Thymic lipofibroadenoma: A case report.

Lipofibroadenoma (LFA) is an epithelial tumor. It has been seen rarely in the thymus, and only a handful of cases have been reported. LFA is usually seen in the anterior mediastinum and is defined as a coalescence of epithelial thymic, adipose, and fibrotic tissue. We present a 30-year-old female who presented due to an unrelated traffic accident. An incidental mass was found in her left anterior superior mediastinum. After performing a complete excision, a histologic examination of the excised mass revealed it to be LFA of the thymus, which is extremely rare. The follow-up period was uneventful. LFA is a slow-growing benign tumor and is very similar to fibroadenoma of the breast. The etiology and clinical findings are yet to be well-defined. It was only seen in men in the prior cases. But recent cases, including this one, have also reported female patients. The tumor is mainly observed in the anterior mediastinum, which was also the case in our patient. The gold standard of diagnosis is pathologic examination. Our examination showed strands and nests of thymic parenchyma, including Hassall corpuscles, which separated fibro adipose tissue. Thymectomy is the treatment of choice. It can be performed by either video-assisted thoracic surgery or open surgery. We performed open surgery. The most important prognostic factor for this tumor is staging.

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.

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.

Bronchial wall necrosis secondary to mucormycosis following SARS-Cov2 infection: A case report.

Coronavirus 2019 infection (COVID-19) has a broad spectrum of clinical complications, some unrecognized. Herein, a case of a diabetic patient with multiple episodes of hemoptysis 2 months following her recovery from SARS-CoV-2 infection is reported. The initial computed tomography (CT scan) revealed the left lower lobe collapsed secondary to bronchial narrowing and obliteration. Bronchoscopy was performed, indicating necrotic endobronchial tissue, which was confirmed histopathologically as invasive mucormycosis. Bronchial necrosis due to mucormycosis is an unusual presentation of COVID-19-associated pulmonary mucormycosis. The accurate diagnosis could be challenging as it can resemble other pathologies such as malignancies. Therefore, it is crucial to identify this fatal complication in patients with prolonged COVID-19 and lung collapse.