FOXP4 is a Direct YAP1 Target that Promotes Gastric Cancer Stemness and Drives Metastasis.

The Hippo-YAP1 pathway is an evolutionally conserved signaling cascade that controls organ size and tissue regeneration. Dysregulation of Hippo-YAP1 signaling promotes initiation and progression of several types of cancer, including gastric cancer (GC). As the Hippo-YAP1 pathway regulates expression of thousands of genes, it is important to establish which target genes contribute to the oncogenic program driven by YAP1 to identify strategies to circumvent it. Here, we identified a vital role of FOXP4 in YAP1-driven gastric carcinogenesis by maintaining stemness and promoting peritoneal metastasis. Loss of FOXP4 impaired GC spheroid formation and reduced stemness marker expression, while FOXP4 upregulation potentiated cancer cell stemness. RNA-seq analysis revealed SOX12 as downstream target of FOXP4, and functional studies established that SOX12 supports stemness in YAP1-induced carcinogenesis. A small molecule screen identified 42-(2-Tetrazolyl)rapamycin as a FOXP4 inhibitor, and targeting FOXP4 suppressed GC tumor growth and enhanced the efficacy of 5-FU chemotherapy in vivo. Collectively, these findings revealed that FOXP4 upregulation by YAP1 in GC regulates stemness and tumorigenesis by upregulating SOX12. Targeting the YAP1-FOXP4-SOX12 axis represents a potential therapeutic strategy for GC.

Broad Multitask Learning System With Group Sparse Regularization.

The broad learning system (BLS) featuring lightweight, incremental extension, and strong generalization capabilities has been successful in its applications. Despite these advantages, BLS struggles in multitask learning (MTL) scenarios with its limited ability to simultaneously unravel multiple complex tasks where existing BLS models cannot adequately capture and leverage essential information across tasks, decreasing their effectiveness and efficacy in MTL scenarios. To address these limitations, we proposed an innovative MTL framework explicitly designed for BLS, named group sparse regularization for broad multitask learning system using related task-wise (BMtLS-RG). This framework combines a task-related BLS learning mechanism with a group sparse optimization strategy, significantly boosting BLS's ability to generalize in MTL environments. The task-related learning component harnesses task correlations to enable shared learning and optimize parameters efficiently. Meanwhile, the group sparse optimization approach helps minimize the effects of irrelevant or noisy data, thus enhancing the robustness and stability of BLS in navigating complex learning scenarios. To address the varied requirements of MTL challenges, we presented two additional variants of BMtLS-RG: BMtLS-RG with sharing parameters of feature mapped nodes (BMtLS-RGf), which integrates a shared feature mapping layer, and BMtLS-RGf and enhanced nodes (BMtLS-RGfe), which further includes an enhanced node layer atop the shared feature mapping structure. These adaptations provide customized solutions tailored to the diverse landscape of MTL problems. We compared BMtLS-RG with state-of-the-art (SOTA) MTL and BLS algorithms through comprehensive experimental evaluation across multiple practical MTL and UCI datasets. BMtLS-RG outperformed SOTA methods in 97.81% of classification tasks and achieved optimal performance in 96.00% of regression tasks, demonstrating its superior accuracy and robustness. Furthermore, BMtLS-RG exhibited satisfactory training efficiency, outperforming existing MTL algorithms by 8.04-42.85 times.

Federated learning using model projection for multi-center disease diagnosis with non-IID data.

Multi-center disease diagnosis aims to build a global model for all involved medical centers. Due to privacy concerns, it is infeasible to collect data from multiple centers for training (i.e., centralized learning). Federated Learning (FL) is a decentralized framework that enables multiple clients (e.g., medical centers) to collaboratively train a global model while retaining patient data locally for privacy. However, in practice, the data across medical centers are not independently and identically distributed (Non-IID), causing two challenging issues: (1) catastrophic forgetting at clients, i.e., the local model at clients will forget the knowledge received from the global model after local training, causing reduced performance; and (2) invalid aggregation at the server, i.e., the global model at the server may not be favorable to some clients after model aggregation, resulting in a slow convergence rate. To mitigate these issues, an innovative Federated learning using Model Projection (FedMoP) is proposed, which guarantees: (1) the loss of local model on global data does not increase after local training without accessing the global data so that the performance will not be degenerated; and (2) the loss of global model on local data does not increase after aggregation without accessing local data so that convergence rate can be improved. Extensive experimental results show that our FedMoP outperforms state-of-the-art FL methods in terms of accuracy, convergence rate and communication cost. In particular, our FedMoP also achieves comparable or even higher accuracy than centralized learning. Thus, our FedMoP can ensure privacy protection while outperforming centralized learning in accuracy and communication cost.

