In-silico analysis of TMEM2 as a pancreatic adenocarcinoma and cancer-associated fibroblast biomarker, and functional characterization of NSC777201, for targeted drug development.

Pancreatic adenocarcinoma (PAAD), known as one of the deadliest cancers, is characterized by a complex tumor microenvironment, primarily comprised of cancer-associated fibroblasts (CAFs) in the extracellular matrix. These CAFs significantly alter the matrix by interacting with hyaluronic acid (HA) and the enzyme hyaluronidase, which degrades HA - an essential process for cancer progression and spread. Despite the critical role of this interaction, the specific functions of CAFs and hyaluronidase in PAAD development are not fully understood. Our study investigates this interaction and assesses NSC777201, a new anti-cancer compound targeting hyaluronidase. This research utilized computational methods to analyze gene expression data from the Gene Expression Omnibus (GEO) database, specifically GSE172096, comparing gene expression profiles of cancer-associated and normal fibroblasts. We conducted in-house sequencing of pancreatic cancer cells treated with NSC777201 to identify differentially expressed genes (DEGs) and performed functional enrichment and pathway analysis. The identified DEGs were further validated using the TCGA-PAAD and Human Protein Atlas (HPA) databases for their diagnostic, prognostic, and survival implications, accompanied by Ingenuity Pathway Analysis (IPA) and molecular docking of NSC777201, in-vitro, and preclinical in-vivo validations. The result revealed 416 DEGs associated with CAFs and 570 DEGs related to NSC777201 treatment, with nine overlapping DEGs. A key finding was the transmembrane protein TMEM2, which strongly correlated with FAP, a CAF marker, and was associated with higher-risk groups in PAAD. NSC777201 treatment showed inhibition of TMEM2, validated by rescue assay, indicating the importance of targeting TMEM2. Further analyses, including IPA, demonstrated that NSC777201 regulates CAF cell senescence, enhancing its therapeutic potential. Both in-vitro and in-vivo studies confirmed the inhibitory effect of NSC777201 on TMEM2 expression, reinforcing its role in targeting PAAD. Therefore, TMEM2 has been identified as a theragnostic biomarker in PAAD, influenced by CAF activity and HA accumulation. NSC777201 exhibits significant potential in targeting and potentially reversing critical processes in PAAD progression, demonstrating its efficacy as a promising therapeutic agent.

Uteroplacental insufficiency decreases leptin expression and impairs lung development in growth-restricted newborn rats.

BACKGROUND: The study aimed to analyze the effect of uteroplacental insufficiency (UPI) on leptin expression and lung development of intrauterine growth restriction (IUGR) rats. METHODS: On day 17 of pregnancy, time-dated Sprague-Dawley rats were randomly divided into either an IUGR group or a control group. Uteroplacental insufficiency surgery (IUGR) and sham surgery (control) were conducted. Offspring rats were spontaneously delivered on day 22 of pregnancy. On postnatal days 0 and 7, rats' pups were selected at random from the control and IUGR groups. Blood was withdrawn from the heart to determine leptin levels. The right lung was obtained for leptin and leptin receptor levels, immunohistochemistry, proliferating cell nuclear antigen (PCNA), western blot, and metabolomic analyses. RESULTS: UPI-induced IUGR decreased leptin expression and impaired lung development, causing decreased surface area and volume in offspring. This results in lower body weight, decreased serum leptin levels, lung leptin and leptin receptor levels, alveolar space, PCNA, and increased alveolar wall volume fraction in IUGR offspring rats. The IUGR group found significant relationships between serum leptin, radial alveolar count, von Willebrand Factor, and metabolites. CONCLUSION: Leptin may contribute to UPI-induced lung development during the postnatal period, suggesting supplementation as a potential treatment. IMPACT: The neonatal rats with intrauterine growth restriction (IUGR) caused by uteroplacental insufficiency (UPI) showed decreased leptin expression and impaired lung development. UPI-induced IUGR significantly decreased surface area and volume in lung offspring. This is a novel study that investigates leptin expression and lung development in neonatal rats with IUGR caused by UPI. If our findings translate to IUGR infants, leptin may contribute to UPI-induced lung development during the postnatal period, suggesting supplementation as a potential treatment.

Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders.

