Chronic exercise interventions for executive function in overweight children: a systematic review and meta-analysis.

Objectives: To systematically evaluate the effectiveness of chronic exercise in physical activity (PA) as an intervention for executive functions (EFs) in children. Methods: We conducted a systematic search in the following online databases: Web of Science, Cochrane Library, PubMed, Embase, and EBSCOhost. The timing is from database inception to July 2023, following PRISMA guidelines. Our inclusion criteria required studies reporting executive function (EF) levels in overweight children (age 0-18 years) before and after interventions. The Cochrane risk of bias tool assessed study bias, and Egger's test examined publication bias. Subgroup analyses considered three moderators: intervention duration, weekly frequency, and session length. Results: The meta-analysis included a total of 10 studies with 843 participants. It revealed a statistically significant yet relatively small overall positive effect (g = 0.3, 95% CI 0.16-0.44, P < 0.01) of chronic exercise on EF in overweight children. Importantly, there was no significant heterogeneity (Q = 11.64, df = 12, P = 0.48; I2 = 0). Conclusions: Chronic exercise interventions had a consistent positive impact on EF, irrespective of intervention duration, weekly frequency, or session length. However, given limitations in the number and design of studies, further high-quality research is needed to strengthen these conclusions. Systematic Review Registration: PROSPERO identifier (CRD42023468588).

Network Pharmacology, Molecular Docking, and Experimental Verification to Reveal the Mitophagy-Associated Mechanism of Tangshen Formula in the Treatment of Diabetic Nephropathy.

Purpose: This study investigated the mechanism of TSF in treating DN through network pharmacology, molecular docking, and experimental validation. Methods: To identify critical active ingredients, targets, and DN genes in TSF, multiple databases were utilized for screening purposes. The drug-compound-target network was constructed using Cytoscape 3.9.1 software for network topological analysis. The protein interaction relationship was analyzed using the String database platform. Metascape database conducted enrichment analysis on the key targets using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. The renoprotective effect was evaluated using a mouse model of diabetic nephropathy (db/db mice) that occurred spontaneously. Validation of the associated targets and pathways was performed using Western Blot (WB), Polymerase Chain Reaction (PCR), and Immunohistochemical methods (IHC). Results: The network analysis showed that the TSF pathway network targeted 24 important targets and 149 significant pathways. TSF might have an impact by focusing on essential objectives such as TP53, PTEN, AKT1, BCL2, BCL2L1, PINK-1, PARKIN, LC3B, and NFE2L2, along with various growth-inducing routes. Our findings demonstrated that TSF effectively repaired the structure of mitochondria in db/db mice. TSF greatly enhanced the mRNA levels of PINK-1. WB and IHC findings indicated that TSF had a notable impact on activating the PINK-1/PARKIN signaling pathway in db/db mice, significantly increasing LC3 and NRF2 expression. Conclusion: Our results indicate that TSF effectively addresses DN by activating the PINK-1/PARKIN signaling pathway and enhancing Mitochondrion structure in experimental diabetic nephropathy.

Minimizing artifact-induced false-alarms for seizure detection in wearable EEG devices with gradient-boosted tree classifiers.

Electroencephalography (EEG) is widely used to monitor epileptic seizures, and standard clinical practice consists of monitoring patients in dedicated epilepsy monitoring units via video surveillance and cumbersome EEG caps. Such a setting is not compatible with long-term tracking under typical living conditions, thereby motivating the development of unobtrusive wearable solutions. However, wearable EEG devices present the challenges of fewer channels, restricted computational capabilities, and lower signal-to-noise ratio. Moreover, artifacts presenting morphological similarities to seizures act as major noise sources and can be misinterpreted as seizures. This paper presents a combined seizure and artifacts detection framework targeting wearable EEG devices based on Gradient Boosted Trees. The seizure detector achieves nearly zero false alarms with average sensitivity values of [Formula: see text] for 182 seizures from the CHB-MIT dataset and [Formula: see text] for 25 seizures from the private dataset with no preliminary artifact detection or removal. The artifact detector achieves a state-of-the-art accuracy of [Formula: see text] (on the TUH-EEG Artifact Corpus dataset). Integrating artifact and seizure detection significantly reduces false alarms-up to [Formula: see text] compared to standalone seizure detection. Optimized for a Parallel Ultra-Low Power platform, these algorithms enable extended monitoring with a battery lifespan reaching 300 h. These findings highlight the benefits of integrating artifact detection in wearable epilepsy monitoring devices to limit the number of false positives.

Nonlinear and machine learning analyses on high-density EEG data of math experts and novices.

Current trend in neurosciences is to use naturalistic stimuli, such as cinema, class-room biology or video gaming, aiming to understand the brain functions during ecologically valid conditions. Naturalistic stimuli recruit complex and overlapping cognitive, emotional and sensory brain processes. Brain oscillations form underlying mechanisms for such processes, and further, these processes can be modified by expertise. Human cortical functions are often analyzed with linear methods despite brain as a biological system is highly nonlinear. This study applies a relatively robust nonlinear method, Higuchi fractal dimension (HFD), to classify cortical functions of math experts and novices when they solve long and complex math demonstrations in an EEG laboratory. Brain imaging data, which is collected over a long time span during naturalistic stimuli, enables the application of data-driven analyses. Therefore, we also explore the neural signature of math expertise with machine learning algorithms. There is a need for novel methodologies in analyzing naturalistic data because formulation of theories of the brain functions in the real world based on reductionist and simplified study designs is both challenging and questionable. Data-driven intelligent approaches may be helpful in developing and testing new theories on complex brain functions. Our results clarify the different neural signature, analyzed by HFD, of math experts and novices during complex math and suggest machine learning as a promising data-driven approach to understand the brain processes in expertise and mathematical cognition.

