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Abstract Against the backdrop of enhancing digital infrastructure to accelerate the development of new quality productivity, this study uses data from 276 prefecture-level cities in China between 2011 and 2020 to construct a static panel model and empirically examine the impact of digital infrastructure on new quality productivity. The baseline regression results indicate that digital infrastructure significantly promotes new quality productivity and remains robust after a series of stability and endogeneity tests. Heterogeneity analyses reveal that the strength of the positive effect varies across cities of different sizes, administrative levels, and geographical regions. Further mechanism tests show that digital infrastructure catalyzes new quality productivity by fostering technological innovation, improving factor allocation efficiency, and upgrading industrial structures. In addition, spatial spillover analyses suggest that digital infrastructure exerts a positive spillover effect on new quality productivity in adjacent regions, thereby benefiting regional development. These findings provide theoretical foundations and empirical insights for addressing key bottlenecks in leveraging digital infrastructure to drive the expansion of new quality productivity.

BackgroundCognitive impairment is an important public health challenge among older adults, particularly in long-term care facilities (LTCFs), where prevalence is higher due to staffing shortages, limited resources, and difficulty maintaining structured exercise programs. Furthermore, older adults often lose interest in repetitive interventions. The exergame “WarioWare: Move It!” (Nintendo) offers a novel solution by combining aerobic exercise, motor coordination, balance training, and cognitive engagement into an immersive experience. ObjectiveThis study aimed to assess the clinical efficacy of an exergame-based training program delivered via “WarioWare: Move It!” in improving physical flexibility, joint range of motion, motor coordination, hand dexterity, and cognitive function in older adults living in LTCFs. MethodsThe training program was conducted across multiple rural LTCFs in Shanxi Province, China. Participants were randomly assigned to the intervention or control group. The intervention protocol encompassed two 60-minute sessions per week over 12 weeks, using motion-sensing exercises such as waving, jumping, arm swinging, rotational movements, and object-mimicking postures with Joy-Con controllers. Primary outcome measures were derived through clinical tests, including the sit and reach test, shoulder flexibility test, trunk rotation flexibility test, shoulder and elbow range of motion, figure-of-8 walk test, standing balance test, hand dexterity test, and cognitive function tests. Statistical analysis was performed using mixed ANOVA, with time as the within-participant factor and intervention group as the between-participant factor, to assess the training effects on the various outcome measures. ResultsA total of 232 participants were recruited, including 32 (13.8%) patients with mild dementia, 18 (7.8%) with moderate dementia, and 182 (78.4%) with mild cognitive impairment, all of whom completed the study. The mixed ANOVA revealed significant group × time interactions across multiple physical flexibility assessments, including the remaining distance between the hands and toes during the forward bend (F2,156=8.484; P

BackgroundThe prompt and accurate identification of mild cognitive impairment (MCI) is crucial for preventing its progression into more severe neurodegenerative diseases. However, current diagnostic solutions, such as biomarkers and cognitive screening tests, prove costly, time-consuming, and invasive, hindering patient compliance and the accessibility of these tests. Therefore, exploring a more cost-effective, efficient, and noninvasive method to aid clinicians in detecting MCI is necessary. ObjectiveThis study aims to develop an ensemble learning framework that adaptively integrates multimodal physiological data collected from wearable wristbands and digital cognitive metrics recorded on tablets, thereby improving the accuracy and practicality of MCI detection. MethodsWe recruited 843 participants aged 60 years and older from the geriatrics and neurology departments of our collaborating hospitals, who were randomly divided into a development dataset (674/843 participants) and an internal test dataset (169/843 participants) at a 4:1 ratio. In addition, 226 older adults were recruited from 3 external centers to form an external test dataset. We measured their physiological signals (eg, electrodermal activity and photoplethysmography) and digital cognitive parameters (eg, reaction time and test scores) using the clinically certified Empatica 4 wristband and a tablet cognitive screening tool. The collected data underwent rigorous preprocessing, during which features in the time, frequency, and nonlinear domains were extracted from individual physiological signals. To address the challenges (eg, the curse of dimensionality and increased model complexity) posed by high-dimensional features, we developed a dynamic adaptive feature selection optimization algorithm to identify the most impactful subset of features for classification performance. Finally, the accuracy and efficiency of the classification model were improved by optimizing the combination of base learners. ResultsThe experimental results indicate that the proposed MCI detection framework achieved classification accuracies of 88.4%, 85.5%, and 84.5% on the development, internal test, and external test datasets, respectively. The area under the curve for the binary classification task was 0.945 (95% CI 0.903-0.986), 0.912 (95% CI 0.859-0.965), and 0.904 (95% CI 0.846-0.962) on these datasets. Furthermore, a statistical analysis of feature subsets during the iterative modeling process revealed that the decay time of skin conductance response, the percentage of continuous normal-to-normal intervals exceeding 50 milliseconds, the ratio of low-frequency to high-frequency (LF/HF) components in heart rate variability, and cognitive time features emerged as the most prevalent and effective indicators. Specifically, compared with healthy individuals, patients with MCI exhibited a longer skin conductance response decay time during cognitive testing (P

