Common outpatient diagnoses and associated treatments logged by osteopathic medical students within a geriatric population.

CONTEXT: Clinical clerkships provide osteopathic medical students the opportunity to participate in the diagnosis and treatment of commonly encountered medical conditions. Appropriate management of these conditions may include pharmacotherapy and/or nonpharmacologic interventions, such as osteopathic manipulative treatment (OMT). Opportunities may exist to expand the utilization of OMT in the management of common conditions, particularly for geriatric patients, who are at increased risk for adverse outcomes from pharmacologic treatments. OBJECTIVES: This study aimed to assess the most common diagnoses and corresponding treatments logged by osteopathic medical students within an ambulatory geriatric population. METHODS: Patient encounters logged electronically by osteopathic medical students were retrospectively reviewed to determine the most commonly reported diagnostic codes and their treatments. Logged interventions were filtered to include patients over the age of 65 years who were seen on family medicine rotations within an ambulatory setting. The top 10 diagnoses were sorted and assessed to determine the associated treatments, including medications, procedures, and OMT. RESULTS: Between January 2018 and June 2020, a total of 11,185 primary diagnoses were logged pertaining to the defined patient population. The most frequently documented diagnoses were essential hypertension (n=1,420; 12.7 %), encounter for well examination (n=1,144; 10.2 %), type 2 diabetes mellitus (n=837; 7.5 %), hyperlipidemia (n=346; 3.1 %), chronic obstructive pulmonary disease (COPD; n=278; 2.5 %), osteoarthritis (OA; n=221; 2.0 %), low back pain (LBP; n=202; 1.8 %), pain in joint (n=187; 1.7 %), hypothyroidism (n=164; 1.5 %), and urinary tract infections (n=160; 1.4 %). Three of the top 10 logged diagnoses were musculoskeletal in nature (OA, LBP, and pain in joint). Pharmacotherapy was reported as the predominant treatment for musculoskeletal conditions, with OMT being logged as a treatment for 10.9 % (n=50) of those cases. The most commonly logged medication class in the management of patients with those musculoskeletal conditions was nonsteroidal anti-inflammatory drugs (NSAIDs; n=128; 27.9 %), while opioids were the second most frequently documented class of medications (n=65; 14.2 %). CONCLUSIONS: Musculoskeletal complaints were commonly logged by osteopathic medical students within the studied population. Opioids were documented as a treatment for musculoskeletal conditions more frequently than OMT. As such, opportunities exist to expand the utilization of OMT during clinical clerkships and to decrease the frequency of prescribed medications for pain management.

Tracing the Genetic Evolution of Canine Parvovirus Type 2 (CPV-2) in Thailand.

Canine parvovirus type 2 (CPV-2) is responsible for hemorrhagic gastroenteritis in dogs worldwide. High genomic substitution rates in CPV-2 contribute to the progressive emergence of novel variants with increased ability to evade the host immune response. Three studies have analyzed the genomic mutations of CPV-2 variants in Thailand. These investigations were independently conducted at different timepoints. Thus, a retrospective integrated analysis of CPV-2 genomic mutations has not been fully performed. Our study aimed at evaluating the evolutionary changes in CPV-2 in Thailand from 2003 to 2019. Two hundred and sixty-eight Thai CPV-2 nucleotide sequences were used for multiple amino acid sequence alignment and phylogenetic analyses. From 2003 to 2010, CPV-2a and -2b were the only variants detected. CPV-2c, emerged in 2014, replacing CPV-2a and -2b, and has become a major variant in 2019. Phylogenetic analysis revealed that the proposed mutation pattern of VP2 amino acid residues could help distinguish Thai CPV-2 variants. This comprehensive examination provides insight into the genomic evolution of CPV-2 in Thailand since its first reporting in 2003, which may facilitate the surveillance of the potential genetic alteration of emergent CPV-2 variants.

Estimating the prevalence of text overlap in biomedical conference abstracts.

