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Venous thromboembolism (VTE) is the third most common cardiovascular disease, affecting ∼ 1,000,000 individuals each year in Europe. VTE is characterized by an annual recurrent rate of ∼ 6%, and ∼ 30% of patients with unprovoked VTE will face a recurrent event after a six-month course of anticoagulant treatment. Even if guidelines recommend life-long treatment for these patients, about ∼ 70% of them will never experience a recurrence and will receive unnecessary lifelong anti-coagulation that is associated with increased risk of bleeding and is highly costly for the society. There is then urgent need to identify biomarkers that could distinguish VTE patients with high risk of recurrence from low-risk patients. Capitalizing on a sample of 913 patients followed up for the risk of VTE recurrence during a median of ∼ 10 years and profiled for 376 plasma proteomic antibodies, we here develop an artificial neural network (ANN) based strategy to identify a proteomic signature that helps discriminating patients at low and high risk of recurrence. In a first stage, we implemented a Repeated Editing Nearest Neighbors algorithm to select a homogeneous sub-sample of VTE patients. This sub-sample was then split in a training and a testing sets. The former was used for training our ANN, the latter for testing its discriminatory properties. In the testing dataset, our ANN led to an accuracy of 0.86 that compared to an accuracy of 0.79 as provided by a random forest classifier. We then applied a Deep Learning Important FeaTures (DeepLIFT) – based approach to identify the variables that contribute the most to the ANN predictions. In addition to sex, the proposed DeepLIFT strategy identified 6 important proteins (DDX1, HTRA3, LRG1, MAST2, NFATC4 and STXBP5) whose exact roles in the etiology of VTE recurrence now deserve further experimental validations. © 2021, Springer Nature Switzerland AG.

Venous thromboembolism is the third common cardiovascular disease and is composed of two entities, deep vein thrombosis (DVT) and its fatal form, pulmonary embolism (PE). While PE is observed in ∼40% of patients with documented DVT, there is limited biomarkers that can help identifying patients at high PE risk.To fill this need, we implemented a two hidden-layers artificial neural networks (ANN) on 376 antibodies and 19 biological traits measured in the plasma of 1388 DVT patients, with or without PE, of the MARTHA study. We used the LIME algorithm to obtain a linear approximate of the resulting ANN prediction model. As MARTHA patients were typed for genotyping DNA arrays, a genome wide association study (GWAS) was conducted on the LIME estimate. Detected single nucleotide polymorphisms (SNPs) were tested for association with PE risk in MARTHA. Main findings were replicated in the EOVT study composed of 143 PE patients and 196 DVT only patients.The derived ANN model for PE achieved an accuracy of 0.89 and 0.79 in our training and testing sets, respectively. A GWAS on the LIME approximate identified a strong statistical association peak (p = 5.3×10−7) at the PLXNA4 locus, with lead SNP rs1424597 at which the minor A allele was further shown to associate with an increased risk of PE (OR = 1.49 [1.12 – 1.98], p = 6.1×10−3). Further association analysis in EOVT revealed that, in the combined MARTHA and EOVT samples, the rs1424597-A allele was associated with increased PE risk (OR = 1.74 [1.27 – 2.38, p = 5.42×10−4) in patients over 37 years of age but not in younger patients (OR = 0.96 [0.65 – 1.41], p = 0.848).Using an original integrated proteomics and genetics strategy, we identified PLXNA4 as a new susceptibility gene for PE whose exact role now needs to be further elucidated.Author SummaryPulmonary embolism is a severe and potentially fatal condition characterized by the presence of a blood clot (or thrombus) in the pulmonary artery. Pulmonary embolism is often the consequence of the migration of a thrombus from a deep vein to the lung. Together with deep vein thrombosis, pulmonary embolism forms the so-called venous thromboembolism, the third most common cardiovascular disease, and its prevalence strongly increases with age. While pulmonary embolism is observed in ∼40% of patients with deep vein thrombosis, there is currenly limited biomarkers that can help predicting which patients with deep vein thrombosis are at risk of pulmonary embolism. We here deployed an Artificial Intelligence based methodology integrating both plasma proteomics and genetics data to identify novel biomarkers for PE. We thus identified the PLXNA4 gene as a novel molecular player involved in the pathophysiology of pulmonary embolism. In particular, using two independent cohorts totalling 1,881 patients with venous thromboembolism among which 467 experienced pulmonary embolism, we identified a genetic polymorphism in the PLXNA4 gene that associates with ∼2 fold increased risk of pulmonary embolism in patients aged more than ∼40 years.

