Toward More Accessible Fully Automated 3D Volumetric MRI Decision Trees for the Differential Diagnosis of Multiple System Atrophy, Related Disorders, and Age-Matched Healthy Subjects.

Differentiating multiple system atrophy (MSA) from related neurodegenerative movement disorders (NMD) is challenging. MRI is widely available and automated decision-tree analysis is simple, transparent, and resistant to overfitting. Using a retrospective cohort of heterogeneous clinical MRIs broadly sourced from a tertiary hospital system, we aimed to develop readily translatable and fully automated volumetric diagnostic decision-trees to facilitate early and accurate differential diagnosis of NMDs. 3DT1 MRI from 171 NMD patients (72 MSA, 49 PSP, 50 PD) and 171 matched healthy subjects were automatically segmented using Freesurfer6.0 with brainstem module. Decision trees employing substructure volumes and a novel volumetric pons-to-midbrain ratio (3D-PMR) were produced and tenfold cross-validation performed. The optimal tree separating NMD from healthy subjects selected cerebellar white matter, thalamus, putamen, striatum, and midbrain volumes as nodes. Its sensitivity was 84%, specificity 94%, accuracy 84%, and kappa 0.69 in cross-validation. The optimal tree restricted to NMD patients selected 3D-PMR, thalamus, superior cerebellar peduncle (SCP), midbrain, pons, and putamen as nodes. It yielded sensitivities/specificities of 94/84% for MSA, 72/96% for PSP, and 73/92% PD, with 79% accuracy and 0.62 kappa. There was correct classification of 16/17 MSA, 5/8 PSP, 6/8 PD autopsy-confirmed patients, and 6/8 MRIs that preceded motor symptom onset. Fully automated decision trees utilizing volumetric MRI data distinguished NMD patients from healthy subjects and MSA from other NMDs with promising accuracy, including autopsy-confirmed and pre-symptomatic subsets. Our open-source methodology is well-suited for widespread clinical translation. Assessment in even more heterogeneous retrospective and prospective cohorts is indicated.

Neuroimaging of COVID-19.

We review the wide variety of common neuroimaging manifestations related to coronavirus disease 2019 (COVID-19) and COVID therapies, grouping the entities by likely pathophysiology, recognizing that the etiology of many entities remains uncertain. Direct viral invasion likely contributes to olfactory bulb abnormalities. COVID meningoencephalitis may represent direct viral infection and/or autoimmune inflammation. Para-infectious inflammation and inflammatory demyelination at the time of infection are likely primary contributors to acute necrotizing encephalopathy, cytotoxic lesion of the corpus callosum, and diffuse white matter abnormality. Later postinfectious inflammation and demyelination may manifest as acute demyelinating encephalomyelitis, Guillain-Barré syndrome, or transverse myelitis. The hallmark vascular inflammation and coagulopathy of COVID-19 may produce acute ischemic infarction, microinfarction contributing to white matter abnormality, space-occupying hemorrhage or microhemorrhage, venous thrombosis, and posterior reversible encephalopathy syndrome. Adverse effects of therapies including zinc, chloroquine/hydroxychloroquine, antivirals, and vaccines, and current evidence regarding "long COVID" is briefly reviewed. Finally, we present a case of bacterial and fungal superinfection related to immune dysregulation from COVID.

The Social Politics of Karl Escherich's 1933 Inaugural Presidential Lecture.

The essay offers a close reading of the inaugural address Termite Craze by the entomologist Karl Escherich, the first German university president to be appointed by the Nazis. Faced with a divided audience and under pressure to politically align the university, Escherich, a former member of the NSDAP, discusses how and to what extent the new regime can recreate the egalitarian perfection and sacrificial predisposition of a termite colony. The paper pays particular attention to the ways in which Escherich tries to appease the various factions in his audience (faculty, students and the Nazi party); in doing so, it also discusses how Escherich depicts his address in the altered versions of his later memoirs.

Identification of α1,2-fucosylated signaling and adhesion molecules in head and neck squamous cell carcinoma.

