Evaluating Debio 1347 in Patients with FGFR Fusion-Positive Advanced Solid Tumors from the FUZE Multicenter, Open-Label, Phase II Basket Trial.

PURPOSE: This multicenter phase II basket trial investigated the efficacy, safety and pharmacokinetics of Debio 1347, an investigational, oral, highly selective, ATP-competitive, small molecule inhibitor of FGFR1-3, in patients with solid tumors harboring a functional FGFR1-3 fusion. PATIENTS AND METHODS: Eligible adults had a previously treated locally advanced (unresectable) or metastatic biliary tract (cohort 1), urothelial (cohort 2) or other histologic cancer type (cohort 3). Debio 1347 was administered at 80 mg once daily, continuously, in 28-day cycles. The primary endpoint was the objective response rate (ORR). Secondary endpoints included duration of response, progression-free survival, overall survival, pharmacokinetics, and incidence of adverse events. RESULTS: Between March 22, 2019 and January 8, 2020, 63 patients were enrolled and treated, 30 in cohort 1, four in cohort 2, and 29 in cohort 3. An unplanned preliminary statistical review showed that the efficacy of Debio 1347 was lower than predicted and the trial was terminated. Three of 58 evaluable patients had partial responses, representing an ORR of 5%, with a further 26 (45%) having stable disease (≥6 weeks duration). Grade ≥3 treatment-related adverse events occurred in 22 (35%) of 63 patients, with the most common being hyperphosphatemia (13%) and stomatitis (5%). Two patients (3%) discontinued treatment due to adverse events. CONCLUSIONS: Debio 1347 had manageable toxicity; however, the efficacy in patients with tumors harboring FGFR fusions did not support further clinical evaluation in this setting. Our transcriptomic-based analysis characterized in detail the incidence and nature of FGFR fusions across solid tumors.

A novel practical method to predict anterior cruciate ligament hamstring graft size using preoperative MRI.

BACKGROUND: Predicting hamstring graft size preoperatively for anterior cruciate ligament (ACL) reconstruction is important for preempting an insufficient diameter in graft size intraoperatively, possibly leading to graft failure. While there are multiple published methods using magnetic resonance imaging (MRI) picture archiving and communication systems (PACS), most are not feasible and practical. Our study aims to (1) practically predict the ACL hamstring graft size in a numerically continuous manner using the preoperative MRI from any native MRI PACS system, (2) determine the degree of correlation between the predicted and actual graft size, and (3) determine the performance of our prediction method if we define an adequate actual graft size as ≥ 8 mm. METHODS: A retrospective review of 112 patients who underwent primary ACL reconstruction with quadrupled hamstring semitendinosus-gracilis grafts at a tertiary institution was conducted between January 2018 and December 2018. Graft diameter can be predicted in a numerically continuous manner as √[2*(AB + CD)], where A and B are the semitendinosus cross-sectional length and breath, respectively, and C and D are the gracilis cross-sectional length and breath, respectively. RESULTS: A moderately positive correlation exists between the predicted and actual graft diameter (r = 0.661 and p < .001). Our method yields a high specificity of 92.6% and a moderate sensitivity of 67.2% if we define an adequate actual graft size as ≥ 8 mm. An area under receiver-operating characteristic curve shows good discrimination (AUC = 0.856). CONCLUSIONS: We present a practical method to predict the ACL hamstring graft size with high specificity using preoperative MRI measurements.

Characterization of a Novel Mouse Model for Fuchs Endothelial Corneal Dystrophy.

