Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.

Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hierarchical clustering tool that fully incorporates nonlinear dimension reduction, negative binomial-based convolutional autoencoder for data fitting, and a self-supervision model for cell similarity enhancement. In comprehensive evaluation using multiple large-scale scRNA-seq datasets, DeepScena consistently outperformed seven popular clustering tools in terms of accuracy. Notably, DeepScena exhibits high proficiency in identifying rare cell populations within large datasets that contain large numbers of clusters. When applied to scRNA-seq data of multiple myeloma cells, DeepScena successfully identified not only previously labeled large cell types but also subpopulations in CD14 monocytes, T cells and natural killer cells, respectively.

Targeting NG2 relieves the resistance of BRAF-mutant thyroid cancer cells to BRAF inhibitors.

BRAFV600E represents a constitutively active onco-kinase and stands as the most prevalent genetic alteration in thyroid cancer. However, the clinical efficacy of small-molecule inhibitors targeting BRAFV600E is often limited by acquired resistance. Here, we find that nerve/glial antigen 2 (NG2), also known as chondroitin sulfate proteoglycan 4 (CSPG4), is up-regulated in thyroid cancers, and its expression is increased with tumor progression in a BRAFV600E-driven thyroid cancer mouse model. Functional studies show that NG2 knockout almost does not affect tumor growth, but significantly improves the response of BRAF-mutant thyroid cancer cells to BRAF inhibitor PLX4720. Mechanistically, the blockade of ERK-dependent feedback by BRAF inhibitor can activate receptor tyrosine kinase (RTK) signaling, causing the resistance to this inhibitor. NG2 knockout attenuates the PLX4720-mediated feedback activation of several RTKs, improving the sensitivity of BRAF-mutant thyroid cancer cells to this inhibitor. Based on this finding, we propose and demonstrate an alternative strategy for targeting NG2 to effectively treat BRAF-mutant thyroid cancers by combining multiple kinase inhibitor (MKI) Sorafenib or Lenvatinib with PLX4720. Thus, this study uncovers a new mechanism in which NG2 contributes to the resistance of BRAF-mutant thyroid cancer cells to BRAF inhibitor, and provides a promising therapeutic option for BRAF-mutant thyroid cancers.

Decoding brain memory formation by single-cell RNA sequencing.

To understand how distinct memories are formed and stored in the brain is an important and fundamental question in neuroscience and computational biology. A population of neurons, termed engram cells, represents the physiological manifestation of a specific memory trace and is characterized by dynamic changes in gene expression, which in turn alters the synaptic connectivity and excitability of these cells. Recent applications of single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) are promising approaches for delineating the dynamic expression profiles in these subsets of neurons, and thus understanding memory-specific genes, their combinatorial patterns and regulatory networks. The aim of this article is to review and discuss the experimental and computational procedures of sc/snRNA-seq, new studies of molecular mechanisms of memory aided by sc/snRNA-seq in human brain diseases and related mouse models, and computational challenges in understanding the regulatory mechanisms underlying long-term memory formation.

RNF115 aggravates tumor progression through regulation of CDK10 degradation in thyroid carcinoma.

BACKGROUND: RING Finger Protein 115 (RNF115), a notable E3 ligase, is known to modulate tumorigenesis and metastasis. In our investigation, we endeavor to unravel the putative function and inherent mechanism through which RNF115 influences the evolution of thyroid carcinoma (THCA). METHODS: We analyzed RNF115 expression in THCA using the Cancer Genome Atlas (TCGA) database. The influence of RNF115 on the progression of THCA was evaluated using both in vitro and in vivo experimental approaches. The protein regulated by RNF115 was identified through bioinformatics analysis, and its biological significance was further explored. RESULTS: In both THCA tissues and cells, RNF115 showed elevated expression levels. Enhanced expression of RNF115 fostered cell proliferation, tumor growth, and the exacerbation of epithelial-mesenchymal transition (EMT) in THCA, while also promoting tumor lung metastasis. Bioinformatics analysis identified cyclin-dependent kinase 10 (CDK10) as a downstream target of RNF115, which was found to be ubiquitinated and degraded by RNF115 in THCA cells. Functionally, overexpression of CDK10 was found to counteract the promotion of malignant phenotype in THCA induced by RNF115. From a mechanistic perspective, RNF115 activated the Raf-1 pathway and enhanced cancer cell cycle progression by degrading CDK10 in THCA cells. CONCLUSION: RNF115 triggers cell proliferation, EMT, and tumor metastasis by ubiquitinating and degrading CDK10. The regulation of the Raf-1 pathway and cell cycle progression in THCA may be profoundly influenced by this process.

