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Abstract Background Identification of active components of traditional Chinese Medicine (TCM) and their respective targets is important for understanding the mechanisms underlying TCM efficacy. However, there are still no effective technical methods to achieve this. Methods Herein, we have established a method for rapidly identifying targets of a specific TCM and interrogating the targets with their corresponding active components based on Isothermal Shift Assay (iTSA) and database interrogation. Results We optimized iTSA workflow and identified 110 targets for Danhong injection (DHI) which is used as an effective remedy for cardiovascular and cerebrovascular diseases. Moreover, we identified the targets of the nine major ingredients found in DHI. Database interrogation found that the potential targets for DHI, in which we verified that ADK as the target for salvianolic acid A and ALDH1B1 as the target for protocatechualdehyde in DHI, respectively. Conclusion Overall, we established a novel paradigm model for the identification of targets and their respective ingredients in DHI, which facilitates the discovery of drug candidates and targets for improving disease management and contributes to revealing the underlying mechanisms of TCM and fostering TCM development and modernization. Graphical Abstract

Abstract As the Internet of Vehicles advances, the demand for timely data acquisition by vehicle users continues to escalate, albeit confronted with the challenge of excessive data retrieval latency. The emergence of edge computing provides technical support for the development of vehicular networks by caching data in advance to reduce data acquisition latency. Therefore, how to effectively cache and query data becomes a key issue in addressing the timeliness of data acquisition in vehicular networks. In this paper, we investigate an efficient query optimization model to minimize data acquisition latency. Firstly, based on the distribution of data query frequencies across different servers, we propose an edge collaborative caching strategy using a tabu search algorithm. This strategy prioritizes high-traffic data by finding two optimal storage nodes for each high-traffic data in descending order of data popularity, ensuring a backup within the collaborative domain for each data segment. This not only reduces data transmission latency between nodes during task execution but also prevents single-point failures. Secondly, we deploy cuckoo filters on edge nodes to enable rapid localization of cached data nodes when users query data, thus reducing data processing latency. Finally, simulation results demonstrate that the proposed query optimization model outperforms other schemes in terms of average data query latency.

Abstract The recent development of nanobiomaterials has shed some light on the field of periodontal tissue regeneration. Laponite (LAP), an artificially synthesized two-dimensional (2D) disk-shaped nanosilicate, has garnered substantial attention in regenerative biomedical applications owing to its distinctive structure, exceptional biocompatibility and bioactivity. This study endeavors to comprehensively evaluate the influence of LAP on periodontal regeneration. The effects of LAP on periodontal ligament cells (PDLCs) on osteogenesis, cementogenesis and angiogenesis were systematically assessed, and the potential mechanism was explored through RNA sequencing. The results indicated that LAP improved osteogenic and cementogenic differentiation of PDLCs, the regulatory effects of LAP on PDLCs were closely correlated with activation of PI3K-AKT signaling pathway. Moreover, LAP enhanced angiogenesis indirectly via manipulating paracrine of PDLCs. Then, LAP was implanted into rat periodontal defect to confirm its regenerative potential. Both micro-CT and histological analysis indicated that LAP could facilitate periodontal tissue regeneration in vivo. These findings provide insights into the bioactivity and underlying mechanism of LAP on PDLCs, highlighting it might be a potential therapeutic option in periodontal therapy.

Abstract Urinary incontinence is a common complication in stroke survivors for whom new interventions are needed. This study investigated the therapeutic effect of low-frequency (LF) repeated transcranial magnetic stimulation (rTMS) on the contralesional primary motor cortex (M1) in patients with poststroke urinary incontinence (PSI). A total of 100 patients were randomly assigned to the rTMS group or sham-rTMS group on basis of the intervention they received. Both groups underwent five treatment sessions per week for 4 weeks. Data from the urodynamic examination were used as the primary outcome. The secondary outcome measures were questionnaires and pelvic floor surface electromyography. After 4 weeks of intervention, the maximum cystometric capacity (MCC), maximum detrusor pressure (Pdet.max), residual urine output, overactive bladder score (OABSS) (including frequency, urgency, and urgency urinary incontinence), and the ICIQ-UI SF improved significantly in the rTMS group compared with those in the sham-rTMS group (P 0.05). Our data confirmed that 4 weeks of LF-rTMS stimulation on the contralateral M1 positively affects poststroke urinary incontinence in several aspects, such as frequency, urgency urinary incontinence, MCC, end-filling Pdet, OABSS, and ICIQ-UI SF scores.

Abstract Object detection plays a vital role in remote sensing applications. Although object detection has achieved proud results in natural images, these methods are difficult to be directly applied to remote sensing images. Remote sensing images often have complex backgrounds and small objects, which results in a highly unbalanced distribution of foreground and complex background information. In order to solve the above problems, this paper proposes a multi-head channel and spatial trans-attention (MCSTA) module, which performs remote pixel interaction from the channel and spatial dimensions respectively to complete the attention feature capture function. It is a plug-and-play module that can be easily embedded in any other natural image object detection convolutional neural network, making it quickly applicable to remote sensing images. First, in order to reduce computational complexity and improve feature richness, we use a special linear convolution to obtain three projection features instead of the simple matrix multiplication transformation in Transformer. Second, we obtain trans-attention maps in different dimensions in a manner similar to the self-attention mechanism to capture the interrelationships of features in channels and spaces. In this process, we use a multi-head mechanism to perform parallel operations to improve speed. Furthermore, in order to avoid large-scale matrix operations, we specially designed an attention blocking mode to reduce computer memory usage and increase operation speed. Finally, we embedded the trans-attention module into YOLOv8, added a new detection head and optimized the feature fusion method, thus designing a lightweight small object detection model named TA-YOLO for remote sensing images. It has fewer parameters than the benchmark model YOLOv8, and its mAP on the PASCAL VOC and VisDrone data sets increased by 1.3% and 6.2% respectively. The experimental results prove the powerful function of the trans-attention module and the excellent performance of TA-YOLO.

Wireless Sensor Network (WSN) is a distributed sensor network composed a large number of nodes with low cost, low performance and self-management. The special structure of WSN brings both convenience and vulnerability. For example, a malicious participant can launch attacks by capturing a physical device. Therefore, node authentication that can resist malicious attacks is very important to network security. Recently, blockchain technology has shown the potential to enhance the security of the Internet of Things (IoT). In this paper, we propose a Blockchain-empowered Authentication Scheme (BAS) for WSN. In our scheme, all nodes are managed by utilizing the identity information stored on the blockchain. Besides, the simulation experiment about worm detection is executed on BAS, and the security is evaluated from detection and infection rate. The experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.

Abstract Blockchain has penetrated in various fields, such as finance, healthcare, supply chain, and intelligent transportation, but the value exchange between different blockchains limits their expansion. Cross-chain technology, such as notary mechanism, enables asset exchanges between different blockchain networks. However, existing research still confronts problems such as single inherent value evaluation, collusion risk, credit evaluation and unreasonable resource allocation, making it difficult to ensure the security of cross-chain asset transactions. So this paper proposes a cross-chain asset trading scheme based on edge cloud storage to improve the reliability of notaries and the security of cross-chain value exchange. Firstly, introduce the entropy weight method to determine indicators and adopt multi indicator evaluation to reduce the risk of collusion between notaries and users; Secondly, design a multi-indicator credit evaluation method to improve the accuracy of the evaluation; Finally, design a new and old notary node share allocation method to improve the rationality of resource allocation.The experiment shows that the scheme designed in this paper can reduce the risk of collusion, more accurately screen out high credit nodes to act as notaries, and make resource allocation more reasonable.

Background Body mass index (BMI) is associated with diabetic nephropathy (DN). However, the mediator factors in the BMI-DN effects remain unclear.Methods Univariate and multivariate Mendelian randomization (MR) analysis were performed to estimate the association between six lifestyles (moderate to vigorous physical activity levels, years of schooling, BMI, nap during day, number of treatments/medications taken and coffee intake) and DN. MR Egger, Weighted median, Simple mode, and Weighted mode was supplemental methods to Inverse variance weighted. Sensitivity analysis included heterogeneity test, horizontal pleiotropy test, and Leave-One-Out. Additionally, mediation MR was conducted to evaluate the mediating role of lifestyles between BMI and DN. Finally, functional enrichment analysis based on the mediation MR results was performed.Results univariate and multivariate Mendelian randomization (MR) analysis were performed to estimate the association between six lifestyles (moderate to vigorous physical activity levels, years of schooling, BMI, nap during day, number of treatments/medications taken and coffee intake) and DN. MR Egger, Weighted median, Simple mode, and Weighted mode was supplemental methods to Inverse variance weighted. Sensitivity analysis included heterogeneity test, horizontal pleiotropy test, and Leave-One-Out. Additionally, mediation MR was conducted to evaluate the mediating role of lifestyles between BMI and DN. Finally, functional enrichment analysis based on the mediation MR results was performed.Conclusion our results supported mediation role of vigorous physical activity level and number of treatments/medications in BMI-DN effects.

