A novel center-based deep contrastive metric learning method for the detection of polymicrogyria in pediatric brain MRI.

Polymicrogyria (PMG) is a disorder of cortical organization mainly seen in children, which can be associated with seizures, developmental delay and motor weakness. PMG is typically diagnosed on magnetic resonance imaging (MRI) but some cases can be challenging to detect even for experienced radiologists. In this study, we create an open pediatric MRI dataset (PPMR) containing both PMG and control cases from the Children's Hospital of Eastern Ontario (CHEO), Ottawa, Canada. The differences between PMG and control MRIs are subtle and the true distribution of the features of the disease is unknown. This makes automatic detection of potential PMG cases in MRI difficult. To enable the automatic detection of potential PMG cases, we propose an anomaly detection method based on a novel center-based deep contrastive metric learning loss function (cDCM). Despite working with a small and imbalanced dataset our method achieves 88.07% recall at 71.86% precision. This will facilitate a computer-aided tool for radiologists to select potential PMG MRIs. To the best of our knowledge, our research is the first to apply machine learning techniques to identify PMG solely from MRI. Our code is available at: https://github.com/RichardChangCA/Deep-Contrastive-Metric-Learning-Method-to-Detect-Polymicrogyria-in-Pediatric-Brain-MRI. Our pediatric MRI dataset is available at: https://www.kaggle.com/datasets/lingfengzhang/pediatric-polymicrogyria-mri-dataset.

Islets-on-Chip: A Tool for Real-Time Assessment of Islet Function Prior to Transplantation.

Islet transplantation improves metabolic control in patients with unstable type 1 diabetes. Clinical outcomes have been improving over the last decade, and the widely used beta-score allows the evaluation of transplantation results. However, predictive pre-transplantation criteria of islet quality for clinical outcomes are lacking. In this proof-of-concept study, we examined whether characterization of the electrical activity of donor islets could provide a criterion. Aliquots of 8 human donor islets from the STABILOT study, sampled from islet preparations before transplantation, were characterized for purity and split for glucose-induced insulin secretion and electrical activity using multi-electrode-arrays. The latter tests glucose concentration dependencies, biphasic activity, hormones, and drug effects (adrenalin, GLP-1, glibenclamide) and provides a ranking of CHIP-scores from 1 to 6 (best) based on electrical islet activity. The analysis was performed online in real time using a dedicated board or offline. Grouping of beta-scores and CHIP-scores with high, intermediate, and low values was observed. Further analysis indicated correlation between CHIP-score and beta-score, although significance was not attained (R = 0.51, p = 0.1). This novel approach is easily implantable in islet isolation units and might provide means for the prediction of clinical outcomes. We acknowledge the small cohort size as the limitation of this pilot study.

Electrophysiological characterisation of iPSC-derived human ß-like cells and an SLC30A8 disease model.

iPSC-derived human ß-like cells (BLC) hold promise for both therapy and disease modelling, but their generation remains challenging and their functional analyses beyond transcriptomic and morphological assessments remain limited. Here, we validate an approach using multicellular and single cell electrophysiological tools to evaluate BLCs functions. The Multi-Electrode Arrays (MEAs) measuring the extracellular electrical activity revealed that BLCs are electrically coupled, produce slow potential (SP) signals like primary ß-cells that are closely linked to insulin secretion. We also used high-resolution single-cell patch-clamp measurements to capture the exocytotic properties, and characterize voltage-gated sodium and calcium currents. These were comparable to those in primary ß and EndoC-ßH1 cells. The KATP channel conductance is greater than in human primary ß cells which may account for the limited glucose responsiveness observed with MEA. We used MEAs to study the impact of the type 2 diabetes protective SLC30A8 allele (p.Lys34Serfs*50) and found that BLCs with this allele have stronger electrical coupling. Our data suggest that with an adapted approach BLCs from pioneer protocol can be used to evaluate the functional impact of genetic variants on ß-cell function and coupling.

