Multiple organ segmentation framework for brain metastasis radiotherapy.

Radiotherapy is a preferred treatment for brain metastases, which kills cancer cells via high doses of radiation meanwhile hardly avoiding damage to surrounding healthy cells. Therefore, the delineation of organs-at-risk (OARs) is vital in treatment planning to minimize radiation-induced toxicity. However, the following aspects make OAR delineation a challenging task: extremely imbalanced organ sizes, ambiguous boundaries, and complex anatomical structures. To alleviate these challenges, we imitate how specialized clinicians delineate OARs and present a novel cascaded multi-OAR segmentation framework, called OAR-SegNet. OAR-SegNet comprises two distinct levels of segmentation networks: an Anatomical-Prior-Guided network (APG-Net) and a Point-Cloud-Guided network (PCG-Net). Specifically, APG-Net handles segmentation for all organs, where multi-view segmentation modules and a deep prior loss are designed under the guidance of prior knowledge. After APG-Net, PCG-Net refines small organs through the mini-segmentation and the point-cloud alignment heads. The mini-segmentation head is further equipped with the deep prior feature. Extensive experiments were conducted to demonstrate the superior performance of the proposed method compared to other state-of-the-art medical segmentation methods.

The Impact of NLRP3 Inflammasome on Osteoblasts and Osteogenic Differentiation: A Literature Review.

Osteoblasts (OBs), which are a crucial type of bone cells, derive from bone marrow mesenchymal stem cells (MSCs). Accumulating evidence suggests inflammatory cytokines can inhibit the differentiation and proliferation of OBs, as well as interfere with their ability to synthesize bone matrix, under inflammatory conditions. NLRP3 inflammasome is closely associated with cellular pyroptosis, which can lead to excessive release of pro-inflammatory cytokines, causing tissue damage and inflammatory responses, however, the comprehensive roles of NLRP3 inflammasome in OBs and their differentiation have not been fully elucidated, making targeting NLRP3 inflammasome approaches to treat diseases related to OBs uncertain. In this review, we provide a summary of NLRP3 inflammasome activation and its impact on OBs. We highlight the significant roles of NLRP3 inflammasome in regulating OBs differentiation and function. Furthermore, current available strategies to affect OBs function and osteogenic differentiation targeting NLRP3 inflammasome are listed and analyzed. Finally, through the prospective discussion, we seek to provide novel insights into the crucial role of NLRP3 inflammasome in diseases related to OBs and offer valuable information for devising treatment strategies.

The role of the Piezo1 channel in osteoblasts under cyclic stretching: A study on osteogenic and osteoclast factors.

OBJECTIVES: Orthodontic tooth movement is a mechanobiological reaction induced by appropriate forces, including bone remodeling. The mechanosensitive Piezo channels have been shown to contribute to bone remodeling. However, information about the pathways through which Piezo channels affects osteoblasts remains limited. Thus, we aimed to investigate the influence of Piezo1 on the osteogenic and osteoclast factors in osteoblasts under mechanical load. MATERIALS AND METHODS: Cyclic stretch (CS) experiments on MC3T3-E1 were conducted using a BioDynamic mechanical stretching device. The Piezo1 channel blocker GsMTx4 and the Piezo1 channel agonist Yoda1 were used 12 h before the application of CS. MC3T3-E1 cells were then subjected to 15% CS, and the expression of Piezo1, Piezo2, BMP-2, OCN, Runx2, RANKL, p-p65/p65, and ALP was measured using quantitative real-time polymerase chain reaction, western blot, alkaline phosphatase staining, and immunofluorescence staining. RESULTS: CS of 15% induced the highest expression of Piezo channel and osteoblast factors. Yoda1 significantly increased the CS-upregulated expression of Piezo1 and ALP activity but not Piezo2 and RANKL. GsMTx4 downregulated the CS-upregulated expression of Piezo1, Piezo2, Runx2, OCN, p-65/65, and ALP activity but could not completely reduce CS-upregulated BMP-2. CONCLUSIONS: The appropriate force is more suitable for promoting osteogenic differentiation in MC3T3-E1. The Piezo1 channel participates in osteogenic differentiation of osteoblasts through its influence on the expression of osteogenic factors like BMP-2, Runx2, and OCN and is involved in regulating osteoclasts by influencing phosphorylated p65. These results provide a foundation for further exploration of osteoblast function in orthodontic tooth movement.