Head and neck cancer is the seventh most common cancer in the world, and most cases manifest as head and neck squamous cell carcinoma. Despite the prominent role of fucosylated carbohydrate antigens in tumor cell adhesion and metastasis, little is known about the functional role of fucose-modified glycoproteins in head and neck cancer pathobiology. Inactivating polymorphisms of the fut2 gene, encoding for the α1,2-fucosyltransferase FUT2, are associated with an increased incidence of head and neck cancer among tobacco users. Moreover, the presence of the α1,2-fucosylated Lewis Y epitope, with both α1,2- and α1,3-linked fucose, has been observed in head and neck cancer tumors while invasive regions lose expression, suggesting a potential role for α1,2-fucosylation in the regulation of aggressive tumor cell characteristics. Here, we report an association between fut2 expression and head and neck cancer survival, document differential surface expression of α1,2-fucosylated epitopes in a panel of normal, dysplastic, and head and neck cancer cell lines, identify a set of potentially α1,2-fucosylated signaling and adhesion molecules including the epidermal growth factor receptor (EGFR), CD44 and integrins via tandem mass spectrometry, and finally, present evidence that EGFR is among the α1,2-fucosylated and LeY-displaying proteins in head and neck cancer. This knowledge will serve as the foundation for future studies to interrogate the role of LeY-modified and α1,2-fucosylated glycoproteins in head and neck cancer pathogenesis. Data are available via ProteomeXchange with identifier PXD029420.

Myeloid Cells Are Enriched in Tonsillar Crypts, Providing Insight into the Viral Tropism of Human Papillomavirus.

Viruses are the second leading cause of cancer worldwide, and human papillomavirus (HPV)-associated head and neck cancers are increasing in incidence in the United States. HPV preferentially infects the crypts of the tonsils rather than the surface epithelium. The present study sought to characterize the unique microenvironment within the crypts to better understand the viral tropism of HPV to a lymphoid-rich organ. Laser-capture microdissection of distinct anatomic areas (crypts, surface epithelium, and germinal centers) of the tonsil, coupled with transcriptional analysis and multiparameter immunofluorescence staining demonstrated that the tonsillar crypts are enriched with myeloid populations that co-express multiple canonical and noncanonical immune checkpoints, including PD-L1, CTLA-4, HAVCR2 (TIM-3), ADORA2A, IDO1, BTLA, LGALS3, CDH1, CEACAM1, PVR, and C10orf54 (VISTA). The resident monocytes may foster a permissive microenvironment that facilitates HPV infection and persistence. Furthermore, the myeloid populations within HPV-associated tonsil cancers co-express the same immune checkpoints, providing insight into potential novel immunotherapeutic targets for HPV-associated head and neck cancers.

Clinical Trial-Ready Patient Cohorts for Multiple System Atrophy: Coupling Biospecimen and iPSC Banking to Longitudinal Deep-Phenotyping.

Multiple system atrophy (MSA) is a fatal neurodegenerative disease of unknown etiology characterized by widespread aggregation of the protein alpha-synuclein in neurons and glia. Its orphan status, biological relationship to Parkinson's disease (PD), and rapid progression have sparked interest in drug development. One significant obstacle to therapeutics is disease heterogeneity. Here, we share our process of developing a clinical trial-ready cohort of MSA patients (69 patients in 2 years) within an outpatient clinical setting, and recruiting 20 of these patients into a longitudinal "n-of-few" clinical trial paradigm. First, we deeply phenotype our patients with clinical scales (UMSARS, BARS, MoCA, NMSS, and UPSIT) and tests designed to establish early differential diagnosis (including volumetric MRI, FDG-PET, MIBG scan, polysomnography, genetic testing, autonomic function tests, skin biopsy) or disease activity (PBR06-TSPO). Second, we longitudinally collect biospecimens (blood, CSF, stool) and clinical, biometric, and imaging data to generate antecedent disease-progression scores. Third, in our Mass General Brigham SCiN study (stem cells in neurodegeneration), we generate induced pluripotent stem cell (iPSC) models from our patients, matched to biospecimens, including postmortem brain. We present 38 iPSC lines derived from MSA patients and relevant disease controls (spinocerebellar ataxia and PD, including alpha-synuclein triplication cases), 22 matched to whole-genome sequenced postmortem brain. iPSC models may facilitate matching patients to appropriate therapies, particularly in heterogeneous diseases for which patient-specific biology may elude animal models. We anticipate that deeply phenotyped and genotyped patient cohorts matched to cellular models will increase the likelihood of success in clinical trials for MSA.

