Protein Profiles and Novel Molecular Biomarkers of Schizophrenia Based on 4D-DIA Proteomics.

Schizophrenia is a severe psychological disorder. The current diagnosis mainly relies on clinical symptoms and lacks laboratory evidence, which makes it very difficult to make an accurate diagnosis especially at an early stage. Plasma protein profiles of schizophrenia patients were obtained and compared with healthy controls using 4D-DIA proteomics technology. Furthermore, 79 DEPs were identified between schizophrenia and healthy controls. GO functional analysis indicated that DEPs were predominantly associated with responses to toxic substances and platelet aggregation, suggesting the presence of metabolic and immune dysregulation in patients with schizophrenia. KEGG pathway enrichment analysis revealed that DEPs were primarily enriched in the chemokine signaling pathway and cytokine receptor interactions. A diagnostic model was ultimately established, comprising three proteins, namely, PFN1, GAPDH and ACTBL2. This model demonstrated an AUC value of 0.972, indicating its effectiveness in accurately identifying schizophrenia. PFN1, GAPDH and ACTBL2 exhibit potential as biomarkers for the early detection of schizophrenia. The findings of our studies provide novel insights into the laboratory-based diagnosis of schizophrenia.

Visual Feedback and Guided Balance Training in an Immersive Virtual Reality Environment for Lower Extremity Rehabilitation.

Balance training is essential for physical rehabilitation procedures, as it can improve functional mobility and enhance cognitive coordination. However, conventional balance training methods may have limitations in terms of motivation, real-time objective feedback, and personalization, which a virtual reality (VR) setup may better provide. In this work, we present an immersive VR training environment for lower extremity balance rehabilitation with real-time guidance and feedback. The VR training environment immerses the user in a 3D ice rink model where a virtual coach (agent) leads them through a series of balance poses, and the user controls a trainee avatar with their own movements. We developed two coaching styles: positive-reinforcement and autonomous-supportive, and two viewpoints of the trainee avatar: first-person and third-person. The proposed environment was evaluated in a user study with healthy, non-clinical participants (n = 16, 24.4 ± 5.7 years old, 9 females). Our results show that participants showed stronger performance in the positive-reinforcement style compared to the autonomous-supportive style. Additionally, in the third-person viewpoint, the participants exhibited more stability in the positive-reinforcement style compared to the autonomous-supportive style. For viewpoint, participants exhibited stronger performance in the first-person viewpoint compared to third-person in the autonomous-supportive style, while they were comparable in the positive-reinforcement style. We observed no significant effects on the foot height and number of mistakes. Furthermore, we report the analysis of user performance with balance training poses and subjective measures based on questionnaires to assess the user experience, usability, and task load. The proposed VR balance training could offer an interactive, adaptive, and engaging environment and open new potential research directions for lower extremity rehabilitation.

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.

Juglone induces ferroptosis in glioblastoma cells by inhibiting the Nrf2-GPX4 axis through the phosphorylation of p38MAPK.

BACKGROUND: Ferroptosis, a non-apoptotic form of cell death induced by accumulation of free iron ions and lipid peroxidation, its importance for cancer treatment is gradually being recognized. Research on the anti-cancer mechanism of juglone is accumulating. However, the specific mechanism by which it directs glioblastoma (GBM) to death is unknown. METHODS: We used in vitro and in vivo experiments to explore the anti-GBM effect generated by juglone through the ferroptosis pathway. RESULTS: Juglone mainly causes cell death by inducing ferroptosis. Mechanistically, juglone can significantly activate the phosphorylation of p38MAPK. According to transcriptome sequencing and protein interaction analysis, the Nrf2-GPX4 signaling pathway is identified as the primary pathway through which juglone mediates ferroptosis. In vitro and in vivo experiments further verified that juglone induces the ferroptosis of GBM by activating the phosphorylation of p38MAPK and negatively regulating the Nrf2-GPX4 signaling pathway. CONCLUSION: Juglone induces ferroptosis and inhibits the growth of GBM by targeting the Nrf2/Gpx4 signaling pathway and thus holds promise as a novel ferroptosis inducer or anti-GBM drug.

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.

Differential diagnosis of lung cancer and tuberculosis based on 18 F-fluorodeoxyglucose PET/CT multi-time points imaging.