Enhanced Multitask Learning for Hash Code Generation of Palmprint Biometrics.

This paper presents a novel multitask learning framework for palmprint biometrics, which optimizes classification and hashing branches jointly. The classification branch within our framework facilitates the concurrent execution of three distinct tasks: identity recognition and classification of soft biometrics, encompassing gender and chirality. On the other hand, the hashing branch enables the generation of palmprint hash codes, optimizing for minimal storage as templates and efficient matching. The hashing branch derives the complementary information from these tasks by amalgamating knowledge acquired from the classification branch. This approach leads to superior overall performance compared to individual tasks in isolation. To enhance the effectiveness of multitask learning, two additional modules, an attention mechanism module and a customized gate control module, are introduced. These modules are vital in allocating higher weights to crucial channels and facilitating task-specific expert knowledge integration. Furthermore, an automatic weight adjustment module is incorporated to optimize the learning process further. This module fine-tunes the weights assigned to different tasks, improving performance. Integrating the three modules above has shown promising accuracies across various classification tasks and has notably improved authentication accuracy. The extensive experimental results validate the efficacy of our proposed framework.

Promoting fast MR imaging pipeline by full-stack AI.

Magnetic resonance imaging (MRI) is a widely used imaging modality in clinics for medical disease diagnosis, staging, and follow-up. Deep learning has been extensively used to accelerate k-space data acquisition, enhance MR image reconstruction, and automate tissue segmentation. However, these three tasks are usually treated as independent tasks and optimized for evaluation by radiologists, thus ignoring the strong dependencies among them; this may be suboptimal for downstream intelligent processing. Here, we present a novel paradigm, full-stack learning (FSL), which can simultaneously solve these three tasks by considering the overall imaging process and leverage the strong dependence among them to further improve each task, significantly boosting the efficiency and efficacy of practical MRI workflows. Experimental results obtained on multiple open MR datasets validate the superiority of FSL over existing state-of-the-art methods on each task. FSL has great potential to optimize the practical workflow of MRI for medical diagnosis and radiotherapy.

Evidence-based uncertainty-aware semi-supervised medical image segmentation.

Semi-Supervised Learning (SSL) has demonstrated great potential to reduce the dependence on a large set of annotated data, which is challenging to collect in clinical practice. One of the most important SSL methods is to generate pseudo labels from the unlabeled data using a network model trained with labeled data, which will inevitably introduce false pseudo labels into the training process and potentially jeopardize performance. To address this issue, uncertainty-aware methods have emerged as a promising solution and have gained considerable attention recently. However, current uncertainty-aware methods usually face the dilemma of balancing the additional computational cost, uncertainty estimation accuracy, and theoretical basis in a unified training paradigm. To address this issue, we propose to integrate the Dempster-Shafer Theory of Evidence (DST) into SSL-based medical image segmentation, dubbed EVidential Inference Learning (EVIL). EVIL performs as a novel consistency regularization-based training paradigm, which enforces consistency on predictions perturbed by two networks with different parameters to enhance generalization Additionally, EVIL provides a theoretically assured solution for precise uncertainty quantification within a single forward pass. By discarding highly unreliable pseudo labels after uncertainty estimation, trustworthy pseudo labels can be generated and incorporated into subsequent model training. The experimental results demonstrate that the proposed approach performs competitively when benchmarked against several state-of-the-art methods on public datasets, i.e., ACDC, MM-WHS, and MonuSeg. The code can be found at https://github.com/CYYukio/EVidential-Inference-Learning.

