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Statin use and low-density lipoprotein cholesterol target achievement for primary prevention of atherosclerotic cardiovascular disease in patients with type 2 diabetes mellitus: a multicenter cross-sectional study in Sri Lanka

Background Statin therapy serves a crucial role as a primary preventive strategy against atherosclerotic cardiovascular disease (ASCVD) in patients with type 2 diabetes mellitus (T2DM). Even though diabetes poses a significant and growing health concern in Sri Lanka, there is a lack of information regarding the prevalence and intensity of statin prescriptions and the achievement of recommended LDL-C targets in diabetic patients for the primary prevention of ASCVD within the nation. We aimed to assess the prevalence and intensity of statin prescriptions, target LDL-C achievement, and factors associated with target LDL-C achievement for the primary prevention of ASCVD in T2DM patients across several tertiary care facilities in Sri Lanka. Methods A multi-centered, cross-sectional study was conducted among T2DM patients without clinical ASCVD attending six tertiary care medical clinics in Sri Lanka. Data on ASCVD risk factors and statin prescription were collected using an interviewer-administered questionnaire. ASCVD risk was calculated using the WHO charts. Atorvastatin 20 mg/ rosuvastatin 10 mg was defined as high-intensity statins and target LDL-C was defined as < 70 mg/dL for moderate to high and < 100 mg/dL for low-risk groups of ASCVD according to the NICE guideline. The independent sample t test, one-way ANOVA and chi-square test were used for data analysis as appropriate. Factors linked to achieving LDL-C targets were determined through multiple logistic regression analysis. Level of significance was considered as 0.05. Results Of the 2013 participants studied, 46.7% were at moderate-high risk and the rest were at low risk of ASCVD. All were eligible for statin therapy, and 84.1% were prescribed statins. High-intensity statins had been prescribed only for 38.5% of moderate-high-risk patients. Nonetheless, high-intensity statins have also been prescribed for 30.7% of low-risk patients. LDL-C target achievement was studied in a randomly selected subsample of 683 and 65.4% (70.7% in low-risk patients and 60.3% in moderate-high-risk patients) achieved LDL-C targets. Of moderate-high-risk patients, 46.3% had not achieved target LDL-C even with high-intensity statin therapy. Female gender (OR = 1.52, 95% CI 1.03-2.24, p = 0.036), poor adherence to statins (OR = 1.67, 95% CI 1.18-2.37, p = 0.004), poor glycemic control (OR = 2.27, 95% CI 1.41-3.65, p = 0.001), and inadequate physical activity (OR = 1.48, 95% CI 1.04-2.10, p = 0.031) were significantly associated with failing to achieve LDL-C targets. Conclusion Only about one third of diabetes patients with moderate-high ASCVD risk received high-intensity statins. Even with high-intensity statin therapy, nearly half of the treated patients failed to meet recommended LDL-C targets.

The molecular dynamic studies of thermal conductivity of SiC ceramic derived from β/α phase transformation

Numerical methods for unraveling inter-particle potentials in colloidal suspensions: A comparative study for two-dimensional suspensions

We compare three model-free numerical methods for inverting structural data to obtain interaction potentials, namely, iterative Boltzmann inversion (IBI), test-particle insertion (TPI), and a machine-learning (ML) approach called ActiveNet. Three archetypal models of two-dimensional colloidal systems are used as test cases: Weeks–Chandler–Anderson short-ranged repulsion, the Lennard-Jones potential, and a repulsive shoulder interaction with two length scales. Additionally, data on an experimental suspension of colloidal spheres are acquired by optical microscopy and used to test the inversion methods. The methods have different merits. IBI is the only choice when the radial distribution function is known but particle coordinates are unavailable. TPI requires snapshots with particle positions and can extract both pair- and higher-body potentials without the need for simulation. The ML approach can only be used when particles can be tracked in time and it returns the force rather than the potential. However, it can unravel pair interactions from any one-body forces (such as drag or propulsion) and does not rely on equilibrium distributions for its derivation. Our results may serve as a guide when a numerical method is needed for application to experimental data and as a reference for further development of the methodology itself.

