Resistant arterial hypertension in a patient with adrenal incidentaloma multiple steno-obstructive vascular lesions and antiphospholipid syndrome

Resistant hypertension is defined as above of blood pressure (≤ 140/90 mmHg) despite therapy with three or more antihypertensive drugs of different classes at maximum tolerable doses with one bling a diuretic. An important consideration in defining a patient with resistant hypertension is the mislabeling of secondary hypertension as resistant hypertension. Here, we report a patients with resistant hypertension caused by multiple stenoocclusive arteries due to antiphospholipid syndrome and coexisting with subclinical Cushing’s syndrome

Hypokalemic rhabdomyolysis: a rare manifestation of primary aldosteronism

Rhabdomyolysis is a rare presentation of hypokalemia, although muscle weakness is a well-known manifestation of hypokalemia. Primary aldosteronism is characterized by hypertension, suppressed plasma renin activity, increased aldosterone excretion and hypokalemia with metabolic alkalosis. Rhabdomyolysis is not common in primary aldosteronism. We present here a 40-year-old woman presenting with rhabdomyolysis accompanied by severe hypokalemia as heralding symptom of primary aldosteronism

Arterial Hypertension, Metabolic Syndrome and Subclinical Cardiovascular Organ Damage in Patients with Asymptomatic Primary Hyperparathyroidism before and after Parathyroidectomy: Preliminary Results

Background. Primary hyperparathyroidism (PHPT) is associated with high cardiovascular morbidity, and the role of calcium and parathyroid hormone is still controversial. Objective. To evaluate the prevalence and outcomes of metabolic syndrome, hypertension, and some cardiovascular alterations in asymptomatic PHPT, and specific changes after successful parathyroidectomy. Material and Methods. We examined 30 newly diagnosed PHPT patients (8 males, 22 females; mean age 56 ± 6 yrs), 30 patients with essential hypertension (EH) (9 males, 21 females; mean age 55 ± 4), and 30 normal subjects (NS) (9 males, 21 females: mean age 55 ± 6). All groups underwent evaluation with ambulatory monitoring blood pressure, echocardiography, and color-Doppler artery ultrasonography and were successively revaluated after one year from parathyroidectomy. Results. PHPT patients presented a higher prevalence of metabolic syndrome (38%) with respect to EH (28%). Prevalence of hypertension in PHPT was 81%, and 57% presented altered circadian rhythm of blood pressure, with respect to EH (35%) and NS (15%). PHPT showed an important myocardial and vascular remodelling. During follow-up in PHPT patients, we found significant reduction of prevalence of metabolic syndrome, blood pressure, and “non-dipping phenomenon.” Conclusions. Cardiovascular and metabolic alterations should be considered as added parameters in evaluation of patients with asymptomatic PHPT

Brown Fat Expresses Adiponectin in Humans

The presence of brown adipose tissue (BAT) in humans is unclear. Pheochromocytomas (PHEO) are rare tumors of neuroectodermal origin which occur in 0.1-0.2% of patients with hypertension. We sought to evaluate the presence and activity of BAT surrounding adrenal PHEO in a well-studied sample of 11 patients who were diagnosed with PHEO and then underwent adrenalectomy. Areas of white fat (WAT) and BAT surrounding PHEO were obtained by Laser Capture Microdissection for analysis of uncoupling protein (UCP)-1 and adiponectin mRNA expression. Adiponectin and UCP-1 mRNA levels were significantly higher in BAT than in WAT (0.62 versus 0.15 and 362.4 versus 22.1, resp., for both). Adiponectin mRNA levels significantly correlated with urinary metanephrines (, ), vanilly mandelic acid (VMA) (, ), and serum adiponectin levels (, ). Serum adiponectin levels significantly decreased ( μg/mL versus  μg/mL, ) after adrenalectomy in PHEO subjects. This study provides the following findings: (1) BAT surrounding PHEO expresses adiponectin and UCP-1 mRNA, (2) expression of adiponectin mRNA is significantly higher in BAT than in WAT surrounding PHEO, and (3) catecholamines and serum adiponectin levels significantly correlate with BAT UCP-1 and adiponectin mRNA

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

Letter of intent for KM3NeT 2.0

The main objectives of the KM3NeT Collaboration are ( i ) the discovery and subsequent observation of high-energy neutrino sources in the Universe and ( ii ) the determination of the mass hierarchy of neutrinos. These objectives are strongly motivated by two recent important discoveries, namely: ( 1 ) the high- energy astrophysical neutrino signal reported by IceCube and ( 2 ) the sizable contribution of electron neutrinos to the third neutrino mass eigenstate as reported by Daya Bay, Reno and others. To meet these objectives, the KM3NeT Collaboration plans to build a new Research Infrastructure con- sisting of a network of deep-sea neutrino telescopes in the Mediterranean Sea. A phased and distributed implementation is pursued which maximises the access to regional funds, the availability of human resources and the syner- gistic opportunities for the Earth and sea sciences community. Three suitable deep-sea sites are selected, namely off-shore Toulon ( France ) , Capo Passero ( Sicily, Italy ) and Pylos ( Peloponnese, Greece ) . The infrastructure will consist of three so-called building blocks. A building block comprises 115 strings, each string comprises 18 optical modules and each optical module comprises 31 photo-multiplier tubes. Each building block thus constitutes a three- dimensional array of photo sensors that can be used to detect the Cherenkov light produced by relativistic particles emerging from neutrino interactions. Two building blocks will be sparsely con fi gured to fully explore the IceCube signal with similar instrumented volume, different methodology, improved resolution and complementary fi eld of view, including the galactic plane. One building block will be densely con fi gured to precisely measure atmospheric neutrino oscillations. Original content from this work may be used under the ter