Tag: nevin manimala

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

MMWR Morb Mortal Wkly Rep. 2021 Oct 15;70(41):1455. doi: 10.15585/mmwr.mm7041a5.

NO ABSTRACT

PMID:34648485 | DOI:10.15585/mmwr.mm7041a5

PLoS Comput Biol. 2021 Oct 14;17(10):e1009468. doi: 10.1371/journal.pcbi.1009468. Online ahead of print.

ABSTRACT

Understanding how immunological memory lasts a lifetime requires quantifying changes in the number of memory cells as well as how their division and death rates change over time. We address these questions by using a statistically powerful mixed-effects differential equations framework to analyze data from two human studies that follow CD8 T cell responses to the yellow fever vaccine (YFV-17D). Models were first fit to the frequency of YFV-specific memory CD8 T cells and deuterium enrichment in those cells 42 days to 1 year post-vaccination. A different dataset, on the loss of YFV-specific CD8 T cells over three decades, was used to assess out of sample predictions of our models. The commonly used exponential and bi-exponential decline models performed relatively poorly. Models with the cell loss following a power law (exactly or approximately) were most predictive. Notably, using only the first year of data, these models accurately predicted T cell frequencies up to 30 years post-vaccination. Our analyses suggest that division rates of these cells drop and plateau at a low level (0.1% per day, ∼ double the estimated values for naive T cells) within one year following vaccination, whereas death rates continue to decline for much longer. Our results show that power laws can be predictive for T cell memory, a finding that may be useful for vaccine evaluation and epidemiological modeling. Moreover, since power laws asymptotically decline more slowly than any exponential decline, our results help explain the longevity of immune memory phenomenologically.

PMID:34648489 | DOI:10.1371/journal.pcbi.1009468

MMWR Morb Mortal Wkly Rep. 2021 Oct 15;70(41):1447-1452. doi: 10.15585/mmwr.mm7041a3.

ABSTRACT

Traumatic brain injuries (TBIs) have contributed to approximately one million deaths in the United States over the last 2 decades (1). CDC analyzed National Vital Statistics System (NVSS) mortality data for a 3-year period (2016-2018) to examine numbers and rates of TBI-related deaths, the percentage difference between each state’s rate and the overall U.S. TBI-related death rate, leading causes of TBI, and the association between TBI and a state’s level of rurality. During 2016-2018, a total of 181,227 TBI-related deaths (17.3 per 100,000 population per year) occurred in the United States. The percentage difference between state TBI-related death rates and the overall U.S. rate during this period ranged from 46.2% below to 101.2% above the overall rate. By state, the lowest rate was in New Jersey (9.3 per 100,000 population per year); the states with the highest rates were Alaska (34.8), Wyoming (32.6), and Montana (29.5). States in the South and those with a higher proportion of residents living in rural areas had higher rates, whereas states in the Northeast and those with a lower proportion of residents living in rural areas had lower TBI-related death rates. In 43 states, suicide was the leading cause of TBI-related deaths; in other states, unintentional falls or unintentional motor vehicle crashes were responsible for the highest numbers and rates of TBI-related deaths. Consistent with previous studies (2), differences in TBI incidence and outcomes were observed across U.S. states; therefore, states can use these findings to develop and implement evidence-based prevention strategies, based on their leading causes of TBI-related deaths. Expanding evidence-based prevention strategies that address TBI-related deaths is warranted, especially among states with high rates due to suicide, unintentional falls, and motor vehicle crashes.

PMID:34648483 | DOI:10.15585/mmwr.mm7041a3

JAMA Ophthalmol. 2021 Oct 14. doi: 10.1001/jamaophthalmol.2021.4139. Online ahead of print.

NO ABSTRACT

PMID:34648026 | DOI:10.1001/jamaophthalmol.2021.4139