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BackgroundEvery year, approximately 42 million people sustain a mild traumatic brain injury (mTBI, also known as concussion), with particularly high rates among college-aged individuals. A substantial proportion of these people (44%-64%) develop persistent symptoms that are challenging to treat, costly, and associated with significant disability. Anxiety has emerged as a risk factor for progression from acute to persistent mTBI symptoms. ObjectiveThis study aims to develop, adapt, and establish the feasibility of the Toolkit for Optimal Recovery after Concussions (TOR-C), an innovative mind-body program aimed at preventing persistent symptoms among young adults with mTBI and comorbid anxiety. Here, we describe the proposed study design, methodology, measurement, and treatment manuals. MethodsIn phase 1, we will conduct individual, live video qualitative interviews (up to n=20) with college-aged individuals with mTBI and comorbid anxiety to inform adaptation of the intervention and study procedures. In phase 2, an open pilot of the live video TOR-C (n=5) with exit interviews will be conducted to explore the initial feasibility, acceptability, and credibility of the program and to refine the study procedures. Phase 3 will involve conducting a feasibility randomized controlled trial (N=50) of the TOR-C versus a health education control (Health Enhancement for Concussions; HE-C), both delivered via live video, to establish feasibility of recruitment procedures (screening, eligibility, and enrollment) and data collection; feasibility, credibility, and acceptability of the live video TOR-C and HE-C (adherence, retention, fidelity, and satisfaction) following prespecified benchmarks; and a signal of improvement in outcomes. ResultsPhase 1 of the study has been approved by the Massachusetts General Hospital Institutional Review Board. Study completion is anticipated by early 2025. ConclusionsWe will develop and test the first mind-body intervention focused on prevention of persistent symptoms following mTBI in young adults with comorbid anxiety problems. This will allow us to establish feasibility markers in postconcussive symptoms, anxiety, disability, and fear avoidance to inform a future efficacy trial of the TOR-C versus HE-C. International Registered Report Identifier (IRRID)PRR1-10.2196/25746

BackgroundCoronavirus disease (COVID-19) has spread exponentially across the United States. Older adults with underlying health conditions are at an especially high risk of developing life-threatening complications if infected. Most intensive care unit (ICU) admissions and non-ICU hospitalizations have been among patients with at least one underlying health condition. ObjectiveThe aim of this study was to develop a model to estimate the risk status of the patients of a nationwide pharmacy chain in the United States, and to identify the geographic distribution of patients who have the highest risk of severe COVID-19 complications. MethodsA risk model was developed using a training test split approach to identify patients who are at high risk of developing serious complications from COVID-19. Adult patients (aged ≥18 years) were identified from the Walgreens pharmacy electronic data warehouse. Patients were considered eligible to contribute data to the model if they had at least one prescription filled at a Walgreens location between October 27, 2019, and March 25, 2020. Risk parameters included age, whether the patient is being treated for a serious or chronic condition, and urban density classification. Parameters were differentially weighted based on their association with severe complications, as reported in earlier cases. An at-risk rate per 1000 people was calculated at the county level, and ArcMap was used to depict the rate of patients at high risk for severe complications from COVID-19. Real-time COVID-19 cases captured by the Johns Hopkins University Center for Systems Science and Engineering (CSSE) were layered in the risk map to show where cases exist relative to the high-risk populations. ResultsOf the 30,100,826 adults included in this study, the average age is 50 years, 15% have at least one specialty medication, and the average patient has 2 to 3 comorbidities. Nearly 28% of patients have the greatest risk score, and an additional 34.64% of patients are considered high-risk, with scores ranging from 8 to 10. Age accounts for 53% of a patient’s total risk, followed by the number of comorbidities (29%); inferred chronic obstructive pulmonary disease, hypertension, or diabetes (15%); and urban density classification (5%). ConclusionsThis risk model utilizes data from approximately 10% of the US population. Currently, this is the most comprehensive US model to estimate and depict the county-level prognosis of COVID-19 infection. This study shows that there are counties across the United States whose residents are at high risk of developing severe complications from COVID-19. Our county-level risk estimates may be used alongside other data sets to improve the accuracy of anticipated health care resource needs. The interactive map can also aid in proactive planning and preparations among employers that are deemed critical, such as pharmacies and grocery stores, to prevent the spread of COVID-19 within their facilities.

