Certifying Network Topologies and Nonlocalities of Triangle Quantum Networks.

Quantum networks promise unprecedented advantages in information processing and open up intriguing new opportunities in fundamental research, where network topology and network nonlocality fundamentally underlie these applications. Hence, the detections of network topology and nonlocality are crucial, which, however, remain an open problem. Here, we conceive and experimentally demonstrate to determine the network topology and network nonlocality hosted by a triangle quantum network comprising three parties, within and beyond Bell theorem, with a general witness operator for the first time. We anticipate that this unique approach may stimulate further studies toward the efficient characterization of large complex quantum networks so as to better harness the advantage of quantum networks for quantum information applications.

The Three-Dimensional Hierarchical Structure of Bi x -Sn1-x O2 Used for Ultrasensitive Detection of Butanone under UV Irradiation.

Recent studies have identified butanone as a promising biomarker in the breath of lung cancer patients, yet the understanding of its gas-sensing properties remains limited. A key challenge has been to enhance the gas-sensing performance of materials toward butanone, particularly under ultraviolet light exposure. Herein, we report the synthesis of a novel three-dimensional composite material composed of SnO2 incorporated with Bi2O3 using facile hydrothermal and impregnation precipitation methods. Detailed physical and chemical characterizations were performed to assess the properties of the developed material. Upon activation with ultraviolet light, our composite exhibited exceptionally high sensitivity to butanone. Remarkably, the butanone response was nearly 3 times greater for the Bi2O3-loaded SnO2 composite than for pristine SnO2, achieving a response value of 70. This substantial improvement is due to the synergistic effect of the material's distinctive three-dimensional architecture and the presence of Bi2O3, which significantly augmented the gas-sensing capability of butanone. To elucidate the underlying gas-sensing mechanism, we conducted first-principles calculations using density functional theory (DFT). The computational analysis revealed that the Bi2O3-containing system possesses superior adsorption energy for butanone. Ultimately, our findings suggest that the Bi-SnO2 composite holds great promise as an optimal sensing material for the detection of butanone under ultraviolet illumination.

Survey on pattern of myopia in school children in Hangzhou after the COVID-19 pandemic: a school-based vision screening study.

BACKGROUND: Myopia is a major health issue around the world. Myopia in children has increased significantly during the COVID-19 pandemic in China, but reports are scarce on the prevalence of myopia following the pandemic. This study collected vision screening data of school children in China for five consecutive years to observe the changes in myopia after the pandemic and compare the observed prevalence of myopia before and after the pandemic. METHODS: A school-based vision screening study used stratified samplings to collect the vision screening data in school children aged 6-13 from 45 primary schools in Hangzhou. Vision screening data including uncorrected visual acuity(UCVA) and spherical equivalent refraction(SER). Calculating the mean of SER and the prevalence of myopia and hyperopia from 2019 to 2023. RESULTS: A total of 79,068 screening results (158,136 eyes) were included in the analysis. A substantial myopic shift (approximately -0.30 diopters [D] on average) was found in 2020 and 2021 compared with 2019 in all age groups and a substantial myopic shift (approximately 0.4 D on average) was found in 2022 compared with 2021. A slight myopic shift (approximately -0.14 D on average) was found in 2023 compared with 2022. The prevalence of myopia in all age groups was the highest for five years in 2020 or 2021, which was 31.3% for 6-year-olds, 43.0% for 7-year-olds, and 53.7% for 8-year-olds. A positive change in the prevalence rate of myopia was found at 6 years old (0.59%, 0.12%, 0.36%, 0.25%, p < 0.001). The change in prevalence rate in myopia was shifted slightly in children aged 10-13 years. Children aged 8 to 13 years had a slight increase in myopia prevalence from 2022 to 2023. The prevalence of hyperopia was low and stable in all grade groups, ranging from 0.7% to 2.2% over five years. CONCLUSION: Myopia in children has increased rapidly during the COVID-19 pandemic. After the pandemic, the prevalence of myopia in children gradually decreased temporarily and then rebounded. Myopic shift was more apparent in younger children. Myopic shift in children may be related to the reduction of outdoor time, less light, and near work habits, and further research is needed.

