Category: Nevin Manimala

Amyotroph Lateral Scler Frontotemporal Degener. 2024 Mar 1:1-10. doi: 10.1080/21678421.2024.2322539. Online ahead of print.

ABSTRACT

BACKGROUND: The Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) total score is a widely used measure of functional status in Amyotrophic Lateral Sclerosis/Motor Neuron Disease (ALS), but recent evidence has raised doubts about its validity. The objective was to examine the measurement properties of the ALSFRS-R, aiming to produce valid measurement from all 12 scale items.

METHOD: Longitudinal ALSFRS-R data were collected between 2013-2020 from 1120 people with ALS recruited from 35 centers, together with other scales in the Trajectories of Outcomes in Neurological Conditions-ALS (TONiC-ALS) study. The ALSFRS-R was analyzed by confirmatory factor analysis (CFA), Rasch Analysis (RA) and Mokken scaling.

RESULTS: No definite factor structure of the ALSFRS-R was confirmed by CFA. RA revealed the raw score total to be invalid even at the ordinal level because of multidimensionality; valid interval level subscale measures could be found for the Bulbar, Fine-Motor and Gross-Motor domains but the Respiratory domain was only valid at an ordinal level. All four domains resolved into a single valid, interval level measure by using a bifactor RA. The smallest detectable difference was 10.4% of the range of the interval scale.

CONCLUSION: A total ALSFRS-R ordinal raw score can lead to inferential bias in clinical trial results due to its non-linear nature. On the interval level transformation, more than 5 points difference is required before a statistically significant detectable difference can be observed. Transformation to interval level data should be mandatory in clinical trials.

PMID:38426231 | DOI:10.1080/21678421.2024.2322539

Brain. 2024 Mar 1:awae067. doi: 10.1093/brain/awae067. Online ahead of print.

ABSTRACT

Frontotemporal Dementia (FTD) is a disease of high heterogeneity, apathy and disinhibition present in all subtypes of FTD and imposes a significant burden on families/society. Traditional neuroimaging analysis has limitations in elucidating the network localization due to individual clinical and neuroanatomical variability. The study aims to identify the atrophy network map associated with different FTD clinical subtypes and determine the specific localization of the network for apathy and disinhibition. Eighty FTD patients [45 behavioral variant FTD (bvFTD) and 35 semantic variant progressive primary aphasia (svPPA)] and 58 healthy controls (HCs) at Xuanwu Hospital were enrolled as Dataset 1; 112 FTD patients including 50 bvFTD, 32 svPPA, and 30 non-fluent variant PPA (nfvPPA) cases, and 110 HCs from Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) dataset were included as Dataset 2. Initially, single-subject atrophy maps were defined by comparing cortical thickness in each FTD patient versus HCs. Next, the network of brain regions functionally connected to each FTD patient’s location of atrophy was determined using seed-based functional connectivity in a large (n = 1000) normative connectome. Finally, we used atrophy network mapping to define clinical subtype-specific network (45 bvFTD, 35 svPPA and 58 HCs in Dataset 1; 50 bvFTD, 32 svPPA, 30 nfvPPA and 110 HCs in Dataset 2) and symptom-specific networks [combined dataset 1 and 2, apathy without depression Vs non-apathy without depression (80:26), disinhibition Vs non-disinhibition (88:68)]. We compare the result with matched symptom networks derived from patients with focal brain lesions or conjunction analysis. Through the analysis of two datasets, we identified heterogeneity in atrophy patterns among FTD patients. However, these atrophy patterns are connected to a common brain network. The primary regions affected by atrophy in FTD included the frontal and temporal lobes, particularly the anterior temporal lobe. bvFTD connects to frontal and temporal cortical areas, svPPA mainly impacts the anterior temporal region, and nfvPPA targets the inferior frontal gyrus and precentral cortex regions. The apathy-specific network was localized in the orbital frontal cortex and ventral striatum, while the disinhibition-specific network was localized in the bilateral orbital frontal gyrus and right temporal lobe. Apathy and disinhibition atrophy networks resemble known motivational and criminal lesion networks respectively. A significant correlation was found between the apathy/disinhibition scores and functional connectivity between atrophy maps and the peak of the networks. This study localizes the common network of clinical subtypes and main symptoms in FTD, guiding future FTD neuromodulation interventions.

PMID:38426222 | DOI:10.1093/brain/awae067

Front Physiol. 2024 Feb 15;15:1293380. doi: 10.3389/fphys.2024.1293380. eCollection 2024.

