publications

2026

1. Learnable Protein Representations in Computational Biology for Predicting Drug-Target Affinity

   Rachit Kumar, Joseph Romano, and Marylyn Ritchie

   Journal of Cheminformatics, Jan 2026

   DOI
2. SAIGE-GPU: Accelerating Genome- and Phenome-Wide Association Studies Using GPUs

   Alex Rodriguez, Youngdae Kim, Tarak Nath Nandi, Karl Keat, Rachit Kumar, Mitchell Conery, Rohan Bhukar, Molei Liu, John Hessington, Ketan Maheshwari,  VA Million Veteran Program, Edmon Begoli, Georgia Tourassi, Sumitra Muralidhar, Pradeep Natarajan, Benjamin F Voight, Kelly Cho, John Michael Gaziano, Scott M Damrauer, Katherine P Liao, Wei Zhou, Jennifer E Huffman, Anurag Verma, and Ravi K Madduri

   Bioinformatics, Mar 2026

   Abs DOI

   Genome-wide association studies (GWAS) at biobank scale are computationally intensive, especially for admixed populations requiring robust statistical models. SAIGE is a widely used method for generalized linear mixed-model GWAS but is limited by its CPU-based implementation, making phenome-wide association studies impractical for many research groups.We developed SAIGE-GPU, a GPU-accelerated version of SAIGE that replaces CPU-intensive matrix operations with GPU-optimized kernels. The core innovation is distributing genetic relationship matrix calculations across GPUs and communication layers. Applied to 2068 phenotypes from 635 969 participants in the Million Veteran Program, including diverse and admixed populations, SAIGE-GPU achieved a 5-fold speedup in mixed model fitting on supercomputing infrastructure and cloud platforms. We further optimized the variant association testing step through multi-core and multi-trait parallelization. Deployed on Google Cloud Platform and Azure, the method provided substantial cost and time savings.Source code and binaries are available for download at https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3. A code snapshot is archived at Zenodo for reproducibility (DOI: [10.5281/zenodo.17642591]). SAIGE-GPU is available in a containerized format for use across HPC and cloud environments and is implemented in R/C++ and runs on Linux systems.

3. Genome-Wide Association Study of Coronary Flow Reserve Assessed by Cardiac Perfusion PET Suggests a Role for NF-kappaB Pathway

   Rasika Venkatesh, Tess Cherlin, Nicole Wayne, Rachit Kumar, Lindsay Guare, Venkata P. Singamneni, Brett Irving, Scott Dudek, Michael G. Levin, Shefali Setia-Verma, and Marie A. Guerraty

   Nature Cardiovascular Research, Jun 2026

   Abs DOI

   Coronary microvascular disease (CMVD) contributes to the large burden of ischemic heart disease (IHD), and there is a need for mechanistic insight and targeted therapies. Perfusion cardiac PET allows for the quantitative assessment of coronary flow reserve (CFR), which reflects coronary microvascular function. Here we use cardiac PET CFR as a measure of CMVD to perform first a gene association study with previously reported IHD loci and, second, an exploratory genome-wide association study. Of 241 known IHD loci, we identify 45 and 18 loci that are associated with CFR in populations genetically similar to African and European populations, respectively. We then perform a genome-wide association study followed by downstream and pathway analyses and identified an association between loci associated with CFR and the NF-κB pathway. We support these associations with targeted proteomic data. Our multi-omic analyses identified potential CMVD loci and suggest a role for the NF-κB pathway in CMVD.

4. Platform-Dependent Effects of Genetic Variants on Plasma APOL1

   Qingbo S. Wang, Jinguo Huang, Leanne J. G. Chan, Nicole Haste, Niclas Olsson, Aleksandr Gaun, Fiona E. McAllister, Deepthi Madhireddy, Amos Baruch, Katie M. Cardone, Rachit Kumar, Marylyn Ritchie, Katalin Susztak, Eugene Melamud, and Anastasia Baryshnikova

   iScience, Mar 2026

   DOI
5. AE-PocketMiner Uses Attention to Simultaneously Predict Cryptic Pockets and Their Allosteric Coupling

