Discovering medication patterns for high-complexity drug-using diseases through electronic medical records

An Electronic Medical Record (EMR) is a professional document that contains all data generated during the treatment process. The EMR can utilize various data formats, such as numerical data, text, and images. Mining the information and knowledge hidden in the huge amount of EMR data is an essential requirement for clinical decision support, such as clinical pathway formulation and evidence-based medical research. In this paper, we propose a machine-learning-based framework to mine the hidden medication patterns in EMR text. The framework systematically integrates the Jaccard similarity evaluation, spectral clustering, the modified Latent Dirichlet Allocation and cross-matching among multiple features to find the residuals that describe additional knowledge and clusters hidden in multiple perspectives of highly complex medication patterns. These methods work together, step by step to reveal the underlying medication pattern. We evaluated the method by using real data from EMR text (patients with cirrhotic ascites) from a large hospital in China. The proposed framework outperforms other approaches for medication pattern discovery, especially for this disease with subtle medication treatment variances. The results also revealed little overlap among the discovered patterns; thus, the distinct features of each pattern are well studied through the proposed framework

MSP-RON Signaling Is Activated in the Transition From Pancreatic Intraepithelial Neoplasia (PanIN) to Pancreatic Ductal Adenocarcinoma (PDAC)

Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest epithelial malignancies and remains difficult to treat. Pancreatic intraepithelial neoplasias (PanINs) represent the majority of the pre-cancer lesions in the pancreas. The PDAC microenvironment consists of activated pancreatic stellate cells (PSCs) and immune cells, which are thought to contribute to neoplastic transformation. However, the signaling events involved in driving the transition from the neoplastic precursor to the more advanced and aggressive forms in the pancreas are not well understood. Recepteur d’Origine Nantais (RON) is a c-MET family receptor tyrosine kinase that is implicated in playing a role in cell proliferation, migration and other aspects of tumorigenesis. Macrophage stimulating protein (MSP) is the ligand for RON and becomes activated upon proteolytic cleavage by matriptase (also known as ST14), a type II transmembrane serine protease. In the current study, by immunohistochemistry (IHC) analysis of human pancreatic tissues, we found that the expression levels MSP and matriptase are drastically increased during the transition from the preneoplastic PanIN stages to the more advanced and aggressive PDAC. Moreover, RON is highly expressed in both PDAC and in cancer-associated stellate cells. In contrast, MSP, RON, and matriptase are expressed at low levels, if any, in normal pancreas. Our study underscores an emerging role of MSP-RON autocrine and paracrine signaling events in driving malignant progression in the pancreas

MLH1-methylated endometrial cancer under 60 years of age as the “sentinel” cancer in female carriers of high-risk constitutional MLH1 epimutation

Objective. Universal screening of endometrial carcinoma (EC) for mismatch repair deficiency (MMRd) and Lynch syndrome uses presence of MLH1 methylation to omit common sporadic cases from follow-up germline testing. However, this overlooks rare cases with high-risk constitutional MLH1 methylation (epimutation), a poorly-recognized mechanism that predisposes to Lynch-type cancers with MLH1 methylation. We aimed to de-termine the role and frequency of constitutional MLH1 methylation among EC cases with MMRd, MLH1- methylated tumors.Methods. We screened blood for constitutional MLH1 methylation using pyrosequencing and real-time methylation-specific PCR in patients with MMRd, MLH1-methylated EC ascertained from (i) cancer clinics (n = 4, <60 years), and (ii) two population-based cohorts; Columbus-area (n = 68, all ages) and Ohio Colo-rectal Cancer Prevention Initiative (OCCPI) (n = 24, <60 years).Results. Constitutional MLH1 methylation was identified in three out of four patients diagnosed between 36 and 59 years from cancer clinics. Two had mono-/hemiallelic epimutation (similar to 50% alleles methylated). One with multiple primaries had low-level mosaicism in normal tissues and somatic second-hits affecting the unmethylated allele in all tumors, demonstrating causation. In the population-based cohorts, all 68 cases from the Columbus-area cohort were negative and low-level mosaic constitutional MLH1 methylation was identified in one patient aged 36 years out of 24 from the OCCPI cohort, representing one of six (similar to 17%) patients <50 years and one of 45 patients (similar to 2%) <60 years in the combined cohorts. EC was the first/dual-first cancer in three pa-tients with underlying constitutional MLH1 methylation.Conclusions. A correct diagnosis at first presentation of cancer is important as it will significantly alter clinical management. Screening for constitutional MLH1 methylation is warranted in patients with early-onset EC or syn-chronous/metachronous tumors (any age) displaying MLH1 methylation.(c) 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/)

Pythagorean Fuzzy Interaction Muirhead Means with Their Application to Multi-Attribute Group Decision-Making

