The role of microbiota in gastric cancer: A comprehensive review.

BACKGROUND: Gastric cancer (GC) continues to pose a significant global threat in terms of cancer-related fatalities. Despite notable advancements in medical research and therapies, further investigation is warranted to elucidate its underlying etiology and risk factors. Recent times have witnessed an escalated emphasis on comprehending the role of the microbiota in cancer development. METHODS: This review briefly delves into recent developments in microbiome-related research pertaining to gastric cancer. RESULTS: According to studies, the microbiota can influence GC growth by inciting inflammation, disrupting immunological processes, and generating harmful microbial metabolites. Furthermore, there is ongoing research into how the microbiome can impact a patient's response to chemotherapy and immunotherapy. CONCLUSION: The utilization of the microbiome for detecting, preventing, and managing stomach cancer remains an active area of exploration.

Chemical and Geological Properties of Shale Gas: In Situ Desorption of Lower Cambrian Niutitang Shale in the Micangshan Tectonic Zone of South Shaanxi, China.

Shale gas was recently found in the Lower Cambrian Niutitang Formation (LCNF) of the Micangshan tectonic zone of south Shaanxi (MTZSS), but not in commercial quantities. To determine the laws governing the generation, enrichment, and desorption of shale gases in overmatured shale strata in the LCNF of MTZSS, we carried out in situ desorption experiments on nine shale core samples and got 168 desorbed gas samples at different phases of desorption. Also measured were the chemical and carbon isotopic compositions of these desorbed gas samples and the geochemical parameters of the shale core samples. CH4 was the predominant hydrocarbon shale gas identified in the 82.06-98.48% range, suggesting that the gases were mainly dry. The nonhydrocarbon gases found were CO2 and H2. The CH4 content of the desorbed gas samples dropped continuously during desorption, lowering the dryness index to 98.48 and 92.26% of the first and last desorbed shale gas, respectively. The change in the gas ratio during shale gas desorption proved that the adsorbability of the LCNF to the various gases follows the trend H2 > CO2 > C2H6 > CH4 > He. Further, δ13C2H6 and δ13CH4 become heavier during desorption, showing isotopic fractionation arising from the desorption-diffusion coeffect. As the desorption temperature increases, the value of δ13CH4 increases because 12CH4 is more sensitive to temperature than 13CH4, so it is with the ethane. Similar to the LCNF shale gas in other areas of China, the desorbed shale gases are characteristic of carbon isotope reversal (CIR) (δ13CH4 > δ13C2H6). The cracking of the residual soluble organic matter at the high overmaturity stage mixed with the cracking of kerogen at the early stage of maturation, causing CIR. Furthermore, the desorbed gas content was proportionally and inversely related to the CIR degree and final dryness index of the desorbed gas, respectively. Moreover, the carbon isotope fractionation degree of CH4 and δ13C1 of the last desorbed gas correlated positively with the desorbed gas content and the desorbed time of the gas. In conclusion, the four parameters are effective parameters for identifying shale gas sweet spots.

Dual-space high-frequency learning for transformer-based MRI super-resolution.

