SAROS: A dataset for whole-body region and organ segmentation in CT imaging.

The Sparsely Annotated Region and Organ Segmentation (SAROS) dataset was created using data from The Cancer Imaging Archive (TCIA) to provide a large open-access CT dataset with high-quality annotations of body landmarks. In-house segmentation models were employed to generate annotation proposals on randomly selected cases from TCIA. The dataset includes 13 semantic body region labels (abdominal/thoracic cavity, bones, brain, breast implant, mediastinum, muscle, parotid/submandibular/thyroid glands, pericardium, spinal cord, subcutaneous tissue) and six body part labels (left/right arm/leg, head, torso). Case selection was based on the DICOM series description, gender, and imaging protocol, resulting in 882 patients (438 female) for a total of 900 CTs. Manual review and correction of proposals were conducted in a continuous quality control cycle. Only every fifth axial slice was annotated, yielding 20150 annotated slices from 28 data collections. For the reproducibility on downstream tasks, five cross-validation folds and a test set were pre-defined. The SAROS dataset serves as an open-access resource for training and evaluating novel segmentation models, covering various scanner vendors and diseases.

CellViT: Vision Transformers for precise cell segmentation and classification.

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in combination with large scale pre-training in this domain. Therefore, we introduce a new method for automated instance segmentation of cell nuclei in digitized tissue samples using a deep learning architecture based on Vision Transformer called CellViT. CellViT is trained and evaluated on the PanNuke dataset, which is one of the most challenging nuclei instance segmentation datasets, consisting of nearly 200,000 annotated nuclei into 5 clinically important classes in 19 tissue types. We demonstrate the superiority of large-scale in-domain and out-of-domain pre-trained Vision Transformers by leveraging the recently published Segment Anything Model and a ViT-encoder pre-trained on 104 million histological image patches - achieving state-of-the-art nuclei detection and instance segmentation performance on the PanNuke dataset with a mean panoptic quality of 0.50 and an F1-detection score of 0.83. The code is publicly available at https://github.com/TIO-IKIM/CellViT.

Addressing the Contrast Media Recognition Challenge: A Fully Automated Machine Learning Approach for Predicting Contrast Phases in CT Imaging.

OBJECTIVES: Accurately acquiring and assigning different contrast-enhanced phases in computed tomography (CT) is relevant for clinicians and for artificial intelligence orchestration to select the most appropriate series for analysis. However, this information is commonly extracted from the CT metadata, which is often wrong. This study aimed at developing an automatic pipeline for classifying intravenous (IV) contrast phases and additionally for identifying contrast media in the gastrointestinal tract (GIT). MATERIALS AND METHODS: This retrospective study used 1200 CT scans collected at the investigating institution between January 4, 2016 and September 12, 2022, and 240 CT scans from multiple centers from The Cancer Imaging Archive for external validation. The open-source segmentation algorithm TotalSegmentator was used to identify regions of interest (pulmonary artery, aorta, stomach, portal/splenic vein, liver, portal vein/hepatic veins, inferior vena cava, duodenum, small bowel, colon, left/right kidney, urinary bladder), and machine learning classifiers were trained with 5-fold cross-validation to classify IV contrast phases (noncontrast, pulmonary arterial, arterial, venous, and urographic) and GIT contrast enhancement. The performance of the ensembles was evaluated using the receiver operating characteristic area under the curve (AUC) and 95% confidence intervals (CIs). RESULTS: For the IV phase classification task, the following AUC scores were obtained for the internal test set: 99.59% [95% CI, 99.58-99.63] for the noncontrast phase, 99.50% [95% CI, 99.49-99.52] for the pulmonary-arterial phase, 99.13% [95% CI, 99.10-99.15] for the arterial phase, 99.8% [95% CI, 99.79-99.81] for the venous phase, and 99.7% [95% CI, 99.68-99.7] for the urographic phase. For the external dataset, a mean AUC of 97.33% [95% CI, 97.27-97.35] and 97.38% [95% CI, 97.34-97.41] was achieved for all contrast phases for the first and second annotators, respectively. Contrast media in the GIT could be identified with an AUC of 99.90% [95% CI, 99.89-99.9] in the internal dataset, whereas in the external dataset, an AUC of 99.73% [95% CI, 99.71-99.73] and 99.31% [95% CI, 99.27-99.33] was achieved with the first and second annotator, respectively. CONCLUSIONS: The integration of open-source segmentation networks and classifiers effectively classified contrast phases and identified GIT contrast enhancement using anatomical landmarks.