ABSTRACT

Background and Purpose: Precisely assessing the likelihood of an intracranial aneurysm rupturing is critical for guiding clinical decision-making. The objective of this study is to construct and validate a deep learning framework utilizing point clouds to forecast the likelihood of aneurysm rupturing. Methods: The dataset included in this study consisted of a total of 623 aneurysms, with 211 of them classified as ruptured and 412 as unruptured, which were obtained from two separate projects within the AneuX morphology database. The HUG project, which included 124 ruptured aneurysms and 340 unruptured aneurysms, was used to train and internally validate the model. For external validation, another project named @neurIST was used, which included 87 ruptured and 72 unruptured aneurysms. A standardized method was employed to isolate aneurysms and a segment of their parent vessels from the original 3D vessel models. These models were then converted into a point cloud format using open3d package to facilitate training of the deep learning network. The PointNet++ architecture was utilized to process the models and generate risk scores through a softmax layer. Finally, two models, the dome and cut1 model, were established and then subjected to a comprehensive comparison of statistical indices with the LASSO regression model built by the dataset authors. Results: The cut1 model outperformed the dome model in the 5-fold cross-validation, with the mean AUC values of 0.85 and 0.81, respectively. Furthermore, the cut1 model beat the morphology-based LASSO regression model with an AUC of 0.82. However, as the original dataset authors stated, we observed potential generalizability concerns when applying trained models to datasets with different selection biases. Nevertheless, our method outperformed the LASSO regression model in terms of generalizability, with an AUC of 0.71 versus 0.67. Conclusion: The point cloud, as a 3D visualization technique for intracranial aneurysms, can effectively capture the spatial contour and morphological aspects of aneurysms. More structural features between the aneurysm and its parent vessels can be exposed by keeping a portion of the parent vessels, enhancing the model’s performance. The point cloud-based deep learning model exhibited good performance in predicting rupture risk while also facing challenges in generalizability.

PMID:38426204 | PMC:PMC10901972 | DOI:10.3389/fphys.2024.1293380

Int J Public Health. 2024 Feb 15;69:1606868. doi: 10.3389/ijph.2024.1606868. eCollection 2024.

ABSTRACT

Objectives: This study aimed to evaluate the joint effects of multiple air pollutants including PM_2.5, PM₁₀, NO₂, and NO_x with dementia and examined the modifying effects of genetic susceptibility. Methods: This study included 220,963 UK Biobank participants without dementia at baseline. Weighted air pollution score reflecting the joint exposure to multiple air pollutants were constructed by cross-validation analyses, and inverse-variance weighted meta-analyses were performed to create a pooled effect. The modifying effect of genetic susceptibility on air pollution score was assessed by genetic risk score and APOE ε4 genotype. Results: The HR (95% CI) of dementia for per interquartile range increase of air pollution score was 1.13 (1.07∼1.18). Compared with the lowest quartile (Q1) of air pollution score, the HR (95% CI) of Q4 was 1.26 (1.13∼1.40) (P _trend = 2.17 × 10^-5). Participants with high air pollution score and high genetic susceptibility had higher risk of dementia compared to those with low air pollution score and low genetic susceptibility. Conclusion: Our study provides evidence that joint exposure to multiple air pollutants substantially increases the risk of dementia, especially among individuals with high genetic susceptibility.

PMID:38426188 | PMC:PMC10901982 | DOI:10.3389/ijph.2024.1606868

Int J Public Health. 2024 Feb 15;69:1606378. doi: 10.3389/ijph.2024.1606378. eCollection 2024.

ABSTRACT

Objectives: Socioeconomic disparities in obesity have been observed in both childhood and adulthood. However, it remains unclear how the role of risk factors influencing these inequalities has evolved over time. Methods: Longitudinal data on 2,866 children and adolescents (6-17 years old) from the China Health and Nutrition Survey were used to track their BMI during childhood, adolescence, and adulthood. Concentration Index was utilized to measure socioeconomic inequalities in obesity, while Oaxaca decomposition was employed to determine the share of different determinants of inequality. Results: The concentration index for obesity during childhood and adulthood were 0.107 (95% CI: 0.023, 0.211) and 0.279 (95% CI: 0.203, 0.355), respectively. Changes in baseline BMI (24.6%), parental BMI (10.4%) and socioeconomic factors (6.7%) were found to be largely responsible for the increasing inequality in obesity between childhood and adulthood. Additionally, mother’s education (-7.4%) was found to contribute the most to reducing these inequalities. Conclusion: Inequalities in obesity during childhood and adulthood are significant and growing. Interventions targeting individuals with higher BMI, especially those who are wealthy, can significantly reduce the gap.