Consensus clustering of single-cell RNA-seq data by enhancing network affinity.

Elucidation of cell subpopulations at high resolution is a key and challenging goal of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data analysis. Although unsupervised clustering methods have been proposed for de novo identification of cell populations, their performance and robustness suffer from the high variability, low capture efficiency and high dropout rates which are characteristic of scRNA-seq experiments. Here, we present a novel unsupervised method for Single-cell Clustering by Enhancing Network Affinity (SCENA), which mainly employed three strategies: selecting multiple gene sets, enhancing local affinity among cells and clustering of consensus matrices. Large-scale validations on 13 real scRNA-seq datasets show that SCENA has high accuracy in detecting cell populations and is robust against dropout noise. When we applied SCENA to large-scale scRNA-seq data of mouse brain cells, known cell types were successfully detected, and novel cell types of interneurons were identified with differential expression of gamma-aminobutyric acid receptor subunits and transporters. SCENA is equipped with CPU + GPU (Central Processing Units + Graphics Processing Units) heterogeneous parallel computing to achieve high running speed. The high performance and running speed of SCENA combine into a new and efficient platform for biological discoveries in clustering analysis of large and diverse scRNA-seq datasets.

MYO5A overexpression promotes invasion and correlates with low lymphocyte infiltration in head and neck squamous carcinoma.

Head and neck squamous carcinoma (HNSC) poses a significant public health challenge due to its substantial morbidity. Nevertheless, despite advances in current treatments, the prognosis for HNSC remains unsatisfactory. To address this, single-cell RNA sequencing (RNA-seq) and bulk RNA-seq data combined with in vitro studies were conducted to examine the role of MYO5A (Myosin VA) in HNSC. Our investigation revealed an overexpression of MYO5A in HNSC that promotes HNSC migration in vitro. Remarkably, knockdown of MYO5A suppressed vimentin expression. Furthermore, analyzing the TCGA database evidenced that MYO5A is a risk factor for human papillomavirus positive (HPV+) HNSC (HR = 0.81, P < 0.001). In high MYO5A expression HNSC, there was a low count of tumor infiltrating lymphocytes (TIL), including activated CD4+ T cells, CD8+ T cells, and B cells. Of note, CD4+ T cells and B cells were positively associated with improved HPV+ HNSC outcomes. Correlation analysis demonstrated a decreased level of immunostimulators in high MYO5A-expressing HNSC. Collectively, these findings suggest that MYO5A may promote HNSC migration through vimentin and involve itself in the process of immune infiltration in HNSC, advancing the understanding of the mechanisms and treatment of HNSC.

IGFL2-AS1 facilitates tongue squamous cell carcinoma progression via Wnt/ß-catenin signaling pathway.