BACKGROUND AND OBJECTIVE: Magnetic resonance imaging (MRI) can provide rich and detailed high-contrast information of soft tissues, while the scanning of MRI is time-consuming. To accelerate MR imaging, a variety of Transformer-based single image super-resolution methods are proposed in recent years, achieving promising results thanks to their superior capability of capturing long-range dependencies. Nevertheless, most existing works prioritize the design of transformer attention blocks to capture global information. The local high-frequency details, which are pivotal to faithful MRI restoration, are unfortunately neglected. METHODS: In this work, we propose a high-frequency enhanced learning scheme to effectively improve the awareness of high frequency information in current Transformer-based MRI single image super-resolution methods. Specifically, we present two entirely plug-and-play modules designed to equip Transformer-based networks with the ability to recover high-frequency details from dual spaces: 1) in the feature space, we design a high-frequency block (Hi-Fe block) paralleled with Transformer-based attention layers to extract rich high-frequency features; while 2) in the image intensity space, we tailor a high-frequency amplification module (HFA) to further refine the high-frequency details. By fully exploiting the merits of the two modules, our framework can recover abundant and diverse high-frequency information, rendering faithful MRI super-resolved results with fine details. RESULTS: We integrated our modules with six Transformer-based models and conducted experiments across three datasets. The results indicate that our plug-and-play modules can enhance the super-resolution performance of all foundational models to varying degrees, surpassing the capabilities of existing state-of-the-art single image super-resolution networks. CONCLUSION: Comprehensive comparison of super-resolution images and high-frequency maps from various methods, clearly demonstrating that our module possesses the capability to restore high-frequency information, showing huge potential in clinical practice for accelerated MRI reconstruction.

3D Multimodal Fusion Network with Disease-induced Joint Learning for Early Alzheimer's Disease Diagnosis.

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging. To address them, we propose a novel multimodal diagnosis network based on multi-fusion and disease-induced learning (MDL-Net) to enhance early AD diagnosis by efficiently fusing multimodal data. Specifically, MDL-Net proposes a multi-fusion joint learning (MJL) module, which effectively fuses multimodal features and enhances the feature representation from global, local, and latent learning perspectives. MJL consists of three modules, global-aware learning (GAL), local-aware learning (LAL), and outer latent-space learning (LSL) modules. GAL via a self-adaptive Transformer (SAT) learns the global relationships among the modalities. LAL constructs local-aware convolution to learn the local associations. LSL module introduces latent information through outer product operation to further enhance feature representation. MDL-Net integrates the disease-induced region-aware learning (DRL) module via gradient weight to enhance interpretability, which iteratively learns weight matrices to identify AD-related brain regions. We conduct the extensive experiments on public datasets and the results confirm the superiority of our proposed method. Our code will be available at: https://github.com/qzf0320/MDL-Net.

Neuropathological characteristics of abnormal white matter functional signaling in adolescents with major depression.

BACKGROUND: Major depression disorder (MDD) constitutes a significant mental health concern. Epidemiological surveys indicate that the lifetime prevalence of depression in adolescents is much higher than that in adults, with a corresponding increased risk of suicide. In studying brain dysfunction associated with MDD in adole-scents, research on brain white matter (WM) is sparse. Some researchers even mistakenly regard the signals generated by the WM as noise points. In fact, studies have shown that WM exhibits similar blood oxygen level-dependent signal fluctuations. The alterations in WM signals and their relationship with disease severity in adolescents with MDD remain unclear. AIM: To explore potential abnormalities in WM functional signals in adolescents with MDD. METHODS: This study involved 48 adolescent patients with MDD and 31 healthy controls (HC). All participants were assessed using the Patient Health Questionnaire-9 Scale and the mini international neuropsychiatric interview (MINI) suicide inventory. In addition, a Siemens Skyra 3.0T magnetic resonance scanner was used to obtain the subjects' image data. The DPABI software was utilized to calculate the WM signal of the fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity, followed by a two-sample t-test between the MDD and HC groups. Independent component analysis (ICA) was also used to evaluate the WM functional signal. Pearson's correlation was performed to assess the relationship between statistical test results and clinical scales. RESULTS: Compared to HC, individuals with MDD demonstrated a decrease in the fALFF of WM in the corpus callosum body, left posterior limb of the internal capsule, right superior corona radiata, and bilateral posterior corona radiata [P < 0.001, family-wise error (FWE) voxel correction]. The regional homogeneity of WM increased in the right posterior limb of internal capsule and left superior corona radiata, and decreased in the left superior longitudinal fasciculus (P < 0.001, FWE voxel correction). The ICA results of WM overlapped with those of regional homo-geneity. The fALFF of WM signal in the left posterior limb of the internal capsule was negatively correlated with the MINI suicide scale (P = 0.026, r = -0.32), and the right posterior corona radiata was also negatively correlated with the MINI suicide scale (P = 0.047, r = -0.288). CONCLUSION: Adolescents with MDD involves changes in WM functional signals, and these differences in brain regions may increase the risk of suicide.

