Unveiling Anoikis-related genes: A breakthrough in the prognosis of bladder cancer.

BACKGROUND: Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA. METHODS: We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS). RESULTS: The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups. CONCLUSIONS: In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.

The Impact of Biosimilar Use on Total Cost of Care and Provider Financial Performance in the Medicare Oncology Care Model: A Population-Based Simulation Study.

INTRODUCTION: Payment for oncology care is increasingly moving from fee-for-service to value-based payment (VBP). VBPs are agreements in which providers are held accountable for total cost of care (TCOC) through risk-sharing arrangements with payers that tie reimbursement levels to TCOC benchmarks. Oncology biosimilars may play an important role in managing financial risk in the VBPs like Medicare's Oncology Care Model (OCM), but there has been limited research in this area. The objective of this study is to estimate the impact of biosimilar adoption on TCOC and oncology provider financial performance under the terms of the Medicare OCM. METHODS: We conducted a population-based simulation study using the Medicare Limited Data Set (LDS) and the methodology of Medicare's OCM. The primary outcome was the simulated average change in TCOC per 6-month episode of care attributable to use of biosimilars as an alternative to reference products. The study population consisted of episodes of care in 2020 and using the reference product or corresponding biosimilar for bevacizumab, rituximab, trastuzumab, epoetin alfa, filgrastim, or pegfilgrastim. TCOC was calculated for each episode of care with use of reference products only and compared with TCOC with corresponding biosimilars. The simulation calculated TCOC outcomes in cohorts of 100 episodes sampled from the Medicare LDS study population using a Monte Carlo simulation with 10,000 iterations. RESULTS: Among the total of 8281 6-month oncology care episodes identified in the study period (initiating January 2020 to July 2020) in Medicare claims, 1586 (19.2%) episodes met OCM and study criteria and were included. Applying the simulation methods to these observed episodes, biosimilar substitution reduced mean TCOC per episode by $1193 (95% CI $583-1840). The cost reduction from biosimilars represented 2.4% of the average TCOC benchmark and led to a 15% reduction in the risk of providers needing to pay recoupments to Medicare for exceeding TCOC benchmarks. CONCLUSIONS: On the basis of our simulation study using observed Medicare claims and OCM criteria, we found that biosimilar substitution for reference products can significantly lower episode TCOC and improve provider financial performance under the terms of the largest value-based payment model implemented to date.

Coordinating single-cell and bulk RNA-seq in deciphering the intratumoral immune landscape and prognostic stratification of prostate cancer patients.

INTRODUCTION: Prostate cancer is a common cancer among male population. The aberrant expression of histone modifiers has been identified as a potential driving force in numerous cancer types. However, the mechanism of histone modifiers in the development of prostate cancer remains unknown. METHODS: Expression profiles and clinical data were obtained from GSE70769, GSE46602, and GSE67980. Seruat R package was utilized to calculate the gene set enrichment of the histone modification pathway and obtain the Histone score. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were employed to identify marker genes with prognostic value. Kaplan-Meier survival analysis was conducted to assess the efficacy of the prognostic model. In addition, microenvironment cell populations counter (MCPcounter), single-sample gene set enrichment analysis (ssGSEA), and xCell algorithms were employed for immune infiltration analysis. Drug sensitivity prediction was performed using oncoPredict R package. RESULTS: We screened differentially expressed genes (DEGs) between Histone-high score (Histone-H) and Histone-low score (Histone-L) groups, which were enriched in RNA splicing and DNA-binding transcription factor binding pathways. We retained four prognostic marker genes, including TACC3, YWHAH, TAF1C and TTLL5. The risk model showed significant efficacy in stratification of the prognosis of prostate cancer patients in both internal and external cohorts (p < .0001 and p = .032, respectively). In addition, prognostic gene YWHAH was infiltrated in abundance of fibroblasts and highly correlated with Entinostat_1593 drug sensitivity score and the value of risk score. CONCLUSION: We innovatively developed a histone modification-related prognostic model with high prognostic potency and identified YWHAH as possible diagnostic and therapeutic biomarkers for prostate cancer. It provides novel insights to address prostate cancer and enhance clinical outcomes, thereby opening up a new avenue for customized treatment alternatives.