Predicting Parkinson’s disease trajectory using clinical and functional MRI features: A reproduction and replication study

Parkinson’s disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability related for instance to cohort selection or image analysis. In this context, an evaluation of the robustness of such biomarkers to variations in the data processing workflow is essential. This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD. Here, we attempt to fully reproduce (reimplementing the experiments with the same methods, including data collection from the same database) and replicate (different data and/or method) the models described in (Nguyen et al., 2021) to predict individual’s PD current state and progression using demographic, clinical and neuroimaging features (fALFF and ReHo extracted from resting-state fMRI). We use the Parkinson’s Progression Markers Initiative dataset (PPMI, ppmi-info.org), as in (Nguyen et al., 2021) and aim to reproduce the original cohort, imaging features and machine learning models as closely as possible using the information available in the paper and the code. We also investigated methodological variations in cohort selection, feature extraction pipelines and sets of input features. Different criteria were used to evaluate the reproduction attempt and compare the results with the original ones. Notably, we obtained significantly better than chance performance using the analysis pipeline closest to that in the original study (R2 > 0), which is consistent with its findings. In addition, we performed a partial reproduction using derived data provided by the authors of the original study, and we obtained results that were close to the original ones. The challenges encountered while attempting to reproduce (fully and partially) and replicating the original work are likely explained by the complexity of neuroimaging studies, in particular in clinical settings. We provide recommendations to further facilitate the reproducibility of such studies in the future.

Model predicted human mobility explains COVID-19 transmission in urban space without behavioral data

Constructing N, B co-doped carbon nanosheets with pyridine N–B sites for boosting sodium-ion storage

Carbonaceous materials have demonstrated extensive potential as anodes for sodium ion batteries (SIBs). Nevertheless, large-scale commercial use is severely hampered by the slow reaction kinetics and rapid capacity fading. Heteroatom doping can create abundant active sites to improve the ion adsorption properties of carbon materials. Here, we report a novel nitrogen/boron co-doped carbon nanosheet (NB-CN) with abundant N–B bonds for efficient Na+ storage. B-doped MIL-68 as a carbon precursor can not only achieve uniform B doping but also serve as the nitrogen doping site to form N–B bonds. N, B co-doping could promote ion adsorption with improved hydrophilicity, while the 2D porous structure can accelerate the Na+ transfer kinetics. Benefitting from the synergistic effect of dual-doping and hierarchical porosity, NB-CN shows improved Na+ storage performance, NB-CN displays a high capacity of 307.1 mA h g−1 in SIBs at 0.1 A g−1, and still has a reversible capacity of 157 mA h g−1 for SIBs at 4 A g−1 after 8000 cycles. Moreover, the assembled NB-CNs//Na3V2(PO4)3/C full cell also exhibits the potential application prospect. This work provides an insight for designing dual-doped carbon materials for high-performance SIBs.

Associations between religiosity/spirituality with insulin resistance and metabolic syndrome in the Midlife in the United States (MIDUS) study

Religiosity and spirituality (R/S) are central aspects to the lives of many people worldwide. Previous research suggests a potentially beneficial relationship between R/S, mostly understood as religious service attendance, and mortality. Though important, this research often fails to account for the complex and multidimensional nature of R/S. Also lacking is an adequate understanding of the physiological mechanisms that may link R/S with mortality and other health outcomes. Insulin resistance and metabolic syndrome, subclinical physiological processes that are influenced by the types of lifestyle factors and psychological factors that R/S addresses, serve as two possible biological mechanisms linking R/S and health outcomes. This study investigated the relations of R/S, defined as service attendance, support from one’s religious community, and composite variables comprised of several diverse R/S indicators, in relation to insulin resistance and metabolic syndrome both cross-sectionally and in longitudinal analyses across 8–10 years in the Midlife in the United States (MIDUS) study. Results, controlling for important covariates (demographic factors, self-rated health, chronic conditions, depressive symptoms for all analyses; diabetes status and body mass index for insulin resistance analyses; antihyperlipidemic medications for metabolic syndrome), demonstrated nonsignificant relationships for all measures of R/S and both insulin resistance and metabolic syndrome in both cross-sectional and longitudinal analyses. Integrating these findings into the limited research on physiological mechanisms in the R/S and health relationship suggests that the area lacks consistent findings. Additional studies that use heterogenous, representative samples and further refine the operationalization of R/S are indicated.

