Machine Learning-based Analysis of Publications Funded by the National Institutes of Health's Initial COVID-19 Pandemic Response.

Background: The National Institutes of Health (NIH) mobilized more than $4 billion in extramural funding for the COVID-19 pandemic. Assessing the research output from this effort is crucial to understanding how the scientific community leveraged federal funding and responded to this public health crisis. Methods: NIH-funded COVID-19 grants awarded between January 2020 and December 2021 were identified from NIH Research Portfolio Online Reporting Tools Expenditures and Results using the "COVID-19 Response" filter. PubMed identifications of publications under these grants were collected and the NIH iCite tool was used to determine citation counts and focus (eg, clinical, animal). iCite and the NIH's LitCOVID database were used to identify publications directly related to COVID-19. Publication titles and Medical Subject Heading terms were used as inputs to a machine learning-based model built to identify common topics/themes within the publications. Results and Conclusions: We evaluated 2401 grants that resulted in 14 654 publications. The majority of these papers were published in peer-reviewed journals, though 483 were published to preprint servers. In total, 2764 (19%) papers were directly related to COVID-19 and generated 252 029 citations. These papers were mostly clinically focused (62%), followed by cell/molecular (32%), and animal focused (6%). Roughly 60% of preprint publications were cell/molecular-focused, compared with 26% of nonpreprint publications. The machine learning-based model identified the top 3 research topics to be clinical trials and outcomes research (8.5% of papers), coronavirus-related heart and lung damage (7.3%), and COVID-19 transmission/epidemiology (7.2%). This study provides key insights regarding how researchers leveraged federal funding to study the COVID-19 pandemic during its initial phase.

Analysis of National Institutes of Health Funding for the COVID-19 Pandemic.

Background: Evaluating the National Institute's Health's (NIH's) response to the coronavirus disease 2019 (COVID-19) pandemic via grants and clinical trials is crucial to determining the impact they had on aiding US citizens. We determined how the NIH's funding for COVID-19 research was disbursed and used by various institutions across the United States. Methods: We queried NIH RePORTER and isolated COVID-19-related grants from January 2020 to December 2021. We analyzed grant type, geographical location, and awardee institution. Manuscripts published from these grants were quantitatively analyzed. COVID-19 clinical trials were mapped and distances from counties to clinical trial sites were calculated using ArcGis. Results: A total of 2401 COVID-19 NIH grants resulted in 14 654 manuscripts from $4.2 billion and generated more than 150 000 citations. R01s make up 32% of grants (763/2401) and 8% of funding ($329 million). UM1 grants account for the majority of funding (30.8%; $1.3 Billion). Five states received 50.6% of funding: North Carolina, Washington, New York, California, and Massachusetts. Finally, of the 1806 clinical trials across 1266 sites in the United States, the majority were in metropolitan areas in close proximity to areas of high COVID-19 disease burden. Conclusions and Relevance: Evaluating the outcome of the NIH's response to the COVID-19 pandemic is of interest to the general public. The present study finds that the NIH disbursed more than $4 billion in funding to large consortiums and clinical trials to develop diagnostics, therapeutics, and vaccines. Approximately 8% of funding was used for R01 grants. Clinical trial sites were generally located in areas of high COVID-19 burden.

Bilateral Vitiligo-Like Depigmentation of Choroid and Retinal Pigment Epithelium Associated with Ipilimumab-Nivolumab Therapy for Metastatic Cutaneous Melanoma.

Introduction: Ipilimumab and nivolumab are checkpoint inhibitors that are known to cause a multitude of inflammatory ocular adverse events. Here we report a patient with poliosis and symptomatic depigmentation of the choroid and retinal pigment epithelium (RPE) associated with checkpoint inhibitor therapy for cutaneous melanoma. Case Presentation: The patient presented with floaters in both eyes and concerns for intraocular metastases of metastatic cutaneous melanoma after 1 month of therapy with ipilimumab and nivolumab. External examination revealed poliosis of her eyebrows and eyelashes. Fundus photography demonstrated multiple 1-3 disc-diameter hypopigmented placoid flat areas in the RPE/choroid exposing underlying choroidal vessels in both eyes. At subsequent evaluation 7 months later (after an additional 6 months of checkpoint inhibitor therapy), the lesions appeared more blanched. Evaluation nearly 20 months after the initial presentation showed no significant changes from her prior visit despite cessation of checkpoint inhibitor therapy for 13 months. Conclusion: Checkpoint inhibitor therapy for cutaneous melanoma metastases can cause depigmentation of the choroid and RPE that must be differentiated from progression of intraocular melanoma.