Regularization for Unsupervised Learning of Optical Flow.

Regularization is an important technique for training deep neural networks. In this paper, we propose a novel shared-weight teacher-student strategy and a content-aware regularization (CAR) module. Based on a tiny, learnable, content-aware mask, CAR is randomly applied to some channels in the convolutional layers during training to be able to guide predictions in a shared-weight teacher-student strategy. CAR prevents motion estimation methods in unsupervised learning from co-adaptation. Extensive experiments on optical flow and scene flow estimation show that our method significantly improves on the performance of the original networks and surpasses other popular regularization methods. The method also surpasses all variants with similar architectures and the supervised PWC-Net on MPI-Sintel and on KITTI. Our method shows strong cross-dataset generalization, i.e., our method solely trained on MPI-Sintel outperforms a similarly trained supervised PWC-Net by 27.9% and 32.9% on KITTI, respectively. Our method uses fewer parameters and less computation, and has faster inference times than the original PWC-Net.

The Past, Present, and Future Role of Artificial Intelligence in Ventilation/Perfusion Scintigraphy: A Systematic Review.

Ventilation-perfusion (V/Q) lung scans constitute one of the oldest nuclear medicine procedures, remain one of the few studies performed in the acute setting, and are amongst the few performed in the emergency setting. V/Q studies have witnessed a long fluctuation in adoption rates in parallel to continuous advances in image processing and computer vision techniques. This review provides an overview on the status of artificial intelligence (AI) in V/Q scintigraphy. To clearly assess the past, current, and future role of AI in V/Q scans, we conducted a systematic Ovid MEDLINE(R) literature search from 1946 to August 5, 2022 in addition to a manual search. The literature was reviewed and summarized in terms of methodologies and results for the various applications of AI to V/Q scans. The PRISMA guidelines were followed. Thirty-one publications fulfilled our search criteria and were grouped into two distinct categories: (1) disease diagnosis/detection (N = 22, 71.0%) and (2) cross-modality image translation into V/Q images (N = 9, 29.0%). Studies on disease diagnosis and detection relied heavily on shallow artificial neural networks for acute pulmonary embolism (PE) diagnosis and were primarily published between the mid-1990s and early 2000s. Recent applications almost exclusively regard image translation tasks from CT to ventilation or perfusion images with modern algorithms, such as convolutional neural networks, and were published between 2019 and 2022. AI research in V/Q scintigraphy for acute PE diagnosis in the mid-90s to early 2000s yielded promising results but has since been largely neglected and thus have yet to benefit from today's state-of-the art machine-learning techniques, such as deep neural networks. Recently, the main application of AI for V/Q has shifted towards generating synthetic ventilation and perfusion images from CT. There is therefore considerable potential to expand and modernize the use of real V/Q studies with state-of-the-art deep learning approaches, especially for workflow optimization and PE detection at both acute and chronic stages. We discuss future challenges and potential directions to compensate for the lag in this domain and enhance the value of this traditional nuclear medicine scan.

Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes.

In diabetes mellitus (DM) treatment, Continuous Glucose Monitoring (CGM) linked with insulin delivery becomes the main strategy to improve therapeutic outcomes and quality of patients' lives. However, Blood Glucose (BG) regulation with CGM is still hampered by limitations of algorithms and glucose sensors. Regarding sensor technology, current electrochemical glucose sensors do not capture the full spectrum of other physiological signals, i.e., lipids, amino acids or hormones, relaying the general body status. Regarding algorithms, variability between and within patients remains the main challenge for optimal BG regulation in closed-loop therapies. This work highlights the simulation benefits to test new sensing and control paradigms which address the previous shortcomings for Type 1 Diabetes (T1D) closed-loop therapies. The UVA/Padova T1DM Simulator is the core element here, which is a computer model of the human metabolic system based on glucose-insulin dynamics in T1D patients. That simulator is approved by the US Food and Drug Administration (FDA) as an alternative for pre-clinical testing of new devices and closed-loop algorithms. To overcome the limitation of standard glucose sensors, the concept of an islet-based biosensor, which could integrate multiple physiological signals through electrical activity measurement, is assessed here in a closed-loop insulin therapy. This investigation has been addressed by an interdisciplinary consortium, from endocrinology to biology, electrophysiology, bio-electronics and control theory. In parallel to the development of an islet-based closed-loop, it also investigates the benefits of robust control theory against the natural variability within a patient population. Using 4 meal scenarios, numerous simulation campaigns were conducted. The analysis of their results then introduces a discussion on the potential benefits of an Artificial Pancreas (AP) system associating the islet-based biosensor with robust algorithms.

