In vitro drug screening models derived from different PC12 cell lines for exploring Parkinson's disease based on electrochemical signals of catecholamine neurotransmitters.

Gold nanostructures and a Nafion modified screen-printed carbon electrode (Nafion/AuNS/SPCE) were developed to assess the cell viability of Parkinson's disease (PD) cell models. The electrochemical measurement of cell viability was reflected by catecholamine neurotransmitter (represented by dopamine) secretion capacity, followed by a traditional tetrazolium-based colorimetric assay for confirmation. Due to the  capacity to synthesize, store, and release catecholamines as well as their unlimited homogeneous proliferation, and ease of manipulation, pheochromocytoma (PC12) cells were used for PD cell modeling. Commercial low-differentiated and highly-differentiated PC12 cells, and home-made nerve growth factor (NGF) induced low-differentiated PC12 cells (NGF-differentiated PC12 cells) were included in the modeling. This approach achieved sensitive and rapid determination of cellular modeling and intervention states. Notably, among the three cell lines, NGF-differentiated PC12 cells displayed the enhanced neurotransmitter secretion level accompanied with attenuated growth rate, incremental dendrites in number and length that were highly resemble with neurons. Therefore, it was selected as the PD-tailorable modeling cell line. In short, the electrochemical sensor can be used to sensitively determine the biological function of neuron-like PC12 cells with negligible destruction and to explore the protective and regenerative impact of various substances on nerve cell model.

Metabolomic analysis of plasma biomarkers in children with autism spectrum disorders.

Autism spectrum disorder (ASD) presents a significant risk to human well-being and has emerged as a worldwide public health concern. Twenty-eight children with ASD and 33 healthy children (HC) were selected for the quantitative determination of their plasma metabolites using an ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) platform. A total of 1997 metabolites were detected in the study cohort, from which 116 metabolites were found to be differentially expressed between the ASD and HC groups. Through analytical algorithms such as least absolute shrinkage selection operator (LASSO), support vector machine (SVM), and random forest (RF), three potential metabolic markers were identified as FAHFA (18:1(9Z)/9-O-18:0), DL-2-hydroxystearic acid, and 7(S),17(S)-dihydroxy-8(E),10(Z),13(Z),15(E),19(Z)-docosapentaenoic acid. These metabolites demonstrated superior performance in distinguishing the ASD group from the HC group, as indicated by the area under curves (AUCs) of 0.935, 0.897, and 0.963 for the three candidate biomarkers, respectively. The samples were divided into training and validation sets according to 7:3. Diagnostic models were constructed using logistic regression (LR), SVM, and RF. The constructed three-biomarker diagnostic model also exhibited strong discriminatory efficacy. These findings contribute to advancing our understanding of the underlying mechanisms involved in the occurrence of ASD and provide a valuable reference for clinical diagnosis.

Identification of pattern recognition receptor genes in peripheral blood mononuclear cells and monocytes as biomarkers for the diagnosis of lupus nephritis.

BACKGROUND: The study aimed to investigate the diagnostic value of lupus-related pattern recognition receptors (PRRs) genes in peripheral blood mononuclear cells (PBMCs) and monocytes (MONs) for lupus nephritis (LN). METHODS: PBMCs were isolated from a cohort with 37 LN patients and 39 healthy controls (HCs), and MONs were derived from another cohort with 70 LN patients and 66 HCs. Q-PCR was used to measure the mRNA levels of CGAS, IFNB1, AIM2, IL1Β, NLRC4, NLRP3, NLRP12 and ZBP1 in the PBMCs and MONs. The Mann-Whitney U test was used to compare the data in LN patients and HCs. Eleven GEO datasets of SLE/LN were used to perform differentially expressed genes (DEGs) analysis to these PRR genes. Receiver operating characteristic (ROC) curve analysis was employed to assess the performance of individual genes or the disease prediction model established by combining multiple genes in LN diagnosis. Spearman correlation method was done to analyze the correlation between these PRRs and other clinical characteristics. RESULTS: The mRNA levels of five genes (AIM2, NLRC4, IL1B, NLRP12 and ZBP1) in PBMCs, and seven genes (CGAS, IFNB1, AIM2, IL1B, NLRP3, NLRP12 and ZBP1) in MONs of LN patients were significantly higher than those of HCs (P < 0.05). DEGs analysis based on the GEO datasets showed that ZBP1, AIM2 and IL1B were significantly increased in several datasets. The ROC curve analysis indicated that the area under curve (AUC) of the LN prediction models derived from PBMCs or MONs were 0.82 or 0.91 respectively. In addition, the expression levels of these PRRs were correlated with other clinical features in LN patients, including Anti-Sm, ESR, serum Cr, and C3. CONCLUSION: Our study suggests that these lupus-related PRRs might be served as potential biomarkers of LN.