PMID:38426185 | PMC:PMC10902784 | DOI:10.3389/ijph.2024.1606378

Front Neurol. 2024 Feb 15;15:1359775. doi: 10.3389/fneur.2024.1359775. eCollection 2024.

ABSTRACT

INTRODUCTION: In acute stroke, identifying early changes (parenchymal hypodensity) on non-contrast CT (NCCT) can be challenging. We aimed to identify whether the accuracy of clinicians in detecting acute hypodensity in ischaemic stroke patients on a non-contrast CT is improved with the use of an Artificial Intelligence (AI) based, automated hypodensity detection algorithm (HDT) using MRI-DWI as the gold standard.

METHODS: The study employed a case-crossover within-clinician design, where 32 clinicians were tasked with identifying hypodensity lesions on NCCT scans for five a priori selected patient cases, before and after viewing the AI-based HDT. The DICE similarity coefficient (DICE score) was the primary measure of accuracy. Statistical analysis compared DICE scores with and without AI-based HDT using mixed-effects linear regression, with individual NCCT scans and clinicians as nested random effects.

RESULTS: The AI-based HDT had a mean DICE score of 0.62 for detecting hypodensity across all NCCT scans. Clinicians’ overall mean DICE score was 0.33 (SD 0.31) before AI-based HDT implementation and 0.40 (SD 0.27) after implementation. AI-based HDT use was associated with an increase of 0.07 (95% CI: 0.02-0.11, p = 0.003) in DICE score accounting for individual scan and clinician effects. For scans with small lesions, clinicians achieved a mean increase in DICE score of 0.08 (95% CI: 0.02, 0.13, p = 0.004) following AI-based HDT use. In a subgroup of 15 trainees, DICE score improved with AI-based HDT implementation [mean difference in DICE 0.09 (95% CI: 0.03, 0.14, p = 0.004)].

DISCUSSION: AI-based automated hypodensity detection has potential to enhance clinician accuracy of detecting hypodensity in acute stroke diagnosis, especially for smaller lesions, and notably for less experienced clinicians.

PMID:38426177 | PMC:PMC10902446 | DOI:10.3389/fneur.2024.1359775

PeerJ. 2024 Feb 26;12:e16963. doi: 10.7717/peerj.16963. eCollection 2024.

ABSTRACT

Global biodiversity is declining at an ever-increasing rate. Yet effective policies to mitigate or reverse these declines require ecosystem condition data that are rarely available. Morphology-based bioassessment methods are difficult to scale, limited in scope, suffer prohibitive costs, require skilled taxonomists, and can be applied inconsistently between practitioners. Environmental DNA (eDNA) metabarcoding offers a powerful, reproducible and scalable solution that can survey across the tree-of-life with relatively low cost and minimal expertise for sample collection. However, there remains a need to condense the complex, multidimensional community information into simple, interpretable metrics of ecological health for environmental management purposes. We developed a riverine taxon-independent community index (TICI) that objectively assigns indicator values to amplicon sequence variants (ASVs), and significantly improves the statistical power and utility of eDNA-based bioassessments. The TICI model training step uses the Chessman iterative learning algorithm to assign health indicator scores to a large number of ASVs that are commonly encountered across a wide geographic range. New sites can then be evaluated for ecological health by averaging the indicator value of the ASVs present at the site. We trained a TICI model on an eDNA dataset from 53 well-studied riverine monitoring sites across New Zealand, each sampled with a high level of biological replication (n = 16). Eight short-amplicon metabarcoding assays were used to generate data from a broad taxonomic range, including bacteria, microeukaryotes, fungi, plants, and animals. Site-specific TICI scores were strongly correlated with historical stream condition scores from macroinvertebrate assessments (macroinvertebrate community index or MCI; R² = 0.82), and TICI variation between sample replicates was minimal (CV = 0.013). Taken together, this demonstrates the potential for taxon-independent eDNA analysis to provide a reliable, robust and low-cost assessment of ecological health that is accessible to environmental managers, decision makers, and the wider community.

PMID:38426140 | PMC:PMC10903356 | DOI:10.7717/peerj.16963

Behav Res Ther. 2024 Feb 24;175:104497. doi: 10.1016/j.brat.2024.104497. Online ahead of print.