BACKGROUND: Scientists communicate progress and exchange information via publication and presentation at scientific meetings. We previously showed that text similarity analysis applied to Medline can identify and quantify plagiarism and duplicate publications in peer-reviewed biomedical journals. In the present study, we applied the same analysis to a large sample of conference abstracts. METHODS: We downloaded 144,149 abstracts from 207 national and international meetings of 63 biomedical conferences. Pairwise comparisons were made using eTBLAST: a text similarity engine. A domain expert then reviewed random samples of highly similar abstracts (1500 total) to estimate the extent of text overlap and possible plagiarism. RESULTS: Our main findings indicate that the vast majority of textual overlap occurred within the same meeting (2%) and between meetings of the same conference (3%), both of which were significantly higher than instances of plagiarism, which occurred in less than .5% of abstracts. CONCLUSIONS: This analysis indicates that textual overlap in abstracts of papers presented at scientific meetings is one-tenth that of peer-reviewed publications, yet the plagiarism rate is approximately the same as previously measured in peer-reviewed publications. This latter finding underscores a need for monitoring scientific meeting submissions - as is now done when submitting manuscripts to peer-reviewed journals - to improve the integrity of scientific communications.

Evaluating disease outbreaks with syndromic surveillance using medical student clinical rotation patient encounter logs.

Context: While the data generated by medical students at schools that require electronic patient encounter logs is primarily used to monitor their training progress, it can also be a great source of public health data. Specifically, it can be used for syndromic surveillance, a method used to analyze instantaneous health data for early detection of disease outbreaks. Objective: To analyze how the International Classification of Diseases, 10th Revision (ICD-10) codes input by medical students at the Edward Via College of Osteopathic Medicine into the Clinical Rotation Evaluation and Documentation Organizer (CREDO) patient encounter logging system could act as a new syndromic surveillance tool. Methods: A CREDO database query was conducted for ICD-10 codes entered between November 1, 2019 and March 13, 2020 using the World Health Organization's 2011 revised case definitions for Influenza Like Illness (ILI). During that period, medical students had an approximated mean of 3,000 patient encounters per day from over 1,500 clinical sites. A cumulative sum technique was applied to the data to generate alert thresholds. Breast cancer, a disease with a stable incidence during the specified timeframe, was used as a control. Results: Total ILI daily ICD-10 counts that exceeded alert thresholds represented unusual levels of disease occurred 11 times from November 20, 2020 through February 28, 2020. This analysis is consistent with the COVID-19 pandemic timeline. The first statistically significant ILI increase occurred nine days prior to the first laboratory confirmed case in the country. Conclusion: Syndromic surveillance can be timelier than traditional surveillance methods, which require laboratory testing to confirm disease. As a result of this study, we are installing a real-time alert for ILI into CREDO, so rates can be monitored continuously as an indicator of possible future new infectious disease outbreaks.

DNA sequencing of anatomy lab cadavers to provide hands-on precision medicine introduction to medical students.

BACKGROUND: Medical treatment informed by Precision Medicine is becoming a standard practice for many diseases, and patients are curious about the consequences of genomic variants in their genome. However, most medical students' understanding of Precision Medicine derives from classroom lectures. This format does little to foster an understanding for the potential and limitations of Precision Medicine. To close this gap, we implemented a hands-on Precision Medicine training program utilizing exome sequencing to prepare a clinical genetic report of cadavers studied in the anatomy lab. The program reinforces Precision Medicine related learning objectives for the Genetics curriculum. METHODS: Pre-embalmed blood samples and embalmed tissue were obtained from cadavers (donors) used in the anatomy lab. DNA was isolated and sequenced and illustrative genetic reports provided to the students. The reports were used to facilitate discussion with students on the implications of pathogenic genomic variants and the potential correlation of these variants in each "donor" with any anatomical anomalies identified during cadaver dissection. RESULTS: In 75% of cases, analysis of whole exome sequencing data identified a variant associated with increased risk for a disease/abnormal condition noted in the donor's cause of death or in the students' anatomical findings. This provided students with real-world examples of the potential relationship between genomic variants and disease risk. Our students also noted that diseases associated with 92% of the pathogenic variants identified were not related to the anatomical findings, demonstrating the limitations of Precision Medicine. CONCLUSION: With this study, we have established protocols and classroom procedures incorporating hands-on Precision Medicine training in the medical student curriculum and a template for other medical educators interested in enhancing their Precision Medicine training program. The program engaged students in discovering variants that were associated with the pathophysiology of the cadaver they were studying, which led to more exposure and understanding of the potential risks and benefits of genomic medicine.

Crossing complexity of space-filling curves reveals entanglement of S-phase DNA.

