Nevin Manimala

PLoS One. 2021 Aug 26;16(8):e0256423. doi: 10.1371/journal.pone.0256423. eCollection 2021.

ABSTRACT

BACKGROUND: Point-of-care (PoC) testing of platelet count (PLT) provides real-time data for rapid decision making. The goal of this study is to evaluate the accuracy and precision of platelet counting using a new microvolume (8 μL), absolute counting, 1.5 kg cytometry-based blood analyzer, the rHEALTH ONE (rHEALTH) in comparison with the International Society of Laboratory Hematology (ISLH) platelet method, which uses a cytometer and an impedance analyzer.

METHODS: Inclusion eligibility were healthy adults (M/F) ages 18-80 for donation of fingerprick and venous blood samples. Samples were from a random N = 31 volunteers from a single U.S. site. Samples were serially diluted to test thrombocytopenic ranges. Interfering substances and conditions were tested, including RBC fragments, platelet fragments, cholesterol, triglycerides, lipids, anti-platelet antibodies, and temperature.

RESULTS: The concordance between the rHEALTH and ISLH methods had a slope = 1.030 and R2 = 0.9684. The rHEALTH method showed a correlation between capillary and venous blood samples (slope = 0.9514 and R2 = 0.9684). Certain interferents changed platelet recovery: RBC fragments and anti-platelet antibodies with the ISLH method; platelet fragments and anti-platelet antibodies on the rHEALTH; and RBC fragments, platelets fragments, triglycerides and LDL on the clinical impedance analyzer. The rHEALTH’s precision ranged from 3.1-8.0%, and the ISLH from 1.0-10.5%.

CONCLUSIONS: The rHEALTH method provides similar results with the reference method and good correlation between adult capillary and venous blood samples. This demonstrates the ability of the rHEALTH to provide point-of-care assessment of normal and thrombocytopenic platelet counts from fingerprick blood with high precision and limited interferences.

PMID:34437590 | DOI:10.1371/journal.pone.0256423

PLoS One. 2021 Aug 26;16(8):e0256195. doi: 10.1371/journal.pone.0256195. eCollection 2021.

ABSTRACT

BACKGROUND: Psychiatric patients are at increased risk of being overweight or obese, and subsequently develop metabolic syndrome. Nevertheless, data regarding associated factors for weight gain are limited and inconsistent.

OBJECTIVE: The present study aimed to determine the risk of metabolic syndrome and its associated factors among psychiatric patients.

METHOD: A cross-sectional quantitative study was conducted among all psychiatric patients at the Psychiatric Unit of the University of Gondar Comprehensive Specialized Hospital from March 1- April 1, 2018. All eligible psychiatric patients were interviewed about their socio-demographic status,and clinical characteristics and useful parameters for the study were recorded from the medical records of the patients and by measuring waist to height ratio. Descriptive statistics were used to summarize baseline information.Binary logistic regression was used to determine the associated factors and P-value <0.05 and confidence interval (CI) of 95% were used as cut off points for determining statistical significance.

RESULT: From 300 patients included in the study, 168(56%) patients were females,and around 50.3% of the study participants had low literacy levels. As per waist to a height ratio scale, 58% (174) of the patients had a risk of metabolic syndrome. The Binary logistic regression analysis indicated that sex (p-<0.0001), occupation (p -0.032), marital status (p-0.006), and distance from the hospital (p<0.0001) were statistically significant determinants of metabolic syndrome risk in the psychiatric patient in our setting.

CONCLUSION: The majority of the psychiatric patients in the study setting had a risk of metabolic syndrome. Sex, marital status, employment status, and distance to the hospital were significantly associated with metabolic syndrome. Routine physical and laboratory investigations to detect metabolic syndrome are indispensable in psychiatric patients to prevent cardiovascular complications.

PMID:34437577 | DOI:10.1371/journal.pone.0256195

PLoS One. 2021 Aug 26;16(8):e0256093. doi: 10.1371/journal.pone.0256093. eCollection 2021.

ABSTRACT

OBJECTIVE: To develop an index assessing the risks of low energy fractures (LEF) in patients prescribed antiepileptic drugs (AED) by exploring five previously suggested risk factors; age, gender, AED-type, epilepsy diagnosis and BMI.

METHODS: In a population-based retrospective open cohort study we used real world data from the Electronic Health Register (EHR) in Region Kalmar County, Sweden. 23 209 patients prescribed AEDs at any time from January 2008 to November 2018 and 23 281 matching controls were followed from first registration in the EHR until the first documented LEF, disenrollment (or death) or until the end of the study period, whichever came first. Risks of LEF measured as hazard rate ratios in relation to the suggested risk factors and in comparison to matched controls were analyzed using Cox regression. The index was developed using a linear combination of the statistically significant variables multiplied by the corresponding regression coefficients.