Quantitative electroencephalography (qEEG), apolipoprotein A-I (APOA-I), and apolipoprotein epsilon 4 (APOE ɛ4) alleles for the diagnosis of mild cognitive impairment and Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) is the most common type of dementia. Amnestic mild cognitive impairment (aMCI), a pre-dementia stage is an important stage for early diagnosis and intervention. This study aimed to investigate the diagnostic value of qEEG, APOA-I, and APOE ɛ4 allele in aMCI and AD patients and found the correlation between qEEG (Delta + Theta)/(Alpha + Beta) ratio (DTABR) and different cognitive domains. METHODS: All participants were divided into three groups: normal controls (NCs), aMCI, and AD, and all received quantitative electroencephalography (qEEG), neuropsychological scale assessment, apolipoprotein epsilon 4 (APOE ɛ4) alleles, and various blood lipid indicators. Different statistical methods were used for different data. RESULTS: The cognitive domains except executive ability were all negatively correlated with DTABR in different brain regions while executive ability was positively correlated with DTABR in several brain regions, although without statistical significance. The consequences confirmed that the DTABR of each brain area were related to MMSE, MoCA, instantaneous memory, and the language ability (p < 0.05), and the DTABR in the occipital area was relevant to all cognitive domains (p < 0.01) except executive function (p = 0.272). Also, occipital DTABR was most correlated with language domain when tested by VFT with a moderate level (r = 0.596, p < 0.001). There were significant differences in T3, T5, and P3 DTABR between both AD and NC and aMCI and NCs. As for aMCI diagnosis, the maximum AUC was achieved when using T3 combined with APOA-I and APOE ε4 (0.855) and the maximum AUC was achieved when using T5 combined with APOA-I and APOE ε4 (0.889) for AD diagnosis. CONCLUSION: These findings highlight that APOA-I, APOE ɛ4, and qEEG play an important role in aMCI and AD diagnosis. During AD continuum, qEEG DTABR should be taken into consideration for the early detection of AD risk.

Hypernetwork-Based Physics-Driven Personalized Federated Learning for CT Imaging.

In clinical practice, computed tomography (CT) is an important noninvasive inspection technology to provide patients' anatomical information. However, its potential radiation risk is an unavoidable problem that raises people's concerns. Recently, deep learning (DL)-based methods have achieved promising results in CT reconstruction, but these methods usually require the centralized collection of large amounts of data for training from specific scanning protocols, which leads to serious domain shift and privacy concerns. To relieve these problems, in this article, we propose a hypernetwork-based physics-driven personalized federated learning method (HyperFed) for CT imaging. The basic assumption of the proposed HyperFed is that the optimization problem for each domain can be divided into two subproblems: local data adaption and global CT imaging problems, which are implemented by an institution-specific physics-driven hypernetwork and a global-sharing imaging network, respectively. Learning stable and effective invariant features from different data distributions is the main purpose of global-sharing imaging network. Inspired by the physical process of CT imaging, we carefully design physics-driven hypernetwork for each domain to obtain hyperparameters from specific physical scanning protocol to condition the global-sharing imaging network, so that we can achieve personalized local CT reconstruction. Experiments show that HyperFed achieves competitive performance in comparison with several other state-of-the-art methods. It is believed as a promising direction to improve CT imaging quality and personalize the needs of different institutions or scanners without data sharing. Related codes have been released at https://github.com/Zi-YuanYang/HyperFed.

SCA-Former: transformer-like network based on stream-cross attention for medical image segmentation.

Objective. Deep convolutional neural networks (CNNs) have been widely applied in medical image analysis and achieved satisfactory performances. While most CNN-based methods exhibit strong feature representation capabilities, they face challenges in encoding long-range interaction information due to the limited receptive fields. Recently, the Transformer has been proposed to alleviate this issue, but its cost is greatly enlarging the model size, which may inhibit its promotion.Approach. To take strong long-range interaction modeling ability and small model size into account simultaneously, we propose a Transformer-like block-based U-shaped network for medical image segmentation, dubbed as SCA-Former. Furthermore, we propose a novel stream-cross attention (SCA) module to enforce the network to focus on finding a balance between local and global representations by extracting multi-scale and interactive features along spatial and channel dimensions. And SCA can effectively extract channel, multi-scale spatial, and long-range information for a more comprehensive feature representation.Main results. Experimental results demonstrate that SCA-Former outperforms the current state-of-the-art (SOTA) methods on three public datasets, including GLAS, ISIC 2017 and LUNG.Significance. This work exhibits a promising method to enhance the feature representation of convolutional neural networks and improve segmentation performance.