Clinical intervention effect of Xuefu Zhuyu decoction on chronic heart failure complicated with depression.

BACKGROUND: The diagnosis and treatment of depression in patients with chronic heart failure (CHF) is challenging, with no ideal treatment at present. AIM: To analyze the clinical intervention effect of Xuefu Zhuyu decoction (XFZYD) on CHF complicated with depression. METHODS: The study cohort comprised 116 patients with CHF complicated with depression who received treatment from July 2020 to July 2023, of which 55 received Western medicine (control group) and 61 received XFZYD (research group). Data on clinical effectiveness, traditional Chinese medicine (TCM) syndrome score, cardiac function, negative emotions, and serum inflammatory factors, were collected for comparative analyses. RESULTS: Compared with the control group, the research group had an evidently higher total effective rate. Furthermore, there were marked reductions in TCM symptom score, left ventricular end-diastolic diameter, left ventricular end-systolic diameter, Self-Rating Depression Scale, Hamilton Depression Scale, high-sensitivity C-reactive protein, monocyte chemoattractant protein-1, and matrix metalloproteinase-9 in the research group after treatment, and these were lower than the corresponding values in the control group. Left ventricular ejection fraction was increased and higher in the research group compared with the control group after treatment. CONCLUSION: Our findings conclusively proved that XFZYD was considerably superior to Western medicine for treating CHF complicated with depression because it significantly alleviated patients' symptoms, improved cardiac function, relieved negative emotions, and reduced the levels of serum inflammatory factors.

Hydraulic system fault diagnosis decoupling method based on 2D time-series modeling and self-attention fusion.

Hydraulic systems play a pivotal and extensive role in mechanics and energy. However, the performance of intelligent fault diagnosis models for multiple components is often hindered by the complexity, variability, strong hermeticity, intricate structures, and fault concealment in real-world conditions. This study proposes a new approach for hydraulic fault diagnosis that leverages 2D temporal modeling and attention mechanisms for decoupling compound faults and extracting features from multisample rate sensor data. Initially, to address the issue of oversampling in some high-frequency sensors within the dataset, variable frequency data sampling is employed during the data preprocessing stage to resample redundant data. Subsequently, two-dimensional convolution simultaneously captures both the instantaneous and long-term features of the sensor signals for the coupling signals of hydraulic system sensors. Lastly, to address the challenge of feature fusion with multisample rate sensor data, where direct merging of features through maximum or average pooling might dilute crucial information, a feature fusion and decoupling method based on a probabilistic sparse self-attention mechanism is designed, avoiding the issue of long-tail distribution in multisample rate sensor data. Experimental validation showed that the proposed model can effectively utilize samples to achieve accurate fault decoupling and classification for different components, achieving a diagnostic accuracy exceeding 97% and demonstrating robust performance in hydraulic system fault diagnosis under noise conditions.

GLGFormer: Global Local Guidance Network for Mucosal Lesion Segmentation in Gastrointestinal Endoscopy Images.

Automatic mucosal lesion segmentation is a critical component in computer-aided clinical support systems for endoscopic image analysis. Image segmentation networks currently rely mainly on convolutional neural networks (CNNs) and Transformers, which have demonstrated strong performance in various applications. However, they cannot cope with blurred lesion boundaries and lesions of different scales in gastrointestinal endoscopy images. To address these challenges, we propose a new Transformer-based network, named GLGFormer, for the task of mucosal lesion segmentation. Specifically, we design the global guidance module to guide single-scale features patch-wise, enabling them to incorporate global information from the global map without information loss. Furthermore, a partial decoder is employed to fuse these enhanced single-scale features, achieving single-scale to multi-scale enhancement. Additionally, the local guidance module is designed to refocus attention on the neighboring patch, thus enhancing local features and refining lesion boundary segmentation. We conduct experiments on a private atrophic gastritis segmentation dataset and four public gastrointestinal polyp segmentation datasets. Compared to the current lesion segmentation networks, our proposed GLGFormer demonstrates outstanding learning and generalization capabilities. On the public dataset ClinicDB, GLGFormer achieved a mean intersection over union (mIoU) of 91.0% and a mean dice coefficient (mDice) of 95.0%. On the private dataset Gastritis-Seg, GLGFormer achieved an mIoU of 90.6% and an mDice of 94.6%.

