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A letter to the editor about a possible treatment approach to aluminum phosphide (AIP) poisoning is presented.

Objective: Aluminum phosphide (ALP) is a solid fumigant pesticide widely used in Iran as a grain preservative. It produces phosphine gas which is a mitochondrial poison that interferes with enzymes and protein synthesis. Over the last 3 years there has been a dramatic increase in the number of cases of ALP poisoning in Iran. Although ALP can cause an increase or decrease in blood glucose levels, we felt bound to examine the role of blood glucose level as a mortality prognostic factor. Methods: Forty-five patients with ALP poisoning due to ingestion were studied in a Loghman-Hakim Hospital Poison Center over a period of 14 months. All of them were treated with the same protocol (sodium bicarbonate, magnesium sulphate, calcium gluconate, and adequate hydration). Patients were divided to dead or alive groups and statistical comparisons were made in various parameters including blood glucose. Results: Between March 2006 and May 2007, 45 patients with ALP poisoning were admitted; of these 47% were female and 53% were male. The mean age of cases was 27.29 ± 11.53 years old with a range of 14-62 years old. All of them were hospitalized in the intensive care unit. The route of exposure to ALP was deliberate ingestion in all patients. The mean of blood glucose was 222.59 ± 20.18 mg/dL and 143.38 ± 13.7 mg/dL in survived and expired groups respectively which was statistically significant (P value < 0.05). Conclusion: Aluminum phosphide can cause either elevation, decrease or no change in blood glucose level. However in non-survived cases, these changes are wider and the mean of blood glucose level is higher than survived group. This hyperglycemic effect of ALP in non-survived group correlated with mortality, and suggested that the use of treatment which increases entrance of glucose to cells may be reducing oxygen consumption and may have useful role in the treatment of these patients. [ABSTRACT FROM AUTHOR]

Background: Aluminum phosphide (ALP) is used as a grain preservative. Fumigation depends on release of phosphine gas from the ALP. Very rare reports describe unintentional exposure secondary to grain fumigation. The majority of case reports involve oral intentional ingestion. Case report: In this presentation, we describe an accidental severe poisoning in a 35 year old woman, her 18 year old daughter and 6 year old son, caused by inhalation of phosphine gas released from 20 tablets of ALP stored in the 15 rice bags. The boy died 2 days after exposure before admission to hospital and any special treatment but the others were admitted approximately 2 days after inhalation. They had signs and symptoms of severe toxicity, and their clinical course included metabolic acidosis, electrocardiographic changes and hypotension. They were treated by intravenous administration of sodium bicarbonate, magnesium sulphate, and calcium gluconate. Conservative and supportive therapies in intensive care unit were also provided. The patients survived following treatment and supportive care and were discharged after 3 days and followed up for one week after discharge. Discussion: ALP is absorbed rapidly by inhalation, dermal exposure or ingestion. Both pesticide applicators and individuals in the vicinity of application are at the risk of accidental exposure and injury from phosphine inhalation; however, acute accidental poisoning happens rarely. The high mortality of ALP should raise the attention of the physicians to the problem of ALP poisoning and also necessitates the awareness of the public to the hazard of this poison. The extensive use of this pesticide poses the hazard of acute accidental and intentional exposure. Among our cases one of the individuals that had no treatment died; the two others that were admitted and treated in hospital recovered. Acute inhalation of phosphine gas if it is not diagnosed and treated can be surely lethal. [ABSTRACT FROM AUTHOR]

