Tag: pubmed

Digit Health. 2024 Oct 29;10:20552076241277693. doi: 10.1177/20552076241277693. eCollection 2024 Jan-Dec.

ABSTRACT

OBJECTIVE: With the increasing global burden of chronic diseases, there is the potential for conversational agents (CAs) to assist people in actively managing their conditions. This paper reviews different types of CAs used for chronic condition management, delving into their characteristics and the chosen study designs. This paper also discusses the potential of these CAs to enhance the health and well-being of people with chronic conditions.

METHODS: A search was performed in February 2023 on PubMed, ACM Digital Library, Scopus, and IEEE Xplore. Studies were included if they focused on chronic disease management or prevention and if systems were evaluated on target user groups.

RESULTS: The 42 selected studies explored diverse types of CAs across 11 health conditions. Personalization varied, with 25 CAs not adapting message content, while others incorporated user characteristics and real-time context. Only 12 studies used medical records in conjunction with CAs for conditions like diabetes, mental health, cardiovascular issues, and cancer. Despite measurement method variations, the studies predominantly emphasized improved health outcomes and positive user attitudes toward CAs.

CONCLUSIONS: The results underscore the need for CAs to adapt to evolving patient needs, customize interventions, and incorporate human support and medical records for more effective care. It also highlights the potential of CAs to play a more active role in helping individuals manage their conditions and notes the value of linguistic data generated during user interactions. The analysis acknowledges its limitations and encourages further research into the use and potential of CAs in disease-specific contexts.

PMID:39484653 | PMC:PMC11526412 | DOI:10.1177/20552076241277693

Spat Stat. 2022 Dec;52:100714. doi: 10.1016/j.spasta.2022.100714. Epub 2022 Nov 9.

ABSTRACT

Spatial aggregation with respect to a population distribution involves estimating aggregate population quantities based on observations from individuals. In this context, a geostatistical workflow must account for three major sources of aggregation error: aggregation weights, fine scale variation, and finite population variation. However, these sources of aggregation error are commonly ignored, and the population instead treated as a fixed population density surface. We improve common practice by introducing a sampling frame model allowing aggregation models to account for aggregation error simply and transparently. This preserves aggregate point estimates while increasing their uncertainties. We compare the proposed and the traditional approach using two simulation studies mimicking neonatal mortality rate (NMR) data from the 2014 Kenya Demographic and Health Survey. In the traditional approach, undercoverage/overcoverage of interval estimates depends arbitrarily on the aggregation grid resolution, while the new approach is resolution robust. Differences between the aggregation approaches increase as an area’s population decreases, and are particularly large at the second administrative level and finer, but also at the first administrative level for some population quantities. These findings are consistent with those of an application to the true NMR data. We demonstrate in a sensitivity analysis that burden estimates and their uncertainties are not robust to changes in population density and census information, while prevalence estimates and uncertainties seem stable.

PMID:39484640 | PMC:PMC11526805 | DOI:10.1016/j.spasta.2022.100714

Toxicol Rep. 2024 Oct 11;13:101767. doi: 10.1016/j.toxrep.2024.101767. eCollection 2024 Dec.

ABSTRACT

Gas stations are one of the sources of benzene, toluene, ethylbenzene and xylene (BTEX) and polyromantic hydrocarbons (PAHs). The present study was conducted with the aim of evaluating the level of breathing exposure of gas station workers to BTEX, PAHs and oxidative stress caused by exposure to these compounds in Karaj city. Oxidative stress and reactive oxygen species (ROS) is one of the mechanisms involved in the toxicity caused by exposure to gas vapors. In this study, all 25 gas stations in the city of Karaj were investigated. Personal sampling and analysis of BTEX and PAHs was done according to National Institute for Occupational Safety and Health (NIOSH) 1501 and 5515 methods, respectively. Finally, oxidative stress markers were investigated in 25 gas station workers and 25 control group. The results showed that the mean age and employment history of gas station workers are 39.96 ± 9.55 and 12.84 ± 6.92, respectively. Also, no significant difference was reported between gas station workers and control subjects in terms of oxidative stress parameters including the level of ROS, oxidized glutathione (GSSG) content, malondialdehyde (MDA) and reduced glutathione (GSH) content. The concentration values of personal exposure of gas station workers to BTEX and PAHs are lower than the occupational exposure limits (OEL). Although the level of oxidative stress parameters in gas station workers is higher than the control group, this difference is not statistically significant (p>0.05). It is recommended to take personal protection measures in case of chronic exposure.

PMID:39484637 | PMC:PMC11525218 | DOI:10.1016/j.toxrep.2024.101767

SSM Popul Health. 2024 Oct 11;28:101716. doi: 10.1016/j.ssmph.2024.101716. eCollection 2024 Dec.

