Facilitating the transition to residency: A resident-as-coach pilot program.

BACKGROUND: The transition from medical school to residency is a critical developmental phase; coaching may help students prepare for this role transition. AIMS: We explored whether near-peer coaching could improve a specific workplace skill prior to residency. METHODS: A resident-as-coach program was piloted for the medicine sub-internship, an advanced acting internship rotation. Between March and June 2021, 26 students were assigned a resident coach (n = 16). Resident coaches completed one training session, and student-coach dyads met for one coaching session on 'pre-rounding'- gathering patient data before rounds. The program was evaluated through surveys and focus groups. RESULTS: 20/26 students and 14/16 residents completed the survey. 19/20 students identified a pre-rounding challenge and reported increased pre-rounding efficiency; all committed to one actionable step for improvement. All 16 residents felt their coaching skills improved. In focus groups, students valued the program's focus on honing a relevant skill in a safe, near-peer setting. Residents expressed their intent to incorporate coaching into their future work. CONCLUSIONS: A resident-as-coach model can be effective in preparing students for residency, while concurrently building residents' coaching skills.

Moving Beyond the Dichotomous Assessment of Professionalism in the Internal Medicine Clerkship: Results of a National Survey of Clerkship Directors.

PURPOSE: This study examines how internal medicine clerkship faculty and leadership conceptualize professionalism and professional behaviors and attitudes, identifies whether and how faculty use metrics to assess professionalism and factor it into clerkship grades, and describes barriers that prevent faculty from feeling prepared to support the development of professional behaviors in students. METHOD: The Clerkship Directors in Internal Medicine opened a call for thematic survey section proposals to its physician-faculty members, blind-reviewed all submissions, and selected 4 based on internal medicine clinical clerkship training experience relevance. The survey launched on October 5 and closed on December 7, 2021. Data were analyzed using descriptive statistics. RESULTS: Of 137 core clerkship directors (CDs) at Liaison Committee on Medical Education-accredited medical schools, 103 (75.2%) responded to the survey. Of 102 respondents (1 nonrespondent), 84 (82.4%) identified professional behavior lapses in involvement and 60 (58.8%) identified introspection lapses. Of 103 respondents, 97 (94.2%) reported that their clerkships ask clinical faculty and residents to formally evaluate professionalism, and 64 (62.1%) reported that they factor professionalism assessments into final clerkship grades. CDs reported multiple barriers to addressing professionalism directly with students, including logistical barriers, professionalism assessment subjectivity concerns, and the possible adverse effect of an unprofessional label for students. CONCLUSIONS: Professionalism assessment and remediation in medical education currently center on a deficit model that seeks to identify and remediate professionalism lapses, rather than a developmental model that seeks to nurture growth. This dichotomous characterization of behaviors as professional or unprofessional limits assessment and can adversely affect the learning environment. The authors propose a shift to a developmental model that considers professionalism as a continuous process parallel to the acquisition of clinical skills and medical knowledge.

Applying deep learning to segmentation of murine lung tumors in pre-clinical micro-computed tomography.

Lung cancer remains a leading cause of cancer-related death, but scientists have made great strides in developing new treatments recently, partly owing to the use of genetically engineered mouse models (GEMMs). GEMM tumors represent a translational model that recapitulates human disease better than implanted models because tumors develop spontaneously in the lungs. However, detection of these tumors relies on in vivo imaging tools, specifically micro-Computed Tomography (micro-CT or µCT), and image analysis can be laborious with high inter-user variability. Here we present a deep learning model trained to perform fully automated segmentation of lung tumors without the interference of other soft tissues. Trained and tested on 100 3D µCT images (18,662 slices) that were manually segmented, the model demonstrated a high correlation to manual segmentations on the testing data (r2=0.99, DSC=0.78) and on an independent dataset (n = 12 3D scans or 2328 2D slices, r2=0.97, DSC=0.73). In a comparison against manual segmentation performed by multiple analysts, the model (r2=0.98, DSC=0.78) performed within inter-reader variability (r2=0.79, DSC=0.69) and close to intra-reader variability (r2=0.99, DSC=0.82), all while completing 5+ hours of manual segmentations in 1 minute. Finally, when applied to a real-world longitudinal study (n = 55 mice), the model successfully detected tumor progression over time and the differences in tumor burden between groups induced with different virus titers, aligning well with more traditional analysis methods. In conclusion, we have developed a deep learning model which can perform fast, accurate, and fully automated segmentation of µCT scans of murine lung tumors.