Artificial materials for the potential treatment of bone defects are very trendy in such research fields. To investigate a hydrogel scaffold with better mechanical strength and osteoinductivity is always a challenge since brittle and soft properties are always the problems in traditional hydrogels. In our study, a novel gelatin/chitosan hydrogel crosslinked by a double network filled with lignin and calcium silicate is fabricated. Enhancements of mechanical strength, low swelling ratio, and improvement in osteoinductivity were achieved in this composite double network hydrogels. The results revealed that lignin was a key factor in raising the compressive strength and stability of the hydrogel because more hydrogen bonds were formed between lignin and the crosslinking network. Even after swelling equilibrium, the compressive strength remained almost unchanged. In addition, the hydrogel groups with a higher ratio of lignin also had lower cytotoxicity and improved osteoinductivity of pre-osteoblasts while the increasing concentration of calcium silicate progressively weakened these performances. Therefore, the gelatin/chitosan double network hydrogel reinforced by lignin would be a potential bone tissue-engineered artificial material.

Abstract Aiming at the problems of identifying storage pest tobacco pest beetles from images that have few object pixels and considerable image noise, and therefore suffer from lack of information and identifiable features, this paper proposes an automatic monitoring method of tobacco beetle based on Multi-scale Global residual Feature Pyramid Network and Dual-path Deformable Attention (MGrFPN-DDrGAM). Firstly, a Multi-scale Global residual Feature Pyramid Network (MGrFPN) is constructed to obtain rich high-level semantic features and more complete information on low-level features to reduce missed detection; Then, a Dual-path Deformable receptive field Guided Attention Module (DDrGAM) is designed to establish long-range channel dependence, guide the effective fusion of features and improve the localization accuracy of tobacco beetles by fitting the spatial geometric deformation features of and capturing the spatial information of feature maps with different scales to enrich the feature information in the channel and spatial. Finally, to simulate a real scene, a multi-scene tobacco beetle dataset is created. The dataset includes 28,080 images and manually labeled tobacco beetle objects. The experimental results show that under the framework of the Faster R-CNN algorithm, the detection precision and recall rate of this method can reach 91.4% and 98.4% when the intersection ratio (IoU) is 0.5. Compared with Faster R-CNN and FPN, when the intersection ratio (IoU) is 0.7, the detection precision is improved by 32.9% and 6.9%, respectively. The proposed method is superior to the current mainstream methods.

Abstract Oncolytic virotherapy holds promise for cancer treatment, but the factors determining its oncolytic activity remain unclear. Neutrophil extracellular traps (NETs) are associated with cancer progression, yet their formation mechanism and role in oncolytic virotherapy remain elusive. In this study, we demonstrate that, in glioma, upregulation of IGF2BP3 enhances the expression of E3 ubiquitin protein ligase MIB1, promoting FTO degradation via the ubiquitin-proteasome pathway. This results in increased m6A-mediated CSF3 release and NET formation. Oncolytic herpes simplex virus (oHSV) stimulates IGF2BP3-induced NET formation in malignant glioma. In glioma models in female mice, a BET inhibitor enhances the oncolytic activity of oHSV by impeding IGF2BP3-induced NETosis, reinforcing virus replication through BRD4 recruitment with the CDK9/RPB-1 complex to HSV gene promoters. Our findings unveil the regulation of m6A-mediated NET formation, highlight oncolytic virus-induced NETosis as a critical checkpoint hindering oncolytic potential, and propose targeting NETosis as a strategy to overcome resistance in oncolytic virotherapy.

Abstract Artificial intelligence and blockchain technology have become indispensable in the era of the digital economy, particularly in the field of financial financing. However, when it comes to supply chain finance (SCF), existing models primarily focus on risk identification and credit evaluation, neglecting the critical aspects of trust transfer continuity and reliability within the chain. To address this issue, this paper proposes a blockchain-based transitive trust model for SCF, which ensures seamless trust transfer from core enterprises to bottom suppliers during financing enterprise credit evaluation. The model utilizes multi-layer metrics to calculate the comprehensive trust value of underlying suppliers, serving as the basis for credit delivery. Additionally, the model stores transitive signature receivable warrants on the blockchain and utilizes splittable delivery of warrants to underlying suppliers. The model’s rationality and correctness are verified through experimental analysis, with results demonstrating that the transitive trust model enhances Small and Medium-Sized Enterprises’ (SMEs) trust at the bottom of the supply chain, thus alleviating financing difficulties for SMEs.

To at the low robustness of the existing model for occluded object detection, an occluded object detection algorithm based on fuzzy sample anchor box IoU Matching degree Deviation Aware(IoU_MDA)is proposed. Firstly, fuzzy samples are defined based on Anchor-based, which reflects the degree of object occlusion. Secondly, IoU_MDA is proposed to quantify the degree of interference experienced by fuzzy samples. Then, IoU_MDA_Loss is constructed based on IoU_MDA, combined with IoU and the balance parameter $\Phi$ . To address class imbalance issues and enhance model generality, intra-class and inter-class fuzzy weights, and fuzzy sample focusing parameters are designed on the basis of the initial IoU_MDA_Loss. An occluded object training scheme is designed based on IoU perception, and non-fuzzy sample weight balancing parameters are constructed. Finally, IoU_MDA_Loss is merged with Focal Loss to obtain IoU_MDA_Focal Loss, simultaneously enhancing the detection performance of fuzzy samples and difficult-to-distinguish samples. The experimental results on the WiderPerson and VOC2007 datasets show that the mAP of IoU_MDA_Loss has increased by 2.04%, 2.36%, and IoU_MDA_Focal Loss has increased by 1.82%, 2.65%, respectively, compared to the baseline model. The detection performance surpasses current mainstream algorithms.

Retinal diseases impair the normal function of the visual system, making accurate segmentation of retinal vessels crucial. This paper proposes an improved U-Net network, namely Mitigating Information Loss U-Net (MILU-Net), for retinal vessel segmentation. The network introduces the Multi-Scale Adaptive Detail Feature Fusion (MSADFF) module, ensuring effective fusion of features at different scales before skip connections to reduce information loss. Simultaneously, the Dual Path Upsampling (DPUS) module is employed to enhance image resolution and compensate for spatial and channel information. Experiments on the DRIVE/STARE datasets demonstrate that MILU-Net outperforms in accuracy, sensitivity, specificity, AUC, and F1-Score metrics. Compared to the original U-Net, MILU-Net shows improvements of 1.44% and 1.84% in AUC, as well as 7.15% and 6.35% in sensitivity. Compared to the advanced Attention U-Net, MILU-Net achieves increases of 1.20% and 0.45% in ACC, as well as 2.63% and 2.71% in F1-Score, respectively. These results indicate the significant advantages of MILU-Net in retinal vessel segmentation tasks.

Abstract Cloud computing has data leakage from all parties, security protection of private data, and existing solutions do not provide a trade-off between security and overhead. With distributed data communication due to data barriers, information interaction security and data computation security have become challenges for secure computing. Combining cloud computing with secure multiparty computation can provide a higher level of data protection while maintaining the benefits of cloud computing. In this case, data can be stored in the cloud and computed through SMPC protocols, thus protecting the privacy and security of the data. However, multiple rounds of information interaction are often required, increasing the communication overhead, and the security strength is limited by the hardness assumption. In this paper, we work to achieve an optimal setting of the number of rounds in secure multi-party computation on the cloud to achieve a sublinear communication overhead and improve the security concept. A 2-round SMPC protocol is constructed in the framework of Universally Composable (UC). A 2-round SMPC protocol is constructed that uses multilinear maps based on the Learning from Errors (LWE) assumption. The participant encodes the input and sends it via broadcast to reduce the interaction, homomorphic computational encoding information for secure access to computational data and secure the SMPC protocol through UC security. This paper extends the participants to multiple parties, reduces the communication rounds to 2, the protocol achieves sublinear communication overhead in poly polynomial time, smaller setup size to poly(k), and static security is achieved.

