Category: Nevin Manimala

J Chem Theory Comput. 2023 Jan 23. doi: 10.1021/acs.jctc.2c00976. Online ahead of print.

ABSTRACT

The dynamics of molecules are governed by rare event transitions between long-lived (metastable) states. To explore these transitions efficiently, many enhanced sampling protocols have been introduced that involve using simulations with biases or changed temperatures. Two established statistically optimal estimators for obtaining unbiased equilibrium properties from such simulations are the multistate Bennett acceptance ratio (MBAR) and the transition-based reweighting analysis method (TRAM). Both MBAR and TRAM are solved iteratively and can suffer from long convergence times. Here, we introduce stochastic approximators (SA) for both estimators, resulting in SAMBAR and SATRAM, which are shown to converge faster than their deterministic counterparts, without significant accuracy loss. Both methods are demonstrated on different molecular systems.

PMID:36689637 | DOI:10.1021/acs.jctc.2c00976

Medwave. 2023 Jan 23;23(1):e2592. doi: 10.5867/medwave.2023.01.2592.

ABSTRACT

Introduction: The SARS-CoV-2 pandemic has affected the entire population, especially vulnerable people with risk factors, such as people over 65 years. Globally and nationally, health protection measures were established to reduce transmission and the impact of the disease on the healthcare system, such as using face masks, hand washing, and social distancing, among others. This led to restrictions on activities outside the home, which affected the cognitive sphere of the population, especially people over 65 years of age. Objective: To demonstrate that social isolation causes changes in the cognitive status of people over 65 years of age. Methods: A longitudinal study was conducted from 2019 to 2020, with the participation of 37 older adults in a parish club of support activities who voluntarily agreed to participate by signing the informed consent form. The Folstein Mini-Mental State Examination was administered to all of them at two points in the study: before the pandemic and after six months of strict social isolation established as a control measure for the SARS-CoV-2 pandemic. We looked for cognitive status differences during this period and studied qualitative-quantitative sociodemographic variables. Results: The club members were older people, predominantly women. Mean age of the participants was 75.4 years; 89.2% had little schooling (less than ten years of formal education). Identified prevalent diseases were arterial hypertension and type-2 diabetes mellitus. In the first evaluation, six out of thirty-seven participants had slight cognitive deficits (16.2%), all females; there were no cases of cognitive impairment; the rest had normal cognitive status (31 out of 37, or 83.8%). After the second evaluation (at the end of strict isolation due to the pandemic), we observed that 11 (29.7%) participants had slight cognitive deficits (ten female and one male), which represents an increase of 13.5%. In addition, four participants (10.8%) showed mild cognitive impairment, all females. Such changes were statistically significant (p-value < 0.05). We conclude that social isolation due to the SARS-CoV-2 pandemic was related to changes in the cognitive status of the elderly.

PMID:36689629 | DOI:10.5867/medwave.2023.01.2592

Diabetes Care. 2023 Jan 23:dc221145. doi: 10.2337/dc22-1145. Online ahead of print.

ABSTRACT

OBJECTIVE: To determine effect of mini-dose, ready-to-use glucagon on incidence of exercise-associated hypoglycemia (EAH) in adults with type 1 diabetes.

RESEARCH DESIGN AND METHODS: Individuals initially participated in the in-clinic training phase for which they were randomly assigned to a crossover design: 150 µg glucagon (treatment arm A) or placebo (arm B) subcutaneously, immediately before exercise, plus 50% reduction in continuous subcutaneous insulin infusion (CSII) basal delivery rate. Completers were then rerandomly assigned in the 12-week outpatient investigational phase: arm A, B, or open-label C, 150 µg glucagon alone. Participants were to undertake their usual aerobic exercise at moderate to high intensity for 30 to 75 min in real-world settings. Data were analyzed for incidence of level 1 hypoglycemia based on self-monitoring blood glucose and for various secondary and exploratory end points.

RESULTS: Of 48 participants who completed the training phase, 45 continued to the outpatient phase. For all exercise sessions in the outpatient phase (n = 795), incidence of level 1 hypoglycemia was lower in both glucagon arms (A, 12% [P < 0.0001]; C, 16% [P = 0.0032]) than in the placebo arm (B, 39%). Times below range, in range, and above range from 0 to 300 min did not significantly differ among treatment arms. Consumed grams of exercise carbohydrates were lower with glucagon use than with placebo use but did not reach statistical significance (P = 0.12). Adverse events were similar among treatment arms.

