Category: Nevin Manimala

PLoS One. 2021 Oct 14;16(10):e0258033. doi: 10.1371/journal.pone.0258033. eCollection 2021.

ABSTRACT

BACKGROUND: Nutritional deficiencies are generally associated with increased risk and severity of tuberculosis. This study investigated the nutritional status, knowledge, and attitudes of tuberculosis (TB) patients receiving treatment in the Tema Metropolis.

METHOD: A cross-sectional design was used to collect data on the nutritional knowledge, attitude, and status of TB patients. Nutritional status was analysed using World Health Organization’s formula for body mass index. Pearson’s chi-square and logistic regression models were used to assess associations between predictor and outcome variables. All statistical analyses were considered significant at p-values < 0.05.

RESULT: The prevalence of malnutrition among TB patients was 39.7%, 14.4%, and 4.8% for underweight, overweight, and obesity respectively. There was a high (61.0%) knowledge of nutrition among the patients. Also, 65.8% had good attitude towards nutrition. There were significant associations between normal nutritional status and age of the TB patients (p = 0.041), highest educational level attained (p = 0.036), employment status (p = 0.019), status of alcohol intake (p = 0.031), number of months on TB treatment (p = 0.021), and attitude towards nutrition (p = 0.028).

CONCLUSION: There was a reasonable nutrition-related knowledge and attitude towards nutrition among the TB patients. However, that did not reflect on their nutritional status. We recommend continuing education on smoking cessation, avoidance of harmful use of alcohol, and the establishment of food aid and other livelihood intervention programs for TB patients.

PMID:34648547 | DOI:10.1371/journal.pone.0258033

PLoS One. 2021 Oct 14;16(10):e0258480. doi: 10.1371/journal.pone.0258480. eCollection 2021.

ABSTRACT

The gray-to-white matter ratio (GWR) has been used to identify brain damage in comatose patients after cardiac arrest. However, Hounsfield units (HUs), the measurement of brain density on computed tomography (CT) images, may vary depending on the machine type or parameter. Therefore, differences in CT scanners may affect the GWR in post-cardiac arrest patients. We performed a retrospective study on comatose post-cardiac arrest patients who visited the hospital from 2007 to 2017. Two CT, Lightspeed and SOMATOM, scanners were used. Two observers independently measured the HUs of the caudate nucleus, putamen, posterior internal capsule, and corpus callosum using regions of interest. We compared the GWR calculated from the HUs measured at different CT scanners. The analysis of different scanners showed statistically significant differences in the measured HUs and GWR. The HUs and GWR of Lightspeed were measured lower than SOMATOM. The difference between the two CT scanners was also evident in groups divided by neurological prognosis. The area under the curve of the receiver operating characteristic curve to predict poor outcomes of Lightspeed was 0.798, and the cut-off value for 100% specificity was 1.172. The SOMATOM was 0.855, and the cut-off value was 1.269. The difference in scanners affects measurements and performance characteristics of the GWR in post-cardiac arrest patients. Therefore, when applying the results of the GWR study to clinical practice, reference values for each device should be presented, and an integrated plan should be prepared.

PMID:34648574 | DOI:10.1371/journal.pone.0258480

PLoS One. 2021 Oct 14;16(10):e0258187. doi: 10.1371/journal.pone.0258187. eCollection 2021.

ABSTRACT

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a cancer of epithelial origin with a high incidence in certain populations. While NPC has a high remission rate with concomitant chemoradiation, recurrences are frequent, and the downstream morbidity of treatment is significant. Thus, it is imperative to find alternative therapies.

METHODS: We employed a Search Tag Analyze Resource (STARGEO) platform to conduct a meta-analysis using the National Center for Biotechnology’s (NCBI) Gene Expression Omnibus (GEO) to define NPC pathogenesis. We identified 111 tumor samples and 43 healthy nasopharyngeal epithelium samples from NPC public patient data. We analyzed associated signatures in Ingenuity Pathway Analysis (IPA), restricting genes that showed statistical significance (p<0.05) and an absolute experimental log ratio greater than 0.15 between disease and control samples.

