Category: Nevin Manimala

Dement Geriatr Cogn Disord. 2021 Nov 4:1-9. doi: 10.1159/000519766. Online ahead of print.

ABSTRACT

BACKGROUND: Subjective cognitive decline (SCD) is a self-perceived cognitive worsening without objective cognitive impairment. Due to its heterogeneity and potential risk of Alzheimer’s disease (AD), baseline biomarkers to predict progression are clinically important. In the present study, cognitive trajectories during a 24-month period were compared between amyloid-positive SCD (A+SCD) and amyloid-negative SCD (A-SCD) subjects, and biomarkers associated with memory decline were investigated.

METHODS: Data from a prospective cohort study in Korea between 2016 and 2019 were analyzed. SCD subjects ≥50 years of age were eligible. All participants underwent neuropsychological tests, brain magnetic resonance imaging, and florbetaben positron emission tomography scans. Amyloid burden and regional volumes were measured. Cognitive changes corrected for age were compared between A+SCD and A-SCD groups. Biomarkers associated with memory decline were assessed.

RESULTS: Forty-seven SCD subjects (69.9 ± 6.7 years, mini-mental state examination (MMSE) score 27.5) were enrolled, and 31 completed at least 1 annual follow-up (mean follow-up: 24.7 months). Baseline characteristics except age, hippocampal atrophy, and white matter hyperintensities were similar between A+SCDs (n = 12, 25.6%) and A-SCDs (n = 35). A+SCD subjects showed greater decline in the verbal memory function compared with the A-SCD subjects after adjustment for age. MMSE scores decreased more in the A+SCD (1.1 in the A+SCD; 0.55 in the A-SCD), although it was not statistically significant. Amyloid burden and baseline memory score were associated with memory decline.

CONCLUSIONS: Within SCD, A+SCD subjects showed faster memory decline compared with the A-SCD subjects and amyloid burden might be associated with future memory decline in SCD.

PMID:34736258 | DOI:10.1159/000519766

Comput Methods Programs Biomed. 2021 Oct 9;212:106458. doi: 10.1016/j.cmpb.2021.106458. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: The number of end-stage renal disease (ESRD) patients treated with hemodialysis (HD) has significantly increased, but the prognosis remains poor. Time-series features have been included in only a few studies to predict HD patient survival, and how to utilize such features effectively remains unclear. This article aims to develop a more accurate, interpretable, and clinically practical personalized survival prediction model for HD patients.

METHODS: This study proposed and evaluated an attention-based Bi-GRU network using time-series features for survival prediction. A distance-based loss function was proposed to improve performance. We used data from 1232 ESRD patients who received regular hemodialysis treatment for ≥ 3 months from 2007 to 2016 at the First Affiliated Hospital of Zhejiang University. The proposed model was compared with representative sequence modeling deep learning architectures and existing survival analysis methods in terms of the C-index and IBS value. Post hoc tests were used to test statistical significance. The attention map was used to assess feature importance over time. The impact of time-series changes on survival was investigated after controlling initial values (using BMI as an example).

RESULTS: The proposed method outperformed other sequence modeling architectures and the state-of-the-art survival analysis approaches in terms of the C-index and the integrated Brier score (IBS) value. Our method achieved a C-index of 0.7680 (95% confidence intervals [CI]: 0.7645, 0.7716) and an IBS of 0.1302 (95% confidence intervals [CI]: 0.1292, 0.1313), showing an improvement of up to 5.4% in terms of the C-index and a decrease of 3.2% in terms of the IBS value. The addition of the distance-based loss function improved the performance. The predicted risk and actual risk levels closely agreed. This study also found that even after controlling the initial body mass index (BMI) values, different 3-month BMI trends could produce different survival outcomes.

CONCLUSIONS: This study proposed a more effective and interpretable method to use time-series information in survival analysis. The proposed method may help promote personalized medicine and improve patient prognosis.

