A foundational transformer leveraging full night, multichannel sleep study data accurately classifies sleep stages.

Study Objectives: To investigate whether a foundational transformer model using 8-hour, multi-channel data from polysomnograms can outperform existing artificial intelligence (AI) methods for sleep stage classification. Methods: We utilized the Sleep Heart Health Study (SHHS) visits 1 and 2 for training and validation and the Multi-Ethnic Study of Atherosclerosis (MESA) for testing of our model. We trained a self-supervised foundational transformer (called PFTSleep) that encodes 8-hour long sleep studies at 125 Hz with 7 signals including brain, movement, cardiac, oxygen, and respiratory channels. These encodings are used as input for training of an additional model to classify sleep stages, without adjusting the weights of the foundational transformer. We compared our results to existing AI methods that did not utilize 8-hour data or the full set of signals but did report evaluation metrics for the SHHS dataset. Results: We trained and validated a model with 8,444 sleep studies with 7 signals including brain, movement, cardiac, oxygen, and respiratory channels and tested on an additional 2,055 studies. In total, we trained and tested 587,944 hours of sleep study signal data. Area under the precision recall curve (AUPRC) scores were 0.82, 0.40, 0.53, 0.75, and 0.82 and area under the receiving operating characteristics curve (AUROC) scores were 0.99, 0.95, 0.96, 0.98, and 0.99 for wake, N1, N2, N3, and REM, respectively, on the SHHS validation set. For MESA, the AUPRC scores were 0.56, 0.16, 0.40, 0.45, and 0.65 and AUROC scores were 0.94, 0.77, 0.87, 0.91, and 0.96, respectively. Our model was compared to the longest context window state-of-the-art model and showed increases in macro evaluation scores, notably sensitivity (3.7% increase) and multi-class REM (3.39% increase) and wake (0.97% increase) F1 scores. Conclusions: Utilizing full night, multi-channel PSG data encodings derived from a foundational transformer improve sleep stage classification over existing methods.

Exposure-Response and Subgroup Analyses to Support Body Weight-Based Dosing of Brentuximab Vedotin in Children and Young Adults with Newly Diagnosed High-risk Classical Hodgkin Lymphoma.

PURPOSE: The purpose of the study was to evaluate the relationships between brentuximab vedotin (BV) pharmacokinetics, age, and body weight (BW) with efficacy and safety in pediatric and young adult patients with previously untreated, high-risk classical Hodgkin lymphoma in the phase III AHOD1331 study. EXPERIMENTAL DESIGN: Overall, 296 patients (age 2-21 years) in the overall population were randomized to and received BV + chemotherapy; the pharmacokinetic subpopulation comprised 24 patients (age <13 years). Age- and/or BW-based (pharmacokinetic surrogates) subgroup analyses of efficacy and safety were conducted for the overall population. Exposure-response analyses were limited to the pharmacokinetic subpopulation. RESULTS: There were no visible trends in disease characteristics across pediatric age subgroups, whereas BW increased with age. Observed antibody-drug conjugate exposures in patients ages <12 years were lower than those in adults administered BV 1.8 mg/kg every 3 weeks, as exposure increased with BW. Nevertheless, no detrimental impact on event-free survival was seen in younger subgroups: 3-year event-free survival rates were 96.2% (2-<12 years) and 92.0% (12-<18 years), with no events observed in those ages <6 years. Neither early response nor lack of need for radiation therapy was associated with high pharmacokinetic exposure. No evidence of exposure-driven grade ≥2 or ≥3 peripheral neuropathy or grade ≥3 neutropenia was seen in exposure-safety and BW-based subgroup analyses; the incidence of these safety events was comparable across pediatric age subgroups, despite lower exposure in younger children. CONCLUSIONS: No further adjustments based on age or BW are required for the BV dosage (1.8 mg/kg every 3 weeks) approved in children.

Evaluation of patient health outcomes of a student-run free clinic in East Harlem.