A New Framework of Designing Iterative Techniques for Image Deblurring.

In this work we present a framework of designing iterative techniques for image deblurring in inverse problem. The new framework is based on two observations about existing methods. We used Landweber method as the basis to develop and present the new framework but note that the framework is applicable to other iterative techniques. First, we observed that the iterative steps of Landweber method consist of a constant term, which is a low-pass filtered version of the already blurry observation. We proposed a modification to use the observed image directly. Second, we observed that Landweber method uses an estimate of the true image as the starting point. This estimate, however, does not get updated over iterations. We proposed a modification that updates this estimate as the iterative process progresses. We integrated the two modifications into one framework of iteratively deblurring images. Finally, we tested the new method and compared its performance with several existing techniques, including Landweber method, Van Cittert method, GMRES (generalized minimal residual method), and LSQR (least square), to demonstrate its superior performance in image deblurring.

Deep-Learning Detection of Cancer Metastases to the Brain on MRI.

BACKGROUND: Approximately one-fourth of all cancer metastases are found in the brain. MRI is the primary technique for detection of brain metastasis, planning of radiotherapy, and the monitoring of treatment response. Progress in tumor treatment now requires detection of new or growing metastases at the small subcentimeter size, when these therapies are most effective. PURPOSE: To develop a deep-learning-based approach for finding brain metastasis on MRI. STUDY TYPE: Retrospective. SEQUENCE: Axial postcontrast 3D T1 -weighted imaging. FIELD STRENGTH: 1.5T and 3T. POPULATION: A total of 361 scans of 121 patients were used to train and test the Faster region-based convolutional neural network (Faster R-CNN): 1565 lesions in 270 scans of 73 patients for training; 488 lesions in 91 scans of 48 patients for testing. From the 48 outputs of Faster R-CNN, 212 lesions in 46 scans of 18 patients were used for training the RUSBoost algorithm (MatLab) and 276 lesions in 45 scans of 30 patients for testing. ASSESSMENT: Two radiologists diagnosed and supervised annotation of metastases on brain MRI as ground truth. This data were used to produce a 2-step pipeline consisting of a Faster R-CNN for detecting abnormal hyperintensity that may represent brain metastasis and a RUSBoost classifier to reduce the number of false-positive foci detected. STATISTICAL TESTS: The performance of the algorithm was evaluated by using sensitivity, false-positive rate, and receiver's operating characteristic (ROC) curves. The detection performance was assessed both per-metastases and per-slice. RESULTS: Testing on held-out brain MRI data demonstrated 96% sensitivity and 20 false-positive metastases per scan. The results showed an 87.1% sensitivity and 0.24 false-positive metastases per slice. The area under the ROC curve was 0.79. CONCLUSION: Our results showed that deep-learning-based computer-aided detection (CAD) had the potential of detecting brain metastases with high sensitivity and reasonable specificity. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1227-1236.

A New Design in Iterative Image Deblurring for Improved Robustness and Performance.

In many applications, image deblurring is a pre-requisite to improve the sharpness of an image before it can be further processed. Iterative methods are widely used for deblurring images but care must be taken to ensure that the iterative process is robust, meaning that the process does not diverge and reaches the solution reasonably fast, two goals that sometimes compete against each other. In practice, it remains challenging to choose parameters for the iterative process to be robust. We propose a new approach consisting of relaxed initialization and pixel-wise updates of the step size for iterative methods to achieve robustness. The first novel design of the approach is to modify the initialization of existing iterative methods to stop a noise term from being propagated throughout the iterative process. The second novel design is the introduction of a vectorized step size that is adaptively determined through the iteration to achieve higher stability and accuracy in the whole iterative process. The vectorized step size aims to update each pixel of an image individually, instead of updating all the pixels by the same factor. In this work, we implemented the above designs based on the Landweber method to test and demonstrate the new approach. Test results showed that the new approach can deblur images from noisy observations and achieve a low mean squared error with a more robust performance.