ABSTRACT

BACKGROUND: The relationship between education and cardiometabolic biomarkers is contextually dependent on both inter-country and intra-country factors. This study aimed to examine educational differences in cardiometabolic biomarkers among middle-aged and older adults in the US, Mexico, China, and India, and whether this relationship is modified by urbanicity of residence.

METHODS: Data were from contemporary cross-sectional waves of the US Health and Retirement Study (HRS; 2016/17, n = 19,608), the Mexican Health and Aging Study (MHAS; 2015, n = 12,356), the China Health and Retirement Longitudinal Study (CHARLS; 2015/16, n = 13,268), and the Longitudinal Aging Study in India (LASI; 2017/19, n = 47,838). To account for substantial variations in educational distribution across the four countries, we measured education attainment in two ways: by categorizing education levels into binary classifications (‘lower education: lower secondary education or below’ vs. ‘higher education: upper secondary education or above’) to assess absolute education attainment, and by using within-country percentile ranks to capture relative education attainment. We assessed educational differences in four cardiometabolic biomarkers: body mass index (BMI), systolic blood pressure (SBP), glycated haemoglobin (HbA1c), and total cholesterol. We tested whether urbanicity of residence modified the relationship between education and these cardiometabolic biomarkers.

RESULTS: The proportion of individuals with higher education was 82.6% in the US, 15.6% in Mexico, 10.6% in China, and 16.8% in India. In the US, higher education was associated with lower SBP (-2.74 mmHg, 95% CI: -3.62, -1.86) and HbA1c (-0.14%, 95% CI: -0.20, -0.08), but higher total cholesterol (3.33 mg/dL, 95% CI: 1.41, 5.25). In Mexico, higher education was associated with lower BMI only (-0.51 kg/m², 95% CI: -0.76, -0.26). In China, higher education was not associated with any biomarker. In India, higher education was associated with higher BMI (1.61 kg/m², 95% CI: 1.49, 1.73), SBP (1.67 mmHg, 95% CI: 1.16, 2.18), and HbA1c (0.35%, 95% CI: 0.19, 0.51). The association between education and cardiometabolic biomarkers was modified by urbanicity in China and India but not in the US or Mexico. In both China and India, relationships between education and cardiometabolic biomarkers were stronger among rural residents compared to those among urban residents. Results based on relative education attainment showed similar patterns in terms of the direction of the effect estimates, despite some discrepancies in statistical significance.

INTERPRETATION: There is a complex relationship between education and cardiometabolic biomarkers across countries and by urbanicity of residence. This complexity underscores the importance of accounting for contextual factors when devising strategies to enhance cardiometabolic health in various settings.

PMID:39484632 | PMC:PMC11525230 | DOI:10.1016/j.ssmph.2024.101716

Int J Hepatol. 2024 Oct 24;2024:3879328. doi: 10.1155/2024/3879328. eCollection 2024.

ABSTRACT

Background and aims: Focal liver lesions (FLL) are one of the most common indications for hepatology and hepatobiliary surgery consultation. In this retrospective study, we aim to assess if contrast-enhanced ultrasound (CEUS) can address diagnostic dilemmas in the evaluation of indeterminate liver lesions by identifying characteristics of indeterminate FLL on CEUS and correlating these with cross-sectional imaging and pathology findings. Methods: We retrospectively reviewed all patients who underwent CEUS evaluation for liver lesions over a 28-month period (Oct 2020 to Jan 2023) at the University of Kentucky. To assess the relationship between CEUS results and the corresponding CT, MRI, and/or pathologic findings, the McNemar-Bowker tests were performed. Results: Twenty-nine patients were included (after two exclusions from a total n of 31). Mean age was 54 years, 62% were female, and 48% had underlying cirrhosis. Of the 29 patients with initial cross-sectional imaging, the initial results showed malignancy or likely malignant lesion in 6 patients and benign or likely benign lesion in 6 patients. The remaining 17 patients had inconclusive/indeterminate results. CEUS clarified an “indeterminate” CT/MRI result 15 times out of 17 (88.2%), moving the diagnosis to “benign” 11 times while suggesting “malignant” only four times. When aggregating indeterminate cross-sectional results with either benign or malignant categories suggested by CEUS, CEUS never reversed a benign CT/MRI diagnosis but often reversed a malignant CT/MRI diagnosis. Conclusion: CEUS provided a definitive diagnosis of indeterminate liver lesions in approximately 90% of patients and avoided the need for biopsy in most patients. In cases where the liver lesions were biopsied, CEUS accurately distinguished malignant versus benign lesions as confirmed by biopsy findings. CEUS, therefore, has the potential to provide a precise diagnosis for the majority of indeterminate lesions.

