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Orthostatic hypotension (OH) is a common inpatient condition associated with falls, syncope, and mortality. However, standardized approaches for inpatient management of OH are lacking and may vary across clinical specialties. In this retrospective observational cohort study, we reviewed the electronic medical records of patients admitted to Beth Israel Deaconess Medical Center between April 1, 2015 and June 1, 2021 with a diagnosis of OH or medication‐related hypotension. Variables of interest included admitting service, presenting symptoms, suspected etiology, and management. Among the 400 inpatients with OH, one‐third had OH documented on admission. Dizziness and lightheadedness were the most common symptoms; medical patients experienced dizziness, falls, and other symptoms more frequently than surgical patients. Volume depletion and medications were the leading suspected causes of OH. Surgical patients were less likely to have medication‐related OH and were more likely to lack an identified etiology. Cardiovascular disease was more frequently implicated in cardiology patients. Volume depletion, neurodegenerative disease, and other conditions were more often suspected among medical patients. Management commonly involved volume resuscitation and medication adjustment, though medication changes were less frequent in surgical patients. Nonpharmacologic interventions were more common among medical patients. By discharge, OH had resolved in only one‐third of patients. In summary, inpatient OH was most often identified after admission, attributed to hypovolemia, treated with fluids, and unresolved at discharge, with differences in symptoms, etiology, and management between specialties. Prospective studies are needed to formalize diagnostic and treatment strategies for OH in the hospital setting.

Multi-center collaborations are crucial in developing robust and generalizable machine learning models in medical imaging. Traditional methods, such as centralized data sharing or federated learning (FL), face challenges, including privacy issues, communication burdens, and synchronization complexities. We present CATegorical and PHenotypic Image SyntHetic learnING (CATphishing), an alternative to FL using Latent Diffusion Models (LDM) to generate synthetic multi-contrast three-dimensional magnetic resonance imaging data for downstream tasks, eliminating the need for raw data sharing or iterative inter-site communication. Each institution trains an LDM to capture site-specific data distributions, producing synthetic samples aggregated at a central server. We evaluate CATphishing using data from 2491 patients across seven institutions for isocitrate dehydrogenase mutation classification and three-class tumor-type classification. CATphishing achieves accuracy comparable to centralized training and FL, with synthetic data exhibiting high fidelity. This method addresses privacy, scalability, and communication challenges, offering a promising alternative for collaborative artificial intelligence development in medical imaging. Methods for developing machine learning models in medical imaging across multi-centre collaborations face important challenges, including technical burdens and privacy issues. Here, the authors introduce CATegorical and PHenotypic Image SyntHetic learnING - CATphishing - as an alternative to Federated Learning to generate synthetic multi-contrast 3D MRI data for downstream tasks.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature ( n  = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here, the authors present the largest FL study to-date to generate an automatic tumor boundary detector for glioblastoma.

Most lawyers in the United States practice in law firms or serve in government, and quite a few, like myself, have crossed between those spheres at least once. Many have been successful leaders in both government and private practice by demonstrating qualities associated with leadership generally-substantive expertise, high ethical standards, a commitment to hard work and the advancement of the larger organization, and respect for colleagues and their opinions. The meaning and means of leadership in each environment are different, however, and success as a leader in one role does not necessarily guarantee success in the other. The qualities that characterize effective leadership in public and private law offices tend to reflect different institutional structures and orientations. What follows are some thoughts about distinctions and commonalities between leadership in government and leadership in large law firms.

Increasing impacts of climatic change and anthropogenic disturbances on natural ecosystems are leading to population declines or extinctions of many species worldwide. In Australia, recent climatic change has caused population declines in some native fauna. The projected increase in mean annual temperature by up to 4°C by the end of the 21st century is expected to exacerbate these trends. The greater glider (Petauroides volans), Australia’s largest gliding marsupial, is widely distributed along the eastern coast, but has recently experienced drastic declines in population numbers. Its association with hollow‐bearing trees, used for nesting, has made it an important species for the conservation of old‐growth forest ecosystems. Fires and timber harvesting have been identified as threats to the species. Greater gliders have disappeared however from areas that have experienced neither raising questions about the role of other factors in their decline. A unique physiology and strict Eucalyptus diet make them vulnerable to high temperatures and low water availability. As such, climatic conditions may drive habitat selection and recent climatic trends may be contributing to observed population declines. Using presence:absence data from across its distribution in Victoria, coupled with high spatial and temporal resolution climatic data and machine‐learning modeling, we tested the influence of climatic, topographic, edaphic, biotic, and disturbance variables on greater glider occupancy and habitat suitability. We found that climatic variables, particularly those related to aridity and extreme weather conditions, such as number of nights warmer than 20°C, were highly significant predictors of greater glider occurrence. Climatic conditions associated with habitat suitability have changed over time, with increasing aridity across much of its southeastern distribution. These changes in climate are closely aligned with observed population declines across this region. At higher elevation, some areas where the greater glider is observed at high densities, conditions have become wetter, which is improving habitat quality. These areas are of growing significance to greater glider conservation as they will become increasingly important as climatic refugia in the coming decades. Protecting these areas of habitat will be critical for facilitating the conservation of greater gliders as the broader landscape becomes less hospitable under future climatic change.