Normalization of ADC does not improve correlation with overall survival in patients with high-grade glioma (HGG).

Mixed reports leave uncertainty about whether normalization of apparent diffusion coefficient (ADC) to a within-subject white matter reference is necessary for assessment of tumor cellularity. We tested whether normalization improves the previously reported correlation of resection margin ADC with 15-month overall survival (OS) in HGG patients. Spin-echo echo-planar DWI was retrieved from 3 T MRI acquired between maximal resection and radiation in 37 adults with new-onset HGG (25 glioblastoma; 12 anaplastic astrocytoma). ADC maps were produced with the FSL DTIFIT tool (Oxford Centre for Functional MRI). 3 neuroradiologists manually selected regions of interest (ROI) in normal appearing white matter (NAWM) and in non-enhancing tumor (NT) < 2 cm from the margin of residual enhancing tumor or resection cavity. Normalized ADC (nADC) was computed as the ratio of absolute NT ADC to NAWM ADC. Reproducibility of nADC and absolute ADC among the readers' ROI was assessed using intra-class correlation coefficient (ICC) and within-subject coefficient of variation (wCV). Correlations of ADC and nADC with OS were compared using receiver operating characteristics (ROC) analysis. A p value 0.05 was considered statistically significant. Both mean ADC and nADC differed significantly between patients subgrouped by 15-month OS (p = 0.0014 and 0.0073 respectively). wCV and ICC among the readers were similar for absolute and normalized ADC. In ROC analysis of correlation with OS, nADC did not perform significantly better than absolute ADC. Normalization does not significantly improve the correlation of absolute ADC with OS in HGG, suggesting that normalization is not necessary for clinical or research ADC analysis in HGG patients.

Neuroimaging in Pregnant Women.

Choosing the most appropriate diagnostic neuroimaging study for a pregnant woman involves assessing the pretest likelihood of serious treatable neurologic disease, the diagnostic utility of various available computed tomography (CT) and magnetic resonance (MR) modalities, and the risks of each. Of these three elements-pretest differential diagnosis, utility of MRI and CT, and risks of MR and CT-the risk component is perhaps the least well understood by most physicians. We provide a basic review of the intrinsic risks of MRI and CT, particularly radiation biology and radiation safety, as well as the risks pertaining to the use of contrast agents, to reduce provider confusion and anxiety and improve quality, safety, and efficiency of neuroimaging diagnosis in pregnant patients. We believe that a better understanding of the associated very low risks with mother and fetus will reassure the reader that CT remains the most appropriate tool for initial rapid diagnosis of acute neurological conditions in pregnancy and that in urgent situations CT should not be withheld or delayed due to exaggerated concern about radiation. Noncontrast MRI, while not without risk, is generally considered safe in pregnancy, as no evidence of fetal adverse effects has been demonstrated to date. Iodinated CT contrast agents are likely safer than gadolinium-based MRI contrast agents because of gadolinium accumulation in the amniotic fluid and fetal tissue, although no harmful effects of tissue gadolinium accumulation are known. In most but not all pregnant patients presenting with a new or worsening neurological abnormality, the risks intrinsic to the disease will outweigh the risks of imaging. In an individual patient, the pretest probability of serious treatable disease and acuity of presentation will usually suggest an optimal imaging strategy and choice of test. This optimal strategy will also depend on the immediate availability and level of sophistication of the scanners, software, technologists, and radiologists. As such, the standard of care for imaging in pregnancy requires direct consultation between the referring clinician and radiologist to determine the most appropriate strategy and brief documentation of the resulting consensus risk-benefit assessment.

Advanced MRI assessment to predict benefit of anti-programmed cell death 1 protein immunotherapy response in patients with recurrent glioblastoma.