AI-based digital histopathology for perihilar cholangiocarcinoma: A step, not a jump.

Introduction: Perihilar cholangiocarcinoma (PHCC) is a rare malignancy with limited survival prediction accuracy. Artificial intelligence (AI) and digital pathology advancements have shown promise in predicting outcomes in cancer. We aimed to improve prognosis prediction for PHCC by combining AI-based histopathological slide analysis with clinical factors. Methods: We retrospectively analyzed 317 surgically treated PHCC patients (January 2009-December 2018) at the University Hospital of Essen. Clinical data, surgical details, pathology, and outcomes were collected. Convolutional neural networks (CNN) analyzed whole-slide images. Survival models incorporated clinical and histological features. Results: Among 142 eligible patients, independent survival predictors were tumor grade (G), tumor size (T), and intraoperative transfusion requirement. The CNN-based model combining clinical and histopathological features demonstrates proof of concept in prognosis prediction, limited by histopathological complexity and feature extraction challenges. However, the CNN-based model generated heatmaps assisting pathologists in identifying areas of interest. Conclusion: AI-based digital pathology showed potential in PHCC prognosis prediction, though refinement is necessary for clinical relevance. Future research should focus on enhancing AI models and exploring novel approaches to improve PHCC patient prognosis prediction.

BOA: A CT-Based Body and Organ Analysis for Radiologists at the Point of Care.

PURPOSE: The study aimed to develop the open-source body and organ analysis (BOA), a comprehensive computed tomography (CT) image segmentation algorithm with a focus on workflow integration. METHODS: The BOA combines 2 segmentation algorithms: body composition analysis (BCA) and TotalSegmentator. The BCA was trained with the nnU-Net framework using a dataset including 300 CT examinations. The CTs were manually annotated with 11 semantic body regions: subcutaneous tissue, muscle, bone, abdominal cavity, thoracic cavity, glands, mediastinum, pericardium, breast implant, brain, and spinal cord. The models were trained using 5-fold cross-validation, and at inference time, an ensemble was used. Afterward, the segmentation efficiency was evaluated on a separate test set comprising 60 CT scans. In a postprocessing step, a tissue segmentation (muscle, subcutaneous adipose tissue, visceral adipose tissue, intermuscular adipose tissue, epicardial adipose tissue, and paracardial adipose tissue) is created by subclassifying the body regions. The BOA combines this algorithm and the open-source segmentation software TotalSegmentator to have an all-in-one comprehensive selection of segmentations. In addition, it integrates into clinical workflows as a DICOM node-triggered service using the open-source Orthanc research PACS (Picture Archiving and Communication System) server to make the automated segmentation algorithms available to clinicians. The BCA model's performance was evaluated using the Sørensen-Dice score. Finally, the segmentations from the 3 different tools (BCA, TotalSegmentator, and BOA) were compared by assessing the overall percentage of the segmented human body on a separate cohort of 150 whole-body CT scans. RESULTS: The results showed that the BCA outperformed the previous publication, achieving a higher Sørensen-Dice score for the previously existing classes, including subcutaneous tissue (0.971 vs 0.962), muscle (0.959 vs 0.933), abdominal cavity (0.983 vs 0.973), thoracic cavity (0.982 vs 0.965), bone (0.961 vs 0.942), and an overall good segmentation efficiency for newly introduced classes: brain (0.985), breast implant (0.943), glands (0.766), mediastinum (0.880), pericardium (0.964), and spinal cord (0.896). All in all, it achieved a 0.935 average Sørensen-Dice score, which is comparable to the one of the TotalSegmentator (0.94). The TotalSegmentator had a mean voxel body coverage of 31% ± 6%, whereas BCA had a coverage of 75% ± 6% and BOA achieved 93% ± 2%. CONCLUSIONS: The open-source BOA merges different segmentation algorithms with a focus on workflow integration through DICOM node integration, offering a comprehensive body segmentation in CT images with a high coverage of the body volume.