SOUL-Net: A Sparse and Low-Rank Unrolling Network for Spectral CT Image Reconstruction.

Spectral computed tomography (CT) is an emerging technology, that generates a multienergy attenuation map for the interior of an object and extends the traditional image volume into a 4-D form. Compared with traditional CT based on energy-integrating detectors, spectral CT can make full use of spectral information, resulting in high resolution and providing accurate material quantification. Numerous model-based iterative reconstruction methods have been proposed for spectral CT reconstruction. However, these methods usually suffer from difficulties such as laborious parameter selection and expensive computational costs. In addition, due to the image similarity of different energy bins, spectral CT usually implies a strong low-rank prior, which has been widely adopted in current iterative reconstruction models. Singular value thresholding (SVT) is an effective algorithm to solve the low-rank constrained model. However, the SVT method requires a manual selection of thresholds, which may lead to suboptimal results. To relieve these problems, in this article, we propose a sparse and low-rank unrolling network (SOUL-Net) for spectral CT image reconstruction, that learns the parameters and thresholds in a data-driven manner. Furthermore, a Taylor expansion-based neural network backpropagation method is introduced to improve the numerical stability. The qualitative and quantitative results demonstrate that the proposed method outperforms several representative state-of-the-art algorithms in terms of detail preservation and artifact reduction.

End-to-End Diverse Metasurface Design and Evaluation Using an Invertible Neural Network.

Employing deep learning models to design high-performance metasurfaces has garnered significant attention due to its potential benefits in terms of accuracy and efficiency. A deep learning-based metasurface design framework typically comprises a forward prediction path for predicting optical responses and a backward retrieval path for generating geometrical configurations. In the forward design path, a specific geometrical configuration corresponds to a unique optical response. However, in the inverse design path, a single performance metric can correspond to multiple potential designs. This one-to-many mapping poses a significant challenge for deep learning models and can potentially impede their performance. Although representing the inverse path as a probabilistic distribution is a widely adopted method for tackling this problem, accurately capturing the posterior distribution to encompass all potential solutions remains an ongoing challenge. Furthermore, in most pioneering works, the forward and backward paths are captured using separate models. However, the knowledge acquired from the forward path does not contribute to the training of the backward model. This separation of models adds complexity to the system and can hinder the overall efficiency and effectiveness of the design framework. Here, we utilized an invertible neural network (INN) to simultaneously model both the forward and inverse process. Unlike other frameworks, INN focuses on the forward process and implicitly captures a probabilistic model for the inverse process. Given a specific optical response, the INN enables the recovery of the complete posterior over the parameter space. This capability allows for the generation of novel designs that are not present in the training data. Through the integration of the INN with the angular spectrum method, we have developed an efficient and automated end-to-end metasurface design and evaluation framework. This novel approach eliminates the need for human intervention and significantly speeds up the design process. Utilizing this advanced framework, we have effectively designed high-efficiency metalenses and dual-polarization metasurface holograms. This approach extends beyond dielectric metasurface design, serving as a general method for modeling optical inverse design problems in diverse optical fields.

Dynamic Corrected Split Federated Learning With Homomorphic Encryption for U-Shaped Medical Image Networks.

U-shaped networks have become prevalent in various medical image tasks such as segmentation, and restoration. However, most existing U-shaped networks rely on centralized learning which raises privacy concerns. To address these issues, federated learning (FL) and split learning (SL) have been proposed. However, achieving a balance between the local computational cost, model privacy, and parallel training remains a challenge. In this articler, we propose a novel hybrid learning paradigm called Dynamic Corrected Split Federated Learning (DC-SFL) for U-shaped medical image networks. To preserve data privacy, including the input, model parameters, label and output simultaneously, we propose to split the network into three parts hosted by different parties. We propose a Dynamic Weight Correction Strategy (DWCS) to stabilize the training process and avoid the model drift problem due to data heterogeneity. To further enhance privacy protection and establish a trustworthy distributed learning paradigm, we propose to introduce additively homomorphic encryption into the aggregation process of client-side model, which helps prevent potential collusion between parties and provides a better privacy guarantee for our proposed method. The proposed DC-SFL is evaluated on various medical image tasks, and the experimental results demonstrate its effectiveness. In comparison with state-of-the-art distributed learning methods, our method achieves competitive performance.

