Retention, adherence, and acceptability testing of a digital health intervention in a 3-group randomized controlled trial for chronic musculoskeletal pain.

OBJECTIVES: Evaluate a digital health intervention using Auricular Point Acupressure (APA) for chronic musculoskeletal pain in terms of participant retention, adherence, acceptability, and satisfaction. Chronic musculoskeletal pain is a global concern and there are persistent challenges in pain management. Despite the value of digital health interventions, these interventions need to be fully evaluated for feasibility. METHODS: We conducted a 3-group, longitudinal, randomized controlled trial (RCT). After Institutional Review Board approval, we posted recruitment flyers in a university, healthcare clinics, and community settings. Participants were randomized into an in-person + app group (n = 8), virtual + app group (n = 7), and a wait-list, education-enhanced control group (n = 8), evaluating our outcomes using standard feasibility measures. The 4-week intervention consisted of virtual sessions, telecommunications, and our APA app, followed by a 3-month follow-up. RESULTS: Data from 22 participants were subsequently analyzed (95.7%). All app participants adhered to the study protocol and used APA at the minimum recommended frequency and duration. The virtual + app group used APA more during the intervention and follow-up periods. All app participants found the intervention to be acceptable and at least 80% overall were satisfied with APA at the 3-month follow-up. There were no adverse events reported. CONCLUSIONS: Our digital health intervention was found to be acceptable and sustainable; participants adhered to and were satisfied with the intervention providing support for a larger RCT. CLINICAL TRIAL: #: NCT05020470.

Adaptation of Chagas Disease Screening Recommendations for a Community of At-risk HIV in the United States.

Chagas disease (CD), caused by Trypanosoma cruzi, is underdiagnosed in the United States. Improved screening strategies are needed, particularly for people at risk for life-threatening sequelae of CD, including people with human immunodeficiency virus (HIV, PWH). Here we report results of a CD screening strategy applied at a large HIV clinic serving an at-risk population.

Machine learning to predict mortality for aneurysmal subarachnoid hemorrhage (aSAH) using a large nationwide EHR database.

Aneurysmal subarachnoid hemorrhage (aSAH) develops quickly once it occurs and threatens the life of patients. We aimed to use machine learning to predict mortality for SAH patients at an early stage which can help doctors make clinical decisions. In our study, we applied different machine learning methods to an aSAH cohort extracted from a national EHR database, the Cerner Health Facts EHR database (2000-2018). The outcome of interest was in-hospital mortality, as either passing away while still in the hospital or being discharged to hospice care. Machine learning-based models were primarily evaluated by the area under the receiver operating characteristic curve (AUC). The population size of the SAH cohort was 6728. The machine learning methods achieved an average of AUCs of 0.805 for predicting mortality with only the initial 24 hours' EHR data. Without losing the prediction power, we used the logistic regression to identify 42 risk factors, -examples include age and serum glucose-that exhibit a significant correlation with the mortality of aSAH patients. Our study illustrates the potential of utilizing machine learning techniques as a practical prognostic tool for predicting aSAH mortality at the bedside.

Nurse-Administered Auricular Point Acupressure for Cancer-Related Pain.

