Tag: statistics

Mil Med. 2021 Jul 9:usab274. doi: 10.1093/milmed/usab274. Online ahead of print.

ABSTRACT

INTRODUCTION: Active duty service women (ADSW) constitute 16% of the force. The prevalence of alopecia, a dermatologic condition characterized by hair loss, is understudied in regard to hairstyle regulations across the U.S. military services. Alopecia has several causes; one of which is due to tension on the scalp secondary to tight hairstyles. In the U.S., alopecia has a lifetime prevalence of 1.7-2.1%; no previous studies which evaluated this condition in service women were found.

MATERIALS AND METHODS: We used the Military Health System Data Repository to perform a retrospective study to assess the prevalence of alopecia in ADSW from fiscal years (FYs) 2010 to 2019. Statistical analyses included descriptive statistics on patient demographics and trend analysis on the prevalence of alopecia over the 10-year study period.

RESULTS: A total of 498,219 ADSW were identified over the 10-year study period, of which 2.40% had a diagnosis of alopecia. Overall, the prevalence of alopecia decreases over the 10-year period, with two observed periods of slight increase (FY 2013 to 2014 and FY 2018 to 2019) when comparing prevalence year-to-year. Of those diagnosed, the majority were young, Black, with a senior enlisted rank, and in the U.S. Army.

CONCLUSION: The prevalence of alopecia in ADSW is slightly higher than that in civilian populations and is most likely underreported. It is more commonly diagnosed in Black women than would be expected based on ratios of this population in military service. Policy changes to ensure that traction alopecia is a qualifying medical condition for Veterans Affairs disability compensation, mechanisms are in place for more specific coding in the electronic medical record, and treatment options to be covered by TRICARE are recommended. All U.S. military services should consider updating and evaluating regulations to improve the health and quality of life of ADSW.

PMID:34244770 | DOI:10.1093/milmed/usab274

Nucleic Acids Res. 2021 Jul 9:gkab457. doi: 10.1093/nar/gkab457. Online ahead of print.

ABSTRACT

Dynamic regulation of gene expression is often governed by progression through transient cell states. Bulk RNA-seq analysis can only detect average change in expression levels and is unable to identify this dynamics. Single cell RNA-seq presents an unprecedented opportunity that helps in placing the cells on a hypothetical time trajectory that reflects gradual transition of their transcriptomes. This continuum trajectory or ‘pseudotime’, may reveal the developmental pathway and provide us with information on dynamic transcriptomic changes and other biological processes. Existing approaches to build pseudotime heavily depend on reducing huge dimension to extremely low dimensional subspaces and may lead to loss of information. We propose PseudoGA, a genetic algorithm based approach to order cells assuming that gene expressions vary according to a smooth curve along the pseudotime trajectory. We observe superior accuracy of our method in simulated as well as benchmarking real datasets. Generality of the assumption behind PseudoGA and no dependence on dimensionality reduction technique make it a robust choice for pseudotime estimation from single cell transcriptome data. PseudoGA is also time efficient when applied to a large single cell RNA-seq data and adaptable to parallel computing. R code for PseudoGA is freely available at https://github.com/indranillab/pseudoga.

PMID:34244782 | DOI:10.1093/nar/gkab457

Int J Epidemiol. 2021 Jul 9:dyab106. doi: 10.1093/ije/dyab106. Online ahead of print.

ABSTRACT

BACKGROUND: The world is experiencing local/regional hotspots and spikes in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19 disease. We aimed to formulate an applicable epidemiological model to accurately predict and forecast the impact of local outbreaks of COVID-19 to guide the local healthcare demand and capacity, policy-making and public health decisions.

METHODS: The model utilized the aggregated daily COVID-19 situation reports (including counts of daily admissions, discharges and bed occupancy) from the local National Health Service (NHS) hospitals and COVID-19-related weekly deaths in hospitals and other settings in Sussex (population 1.7 million), Southeast England. These data sets corresponded to the first wave of COVID-19 infections from 24 March to 15 June 2020. A novel epidemiological predictive and forecasting model was then derived based on the local/regional surveillance data. Through a rigorous inverse parameter inference approach, the model parameters were estimated by fitting the model to the data in an optimal sense and then subsequent validation.

RESULTS: The inferred parameters were physically reasonable and matched up to the widely used parameter values derived from the national data sets by Biggerstaff M, Cowling BJ, Cucunubá ZM et al. (Early insights from statistical and mathematical modeling of key epidemiologic parameters of COVID-19, Emerging infectious diseases. 2020;26(11)). We validate the predictive power of our model by using a subset of the available data and comparing the model predictions for the next 10, 20 and 30 days. The model exhibits a high accuracy in the prediction, even when using only as few as 20 data points for the fitting.