The role of trace elements and toxic metals on human health has been extensively discussed concerning disease pathogenesis and risk factors of diseases. In this systematic review and meta-analysis, we aimed to investigate arsenic (As), cadmium (Cd), lead (Pb), iron (Fe), zinc (Zn), and magnesium (Mg) levels in individuals with multiple sclerosis (MS) and healthy controls. We searched different databases/search engines for this systematic review and meta-analysis, including Web of Science, PubMed, Scopus, and Google Scholar, until June 27, 2024. Out of 5466 studies identified, 65 met our eligibility criteria and were included in the systematic review. For the meta-analysis, 58 studies with 10420 participants (5316 multiple sclerosis patients and 5104 healthy controls) had adequate data for inclusion. Results from the pooled data, analyzed using a random-effects model, revealed higher levels of As (Hedge's g = 4.00 μg/l, 95% CI = 2.03 to 5.98, P <0.001; I2 = 97.69%, P<0.001) and Cd (Hedge's g = 1.20 μg/l, 95% CI = 0.13 to 2.27, P = 0.028; I2 = 97.99%, P<0.001) in multiple sclerosis patients compared to healthy ones. However, no significant differences were observed in the concentrations of Zn, Fe, Mg, and Pb between the two groups. This study identified elevated As and Cd levels in MS patients, indicating the need for targeted interventions and public health guidelines for toxic metal exposure. Limiting exposure to contaminated environments and maintaining essential element levels through natural resources or supplements is essential, as there may be a possible relationship between multiple sclerosis and the concentrations of these elements in humans., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kooshki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The present study aimed to investigate the efficacy of different doses of Eucerin-based ointment containing opium compared to routine treatment on experimentally induced burn injury. Male Wistar rats were divided into five experimental groups for topical application: silver sulfadiazine 1% (SSD), Eucerin-based ointment containing opium at concentrations of 0.05%, 0.5%, and 5%, and a Eucerin-based ointment containing 0.05% opium plus SSD (mixed group), following burn wound induction using the comb burn model. An equal volume of different ointments was administered topically. The wound appearances of different groups were photographed at different time points for 21 days. In addition, blood and skin samples were taken 2 and 10 days after thermal injury to assess inflammatory parameters and oxidative stress markers. Also, the liver enzyme activity and kidney function tests were assessed on day 10. The body weight in Opium 5% significantly decreased compared to all other groups after ten days. The wound area was significantly reduced (P<0.05) in three groups: Opium 0.05%, Opium 0.5%, and mixed, compared to the SSD group on days 14 and 21. On day 21, the wound size in the Opium 5% group was significantly larger than that in the SSD group. Significantly lower serum levels of MDA were observed in all groups compared to the SSD group on days 2 and 10. There were no significant differences between treatment groups for concentrations of TNF-α in serum and burned skin samples (p>0.05). The serum concentrations of interleukin-10 in the mixed group were significantly higher compared to the SSD group on day 10. After ten days, groups were not different regarding AST, ALT, and creatinine concentrations (p>0.05). Histopathological analysis revealed that the wound healing efficacy of opium at its lowest concentration (0.05%) surpassed that of silver sulfadiazine (SSD). Furthermore, the combination of 0.05% opium with SSD enhanced the wound repair potential of SSD in burn injuries. This study showed that opium ointment delayed wound closure dose-dependently. Low doses of topical opium ointment and its mixed application with silver sulfadiazine had a protective role in thermal-induced injury., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Mehrpour et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to compare different machine learning models to predict the outcomes following methadone poisoning. This six-year retrospective longitudinal study utilizes National Poison Data System (NPDS) data. The severity of outcomes was derived from the NPDS Coding Manual. Our database was divided into training (70%) and test (30%) sets. We used a light gradient boosting machine (LGBM), extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) to predict the outcomes of methadone poisoning. A total of 3847 patients with methadone exposures were included. Our results demonstrated that machine learning models conferred high accuracy and reliability in determining the outcomes of methadone poisoning cases. The performance evaluation showed all models had high accuracy, precision, specificity, recall, and F 1-score values. All models could reach high specificity (more than 96%) and high precision (80% or more) for predicting major outcomes. The models could also achieve a high sensitivity to predict minor outcomes. Finally, the accuracy of all models was about 75%. However, XGBoost and LGBM models achieved the best performance among all models. This study showcased the accuracy and reliability of machine learning models in the outcome prediction of methadone poisoning.

Introduction: Attention-Deficit/Hyperactivity Disorder (ADHD) is a recognized neurodevelopmental disorder with a complex, multifactorial origin. Lead (Pb) and mercury (Hg) are highly toxic substances that can potentially impair brain development and have been implicated in the development of ADHD. This systematic review aims to analyze the epidemiological literature regarding the association between Pb and Hg exposure and the diagnosis of ADHD., Methods: From November 1983 to June 2, 2023, a comprehensive search was conducted in multiple databases and search engines, including PubMed, Web of Science, Scopus, and Google Scholar. Observational studies (case-control, cohort, and cross-sectional) measuring Pb and Hg levels in various biological samples (blood, hair, urine, nail, saliva, teeth, and bone) of children with ADHD or their parents and their association with ADHD symptoms were included., Results: Out of 2059 studies, 87 met the inclusion criteria and were included in this systematic review. Approximately two-thirds of the 74 studies investigating Pb levels in different biological samples reported associations with at least one subtype of ADHD. However, most studies examining Hg levels in various biological samples found no significant association with any ADHD subtype, although there were variations in exposure periods and diagnostic criteria., Conclusion: The evidence gathered from the included studies supports an association between Pb exposure and the diagnosis of ADHD, while no significant association was found with Hg exposure. Importantly, even low levels of Pb were found to elevate the risk of ADHD. Further research is needed to explore the comprehensive range of risk factors for ADHD in children, considering its significance as a neurodevelopmental disorder., (© 2024. The Author(s).)

