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Purpose: Upper-tract urothelial carcinoma (UTUC) is a relatively uncommon disease with limited available evidence on specific topics. The purpose of this article was to review the previous literature to summarize the current knowledge about UTUC epidemiology, diagnosis, preoperative evaluation and prognostic assessment. Methods: Using MEDLINE, a non-systematic review was performed including articles between January 2000 and February 2016. English language original articles, reviews and editorials were selected based on their clinical relevance. Results: UTUC accounts for 5-10 % of all urothelial cancers, with an increasing incidence. UTUC and bladder cancer share some common risk factors, even if they are two different entities regarding practical, biological and clinical characteristics. Aristolochic acid plays an important role in UTUC pathogenesis in certain regions. It is further estimated that approximately 10 % of UTUC are part of the hereditary non-polyposis colorectal cancer spectrum disease. UTUC diagnosis remains mainly based on imaging and endoscopy, but development of new technologies is rapidly changing the diagnosis algorithm. To help the decision-making process regarding surgical treatment, extent of lymphadenectomy and selection of neoadjuvant systemic therapies, predictive tools based on preoperative patient and tumor characteristics have been developed. Conclusions: Awareness regarding epidemiology, diagnosis, preoperative evaluation and prognostic assessment changes is essential to correctly diagnose and manage UTUC patients, thereby potentially improving their outcomes. [ABSTRACT FROM AUTHOR]

Background: Upper tract urothelial carcinoma (UTUC) is a rare and heterogeneous disease. Several clinical and biological prognostic factors have been identified in multi-institutional collaborative works with the aim of helping decision-making in pursuit of tailored individual patient care. This review provides an overview of these existing prognostic factors and predictive tools for the management of patients with UTUC. Methods: A systematic literature search was performed using PubMed/MEDLINE, Web of Science and Scopus databases regarding articles published in English between January 2000 and November 2015 according to PRISMA guidelines. Thresholds of 100 and 300 patients were applied for studies on biomarkers and clinical studies, respectively. All the studies on predictive tools were included for analysis. Outcomes of interest were features associated with advanced-stage UTUC, disease recurrence and survival. Results: A total of 116 studies were included in this review. These large and/or multi-institutional studies have confirmed the prognostic value of standard pathological factors (i.e., tumor stage, grade and lymph node metastasis) and identified novel features such as lymphovascular invasion, tumor architecture, multifocality, concomitant CIS, variant histology and biomarker status among others. Based on these variables, several predictive tools have been developed; however, they often lack of validation. The value of these features and tools needs prospective testing. Conclusion: Efforts provided by international collaboration groups have permitted to validate established features and identify new features of biologically and clinically aggressive UTUC. Further investigation on prognostic factors and biomarkers is still needed to assess the benefit of these features and tools on clinical decision-making. [ABSTRACT FROM AUTHOR]

There is a developing trend of using wearable electronic devices as health aides, spurred on by telecommunication companies as fitness devices and marketed as such. They have been shown to count steps, pulse, and record arrhythmias, doubling as communication devices and prompting healthcare providers in some instances. We sought to determine if there was a direct correlation between device use and increased physical activity as recommended by the World Health Organization, or if this physical activity increase was only marginal at best. In addition, we sought to investigate if there were additional benefits to using these devices besides increased self-awareness of health. This systematic review used Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Keywords for searching articles centered around cardiovascular disease, wearable electronic devices, and their synonyms. Most of the data were obtained from PubMed, although other contributing databases were used, including ResearchGate, Science.gov, ScienceDirect, and PubMed Medical Subject Headings database. Only full-text articles were used. We identified 62 articles that met our search criteria but narrowed them down to 19 following qualitative assessment. Increased physical activity was found to be the one parameter that stood out by way of benefit from the device. Other findings, such as reduced waist circumference, obesity, glycated hemoglobin, and lipid levels, shared mixed results. At this time, we do not have a definition of what duration of device use is deemed standard for health. We have no consensus on which devices are superior health-wise. Our study, however, indicates that these devices, used with adequate health professional supervision, have a role to play in motivation and increased physical activity, enough to cause impactful gains in cardiovascular health., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Seffah et al.)

