Search

Background: Dermatoporosis (DP) is a condition associated with thinning skin layers and resultant fragility. Much of the thinning is related to fibroblast dysfunction, production of destructive inflammatory cytokines, breakdown of the extracellular matrix (ECM), and weakening of the dermo-epidermal junction. A major contributor to this change in the ECM milieu, previously under-considered, is cellular senescence, particularly involving the papillary dermal fibroblasts., Methods: A series of experiments were undertaken to explore the impact of a combination of known actives on senescent cell status. Human keratinocytes and fibroblasts were cultured, and cytotoxicity tests were performed to determine the ideal concentration to avoid cell toxicity. Microdoses of Centella asiatica (0.005%) and mandelic acid (0.05%) were found to be ideal in avoiding any cytotoxicity. However, the challenge was then to assess the efficacy of these actives in this microdosed form. After exposing the cells to the compounds, RNA was isolated and sequenced. Moreover, a well-described ex vivo model using photodamaged skin was subjected to immunofluorescence to identify senescent cells (via p16INK4a), particularly in the papillary dermis, using the microdose formulation compared to untreated skin. In addition, JAG/NOTCH expression in the epidermal basal cells was evaluated to further understand the cellular senescence signaling mechanism., Results: Microdosing these two well-known agents had surprisingly significant synergistic effects in vitro, decreasing senescence-associated secretory phenotype (SASP) cytokines and the associated inflammation involved in the process. The ex vivo model revealed a significant (P<0.05) decrease in senescent cells in the papillary dermis and a significant increase (P<0.001) of JAG/NOTCH expression in the basal cells of the epidermis., Conclusion: Using microdoses of two known agents, a novel approach produced an unexpected effect of reversal of dermal senescent cells and promoting an anti-inflammatory milieu. A gene expression analysis of the individual and combined actives validated these observations, followed by full formulation testing in an ex vivo model. The approach of limiting cellular senescence in dermal fibroblasts for managing DP is novel and provides an exciting new direction to address dermatoporosis. Clinical studies will follow. J Drugs Dermatol. 2024;23(9):748-756. doi:10.36849/JDD.8388.

Background: The demand for liver transplants (LT) in the United States far surpasses the availability of allografts. New allocation schemes have resulted in occasional difficulties with allograft placement and increased intraoperative turndowns. We aimed to evaluate the outcomes related to use of late-turndown liver allografts., Methods: A review of prospectively collected data of LTs at a single center from July 2019 to July 2023 was performed. Late-turndown placement was defined as an open offer 6 h prior to donation, intraoperative turndown by primary center, or post-cross-clamp turndown., Results: Of 565 LTs, 25.1% (n = 142) received a late-turndown liver allograft. There were no significant differences in recipient age, gender, BMI, or race (all p > 0.05), but MELD was lower for the late-turndown LT recipient group (median 15 vs 21, p < 0.001). No difference in 30-day, 6-month, or 1-year survival was noted on logistic regression, and no difference in patient or graft survival was noted on Cox proportional hazard regression. Late-turndown utilization increased during the study from 17.2% to 25.8%, and median waitlist time decreased from 77 days in 2019 to 18 days in 2023 (p < 0.001)., Conclusion: Use of late-turndown livers has increased and can increase transplant rates without compromising post-transplant outcomes with appropriate selection., Competing Interests: Conflict of interest None declared., (Copyright © 2024 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.)

Background: Bleeding during cardiac surgery may be refractory to standard interventions. Off-label use of Factor Eight Inhibitor Bypass Activity (FEIBA) has been described to treat such bleeding. However, reports of safety, particularly thromboembolic outcomes, show mixed results and reported cohorts have been small., Methods: Adult patients undergoing cardiac surgery on cardiopulmonary bypass between July 1, 2018 and June 30, 2023 at Stanford Hospital were reviewed (n=3335). Patients who received FEIBA to treat post-cardiopulmonary bypass bleeding were matched with those who did not by propensity scores in a 1:1 ratio using nearest neighbor matching (n= 352 per group). The primary outcome was a composite outcome of thromboembolic complications including any one of deep vein thrombosis (DVT), pulmonary embolism (PE), unplanned coronary artery intervention, ischemic stroke, and acute limb ischemia, in the postoperative period. Secondary outcomes included renal failure, reoperation, postoperative transfusion, ICU length of stay (LOS), and 30-day mortality., Results: 704 encounters were included in our propensity matched analysis. The mean dose of FEIBA administered was 7.3 ±5.5 units/kg. In propensity matched multivariate logistic regression models there was no statistically significant difference in odds ratios for thromboembolic outcomes, ICU LOS, or mortality. Patients who received >750 units of FEIBA had an increased odds ratio for acute renal failure (OR 4.14; 95% CI 1.61 to 10.36, p <0.001). In multivariate linear regression, patients receiving FEIBA were transfused more plasma and cryoprecipitate postoperatively. However, only the dose range of 501-750 units was associated with an increase in transfusion of RBCs (β 2.73; 95% CI 0.68 to 4.78; p=0.009), and platelets (β 1.74; 95% CI 0.85 to 2.63; p <0.001)., Conclusions: Low dose FEIBA administration during cardiac surgery does not increase risk of thromboembolic events, ICU LOS, or mortality in a propensity matched cohort. Higher doses were associated with increased acute renal failure and postoperative transfusion. Further studies are required to establish the efficacy of activated factor concentrates to treat refractory bleeding during cardiac surgery., Competing Interests: Conflicts of Interest: The authors declare no competing interests., (Copyright © 2024 American Society of Anesthesiologists. All Rights Reserved.)