ABSTRACT

The onset of the COVID-19 pandemic resulted in a dramatic increase in the salience and importance of information relating to both the risk of infection, and factors that could mitigate against such risk. This is likely to have contributed to elevated contamination fear concerns in the general population. Biased attention for contamination-related information has been proposed as a potential mechanism underlying contamination fear, though evidence regarding the presence of such biased attention has been inconsistent. A possible reason for this is that contamination fear may be characterised by variability in attention bias that has not yet been examined. The current study examined the potential association between attention bias variability for both contamination-related and mitigation-related stimuli, and contamination fear during the early stages of the COVID-19 pandemic. A final sample of 315 participants completed measures of attention bias and contamination fear. The measure of average attention bias for contamination-related stimuli and mitigation-related stimuli was not associated with contamination fear (r = 0.055 and r = 0.051, p > 0.10), though both attention bias variability measures did show a small but statistically significant relationship with contamination fear (r = 0.133, p < 0.05; r = 0.147, p < 0.01). These attention bias variability measures also accounted for significant additional variance in contamination fear above the average attention bias measure (and controlling for response time variability). These findings provide initial evidence for the association between attention bias variability and contamination fear, underscoring a potential target for cognitive bias interventions for clinical contamination fear.

PMID:38422560 | DOI:10.1016/j.brat.2024.104497

Am J Otolaryngol. 2024 Feb 23;45(3):104230. doi: 10.1016/j.amjoto.2024.104230. Online ahead of print.

ABSTRACT

OBJECTIVE: Previous studies have focused on the balance system’s involvement in sleep deprivation or disorders. This study investigated how daily routine sleep quality affects the balance system of people without sleep deprivation or diagnosed sleep disorders.

METHODS: The study included 45 participants with a BMI score of <25. The PSQI was used to determine sleep quality. The SOT, HS-SOT, and ADT evaluated the vestibular system’s functionality.

RESULTS: In SOT, condition 3, 4, 5, and 6 composite scores, VIS and VEST composite balance scores, and HS-SOT 5 scores were lower in the HPSQI group. At the same time, there is a statistically significant negative correlation between these scores and PSQI scores.

CONCLUSION: Poor sleep quality may be a factor influencing the balance system. Sleep quality affects the visual and vestibular systems rather than the somatosensory system. The population should be made aware of this issue, and clinicians should consider the potential impact of sleep quality when evaluating the balance system.

PMID:38422556 | DOI:10.1016/j.amjoto.2024.104230

Phys Med Biol. 2024 Feb 29. doi: 10.1088/1361-6560/ad2ee8. Online ahead of print.

ABSTRACT

OBJECTIVE: &#xD;An algorithm was developed for automated positioning of lattice points within volumetric modulated arc lattice radiation therapy (VMATLRT) planning. These points are strategically placed within the gross tumor volume(GTV) to receive high doses, adhering to specific separation rules from adjacent organs at risk(OARs). The study goals included enhancing planning safety, consistency, and efficiency while emulating human performance.

APPROACH: A Monte Carlo-based algorithm was designed to optimize the number and arrangement of lattice points within the GTV while considering placement constraints and objectives. These constraints encompassed minimum spacing between points, distance from OARs, and longitudinal separation along the z-axis. Additionally, the algorithm included an objective to permit, at the user’s discretion, solutions with more centrally placed lattice points within the GTV. To validate its effectiveness, the automated approach was compared with manually planned treatments for 24 previous patients. Prior to clinical implementation, a failure mode and effects analysis (FMEA) was conducted to identify potential shortcomings.&#xD;Main results.&#xD;The automated program successfully met all placement constraints with an average execution time of 0.29 ±0.07 minutes per lattice point. The average lattice point density(# points per cc of GTV) was similar for automated(0.00725) compared to manual placement(0.00704). The dosimetric differences between the automated and manual plans were minimal, with statistically significant differences in certain metrics like minimum dose(1.9% vs. 1.4%), D5%(52.8% vs. 49.4%), and Body-GTV V30%(20.7 cc vs. 19.7 cc).

SIGNIFICANCE: This study underscores the feasibility of employing a straightforward Monte Carlo-based algorithm to automate the creation of spherical target structures for VMAT LRT planning. The automated method yields similar dose metrics, enhances inter-planner consistency for larger targets, and requires fewer resources and less time compared to manual placement. This approach holds promise for standardizing treatment planning in prospective patient trials and facilitating its adoption across centers seeking to implement VMATLRT techniques.&#xD.

PMID:38422544 | DOI:10.1088/1361-6560/ad2ee8