Category: Nevin Manimala

J Sleep Res. 2023 Feb 20:e13854. doi: 10.1111/jsr.13854. Online ahead of print.

ABSTRACT

People with disrupted circadian rhythms, such as shift workers, have shown a higher risk of hypertension. However, it is unclear whether more subtle differences in diurnal rest-activity rhythms in the population are associated with hypertension. Clarifying the association between the rest-activity rhythm, a modifiable behavioural factor, and hypertension could provide insight into preventing hypertension and possibly cardiovascular diseases. In this study, we investigated the association between rest-activity rhythm characteristics and hypertension in a large representative sample of United States adults. Cross-sectional data were obtained from the National Health and Nutrition Examination Survey 2011-2014 (N = 6726; mean [range] age 49 [20-79] years; 52% women). Five rest-activity rhythm parameters (i.e., pseudo F statistic, amplitude, mesor, amplitude:mesor ratio, and acrophase) were derived from 24-h actigraphy data using the extended cosine model. We performed multiple logistic regression to assess the associations between the rest-activity rhythm parameters and hypertension. Subgroup analysis stratified by age, gender, race/ethnicity, body mass index and diabetes status was also conducted. A weakened overall rest-activity rhythm, characterised by a lower F statistic, was associated with higher odds of hypertension (odds ratio quintile 1 versus quintile 5 [OR _Q1vs.Q5 ] 1.61, 95% confidence interval [CI] 1.26-2.05; p trend < 0.001). Similar results were found for lower amplitude (OR _Q1vs.Q5 1.51, 95% CI 1.13-2.03; p trend = 0.01) and amplitude:mesor ratio (OR _Q1vs.Q5 1.34, 95% CI 1.01-1.78; p trend = 0.03). The results were robust to the adjustment of confounders, individual behaviours including physical activity levels and sleep duration and appeared consistent across subgroups. Possible interaction between the rest-activity rhythm and body mass index was found. Our results support an association between weakened rest-activity rhythms and higher odds of hypertension.

PMID:36807441 | DOI:10.1111/jsr.13854

Phys Chem Chem Phys. 2023 Feb 20. doi: 10.1039/d2cp05286e. Online ahead of print.

ABSTRACT

NMR spectroscopy was used to measure the rates of the first and second substitution reactions between iodoalkane (R = Me, 1-butyl) and DABCO in methanol, acetonitrile and DMSO. Most of the reactions were recorded at three different temperatures, which permitted calculation of the activation parameters from Eyring and Arrhenius plots. Additionally, the reaction rate and heat of reaction for 1-iodobutane + DABCO in acetonitrile and DMSO were also measured using calorimetry. To help interpret experimental results, ab initio calculations were performed on the reactant, product, and transition state entities to understand structures, reaction enthalpies and activation parameters. Markov chain Monte Carlo statistical sampling was used to determine a distribution of kinetic rates with respect to the uncertainties in measured concentrations and correlations between parameters imposed by a kinetics model. The reactions with 1-iodobutane are found to be slower in all cases compared to reactions under similar conditions for iodomethane. This is due to steric crowding around the reaction centre for the larger butyl group compared to methyl which results in a larger activation energy for the reaction.

PMID:36807434 | DOI:10.1039/d2cp05286e

Angew Chem Int Ed Engl. 2023 Feb 21:e202300662. doi: 10.1002/anie.202300662. Online ahead of print.

ABSTRACT

Cancer is one of the deadliest diseases worldwide. Recent statistics have shown that metastases and tumor relapse are the leading causes of cancer-associated deaths. While traditional treatments are able to efficiently remove the primary tumor, secondary tumors remain poorly accessible. Capitalizing on this there is an urgent need for novel treatment modalities. Among the most promising approaches, increasing research interest has been devoted to immunogenic cell death inducing agents that are able to trigger localized cell death of the cancer cells as well as induce an immune response inside the whole organism. Preliminary studies have shown that immunogenic cell death inducing compounds could be able to overcome metastatic and relapsing tumors. Herein, the application of metal complexes as immunogenic cell death inducing compounds is systematically reviewed.

PMID:36807420 | DOI:10.1002/anie.202300662

Genet Epidemiol. 2023 Feb 19. doi: 10.1002/gepi.22518. Online ahead of print.