Behavioral (reaction time) and prefrontal cortex response revealed differences in grief vs. sadness perception

The Gibbs method extended to nanothermodynamics and exemplified by evaluations of the surface, line, and point excess energies for icosahedral metal nanoclusters

The Gibbs method of surface excesses was extended to nanosized objects and exemplified by evaluations of the specific (per unit area) surface excess energies of Ih nanoclusters of fcc metals (Ag, Au, Cu, Ni, Pb, and Pt), the specific (per unit length) line energies of edges of Ih nanoclusters, and excess point energies of their vertices. In particular, for this purpose, an original interpretation of the Gibbs equimolecular surface concept has been employed. To perform all the above-mentioned evaluations, the extended Gibbs method was combined with the nearest neighbor interaction model. The results of our evaluations of the specific surface energy agree with the experimental values of the specific surface energy for corresponding solid bulk fcc metals. Then, we have found that the values of the specific excess line energy of the Ih nanocluster edges are positive and fall in order of magnitude within the range of 10−10 to 10−9 J/m, which agrees with the available evaluations for other types of linear boundaries. The vertex point energy was found to be on the order of 10−20 J and positive as well. A hypothesis is put forward that the positivity of the excess energies of the edges and vertices results in their instability, i.e., in a trend for the formation of a more rounded shape of polyhedral nanoparticles, especially in the vicinity of their melting temperatures. In addition, some molecular dynamics results on Ih metal nanoclusters are discussed. For Au and Pt Ih nanoclusters, the theoretically calculated values of the specific surface energy are compared with those obtained by combining the Gibbs method with our molecular dynamics results on the size dependence of the potential term into the specific (per atom) internal energy of Ih nanoparticles.

Inequality of opportunity in child nutrition in Pakistan

Introduction Malnutrition among children is one of the major health challenges in Pakistan. The National Nutritional Survey 2018 revealed that 44% of children are stunted. Different circumstances surrounding a child’s birth can lead to inequality of opportunity in early childhood, with significant nutritional inequalities between rural and urban areas. This study aims to identify the drivers of inequality of opportunity in stunting among children under-five years of age in Pakistan. Methods This study used Pakistan Demographic and Health Survey, 2017–18 to identify the factors contributing to inequality of opportunity in child’s stunting. The Dissimilarity index (D-index), along with Oaxaca decomposition, and Shapely decomposition were employed to measure and decompose inequality in opportunity in stunting. Regional variations in stunting among children under various circumstances were analyzed using Geographic Information System or GIS. Results The burden of stunting is exceptionally high in Pakistan, with the prevalence in rural areas significantly exceeding that in urban areas from 1990 to 2018. Shapley decomposition of the contributors to inequality in opportunity indicates that maternal education accounted for 24% of total inequality among rural children and 44% among urban children. Water and sanitation contributed 22% to overall inequality in rural areas but only 2% in urban areas, highlighting the critical role of inadequate water and sanitation in rural settings. The wealth index was a predominant contributor to inequality both nationally and in urban areas. Southern regions exhibit a higher prevalence of stunting and a greater proportion of households lacking adequate water and sanitation. Additionally, the concentration of uneducated mothers and stunted children is notably high in Balochistan and Sindh. Conclusions The lack of maternal education, inadequate access to water and sanitation services, and lower socio-economic status are key factors contributing to inequality of opportunity in stunting among children under five in Pakistan. Understanding the critical role of these circumstances can help policymakers address the situation and implement concrete steps to enhance equal opportunities for child health.

Early detection of retinal dysfunction in type 1 diabetes without retinopathy using multifocal electroretinography

Surface structures of gold epitaxial overlayers grown on Ag(111)

Surface reconstructions and electronic structures of Au thin films formed on the Ag(111) surface have been investigated using low-temperature scanning tunneling microscopy. We find that striped patterns form even at a one-monolayer (ML) coverage of Au on the Ag(111) surface. In contrast to the striped patterns, hexagonal ring structures are observed on the 2 ML Au surface, where four rings are connected in a triangular arrangement as a single unit. The ring patterns disappear above 3 MLs, and the striped patterns reappear.