Abstract With the development of cloud computing and deep learning, an increasing number of artificial intelligence models have been applied to reality. Such as videos on cell phones can be uploaded to the cloud for storage, which is detected by cloud arithmetic. Nevertheless, achieving this goal requires frequent consideration of the security of the model, since videos or images that go to the cloud, it is very likely to receive an adversarial attack. Regarding object detection, there has however been slow advancement in robustness research in this area. This is because training a target detection model requires a lot of arithmetic and time. Moreover, the current research has only slightly reduced the gap between clean and adversarial samples. To alleviate this problem, we propose a uniform perspective object detection robustness model based on bilinear interpolation that can accurately identify clean and adversarial samples. We propose the robustness optimization based on uniform metric perspective (RPU) for feature learning of clean and adversarial samples, drawing on the fine-grained idea. Following this, we analyze the fragility of the adversarial samples and consequently use the proposed perturbation filtering verification (PVB) based on bilinear interpolation. With slightly degraded clean sample detection performance, it substantially improves the robustness of object detection. Extensive experiments on PASCAL VOC and MS COCO show that our model guarantees the detection performance of clean samples and increases the detection performance of adversarial samples. The work we did has been open-sourced on GitHub: https://github.com/KujouRiu/RPU-PVB .

BackgroundThe human gut microbiota has been identified as a potentially important factor influencing the development of COVID-19. It is believed that the disease primarily affects the organism through inflammatory pathways. With the aim of improving early diagnosis and targeted therapy, it is crucial to identify the specific gut microbiota associated with COVID-19 and to gain a deeper understanding of the underlying processes. The present study sought to investigate the potential causal relationship between the gut microbiota and COVID-19, and to determine the extent to which inflammatory proteins act as mediators in this relationship.MethodsBidirectional mendelian randomization (MR) and Two-step mediated MR analyses were applied to examine causative associations among 196 gut microbiota, 91 inflammatory proteins and COVID-19. The main analytical method used in the MR was the random effects inverse variance weighted (IVW) method. This was complemented by the Bayesian weighted Mendelian randomization (BWMR) method, which was utilized to test the hypothesis of MR. In order for the results to be deemed reliable, statistical significance was required for both methods. Validation was then carried out using an external dataset, and further meta-analyses were conducted to authenticate that the association was reliable.ResultsResults of our research indicated that seven gut microbiota were actively associated to the COVID-19 risk. Five inflammatory proteins were associated with COVID-19 risk, of which three were positively and two were negatively identified with COVID-19. Further validation was carried out using sensitivity analyses. Mediated MR results revealed that CCL2 was a possible mediator of causality of family Bifidobacteriaceae and order Bifidobacteriales with COVID-19, mediating at a ratio of 12.73%.ConclusionSuggesting a genetic causation between specific gut microbiota and COVID-19, our present research emphasizes the underlying mediating role of CCL2, an inflammatory factor, and contributes to a deeper understanding of the mechanism of action underlying COVID-19.

Abiotic stresses can increase the total fatty acid (TFA) and astaxanthin accumulation in microalgae. However, it remains unknown whether a unified signal transduction mechanism exists under different stresses. This study explored the link between nicotinamide adenine dinucleotide phosphate (NADPH) oxidase-derived reactive oxygen species (ROS) and the accumulation of fatty acids and astaxanthin in Chromochloris zofingiensis under three abiotic stresses. Results showed significant increases in fatty acid, astaxanthin, and ROS levels under nitrogen deficiency, phosphorus deficiency, and high-salinity stress. The introduction of the NADPH oxidase inhibitor diphenyleneiodonium (DPI) decreased the content of these components. This underscores the pivotal role of NADPH oxidase-derived ROS in the accumulation of fatty acid and astaxanthin under abiotic stress. Analysis of transcriptomes across three conditions following DPI addition revealed 1,445 shared differentially expressed genes (DEGs). Enrichment analysis revealed that biotin, betalain, thiamine, and glucosinolate may be important in stress responses. The heatmap demonstrated that DPI notably suppressed gene expression in the fatty acid and carotenoid biosynthesis pathways. Our findings underscore the pivotal role of NADPH oxidase-derived ROS in the accumulation of fatty acid and astaxanthin under abiotic stresses.

Background Asian people in the United States have different sociodemographic and health‐related characteristics that might affect cardiovascular disease (CVD) risk by ethnicity and birthplace. However, they are often studied as a monolithic group in health care research. This study aimed to examine heterogeneity in CVD risk factors on the basis of birthplace among the 3 largest Asian subgroups (Chinese, Asian Indian, and Filipino) compared with US‐born non‐Hispanic White (NHW) adults. Methods and Results A cross‐sectional analysis was conducted using the 2010 to 2018 National Health Interview Survey data from 125 008 US‐born and foreign‐born Chinese, Asian Indian, Filipino, and US‐born NHW adults. Generalized linear models with Poisson distribution were used to examine the prevalence and prevalence ratios of self‐reported hypertension, diabetes, high cholesterol, physical inactivity, smoking, and overweight/obesity among Asian subgroups compared with US‐born NHW adults. The study included 118 979 US‐born NHW and 6029 Asian adults who self‐identified as Chinese (29%), Asian Indian (33%), and Filipino (38%). Participants' mean (±SD) age was 49±0.1 years, and 53% were females. In an adjusted analysis, foreign‐born Asian Indians had significantly higher prevalence of diabetes, physical inactivity, and overweight/obesity; foreign‐born Chinese had higher prevalence of physical inactivity, and foreign‐born Filipinos had higher prevalence of all 5 CVD risk factors except smoking compared with NHW adults. Conclusions This study revealed significant heterogeneity in the prevalence of CVD risk factors among Asian subgroups by ethnicity and birthplace, stressing the necessity of disaggregating Asian subgroup data. Providers should consider this heterogeneity in CVD risk factors and establish tailored CVD prevention plans for Asian subgroups.

Pancreatic ductal adenocarcinoma (PDAC) is a highly prevalent digestive system malignancy, with a significant impact on public health, especially in the elderly population. The advent of the Human Genome Project has opened new avenues for precision medicine, allowing researchers to explore genetic markers and molecular targets for cancer diagnosis and treatment. Despite significant advances in genomic research, early diagnosis of pancreatic cancer remains elusive due to the lack of highly sensitive and specific markers. Therefore, there is a need for in-depth research to identify more precise and reliable diagnostic markers for pancreatic cancer. In this study, we utilized a combination of public databases from different sources to meticulously screen genes associated with prognosis in pancreatic cancer. We used gene differential analysis, univariate cox regression analysis, least absolute selection and shrinkage operator (LASSO) regression, and multivariate cox regression analysis to identify genes associated with prognosis. Subsequently, we constructed a scoring system, validated its validity using survival analysis and ROC analysis, and further confirmed its reliability by nomogram and decision curve analysis (DCA). We evaluated the diagnostic value of this scoring system for pancreatic cancer prognosis and validated the function of the genes using single cell data analysis. Our analysis identifies six genes, including GABRA3, IL20RB, CDK1, GPR87, TTYH3, and KCNA2, that were strongly associated with PDAC prognosis. Clinical prognostic models based on these genes showed strong predictive power not only in the training set but also in external datasets. Functional enrichment analysis revealed significant differences between high- and low-risk groups mainly in immune-related functions. Additionally, we explored the potential of the risk score as a marker for immunotherapy response and identified key factors within the tumor microenvironment. The single-cell RNA sequencing analysis further enriched our understanding of cell clusters and six hub genes expressions. This comprehensive investigation provides valuable insights into pancreatic PDAC and its intricate immune landscape. The identified genes and their functional significance underscore the importance of continued research into improving diagnosis and treatment strategies for PDAC.

