Nevin Manimala

PLoS One. 2021 Feb 25;16(2):e0247459. doi: 10.1371/journal.pone.0247459. eCollection 2021.

ABSTRACT

BACKGROUND: Effective management of the vaccine cold chain system at all levels is one of the crucial factors for maintaining vaccine potency. Vaccines require more complex handling and storage requirements due to increased temperature sensitivity and complicated immunization schedules. This urges adequate knowledge, attitude, and practice. This study assessed the knowledge, attitude, and practice of vaccinators and vaccine handlers’ in public health facilities.

METHODOLOGY: An institutional-based cross-sectional study design was used to assess the knowledge, attitude, and practice of 127 vaccinators and vaccine handlers in public health facilities of Oromia Special Zone, from September 1 to 30, 2019. Data were collected using self-administered questionnaires and a structured observation checklist. Descriptive and inferential statistics were made using the statistical package for social sciences version 20. Variables with a p-value <0.05 were taken as statistically significant.

RESULT: The response rate was (96.94%). Sixty-eight (53.5%; 95% CI: 46.5%, 61.4%), 58 (45.7%; 95% CI: 37.8%, 53.5%) and 62 (48.8%: 95% CI; 41.7%, 56.7%) vaccinators and vaccine handlers had satisfactory knowledge, positive attitude and good practice respectively. Receiving training on cold chain management had a statistically significant association with the level of knowledge on cold chain management (AOR = 3.04, 95% CI: 1.04-8.88).

CONCLUSIONS: More than half of vaccinators and vaccine handlers had satisfactory knowledge, while below half of vaccinators and vaccine handlers had a positive attitude and good practice. The determinants of knowledge in cold chain management were receiving training on cold chain management. Providing regular technical support and on the job training on vaccine cold chain management will improve the knowledge, attitude, and practice of vaccinators and vaccine handlers.

PMID:33630946 | DOI:10.1371/journal.pone.0247459

PLoS One. 2021 Feb 25;16(2):e0247544. doi: 10.1371/journal.pone.0247544. eCollection 2021.

ABSTRACT

Six common beliefs about the usage of active learning in introductory STEM courses are investigated using survey data from 3769 instructors. Three beliefs focus on contextual factors: class size, classroom setup, and teaching evaluations; three focus on individual factors: security of employment, research activity, and prior exposure. The analysis indicates that instructors in all situations can and do employ active learning in their courses. However, with the exception of security of employment, trends in the data are consistent with beliefs about the impact of these factors on usage of active learning. We discuss implications of these results for institutional and departmental policies to facilitate the use of active learning.

PMID:33630945 | DOI:10.1371/journal.pone.0247544

PLoS One. 2021 Feb 25;16(2):e0247634. doi: 10.1371/journal.pone.0247634. eCollection 2021.

ABSTRACT

BACKGROUND: Diabetes is one of the most prevalent non-communicable diseases globally, which rapidly is increasing in developing countries. Ethiopia is also facing growing morbidity and mortality related to diabetes complications. Thus, dealing with glycemic control is essential for controlling the development of devastating acute and chronic complications related to diabetes. Therefore, this study aims to assess the magnitude and predictors of poor glycemic control among diabetic patients in western Ethiopia.

METHODS: The cross-sectional study design was employed on a sample of 423 diabetic patients. A systematic random sampling method was employed. An interviewer-administered structured questionnaire was used. The data entered into Epi data version 3.1 and exported into Statistical Package for the Social Sciences window version 24 for analysis. All variables significant at p-<0.25 in bivariate were entered into multivariate analysis. The multivariable logistic regressions were used to determine predictors’ poor glycemic control by considering the Adjusted Odds Ratio at CI 95% and the significance level was set at p <0.05.

RESULTS: The magnitude of poor glycemic control was 64.1%. Being females (AOR = 1.684,95%CI = 1.066,2.662), duration of diabetes >8years (AOR = 2.552,95%CI = 1.397, 4.665), presence of diabetes complication (AOR = 2.806,95%CI = 1.594,4.941), negligence of blood glucose test at home (AOR = 1.720, 95%CI = 1.078, 2.743), poor self-care behavior (AOR = 1.787, 95%CI = 1.083,2.959) and poor self-efficacy (AOR = 1.934, 95%CI = 1.078,3.469) were significant predictors of poor glycemic control.