INTRODUCTION: We describe the imaging findings encountered in GBM patients receiving immune checkpoint blockade and assess the potential of quantitative MRI biomarkers to differentiate patients who derive therapeutic benefit from those who do not. METHODS: A retrospective analysis was performed on longitudinal MRIs obtained on recurrent GBM patients enrolled on clinical trials. Among 10 patients with analyzable data, bidirectional diameters were measured on contrast enhanced T1 (pGd-T1WI) and volumes of interest (VOI) representing measurable abnormality suggestive of tumor were selected on pGdT1WI (pGdT1 VOI), FLAIR-T2WI (FLAIR VOI), and ADC maps. Intermediate ADC (IADC) VOI represented voxels within the FLAIR VOI having ADC in the range of highly cellular tumor (0.7-1.1 × 10-3 mm2/s) (IADC VOI). Therapeutic benefit was determined by tissue pathology and survival on trial. IADC VOI, pGdT1 VOI, FLAIR VOI, and RANO assessment results were correlated with patient benefit. RESULTS: Five patients were deemed to have received therapeutic benefit and the other five patients did not. The average time on trial for the benefit group was 194 days, as compared to 81 days for the no benefit group. IADC VOI correlated well with the presence or absence of clinical benefit in 10 patients. Furthermore, pGd VOI, FLAIR VOI, and RANO assessment correlated less well with response. CONCLUSION: MRI reveals an initial increase in volumes of abnormal tissue with contrast enhancement, edema, and intermediate ADC suggesting hypercellularity within the first 0-6 months of immunotherapy. Subsequent stabilization and improvement in IADC VOI appear to better predict ultimate therapeutic benefit from these agents than conventional imaging.

Association Between Learning Environment Interventions and Medical Student Well-being: A Systematic Review.

Importance: Concerns exist about the current quality of undergraduate medical education and its effect on students' well-being. Objective: To identify best practices for undergraduate medical education learning environment interventions that are associated with improved emotional well-being of students. Data Sources: Learning environment interventions were identified by searching the biomedical electronic databases Ovid MEDLINE, EMBASE, the Cochrane Library, and ERIC from database inception dates to October 2016. Studies examined any intervention designed to promote medical students' emotional well-being in the setting of a US academic medical school, with an outcome defined as students' reports of well-being as assessed by surveys, semistructured interviews, or other quantitative methods. Data Extraction and Synthesis: Two investigators independently reviewed abstracts and full-text articles. Data were extracted into tables to summarize results. Study quality was assessed by the Medical Education Research Study Quality Instrument (MERQSI), which has a possible range of 5 to 18; higher scores indicate higher design and methods quality and a score of 14 or higher indicates a high-quality study. Findings: Twenty-eight articles including at least 8224 participants met eligibility criteria. Study designs included single-group cross-sectional or posttest only (n = 10), single-group pretest/posttest (n = 2), nonrandomized 2-group (n = 13), and randomized clinical trial (n = 3); 89.2% were conducted at a single site, and the mean MERSQI score for all studies was 10.3 (SD, 2.11; range, 5-13). Studies encompassed a variety of interventions, including those focused on pass/fail grading systems (n = 3; mean MERSQI score, 12.0), mental health programs (n = 4; mean MERSQI score, 11.9), mind-body skills programs (n = 7; mean MERSQI score, 11.3), curriculum structure (n = 3; mean MERSQI score, 9.5), multicomponent program reform (n = 5; mean MERSQI score, 9.4), wellness programs (n = 4; mean MERSQI score, 9.0), and advising/mentoring programs (n = 3; mean MERSQI score, 8.2). Conclusions and Relevance: In this systematic review, limited evidence suggested that some specific learning environment interventions were associated with improved emotional well-being among medical students. However, the overall quality of the evidence was low, highlighting the need for high-quality medical education research.

Spin-echo echo-planar perfusion prior to chemoradiation is a strong independent predictor of progression-free and overall survival in newly diagnosed glioblastoma.