Abstract Background Emerging evidence suggests the critical roles of N6-methyladenosine (m6A) RNA modification in tumorigenesis and tumor progression. However, the role of m6A in non-small cell lung cancer (NSCLC) is still unclear. This study aimed to explore the role of the m6A demethylase fat mass and obesity-associated protein (FTO) in the tumor metastasis of NSCLC. Methods A human m6A epitranscriptomic microarray analysis was used to identify downstream targets of FTO. Quantitative real-time PCR (qRT‒PCR) and western blotting were employed to evaluate the expression levels of FTO and FAP in NSCLC cell lines and tissues. Gain-of-function and loss-of-function assays were conducted in vivo and in vitro to assess the effects of FTO and FAP on NSCLC metastasis. M6A-RNA immunoprecipitation (MeRIP), RNA immunoprecipitation (RIP), luciferase reporter assays, and RNA stability assays were used to explore the mechanism of FTO action. Co-immunoprecipitation (co-IP) assays were used to determine the mechanism of FAP in NSCLC metastasis. Results FTO was upregulated and predicted poor prognosis in patients with NSCLC. FTO promoted cell migration and invasion in NSCLC, and the FAK inhibitor defactinib (VS6063) suppressed NSCLC metastasis induced by overexpression of FTO. Mechanistically, FTO facilitated NSCLC metastasis by modifying the m6A level of FAP in a YTHDF2-dependent manner. Moreover, FTO-mediated metastasis formation depended on the interactions between FAP and integrin family members, which further activated the FAK signaling. Conclusion Our current findings provided valuable insights into the role of FTO-mediated m6A demethylation modification in NSCLC metastasis. FTO was identified as a contributor to NSCLC metastasis through the activation of the FAP/integrin/FAK signaling, which may be a potential therapeutic target for NSCLC. Video Abstract

Abstract Despite improvements in medical and surgical therapies, a significant portion of patients with critical limb ischemia (CLI) are considered as “no option” for revascularization. In this work, a nitric oxide (NO)-boosted and activated nanovesicle regeneration kit (n-BANK) is constructed by decorating stem cell-derived nanoscale extracellular vesicles with NO nanocages. Our results demonstrate that n-BANKs could store NO in endothelial cells for subsequent release upon pericyte recruitment for CLI revascularization. Notably, n-BANKs enable endothelial cells to trigger eNOS activation and form tube-like structures. Subsequently, eNOS-derived NO robustly recruits pericytes to invest nascent endothelial cell tubes, giving rise to mature blood vessels. Consequently, n-BANKs confer complete revascularization in female mice following CLI, and thereby achieve limb preservation and restore the motor function. In light of n-BANK evoking pericyte-endothelial interactions to create functional vascular networks, it features promising therapeutic potential in revascularization to reduce CLI-related amputations, which potentially impact regeneration medicine.

Abstract IDH1 mutations frequently occur early in human glioma. While IDH1 mutation has been shown to promote gliomagenesis via DNA and histone methylation, little is known regarding its regulation in antiviral immunity. Here, we discover that IDH1 mutation inhibits virus-induced interferon (IFN) antiviral responses in glioma cells. Mechanistically, D2HG produced by mutant IDH1 enhances the binding of DNMT1 to IRF3/7 promoters such that IRF3/7 are downregulated, leading to impaired type I IFN response in glioma cells, which enhances the susceptibility of gliomas to viral infection. Furthermore, we identify DNMT1 as a potential biomarker predicting which IDH1mut gliomas are most likely to respond to oncolytic virus. Finally, both D2HG and ectopic mutant IDH1 can potentiate the replication and oncolytic efficacy of VSVΔ51 in female mouse models. These findings reveal a pivotal role for IDH1 mutation in regulating antiviral response and demonstrate that IDH1 mutation confers sensitivity to oncolytic virotherapy.

Abstract Automatic segmentation of skin lesions is a critical step in Computer Aided Diagnosis (CAD) of melanoma. However, due to the blurring of the lesion boundary, uneven color distribution, and low image contrast, resulting in poor segmentation result. Aiming at the problem of difficult segmentation of skin lesions, this paper proposes an Attention-based Dual-path Feature Fusion Network (ADFFNet) for automatic skin lesion segmentation. Firstly, in the spatial path, a Boundary Refinement (BR) module is designed for the output of low-level features to filter out irrelevant background information and retain more boundary details of the lesion area. Secondly, in the context path, a Multi-scale Feature Selection (MFS) module is constructed for high-level feature output to capture multi-scale context information and use the attention mechanism to filter out redundant semantic information. Finally, we design a Dual-path Feature Fusion (DFF) module, which uses high-level global attention information to guide the step-by-step fusion of high-level semantic features and low-level detail features, which is beneficial to restore image detail information and further improve the pixel-level segmentation accuracy of skin lesion. In the experiment, the ISIC 2018 and PH2 datasets are employed to evaluate the effectiveness of the proposed method. It achieves a performance of 0.890/ 0.925 and 0.933 /0.954 on the F1-score and SE index, respectively. Comparative analysis with state-of-the-art segmentation methods reveals that the ADFFNet algorithm exhibits superior segmentation performance.

Carbon sequestration management of plantation forests has become an important topic in the current context of vigorously promoting carbon peaking and carbon neutrality goals and will be the goal and task of the forest industry for a long time. The objective of this study was to explore the applications of genetic algorithm (GA) in both near-optimal thinning regimes at stand level and near-optimal forest management planning at the regional level under the forest management objectives of carbon sequestration. This research integrates a carbon assessment technique with GA optimization to effectively enhance the management of carbon sequestration within plantation forests. Results indicate that the density effect model was an accurate and reliable carbon assessment method (R2 = 0.8701, RMSE = 7.548). The GA optimization approach is efficient in the near-optimal thinning regime and the appropriate forest management planning schedule under the forest management objectives of carbon sequestration. In the research area, the near-optimal carbon sequestration is 38,045.71 t, and in the 15 years from 2016 to 2030, the carbon sequestration of 20 Chinese fir stands should meet the annual thinning constraint condition of not less than 50 t. A near-optimal decision of the carbon sequestration management of plantation forests based on GA provides a theoretical basis and technical support for the compilation of a forest management plan at the stand and regional scales in the plantation operation management of carbon sequestration.

With the increase in port throughput and the development of the trend of large-scale ships, selecting applicable anchor positions for ships and ensuring the rational and comprehensive utilization of anchorage areas have become a key issue in utilizing the rate of anchorage resources, ensuring the safety of ships anchoring operations and promoting the development of the shipping industry. Existing anchor position selection and detection algorithm studies are limited to a two-dimensional plane for ship anchor position selection, with few studies considering intelligent detection algorithms for safe ship anchoring water depths based on three-dimensional space, considering conditions such as wind and waves. By considering water depth conditions and the objectives of anchorage safety issues, this study proposes an intelligent detection method for ship anchor detection to find the ship’s ideal anchor location in the anchorages by applying the Monte Carlo algorithm. In the processing step, in combination with the Monte Carlo random plane anchor position detection algorithm and Monte Carlo random sampling water depth detection method, the anchor position circle radius model, safe spacing model, anchoring area detection model and safe water depth model are used for examining the anchorage area for awaiting the ship in three-dimensions. To verify the accuracy of the proposed model, based on the scale of common ship types and considering the most conservative parameters, a series of simulation experiments are conducted to check whether the water depth meets the requirements and fully ensure the safety of the experimental results. The research results indicate that the detection almost cover the whole anchorage area and obtain safe water depth restrictions. This method helps to improve the efficiency of ship anchoring and makes actual anchoring operations safer. Under the premise of ensuring sufficient safe spacing between ships, the anchorage ground can accommodate more ships and simulate multi-type ship anchor position detection operations concerning various ship-type parameters to further ensure the safety of ship anchoring.

This study aims to investigate the potential and advantages of multi-agent reinforcement learning (MARL) in forest management, offering innovative insights and methodologies for achieving sustainable management of forest ecosystems. Focusing on the Pinus yunnanensis secondary forests in Southwest China, we formulated the objective function and constraints based on both spatial and non-spatial structural indices of the forest stand structure (FSS). The value of the objective function (VOF) served as an indicator for assessing FSS. Leveraging the random selection method (RSM) to select harvested trees, we propose the replanting foreground index (RFI) to enhance replanting optimization. The decision-making processes involved in selection harvest optimization and replanting were modeled as actions within MARL. Through iterative trial-and-error and collaborative strategies, MARL optimized agent actions and collaboration to address the collaborative optimization problem of FSS. We conducted optimization experiments for selection felling and replanting across four circular sample plots, comparing MARL with traditional combinatorial optimization (TCO) and single-agent reinforcement learning (SARL). The findings illustrate the superior practical efficacy of MARL in collaborative optimization of FSS. Specifically, replanting optimization based on RFI outperformed the classical maximum Delaunay generator area method (MDGAM). Across different plots (P1, P2, P3, and P4), MARL consistently improved the maximum VOFs by 54.87%, 88.86%, 41.34%, and 22.55%, respectively, surpassing those of the TCO (38.81%, 70.04%, 41.23%, and 18.73%) and SARL (54.38%, 70.04%, 41.23%, and 18.73%) schemes. The RFI demonstrated superior performance in replanting optimization experiments, emphasizing the importance of considering neighboring trees’ influence on growth space and replanting potential. Following selective logging and replanting adjustments, the FSS of each sample site exhibited varying degrees of improvement. MARL consistently achieved maximum VOFs across different sites, underscoring its superior performance in collaborative optimization of logging and replanting within FSS. This study presents a novel approach to optimizing FSS, contributing to the sustainable management of Pinus yunnanensis secondary forests in southwestern China.

Abstract The traditional threshold secret sharing scheme only allows the participants’ sub-secret shares to be used once in the reconstruction process. Several multi-secret sharing schemes have been proposed that are related to cloud computing, aiming to improve reconstruction efficiency. Rational secret sharing is a technique that combines secret sharing with game theory. In traditional rational multi-secret sharing, participants must reconstruct all secrets, resulting in unnecessary overhead. Rational participants will act dishonestly to maximize their own interests, leading to a prisoner’s dilemma and incomplete secret reconstruction. Additionally, when sharing multiple secrets, the Dealer must distribute the sub-secret shares of all secrets to the participants, increasing overhead. In this paper, we propose a rational optional multi-secret sharing scheme based on a reputation mechanism that selectively reconstructs secrets according to participants’ needs in the context of cloud computing. Our scheme introduces a reputation mechanism to evaluate participants’ reputation values to avoid their dishonest behaviors. Furthermore, we adopt a broadcast encryption matrix so that participants only need to receive a single sub-secret share to participate in multi-secret reconstruction. Our security analysis shows that the proposed scheme can effectively constrain the self-interested behavior of rational participants and reduce the overhead in the process, thus multi-secret sharing scheme can provide more efficient and secure solutions for secret sharing in key management and distributive storage for the cloud scenarios.

Abstract Background The oocyte-to-embryo transition (OET) converts terminally differentiated gametes into a totipotent embryo and is critically controlled by maternal mRNAs and proteins, while the genome is silent until zygotic genome activation. How the transcriptome, translatome, and proteome are coordinated during this critical developmental window remains poorly understood. Results Utilizing a highly sensitive and quantitative mass spectrometry approach, we obtain high-quality proteome data spanning seven mouse stages, from full-grown oocyte (FGO) to blastocyst, using 100 oocytes/embryos at each stage. Integrative analyses reveal distinct proteome reprogramming compared to that of the transcriptome or translatome. FGO to 8-cell proteomes are dominated by FGO-stockpiled proteins, while the transcriptome and translatome are more dynamic. FGO-originated proteins frequently persist to blastocyst while corresponding transcripts are already downregulated or decayed. Improved concordance between protein and translation or transcription is observed for genes starting translation upon meiotic resumption, as well as those transcribed and translated only in embryos. Concordance between protein and transcription/translation is also observed for proteins with short half-lives. We built a kinetic model that predicts protein dynamics by incorporating both initial protein abundance in FGOs and translation kinetics across developmental stages. Conclusions Through integrative analyses of datasets generated by ultrasensitive methods, our study reveals that the proteome shows distinct dynamics compared to the translatome and transcriptome during mouse OET. We propose that the remarkably stable oocyte-originated proteome may help save resources to accommodate the demanding needs of growing embryos. This study will advance our understanding of mammalian OET and the fundamental principles governing gene expression.

Abstract Although various new biomaterials have enriched the methods for periodontal regeneration, their efficacy is still controversial, and the regeneration of damaged support tissue in the periodontium remains challenging. Laponite (LAP) nanosilicate is a layered two-dimensional nanoscale, ultrathin nanomaterial with a unique structure and brilliant biocompatibility and bioactivity. This study aimed to investigate the effects of nanosilicate-incorporated PCL (PCL/LAP) nanofibrous membranes on periodontal ligament cells (PDLCs) in vitro and periodontal regeneration in vivo. A PCL/LAP nanofibrous membrane was fabricated by an electrospinning method. The characterization of PCL/LAP nanofibrous membrane were determined by scanning electron microscopy (SEM), energy dispersive spectrum of X-ray (EDS), inductively coupled plasma mass spectrometry (ICP-MS) and tensile test. The proliferation and osteogenic differentiation of PDLCs on the PCL/LAP nanofibrous membrane were evaluated. A PDLCs and macrophage coculture system was used to explore the immunomodulatory effects of the PCL/LAP nanofibrous membrane. PCL/LAP nanofibrous membrane was implanted into rat calvarial and periodontal defects, and the regenerative potential was evaluated by microcomputed topography (micro-CT) and histological analysis. The PCL/LAP nanofibrous membrane showed good biocompatibility and bioactivity. It enhanced the proliferation and osteogenic differentiation of PDLCs. The PCL/LAP nanofibrous membrane also stimulated anti-inflammatory and pro-remodeling N2 neutrophil formation, regulated inflammatory responses and induced M2 macrophage polarization by orchestrating the immunomodulatory effects of PDLCs. The PCL/LAP nanofibrous membrane promoted rat calvarial defect repair and periodontal regeneration in vivo. LAP nanosilicate-incorporated PCL membrane is capable of mediating osteogenesis and immunomodulation of PDLCs in vitro and accelerating periodontal regeneration in vivo. It could be a promising biomaterial for periodontal regeneration therapy.

Abstract As a common tumor with high incidence, osteosarcoma possesses extremely poor prognosis and high mortality. Improving the survival of osteosarcoma patients is still a great challenge due to the precipice of advancement in treatment. In this study, a combination strategy of gene therapy and photothermal therapy (PTT) is developed for efficient treatment of osteosarcoma. Two-dimensional (2D) FePS3 nanosheets are synthesized and functionalized by poly-L-lysine-PEG-folic acid (PPF) to fabricate a multifunctional nanoplatform (FePS@PPF) for further loading microRNAs inhibitor, miR-19a inhibitor (anti-miR-19a). The photothermal conversion efficiency of FePS@PPF is up to 47.1% under irradiation by 1064 nm laser. In vitro study shows that anti-miR-19a can be efficiently internalized into osteosarcoma cells through the protection and delivery of FePS@PPF nanaocarrier, which induces up-regulation of PTEN protein and down-regulation p-AKT protein. After intravenous injection, the FePS@PPF nanoplatform specifically accumulates to tumor site of osteosarcoma-bearing mice. The in vitro and in vivo investigations reveal that the combined PTT-gene therapy displays most significant tumor ablation compared with monotherapy. More importantly, the good biodegradability promotes FePS@PPF to be cleared from body avoiding potential toxicity of long-term retention. Our work not only develops a combined strategy of NIR-II PTT and gene therapy mediated by anti-miR-19a/FePS@PPF but also provides insights into the design and applications of other nanotherapeutic platforms.

Abstract At present, iron oxide nanoparticles (IONPs) are widely used in the biomedical field. They have unique advantages in targeted drug delivery, imaging and disease treatment. However, there are many things to pay attention to. In this paper, we reviewed the fate of IONPs in different cells and the influence on the production, separation, delivery and treatment of extracellular vesicles. It aims to provide cutting-edge knowledge related to iron oxide nanoparticles. Only by ensuring the safety and effectiveness of IONPs can their application in biomedical research and clinic be further improved.

