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. RESULTS: The present study included 16,657 (57.27%) males and 12,426 (42.73%) females, resulting in 29,083 patients. The median age of men and women was 29 and 26 years, respectively (p < 0.0001). There were 12,071 (72.47%) men and 10,326 (83.10%) women under the age of 40 (p < 0.001). Most cases were intentional (69.38% in men and 79.00% in women, p < 0.001) and 44.10% had a history of poisoning. The proportions of men and women varied significantly between different age groups and nervous system agents. For women, the most common agent was alprazolam, whereas for men, methadone. The overall trend of acute poisoning with drug used in addictive disorders, opioids and alcohol was increasing but decreasing with benzodiazepines and antidepressants. Acute poisoning by nervous system agents led to more deaths in men (1.95% vs. 0.56%; p < 0.001). CONCLUSIONS: Methadone intoxication was common especially among young men and most of these intoxications were intentional. Women and men aged 20-29 most frequently suffer poisoning from alprazolam and clonazepam, respectively. Women over 60 and men over 30 used opium. Illicit drugs caused more than half of the deaths, and opium dominated. This study may create awareness and develop educational and preventive gender and age-specific local programs.

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. This study analyzed a dataset of 897 patient records from Loghman Hakim Hospital in Tehran, Iran, encompassing cases of methanol poisoning, including those requiring intubation (202 cases) and those not requiring it (695 cases). Eight established ML (SVM, XGB, DT, RF) and DL (DNN, FNN, LSTM, CNN) models were used. Techniques such as tenfold cross-validation and hyperparameter tuning were applied to prevent overfitting. The study also focused on interpretability through SHAP and LIME methods. Model performance was evaluated based on accuracy, specificity, sensitivity, F1-score, and ROC curve metrics. Among DL models, LSTM showed superior performance in accuracy (94.0%), sensitivity (99.0%), specificity (94.0%), and F1-score (97.0%). CNN led in ROC with 78.0%. For ML models, RF excelled in accuracy (97.0%) and specificity (100%), followed by XGB with sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%). Overall, RF and XGB outperformed other models, with accuracy (97.0%) and specificity (100%) for RF, and sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%) for XGB. ML models surpassed DL models across all metrics, with accuracies from 93.0% to 97.0% for DL and 93.0% to 99.0% for ML. Sensitivities ranged from 98.0% to 99.37% for DL and 93.0% to 99.0% for ML. DL models achieved specificities from 78.0% to 94.0%, while ML models ranged from 93.0% to 100%. F1-scores for DL were between 93.0% and 97.0%, and for ML between 96.0% and 98.27%. DL models scored ROC between 68.0% and 78.0%, while ML models ranged from 84.0% to 96.08%. Key features for predicting intubation necessity include GCS at admission, ICU admission, age, longer folic acid therapy duration, elevated BUN and AST levels, VBG_HCO3 at initial record, and hemodialysis presence. This study as the showcases XAI's effectiveness in predicting intubation necessity in methanol-poisoned patients. ML models, particularly RF and XGB, outperform DL counterparts, underscoring their potential for clinical decision-making.

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). The duration of administration was divided into two categories: A (which includes a duration of less than or equal to 24 h of infusion) and B (more than 24 h of naloxone infusion). XGBoost algorithm model with an accuracy rate of 91.04%, a prediction rate of 91.34%, and a sensitivity rate of 91.04% and area under the Curve (AUC) 0.97 was best model for classification patients. Also, the best maintenance dose of naloxone was obtained with XGBoost algorithm with R2 = 0.678. Based on the selected algorithm, the most important features for classifying patients for the duration of treatment were bicarbonate, respiration rate, physical sign, The partial pressure of carbon dioxide (PCO2), diastolic blood pressure, pulse rate, naloxone bolus dose, Blood Creatinine(Cr), Body temperature (T). The most important characteristics for determining the maintenance dose of naloxone were physical signs, bolus dose of 4.5 mg/kg, Glasgow Coma Scale (GCS), Creatine Phosphokinase (CPK) and intensive care unit (ICU) add. CONCLUSION: A predictive model can significantly enhance the decision-making and clinical care provided by emergency physicians in hospitals and medical settings. XGBoost was found to be the superior model.

