Nevin Manimala

Sci Rep. 2023 May 13;13(1):7779. doi: 10.1038/s41598-023-34906-1.

ABSTRACT

Ethiopia has been challenged by the growing magnitude of diabetes in general and type-2 diabetes in particular. Knowledge extraction from stored dataset can be an important base for better decision on diabetes rapid diagnosis, suggestive on prediction for early intervention. Thus, this study was addressed these problem by application of supervised machine learning algorithms for classification and prediction of type 2 diabetes disease status and might provide context-specific information to program planners and policy makers so that, priority will be given to the more affected groups. To apply supervised machine learning algorithms; compare these algorithms and select the best algorithm based on their performance for classification and prediction of type-2 diabetic disease status (positive or negative) in public hospitals of Afar regional state, Northeastern Ethiopia. This study was conducted at Afar regional state from February to June of 2021. Decision tree; pruned J 48, Artificial neural network, K-nearest neighbor, Support vector machine, Binary logistic regression, Random forest, and Naïve Bayes supervised machine learning algorithms were applied using secondary data from the medical database record review. A total of 2239 sample Dataset diagnosed for diabetes from 2012 to April 22/2020 (1523 with type-2 diabetes and 716 without type-2 diabetes) was checked for its completeness prior to analysis. For all algorithms, WEKA3.7 tool was used for analysis purposes. Moreover, all algorithms were compared based on their correctly classification rate, kappa statistics, confusion matrix, area under the curve, sensitivity, and specificity. From the seven major supervised machine learning algorithms, the best classification and prediction results were obtained from random forest [correctly classified rate (93.8%), kappa statistics (0.85), sensitivity (0.98), area under the curve (0.97) and confusion matrix (out of 454 actual positive prediction for 446)] which was followed by decision tree pruned J 48 [correctly classified rate (91.8%), kappa statistics (0.80), sensitivity (0.96), area under the curve (0.91) and confusion matrices (out of 454 actual positive prediction for 438)] and k-nearest neighbor [correctly classified rate (89.8%), kappa statistics (0.76), sensitivity (0.92), area under the curve (0.88) and confusion matrices (out of 454 actual positive prediction for 421)]. Random forest, Decision tree pruned J48 and k-nearest neighbor algorithms have better classification and prediction performance for classifying and predicting type-2 diabetes disease status. Therefore, based on this performance, random forest algorithm can be judged as suggestive and supportive for clinicians at the time of type-2 diabetes diagnosis.

PMID:37179444 | DOI:10.1038/s41598-023-34906-1

Br J Cancer. 2023 May 13. doi: 10.1038/s41416-023-02290-2. Online ahead of print.

ABSTRACT

BACKGROUND: We sought to assess the influences of sleep duration, sleep adequacy, and daytime sleepiness on survival outcomes among Stage III colon cancer patients.

METHODS: We conducted a prospective observational study of 1175 Stage III colon cancer patients enrolled in the CALGB/SWOG 80702 randomised adjuvant chemotherapy trial who completed a self-reported questionnaire on dietary and lifestyle habits 14-16 months post-randomisation. The primary endpoint was disease-free survival (DFS), and secondary was overall survival (OS). Multivariate analyses were adjusted for baseline sociodemographic, clinical, dietary and lifestyle factors.

RESULTS: Patients sleeping ≥9 h-relative to 7 h-experienced a worse hazard ratio (HR) of 1.62 (95% confidence interval (CI), 1.01-2.58) for DFS. In addition, those sleeping the least (≤5 h) or the most (≥ 9 h) experienced worse HRs for OS of 2.14 (95% CI, 1.14-4.03) and 2.34 (95% CI, 1.26-4.33), respectively. Self-reported sleep adequacy and daytime sleepiness showed no significant correlations with outcomes.

CONCLUSIONS: Among resected Stage III colon cancer patients who received uniform treatment and follow-up within a nationwide randomised clinical trial, very long and very short sleep durations were significantly associated with increased mortality. Interventions targeting optimising sleep health among indicated colon cancer patients may be an important method by which more comprehensive care can be delivered.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01150045.