CONCLUSION: The proportion of poor glycemic control was high which was nearly comparable to that reported from many countries. This could be due to factors that were significantly associated with poor glycemic control like lack of home blood glucose test, increased duration of diabetes, presence of diabetes complications, poor self-efficacy, and poor self-care behaviors. Each were significant independent predictors of poor glycemic control. Thus, we recommend patients with diabetes and health care providers enhancing self-monitoring practices, and preventing potential complications should be a priority concern to improve blood glucose levels. Further studies are also recommended to explore important factors which were not identified by the current study.

PMID:33630936 | DOI:10.1371/journal.pone.0247634

PLoS One. 2021 Feb 25;16(2):e0247499. doi: 10.1371/journal.pone.0247499. eCollection 2021.

ABSTRACT

INTRODUCTION: Medicines used at Danish public hospitals are purchased through tendering. Together with drug shortage, tendering result in drug changes, known to compromise patient safety, increase medicine errors and to be resource demanding for healthcare personnel. Details on actual resources required in the clinic setting to manage drug changes are unknown. The aim of the study is to explore time spend by hospital personnel in a drug change situation when dispensing medicine to in- and outpatients in a hospital setting in the Capital Region of Denmark.

METHOD: A time and motion study, using direct observation combined with time-registration tools, such as eye-tracking, video recording and manual time tracking. Data were obtained from observing nurses and social and health care assistants with dispensing authority while dispensing or extraditing medicine before and after the implementation of drug changes in two clinical setting; a cardiology ward and a rheumatology outpatient clinic.

RESULTS: Hospital personnel at the cardiology inpatient ward spent 20.5 seconds on dispensing a drug, which was increased up to 28.4 seconds by drug changes. At the rheumatology outpatient clinic, time to extradite medicine increased from 8 minutes and 6 seconds to 15 minutes and 36 seconds by drug changes due to tender. Similarly, drug changes due to drug shortage prolonged the extradition time to 16 minutes and 54 seconds. Statistical analysis reveal that drug changes impose a significant increase in time to dispense a drug for both in- and outpatients.

CONCLUSION: Clinical hospital personnel spent significantly longer time on drug change situations in the dispensing of medicine to in- and outpatients in a hospitals. This study emphasizes that implementing drug changes do require extra time, thus, the hospital management should encounter this and ensure that additional time is available for the hospital personnel to ensure a safe drug dispensing process.

PMID:33630933 | DOI:10.1371/journal.pone.0247499

PLoS One. 2021 Feb 25;16(2):e0247661. doi: 10.1371/journal.pone.0247661. eCollection 2021.

ABSTRACT

OBJECTIVES: In Jang-jeom, a small village in Hamra-myeon, Iksan-si, Jeollabuk-do, South Korea, residents raised concerns about a suspected cancer cluster that they attributed to a fertilizer plant near the village. We aimed to investigate whether the cancer incidence in the village was higher than that in the general Korean population when the factory was in operation (2001-2017) and whether living in the village was associated with a higher risk of cancer.

METHODS: Using national population data and cancer registration data of South Korea, we estimated the standardized incidence ratios (SIRs) in the village to investigate whether more cancer cases occurred in the village compared to other regions. The SIRs were standardized by age groups of 5 years and sex. In order to investigate whether residence in the village increased the risk of cancer, a retrospective cohort was constructed using National Health Insurance Service (NHIS) databases. We estimated the cancer hazard ratios (HRs) using the Cox proportional hazard model, and defined the exposed area as the village of Jang-jeom, and the unexposed or control area as the village neighborhood in Hamra-myeon. We considered potential confounding variables such as age, sex, and income index in the models. Additionally, we measured polycyclic aromatic hydrocarbons (PAHs) and tobacco-specific nitrosamines (TSNAs), suspected carcinogens that may have caused the cancer cluster, in samples collected from the plant and the village.