Hot-Pressing Metal Covalent Organic Frameworks as Personal Protection Films.

Effective personal protection is crucial for controlling infectious disease spread. However, commonly used personal protective materials such as disposable masks lack antibacterial/antiviral function and may lead to cross infection. Herein, a polyethylene glycol-assisted solvent-free strategy is proposed to rapidly synthesize a series of the donor-acceptor metal-covalent organic frameworks (MCOFs) (i.e., GZHMU-2, JNM-1, and JNM-2) under air atmosphere and henceforth extend it via in situ hot-pressing process to prepare MCOFs based films with photocatalytic disinfect ability. Best of them, the newly designed GZHMU-2 has a wide absorption spectrum (200 to 1500 nm) and can efficiently produce reactive oxygen species under sunlight irradiation, achieving excellent photocatalytic disinfection performance. After in situ hot-pressing as a film material, the obtained GZHMU-2/NMF can effectively kill E. coli (99.99%), S. aureus (99%), and H1N1 (92.5%), meanwhile possessing good reusability. Noteworthy, the long-term use of a GZHMU-2/NWF-based mask has verified no damage to the living body by measuring the expression of mouse blood routine, lung tissue, and inflammatory factors at the in-vivo level.

Identification of novel candidate biomarkers for acute myocardial infarction by the Olink proteomics platform.

BACKGROUND: Both pathological and normal processes depend on proteins. In this study, plasma protein profiles were analyzed by a novel proximity extension assay (PEA) to identify potential pathogenic mechanisms and diagnostic biomarkers in patients diagnosed with acute myocardial infarction (AMI). METHODS: In this study, we identified a total of 92 plasma proteins using the Olink Target 96 Cardiovascular III panel in a cohort consisting of 30 healthy controls (HC), 28 patients with unstable angina (UA) and 30 patients with AMI. Subsequently, we conducted a differential expression analysis to identify protein molecules that were specifically expressed in patients with AMI. To gain insights into the potential functional mechanisms of these differentially expressed molecules, we performed Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Following that, the utilization of least absolute shrinkage and selection operator (LASSO) regression facilitated the identification of potential protein biomarkers, enabling the differentiation between AMI and UA. A diagnostic model was subsequently developed through logistic regression, and the effectiveness of these markers was assessed using receiver operating characteristic (ROC) analysis. Ultimately, the diagnostic capabilities of these potential biomarkers were validated in an independent validation cohort consisting of 30 UA cases and 30 AMI cases. RESULTS: In this study, a comprehensive analysis of plasma proteins identified a total of 92 proteins. Further analysis using analysis of variance revealed that 25 proteins exhibited specific expression in the AMI group compared to the HC and UA groups. Additionally, KEGG enrichment analysis indicated that these differentially expressed proteins were primarily associated with the activation of cytokine-cytokine receptor interaction, PI3K-Akt signaling pathway, and GnRH signaling pathway. AGRP, TGM2, IL6, GH1, and CA5A were identified through LASSO regression as prospective protein biomarkers for distinguishing between UA and AMI. The diagnostic model comprising these five proteins exhibited exceptional performance in both the discovery and validation datasets, surpassing AUC values of 0.9. CONCLUSION: The findings of our study provide additional insights into the involvement of the inflammatory response and AKT cascade response in the development of AMI. Moreover, we have identified potential protein markers that could be utilized for the accurate diagnosis of AMI. These results offer a fresh perspective for clinical decision-making in the context of AMI.

