Toll-like receptors: exploring their potential connection with post-operative infectious complications and cancer recurrence.

Cancer is the leading cause of death in North America. Despite modern advances in cancer therapy, many patients will ultimately develop cancer metastasis resulting in mortality. Surgery to resect early stage solid malignancies remains the cornerstone of cancer treatment. However, surgery places patients at risk of developing post-operative infectious complications that are linked to earlier cancer metastatic recurrence and cancer mortality. Toll-like receptors (TLRs) are evolutionarily-conserved sentinel receptors of the innate immune system that are activated by microbial products present during infection, leading to activation of innate immunity. Numerous types of solid cancer cells also express TLRs, with their activation augmenting their ability to metastasize. Similarly, healthy host-tissue TLRs activated during infection induce a prometastatic environment in the host. Cancer cells additionally secrete TLR activating ligands that activate both cancer TLRs and host TLRs to promote metastasis. Consequently, TLRs are an attractive therapeutic candidate to target infection-induced cancer metastasis and progression.

Esophagectomy in esophageal perforations: an analysis.

This study aimed to study the factors that are associated with urgent esophagectomy for the treatment of esophageal perforations and the impact of this therapy. A retrospective review of all esophageal perforations treated at a tertiary care hospital from January 1984 to January 2012 was performed. Compiling demographics, cause and site of perforations, time to presentation, comorbidities, radiological tests, the length of perforation, the hemodynamic status of the patient, type of treatment required, and outcomes were performed. Univariate, multivariate, and Cox regression analyses were conducted. Of 127 cases of esophageal perforation, it was spontaneous in 44 (35%), iatrogenic in 53 (44%), foreign body ingestion in 22 (17%), and traumatic perforation in 7 (6%) cases. Overall, 85 of the 127 (67%) patients were managed operatively, 35 (27.6%) patients were treated conservatively, and 7 (6.3%) patients were treated by endoscopic stent placement. Of the 85 patients who were managed operatively, 21 (16.5%) required esophagectomies, 13 (15.3%) had esophagectomy with immediate reconstruction, 5 (5.9%) patients had esophagectomy followed by delayed reconstruction, and 3 (3.5%) patients failed primary repair and required an esophagectomy as a secondary definitive procedure. Multivariate analysis revealed that esophagectomy in esophageal perforations was associated with the presence of benign or malignant esophageal stricture (P = 0.001) and a perforation >5 cm (P = 0.001). Mortality was mainly associated with the presence of a benign or malignant esophageal stricture (P = 0.04). The presence of pre-existing benign or malignant stricture or large perforation (>5 cm) is associated with the need for an urgent esophagectomy with or without immediate reconstruction. Performing esophagectomy was not found to be a significant prognosticator for mortality.

Segmentation and classification of capnograms: application in respiratory variability analysis.

Variability analysis of respiratory waveforms has been shown to provide key insights into respiratory physiology and has been used successfully to predict clinical outcomes. The current standard for quality assessment of the capnogram signal relies on a visual analysis performed by an expert in order to identify waveform artifacts. Automated processing of capnograms is desirable in order to extract clinically useful features over extended periods of time in a patient monitoring environment. However, the proper interpretation of capnogram derived features depends upon the quality of the underlying waveform. In addition, the comparison of capnogram datasets across studies requires a more practical approach than a visual analysis and selection of high-quality breath data. This paper describes a system that automatically extracts breath-by-breath features from capnograms and estimates the quality of individual breaths derived from them. Segmented capnogram breaths were presented to expert annotators, who labeled the individual physiological breaths into normal and multiple abnormal breath types. All abnormal breath types were aggregated into the abnormal class for the purpose of this manuscript, with respiratory variability analysis as the end-application. A database of 11,526 breaths from over 300 patients was created, comprising around 35% abnormal breaths. Several simple classifiers were trained through a stratified repeated ten-fold cross-validation and tested on an unseen portion of the labeled breath database, using a subset of 15 features derived from each breath curve. Decision Tree, K-Nearest Neighbors (KNN) and Naive Bayes classifiers were close in terms of performance (AUC of 90%, 89% and 88% respectively), while using 7, 4 and 5 breath features, respectively. When compared to airflow derived timings, the 95% confidence interval on the mean difference in interbreath intervals was ± 0.18 s. This breath classification system provides a fast and robust pre-processing of continuous respiratory waveforms, thereby ensuring reliable variability analysis of breath-by-breath parameter time series.