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. The data pertaining to individuals afflicted with methanol poisoning was retrieved retrospectively and divided into training and test groups at a ratio of 70:30. The selected features were then inputted into various machine learning methods. The models were implemented using the Scikit-learn library in the Python programming language. Ultimately, the efficacy of the developed models was assessed through ten-fold cross-validation techniques and specific evaluation criteria, with a confidence level of 95%. A total number of 897 patients were included and divided in three groups including without sequel (n = 573), with sequel (n = 234), and patients who died (n = 90). The two-step feature selection was yielded 43 features in first step and 23 features in second step. In best model (Gradient Boosting Classifier) test dataset metric by 32 features younger age, higher methanol ingestion, respiratory symptoms, lower GCS scores, type of visual symptom, duration of therapeutic intervention, ICU admission, and elevated CPK levels were among the most important features predicting the prognosis of methanol poisoning. The Gradient Boosting Classifier demonstrated the highest predictive capability, achieving AUC values of 0.947 and 0.943 in the test dataset with 43 and 23 features, respectively. This research introduces a machine learning-driven prognostic model for methanol poisoning, demonstrating superior predictive capabilities compared to traditional statistical methods. The identified features provide valuable insights for early intervention and personalized treatment strategies.

Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms.

BACKGROUND: Hemodialysis is a life-saving treatment used to eliminate toxins and metabolites from the body during poisoning. Despite its effectiveness, there needs to be more research on this method precisely, with most studies focusing on specific poisoning. This study aims to bridge the existing knowledge gap by developing a machine-learning prediction model for forecasting the prognosis of the poisoned patient undergoing hemodialysis. METHODS: Using a registry database from 2016 to 2022, this study conducted a retrospective cohort study at Loghman Hakim Hospital. First, the relief feature selection algorithm was used to identify the most important variables influencing the prognosis of poisoned patients undergoing hemodialysis. Second, four machine learning algorithms, including extreme gradient boosting (XGBoost), histgradient boosting (HGB), k-nearest neighbors (KNN), and adaptive boosting (AdaBoost), were trained to construct predictive models for predicting the prognosis of poisoned patients undergoing hemodialysis. Finally, the performance of paired feature selection and machine learning (ML) algorithm were evaluated to select the best models using five evaluation metrics including accuracy, sensitivity, specificity the area under the curve (AUC), and f1-score. RESULT: The study comprised 980 patients in total. The experimental results showed that ten variables had a significant influence on prognosis outcomes including age, intubation, acidity (PH), previous medical history, bicarbonate (HCO3), Glasgow coma scale (GCS), intensive care unit (ICU) admission, acute kidney injury, and potassium. Out of the four models evaluated, the HGB classifier stood out with superior results on the test dataset. It achieved an impressive mean classification accuracy of 94.8%, a mean specificity of 93.5 a mean sensitivity of 94%, a mean F-score of 89.2%, and a mean receiver operating characteristic (ROC) of 92%. CONCLUSION: ML-based predictive models can predict the prognosis of poisoned patients undergoing hemodialysis with high performance. The developed ML models demonstrate valuable potential for providing frontline clinicians with data-driven, evidence-based tools to guide time-sensitive prognosis evaluations and care decisions for poisoned patients in need of hemodialysis. Further large-scale multi-center studies are warranted to validate the efficacy of these models across diverse populations.

Incidence of rhabdomyolysis occurrence in psychoactive substances intoxication: a systematic review and meta-analysis.

Rhabdomyolysis is a potentially life-threatening condition induced by diverse mechanisms including drugs and toxins. We aimed to investigate the incidence of rhabdomyolysis occurrence in intoxicated patients with psychoactive substances. In this review, three databases (PubMed, Scopus, Web of Science) and search engine (Google Scholar) were searched by various keywords. After the screening of retrieved documents, related data of included studies were extracted and analyzed with weighted mean difference (WMD) in random effect model. The highest incidence of rhabdomyolysis was observed in intoxication with heroin (57.2 [95% CI 22.6-91.8]), amphetamines (30.5 [95% CI 22.6-38.5]), and cocaine (26.6 [95% CI 11.1-42.1]). The pooled effect size for blood urea nitrogen (WMD = 8.78, p = 0.002), creatinine (WMD = 0.44, p < 0.001), and creatinine phosphokinase (WMD = 2590.9, p < 0.001) was high in patients with rhabdomyolysis compared to patients without rhabdomyolysis. Our results showed a high incidence of rhabdomyolysis induced by psychoactive substance intoxication in ICU patients when compared to total wards. Also, the incidence of rhabdomyolysis occurrence was high in ICU patients with heroin and amphetamine intoxication. Therefore, clinicians should anticipate this complication, monitor for rhabdomyolysis, and institute appropriate treatment protocols early in the patient's clinical course.

