The value of total tumor diameter in unilateral multifocal papillary thyroid carcinoma: a propensity score matching analysis

BackgroundTumor multifocality is frequently observed in papillary thyroid carcinoma (PTC). However, the maximum tumor diameter (MTD), currently utilized in various staging schemes, might not accurately indicate the level of aggressiveness exhibited by multifocal tumors. We aimed to investigate the relationship between total tumor diameter (TTD) and clinicopathological features of papillary thyroid carcinoma.MethodsRetrospective data analysis was done on 1936 individuals who underwent complete thyroidectomy for PTC. Patients were classified into subgroups according to unilateral multifocality, central lymph node metastasis (CLNM) and lateral lymph node metastasis (LLNM). The relationships of clinicopathological features among these groups were analyzed.ResultsUnilateral multifocality was observed in 117 patients. The clinicopathological features of the unilateral multifocal PTC were similar to the unifocal PTC with approximate TTD. The unilateral multifocality played no independent role in CLNM and LLNM. Moreover, the efficiency of TTD in predicting CLNM and LLNM was significantly higher than that of MTD.ConclusionIn the case of unilateral multifocal PTC, TTD is a more accurate indicator of the biological characteristics of the tumor than MTD

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Integrated Analysis of Single-Cell and Bulk RNA-Sequencing Reveals a Tissue-Resident Macrophage-Related Signature for Predicting Immunotherapy Response in Breast Cancer Patients

Immune checkpoint therapy (ICT) is among the widely used treatments for breast cancer (BC), but most patients do not respond to ICT and the availability of the predictive biomarkers is limited. Emerging evidence indicates that tissue-resident macrophages (RTMs) inhibit BC progression, suggesting that their presence may predict immunotherapy response. A single-cell RNA-sequencing analysis of BC samples was performed to identify five RTM clusters with a mixed phenotype of M1-M2 macrophages. The comprehensive results showed that a high score of each RTM cluster was associated with a high infiltration of CD8+ T cells, M1 macrophages, and dendritic cells, and improved overall survival. In addition, a low score of each RTM cluster was associated with a high infiltration of M0 macrophages, naïve B cells and Tregs, and poor overall survival. Gene signatures from each RTM cluster were significantly enriched in responders compared with nonresponders. Each RTM cluster expression was significantly higher in responders than in nonresponders. The analyses of bulk RNA-seq datasets of BC samples led to identification and validation of a gene expression signature, named RTM.Sig, which contained the related genes of RTM clusters for predicting response to immunotherapy. This study highlights RTM.Sig could provide a valuable tool for clinical decisions in administering ICT

Adaptive Output Synchronization of Coupled Fractional-Order Memristive Reaction-Diffusion Neural Networks

This article discusses the adaptive output synchronization problem of coupled fractional-order memristive reaction-diffusion neural networks (CFOMRDNNs) with multiple output couplings or multiple output derivative couplings. Firstly, by using Lyapunov functional and inequality techniques, an adaptive output synchronization criterion for CFOMRDNNs with multiple output couplings is proposed. Then, an adaptive controller is designed for ensuring the output synchronization of CFOMRDNNs with multiple output derivative couplings. Finally, two numerical examples are given to verify the effectiveness of the theoretical results

Three New Species of Hypoxylon (Xylariales, Ascomycota) on a Multigene Phylogeny from Medog in Southwest China

During a survey of hypoxylaceous fungi in Medog county (Tibet Autonomous Region, China), three new species, including Hypoxylon damuense, Hypoxylon medogense, and Hypoxylon zangii, were described and illustrated based on morphological and multi-gene phylogenetic analyses. Hypoxylon damuense is characterized by its yellow-brown stromatal granules, light-brown to brown ascospores, and frequently indehiscent perispore. Hypoxylon medogense is morphologically and phylogenetically related to H. erythrostroma but differs in having larger ascospores with straight spore-length germ slit and conspicuously coil-like perispore ornamentation. Hypoxylon zangii shows morphological similarities to H. texense but differs in having Amber (47), Fulvous (43) and Sienna (8) KOH-extractable pigments and larger ascospores with straight spore-length germ slit. The multi-gene phylogenetic analyses inferred from the datasets of ITS-RPB2-LSU-TUB2 supported the three new taxa as separate lineages within Hypoxylon. A key to all known Hypoxylon species from China and related species worldwide is provided

