Faculty Bibliography

PURPOSE/OBJECTIVE:The purpose of this study is to compare the quality and clinical outcomes of patients who underwent open reduction internal fixation for a proximal humerus fracture in a "timely manner" which was defined to be within 5 days of injury compared to those with "delayed intervention" (>5 Days) to determine the effect this had. METHODS:This IRB-approved study evaluated patients who sustained a proximal humerus fracture treated with plate and screw fixation (ORIF) between January 2004 and October 2022 and had time from injury to surgery documented. Patients were grouped based on the time to surgery (TTS) - Less than 5 Days (L5) vs. More than 5 Days (M5). TTS was also evaluated as a continuous variable. Univariable and multivariable analysis compared patient demographics, injury/surgical characteristics, postoperative complications, and clinical outcomes to determine effect of TTS. Clinical outcomes included shoulder range of motion (ROM) and Disabilities of the Arm, Shoulder, and Hand (DASH) score at least 1 year following the date of injury. Standard statistical tests were used (p<0.05 considered significant). RESULTS:, p=0.03, β= -0.27, 95% CI = -41.71- -2.89) surgery was associated with less passive forward elevation. CONCLUSION/CONCLUSIONS:Timing of surgery did not impact outcomes of patients who underwent open reduction internal fixation for proximal humerus fractures. Surgical intervention after 14 days was associated with diminished passive forward elevation only.

High-dimensional multiplexed imaging can reveal the spatial organization of tumour tissues at the molecular level. However, owing to the scale and information complexity of the imaging data, it is challenging to discover and thoroughly characterize the heterogeneity of tumour microenvironments. Here we show that self-supervised representation learning on data from imaging mass cytometry can be leveraged to distinguish morphological differences in tumour microenvironments and to precisely characterize distinct microenvironment signatures. We used self-supervised masked image modelling to train a vision transformer that directly takes high-dimensional multiplexed mass-cytometry images. In contrast with traditional spatial analyses relying on cellular segmentation, the vision transformer is segmentation-free, uses pixel-level information, and retains information on the local morphology and biomarker distribution. By applying the vision transformer to a lung-tumour dataset, we identified and validated a monocytic signature that is associated with poor prognosis.

UNLABELLED: HIGHLIGHTS/UNASSIGNED:Communicating relative risk differences as opposed to absolute risk differences, using numerator-only instead of part-to-whole graphics, and including anecdotes or information about others' decisions will all increase intentions to engage in a behavior.Relative risks (rather than absolute risk differences) and numerator-only graphics (rather than part-to-whole) will also increase felt and perceived effectiveness.To illustrate probability differences, people tend to prefer bar charts over icon arrays and graphics with labels over those without.All findings regarding the impact of different presentation formats for probability differences on trust produced insufficient evidence.

Existing deep learning algorithms for electrocardiogram (ECG) classification rely on supervised training approaches requiring large volumes of reliably labeled data. This limits their applicability to rare cardiac diseases like Brugada syndrome (BrS), often lacking accurately labeled ECG examples. To address labeled data constraints and the resulting limitations of supervised training approaches, we developed a novel deep learning model for BrS ECG classification using the Variance-Invariance-Covariance Regularization (VICReg) architecture for self-supervised pre-training. The VICReg model outperformed a state-of-the-art neural network in all calculated metrics, achieving an area under the receiver operating and precision-recall curves of 0.88 and 0.82, respectively. We used the VICReg model to identify missed BrS cases and hence refine the previously underestimated institutional BrS prevalence and patient outcomes. Our results provide a novel approach to rare cardiac disease identification and challenge existing BrS prevalence estimates offering a framework for other rare cardiac conditions.

The mechanisms behind comorbid symptoms of depression in persons with epilepsy (PWE) remain largely unknown. Our study aimed to learn whether seizures moderate fluctuations in depressive symptoms in PWE when controlling for preictal symptoms of depression. We enrolled 57 adult PWE admitted to the New York University (NYU) Langone Epilepsy Monitoring Unit (EMU) from 2021 to 2024. Thirty-seven participants had a seizure. Twenty of the admitted patients did not have seizures during the admission period and therefore served as controls. All participants were seizure free for > 7 days prior to participation. Upon admission, all participants completed the Montgomery-Asberg Depression Rating Scale (MADRS) to evaluate baseline mood. The MADRS was repeated acutely (4-24 h post seizure or admission) and subacutely (2-7 days post seizure or discharge) for both groups. Linear regression models revealed that individuals with higher baseline MADRS scores (indicating higher depressive symptoms) experienced worse mood acutely post-seizure, while lower baseline MADRS scores were associated with acute mood improvement (R² = 0.59, p < 0.001). Experiencing a seizure was not associated with subacute mood outcomes, which were instead driven by acute mood state (R² = 0.56, p < 0.001). In conclusion, we found that seizures exacerbate pre-ictal depressive symptoms and that post-ictal depressive symptoms persist up to 7 days after seizure resolution. This study may provide evidence for a bidirectional relationship and demonstrate a vicious cycle between depression and epilepsy.

