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My Family's Accessibility and Community Engagement (My FACE) measures mothers' perceptions of community accessibility and engagement for families raising children with a disability. This study investigated the construct validity including the factor structure and internal reliability of the MyFace scale.

Construct validity was evaluated using hypothesis testing. Structural validity was confirmed with factor analysis. Internal reliability was measured using Cronbach alpha. The nine-item MyFACE includes items representing common community destinations. A 5-point Likert scale measured perceptions of need for change and inclusion.

Mothers (N = 83) completed an online survey with MyFACE, maternal and childhood disability scales. Hypothesis testing revealed correlations with MyFACE Depression Anxiety Stress Scales (DASS)-stress (r = -.25, n = 72, p = .037), DASS-anxiety (r = -.41, n = 70, p < .001,), and DASS-depression (r = -.27, n = 72, p = .023,) scales. MyFACE scores correlated with mothers' total Health Promoting Activity Scale (HPAS) scores (r = .40, n = 74, p < .001). HPAS was the strongest predictor of variation in MyFACE scores F(5, 66) = 5.68, p < .001. Factor analysis demonstrated unidimensionality. Internal reliability was excellent (Cronbach alpha = .80).

The MyFACE tool is psychometrically sound. Compared to child factors, maternal mental health and health promoting behaviour had more influence on mothers' perceptions of family community accessibility and engagement. The MyFACE measures a unique, previously unmeasurable family construct.

The MyFACE tool is psychometrically sound. Compared to child factors, maternal mental health and health promoting behaviour had more influence on mothers' perceptions of family community accessibility and engagement. The MyFACE measures a unique, previously unmeasurable family construct.Developing strategies against the antibiotic resistance is a major global challenge for public health. Here, we report the synergy of the combination of Preyssler-type polyoxometalates (POMs) ([NaP5W30O110]14- or [AgP5W30O110]14-) and ribosome-targeting antibiotics for high antibacterial efficiency with low risk of antibiotic resistance. Due to their ultra-small sizes and active surface ligands, POM anions show strong affinity to bacterial cell membrane and impose hyperpolarization of the bacterial cells as well as the decrease of Mg2+ influx by blocking Mg2+ transporters, which finally lead to the structural perturbations of ribosomes and instability of bacterial structures. The bacterial growth can, therefore, be regulated by the presence of POMs a fraction of Bacillus subtilis shifted to a 'dormant', slow-growing cellular state (an extended lag phase) upon the application of subinhibitory concentration of POMs. mTOR inhibitor An approach to combat antibiotic resistant bacteria by applying POMs at their early growth phase followed by antibiotic exposure is validated, and its high efficiency for bacterial control is confirmed.The reactivity of inorganic sulfide towards ferric bis(N-acetyl)- microperoxidase 11 in sodium dodecyl sulfate has been explored by means of visible absorption and resonance Raman spectroscopies. The reaction has been previously studied in buffered solutions at neutral pH and in the presence of excess sulfide, revealing the formation of a moderately stable hexacoordinated low spin ferric sulfide complex that yields the ferrous form in the hour's timescale. In the surfactant solution, instead, the ferrous form is rapidly formed. The spectroscopic characterization of the heme structure in the surfactant milieu revealed the stabilization of a major ferric mono-histidyl high spin heme, which may be ascribed to out of plane distortions prompting the detachment of the axially ligated water molecule, thus leading to a differential reactivity. The ferric bis(N-acetyl)- microperoxidase 11 in sodium dodecyl sulfate provides a model for pentacoordinated heme platforms with an imidazole-based ligand.

Diabetes mellitus (DM) and thyroid disorders are the most common endocrine disorders in clinical practice. Unrecognized thyroid disorders have an adverse effect on metabolic functions. The aim of the study is to demonstrate the prevalence of thyroid disorders in individuals with diabetes mellitus.

A prospective observational study, conducted at Sree Sidhi Vinayaka Diabetic Center, between September 2013 to December 2019. link2 A total of 5037 patients attended the outpatient clinic, among which 2470 met the inclusion criteria. All patients underwent a clinical and laboratory evaluation.

A total of 2321 individuals with diabetes had consented to be the part of the study, 102 had Type 1 diabetes mellitus (T1DM) and 2219 Type 2 diabetes mellitus (T2DM). The mean age was 48.4±10.7, among which 1128 females and 1193 are males. 79.9% (1853) individuals with diabetes were euthyroid; 13.8% (321) subclinical hypothyroidism; 3.4% (79) clinical hypothyroidism, and 2.9% (68) were having hyperthyroidism. 14.1% of T2DM had subclinical hypothyroidism, in contrast, clinical hypothyroidism was common in T1DM (6.9%).

A high index of suspicion for thyroid dysfunction in diabetics should be considered to screen for thyroid function in them for early detection and effective management of both the conditions.

