CHNet: A multi-task global-local Collaborative Hybrid Network for KRAS mutation status prediction in colorectal cancer.

Accurate prediction of Kirsten rat sarcoma (KRAS) mutation status is crucial for personalized treatment of advanced colorectal cancer patients. However, despite the excellent performance of deep learning models in certain aspects, they often overlook the synergistic promotion among multiple tasks and the consideration of both global and local information, which can significantly reduce prediction accuracy. To address these issues, this paper proposes an innovative method called the Multi-task Global-Local Collaborative Hybrid Network (CHNet) aimed at more accurately predicting patients' KRAS mutation status. CHNet consists of two branches that can extract global and local features from segmentation and classification tasks, respectively, and exchange complementary information to collaborate in executing these tasks. Within the two branches, we have designed a Channel-wise Hybrid Transformer (CHT) and a Spatial-wise Hybrid Transformer (SHT). These transformers integrate the advantages of both Transformer and CNN, employing cascaded hybrid attention and convolution to capture global and local information from the two tasks. Additionally, we have created an Adaptive Collaborative Attention (ACA) module to facilitate the collaborative fusion of segmentation and classification features through guidance. Furthermore, we introduce a novel Class Activation Map (CAM) loss to encourage CHNet to learn complementary information between the two tasks. We evaluate CHNet on the T2-weighted MRI dataset, and achieve an accuracy of 88.93% in KRAS mutation status prediction, which outperforms the performance of representative KRAS mutation status prediction methods. The results suggest that our CHNet can more accurately predict KRAS mutation status in patients via a multi-task collaborative facilitation and considering global-local information way, which can assist doctors in formulating more personalized treatment strategies for patients.

Trace elements in pancreatic cancer.

BACKGROUND: Pancreatic cancer (PCA) is an extremely aggressive malignant cancer with an increasing incidence and a low five-year survival rate. The main reason for this high mortality is that most patients are diagnosed with PCA at an advanced stage, missing early treatment options and opportunities. As important nutrients of the human body, trace elements play an important role in maintaining normal physiological functions. Moreover, trace elements are closely related to many diseases, including PCA. REVIEW: This review systematically summarizes the latest research progress on selenium, copper, arsenic, and manganese in PCA, elucidates their application in PCA, and provides a new reference for the prevention, diagnosis and treatment of PCA. CONCLUSION: Trace elements such as selenium, copper, arsenic and manganese are playing an important role in the risk, pathogenesis, diagnosis and treatment of PCA. Meanwhile, they have a certain inhibitory effect on PCA, the mechanism mainly includes: promoting ferroptosis, inducing apoptosis, inhibiting metastasis, and inhibiting excessive proliferation.

Detection of Mild Cognitive Impairment Through Hand Motor Function Under Digital Cognitive Test: Mixed Methods Study.

BACKGROUND: Early detection of cognitive impairment or dementia is essential to reduce the incidence of severe neurodegenerative diseases. However, currently available diagnostic tools for detecting mild cognitive impairment (MCI) or dementia are time-consuming, expensive, or not widely accessible. Hence, exploring more effective methods to assist clinicians in detecting MCI is necessary. OBJECTIVE: In this study, we aimed to explore the feasibility and efficiency of assessing MCI through movement kinetics under tablet-based "drawing and dragging" tasks. METHODS: We iteratively designed "drawing and dragging" tasks by conducting symposiums, programming, and interviews with stakeholders (neurologists, nurses, engineers, patients with MCI, healthy older adults, and caregivers). Subsequently, stroke patterns and movement kinetics were evaluated in healthy control and MCI groups by comparing 5 categories of features related to hand motor function (ie, time, stroke, frequency, score, and sequence). Finally, user experience with the overall cognitive screening system was investigated using structured questionnaires and unstructured interviews, and their suggestions were recorded. RESULTS: The "drawing and dragging" tasks can detect MCI effectively, with an average accuracy of 85% (SD 2%). Using statistical comparison of movement kinetics, we discovered that the time- and score-based features are the most effective among all the features. Specifically, compared with the healthy control group, the MCI group showed a significant increase in the time they took for the hand to switch from one stroke to the next, with longer drawing times, slow dragging, and lower scores. In addition, patients with MCI had poorer decision-making strategies and visual perception of drawing sequence features, as evidenced by adding auxiliary information and losing more local details in the drawing. Feedback from user experience indicates that our system is user-friendly and facilitates screening for deficits in self-perception. CONCLUSIONS: The tablet-based MCI detection system quantitatively assesses hand motor function in older adults and further elucidates the cognitive and behavioral decline phenomenon in patients with MCI. This innovative approach serves to identify and measure digital biomarkers associated with MCI or Alzheimer dementia, enabling the monitoring of changes in patients' executive function and visual perceptual abilities as the disease advances.