Olink proteomics profiling platform reveals non-invasive inflammatory related protein biomarkers in autism spectrum disorder.

Background: Owing to the lack of valid biomarkers, the diagnosis of autism spectrum disorder (ASD) diagnosis relies solely on the behavioral phenotypes of children. Several researchers have suggested an association between ASD and inflammation; however, the complex relationship between the two is unelucidated to date. Therefore, the current study aims to comprehensively identify novel circulating ASD inflammatory biomarkers. Methods: Olink proteomics was applied to compare the plasma inflammation-related protein changes in a group of the healthy children (HC, n = 33) and another with ASD (n = 31). The areas under the receiver operating characteristic curves (AUCs) of the differentially expressed proteins (DEPs) were calculated. The functional analysis of the DEPs was performed using Gene Ontology and Kyoto Encyclopedia Genes and Genomes. Pearson correlation tests were used employed to analyze the correlation between the DEPs and clinical features. Results: A total of 13 DEPs were significantly up-regulated in the ASD group compared with the HC group. The four proteins, namely, STAMBP, ST1A1, SIRT2, and MMP-10 demonstrated good diagnostic accuracy with the corresponding AUCs (95% confidence interval, CI) of 0.7218 (0.5946-0.8489), 0.7107 (0.5827-0.8387), 0.7016 (0.5713-0.8319), and 0.7006 (0.568-0.8332). Each panel of STAMBP and any other differential protein demonstrated a better classification performance [AUC values from 0.7147 (0.5858-0.8436, STAMBP/AXIN1) to 0.7681 (0.6496-0.8867, STAMBP/MMP-10)]. These DEP profiles were enriched in immune and inflammatory response pathways, including TNF and NOD-like receptor signaling pathways. The interaction between STAMBP and SIRT2 (R = 0.97, p = 8.52 × 10-39) was found to be the most significant. In addition, several DEPs related to clinical features in patients with ASD, particularly AXIN1 (R = 0.36, p = 0.006), SIRT2 (R = 0.34, p = 0.010) and STAMBP (R = 0.34, p = 0.010), were positively correlated with age and parity, indicating that older age and higher parity may be the inflammation-related clinical factors in ASD. Conclusion: Inflammation plays a crucial role in ASD, and the up-regulated inflammatory proteins may serve as potential early diagnostic biomarkers for ASD.

Research status and prospects of acupuncture for autism spectrum disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder and has a predilection for children. Its symptoms, such as lifelong social communication deficits and repetitive sensory-motor behaviors, put a huge burden on the patient's family and society. Currently, there is no cure for ASD, and some medications that can improve its symptoms are often accompanied by adverse effects. Among many complementary and alternative medicine (CAM) therapies, acupuncture has shown promising application potential, but after years of practice, it has not been recognized as the preferred CAM therapy for ASD. Therefore, we analyzed and discussed the clinical study reports of acupuncture in the treatment of ASD in the past 15 years from the aspects of study subjects, group setting, intervention modalities, acupoint selection, outcome evaluation, and safety. The data accumulated at present are not sufficient to support the clinical effectiveness of acupuncture in ASD and to justify its use in clinical practice. They provide, however, initial evidence of possible effectiveness and encourage further investigation in order to reach firm conclusions. Based on a comprehensive analysis, we believed that following the Standards for Reporting Interventions in Clinical Trials of Acupuncture (STRICTA) and Consolidated Standards of Reporting Trials (CONSORT), screening the optimal combination of acupoints applying a rigorous scientific study design, and performing the related functional experiments may be the effective way to convincingly test the hypothesis that acupuncture may be beneficial in ASD patients. The significance of this review is to provide a reference for researchers to carry out high-quality clinical trials of acupuncture in the treatment of ASD from the perspective of the combination of modern medicine and traditional Chinese medicine.

Traditional Chinese medicine in human diseases treatment: New insights of their potential mechanisms.