Vertical Organic Electrochemical Transistors and Electronics for Low Amplitude Micro-Organ Signals.

Electrical signals are fundamental to key biological events such as brain activity, heartbeat, or vital hormone secretion. Their capture and analysis provide insight into cell or organ physiology and a number of bioelectronic medical devices aim to improve signal acquisition. Organic electrochemical transistors (OECT) have proven their capacity to capture neuronal and cardiac signals with high fidelity and amplification. Vertical PEDOT:PSS-based OECTs (vOECTs) further enhance signal amplification and device density but have not been characterized in biological applications. An electronic board with individually tuneable transistor biases overcomes fabrication induced heterogeneity in device metrics and allows quantitative biological experiments. Careful exploration of vOECT electric parameters defines voltage biases compatible with reliable transistor function in biological experiments and provides useful maximal transconductance values without influencing cellular signal generation or propagation. This permits successful application in monitoring micro-organs of prime importance in diabetes, the endocrine pancreatic islets, which are known for their far smaller signal amplitudes as compared to neurons or heart cells. Moreover, vOECTs capture their single-cell action potentials and multicellular slow potentials reflecting micro-organ organizations as well as their modulation by the physiological stimulator glucose. This opens the possibility to use OECTs in new biomedical fields well beyond their classical applications.

Integrating an Islet-Based Biosensor in the Artificial Pancreas: In Silico Proof-of-Concept.

OBJECTIVE: Current treatment of type 1 diabetes by closed-loop therapy depends on continuous glucose monitoring. However, glucose readings alone are insufficient for an artificial pancreas to truthfully restore nutrient homeostasis where additional physiological regulators of insulin secretion play a considerable role. Previously, we have developed an electrophysiological biosensor of pancreatic islet activity, which integrates these additional regulators through electrical measurements. This work aims at investigating the performance of the biosensor in a blood glucose control loop as potential in silico proof-of-concept. METHODS: Two islet algorithm models were identified on experimental data recorded with the biosensor. First, we validated electrical measurement as a means to exploit the inborn regulation capabilities of islets for intravenous glucose measurement and insulin infusion. Subsequently, an artificial pancreas integrating the islet-based biosensor was compared to standard treatment approaches using subcutaneous routes. The closed-loop simulations were performed in the UVA/Padova T1DM Simulator where a series of realistic meal scenarios were applied to virtual diabetic patients. RESULTS: With intravenous routes, the endogenous islet algorithms successfully restored glucose homeostasis for all patient categories (mean time in range exceeds 90%) while mitigating the risk of adverse glycaemic events (mean BGI < 2). Using subcutaneous routes, the biosensor-based artificial pancreas was as efficient as standard treatments, and outperformed them under challenging conditions. CONCLUSION: This work validates the concept of using inborn pancreatic islets algorithms in an artificial pancreas in silico. SIGNIFICANCE: Pancreatic islet endogenous algorithms obtained via an electrophysiological biosensor successfully regulate blood glucose levels of virtual type 1 diabetic patients.

Semi-Supervised Training for Positioning of Welding Seams.