Background: Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hindering more precise diagnosis and research. The objective of this study was to take a fresh look at neonatal intestinal disease classification using unsupervised machine learning., Methods: Patients admitted to the University of Florida Shands Neonatal Intensive Care Unit January 2013-September 2019 diagnosed with an intestinal injury, or had imaging findings of portal venous gas, pneumatosis, abdominal free air, or had an abdominal drain placed or exploratory laparotomy during admission were included. Congenital gastroschisis, omphalocele, intestinal atresia, malrotation were excluded. Data was collected via retrospective chart review with subsequent hierarchal, unsupervised clustering analysis., Results: Five clusters of intestinal injury were identified: Cluster 1 deemed the "Low Mortality" cluster, Cluster 2 deemed the "Mature with Inflammation" cluster, Cluster 3 deemed the "Immature with High Mortality" cluster, Cluster 4 deemed the "Late Injury at Full Feeds" cluster, and Cluster 5 deemed the "Late Injury with High Rate of Intestinal Necrosis" cluster., Conclusion: Unsupervised machine learning can be used to cluster acquired neonatal intestinal injuries. Future study with larger multicenter datasets is needed to further refine and classify types of intestinal diseases., Impact: Unsupervised machine learning can be used to cluster types of acquired neonatal intestinal injury. Five major clusters of acquired neonatal intestinal injury are described, each with unique features. The clusters herein described deserve future, multicenter study to determine more specific early biomarkers and tailored therapeutic interventions to improve outcomes of often devastating neonatal acquired intestinal injuries., (© 2024. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

Background: Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field., Methods: The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test., Results: The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, "Cells & Proteomics" had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000-2022. However, when focusing on the period from 2015 to 2022, topics "Cells & Proteomics" and "Pulmonology" exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in "Aneurysms", "Covid 19 Pandemic", and "Artificial intelligence & Machine Learning"., Conclusion: Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Competing Interests: The authors declare no conflicts of interest.

Introduction: The purpose of this study is to analyze the impact of a virtual multidisciplinary sarcoma case conference (VMSCC) on the outcomes of dermatofibrosarcoma protuberans (DFSP)., Methods: We compared margin status after surgery and disease-free survival (DFS) on two cohorts of patients with DFSP, one diagnosed from 2010 to 2015 and one from 2016 to 2020 (before and after virtual multidisciplinary sarcoma case conference (VMSCC) within Kaiser Permanente Northern California (KPNC), using Kaplan-Meier curves and Cox proportional hazard regression models., Results: There was no significant difference between the two cohorts on demographics, tumor location, type of surgery, receipt of radiation, receipt of imatinib, or size of tumor. However, the percent of patients with positive margin after final surgery and the percent of local recurrence were significantly different: 6.5% and 6.3% for the 2010-2015 cohort, and .8% and 0% for the 2016-2020 cohort, respectively., Conclusion: Our data suggest that the outcomes of DFSP improved significantly after the implementation of VMSCC., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: We have proposed that cognitive resilience (CR) counteracts brain damage from Alzheimer's disease (AD) or AD-related dementias such that older individuals who harbor neurodegenerative disease burden sufficient to cause dementia remain cognitively normal. However, CR traditionally is considered a binary trait, capturing only the most extreme examples, and is often inconsistently defined., Methods: This study addressed existing discrepancies and shortcomings of the current CR definition by proposing a framework for defining CR as a continuous variable for each neuropsychological test. The linear equations clarified CR's relationship to closely related terms, including cognitive function, reserve, compensation, and damage. Primarily, resilience is defined as a function of cognitive performance and damage from neuropathologic damage. As such, the study utilized data from 844 individuals (age = 79 ± 12, 44% female) in the National Alzheimer's Coordinating Center cohort that met our inclusion criteria of comprehensive lesion rankings for 17 neuropathologic features and complete neuropsychological test results. Machine learning models and GWAS then were used to identify medical and genetic factors that are associated with CR., Results: CR varied across five cognitive assessments and was greater in female participants, associated with longer survival, and weakly associated with educational attainment or APOE ε4 allele. In contrast, damage was strongly associated with APOE ε4 allele (P value < 0.0001). Major predictors of CR were cardiovascular health and social interactions, as well as the absence of behavioral symptoms., Conclusions: Our framework explicitly decoupled the effects of CR from neuropathologic damage. Characterizations and genetic association study of these two components suggest that the underlying CR mechanism has minimal overlap with the disease mechanism. Moreover, the identified medical features associated with CR suggest modifiable features to counteract clinical expression of damage and maintain cognitive function in older individuals., (© 2023. The Author(s).)