OBJECTIVES: Tongue squamous cell carcinoma (TSCC) is the most common malignancy in oral cancer. Long noncoding RNAs (lncRNAs) are important regulators in cancer biology. In our present study, we investigated a novel lncRNA IGF-like family member 2 antisense RNA 1 (IGFL2-AS1) in TSCC. METHODS: RT-qPCR analyzed IGFL2-AS1 expression in TSCC cells. Functional assays assessed the impact of IGFL2-AS1 on TSCC cell proliferation, migration, and invasion. Western blot analyzed the protein levels of EMT-related markers. Mechanism assays analyzed the regulatory mechanism of IGFL2-AS1 in TSCC cells. In-vivo experiments were conducted to prove the role of IGFL2-AS1 in TSCC progression. RESULTS: IGFL2-AS1 was significantly up-regulated in TSCC cells and tissues, and IGFL2-AS1 knockdown inhibited cell proliferation, migration, invasion and EMT in TSCC. Moreover, IGFL2-AS1 functioned as a competing endogenous RNA (ceRNA) to sponge miR-1224-5p and thereby modulated SATB homeobox 1 (SATB1) expression. Additionally, SATB1 activated the Wnt/ß-catenin signaling pathway in TSCC cells and IGFL2-AS1 regulated the Wnt/ß-catenin signaling pathway and TSCC progression via elevating SATB1 expression. CONCLUSIONS: The data revealed that IGFL2-AS1 played a cancer promoting role in TSCC and may aid in exploring a brand new biomarker that might contribute to TSCC treatment.

Efficacy and safety of Artemisia annua sublingual immunotherapy in patients with seasonal allergic rhinoconjunctivitis over two pollen seasons.

OBJECTIVE: This study investigates the efficacy and safety of sublingual immunotherapy (SLIT) with A. annua allergens in patients with seasonal allergic rhinoconjunctivitis over two pollen seasons. METHODS: Seventy patients with moderate-severe seasonal allergic rhinoconjunctivitis were divided evenly into the SLIT and control groups. The SLIT last from 3 months before the summer-autumn pollen season in 2021 till the end of the summer-autumn pollen season in 2022. The daily individual symptom score, total rhinoconjunctivitis symptom score (dTRSS), total medication score (dTMS), combined score of medication and rhinoconjunctivitis symptom (dCSMRS), visual analog scale (VAS) score, and adverse events (AEs) were evaluated. RESULTS: The average pollen concentration in 2022 was twice that previous two-year during the pollen season. Fifty-six patients completed treatments (SLIT group: 29, control group: 27). Compared with baseline, the individual symptoms, dTRSS, dTMS, dCSMRS, and VAS scores of SLIT group declined in 2021. After 16 months of SLIT, all efficacy indexes in 2022 were still lower than baseline and equivalent to those in 2021. In control group, the efficacy indexes in 2022 were higher than that in 2020 and 2021. The efficacy indexes of SLIT group were lower than those of control group in 2021 and 2022. SLIT is effective for both mono- and poly-sensitized patients. AEs incidence in SLIT group was 82.7% without severe AEs. CONCLUSIONS: The A. annua-SLIT can obtain efficacy and safety over two pollen seasons for patients with moderate-severe seasonal allergic rhinoconjunctivitis.

scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets.

Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and none infer GRNs by directly using the paired datasets of case-versus-control experiments. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. scTIGER employs cell-type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among gene modules. As state-of-the-art applications, we first applied scTIGER to scRNA-seq datasets of prostate cancer cells, and successfully identified the dynamic regulatory networks of AR, ERG, PTEN and ATF3 for same-cell type between prostatic cancerous and normal conditions, and two-cell types within the prostatic cancerous environment. We then applied scTIGER to scRNA-seq data from neurons with and without fear memory and detected specific regulatory networks for BDNF, CREB1 and MAPK4. Additionally, scTIGER demonstrates robustness against high levels of dropout noise in scRNA-seq data.

Novel recombinant human thyroid-stimulating hormone in aiding postoperative assessment of patients with differentiated thyroid cancer-phase I/II study.