Robotic welding often uses vision-based measurement to find the correct placement of the welding seam. Traditional machine vision methods work well in many cases but lack robustness when faced with variations in the manufacturing process or in the imaging conditions. While supervised deep neural networks have been successful in increasing accuracy and robustness in many real-world measurement applications, their success relies on labeled data. In this paper, we employ semi-supervised learning to simultaneously increase accuracy and robustness while avoiding expensive and time-consuming labeling efforts by a domain expert. While semi-supervised learning approaches for various image classification tasks exist, we purpose a novel algorithm for semi-supervised key-point detection for seam placement by a welding robot. We demonstrate that our approach can work robustly with as few as fifteen labeled images. In addition, our method utilizes full image resolution to enhance the accuracy of the key-point detection in seam placement.

3D shearlet-based descriptors combined with deep features for the classification of Alzheimer's disease based on MRI data.

Alzheimer's disease (AD) is a neurodegenerative disease that afflicts millions of people worldwide. Early detection of AD is critical, as drug trials show a promising advantage to those patients with early diagnoses. In this study, magnetic resonance imaging (MRI) datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and The Open Access Series of Imaging Studies are used. Our method for performing the classification of AD is to combine a set of shearlet-based descriptors with deep features. A major challenge in classifying such MRI datasets is the high dimensionality of feature vectors because of the large number of slices of each MRI sample. Given the volumetric nature of the MRI data, we propose using the 3D shearlet transform (3D-ST), but we obtain the average of all directionalities, which reduces the dimensionality. On the other hand, we propose to leverage the capabilities of convolutional neural networks (CNN) to learn feature maps from stacked MRI slices, which generate a very compact feature vector for each MRI sample. The 3D-ST and CNN feature vectors are combined for the classification of AD. After the concatenation of the feature vectors, they are used to train a classifier. Alternatively, a custom CNN model is utilized, in which the descriptors are further processed end to end to obtain the classification model. Our experimental results show that the fusion of shearlet-based descriptors and deep features improves classification performance, especially on the ADNI dataset.

[Pancreatic α and ß cells: Best enemies or partners for life?] / Cellules α et ß du pancréas - Meilleures ennemies ou partenaires pour la vie ?

Diabetes are major metabolic diseases constantly increasing in the population, caused by reduced secretion and action of insulin, the only hormone lowering efficiently the glycaemia. Insulin is secreted by ß cells within the pancreatic islets of Langerhans. The islet micro-organs also contain 15 to 35% of α cells, well-known for their opposite effects on glycaemia. Considered until now as potentially harmful in diabetes, α cells are emerging as potent enhancers of ß cell activity when studied in physiological nutritional setting and should therefore be reconsidered in a therapeutic point of view. This review summarizes the latest concepts regarding ß cell function in physiological states and the involvement of dynamic functional interactions between α and ß cells for the regulation of nutrient homeostasis.

TITLE: Cellules α et ß du pancréas - Meilleures ennemies ou partenaires pour la vie ? ABSTRACT: Les diabètes sucrés sont des maladies métaboliques graves en constante augmentation. Ils sont dus à des déficits de sécrétion et d'action de l'insuline, la seule hormone qui diminue efficacement la glycémie. L'insuline est sécrétée par les cellules ß des îlots pancréatiques. Les cellules α, également présentes dans les îlots, libèrent du glucagon et ont des effets opposés à ceux des cellules ß sur la glycémie. Longtemps considérée comme néfaste dans le diabète, la cellule α apparaît désormais comme un modulateur des cellules ß, ce qui nécessite de prendre désormais en compte cette cellule sur le plan thérapeutique. Cette revue présente le fonctionnement des cellules ß et des cellules α. L'implication des interactions dynamiques entre ces deux types cellulaires dans l'homéostasie du glucose, mais aussi celle des autres nutriments, est également décrite.

DOE-SLAM: Dynamic Object Enhanced Visual SLAM.

