Parenclitic and Synolytic Networks Revisited

Parenclitic networks provide a powerful and relatively new way to coerce multidimensional data into a graph form, enabling the application of graph theory to evaluate features. Different algorithms have been published for constructing parenclitic networks, leading to the question-which algorithm should be chosen? Initially, it was suggested to calculate the weight of an edge between two nodes of the network as a deviation from a linear regression, calculated for a dependence of one of these features on the other. This method works well, but not when features do not have a linear relationship. To overcome this, it was suggested to calculate edge weights as the distance from the area of most probable values by using a kernel density estimation. In these two approaches only one class (typically controls or healthy population) is used to construct a model. To take account of a second class, we have introduced synolytic networks, using a boundary between two classes on the feature-feature plane to estimate the weight of the edge between these features. Common to all these approaches is that topological indices can be used to evaluate the structure represented by the graphs. To compare these network approaches alongside more traditional machine-learning algorithms, we performed a substantial analysis using both synthetic data with a priori known structure and publicly available datasets used for the benchmarking of ML-algorithms. Such a comparison has shown that the main advantage of parenclitic and synolytic networks is their resistance to over-fitting (occurring when the number of features is greater than the number of subjects) compared to other ML approaches. Secondly, the capability to visualise data in a structured form, even when this structure is not a priori available allows for visual inspection and the application of well-established graph theory to their interpretation/application, eliminating the "black-box" nature of other ML approaches

Mathematical model for the impact of awareness on the dynamics of infectious diseases

This paper analyses an SIRS-type model for infectious diseases with account for behavioural changes associated with the simultaneous spread of awareness in the population. Two types of awareness are included into the model: private awareness associated with direct contacts between unaware and aware populations, and public information campaign. Stability analysis of different steady states in the model provides information about potential spread of disease in a population, and well as about how the disease dynamics is affected by the two types of awareness. Numerical simulations are performed to illustrate the behaviour of the system in different dynamical regimes

Dynamics of vaccination in a time-delayed epidemic model with awareness

This paper investigates the effects of vaccination on the dynamics of infectious disease, which is spreading in a population concurrently with awareness. The model considers contributions to the overall awareness from a global information campaign, direct contacts between unaware and aware individuals, and reported cases of infection. It is assumed that there is some time delay between individuals becoming aware and modifying their behaviour. Vaccination is administered to newborns, as well as to aware individuals, and it is further assumed that vaccine-induced immunity may wane with time. Feasibility and stability of the disease-free and endemic equilibria are studied analytically, and conditions for the Hopf bifurcation of the endemic steady state are found in terms of system parameters and the time delay. Analytical results are supported by numerical continuation of the Hopf bifurcation and numerical simulations of the model to illustrate different types of dynamical behaviour

Control of mosaic disease using microbial biostimulants: insights from mathematical modelling

A major challenge to successful crop production comes from viral diseases of plants that cause significant crop losses, threatening global food security and the livelihoods of countries that rely on those crops for their staple foods or source of income. One example of such diseases is a mosaic disease of plants, which is caused by begomoviruses and is spread to plants by whitefly. In order to mitigate negative impact of mosaic disease, several different strategies have been employed over the years, including roguing/replanting of plants, as well as using pesticides, which have recently been shown to be potentially dangerous to the environment and humans. In this paper we derive and analyse a mathematical model for control of mosaic disease using natural microbial biostimulants that, besides improving plant growth, protect plants against infection through a mechanism of RNA interference. By analysing the stability of the system’s steady states, we will show how properties of biostimulants affect disease dynamics, and in particular, how they determine whether the mosaic disease is eradicated or is rather maintained at some steady level. We will also present the results of numerical simulations that illustrate the behaviour of the model in different dynamical regimes, and discuss biological implications of theoretical results for the practical purpose of control of mosaic disease

Identification of a serum biomarker panel for the differential diagnosis of cholangiocarcinoma and primary sclerosing cholagnitis

The non-invasive differentiation of malignant and benign biliary disease is a clinical challenge. Carbohydrate antigen 19-9 (CA19-9), leucine-rich α2-glycoprotein (LRG1), interleukin 6 (IL6), pyruvate kinase M2 (PKM2), cytokeratin 19 fragment (CYFRA21.1) and mucin 5AC (MUC5AC) have reported utility for differentiating cholangiocarcinoma (CCA) from benign biliary disease. Herein, serum levels of these markers were tested in 66 cases of CCA and 62 cases of primary sclerosing cholangitis (PSC) and compared with markers of liver function and inflammation. Markers panels were assessed for their ability to discriminate malignant and benign disease. Several of the markers were also assessed in pre-diagnosis biliary tract cancer (BTC) samples with performances evaluated at different times prior to diagnosis. We show that LRG1 and IL6 were unable to accurately distinguish CCA from PSC, whereas CA19-9, PKM2, CYFRA21.1 and MUC5AC were significantly elevated in malignancy. Area under the receiver operating characteristic curves for these individual markers ranged from 0.73–0.84, with the best single marker (PKM2) providing 61% sensitivity at 90% specificity. A panel combining PKM2, CYFRA21.1 and MUC5AC gave 76% sensitivity at 90% specificity, which increased to 82% sensitivity by adding gamma-glutamyltransferase (GGT). In the pre-diagnosis setting, LRG1, IL6 and PKM2 were poor predictors of BTC, whilst CA19-9 and C-reactive protein were elevated up to 2 years before diagnosis. In conclusion, LRG1, IL6 and PKM2 were not useful for early detection of BTC, whilst a model combining PKM2, CYFRA21.1, MUC5AC and GGT was beneficial in differentiating malignant from benign biliary disease, warranting validation in a prospective trial