Optimizing platelet transfusion through a personalized deep learning risk assessment system for demand management.

ABSTRACT: Platelet demand management (PDM) is a resource-consuming task for physicians and transfusion managers of large hospitals. Inpatient numbers and institutional standards play significant roles in PDM. However, reliance on these factors alone commonly results in platelet shortages. Using data from multiple sources, we developed, validated, tested, and implemented a patient-specific approach to support PDM that uses a deep learning-based risk score to forecast platelet transfusions for each hospitalized patient in the next 24 hours. The models were developed using retrospective electronic health record data of 34 809 patients treated between 2017 and 2022. Static and time-dependent features included demographics, diagnoses, procedures, blood counts, past transfusions, hematotoxic medications, and hospitalization duration. Using an expanding window approach, we created a training and live-prediction pipeline with a 30-day input and 24-hour forecast. Hyperparameter tuning determined the best validation area under the precision-recall curve (AUC-PR) score for long short-term memory deep learning models, which were then tested on independent data sets from the same hospital. The model tailored for hematology and oncology patients exhibited the best performance (AUC-PR, 0.84; area under the receiver operating characteristic curve [ROC-AUC], 0.98), followed by a multispecialty model covering all other patients (AUC-PR, 0.73). The model specific to cardiothoracic surgery had the lowest performance (AUC-PR, 0.42), likely because of unexpected intrasurgery bleedings. To our knowledge, this is the first deep learning-based platelet transfusion predictor enabling individualized 24-hour risk assessments at high AUC-PR. Implemented as a decision-support system, deep-learning forecasts might improve patient care by detecting platelet demand earlier and preventing critical transfusion shortages.

FHIR-PYrate: a data science friendly Python package to query FHIR servers.

BACKGROUND: We present FHIR-PYrate, a Python package to handle the full clinical data collection and extraction process. The software is to be plugged into a modern hospital domain, where electronic patient records are used to handle the entire patient's history. Most research institutes follow the same procedures to build study cohorts, but mainly in a non-standardized and repetitive way. As a result, researchers spend time writing boilerplate code, which could be used for more challenging tasks. METHODS: The package can improve and simplify existing processes in the clinical research environment. It collects all needed functionalities into a straightforward interface that can be used to query a FHIR server, download imaging studies and filter clinical documents. The full capacity of the search mechanism of the FHIR REST API is available to the user, leading to a uniform querying process for all resources, thus simplifying the customization of each use case. Additionally, valuable features like parallelization and filtering are included to make it more performant. RESULTS: As an exemplary practical application, the package can be used to analyze the prognostic significance of routine CT imaging and clinical data in breast cancer with tumor metastases in the lungs. In this example, the initial patient cohort is first collected using ICD-10 codes. For these patients, the survival information is also gathered. Some additional clinical data is retrieved, and CT scans of the thorax are downloaded. Finally, the survival analysis can be computed using a deep learning model with the CT scans, the TNM staging and positivity of relevant markers as input. This process may vary depending on the FHIR server and available clinical data, and can be customized to cover even more use cases. CONCLUSIONS: FHIR-PYrate opens up the possibility to quickly and easily retrieve FHIR data, download image data, and search medical documents for keywords within a Python package. With the demonstrated functionality, FHIR-PYrate opens an easy way to assemble research collectives automatically.

