Background Definition: Wunderlich syndrome (WS) is defined as spontaneous, nontraumatic haemorrhage into the renal subcapsular, and retroperitoneal spaces. It was described first by Carl Reinhold August Wunderlich in 1858 in "Grundriss der speciellen Pathologie und Therapie"[1]. Epidemiology: Rare disease, a total of 267 cases were reported from 1985 to 2016 [2], including 102 cases described since 2000 [3]. Aetiology and pathogenesis: Causes of WS can be divided into neoplastic, which are more frequent, and non-neoplastic [2]. Neoplastic causes: 1. Renal angiomyolipoma (AML)-a benign tumour of the kidney, is the most common cause of WS, found in 57-73% of cases of WS [4,5]. Frequency of AML in the general population is estimated to be between 0.2% and 0.6%, with a higher prevalence among women. In 20% of cases, AML coexists with tuberous sclerosis complex or pulmonary lymphangioleiomyomatosis. Usually, AML is an asymptomatic, incidental finding [6]. 2. Malignant primary renal tumours e.g. renal cell carcinoma [2]. 3. Metastases of malignant tumours to the kidney e.g. choriocarcinoma, gastric sarcoma [7]. Other causes:

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Background During the coronavirus disease 2019 (COVID‐19) pandemic, it has become a pressing need to be able to diagnose aspirin hypersensitivity in patients with asthma without the need to use oral aspirin challenge (OAC) testing. OAC is time consuming and is associated with the risk of severe hypersensitive reactions. In this study, we sought to investigate whether machine learning (ML) based on some clinical and laboratory procedures performed during the pandemic might be used for discriminating between patients with aspirin hypersensitivity and those with aspirin‐tolerant asthma. Methods We used a prospective database of 135 patients with non‐steroidal anti‐inflammatory drug (NSAID)–exacerbated respiratory disease (NERD) and 81 NSAID‐tolerant (NTA) patients with asthma who underwent OAC. Clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant and urine were extracted for the purpose of applying ML techniques. Results The overall best ML model, neural network (NN), trained on a set of best features, achieved a sensitivity of 95% and a specificity of 76% for diagnosing NERD. The 3 promising models (i.e., multiple logistic regression, support vector machine, and NN) trained on a set of easy‐to‐obtain features including only clinical characteristics and laboratory data achieved a sensitivity of 97% and a specificity of 67%. Conclusions ML techniques are becoming a promising tool for discriminating between patients with NERD and NTA. The models are easy to use, safe, and achieve very good results, which is particularly important during the COVID‐19 pandemic.