Category: Nevin Manimala

Clin Exp Immunol. 2023 Feb 1:uxad011. doi: 10.1093/cei/uxad011. Online ahead of print.

ABSTRACT

Inflammation plays a fundamental role in the development of several metabolic diseases, including obesity and type 2 diabetes (T2D); the complement system has been implicated in their development. People of Black African (BA) ethnicity are disproportionately affected by T2D and other metabolic diseases but the impact of ethnicity on the complement system has not been explored. We investigated ethnic differences in complement biomarkers and activation status between men of BA and White European (WE) ethnicity and explored their association with parameters of metabolic health. We measured a panel of 15 complement components, regulators and activation products in fasting plasma from 89 BA and 96 WE men. Ethnic differences were statistically validated. Association of complement biomarkers with metabolic health indices (BMI, waist circumference, insulin resistance and HbA1c) were assessed in the groups. Plasma levels of the key complement components C3 and C4, the regulators clusterin and properdin and the activation marker iC3b were significantly higher in BA compared to WE men after age adjustment, while FD levels were significantly lower. C3 and C4 levels positively correlated with some or all markers of metabolic dysfunction in both ethnic groups while FD was inversely associated with HbA1c in both groups, and clusterin and properdin were inversely associated with some markers of metabolic dysfunction only in the WE group. Our findings of increased levels of complement components and activation products in BA compared to WE men suggest differences in complement regulation that may impact susceptibility to poor metabolic health.

PMID:36722378 | DOI:10.1093/cei/uxad011

Nutr Bull. 2023 Feb 1. doi: 10.1111/nbu.12603. Online ahead of print.

ABSTRACT

Adequate nutritional intake in the first years of life is crucial for future health. The purpose of this study is to assess the adequacy of nutritional intake in Portuguese toddlers. The EPACI Portugal 2012 is a cross-sectional study of a representative sample of toddlers (n = 2230), aged between 12 and 36 months. Data on diets were collected by trained interviewers. The current analysis included 853 children with full data from 3-day food diaries completed by parents/caregivers. Intakes of energy, macro- and micronutrients were estimated through Statistical Program to Assess Dietary Exposure (SPADE). Nutritional adequacy was evaluated using Dietary Reference Values established by the European Food Safety Authority. A large proportion of children exceeded the recommended energy intake. The median daily protein intake was 4.7 g/kg/day, five times more than that recommended. About 9% and 90% of the children consumed a lower proportion of energy than the lower limit of the Reference Intake range for carbohydrates and fat, respectively. Around a third consumed less fibre and magnesium and 100% less vitamin D than the recommended Adequate Intake (AI). Almost a third consumed less vitamin A than the recommended Average Requirement (AR) and 86% of the children showed excessive sodium consumption. Portuguese toddlers consumed a low proportion of energy from fat, had energy and protein intakes above the recommendations and excessive intakes of sodium, and inadequate intakes of vitamin A. Every child consumed less than the recommended AI for vitamin D.

PMID:36722373 | DOI:10.1111/nbu.12603

Clin J Am Soc Nephrol. 2023 Feb 1. doi: 10.2215/CJN.0000000000000063. Online ahead of print.

ABSTRACT

BACKGROUND: Childhood kidney failure is a rare condition with worldwide clinical variability. We used a nationwide multicenter analysis to study the pretransplant course of the entire Israeli pediatric kidney failure population over 30 years.

METHODS: In this nationwide, population-based, historical cohort study, we analyzed medical and demographic data of all children treated with KRT and reported to the Israeli kidney failure registry in 1990-2020. Statistical analysis was performed with incidence rate corrected for age, ethnicity, and calendar year, using the appropriate age-related general population as denominator.

RESULTS: During the last 30 years, childhood incidence of kidney failure decreased. Average incidence in 2015-2019 was 9.1 cases per million age-related population (pmarp). Arab and Druze children exhibited higher kidney failure incidence rates than Jewish children (18.4 versus 7.0 cases pmarp for minorities versus Jews). The most common kidney failure etiologies among Arab and Jewish children were congenital anomalies of the kidney and urinary tract (approximately 27%), followed by cystic kidney diseases among Arab children (13%) and glomerulonephritis among Jewish children (16%). The most common etiology among Druze children was primary hyperoxaluria type 1 (33%). Israel’s national health insurance provides access to primary health care to all citizens. Accordingly, waiting time for deceased donor transplantation was equal between all ethnicities. Living-donor kidney transplantation rates among minority populations remained low in comparison with Jews over the entire study period. Although all patient groups demonstrated improvement in survival, overall survival rates were mainly etiology dependent.