The role of heavy metals on human health has often been discussed regarding disease pathogenesis and risk factors. Alzheimer's disease (AD), a prevalent neurodegenerative disease, is no exception. We conducted a comprehensive examination to assess the concentrations of lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg) in biological samples of AD patients in comparison with a group of individuals without AD, with the objective of our study being to perform a systematic review on this topic. We performed this systematic review by searching various databases/search engines, including Web of Science, Scopus, PubMed, and Google Scholar, until December 7, 2022. Relevant studies were scrutinized for eligibility. Meta-analysis was performed on studies with sufficient data using STATA software. Twenty-seven out of 3349 studies met pre-defined eligibility criteria and were included in our systematic review. For the meta-analysis, 22 studies with 3346 participants (1291 AD patients and 2055 healthy controls) had sufficient data for inclusion in the analysis. Using a random-effects model, we found that pooled data indicated patients with AD had significantly higher levels of Cd in their biological samples compared to controls (Hedges' g, 0.83; 95% CI, 0.11, 1.54; p = 0.023). However, other heavy metals were not significantly different in circulatory samples of AD patients compared to healthy controls (p > 0.05). This systematic review and meta-analysis indicated that Cd concentrations in AD patients were significantly higher than in healthy controls. Other included heavy metals may not be directly associated with increased risk of AD., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Introduction: Hydroxychloroquine and azathioprine have been routinely used to control and treat primary and secondary Sjögren's syndrome, which potentially triggered some overdoses by these drugs. Toxicity from hydroxychloroquine and azathioprine manifests in the form of cardiac conduction abnormalities, nausea, vomiting, and muscle weakness. Recognizing these unique drug overdoses and management of these toxicities is important. This case report aims to expand our current understanding of these drug overdoses and their management and also underscores the importance of anticipating and identifying fewer common complications, such as hypocalcemia., Case Report: A 34-year-old Persian woman with a history of Sjögren's syndrome presented to the emergency department 3.5-4 hours after an intentional overdose of hydroxychloroquine and azathioprine and severe hypotension and loss of consciousness. Although the patient was regularly taking other medications, such as fluoxetine, naproxen, and prednisolone, she explicitly clarified that these were not the substances involved in her overdose. Early investigations showed hypokalemia (2.4 mEq/L), hypocalcemia (7.5 mg/dL), and hypoglycemia (65 mg/dL). She was also diagnosed with metabolic acidosis and respiratory alkalosis. The electrocardiogram showed changes in favor of hypokalemia; other lab tests were run on the patient. Supportive treatments were applied, including rapid intravenous fluid dextrose 5%, normal saline, potassium chloride 30 mEq, and calcium gluconate 100 mg. The patient was managed and monitored overnight in the emergency room and recovered without residual side effects., Conclusion: Hydroxychloroquine and azathioprine toxicity are considered rare, but it is likely to increase in frequency given the prevalence and increase in autoimmune diseases and the increasing usage of these drugs in treating such diseases. We found hypocalcemia as the presentation to this patient, which needs further investigation into the probable mechanism. Clinicians need to consider the unique effects of hydroxychloroquine and azathioprine poisoning and initiate appropriate emergency interventions to improve the outcomes in similar patients., (© 2024. The Author(s).)

Background: Previous research has emphasized the importance of efficient ventilation in suppressing COVID-19 transmission in indoor spaces, yet suitable ventilation rates have not been suggested., Materials and Methods: This study investigated the impacts of mechanical, natural, single-sided, cross-ventilation, and three mask types (homemade, surgical, N95) on COVID-19 spread across eight common indoor settings. Viral exposure was quantified using a mass balance calculation of inhaled viral particles, accounting for initial viral load, removal via ventilation, and mask filtration efficiency., Results: Results demonstrated that natural cross-ventilation significantly reduced viral load, decreasing from 10,000 to 0 viruses over 15 minutes in a 100 m2 space by providing ~1325 m3/h of outdoor air via two 0.6 m2 openings at 1.5 m/s wind speed. In contrast, single-sided ventilation only halved viral load at best., Conclusion: Natural cross-ventilation with masks effectively suppressed airborne viruses, lowering potential infections and disease transmission. The study recommends suitable ventilation rates to reduce COVID-19 infection risks in indoor spaces., Competing Interests: There are no conflicts of interest., (Copyright: © 2024 Journal of Research in Medical Sciences.)

Cinnamon is the inner bark of trees named Cinnamomum. Studies have shown that cinnamon and its bioactive compounds can influence brain function and affect behavioral characteristics. This study aimed to systematically review studies about the relationship between cinnamon and its key components in memory and learning. Two thousand six hundred five studies were collected from different databases (PubMed, Scopus, Google Scholar, and Web of Science) in September 2021 and went under investigation for eligibility. As a result, 40 studies met our criteria and were included in this systematic review. Among the included studies, 33 were In vivo studies, five were In vitro , and two clinical studies were also accomplished. The main outcome of most studies (n = 40) proved that cinnamon significantly improves cognitive function (memory and learning). In vivo studies showed that using cinnamon or its components, such as eugenol, cinnamaldehyde, and cinnamic acid, could positively alter cognitive function. In vitro studies also showed that adding cinnamon or cinnamaldehyde to a cell medium can reduce tau aggregation, Amyloid β and increase cell viability. For clinical studies, one study showed positive effects, and another reported no changes in cognitive function. Most studies reported that cinnamon might be useful for preventing and reducing cognitive function impairment. It can be used as an adjuvant in the treatment of related diseases. However, more studies need to be done on this subject.