Association between intoxication with psychoactive substances and adverse effects occurrence in consumers.

Background: The prevalence of psychoactive substance use is increasing worldwide and identifying adverse effects of these types of drugs is necessary in intoxicated patients. Objective: We aimed to investigate the association of psychoactive substance intoxication with their adverse effects on the functioning of the bodily organs. Methods: This was a single-center study between March 2019 and April 2022 on intoxicated patients with psychoactive substances. Inclusion criteria were intoxication with alcohol, opioids, and stimulants, and having available results of laboratory biomarkers. Demographic and clinical data of patients at the time of admission as well as during hospitalization were reviewed, retrospectively. Data were analyzed using a generalized linear mixed model in R software and the Adjusted Odds Ratio (AOR) was estimated. Results: A total of 800 hospitalized patients in the ICU (n = 400) and general ward (n = 400) were divided into two groups of intoxicated with alcohol (n = 200) and opioids or stimulants (n = 200). Liver (AOR = 0.15, p = 0.033; AOR = 0.13, p = 0.007) and kidney (AOR = 0.46, p = 0.004; AOR = 0.24, p = 0.021) dysfunction occurred less in the ICU and general ward, respectively, in opioids or stimulants intoxication compared to alcohol. Cardiovascular dysfunctions occurred more in opioids or stimulants intoxication compared to alcohol in both ICU (AOR = 10.32, p < 0.0001) and general ward (AOR = 4.74, p < 0.0001). Conclusion: Kidney dysfunctions had a greater effect on mortality compared to other dysfunctions. During the follow-up, the incidence of dysfunctions increased in those intoxicated with opioids or stimulants. Men experienced more liver and kidney dysfunctions as well as mortality, but psychoactive substance experience was a protective factor in cardiovascular dysfunctions and mortality.

Assessment of renal function indexes in methamphetamine or tramadol intoxication adults to the emergency departments: a systematic review and meta-analysis.

BACKGROUND: Renal dysfunction is one of the adverse effects observed in methamphetamine (MET) or tramadol abusers. In this study, we aimed to review articles involving intoxication with MET or tramadol to assess the occurrence of renal dysfunction. METHODS: Two researchers systematically searched PubMed, Scopus, Web of Sciences, and Google Scholar databases from 2000 to 2022. All articles that assessed renal function indexes including creatine, Blood Urea Nitrogen (BUN), and Creatine phosphokinase (CPK) in MET and tramadol intoxication at the time of admission in hospitals were included. We applied random effect model with Knapp-Hartung adjustment for meta-analysis using STATA.16 software and reported outcomes with pooled Weighted Mean (WM). RESULTS: Pooled WM for BUN was 29.85 (95% CI, 21.25-38.46) in tramadol intoxication and 31.64(95% CI, 12.71-50.57) in MET intoxication. Pooled WM for creatinine in tramadol and MET intoxication was respectively 1.04 (95% CI, 0.84-1.25) and 1.35 (95% CI, 1.13-1.56). Also, pooled WM for CPK was 397.68(376.42-418.94) in tramadol and 909.87(549.98-1269.76) in MET intoxication. No significance was observed in publication bias and heterogeneity tests. CONCLUSION: Our findings showed that tramadol or MET intoxication is associated with a considerably increased risk of renal dysfunction that may result in organ failure.

Comparing Two Naloxone Tapering Methods in Management of Methadone Intoxication; a Quasi-experimental Study.

