Transfer Learning Reveals Cancer-Associated Fibroblasts Are Associated with Epithelial-Mesenchymal Transition and Inflammation in Cancer Cells in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by an immunosuppressive tumor microenvironment enriched with cancer-associated fibroblasts (CAF). This study used a convergence approach to identify tumor cell and CAF interactions through the integration of single-cell data from human tumors with human organoid coculture experiments. Analysis of a comprehensive atlas of PDAC single-cell RNA sequencing data indicated that CAF density is associated with increased inflammation and epithelial-mesenchymal transition (EMT) in epithelial cells. Transfer learning using transcriptional data from patient-derived organoid and CAF cocultures provided in silico validation of CAF induction of inflammatory and EMT epithelial cell states. Further experimental validation in cocultures demonstrated integrin beta 1 (ITGB1) and vascular endothelial factor A (VEGFA) interactions with neuropilin-1 mediating CAF-epithelial cell cross-talk. Together, this study introduces transfer learning from human single-cell data to organoid coculture analyses for experimental validation of discoveries of cell-cell cross-talk and identifies fibroblast-mediated regulation of EMT and inflammation. SIGNIFICANCE: Adaptation of transfer learning to relate human single-cell RNA sequencing data to organoid-CAF cocultures facilitates discovery of human pancreatic cancer intercellular interactions and uncovers cross-talk between CAFs and tumor cells through VEGFA and ITGB1.

Genome-wide repeat landscapes in cancer and cell-free DNA.

Genetic changes in repetitive sequences are a hallmark of cancer and other diseases, but characterizing these has been challenging using standard sequencing approaches. We developed a de novo kmer finding approach, called ARTEMIS (Analysis of RepeaT EleMents in dISease), to identify repeat elements from whole-genome sequencing. Using this method, we analyzed 1.2 billion kmers in 2837 tissue and plasma samples from 1975 patients, including those with lung, breast, colorectal, ovarian, liver, gastric, head and neck, bladder, cervical, thyroid, or prostate cancer. We identified tumor-specific changes in these patients in 1280 repeat element types from the LINE, SINE, LTR, transposable element, and human satellite families. These included changes to known repeats and 820 elements that were not previously known to be altered in human cancer. Repeat elements were enriched in regions of driver genes, and their representation was altered by structural changes and epigenetic states. Machine learning analyses of genome-wide repeat landscapes and fragmentation profiles in cfDNA detected patients with early-stage lung or liver cancer in cross-validated and externally validated cohorts. In addition, these repeat landscapes could be used to noninvasively identify the tissue of origin of tumors. These analyses reveal widespread changes in repeat landscapes of human cancers and provide an approach for their detection and characterization that could benefit early detection and disease monitoring of patients with cancer.

Forward dynamics computational modelling of a cyclist fall with the inclusion of protective response using deep learning-based human pose estimation.

Single bicycle crashes, i.e., falls and impacts not involving a collision with another road user, are a significantly underestimated road safety problem. The motions and behaviours of falling people, or fall kinematics, are often investigated in the injury biomechanics research field. Understanding the mechanics of a fall can help researchers develop better protective gear and safety measures to reduce the risk of injury. However, little is known about cyclist fall kinematics or dynamics. Therefore, in this study, a video analysis of cyclist falls is performed to investigate common kinematic forms and impact patterns. Furthermore, a pipeline involving deep learning-based human pose estimation and inverse kinematics optimisation is created for extracting human motion from real-world footage of falls to initialise forward dynamics computational human body models. A bracing active response is then optimised for using a genetic algorithm. This is then applied to a case study of a cyclist fall. The kinematic forms characterised in this study can be used to inform initial conditions for computational modelling and injury estimation in cyclist falls. Findings indicate that protective response is an important consideration in fall kinematics and dynamics, and should be included in computational modelling. Furthermore, the novel reconstruction pipeline proposed here can be applied more broadly for traumatic injury biomechanics tasks. The tool developed in this study is available at https://kevgildea.github.io/KinePose/.