Barriers and Stimulus in Shared Decision Making Among Aesthetic Dermatologists in China: Findings from a Cross-Sectional Study.

Introduction: Shared decision making (SDM) is a collaborative process involving both healthcare providers and patients in making medical decisions, which gains increasing prominence in healthcare practice. But evidence on the level of SDM in medical practice and barriers as well as stimulus during the SDM implementation among aesthetic dermatologists is limited in China. Methods: From July to August 2023, 1938 dermatologists were recruited online in China. Data were collected through an electronic questionnaire covering: (1) demographic features; (2) SDM questionnaire physician version (SDM-Q-Doc); and (3) stimulus and barriers in SDM implementation. Logistic regression was applied to explore factors associated with SDM practice, barriers, and stimulus of SDM implementation, respectively. Results: The 1938 dermatologists included 1329 females (68.6%), with an average age of 35 years. The total SDM score ranged from 0 to 45, with a median value of 40 (IQR: 35-44), and the median stimulus score and barriers scores were 28 (IQR: 24-32) and 19 (IQR: 13-26), respectively. The prevalence of good SDM was 27.2%, logistic regression indicated that female dermatologists (odds ratio, OR=1.21, 95% confidence interval, CI: 0.96-1.51), and dermatologists with more years of aesthetic practice had a higher proportion of good SDM practice (OR was 1.44 for 5-9 years, 1.58 for 10-15 years and 1.77 for over 15 years). Moreover, female dermatologists and dermatologists with higher education level and serviced in private settings had lower barrier scores; female dermatologists and dermatologists with more years of aesthetic practice had higher stimulus scores. Conclusion: Chinese aesthetic dermatologists appear to implement SDM at an active level, with more stimulus and less barriers in SDM implementation. The integration of SDM into clinical practice among dermatologists is beneficial both for patients and dermatologists. Moreover, SDM practice should be strongly promoted and enhanced during medical aesthetics, especially among male dermatologists, dermatologists with less working experience, and those who work at public institutions.

Tobacco smoking negatively influences the achievement of greater than three-quarters reduction in psoriasis area and severity index after eight weeks of treatment among patients with psoriasis: Findings from a prospective study.

INTRODUCTION: Smoking is an independent and modifiable risk factor for the onset and development of psoriasis; however, evidence on the association between tobacco smoking and psoriasis treatment efficacy is limited. This study aimed to explore the influence of smoking on treatment efficacy in a cohort of patients with psoriasis in Shanghai, China. METHODS: Patients with psoriasis were recruited from the Shanghai Skin Disease Hospital between 2021 and 2022. The treatment for patients with psoriasis includes acitretin, methotrexate, narrow-band ultraviolet/benvitimod, and biologics. Data were collected using a structured questionnaire, physical examination, and disease severity estimation at baseline, week four, and week eight. The achievement of a ≥75% reduction in psoriasis area and severity index (PASI75) score from baseline to week 8 was set as the primary outcome for treatment efficacy estimation. Data were analyzed using SAS 9.4. RESULTS: A total of 560 patients with psoriasis were enrolled in this study, who were predominantly males (72.9%). The average age of patients was 48.4 years, and 38.8% of them were current smokers, 5.0% of them were former smokers. The median score of PASI among patients changed from 11.1 (interquartile range, IQR: 7.9-16.6) at baseline to 6.2 at week 4 and 3.1 at week 8, and 13.8% and 47.3% of patients with psoriasis achieved PASI75 at weeks 4 and 8, respectively. Logistic regression indicated that patients without tobacco smoking had a higher proportion of PASI75 achievement at week 8. The adjusted odds ratio (AOR) was 11.43 (95% CI: 6.91-18.89), 14.14 (95% CI: 8.27-24.20), and 3.05 (95% CI: 1.20-7.76) for non-smokers compared with smokers, current smokers, and former smokers, respectively. Moreover, former smokers had higher PASI75 achievement than current smokers (AOR=3.37), and patients with younger smoking initiation age, longer smoking duration, and higher smoking intensity had lower PASI75 achievement. CONCLUSIONS: Tobacco smoking was negatively associated with PASI75 achievement both in current and former smokers, and former smokers had higher PASI75 achievement than current smokers. The implementation of tobacco control measures is beneficial for improving treatment responses.