A Nationwide Evaluation of Cardiothoracic Resident Research Productivity.

BACKGROUND: Evaluating the research productivity of cardiothoracic surgery residents during their training and early career is crucial for tracking their academic development. To this end, the training pathway of residents and the characteristics of their program in relation to their productivity were evaluated. METHODS: Alumni lists from integrated 6-year thoracic surgery (I-6) and traditional thoracic surgery residency programs were collected. A Python script was used to search PubMed for publications and the iCite database for citations from each trainee. Publications during a 20-year time span were stratified by the year of publication in relation to the trainee's graduation from thoracic surgery residency. Trainees were analyzed by training program type, institutional availability of a cardiothoracic surgery T32 training grant, and protected academic development time. RESULTS: A total of 741 cardiothoracic surgery graduates (I-6, 70; traditional, 671) spanning 1971 to 2021 from 57 programs published >23,000 manuscripts. I-6 trainees published significantly more manuscripts during medical school and residency compared with traditional trainees. Trainees at institutions with cardiothoracic surgery T32 training grants published significantly more manuscripts than those at non-T32 institutions (13 vs 9; P = .0048). I-6 trainees published more manuscripts at programs with dedicated academic development time compared with trainees at programs without protected time (22 vs 9; P = .004). CONCLUSIONS: I-6 trainees publish significantly more manuscripts during medical school and residency compared with their traditional colleagues. Trainees at institutions with T32 training grants and dedicated academic development time publish a higher number of manuscripts than trainees without those opportunities.

Belzutifan for treatment of giant retinal hemangioblastoma with extrascleral extension associated with von Hippel-Lindau syndrome.

PURPOSE: To describe the clinical response and safety profile of the novel HIF-2ɑ inhibitor belzutifan in treating a giant retinal hemangioblastoma (RHB) with extrascleral extension associated with von Hippel-Lindau syndrome (VHL). METHODS: A 71-year-old woman with VHL presented with a giant RHB with extrascleral extension in her only remaining eye. She had no light perception OD and intraocular pressure (IOP) was 48. She requested enucleation due to chronic pain, but, due to concern for significant bleeding given the size of the neoplasm, a trial of belzutifan was initiated. RESULTS: Within three months of treatment initiation, the patient reported an 80% reduction in pain. MRI showed 30% reduction in longest tumor diameter. Dose adjustments were guided by serum hemoglobin levels, allowing the patient to remain on the medication for over a year with continued tumor regression on MRI and avoid enucleation. CONCLUSION: RHB with extrascleral extension is exceedingly rare and its treatment is complex, often requiring enucleation or external beam radiotherapy. This report demonstrates the use of belzutifan to safely and successfully reduce ocular tumor burden of complicated RHB with extrascleral extension, ultimately decreasing the need for enucleation.

Clinical Applications of Machine Learning in the Management of Intraocular Cancers: A Narrative Review.

Purpose: There is great promise in use of machine learning (ML) for the diagnosis, prognosis, and treatment of various medical conditions in ophthalmology and beyond. Applications of ML for ocular neoplasms are in early development and this review synthesizes the current state of ML in ocular oncology. Methods: We queried PubMed and Web of Science and evaluated 804 publications, excluding nonhuman studies. Metrics on ML algorithm performance were collected and the Prediction model study Risk Of Bias ASsessment Tool was used to evaluate bias. We report the results of 63 unique studies. Results: Research regarding ML applications to intraocular cancers has leveraged multiple algorithms and data sources. Convolutional neural networks (CNNs) were one of the most commonly used ML algorithms and most work has focused on uveal melanoma and retinoblastoma. The majority of ML models discussed here were developed for diagnosis and prognosis. Algorithms for diagnosis primarily leveraged imaging (e.g., optical coherence tomography) as inputs, whereas those for prognosis leveraged combinations of gene expression, tumor characteristics, and patient demographics. Conclusions: ML has the potential to improve the management of intraocular cancers. Published ML models perform well, but were occasionally limited by small sample sizes owing to the low prevalence of intraocular cancers. This could be overcome with synthetic data enhancement and low-shot ML techniques. CNNs can be integrated into existing diagnostic workflows, while non-neural networks perform well in determining prognosis.

Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies.

Stroke is an emergency in which delays in treatment can lead to significant loss of neurological function and be fatal. Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. No resource exists that comprehensively assesses artificial intelligence/machine learning (AI/ML)-enabled technologies indicated for the management of ischemic and hemorrhagic stroke. We queried a United States Food and Drug Administration (FDA) database, along with PubMed and private company websites, to identify the recent literature assessing the clinical performance of FDA-approved AI/ML-enabled technologies. The FDA has approved 22 AI/ML-enabled technologies that triage brain imaging for more immediate diagnosis or promote post-stroke neurological/functional recovery. Technologies that assist with diagnosis predominantly use convolutional neural networks to identify abnormal brain images (e.g., CT perfusion). These technologies perform comparably to neuroradiologists, improve clinical workflows (e.g., time from scan acquisition to reading), and improve patient outcomes (e.g., days spent in the neurological ICU). Two devices are indicated for post-stroke rehabilitation by leveraging neuromodulation techniques. Multiple FDA-approved technologies exist that can help clinicians better diagnose and manage stroke. This review summarizes the most up-to-date literature regarding the functionality, performance, and utility of these technologies so clinicians can make informed decisions when using them in practice.

Postdoctoral National Institutes of Health F32 Grants: Broken Pipeline in the Development of Surgeon-Scientists.

OBJECTIVE: We examined trainees in surgery and internal medicine who received National Institutes of Health (NIH) F32 postdoctoral awards to determine their success rates in obtaining future NIH funding. BACKGROUND: Trainees participate in dedicated research years during residency (surgery) and fellowship (internal medicine). They can obtain an NIH F32 grant to fund their research time and have structured mentorship. METHODS: We collected NIH F32 grants (1992-2021) for Surgery Departments and Internal Medicine Departments from NIH RePORTER, an online database of NIH grants. Nonsurgeons and noninternal medicine physicians were excluded. We collected demographic information on each recipient, including gender, current specialty, leadership positions, graduate degrees, and any future NIH grants they received. A Mann-Whitney U test was used for continuous variables, and a χ 2 test was utilized to analyze categorical variables. An alpha value of 0.05 was used to determine significance. RESULTS: We identified 269 surgeons and 735 internal medicine trainees who received F32 grants. A total of 48 surgeons (17.8%) and 339 internal medicine trainees (50.2%) received future NIH funding ( P < 0.0001). Similarly, 24 surgeons (8.9%) and 145 internal medicine trainees (19.7%) received an R01 in the future ( P < 0.0001). Surgeons who received F32 grants were more likely to be department chair or division chiefs ( P =0.0055 and P < 0.0001). CONCLUSIONS: Surgery trainees who obtain NIH F32 grants during dedicated research years are less likely to receive any form of NIH funding in the future compared with their internal medicine colleagues who received F32 grants.

Landscape and future directions of machine learning applications in closed-loop brain stimulation.