Prebiotics modulate the microbiota-gut-brain axis and ameliorate cognitive impairment in APP/PS1 mice.

PURPOSE: Prebiotics, including fructo-oligosaccharides (FOS) and galacto-oligosaccharides (GOS), stimulate beneficial gut bacteria and may be helpful for patients with Alzheimer's disease (AD). This study aimed to compare the effects of FOS and GOS, alone or in combination, on AD mice and to identify their underlying mechanisms. METHODS: Six-month-old APP/PS1 mice and wild-type mice were orally administered FOS, GOS, FOS + GOS or water by gavage for 6 weeks and then subjected to relative assays, including behavioral tests, biochemical assays and 16S rRNA sequencing. RESULTS: Through behavioral tests, we found that GOS had the best effect on reversing cognitive impairment in APP/PS1 mice, followed by FOS + GOS, while FOS had no effect. Through biochemical techniques, we found that GOS and FOS + GOS had effects on multiple targets, including diminishing Aß burden and proinflammatory IL-1ß and IL-6 levels, and changing the concentrations of neurotransmitters GABA and 5-HT in the brain. In contrast, FOS had only a slight anti-inflammatory effect. Moreover, through 16S rRNA sequencing, we found that prebiotics changed composition of gut microbiota. Notably, GOS increased relative abundance of Lactobacillus, FOS increased that of Bifidobacterium, and FOS + GOS increased that of both. Furthermore, prebiotics downregulated the expression levels of proteins of the TLR4-Myd88-NF-κB pathway in the colons and cortexes, suggesting the involvement of gut-brain mechanism in alleviating neuroinflammation. CONCLUSION: Among the three prebiotics, GOS was the optimal one to alleviate cognitive impairment in APP/PS1 mice and the mechanism was attributed to its multi-target role in alleviating Aß pathology and neuroinflammation, changing neurotransmitter concentrations, and modulating gut microbiota.

Mechanism of ERK/CREB pathway in pain and analgesia.

Research has long centered on the pathophysiology of pain. The Transient Receiver Potential (TRP) protein family is well known for its function in the pathophysiology of pain, and extensive study has been done in this area. One of the significant mechanisms of pain etiology and analgesia that lacks a systematic synthesis and review is the ERK/CREB (Extracellular Signal-Regulated Kinase/CAMP Response Element Binding Protein) pathway. The ERK/CREB pathway-targeting analgesics may also cause a variety of adverse effects that call for specialized medical care. In this review, we systematically compiled the mechanism of the ERK/CREB pathway in the process of pain and analgesia, as well as the potential adverse effects on the nervous system brought on by the inhibition of the ERK/CREB pathway in analgesic drugs, and we suggested the corresponding solutions.

KIF18A interacts with PPP1CA to promote the malignant development of glioblastoma.

Glioblastoma (GBM), which has poor prognosis and low 5-year survival rate, is the most common primary central nervous system malignant tumour in adults. Kinesin family member 18A (KIF18A) plays an important role in multiple tumours and is potential therapeutic target for GBM. Therefore, the present study investigated the role of KIF18A in GBM. The expression level and survival prognosis of KIF18A and protein phosphatase 1 catalytic subunit α (PPP1CA) in GBM patients were analysed using the Chinese Glioma Genome Atlas (CGGA) database. Reverse transcription-quantitative PCR and western blot analysis were applied to measure the expression of KIF18A and PPP1CA in normal and GBM cell lines. KIF18A expression was inhibited through cell transfection with a KIF18A-targeting short hairpin RNA. Cell proliferation was detected with the Cell Counting Kit-8 assay. Flow cytometry was used to detect cell cycle changes. Transwell and wound healing assays were used to measure cell invasion and migration. Western blotting was utilized for the detection of invasion- and migration-related proteins MMP9 and MMP2. Biological General Repository for Interaction Datasets and GeneMANIA databases were used to analyse the interaction between KIF18A and PPP1CA. The correlation between PPP1CA and KIF18A was examined using data from the CGGA database. Immunoprecipitation was used to demonstrate the binding relationship between KIF18A and PPP1CA. PPP1CA was overexpressed using cell transfection technology and its mechanism was further examined. The results demonstrated that KIF18A was upregulated in GBM cells compared with normal microglia HMC3. Compared with that in sh-NC group, silencing of KIF18A reduced cell proliferation, induced G2/M cycle arrest and inhibited the migration and the invasion of A172 GBM cells by interacting with PPP1CA. In conclusion, KIF18A interacted with PPP1CA to promote the proliferation, cycle arrest, migration and invasion of GBM cells.