SG-Transunet: A segmentation-guided Transformer U-Net model for KRAS gene mutation status identification in colorectal cancer.

Accurately identifying the Kirsten rat sarcoma virus (KRAS) gene mutation status in colorectal cancer (CRC) patients can assist doctors in deciding whether to use specific targeted drugs for treatment. Although deep learning methods are popular, they are often affected by redundant features from non-lesion areas. Moreover, existing methods commonly extract spatial features from imaging data, which neglect important frequency domain features and may degrade the performance of KRAS gene mutation status identification. To address this deficiency, we propose a segmentation-guided Transformer U-Net (SG-Transunet) model for KRAS gene mutation status identification in CRC. Integrating the strength of convolutional neural networks (CNNs) and Transformers, SG-Transunet offers a unique approach for both lesion segmentation and KRAS mutation status identification. Specifically, for precise lesion localization, we employ an encoder-decoder to obtain segmentation results and guide the KRAS gene mutation status identification task. Subsequently, a frequency domain supplement block is designed to capture frequency domain features, integrating it with high-level spatial features extracted in the encoding path to derive advanced spatial-frequency domain features. Furthermore, we introduce a pre-trained Xception block to mitigate the risk of overfitting associated with small-scale datasets. Following this, an aggregate attention module is devised to consolidate spatial-frequency domain features with global information extracted by the Transformer at shallow and deep levels, thereby enhancing feature discriminability. Finally, we propose a mutual-constrained loss function that simultaneously constrains the segmentation mask acquisition and gene status identification process. Experimental results demonstrate the superior performance of SG-Transunet over state-of-the-art methods in discriminating KRAS gene mutation status.

Investigating the influence of the gut microbiome on cholelithiasis: unveiling insights through sequencing and predictive modeling.

BACKGROUND: Cholelithiasis is one of the most common disorders of hepatobiliary system. Gut bacteria may be involved in the process of gallstone formation and are, therefore considered as potential targets for cholelithiasis prediction. OBJECTIVE: To reveal the correlation between cholelithiasis and gut bacteria. METHODS: Stool samples were collected from 100 cholelithiasis and 250 healthy individuals from Huzhou Central Hospital; The 16S rRNA of gut bacteria in the stool samples was sequenced using the third-generation Pacbio sequencing platform; Mothur v.1.21.1 was used to analyze the diversity of gut bacteria; Wilcoxon rank-sum test and linear discriminant analysis of effect sizes (LEfSe) were used to analyze differences in gut bacteria between patients suffering from cholelithiasis and healthy individuals; Chord diagram and Plot-related heat maps were used to analyze the correlation between cholelithiasis and gut bacteria; six machine algorithms were used to construct models to predict cholelithiasis. RESULTS: There were differences in the abundance of gut bacteria between cholelithiasis and healthy individuals, but there were no differences in their community diversity. Increased abundance of Costridia, Escherichia flexneri, and Klebsiella pneumonae were found in cholelithiasis, while Bacteroidia, Phocaeicola, and Phocaeicola vulgatus were more abundant in healthy individuals. The top four bacteria that were most closely associated with cholelithiasis were Escherichia flexneri, Escherichia dysenteriae, Streptococcus salivarius, and Phocaeicola vulgatus. The cholelithiasis model based on CatBoost algorithm had the best prediction effect (sensitivity: 90.48%, specificity: 88.32%, and AUC: 0.962). CONCLUSION: The identification of characteristic gut bacteria may provide new predictive targets for gallstone screening. As being screened by the predictive model, people at high risk of cholelithiasis can determine the need for further testing, thus enabling early warning of cholelithiasis.

MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction.

Drug combination therapy is generally more effective than monotherapy in the field of cancer treatment. However, screening for effective synergistic combinations from a wide range of drug combinations is particularly important given the increase in the number of available drug classes and potential drug-drug interactions. Existing methods for predicting the synergistic effects of drug combinations primarily focus on extracting structural features of drug molecules and cell lines, but neglect the interaction mechanisms between cell lines and drug combinations. Consequently, there is a deficiency in comprehensive understanding of the synergistic effects of drug combinations. To address this issue, we propose a drug combination synergy prediction model based on multi-source feature interaction learning, named MFSynDCP, aiming to predict the synergistic effects of anti-tumor drug combinations. This model includes a graph aggregation module with an adaptive attention mechanism for learning drug interactions and a multi-source feature interaction learning controller for managing information transfer between different data sources, accommodating both drug and cell line features. Comparative studies with benchmark datasets demonstrate MFSynDCP's superiority over existing methods. Additionally, its adaptive attention mechanism graph aggregation module identifies drug chemical substructures crucial to the synergy mechanism. Overall, MFSynDCP is a robust tool for predicting synergistic drug combinations. The source code is available from GitHub at https://github.com/kkioplkg/MFSynDCP .

Ultra-short-term forecasting model of power load based on fusion of power spectral density and Morlet wavelet.

An accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low prediction accuracy, difficulty capturing the local mutation features, poor stability, and others. From the perspective of series decomposition, a multi-scale sequence decomposition model (TFDNet) based on power spectral density and the Morlet wavelet transform is proposed that combines the multidimensional correlation feature fusion strategy in the time and frequency domains. By introducing the time-frequency energy selection module, the "prior knowledge" guidance module, and the sequence denoising decomposition module, the model not only effectively delineates the global trend and local seasonal features, completes the in-depth information mining of the smooth trend and fluctuating seasonal features, but more importantly, realizes the accurate capture of the local mutation seasonal features. Finally, on the premise of improving the forecasting accuracy, single-point load forecasting and quantile probabilistic load forecasting for ultra-short-term load forecasting are realized. Through the experiments conducted on three public datasets and one private dataset, the TFDNet model reduces the mean square error (MSE) and mean absolute error (MAE) by 19.80 and 11.20% on average, respectively, as compared with the benchmark method. These results indicate the potential applications of the TFDNet model.

Integrating image and gene-data with a semi-supervised attention model for prediction of KRAS gene mutation status in non-small cell lung cancer.

KRAS is a pathogenic gene frequently implicated in non-small cell lung cancer (NSCLC). However, biopsy as a diagnostic method has practical limitations. Therefore, it is important to accurately determine the mutation status of the KRAS gene non-invasively by combining NSCLC CT images and genetic data for early diagnosis and subsequent targeted therapy of patients. This paper proposes a Semi-supervised Multimodal Multiscale Attention Model (S2MMAM). S2MMAM comprises a Supervised Multilevel Fusion Segmentation Network (SMF-SN) and a Semi-supervised Multimodal Fusion Classification Network (S2MF-CN). S2MMAM facilitates the execution of the classification task by transferring the useful information captured in SMF-SN to the S2MF-CN to improve the model prediction accuracy. In SMF-SN, we propose a Triple Attention-guided Feature Aggregation module for obtaining segmentation features that incorporate high-level semantic abstract features and low-level semantic detail features. Segmentation features provide pre-guidance and key information expansion for S2MF-CN. S2MF-CN shares the encoder and decoder parameters of SMF-SN, which enables S2MF-CN to obtain rich classification features. S2MF-CN uses the proposed Intra and Inter Mutual Guidance Attention Fusion (I2MGAF) module to first guide segmentation and classification feature fusion to extract hidden multi-scale contextual information. I2MGAF then guides the multidimensional fusion of genetic data and CT image data to compensate for the lack of information in single modality data. S2MMAM achieved 83.27% AUC and 81.67% accuracy in predicting KRAS gene mutation status in NSCLC. This method uses medical image CT and genetic data to effectively improve the accuracy of predicting KRAS gene mutation status in NSCLC.

COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge.

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.

Effective lung nodule detection using deep CNN with dual attention mechanisms.

Novel methods are required to enhance lung cancer detection, which has overtaken other cancer-related causes of death as the major cause of cancer-related mortality. Radiologists have long-standing methods for locating lung nodules in patients with lung cancer, such as computed tomography (CT) scans. Radiologists must manually review a significant amount of CT scan pictures, which makes the process time-consuming and prone to human error. Computer-aided diagnosis (CAD) systems have been created to help radiologists with their evaluations in order to overcome these difficulties. These systems make use of cutting-edge deep learning architectures. These CAD systems are designed to improve lung nodule diagnosis efficiency and accuracy. In this study, a bespoke convolutional neural network (CNN) with a dual attention mechanism was created, which was especially crafted to concentrate on the most important elements in images of lung nodules. The CNN model extracts informative features from the images, while the attention module incorporates both channel attention and spatial attention mechanisms to selectively highlight significant features. After the attention module, global average pooling is applied to summarize the spatial information. To evaluate the performance of the proposed model, extensive experiments were conducted using benchmark dataset of lung nodules. The results of these experiments demonstrated that our model surpasses recent models and achieves state-of-the-art accuracy in lung nodule detection and classification tasks.

Two-stream vision transformer based multi-label recognition for TCM prescriptions construction.

Traditional Chinese medicine (TCM) observation diagnosis images (including facial and tongue images) provide essential human body information, holding significant importance in clinical medicine for diagnosis and treatment. TCM prescriptions, known for their simplicity, non-invasiveness, and low side effects, have been widely applied worldwide. Exploring automated herbal prescription construction based on visual diagnosis holds vital value in delving into the correlation between external features and herbal prescriptions and offering medical services in mobile healthcare systems. To effectively integrate multi-perspective visual diagnosis images and automate prescription construction, this study proposes a multi-herb recommendation framework based on Visual Transformer and multi-label classification. The framework comprises three key components: image encoder, label embedding module, and cross-modal fusion classification module. The image encoder employs a dual-stream Visual Transformer to learn dependencies between different regions of input images, capturing both local and global features. The label embedding module utilizes Graph Convolutional Networks to capture associations between diverse herbal labels. Finally, two Multi-Modal Factorized Bilinear modules are introduced as effective components to fuse cross-modal vectors, creating an end-to-end multi-label image-herb recommendation model. Through experimentation with real facial and tongue images and generating prescription data closely resembling real samples. The precision is 50.06 %, the recall rate is 48.33 %, and the F1-score is 49.18 %. This study validates the feasibility of automated herbal prescription construction from the perspective of visual diagnosis. Simultaneously, it provides valuable insights for constructing herbal prescriptions automatically from more physical information.

Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network.