Steganography, the use of algorithms to embed secret information in a carrier image, is widely used in the field of information transmission, but steganalysis tools built using traditional steganographic algorithms can easily identify them. Steganography without embedding (SWE) can effectively resist detection by steganography analysis tools by mapping noise onto secret information and generating secret images from secret noise. However, most SWE still have problems with the small capacity of steganographic data and the difficulty of extracting the data. Based on the above problems, this paper proposes image steganography without embedding carrier secret information. The objective of this approach is to enhance the capacity of secret information and the accuracy of secret information extraction for the purpose of improving the performance of security network communication. The proposed technique exploits the carrier characteristics to generate the carrier secret tensor, which improves the accuracy of information extraction while ensuring the accuracy of secret information extraction. Furthermore, the Wasserstein distance is employed as a constraint for the discriminator, and weight clipping is introduced to enhance the secret information capacity and extraction accuracy. Experimental results show that the proposed method can improve the data extraction accuracy by 10.03% at the capacity of 2304 bits, which verifies the effectiveness and universality of the method. The research presented here introduces a new intelligent information steganography secure communication model for secure communication in networks, which can improve the information capacity and extraction accuracy of image steganography without embedding.

BackgroundNumerous studies have suggested that the relationship between cardiovascular disease (CVD) risk and the usage of mobile health (mHealth) technology may vary depending on the total number of CVD risk factors present. However, whether higher CVD risk is associated with a greater likelihood of engaging in specific mHealth use among US adults is currently unknown. ObjectiveWe aim to assess the associations between the composite CVD risk and each component of mHealth use among US adults regardless of whether they have a history of CVD or not. MethodsThis study used cross-sectional data from the 2017 to 2020 Health Information National Trends Survey. The exposure was CVD risk (diabetes, hypertension, smoking, physical inactivity, and overweight or obesity). We defined low, moderate, and high CVD risk as having 0-1, 2-3, and 4-5 CVD risk factors, respectively. The outcome variables of interest were each component of mHealth use, including using mHealth to make health decisions, track health progress, share health information, and discuss health decisions with health providers. We used multivariable logistic regression models to examine the association between CVD risk and mHealth use adjusted for demographic factors. ResultsWe included 10,531 adults, with a mean age of 54 (SD 16.2) years. Among the included participants, 50.2% were men, 65.4% were non-Hispanic White, 41.9% used mHealth to make health decisions, 50.8% used mHealth to track health progress toward a health-related goal, 18.3% used mHealth to share health information with health providers, and 37.7% used mHealth to discuss health decisions with health providers (all are weighted percentages). Adults with moderate CVD risk were more likely to use mHealth to share health information with health providers (adjusted odds ratio 1.49, 95% CI 1.24-1.80) and discuss health decisions with health providers (1.22, 95% CI 1.04-1.44) compared to those with low CVD risk. Similarly, having high CVD risk was associated with higher odds of using mHealth to share health information with health providers (2.61, 95% CI 1.93-3.54) and discuss health decisions with health providers (1.56, 95% CI 1.17-2.10) compared to those with low CVD risk. Upon stratifying by age and gender, we observed age and gender disparities in the relationship between CVD risk and the usage of mHealth to discuss health decisions with health providers. ConclusionsAdults with a greater number of CVD risk factors were more likely to use mHealth to share health information with health providers and discuss health decisions with health providers. These findings suggest a promising avenue for enhancing health care communication and advancing both primary and secondary prevention efforts related to managing CVD risk factors through the effective usage of mHealth technology.

The mitochondrial genome of Camellia nitidissima was sequenced by Illumina and Pacbio sequencing. The results of sequences showed that a total length was 949,915 bp, and the GC content was 45.7% in assembled mitochondrial genome of C. nitidissima. 71 unigenes had been found, including 36 coding proteins and 35 non-coding proteins. Subsequently, the phylogenetic tree was built on 24 plants with the maximum-likelihood method, which had high bootstrap value and fited to the angiosperm phylogeny group classification (APG IV). The study’s findings unravel the taxonomic status of C. nitidissima and benefit the evolution study.

Rapeseed (Brassica napus) is an oil crop grown worldwide, making it a key plant species in molecular breeding research. However, the complexity of its polyploid genome increases sequencing costs and reduces sequencing accuracy. Target capture coupled with high-throughput sequencing is an efficient approach for detecting genetic variation at genomic regions or loci of interest. In this study, 588 resequenced accessions of rapeseed were used to develop a target capture sequencing SNP genotyping platform named BnaPan50T. The platform comprised 54,765, with 54,058 resequenced markers from the pan-genome, and 855 variant trait-associated markers for 12 agronomic traits. The capture quality of BnaPan50T was demonstrated well in 12 typical accessions. Compared with a conventional genotyping array, BnaPan50T has a high SNP density and a high proportion of SNPs in unique physical positions and in annotated functional genes, promising wide application. Target capture sequencing and whole-genome resequencing in 90 doubled-haploid lines yielded 60% specificity, 78% uniformity within tenfold coverage range, and 93% genotyping accuracy for the platform. BnaPan50T was used to construct a genetic map for quantitative trait loci (QTL) mapping, identify 21 unique QTL, and predict several candidate genes for yield-related traits in multiple environments. A set of 132 core SNP loci was selected from BnaPan50T to construct DNA fingerprints and germplasm identification resources. This study provides genomics resources to support target capture sequencing, genetic analysis and genomic breeding of rapeseed.

Background: Mounting evidence indicates that complement components play a crucial role in cancer progression. Recent findings indicate that certain complement components display a significant rise in expression within esophageal squamous cell carcinoma (ESCC). However, the specific tumorigenic functions of these components remain unclear. This study focuses on investigating the expression pattern of C1r, elucidating a role for C1r in ESCC, as well as exploring underlying mechanisms controlled by C1r. Methods: The expression of C1r in ESCC tissues, malignant epithelial cells, and its relationship with survival were analyzed using the Gene Expression Omnibus (GEO) database and tissue microarrays. Single-cell RNA sequencing (scRNA-seq) was used to study the expression of C1r in malignant epithelial cells. C1r knockdown or C1r overexpression in cultured ESCC cells were used to assess the effects of C1r on proliferation, migration, invasion, cell-matrix adhesion, apoptosis, and growth of xenografted tumors in immunocompromised (nude) mice. Western blotting was used to detect the expression of MMP-1 and MMP-10 in C1r knockdown or C1r overexpressing ESCC cells. Results: C1r was highly expressed in ESCC tissues, malignant epithelial cells, and cultured ESCC cell lines. High C1r expression indicated a poor prognosis. Knockdown of C1r significantly suppressed the proliferation, migration, invasion, cell-matrix adhesion, and promoted apoptosis in cultured ESCC cells. Additionally, knockdown of C1r markedly inhibited tumor growth in nude mice. Overexpression of C1r had the opposite effects. C1r induced the expression of MMP-1 and MMP-10. Conclusions: C1r is highly expressed in ESCC and promotes the progression of this tumor type. Our findings suggest that C1r may serve as a novel prognostic biomarker and therapeutic target in ESCC.

The National Forestland “One Map” applies the boundaries and attributes of sub-elements to mountain plots by means of spatial data to achieve digital management of forest resources. The change detection and analysis of forest space and property is the key to determining the change characteristics, evolution trend and management effectiveness of forest land. The existing spatial overlay method, rasterization method, object matching method, etc., cannot meet the requirements of high efficiency and high precision at the same time. In this paper, we investigate a fast algorithm for the detection of changes in “One Map”, taking Sichuan Province as an example. The key spatial characteristic extraction method is used to uniquely determine the sub-compartments. We construct an unbalanced quadtree based on the number of maximum leaf node elements (NLNE Quad-Tree) to narrow down the query range of the target sub-compartments and quickly locate the sub-compartments. Based on NLNE Quad-Tree, we establish a change detection model for “One Map” (NQT-FCDM). The results show that the spatial feature combination of barycentric coordinates and area can ensure the spatial uniqueness of 44.45 million sub-compartments in Sichuan Province with 1 m~0.000001 m precision. The NQT-FCDM constructed with 1000–6000 as the maximum number of leaf nodes has the best retrieval efficiency in the range of 100,000–500,000 sub-compartments. The NQT-FCDM shortens the time by about 75% compared with the traditional spatial union analysis method, shortens the time by about 50% compared with the normal quadtree and effectively solves the problem of generating a large amount of intermediate data in the spatial union analysis method. The NQT-FCDM proposed in this paper improves the efficiency of change detection in “One Map” and can be generalized to other industries applying geographic information systems to carry out change detection, providing a basis for the detection of changes in vector spatial data.