A high index of suspicion for thyroid dysfunction in diabetics should be considered to screen for thyroid function in them for early detection and effective management of both the conditions.Aortic aneurysm is associated with aberrant blood flow and wall shear stress (WSS). This can be studied by coupling magnetic resonance imaging (MRI) with computational fluid dynamics (CFD). For patient-specific simulations, extra attention should be given to the variation in segmentation of the MRI data-set and its effect on WSS. link3 We performed CFD simulations of blood flow in the aorta for ten different volunteers and provided corresponding WSS distributions. The aorta of each volunteer was segmented four times. The same inlet and outlet boundary conditions were applied for all segmentation variations of each volunteer. Steady-state CFD simulations were performed with inlet flow based on phase-contrast MRI during peak systole. We show that the commonly used comparison of mean and maximal values of WSS, based on CFD in the different segments of the thoracic aorta, yields good to excellent correlation (0.78-0.95) for rescan and moderate to excellent correlation (0.64-1.00) for intra- and interobserver reproducibility. However, the effect of geometrical variations is higher for the voxel-to-voxel comparison of WSS. With this analysis method, the correlation for different segments of the whole aorta is poor to moderate (0.43-0.66) for rescan and poor to good (0.48-0.73) for intra- and interobserver reproducibility. Therefore, we advise being critical about the CFD results based on the MRI segmentations to avoid possible misinterpretation. While the global values of WSS are similar for different modalities, the variation of results is high when considering the local distributions.Medical image segmentation is a complex yet one of the most essential tasks for diagnostic procedures such as brain tumor detection. Several 3D Convolutional Neural Network (CNN) architectures have achieved remarkable results in brain tumor segmentation. However, due to the black-box nature of CNNs, the integration of such models to make decisions about diagnosis and treatment is high-risk in the domain of healthcare. It is difficult to explain the rationale behind the model's predictions due to the lack of interpretability. Hence, the successful deployment of deep learning models in the medical domain requires accurate as well as transparent predictions. In this paper, we generate 3D visual explanations to analyze the 3D brain tumor segmentation model by extending a post-hoc interpretability technique. We explore the advantages of a gradient-free interpretability approach over gradient-based approaches. Moreover, we interpret the behavior of the segmentation model with respect to the input Magnetic Resonance Imaging (MRI) images and investigate the prediction strategy of the model. We also evaluate the interpretability methodology quantitatively for medical image segmentation tasks. To deduce that our visual explanations do not represent false information, we validate the extended methodology quantitatively. We learn that the information captured by the model is coherent with the domain knowledge of human experts, making it more trustworthy. We use the BraTS-2018 dataset to train the 3D brain tumor segmentation network and perform interpretability experiments to generate visual explanations.

COVID-19, declared a pandemic in March 2020 by the World Health Organization is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The virus has already killed more than 2.3 million people worldwide.

The principal intent of this work was to investigate lead compounds by screening natural product library (NPASS) for possible treatment of COVID-19.

Pharmacophore features were used to screen a large database to get a small dataset for structure-based virtual screening of natural product compounds. In the structure-based screening, molecular docking was performed to find a potent inhibitor molecule against the main protease (M

) of SARS-CoV-2 (PDB ID 6Y7M). The predicted lead compound was further subjected to Molecular Dynamics (MD) simulation to check the stability of the leads compound with the evolution of time.

In pharmacophore-based virtual screening, 2,361 compounds were retained out of 30,927. In the structure-based screening, the lead compounds were filtered based on their doID-19.The software tool POSEIDON-R was developed for modelling the concentration of radionuclides in water and sediments as well as uptake and fate in the aquatic environment and marine organisms. The software has been actively advanced in the aftermath of the Fukushima Dai-ichi accident. This includes development of an uptake model for the benthic food chain, a kinetic-allometric compartment model for fish and recent advancements for the application of 3H. This work will focus on the food chain model development and its extension to key artificial radionuclides in radioecology such as 3H. Subsequently, the model will be applied to assess the radiological dose for marine biota from 3H, 90Sr, 131I, 134Cs and 137Cs released during and after the Fukushima Dai-ichi accident. The simulation results for 3H, 90Sr, 131I, 134Cs and 137Cs obtained from the coastal box (4-4 km) located at the discharge area of the Fukushima Dai-ichi NPP, and the surrounding regional box (15-30 km) are compared with measurements. The predictioound tritium (OBT) is modelled and shows some accumulation of OBT in the marine organism. However, dose rates from tritium, even during the accident, are low.The behaviors of U(VI) in environmental media around radioactive waste disposal site are important for safety assessment of geological repositories. However, the estimation of environmental behaviors of U(VI) in natural media was insufficient. This work aimed to determine the adsorption of U(VI) on natural soil surrounding a candidate very low-level radioactive waste (VLLW) disposal site in southwest China. Results showed that the adsorption process of U(VI) on soils could be well supported by pseudo-second-order kinetic and Freundlich model. The adsorption of U(VI) was pH-dependent but temperature-independent. High ionic strength (NaCl) strongly affected the adsorption process at low pH (2.0-5.5). CO32- remarkably inhibited the U(VI) adsorption, while the adsorption of U(VI) was promoted by PO43- and SO42-. Naturally occurred soil organic matters (SOMs) showed high affinity for U(VI), while the presence of additional humic acid (HA) strongly inhibited U(VI) adsorption. The occurrence of ferrous iron could result in the reduction of U(VI) at low pH values (pH less then 4), leading to the promotion of immobilization of U(VI).