Background: Sepsis is a significant cause of mortality worldwide. This study aimed to compare clinical and laboratory characteristics of sepsis in patients addicted to illicit drugs versus patients with no illicit drug addiction., Methods: In this cross-sectional study, all patients hospitalized with sepsis diagnosis were recruited within six months from September to March 2019. Sixty patients for each group (illicit drugaddicted and non-addicted individuals) were selected. The data relating to illicit drug consumption, serum indices, the current focus of infection, duration of hospitalization, and disease outcomes were collected. Patients who had an illicit drug addiction were compared with non-addicted patients in terms of clinical and laboratory parameters. The data collected were analyzed using SPSS software (version 19)., Results: The bacterial load in the urine culture was statistically significant in both groups and higher in the non-addicted group. The frequency distributions of focus of infection, duration of hospitalization, and outcome were not significantly different between the two groups. The serum sodium and total neutrophils were significantly higher in the addicted group. However, the MCHC level was significantly lower (p < 0.05)., Conclusion: Opium may have stimulated the immune system and reduced bacterial infection in septic patient users., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Due to the presence of large surfaces and high blood supply, drug delivery through the nasal route of administration is the appropriate route to administrate drugs with rapid onsets of action. Bypassing first-pass metabolism can increase drug bioavailability. The physicochemical properties of fentanyl led to a need to develop formulations for delivery by multiple routes. Several approved inter-nasal fentanyl products in Europe and the USA have been used in prehospital and emergency departments to treat chronic cancer pain and used to treat severe acute abdominal and flank pain. Analgesia durations and onsets were not significantly different between intranasal and intravenous fentanyl in patients with cancer breakthrough pain and were well-tolerated in the long term. Intranasal Fentanyl (INF) at a 50 μg/ml concentration decreased renal colic pain to the lowest level in 30 min. Possible adverse effects specific to INF are epistaxis, nasal wall ulcer, rhinorrhea, throat irritation, dysgeusia, nausea, and vomiting. However, there is limited available literature about the serious adverse effects of INF in adults and children. Intranasal Fentanyl Spray (INFS) results in significantly higher plasma concentrations and has a lower T max than oral transmucosal formulation, and the bioavailability of fentanyl in intranasal formulations is very high (89 %), particularly in pectin-containing formulations such as PecFent and Lazanda., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Ltd.)

COVID-19 was responsible for many deaths and economic losses around the globe since its first case report. Governments implemented a variety of policies to combat the pandemic in order to protect their citizens and save lives. Early in 2020, the first cases were reported in Arizona State and continued to rise until the discovery of the vaccine in 2021. A variety of strategies and interventions to stop or decelerate the spread of the pandemic has been considered. It is recommended to define which strategy was successful for disease propagation prevention and could be used in further similar situations. This study aimed to evaluate the effect of people's contact interventions strategies which were implemented in Arizona State and their effect on reducing the daily new COVID-19 cases and deaths. Their effect on daily COVID-19 cases and deaths were evaluated using an interrupted time series analysis during the pandemic's first peaks to better understand the onward situation. Canceling the order of staying at home (95% CI, 1718.52 to 6218.79; p<0.001) and expiring large gatherings (95% CI, 1984.99 to 7060.26; p<0.001) on June 30 and August 17, 2020, respectively, had a significant effect on the pandemic, leading to the daily cases to grow rapidly. Moreover, canceling the stay at home orders led to an increase in the number of COVID-19 daily deaths by 67.68 cases (95% CI, 27.96 to 107.40; p<0.001) after about 21 days while prohibiting large gatherings significantly decreased 66.76 (95% CI: 20.56 to 112.96; p = 0.004) the number of daily deaths with about 21 days' lag. The results showed that strategies aimed at reducing people's contact with one another could successfully help fight the pandemic. Findings from this study provide important evidence to support state-level policies that require observance of social distancing by the general public for future pandemics., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

This study is aimed at establishing the outcome of RSTI exposure to acetaminophen based on a decision tree algorithm for the first time. This study used the National Poison Data System (NPDS) to conduct a six-year retrospective cohort analysis, which included 4522 individuals. The patients had a mean age of 26.75 ± 16.3 years (1-89). 3160 patients (70%) were females. Most patients had intentional exposure to acetaminophen. Almost all the patients had acetaminophen exposure via ingestion. In addition, 400 (8.8%) experienced major outcomes, 1500 (33.2%) experienced moderate outcomes, and 2622 (58%) of the patients experienced mild ones. The decision tree model performed well in the training and test groups. In the test group, the accuracy was 0.813, precision of 0.827, recall being 0.798, specificity 0.898, and an F1 score 0.80. In the training group, accuracy was 0.831, recall was 0.825, precision was 0.837, specificity was 0.90, and F1 score was 0.829. Our results showed that serum liver enzymes being present at elevated levels (Alanine aminotransferase (ALT), Aspartate aminotransferase (AST) greater than 1000 U/L followed by ALT, AST between 100 and 1000 U/L), prothrombin time (PT) prolongation, bilirubin increase, renal failure, confusion, age, hypotension, other coagulopathy (such as partial thromboplastin time (PTT) prolongation), acidosis, and electrolyte abnormality were the effective factors in determining the outcomes in these patients. The decision tree algorithm is a dependable method for establishing the prognosis of patients who have been exposed to RSTI acetaminophen and can be used throughout the patients' hospitalization period.

