A genomic signature for accurate classification and prediction of clinical outcomes in cancer patients treated with immune checkpoint blockade immunotherapy

Tumor mutational burden (TMB) is associated with clinical response to immunotherapy, but application has been limited to a subset of cancer patients. We hypothesized that advanced machine-learning and proper modeling could identify mutations that classify patients most likely to derive clinical benefits. Training data: Two sets of public whole-exome sequencing (WES) data for metastatic melanoma. Validation data: One set of public non-small cell lung cancer (NSCLC) data. Least Absolute Shrinkage and Selection Operator (LASSO) machine-learning and proper modeling were used to identify a set of mutations (biomarker) with maximum predictive accuracy (measured by AUROC). Kaplan-Meier and log-rank methods were used to test prediction of overall survival. The initial model considered 2139 mutations. After pruning, 161 mutations (11%) were retained. An optimal threshold of 0.41 divided patients into high-weight (HW) or low-weight (LW) TMB groups. Classification for HW-TMB was 100% (AUROC = 1.0) on melanoma learning/testing data; HW-TMB was a prognostic marker for longer overall survival. In validation data, HW-TMB was associated with survival (p = 0.0057) and predicted 6-month clinical benefit (AUROC = 0.83) in NSCLC. In conclusion, we developed and validated a 161-mutation genomic signature with outstanding 100% accuracy to classify melanoma patients by likelihood of response to immunotherapy. This biomarker can be adapted for clinical practice to improve cancer treatment and care

A survey of educational articles appearing in sixteen popular magazines for an eleven year period from January, 1943 through December, 1953.

Thesis (Ed.M.)--Boston Universit

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Barriers and strategies for recruiting study participants in clinical settings

Recruiting participants for research studies is often a challenging task. Recruitment requires careful planning, collaboration, and flexibility on the part of researchers and health care providers at the recruitment sites. This article describes six major barriers to recruiting study participants as identified from a review of the literature and from the coauthors\u27 research experiences. These barriers include challenges related to regulations of the Health Insurance Portabililty and Accountability Act (HIPAA), health care providers\u27 work burden, providers\u27 financial disincentives, competition, health care provider concerns regarding research, and provider protection of patients. Each barrier is described, and specific strategies are suggested based on the empirical literature. In some instances, the coauthors\u27 experiences are also shared

Response scale selection in adult pain measures: results from a literature review

Abstract Background The purpose of this literature review was to examine the existing patient-reported outcome measurement literature to understand the empirical evidence supporting response scale selection in pain measurement for the adult population. Methods The search strategy involved a comprehensive, structured, literature review with multiple search objectives and search terms. Results The searched yielded 6918 abstracts which were reviewed against study criteria for eligibility across the adult pain objective. The review included 42 review articles, consensus guidelines, expert opinion pieces, and primary research articles providing insights into optimal response scale selection for pain assessment in the adult population. Based on the extensive and varied literature on pain assessments, the adult pain studies typically use simple response scales with single-item measures of pain—a numeric rating scale, visual analog scale, or verbal rating scale. Across 42 review articles, consensus guidelines, expert opinion pieces, and primary research articles, the NRS response scale was most often recommended in these guidance documents. When reviewing the empirical basis for these recommendations, we found that the NRS had slightly superior measurement properties (e.g., reliability, validity, responsiveness) across a wide variety of contexts of use as compared to other response scales. Conclusions Both empirical studies and review articles provide evidence that the 11-point NRS is likely the optimal response scale to evaluate pain among adult patients without cognitive impairment