Space-filling curves have been used for decades to study the folding principles of globular proteins, compact polymers, and chromatin. Formally, space-filling curves trace a single circuit through a set of points (x,y,z); informally, they correspond to a polymer melt. Although not quite a melt, the folding principles of Human chromatin are likened to the Hilbert curve: a type of space-filling curve. Hilbert-like curves in general make biologically compelling models of chromatin; in particular, they lack knots which facilitates chromatin folding, unfolding, and easy access to genes. Knot complexity has been intensely studied with the aid of Alexander polynomials; however, the approach does not generalize well to cases of more than one chromosome. Crossing complexity is an understudied alternative better suited for quantifying entanglement between chromosomes. Do Hilbert-like configurations limit crossing complexity between chromosomes? How does crossing complexity for Hilbert-like configurations compare to equilibrium configurations? To address these questions, we extend the Mansfield algorithm to enable sampling of Hilbert-like space filling curves on a simple cubic lattice. We use the extended algorithm to generate equilibrium, intermediate, and Hilbert-like configurational ensembles and compute crossing complexity between curves (chromosomes) in each configurational snapshot. Our main results are twofold: (a) Hilbert-like configurations limit entanglement between chromosomes and (b) Hilbert-like configurations do not limit entanglement in a model of S-phase DNA. Our second result is particularly surprising yet easily rationalized with a geometric argument. We explore ergodicity of the extended algorithm and discuss our results in the context of more sophisticated models of chromatin.

Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU.

Despite decades of research, effective treatments for most cancers remain elusive. One reason is that different instances of cancer result from different combinations of multiple genetic mutations (hits). Therefore, treatments that may be effective in some cases are not effective in others. We previously developed an algorithm for identifying combinations of carcinogenic genes with mutations (multi-hit combinations), which could suggest a likely cause for individual instances of cancer. Most cancers are estimated to require three or more hits. However, the computational complexity of the algorithm scales exponentially with the number of hits, making it impractical for identifying combinations of more than two hits. To identify combinations of greater than two hits, we used a compressed binary matrix representation, and optimized the algorithm for parallel execution on an NVIDIA V100 graphics processing unit (GPU). With these enhancements, the optimized GPU implementation was on average an estimated 12,144 times faster than the original integer matrix based CPU implementation, for the 3-hit algorithm, allowing us to identify 3-hit combinations. The 3-hit combinations identified using a training set were able to differentiate between tumor and normal samples in a separate test set with 90% overall sensitivity and 93% overall specificity. We illustrate how the distribution of mutations in tumor and normal samples in the multi-hit gene combinations can suggest potential driver mutations for further investigation. With experimental validation, these combinations may provide insight into the etiology of cancer and a rational basis for targeted combination therapy.

Germline microsatellite genotypes differentiate children with medulloblastoma.

BACKGROUND: The germline genetic events underpinning medulloblastoma (MB) initiation, and therefore the ability to determine who is at risk, are still unknown for the majority of cases. Microsatellites are short repeated sequences that make up ~3% of the genome. Repeat lengths vary among individuals and are often nonrandomly associated with disease, including several cancers such as breast, glioma, lung, and ovarian. Due to their effects on gene function, they have been called the "tuning knobs of the genome." METHODS: We have developed a novel approach for identifying a microsatellite-based signature to differentiate MB patients from controls using germline DNA. RESULTS: Analyzing germline whole exome sequencing data from a training set of 120 MB subjects and 425 controls, we identified 139 individual microsatellite loci whose genotypes differ significantly between the groups. Using a genetic algorithm, we identified a subset of 43 microsatellites that distinguish MB subjects from controls with a sensitivity and specificity of 92% and 88%, respectively. This microsatellite signature was validated in an independent dataset consisting of 102 subjects and 428 controls, with comparable sensitivity and specificity of 95% and 90%, respectively. Analysis of the allele genotypes of those 139 informative loci demonstrates that their association with MB is a consequence of individual microsatellites' genotypes rather than their hypermutability. Finally, an analysis of the genes harboring these microsatellite loci reveals cellular functions important for tumorigenesis. CONCLUSION: This study demonstrates that MB-specific germline microsatellite variations mark those at risk for MB development and suggests mechanisms of predisposition.

Abundance of ethnically biased microsatellites in human gene regions.