PMID:37179438 | DOI:10.1038/s41416-023-02290-2

Sci Rep. 2023 May 13;13(1):7788. doi: 10.1038/s41598-023-35044-4.

ABSTRACT

The social ecological model provides a comprehensive framework for understanding the multiple-level determinants of physical activity. This study explores the significant individual, social, and environmental variables and their interactions in relation to physical activity in middle-aged and older adults in Taiwan. A cross-sectional study design was implemented. Healthy middle-aged and older adults were recruited (n = 697) through face-to-face and online surveys. The data collected comprised self-efficacy, social support, neighbourhood environment, and demographic characteristics. Hierarchical regression was used for statistical analysis. Self-rated health (B = 74.74, p < .001; B = 101.45, p = .022) and self-efficacy (B = 17.93, p < .001; B = 14.95, p = .020) were the significant individual variables in both middle-aged and older adults. Neighbourhood environment (B = 6.90, p = .015) and the interaction between self-efficacy and neighbourhood environment (B = 1.56, p = .009) were significant in middle-aged adults. Self-efficacy was the most significant predictor for all participants, with the positive correlations of neighbourhood environment arising only for middle-aged adults with high self-efficacy. Policy making or project design should consider multilevel factors in order to facilitate their physical activity.

PMID:37179430 | DOI:10.1038/s41598-023-35044-4

Sci Rep. 2023 May 13;13(1):7780. doi: 10.1038/s41598-023-34093-z.

ABSTRACT

Heart rate variability (HRV) is a simple tool to monitor cardiovascular stress. The proper function of the cardiovascular system is a problem among firefighters. Physical activity has health benefits correlated with psychological stress. Physically active people should be more resilient to psychological stress, but this has not always been demonstrated. The aim of this study was to determine whether cranial techniques would have an effect on HRV parameters. Osteopathy in the cranium reduces stress and improves cardiovascular function. Fifty-seven firefighter cadets aged 18-24 years (21.63 ± 1.41) participated in the study. All subjects had their heart rate variability measured and were randomly assigned either to the cranial techniques (CS) group, with therapy performed once a week for 5 weeks), or to the control group (CO). After 5 weeks, heart rate variability was measured again in both groups. In the Friedman test, in the CS group there was a statistically significant effect of cranial techniques on Heart Rate (HR) and Low Frequency (LF), but not on High Frequency (HF); in the CO group, a statistically significant difference was observed for HR, HF and LF. In the Nemenyi test, in the CS group there was a statistically significant difference for HR and LF and in the CO group for HR, HF and LF. After applying hierarchical clustering with Euclidean measure and the complete method, dendrograms were drawn up showing similarities for HR, HF and LF values. The cranial techniques and touch might exert a beneficial effect on HRV. Both factors can be used in stressful situations to lower HRV.

PMID:37179419 | DOI:10.1038/s41598-023-34093-z

Sci Rep. 2023 May 13;13(1):7784. doi: 10.1038/s41598-023-33664-4.

ABSTRACT

The prevalence of high birth weight or large for gestational age (LGA) infants is increasing, with increasing evidence of pregnancy-related factors that may have long-term impacts on the health of the mother and baby. We aimed to determine the association between excessive fetal growth, specifically LGA and macrosomia, and subsequent maternal cancer by performing a prospective population-based cohort study. The data set was based on the Shanghai Birth Registry and Shanghai Cancer Registry, with medical records from the Shanghai Health Information Network as a supplement. Macrosomia and LGA prevalence was higher in women who developed cancer than in women who did not. Having an LGA child in the first delivery was associated with a subsequently increased risk of maternal cancer (hazard ratio [HR] = 1.08, 95% confidence interval [CI] 1.04-1.11). Additionally, in the last and heaviest deliveries, there were similar associations between LGA births and maternal cancer rates (HR = 1.08, 95% CI 1.04-1.12; HR = 1.08, 95% CI 1.05-1.12, respectively). Furthermore, a substantially increased trend in the risk of maternal cancer was associated with birth weights exceeding 2500 g. Our study supports the association between LGA births and increased risks of maternal cancer, but this risk requires further investigation.