Connected multi-vehicle crash risk assessment considering probability and intensity

Accurate driving risk assessments are essential in vehicle collision avoidance and traffic safety. The uncertainty in driving intentions and behavior, coupled with the difficulty in accurately predicting future trajectories of vehicles, poses challenges in assessing collision risk among vehicles. Existing research on collision risk assessment has been limited to focusing on pre-crashes (e.g., time-to-collision) and ignoring the impact of crash severity on risk. Research integrating pre- and post-crash is needed to assess the collision risk comprehensively. Therefore, the objective of this study was to propose an assessment model for collision risk in a vehicle-to-vehicle communication environment to achieve a more scientific assessment of driving risk by integrating probability (pre-crash) and intensity (post-crash). The proposed trajectory prediction model takes driving intentions into account and employs a social tensor pool to integrate interactions between vehicles, thereby achieving improved prediction accuracy. The likelihood of collision is obtained by analyzing the conflict relationship between the predicted and candidate trajectories of different vehicles. This study proposes a risk assessment model comprising two parts: one assesses the likelihood of collision by analyzing the conflicted relationship between predicted and candidate trajectories of different vehicles, and the other determines collision intensity through analysis of vehicle driving states. Finally, publicly available unmanned aerial vehicle (UAV)-based traffic data are used to validate the models. The prediction errors of the proposed trajectory prediction model for three-second trajectories are 0.68 m and 1.34 for the root mean square error and negative log-likelihood, respectively. The quantitative experimental results illustrate that the proposed model outperforms existing models and can scientifically assess the risk of vehicle travel.

Gully erosion is a serious obstacle in India’s land degradation neutrality mission

Abstract India is famous for her badlands. These vast, intensely degraded landscapes occur extensively across Central and Western India, wherein they have had several adverse effects on both environment and society. However, accurate information on their current spatial extents, as well as the spatial distribution and severity of gully erosion elsewhere in the country was hitherto lacking. Considering that India has planned to effectively halt land degradation by 2030 in line with the agenda of the United Nations, and as precise spatial data is indispensable in planning and implementing land management interventions, we have created an exhaustive spatial inventory of gully erosion features in India by recording their location, extents and current management status from high-resolution satellite imagery available on Google Earth Pro. Through this first of its kind mapping endeavour and attendant spatial analysis, we have recorded the presence of gully landforms in 19 of India’s 28 states and the National Capital Region of Delhi and have estimated the total gullied area in the country between 7,451 and 8,157 km2. According to our results, states occupying 38% of Indian territory (viz., Rajasthan, Uttar Pradesh, Madhya Pradesh, Jharkhand, Gujarat and Chhattisgarh) are affected by 92% of the total gullied area of the country. We have noted a clear east-west divide in terms of the relative dominance of the mapped gully erosion features, with badlands being common in Western India and gully systems being the dominant gully feature in the east. A similar observation has interestingly also been made as regards gully management, with the major proportion of unmanaged gully erosion features occurring in Eastern India. Ultimately, we have identified 77 districts across India where urgent rehabilitative intervention is required, more than 70% of which are in Eastern and Southern India where unmanaged (active) gullies are common. That contemporary gully erosion in Eastern India poses a more serious land management challenge than that of the vast badlands of Central and Western India is a truly unexpected finding of our analysis. Our mapped data and analytical results shall be integral to efforts aiming to ameliorate the land degradation caused by gully erosion across India by supporting policymaking and planning at the governmental level and serving as useful guidance for land managers and practitioners on the ground.

