Autism spectrum disorder: Cadmium and mercury concentrations in different biological samples, a systematic literature review and meta-analysis of human studies.

The present study was conducted to investigate the differences in cadmium (Cd) and mercury (Hg) concentrations between children with autism spectrum disorder (ASD) and controls. In this systematic review and meta-analysis study, three thousand one hundred forty-five studies were collected from scientific databases including Web of Science, Scopus, PubMed, and Google Scholar from January 2000 to October 2022 and were investigated for eligibility. As a result, 37 studies published in the period from 2003 to 2022 met our inclusion criteria and were considered in the meta-analysis. The heterogeneity assumption was evaluated using the Chi-squared-based Q-test and I-squared (I2) statistics. The pooled estimates were shown in the forest plots with Hedges' g (95% confidence interval) values. The random effects model demonstrated that there is no significant difference in the blood (Hedges' g: 0.14, 95% CI: 0.45, 0.72, p > 0.05), hair (Hedges' g: 0.12, 95% CI: 0.26, 0.50, p > 0.05), and urinary (Hedges' g: 0.05, 95% CI: 0.86, 0.76, p > 0.05) Cd levels of the case group versus control subjects. Moreover, the pooled findings of studies showed no significant difference in the blood (Hedges' g: 1.69, 95% CI: 0.09, 3.48, p > 0.05), hair (Hedges' g: 3.42, 95% CI: 1.96, 8.80, p > 0.05), and urinary (Hedges' g: 0.49, 95% CI: 1.29 - 0.30, p > 0.05) Hg concentrations. The results demonstrated no significant differences in Hg and Cd concentrations in different biological samples of children with ASD compared to control subjects.

Evaluation of urinary trace element levels in patients with opioid use disorder undergoing methadone treatment in western Iran.

The monitoring of essential and toxic elements in patients with Opioid Use Disorder (OUD) undergoing methadone treatment (MT) is important, and there is limited previous research on the urinary levels of these elements in MT patients. Therefore, the present study aimed to analyze certain elements in the context of methadone treatment compared to a healthy group. In this study, patients with opioid use disorder undergoing MT (n = 67) were compared with a healthy group of companions (n = 62) in terms of urinary concentrations of some essential elements (selenium (Se), zinc (Zn), copper (Cu), iron (Fe), manganese (Mn), calcium (Ca)) and toxic elements (lead (Pb), cadmium (Cd), arsenic (As), and chromium (Cr)). Urine samples were prepared using the acid digestion method with a mixture of nitric acid and perchloric acid and assessed using the ICP-MS method. Our results showed that the two groups had no significant differences in terms of gender, education level, occupation, and smoking status. Urinary concentrations of Se, Cu, and Fe levels were significantly lower in the MT group compared to the healthy subjects. However, the concentrations of Pb, Cd, As, Mn, Cr, and Ca in the MT group were higher than in the healthy group (p < 0.05). No significant difference was established between the levels of Zn in the two groups (p = 0.232). The results of regression analysis revealed that the differences between the concentration levels of all metals (except Zn) between two groups were still remained significant after adjusting for all variables (p < 0.05). The data obtained in the current study showed lower urinary concentrations of some essential elements and higher levels of some toxic elements in the MT group compared to the healthy subjects. These findings should be incorporated into harm-reduction interventions.

Mixture effects of trace element levels on cardiovascular diseases and type 2 diabetes risk in adults using G-computation analysis.

