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Abstract Background To support the development of social medicine curricula that empower medical school graduates to redress health inequities, we conducted a mixed methods student and faculty evaluation of an expanded and innovative preclinical social medicine curriculum. Methods We implemented a longitudinal, interactive preclinical social medicine curriculum that was closely integrated with foundational science teaching then conducted a survey-based mixed methods student and faculty curriculum evaluation. Based on these results, we propose a novel conceptual roadmap for social medicine curriculum design. Results Student and faculty evaluations of an expanded and innovative longitudinal preclinical social medicine curriculum were strongly favorable. Both student and faculty respondents indicated a particular desire for deeper coverage of race and poverty among other social medicine domains. Qualitative student evaluations highlighted the importance of faculty champions to social medicine teaching as well as the educational impact of stories that exemplify the practical impact of the social determinants of health on specific patient experiences. Qualitative faculty evaluations pointed to the challenges of curriculum integration and the need for faculty career development in social medicine teaching. Conclusions Based on mixed methods student and faculty curriculum evaluation data, we propose a novel conceptual roadmap for the design of social medicine curricula at other institutions.

Brian De,1,* Sweet Ping Ng,1,2,* Amy Y Liu,1 Santiago Avila,1 Randa Tao,3 Emma B Holliday,1 Zachary Brownlee,4 Ahmed Kaseb,5 Sunyoung Lee,3 Kanwal Raghav,5 Jean-Nicolas Vauthey,6 Bruce D Minsky,1 Joseph M Herman,1 Prajnan Das,1 Grace L Smith,1 Cullen M Taniguchi,1 Sunil Krishnan,7 Christopher H Crane,8 Clemens Grassberger,9 Theodore S Hong,9 Steven H Lin,1 Albert C Koong,1 Radhe Mohan,1 Eugene J Koay1 1Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; 2Department of Radiation Oncology, Austin Health, Melbourne, Victoria, Australia; 3Department of Radiation Oncology, University of Utah, Huntsman Cancer Institute, Salt Lake City, UT, USA; 4Department of Radiation Oncology, Tufts Medical Center, Boston, MA, USA; 5Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; 6Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; 7Department of Radiation Oncology, Mayo Clinic Jacksonville, Jacksonville, FL, USA; 8Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; 9Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA*These authors contributed equally to this workCorrespondence: Eugene J KoayMD Anderson Cancer Center, Department of Radiation Oncology, 1400 Pressler St. Unit 1422, Houston, TX, 77030, USATel + 1 713-563-2381Fax +1 713-563-2331Email EKoay@mdanderson.orgBackground: The immune system plays a crucial role in cancer surveillance. Previous studies have shown that lymphopenia associated with radiotherapy (RT) portends a poor prognosis. We sought to differentiate the effects of proton and photon RT on changes in absolute lymphocyte count (ALC) for patients with hepatocellular carcinoma (HCC).Patients and Methods: Patients with HCC treated with definitive RT from 2006 to 2016 were studied. Serial ALCs were graded according to CTCAE v4.0. Overall survival (OS), disease-free survival, and distant metastasis-free survival were analyzed using the Kaplan–Meier method. Univariable and multivariable Cox-proportional hazards analyses were used to identify predictors of OS. A cohort analysis matched for treatment volume was performed to investigate differences in ALC dynamics between photon and proton therapy.Results: Of 143 patients identified, the median age was 66 (range, 19– 90) years. The treatment modality was photon in 103 (72%) and proton in 40 (28%). Median follow-up was 17 months (95% confidence interval, 13– 25 months). The median time to ALC nadir after initiation of RT was 17 days with a median relative decrease of 67%. Those who received proton RT had a higher median ALC nadir (0.41 vs 0.32 k/μL, p=0.002) and longer median OS (33 vs 13 months, p=0.002) than those who received photon RT. Matched cohort analyses revealed a larger low-dose liver volume in the photon group, which correlated with lower ALC. On multivariable Cox analysis, Grade 3 or higher lymphopenia prior to or after RT, portal venous tumor thrombus, larger planning target volumes, Child-Pugh (CP) Class B, and increased CP score after RT were associated with a higher risk of death, whereas the use of proton therapy was associated with lower risk.Conclusion: Grade 3 or higher lymphopenia may be associated with poorer outcomes in patients receiving RT for HCC. Protons may mitigate lymphopenia compared with photons, potentially due to reduced dose exposure of sites of lymphopoiesis.Keywords: lymphocyte count, circulating lymphocytes, splenic dose, liver dose, overall survival