CONCLUSIONS: Mini-dose glucagon with or without 50% reduction in CSII basal delivery rate may help to decrease EAH incidence in adults with type 1 diabetes.

PMID:36689626 | DOI:10.2337/dc22-1145

Stroke. 2023 Feb;54(2):476-487. doi: 10.1161/STROKEAHA.122.040065. Epub 2023 Jan 23.

ABSTRACT

BACKGROUND: Symptomatic intracranial hemorrhage (sICH) is a severe complication of reperfusion therapy for ischemic stroke. Multiple models have been developed to predict sICH or intracranial hemorrhage (ICH) after reperfusion therapy. We provide an overview of published models and validate their ability to predict sICH in patients treated with endovascular treatment in daily clinical practice.

METHODS: We conducted a systematic search to identify models either developed or validated to predict sICH or ICH after reperfusion therapy (intravenous thrombolysis and/or endovascular treatment) for ischemic stroke. Models were externally validated in the MR CLEAN Registry (n=3180; Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands). The primary outcome was sICH according to the Heidelberg Bleeding Classification. Model performance was evaluated with discrimination (c-statistic, ideally 1; a c-statistic below 0.7 is considered poor in discrimination) and calibration (slope, ideally 1, and intercept, ideally 0).

RESULTS: We included 39 studies describing 40 models. The most frequently used predictors were baseline National Institutes of Health Stroke Scale (NIHSS; n=35), age (n=22), and glucose level (n=22). In the MR CLEAN Registry, sICH occurred in 188/3180 (5.9%) patients. Discrimination ranged from 0.51 (SPAN-100 [Stroke Prognostication Using Age and National Institutes of Health Stroke Scale]) to 0.61 (SITS-SICH [Safe Implementation of Treatments in Stroke Symptomatic Intracerebral Hemorrhage] and STARTING-SICH [STARTING Symptomatic Intracerebral Hemorrhage]). Best calibrated models were IST-3 (intercept, -0.15 [95% CI, -0.01 to -0.31]; slope, 0.80 [95% CI, 0.50-1.09]), SITS-SICH (intercept, 0.15 [95% CI, -0.01 to 0.30]; slope, 0.62 [95% CI, 0.38-0.87]), and STARTING-SICH (intercept, -0.03 [95% CI, -0.19 to 0.12]; slope, 0.56 [95% CI, 0.35-0.76]).

CONCLUSIONS: The investigated models to predict sICH or ICH discriminate poorly between patients with a low and high risk of sICH after endovascular treatment in daily clinical practice and are, therefore, not clinically useful for this patient population.

PMID:36689584 | DOI:10.1161/STROKEAHA.122.040065

Stroke. 2023 Feb;54(2):448-456. doi: 10.1161/STROKEAHA.122.040542. Epub 2023 Jan 23.

ABSTRACT

BACKGROUND: Distal embolization (DE) is a common complication of endovascular treatment (EVT). We investigated the association of radiological thrombus characteristics and treatment details with DE.

METHODS: Patients with thin-slice (≤2.5 mm) baseline noncontrast computed tomography and computed tomography angiography from the ESCAPE-NA1 trial (Efficacy and Safety of Nerinetide for the Treatment of Acute Ischemic Stroke) were included. Thrombus annotation was performed manually on coregistered scans by experienced readers. We assessed thrombus location, distance from internal carotid artery terminus, length, perviousness, absolute attenuation, and hyperdense artery sign. In addition, we evaluated balloon guide catheter use during EVT, first-line EVT approach, the number of thrombectomy passes, and prior intravenous thrombolysis administration. DE was defined as the occurrence of emboli distal to the target artery or in new territories during EVT. The association between thrombus characteristics, treatment details, and DE was evaluated using descriptive statistics and multivariable mixed-effects logistic regression, resulting in adjusted odds ratios (aOR) with 95% CI. Interaction between IVT and radiological thrombus characteristics was assessed by adding interaction terms in separate models.

RESULTS: In total, 496 out of 1105 (44.9%) ESCAPE-NA1 patients were included. DE was detected in 251 out of 496 patients (50.6%). Patients with DE had longer thrombi (median, 28.5 [interquartile range, 20.8-42.3] mm versus 24.4 [interquartile range, 17.1-32.4] mm; P<0.01). There were no statistically significant differences in the other thrombus characteristics. Factors associated with DE were thrombus length (aOR, 1.02 [95% CI, 1.01-1.04]), balloon guide catheter use (aOR, 0.49 [95% CI, 0.29-0.85]), and number of passes (aOR, 1.24 [95% CI, 1.04-1.47]). In patients with hyperdense artery sign, IVT was associated with reduced odds of DE (aOR, 0.55 [95% CI, 0.31-0.97]), P for interaction=0.04.