BACKGROUND: Reducing the radiation dose from computed tomography (CT) can significantly reduce the radiation risk to patients. However, low-dose CT (LDCT) suffers from severe and complex noise interference that affects subsequent diagnosis and analysis. Recently, deep learning-based methods have shown superior performance in LDCT image-denoising tasks. However, most methods require many normal-dose and low-dose CT image pairs, which are difficult to obtain in clinical applications. Unsupervised methods, on the other hand, are more general. PURPOSE: Deep learning methods based on GAN networks have been widely used for unsupervised LDCT denoising, but the additional memory requirements of the model also hinder its further clinical application. To this end, we propose a simpler multi-stage denoising framework trained using unpaired data, the progressive cyclical convolutional neural network (PCCNN), which can remove the noise from CT images in latent space. METHODS: Our proposed PCCNN introduces a noise transfer model that transfers noise from LDCT to normal-dose CT (NDCT), denoised CT images generated from unpaired CT images, and noisy CT images. The denoising framework also contains a progressive module that effectively removes noise through multi-stage wavelet transforms without sacrificing high-frequency components such as edges and details. RESULTS: Compared with seven LDCT denoising algorithms, we perform a quantitative and qualitative evaluation of the experimental results and perform ablation experiments on each network module and loss function. On the AAPM dataset, compared with the contrasted unsupervised methods, our denoising framework has excellent denoising performance increasing the peak signal-to-noise ratio (PSNR) from 29.622 to 30.671, and the structural similarity index (SSIM) was increased from 0.8544 to 0.9199. The PCCNN denoising results were relatively optimal and statistically significant. In the qualitative result comparison, PCCNN without introducing additional blurring and artifacts, the resulting image has higher resolution and complete detail preservation, and the overall structural texture of the image is closer to NDCT. In visual assessments, PCCNN achieves a relatively balanced result in noise suppression, contrast retention, and lesion discrimination. CONCLUSIONS: Extensive experimental validation shows that our scheme achieves reconstruction results comparable to supervised learning methods and has performed well in image quality and medical diagnostic acceptability.

Host plant-mediated effects on Buchnera symbiont: implications for biological characteristics and nutritional metabolism of pea aphids (Acyrthosiphon pisum).

Introduction: The pea aphid, Acyrthosiphon pisum, is a typical sap-feeding insect and an important worldwide pest. There is a primary symbiont-Buchnera aphidicola, which can synthesize and provide some essential nutrients for its host. At the same time, the hosts also can actively adjust the density of bacterial symbiosis to cope with the changes in environmental and physiological factors. However, it is still unclear how symbionts mediate the interaction between herbivorous insects' nutrient metabolism and host plants. Methods: The current study has studied the effects of different host plants on the biological characteristics, Buchnera titer, and nutritional metabolism of pea aphids. This study investigated the influence of different host plants on biological characteristics, Buchnera titer, and nutritional metabolism of pea aphids. Results and discussion: The titer of Buchnera was significantly higher on T. Pretense and M. officinalis, and the relative expression levels were 1.966±0.104 and 1.621±0.167, respectively. The content of soluble sugar (53.46±1.97µg/mg), glycogen (1.12±0.07µg/mg) and total energy (1341.51±39.37µg/mg) of the pea aphid on V. faba were significantly higher and showed high fecundity (143.86±11.31) and weight (10.46±0.77µg/mg). The content of total lipids was higher on P. sativum and T. pretense, which were 2.82±0.03µg/mg and 2.92±0.07µg/mg, respectively. Correlation analysis found that the difference in Buchnera titer was positively correlated with the protein content in M. officinalis and the content of total energy in T. pratense (P < 0.05). This study confirmed that host plants not only affected the biological characteristics and nutritional metabolism of pea aphids but also regulated the symbiotic density, thus interfering with the nutritional function of Buchnera. The results can provide a theoretical basis for further studies on the influence of different host plants on the development of pea aphids and other insects.

Cholesterol induction in CD8+ T cell exhaustion in colorectal cancer via the regulation of endoplasmic reticulum-mitochondria contact sites.