The expression profiles of signature genes from CD103+LAG3+ tumour-infiltrating lymphocyte subsets predict breast cancer survival

Abstract Background Tumour-infiltrating lymphocytes (TILs), including T and B cells, have been demonstrated to be associated with tumour progression. However, the different subpopulations of TILs and their roles in breast cancer remain poorly understood. Large-scale analysis using multiomics data could uncover potential mechanisms and provide promising biomarkers for predicting immunotherapy response. Methods Single-cell transcriptome data for breast cancer samples were analysed to identify unique TIL subsets. Based on the expression profiles of marker genes in these subsets, a TIL-related prognostic model was developed by univariate and multivariate Cox analyses and LASSO regression for the TCGA training cohort containing 1089 breast cancer patients. Multiplex immunohistochemistry was used to confirm the presence of TIL subsets in breast cancer samples. The model was validated with a large-scale transcriptomic dataset for 3619 breast cancer patients, including the METABRIC cohort, six chemotherapy transcriptomic cohorts, and two immunotherapy transcriptomic cohorts. Results We identified two TIL subsets with high expression of CD103 and LAG3 (CD103+LAG3+), including a CD8+ T-cell subset and a B-cell subset. Based on the expression profiles of marker genes in these two subpopulations, we further developed a CD103+LAG3+ TIL-related prognostic model (CLTRP) based on CXCL13 and BIRC3 genes for predicting the prognosis of breast cancer patients. CLTRP-low patients had a better prognosis than CLTRP-high patients. The comprehensive results showed that a low CLTRP score was associated with a high TP53 mutation rate, high infiltration of CD8 T cells, helper T cells, and CD4 T cells, high sensitivity to chemotherapeutic drugs, and a good response to immunotherapy. In contrast, a high CLTRP score was correlated with a low TP53 mutation rate, high infiltration of M0 and M2 macrophages, low sensitivity to chemotherapeutic drugs, and a poor response to immunotherapy. Conclusions Our present study showed that the CLTRP score is a promising biomarker for distinguishing prognosis, drug sensitivity, molecular and immune characteristics, and immunotherapy outcomes in breast cancer patients. The CLTRP could serve as a valuable tool for clinical decision making regarding immunotherapy

Serum Proteome Alterations in Patients with Cognitive Impairment after Traumatic Brain Injury Revealed by iTRAQ-Based Quantitative Proteomics

Background. Cognitive impairment is the leading cause of traumatic brain injury- (TBI-) related disability; however, the underlying pathogenesis of this dysfunction is not completely understood. Methods. Using an isobaric tagging for relative and absolute quantitation- (iTRAQ-) based quantitative proteomic approach, serum samples from healthy control subjects, TBI patients with cognitive impairment, and TBI patients without cognitive impairment were analysed to identify differentially expressed proteins (DEPs) related to post-TBI cognitive impairment. In addition, DEPs were further analysed using bioinformatic platforms and validated using enzyme-linked immunosorbent assays (ELISA). Results. A total of 56 DEPs were identified that were specifically related to TBI-induced cognitive impairment. Bioinformatic analysis revealed that a wide variety of cellular and metabolic processes and some signaling pathways were involved in the pathophysiology of cognitive deficits following TBI. Five randomly selected DEPs were validated using ELISA in an additional 105 cases, and the results also supported the experimental findings. Conclusions. Despite limitations, our findings will facilitate further studies of the pathological mechanisms underlying TBI-induced cognitive impairment and provide new methods for the research and development of neuroprotective agents. However, further investigation on a large cohort is warranted