RESULTS: Our meta-analysis identified activation of lipopolysaccharide (LPS)-induced tissue injury in NPC tissue. Additionally, interleukin-1 (IL-1) and SB203580 were the top upstream regulators. Tumorigenesis-related genes such as homeobox A10 (HOXA10) and prostaglandin-endoperoxide synthase 2 (PTGS2 or COX-2) as well as those associated with extracellular matrix degradation, such as matrix metalloproteinases 1 and 3 (MMP-1, MMP-3) were also upregulated. Decreased expression of genes that encode proteins associated with maintaining healthy nasal respiratory epithelium structural integrity, including sentan-cilia apical structure protein (SNTN) and lactotransferrin (LTF) was documented. Importantly, we found that etanercept inhibits targets upregulated in NPC and LPS induction, such as MMP-1, PTGS2, and possibly MMP-3.

CONCLUSIONS: Our analysis illustrates that nasal epithelial barrier dysregulation and maladaptive immune responses are key components of NPC pathogenesis along with LPS-induced tissue damage.

PMID:34648530 | DOI:10.1371/journal.pone.0258187

PLoS One. 2021 Oct 14;16(10):e0258429. doi: 10.1371/journal.pone.0258429. eCollection 2021.

ABSTRACT

Static light scattering is a popular physical chemistry technique that enables calculation of physical attributes such as the radius of gyration and the second virial coefficient for a macromolecule (e.g., a polymer or a protein) in solution. The second virial coefficient is a physical quantity that characterizes the magnitude and sign of pairwise interactions between particles, and hence is related to aggregation propensity, a property of considerable scientific and practical interest. Estimating the second virial coefficient from experimental data is challenging due both to the degree of precision required and the complexity of the error structure involved. In contrast to conventional approaches based on heuristic ordinary least squares estimates, Bayesian inference for the second virial coefficient allows explicit modeling of error processes, incorporation of prior information, and the ability to directly test competing physical models. Here, we introduce a fully Bayesian model for static light scattering experiments on small-particle systems, with joint inference for concentration, index of refraction, oligomer size, and the second virial coefficient. We apply our proposed model to study the aggregation behavior of hen egg-white lysozyme and human γS-crystallin using in-house experimental data. Based on these observations, we also perform a simulation study on the primary drivers of uncertainty in this family of experiments, showing in particular the potential for improved monitoring and control of concentration to aid inference.

PMID:34648536 | DOI:10.1371/journal.pone.0258429

PLoS Negl Trop Dis. 2021 Oct 14;15(10):e0009858. doi: 10.1371/journal.pntd.0009858. Online ahead of print.

ABSTRACT

BACKGROUND: Scrub typhus is an acute febrile illness caused by intracellular bacteria from the genus Orientia. It is estimated that one billion people are at risk, with one million cases annually mainly affecting rural areas in Asia-Oceania. Relative to its burden, scrub typhus is understudied, and treatment recommendations vary with poor evidence base. These knowledge gaps could be addressed by establishing an individual participant-level data (IPD) platform, which would enable pooled, more detailed and statistically powered analyses to be conducted. This study aims to assess the characteristics of scrub typhus treatment studies and explore the feasibility and potential value of developing a scrub typhus IPD platform to address unanswered research questions.

METHODOLOGY/PRINCIPAL FINDINGS: We conducted a systematic literature review looking for prospective scrub typhus clinical treatment studies published from 1998 to 2020. Six electronic databases (Ovid Embase, Ovid Medline, Ovid Global Health, Cochrane Library, Scopus, Global Index Medicus), ClinicalTrials.gov, and WHO ICTRP were searched. We extracted data on study design, treatment tested, patient characteristics, diagnostic methods, geographical location, outcome measures, and statistical methodology. Among 3,100 articles screened, 127 were included in the analysis. 12,079 participants from 12 countries were enrolled in the identified studies. ELISA, PCR, and eschar presence were the most commonly used diagnostic methods. Doxycycline, azithromycin, and chloramphenicol were the most commonly administered antibiotics. Mortality, complications, adverse events, and clinical response were assessed in most studies. There was substantial heterogeneity in the diagnostic methods used, treatment administered (including dosing and duration), and outcome assessed across studies. There were few interventional studies and limited data collected on specific groups such as children and pregnant women.

CONCLUSIONS/SIGNIFICANCE: There were a limited number of interventional trials, highlighting that scrub typhus remains a neglected disease. The heterogeneous nature of the available data reflects the absence of consensus in treatment and research methodologies and poses a significant barrier to aggregating information across available published data without access to the underlying IPD. There is likely to be a substantial amount of data available to address knowledge gaps. Therefore, there is value for an IPD platform that will facilitate pooling and harmonisation of currently scattered data and enable in-depth investigation of priority research questions that can, ultimately, inform clinical practice and improve health outcomes for scrub typhus patients.

