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Background: There is still limited research on the prognostic value of Presepsin as a biomarker for predicting the outcome of COVID-19 patients. Additionally, research on the combined predictive value of Presepsin with clinical scoring systems and inflammation markers for disease prognosis is lacking., Methods: A total of 226 COVID-19 patients admitted to Beijing Youan Hospital's emergency department from May to November 2022 were screened. Demographic information, laboratory measurements, and blood samples for Presepsin levels were collected upon admission. The predictive value of Presepsin, clinical scoring systems, and inflammation markers for 28-day mortality was analyzed., Results: A total of 190 patients were analyzed, 83 (43.7%) were mild, 61 (32.1%) were moderate, and 46 (24.2%) were severe/critically ill. 23 (12.1%) patients died within 28 days. The Presepsin levels in severe/critical patients were significantly higher compared to moderate and mild patients (p < 0.001). Presepsin showed significant predictive value for 28-day mortality in COVID-19 patients, with an area under the ROC curve of 0.828 (95% CI: 0.737-0.920). Clinical scoring systems and inflammation markers also played a significant role in predicting 28-day outcomes. After Cox regression adjustment, Presepsin, qSOFA, NEWS2, PSI, CURB-65, CRP, NLR, CAR, and LCR were identified as independent predictors of 28-day mortality in COVID-19 patients (all p-values < 0.05). Combining Presepsin with clinical scoring systems and inflammation markers further enhanced the predictive value for patient prognosis., Conclusion: Presepsin is a favorable indicator for the prognosis of COVID-19 patients, and its combination with clinical scoring systems and inflammation markers improved prognostic assessment., (© 2024. The Author(s).)

Background: Quality control of traditional Chinese medicine (TCM) is the basis of clinical efficacy. Due to the complexity of TCM, it is difficult to unify the quality control, and hinders the further implementation of the quality standardization of TCM. As a new concept, quality-marker (Q-marker) plays a powerful role in promoting the standardization of quality control system of TCM., Hypothesis/purpose: The present review aims to provide reference and scientific basis for further development of Q-marker and assist standardization of quality control of TCM., Methods: Extensive search of various documents and electronic databases such as Pubmed, Royal Society of Chemistry, Science Direct, Springer, Web of Science, and Wiley, etc., were used to search scientific contributions. Other online academic libraries, e.g. Google Scholars, Scopus and national pharmacology literature were also been employed to learn more relevant information about Q-marker., Results: Q-markers play vital role in promoting the standardization of quality control of TCM. The factors that affect the quality of TCM, the advantages and disadvantages of the analytical techniques commonly used in Q-marker research were reviewed, as well as the systematic research strategies, which were verified by practices., Conclusion: The proposal of Q-marker not only provided a new perspective to break through the bottleneck of current quality control, but also can be used in the evaluation of pharmacological efficiency, therapeutic discovery, toxicology, etc. In addition, the Q-marker analysis strategies summarized in this paper is helpful to standardize the quality control of TCM and promote the internationalization of TCM., Competing Interests: Declaration of Competing Interest None, (Copyright © 2020 Elsevier GmbH. All rights reserved.)

Lipidomics, which focuses on the global study of molecular lipids in biological systems, could provide valuable insights about disease mechanisms. In this study, we present a nontargeted lipidomics strategy to determine cellular lipid alterations after scoparone exposure in primary hepatocytes. Lipid metabolic profiles were analyzed by high-performance liquid chromatography coupled with time-of-flight mass spectrometry, and a novel imaging TransOmics tool has been developed for the analysis of high-resolution MS data, including the data pretreatment, visualization, automated identification, deconvolution and quantification of lipid species. Chemometric and statistical analyses of the obtained lipid fingerprints revealed the global lipidomic alterations and tested the therapeutic effects of scoparone. Identification of ten proposed lipids contributed to the better understanding of the effects of scoparone on lipid metabolism in hepatocytes. The most striking finding was that scoparone caused comprehensive lipid changes, as represented by significant changes of the identificated lipids. The levels of identified PG(19:1(9Z)/14:0), PE(17:1(9Z)/0:0), PE(19:1(9Z)/0:0) were found to be upregulated in ethanol-induced group, whereas the levels in scoparone group were downregulated. Lipid metabolism in primary hepatocytes was changed significantly by scoparone treatment. We believe that this novel approach could substantially broaden the applications of high mass resolution mass spectrometry for cellular lipidomics.

Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information. Deciphering the molecular networks that distinguish diseases may lead to the identification of critical biomarkers for disease aggressiveness. However, current diagnostic methods cannot predict typical Jaundice syndrome (JS) in patients with liver disease and little is known about the global metabolomic alterations that characterize JS progression. Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication, and therapy. Therefore, the aim of this study is to find the potential biomarkers from JS disease by using a nontarget metabolomics method, and test their usefulness in human JS diagnosis. Multivariate data analysis methods were utilized to identify the potential biomarkers. Interestingly, 44 marker metabolites contributing to the complete separation of JS from matched healthy controls were identified. Metabolic pathways (Impact-value≥0.10) including alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies were found to be disturbed in JS patients. This study demonstrates the possibilities of metabolomics as a diagnostic tool in diseases and provides new insight into pathophysiologic mechanisms.

Objective To compare the differences in plasma lipid metabolites between the patients with acute myocardial infarction (AMI) and those with normal coronary artery in order to explore the pathophysiologic mechanism and search for possible new biomarkers of AMI. Methods The patients undergoing coronary angiography in our hospital from September 20, 2019 to January 20, 2020 were enrolled in this study. According to the results, they were divided into myocardial infarction group (AMI group, n=32) and control group (age- and sex-matched with the AMI group, n=32). Non-targeted lipidomics analysis was performed on the blood samples to find the differences of lipid metabolites between the 2 groups. Univariate and multivariate statistical analyses were applied to explore the meaningful differential metabolites, and receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the selected differential metabolites. Finally, how to disturb metabolic pathways was investigated in the AMI patients. Results ① The orthogonal projection to latent structure discriminant analysis (OPLS-DA) and cluster heat map showed that there was a significant trend of separation between AMI group and the control group. Multivariate statistical analysis identified totally 28 differential plasma lipid metabolites, including 16 of them significantly increased and 12 decreased in the AMI group. ②There were 10 lipid metabolites such as diglyceride (DG) and phosphatidyl serine (PS) associated with an increased risk of AMI, while 13 negative predictors, including phosphatidylinositol (PI), phosphatidylcholine (PC), phosphatidylserine (PS), lytic phosphatidylcholine (LPC) and triglyceride (TG), were associated with a reduced risk of AMI. ③ROC curve identified 2 possible biomarkers of AMI, namely, PS (27∶0) (AUC=0.976) and DG(16∶0/18∶1) (AUC=0.807); ④The disturbed metabolic pathways included metabolism of glycerol phospholipid, sphingomyelin, purine and pyrimidine in the AMI patients, among which the disorder of glycerophospholipid metabolism was the most signficant. Conclusion The characteristics of lipid metabolism are significantly changed in AMI patients; a total of 28 differential lipid metabolites are identified, among which the disorder of glycerol phospholipid metabolism pathway indicates the most significant. PS and DG may act as the potential biomarkers for the diagnosis of AMI.

As an important part of medical science, Traditional Chinese Medicine (TCM) attracts much public attention due to its multi-target and multi-pathway characteristics in treating diseases. However, the limitations of traditional research methods pose a dilemma for the evaluation of clinical efficacy, the discovery of active ingredients and the elucidation of the mechanism of action. Therefore, innovative approaches that are in line with the characteristics of TCM theory and clinical practice are urgently needed. Chinmendomics, a newly emerging strategy for evaluating the efficacy of TCM, is proposed. This strategy combines systems biology, serum pharmacochemistry of TCM and bioinformatics to evaluate the efficacy of TCM with a holistic view by accurately identifying syndrome biomarkers and monitoring their complex metabolic processes intervened by TCM, and finding the agents associated with the metabolic course of pharmacodynamic biomarkers by constructing a bioinformatics-based correlation network model to further reveal the interaction between agents and pharmacodynamic targets. In this article, we review the recent progress of Chinmedomics to promote its application in the modernisation and internationalisation of TCM. [ABSTRACT FROM AUTHOR]