Enhanced H2O2 formation and norfloxacin removal by electro-Fenton process using a surface-reconstructed graphite felt cathode: New insight into synergistic mechanism of defective active sites.

The efficient catalytic activity and strong durability possibility of carbon-based three-dimensional fiber materials remains an important challenge in Electro-Fenton advanced oxidation technology. Graphite felt (GF) is a promising electrode material for 2-electron oxygen reduction reaction but with higher catalytic inertia. Anodizing modification of GF has been proved to enhance it electro-catalytic property, but the disadvantages of excessive or insufficient oxidation of GF need further improved. Herein, the surface reconstituted graphite felt by anodizing and HNO3 ultrasonic integrated treatment was used as cathode to degrade norfloxacin (NOR) and the substantial role of different modification processes was essentially investigated. Compared with the single modification process, the synergistic interaction between these two methods can generate more defective active sites (DASs) on GF surface and greatly improved 2-electron ORR activity. The H2O2 can be further co-activated by Fe2+ and DASs into •OH(ads and free) and •O2- to efficiently degrade NOR. The treated GF with 20 min anodizing and 1 h HNO3 ultrasound had the highest electrocatalytic activity in a wide electric potential (-0.4 V to -0.8 V) and pH range (3-9) in system and the efficient removal rate of NOR was basically maintained after 5 cycles. Under optimal reaction conditions, 50 mg L-1 NOR achieved 93% degradation and almost 63% of NOR was completely mineralized within 120 min. The possible NOR degradation pathways and ecotoxicity of intermediates were analyzed by LC-MS and T.E.S.T. theoretical calculation. This paper provided the underlying insights into designing a high-efficiency carbon-based cathode materials for commercial antibiotic wastewater treatment.

Prognostic risk assessment model and drug sensitivity analysis of colon adenocarcinoma (COAD) based on immune-related lncRNA pairs.

PURPOSE: The aim of this study was to identify and screen long non-coding RNA (lncRNA) associated with immune genes in colon cancer, construct immune-related lncRNA pairs, establish a prognostic risk assessment model for colon adenocarcinoma (COAD), and explore prognostic factors and drug sensitivity. METHOD: Our method was based on data from The Cancer Genome Atlas (TCGA). To begin, we obtained all pertinent demographic and clinical information on 385 patients with COAD. All lncRNAs significantly related to immune genes and with differential expression were identified to construct immune lncRNA pairs. Subsequently, least absolute shrinkage and selection operator and Cox models were used to screen out prognostic-related immune lncRNAs for the establishment of a prognostic risk scoring formula. Finally, We analysed the functional differences between subgroups and screened the drugs, and establish an individual prediction nomogram model. RESULTS: Our final analysis confirmed eight lncRNA pairs to construct prognostic risk assessment model. Results showed that the high-risk and low-risk groups had significant differences (training (n = 249): p < 0.001, validation (n = 114): p = 0.022). The prognostic model was certified as an independent prognosis model. Compared with the common clinicopathological indicators, the prognostic model had better predictive efficiency (area under the curve (AUC) = 0.805). Finally, We have analysed highly differentiated cellular pathways such as mucosal immune response, identified 9 differential immune cells, 10 sensitive drugs, and establish an individual prediction nomogram model (C-index = 0.820). CONCLUSION: Our study verified that the eight lncRNA pairs mentioned can be used as biomarkers to predict the prognosis of COAD patients. Identified cells, drugs may have an positive effect on colon cancer prognosis.

