Transcription factor Twist1 drives fibroblast activation to promote kidney fibrosis via signaling proteins Prrx1/TNC.

The transcription factor Twist1 plays a vital role in normal development in many tissue systems and continues to be important throughout life. However, inappropriate Twist1 activity has been associated with kidney injury and fibrosis, though the underlying mechanisms involved remain incomplete. Here, we explored the role of Twist1 in regulating fibroblast behaviors and the development kidney fibrosis. Initially Twist1 protein and activity was found to be markedly increased within interstitial myofibroblasts in fibrotic kidneys in both humans and rodents. Treatment of rat kidney interstitial fibroblasts with transforming growth factor-ß1 (a profibrotic factor) also induced Twist1 expression in vitro. Gain- and loss-of-function experiments supported that Twist1 signaling was responsible for transforming growth factor-ß1-induced fibroblast activation and fetal bovine serum-induced fibroblast proliferation. Mechanistically, Twist1 protein promoted kidney fibroblast activation by driving the expression of downstream signaling proteins, Prrx1 and Tnc. Twist1 directly enhanced binding to the promoter of Prrx1 but not TNC, whereas the promoter of TNC was directly bound by Prrx1. Finally, mice with fibroblast-specific deletion of Twist1 exhibited less Prrx1 and TNC protein abundance, interstitial extracellular matrix deposition and kidney inflammation in both the unilateral ureteral obstruction and ischemic-reperfusion injury-induced-kidney fibrotic models. Inhibition of Twist1 signaling with Harmine, a ß-carboline alkaloid, improved extracellular matrix deposition in both injury models. Thus, our results suggest that Twist1 signaling promotes the activation and proliferation of kidney fibroblasts, contributing to the development of interstitial fibrosis, offering a potential therapeutic target for chronic kidney disease.

Radiomics-Guided Deep Learning Networks Classify Differential Diagnosis of Parkinsonism.

The differential diagnosis between atypical Parkinsonian syndromes may be challenging and critical. We aimed to proposed a radiomics-guided deep learning (DL) model to discover interpretable DL features and further verify the proposed model through the differential diagnosis of Parkinsonian syndromes. We recruited 1495 subjects for 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scanning, including 220 healthy controls and 1275 patients diagnosed with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA), or progressive supranuclear palsy (PSP). Baseline radiomics and two DL models were developed and tested for the Parkinsonian diagnosis. The DL latent features were extracted from the last layer and subsequently guided by radiomics. The radiomics-guided DL model outperformed the baseline radiomics approach, suggesting the effectiveness of the DL approach. DenseNet showed the best diagnosis ability (sensitivity: 95.7%, 90.1%, and 91.2% for IPD, MSA, and PSP, respectively) using retained DL features in the test dataset. The retained DL latent features were significantly associated with radiomics features and could be interpreted through biological explanations of handcrafted radiomics features. The radiomics-guided DL model offers interpretable high-level abstract information for differential diagnosis of Parkinsonian disorders and holds considerable promise for personalized disease monitoring.

The Effect of Human Umbilical Cord Mesenchymal Stem Cell on Premature Ovarian Cell Senilism Through miR-10a.

Objective: To investigate the potential protective impact of miR-10a-modified HUMSCs-derived exosomes on both premature ovarian failure and the functionality of ovarian granulosa cells in a POF model. Methods: KGN cells were co-cultured with cisplatin-diaminedichloroplatinum (II) (10 µM) for 24 h to establish an in vitro POF model. The cells were distributed into three distinct groups: the control group, the POF group, and the POF + HUCMSC group. The plasmid sh-NC, sh-miR-10 a and miR-10 a mimic were transfected into KGN cells. After co-cultured with HUCMSC-EVs for 48 h, they were divided into HUCMSC group, sh-miR-10 a-HUMSCs-exosomes group and miR-10 a-HUMSCs-exosomes group. Flow cytometry was adopted to assess the impact of HUMSCs surface immune antigens and miR-10a-HUCMSCs-exosomes on KGN cell apoptosis. Additionally, the evaluation of cell proliferation was carried out through CCK-8 and EDU assays. Western blot analysis was utilized to detect the Caspase-3, Bax, and Bcl-2 proteins levels. Furthermore, the levels of TNF-α, IL-6, IL-10, MDA, SOD, and CAT were quantified using ELISA. Results: Compared with the Control group, the POF group inhibited the growth of ovarian granulosa cells (P<0.01), reduced the number of EDU cells (P<0.01), and increased the protein expression of Caspase-3 (P<0.05) and Bax (P<0.01). HUMSCs treatment significantly down-regulated the expression of IL-6, TNF-α and MDA, while up-regulating the expression of IL-10, SOD and CAT (P<0.01); the overexpression of miR-10a promoted cell growth, besides, the introduction of miR-10a-HUMSCs-derived exosomes led to an elevation in the proliferation rate of OGCs affected by POF and concurrently suppressed the apoptosis rate. Conclusion: HUMSCs-derived exosomes modified by miR-10a have protective effects on premature ovarian failure and ovarian granulosa cell function in POF model.