The single-cell evolution trajectory presented different hypoxia heterogeneity to reveal the carcinogenesis of genes in clear cell renal cell carcinoma: Based on multiple omics and real experimental verification.

INTRODUCTION: Clear cell renal cell carcinoma (ccRCC) is the most prevalent and aggressive subtype of renal cell carcinoma, originating from renal tubular epithelial cells in the kidney. Hypoxia proves to be a feature commonly observed in solid tumors, leading to increased resistance to treatment and tumor progression. METHODS: scRNA-seq data were procured from GSE159115 data set. We utilized UMAP and NMF algorithm for clustering and dimensionality reduction. The FindAllMarkers function was used to compare various groups and identify potential hypoxia marker genes. A series of in vitro experiments, including CFA, flow cytometry targeting cell cycle, CCK-8, and EDU, was applied to investigate how ANGPTL4 regulated the ccRCC progression. Two cell lines of ccRCC cells, 786-O and Caki, were used for si-ANGPTL4 transfection. RESULTS: We annotated a total of a total of 6 cell clusters, namely ccRCC malignant cells, T cells, endothelial cells, myeloid cells, smooth muscle cells, and B cells. We observed higher levels of hypoxia-score in the ccRCC malignant cells, while lowest hypoxia-score in T and B cells. We detected multiple hypoxia-related subclusters of TME cells in ccRCC, among which S100A4 CD8+ T cells and nonhypoxia CD8+ T cells were found with a marked elevation of T cell inhibitory gene score. We identified that ANGPTL4+ endothelial cells might function as an integrative role in tumor angiogenesis. Multiple TME subclusters showed high potency in stratification of the prognosis of ccRCC patients. Moreover, by a series of in vitro experiment, we found ANGPTL4 regulated the ccRCC cell proliferation, probably through ERK/P38 pathway. CONCLUSION: We discerned multiple hypoxia-related subclusters of TME cells in ccRCC, which displayed distinct functional features and great potency in predicting prognosis of ccRCC patients. We identified the role of ANGPTL4 in regulating ccRCC proliferation via ERK/p38 pathway.

Investigating the prognostic role of lncRNAs associated with disulfidptosis-related genes in clear cell renal cell carcinoma.

INTRODUCTION: Renal cell carcinoma (RCC) is a grave malignancy that poses a significant global health burden with over 400,000 new cases annually. Disulfidptosis, a newly discovered programmed cell death process, is linked to the actin cytoskeleton, which plays a vital role in maintaining cell shape and survival. The role of disulfidptosis is poorly depicted in the clear cell histologic variant of RCC (ccRCC). METHODS: Three sets of ccRCC cohorts, ICGC_RECA-EU (n = 91), GSE76207 (n = 32) and TCGA-KIRC (n = 607), were included in our study, the batch effect of which was removed using the "combat" function. Correlation was calculated using the "rcorr" function of the "Hmisc" package for Pearson analysis, which was visualized using the "pheatmap" package. Principal component analysis was performed by the "vegan" package, visualized using the "scatterplot3d" package. Long non-coding RNAs (lncRNAs) associated with disulfidptosis were screened out using least absolute shrinkage and selection operator (LASSO) and COX analysis. Tumor mutation, immune landscaping and immunotherapy prediction were performed for further characterization of two risk groups. RESULTS: A total of 1822 disulfidptosis-related lncRNAs was selected, among which 308 lncRNAs were found to be significantly associated with the clinical outcome of ccRCC patients. We retained 11 disulfidptosis-related lncRNAs, namely, AP000439.3, RP11-417E7.1, RP11-119D9.1, LINC01510, SNHG3, AC156455.1, RP11-291B21.2, EMX2OS, AC093850.2, HAGLR and RP11-389C8.2, through LASSO and COX analysis for prognosis model construction, which displayed satisfactory accuracy (area under the curve, AUC, values all above 0.6 in multiple cohorts) in stratification of ccRCC prognosis. A nomogram model was constructed by integrating clinical factors with risk score, which further enhanced the prediction efficacy (AUC values all above 0.7 in multiple cohorts). We found that patients of male gender, higher clinical stages and advanced pathological T stage were inclined to have higher risk score values. Dactinomycin_1911, Vinblastine_1004, Daporinad_1248 and Vinorelbine_2048 were identified as promising candidate drugs for treating ccRCC patients of higher risk score value. Moreover, patients of higher risk value were prone to be resistant to immunotherapy. CONCLUSION: We developed a prognosis predicting model based on 11 selected disulfidptosis-related lncRNAs, the efficacy of which was verified in different cohorts. Furthermore, we delineated an intricate portrait of tumor mutation, immune topography and pharmacosensitivity evaluations within disparate risk stratifications.