Background: Despite surgical fixation, the scaphoid nonunion rate remains at 3% to 5%. Recent biomechanical studies have demonstrated increased stability with 2-screw constructs. The objective of our study is to determine the preliminary union rate and anatomic feasibility of 2-screw surgical fixation for scaphoid fractures., Methods: This study is a retrospective case series of 25 patients (average age 32 years) with scaphoid fractures treated with 2 parallel headless compression screws (HCS). Postoperative evaluation included Mayo Wrist Score (MWS), range of motion, time to union, and return to activity. Bivariate analysis for gender and Pearson correlation coefficient for body size (height, weight, and body mass index) was conducted against radiographically measured scaphoid width, screw lengths, and the distance between the 2 screws., Results: All fractures healed with an average time to union of 9.9 weeks (median 7.6 weeks; range: 4.1-28.3). The mean MWS was 93.3 (range: 55-100), with 3 complications (12%), one of which affected the outcome of the surgery. The bivariate analysis demonstrated that the female gender was associated with significantly smaller scaphoid width ( P = .004) but a similar distance between the 2 screws ( P = .281). The distance between the 2 screws and the body size demonstrated a weak-to-no correlation., Conclusions: The 2-screw construct for scaphoid fracture achieved a favorable union rate and clinical outcome. Gender was the only variable significantly associated with scaphoid width and screw length. The distance between the screws was constant regardless of gender and body size, indicating that the technique for parallel screw placement can remain consistent., Type of Study: Therapeutic., Level of Evidence: Level IV., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Temporary abdominal closure is commonly employed in liver transplantation when patient factors make primary fascial closure challenging. However, there is minimal data evaluating long-term survival and patient outcomes after temporary abdominal closure., Methods: A single-center, retrospective review of patients undergoing liver transplantation from January 2013 through December 2017 was performed with a 5-year follow-up. Patients were characterized as either requiring temporary abdominal closure or immediate primary fascial closure at the time of liver transplantation., Results: Of 422 patients who underwent 436 liver transplantations, 17.2% (n = 75) required temporary abdominal closure, whereas 82.8% (n = 361) underwent primary fascial closure. Patients requiring temporary abdominal closure had higher Model for End-Stage Liver Disease scores preoperatively (27 [22-36] vs 23 [20-28], P = .0002), had higher rates of dialysis preoperatively (28.0% vs 12.5%, P = .0007), and were more likely to be hospitalized within 90 days of liver transplantation (64.0% vs 47.5%, P = .0093). On univariable analysis, survival at 1 year was different between the groups (90.9% surviving at 1 year for primary fascial closure versus 82.7% for temporary abdominal closure, P = .0356); however, there was no significant difference in survival at 5 years (83.7% vs 76.0%, P = .11). On multivariable analysis, there was no difference in survival after adjusting for multiple factors. Patients requiring temporary abdominal closure were more likely to have longer hospital stays (median 16 days [9.75-29.5] vs 8 days [6-14], P < .0001), more likely to be readmitted within 30 days (45.3% vs 32.2%, P = .03), and less likely to be discharged home (36.5% vs 74.2%, P < .0001)., Conclusions: Temporary abdominal closure after liver transplantation appears safe and has similar outcomes to primary fascial closure, though it is used more commonly in complex patients., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs., (© 2023. The Author(s).)

Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.5 million single presynapses among normal human, macaque, and mouse samples. An extensive validation showed the feasibility of performing cross-species comparison using SynTOF profiling. Integrative analysis of the abundance of 20 presynaptic proteins revealed near-complete separation between primates and mice involving synaptic pruning, cellular energy, lipid metabolism, and neurotransmission. In addition, our analysis revealed a strong overlap between the presynaptic composition of human and macaque in the cerebral cortex and neostriatum. Our unique approach illuminates species- and region-specific variation in presynapse molecular composition., (© 2023. Springer Nature Limited.)

Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data., (© 2023. Springer Nature Limited.)

Introduction: The purpose of this study is to examine pancreatoduodenectomy (PD) perioperative outcomes and consider how age may be related to overall survival in an integrated health system., Materials and Methods: A retrospective review was performed of 309 patients who underwent PD between December 2008 and December 2019. Patients were divided into two groups: aged 75 y or less and more than 75 y, defined as senior surgical patients. Univariate and multivariable analyses of predictive clinicopathologic factors associated with overall survival at 5 y were performed., Results: In both groups, the majority underwent PD for malignant disease. The proportion of senior surgical patients alive at 5 y was 33.3% compared to 53.6% of younger patients (P = 0.003). There were also statistically significant differences between the two groups with respect to body mass index, cancer antigen 19-9, Eastern Cooperative Oncology Group performance status, and Charlson comorbidity index. On multivariable analysis, disease type, cancer antigen 19-9, hemoglobin A1c, length of surgery, length of stay, Charlson comorbidity index, and Eastern Cooperative Oncology Group performance status were found to be statistically significant factors for overall survival. Age was not significantly related to overall survival on multivariable logistic regression and when the analysis was limited to pancreatic cancer patients., Conclusions: Although the difference in overall survival between patients aged less than and more than 75 years was significant, age was not an independent risk factor for overall survival on multivariable analysis. Rather than a patient's chronological age, his/her physiologic age including medical comorbidities and functional status may be more correlated to overall survival., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Introduction: Post-mortem analysis provides definitive diagnoses of neurodegenerative diseases; however, only a few can be diagnosed during life., Methods: This study employed statistical tools and machine learning to predict 17 neuropathologic lesions from a cohort of 6518 individuals using 381 clinical features (Table S1). The multisite data allowed validation of the model's robustness by splitting train/test sets by clinical sites. A similar study was performed for predicting Alzheimer's disease (AD) neuropathologic change without specific comorbidities., Results: Prediction results show high performance for certain lesions that match or exceed that of research annotation. Neurodegenerative comorbidities in addition to AD neuropathologic change resulted in compounded, but disproportionate, effects across cognitive domains as the comorbidity number increased., Discussion: Certain clinical features could be strongly associated with multiple neurodegenerative diseases, others were lesion-specific, and some were divergent between lesions. Our approach could benefit clinical research, and genetic and biomarker research by enriching cohorts for desired lesions., (© 2023 the Alzheimer's Association.)

Melanoma is an aggressive malignant disease with a high rate of cardiac metastasis. There is a reported association between myocardial tumor invasion and ventricular arrhythmias. We present a case of cardiac metastatic melanoma causing ventricular arrhythmias through a novel mechanism of encasement of coronary arteries leading to reduced myocardial perfusion. ( Level of Difficulty: Intermediate. )., Competing Interests: The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2023 The Authors.)

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery ( r = 0.65), maternal age ( r = 0.59), gravidity ( r = 0.56), and BMI ( r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches to understand the role of the immune system in various diseases. However, the performance of these approaches and the generalizability of the findings have been hindered by limited cohort sizes in translational studies, partially due to logistical demands and costs associated with longitudinal data collection in sufficiently large patient cohorts. An evolving challenge is the requirement for ever-increasing cohort sizes as the dimensionality of datasets grows. We propose a deep learning model derived from a novel pipeline of optimal temporal cell matching and overcomplete autoencoders that uses data from a small subset of patients to learn to forecast an entire patient's immune response in a high dimensional space from one timepoint to another. In our analysis of 1.08 million cells from patients pre- and post-surgical intervention, we demonstrate that the generated patient-specific data are qualitatively and quantitatively similar to real patient data by demonstrating fidelity, diversity, and usefulness., (© 2022 International Society for Advancement of Cytometry.)

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies., Competing Interests: Competing interests The authors declare no competing interests.

