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Nevin Manimala Statistics

Effects of exercise or metformin on myokine concentrations in patients with breast and colorectal cancer: A phase II multi-centre factorial randomized trial

J Cachexia Sarcopenia Muscle. 2024 Jun 18. doi: 10.1002/jcsm.13509. Online ahead of print.

ABSTRACT

BACKGROUND: Physical activity and metformin pharmacotherapy are associated with improved clinical outcomes in breast and colorectal cancer survivors. Myokines are cytokines secreted from skeletal muscle that may mediate these associations.

METHODS: This hypothesis-generating analysis used biospecimens collected from a multi-centre 2 × 2 factorial randomized design of 116 patients with stage I-III breast and colorectal cancer who were randomized to 12 weeks of (1) aerobic exercise (moderate intensity titrated to 220 min/week); (2) metformin (850 mg daily for 2 weeks and then titrated to 850 mg twice per day); (3) aerobic exercise and metformin; or (4) control. Fourteen myokines were quantified using a multiplex panel. Myokine concentrations were log-transformed, and main effects analyses were conducted using linear mixed-effects regression models. The type I error rate was controlled with the Holm sequential testing procedure.

RESULTS: Randomization to exercise increased leukaemia inhibitory factor (1.26 pg/mL, 95% confidence interval [CI]: 0.69, 1.84; adjusted P = 0.001) and interleukin-15 (2.23 pg/mL, 95% CI: 0.87, 3.60; adjusted P = 0.013) compared with randomization to no exercise. Randomization to metformin decreased apelin (-2.69 pg/mL, 95% CI: -4.31, -1.07; adjusted P = 0.014) and interleukin-15 (-1.74 pg/mL, 95% CI: -2.79, -0.69; adjusted P = 0.013) compared with randomization to no metformin. Metformin decreased myostatin, irisin, oncostatin M, fibroblast growth factor 21 and osteocrin; however, these changes were not statistically significant after correction for multiple comparisons.

CONCLUSIONS: This pilot study demonstrates that randomization to exercise and metformin elicit unique effects on myokine concentrations in cancer patients. This hypothesis-generating observation warrants further basic, translational and clinical investigation and replication.

PMID:38887915 | DOI:10.1002/jcsm.13509

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Nevin Manimala Statistics

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes

Biostatistics. 2024 Jun 17:kxae017. doi: 10.1093/biostatistics/kxae017. Online ahead of print.

ABSTRACT

Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g. healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, ie reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, vs. state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. DifferentialRegulation is distributed as a Bioconductor R package.

PMID:38887902 | DOI:10.1093/biostatistics/kxae017

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Nevin Manimala Statistics

Cardiac fibrosis as a predictor for sudden cardiac death after transcatheter aortic valve implantation

EuroIntervention. 2024 Jun 17;20(12):e760-e769. doi: 10.4244/EIJ-D-23-01068.

ABSTRACT

BACKGROUND: Cardiac fibrosis plays a major pathophysiological role in any form of chronic heart disease, and high levels are associated with poor outcome. Diffuse and focal cardiac fibrosis are different subtypes, which have different pathomechanisms and prognostic implications. The total fibrosis burden in endomyocardial biopsy tissue was recently proved to play an independent prognostic role in aortic stenosis patients after transcatheter aortic valve implantation (TAVI).

AIMS: Here, for the first time, we aim to assess the specific impact of different fibrosis subtypes on sudden cardiac death (SCD) as a primary reason for cardiovascular mortality after TAVI.

METHODS: The fibrosis pattern was assessed histologically in the left ventricular biopsies obtained during TAVI interventions in 161 patients, who received a structured follow-up thereafter.

RESULTS: Receiver operating characteristic analyses, performed 6, 12, 24 and 48 months after TAVI, showed diffuse, but not focal, fibrosis as a significant predictor for SCD at all timepoints, with the highest area under the curve at the first time point and a decrease in its SCD predictivity over time. In both multivariate Cox proportional hazards and Fine-Gray competing risk models, including both fibrosis subtypes, as well as age, sex and ejection fraction, high diffuse fibrosis remained statistically significant. Accordingly, it represents an independent SCD predictor, most importantly for the occurrence of early events.