Post-Stroke Detection of Subclinical Paroxysmal Atrial Fibrillation in Patients With Embolic Stroke of Undetermined Source in the Real World Practice: The Empoli ESUS Atrial Fibrillation (E 2 AF) Study.

BACKGROUND: Subclinical paroxysmal atrial fibrillation (AF) is one of the main occult causative mechanisms of embolic stroke of undetermined source (ESUS). Aim of this study was to identify AF predictors, and to develop a score to predict the probability of AF detection in ESUS. METHODS: We retrospectively analyzed ESUS patients undergoing 2-week external electrocardiographic monitoring. Patients with and without AF detection were compared. On the basis of multivariate analysis, predictors of AF were identified and used to develop a predictive score, which was then compared with other existing literature scores. RESULTS: Eighty-two patients, 48 females, mean age±SD 72±10 years, were included. In 36 patients (43.9%) AF was detected. The frequency of age 75 years or above and arterial hypertension, and the median CHA 2 DS 2 -VASc score were significantly higher in patients with AF compared with those without. National Institutes of Health Stroke Scale (NIHSS) score ≥8 was the only independent variable associated with AF detection. We derived the Empoli ESUS-AF (E 2 AF) score (NIHSS ≥8 5 points, arterial hypertension 3 points, age 75 years or above 2 points, age 65 to 74 years 1 point, history of coronary/peripheral artery disease 1 point, left atrial enlargement 1 point, posterior lesion 1 point, cortical or cortical-subcortical lesion 1 point), whose predictive power in detecting AF was good (area under the curve: 0.746, 95% confidence interval: 0.638-0.836) and higher than that of CHA 2 DS 2 -VASc and other scores. CONCLUSIONS: In our study NIHSS score ≥8 was the only independent predictor of post-ESUS-AF detection. The E 2 AF score appears to have a good predictive power for detecting AF. External validations are required.

Design of a modular and compliant wrist module for upper limb prosthetics.

In the last decades, there have been great efforts in the development of advanced polyarticulated prosthetic hands; in contrast, prosthetic wrists have drawn less interest. Nevertheless, increasing the dexterity of the wrist improves handling skills because the wrist allows the prepositioning of the hand before carrying out a task, avoiding the onset of unwanted trunk or shoulders compensatory movements and potential onset or exacerbation of articular injuries. This study presents a novel 2-degree-of-freedom prosthetic wrist module with active pronation/supination and passive elastic flexion/extension. This system is suitable to be included in hand prostheses to improve anthropomorphism and produce a more physiological motion. The first submodule within the socket is able to rotate a prosthetic hand and an external load of 3 kg at 2.6 rad/s. The second one can guarantee a range of motion of ±75° with a centering elastic torque (compliant mode) or it can keep firms grasps (fixed mode). Compliant mode is based on a Scotch-Yoke mechanism converting wrist flexion/extension into the linear motion of a crossbeam acting on compression springs, while fixed mode is achieved by means of a piston that can be engaged/disengaged. The whole module fits with anthropometry and the modular design ensures the proposed system can be used in a stand-alone way and adapted to different hand prostheses. This device is expected to favor a more physiological dexterity with respect to simpler fixed prostheses that can potentially induce harmful motion of body districts not naturally involved in the reaching and grasping tasks.

CT-derived body composition analysis could possibly replace DXA and BIA to monitor NET-patients.

