Changes in speech have the potential to provide important information on the diagnosis and progression of various neurological diseases. Many researchers have relied on open-source speech features to develop algorithms for measuring speech changes in clinical populations as they are convenient and easy to use. However, the repeatability of open-source features in the context of neurological diseases has not been studied.

We used a longitudinal sample of healthy controls, individuals with amyotrophic lateral sclerosis, and individuals with suspected frontotemporal dementia, and we evaluated the repeatability of acoustic and language features separately on these 3 data sets.

Repeatability was evaluated using intraclass correlation (ICC) and the within-subjects coefficient of variation (WSCV). In 3 sets of tasks, the median ICC were between 0.02 and 0.55, and the median WSCV were between 29 and 79%.

Our results demonstrate that the repeatability of speech features extracted using open-source tool kits is low. Researchers should exercise caution when developing digital health models with open-source speech features. We provide a detailed summary of feature-by-feature repeatability results (ICC, WSCV, SE of measurement, limits of agreement for WSCV, and minimal detectable change) in the online supplementary material so that researchers may incorporate repeatability information into the models they develop.

Our results demonstrate that the repeatability of speech features extracted using open-source tool kits is low. Researchers should exercise caution when developing digital health models with open-source speech features. We provide a detailed summary of feature-by-feature repeatability results (ICC, WSCV, SE of measurement, limits of agreement for WSCV, and minimal detectable change) in the online supplementary material so that researchers may incorporate repeatability information into the models they develop.During the last 30 y, a gluten-free diet has been classified among the most popular fad diets mainly due to the ambiguous notion that gluten avoidance promotes health. Gluten intolerance has been implicated in non-celiac gluten sensitivity (NCGS) and irritable bowel syndrome (IBS), 2 disorders with overlapping symptoms and increasing trend. Together with gluten, other wheat components; fermentable oligo-, di-, monosaccharide, and polyols (FODMAPs); and amylase trypsin inhibitors (ATIs), are implicated in the pathogenesis of both disorders. Gut microflora alterations in IBS and NCGS have been described, while microbiota manipulations have been shown to be promising in some IBS cases. This literature review summarizes our current knowledge on the impact of wheat ingredients (gluten, FODMAPs, and ATIs) in IBS and NCGS. In both disorders, FODMAPs and ATIs trigger gut dysbiosis, suggesting that gluten may not be the culprit, and microbiota manipulations can be applied in diagnostic and intervention approaches.

Lipodystrophy syndromes cause hypertriglyceridemia that improves with leptin treatment using metreleptin. Mechanisms causing hypertriglyceridemia and improvements after metreleptin are incompletely understood.

Determine relationship of circulating lipoprotein lipase (LPL) modulators with hypertriglyceridemia in healthy controls and in patients with lipodystrophy before and after metreleptin.

Cross-sectional comparison of patients with lipodystrophy (generalized lipodystrophy n = 3; partial lipodystrophy n = 11) vs age/sex-matched healthy controls (n = 28), and longitudinal analyses in patients before and after 2 weeks and 6 months of metreleptin. The study was carried out at the National Institutes of Health, Bethesda, Maryland. Outcomes were LPL stimulators apolipoprotein (apo) C-II and apoA-V and inhibitors apoC-III and angiopoietin-like proteins (ANGPTLs) 3, 4, and 8; ex vivo activation of LPL by plasma.

Patients with lipodystrophy were hypertriglyceridemic and had higher levels of all LPL stimulat circulating and hepatic triglycerides after metreleptin. These therefore are strong candidates for therapies to lower triglycerides in these patients.A bottom-illuminated orbital shaker designed for the cultivation of microalgae suspensions is described in this open-source hardware report. The instrument agitates and illuminates microalgae suspensions grown inside flasks. It was optimized for low production cost, simplicity, low power consumption, design flexibility, consistent, and controllable growth light intensity. The illuminated orbital shaker is especially well suited for low-resource research laboratories and education. It is an alternative to commercial instruments for microalgae cultivation. It improves on typical do-it-yourself microalgae growth systems by offering consistent and well characterized illumination light intensity. The illuminated growth area is 20 cm × 15 cm, which is suitable for three T75 tissue culture flasks or six 100 ml Erlenmeyer flasks. The photosynthetic photon flux density, is variable in eight steps ( 26 – 800 μ mol · m – 2 · s – 1 ) and programmable in a 24-h light/dark cycle. The agitation speed is variable ( 0 – 210 RPM ). The overall material cost is around £300, including an entry-level orbital shaker. The build takes two days, requiring electronics and mechanical assembly capabilities. The instrument build is documented in a set of open-source protocols, design files, and source code. The design can be readily modified, scaled, and adapted for other orbital shakers and specific experimental requirements. The instrument function was validated by growing fresh-water microalgae Desmodesmus quadricauda and Chlorella vulgaris. The cultivation protocols, microalgae growth curves, and doubling times are included in this report.