INTRODUCTION: Conflicting results persist regarding the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in post-stroke survivors. We used several machine learning algorithms to construct prediction models for the functional outcomes of robotic neurorehabilitation in adult patients. METHODS AND MATERIALS: Data of 139 patients who underwent Lokomat training at Taipei Medical University Hospital were retrospectively collected. After screening for data completeness, records of 91 adult patients with acute or chronic neurological disorders were included in this study. Patient characteristics and quantitative data from Lokomat were incorporated as features to construct prediction models to explore early responses and factors associated with patient recovery. RESULTS: Experimental results using the random forest algorithm achieved the best area under the receiver operating characteristic curve of 0.9813 with data extracted from all sessions. Body weight (BW) support played a key role in influencing the progress of functional ambulation categories. The analysis identified negative correlations of BW support, guidance force, and days required to complete 12 Lokomat sessions with the occurrence of progress, while a positive correlation was observed with regard to speed. CONCLUSIONS: We developed a predictive model for ambulatory outcomes based on patient characteristics and quantitative data on impairment reduction with early-stage robotic neurorehabilitation. RAGT is a customized approach for patients with different conditions to regain walking ability. To obtain a more-precise and clearer predictive model, collecting more RAGT training parameters and analyzing them for each individual disorder is a possible approach to help clinicians achieve a better understanding of the most efficient RAGT parameters for different patients. TRIAL REGISTRATION: Retrospectively registered.

Deep learning for abdominal ultrasound: A computer-aided diagnostic system for the severity of fatty liver.

BACKGROUND: The prevalence of nonalcoholic fatty liver disease is increasing over time worldwide, with similar trends to those of diabetes and obesity. A liver biopsy, the gold standard of diagnosis, is not favored due to its invasiveness. Meanwhile, noninvasive evaluation methods of fatty liver are still either very expensive or demonstrate poor diagnostic performances, thus, limiting their applications. We developed neural network-based models to assess fatty liver and classify the severity using B-mode ultrasound (US) images. METHODS: We followed standards for reporting of diagnostic accuracy guidelines to report this study. In this retrospective study, we utilized B-mode US images from a consecutive series of patients to develop four-class, two-class, and three-class diagnostic prediction models. The images were eligible if confirmed by at least two gastroenterologists. We compared pretrained convolutional neural network models, consisting of visual geometry group (VGG)19, ResNet-50 v2, MobileNet v2, Xception, and Inception v2. For validation, we utilized 20% of the dataset resulting in >100 images for each severity category. RESULTS: There were 21,855 images from 2,070 patients classified as normal (N = 11,307), mild (N = 4,467), moderate (N = 3,155), or severe steatosis (N = 2,926). We used ResNet-50 v2 for the final model as the best ones. The areas under the receiver operating characteristic curves were 0.974 (mild steatosis vs others), 0.971 (moderate steatosis vs others), 0.981 (severe steatosis vs others), 0.985 (any severity vs normal), and 0.996 (moderate-to-severe steatosis/clinically abnormal vs normal-to-mild steatosis/clinically normal). CONCLUSION: Our deep learning models achieved comparable predictive performances to the most accurate, yet expensive, noninvasive diagnostic methods for fatty liver. Because of the discriminative ability, including for mild steatosis, significant impacts on clinical applications for fatty liver are expected. However, we need to overcome machine-dependent variation, motion artifacts, lacking of second confirmation from any other tools, and hospital-dependent regional bias.

Clinical Assay for the Early Detection of Colorectal Cancer Using Mass Spectrometric Wheat Germ Agglutinin Multiple Reaction Monitoring.