CONCLUSIONS: We have demonstrated that by using local/regional data, our predictive and forecasting model can be utilized to guide the local healthcare demand and capacity, policy-making and public health decisions to mitigate the impact of COVID-19 on the local population. Understanding how future COVID-19 spikes/waves could possibly affect the regional populations empowers us to ensure the timely commissioning and organization of services. The flexibility of timings in the model, in combination with other early-warning systems, produces a time frame for these services to prepare and isolate capacity for likely and potential demand within regional hospitals. The model also allows local authorities to plan potential mortuary capacity and understand the burden on crematoria and burial services. The model algorithms have been integrated into a web-based multi-institutional toolkit, which can be used by NHS hospitals, local authorities and public health departments in other regions of the UK and elsewhere. The parameters, which are locally informed, form the basis of predicting and forecasting exercises accounting for different scenarios and impacts of COVID-19 transmission.

PMID:34244764 | DOI:10.1093/ije/dyab106

Nat Protoc. 2021 Jul 9. doi: 10.1038/s41596-021-00566-6. Online ahead of print.

ABSTRACT

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

PMID:34244696 | DOI:10.1038/s41596-021-00566-6

Ann Behav Med. 2021 Jul 9:kaab057. doi: 10.1093/abm/kaab057. Online ahead of print.

ABSTRACT

BACKGROUND: Cancer impacts both patients and their family caregivers. Evidence suggests that caregiving stress, including the strain of taking on a new role, can elevate the risk of numerous health conditions, including high blood pressure (BP). However, the caregiver’s psychosocial experiences, including their interpersonal relationship with the patient, may buffer some of the negative physiological consequences of caregiving.

PURPOSE: To examine the influence of psychosocial contextual variables on caregiver ambulatory BP.

METHODS: Participants were 81 spouse-caregivers of patients with advanced gastrointestinal or thoracic cancer. For an entire day at home with the patient, caregivers wore an ambulatory BP monitor that took readings at random intervals. Immediately after each BP reading, caregivers reported on physical circumstances (e.g., posture, activity) and psychosocial experiences since the last BP measurement, including affect, caregiver and patient disclosure, and role perceptions (i.e., feeling more like a spouse vs. caregiver). Multilevel modeling was used to examine concurrent and lagged effects of psychosocial variables on systolic and diastolic BP, controlling for momentary posture, activity, negative affect, and time.

RESULTS: Feeling more like a caregiver (vs. spouse) was associated with lower systolic BP at the same time point. Patient disclosure to the caregiver since the previous BP reading was associated with higher diastolic BP. No lagged effects were statistically significant.

CONCLUSIONS: Caregivers’ psychosocial experiences can have immediate physiological effects. Future research should examine possible cognitive and behavioral mechanisms of these effects, as well as longer-term effects of caregiver role perceptions and patient disclosure on caregiver psychological and physical health.

PMID:34244701 | DOI:10.1093/abm/kaab057

Commun Biol. 2021 Jul 9;4(1):856. doi: 10.1038/s42003-021-02385-7.

ABSTRACT

Mosquito vectors transmit various diseases through blood feeding, required for their egg development. Hence, blood feeding is a major physiological event in their life cycle, during which hundreds of genes are tightly regulated. Blood is a rich source of proteins for mosquitoes, but also contains many other molecules including microRNAs (miRNAs). Here, we found that human blood miRNAs are transported abundantly into the fat body tissue of Aedes aegypti, a key metabolic center in post-blood feeding reproductive events, where they target and regulate mosquito genes. Using an artificial diet spiked with the mimic of an abundant and stable human blood miRNA, hsa-miR-21-5p, and proteomics analysis, we found over 40 proteins showing differential expression in female Ae. aegypti mosquitoes after feeding. Of interest, we found that the miRNA positively regulates the vitellogenin gene, coding for a yolk protein produced in the mosquito fat body and then transported to the ovaries as a protein source for egg production. Inhibition of hsa-miR-21-5p followed by human blood feeding led to a statistically insignificant reduction in progeny production. The results provide another example of the involvement of small regulatory molecules in the interaction of taxonomically vastly different taxa.

PMID:34244602 | DOI:10.1038/s42003-021-02385-7

Nat Med. 2021 Jul 9. doi: 10.1038/s41591-021-01446-y. Online ahead of print.

