COMPUTATION OF ANKLE TORQUE IN DIFFERENT STRATEGIES OF LANDING BY DYNAMIC MODELING

Ankle joint injury often occurs in sports. The purpose of this study was to compute ankle torque under different strategies of landing using human body movement simulation during landing for males and females. Kinematic and kinetic data were given to model and ankle torque under three strategies of landing was computed. Results showed that in first strategy there was a minimum value of peak ankle torque for both males and females. In second and third strategies by increasing plantar flexion of ankle, peak ankle torque was increased for both males and females. Results were in agreement with previous experimental investigations

BIOMECHANICAL MODELLING FOR COMPUTATION OF KNEE JOINT TORQUE IN DIFFERENT STRATEGIES OF STOP-JUMP LANDING

Knee injuries especially non-contact anterior cruciate ligament injury, is very common in sport activities and often occurs in landing. The purpose of this study was to recognize the risk factors and to recommend better landing strategies using dynamic modeling. For this purpose, the stop-jump landing was modeled using anthropometric, kinematic and kinetic data. The knee torque was calculated for different landing strategies. The result showed that the maximum knee torque for women was 42% more than men. When the knee flexion at the initial foot contact increased, the peak knee torque decreased. Finally, increasing the knee and hip angular displacement, the maximum knee torque decreased at first and then increased again. Therefore the knee flexion angle at initial foot contact and the knee and hip angular displacement are effective factors in reducing knee injuries

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

A Comparison Study on the Removal of Phenol From Aqueous Solution Using Organomodified Bentonite and Commercial Activated Carbon

The potential of bentonite modified with cationic surfactant (CTAB-Bent) and commercial Activated Carbon (AC) for the removal of phenol removal was assessed. Batch kinetics and isotherm studies were carried out to evaluate the effects of contact time (t), phenol initial concentration (C0), adsorbent dose (Cads), and pH of the solutions. Kinetics and isotherm of the adsorption were also determined using the most frequent models. The results of the study showed that increasing C0 could result in the increase of adsorption capacity of both types of adsorbents. The optimum pHs for CTAB-Bent and AC were determined as 10 and 8, respectively. The equilibrium data of the phenol adsorption from the aqueous solutions was well fitted by the Langmuir isotherm models. The kinetics study revealed that pseudo-second-order kinetics gives better description of adsorption process. The adsorption capacities, as deduced from the adsorption isotherm were 22.68 and 112.36 mg/g for CTAB-Bent and activated carbon, respectively. However, considering the market price of these adsorbents, low-cost modified bentonite can be a promising adsorption technology and an alternative adsorbent to commercial activated carbon in the future

Evaluation of Pollution and Carcinogenic Risk of Heavy Metals in Agricultural Soil, Wheat and Flour in Bijar City

Background and Aim: The purpose of this study was to investigate the level of pollution and concentration of heavy metals in agricultural soil, wheat and flour and to evaluate the consequent health risks in the residents of Bijar city in 2021. Materials and Methods: To determine the total content of heavy metals, after acid digestion, the dry soil samples were converted into powder and prepared according to the steps of acid digestion for reading by graphite furnace atomic absorption spectrometry (GFAAS). Extraction from the samples was also done by acid digestion method. In the flour sample, direct acid digestion was done. For non-carcinogenic risk assessment studies, two routes including direct consumption (ingestion) and dermal absorption of heavy metals were considered. To calculate carcinogenic and non-carcinogenic risks, we considered heavy metals including cadmium, lead, zinc and arsenic. Results: The concentrations of heavy metals studied in the soil, wheat and flour samples of four regions were similar to one another and no significant differences were observed. The results of the risk assessment showed that the soil samples of all four studied areas were in class III (moderate risk) in regard to the carcinogenic risk of arsenic. Also, the soil samples of all four regions showed a low cancer risk in relation to cadmium. All other samples showed a low risk less than 10-6. In all of the samples of flour, wheat and soil from the four regions, we found no risk higher than 10-4 and, thus they were considered safe. Conclusion: The results of this study showed non-carcinogenic risk in relation to zinc, lead, arsenic, and cadmium based on the results of examined samples, but the concentration of some of these metals, such as arsenic, is close to the risk level for the vulnerable populations such as children, and appropriate measures should be taken to evaluate and reduce these heavy metals concentrations

Relationship of abnormal profiles and the prevalence of musculoskeletal disorders among academic staff men and women

Introduction: Work-related musculoskeletal abnormalities are one of the main causes of musculoskeletal injuries, followed by job-disability among employees. The purpose of this study was to investigate the relationship between abnormal profiles and the prevalence of musculoskeletal disorders (MSDs) among academic staff men and women. Material and Methods: The present study is of a causal nature after occurrence. The statistical population consisted of all employees of Isfahan University of Technology in 2018. A random sample of 64 men and 59 women were recruited as the study sample. A New-York-based assessment questionnaire was used to assess musculoskeletal-abnormalities and a Nordic questionnaire was used to assess the extent of MSDs. For data analysis, descriptive statistics and Spearman test were used in SPSS version24 software. Results: The highest musculoskeletal abnormality in the upper limb was observed in shoulder area (0.37%) and among the lower limbs the ankle (0.82%) was reported as the highest. In terms of correlation between pain, disability and injuries and MSDs with gender in the last 7 days, there was the highest correlation with shoulder (r=0.377), wrist (r=0.401) in the last 12 months and musculoskeletal abnormalities with shoulder area (r = 0.411). There was a significant difference in the prevalence of pain and disability with injury in the shoulder area (P = 0.001), wrist (P = 0.001), back (P = 0.002), buttocks (P = 0.032) and knees (P = 0.006). Conclusion: The results indicate a high risk of MSDs and abnormalities in male and female employees. In order to reduce the incidence of abnormalities, it is necessary to perform corrective exercises and improve the ergonomics conditions of the work environment

A deep learning-based framework for predicting survival-associated groups in colon cancer by integrating multi-omics and clinical data

Precise prognostic classification of patients and identifying survival subgroups and their associated genes can be important clinical references when designing treatment strategies for cancer patients. Multi-omics and data integration techniques are powerful tools to achieve this goal. This study aimed to introduce a machine learning method to integrate three types of biological data, and investigate the performance of two other methods, in identifying the survival dependency of patients. The data included TCGA RNA-seq gene expression, DNA methylation, and clinical data from 368 patients with colon cancer also we use an independent external validation data set, containing 232 samples. Three methods including, hyper-parameter optimized autoencoders (HPOAE), normal autoencoder, and penalized principal component analysis (PPCA) were used for simultaneous data integration and estimation under a COX hazards model. The HPOAE was thought to outperform other methods. The HPOAE had the Log Rank Mantel-Cox value of 14.27 ± 2, and a Breslow-Generalized Wilcoxon value of 13.13 ± 1. Ten miRNA, 11 methylated genes, and 28 mRNA all by (importance of marginal cutoff > 0.95) were identified. The study demonstrated that hsa-miR-485-5p targets both ZMYM1 and tp53, the latter of which has been previously associated with cancer in numerous studies. Furthermore, compared to other methods, the HPOAE exhibited a greater capacity for identifying survival subgroups and the genes associated with them in patients with colon cancer. However, all of the results were obtained by computational methods, and clinical and experimental studies are needed to validate these results