Colorectal cancer (CRC) is currently the third leading cause of cancer-related mortality in the world. U.S. Food and Drug Administration-approved circulating tumor markers, including carcinoembryonic antigen, carbohydrate antigen (CA) 19-9 and CA125 were used as prognostic biomarkers of CRC that attributed to low sensitivity in diagnosis of CRC. Therefore, our purpose is to develop a novel strategy for novel clinical biomarkers for early CRC diagnosis. We used mass spectrometry (MS) methods such as nanoLC-MS/MS, targeted LC-MS/MS, and stable isotope-labeled multiple reaction monitoring (MRM) MS coupled to test machine learning algorithms and logistic regression to analyze plasma samples from patients with early-stage CRC, late-stage CRC, and healthy controls (HCs). On the basis of our methods, 356 peptides were identified, 6 differential expressed peptides were verified, and finally three peptides corresponding wheat germ agglutinin (WGA)-captured proteins were semi-quantitated in 286 plasma samples (80 HCs and 206 CRCs). The novel peptide biomarkers combination of PF454-62, ITIH4429-438, and APOE198-207 achieved sensitivity 84.5%, specificity 97.5% and an AUC of 0.96 in CRC diagnosis. In conclusion, our study demonstrated that WGA-captured plasma PF454-62, ITIH4429-438, and APOE198-207 levels in combination may serve as highly effective early diagnostic biomarkers for patients with CRC.

Predicting Hyperoxia-Induced Lung Injury from Associated Intestinal and Lung Dysbiosis in Neonatal Mice.

BACKGROUND: Preclinical studies have demonstrated that hyperoxia disrupts the intestinal barrier, changes the intestinal bacterial composition, and injures the lungs of newborn animals. OBJECTIVES: The aim of the study was to investigate the effects of hyperoxia on the lung and intestinal microbiota and the communication between intestinal and lung microbiota and to develop a predictive model for the identification of hyperoxia-induced lung injury from intestinal and lung microbiota based on machine learning algorithms in neonatal mice. METHODS: Neonatal C57BL/6N mice were reared in either room air or hyperoxia (85% O2) from postnatal days 1-7. On postnatal day 7, lung and intestinal microbiota were sampled from the left lung and lower gastrointestinal tract for 16S ribosomal RNA gene sequencing. Tissue from the right lung and terminal ileum were harvested for Western blot and histology analysis. RESULTS: Hyperoxia induced intestinal injury, decreased intestinal tight junction expression, and impaired lung alveolarization and angiogenesis in neonatal mice. Hyperoxia also altered intestinal and lung microbiota and promoted bacterial translocation from the intestine to the lung as evidenced by the presence of intestinal bacteria in the lungs of hyperoxia-exposed neonatal mice. The relative abundance of these bacterial taxa was significantly positively correlated with the increased lung cytokines. CONCLUSIONS: Neonatal hyperoxia induced intestinal and lung dysbiosis and promoted bacterial translocation from the intestine to the lung. Further studies are needed to clarify the pathophysiology of bacterial translocation to the lung.

Hiltonol Cocktail Kills Lung Cancer Cells by Activating Cancer-Suppressors, PKR/OAS, and Restraining the Tumor Microenvironment.

NSCLC (non-small cell lung cancer) is a leading cause of cancer-related deaths worldwide. Clinical trials showed that Hiltonol, a stable dsRNA representing an advanced form of polyI:C (polyinosinic-polycytidilic acid), is an adjuvant cancer-immunomodulator. However, its mechanisms of action and effect on lung cancer have not been explored pre-clinically. Here, we examined, for the first time, how a novel Hiltonol cocktail kills NSCLC cells. By retrospective analysis of NSCLC patient tissues obtained from the tumor biobank; pre-clinical studies with Hiltonol alone or Hiltonol+++ cocktail [Hiltonol+anti-IL6+AG490 (JAK2 inhibitor)+Stattic (STAT3 inhibitor)]; cytokine analysis; gene knockdown and gain/loss-of-function studies, we uncovered the mechanisms of action of Hiltonol+++. We demonstrated that Hiltonol+++ kills the cancer cells and suppresses the metastatic potential of NSCLC through: (i) upregulation of pro-apoptotic Caspase-9 and Caspase-3, (ii) induction of cytosolic cytochrome c, (iii) modulation of pro-inflammatory cytokines (GRO, MCP-1, IL-8, and IL-6) and anticancer IL-24 in NSCLC subtypes, and (iv) upregulation of tumor suppressors, PKR (protein kinase R) and OAS (2'5' oligoadenylate synthetase). In silico analysis showed that Lys296 of PKR and Lys66 of OAS interact with Hiltonol. These Lys residues are purportedly involved in the catalytic/signaling activity of the tumor suppressors. Furthermore, knockdown of PKR/OAS abrogated the anticancer action of Hiltonol, provoking survival of cancer cells. Ex vivo analysis of NSCLC patient tissues corroborated that loss of PKR and OAS is associated with cancer advancement. Altogether, our findings unraveled the significance of studying tumor biobank tissues, which suggests PKR and OAS as precision oncological suppressor candidates to be targeted by this novel Hiltonol+++ cocktail which represents a prospective drug for development into a potent and tailored therapy for NSCLC subtypes.