PMID:39484627 | PMC:PMC11527524 | DOI:10.1155/2024/3879328

bioRxiv [Preprint]. 2024 Oct 21:2024.10.17.618550. doi: 10.1101/2024.10.17.618550.

ABSTRACT

Functional Positron Emission Tomography (fPET) with (bolus plus) constant infusion of [18F]-fluorodeoxyglucose FDG), known as fPET-FDG, is a recently introduced technique in human neuroimaging, enabling the detection of dynamic glucose metabolism changes within a single scan. However, the statistical analysis of fPET-FDG data remains challenging because its signal and noise characteristics differ from both classic bolus-administration FDG PET and from functional Magnetic Resonance Imaging (fMRI), which together compose the primary sources of inspiration for analytical methods used by fPET-FDG researchers. In this study, we present an investigate of how inaccuracies in modeling baseline FDG uptake can introduce artifactual patterns to detrended TAC residuals, potentially introducing spurious (de)activations to general linear model (GLM) analyses. By combining simulations and empirical data from both constant infusion and bolus-plus-constant infusion protocols, we evaluate the effects of various baseline modeling methods, including polynomial detrending, regression against the global mean time-activity curve, and two analytical methods based on tissue compartment model kinetics. Our findings indicate that improper baseline removal can introduce statistically significant artifactual effects, although these effects characterized in this study (~2-8%) are generally smaller than those reported by previous literature employing robust sensory stimulation (~10-30%). We discuss potential strategies to mitigate this issue, including informed baseline modeling, optimized tracer administration protocols, and careful experimental design. These insights aim to enhance the reliability of fPET-FDG in capturing true metabolic dynamics in neuroimaging research.

PMID:39484579 | PMC:PMC11526866 | DOI:10.1101/2024.10.17.618550

bioRxiv [Preprint]. 2024 Oct 26:2024.10.24.619733. doi: 10.1101/2024.10.24.619733.

ABSTRACT

BACKGROUND: Schizophrenia (SCZ) is characterized by a disconnect from reality that manifests as various clinical and cognitive symptoms, and persistent neurobiological abnormalities. Sex-related differences in clinical presentation imply separate brain substrates. The present study characterized deep brain morphology using shape features to understand the independent effects of diagnosis and sex on the brain, and to determine whether the neurobiology of schizophrenia varies as a function of sex.

METHODS: This study analyzed multi-site archival data from 1,871 male (M) and 955 female (F) participants with SCZ, and 2,158 male and 1,877 female healthy controls (CON) from twenty-three cross-sectional samples from the ENIGMA Schizophrenia Workgroup. Harmonized shape analysis protocols were applied to each site’s data for seven deep brain regions obtained from T1-weighted structural MRI scans. Effect sizes were calculated for the following main contrasts: 1) Sex effects;2) Diagnosis-by-Sex interaction; 3) within sex tests of diagnosis; 4) within diagnosis tests of sex differences. Meta-regression models between brain structure and clinical variables were also computed separately in men and women with schizophrenia.

RESULTS: Mass univariate meta-analyses revealed more concave-than-convex shape differences in all regions for women relative to men, across diagnostic groups ( d = -0.35 to 0.20, SE = 0.02 to 0.07); there were no significant diagnosis-by-sex interaction effects. Within men and women separately, we identified more-concave-than-convex shape differences for the hippocampus, amygdala, accumbens, and thalamus, with more-convex-than-concave differences in the putamen and pallidum in SCZ ( d = -0.30 to 0.30, SE = 0.03 to 0.10). Within CON and SZ separately, we found more-concave-than-convex shape differences in the thalamus, pallidum, putamen, and amygdala among females compared to males, with mixed findings in the hippocampus and caudate ( d = -0.30 to 0.20, SE = 0.03 to 0.09). Meta-regression models revealed similarly small, but significant relationships, with medication and positive symptoms in both SCZ-M and SCZ-F.

CONCLUSIONS: Sex-specific variation is an overriding feature of deep brain shape regardless of disease status, underscoring persistent patterns of sex differences observed both within and across diagnostic categories, and highlighting the importance of including it as a critical variable in studies of neurobiology. Future work should continue to explore these dimensions independently to determine whether these patterns of brain morphology extend to other aspects of neurobiology in schizophrenia, potentially uncovering broader implications for diagnosis and treatment.