Age-associated Senescent - T Cell Signaling Promotes Type 3 Immunity that Inhibits the Biomaterial Regenerative Response.

Aging is associated with immunological changes that compromise response to infections and vaccines, exacerbate inflammatory diseases and can potentially mitigate tissue repair. Even so, age-related changes to the immune response to tissue damage and regenerative medicine therapies remain unknown. Here, it is characterized how aging induces changes in immunological signatures that inhibit tissue repair and therapeutic response to a clinical regenerative biological scaffold derived from extracellular matrix. Signatures of inflammation and interleukin (IL)-17 signaling increased with injury and treatment both locally and regionally in aged animals, and computational analysis uncovered age-associated senescent-T cell communication that promotes type 3 immunity in T cells. Local inhibition of type 3 immune activation using IL17-neutralizing antibodies improves healing and restores therapeutic response to the regenerative biomaterial, promoting muscle repair in older animals. These results provide insights into tissue immune dysregulation that occurs with aging that can be targeted to rejuvenate repair.

Integration of Patient-Derived Organoids and Organ-on-Chip Systems: Investigating Colorectal Cancer Invasion within the Mechanical and GABAergic Tumor Microenvironment.

Three-dimensional (3D) in vitro models are essential in cancer research, but they often neglect physical forces. In our study, we combined patient-derived tumor organoids with a microfluidic organ-on-chip system to investigate colorectal cancer (CRC) invasion in the tumor microenvironment (TME). This allowed us to create patient-specific tumor models and assess the impact of physical forces on cancer biology. Our findings showed that the organoid-on-chip models more closely resembled patient tumors at the transcriptional level, surpassing organoids alone. Using 'omics' methods and live-cell imaging, we observed heightened responsiveness of KRAS mutant tumors to TME mechanical forces. These tumors also utilized the γ-aminobutyric acid (GABA) neurotransmitter as an energy source, increasing their invasiveness. This bioengineered model holds promise for advancing our understanding of cancer progression and improving CRC treatments.

Lung tumor-infiltrating Treg have divergent transcriptional profiles and function linked to checkpoint blockade response.

Regulatory T cells (Treg) are conventionally viewed as suppressors of endogenous and therapy-induced antitumor immunity; however, their role in modulating responses to immune checkpoint blockade (ICB) is unclear. In this study, we integrated single-cell RNA-seq/T cell receptor sequencing (TCRseq) of >73,000 tumor-infiltrating Treg (TIL-Treg) from anti-PD-1-treated and treatment-naive non-small cell lung cancers (NSCLC) with single-cell analysis of tumor-associated antigen (TAA)-specific Treg derived from a murine tumor model. We identified 10 subsets of human TIL-Treg, most of which have high concordance with murine TIL-Treg subsets. Only one subset selectively expresses high levels of TNFRSF4 (OX40) and TNFRSF18 (GITR), whose engangement by cognate ligand mediated proliferative programs and NF-κB activation, as well as multiple genes involved in Treg suppression, including LAG3. Functionally, the OX40hiGITRhi subset is the most highly suppressive ex vivo, and its higher representation among total TIL-Treg correlated with resistance to PD-1 blockade. Unexpectedly, in the murine tumor model, we found that virtually all TIL-Treg-expressing T cell receptors that are specific for TAA fully develop a distinct TH1-like signature over a 2-week period after entry into the tumor, down-regulating FoxP3 and up-regulating expression of TBX21 (Tbet), IFNG, and certain proinflammatory granzymes. Transfer learning of a gene score from the murine TAA-specific TH1-like Treg subset to the human single-cell dataset revealed a highly analogous subcluster that was enriched in anti-PD-1-responding tumors. These findings demonstrate that TIL-Treg partition into multiple distinct transcriptionally defined subsets with potentially opposing effects on ICB-induced antitumor immunity and suggest that TAA-specific TIL-Treg may positively contribute to antitumor responses.

Distinct Myeloid Derived Suppressor Cell Populations Promote Tumor Aggression in Glioblastoma.

