CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI.

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The 'CMRxRecon' dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.

Network Pharmacology Analysis of the Potential Pharmacological Mechanism of a Sleep Cocktail.

Insomnia, also known as sleeplessness, is a sleep disorder due to which people have trouble sleeping, followed by daytime sleepiness, low energy, irritability, and a depressed mood. It may result in an increased risk of accidents of all kinds as well as problems focusing and learning. Dietary supplements have become popular products for alleviating insomnia, while the lenient requirements for pre-market research result in unintelligible mechanisms of different combinations of dietary supplements. In this study, we aim to systematically identify the molecular mechanisms of a sleep cocktail's pharmacological effects based on findings from network pharmacology and molecular docking. A total of 249 targets of the sleep cocktail for the treatment of insomnia were identified and enrichment analysis revealed multiple pathways involved in the nervous system and inflammation. Protein-protein interaction (PPI) network analysis and molecular complex detection (MCODE) analysis yielded 10 hub genes, including AKT1, ADORA1, BCL2, CREB1, IL6, JUN, RELA, STAT3, TNF, and TP53. Results from weighted correlation network analysis (WGCNA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of insomnia-related transcriptome data from peripheral blood mononuclear cells (PBMCs) showed that a sleep cocktail may also ease insomnia via regulating the inflammatory response. Molecular docking results reveal good affinity of Sleep Cocktail to 9 selected key targets. It is noteworthy that the crucial target HSP90AA1 binds to melatonin most stably, which was further validated by MD simulation.

Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network.

Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and critical to study AD through multi-modal data. Existing learning methods, however, usually ignore the influence of feature heterogeneity and directly fuse features in the last stages. Furthermore, most of these methods only focus on local fusion features or global fusion features, neglecting the complementariness of features at different levels and thus not sufficiently leveraging information embedded in multi-modal data. To overcome these shortcomings, we propose a novel framework for AD diagnosis that fuses gene, imaging, protein, and clinical data. Our framework learns feature representations under the same feature space for different modalities through a feature induction learning (FIL) module, thereby alleviating the impact of feature heterogeneity. Furthermore, in our framework, local and global salient multi-modal feature interaction information at different levels is extracted through a novel dual multilevel graph neural network (DMGNN). We extensively validate the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and experimental results demonstrate our method consistently outperforms other state-of-the-art multi-modal fusion methods. The code is publicly available on the GitHub website. (https://github.com/xiankantingqianxue/MIA-code.git).

Hand Hygiene Education Components Among First-Year Nursing Students: A Cluster Randomized Clinical Trial.

Importance: Few studies have directly and objectively measured the individual and combined effects of multifaceted hand hygiene education programs. Objective: To evaluate the individual and combined immediate effects of an instructional video and hand scan images on handwashing quality, decontamination, and knowledge improvement. Design, Setting, and Participants: This cluster randomized clinical trial was conducted in June to July 2023 among first-year nursing students at a university in Hong Kong. The study used an intention-to-treat analysis. Intervention: Hand hygiene education sessions featuring an instructional video, hand scan images, or both. Main Outcomes and Measures: The primary outcome was the change in residue from fluorescent lotion remaining on participants' hands after handwashing before and after the intervention. The secondary outcomes included handwashing quality and knowledge of hand hygiene. Results: A total of 270 of 280 students (mean [SD] age, 19 [1] years; 182 [67.4%] female) participated in the trial (96.4% participation rate). Participants were randomized to a control group (66 participants), hand scan image group (68 participants), instructional video group (67 participants), and hand scan image with instructional video group (69 participants). All intervention groups had greater reductions in residue after the intervention compared with the control group, although none reached statistical significance (hand scan image group: 3.9 [95% CI, 2.0-5.8] percentage points; instructional video group: 4.8 [95% CI, 2.9-6.7] percentage points; hand scan image with instructional video: 3.5 [95% CI, 1.6-5.4] percentage points; control group: 3.2 [95% CI, 1.3-5.2] percentage points). The instructional video group showed a significant improvement in their handwashing performance, with a higher percentage of participants correctly performing all 7 steps compared with the control group (22.4% [95% CI, 13.1% to 31.6%] vs 1.5% [-7.9% to 10.9%]; P < .001). Hand scan images revealed that wrists, fingertips, and finger webs were the most commonly ignored areas in handwashing. Conclusions and Relevance: In this cluster randomized clinical trial of an education program for hand hygiene, a handwashing instructional video and hand scan images did not enhance the level of decontamination. The intervention group had improved handwashing techniques compared with the control group, a secondary outcome. Trial Registration: ClinicalTrials.gov Identifier: NCT05872581.

