CD28 Costimulation Regulates Genome-Wide Effects on Alternative Splicing

CD28 is the major costimulatory receptor required for activation of naïve T cells, yet CD28 costimulation affects the expression level of surprisingly few genes over those altered by TCR stimulation alone. Alternate splicing of genes adds diversity to the proteome and contributes to tissue-specific regulation of genes. Here we demonstrate that CD28 costimulation leads to major changes in alternative splicing during activation of naïve T cells, beyond the effects of TCR alone. CD28 costimulation affected many more genes through modulation of alternate splicing than by modulation of transcription. Different families of biological processes are over-represented among genes alternatively spliced in response to CD28 costimulation compared to those genes whose transcription is altered, suggesting that alternative splicing regulates distinct biological effects. Moreover, genes dependent upon hnRNPLL, a global regulator of splicing in activated T cells, were enriched in T cells activated through TCR plus CD28 as compared to TCR alone. We show that hnRNPLL expression is dependent on CD28 signaling, providing a mechanism by which CD28 can regulate splicing in T cells and insight into how hnRNPLL can influence signal-induced alternative splicing in T cells. The effects of CD28 on alternative splicing provide a newly appreciated means by which CD28 can regulate T cell responses

Do serum biomarkers really measure breast cancer?

Background Because screening mammography for breast cancer is less effective for premenopausal women, we investigated the feasibility of a diagnostic blood test using serum proteins. Methods This study used a set of 98 serum proteins and chose diagnostically relevant subsets via various feature-selection techniques. Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance. We assessed generalization performance using leave-one-out cross-validation (LOOCV) and receiver operating characteristic (ROC) curve analysis. Results The classifiers were able to distinguish normal tissue from breast cancer with a classification performance of AUC = 0.82 ± 0.04 with the proteins MIF, MMP-9, and MPO. The classifiers distinguished normal tissue from benign lesions similarly at AUC = 0.80 ± 0.05. However, the serum proteins of benign and malignant lesions were indistinguishable (AUC = 0.55 ± 0.06). The classification tasks of normal vs. cancer and normal vs. benign selected the same top feature: MIF, which suggests that the biomarkers indicated inflammatory response rather than cancer. Conclusion Overall, the selected serum proteins showed moderate ability for detecting lesions. However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.United States. Dept. of Defense. Breast Cancer Research Program (Grant No. W81XWH-05-1-0292)National Institutes of Health (U.S.) (R01 CA-112437-01)National Institutes of Health (U.S.) (NIH CA 84955

Novel Educational Information Management Platform Improves the Surgical Skill Evaluation Process of Surgical Residents

OBJECTIVE: We sought to increase compliance and timeliness of surgery resident operative evaluation, by providing faculty and residents with a Platform-linking evaluation to analytics and machine-learning-facilitated case logging. DESIGN: We built a HIPAA-compliant web-based Platform for comprehensive management of resident education information, including resident operative performance evaluations. To assess evaluation timeliness, we compared the lag time for Platform-based evaluations to that of end-of-rotation evaluations. We also assessed evaluation compliance, based on a time threshold of 5 days for Platform evaluations and 2 weeks for end-of-rotation evaluations. SETTING: University of Massachusetts, Baystate Medical Center, General Surgery Residency. PARTICIPANTS: Twenty three attendings and 43 residents for the Platform cohort; 15 services and 45 residents for the end-of-rotation cohort. RESULTS: Three hundred and fifty-eight Platform evaluations were completed by 23 attendings for 43 residents for March through October 2017. Six hundred and ten end-of-rotation evaluations by 15 attendings for 45 residents were used for comparison (September 2015 through June 2017). Of Platform evaluations, 41.3% were completed within 24 hours of the operation (16.5% in 6 hours, 33.3% in 12 hours, and 62.2% in 48 hours), with 24.3% of evaluations completed within 3 hours after e-mail reminders. In the first 6 weeks (March 1 through April 12) 4.5 ± 3.7 evaluations were completed per week compared to 18.8 ± 5.8 in the last (September 18 through October 31). Evaluation lag times improved with the use of the Platform, both for median lag of 35 days earlier (1 ± 1.5 days Platform, 36 ± 28.2 days traditional, p \u3c 0.0001) and a mean lag of 41 days earlier (3.0 ± 4.7 days Platform, 44.0 ± 32.6 days traditional, p \u3c 0.0001). CONCLUSIONS: Our comprehensive Platform facilitated faculty compliance with evaluation requirements and timeliness of availability of performance information (often in near real time) for both residents and residency leadership. The added value of the Platform\u27s integration of evaluations with resident and attending case logging may account for the rapidly increasing number of operative skill evaluations over the short time span since implementation

Education Management Platform Enables Delivery and Comparison of Multiple Evaluation Types