Machine Learning-Based Prediction of Helicobacter pylori Infection Study in Adults.

BACKGROUND Helicobacter pylori has a high infection rate worldwide, and epidemiological study of H. pylori is important. Artificial intelligence has been widely used in the field of medical research and has become a hotspot in recent years. This paper proposed a prediction model for H. pylori infection based on machine learning in adults. MATERIAL AND METHODS Adult patients were selected as research participants, and information on 30 factors was collected. The chi-square test, mutual information, ReliefF, and information gain were used to screen the feature factors and establish 2 subsets. We constructed an H. pylori infection prediction model based on XGBoost and optimized the model using a grid search by analyzing the correlation between features. The performance of the model was assessed by comparing its accuracy, recall, precision, F1 score, and AUC with those of 4 other classical machine learning methods. RESULTS The model performed better on the part B subset than on the part A subset. Compared with the other 4 machine learning methods, the model had the highest accuracy, recall, F1 score, and AUC. SHAP was used to evaluate the importance of features in the model. It was found that H. pylori infection of family members, living in rural areas, poor washing hands before meals and after using the toilet were risk factors for H. pylori infection. CONCLUSIONS The model proposed in this paper is superior to other models in predicting H. pylori infection and can provide a scientific basis for identifying the population susceptible to H. pylori and preventing H. pylori infection.

Structure-aware diffusion for low-dose CT imaging.

Reducing the radiation dose leads to the x-ray computed tomography (CT) images suffering from heavy noise and artifacts, which inevitably interferes with the subsequent clinic diagnostic and analysis. Leading works have explored diffusion models for low-dose CT imaging to avoid the structure degeneration and blurring effects of previous deep denoising models. However, most of them always begin their generative processes with Gaussian noise, which has little or no structure priors of the clean data distribution, thereby leading to long-time inference and unpleasant reconstruction quality. To alleviate these problems, this paper presents a Structure-Aware Diffusion model (SAD), an end-to-end self-guided learning framework for high-fidelity CT image reconstruction. First, SAD builds a nonlinear diffusion bridge between clean and degraded data distributions, which could directly learn the implicit physical degradation prior from observed measurements. Second, SAD integrates the prompt learning mechanism and implicit neural representation into the diffusion process, where rich and diverse structure representations extracted by degraded inputs are exploited as prompts, which provides global and local structure priors, to guide CT image reconstruction. Finally, we devise an efficient self-guided diffusion architecture using an iterative updated strategy, which further refines structural prompts during each generative step to drive finer image reconstruction. Extensive experiments on AAPM-Mayo and LoDoPaB-CT datasets demonstrate that our SAD could achieve superior performance in terms of noise removal, structure preservation, and blind-dose generalization, with few generative steps, even one step only.

Thoracic high resolution computed tomography evaluation of imaging abnormalities of 108 lung cancer patients with different pulmonary function.

PURPOSE: Preserved ratio impaired spirometry (PRISm) and chronic obstructive pulmonary disease (COPD) belong to lung function injury. PRISm is a precursor to COPD. We compared and evaluated the different basic information, imaging findings and survival curves of 108 lung cancer patients with different pulmonary function based on high resolution computed tomography (HRCT). METHODS: This retrospective study was performed on 108 lung cancer patients who did pulmonary function test (PFT) and thoracic HRCT. The basic information was evaluated: gender, age, body mass index (BMI), smoke, smoking index (SI). The following pulmonary function findings were evaluated: forced expiratory volume in 1s (FEV1), forced vital capacity (FVC), FEV1/FVC ratio. The following computed tomography (CT) findings were evaluated: appearance (bronchiectasis, pneumonectasis, atelectasis, ground-glass opacities [GGO], interstitial inflammation, thickened bronchial wall), diameter (aortic diameter, pulmonary artery diameter, MPAD/AD ratio, inferior vena cava diameter [IVCD]), tumor (volume, classification, distribution, staging [I, II, III, IV]). Mortality rates were calculated and survival curves were estimated using the Kaplan-Meier method. RESULTS: Compared with normal pulmonary function group, PRISm group and COPD group were predominantly male, older, smoked more, poorer lung function and had shorter survival time after diagnosis. There were more abnormal images in PRISm group and COPD group than in normal lung function group (N-C group). In PRISm group and COPD group, lung cancer was found late, and the tumor volume was larger, mainly central squamous carcinoma. But the opposite was true for the N-C group. The PRISm group and COPD group had significant poor survival probability compared with the normal lung function group. CONCLUSIONS: Considerable differences regarding basic information, pulmonary function, imaging findings and survival curves are found between normal lung function group and lung function injury group. Lung function injury (PRISm and COPD) should be taken into account in future lung cancer screening studies.