Collaborating single-cell and bulk RNA sequencing for comprehensive characterization of the intratumor heterogeneity and prognostic model development for bladder cancer.

INTRODUCTION: Gaining a deeper insight into the single-cell RNA sequencing (scRNA-seq) results of bladder cancer (BLCA) provides a transcriptomic profiling of individual cancer cells, which may disclose the molecular mechanisms involved in BLCA carcinogenesis. METHODS: scRNA data were obtained from GSE169379 dataset. We used the InferCNV software to determine the copy number variant (CNV) with normal epithelial cells serving as the reference, and performed the pseudo-timing analysis on subsets of epithelial cell using Monocle3 software. Transcription factor analysis was conducted using the Dorothea software. Intercellular communication analysis was performed using the Liana software. Cox analysis and LASSO regression were applied to establish a prognostic model. RESULTS: We investigated the heterogeneity of tumors in four distinct cell types of BLCA cancer, namely immune cells, endothelial cells, epithelial cells, and fibroblasts. We evaluated the transcription factor activity of different immune cells in BLCA and identified significant enrichment of TCF7 and TBX21 in CD8+ T cells. Additionally, we identified two distinct subtypes of cancer-associated fibroblasts (CAFs), namely iCAFs and myoCAFs, which exhibited distinct communication patterns. Using sub-cluster and cell trajectory analyses, we identified different states of normal-to-malignant cell transformation in epithelial cells. TF analysis further revealed high activation of MYC and SOX2 in tumor cells. Finally, we identified five model genes (SLCO3A1, ANXA1, TENM3, EHBP1, LSAMP) for the development of a prognostic model, which demonstrated high effectiveness in stratifying patients across seven different cohorts. CONCLUSIONS: We have developed a prognostic model that has demonstrated significant efficacy in stratifying patients with BLCA.

Exploring oncogenes for renal clear cell carcinoma based on G protein-coupled receptor-associated genes.

G protein-coupled receptors (GPCRs) are a class of receptors on cell membranes that regulate various biological processes in cells, such as cell proliferation, differentiation, migration, apoptosis, and metabolism, by interacting with G proteins. However, the role of G protein-coupled receptors in predicting the prognosis of renal clear cell carcinoma is still unknown. The transcriptome data and clinical profiles of renal clear cell carcinoma patients, were downloaded from TCGA databases, and the validation group data were downloaded from number GSE167573, including 63 tumor samples and 14 normal samples. Single-cell RNA sequencing data were downloaded from the GEO database, No. GSE152938 and selected samples were used for GSEA enrichment analysis, WGCNA subgroup analysis, single-cell data analysis, and mutation analysis to explore the role of G protein-coupled receptor-related genes in the diagnosis and prognosis of renal clear cell carcinoma and to verify their reliability with cellular experiments. Finally, this study establishes a disease model based on G protein-coupled receptor-related genes, which may help to propose targeted therapeutic regimens in different strata of renal cell carcinoma patients.Author names: Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author: Given name [Lisa Jia] Last name [Tran].It's ok!

Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay.

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC.

Single-cell transcriptome analysis revealing the intratumoral heterogeneity of ccRCC and validation of MT2A in pathogenesis.

Clear-cell renal cell carcinoma (ccRCC) appears as the most common type of kidney cancer, the carcinogenesis of which has not been fully elucidated. Tumor heterogeneity plays a crucial role in cancer progression, which could be largely deciphered by the implement of scRNA-seq. The bulk and single-cell RNA expression profile is obtained from TCGA and study conducted by Young et al. We utilized UMAP, TSNE, and clustering algorithm Louvain for dimensionality reduction and FindAllMarkers function for determining the DEGs. Monocle2 was utilized to perform pseudo-time series analysis. SCENIC was implemented for transcription factor analysis of each cell subgroup. A series of WB, CFA, CCK-8, and EDU analysis was utilized for the validation of the role of MT2A in ccRCC carcinogenesis. We observed higher infiltration of T/NK and B cells in tumorous tissues, indicating the role of immune cells in ccRCC carcinogenesis. Transcription factor analysis revealed the activation of EOMES and ETS1 in CD8 + T cells, while CAFs were divided into myo-CAFs and i-CAFs, with i-CAFs showing distinct enrichment of ATF3, JUND, JUNB, EGR1, and XBP1. Through cell trajectory analysis, we discerned three distinct stages of cellular evolution, where State2 symbolizes normal renal tubular cells that underwent transitions into State1 and State3 as the CNV score ascended. Functional enrichment examination revealed an amplification of interferon gamma and inflammatory response pathways within tumor cells. The consensus clustering algorithm yielded two molecular subtypes, with cluster 2 being associated with advanced tumor stages and an abundance of infiltrated immune cells. We identified 17 prognostic genes through Cox and LASSO regression models and used them to construct a prognostic model, the efficacy of which was verified in multiple cohorts. Furthermore, we investigated the role of MT2A, one of our hub genes, in ccRCC carcinogenesis, and found it to regulate proliferation and migration of malignant cells. We depicted a detailed single-cell landscape of ccRCC, with special focus on CAFs, endothelial cells, and renal tubular cells. A prognostic model of high stability and accuracy was constructed based on the DEGs. MT2A was found to be actively implicated in ccRCC carcinogenesis, regulating proliferation and migration of the malignant cells.

Innovative breakthroughs facilitated by single-cell multi-omics: manipulating natural killer cell functionality correlates with a novel subcategory of melanoma cells.

Background: Melanoma is typically regarded as the most dangerous form of skin cancer. Although surgical removal of in situ lesions can be used to effectively treat metastatic disease, this condition is still difficult to cure. Melanoma cells are removed in great part due to the action of natural killer (NK) and T cells on the immune system. Still, not much is known about how the activity of NK cell-related pathways changes in melanoma tissue. Thus, we performed a single-cell multi-omics analysis on human melanoma cells in this study to explore the modulation of NK cell activity. Materials and methods: Cells in which mitochondrial genes comprised > 20% of the total number of expressed genes were removed. Gene ontology (GO), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and AUCcell analysis of differentially expressed genes (DEGs) in melanoma subtypes were performed. The CellChat package was used to predict cell-cell contact between NK cell and melanoma cell subtypes. Monocle program analyzed the pseudotime trajectories of melanoma cells. In addition, CytoTRACE was used to determine the recommended time order of melanoma cells. InferCNV was utilized to calculate the CNV level of melanoma cell subtypes. Python package pySCENIC was used to assess the enrichment of transcription factors and the activity of regulons in melanoma cell subtypes. Furthermore, the cell function experiment was used to confirm the function of TBX21 in both A375 and WM-115 melanoma cell lines. Results: Following batch effect correction, 26,161 cells were separated into 28 clusters and designated as melanoma cells, neural cells, fibroblasts, endothelial cells, NK cells, CD4+ T cells, CD8+ T cells, B cells, plasma cells, monocytes and macrophages, and dendritic cells. A total of 10137 melanoma cells were further grouped into seven subtypes, i.e., C0 Melanoma BIRC7, C1 Melanoma CDH19, C2 Melanoma EDNRB, C3 Melanoma BIRC5, C4 Melanoma CORO1A, C5 Melanoma MAGEA4, and C6 Melanoma GJB2. The results of AUCell, GSEA, and GSVA suggested that C4 Melanoma CORO1A may be more sensitive to NK and T cells through positive regulation of NK and T cell-mediated immunity, while other subtypes of melanoma may be more resistant to NK cells. This suggests that the intratumor heterogeneity (ITH) of melanoma-induced activity and the difference in NK cell-mediated cytotoxicity may have caused NK cell defects. Transcription factor enrichment analysis indicated that TBX21 was the most important TF in C4 Melanoma CORO1A and was also associated with M1 modules. In vitro experiments further showed that TBX21 knockdown dramatically decreases melanoma cells' proliferation, invasion, and migration. Conclusion: The differences in NK and T cell-mediated immunity and cytotoxicity between C4 Melanoma CORO1A and other melanoma cell subtypes may offer a new perspective on the ITH of melanoma-induced metastatic activity. In addition, the protective factors of skin melanoma, STAT1, IRF1, and FLI1, may modulate melanoma cell responses to NK or T cells.