Purpose: Fuchs endothelial corneal dystrophy (FECD) is a progressive blinding disorder, characterized by increased corneal endothelial excrescences (guttae), corneal endothelial cell loss, and edema. These symptoms are hypothesized to be caused by changes in the extracellular matrix (ECM) and mitochondrial dysfunction in the corneal endothelium. Despite this clinical and biological relevance, a comprehensive animal model that recapitulates all the major disease characteristics is currently unavailable. In this study, we develop such a model to improve our understanding of the signaling pathways involved in the FECD progression and develop strategies for early intervention. Method: To generate a comprehensive FECD model, we generated a double mutant mouse bearing tamoxifen-inducible knockdown of Slc4a11 and the Col8a2 (Q455K) mutation. We performed optical coherence tomography (OCT) and in vivo confocal microscopy using the Heidelberg Retinal Tomography 3 - Rostock Cornea module (HRT3-RCM) on the mice at 5 weeks of age before tamoxifen feeding to establish baseline values for corneal thickness, endothelial cell density, and test for the presence of guttae. We measured these parameters again post-tamoxifen treatment at 16 weeks of age. We collected corneas at 16 weeks to perform histopathology, immunofluorescence staining for tight junctions, adherens junctions, and oxidative stress. We evaluated endothelial pump function using a lactate assay. Results: The double mutant tamoxifen-fed animals showed the presence of guttae, and displayed increased corneal thickness and decreased endothelial cell density. Endothelial cells showed altered morphology with disrupted adherens junctions and elevated reactive oxygen species (ROS). Finally, we found that stromal lactate concentrations were elevated in the double mutant mice, indicative of compromised endothelial pump function. Conclusions: Overall, this mouse model recapitulates all the important phenotypic features associated with FECD.

Machine Learning and Bayesian Network Analyses Identifies Psychiatric Disorders and Symptom Associations with Insomnia in a national sample of 31,285 Treatment-Seeking College Students.

Background: A better understanding of the structure of relations among insomnia and anxiety, mood, eating, and alcohol-use disorders is needed, given its prevalence among young adults. Supervised machine learning provides the ability to evaluate the discriminative accuracy of psychiatric disorders associated with insomnia. Combined with Bayesian network analysis, the directionality between symptoms and their associations may be illuminated. Methods: The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. Firstly, an elastic net regularization model was trained to predict, via repeated 10-fold cross-validation, which psychiatric disorders were associated with insomnia severity. Seven disorders were included: major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder. Secondly, using a Bayesian network approach, completed partially directed acyclic graphs (CPDAG) built on training and holdout samples were computed via a Bayesian hill-climbing algorithm to determine symptom-level interactions of disorders most associated with insomnia [based on SHAP (SHapley Additive exPlanations) values)] and were evaluated for stability across networks. Results: Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .449 (.016); RMSE = 5.00 [.081]), with comparable performance in accounting for variance explained in the holdout sample [R2 = .33; RMSE = 5.47). SHAP indicated the presence of any psychiatric disorder was associated with higher insomnia severity, with major depressive disorder demonstrated to be the most associated disorder. CPDAGs showed excellent fit in the holdout sample and suggested that depressed mood, fatigue, and self-esteem were the most important depression symptoms that presupposed insomnia. Conclusion: These findings offer insights into associations between psychiatric disorders and insomnia among college students and encourage future investigation into the potential direction of causality between insomnia and major depressive disorder. Trial registration: Trial may be found on the National Institute of Health RePORTER website: Project Number: R01MH115128-05.

Positive reappraisal coping mediates the relationship between parental abuse and lack of affection on adulthood generalized anxiety severity.

Exposure to parental abuse and lack of parental affection during childhood are risk factors for adulthood psychopathology. Tendency to engage in positive reappraisal may be a plausible mechanism underlying this relationship. The current study examined if positive reappraisal coping mediated the relationship between maternal/paternal abuse/affection and adulthood generalized anxiety disorder (GAD) symptoms. Participant data (N = 3294) from the Midlife Development in the United States study was collected in three waves, spaced nine years apart. Longitudinal structural equation mediation modeling examined whether positive reappraisal coping at Time 2 mediated the relationship between maternal/paternal abuse/affection at Time 1 and GAD symptoms at Time 3, controlling for GAD symptoms at Time 1. Positive reappraisal coping mediated maternal/paternal childhood abuse - GAD symptom severity and maternal/paternal childhood affection - GAD severity relations. Maternal and paternal abuse was associated with lower positive reappraisal tendencies, predicting increased GAD symptom severity. Conversely, higher maternal/paternal affection was associated with increased positive reappraisal, predicting lower GAD severity. Incremental prediction revealed that childhood abuse to GAD severity via positive reappraisal path was significant for maternal but not paternal abuse, whereas affection from both parents remained significant. Positive reappraisal coping may be a possible mechanism linking childhood experiences to adulthood GAD severity.