Population pharmacokinetic/pharmacodynamic modeling of nifekalant injection with varies dosing plan in Chinese volunteers: a randomized, blind, placebo-controlled study.

Nifekalant hydrochloride is a class III antiarrhythmic agent which could increase the duration of the action potential and the effective refractory period of ventricular and atrial myocytes by blocking the K+ current. Nifekalant is used to prevent ventricular tachycardia/ventricular fibrillation. QT interval prolongation is the main measurable drug effect. However, due to the complicated dosing plan in clinic, the relationship among dosage, time, drug concentration and efficacy is not fully understood. In this study, a single-center, randomized, blind, dose-ascending, placebo-controlled study was conducted to explore the intrinsic characteristics of nifekalant injection in healthy Chinese volunteers by a population pharmacokinetic (PK)-pharmacodynamic (PD) model approach. 42 subjects were enrolled in this study and received one of three dose plans (loading dose on Day 1 (0.15, 0.3 or 0.5 mg/kg), loading dose followed by maintenance dose (0.2, 0.4 or 0.8 mg/kg/h) on Day 4) or vehicle. Blood samples were drawn for PK evaluation, and ECGs were recorded for QTc calculation at the designed timepoints. No Torsades de Pointes occurred during the study. The popPK model of nifekalant injection could be described by a two-compartment model with first-order elimination. The population mean clearance (CL) was 53.8 L/h. The population mean distribution volume of the central (Vc) and peripheral (Vp) compartments was 8.27 L and 45.6 L, respectively. A nonlinear dose-response (Emax) model well described the pharmacodynamic effect (QTc interval prolongation) of nifekalant. The Emax and EC50 from current study were 101 ms and 342 ng/mL, respectively.

Bidirectional Mapping Perception-enhanced Cycle-consistent Generative Adversarial Network for Super-resolution of Brain MRI images.

As an effective tool for visualizing neurodegeneration, high-resolution structural magnetism facilitates quantitative image analysis and clinical applications. Super-resolution reconstruction technology allows to improve the resolution of images without upgrading the scanning hardware. However, existing super-resolution techniques relied on paired image data sets and lacked further quantitative analysis of the generated images. In this study, we proposed a semi-supervised generative adversarial network (GAN) model for super-resolution of brain MRI, and the synthetic images were evaluated using various quantitative measures. This model adopted the cycle-consistency structure to allow for a mixture of unpaired data for training. Perceptual loss was further introduced into the model to preserve detailed texture features at high frequencies. 363 subjects with both high-resolution (HR) and low-resolution (LR) scans and 217 subjects with HR scans only were used for model derivation, training, and validation. We extracted multiple voxel-based and surface-based morphological features of the synthetic and real 3D HR images for comparison. We further evaluated the synthetic images in the differential diagnosis of diseases. Our model achieved superior mean absolute error (0.049±0.021), mean squared error (0.0059±0.0043), peak signal-to-noise ratio (29.41±3.71), structural similarity index measure (0.914±0.048). Eight morphological metrics, both voxel-based and surface-based, showed significant agreement (P<0.0001). The gap of accuracy in disease diagnosis between synthetic and real HR images was within 5% and significantly outperformed the LR images. Our proposed model enables the reconstruction of HR MRI and could be used accurately for image quantification.Clinical relevance- Quantitative evaluation of the synthetic high-resolution images was used to determine whether the synthetic images have sufficient realism and diversity.