To forecast marine disease outbreaks as oceans warm requires new environmental surveillance tools. We describe an iterative process for developing these tools that combines research, development and deployment for suitable systems. The first step is to identify candidate host–pathogen systems. The 24 candidate systems we identified include sponges, corals, oysters, crustaceans, sea stars, fishes and sea grasses (among others). To illustrate the other steps, we present a case study of epizootic shell disease (ESD) in the American lobster. Increasing prevalence of ESD is a contributing factor to lobster fishery collapse in southern New England (SNE), raising concerns that disease prevalence will increase in the northern Gulf of Maine under climate change. The lowest maximum bottom temperature associated with ESD prevalence in SNE is 12°C. Our seasonal outlook for 2015 and long-term projections show bottom temperatures greater than or equal to 12°C may occur in this and coming years in the coastal bays of Maine. The tools presented will allow managers to target efforts to monitor the effects of ESD on fishery sustainability and will be iteratively refined. The approach and case example highlight that temperature-based surveillance tools can inform research, monitoring and management of emerging and continuing marine disease threats. [ABSTRACT FROM AUTHOR]

The term drug hypersensitivity refers to a category of adverse drug reactions mediated by various immunological and nonimmunological mechanisms. Small-molecule drugs and biotherapeutics have been associated with drug hypersensitivity reactions (DHRs), and the mechanisms driving the responses vary. Depending on the mechanism, some DHRs may be detected in nonclinical toxicology studies, and there may be tools and models in place that can be used as part of a risk assessment strategy. In contrast, for other mechanisms, particularly those that are not readily detected during nonclinical development, predictive tools and strategies for risk assessment are not well defined. This chapter provides an overview of the nonclinical tools currently available to assess the risk for developing DHRs. [ABSTRACT FROM AUTHOR]

Emollient oils are ubiquitous ingredients of personal care products, especially skin care and hair care formulations. They offer excellent spreading properties and give end-use products a soft, pleasant and non-sticky after-feel. Emollients belong to various petro- or bio-based chemical families among which silicone oils, hydrocarbons and esters are the most prominent. Silicones have exceptional physicochemical and sensory properties but their high chemical stability results in very low biodegradability and a high bioaccumulation potential. Nowadays, consumers are increasingly responsive to environmental issues and demand more environmentally friendly products. This awareness strongly encourages cosmetics industries to develop bio-based alternatives to silicone oils. Finding effective silicon-free emollients requires understanding the molecular origin of emollience. This review details the relationships between the molecular structures of emollients and their physicochemical properties as well as the resulting functional performances in order to facilitate the design of alternative oils with suitable physicochemical and sensory properties. The molecular profile of an ideal emollient in terms of chemical function (alkane, ether, ester, carbonate, alcohol), optimal number of carbons and branching is established to obtain an odourless oil with good spreading on the skin. Since none of the carbon-based emollients alone can imitate the non-sticky and dry feel of silicone oils, it is judicious to blend alkanes and esters to significantly improve both the sensory properties and the solubilizing properties of the synergistic mixture towards polar ingredients (sun filters, antioxidants, fragrances). Finally, it is shown how modelling tools (QSPR, COSMO-RS and neural networks) can predict in silico the key properties of hundreds of virtual candidate molecules in order to synthesize only the most promising whose predicted properties are close to the specifications. [Display omitted] • Bio-based oils as alternatives for volatile silicones. • Evolution of the viscosity and the surface tension of oils according to their chemical structure. • Relationships between molecular structures of emollients and their physicochemical and sensory properties. • Optimal number of carbons is established for obtaining an odourless oil with good spreadability on the skin. • Synergistic mixtures of alkanes and esters improve the sensory performance of oils. [ABSTRACT FROM AUTHOR]

Adequate Knowledge of reservoir fluid characteristics (e.g., bubble point pressure) plays a crucial role while conducting modeling/simulation of production processes in petroleum reservoirs. Although many efforts have been made to obtain proper correlations for prediction of bubble point pressure (BPP) of reservoir fluids, there is still relatively high magnitude of error with the developed predictive tools available in the literature. To fill this lacuna, a robust and effective technique, called gene expression programming (GEP), is employed to determine BPP of crude oil samples as a function of temperature, oil composition, molecular weight of C 7+ , and specific gravity of C 7+ . The GEP method is built based on the experimental (or real) data used for training and testing phases in order to develop an appropriate correlation. The previous predictive methods are also reported in this study and employed to calculate BPP as a function of independent parameters when the same data bank is utilized. Comparing the outputs obtained from the previous models with the BPP values predicted by the GEP technique, it was found that the GEP approach exhibits higher accuracy and lower uncertainty on the basis of statistical analysis in terms of coefficient of determination ( R 2 ) and mean squared error (MSE). Great precision attained in this study through using GEP recommends linking reservoir simulator packages with the GEP tool when thermodynamic properties such as BPP are required for modeling and optimization purposes. [ABSTRACT FROM AUTHOR]