Abstract Background Despite an abundant literature advocating that social determinants of health (SDH) be taught during undergraduate medical education, there are few detailed descriptions of how to design and implement longitudinal core curricula that is delivered to all students and accomplishes this goal. Methods In this paper, we describe the design and implementation of a social medicine curriculum at the University of Vermont’s Larner College of Medicine (UVM Larner). Using Kern’s principles, we designed a longitudinal curriculum that extends through both preclinical and clinical training for all students and focused on integrating SDH material directly into basic science and clinical training. Results We successfully developed and implemented two primary tools, a “Social Medicine Theme of the Week” (SMTW) in preclinical training, and SDH rounds in the clinical setting to deliver SDH content to all learners at UVM Larner. Conclusions Extensive student-faculty partnerships, robust needs assessment, and focusing on longitudinal and integrated SDH content delivery to all students were key features that contributed to successful design and implementation.

An amendment to this paper has been published and can be accessed via the original article.

A custom-built objective lens called the Mesolens allows relatively large biological specimens to be imaged with cellular resolution.

Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.

The human mammary gland is a complex and heterogeneous organ, where the interactions between mammary epithelial cells (MEC) and stromal fibroblasts are known to regulate normal biology and tumorigenesis. We aimed to longitudinally evaluate morphology and size of organoids in 3D co-cultures of normal (MCF10A) or pre-malignant (MCF10DCIS.com) MEC and hTERT-immortalized fibroblasts from reduction mammoplasty (RMF). This co-culture model, based on an isogenic panel of cell lines, can yield insights to understand breast cancer progression. However, 3D cultures pose challenges for quantitative assessment and imaging, especially when the goal is to measure the same organoid structures over time. Using optical coherence tomography (OCT) as a non-invasive method to longitudinally quantify morphological changes, we found that OCT provides excellent visualization of MEC-fibroblast co-cultures as they form ductal acini and remodel over time. Different concentrations of fibroblasts and MEC reflecting reported physiological ratios [1] were evaluated, and we found that larger, hollower, and more aspherical acini were formed only by pre-malignant MEC (MCF10DCIS.com) in the presence of fibroblasts, whereas in comparable conditions, normal MEC (MCF10A) acini remained smaller and less aspherical. The ratio of fibroblast to MEC was also influential in determining organoid phenotypes, with higher concentrations of fibroblasts producing more aspherical structures in MCF10DCIS.com. These findings suggest that stromal-epithelial interactions between fibroblasts and MEC can be modeled in vitro, with OCT imaging as a convenient means of assaying time dependent changes, with the potential for yielding important biological insights about the differences between benign and pre-malignant cells.

BackgroundThis retrospective study aims to investigate the activity of retreatment with anti-EGFR-based therapies in order to explore the concept of clonal evolution by evaluating the impact of prior activity and intervening time interval.MethodsEighty-nine KRAS exon 2-wild-type metastatic colorectal patients were retreated on phase I/II clinical trials containing anti-EGFR therapies after progressing on prior cetuximab or panitumumab. Response on prior anti-EGFR therapy was defined retrospectively per physician-records as response or stable disease ≥6 months. Multivariable statistical methods included a multiple logistic regression model for response, and Cox proportional hazards model for progression-free survival.ResultsRetreatment anti-EGFR agents were cetuximab (n = 76) or cetuximab plus erlotinib (n = 13). The median interval time between prior and retreatment regimens was 4.57 months (range: 0.46-58.7). Patients who responded to the prior cetuximab or panitumumab were more likely to obtain clinical benefit to the retreatment compared to the non-responders in both univariate (p = 0.007) and multivariate analyses (OR: 3.38, 95 % CI: 1.27, 9.31, p = 0.019). The clinical benefit rate on retreatment also showed a marginally significant association with interval time between the two anti-EGFR based therapies (p = 0.053). Median progression-free survival on retreatment was increased in prior responders (4.9 months, 95 % CI: 3.6, 6.2) compared to prior non-responders (2.5 months, 95 % CI, 1.58, 3.42) in univariate (p = 0.064) and multivariate analysis (HR: 0.70, 95 % CI: 0.43-1.15, p = 0.156).ConclusionOur data lends support to the concept of clonal evolution, though the clinical impact appears less robust than previously reported. Further work to determine which patients benefit from retreatment post progression is needed.