BACKGROUND: Most United States medical schools have affiliated student-run free clinics, but the quality of services provided in such contexts compared to national metrics is unknown. This study determines whether a student-run, attending-supervised free clinic servicing a low-income and minority race patient population in New York City can meet national metrics of care. METHODS: Through chart review from January 1, 2020 to December 31, 2020, patient outcomes and service utilization in the Healthcare Effectiveness Data and Information Set were examined and compared to national rates of patients using Medicaid HMO or Medicare. Patients are ≥ 21 years of age, residents of East Harlem, and ineligible for health insurance because of legal residency requirements. The majority identify as Hispanic and speak Spanish as their primary language. All patients who were seen in the clinic during the 2020 calendar year were included. The primary study outcome is the number of Healthcare Effectiveness Data and Information Set measures in which patients, seen in a student-run free clinic, meet or exceed national comparisons. RESULTS: The healthcare outcomes of 238 patients, mean age 47.8 years and 54.6% female, were examined in 18 Healthcare Effectiveness Data and Information Set measures. The student-run free clinic met or exceeded national metrics in 16 out of 18 categories. CONCLUSIONS: The student-run free clinic met or exceeded the national standard of care according to national metrics. Evidence-based priorities have been clarified for future improvement. Other student-run free clinics should similarly evaluate the quality of their services.

Peripheral Transcriptomics in Acute and Long-Term Kidney Dysfunction in SARS-CoV2 Infection.

Background: Acute kidney injury (AKI) is common in hospitalized patients with SARS-CoV2 infection despite vaccination and leads to long-term kidney dysfunction. However, peripheral blood molecular signatures in AKI from COVID-19 and their association with long-term kidney dysfunction are yet unexplored. Methods: In patients hospitalized with SARS-CoV2, we performed bulk RNA sequencing using peripheral blood mononuclear cells(PBMCs). We applied linear models accounting for technical and biological variability on RNA-Seq data accounting for false discovery rate (FDR) and compared functional enrichment and pathway results to a historical sepsis-AKI cohort. Finally, we evaluated the association of these signatures with long-term trends in kidney function. Results: Of 283 patients, 106 had AKI. After adjustment for sex, age, mechanical ventilation, and chronic kidney disease (CKD), we identified 2635 significant differential gene expressions at FDR<0.05. Top canonical pathways were EIF2 signaling, oxidative phosphorylation, mTOR signaling, and Th17 signaling, indicating mitochondrial dysfunction and endoplasmic reticulum (ER) stress. Comparison with sepsis associated AKI showed considerable overlap of key pathways (48.14%). Using follow-up estimated glomerular filtration rate (eGFR) measurements from 115 patients, we identified 164/2635 (6.2%) of the significantly differentiated genes associated with overall decrease in long-term kidney function. The strongest associations were 'autophagy', 'renal impairment via fibrosis', and 'cardiac structure and function'. Conclusions: We show that AKI in SARS-CoV2 is a multifactorial process with mitochondrial dysfunction driven by ER stress whereas long-term kidney function decline is associated with cardiac structure and function and immune dysregulation. Functional overlap with sepsis-AKI also highlights common signatures, indicating generalizability in therapeutic approaches. SIGNIFICANCE STATEMENT: Peripheral transcriptomic findings in acute and long-term kidney dysfunction after hospitalization for SARS-CoV2 infection are unclear. We evaluated peripheral blood molecular signatures in AKI from COVID-19 (COVID-AKI) and their association with long-term kidney dysfunction using the largest hospitalized cohort with transcriptomic data. Analysis of 283 hospitalized patients of whom 37% had AKI, highlighted the contribution of mitochondrial dysfunction driven by endoplasmic reticulum stress in the acute stages. Subsequently, long-term kidney function decline exhibits significant associations with markers of cardiac structure and function and immune mediated dysregulation. There were similar biomolecular signatures in other inflammatory states, such as sepsis. This enhances the potential for repurposing and generalizability in therapeutic approaches.

Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study.

BACKGROUND: Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models. OBJECTIVE: To estimate changes in predictive model performance with use through 3 common scenarios: model retraining, sequentially implementing 1 model after another, and intervening in response to a model when 2 are simultaneously implemented. DESIGN: Simulation of model implementation and use in critical care settings at various levels of intervention effectiveness and clinician adherence. Models were either trained or retrained after simulated implementation. SETTING: Admissions to the intensive care unit (ICU) at Mount Sinai Health System (New York, New York) and Beth Israel Deaconess Medical Center (Boston, Massachusetts). PATIENTS: 130 000 critical care admissions across both health systems. INTERVENTION: Across 3 scenarios, interventions were simulated at varying levels of clinician adherence and effectiveness. MEASUREMENTS: Statistical measures of performance, including threshold-independent (area under the curve) and threshold-dependent measures. RESULTS: At fixed 90% sensitivity, in scenario 1 a mortality prediction model lost 9% to 39% specificity after retraining once and in scenario 2 a mortality prediction model lost 8% to 15% specificity when created after the implementation of an acute kidney injury (AKI) prediction model; in scenario 3, models for AKI and mortality prediction implemented simultaneously, each led to reduced effective accuracy of the other by 1% to 28%. LIMITATIONS: In real-world practice, the effectiveness of and adherence to model-based recommendations are rarely known in advance. Only binary classifiers for tabular ICU admissions data were simulated. CONCLUSION: In simulated ICU settings, a universally effective model-updating approach for maintaining model performance does not seem to exist. Model use may have to be recorded to maintain viability of predictive modeling. PRIMARY FUNDING SOURCE: National Center for Advancing Translational Sciences.