Microsatellites-a type of short tandem repeat (STR)-have been used for decades as putatively neutral markers to study the genetic structure of diverse human populations. However, recent studies have demonstrated that some microsatellites contribute to gene expression, cis heritability, and phenotype. As a corollary, some microsatellites may contribute to differential gene expression and RNA/protein structure stability in distinct human populations. To test this hypothesis, we investigate genotype frequencies, functional relevance, and adaptive potential of microsatellites in five super-populations (ethnicities) drawn from the 1000 Genomes Project. We discover 3,984 ethnically-biased microsatellite loci (EBML); for each EBML at least one ethnicity has genotype frequencies statistically different from the remaining four. South Asian, East Asian, European, and American EBML show significant overlap; on the contrary, the set of African EBML is mostly unique. We cross-reference the 3,984 EBML with 2,060 previously identified expression STRs (eSTRs); repeats known to affect gene expression (64 total) are over-represented. The most significant pathway enrichments are those associated with the matrisome: a broad collection of genes encoding the extracellular matrix and its associated proteins. At least 14 of the EBML have established links to human disease. Analysis of the 3,984 EBML with respect to known selective sweep regions in the genome shows that allelic variation in some of them is likely associated with adaptive evolution.

Author Correction: Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Genomic divergence and adaptive convergence in Drosophila simulans from Evolution Canyon, Israel.

Biodiversity refugia formed by unique features of the Mediterranean arid landscape, such as the dramatic ecological contrast of "Evolution Canyon," provide a natural laboratory in which local adaptations to divergent microclimate conditions can be investigated. Significant insights have been provided by studies of Drosophila melanogaster diversifying along the thermal gradient in Evolution Canyon, but a comparative framework to survey adaptive convergence across sister species at the site has been lacking. To fill this void, we present an analysis of genomic polymorphism and evolutionary divergence of Drosophila simulans, a close relative of Drosophila melanogaster with which it co-occurs on both slopes of the canyon. Our results show even deeper interslope divergence in D. simulans than in D. melanogaster, with extensive signatures of selective sweeps present in flies from both slopes but enhanced in the population from the hotter and drier south-facing slope. Interslope divergence was enriched for genes related to electrochemical balance and transmembrane transport, likely in response to increased selection for dehydration resistance on the hotter slope. Both species shared genomic regions that underwent major selective sweeps, but the overall level of adaptive convergence was low, demonstrating no shortage of alternative genomic solutions to cope with the challenges of the microclimate contrast. Mobile elements were a major source of genetic polymorphism and divergence, affecting all parts of the genome, including coding sequences of mating behavior-related genes.

Estimating the number of genetic mutations (hits) required for carcinogenesis based on the distribution of somatic mutations.

Individual instances of cancer are primarily a result of a combination of a small number of genetic mutations (hits). Knowing the number of such mutations is a prerequisite for identifying specific combinations of carcinogenic mutations and understanding the etiology of cancer. We present a mathematical model for estimating the number of hits based on the distribution of somatic mutations. The model is fundamentally different from previous approaches, which are based on cancer incidence by age. Our somatic mutation based model is likely to be more robust than age-based models since it does not require knowing or accounting for the highly variable mutation rate, which can vary by over three orders of magnitude. In fact, we find that the number of somatic mutations at diagnosis is weakly correlated with age at cancer diagnosis, most likely due to the extreme variability in mutation rates between individuals. Comparing the distribution of somatic mutations predicted by our model to the actual distribution from 6904 tumor samples we estimate the number of hits required for carcinogenesis for 17 cancer types. We find that different cancer types exhibit distinct somatic mutational profiles corresponding to different numbers of hits. Why might different cancer types require different numbers of hits for carcinogenesis? The answer may provide insight into the unique etiology of different cancer types.

Improving Medical Education by Coupling Basic Science Lectures With ICD-10 Codes.

At the Edward Via College of Osteopathic Medicine (VCOM), students are taught through a systems-based block education process organized according to separate organ systems. The block education lectures provide instruction on these various organ systems and their associated diseases and potential for diagnosis and treatment. A curricular initiative implemented at VCOM incorporates International Classification of Diseases, 10th Revision (ICD-10) codes into the preclinical curriculum to enhance student learning and recall of basic science information and to prepare them for patient encounters during clinical rotations. In constructing this curricular initiative, diseases and procedures mentioned in all lectures during the first 2 years were evaluated and matched with their corresponding ICD-10 diagnostic and procedural codes to illustrate to students how this information would be used in a clinical setting. Of 994 lectures with 36,105 slides, 4331 opportunities to associate ICD-10 codes were identified. Information was given to instructors to update their future lectures. This initiative aims to enhance the preclinical educational experience and prepare preclinical students for documenting patient care. After students have been fully exposed to this new learning component, a study is planned to analyze the effects of the curriculum.

Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations.