Background: Sleep disorders are prevalent among patients with Alzheimer's disease (AD), and the APOE ε4 genotype is a key genetic risk factor for sporadic AD. However, the combined effect of the genotype and sleep disorders on cognitive decline remains uncertain. Methods: A total of 972 participants were drawn from the SILCODE cohort, comprising 655 without the ε4 allele (APOE−) and 317 with ε4 allele (APOE+). Data were collected, including neuropsychological assessments, sleep measurements, plasma biomarkers, and PET imaging. A Sleep Composite Index (SCI) was created, categorizing participants into high risk (Sleep+) and low risk (Sleep−). Results: Significant predictions of dementia risk associated with plasma p‐tau181, neurofilament light chain (NfL), and SCI. Individuals with both Sleep+ and APOE+ had a higher risk of dementia compared to those with Sleep‐. The Sleep+/APOE+ group had higher plasma NfL levels than the Sleep−/APOE− group. Similar trends emerged in plasma NfL levels among the Aβ PET‐positive subgroup. Plasma NfL levels explained 23% of the relationship between SCI and cognitive impairment. Conclusion: Our study highlights sleep disorder was associated with cognitive decline, with plasma NfL playing a partial mediating role. These findings explain how sleep disorders affect cognitive function and emphasize the importance of healthy sleep for older adults. [ABSTRACT FROM AUTHOR]

Simple Summary: Objective biomarkers for the diagnosis of subjective cognitive decline (SCD) are critical for intervention in the Alzheimer's disease (AD) disease process. Previous research has shown that functional magnetic resonance imaging (fMRI) is a significant tool for detecting abnormal interregional functional connectivity (FC) in SCD populations. However, traditional FC calculations do not reveal the high individual variation in the SCD population. We proposed a new framework for individual proportion loss of functional connectivity strength (IPLFCS) based on FC to identify SCD biomarkers, and further explore the relationship between biomarkers and amyloid deposition as well as neuropsychological performance. The study findings indicated that IPLFCS in the left middle temporal gyrus (LMTG) was identified as a potential biomarker that correlated with cortical amyloid deposition and cognitive performance. High individual variation in the subjective cognitive decline (SCD) population makes functional connectivity (FC) biomarkers unstable. This study proposed a novel individual FC index, named individual proportion loss of functional connectivity strength (IPLFCS), and explored potential biomarkers for SCD using this new index. We proposed an IPLFCS analysis framework and compared it with traditional FC in Chinese and Western cohorts. Post hoc tests were used to determine biomarkers. Pearson's correlation analysis was used to investigate the correlation between neuropsychological scores or cortical amyloid deposits and IPLFCS biomarkers. Receiver operating characteristic curves were utilized to evaluate the ability of potential biomarkers to distinguish between groups. IPLFCS of the left middle temporal gyrus (LMTG) was identified as a potential biomarker. The IPLFC was correlated with the traditional FC (r = 0.956, p < 0.001; r = 0.946, p < 0.001) and cortical amyloid deposition (r = −0.245, p = 0.029; r = −0.185, p = 0.048) in both cohorts. Furthermore, the IPLFCS decreased across the Alzheimer's disease (AD) continuum. Its diagnostic efficiency was superior to that of existing fMRI biomarkers. These findings suggest that IPLFCS of the LMTG could be a potential biomarker of SCD. [ABSTRACT FROM AUTHOR]