For millennia, traditional Chinese medicine (TCM) has relieved the pain of countless patients with its unique theory and treatment method, which has provoked researchers' interest for exploring the biological and molecular mechanisms. This special issue highlights recent advances of this ancient and mysterious medical system in the basic science research field. The authors in this volume explored the molecular characteristics of TCM syndromes and the disease-resistant mechanisms of acupuncture and Chinese herbs in the diseases effecting the human motor system, digestive system, nervous system, and other organ systems by applying high-throughput omics technologies, molecular biology experiments, animal models and other methods. Alongside enhancing their perception of TCM from these latest findings, readers can also understand how to cross the systematic theory of TCM with modern molecular biology techniques. These studies advance our understanding of the potential mechanisms of TCM in treating human diseases, and also provide inspiration for the development of novel TCM-based therapeutic strategies. We hope these efforts will promote extensive development in TCM research.

Biomarker discovery for tuberculosis using metabolomics.

Tuberculosis (TB) is the leading cause of death among infectious diseases, and the ratio of cases in which its pathogen Mycobacterium tuberculosis (Mtb) is drug resistant has been increasing worldwide, whereas latent tuberculosis infection (LTBI) may develop into active TB. Thus it is important to understand the mechanism of drug resistance, find new drugs, and find biomarkers for TB diagnosis. The rapid progress of metabolomics has enabled quantitative metabolite profiling of both the host and the pathogen. In this context, we provide recent progress in the application of metabolomics toward biomarker discovery for tuberculosis. In particular, we first focus on biomarkers based on blood or other body fluids for diagnosing active TB, identifying LTBI and predicting the risk of developing active TB, as well as monitoring the effectiveness of anti-TB drugs. Then we discuss the pathogen-based biomarker research for identifying drug resistant TB. While there have been many reports of potential candidate biomarkers, validations and clinical testing as well as improved bioinformatics analysis are needed to further substantiate and select key biomarkers before they can be made clinically applicable.

Identification of three potential novel biomarkers for early diagnosis of acute ischemic stroke via plasma lipidomics.

INTRODUCTION: Acute ischemic stroke (AIS) accounts for the majority of all stroke, globally the second leading cause of death. Due to its rapid development after onset, its early diagnosis is crucial. OBJECTIVES: We aim to identify potential highly reliable blood-based biomarkers for early diagnosis of AIS using quantitative plasma lipid profiling via a machine learning approach. METHODS: Lipidomics was used for quantitative plasma lipid profiling, based on ultra-performance liquid chromatography tandem mass spectrometry. Our samples were divided into a discovery and a validation set, each containing 30 AIS patients and 30 health controls (HC). Differentially expressed lipid metabolites were screened based on the criteria VIP > 1, p < 0.05, and fold change > 1.5 or < 0.67. The least absolute shrinkage and selection operator (LASSO) and random forest algorithms in machine learning were used to select differential lipid metabolites as potential biomarkers. RESULTS: Three key differential lipid metabolites, CarnitineC10:1, CarnitineC10:1-OH and Cer(d18:0/16:0), were identified as potential biomarkers for early diagnosis of AIS. The former two, associated with thermogenesis, were down-regulated, whereas the latter, associated with necroptosis and sphingolipd metabolism, was upregulated. Univariate and multivariate logistic regressions showed that these three lipid metabolites and the resulting diagnostic model exhibited a strong ability in discriminating between AIS patients and HCs in both the discovery and validation sets, with an area under the curve above 0.9. CONCLUSIONS: Our work provides valuable information on the pathophysiology of AIS and constitutes an important step toward clinical application of blood-based biomarkers for diagnosing AIS.

Review on the systems biology research of Yin-deficiency-heat syndrome in traditional Chinese medicine.

Traditional Chinese medicine (TCM) is a systematic medical method that has existed for more than 3,000 years. Unlike Western medicine, the disease diagnosis in TCM is carried out by inspection, auscultation, olfaction, interrogation, and palpation. The patient is then treated according to the disease and corresponding TCM syndrome. However, the development of Chinese medicine is stagnated, partially because it can be influenced by subjective factors, such as the experience and knowledge of TCM practitioners, and there is a lack of relevant biological research on TCM syndromes. Yin-deficiency-heat (YDH) syndrome in TCM is characterized by a series of pathological changes caused by the insufficiency of Yin-fluid, inability to moisturize, and the failure to suppress Yang. In recent years, systems biology research on TCM syndromes has gradually become the focus of TCM research, including syndrome differentiation and functional research using systems biology methodologies such as proteomics, transcriptomics, and metabolomics. This journal aims to publish a series of issues on the systems biology research of TCM syndromes that can provide biological indicators for the syndrome differentiation of YDH syndrome and can provide perspectives on the biological research of YDH syndrome.

