Tag: nevin manimala

Gait Posture. 2021 Jul 16;89:126-131. doi: 10.1016/j.gaitpost.2021.07.007. Online ahead of print.

ABSTRACT

BACKGROUND: Increased terminal swing knee flexion (TSKF) impacts on step length, walking efficiency and may lead to knee flexion in stance in cerebral palsy (CP). Surgical lengthening of the hamstrings is often used to address this issue, but outcomes are inconsistent. There is an established association between TSKF and functional shortening or reduced lengthening velocity of the hamstrings. However, the aetiology of increased TSKF in CP is complex and additional associated factors are not well understood. An examination of clinical and kinematic factors associated with increased TSKF may demonstrate this complexity, highlight the multifactorial nature of this feature and provide a basis for enhanced treatment decision making.

RESEARCH QUESTION: What kinematic and clinical factors are associated with TSKF in individuals with CP?.

METHODS: A retrospective database review was conducted. Individuals with bilateral CP were identified and a subset was extracted which represented the full spectrum of degree of TSKF in the database. The total dataset for analysis was n = 88. Associations between absolute clinical and kinematic data and TSKF were explored using correlation analysis, linear and multivariate regression. Time series data were examined across quartiles using statistical parametric mapping analysis of variance (SPM ANOVA).

RESULTS: Increased TSKF was associated with overall gait impairment (GDI), degree of knee flexion throughout the stride, knee extension velocity, hamstring lengthening characteristics and functional status (GMFCS). There was no relationship to walking speed or clinical measures of hamstring extensibility on clinical assessment.

SIGNIFICANCE: TSKF is associated with multiple factors which clinicians need to consider when devising treatment strategies. Caution is advised when relying on degree of TSKF to independently guide surgical decision-making.

PMID:34280883 | DOI:10.1016/j.gaitpost.2021.07.007

Comput Biol Med. 2021 Jul 12;135:104648. doi: 10.1016/j.compbiomed.2021.104648. Online ahead of print.

ABSTRACT

BACKGROUND: Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools.

METHOD: Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death.

RESULTS: The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively.

CONCLUSIONS: The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general.

PMID:34280775 | DOI:10.1016/j.compbiomed.2021.104648

Spectrochim Acta A Mol Biomol Spectrosc. 2021 Jul 12;263:120171. doi: 10.1016/j.saa.2021.120171. Online ahead of print.

ABSTRACT

Spectral-fluorescent and photochemical properties (photoisomerization and generation of the triplet state) of meso-substituted cationic carbocyanine dyes, 3,3′-di-(β-hydroxyethyl)-5,5′-dimethoxy-9-ethylthiacarbocyanine iodide (K1) and 3,3′-di-(β-hydroxyethyl)-9-methylthiacarbocyanine iodide (K2), have been studied in solutions and in the presence of DNA. In solutions, on passing from acetonitrile to dioxane, a growth of fluorescence of the dyes is observed due to a shift of the equilibrium of cis/trans isomers toward the fluorescent trans-isomer. Upon flash photolysis of dye solutions in dioxane, the formation and subsequent decay of the cis-photoisomers of the dyes are observed. In aqueous solutions, the interaction with DNA leads to the formation of noncovalent complexes of K1 and K2 with DNA, which is accompanied by a significant increase in the fluorescence intensity. The results of the molecular docking experiments showed the possibility of several types of binding, which was confirmed by the data obtained from other experiments. The effects of temperature and additions of NaCl on the stability of the dye-DNA complexes were studied. The spectral-fluorescent data were used to estimate the binding constants of the dyes with DNA and other characteristics of the dyes that are important for their use as probes. Upon flash photolysis of the dyes in complexes with DNA, photoisomerization is not observed, but the quantum yield of intersystem crossing to the triplet state increases. The decay of the triplet states occurs by a two-exponential law. The rate constants for quenching of the triplet states of the dyes complexed with DNA by oxygen were found to be lower than the expected values for diffusion-controlled quenching (taking into account the spin statistical factor 1/9), which is explained by the steric factor of complexation.

PMID:34280796 | DOI:10.1016/j.saa.2021.120171

J Environ Manage. 2021 Jul 16;297:113283. doi: 10.1016/j.jenvman.2021.113283. Online ahead of print.