Early Diagnosis and Biomarkers of Alzheimer's Disease Based on Spatio-temporal Graph Convolution Network.

Functional magnetic resonance imaging (fMRI) could detect the dynamic activity of brain function and communication. Previous studies have found reduced brain functional connectivity in Alzheimer's disease (AD) patients. In this study, we proposed to process fMRI data by spatio-temporal graph convolution network (ST-GCN) to achieve an early differential diagnosis of AD and to extract image markers using gradient-weighted class activation mapping (Grad-CAM). The data used in this study were from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, Xuanwu Hospital, and Tongji Hospital. The study included 1105 normal controls and 790 patients with mild cognitive impairment (MCI). The grid search method of K-fold cross-validation was used to train the model. In addition, we used Grad-CAM to extract image markers and carried out visualization analysis. This model obtains better AD diagnosis power: accuracy = 0.92, sensitivity = 0.97, specificity = 0.89, and area under the curve=0.96. Salient brain regions extracted by Grad-CAM include the paracentral lobule, inferior occipital gyrus, middle frontal gyrus, superior temporal gyrus, cuneus, posterior cingulate gyrus, and superior parietal gyrus. Our proposed ST-GAN model will help to explore objective markers that can be used for the early diagnosis of AD.Clinical relevance- Our proposed model shows great potential for enhancing the understanding of the pathology of AD by detecting functional connectivity interruptions.

The efficacy and safety of intravenous administration of tranexamic acid in patients undergoing cardiac surgery: Evidence from a single cardiovascular center.

BACKGROUND: The current study was performed to systemically review the efficacy and safety of tranexamic acid (TXA) in patients undergoing cardiac surgery at a single large-volume cardiovascular center. METHODS: A computerized search of electronic databases was performed to identify all relevant studies using search terms till December 31st, 2021. The primary outcomes were postoperative blood loss and the composite incidence of mortality and morbidities during hospitalization. Secondary outcomes included postoperative massive bleeding and transfusion, postoperative recovery profiles, coagulation functions, inflammatory variables, and biomarkers of vital organ injury. RESULTS: Database search yielded 23 qualified studies including 27,729 patients in total. Among them, 14,136 were allocated into TXA group and 13,593 into Control group. The current study indicated that intravenous TXA significantly reduced total volume of postoperative bleeding in both adult and pediatric patients, and that medium- and high-dose TXA were more effective than low-dose TXA in adult patients (P < .05). The current study also demonstrated that intravenous TXA, as compared to Control, remarkably reduced postoperative transfusion incidences and volume of red blood cell and fresh frozen plasma, and reduced postoperative transfusion incidence of platelet concentrates (PC) (P < .05) without obvious dose-effects (P > .05), but TXA did not reduce PC transfusion volume postoperatively in adult patients (P > .05). For pediatrics, TXA did not significantly reduce postoperative transfusion incidence and volume of allogenic red blood cell, fresh frozen plasma and PC (P > .05). Additionally, the current study demonstrated that intravenous TXA did not influence the composite incidence of postoperative mortality and morbidities in either adults or pediatrics during hospitalization (P > .05), and that there was no obvious dose-effect of TXA in adult patients (P > .05). CONCLUSIONS: This current study suggested that intravenous TXA significantly reduced total volume of postoperative bleeding in both adult and pediatric patients undergoing cardiac surgery at the single cardiovascular center without increasing the composite incidence of mortality and morbidities.

Qualitative and quantitative determination of chemical constituents in Jinbei oral liquid, a modern Chinese medicine for coronavirus disease 2019, by ultra-performance liquid chromatography coupled with mass spectrometry.