PURPOSE: Thyroid hormone withdrawal (THW) inevitably induced hypothyroidism in patients with differentiated thyroid cancer (DTC), and we aimed to evaluate the safety and efficacy of a novel recombinant human thyroid-stimulating hormone (rhTSH, ZGrhTSH) as an alternative of THW in China. METHODS: Totally, 64 DTC patients were enrolled with 24 in the dose-escalation cohort equally grouped into 0.9 mg × 1 day, 0.9 mg × 2 day, 1.8 mg × 1 day, and 1.8 mg × 2 day dosage, and 40 further enrolled into 0.9 mg × 2 day dose-expansion cohort. All patients underwent both ZGrhTSH phase and levothyroxine (L-T4) withdrawal phase for self-comparison in terms of TSH levels, the radioactive iodine (RAI) uptake, stimulated thyroglobulin level, and the quality of life (QoL). RESULTS: In ZGrhTSH phase, no major serious adverse events were observed, and mild symptoms of headache were observed in 6.3%, lethargy in 4.7%, and asthenia in 3.1% of the patients, and mostly resolved spontaneously within 2 days. Concordant RAI uptake was noticed in 89.1% (57/64) of the patients between ZGrhTSH and L-T4 withdrawal phases. The concordant thyroglobulin level with a cut-off of 1 µg/L was noticed in 84.7% (50/59) of the patients without the interference of anti-thyroglobulin antibody. The QoL was far better during ZGrhTSH phase than L-T4 withdrawal phase, with lower Billewicz (- 51.30 ± 4.70 vs. - 39.10 ± 16.61, P < 0.001) and POMS (91.70 ± 16.70 vs. 100.40 ± 22.11, P = 0.011) scores which indicate the lower the better. Serum TSH level rose from basal 0.11 ± 0.12 mU/L to a peak of 122.11 ± 42.44 mU/L 24 h after the last dose of ZGrhTSH. In L-T4 withdrawal phase, a median of 23 days after L-T4 withdrawal was needed, with the mean TSH level of 82.20 ± 31.37 mU/L. The half-life for ZGrhTSH clearance was about 20 h. CONCLUSION: The ZGrhTSH held the promise to be a safe and effective modality in facilitating RAI uptake and serum thyroglobulin stimulation, with better QoL of patients with DTC compared with L-T4 withdrawal.

Supervised Contrastive Learning and Intra-Dataset Adversarial Adaptation for Iris Segmentation.

Precise iris segmentation is a very important part of accurate iris recognition. Traditional iris segmentation methods require complex prior knowledge and pre- and post-processing and have limited accuracy under non-ideal conditions. Deep learning approaches outperform traditional methods. However, the limitation of a small number of labeled datasets degrades their performance drastically because of the difficulty in collecting and labeling irises. Furthermore, previous approaches ignore the large distribution gap within the non-ideal iris dataset due to illumination, motion blur, squinting eyes, etc. To address these issues, we propose a three-stage training strategy. Firstly, supervised contrastive pretraining is proposed to increase intra-class compactness and inter-class separability to obtain a good pixel classifier under a limited amount of data. Secondly, the entire network is fine-tuned using cross-entropy loss. Thirdly, an intra-dataset adversarial adaptation is proposed, which reduces the intra-dataset gap in the non-ideal situation by aligning the distribution of the hard and easy samples at the pixel class level. Our experiments show that our method improved the segmentation performance and achieved the following encouraging results: 0.44%, 1.03%, 0.66%, 0.41%, and 0.37% in the Nice1 and 96.66%, 98.72%, 93.21%, 94.28%, and 97.41% in the F1 for UBIRIS.V2, IITD, MICHE-I, CASIA-D, and CASIA-T.

KLK11 acts as a tumor-inhibitor in laryngeal squamous cell carcinoma through the inactivation of Akt/Wnt/ß-catenin signaling.