Background: The prediction value of prostate-specific antigen (PSA) isoform [-2]proPSA (p2PSA) for detecting advanced prostate cancer (PCa) remains unclear. Our objective was to evaluate the additional clinical utility of p2PSA compared with total PSA (tPSA), free PSA (fPSA), and preoperative Gleason score (Gls) in predicting locally advanced PCa (pT3/T4) with high-accuracy discrimination. The aim was to develop a novel classification based on p2PSA and preoperative Gls for predicting advanced PCa.Materials and Methods: In 208 consecutive men diagnosed with clinically localized PCa who underwent radical prostatectomy, we determined the predictive and discriminatory accuracy of serum tPSA, fPSA, percentage of fPSA to tPSA, p2PSA, p2PSA density, percentage of p2PSA to fPSA, and the Prostate Health Index. The cutoff level of p2PSA with best accuracy was estimated. The novel classification was developed by analyzing the interaction between p2PSA and Gls in predicting pathologic outcomes using a chi-square automatic interaction detection analysis. Decision curve analysis was applied to test the clinical consequences of using the novel classification.Results: On univariate analyses, p2PSA, p2PSA density, percentage of p2PSA to fPSA, and Prostate Health Index were accurate but were not independent predictors by multivariate analysis. The p2PSA cutoff level of 22.5 pg/ml showed the best accuracy level for predicting and discriminating advanced diseases (area under the curve [AUC] = 0.725, sensitivity = 51.4%, specificity = 81.8%). By chi-square automatic interaction detection, univariate and multivariate analysis, a p2PSA level > 22.5 pg/ml was significantly associated with an increased frequency and risk of advanced disease. In patients with a p2PSA level ≤ 22.5 pg/ml, 91.8% of Gleason sum 6 PCa was organ confined. The combination of p2PSA and Gls enhanced slightly but significantly the predictive and discriminatory accuracy for advanced disease (0.6%-3.6%).Conclusions: The p2PSA cutoff level of 22.5 pg/ml can accurately discriminate between organ-confined and advanced PCa. The additional use of p2PSA enhanced slightly the predictive accuracy for advanced PCa (pT3/pT4) and has limited additional predictive value in identifying aggressive PCa (Gls > 7a). [ABSTRACT FROM AUTHOR]

The basic structure of historic sites and their associated interior artefacts can be damaged or even destroyed by climate change. The evaluation of combined heat and moisture induced stress and strain (HMSS) can predict possible damage-related processes. In this paper, the development of one- and two-dimensional HMSS models of building materials and artefacts in COMSOL Multiphysics Version 4, a commercial finite element software, is presented. The validation of the numerical models is revealed using analytical, numerical and experimental solutions. As a result, the HMSS model was shown to be an adequate predictive tool to determine possible damage-related processes in building assemblies and artefacts. [ABSTRACT FROM AUTHOR]

Precipitation of asphaltene is considered as an undesired process during oil production via natural depletion and gas injection as it blocks the pore space and reduces the oil flow rate. In addition, it lessens the efficiency of the gas injection into oil reservoirs. This paper presents static and dynamic experiments conducted to investigate the effects of temperature, pressure, pressure drop, dilution ratio, and mixture compositions on asphaltene precipitation and deposition. Important technical aspects of asphaltene precipitation such as equation of state, analysis tools, and predictive methods are also discussed. Different methodologies to analyze asphaltene precipitation are reviewed, as well. Artificial neural networks (ANNs) joined with imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) are employed to approximate asphaltene precipitation and deposition with and without CO2 injection. The connectionist model is built based on experimental data covering wide ranges of process and thermodynamic conditions. A good match was obtained between the real data and the model predictions. Temperature and pressure drop have the highest influence on asphaltene deposition during dynamic tests. ICA-ANN attains more reliable outputs compared with PSO-ANN, the conventional ANN, and scaling models. In addition, high pressure microscopy (HPM) technique leads to more accurate results compared with quantitative methods when studying asphaltene precipitation. [ABSTRACT FROM AUTHOR]

Sentinel node biopsy has replaced routine axillary node dissection for women with clinically node-negative breast cancer. The development of nomograms, predictive models that enable individualized risk estimation, has enabled identification of women that are at low or high risk of residual non-sentinel node disease after a positive sentinel node biopsy, and have been widely validated. Furthermore, such models have been shown to predict risk of regional recurrence, risk of non-sentinel node involvement after neoadjuvant chemotherapy, and risk of four or more positive axillary nodes. Together with the panoply of other data, these mathematical models contribute to optimum and individualized decision making for a woman with breast cancer. [ABSTRACT FROM AUTHOR]