BACKGROUND: Hypercholesterolemia is one of the risk factors for colorectal cancer (CRC). Cholesterol can participate in the regulation of human T cell function and affect the occurrence and development of CRC. OBJECTIVE: To elucidate the pathogenesis of CRC immune escape mediated by CD8+ T cell exhaustion induced by cholesterol. METHODS: CRC samples (n = 217) and healthy individuals (n = 98) were recruited to analyze the relationship between peripheral blood cholesterol levels and the clinical features of CRC. An animal model of CRC with hypercholesterolemia was established. Intraperitoneal intervention with endoplasmic reticulum stress (ERS) inhibitors in hypercholesterolemic CRC mice was performed. CD69, PD1, TIM-3, and CTLA-4 on CD8+ T cells of spleens from C57BL/6 J mice were detected by flow cytometry. CD8+ T cells were cocultured with MC38 cells (mouse colon cancer cell line). The proliferation, apoptosis, migration and invasive ability of MC38 cells were detected by CCK-8 assay, Annexin-V APC/7-AAD double staining, scratch assay and transwell assay, respectively. Transmission electron microscopy was used to observe the ER structure of CD8+ T cells. Western blotting was used to detect the expression of ERS and mitophagy-related proteins. Mitochondrial function and energy metabolism were measured. Immunoprecipitation was used to detect the interaction of endoplasmic reticulum-mitochondria contact site (ERMC) proteins. Immunofluorescence colocalization was used to detect the expression and intracellular localization of ERMC-related molecules. RESULTS: Peripheral blood cholesterol-related indices, including Tc, low density lipoproteins (LDL) and Apo(a), were all increased, and high density lipoprotein (HDL) was decreased in CRCs. The proliferation, migration and invasion abilities of MC38 cells were enhanced, and the proportion of tumor cell apoptosis was decreased in the high cholesterol group. The expression of IL-2 and TNF-α was decreased, while IFN-γ was increased in the high cholesterol group. It indicated high cholesterol could induce exhaustion of CD8+ T cells, leading to CRC immune escape. Hypercholesterolemia damaged the ER structure of CD8+ T cells and increased the expression of ER stress molecules (CHOP and GRP78), lead to CD8+ T cell exhaustion. The expression of mitophagy-related proteins (BNIP3, PINK and Parkin) in exhausted CD8+ T cells increased at high cholesterol levels, causing mitochondrial energy disturbance. High cholesterol enhanced the colocalization of Fis1/Bap31, MFN2/cox4/HSP90B1, VAPB/PTPIP51, VDAC1/IPR3/GRP75 in ERMCs, indicated that high cholesterol promoted the intermolecular interaction between ER and mitochondrial membranes in CD8+ T cells. CONCLUSION: High cholesterol regulated the ERS-ERMC-mitophagy axis to induce the exhaustion of CD8+ T cells in CRC.

Diagnosis of Alzheimer's disease by joining dual attention CNN and MLP based on structural MRIs, clinical and genetic data.

Alzheimer's disease (AD) is an irreversible central nervous degenerative disease, while mild cognitive impairment (MCI) is a precursor state of AD. Accurate early diagnosis of AD is conducive to the prevention and early intervention treatment of AD. Although some computational methods have been developed for AD diagnosis, most employ only neuroimaging, ignoring other data (e.g., genetic, clinical) that may have potential disease information. In addition, the results of some methods lack interpretability. In this work, we proposed a novel method (called DANMLP) of joining dual attention convolutional neural network (CNN) and multilayer perceptron (MLP) for computer-aided AD diagnosis by integrating multi-modality data of the structural magnetic resonance imaging (sMRI), clinical data (i.e., demographics, neuropsychology), and APOE genetic data. Our DANMLP consists of four primary components: (1) the Patch-CNN for extracting the image characteristics from each local patch, (2) the position self-attention block for capturing the dependencies between features within a patch, (3) the channel self-attention block for capturing dependencies of inter-patch features, (4) two MLP networks for extracting the clinical features and outputting the AD classification results, respectively. Compared with other state-of-the-art methods in the 5CV test, DANMLP achieves 93% and 82.4% classification accuracy for the AD vs. MCI and MCI vs. NC tasks on the ADNI database, which is 0.2%∼15.2% and 3.4%∼26.8% higher than that of other five methods, respectively. The individualized visualization of focal areas can also help clinicians in the early diagnosis of AD. These results indicate that DANMLP can be effectively used for diagnosing AD and MCI patients.

A novel cross-layer dual encoding-shared decoding network framework with spatial self-attention mechanism for hippocampus segmentation.