Bupropion is widely used for the treatment of major depressive disorder and for smoking cessation assistance. Unfortunately, there are no practical systems to assist clinicians or poison centres in predicting outcomes based on clinical features. Hence, the purpose of this study was to use a decision tree approach to inform early diagnosis of outcomes secondary to bupropion overdose. This study utilized a dataset from the National Poison Data System, a 6-year retrospective cohort study on toxic exposures and patient outcomes. A machine learning algorithm (decision tree) was applied to the dataset using the sci-kit-learn library in Python. Shapley Additive exPlanations (SHAP) were used as an explainable method. Comparative analysis was performed using random forest (RF), Gradient Boosting classification, eXtreme Gradient Boosting, Light Gradient Boosting (LGM) and voting ensembling. ROC curve and precision-recall curve were used to analyse the performance of each model. LGM and RF demonstrated the highest performance to predict outcome of bupropion exposure. Multiple seizures, conduction disturbance, intentional exposure, and confusion were the most influential factors to predict the outcome of bupropion exposure. Coma and seizure, including single, multiple and status, were the most important factors to predict major outcomes., (© 2023 Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society). Published by John Wiley & Sons Ltd.)

Snakebite is a relatively common health condition in Iran with a diverse snake fauna, especially in tropical southern and mountainous western areas of the country with a plethora of snake species. The list of medically important snakes, circumstances and effects of their bite, and necessary medical care require critical appraisal and should be updated regularly. This study aims to review and map the distributions of medically important snake species of Iran, re-evaluate their taxonomy, review their venomics, describe the clinical effects of envenoming, and discuss medical management and treatment, including the use of antivenom. Nearly 350 published articles and 26 textbooks with information on venomous and mildly venomous snake species and snakebites of Iran, were reviewed, many in Persian (Farsi) language, making them relatively inaccessible to an international readership. This has resulted in a revised updated list of Iran's medically important snake species, with taxonomic revisions of some, compilation of their morphological features, remapping of their geographical distributions, and description of species-specific clinical effects of envenoming. Moreover, the antivenom manufactured in Iran is discussed, together with treatment protocols that have been developed for the hospital management of envenomed patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Diphenhydramine (DPH) is an antihistamine medication that in overdose can result in anticholinergic symptoms and serious complications, including arrhythmia and coma. We aimed to compare the value of various machine learning (ML) models, including light gradient boosting machine (LGBM), logistic regression (LR), and random forest (RF), in the outcome prediction of DPH poisoning., Materials and Methods: We used the National Poison Data System database and included all of the human exposures of DPH from January 01, 2017 to December 31, 2017, and excluded those cases with missing information, duplicated cases, and those who reported co-ingestion. Data were split into training and test datasets, and three ML models were compared. We developed confusion matrices for each, and standard performance metrics were calculated., Results: Our study population included 53,761 patients with DPH exposure. The most common reasons for exposure, outcome, chronicity of exposure, and formulation were captured. Our results showed that the average precision-recall area under the curve (AUC) of 0.84. LGBM and RF had the highest performance (average AUC of 0.91), followed by LR (average AUC of 0.90). The specificity of the models was 87.0% in the testing groups. The precision of models was 75.0%. Recall (sensitivity) of models ranged between 73% and 75% with an F1 score of 75.0%. The overall accuracy of LGBM, LR, and RF models in the test dataset was 74.8%, 74.0%, and 75.1%, respectively. In total, just 1.1% of patients (mostly those with major outcomes) received physostigmine., Conclusion: Our study demonstrates the application of ML in the prediction of DPH poisoning., Competing Interests: Dr. Goss receives consulting fees from Credo Health. Dr. Goss's financial interests have been reviewed by University of Colorado Hospital and University of Colorado School of Medicine in accordance with their institutional policies., (Copyright: © 2023 Journal of Research in Medical Sciences.)

Background: This study aimed to compare clinical and laboratory characteristics of supra-therapeutic (RSTI) and acute acetaminophen exposures using a predictive decision tree (DT) algorithm., Methods: We conducted a retrospective cohort study using the National Poison Data System (NPDS). All patients with RSTI acetaminophen exposure (n = 4,522) between January 2012 and December 2017 were included. Additionally, 4,522 randomly selected acute acetaminophen ingestion cases were included. After that, the DT machine learning algorithm was applied to differentiate acute acetaminophen exposure from supratherapeutic exposures., Results: The DT model had accuracy, precision, recall, and F1-scores of 0.75, respectively. Age was the most relevant variable in predicting the type of acetaminophen exposure, whether RSTI or acute. Serum aminotransferase concentrations, abdominal pain, drowsiness/lethargy, and nausea/vomiting were the other most important factors distinguishing between RST and acute acetaminophen exposure., Conclusion: DT models can potentially aid in distinguishing between acute and RSTI of acetaminophen. Further validation is needed to assess the clinical utility of this model., (© 2023. The Author(s).)