Abstract: To provide the world's population with energy for mobility is a major challenge for the 21st century because of global warming and depletion of fossil fuels. Combustion engines will remain the most used propulsion mode during several decades; therefore both cleaner conventional fuels and alternative resources are required calling in turn for the development of new transformation and process pathways. IFPEN is strongly involved in this challenge, with R&D activities aiming at the improvement of catalysts performance or dealing with the process design for biofuels production. Simulation work in the field of thermodynamics is a key technology in this undertaking, like e.g. the “in silico” description of mechanisms at the atomic scale, or the development and use of predictive methods that allow to limit the experimental work. The present paper illustrates this ambition of providing more sustainable fuels through examples of IFPEN ongoing research activities dealing with thermodynamics. [Copyright &y& Elsevier]

BACKGROUND This study sought to develop prognostic tools that will accurately predict overall and cancer-related mortality and risk of recurrence in individual patients with oral cancer based on host and tumor characteristics. These tools would take into account numerous prognosticators beyond those covered by the traditional TNM (tumor-node-metastasis) staging system. METHODS Demographic, host, and tumor characteristics of 1617 patients with cancer of the oral cavity, who were treated primarily with surgery at a single-institution tertiary care cancer center between 1985 and 2009, were reviewed from a preexisting database. Recurrent disease was recorded in 509 patients (456 locoregional and 116 distant); 328 patients died of cancer-related causes, and 542 died of other causes. The median follow-up was 42 months (range, 1-300 months). The following variables were analyzed as predictors of prognosis: age, sex, race, alcohol and tobacco use, oral cavity subsite, invasion of other structures, comorbidity, tumor size, and clinical nodal status. The stepdown method was used to select the statistically most influential predictors for inclusion in the final nomogram for each outcome of interest. RESULTS The most influential predictors of both recurrence and cancer-specific mortality probability (CSMP) were tumor size, nodal status, subsite, and bone invasion. Nomograms were generated for prediction of overall survival (OS), CSMP, and locoregional recurrence-free probability (LRRFP). The nomograms were internally validated with an overfit-corrected predictive discrimination metric (concordance index) for OS of 67%, CSMP of 66%, and LRRFP of 60%. CONCLUSIONS Nomograms have been developed that can reasonably estimate OS, CSMP, and LRRFP based on specific tumor and host characteristics in patients with oral cancer. Cancer 2014;120:214-221. © 2013 American Cancer Society. [ABSTRACT FROM AUTHOR]

We present a new hydro-morphological index of diversity (HMID), a tool aimed for use in river engineering projects and firstly developed at gravel-bed streams in Switzerland, but intended for a broader use. We carried out field work with extensive hydraulic and geomorphic data collection, conducted correlation analysis with hydro-morphological variables, formulated the HMID, and analyzed the correlation between HMID and a visual habitat assessment method. The HMID is calculated by means of the coefficient of variation of the hydraulic variables flow velocity and water depth, which have been demonstrated to sufficiently represent the hydro-morphological heterogeneity of alpine gravel-bed stream reaches. Based on numerical modeling, the HMID can be calculated easily for a comparison of different alternatives in river engineering projects and thus achieves predictive power for design decisions. HMID can be applied at a reach-related scale in engineering programs involving geomorphic measures that aim at the enhancement of habitat heterogeneity of a stream. However, the application of HMID has to be integrated with evaluations of the long-term streambed evolvements that are considered at a catchment scale and strongly related to the sediment regime of the stream under study. [ABSTRACT FROM AUTHOR]

Background: Upper tract urothelial carcinoma (UTUC) is a rare disease with a highly heterogeneous biologic behavior. Accurate individualized prediction of the behavior of UTUC could help guide personalized clinical decision-making regarding optimal therapy. Methods: A MEDLINE literature search was performed on UTUC predictive tools. We recorded input variables, prediction form, number of patients used to develop the prediction tools, outcomes being predicted, prediction tool-specific features, predictive accuracy, and whether internal or external validations were performed. Each prediction tool was classified according to the clinical disease state it addressed and the outcome it predicted. Results: The literature search generated five published tools for UTUC staging and prognostication. None of these prediction tools have undergone external validation yet. Two tools focused on the clinical decision-making regarding conservative management versus radical nephroureterectomy (RNU), lymphadenectomy versus not, and neoadjuvant systemic therapy versus not. Three tools focused on the prognosis after RNU, thereby helping in the decision-making regarding adjuvant systemic chemotherapy. Conclusions: Management of UTUC is challenging, and there are no high-level data to guide physicians and patients. Prognostic tools relying on data from large cohorts of patients are currently the best source of information for evidence-based management of UTUC patients. [ABSTRACT FROM AUTHOR]