PMID:34648517 | DOI:10.1371/journal.pntd.0009858

PLoS One. 2021 Oct 14;16(10):e0257249. doi: 10.1371/journal.pone.0257249. eCollection 2021.

ABSTRACT

OBJECTIVES: Antibiotics are important for improving animal health and production. However, the deposition of its residues in food of animal origin intended for human consumption at non-permissible levels has generated global health concern and the need to tackle this using the “One Health Approach”. This study assessed the knowledge and practice of 286 cattle handlers in Kwara State, Nigeria.

METHODS: A web-based cross sectional online survey using a semi-structured questionnaire was conducted from November to December, 2019. Univariate, bivariate and multivariate analyses were performed at 95% confidence interval to determine predictors of good knowledge and practices towards Antibiotic Residues in Meat and Milk among cattle handlers.

RESULTS: This study revealed that majority (52.7% n = 165/286) of the cattle handlers were not aware of antibiotic residues. Knowledge and practices regarding antibiotic residues were generally poor among the study population; 36.7% and 35.5% had satisfactory knowledge and practice respectively. The age (p = 0.026), gender (p = 0.006) and business duration (p = 0.001) of participants were significantly associated with their knowledge of antimicrobial residues. The effect of education on knowledge was modified by age. The odds of having poor knowledge on antibiotic residues increased 4 times among participants who were ≤40 years old than those above 40 years (Stratum Specific OR = 3.65; CI = 1.2, 11.1; p = 0.026). Knowledge levels of participants were statistically associated with their practice levels p<0.05 (OR = 2.43; CI = 1.45. 4.06; p = 0.0006).

CONCLUSION: This implies that poor knowledge is a risk factor to having poor practice among cattle handlers. Deliberate efforts towards educating cattle farmers on best farm practices in antibiotic use would prevent antibiotic residues in meat and milk. Also, an effective surveillance system for monitoring the use of veterinary drugs in Kwara State, Nigeria is crucial.

PMID:34648524 | DOI:10.1371/journal.pone.0257249

PLoS One. 2021 Oct 14;16(10):e0255181. doi: 10.1371/journal.pone.0255181. eCollection 2021.

ABSTRACT

BACKGROUND: During the COVID-19 pandemic, many people had to shift their social and work life online. A few researchers and journalists described a new form of fatigue associated with a massive use of technology, including videoconferencing platforms. In this study, this type of fatigue was referred to as Online Fatigue. A new tool (the Online Fatigue Scale) was developed, and its psychometric properties were evaluated. This tool was used to assess Online Fatigue among Italian academics and to examine its associations with psychological and physical health.

METHODS: An online survey was conducted in December 2020 on a sample of Italian academics. Besides the Online Fatigue Scale (11 items) used to assess Online Fatigue, the survey was composed of questionnaires (including validated measures) focused on sociodemographic and job-related information, technostress creators, health status, psychological well-being, and COVID-related perceived distress. The psychometric properties of the Online Fatigue Scale were evaluated, and statistical analyses were conducted to examine the associations between Online Fatigue and all the other variables.

RESULTS: Participants were 307 academics aged 24-70 years old (mean age = 40.7; SD = 10.1). The Online Fatigue Scale showed good psychometric properties. Two subscales were identified: Off-Balance Fatigue and Virtual Relations Fatigue. High levels of Off-Balance Fatigue were associated with a greater use of technology, female gender, and presence of minor children. Participants with high scores on both subscales reported a greater frequency of psychosomatic symptoms, unhealthy habits, poorer psychological well-being, and greater Covid-related perceived distress.

CONCLUSIONS: The Online Fatigue Scale can be considered a reliable tool to assess Online Fatigue, which was significantly detected in our sample of Italian academics, along with its negative effects on physical and psychological health. Being a woman and having young children represent important risk factors. Universities should promote the separation between work and private life by encouraging self-care activities.

PMID:34648507 | DOI:10.1371/journal.pone.0255181

PLoS One. 2021 Oct 14;16(10):e0257857. doi: 10.1371/journal.pone.0257857. eCollection 2021.