LA-ViT: A Network with Transformers Constrained by Learned-Parameter-Free Attention for Interpretable Grading in a New Laryngeal Histopathology Image Dataset.

Grading laryngeal squamous cell carcinoma (LSCC) based on histopathological images is a clinically significant yet challenging task. However, more low-effect background semantic information appeared in the feature maps, feature channels, and class activation maps, which caused a serious impact on the accuracy and interpretability of LSCC grading. While the traditional transformer block makes extensive use of parameter attention, the model overlearns the low-effect background semantic information, resulting in ineffectively reducing the proportion of background semantics. Therefore, we propose an end-to-end network with transformers constrained by learned-parameter-free attention (LA-ViT), which improve the ability to learn high-effect target semantic information and reduce the proportion of background semantics. Firstly, according to generalized linear model and probabilistic, we demonstrate that learned-parameter-free attention (LA) has a stronger ability to learn highly effective target semantic information than parameter attention. Secondly, the first-type LA transformer block of LA-ViT utilizes the feature map position subspace to realize the query. Then, it uses the feature channel subspace to realize the key, and adopts the average convergence to obtain a value. And those construct the LA mechanism. Thus, it reduces the proportion of background semantics in the feature maps and feature channels. Thirdly, the second-type LA transformer block of LA-ViT uses the model probability matrix information and decision level weight information to realize key and query, respectively. And those realize the LA mechanism. So, it reduces the proportion of background semantics in class activation maps. Finally, we build a new complex semantic LSCC pathology image dataset to address the problem, which is less research on LSCC grading models because of lacking clinically meaningful datasets. After extensive experiments, the whole metrics of LA-ViT outperform those of other state-of-the-art methods, and the visualization maps match better with the regions of interest in the pathologists' decision-making. Moreover, the experimental results conducted on a public LSCC pathology image dataset show that LA-ViT has superior generalization performance to that of other state-of-the-art methods.

Mangiferin, a Potential Supplement to Improve Metabolic Syndrome: Current Status and Future Opportunities.

Metabolic syndrome (MetS) represents a considerable clinical and public health burden worldwide. Mangiferin (MF), a flavonoid compound present in diverse species such as mango (Mangifera indica L.), papaya (Pseudocydonia sinensis (Thouin) C. K. Schneid.), zhimu (Anemarrhena asphodeloides Bunge), and honeybush tea (Cyclopia genistoides), boasts a broad array of pharmacological effects. It holds promising uses in nutritionally and functionally targeted foods, particularly concerning MetS treatment. It is therefore pivotal to systematically investigate MF's therapeutic mechanism for MetS and its applications in food and pharmaceutical sectors. This review, with the aid of a network pharmacology approach complemented by this experimental studies, unravels possible mechanisms underlying MF's MetS treatment. Network pharmacology results suggest that MF treats MetS effectively through promoting insulin secretion, targeting obesity and inflammation, alleviating insulin resistance (IR), and mainly operating via the phosphatidylinositol 3 kinase (PI3K)/Akt, nuclear factor kappa-B (NF-[Formula: see text]B), microtubule-associated protein kinase (MAPK), and oxidative stress signaling pathways while repairing damaged insulin signaling. These insights provide a comprehensive framework to understand MF's potential mechanisms in treating MetS. These, however, warrant further experimental validation. Moreover, molecular docking techniques confirmed the plausibility of the predicted outcomes. Hereafter, these findings might form the theoretical bedrock for prospective research into MF's therapeutic potential in MetS therapy.

Recent advances in metal oxide nanozyme-based optical biosensors for food safety assays.