PMID:37179417 | DOI:10.1038/s41598-023-33664-4

J Expo Sci Environ Epidemiol. 2023 May 13. doi: 10.1038/s41370-023-00550-0. Online ahead of print.

ABSTRACT

BACKGROUND: Evidence of the acute effects of high-level air pollution on small airway function and systemic inflammation in adults is scarce.

OBJECTIVE: To examined the associations of short-term (i.e., daily) exposure to multiple air pollutants with lung function and inflammatory markers.

METHODS: We assessed short-term (daily) effects of air pollutants, including particulate matter with aerodynamic diameter less than 2.5 μm (PM_2.5) and 10 μm (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and carbon monoxide (CO), on lung function and peripheral immune cell counts over various lag times using generalized linear regression models.

RESULTS: A total of 4764 adults were included from the general community-dwelling population in Shanghai, China. Exposure to air pollutants and lung function were negatively correlated. Decline in FEF between 25% and 75% of vital capacity (FEF_25-75%) were found associated with PM_2.5, SO₂, and CO, and decline in forced expiratory volume in 3 s (FEV₃) to forced vital capacity (FVC) ratio were associated with all examined pollutants, indicating obstruction in small airways. Obstructed airflow in large and middle airways as indicated by decline in FEV₁/FVC were also associated with all pollutants. In subgroup analysis, significant negative associations between the five pollutants and SAD parameters were found only in males but not in females. The difference in the associations of SO₂ with FEF_75% between males and females achieved statistical significance. Additionally, all examined pollutants were significantly associated with lower peripheral neutrophil count.

IMPACT STATEMENT: Acute exposure to air pollutants were associated with airflow-limitation. Both small airways and proximal airways were affected. Acute exposure to air pollutants were accompanied with a lower neutrophil count.

PMID:37179406 | DOI:10.1038/s41370-023-00550-0

NPJ Prim Care Respir Med. 2023 May 13;33(1):20. doi: 10.1038/s41533-023-00342-x.

ABSTRACT

Cognitive screening has been proposed for older adults diagnosed with chronic obstructive pulmonary disease (COPD). Therefore, we examined the change over time in cognitive function and the risk of incident dementia in older adults after COPD diagnosis. A sample of 3,982 participants from the population-based cohort study Good Aging in Skåne was followed for 19 years, and 317 incident COPD cases were identified. The cognitive domains of episodic memory, executive function, and language were assessed using neuropsychological tests. Mixed models for repeated measures and a Cox model were implemented. Participants performed, on average, worse over time on all neuropsychological tests after COPD diagnosis in comparison to those without COPD, although statistical significance differences were only observed for episodic memory and language. The groups had a comparable risk of developing dementia. In conclusion, our results indicate that cognitive screening in the early stages of COPD may be of limited clinical relevance.

PMID:37179395 | DOI:10.1038/s41533-023-00342-x

BMC Chem. 2023 May 13;17(1):47. doi: 10.1186/s13065-023-00956-9.