Objectives: To identify the predictive tools which have emerged recently in the field of urothelial carcinomas. Materials and methods: We performed a thorough MEDLINE literature review using a combination of the following keywords: urothelial carcinoma, transitional cell carcinoma, bladder, renal pelvis, ureter, predictive tools, predictive models and nomograms. We found 117 articles, but only the relevant reports were selected. Results: The majority of available tools are prediction models, particularly nomograms. These models combine good performance accuracy with ease of use. They appear to be more accurate than risk grouping or tree modeling and are more suitable for clinicians than artificial intelligence. The most recent nomograms have been designed to be used in daily clinical practice and are even available as computer or smartphone applications. They focus on pathological outcomes or more frequently on survival statistics or recurrence risk after surgery. They provide an accurate prediction of disease evolution and may help clinicians to choose the most appropriate treatment option. However, these prediction tools still need to be validated and regularly utilized. Conclusion: Predictive tools represent very helpful clinical decision-making aids but need to be validated in larger populations. [ABSTRACT FROM AUTHOR]

Haemophilia B is an X-linked recessive disorder caused by deficiency of functional coagulation factor IX, which results almost exclusively from mutations in the F9 gene. We sought to determine features, which could distinguish between mutations that cause severe disease symptoms from those that cause non-severe disease symptoms. Towards this objective, we have performed a statistical analysis of reported point mutations in F9. These include: potential local changes in mRNA free energy, codon usage, charge and type of mutated amino acid, location of the mutation with regard to protein secondary structure and functional domain and amino acids' evolutionary conservation scores. Wilcoxon signed-rank tests showed highly significant differences between severe and non-severe disease causing mutations in their effect on free energy of small mRNA fragments and evolutionarily conserved amino acids. Our results suggest that information at the mRNA level as well as conservation of the amino acid correlate well with disease severity. This study demonstrates that computational tools may be used to characterize the severity of haemophilia B associated with point mutations and suggests their utility in predicting the outcome of sequence changes in recombinant proteins. [ABSTRACT FROM AUTHOR]

Abstract: Background: Statistical prediction tools are increasingly common, but there is considerable disagreement about how they should be evaluated. Three tools—Partin tables, the European Society for Urological Oncology (ESUO) criteria, and the Gallina nomogram—have been proposed for the prediction of seminal vesicle invasion (SVI) in patients with clinically localized prostate cancer who are candidates for a radical prostatectomy. Objectives: Using different statistical methods, we aimed to determine which of these tools should be used to predict SVI. Design, settings, and participants: The independent validation cohort consisted of 2584 patients treated surgically for clinically localized prostate cancer at four North American tertiary care centers between 2002 and 2007. Interventions: Robot-assisted laparoscopic radical prostatectomy. Outcome measurements and statistical analysis: Primary outcome was the presence of SVI. Traditional (area under the receiver operating characteristic [ROC] curve, calibration plots, the Brier score, sensitivity and specificity, positive and negative predictive value) and novel (decision curve analysis and predictiveness curves) statistical methods quantified the predictive abilities of the three models. Results and limitations: Traditional statistical methods (ie, ROC plots and Brier scores) could not clearly determine which one of the three SVI prediction tools should be preferred. For example, ROC plots and Brier scores seemed biased against the binary decision tool (ESUO criteria) and gave discordant results for the continuous predictions of the Partin tables and the Gallina nomogram. The results of the calibration plots were discordant with those of the ROC plots. Conversely, the decision curve indicated that the Partin tables represent the best strategy for stratifying the risk of SVI, resulting in the highest net benefit within the whole range of threshold probabilities. Conclusions: When predicting SVI, surgeons should prefer the Partin tables over the ESUO criteria and the Gallina nomogram because this tool provided the highest net benefit. In contrast to traditional statistical methods, decision curve analysis gave an unambiguous result applicable to both continuous and binary models, providing an insight into clinical utility. [Copyright &y& Elsevier]

Purpose Many countries around the world have been undertaking initiatives to promote the improvement of care for people with chronic diseases. The purpose of this article is to showcase some of the innovations developed in Spain, with emphasis on the societal burden of chronicity and the emergent challenge of polypathology. Methods The database of the Observatory of Innovative Practices on Complex Chronic Disease Management (known in Spanish as OPIMEC and available at www.opimec.org) was used to identify innovations in Spain. Those that met five requirements were selected for this article. The person in charge of each innovation selected was contacted and asked to provide further information. All data gathered were organized in a similar way to facilitate a better understanding. Findings Six out of 45 potentially eligible innovations were selected. The innovations are related to strategies, predictive tools, case management, home care and patient education. Conclusion The innovations showed in this article are breaking ground in the Spanish National Health System. They appear to be improving chronic disease management, while transforming current healthcare delivery models. [ABSTRACT FROM AUTHOR]