Metal oxide nanozymes are emerging as promising materials for food safety detection, offering several advantages over natural enzymes, including superior stability, cost-effectiveness, large-scale production capability, customisable functionality, design options, and ease of modification. Optical biosensors based on metal oxide nanozymes have significantly accelerated the advancement of analytical research, facilitating the rapid, effortless, efficient, and precise detection and characterisation of contaminants in food. However, few reviews have focused on the application of optical biosensors based on metal oxide nanozymes for food safety detection. In this review, the catalytic mechanisms of the catalase, oxidase, peroxidase, and superoxide dismutase activities of metal oxide nanozymes are characterized. Research developments in optical biosensors based on metal oxide nanozymes, including colorimetric, fluorescent, chemiluminescent, and surface-enhanced Raman scattering biosensors, are comprehensively summarized. The application of metal oxide nanozyme-based biosensors for the detection of nitrites, sulphites, metal ions, pesticides, antibiotics, antioxidants, foodborne pathogens, toxins, and other food contaminants has been highlighted. Furthermore, the challenges and future development prospects of metal oxide nanozymes for sensing applications are discussed. This review offers insights and inspiration for further investigations on optical biosensors based on metal oxide nanozymes for food safety detection.

Synergizing economic growth and carbon emission reduction in China: A path to coupling the MFLP and PLUS models for optimizing the territorial spatial functional pattern.

Carbon emissions caused by economic growth are the main cause of global warming, but controlling economic growth to reduce carbon emissions does not meet China's conditions. Therefore, how to synergize economic growth and carbon emission reduction is not only a sustainable development issue for China, but also significant for mitigating global warming. The territorial spatial functional pattern (TSFP) is the spatial carrier for coordinating economic development and carbon emissions, but how to establish the TSFP to synergize economic growth and carbon emission reduction remains unresolved. We propose a decision framework for optimizing TSFP coupled with the multi-objective fuzzy linear programming and the patch-generating land use simulation model, to provide a new path to synergize economic growth and carbon emission reduction in China. To confirm the reliability, we took Qionglai City as the demonstration. The results found a significant spatiotemporal coupling between TSFP and the synergistic effect between economic growth and carbon emission reduction (q ≥ 0.8220), which resolves the theoretical uncertainty about synergizing economic growth and carbon emission reduction through optimizing TSFP. The urban space of Qionglai City in 2025 and 2030 obtained by the decision framework was 6497.57 hm2 and 6628.72 hm2 respectively, distributed in the central and eastern regions; the rural space was 60,132.92 hm2 and 56,084.97 hm2, concentrated in the east, with a few located in the west; and the ecological space was 71,072.52 hm2 and 74,998.31 hm2, mainly located in the western and southeastern areas. Compared with 2020, the carbon emission intensity of the TSFP that realized the synergy (decoupling index was 0.25 and 0.21, respectively) was reduced by 0.7 and 4.7 tons/million yuan, respectively. Further confirming that optimizing TSFP is an effective way to synergize economic growth and carbon emission reduction, which can provide policy implications for coordinating economic growth and carbon emissions for China and even similar developing countries.

Intercorrelated Ferroelectricity and Bulk Photovoltaic Effect in Two-Dimensional Sn2P2S6 Semiconductor for Polarization-Sensitive Photodetection.

A two-dimensional (2D) ferroelectric semiconductor, which is coupled with photosensitivity and room-temperature ferroelectricity, provides the possibility of coordinated conductance modulation by both electric field and light illumination and is promising for triggering the revolution of optoelectronics for monolithic multifunctional integration. Here, we report that semiconducting Sn2P2S6 crystals can be achieved in a 2D morphology using a chemical vapor transport approach with the assistant of space confinement and experimentally demonstrate the robust ferroelectricity in atomic-thin Sn2P2S6 nanosheet at room temperature. The intercorrelated programming of ferroelectric order along out-of-plane (OOP) and in-plane (IP) directions enables a tunable bulk photovoltaic (BPV) effect through multidirectional electrical control. By combining the capability of anisotropic in-plane optical absorption, a highly integrated Sn2P2S6 optoelectronic device vertically sandwiched with graphene electrodes yields the polarization-dependent open-circuit photovoltage with a dichroic ratio of 2.0 under 405 nm light illumination. The reintroduction of ferroelectric Sn2P2S6 to the 2D asymmetric semiconductor family provides possibilities to hardware implement of the self-powered polarization-sensitive photodetection and spotlights the promising applications for next-generation photovoltaic devices.

