No abstract

Gene-expression deconvolution is used to quantify different types of cells in a mixed population. It provides a highly promising solution to rapidly characterize the tumor-infiltrating immune landscape and identify cold cancers. However, a major challenge is that gene-expression data are frequently contaminated by many outliers that decrease the estimation accuracy. Thus, it is imperative to develop a robust deconvolution method that automatically decontaminates data by reliably detecting and removing outliers. We developed a new machine learning tool, F ast A nd R obust DE convolution of E xpression P rofiles (FARDEEP), to enumerate immune cell subsets from whole tumor tissue samples. To reduce noise in the tumor gene expression datasets, FARDEEP utilizes an adaptive least trimmed square to automatically detect and remove outliers before estimating the cell compositions. We show that FARDEEP is less susceptible to outliers and returns a better estimation of coefficients than the existing methods with both numerical simulations and real datasets. FARDEEP provides an estimate related to the absolute quantity of each immune cell subset in addition to relative percentages. Hence, FARDEEP represents a novel robust algorithm to complement the existing toolkit for the characterization of tissue-infiltrating immune cell landscape. The source code for FARDEEP is implemented in R and available for download at https://github.com/YuningHao/FARDEEP.git .

Gene-expression deconvolution is used to quantify different types of cells in a mixed population. It provides a highly promising solution to rapidly characterize the tumor-infiltrating immune landscape and identify cold cancers. However, a major challenge is that gene-expression data are frequently contaminated by many outliers that decrease the estimation accuracy. Thus, it is imperative to develop a robust deconvolution method that automatically decontaminates data by reliably detecting and removing outliers. We developed a new machine learning tool, Fast And Robust DEconvolution of Expression Profiles (FARDEEP), to enumerate immune cell subsets from whole tumor tissue samples. To reduce noise in the tumor gene expression datasets, FARDEEP utilizes an adaptive least trimmed square to automatically detect and remove outliers before estimating the cell compositions. We show that FARDEEP is less susceptible to outliers and returns a better estimation of coefficients than the existing methods with both numerical simulations and real datasets. FARDEEP provides the absolute quantitation of each immune cell subset in addition to relative percentages. Hence, FARDEEP represents a novel robust algorithm to complement the existing toolkit for the characterization of tissueinfiltrating immune cell landscape. The source code for FARDEEP as implemented in R is available for download at https://goo.gl/SqGKuo.

Rationale: The endemic of peri-implantitis affects over 25% of dental implants. Current treatment depends on empirical patient and site-based stratifications and lacks a consistent risk grading system. Methods: We investigated a unique cohort of peri-implantitis patients undergoing regenerative therapy with comprehensive clinical, immune, and microbial profiling. We utilized a robust outlier-resistant machine learning algorithm for immune deconvolution. Results: Unsupervised clustering identified risk groups with distinct immune profiles, microbial colonization dynamics, and regenerative outcomes. Low-risk patients exhibited elevated M1/M2-like macrophage ratios and lower B-cell infiltration. The low-risk immune profile was characterized by enhanced complement signaling and higher levels of Th1 and Th17 cytokines. Fusobacterium nucleatum and Prevotella intermedia were significantly enriched in high-risk individuals. Although surgery reduced microbial burden at the peri-implant interface in all groups, only low-risk individuals exhibited suppression of keystone pathogen re-colonization. Conclusion: Peri-implant immune microenvironment shapes microbial composition and the course of regeneration. Immune signatures show untapped potential in improving the risk-grading for peri-implantitis.

A workforce that understands principles of geriatric medicine is critical to addressing the care needs of the growing elderly population. This will be impossible without a substantial increase in academicians engaged in education and aging research. Limited support of early‐career clinician–educators is a major barrier to attaining this goal. The Geriatric Academic Career Award (GACA) was a vital resource that benefitted 222 junior faculty members. GACA availability was interrupted in 2006, followed by permanent discontinuation after the Geriatrics Workforce Education Program (GWEP) subsumed it in 2015, leaving aspiring clinician–educators with no similar alternatives. GACA recipients were surveyed in this cross‐sectional, multimethod study to assess the effect of the award on career development, creation and dissemination of educational products, funding discontinuation consequences, and implications of program closure for the future of geriatric health care. Uninterrupted funding resulted in fulfillment of GACA goals (94%) and overall career success (96%). Collectively, awardees reached more than 40,700 learners. Funding interruption led to 55% working additional hours over and above an increased clinical workload to continue their GACA‐related research and scholarship. Others terminated GACA projects (36%) or abandoned academic medicine altogether. Of respondents currently at GWEP sites (43%), only 13% report a GWEP budget including GACA‐like support. Those with GWEP roles attributed their current standing to experience gained through GACA funding. These consequences are alarming and represent a major setback to academic geriatrics. GACA's singular contribution to the mission of geriatric medicine must prompt vigorous efforts to restore it as a distinct funding opportunity.