Genetic loci regulate Sarbecovirus pathogenesis: A comparison across mice and humans.

Coronavirus (CoV) cause considerable morbidity and mortality in humans and other mammals, as evidenced by the emergence of Severe Acute Respiratory CoV (SARS-CoV) in 2003, Middle East Respiratory CoV (MERS-CoV) in 2012, and SARS-CoV-2 in 2019. Although poorly characterized, natural genetic variation in human and other mammals modulate virus pathogenesis, as reflected by the spectrum of clinical outcomes ranging from asymptomatic infections to lethal disease. Using multiple human epidemic and zoonotic Sarbecoviruses, coupled with murine Collaborative Cross genetic reference populations, we identify several dozen quantitative trait loci that regulate SARS-like group-2B CoV pathogenesis and replication. Under a Chr4 QTL, we deleted a candidate interferon stimulated gene, Trim14 which resulted in enhanced SARS-CoV titers and clinical disease, suggesting an antiviral role during infection. Importantly, about 60 % of the murine QTL encode susceptibility genes identified as priority candidates from human genome-wide association studies (GWAS) studies after SARS-CoV-2 infection, suggesting that similar selective forces have targeted analogous genes and pathways to regulate Sarbecovirus disease across diverse mammalian hosts. These studies provide an experimental platform in rodents to investigate the molecular-genetic mechanisms by which potential cross mammalian susceptibility loci and genes regulate type-specific and cross-SARS-like group 2B CoV replication, immunity, and pathogenesis in rodent models. Our study also provides a paradigm for identifying susceptibility loci for other highly heterogeneous and virulent viruses that sporadically emerge from zoonotic reservoirs to plague human and animal populations.

Beyond mathematics, statistics, and programming: data science, machine learning, and artificial intelligence competencies and curricula for clinicians, informaticians, science journalists, and researchers.

Data science, machine learning and artificial intelligence applications impact clinicians, informaticians, science journalists, and researchers. Most biomedical data science training focuses on learning a programming language in addition to higher mathematics and advanced statistics. This approach is appropriate for graduate students but greatly reduces the number of individuals in healthcare who can be involved in data science. To serve these four stakeholder audiences, we describe several curricular strategies focusing on solving real problems of interest to these audiences. Relevant competencies for these audiences include using intuitive programming tools that facilitate data exploration with minimal programming background, creating data models, evaluating results of data analyses, and assessing data science research reports, among others. Offering the curricula described here more broadly could broaden the stakeholder groups knowledgeable about and engaged in data science.

Patients' lived experiences and recommendations for enhanced awareness and use of integrative oncology services in cancer care.

BACKGROUND: Consuming educational content, adhering to treatment plans and managing symptoms and side-effects can be overwhelming to new oncology patients. OBJECTIVE: The purpose of this study is to engage patients in conceptualization of enhanced clinic processes and digital health tools to support awareness and use of integrative oncology services. PATIENT INVOLVEMENT: We engaged patients in participatory design to understand lived experiences surrounding use of integrative oncology services during and after conventional cancer treatment. METHODS: Ten participatory design sessions were held with individual participants. Sessions began with patient story telling regarding diagnosis and paths to awareness and use of integrative oncology services. We then reviewed prototype mobile app screens to solicit feedback regarding digital health functionality to support patient navigation of symptom-alleviating options. RESULTS: Oncology patients are active participants in the management of symptoms and side effects. Patients who utilize yoga, acupuncture, and massage report a need for earlier patient education about these services. Patients express interest in digital health tools to match symptoms to options for relief, provide access to searchable information, and facilitate streamlined access to in-person and remote services. DISCUSSION: Patients co-produce wellbeing by seeking solutions to daily challenges and consuming educational content. Clinics can collaborate with patients to identify high priority needs and challenges. PRACTICAL VALUE: Active collaboration with patients is needed to identify unmet needs and guide development of clinic processes and digital health tools to enhance awareness and use of IO services in conventional cancer care. FUNDING: The principal investigator was supported by the U.S. Agency for Healthcare Research and Quality (AHRQ K12HS026370). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ. The sponsor had no role in the study design, data collection, analysis, report writing, or decision to submit for publication.

Common Mechanism of SARS-CoV and SARS-CoV-2 Pathogenesis across Species.