Objective: The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical outcome. However, physical recovery from surgery itself remains poorly defined and the utility of wearable technologies to study recovery after surgery has not been established., Background: Prolonged postoperative recovery is often associated with long-lasting impairment of physical, mental, and social functions. Although phenotypical and clinical patient characteristics account for some variation of individual recovery trajectories, biological differences likely play a major role. Specifically, patient-specific immune states have been linked to prolonged physical impairment after surgery. However, current methods of quantifying physical recovery lack patient specificity and objectivity., Methods: Here, a combined high-fidelity accelerometry and state-of-the-art deep immune profiling approach was studied in patients undergoing major joint replacement surgery. The aim was to determine whether objective physical parameters derived from accelerometry data can accurately track patient-specific physical recovery profiles (suggestive of a 'clock of postoperative recovery'), compare the performance of derived parameters with benchmark metrics including step count, and link individual recovery profiles with patients' preoperative immune state., Results: The results of our models indicate that patient-specific temporal patterns of physical function can be derived with a precision superior to benchmark metrics. Notably, 6 distinct domains of physical function and sleep are identified to represent the objective temporal patterns: ''activity capacity'' and ''moderate and overall activity (declined immediately after surgery); ''sleep disruption and sedentary activity (increased after surgery); ''overall sleep'', ''sleep onset'', and ''light activity'' (no clear changes were observed after surgery). These patterns can be linked to individual patients preopera-tive immune state using cross-validated canonical-correlation analysis. Importantly, the pSTAT3 signal activity in monocytic myeloid-derived suppressor cells predicted a slower recovery., Conclusions: Accelerometry-based recovery trajectories are scalable and objective outcomes to study patient-specific factors that drive physical recovery., Competing Interests: R.F., F.V., E.G., N.S., I.M., A.L.C., M.B., T.P., M.X., D.D.F., C.E., P.S., D.F.A., J.I.H., S.B.G., B.G., M.S.A., and N.A. contributed to conception and design. E.G., X.G., A.T., M.T., B.G., M.S.A, and N.A. acquired the data. R.F., F.V., A.C., B.G., M.S.A, and N.A. analyzed the data and interpreted the results. R.F., F.V., B.G., M.S.A., and N.A. wrote the manuscript with input from all authors. The authors report no conflicts of interest., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)

Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children's Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny&#95;app/.

First described in 1955 as "gamekeeper's thumb," injuries to the ulnar collateral ligament (UCL) of the thumb metacarpophalangeal joint are common and can cause pain and instability, especially during key pinch and grasp. Although primarily diagnosed on physical examination, stress radiographs, ultrasonography, and magnetic resonance imaging can be used to diagnose UCL injuries and distinguish partial from complete tears. If complete rupture occurs, the adductor aponeurosis can become interposed between the retracted UCL stump and its insertion on the proximal phalanx, known as a "Stener lesion." When instability persists after a trial of nonsurgical management or in the setting of complete rupture, there are various methods of repair or reconstruction. Biomechanically, there are no treatments of repair or reconstruction using native tissues that provide equivalent strength to the preinjured ligament. Recently, suture tape augmentation has been used for the repair or reconstruction with excellent short-term results and earlier return to function, although there is a paucity of literature on longer term outcomes. The various methods of surgical treatment yield excellent outcomes with a low incidence of complications., (Copyright © 2022 by the American Academy of Orthopaedic Surgeons.)

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches., Objectives: The primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions., Materials and Methods: In a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF)., Results: Jointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs., Conclusions: Elucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2022 Becker, Dai, Chang, Feyaerts, Stelzer, Zhang, Berson, Saarunya, De Francesco, Espinosa, Kim, Marić, Mataraso, Payrovnaziri, Phongpreecha, Ravindra, Shome, Tan, Thuraiappah, Xue, Mayo, Quaintance, Laborde, King, Dhabhar, Gotlib, Wong, Angst, Shaw, Stevenson, Gaudilliere and Aghaeepour.)

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia., Competing Interests: S.R.Q. is a founder, consultant, and shareholder of Mirvie. M.N.M. is a shareholder of Mirvie. D.A.R. is a shareholder of Blue Willow Biologics, Cantata Bio, Evelo Biosciences, Karius, Proderm IQ, and Second Genome. D.A.R. is an advisor to Cantata Bio and Visby Medical. K.G.S. is paid advisor of Mission Biocapital and Infinant Health; equity holder and paid advisor of Avexegen; and equity holder of mProbe. M.P.S. is a co-founder and scientific advisor of Personalis, SensOmics, Qbio, January AI, Fodsel, Filtricine, Protos, RTHM, Iollo, Marble Therapeutics, and Mirvie, and a scientific advisor of Genapsys, Jupiter, Neuvivo, Swaza, and Mitrix., (© 2022 The Authors.)