Kallikrein-associated peptidase 11 (KLK11) has emerged as a key tumor-associated protein that is implicated in a wide spectrum of tumor types. However, the detailed involvement of KLK11 in laryngeal squamous cell carcinoma (LSCC) has not been well studied. The aims of our work were to evaluate whether KLK11 plays a role in LSCC. We found that both the mRNA and protein expression of KLK11 were significantly lower in LSCC tissues than in normal tissues. Low expression of KLK11 was also observed in LSCC cell lines, and the up-regulation of KLK11 caused a significant inhibitory effect on the proliferation, colony formation and invasion of LSCC cells. On the contrary, the knockdown of KLK11 markedly accelerated the proliferative and invasive abilities of LSCC cells. Molecular mechanism research revealed that KLK11 overexpression decreased the phosphorylation of glycogen synthase kinase-3ß (GSK-3ß) and down-regulated the expression of active ß-catenin, leading to the inactivation of Wnt/ß-catenin signaling in LSCC cells. Furthermore, GSK-3ß inhibition markedly abrogated the KLK11-mediated suppressive effect on Wnt/ß-catenin signaling. Notably, the reactivation of Wnt/ß-catenin partially reversed KLK11-mediated tumor-inhibition effect in LSCC. In addition, the xenograft tumor assay demonstrated that the up-regulation of KLK11 retarded tumor formation and the growth of LSCC cells in vivo. Taken together, the findings of our work demonstrate that KLK11 exerts a tumor-inhibition role in LSCC by down-regulating Wnt/ß-catenin signaling. Our work highlights a pivotal role of KLK11 in LSCC progression and suggests it as an attractive anticancer target for LSCC treatment.

Long non-coding RNA AGAP2-AS1 promotes proliferation and metastasis in papillary thyroid cancer by miR-628-5p/KLF12 axis.

Long non-coding RNA (lncRNA) AGAP2-AS1 acts as an oncogene in several types of cancers. However, the role and mechanism of AGAP2-AS1 in papillary thyroid carcinoma (PTC) remain unclear. Thus, in this study, we aimed to explore the role of AGAP2-AS1 in PTC. Our results showed that AGAP2-AS1 was significantly upregulated in PTC tissues. Knockdown of AGAP2-AS1 inhibited the proliferation, migration and invasion of PTC cells. In vivo experiment showed that AGAP2-AS1 knockdown inhibited the tumorigenesis of PTC. MiR-628-5p was found to act as a target miRNA of AGAP2-AS1 in PTC. The expression level of miR-628-5p in PTC tissues was negatively associated with that of AGAP2-AS1. Inhibition of miR-628-5p attenuated the effects of AGAP2-AS1 knockdown on PTC. Moreover, miR-628-5p directly bound to the 3'UTR of KLF12 and inhibited the expression of KLF12. Knockdown of KLF12 enhanced the inhibitory effects of miR-628-5p on PTC cell proliferation and metastasis. In conclusion, these findings indicated that AGAP2-AS1 exerted an oncogenic role in PTC progression and metastasis. The effects of AGAP2-AS1 might be mediated by the regulation of miR-628-5p/KLF12 axis.

Oridonin inhibits epithelial-mesenchymal transition of human nasopharyngeal carcinoma cells by negatively regulating AKT/STAT3 signaling pathway.

Oridonin, derived from Rabdosia rubescens, has exhibited anticancer activity in a variety of cancers. However, few studies have explored the effect of oridonin (ORI) on migration, invasion and epithelial-mesenchymal transition (EMT) in nasopharyngeal carcinoma. In our study, the results demonstrated that oridonin significantly inhibited migration and invasion of human nasopharyngeal carcinoma CNE-2Z and HNE-1 cell lines, as depicted by wound healing and Transwell assays. In addition, oridonin increased the expression of E-Cadherin while decreased the expressions of vimentin and twist1 at the mRNA and protein levels in a dose-dependent manner. Interestingly, oridonin also decreased cell mobility in nasopharyngeal carcinoma. The subsequent results of western blotting uncovered that the phosphorylation levels of AKT and signal transducer and activator of transcription 3 (STAT3) were decreased upon oridonin treatment. Furthermore, co-treatment with the AKT activator SC-79 attenuated the anti-metastatic effect of oridonin on nasopharyngeal carcinoma and partially abolished the high expression of E-cadherin and the low expression of twist1 mediated by oridonin. In conclusion, the results revealed that oridonin could repress metastatic phenotype and reverse epithelial-mesenchymal transition (EMT) in nasopharyngeal carcinoma by negatively regulating AKT/STAT3 signaling pathway, suggesting that AKT/STAT3 signaling may be the potential therapeutic target of oridonin against nasopharyngeal carcinoma.