Introduction: Traditional Chinese medicine (TCM) has the advantages of syndrome differentiation and rapid determination of etiology, and many TCM prescriptions have been applied to the clinical treatment of coronavirus disease 2019 (COVID-19). Among them, Jinbei Oral Liquid (Jb.L) has also shown an obvious curative effect in the clinic, but the related material basic research is relatively limited. Methods: Therefore, in this process, a systematic data acquisition and mining strategy was established using ultra-high- performance liquid chromatography coupled with quadruple time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Results and Discussion: With the optimized conditions, a total of 118 peaks were tentatively characterized, including 43 flavonoids, 26 phenylpropanoids, 14 glycosides, 9 phthalides, 8 alkaloids and others. To determine the content of relevant pharmacological ingredients, we firstly exploited the ultra-performance liquid chromatography method coupled with triple-quadrupole tandem mass spectrometry (UPLC-QqQ-MS/MS) method for simultaneous detection of 31 active ingredients within 17 min, and the validation of methodology showed that this method has good precision and accuracy. Moreover, analyzing the pharmacology of 31 individual of the medicinal material preliminarily confirmed the efficacy of Jb.L and laid a foundation for an in-depth study of network pharmacology.

Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening.

Background: Mild cognitive impairment (MCI) is a transitional stage between normal aging and probable Alzheimer's disease. It is of great value to screen for MCI in the community. A novel machine learning (ML) model is composed of electroencephalography (EEG), eye tracking (ET), and neuropsychological assessments. This study has been proposed to identify MCI subjects from normal controls (NC). Methods: Two cohorts were used in this study. Cohort 1 as the training and validation group, includes184 MCI patients and 152 NC subjects. Cohort 2 as an independent test group, includes 44 MCI and 48 NC individuals. EEG, ET, Neuropsychological Tests Battery (NTB), and clinical variables with age, gender, educational level, MoCA-B, and ACE-R were selected for all subjects. Receiver operating characteristic (ROC) curves were adopted to evaluate the capabilities of this tool to classify MCI from NC. The clinical model, the EEG and ET model, and the neuropsychological model were compared. Results: We found that the classification accuracy of the proposed model achieved 84.5 ± 4.43% and 88.8 ± 3.59% in Cohort 1 and Cohort 2, respectively. The area under curve (AUC) of the proposed tool achieved 0.941 (0.893-0.982) in Cohort 1 and 0.966 (0.921-0.988) in Cohort 2, respectively. Conclusions: The proposed model incorporation of EEG, ET, and neuropsychological assessments yielded excellent classification performances, suggesting its potential for future application in cognitive decline prediction.

Specnuezhenide suppresses diabetes-induced bone loss by inhibiting RANKL-induced osteoclastogenesis.

Diabetes osteoporosis is a chronic complication of diabetes mellitus (DM) and is associated with osteoclast formation and enhanced bone resorption. Specnuezhenide (SPN) is an active compound with anti-inflammatory and immunomodulatory properties. However, the roles of SPN in diabetic osteoporosis remain unknown. In this study, primary bone marrow macrophages (BMMs) were pretreated with SPN and were stimulated with receptor activator of nuclear factor kappa B ligand (RANKL; 50 ng/mL) to induce osteoclastogenesis. The number of osteoclasts was detected by tartrate-resistant acid phosphatase (TRAP) staining. The protein levels of cellular oncogene fos/nuclear factor of activated T cells c1 (c-Fos/NFATc1), nuclear factor kappa-B (NF-κB), and mitogen-activated protein kinases (MAPKs) were evaluated by western blot analysis. NF-κB luciferase assays were used to examine the role of SPN in NF-κB activation. The DM model group received a high-glucose, high-fat diet and was then intraperitoneally injected with streptozotocin (STZ). Micro-CT scanning, serum biochemical analysis, histological analysis were used to assess bone loss. We found that SPN suppressed RANKL-induced osteoclast formation and that SPN inhibited the expression of osteoclast-related genes and c-Fos/ NFATc1. SPN inhibited RANKL-induced activation of NF-κB and MAPKs. In vivo experiments revealed that SPN suppressed diabetes-induced bone loss and the number of osteoclasts. Furthermore, SPN decreased the levels of bone turnover markers and increased the levels of runt-related transcription factor 2 (RUNX2), osteoprotegerin (OPG), calcium (Ca) and phosphorus (P). SPN also regulated diabetes-related markers. This study suggests that SPN suppresses diabetes-induced bone loss by inhibiting RANKL-induced osteoclastogenesis, and provides an experimental basis for the treatment of diabetic osteoporosis.

Investigation of the Pharmacological Effect and Mechanism of Jinbei Oral Liquid in the Treatment of Idiopathic Pulmonary Fibrosis Using Network Pharmacology and Experimental Validation.