Transurethral En Bloc Resection Versus Standard Resection of Bladder Tumour: A Randomised, Multicentre, Phase 3 Trial.

BACKGROUND AND OBJECTIVE: Conventionally, standard resection (SR) is performed by resecting the bladder tumour in a piecemeal manner. En bloc resection of the bladder tumour (ERBT) has been proposed as an alternative technique in treating non-muscle-invasive bladder cancer (NMIBC). The objective of this study is to investigate whether ERBT could improve the 1-yr recurrence rate of NMIBC, as compared with SR. METHODS: A multicentre, randomised, phase 3 trial was conducted in Hong Kong. Adults with bladder tumour(s) of ≤ 3cm were enrolled from April 2017 to December 2020, and followed up until 1 yr after surgery. Patients were randomly assigned to receive either ERBT or SR in a 1:1 ratio. The primary outcome was 1-yr recurrence rate. A modified intention-to-treat analysis on patients with histologically confirmed NMIBC was performed. The main secondary outcomes included detrusor muscle sampling rate, operative time, hospital stay, 30-d complications, any residual or upstaging of disease upon second-look transurethral resection, and 1-yr progression rate. KEY FINDINGS AND LIMITATIONS: A total of 350 patients underwent randomisation, and 276 patients were histologically confirmed to have NMIBC. At 1 yr, 31 patients in the ERBT group and 46 in the SR group developed recurrence; the Kaplan-Meier estimate of 1- yr recurrence rates were 29% (95% confidence interval, 18-37) in the ERBT group and 38% (95% confidence interval, 28-46) in the SR group (p = 0.007). Upon a subgroup analysis, patients with 1-3 cm tumour, single tumour, Ta disease, or intermediate-risk NMIBC had a significant benefit from ERBT. None of the patients in the ERBT group and three patients in the SR group developed progression to muscle-invasive bladder cancer; the Kaplan-Meier estimates of 1-yr progression rates were 0% in the ERBT group and 2.6% (95% confidence interval, 0-5.5) in the SR group (p = 0.065). The median operative time was 28 min (interquartile range, 20-45) in the ERBT group and 22 min (interquartile range, 15-30) in the SR group (p < 0.001). All other secondary outcomes were similar in the two groups. CONCLUSIONS AND CLINICAL IMPLICATIONS: In patients with NMIBC of ≤ 3cm, ERBT resulted in a significant reduction in the 1-yr recurrence rate when compared with SR. The study results support ERBT as the first-line surgical treatment for patients with bladder tumours of≤ 3cm.

Synthetic BZLF1-targeted transcriptional activator for efficient lytic induction therapy against EBV-associated epithelial cancers.

The unique virus-cell interaction in Epstein-Barr virus (EBV)-associated malignancies implies targeting the viral latent-lytic switch is a promising therapeutic strategy. However, the lack of specific and efficient therapeutic agents to induce lytic cycle in these cancers is a major challenge facing clinical implementation. We develop a synthetic transcriptional activator that specifically activates endogenous BZLF1 and efficiently induces lytic reactivation in EBV-positive cancer cells. A lipid nanoparticle encapsulating nucleoside-modified mRNA which encodes a BZLF1-specific transcriptional activator (mTZ3-LNP) is synthesized for EBV-targeted therapy. Compared with conventional chemical inducers, mTZ3-LNP more efficiently activates EBV lytic gene expression in EBV-associated epithelial cancers. Here we show the potency and safety of treatment with mTZ3-LNP to suppress tumor growth in EBV-positive cancer models. The combination of mTZ3-LNP and ganciclovir yields highly selective cytotoxic effects of mRNA-based lytic induction therapy against EBV-positive tumor cells, indicating the potential of mRNA nanomedicine in the treatment of EBV-associated epithelial cancers.

How hospital autonomy affects provider payment reform effectiveness.

