Identification of Genomic Signatures for Colorectal Cancer Survival Using Exploratory Data Mining.

Clinicopathological presentations are critical for establishing a postoperative treatment regimen in Colorectal Cancer (CRC), although the prognostic value is low in Stage 2 CRC. We implemented a novel exploratory algorithm based on artificial intelligence (explainable artificial intelligence, XAI) that integrates mutational and clinical features to identify genomic signatures by repurposing the FoundationOne Companion Diagnostic (F1CDx) assay. The training data set (n = 378) consisted of subjects with recurrent and non-recurrent Stage 2 or 3 CRC retrieved from TCGA. Genomic signatures were built for identifying subgroups in Stage 2 and 3 CRC patients according to recurrence using genomic parameters and further associations with the clinical presentation. The summarization of the top-performing genomic signatures resulted in a 32-gene genomic signature that could predict tumor recurrence in CRC Stage 2 patients with high precision. The genomic signature was further validated using an independent dataset (n = 149), resulting in high-precision prognosis (AUC: 0.952; PPV = 0.974; NPV = 0.923). We anticipate that our genomic signatures and NCCN guidelines will improve recurrence predictions in CRC molecular stratification.

Understanding common key indicators of successful and unsuccessful cancer drug trials using a contrast mining framework on ClinicalTrials.gov.

Clinical trials are essential to the process of new drug development. As clinical trials involve significant investments of time and money, it is crucial for trial designers to carefully investigate trial settings prior to designing a trial. Utilizing trial documents from ClinicalTrials.gov, we aim to understand the common characteristics of successful and unsuccessful cancer drug trials to provide insights about what to learn and what to avoid. In this research, we first computationally classified cancer drug trials into successful and unsuccessful cases and then utilized natural language processing to extract eligibility criteria information from the trial documents. To provide explainable and potentially modifiable recommendations for new trial design, contrast mining was applied to discoverhighly contrasted patterns with a significant difference in prevalence between successful (completion with advancement to the next phase) and unsuccessful (suspended, withdrawn, or terminated) groups. Our method identified contrast patterns consisting of combinations of drug categories, eligibility criteria, study organization, and study design for nine major cancers. In addition to a literature review for the qualitative validation of mined contrast patterns, we found that contrast-pattern-based classifiers using the top 200 contrast patterns as feature representations can achieve approximately 80% F1 score for eight out of ten cancer types in our experiments. In summary, aligning with the modernization efforts of ClinicalTrials.gov, our study demonstrates that understanding the contrast characteristics of successful and unsuccessful cancer trials may provide insights into the decision-making process for trial investigators and therefore facilitate improved cancer drug trial design.

Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential.

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. This shows the importance of developing data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Contrast pattern mining and network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The proposed method represents a human-in-the-loop framework, where medical experts use the data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. Colorectal cancer (CRC) was used as a case study. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups identified by medical experts showed that most of these drugs are cancer-related, and most of them have the potential to be a CRC regimen based on studies in the literature.

Investigating Health Context Using a Spatial Data Analytical Tool: Development of a Geospatial Big Data Ecosystem.

BACKGROUND: Enabling the use of spatial context is vital to understanding today's digital health problems. Any given location is associated with many different contexts. The strategic transformation of population health, epidemiology, and eHealth studies requires vast amounts of integrated digital data. Needed is a novel analytical framework designed to leverage location to create new contextual knowledge. The Geospatial Analytical Research Knowledgebase (GeoARK), a web-based research resource has robust, locationally integrated, social, environmental, and infrastructural information to address today's complex questions, investigate context, and spatially enable health investigations. GeoARK is different from other Geographic Information System (GIS) resources in that it has taken the layered world of the GIS and flattened it into a big data table that ties all the data and information together using location and developing its context. OBJECTIVE: It is paramount to build a robust spatial data analytics framework that integrates social, environmental, and infrastructural knowledge to empower health researchers' use of geospatial context to timely answer population health issues. The goal is twofold in that it embodies an innovative technological approach and serves to ease the educational burden for health researchers to think spatially about their problems. METHODS: A unique analytical tool using location as the key was developed. It allows integration across source, geography, and time to create a geospatial big table with over 162 million individual locations (X-Y points that serve as rows) and 5549 attributes (represented as columns). The concept of context (adjacency, proximity, distance, etc) is quantified through geoanalytics and captured as new distance, density, or neighbor attributes within the system. Development of geospatial analytics permits contextual extraction and investigator-initiated eHealth and mobile health (mHealth) analysis across multiple attributes. RESULTS: We built a unique geospatial big data ecosystem called GeoARK. Analytics on this big table occur across resolution groups, sources, and geographies for extraction and analysis of information to gain new insights. Case studies, including telehealth assessment in North Carolina, national income inequality and health outcome disparity, and a Missouri COVID-19 risk assessment, demonstrate the capability to support robust and efficient geospatial understanding of a wide spectrum of population health questions. CONCLUSIONS: This research identified, compiled, transformed, standardized, and integrated multifaceted data required to better understand the context of health events within a large location-enabled database. The GeoARK system empowers health professionals to engage more complex research where the synergisms of health and geospatial information will be robustly studied beyond what could be accomplished today. No longer is the need to know how to perform geospatial processing an impediment to the health researcher, but rather the development of how to think spatially becomes the greater challenge.