Investigating the Pharmacological Mechanisms of Total Flavonoids from Eucommia ulmoides Oliver Leaves for Ischemic Stroke Protection.

The aim of this study was to explore how the total flavonoids from Eucommia ulmoides leaves (EULs) regulate ischemia-induced nerve damage, as well as the protective effects mediated by oxidative stress. The cell survival rate was significantly improved compared to the ischemic group (p < 0.05) after treatment with the total flavonoids of EULs. The levels of reactive oxygen species (ROS), lactate dehydrogenase (LDH), and malondialdehyde (MDA) decreased, while catalase (CAT) and glutathione (GSH) increased, indicating that the total flavonoids of EULs can significantly alleviate neurological damage caused by ischemic stroke by inhibiting oxidative stress (p < 0.01). The mRNA expression level of VEGF increased (p < 0.01), which was consistent with the protein expression results. Meanwhile, the protein expression of ERK and CCND1 increased (p < 0.01), suggesting that the total flavonoids of EULs could protect PC12 cells from ischemic injury via VEGF-related pathways. MCAO rat models indicated that the total flavonoids of EULs could reduce brain ischemia-reperfusion injury. In conclusion, this study demonstrates the potential mechanisms of the total flavonoids of EULs in treating ischemic stroke and their potential therapeutic effects in reducing ischemic injury, which provides useful information for ischemic stroke drug discovery.

Peripheral CD8+PD-1+ T cells as novel biomarker for neoadjuvant chemoimmunotherapy in humanized mice of non-small cell lung cancer.

Neoadjuvant immunotherapy has shown promising clinical activity in the treatment of early non-small cell lung cancer (NSCLC); however, further clarification of the specific mechanism and identification of biomarkers are imperative prior to implementing it as a daily practice. The study investigated the reprogramming of T cells in both tumor and peripheral blood following neoadjuvant chemoimmunotherapy in a preclinical NSCLC mouse model engrafted with a human immune system. Samples were also collected from 21 NSCLC patients (Stage IA-IIIB) who received neoadjuvant chemoimmunotherapy, and the dynamics of potential biomarkers within these samples were measured and further subjected to correlation analysis with prognosis. Further, we initially investigated the sources of the potential biomarkers. We observed in the humanized mouse model, neoadjuvant chemoimmunotherapy could prevent postoperative recurrence and metastasis by increasing the frequency and cytotoxicity of CD8+ T cells in both peripheral blood (p < 0.001) and tumor immune microenvironment (TIME) (p < 0.001). The kinetics of peripheral CD8+PD-1+ T cells reflected the changes in the TIME and pathological responses, ultimately predicting survival outcome of mice. In the clinical cohort, patients exhibiting an increase in these T cells post-treatment had a higher rate of complete or major pathological response (p < 0.05) and increased immune infiltration (p = 0.0012, r = 0.792). We identified these T cells originating from tumor draining lymph nodes and subsequently entering the TIME. In conclusion, the kinetics of peripheral CD8+PD-1+ T cells can serve as a predictor for changes in TIME and optimal timing for surgery, ultimately reflecting the outcomes of neoadjuvant chemoimmunotherapy in both preclinical and clinical setting.

Bioinformatics analysis of potential common pathogenic mechanisms for COVID-19 and venous thromboembolism.