Spin-echo echo planar (EP) perfusion weighted imaging (SE-PWI) has been demonstrated to be more selective than gradient-echo EP PWI for blood volume in microvessels the size of glioma neocapillaries, but it has not been comprehensively studied in human clinical use. We assessed whether SE-PWI before and after initiating chemoradiation can stratify patients with respect to progression free survival (PFS) and overall survival (OS). Sixty-eight patients with newly diagnosed glioblastoma (mean age 58.3, 36 males) were included in analysis. SE EP cerebral blood volumes (SE-CBVs) in enhancing and nonenhancing tumor, normalized to contralateral normal appearing white matter (SE-nCBV), were assessed at baseline and after initial chemoradiation. SE-nCBV parameters predictive of PFS and OS were identified in univariate and multivariate Cox proportional hazards models. Multivariate analysis demonstrated that baseline tumor mean SE-nCBV was predictive of PFS (p = 0.038) and OS (p = 0.004). Within the patient sample, baseline tumor mean SE-nCBV <2.0 predicted longer patient PFS (median 47.0 weeks, p < 0.001) and OS (median 98.6 weeks, p = 0.003) compared with baseline mean SE-nCBV >2.0 (median PFS 25.3, median OS 56.0 weeks). Exploratory multi-group stratification demonstrated that very high (>4.0) tumor SE-nCBV was associated with worse patient OS than intermediate high (>2.0, <4.0) SE-nCBV (p = 0.025). Baseline mean SE-nCBV can stratify patients for PFS and OS prior to initiation of chemoradiation, which may help select patients who require closer surveillance. Our exploratory analysis indicates a magnitude-dependent relationship between baseline SE-nCBV and OS.

The impact of COVID-19 on patients diagnosed with melanoma, breast, and colorectal cancer.

BACKGROUND: The COVID-19 pandemic impacted healthcare resource allocation and utilization of preventative medical services. It is unknown if there is resultant stage migration of melanoma, breast, and colorectal cancer when comparing extended time periods before and after the pandemic onset. METHODS: A retrospective cohort study of melanoma, breast, and colorectal cancer patients was completed. Clinical and pathological staging was compared utilizing 12 and 22-month timeframes before and after the pandemic outbreak. RESULTS: Between the 22-month pre- and post-COVID-19 groups, breast cancer clinical stage T2 significantly increased, and pathological stage 2 decreased. Colorectal cancer clinical stage T1 decreased, stage T4 increased, and stage 0 decreased in the 22-month groups. In the 12-month groups, melanoma clinical stage T1 increased, and colorectal cancer clinical stage N2 increased. CONCLUSIONS: Evaluating extended timeframes beyond the immediate pre- and post-COVID-19 period revealed significant increases in clinical staging of breast and colorectal cancer, suggesting advanced disease is becoming more evident as time progresses.

Auto-segmentation of Adult-Type Diffuse Gliomas: Comparison of Transfer Learning-Based Convolutional Neural Network Model vs. Radiologists.

Segmentation of glioma is crucial for quantitative brain tumor assessment, to guide therapeutic research and clinical management, but very time-consuming. Fully automated tools for the segmentation of multi-sequence MRI are needed. We developed and pretrained a deep learning (DL) model using publicly available datasets A (n = 210) and B (n = 369) containing FLAIR, T2WI, and contrast-enhanced (CE)-T1WI. This was then fine-tuned with our institutional dataset (n = 197) containing ADC, T2WI, and CE-T1WI, manually annotated by radiologists, and split into training (n = 100) and testing (n = 97) sets. The Dice similarity coefficient (DSC) was used to compare model outputs and manual labels. A third independent radiologist assessed segmentation quality on a semi-quantitative 5-scale score. Differences in DSC between new and recurrent gliomas, and between uni or multifocal gliomas were analyzed using the Mann-Whitney test. Semi-quantitative analyses were compared using the chi-square test. We found that there was good agreement between segmentations from the fine-tuned DL model and ground truth manual segmentations (median DSC: 0.729, std-dev: 0.134). DSC was higher for newly diagnosed (0.807) than recurrent (0.698) (p < 0.001), and higher for unifocal (0.747) than multi-focal (0.613) cases (p = 0.001). Semi-quantitative scores of DL and manual segmentation were not significantly different (mean: 3.567 vs. 3.639; 93.8% vs. 97.9% scoring ≥ 3, p = 0.107). In conclusion, the proposed transfer learning DL performed similarly to human radiologists in glioma segmentation on both structural and ADC sequences. Further improvement in segmenting challenging postoperative and multifocal glioma cases is needed.

Association Between a Medical School Applicant's Community College Attendance and the Likelihood of Application Acceptance: An Investigation of Select Medical School Characteristics.