Introduction: Implementing enhanced recovery after surgery (ERAS) protocols for major abdominal surgery has been shown to decrease length of stay (LOS) and postoperative complications, including mortality and readmission. Little is known to guide which patients undergoing pancreaticoduodenectomy (PD) should be eligible for ERAS protocols., Methods and Procedures: A retrospective chart review of all PD performed from 2010 to 2018 within an integrated healthcare system was conducted. A predictive score that ranges from 0 to 4 was developed, with one point assigned to each of the following: obesity (BMI > 30), operating time > 400 min, estimated blood loss (EBL) > 400 mL, low- or high-risk pancreatic remnant (based on the presence of soft gland or small duct). Chi-squared tests and ANOVA were used to assess the relationship between this score and LOS, discharge before postoperative day 7, readmission, mortality, delayed gastric emptying (DGE), and pancreatic leak/fistula., Results: 291 patients were identified. Mean length of stay was 8.5 days in those patients who scored 0 compared to 16.2 days for those who scored 4 (p = 0.001). 30% of patients who scored 0 were discharged before postoperative day 7 compared to 0% of those who scored 4 (p = 0.019). Readmission rates for patients who scored 0 and 4 were 12% and 33%, respectively (p = 0.017). Similarly, postoperative pancreatic fistula occurred in 2% versus 25% in these groups (p = 0.007)., Conclusion: A simple scoring system using BMI, operating time, EBL, and pancreatic remnant quality can help risk-stratify postoperative PD patients. Those with lower scores could potentially be managed via an ERAS protocol. Patients with higher scores required longer hospitalizations, and adjunctive therapy such as medication and surgical technique to decrease risk of delayed gastric emptying and pancreatic fistula could be considered., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: Many patients presenting with suspected acute coronary syndrome (ACS) have high-sensitivity cardiac troponin (hs-cTn) concentrations between rule-in and rule-out thresholds and hence need serial testing, which is time consuming. The Prospective RandOmised Trial of Emergency Cardiac Computerised Tomography (PROTECCT) assessed the utility of coronary CT angiography (CCTA) in patients with suspected ACS, non-ischaemic ECG and intermediate initial hs-cTn concentration., Methods: Patients were randomised to CCTA-guided management versus standard of care (SOC). The primary outcome was hospital length of stay (LOS). Secondary outcomes included cost of in-hospital stay and major adverse cardiac events (MACE) at 12 months of follow-up. Data are mean (SD); for LOS harmonic means, IQRs are shown., Results: 250 (aged 55 (14) years, 25% women) patients were randomised. Harmonic mean (IQR) LOS was 7.53 (6.0-9.6) hours in the CCTA arm and 8.14 (6.3-9.8) hours in the SOC arm (p=0.13). Inpatient cost was £1285 (£2216) and £1108 (£3573), respectively, p=0.68. LOS was shorter in the CCTA group in patients with <25% stenosis, compared with SOC; 6.6 (5.6-7.8) hours vs 7.5 (6.1-9.4) hours, respectively; p=0.021. More referrals for cardiology outpatient clinic review and cardiac CT-related outpatient referrals occurred in the SOC arm (p=0.01). 12-month MACE rates were similar between the two arms (7 (5.6%) in the CCTA arm and 8 (6.5%) in the SOC arm-log-rank p=0.78)., Conclusions: CCTA did not lead to reduced hospital LOS or cost, largely because these outcomes were influenced by the detection of ≥25% grade stenosis in a proportion of patients., Trial Registration Number: NCT03583320., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: Given the complex surgical management and infrequency of pancreatic neuroendocrine tumor, we hypothesized that treatment at a center of excellence improves survival., Methods: Retrospective review identified 354 patients with pancreatic neuroendocrine tumor treated between 2010 and 2018. Four hepatopancreatobiliary centers of excellence were created from 21 hospitals throughout Northern California. Univariate and multivariate analyses were performed. The χ2 test of clinicopathologic factors determined which were predictive for overall survival (OS)., Results: Localized disease was seen in 51% of patients, and metastatic disease was seen in 32% of patients with mean OS of 93 and 37 months, respectively (P < 0.001). On multivariate survival analysis, stage, tumor location, and surgical resection were significant for OS (P < 0.001). All stage OS for patients treated at designated centers was 80 and 60 months for noncenters (P < 0.001). Surgery was more common across stages at the centers of excellence versus noncenters at 70% and 40%, respectively (P < 0.001)., Conclusions: Pancreatic neuroendocrine tumors are indolent but have malignant potential at any size with management often requiring complex surgeries. We showed survival was improved for patients treated at a center of excellence, where surgery was more frequently utilized., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Purpose: Proton therapy (PT) for partial breast irradiation (PBI) in early-stage breast cancer can decrease morbidity versus photon PBI with superior organs-at-risk sparing. We report 3-year outcomes of the first prospective, multicenter, phase II trial of proton PBI., Methods and Materials: This Proton Collaborative Group phase II trial (PCG BRE007-12) recruited women ≥ 50 years with node-negative, estrogen receptor (ER)-positive, ≤3cm, invasive ductal carcinoma (IDC) or ductal carcinoma in situ undergoing breast conserving surgery followed by proton PBI (40 Gy(RBE), 10 daily fractions). Primary endpoint was freedom from ipsilateral breast cancer recurrence. Adverse events were prospectively graded using CTCAEv4.0. Breast Cancer Treatment Outcome Scale (BCTOS) assessed patient-reported quality of life (PRQOL)., Results: Thirty-eight evaluable patients enrolled between 2/2013-11/2016. Median age was 67 years (range 50-79); 55 % had left-sided disease, and median tumor size was 0.9 cm. Treatment was delivered in ≥ 2 fields predominantly with uniform scanning PT (n = 37). At 35-month median follow-up (12-62), all patients were alive, and none had local, regional or distant disease progression. One patient developed an ER-negative contralateral IDC. Seven grade 2 adverse events occurred; no radiotherapy-related grade ≥ 3 toxicities occurred. Changes in BCTOS subdomain mean scores were maximum 0.36, indicating no meaningful change in PRQOL. Median heart volume receiving 5 Gy (V5Gy), lung V20Gy, and lung V10Gy were 0 %, 0 % and 0.19 %, respectively., Conclusion: At 3 years, proton PBI provided 100 % cancer control for early-stage, ER-positive breast cancer. Toxicities are minimal, and PRQOL remains acceptable with continued follow-up. These findings support PT as a safe and effective PBI delivery option., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Background: Hypertensive disorders of pregnancy and maternal diabetes profoundly affect fetal and newborn growth, yet disturbances in intermediate metabolism and relevant mediators of fetal growth alterations remain poorly defined. We sought to determine whether there are distinct newborn screen metabolic patterns among newborns affected by maternal hypertensive disorders or diabetes in utero., Methods: A retrospective observational study investigating distinct newborn screen metabolites in conjunction with data linked to birth and hospitalization records in the state of California between 2005 and 2010., Results: A total of 41,333 maternal-infant dyads were included. Infants of diabetic mothers demonstrated associations with short-chain acylcarnitines and free carnitine. Infants born to mothers with preeclampsia with severe features and chronic hypertension with superimposed preeclampsia had alterations in acetylcarnitine, free carnitine, and ornithine levels. These results were further accentuated by size for gestational age designations., Conclusions: Infants of diabetic mothers demonstrate metabolic signs of incomplete beta oxidation and altered lipid metabolism. Infants of mothers with hypertensive disorders of pregnancy carry analyte signals that may reflect oxidative stress via altered nitric oxide signaling. The newborn screen analyte composition is influenced by the presence of these maternal conditions and is further associated with the newborn size designation at birth., Impact: Substantial differences in newborn screen analyte profiles were present based on the presence or absence of maternal diabetes or hypertensive disorder of pregnancy and this finding was further influenced by the newborn size designation at birth. The metabolic health of the newborn can be examined using the newborn screen and is heavily impacted by the condition of the mother during pregnancy. Utilizing the newborn screen to identify newborns affected by common conditions of pregnancy may help relate an infant's underlying biological disposition with their clinical phenotype allowing for greater risk stratification and intervention., (© 2021. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.)