Brain stimulation (BStim) encompasses multiple modalities (e.g., deep brain stimulation, responsive neurostimulation) that utilize electrodes implanted in deep brain structures to treat neurological disorders. Currently, BStim is primarily used to treat movement disorders such as Parkinson's, though indications are expanding to include neuropsychiatric disorders like depression and schizophrenia. Traditional BStim systems are "open-loop" and deliver constant electrical stimulation based on manually-determined parameters. Advancements in BStim have enabled development of "closed-loop" systems that analyze neural biomarkers (e.g., local field potentials in the sub-thalamic nucleus) and adjust electrical modulation in a dynamic, patient-specific, and energy efficient manner. These closed-loop systems enable real-time, context-specific stimulation adjustment to reduce symptom burden. Machine learning (ML) has emerged as a vital component in designing these closed-loop systems as ML models can predict / identify presence of disease symptoms based on neural activity and adaptively learn to modulate stimulation. We queried the US National Library of Medicine PubMed database to understand the role of ML in developing closed-loop BStim systems to treat epilepsy, movement disorders, and neuropsychiatric disorders. Both neural and non-neural network ML algorithms have successfully been leveraged to create closed-loop systems that perform comparably to open-loop systems. For disorders in which the underlying neural pathophysiology is relatively well understood (e.g., Parkinson's, essential tremor), most work has involved refining ML models that can classify neural signals as aberrant or normal. The same is seen for epilepsy, where most current research has focused on identifying optimal ML model design and integrating closed-loop systems into existing devices. For neuropsychiatric disorders, where the underlying pathologic neural circuitry is still being investigated, research is focused on identifying biomarkers (e.g., local field potentials from brain nuclei) that ML models can use to identify onset of symptoms and stratify severity of disease.

Fewer Than One in 20 Current Academic Orthopaedic Surgeons Have Obtained National Institutes of Health Funding.

BACKGROUND: National Institutes of Health (NIH) funding is a key driver of orthopaedic research, but it has become increasingly difficult to obtain in recent years. An understanding of the types of grants that are commonly funded, how productive they are, and the factors associated with obtaining funding may help orthopaedic surgeons better understand how to earn grants. QUESTIONS/PURPOSES: In this study, we sought to determine (1) the proportion of current academic orthopaedic surgeons who have obtained NIH grant funding, (2) the productivity of these grants by calculating grant productivity metrics, and (3) the factors (such as gender, subspecialty, and additional degrees) that are associated with obtaining grant funding. METHODS: Current academic orthopaedic surgeons at the top 140 NIH-funded institutions were identified via faculty webpages; 3829 surgeons were identified. Demographic information including gender (men constituted 88% of the group [3364 of 3829]), academic rank (full professors constituted 22% [856 of 3829]), additional degrees (those with MD-PhD degrees constituted 3% [121 of 3829]), leadership positions, and orthopaedic subspecialty was collected. Funding histories from 1985 through 2021 were collected using the NIH Research Portfolio Online Reporting Tools Expenditures and Results. Grant type, funding, publications, and citations of each article were collected. A previously used grant impact metric (total citations per USD 0.1 million) was calculated to assess grant productivity. Multivariable binomial logistic regression was used to evaluate factors associated with obtaining funding. RESULTS: Four percent (150 of 3829) of academic orthopaedic surgeons obtained USD 338.3 million in funding across 301 grants, resulting in 2887 publications over the entire study period. The R01 was the most commonly awarded grant in terms of the total number awarded, at 36% (108 of 301), as well as by funding, publications, and citations, although other grant types including T32, F32, R03, R13, and R21 had higher mean grant impact metrics. There was no difference between men and women in the by-gender percentage of academic orthopaedic surgeons who obtained funding (4% [135 of 3229] versus 3% [15 of 450]; odds ratio 0.9 [95% confidence interval 0.5 to 1.7]; p = 0.80). A department having a single funded PhD researcher may be associated with surgeon-scientists obtaining grant funding, but with the numbers available, we could not demonstrate this was the case (OR 1.4 [95% CI 0.9 to 2.2]; p = 0.12). CONCLUSION: Fewer than one in 20 academic orthopaedic surgeons have received NIH funding. R01s are the most commonly awarded grant, although others demonstrate increased productivity metrics. Future studies should investigate the role of co-principal investigators on productivity and the role of different funding sources. CLINICAL RELEVANCE: Individuals should pursue both R01 and non-R01 grants, and departments should consider cultivating relationships with funded PhDs. The specific research infrastructure and departmental policies of the most productive institutions and grants should be surveyed and emulated.

The impact of the American Association for Thoracic Surgery on National Institutes of Health grant funding for cardiothoracic surgeons.