Robustness of the synergistic partial-denitrification, anammox, and fermentation process for treating domestic and nitrate wastewaters under fluctuating C/N ratios.

The synergistic partial-denitrification, anammox, and fermentation (SPDAF) process presents a promising solution to treat domestic and nitrate wastewaters. However, its capability to handle fluctuating C/N ratios (the ratios of COD to total inorganic nitrogen) in practical applications remains uncertain. In this study, the SPDAF process was operated for 236 days with C/N ratios of 0.7-3.5, and a high and stable efficiency of nitrogen removal (84.9 ± 7.8%) was achieved. The denitrification and anammox contributions were 6.1 ± 7.1% and 93.9 ± 7.1%, respectively. Batch tests highlighted the pivotal role of in situ fermentation at low biodegradable chemical oxygen demand (BCOD)/NO3- ratios. As the BCOD/NO3- ratios increased from 0 to 6, the NH4+ and NO3- removal rates increased, while the anammox contribution decreased from 100% to 80.1% but remained the primary pathway of nitrogen removal. The cooperation and balanced growth of denitrifying bacteria, anammox bacteria, and fermentation bacteria contributed to the system's robustness under fluctuating C/N ratios.

MHD-Net: Memory-aware Hetero-modal Distillation Network for Thymic Epithelial Tumor Typing with Missing Pathology Modality.

Fusing multi-modal radiology and pathology data with complementary information can improve the accuracy of tumor typing. However, collecting pathology data is difficult since it is high-cost and sometimes only obtainable after the surgery, which limits the application of multi-modal methods in diagnosis. To address this problem, we propose comprehensively learning multi-modal radiology-pathology data in training, and only using uni-modal radiology data in testing. Concretely, a Memory-aware Hetero-modal Distillation Network (MHD-Net) is proposed, which can distill well-learned multi-modal knowledge with the assistance of memory from the teacher to the student. In the teacher, to tackle the challenge in hetero-modal feature fusion, we propose a novel spatial-differentiated hetero-modal fusion module (SHFM) that models spatial-specific tumor information correlations across modalities. As only radiology data is accessible to the student, we store pathology features in the proposed contrast-boosted typing memory module (CTMM) that achieves type-wise memory updating and stage-wise contrastive memory boosting to ensure the effectiveness and generalization of memory items. In the student, to improve the cross-modal distillation, we propose a multi-stage memory-aware distillation (MMD) scheme that reads memory-aware pathology features from CTMM to remedy missing modal-specific information. Furthermore, we construct a Radiology-Pathology Thymic Epithelial Tumor (RPTET) dataset containing paired CT and WSI images with annotations. Experiments on the RPTET and CPTAC-LUAD datasets demonstrate that MHD-Net significantly improves tumor typing and outperforms existing multi-modal methods on missing modality situations.

Breast Cancer Classification from Digital Pathology Images via Connectivity-aware Graph Transformer.