PURPOSE: The study aimed to (1) examine the feasibility of providing a training course on auricular point acupressure (APA) for clinical oncology nurses to integrate APA into real-world nursing care settings, and (2) examine the effectiveness of APA on cancer-related pain (CRP) under usual inpatient oncology ward conditions. METHODS: This was a 2-phase feasibility study. Phase 1, an in-person, 8 hour training program was provided to oncology nurses. Phase 2, a prospective and feasibility study was conducted to evaluate the integration of APA into nursing care activities to manage CRP. Oncology patients were included if their pain was rated at ≥4 on a 0 to 10 numeric rating scale in the past 24 hours. Patients received 1 APA treatment administered by the nurses and were instructed to stimulate the points for 3 days. Study outcomes (pain intensity, fatigue, and sleep disturbance), pain medication use, and APA practice were measured by a phone survey daily. RESULTS: Ten oncology nurses received APA training in phase 1. APA had been added to the hospital's electronic health records (EHRs) as a pain treatment. In phase 2, 33 oncology patients received APA treatment with a 100% adherence rate (pressing the seeds 3 times per day, 3 minutes per time based on the suggestion). The side effects of APA were minimal (~8%-12% felt tenderness on the ear). After 3 days of APA, patients reported 38% pain relief, 39% less fatigue, and 45% improvement in sleep disturbance; 24% reduced any type of pain medication use and 19% reduced opioid use (10 mg opioids using milligram morphine equivalent). The major barrier to integrating APA into routine nursing practice was time management (how to include APA in a daily workflow). CONCLUSION: It is feasible to provide 8-hour training to oncology nurses for mastering APA skill and then integrating APA into their daily nursing care for patients with CRP. Based on the promising findings (decreased pain, improved fatigue and sleep disturbance, and less opioid use), the next step is to conduct a randomized clinical trial with a larger sample to confirm the efficacy of APA for oncology nurses to treat CRP in real-world practice.ClinicalTrial.gov identifier number: NCT04040140.

Data Mining Pipeline for COVID-19 Vaccine Safety Analysis Using a Large Electronic Health Record.

We developed a novel data mining pipeline that automatically extracts potential COVID-19 vaccine-related adverse events from a large Electronic Health Record (EHR) dataset. We applied this pipeline to Optum® de-identified COVID-19 EHR dataset containing COVID-19 vaccine records between December 11, 2020 and January 20, 2022. We compared post-vaccination diagnoses between the COVID-19 vaccine group and the influenza vaccine group among 553,682 individuals without COVID-19 infection. We extracted 1,414 ICD-10 diagnosis categories (first three ICD10 digits) within 180 days after the first dose of the COVID-19 vaccine. We then ranked the diagnosis codes using the adverse event rates and adjusted odds ratio based on the self-controlled case series analysis. Using inverse probability of censoring weighting, we estimated the right-censored time-to-event records. Our results show that the COVID-19 vaccine has a similar adverse events rate to the influenza vaccine. We found 20 types of potential COVID-19 vaccine-related adverse events that may need further investigation.

A National, Electronic Health Record-Based Study of Perinatal Hemorrhagic and Ischemic Stroke.

BACKGROUND: Perinatal stroke occurs in approximately 1 in 1100 live births. Large electronic health record (EHR) data can provide information on exposures associated with perinatal stroke in a larger number of patients than is achievable through traditional clinical studies. The objective of this study is to assess prevalence and odds ratios of known and theorized comorbidities with perinatal ischemic and hemorrhagic stroke. METHODS: The data for patients aged 0-28 days with a diagnosis of either ischemic or hemorrhagic stroke were extracted from the Cerner Health Facts Electronic Medical Record (EMR) database. Incidence of birth demographics and perinatal complications were recorded. Odds ratios were calculated against a control group. RESULTS: A total of 535 (63%) neonates were identified with ischemic stroke and 312 (37%) with hemorrhagic stroke. The most common exposures for ischemic stroke were sepsis (n = 82, 15.33%), hypoxic injury (n = 61, 11.4%), and prematurity (n = 49, 9.16%). The most common comorbidities for hemorrhagic stroke were prematurity (n = 81, 26%) and sepsis (n = 63, 20%). No perinatal ischemic stroke patients had diagnosis codes for cytomegalovirus disease. Procedure and diagnosis codes related to critical illness, including intubation and resuscitation, were prominent in both hemorrhagic (n = 46, 15%) and ischemic stroke (n = 45, 8%). CONCLUSION: This electronic health record-based study of perinatal stroke, the largest of its kind, demonstrated a wide variety of comorbid conditions with ischemic and hemorrhagic stroke. Sepsis, prematurity, and hypoxic injury are associated with perinatal hemorrhagic and ischemic stroke, though prevalence varies between types. Much of our data were similar to prior studies, which lends validity to the electronic health record database in studying perinatal stroke.