CD8 + T-cell marker genes reveal different immune subtypes of oral lichen planus by integrating single-cell RNA-seq and bulk RNA-sequencing.

BACKGROUND: Oral lichen planus (OLP) is a local autoimmune disease induced by T-cell dysfunction that frequently affects middle-aged or elderly people, with a higher prevalence in women. CD8 + T cells, also known as killer T cells, play an important role in the progression and persistence of OLP. In order to identify different OLP subtypes associated with CD8 + T cell pathogenesis, consensus clustering was used. METHODS: In this study, we preprocessed and downscaled the OLP single-cell dataset GSE211630 cohort downloaded from Gene Expression Omnibus (GEO) to finally obtain the marker genes of CD8 + T cells. Based on the expression of marker genes, we classified OLP patients into CMGs subtypes using unsupervised clustering analysis. The gene expression profiles were analyzed by WGCNA using the "WGCNA" R package based on the clinical disease traits and typing results, and 108 CD8 + T-cell related OLP pathogenicity-related genes were obtained from the intersection. Patients were once again classified into gene subtypes based on intersection gene expression using unsupervised clustering analysis. RESULTS: After obtaining the intersecting genes of CD8 + T cells related to pathogenesis, OLP patients can be precisely classified into two different subtypes based on unsupervised clustering analysis, and subtype B has better immune infiltration results, providing clinicians with a reference for personalized treatment. CONCLUSIONS: Classification of OLP into different subtypes improve our current understanding of the underlying pathogenesis of OLP and provides new insights for future studies.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC.

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.

Revolutionizing anti-tumor therapy: unleashing the potential of B cell-derived exosomes.

B cells occupy a vital role in the functioning of the immune system, working in tandem with T cells to either suppress or promote tumor growth within the tumor microenvironment(TME). In addition to direct cell-to-cell communication, B cells and other cells release exosomes, small membrane vesicles ranging in size from 30-150 nm, that facilitate intercellular signaling. Exosome research is an important development in cancer research, as they have been shown to carry various molecules such as major histocompatibility complex(MHC) molecules and integrins, which regulate the TME. Given the close association between TME and cancer development, targeting substances within the TME has emerged as a promising strategy for cancer therapy. This review aims to present a comprehensive overview of the contributions made by B cells and exosomes to the tumor microenvironment (TME). Additionally, we delve into the potential role of B cell-derived exosomes in the progression of cancer.

Convection Confounds Measurements of Osmophoresis for Lipid Vesicles in Solute Gradients.