   Si Zhang, Prajna Mishra, Devin Kelly, Rachit Kumar, and Gregory R. Bowman

   bioRxiv, Mar 2026

2025

1. The Moment AI Arrived in the Clinic: Insights from the SAIL 2025 Year in Review

   Pierre Elias, Kameron Collin Black, Payal Chandak, Elizabeth Healey, Rachit Kumar, Michelle M. Li, Hugo Morales, Brett Beaulieu Jones, and Emily Alsentzer

   NEJM AI, Oct 2025

   DOI
2. CASTER-DTA: Equivariant Graph Neural Networks for Predicting Drug–Target Affinity

   Rachit Kumar, Joseph D Romano, and Marylyn D Ritchie

   Briefings in Bioinformatics, Sep 2025

   Abs DOI

   Accurately determining the binding affinity of a ligand with a protein is important for drug design, development, and screening. With the advent of accessible protein structure prediction methods such as AlphaFold, predicted protein 3D structures are readily available; however, scalable methods for predicting binding affinity currently do not take full advantage of 3D protein information. Here, we present CASTER-DTA (Cross-Attention with Structural Target Equivariant Representations for Drug–Target Affinity), which uses an equivariant graph neural network (GNN) to learn more robust protein representations alongside a standard GNN to learn molecular representations to predict DTA. We augment these representations by incorporating an attention-based mechanism between protein residues and drug atoms to improve interpretability. We show that CASTER-DTA represents a state-of-the-art improvement on multiple benchmarks for predicting DTA, and that it generates novel insights for several related tasks. We then apply CASTER-DTA to create a large resource of the binding affinities of every drug approved by the U.S. Food and Drug Administration (FDA) against every protein in the human proteome and make these predictions freely available for download. We also make available a web server for researchers to apply a pretrained CASTER-DTA model for predicting binding affinities between arbitrary proteins and drugs.

3. Graph Neural Networks for Interpretable Biomedical Data Analysis in Genomics and Structural Biology

   Rachit Kumar

   University of Pennsylvania, Sep 2025
4. Network-Based Analyses of Multiomics Data in Biomedicine

   Rachit Kumar, Joseph D. Romano, and Marylyn D. Ritchie

   BioData Mining, May 2025

   DOI
5. Quantitative Electroencephalogram and Machine Learning to Predict Expired Sevoflurane Concentration in Infants

   Rachit Kumar, Justin Skowno, Britta S. Von Ungern-Sternberg, Andrew Davidson, Ting Xu, Jianmin Zhang, XingRong Song, Mazhong Zhang, Ping Zhao, Huacheng Liu, Yifei Jiang, Yunxia Zuo, Jurgen C. De Graaff, Laszlo Vutskits, Vanessa A. Olbrecht, Peter Szmuk, Allan F. Simpao, Fuchiang Rich Tsui, Jayant Nick Pratap, Asif Padiyath, Olivia Nelson, Charles D. Kurth, Ian Yuan,  BRAIN Collaborative Investigators, Minal Menezes, and 17 more authors

   Journal of Clinical Monitoring and Computing, May 2025

   Abs DOI

   Abstract Processed electroencephalography (EEG) indices used to guide anesthetic dosing in adults are not validated in young infants. Raw EEG can be processed mathematically, yielding quantitative EEG parameters (qEEG). We hypothesized that machine learning combined with qEEG can accurately classify expired sevoflurane concentrations in young infants. Knowledge from this may contribute to development of future infant-specific EEG algorithms. Frontal EEG collected from infants\,≤ 3 months were time-matched as one-minute epochs to expired sevoflurane (eSevo). Fifteen qEEG parameters were extracted from each epoch and eight machine learning models combined the qEEG to classify each epoch into one of four eSevo levels (%): 0.1–1.0, 1.0–2.1, 2.1–2.9, and\,> 2.9. 64 epochs formed the p ost hoc SHAP dataset to determine the qEEG that contributed most to the model. The remaining epochs were randomly split 50 times into 80/20 training/testing sets. Accuracy and F1-score determined model performance. 42 infants provided 4574 epochs. The top classifiers K-nearest neighbors, default multi-layer perceptron, and support vector machine achieved 67.5–68.7% accuracy. Burst suppression ratio and entropy β were the top contributors to the models. Post hoc analysis performed without burst suppression ratio yielded similar prediction performance. In young infants, machine learning applied to qEEG predicted eSevo levels with moderate success. Burst suppression ratio, the most important contributor, represented an efficient EEG feature that encapsulated underlying EEG changes seen on other qEEG features. These results provided insight into EEG parameter selection and optimal machine learning models used for future development of infant-specific EEG algorithms.