Does Spasticity Correlate With Motor Impairment in the Upper and Lower Limbs in Ambulatory Chronic Stroke Survivors?

OBJECTIVE: This study aimed to explore correlations between spasticity and motor impairments in the upper and lower limbs in ambulatory chronic stroke survivors. DESIGN: We performed clinical assessments in 28 ambulatory chronic stroke survivors with spastic hemiplegia (female: 12; male: 16; mean ages = 57.8 ± 11.8 yrs; 76 ± 45 mos after stroke). RESULTS: In the upper limb, spasticity index and Fugl-Meyer Motor Assessment showed a significant correlation. Spasticity index for the upper limb showed a significant negative correlation with handgrip strength of the affected side ( r = -0.4, P = 0.035) while Fugl-Meyer Motor Assessment for the upper limb had a significant positive correlation ( r = 0.77, P < 0.001). In the LL, no correlation was found between SI_LL and FMA_LL. There was a significant and high correlation between timed up and go test and gait speed ( r = 0.93, P < 0.001). Gait speed was positively correlated with Spasticity index for the lower limb ( r = 0.48, P = 0.01), and negatively correlated with Fugl-Meyer Motor Assessment for the lower limb ( r = -0.57, P = 0.002). Age and time since stroke showed no association in analyses for both upper limb and lower limb. CONCLUSIONS: Spasticity has a negative correlation on motor impairment in the upper limb but not in the lower limb. Motor impairment was significantly correlated with grip strength in the upper limb and gait performance in the lower limb of ambulatory stroke survivors.

A novel NIH research grant recommender using BERT.

Research grants are important for researchers to sustain a good position in academia. There are many grant opportunities available from different funding agencies. However, finding relevant grant announcements is challenging and time-consuming for researchers. To resolve the problem, we proposed a grant announcements recommendation system for the National Institute of Health (NIH) grants using researchers' publications. We formulated the recommendation as a classification problem and proposed a recommender using state-of-the-art deep learning techniques: i.e. Bidirectional Encoder Representations from Transformers (BERT), to capture intrinsic, non-linear relationship between researchers' publications and grants announcements. Internal and external evaluations were conducted to assess the system's usefulness. During internal evaluations, the grant citations were used to establish grant-publication ground truth, and results were evaluated against Recall@k, Precision@k, Mean reciprocal rank (MRR) and Area under the Receiver Operating Characteristic curve (ROC-AUC). During external evaluations, researchers' publications were clustered using Dirichlet Process Mixture Model (DPMM), recommended grants by our model were then aggregated per cluster through Recency Weight, and finally researchers were invited to provide ratings to recommendations to calculate Precision@k. For comparison, baseline recommenders using Okapi Best Matching (BM25), Term-Frequency Inverse Document Frequency (TF-IDF), doc2vec, and Naïve Bayes (NB) were also developed. Both internal and external evaluations (all metrics) revealed favorable performances of our proposed BERT-based recommender.

Variable importance evaluation with personalized odds ratio for machine learning model interpretability with applications to electronic health records-based mortality prediction.