Sub-100 µW Multispectral Riemannian Classification for EEG-Based Brain-Machine Interfaces.

Motor imagery (MI) brain-machine interfaces (BMIs) enable us to control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution where the classification of the brain signals is done locally near the sensor using machine learning models embedded on energy-efficient microcontroller units (MCUs), for assured privacy, user comfort, and long-term usage. In this work, we provide practical insights on the accuracy-cost trade-off for embedded BMI solutions. Our multispectral Riemannian classifier reaches 75.1% accuracy on a 4-class MI task. The accuracy is further improved by tuning different types of classifiers to each subject, achieving 76.4%. We further scale down the model by quantizing it to mixed-precision representations with a minimal accuracy loss of 1% and 1.4%, respectively, which is still up to 4.1% more accurate than the state-of-the-art embedded convolutional neural network. We implement the model on a low-power MCU within an energy budget of merely 198 µJ and taking only 16.9 ms per classification. Classifying samples continuously, overlapping the 3.5 s samples by 50% to avoid missing user inputs allows for operation at just 85 µW. Compared to related works in embedded MI-BMIs, our solution sets the new state-of-the-art in terms of accuracy-energy trade-off for near-sensor classification.

Seasonal deformation monitoring of the northern Yellow River Delta based on InSAR technology.

Due to overexploitation of natural resources in the northern Yellow River Delta and the resulting land subsidence, interferometric synthetic aperture radar (InSAR) technology is utilized to analyze the deformation trend. Based on Advanced Land Observation Satellite PHASE Array Type L-band Synthetic Aperture Radar and Sentinel-1 data, this paper obtains the annual subsidence rate in two time periods (2007-2010 and 2017-2020) and verifies the correctness by comparing the ascending and descending orbit results. Subsequently, the 11 deformation interference pairs of the three-month interval are extracted to analyze the time series displacement of deformation areas. The results show that there are three large-scale subsidence areas with maximum annual subsidence of 250 mm, all of which are located in the oil or brine exploitation areas, and each deformation area displays a larger linear rate from January to May and then displays different nonlinear deformation from June to December.

Evaluation of intestinal permeation enhancement with carboxymethyl chitosan-rhein polymeric micelles for oral delivery of paclitaxel.

Polymeric micelles (PMs) are currently under investigation as potential nanocarriers for oral administration of paclitaxel (PTX). Previously, we developed amphiphilic carboxymethyl chitosan-rhein (CR) conjugate for oral delivery of PTX. PTX-loaded CR PMs exhibited a homogeneous and small size (<200 nm) with a drug loading capacity (DL) of 35.46 ±â€¯1.07%. However, The absorption parameters of PTX using CR PMs have not been studied before. Here, we evaluated the intestinal permeation of CR PMs by in situ intestinal absorption experiments. PTX-loaded CR PMs enhanced the absorption of PTX in the intestine without causing significant intestinal villi injury. Compared to the P-glycoprotein (P-gp) inhibition of verapamil, the transport mechanism of CR PMs across intestinal epithelial cells may bypass P-gp efflux. Caco-2 cell uptake assays also confirmed that CR PMs can be taken up into the enterocyte as whole and independent of P-gp. Local biodistribution evaluation showed that fluorescence-labeled CR PMs were absorbed into the intestinal villi. In vivo bioimaging of tumor-bearing mice verified a significant portion of CR PMs were intactly absorbed through the intestine, then distributed and accumulated at the tumor site. For their significant intestinal permeation enhancement, CR PMs might be considered as promising oral delivery carriers for PTX and other water-insoluble drugs.

Preparation and evaluation of carboxymethyl chitosan-rhein polymeric micelles with synergistic antitumor effect for oral delivery of paclitaxel.

An amphiphilic carboxymethyl chitosan-rhein (CR) conjugate was prepared, characterized, and evaluated as a potential carrier material for oral delivery of paclitaxel (PTX). CR conjugate self-assembled in aqueous environment into CR polymeric micelles (CR PMs). The drug loading capacity and entrapment efficiency of PTX-loaded CR PMs were 35.24 ± 1.58% and 86.99 ± 12.26%, respectively. Pharmacokinetic results indicate that PTX-loaded CR PMs could significantly enhance the oral bioavailability of PTX. Confocal imaging of intestinal sections verified many of CR PMs were absorbed as whole through the intestinal membrane. The cytotoxicity assays in Caco-2 cells and in vivo antitumor efficacy showed that PTX-loaded CR PMs had a stronger antitumor efficacy. A synergistic antitumor effect between CR conjugate and PTX was proven in MCF-7 cells and antitumor efficacy studies. The investigation of CR conjugate developed in this study showed that CR PMs are promising for oral delivery of water-insoluble antitumor drugs.

[Application of artificial neural network to diagnosis of prostate cancer].

Prostatic carcinoma is the fifth most common cancer in the world and the second most common in men. It is quite important to early detect and diagnose prostate cancer to reduce the mortality. With the increasing of the diagnosis and treatment tasks of prostate cancer and the development of medical techniques, more and more clinical and lab examinations, biopsy and medical imaging techniques are included in the diagnosis of prostate cancer. Although these examination results are supplement to each other, there are contradictions among them at the same time. Artificial neural networks (ANNs) which can perform multifactorial analysis based on computational methodologies have been widely used in the prognosis of prostate cancer. The current application of ANNs is reviewed.