Comparative proteomics reveals that fatty acid metabolism is involved in myocardial adaptation to chronic hypoxic injury.

Congenital heart disease (CHD) is the most serious form of heart disease, and chronic hypoxia is the basic physiological process underlying CHD. Some patients with CHD do not undergo surgery, and thus, they remain susceptible to chronic hypoxia, suggesting that some protective mechanism might exist in CHD patients. However, the mechanism underlying myocardial adaptation to chronic hypoxia remains unclear. Proteomics was used to identify the differentially expressed proteins in cardiomyocytes cultured under hypoxia for different durations. Western blotting assays were used to verify protein expression. A Real-Time Cell Analyzer (RTCA) was used to analyze cell growth. In this study, 3881 proteins were identified by proteomics. Subsequent bioinformatics analysis revealed that proteins were enriched in regulating oxidoreductase activity. Functional similarity cluster analyses showed that chronic hypoxia resulted in proteins enrichment in the mitochondrial metabolic pathway. Further KEGG analyses found that the proteins involved in fatty acid metabolism, the TCA cycle and oxidative phosphorylation were markedly upregulated. Moreover, knockdown of CPT1A or ECI1, which is critical for fatty acid degradation, suppressed the growth of cardiomyocytes under chronic hypoxia. The results of our study revealed that chronic hypoxia activates fatty acid metabolism to maintain the growth of cardiomyocytes.

Observation of Non-Hermitian Edge Burst Effect in One-Dimensional Photonic Quantum Walk.

Non-Hermitian systems can exhibit unique quantum phases without any Hermitian counterparts. For example, the latest theoretical studies predict a new surprising phenomenon that bulk bands can localize and dissipate prominently at the system boundary, which is dubbed the non-Hermitian edge burst effect. Here we realize a one-dimensional non-Hermitian Su-Schrieffer-Heeger lattice with bulk translation symmetry implemented with a photonic quantum walk. Employing time-resolved single-photon detection to characterize the chiral motion and boundary localization of bulk bands, we determine experimentally that the dynamics underlying the non-Hermitian edge burst effect is due to the interplay of non-Hermitian skin effect and imaginary band gap closing. This new non-Hermitian physical effect deepens our understanding of quantum dynamics in open quantum systems.

On-mask detection of SARS-CoV-2 related substances by surface enhanced Raman scattering.

We have developed a convenient surface-enhanced Raman scattering (SERS) platform based on vertical standing gold nanowires (v-AuNWs) which enabled the on-mask detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) related substances such as the Spike-1 protein and the corresponding pseudo-virus. The Spike-1 protein was clearly distinguished from BSA protein with an accuracy above 99 %, and the detection limit could be achieved down to 0.01 µg/mL. Notably, a similar accuracy was achieved for the pseudo-SARS-CoV-2 (pSARS-2) virus as compared to the pseudo-influenza H7N9 (pH7N9) virus. The sensing strategy and setups could be easily adapted to the real SARS-CoV-2 virus and other highly contagious viruses. It provided a promising way to screen the virus carriers by a fast evaluation of their wearing v-AuNWs integrated face-mask which was mandatory during the pandemic.

A Prospective Randomized Controlled Trial Assessing the Impact of Preoperative Combined with Postoperative Progressive Resistance Training on Muscle Strength, Gait, Balance and Function in Patients Undergoing Total Hip Arthroplasty.