CONCLUSIONS: In Israel, Arab and Druze children had a higher incidence of kidney failure, a unique etiological distribution, and a lower rate of living-donor kidney transplantations compared with Jewish children.

PMID:36722361 | DOI:10.2215/CJN.0000000000000063

J Magn Reson Imaging. 2023 Feb 1. doi: 10.1002/jmri.28621. Online ahead of print.

ABSTRACT

BACKGROUND: The prognosis of advanced gastric cancer (AGC) patients has attracted much attention, but there is a lack of evaluation method. MRI-based radiomics has the potential to evaluate AGC patients’ prognosis.

PURPOSE: To identify and validate the risk stratification and overall survival (OS) in AGC patients using MRI-based radiomics.

STUDY TYPE: Retrospective.

SUBJECTS: A total of 233 patients (168 males, 63.6 ± 11.1 years; 65 females, 59.7 ± 11.8 years) confirmed AGC were collected. The data were randomly divided into a training (164) and validation set (69).

SEQUENCE: A 3.0 T, axial T2-weighted, diffusion-weighted imaging, and contrast-enhanced T1-weighted (CE-T1WI).

ASSESSMENT: Radiologist 1 segmented 233 patients and radiologist 2 segmented randomly 50 patients on CE-T1WI. The risk score (RS) was summed by each sample based on the radiomics features and correlation coefficients. Patients were followed up for 7-67 months (median 41; 138 dead and 95 alive).

STATISTICAL TESTS: The intraclass correlation coefficient (ICC) and Kappa value were calculated. Differences in survival analysis were assessed by Kaplan-Meier curves and log-rank test. Cox-regression analysis was performed to identify the radiomics features and clinical indicators associated with OS. The calibration curves were built to assess the model. A two-tailed P value < 0.05 was considered statistically significant.

RESULTS: Integrated with age, lymphovascular invasion (LVI) and RS, a survival combined model was built. The area under the curve (AUC) for predicting 3-year and 5-year OS was 0.765 and 0.788 in the training set, 0.757 and 0.729 in the validation set. There was no significant difference between the radiomics model and survival combined model for 3-year (0.690 vs. 0.757, P = 0.425) and 5-year OS (0.687 vs. 729, P = 0.412) in the validation set. The calibration curves showed a high degree of fit for the survival combined model.

DATA CONCLUSION: This study established a survival combined model that might help AGC patients in future clinical decision-making.

EVIDENCE LEVEL: 33 TECHNICAL EFFICACY: Stage 5.

PMID:36722356 | DOI:10.1002/jmri.28621

J Med Internet Res. 2023 Jan 31. doi: 10.2196/35568. Online ahead of print.

ABSTRACT

BACKGROUND: Assessment of the quality of medical evidence available online is a critical step in the systematic review of clinical evidence. Existing tools that automate parts of this task validate the quality of individual studies but not of entire bodies of evidence, and focus on a restricted set of quality criteria.

OBJECTIVE: We propose a quality assessment task that consists of providing an overall quality rating for each outcome, as well as finer-grained justification for different quality criteria according to the GRADE formalisation framework. For this, we construct a new dataset and develop a machine-learning baseline system (EvidenceGRADEr). Our goal is to work towards evaluating the quality of a body of evidence (BoE) for a specific clinical question, rather than assessing the quality of individual primary studies.

METHODS: We algorithmically extracted quality-related data from all summaries of findings found in the Cochrane Database of Systematic Reviews (CDSR). Each BoE is defined by a set of PICO criteria (population-intervention-comparison-outcome) and assigned a quality grade (high/moderate/low/very low) together with quality criteria (justification) that influenced that decision. Different statistical data, metadata about the review, and parts of review text are extracted as support for grading each BoE. After pruning the resulting dataset with various quality checks, we used it to train several variants of a feature-rich neural model. The predictions were compared against the labels originally assigned by the authors of the systematic reviews.

RESULTS: Our quality assessment dataset, CDSR-QoE, contains 13,440 instances, or BoEs labelled for quality, originating from 2,252 systematic reviews published on the Internet in the years 2002–2020. Based on 10-fold cross-validation, the best neural binary classifiers for quality criteria detect risk of bias at .78 F1 (P: .68, R: .92) and imprecision at .75 F1 (P: .66, R: .86), while the performance on inconsistency, indirectness and publication bias criteria is lower (F1 in the range of .3-.4). The prediction of the overall quality grade into one of the four levels results in 0.5 F1. When casting the task as a binary problem by merging the GRADE classes (high+moderate vs. low+very low quality evidence), we attain .74 F1. We also find that the results vary depending on what supporting information is provided as input to the models.