BackgroundAllergic rhinitis (AR) is a common nasal inflammatory disorder that severely affects an individual's quality of life (QoL) and poses a heavy financial burden. In addition to routine treatments, probiotic intervention has emerged as a promising strategy for preventing and alleviating allergic diseases. The main objective of this study was to determine the effect of a novel multi-strain probiotic mixture on AR symptoms and investigate potential targets underlying the probiotic intervention.MethodsA randomized, double-blind, placebo-controlled clinical study was conducted on AR patients who were allergic to autumnal pollens (n = 31). Placebo or a novel probiotic mixture, composed of Lactobacillus rhamnosus (L. rhamnosus) HN001, L. acidophilus NCFM, Bifidobacterium lactis (B. lactis) Bi-07, L. paracasei LPC-37, and L. reuteri LE16, was administered after 2 months. The therapeutic efficacy was evaluated by a symptom assessment scale. Before and during the pollen season, blood samples were collected, and peripheral blood mononuclear cells (PBMCs) were isolated for further tandem mass tags (TMTs)-based quantitative proteomic analyses. Potential targets and underlying pathological pathways were explored using bioinformatics methods.ResultsDuring the pollen season, the rhinoconjunctivitis symptom score of participants who were administered probiotics (probiotic group, n = 15) was significantly lower than those administered placebo (placebo group, n = 15) (P = 0.037). The proteomic analyses identified 60 differentially expressed proteins (DEPs) in the placebo group, and subsequent enrichment analyses enriched a series of pathways and biological processes, including signaling pathways of inflammation, coagulation cascade, lipid, carbohydrate and amino acid metabolic pathways, and transcription and translation processes. Least Absolute Shrinkage and Selection Operator (LASSO) regression extracted five main elements, namely, GSTO1, ATP2A2, MCM7, PROS1, and TRIM58, as signature proteins. A total of 17 DEPs were identified in the probiotic group, and there was no pathway enriched. Comparison of DEPs in the two groups revealed that the expression levels of the high-mobility group nucleosome-binding domain-containing protein 2 (HMGN2) and Histone H1.2 presented an opposite trend with different interventions.ConclusionOur data showed that AR symptoms alleviated after treatment with the novel multi-strain probiotic mixture, and the proteomic analyses suggested that HMGN2 and Histone H1.2 might be targets of probiotic intervention for seasonal AR.

BackgroundMobile health (mHealth) technology has the potential to support shared decision-making (SDM) and improve hypertension control. However, our understanding of the variations in individuals’ involvement in SDM and mHealth usage across different racial and ethnic groups in the United States is still limited. ObjectiveThis study aimed to investigate the extent of involvement in SDM and the usage of mHealth technology in health-related activities among US adults with hypertension from diverse racial and ethnic backgrounds and to examine whether the mHealth usage differed by individuals’ level of engagement in SDM. MethodsThis study used cross-sectional data from the 2017 to 2020 Health Information National Trends Survey, which was conducted on US adults with self-reported hypertension, and race and ethnicity data were included. The exposure of interest was race and ethnicity. The outcomes were SDM and mHealth usage. SDM was assessed using an item: “In the past 12 months, how often did your health professional: involve you in decisions about your healthcare as much as you wanted?” mHealth usage was defined as using a smartphone or tablet to engage in (1) making health decisions, (2) discussing health decisions with health providers, (3) tracking health progress, and (4) sharing health information. Weighted multivariable logistic regression models were used to examine the association between race and ethnicity and SDM or mHealth usage adjusted for covariates and stratified by the level of engagement in SDM. ResultsThis study included 4893 adults with hypertension, and the mean age was 61 (SD 13) years. The sample was 53% female, 61% (n=3006) non-Hispanic White, 19% (n=907) non-Hispanic Black or African American, 12% (n=605) Hispanic, 4% (n=193) non-Hispanic Asian, and 4% (n=182) non-Hispanic other. Compared to the non-Hispanic White adults, non-Hispanic Black adults were more likely to use mHealth to make health decisions (adjusted odds ratio [aOR] 1.70, 95% CI 1.23-2.34), share health information (aOR 1.46, 95% CI 1.02-2.08), and discuss health decisions with health providers (aOR 1.38, 95% CI 1.02-1.87). Significant associations were observed specifically among those who were always involved in SDM. Asian adults were less likely to be involved in SDM (aOR 0.51, 95% CI 0.26-0.99) and were more likely to use mHealth to track progress on a health-related goal (aOR 2.07, 95% CI 1.28-3.34) than non-Hispanic White adults. Hispanic adults were less likely to use mHealth to share health information (aOR 0.47, 95% CI 0.33-0.67) and discuss health decisions with health providers (aOR 0.65, 95% CI 0.46-0.94) compared to non-Hispanic White adults. ConclusionsThis study observed racial and ethnic disparities in SDM and mHealth usage among US adults with hypertension. These findings emphasize the significance of comprehending the involvement of SDM and the usage of mHealth technology within racially and ethnically diverse populations.

An increasing body of research suggests cancer-induced cardiovascular diseases, leading to the appearance of an interdisciplinary study known as onco-cardiology. Lung cancer has the highest incidence and mortality. Cardiac dysfunction constitutes a major cause of death in lung cancer patients. However, its mechanism has not been elucidated because suitable animal models that adequately mimic clinical features are lacking. Here, we established a novel chemically induced lung cancer mouse model using benzo[a]pyrene and urethane to recapitulate the general characteristics of cardiac dysfunction caused by lung cancer, the cardiac disorders in the context of the progression of lung cancer were evaluated using echocardiographic and histological approaches. The pathological changes included myocardial ischaemia, pericarditis, cardiac pre-cachexia, and pulmonary artery hypertension. We performed sequencing to detect the tRNA-derived fragments and tRNA-derived stress-induced RNAs (tRFs/tiRNAs) expressions in mouse heart tissue. 22 upregulated and 16 downregulated tRFs/tiRNAs were identified. Subsequently, the top 10 significant results of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were presented. The in vitro model was established by exposing neonatal rat cardiomyocytes and myocardial fibroblasts to lung tumour cell-conditioned medium, respectively. Western blotting revealed significant changes in cardiac failure markers (atrial natriuretic peptide and α-myosin heavy chain) and cardiac fibrosis markers (Collagen-1 and Collagen-3). Our model adequately reflects the pathological features of lung cancer-induced cardiac dysfunction. Furthermore, the altered tRF/tiRNA profiles showed great promise as novel targets for therapies. These results might pave the way for research on therapeutic targets in onco-cardiology.

Abstract In oral squamous cell carcinoma (OSCC), a highly aggressive and frequently lethal malignancy, the role and action mechanism of the microtubule regulatory protein CDK5RAP2 have not been fully understood. Here, we show that CDK5RAP2 is highly expressed in OSCC and its expression correlates with clinical stage and lymph node metastasis of the disease. The expression of CDK5RAP2 is regulated by the Wnt signaling pathway. Depletion of CDK5RAP2 inhibits the tumorigenesis and migration of OSCC cells and alters the OSCC cancer stem (-like) cell (CSC) signature. Notably, suppression of CDK5RAP2 expression disrupts spindle orientation during mitosis. Collectively, these results identify CDK5RAP2 as a potential CSC marker and reveal a mechanism that controls the CSC population in OSCC.

[INLINE:1] Targeted muscle reinnervation has been proposed for reconstruction of neuromuscular function in amputees. However, it is unknown whether performing delayed targeted muscle reinnervation after nerve injury will affect restoration of function. In this rat nerve injury study, the median and musculocutaneous nerves of the forelimb were transected. The proximal median nerve stump was sutured to the distal musculocutaneous nerve stump immediately and 2 and 4 weeks after surgery to reinnervate the biceps brachii. After targeted muscle reinnervation, intramuscular myoelectric signals from the biceps brachii were recorded. Signal amplitude gradually increased with time. Biceps brachii myoelectric signals and muscle fiber morphology and grooming behavior did not significantly differ among rats subjected to delayed target muscle innervation for different periods. Targeted muscle reinnervation delayed for 4 weeks can acquire the same nerve function restoration effect as that of immediate reinnervation.