Identification of hsa-miR-365b-5p's role in Alzheimer's disease: A combined analysis of miRNA and mRNA microarrays.

BACKGROUND: Alzheimer's disease is a prevalent health problem with a heavy global burden. Definitely diagnosed by autopsy, the clear mechanism of Alzheimer's disease pathogenesis process needs to be illustrated. MicroRNAs are suggested to be involved in many diseases. We aimed to investigate the role of microRNA in Alzheimer's disease. METHODS: We attempted to discover the role of microRNA in Alzheimer's disease by microarray bioinformatics analysis using autopsy sample data from the GEO database. Temporal cortex samples were included in this study. Bioinformatics analyses and visualization were processed based on R. RESULTS: After filtering out significantly differential expressed microRNAs and genes, enrichment analyses of both microRNAs and genes were conducted, respectively. Then, we constructed a transcription factor-microRNA-mRNA network and a protein-protein interaction network. In parallel, we used the receiver operating characteristic curve to evaluate the diagnostic value of microRNA. Based on the evidence, we finally identified hsa-miR-365b-5p as a key target in Alzheimer's disease. CONCLUSIONS: Hsa-miR-365b-5p act as a key target in Alzheimer's disease. It regulates Alzheimer's disease pathogenesis process via neuroinflammation, Wnt and oxidative stress pathway which provides a potential target for Alzheimer's disease treatment.

An xception model based on residual attention mechanism for the classification of benign and malignant gastric ulcers.

To explore the application value of convolutional neural network combined with residual attention mechanism and Xception model for automatic classification of benign and malignant gastric ulcer lesions in common digestive endoscopy images under the condition of insufficient data. For the problems of uneven illumination and low resolution of endoscopic images, the original image is preprocessed by Sobel operator, etc. The algorithm model is implemented by Pytorch, and the preprocessed image is used as input data. The model is based on convolutional neural network for automatic classification and diagnosis of benign and malignant gastric ulcer lesions in small number of digestive endoscopy images. The accuracy, F1 score, sensitivity, specificity and precision of the Xception model improved by the residual attention module for the diagnosis of benign and malignant gastric ulcer lesions were 81.411%, 81.815%, 83.751%, 76.827% and 80.111%, respectively. The superposition of residual attention modules can effectively improve the feature learning ability of the model. The pretreatment of digestive endoscopy can remove the interference information on the digestive endoscopic image data extracted from the database, which is beneficial to the training of the model. The residual attention mechanism can effectively improve the classification effect of Xception convolutional neural network on benign and malignant lesions of gastric ulcer on common digestive endoscopic images.

Plasma tau proteins for the diagnosis of mild cognitive impairment and Alzheimer's disease: A systematic review and meta-analysis.

Objective: Detecting plasma tau biomarkers used to be impossible due to their low concentrations in blood samples. Currently, new high-sensitivity assays made it a reality. We performed a systematic review and meta-analysis in order to test the accuracy of plasma tau protein in diagnosing Alzheimer's disease (AD) or mild cognitive impairment (MCI). Methods: We searched PubMed, Cochrane, Embase and Web of Science databases, and conducted correlation subgroup analysis, sensitivity analysis and publication bias analysis using R Programming Language. Results: A total of 56 studies were included. Blood t-tau and p-tau levels increased from controls to MCI to AD patients, and showed significant changes in pairwise comparisons of AD, MCI and normal cognition. P-tau217 was more sensitive than p-tau181 and p-tau231 in different cognition periods. In addition, ultrasensitive analytical platforms, immunomagnetic reduction (IMR), increased the diagnostic value of tau proteins, especially the diagnostic value of t-tau. Conclusion: Both t-tau and p-tau are suitable AD blood biomarkers, and p-tau217 is more sensitive than other tau biomarkers to differentiate MCI and AD. Detection techniques also have an impact on biomarkers' results. New ultrasensitive analytical platforms of IMR increase the diagnostic value of both t-tau and p-tau biomarkers. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, registration number: CRD42021264701.