Visual light perceptions caused by medical linear accelerator: Findings of machine-learning algorithms in a prospective questionnaire-based case-control study.

This study aimed to investigate the possible incidence of visual light perceptions (VLPs) during radiation therapy (RT). We analyzed whether VLPs could be affected by differences in the radiation energy, prescription doses, age, sex, or RT locations, and whether all VLPs were caused by radiation. From November 2016 to August 2018, a total of 101 patients who underwent head-and-neck or brain RT were screened. After receiving RT, questionnaires were completed, and the subjects were interviewed. Random forests (RF), a tree-based machine learning algorithm, and logistic regression (LR) analyses were compared by the area under the curve (AUC), and the algorithm that achieved the highest AUC was selected. The dataset sample was based on treatment with non-human units, and a total of 293 treatment fields from 78 patients were analyzed. VLPs were detected only in 122 of the 293 exposure portals (40.16%). The dataset was randomly divided into 80% and 20% as the training set and test set, respectively. In the test set, RF achieved an AUC of 0.888, whereas LR achieved an AUC of 0.773. In this study, the retina fraction dose was the most important continuous variable and had a positive effect on VLP. Age was the most important categorical variable. In conclusion, the visual light perception phenomenon by the human body during RT is induced by radiation rather than being a self-suggested hallucination or induced by phosphenes.

Isotypes of autoantibodies against novel differential 4-hydroxy-2-nonenal-modified peptide adducts in serum is associated with rheumatoid arthritis in Taiwanese women.

BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune disorder with systemic inflammation and may be induced by oxidative stress that affects an inflamed joint. Our objectives were to examine isotypes of autoantibodies against 4-hydroxy-2-nonenal (HNE) modifications in RA and associate them with increased levels of autoantibodies in RA patients. METHODS: Serum samples from 155 female patients [60 with RA, 35 with osteoarthritis (OA), and 60 healthy controls (HCs)] were obtained. Four novel differential HNE-modified peptide adducts, complement factor H (CFAH)1211-1230, haptoglobin (HPT)78-108, immunoglobulin (Ig) kappa chain C region (IGKC)2-19, and prothrombin (THRB)328-345, were re-analyzed using tandem mass spectrometric (MS/MS) spectra (ProteomeXchange: PXD004546) from RA patients vs. HCs. Further, we determined serum protein levels of CFAH, HPT, IGKC and THRB, HNE-protein adducts, and autoantibodies against unmodified and HNE-modified peptides. Significant correlations and odds ratios (ORs) were calculated. RESULTS: Levels of HPT in RA patients were greatly higher than the levels in HCs. Levels of HNE-protein adducts and autoantibodies in RA patients were significantly greater than those of HCs. IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgM anti-IGKC2-19 HNE may be considered as diagnostic biomarkers for RA. Importantly, elevated levels of IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgG anti-THRB328-345 were positively correlated with the disease activity score in 28 joints for C-reactive protein (DAS28-CRP). Further, the ORs of RA development through IgM anti-HPT78-108 HNE (OR 5.235, p < 0.001), IgM anti-IGKC2-19 (OR 12.655, p < 0.001), and IgG anti-THRB328-345 (OR 5.761, p < 0.001) showed an increased risk. Lastly, we incorporated three machine learning models to differentiate RA from HC and OA, and performed feature selection to determine discriminative features. Experimental results showed that our proposed method achieved an area under the receiver operating characteristic curve of 0.92, which demonstrated that our selected autoantibodies combined with machine learning can efficiently detect RA. CONCLUSIONS: This study discovered that some IgG- and IgM-NAAs and anti-HNE M-NAAs may be correlated with inflammation and disease activity in RA. Moreover, our findings suggested that IgM anti-HPT78-108 HNE, IgM anti-IGKC2-19, and IgG anti-THRB328-345 may play heavy roles in RA development.