Due to the increased complexity of real decision-making problems, representing attribute values correctly and appropriately is always a challenge. The recently proposed Pythagorean fuzzy set (PFS) is a powerful and useful tool for handling fuzziness and vagueness. The feature of PFS that the square sum of membership and non-membership degrees should be less than or equal to one provides more freedom for decision makers to express their assessments and further results in less information loss. The aim of this paper is to develop some Pythagorean fuzzy aggregation operators to aggregate Pythagorean fuzzy numbers (PFNs). Additionally, we propose a novel approach to multi-attribute group decision-making (MAGDM) based on the proposed operators. Considering the Muirhead mean (MM) can capture the interrelationship among all arguments, and the interaction operational rules for PFNs can make calculation results more reasonable, to take full advantage of both, we extend MM to PFSs and propose a family of Pythagorean fuzzy interaction Muirhead mean operators. Some desirable properties and special cases of the proposed operators are also investigated. Further, we present a novel approach to MAGDM with Pythagorean fuzzy information. Finally, we provide a numerical instance to illustrate the validity of the proposed model. In addition, we perform a comparative analysis to show the superiorities of the proposed method

Some q-Rung Dual Hesitant Fuzzy Heronian Mean Operators with Their Application to Multiple Attribute Group Decision-Making

The q-rung orthopair fuzzy sets (q-ROFSs), originated by Yager, are good tools to describe fuzziness in human cognitive processes. The basic elements of q-ROFSs are q-rung orthopair fuzzy numbers (q-ROFNs), which are constructed by membership and nonmembership degrees. As realistic decision-making is very complicated, decision makers (DMs) may be hesitant among several values when determining membership and nonmembership degrees. By incorporating dual hesitant fuzzy sets (DHFSs) into q-ROFSs, we propose a new technique to deal with uncertainty, called q-rung dual hesitant fuzzy sets (q-RDHFSs). Subsequently, we propose a family of q-rung dual hesitant fuzzy Heronian mean operators for q-RDHFSs. Further, the newly developed aggregation operators are utilized in multiple attribute group decision-making (MAGDM). We used the proposed method to solve a most suitable supplier selection problem to demonstrate its effectiveness and usefulness. The merits and advantages of the proposed method are highlighted via comparison with existing MAGDM methods. The main contribution of this paper is that a new method for MAGDM is proposed

A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome

Abstract Gene expression signatures are commonly used as predictive biomarkers, but do not capture structural features within the tissue architecture. Here we apply a 2-step machine learning framework for quantitative imaging of tumor vasculature to derive a spatially informed, prognostic gene signature. The trained algorithms classify endothelial cells and generate a vascular area mask (VAM) in H&E micrographs of clear cell renal cell carcinoma (ccRCC) cases from The Cancer Genome Atlas (TCGA). Quantification of VAMs led to the discovery of 9 vascular features (9VF) that predicted disease-free-survival in a discovery cohort (n = 64, HR = 2.3). Correlation analysis and information gain identified a 14 gene expression signature related to the 9VF’s. Two generalized linear models with elastic net regularization (14VF and 14GT), based on the 14 genes, separated independent cohorts of up to 301 cases into good and poor disease-free survival groups (14VF HR = 2.4, 14GT HR = 3.33). For the first time, we successfully applied digital image analysis and targeted machine learning to develop prognostic, morphology-based, gene expression signatures from the vascular architecture. This novel morphogenomic approach has the potential to improve previous methods for biomarker development

Receptor-interacting protein kinase 2 (RIPK2) stabilizes c-Myc and is a therapeutic target in prostate cancer metastasis.

Despite progress in prostate cancer (PC) therapeutics, distant metastasis remains a major cause of morbidity and mortality from PC. Thus, there is growing recognition that preventing or delaying PC metastasis holds great potential for substantially improving patient outcomes. Here we show receptor-interacting protein kinase 2 (RIPK2) is a clinically actionable target for inhibiting PC metastasis. RIPK2 is amplified/gained in ~65% of lethal metastatic castration-resistant PC. Its overexpression is associated with disease progression and poor prognosis, and its genetic knockout substantially reduces PC metastasis. Multi-level proteomics analyses reveal that RIPK2 strongly regulates the stability and activity of c-Myc (a driver of metastasis), largely via binding to and activating mitogen-activated protein kinase kinase 7 (MKK7), which we identify as a direct c-Myc-S62 kinase. RIPK2 inhibition by preclinical and clinical drugs inactivates the noncanonical RIPK2/MKK7/c-Myc pathway and effectively impairs PC metastatic outgrowth. These results support targeting RIPK2 signaling to extend metastasis-free and overall survival