BACKGROUND: A growing body of research has shown that patients with coronavirus disease 2019 (COVID-19) have significantly higher rates of venous thromboembolism (VTE) than healthy. However, the mechanism remains incompletely elucidated. This study aimed to further investigate the molecular mechanisms underlying the development of this complication. METHODS: The gene expression profiles of COVID-19 and VTE were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) for COVID-19 and VTE, functional annotation, a protein-protein interactions (PPI) network, module construction, and hub gene identification were performed. Finally, we constructed a transcription factor (TF)-gene regulatory network and a TF-miRNA regulatory network for hub genes. RESULTS: A total of 42 common DEGs were selected for subsequent analyses. Functional analyses showed that biological function and signaling pathways collectively participated in the development and progression of VTE and COVID-19. Finally, 8 significant hub genes were identified using the cytoHubba plugin, including RSL24D1, RPS17, RPS27, HINT1, COX7C, RPL35, RPL34, and NDUFA4, which had preferable values as diagnostic markers for COVID-19 and VTE. CONCLUSIONS: Our study revealed the common pathogenesis of COVID-19 and VTE. These common pathways and pivotal genes may provide new ideas for further mechanistic studies.

Loss of ß-arrestin2 aggravated condylar cartilage degeneration at the early stage of temporomandibular joint osteoarthritis.

OBJECTIVE: Temporomandibular joint osteoarthritis (TMJOA) is a chronic degenerative joint disorder characterized by extracellular matrix degeneration and inflammatory response of condylar cartilage. ß-arrestin2 is an important regulator of inflammation response, while its role in TMJOA remains unknown. The objective of this study was to investigate the role of ß-arrestin2 in the development of TMJOA at the early stage and the underlying mechanism. METHODS: A unilateral anterior crossbite (UAC) model was established on eight-week-old wild-type (WT) and ß-arrestin2 deficiency mice to simulate the progression of TMJOA. Hematoxylin-eosin (HE) staining and microcomputed tomography (micro-CT) analysis were used for histological and radiographic assessment. Immunohistochemistry was performed to detect the expression of inflammatory and degradative cytokines, as well as autophagy related factors. Terminal-deoxynucleotidyl transferase mediated nick end labeling (TUNEL) assay was carried out to assess chondrocyte apoptosis. RESULTS: The loss of ß-arrestin2 aggravated cartilage degeneration and subchondral bone destruction in the model of TMJOA at the early stage. Furthermore, in UAC groups, the expressions of degradative (Col-X) and inflammatory (TNF-α and IL-1ß) factors in condylar cartilage were increased in ß-arrestin2 null mice compared with WT mice. Moreover, the loss of ß-arrestin2 promoted apoptosis and autophagic process of chondrocytes at the early stage of TMJOA. CONCLUSION: In conclusion, we demonstrated for the first time that ß-arrestin2 plays a protective role in the development of TMJOA at the early stage, probably by inhibiting apoptosis and autophagic process of chondrocytes. Therefore, ß-arrestin2 might be a potential therapeutic target for TMJOA, providing a new insight for the treatment of TMJOA at the early stage.

Effectiveness of high-risk human papillomavirus genotyping for cervical cancer screening. A multicentre screening cohort study in rural China.