Background: Physicians lack a robust and validated method of measuring severity or predicting poor outcome in patients with acute exacerbation of COPD (AECOPD). Such a predictive tool would allow optimisation of treatment plans for these patients, as well as best use of health care resources. Objective: To determine predictors of in-hospital mortality in AECOPD, and develop a predictive scoring system to identify patients at higher risk of in-hospital mortality. Methods: Analysis of clinical patient data from the exacerbations of obstructive lung disease managed in UK Secondary care [EXODUS] study database, collected from 11 UK hospitals. Results: A total of 1031 patients were included in the validation cohort. The in-hospital mortality rate was 5.2%. Independent predictors of mortality were identified and a new scoring system ("CAUDA70"), for prediction of in-hospital mortality in AECOPD was derived. The score incorporated 6 easily obtained clinical variables: acidosis, albumin, urea, the presence of confusion, MRC dyspnoea score and age. The score displayed strong discrimination, with an area under the receiver operating characteristic (ROC) curve of 0.84. This performance was reproduced in a further validation dataset of 312 patients. The discrimination of the new score exceeds that of existing scores validated for use in AECOPD (CURB65, CRB65 and BAP- 65). Conclusion: A new scoring system composed of six readily available clinical variables can accurately predict in-hospital mortality in AECOPD. [ABSTRACT FROM AUTHOR]

Abstract: By employing fluids of the same composition (regarding foulants and ionic strength) specific fouling resistances are measured in the constant flux filtration mode, in dead-end UF and cross-flow RO tests, and compared with the respective resistances determined under constant pressure. Sodium alginate (SA) alone and mixtures with humic acids (HA), in presence of calcium, were used as typical organic foulants in feedwaters of two ionic strengths (TDS=500 and 2000mg/L). Plotting resistances α, from UF (or RO) constant pressure and constant flux tests, versus pressure drop across the cake ΔP c , estimated at the same deposited mass density, very good agreement was obtained; increasing resistance α with ΔP c was observed due to cake compressibility, as expected. Regarding cake structure, the dominant role of alginates, in proportion greater than 25% in mixtures with HA, is reflected in the resistances, which exhibit approximately the same compressibility index. Moreover, at the higher alginate proportions and for the greater salinity (TDS 2000mg/L), the resistances from both UF and RO tests are correlated satisfactorily, versus ΔP c , by a single expression. These results significantly contribute towards developing reliable tools for assessing water fouling potential and predicting RO membrane fouling evolution in desalination plants. [Copyright &y& Elsevier]

A development of a new drug is an extremely complex process, with average costs over $900 million and a time span up to 15 years. Regulatory hurdles are progressively higher, costs are escalating and the competition is ever tougher. All these factors drive pharmaceutical companies to shorten the Research & Development (R&D) process as much as possible. Consequently, methodologies for increasing productivity of the R&D processes become more and more important. One of them, a linear drug discovery paradigm starts from numerous genes and ends with prospective drugs. It consists of four inter-related building blocks that together provide a whole new platform that drives a focus on R&D efforts and commercial capabilities, on use of product and capability partnerships, on provision of customer solutions (prevention, prediction and follow-up, not just treatment), and finally on preference of organizations based on a business unit model instead of a functional one. Companies are to find a combination of these building blocks that best fits their strengths, improves returns and minimizes involved risks. Predictive methodology, together with other prediction approaches applied in drug discovery, is powerful tool that is quickly becoming more and more important, if not essential, in modern drug development. In this review, a brief description of new trends, as well as new challenges of today's drug discovery is presented from the insider perspective. [ABSTRACT FROM AUTHOR]

Renal cell carcinoma (RCC) is a very heterogeneous disease with widely varying prognosis. An accurate knowledge of the individual risk of disease progression and mortality after treatment is essential to counsel patients, plan individualized surveillance protocols and select patients for adapted treatment schedules and new clinical trials. A systematic review of the literature on prognostic factors of localized and metastatic RCC was performed. Prognostic factors in RCC include anatomical (TNM classification, tumor size), histological (Fuhrman grade, histologic subtype), clinical (symptoms and performance status), and molecular features. All these features are not perfectly accurate when used alone. Therefore an increasing number of prognostic models or nomograms that include several combined prognostic features have been designed in order to improve predictive accuracy. UCLA Integrated Staging System (UISS) and the Mayo Clinic’s SSIGN score are the two most used prognostic models for localized RCC. In the setting of metastatic RCC the classical anatomical and histological tumor features have little predictive value. However, accurate prognostic models have been designed to predict response to therapy, and progression-free and overall survival. The two most used tools to predict response to immunotherapy are the model designed by the French Group of Immunotherapy and the Motzer’s model. The advent of tyrosine kinase inhibitors and antiangiogenic drugs have deeply changed the treatment of metastatic RCC. Predictive tools that are adapted to the modern targeted therapies are now needed. There is increasing knowledge on prognostic factors of localized and metastatic RCC. Several predictive models have been developed by combining different prognostic features and are valuable tools for patient counseling, treatment decision-making and trial design. Further research is needed to assess whether the combination of classical prognostic factors with molecular features and information from gene and protein expression profiling can increase the predictive accuracy of the current prognostic models. [ABSTRACT FROM AUTHOR]