CONCLUSIONS: The burden of diffuse cardiac fibrosis plays an important and independent prognostic role regarding SCD early after TAVI. Therefore, the histological evaluation of fibrosis topography has value as a prognostic tool for TAVI patients and may help to tailor individualised approaches to optimise their postinterventional management.

PMID:38887885 | DOI:10.4244/EIJ-D-23-01068

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Nevin Manimala Statistics

Individual HLAs affect survival after allogeneic stem cell transplantation in adult T-cell leukaemia/lymphoma

HLA. 2024 Jun;103(6):e15555. doi: 10.1111/tan.15555.

ABSTRACT

Allogeneic haematopoietic stem cell transplantation (allo-HSCT) is the only curative therapy for adult T-cell leukaemia/lymphoma (ATL). Specific HLAs are associated with outcomes of immunotherapy and allo-HSCT. We hypothesised that individual HLAs would affect the clinical outcomes of ATL patients after allo-HSCT. Using data from a Japanese registry, we retrospectively analysed 829 patients with ATL who received transplants from HLA-identical sibling donors or HLA-A, -B, -C or -DRB1 allele-matched unrelated donors between 1996 and 2015. We evaluated the overall mortality risk of HLA-A, -B and -DR antigens with frequencies exceeding 3%. Outcomes were compared between transplants with or without specific HLA antigens. Of the 25 HLAs, two candidates were identified but showed no statistically significant differences by multiple comparison. HLA-B62 was associated with a lower risk of mortality (hazard ratio [HR], 0.68; 95% confidence interval [CI]: 0.51-0.90; p = 0.008), whereas HLA-B60 was associated with a higher risk of mortality (HR, 1.64; 95% CI: 1.19-2.27; p = 0.003). In addition, HLA-B62 was associated with a lower risk of transplant-related mortality (TRM) (HR, 0.52; 95% CI: 0.32-0.85, p = 0.009), whereas HLA-B60 was associated with a higher risk of grades III-IV acute graft-versus-host disease (HR, 2.63; 95% CI: 1.62-4.27; p < 0.001). Neither HLA influenced relapse. The higher risk of acute GVHD in HLA-B60-positive patients and the lower risk of TRM in HLA-B62-positive patients were consistent with previously obtained results from patients with other haematological malignancies. Consideration of HLA in ATL patients may help to predict risk and outcomes after allo-HSCT.

PMID:38887872 | DOI:10.1111/tan.15555

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Nevin Manimala Statistics

Label-free imaging diagnosis and collagen-optical evaluation of endometrioid adenocarcinoma with multiphoton microscopy

J Biophotonics. 2024 Jun 17:e202400177. doi: 10.1002/jbio.202400177. Online ahead of print.

ABSTRACT

The assessment of tumor grade and pathological stage plays a pivotal role in determining the treatment strategy and predicting the prognosis of endometrial cancer. In this study, we employed multiphoton microscopy (MPM) to establish distinctive optical pathological signatures specific to endometrioid adenocarcinoma (EAC), while also assessing the diagnostic sensitivity, specificity, and accuracy of MPM for this particular malignancy. The MPM technique exhibits robust capability in discriminating between benign hyperplasia and various grades of cancer tissue, with statistically significant differences observed in nucleocytoplasmic ratio and second harmonic generation/two-photon excited fluorescence intensity. Moreover, by utilizing semi-automated image analysis, we identified notable disparities in six collagen signatures between benign and malignant endometrial stroma. Our study demonstrates that MPM can differentiate between benign endometrial hyperplasia and EAC without labels, while also quantitatively assessing changes in the tumor microenvironment by analyzing collagen signatures in the endometrial stromal tissue.

PMID:38887864 | DOI:10.1002/jbio.202400177

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Nevin Manimala Statistics

Phase-Resolved Functional Lung (PREFUL) MRI May Reveal Distinct Pulmonary Perfusion Defects in Postacute COVID-19 Syndrome: Sex, Hospitalization, and Dyspnea Heterogeneity

J Magn Reson Imaging. 2024 Jun 17. doi: 10.1002/jmri.29458. Online ahead of print.

