Orthopedic Injections: A Longitudinal Musculoskeletal Curriculum in a Family Medicine Residency.

BACKGROUND AND OBJECTIVES: Musculoskeletal (MSK) complaints comprise more than 20% of all visits to health care providers each year. Despite required experiences in MSK care, family physicians report low confidence in diagnosing and treating MSK conditions. The purpose of this study was to analyze the effects of early and longitudinal exposure to MSK education on residents' confidence in and likelihood of performing MSK physical exams and injections in future practice. METHODS: From 2017 to 2019, residents completed an annual survey assessing confidence in, frequency of, and future intentions to perform exams and injections for MSK conditions. We compared responses between family medicine residents who completed a 176-hour longitudinal sports medicine (LSM) curriculum distributed over all 3 years of residency and a comparable cohort of family medicine residents who completed a 188-hour concentrated MSK curriculum primarily in the final year of residency. We made comparisons using the Fisher exact test for categorical variables and an independent samples t test for numeric variables. RESULTS: We analyzed the 98 total responses from 50 residents. The proportion of residents reporting high ratings of their residency MSK education (26% to 60%), performing >5 injections (38% to 73%), reporting confidence in performing injections (12% to 40%), and indicating likelihood to perform MSK injections in the future (52% to 65%) were all greater in the LSM versus concentrated MSK curriculum cohorts (P<.05 for all). CONCLUSIONS: Early and longitudinal exposure to MSK care and sports medicine in family medicine residency led to both an increase in MSK injections during residency training and a greater desire to perform these injections in postresidency practice.

Impact of Genetic Ancestry on T-cell Acute Lymphoblastic Leukemia Outcomes.

The influence of genetic ancestry on biology, survival outcomes, and risk stratification in T-cell Acute Lymphoblastic Leukemia (T-ALL) has not been explored. Genetic ancestry was genomically-derived from DNA-based single nucleotide polymorphisms in children and young adults with T-ALL treated on Children's Oncology Group trial AALL0434. We determined associations of genetic ancestry, leukemia genomics and survival outcomes; co-primary outcomes were genomic subtype, pathway alteration, overall survival (OS), and event-free survival (EFS). Among 1309 patients, T-ALL molecular subtypes varied significantly by genetic ancestry, including increased frequency of genomically defined ETP-like, MLLT10, and BCL11B-activated subtypes in patients of African ancestry. In multivariable Cox models adjusting for high-risk subtype and pathways, patients of Admixed American ancestry had superior 5-year EFS/OS compared with European; EFS/OS for patients of African and European ancestry were similar. The prognostic value of five commonly altered T-ALL genes varied by ancestry - including NOTCH1 , which was associated with superior OS for patients of European and Admixed American ancestry but non-prognostic among patients of African ancestry. Furthermore, a published five-gene risk classifier accurately risk stratified patients of European ancestry, but misclassified patients of African ancestry. We developed a penalized Cox model which successfully risk stratified patients across ancestries. Overall, 80% of patients had a genomic alteration in at least one gene with differential prognostic impact by genetic ancestry. T-ALL genomics and prognostic associations of genomic alterations vary by genetic ancestry. These data demonstrate the importance of incorporating genetic ancestry into analyses of tumor biology for risk classification algorithms.

The genomic basis of childhood T-lineage acute lymphoblastic leukaemia.