PMID:34736175 | DOI:10.1016/j.cmpb.2021.106458

Cell Calcium. 2021 Oct 21;100:102494. doi: 10.1016/j.ceca.2021.102494. Online ahead of print.

ABSTRACT

We previously described that cell-wide cytosolic Ca²⁺ transients evoked by inositol trisphosphate (IP₃) are generated by two modes of Ca²⁺ liberation from the ER; ‘punctate’ release via an initial flurry of transient Ca²⁺ puffs from local clusters of IP₃ receptors, succeeded by a spatially and temporally ‘diffuse’ Ca²⁺ liberation. Those findings were derived using statistical fluctuation analysis to monitor puff activity which is otherwise masked as global Ca²⁺ levels rise. Here, we devised imaging approaches to resolve individual puffs during global Ca²⁺ elevations to better investigate the mechanisms terminating the puff flurry. We find that puffs contribute about 40% (∼90 attomoles) of the total Ca²⁺ liberation, largely while the global Ca²⁺ signal rises halfway to its peak. The major factor terminating punctate Ca²⁺ release is an abrupt decline in puff frequency. Although the amplitudes of large puffs fall during the flurry, the amplitudes of more numerous small puffs remain steady, so overall puff amplitudes decline only modestly (∼30%). The Ca²⁺ flux through individual IP₃ receptor/channels does not measurably decline during the flurry, or when puff activity is depressed by pharmacological lowering of Ca²⁺ levels in the ER lumen, indicating that the termination of punctate release is not a simple consequence of reduced driving force for Ca²⁺ liberation. We propose instead that the gating of IP₃ receptors at puff sites is modulated such that their openings become suppressed as the bulk [Ca²⁺] in the ER lumen falls during global Ca²⁺ signals.

PMID:34736161 | DOI:10.1016/j.ceca.2021.102494

Comput Methods Programs Biomed. 2021 Oct 22;212:106484. doi: 10.1016/j.cmpb.2021.106484. Online ahead of print.

ABSTRACT

BACKGROUND: Mobile health (mHealth) have significantly advanced evaluating neurocognitive functions; but, few reports have documented whether they validate neurocognitive impairments as well as paper-and-pencil neuropsychological tests.

OBJECTIVE: To meta-analyze the correlation between mobile applications for neuropsychological tests and validated paper-and-pencil neuropsychological tests for evaluating neurocognitive impairments.

METHOD: We used PubMed, Embase, Cochrane, Web of Science, and IEEE Explorer through January 2020 to identify studies that compared mobile applications for neuropsychological tests vs. paper-and-pencil neurophysiological tests. We used random-effects models via the DerSimonian and Laird method to extract pooled Pearson’s correlation coefficients and we stratified by study design.

RESULT: Nine out of 4639 screened articles (one RCT and eight prospective longitudinal case series) were included. For the observational studies, there was a statistically significant strong and direct correlation between mobile applications for neuropsychological test scores and validated paper-and-pencil neuropsychological assessment scores (r = 0.70; 95% CI 0.59, 0.79; I² = 74.5%; p- heterogeneity <0.001). Stronger results were seen for the RCT (r = 0.92; 95% CI 0.77, 0.97).

CONCLUSION: This meta-analysis showed a statistically significant correlation between mobile applications and the validated paper-and-pencil neuropsychological assessments analyzed for the evaluation of neurocognitive impairments.

PMID:34736169 | DOI:10.1016/j.cmpb.2021.106484

Parkinsonism Relat Disord. 2021 Oct 12;92:94-100. doi: 10.1016/j.parkreldis.2021.10.007. Online ahead of print.

ABSTRACT

INTRODUCTION: Individuals with progressive supranuclear palsy (PSP) experience cognitive changes that are challenging to follow without a validated neuropsychological test battery to measure progression. This study describes a composite measure to evaluate cognition in individuals with PSP.