ABSTRACT

BACKGROUND: Pulmonary perfusion defects have been observed in patients with coronavirus disease 2019 (COVID-19). Currently, there is a need for further data on non-contrast-enhanced MRI in COVID patients. The early identification of heterogeneity in pulmonary perfusion defects among COVID-19 patients is beneficial for their timely clinical intervention and management.

PURPOSE: To investigate the utility of phase-resolved functional lung (PREFUL) MRI in detecting pulmonary perfusion disturbances in individuals with postacute COVID-19 syndrome (PACS).

STUDY TYPE: Prospective.

SUBJECTS: Forty-four participants (19 females, mean age 64.1 years) with PACS and 44 healthy subjects (19 females, mean age 59.5 years). Moreover, among the 44 patients, there were 19 inpatients and 25 outpatients; 19 were female and 25 were male; 18 with non-dyspnea and 26 with dyspnea.

FIELD STRENGTH/SEQUENCE: 3-T, two-dimensional (2D) spoiled gradient-echo sequence.

ASSESSMENT: Ventilation and perfusion-weighted maps were extracted from five coronal slices using PREFUL analysis. Subsequently, perfusion defect percentage (QDP), ventilation defect percentage (VDP), and ventilation-perfusion match healthy (VQM) were calculated based on segmented lung parenchyma ventilation and perfusion-weighted maps. Additionally, clinical features, including demographic data (such as sex and age) and serum biomarkers (such as D-dimer levels), were evaluated.

STATISTICAL TESTS: Spearman correlation coefficients to explore relationships between clinical features and QDP, VDP, and VQM. Propensity score matching analysis to reduce the confounding bias between patients with PACS and healthy controls. The Mann-Whitney U tests and Chi-squared tests to detect differences between groups. Multivariable linear regression analyses to identify factors related to QDP, VDP, and VQM. A P-value <0.05 was considered statistically significant.

RESULTS: QDP significantly exceeded that of healthy controls in individuals with PACS (39.8% ± 15.0% vs. 11.0% ± 4.9%) and was significantly higher in inpatients than in outpatients (46.8% ± 17.0% vs. 34.5% ± 10.8%). Moreover, males exhibited pulmonary perfusion defects significantly more frequently than females (43.9% ± 16.8% vs. 34.4% ± 10.2%), and dyspneic participants displayed significantly higher perfusion defects than non-dyspneic patients (44.8% ± 15.8% vs. 32.6% ± 10.3%). QDP showed a significant positive relationship with age (β = 0.50) and D-dimer level (β = 0.72).

DATA CONCLUSION: PREFUL MRI may show pulmonary perfusion defects in patients with PACS. Furthermore, perfusion impairments may be more pronounced in males, inpatients, and dyspneic patients.

EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

PMID:38887850 | DOI:10.1002/jmri.29458

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Nevin Manimala Statistics

Impact of SARS-CoV-2 Pandemic on Emergency Hospitalizations for Acute Respiratory Infections: The Experience of a Paediatric Tertiary Care Hospital in Italy

Influenza Other Respir Viruses. 2024 Jun;18(6):e13335. doi: 10.1111/irv.13335.

ABSTRACT

BACKGROUND: Acute respiratory infections (ARIs) are a major healthcare issue in children. The SARS-CoV-2 pandemic changed the epidemiology of ARIs; the aims of this study are to characterize the epidemiological trend of ARI emergency hospitalizations and virology results and to estimate the association of ARI emergency hospitalizations with respiratory viruses from January 2018 to June 2023.

METHODS: This study was carried out in an Italian tertiary care children’s hospital (Bambino Gesù Children’s Hospital). The demographic and clinical information of children who accessed the Emergency Department (ED) with ARI and were hospitalized were retrospectively extracted from the electronic health records. Multivariate linear regression model was used to compare the number of ARI hospital admissions with the reported temporal trends in viruses diagnosed from respiratory samples throughout the same time period.

RESULTS: During the study period, there were 92,140 ED visits and 10,541 hospitalizations due to ARIs, reflecting an admission rate of 11.4%. The highest proportion of hospitalizations occurred in infants ≤ 1 year of age (n = 4840, 45.9% of total admissions), with a hospitalization rate of 22.6%. Emergency hospitalizations aligned closely with the predictions made by the multivariate regression model; peaks in hospitalizations reflected Respiratory Syncytial Virus (RSV) circulation.

CONCLUSIONS: ARI hospital urgent admissions are a relevant component of ARI disease burden in children. RSV prevention and control are crucial to limit the risk of urgent hospitalizations due to ARIs.

PMID:38887843 | DOI:10.1111/irv.13335

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Nevin Manimala Statistics

Assessment of the fetal thymic-thoracic ratio in pregnant women with intrahepatic cholestasis: a prospective case-control study

J Perinat Med. 2024 Jun 19. doi: 10.1515/jpm-2024-0191. Online ahead of print.

ABSTRACT

OBJECTIVES: To examine the fetal thymic-thoracic ratio (TTR) in intrahepatic cholestasis of pregnancy (ICP).

METHODS: This prospective case-control study was conducted in a single tertiary center. The sample consisted of 86 pregnant women at 28-37 weeks of gestation, including 43 women with ICP and 43 healthy controls. TTR was calculated for each patient using the anterior-posterior measurements of the thymus and intrathoracic mediastinal measurements.

RESULTS: The median TTR value was found to be smaller in the ICP group compared to the control group (0.32 vs. 0.36, p<0.001). The ICP group had a greater rate of admission to the neonatal intensive care unit (NICU) (p<0.001). Univariate regression analysis revealed that lower TTR values increased the possibility of NICU admission six times (95 % confidence interval: 0.26-0.39, p=0.01). A statistically significant negative correlation was detected between TTR and the NICU requirement (r: -0.435, p=0.004). As a result of the receiver operating characteristic analysis, in predicting NICU admission, the optimal cut-off value of TTR was determined to be 0.31 with 78 % sensitivity and 67 % specificity (area under the curve=0.819; p<0.001).

CONCLUSIONS: We determined that the fetal TTR may be affected by the inflammatory process caused by the maternal-fetal immune system and increased serum bile acid levels in fetal organs in the presence of ICP. We consider that TTR can be used to predict adverse pregnancy outcomes in patients with ICP.

PMID:38887817 | DOI:10.1515/jpm-2024-0191

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Nevin Manimala Statistics

Pour une représentation fidèle de la diversité des communautés autochtones au Canada dans les nouvelles cohortes d’étudiantes et d’étudiants en médecine

CMAJ. 2024 Jun 16;196(23):E803-E805. doi: 10.1503/cmaj.231272-f.

NO ABSTRACT

PMID:38885978 | DOI:10.1503/cmaj.231272-f

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Nevin Manimala Statistics

Unsupervised machine learning cluster analysis to identification EVAR patients clinical phenotypes based on radiomics

Vascular. 2024 Jun 17:17085381241262575. doi: 10.1177/17085381241262575. Online ahead of print.

ABSTRACT

OBJECTIVE: This study used unsupervised machine learning (UML) cluster analysis to explore clinical phenotypes of endovascular aortic repair (EVAR) for abdominal aortic aneurysm (AAA) patients based on radiomics.

METHOD: We retrospectively reviewed 1785 patients with infra-renal AAA who underwent elective EVAR procedures between January 2010 and December 2020. Pyradiomics was used to extract the radiomics features. Statistical analysis was applied to determine the radiomics features that related to severe adverse events (SAEs) after EVAR. The selected features were used for UML cluster analysis in training set and validation in test set. Comparison of basic characteristics and radiomics features of different clusters. The Kaplan-Meier analysis was conducted to generate the cumulative incidence of freedom from SAEs rate.

RESULT: A total of 1180 patients were enrolled. During the follow-up, 353 patients experienced EVAR-related SAEs. In total, 1223 radiomics features were extracted from each patient, of which 23 radiomics features were finally preserved to identify different clinical phenotypes. 944 patients were allocated to the training set. Three clusters were identified in training set, in which patients had identical clinical characteristics and morphological features, while varied considerably of selected radiomics features. This encouraging performance was further approved in the test set. In addition, each cluster was well differentiated from other clusters and Kaplan-Meier analysis showed significant differences of freedom from SAEs rate between different clusters both in the training (p = .0216) and test sets (p = .0253).

CONCLUSION: Based on radiomics, UML cluster analysis can identify clinical phenotypes in EVAR patients with distinct long-term outcomes.

PMID:38885967 | DOI:10.1177/17085381241262575