Identifying autism using EEG: unleashing the power of feature selection and machine learning.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is characterized by communication barriers, societal disengagement, and monotonous actions. Currently, the diagnosis of ASD is made by experts through a subjective and time-consuming qualitative behavioural examination using internationally recognized descriptive standards. In this paper, we present an EEG-based three-phase novel approach comprising 29 autistic subjects and 30 neurotypical people. In the first phase, preprocessing of data is performed from which we derived one continuous dataset and four condition-based datasets to determine the role of each dataset in the identification of autism from neurotypical people. In the second phase, time-domain and morphological features were extracted and four different feature selection techniques were applied. In the last phase, five-fold cross-validation is used to evaluate six different machine learning models based on the performance metrics and computational efficiency. The neural network outperformed when trained with maximum relevance and minimum redundancy (MRMR) algorithm on the continuous dataset with 98.10% validation accuracy and 0.9994 area under the curve (AUC) value for model validation, and 98.43% testing accuracy and AUC test value of 0.9998. The decision tree overall performed the second best in terms of computational efficiency and performance accuracy. The results indicate that EEG-based machine learning models have the potential for ASD identification from neurotypical people with a more objective and reliable method.

Effect of Aggregation Operators on Network-Based Disease Gene Prioritization: A Case Study on Blood Disorders.

Owing to the innate noise in the biological data sources, a single source or a single measure do not suffice for an effective disease gene prioritization. So, the integration of multiple data sources or aggregation of multiple measures is the need of the hour. The aggregation operators combine multiple related data values to a single value such that the combined value has the effect of all the individual values. In this paper, an attempt has been made for applying the fuzzy aggregation on the network-based disease gene prioritization and investigate its effect under noise conditions. This study has been conducted for a set of 15 blood disorders by fusing four different network measures, computed from the protein interaction network, using a selected set of aggregation operators and ranking the genes on the basis of the aggregated value. The aggregation operator-based rankings have been compared with the "Random walk with restart" gene prioritization method. The impact of noise has also been investigated by adding varying proportions of noise to the seed set. The results reveal that for all the selected blood disorders, the Mean of Maximal operator has relatively outperformed the other aggregation operators for noisy as well as non-noisy data.

Endotracheobronchial lymphoma: Two unusual case reports and review of article.

The tracheobronchial origin of non-Hodgkin's lymphoma (NHL) is a very rare presentation, and there are only a few case reports of primary tracheal or endobronchial NHL. We have two cases of primary tracheobronchial NHL; one case was incidentally diagnosed as anaplastic large cell lymphoma of endobronchial origin when a comprehensive workup and surgery were carried out for an endobronchial aspergilloma which was actually sitting on top of lymphoma. The second patient was a case of myelodysplastic syndrome who presented with acute respiratory distress; on thorough workup, he was found to have endotracheal B-cell lymphoma. Both cases were responding well with standard chemotherapy. The mortality in these kinds of patients is due to disease progression or airway compromise and treatment complications.

Evaluation of postoperative analgesic efficacy of transversus abdominis plane block after abdominal surgery: A comparative study.

BACKGROUND: The transversus abdominis plane (TAP) block is an effective method of providing postoperative analgesia in patients undergoing midline abdominal wall incisions, by blocking the abdominal wall neural afferents via the bilateral lumbar triangles of Petit. We evaluated its analgesic efficacy in patients during the first 48 postoperative hours after abdominal surgery, in a randomized, controlled single-blind clinical trial. MATERIALS AND METHODS: Sixty patients (mean age 36.2 ± 9.6 years) of either sex of ASA grade 1 and 2 who underwent major gynecological or surgical operation were randomized either to receive standard care, including patient-controlled tramadol analgesia (n = 30), or to undergo TAP block (n = 30) in addition to standard care. After completion of surgery, 20 ml of 0.375% levobupivacaine was deposited into the transversus abdominis neurofascial plane via the bilateral lumbar triangles of Petit. Each patient was assessed in the postanesthesia care unit and at 2, 4, 6, 12, 24, and 48 h postoperatively. RESULTS: The TAP block reduced Visual Analog Scale pain scores at most (2, 4, 6, 12, 24 h), but not at all time (36, 48 h) points assessed. Patients undergoing TAP block had reduced tramadol requirement in 24 h (210.05 ± 20.5 vs. 320.05 ± 10.6; P < 0.01) and 48 h (508.25 ± 20.6 vs. 550.25 ± 20.6; P < 0.01), and a longer time to the first PCA tramadol request (in minutes), compared to the control group (178.5 ± 45.6 vs. 23.5 ± 3.8; P < 0.001). CONCLUSION: The TAP block provided highly effective postoperative analgesia in the first 24 postoperative hours after major abdominal surgery, and no complications due to the TAP block were detected.

Pycnodysostosis: an anaesthetic approach to this rare genetic disorder.

Pycnodysostosis (the Toulouse-Lautrec syndrome) is a rare autosomal-recessive disorder of osteoclast dysfunction. This disorder was first described by Maroteaux and Lamy in 1962. We describe anaesthetic management of a 35-year-old female having pyknodysostosis with fracture shaft left femur with anticipated difficult intubation. Therefore, spinal anesthesia was planned for her fracture fixation. The intra- and postoperative period remains uneventful.

Tension Gastrothorax Complicating Third Trimester of Pregnancy: Suspicion is the Key.

Gastrothorax is characterized by herniation of the stomach and other abdominal contents into the thoracic cavity either through the oesophageal hiatus or ruptured diaphragm. When gastrothorax causes pulmonary and hemodynamic compromise, due to compression of lungs and mediastinal structures, it is named as tension gastrothorax. Diagnosis of tension gastrothorax is often complicated during late pregnancy, because of unusual presentation, altered physiology, absence of trauma, hesitation about radiation exposure, and rarity of the condition. We report a case of a patient, in her 32nd week of pregnancy, who presented with left tension gastrothorax. Lower segment caesarean section was planned after steroid therapy, with all the preparations for thoracotomy. Intra-operatively, stomach, spleen, and colon were found herniated in the left hemithorax, through a ruptured left hemidiaphragm. Thoracotomy was done immediately after caesarean section, with reduction of herniated contents and repair of the defect in the diaphragm. The patient and her baby were discharged in stable condition 2 weeks after thoracotomy.

Recurrent pneumothorax developing during chemotherapy in a patient with miliary tuberculosis.

Despite the fact that miliary tuberculosis is frequently seen, associated pneumothorax developing during antitubercular chemotherapy for miliary tuberculosis is rare. Pneumothorax is potentially life threatening in association with miliary tuberculosis; and its symptoms may be masked by those of miliary tuberculosis, leading to avoidable delay in the diagnosis of pneumothorax. Here we describe a 24-year-old female patient developing recurrent pneumothorax while on antitubercular chemotherapy for miliary tuberculosis.