T-lineage acute lymphoblastic leukaemia (T-ALL) is a high-risk tumour1 that has eluded comprehensive genomic characterization, which is partly due to the high frequency of noncoding genomic alterations that result in oncogene deregulation2,3. Here we report an integrated analysis of genome and transcriptome sequencing of tumour and remission samples from more than 1,300 uniformly treated children with T-ALL, coupled with epigenomic and single-cell analyses of malignant and normal T cell precursors. This approach identified 15 subtypes with distinct genomic drivers, gene expression patterns, developmental states and outcomes. Analyses of chromatin topology revealed multiple mechanisms of enhancer deregulation that involve enhancers and genes in a subtype-specific manner, thereby demonstrating widespread involvement of the noncoding genome. We show that the immunophenotypically described, high-risk entity of early T cell precursor ALL is superseded by a broader category of 'early T cell precursor-like' leukaemia. This category has a variable immunophenotype and diverse genomic alterations of a core set of genes that encode regulators of hematopoietic stem cell development. Using multivariable outcome models, we show that genetic subtypes, driver and concomitant genetic alterations independently predict treatment failure and survival. These findings provide a roadmap for the classification, risk stratification and mechanistic understanding of this disease.

Stochastic EM algorithm for partially observed stochastic epidemics with individual heterogeneity.

We develop a stochastic epidemic model progressing over dynamic networks, where infection rates are heterogeneous and may vary with individual-level covariates. The joint dynamics are modeled as a continuous-time Markov chain such that disease transmission is constrained by the contact network structure, and network evolution is in turn influenced by individual disease statuses. To accommodate partial epidemic observations commonly seen in real-world data, we propose a stochastic EM algorithm for inference, introducing key innovations that include efficient conditional samplers for imputing missing infection and recovery times which respect the dynamic contact network. Experiments on both synthetic and real datasets demonstrate that our inference method can accurately and efficiently recover model parameters and provide valuable insight at the presence of unobserved disease episodes in epidemic data.

Multiplexed Imaging Mass Cytometry Analysis Characterizes the Vascular Niche in Pancreatic Cancer.

Oncogenesis and progression of pancreatic ductal adenocarcinoma (PDAC) are driven by complex interactions between the neoplastic component and the tumor microenvironment, which includes immune, stromal, and parenchymal cells. In particular, most PDACs are characterized by a hypovascular and hypoxic environment that alters tumor cell behavior and limits the efficacy of chemotherapy and immunotherapy. Characterization of the spatial features of the vascular niche could advance our understanding of inter- and intratumoral heterogeneity in PDAC. In this study, we investigated the vascular microenvironment of PDAC by applying imaging mass cytometry using a 26-antibody panel on 35 regions of interest across 9 patients, capturing more than 140,000 single cells. The approach distinguished major cell types, including multiple populations of lymphoid and myeloid cells, endocrine cells, ductal cells, stromal cells, and endothelial cells. Evaluation of cellular neighborhoods identified 10 distinct spatial domains, including multiple immune and tumor-enriched environments as well as the vascular niche. Focused analysis revealed differential interactions between immune populations and the vasculature and identified distinct spatial domains wherein tumor cell proliferation occurs. Importantly, the vascular niche was closely associated with a population of CD44-expressing macrophages enriched for a proangiogenic gene signature. Taken together, this study provides insights into the spatial heterogeneity of PDAC and suggests a role for CD44-expressing macrophages in shaping the vascular niche. Significance: Imaging mass cytometry revealed that pancreatic ductal cancers are composed of 10 distinct cellular neighborhoods, including a vascular niche enriched for macrophages expressing high levels of CD44 and a proangiogenic gene signature.

Enrollment Trends Among Patients with Melanoma Brain Metastasis in Active Clinical Trials.

The CNS is a common site for distant metastasis and treatment failure in melanoma patients. This study aimed to evaluate the inclusion rate of patients with melanoma brain metastases (MBM) in prospective clinical trials. 69.3% of trials excluded MBM patients based on their CNS disease. In univariate analysis, trials not employing immunotherapy (p = 0.0174), inclusion of leptomeningeal disease (p < 0.0001) and non-pharmaceutical sponsor trials (p = 0.0461) were more likely to enroll patients with MBM. Thoughtful reconsideration of clinical trial designs is needed to give patients with MBMs access to promising investigational agents and improve outcomes for patients with MBM.

Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging.

Non-hematopoietic cells are essential contributors to hematopoiesis. However, heterogeneity and spatial organization of these cells in human bone marrow remain largely uncharacterized. We used single-cell RNA sequencing (scRNA-seq) to profile 29,325 non-hematopoietic cells and discovered nine transcriptionally distinct subtypes. We simultaneously profiled 53,417 hematopoietic cells and predicted their interactions with non-hematopoietic subsets. We employed co-detection by indexing (CODEX) to spatially profile over 1.2 million cells. We integrated scRNA-seq and CODEX data to link predicted cellular signaling with spatial proximity. Our analysis revealed a hyperoxygenated arterio-endosteal neighborhood for early myelopoiesis, and an adipocytic localization for early hematopoietic stem and progenitor cells (HSPCs). We used our CODEX atlas to annotate new images and uncovered mesenchymal stromal cell (MSC) expansion and spatial neighborhoods co-enriched for leukemic blasts and MSCs in acute myeloid leukemia (AML) patient samples. This spatially resolved, multiomic atlas of human bone marrow provides a reference for investigation of cellular interactions that drive hematopoiesis.

Diagnosis, management, and outcomes of immune checkpoint inhibitor induced acute interstitial nephritis: A single-center experience.

BACKGROUND: Immune checkpoint inhibitor (ICI)-associated acute interstitial nephritis (AIN) is a recognized complication of immunotherapy (IO), but literature on its management and outcomes is limited. METHODS: We retrospectively reviewed patients who received ICIs and developed biopsy-proven or clinically-suspected ICI-associated AIN at the University of Virginia Comprehensive Cancer Center from 2012-2023. We analyzed baseline characteristics and clinical outcomes, including treatment interruption and rechallenge rates. Acute kidney injury (AKI) was defined as a ≥ 1.5-fold increase in baseline creatinine under seven days, a two-fold increase above the upper limit of normal, or an increase by ≥0.3 mg/dL. Kidney function returning to within 0.3 mg/dL or less than twice baseline was considered complete (CRc) and partial (PRc) recovery, respectively. RESULTS: We identified 12 cases of ICI-AIN: four by biopsy (33%) and eight (67%) by clinical suspicion. Two patients received anti-CTLA-4 and anti-PD1, six received anti-PD1 alone, and four received chemo-immunotherapy. The majority (58%) of patients developed AIN within the first 5 cycles. Eight patients developed ≥ Grade 3 AKI, and six developed multiple irAEs. ICI was permanently discontinued in seven patients (58%) and temporarily interrupted in four (30%). The CRc and PRc rates were 67% and 8%, respectively. Upon AIN onset, the best disease response was stable disease in five patients, partial response in three, and progressive disease in three. Median overall survival was 4.87 years, and progression-free survival was 1.5 years. CONCLUSIONS: Rechallenge with IO after kidney irAE may be possible in some patients but requires careful evaluation on an individual basis.

Multiomics Analysis of PCB126's Effect on a Mouse Chronic-Binge Alcohol Feeding Model.