The interpretability of machine learning models, even though with an excellent prediction performance, remains a challenge in practical applications. The model interpretability and variable importance for well-performed supervised machine learning models are investigated in this study. With the commonly accepted concept of odds ratio (OR), we propose a novel and computationally efficient Variable Importance evaluation framework based on the Personalized Odds Ratio (VIPOR). It is a model-agnostic interpretation method that can be used to evaluate variable importance both locally and globally. Locally, the variable importance is quantified by the personalized odds ratio (POR), which can account for subject heterogeneity in machine learning. Globally, we utilize a hierarchical tree to group the predictors into five groups: completely positive, completely negative, positive dominated, negative dominated, and neutral groups. The relative importance of predictors within each group is ranked based on different statistics of PORs across subjects for different application purposes. For illustration, we apply the proposed VIPOR method to interpreting a multilayer perceptron (MLP) model, which aims to predict the mortality of subarachnoid hemorrhage (SAH) patients using real-world electronic health records (EHR) data. We compare the important variables derived from MLP with other machine learning models, including tree-based models and the L1-regularized logistic regression model. The top importance variables are consistently identified by VIPOR across different prediction models. Comparisons with existing interpretation methods are also conducted and discussed based on publicly available data sets.

Biological Correlates of the Effects of Auricular Point Acupressure on Pain.

BACKGROUND: To identify candidate inflammatory biomarkers for the underlying mechanism of auricular point acupressure (APA) on pain relief and examine the correlations among pain intensity, interference, and inflammatory biomarkers. DESIGN: This is a secondary data analysis. METHODS: Data on inflammatory biomarkers collected via blood samples and patient self-reported pain intensity and interference from three pilot studies (chronic low back pain, n = 61; arthralgia related to aromatase inhibitors, n = 20; and chemotherapy-induced neuropathy, n = 15) were integrated and analyzed. This paper reports the results based on within-subject treatment effects (change in scores from pre- to post-APA intervention) for each study group (chronic low back pain, cancer pain), between-group differences (changes in scores from pre- to post-intervention between targeted-point APA [T-APA] and non-targeted-point APA [NT-APA]), and correlations among pain intensity, interference, and biomarkers. RESULTS: Within-group analysis (the change score from pre- to post-APA) revealed statistically significant changes in three biomarkers: TNF-α (cancer pain in the APA group, p = .03), ß-endorphin (back pain in the APA group, p = .04), and IL-2 (back pain in the NT-APA group, p = .002). Based on between-group analysis in patients with chronic low back pain (T-APA vs NT-APA), IL-4 had the largest effect size (0.35), followed by TNF-α (0.29). A strong positive monotonic relationship between IL-1ß and IL-2 was detected. CONCLUSIONS: The current findings further support the potential role of inflammatory biomarkers in the analgesic effects of APA. More work is needed to gain a comprehensive understanding of the underlying mechanisms of APA on chronic pain. Because it is simple, inexpensive, and has no negative side effects, APA can be widely disseminated as an alternative to opioids.

Effect of Temperature Cycling Pretreatment on the Thermal Stability of Sm2(Co, Fe, Zr, Cu)17 Magnets in the Mild Temperature Range.

The irredeemable magnetic losses of Sm(Co, Fe, Zr, Cu)7.8 permanent magnets caused by oxidation are very important for their practical application. In this work, the simulated results with R2 ≥ 98% based on the data of the temperature cycling test and the long-term isothermal test for the original samples confirmed that the magnetic flux losses reached 9.38% after the 5000th cycle in range R.T.-300 °C, and 7.15% after oxidated at 180 °C for 10 years, respectively. Demagnetization curves showed that the low-temperature oxidation mainly led to the remanence attenuation, while the coercivity remained relatively stable. SEM observation and EDS analysis revealed that an oxide outer layer with a thickness of 1.96 µm was formed on the surface of the original sample at 180 °C for 180 days, in which there was no enrichment or precipitation of metal elements. However, once a Cu, O-rich outer layer with a thickness of 0.72 µm was grown by using a temperature cycling from -50-250 °C for three cycles, the attenuation of magnetic properties could be inhibited under the low-temperature oxidation. This work suggested that the magnetic attenuation of Sm2Co17-type permanent magnets in the low-temperature field could not be ignored, and provided a simple method to suppress this attenuation of magnetic properties below 300 °C.

Self-Guided Smartphone Application to Manage Chronic Musculoskeletal Pain: A Randomized, Controlled Pilot Trial.