ABSTRACT

Gene-environment (G-E) interaction analysis plays an important role in studying complex diseases. Extensive methodological research has been conducted on G-E interaction analysis, and the existing methods are mostly based on regression techniques. In many fields including biomedicine and omics, it has been increasingly recognized that deep learning may outperform regression with its unique flexibility (e.g., in accommodating unspecified nonlinear effects) and superior prediction performance. However, there has been a lack of development in deep learning for G-E interaction analysis. In this article, we fill this important knowledge gap and develop a new analysis approach based on deep neural network in conjunction with penalization. The proposed approach can simultaneously conduct model estimation and selection (of important main G effects and G-E interactions), while uniquely respecting the “main effects, interactions” variable selection hierarchy. Simulation shows that it has superior prediction and feature selection performance. The analysis of data on lung adenocarcinoma and skin cutaneous melanoma overall survival further establishes its practical utility. Overall, this study can advance G-E interaction analysis by delivering a powerful new analysis approach based on modern deep learning.

PMID:36807383 | DOI:10.1002/gepi.22518

J Magn Reson Imaging. 2023 Feb 20. doi: 10.1002/jmri.28649. Online ahead of print.

ABSTRACT

BACKGROUND: Diagnosing anterior talofibular ligament (ATFL) injuries differs among radiologists. Further assessment of ATFL tears is valuable for clinical decision-making.

PURPOSE: To establish a deep learning method for classifying ATFL injuries based on magnetic resonance imaging (MRI).

STUDY TYPE: Retrospective.

POPULATION: One thousand seventy-three patients from a single center with ankle MRI within 1 month of reference standard arthroscopy (in-group dataset), were divided into training, validation, and test sets in a ratio of 8:1:1. Additionally, 167 patients from another center were used as an independent out-group dataset.

FIELD STRENGTH/SEQUENCE: Fat-saturation proton density-weighted fast spin-echo sequence at 1.5/3.0 T.

ASSESSMENT: Patients were divided into normal, strain and degeneration, partial tear and complete tear groups (groups 0-3). The complete tear group was divided into five sub-groups by location and the potential avulsion fracture (groups 3.1-3.5). All images were input into AlexNet, VGG11, Small-Sample-Attention Net (SSA-Net), and SSA-Net + Weight Loss for classification. The results were compared with four radiologists with 5-30 years of experience.

STATISTICAL TESTS: Model performance was evaluated by the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and so on. McNemar’s test was used to compare performance among the different models, and between the radiologists and models. The intraclass correlation coefficient (ICC) was used to assess the reliability of the radiologists. P < 0.05 was considered statistically significant.

RESULTS: The average AUC of AlexNet, VGG11, SAA-Net, and SSA-Net + Weight Loss was 0.95, 0.99, 0.99, 0.99 in groups 0-3 and 0.96, 0.99, 0.99, 0.99 in groups 3.1-3.5. The effect of SSA-Net + Weight Loss was similar to SSA-Net but better than AlexNet and VGG11. In the out-group test set, the AUC of SSA-Net + Weight Loss ranged from 0.89 to 0.99. The ICC of radiologists was 0.97-1.00. The effect of SSA-Net + Weight Loss was better than each radiologist in the in-group and out-group test sets.

DATA CONCLUSION: Deep learning has potential to be used for classifying ATFL injuries. SSA-Net + Weight Loss has a better diagnostic effect than radiologists with different experience levels.

LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

PMID:36807381 | DOI:10.1002/jmri.28649

Phys Chem Chem Phys. 2023 Feb 20. doi: 10.1039/d2cp05523f. Online ahead of print.

ABSTRACT

Topological superconductors (TSCs) are an exotic field due to the existence of Majorana zero-modes (MZM) in the edge states that obey non-Abelian statistics and can be used to implement topological quantum computations, especially for two-dimensional (2D) materials. Here we predict manganese diboride (Mn₂B₂) as an intrinsic 2D anti-ferromagnetic (AFM) TSC based on the magnetic and electronic structures of Mn and B atoms. Once Mn₂B₂ ML enters a superconducting state, MZM will be induced by the spin-polarized helical gapless edge states. The Z₂ topological non-trivial properties are confirmed by Wannier charge centers (WCC) and the platform of the spin Hall conductivity near the Fermi level. Phonon-electron coupling (EPC) implies s-wave superconductivity and the critical temperature (T_c) is 6.79 K.

PMID:36807355 | DOI:10.1039/d2cp05523f

Br J Cancer. 2023 Feb 17. doi: 10.1038/s41416-023-02188-z. Online ahead of print.

ABSTRACT

BACKGROUND: Treatment of hepatocellular carcinoma (HCC) is predicated on early diagnosis such that ‘curative therapies’ can be successfully applied. The term ‘curative’ is, however, poorly quantitated. We aimed to complement our previous work by developing a statistical model to predict cure after ablation and to use this analysis to compare the true curative potential of the various ‘curative’ therapies.

METHODS: We accessed data from 1571 HCC patients treated in 5 centres receiving radiofrequency (RFA) or microwave (MWA) ablation and used flexible parametric modelling to determine the curative fraction. The results of this analysis were then combined with our previous estimations to provide a simple calculator applicable to all patients undergoing potentially curative therapies.