CONCLUSIONS: DE was associated with longer thrombi, no balloon guide catheter use, and more EVT passes. IVT was associated with a reduced risk of DE in patients with hyperdense artery sign. These findings may support treatment decisions on IVT and EVT approaches.

PMID:36689583 | DOI:10.1161/STROKEAHA.122.040542

PLoS Genet. 2023 Jan 23;19(1):e1010620. doi: 10.1371/journal.pgen.1010620. Online ahead of print.

ABSTRACT

Estimation of heritability and genetic covariance is crucial for quantifying and understanding complex trait genetic architecture and is employed in almost all recent genome-wide association studies (GWAS). However, many existing approaches for heritability estimation and almost all methods for estimating genetic correlation ignore the presence of indirect genetic effects, i.e., genotype-phenotype associations confounded by the parental genome and family environment, and may thus lead to incorrect interpretation especially for human sociobehavioral phenotypes. In this work, we introduce a statistical framework to decompose heritability and genetic covariance into multiple components representing direct and indirect effect paths. Applied to five traits in UK Biobank, we found substantial involvement of indirect genetic components in shared genetic architecture across traits. These results demonstrate the effectiveness of our approach and highlight the importance of accounting for indirect effects in variance component analysis of complex traits.

PMID:36689559 | DOI:10.1371/journal.pgen.1010620

PLoS One. 2023 Jan 23;18(1):e0280887. doi: 10.1371/journal.pone.0280887. eCollection 2023.

ABSTRACT

BACKGROUND: Despite the progress in achieving gender equality to a certain extent, women are found to be more susceptible to health disadvantages compared to men in the older ages. However, research in the Indian context has mainly remained restricted to subjective health that heavily depends on the individual’s perception, which may affect the validity of results. This study addresses this gap by complementing the investigation of the gender differentials in self-reported health outcomes (mobility and functional limitations) with that of objectively measured health status (hand-grip strength and static balance) among the older population of India. Besides, there is a dearth of literature that considers financial empowerment in explaining the gender differentials in health. Women’s ability to participate in household decision-making, especially for important matters like major purchases, including property, indicates their empowerment status. Furthermore, the ability to extend financial support can be considered an important ‘non-altruistic’ driver for kins to care for older adults, indirectly affecting their health and well-being. Thus, the present paper explores the influence of financial empowerment on gender differentials in poor health outcomes.

METHODS: Using the Longitudinal Aging Study in India, Wave-1 (2017-18), six logistic regression models have been specified to capture the adjusted association between gender and poor health outcomes. The first three models successively control for the demographic and social support factors; socioeconomic factors and pre-existing health conditions; and financial empowerment indicators. The last three models investigate the interactions between gender and marital status, living arrangement and involvement in financial decisions, respectively.

RESULTS: The findings reveal that women tend to be more perceptive about their physical discomfort than men and reported a higher prevalence of poor subjective health. In terms of objectively measured health status, older men had a higher prevalence of low hand-grip strength but a lower prevalence of poor balance. Gender demonstrated a strong, adjusted association with poor health outcomes among older adults. However, the magnitude of gender difference either shrunk considerably or became statistically insignificant for all the poor health outcomes after controlling the effect of indicators of financial empowerment. Further, the interaction between gender and involvement in financial matters demonstrated a stronger effect for men in reversing poor subjective health.

CONCLUSION: The study reinforced the positive effect of financial empowerment in mitigating gender disparity in health among older adults.

PMID:36689542 | DOI:10.1371/journal.pone.0280887

PLoS One. 2023 Jan 23;18(1):e0280761. doi: 10.1371/journal.pone.0280761. eCollection 2023.

ABSTRACT

Using solid waste in building materials is an efficient approach to achieving sustainability goals. Also, the application of modern methods like artificial intelligence is gaining attention. In this regard, the flexural strength (FS) of cementitious composites (CCs) incorporating waste glass powder (WGP) was evaluated via both experimental and machine learning (ML) methods. WGP was utilized to partially substitute cement and fine aggregate separately at replacement levels of 0%, 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. At first, the FS of WGP-based CCs was determined experimentally. The generated data, which included six inputs, was then used to run ML techniques to forecast the FS. For FS estimation, two ML approaches were used, including a support vector machine and a bagging regressor. The effectiveness of ML models was assessed by the coefficient of determination (R2), k-fold techniques, statistical tests, and examining the variation amongst experimental and forecasted FS. The use of WGP improved the FS of CCs, as determined by the experimental results. The highest FS was obtained when 10% and 15% WGP was utilized as a cement and fine aggregate replacement, respectively. The modeling approaches’ results revealed that the support vector machine method had a fair level of accuracy, but the bagging regressor method had a greater level of accuracy in estimating the FS. Using ML strategies will benefit the building industry by expediting cost-effective and rapid solutions for analyzing material characteristics.

