included. A random effects model was used for the meta-analysis. The GRADE method was used to determine the quality of the evidence.

RESULTS: We screened 168 RCTs related to young permanent tooth pulp necrosis through electronic and manual retrieval. A total of 28 RCTs were related to regenerative endodontic procedures. Ultimately, 12 articles met the inclusion criteria and were included in the relevant meta-analysis. Only 2 studies were assessed to have a low risk of bias. High quality evidence indicated that there was no statistically significant difference in the success rate between the two groups (RR=0.99, 95% CI=0.96 to 1.03; 434 participants, 12 studies); low-quality evidence indicated that there was no statistically significant difference in the increase in root length or root canal wall thickness between the two groups. Medium quality evidence indicated that there was no statistically significant difference in pulp vitality testing between the two groups.

CONCLUSIONS: For clinical regenerative endodontic procedures, the most commonly used scaffolds include BC, PRP, and PRF. All the different scaffolds had fairly high clinical success rates, and the difference was not significant. For regenerative endodontic procedures involving young permanent teeth with pulp necrosis, clinical practitioners could choose a reasonable scaffold considering the conditions of the equipment and patients.

PMID:38461281 | DOI:10.1186/s12903-024-04064-5

Alzheimers Res Ther. 2024 Mar 9;16(1):54. doi: 10.1186/s13195-024-01421-y.

ABSTRACT

BACKGROUND: Due to the heterogeneity among patients with Mild Cognitive Impairment (MCI), it is critical to predict their risk of converting to Alzheimer’s disease (AD) early using routinely collected real-world data such as the electronic health record data or administrative claim data.

METHODS: The study used MarketScan Multi-State Medicaid data to construct a cohort of MCI patients. Logistic regression with tree-guided lasso regularization (TGL) was proposed to select important features and predict the risk of converting to AD. A subsampling-based technique was used to extract robust groups of predictive features. Predictive models including logistic regression, generalized random forest, and artificial neural network were trained using the extracted features.

RESULTS: The proposed TGL workflow selected feature groups that were robust, highly interpretable, and consistent with existing literature. The predictive models using TGL selected features demonstrated higher prediction accuracy than the models using all features or features selected using other methods.

CONCLUSIONS: The identified feature groups provide insights into the progression from MCI to AD and can potentially improve risk prediction in clinical practice and trial recruitment.

PMID:38461266 | DOI:10.1186/s13195-024-01421-y

BMC Oral Health. 2024 Mar 9;24(1):313. doi: 10.1186/s12903-024-04012-3.

ABSTRACT

OBJECTIVES: To predict and identify the key demographic and clinical exposure factors associated with dental anxiety among young adults, and to compare if the traditional statistical modelling approach provides similar results to the machine learning (ML) approach in predicting factors for dental anxiety.

METHODS: A cross-sectional study of Western Illinois University students. Three survey instruments (sociodemographic questionnaire, modified dental anxiety scale (MDAS), and dental concerns assessment tool (DCA)) were distributed via email to the students using survey monkey. The dependent variable was the mean MDAS scores, while the independent variables were the sociodemographic and dental concern assessment variables. Multivariable analysis was done by comparing the classical statistical model and the machine learning model. The classical statistical modelling technique was conducted using the multiple linear regression analysis and the final model was selected based on Akaike information Criteria (AIC) using the backward stepwise technique while the machine learining modelling was performed by comparing two ML models: LASSO regression and extreme gradient boosting machine (XGBOOST) under 5-fold cross-validation using the resampling technique. All statistical analyses were performed using R version 4.1.3.

RESULTS: The mean MDAS was 13.73 ± 5.51. After careful consideration of all possible fitted models and their interaction terms the classical statistical approach yielded a parsimonious model with 13 predictor variables with Akaike Information Criteria (AIC) of 2376.4. For the ML approach, the Lasso regression model was the best-performing model with a mean RMSE of 0.617, R² of 0.615, and MAE of 0.483. Comparing the variable selection of ML versus the classical statistical model, both model types identified 12 similar variables (out of 13) as the most important predictors of dental anxiety in this study population.

CONCLUSION: There is a high burden of dental anxiety within this study population. This study contributes to reducing the knowledge gap about the impact of clinical exposure variables on dental anxiety and the role of machine learningin the prediction of dental anxiety. The predictor variables identified can be used to inform public health interventions that are geared towards eliminating the individual clinical exposure triggers of dental anxiety are recommended.

PMID:38461263 | DOI:10.1186/s12903-024-04012-3

BMC Public Health. 2024 Mar 9;24(1):750. doi: 10.1186/s12889-024-18254-x.

ABSTRACT

BACKGROUND: Women in their reproductive age have tremendous health implications that affect their health and well-being. Anaemia is an indicator of inadequate dietary intake and poor health. Maternal malnutrition significantly impacts maternal and child health outcomes, increasing the mother’s risk of dying during delivery. High-risk fertility behaviour is a barrier to reducing mother and child mortality. This study aims to examine the level of high-risk fertility behaviour and anaemia among ever-married urban Indian women and also examine the linkages between the both.

METHODS: Based on the National Family Health Survey’s fifth round of data, the study analyzed 44,225 samples of ever-married urban women. Univariate and bivariate analysis and binary logistic regression have been used for the analysis.

RESULTS: Findings suggested that more than half (55%) of the urban women were anaemic, and about one-fourth (24%) of women had any high-risk fertility behaviour. Furthermore, the results suggest that 20% of women were more vulnerable to anaemia due to high-risk fertility behaviour. For the specific category, 19% and 28% of women were more likely to be anaemic due to single and multiple high-risk fertility. However, after controlling for sociodemographic factors, the findings showed a statistically significant link between high-risk fertility behaviour and anaemia. As a result, 16% of the women were more likely to be anaemic due to high-risk fertility behaviour, and 16% and 24% were more likely to be anaemic due to single and multiple high-risk fertility behaviour, respectively.

CONCLUSIONS: The findings exposed that maternal high-risk fertility behaviour is a significant factor in raising the chance of anaemia in ever-married urban women of reproductive age in forms of the short birth interval, advanced maternal age, and advanced maternal age & higher order. Policy and choice-based family planning techniques should be employed to minimize the high-risk fertility behaviour among Indian urban women. This might aid in the reduction of the malnutrition status of their children.

PMID:38461259 | DOI:10.1186/s12889-024-18254-x