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Lung cancer is the leading cause of cancer-related deaths in the United States, with estimates of 236,740 new cases and 118,830 deaths in 2022 secondary to the disease. Blood-based liquid biopsies hold promise to reduce morbidity and mortality from lung cancer by enabling early detection to downstage disease at diagnosis, theragnostic identification of patients most likely to be helped or harmed by therapy, monitoring of therapeutic efficacy, and detection of residual disease. PrognomiQ’s multi-omics platform comprehensively profiles proteins, metabolites, lipids, mRNA, and cfDNA in blood samples which can be used for the development of liquid biopsy tests with high sensitivity and specificity for lung cancer. We conducted a case-control study comprising 1031 subjects: 361 subjects with untreated non-small-cell lung cancer (NSCLC) and 670 matched controls which included 340 subjects with salient pulmonary and gastrointestinal co-morbidities. Blood samples from each subject were processed to provide 7 different `omics readouts. LCMS was used to detect and quantify proteins, metabolites, and lipids. In addition, cfDNA and mRNA were assayed using next-generation sequencing. cfDNA reads were analyzed to estimate fragment-lengths, copy-number variation, and CpG site methylation. All molecular data were normalized using standard methods specific to each assay. Univariate analyses of cases vs controls were performed to identify differentially abundant features on all available samples per assay. We detected 9,868 proteins, 605 lipids, 329 metabolites, and 109,070 mRNA transcripts. Of these, 3,098 proteins, 210 lipids, 57 metabolites, and 30,236 mRNA transcripts were significantly different (FWER < 0.05) in cases versus controls. Gene set enrichment analysis on statistically significant transcripts and proteins identified multiple gene-ontology terms associated with cancer including the Wnt signaling process and IgA immunoglobulin complex, respectively. From cfDNA data, we identified 234 non-contiguous genomic regions associated with the fragment-length disorder, 4,790 with copy-number variation, and 74 differentially methylated genomic regions spanning 184 CpG sites (FWER < 0.05). With the premise that deviations from copy number neutrality are more likely to indicate a tumor contribution, we then focused our examination on those differentially expressed proteins that overlap with differentially expressed mRNA transcripts as well as CNV genomic regions. We identified 52 protein coding genes including E-cadherin (associated with EMT) and related binding proteins such as RAB11B, CAPZB, EPS15, FLNB, MYH9, STK24 and YWHAE. Ongoing machine-learning-based classifier training to distinguish between cancer and non-cancer can serve as the basis for the development of high-sensitivity liquid-biopsy tests for lung cancer. Citation Format: Jinlyung Choi, Ajinkya Kokate, Ehdieh Khaledian, Manway Liu, Preethi Prasad, John Blume, Jessica Chan, Rea Cuaresma, Kevin Dai, Manoj Khadka, Thidar Khin, Yuya Kodama, Joon-Yong Lee, Hoda Malekpour, Megan Mora, Nithya Mudaliar, Sara Nouri Golmaei, Madhuvanthi Ramaiah, Saividya Ramaswamy, Peter Spiro, Dijana Vitko, Kavya Swaminathan, James Yee, Brian Young, Chinmay Belthangady, Bruce Wilcox, Brian Koh, Philip Ma. Biomarker discovery in non-small-cell lung cancer enabled by deep multi-omics profiling of proteins, metabolites, transcripts, and genes in blood. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6606.