BACKGROUND: Provider payment reforms (PPRs) have demonstrated mixed results for improving health system efficiency. Since PPRs require health care organisations to interpret and implement policies, the organizational characteristics of hospitals may affect the effectiveness of PPRs. Hospitals with more autonomy have the flexibility to respond to PPRs more efficiently, but they may not if the autonomy previously facilitated behaviours that counter the PPR's objective. This study examines whether hospitals with higher autonomy responds to PPRs more effectively. METHODS: We used data from a matched-pair, cluster randomized controlled PPR intervention in a resource-limited Chinese province between 2014 and 2018. The intervention reformed the reimbursement method from the publicly administered New Cooperative Medical Scheme (NCMS) from fee-for-service to global budget. We interacted measures of hospital autonomy over surplus, hiring, and procurement (drugs, consumables, equipment, and overall index) with the difference-in-difference estimator to examine how autonomy moderated the intervention's effect. RESULTS: Autonomy over surplus (p < 0.01) and procurement of equipment (p < 0.01) were associated with relatively faster NCMS expenditure growth, demonstrating worse PPR response. They were also associated with higher expenditure shifting to out-of-pocket expenditures (p > 0.05). Post hoc analysis suggests that hospitals with surplus autonomy had higher OOP per admission (p < 0.01), suggesting profiteering tendencies. Other dimensions of autonomy demonstrated imprecise association. DISCUSSION: Hospitals with more autonomy may not necessarily respond more effectively to PPRs that incentivise efficiency when they had previously been encouraged to maximise profit. Policymakers should assess the extent of perverse incentives before granting autonomy and adjust the incentives accordingly.

Diversity pattern of insects from Macao based on an updated species checklist after 25 years.

Background: Insects represent one of the most diverse groups in the organism world with extremely rich species and morphological diversity, playing important roles in natural and city ecosystems. Regional compilation of insect species lists helps to clarify the richness of insect species in a region, enhances our understanding the structure and function of a local ecosystem and promotes the protection and development of insect resources. Moreover, it also serves as a valuable reference for cities with small area, large population and high urbanisation like Macao. Macao (Macau) Special Administrative Region (SAR) is situated at the Pearl River Delta on the southeast coast of mainland China. With urban development accelerating at great rate in a quite restricted area, Macao still has rich fauna, within which the insect diversity is surprisingly high. New information: In this study, we systematically sorted out major references items of manuals or handbooks, monographs, articles, dissertations, official websites and other publicly available information sources about the insects recorded in Macao and, thus, generated a checklist of 15 orders, 166 families, 868 genera, 1,339 species and 118 subspecies. During this process, the preliminarily summarised list was re-examined to eliminate synonyms and invalid species, based on many more extensive literature reviews. Besides, spelling errors of scientific names, authors and years were corrected. Meanwhile, the catalogue revealed a different composition pattern of species diversity between orders from those of the world and China. Even based on the most conservative estimates, the number of insect species in Macao should not be lower than 3,340 species, which hints at the necessity of deeper investigations with adequate collecting in the future to achieve more comprehensive recognition and understanding of Macao's insect biodiversity.

Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition.

Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing subject-specific cross-session emotion recognition models. To simultaneously address these challenges, we propose a unified pre-training framework based on multi-scale masked autoencoders (MSMAE), which utilizes large-scale unlabeled EEG signals from multiple subjects and sessions to extract noise-robust, subject-invariant, and temporal-invariant features. We subsequently fine-tune the obtained generalized features with only a small amount of labeled data from a specific subject for personalization and enable cross-session emotion recognition. Our framework emphasizes: 1) Multi-scale representation to capture diverse aspects of EEG signals, obtaining comprehensive information; 2) An improved masking mechanism for robust channel-level representation learning, addressing missing channel issues while preserving inter-channel relationships; and 3) Invariance learning for regional correlations in spatial-level representation, minimizing inter-subject and inter-session variances. Under these elaborate designs, the proposed MSMAE exhibits a remarkable ability to decode emotional states from a different session of EEG data during the testing phase. Extensive experiments conducted on the two publicly available datasets, i.e., SEED and SEED-IV, demonstrate that the proposed MSMAE consistently achieves stable results and outperforms competitive baseline methods in cross-session emotion recognition.

Comparison of carbapenem MIC for NDM-producing Enterobacterales by different AST methods.