Understanding which are the key components of a system that distinguish a normal from a cancerous cell has been approached widely in the recent years using machine learning and statistical approaches to detect gene signatures and predict cell growth. Recently, the idea of using gene regulatory and signaling networks, in the form of logic programs has been introduced in order to detect the mechanisms that control cells state change. Complementary to this, a large literature deals with constraint-based methods for analyzing genome-scale metabolic networks. One of the major outcome of these methods concern the quantitative prediction of growth rates under both given environmental conditions and the presence or absence of a given set of enzymes which catalyze biochemical reactions. It is of high importance to plug logic regulatory models into metabolic networks by using a gene-enzyme logical interaction rule. In this work, our aim is first to review previously proposed logic programs to discover key components in the graph-based causal models that distinguish different variants of cell types. These variants represent either cancerous versus healthy cell types, multiple cancer cell lines, or patients with different treatment response. With these approaches, we can handle experimental data coming from transcriptomic profiles, gene expression micro-arrays or RNAseq, and (multi-perturbation) phosphoproteomics measurements. In a second part, we deal with the problem of combining both, regulatory and signaling, logic models within metabolic networks. Such a combination allows us to obtain quantitative prediction of tumor cell growth. Our results point to logic program models built for three cancer types: Multiple Myeloma, Acute Myeloid Leukemia, and Breast Cancer. Experimental data for these studies was collected through DREAM challenges and in collaboration with biologists that produced them. The networks were built using several publicly available pathway databases, such as Pathways Interaction Database [39], KEGG [18], Reactome [10], and Trrust [13]. We show how these models allow us to identify the key mechanisms distinguishing a cancerous cell. In complement to this, we sketch a methodology, based on currently available frameworks and datasets, that relates both the linear component of the metabolic networks and the logical part of logic programing-based methods.

Introduction: The rising number of overweight and obese children and adolescents has been well documented in many developed countries. Several studies have provided robust evidence that being overweight in childhood increases the risk of atherosclerosis, a risk that continues in to adulthood. Aims and objectives: The basic aim of the study is to analyze the increase in childhood obesity due to high rates of under nutrition in Pakistan. Material and methods: This study was conducted at Allama Iqbal Memorial Teaching Hospital, Sialkot in 2018. The data was collected from local population of Pakistan. Data on demographic, lifestyle, socioeconomic and health-related variables were collected using a questionnaire validated in local languages. Physicians at mobile examination centres performed a standardized physical examination. Mothers were proxy respondents for children aged under 12 years. Trained technicians performed anthropometric examinations. Results: About 27.9% (95% CI) (26–30%) of children were underweight: 29% boys versus 27% girls (p = 0.39), and 14.6% (13–16%) were stunted: 15.5% boys versus 13.8% girls (p = 0.36). The pattern with age was similar to that seen in the NHSP, remaining fairly stable until the age of 11–12 years (prevalence of 16%) and then decreasing with a prevalence of underweight and stunted children among those aged 13–14 years, respectively (prevalence of 9%). Conclusion: It is concluded that the rapidly rising burden of obesity with persistent levels of under nutrition among children is a unique and complex challenge and represents a major threat to the healthcare services. Thus, there is a clear need to focus health policies on combating this rising epidemic of energy imbalance, which is shifting the pendulum towards overweight and obese children in Indo-Asian countries, while paying attention to the needs of the ones who are still undernourished.

Introduction: Overweight is a body mass index between 25.0 to 29.9 kg/m2. Obesity is a body mass index of 30 kg/m2or higher. Over weight and obesity are risk factors for hypertension, dyslipidemia, and diabetes mellitus. The Framingham Study demonstrated that obesity was about twice as prevalent in obese men and in obese women as in men and in women with a normal Metropolitan relative weight. Aims and objectives: The basic aim of the study is to find relationship of obesity with hypertension among local population of Pakistan. Methodology of the study: This study was conducted at Islam Medical College, Sialkot during 2018. In this study we selected 100 patients who was suffering from obesity and hypertension. For this purpose we select the patients from both genders. We design an analysis survey for the collection of data. Data on demographic, socioeconomic and health-related variables were collected with a questionnaire validated in local languages. Dietary data were collected with a food-frequency questionnaire. Results: The mean and median ages of the study participants were 35 and 32years old respectively (Table01). Regarding the self or family history of any chronic disease; 50 (10.3%), and 16 (3.3%) of the total study participants were known hypertensive, and diabetes mellitus (DM) patients respectively, while 82 (16.8%) and 64 (13.1%) have family history of hypertension and DM respectively. Conclusion: It is concluded that there is an alarming situation of obesity among local population of Pakistan which leads to many health issues like hypertension and CVD. Immediate efforts are needed at a national level to control this problem in Pakistan and possibly the neighboring developing countries.