BACKGROUND: Environmental pollutants, including polychlorinated biphenyls (PCBs) have been implicated in the pathogenesis of liver disease. Our group recently demonstrated that PCB126 promoted steatosis, hepatomegaly, and modulated intermediary metabolism in a rodent model of alcohol-associated liver disease (ALD). OBJECTIVE: To better understand how PCB126 promoted ALD in our previous model, the current study adopts multiple omics approaches to elucidate potential mechanistic hypotheses. METHODS: Briefly, male C57BL/6J mice were exposed to 0.2mg/kg polychlorinated biphenyl (PCB) 126 or corn oil vehicle prior to ethanol (EtOH) or control diet feeding in the chronic-binge alcohol feeding model. Liver tissues were collected and prepared for mRNA sequencing, phosphoproteomics, and inductively coupled plasma mass spectrometry for metals quantification. RESULTS: Principal component analysis showed that PCB126 uniquely modified the transcriptome in EtOH-fed mice. EtOH feeding alone resulted in >4,000 differentially expressed genes (DEGs), and PCB126 exposure resulted in more DEGs in the EtOH-fed group (907 DEGs) in comparison with the pair-fed group (503 DEGs). Top 20 significant gene ontology (GO) biological processes included "peptidyl tyrosine modifications," whereas top 25 significantly decreasing GO molecular functions included "metal/ion/zinc binding." Quantitative, label-free phosphoproteomics and western blot analysis revealed no major significant PCB126 effects on total phosphorylated tyrosine residues in EtOH-fed mice. Quantified hepatic essential metal levels were primarily significantly lower in EtOH-fed mice. PCB126-exposed mice had significantly lower magnesium, cobalt, and zinc levels in EtOH-fed mice. DISCUSSION: Previous work has demonstrated that PCB126 is a modifying factor in metabolic dysfunction-associated steatotic liver disease (MASLD), and our current work suggests that pollutants also modify ALD. PCB126 may, in part, be contributing to the malnutrition aspect of ALD, where metal deficiency is known to contribute and worsen prognosis. https://doi.org/10.1289/EHP14132.

All-trans retinoic acid induces durable tumor immunity in IDH-mutant gliomas by rescuing transcriptional repression of the CRBP1-retinoic acid axis.

Diffuse gliomas are epigenetically dysregulated, immunologically cold, and fatal tumors characterized by mutations in isocitrate dehydrogenase (IDH). Although IDH mutations yield a uniquely immunosuppressive tumor microenvironment, the regulatory mechanisms that drive the immune landscape of IDH mutant (IDHm) gliomas remain unknown. Here, we reveal that transcriptional repression of retinoic acid (RA) pathway signaling impairs both innate and adaptive immune surveillance in IDHm glioma through epigenetic silencing of retinol binding protein 1 (RBP1) and induces a profound anti-inflammatory landscape marked by loss of inflammatory cell states and infiltration of suppressive myeloid phenotypes. Restorative retinoic acid therapy in murine glioma models promotes clonal CD4 + T cell expansion and induces tumor regression in IDHm, but not IDH wildtype (IDHwt), gliomas. Our findings provide a mechanistic rationale for RA immunotherapy in IDHm glioma and is the basis for an ongoing investigator-initiated, single-center clinical trial investigating all-trans retinoic acid (ATRA) in recurrent IDHm human subjects.

The FDA Reclassification of Cervical Pedicle and Lateral Mass Screws: A Case Study in Regulatory History.

The classification of medical devices by the Food and Drug Administration (FDA) involves rigorous scrutiny from specialized panels that designate devices as Class I, II, or III depending on their levels of relative risk to patient health. Posterior rigid pedicle screw systems were first classified by the FDA in 1984 and have since revolutionized the treatment of many spine pathologies. Despite this early classification by the FDA, posterior cervical pedicle and lateral mass screws were not reclassified from unclassified to Class III and then to Class II until 2019, nearly 35 years after their initial classification. This reclassification process involved a decades-long interplay between the FDA, formal panels, manufacturers, academic leaders, practicing physicians, and patients. It was delayed by lawsuits and a paucity of data demonstrating the ability to improve outcomes for cervical spinal pathologies. The off-label use of thoracolumbar pedicle screw rigid fixation systems by early adopters assisted manufacturers and professional organizations in providing the necessary data for the reclassification process. This case study highlights the collaboration between physicians and professional organizations in facilitating FDA reclassification and underscores changes to the current classification process that could avoid the prolonged dichotomy between common medical practice and FDA guidelines.

Mapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics and Proteomic Imaging.