OBJECTIVE: The purpose of this study was to determine whether an automated platform for evaluation selection and delivery would increase participation from surgical teaching faculty in submitting resident operative performance evaluations. DESIGN: We built a HIPAA-compliant, web-based platform to track resident operative assignments and to link embedded evaluation instruments to procedure type. The platform matched appropriate evaluations to surgeons\u27 scheduled procedures, and delivered multiple evaluation types, including Ottawa Surgical Competency Operating Room Evaluation (O-Score) evaluations and Operative Performance Rating System (OPRS) evaluations. Prompts to complete evaluations were made through a system of automatic electronic notifications. We compared the time spent in the platform to achieve evaluation completion. As a metric for the platform\u27s effect on faculty participation, we considered a task that would typically be infeasible without workflow optimization: the evaluator could choose to complete multiple, complementary evaluations for the same resident in the same case. For those cases with multiple evaluations, correlation was analyzed by Spearman rank test. Evaluation data were compared between PGY levels using repeated measures ANOVA. SETTING: The study took place at 4 general surgery residency programs: The University of Massachusetts Medical School-Baystate, the University of Connecticut School or Medicine, the University of Iowa Carver College of Medicine, and Maimonides Medical Center. PARTICIPANTS: From March 2017 to February 2019, the study included 70 surgical teaching faculty and 101 general surgery residents. RESULTS: Faculty completed 1230 O-Score evaluations and 106 OPRS evaluations. Evaluations were completed quickly, with a median time of 36 ± 18 seconds for O-Score evaluations, and 53 ± 51 seconds for OPRS evaluations. 89% of O-Score and 55% of OPRS evaluations were completed without optional comments within one minute, and 99% of O-Score and 82% of OPRS evaluations were completed within 2 minutes. For cases eligible for both evaluation types, attendings completed both evaluations on 74 of 221 (33%) of these cases. These paired evaluations strongly correlated on resident performance (Spearman coefficient = 0.84, p \u3c 0.00001). Both evaluation types stratified operative skill level by program year (p \u3c 0.00001). CONCLUSIONS: Evaluation initiatives can be hampered by the challenge of making multiple surgical evaluation instruments available when needed for appropriate clinical situations, including specific case types. As a test of the optimized evaluation workflow, and to lay the groundwork for future data-driven design of evaluations, we tested the impact of simultaneously delivering 2 evaluation instruments via a secure web-based education platform. We measured the evaluation completion rates of faculty surgeon evaluators when rating resident operative performance, and how effectively the results of evaluation could be analyzed and compared, taking advantage of a highly integrated management of the evaluative information

CD28 costimulation regulates genome-wide effects on alternative splicing.

CD28 is the major costimulatory receptor required for activation of naïve T cells, yet CD28 costimulation affects the expression level of surprisingly few genes over those altered by TCR stimulation alone. Alternate splicing of genes adds diversity to the proteome and contributes to tissue-specific regulation of genes. Here we demonstrate that CD28 costimulation leads to major changes in alternative splicing during activation of naïve T cells, beyond the effects of TCR alone. CD28 costimulation affected many more genes through modulation of alternate splicing than by modulation of transcription. Different families of biological processes are over-represented among genes alternatively spliced in response to CD28 costimulation compared to those genes whose transcription is altered, suggesting that alternative splicing regulates distinct biological effects. Moreover, genes dependent upon hnRNPLL, a global regulator of splicing in activated T cells, were enriched in T cells activated through TCR plus CD28 as compared to TCR alone. We show that hnRNPLL expression is dependent on CD28 signaling, providing a mechanism by which CD28 can regulate splicing in T cells and insight into how hnRNPLL can influence signal-induced alternative splicing in T cells. The effects of CD28 on alternative splicing provide a newly appreciated means by which CD28 can regulate T cell responses

Do serum biomarkers really measure breast cancer?

Abstract Background Because screening mammography for breast cancer is less effective for premenopausal women, we investigated the feasibility of a diagnostic blood test using serum proteins. Methods This study used a set of 98 serum proteins and chose diagnostically relevant subsets via various feature-selection techniques. Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance. We assessed generalization performance using leave-one-out cross-validation (LOOCV) and receiver operating characteristic (ROC) curve analysis. Results The classifiers were able to distinguish normal tissue from breast cancer with a classification performance of AUC = 0.82 ± 0.04 with the proteins MIF, MMP-9, and MPO. The classifiers distinguished normal tissue from benign lesions similarly at AUC = 0.80 ± 0.05. However, the serum proteins of benign and malignant lesions were indistinguishable (AUC = 0.55 ± 0.06). The classification tasks of normal vs. cancer and normal vs. benign selected the same top feature: MIF, which suggests that the biomarkers indicated inflammatory response rather than cancer. Conclusion Overall, the selected serum proteins showed moderate ability for detecting lesions. However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.</p