METHODS: Baseline cognitive test data from 486 participants with PSP in the PASSPORT (NCT03068468) study included the Repeatable Battery for Assessment of Neuropsychological Status (RBANS), Color Trails Test (CTT) parts 1 and 2, letter-number sequencing, and letter fluency. Data were analyzed using summary statistics and a matrix of Pearson correlations. A hypothetical factor structure was constructed and validated.

RESULTS: Observed correlations were highest for scores between story memory and story recall (correlation coefficient = 0.78) and lowest for scores between letter fluency and picture naming (correlation coefficient = 0.11), and picture naming and figure copy (correlation coefficient = 0.12). After excluding picture naming and Color Trails Test (CTT) parts 1 and 2, a 3-factor structure was hypothesized for the remaining 13 tests. Confirmatory factor analysis demonstrated goodness of fit within acceptable limits (comparative fit index and Tucker-Lewis index = 0.98, standardized root-mean-square residual and root-mean-square error of approximation = 0.05-0.06). Mixed-model repeated-measures analysis of change from baseline to week 52 in RBANS and PSP cognitive composite score produced mean-to-standard-deviation ratios of 0.418 and 0.780, respectively.

CONCLUSIONS: This novel composite endpoint, based on RBANS and designed to account for motor impairments in PSP, improves on current cognitive assessments PSP.

PMID:34736158 | DOI:10.1016/j.parkreldis.2021.10.007

Health Place. 2021 Oct 30;72:102692. doi: 10.1016/j.healthplace.2021.102692. Online ahead of print.

ABSTRACT

Spatially varying baseline data can help identify and prioritise actions directed to determinants of intra-urban health inequalities. Twenty-seven years (1990-2016) of cause-specific mortality data in British Columbia, Canada were linked to three demographic data sources. Bayesian small area estimation models were used to estimate life expectancy (LE) at birth and 20 cause-specific mortality rates by sex and year. The gaps in LE for males and females ranged from 6.9 years to 9.5 years with widening inequality in more recent years. Inequality ratios increased for almost all causes, especially for HIV/AIDS and sexually transmitted infections, maternal and neonatal disorders, and neoplasms.

PMID:34736154 | DOI:10.1016/j.healthplace.2021.102692

Biomaterials. 2021 Oct 22;279:121223. doi: 10.1016/j.biomaterials.2021.121223. Online ahead of print.

ABSTRACT

Harnessing developmental processes for tissue engineering represents a promising yet challenging approach to regenerative medicine. Tooth avulsion is among the most serious traumatic dental injuries, whereas functional tooth regeneration remains uncertain. Here, we established a strategy using decellularized tooth matrix (DTM) combined with human dental pulp stem cell (hDPSC) aggregates to simulate an odontogenesis-related developmental microenvironment. The bioengineered teeth reconstructed by this strategy regenerated three-dimensional pulp and periodontal tissues equipped with vasculature and innervation in a preclinical pig model after implantation into the alveolar bone. These results prompted us to enroll 15 patients with avulsed teeth after traumatic dental injuries in a pilot clinical trial. At 12 months after implantation, bioengineered teeth led to the regeneration of functional teeth, which supported continued root development, in humans. Mechanistically, exosomes derived from hDPSC aggregates mediated the tooth regeneration process by upregulating the odontogenic and angiogenic ability of hDPSCs. Our findings suggest that odontogenic microenvironment engineering by DTM and stem cell aggregates initiates functional tooth regeneration and serves as an effective treatment for tooth avulsion.

PMID:34736149 | DOI:10.1016/j.biomaterials.2021.121223

J Surg Res. 2021 Nov 1;270:369-375. doi: 10.1016/j.jss.2021.09.017. Online ahead of print.

ABSTRACT

BACKGROUND: Damage control resuscitation has become the standard of care in military and civilian trauma. Early identification of blood product requirements may aid in optimizing the clinical decision-making process while improving trauma related outcomes. This study aimed to assess and compare multiple machine learning models for predicting patients at highest risk for massive transfusion on the battlefield.

