Duodenocolic and Cholecystocolonic Fistula: A Case Report of an Unusual Presentation.

Laparoscopic cholecystectomy is the established standard of care for addressing symptomatic gallstones, typically representing a straightforward and uncomplicated surgical procedure. However, patients exhibiting variant anatomy or local inflammation can present challenges to the surgeon, potentially leading to complications. In this context, we present the case of a 55-year-old woman who underwent a laparoscopic cholecystectomy for symptomatic gallstone disease at a different medical facility. Postoperatively, she was diagnosed with a case of duodenocolic fistula and cholecystocolonic fistula. Conservative treatment ensued with intravenous antibiotic administration, as well as enteral and parenteral feeding. Diagnosing cholecystocolonic fistula before surgery proves challenging, even with modern diagnostic and imaging tools. Despite its significance, there is limited information in the literature regarding the management of this infrequent finding. The approach to diagnosis and management is elaborated upon in the case report.

Robust extraction of P300 using constrained ICA for BCI applications.

P300 is a positive event-related potential used by P300-brain computer interfaces (BCIs) as a means of communication with external devices. One of the main requirements of any P300-based BCI is accuracy and time efficiency for P300 extraction and detection. Among many attempted techniques, independent component analysis (ICA) is currently the most popular P300 extraction technique. However, since ICA extracts multiple independent components (ICs), its use requires careful selection of ICs containing P300 responses, which limits the number of channels available for computational efficiency. Here, we propose a novel procedure for P300 extraction and detection using constrained independent component analysis (cICA) through which we can directly extract only P300-relevant ICs. We tested our procedure on two standard datasets collected from healthy and disabled subjects. We tested our procedure on these datasets and compared their respective performances with a conventional ICA-based procedure. Our results demonstrate that the cICA-based method was more reliable and less computationally expensive, and was able to achieve 97 and 91.6% accuracy in P300 detection from healthy and disabled subjects, respectively. In recognizing target characters and images, our approach achieved 95 and 90.25% success in healthy and disabled individuals, whereas use of ICA only achieved 83 and 72.25%, respectively. In terms of information transfer rate, our results indicate that the ICA-based procedure optimally performs with a limited number of channels (typically three), but with a higher number of available channels (>3), its performance deteriorates and the cICA-based one performs better.

Extraction of P300 using constrained independent component analysis.

A brain computer interface (BCI) uses electrophysiological activities of the brain such as natural rhythms and evoked potentials to communicate with some external devices. P300 is a positive evoked potential (EP), elicited approximately 300 ms after an attended external stimulus. A P300-based BCI uses this evoked potential as a means of communication with the external devices. Until now this P300-based BCI has been rather slow, as it is difficult to detect a P300 response without averaging over a number of trials. Previously, independent component analysis (ICA) has been used in the extraction of P300. However, the drawback of ICA is that it extracts not only P300 but also non-P300 related components requiring a proper selection of P300 ICs by the system. In this study we propose an algorithm based on constrained independent component analysis (cICA) for P300 extraction which can extract only the relevant component by incorporating a priori information. A reference signal is generated as this a priori information of P300 and cICA is applied to extract the P300 related component. Then the extracted P300 IC is segmented, averaged, and classified into target and non-target events by means of a linear classifier. The method is fast, reliable, computationally inexpensive as compared to ICA and achieves an accuracy of 98.3% in the detection of P300.