Introduction: As SARS-CoV-2 spread around the world, Australia was no exception. Part of the Australian response was a robust primary care approach, involving changes to care models (including telehealth) and the widespread use of data to inform the changes. This paper outlines how a large primary care database responded to provide real-time data to inform policy and practice. Simply extracting the data is not sufficient. Understanding the data is. The POpulation Level Analysis and Reporting (POLAR) program is designed to use GP data for multiple objectives and is built on a pre-existing engagement framework established over a fifteen-year period. Initially developed to provide QA activities for general practices and population level data for General Practice support organisations, the POLAR platform has demonstrated the critical ability to design and deploy real-time data analytics solutions during the COVID-19 pandemic for a variety of stakeholders including state and federal government agencies.Methods: The system extracts and processes data from over 1,300 general practices daily. Data is de-identified at the point of collection and encrypted before transfer. Data cleaning for analysis uses a variety of techniques, including Natural Language Processing and coding of free text information. The curated dataset is then distilled into several analytic solutions designed to address specific areas of investigation of interest to various stakeholders. One such analytic solution was a model we created that used multiple data inputs to rank patient geographic areas by the likelihood of a COVID-19 outbreak. The model utilised pathology ordering, COVID-19 related diagnoses, indication of COVID-19 related concern (via progress notes) and also incorporated state based actual confirmed case figures.Results: Using the methods described, we were able to deliver real-time data feeds to practices, Primary Health Networks (PHN) and other agencies. In addition, we developed a COVID-19 geographic risk stratification based on local government areas (LGAs) to pro-actively inform the primary care response. Providing PHNs with a list of geographic priority hotspots allowed for better targeting and response of Personal Protective Equipment allocation and pop-up clinic placement.Conclusions: The program summarised here demonstrates the ability of a well-designed system underpinned by accurate and reliable data, to respond in real-time to a rapidly evolving public health emergency in a way which supports and enhances the health system response. [ABSTRACT FROM AUTHOR]

Abstract: Models capable of predicting the possible involvement of cytochromes P450 in the metabolism of drugs or drug candidates are important tools in drug discovery and development. Ideally, functional information would be obtained from crystal structures of all the cytochromes P450 of interest. Initially, only crystal structures of distantly related bacterial cytochromes P450 were available—comparative modeling techniques were used to bridge the gap and produce structural models of human cytochromes P450, and thereby obtain some useful functional information. A significant step forward in the reliability of these models came four years ago with the first crystal structure of a mammalian cytochrome P450, rabbit CYP2C5, followed by the structures of two human enzymes, CYP2C8 and CYP2C9, and a second rabbit enzyme, CYP2B4. The evolution of a CYP2D6 model, leading to the validation of the model as an in silico tool for predicting binding and metabolism, is presented as a case study. [Copyright &y& Elsevier]

Presents a study that examined the improvement of the task of capturing institutional knowledge as a tool in the development of pest animal management strategies in New South Wales and Western Australia. Information on minimizing the perceptual biases in human-wildlife situations; Increase use of data from geographic information system; Discussion on the standardized definitions of animal abundance and distribution.

Novel biomarkers are needed to continue to improve breast cancer clinical management and outcome. IL6-like cytokines, whose pleiotropic functions include roles in many hallmarks of malignancy, rely on the signal transducer IL6ST (gp130) for all their signalling. To date, 10 separate independent studies based on the analysis of clinical breast cancer samples have identified IL6ST as a predictor. Consistent findings suggest that IL6ST is a positive prognostic factor and is associated with ER status. Interestingly, these studies include 4 multigene signatures (EndoPredict, EER4, IRSN-23 and 42GC) that incorporate IL6ST to predict risk of recurrence or outcome from endocrine or chemotherapy. Here we review the existing evidence on the promising predictive and prognostic value of IL6ST. We also discuss how this potential could be further translated into clinical practice beyond the EndoPredict tool, which is already available in the clinic. The most promising route to further exploit IL6ST's promising predicting power will likely be through additional hybrid multifactor signatures that allow for more robust stratification of ER+ breast tumours into discrete groups with distinct outcomes, thus enabling greater refinement of the treatment-selection process. [ABSTRACT FROM AUTHOR]