Introduction: This study compared the performance of MIC test strip (ETEST), automated AST card (Vitek 2) and broth microdilution (BMD) in determining carbapenem susceptibility and MIC values of NDM-producing Enterobacterales. Methods: NDM-producing Enterobacterales recovered from clinical specimens were included. The presence of blaNDM was confirmed by PCR. Identification of bacterial isolates was done by MALDI-TOF. Phenotypic susceptibility to three carbapenems (ertapenem, imipenem and meropenem) was tested by BMD, ETEST and Vitek 2. MIC values were interpreted in accordance with CLSI M100 (2022 edition). Using BMD as the reference standard, the essential agreement (EA), categorical agreement (CA), very major error (VME) and major error (ME) rates were evaluated. Results: Forty-seven NDM-producing Enterobacterales isolates were included, 44 of which were Escherichia coli. The EA of Vitek 2 was 97.9% for ertapenem, 25.5% for meropenem and 42.6% for imipenem. Using Vitek 2, there were 0% VMEs across all three carbapenems tested. The EA of ETEST was 53.2% for ertapenem, 55.3% for imipenem and 36.2% for meropenem. The rates of VMEs for ETEST were high too (ertapenem 8.5%, meropenem 36.2%, imipenem 26.1%). The MIC values obtained from Vitek 2 were consistently higher than those from BMD, while MICs from ETEST were consistently lower than those from BMD. Conclusions: The VME rate for ETEST was unacceptably high when BMD was used as the standard for comparison. Vitek 2 had acceptable EA and CA for ertapenem when BMD was used as the standard for comparison. For meropenem and imipenem, neither of the methods (ETEST, Vitek 2) showed acceptable EA and CA when compared with BMD.

CTNet: Contrastive Transformer Network for Polyp Segmentation.

Segmenting polyps from colonoscopy images is very important in clinical practice since it provides valuable information for colorectal cancer. However, polyp segmentation remains a challenging task as polyps have camouflage properties and vary greatly in size. Although many polyp segmentation methods have been recently proposed and produced remarkable results, most of them cannot yield stable results due to the lack of features with distinguishing properties and those with high-level semantic details. Therefore, we proposed a novel polyp segmentation framework called contrastive Transformer network (CTNet), with three key components of contrastive Transformer backbone, self-multiscale interaction module (SMIM), and collection information module (CIM), which has excellent learning and generalization abilities. The long-range dependence and highly structured feature map space obtained by CTNet through contrastive Transformer can effectively localize polyps with camouflage properties. CTNet benefits from the multiscale information and high-resolution feature maps with high-level semantic obtained by SMIM and CIM, respectively, and thus can obtain accurate segmentation results for polyps of different sizes. Without bells and whistles, CTNet yields significant gains of 2.3%, 3.7%, 3.7%, 18.2%, and 10.1% over classical method PraNet on Kvasir-SEG, CVC-ClinicDB, Endoscene, ETIS-LaribPolypDB, and CVC-ColonDB respectively. In addition, CTNet has advantages in camouflaged object detection and defect detection. The code is available at https://github.com/Fhujinwu/CTNet.

Coordination Environment Engineering to Regulate the Adsorption Strength of Intermediates in Single Atom Catalysts for High-performance CO2 Reaction Reduction.

The modulation of the coordination environment of single atom catalysts (SACs) plays a vital role in promoting CO2 reduction reaction (CO2RR). Herein, N or B doped Fe-embedded graphyne (Fe-GY), Fe-nXGYm (n = 1, 2, 3; X = N, B; m = 1, 2, 3), are employed as probes to reveal the effect of the coordination environment engineering on CO2RR performance via heteroatom doping in SACs. The results show that the doping position and number of N or B in Fe-GY significantly affects catalyst activity and CO2RR product selectivity. In comparison, Fe-1NGY exhibits high-performance CO2RR to CH4 with a low limiting potential of -0.17 V, and Fe-2NGY3 is demonstrated as an excellent CO2RR electrocatalyst for producing HCOOH with a low limiting potential of -0.16 V. With applied potential, Fe-GY, Fe-1NGY, and Fe-2NGY3 exhibit significant advantages in CO2RR to CH4 while hydrogen evolution reaction is inhibited. The intrinsic essence analysis illustrates that heteroatom doping modulates the electronic structure of active sites and regulates the adsorption strength of the intermediates, thereby rendering a favorable coordination environment for CO2RR. This work highlights Fe-nXGYm as outstanding SACs for CO2RR, and provides an in-depth insight into the intrinsic essence of the promotion effect from heteroatom doping.

Learning From Incorrectness: Active Learning With Negative Pre-Training and Curriculum Querying for Histological Tissue Classification.