ABSTRACT

Severe drought events in recent decades and their catastrophic effects have called for drought prediction and monitoring needed for developing drought readiness plans and mitigation measures. This study used a fusion-based framework for meteorological drought modeling for the historical (1983-2016) and future (2020-2050) periods using remotely sensed datasets versus ground-based observations and climate change scenarios. To this aim, high-resolution remotely sensed precipitation datasets, including PERSIANN-CDR and CHIRPS (multi-source products), ERA5 (reanalysis datasets), and GPCC (gauge-interpolated datasets), were employed to estimate non-parametric SPI (nSPI) as a meteorological drought index against local observations. For more accurate drought evaluation, all stations were classified into different clusters using the K-means clustering algorithm based on ground-based nSPI. Then, four Individual Artificial Intelligence (IAI) models, including Adaptive Neuro-Fuzzy Inference System (ANFIS), Group Method of Data Handling (GMDH), Multi-Layer Perceptron (MLP), and General Regression Neural Network (GRNN), were developed for drought modeling within each cluster. Finally, two advanced fusion-based methods, including Multi-Model Super Ensemble (MMSE) as a linear weighted model and a nonlinear model called machine learning Random Forest (RF), combined results by IAI models using different remotely sensed datasets. The proposed framework was implemented to simulate each remotely sensed precipitation data for the future based on CORDEX regional climate models (RCMs) under RCP4.5 and RCP8.5 scenarios for drought projection. The efficiency of IAI and fusion models was evaluated using statistical error metrics, including the coefficient of determination (R²), Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The proposed methodology was employed in the Gavkhooni basin of Iran, and results showed that the RF model with the lowest estimation error (RMSE of 0.391 and R² of 0.810) had performed well compared to all other models. Finally, the resilience, vulnerability, and frequency of probability metrics indicated that the 12-month time scale of drought affected the basin more severely than other time scales.

PMID:34280857 | DOI:10.1016/j.jenvman.2021.113283

J Health Econ. 2021 Jul 9;79:102494. doi: 10.1016/j.jhealeco.2021.102494. Online ahead of print.

ABSTRACT

We examine whether the least educated population groups experienced the worst mortality trends at the beginning of the 21st century by measuring changes in mortality across education quartiles. We document sharply differing gender patterns. Among women, mortality trends improved fairly monotonically with education. Conversely, male trends for the lowest three education quartiles were often similar. For both sexes, the gap in mortality between the top 25 percent and the bottom 75 percent is growing. However, there are many groups for whom these patterns are reversed – with better experiences for the less educated – or where the differences are statistically indistinguishable.

PMID:34280727 | DOI:10.1016/j.jhealeco.2021.102494

Diabetes Metab Syndr. 2021 Jul 12;15(5):102207. doi: 10.1016/j.dsx.2021.102207. Online ahead of print.

ABSTRACT

AIMS: the objectives of this study are to reveal the potential side effects after taking the covid19 vaccines, associated risk factors with severe side effects, and to compare the three COVID-19 vaccines available in Iraq (Sinopharm, AstraZeneca-Oxford and Pfizer- BioNTech).

METHODS: a randomized cross-sectional study was conducted in April 2021. A standardized questionnaire platform was utilized to collect information about the Iraqi population.

RESULTS: 1012 were enrolled in the study, 60.2% were male and 39.8% were female. 84% were symptomatic post vaccination. Young aged participants, females, participants with history of COVID19 infection, those with comorbid diseases and AstraZeneca vaccine receivers were statistically significant risk factors for having adverse reactions post vaccination, P value (0.03, 0.028, 0.007, 0.019 and 0.0001) respectively. Regarding severity of symptoms, most symptoms were mild and moderate. Residency in Kurdistan Region of Iraq and AstraZeneca vaccine were the statistically significant risk factors for getting severe symptoms P value < 0.0001 of both. Females were an associated risk factor for D-dimer elevation P value = 0.05.

CONCLUSION: fatigue, injection site reactions, fever, myalgia, headache and chills were the most reported side effects. Most symptoms were mild to moderate in term of severity.

PMID:34280733 | DOI:10.1016/j.dsx.2021.102207

J Chromatogr B Analyt Technol Biomed Life Sci. 2021 Jul 10;1178:122857. doi: 10.1016/j.jchromb.2021.122857. Online ahead of print.

ABSTRACT

The complexity of ingredients in traditional Chinese medicine (TCM) makes it challenging to clarify its efficacy in an acceptable and scientific approach. The present study was aimed to use quantification results from targeted cellular metabolomics to evaluate anti-aging efficacy of a famous Chinese medicine formula, Erzhi Wan (EZW), and screen possible effective extracts, depending on the developed strategy integrating multivariate receiver operating characteristic (ROC) curve and analytic hierarchy process (AHP). In this study, senescent NRK cells induced by D-galactose were treated with drug-containing serum of EZW and four kinds of extracts (petroleum ether, ethyl acetate, butanol and water). Intermediates of two major metabolic pathways for energy synthesis, tricarboxylic acid (TCA) cycle and glycolysis, were accurately quantified by GC-MS/MS to identify discriminate metabolites for clarifying therapeutic mechanism of EZW based on multivariate statistical analysis. Senescent and non-senescent cells were successfully distinguished using these metabolites by ROC curve analysis. Next, these metabolites were used as evaluation indexes to quantitatively reflect different effect of EZW and its extracts, according to the role of them in distinguishing groups and in conjunction with AHP. In vitro detection of senescence-associated β-galactosidase (SA-β-gal) activity was used to verify the reliability of evaluation results. The reversal after treatment of drug-containing serum of EZW and extracts was observed, and the petroleum ether extract might be the potential active extract responsible for the major anti-aging effect of EZW, which was in agreement with in vitro experiments. Altogether, metabolomics was a powerful approach for evaluation efficacy and elucidation action mechanisms of TCM. The integrated evaluation strategy in this paper with properties of high practicality, feasibility and effectivity was expected to provide a new insight into comprehensive and quantitative efficacy evaluation.