In this paper, we formulate a novel strategy to adapt monocular-vision-based simultaneous localization and mapping (vSLAM) to dynamic environments. When enough background features can be captured, our system not only tracks the camera trajectory based on static background features but also estimates the foreground object motion from object features. In cases when a moving object obstructs too many background features for successful camera tracking from the background, our system can exploit the features from the object and the prediction of the object motion to estimate the camera pose. We use various synthetic and real-world test scenarios and the well-known TUM sequences to evaluate the capabilities of our system. The experiments show that we achieve higher pose estimation accuracy and robustness over state-of-the-art monocular vSLAM systems.

Dynamic Uni- and Multicellular Patterns Encode Biphasic Activity in Pancreatic Islets.

Biphasic secretion is an autonomous feature of many endocrine micro-organs to fulfill physiological demands. The biphasic activity of islet ß-cells maintains glucose homeostasis and is altered in type 2 diabetes. Nevertheless, underlying cellular or multicellular functional organizations are only partially understood. High-resolution noninvasive multielectrode array recordings permit simultaneous analysis of recruitment, of single-cell, and of coupling activity within entire islets in long-time experiments. Using this unbiased approach, we addressed the organizational modes of both first and second phase in mouse and human islets under physiological and pathophysiological conditions. Our data provide a new uni- and multicellular model of islet ß-cell activation: during the first phase, small but highly active ß-cell clusters are dominant, whereas during the second phase, electrical coupling generates large functional clusters via multicellular slow potentials to favor an economic sustained activity. Postprandial levels of glucagon-like peptide 1 favor coupling only in the second phase, whereas aging and glucotoxicity alter coupled activity in both phases. In summary, biphasic activity is encoded upstream of vesicle pools at the micro-organ level by multicellular electrical signals and their dynamic synchronization between ß-cells. The profound alteration of the electrical organization of islets in pathophysiological conditions may contribute to functional deficits in type 2 diabetes.

Texture features in the Shearlet domain for histopathological image classification.

BACKGROUND: A various number of imaging modalities are available (e.g., magnetic resonance, x-ray, ultrasound, and biopsy) where each modality can reveal different structural aspects of tissues. However, the analysis of histological slide images that are captured using a biopsy is considered the gold standard to determine whether cancer exists. Furthermore, it can reveal the stage of cancer. Therefore, supervised machine learning can be used to classify histopathological tissues. Several computational techniques have been proposed to study histopathological images with varying levels of success. Often handcrafted techniques based on texture analysis are proposed to classify histopathological tissues which can be used with supervised machine learning. METHODS: In this paper, we construct a novel feature space to automate the classification of tissues in histology images. Our feature representation is to integrate various features sets into a new texture feature representation. All of our descriptors are computed in the complex Shearlet domain. With complex coefficients, we investigate not only the use of magnitude coefficients, but also study the effectiveness of incorporating the relative phase (RP) coefficients to create the input feature vector. In our study, four texture-based descriptors are extracted from the Shearlet coefficients: co-occurrence texture features, Local Binary Patterns, Local Oriented Statistic Information Booster, and segmentation-based Fractal Texture Analysis. Each set of these attributes captures significant local and global statistics. Therefore, we study them individually, but additionally integrate them to boost the accuracy of classifying the histopathology tissues while being fed to classical classifiers. To tackle the problem of high-dimensionality, our proposed feature space is reduced using principal component analysis. In our study, we use two classifiers to indicate the success of our proposed feature representation: Support Vector Machine (SVM) and Decision Tree Bagger (DTB). RESULTS: Our feature representation delivered high performance when used on four public datasets. As such, the best achieved accuracy: multi-class Kather (i.e., 92.56%), BreakHis (i.e., 91.73%), Epistroma (i.e., 98.04%), Warwick-QU (i.e., 96.29%). CONCLUSIONS: Our proposed method in the Shearlet domain for the classification of histopathological images proved to be effective when it was investigated on four different datasets that exhibit different levels of complexity.