RESULTS: Data from 23 209 patients prescribed AEDs and 2084 documented LEFs during a follow-up time of more than 10 years resulted in the Kalmar Epilepsy Fracture Risk Index (KEFRI). KEFRI = Age-category x (1.18) + Gender x (-0.51) + AED-type x (0.29) + Epilepsy diagnosis-category x (0.31) + BMI-category x (-0.35). All five previously suggested risk factors were confirmed. Women aged 75 years and older treated with an inducing AED against epilepsy and BMIs of 25 kg/m2 or below had 48 times higher LEF rates compared to men aged 50 years or younger, treated with a non-inducing AED for a condition other than epilepsy and BMIs above 25 kg/m2.

CONCLUSION: The KEFRI is the first weighted multifactorial assessment tool estimating risks of LEF in patients prescribed AEDs and could serve as a feasible guide within clinical practice.

PMID:34437569 | DOI:10.1371/journal.pone.0256093

PLoS One. 2021 Aug 26;16(8):e0256379. doi: 10.1371/journal.pone.0256379. eCollection 2021.

ABSTRACT

INTRODUCTION: The COVID-19 pandemic caused by SARS-CoV-2 has now affected tens of millions of people globally. It is the hope that vaccines against SARS-CoV-2 will deliver a comprehensive solution to this global pandemic; however, this will require extensive national vaccination programs. Ultimately, clinical conditions and even sudden unexplained death will occur around the time of vaccination, thus a distinction needs to be made between events that are causally related to the vaccine or temporally related to vaccination. This study aimed to estimate the background occurrence of 43 clinical conditions in the Japanese population.

METHODS: A retrospective cohort study was conducted from 2013 to 2019 using data from two large healthcare claims databases (MDV and JMDC) in Japan. The estimated number of new cases and incidence were calculated based on the actual number of new cases identified in the databases. The PubMed and Ichushi-web databases, as well as grey literature such as guidelines and government statistics, were also searched to identify any publications related to incidence of these conditions in Japan.

RESULTS AND CONCLUSION: The estimates of the number of total cases and incidence were similar for the MDV and JMDC databases for some diseases. In addition, some estimates were similar to those in the scientific literature. For example, from the MDV and JMDC databases, estimates of incidence of confirmed Bell’s palsy in 2019 were 41.7 and 47.9 cases per 100,000 population per year, respectively. These estimates were of the same order from the scientific publication. Determining whether clinical conditions occurring around the time of vaccination are causally or only temporally related to vaccination will be critical for public health decision makers as well as for the general public. Comparison of background occurrence at the population level may provide some additional objective evidence for the evaluation of temporality or causality.

PMID:34437567 | DOI:10.1371/journal.pone.0256379

PLoS One. 2021 Aug 26;16(8):e0252540. doi: 10.1371/journal.pone.0252540. eCollection 2021.

ABSTRACT

Probability matching, also known as the “matching law” or Herrnstein’s Law, has long puzzled economists and psychologists because of its apparent inconsistency with basic self-interest. We conduct an experiment with real monetary payoffs in which each participant plays a computer game to guess the outcome of a binary lottery. In addition to finding strong evidence for probability matching, we document different tendencies towards randomization in different payoff environments-as predicted by models of the evolutionary origin of probability matching-after controlling for a wide range of demographic and socioeconomic variables. We also find several individual differences in the tendency to maximize or randomize, correlated with wealth and other socioeconomic factors. In particular, subjects who have taken probability and statistics classes and those who self-reported finding a pattern in the game are found to have randomized more, contrary to the common wisdom that those with better understanding of probabilistic reasoning are more likely to be rational economic maximizers. Our results provide experimental evidence that individuals-even those with experience in probability and investing-engage in randomized behavior and probability matching, underscoring the role of the environment as a driver of behavioral anomalies.

PMID:34437550 | DOI:10.1371/journal.pone.0252540

PLoS Genet. 2021 Aug 26;17(8):e1009762. doi: 10.1371/journal.pgen.1009762. Online ahead of print.

ABSTRACT

The development of genome-informed methods for identifying quantitative trait loci (QTL) and studying the genetic basis of quantitative variation in natural and experimental populations has been driven by advances in high-throughput genotyping. For many complex traits, the underlying genetic variation is caused by the segregation of one or more ‘large-effect’ loci, in addition to an unknown number of loci with effects below the threshold of statistical detection. The large-effect loci segregating in populations are often necessary but not sufficient for predicting quantitative phenotypes. They are, nevertheless, important enough to warrant deeper study and direct modelling in genomic prediction problems. We explored the accuracy of statistical methods for estimating the fraction of marker-associated genetic variance (p) and heritability ([Formula: see text]) for large-effect loci underlying complex phenotypes. We found that commonly used statistical methods overestimate p and [Formula: see text]. The source of the upward bias was traced to inequalities between the expected values of variance components in the numerators and denominators of these parameters. Algebraic solutions for bias-correcting estimates of p and [Formula: see text] were found that only depend on the degrees of freedom and are constant for a given study design. We discovered that average semivariance methods, which have heretofore not been used in complex trait analyses, yielded unbiased estimates of p and [Formula: see text], in addition to best linear unbiased predictors of the additive and dominance effects of the underlying loci. The cryptic bias problem described here is unrelated to selection bias, although both cause the overestimation of p and [Formula: see text]. The solutions we described are predicted to more accurately describe the contributions of large-effect loci to the genetic variation underlying complex traits of medical, biological, and agricultural importance.