Transient elastography with controlled attenuation parameter (CAP) for diagnosis of moderate or severe steatosis in people with suspected non-alcoholic fatty liver disease

Objectives: This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows: To determine the diagnostic accuracy of transient elastography with CAP for diagnosis of moderate and severe hepatic steatosis in people with suspected NAFLD when compared with liver biopsy as reference standard. To achieve this, we will compare none/mild hepatic steatosis (S0 to S1) versus moderate/severe hepatic steatosis (S2 to S3); and none/mild/moderate hepatic steatosis (S0 to S2) versus severe hepatic steatosis (S3). Secondary objectives: We aim to also identify the pooled sensitivity and specificity for the most common cut‐off values of CAP for diagnosis of none/mild hepatic steatosis (S0 to S1) and moderate/severe hepatic steatosis (S2 to S3); or none/mild/moderate hepatic steatosis (S0 to S2) and severe hepatic steatosis (S3) in people with suspected NAFLD, and to explore potential sources of heterogeneity influencing the diagnostic test accuracy of CAP (see: Investigations of heterogeneity)

Serial Patterns of Ovarian Cancer Biomarkers in a Prediagnosis Longitudinal Dataset

Early detection of ovarian cancer through screening may have impact on mortality from the disease. Approaches based on CA125 cut-off have not been effective. Longitudinal algorithms such as the Risk of Ovarian Cancer Algorithm (ROCA) to interpret CA125 have been shown to have higher sensitivity and specificity than a single cut-off. The aim of this study was to investigate whether other ovarian cancer-related biomarkers, Human Epididymis 4 (HE4), glycodelin, mesothelin, matrix metalloproteinase 7 (MMP7), and cytokeratin 19 fragment (CYFRA 21-1), could improve the performance of CA125 in detecting ovarian cancer earlier. Serum samples (single and serial) predating diagnosis from 47 women taking part in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) who went on to develop primary invasive ovarian, fallopian tube, or peritoneal cancer (index cancer) (170 samples) and 179 matched controls (893 samples) were included in the study. A multiplex immunobased assay platform (Becton Dickinson) allowing simultaneous measurement of the six serum markers was used. The area under the ROC curve for the panel of three biomarkers (CA125, HE4, and glycodelin) was higher than for CA125 alone for all analysed time groups, indicating that these markers can improve on sensitivity of CA125 alone for ovarian cancer detection

Long-term excess risk of breast cancer after a single breast density measurement.

AIM: Breast density is a risk factor for breast cancer. As density changes across a woman's life span, we studied for how long a single density measurement taken in (post-)menopausal women remains informative. METHODS: We used data from Singaporean women who underwent a single mammography screen at age 50-64 years. For each case with breast cancer diagnosed at screening or in the subsequent 10 years, whether screen detected or diagnosed following symptoms, two age-matched controls were selected. We studied the excess risk of breast cancer, calculated as an odds ratio (OR) with conditional logistic regression and adjusted for body mass index, associated with 26-50% and with 51-100% density compared with ≤25% density by time since screening. RESULTS: In total, 490 women had breast cancer, of which 361 were diagnosed because of symptoms after screening. Women with 51-100% breast density had an excess risk of breast cancer that did not seem to attenuate with time. In 1-3 years after screening, the OR was 2.22 (95% confidence interval [CI]: 1.07-4.61); in 4-6 years after screening, the OR was 4.09 (95% CI: 2.21-7.58), and in 7-10 years after screening, the OR was 5.35 (95% CI: 2.57-11.15). Excess risk with a stable OR of about 2 was also observed for women with 26-50% breast density. These patterns were robust when the analyses were limited to post-menopausal women, non-users of hormonal replacement therapy and after stratification by age at density measurement. CONCLUSION: A single breast density measurement identifies women with an excess risk of breast cancer during at least the subsequent 10 years.Cancer Research UK (grant number: C8162/A16892)Singapore National Medical Research CouncilDepartment of Health Policy Research Programme (106/0001

Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance

Objectives: To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). // Methods: The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5–16 years’ experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong’s test. // Results: The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34–0.77). // Conclusions: PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients