Relationship between intermittency and stratification

A formal analogy exists between 2D turbulence and 3D turbulence with stratification and rotation. Although the effect of the rotation, to the scale typical of the turbulence, is negligible in the atmosphere, we have found a relationship between the behavior of the intermittency and that of the atmospheric stratification. In order to do that, the intermittency has been characterized through the flatness of the PDFs of velocity increments, for the smallest possible scale, present in our measurements

Can a Dinosaur Think? Implementation of Artificial Intelligence in Extracorporeal Shock Wave Lithotripsy

Background: Extracorporeal shock wave lithotripsy (ESWL) of kidney stones is losing ground to more expensive and invasive endoscopic treatments. Objective: This proof-of-concept project was initiated to develop artificial intelligence (AI)-augmented ESWL and to investigate the potential for machine learning to improve the efficacy of ESWL. Design, setting, and participants: Two-dimensional ultrasound videos were captured during ESWL treatments from an inline ultrasound device with a video grabber. An observer annotated 23 212 images from 11 patients as either in or out of focus. The median hit rate was calculated on a patient level via bootstrapping. A convolutional neural network with U-Net architecture was trained on 57 ultrasound images with delineated kidney stones from the same patients annotated by a second observer. We tested U-Net on the ultrasound images annotated by the first observer. Cross-validation with a training set of nine patients, a validation set of one patient, and a test set of one patient was performed. Outcome measurements and statistical analysis: Classical metrics describing classifier performance were calculated, together with an estimation of how the algorithm would affect shock wave hit rate. Results and limitations: The median hit rate for standard ESWL was 55.2% (95% confidence interval [CI] 43.2–67.3%). The performance metrics for U-Net were accuracy 63.9%, sensitivity 56.0%, specificity 74.7%, positive predictive value 75.3%, negative predictive value 55.2%, Youden’s J statistic 30.7%, no-information rate 58.0%, and Cohen’s κ 0.2931. The algorithm reduced total mishits by 67.1%. The main limitation is that this is a proof-of-concept study involving only 11 patients. Conclusions: Our calculated ESWL hit rate of 55.2% (95% CI 43.2–67.3%) supports findings from earlier research. We have demonstrated that a machine learning algorithm trained on just 11 patients increases the hit rate to 75.3% and reduces mishits by 67.1%. When U-Net is trained on more and higher-quality annotations, even better results can be expected. Patient summary: Kidney stones can be treated by applying shockwaves to the outside of the body. Ultrasound scans of the kidney are used to guide the machine delivering the shockwaves, but the shockwaves can still miss the stone. We used artificial intelligence to improve the accuracy in hitting the stone being treated.publishedVersio

Clinical markers of morbidity, mortality and survival in bladder cancer patients treated with radical cystectomy. A systematic review.

To access publisher's full text version of this article click on the hyperlink belowContext: Radical cystectomy and pelvic lymph node dissection (RC and PLND) are an essential part of the treatment paradigm in high risk bladder cancer. However, these patients have high rates of morbidity and mortality related both to the treatment and to the disease.Objective: To provide overview of current literature about clinical markers that can be used to predict and improve BC-patient outcomes at the time of RC and PLND and to study if they are properly validated.Evidence acquisition: A systematic literature search was conducted according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria between January 1990 and October 2018 to identify English written original and review articles relevant to this topic. Prospective and retrospective studies were included.Evidence synthesis: There are several risk factors identified from non-randomised trials that can be improved before surgery to reduce perioperative mortality and morbidity. These include poor nutritional status, anaemia, renal function and smoking. Preoperative nomograms have also been developed to help decision-making and to inform patients about the risks of surgery. They can be used to estimate risk of postoperative mortality after RC and PLND with accuracy varying from 70 to 86%. These nomograms are largely based on retrospective data. Likewise, nomograms developed to calculate estimates about patient's overall and cancer specific survival have the same limitations.Conclusion: Clinical markers to predict morbidity, mortality and survival in patients with bladder cancer treated with RC and PLND may help to improve patient outcomes and treatment decision-making, but available data come from small retrospective trials and have not been properly validated. Prospective, multi-centre studies are needed to implement and disseminate predictive clinical markers and nomograms such that they can be utilised in treatment decision-making in daily practice

tRNA-Derived Fragments as Biomarkers in Bladder Cancer

Bladder cancer (BC) diagnosis is reliant on cystoscopy, an invasive procedure associated with urinary tract infections. This has sparked interest in identifying noninvasive biomarkers in body fluids such as blood and urine. A source of biomarkers in these biofluids are extracellular vesicles (EVs), nanosized vesicles that contain a wide array of molecular cargo, including small noncoding RNA such as transfer RNA-derived fragments (tRF) and microRNA. Here, we performed small-RNA next-generation sequencing from EVs from urine and serum, as well as from serum supernatant. RNA was extracted from 15 non-cancer patients (NCPs) with benign findings in cystoscopy and 41 patients with non-muscle invasive BC. Urine and serum were collected before transurethral resection of bladder tumors (TUR-b) and at routine post-surgery check-ups. We compared levels of tRFs in pre-surgery samples to samples from NCPs and post-surgery check-ups. To further verify our findings, samples from 10 patients with stage T1 disease were resequenced. When comparing tRF expression in urine EVs between T1 stage BC patients and NCPs, 14 differentially expressed tRFs (DEtRFs) were identified. In serum supernatant, six DEtRFs were identified among stage T1 patients when comparing pre-surgery to post-surgery samples and four DEtRFs were found when comparing pre-surgery samples to NCPs. By performing a blast search, we found that sequences of DEtRFs aligned with genomic sequences pertaining to processes relevant to cancer development, such as enhancers, regulatory elements and CpG islands. Our findings display a number of tRFs that may hold potential as biomarkers for the diagnosis and recurrence-free survival of BC

Cancer Immunoediting from Immunosurveillance to Tumor Escape in Microvillus-Formed Niche: A Study of Syngeneic Orthotopic Rat Bladder Cancer Model in Comparison with Human Bladder Cancer1

Cancer cells can develop an attenuated immunogenicity and/or create an immunosuppressive microenvironment to prevent tumor eradication by host immune system, the so-called “cancer immunoediting” hypothesis. The aim of the present study was to find evidence for this hypothesis by using a rat orthotopic bladder cancer model. Fisher rats were inoculated with AY-27 cells (a Fisher rat bladder cancer cell line). Cultured cancer cells, rat and human bladder cancer tissues, and publicly available microarray data from human bladder cancer were analyzed by means of bioinformatics and morphology. Results showed that 12 of 24 differentially expressed pathways were concordant in connection to cell cycle and proliferation between rats and humans (both non-muscle-invasive and muscle-invasive tumors) and that 11 of the 24 pathways, including major histocompatibility complex, were related to host immunosurveillance with activations of T cells and natural killer cells in rats. The altered pathways and morphogenesis of this rat model corresponded more closely with those of human muscle-invasive rather than non-muscle-invasive tumors. A unique ultrastructure displaying microvillus-formed niches was found in small areas within the tumor of both rats and humans. These niches were interconnected with desmosomes between cancer cells and without infiltration of lymphocytes. The expression of E-cadherin, selectins, PGP9.5, vascular endothelial growth factor, caspase-3, CD133, Oct-4, nestin, CD3, and CD45RA was lower in the tumor than in the adjacent normal epithelium. We suggest that the microvillus-formed niche that harbors a few implanted cancer cells might be the compartment that prevents the tumor eradication by the host immune system

Increased Anticancer Efficacy of Intravesical Mitomycin C Therapy when Combined with a PCNA Targeting Peptide

Non–muscle-invasive bladder cancers (NMIBCs) are tumors confined to the mucosa or the mucosa/submucosa. An important challenge in treatment of NMIBC is both high recurrence and high progression rates. Consequently, more efficacious intravesical treatment regimes are in demand. Inhibition of the cell’s DNA repair systems is a new promising strategy to improve cancer therapy, and proliferating cell nuclear antigen (PCNA) is a new promising target. PCNA is an essential scaffold protein in multiple cellular processes including DNA replication and repair. More than 200 proteins, many involved in stress responses, interact with PCNA through the AlkB homologue 2 PCNA-interacting motif (APIM), including several proteins directly or indirectly involved in repair of DNA interstrand crosslinks (ICLs). In this study, we targeted PCNA with a novel peptide drug containing the APIM sequence, ATX-101, to inhibit repair of the DNA damage introduced by the chemotherapeutics. A bladder cancer cell panel and two different orthotopic models of bladder cancer in rats, the AY-27 implantation model and the dietary BBN induction model, were applied. ATX-101 increased the anticancer efficacy of the ICL-inducing drug mitomycin C (MMC), as well as bleomycin and gemcitabine in all bladder cancer cell lines tested. Furthermore, we found that ATX-101 given intravesically in combination with MMC penetrated the bladder wall and further reduced the tumor growth in both the slow growing endogenously induced and the rapidly growing transplanted tumors. These results suggest that ATX-101 has the potential to improve the efficacy of current MMC treatment in NMIBC