Introduction: Even though naloxone is the main treatment for methadone poisoning treatment there are controversies about the proper method of its tapering. This study aimed to compare two methods in this regard. Method: This study was a prospective, single-blind pilot quasi-experimental study on non-addicted adult patients poisoned with methadone. Patients were randomly divided into 2 groups. In one group, after stabilization of respiratory conditions and consciousness, naloxone was tapered using the half-life of methadone and in the other group, naloxone was tapered using the half-life of naloxone. Recurrence of symptoms and changes in venous blood gas parameters were compared between groups as outcome. Results: 52 patients were included (51.92% female). 31 cases entered Group A (tapering based on methadone's half-life) and 21 cases entered Group B (tapering based on naloxone's half-life). The two groups were similar regarding mean age (p = 0.575), gender distribution (p = 0.535), the cause of methadone use (p = 0.599), previous medical history (p = 0.529), previous methadone use (p = 0.654), drug use history (p = 0.444), and vital signs on arrival to emergency department (p = 0.054). The cases of re-decreasing consciousness during tapering (52.38% vs. 25.81%; p = 0.049) and after discontinuation of naloxone (72.73% vs. 37.50%; p = 0.050) were higher in the tapering based on naloxone half-life group. The relative risk reduction (RRR) for naloxone half-life group was -1.03 and for methadone half-life group was 0.51. The absolute risk reduction (ARR) was 0.27 (95% confidence interval (CI) = 0.01-0.53) and the number needed to treat (NNT) was 3.7 (95% CI= 1.87- 150.53). There was not any statistically significant difference between groups regarding pH, HCO3, and PCO2 changes during tapering and after naloxone discontinuation (p > 0.05). However, repeated measures analysis of variance (ANOVA), showed that in the tapering based on methadone's half-life group, the number of changes and stability in the normal range were better (p < 0.001). Conclusion: It seems that, by tapering naloxone based on methadone's half-life, not only blood acid-base disorders are treated, but they also remain stable after discontinuation and the possibility of symptom recurrence is reduced.

Montelukast Is Effective in Treating Rhabdomyolysis Due to Intoxication: A Randomized Clinical Trial.

INTRODUCTION: Rhabdomyolysis is a clinical syndrome accompanied with biochemical changes that is diagnosed in some patients with acute chemical or drug poisoning. In this regard, the present study aimed to evaluate the effects of Montelukast in the treatment of intoxication-induced rhabdomyolysis. METHODS: This single-blind randomized clinical trial study was conducted in Loghman Hakim Hospital from March 2021 to March 2022. The study participants were 60 individuals evenly distributed into experimental and control groups. The experimental group received Montelukast plus routine treatment and the control group Creatine phosphokinase (CPK), urea, creatinine, aspartate aminotransferase (AST) and alanine transaminase (ALT) levels were monitored daily in both groups for seven days. The variables of age, gender and history of diabetes mellitus and kidney diseases were recorded. RESULTS: The mean age was 39.9 ± 16.87 and 38.2 ± 16.3 years in the control and intervention groups, respectively. Montelukast significantly (P < .05) reduced CPK levels on days five and seven, urea on days three, four, five and seven, and creatinine on days two to seven. The AST and ALT levels, unlike the control group which has a decreasing trend, increased first in the Montelukast group and then decreased on the sixth and seventh days. CONCLUSION: The results showed that Montelukast effectively reduced CPK, urea and creatinine levels, as well as the recovery time in patients with poison-induced rhabdomyolysis. In other words, Montelukast is effective in the treatment of rhabdomyolysis.  DOI: 10.52547/ijkd.7222.

A rare case report of lead encephalopathy due to high blood lead level.

Here we report a case of lead poisoning having a serum lead level of 412 mcg dL-1 who presented with decreasing level of consciousness and recurrent seizures. He responded well to treatment with chelation therapy.

Visual impairment following aluminum phosphide poisoning: A rare case.

Key Clinical Message: Aluminum phosphide poisoning may cause rare visual impairment. In a case, a 31-year-old female, visual loss was linked to shock-induced hypoperfusion, causing oxygen lack and cerebral atrophy, emphasizing the need for identifying atypical symptoms. Abstract: This case report describes the multidisciplinary evaluation of a 31-year-old female patient who suffered from visual impairment as a result of aluminum phosphide (AlP) poisoning. Phosphine, which is formed in the body when AlP reacts with water, cannot cross the blood-brain barrier; therefore, visual impairment seems unlikely to be the direct result of phosphine. To our knowledge, it is the first documented report of such impairment due to AlP.

Prediction of acute organophosphate poisoning severity using machine learning techniques.