How Are We Doing? A Scoping Review of Published Patient-Centered Outcomes Research in United States Student-Run Free Clinics.

Phenomenon: Student-run free clinics (SRFCs) serve an integral role in most United States (US) medical schools and contribute substantially to literature on the quality of care to uninsured persons. There has been substantial growth over the past decade of scholarly work produced by SRFCs as they have increased in size and number. Research on patient care outcomes informs better care structures for patients, however there is no current synthesis of patient care outcomes research among SRFCs. This article provides an overview of SRFC research on patient outcomes to understand current research domains and to identify gaps in the literature. Approach: We completed a scoping review by searching Scopus, PubMed, and Journal of Student Run Clinics in June 2021. All peer-reviewed, English-language articles focused on patient-centered outcomes at SRFCs in the US were included. Two independent reviewers performed title, abstract, and full-text screening of relevant works, and eight reviewers conducted data extraction. Descriptive data analysis was performed along with relevant content analysis of patient-centered outcomes. Findings: The search strategy identified 784 studies, of which 87 met inclusion criteria. Most studies were published within the last six years (81.6%), located in California, New York, or Florida (43.7%), and intervention based (33.3%). Many studies (46.0%) had a specific disease of focus of which diabetes was the most researched(19.5%). Patient-centered studies were the leading focus of the study aims (40.2%), where key findings demonstrated primarily improved outcomes in clinic metrics post-intervention (36.8%) or equivalent/better clinical performance than national metrics (20.7%). Insights: This review brings to light gaps in the literature reporting research in SRFCs and can be applied to other low-resource settings. Future efforts to expand SRFC outcomes research should focus on community relationship building, understanding institutional support, and ensuring education on best practices for research within SRFCs. Doing so informs patient care improvement as SRFCs continue to operate as safety net clinics for marginalized populations.

A foundational vision transformer improves diagnostic performance for electrocardiograms.

The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches for biomedical problems may result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create a vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We pre-trained this model on 8.5 million ECGs and then compared performance vs. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction and ST elevation myocardial infarction using differing training sample sizes and independent validation datasets. We find that HeartBEiT has significantly higher performance at lower sample sizes compared to other models. We also find that HeartBEiT improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. standard CNNs. Domain specific pre-trained transformer models may exceed the classification performance of models trained on natural images especially in very low data regimes. The combination of the architecture and such pre-training allows for more accurate, granular explainability of model predictions.

Impact of Vaccination on Major Adverse Cardiovascular Events in Patients With COVID-19 Infection.

Taken from the largest U.S. cohort of patients with SARS-CoV2, our results demonstrate the association of even partial vaccination with lower risk of MACE after SARS-CoV-2 infection.

Shc Is Implicated in Calreticulin-Mediated Sterile Inflammation in Alcoholic Hepatitis.