Automated classification of breast cancer subtypes from digital pathology images has been an extremely challenging task due to the complicated spatial patterns of cells in the tissue micro-environment. While newly proposed graph transformers are able to capture more long-range dependencies to enhance accuracy, they largely ignore the topological connectivity between graph nodes, which is nevertheless critical to extract more representative features to address this difficult task. In this paper, we propose a novel connectivity-aware graph transformer (CGT) for phenotyping the topology connectivity of the tissue graph constructed from digital pathology images for breast cancer classification. Our CGT seamlessly integrates connectivity embedding to node feature at every graph transformer layer by using local connectivity aggregation, in order to yield more comprehensive graph representations to distinguish different breast cancer subtypes. In light of the realistic intercellular communication mode, we then encode the spatial distance between two arbitrary nodes as connectivity bias in self-attention calculation, thereby allowing the CGT to distinctively harness the connectivity embedding based on the distance of two nodes. We extensively evaluate the proposed CGT on a large cohort of breast carcinoma digital pathology images stained by Haematoxylin & Eosin. Experimental results demonstrate the effectiveness of our CGT, which outperforms state-of-the-art methods by a large margin. Codes are released on https://github.com/wang-kang-6/CGT.

Bone metastasis in non-small-cell lung cancer: genomic characterization and exploration of potential targets.

Background: Bone metastasis (BM) seriously affects the quality of life and reduces the survival time of patients with non-small-cell lung cancer (NSCLC). The genomic characteristics and potential targets of BMs are yet to be fully explored. Objective: To explore the genetic characteristics and potential targets of BM in NSCLC. Design: In all, 83 patients with NSCLC were retrospectively selected in this study. Genomic characterization of BMs was explored with the analysis of NGS results from primary tumors and BMs in 6 patients, then combined with NGS results of lung tumors in 16 patients with initial recurrence in bone to analyze mutations potentially associated with BMs, and finally, the correlation was further validated in 61 postoperative patients. Methods: The next generation sequencing (NGS) was performed to identify genomic differences between pulmonary primary tumors and BM. Fluorescence in situ hybridization and immunohistochemistry were performed in postoperative tumor tissues from patients who had undergone radical surgery to validate the predictive role of molecular targets for BM. The correlation between cyclin-dependent kinase 4 (CDK4) and BM was evaluated by Pearson's chi-square test. The university of alabama at birminghan cancer data analysis portal (UALCAN) was carried out for the detection of CDK4 expression in lung cancer and the relationship between CDK4 and clinicopathological parameters. The relationship between prognosis and CDK4 expression was analyzed by the Kaplan-Meier plotter. Results: The rate of gene amplification was increased (24% versus 36%) while gene substitution/indel was decreased (64% versus 52%) in BMs. The BM-specific mutations were analyzed in 16 recurrent patients which revealed the highest incidence of CDK4 amplification (18.8%). According to the Kaplan-Meier plotter database, the NSCLC patients with high CDK4 gene expression showed poor overall survival (OS) and recurrence-free survival (RFS) (p < 0.05). The incidence of CDK4 amplification tended to be higher in recurrent patients compared to the patients without BM (18.8% versus 4.7%, p = 0.118). Conclusion: Compared to the primary tumors of NSCLC, the genome of BMs showed an increased proportion of amplification and a decreased proportion of gene substitution/indel. Furthermore, the CDK4 amplification ratio seemed to be elevated in NSCLC patients with BM which may be associated with poor OS and RFS.

Genomic characterization and potential targets of bone metastasis in non-small cell lung cancer NGS was performed on the matched primary tumors and bone metastases to explore the differences in the genomes of bone metastases, and it was found that gene amplification increased in bone metastases. Combined with the results of NGS in NSCLC patients with the first postoperative recurrence site in the bone, it was found that CDK4 amplification expression increased in bone metastases. Finally, the correlation between bone metastasis and CDK4 amplification was verified by expanding the sample.

Seafloor primary production in a changing Arctic Ocean.