There is an increasing concern about the health effects of exposure to a mixture of pollutants. This study aimed to evaluate the associations between serum levels of heavy/essential metals ([Arsenic (As), Cadmium (Cd), Mercury (Hg), Lead (Pb), Nickel (Ni), Chromium (Cr), Copper (Cu), Iron (Fe), and Zinc (Zn)]) and the risk of developing cardiovascular diseases (CVDs) and type 2 diabetes mellitus (T2D). Data were collected from 450 participants (150 with CVDs, 150 with T2D, and 150 healthy subjects) randomly selected from the Ravansar Non-Communicable Disease (RaNCD) cohort in Western Iran, covering the years 2018-2023. Trace element levels in the serum samples were assayed using ICP-MS. Logistic regression was performed to estimate the adjusted risk of exposure to single and multi-metals and CVD/T2D. Odds ratios were adjusted for age, sex, education, residential areas, hypertension, and BMI. The mixture effect of exposure to multi-metals and CVD/T2D was obtained using Quantile G-computation (QGC). In the logistic regression model, chromium, nickel, and zinc levels were associated with CVD, and significant trends were observed for these chemical quartiles (P < 0.001). Arsenic, chromium, and copper levels were also associated with T2D. The weight quartile sum (WQS) index was significantly associated with both CVD (OR 4.17, 95% CI 2.16-7.69) and T2D (OR 11.96, 95% CI 5.65-18.26). Cd, Pb, and Ni were the most heavily weighed chemicals in these models.The Cd had the highest weight among the metals in the CVD model (weighted at 0.78), followed by Hg weighted at 0.197. For T2D, the serum Pb (weighted at 0.32), Ni (weighted at 0.19), Cr (weighted at 0.17), and Cd (weighted at 0.14) were the most weighted in the G-computation model. The results showed the significant role of toxic and essential elements in CVDs and T2D risk. This association may be driven primarily by cadmium and mercury for CVDs and Pb, Ni, Cr, and Cd for T2D, respectively. Prospective studies with higher sample sizes are necessary to confirm or refute our preliminary results as well as to determine other important elements.

Azathioprine and hydroxychloroquine overdose in Sjögren's syndrome patient with hypocalcemia: a case report.

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.

Trace element levels: How Substance Use Disorder (SUD) contributes to the alteration of urinary essential and toxic element levels.

Increasing illicit drug use is one of the main problems in most countries or societies. Monitoring heavy metals and trace elements in this vulnerable group seems to be necessary. Therefore, we assessed the urinary trace element and toxic metals/metalloids concentrations (Zinc (Zn), Iron (Fe), Copper (Cu), Chromium (Cr), Lead (Pb), Cadmium (Cd), Arsenic (As), Nickel (Ni), and Mercury (Hg)) in opium, tramadol, and cannabis users compared to healthy subjects. In this cross-sectional study, patients with substance use disorder (SUD) (n = 74) were divided into four groups: cannabis, tramadol, opium, and mixed (simultaneous use of more than one of the three studied substances), along with a healthy group (n = 60). Urine samples were prepared by dispersive liquid-liquid microextraction method so that heavy metals/metalloids could be measured by ICP-MS. The mean urinary concentration of Cu (48.15 vs. 25.45; 89.2%, p<0.001), Hg (1.3 vs. 0.10; 1200%, p < 0.001), and Zn (301.95 vs. 210; 43.8%, p < 0.001) was markedly lower among patients with SUD. The mean urinary concentration of other elements including As (1.9 vs. 4.1; 115.8%), Cd (0.1 vs. 1.10; 1000%), Cr (6.80 vs. 11.65; 71.3%), Ni (2.95 vs. 4.95; 67.8%), and Pb (1.5 vs. 7.9; 426.6%) were significantly higher among patients with SUD compared to healthy subjects. When sub-groups were compared, no significant differences were observed between their trace element levels (Kruskal-Wallis test, p > 0.05). This can be an indication that regardless of the type of drug, the levels of trace elements are changed with respect to healthy individuals. Our results showed that illicit drug use causes changes in urinary trace element/heavy metal/metalloid levels and highlights the need for monitoring heavy metals and trace elements in individuals with substance use disorder. Assessment of different elements in biological samples of drug dependents may be useful for implementing new prevention and treatment protocols. In case of changes in their levels, complementary recommendations, attention to diet, and periodic assessment of toxic metal levels within treatment programs will be needed.

Cinnamon and cognitive function: a systematic review of preclinical and clinical studies.

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 ß 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.

Clinical and pharmacokinetics overview of intranasal administration of fentanyl.