Object. L6 cells were cultured to explore the possible mechanism underlying the improvement of insulin resistance by Liraglutide (LR). Methods. Cells were divided into 5 groups—control, high-fat, 10 nmol/L LR + 0.6 mmol/L palmitic acid (PA) (10LR), 100 nmol/L LR + 0.6 mmol/L PA (100LR), and 1000 nmol/L LR + 0.6 mmol/L PA (1000LR). CCK-8 method to detect cell viability, GPO-PAP enzymatic method to detect intracellular triglyceride content, and reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) and western blotting methods to detect fatty acid translocase CD36 (FAT/CD36) and fatty acid binding protein 4 (FABP4) in L6 cells, glucose-regulated protein 78 (GRP78), glucose transporter 4 (GLUT4) expression at the mRNA and protein levels, respectively, were performed. Results. We found that after PA intervention for 24 h, the cell viability decreased significantly; the cell viability of the LR group was higher than that of the high-fat group (P < 0.01). After PA intervention, compared with those in the high-fat group, GRP-78, FAT/CD36, FABP4 mRNA ((4.36 ± 0.32 vs. 8.15 ± 0.35); (1.00 ± 0.04 vs. 2.46 ± 0.08); (2.88 ± 0.55 vs. 8.29 ± 0.52), P < 0.01) and protein ((3338.13 ± 333.15 vs. 4963.98 ± 277.29); (1978.85 ± 124.24 vs. 2676.07 ± 100.64); (3372.00 ± 219.84 vs. 6083.20 ± 284.70), both P < 0.01) expression decreased in the LR group. The expression levels of GLUT4 mRNA ((0.75 ± 0.04 vs. 0.34 ± 0.03), P < 0.01) and protein ((3443.71 ± 191.89 vs. 2137.79 ± 118.75), P < 0.01) increased. Conclusion. Therefore, we conclude that LR can reverse PA-induced cell inactivation and lipid deposition, which may be related to the change in GRP-78, FAT/CD36, FABP4, GLUT4, and other factors. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology. Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses. This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner. In this review, we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies: (1) exploring novel biomarkers and seeking mutual interpretability and (2) providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics. Importantly, we highlight the necessity of brain imaging biomarker genomics, discuss the strengths and limitations of current methods, and propose directions for development of this research field. [ABSTRACT FROM AUTHOR]

Background: Centromere protein L (CENPL) is an important member of the centromere protein (CENP) family. However, the correlation between CENPL expression and cancer development and immune infiltration has rarely been studied. Here, we studied the role of CENPL in pan-cancer and further verified the results in lung adenocarcinoma (LUAD) through in vitro experiments. Methods: The CENPL expression level was studied with TIMER 2.0 and Oncomine databases. The potential value of CENPL as a diagnostic and prognostic biomarker in pan-cancer was evaluated with the TCGA database and GEPIA. The CENPL mutation character was analyzed using the cBioPortal database. The LinkedOmics and CancerSEA databases were used to carry out the function analysis of CENPL. The role of CENPL in immune infiltration was studied using the TIMER and TISIDB websites. Moreover, the expression of CENPL was detected through RT-qPCR and Western blotting. Immunohistochemistry was used to evaluate the infiltration level of CD8+ T cells. Cell proliferation was detected by EdU and CCK8. A flow cytometer was used to analyze the influence of CENPL in cell cycle and apoptosis. Results: CENPL was increased in most of the cancers. The upregulation and mutation of CENPL were associated with a poorer prognosis in many cancers. The results showed a significant positive correlation between CENPL and myeloid-derived suppressor cell (MDSC) infiltration and a negative correlation between CENPL and T-cell NK infiltration in most of the cancers. CENPL regulated cell proliferation and cell cycle, and was negatively correlated with the inflammation level of LUAD. The in vitro experiments suggested that CENPL was increased in LUAD tissue and cell lines. There was a negative correlation between CENPL expression and CD8+ T-cell infiltration. The knockdown of CENPL significantly suppressed the expression of CDK2 and CCNE2, and induced G0/G1 arrest and apoptosis of LUAD. Conclusions: CENPL may function as a potential biomarker and oncogene in pan-cancer, especially LUAD. Furthermore, CENPL was associated with immune cell infiltration in pan-cancer, providing a potential immune therapy target for tumor treatment. [ABSTRACT FROM AUTHOR]