Patch-level histological tissue classification is an effective pre-processing method for histological slide analysis. However, the classification of tissue with deep learning requires expensive annotation costs. To alleviate the limitations of annotation budgets, the application of active learning (AL) to histological tissue classification is a promising solution. Nevertheless, there is a large imbalance in performance between categories during application, and the tissue corresponding to the categories with relatively insufficient performance are equally important for cancer diagnosis. In this paper, we propose an active learning framework called ICAL, which contains Incorrectness Negative Pre-training (INP) and Category-wise Curriculum Querying (CCQ) to address the above problem from the perspective of category-to-category and from the perspective of categories themselves, respectively. In particular, INP incorporates the unique mechanism of active learning to treat the incorrect prediction results that obtained from CCQ as complementary labels for negative pre-training, in order to better distinguish similar categories during the training process. CCQ adjusts the query weights based on the learning status on each category by the model trained by INP, and utilizes uncertainty to evaluate and compensate for query bias caused by inadequate category performance. Experimental results on two histological tissue classification datasets demonstrate that ICAL achieves performance approaching that of fully supervised learning with less than 16% of the labeled data. In comparison to the state-of-the-art active learning algorithms, ICAL achieved better and more balanced performance in all categories and maintained robustness with extremely low annotation budgets. The source code will be released at https://github.com/LactorHwt/ICAL.

ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain-Computer Interface.

Convolutional neural networks (CNNs) have been successfully applied to motor imagery (MI)-based brain-computer interface (BCI). Nevertheless, single-scale CNN fail to extract abundant information over a wide spectrum from EEG signals, while typical multi-scale CNNs cannot effectively fuse information from different scales with concatenation-based methods. To overcome these challenges, we propose a new scheme equipped with attention-based dual-scale fusion convolutional neural network (ADFCNN), which jointly extracts and fuses EEG spectral and spatial information at different scales. This scheme also provides novel insight through self-attention for effective information fusion from different scales. Specifically, temporal convolutions with two different kernel sizes identify EEG µ and ß rhythms, while spatial convolutions at two different scales generate global and detailed spatial information, respectively, and the self-attention mechanism performs feature fusion based on the internal similarity of the concatenated features extracted by the dual-scale CNN. The proposed scheme achieves the superior performance compared with state-of-the-art methods in subject-specific motor imagery recognition on BCI Competition IV dataset 2a, 2b and OpenBMI dataset, with the cross-session average classification accuracies of 79.39% and significant improvements of 9.14% on BCI-IV2a, 87.81% and 7.66% on BCI-IV2b, 65.26% and 7.2% on OpenBMI dataset, and the within-session average classification accuracies of 86.87% and significant improvements of 10.89% on BCI-IV2a, 87.26% and 8.07% on BCI-IV2b, 84.29% and 5.17% on OpenBMI dataset, respectively. What is more, ablation experiments are conducted to investigate the mechanism and demonstrate the effectiveness of the dual-scale joint temporal-spatial CNN and self-attention modules. Visualization is also used to reveal the learning process and feature distribution of the model.

Quaternion Cross-Modality Spatial Learning for Multi-Modal Medical Image Segmentation.

Recently, the Deep Neural Networks (DNNs) have had a large impact on imaging process including medical image segmentation, and the real-valued convolution of DNN has been extensively utilized in multi-modal medical image segmentation to accurately segment lesions via learning data information. However, the weighted summation operation in such convolution limits the ability to maintain spatial dependence that is crucial for identifying different lesion distributions. In this paper, we propose a novel Quaternion Cross-modality Spatial Learning (Q-CSL) which explores the spatial information while considering the linkage between multi-modal images. Specifically, we introduce to quaternion to represent data and coordinates that contain spatial information. Additionally, we propose Quaternion Spatial-association Convolution to learn the spatial information. Subsequently, the proposed De-level Quaternion Cross-modality Fusion (De-QCF) module excavates inner space features and fuses cross-modality spatial dependency. Our experimental results demonstrate that our approach compared to the competitive methods perform well with only 0.01061 M parameters and 9.95G FLOPs.

QMLS: quaternion mutual learning strategy for multi-modal brain tumor segmentation.