ALB, HP, OAF and RBP4 as novel protein biomarkers for identifying cured patients with pulmonary tuberculosis by DIA.

BACKGROUND: Pulmonary tuberculosis (TB) is a serious infectious disease that lacks robust blood-based biomarkers to identify cured TB. Some discharged patients are not fully cured and may relapse or even develop multidrug-resistant TB. This study is committed to finding proteomic-based plasma biomarkers to support establishing laboratory standards for clinical TB cure. METHODS: Data-independent acquisition (DIA) was used to obtain the plasma protein expression profiles of TB patients at different treatment stages compared with healthy controls. Multivariate statistical methods and bioinformatics were used to analyze the data. RESULTS: Bioinformatic analysis suggests coagulation dysfunction and vitamin and lipid metabolism disturbances in TB. Albumin (ALB), haptoglobin (HP), out at first protein homolog (OAF), and retinol-binding protein 4 (RBP4) can be used to establish a diagnostic model for the efficacy evaluation of TB with an area under the curve of 0.963, which could effectively distinguish untreated TB patients from cured patients. CONCLUSIONS: Our research demonstrated that ALB, HP, OAF and RBP4 can be potential biomarkers for evaluating the efficacy of TB. These findings may provide experimental data for establishing the laboratory indicators of clinical TB cure and providing clinicians with new targets for exploring the underlying mechanisms of TB pathogenesis.

Metabolite profile of COVID-19 revealed by UPLC-MS/MS-based widely targeted metabolomics.

The metabolic characteristics of COVID-19 disease are still largely unknown. Here, 44 patients with COVID-19 (31 mild COVID-19 patients and 13 severe COVID-19 patients), 42 healthy controls (HC), and 42 patients with community-acquired pneumonia (CAP), were involved in the study to assess their serum metabolomic profiles. We used widely targeted metabolomics based on an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The differentially expressed metabolites in the plasma of mild and severe COVID-19 patients, CAP patients, and HC subjects were screened, and the main metabolic pathways involved were analyzed. Multiple mature machine learning algorithms confirmed that the metabolites performed excellently in discriminating COVID-19 groups from CAP and HC subjects, with an area under the curve (AUC) of 1. The specific dysregulation of AMP, dGMP, sn-glycero-3-phosphocholine, and carnitine was observed in the severe COVID-19 group. Moreover, random forest analysis suggested that these metabolites could discriminate between severe COVID-19 patients and mild COVID-19 patients, with an AUC of 0.921. This study may broaden our understanding of pathophysiological mechanisms of COVID-19 and may offer an experimental basis for developing novel treatment strategies against it.

Novel Plasma Proteomic Biomarkers for Early Identification of Induction Chemotherapy Beneficiaries in Locoregionally Advanced Nasopharyngeal Carcinoma.