Qualitative systematic review: The lived experiences of males in the nursing profession on gender discrimination encounters.

AIMS: This review aims to synthesise the best available evidence exploring the lived experiences of males in the nursing profession on gender discrimination encounters. BACKGROUND: The underrepresentation of males within the nursing profession could potentially cause discrimination and stereotypes against them, causing barriers to entry into, and retention within, the profession. With a shortage in manpower, the deterrence of males to nursing might exacerbate the existing manpower shortage. This review consolidates and highlights the lived experiences of males in the profession. METHODS: The review was synthesised according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Employing the meta-synthesis approach, two independent reviewers conducted critical appraisal and data extraction of included studies, using the standard Joanna Briggs Institute (JBI) Critical Appraisal Instrument for Qualitative Research and a standardised data extraction form, respectively. RESULTS: Twenty-three studies were included. Extracted findings were aggregated into 14 categories and synthesised into three themes: stereotypes, discrimination and challenges faced by males. DISCUSSION: The synthesised findings showed that male nurses faced gender discrimination in multiple facets. These caused them to have difficulty in carrying out their nursing duties, missing out of clinical opportunities and causing them multiple social and mental stressors. CONCLUSION: This review informed the discriminatory experiences faced by males within the nursing profession. With males already underrepresented within this profession, it is imperative for interventions, policies and research to be made to address this issue to improve experiences of male nurses, thereby limiting the deterrence of males into the profession. IMPLICATIONS: Findings of this study showed that males are being stereotyped and discriminated, resulting in challenges faced solely because of their gender. Effective interventions and policies could be implemented to alleviate the negative experiences.

Explainable Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Machine Learning Techniques in a Population of 1780 Patients.

Post-stroke depression and anxiety, collectively known as post-stroke adverse mental outcome (PSAMO) are common sequelae of stroke. About 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with PSAMO have poorer health outcomes with higher mortality and greater functional disability. In this study, we aimed to develop a machine learning (ML) model to predict the risk of PSAMO. We retrospectively studied 1780 patients with stroke who were divided into PSAMO vs. no PSAMO groups based on results of validated depression and anxiety questionnaires. The features collected included demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. Recursive feature elimination was used to select features to input in parallel to eight ML algorithms to train and test the model. Bayesian optimization was used for hyperparameter tuning. Shapley additive explanations (SHAP), an explainable AI (XAI) method, was applied to interpret the model. The best performing ML algorithm was gradient-boosted tree, which attained 74.7% binary classification accuracy. Feature importance calculated by SHAP produced a list of ranked important features that contributed to the prediction, which were consistent with findings of prior clinical studies. Some of these factors were modifiable, and potentially amenable to intervention at early stages of stroke to reduce the incidence of PSAMO.

Exploiting frequent and specific expression of PRL3 in pediatric solid tumors for first-in-child use of PRL3-zumab humanized antibody.

