Case Report: Hexachloroethane Smoke Inhalation: A Rare Cause of Severe Hepatic Injuries

CONTEXT: We report on two patients, a 23-year-old man and a 24-year-old man, who had chemical pneumonitis and respiratory distress after inhaling hexachloroethane/zinc oxide (HC/ZnO) smoke during military training. CASE PRESENTATION: The patients had been healthy previously and denied any history of alcohol or drug abuse. Hematologic tests revealed leukocytosis with neutrophils predominant. The respiratory conditions of both patients improved after steroid therapy and oxygen support, but deterioration of liver function was found. The laboratory results showed that alanine aminotransferase (ALT) and γ-glutamyl transpeptidase levels were elevated about 1.5-fold the normal limits and that aspartate aminotransferase (AST) levels were marginally elevated. The elevation of liver aminotransferase started from day 1 and day 2 and peaked from day 18 to day 22. ALT/AST levels then returned to normal in 6 weeks. Common viral hepatitis was ruled out after serologic tests. Abdominal sonography and physical examination failed to show any specific findings. DISCUSSION: The hepatotoxic effect was attributed to inhalation of high-concentration HC/ZnO smoke in an enclosed area, where several hepatotoxicants, including ZnCl(2), HC, and chlorinated vapors, could have been generated and mixed in the smoke. RELEVANCE TO CLINICAL PRACTICE: These case reports elaborate the hepatic effects that may occur in addition to pulmonary effects of HC/ZnO smoke

Spontaneous regression of advanced hepatocellular carcinoma: a case report

Spontaneous regression of advanced hepatocellular carcinoma is extremely rare. A 66-year-old Taiwanese male patient with liver cirrhosis related to chronic hepatitis C presented with hepatocellular carcinoma with portal vein thrombosis. At first, he refused curative therapy, except for silymarin medicine. Spontaneous regression of hepatocellular carcinoma occurred with a decline in tumour size and tumour marker in imaging studies. The patient agreed to undergo surgery approximately 14 months after presentation because of no further decrease in tumour size and an increase in tumour marker in the imaging studies. The resected tumour was hepatocellular carcinoma with portal vein thromboses. Presently, the patient is alive and in good condition without any symptoms or tumour recurrence. We concluded that this was a rare case of spontaneous regression of advanced hepatocellular carcinoma

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation

Fault detection for medium voltage switchgear using a deep learning hybrid 1D-CNN-LSTM model

Medium voltage (MV) switchgear is a vital part of modern power systems, responsible for regulating the flow of electrical power and ensuring the safety of equipment and personnel. However, switchgear can experience various types of faults that can compromise its reliability and safety. Common faults in switchgear include arcing, tracking, corona, normal cases, and mechanical faults. Accurate detection of these faults is essential for maintaining the safety of MV switchgear. In this paper, we propose a novel approach for fault detection using a hybrid model (1D-CNN-LSTM) in both the time domain (TD) and frequency domain (FD). The proposed approach involves gathering a dataset of switchgear operation data and pre-processing it to prepare it for training. The hybrid model is then trained on this dataset, and its performance is evaluated in the testing phase. The results of the testing phase demonstrate the effectiveness of the hybrid model in detecting faults. The model achieved 100% accuracy in both the time and frequency domains for classifying faults in Switchgear, including arcing, tracking, and mechanical faults. Additionally, the model achieved 98.4% accuracy in detecting corona faults in the TD. The hybrid model proposed in this study provides an effective and efficient approach for fault detection in MV switchgear. By learning spatial and temporal features simultaneously, this model can accurately classify faults in both the TD and FD. This approach has significant potential to improve the safety of MV switchgear as well as other industrial applications

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

Background: A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. Methods: The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80 % of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20 % of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. Conclusions: The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection

Comparative Study on the Synthesis of Path-Generating Four-Bar Linkages Using Metaheuristic Optimization Algorithms

Four-bar linkages are one of the most widely used mechanisms in industries. This paper presents a comparative study on the accuracy and efficiency of the optimum synthesis of path-generating four-bar linkages using five metaheuristic optimization algorithms. The utilized metaheuristic methods included two swarm intelligence-based algorithms, i.e., particle swarm optimization and hybrid particle swarm optimization, and three evolutionary-based algorithms, i.e., differential evolution, ensemble of parameters and mutation strategies in differential evolution, and linearly ensemble of parameters and mutation strategies in differential evolution. The objective function to be minimized is the sum of squares of the distance between the generated points and the precision points of a coupler point. The optimal design of four-bar linkages must meet the Grashof’s criteria and exhibit sequential constraints that can prevent the occurrence of order defect. This study investigated five representative cases of the dimensional synthesis of four-bar path generators with and without prescribed timing and compared the optimal solutions of the utilized five metaheuristic methods to those of previously reported algorithms in literature. The improved metaheuristic methods exhibited superior optimal solution and enhanced reliability compared to the original methods. Moreover, three improved metaheuristic methods were not only easy implemented, but also more efficient for solving the optimal synthesis problems, particularly for high dimensional problems