Eviscerated liver: an extremely rare complication of abdominal wound dehiscense through a midline incision.

Abdominal wound dehiscense, or burst abdomen, is a critical postoperative complication necessitating immediate intervention. We present an extremely rare case of left hepatic lobe evisceration through wound dehiscense in a 65-year-old female receiving palliative care for hypopharyngeal squamous cell carcinoma. The patient's midline incision that was performed for feeding jejunostomy tube displayed liver protrusion on Day 14 postoperatively. Surgical exploration revealed a healthy liver, prompting reduction and secondary sutures to prevent complications. Abdominal wound dehiscense risk factors, including advanced age, poor nutrition, and medical illness, contribute to its occurrence. Although guidelines for liver evisceration management are lacking, our case emphasizes proper technique, wound care, and nutritional support to aid the healing process and to ensure a better outcome for the patients.

An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm.

Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew's correlation coefficient.