ABCD1 as a Novel Diagnostic Marker for Solid Pseudopapillary Neoplasm of the Pancreas.

The diagnosis of solid pseudopapillary neoplasm of the pancreas (SPN) can be challenging due to potential confusion with other pancreatic neoplasms, particularly pancreatic neuroendocrine tumors (NETs), using current pathological diagnostic markers. We conducted a comprehensive analysis of bulk RNA sequencing data from SPNs, NETs, and normal pancreas, followed by experimental validation. This analysis revealed an increased accumulation of peroxisomes in SPNs. Moreover, we observed significant upregulation of the peroxisome marker ABCD1 in both primary and metastatic SPN samples compared with normal pancreas and NETs. To further investigate the potential utility of ABCD1 as a diagnostic marker for SPN via immunohistochemistry staining, we conducted verification in a large-scale patient cohort with pancreatic tumors, including 127 SPN (111 primary, 16 metastatic samples), 108 NET (98 nonfunctional pancreatic neuroendocrine tumor, NF-NET, and 10 functional pancreatic neuroendocrine tumor, F-NET), 9 acinar cell carcinoma (ACC), 3 pancreatoblastoma (PB), 54 pancreatic ductal adenocarcinoma (PDAC), 20 pancreatic serous cystadenoma (SCA), 19 pancreatic mucinous cystadenoma (MCA), 12 pancreatic ductal intraepithelial neoplasia (PanIN) and 5 intraductal papillary mucinous neoplasm (IPMN) samples. Our results indicate that ABCD1 holds promise as an easily applicable diagnostic marker with exceptional efficacy (AUC=0.999, sensitivity=99.10%, specificity=100%) for differentiating SPN from NET and other pancreatic neoplasms through immunohistochemical staining.

Therapeutic Potential of Adipose-Derived Stem Cell-Conditioned Medium and Extracellular Vesicles in an In Vitro Radiation-Induced Skin Injury Model.

Radiotherapy (RT) is one of three major treatments for malignant tumors, and one of its most common side effects is skin and soft tissue injury. However, the treatment of these remains challenging. Several studies have shown that mesenchymal stem cell (MSC) treatment enhances skin wound healing. In this study, we extracted human dermal fibroblasts (HDFs) and adipose-derived stem cells (ADSCs) from patients and generated an in vitro radiation-induced skin injury model with HDFs to verify the effect of conditioned medium derived from adipose-derived stem cells (ADSC-CM) and extracellular vesicles derived from adipose-derived stem cells (ADSC-EVs) on the healing of radiation-induced skin injury. The results showed that collagen synthesis was significantly increased in wounds treated with ADSC-CM or ADSC-EVs compared with the control group, which promoted the expression of collagen-related genes and suppressed the expression of inflammation-related genes. These findings indicated that treatment with ADSC-CM or ADSC-EVs suppressed inflammation and promoted extracellular matrix deposition; treatment with ADSC-EVs also promoted fibroblast proliferation. In conclusion, these results demonstrate the effectiveness of ADSC-CM and ADSC-EVs in the healing of radiation-induced skin injury.

Improved lung nodule diagnosis accuracy using lung CT images with uncertain class.

BACKGROUND AND OBJECTIVE: Among all malignant tumors, lung cancer ranks in the top in mortality rate. Pulmonary nodule is the early manifestation of lung cancer, and plays an important role in its discovery, diagnosis and treatment. The technology of medical imaging has encountered a rapid development in recent years, thus the amount of pulmonary nodules can be discovered are on the raise, which means even tiny or minor changes in lung can be recorded by the CT images. This paper proposes a pulmonary nodule computer aided diagnosis (CAD) based on semi-supervised extreme learning machine(SS-ELM). METHODS: First, the feature model based on the pulmonary nodules regions of lung CT images is established. After that, the same feature data sets have been put into ELM, support vector machine (SVM) methods, probabilistic neural network (PNN) and multilayer perceptron (MLP) so as to compare the performance of the methods. ELM turned out to have better performance in training time and testing accuracy compared with SVM, PNN and MLP. Then, we propose a pulmonary nodules computer aided diagnosis algorithm based on semi-supervised ELM (SS-ELM), which enables both certain class feature sets with labels and unlabeled feature sets to be input for training and computer aided diagnosing. This algorithm has provided a solution for the using of uncertain class data and improve the testing accuracy of benign and malignant diagnosis. RESULTS: 1018 sets of thoracic CT images from the Lung Database Consortium and Image Database Resource Initiative (LIDC-IDRI) have been used in experiment in order to test the effectiveness of the algorithm. Compared with ELM, the pulmonary nodules CAD based on SS-ELM has better testing accuracy performance. CONCLUSIONS: We have proposed a pulmonary nodule CAD system based on SS-ELM, which achieving better generalization performance at faster learning speed and higher testing accuracy than ELM, SVM, PNN and MLP. The SS-ELM based pulmonary nodules CAD has been proposed to solve the problem of uncertain class data using.

Label-free sensitive luminescence biosensor for immunoglobulin G based on Ag6Au6 ethisterone cluster-estrogen receptor α aggregation and graphene.

A specific and label-free "on-off-on" luminescence biosensor based on a novel heterometallic cluster [Ag6Au6(ethisterone)12]-estrogen receptor α (Ag6Au6Eth-ERα) aggregation utilizing graphene oxide (GO) as a quencher to lead a small background signal was firstly constructed to detect immunoglobulin G (IgG) with a simple process and high selectivity. The efficient photoluminescent (PL) Ag6Au6Eth-ERα aggregation is strongly quenched by GO. In the presence of IgG, the PL of this system will be restored, and perceivable by human eyes under UV lamp excitation (365 nm). The quenching mechanism of GO on Ag6Au6Eth-ERα and enhancement mechanism of IgG on Ag6Au6Eth-ERα-GO were investigated in detail. Under the optimum conditions, the biosensor for high sensitive IgG detection expressed a wider linear range of 0.0078-10 ng/mL and a lower detection limit of 0.65 pg/mL with good stability and repeatability, which provided a new approach for label-free IgG detection.

microRNA-365-targeted nuclear factor I/B transcriptionally represses cyclin-dependent kinase 6 and 4 to inhibit the progression of cutaneous squamous cell carcinoma.

Cyclin-dependent kinases are either post-transcriptionally regulated by interacting with cyclins and cyclin-dependent kinase inhibitors or are transcriptionally regulated by transcription factors, but the latter mechanism has not been extensively investigated. Dysregulated transcription factors resulting from aberrantly expressed microRNAs play critical roles in tumor development and progression. Our previous work identified miR-365 as an oncogenic microRNA that promotes the development of cutaneous squamous cell carcinoma via repression of cyclin-dependent kinase 6, while miR-365 also targets nuclear factor I/B. However, the underlying mechanism(s) of the interaction between nuclear factor I/B and cyclin-dependent kinase 6 are unclear. In this work, we demonstrate that miR-365-regulated nuclear factor I/B transcriptionally inhibits cyclin-dependent kinases 6 and 4 by binding to their promoter regions. In vivo and in vitro experiments demonstrate that the loss of nuclear factor I/B after miR-365 expression or treatment with small interfering RNAs results in the upregulation of cyclin-dependent kinases 6 and 4. This upregulation, in turn, enhances the phosphorylation of retinoblastoma protein and tumor progression. Characterizing this transcriptional repression of cyclin-dependent kinases 6 and 4 by nuclear factor I/B contributes to the understanding of the transcriptional regulation of cyclin-dependent kinases by transcription factors and also facilitates the development of new therapeutic regimens to improve the clinical treatment of cutaneous squamous cell carcinoma.