Phosphatase of regenerating liver 3 (PRL3) is a specific tumor antigen overexpressed in a broad range of adult cancer types. However, its physiological expression in pediatric embryonal and mesenchymal tumors and its association with clinical outcomes in children is unknown. We sought to profile the expression of PRL3 in pediatric tumors in relation to survival outcomes, expression of angiogenesis markers, and G-protein-coupled receptor (GPCR)-mitogen-activated protein kinase (MAPK) signaling targets. PRL3-zumab, a first-in-class humanized antibody, was administered in a dose escalation schedule in a first-in-child clinical trial to study toxicity, pharmacokinetics, and clinical outcomes. Among 64 pediatric tumors, PRL3 was most frequently expressed in neuroblastoma (100%), rhabdomyosarcoma and non-rhabdomyosarcoma soft tissue sarcomas (71%), and renal sarcomas (60%) but absent in paired normal tissues. PRL3 was expressed in 75% of relapsed tumors and associated with shorter median event-free survival. Microarray profiling of PRL3-positive tumors showed elevation of angiogenin, TIMP1 and TIMP2, and GPCR-MAPK signaling proteins that commonly interacted with PRL3. The first use of PRL3-zumab in a pediatric patient saw no adverse events. A 28.6% reduction in maximum target lesion diameter was achieved when PRL3-zumab was administered concurrently with hypofractionated radiation. These findings support wider exploration of PRL3 expression in embryonal and mesenchymal tumors and further clinical application of PRL3-zumab in pediatric patients.

Knowledge, attitudes, and perceptions of hidradenitis suppurativa among young adults in Singapore.

Background: The prevalence of hidradenitis suppurativa (HS) is 0.00033% to 4.10% globally. Few epidemiological studies derive from Asia, with social stigmatization postulated to result in under-diagnosis. Objective: This study aimed to assess the self-reported prevalence of HS, and the knowledge, attitudes, and perceptions towards HS among Singaporean young-adults. Methods: A cross-sectional study (n = 158) was conducted by anonymous online questionnaire. The association between demographic factors and risk of potentially undiagnosed HS was evaluated using multivariable logistic regression. Differences between attitude-perception scores by demographic factors and knowledge of HS were tested using two-sample t-tests. Results: The prevalence of diagnosed and potentially undiagnosed HS was 0.63% and 8.9%, respectively. Non-Chinese had significantly higher social attitude-perception scores than Chinese (P = .029). Females had significantly higher social (P = .048) as well as economic and work (P = .037) attitude-perception scores than males. Those with knowledge of HS had significantly higher attitude-perception scores for interpersonal (P = .031) and social (P = .0052) subsections. Limitations: Small sample size, low frequency of HS cases, and self-reported prevalence may not generalize to the broader population in Singapore. Conclusion: Our results suggest a potential underdiagnosis of HS. Non-Chinese stigmatize HS less than Chinese, and females less than males. Individuals with knowledge of HS might be more open to interpersonal and social interactions with HS sufferers.

Evaluating TESLA-G, a gamified, telegram-delivered, quizzing platform for surgical education in medical students: protocol for a pilot randomised controlled trial.

INTRODUCTION: Online multiple-choice question (MCQ) quizzes are popular in medical education due to their ease of access and ability for test-enhanced learning. However, a general lack of motivation among students often results in decreasing usage over time. We aim to address this limitation by developing Telegram Education for Surgical Learning and Application Gamified (TESLA-G), an online platform for surgical education that incorporates game elements into conventional MCQ quizzes. METHODS AND ANALYSIS: This online, pilot randomised control trial will be conducted over 2 weeks. Fifty full-time undergraduate medical students from a medical school in Singapore will be recruited and randomised into an intervention group (TESLA-G) and an active control group (non-gamified quizzing platform) with a 1:1 allocation ratio, stratified by year of study.We will evaluate TESLA-G in the area of endocrine surgery education. Our platform is designed based on Bloom's taxonomy of learning domains: questions are created in blocks of five questions per endocrine surgery topic, with each question corresponding to one level on Bloom's taxonomy. This structure promotes mastery while boosting student engagement and motivation. All questions are created by two board-certified general surgeons and one endocrinologist, and validated by the research team. The feasibility of this pilot study will be determined quantitatively by participant enrolment, participant retention and degree of completion of the quizzes. The acceptability of the intervention will be assessed quantitatively by a postintervention learner satisfaction survey consisting of a system satisfaction questionnaire and a content satisfaction questionnaire. The improvement of surgical knowledge will be assessed by comparing the scores of preintervention and postintervention knowledge tests, which consist of separately created questions on endocrine surgery. Retention of surgical knowledge will be measured using a follow-up knowledge test administered 2 weeks postintervention. Finally, qualitative feedback from participants regarding their experience will be obtained and thematically analysed. ETHICS AND DISSEMINATION: This research is approved by Singapore Nanyang Technological University (NTU) Institutional Review Boards (Reference Number: IRB-2021-732). All participants will be expected to read and sign a letter of informed consent before they are considered as recruited into the study. This study poses minimal risk to participants. Study results will be published in peer-reviewed open-access journals and presented in conference presentations. TRIAL REGISTRATION NUMBER: NCT05520671.