Background: Induction chemotherapy (IC) can alleviate locoregionally advanced nasopharyngeal carcinoma (LA-NPC), but effectiveness differs between patients, toxicity is problematic, and effective blood-based IC efficacy predictors are lacking. Here, we aimed to identify biomarkers for early identification of IC beneficiaries. Methods: Sixty-four pairs of matched plasma samples collected before and after IC from LA-NPC patients including 34 responders and 30 non-responders, as well as 50 plasma samples of healthy individuals, were tested using data-independent acquisition mass spectrometry. The proteins associated with clinical traits or IC benefits were investigated by weighted gene co-expression network analysis (WGCNA) and soft cluster analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional annotations were performed to determine the potential function of the identified proteins. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of candidate biomarkers in predicting IC beneficiaries. Results: Compared with healthy individuals, 1027 differentially expressed proteins (DEPs) were found in the plasma of LA-NPC patients. Based on feedback from IC outcomes, 463 DEPs were identified in the pre-IC plasma between responders and non-responders. A total of 1212 DEPs represented the proteomic changes before and after IC in responders, while 276 DEPs were identified in post-IC plasma between responders and non-responders. WGCNA identified nine protein co-expression modules correlated with clinical traits. Soft cluster analysis identified four IC benefits-related protein clusters. Functional enrichment analysis showed that these proteins may play a role in IC via immunity, complement, coagulation, glycosaminoglycan and serine. Four proteins differentially expressed in all group comparisons, paraoxonase/arylesterase 1 (PON1), insulin-like growth factor-binding protein 3 (IGFBP-3), rheumatoid factor D5 light chain (v-kappa-3) and RNA helicase (DDX55), were associated with clinical traits or IC benefits. A four-protein model accurately identified potential IC beneficiaries (AUC=0.95) while diagnosing LA-NPC (AUC=0.92), and the prediction performance was verified using the models to confirm the effective IC (AUC=0.97) and evaluate IC outcome (AUC=0.94). Conclusion: The plasma protein profiles among IC responders and non-responders were different. PON1, IGFBP3, v-kappa-3 and DDX55 could serve as potential biomarkers for early identification of IC beneficiaries for individualised treatment of LA-NPC.

Plasma Quantitative Lipid Profiles: Identification of CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1) as Novel Biomarkers for Pre-warning and Prognosis in Acute Myocardial Infarction.

This study was aimed to determine the association between potential plasma lipid biomarkers and early screening and prognosis of Acute myocardial infarction (AMI). In the present study, a total of 795 differentially expressed lipid metabolites were detected based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Out of these metabolites, 25 lipid metabolites were identified which showed specifical expression in the AMI group compared with the healthy control (HC) group and unstable angina (UA) group. Then, we applied the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) methods to obtain three lipid molecules, including CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1). The three lipid metabolites and the diagnostic model exhibited well predictive ability in discriminating between AMI patients and UA patients in both the discovery and validation sets with an area under the curve (AUC) of 0.9. Univariate and multivariate logistic regression analyses indicated that the three lipid metabolites may serve as potential biomarkers for diagnosing AMI. A subsequent 1-year follow-up analysis indicated that the three lipid biomarkers also had prominent performance in predicting re-admission of patients with AMI due to cardiovascular events. In summary, we used quantitative lipid technology to delineate the characteristics of lipid metabolism in patients with AMI, and identified potential early diagnosis biomarkers of AMI via machine learning approach.

Pyridoxal phosphate, pyridoxamine phosphate, and folic acid based on ceRNA regulatory network as potential biomarkers for the diagnosis of pulmonary tuberculosis.

BACKGROUND: Pulmonary tuberculosis (TB) is a serious disease burden worldwide, and its effective early diagnosis is still facing challenges. Knowledge, acquired from multi-omics integration analysis about the association between different types of differentially expressed molecules in the plasma of TB patients and the disease traits, is anticipated to improve the accuracy of TB diagnosis through the "integrative pattern". METHODS: In this study, the lncRNA-miRNA-mRNA interaction network was constructed based on the competing endogenous RNA (ceRNA) hypothesis by integrating our previous data sets of lncRNA, mRNA, miRNA, and metabolites. Moreover, the key regulatory axis was established by co-expression analysis and verified at the level of metabolites. RESULTS: A ceRNA regulatory network consisting of 23 lncRNAs, 10 miRNAs, and 113 mRNAs was constructed. The analysis results suggested that lncRNA (OSBPL10-AS1), miRNA (has-miR-485-5p), and mRNA (SLC23A2) might be involved in the regulation of vitamin metabolism in patients with TB. Metabolite analysis showed that compared with the normal control group, TB patients had abnormal vitamin metabolism, and the expression levels of pyridoxal phosphate, pyridoxamine phosphate, and folic acid were significantly different between the two groups (p < 0.05). CONCLUSION: Integrated multi-omics analysis showed that vitamin metabolism disorder may be one of the pathological characteristic of TB. OSBPL10-AS1, hsa-miR-485-5p, SLC23A2, pyridoxal phosphate, pyridoxamine phosphate, and folic acid may collectively constitute the "integrative pattern" of multiple biomarkers, which may provide an accurate diagnosis of TB.