Poisoning with organophosphate compounds is a significant public health risk, especially in developing countries. Considering the importance of early and accurate prediction of organophosphate poisoning prognosis, the aim of this study was to develop a machine learning-based prediction model to predict the severity of organophosphate poisoning. The data of patients with organophosphate poisoning were retrospectively extracted and split into training and test sets in a ratio of 70:30. The feature selection was done by least absolute shrinkage and selection operator method. Selected features were fed into five machine learning techniques, including Histogram Boosting Gradient, eXtreme Gradient Boosting, K-Nearest Neighborhood, Support Vector Machine (SVM) (kernel = linear), and Random Forest. The Scikit-learn library in Python programming language was used to implement the models. Finally, the performance of developed models was measured using ten-fold cross-validation methods and some evaluation criteria with 95 % confidence intervals. A total of 1237 patients were used to train and test the machine learning models. According to the criteria determining severe organophosphate poisoning, 732 patients were assigned to group 1 (patients with mild to moderate poisoning) and 505 patients were assigned to group 2 (patients with severe poisoning). With an AUC value of 0.907 (95 % CI 0.89-0.92), the model developed using XGBoost outperformed other models. Feature importance evaluation found that venous blood gas-pH, white blood cells, and plasma cholinesterase activity were the top three variables that contribute the most to the prediction performance of the prognosis in patients with organophosphate poisoning. XGBoost model yield an accuracy of 90.1 % (95 % CI 0.891-0.918), specificity of 91.4 % (95 % CI 0.90-0.92), a sensitivity of 89.5 % (95 % CI 0.87-0.91), F-measure of 91.2 % (95 % CI 0.90-0.921), and Kappa statistic of 91.2 % (95 % CI 0.90-0.92). The machine learning-based prediction models can accurately predict the severity of organophosphate poisoning. Based on feature selection techniques, the most important predictors of organophosphate poisoning were VBG-pH, white blood cell count, plasma cholinesterase activity, VBG-BE, and age. The best algorithm with the highest predictive performance was the XGBoost classifier.

Sumatriptan poisoning and its clinical presentation in humans: A case report.

This case report displays some of the possible complications of sumatriptan poisoning, including nephritic syndrome.

Colchicine poisoning treated with hemoperfusion and hemodialysis: A case report.

This case report described an improved case of colchicine poisoning using hemoperfusion and hemodialysis.

Increased levels of neurofilament light chain in suicide attempters' serum.

Background: A specific biological vulnerability underlies suicidal behavior. Recent findings have suggested a possible role of inflammation and neuroaxonal injury. However, the relationship between inflammation and clinical symptoms in this disorder is still unclear. The objective of this study is applying novel blood markers of neuroaxonal integrity such as neurofilament light chain (NfL) and comparing the results with the healthy control subjects. Methods: In this cross-sectional study patients with suicide attempts were evaluated. The serum concentration of NfL on admission was measured by enzyme-linked immunosorbent assays. Results: A total of 50 patients with a suicide attempts and 35 healthy controls were included in the study. The levels of NfL in attempted suicide patients were significantly higher in comparison with healthy controls (40.52 ± 33.54 vs 13.73 ± 5.11, P < 0.001). A significant association between serum levels of NfL and risk factors for suicide was not found. Conclusion: These findings indicate that axonal damage may be an underlying neuropathological component of suicide attempt patients, although no correlation was observed with clinical features. This line of work could lead to new horizons in understanding the neurobiology of suicidal attempts and the development of better management strategies for these patients.

Demographic and Clinical Characteristics of 907 Cases with Naltrexone Intoxication; a 14-Year Cross-Sectional Study.

Introduction: Opioids have been the leading cause of death from poisoning in Iran for several years. This study aimed to evaluate the clinical and para-clinical presentations of naltrexone intoxication, its toxic dose, and its epidemiological properties. Methods: This retrospective cross-sectional study was conducted on medical records of patients presenting to Toxicology Department of Loghman Hakim Hospital, Tehran, Iran, following naltrexone intoxication, from 2002 to 2016. Patients' demographic and laboratory data, clinical signs, supposed ingested dose, and intent of naltrexone consumption were collected, analyzed, and then interpreted. Results: 907 patients with the mean age of 36.6 ±11.7 years were evaluated (94.3% male). The mean amount of naltrexone consumed by the intoxicated patients reported in the medical records was 105.8 ± 267.8 mg. One hundred thirty patients (14.3%) used naltrexone to treat substance use disorder. Two hundred eighty-seven poisoned patients (31.6%) were current opium users who intentionally or unintentionally used naltrexone concomitantly. The most common symptoms observed in these patients were agitation (41.8%), vomiting (16.4%), and nausea (14.8%). Among patients with naltrexone poisoning, 25 patients were intubated (2.8%), and three passed away. Aspartate aminotransferase (AST) levels were significantly higher in patients intoxicated with naltrexone who needed intubation (p = 0.02). Conclusion: The probability of intubation of cases with naltrexone intoxication was associated with AST elevation. It seems that, the number of intensive care unit (ICU) admissions and mortality rates are not high among these patients.