ABSTRACT

Triamterene (TRI) and xipamide (XIP) mixture is used as a binary medication of antihypertension which is considered as a major cause of premature death worldwide. The purpose of this research is the quantitative and qualitative analysis of this binary mixture by green univariate and multivariate spectrophotometric methods. Univariate methods were zero order absorption spectra method (D⁰) and Fourier self-deconvolution (FSD), as TRI was directly determined by D⁰ at 367.0 nm in the range (2.00-10.00 µg/mL), where XIP show no interference. While XIP was determined by FSD at 261.0 nm in the range (2.00-8.00 µg/mL), where TRI show zero crossing. Multivariate methods were Partial Least Squares, Principal Component Regression, Artificial Neural Networks, and Multivariate Curve Resolution-Alternating Least Squares. A training set of 25 mixtures with different quantities of the tested components was used to construct and evaluate them, 3 latent variables were displayed using an experimental design. A set of 18 synthetic mixtures with concentrations ranging from (3.00-7.00 µg/mL) for TRI and (2.00-6.00 µg/mL) for XIP, were used to construct the calibration models. A collection of seven synthetic mixtures with various quantities was applied to build the validation models. All the proposed approaches quantitative analyses were evaluated using recoveries as a percentage, root mean square error of prediction, and standard error of prediction. Strong multivariate statistical tools were presented by these models, and they were used to analyze the combined dosage form available on the Egyptian market. The proposed techniques were evaluated in accordance with ICH recommendations, where they are capable of overcoming challenges including spectral overlaps and collinearity. When the suggested approaches and the published one were statistically compared, there was no discernible difference between them. The green analytical method index and eco-scale tools were applied for assessment of the established models greenness. The suggested techniques can be used in product testing laboratories for standard pharmaceutical analysis of the substances being studied.

PMID:37179391 | DOI:10.1186/s13065-023-00956-9

NPJ Prim Care Respir Med. 2023 May 13;33(1):21. doi: 10.1038/s41533-023-00341-y.

ABSTRACT

Despite national and international guidelines, asthma is frequently misdiagnosed, control is poor and unnecessary deaths are far too common. Large scale asthma management programme such as that undertaken in Finland, can improve asthma outcomes. A primary care asthma management quality improvement programme was developed with the support of the British Lung Foundation (now Asthma + Lung UK) and Optimum Patient Care (OPC) Limited. It was delivered and cascaded to all relevant staff at participating practices in three Clinical Commissioning Groups. The programme focussed on improving diagnostic accuracy, management of risk and control, patient self-management and overall asthma control. Patient data were extracted by OPC for the 12 months before (baseline) and after (outcome) the intervention. In the three CCGs, 68 GP practices participated in the programme. Uptake from practices was higher in the CCG that included asthma in its incentivised quality improvement programme. Asthma outcome data were successfully extracted from 64 practices caring for 673,593 patients. Primary outcome (Royal College of Physicians Three Questions [RCP3Q]) data were available in both the baseline and outcome periods for 10,328 patients in whom good asthma control (RCP3Q = 0) increased from 36.0% to 39.2% (p < 0.001) after the intervention. The odds ratio of reporting good asthma control following the intervention was 1.15 (95% CI 1.09-1.22), p < 0.0001. This asthma management programme produced modest but highly statistically significant improvements in asthma outcomes. Key lessons learnt from this small-scale implementation will enable the methodology to be improved to maximise benefit in a larger scale role out.

PMID:37179388 | DOI:10.1038/s41533-023-00341-y

NPJ Breast Cancer. 2023 May 13;9(1):38. doi: 10.1038/s41523-023-00535-0.

ABSTRACT

We assessed the predictive value of an image analysis-based tumor-infiltrating lymphocytes (TILs) score for pathologic complete response (pCR) and event-free survival in breast cancer (BC). About 113 pretreatment samples were analyzed from patients with stage IIB-IIIC HER-2-negative BC randomized to neoadjuvant chemotherapy ± bevacizumab. TILs quantification was performed on full sections using QuPath open-source software with a convolutional neural network cell classifier (CNN11). We used easTILs% as a digital metric of TILs score defined as [sum of lymphocytes area (mm²)/stromal area(mm²)] × 100. Pathologist-read stromal TILs score (sTILs%) was determined following published guidelines. Mean pretreatment easTILs% was significantly higher in cases with pCR compared to residual disease (median 36.1 vs.14.8%, p < 0.001). We observed a strong positive correlation (r = 0.606, p < 0.0001) between easTILs% and sTILs%. The area under the prediction curve (AUC) was higher for easTILs% than sTILs%, 0.709 and 0.627, respectively. Image analysis-based TILs quantification is predictive of pCR in BC and had better response discrimination than pathologist-read sTILs%.

PMID:37179362 | DOI:10.1038/s41523-023-00535-0