PMID:34280712 | DOI:10.1016/j.jchromb.2021.122857

Health Place. 2021 Jul 16;70:102628. doi: 10.1016/j.healthplace.2021.102628. Online ahead of print.

ABSTRACT

Inspired by the influential “deaths of despair” narrative, which emphasizes the role of worsening economic opportunity in driving the increasing mortality for non-Hispanic Whites in the recent decades, a rising number of studies have provided suggestive evidence that upward mobility levels across counties may partly explain variations in mortality rates. A gap in the literature is the lack of life-course studies examining the relationship between early-life upward mobility and later-life mortality across counties. Another gap is the lack of studies on how the relationship between upward mobility and mortality across counties varies across diverse sociodemographic populations. This study examines differences across race and sex in the relationship between early-life intergenerational upward mobility and early adulthood mortality at the county level. We use administrative data on upward mobility and vital statistics data on mortality across 3030 counties for those born between 1978 and 1983. We control for a variety of county-level socioeconomic variables in a model with fixed effects for state and year. Subgroup analyses by educational attainment and urban status were also performed for each race-sex combination. Results show strong negative relationships between early-life upward mobility and early adulthood mortality across racial-sex combinations, with a particularly greater magnitude for non-Hispanic Black males. In addition, individuals without a college degree and living in urban counties are particularly affected by early life upward mobility. The findings of this study highlight the vulnerability of less-educated, young urban Black males, due to the intersecting effects of the urban context, education, race, and sex.

PMID:34280713 | DOI:10.1016/j.healthplace.2021.102628

Accid Anal Prev. 2021 Jul 16;159:106297. doi: 10.1016/j.aap.2021.106297. Online ahead of print.

ABSTRACT

Making motorcycle rides safer by advanced technology is an ongoing challenge in the context of developing driving assistant systems and safety infrastructure. Determining which section of a road and which driving behaviour is “safe” or “unsafe” is rarely possible due to the individual differences in driving experience, driving style, fitness and potentially available assistant systems. This study investigates the feasibility of a new approach to quantify motorcycle riding risk for an experimental sample of bikers by collecting motorcycle-specific dynamic data of several riders on selected road sections. Comparing clustered dynamics with the observed dynamic data at known risk spots, we provide a method to represent individual risk estimates in a single risk map for the investigated road section. This yields a map of potential risk spots, based on an aggregation of individual risk estimates. The risk map is optimized to include most of the previous accident sites, while keeping the overall area classified as risky small. As such, with data collected on a large scale, the presented methodology could guide safety inspections at the highlighted areas of a risk map and be the basis of further studies into the safety relevant differences in driving styles.

PMID:34280694 | DOI:10.1016/j.aap.2021.106297

Clin Neurol Neurosurg. 2021 Jul 10;207:106805. doi: 10.1016/j.clineuro.2021.106805. Online ahead of print.

ABSTRACT

BACKGROUND: Multiple sclerosis (MS) is a demyelinating disease of the central nervous system, predominating within young adults. Cognitive disorders are common in MS and have are associated with several Magnetic Resonance Imaging (MRI) markers, especially brain atrophy. Many have found the symbol digit modalities test (SDMT) to be the most sensitive individual cognitive measure relevant to MS. However, the relationship between SDMT and regional brain cortex thickness in young adults with relapsing-remitting multiple sclerosis (YA-RRMS) has been little explored. The purpose of this study was to investigate the association between the SDMT and regional cortex thickness in YA-RRMS by FreeSurfer, which is an automatic brain structure segmentation method.

METHOD: Twenty-eight YA-RRMS patients (18-35 years old) were enrolled in the present study. Informed consent and information including gender, age, disease duration, number of relapses, annual relapse rate was collected from all patients. Clinical cognitive evaluations (SDMT and auditory verbal learning test (AVLT)) and daily performance: activities of daily living (ADL) were assessed in the present study. MRI scans were performed at the Institute of Neurosurgery of Tiantan Hospital. Twenty-eight matched healthy controls (HC) MRI data were obtained from Tiantan Hospital database. Data on thirty-four points of bilateral cortical structure thickness using statistically defined brain regions-of-interest from FreeSurfer were obtained from all participants.

RESULTS: Patients with RRMS exhibited extensively thinner cerebellar cortex compared with HC. SDMT scores were significantly correlated with AVLT subentries (IM, immediate memory; DRM, delayed recall memory; LTRM, long-term recognition memory) in YA-RRMS patients (P < 0.05). SDMT was strongly correlated with regional cortex thickness differences of the right temporal pole (r = 0.68) and bilateral parahippocampal areas (right r = 0.62; left r = 0.60), and moderately correlated with regional cortex thickness differences including the left superior temporal and right insula (r = 0.57 and 0.56, respectively) in YA-RRMS patients.

CONCLUSION: The present study has shown the SDMT is strongly correlated with selected cortex regions including the bilateral parahippocampal area and the right temporal pole which are involved in geometric structures processing.

PMID:34280674 | DOI:10.1016/j.clineuro.2021.106805