ABSTRACT

The SARS-CoV-2 pandemic continues to be a global health concern. The mRNA-1273 (Moderna) vaccine was reported to have an efficacy of 94.1% at preventing symptomatic COVID-19 due to infection with ‘wild-type’ variants in a randomized clinical trial. Here, we assess the real-world effectiveness of this vaccine against SARS-CoV-2 variants of concern, specifically B.1.1.7 (Alpha) and B.1.351 (Beta), in Qatar, a population that comprises mainly working-age adults, using a matched test-negative, case-control study design. We show that vaccine effectiveness was negligible for 2 weeks after the first dose, but increased rapidly in the third and fourth weeks immediately before administration of a second dose. Effectiveness against B.1.1.7 infection was 88.1% (95% confidence interval (CI): 83.7-91.5%) ≥14 days after the first dose but before the second dose, and was 100% (95% CI: 91.8-100.0%) ≥14 days after the second dose. Analogous effectiveness against B.1.351 infection was 61.3% after the first dose (95% CI: 56.5-65.5%) and 96.4% after the second dose (95% CI: 91.9-98.7%). Effectiveness against any severe, critical or fatal COVID-19 disease due to any SARS-CoV-2 infection (predominantly B.1.1.7 and B.1.351) was 81.6% (95% CI: 71.0-88.8%) and 95.7% (95% CI: 73.4-99.9%) after the first and second dose, respectively. The mRNA-1273 vaccine is highly effective against B.1.1.7 and B.1.351 infections, whether symptomatic or asymptomatic, and against any COVID-19 hospitalization and death, even after a single dose.

PMID:34244681 | DOI:10.1038/s41591-021-01446-y

Sci Rep. 2021 Jul 9;11(1):14250. doi: 10.1038/s41598-021-93719-2.

ABSTRACT

Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9-95.8%; Sensitivity: 55.5-77.8%; Specificity: 91.5-98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.

PMID:34244563 | DOI:10.1038/s41598-021-93719-2

Sci Rep. 2021 Jul 9;11(1):14179. doi: 10.1038/s41598-021-93483-3.

ABSTRACT

The main aim of the study was to assess the risk of falls, and physical fitness in the group of women aged 60 to 65 years of age suffering from an identified osteoporosis in comparison to a similar group of healthy women. The main question was: What is the level of physical fitness and risk of fall among women with osteoporosis compared to healthy women? The research included 262 women aged 60 to 65 of age: 135 with osteoporosis and 127 healthy ones, living in the Małopolskie and the Świętokrzyskie Provinces of Poland. To assess the level of physical fitness, the Senior Fitness Test (SFT) was used, while the Tinetti POMA (Performance Oriented Mobility Assessment) and Timed Up&Go test (TUG) were used to asses the risk of fall. Significant statistical differences in average results of physical fitness assessment were noticed as regards the following aspects: flexibility of the lower body part p < 0.001; flexibility of the upper body part p < 0.001. Essential differences were demonstrated in assessing the risk of falling with p < 0.01. Women with osteoporosis are marked by a lower physical fitness than healthy women. A higher percentage of great and serious risk of fall was demonstrated among women with osteoporosis.

PMID:34244566 | DOI:10.1038/s41598-021-93483-3

Sci Rep. 2021 Jul 9;11(1):14180. doi: 10.1038/s41598-021-92964-9.

ABSTRACT

High resistance towards traditional antibiotics has urged the development of new, natural therapeutics against methicillin-resistant Staphylococcus aureus (MRSA). Prenylated (iso)flavonoids, present mainly in the Fabaceae, can serve as promising candidates. Herein, the anti-MRSA properties of 23 prenylated (iso)flavonoids were assessed in-vitro. The di-prenylated (iso)flavonoids, glabrol (flavanone) and 6,8-diprenyl genistein (isoflavone), together with the mono-prenylated, 4′-O-methyl glabridin (isoflavan), were the most active anti-MRSA compounds (Minimum Inhibitory Concentrations (MIC) ≤ 10 µg/mL, 30 µM). The in-house activity data was complemented with literature data to yield an extended, curated dataset of 67 molecules for the development of robust in-silico prediction models. A QSAR model having a good fit (R²_adj 0.61), low average prediction errors and a good predictive power (Q²) for the training (4% and Q²_LOO 0.57, respectively) and the test set (5% and Q²_test 0.75, respectively) was obtained. Furthermore, the model predicted well the activity of an external validation set (on average 5% prediction errors), as well as the level of activity (low, moderate, high) of prenylated (iso)flavonoids against other Gram-positive bacteria. For the first time, the importance of formal charge, besides hydrophobic volume and hydrogen-bonding, in the anti-MRSA activity was highlighted, thereby suggesting potentially different modes of action of the different prenylated (iso)flavonoids.

PMID:34244528 | DOI:10.1038/s41598-021-92964-9