Volumetric imaging of optically cleared and fluorescently labeled animal tissue (VIOLA) for quantifying the 3D biodistribution of nanoparticles at cellular resolution in tumor tissue.

Nanoparticle (NP) technology holds significant promise to mediate targeted drug delivery to specific organs in the body. Understanding the 3D biodistribution of NPs in heterogeneous environments such as the tumor tissue can provide crucial information on efficacy, safety and potential clinical outcomes. Here we present a novel end-to-end workflow, VIOLA, which makes use of tissue clearing methodology in conjunction with high resolution imaging and advanced 3D image processing to quantify the spatiotemporal 3D biodistribution of fluorescently labeled ACCURIN® NPs. Specifically, we investigate the spatiotemporal biodistribution of NPs in three different murine tumor models (CT26, EMT6, and KPC-GEM) of increasing complexity and translational relevance. We have developed new endpoints to characterize NP biodistribution at multiple length scales. Our observations reveal that the macroscale NP biodistribution is spatially heterogeneous and exhibits a gradient with relatively high accumulation at the tumor periphery that progressively decreases towards the tumor core in all the tumor models. Microscale analysis revealed that NP extravasation from blood vessels increases in a time dependent manner and plateaus at 72 h post injection. Volumetric analysis and pharmacokinetic modeling of NP biodistribution in the vicinity of the blood vessels revealed that the local NP density exhibits a distance dependent spatiotemporal biodistribution which provide insights into the dynamics of NP extravasation in the tumor tissue. Our data represents a comprehensive analysis of NP biodistribution at multiple length scales in different tumor models providing unique insights into their spatiotemporal dynamics. Specifically, our results show that NPs exhibit a dynamic equilibrium with macroscale heterogeneity combined with microscale homogeneity.

Fully automated mouse echocardiography analysis using deep convolutional neural networks.

Echocardiography (echo) is a translationally relevant ultrasound imaging modality widely used to assess cardiac structure and function in preclinical models of heart failure (HF) during research and drug development. Although echo is a very valuable tool, the image analysis is a time-consuming, resource-demanding process, and is susceptible to interreader variability. Recent advancements in deep learning have enabled researchers to automate image processing and reduce analysis time and interreader variability in the field of medical imaging. In the present study, we developed a fully automated tool, mouse-echocardiography neural net (MENN), for the analysis of both long-axis brightness (B)-mode and short-axis motion (M)-mode images of left ventricle. MENN is a series of fully convolutional neural networks that were trained and validated using manually segmented B-mode and M-mode echo images of the left ventricle. The segmented images were then used to compute cardiac structural and functional metrics. The performance of MENN was further validated in two preclinical models of HF. MENN achieved excellent correlations (Pearson's r = 0.85-0.99) and good-to-excellent agreement between automated and manual analyses. Further interreader variability analysis showed that MENN has better agreements with an expert analyst than both a trained analyst and a novice. Notably, the use of MENN reduced manual analysis time by >92%. In conclusion, we developed an automated echocardiography analysis tool that allows for fast and accurate analysis of B-mode and M-mode mouse echo data and mitigates the issue of interreader variability in manual analysis.NEW & NOTEWORTHY Echocardiography is commonly used in preclinical research to evaluate cardiac structure and function. Despite the broad applications across therapeutic areas, the analysis of echo data is laborious and susceptible to interreader variability. In this study, we developed a fully automated mouse-echocardiography neural net (MENN). Cardiac measurements from MENN showed excellent correlations with manual analysis. Furthermore, the use of MENN leads to >92% reduction in analysis time and potentially eliminates the interobserver variability issue.