OBJECTIVE: The goal of this study is to evaluate the feasibility and efficacy of an auricular point acupressure smartphone app (mAPA) to self-manage chronic musculoskeletal pain. METHODS: A prospective, longitudinal, randomized, controlled pilot trial was conducted using a three-group design (self-guided mAPA (n = 14); in-person mAPA (n = 12); and control (n = 11)). The primary outcomes included physical function and pain intensity. RESULTS: After a 4-week APA intervention, participants in the in-person mAPA group had improved physical function of 32% immediately post-intervention and 29% at the 1M follow-up. Participants in the self-guided mAPA group had higher improvement (42% at post-intervention and 48% at the 1M follow-up). Both mAPA groups had similar degrees of pain intensity relief at post-intervention (45% for in-person and 48% for the self-guided group) and the 1M follow-up (42% for in-person and 45% for the self-guided group). Over 50% of the participants in each group reached at least 30% reduced pain intensity at post-intervention, and this was sustained in the mAPA groups at the 1M follow-up. Approximately 80% of the participants in both mAPA groups were satisfied with the treatment outcomes and adhered to the suggested APA practice; however, participants in the self-guided group had higher duration and more frequency in APA use. The attrition rate was 16% at the 1M follow-up. No adverse effects of APA were reported, and participants found APA to be beneficial and the app to be valuable. CONCLUSIONS: The study findings indicate that participants effectively learned APA using a smartphone app, whether they were self-guided or received in-person training. They were able to self-administer APA to successfully manage their pain. Participants found APA to be valuable in their pain self-management and expressed satisfaction with the intervention using the app.

Preparation of Core-Shell-Structured RDX@PVDF Microspheres with Improved Thermal Stability and Decreased Mechanical Sensitivity.

Reducing the sensitivity of high-energy simple explosives is the key technology in improving the practical application of high-energy insensitive powder. As the most widely used high-energy explosive, hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) is limited in application due to its high sensitivity. In this work, polyvinylidene fluoride (PVDF) was used as an energetic binder. Core-shell-structured RDX@PVDF microspheres are produced using electrospray assembly technology and fully characterized by thermogravimetric analysis, X-ray diffraction, scanning electron microscopy, transmission electron microscopy, energy dispersive spectroscopy, and mechanical sensitivity. Their thermal stability and mechanical sensitivity are directly related to the weight fraction of the added PVDF. Moreover, core-shell-structured RDX@PVDF microspheres with RDX and PVDF in the proportion three to one possess a spherical-like morphology, the lowest impact sensitivity, the lowest friction sensitivity, and the highest thermal stability. This work provides a facile method for the positive design energetic materials and the prediction of their environmental adaptability.

Aging after stroke: how to define post-stroke sarcopenia and what are its risk factors?

BACKGROUND: Sarcopenia, generally described as "aging-related loss of skeletal muscle mass and function", can occur secondary to a systemic disease. AIM: This project aimed to study the prevalence of sarcopenia in chronic ambulatory stroke survivors and its associated risk factors using the two most recent diagnostic criteria. DESIGN: A cross-sectional observational study. SETTING: A scientific laboratory. POPULATION: Chronic stroke. METHODS: Twenty-eight ambulatory chronic stroke survivors (12 females; mean age=57.8±11.8 years; time after stroke=76±45 months), hand-grip strength, gait speed, and appendicular skeletal muscle mass (ASM) were measured to define sarcopenia. Risk factors, including motor impairment and spasticity, were identified using regression analysis. RESULTS: The prevalence of sarcopenia varied between 18% and 25% depending on the diagnostic criteria used. A significant difference was seen in the prevalence of low hand grip strength on the affected side (96%) when compared to the contralateral side (25%). The prevalence of slow gait speed was 86% while low ASM was present in 89% of the subjects. Low ASM was marginally negatively correlated with time since stroke and gait speed, but no correlation was observed with age, motor impairment, or spasticity. ASM loss, bone loss and fat deposition were significantly greater in the affected upper limb than in the affected lower limb. Regression analyses showed that time since stroke was a factor associated with bone and muscle loss in the affected upper limb, spasticity had a protective role for muscle loss in the affected lower limb, and walking had a protective role for bone loss in the lower limb. CONCLUSIONS: The prevalence of sarcopenia in stroke survivors is high and is a multifactorial process that is not age-related. Different risk factors contribute to muscle loss in the upper and lower limbs after stroke. CLINICAL REHABILITATION IMPACT: Clinicians need to be aware of high prevalence of sarcopenia in chronic stroke survivors. Sarcopenia is more evident in the upper than lower limbs. Clinicians also need to understand potential protective roles of some factors, such as spasticity and walking for the muscles in the lower limb.