Purpose: The aim of this study is to investigate the effects of a preoperative combined with postoperative moderate-intensity progressive resistance training (PRT) of the operative side in patients with hip osteoarthritis (HOA) who are undergoing total hip arthroplasty (THA). The study seeks to evaluate the impact of this combined intervention on muscle strength, gait, balance, and hip joint function in a controlled, measurable, and objective manner. Additionally, the study aims to compare the outcomes of this combined intervention with those of preoperative or postoperative muscle strength training conducted in isolation. Methods: A total of 90 patients with HOA scheduled for unilateral primary THA were randomly assigned to three groups: Pre group (preoperative PRT), Post group (postoperative PRT), and Pre& Post group (preoperative combined with postoperative PRT) focusing on hip flexion, extension, adduction, and abduction of operated side. Muscle strength, gait parameters, balance, and hip function were assessed at specific time points during a 12-month follow-up period. Results: All three groups showed significant improvements in muscle strength, with the Pre& Post group demonstrating the most pronounced and sustained gains. Gait velocity and cadence were significantly improved in the Pre& Post group at 1-month and 3-month postoperative follow-ups compared to the other groups. Similarly, the Pre& Post group exhibited superior balance performance at 3-month and 12-month postoperative follow-ups. The Harris Hip Score also showed better outcomes in the Pre& Post group at all follow-up intervals. Conclusion: Preoperative combined with postoperative moderate-intensity PRT in HOA patients undergoing THA led to superior improvements in muscle strength, gait, balance, and hip joint function compared to preoperative or postoperative PRT alone. This intervention shows significant promise in optimizing postoperative rehabilitation and enhancing patient outcomes following THA.

Genomic and transcriptomic significance of multiple primary lung cancers detected by next-generation sequencing in clinical settings.

Effective diagnosis and understanding of the mechanism of intrapulmonary metastasis (IM) from multiple primary lung cancers (MPLC) aid clinical management. However, the actual detection panels used in the clinic are variable. Current research on tumor microenvironment (TME) of MPLC and IM is insufficient. Therefore, additional investigation into the differential diagnosis and discrepancies in TME between two conditions is crucial. Two hundred and fourteen non-small cell lung cancer patients with multiple tumors were enrolled and 507 samples were subjected to DNA sequencing (NGS 10). Then, DNA and RNA sequencing (master panel) were performed on the specimens from 32 patients, the TME profiles between tumors within each patient and across patients and the differentially expressed genes were compared. Four patients were regrouped with NGS 10 results. Master panel resolved the classifications of six undetermined patients. The TME in MPLC exhibited a high degree of infiltration by natural killer (NK) cells, CD56dim NK cells, endothelial cells, etc., P < 0.05. Conversely, B cells, activated B cells, regulatory cells, immature dendritic cells, etc., P < 0.001, were heavily infiltrated in the IM. NECTIN4 and LILRB4 mRNA were downregulated in the MPLC (P < 0.0001). Additionally, NECTIN4 (P < 0.05) and LILRB4 were linked to improved disease-free survival in the MPLC. In conclusion, IM is screened from MPLC by pathology joint NGS 10 detections, followed by a large NGS panel for indistinguishable patients. A superior prognosis of MPLC may be associated with an immune-activating TME and the downregulation of NECTIN4 and LILRB4 considered as potential drug therapeutic targets.

Roles of mir155hg and TNF-α in evaluation of prognosis of patients with systemic lupus erythematosus.