6. Integrative Multi-omics Approaches Identify Molecular Pathways and Improve Alzheimer’s Disease Risk Prediction

   Rasika Venkatesh, Katie M. Cardone, Yuki Bradford, Anni K. Moore, Rachit Kumar, Jason H. Moore, Li Shen, Dokyoon Kim, and Marylyn D. Ritchie

   Alzheimer’s & Dementia, Nov 2025

   Abs DOI

   Abstract INTRODUCTION Alzheimer’s disease (AD) is a complex neurodegenerative disorder with heterogeneous genetic and molecular underpinnings. Polygenic scores (PGS) capture little of this complexity. METHODS We conducted genome-, transcriptome-, and proteome-wide association studies (G/T/PWAS) on 15,480 individuals from the Alzheimer’s Disease Sequencing Project R4 (ADSP) to identify AD-associated signals, followed by pathway enrichment analysis. Integrative risk models (IRMs) were developed using genetically regulated components of gene and protein expression and clinical covariates. Elastic-net logistic regression and random forest classifiers were evaluated using standard metrics and compared against baseline PGS. RESULTS Known and novel signals were identified via G/T/PWAS. Enrichment analyses highlighted cholesterol and immune signaling pathways. The best-performing IRM, random forest with transcriptomic and covariate features, achieved area under the receiver operating characteristic (AUROC) of 0.703 and area under the precision-recall curve (AUPRC) of 0.622, significantly outperforming PGS and baseline models. DISCUSSION Integrating univariate discovery approaches with multivariate modeling enhances AD risk prediction and offers novel insights into underlying biological processes. Highlights Identified novel contributions to Alzheimer’s disease (AD) from a multi-omics perspective. Integrated genome-wide association studies (GWAS), transcriptome-wide association studies (TWAS), and proteome-wide association studies (PWAS) in a unified association study framework. Developed a method for predicting heritable risk of late-onset AD. Demonstrated that ancestry-aware modeling improves AD risk prediction accuracy.

7. Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates

   Jacob D Washburn, José Ignacio Varela, Alencar Xavier, Qiuyue Chen, David Ertl, Joseph L Gage, James B Holland, Dayane Cristina Lima, Maria Cinta Romay, Marco Lopez-Cruz, Gustavo de los Campos, Wesley Barber, Cristiano Zimmer, Ignacio Trucillo Silva, Fabiani Rocha, Renaud Rincent, Baber Ali, Haixiao Hu, Daniel E Runcie, Kirill Gusev, Andrei Slabodkin, Phillip Bax, Julie Aubert, Hugo Gangloff, Tristan Mary-Huard, and 34 more authors

   Genetics, Feb 2025

   Abs DOI

   Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023, the first open-to-the-public Genomes to Fields initiative Genotype by Environment prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements, and field management notes gathered by the project over 9 years. The competition attracted registrants from around the world with representation from academic, government, industry, and nonprofit institutions as well as unaffiliated. These participants came from diverse disciplines, including plant science, animal science, breeding, statistics, computational biology, and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner’s strategy involved 2 models combining machine learning and traditional breeding tools: 1 model emphasized environment using features extracted by random forest, ridge regression, and least squares, and 1 focused on genetics. Other high-performing teams’ methods included quantitative genetics, machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics, weather, and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

2024

1. Pathways and Barriers to Becoming Physician-Scientists for First-Generation Individuals

   Briana Christophers, Briana Macedo, Jessica Weng, Michael C. Granovetter, Rachit Kumar, Chynna Smith, Olaf S. Andersen, and Catharine Boothroyd

   medRxiv, Feb 2024
2. PGxQA: A Resource for Evaluating LLM Performance for Pharmacogenomic QA Tasks

   Karl Keat, Rasika Venkatesh, Yidi Huang, Rachit Kumar, Sony Tuteja, Katrin Sangkuhl, Binglan Li, Li Gong, Michelle Whirl-Carrillo, Teri E. Klein, Marylyn D. Ritchie, and Dokyoon Kim

   In Biocomputing 2025, Dec 2024

   DOI
3. A Comprehensive Bibliometric Analysis: Celebrating the Thirtieth Anniversary of the Pacific Symposium on Biocomputing