BACKGROUND: This study aimed to assess the effectiveness of high-risk human papillomavirus (HR-HPV) primary testing for cervical cancer screening in China's rural areas. METHODS: Women aged 21-64 years were recruited. Cervical cytology was diagnosed following the Bethesda 2001 classification system, HPV infection (HR-HPV, HPV-16, HPV-18, and other 12 genotypes) identified by Cobas-4800, and colposcopy and biopsy performed when required. Primary outcomes were defined as the cumulative incidence of cervical intraepithelial neoplasia grade 2/3/higher (CIN2/3+) and its relative risk at baseline and at the 36-month follow-up. RESULTS: The study included 9,218 women; mean age was 45.15 years (SD: 8.74); 81% completed the follow-up. The most frequent type of cytological lesions (12.4% ) were ASCUS (8.4%) and LSIL (2.2%). HR-HPV infection (16.3%) was more prevalent in HPV-16 than in HPV-18 (3 vs 1.5%); a positive relationship with the severity of the lesions, from 29.8% in ASCUS to 89.6% in HSIL was found. At baseline, 3.5% of the patients underwent colposcopy; 20% had a positive diagnosis. At the 36-month follow-up, the cumulative incidences of CIN2+ and CIN3+ were higher in women with HR-HPV infection (16.9 vs 0.5% and 8.2 vs 0.2%). The relative risk of CIN2/3+ was lower in HR-HPV-negative women compared to those with a negative cytology at baseline (0.4; 95%CI: 0.3-0.4). CONCLUSIONS: High-risk HPV-based screening may significantly reduce the risk of CIN2/3+ compared with cytology testing. This may be a new resource for public health demands in China's rural areas.

An automated approach for predicting HAMD-17 scores via divergent selective focused multi-heads self-attention network.

This study introduces the Divergent Selective Focused Multi-heads Self-Attention Network (DSFMANet), an innovative deep learning model devised to automatically predict Hamilton Depression Rating Scale-17 (HAMD-17) scores in patients with depression. This model introduces a multi-branch structure for sub-bands and artificially configures attention focus factors on various branches, resulting in distinct attention distributions for different sub-bands. Experimental results demonstrate that when DSFMANet processes sub-band data, its performance surpasses current benchmarks in key metrics such as mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). This success is particularly evident in terms of MSE and MAE, where the performance of sub-band data is significantly superior, highlighting the model's potential in accurately predicting HAMD-17 scores. Concurrently, the experiment also compared the model's performance with sub-band and full-band data, affirming the superiority of the selective focus attention mechanism in electroencephalography (EEG) signal processing. DSFMANet, when utilizing sub-band data, exhibits higher data processing efficiency and reduced model complexity. The findings of this study underscore the significance of employing deep learning models based on sub-band analysis in depression diagnosis. The DSFMANet model not only effectively enhances the accuracy of depression diagnosis but also offers valuable research directions for similar applications in the future. This deep learning-based automated approach can effectively ascertain the HAMD-17 scores of patients with depression, thus offering more accurate and reliable support for clinical decision-making.

gCTRP3 inhibits oophorectomy­induced osteoporosis by activating the AMPK/SIRT1/Nrf2 signaling pathway in mice.

C1q/tumor necrosis factor­related protein 3 (CTRP3) expression is markedly reduced in the serum of patients with osteoporosis. The present study aimed to investigate whether CTRP3 reduces bone loss in oophorectomy (OVX)­induced mice via the AMP­activated protein kinase (AMPK)/sirtuin 1 (SIRT1)/nuclear factor E2­related factor 2 (Nrf2) signaling pathway. Female C57BL/6J mice and MC3T3­E1 cells were used to construct in vivo and in vitro models of osteoporosis, respectively. The left femurs of mice were examined using micro­computed tomography scans and bone­related quantitative morphological evaluation was performed. Pathological changes and the number of osteoclasts in the left femurs of mice were detected using hematoxylin and eosin, and tartrate­resistant acid phosphatase (TRAP) staining. Runt­related transcription factor­2 (RUNX2) expression in the left femurs was detected using immunofluorescence analysis, and the serum levels of bone resorption markers (C­telopeptide of type I collagen and TRAP) and bone formation markers [osteocalcin (OCN) and procollagen type 1 N­terminal propeptide] were detected. In addition, osteoblast differentiation and calcium deposits were examined in MC3T3­E1 cells using alkaline phosphatase (ALP) and Alizarin red staining, respectively. Moreover, RUNX2, ALP and OCN expression levels were detected using reverse transcription­quantitative PCR, and the expression levels of proteins associated with the AMPK/SIRT1/Nrf2 signaling pathway were detected using western blot analysis. The results revealed that globular CTRP3 (gCTRP3) alleviated bone loss and promoted bone formation in OVX­induced mice. gCTRP3 also facilitated the osteogenic differentiation of MC3T3­E1 cells through the AMPK/SIRT1/Nrf2 signaling pathway. The addition of an AMPK inhibitor (Compound C), SIRT1 inhibitor (EX527) or Nrf2 inhibitor (ML385) reduced the osteogenic differentiation of MC3T3­E1 cells via inhibition of gCTRP3. In conclusion, gCTRP3 inhibits OVX­induced osteoporosis by activating the AMPK/SIRT1/Nrf2 signaling pathway.