Patients with neuroendocrine tumors of gastro-entero-pancreatic origin (GEP-NET) experience changes in fat and muscle composition. Dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) are currently used to analyze body composition. Changes thereof could indicate cancer progression or response to treatment. This study examines the correlation between CT-based (computed tomography) body composition analysis (BCA) and DXA or BIA measurement. 74 GEP-NET-patients received whole-body [68Ga]-DOTATOC-PET/CT, BIA, and DXA-scans. BCA was performed based on the non-contrast-enhanced, 5 mm, whole-body-CT images. BCA from CT shows a strong correlation between body fat ratio with DXA (r = 0.95, ρC = 0.83) and BIA (r = 0.92, ρC = 0.76) and between skeletal muscle ratio with BIA: r = 0.81, ρC = 0.49. The deep learning-network achieves highly accurate results (mean Sørensen-Dice-score 0.93). Using BCA on routine Positron emission tomography/CT-scans to monitor patients' body composition in the diagnostic workflow can reduce additional exams whilst substantially amplifying measurement in slower progressing cancers such as GEP-NET.

Primary acute lymphoblastic leukemia cells are susceptible to microtubule depolymerization in G1 and M phases through distinct cell death pathways.

Microtubule targeting agents (MTAs) are widely used cancer chemotherapeutics which conventionally exert their effects during mitosis, leading to mitotic or postmitotic death. However, accumulating evidence suggests that MTAs can also generate death signals during interphase, which may represent a key mechanism in the clinical setting. We reported previously that vincristine and other microtubule destabilizers induce death not only in M phase but also in G1 phase in primary acute lymphoblastic leukemia cells. Here, we sought to investigate and compare the pathways responsible for phase-specific cell death. Primary acute lymphoblastic leukemia cells were subjected to centrifugal elutriation, and cell populations enriched in G1 phase (97%) or G2/M phases (80%) were obtained and treated with vincristine. We found death of M phase cells was associated with established features of mitochondrial-mediated apoptosis, including Bax activation, loss of mitochondrial transmembrane potential, caspase-3 activation, and nucleosomal DNA fragmentation. In contrast, death of G1 phase cells was not associated with pronounced Bax or caspase-3 activation but was associated with loss of mitochondrial transmembrane potential, parylation, nuclear translocation of apoptosis-inducing factor and endonuclease G, and supra-nucleosomal DNA fragmentation, which was enhanced by inhibition of autophagy. The results indicate that microtubule depolymerization induces distinct cell death pathways depending on during which phase of the cell cycle microtubule perturbation occurs. The observation that a specific type of drug can enter a single cell type and induce two different modes of death is novel and intriguing. These findings provide a basis for advancing knowledge of clinical mechanisms of MTAs.

An Atlas for the Inkjet Printing of Large-Area Tactile Sensors.

This review aims to discuss the inkjet printing technique as a fabrication method for the development of large-area tactile sensors. The paper focuses on the manufacturing techniques and various system-level sensor design aspects related to the inkjet manufacturing processes. The goal is to assess how printed electronics simplify the fabrication process of tactile sensors with respect to conventional fabrication methods and how these contribute to overcoming the difficulties arising in the development of tactile sensors for real robot applications. To this aim, a comparative analysis among different inkjet printing technologies and processes is performed, including a quantitative analysis of the design parameters, such as the costs, processing times, sensor layout, and general system-level constraints. The goal of the survey is to provide a complete map of the state of the art of inkjet printing, focusing on the most effective topics for the implementation of large-area tactile sensors and a view of the most relevant open problems that should be addressed to improve the effectiveness of these processes.

Liraglutide Counteracts Endoplasmic Reticulum Stress in Palmitate-Treated Hypothalamic Neurons without Restoring Mitochondrial Homeostasis.

