A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

Evaluating protein cross-linking as a therapeutic strategy to stabilize SOD1 variants in a mouse model of familial ALS

Mutations in the gene encoding Cu-Zn superoxide dismutase 1 (SOD1) cause a subset of familial amyotrophic lateral sclerosis (fALS) cases. A shared effect of these mutations is that SOD1, which is normally a stable dimer, dissociates into toxic monomers that seed toxic aggregates. Considerable research effort has been devoted to developing compounds that stabilize the dimer of fALS SOD1 variants, but unfortunately, this has not yet resulted in a treatment. We hypothesized that cyclic thiosulfinate cross-linkers, which selectively target a rare, 2 cysteine-containing motif, can stabilize fALS-causing SOD1 variants in vivo. We created a library of chemically diverse cyclic thiosulfinates and determined structure-cross-linking-activity relationships. A pre-lead compound, “S-XL6,” was selected based upon its cross-linking rate and drug-like properties. Co-crystallographic structure clearly establishes the binding of S-XL6 at Cys 111 bridging the monomers and stabilizing the SOD1 dimer. Biophysical studies reveal that the degree of stabilization afforded by S-XL6 (up to 24°C) is unprecedented for fALS, and to our knowledge, for any protein target of any kinetic stabilizer. Gene silencing and protein degrading therapeutic approaches require careful dose titration to balance the benefit of diminished fALS SOD1 expression with the toxic loss-of-enzymatic function. We show that S-XL6 does not share this liability because it rescues the activity of fALS SOD1 variants. No pharmacological agent has been proven to bind to SOD1 in vivo. Here, using a fALS mouse model, we demonstrate oral bioavailability; rapid engagement of SOD1G93A by S-XL6 that increases SOD1G93A’s in vivo half-life; and that S-XL6 crosses the blood–brain barrier. S-XL6 demonstrated a degree of selectivity by avoiding off-target binding to plasma proteins. Taken together, our results indicate that cyclic thiosulfinate-mediated SOD1 stabilization should receive further attention as a potential therapeutic approach for fALS

Predictors of surgical site infection after open lower extremity revascularization

ObjectivesSurgical site infection (SSI) after open surgery for lower extremity revascularization is a serious complication that may lead to graft infection, prolonged hospitalization, and increased cost. Rates of SSI after revascularization vary widely, with most studies reported from single institutions. The objective of this study was to describe the rate and predictors of SSI after surgery for arterial occlusive disease using national data, and to identify any association between SSI and length of hospital stay, reoperation, graft loss, and mortality.MethodsPatients who underwent lower extremity arterial bypass or thromboendarterectomy from 2005-2008 were identified from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) participant use files. Multivariate logistic regression identified predictors of SSI. Odds ratios were adjusted for patient demographics, comorbidities, preoperative laboratory values, and operative factors. The association between SSI and other 30-day outcomes such as mortality and graft failure was determined.ResultsOf 12,330 patients who underwent revascularization, 1367 (11.1%) were diagnosed with an SSI within 30 days. Multivariate predictors of SSI included female gender (odds ratio [OR], 1.4; 95% confidence interval [CI], 1.3-1.6), obesity (OR, 2.1; 95% CI, 1.8-2.4), chronic obstructive pulmonary disease (OR, 1.2; 95% CI, 1.0-1.5), dialysis (OR, 1.5; 95% CI, 1.1-2.1), preoperative hyponatremia (OR, 1.2; 95% CI, 1.0-1.4), and length of operation >4 hours (OR, 1.4; 95% CI, 1.2-1.6). SSI was associated with prolonged (>10 days) hospital stay (OR, 1.8; 95% CI, 1.4-2.1) and higher rates of 30-day graft loss (OR, 2.3; 95% CI, 1.7-3.1) and reoperation (OR, 3.7; 95% CI, 3.1-4.6). SSI was not associated with increased 30-day mortality.ConclusionSSI is a common complication after open revascularization and is associated with a more than twofold increased risk of early graft loss and reoperation. Several patient and operation-related risk factors that predict postoperative SSI were identified, suggesting that targeted improvements in perioperative care may decrease complications and improve outcomes in this patient population