Clinical outcomes after IL-6 blockade in patients with COVID-19 and HIV: a case series.

BACKGROUND: In hospitalized people with HIV (PWH) there is an increased risk of mortality from COVID-19 among hospitalized PWH as compared to HIV-negative individuals. Evidence suggests that tocilizumab-a humanized monoclonal interleukin (IL)-6 receptor inhibitor (IL-6ri) antibody-has a modest mortality benefit when combined with corticosteroids in select hospitalized COVID-19 patients who are severely ill. Data on clinical outcomes after tocilizumab use in PWH with severe COVID-19 are lacking. CASE PRESENTATION: We present a multinational case series of 18 PWH with COVID-19 who were treated with IL-6ri's during the period from April to June 2020. Four patients received tocilizumab, six sarilumab, and eight received an undocumented IL-6ri. Of the 18 patients in the series, 4 (22%) had CD4 counts < 200 cells/mm3; 14 (82%) had a suppressed HIV viral load. Eight patients (44%), all admitted to ICU, were treated for secondary infection; 5 had a confirmed organism. Of the four patients with CD4 counts < 200 cells/mm3, three were treated for secondary infection, with 2 confirmed organisms. Overall outcomes were poor-12 patients (67%) were admitted to the ICU, 11 (61%) required mechanical ventilation, and 7 (39%) died. CONCLUSIONS: In this case series of hospitalized PWH with COVID-19 and given IL-6ri prior to the common use of corticosteroids, there are reports of secondary or co-infection in severely ill patients. Comprehensive studies in PWH, particularly with CD4 counts < 200 cells, are warranted to assess infectious and other outcomes after IL-6ri use, particularly in the context of co-administered corticosteroids.

Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.

Unlike the majority of cancers, survival for lung cancer has not shown much improvement since the early 1970s and survival rates remain low. Genetically engineered mice tumor models are of high translational relevance as we can generate tissue specific mutations which are observed in lung cancer patients. Since these tumors cannot be detected and quantified by traditional methods, we use micro-computed tomography imaging for longitudinal evaluation and to measure response to therapy. Conventionally, we analyze microCT images of lung cancer via a manual segmentation. Manual segmentation is time-consuming and sensitive to intra- and inter-analyst variation. To overcome the limitations of manual segmentation, we set out to develop a fully-automated alternative, the Mouse Lung Automated Segmentation Tool (MLAST). MLAST locates the thoracic region of interest, thresholds and categorizes the lung field into three tissue categories: soft tissue, intermediate, and lung. An increase in the tumor burden was measured by a decrease in lung volume with a simultaneous increase in soft and intermediate tissue quantities. MLAST segmentation was validated against three methods: manual scoring, manual segmentation, and histology. MLAST was applied in an efficacy trial using a Kras/Lkb1 non-small cell lung cancer model and demonstrated adequate precision and sensitivity in quantifying tumor growth inhibition after drug treatment. Implementation of MLAST has considerably accelerated the microCT data analysis, allowing for larger study sizes and mid-study readouts. This study illustrates how automated image analysis tools for large datasets can be used in preclinical imaging to deliver high throughput and quantitative results.

Lead Poisoning in South African Children: The Hazard is at Horne.

Lead is a toxic heavy metal that has been used extensively in modern society, causing widespread environmental contamination even in isolated parts of the world. Irrefutable evidence associates lead at different exposure levels with a wide spectrum of health and social effects, including mild intellectual impairment, hyperactivity, shortened concentration span, poor school performance, violent/aggressive behavior, and hearing loss. Lead has an impact on virtually all organ systems, including the heart, brain, liver, kidneys, and circulatory system, resulting in coma and death in severe cases. In recent years, a consensus was reached regarding the absence of a threshold for the key health effects associated with lead exposure and the permanent and irreversible nature of many health and social consequences of lead exposure. The public health problem of environmental lead exposure has been widely investigated in developed countries like the United States of America, where actions taken have led to significant reductions in children's blood lead concentrations. In contrast, there is a relative dearth of information and action regarding lead poisoning in developing countries, particularly in African countries, despite evidence of widespread and excessive childhood lead exposure. In this paper, we will review the information from available published papers, the 'grey Literature', and unpublished reports to give an overview of lead exposure in South African children over the past two decades, with particular emphasis on sources of exposure in the home environment.