The bone marrow is the organ responsible for blood production. Diverse non-hematopoietic cells contribute essentially to hematopoiesis. However, these cells and their spatial organization remain largely uncharacterized as they have been technically challenging to study in humans. Here, we used fresh femoral head samples and performed single-cell RNA sequencing (scRNA-Seq) to profile 29,325 enriched non-hematopoietic bone marrow cells and discover nine transcriptionally distinct subtypes. We next employed CO-detection by inDEXing (CODEX) multiplexed imaging of 18 individuals, including both healthy and acute myeloid leukemia (AML) samples, to spatially profile over one million single cells with a novel 53-antibody panel. We discovered a relatively hyperoxygenated arterio-endosteal niche for early myelopoiesis, and an adipocytic, but not endosteal or perivascular, niche for early hematopoietic stem and progenitor cells. We used our atlas to predict cell type labels in new bone marrow images and used these predictions to uncover mesenchymal stromal cell (MSC) expansion and leukemic blast/MSC-enriched spatial neighborhoods in AML patient samples. Our work represents the first comprehensive, spatially-resolved multiomic atlas of human bone marrow and will serve as a reference for future investigation of cellular interactions that drive hematopoiesis.

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.

Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities to adult glioma, it is increasingly recognized as a molecularly distinct, yet highly heterogeneous disease. In this study, we longitudinally profiled a molecularly diverse cohort of 16 pHGG patients before and after standard therapy through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of the tumor microenvironment during progression following treatment. We found that the canonical neoplastic cell phenotypes of adult glioblastoma are insufficient to capture the range of tumor cell states in a pediatric cohort and observed differential tumor-myeloid interactions between malignant cell states. We identified key transcriptional regulators of pHGG cell states and did not observe the marked proneural to mesenchymal shift characteristic of adult glioblastoma. We showed that essential neuromodulators and the interferon response are upregulated post-therapy along with an increase in non-neoplastic oligodendrocytes. Through in vitro pharmacological perturbation, we demonstrated novel malignant cell-intrinsic targets. This multiomic atlas of longitudinal pHGG captures the key features of therapy response that support distinction from its adult counterpart and suggests therapeutic strategies which are targeted to pediatric gliomas.

Chronic Aroclor 1260 exposure alters the mouse liver proteome, selenoproteins, and metals in steatotic liver disease.

The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to increase due in part to the obesity epidemic and to environmental exposures to metabolism disrupting chemicals. A single gavage exposure of male mice to Aroclor 1260 (Ar1260), an environmentally relevant mixture of non-dioxin-like polychlorinated biphenyls (PCBs), resulted in steatohepatitis and altered RNA modifications in selenocysteine tRNA 34 weeks post-exposure. Unbiased approaches identified the liver proteome, selenoproteins, and levels of 25 metals. Ar1260 altered the abundance of 128 proteins. Enrichment analysis of the liver Ar1260 proteome included glutathione metabolism and translation of selenoproteins. Hepatic glutathione peroxidase 4 (GPX4) and Selenoprotein O (SELENOO) were increased and Selenoprotein F (SELENOF), Selenoprotein S (SELENOS), Selenium binding protein 2 (SELENBP2) were decreased with Ar1260 exposure. Increased copper, selenium (Se), and zinc and reduced iron levels were detected. These data demonstrate that Ar1260 exposure alters the (seleno)proteome, Se, and metals in MASLD-associated pathways.

Inferring HIV transmission patterns from viral deep-sequence data via latent typed point processes.

Viral deep-sequencing data play a crucial role toward understanding disease transmission network flows, providing higher resolution compared to standard Sanger sequencing. To more fully utilize these rich data and account for the uncertainties in outcomes from phylogenetic analyses, we propose a spatial Poisson process model to uncover human immunodeficiency virus (HIV) transmission flow patterns at the population level. We represent pairings of individuals with viral sequence data as typed points, with coordinates representing covariates such as gender and age and point types representing the unobserved transmission statuses (linkage and direction). Points are associated with observed scores on the strength of evidence for each transmission status that are obtained through standard deep-sequence phylogenetic analysis. Our method is able to jointly infer the latent transmission statuses for all pairings and the transmission flow surface on the source-recipient covariate space. In contrast to existing methods, our framework does not require preclassification of the transmission statuses of data points, and instead learns them probabilistically through a fully Bayesian inference scheme. By directly modeling continuous spatial processes with smooth densities, our method enjoys significant computational advantages compared to previous methods that rely on discretization of the covariate space. We demonstrate that our framework can capture age structures in HIV transmission at high resolution, bringing valuable insights in a case study on viral deep-sequencing data from Southern Uganda.