Sarbecovirus (CoV) infections, including Severe Acute Respiratory CoV (SARS-CoV) and SARS-CoV-2, are considerable human threats. Human GWAS studies have recently identified loci associated with variation in SARS-CoV-2 susceptibility. However, genetically tractable models that reproduce human CoV disease outcomes are needed to mechanistically evaluate genetic determinants of CoV susceptibility. We used the Collaborative Cross (CC) and human GWAS datasets to elucidate host susceptibility loci that regulate CoV infections and to identify host quantitative trait loci that modulate severe CoV and pan-CoV disease outcomes including a major disease regulating loci including CCR9. CCR9 ablation resulted in enhanced titer, weight loss, respiratory dysfunction, mortality, and inflammation, providing mechanistic support in mitigating protection from severe SARS-CoV-2 pathogenesis across species. This study represents a comprehensive analysis of susceptibility loci for an entire genus of human pathogens conducted, identifies a large collection of susceptibility loci and candidate genes that regulate multiple aspects type-specific and cross-CoV pathogenesis, and also validates the paradigm of using the CC platform to identify common cross-species susceptibility loci and genes for newly emerging and pre-epidemic viruses.

Evaluation of patient-level retrieval from electronic health record data for a cohort discovery task.

OBJECTIVE: Growing numbers of academic medical centers offer patient cohort discovery tools to their researchers, yet the performance of systems for this use case is not well understood. The objective of this research was to assess patient-level information retrieval methods using electronic health records for different types of cohort definition retrieval. MATERIALS AND METHODS: We developed a test collection consisting of about 100 000 patient records and 56 test topics that characterized patient cohort requests for various clinical studies. Automated information retrieval tasks using word-based approaches were performed, varying 4 different parameters for a total of 48 permutations, with performance measured using B-Pref. We subsequently created structured Boolean queries for the 56 topics for performance comparisons. In addition, we performed a more detailed analysis of 10 topics. RESULTS: The best-performing word-based automated query parameter settings achieved a mean B-Pref of 0.167 across all 56 topics. The way a topic was structured (topic representation) had the largest impact on performance. Performance not only varied widely across topics, but there was also a large variance in sensitivity to parameter settings across the topics. Structured queries generally performed better than automated queries on measures of recall and precision but were still not able to recall all relevant patients found by the automated queries. CONCLUSION: While word-based automated methods of cohort retrieval offer an attractive solution to the labor-intensive nature of this task currently used at many medical centers, we generally found suboptimal performance in those approaches, with better performance obtained from structured Boolean queries. Future work will focus on using the test collection to develop and evaluate new approaches to query structure, weighting algorithms, and application of semantic methods.

Correction: Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.

[This corrects the article DOI: 10.1371/journal.pone.0235574.].

Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.

BACKGROUND: With the growing adoption of the electronic health record (EHR) worldwide over the last decade, new opportunities exist for leveraging EHR data for detection of rare diseases. Rare diseases are often not diagnosed or delayed in diagnosis by clinicians who encounter them infrequently. One such rare disease that may be amenable to EHR-based detection is acute hepatic porphyria (AHP). AHP consists of a family of rare, metabolic diseases characterized by potentially life-threatening acute attacks and chronic debilitating symptoms. The goal of this study was to apply machine learning and knowledge engineering to a large extract of EHR data to determine whether they could be effective in identifying patients not previously tested for AHP who should receive a proper diagnostic workup for AHP. METHODS AND FINDINGS: We used an extract of the complete EHR data of 200,000 patients from an academic medical center and enriched it with records from an additional 5,571 patients containing any mention of porphyria in the record. After manually reviewing the records of all 47 unique patients with the ICD-10-CM code E80.21 (Acute intermittent [hepatic] porphyria), we identified 30 patients who were positive cases for our machine learning models, with the rest of the patients used as negative cases. We parsed the record into features, which were scored by frequency of appearance and filtered using univariate feature analysis. We manually choose features not directly tied to provider attributes or suspicion of the patient having AHP. We trained on the full dataset, with the best cross-validation performance coming from support vector machine (SVM) algorithm using a radial basis function (RBF) kernel. The trained model was applied back to the full data set and patients were ranked by margin distance. The top 100 ranked negative cases were manually reviewed for symptom complexes similar to AHP, finding four patients where AHP diagnostic testing was likely indicated and 18 patients where AHP diagnostic testing was possibly indicated. From the top 100 ranked cases of patients with mention of porphyria in their record, we identified four patients for whom AHP diagnostic testing was possibly indicated and had not been previously performed. Based solely on the reported prevalence of AHP, we would have expected only 0.002 cases out of the 200 patients manually reviewed. CONCLUSIONS: The application of machine learning and knowledge engineering to EHR data may facilitate the diagnosis of rare diseases such as AHP. Further work will recommend clinical investigation to identified patients' clinicians, evaluate more patients, assess additional feature selection and machine learning algorithms, and apply this methodology to other rare diseases. This work provides strong evidence that population-level informatics can be applied to rare diseases, greatly improving our ability to identify undiagnosed patients, and in the future improve the care of these patients and our ability study these diseases. The next step is to learn how best to apply these EHR-based machine learning approaches to benefit individual patients with a clinical study that provides diagnostic testing and clinical follow up for those identified as possibly having undiagnosed AHP.

