Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.

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

JAK Inhibitors in Cutaneous T-Cell Lymphoma: Friend or Foe? A Systematic Review of the Published Literature.

Cutaneous T-cell lymphomas (CTCLs) are a group of lymphoid neoplasms with high relapse rates and no curative treatment other than allogeneic stem cell transplantation (allo-SCT). CTCL is significantly influenced by disruption of JAK/STAT signaling. Therefore, Janus kinase (JAK) inhibitors may be promising for CTCL treatment. This study is a systematic review aiming to investigate the role of JAK inhibitors in the treatment of CTCL, including their efficacy and safety. Out of 438 initially searched articles, we present 13 eligible ones. The overall response rate (ORR) in the treatment with JAK inhibitors in clinical trials was 11-35%, although different subtypes of CTCL showed different ORRs. Mycosis fungoides showed an ORR of 14-45%, while subcutaneous-panniculitis-like T-cell lymphoma (SPTCL) displayed an ORR ranging from 75% to 100%. Five cases were reported having a relapse/incident of CTCL after using JAK inhibitors; of these, three cases were de novo CTCLs in patients under treatment with a JAK inhibitor due to refractory arthritis, and two cases were relapsed disease after graft-versus-host disease treatment following allo-SCT. In conclusion, using JAK inhibitors for CTCL treatment seems promising with acceptable side effects, especially in patients with SPTCL. Some biomarkers, like pS6, showed an association with better responses. Caution should be taken when treating patients with an underlying autoimmune disease and prior immunosuppression.

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.

Co-occurrence of celiac disease and glycogen storage disease in a five-year-old patient with diabetes mellitus; a case report.

A patient presented with edema, ascites and jaundice. Histologic report was consistent with Celiac Disease. Liver biopsy commensurate with Glycogen storage disease III, which was confirmed by genetic testing. A gluten-free diet was initiated. After 2 months, ascites was relieved, hepatic function was improved, and hepatic size reduced.

Investigation of sexual dysfunction and depression prevalence in neuromyelitis optica spectrum disorder.

OBJECTIVE: To investigate the prevalence of sexual dysfunction (SD) and depression in patients with neuromyelitis optica (NMO), a demyelinating disorder of the central nervous system. METHODS: A total of 110 NMO patients and 112 healthy individuals were included as a control group, and their SD was assessed using the Female Sexual Function Inventory (FSFI) and the International Index of Erectile Function (IIEF) for women and men, respectively. The FSFI categorizes female sexual dysfunction into six subscores, including libido, arousal, lubrication, orgasm, sexual satisfaction, and pain, while the IIEF categorizes male sexual dysfunction into five subscores, including sexual desire, erection, orgasm, intercourse satisfaction, and overall satisfaction. RESULTS: SD was prevalent among NMO patients, with 78% of female patients and 63.2% of male patients reporting SD in at least one subscore. The severity of the disease, as measured by the Expanded Disability Status Scale (EDSS), was found to be significantly correlated with SD in all subscores, while the duration of the disease was only correlated with the overall satisfaction subscore in men and the pain subscore in women. Furthermore, SD was found to be significantly correlated with depression in these patients. CONCLUSION: The study highlights the importance of addressing SD and depression in NMO patients, as they adversely affect the quality of life. The findings suggest that the physical aspects of SD are mostly affected by the severity of the disease, while psychological aspects are highly correlated with the chronicity of the disease.