A key conceptual issue in the resilience literature centers on whether the presence of resilience in one domain corresponds to positive adaptation in other areas. The present studies investigated whether an individual's likelihood of demonstrating resilience in their trajectory of depressive symptoms would be associated with positive adjustment in psychological, functional, and health-related domains following exposure to spinal cord injury, bereavement, and heart attack. In each study, we utilized growth mixture and robust linear mixed-effects modeling to examine the associations between depression-based trajectories and multiple domains of positive adjustment. Results from all three studies indicated that, on average, individuals who exhibited trajectories of resilience in relation to depressive symptoms concurrently experienced better quality of life, perceived manageability, self-esteem, cognition, and body mass index (BMI). Further, a higher probability of belonging to the resilient trajectory class was linked to higher quality of life, B = 33.78, 95% CI [24.31, 42.91]; perceived manageability, B = 3.44, 95% CI [1.54, 5.21]; cognitive functioning, B = 2.30, 95% CI [1.32, 3.27]; and healthier BMI, B = -1.02, 95% CI [-1.89, -0.17]. Together, these findings illustrate that it is possible to utilize symptoms of depression to predict patterns of resilience across several clinically meaningful domains.

Xylose constitutes the second major sugar fraction of the plant-derived lignocellulosic biomass, which is the most abundantly available and renewable feedstock for microbial fermentations. Hence, comprehensive utilization of xylose is crucial from the perspective of sustainable development of bio-based products, such as fuels, fine chemicals, and high-value compounds. Due to several inherent advantages, various species and strains of yeast are employed to produce these biomolecules. With the advancement of genetic engineering in yeast, lignocellulosic biomass has begun to be commercialized for producing various bioproducts required in the food, fuel, pharmaceutical, chemical, and cosmetics industries. The increasing demands of these bioproducts worldwide lead to a necessity of utilizing xylose efficiently for yeast fermentation strategies together with/replacing glucose for more economic sustainability. However, yeast fermentation processes mostly employ glucose; hence, our understanding of xylose utilization by yeast has not been as scrupulous as it should have been. There has been a remarkable increase in the number of studies conducted on xylose utilization and metabolism in yeasts in the past decade. Our objective in this chapter is to highlight the key advancements and novel approaches in this area and to integrate our understanding of xylose metabolism in yeasts, which can help culminate into commercializing strategies in the future for the development of important bioproducts.

Acute respiratory distress syndrome (ARDS) is a heterogeneous group of disease entities that are associated with acute hypoxic respiratory failure and significant morbidity and mortality. With a better understanding and phenotyping of lung injury, novel pathophysiologic mechanisms demonstrate the impact of a patient's excessive spontaneous breathing effort on perpetuating lung injury. Patient self-inflicted lung injury (P-SILI) is a recently identified phenomenon that delves into the impact of spontaneous breathing on respiratory mechanics in patients with lung injury. While the studies are hypothesis-generating and have been demonstrated in animal and human studies, further clinical trials are needed to identify its impact on ARDS management. The purpose of this review article is to highlight the physiologic mechanisms of P-SILI, novel tools and methods to detect P-SILI, and to review the current literature on non-invasive and invasive respiratory management in patients with ARDS.

Cancer is a systemic disease with complications beyond the primary tumor site. Among them, thrombosis is the second leading cause of death in patients with certain cancers (e.g., pancreatic ductal adenocarcinoma [PDAC]) and advanced-stage disease. Here, we demonstrate that pro-thrombotic small extracellular vesicles (sEVs) are secreted by C-X-C motif chemokine 13 (CXCL13)-reprogrammed interstitial macrophages in the non-metastatic lung microenvironment of multiple cancers, a niche that we define as the pro-thrombotic niche (PTN). These sEVs package clustered integrin β₂ that dimerizes with integrin α_X and interacts with platelet-bound glycoprotein (GP)Ib to induce platelet aggregation. Blocking integrin β₂ decreases both sEV-induced thrombosis and lung metastasis. Importantly, sEV-β₂ levels are elevated in the plasma of PDAC patients prior to thrombotic events compared with patients with no history of thrombosis. We show that lung PTN establishment is a systemic consequence of cancer progression and identify sEV-β₂ as a prognostic biomarker of thrombosis risk as well as a target to prevent thrombosis and metastasis.

Early antibody therapy can prevent severe SARS-CoV-2 infection (COVID-19). However, the effectiveness of COVID-19 convalescent plasma (CCP) therapy in treating severe COVID-19 remains inconclusive. To test a hypothesis that some CCP units are associated with a coagulopathy hazard in severe disease that offsets its benefits, we tracked 304 CCP units administered to 414 hospitalized COVID-19 patients to assess their association with the onset of unfavorable post-transfusion D-dimer trends. CCP recipients with increasing or persistently elevated D-dimer trajectories after transfusion experienced higher mortality than those whose D-dimer levels were persistently low or decreasing after transfusion. Within the CCP donor-recipient network, recipients with increasing or persistently high D-dimer trajectories were skewed toward association with a minority of CCP units. In in vitro assays, CCP from "higher-risk" units had higher cross-reactivity with the spike protein of human seasonal betacoronavirus OC43. "Higher-risk" CCP units also mediated greater Fcγ receptor IIa signaling against cells expressing SARS-CoV-2 spike compared with "lower-risk" units. This study finds that post-transfusion activation of coagulation pathways during severe COVID-19 is associated with specific CCP antibody profiles and supports a potential mechanism of immune complex-activated coagulopathy.