BACKGROUND & AIMS: Src homology and collagen (Shc) proteins are major adapters to extracellular signals, however, the regulatory role of Shc isoforms in sterile inflammatory responses in alcoholic hepatitis (AH) has not been fully investigated. We hypothesized that in an isoform-specific manner Shc modulates pre-apoptotic signals, calreticulin (CRT) membrane exposure, and recruitment of inflammatory cells. METHODS: Liver biopsy samples from patients with AH vs healthy subjects were studied for Shc expression using DNA microarray data and immunohistochemistry. Shc knockdown (hypomorph) and age-matched wild-type mice were pair-fed according to the chronic-plus-binge alcohol diet. To analyze hepatocyte-specific effects, adeno-associated virus 8-thyroxine binding globulin-Cre (hepatocyte-specific Shc knockout)-mediated deletion was performed in flox/flox Shc mice. Lipid peroxidation, proinflammatory signals, redox radicals, reduced nicotinamide adenine dinucleotide/oxidized nicotinamide adenine dinucleotide ratio, as well as cleaved caspase 8, B-cell-receptor-associated protein 31 (BAP31), Bcl-2-associated X protein (Bax), and Bcl-2 homologous antagonist killer (Bak), were assessed in vivo. CRT translocation was studied in ethanol-exposed p46ShcẟSH2-transfected hepatocytes by membrane biotinylation in conjunction with phosphorylated-eukaryotic initiation factor 2 alpha, BAP31, caspase 8, and Bax/Bak. The effects of idebenone, a novel Shc inhibitor, was studied in alcohol/pair-fed mice. RESULTS: Shc was significantly induced in patients with AH (P < .01). Alanine aminotransferase, reduced nicotinamide adenine dinucleotide/oxidized nicotinamide adenine dinucleotide ratios, production of redox radicals, and lipid peroxidation improved (P < .05), and interleukin 1ß, monocyte chemoattractant protein 1, and C-X-C chemokine ligand 10 were reduced in Shc knockdown and hepatocyte-specific Shc knockout mice. In vivo, Shc-dependent induction, and, in hepatocytes, a p46Shc-dependent increase in pre-apoptotic proteins Bax/Bak, caspase 8, BAP31 cleavage, and membrane translocation of CRT/endoplasmic reticulum-resident protein 57 were seen. Idebenone protected against alcohol-mediated liver injury. CONCLUSIONS: Alcohol induces p46Shc-dependent activation of pre-apoptotic pathways and translocation of CRT to the membrane, where it acts as a damage-associated molecular pattern, instigating immunogenicity. Shc inhibition could be a novel treatment strategy in AH.

Automated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis.

BACKGROUND AND OBJECTIVES: Left ventricular ejection fraction is disrupted in patients on maintenance hemodialysis and can be estimated using deep learning models on electrocardiograms. Smaller sample sizes within this population may be mitigated using transfer learning. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We identified patients on hemodialysis with transthoracic echocardiograms within 7 days of electrocardiogram using diagnostic/procedure codes. We developed four models: (1) trained from scratch in patients on hemodialysis, (2) pretrained on a publicly available set of natural images (ImageNet), (3) pretrained on all patients not on hemodialysis, and (4) pretrained on patients not on hemodialysis and fine-tuned on patients on hemodialysis. We assessed the ability of the models to classify left ventricular ejection fraction into clinically relevant categories of ≤40%, 41% to ≤50%, and >50%. We compared performance by area under the receiver operating characteristic curve. RESULTS: We extracted 705,075 electrocardiogram:echocardiogram pairs for 158,840 patients not on hemodialysis used for development of models 3 and 4 and n=18,626 electrocardiogram:echocardiogram pairs for 2168 patients on hemodialysis for models 1, 2, and 4. The transfer learning model achieved area under the receiver operating characteristic curves of 0.86, 0.63, and 0.83 in predicting left ventricular ejection fraction categories of ≤40% (n=461), 41%-50% (n=398), and >50% (n=1309), respectively. For the same tasks, model 1 achieved area under the receiver operating characteristic curves of 0.74, 0.55, and 0.71, respectively; model 2 achieved area under the receiver operating characteristic curves of 0.71, 0.55, and 0.69, respectively, and model 3 achieved area under the receiver operating characteristic curves of 0.80, 0.51, and 0.77, respectively. We found that predictions of left ventricular ejection fraction by the transfer learning model were associated with mortality in a Cox regression with an adjusted hazard ratio of 1.29 (95% confidence interval, 1.04 to 1.59). CONCLUSION: A deep learning model can determine left ventricular ejection fraction for patients on hemodialysis following pretraining on electrocardiograms of patients not on hemodialysis. Predictions of low ejection fraction from this model were associated with mortality over a 5-year follow-up period. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_06_06_CJN16481221.mp3.

The promise of artificial intelligence for kidney pathophysiology.