RESULTS: Twenty-three cancer cases occurred in Jang-jeom from 2001 to 2017. Between 2010 and 2016, the incidence rates of all cancers (SIR: 2.05, except thyroid cancer: 2.22), non-melanoma skin cancer (SIR: 21.14, female: 25.41), and gallbladder (GB) and biliary tract cancer in men (SIR: 16.01) in the village were higher than those in the national population in a way that was statistically significant. In our cohort analysis that included only Hamra-myeon residents who have lived there for more than 7 years, we found a statistically significant increase in the risk of all cancers (HR: 1.99, except thyroid cancer: 2.20), non-melanoma skin cancer (HR: 11.60), GB and biliary tract cancer (HR: 15.24), liver cancer (HR: 6.63), and gastric cancer (HR: 3.29) for Jang-jeom residents compared to other Hamra area residents. We identified PAHs and TSNAs in samples of deposited dust and residual fertilizer from the plant and TSNAs in dust samples from village houses.

CONCLUSIONS: The results of the SIR calculation and cancer risk analyses of Jang-jeom village residents from the retrospective cohort design showed consistency in the effect size and direction, suggesting that there was a cancer cluster in Jang-jeom. This study would be a good precedent for cancer cluster investigation.

PMID:33630917 | DOI:10.1371/journal.pone.0247661

PLoS One. 2021 Feb 25;16(2):e0247419. doi: 10.1371/journal.pone.0247419. eCollection 2021.

ABSTRACT

Statins have been recommended for use in atherosclerotic cardio-cerebrovascular disease (CCVD). The purpose of this study was to investigate the efficacy of five different types of statin in the secondary prevention of CCVD in patients. This study retrospectively designed and analyzed data from the National Health Insurance Service-National Health in Korea. Participants aged 40 to 69 years were categorized into five statin groups (atorvastatin, rosuvastatin, pitavastatin, simvastatin, and pravastatin). The primary composite outcome was defined as recurrence of CCVD or all causes of death. Cox proportional hazard regression models were adopted after stepwise adjustments for confounders to investigate the difference in efficacy among the different statins. Of the 755 final participants, 48 patients experienced primary composite outcomes. After adjustments, the hazard ratios (95% confidence intervals) for primary composite outcomes of atorvastatin, pitavastatin, and rosuvastatin groups were 0.956 (0.456-2.005), 1.347 (0.354-5.116), and 0.943 (0.317-2.803), respectively, when compared with the simvastatin group. There were no significant differences between the statins in efficacy for preventing recurrence of CCVD events and/or death in CCVD patients.

PMID:33630898 | DOI:10.1371/journal.pone.0247419

PLoS One. 2021 Feb 25;16(2):e0246266. doi: 10.1371/journal.pone.0246266. eCollection 2021.

ABSTRACT

Male lower urinary tract symptoms (LUTS) comprise a common syndrome of aging that negatively impacts quality of life. The etiology of LUTS is multifactorial, involving benign prostatic hyperplasia, smooth muscle and neurologic dysfunction, inflammation, sexually transmitted infections, fibrosis, and potentially dysbiosis, but this aspect remains poorly explored. We investigated whether the presence of infectious agents in urine might be associated with LUTS by combining next-generation DNA sequencing for virus discovery, microbiome analysis for characterization of bacterial communities, and mass spectrometry-based metabolomics. In urine from 29 LUTS cases and 9 controls from Wisconsin, we found a statistically significant association between a diagnosis of LUTS and the presence of JC virus (JCV), a common neurotropic human polyomavirus (Polyomaviridae, Betapolyomavirus) linked to severe neurologic disease in rare cases. This association (based on metagenomics) was not borne out when specific polymerase chain reaction (PCR) testing was applied to this set of samples, likely due to the greater sensitivity of PCR. Interestingly, urine metabolomics analysis identified dysregulation of metabolites associated with key LUTS processes. Microbiome analysis found no evidence of microbial community dysbiosis in LUTS cases, but JCV-positive samples contained more Anaerococcus species, which are involved in polymicrobial infections of the urinary tract. Neither age nor body mass index were significantly associated with the presence of urinary JCV-in the initial group or in an additional, regionally distinct group. These data provide preliminary support the hypothesis that viruses such as JCV may play a role in the development or progression of LUTS, together with other infectious agents and host metabolic responses.

PMID:33630889 | DOI:10.1371/journal.pone.0246266

PLoS One. 2021 Feb 25;16(2):e0246485. doi: 10.1371/journal.pone.0246485. eCollection 2021.