PMID:36689541 | DOI:10.1371/journal.pone.0280761

PLoS One. 2023 Jan 23;18(1):e0280633. doi: 10.1371/journal.pone.0280633. eCollection 2023.

ABSTRACT

INTRODUCTION: Many countries around the world are still affected by the global pandemic of coronavirus disease. The vaccine is the most effective method of controlling Coronavirus Disease 2019 (COVID-19). However, attitudes toward vaccination are heavily affected by different factors besides vaccine availability.

OBJECTIVES: This study aimed to determine community attitudes toward the COVID-19 vaccine in Gurage Zone, Ethiopia.

METHODS: A community-based cross-sectional study was conducted from November 15th to December 15th, 2021. A simple random sampling technique was used to select 364 participants in the study area. An interview-administered structured questionnaire was used to collect the data; the data was entered into Epidata 3.1 version, and then exported to SPSS version 23 for further analysis. Descriptive statistics were used to determine the characteristics of study participants. Binary and multivariable logistic regression analyses with a p-value of less than 0.05 were used as a measure of significance.

RESULTS: In this study, 44.7% of study participants had a favorable attitude toward the COVID-19 vaccine. Perceived potential vaccine harm [AOR: 1.85; 95% CI (1.15-2.96)], Having ever had a chronic disease [AOR: 3.22; 95% CI (2.02-5.14)], community belief on the effectiveness of the vaccine [AOR: 2.02; 95% CI (1.27-3.22)], and average monthly income 3001-5000 ETB [AOR: 0.54; 95% CI (0.30-0.97)], average monthly income 5001-10000 ETB [AOR: 0.48; 95% CI(0.27-0.86)] were statistically significantly towards COVID-19 vaccination.

CONCLUSIONS: Overall, less than half of the participants had a favorable attitude toward the COVID-19 vaccine. Perceived potential vaccine harm, having ever had a chronic disease, community belief in the effectiveness of the vaccine, and average monthly income were determinant factors of the community’s attitude toward COVID-19 vaccination. As a result, information conversation with the community’s awareness of the COVID-19 vaccination in reducing vaccine-related suspicion.

PMID:36689539 | DOI:10.1371/journal.pone.0280633

PLoS Comput Biol. 2023 Jan 23;19(1):e1010855. doi: 10.1371/journal.pcbi.1010855. Online ahead of print.

ABSTRACT

How the connectivity of cortical networks determines the neural dynamics and the resulting computations is one of the key questions in neuroscience. Previous works have pursued two complementary approaches to quantify the structure in connectivity. One approach starts from the perspective of biological experiments where only the local statistics of connectivity motifs between small groups of neurons are accessible. Another approach is based instead on the perspective of artificial neural networks where the global connectivity matrix is known, and in particular its low-rank structure can be used to determine the resulting low-dimensional dynamics. A direct relationship between these two approaches is however currently missing, and in particular it remains to be clarified how local connectivity statistics and the global low-rank connectivity structure are inter-related and shape the low-dimensional activity. To bridge this gap, here we develop a method for mapping local connectivity statistics onto an approximate global low-rank structure. Our method rests on approximating the global connectivity matrix using dominant eigenvectors, which we compute using perturbation theory for random matrices. We demonstrate that multi-population networks defined from local connectivity statistics for which the central limit theorem holds can be approximated by low-rank connectivity with Gaussian-mixture statistics. We specifically apply this method to excitatory-inhibitory networks with reciprocal motifs, and show that it yields reliable predictions for both the low-dimensional dynamics, and statistics of population activity. Importantly, it analytically accounts for the activity heterogeneity of individual neurons in specific realizations of local connectivity. Altogether, our approach allows us to disentangle the effects of mean connectivity and reciprocal motifs on the global recurrent feedback, and provides an intuitive picture of how local connectivity shapes global network dynamics.

PMID:36689488 | DOI:10.1371/journal.pcbi.1010855