BackgroundGluten avoidance among patients without celiac disease has become increasingly popular, especially among young and female demographics; however, no research has explored gluten avoidance during pregnancy, when nutrition is particularly important.AimsTo determine whether avoiding gluten in pregnancy is associated with any medical, obstetric, or neonatal characteristics.MethodsIn this single-center retrospective cohort study, we identified women with singleton pregnancies who avoid gluten based on antenatal intake questionnaire responses and inpatient dietary orders, excluding those with celiac disease. Certain demographic, medical, obstetric, and neonatal characteristics were compared to matched controls who do not avoid gluten.ResultsFrom July 1, 2011 to July 1, 2019, 138 pregnant women who avoid gluten were admitted for delivery of singleton gestations. Compared to controls, gluten-avoidant women had fewer prior pregnancies (p = 0.005), deliveries (p < 0.0005), and living children (p < 0.0005), higher rates of hypothyroidism (OR = 3.22; p = 0.001) and irritable bowel syndrome (OR = 6.00; p = 0.019), higher second trimester hemoglobin (p = 0.018), and lower body mass index at delivery (p = 0.045). Groups did not differ in any obstetric or fetal characteristics.ConclusionsGluten avoidance in pregnancy is common and, in women without celiac disease, is associated with higher rates of hypothyroidism and irritable bowel syndrome, fewer pregnancies, term births, and living children, and lower peripartum BMI, but is not associated with any obstetric or neonatal comorbidities. Avoiding gluten does not appear to adversely affect maternal or fetal health, but reasons for gluten avoidance, as well as long-term maternal and pediatric outcomes after gluten avoidance in pregnancy, warrant further study.

Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. This study sought to develop deep learning networks for non-invasive IDH classification using T2w MR images while comparing their performance to a multi-contrast network. Methods: Multi-contrast brain tumor MRI and genomic data were obtained from The Cancer Imaging Archive (TCIA) and The Erasmus Glioma Database (EGD). Two separate 2D networks were developed using nnU-Net, a T2w-image-only network (T2-net) and a multi-contrast network (MC-net). Each network was separately trained using TCIA (227 subjects) or TCIA + EGD data (683 subjects combined). The networks were trained to classify IDH mutation status and implement single-label tumor segmentation simultaneously. The trained networks were tested on over 1100 held-out datasets including 360 cases from UT Southwestern Medical Center, 136 cases from New York University, 175 cases from the University of Wisconsin–Madison, 456 cases from EGD (for the TCIA-trained network), and 495 cases from the University of California, San Francisco public database. A receiver operating characteristic curve (ROC) was drawn to calculate the AUC value to determine classifier performance. Results: T2-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 85.4% and 87.6% with AUCs of 0.86 and 0.89, respectively. MC-net trained on TCIA and TCIA + EGD datasets achieved an overall accuracy of 91.0% and 92.8% with AUCs of 0.94 and 0.96, respectively. We developed reliable, high-performing deep learning algorithms for IDH classification using both a T2-image-only and a multi-contrast approach. The networks were tested on more than 1100 subjects from diverse databases, making this the largest study on image-based IDH classification to date.

Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider (Petauroides volans), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high‐resolution multispectral imagery, collected by a lightweight and low‐cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand‐scale from leaf‐scale chemistry measurements across a gradient of mixed‐species Eucalyptus forests in southeastern Australia. We surveyed temperate Eucalyptus forests across an elevational and topographic gradient from sea level to high elevation (50–1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 Eucalyptus species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross‐validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate Eucalyptus species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia. For arboreal folivores such as the greater glider, which is threatened by habitat loss and climate change, the abundance of suitable forage trees can influence habitat suitability and species occurrence. We developed a method to quantify spatial variability in foliar nitrogen, a key metric in forage suitability using UAV multispectral imagery. Our approach allowed us to scale leaf‐level measures of foliar nitrogen to the stand level. This method can provide critical habitat assessments to support management and conservation of this vulnerable species across southeastern Australia.

Recent studies report that STAT3 signaling is a master regulator of mesenchymal transformation of gliomas and that STAT3 modulated genes are highly expressed in the mesenchymal transcriptome of gliomas. A currently studied experimental treatment for gliomas consists of intratumoral injection of oncolytic viruses (OV), such as oncolytic herpes simplex virus type 1 (oHSV). We have described one particular oHSV (rQNestin34.5) that exhibits potent anti-glioma activity in animal models. Here, we hypothesized that alterations in STAT3 signaling in glioma cells may affect the replicative ability of rQNestin34.5. In fact, human U251 glioma cells engineered to either over-express STAT3 or with genetic down-regulation of STAT3 supported oHSV replication to a significantly higher or lesser degree, respectively, when compared to controls. Administration of pharmacologic agents that increase STAT3 phosphorylation/activation (Valproic Acid) or increase STAT3 levels (Interleukin 6) also significantly enhanced oHSV replication. Instead, administration of inhibitors of STAT3 phosphorylation/activation (LLL12) significantly reduced oHSV replication. STAT3 led to a reduction in interferon signaling in oHSV infected cells and inhibition of interferon signaling abolished the effect of STAT3 on oHSV replication. These data thus indicate that STAT3 signaling in malignant gliomas enhances oHSV replication, likely by inhibiting the interferon response in infected glioma cells, thus suggesting avenues for possible potentiation of oncolytic virotherapy.