Abstract Confronted with the concurrent challenges of economic advancement and environmental management, this study explores whether implementing Intellectual Property Demonstration Policies (IPDP) can be a covert force in enhancing carbon emission efficiency. Utilizing panel data from 280 prefecture-level cities in China over the period 2007–2019, we employ a quasi-natural experimental design, incorporating multiple-period difference-in-differences models, mediation effect models, and spatial Durbin difference-in-differences models to assess the impacts of IPDP on carbon emission efficiency, its mechanisms of action, and its spatial spillover effects. The regression results of the multi-period difference-in-differences model reveal a statistically significant enhancement in carbon emission efficiency due to IPDP, with an impact coefficient of 0.044. Through heterogeneity tests, it is observed that the influence of IPDP on carbon emission efficiency varies based on regional characteristics, carbon emission levels, and the extent of marketization. The mediation effect model demonstrates that IPDP enhances carbon emission efficiency by fostering green technological innovation and facilitating the transformation of industrial structures. Furthermore, the spatial Durbin difference-in-differences model illustrates that IPDP positively influences the carbon emission efficiency of neighboring regions, indicating favorable spatial spillover effects. Notably, the indirect effect coefficients in the geographical distance matrix, economic distance matrix, and economic-geographical nested matrix are calculated as 0.673, 0.250, and 0.386, respectively. These findings offer compelling theoretical and empirical support for strengthening the intellectual property framework to optimize its environmental impact.

BackgroundEarly detection of cognitive impairment or dementia is essential to reduce the incidence of severe neurodegenerative diseases. However, currently available diagnostic tools for detecting mild cognitive impairment (MCI) or dementia are time-consuming, expensive, or not widely accessible. Hence, exploring more effective methods to assist clinicians in detecting MCI is necessary. ObjectiveIn this study, we aimed to explore the feasibility and efficiency of assessing MCI through movement kinetics under tablet-based “drawing and dragging” tasks. MethodsWe iteratively designed “drawing and dragging” tasks by conducting symposiums, programming, and interviews with stakeholders (neurologists, nurses, engineers, patients with MCI, healthy older adults, and caregivers). Subsequently, stroke patterns and movement kinetics were evaluated in healthy control and MCI groups by comparing 5 categories of features related to hand motor function (ie, time, stroke, frequency, score, and sequence). Finally, user experience with the overall cognitive screening system was investigated using structured questionnaires and unstructured interviews, and their suggestions were recorded. ResultsThe “drawing and dragging” tasks can detect MCI effectively, with an average accuracy of 85% (SD 2%). Using statistical comparison of movement kinetics, we discovered that the time- and score-based features are the most effective among all the features. Specifically, compared with the healthy control group, the MCI group showed a significant increase in the time they took for the hand to switch from one stroke to the next, with longer drawing times, slow dragging, and lower scores. In addition, patients with MCI had poorer decision-making strategies and visual perception of drawing sequence features, as evidenced by adding auxiliary information and losing more local details in the drawing. Feedback from user experience indicates that our system is user-friendly and facilitates screening for deficits in self-perception. ConclusionsThe tablet-based MCI detection system quantitatively assesses hand motor function in older adults and further elucidates the cognitive and behavioral decline phenomenon in patients with MCI. This innovative approach serves to identify and measure digital biomarkers associated with MCI or Alzheimer dementia, enabling the monitoring of changes in patients’ executive function and visual perceptual abilities as the disease advances.

Abstract Breast cancer is one of the most common cancer types which is described as the leading cause of cancer death in women. After competitive endogenous RNA (ceRNA) hypothesis was proposed, this triple regulatory network has been observed in various cancers, and increasing evidences reveal that ceRNA network plays a significant role in the migration, invasion, proliferation of cancer cells. In the current study, our target is to construct a CD24-associated ceRNA network, and to further identify key prognostic biomarkers in breast cancer. Using the transcriptom profiles from TCGA database, we performed a comprehensive analysis between CD24high tumor samples and CD24low tumor samples, and identified 132 DElncRNAs, 602 DEmRNAs and 26 DEmiRNAs. Through comprehensive analysis, RP1-228H13.5/miR-135a-5p/BEND3 and SIM2 were identified as key CD24-associated biomarkers, which exhibited highly significance with overall survival, immune microenvironment as well as clinical features. To sum up the above, the current study constructed a CD24-associated ceRNA network, and RP1-228H13.5/miR-135a-5p/BEND3 and SIM2 axis worked as a potential therapeutic target and a predictor for BRCA diagnosis and prognosis.

