Search

Objective:: Research grants, which are available from several sources, are essential for scholars to sustain a good standing in academia. Although securing grant funds for research is very competitive, being able to locate and find previously funded grants and projects that are relevant to researchers’ interests would be very helpful. In this work, we developed a funded-grants/projects recommendation system for the National Institute of Health (NIH) grants. Methods:: Our system aims to recommend funded grants to researchers based on their publications or input keywords. By extracting summary information from funded grants and their associated applications, we employed two embedding models for biomedical words and sentences (biowordvec and biosentvec), and compare multiple recommendation methods to recommend the most relevant funded grants for researchers’ input Results:: Compared to a baseline method, the recommendation system based on biomedical word embedding models provided higher performance. The system also received an average rate of 3.53 out of 5, based on the relevancy evaluation results from biomedical researchers. Conclusion:: Both internal and external evaluation results prove the effectiveness of our recommendation system. The system would be helpful for biomedical researchers to locate and find previously funded grants related to their interests.

Background: Stroke is an important cause of morbidity in pediatrics. Large studies are needed to better understand the epidemiology, pathogenesis and risk factors associated with pediatric stroke. Large administrative datasets can provide information on risk factors in perinatal and childhood stroke at low cost. The aim of this hypothesis-generating study was to use a large administrative dataset to assess for prevalence and odds-ratios of rare exposures associated with pediatric stroke. Methods: The data for patients aged 0–18 with a diagnosis of either ischemic stroke or intracranial hemorrhage were extracted from the Cerner Health Facts EMR Database from 2000 to 2018. Prevalence of various possible risk factors for pediatric and adult stroke was assessed using ICD 9 and 10 codes. Odds ratios were calculated using a control group of patients without stroke. Results: 10,688 children were identified with stroke. 6339 (59 %) were ischemic and 4349 (41 %) were hemorrhagic. The most frequently identified risk factors for ischemic stroke across age groups were hypertension (29–44 %), trauma (19–33 %), and malignancy (11–24 %). The most common risk factors seen with hemorrhagic stroke were trauma (32–64 %), malignancy (5–19 %) and arrhythmia (9–12 %). Odds ratios across all age groups for dyslipidemia (17–64), hypertension (20–63), and tobacco exposure (3–59) were high in the ischemic stroke cohort. Conclusion: This is the largest retrospective study of pediatric stroke of its kind from hospitals across the US in both academic and non-academic clinical settings. Much of our data was consistent with prior studies. ICD codes for tobacco exposure, hyperlipidemia, diabetes, and hypertension all had high odds ratios for stroke in children, which suggest a relationship between these conditions and pediatric stroke. However, ascertainment bias is a major concern with electronic health record-based studies. More focused study is needed into the role of these exposures into the pathogenesis of pediatric stroke.

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

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.

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.

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.

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.

Abstract Objective Subarachnoid hemorrhage (SAH) is often devastating with increased early mortality, particularly in those with presumed delayed cerebral ischemia (DCI). The ability to accurately predict survival for SAH patients during the hospital course would provide valuable information for healthcare providers, patients, and families. This study aims to utilize electronic health record (EHR) data and machine learning approaches to predict the adverse outcome for nontraumatic SAH adult patients. Methods The cohort included nontraumatic SAH patients treated with vasopressors for presumed DCI from a large EHR database, the Cerner Health Facts® EMR database (2000–2014). The outcome of interest was the adverse outcome, defined as death in hospital or discharged to hospice. Machine learning‐based models were developed and primarily assessed by area under the receiver operating characteristic curve (AUC). Results A total of 2467 nontraumatic SAH patients (64% female; median age [interquartile range]: 56 [47–66]) who were treated with vasopressors for presumed DCI were included in the study. 934 (38%) patients died or were discharged to hospice. The model achieved an AUC of 0.88 (95% CI, 0.84–0.92) with only the initial 24 h EHR data, and 0.94 (95% CI, 0.92–0.96) after the next 24 h. Interpretation EHR data and machine learning models can accurately predict the risk of the adverse outcome for critically ill nontraumatic SAH patients. It is possible to use EHR data and machine learning techniques to help with clinical decision‐making.

Previous results reported that SiCp (Silicon Carbide particles) particle doping proved to be effective in enhancing the wear performance of WE43 magnesium alloy. In this work, finite element simulation was employed to investigate the effect of SiCp particle shape on the mechanical behaviors of SiCp/WE43 magnesium matrix composites. SiCp particles underwent larger load internally and a smaller plastic deformation under tensile loading, leading to the enhanced strength and stiffness of the composites. Polygonal SiCp particles provided a better enhancement in strength for the composites than round SiCp particles, but the enhancement in stiffness was opposite. Meanwhile, the damage is likely to initiate at the interface between the matrix and particle, at the location of the highest stress concentration. This phenomenon was more prominent in polygonal particle-reinforced composites. These current findings provide a comprehensive understanding of the effect of SiCp particle shape on the mechanical behaviors of magnesium matrix composites.