Nasal Air Puff Promotes Default Mode Network Activity in Mechanically Ventilated Comatose Patients: A Noninvasive Brain Stimulation Approach.

OBJECTIVES: Coma state and loss of consciousness are associated with impaired brain activity, particularly gamma oscillations, that integrate functional connectivity in neural networks, including the default mode network (DMN). Mechanical ventilation (MV) in comatose patients can aggravate brain activity, which has decreased in coma, presumably because of diminished nasal airflow. Nasal airflow, known to drive functional neural oscillations, synchronizing distant brain networks activity, is eliminated by tracheal intubation and MV. Hence, we proposed that rhythmic nasal air puffing in mechanically ventilated comatose patients may promote brain activity and improve network connectivity. MATERIALS AND METHODS: We recorded electroencephalography (EEG) from 15 comatose patients (seven women) admitted to the intensive care unit because of opium poisoning and assessed the activity, complexity, and connectivity of the DMN before and during the nasal air-puff stimulation. Nasal cavity air puffing was done through a nasal cannula controlled by an electrical valve (open duration of 630 ms) with a frequency of 0.2 Hz (ie, 12 puff/min). RESULTS: Our analyses demonstrated that nasal air puffing enhanced the power of gamma oscillations (30-100 Hz) in the DMN. In addition, we found that the coherence and synchrony between DMN regions were increased during nasal air puffing. Recurrence quantification and fractal dimension analyses revealed that EEG global complexity and irregularity, typically seen in wakefulness and conscious state, increased during rhythmic nasal air puffing. CONCLUSIONS: Rhythmic nasal air puffing, as a noninvasive brain stimulation method, opens a new window to modifying the brain connectivity integration in comatose patients. This approach may potentially influence comatose patients' outcomes by increasing brain reactivity and network connectivity.

Organ Procurement From Poisoned Patients: A 14-Year Survey in 2 Academic Centers.

OBJECTIVES: Organ transplant from poisoned donors is an issue that has received much attention, especially over the past decade. Unfortunately, there are still opponents to this issue who emphasize that toxins and drugs affect the body's organs and do not consider organs from poisoned donors appropriate for transplantation. MATERIALS AND METHODS: Cases of brain death due to poisoning were collected from 2 academic centers in Tehran, Iran during a period from 2006 to 2020. Donor information and recipient condition at 1 month and 12 months after transplant and the subsequent transplant success rates were investigated. RESULTS: From 102 poisoned donors, most were 30 to 40 years old (33.4%) and most were men (55.9%). The most common causes of poisoning among donors were opioids (28.4%). Six candidate donors had been referred with cardiorespiratory arrest; these patients had organs that were in suitable condition, and transplant was successful. Acute kidney injury was seen in 30 donors, with emergency dialysis performed in 23 cases. For 51% of donors, cardiopulmonary resuscitation was performed. The most donated organs were the liver (81.4%), left kidney (81.4%), and right kidney (80.4%). Survival rate of recipients at 1 month and 12 months was 92.5% and 91.4%, respectively. Graft rejection rate at 1 month and 12 months after transplant was 0.7% and 2.21%, respectively. CONCLUSIONS: Organ donation from poisoning-related brain deaths is one of the best sources of organ supply for people in need. If the organ is in optimal condition before transplant, there are no exclusions for use of the graft.

The outbreak of methanol intoxication during COVID-19 pandemic: prevalence of brain lesions and its predisposing factors.

During the COVID-19 pandemic, methanol-containing beverages' consumption has risen because people mistakenly believed that alcohol might protect them against the virus. This study aimed to evaluate the prevalence and predisposing factors of brain lesions in patients with methanol toxicity and its outcome. A total of 516 patients with confirmed methanol poisoning were enrolled in this retrospective study, of which 40 patients underwent spiral brain computed tomography (CT) scan. The presence of unilateral or bilateral brain necrosis was significantly higher in the non-survival group (p = 0.001). Also, intracerebral hemorrhage (ICH) and brain edema were prevalent among patients that subsequently died (p = 0.004 and p = 0.002, respectively). Lower Glasgow Coma Scale (GCS) was related to a higher mortality rate (p = 0.001). The mortality rate in chronic alcohol consumption was lower than the patients who drank alcohol for the first time (p = 0.014). In conclusion, increasing the number of methanol poisoning and its associated mortality and morbidity should be considered a threat during the COVID-19 pandemic.