Proteomics-based screening and validation of key targets in chronic obstructive pulmonary disease complicated with lung cancer

Objective: To preliminarily predict and verify the biological mechanism and potential core therapeutic targets of chronic obstructive pulmonary disease (COPD)-associated lung cancer （COPD-LC） based on proteomics analysis combined with bioinformatics technology. Methods: Urine samples of COPD, lung cancer patients and healthy people who visited the outpatient clinic of the Fourth Clinical Medical College of Xinjiang Medical University from December 2018 to August 2021 were collected and sequenced by high-throughput sequencing. Differential proteins were screened, protein-protein interaction (PPI) network diagrams were constructed, functional enrichment analysis was carried out to further predict the key targets of COPD-LC, and finally the above molecules were verified in the Gene Expression Omnibus (GEO) database. Results: Proteomics results showed that there were 157 differential proteins in the COPD group compared with the normal control group, including 67 up-regulated proteins and 90 down-regulated proteins. Compared with the normal control group, there were 306 differential proteins in the lung cancer group, including 132 up-regulated and 174 down-regulated proteins. In addition, PPI analysis was performed for the above differential proteins based on the Search Tool for the Retrieval of Interacting Genes (STRING) platform. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes （KEGG） analysis showed that COPD-LC was mainly enriched in extracellular region, lysosomes, extracellular space, amylase activity, protease inhibitor activity, defense/immune protein activity and other aspects. The microarray data of COPD and lung cancer were searched in the GEO database, and GSE8581 and GSE43346 were finally included. A total of 13 605 differential genes were identified in GSE8581 dataset, and 3 403 differential genes were identified in GSE43346 dataset. The degree of overlap between the differential genes in the GSE8581 and GSE43346 datasets was further analyzed, and 4 overlapping proteins were found in the two, including latent TGF-β binding protein (LTBP) 4, N-acetyl-alpha-glucosaminidase (NAGLU), ubiquitin protein ligase E3 component N-recognin (UBR) 4, and DNA damage binding protein 1 and cullin-ring ligase 4 associated factor (DCAF) 5, which were verified in the GEO database finally. Conclusion: This study has initially revealed 4 potential therapeutic targets for COPD-LC, including LTBP4, NAGLU, UBR4 and DCAF5, among which NAGLU, UBR4 and DCAF5 are rarely reported in the research of COPD and lung cancer. The above proteins are significantly low expressed in COPD and lung cancer, and may become significant biomarkers for the diagnosis and treatment of COPD-LC

The Effects of Uygur Herb Hyssopus officinalis

It has been proved that Uygur herb Hyssopus offcinalis L. could affect the levels of some cytokines (such as IL-4, IL-6, IL-17, and IFN-γ) in asthmatic mice. By detection of the expressions of MMP-9 and TIMP-1 and the morphological changes, the aim of this research is to reveal the mechanism of Uygur herb Hyssopus offcinalis L. in the process of airway remodeling. It was observed that the expressions of MMP-9 and TIMP-1 increased, but the ratio of MMP-9/TIMP-1 decreased in airway remodeling group. However, the expression of both MMP-9 and TIMP-1 decreased after being treated with dexamethasone and Hyssopus offcinalis L., accompanied by the relieved pathological changes, including collagen deposition, mucus secretion, and smooth muscle proliferation. It is suggested that Uygur herb Hyssopus offcinalis L. could inhibit airway remodeling by correcting imbalance of MMP-9/TIMP-1 ratio

Cross-Layer Optimization-Based Asymmetric Medical Video Transmission in IoT Systems

At present, Internet of Things (IoT) networks are attracting much attention since they provide emerging opportunities and applications. In IoT networks, the asymmetric and symmetric studies on medical and biomedical video transmissions have become an interesting topic in both academic and industrial communities. Especially, the transmission process shows the characteristics of asymmetry: the symmetric video-encoding and -decoding processes become asymmetric (affected by modulation and demodulation) once a transmission error occurs. In such an asymmetric condition, the quality of service (QoS) of such video transmissions is impacted by many different factors across the physical (PHY-), medium access control (MAC-), and application (APP-) layers. To address this, we propose a cross-layer optimization-based strategy for asymmetric medical video transmission in IoT systems. The proposed strategy jointly utilizes the video-coding structure in the APP- layer, the power control and channel allocation in the MAC- layer, and the modulation and coding schemes in the PHY- layer. To obtain the optimum configuration efficiently, the proposed strategy is formulated and proofed by a quasi-convex problem. Consequently, the proposed strategy could not only outperform the classical algorithms in terms of resource utilization but also improve the video quality under the resource-limited network efficiently

Cross-Layer Optimization-Based Asymmetric Medical Video Transmission in IoT Systems