Background: Higher-volume centers for pancreatic cancer surgeries have been shown to have improved outcomes such as length of stay. We examined how centralization of pancreatic cancer care within a regional integrated healthcare system improves overall survival., Methods: We conducted a retrospective study of 1621 patients treated for pancreatic cancer from February 2010 to December 2018. Care was consolidated into 4 Centers of Excellence (COE) in surgery, medical oncology, and other specialties. Descriptive statistics, bivariate analysis, Chi-square tests, and Kaplan-Meier analysis were performed., Results: Neoadjuvant chemotherapy use rose from 10% to 31% (p < .001). The median overall survival (OS) improved by 3 months after centralization (p < .001), but this did not reach significance on multivariate analysis., Conclusions: Our results suggest that in a large integrated healthcare system, centralization improves overall survival and neoadjuvant therapy utilization for pancreatic cancer patients., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Preterm newborns are exposed to several risk factors for developing brain injury. Clinical studies have suggested that the presence of intrauterine infection is a consistent risk factor for preterm birth and white matter injury. Animal models have confirmed these associations by identifying inflammatory cascades originating at the maternofetal interface that penetrate the fetal blood-brain barrier and result in brain injury. Acquired diseases of prematurity further potentiate the risk for cerebral injury. Systems biology approaches incorporating ante- and post-natal risk factors and analyzing omic and multiomic data using machine learning are promising methodologies for further elucidating biologic mechanisms of fetal and neonatal brain injury., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Introduction: With the COVID-19 epidemic ever-expanding, nonemergent access to health care resources has been reduced to decrease the exposure for patients and health care providers. Alternatives to in-office outpatient medical evaluations are necessary. We aimed to analyze how quickly orthopedic surgery providers at a large academic institution adopted telemedicine, and identify any factors that were associated with earlier or "faster" telemedicine adoption. Methods: We analyzed the telemedicine activity of 39 providers within the Department of Orthopedic Surgery between March 16, 2020, and May 30, 2020, and constructed logistic regression models to identify characteristics with significant association to earlier or faster telemedicine adoption. Results: No significant predictors of percentage of visits conducted via telemedicine were found. However, increased experience and practice at multiple locations was associated with slower telemedicine adoption time, while Professor level academic rank was associated with a faster time to achieving 10% of pre-COVID visit volumes via telemedicine. Higher pre-COVID visit volumes were also significantly associated with faster telemedicine adoption. Demographic factors, including, age, gender, practice locations, academic degrees, pediatric specialty, and use of physician assistants/nurse practitioners, were not found to have significant associations with telemedicine use. Conclusions: These results indicate that telemedicine has an important role to play within academic orthopedic surgery practices, with a wide and diverse range of orthopedic surgery providers choosing to utilize it during the COVID-19 pandemic. Given the rapid expansion and urgency driving the adoption of telemedicine, these results illustrate the importance of considering provider-side characteristics in ensuring that providers are well equipped to utilize telemedicine.