PURPOSE OF REVIEW: We seek to determine recent advances in kidney pathophysiology that have been enabled or enhanced by artificial intelligence. We describe some of the challenges in the field as well as future directions. RECENT FINDINGS: We first provide an overview of artificial intelligence terminologies and methodologies. We then describe the use of artificial intelligence in kidney diseases to discover risk factors from clinical data for disease progression, annotate whole slide imaging and decipher multiomics data. We delineate key examples of risk stratification and prognostication in acute kidney injury (AKI) and chronic kidney disease (CKD). We contextualize these applications in kidney disease oncology, one of the subfields to benefit demonstrably from artificial intelligence using all if these approaches. We conclude by elucidating technical challenges and ethical considerations and briefly considering future directions. SUMMARY: The integration of clinical data, patient derived data, histology and proteomics and genomics can enhance the work of clinicians in providing more accurate diagnoses and elevating understanding of disease progression. Implementation research needs to be performed to translate these algorithms to the clinical setting.

A Three-Gene Signature Predicts Response to Selinexor in Multiple Myeloma.

PURPOSE: Selinexor is the first selective inhibitor of nuclear export to be approved for the treatment of relapsed or refractory multiple myeloma (MM). Currently, there are no known genomic biomarkers or assays to help select MM patients at higher likelihood of response to selinexor. Here, we aimed to characterize the transcriptomic correlates of response to selinexor-based therapy. METHODS: We performed RNA sequencing on CD138+ cells from the bone marrow of 100 patients with MM who participated in the BOSTON study, followed by differential gene expression and pathway analysis. Using the differentially expressed genes, we used cox proportional hazard models to identify a gene signature predictive of response to selinexor, followed by validation in external cohorts. RESULTS: The three-gene signature predicts response to selinexor-based therapy in patients with MM in the BOSTON cohort. Then, we validated this gene signature in 64 patients from the STORM cohort of triple-class refractory MM and additionally in an external cohort of 35 patients treated in a real-world setting outside of clinical trials. We found that the signature tracks with both depth and duration of response, and it also validates in a different tumor type using a cohort of pretreatment tumors from patients with recurrent glioblastoma. Furthermore, the genes involved in the signature, WNT10A, DUSP1, and ETV7, reveal a potential mechanism through upregulated interferon-mediated apoptotic signaling that may prime tumors to respond to selinexor-based therapy. CONCLUSION: In this study, we present a present a novel, three-gene expression signature that predicts selinexor response in MM. This signature has important clinical relevance as it could identify patients with cancer who are most likely to benefit from treatment with selinexor-based therapy.

The Association Between Moderate and Serious Mental Health Distress and General Health Services Utilization Among Chinese, Filipino, Japanese, Korean, and Vietnamese Adults in California.

INTRODUCTION: A growing body of literature has indicated that disaggregated analyses using distinct Asian subgroups allow for identification of varying mental health challenges and health services utilization. In this study, we examined the associations between distress and health services utilization among five Asian subgroups: Chinese, Korean, Japanese, Filipino, and Vietnamese adults in California. MATERIALS AND METHODS: Using a combined dataset using the 2011-2018 cross-sectional cycles of the California Health Interview survey, we assessed moderate and serious distress and four health services utilization indicators in a set of disaggregated analyses among adults 18 years of age and older in five Asian subgroups. We performed bivariate and multivariable analyses. RESULTS: The prevalence of and associations between moderate and serious distress and gaps in health services utilization varied among each Asian subgroup. Koreans had the highest prevalence of moderate and serious distress and the most gaps in health services utilization. Compared to those without moderate distress (p < .05), Japanese adults were more likely to delay care. Compared to those without serious distress (p < .05), Chinese adults who experienced serious distress were more likely to delay both medications and care, whereas Filipino and Vietnamese adults were more likely to delay medications. DISCUSSION: Disaggregating health data elucidates the impact of mental distress on healthcare-seeking behaviors among specific Asian subgroups. Identifying these influences can facilitate future tailored interventions, yet fully understanding the mechanism linking mental distress and healthcare usage will necessitate a comprehensive assessment of structural influences and Asian American experiences without otherization.

Shc inhibitor idebenone ameliorates liver injury and fibrosis in dietary NASH in mice.