Design of Potassium-Selective Mixed Ion/Electron Conducting Polymers.

An approach providing cation-selective poly-(3,4-ethylenedioxythiophene)(PEDOT):polyelectrolyte-mixed conductors is presented in this communication based on the structural modification of this ambivalent (ionic and electronic conductive) polymer complex. First, an 18-crown-6 moiety is integrated into the styrene sulfonate monomer structure as a specific metal cation scavenger particularly targeting K+ versus Na+ detection. This newly functionalized monomer is characterized by 1 H NMR titration to evaluate the ion selectivity. Aqueous PEDOT dispersion inks containing the polymeric ion-selective moieties are designed and their electrical and electrochemical properties analyzed. These biocompatible inks are the first proof-of-concept step towards ion selectivity in view of their interfacing with biological cells and microorgans of interest in the field of biosensors and physiology.

The glutamate receptor GluK2 contributes to the regulation of glucose homeostasis and its deterioration during aging.

OBJECTIVE: Islets secrete neurotransmitters including glutamate which participate in fine regulation of islet function. The excitatory ionotropic glutamate receptor GluK2 of the kainate receptor family is widely expressed in brain and also found in islets, mainly in α and γ cells. α cells co-release glucagon and glutamate and the latter increases glucagon release via ionotropic glutamate receptors. However, neither the precise nature of the ionotropic glutamate receptor involved nor its role in glucose homeostasis is known. As isoform specific pharmacology is not available, we investigated this question in constitutive GluK2 knock-out mice (GluK2-/-) using adult and middle-aged animals to also gain insight in a potential role during aging. METHODS: We compared wild-type GluK2+/+ and knock-out GluK2-/- mice using adult (14-20 weeks) and middle-aged animals (40-52 weeks). Glucose (oral OGTT and intraperitoneal IPGTT) and insulin tolerance as well as pyruvate challenge tests were performed according to standard procedures. Parasympathetic activity, which stimulates hormones secretion, was measured by electrophysiology in vivo. Isolated islets were used in vitro to determine islet ß-cell electrical activity on multi-electrode arrays and dynamic secretion of insulin as well as glucagon was determined by ELISA. RESULTS: Adult GluK2-/- mice exhibit an improved glucose tolerance (OGTT and IPGTT), and this was also apparent in middle-aged mice, whereas the outcome of pyruvate challenge was slightly improved only in middle-aged GluK2-/- mice. Similarly, insulin sensitivity was markedly enhanced in middle-aged GluK2-/- animals. Basal and glucose-induced insulin secretion in vivo was slightly lower in GluK2-/- mice, whereas fasting glucagonemia was strongly reduced. In vivo recordings of parasympathetic activity showed an increase in basal activity in GluK2-/- mice which represents most likely an adaptive mechanism to counteract hypoglucagonemia rather than altered neuronal mechanism. In vitro recording demonstrated an improvement of glucose-induced electrical activity of ß-cells in islets obtained from GluK2-/- mice at both ages. Finally, glucose-induced insulin secretion in vitro was increased in GluK2-/- islets, whereas glucagon secretion at 2 mmol/l of glucose was considerably reduced. CONCLUSIONS: These observations indicate a general role for kainate receptors in glucose homeostasis and specifically suggest a negative effect of GluK2 on glucose homeostasis and preservation of islet function during aging. Our observations raise the possibility that blockade of GluK2 may provide benefits in glucose homeostasis especially during aging.

The transmembrane domain of the SNARE protein VAMP2 is highly sensitive to its lipid environment.