RESULTS: The cure fraction was 18.3% rising to about 40% in patients with good liver function and very small tumours.

CONCLUSION: Cure for HCC treated with ablation occurs in the order of 20% to 30%, similar to that achievable by resection but much inferior to transplantation where the analogous figure is >70%. We provide a ‘calculator’ that permits clinicians to estimate the chance of cure for any individual patient, based on readily available clinical features.

PMID:36807338 | DOI:10.1038/s41416-023-02188-z

Genet Epidemiol. 2023 Feb 19. doi: 10.1002/gepi.22519. Online ahead of print.

ABSTRACT

The application of causal mediation analysis (CMA) considering the mediation effect of a third variable is increasing in epidemiological studies; however, this requires fitting strong assumptions on confounding bias. To address this limitation, we propose an extension of CMA combining it with Mendelian randomization (MRinCMA). We applied the new approach to analyse the causal effect of obesity and diabetes on pancreatic cancer, considering each factor as potential mediator. To check the performance of MRinCMA under several conditions/scenarios, we used it in different simulated data sets and compared it with structural equation models. For continuous variables, MRinCMA and structural equation models performed similarly, suggesting that both approaches are valid to obtain unbiased estimates. When noncontinuous variables were considered, MRinCMA presented, overall, lower bias than structural equation models. By applying MRinCMA, we did not find any evidence of causality of obesity or diabetes on pancreatic cancer. With this new methodology, researchers would be able to address CMA hypotheses by appropriately accounting for the confounding bias assumption regardless of the conditions used in their studies in different settings.

PMID:36807329 | DOI:10.1002/gepi.22519

Sex Transm Dis. 2023 Feb 20. doi: 10.1097/OLQ.0000000000001783. Online ahead of print.

ABSTRACT

BACKGROUND: The prevalence of sexually transmitted infections (STIs) is at an all-time high. Direct-to-consumer STI testing services may help alleviate this undue health burden. These products are sold online and rarely require interaction with a healthcare professional (HCP). Vendors offer STI self-collection kits or prescriptions for HCP specimen collection. The objective was to understand the scope of direct-to-consumer STI testing services offered and provide recommendations for consumers and industry.

METHODS: Seven volunteers searched for “STD tests” on Google from February 1 through March 31, 2021 and shared their top three results. The study team extracted data from consumer-facing information on each website. Descriptive statistics and thematic qualitative analyses were performed.

RESULTS: Twenty vendors were identified. Most vendors (95%) used Clinical Laboratory Improvement Amendments (CLIA)-certified or College of American Pathologists (CAP) accredited laboratories. Analyses distinguished between STI self-collection kits (n = 9) using independent laboratories and HCP specimen collection (n = 10) which used commercial laboratories (n = 1 offered both). The STI self-collection kits were cheaper per test and bundle on average (eg, $79.00 vs $106.50 for chlamydia/gonorrhea), and more closely aligned with clinical recommendations compared with the HCP specimen collection options. Websites often contained inaccurate or misleading information (n = 13), often promoting testing outside of the recommendations.

CONCLUSIONS: Direct-to-consumer STI testing services are part of an emerging market lacking regulation. Consumers should select vendors offering prescriptions for HCP specimen collection at CAP accredited and CLIA-certified laboratories. Vendors should provide a screening tool to assess individual patient risk prior to test purchase.

PMID:36807311 | DOI:10.1097/OLQ.0000000000001783

Biometrics. 2023 Feb 17. doi: 10.1111/biom.13843. Online ahead of print.

ABSTRACT

Bayesian networks have been widely used to generate causal hypotheses from multivariate data. Despite their popularity, the vast majority of existing causal discovery approaches make the strong assumption of a (partially) homogeneous sampling scheme. However, such assumption can be seriously violated, causing significant biases when the underlying population is inherently heterogeneous. To this end, we propose a novel causal Bayesian network model, termed BN-LTE, that embeds heterogeneous samples onto a low-dimensional manifold and builds Bayesian networks conditional on the embedding. This new framework allows for more precise network inference by improving the estimation resolution from population level to observation level. Moreover, while causal Bayesian networks are in general not identifiable with purely observational, cross-sectional data due to Markov equivalence, with the blessing of causal effect heterogeneity, we prove that the proposed BN-LTE is uniquely identifiable under relatively mild assumptions. Through extensive experiments, we demonstrate the superior performance of BN-LTE in causal structure learning as well as inferring observation-specific gene regulatory networks from observational data. This article is protected by copyright. All rights reserved.

PMID:36807295 | DOI:10.1111/biom.13843