The impact of the new WHO Classification of renal cell carcinoma on the diagnosis of hereditary leiomyomatosis and renal cell carcinoma.

Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome is caused by heterozygous germline variants in the fumarate hydratase (FH) gene [1,2]. Inheritance follows an autosomal dominant pattern. Loss of FH confers a predisposition for various benign and malignant neoplasms, including cutaneous leiomyomas, uterine fibroids and FH-deficient renal cell carcinoma [3].

Machine learning to detect recent recreational drug use in intensive cardiac care units.

Background: Although recreational drug use is a strong risk factor for acute cardiovascular events, systematic testing is currently not performed in patients admitted to intensive cardiac care units, with a risk of underdetection. To address this issue, machine learning methods could assist in the detection of recreational drug use.

Aims: To investigate the accuracy of a machine learning model using clinical, biological and echocardiographic data for detecting recreational drug use in patients admitted to intensive cardiac care units.

Transition Metal-Free Direct Electrochemical Carboxylation of Organic Halides Using a Sacrificial Magnesium Anode: Straightforward Synthesis of Carboxylic Acids.

The direct electrochemical carboxylation of aryl, benzyl and alkyl halides by CO is described using a magnesium anode and a nickel foam cathode in an undivided cell. The process employs a sacrificial anode and does not require the additional use of a transition metal catalyst or demanding conditions, as the reactions are carried out under galvanostatic mode, at -10 °C and with commercial DMF. Under these operationally simple conditions, an important range of carboxylic acids are affordable.

Prognostic value of coronary plaque composition in symptomatic patients with obstructive coronary artery disease.

Objectives: To determine whether plaque composition analysis defined by cardiac CT can provide incremental prognostic value above coronary artery disease (CAD) burden markers in symptomatic patients with obstructive CAD.

Materials And Methods: Between 2009 and 2019, a multicentric registry included all consecutive symptomatic patients with obstructive CAD (at least one ≥ 50% stenosis on CCTA) and was followed for major adverse cardiovascular (MACE) defined by cardiovascular death or nonfatal myocardial infarction. Each coronary segment was scored visually for both the degree of stenosis and composition of plaque, which were classified as non-calcified, mixed, or calcified.

A Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease.

Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.