Transarterial radioembolization (TARE) is a widely utilized therapeutic approach for hepatocellular carcinoma (HCC), however, the clinical implementation is constrained by the stringent preparation conditions of radioembolization agents. Herein, we incorporated the superstable homogeneous iodinated formulation technology (SHIFT), simultaneously utilizing an enhanced solvent form in a carbon dioxide supercritical fluid environment, to encapsulate radionuclides (such as ¹³¹I,¹⁷⁷Lu, or ¹⁸F) with lipiodol for the preparation of radiolipiodol. The resulting radiolipiodol exhibited exceptional stability and ultra-high labeling efficiency (≥99%) and displayed notable intratumoral radionuclide retention and in vivo stability more than 2 weeks following locoregional injection in subcutaneous tumors in mice and orthotopic liver tumors in rats and rabbits. Given these encouraging findings, ¹⁸F was authorized as a radiotracer in radiolipiodol for clinical trials in HCC patients, and showed a favorable tumor accumulation, with a tumor-to-liver uptake ratio of ≥50 and minimal radionuclide leakage, confirming the feasibility of SHIFT for TARE applications. In the context of transforming from preclinical to clinical screening, the preparation of radiolipiodol by SHIFT represents an innovative physical strategy for radionuclide encapsulation. Hence, this work offers a reliable and efficient approach for TARE in HCC, showing considerable promise for clinical application (ChiCTR2400087731).

Objective To investigate the expression levels of selenoprotein genes in the patients with coronavirus disease 2019(COVID-19)and the possible regulatory mechanisms.Methods The dataset GSE177477 was obtained from the Gene Expression Omnibus,consisting of a symptomatic group(n=11),an asymptomatic group(n=18),and a healthy control group(n=18).The dataset was preprocessed to screen the differentially expressed genes(DEG)related to COVID-19,and gene ontology functional annotation and Kyoto encyclopedia of genes and genomes enrichment analysis were performed for the DEGs.The protein-protein interaction network of DEGs was established,and multivariate Logistic regression was employed to analyze the effects of selenoprotein genes on the presence/absence of symptoms in the patients with COVID-19.Results Compared with the healthy control,the symptomatic COVID-19 patients presented up-regulated expression of GPX1,GPX4,GPX6,DIO2,TXNRD1,SELENOF,SELENOK,SELENOS,SELENOT,and SELENOW and down-regulated ex-pression of TXNRD2 and SELENON(all P＜0.05).The asymptomatic patients showcased up-regulated expres-sion of GPX2,SELENOI,SELENOO,SELENOS,SELENOT,and SELENOW and down-regulated expression of SELP(all P＜0.05).The results of multivariate Logistic regression analysis showed that the abnormally high expression of GPX1(OR=0.067,95％CI=0.005-0.904,P=0.042)and SELENON(OR=56.663,95％CI=3.114-856.999,P=0.006)was the risk factor for symptomatic COVID-19,and the abnormally high expres-sion of SELP was a risk factor for asymptomatic COVID-19(OR=15.000,95％CI=2.537-88.701,P=0.003).Conclusions Selenoprotein genes with differential expression are involved in the regulation of COVID-19 development.The findings provide a new reference for the prevention and treatment of COVID-19.