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 Tmax 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.

The effects of active and passive smoking on selected trace element levels in human milk.

Our study aimed to compare levels of six micro-elements and six potentially toxic elements in the breast milk of non-smoking women compared to those found in women who smoke tobacco and women exposed to second-hand smoke during pregnancy and lactation. This was a cross-sectional study conducted on 100 lactating women in western Iran. The studied subjects were in three groups: passive smokers, active smokers, and a control group. Concentrations of selected trace elements in breast milk (essential and non-essential metals) were determined using ICP-MS. Our results indicated that the parameters of education, fruit consumption, and cosmetics usage had a significant difference among the groups (p < 0.05). Moreover, for trace elements, the Kruskal-Wallis test was statistically significant for arsenic (As), cadmium (Cd), mercury (Hg), and lead (Pb) (p < 0.05). The post hoc Dunn test revealed a significant difference in the levels of As, Cd, Hg, and Pb between non-smoker and passive/active smoker groups (p < 0.05). Our findings illustrate that exposure to cigarette smoke can cause an increase in the level of potentially toxic elements in human milk, which is dangerous for the consumption of premature newborns, but more research is needed to evaluate the potential toxic mechanisms of toxic metals.

Hepatic regenerative response to long-term consumption of cinnamon-rich diet in aged rats.

Objective: The present study aimed to investigate the impact of cinnamon on liver regeneration in a rat model of partial hepatectomy (PH). Materials and Methods: Thirty-two old male Sprague-Dawley rats (12 weeks old) were randomly divided into two equal groups (n=16). One group was fed with a standard diet (control) while the other group was fed with the same diet containing 1% cinnamon for 41 weeks. Then, all animals were subjected to the PH procedure and their livers were studied on postoperative days 2, 10 and 28. The liver contents of hepatic growth factor (HGF), insulin, malondialdehyde (MDA), nitric oxide metabolites (NOx), superoxide dismutase (SOD) and tumor necrosis factor-alpha (TNF-α) were evaluated. Also, the serum levels of liver function markers (alanine aminotransferase (ALT) and aspartate aminotransferase (AST), MDA, NOx and SOD activity were measured. Results: The regenerated liver weight was significantly higher in cinnamon-treated animals than the controls on both day 10 and 28 post hepatectomy. The hepatic MDA levels in the cinnamon-treated animals were significantly lower than the control rats. Cinnamon led to a significant increase of SOD on day 2 after hepatectomy in serum and liver content. The basal level of HGF in the liver of cinnamon-consuming rats was significantly higher than in the control rats. Hepatic insulin level was significantly increased relative to baseline and control on day 2 in the cinnamon-consuming rats. Hepatic TNF-α levels dramatically decreased on postoperative days (POD) 2 relative to baseline in the control and cinnamon-treated rats. Conclusion: Long-term cinnamon consumption enhanced liver regeneration outcomes in old rats.

Deep learning neural network derivation and testing to distinguish acute poisonings.

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.

Using a decision tree algorithm to distinguish between repeated supra-therapeutic and acute acetaminophen exposures.

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.

Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System.

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_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.

Human health risk assessment of potentially toxic elements in the breast milk consumed by infants in Western Iran.

This study aimed to assess the human health risk of some toxic metals/metalloids [lead (Pb), mercury (Hg), cadmium (Cd), nickel (Ni), chromium (Cr), and arsenic (As)] on infants via consumption of the breast milk of women living in urban areas of Kermanshah city, west of Iran. After collecting milk samples, the carcinogenic and non-carcinogenic risk assessment as well as uncertainty analysis of toxic metal levels were carried out. The order of concentration of heavy metals/metalloids in the breast milk samples was Cr (41.07 ± 23.19) > Ni (19.25 ± 11.81) > Pb (11.5 ± 4.48) > As (1.96 ± 2.04) > Cd (.72 ± 0.42) > Hg (0.31 ± 0.26). The results revealed that the levels of Cr and Pb in the breast milk samples were exceeded the World Health Organization (WHO) tolerable daily intake. In the breast milk samples a high levels of one of the trace elements As, Cd, Cr, Pb, and Ni were observed (over 73%) and in 40% of them the levels of Cr, Pb, Cd, As, and Ni were all above WHO tolerable daily intake. Moreover, the As-related point assessment of target risk factor (THQ) was higher than the allowable limit only for 1-month-old male neonates and 2-month-old female neonates (THQ > 1). In addition, Cr-related THQ scores were higher at all age and gender groups (THQ > 1). In conclusion, our findings suggest a potential risk of some metals for infants via the consumption of mothers' breast milk.