Overview: Idiopathic pulmonary fibrosis (IPF) is a disease caused by many factors, eventually resulting in lung function failure. Jinbei oral liquid (JBOL) is a traditional Chinese clinical medicine used to treat pulmonary diseases. However, the pharmacological effects and mechanism of the action of JBOL on IPF remain unclear. This study investigated the protective effects and mechanism of the action of JBOL on IPF using network pharmacology analysis, followed by in vivo and in vitro experimental validation. Methods: The components of JBOL and their targets were screened using the TCMSP database. IPF-associated genes were obtained using DisGeNET and Drugbank. The common targets of JBOL and IPF were identified with the STRING database, and a protein-protein interaction (PPI) network was constructed. GO and KEGG analyses were performed. Sprague-Dawley rats were injected with bleomycin (BLM) to establish an IPF model and treated orally with JBOL at doses of 5.4, 10.8, and 21.6 ml/kg. A dose of 54 mg/kg of pirfenidone was used as a control. All rats were treated for 28 successive days. Dynamic pulmonary compliance (Cdyn), minute ventilation volume (MVV), vital capacity (VC), and lung resistance (LR) were used to evaluate the efficacy of JBOL. TGF-ß-treated A549 cells were exposed to JBOL, and epithelial-to-mesenchymal transition (EMT) changes were assessed. Western blots were performed. Results: Two hundred seventy-eight compounds and 374 targets were screened, and 103 targets related to IPF were identified. Core targets, including MAPK1 (ERK2), MAPK14 (p38), JUN, IL-6, AKT, and others, were identified by constructing a PPI network. Several pathways were involved, including the MAPK pathway. Experimentally, JBOL increased the levels of the pulmonary function indices (Cdyn, MVV, and VC) in a dose-dependent manner and reduced the RL level in the BLM-treated rats. JBOL increased the epithelial marker E-cadherin and suppressed the mesenchymal marker vimentin expression in the TGF-ß-treated A549 cells. The suppression of ERK1/2, JNK, and p38 phosphorylation by JBOL was validated. Conclusion: JBOL had therapeutic effects against IPF by regulating pulmonary function and EMT through a systemic network mechanism, thus supporting the need for future clinical trials of JBOL.

CircRNA circ-ITCH improves renal inflammation and fibrosis in streptozotocin-induced diabetic mice by regulating the miR-33a-5p/SIRT6 axis.

Diabetic nephropathy (DN) seriously affects the people's life and health in China. This study aimed to investigate the effect of circRNA circ-ITCH on improving DN by regulating the miR-33a-5p/SIRT6 axis and the possible mechanism of action. High glucose (HG)-induced rat mesangial cells (RMCs) were used to simulate the DN in vitro. Reverse transcription-quantitative PCR (RT-qPCR) and western blot analysis were conducted to detect the gene or protein expression. Cell Counting Kit-8 (CCK-8) and wound healing assays were performed to estimate the cell viability and migration capability. Immunofluorescence and enzyme-linked immunosorbent assay (ELISA) were used to detect the α-Smooth Muscle Actin (α-SMA) expression and levels of inflammatory factors. The potential associations between circ-ITCH and miR-33a-5p, miR-33a-5p and SIRT6 in RMCs were measured via dual-luciferase reporter assay. Streptozotocin (STZ) was used to induce the diabetic mice. Blood glucose and serum insulin of mice were determined by corresponding kits, and blood urea nitrogen (BUN) and serum creatinine (SCr) were measured using an automatic biochemical analyzer. Hematoxylin and eosin (H&E) staining and periodic acid-Schiff (PAS) staining were applied to observe the degree of pathological injury and fibrosis of renal tissues. The results of the present study revealed that circ-ITCH expression was obviously decreased in HG-induced RMCs. In addition, circ-ITCH overexpression inhibited the viability, migration, fibrosis and inflammatory response of HG-induced RMCs. Further experiments confirmed that miR-33a-5p may be a direct target of circ-ITCH and SIRT6 may be a direct target of miR-33a-5p. Notably, the miR-33a-5p mimic or shRNA-SIRT6 were discovered to reverse the inhibitory effects of circ-ITCH on the proliferation, migration, fibrosis and inflammatory response of HG-induced RMCs. Furthermore, circ-ITCH overexpression ameliorated renal inflammation and fibrosis in STZ-induced diabetic mice. In conclusion, circ-ITCH alleviated renal inflammation and fibrosis in STZ-induced diabetic mice by regulating the miR-33a-5p/SIRT6 axis.Author names: Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [ChunYang] Last name [Xu]. Author 2 Given name: [DingBo] Last name [Xu]. Author 3 Given name: [YongHua] Last name [Liu]. Author 4 Given name: [Juanjuan] Last name [Jiang]. Also, kindly confirm the details in the metadata are correct.ok.