Identification of Immuno-Targeted Combination Therapies Using Explanatory Subgroup Discovery for Cancer Patients with EGFR Wild-Type Gene.

(1) Background: Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients' heterogeneity, immune checkpoint inhibitors (ICIs) represent some the most promising therapeutic approaches. However, approximately 50% of cancer patients that are eligible for treatment with ICIs do not respond well, especially patients with no targetable mutations. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although matching a patient subgroup to a treatment option that can improve patients' health outcomes remains a challenging task. (2) Methods: We extended our Subgroup Discovery algorithm to identify patient subpopulations that could potentially benefit from immuno-targeted combination therapies in four cancer types: head and neck squamous carcinoma (HNSC), lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), and skin cutaneous melanoma (SKCM). We employed the proportional odds model to identify significant drug targets and the corresponding compounds that increased the likelihood of stable disease versus progressive disease in cancer patients with the EGFR wild-type (WT) gene. (3) Results: Our pipeline identified six significant drug targets and thirteen specific compounds for cancer patients with the EGFR WT gene. Three out of six drug targets-FCGR2B, IGF1R, and KIT-substantially increased the odds of having stable disease versus progressive disease. Progression-free survival (PFS) of more than 6 months was a common feature among the investigated subgroups. (4) Conclusions: Our approach could help to better select responders for immuno-targeted combination therapies and improve health outcomes for cancer patients with no targetable mutations.

Exploratory Data Mining for Subgroup Cohort Discoveries and Prioritization.

Finding small homogeneous subgroup cohorts in large heterogeneous populations is a critical process for hypothesis development in biomedical research. Concurrent computational approaches are still lacking in robust answers to the question "what hypotheses are likely to be novel and to produce clinically relevant results with well thought-out study designs?" We have developed a novel subgroup discovery method which employs a deep exploratory mining process to slice and dice thousands of potential subpopulations and prioritize potential cohorts based on their explainable contrast patterns and which may provide interventionable insights. We conducted computational experiments on both synthesized data and a clinical autism data set to assess performance quantitatively for coverage of pre-defined cohorts and qualitatively for novel knowledge discovery, respectively. We also conducted a scaling analysis using a distributed computing environment to suggest computational resource needs for when the subpopulation number increases. This work will provide a robust data-driven framework to automatically tailor potential interventions for precision health.

The environmental friendliness of fuzzy-ball drilling fluids during their entire life-cycles for fragile ecosystems in coalbed methane well plants.

To protect the vulnerable ecosystems in coalbed methane(CBM) well plants from the pollution of drilling fluids, the environmental friendliness of fuzzy-ball drilling fluids was evaluated. Further, the combination of solidification and in-situ landfill were proposed to optimize their waste disposal. Firstly, the samples for tests were collected from the well plants. Then, their environment-related properties were evaluated with the indicator vector from wastewater perspective to monitor their environmental friendliness. Lastly, comparative analysis of two wells in Linfen, China was conducted concerning engineering and economic parameters. Results showed that these indicators of the new fuzzy-ball drilling fluids and their raw materials were within the ceiling limits of related national standard, whereas those of waste fuzzy-ball drilling fluids exceeded the limits. The mechanical strengths of the waste drilling fluids solidified directly could meet the practical transport demand, and their leachates met the standardized requirements due to the strong biodegradability of fuzzy-ball fluids. Field studies demonstrated the economic effectiveness of fuzzy-ball drilling fluids, reconciling the conflict between environment-protecting and cost-reducing. In conclusion, fuzzy-ball drilling fluids are feasible for fragile ecosystem protection in CBM well plants. The environmental friendliness relies on their material, structural and functional basis.

Quality assessment and factor analysis of systematic reviews and meta-analyses of endoscopic ultrasound diagnosis.