Objective:To investigate the relationship between glutathione peroxidases (GPXs) gene expression in colorectal cancer tissues and survival prognosis, and to construct and evaluate a nomogram prediction model of GPXs for survival prognosis of colorectal cancer patients.Methods:The GPXs gene expresion data and other clinical data of 620 patients with colorectal cancer (455 cases of colon cancer and 165 cases of rectal cancer) were downloaded from The Cancer Genome Atlas (TCGA) database, and the GPXs gene expression data of 820 normal people were downloaded as controls, preprocessed by R language, and the gene expression data were analyzed for differential expression. Spearman rank correlation was used to analyze the correlation between GPXs gene expression and tumor mutation burden (TMB) in colorectal cancer tissues. Cox risk regression model was used to analyze the influencing factors of survival and prognosis of colorectal cancer patients. Nomogram models were constructed to predict overall survival (OS) of colon cancer and rectal cancer patients, and its predictive performance was evaluated by calibration curve.Results:In the GPXs family, there were statistically significant differences in the mRNA expressions of GPX1, GPX2, GPX3, GPX4, GPX5, GPX7 and GPX8 between colon cancer patients and normal population, and the mRNA expressions of GPX1, GPX2, GPX4 and GPX8 in colon cancer patients were higher than those in normal population (all P<0.05) . There were statistically significant differences in the mRNA expressions of GPX1, GPX2, GPX3, GPX4, GPX7 and GPX8 between rectal cancer patients and normal population, and the mRNA expressions of GPX1, GPX2, GPX4, GPX7 and GPX8 in rectal cancer patients were higher than those in normal population (all P<0.05) . Spearman rank correlation analysis showed that GPX2 ( r s=-0.27, P<0.001) and GPX7 ( r s=-0.11, P=0.043) expressions were negatively correlated with TMB in colon cancer. There were no significant correlations between GPXs genes expressions and TMB in rectal cancer tissues (all P>0.05) . In colon cancer, univariate analysis showed that GPX3 ( HR=1.22, 95% CI: 1.05-1.43, P=0.012) , GPX4 ( HR=1.39, 95% CI: 1.01-1.92, P=0.045) , age ( HR=1.02, 95% CI: 1.01-1.04, P=0.010) and pTNM-stage ( HR=1.78, 95% CI: 1.43-2.21, P<0.001) were the influencing factors of OS. Multivariate analysis showed that GPX4 ( HR=1.96, 95% CI: 1.09-3.51, P=0.024) , age ( HR=1.02, 95% CI: 1.00-1.04, P=0.042) and pTNM-stage ( HR=1.61, 95% CI: 1.21-2.15, P=0.001) were the independent risk factors of OS. In rectal cancer, univariate analysis showed that age ( HR=1.08, 95% CI: 1.04-1.13, P<0.001) was the influencing factor of OS. Multivariate analysis showed that GPX7 ( HR=0.44, 95% CI: 0.22-0.88, P=0.020) , GPX8 ( HR=3.17, 95% CI: 1.63-6.17, P=0.001) and age ( HR=1.10, 95% CI: 1.04-1.16, P=0.001) were the independent influencing factors of OS. The consistency index (C-index) of the nomogram model for predicting OS in patients with colon cancer and rectal cancer were 0.71 (95% CI: 0.63-0.79) and 0.88 (95% CI: 0.82-0.94) respectively. The calibration curve showed that the prediction curve of the two models had a good fit with the real curve. Conclusion:GPX4 is an independent risk factor affecting the prognosis of colon cancer patients. Patients with high GPX4 expression have a poor prognosis. GPX7 and GPX8 are independent prognostic factors for rectal cancer patients, and the rectal cancer patients with low GPX7 expression and high GPX8 expression have poor prognosis. The nomogram constructed based on the above factors can better predict the prognosis of patients with colon cancer and rectal cancer.

Closed-loop hospital management can effectivly cope with the COVID-19 pandemic. In order to ensure the continuity of treatments for hemodialysis patients under closed-loop management and minimize possible medical and infection risks, Huashan Hospital affiliated to Fudan University and 9 hospitals in Shanghai established a hemodialysis alliance in January 2021.The alliance optimized hemodialysis resources within the region through overall planning by preparing sites, materials and personnel shifts in advance, and establishing management systems and work processes to ensure that patients could be quickly and orderly diverted to other blood dialysis centers for uninterrupted high-quality hemodialysis services, in case that some hemodialysis centers in the alliance under closed-loop management.From November 2021 to April 2022, 317 of 1 459 hemodialysis patients in the alliance were diverted to other centers for treatment, accumulating 1 215 times/cases of treatments without obvious adverse reactions. The practice could provide a reference for medical institutions to quickly establish mutual support mode under major public health events.

