Iatrogenic Botulism After Cosmetic Use of Botulinum Toxin-A: A Case Series.

Botulism symptoms after cosmetic botulinum toxin-A (BTX-A) injections happen very rarely, and it needs careful attention since it can be life-threatening. Hence, it is advised to meticulously check the technique, dose, and authenticity of the BTX-A before injections to reduce the adverse effects.

Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.

The need for intubation in methanol-poisoned patients, if not predicted in time, can lead to irreparable complications and even death. Artificial intelligence (AI) techniques like machine learning (ML) and deep learning (DL) greatly aid in accurately predicting intubation needs for methanol-poisoned patients. So, our study aims to assess Explainable Artificial Intelligence (XAI) for predicting intubation necessity in methanol-poisoned patients, comparing deep learning and machine learning models.

Age- and gender-specific acute poisoning with drugs and medications affecting nervous system.

Background: We investigated acute poisonings resulting from medications affecting the nervous system and illicit substances at Loghman Hakim Hospital in Tehran.

Methods: We retrospectively reviewed patient records at Iran's largest tertiary toxicology referral center between January 2010 and December 2015. We analyzed the prevalence, trend, age and gender distribution of acute poisoning caused by nervous system agents.

Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).

Background: Treatment management for opioid poisoning is critical and, at the same time, requires specialized knowledge and skills. This study was designed to develop and evaluate machine learning algorithms for predicting the maintenance dose and duration of hospital stay in opioid poisoning, in order to facilitate appropriate clinical decision-making.

Method And Results: This study used artificial intelligence technology to predict the maintenance dose and duration of administration by selecting clinical and paraclinical features that were selected by Pearson correlation (filter method) (Stage 1) and then the (wrapper method) Recursive Feature Elimination Cross-Validated (RFECV) (Stage2).

Prediction of acute methanol poisoning prognosis using machine learning techniques.

Methanol poisoning is a global public health concern, especially prevalent in developing nations. This study focuses on predicting the severity of methanol intoxication using machine learning techniques, aiming to improve early identification and prognosis assessment. The study, conducted at Loghman Hakim Hospital in Tehran, Iran.