BACKGROUND: Comprehensive monitoring of the quality of systematic reviews (SRs) and meta-analyses (MAs) of endoscopic ultrasound (EUS) requires complete and accurate reporting and methodology. OBJECTIVE: To assess the reporting and methodological quality of SRs/MAs on EUS diagnosis and to explore the potential factors influencing articles' quality. METHODS: The quality of the reporting and methodology was evaluated in relation to the adherence of papers to the PRISMA checklist and the AMSTAR quality scale. The total scores for every criterion and for every article on the two standards were calculated. Data were evaluated and analyzed using SPSS17.0 and RevMan 5.1 in terms of publication time, category of reviews, category of journals, and funding resource. RESULTS: A total of 72 SRs/MAs was included, but no Cochrane Systematic Reviews (CSRs) were obtained. The number of SRs/MAs ranged from 1 in 1998 to 15 in 2013; 88.1% used the QUADAS tool; the average overall scores by PRISMA statement and AMSTAR tool were 19.9 and 5.4, respectively. Scores on some items showed substantial improvement after publication of PRISMA and AMSTAR. However, no reviews followed the criterion of protocol and registration, and only 11.1% of articles fulfilled the criterion of literature search. SRs/MAs from the Science Citation Index (SCI) were of better quality than non-SCI studies. Funding resource made no difference to quality. Regression analysis showed that time of publication and inclusion in the SCI were significantly correlated with total scores on the two standards. CONCLUSION: The reporting and methodological quality of SRs/MAs on EUS diagnosis has improved measurably since PRISMA and AMSTAR checklists released. It is hoped that CSR in this field will be produced. Literature searching and protocol criteria, as well as QUADAS-2 tool need to be addressed more in the future. Time of publication and SCI relate more to the overall quality of SRs/MAs than does funding resource.

Aging and the optimal viewing position effect in Chinese.

Substantial evidence indicates that where readers fixate within a word affects the efficiency with which that word is recognized. Indeed, words in alphabetic languages (e.g., English, French) are recognized most efficiently when fixated at their optimal viewing position (OVP), which is near the word center. However, little is known about the effects of fixation location on word recognition in non-alphabetic languages, such as Chinese. Moreover, studies to date have not investigated if effects of fixation location vary across adult age-groups, although it is well-established that older readers experience greater difficulty recognizing words due to visual and cognitive declines. Accordingly, the present research examined OVP effects by young and older adult readers when recognizing Chinese words presented in isolation. Most words in Chinese are formed from two or more logograms called characters and so the present experiment investigated the influence of fixation location on the recognition of 2-, 3-, and 4-character words (and nonwords). The older adults experienced generally greater word recognition difficulty. But whereas the young adults recognized words most efficiently when initially fixating the first character of 2-character words and second character of 3- and 4-character words, the older adults recognized words most efficiently when initially fixating the first character for words of each length. The findings therefore reveal subtle but potentially important adult age differences in the effects of fixation location on Chinese word recognition. Moreover, the similarity in effects for words and nonwords implies a more general age-related change in oculomotor strategy when processing Chinese character-strings.

Reporting quality of systematic reviews/meta-analyses of acupuncture.

BACKGROUND: The QUOROM and PRISMA statements were published in 1999 and 2009, respectively, to improve the consistency of reporting systematic reviews (SRs)/meta-analyses (MAs) of clinical trials. However, not all SRs/MAs adhere completely to these important standards. In particular, it is not clear how well SRs/MAs of acupuncture studies adhere to reporting standards and which reporting criteria are generally ignored in these analyses. OBJECTIVES: To evaluate reporting quality in SRs/MAs of acupuncture studies. METHODS: We performed a literature search for studies published prior to 2014 using the following public archives: PubMed, EMBASE, Web of Science, the Cochrane Database of Systematic Reviews (CDSR), the Chinese Biomedical Literature Database (CBM), the Traditional Chinese Medicine (TCM) database, the Chinese Journal Full-text Database (CJFD), the Chinese Scientific Journal Full-text Database (CSJD), and the Wanfang database. Data were extracted into pre-prepared Excel data-extraction forms. Reporting quality was assessed based on the PRISMA checklist (27 items). RESULTS: Of 476 appropriate SRs/MAs identified in our search, 203, 227, and 46 were published in Chinese journals, international journals, and the Cochrane Database, respectively. In 476 SRs/MAs, only 3 reported the information completely. By contrast, approximately 4.93% (1/203), 8.81% (2/227) and 0.00% (0/46) SRs/Mas reported less than 10 items in Chinese journals, international journals and CDSR, respectively. In general, the least frequently reported items (reported≤50%) in SRs/MAs were "protocol and registration", "risk of bias across studies", and "additional analyses" in both methods and results sections. CONCLUSIONS: SRs/MAs of acupuncture studies have not comprehensively reported information recommended in the PRISMA statement. Our study underscores that, in addition to focusing on careful study design and performance, attention should be paid to comprehensive reporting standards in SRs/MAs on acupuncture studies.