The Relationship Between Applicant Gender and Internal Medicine Residency Interview Scores.

BACKGROUND: Gender inequity is widespread in academic medicine, including in the promotion, academic recognition, and compensation of female faculty. OBJECTIVE: To assess whether these inequities extend to the GME intern selection process, this study examines differences in the interview scores assigned to male and female applicants at one large internal medicine residency program. METHODS: Subjects include 1399 applicants who completed 3099 interviews for internship positions for the Brigham and Women's Hospital internal medicine residency in Electronic Residency Application Service (ERAS) cycles 2015-2016, 2017-2018, 2018-2019, and 2019-2020. Unadjusted and multivariable linear regressions were used to assess the simultaneous effect of applicant gender, interviewer gender, and applicant academic characteristics on pre-interview, post-interview, and change in interview scores. RESULTS: Our analysis included 3027 interviews (97.7%) of 1359 applicants (97.1%). There were no statistically significant differences in the interview scores assigned to female versus male applicants. This was true across pre-interview scores (difference = 0.03, P = .61), post-interview scores (difference = 0.00, P = .98), and change in interview scores (difference = 0.01, P = .24) as well as when adjusting for the baseline academic characteristics of both male and female applicants. This was also true when analyzing individual application years, individual residency tracks, and accounting for the gender of the faculty interviewers. CONCLUSIONS: The findings do not support the presence of gender inequity in the interview scores assigned to male and female applicants included in this study.

Characteristics, Comorbidities, and Outcomes in a Multicenter Registry of Patients With Human Immunodeficiency Virus and Coronavirus Disease 2019.

BACKGROUND: People living with human immunodeficiency virus (HIV) may have numerous risk factors for acquiring coronavirus disease 2019 (COVID-19) and developing severe outcomes, but current data are conflicting. METHODS: Health-care providers enrolled consecutively, by nonrandom sampling, people living with HIV (PWH) with lab-confirmed COVID-19, diagnosed at their facilities between 1 April and 1 July 2020. Deidentified data were entered into an electronic Research Electronic Data Capture (REDCap) system. The primary endpoint was a severe outcome, defined as a composite endpoint of intensive care unit (ICU) admission, mechanical ventilation, or death. The secondary outcome was the need for hospitalization. RESULTS: There were 286 patients included; the mean age was 51.4 years (standard deviation, 14.4), 25.9% were female, and 75.4% were African American or Hispanic. Most patients (94.3%) were on antiretroviral therapy, 88.7% had HIV virologic suppression, and 80.8% had comorbidities. Within 30 days of testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 164 (57.3%) patients were hospitalized, and 47 (16.5%) required ICU admission. Mortality rates were 9.4% (27/286) overall, 16.5% (27/164) among those hospitalized, and 51.5% (24/47) among those admitted to an ICU. The primary composite endpoint occurred in 17.5% (50/286) of all patients and 30.5% (50/164) of hospitalized patients. Older age, chronic lung disease, and hypertension were associated with severe outcomes. A lower CD4 count (<200 cells/mm3) was associated with the primary and secondary endpoints. There were no associations between the ART regimen or lack of viral suppression and the predefined outcomes. CONCLUSIONS: Severe clinical outcomes occurred commonly in PWH with COVID-19. The risks for poor outcomes were higher in those with comorbidities and lower CD4 cell counts, despite HIV viral suppression. CLINICAL TRIALS REGISTRATION: NCT04333953.

Feasibility and Safety of Outpatient Parenteral Antimicrobial Therapy in Conjunction With Addiction Treatment for People Who Inject Drugs.