Background: Systemic lupus erythematosus (SLE) is a chronic inflammatory autoimmune disease characterized by multi-organ multi-system inflammation, causing severe damage to various organs or systems. Recent studies have shown that miR-155 can affect the progression of Lupus Nephritis via regulating TNF-a. The present study aims to explore the roles of MIR155HG and TNF-a in the evaluation of prognosis of patients with SLE, so as to provide a basis for clinical work. Methods: A total of 130 patients with SLE admitted to our hospital were selected, were selected from June 2015 to December 2017., and the SLE disease activity index (SLEDAI) score was given. The expressions of MIR155HG and TNF-a were detected via quantitative reverse transcription-polymerase chain reaction (qRT-PCR), the incidence of complications during treatment was observed, and the associations of MIR155HG and TNF-a with SLEDAI before treatment and complications were analyzed. All patients were followed up after discharge, and the related factors to the prognosis of patients were analyzed via Cox regression analysis. Results: The levels of MIR155HG and TNF-a were higher in patients with an SLEDAI score of 10-14 points than those in patients with an SLEDAI score of 5-9 points and 0-4 points. MIR155HG and TNF-a were positively correlated with the incidence of infection, renal damage and cardiac damage (r=0.623, 0.533 and 0.621; r=0.431, 0.498 and 0.552) (P<0.05). Moreover, there was also a positive correlation (r=0.3398, P<0.001) between the expressions of serum MIR155HG and TNF-a in SLE patients. SLEDAI score ≥10 points, complications during hospitalization, and highly-expressed MIR155HG and TNFa were risk factors related to the prognosis of patients. Conclusions: MIR155HG and TNF-a affect the activity of SLE, and the high expressions of them promote the occurrence of such complications as infection, renal damage and cardiac damage, harming the prognosis.

Development and Assessment of a Prediction Model for Alzheimer's Disease Diagnosis Based on Thermoregulation-Related Genes.

BACKGROUND: Alzheimer's disease (AD) is a prevalent neurodegenerative condition among the elderly population and the most common form of dementia, however, we lack potent interventions to arrest its inherent pathogenic vectors. Robust evidence indicates thermoregulatory perturbations during and before the onset of symptoms. Therefore, temperature-regulated biomarkers may offer clues to therapeutic targets during the presymptomatic stage. OBJECTIVE: The purpose of this study is to develop and assess a thermoregulation-related gene prediction model for Alzheimer's Disease diagnosis. METHOD: This study aims to utilize microarray bioinformatic analysis to identify the potential biomarkers of AD by analyzing four microarray datasets (GSE48350, GSE5281, GSE122063, and GSE181279) of AD patients. Furthermore, thermoregulation-associated hub genes were identified, and the expression patterns in the brain were explored. In addition, we explored the infiltration of immune cells with thermoregulation-related hub genes. Diagnostic marker validation was then performed at the single-cell level. Finally, the prediction of targeted drugs was performed based on the hub genes. RESULTS: Through the analysis of four datasets pertaining to AD, a total of five genes associated with temperature regulation were identified. Notably, CCK, CXCR4, SLC27A4, and SLC17A6 emerged as diagnostic markers indicative of AD-related brain injury. Furthermore, in the examination of peripheral blood samples from AD patients, SLC27A4 and CXCR4 were identified as pivotal diagnostic indicators. Regrettably, animal experimentation was not pursued to validate the data; rather, an assessment of temperature regulation-related genes was conducted. Future investigations will be undertaken to establish the correlation between these genes and AD pathology. CONCLUSION: Overall, CCK, CXCR4, SLC27A4, and SLC17A6 can be considered pivotal biomarkers for diagnosing the pathogenesis and molecular functions of AD.

Efficient complete denture metal base design via a dental feature-driven segmentation network.