Fatigue is a common complaint and shares many risk factors with falls, yet the independent contribution of fatigue on fall risk is unclear. This study’s primary aim was to assess the association between fatigue and prospective fall risk in 5642 men aged 64-100 enrolled in the Osteoporotic Fractures in Men Study (MrOS). The secondary aim was to examine the association between fatigue and recurrent fall risk.

Fatigue was measured at baseline using the Medical Outcomes Study (short form) single-item question „During the past four weeks, how much of the time did you feel energetic?” Responses were then classified higher fatigue = „none,” „a little,” or „some” of the time and lower fatigue = „a good bit,” „most,” or „all” of the time. We assessed falls using triannual questionnaires. Fall risk was examined prospectively over 3 years; recurrent falling was defined as at least 2 falls within the first year. Generalized estimating equations and multinomial logistic regression modeled prospective and recurrent faltigue (ie, increased energy) may lessen the burden of falls in older men and provide a novel avenue for fall risk intervention.

Studies evaluating self-reported cognitive impairment among Arab American immigrants have not been conducted. Our objective was 2-fold (a) to estimate and compare the age- and sex-adjusted prevalence of self-reported cognitive impairment between Arab American immigrants and U.S.- and immigrant non-Hispanic Whites, non-Hispanic Blacks, Hispanics and non-Hispanic Asians and (b) to examine associations between race, ethnicity, nativity status, and cognitive impairment among Arab American immigrants and non-Hispanic Whites (U.S.- and foreign-born) after controlling for explanatory factors.

We used 18 years (2000-2017) of National Health Interview Survey data (

= 228 985; ages ≥ 45 years). Weighted percentages, prevalence estimates, and multivariable logistic regression models were calculated.

The age- and sex-adjusted prevalence of self-reported cognitive impairment was significantly higher among Arab American immigrants (9.7%) compared to U.S.-born and non-Hispanic White immigrants (~7.4%).

This is the first study to indicate that ethnic disparities in self-reported cognitive impairment may extend to Arab American immigrants. Additional studies need to be conducted to better understand the prevalence of cognitive impairment.

This is the first study to indicate that ethnic disparities in self-reported cognitive impairment may extend to Arab American immigrants. Additional studies need to be conducted to better understand the prevalence of cognitive impairment.Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research. Conventional approaches in ML use cross-entropy loss (CEL) that often suffers from poor margin classification. For the first time, we show that contrastive loss (CL) improves the performance of CEL especially for imbalanced EHR data and the related COVID-19 analyses. This study has been approved by the Institutional Review Board at the Icahn School of Medicine at Mount Sinai. We use EHR data from five hospitals within the Mount Sinai Health System (MSHS) to predict mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over 24 and 48 hour time windows. We train two sequential architectures (RNN and RETAIN) using two loss functions (CEL and CL). Models are tested on full sample data set which contain all available data and restricted data set to emulate higher class imbalance.CL models consistently outperform CEL models with the restricted data set on these tasks with differences ranging from 0.04 to 0.15 for AUPRC and 0.05 to 0.1 for AUROC. For the restricted sample, only the CL model maintains proper clustering and is able to identify important features, such as pulse oximetry. CL outperforms CEL in instances of severe class imbalance, on three EHR outcomes with respect to three performance metrics predictive power, clustering, and feature importance. We believe that the developed CL framework can be expanded and used for EHR ML work in general.With the severity of the COVID-19 outbreak, we characterize the nature of the growth trajectories of counties in the United States using a novel combination of spectral clustering and the correlation matrix. As the U.S. and the rest of the world are experiencing a severe second wave of infections, the importance of assigning growth membership to counties and understanding the determinants of the growth are increasingly evident. Subsequently, we select the demographic features that are most statistically significant in distinguishing the communities. Lastly, we effectively predict the future growth of a given county with an LSTM using three social distancing scores. This comprehensive study captures the nature of counties’ growth in cases at a very micro-level using growth communities, demographic factors, and social distancing performance to help government agencies utilize known information to make appropriate decisions regarding which potential counties to target resources and funding to.Factors such as non-uniform definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US is 13$\%$ higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find negligible or negative excess deaths for part and all of 2020 for Denmark, Germany, and Norway.