Low-frequency Raman signatures of transient polyamorphic situation in linezolid: A competition between conformational polymorphs

This paper reports two bimodal first-order transformations in the disordered form III of linezolid. The most notable result was the identification of a transient apparently amorphous state, different from the glass, during slow heating from the glass. This transient state was interpreted as resulting from the competition between two organizations, i.e., a locally preferred structure reminiscent of the ordered form and the long-range order of the disordered form, energetically closer to the undercooled liquid than the ordered form. The first-order transformation of the undercooled liquid in this short lifetime amorphous state reflects a polyamorphism in a pharmaceutical active ingredient, which is a very unusual feature in the domain of solid-state transformations in molecular materials. Meanwhile, the bimodal polymorphic transformation of the stable crystalline form II into form III via a transient liquid state during rapid heating can also be considered as an uncommon feature for a polymorphic transition. It was found that both atypical transformations assisted by thermally activated conformational motions into the disordered crystalline form III could be kinetically hindered by the high energy barrier between conformational polymorphs. As a consequence, the polyamorphic transformation can only be detected using the rapid acquisition capability of a low-frequency Raman spectrometer, probing the molecular organization from short- to long-range order.

Prediction of Poisson’s ratio for a petroleum engineering application: Machine learning methods

Static Poisson’s ratio (νs) is an essential property used in petroleum calculations, namely fracture pressure (FP). The νs is often determined in the laboratory; however, due to time and cost constraints, quicker and cheaper alternatives are sought, such as data-driven models. However, existing methods lack the accuracy needed for critical applications, necessitating the need to explore more accurate methods. In addition, the previous studies used limited datasets and they do not show the relationships between the inputs and output. Therefore, this study developed a reliable model to predict the νs accurately using the nineteen most common learning methods. The proposed models were created based on a large data of 1691 datasets from different countries. The best-performing model of the nineteen models was selected and further enhanced using various approaches such as trend analysis to improve the model’s performance and robustness as some models show high accuracy but show incorrect relationships between the inputs and output because the machine learning model only built based on the data and do not consider the physical behavior of the model. The proposed Gaussian process regression (GPR) model was also compared with published models. After the proposed GPR model was developed, the FP was determined based on the proposed GPR νs model and the previous νs models to evaluate their accuracy on the FP determinations. The best approach out of the published and proposed methods was GPR with a coefficient of determination (R2) and average-absolute-percentage-relative-error (AAPRE) of 0.95 and 2.73%. The GPR model showed proper trends for all inputs. The cross-plotting and group error analyses also confirmed that the proposed GPR approach had high precision and surpassed other methods within all practical ranges. The GPR model decreased the residual error of FP from 87% to 26%. It is believed that such a significant improvement in the accuracy of the GPR model will have a significant effect on realistic FP determination.

Effect of PM2.5 and its constituents on hospital admissions for cardiometabolic multimorbidity in Urumqi, China

Cold H + O2 collisions: Impact of resonances, geometric phase, and alignment

We report a quantum mechanical investigation of cold inelastic collisions between H and O2 (Ec ≤ 10 K) using a recently developed diabatic potential energy matrix for the lowest two 2A″ states coupled by conical intersections. Time-independent close coupling calculations were carried out in both the adiabatic and nonadiabatic representations in order to delineate the impact of the geometric phase (GP) on scattering. Both adiabatic and nonadiabatic results show many resonance peaks dominated by single partial waves. The inclusion of GP is found to have a large impact on the scattering resonances and more generally on both the integral cross section (ICS) and differential cross section (DCS). In addition, our investigations show that both ICS and DCS could be controlled by the initial alignment of O2, and the effect of the GP also manifest in the stereodynamics of the H + O2 collisions.

Understanding disability from a secondary data lens perspective: Evidence from consultations with members of the public with disabilities in the UK

Disability is a multifaceted phenomenon, which complicates data collection about people with disabilities in surveys and censuses. A central issue is that the multiple underlying theoretical models about disability are seldomly made explicit yet strongly determine how data are collected and analysed by governments and organisations. It is crucial that such models together with other information about disability and its measurement are accessible and understood by everyone. This study comprised several UK survey searches for disability or disability-related questions and a series of consultations with members of the public with lived experience of disability to understand their perceptions of theoretical models of disability in survey questions. The findings highlighted the importance of continued involvement of people with lived experience in technical research activities. They further revealed that members of the public with lived experience can effectively become familiar with theoretical models of disability and how to analyse them in relation to survey questions subject to careful preparation, including practical examples.

Perception of psychosocial burden in mothers of children with rare pediatric neurological diseases