Lipid vesicles immersed in solute gradients are predicted to migrate from regions of high to low solute concentration due to osmotic flows induced across their semipermeable membranes. This process─known as osmophoresis─is potentially relevant to biological processes such as vesicle trafficking and cell migration; however, there exist significant discrepancies (several orders of magnitude) between experimental observations and theoretical predictions for the vesicle speed. Here, we seek to reconcile predictions of osmophoresis with observations of vesicle motion in osmotic gradients. We prepare quasi-steady solute gradients in a microfluidic chamber using density-matched solutions of sucrose and glucose to eliminate buoyancy-driven flows. We quantify the motions of giant DLPC vesicles and Brownian tracer particles in such gradients using Bayesian analysis of particle tracking data. Despite efforts to mitigate convective flows, we observe directed motion of both lipid vesicles and tracer particles in a common direction at comparable speeds of order 10 nm/s. These observations are not inconsistent with models of osmophoresis, which predict slower motion at ca. 1 nm/s; however, experimental uncertainty and the confounding effects of fluid convection prohibit a quantitative comparison. In contrast to previous reports, we find no evidence for anomalously fast osmophoresis of lipid vesicles when fluid convection is mitigated and quantified. We discuss strategies for enhancing the speed of osmophoresis using high permeability membranes and geometric confinement.

Multiple BCG vaccinations for the prevention of COVID-19 and other infectious diseases in type 1 diabetes.

There is a need for safe and effective platform vaccines to protect against coronavirus disease 2019 (COVID-19) and other infectious diseases. In this randomized, double-blinded, placebo-controlled phase 2/3 trial, we evaluate the safety and efficacy of a multi-dose Bacillus Calmette-Guérin (BCG) vaccine for the prevention of COVID-19 and other infectious disease in a COVID-19-unvaccinated, at-risk-community-based cohort. The at-risk population is made of up of adults with type 1 diabetes. We enrolled 144 subjects and randomized 96 to BCG and 48 to placebo. There were no dropouts over the 15-month trial. A cumulative incidence of 12.5% of placebo-treated and 1% of BCG-treated participants meets criteria for confirmed COVID-19, yielding an efficacy of 92%. The BCG group also displayed fewer infectious disease symptoms and lesser severity and fewer infectious disease events per patient, including COVID-19. There were no BCG-related systemic adverse events. BCG's broad-based infection protection suggests that it may provide platform protection against new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants and other pathogens.

Cytotoxicity of 2D engineered nanomaterials in pulmonary and corneal epithelium.

Two-dimensional (2D) engineered nanomaterials are widely used in consumer and industrial goods due to their unique chemical and physical characteristics. Engineered nanomaterials are incredibly small and capable of being aerosolized during manufacturing, with the potential for biological interaction at first-contact sites such as the eye and lung. The unique properties of 2D nanomaterials that make them of interest to many industries may also cause toxicity towards epithelial cells. Using murine and human respiratory epithelial cell culture models, we tested the cytotoxicity of eight 2D engineered nanomaterials: graphene (110 nm), graphene oxide (2 um), graphene oxide (400 nm), reduced graphene oxide (2 um), reduced graphene oxide (400 nm), partially reduced graphene oxide (400 nm), molybdenum disulfide (400 nm), and hexagonal boron nitride (150 nm). Non-graphene nanomaterials were also tested in human corneal epithelial cells for ocular epithelial cytotoxicity. Hexagonal boron nitride was found to be cytotoxic in mouse tracheal, human alveolar, and human corneal epithelial cells. Hexagonal boron nitride was also tested for inhibition of wound healing in alveolar epithelial cells; no inhibition was seen at sub-cytotoxic doses. Nanomaterials should be considered with care before use, due to specific regional cytotoxicity that also varies by cell type. Supported by U01ES027288 and T32HL007013 and T32ES007059.

Clinicopathologic and molecular characteristics of EGFR-mutant lung adenocarcinomas that transform to small cell lung cancer after TKI therapy.