Background: Biguanides and sulfonylurea are two classes of anti-diabetic medications that have commonly been prescribed all around the world. Diagnosis of biguanide and sulfonylurea exposures is based on history taking and physical examination; thus, physicians might misdiagnose these two different clinical settings. We aimed to conduct a study to develop a model based on decision tree analysis to help physicians better diagnose these poisoning cases., Methods: The National Poison Data System was used for this six-year retrospective cohort study.The decision tree model, common machine learning models multi layers perceptron, stochastic gradient descent (SGD), Adaboosting classiefier, linear support vector machine and ensembling methods including bagging, voting and stacking methods were used. The confusion matrix, precision, recall, specificity, f1-score, and accuracy were reported to evaluate the model's performance., Results: Of 6183 participants, 3336 patients (54.0%) were identified as biguanides exposures, and the remaining were those with sulfonylureas exposures. The decision tree model showed that the most important clinical findings defining biguanide and sulfonylurea exposures were hypoglycemia, abdominal pain, acidosis, diaphoresis, tremor, vomiting, diarrhea, age, and reasons for exposure. The specificity, precision, recall, f1-score, and accuracy of all models were greater than 86%, 89%, 88%, and 88%, respectively. The lowest values belong to SGD model. The decision tree model has a sensitivity (recall) of 93.3%, specificity of 92.8%, precision of 93.4%, f1&#95;score of 93.3%, and accuracy of 93.3%., Conclusion: Our results indicated that machine learning methods including decision tree and ensembling methods provide a precise prediction model to diagnose biguanides and sulfonylureas exposure., (© 2023. The Author(s).)

Clinical effects of antihyperglycemic agents poisoning may overlap each other. So, distinguishing exposure to these pharmaceutical drugs may take work. This study examined the application of machine learning techniques in identifying antihyperglycemic agent exposure using the national poisoning database in the USA. In this study, the data of single exposure due to Biguanides and Sulfonylureas (n=6183) was requested from the National Poison Data System (NPDS) for 2014-2018. We have tried five machine learning models (random forest classifier, k-nearest neighbors, Xgboost classifier, logistic regression, neural network Keras). For the multiclass classification modeling, we have divided the dataset into two parts: train (75%) and test (25%). The performance metrics used were accuracy, specificity, precision, recall, and F1-score. The algorithms used to get the classification results of different models to diagnose antihyperglycemic agents were very accurate. The accuracy of our model in determining these two antihyperglycemic agents was 91-93%. The precision-recall curve showed average precision of 0.91, 0.97, 0.97, and 0.98 for k-nearest neighbors, logistic regression, random forest, and XGB, respectively. The logistic regression, random forest, and XGB had the highest AUC (AUC=0.97) among both biguanides and sulfonylureas groups. The negative predictive values (NPV) for all the models were between 89 and 93%. We introduced a practical web application to help physicians distinguish between these agents. Despite variations in accuracy among the different types of algorithms used, all of them could accurately determine the specific exposure to biguanides and sulfonylureas retrospectively. Machine learning can distinguish antihyperglycemic agents, which may be useful for physicians without any background in medical toxicology. Besides, Our suggested ML-based Web application might help physicians in their diagnosis., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Alcoholic hepatitis (AH) is characterized by acute symptomatic hepatitis associated with heavy alcohol use. This study was designed to assess the impact of metabolic syndrome on high-risk patients with AH with discriminant function (DF) score ≥ 32 and its effect on mortality., Methods: We searched the hospital database for ICD-9 diagnosis codes of acute AH, alcoholic liver cirrhosis, and alcoholic liver damage. The entire cohort was categorized into two groups: AH and AH with metabolic syndrome. The effect of metabolic syndrome on mortality was evaluated. Also, an exploratory analysis was used to create a novel risk measure score to assess mortality., Results: A large proportion (75.5%) of the patients identified in the database who had been treated as AH had other etiologies and did not meet the American College of Gastroenterology (ACG)-defined diagnosis of acute AH, thus had been misdiagnosed as AH. Such patients were excluded from analysis. The mean body mass index (BMI), hemoglobin (Hb), hematocrit (HCT), and alcoholic liver disease/non-alcoholic fatty liver disease index (ANI) were significantly different between two groups (P < 0.05). The results of a univariate Cox regression model showed that age, BMI, white blood cells (WBCs), creatinine (Cr), international normalized ratio (INR), prothrombin time (PT), albumin levels, albumin < 3.5, total bilirubin, Na, Child-Turcotte-Pugh (CTP), model for end-stage liver disease (MELD), MELD ≥ 21, MELD ≥ 18, DF score, and DF ≥ 32 had a significant effect on mortality. Patients with a MELD greater than 21 had a hazard ratio (HR) (95% confidence interval (CI) of 5.81 (2.74 - 12.30) (P < 0.001). The adjusted Cox regression model results showed that age, Hb, Cr, INR, Na, MELD score, DF score, and metabolic syndrome were independently associated with high patient mortality. However, the increase in BMI and mean corpuscular volume (MCV) and sodium significantly reduced the risk of death. We found that a model including age, MELD ≥ 21, and albumin < 3.5 was the best model in identifying patient mortality. Our study showed that patients admitted with a diagnosis of alcoholic liver disease with metabolic syndrome had an increased mortality risk compared to patients without metabolic syndrome, in high-risk patients with DF ≥ 32 and MELD ≥ 21. A bivariate correlation analysis revealed that patients with AH with metabolic syndrome were more likely to have infection (43%) compared to AH (26%) with correlation coefficient of 0.176 (P = 0.03, CI: 0.018 - 1.0)., Conclusion: In clinical practice, the diagnosis of AH is inaccurately applied. Metabolic syndrome significantly increases the mortality risk in high-risk AH. It signifies that the presence of features of metabolic syndrome modifies the behavior of AH in acute settings, warranting different therapeutic strategies. We propose that in defining AH, patients overlapping with metabolic syndrome may need to be excluded as their outcome is different with regard to risk of renal dysfunctions, infections and death., Competing Interests: None to declare., (Copyright 2023, Habib et al.)