OBJECTIVES: The American Association for Thoracic Surgery, through its annual meeting, pilot grant funding, Scientific Affairs and Government Relations Committee activity, and academic development programs (Grant Writing Workshop, Clinical Trials Course, Innovation Summit), has aimed to develop the research careers of cardiothoracic surgeons. We hypothesized that American Association for Thoracic Surgery activities have helped increase National Institutes of Health grants awarded to cardiothoracic surgeons. METHODS: A database of 1869 academic cardiothoracic surgeons in the United States was created in December 2020. National Institutes of Health grant records from 1985 to 2020 were obtained for each surgeon using National Institutes of Health Research Portfolio Online Reporting Tools Expenditures and Results. Analyses were normalized to the number of active surgeons per year, based on the year of each surgeon's earliest research publication on Scopus. RESULTS: A total of 346 cardiothoracic surgeons have received 696 National Institutes of Health grants totaling more than $1.5 billion in funding, with 48 surgeons actively serving as principal investigator of 66 R01 grants in 2020. The prevalence of research grants (7.4 vs 5.6 grants per 100 active surgeons, P < .0001), percentage of surgeons with a research grant (5.3% vs 4.7%, P = .0342), and number of research grants per funded surgeon (1.4 vs 1.2 grants, P < .0001) were significantly greater during the Scientific Affairs and Government Relations era (2003-2020) than the pre-Scientific Affairs and Government Relations era (1985-2002). The incidence of new research grants after surgeon participation in an American Association for Thoracic Surgery academic development program was significantly greater than that in the absence of participation (3.5 vs 1.1 new grants per 100 surgeons per year, P < .0001). CONCLUSIONS: Through dedicated efforts and programs, the American Association for Thoracic Surgery has provided effective support to help increase National Institutes of Health grant funding awarded to cardiothoracic surgeons.

Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current protocols assess PD symptoms during clinic visits and can be subjective. Patient diaries can help clinicians evaluate at-home symptoms, but can be incomplete or inaccurate. Therefore, researchers have developed in-home automated methods to monitor PD symptoms to enable data-driven PD diagnosis and management. We queried the US National Library of Medicine PubMed database to analyze the progression of the technologies and computational/machine learning methods used to monitor common motor PD symptoms. A sub-set of roughly 12,000 papers was reviewed that best characterized the machine learning and technology timelines that manifested from reviewing the literature. The technology used to monitor PD motor symptoms has advanced significantly in the past five decades. Early monitoring began with in-lab devices such as needle-based EMG, transitioned to in-lab accelerometers/gyroscopes, then to wearable accelerometers/gyroscopes, and finally to phone and mobile & web application-based in-home monitoring. Significant progress has also been made with respect to the use of machine learning algorithms to classify PD patients. Using data from different devices (e.g., video cameras, phone-based accelerometers), researchers have designed neural network and non-neural network-based machine learning algorithms to categorize PD patients across tremor, gait, bradykinesia, and dyskinesia. The five-decade co-evolution of technology and computational techniques used to monitor PD motor symptoms has driven significant progress that is enabling the shift from in-lab/clinic to in-home monitoring of PD symptoms.

Longitudinal analysis of National Institutes of Health funding for academic thoracic surgeons.

OBJECTIVE: National Institutes of Health (NIH) funding for academic (noncardiac) thoracic surgeons at the top-140 NIH-funded institutes in the United States was assessed. We hypothesized that thoracic surgeons have difficulty in obtaining NIH funding in a difficult funding climate. METHODS: The top-140 NIH-funded institutes' faculty pages were searched for noncardiac thoracic surgeons. Surgeon data, including gender, academic rank, and postfellowship training were recorded. These surgeons were then queried in NIH Research Portfolio Online Reporting Tools Expenditures and Results for their funding history. Analysis of the resulting grants (1980-2019) included grant type, funding amount, project start/end dates, publications, and a citation-based Grant Impact Metric to evaluate productivity. RESULTS: A total of 395 general thoracic surgeons were evaluated with 63 (16%) receiving NIH funding. These 63 surgeons received 136 grants totaling $228 million, resulting in 1772 publications, and generating more than 50,000 citations. Thoracic surgeons have obtained NIH funding at an increasing rate (1980-2019); however, they have a low percentage of R01 renewal (17.3%). NIH-funded thoracic surgeons were more likely to have a higher professorship level. Thoracic surgeons perform similarly to other physician-scientists in converting K-Awards into R01 funding. CONCLUSIONS: Contrary to our hypothesis, thoracic surgeons have received more NIH funding over time. Thoracic surgeons are able to fill the roles of modern surgeon-scientists by obtaining NIH funding during an era of increasing clinical demands. The NIH should continue to support this mission.