Objective.Due to non-invasive imaging and the multimodality of magnetic resonance imaging (MRI) images, MRI-based multi-modal brain tumor segmentation (MBTS) studies have attracted more and more attention in recent years. With the great success of convolutional neural networks in various computer vision tasks, lots of MBTS models have been proposed to address the technical challenges of MBTS. However, the problem of limited data collection usually exists in MBTS tasks, making existing studies typically have difficulty in fully exploring the multi-modal MRI images to mine complementary information among different modalities.Approach.We propose a novel quaternion mutual learning strategy (QMLS), which consists of a voxel-wise lesion knowledge mutual learning mechanism (VLKML mechanism) and a quaternion multi-modal feature learning module (QMFL module). Specifically, the VLKML mechanism allows the networks to converge to a robust minimum so that aggressive data augmentation techniques can be applied to expand the limited data fully. In particular, the quaternion-valued QMFL module treats different modalities as components of quaternions to sufficiently learn complementary information among different modalities on the hypercomplex domain while significantly reducing the number of parameters by about 75%.Main results.Extensive experiments on the dataset BraTS 2020 and BraTS 2019 indicate that QMLS achieves superior results to current popular methods with less computational cost.Significance.We propose a novel algorithm for brain tumor segmentation task that achieves better performance with fewer parameters, which helps the clinical application of automatic brain tumor segmentation.

H. pylori-induced NF-κB-PIEZO1-YAP1-CTGF axis drives gastric cancer progression and cancer-associated fibroblast-mediated tumour microenvironment remodelling.

BACKGROUND: Gastric cancer (GC) is one of the most common tumours in East Asia countries and is associated with Helicobacter pylori infection. H. pylori utilizes virulence factors, CagA and VacA, to up-regulate pro-inflammatory cytokines and activate NF-κB signaling. Meanwhile, the PIEZO1 upregulation and cancer-associated fibroblast (CAF) enrichment were found in GC progression. However, the mechanisms of PIEZO1 upregulation and its involvement in GC progression have not been fully elucidated. METHODS: The CAF enrichment and clinical significance were investigated in animal models and primary samples. The expression of NF-κB and PIEZO1 in GC was confirmed by immunohistochemistry staining, and expression correlation was analysed in multiple GC datasets. GSEA and Western blot analysis revealed the YAP1-CTGF axis regulation by PIEZO1. The stimulatory effects of CTGF on CAFs were validated by the co-culture system and animal studies. Patient-derived organoid and peritoneal dissemination models were employed to confirm the role of the PIEZO1-YAP1-CTGF cascade in GC. RESULTS: Both CAF signature and PIEZO1 were positively correlated with H. pylori infection. PIEZO1, a mechanosensor, was confirmed as a direct downstream of NF-κB to promote the transformation from intestinal metaplasia to GC. Mechanistic studies revealed that PIEZO1 transduced the oncogenic signal from NF-κB into YAP1 signaling, a well-documented oncogenic pathway in GC progression. PIEZO1 expression was positively correlated with the YAP1 signature (CTGF, CYR61, and c-Myc, etc.) in primary samples. The secreted CTGF by cancer cells stimulated the CAF infiltration to form a stiffened collagen-enrichment microenvironment, thus activating PIEZO1 to form a positive feedback loop. Both PIEZO1 depletion by shRNA and CTGF inhibition by Procyanidin C1 enhanced the efficacy of 5-FU in suppressing the GC cell peritoneal metastasis. CONCLUSION: This study elucidates a novel driving PIEZO1-YAP1-CTGF force, which opens a novel therapeutic avenue to block the transformation from precancerous lesions to GC. H. pylori-NF-κB activates the PIEZO1-YAP1-CTGF axis to remodel the GC microenvironment by promoting CAF infiltration. Targeting PIEZO1-YAP1-CTGF plus chemotherapy might serve as a potential therapeutic option to block GC progression and peritoneal metastasis.

Eco-Friendly Cerium-Cobalt Counter-Doped Bi2Se3 Nanoparticulate Semiconductor: Synergistic Doping Effect for Enhanced Thermoelectric Generation.