PURPOSE: One-third of medical school applicants attend a community college (CC), and they represent a diverse group of applicants. However, they have a lower likelihood of being accepted to medical school. Using application-level data, this study examines how an applicant's CC attendance impacts the likelihood of application acceptance and how 3 medical school characteristics moderate this association. METHOD: Data examined were from 2,179,483 applications submitted to at least one of 146 U.S. Liaison Committee on Medical Education-accredited medical schools by 124,862 applicants between 2018 and 2020. The outcome was application acceptance. The main measures were applicants' CC attendance (no, lower [> 0%-19% of college course hours], or higher [≥ 20%]) and 3 medical school characteristics: geographic region, private versus public control, and admissions policy regarding CC coursework. Multilevel logistic regression models estimated the association between CC attendance and the likelihood of application acceptance and how this association was moderated by school characteristics. RESULTS: Among applicants, 23.8% (29,704/124,862) had lower CC attendance and 10.3% (12,819/124,862) had higher CC attendance. Regression results showed that, relative to no CC attendance, applications with lower (adjusted odds ratio [AOR] = .96; 95% confidence internal [CI], .94-.97) and higher (AOR = .78; 95% CI, .76-.81) CC attendance had significantly decreased odds of acceptance when the 3 school characteristics were included. Each of the 3 medical school characteristics significantly moderated the association between an applicant's CC attendance and the likelihood of application acceptance. CONCLUSIONS: The negative association between CC attendance and medical school application acceptance varies by medical school characteristics. Professional development for admissions officers focused on understanding the CC pathway and potential biases related to CC attendance would likely be beneficial in terms of trying to attract and select a diverse cohort of medical students in a holistic and equitable manner.

Increasing the Representation of Black Men in Medicine by Addressing Systems Factors.

In 2015, data released by the Association of American Medical Colleges (AAMC) showed that there were more Black men applying and matriculating to medical school in 1978 than 2014. The representation of Black men in medicine is a troubling workforce issue that was identified by the National Academies of Sciences, Engineering, and Medicine as a national crisis. While premedical pathway programs have contributed to increased workforce diversity, alone they are insufficient to accelerate change. In response, the AAMC and the National Medical Association launched a new initiative in August 2020, the Action Collaborative for Black Men in Medicine, to address the systems factors that influence the trajectory to medicine for Black men. The authors provide a brief overview of the educational experiences of Black boys and men in the United States and, as members of the Action Collaborative, describe their early work. Using research, data, and collective lived experiences, the Action Collaborative members identified premedical and academic medicine systems factors that represented opportunities for change. The premedical factors include financing and funding, information access, pre-health advisors, the Medical College Admission Test, support systems, foundational academics, and alternative career paths. The academic medicine factors include early identification, medical school recruitment and admissions, and leadership accountability. The authors offer several points of intervention along the medical education continuum, starting as early as elementary school through medical school matriculation, for institutional leaders to address these factors as part of their diversity strategy. The authors also present the Action Collaborative's process for leveraging collective impact to build an equity-minded action agenda focused on Black men. They describe their initial focus on pre-health advising and leadership accountability and next steps to develop an action agenda. Collective impact and coalition building will facilitate active, broad engagement of partners across sectors to advance long-term systems change.

Multicolored stain-free histopathology with coherent Raman imaging.

Conventional histopathology with hematoxylin & eosin (H&E) has been the gold standard for histopathological diagnosis of a wide range of diseases. However, it is not performed in vivo and requires thin tissue sections obtained after tissue biopsy, which carries risk, particularly in the central nervous system. Here we describe the development of an alternative, multicolored way to visualize tissue in real-time through the use of coherent Raman imaging (CRI), without the use of dyes. CRI relies on intrinsic chemical contrast based on vibrational properties of molecules and intrinsic optical sectioning by nonlinear excitation. We demonstrate that multicolor images originating from CH(2) and CH(3) vibrations of lipids and protein, as well as two-photon absorption of hemoglobin, can be obtained with subcellular resolution from fresh tissue. These stain-free histopathological images show resolutions similar to those obtained by conventional techniques, but do not require tissue fixation, sectioning or staining of the tissue analyzed.