Background and Aims: Euthanasia is a controversial issue related to the right to die. Although euthanasia is mostly requested by terminally sick individuals, even in societies where it is legal, it is unclear what medical conditions lead to euthanasia requests. In this scoping review, we aimed to compile medical conditions for which euthanasia has been requested or performed around the world.Methods: The review was preferred reporting items for systematic reviews and meta‐analysis for scoping reviews (PRISMA‐ScR) checklist. Retrieved search results were screened and unrelated documents were excluded. Data on reasons for conducting or requesting euthanasia along with the study type, setting, and publication year were extracted from documents. Human development index and euthanasia legality were also extracted. Major medical fields were used to categorize reported reasons. Group discussions were conducted if needed for this categorization. An electronic search was undertaken in MEDLINE through PubMed for published documents covering the years January 2000 to September 2022.Results: Out of 3323 records, a total of 197 papers were included. The most common medical conditions in euthanasia requests are cancer in a terminal phase (45.4%), Alzheimer's disease and dementia (19.8%), constant unbearable physical or mental suffering (19.8%), treatment‐resistant mood disorders (12.2%), and advanced cardiovascular disorders (12.2%).Conclusion: Reasons for euthanasia are mostly linked to chronic or terminal physical conditions. Psychiatric disorders also lead to a substantial proportion of euthanasia requests. This review can help to identify the features shared by conditions that lead to performing or requesting euthanasia

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. Misdiagnosis might lead to several systemic conditions, including hypertension, vascular damage, and cognitive impairment. The gold-standard diagnostic tool for OSA is polysomnography, which is expensive, time-consuming, and not accessible everywhere. Artificial intelligence (AI) algorithms can facilitate diagnosis by detecting patients’ signs and symptoms. In this systematic review, we evaluated the diagnostic accuracy of AI models in detecting sleep apnea. We searched six major databases, PubMed^®, Cochrane, Web of Science, Scopus, Embase, and IEEE Xplore, using keywords related to AI and OSA. Eligible studies focused on adult populations, used in-laboratory PSG as the reference standard, and applied AI models trained on multiple clinical features. Reviews, pediatric studies, and articles lacking accuracy metrics were excluded. From the included articles, data were extracted regarding patients and datasets, type of AI model applied, accuracy report, and explainability of the AI model. A risk of bias assessment was done using the QUADAS-2 checklist. Thirteen studies were included in our final analysis. The AI models consisted of deep learning, machine learning, and hybrid models with various architectures. The reported accuracy of studies ranged from 67.03 to 98.6%, with the highest being related to hybrid and deep learning models. Risk of bias assessment showed that 7 of the studies had a low risk of bias, indicating high reliability. AI-driven models, particularly deep learning and hybrid architectures, show significant promise in diagnosing obstructive sleep apnea. However, challenges such as transparency, explainability, and variability in performance necessitate diverse training datasets to improve generalizability for clinical adoption. The protocol of this systematic review was registered in PROSPERO (CRD42023453789), available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023453789. The online version contains supplementary material available at 10.1186/s12911-025-03129-x.

Background: Polypharmacy in patients with cardiovascular diseases (CVDs) has been linked to several adverse outcomes. This study aimed to investigate the pattern of medication use and prevalence of polypharmacy among CVDs patients in Iran. Method: We used the baseline data of the Pars cohort study (PCS). The participants were asked to bring their medication bags; then, the medications were classified using the Anatomical Therapeutic Chemical classification. Polypharmacy was defined as using five or more medications concurrently. Poisson regression modeling was applied. The adjusted prevalence ratios (PR) and its 95% confidence interval (CI) were estimated. Results: Totally, 9262 participants were enrolled in the PCS, of whom 961 had CVDs. The prevalence of polypharmacy in participants with and without CVDs was 38.9% and 7.1%, respectively. The highest prevalence of polypharmacy (51.5%) was among obese patients. Abnormal waist-hip ratio (PR: 2.79; 95% CI 1.57–4.94), high socioeconomic status (PR: 1.65; 95% CI 1.07–2.54), tobacco-smoking (PR: 1.35; 95% CI 1.00–1.81), patients with more than three co-morbidities (PR: 1.41; 95% CI 1.30–1.53), high physical activity (PR: 0.66; 95% CI 0.45–0.95), use of opiate ever (PR: 0.46; 95% CI 0.26–0.82), and healthy overweight subjects (PR: 0.22; 95% CI 0.12–0.39) were associated with polypharmacy. Cardiovascular drugs (76.1%), drugs acting on blood and blood-forming organs (50.4%), and alimentary tract and metabolism drugs (33.9%) were the most frequently used drugs. Agents acting on the renin-angiotensin system were the mostly used cardiovascular system drugs among men and those above 60 years old, while beta-blocking agents were mostly prevalent among cardiovascular system drugs in women with CVDs. Conclusion: Given the high prevalence of polypharmacy among CVDs patients, and subsequent complications, programs to educate both physicians and patients to prevent this issue is crucial.

Peganum harmala L. and Lavandula angustifolia are two traditional herbs with probable antiseizure effects. This study evaluated the effects of these two herbal extracts on pentylenetetrazol‐ (PTZ‐) induced seizures in mice. We prepared hydroalcoholic extracts using P. harmala seeds and the aerial parts of L. angustifolia and then randomly divided 190 mice into 19 groups. Normal saline (10 mg/kg), diazepam (2 mg/kg), P. harmala (2.5, 5, 10, 15, 30, 45, and 60 mg/kg), and L. angustifolia (200, 400, 600, and 800 mg/kg) were intraperitoneally (IP) administrated 30 min before an IP administration of PTZ (90 mg/kg). Animals were observed for behavioral changes for one hour. In addition, the effects of flumazenil and naloxone on the antiseizure activity of P. harmala and L. angustifolia were assessed. P. harmala showed antiseizure activity at the dose of 10 mg/kg; it prolonged the seizure latency and decreased the seizure duration. The mortality protection rate was 90% for this herbal extract. L. angustifolia (600 mg/kg) prolonged the seizure latency and decreased both seizure duration and mortality. Neither flumazenil nor naloxone significantly reversed the antiseizure activities of P. harmala and L. angustifolia. In mice, the hydroalcoholic extracts of P. harmala and L. angustifolia showed antiseizure activity against PTZ‐induced seizures. We could not delineate the exact antiseizure mechanisms of these extracts in the current study.