Abstract High expression of CD38 in tissues is a characteristic of aging, resulting in a decline in nicotinamide adenine dinucleotide (NAD) and increasing cellular reactive oxygen species (ROS). However, whether CD38 increases susceptibility to ferroptosis remains largely unexplored. Our previous study showed that CD38 overexpression decreased dihydrofolate reductase (DHFR). In the present study, we confirmed that high expression of CD38 increased ROS levels and induced DHFR degradation, which was prevented by nicotinamide mononucleotide (NMN) replenishment. We further revealed that ROS-mediated sulfonation on Cys7 of DHFR induced its degradation via the autophagy and non-canonical proteasome pathways. Mutation of Cys7 to alanine abolished ROS-induced DHFR degradation. Moreover, oxidative degradation of DHFR was responsible for the increased ferroptosis susceptibility of cells in which CD38 was highly expressed. We also found that CD38 expression was higher in bone-marrow-derived macrophages (BMDMs) from aged mice than those from young mice, while the DHFR level was lower. Consequently, we demonstrated that BMDMs from aged mice were more susceptible to ferroptosis that can be reverted by NMN replenishment, suggesting that CD38 high expression rendered cells more susceptible to ferroptosis. Taken together, these results indicated that CD38-mediated NAD+ decline promoted DHFR oxidative degradation, thus resulting in increased cellular susceptibility to ferroptosis and suggesting that NMN replenishment may protect macrophages from ferroptosis in aged mice.

Abstract Multi-cloud computing provides services by used different clouds simultaneously multi-signature can be used as the interactive technology between multi-cloud and users. However, the limited resources of some terminal devices make multi-signature, which based on bilinear map, is not suitable for multi-cloud computing environment. In addition, the signers are disclosure in multi-signature so there is the risk of attack. To solve this issues, this paper proposes a certificateless designated verifier multi-signature scheme based on multivariable public key cryptography (MPKC). Firstly, the formalized definition and security model of the proposed scheme are given. Secondly, it is proved that the proposed scheme is against adaptive chosen-message attacks. Finally, the analysis shows that the proposed scheme is more efficiency and suitable for multi-cloud. Moreover, the proposed scheme can hidding signature source to achieve privacy protection.

ABSTRACT Increasing evidence shows that protein lysine acetylation is involved in almost every aspect of cellular physiology in bacteria. Yersinia pestis is a flea-borne pathogen responsible for millions of human deaths in three global pandemics. However, the functional role of lysine acetylation in this pathogen remains unclear. Here, we found more acetylated proteins and a higher degree of acetylation in Y. pestis grown under mammalian host (Mh) conditions than under flea vector (Fv) conditions, suggesting that protein acetylation could significantly change during fleabite transmission. Comparative acetylome analysis of mutants of YfiQ and CobB, the major acetyltransferase and deacetylase of Y. pestis, respectively, identified 23 YfiQ-dependent and 315 CobB-dependent acetylated proteins. Further results demonstrated that acetylation of Lys73 of the SlyA protein, a MarR-family transcriptional regulator, inhibits its binding to the promoter of target genes, including hmsT that encodes diguanylate cyclase responsible for the synthesis of c-di-GMP, and significantly enhances biofilm formation of Y. pestis. Our study presents the first extensive acetylome data of Y. pestis and a critical resource for the functional study of lysine acetylation in this pathogen. IMPORTANCE Yersinia pestis is the etiological agent of plague, historically responsible for three global pandemics. The 2017 plague epidemic in Madagascar was a reminder that Y. pestis remains a real threat in many parts of the world. Plague is a zoonotic disease that primarily infects rodents via fleabite, and transmission of Y. pestis from infected fleas to mammals requires rapid adaptive responses to adverse host environments to establish infection. Our study provides the first global profiling of lysine acetylation derived from mass spectrometry analysis in Y. pestis. Our data set can serve as a critical resource for the functional study of lysine acetylation in Y. pestis and provides new molecular insight into the physiological role of lysine acetylation in proteins. More importantly, we found that acetylation of Lys73 of SlyA significantly promotes biofilm formation of Y. pestis, indicating that bacteria can use lysine acetylation to fine-tune the expression of genes to improve adaptation.

Four deep eutectic solvents were used to produce plasticized thermoplastic starch films by a solution casting method. The mechanical properties of thin films, the stability of these properties over time, and the effects of humidity were investigated with the quartz crystal microbalance. The crystallinity, glass transition temperature, modulus and elongation at break for thicker films was measured using a combination of x-ray diffraction, differential scanning calorimetry and tensile testing. The films had very low glass transition temperatures (-50 ∘C or lower), with mechanical properties determined by the crystallinity of the films. The crystallinity did not change significantly over a period of one week, giving materials with a stable mechanical response. Deep eutectic solvents containing ammonium acetate, glycerol, and urea produced films with a crystalline structure that was much more stable at high humidity values than films plasticized only with glycerol or a traditional ionic liquid. These effects are attributed to the complex hydrogen bonding interactions between the different components of the deep eutectic solvents, and between these components and the starch polymer.

MicroRNAs (miRNAs) in oral squamous cell carcinoma (OSCC)-derived small extracellular vesicles (sEVs) play a pivotal role in modulating intercellular communications between tumor cells and other cells in the microenvironment, thereby influencing tumor progression and the efficacy of therapeutic interventions. However, a comprehensive inventory of these secretory miRNAs in sEVs and their biological and clinical implications remains elusive. This study aims to profile the miRNA content of OSCC cell line sEVs and computationally elucidate their biological and clinical relevance. We conducted miRNA sequencing to compare the miRNA profiles of OSCC cells and their corresponding sEVs. Our motif enrichment analysis identified specific sorting motifs that are implicated in either cellular retention or preferential sEV secretion. Target cell analysis suggested that the sEV miRNAs potentially interact with various immune cell types, including natural killer cells and dendritic cells. Additionally, we explored the clinical relevance of these miRNAs by correlating their expression levels with TNM stages and patient survival outcomes. Intriguingly, our findings revealed that a distinct sEV miRNA signature is associated with lymph node metastasis and poorer survival in patients in TCGA-HNSC dataset. Collectively, this research furthers our understanding of the miRNA sorting mechanisms in OSCC and underscores their clinical implications.

Aiming to enhance the efficiency and precision of multi-objective optimization in southwestern secondary growth of Pinus yunnanensis forests, this study integrated spatial and non-spatial structural indicators to establish objective functions and constraints for assessing forest structure. Felling decisions were made using the random selection method (RSM), Q-value method (QVM), and V-map method (VMM). Actions taken to optimize the forest stand structure (FSS) through tree selection were approached as decisions by a reinforcement learning (RL) agent. Leveraging RL’s trial-and-error strategy, we continually refined the agent’s decision-making process, applying it to multi-objective optimization. Simulated felling experiments conducted across circular sample plots (P1–P4) compared RL, Monte Carlo (MC), and particle swarm optimization (PSO) in FSS optimization. Notable enhancements in the values of the objective function (VOFs) were observed across all plots. RL-based strategies exhibited improvements, achieving VOF increases of 17.24%, 44.92%, 34.66%, and 17.10% for P1–P4, respectively, outperforming MC-based (10.73%, 41.54%, 30.39%, and 15.07%, respectively) and PSO-based (14.08%, 37.78%, 26.17%, and 16.23%, respectively) approaches. The hybrid M7 scheme, integrating RL with the RSM, consistently outperformed other schemes across all plots, yielding an average 26.81% increase in VOF compared to the average enhancement of all schemes (17.42%). This study significantly advances the efficacy and precision of multi-objective optimization strategies for Pinus yunnanensis secondary forests, emphasizing RL’s superior optimization performance, particularly when combined with the RSM, highlighting its potential for optimizing sustainable forest management strategies.

The emerging cloud storage technology has significantly improved efficiency and productivity in the traditional electronic healthcare field. However, it has also brought about many security concerns. Ciphertext policy attribute-based encryption (CP-ABE) holds immense potential in achieving fine-grained access control, providing robust security for electronic healthcare data in the cloud. However, current CP-ABE schemes still face issues such as inflexible attribute revocation, relatively lower computational capabilities, and key management. To address these issues, this paper introduces a revocable and traceable undeniable ciphertext policy attribute-based encryption scheme (MA-RUABE). MA-RUABE not only enables fast and accurate data traceability, effectively preventing malicious user key leakage, but also includes a direct revocation feature, significantly enhancing computational efficiency. Furthermore, the introduction of a multi-permission mechanism resolves the issue of centralization of power caused by single-attribute permissions. Furthermore, a security analysis demonstrates that our system ensures resilience against chosen plaintext attacks. Experimental results demonstrate that MA-RUABE incurs lower computational overhead, effectively enhancing system performance and ensuring data-sharing security in cloud-based electronic healthcare systems.