METHOD: This study proposed a single-scale multi-input convolutional neural network (SSMICNN) method to classify ERP signals between aMCI patients with T2DM and the control group. Firstly, the 18-electrode ERP signal on alpha, beta, and theta frequency bands was extracted by using the fast Fourier transform, and then the mean, sum of squares, and absolute value feature of each frequency band were calculated. Finally, these three features are converted into multispectral images respectively and used as the input of the SSMICNN network to realize the classification task.
RESULTS: The results show that the SSMICNN can fuse MSI formed by different features, SSMICNN enriches the feature quantity of the neural network input layer and has excellent robustness, and the errors of SSMICNN can be simultaneously transmitted to the three convolution channels in the back-propagation phase. Comparison with Existing Method(s): SSMICNN could more effectively identify ERP signals from aMCI with T2DM from the control group compared to existing classification methods, including convolution neural network, support vector machine, and logistic regression.
CONCLUSIONS: The combination of SSMICNN and MSI can be used as an effective biological marker to distinguish aMCI patients with T2DM from the control group.
OBJECTIVE: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.
METHODS: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.
RESULTS: The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.
CONCLUSION: A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.
DESIGN: Network meta-analysis.
DATA SOURCES: PubMed, Embase, Scopus, Cochrane Library and Web of Science from database inception to January 2022.
ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Randomised controlled trials (RCTs) comparing exercise therapy with oral NSAIDs and paracetamol directly or indirectly in knee or hip OA.
RESULTS: A total of n=152 RCTs (17 431 participants) were included. For pain relief, there was no difference between exercise and oral NSAIDs and paracetamol at or nearest to 4 (standardised mean difference (SMD)=-0.12, 95% credibility interval (CrI) -1.74 to 1.50; n=47 RCTs), 8 (SMD=0.22, 95% CrI -0.05 to 0.49; n=2 RCTs) and 24 weeks (SMD=0.17, 95% CrI -0.77 to 1.12; n=9 RCTs). Similarly, there was no difference between exercise and oral NSAIDs and paracetamol in functional improvement at or nearest to 4 (SMD=0.09, 95% CrI -1.69 to 1.85; n=40 RCTs), 8 (SMD=0.06, 95% CrI -0.20 to 0.33; n=2 RCTs) and 24 weeks (SMD=0.05, 95% CrI -1.15 to 1.24; n=9 RCTs).
CONCLUSIONS: Exercise has similar effects on pain and function to that of oral NSAIDs and paracetamol. Given its excellent safety profile, exercise should be given more prominence in clinical care, especially in older people with comorbidity or at higher risk of adverse events related to NSAIDs and paracetamol.CRD42019135166.
METHODS: In vitro cell model of IDD was established by treating human nucleus pulposus cells (HNPCs) with interleukin-1β (IL-1β). The level of peroxisome proliferator-activated receptor γ (PPARγ) was examined in the IDD cell model by Western blot and quantification real-time reverse transcription-polymerase chain reaction (qRT-PCR). The expression level of miR-96-5p was detected by RT-qPCR. Effects of PPARγ or/and PPARγ agonist on inflammatory factors, extracellular matrix (ECM), apoptosis, and nuclear factor-kappaB (NF-κB) nuclear translocation were examined through enzyme-linked immunosorbent assay (ELISA), Western blot, flow cytometry assay, and immunofluorescence staining. The Starbase database and dual luciferase reporter assay were used to predict and validate the targeting relationship between miR-96-5p and PPARγ, and rescue assay was performed to gain insight into the role of miR-96-5p on IDD through PPARγ/NF-κB signaling.
RESULTS: PPARγ expression reduced with concentration and time under IL-1β stimulation, while miR-96-5p expression showed the reverse trend (P
METHODS: Expression of SPRY genes in human and mice PDAC was analyzed using The Cancer Genome Atlas and Gene Expression Omnibus datasets, and by immunohistochemistry analysis. Gain-of-function, loss-of-function of Spry1 and orthotopic xenograft model were adopted to investigate the function of Spry1 in mice PDAC. Bioinformatics analysis, transwell and flowcytometry analysis were used to identify the effects of SPRY1 on immune cells. Co-immunoprecipitation and K-ras4B G12V overexpression were used to identify molecular mechanism.
RESULTS: SPRY1 expression was remarkably increased in PDAC tissues and positively associated with poor prognosis of PDAC patients. SPRY1 knockdown suppressed tumor growth in mice. SPRY1 was found to promote CXCL12 expression and facilitate neutrophil and macrophage infiltration via CXCL12-CXCR4 axis. Pharmacological inhibition of CXCL12-CXCR4 largely abrogated the oncogenic functions of SPRY1 by suppressing neutrophil and macrophage infiltration. Mechanistically, SPRY1 interacted with ubiquitin carboxy-terminal hydrolase L1 to induce activation of nuclear factor κB signaling and ultimately increase CXCL12 expression. Moreover, SPRY1 transcription was dependent on KRAS mutation and was mediated by MAPK-ERK signaling.
CONCLUSION: High expression of SPRY1 can function as an oncogene in PDAC by promoting cancer-associated inflammation. Targeting SPRY1 might be an important approach for designing new strategy of tumor therapy.