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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 of synergizing 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 states between economic growth and carbon emission reduction (q ≥ 0.8220), which resolves the theoretical uncertainty about synergizing economic growth and carbon emission reduction through the path of TSFP optimization. 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 the TSFP in 2020, the carbon emission intensity of the TSFP obtained by the decision framework was reduced by 0.7 and 4.7 tons/million yuan, respectively, and realized the synergy between economic growth and carbon emission reduction (decoupling index was 0.25 and 0.21). Further confirming that TSFP optimization 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.

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.