Metal chalcogenides are primarily used for thermoelectric applications due to their enormous potential to convert waste heat into valuable energy. Several studies focused on single or dual aliovalent doping techniques to enhance thermoelectric properties in semiconductor materials; however, these dopants enhance one property while deteriorating others due to the interdependency of these properties or may render the host material toxic. Therefore, a strategic doping approach is vital to harness the full potential of doping to improve the efficiency of thermoelectric generation while restoring the base material eco-friendly. Here, we report a well-designed counter-doped eco-friendly nanomaterial system (~70 nm) using both isovalent (cerium) and aliovalent (cobalt) in a Bi2Se3 system for enhancing energy conversion efficiency. Substituting cerium for bismuth simultaneously enhances the Seebeck coefficient and electrical conductivity via ionized impurity minimization. The boost in the average electronegativity offered by the self-doped transitional metal cobalt leads to an improvement in the degree of delocalization of the valence electrons. Hence, the new energy state around the Fermi energy serving as electron feed to the conduction band coherently improves the density of the state of conducting electrons. The resulting high power factor and low thermal conductivity contributed to the remarkable improvement in the figure of merit (zT = 0.55) at 473 K for an optimized doping concentration of 0.01 at. %. sample, and a significant nanoparticle size reduction from 400 nm to ~70 nm, making the highly performing materials in this study (Bi2-xCexCo2x3Se3) an excellent thermoelectric generator. The results presented here are higher than several Bi2Se3-based materials already reported.

QGD-Net: A Lightweight Model Utilizing Pixels of Affinity in Feature Layer for Dermoscopic Lesion Segmentation.

RESPONSE: Pixels with location affinity, which can be also called "pixels of affinity," have similar semantic information. Group convolution and dilated convolution can utilize them to improve the capability of the model. However, for group convolution, it does not utilize pixels of affinity between layers. For dilated convolution, after multiple convolutions with the same dilated rate, the pixels utilized within each layer do not possess location affinity with each other. To solve the problem of group convolution, our proposed quaternion group convolution uses the quaternion convolution, which promotes the communication between to promote utilizing pixels of affinity between channels. In quaternion group convolution, the feature layers are divided into 4 layers per group, ensuring the quaternion convolution can be performed. To solve the problem of dilated convolution, we propose the quaternion sawtooth wave-like dilated convolutions module (QS module). QS module utilizes quaternion convolution with sawtooth wave-like dilated rates to effectively leverage the pixels that share the location affinity both between and within layers. This allows for an expanded receptive field, ultimately enhancing the performance of the model. In particular, we perform our quaternion group convolution in QS module to design the quaternion group dilated neutral network (QGD-Net). Extensive experiments on Dermoscopic Lesion Segmentation based on ISIC 2016 and ISIC 2017 indicate that our method has significantly reduced the model parameters and highly promoted the precision of the model in Dermoscopic Lesion Segmentation. And our method also shows generalizability in retinal vessel segmentation.

Clinical characteristics of unvaccinated or incompletely vaccinated children with neurological manifestations due to SARS-CoV-2 Omicron infection.

Omicron generally causes milder disease than previous strains of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), especially in fully vaccinated individuals. However, incompletely vaccinated children may develop Omicron-related complications such as those affecting the central nervous system. To characterize the spectrum of clinical manifestations of neuro-COVID and to identify potential biomarkers associated with clinical outcomes, we recruited 15 children hospitalized for Omicron-related neurological manifestations in three hospitals in Hong Kong (9 boys and 6 girls aged 1-13 years). All were unvaccinated or incompletely vaccinated. Fourteen (93.3%) were admitted for convulsion, including benign febrile seizure (n = 7), complex febrile seizure (n = 2), seizure with fever (n = 3), and recurrent breakthrough seizure (n = 2), and the remaining nonconvulsive patient developed encephalopathic state with impaired consciousness. None of the seven children with benign febrile seizure and six of eight children with other neurological manifestations had residual deficits at 9-month follow-up. SARS-CoV-2 RNA was undetectable in the cerebrospinal fluid (CSF) specimens of seven patients who underwent lumbar puncture. Spike-and-wave/sharp waves affecting the frontal lobes were detected in four of seven (57.1%) patients who underwent electroencephalogram. Children with Omicron-related neurological manifestations had significantly higher blood levels of IL-6 (p < 0.001) and CHI3L1 (p = 0.022) than healthy controls, and higher CSF levels of IL-6 (p = 0.002) than children with non-COVID-19-related febrile illnesses. Higher CSF-to-blood ratios of IL-8 and CHI3L1 were associated with longer length of stay, whereas higher ratios of IL-6 and IL-8 were associated with higher blood tau level. The role of CSF:blood ratio of IL-6, IL-8, and CHI3L1 as prognostic markers for neuro-COVID should be further evaluated.