CONCLUSIONS: There are different factors affecting the quality of evidence in the context of systematic reviews of medical evidence. Some of them (risk of bias and imprecision) can be automated with reasonable accuracy. Other quality dimensions such as indirectness, inconsistency, and publication bias prove more challenging for machine learning, largely because they are much rarer. This technology could substantially reduce reviewer workload in the future and expedite quality assessment as part of evidence synthesis.

PMID:36722350 | DOI:10.2196/35568

Stat Med. 2023 Feb 1. doi: 10.1002/sim.9669. Online ahead of print.

ABSTRACT

Interrupted time series are increasingly being used to assess the population impact of public health interventions. These data are usually correlated over time (auto correlated) and this must be accounted for in the analysis. Typically, this is done using either the Prais-Winsten method, the Newey-West method, or autoregressive-moving-average (ARMA) modeling. In this paper, we illustrate these methods via a study of pneumococcal vaccine introduction and explore their performance under 20 simulated autocorrelation scenarios with sample sizes ranging between 20 and 300. We show that in terms of mean square error, the Prais-Winsten and ARMA methods perform best, while in terms of coverage the Prais-Winsten method generally performs better than other methods. All three methods are unbiased. As well as having good statistical properties, the Prais-Winsten method is attractive because it is decision-free and produces a single measure of autocorrelation that can be compared between studies and used to guide sample size calculations. We would therefore encourage analysts to consider using this simple method to analyze interrupted time series.

PMID:36722328 | DOI:10.1002/sim.9669

Phys Sportsmed. 2023 Feb 1. doi: 10.1080/00913847.2023.2176161. Online ahead of print.

ABSTRACT

OBJECTIVES: Currently there are five international screening tools that are recommended to identify individuals who require pre-exercise medical clearance to reduce the risk of medical encounters during exercise. Therefore, the aim was to determine the percentage of race entrants who are advised to obtain pre-exercise medical clearance and the observed agreement between these five different international pre-exercise medical screening tools.

METHODS: 76654 race entrants from the Two Oceans Marathon (2012-2015) that completed an online pre-race screening questionnaire. Five pre-exercise medical screening tools (American Heart Association (AHA), pre-2015 American College of Sport Medicine (ACSM), post-2015 ACSM, Physical Activity Readiness Questionnaire (PAR-Q), and the European Association of Cardiovascular Prevention and Rehabilitation (EACPR)) were retrospectively applied to all participants. The % (95%CI) race entrants requiring medical clearance identified by each tool and the observed agreement between tools (%) was determined.

RESULTS: The % entrants requiring medical clearance varied from 6.7-33.9% between the five tools: EACPR (33.9%; 33.5-34.3); pre-2015 ACSM (33.9%; 33.5-34.3); PAR-Q (23.2%; 22.9-23.6); AHA (10.0%; 9.7-10.2); post-2015 ACSM (6.7%; 6.5-6.9). The observed agreement was highest between the pre-2015 ACSM and EACPR (35.4%), for pre-2015 ACSM and PAR-Q (24.8%), PAR-Q and EACPR (24.8%), and lowest between the post-2015 ACSM and AHA (4.1%).

CONCLUSION: The percentage of race entrants identified to seek medical clearance (and observed agreement), varied considerably between pre-exercise medical screening tools. Further research should determine which tool has the best predictive ability in identifying those at higher risk of medical encounters during exercise.

PMID:36722299 | DOI:10.1080/00913847.2023.2176161

Int J Med Robot. 2023 Feb 1:e2503. doi: 10.1002/rcs.2503. Online ahead of print.

ABSTRACT

BACKGROUND: This systematic review aims to ascertain how accurately 3D models can be predicted from two-dimensional (2D) imaging utilising statistical shape modelling.

METHODS: A systematic search of published literature was conducted in September 2022. All papers which assessed the accuracy of 3D models predicted from 2D imaging utilising statistical shape models and which validated the models against the ground truth were eligible.

RESULTS: 2127 papers were screened and a total of 34 studies were included for final data extraction. The best overall achievable accuracy was 0.45 mm (root mean square error) and 0.16 mm (average error).

CONCLUSION: Statistical shape modelling can predict detailed 3D anatomical models from minimal 2D imaging. Future studies should report the intended application domain of the model, the level of accuracy required, the underlying demographics of subjects, and the method in which accuracy was calculated, with root mean square error recommended if appropriate. This article is protected by copyright. All rights reserved.

PMID:36722297 | DOI:10.1002/rcs.2503

Expert Rev Neurother. 2023 Feb 1. doi: 10.1080/14737175.2023.2174017. Online ahead of print.