Illuminating biological pathways for drug targeting in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) remains a morbid disease with poor prognosis and treatment that typically leaves patients with permanent damage to critical functions such as eating and talking. Currently only three targeted therapies are FDA approved for use in HNSCC, two of which are recently approved immunotherapies. In this work, we identify biological pathways involved with this disease that could potentially be targeted by current FDA approved cancer drugs and thereby expand the pool of potential therapies for use in HNSCC treatment. We analyzed 508 HNSCC patients with sequencing information from the Genomic Data Commons (GDC) database and assessed which biological pathways were significantly enriched for somatic mutations or copy number alterations. We then further classified pathways as either "light" or "dark" to the current reach of FDA-approved cancer drugs using the Cancer Targetome, a compendium of drug-target information. Light pathways are statistically enriched with somatic mutations (or copy number alterations) and contain one or more targets of current FDA-approved cancer drugs, while dark pathways are enriched with somatic mutations (or copy number alterations) but not currently targeted by FDA-approved cancer drugs. Our analyses indicated that approximately 35-38% of disease-specific pathways are in scope for repurposing of current cancer drugs. We further assess light and dark pathways for subgroups of patient tumor samples according to HPV status. The framework of light and dark pathways for HNSCC-enriched biological pathways allows us to better prioritize targeted therapies for further research in HNSCC based on the HNSCC genetic landscape and FDA-approved cancer drug information. We also highlight the importance in the identification of sub-pathways where targeting and cross targeting of other pathways may be most beneficial to predict positive or negative synergy with potential clinical significance. This framework is ideal for precision drug panel development, as well as identification of highly aberrant, untargeted candidates for future drug development.

Natural Product Target Network Reveals Potential for Cancer Combination Therapies.

A body of research demonstrates examples of in vitro and in vivo synergy between natural products and anti-neoplastic drugs for some cancers. However, the underlying biological mechanisms are still elusive. To better understand biological entities targeted by natural products and therefore provide rational evidence for future novel combination therapies for cancer treatment, we assess the targetable space of natural products using public domain compound-target information. When considering pathways from the Reactome database targeted by natural products, we found an increase in coverage of 61% (725 pathways), relative to pathways covered by FDA approved cancer drugs collected in the Cancer Targetome, a resource for evidence-based drug-target interactions. Not only is the coverage of pathways targeted by compounds increased when we include natural products, but coverage of targets within those pathways is also increased. Furthermore, we examined the distribution of cancer driver genes across pathways to assess relevance of natural products to critical cancer therapeutic space. We found 24 pathways enriched for cancer drivers that had no available cancer drug interactions at a potentially clinically relevant binding affinity threshold of < 100nM that had at least one natural product interaction at that same binding threshold. Assessment of network context highlighted the fact that natural products show target family groupings both distinct from and in common with cancer drugs, strengthening the complementary potential for natural products in the cancer therapeutic space. In conclusion, our study provides a foundation for developing novel cancer treatment with the combination of drugs and natural products.

Naturopathic practice at north american academic institutions: description of 300,483 visits and comparison to conventional primary care.

This study collected patient visit data to explore similarities and differences between conventional and naturopathic primary care (PC). Administrative data from practice management software systems from the main teaching clinics of four of the eight accredited North American naturopathic academic institutions were abstracted into an integrated database containing five years (2006-2010) of visit, patient, laboratory, and prescribing data. Descriptive analyses of healthcare services were compared to the National Ambulatory Medical Care Survey (NAMCS). Over the five-year period, 300,483 patient visits to naturopathic doctors occurred at clinics, excluding visits at clinics operated by the schools in community settings. Patients were 69% female; mean age was 39 (SE 0.09). Older adults (>65) comprised 9% of the population and children (<16) comprised 8%. Comparing academic naturopathic clinics to national conventional PC (NAMCS), we found more patients paid out of pocket at naturopathic clinics (50 vs. 4%) and naturopathic clinics more frequently offered discounted care (26 vs. 0.3%). There was a 44% overlap in the most frequent 25 diagnoses for PC at conventional community clinics. Overall, these data suggest substantial similarities in care offered by academic naturopathic clinics, at which most Naturopathic Doctor (ND) students are trained, and by conventional PC practices.