Detecting changes in the transmission rate of a stochastic epidemic model.

Throughout the course of an epidemic, the rate at which disease spreads varies with behavioral changes, the emergence of new disease variants, and the introduction of mitigation policies. Estimating such changes in transmission rates can help us better model and predict the dynamics of an epidemic, and provide insight into the efficacy of control and intervention strategies. We present a method for likelihood-based estimation of parameters in the stochastic susceptible-infected-removed model under a time-inhomogeneous transmission rate comprised of piecewise constant components. In doing so, our method simultaneously learns change points in the transmission rate via a Markov chain Monte Carlo algorithm. The method targets the exact model posterior in a difficult missing data setting given only partially observed case counts over time. We validate performance on simulated data before applying our approach to data from an Ebola outbreak in Western Africa and COVID-19 outbreak on a university campus.

Identification and targeting of treatment resistant progenitor populations in T-cell Acute Lymphoblastic Leukemia.

Refractoriness to initial chemotherapy and relapse after remission are the main obstacles to cure in T-cell Acute Lymphoblastic Leukemia (T-ALL). Biomarker guided risk stratification and targeted therapy have the potential to improve outcomes in high-risk T-ALL; however, cellular and genetic factors contributing to treatment resistance remain unknown. Previous bulk genomic studies in T-ALL have implicated tumor heterogeneity as an unexplored mechanism for treatment failure. To link tumor subpopulations with clinical outcome, we created an atlas of healthy pediatric hematopoiesis and applied single-cell multiomic (CITE-seq/snATAC-seq) analysis to a cohort of 40 cases of T-ALL treated on the Children's Oncology Group AALL0434 clinical trial. The cohort was carefully selected to capture the immunophenotypic diversity of T-ALL, with early T-cell precursor (ETP) and Near/Non-ETP subtypes represented, as well as enriched with both relapsed and treatment refractory cases. Integrated analyses of T-ALL blasts and normal T-cell precursors identified a bone-marrow progenitor-like (BMP-like) leukemia sub-population associated with treatment failure and poor overall survival. The single-cell-derived molecular signature of BMP-like blasts predicted poor outcome across multiple subtypes of T-ALL within two independent patient cohorts using bulk RNA-sequencing data from over 1300 patients. We defined the mutational landscape of BMP-like T-ALL, finding that NOTCH1 mutations additively drive T-ALL blasts away from the BMP-like state. We transcriptionally matched BMP-like blasts to early thymic seeding progenitors that have low NR3C1 expression and high stem cell gene expression, corresponding to a corticosteroid and conventional cytotoxic resistant phenotype we observed in ex vivo drug screening. To identify novel targets for BMP-like blasts, we performed in silico and in vitro drug screening against the BMP-like signature and prioritized BMP-like overexpressed cell-surface (CD44, ITGA4, LGALS1) and intracellular proteins (BCL-2, MCL-1, BTK, NF-κB) as candidates for precision targeted therapy. We established patient derived xenograft models of BMP-high and BMP-low leukemias, which revealed vulnerability of BMP-like blasts to apoptosis-inducing agents, TEC-kinase inhibitors, and proteasome inhibitors. Our study establishes the first multi-omic signatures for rapid risk-stratification and targeted treatment of high-risk T-ALL.

Towards a comprehensive regulatory map of Mammalian Genomes.