METHODS: Supervised machine learning approaches using logistic regression, support vector machine, neural network, and random forest techniques were used to create predictive models for massive transfusion using standard prehospital and arrival data points from the Department of Defense Trauma Registry, 2008-2016. Seventy percent of the population was used for model development and performance was validated using the remaining 30%. Models were tested for accuracy and compared by standard performance statistics.

RESULTS: A total of 22,158 patients (97% male, 58% penetrating injury, median age 25-29 y/o, average Injury Severity Score 9, with an overall mortality of 3%) were included in the analysis. Massive transfusion was required by 7.4% of patients. Overall accuracy was found to be above 90% in all models tested. Following cross validation and model training, the random forest model outperformed the alternatively tested models for precision, recall, and area under the curve.

CONCLUSION: Machine learning techniques may allow for more optimal and rapid identification of combat trauma patients at highest risk for massive transfusion. These powerful approaches may uncover novel correlations and help improve triage, activation of massive transfusion resources, and trauma-related outcomes. Further research seeking to optimize and apply these algorithms to trauma-centered research should be pursued.

PMID:34736129 | DOI:10.1016/j.jss.2021.09.017

Leg Med (Tokyo). 2021 Oct 23;54:101983. doi: 10.1016/j.legalmed.2021.101983. Online ahead of print.

ABSTRACT

In the current scenario, DNA typing is the need of forensic science field due to its ability to provide results in much shorter time. In view of advancement of forensic DNA typing and incensement in the number of STRs markers, Promega offered a new VersaPlex™ 27PY system with 27 loci (23 autosomal STR loci, Amelogenin, DYS391 and two rapidly mutating Y-STR loci (DYS570 and DYS576)). In this study, the efficacy of “23 autosomal STR loci” for paternity testing and personal identification was demonstrated in Indian population. For this, 217 central Indians were tested and all the statistical parameters of forensic and population genetic interest were calculated. In addition, sensitivity of the kit was also tested for forensic casework. During investigation with VersaPlex™ 27PY system, allele 11 at locus TPOX was observed to be most frequent with the highest allelic frequency 0.432. Studied 23 loci showed valuable together with highest value of combined power of discrimination (CPD = 1), combined power of exclusion (CPI = 0.9999999989) and lowest value of combined matching probability (CPM = 7.92×10^-28).

PMID:34736143 | DOI:10.1016/j.legalmed.2021.101983

J Neurol Sci. 2021 Oct 28;431:120041. doi: 10.1016/j.jns.2021.120041. Online ahead of print.

ABSTRACT

BACKGROUND: To estimate King’s college clinical stage progression rate (ΔKC) at first clinical evaluation in order to define its predictive and prognostic role on survival in a large cohort of Amyotrophic Lateral Sclerosis (ALS) patients.

METHODS: The ΔKC was calculated with the following formula: 0 – KC clinical stage at first clinical evaluation/disease duration from onset to first evaluation, and each result was reported as absolute value. All the evaluations were performed in two cohorts: one from our tertiary centre for motor neuron disease and the other one from a pooled resource open-access ALS clinical trials (PRO-ACT) database. C-statistic was used to evaluate the model discrimination of survival at different time points (1-3 years). Cox proportional hazard model was used to identify factors associated with survival.

RESULTS: ΔKC predicted survival at three years in our centre and in the PRO-ACT cohort (C-statistic 0.83, 95% CI 0.8-0.86, p < 0.0001; 0.7, 95% CI 0.68-0.73, p < 0.0001, respectively). At multivariate analysis, ΔKC was independently associated with survival both in our cohort (HR 3.62 95% CI 2.71-4.83 p = 0.001) and in the PRO-ACT cohort (HR 2.75 95% CI 2.1-3.6 p = 0.001).

CONCLUSIONS: Based on our results, ΔKC could be used as a novel measure of disease progression, hence as an accurate predictor of survival in ALS patients. Indeed, greater values of ΔKC were associated with a 3.5-fold higher risk to experience the event, confirming its robust prognostic value.

PMID:34736124 | DOI:10.1016/j.jns.2021.120041