Pancreatic ductal adenocarcinoma (PDAC) is currently the 3rd leading cause of cancer-related deaths in the US. Although the all-stage 5-year survival rate is ~10%, early-stage 5-year survival is markedly superior and in excess of 40%. Hence, early detection of PDAC via blood-based liquid biopsies holds promise to reduce morbidity and mortality. PrognomiQ’s multi-omics platform performs deep and unbiased molecular profiling of blood samples to detect proteins, metabolites, lipids, mRNA, miRNA, cfDNA fragmentation and copy-number, and CpG methylation. Here we report results from training and validation of a classifier on a subset of that multi-omic data with the potential to enable the development of high sensitivity and specificity tests for early detection of PDAC.We conducted a case-control study comprising 146 subjects across 16 clinical sites, including 63 pathology-confirmed, untreated PDAC cases (12 stage I, 8 stage II, 4 stage III, 36 stage IV, and 3 stage unknown) and 83 age- and gender- matched controls without any known cancer. For each subject, venous blood samples including plasma were collected. Unbiased LCMS was used to detect and quantify proteins, and targeted, multiplexed MRM-LCMS assays were used for both metabolites and lipids. After data processing, we detected 54,114 proteomic features, 898 lipids, and 373 metabolites. 445 proteomic features, 170 lipids, and 37 metabolites were found to be significantly different as determined by Bonferroni-corrected Wilcoxon tests with FWER < 0.05. For classification, the dataset was split into training (37 cases and 37 controls) and validation (26 cases and 46 controls) sets, with control for collection site and date, age, and gender. XGBoost models were constructed for each analyte class using ten repeats of 10-fold cross-validation. To improve specificity to PDAC, all proteomic features which mapped to GOBP terms associated with acute-phase response, inflammation, and immune response were excluded prior to training. The best-performing hyperparameters were used for a final model built on the full training set and then used for inference on the validation set. At 99% specificity, the proteomic classifier had sensitivities of 77%, 57%, and 88% for Stages 1-4, Stages 1-2, and Stages 3-4, respectively, estimated by bootstrap re-sampling of the validation results. Metabolomics had sensitivities of 81%, 71%, and 88%. Lipidomics had sensitivities of 65%, 71%, and 65%. A joint, multi-omic model was constructed by averaging the scaled probabilities of all models. This joint model improved performance at 99% specificity with sensitivities of 92%, 86%, and 94%, highlighting the synergy of multi-omics data, particularly phenotypically related omics such as those described here. Multi-omic classifiers such as these can serve as the foundation for blood-based liquid biopsies for the early detection of PDAC. Citation Format: John Blume, Ghristine Bundalian, Jessica Chan, Connie Chao-Shern, Jinlyung Choi, Rea Cuaresma, Kevin Dai, Sara N. Golmaei, Jun Heok Jang, Manoj Khadka, Ehdieh Khaledian, Thidar Khin, Yuya Kodama, Ajinkya Kokate, Joon-Yong Lee, Manway Liu, Hoda Malekpour, Megan Mora, Nithya Mudaliar, Preethi Prasad, Madhuvanthi Ramaiah, Saividya Ramaswamy, Peter Spiro, Kavya Swaminathan, Dijana Vitko, James Yee, Brian Young, Susan Zhang, Chinmay Belthangady, Bruce Wilcox, Brian Koh, Philip Ma. A multi-omics classifier achieves high sensitivity and specificity for pancreatic ductal adenocarcinoma in a case-control study of 146 subjects [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6597.