Phytoplankton and sea ice algae are traditionally considered to be the main primary producers in the Arctic Ocean. In this Perspective, we explore the importance of benthic primary producers (BPPs) encompassing microalgae, macroalgae, and seagrasses, which represent a poorly quantified source of Arctic marine primary production. Despite scarce observations, models predict that BPPs are widespread, colonizing ~3 million km2 of the extensive Arctic coastal and shelf seas. Using a synthesis of published data and a novel model, we estimate that BPPs currently contribute ~77 Tg C y-1 of primary production to the Arctic, equivalent to ~20 to 35% of annual phytoplankton production. Macroalgae contribute ~43 Tg C y-1, seagrasses contribute ~23 Tg C y-1, and microalgae-dominated shelf habitats contribute ~11 to 16 Tg C y-1. Since 2003, the Arctic seafloor area exposed to sunlight has increased by ~47,000 km2 y-1, expanding the realm of BPPs in a warming Arctic. Increased macrophyte abundance and productivity is expected along Arctic coastlines with continued ocean warming and sea ice loss. However, microalgal benthic primary production has increased in only a few shelf regions despite substantial sea ice loss over the past 20 y, as higher solar irradiance in the ice-free ocean is counterbalanced by reduced water transparency. This suggests complex impacts of climate change on Arctic light availability and marine primary production. Despite significant knowledge gaps on Arctic BPPs, their widespread presence and obvious contribution to coastal and shelf ecosystem production call for further investigation and for their inclusion in Arctic ecosystem models and carbon budgets.

STADNet: Spatial-Temporal Attention-Guided Dual-Path Network for cardiac cine MRI super-resolution.

Cardiac cine magnetic resonance imaging (MRI) is a commonly used clinical tool for evaluating cardiac function and morphology. However, its diagnostic accuracy may be compromised by the low spatial resolution. Current methods for cine MRI super-resolution reconstruction still have limitations. They typically rely on 3D convolutional neural networks or recurrent neural networks, which may not effectively capture long-range or non-local features due to their limited receptive fields. Optical flow estimators are also commonly used to align neighboring frames, which may cause information loss and inaccurate motion estimation. Additionally, pre-warping strategies may involve interpolation, leading to potential loss of texture details and complicated anatomical structures. To overcome these challenges, we propose a novel Spatial-Temporal Attention-Guided Dual-Path Network (STADNet) for cardiac cine MRI super-resolution. We utilize transformers to model long-range dependencies in cardiac cine MR images and design a cross-frame attention module in the location-aware spatial path, which enhances the spatial details of the current frame by using complementary information from neighboring frames. We also introduce a recurrent flow-enhanced attention module in the motion-aware temporal path that exploits the correlation between cine MRI frames and extracts the motion information of the heart. Experimental results demonstrate that STADNet outperforms SOTA approaches and has significant potential for clinical practice.

Blockade of V-domain immunoglobulin suppressor of T-cell activation reprograms tumour-associated macrophages and improves efficacy of PD-1 inhibitor in gastric cancer.

BACKGROUND AND AIMS: In gastric cancer, the response rate of programmed cell death protein-1 (PD-1) inhibitor is far from satisfactory, indicating additional nonredundant pathways might hamper antitumour immunity. V-domain immunoglobulin suppressor of T-cell activation (VISTA) has been reported in several malignancies as a novel immune-checkpoint. Nevertheless, the role of VISTA in gastric cancer still remains obscure. Our purpose is to explore the clinical significance and potential mechanism of VISTA in affecting gastric cancer patients' survival and immunotherapeutic responsiveness. METHODS: Our study recruited eight independent cohorts with a total of 1403 gastric cancer patients. Immunohistochemistry, multiplex immunofluorescence, flow cytometry or intracellular flow cytometry, quantitative polymerase chain reaction, western blotting, fluorescence-activated cell sorting, magnetic-activated cell sorting, smart-seq2, in vitro cell co-culture and ex vivo tumour inhibition assays were applied to investigate the clinical significance and potential mechanism of VISTA in gastric cancer. RESULTS: VISTA was predominantly expressed on tumour-associated macrophages (TAMs), and indicated poor clinical outcomes and inferior immunotherapeutic responsiveness. VISTA+ TAMs showed a mixed phenotype. Co-culture of TAMs and CD8+ T cells indicated that VISTA+ TAMs attenuated effective function of CD8+ T cells. Blockade of VISTA reprogrammed TAMs to a proinflammatory phenotype, reactivated CD8+ T cells and promoted apoptosis of tumour cells. Moreover, blockade of VISTA could also enhance the efficacy of PD-1 inhibitor, suggesting that blockade of VISTA might synergise with PD-1 inhibitor in gastric cancer. CONCLUSIONS: Our data revealed that VISTA was an immune-checkpoint associated with immunotherapeutic resistance. Blockade of VISTA reprogrammed TAMs, promoted T-cell-mediated antitumour immunity, and enhanced efficacy of PD-1 inhibitor, which might have implications in the treatment of gastric cancer.