OBJECTIVE: To investigate the value of 18 F-fluorodeoxyglucose(FDG) PET/CT multi-time points imaging (MTPI) on the differential diagnosis between lung cancer (LC) and tuberculosis (TB). METHODS: Sixty-four patients underwent 18 F-FDG PET/CT MTPI. The stdSUVmax, stdSUVavg, retention index, metabolic tumor volume, total lesion glycolysis at four-time points and slope of metabolic curve were measured and calculated, and the sex, age, and uniformity of FDG uptake were recorded. The difference in each index between LC and TB was analyzed, and dynamic metabolic curves (DMCs) of LC and TB were fitted by significance indexes. Artificial neural network (ANN) prediction models were established between squamous cell carcinoma (SCC) and TB, as well as between adenocarcinomas and TB. RESULTS: Differences between SCC and TB, stdSUVmax/avg at four-time points, total lesion glycolysis, stdSUVmax/avg slope (1-2 h,1-3 h and 1-4 h), uniformity of FDG uptake and age were significant. stdSUVavg has the largest area under the 4 h curve; age was only significant between adenocarcinomas and TB. DMCs at 1-4 h fitted by stdSUVavg were more helpful in differentiating LC and TB than stdSUVmax. stdSUVavg(1 h and 4 h), stdSUVavg slope 1-4 h, age, and uniformity of FDG uptake were selected to establish an ANN prediction model between SCC and TB; the area under the curve (AUC) was 100.0%. The same indices were used to establish the prediction model between adenocarcinomas and TB; the AUC was up to 83.5, and after adding stdSUVavg (2 and 4 h) to adenocarcinomas and TB models, the AUC was 87.7%. CONCLUSION: 18 F-FDG PET/CT MTPI fitting DMCs and establishing an ANN prediction model would distinguish SCC from TB relatively accurately and provide certain help in the differentiation between adenocarcinomas and TB.

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.

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.

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.

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.

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.

AJ76 and UH232 as potential agents for diagnosing early-stage Parkinson's disease.

We recently reported that the "Dopamine Neuron Challenge Test" (DNC Test), a diagnostic method that measures the levels of dopamine metabolites in cerebrospinal fluid (CSF) and plasma samples after pharmacologically inducing a transient dopamine release, can detect early-stage Parkinson's disease (PD) with high sensitivity and selectivity in mouse models. The use of haloperidol in the original DNC test to challenge dopamine neurons was less than ideal, as it may cause extrapyramidal motor symptoms. Here we report an improved DNC Test, in which the original challenging agents, haloperidol and methylphenidate, are replaced by a single challenging agent, a dopamine autoreceptor preferring antagonist AJ76 or UH232. We show that the improved DNC Test can achieve the same level of sensitivity and selectivity in detecting early PD in a mouse model without causing motor side effects. These findings significantly improve the practicality of using the DNC Test as a screening or diagnostic test for detecting early-stage PD in the high-risk population in humans.

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.

Value of dynamic metabolic curves and artificial neural network prediction models based on 18F-FDG PET/CT multiphase imaging in differentiating nonspecific solitary pulmonary lesions: a pilot study.

OBJECTIVE: To investigate the value of dynamic metabolic curves and artificial neural network prediction models based on 18F-FDG PET multiphase imaging in differentiating nonspecific solitary pulmonary lesions. METHODS: This study enrolled 71 patients with solitary pulmonary lesions (48 malignant and 23 benign lesions) who underwent multiphase 18F-fluorodeoxyglucose (18F-FDG)-PET/CT imaging. We recorded information on age, sex and uniformity of FDG uptake, measured standardized uptake value, metabolic tumor volume and total lesion glycolysis at various time points, and calculated individual standardized uptake values, retention index (RI) and slope of metabolic curve. Variables with high diagnostic efficiency were selected to fit dynamic metabolic curves for various lesions and establish different artificial neural network prediction models. RESULTS: There were no significant differences in the retention index, metabolic tumor volume, total lesion glycolysis or sex between benign and malignant lesions; standardized uptake values, the slopes of five metabolic curves, uniformity of FDG uptake, and age showed significant differences. Dynamic metabolic curves for various solitary pulmonary lesions exhibited characteristic findings. Model-1 was established using metabolic parameters with high diagnostic efficacy (area under the curve, 83.3%). Model-2 was constructed as Model-1 + age (area under the curve, 86.7%), whereas Model-3 was established by optimizing Model-2 (area under the curve, 86.0%). CONCLUSIONS: Dynamic metabolic curves showed varying characteristics for different lesions. Referring to these findings in clinical work may facilitate the differential diagnosis of nonspecific solitary pulmonary lesions. Establishing an artificial neural network prediction model would further improve diagnostic efficiency.

Topology Optimization for Hybrid Lattice Compliant Mechanisms with Multiple Microstructures.

Hybrid lattice compliant mechanisms (HLCMs) composed of multiple microstructures have attracted widespread interest due to their superior compliant performance compared to the traditional solid compliant mechanisms. A novel optimization scheme for HLCMs is presented using the independent continuous mapping (ICM) method. Firstly, the effective properties of multiple orthogonal and anisotropic lattice microstructures are obtained by taking advantage of homogenization theory, which are used to bridge the relationship between the macrostructure layout and microstructure recognition. Then, a new parallel topology optimization model for optimizing HLCMs is built via a generalized multi-material, recognizing interpolation scheme with filter functions. In addition, the characterization relationship between independent continuous variables and performance of different elements is established. Sensitivity analysis and linear programming are utilized to solve the optimal model. Lastly, numerical examples with a displacement inverter mechanism and compliant gripper mechanism demonstrate the effectiveness of the proposed method for designing HLCMs with various lattice microstructures. Anisotropic lattice microstructures (ALMs) significantly facilitate the efficient use of constitutive properties of materials. Hence, HLCMs consisting of various ALMs achieve superior compliant performance than counterparts comprising different orthogonal lattice microstructures (OLMs). The presented method offers a reference to optimize HLCMs, as well as promotes the theoretical development and application of the ICM method.

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.

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.