Pancreatic cancer is the third leading cause of cancer-related deaths in the United States. Disease biomarkers quantified from blood-based assays may help reduce mortality by enabling early detection, treatment selection, or response and resistance assessment. PrognomiQ has developed a multi-omics assay and analysis platform that comprehensively profiles blood samples to detect proteins, metabolites, lipids, mRNA, miRNA, cfDNA fragments, and methylation at CpG sites. This platform can provide deep insights into the biology of pancreatic cancer and could enable the development of high sensitivity and specificity tests for the early detection of pancreatic cancer. We conducted a case-control study comprising 196 subjects: 92 with untreated pancreatic cancer and 104 matched controls without pancreatic cancer. For each subject, blood was collected in assay-specific tubes and processed to provide 7 different `omics readouts. cfDNA and mRNA were isolated from samples and assayed following standard NGS protocols. cfDNA fragments were processed to estimate fragment-length disorder and copy-number variation along with CpG site methylation. In addition, targeted and untargeted LCMS were used to detect and quantify proteins, metabolites, and lipids. After normalization, non-parametric univariate analyses of cases versus controls were performed to identify differentially abundant features on all available samples for each assay. Unsupervised learning was used to investigate the separation of subjects into groups based on disease status for the subset of 157 subjects for which complete data on all 7 readouts were available. We detected 2,812 proteins, 811 lipids, 373 metabolites, and 110,864 mRNA transcripts in all samples where data for each assay was available. Of these, 275 proteins, 232 lipids, 49 metabolites, and 3385 mRNA transcripts were significantly different (FWER < 0.05) in cases versus controls. From cfDNA data, we identified 35 non-contiguous genomic regions associated with fragment-length disorder, 557 with copy-number variation, and 5 with multiple, differentially methylated CpGs (FWER < 0.05) that aggregately span 307 protein-coding genes; of these, the overlap with the differentially expressed proteins included E-cadherin (tumor suppressor) and N-cadherin (involved in epithelial-to-mesenchymal transition). Statistically significant genes and proteins were found to be associated with processes including Wnt signaling, regulation of focal adhesion assembly, and actin cytoskeleton organization. Multi-omics, unsupervised learning showed separation of early- and late-stage cases and controls. High-dimensional bioinformatics analyses systematically decomposed each `omics data type into joint and orthogonal components associated with pancreatic cancer. Ongoing multivariate analyses, including supervised machine learning, will further elucidate the biology of pancreatic cancer development, and serve as the foundation for high-sensitivity blood tests for the early detection and monitoring of pancreatic cancer. Citation Format: Ehdieh Khaledian, Preethi Prasad, John Blume, Ghristine Bundalian, Connie Chao-Shern, Jinlyung Choi, Rea Cuaresma, Jared Deyarmin, Jun Heok Jang, Manoj Khadka, Thidar Khin, Yuya Kodama, Ajinkya Kokate, Joon-Yong Lee, Manway Liu, Nithya Mudaliar, Madhuvanthi Ramaiah, Saividya Ramaswamy, Peter Spiro, Kavya Swaminathan, Preston Williams, Mi Yang, James Yee, Brian Young, Robert Zawada, Susan Zhang, Chinmay Belthangady, Bruce Wilcox, Philip Ma. High-dimensional, multi-omics analyses of proteins, metabolites, transcripts, and genes enable biomarker discovery in early- and late-stage pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A038.

Albuterol is an uncommonly recognized culprit in causing lactic acidosis. While treating asthmatics, lactic acidosis creates a paradoxical situation in which, despite improvement in bronchospasm, patients look more dyspneic as a compensatory mechanism for metabolic acidosis. This might lead to

Combined pulmonary fibrosis and emphysema (CPFE) is a distinct clinical entity characterized by the simultaneous coexistence of both upper lobe emphysema and lower lobe pulmonary fibrosis. Although it was first described in 1990, it has not received enough recognition. Due to its unique lung

TRPV4, a close relative of the vanilloid receptor TRPV1, is activated by diverse modalities such as endogenous lipid ligands, hypotonicity, protein kinases and, possibly, mechanical inputs. While its multiple roles in vivo are being explored with KO mice and selective agonists, there is a dearth of selective antagonists available to examine TRPV4 function. Herein we detail the use of a focused library of commercial compounds in order to identify RN-1747 and RN-1734, a pair of structurally related small molecules endowed with TRPV4 agonist and antagonist properties, respectively. Their activities against human, rat and mouse TRPV4 were characterized using electrophysiology and intracellular calcium influx. Significantly, antagonist RN-1734 was observed to completely inhibit both ligand- and hypotonicity-activated TRPV4. In addition, RN-1734 was found to be selective for TRPV4 in a TRP selectivity panel including TRPV1, TRPV3 and TRPM8, and could thus be a valuable pharmacological probe for TRPV4 studies.