Efficient data integration under prior probability shift.

Conventional supervised learning usually operates under the premise that data are collected from the same underlying population. However, challenges may arise when integrating new data from different populations, resulting in a phenomenon known as dataset shift. This paper focuses on prior probability shift, where the distribution of the outcome varies across datasets but the conditional distribution of features given the outcome remains the same. To tackle the challenges posed by such shift, we propose an estimation algorithm that can efficiently combine information from multiple sources. Unlike existing methods that are restricted to discrete outcomes, the proposed approach accommodates both discrete and continuous outcomes. It also handles high-dimensional covariate vectors through variable selection using an adaptive least absolute shrinkage and selection operator penalty, producing efficient estimates that possess the oracle property. Moreover, a novel semiparametric likelihood ratio test is proposed to check the validity of prior probability shift assumptions by embedding the null conditional density function into Neyman's smooth alternatives (Neyman, 1937) and testing study-specific parameters. We demonstrate the effectiveness of our proposed method through extensive simulations and a real data example. The proposed methods serve as a useful addition to the repertoire of tools for dealing dataset shifts.

Revealing the potential bioactive components and mechanism of Qianhua Gout Capsules in the treatment of gouty arthritis through network pharmacology, molecular docking and pharmacodynamic study strategies.

Recent clinical studies have confirmed the effectiveness of Qianhua Gout Capsules (QGC) in the treatment of gouty arthritis (GA). However, the specific regulatory targets and mechanisms of action of QGC are still unclear. To address this gap, we utilized network pharmacology, molecular docking, and pharmacodynamic approaches to investigate the bioactive components and associated mechanisms of QGC in the treatment of GA. By employing UPLC-Q Exactive-MS, we identified the compounds present in QGC, with active ingredients defined as those with oral bioavailability ≥30 % and drug similarity ≥0.18. Subsequently, the targets of these active compounds were determined using the TCMSP database, while GA-related targets were identified from DisGeNET, GeneCards, TTD, OMIM, and DrugBank databases. Further analysis including PPI analysis, GO analysis, and KEGG pathway enrichment was conducted on the targets. Validation of the predicted results was performed using a GA rat model, evaluating pathological changes, inflammatory markers, and pathway protein expression. Our results revealed a total of 130 components, 44 active components, 16 potential shared targets, GO-enriched terms, and 47 signaling pathways related to disease targets. Key active ingredients included quercetin, kaempferol, ß-sitosterol, luteolin, and wogonin. The PPI analysis highlighted five targets (PPARG, IL-6, MMP-9, IL-1ß, CXCL-8) with the highest connectivity, predominantly enriched in the IL-17 signaling pathway. Molecular docking experiments demonstrated strong binding of CXCL8, IL-1ß, IL-6, MMP9, and PPARG targets with the top five active compounds. Furthermore, animal experiments confirmed the efficacy of QGC in treating GA in rats, showing reductions in TNF-α, IL-6, and MDA levels, and increases in SOD levels in serum. In synovial tissues, QGC treatment upregulated CXCL8 and PPARG expression, while downregulating IL-1ß, MMP9, and IL-6 expression. In conclusion, this study applied a network pharmacology approach to uncover the composition of QGC, predict its pharmacological interactions, and demonstrate its in vivo efficacy, providing insights into the anti-GA mechanisms of QGC. These findings pave the way for future investigations into the therapeutic mechanisms underlying QGC's effectiveness in the treatment of GA.

