Acute and chronic blood serum proteome changes in patients with methanol poisoning

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.

Comprehensive spectroscopic, metabolomic, and proteomic liquid biopsy in the diagnostics of hepatocellular carcinoma

Liquid biopsy is a very topical issue in clinical diagnostics research nowadays. In this study, we explored and compared various analytical approaches to blood plasma analysis. Finally, we proposed a comprehensive procedure, which, thanks to the utilization of multiple analytical techniques, allowed the targeting of various biomolecules in blood plasma reflecting diverse biological processes underlying disease development. The potential of such an approach, combining proteomics, metabolomics, and vibrational spectroscopy along with preceding blood plasma fractionation, was demonstrated on blood plasma samples of patients suffering from hepatocellular carcinoma in cirrhotic terrain (n = 20) and control subjects with liver cirrhosis (n = 20) as well as healthy subjects (n = 20). Most of the applied methods allowed the classification of the samples with an accuracy exceeding 80.0 % and therefore have the potential to be used as a stand-alone method in clinical diagnostics. Moreover, a final panel of 48 variables obtained by a combination of the utilized analytical methods enabled the discrimination of the hepatocellular carcinoma samples from cirrhosis with 94.3 % cross-validated accuracy. Thus, this study, although limited by the cohort size, clearly demonstrated the benefit of the multimethod approach in clinical diagnosis.

Cannabidiol nanoemulsion for eye treatment - Anti-inflammatory, wound healing activity and its bioavailability using in vitro human corneal substitute

Cannabidiol (CBD) is the non-psychoactive component of the plant Cannabis sativa (L.) that has great anti-inflammatory benefits and wound healing effects. However, its high lipophilicity, chemical instability, and extensive metabolism impair its bioavailability and clinical use. Here, we report on the preparation of a human cornea substitute in vitro and validate this substitute for the evaluation of drug penetration. CBD nanoemulsion was developed and evaluated for stability and biological activity. The physicochemical properties of CBD nanoemulsion were maintained during storage for 90 days under room conditions. In the scratch assay, nanoformulation showed significantly ameliorated wound closure rates compared to the control and pure CBD. Due to the lower cytotoxicity of nanoformulated CBD, a higher anti-inflammatory activity was demonstrated. Neither nanoemulsion nor pure CBD can penetrate the cornea after the four-hour apical treatment. For nanoemulsion, 94 % of the initial amount of CBD remained in the apical compartment while only 54 % of the original amount of pure CBD was detected in the apical medium, and 7 % in the cornea, the rest was most likely metabolized. In summary, the nanoemulsion developed in this study enhanced the stability and biological activity of CBD.