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Purpose. Urinary tract infections (UTIs) are the most common infections among hospitalized patients. Cystoscopy is a minimally invasive procedure to check bladder disease, among the patients receiving procedure, approximately 10% of patients may experience UTI. In this study, a neural network model with high accuracy, sensitivity, and specificity was developed to predict the probability of UTIs caused by cystoscopic procedures. To reduce antibiotic overuse during cystoscopic procedures, the model can provide clinicians with a rapid assessment of whether patients require prophylactic antibiotics. Materials and Methods. Patients who underwent cystoscopic procedures at China Medical University Beigang Hospital from 2016 to 2019 were retrospectively reviewed. A total of 1647 patients were enrolled, and 147 cases of urinary tract infection occurred. An artificial neural network (ANN) and logistic regression analysis were used to develop the prediction models, and the two models were compared. Results. The logistic regression analysis model had an accuracy of 91%, sensitivity of 2%, and specificity of 99%, indicating that the logistic regression model predicted that most patients had a low risk of infection. The neural network model had a high accuracy of 85%, sensitivity of 80%, and specificity of 88%. Conclusions. Because the logistic regression model had low sensitivity and missed most cases of UTI, the logistic regression model is inappropriate for clinical application. The neural network model has superior predictive ability and can be considered a tool in clinical practice.

Objective. To kill urothelial cancer cells while preserving healthy cells, this study used photothermal therapy (PTT). PTT techniques target urothelial cancer cells using gold nanoparticles (GNPs) and a green light laser.Materials and Methods. The GNPs were conjugated with anti-Mucin 7 antibodies, which acted as a probe for targeting tumor cells. Conjugated GNPs were exposed to a green light laser (532 nm) with sufficient thermal energy to kill the transitional cell carcinomas (TCCs).Results. According to our results, nanoparticles conjugated with Mucin 7 antibodies damaged all types of cancer cells (MBT2, T24, 9202, and 8301) at relatively low energy levels (i.e., 500 laser shots at 10 W/cm2in power, 1.6 Hz in frequency, and 300 ms in duration). Nonconjugated nanoparticles required 30 W/cm2or more to achieve the same effect. Cell damage was directly related to irradiation time and applied laser energy.Conclusions. The minimally invasive PTT procedure combined with Mucin 7 targeted GNPs is able to kill cancer cells and preserve healthy cells. The success of this treatment technique can likely be attributed to the lower amount of energy required to kill targeted cancer cells compared with that required to kill nontargeted cancer cells. Our in vitro pilot study yielded promising results; however, additional animal studies are required to confirm these findings.