One feature of high-fat diet-induced neurodegeneration in the hypothalamus is an increased level of palmitate, which is associated with endoplasmic reticulum (ER) stress, loss of CoxIV, mitochondrial fragmentation, and decreased abundance of MC4R. To determine whether antidiabetic drugs protect against ER and/or mitochondrial dysfunction by lipid stress, hypothalamic neurons derived from pre-adult mice and neuronal Neuro2A cells were exposed to elevated palmitate. In the hypothalamic neurons, palmitate exposure increased expression of ER resident proteins, including that of SERCA2, indicating ER stress. Liraglutide reverted such altered ER proteostasis, while metformin only normalized SERCA2 expression. In Neuro2A cells liraglutide, but not metformin, also blunted dilation of the ER induced by palmitate treatment, and enhanced abundance and expression of MC4R at the cell surface. Thus, liraglutide counteracts, more effectively than metformin, altered ER proteostasis, morphology, and folding capacity in neurons exposed to fat. In palmitate-treated hypothalamic neurons, mitochondrial fragmentation took place together with loss of CoxIV and decreased mitochondrial membrane potential (MMP). Metformin, but not liraglutide, reverted mitochondrial fragmentation, and both liraglutide and metformin did not protect against either loss of CoxIV abundance or MMP. Thus, ER recovery from lipid stress can take place in hypothalamic neurons in the absence of recovered mitochondrial homeostasis.

Selective Survival of Sim1/MC4R Neurons in Diet-Induced Obesity.

In the melanocortin pathway, melanocortin-4 receptor (MC4R) functions to control energy homeostasis. MC4R is expressed in a sub-population of Sim1 neurons (Sim1/MC4R neurons) and functions in hypothalamic paraventricular nuclei (PVN) to control food intake. Mapping sites of hypothalamic injury in obesity is essential to counteract the disease. In the PVN of male and female mice with diet-induced obesity (DIO) there is neuronal loss. However, the existing subpopulation of PVN Sim1/MC4R neurons is unchanged, but has a loss of mitochondria and MC4R protein. In mice of both sexes with DIO, dietary intervention to re-establish normal weight restores abundance of MC4R protein in Sim1/MC4R neurons and neurogenesis in the PVN. However, the number of non-Sim1/MC4R neurons in the PVN continues to remain decreased. Selective survival and recovery of Sim1/MC4R neurons after DIO suggests these neurons as preferential target to restore energy homeostasis and of therapy against obesity.

Elevation of the unfolded protein response increases RANKL expression.

Increased production of the osteoclastogenic cytokine RANKL is a common feature of pathologic bone loss, but the underlying cause of this increase is poorly understood. The unfolded protein response (UPR) is activated in response to accumulation of misfolded proteins in the endoplasmic reticulum (ER). Failure to resolve misfolding results in excess UPR signaling that stimulates cytokine production and cell death. We therefore investigated whether RANKL is one of the cytokines stimulated in response to elevated UPR in bone cells. Pharmacologic induction of UPR with tunicamycin (Tm)-stimulated RANKL expression in cultures of primary osteoblastic cells and in osteoblast and osteocyte cell lines. Pharmacologic inhibition of the UPR blunted Tm-induced RANKL production. Silencing Edem1 or Sel1l, proteins that aid in degradation of misfolded proteins, also induced UPR and increased RANKL mRNA. Moreover, Tm or hypoxia increased RANKL and bone resorption in cultures of neonatal murine calvaria. Administration of Tm to adult mice caused dilation of ER in osteoblasts and osteocytes, elevated the UPR, and increased RANKL expression and osteoclast number. These findings support the hypothesis that excessive UPR signaling stimulates the expression of RANKL by osteoblasts and osteocytes, and thereby facilitates excessive bone resorption and bone loss in pathologic conditions.

Delivery of phosphatidylethanolamine blunts stress in hepatoma cells exposed to elevated palmitate by targeting the endoplasmic reticulum.