Objective Iodothyronine deiodinases (DIOs) are important selenoproteins that play a key role in the bone and joint diseases. Osteoarthritis (OA) is the most prevalent joint disease especially in elders. This bioinformatic analysis was performed to explore the role of DIOs in OA pathogenesis. Methods The biological functions of selenoprotein DIOs were analyzed by bioinformatic techniques, including GenCLip 3.0, Database for Annotation, Visualization and Integrated Discovery (DAVID), STRING, Cytoscape, and Network Analyst. The expression of DIOs in the healthy individuals and OA patients was determined by mining OA-related microarray data in the gene expression omnibus (GEO) database of National Center for Biotechnology Information and performing a Meta-analysis of the data with Review Manager 5.3. Results Cluster analysis revealed that the function of the DIOs was associated with thyroid hormone receptor and iodothyronine; GO analysis showed that DIOs were mainly involved in biological processes, such as ethanol metabolism and phenol-containing compound metabolism and primarily involved in the cytochrome P450 metabolism of exogenous organisms and thyroid hormone signaling; SULT1A1 was the core node of the PPI network; miRNAs and thyroid hormones had some iterations with DIO1 and DIO2; Meta-analysis showed that DIO3 expression was significantly up-regulated in OA patients (SMD = 0.31, 95％CI: 0.03, 0.59, P = 0.03). Conclusions The main biological functions of DIOs were closely associated with the regulation of thyroid hormone. And the up-regulated expression of DIO3 may have crucial impact on the occurrence of OA.

【Objective】 Based on the high-throughput sequencing data of the whole genome， genomics and bioinformatics analyses were made to analyze the gene expression changes in the epithelial cells of the lung tissue from patients with coronavirus disease 2019 （COVID-19）， and explore the effects of the new coronavirus SARS-CoV-2 on human lungs. This study can provide a theoretical basis for the exploration of SARS-CoV-2 on the pathogenesis of lung tissue. 【Methods】 The public data set GSE160435 was retrieved. The data were analyzed by Network analyst， Cytoscape 3.7.2， String 11.0， and other software. The differentially expressed genes were screened， gene function （Gene Ontology， GO） and signal pathway KEGG （Kyoto Encyclopedia of Genes and Genomes） enrichment analysis were carried out. We established the Protein-protein Interactions Network （PPI）， PPI of lung tissue-specific DEGs， DEG microRNA regulatory network， Transcription Factor （TF）-DEG regulatory network， and environmental chemicals DEGs regulatory network. 【Results】 We found 324 DEGs in the lung epithelial cells of patients with COVID-19， of which 112 （34.57%） were upregulated and 212 （65.43%） were downregulated. Enrichment analysis showed that DEGs were mainly involved in biological processes such as virus-related defense response， mainly involved in protein digestion and absorption， anti-human papillomavirus infection and other signaling pathways. Specific PPI network closely related to DEGs and lung tissue showed that PDGFRB and KIT were core proteins； hsa-mir-340 had targeted interaction with DEGs. It indicated that HOXB4， ISG15 and other related genes were regulated by transcription factors； DEGs interacted with environmental chemicals such as nickel and estradiol. 【Conclusion】 The gene expression pattern of lung epithelial cells in lung tissue of COVID-19 patients has changed significantly. Proteins or genes such as PDGFRB， MMP9 and KIT may play a vital role in the defense immunity of lung tissue. Micro-RNA， TF， signaling pathway molecules， environmental chemicals， and lung tissue-specific genes also play a role in the above-mentioned process. This study provides new ideas for exploring the pathogenic mechanism of SARS-CoV-2 on lung tissue and formulating clinical prevention， diagnosis and treatment measures.