Human FBXL8 Is a Novel E3 Ligase Which Promotes BRCA Metastasis by Stimulating Pro-Tumorigenic Cytokines and Inhibiting Tumor Suppressors.

The initiation and progression of breast cancer (BRCA) is associated with inflammation and immune-overactivation, which is critically modulated by the E3 ubiquitin ligase. However, the underlying mechanisms and key factors involved in BRCA formation and disease advancement remains under-explored. By retrospective studies of BRCA patient tissues; and gene knockdown and gain/loss-of-function studies, we uncovered a novel E3 ligase, FBXL8, in BRCA. A signature expression profile of F-box factors that specifically target and degrade proteins involved in cell death/survival, was identified. FBXL8 emerged as a prominent member of the F-box factors. Ex vivo analysis of 1349 matched BRCA tissues indicated that FBXL8 promotes cell survival and tumorigenesis, and its level escalates with BRCA progression. Knockdown of FBXL8 caused: (i) intrinsic apoptosis, (ii) inhibition of cell migration and invasion, (iii) accumulation of two tumor-suppressors, CCND2 and IRF5, and (iv) downregulation of cancer-promoting cytokines/chemokines; all of which curtailed the tumor microenvironment and displayed potential to suppress cancer progression. Co-IP study suggests that two tumor-suppressors, CCND2 and IRF5 are part of the immune-complex of FBXL8. The protein levels of CCND2 and IRF5 inversely correlated with FBXL8 expression, implying that FBXL8 E3 ligase was associated with the degradation of CCND2 and IRF5. Altogether, we propose the exploitation of the ubiquitin signaling axis of FBXL8-CCND2-IRF5 for anti-cancer strategies and potential therapeutics.

Ovatodiolide, isolated from Anisomeles indica, suppresses bladder carcinogenesis through suppression of mTOR/ß-catenin/CDK6 and exosomal miR-21 derived from M2 tumor-associated macrophages.

Bladder cancer (BCa) is the fourth leading cause of cancer deaths worldwide due to its aggressiveness and resistance against therapies. Intricate interactions between cancer cells and the tumor microenvironment (TME) are essential for both disease progression and regression. Thus, interrupting molecular communications within the TME could potentially provide improved therapeutic efficacies. M2-polarized tumor-associated macrophages (M2 TAMs) were shown to contribute to BCa progression and drug resistance. We attempted to provide evidence for ovatodiolide (OV) as a potential therapeutic agent that targets both TME and BCa cells. First, tumor-suppressing functions of OV were determined by cell viability, colony, and tumor-sphere formation assays using a coculture system composed of M2 TAMs/BCa cells. Subsequently, we demonstrated that extracellular vesicles (EVs) isolated from M2 TAMs containing oncomiR-21 and mRNAs, including Akt, STAT3, mTOR, and ß-catenin, promoted cisplatin (CDDP) resistance, migration, and tumor-sphere generation in BCa cells, through increasing CDK6, mTOR, STAT3, and ß-catenin expression. OV treatment also prevented M2 polarization and reduced EV cargos from M2 TAMs. Finally, in vivo data demonstrated that OV treatment overcame CDDP resistance. OV only and the OV + CDDP combination both resulted in significant reductions in mTOR, ß-catenin, CDK6, and miR-21 expression in tumor samples and EVs isolated from serum. Collectively, we demonstrated that M2 TAMs induced malignant properties in BCa cells, in part via oncogenic EVs. OV treatment prevented M2 TAM polarization, reduced EV cargos derived from M2 TAMs, and suppressed ß-catenin/mTOR/CDK6 signaling. These findings provide preclinical evidence for OV as a single or adjuvant agent for treating drug-resistant BCa.

Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia.