Genetic obesity increases in liver phosphatidylcholine (PC)/phosphatidylethanolamine (PE) ratio, inducing endoplasmic reticulum (ER) stress without concomitant increase of ER chaperones. Here, it is found that exposing mice to a palm oil-based high fat (HF) diet induced obesity, loss of liver PE, and loss of the ER chaperone Grp78/BiP in pericentral hepatocytes. In Hepa1-6 cells treated with elevated concentration of palmitate to model lipid stress, Grp78/BiP mRNA was increased, indicating onset of stress-induced Unfolded Protein Response (UPR), but Grp78/BiP protein abundance was nevertheless decreased. Exposure to elevated palmitate also induced in hepatoma cells decreased membrane glycosylation, nuclear translocation of pro-apoptotic C/EBP-homologous-protein-10 (CHOP), expansion of ER-derived quality control compartment (ERQC), loss of mitochondrial membrane potential (MMP), and decreased oxidative phosphorylation. When PE was delivered to Hepa1-6 cells exposed to elevated palmitate, effects by elevated palmitate to decrease Grp78/BiP protein abundance and suppress membrane glycosylation were blunted. Delivery of PE to Hepa1-6 cells treated with elevated palmitate also blunted expansion of ERQC, decreased nuclear translocation of CHOP and lowered abundance of reactive oxygen species (ROS). Instead, delivery of the chemical chaperone 4-phenyl-butyrate (PBA) to Hepa1-6 cells treated with elevated palmitate, while increasing abundance of Grp78/BiP protein and restoring membrane glycosylation, also increased ERQC, expression and nuclear translocation of CHOP, non-mitochondrial oxygen consumption, and generation of ROS. Data indicate that delivery of PE to hepatoma cells under lipid stress recovers cell function by targeting the secretory pathway and by blunting pro-apoptotic branches of the UPR.

The melanocortin pathway and control of appetite-progress and therapeutic implications.

The initial discovery that ob/ob mice become obese because of a recessive mutation of the leptin gene has been crucial to discover the melanocortin pathway to control appetite. In the melanocortin pathway, the fed state is signaled by abundance of circulating hormones such as leptin and insulin, which bind to receptors expressed at the surface of pro-opiomelanocortin (POMC) neurons to promote processing of POMC to the mature hormone α-melanocyte-stimulating hormone (α-MSH). The α-MSH released by POMC neurons then signals to decrease energy intake by binding to melanocortin-4 receptor (MC4R) expressed by MC4R neurons to the paraventricular nucleus (PVN). Conversely, in the 'starved state' activity of agouti-related neuropeptide (AgRP) and of neuropeptide Y (NPY)-expressing neurons is increased by decreased levels of circulating leptin and insulin and by the orexigenic hormone ghrelin to promote food intake. This initial understanding of the melanocortin pathway has recently been implemented by the description of the complex neuronal circuit that controls the activity of POMC, AgRP/NPY and MC4R neurons and downstream signaling by these neurons. This review summarizes the progress done on the melanocortin pathway and describes how obesity alters this pathway to disrupt energy homeostasis. We also describe progress on how leptin and insulin receptors signal in POMC neurons, how MC4R signals and how altered expression and traffic of MC4R change the acute signaling and desensitization properties of the receptor. We also describe how the discovery of the melanocortin pathway has led to the use of melanocortin agonists to treat obesity derived from genetic disorders.

Injury to hypothalamic Sim1 neurons is a common feature of obesity by exposure to high-fat diet in male and female mice.

The hypothalamus is essential for regulation of energy homeostasis and metabolism. Feeding hypercaloric, high-fat (HF) diet induces hypothalamic arcuate nucleus injury and alters metabolism more severely in male than in female mice. The site(s) and extent of hypothalamic injury in male and female mice are not completely understood. In the paraventricular nucleus (PVN) of the hypothalamus, single-minded family basic helix-loop helix transcription factor 1 (Sim1) neurons are essential to control energy homeostasis. We tested the hypothesis that exposure to HF diet induces injury to Sim1 neurons in the PVN of male and female mice. Mice expressing membrane-bound enhanced green fluorescent protein (mEGFP) in Sim1 neurons (Sim1-Cre:Rosa-mEGFP mice) were generated to visualize the effects of exposure to HF diet on these neurons. Male and female Sim1-Cre:Rosa-mEGFP mice exposed to HF diet had increased weight, hyperleptinemia, and developed hepatosteatosis. In male and female mice exposed to HF diet, expression of mEGFP was reduced by > 40% in Sim1 neurons of the PVN, an effect paralleled by cell apoptosis and neuronal loss, but not by microgliosis. In the arcuate nucleus of the Sim1-Cre:Rosa-mEGFP male mice, there was decreased alpha-melanocyte-stimulating hormone in proopiomelanocortin neurons projecting to the PVN, with increased cell apoptosis, neuronal loss, and microgliosis. These defects were undetectable in the arcuate nucleus of female mice exposed to the HF diet. Thus, injury to Sim1 neurons of the PVN is a shared feature of exposure to HF diet in mice of both sexes, while injury to proopiomelanocortin neurons in arcuate nucleus is specific to male mice. OPEN SCIENCE BADGES: This article has received a badge for *Open Materials* because it provided all relevant information to reproduce the study in the manuscript. The complete Open Science Disclosure form for this article can be found at the end of the article. More information about the Open Practices badges can be found at https://cos.io/our-services/open-science-badges/.