A semi-dry chemistry hydrogel-based smart biosensing platform for on-site detection of metal ions.

Balancing operability and performance has long been a focus of research in bioanalysis and biosensing. In this work, between the traditional wet chemistry and dry chemistry, we develop a semi-dry smart biosensing platform with favourable operability and performance for metal ions detection. This platform is based on the integration of a stimuli-responsive hydrogel with intelligent image recognition. The hydrogel consists of agarose as a matrix and well-designed fluorescent DNA probes as response elements. Target metal ions in a test sample can diffuse into the hydrogel and activate the DNA probes, outputting fluorescence signals for intelligent imaging. In this way, sensitive and convenient detection of metal ions such as potassium ions (K+) and mercury ions (Hg2+) can be achieved without the assistance of huge instruments and professional workers. The detection limits for K+ and Hg2+ are 0.34 mM and 5.6 nM, respectively. Detection of ions in serum and lake water is also available. Moreover, the hydrogel-based biosensing platform exhibits favorable selectivity, anti-degradation ability, and long-term stability. High-throughput testing can be also achieved by punching multiple test microwells in a single piece of hydrogel. The concept and successful practice of a semi-dry chemistry-based strategy make up for the shortcomings of wet chemistry and dry chemistry, and provide a promising approach for on-site testing.

Radiomics Analysis of Magnetic Resonance Imaging Facilitates the Identification of Preclinical Alzheimer's Disease: An Exploratory Study.

Diagnosing Alzheimer's disease (AD) in the preclinical stage offers opportunities for early intervention; however, there is currently a lack of convenient biomarkers to facilitate the diagnosis. Using radiomics analysis, we aimed to determine whether the features extracted from multiparametric magnetic resonance imaging (MRI) can be used as potential biomarkers. This study was part of the Sino Longitudinal Study on Cognitive Decline project (NCT03370744), a prospective cohort study. All participants were cognitively healthy at baseline. Cohort 1 (n = 183) was divided into individuals with preclinical AD (n = 78) and controls (n = 105) using amyloid-positron emission tomography, and this cohort was used as the training dataset (80%) and validation dataset (the remaining 20%); cohort 2 (n = 51) was selected retrospectively and divided into "converters" and "nonconverters" according to individuals' future cognitive status, and this cohort was used as a separate test dataset; cohort three included 37 converters (13 from the Alzheimer's Disease Neuroimaging Initiative) and was used as another test set for independent longitudinal research. We extracted radiomics features from multiparametric MRI scans from each participant, using t-tests, autocorrelation tests, and three independent selection algorithms. We then established two classification models (support vector machine [SVM] and random forest [RF]) to verify the efficiency of the retained features. Five-fold cross-validation and 100 repetitions were carried out for the above process. Furthermore, the acquired stable high-frequency features were tested in cohort three by paired two-sample t-tests and survival analyses to identify whether their levels changed with cognitive decline and impact conversion time. The SVM and RF models both showed excellent classification efficiency, with an average accuracy of 89.7-95.9% and 87.1-90.8% in the validation set and 81.9-89.1% and 83.2-83.7% in the test set, respectively. Three stable high-frequency features were identified, all based on the structural MRI modality: the large zone high-gray-level emphasis feature of the right posterior cingulate gyrus, the variance feature of the left superior parietal gyrus, and the coarseness feature of the left posterior cingulate gyrus; their levels were correlated with amyloid-ß deposition and predicted future cognitive decline (areas under the curve 0.649-0.761). In addition, levels of the variance feature at baseline decreased with cognitive decline and could affect the conversion time (p < 0.05). In conclusion, this exploratory study shows that the radiomics features of multiparametric MRI scans could represent potential biomarkers of preclinical AD.