The diversity of genetic programs and cellular plasticity of glioma-associated myeloid cells, and thus their contribution to tumor growth and immune evasion, is poorly understood. We performed single cell RNA-sequencing of immune and tumor cells from 33 glioma patients of varying tumor grades. We identified two populations characteristic of myeloid derived suppressor cells (MDSC), unique to glioblastoma (GBM) and absent in grades II and III tumors: i) an early progenitor population (E-MDSC) characterized by strong upregulation of multiple catabolic, anabolic, oxidative stress, and hypoxia pathways typically observed within tumor cells themselves, and ii) a monocytic MDSC (M-MDSC) population. The E-MDSCs geographically co-localize with a subset of highly metabolic glioma stem-like tumor cells with a mesenchymal program in the pseudopalisading region, a pathognomonic feature of GBMs associated with poor prognosis. Ligand-receptor interaction analysis revealed symbiotic cross-talk between the stemlike tumor cells and E-MDSCs in GBM, whereby glioma stem cells produce chemokines attracting E-MDSCs, which in turn produce growth and survival factors for the tumor cells. Our large-scale single-cell analysis elucidated unique MDSC populations as key facilitators of GBM progression and mediators of tumor immunosuppression, suggesting that targeting these specific myeloid compartments, including their metabolic programs, may be a promising therapeutic intervention in this deadly cancer. One-Sentence Summary: Aggressive glioblastoma harbors two unique myeloid populations capable of promoting stem-like properties of tumor cells and suppressing T cell function in the tumor microenvironment.

Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies.

Cellular senescence is a state of permanent growth arrest that plays an important role in wound healing, tissue fibrosis, and tumor suppression. Despite senescent cells' (SnCs) pathological role and therapeutic interest, their phenotype in vivo remains poorly defined. Here, we developed an in vivo-derived senescence signature (SenSig) using a foreign body response-driven fibrosis model in a p16-CreERT2;Ai14 reporter mouse. We identified pericytes and "cartilage-like" fibroblasts as senescent and defined cell type-specific senescence-associated secretory phenotypes (SASPs). Transfer learning and senescence scoring identified these two SnC populations along with endothelial and epithelial SnCs in new and publicly available murine and human data single-cell RNA sequencing (scRNAseq) datasets from diverse pathologies. Signaling analysis uncovered crosstalk between SnCs and myeloid cells via an IL34-CSF1R-TGFßR signaling axis, contributing to tissue balance of vascularization and matrix production. Overall, our study provides a senescence signature and a computational approach that may be broadly applied to identify SnC transcriptional profiles and SASP factors in wound healing, aging, and other pathologies.

FOXM1 acts sexually dimorphically to regulate functional ß-cell mass.

The transcription factor FOXM1 regulates ß-cell proliferation and insulin secretion. Our previous work demonstrates that expressing an activated form of FOXM1 (FOXM1*) in ß cells increases ß-cell proliferation and mass in aged male mice. Additionally, FOXM1* enhances ß-cell function even in young mice, in which no ß-cell mass elevation occurs. Here, we demonstrate that FOXM1 acts in a sexually dimorphic manner in the ß cell. Expression of FOXM1* in female mouse ß cells does not affect ß-cell proliferation or glucose tolerance. Transduction of male but not female human islets with FOXM1* enhances insulin secretion in response to elevated glucose. Estrogen contributes to diabetes susceptibility differences between males and females, and the estrogen receptor (ER)α is the primary mediator of ß-cell estrogen signaling. We show that FOXM1* can rescue impaired glucose tolerance in female mice with a pancreas-wide ERα deletion. Further, FOXM1 and ERα binding sites overlap with each other and with other ß-cell-enriched transcription factors, including ISL1, PAX6, MAF, and GATA. These data indicate that FOMX1 and ERα cooperate to regulate ß-cell function and suggest a general mechanism contributing to the lower incidence of diabetes observed in women.

Persistent mutation burden drives sustained anti-tumor immune responses.