BACKGROUND AND OBJECTIVE: Complete denture is a common restorative treatment in dental patients and the design of the core components (major connector and retentive mesh) of complete denture metal base (CDMB) is the basis of successful restoration. However, the automated design process of CDMB has become a challenging task primarily due to the complexity of manual interaction, low personalization, and low design accuracy. METHODS: To solve the existing problems, we develop a computer-aided Segmentation Network-driven CDMB design framework, called CDMB-SegNet, to automatically generate personalized digital design boundaries for complete dentures of edentulous patients. Specifically, CDMB-SegNet consists of a novel upright-orientation adjustment module (UO-AM), a dental feature-driven segmentation network, and a specific boundary-optimization design module (BO-DM). UO-AM automatically identifies key points for locating spatial attitude of the three-dimensional dental model with arbitrary posture, while BO-DM can result in smoother and more personalized designs for complete denture. In addition, to achieve efficient and accurate feature extraction and segmentation of 3D edentulous models with irregular gingival tissues, the light-weight backbone network is also incorporated into CDMB-SegNet. RESULTS: Experimental results on a large clinical dataset showed that CDMB-SegNet can achieve superior performance over the state-of-the-art methods. Quantitative evaluation (major connector/retentive mesh) showed improved Accuracy (98.54 ± 0.58 %/97.73 ± 0.92 %) and IoU (87.42 ± 5.48 %/70.42 ± 7.95 %), and reduced Maximum Symmetric Surface Distance (4.54 ± 2.06 mm/4.62 ± 1.68 mm), Average Symmetric Surface Distance (1.45 ± 0.63mm/1.28 ± 0.54 mm), Roughness Rate (6.17 ± 1.40 %/6.80 ± 1.23 %) and Vertices Number (23.22 ± 1.85/43.15 ± 2.72). Moreover, CDMB-SegNet shortened the overall design time to around 4 min, which is one tenth of the comparison methods. CONCLUSIONS: CDMB-SegNet is the first intelligent neural network for automatic CDMB design driven by oral big data and dental features. The designed CDMB is able to couple with patient's personalized dental anatomical morphology, providing higher clinical applicability compared with the state-of-the-art methods.

Multi-omics delineate growth factor network underlying exercise effects in an Alzheimer's mouse model.

Physical exercise represents a primary defense against age-related cognitive decline and neurodegenerative disorders like Alzheimer's disease (AD). To impartially investigate the underlying mechanisms, we conducted single-nucleus transcriptomic and chromatin accessibility analyses (snRNA-seq and ATAC-seq) on the hippocampus of mice carrying AD-linked NL-G-F mutations in the amyloid precursor protein gene (APPNL-G-F) following prolonged voluntary wheel-running exercise. Our study reveals that exercise mitigates amyloid-induced changes in both transcriptomic expression and chromatin accessibility through cell type-specific transcriptional regulatory networks. These networks converge on the activation of growth factor signaling pathways, particularly the epidermal growth factor receptor (EGFR) and insulin signaling, correlating with an increased proportion of immature dentate granule cells and oligodendrocytes. Notably, the beneficial effects of exercise on neurocognitive functions can be blocked by pharmacological inhibition of EGFR and the downstream phosphoinositide 3-kinases (PI3K). Furthermore, exercise leads to elevated levels of heparin-binding EGF (HB-EGF) in the blood, and intranasal administration of HB-EGF enhances memory function in sedentary APPNL-G-F mice. These findings offer a panoramic delineation of cell type-specific hippocampal transcriptional networks activated by exercise and suggest EGF-related growth factor signaling as a druggable contributor to exercise-induced memory enhancement, thereby suggesting therapeutic avenues for combatting AD-related cognitive decline.

Enhanced Mechanical Strength and Sustained Drug Release in Carrier-Free Silver-Coordinated Anthraquinone Natural Antibacterial Anti-Inflammatory Hydrogel for Infectious Wound Healing.

The persistent challenge of healing infectious wounds and the rise of bacterial resistance represent significant hurdles in contemporary medicine. In this study, based on the natural small molecule drug Rhein self-assembly to form hydrogels and coordinate assembly with silver ions (Ag+), a sustained-release carrier-free hydrogel with compact structure is constructed to promote the repair of bacterial-infected wounds. As a broad-spectrum antimicrobial agent, Ag+ can avoid the problem of bacterial resistance caused by the abuse of traditional antibiotics. In addition, due to the slow-release properties of Rhein hydrogel, continuous effective concentration of Ag+ at the wound site can be ensured. The assembly of Ag+ and Rhein makes the hydrogel system with enhanced mechanical stability. More importantly, it is found that Rhein effectively promotes skin tissue regeneration and wound healing by reprogramming M1 macrophages into M2 macrophages. Further mechanism studies show that Rhein realizes its powerful anti-inflammatory activity through NRF2/HO-1 activation and NF-κB inhibition. Thus, the hydrogel system combines the excellent antibacterial properties of Ag+ with the excellent anti-inflammatory and tissue regeneration ability of Rhein, providing a new strategy for wound management with dual roles.