Association of tau accumulation and atrophy in mild cognitive impairment: a longitudinal study.

OBJECTIVE: To examine the patterns of longitudinal tau accumulation and cortical atrophy and their association in subjects with mild cognitive impairment (MCI). METHODS: We collected 23 participants (60-89 years old, 11 males/12 females) with MCI from the Alzheimer's Disease Neuroimaging Initiative database. All participants underwent 18F flortaucipir (FTP) positron emission tomography (PET) and structural magnetic resonance imaging (MRI) scans at the baseline and follow-up visits (12-36 months). General linear models with covariates (baseline age, sex) were used to detect brain areas of significant tau accumulation and atrophy over time. Mediation analysis was employed to explore the potential reason for sequential biomarker changes in MCI progression, adjusting for baseline age, sex, and education level. RESULTS: Voxel-wise tau accumulation in MCI subjects was predominantly located in the inferior temporal cortex, middle temporal cortex, parietal cortex, posterior cingulate, precuneus, and temporoparietal regions (P < 0.001), and MRI atrophy included the inferior-middle temporal lobe, parietal lobe, and precuneus (P < 0.001). Longitudinal FTP accumulation was moderately associated with annualized MRI cortical atrophy (r = 0.409, 95% CI: 0.405-0.414, P < 0.01). Regional analyses indicated significant bivariate associations between annualized MRI cortical atrophy and FTP accumulation (baseline FTP cortical uptake and longitudinal FTP change). The results of the mediation analysis showed that the relationship between baseline FTP uptake and longitudinal cortical atrophy was partly mediated by the longitudinal FTP cortical change (indirect effect: 0.0107, P = 0.04). CONCLUSIONS: Our findings provide a preliminary description of the patterns of longitudinal FTP accumulation and annualized cortical atrophy in MCI progression, and MCI subjects with high tau binding levels show an increase risk of longitudinal tau accumulation, atrophy, and cognitive decline. Trial registration NCT00106899. Registered 1 April 2005, https://clinicaltrials.gov/ct2/show/study/NCT00106899.

LncRNA ANRIL Silencing Alleviates High Glucose-Induced Inflammation, Oxidative Stress, and Apoptosis via Upregulation of MME in Podocytes.

Diabetic nephropathy (DN), characterized by glomerular injury, is a common complication of both type 1 and type 2 diabetes, accompanied by massive proteinuria. Podocytes are reported to play pivotal roles in maintaining the glomerular filtration barrier. In addition, the expression of long non-coding RNAs (lncRNAs) ANRIL was upregulated in type 2 diabetes patients. Hence, the aim of this study was to investigate the underlying mechanisms implicated the role of LncRNA ANRIL in podocyte injury in DN. The concentration of inflammatory cytokines was quantified by the corresponding enzyme-linked immunosorbent assay (ELISA) kits. The mRNA levels of the target gene were determined by reverse transcription and real-time quantitative PCR (RT-qPCR). The expressions of proteins were evaluated by Western blot. The activities of lactate dehydrogenase (LDH), superoxide dismutase (SOD), and malondialdehyde (MDA) level were measured by corresponding commercial kits. Finally, the apoptosis of podocytes was analyzed by TUNEL assay. In our study, LncRNA ANRIL was highly expressed in high glucose (HG)-induced podocytes. Moreover, LncRNA ANRIL silencing attenuated HG-induced inflammation, oxidative stress, and apoptosis and induced MME overexpression in podocytes. Interestingly, MME knockdown abolished the suppressive effect of LncRNA ANRIL silencing on HG-induced inflammation, oxidative stress, and apoptosis in podocytes. LncRNA ANRIL silencing alleviates HG-induced inflammation, oxidative stress, and apoptosis via upregulation of MME in podocytes. Hence, LncRNA ANRIL may be a novel and effective target to ameliorate podocyte injury in DN.

Brain Functional and Structural Signatures in Parkinson's Disease.

