Category: Nevin Manimala

J Clin Endocrinol Metab. 2024 Jan 5:dgae006. doi: 10.1210/clinem/dgae006. Online ahead of print.

ABSTRACT

INTRODUCTION: Studies addressing the methylation pattern in adamantinomatous craniopharyngioma (ACP) are lacking.

OBJECTIVE: To identify methylation signatures in ACPs regarding clinical presentation and outcome.

METHODS: Clinical and pathology data were collected from 35 ACP patients (54% male; 18.1 years [2-68]). CTNNB1 mutations and methylation profile (MethylationEPIC/Array-Illumina) were analyzed in tumoral DNA. Unsupervised machine learning analysis of this comprehensive methylome sample was achieved using hierarchical clustering and multi-dimensional scaling. Statistical associations between clusters and clinical features were achieved using Fisher’s test and global biological process interpretations were aided by Gene Ontology enrichment analyses.

RESULTS: Two clusters were revealed consistently by all unsupervised methods (ACP-1: n = 18; ACP-2: n = 17) with strong bootstrap statistical support. ACP-2 was enriched by CTNNB1 mutations (100% vs 56%, P = 0.0006), hypomethylated in CpG Island (CGI), non-CGI sites, and globally (P < 0.001), and associated with greater tumor size (24.1 vs 9.5cm3, P = 0.04). Enrichment analysis highlighted pathways on signaling transduction, transmembrane receptor, development of anatomical structures, cell-adhesion, cytoskeleton organization, and cytokine binding, and also cell-type specific biological processes as regulation of oligodendrocytes, keratinocyte, and epithelial cells differentiation.

CONCLUSION: Two clusters of ACP patients were consistently revealed by unsupervised machine learning methods, being one of them significantly hypomethylated, enriched by CTNNB1 mutated ACPs, and associated with increased tumor size. Enrichment analysis reinforced pathways involved in tumor proliferation and in cell-specific tumoral microenvironment.

PMID:38181427 | DOI:10.1210/clinem/dgae006

ACS Appl Mater Interfaces. 2024 Jan 5. doi: 10.1021/acsami.3c17113. Online ahead of print.

ABSTRACT

Extracellular matrices interface with cells to promote cell growth and tissue development. Given this critical role, matrix mimetics are introduced to enable biomedical materials ranging from tissue engineering scaffolds and tumor models to organoids for drug screening and implant surface coatings. Traditional microscopy methods are used to evaluate such materials in their ability to support exploitable cell responses, which are expressed in changes in cell proliferation rates and morphology. However, the physical imaging methods do not capture the chemistry of cells at cell-matrix interfaces. Herein, we report hyperspectral imaging to map the chemistry of human primary and embryonic stem cells grown on matrix materials, both native and artificial. We provide the statistical analysis of changes in lipid and protein content of the cells obtained from infrared spectral maps to conclude matrix morphologies as a major determinant of biochemical cell responses. The study demonstrates an effective methodology for evaluating bespoke matrix materials directly at cell-matrix interfaces.

PMID:38181419 | DOI:10.1021/acsami.3c17113

ASAIO J. 2024 Jan 5. doi: 10.1097/MAT.0000000000002136. Online ahead of print.

ABSTRACT

The purpose of this study was to evaluate left ventricular (LV) unloading strategies in patients supported with peripheral venoarterial extracorporeal membrane oxygenation (VA-ECMO). A retrospective review was conducted of all consecutive patients requiring VA-ECMO support for any indication, who underwent novel LV unloading strategies with either direct left atrial venoarterial (LAVA) cannulation or pulmonary artery venoarterial (PAVA) venting, in comparison to Impella and intra-aortic balloon pump (IABP). The primary outcome was successful bridge to transplant, LV assist device, or myocardial recovery. Forty-six patients (63% male, mean age 52.8 ± 17.6 years) were included. Fourteen patients (30%) underwent novel unloading with either LAVA or PAVA, 11 patients (24%) underwent IABP placement, and 21 patients (46%) underwent Impella insertion. In the novel LV unloading cohort, 10 patients (71%) survived to hospital discharge. Four patients (29%) were weaned from ECMO and eight patients (57%) underwent cardiac transplantation. Although a trend favoring cannula-based unloading for the primary outcome was noted, the cohort was too small for statistical significance (79% LAVA/PAVA, 57% Impella, 45% IABP; p = 0.21). However, probability of survival was greater in the LAVA/PAVA cohort compared to Impella and IABP (p < 0.05). Thus, we demonstrate the efficacy of LA and PA cannulation as an alternative LV unloading strategy for patients supported with peripheral VA-ECMO.

PMID:38181416 | DOI:10.1097/MAT.0000000000002136

ASAIO J. 2024 Jan 8. doi: 10.1097/MAT.0000000000002128. Online ahead of print.

ABSTRACT

The Extracorporeal Life Support Organization (ELSO) maintains the world’s largest extracorporeal membrane oxygenation (ECMO) registry by volume, center participation, and international scope. This 2022 ELSO Registry Report describes the program characteristics of ECMO centers, processes of ECMO care, and reported outcomes. Neonates (0-28 days), children (29 days-17 years), and adults (≥18 years) supported with ECMO from 2009 through 2022 and reported to the ELSO Registry were included. This report describes adjunctive therapies, support modes, treatments, complications, and survival outcomes. Data are presented descriptively as counts and percent or median and interquartile range (IQR) by year, group, or level. Missing values were excluded before calculating descriptive statistics. Complications are reported per 1,000 ECMO hours. From 2009 to 2022, 154,568 ECMO runs were entered into the ELSO Registry. Seven hundred and eighty centers submitted data during this time (557 in 2022). Since 2009, the median annual number of adult ECMO runs per center per year increased from 4 to 15, whereas for pediatric and neonatal runs, the rate decreased from 12 to 7. Over 50% of patients were transferred to the reporting ECMO center; 20% of these patients were transported with ECMO. The use of prone positioning before respiratory ECMO increased from 15% (2019) to 44% (2021) for adults during the coronavirus disease-2019 (COVID-19) pandemic. Survival to hospital discharge was greatest at 68.5% for neonatal respiratory support and lowest at 29.5% for ECPR delivered to adults. By 2022, the Registry had enrolled its 200,000th ECMO patient and 100,000th patient discharged alive. Since its inception, the ELSO Registry has helped centers measure and compare outcomes across its member centers and strategies of care. Continued growth and development of the Registry will aim to bolster its utility to patients and centers.

PMID:38181413 | DOI:10.1097/MAT.0000000000002128

Phys Rev Lett. 2023 Dec 22;131(25):250401. doi: 10.1103/PhysRevLett.131.250401.

ABSTRACT

Ergodicity of quantum dynamics is often defined through statistical properties of energy eigenstates, as exemplified by Berry’s conjecture in single-particle quantum chaos and the eigenstate thermalization hypothesis in many-body settings. In this work, we investigate whether quantum systems can exhibit a stronger form of ergodicity, wherein any time-evolved state uniformly visits the entire Hilbert space over time. We call such a phenomenon complete Hilbert-space ergodicity (CHSE), which is more akin to the intuitive notion of ergodicity as an inherently dynamical concept. CHSE cannot hold for time-independent or even time-periodic Hamiltonian dynamics, owing to the existence of (quasi)energy eigenstates which precludes exploration of the full Hilbert space. However, we find that there exists a family of aperiodic, yet deterministic drives with minimal symbolic complexity-generated by the Fibonacci word and its generalizations-for which CHSE can be proven to occur. Our results provide a basis for understanding thermalization in general time-dependent quantum systems.

PMID:38181361 | DOI:10.1103/PhysRevLett.131.250401

Phys Rev Lett. 2023 Dec 22;131(25):257301. doi: 10.1103/PhysRevLett.131.257301.

ABSTRACT

The Hopfield model is a paradigmatic model of neural networks that has been analyzed for many decades in the statistical physics, neuroscience, and machine learning communities. Inspired by the manifold hypothesis in machine learning, we propose and investigate a generalization of the standard setting that we name random-features Hopfield model. Here, P binary patterns of length N are generated by applying to Gaussian vectors sampled in a latent space of dimension D a random projection followed by a nonlinearity. Using the replica method from statistical physics, we derive the phase diagram of the model in the limit P,N,D→∞ with fixed ratios α=P/N and α_{D}=D/N. Besides the usual retrieval phase, where the patterns can be dynamically recovered from some initial corruption, we uncover a new phase where the features characterizing the projection can be recovered instead. We call this phenomena the learning phase transition, as the features are not explicitly given to the model but rather are inferred from the patterns in an unsupervised fashion.

PMID:38181351 | DOI:10.1103/PhysRevLett.131.257301

Phys Rev Lett. 2023 Dec 22;131(25):251803. doi: 10.1103/PhysRevLett.131.251803.

ABSTRACT

We present the first data-driven result for a_{μ}^{win,lqc}, the isospin-limit light-quark connected component of the intermediate-window Hadronic-vacuum-polarization contribution to the muon anomalous magnetic moment. Our result, (198.8±1.1)×10^{-10}, is in significant tension with eight recent mutually compatible high-precision lattice-QCD determinations, and provides enhanced evidence for a puzzling discrepancy between lattice and data-driven determinations of the intermediate-window quantity, one driven largely by a difference in the light-quark connected component.

PMID:38181347 | DOI:10.1103/PhysRevLett.131.251803

Phys Rev Lett. 2023 Dec 22;131(25):254201. doi: 10.1103/PhysRevLett.131.254201.

ABSTRACT

Evaporation of cloud droplets accelerates when turbulence mixes dry air into the cloud, affecting droplet-size distributions in atmospheric clouds, combustion sprays, and jets of exhaled droplets. The challenge is to model local correlations between droplet numbers, sizes, and supersaturation, which determine supersaturation fluctuations along droplet paths (Lagrangian fluctuations). We derived a statistical model that accounts for these correlations. Its predictions are in quantitative agreement with results of direct numerical simulations, and explain the key mechanisms at play.