BACKGROUND: Research is limited on combining outpatient parenteral antimicrobial therapy (OPAT) with addiction treatment for people who inject drugs (PWID) with serious infections. METHODS: This is a retrospective study of PWID (n = 68) requiring intravenous antibiotics evaluated for suitability for our OPAT program with concurrent addiction treatment. RESULTS: Most common infections were bacteremia and/or endocarditis (73.5%), bone and/or joint infections (32.4%), and epidural abscess (22.1%). Of the 20 patients (29.4%) who qualified, 100.0% completed the course of antibiotics, 30.0% experienced a 30-day readmission, and 15.0% relapsed. No overdoses, deaths, or peripherally inserted central catheter-line complications were reported. CONCLUSIONS: Outpatient parenteral antimicrobial therapy with addiction treatment may be feasible and safe for PWID with serious infections.

Glioma-Induced Alterations in Neuronal Activity and Neurovascular Coupling during Disease Progression.

Diffusely infiltrating gliomas are known to cause alterations in cortical function, vascular disruption, and seizures. These neurological complications present major clinical challenges, yet their underlying mechanisms and causal relationships to disease progression are poorly characterized. Here, we follow glioma progression in awake Thy1-GCaMP6f mice using in vivo wide-field optical mapping to monitor alterations in both neuronal activity and functional hemodynamics. The bilateral synchrony of spontaneous neuronal activity gradually decreases in glioma-infiltrated cortical regions, while neurovascular coupling becomes progressively disrupted compared to uninvolved cortex. Over time, mice develop diverse patterns of high amplitude discharges and eventually generalized seizures that appear to originate at the tumors' infiltrative margins. Interictal and seizure events exhibit positive neurovascular coupling in uninfiltrated cortex; however, glioma-infiltrated regions exhibit disrupted hemodynamic responses driving seizure-evoked hypoxia. These results reveal a landscape of complex physiological interactions occurring during glioma progression and present new opportunities for exploring novel biomarkers and therapeutic targets.

Evidence of Failure of Oral Third-Generation Cephalosporin Treatment for Shigella sonnei Infection.

In 2017, state health departments notified the Centers for Disease Control and Prevention about 4 patients with shigellosis who experienced persistent illness after treatment with oral third-generation cephalosporins. Given increasing antibiotic resistance among Shigella, these cases highlight the need to evaluate the efficacy of oral cephalosporins for shigellosis.

Superficial Cutaneous Zygomycosis Presenting as Resistant Intertrigo: A Case Report.

Zygomycosis is an angioinvasive fungal infection with a high mortality rate. Cutaneous zygomycosis is the second most common form of the disease, typically characterized by necrotic eschars in an immunocompromised host. We report an unusual case of superficial intertrigo resistant to conventional therapies caused by Mucor circinelloides in a patient with HIV and diabetes.

Impact of Medications for Opioid Use Disorder on Discharge Against Medical Advice Among People Who Inject Drugs Hospitalized for Infective Endocarditis.

BACKGROUND AND OBJECTIVES: The impact of medications for opioid use disorder (MOUD) on against medical advice (AMA) discharges among people who inject drugs (PWID) hospitalized for endocarditis is unknown. METHODS: A retrospective review of all PWID hospitalized for endocarditis at our institution between 2016 and 2018 (n = 84). RESULTS: PWID engaged with MOUD at admission, compared with those who were not, were less likely to be discharged AMA but this did not reach statistical significance in adjusted analysis (odds ratio [OR], 0.22; 95% confidence interval [CI], 0.033-1.41; P = .11). Among out-of-treatment individuals, newly initiating MOUD did not lead to significantly fewer AMA discharges (OR, 0.98; 95% CI, 0.26-3.7; P = .98). CONCLUSION AND SCIENTIFIC SIGNIFICANCE: PWID hospitalized for endocarditis are at high risk for discharge AMA but more research is needed to understand the impact of MOUD. (Am J Addict 2020;29:155-159).