Low-latency inference for machine learning models is increasingly becoming a necessary requirement, as these models are used in mission-critical applications such as autonomous driving, military defense (e.g., target recognition), and network traffic analysis. A widely studied and used technique to overcome this challenge is to offload some or all parts of the inference tasks onto specialized hardware such as graphic processing units. More recently, offloading machine learning inference onto programmable network devices, such as programmable network interface cards or a programmable switch, is gaining interest from both industry and academia, especially due to the latency reduction and computational benefits of performing inference directly on the data plane where the network packets are processed. Yet, current approaches are relatively limited in scope, and there is a need to develop more general approaches for mapping offloading machine learning models onto programmable network devices. To fulfill such a need, this work introduces a novel framework, called ML-NIC, for deploying trained machine learning models onto programmable network devices' data planes. ML-NIC deploys models directly into the computational cores of the devices to efficiently leverage the inherent parallelism capabilities of network devices, thus providing huge latency and throughput gains. Our experiments show that ML-NIC reduced inference latency by at least 6 × on average and in the 99th percentile and increased throughput by at least 16x with little to no degradation in model effectiveness compared to the existing CPU solutions. In addition, ML-NIC can provide tighter guaranteed latency bounds in the presence of other network traffic with shorter tail latencies. Furthermore, ML-NIC reduces CPU and host server RAM utilization by 6.65% and 320.80 MB. Finally, ML-NIC can handle machine learning models that are 2.25 × larger than the current state-of-the-art network device offloading approaches.

Background: Erectile dysfunction (ED) is a very common complication in men with diabetes mellitus (DM). Low-intensity extracorporeal shockwave therapy (Li-ESWT) offers a promising nonsurgical treatment option for ED. A systematic scoping review investigating the outcomes of Li-ESWT in diabetic men with ED has not yet been performed. Objectives: To systematically review animal and clinical studies related to the use of Li-ESWT for treatment of DM-related ED. Data sources: PubMed, Embase, The Cochrane Library, Scopus, and Web of Science were searched, unrestricted by dates or study design. Materials and methods: We included qualitative studies, quantitative studies, primary research studies, meta-analyses, and research letters written in English. Full text reviewing was completed in all animal and human studies discussing Li-ESWT for the treatment of ED in subjects with DM. Data extracted included the journal citation, publication year, country of origin, study design, and a summary of the pertinent findings. Results: Our search yielded nine clinical studies and 10 animal studies. The results of the clinical studies suggest that Li-ESWT is a safe and effective treatment in men with well-controlled DM and moderate or better ED. However, the benefit is less durable in diabetic men than nondiabetic men. The results of the animal studies suggest that Li-ESWT can significantly improve erectile function in diabetic rat models with ED. Conclusions: The examined studies present encouraging results for the use of Li-ESWT to treat diabetic men with ED. Future studies, particularly robust randomized controlled trials, are necessary to confirm these findings and provide long-term follow-up. [ABSTRACT FROM AUTHOR]

Monkeypox virus, a close relative of variola virus, has significantly increased the incidence of monkeypox disease in humans, with several clinical symptoms. The sporadic spread of the disease outbreaks has resulted in the need for a comprehensive understanding of the molecular mechanisms underlying disease infection and potential therapeutic targets. Protein–protein interactions play a crucial role in various cellular processes and regulate different immune signals during virus infection. Computational algorithms have gained high significance in the prediction of potential protein interaction pairs. Here, we developed a comprehensive database called HuPoxNET (https://kaabil.net/hupoxnet/) using the state-of-the-art MERN stack technology. The database leverages two sequence-based computational models to predict strain-specific protein–protein interactions between human and monkeypox virus proteins. Furthermore, various protein annotations of the human and viral proteins such as gene ontology, KEGG pathways, subcellular localization, protein domains, and novel drug targets identified from our study are also available on the database. HuPoxNET is a user-friendly platform for the scientific community to gain more insights into the monkeypox disease infection and aid in the development of therapeutic drugs against the disease.