PMID:38181342 | DOI:10.1103/PhysRevLett.131.254201

Neurology. 2024 Feb 13;102(3):e208035. doi: 10.1212/WNL.0000000000208035. Epub 2024 Jan 5.

ABSTRACT

BACKGROUND AND OBJECTIVES: Risk of readmission after stroke differs by stroke (sub)type and etiology, with higher risks reported for hemorrhagic stroke and cardioembolic stroke. We examined the risk and cause of first readmission by stroke subtype over the years post incident stroke.

METHODS: Atherosclerosis Risk in Communities (ARIC) study participants (n = 1,412) with first-ever stroke were followed up for all-cause readmission after incident stroke. Risk of first readmission was examined by stroke subtypes (cardioembolic, thrombotic/lacunar, and hemorrhagic [intracerebral and subarachnoid]) using Cox and Fine-Gray proportional hazards models, adjusting for sociodemographic and cardiometabolic risk factors.

RESULTS: Among 1,412 participants (mean [SD] age 72.4 [9.3] years, 52.1% women, 35.3% Black), 1,143 hospitalizations occurred over 41,849 person-months. Overall, 81% of participants were hospitalized over a maximum of 26.6 years of follow-up (83% of participants with thrombotic/lacunar stroke, 77% of participants with cardioembolic stroke, and 78% of participants with hemorrhagic stroke). Primary cardiovascular and cerebrovascular diagnoses were reported for half of readmissions. Over the entire follow-up period, compared with cardioembolic stroke, readmission risk was lower for thrombotic/lacunar stroke (hazard ratio [HR] 0.82, 95% CI 0.71-0.95) and hemorrhagic stroke (HR 0.74, 95% CI 0.58-0.93) in adjusted Cox proportional hazards models. By contrast, there was no statistically significant difference among subtypes when adjusting for atrial fibrillation and competing risk of death. Compared with cardioembolic stroke, thrombotic/lacunar stroke was associated with lower readmission risk within 1 month (HR 0.66, 95% CI 0.46-0.93) and during 1 month-1 year (HR 0.78, 95% CI 0.62-0.97), and hemorrhagic stroke was associated with lower risk during 1 month-1 year (HR 0.60, 95% CI 0.41-0.87). There was no significant difference between subtypes in readmission risk during later periods.

DISCUSSION: Over 26 years of follow-up, 81% of stroke participants experienced a readmission. Cardiovascular and cerebrovascular diagnoses at readmission were most common across stroke subtypes. Though cardioembolic stroke has previously been reported to confer higher risk of readmission, in this study, the readmission risk was not statistically significantly different between stroke subtypes or over different periods when accounting for the competing risk of death.

PMID:38181329 | DOI:10.1212/WNL.0000000000208035

JCO Clin Cancer Inform. 2024 Jan;8:e2300118. doi: 10.1200/CCI.23.00118.

ABSTRACT

PURPOSE: Limitations from commercial software applications prevent the implementation of a robust and cost-efficient high-throughput cancer imaging radiomic feature extraction and perfusion analysis workflow. This study aimed to develop and validate a cancer research computational solution using open-source software for vendor- and sequence-neutral high-throughput image processing and feature extraction.

METHODS: The Cancer Radiomic and Perfusion Imaging (CARPI) automated framework is a Python-based software application that is vendor- and sequence-neutral. CARPI uses contour files generated using an application of the user’s choice and performs automated radiomic feature extraction and perfusion analysis. This workflow solution was validated using two clinical data sets, one consisted of 40 pelvic chondrosarcomas and 42 sacral chordomas with a total of 82 patients, and a second data set consisted of 26 patients with undifferentiated pleomorphic sarcoma (UPS) imaged at multiple points during presurgical treatment.

RESULTS: Three hundred sixteen volumetric contour files were processed using CARPI. The application automatically extracted 107 radiomic features from multiple magnetic resonance imaging sequences and seven semiquantitative perfusion parameters from time-intensity curves. Statistically significant differences (P < .00047) were found in 18 of 107 radiomic features in chordoma versus chondrosarcoma, including six first-order and 12 high-order features. In UPS postradiation, the apparent diffusion coefficient mean increased 41% in good responders (P = .0017), while firstorder_10Percentile (P = .0312) was statistically significant between good and partial/nonresponders.

CONCLUSION: The CARPI processing of two clinical validation data sets confirmed the software application’s ability to differentiate between different types of tumors and help predict patient response to treatment on the basis of radiomic features. Benchmark comparison with five similar open-source solutions demonstrated the advantages of CARPI in the automated perfusion feature extraction, relational database generation, and graphic report export features, although lacking a user-friendly graphical user interface and predictive model building.

PMID:38181324 | DOI:10.1200/CCI.23.00118