ABSTRACT

CD36 (cluster of differentiation 36) is a membrane protein involved in lipid metabolism and has been linked to pathological conditions associated with metabolic disorders, such as diabetes and dyslipidemia. A case-control study was conducted and included 177 patients with type-2 diabetes mellitus (T2DM) and 173 control subjects to study the involvement of CD36 gene rs1761667 (G>A) and rs1527483 (C>T) polymorphisms in the pathogenesis of T2DM and dyslipidemia among Jordanian population. Lipid profile, blood sugar, gender and age were measured and recorded. Also, genotyping analysis for both polymorphisms was performed. Following statistical analysis, 10 different neural networks and machine learning (ML) tools were used to predict subjects with diabetes or dyslipidemia. Towards further understanding of the role of CD36 protein and gene in T2DM and dyslipidemia, a protein-protein interaction network and meta-analysis were carried out. For both polymorphisms, the genotypic frequencies were not significantly different between the two groups (p > 0.05). On the other hand, some ML tools like multilayer perceptron gave high prediction accuracy (≥ 0.75) and Cohen’s kappa (κ) (≥ 0.5). Interestingly, in K-star tool, the accuracy and Cohen’s κ values were enhanced by including the genotyping results as inputs (0.73 and 0.46, respectively, compared to 0.67 and 0.34 without including them). This study confirmed, for the first time, that there is no association between CD36 polymorphisms and T2DM or dyslipidemia among Jordanian population. Prediction of T2DM and dyslipidemia, using these extensive ML tools and based on such input data, is a promising approach for developing diagnostic and prognostic prediction models for a wide spectrum of diseases, especially based on large medical databases.

PMID:34648514 | DOI:10.1371/journal.pone.0257857

PLoS Comput Biol. 2021 Oct 14;17(10):e1009363. doi: 10.1371/journal.pcbi.1009363. eCollection 2021 Oct.

ABSTRACT

The spread of a communicable disease is a complex spatio-temporal process shaped by the specific transmission mechanism, and diverse factors including the behavior, socio-economic and demographic properties of the host population. While the key factors shaping transmission of influenza and COVID-19 are beginning to be broadly understood, making precise forecasts on case count and mortality is still difficult. In this study we introduce the concept of a universal geospatial risk phenotype of individual US counties facilitating flu-like transmission mechanisms. We call this the Universal Influenza-like Transmission (UnIT) score, which is computed as an information-theoretic divergence of the local incidence time series from an high-risk process of epidemic initiation, inferred from almost a decade of flu season incidence data gleaned from the diagnostic history of nearly a third of the US population. Despite being computed from the past seasonal flu incidence records, the UnIT score emerges as the dominant factor explaining incidence trends for the COVID-19 pandemic over putative demographic and socio-economic factors. The predictive ability of the UnIT score is further demonstrated via county-specific weekly case count forecasts which consistently outperform the state of the art models throughout the time-line of the COVID-19 pandemic. This study demonstrates that knowledge of past epidemics may be used to chart the course of future ones, if transmission mechanisms are broadly similar, despite distinct disease processes and causative pathogens.

PMID:34648492 | DOI:10.1371/journal.pcbi.1009363

PLoS One. 2021 Oct 14;16(10):e0249501. doi: 10.1371/journal.pone.0249501. eCollection 2021.

ABSTRACT

The aim of this systematic review and meta-analysis was to identify a list of common, candidate genes associated with the three components of fitness, specifically cardiovascular fitness, muscular strength, and anaerobic power, and how these genes are associated with exercise response phenotype variability, in previously untrained participants. A total of 3,969 potentially relevant papers were identified and processed for inclusion. After eligibility and study selection assessment, 24 studies were selected for meta-analysis, comprising a total of 3,012 participants (male n = 1,512; females n = 1,239; not stated n = 261; age 28 ± 9 years). Meta-Essentials spreadsheet 1.4 (Microsoft Excel) was used in creating the forest plots and meta-analysis. IBM SPSS statistics V24 was implemented for the statistical analyses and the alpha was set at p ≤ 0.05. 13 candidate genes and their associated alleles were identified, which were associated with the phenotypes of interest. Analysis of training group data showed significant differential phenotypic responses. Subgroup analysis showed; 44%, 72% and 10% of the response variance in aerobic, strength and power phenotypes, respectively, were explained by genetic influences. This analysis established that genetic variability explained a significant proportion of the adaptation differences across the three components of fitness in the participants post-training. The results also showed the importance of analysing and reporting specific gene alleles. Information obtained from these findings has the potential to inform and influence future exercise-related genes and training studies.

PMID:34648504 | DOI:10.1371/journal.pone.0249501