Information on the pattern of acute poisonings in hospitals of Birjand city, Iran, is limited. This study aimed to address this knowledge gap by examining the admissions in a major poisoning center in eastern Iran. This cross-sectional study included patients admitted to the Imam Reza Hospital in Birjand over 12 months. Medical records of the poisoned patients were reviewed, and the study variables were used for data analysis. During the study period, 534 cases of acute poisonings were evaluated. The patient's ages ranged from 12 to 84 years, with a high rate of poisonings between 15 and 35 years. The female predominance in poisoning cases was 52.1%. Most cases of poisonings occurred in spring, and the common route of exposure was oral (93.1%). The incidence of poisoning in married couples, uneducated patients, and residents of urban areas was 56.5%, 90.1%, and 74.6%, respectively. Patients with a previous medical history experienced addiction and psychiatric disorders. Intentional poisoning accounted for 23.4% of acute poisoning cases referred to the hospital in the current study. The main groups of toxicants were pharmaceutical products (48.1%), narcotics (25.8%), chemical products (10.1%), envenomation (7.1%), and alcohol (1.7%). The mean hospital stay was 2.5 ± 3.0 days, and the final treatment outcome was complete recovery, except for one patient intoxicated by warfarin and alprazolam. Our results indicate that the high toxicity cases were related to pharmaceutical product and opioids abuse, especially methadone (8.4%), alprazolam (7.9%), clonazepam (7.5%), and acetaminophen (9.9%) taken orally and more commonly happened at home. Due to the high rate of deliberate poisonings, especially among young adults and students, monitoring drug distribution and exceptional attention to mental health should be seriously considered by national health authorities to prevent suicide attempts., (© 2023. The Author(s).)

Introduction: Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was exposed to from a pre-specified list of drugs., Research Design & Methods: Data were queried from the National Poison Data System (NPDS) from 2014 through 2018 for eight single-agent poisonings (acetaminophen, diphenhydramine, aspirin, calcium channel blockers, sulfonylureas, benzodiazepines, bupropion, and lithium). Two Deep Neural Networks (PyTorch and Keras) designed for multi-class classification tasks were applied., Results: There were 201,031 single-agent poisonings included in the analysis. For distinguishing among selected poisonings, PyTorch model had specificity of 97%, accuracy of 83%, precision of 83%, recall of 83%, and a F1-score of 82%. Keras had specificity of 98%, accuracy of 83%, precision of 84%, recall of 83%, and a F1-score of 83%. The best performance was achieved in the diagnosis of single-agent poisoning in diagnosing poisoning by lithium, sulfonylureas, diphenhydramine, calcium channel blockers, then acetaminophen, in PyTorch (F1-score = 99%, 94%, 85%, 83%, and 82%, respectively) and Keras (F1-score = 99%, 94%, 86%, 82%, and 82%, respectively)., Conclusion: Deep neural networks can potentially help in distinguishing the causative agent of acute poisoning. This study used a small list of drugs, with polysubstance ingestions excluded.Reproducible source code and results can be obtained at https://github.com/ashiskb/npds-workspace.git.

The primary aim of this pilot study was to develop a machine learning algorithm to predict and distinguish eight poisoning agents based on clinical symptoms. Data were used from the National Poison Data System from 2014 to 2018, for patients 0-89 years old with single-agent exposure to eight drugs or drug classes (acetaminophen, aspirin, benzodiazepines, bupropion, calcium channel blockers, diphenhydramine, lithium and sulfonylureas). Four classifier prediction models were applied to the data: logistic regression, LightGBM, XGBoost, and CatBoost. There were 201 031 cases used to develop and test the algorithms. Among the four models, accuracy ranged 77%-80%, with precision and F1 scores of 76%-80% and recall of 77%-78%. Overall specificity was 92% for all models. Accuracy was highest for identifying sulfonylureas, acetaminophen, benzodiazepines and diphenhydramine poisoning. F1 scores were highest for correctly classifying sulfonylureas, acetaminophen and benzodiazepine poisonings. Recall was highest for sulfonylureas, acetaminophen, and benzodiazepines, and lowest for bupropion. Specificity was >99% for models of sulfonylureas, calcium channel blockers, lithium and aspirin. For single-agent poisoning cases among the eight possible exposures, machine learning models based on clinical signs and symptoms moderately predicted the causal agent. CatBoost and LightGBM classifier models had the highest performance of those tested., (© 2022 Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society). Published by John Wiley & Sons Ltd.)