Shc expression rises in human nonalcoholic steatohepatitis (NASH) livers, and Shc-deficient mice are protected from NASH-thus Shc inhibition could be a novel therapeutic strategy for NASH. Idebenone was recently identified as the first small-molecule Shc inhibitor drug. We tested idebenone in the fibrotic methionine-choline deficient (MCD) diet and the metabolic fast food diet (FFD) mouse models of NASH. In the fibrotic MCD NASH model, idebenone reduced Shc expression and phosphorylation in peripheral blood mononuclear cells and Shc expression in the liver; decreased serum alanine aminotransferase and aspartate aminotransferase; and attenuated liver fibrosis as observed by quantitative polymerase chain reaction (qPCR) and hydroxyproline quantification. In the metabolic FFD model, idebenone administration improved insulin resistance, and reduced inflammation and fibrosis shown with qPCR, hydroxyproline measurement, and histology. Thus, idebenone ameliorates NASH in two mouse models. As an approved drug with a benign safety profile, Idebenone could be a reasonable human NASH therapy.

Death certificates compared to SEER-Medicare data for surveillance of liver cancer mortality due to hepatitis B or hepatitis C infection.

Hepatocellular carcinoma (HCC) is often caused by hepatitis B virus (HBV) or hepatitis C virus (HCV) infection. To investigate the completeness of death certificates for recording viral hepatitis in HCC death, we compared the proportion of HCC deaths with hepatitis virus infection reported on death certificates to that reported as claims in the Surveillance, Epidemiology, and End Results (SEER)-Medicare database among individuals ≥66 years of age. For 2001-2015, we tabulated proportions of HCC deaths with HBV or HCV infection in each database overall, and by demographic factors. To correct for under ascertainment of viral hepatitis-associated HCC on death certificates, we multiplied by the reciprocal ratio of death certificates to SEER-Medicare. Among HCC decedents, HBV infection was reported on 3.6% of death certificates and 17.2% of Medicare claims. For HCV, corresponding proportions were 14.9% and 26.9%. The ratio of HBV-attributable HCC deaths in death certificates to SEER-Medicare remained ~0.21 over time. The ratio of HCV-attributable HCC deaths decreased 22.1% per year, from 0.70 in 2001 to 0.37 in 2003, and increased 4.1% per year, from 0.47 in 2004 to 0.66 in 2015. Following correction, the 2015 mortality rate from death certificate data increased from 0.2 to 0.9 per 100,000 for HBV-attributable HCC and from 2.3 to 3.5 per 100,000 for HCV-attributable HCC. In conclusion, among older Americans dying from HCC, death certificates captured 21% of HBV and 55% of HCV infections compared to Medicare claims. Our results suggest that death certificates provide incomplete data for viral hepatitis-associated HCC surveillance.

Vimentin filaments drive migratory persistence in polyploidal cancer cells.

Polyploidal giant cancer cells (PGCCs) are multinucleated chemoresistant cancer cells found in heterogeneous solid tumors. Due in part to their apparent dormancy, the effect of PGCCs on cancer progression has remained largely unstudied. Recent studies have highlighted the critical role of PGCCs as aggressive and chemoresistant cancer cells, as well as their ability to undergo amitotic budding to escape dormancy. Our recent study demonstrated the unique biophysical properties of PGCCs, as well as their unusual migratory persistence. Here we unveil the critical function of vimentin intermediate filaments (VIFs) in maintaining the structural integrity of PGCCs and enhancing their migratory persistence. We performed in-depth single-cell analysis to examine the distribution of VIFs and their role in migratory persistence. We found that PGCCs rely heavily on their uniquely distributed and polarized VIF network to enhance their transition from a jammed to an unjammed state to allow for directional migration. Both the inhibition of VIFs with acrylamide and small interfering RNA knockdown of vimentin significantly decreased PGCC migration and resulted in a loss of PGCC volume. Because PGCCs rely on their VIF network to direct migration and to maintain their enlarged morphology, targeting vimentin or vimentin cross-linking proteins could provide a therapeutic approach to mitigate the impact of these chemoresistant cells in cancer progression and to improve patient outcomes with chemotherapy.

Alcohol Consumption among Samoan Adults in 2010: Patterns, Correlates and Health Implications.