Genome mapping studies have generated a nearly complete collection of genes for the human genome, but we still lack an equivalently vetted inventory of human regulatory sequences. Cis-regulatory modules (CRMs) play important roles in controlling when, where, and how much a gene is expressed. We developed a training data-free CRM-prediction algorithm, the Mammalian Regulatory MOdule Detector (MrMOD) for accurate CRM prediction in mammalian genomes. MrMOD provides genome position-fixed CRM models similar to the fixed gene models for the mouse and human genomes using only genomic sequences as the inputs with one adjustable parameter - the significance p-value. Importantly, MrMOD predicts a comprehensive set of high-resolution CRMs in the mouse and human genomes including all types of regulatory modules not limited to any tissue, cell type, developmental stage, or condition. We computationally validated MrMOD predictions used a compendium of 21 orthogonal experimental data sets including thousands of experimentally defined CRMs and millions of putative regulatory elements derived from hundreds of different tissues, cell types, and stimulus conditions obtained from multiple databases. In ovo transgenic reporter assay demonstrates the power of our prediction in guiding experimental design. We analyzed CRMs located in the chromosome 17 using unsupervised machine learning and identified groups of CRMs with multiple lines of evidence supporting their functionality, linking CRMs with upstream binding transcription factors and downstream target genes. Our work provides a comprehensive base pair resolution annotation of the functional regulatory elements and non-functional regions in the mammalian genomes.

Fitting a stochastic model of intensive care occupancy to noisy hospitalization time series during the COVID-19 pandemic.

Intensive care occupancy is an important indicator of health care stress that has been used to guide policy decisions during the COVID-19 pandemic. Toward reliable decision-making as a pandemic progresses, estimating the rates at which patients are admitted to and discharged from hospitals and intensive care units (ICUs) is crucial. Since individual-level hospital data are rarely available to modelers in each geographic locality of interest, it is important to develop tools for inferring these rates from publicly available daily numbers of hospital and ICU beds occupied. We develop such an estimation approach based on an immigration-death process that models fluctuations of ICU occupancy. Our flexible framework allows for immigration and death rates to depend on covariates, such as hospital bed occupancy and daily SARS-CoV-2 test positivity rate, which may drive changes in hospital ICU operations. We demonstrate via simulation studies that the proposed method performs well on noisy time series data and apply our statistical framework to hospitalization data from the University of California, Irvine (UCI) Health and Orange County, California. By introducing a likelihood-based framework where immigration and death rates can vary with covariates, we find, through rigorous model selection, that hospitalization and positivity rates are crucial covariates for modeling ICU stay dynamics and validate our per-patient ICU stay estimates using anonymized patient-level UCI hospital data.

Dissecting the tumor microenvironment of epigenetically driven gliomas: Opportunities for single-cell and spatial multiomics.

Malignant gliomas are incurable brain neoplasms with dismal prognoses and near-universal fatality, with minimal therapeutic progress despite billions of dollars invested in research and clinical trials over the last 2 decades. Many glioma studies have utilized disparate histologic and genomic platforms to characterize the stunning genomic, transcriptomic, and immunologic heterogeneity found in gliomas. Single-cell and spatial omics technologies enable unprecedented characterization of heterogeneity in solid malignancies and provide a granular annotation of transcriptional, epigenetic, and microenvironmental states with limited resected tissue. Heterogeneity in gliomas may be defined, at the broadest levels, by tumors ostensibly driven by epigenetic alterations (IDH- and histone-mutant) versus non-epigenetic tumors (IDH-wild type). Epigenetically driven tumors are defined by remarkable transcriptional programs, immunologically distinct microenvironments, and incompletely understood topography (unique cellular neighborhoods and cell-cell interactions). Thus, these tumors are the ideal substrate for single-cell multiomic technologies to disentangle the complex intra-tumoral features, including differentiation trajectories, tumor-immune cell interactions, and chromatin dysregulation. The current review summarizes the applications of single-cell multiomics to existing datasets of epigenetically driven glioma. More importantly, we discuss future capabilities and applications of novel multiomic strategies to answer outstanding questions, enable the development of potent therapeutic strategies, and improve personalized diagnostics and treatment via digital pathology.