Controlling Multiple COVID-19 Epidemic Waves: An Insight from a Multi-scale Model Linking the Behaviour Change Dynamics to the Disease Transmission Dynamics.

COVID-19 epidemics exhibited multiple waves regionally and globally since 2020. It is important to understand the insight and underlying mechanisms of the multiple waves of COVID-19 epidemics in order to design more efficient non-pharmaceutical interventions (NPIs) and vaccination strategies to prevent future waves. We propose a multi-scale model by linking the behaviour change dynamics to the disease transmission dynamics to investigate the effect of behaviour dynamics on COVID-19 epidemics using game theory. The proposed multi-scale models are calibrated and key parameters related to disease transmission dynamics and behavioural dynamics with/without vaccination are estimated based on COVID-19 epidemic data (daily reported cases and cumulative deaths) and vaccination data. Our modeling results demonstrate that the feedback loop between behaviour changes and COVID-19 transmission dynamics plays an essential role in inducing multiple epidemic waves. We find that the long period of high-prevalence or persistent deterioration of COVID-19 epidemics could drive almost all of the population to change their behaviours and maintain the altered behaviours. However, the effect of behaviour changes fades out gradually along the progress of epidemics. This suggests that it is essential to have not only persistent, but also effective behaviour changes in order to avoid subsequent epidemic waves. In addition, our model also suggests the importance to maintain the effective altered behaviours during the initial stage of vaccination, and to counteract relaxation of NPIs, it requires quick and massive vaccination to avoid future epidemic waves.

Assessing Age-Specific Vaccination Strategies and Post-vaccination Reopening Policies for COVID-19 Control Using SEIR Modeling Approach.

As the availability of COVID-19 vaccines, it is badly needed to develop vaccination guidelines to prioritize the vaccination delivery in order to effectively stop COVID-19 epidemic and minimize the loss. We evaluated the effect of age-specific vaccination strategies on the number of infections and deaths using an SEIR model, considering the age structure and social contact patterns for different age groups for each of different countries. In general, the vaccination priority should be given to those younger people who are active in social contacts to minimize the number of infections, while the vaccination priority should be given to the elderly to minimize the number of deaths. But this principle may not always apply when the interaction of age structure and age-specific social contact patterns is complicated. Partially reopening schools, workplaces or households, the vaccination priority may need to be adjusted accordingly. Prematurely reopening social contacts could initiate a new outbreak or even a new pandemic out of control if the vaccination rate and the detection rate are not high enough. Our result suggests that it requires at least nine months of vaccination (with a high vaccination rate > 0.1%) for Italy and India before fully reopening social contacts in order to avoid a new pandemic.

Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database.

OBJECTIVE: A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes-related symptoms using a large nationwide electronic health record (EHR) database. Methods: We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (n = 1,136,301 patients) was identified using a rule-based phenotype method. A multistep procedure was then used to identify type 2 diabetes-related symptoms based on International Classification of Diseases, 9th and 10th revisions, diagnosis codes. Type 2 diabetes-related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. Results: Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21-60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies. Conclusion: To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes-related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified.