Unsupervised lung CT image registration via stochastic decomposition of deformation fields.

We address the problem of lung CT image registration, which underpins various diagnoses and treatments for lung diseases. The main crux of the problem is the large deformation that the lungs undergo during respiration. This physiological process imposes several challenges from a learning point of view. In this paper, we propose a novel training scheme, called stochastic decomposition, which enables deep networks to effectively learn such a difficult deformation field during lung CT image registration. The key idea is to stochastically decompose the deformation field, and supervise the registration by synthetic data that have the corresponding appearance discrepancy. The stochastic decomposition allows for revealing all possible decompositions of the deformation field. At the learning level, these decompositions can be seen as a prior to reduce the ill-posedness of the registration yielding to boost the performance. We demonstrate the effectiveness of our framework on Lung CT data. We show, through extensive numerical and visual results, that our technique outperforms existing methods.

Microstructure and Texture Evolution in Cold-Rolled and Annealed Oxygen-Free Copper Sheets.

Commercial oxygen-free copper sheets were cold-rolled with reduction rates ranging from 20% to 87% and annealed at 400, 500 and 600 °C. The microstructure and texture evolution during the cold-rolling and annealing processes were studied using optical microscopy (OM), scanning electron microscopy (SEM) and electron back-scattered diffraction (EBSD). The results show that the deformation textures of {123}<634> (S), {112}<111> (Copper) and {110}<112> (Brass) were continuously enhanced with the increase in cold-rolling reduction. The orientations along the α-oriented fiber converged towards Brass, and the orientation density of ß fiber obviously increased when the rolling reduction exceeded 60%. The recrystallization texture was significantly affected by the cold-rolling reduction. After 60% cold-rolling reduction, Copper and S texture components gradually decreased, and the {011}<511> recrystallization texture component formed with the increase in annealing temperature. After 87% cold-rolling reduction, a strong Cube texture formed, and other textures were inhibited with the increase in annealing temperature. The strong Brass and S deformation texture was conducive to the formation of a strong Cube annealing texture. The density of the annealing twin boundary decreased with the increase in annealing temperature, and more annealing twin boundaries formed in the oxygen-free copper sheets with the increase in cold-rolling reduction.

CENTRAL VENOUS-TO-ARTERIAL CARBON DIOXIDE PARTIAL PRESSURE DIFFERENCE AS A GUIDING PARAMETER FOR CARDIOTONIC DRUG ADMINISTRATION IN PATIENTS WITH EARLY-STAGE SEPTIC SHOCK.

ABSTRACT: Objective: This study aimed to investigate the effect of the central venous-to-arterial carbon dioxide partial pressure difference (Pcv-aCO2) on the administration of cardiotonic drugs in patients with early-stage septic shock. Methods: A retrospective study was conducted on 120 patients suffering from septic shock. At admission, the left ventricular ejection fraction (LVEF) and Pcv-aCO2 of the patients were obtained. On the premise of mean arterial pressure ≥ 65 mm Hg, the patients were divided into two groups according to the treatment approaches adopted by different doctors-control group: LVEF ≤50% and observation group: Pcv-aCO2 ≥ 6. Both groups received cardiotonic therapy. Results: The two groups of patients had similar general conditions and preresuscitation conditions ( P > 0.05). Compared with the control group, the observation group had a higher mean arterial pressure, lactic acid clearance rate, and urine output after 6 h of resuscitation ( P < 0.05), but a lower absolute value of lactic acid, total fluid intake in 24 h, and a lower number of patients receiving renal replacement therapy during hospitalization ( P < 0.05). After 6 hours of resuscitation, the percentages of patients meeting central venous oxygen saturation and central venous pressure targets were not significantly different between the control and observation groups ( P > 0.05). There was no difference in the 28-day mortality rate between the two groups ( P > 0.05). Conclusion: Pcv-aCO2 is more effective than LVEF in guiding the administration of cardiotonic drugs in the treatment of patients with septic shock.

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.

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.

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.