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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.

Stimuli-responsive hydrogels are candidate building blocks for soft robotic applications due to many of their unique properties, including tunable mechanical properties and biocompatibility. Over the past decade, there has been significant progress in developing soft and biohybrid actuators using naturally occurring and synthetic hydrogels to address the increasing demands for machines capable of interacting with fragile biological systems. Recent advancements in three-dimensional (3D) printing technology, either as a standalone manufacturing process or integrated with traditional fabrication techniques, have enabled the development of hydrogel-based actuators with on-demand geometry and actuation modalities. This mini-review surveys existing research efforts to inspire the development of novel fabrication techniques using hydrogel building blocks and identify potential future directions. In this article, existing 3D fabrication techniques for hydrogel actuators are first examined. Next, existing actuation mechanisms, including pneumatic, hydraulic, ionic, dehydration-rehydration, and cell-powered actuation, are reviewed with their benefits and limitations discussed. Subsequently, the applications of hydrogel-based actuators, including compliant handling of fragile items, micro-swimmers, wearable devices, and origami structures, are described. Finally, challenges in fabricating functional actuators using existing techniques are discussed.

Niemann–Pick C1-like protein (NPC1L1) mediates the absorption of dietary cholesterol in the proximal region of the intestine, a process that is blocked by cholesterol absorption inhibitors (CAIs), including ezetimibe (EZE). Using a proteomic approach, we demonstrate that NPC1L1 is the protein to which EZE and its analogs bind. Next, we determined the site of interaction of EZE analogs with NPC1L1 by exploiting the different binding affinities of mouse and dog NPC1L1 for the radioligand analog of EZE, [ 3 H]AS. Chimeric and mutational studies indicate that high-affinity binding of [ 3 H]AS to dog NPC1L1 depends on molecular determinants present in a 61-aa region of a large extracellular domain (loop C), where Phe-532 and Met-543 appear to be key contributors. These data suggest that the [ 3 H]AS-binding site resides in the intestinal lumen and are consistent with preclinical data demonstrating in vivo efficacy of a minimally bioavailable CAI. Furthermore, these determinants of [ 3 H]AS binding lie immediately adjacent to a hotspot of human NPC1L1 polymorphisms correlated with hypoabsorption of cholesterol. These observations, taken together with the recently described binding of cholesterol to the N terminus (loop A) of the close NPC1L1 homologue, NPC1, may provide a molecular basis for understanding EZE inhibition of NPC1L1-mediated cholesterol absorption. Specifically, EZE binding to an extracellular site distinct from where cholesterol binds prevents conformational changes in NPC1L1 that are necessary for the translocation of cholesterol across the membrane.

A unique laser resonator containing an internal phase grating and a diffractive mode-selecting mirror provides high modal discrimination in a Nd:YAG laser cavity. Single-spatial-mode lasers with high Fresnel numbers are possible, with negligible loss to the fundamental mode.

This article reports the fabrication and preliminary photoluminescence (PL) study of free‐standing Si pillars with diameters of about 10 nm and aspect ratios greater than 15. The pillars were fabricated using electron‐beam lithography, chlorine based reactive ion etching (RIE), and subsequent HF wet etching. Using HF etching offers several advantages: (a) it is a relatively stress independent process and therefore preserves the original shape of the structure; (b) it is a room temperature process; (c) it has a very controllable etch rate, ∼1.9 nm/h; and (d) it can remove RIE damage and passivate the Si surface. PL with a peak at 720 nm was repeatedly observed from an array of nanoscale pillars with ∼20 nm diameters. However, the cause of such PL is still unclear.