[Serological and molecular study of a novel B(A) allele with multiple missense mutations].

OBJECTIVE: To explore the molecular basis for an individual suspected as AwB subtype through DNA sequencing. METHODS: ABO serology was carried out with the standard tube method. To identify the ABO gene haplotype, the amplicons of exon 7 were cloned and sequenced. RESULTS: Serological results showed that the forward typing was AwB and the reverse typing was B. Sequencing analysis revealed that the sample has contained an O01 allele in addition with c.297A>G, c.657C>T, c.796C>A, c.803G>C, c.930G>A variants as compared with the A101 allele. CONCLUSION: Through sequencing analysis, the sample with an AwB subtype by serological testing was identified as a novel B(A) phenotype, which was unreported previously.

ProSampler: an ultrafast and accurate motif finder in large ChIP-seq datasets for combinatory motif discovery.

MOTIVATION: The availability of numerous ChIP-seq datasets for transcription factors (TF) has provided an unprecedented opportunity to identify all TF binding sites in genomes. However, the progress has been hindered by the lack of a highly efficient and accurate tool to find not only the target motifs, but also cooperative motifs in very big datasets. RESULTS: We herein present an ultrafast and accurate motif-finding algorithm, ProSampler, based on a novel numeration method and Gibbs sampler. ProSampler runs orders of magnitude faster than the fastest existing tools while often more accurately identifying motifs of both the target TFs and cooperators. Thus, ProSampler can greatly facilitate the efforts to identify the entire cis-regulatory code in genomes. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are freely available for download at https://github.com/zhengchangsulab/prosampler. It was implemented in C++ and supported on Linux, macOS and MS Windows platforms. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

Current treatment approaches for COVID-19 and the clinical value of transfusion-related technologies.

COVID-19 is caused by SARS-CoV-2 which is a new enveloped virus that belongs to the Beta coronavirus genus. As a major health crisis, SARS-CoV-2 has infected over a million people around the world. There is currently no specific treatment available for patients with COVID-19 infection. Numerous potential therapies, including supportive intervention, immunomodulatory agents, antiviral therapy, and convalescent plasma transfusion, have been used in clinical practice. Herein, we summarize the current potential therapeutic approaches for diseases related to COVID-19 infection and discusses the clinical value of blood transfusion-related technologies used in COVID-19 treatment.

Do carbon nanoparticles really improve thyroid cancer surgery? A retrospective analysis of real-world data.

BACKGROUND: Parathyroid protection and central neck dissection (CND) are basic points of thyroid cancer surgery and draw persistent concern. We aimed to evaluate the value of carbon nanoparticles (CNs) for parathyroid gland protection and CND in thyroid surgery for thyroid cancer patients. METHODS: A total of 386 consecutive thyroid cancer patients were enrolled in the retrospective study. Three hundred thirty-four patients using CNs intraoperatively were included in the CN group, and 52 patients without using CNs or any other helping agent were included in the control group. Intact parathyroid hormone (iPTH) was examined. Medical records and histopathologic reports were reviewed. Histopathologic examination was performed. RESULTS: There were no statistical significances in demographic and basic surgical information, preoperative iPTH, and serum calcium between the two groups (P > 0.05). In the CN group, the thyroid tissue and central neck lymph nodes were stained black by CNs, while the parathyroid glands were not. Histopathological examination showed that the carbon nanoparticles might accumulated in the subcapsular sinus of lymph nodes compared with the none-stained samples. The staining with CNs did not impact the histopathological examination. There were no significant differences in postoperative hypocalcemia and hypoPT at day 1, 1 month, and half year after surgery between the two groups, respectively. There was a big decline of iPTH level after surgery, whereas the perioperative decreasing amplitude of PTH was not statistically different between the CNs and control group (57.2 ± 28.6 vs 55.7 ± 27.8, P = 0.710). There were 43 patients occurring incidental parathyroidectomy in the CN group (43/334, 12.9%) and 7 patients in the control group (7/52, 13.5%), without significant difference (P = 0.907). There was no significant difference in the number of lymph nodes identified by pathology per patient between the CNs and control group regardless of unilateral and bilateral CND. CONCLUSIONS: Carbon nanoparticles help highlight parathyroid glands and lymph nodes in thyroidectomy, but generate no significant benefit for parathyroid glands protection and lymph node dissection. The value of carbon nanoparticles in thyroid cancer surgery should not be exaggerated and needs further evaluation.