Introduction: Subjective cognitive decline (SCD) represents a cognitively normal state but at an increased risk for developing Alzheimer's disease (AD). Recognizing the glucose metabolic biomarkers of SCD could facilitate the location of areas with metabolic changes at an ultra-early stage. The objective of this study was to explore glucose metabolic biomarkers of SCD at the region of interest (ROI) level. Methods: This study was based on cohorts from two tertiary medical centers, and it was part of the SILCODE project (NCT03370744). Twenty-six normal control (NC) cases and 32 SCD cases were in cohort 1; 36 NCs, 23 cases of SCD, 32 cases of amnestic mild cognitive impairment (aMCIs), 32 cases of AD dementia (ADDs), and 22 cases of dementia with Lewy bodies (DLBs) were in cohort 2. Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) imaging and magnetic resonance imaging (MRI), and subjects from cohort 1 additionally underwent amyloid-PET scanning. The ROI analysis was based on the Anatomical Automatic Labeling (AAL) template; multiple permutation tests and repeated cross-validations were conducted to determine the metabolic differences between NC and SCD cases. In addition, receiver operating characteristic curves were used to evaluate the capabilities of potential glucose metabolic biomarkers in distinguishing different groups. Pearson correlation analysis was also performed to explore the correlation between glucose metabolic biomarkers and neuropsychological scales or amyloid deposition. Results: Only the right middle temporal gyrus (RMTG) passed the methodological verification, and its metabolic levels were correlated with the degrees of complaints (R = − 0.239, p = 0.009), depression (R = − 0.200, p = 0.030), and abilities of delayed memory (R = 0.207, p = 0.025), and were weakly correlated with cortical amyloid deposition (R = − 0.246, p = 0.066). Furthermore, RMTG metabolism gradually decreased across the cognitive continuum, and its diagnostic efficiency was comparable (NC vs. ADD, aMCI, or DLB) or even superior (NC vs. SCD) to that of the metabolism of the posterior cingulate cortex or precuneus. Conclusions: These findings suggest that the hypometabolism of RMTG could be a typical feature of SCD, and the large-scale hypometabolism in patients with symptomatic stages of AD may start from the RMTG, which gradually progresses starting in the preclinical stage. The specificity of identifying SCD from the perspective of self-perceived symptoms is likely to be increased by the detection of RMTG metabolism. [ABSTRACT FROM AUTHOR]

Rectal cancer (RC) is the leading cause of tumor-related death among both men and women. The efficacy of immunotherapy for rectal cancer is closely related to the immune infiltration level. The N6-methyladenosine (m6A) modification may play a pivotal role in tumor-immune interactions. However, the roles of m6A-related genes in tumor-immune interactions of rectal cancer remain largely unknown. After an evaluation on the expression levels of m6A-related genes and their correlations with the prognosis of rectal cancer patients, we found that METTL14 was the only gene to be significantly correlated with prognosis in rectal cancer patients. Therefore, we further observed the impact of METTL14 expression and m6A modification on the immune infiltration in rectal cancer. Our study indicates that low expression of the m6A "writer" gene METTL14 in rectal cancer may lead to the downregulation of m6A RNA modification, thus reducing the level of immune cell infiltration and resulting in poor prognosis. METTL14 expression level is an independent prognostic factor in rectal cancer and is positively correlated with the immune infiltration level. Our study identified METTL14 as a potential target for enhancing immunotherapy efficacy in rectal cancer. [ABSTRACT FROM AUTHOR]

CLEC10A, (C‐type lectin domain family 10, member A), as the member of C‐type lectin receptors (CLRs), plays a vital role in modulating innate immunity and adaptive immunity and has shown great potential as an immunotherapy target for cancers. However, there is no functional research of CLEC10A in prognostic risk, immunotherapy or any other treatment of lung adenocarcinoma (LUAD). We performed bioinformatics analysis on LUAD data downloaded from TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus), and jointly analysed with online databases such as HPA, LinkedOmics, TIMER, ESTIMATE and TISIDB. We found that lower expression of CLEC10A was accompanied with worse outcomes of LUAD patients. Moreover, CLEC10A expression was significantly correlated with a variety of the tumour‐infiltrating immune cells (TIICs). As a promising prognosis predictor and potential immunotherapy target, the potential influence and mechanisms of CLEC10A in LUAD deserve further exploring. [ABSTRACT FROM AUTHOR]

