Multi-Omics Analysis in Mouse Primary Cortical Neurons Reveals Complex Positive and Negative Biological Interactions Between Constituent Compounds of .

A water extract of the Ayurvedic plant (L.) Urban, family Apiaceae (CAW), improves cognitive function in mouse models of aging and Alzheimer's disease and affects dendritic arborization, mitochondrial activity, and oxidative stress in mouse primary neurons. Triterpenes (TT) and caffeoylquinic acids (CQA) are constituents associated with these bioactivities of CAW, although little is known about how interactions between these compounds contribute to the plant's therapeutic benefit.

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.

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.

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.

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.

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.

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).

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.

Natural Product Target Network Reveals Potential for Cancer Combination Therapies.

A body of research demonstrates examples of and 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.

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).