Utility of artificial intelligence to identify antihyperglycemic agents poisoning in the USA: introducing a practical web application using National Poison Data System (NPDS).

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.

Association Between Biological Lead Concentrations and Autism Spectrum Disorder (ASD) in Children: a Systematic Review and Meta-Analysis.

Studies have been conducted in different countries of the world to illustrate a link between autism spectrum disorder (ASD) and lead (Pb) in different specimens such as hair, blood, and urine. Therefore, we carried out a systematic review and meta-analysis to determine the association between Pb concentration in biological samples (blood, urine, and hair) and ASD in children through case-control and cross-sectional studies. In this systematic review, PubMed, Web of Sciences, Scopus, and Google Scholar were searched for relevant studies from January 2000 to February 2022. A random-effects model was used to pool the results. The effect sizes were standardized mean differences (proxied by Hedges' g) followed by a 95% confidence interval. Pooling data under the random effect model from blood and hair studies showed a significant difference between the children in the ASD group and the control group in blood lead level (Hedges' g: 1.21, 95% CI: 0.33-2.09, P = 0.01) and hair level (Hedges' g: 2.20, 95% CI: 0.56-3.85, P = 0.01). For urine studies, pooling data under the random effect model from eight studies indicated no significant difference between the children in the ASD group and control group in urinary lead level (Hedges' g: - 0.34, 95% CI: - 1.14,0.45, P = 0.40). Moreover, the funnel plot and the results of the Egger test for the blood and urine samples showed no publication bias, while, for the hair samples, the funnel plot illustrated the existence of publication bias.

Decision tree algorithm can determine the outcome of repeated supratherapeutic ingestion (RSTI) exposure to acetaminophen: review of 4500 national poison data system cases.

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.

Evaluation of some toxic metals in breast milk samples with dietary and sociodemographic characteristics: a case study of Kermanshah, Western Iran.

This study aimed to investigate the concentration of some toxic metals (gold (Au), cadmium (Cd), chromium (Cr), mercury (Hg), nickel (Ni), lead (Pb), tin (Sn), and vanadium (V)) and arsenic (As) in breast milk based on demographic characteristics and the diet of mothers. In this cross-sectional study, 100 lactating mothers from Kermanshah, Western Iran, were included. The mean age of the participants was 29.5 (ranging from 16 to 43 years) with a mean BMI of 26.9 (± 3.81) kg (range: 17.0-39.1). The results of the pairwise correlation coefficient of trace elements illustrated that correlation was mostly positive and weak to moderate. A few exceptions of strong correlations were Cr-Ni (r = 0.82), Au-As (r = 0.64), Cr-V (r = 0.64), and Ni-V (r = 0.58). Moreover, results indicated that BMI (p = 0.008), cooking oil (0.042), and potato intake (p = 0.010) affected the trace element levels significantly. The concentrations of V (p = 0.044), Sn (p = 0.036), Au (p < 0.001), and As (p < 0.001) in the breast milk of women was affected by the BMI. The results of univariate linear regression analysis showed that the concentration of Pb in the milk of mothers who use cosmetics was significant (p < 0.05). Since the lifestyle of lactating women, such as cosmetics usage can impact the content of some elements in breast milk, they should be educated in this part.

How much natural ventilation rate can suppress COVID-19 transmission in occupancy zones?

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.

Classification of acute poisoning exposures with machine learning models derived from the National Poison Data System.

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.