Background: Clinical trials report occurrence of nonmelanoma skin cancers (NMSCs) with ruxolitinib in patients with polycythemia vera (PV) or myelofibrosis (MF); however, the level of risk and effect of covariates are not known in the real-world setting., Objective: To systematically assess the risk of developing NMSC after ruxolitinib exposure in patients with PV or MF., Methods: A 10-year retrospective cohort of patients with PV or MF at Stanford Medical Center was identified and matched according to age, gender, race, Charlson Comorbidity Index, disease diagnosis, and follow-up time. The main outcome measure was hazard ratio (HR) for NMSC (comprised of basal cell carcinoma and squamous cell carcinoma [SCC]) after ruxolitinib exposure, adjusted for covariates., Results: The study cohort consisted of 564 patients (188 exposed to ruxolitinib for at least 4 weeks, 376 unexposed). Ruxolitinib-exposed patients with PV or MF had an adjusted NMSC HR of 2.69 (95% CI, 1.03-7.02). In particular, ruxolitinib exposure was associated with SCC (HR, 3.24; 95% CI, 1.45-7.22), with non-Janus kinase 2-mutated patients showing even higher SCC risk (HR, 7.40; 95% CI, 2.54-21.63)., Limitations: Retrospective design., Conclusions: Our real-world results indicate that SCC risk is increased in patients with PV or MF taking ruxolitinib and support consideration of skin cancer monitoring., Competing Interests: Conflicts of interest Dr Chang was an advisory board member and clinical investigator for Merck and Regeneron and a consultant for Jounce Therapeutics and Sun Pharma, none of which are related to the current study. Dr Kwong was consultant for Genentech, Oncoderm, and Happy 2nd Birthday and was on the advisory board for Kyowa Kirin. Authors Lin, S. Q. Li, S. Li, and Kiamanesh and Dr Aasi have no conflicts of interest to declare., (Copyright © 2021 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.)

Synaptic molecular characterization is limited for Alzheimer’s disease (AD). Our newly invented mass cytometry–based method, synaptometry by time of flight (SynTOF), was used to measure 38 antibody probes in approximately 17 million single-synapse events from human brains without pathologic change or with pure AD or Lewy body disease (LBD), nonhuman primates (NHPs), and PS/APP mice. Synaptic molecular integrity in humans and NHP was similar. Although not detected in human synapses, Aβ was in PS/APP mice single-synapse events. Clustering and pattern identification of human synapses showed expected disease-specific differences, like increased hippocampal pathologic tau in AD and reduced caudate dopamine transporter in LBD, and revealed previously unidentified findings including increased hippocampal CD47 and lowered DJ1 in AD and higher ApoE in AD with dementia. Our results were independently supported by multiplex ion beam imaging of intact tissue. This highlights the higher depth and breadth of insight on neurodegenerative diseases obtainable through SynTOF.