Near-infrared auto-fluorescence spectroscopy combining with Fisher's linear discriminant analysis improves intraoperative real-time identification of normal parathyroid in thyroidectomy.

BACKGROUND: To evaluate the efficacy of a sensitive, real-time tool for identification and protection for parathyroid glands during thyroidectomy. METHODS: Near-infrared (NIR) auto-fluorescence was measured intraoperatively from 20 patients undergoing thyroidectomy. Spectra were measured from suspicious parathyroid glands and surrounding neck tissues during the operation with a NIR fluorescence system. Fast frozen sections were performed on the suspicious parathyroid glands. Accuracy was evaluated by comparison with histology and NIR identification. Data were attracted for Fisher's linear discriminant analysis. RESULTS: The auto-fluorescence intensity of parathyroid was significantly higher than that of thyroid, fat and lymph node. The peak intensity of auto-fluorescence from parathyroid was 5.55 times of that from thyroid at the corresponding wave number. Of the 20 patients, the parathyroid was accurately detected and identified in 19 patients by NIR system, compared with their histologic results. One suspicious parathyroid did not exhibit typical spectra, and was proved to be fat tissue by histology. The NIR auto-fluorescence method had a 100% sensitivity of parathyroid glands identification and a high accuracy of 95%. The positive predictive value was 95%. The parathyroid gland have specific auto-fluorescence spectrum and can be separated from the other three samples through the Fisher's linear discriminant analysis. CONCLUSIONS: NIR auto-fluorescence spectroscopy can accurately identify normal parathyroid gland during thyroidectomy. The Fisher's linear discriminant analysis demonstrated the specificity of the NIR auto-fluorescence of parathyroid tissue and its efficacy in parathyroid discrimination.

Application of a sensitive and specific LC-ESI-MS/MS method for the simultaneous quantification of twelve bioactive components in dog plasma for an intravenous pharmacokinetic study of Yiqifumai Injection in beagle dogs.

Yiqifumai Injection is a lyophilized powder preparation widely used to treat coronary heart disease. However, its in vivo bioactive components and pharmacokinetic behavior remain unknown. Therefore a sensitive and specific LC-MS/MS was developed and validated for the simultaneous quantification of eight saponins and four lignans in beagle dog plasma. The plasma samples were pretreated by protein precipitation with methanol-acetonitrile (1:1, v/v). Chromatographic separation of all the 12 analytes and estazolam (internal standard, IS) was successfully accomplished on an Ultimate® XB-C8 column (100 × 2.1 mm, 3 µm) with a gradient elution system. The total running time was 8 min with a flow rate of 0.40 mL/min. Acquisition of mass spectrometric data was performed via positive electrospray ionization in multiple reaction monitoring mode. The assay was fully validated in terms of selectivity, linear range, lower limit of quantitation, precision, accuracy, matrix effect, recovery and stability. This validated method was successfully applied to the pharmacokinetics of 12 bioactive components after intravenous administration of Yiqifumai Injection to beagle dogs at a dose of 0.541 g/kg.