PMID:34437540 | DOI:10.1371/journal.pgen.1009762

PLoS Genet. 2021 Aug 26;17(8):e1009723. doi: 10.1371/journal.pgen.1009723. eCollection 2021 Aug.

ABSTRACT

Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Previous work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the longitudinal relationship between genetic relative risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic relative risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their relative risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying relative risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic relative risk. The predominant pattern shows genetic risk factors having the greatest relative impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants, although the magnitude and form of the decrease varies among diseases. As a consequence, for diseases where genetic relative risk decreases over age, genetic risk factors have stronger explanatory power among younger populations, compared to older ones. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction.

PMID:34437535 | DOI:10.1371/journal.pgen.1009723

J Gerontol Soc Work. 2021 Aug 26:1-26. doi: 10.1080/01634372.2021.1939219. Online ahead of print.

ABSTRACT

This study deals with key factors affecting mental health during the COVID-19 pandemic. The results are based on the longitudinal representative 50+ population-based multi-country panel study Survey of Health, Aging and Retirement in Europe (SHARE) in which representative samples of individuals in European countries plus Israel were collected. The goal is to turn the challenges of aging into opportunities as well as provide valid and reliable data for evidence-based policies. A total of 2,631 participants were interviewed by telephone in the Czech Republic during the initial stages of the COVID-19 outbreak in 2020. After a descriptive analysis showed that participants complied well with most preventative and protective COVID-19 measures, subsequent modeling using logistic regression analysis and a decision tree algorithm identified key determinants that have contributed to an understanding of variation in declared feelings of depression. We found that nervousness, previously unexperienced trouble with sleep, and self-rated health status before the COVID-19 outbreak and gender remained significant predictors of depression, from which statistically significant different categories concerning the percentage of depression could be formed. These identified potential risk factors should be considered in planning further supportive strategies for high-risk 50+ population.

PMID:34435934 | DOI:10.1080/01634372.2021.1939219

Hum Vaccin Immunother. 2021 Aug 26:1-6. doi: 10.1080/21645515.2021.1968216. Online ahead of print.

ABSTRACT

Kuwait started immunizing children <2 y age with the 7-valent pneumococcal conjugate vaccine, PCV7 from August 2007. PCV7 was replaced by the 13-valent conjugate vaccine, PCV13 from August 2010. In a previous analysis of the results for the period, August 2010-July 2013 (period II), there was no evidence of serotype-specific protection for invasive disease against the additional six serotypes to PCV7 present in PCV13 (non-PCV7 serotypes) as evidenced by isolation from blood and cerebrospinal fluid in any of the age groups, <2 y, 2-5 y, 6-50 y, 51-65 y, and >65 y and all ages, compared to the pre-vaccination period, August 2003-July 2006 (period I). In the current study, we allowed additional time, August 2013-July 2019 (period III) for better vaccine effect and repeated the analysis. We did not find any significant decrease of invasive disease due to the non-PCV7 serotypes of PCV13 in period III and combined II and III periods compared to period I. However, these comparisons showed significant reductions for four of the six and total serotypes of PCV7, and total serotypes of PCV13. Reduction for total PCV13 serotypes was contributed by serotypes of PCV7. It appears that the six non-PCV7 serotypes in PCV13 do not offer much protection. Some contributory factors for the poor effect of the non-PCV7 serotypes may be related to few cases with underpowered statistical analysis, lack of vaccine coverage data, method of vaccine efficacy analysis based on vaccine serotypes relative to all serotypes and unusual rise in non-typeable isolates post vaccination that would have masked true serotypes.

PMID:34435932 | DOI:10.1080/21645515.2021.1968216

Ecol Food Nutr. 2021 Aug 26:1-20. doi: 10.1080/03670244.2021.1968849. Online ahead of print.

ABSTRACT

In total, 1224 Turkish adults (27.5 ± 9.6 years; 80.6% female) answered sociodemographic questions, and three instruments: Mindful Eating Questionnaire (MEQ), Intuitive Eating Scale-2 (IES-2), and COVID-19 Perception and Attitude Scale. Women’s perceptions and attitudes toward COVID-19 indicated higher concerns than men (p < .001). It was found that IES-2 scores of all subjects increased as body mass index (BMI) decreased and education level increased (p < .001; p = .033, respectively). During the social isolation of COVID-19, the MEQ scores of married couples and those who did not eat take-out foods were higher (p = .027; p = .006, respectively). Interestingly, it was found that as the BMI of the subjects increased, their MEQ scores increased (p < .001). The COVID-19 pandemic, which has social and economic consequences, has a great impact on human health and causes sudden lifestyle changes through social distance and isolation at home. Although social isolation during the epidemic is a necessary precaution to protect public health, the results of this study support that it causes changes in intuitive eating, and mindful eating behaviors.

PMID:34435919 | DOI:10.1080/03670244.2021.1968849