Circulating Tumor DNA as a Predictive Biomarker for Clinical Outcomes With Margetuximab and Pembrolizumab in Pretreated HER2-Positive Gastric/ Gastroesophageal Adenocarcinoma.

PURPOSE: To assess the ability of circulating tumor DNA (ctDNA)-based testing to identify patients with HER2 (encoded by ERBB2)-positive gastric/gastroesophageal adenocarcinoma (GEA) who progressed on or after trastuzumab-containing treatments were treated with combination therapy of anti-HER2 and anti-PD-1 agents. METHODS: ctDNA analysis was performed retrospectively using plasma samples collected at study entry from 86 patients participating in the phase 1/2 CP-MGAH22-05 study (NCT02689284). RESULTS: Objective response rate (ORR) was significantly higher in evaluable ERBB2 amplification-positive vs - negative patients based on ctDNA analysis at study entry (37% vs 6%, respectively; P = .00094). ORR was 23% across all patients who were evaluable for response. ERBB2 amplification was detected at study entry in 57% of patients (all HER2 positive at diagnosis), and detection was higher (88%) when HER2 status was determined by immunohistochemistry fewer than 6 months before study entry. ctDNA was detected in 98% (84/86) of patients tested at study entry. Codetected ERBB2-activating mutations were not associated with response. CONCLUSIONS: Current ERBB2 status may be more effective than archival status at predicting clinical benefit from margetuximab plus pembrolizumab therapy. ctDNA testing for ERBB2 status prior to treatment will spare patients from repeat tissue biopsies, which may be reserved for reflex testing when ctDNA is not detected.

Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study.

OBJECTIVE: Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world dataset, it is important to quantify segmentation uncertainty and identify segmentations which could be problematic. In this work, we performed a systematic study of Bayesian and non-Bayesian methods for estimating uncertainty in segmentation neural networks. METHODS: We evaluated Bayes by Backprop, Monte Carlo Dropout, Deep Ensembles, and Stochastic Segmentation Networks in terms of segmentation accuracy, probability calibration, uncertainty on out-of-distribution images, and segmentation quality control. RESULTS: We observed that Deep Ensembles outperformed the other methods except for images with heavy noise and blurring distortions. We showed that Bayes by Backprop is more robust to noise distortions while Stochastic Segmentation Networks are more resistant to blurring distortions. For segmentation quality control, we showed that segmentation uncertainty is correlated with segmentation accuracy for all the methods. With the incorporation of uncertainty estimates, we were able to reduce the percentage of poor segmentation to 5% by flagging 31-48% of the most uncertain segmentations for manual review, substantially lower than random review without using neural network uncertainty (reviewing 75-78% of all images). CONCLUSION: This work provides a comprehensive evaluation of uncertainty estimation methods and showed that Deep Ensembles outperformed other methods in most cases. SIGNIFICANCE: Neural network uncertainty measures can help identify potentially inaccurate segmentations and alert users for manual review.

What is hemoglobin, albumin, lymphocyte, platelet (HALP) score? A comprehensive literature review of HALP's prognostic ability in different cancer types.

Since its inception, the Hemoglobin, Albumin, Lymphocyte, Platelet Score (HALP) has gained attention as a new prognostic biomarker to predict several clinical outcomes in a multitude of cancers. In our review, we searched PubMed for articles between the first paper on HALP in 2015 through September 2022, yielding 32 studies in total that evaluated HALP's association with various cancers, including Gastric, Colorectal, Bladder, Prostate, Kidney, Esophageal, Pharyngeal, Lung, Breast, and Cervical cancers, among others. This review highlights the collective association HALP has with demographic factors such as age and sex in addition to TNM staging, grade, and tumor size. Furthermore, this review summarizes HALP's prognostic ability to predict overall survival, progression-free survival, recurrence-free survival, among other outcomes. In some studies, HALP has also been able to predict response to immunotherapy and chemotherapy. This review article also aims to serve as a comprehensive and encyclopedic report on the literature that has evaluated HALP as a biomarker in various cancers, highlighting the heterogeneity surrounding HALP's utilization. Because HALP requires only a complete blood count and albumin - already routinely collected for cancer patients - HALP shows potential as a cost-effective biomarker to aid clinicians in improving outcomes for immuno-nutritionally deficient patients.

Regulation of autophagosome biogenesis and mitochondrial bioenergetics by the cholesterol transport protein GRAMD1C.

Autophagosomes are crucial components of the cellular recycling machinery that form at endoplasmic reticulum (ER)-associated sites. As the autophagosome membrane is largely devoid of transmembrane proteins, autophagosome biogenesis is thought to be largely regulated by lipid transfer and lipid modifications, as well as membrane-associated proteins. While the membrane origin of autophagosomes and their lipid composition are still incompletely understood, previous studies have found the autophagosome membrane to be enriched in unsaturated fatty acids and have little cholesterol, suggesting that cholesterol removal is an integral step during autophagosome biogenesis. In our study, we demonstrate that short term cholesterol depletion leads to a rapid induction of autophagy and identify the ER-localized cholesterol transport protein GRAMD1C as a negative regulator of starvation-induced macroautophagy/autophagy. Abbreviations: ATG: autophagy related; ccRCC: clear cell renal cell carcinoma; ER: endoplasmic reticulum; GRAM: glucosyltransferases, RAB-like GTPase activators and myotubularins; GRAMD: GRAM domain containing; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MCBD: methyl-cyclodextrin; MTOR: mechanistic target of rapamycin kinase; VASt: VAD1 analog of StAR-related lipid transfer.

Safety of robotic surgical management of non-elective colectomies for diverticulitis compared to laparoscopic surgery.

Non-elective minimally invasive surgery (MIS) remains controversial, with minimal focus on robotics. This study aims to evaluate the short-term outcomes for non-elective robotic colectomies for diverticulitis. All colectomies for diverticulitis in ACS-NSQIP between 2012 and 2019 were identified by CPT and diagnosis codes. Open and elective cases were excluded. Patients with disseminated cancer, ascites, and ventilator-dependence were excluded. Procedures were grouped by approach (laparoscopic and robotic). Demographics, operative variables, and postoperative outcomes were compared between groups. Covariates with p < .1 were entered into multivariable logistic regression models for 30 day mortality, postoperative septic shock and reoperation. 6880 colectomies were evaluated (Laparoscopic = 6583, Robotic = 297). The laparoscopic group included more preoperative sepsis (31.6% vs. 10.8%), emergency cases (32.3% vs. 6.7%), and grade 3/4 wound classifications (53.3% vs. 42.8%). There was no difference in mortality, anastomotic leak, SSI, reoperation, readmission, or length of stay. The laparoscopic group had more postoperative sepsis (p = 0.001) and the robotic group showed increased bleeding (p = 0.011). In a multivariate regression model, increased age (OR = 1.083, p < 0.001), COPD (OR = 2.667, p = 0.007), dependent functional status (OR = 2.657, p = 0.021), dialysis (OR = 4.074, p = 0.016), preoperative transfusions (OR = 3.182, p = 0.019), emergency status (OR = 2.241, p = 0.010), higher ASA classification (OR = 3.170, p = 0.035), abnormal WBC (OR = 1.883, p = 0.046) were independent predictors for mortality. When controlling for confounders, robotic approach was not statistically significantly associated with septic shock or reoperation. When controlling for confounders, robotic approach was not a predictor for mortality, reoperation or septic shock. Robotic surgery is a feasible option for the acute management of diverticulitis.