Increased Anticancer Efficacy of Intravesical Mitomycin C Therapy when combined with a PCNA Targeting Peptide

Non–muscle-invasive bladder cancers (NMIBCs) are tumors confined to the mucosa or the mucosa/submucosa. An important challenge in treatment of NMIBC is both high recurrence and high progression rates. Consequently, more efficacious intravesical treatment regimes are in demand. Inhibition of the cell’s DNA repair systems is a new promising strategy to improve cancer therapy, and proliferating cell nuclear antigen (PCNA) is a new promising target. PCNA is an essential scaffold protein in multiple cellular processes including DNA replication and repair. More than 200 proteins, many involved in stress responses, interact with PCNA through the AlkB homologue 2 PCNA-interacting motif (APIM), including several proteins directly or indirectly involved in repair of DNA interstrand crosslinks (ICLs). In this study, we targeted PCNA with a novel peptide drug containing the APIM sequence, ATX- 101, to inhibit repair of the DNA damage introduced by the chemotherapeutics. A bladder cancer cell panel and two different orthotopic models of bladder cancer in rats, the AY-27 implantation model and the dietary BBN induction model, were applied. ATX-101 increased the anticancer efficacy of the ICL-inducing drug mitomycin C (MMC), as well as bleomycin and gemcitabine in all bladder cancer cell lines tested. Furthermore, we found that ATX-101 given intravesically in combination with MMC penetrated the bladder wall and further reduced the tumor growth in both the slow growing endogenously induced and the rapidly growing transplanted tumors. These results suggest that ATX-101 has the potential to improve the efficacy of current MMC treatment in NMIBC

Can a Dinosaur Think? Implementation of Artificial Intelligence in Extracorporeal Shock Wave Lithotripsy

Background: Extracorporeal shock wave lithotripsy (ESWL) of kidney stones is losing ground to more expensive and invasive endoscopic treatments. Objective: This proof-of-concept project was initiated to develop artificial intelligence (AI)-augmented ESWL and to investigate the potential for machine learning to improve the efficacy of ESWL. Design, setting, and participants: Two-dimensional ultrasound videos were captured during ESWL treatments from an inline ultrasound device with a video grabber. An observer annotated 23 212 images from 11 patients as either in or out of focus. The median hit rate was calculated on a patient level via bootstrapping. A convolutional neural network with U-Net architecture was trained on 57 ultrasound images with delineated kidney stones from the same patients annotated by a second observer. We tested U-Net on the ultrasound images annotated by the first observer. Cross-validation with a training set of nine patients, a validation set of one patient, and a test set of one patient was performed. Outcome measurements and statistical analysis: Classical metrics describing classifier performance were calculated, together with an estimation of how the algorithm would affect shock wave hit rate. Results and limitations: The median hit rate for standard ESWL was 55.2% (95% confidence interval [CI] 43.2–67.3%). The performance metrics for U-Net were accuracy 63.9%, sensitivity 56.0%, specificity 74.7%, positive predictive value 75.3%, negative predictive value 55.2%, Youden’s J statistic 30.7%, no-information rate 58.0%, and Cohen’s k 0.2931. The algorithm reduced total mishits by 67.1%. The main limitation is that this is a proof-of-concept study involving only 11 patients. Conclusions: Our calculated ESWL hit rate of 55.2% (95% CI 43.2–67.3%) supports findings from earlier research. We have demonstrated that a machine learning algorithm trained on just 11 patients increases the hit rate to 75.3% and reduces mishits by 67.1%. When U-Net is trained on more and higher-quality annotations, even better results can be expected. Patient summary: Kidney stones can be treated by applying shockwaves to the outside of the body. Ultrasound scans of the kidney are used to guide the machine delivering the shockwaves, but the shockwaves can still miss the stone. We used artificial intelligence to improve the accuracy in hitting the stone being treated