Ovarian carcinoma (OV) is a lethal gynecological malignancy. Most OV patients develop resistance to platinum-based chemotherapy and recurrence. Peroxisome proliferator-activated receptors (PPARs) are the ligand activating transcription factor of the nuclear receptor superfamily. PPARs as important transcriptional regulators regulate important physiological processes such as lipid metabolism, inflammation, and wound healing. Several reports point out that PPARs can also have an effect on the sensitivity of tumor cells to platinum-based chemotherapy drugs. However, the role of PPAR-target related genes (PPAR-TRGs) in chemotherapeutic resistance of OV remains unclear. The present study is aimed at optimizing candidate genes by integrating platinum-chemotherapy expression data and PPAR family genes with their targets. The gene expression profiles were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. A total of 4 genes (AP2A2, DOCK4, HSDL2, and PDK4) were the candidate differentially expressed genes (DEGs) of PPAR-TRGs with platinum chemosensitivity. After conducting numerous survival analyses using different cohorts, we found that only the upexpression of DOCK4 has important significance with the poor prognosis of OV patients. Meanwhile, DOCK4 is detected in plasma and enriched in neutrophil and monocyte cells of the blood. We further found that there were significant correlations between DOCK4 expression and the levels of CD4+ T cell infiltration, dendritic cell infiltration, and neutrophil infiltration in OV. In addition, we verified the expression level of DOCK4 in OV cell lines treated with platinum drugs and found that DOCK4 is potentially responsive to platinum drugs. In conclusion, DOCK4 is potentially associated with immune cell infiltration and represents a valuable prognostic biomarker in ovarian cancer patients. [ABSTRACT FROM AUTHOR]

Background: Major depressive disorder (MDD) is characterized by core functional deficits in cognitive inhibition, which is crucial for emotion regulation. To assess the response to ruminative and negative mood states, it was hypothesized that MDD patients have prolonged disparities in the oscillatory dynamics of the frontal cortical regions across the life course of the disease. Method: A "go/no-go" response inhibition paradigm was tested in 31 MDD patients and 19 age-matched healthy controls after magnetoencephalography (MEG) scanning. The use of minimum norm estimates (MNE) examined the changes of inhibitory control network which included the right inferior frontal gyrus (r IFG), pre-supplementary motor area (preSMA), and left primary motor cortex (l M1). The power spectrum (PS) within each node and the functional connectivity (FC) between nodes were compared between two groups. Furthermore, Pearson correlation was calculated to estimate the relationship between altered FC and clinical features. Result: PS was significantly reduced in left motor and preSMA of MDD patients in both beta (13–30 Hz) and low gamma (30–50 Hz) bands. Compared to the HC group, the MDD group demonstrated higher connectivity between l M1 and preSMA in the beta band (t = 3.214, p = 0.002, FDR corrected) and showed reduced connectivity between preSMA and r IFG in the low gamma band (t = −2.612, p = 0.012, FDR corrected). The FC between l M1 and preSMA in the beta band was positively correlated with illness duration (r = 0.475, p = 0.005, FDR corrected), while the FC between preSMA and r IFG in the low gamma band was negatively correlated with illness duration (r = −0.509, p = 0.002, FDR corrected) and retardation factor scores (r = −0.288, p = 0.022, uncorrected). Conclusion: In this study, a clinical neurophysiological signature of cognitive inhibition leading to sustained negative affect as well as functional non-recovery in MDD patients is highlighted. Duration of illness (DI) plays a key role in negative emotional processing, heighten rumination, impulsivity, and disinhibition. [ABSTRACT FROM AUTHOR]

Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end‐to‐end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross‐validation on in‐house, multicenter (n = 716), and public (n = 1116) databases with an accuracy up to 92%. Significant associations between the classification output and clinical characteristics of AD and mild cognitive impairment (MCI, a middle stage of dementia) groups provide solid neurobiological support for the 3DAN model. The effectiveness of the 3DAN model is further validated by its good performance in predicting the MCI subjects who progress to AD with an accuracy of 72%. Collectively, the findings highlight the potential for structural brain imaging to provide a generalizable, and neuroscientifically interpretable imaging biomarker that can support clinicians in the early diagnosis of AD. [ABSTRACT FROM AUTHOR]

Neurodegeneration due to Alzheimer's disease ( AD) can progress over decades before dementia becomes apparent. Indeed, patients with mild cognitive impairment ( MCI) already demonstrate significant lesion loads. In most cases, MCI is preceded by subjective cognitive decline ( SCD), which is applied to individuals who have self-reported memory-related complaints and has been associated with a higher risk of future cognitive decline and conversion to dementia. Based on the schema of a well-received model of biomarker dynamics in AD pathogenesis, it has been postulated that SCD symptoms may result from compensatory changes in response to β-amyloid accumulation and neurodegeneration. Although SCD is considered a prodromal stage of MCI, it is also a common manifestation in old age, independent of AD, and the predictive value of SCD for AD pathology remains controversial. Here, we provide a review focused on the contributions of cross-sectional and longitudinal analogical studies of biomarkers and neuroimaging evidence in disentangling under what conditions SCD may be attributable to AD pathology. In conclusion, there is promising evidence indicating that clinicians should be able to differentiate pre- AD SCD based on the presence of pathophysiological biomarkers in cerebrospinal fluid ( CSF) and neuroimaging. However, this neuroimaging approach is still at an immature stage without an established rubric of standards. A substantial amount of work remains in terms of replicating recent findings and validating the clinical utility of identifying SCD. [ABSTRACT FROM AUTHOR]