E-bibliotherapy for improving the psychological well-being of informal caregivers of people with dementia: a randomized controlled trial protocol.

BACKGROUND: Providing informal care for individuals with dementia is frequently a challenging and demanding experience that can have detrimental effects on the psychological well-being of caregivers. Regrettably, community-based caregiver services often prove inadequate, highlighting the necessity for innovative approaches to support caregivers. AIM: To test the efficacy of e-bibliotherapy in improving the psychological well-being of informal caregivers of people with dementia. METHOD: The study is divided into two phases. In phase 1, the research team will co-design the e-bibliotherapy app with caregivers. In phase 2, a randomized controlled trial will be conducted among 192 informal caregivers of people with dementia in Hong Kong. Caregivers will be randomly assigned to either the e-bibliotherapy group or the control group using simple randomization. Outcome measures will encompass caregivers' psychological well-being, caregiving appraisal, mental health, saliva cortisol levels as an indicator of stress, and health-related quality of life for caregivers. Data will be collected at baseline, immediately post intervention, and 3 months and 6 months post intervention. General linear mixed model will be employed to analyze intervention effects. Qualitative interviews will be undertaken to explore caregiver experiences within this study and evaluate intervention acceptability using conventional content analysis methods. DISCUSSION: This study represents a pioneering effort in utilizing e-bibliotherapy to enhance the psychological well-being of informal caregivers of individuals with dementia, addressing the existing gap in caregiver services and facilitating knowledge dissemination within the community. TRIAL REGISTRATION: The trial has been registered on ClinicalTrial.gov (Ref: NCT05927805).

Path planning algorithm for percutaneous puncture lung mass biopsy procedure based on the multi-objective constraints and fuzzy optimization.

Objective. The percutaneous puncture lung mass biopsy procedure, which relies on preoperative CT (Computed Tomography) images, is considered the gold standard for determining the benign or malignant nature of lung masses. However, the traditional lung puncture procedure has several issues, including long operation times, a high probability of complications, and high exposure to CT radiation for the patient, as it relies heavily on the surgeon's clinical experience.Approach.To address these problems, a multi-constrained objective optimization model based on clinical criteria for the percutaneous puncture lung mass biopsy procedure has been proposed. Additionally, based on fuzzy optimization, a multidimensional spatial Pareto front algorithm has been developed for optimal path selection. The algorithm finds optimal paths, which are displayed on 3D images, and provides reference points for clinicians' surgical path planning.Main results.To evaluate the algorithm's performance, 25 data sets collected from the Second People's Hospital of Zigong were used for prospective and retrospective experiments. The results demonstrate that 92% of the optimal paths generated by the algorithm meet the clinicians' surgical needs.Significance.The algorithm proposed in this paper is innovative in the selection of mass target point, the integration of constraints based on clinical standards, and the utilization of multi-objective optimization algorithm. Comparison experiments have validated the better performance of the proposed algorithm. From a clinical standpoint, the algorithm proposed in this paper has a higher clinical feasibility of the proposed pathway than related studies, which reduces the dependency of the physician's expertise and clinical experience on pathway planning during the percutaneous puncture lung mass biopsy procedure.