This document was developed through the collaborative efforts of the Society of Critical Care Medicine, the American College of Chest Physicians, and the Association of Organ Procurement Organizations. Under the auspices of these societies, a multidisciplinary, multi-institutional task force was convened, incorporating expertise in critical care medicine, organ donor management, and transplantation. Members of the task force were divided into 13 subcommittees, each focused on one of the following general or organ-specific areas: death determination using neurologic criteria, donation after circulatory death determination, authorization process, general contraindications to donation, hemodynamic management, endocrine dysfunction and hormone replacement therapy, pediatric donor management, cardiac donation, lung donation, liver donation, kidney donation, small bowel donation, and pancreas donation. Subcommittees were charged with generating a series of management-related questions related to their topic. For each question, subcommittees provided a summary of relevant literature and specific recommendations. The specific recommendations were approved by all members of the task force and then assembled into a complete document. Because the available literature was overwhelmingly comprised of observational studies and case series, representing low-quality evidence, a decision was made that the document would assume the form of a consensus statement rather than a formally graded guideline. The goal of this document is to provide critical care practitioners with essential information and practical recommendations related to management of the potential organ donor, based on the available literature and expert consensus.

Diameter, height, crown shape, and crown area were measured on 23-42 trees ranging in size from saplings to large adults for each of eight common dicotyledonous tree species in a neotropical forest on Barro Colorado Island, Panama. Six species were canopy trees, one species was an emergent tree, and the remaining species was an understory tree. Crown areas and shapes were quantified by eight radii measured every 450 from the trunk to the vertically projected edge of the crown. Crown areas were calculated from the areas of circles with the average radius; crown shapes were measured by the coefficients of variation of the eight crown radii. Age-diameter relationships were estimated from diameter growth increments over an 8-yr period. Observed height-diameter relationships were compared to expectations based on the theories of elastic similarity, constant stress, and geometric similarity. Slopes of log-transformed height-diameter relationships differed from the theoretically expected value of 2/3 for elastic similarity in three of eight species; two canopy species had higher slopes, but not as high as the expected value of 1.0 for geometric similarity; one canopy species was shorter, but the slope was greater than the slope of 0.5 predicted by the constant stress theory. The theory of elastic similarity also predicts that canopy mass to trunk mass ratio should remain constant during tree growth. Observed ratios based on proxy variables for the masses were constant in six of eight species- the two deviant species had heavier crowns in large trees than expected from their diameters. Crown shapes were much more variable in some species than others. The understory species had much lower r2 values for height-diameter and crown-diameter relationships, suggesting that these relationships may be more variable in trees that live in the less windy understory than in trees that reach the canopy. The allometric relationships for the species in this study were not unique for each species, suggesting that it may be possible to model the allometric relationships of many species with fewer equations than species. If the primary adaptive forces acting on tree species in a diverse forest are their physical environment and the sum of competitive interactions with an ever-changing mix of neighbors, then different species may have similar resource allocation patterns. Thus, using the age-size relationships, we found three groups of species among the seven canopy species. Two groups had three species each and one species (Ocotea whitei) was in a group by itself. The understory species, Faramea occidentalis, was also in a group by itself.

Although some earlier expectations of widespread use of glass reinforced resin for (stressed skin) parts of aircraft were not fulfilled (quality control difficulty with a hand laid up material), glass and other fibre reinforced plastics, “GRP”, plays an increasing role in the manufacture of light as well as large 'planes. There is an incentive to use GRP whenever not only lightness but also freedom to use complex curved shapes is needed, permeability to radar sometimes forming a further attraction.

Citizenship is a tough subject to address as an academic or otherwise. The popular ivory-tower conception of academics notwithstanding, most have nationality and are proud of it (liberals and conservatives alike). Most academics addressing American citizenship as an institution are themselves American. That makes it tough to have the sort of dispassionate discussion one might be able to have with respect to, say, copyright law. Everyone's got something invested in citizenship. Nobody's "against" it, in contrast to the related but distinct issue of immigration policy; it's a consensus institution. [ABSTRACT FROM PUBLISHER]