Cancer is known to result from a combination of a small number of genetic defects. However, the specific combinations of mutations responsible for the vast majority of cancers have not been identified. Current computational approaches focus on identifying driver genes and mutations. Although individually these mutations can increase the risk of cancer they do not result in cancer without additional mutations. We present a fundamentally different approach for identifying the cause of individual instances of cancer: we search for combinations of genes with carcinogenic mutations (multi-hit combinations) instead of individual driver genes or mutations. We developed an algorithm that identified a set of multi-hit combinations that differentiate between tumor and normal tissue samples with 91% sensitivity (95% Confidence Interval (CI) = 89-92%) and 93% specificity (95% CI = 91-94%) on average for seventeen cancer types. We then present an approach based on mutational profile that can be used to distinguish between driver and passenger mutations within these genes. These combinations, with experimental validation, can aid in better diagnosis, provide insights into the etiology of cancer, and provide a rational basis for designing targeted combination therapies.

CAGm: a repository of germline microsatellite variations in the 1000 genomes project.

The human genome harbors an abundance of repetitive DNA; however, its function continues to be debated. Microsatellites-a class of short tandem repeat-are established as an important source of genetic variation. Array length variants are common among microsatellites and affect gene expression; but, efforts to understand the role and diversity of microsatellite variation has been hampered by several challenges. Without adequate depth, both long-read and short-read sequencing may not detect the variants present in a sample; additionally, large sample sizes are needed to reveal the degree of population-level polymorphism. To address these challenges we present the Comparative Analysis of Germline Microsatellites (CAGm): a database of germline microsatellites from 2529 individuals in the 1000 genomes project. A key novelty of CAGm is the ability to aggregate microsatellite variation by population, ethnicity (super population) and gender. The database provides advanced searching for microsatellites embedded in genes and functional elements. All data can be downloaded as Microsoft Excel spreadsheets. Two use-case scenarios are presented to demonstrate its utility: a mononucleotide (A) microsatellite at the BAT-26 locus and a dinucleotide (CA) microsatellite in the coding region of FGFRL1. CAGm is freely available at http://www.cagmdb.org/.

Genomic signatures of experimental adaptive radiation in Drosophila.

Abiotic environmental factors play a fundamental role in determining the distribution, abundance and adaptive diversification of species. Empowered by new technologies enabling rapid and increasingly accurate examination of genomic variation in populations, researchers may gain new insights into the genomic background of adaptive radiation and stress resistance. We investigated genomic variation across generations of large-scale experimental selection regimes originating from a single founder population of Drosophila melanogaster, diverging in response to ecologically relevant environmental stressors: heat shock, heat knock down, cold shock, desiccation and starvation. When compared to the founder population, and to parallel unselected controls, there were more than 100,000 single nucleotide polymorphisms (SNPs) displaying consistent allelic changes in response to selective pressures across generations. These SNPs were found in both coding and noncoding sequences, with the highest density in promoter regions, and involved a broad range of functionalities, including molecular chaperoning by heat-shock proteins. The SNP patterns were highly stressor-specific despite considerable variation among line replicates within each selection regime, as reflected by a principal component analysis, and co-occurred with selective sweep regions. Only ~15% of SNPs with putatively adaptive changes were shared by at least two selective regimes, while less than 1% of SNPs diverged in opposite directions. Divergent stressors driving evolution in the experimental system of adaptive radiation left distinct genomic signatures, most pronounced in starvation and heat-shock selection regimes.

Dysfunctional DNA repair pathway via defective FANCD2 gene engenders multifarious exomic and transcriptomic effects in Fanconi anemia.

BACKGROUND: Fanconi anemia (FA) affects only one in 130,000 births, but has severe and diverse clinical consequences. It has been theorized that defects in the FA DNA cross-link repair complex lead to a spectrum of variants that are responsible for those diverse clinical phenotypes. METHODS: Using NextGen sequencing, we show that a clinically derived FA cell line had accumulated numerous genetic variants, including high-impact mutations, such as deletion of start codons, introduction of premature stop codons, missense mutations, and INDELs. RESULTS: About 65% of SNPs and 55% of INDELs were found to be commonly present in both the FA dysfunctional and retrovirally corrected cell lines, showing their common origin. The number of INDELs, but not SNPs, is decreased in FANCD2-corrected samples, suggesting that FANCD2 deficiency preferentially promotes the origin of INDELs. These genetic modifications had a considerable effect on the transcriptome, with statistically significant changes in the expression of 270 genes. These genetic and transcriptomic variants significantly impacted pathways and molecular functions, spanning a diverse spectrum of disease phenotypes/symptoms, consistent with the disease diversity seen in FA patients. CONCLUSION: These results underscore the consequences of defects in the DNA cross-link repair mechanism and indicate that accumulating diverse mutations from individual parent cells may make it difficult to anticipate the longitudinal clinical behavior of emerging disease states in an individual with FA.