OBJECTIVES: To study the gene expression of adipose tissue CD14 METHODS: The data of GSE54350 were obtained from the public database of gene expression profiling. The data were pre-processed by Network Analyst, String 11.0, Cytoscape 3.7.1, and other analytical software. The differentially expressed genes were analyzed by gene ontology biological function and kyoto encycopedia of genes and genomes (KEGG) signaling pathway to establish differential gene protein interaction network, transcription factor-gene regulatory network, microRNA-gene regulatory network, environmental factors-gene regulatory network, and other interaction systems. RESULTS: The gene expression pattern of CD14 CONCLUSIONS The gene expression of adipose tissue CD14
Adipose Tissue ; Computational Biology ; DNA-Binding Proteins ; Diabetes Mellitus, Type 2/genetics* ; Gene Expression ; Gene Expression Profiling ; Gene Regulatory Networks ; Humans ; MicroRNAs/genetics* ; Muscle Proteins

Objective    To explore the value of artificial intelligence (AI) diagnostic imaging system and three dimensional computed tomographic bronchoangiography (3D-CTBA) surgical planning system in the management of multiple primary lung cancer (MPLC). Methods    The clinical data of 53 patients with MPLC treated surgically in our hospital from January 2018 to August 2020 were retrospectively analyzed, including 16 males and 37 females, with a median age of 60 (39-75) years. The patients' preoperative CT was analyzed by AI and manually, and the data of patients who underwent 3D-CTBA were compiled to evaluate the value of AI and 3D-CTBA in the diagnosis and treatment of MPLC, respectively. Results     The sensitivity of AI screening for MPLC was 84.91%. The sensitivity (91.90% vs. 83.78%) and accuracy (85.60% vs. 84.00%) of AI diagnosis of high-risk MPLC infiltrative lesions were better than those of manual diagnosis. 3D-CTBA was used for planning the surgery in 12 patients, and the intraoperative situation was generally consistent with the reconstructed results. Conclusion    AI is of high value in identifying infiltrative lesions of MPLC. 3D-CTBA reconstruction of anatomical structures is accurate and can guide preoperative planning.

Mixed reality is a new digital hologram technology after virtual reality and augmented reality, which combines the real world with the virtual world to form a new visualization environment. At present, mixed reality has been applied in various fields, but its application in medical field is still in the exploratory stage. With the rapid development of the digital age, the prospect of the combination of mixed reality and medicine is boundless. It is believed that mixed reality will bring subversive changes in medical training, disease diagnosis, doctor-patient communication, clinical diagnosis, treatment and so on in the near future. In this paper, the application of mixed reality in medicine was summarized.

OBJECTIVES: Coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 can damage the myocardium directly, or activate the immune system, trigger a cytokine storm, and cause inflammatory cells to infiltrate the myocardial tissue and damage the myocardium. This study is based on the sequencing data to analyze the changes in gene expression of cardiomyocytes and macrophages after SARS-CoV-2 infection, and explore the potential effects of SARS-CoV-2 on the heart and immune system. METHODS: The public data set GSE151879 was retrieved. The online software Network Analyst was used to preprocess the data, and the differentially expressed genes (DEGs) [log RESULTS: After data standardization, the data quality was excellent and it can ensure reliable results. Myocardial cell infection with SARS-CoV-2 and gene expression spectrum were changed significantly, including a total of 484 DEGs in adult cardiomyoblasts, a total of 667 DEGs in macrophages, and a total of 1 483 DEGs in human embryo source of cardiomyopathy. The Stum, mechanosensory transduction mediator homolog (STUM), dehydrogenase/reductase 9 (DHRS9), calcium/calmodulin dependent protein kinase II beta (CAMK2B), claudin 1(CLDN1), C-C motif chemokine ligand 2 (CCL2), TNFAIP3 interacting protein 3 (TNIP3), G protein-coupled receptor 84 (GPR84), and C-X-C motif chemokine ligand 1 (CXCL1) were identical in expression patterns in 3 types of cells. The protein-protein interaction suggested that CAMK2B proteins may play a key role in the antiviral process in 3 types of cells; and silicon dioxide (SiO CONCLUSIONS CAMK2B, CLDN1, CCL2, and DHRS9 genes play important roles in the immune response of cardiomyocytes against SARS-CoV-2. SiO
COVID-19 ; Humans ; Macrophages ; Myocytes, Cardiac ; SARS-CoV-2 ; Silicon Dioxide ; Transcriptome