ABSTRACT

q-Rung orthopair fuzzy set (qROFS) and m-polar fuzzy set (mPFS) are rudimentary concepts in the computational intelligence, which have diverse applications in fuzzy modeling and decision making under uncertainty. The aim of this paper is to introduce the hybrid concept of q-rung orthopair m-polar fuzzy set (qROmPFS) as a hybrid model of q-rung orthopair fuzzy set and m-polar fuzzy set. A qROmPFS has the ability to deal with real life situations when decision experts are interested to deal with multi-polarity as well as membership and non-membership grades to the alternatives in an extended domain with q-ROF environment. Certain operations on qROmPFSs and several new notions like support, core, height, concentration, dilation, α-cut and (α, β)-cut of qROmPFS are defined. Additionally, grey relational analysis (GRA) and choice value method (CVM) are presented under qROmPFSs for multi-criteria decision making (MCDM) in robotic agri-farming. The proposed methods are suitable to find out an appropriate mode of farming among several kinds of agri-farming. The applications of proposed MCDM approaches are illustrated by respective numerical examples. To justify the feasibility, superiority and reliability of proposed techniques, the comparison analysis of the final ranking in the robotic agri-farming computed by the proposed techniques with some existing MCDM methods is also given.

PMID:33630877 | DOI:10.1371/journal.pone.0246485

PLoS One. 2021 Feb 25;16(2):e0245579. doi: 10.1371/journal.pone.0245579. eCollection 2021.

ABSTRACT

Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia’s neural-biomarkers from MRI datasets sourced from publicly available open databases. The proposed system utilized a modified histogram normalization (MHN) method to improve dyslexia neural-biomarker interpretations by mapping the pixels’ intensities of low-quality input neuroimages to range between the low-intensity region of interest (ROIlow) and high-intensity region of interest (ROIhigh) of the high-quality image. This was achieved after initial image smoothing using the Gaussian filter method with an isotropic kernel of size 4mm. The performance of the proposed smoothing and normalization methods was evaluated based on three image post-processing experiments: ROI segmentation, gray matter (GM) tissues volume estimations, and deep learning (DL) classifications using Computational Anatomy Toolbox (CAT12) and pre-trained models in a MATLAB working environment. The three experiments were preceded by some pre-processing tasks such as image resizing, labelling, patching, and non-rigid registration. Our results showed that the best smoothing was achieved at a scale value, σ = 1.25 with a 0.9% increment in the peak-signal-to-noise ratio (PSNR). Results from the three image post-processing experiments confirmed the efficacy of the proposed methods. Evidence emanating from our analysis showed that using the proposed MHN and Gaussian smoothing methods can improve comparability of image features and neural-biomarkers of dyslexia with a statistically significantly high disc similarity coefficient (DSC) index, low mean square error (MSE), and improved tissue volume estimations. After 10 repeated 10-fold cross-validation, the highest accuracy achieved by DL models is 94.7% at a 95% confidence interval (CI) level. Finally, our finding confirmed that the proposed MHN method significantly outperformed the normalization method of the state-of-the-art histogram matching.

PMID:33630876 | DOI:10.1371/journal.pone.0245579

PLoS One. 2021 Feb 25;16(2):e0246790. doi: 10.1371/journal.pone.0246790. eCollection 2021.

ABSTRACT

Falling in Parkinsonian syndromes (PS) is associated with postural instability and consists a common cause of disability among PS patients. Current posturographic practices record the body’s center-of-pressure displacement (statokinesigram) while the patient stands on a force platform. Statokinesigrams, after appropriate processing, can offer numerous posturographic features. This fact, although beneficial, challenges the efforts for valid statistics via standard univariate approaches. In this work, 123 PS patients were classified into fallers (PSF) or non-faller (PSNF) based on the clinical assessment, and underwent simple Romberg Test (eyes open/eyes closed). We developed a non-parametric multivariate two-sample test (ts-AUC) based on machine learning, in order to examine statokinesigrams’ differences between PSF and PSNF. We analyzed posturographic features using both multiple testing with p-value adjustment and ts-AUC. While ts-AUC showed significant difference between groups (p-value = 0.01), multiple testing did not agree with this result (eyes open). PSF showed significantly increased antero-posterior movements as well as increased posturographic area compared to PSNF. Our study highlights the superiority of ts-AUC compared to standard statistical tools in distinguishing PSF and PSNF in multidimensional space. Machine learning-based statistical tests can be seen as a natural extension of classical statistics and should be considered, especially when dealing with multifactorial assessments.

PMID:33630865 | DOI:10.1371/journal.pone.0246790