Towards the development of a probabilistic approach to informal settlement fire spread using ignition modelling and spatial metrics

CITATION: Cicione, A. et al. 2020. Towards the development of a probabilistic approach to informal settlement fire spread using ignition modelling and spatial metrics. Fire, 3(4):67, doi:10.3390/fire3040067.The original publication is available at https://www.mdpi.comENGLISH ABSTRACT: Large conflagrations of informal settlements occur regularly, leaving thousands of people homeless daily and taking tens of thousands of lives annually. Over the past few years, a large amount of data has been collected from a number of full-scale informal settlement fire experiments. This paper uses that data with a semi-probabilistic fire model previously proposed by the authors, to illustrate the potential applications of the fire spread method proposed. The current model is benchmarked against a 20-dwelling full-scale informal settlement fire experiment, and the effects of the (a) ignition criteria, (b) wind direction, and (c) wind speeds on the predicted fire spread rates are investigated through the use of a parametric study. Colour maps of the fire spread rates and patterns are then used to visually interpret the effects of different types of fire scenarios and fire breaks. Finally, the fire spread capability within B-RISK is used to derive a linear equation for the potential fire spread rate as a function of the settlement spatial metrics (e.g., density and distance to nearest neighbour). To further illustrate the potential application of this work, the fire spread rate equation is then applied across the whole of Cape Town, South Africa, to show the 10 informal settlement areas most at “risk” of large conflagrations.Lloyd’s Register FoundationUK Engineering and Physical Sciences Research CouncilRoyal Fire Academy of Engineering / Lloyd’s Register Foundationhttps://www.mdpi.com/2571-6255/3/4/67Publisher's versio

Safe introduction of laparoscopic and retroperitoneoscopic nephrectomy in clinical practice: impact of a modular training program

Purpose: To describe and validate a novel modular training scheme (MTS) for trans-peritoneal laparoscopic nephrectomy (LN) and retroperitoneoscopic nephrectomy (RN). Methods: Four consultant urologists attended a Masterclass in âAdvanced Laparoscopic and Robotic Surgery,â certified by the University of Turin (IT). The Masterclass was based on a supervised MTS, which involved progressive, proficiency-based training through nine and seven steps for LN and RN, respectively. After becoming proficient in all the steps, each trainee performed a minimum of five procedures as first operator under direct observation of the mentor in the training centre. Then, each trainee independently performed 10 LN and 10 RN at his home institution. The surgical outcomes were compared with those from a contemporary series of procedures performed by the mentor. Results: All trainees successfully completed the 12-week MTS program. Median number of training cases to become competent in trans-peritoneal LN and RN was 13.0 (IQR 11.5â20.5) and 23.5 (IQR 19.5â32.0), respectively. A significantly higher rate of conversion to open surgery was observed for RNs independently performed by the trainees in their hospital compared to the mentor (p = 0.033). Failure to progress due to difficult anatomical orientation and abdominal wall bleeding during dissection of retroperitoneal space were the most frequent reasons of conversion. Conclusions: A 12-week intensive modular program allows to achieve proficiency in performing independently LN and a RN after a median of 13 and 23.5 cases, respectively. Therefore, these procedures can be safely introduced and implemented in clinical practice within a relatively short time

Presence and severity of lower urinary tract symptoms are inversely correlated with the risk of prostate cancer on prostate biopsy

BACKGROUND: The assessment of lower urinary tract symptoms (LUTS) is common part of urological investigation. Furthermore, patients bother of prostate cancer (PCa) when they are affected of LUTS. This study was aimed to determine whether the presence and severity of LUTS, as assessed by the International Prostate Symptoms Score (IPSS), could help to identify patients at higher risk of prostate cancer (PCa) on prostate biopsy (PBx). In this effort, an initial PCa predictive model was calculated and IPSS was subsequently added. The diagnostic accuracy of both models was compared. METHODS: The analysis of prospectively collected data of patients scheduled for PBx at four academic hospitals between January 2012 and June 2015 was performed. Univariate and multivariate analysis assessed the correlation between the IPSS and the risk of being diagnosed with PCa; Receiver operator characteristic curve (ROC) analysis evaluated the predictive models including or not the IPSS. RESULTS: Of the 1366 enrolled patients, 706 (52%) were diagnosed with PCa. Patients with PCa had a significantly lower IPSS (10.6 +/- 7.4 vs. 12.7 +/- 8.1) than those with benign diagnosis. Multivariate logistic regression analysis showed that age, prostate-specific antigen (PSA), prostate volume and IPSS were the most significant predictors of PBx outcome, (OR 1.61, P=0.001; OR 1.20, P=0.001; OR 0.97, P=0.001; OR 0.74, P=0.004; respectively). ROC curve analysis showed that the addition of IPSS to the predictive model based on age, PSA, DRE and prostate volume significantly improved the model diagnostic accuracy (AUC: 0.776 vs. 0.652; P=0.001). CONCLUSIONS: Presence and severity of LUTS are inversely correlated with the risk of being diagnosed with PCa at PBx. Incorporating the IPSS into predictive models may reduce the risk of unnecessary PBxs.info:eu-repo/semantics/publishedVersio

Prognostic accuracy of Prostate Health Index and urinary Prostate Cancer Antigen 3 in predicting pathologic features after radical prostatectomy

Objective: To compare the prognostic accuracy of Prostate Health Index (PHI) and Prostate Cancer Antigen 3 in predicting pathologic features in a cohort of patients who underwent radical prostatectomy (RP) for prostate cancer (PCa). Methods and materials: We evaluated 156 patients with biopsy-proven, clinically localized PCa who underwent RP between January 2013 and December 2013 at 2 tertiary care institutions. Blood and urinary specimens were collected before initial prostate biopsy for [-2] pro-prostate-specific antigen (PSA), its derivates, and PCA3 measurements. Univariate and multivariate logistic regression analyses were carried out to determine the variables that were potentially predictive of tumor volume >0.5. ml, pathologic Gleason sum 657, pathologically confirmed significant PCa, extracapsular extension, and seminal vesicles invasions. Results: On multivariate analyses and after bootstrapping with 1,000 resampled data, the inclusion of PHI significantly increased the accuracy of a baseline multivariate model, which included patient age, total PSA, free PSA, rate of positive cores, clinical stage, prostate volume, body mass index, and biopsy Gleason score (GS), in predicting the study outcomes. Particularly, to predict tumor volume>0.5, the addition of PHI to the baseline model significantly increased predictive accuracy by 7.9% (area under the receiver operating characteristics curve [AUC] = 89.3 vs. 97.2, P>0.05), whereas PCA3 did not lead to a significant increase.Although both PHI and PCA3 significantly improved predictive accuracy to predict extracapsular extension compared with the baseline model, achieving independent predictor status (all P's<0.01), only PHI led to a significant improvement in the prediction of seminal vesicles invasions (AUC = 92.2, P<0.05 with a gain of 3.6%).In the subset of patients with GS 646, PHI significantly improved predictive accuracy by 7.6% compared with the baseline model (AUC = 89.7 vs. 97.3) to predict pathologically confirmed significant PCa and by 5.9% compared with the baseline model (AUC = 83.1 vs. 89.0) to predict pathologic GS 657. For these outcomes, PCA3 did not add incremental predictive value. Conclusions: In a cohort of patients who underwent RP, PHI is significantly better than PCA3 in the ability to predict the presence of both more aggressive and extended PCa