Whole-exome sequencing reveals microsatellite DNA markers for response to dofetilide initiation in patients with persistent atrial fibrillation: A pilot study.

BACKGROUND: Dofetilide is a class III antiarrhythmic drug effective for the treatment of atrial fibrillation (AF). Dofetilide initiation (DI) associates with corrected QT interval (QTc) prolongation. Significant QTc prolongation during DI mandates dose adjustment or discontinuation of the drug. Microsatellite DNA are novel genetic markers associated with congenital and acquired health conditions. HYPOTHESIS: DNA microsatellite polymorphism may associate with QTc response to dofetilide initiation in patients with persistent AF. METHODS: We performed whole-exome sequencing in a cohort of patients with persistent AF undergoing DI. Electrocardiographic variables and clinical data were assessed. We defined patients as eligible for DI when no significant QTc prolongation (>20% compared with baseline) was seen with a 500-µg dose. We defined patients as ineligible for DI when significant QTc prolongation was seen during DI with 500 µg. We investigated polymorphisms for 11 919 DNA microsatellite loci in relation to QTc response to DI. RESULTS: During the study, 14 consecutive patients with persistent AF presenting for DI were enrolled. Whole-exome sequencing revealed 14 different microsatellite loci in the 2 groups. All genes or proximal genes that harbor these loci are known to have expression in the human heart. Two genes, MYH6 and TRAK2, are known to have expression in the atria. TRAK2 is known to interact with KCNJ2, the inward-rectifier potassium channel 1. CONCLUSIONS: Microsatellite DNA polymorphisms seem to associate with QTc response to DI therapy in patients with persistent AF who are deemed otherwise eligible for dofetilide therapy.

Quantifying medical student clinical experiences via an ICD Code Logging App.

OBJECTIVES: The logging of ICD Diagnostic, Procedure and Drug codes is one means of tracking the experience of medical students' clinical rotations. The goal is to create a web-based computer and mobile application to track the progress of trainees, monitor the effectiveness of their training locations and be a means of sampling public health status. MATERIALS AND METHODS: We have developed a web-based app in which medical trainees make entries via a simple and quick interface optimized for both mobile devices and personal computers. For each patient interaction, users enter ICD diagnostic, procedure, and drug codes via a hierarchical or search entry interface, as well as patient demographics (age range and gender, but no personal identifiers), and free-text notes. Users and administrators can review and edit input via a series of output interfaces. The user interface and back-end database are provided via dual redundant failover Linux servers. RESULTS AND DISCUSSION: Students master the interface in ten minutes, and thereafter complete entries in less than one minute. Five hundred-forty 3rd year VCOM students each averaged 100 entries in the first four week clinical rotation. Data accumulated in various Appalachian clinics and Central American medical mission trips has demonstrated the public health surveillance utility of the application. CONCLUSION: PC and mobile apps can be used to collect medical trainee experience in real time or near real-time, quickly, and efficiently. This system has collected 75,596 entries to date, less than 2% of trainees have needed assistance to become proficient, and medical school administrators are using the various summaries to evaluate students and compare different rotation sites.

ZDHHC3 as a Risk and Mortality Marker for Breast Cancer in African American Women.

African American woman are 43% more likely to die from breast cancer than white women and have increased the risk of tumor recurrence despite lower incidence. We investigate variations in microsatellite genomic regions-a type of repetitive DNA-and possible links to the breast cancer mortality gap. We screen 33 854 microsatellites in germline DNA of African American women with and without breast cancer: 4 are statistically significant. These are located in the 3' UTR (untranslated region) of gene ZDHHC3, an intron of transcribed pseudogene INTS4L1, an intron of ribosomal gene RNA5-8S5, and an intergenic region of chromosome 16. The marker in ZDHHC3 is interesting for 3 reasons: (a) the ZDHHC3 gene is located in region 3p21 which has already been linked to early invasive breast cancer, (b) the Kaplan-Meier estimator demonstrates that ZDHHC3 alterations are associated with poor breast cancer survival in all racial/ethnic groups combined, and (c) data from cBioPortal suggest that ZDHHC3 messenger RNA expression is significantly lower in African Americans compared with whites. These independent lines of evidence make ZDHHC3 a candidate for further investigation.