Tumor mutation burden is an imperfect proxy of tumor foreignness and has therefore failed to consistently demonstrate clinical utility in predicting responses in the context of immunotherapy. We evaluated mutations in regions of the genome that are unlikely to undergo loss in a pan-cancer analysis across 31 tumor types (n = 9,242) and eight immunotherapy-treated cohorts of patients with non-small-cell lung cancer, melanoma, mesothelioma, and head and neck cancer (n = 524). We discovered that mutations in single-copy regions and those present in multiple copies per cell constitute a persistent tumor mutation burden (pTMB) which is linked with therapeutic response to immune checkpoint blockade. Persistent mutations were retained in the context of tumor evolution under selective pressure of immunotherapy and tumors with a high pTMB content were characterized by a more inflamed tumor microenvironment. pTMB imposes an evolutionary bottleneck that cancer cells cannot overcome and may thus drive sustained immunologic tumor control in the context of immunotherapy.

Genomic Landscapes and Hallmarks of Mutant RAS in Human Cancers.

The RAS family of small GTPases represents the most commonly activated oncogenes in human cancers. To better understand the prevalence of somatic RAS mutations and the compendium of genes that are coaltered in RAS-mutant tumors, we analyzed targeted next-generation sequencing data of 607,863 mutations from 66,372 tumors in 51 cancer types in the AACR Project GENIE Registry. Bayesian hierarchical models were implemented to estimate the cancer-specific prevalence of RAS and non-RAS somatic mutations, to evaluate co-occurrence and mutual exclusivity, and to model the effects of tumor mutation burden and mutational signatures on comutation patterns. These analyses revealed differential RAS prevalence and comutations with non-RAS genes in a cancer lineage-dependent and context-dependent manner, with differences across age, sex, and ethnic groups. Allele-specific RAS co-mutational patterns included an enrichment in NTRK3 and chromatin-regulating gene mutations in KRAS G12C-mutant non-small cell lung cancer. Integrated multiomic analyses of 10,217 tumors from The Cancer Genome Atlas (TCGA) revealed distinct genotype-driven gene expression programs pointing to differential recruitment of cancer hallmarks as well as phenotypic differences and immune surveillance states in the tumor microenvironment of RAS-mutant tumors. The distinct genomic tracks discovered in RAS-mutant tumors reflected differential clinical outcomes in TCGA cohort and in an independent cohort of patients with KRAS G12C-mutant non-small cell lung cancer that received immunotherapy-containing regimens. The RAS genetic architecture points to cancer lineage-specific therapeutic vulnerabilities that can be leveraged for rationally combining RAS-mutant allele-directed therapies with targeted therapies and immunotherapy. SIGNIFICANCE: The complex genomic landscape of RAS-mutant tumors is reflective of selection processes in a cancer lineage-specific and context-dependent manner, highlighting differential therapeutic vulnerabilities that can be clinically translated.

Peripheral blood immune cell dynamics reflect antitumor immune responses and predict clinical response to immunotherapy.

BACKGROUND: Despite treatment advancements with immunotherapy, our understanding of response relies on tissue-based, static tumor features such as tumor mutation burden (TMB) and programmed death-ligand 1 (PD-L1) expression. These approaches are limited in capturing the plasticity of tumor-immune system interactions under selective pressure of immune checkpoint blockade and predicting therapeutic response and long-term outcomes. Here, we investigate the relationship between serial assessment of peripheral blood cell counts and tumor burden dynamics in the context of an evolving tumor ecosystem during immune checkpoint blockade. METHODS: Using machine learning, we integrated dynamics in peripheral blood immune cell subsets, including neutrophil-lymphocyte ratio (NLR), from 239 patients with metastatic non-small cell lung cancer (NSCLC) and predicted clinical outcome with immune checkpoint blockade. We then sought to interpret NLR dynamics in the context of transcriptomic and T cell repertoire trajectories for 26 patients with early stage NSCLC who received neoadjuvant immune checkpoint blockade. We further determined the relationship between NLR dynamics, pathologic response and circulating tumor DNA (ctDNA) clearance. RESULTS: Integrated dynamics of peripheral blood cell counts, predominantly NLR dynamics and changes in eosinophil levels, predicted clinical outcome, outperforming both TMB and PD-L1 expression. As early changes in NLR were a key predictor of response, we linked NLR dynamics with serial RNA sequencing deconvolution and T cell receptor sequencing to investigate differential tumor microenvironment reshaping during therapy for patients with reduction in peripheral NLR. Reductions in NLR were associated with induction of interferon-γ responses driving the expression of antigen presentation and proinflammatory gene sets coupled with reshaping of the intratumoral T cell repertoire. In addition, NLR dynamics reflected tumor regression assessed by pathological responses and complemented ctDNA kinetics in predicting long-term outcome. Elevated peripheral eosinophil levels during immune checkpoint blockade were correlated with therapeutic response in both metastatic and early stage cohorts. CONCLUSIONS: Our findings suggest that early dynamics in peripheral blood immune cell subsets reflect changes in the tumor microenvironment and capture antitumor immune responses, ultimately reflecting clinical outcomes with immune checkpoint blockade.