A novel group of genes that cause endocrine resistance in breast cancer identified by dynamic gene expression analysis.

Breast cancer (BC) is the most common type of cancer diagnosed in women. Among female cancer deaths, BC is the second leading cause of death worldwide. For estrogen receptor-positive (ER-positive) breast cancers, endocrine therapy is an effective therapeutic approach. However, in many cases, an ER-positive tumor becomes unresponsive to endocrine therapy, and tumor regrowth occurs after treatment. While some genetic mutations contribute to resistance in some patients, the underlying causes of resistance to endocrine therapy are mostly undetermined. In this study, we utilized a recently developed statistical approach to investigate the dynamic behavior of gene expression during the development of endocrine resistance and identified a novel group of genes whose time course expression significantly change during cell modelling of endocrine resistant BC development. Expression of a subset of these genes was also differentially expressed in microarray analysis of endocrine-resistant and endocrine-sensitive tumor samples. Surprisingly, a subset of those genes was also differentially genes expressed in triple-negative breast cancer (TNBC) as compared with ER-positive BC. The findings suggest shared genetic mechanisms may underlie the development of endocrine resistant BC and TNBC. Our findings identify 34 novel genes for further study as potential therapeutic targets for treatment of endocrine-resistant BC and TNBC.

ImmuneData: an integrated data discovery system for immunology data repositories.

To meet the increasing demand for data sharing, data reuse and meta-analysis in the immunology research community, we have developed the data discovery system ImmuneData. The system provides integrated access to five immunology data repositories funded by the National Institute of Allergy and Infectious Diseases, Division of Allergy, Immunology and Transplantation, including ImmPort, ImmuneSpace, ITN TrialShare, ImmGen and IEDB. ImmuneData restructures the data repositories' metadata into a uniform schema using domain experts' knowledge and state-of-the-art Natural Language Processing (NLP) technologies. It comes with a user-friendly web interface, accessible at http://www.immunedata.org/, and a Google-like search engine for biological researchers to find and access data easily. The vast quantity of synonyms used in biomedical research increase the likelihood of incomplete search results. Thus, our search engine converts queries submitted by users into ontology terms, which are then expended by NLP technologies to ensure that the search results will include all synonyms for a particular concept. The system also includes an advanced search function to build customized queries to meet higher-level users' needs. ImmuneData ensures the FAIR principle (Findability, Accessibility, Interoperability and Reusability) of the five data repositories to benefit data reuse in the immunology research community. The data pipeline constructing our system can be extended to other data repositories to build a more comprehensive biological data discovery system. DATABASE URL: http://www.immunedata.org/.

Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model.

While the Coronavirus Disease 2019 (COVID-19) pandemic continues to threaten public health and safety, every state has strategically reopened the business in the United States. It is urgent to evaluate the effect of reopening policies on the COVID-19 pandemic to help with the decision-making on the control measures and medical resource allocations. In this study, a novel SEIR model was developed to evaluate the effect of reopening policies based on the real-world reported COVID-19 data in Texas. The earlier reported data before the reopening were used to develop the SEIR model; data after the reopening were used for evaluation. The simulation results show that if continuing the "stay-at-home order" without reopening the business, the COVID-19 pandemic could end in December 2020 in Texas. On the other hand, the pandemic could be controlled similarly as the case of no-reopening only if the contact rate was low and additional high magnitude of control measures could be implemented. If the control measures are only slightly enhanced after reopening, it could flatten the curve of the COVID-19 epidemic with reduced numbers of infections and deaths, but it might make the epidemic last longer. Based on the reported data up to July 2020 in Texas, the real-world epidemic pattern is between the cases of the low and high magnitude of control measures with a medium risk of contact rate after reopening. In this case, the pandemic might last until summer 2021 to February 2022 with a total of 4-10 million infected cases and 20,080-58,604 deaths.