A novel three-microRNA signature for predicting survival in patients with nasopharyngeal carcinoma.

BACKGROUND/PURPOSE: Nasopharyngeal carcinoma (NPC) is a malignant neoplasm of the head and neck. This study aims to use integrated bioinformatics technologies to develop a predictive miRNA-signature correlated with the prognosis of NPC. MATERIALS AND METHODS: Initially, the differentially expressed miRNAs (DEMs) in NPC were identified, and then DEMs related to the prognosis of NPC were further screened. Subsequently, the relatively important DEMs identified by random forest algorithm were used to construct a predictive signature by multivariate COX regression analysis. Moreover, PCA, Kaplan-Meier analysis, time-dependent ROC analysis, and univariate and multivariate COX regression analysis were performed to evaluate the ability of the signature in risk identification and prognosis prediction in NPC. RESULTS: Hsa-miR-29c, hsa-miR-30e and hsa-miR-93 were selected from DEMs to construct a signature, and their abnormal expression was significantly associated with poor prognosis of NPC. The average AUC values of 1- to 5-year OS, DFS and DMFS predicted by the signature were all above 0.7, and showed better clinical independence than other indexes. In addition, 295 differentially expressed mRNAs could be used as potential target genes of the 3 DEMs. Among them, 56 differentially expressed mRNAs were related to PFS. GO and KEGG enrichment analysis indicated that the poor prognosis of NPC was related to the abnormality of chromosomes, cytokines, and chemokines. CONCLUSION: We constructed a three-miRNA signature with good independent performance in predicting the prognosis for NPC. This study may lay the foundation for exploring new therapeutic targets and improving survival outcomes in NPC patients.

Screening and identification of potential novel lipid biomarkers for non-small cell lung cancer using ultra-high performance liquid chromatography tandem mass spectrometry.

Lung cancer is characterized by a high incidence rate and low survival rate. It is important to achieve early diagnosis of the disease. We applied ultra-high performance liquid chromatography tandem mass spectrometry to screen plasma lipid spectrum in non-small cell lung cancer (NSCLC) patients, healthy controls (HC), and community-acquired pneumonia (CAP) patients. Modeling employing orthogonal partial least squares-discriminant analysis combined with t-test was used to screen the differential lipids. Logistic regression analysis was used to establish the diagnostic model, while the accuracy was verified by 10-fold cross-validation. The results showed that the abnormal metabolism of lipid in NSCLC mainly comprised fatty acid metabolism, phospholipid metabolism, and glyceride metabolism. Four potential biomarkers, including LPC (14:0/0:0), LPI (14:1/0:0), DG (14:0/18:2/0:0), and LPC (16:1/0:0), were fitted by the receiver operating characteristic curve model with the area under curve (AUC) value of 0.856, and the specificity and sensitivity were 87.0 and 78.0%, respectively. The results of cross validation showed that the AUC value of the model was 0.812, the sensitivity was 72.9%, and the specificity was 82.6%. The positive rate of four potential lipid biomarkers in this study (>60.0%) was higher than that of existing tumor biomarkers in the clinical application. We investigated the plasma lipid profile of NSCLC patients and identified lipid biomarkers with potential diagnostic values. From the lipidomics perspective, our study may lay a foundation for the biomarker-based early diagnosis of lung cancer.

An introduction to traditional Chinese medicine, including acupuncture.

As an independent medical system, the scientific value behind traditional Chinese medicine (TCM) is gaining appreciation because of growing evidence about mechanisms and efficacy. The aim of this special issue is to introduce this ancient medicine to readers through the compiled research papers focusing on TCM. The papers in this issue cover many research fields such as TCM theory, traditional Chinese prescription, syndrome biomarkers, and acupuncture. In particular for acupuncture, the focus on the history of acupuncture, the importance of precise acupoint positioning, the effect of Fu's subcutaneous needling among patients with shoulder pain, and the effect of electroacupuncture on senile plaque and insulin signaling pathway in the olfactory bulb of transgenic mice. By combining, the papers in this issue and TCM papers published elsewhere provide scientific evidence to improve understanding this ancient Oriental form of medicine.