Durvalumab with platinum-pemetrexed for unresectable pleural mesothelioma: survival, genomic and immunologic analyses from the phase 2 PrE0505 trial.

Mesothelioma is a rare and fatal cancer with limited therapeutic options until the recent approval of combination immune checkpoint blockade. Here we report the results of the phase 2 PrE0505 trial ( NCT02899195 ) of the anti-PD-L1 antibody durvalumab plus platinum-pemetrexed chemotherapy for 55 patients with previously untreated, unresectable pleural mesothelioma. The primary endpoint was overall survival compared to historical control with cisplatin and pemetrexed chemotherapy; secondary and exploratory endpoints included safety, progression-free survival and biomarkers of response. The combination of durvalumab with chemotherapy met the pre-specified primary endpoint, reaching a median survival of 20.4 months versus 12.1 months with historical control. Treatment-emergent adverse events were consistent with known side effects of chemotherapy, and all adverse events due to immunotherapy were grade 2 or lower. Integrated genomic and immune cell repertoire analyses revealed that a higher immunogenic mutation burden coupled with a more diverse T cell repertoire was linked to favorable clinical outcome. Structural genome-wide analyses showed a higher degree of genomic instability in responding tumors of epithelioid histology. Patients with germline alterations in cancer predisposing genes, especially those involved in DNA repair, were more likely to achieve long-term survival. Our findings indicate that concurrent durvalumab with platinum-based chemotherapy has promising clinical activity and that responses are driven by the complex genomic background of malignant pleural mesothelioma.

Computational reconstruction of the signalling networks surrounding implanted biomaterials from single-cell transcriptomics.

The understanding of the foreign-body responses to implanted biomaterials would benefit from the reconstruction of intracellular and intercellular signalling networks in the microenvironment surrounding the implant. Here, by leveraging single-cell RNA-sequencing data from 42,156 cells collected from the site of implantation of either polycaprolactone or an extracellular-matrix-derived scaffold in a mouse model of volumetric muscle loss, we report a computational analysis of intercellular signalling networks reconstructed from predictions of transcription-factor activation. We found that intercellular signalling networks can be clustered into modules associated with specific cell subsets, and that biomaterial-specific responses can be characterized by interactions between signalling modules for immune, fibroblast and tissue-specific cells. In a Il17ra-/- mouse model, we validated that predicted interleukin-17-linked transcriptional targets led to concomitant changes in gene expression. Moreover, we identified cell subsets that had not been implicated in the responses to implanted biomaterials. Single-cell atlases of the cellular responses to implanted biomaterials will facilitate the design of implantable biomaterials and the understanding of the ensuing cellular responses.

Comparison of motor vehicle-involved e-scooter and bicycle crashes using standardized crash typology.