Metabolomics represents an emerging discipline concerned with comprehensive assessment of small molecule endogenous metabolites in biological systems and provides a powerful approach insight into the mechanisms of diseases. Type 2 diabetes (T2D), called the burden of the 21st century, is growing with an epidemic rate. However, its precise molecular mechanism has not been comprehensively explored. In this study, we applied urinary metabolomics based on the UPLC/MS integrated with pattern recognition approaches to discover differentiating metabolites, to characterize and explore metabolic pathway disruption in an experimental model for high-fat-diet induced T2D. Six differentiating urinary metabolites were found in the negative mode, and two (2-(4-hydroxy-3-methoxy-phenyl) acetaldehyde sulfate, 2-phenylethanol glucuronide) of which were identified involving the key metabolic pathways linked to pentose and glucuronate interconversions, starch, sucrose metabolism and tyrosine metabolism. Our study provides new insight into pathophysiologic mechanisms and may enhance the understanding of T2D pathogenesis. [ABSTRACT FROM AUTHOR]

Hepatitis B virus (HBV) is the fatal consequence of chronic hepatitis, and lack of biomarkers has been a long standing bottleneck in the clinical diagnosis. Metabolomics concerns with comprehensive analysis of small molecules and provides a powerful approach to discover biomarkers in biological systems. Here, we present metabolomics analysis applying ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry. (UPLC-Q-TOF-HDMS) to determine metabolite alterations in HBV patients. Most important permutations are elaborated using multivariate statistical analysis and network analysis that was used to select the metabolites for the noninvasive diagnosis of HBV. In this study, the total 11 urinary differential metabolites were identified and contributed to HBV progress involving several key metabolic pathways by using pathway analysis with MetPA, which are promising biomarker candidates for diagnostic research. More importantly, of 11 altered metabolites, 4 metabolite markers were effective for the diagnosis of human HBV, achieved a satisfactory accuracy, sensitivity and specificity, respectively. It demonstrates that metabolomics has the potential as a non-invasive tool to evaluate the potential of these metabolites in the early diagnosis of HBV patients. These findings may be promising to yield a valuable insight into the pathophysiology of HBV and to advance the approaches of diagnosis, treatment, and prevention. [ABSTRACT FROM AUTHOR]

• A multi-dimensional index combining multiscale FuzzyEn and PLV is proposed for the early diagnosis of aMCI. • The ELM method is used to classify aMCI patients based on the multi-dimensional index. • The prefrontal lobe is a sensitive brain region for the diagnosis of aMCI patients. To achieve the early diagnosis of amnestic mild cognitive impairment (aMCI), this paper proposes a multi-dimensional index, which combines the advantages of the multiscale fuzzy entropy (FuzzyEn) and phase locking value (PLV) based on electroencephalography (EEG). The complexity and synchronization of the EEG were characterized using FuzzyEn and PLV in five frequency bands, respectively. By combining the two methods, the changes in the health of brain function were comprehensively analyzed. The extreme learning machine (ELM) method was used to classify aMCI patients based on a multi-dimensional index. Compared with aMCI patients, the multiscale FuzzyEn and PLV of normal controls (NC) were higher and statistically significant (P < 0.05) in the Fp1 and Fp2 channels. Moreover, significant correlation existed between the multiscale FuzzyEn or PLV and the MoCA scores in the Fp1 and Fp2 channels. The classification accuracy and running time based on ELM in the prefrontal lobe were 83.34% and 0.003 s, respectively. The multi-dimensional index based on prefrontal lobe could diagnosis cognitive decline of aMCI patients. The results showed that features integrated multiscale FuzzyEn and PLV could be used as a biomarker of cognitive decline and help realize the early diagnosis of aMCI patients. [ABSTRACT FROM AUTHOR]