A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology.

BackgroundTesting a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified.MethodsThe PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system.ResultsThe search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible.ConclusionES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research

Dual acting therapeutic proteins for intraocular use

Antibody-based medicines that target vascular endothelial growth factor (VEGF) are administered by intravitreal injection to treat chronic neovascular retinal diseases. Much ongoing effort is focused on enhancing therapeutic outcome of these medicines. One strategy is the use of dual acting drugs (e.g. bispecific antibodies) to simultaneously bind to more than one intraocular biological target. A dual acting molecule targeting components within the vitreal cavity could also potentially extend vitreous residence time. In this review, the applications of bispecific antibodies within the eye are described with consideration to potential targets, applications and suitable bispecific formats

Cytokine storm of a different flavor: the different cytokine signature of SARS-CoV2 the cause of COVID-19 from the original SARS outbreak.

We present a case series of three patients with COVID-19 who had a cytokine panel which revealed elevation of interleukin-6 (IL-6), but normal levels of interleukin-10 (IL-10), interferon-gamma (INF-γ) and interleukin-8 (IL-8) in contrast to the cytokine signature described in Severe Acute Respiratory Syndrome (SARS) and Middle Eastern Respiratory Syndrome (MERS). We also documented evidence of a compromised T-cell IFN-gamma response in two of these patients

Consumption of Whole Grains, Refined Cereals and Legumes and its Association with Colorectal Cancer among Jordanians

Background The role of whole grains, refined cereals, and legumes in preventing or initiating colorectal cancer (CRC) is still uncertain. The aim of this study is to examine the possible association between the consumption of whole grains, refined cereals, and legumes and the risk of developing CRC among Jordanian population. Methods A validated food frequency questionnaire was used to collect dietary data with regard to intake of whole grains, refined cereals, and legumes. A total of 220 diagnosed CRC participants and 281 CRC-free control participants matched by age, gender, occupation, and marital status were recruited. Logistic regression was used to estimate the odds of developing CRC in relation to the consumption of different types of whole grains, refined cereals, and legumes. Results The odds ratio (OR) for developing CRC among cases consumed refined wheat bread at all meals was 3.1 compared with controls (95% CI: 1.2-7.9, P-Trend = 0.001); whereas the OR associated with whole wheat bread was 0.44 (95% CI: 0.22-0.92, P-Trend = 0.001). The statistical evaluation for daily consumption of rice suggested a direct association with the risk of developing CRC, OR = 3.0 (95% CI: 0.27-33.4, P-Trend = 0.020). Weekly consumption of macaroni was associated with CRC with OR of 2.4 (95% CI: 1.1-5.3, P-Trend = 0.001). The consumption of corn, bulgur, lentils, and peas suggested a protective trend, although the trend was not statistically significant. Conclusion This study provides additional indicators of the protective role of whole grains and suggests a direct association between consumption of refined grains and higher possibility for developing CRC.Higher Council of Science and Technology-Jorda

Current and projected gaps in the availability of radiotherapy in the Asia-Pacific region: a country income-group analysis

Background: Cancer incidence and mortality is increasing rapidly worldwide, with a higher cancer burden observed in the Asia-Pacific region than in other regions. To date, evidence-based modelling of radiotherapy demand has been based on stage data from high-income countries (HIC) that do not account for the later stage at presentation seen in many low-income and middle-income countries (LMICs). We aimed to estimate the current and projected demand and supply in megavoltage radiotherapy machines in the Asia-Pacific region, using a national income-group adjusted model. Methods: Novel LMIC radiotherapy demand and outcome models were created by adjusting previously developed models that used HIC cancer staging data. These models were applied to the cancer case mix (ie, the incidence of each different cancer) in each LMIC in the Asia-Pacific region to estimate the current and projected optimal radiotherapy utilisation rate (ie, the proportion of cancer cases that would require radiotherapy on the basis of guideline recommendations), and to estimate the number of megavoltage machines needed in each country to meet this demand. Information on the number of megavoltage machines available in each country was retrieved from the Directory of Radiotherapy Centres. Gaps were determined by comparing the projected number of megavoltage machines needed with the number of machines available in each region. Megavoltage machine numbers, local control, and overall survival benefits were compared with previous data from 2012 and projected data for 2040. Findings: 57 countries within the Asia-Pacific region were included in the analysis with 9·48 million new cases of cancer in 2020, an increase of 2·66 million from 2012. Local control was 7·42% and overall survival was 3·05%. Across the Asia-Pacific overall, the current optimal radiotherapy utilisation rate is 49·10%, which means that 4·66 million people will need radiotherapy in 2020, an increase of 1·38 million (42%) from 2012. The number of megavoltage machines increased by 1261 (31%) between 2012 and 2020, but the demand for these machines increased by 3584 (42%). The Asia-Pacific region only has 43·9% of the megavoltage machines needed to meet demand, ranging from 9·9–40·5% in LMICs compared with 67·9% in HICs. 12 000 additional megavoltage machines will be needed to meet the projected demand for 2040. Interpretation: The difference between supply and demand with regard to megavoltage machine availability has continued to widen in LMICs over the past decade and is projected to worsen by 2040. The data from this study can be used to provide evidence for the need to incorporate radiotherapy in national cancer control plans and to inform governments and policy makers within the Asia-Pacific region regarding the urgent need for investment in this sector. Funding: The Regional Cooperative Agreement for Research, Development and Training Related to Nuclear Science and Technology for Asia and the Pacific (RCA) Regional Office (RCARP03)

Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors

BACKGROUND AND PURPOSE: Qualitative radiologic MR imaging review affords limited differentiation among types of pediatric posterior fossa brain tumors and cannot detect histologic or molecular subtypes, which could help to stratify treatment. This study aimed to improve current posterior fossa discrimination of histologic tumor type by using support vector machine classifiers on quantitative MR imaging features. MATERIALS AND METHODS: This retrospective study included preoperative MRI in 40 children with posterior fossa tumors (17 medulloblastomas, 16 pilocytic astrocytomas, and 7 ependymomas). Shape, histogram, and textural features were computed from contrast-enhanced T2WI and T1WI and diffusivity (ADC) maps. Combinations of features were used to train tumor-type-specific classifiers for medulloblastoma, pilocytic astrocytoma, and ependymoma types in separation and as a joint posterior fossa classifier. A tumor-subtype classifier was also produced for classic medulloblastoma. The performance of different classifiers was assessed and compared by using randomly selected subsets of training and test data. RESULTS: ADC histogram features (25th and 75th percentiles and skewness) yielded the best classification of tumor type (on average >95.8% of medulloblastomas, >96.9% of pilocytic astrocytomas, and >94.3% of ependymomas by using 8 training samples). The resulting joint posterior fossa classifier correctly assigned >91.4% of the posterior fossa tumors. For subtype classification, 89.4% of classic medulloblastomas were correctly classified on the basis of ADC texture features extracted from the Gray-Level Co-Occurence Matrix. CONCLUSIONS: Support vector machine–based classifiers using ADC histogram features yielded very good discrimination among pediatric posterior fossa tumor types, and ADC textural features show promise for further subtype discrimination. These findings suggest an added diagnostic value of quantitative feature analysis of diffusion MR imaging in pediatric neuro-oncology