INTRODUCTION: The market share of e-scooters in the United States has proliferated in cities: 86 million trips were made on shared e-scooters in 2019, a more than 100% increase compared to 2018. However, the interaction of e-scooters with other road users and infrastructure remains uncertain. METHOD: This study scrutinized 52 e-scooter and 79 bicycle police-reported crashes in Nashville, Tennessee, from April 2018 to April 2020 from the Tennessee Integrated Traffic Analysis Network (TITAN) database. We used descriptive analysis and a recent prototype version of the Pedestrian and Bicycle Crash Analysis Tool (PBCAT) to classify crashes based on the locations of the crashes relative to roadway segments or intersections, as well as the maneuver of the motor vehicle and e-scooter/bicycle relative to the motor vehicle. RESULTS: Two crash typologies can explain the majority of e-scooter crashes, while bicycle crashes are distributed over several crash typologies. Additionally, 1 in 10 e-scooter- and bicycle-motor vehicle crashes leads to the injury or fatality of the e-scooter rider or bicyclist. Furthermore, we noted statistically significant differences in spatial and temporal distribution, demographics, lighting conditions, and crash distance from home for e-scooter and bicycle crashes. CONCLUSIONS: The police crash report provides a comprehensive picture of e-scooter safety complementing existing literature. We found that e-scooter crash characteristics do not fully overlap with features of bicycle crashes. PRACTICAL IMPLICATIONS: A generalized engineering, education, and enforcement treatment to reduce and prevent e-scooter and bicycle crashes, injuries, and fatalities might not result in equal outcomes for each mode. More rigorous enforcement could be implemented to deter e-scooters riders under the age of 18 years and e-scooter safety campaigns could target female riders.

Not gendered but different from each other? A structural equation model for explaining risky road behaviors of female and male pedestrians.

As alternative transportation is getting more and more fashionable, and more people worldwide are "shifting" to walking trips, even for their daily commuting, traffic crashes suffered by pedestrians are still a great concern for road safety and public health researchers and practitioners. In this regard, risky or "aberrant" road behaviors have emerged, during the last few years, as a key issue to be considered for crash prevention. Nevertheless, the idea of a "generic pedestrian" is getting re-evaluated, and analyzing key features, such as gender, seems to be crucial for understanding pedestrians' performance and safety outcomes. OBJECTIVE: The objective of this study was to examine the effect of gender on pedestrians' both deliberate (traffic violations) and undeliberate (errors) risky walking behaviors, considering a set of theoretically based demographic and psychosocial variables as their potential predictors. METHOD: For this cross-sectional study, data from 1070 Spanish pedestrians (60 % females and 40 % males, aged between 16 and 79) from the 17 regions of Spain, responding to an electronic questionnaire, were analyzed through a multi-group structural equation modeling (MGSEM) approach. RESULTS: Although age, handheld device-interaction, and sensation-seeking seem to have a similar effect on the errors and violations reported by both genders (similarities), factors such as risk perception, educational level and the misbehaviors observed in other road users are significant predictors only in the case of male pedestrians. On the other hand, road distractions have been shown to play a significant role in females' errors and violations, while males' road distractions seem to only affect their involuntary risky behaviors. CONCLUSION: The findings of this study support the influence of gender in the statistical explanation of both deliberate and undeliberate walking risky road behaviors, also depicting the differential role of certain demographic and psychosocial factors when we compare male and female pedestrians.

A home-based approach to understanding seatbelt use in single-occupant vehicles in Tennessee: Application of a latent class binary logit model.