Neurotransmitter and hormone exocytosis depends on SNARE protein transmembrane domains and membrane lipids but their interplay is poorly understood. We investigated the interaction of the structure of VAMP2, a vesicular transmembrane SNARE protein, and membrane lipid composition by infrared spectroscopy using either the wild-type transmembrane domain (TMD), VAMP2TM22, or a peptide mutated at the central residues G100/C103 (VAMP2TM22VV) previously identified by us as being critical for exocytosis. Our data show that the structure of VAMP2TM22, in terms of α-helices and ß-sheets is strongly influenced by peptide/lipid ratios, by lipid species including cholesterol and by membrane surface charges. Differences observed in acyl chain alignments further underscore the role of the two central small amino acid residues G100/C103 within the transmembrane domain during lipid rearrangements in membrane fusion.

Multimed: An Integrated, Multi-Application Platform for the Real-Time Recording and Sub-Millisecond Processing of Biosignals.

Enhanced understanding and control of electrophysiology mechanisms are increasingly being hailed as key knowledge in the fields of modern biology and medicine. As more and more excitable cell mechanics are being investigated and exploited, the need for flexible electrophysiology setups becomes apparent. With that aim, we designed Multimed, which is a versatile hardware platform for the real-time recording and processing of biosignals. Digital processing in Multimed is an arrangement of generic processing units from a custom library. These can freely be rearranged to match the needs of the application. Embedded onto a Field Programmable Gate Array (FPGA), these modules utilize full-hardware signal processing to lower processing latency. It achieves constant latency, and sub-millisecond processing and decision-making on 64 channels. The FPGA core processing unit makes Multimed suitable as either a reconfigurable electrophysiology system or a prototyping platform for VLSI implantable medical devices. It is specifically designed for open- and closed-loop experiments and provides consistent feedback rules, well within biological microseconds timeframes. This paper presents the specifications and architecture of the Multimed system, then details the biosignal processing algorithms and their digital implementation. Finally, three applications utilizing Multimed in neuroscience and diabetes research are described. They demonstrate the system’s configurability, its multi-channel, real-time processing, and its feedback control capabilities.

Loss-of-function mutations in ADCY3 cause monogenic severe obesity.

Study of monogenic forms of obesity has demonstrated the pivotal role of the central leptin-melanocortin pathway in controlling energy balance, appetite and body weight 1 . The majority of loss-of-function mutations (mostly recessive or co-dominant) have been identified in genes that are directly involved in leptin-melanocortin signaling. These genes, however, only explain obesity in <5% of cases, predominantly from outbred populations 2 . We previously showed that, in a consanguineous population in Pakistan, recessive mutations in known obesity-related genes explain ~30% of cases with severe obesity3-5. These data suggested that new monogenic forms of obesity could also be identified in this population. Here we identify and functionally characterize homozygous mutations in the ADCY3 gene encoding adenylate cyclase 3 in children with severe obesity from consanguineous Pakistani families, as well as compound heterozygous mutations in a severely obese child of European-American descent. These findings highlight ADCY3 as an important mediator of energy homeostasis and an attractive pharmacological target in the treatment of obesity.

Fusion pore in exocytosis: More than an exit gate? A ß-cell perspective.

Secretory vesicle exocytosis is a fundamental biological event and the process by which hormones (like insulin) are released into the blood. Considerable progress has been made in understanding this precisely orchestrated sequence of events from secretory vesicle docked at the cell membrane, hemifusion, to the opening of a membrane fusion pore. The exact biophysical and physiological regulation of these events implies a close interaction between membrane proteins and lipids in a confined space and constrained geometry to ensure appropriate delivery of cargo. We consider some of the still open questions such as the nature of the initiation of the fusion pore, the structure and the role of the Soluble N-ethylmaleimide-sensitive-factor Attachment protein REceptor (SNARE) transmembrane domains and their influence on the dynamics and regulation of exocytosis. We discuss how the membrane composition and protein-lipid interactions influence the likelihood of the nascent fusion pore forming. We relate these factors to the hypothesis that fusion pore expansion could be affected in type-2 diabetes via changes in disease-related gene transcription and alterations in the circulating lipid profile. Detailed characterisation of the dynamics of the fusion pore in vitro will contribute to understanding the larger issue of insulin secretory defects in diabetes.