   Rachit Kumar, Rasika Venkatesh, David Y. Zhang, Teri E. Klein, and Marylyn D. Ritchie

   In Biocomputing 2025, Dec 2024

   DOI
4. Genetic Algorithm Selection of Interacting Features (GASIF) for Selecting Biological Gene-Gene Interactions

   Rachit Kumar, David Zhang, and Marylyn DeRiggi Ritchie

   In Proceedings of the Genetic and Evolutionary Computation Conference, Jul 2024

   DOI
5. SABER: Statistical Identification of Loci of Interest in GWAS Summary Statistics Using a Bayesian Gaussian Mixture Model

   Rachit Kumar, Rasika Venkatesh, and Marylyn D. Ritchie

   AMIA Summits on Translational Science Proceedings, Jul 2024
6. Known and Novel Genes Significant to AD across Tissues May Help to Uncover Comprehensive Changes of Disease

   Anni Moore, Yuki Bradford, Rachit Kumar, Rasika Venkatesh, Manu Shivakumar, Jingxuan Bao, Li Shen, Dokyoon Kim, and Marylyn D. Ritchie

   Alzheimer’s & Dementia, Dec 2024

   Abs DOI

   Abstract Background Alzheimer’s disease (AD) is neurodegenerative disease brought on by a combination of changes in multiple pathways that conglomerate to promote disease progression. AD often occurs alongside comorbid diseases, most often immune or vascular in nature, which have been shown to further increase AD risk. We previously showed that known AD variants also associate with secondary diseases in these categories, including rheumatoid arthritis, ischemic myocardial infarction, and both Type 1 and Type 2 diabetes. To better understand which pathways play a significant role in AD etiology and how comorbid diseases may contribute to risk through shared pathways, we aimed to identify gene expression changes associated with AD status in tissues throughout the body. Method We conducted a transcriptome-wide association study (TWAS) using genotypes from the Alzheimer’s Disease Sequencing Project (ADSP) including 11,074 cases and 14,310 controls from multiple ancestry groups and reference expression-trait quantitative loci (eQTLs) from 49 tissues in Genotype-Tissue Expression (GTEx) Project (v8). Using an ensemble approach we first predicted genetically regulated gene expression (GREx) and gene-associations per tissue within each of the 49 tissues available in GTEx using PrediXcan. Secondly, to better predict cross-tissue signals we used MulTiXcan with GREx to test for gene-associations across tissues. Result Both known and novel genes appeared in the single tissue and cross tissue gene associations. Previously AD-associated genes PET117 , RAB35 , NECTIN2 , APOC1 , and TOMM40 were significantly associated (p<1.51e-7) with AD across tissues, with many of these known to lie within the APOE region. Additionally, genes previously unreported with AD, but involved in relevant disease processes including macro-autophagy ( SUPT20H ), nucleosome binding ( HMGN2 ), histone acetylation ( KAT14 ), DNA damage repair ( HFM1 ), and immune response ( TSPAN2 ) showed significance. Many of these novel genes have previously been associated with immune, vascular and other neurological diseases, many of which are better known to appear as AD comorbidities. Conclusion In addition to known APOE region genes, novel genes with implications relevant to AD etiology and ties to other immune, vascular, and neurological diseases also appeared significant across tissues. Next we will investigate how these gene expression changes apply to pathways within relevant tissues.

7. Biomechanical Analysis of Complications Following T10-Pelvis Spinal Fusion: A Population Based Computational Study

   Austin Q. Nguyen, Christian Rodriguez, Rachit Kumar, Sachin Gupta, Dennis E. Anderson, and Comron Saifi

   Journal of Biomechanics, Dec 2024
8. The Alzheimer’s Knowledge Base: A Knowledge Graph for Alzheimer Disease Research

   Joseph D. Romano, Van Truong, Rachit Kumar, Mythreye Venkatesan, Britney E. Graham, Yun Hao, Nick Matsumoto, Xi Li, Zhiping Wang, and Marylyn D. Ritchie

   Journal of Medical Internet Research, Dec 2024

2023

1. The Fourth Annual Carnegie Mellon Libraries Hackathon for Biomedical Data Management, Knowledge Graphs, and Deep Learning