Network model-based screen for FDA-approved drugs affecting cardiac fibrosis.

Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and α-SMA expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFß expression and MMP2-driven TGFß activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drug-target mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.

A 30-year analysis of National Institutes of Health-funded cardiac transplantation research: Surgeons lead the way.

OBJECTIVES: Obtaining National Institutes of Health funding for heart transplant research is becoming increasingly difficult, especially for surgeons. We sought to determine the impact of National Institutes of Health-funded cardiac transplantation research over the past 30 years. METHODS: National Institutes of Health Research Portfolio Online Reporting Tools Expenditures and Results was queried for R01s using 10 heart transplant-related terms. Principal Investigator, total grant funding amount, number of publications, and citations of manuscripts were collected. A citation-based Grant Impact Metric was assigned to each grant: sum of citations for each manuscript normalized by the funding of the respective grant (per $100K). The department and background degree(s) (MD, PhD, MD/PhD) for each funded Principal Investigator were identified from institutional faculty profiles. RESULTS: A total of 321 cardiac transplantation R01s totaling $723 million and resulting in 6513 publications were analyzed. Surgery departments received more grants and more funding dollars to study cardiac transplantation than any other department (n = 115, $249 million; Medicine: n = 93, $208 million; Pathology: 26, $55 million). Surgeons performed equally well compared with all other Principal Investigators with respect to Grant Impact Metric (15.1 vs 20.6; P = .19) and publications per $1 million (7.5 vs 6.8; P = .75). Finally, all physician-scientists (MDs) have a significantly higher Grant Impact Metric compared with nonclinician researchers (non-MDs) (22.3 vs 16.3; P = .028). CONCLUSIONS: Surgeon-scientists are equally productive and impactful compared with nonsurgeons despite decreasing funding rates at the National Institutes of Health and greater pressure from administrators to increase clinical productivity.

The Changing Face of Academic Surgery: Overrepresentation of Women among Surgeon-Scientists with R01 Funding.

BACKGROUND: There has been a recent focus on sex-based disparities within the field of academic surgery. However, the proportion of female surgeons conducting NIH-funded research is unknown. STUDY DESIGN: The NIH RePORTER (Research Portfolio Online Reporting Tools Expenditures and Results) was queried for R01 grants from surgery departments for which the principal investigator (PI) had a primary medical degree, as of October 2018. Characteristics of the PI and their respective grants were collected. Institutional faculty profiles were reviewed for PI and departmental characteristics. PIs were stratified by sex and compared using standard univariate statistics. RESULTS: There were a total of 212 R01 grants in surgery departments held by 159 PIs. Of these, 26.4% (n = 42) of R01-funded surgeons were female compared with the reported 19% of academic surgery female faculty (as reported by the Association of American Medical Colleges; p = 0.02). Women with R01 grants were more likely to be first-time grant recipients with no concurrent or previous NIH funding (21.4% vs 8.6%; p = 0.03) and less likely to have a previous R01 or equivalent grant (54.8% vs 73.5%; p = 0.03). Women were more likely to be from departments with a female surgery chair (31.0% vs 13.7%; p = 0.01) or a department with > 30% female surgeons (35.0% vs 18.2%; p = 0.03). CONCLUSIONS: Although female surgeons remain a minority in academic surgery, they hold a greater than anticipated proportion of NIH funding, with a high number of first-time grants, forming a crucial component of the next generation of surgeon-scientists.

Computational model predicts paracrine and intracellular drivers of fibroblast phenotype after myocardial infarction.

The fibroblast is a key mediator of wound healing in the heart and other organs, yet how it integrates multiple time-dependent paracrine signals to control extracellular matrix synthesis has been difficult to study in vivo. Here, we extended a computational model to simulate the dynamics of fibroblast signaling and fibrosis after myocardial infarction (MI) in response to time-dependent data for nine paracrine stimuli. This computational model was validated against dynamic collagen expression and collagen area fraction data from post-infarction rat hearts. The model predicted that while many features of the fibroblast phenotype at inflammatory or maturation phases of healing could be recapitulated by single static paracrine stimuli (interleukin-1 and angiotensin-II, respectively), mimicking the reparative phase required paired stimuli (e.g. TGFß and endothelin-1). Virtual overexpression screens simulated with either static cytokine pairs or post-MI paracrine dynamic predicted phase-specific regulators of collagen expression. Several regulators increased (Smad3) or decreased (Smad7, protein kinase G) collagen expression specifically in the reparative phase. NADPH oxidase (NOX) overexpression sustained collagen expression from reparative to maturation phases, driven by TGFß and endothelin positive feedback loops. Interleukin-1 overexpression had mixed effects, both enhancing collagen via the TGFß positive feedback loop and suppressing collagen via NFκB and BAMBI (BMP and activin membrane-bound inhibitor) incoherent feed-forward loops. These model-based predictions reveal network mechanisms by which the dynamics of paracrine stimuli and interacting signaling pathways drive the progression of fibroblast phenotypes and fibrosis after myocardial infarction.

Comprehensive National Institutes of Health funding analysis of academic cardiac surgeons.

OBJECTIVE: To determine trends in National Institutes of Health (NIH) funding for cardiac surgeons, hypothesizing they are at a disadvantage in obtaining funding owing to intensive clinical demands. METHODS: Cardiac surgeons (adult/congenital) currently at the top 141 NIH-funded institutions were identified using institutional websites. The NIH funding history for each cardiac surgeon was queried using the NIH Research Portfolio Online Reporting Tools Expenditures and Results (RePORTER). Total grant funding, publications, and type was collected. Academic rank, secondary degrees, and fellowship information was collected from faculty pages. Grant productivity was calculated using a validated grant impact metric. RESULTS: A total of 818 academic cardiac surgeons were identified, of whom 144 obtained 293 NIH grants totaling $458 million and resulting in 6694 publications. We identified strong associations between an institution's overall NIH funding rank and the number of cardiac surgeons, NIH grants to cardiac surgeons, and amount of NIH funding to cardiac surgeons (P < .0001 for all). The majority of NIH funding to cardiac surgeons is concentrated in the top quartile of institutions. Cardiac surgeons had a high conversion rates from K awards (mentored development awards) to R01s (6 of 14; 42.9%). Finally, we demonstrate that the rate of all NIH grants awarded to cardiac surgeons has increased, driven primarily by P and U (collaborative project) grants. CONCLUSIONS: NIH-funded cardiac surgical research has had a significant impact over the last 3 decades. Aspiring cardiac surgeon-scientists may be more successful at top quartile institutions owing to better infrastructure and mentorship.

Cardiothoracic and Vascular Surgeons Achieve High Rates of K Award Conversion Into R01 Funding.

BACKGROUND: Obtaining National Institutes of Health (NIH) R01 funding remains extremely difficult. The utility of career development grants (K awards) for achieving the goal of R01 funding remains debated, particularly for surgeon-scientists. We examined the success rate for cardiothoracic and vascular (CTV) surgeons compared with other specialties in converting K-level grants into R01 equivalents. METHODS: All K (K08 and K23) grants awarded to surgeons by the NIH between 1992 and 2017 were identified through NIH Research Portfolio Online Report Tools (RePORTER), an online database combining funding, publications, and patents. Only grants awarded to CTV surgeons were included. Grants active within the past year were excluded. Mann-Whitney U tests and χ2 tests were used to compare groups. RESULTS: During this period, 62 K grants were awarded to CTV surgeons. The analysis excluded 16 grants that were still active within the last year. Twenty-two (48%) of the remaining K awardees successfully transitioned to an R01 or equivalent grant. Awardees with successful conversion published nine publications per K grant compared with four publications for those who did not convert successfully (p = 0.01). The median time for successful conversion to an R grant was 5.0 years after the K award start date. Importantly, the 10-year conversion rate to R01 was equal for CTV surgeons compared with other clinician-investigators (52.6% vs 42.5%). CONCLUSIONS: CTV surgeons have an equal 10-year conversion rate to the first R01 award compared with other clinicians. These data suggest that NIH achieves a good return on investment when funding CTV surgeon-scientists with K-level funding.