A Deep Learning Segmentation Pipeline for Cardiac T1 Mapping Using MRI Relaxation-based Synthetic Contrast Augmentation.

Purpose: To design and evaluate an automated deep learning method for segmentation and analysis of cardiac MRI T1 maps with use of synthetic T1-weighted images for MRI relaxation-based contrast augmentation. Materials and Methods: This retrospective study included MRI scans acquired between 2016 and 2019 from 100 patients (mean age ± SD, 55 years ± 13; 72 men) across various clinical abnormalities with use of a modified Look-Locker inversion recovery, or MOLLI, sequence to quantify native T1 (T1native), postcontrast T1 (T1post), and extracellular volume (ECV). Data were divided into training (n = 60) and internal (n = 40) test subsets. "Synthetic" T1-weighted images were generated from the T1 exponential inversion-recovery signal model at a range of optimal inversion times, yielding high blood-myocardium contrast, and were used for contrast-based image augmentation during training and testing of a convolutional neural network for myocardial segmentation. Automated segmentation, T1, and ECV were compared with experts with use of Dice similarity coefficients (DSCs), correlation coefficients, and Bland-Altman analysis. An external test dataset (n = 147) was used to assess model generalization. Results: Internal testing showed high myocardial DSC relative to experts (0.81 ± 0.08), which was similar to interobserver DSC (0.81 ± 0.08). Automated segmental measurements strongly correlated with experts (T1native, R = 0.87; T1post, R = 0.91; ECV, R = 0.92), which were similar to interobserver correlation (T1native, R = 0.86; T1post, R = 0.94; ECV, R = 0.95). External testing showed strong DSC (0.80 ± 0.09) and T1native correlation (R = 0.88) between automatic and expert analysis. Conclusion: This deep learning method leveraging synthetic contrast augmentation may provide accurate automated T1 and ECV analysis for cardiac MRI data acquired across different abnormalities, centers, scanners, and T1 sequences.Keywords: MRI, Cardiac, Tissue Characterization, Segmentation, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms, Supervised Learning Supplemental material is available for this article. © RSNA, 2022.

The cholesterol transport protein GRAMD1C regulates autophagy initiation and mitochondrial bioenergetics.

During autophagy, cytosolic cargo is sequestered into double-membrane vesicles called autophagosomes. The contributions of specific lipids, such as cholesterol, to the membranes that form the autophagosome, remain to be fully characterized. Here, we demonstrate that short term cholesterol depletion leads to a rapid induction of autophagy and a corresponding increase in autophagy initiation events. We further show that the ER-localized cholesterol transport protein GRAMD1C functions as a negative regulator of starvation-induced autophagy and that both its cholesterol transport VASt domain and membrane binding GRAM domain are required for GRAMD1C-mediated suppression of autophagy initiation. Similar to its yeast orthologue, GRAMD1C associates with mitochondria through its GRAM domain. Cells lacking GRAMD1C or its VASt domain show increased mitochondrial cholesterol levels and mitochondrial oxidative phosphorylation, suggesting that GRAMD1C may facilitate cholesterol transfer at ER-mitochondria contact sites. Finally, we demonstrate that expression of GRAMD family proteins is linked to clear cell renal carcinoma survival, highlighting the pathophysiological relevance of cholesterol transport proteins.