   Jędrzej Kubica, Rachit Kumar, Glenda Tan, Van Q. Truong, David Enoma, Nicholas Cooley, Minhyek Jeon, C. Lin, Minh Tran, and Amrita Roy Choudhury

   Dec 2023
2. Extending Tree-Based Automated Machine Learning to Biomedical Image and Text Data Using Custom Feature Extractors

   Rachit Kumar, Joseph Romano, Marylyn Ritchie, and Jason Moore

   In Proceedings of the Companion Conference on Genetic and Evolutionary Computation, Jul 2023

   DOI
3. Aging and Visual Presentations in MRI Environments

   Jenny Walker, Rachit Kumar, Marvin Hoo, and Mark E. Wheeler

   Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, Mar 2023

   Abs DOI

   As individuals age, their vision tends to decline. These changes are natural, but often neglected when designing tasks for both older and younger adults. For instance, many MRI machine set-ups include in-bore displays. Ongoing work in our lab suggests that some older adults have issues seeing stimuli on these displays if there is a visual noise component. This is a problem that did not occur with younger adult samples. Therefore, this work provides an example of how this concern was addressed using a psychophysical thresholding technique. We hope that our experience will inform others who are facing similar issues and/or seeking suggestions for improving their patient and participants’ scanning experiences.

2022

1. Intraoperative Hypotension Is Not Correlated with Acute Kidney Injury during Spinal Fusion Surgery

   Rachel Blue, Alexis Gutierrez, Hasan S. Ahmad, Maya Alexis, Rachit Kumar, Michael Spadola, Connor Wathen, Mitchell Weinstein, and Dmitriy Petrov

   International Journal of Spine Surgery, Mar 2022
2. The Virtual Summer Research Program: Supporting Future Physician-Scientists from Underrepresented Backgrounds

   Briana Macedo, Briana Christophers, Rio Barrere-Cain, Yentli Soto Albrecht, Michael C. Granovetter, Rachit Kumar, Dania Daye, Elizabeth Bhoj, Lawrence Brass, and Jose Alexandre Rodrigues

   Journal of Clinical and Translational Science, Mar 2022
3. Environmental Enrichment Leads to Behavioral Circadian Shifts Enhancing Brain-Wide Functional Connectivity between Sensory Cortices and Eliciting Increased Hippocampal Spiking

   Francis AM Manno, Ziqi An, Rachit Kumar, Junfeng Su, Jiaming Liu, Ed X. Wu, Jufang He, Yanqiu Feng, and Condon Lau

   Neuroimage, Mar 2022
4. Structural and Functional Hippocampal Correlations in Environmental Enrichment during the Adolescent to Adulthood Transition in Mice

   Francis AM Manno, Rachit Kumar, Ziqi An, Muhammad Shehzad Khan, Junfeng Su, Jiaming Liu, Ed X. Wu, Jufang He, Yanqiu Feng, and Condon Lau

   Frontiers in Systems Neuroscience, Mar 2022
5. Quality Control Procedures for Genome-Wide Association Studies

   Van Q. Truong, Jakob A. Woerner, Tess A. Cherlin, Yuki Bradford, Anastasia M. Lucas, Chelsea C. Okeh, Manu K. Shivakumar, Daniel H. Hui, Rachit Kumar, Milton Pividori, S. Chris Jones, Abigail C. Bossa, Stephen D. Turner, Marylyn D. Ritchie, and Shefali S. Verma

   Current Protocols, Nov 2022

   Abs DOI

   Abstract Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of many complex diseases. Regardless of the context, the practical utility of this information ultimately depends upon the quality of the data used for statistical analyses. Quality control (QC) procedures for GWAS are constantly evolving. Here, we enumerate some of the challenges in QC of genotyped GWAS data and describe the approaches involving genotype imputation of a sample dataset along with post-imputation quality assurance, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of the GWAS data (genotyped and imputed), including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We provide detailed guidelines along with a sample dataset to suggest current best practices and discuss areas of ongoing and future research. \copyright 2022 Wiley Periodicals LLC.