The aim of this study is to explore functional and structural properties of abnormal brain networks associated with Parkinson's disease (PD). 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) and T1-weighted magnetic resonance imaging from 20 patients with moderate-stage PD and 20 age-matched healthy controls were acquired to identify disease-related patterns in functional and structural networks. Dual-modal images from another prospective subject of 15 PD patients were used as the validation group. Scaled Subprofile Modeling based on principal component analysis method was applied to determine disease-related patterns in both modalities, and brain connectome analysis based on graph theory was applied to verify these patterns. The results showed that the expressions of the metabolic and structural patterns in PD patients were significantly higher than healthy controls (PD1-HC, p = 0.0039, p = 0.0058; PD2-HC, p < 0.001, p = 0.044). The metabolic pattern was characterized by relative increased metabolic activity in pallidothalamic, pons, putamen, and cerebellum, associated with metabolic decreased in parietal-occipital areas. The structural pattern was characterized by relative decreased gray matter (GM) volume in pons, transverse temporal gyrus, left cuneus, right superior occipital gyrus, and right superior parietal lobule, associated with preservation in GM volume in pallidum and putamen. In addition, both patterns were verified in the connectome analysis. The findings suggest that significant overlaps between metabolic and structural patterns provide new evidence for elucidating the neuropathological mechanisms of PD.

A Novel Detection Tool for Mild Cognitive Impairment Patients Based on Eye Movement and Electroencephalogram.

BACKGROUND: Detecting subtle changes in visual attention from electroencephalography (EEG) and the perspective of eye movement in mild cognitive impairment (MCI) patients can be of great significance in screening early Alzheimer's disease (AD) in a large population at primary care. OBJECTIVE: We proposed an automatic, non-invasive, and quick MCI detection approach based on multimodal physiological signals for clinical decision-marking. METHODS: The proposed model recruited 152 patients with MCI and 184 healthy elderly controls (HC) who underwent EEG and eye movement signal recording under a visual stimuli task, as well as other neuropsychological assessments. Forty features were extracted from EEG and eye movement signals by linear and nonlinear analysis. The features related to MCI were selected by logistic regression analysis. To evaluate the efficacy of this MCI detection approach, we applied the same procedures to achieve the Clinical model, EEG model, Eye movement model, EEG+ Clinical model, Eye movement+ Clinical model, and Combined model, and compared the classification accuracy between the MCI and HC groups with the above six models. RESULTS: After the penalization of logistic regression analysis, five features from EEG and eye movement features exhibited significant differences (p < 0.05). In the classification experiment, the combined model resulted in the best accuracy. The average accuracy for the Clinical/EEG/Eye movement/EEG+ Clinical/Eye movement+ Clinical/Combined model was 68.69%, 61.79%, 73.13%, 69.46%, 75.61%, and 81.51%, respectively. CONCLUSION: These results suggest that the proposed MCI detection tool has the potential to screen MCI patients from HCs and may be a powerful tool for personalized precision MCI screening in the large-scale population under primary care condition.

Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment.

BACKGROUND: Alzheimer's disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using 18F-fluorodeoxy-glucose positron emission tomography (18F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance. This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information. METHODS: In this study, 18F-FDG PET and clinical assessments were collected in a cohort of 422 individuals [including 130 with AD, 130 with MCI, and 162 healthy controls (HCs)] from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 44 individuals (including 22 with AD, and 22 HCs) from Huashan Hospital, Shanghai, China. First, we performed a group comparison using a two-sample Student's t test to determine the regions of interest (ROIs) based on 30 AD patients and 30 HCs from ADNI cohorts. Second, based on two time scans of 32 HCs from ADNI cohorts, we used Cronbach's alpha coefficient for radiomic feature stability analyses. Pearson's correlation coefficients were regarded as a feature selection criterion, to select effective features associated with the clinical cognitive scale [clinical dementia rating scale in its sum of boxes (CDRSB); Alzheimer's disease assessment scale (ADAS)] with 500-times cross-validation. Finally, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients. RESULTS: As a result, we identified brain regions which were mainly distributed in the temporal, occipital and frontal areas as ROIs. A total of 168 radiomic features of AD were stable (alpha > 0.8). The classification experiment led to maximal accuracies of 91.5%, 83.1% and 85.9% for classifying AD versus HC, MCI versus HCs and AD versus MCI. CONCLUSION: The research in this paper proved that the novel approach based on high-order radiomic features extracted from 18F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis.