Factors affecting the severity and prognosis of visual damage in inhalational methanol poisoning

Methanol Induced Optic Neuropathy: Molecular Mysteries, Public Health Perspective, Clinical Insights and Treatment Strategies

Methanol-induced optic neuropathy (MION) represents a critical public health issue, particularly prevalent in lower socioeconomic populations and regions with restricted alcohol access. MION, characterized by irreversible visual impairment, arises from the toxic metabolization of methanol into formaldehyde and formic acid, leading to mitochondrial oxidative phosphorylation inhibition, oxidative stress, and subsequent neurotoxicity. The pathogenesis involves axonal and glial cell degeneration within the optic nerve and potential retinal damage. Despite advancements in therapeutic interventions, a significant proportion of affected individuals endure persistent visual sequelae. The study comprehensively investigates the pathophysiology of MION, encompassing the absorption and metabolism of methanol, subsequent systemic effects, and ocular impacts. Histopathological changes, including alterations in retinal layers and proteins, Müller cell dysfunction, and visual symptoms, are meticulously examined to provide insights into the disease mechanism. Furthermore, preventive measures and public health perspectives are discussed to highlight the importance of awareness and intervention strategies. Therapeutic approaches, such as decontamination procedures, ethanol and fomepizole administration, hemodialysis, intravenous fluids, electrolyte balance management, nutritional therapy, corticosteroid therapy, and erythropoietin (EPO) treatment, are evaluated for their efficacy in managing MION. This comprehensive review underscores the need for increased awareness, improved diagnostic strategies, and more effective treatments to mitigate the impact of MION on global health.

Outcome assessment of acute methanol poisoning: A risk-prediction nomogram approach for in-hospital mortality

Acute methanol poisoning could be associated with high morbidities and fatalities. Stratifying high-risk patients is crucial in improving their prognosis. Hence, this study aimed to identify patients with methanol poisoning at high risk of in-hospital mortality. Also, the risk factors for blindness were assessed. The study included 180 acutely methanol-poisoned patients who received standard medical care. Out of 180 patients, 52 (28.9 %) patients presented with blindness, and 43 (23.9 %) patients died. The predictive model was based on four significant variables, including blindness, mean arterial pressure, serum bicarbonate, and serum creatinine. The presence of blindness and elevated serum creatinine significantly increased the likelihood of mortality by 14.274 and 5.670 times, respectively. Likewise, decreases in mean arterial pressure and serum bicarbonate significantly increased mortality risk by 0.908 and 0.407 times, respectively. The proposed nomogram exhibited excellent discriminatory power (area under the curve (AUC)=0.978, accuracy=93.3 %), which outperforms the AUCs of individual predictors. The provided nomogram is easily applicable with outstanding discrimination, making it clinically helpful in predicting in-hospital mortality in acutely methanol-poisoned patients. Regarding the risk factors for blindness, multivariable regression analysis revealed that delayed time for admission (OR=1.039; 95 % CI=1.010-1.069; p= 0.009) and elevated anion gap (OR=1.053; 95 % CI=1.007-1.101; p= 0.023) were significant risk factors. The current study assists physicians in identifying methanol-poisoned patients with a high probability of mortality or blindness on admission. Future studies are recommended for external validation of the created nomogram, in addition to follow-up for patients with visual impairment.

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.

Qualitative assessment of regular and premium gasoline available in Bangladesh markets

Assessing the quality of fuel is essential to comprehend its impact on the environment and human health. In this study, the evaluation of fuel quality standards at the consumer level was conducted by analyzing the motor fuels in Khulna, Bangladesh. A total of 32 samples of petrol (regular gasoline), and octane (premium gasoline) were collected from the fuel stations in the Khulna City Corporation area and analyzed with an FTIR-Fuel Analyzer. Fuel properties, such as research octane number (RON), motor octane number (MON), ethanol content, olefins content, and oxygen content were analyzed. For petrol, the average RON, MON, olefins, and oxygen content were 95.34, 85.70, 8.23 %v/v, and 0.78 %m/m, respectively, and for octane, they were 96.96, 85.39, 1.25 %v/v, and 0.09 %m/m, respectively. Almost all of these parameters complied with both Bangladesh standard and Euro 5 fuel specifications, and those that did not comply were very close to their standard values. However, benzene concentration, which was not specified in Bangladesh Standard, was the most alarming metric for octane since none of the samples matched the Euro 5 fuel specifications of the maximum concentration of 1 %v/v benzene; on average it was 3.70 %v/v. Although petrol benzene content (average 1.50 %v/v) was not as bad as it was for octane, it was still nowhere near good enough, with only 25% of the samples within the recommended level among the studied sample. This information holds significance in establishing the fuel profile and facilitating the identification of distinct samples linked to adulteration. Therefore, the analysis of motor fuel qualities is essential for maintaining the environment, human health, and the economy of a country.