At present, Internet of Things (IoT) networks are attracting much attention since they provide emerging opportunities and applications. In IoT networks, the asymmetric and symmetric studies on medical and biomedical video transmissions have become an interesting topic in both academic and industrial communities. Especially, the transmission process shows the characteristics of asymmetry: the symmetric video-encoding and -decoding processes become asymmetric (affected by modulation and demodulation) once a transmission error occurs. In such an asymmetric condition, the quality of service (QoS) of such video transmissions is impacted by many different factors across the physical (PHY-), medium access control (MAC-), and application (APP-) layers. To address this, we propose a cross-layer optimization-based strategy for asymmetric medical video transmission in IoT systems. The proposed strategy jointly utilizes the video-coding structure in the APP- layer, the power control and channel allocation in the MAC- layer, and the modulation and coding schemes in the PHY- layer. To obtain the optimum configuration efficiently, the proposed strategy is formulated and proofed by a quasi-convex problem. Consequently, the proposed strategy could not only outperform the classical algorithms in terms of resource utilization but also improve the video quality under the resource-limited network efficiently

基于纳米材料的微针阵列技术及其应用 [Nanomaterial-based Microneedle Arrays Technology and Its Application: a Review]

In recent years, microneedles have received a lot of attention from researchers because of its advantages of being painless and minimally invasive, safe and efficient. Microneedles have been widely used in the fields of transdermal drug delivery, medical aesthetics and biological diagnosis. 匀owever, traditional microneedles, especially those prepared directly from polymer materials, generally have problems such as low mechanical strength, single drug release mode, poor biological sensing performance and simple functions. The clever combination of nanotechnology and microneedles can effectively improve the above problems, due to the unique nano⁃size, mechanical strength and photoelectric effects of nanoparticles. This paper reviews nanoparticles for microneedles, including inorganic nanoparticles (metal, inorganic non⁃ metal), organic nanoparticles (polymer, lipid) and drug nanoparticles (nanocrystalline drugs, virus⁃like particles), and describes the role of nanoparticles in increasing mechanical property, synergistic improvement of drug release and immune enhancement are introduced. Finally, the urgent problems need to be solved in this field and the future research directions are discussed.</p

Identification of 5 Gene Signatures in Survival Prediction for Patients with Lung Squamous Cell Carcinoma Based on Integrated Multiomics Data Analysis

Background. Lung squamous cell carcinoma (LSCC) is a frequently diagnosed cancer worldwide, and it has a poor prognosis. The current study is aimed at developing the prediction of LSCC prognosis by integrating multiomics data including transcriptome, copy number variation data, and mutation data analysis, so as to predict patients’ survival and discover new therapeutic targets. Methods. RNASeq, SNP, CNV data, and LSCC patients’ clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and the samples were randomly divided into two groups, namely, the training set and the validation set. In the training set, the genes related to prognosis and those with different copy numbers or with different SNPs were integrated to extract features using random forests, and finally, robust biomarkers were screened. In addition, a gene-related prognostic model was established and further verified in the test set and GEO validation set. Results. We obtained a total of 804 prognostic-related genes and 535 copy amplification genes, 621 copy deletions genes, and 388 significantly mutated genes in genomic variants; noticeably, these genomic variant genes were found closely related to tumor development. A total of 51 candidate genes were obtained by integrating genomic variants and prognostic genes, and 5 characteristic genes (HIST1H2BH, SERPIND1, COL22A1, LCE3C, and ADAMTS17) were screened through random forest feature selection; we found that many of those genes had been reported to be related to LSCC progression. Cox regression analysis was performed to establish 5-gene signature that could serve as an independent prognostic factor for LSCC patients and can stratify risk samples in training set, test set, and external validation set (p 0.67. Conclusion. In the current study, 5 gene signatures were constructed as novel prognostic markers to predict the survival of LSCC patients. The present findings provide new diagnostic and prognostic biomarkers and therapeutic targets for LSCC treatment

Bioimaging of Dissolvable Microneedle Arrays: Challenges and Opportunities

The emergence of microneedle arrays (MNAs) as a novel, simple, and minimally invasive administration approach largely addresses the challenges of traditional drug delivery. In particular, the dissolvable MNAs act as a promising, multifarious, and well-controlled platform for micro-nanotransport in medical research and cosmetic formulation applications. The effective delivery mostly depends on the behavior of the MNAs penetrated into the body, and accurate assessment is urgently needed. Advanced imaging technologies offer high sensitivity and resolution visualization of cross-scale, multidimensional, and multiparameter information, which can be used as an important aid for the evaluation and development of new MNAs. The combination of MNA technology and imaging can generate considerable new knowledge in a cost-effective manner with regards to the pharmacokinetics and bioavailability of active substances for the treatment of various diseases. In addition, noninvasive imaging techniques allow rapid, receptive assessment of transdermal penetration and drug deposition in various tissues, which could greatly facilitate the translation of experimental MNAs into clinical application. Relying on the recent promising development of bioimaging, this review is aimed at summarizing the current status, challenges, and future perspective on in vivo assessment of MNA drug delivery by various imaging technologies