Accurate segmentation of the hippocampus from the brain magnetic resonance images (MRIs) is a crucial task in the neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, such as Alzheimer's disease (AD). Automatic segmentation of the hippocampus structures is challenging due to the small volume, complex shape, low contrast and discontinuous boundaries of hippocampus. Although some methods have been developed for the hippocampus segmentation, most of them paid too much attention to the hippocampus shape and volume instead of considering the spatial information. Additionally, the extracted features are independent of each other, ignoring the correlation between the global and local information. In view of this, here we proposed a novel cross-layer dual Encoding-Shared Decoding network framework with Spatial self-Attention mechanism (called ESDSA) for hippocampus segmentation in human brains. Considering that the hippocampus is a relatively small part in MRI, we introduced the spatial self-attention mechanism in ESDSA to capture the spatial information of hippocampus for improving the segmentation accuracy. We also designed a cross-layer dual encoding-shared decoding network to effectively extract the global information of MRIs and the spatial information of hippocampus. The spatial features of hippocampus and the features extracted from the MRIs were combined to realize the hippocampus segmentation. Results on the baseline T1-weighted structural MRI data show that the performance of our ESDSA is superior to other state-of-the-art methods, and the dice similarity coefficient of ESDSA achieves 89.37%. In addition, the dice similarity coefficient of the Spatial Self-Attention mechanism (SSA) strategy and the dual Encoding-Shared Decoding (ESD) strategy is 9.47%, 5.35% higher than that of the baseline U-net, respectively, indicating that the strategies of SSA and ESD can effectively enhance the segmentation accuracy of human brain hippocampus.

Graph Neural Network for representation learning of lung cancer.

The emergence of image-based systems to improve diagnostic pathology precision, involving the intent to label sets or bags of instances, greatly hinges on Multiple Instance Learning for Whole Slide Images(WSIs). Contemporary works have shown excellent performance for a neural network in MIL settings. Here, we examine a graph-based model to facilitate end-to-end learning and sample suitable patches using a tile-based approach. We propose MIL-GNN to employ a graph-based Variational Auto-encoder with a Gaussian mixture model to discover relations between sample patches for the purposes to aggregate patch details into an individual vector representation. Using the classical MIL dataset MUSK and distinguishing two lung cancer sub-types, lung cancer called adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we exhibit the efficacy of our technique. We achieved a 97.42% accuracy on the MUSK dataset and a 94.3% AUC on the classification of lung cancer sub-types utilizing features.

Screening for Mild Cognitive Impairment with Speech Interaction Based on Virtual Reality and Wearable Devices.

Significant advances in sensor technology and virtual reality (VR) offer new possibilities for early and effective detection of mild cognitive impairment (MCI), and this wealth of data can improve the early detection and monitoring of patients. In this study, we proposed a non-invasive and effective MCI detection protocol based on electroencephalogram (EEG), speech, and digitized cognitive parameters. The EEG data, speech data, and digitized cognitive parameters of 86 participants (44 MCI patients and 42 healthy individuals) were monitored using a wearable EEG device and a VR device during the resting state and task (the VR-based language task we designed). Regarding the features selected under different modality combinations for all language tasks, we performed leave-one-out cross-validation for them using four different classifiers. We then compared the classification performance under multimodal data fusion using features from a single language task, features from all tasks, and using a weighted voting strategy, respectively. The experimental results showed that the collaborative screening of multimodal data yielded the highest classification performance compared to single-modal features. Among them, the SVM classifier using the RBF kernel obtained the best classification results with an accuracy of 87%. The overall classification performance was further improved using a weighted voting strategy with an accuracy of 89.8%, indicating that our proposed method can tap into the cognitive changes of MCI patients. The MCI detection scheme based on EEG, speech, and digital cognitive parameters proposed in this study provides a new direction and support for effective MCI detection, and suggests that VR and wearable devices will be a promising direction for easy-to-perform and effective MCI detection, offering new possibilities for the exploration of VR technology in the field of language cognition.