2021

1. Supporting Equity and Inclusion of Deaf and Hard-of-Hearing Individuals in Professional Organizations

   Julia Jones Huyck, Kelsey L. Anbuhl, Brad N. Buran, Henry J. Adler, Samuel R. Atcherson, Ozan Cakmak, Robert T. Dwyer, Morgan Eddolls, Fadhel El May, Juergen-Theodor Fraenzer, Rebekah Funkhouser, Mathilde Gagliardini, Frederick J. Gallun, Raymond L. Goldsworthy, Samir Gouin, Joseph Heng, Ariel Edward Hight, Zina Jawadi, Damir Kovacic, Rachit Kumar, Santosh Kumar, Stacey R. Lim, Chengeng Mo, Lisa S. Nolan, Alexandra Parbery-Clark, and 17 more authors

   Frontiers in Education, Oct 2021

   Abs DOI

   Disability is an important and often overlooked component of diversity. Individuals with disabilities bring a rare perspective to science, technology, engineering, mathematics, and medicine (STEMM) because of their unique experiences approaching complex issues related to health and disability, navigating the healthcare system, creatively solving problems unfamiliar to many individuals without disabilities, managing time and resources that are limited by physical or mental constraints, and advocating for themselves and others in the disabled community. Yet, individuals with disabilities are underrepresented in STEMM. Professional organizations can address this underrepresentation by recruiting individuals with disabilities for leadership opportunities, easing financial burdens, providing equal access, fostering peer-mentor groups, and establishing a culture of equity and inclusion spanning all facets of diversity. We are a group of deaf and hard-of-hearing (D/HH) engineers, scientists, and clinicians, most of whom are active in clinical practice and/or auditory research. We have worked within our professional societies to improve access and inclusion for D/HH individuals and others with disabilities. We describe how different models of disability inform our understanding of disability as a form of diversity. We address heterogeneity within disabled communities, including intersectionality between disability and other forms of diversity. We highlight how the Association for Research in Otolaryngology has supported our efforts to reduce ableism and promote access and inclusion for D/HH individuals. We also discuss future directions and challenges. The tools and approaches discussed here can be applied by other professional organizations to include individuals with all forms of diversity in STEMM.

2. Hearing Loss Impacts Gray and White Matter across the Lifespan: Systematic Review, Meta-Analysis and Meta-Regression

   Francis AM Manno, Raul Rodríguez-Cruces, Rachit Kumar, J. Tilak Ratnanather, and Condon Lau

   NeuroImage, Oct 2021
3. Structural Alterations in a Rat Model of Short-Term Conductive Hearing Loss Are Associated with Reduced Resting State Functional Connectivity

   Francis AM Manno, Ziqi An, Rachit Kumar, Ed X. Wu, Jufang He, Yanqiu Feng, and Condon Lau

   Frontiers in Systems Neuroscience, Oct 2021

2020

1. Challenges and Advice for MD/PhD Applicants Who Are Underrepresented in Medicine

   Carl Bannerman, Natalie Guzman, Rachit Kumar, Chelsea Nnebe, Jordan Setayesh, Amitej Venapally, and Jonathan H. Sussman

   Molecular Biology of the Cell, Nov 2020

   Abs DOI

   The importance of diversity is self-evident in medicine and medical research. Not only does diversity result in more impactful scientific work, but diverse teams of researchers and clinicians are necessary to address health disparities and improve the health of underserved communities. MD/PhD programs serve an important role in training physician-scientists, so it is critical to ensure that MD/PhD students represent diverse backgrounds and experiences. Groups who are underrepresented in medicine and the biomedical sciences include individuals from certain racial and ethnic backgrounds, individuals with disabilities, individuals from disadvantaged backgrounds, and women. However, underrepresented students are routinely discouraged from applying to MD/PhD programs due to a range of factors. These factors include the significant cost of applying, which can be prohibitive for many students, the paucity of diverse mentors who share common experiences, as well as applicants’ perceptions that there is inadequate support and inclusion from within MD/PhD programs. By providing advice to students who are underrepresented in medicine and describing steps programs can take to recruit and support minority applicants, we hope to encourage more students to consider the MD/PhD career path that will yield a more productive and equitable scientific and medical community.

2. Glymphatic Clearance of Simulated Silicon Dispersion in Mouse Brain Analyzed by Laser Induced Breakdown Spectroscopy

   Muhammad Shehzad Khan, Rachit Kumar, Sinai HC Manno, Irfan Ahmed, Alan Wing Lun Law, Raul R. Cruces, Victor Ma, William C. Cho, Shuk Han Cheng, and Condon Lau

   Heliyon, Nov 2020