ABSTRACT

OBJECTIVES: Headache disorders are a common cause of disability and reduced health-related quality of life globally. Growing evidence supports the use of cannabis-based medicinal products (CBMPs) for chronic pain; however, a paucity of research specifically focuses on CBMPs’ efficacy and safety in headache disorders. This study aims to assess changes in validated patient-reported outcome measures (PROMs) in patients with headaches prescribed CBMPs and investigate the clinical safety in this population.

METHODS: A case series of the UK Medical Cannabis Registry was conducted. Primary outcomes were changes from baseline in PROMs (Headache Impact Test-6 (HIT-6), Migraine Disability Assessment (MIDAS), EQ-5D-5L, Generalized Anxiety Disorder-7 (GAD-7) questionnaire and Single-Item Sleep Quality Scale (SQS)) at 1-, 3-, and 6-months follow-up. P-values<0.050 were deemed statistically significant.

RESULTS: 97 patients were identified for inclusion. Improvements in HIT-6, MIDAS, EQ-5D-5L and SQS were observed at 1-, 3-, and 6-months (p<0.005) follow-up. GAD-7 improved at 1- and 3-months (p<0.050). 17 (17.5%) patients experienced a total of 113 (116.5%) adverse events.Conclusion: Improvements in headache/migraine-specific PROMs and general health-related quality of life were associated with the initiation of CBMPs in patients with headache disorders. Cautious interpretation of results is necessary and randomized control trials are required to ascertain causality.

PMID:36722292 | DOI:10.1080/14737175.2023.2174017

Front Biosci (Landmark Ed). 2023 Jan 10;28(1):3. doi: 10.31083/j.fbl2801003.

ABSTRACT

BACKGROUND: Brewer’s spent grain (BSG) is one of the main by-products of beer industry, little used because of its high moisture making it difficult to transport and store. Mainly used as animal feed and for energy production, the agro-industrial waste have recently attracted attention as source of bioactive compounds, with potential applications in many sectors as food, nutraceutical, pharmaceutical, cosmetic, food packaging. The present work focuses on BSG as potential source of valuable small-size bioactive compounds.

METHODS: Laboratory-made BSG was obtained by using four base malts for mashing. After drying, BSG was eco-friendly extracted with water and the extracts analyzed by untargeted ElectroSpray Ionization (ESI)-Mass Spectrometry (MS)/Mass Spectrometry (MS) (ESI-MS/MS) infusion experiments and by targeted High Performance Liquid Chromatography-PhotoDiodeArray-ElectroSpray Ionization-Mass Spectrometry (HPLC-PDA-ESI-MS) in Selected Ion Recording (SIR) mode analysis, to investigate the metabolic profile, the phenolic profile, the individual phenolic content, and tryptophan content. Aqueous extracts of malts and wort samples were also analyzed for a comparison. Data were statistically analyzed by ANOVA test. An explorative analysis based on Principal Component Analysis (PCA) was also carried out on malts, wort and threshes, in order to study correlation among samples and between samples and variables.

RESULTS: The untargeted ESI-MS/MS infusion experiments provided the mass spectral fingerprint of BSG, evidencing amino acids (γ-aminobutyric acid, proline, valine, threonine, leucine/isoleucine, lysine, histidine, phenylalanine and arginine) and organic and inorganic acids (pyruvic, lactic, phosphoric, valerianic, malonic, 2-furoic, malic, citric and gluconic acids), besides sugars. γ-Aminobutyric acid and lactic acid resulted predominant among the others. The targeted HPLC-PDA-ESI-MS in SIR mode analysis provided the phenolic profile of the polar fraction of BSG, evidenced tryptophan as the main residual metabolite in BSG (62.33-75.35 μg/g dry BSG), and catechin (1.13-4.24 μg/g dry BSG) as the representative phenolic antioxidant of not pre-treated BSG samples. The chemometric analysis of the individual compounds content in BSG, malt and wort evidenced similarities and differences among the samples.

CONCLUSIONS: As main goal, the phytochemical characterization of BSG from base malts highlighted BSG as a potential source of small biomolecules, as tryptophan and catechin, besides γ-aminobutyric acid and lactic acid, opening to new perspectives of application for BSG. Strategies for their recovery are a future challenge. Moreover, ESI-MS/MS analysis was confirmed as a powerful tool for fast characterization of complex matrix. Last, results obtained by chemometric elaboration of data demonstrated the possibility to monitor a small number of molecules to ensure the quality of a final product.

PMID:36722277 | DOI:10.31083/j.fbl2801003