KEY POINTS: Statistical analyses revealed significant main effects for diagnosis and sex in deep brain shape morphology. Among patients with schizophrenia, there was a pattern of thinning and surface contraction in the bilateral hippocampus, amygdala, accumbens, and thalamus, and a pattern of significant thickening and surface expansion in the bilateral putamen and pallidum compared to healthy control participants. Between males and females, there was a pattern of significant thinning and surface contraction in the bilateral thalamus, pallidum, putamen, and amygdala in females compared to males.There was no significant interaction between diagnosis and biological sex, suggesting that sex differences in deep brain shape and asymmetry among patients with schizophrenia reflect those observed in healthy individuals.Small but statistically significant relationships exist between brain structure and clinical correlates of schizophrenia were similar for both men and women with the disease, such that higher CPZ was associated with shape-derived thinning and surface contraction in the caudate, accumbens, hippocampus, amygdala, and thalamus, and elevated positive symptoms were associated with shape-derived thinning and surface contraction in the bilateral caudate, right hippocampus, and right amygdala.

PMID:39484539 | PMC:PMC11526939 | DOI:10.1101/2024.10.24.619733

bioRxiv [Preprint]. 2024 Oct 26:2024.10.25.619982. doi: 10.1101/2024.10.25.619982.

ABSTRACT

Notwithstanding advances in computational models of neuromodulation, there are mismatches between simulated and experimental activation thresholds. Transcranial Magnetic Stimulation (TMS) of the primary motor cortex generates motor evoked potentials (MEPs). At the threshold of MEP generation, whole-head models predict macroscopic (at millimeter scale) electric fields (50-70 V/m) which are considerably below conventionally simulated cortical neuron thresholds (200-300 V/m). We hypothesize that this apparent contradiction is in part a consequence of electrical field warping by brain microstructure. Classical neuronal models ignore the physical presence of neighboring neurons and microstructure and assume that the macroscopic field directly acts on the neurons. In previous work, we performed advanced numerical calculations considering realistic microscopic compartments (e.g., cells, blood vessels), resulting in locally inhomogeneous (micrometer scale) electric field and altered neuronal activation thresholds. Here we combine detailed neural threshold simulations under homogeneous field assumptions with microscopic field calculations, leveraging a novel statistical approach. We show that, provided brain-region specific microstructure metrics, a single statistically derived scaling factor between microscopic and macroscopic electric fields can be applied in predicting neuronal thresholds. For the cortical sample considered, the statistical methods match TMS experimental thresholds. Our approach can be broadly applied to neuromodulation models, where fully coupled microstructure scale simulations may not be practical.

PMID:39484517 | PMC:PMC11527135 | DOI:10.1101/2024.10.25.619982

bioRxiv [Preprint]. 2024 Oct 22:2024.10.21.619532. doi: 10.1101/2024.10.21.619532.

ABSTRACT

Quality control of MRI data prior to preprocessing is fundamental, as substandard data are known to increase variability spuriously. Currently, no automated or manual method reliably identifies subpar images, given pre-specified exclusion criteria. In this work, we propose a protocol describing how to carry out the visual assessment of T1-weighted, T2-weighted, functional, and diffusion MRI scans of the human brain with the visual reports generated by MRIQC. The protocol describes how to execute the software on all the images of the input dataset using typical research settings (i.e., a high-performance computing cluster). We then describe how to screen the visual reports generated with MRIQC to identify artifacts and potential quality issues and annotate the latter with the “rating widget” – a utility that enables rapid annotation and minimizes bookkeeping errors. Integrating proper quality control checks on the unprocessed data is fundamental to producing reliable statistical results and crucial to identifying faults in the scanning settings, preempting the acquisition of large datasets with persistent artifacts that should have been addressed as they emerged.

PMID:39484445 | PMC:PMC11526949 | DOI:10.1101/2024.10.21.619532

bioRxiv [Preprint]. 2024 Oct 23:2024.10.23.619767. doi: 10.1101/2024.10.23.619767.

ABSTRACT

The NHGRI-EBI GWAS Catalog serves as a vital resource for the genetic research community, providing access to the most comprehensive database of human GWAS results. Currently, it contains close to 7,000 publications for more than 15,000 traits, from which more than 625,000 lead associations have been curated. Additionally, 85,000 full genome-wide summary statistics datasets – containing association data for all variants in the analysis – are available for downstream analyses such as meta-analysis, fine-mapping, Mendelian randomisation or development of polygenic risk scores. As a centralised repository for GWAS results, the GWAS Catalog sets and implements standards for data submission and harmonisation, and encourages the use of consistent descriptors for traits, samples and methodologies. We share processes and vocabulary with the PGS Catalog, improving interoperability for a growing user group. Here, we describe the latest changes in data content, improvements in our user interface, and the implementation of the GWAS-SSF standard format for summary statistics. We address the challenges of handling the rapid increase in large-scale molecular quantitative trait GWAS and the need for sensitivity in the use of population and cohort descriptors while maintaining data interoperability and reusability.

PMID:39484403 | PMC:PMC11526975 | DOI:10.1101/2024.10.23.619767