Background: With diabetes incidence growing globally and metformin still being the first-line for its treatment, metformin's toxicity and overdose have been increasing. Hence, its mortality rate is increasing. For the first time, we aimed to study the efficacy of machine learning algorithms in predicting the outcome of metformin poisoning using two well-known classification methods, including support vector machine (SVM) and decision tree (DT)., Methods: This study is a retrospective cohort study of National Poison Data System (NPDS) data, the largest data repository of poisoning cases in the United States. The SVM and DT algorithms were developed using training and test datasets. We also used precision-recall and ROC curves and Area Under the Curve value (AUC) for model evaluation., Results: Our model showed that acidosis, hypoglycemia, electrolyte abnormality, hypotension, elevated anion gap, elevated creatinine, tachycardia, and renal failure are the most important determinants in terms of outcome prediction of metformin poisoning. The average negative predictive value for the decision tree and SVM models was 92.30 and 93.30. The AUC of the ROC curve of the decision tree for major, minor, and moderate outcomes was 0.92, 0.92, and 0.89, respectively. While this figure of SVM model for major, minor, and moderate outcomes was 0.98, 0.90, and 0.82, respectively., Conclusions: In order to predict the prognosis of metformin poisoning, machine learning algorithms might help clinicians in the management and follow-up of metformin poisoning cases., (© 2022. The Author(s).)

Lead (Pb) is a prevalent environmental toxic metal that may harmfully affect the function of the thyroid gland. Our information about the effects of lead on thyroid function in lead poisoning is limited. The present study aimed to evaluate thyroid function in lead-poisoned patients compared with the healthy group. In this study, two groups of lead poisoning subjects (lead concentrations ≥ 25 μg/dL) (n = 56) (HBLC group) and randomly selected age and gender-matched healthy individuals with lower blood lead concentrations (BLC < 10 μg/dL) (n = 58) (LBLC group) were included. Thyroid function tests, including thyroxine (T4), triiodothyronine (T3), and thyroid-stimulating hormone (TSH) concentration, were measured for each patient in both groups (for the HBLC group, it was taken on admission). The linear regression model was used to investigate the effects of lead poisoning on thyroid function tests. A p-value of less than 0.05 was considered significant. Our study showed no significant difference between the two groups in terms of age and gender. The mean concentration of T3 and TSH in the HBLC group was significantly lower than the LBLC group, but the mean levels of T4 were significantly higher in the HBLC group (p < 0.05). Based on the linear regression model results, the T3 and TSH levels in the LBLC group were significantly higher by 13.86 and 0.43 units than the HBLC group, respectively. The current study results showed that lead poisoning affects thyroid function, reduces TSH, and increases T4., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objectives: Biguanides and sulfonylureas are anti-hyperglycemic drugs commonly used in the United States. Poisoning with these drugs may lead to serious consequences. The diagnosis of biguanide and sulfonylurea poisoning is based on history, clinical manifestations, and laboratory studies., Methods: This study is a six-year retrospective cohort analysis based on the National Poison Data System. Clinical effects of 6183 biguanide and sulfonylurea exposures were identified using binary logistic regression., Results: The mean age of patients with biguanide and sulfonylurea exposure was 39.27 ± 28.91 and 28.91 ± 30.41 years, respectively. Sulfonylurea exposure is most commonly seen via unintentional exposure, while biguanide exposure frequently occurs as a result of intentional ingestion. Minor and moderate outcomes commonly developed following biguanide and sulfonylurea exposure, respectively. Sulfonylurea exposure was less likely to develop clinical effects abdominal pain, metabolic acidosis, diarrhea, nausea, vomiting, and elevated creatinine than patients ingesting biguanides. However, sulfonylurea exposure was more likely to cause dizziness or vertigo, tremor, drowsiness or lethargy, agitation, diaphoresis, and hypoglycemia., Conclusions: Our study is the first to use a wide range of national data to describe the clinical characteristics that differentiate the toxicologic exposure to biguanides and sulfonylureas. Sulfonylurea exposure is commonly seen via unintentional exposure, while metformin exposure is frequently seen via intentional exposure. Sulfonylurea toxicity is more likely to cause agitation, dizziness or vertigo, tremor, diaphoresis, and hypoglycemia, while metformin exposure induces abdominal pain, acidosis, diarrhea, nausea, vomiting, and elevated creatinine., Competing Interests: Declaration of Competing Interest Authors declare no conflict of interest., (Copyright © 2022. Published by Elsevier Inc.)

Congenital heart disease (CHD) is the most prevalent congenital defect that causes several problems for patients and their families and medical specialists. It is important to investigate CHD risk factors due to their significantly destructive load on society. This study aims to determine the association between maternal lead exposure and conceiving a child with CHD. This case-control study was performed on a total of 246 mothers in the pediatric clinic of Vali-e-Asr Hospital in Birjand, Iran. One hundred forty-six mothers with CHD children were defined as the case group, and 100 age-matched mothers with healthy children were considered the control group. All the mothers were between 20 and 40 years old, and their children were under the age of 6 months. Demographic data was collected from mothers using a well-designed questionnaire. In addition, all mothers were referred to the laboratory for measuring blood lead concentrations. The mean blood lead concentration (BLC) of mothers in the group of children with heart problems was 4.11 ± 10.02 with a median of 2.50 μg/dL and in the control group was 2.66 ± 2.06 with a median of 2.30 μg/dL. The Mann-Whitney test results showed that mothers' lead concentration in the group of children with heart problems was significantly higher than the control group (z = 2.13, p = 0.03). The chi-square test results showed that lead concentrations in the two groups were significantly different from each other (χ 2  = 9.11, p = 0.01). The results of our study showed that mothers of children with CHD had higher blood concentrations than mothers of healthy children., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)