The objective of this work was to study the corrosion behavior of 2xxx aluminum alloys in the marine environment and the degradation of mechanical properties caused by corrosion as well as to provide support for the lightweight design of marine equipment. The corrosion characteristics of 2297-T8 and 2195-T8 were observed by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) at 3, 6, 9, and 12 months of exposure, and the mechanical properties were obtained via uniaxial tensile testing. The results of the study showed the following: (1) With an increase in the exposure time, the corrosion products on the surface of 2297-T8 gradually increase, and the corrosion products on the surface of 2195-T8 accumulate rapidly. (2) The results of EDS show that the corrosion products of 2297 are mainly Al oxides and that those of 2195 are composed of Al, Fe, Mn, Si, etc. (3) Through the corrosion pit diameter, corrosion pit depth, and corrosion mass reduction rate surface, the trend of an increasing average radius of the corrosion pits and the corrosion mass reduction rate surface varies linearly, and the average depth of the corrosion pits increases dramatically after 9 months of exposure time. (4) According to the mechanical properties of the study surface, in a marine atmosphere, the degradation of 2297-T8 ductility is higher than that of 2195-T8, while the degradation of the yield stress is lower than that of 2195-T8.

Many pragmatic clustering methods have been developed to group data vectors or objects into clusters so that the objects in one cluster are very similar and objects in different clusters are distinct based on some similarity measure. The availability of time course data has motivated researchers to develop methods, such as mixture and mixed-effects modelling approaches, that incorporate the temporal information contained in the shape of the trajectory of the data. However, there is still a need for the development of time-course clustering methods that can adequately deal with inhomogeneous clusters (some clusters are quite large and others are quite small). Here we propose two such methods, hierarchical clustering (IHC) and iterative pairwise-correlation clustering (IPC). We evaluate and compare the proposed methods to the Markov Cluster Algorithm (MCL) and the generalised mixed-effects model (GMM) using simulation studies and an application to a time course gene expression data set from a study containing human subjects who were challenged by a live influenza virus. We identify four types of temporal gene response modules to influenza infection in humans, i.e., single-gene modules (SGM), small-size modules (SSM), medium-size modules (MSM) and large-size modules (LSM). The LSM contain genes that perform various fundamental biological functions that are consistent across subjects. The SSM and SGM contain genes that perform either different or similar biological functions that have complex temporal responses to the virus and are unique to each subject. We show that the temporal response of the genes in the LSM have either simple patterns with a single peak or trough a consequence of the transient stimuli sustained or state-transitioning patterns pertaining to developmental cues and that these modules can differentiate the severity of disease outcomes. Additionally, the size of gene response modules follows a power-law distribution with a consistent exponent across all subjects, which reveals the presence of universality in the underlying biological principles that generated these modules. Keywords: Clustering, Inhomogeneous clusters, Power law

Background: Gene regulatory networks are complex dynamic systems and the reverse-engineering of such networks from high-dimensional time course transcriptomic data have attracted researchers from various fields. It is also interesting and important to study the behavior of the reconstructed networks on the basis of dynamic models and the biological mechanisms. We focus on the gene regulatory networks reconstructed using the ordinary differential equation (ODE) modelling approach and investigate the properties of these networks. Results: Controllability and stability analyses are conducted for the reconstructed gene response networks of 17 influenza infected subjects based on ODE models. Symptomatic subjects tend to have larger numbers of driver nodes, higher proportions of critical links and lower proportions of redundant links than asymptomatic subjects. We also show that the degree distribution, rather than the structure of networks, plays an important role in controlling the network in response to influenza infection. In addition, we find that the stability of high-dimensional networks is very sensitive to randomness in the reconstructed systems brought by errors in measurements and parameter estimation. Conclusions: The gene response networks of asymptomatic subjects are easier to be controlled than those of symptomatic subjects. This may indicate that the regulatory systems of asymptomatic subjects are easier to recover from disease stimulations, so these subjects are less likely to develop symptoms. Our results also suggest that stability constraint should be considered in the modelling of high-dimensional networks and the estimation of network parameters.

Medical researchers are increasingly interested in knowing how the complex community of micro-organisms living on human body impacts human health. Key to this is to understand how the microbes interact with each other. Time-course studies on human microbiome indicate that the composition of microbiome changes over short time periods, primarily as a consequence of synergistic and antagonistic interactions of the members of the microbiome with each other and with the environment. Knowledge of the abundance of bacteria-which are the predominant members of the human microbiome-in such time-course studies along with appropriate mathematical models will allow us to identify key dynamic interaction networks within the microbiome. However, the high-dimensional nature of these data poses significant challenges to the development of such mathematical models. We propose a high-dimensional linear State Space Model (SSM) with a new Expectation-Regularization-Maximization (ERM) algorithm to construct a dynamic Microbial Interaction Network (MIN). System noise and measurement noise can be separately specified through SSMs. In order to deal with the problem of high-dimensional parameter space in the SSMs, the proposed new ERM algorithm employs the idea of the adaptive LASSO-based variable selection method so that the sparsity property of MINs can be preserved. We performed simulation studies to evaluate the proposed ERM algorithm for variable selection. The proposed method is applied to identify the dynamic MIN from a time-course vaginal microbiome study of women. This method is amenable to future developments, which may include interactions between microbes and the environment.