BACKGROUND: We developed and validated an artificial intelligence (AI)-assisted prediction of preeclampsia applied to a nationwide health insurance dataset in Indonesia. METHODS: The BPJS Kesehatan dataset have been preprocessed using a nested case-control design into preeclampsia/eclampsia (n = 3318) and normotensive pregnant women (n = 19,883) from all women with one pregnancy. The dataset provided 95 features consisting of demographic variables and medical histories started from 24 months to event and ended by delivery as the event. Six algorithms were compared by area under the receiver operating characteristics curve (AUROC) with a subgroup analysis by time to the event. We compared our model to similar prediction models from systematically reviewed studies. In addition, we conducted a text mining analysis based on natural language processing techniques to interpret our modeling results. FINDINGS: The best model consisted of 17 predictors extracted by a random forest algorithm. Nine∼12 months to the event was the period that had the best AUROC in external validation by either geographical (0.88, 95% confidence interval (CI) 0.88-0.89) or temporal split (0.86, 95% CI 0.85-0.86). We compared this model to prediction models in seven studies from 869 records in PUBMED, EMBASE, and SCOPUS. This model outperformed the previous models in terms of the precision, sensitivity, and specificity in all validation sets. INTERPRETATION: Our low-cost model improved preliminary prediction to decide pregnant women that will be predicted by the models with high specificity and advanced predictors. FUNDING: This work was supported by grant no. MOST108-2221-E-038-018 from the Ministry of Science and Technology of Taiwan.

RNA editing-based classification of diffuse gliomas: predicting isocitrate dehydrogenase mutation and chromosome 1p/19q codeletion.

BACKGROUND: Accurate classification of diffuse gliomas, the most common tumors of the central nervous system in adults, is important for appropriate treatment. However, detection of isocitrate dehydrogenase (IDH) mutation and chromosome1p/19q codeletion, biomarkers to classify gliomas, is time- and cost-intensive and diagnostic discordance remains an issue. Adenosine to inosine (A-to-I) RNA editing has emerged as a novel cancer prognostic marker, but its value for glioma classification remains largely unexplored. We aim to (1) unravel the relationship between RNA editing and IDH mutation and 1p/19q codeletion and (2) predict IDH mutation and 1p/19q codeletion status using machine learning algorithms. RESULTS: By characterizing genome-wide A-to-I RNA editing signatures of 638 gliomas, we found that tumors without IDH mutation exhibited higher total editing level compared with those carrying it (Kolmogorov-Smirnov test, p < 0.0001). When tumor grade was considered, however, only grade IV tumors without IDH mutation exhibited higher total editing level. According to 10-fold cross-validation, support vector machines (SVM) outperformed random forest and AdaBoost (DeLong test, p < 0.05). The area under the receiver operating characteristic curve (AUC) of SVM in predicting IDH mutation and 1p/19q codeletion were 0.989 and 0.990, respectively. After performing feature selection, AUCs of SVM and AdaBoost in predicting IDH mutation were higher than that of random forest (0.985 and 0.983 vs. 0.977; DeLong test, p < 0.05), but AUCs of the three algorithms in predicting 1p/19q codeletion were similar (0.976-0.982). Furthermore, 67% of the six continuously misclassified samples by our 1p/19q codeletion prediction models were misclassifications in the original labelling after inspection of 1p/19q status and/or pathology report, highlighting the accuracy and clinical utility of our models. CONCLUSIONS: The study represents the first genome-wide analysis of glioma editome and identifies RNA editing as a novel prognostic biomarker for glioma. Our prediction models provide standardized, accurate, reproducible and objective classification of gliomas. Our models are not only useful in clinical decision-making, but also able to identify editing events that have the potential to serve as biomarkers and therapeutic targets in glioma management and treatment.

Data-Driven Approach to Improving the Risk Assessment Process of Medical Failures.

In recent decades, many researchers have focused on the issue of medical failures in the healthcare industry. A variety of techniques have been employed to assess the risk of medical failure and to generate strategies to reduce the frequency of medical failures. Considering the limitations of the traditional method-failure mode and effects analysis (FMEA)-for risk assessment and quality improvement, this paper presents two models developed using data envelopment analysis (DEA). One is called the slacks-based measure DEA (SBM-DEA) model, and the other is a novel data-driven approach (NDA) that combines FMEA and DEA. The relative advantages of the three models are compared. In this paper, an infant security case consisting of 16 failure modes at Western Wake Medical Center in Raleigh, North Carolina, U.S., was employed. The results indicate that both SBM-DEA and NDA may improve the discrimination and accuracy of detection compared to the traditional method of FMEA. However, NDA was found to have a relative advantage over SBM-DEA due to its risk assessment capability and precise detection of medical failures.