Background: Small cell lung cancer (SCLC) transformation is one of the mechanisms of drug resistance to tyrosine kinase inhibitors (TKIs) in advanced epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) and represents an increasingly recognized clinical dilemma. Methods: We performed a retrospective review of 964 cases at the University of California, San Diego of patients with EGFR sensitizing mutations. Nine patients had a biopsy-confirmed small cell transformation. The unique gene alterations and clinicopathologic features were collected and analyzed. Results: Nine cases (9/964, 0.9%) were identified, all with stage IV adenocarcinoma (ADC) at diagnosis, 7 were poorly differentiated, and 7 had an EGFR exon 19 deletion. All nine patients had tumor protein p53 (TP53) mutation. Among seven cases that had next-generation sequencing (NGS), 5 harbored retinoblastoma 1 (RB1) loss. WNK lysine deficient protein kinase 1 (WNK1) mutation was found in two patients that had longer survival. The median time from the initial diagnosis to transformation was 22.7 months (IQR: 15.1-25.1). After small cell transformation on EGFR inhibition, all patients were treated with etoposide/platinum, conferring a median progression-free survival (PFS) of 3.2 months (IQR, 2.2-6.5 months) and post-chemotherapy survival of 8.6 months (IQR, 4.0-19.0 months). Six patients, as they retained the initial EGFR mutations, resumed (did so after terminating chemotherapy)/continued (did so concomitantly with chemotherapy) TKIs with a median duration of 13.8 months (IQR, 3.8-27.7 months). Two patients received immunotherapy but had no benefit. Conclusions: In our series, most patients with small cell transformation had poorly differentiated adenocarcinomas at baseline. RB1 loss was not universal in transformed patients in this series, though TP53 mutation was present in all tumor samples. WNK1 mutation may be a new resistance mechanism to TKIs that may be associated with improved survival.

Sampling efficiency of Candida auris from healthcare surfaces: culture and nonculture detection methods.

Sponges and swabs were evaluated for their ability to recover Candida auris dried 1 hour on steel and plastic surfaces. Culture recovery ranged from <0.1% (sponges) to 8.4% (swabs), and cells detected with an esterase activity assay revealed >50% recovery (swabs), indicating that cells may enter a viable but nonculturable state.

Prevalence of Recombinant Strains of Potato Virus Y in Seed Potato Planted in Idaho and Washington States Between 2011 and 2021.

Potato virus Y (PVY) has emerged as the main reason for potato seed lot rejections, seriously affecting seed potato production in the United States throughout the past 20 years. The dynamics of PVY strain abundance and composition in various potato growing areas of the United States has not been well documented or understood up to now. The objective of this study was to find out the prevalence of PVY strains in potato fields in the Pacific Northwest (PNW), including seed potato production systems in the State of Idaho and commercial potato fields in the Columbia Basin of Washington State between 2011 and 2021. Based on the testing of >10,000 foliar samples during Idaho seed certification winter grow-out evaluations of seed potato lots and seed lot trials in Washington State, a dramatic shift in the PVY strain composition was revealed in the PNW between 2011 and 2016. During this time period, the prevalence of the ordinary, PVYO strain in seed potato dropped 8- to 10-fold, concomitantly with the rise of recombinant strains PVYN-Wi and PVYNTNa, which together accounted for 98% of all PVY positives by 2021. In Idaho seed potato, PVYNTNa strain associated with the potato tuber necrotic ringspot disease (PTNRD) was found to increase threefold between 2011 and 2019, accounting for 24% of all PVY positives in 2019. Mild foliar symptoms induced by recombinant PVY strains may be partially responsible for the proliferation of PVYN-Wi and PVYNTNa in potato crops. A spike of another PTNRD-associated recombinant, PVY-NE11, was recorded in the PNW between 2012 and 2016, but after reaching a 7 to 10% level in 2012 to 2013 this recombinant disappeared from the PNW potato by 2019. Whole genome sequence analysis of the PVY-NE11 suggested this recombinant was introduced in the United States at least three times. The data on PVY strain abundance in the PNW potato crops suggest that virus management strategies must consider the current dominance of the two recombinant PVY strains, PVYN-Wi and PVYNTNa.