Current approaches to study transcriptional profiles post influenza infection typically rely on tissue sampling from one or two sites at a few time points, such as spleen and lung in murine models. In this study, we infected female C57/BL6 mice intranasally with mouse-adapted H3N2/Hong Kong/X31 avian influenza A virus, and then analyzed the gene expression profiles in four different compartments (blood, lung, mediastinal lymph nodes, and spleen) over 11 consecutive days post infection. These data were analyzed by an advanced statistical procedure based on ordinary differential equation (ODE) modeling. Vastly different lists of significant genes were identified by the same statistical procedure in each compartment. Only 11 of them are significant in all four compartments. We classified significant genes in each compartment into co-expressed modules based on temporal expression patterns. We then performed functional enrichment analysis on these co-expression modules and identified significant pathway and functional motifs. Finally, we used an ODE based model to reconstruct gene regulatory network (GRN) for each compartment and studied their network properties.

The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.

The B cell response to influenza infection of the respiratory tract contributes to viral clearance and establishes profound resistance to reinfection by related viruses. Numerous studies have measured virus-specific antibody-secreting cell (ASC) frequencies in different anatomical compartments after influenza infection and provided a general picture of the kinetics of ASC formation and dispersion. However, the dynamics of ASC populations are difficult to determine experimentally and have received little attention. Here, we applied mathematical modeling to investigate the dynamics of ASC growth, death, and migration over the 2-week period following primary influenza infection in mice. Experimental data for model fitting came from high frequency measurements of virus-specific IgM, IgG, and IgA ASCs in the mediastinal lymph node (MLN), spleen, and lung. Model construction was based on a set of assumptions about ASC gain and loss from the sampled sites, and also on the directionality of ASC trafficking pathways. Most notably, modeling results suggest that differences in ASC fate and trafficking patterns reflect the site of formation and the expressed antibody class. Essentially all early IgA ASCs in the MLN migrated to spleen or lung, whereas cell death was likely the major reason for IgM and IgG ASC loss from the MLN. In contrast, the spleen contributed most of the IgM and IgG ASCs that migrated to the lung, but essentially none of the IgA ASCs. This finding points to a critical role for regional lymph nodes such as the MLN in the rapid generation of IgA ASCs that seed the lung. Results for the MLN also suggest that ASC death is a significant early feature of the B cell response. Overall, our analysis is consistent with accepted concepts in many regards, but it also indicates novel features of the B cell response to influenza that warrant further investigation.

Human CD4 T cell recall responses to influenza virus are strongly biased towards Type 1 cytokines, producing IFNγ, IL-2 and TNFα. We have now examined the effector phenotypes of CD4 T cells in more detail, particularly focusing on differences between recent versus long-term, multiply-boosted responses. Peptides spanning the proteome of temporally distinct influenza viruses were distributed into pools enriched for cross-reactivity to different influenza strains, and used to stimulate antigen-specific CD4 T cells representing recent or long-term memory. In the general population, peptides unique to the long-circulating influenza A/New Caledonia/20/99 (H1N1) induced Th1-like responses biased toward the expression of IFNγ(+)TNFα(+) CD4 T cells. In contrast, peptide pools enriched for non-cross-reactive peptides of the pandemic influenza A/California/04/09 (H1N1) induced more IFNγ(-)IL-2(+)TNFα(+) T cells, similar to the IFNγ(-)IL-2(+) non-polarized, primed precursor T cells (Thpp) that are a predominant response to protein vaccination. These results were confirmed in a second study that compared samples taken before the 2009 pandemic to samples taken one month after PCR-confirmed A/California/04/09 infection. There were striking increases in influenza-specific TNFα(+), IFNγ(+), and IL-2(+) cells in the post-infection samples. Importantly, peptides enriched for non-cross-reactive A/California/04/09 specificities induced a higher proportion of Thpp-like IFNγ(-)IL-2(+)TNFα(+) CD4 T cells than peptide pools cross-reactive with previous influenza strains, which induced more Th1 (IFNγ(+)TNFα(+)) responses. These IFNγ(-)IL-2(+)TNFα(+) CD4 T cells may be an important target population for vaccination regimens, as these cells are induced upon infection, may have high proliferative potential, and may play a role in providing future effector cells during subsequent infections.