Ovarian cancer (OC) is highly lethal due to late detection and frequent recurrence. Initial treatments, comprising surgery and chemotherapy, lead to disease remission but are invariably associated with subsequent relapse. The identification of novel therapies and an improved understanding of the molecular and cellular characteristics of OC are urgently needed. Here, we conducted a comprehensive analysis of primary tumor cells and their microenvironment from 16 chemonaive and 10 recurrent OC patient samples. Profiling OC tumor biomarkers allowed for the identification of potential molecular targets for developing immunotherapies, while profiling the microenvironment yielded insights into its cellular composition and property changes between chemonaive and recurrent samples. Notably, we identified CD1d as a biomarker of the OC microenvironment and demonstrated its targeting by invariant natural killer T (iNKT) cells. Overall, our study presents a comprehensive immuno-profiling of OC tumor and microenvironment during disease progression, guiding the development of immunotherapies for OC treatment, especially for recurrent disease.

One of the major challenges in the treatment of cancer are differential responses of patients to existing standard of care anti-cancer drugs. These differential responses may, in part, be due to a diverse range of genomic, epigenomic, proteomic, and metabolic alterations among individuals suffering from the same type of cancer. Precision medicine is an emerging approach in cancer therapeutics that takes into account specific molecular alterations, environmental factors as well as lifestyle of individual patients. This approach allows clinicians and researchers to select or predict treatments that would most likely benefit the patient based on their individual tumor characteristics. One class of precision medicine tools are predictive, in vitro drug-response assays designed to test the sensitivity of patient tumor cells to existing or novel therapies. These assays have the potential to rapidly identify the most effective treatments for cancer patients and thus hold great promise in the field of precision medicine. In this review, we have highlighted several drug-response assays developed in ovarian cancer and discussed the current challenges and future prospects of these assays in the clinical management of this disease.

Alzheimer's disease (AD) begins several decades before the onset of clinical symptoms, at a time when women may still undergo reproductive cycling. Whether ovarian functions alter substrates of AD pathogenesis is unknown. Here we show that ovarian cycle stages significantly modulate AD-related alterations in neural network patterns, cognitive impairments, and pathogenic protein production in the hAPP-J20 mouse model of AD. Female hAPP mice spent more time in estrogen-dominant cycle stages and these ovarian stages worsened AD-related network dysfunction and cognitive impairments. In contrast, progesterone-dominant stages and gonadectomy attenuated these AD-related deficits. Further studies revealed a direct role for estradiol in stimulating neural network excitability and susceptibility to seizures in hAPP mice and increasing amyloid beta levels. Understanding dynamic effects of the ovarian cycle on the female nervous system in disease, including AD, is of critical importance and may differ from effects on a healthy brain. The pattern of ovarian cycle effects on disease-related networks, cognition, and pathogenic protein expression may be relevant to young women at risk for AD.

Single nucelotide polymorphisms (SNPs) at the FAM13A locus are among the most commonly reported risk alleles associated with chronic obstructive pulmonary disease (COPD) and other respiratory diseases; however, the physiological role of FAM13A is unclear. In humans, two major protein isoforms are expressed at the FAM13A locus: "long" and "short," but their functions remain unknown, partly because of a lack of isoform conservation in mice. We performed in-depth characterization of organotypic primary human airway epithelial cell subsets and show that multiciliated cells predominantly express the FAM13A long isoform containing a putative N-terminal Rho GTPase-activating protein (RhoGAP) domain. Using purified proteins, we directly demonstrate the RhoGAP activity of this domain. In Xenopus laevis, which conserve the long-isoform, Fam13a deficiency impaired cilia-dependent embryo motility. In human primary epithelial cells, long-isoform deficiency did not affect multiciliogenesis but reduced cilia coordination in mucociliary transport assays. This is the first demonstration that FAM13A isoforms are differentially expressed within the airway epithelium, with implications for the assessment and interpretation of SNP effects on FAM13A expression levels. We also show that the long FAM13A isoform coordinates cilia-driven movement, suggesting that FAM13A risk alleles may affect susceptibility to respiratory diseases through deficiencies in mucociliary clearance. [ABSTRACT FROM AUTHOR]