Lipid stress inhibits endocytosis of melanocortin-4 receptor from modified clathrin-enriched sites and impairs receptor desensitization.

Melanocortin-4 receptor (MC4R) is a G-protein-coupled receptor expressed in the brain's hypothalamus where it regulates energy homeostasis. MC4R agonists function to lower food intake and weight. In this respect, although obesity promotes hyperlipidemia and hypothalamic injury, MC4R agonists are nevertheless more effective to reduce food intake within hours of administration in overweight, rather than lean, mice. MC4R undergoes constitutive internalization and recycling to the plasma membrane with agonist binding inducing receptor retention along the intracellular route and, under prolonged exposure, desensitization. Here, we found that, in neuronal cells, lipid stress by exposure to elevated palmitate leaves unchanged the rate by which MC4R and transferrin receptor are constitutively excluded from the cell surface. However, lipid stress disrupted later steps of MC4R and transferrin receptor internalization to endosomes as well as traffic of agonist-occupied MC4R to lysosomes and MC4R desensitization. In the lipid-stressed cells, MC4R and clathrin were redistributed to the plasma membrane where they colocalized to sites that appeared by super-resolution microscopy to be modified and to have higher clathrin content than those of cells not exposed to elevated palmitate. The data suggest that lipid stress disrupts steps of endocytosis following MC4R localization to clathrin-coated sites and exclusion of the receptor from the extracellular medium. We conclude that increased effectiveness of MC4R agonists in obesity may be an unexpected outcome of neuronal injury with disrupted clathrin-dependent endocytosis and impaired receptor desensitization.

Indicators of responsiveness to immune checkpoint inhibitors.

Modulation of the immune system can produce anti-tumor responses in various cancer types, including melanoma. Recently, immune checkpoint inhibitors (ICI), in single agent and combination regimens, have produced durable and long-lasting clinical responses in a subset of metastatic melanoma patients. These monoclonal antibodies, developed against CTLA-4 and PD-1, block immune-inhibitory receptors on activated T-cells, amplifying the immune response. However, even when using anti-CTLA-4 and anti-PD-1 in combination, approximately half of patients exhibit innate resistance and suffer from disease progression. Currently, it is impossible to predict therapeutic response. Here, we report the first proteomic and histone epigenetic analysis of patient metastatic melanoma tumors taken prior to checkpoint blockade, which revealed biological signatures that can stratify patients as responders or non-responders. Furthermore, our findings provide evidence of mesenchymal transition, a known mechanism of immune-escape, in non-responding melanoma tumors. We identified elevated histone H3 lysine (27) trimethylation (H3K27me3), decreased E-cadherin, and other protein features indicating a more mesenchymal phenotype in non-responding tumors. Our results have implications for checkpoint inhibitor therapy as patient specific responsiveness can be predicted through readily assayable proteins and histone epigenetic marks, and pathways activated in non-responders have been identified for therapeutic development to enhance responsiveness.