Although the enforcement of seatbelt use is considered to be an effective strategy in reducing road injuries and fatalities, lack of seatbelt use still accounts for a substantial proportion of fatal crashes in Tennessee, United States. This problem has raised the need to better understand factors influencing seatbelt use. These factors may arise from spatial/temporal characteristics of a driving location, type of vehicle, demographic and socioeconomic attributes of the vehicle occupants, driver behaviours, attitudes, and social norms. However, the above factors may not have the same effects on seatbelt use across different individuals. In addition, the behavioural factors are usually difficult to measure and may not always be readily available. Meanwhile, residential locations of vehicle occupants have been shown to be associated with their behavioural patterns and thus may serve as a proxy for behavioural factors. However, the suitability of geographic and residential locations of vehicle occupants to understand the seatbelt use behaviour is not known to date. This study aims to fill the above gaps by incorporating the residential location characteristics of vehicle occupants in addition to their demographics and crash characteristics into their seatbelt use while accounting for the varying effects of these factors on individual seatbelt use choices. To achieve this goal, empirical data are collected for vehicular crashes in Tennessee, United States, and the home addresses of vehicle occupants at the time of the crash are geocoded and linked with the census tract information. The resulting data is then used as explanatory variables in a latent class binary logit model to investigate the determinants of vehicle occupants' seatbelt use at the time of the crash. The latent class specification is employed to capture the unobserved heterogeneity in data. Results show that Tennessean drivers belong to two general categories-conformist and eccentric-with gender, vehicle type, and income per capita determining the likelihood of these categories. Overall, male drivers, younger drivers, and drivers who have consumed drugs are less likely to wear a seatbelt, whereas drivers who come from areas with higher population density, travel time, and income per capita are more likely to wear a seatbelt. In addition, driving during the day and in rainy weather are associated with an increased likelihood of seatbelt use. The findings of this study will help developing effective policies to increase seatbelt use rate and improve safety.

Interleukin-36γ-producing macrophages drive IL-17-mediated fibrosis.

Biomaterials induce an immune response and mobilization of macrophages, yet identification and phenotypic characterization of functional macrophage subsets in vivo remain limited. We performed single-cell RNA sequencing analysis on macrophages sorted from either a biologic matrix [urinary bladder matrix (UBM)] or synthetic biomaterial [polycaprolactone (PCL)]. Implantation of UBM promotes tissue repair through generation of a tissue environment characterized by a T helper 2 (TH2)/interleukin (IL)-4 immune profile, whereas PCL induces a standard foreign body response characterized by TH17/IL-17 and fibrosis. Unbiased clustering and pseudotime analysis revealed distinct macrophage subsets responsible for antigen presentation, chemoattraction, and phagocytosis, as well as a small population with expression profiles of both dendritic cells and skeletal muscle after UBM implantation. In the PCL tissue environment, we identified a CD9hi+IL-36γ+ macrophage subset that expressed TH17-associated molecules. These macrophages were virtually absent in mice lacking the IL-17 receptor, suggesting that they might be involved in IL-17-dependent immune and autoimmune responses. Identification and comparison of the unique phenotypical and functional macrophage subsets in mouse and human tissue samples suggest broad relevance of the new classification. These distinct macrophage subsets demonstrate previously unrecognized myeloid phenotypes involved in different tissue responses and provide targets for potential therapeutic modulation in tissue repair and pathology.

A geographically weighted regression to estimate the comprehensive cost of traffic crashes at a zonal level.

Global road safety records demonstrate spatial variation of comprehensive cost of traffic crashes across countries. To the best of our knowledge, no study has explored the variation of this matter at a local geographical level. This study proposes a method to estimate the comprehensive crash cost at the zonal level by using person-injury cost. The current metric of road safety attributes safety to the location of the crash, which makes it challenging to assign the crash cost to home-location of the individuals who were involved in traffic crashes. To overcome this limitation, we defined Home-Based Approach crash frequency as the expected number of crashes by severity that road users who live in a certain geographic area have during a specified period. Using crash data from Tennessee, we assign those involved in traffic crashes to the census tract corresponding to their home address. The average Comprehensive Crash Cost at the Zonal Level (CCCAZ) for the period of the study was $18.2 million (2018 dollars). Poisson and Geographically Weighted Poisson Regression (GWPR) models were used to analyzing the data. The GWPR model was more suitable compared to the global model to address spatial heterogeneity. Findings indicate population of people over 60-years-old, the proportion of residents that use non-motorized transportation, household income, population density, household size, and metropolitan indicator have a negative association with CCCAZ. Alternatively, VMT, vehicle per capita, percent educated over 25-year-old, population under 16-year-old, and proportion of non-white races and individuals who use a motorcycle as their commute mode have a positive association with CCCAZ. Findings are discussed in line with road safety literature.