Background: In primary ciliary dyskinesia (PCD) there is no single diagnostic test. Different predictive tools have been proposed to guide referral of high-risk patients for further diagnostic workup. We aimed to test clinical index (CI) on a large unselected cohort and compare its characteristics with other widely used tools—PICADAR and NA-CDCF. Methods: CI, PICADAR, and NA-CDCF scores were calculated in 1401 patients with suspected PCD referred to our center. Their predictive characteristics were analyzed using receiver operating characteristics (ROC) curves and compared to each other. Nasal nitric oxide (nNO) was measured in 569 patients older than 3 years. Results: PCD was diagnosed in 67 (4.8%) patients. CI, PICADAR, and NA-CDCF scores were higher in PCD than in nonPCD group (all p < 0.001). The area under the ROC curve (AUC) for CI was larger than for NA-CDCF (p = 0.005); AUCPICADAR and AUCNA-CDCF did not differ (p = 0.093). An overlap in signs and symptoms among tools was identified. PICADAR could not be assessed in 86 (6.1%) patients without chronic wet cough. For CI laterality or congenital heart defects assessment was not necessary. nNO further improved predictive power of all three tools. Conclusion: CI is a feasible predictive tool for PCD that may outperform PICADAR and NA-CFCD. [ABSTRACT FROM AUTHOR]

The author, among others, has developed a system that empowers communities to organise themselves to engage in outcome focused prevention planning. This system for prevention planning using the tools of prevention science is Communities That Care (CTC). In a collaborative US project 22 reliable and valid archival indicators of 12 major risk factors that predict rates of adolescent drug use at the county level were found. This collaborative project has also developed a validated school survey that measures risk, protection, delinquency and drug use outcomes reliably across states and ethnic groups. It allows a diagnosis of risk and protection levels in any geographical area, from a state to a neighbourhood. With these tools, communities can organise themselves to engage in outcome focused prevention planning, objectively assess their own profiles of risk and protection, and choose and implement effective strategies to address their unique strengths and needs. In Europe CTC has been currently introduced in England and Wales, Scotland and in the Netherlands. [ABSTRACT FROM AUTHOR]

Childhood asthma develops in 30-40% of children with severe bronchiolitis but accurate prediction remains challenging. In a severe bronchiolitis cohort, we applied the Asthma Predictive Index (API), the modified Asthma Predictive Index (mAPI), and the Pediatric Asthma Risk Score (PARS) to predict asthma at age 5 years. We applied the API, mAPI, and PARS to the 17-center cohort of infants hospitalized with severe bronchiolitis during 2011-2014 (35th Multicenter Airway Research Collaboration, MARC-35). We used data from the first 3 years of life including parent interviews, chart review, and specific IgE testing to predict asthma at age 5 years, defined as parent report of clinician-diagnosed asthma. Among 875/921 (95%) children with outcome data, parent-reported asthma was 294/875 (34%). In MARC-35, a positive index/score for stringent and loose API, mAPI, and PARS were 24, 68, 6, and 55%, respectively. The prediction tools' AUCs (95%CI) ranged from 0.57 (95%CI 0.54-0.59) to 0.68 (95%CI 0.65-0.71). The positive likelihood ratios were lower in MARC-35 compared to the published results from the original cohorts. In this high-risk population of infants hospitalized with severe bronchiolitis, API, mAPI, and PARS had sub-optimal performance (AUC <0.8). Highly accurate (AUC >0.8) asthma prediction tools are desired in infants hospitalized with severe bronchiolitis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 C. Fabiano Filho, Geller, Candido Santos, Espinola, Robinson, Hasegawa and Camargo.)

Web-based personalized predictive tools in orthopedic surgery are becoming more widely available. Despite rising numbers of these tools, many orthopedic surgeons may not know what tools are available, how these tools were developed, and how they can be utilized. The aim of this scoping review is to compile and synthesize the profile of existing web-based orthopedic tools. We conducted two separate PubMed searches—one a broad search and the second a more targeted one involving high impact journals—with the aim of comprehensively identifying all existing tools. These articles were then screened for functional tool URLs, methods regarding the tool's creation, and general inputs and outputs required for the tool to function. We identified 57 articles, which yielded 31 unique web-based tools. These tools involved various orthopedic conditions (e.g., fractures, osteoarthritis, musculoskeletal neoplasias); interventions (e.g., fracture fixation, total joint arthroplasty); outcomes (e.g., mortality, clinical outcomes). This scoping review highlights the availability and utility of a vast array of web-based personalized predictive tools for orthopedic surgeons. Increased awareness and access to these tools may allow for better decision support, surgical planning, post-operative expectation management, and improved shared decision-making. [ABSTRACT FROM AUTHOR]