Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Inflammatory biomarkers of ischemic stroke

Ischemic stroke remains the second leading cause of death and among the major causes of morbidity worldwide. Therapeutic options are currently limited to early reperfusion strategies, while pharmacological neuroprotective strategies despite showing promising results in the experimental setting constantly failed to enter the clinical arena. Inflammation plays an important role in the pathophysiology of ischemic stroke and mediators of inflammation have been longtime investigated as possible prognostic marker and therapeutic target for stroke patients. Here, we summarized available evidence on the role of cytokines, soluble adhesion molecules and adipokines in the pathophysiology, prognosis and therapy of ischemic stroke

Artificial intelligence in scientific medical writing: Legitimate and deceptive uses and ethical concerns

: The debate surrounding the integration of artificial intelligence (AI) into scientific writing has already attracted significant interest in medical and life sciences. While AI can undoubtedly expedite the process of manuscript creation and correction, it raises several criticisms. The crossover between AI and health sciences is relatively recent, but the use of AI tools among physicians and other scientists who work in the life sciences is growing very fast. Within this whirlwind, it is becoming essential to realize where we are heading and what the limits are, including an ethical perspective. Modern conversational AIs exhibit a context awareness that enables them to understand and remember any conversation beyond any predefined script. Even more impressively, they can learn and adapt as they engage with a growing volume of human language input. They all share neural networks as background mathematical models and differ from old chatbots for their use of a specific network architecture called transformer model [1]. Some of them exceed 100 terabytes (TB) (e.g., Bloom, LaMDA) or even 500 TB (e.g., Megatron-Turing NLG) of text data, the 4.0 version of ChatGPT (GPT-4) was trained with nearly 45 TB, but stays updated by the internet connection and may integrate with different plugins that enhance its functionality, making it multimodal

Statin-Induced Necrotizing Autoimmune Myopathy: Case Report of a Patient under Chronic Treatment

Introduction. 3-Hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) inhibitors are widely used worldwide to treat dyslipidaemia and prevent cardiovascular events. Statins can cause a wide variety of muscle injuries ranging from myalgia to severe rhabdomyolysis. In most cases, these symptoms are mild and self-limiting and do not require specific treatment besides drug withdrawal. Statin-induced autoimmune necrotizing myopathy (SINAM) is a rare but potentially fatal complication, characterized by the subacute onset of progressive proximal muscle weakness and considerably high creatine phosphokinase (CK) levels in patients exposed to statins. The diagnosis is supported by the presence of antibodies HMGCR, which allows the differentiation from other forms of necrotizing autoimmune myopathies. Symptoms usually progress even after statin discontinuation and can determine severe muscle damage. Summary. We describe the case of a 77-year-old man who developed SINAM after 5 years of statin use. He suffered from muscle functional impairment mainly involving proximal lower limb muscles which progressed to the point that he almost became bedridden. Initial treatment with prednisone alone was not effective, and he required a combination therapy with steroids, methotrexate, and intravenous immunoglobulins. After 5 months of therapy and rehabilitation, he showed complete laboratory response and muscle strength recovery. Conclusion. Recognizing SINAM is paramount in order to promptly start treatment and avoid permanent muscle damage. Using a combination therapy from the beginning could contribute to a better outcome. Prompt statin cessation, categorization of the muscle disease by autoantibody testing, imaging, and histology, exclusion of malignancy, and anti-inflammatory therapy with corticosteroids, antimetabolites, immunoglobulins, and in some cases rituximab are currently accepted approaches to this entity

Osteopontin levels correlate with severity of diabetic cardiomyopathy in early stage of diabetes

Diabetic cardiomyopathy (DbCM) is characterized by restrictive pattern and consistent risk of overt heart failure. We here focused osteopontin (OPN), which was tested independently associated with left ventricular diastolic dysfunction (LVDD). Overall, OPN increased with DbCM severity according with the presence of left atrial dilatation, LV hypertrophy and LVDD

Plasma levels of myeloperoxidase and resistin independently predict mortality in dialysis patients

Background: In patients with kidney failure (KF) undergoing dialysis, neutrophils are dysfunctionally activated. Such chronic activation does not correspond to increased protection against infections and is thought to cause direct vascular damage accounting for the higher incidence of cardiovascular (CV) events. We hypothesized that circulating levels of neutrophil degranulation products (i.e. myeloperoxidase (MPO) and resistin) can predict overall and CV-specific mortality in dialysis patients. Methods: MPO and resistin levels were assessed in plasma samples from n = 1182 dialysis patients who were followed-up for median 2.9 years (IQR: 1.7-4.2). Results: Patients were 65 ± 14 (SD) years old and 36 % women. Median value of MPO and resistin were 78 ng/mL (IQR: 54 - 123) and 72 ng/mL (IQR: 46 - 110), respectively. MPO and resistin levels correlated with biomarkers of organ damage, nutritional status and inflammation. Both MPO and resistin levels predicted all-cause mortality even after adjustment for traditional risk factors and inflammation, nutritional and KF-related indexes (MPO, HRfor 1 ln unit increase: 1.26, 95 %CI 1.11 - 1.42, P < 0.001; Resistin, HRfor 1 ln unit increase: 1.25, 95 %CI 1.09 - 1.44, P = 0.001). Similarly, their predictive ability held true also for CV death (MPO, HRfor 1 ln unit increase: 1.19, 95 %CI 1.01 - 1.41, P = 0.04; Resistin, HRfor 1 ln unit increase: 1.29, 95 %CI 1.07 - 1.56, P = 0.007). Conclusion: Plasma levels of MPO and resistin correlate with prospective overall and CV-specific mortality risk in KF patients undergoing dialysis and might be useful prognostic tools. Mediators of inflammation may be potential target to improve survival of those patients

Osteopontin levels correlate with severity of diabetic cardiomyopathy in early stage of diabetes

Diabetic cardiomyopathy (DbCM) is characterized by restrictive pattern and consistent risk of overt heart failure. We here focused osteopontin (OPN), which was tested independently associated with left ventricular diastolic dysfunction (LVDD). Overall, OPN increased with DbCM severity according with the presence of left atrial dilatation, LV hypertrophy and LVDD

KM3NeT front-end and readout electronics system: hardware, firmware, and software

he KM3NeT research infrastructure being built at the bottom of the Mediterranean Sea will host water-Cherenkov telescopes for the detection of cosmic neutrinos. The neutrino telescopes will consist of large volume three-dimensional grids of optical modules to detect the Cherenkov light from charged particles produced by neutrino-induced interactions. Each optical module houses 31 3-in. photomultiplier tubes, instrumentation for calibration of the photomultiplier signal and positioning of the optical module, and all associated electronics boards. By design, the total electrical power consumption of an optical module has been capped at seven Watts. We present an overview of the front-end and readout electronics system inside the optical module, which has been designed for a 1-ns synchronization between the clocks of all optical modules in the grid during a life time of at least 20 years