AIMS: We aim to describe alcohol consumption and related problems from a nationwide survey in 2010 in Samoa in association with sociodemographic variables as part of an intervention development. METHODS: The sample consisted of 3463 adults, 25-65 years of age. Participants self-reported alcohol consumption in the previous 12 months, patterns of drinking and alcohol-related psychosocial problems. Data about age, census region of residence, highest attained education level, employment, marital status, household assets score and current smoking status were gathered. RESULTS: More than one-third of men, 36.1%, and 4.1% of women consumed alcohol in the past year. There were greater proportions of alcohol users among younger adults, <45 years, in both men and women. Among men, being unemployed and residing outside of rural Savai'i and smoking cigarettes were associated with current alcohol use. Among women, tertiary education and cigarette smoking were strongly associated with alcohol use. Among alcohol consumers, almost 75% of both men and women reported being drunk more than once in the prior month, and 58% of men and 81% of women drank heavily, consuming >4 drinks for women and >5 drinks for men at least once per episode in the prior week. More men than women, 51% versus 26%, felt that alcohol consumption had interfered with their daily life. CONCLUSION: Our analyses identified correlates of alcohol consumption and associated problems that can help guide the development of targeted interventions for different sex and age groups to mitigate the social and physiological harms of alcohol misuse.

AGER1 downregulation associates with fibrosis in nonalcoholic steatohepatitis and type 2 diabetes.

Type 2 diabetes is clinically associated with progressive necroinflammation and fibrosis in nonalcoholic steatohepatitis (NASH). Advanced glycation end-products (AGEs) accumulate during prolonged hyperglycemia, but the mechanistic pathways that lead to accelerated liver fibrosis have not been well defined. In this study, we show that the AGEs clearance receptor AGER1 was downregulated in patients with NASH and diabetes and in our NASH models, whereas the proinflammatory receptor RAGE was induced. These findings were associated with necroinflammatory, fibrogenic, and pro-oxidant activity via the NADPH oxidase 4. Inhibition of AGEs or RAGE deletion in hepatocytes in vivo reversed these effects. We demonstrate that dysregulation of NRF2 by neddylation of cullin 3 was linked to AGER1 downregulation and that induction of NRF2 using an adeno-associated virus-mediated approach in hepatocytes in vivo reversed AGER1 downregulation, lowered the level of AGEs, and improved proinflammatory and fibrogenic responses in mice on a high AGEs diet. In patients with NASH and diabetes or insulin resistance, low AGER1 levels were associated with hepatocyte ballooning degeneration and ductular reaction. Collectively, prolonged exposure to AGEs in the liver promotes an AGER1/RAGE imbalance and consequent redox, inflammatory, and fibrogenic activity in NASH.

Nonphagocytic Activation of NOX2 Is Implicated in Progressive Nonalcoholic Steatohepatitis During Aging.

BACKGROUND AND AIMS: Older patients with obesity/type II diabetes mellitus frequently present with advanced NASH. Whether this is due to specific molecular pathways that accelerate fibrosis during aging is unknown. Activation of the Src homology 2 domain-containing collagen-related (Shc) proteins and redox stress have been recognized in aging; however, their link to NASH has not been explored. APPROACH AND RESULTS: Shc expression increased in livers of older patients with NASH, as assessed by real time quantitative PCR (RT-qPCR) or western blots. Fibrosis, Shc expression, markers of senescence, and nicotinamide adenine dinucleotide phosphate, reduced form oxidases (NOXs) were studied in young/old mice on fast food diet (FFD). To inhibit Shc in old mice, lentiviral (LV)-short hairpin Shc versus control-LV were used during FFD. For hepatocyte-specific effects, floxed (fl/fl) Shc mice on FFD were injected with adeno-associated virus 8-thyroxine-binding globulin-Cre-recombinase versus control. Fibrosis was accelerated in older mice on FFD, and Shc inhibition by LV in older mice or hepatocyte-specific deletion resulted in significantly improved inflammation, reduction in senescence markers in older mice, lipid peroxidation, and fibrosis. To study NOX2 activation, the interaction of p47phox (NOX2 regulatory subunit) and p52Shc was evaluated by proximity ligation and coimmunoprecipitations. Palmitate-induced p52Shc binding to p47phox , activating the NOX2 complex, more so at an older age. Kinetics of binding were assessed in Src homology 2 domain (SH2) or phosphotyrosine-binding (PTB) domain deletion mutants by biolayer interferometry, revealing the role of SH2 and the PTB domains. Lastly, an in silico model of p52Shc/p47phox interaction using RosettaDock was generated. CONCLUSIONS: Accelerated fibrosis in the aged is modulated by p52Shc/NOX2. We show a pathway for direct activation of the phagocytic NOX2 in hepatocytes by p52Shc binding and activating the p47phox subunit that results in redox stress and accelerated fibrosis in the aged.