The escalation of jellyfish stings has drawn attention to severe skin reactions, underscoring the necessity for novel treatments. This investigation assesses the potential of hydroxybenzoic acid derivatives, specifically protocatechuic acid (PCA) and gentisic acid (DHB), for alleviating Nemopilema nomurai Nematocyst Venom (NnNV)-induced injuries. By employing an in vivo mouse model, the study delves into the therapeutic efficacy of these compounds. Through a combination of ELISA and Western blot analyses, histological examinations, and molecular assays, the study scrutinizes the inflammatory response, assesses skin damage and repair mechanisms, and investigates the compounds’ ability to counteract venom effects. Our findings indicate that PCA and DHB significantly mitigate inflammation by modulating critical cytokines and pathways, altering collagen ratios through topical application, and enhancing VEGF and bFGF levels. Furthermore, both compounds demonstrate potential in neutralizing NnNV toxicity by inhibiting metalloproteinases and phospholipase-A2, showcasing the viability of small-molecule compounds in managing toxin-induced injuries.

Abstract Background Long non-coding RNA X-inactive specific transcript (XIST) regulates the progression of a variety of tumors, including osteosarcoma. Bone marrow mesenchymal stem cells (BMSCs) can be recruited into osteosarcoma tissue and affect the progression by secreting exosomes. However, whether BMSCs derived exosomes transmit XIST to regulate the growth and metastasis of osteosarcoma and the related mechanism are still unclear. Method In this study, BMSCs derived exosomes were used to treat human osteosarcoma cells MG63 and 143B, and the level of XIST in BMSCs was intervened by siRNA. CCK-8, EdU, transwell assays were used to analyze the changes of cell proliferation, migration and invasion. Bioinformatics analysis, RNA pulldown and dual-luciferase reporter gene assays validated the targeted relationship of XIST with miR-655 and the interaction between miR-655 and ACLY 3’-UTR. 143B/LUC cell line was used to establish an animal model of in situ osteosarcoma to verify the found effects of XIST on osteosarcoma. Oil Red O staining, Western blot and so on were used to detect the changes of lipid deposition and protein expression. Results It was found that BMSCs derived exosomes promoted the proliferation, migration and invasion of osteosarcoma cells, and the down-regulation of XIST inhibited this effect. miR-655 mediated the role of BMSCs derived exosomal XIST in promoting the progression of osteosarcoma and down-regulation of miR-655 could reverse the effects of inhibiting XIST on the proliferation, migration and invasion of osteosarcoma cells. Meanwhile, animal level results confirmed that BMSCs derived exosomal XIST could promote osteosarcoma growth and lung metastasis by combining with miR-655. In-depth mechanism study showed that BMSCs derived exosomal XIST combined with miR-655 to increase the protein level of ACLY, which led to lipid deposition and activate β-catenin signal to promote the proliferation, migration and invasion of osteosarcoma cells. Conclusion This study showed that BMSCs derived exosomal XIST could enter osteosarcoma cells, bind and down-regulates the level of miR-655, resulting in an increase in the level of ACLY, thus increasing the lipid deposition and the activity of β-catenin signal to promote the growth and metastasis of osteosarcoma.

IntroductionAdvances in mobile computing platforms and the rapid development of wearable devices have made possible the continuous monitoring of patients with mild cognitive impairment (MCI) and their daily activities. Such rich data can reveal more subtle changes in patients’ behavioral and physiological characteristics, providing new ways to detect MCI anytime, anywhere. Therefore, we aimed to investigate the feasibility and validity of digital cognitive tests and physiological sensors applied to MCI assessment.MethodsWe collected photoplethysmography (PPG), electrodermal activity (EDA) and electroencephalogram (EEG) signals from 120 participants (61 MCI patients, 59 healthy controls) during rest and cognitive testing. The features extracted from these physiological signals involved the time domain, frequency domain, time-frequency domain and statistics. Time and score features during the cognitive test are automatically recorded by the system. In addition, selected features of all modalities were classified by tenfold cross-validation using five different classifiers.ResultsThe experimental results showed that the weighted soft voting strategy combining five classifiers achieved the highest classification accuracy (88.9%), precision (89.9%), recall (88.2%), and F1 score (89.0%). Compared to healthy controls, the MCI group typically took longer to recall, draw, and drag. Moreover, during cognitive testing, MCI patients showed lower heart rate variability, higher electrodermal activity values, and stronger brain activity in the alpha and beta bands.DiscussionIt was found that patients’ classification performance improved when combining features from multiple modalities compared to using only tablet parameters or physiological features, indicating that our scheme could reveal MCI-related discriminative information. Furthermore, the best classification results on the digital span test across all tasks suggest that MCI patients may have deficits in attention and short-term memory that came to the fore earlier. Finally, integrating tablet cognitive tests and wearable sensors would provide a new direction for creating an easy-to-use and at-home self-check MCI screening tool.

Abstract Introduction Melanoma is a highly aggressive malignant skin tumor as well as the primary reason for skin cancer‐specific deaths. We first identified immune‐related long noncoding RNA (lncRNA) prognostic signature and found potential immunotherapeutic targets for melanoma cancer. Methods RNA‐seq data and clinical features of melanoma samples were obtained from The Cancer Genome Atlas. Samples of melanoma were randomly assigned to the training and testing cohort. The immune‐related lncRNA signature was then obtained via using univariate, LASSO, and multivariate Cox analysis of patients in the training cohort. Eight significant immune‐related lncRNA signature was then subsequently obtained through correlation analysis between immune‐related genes and lncRNAs. The association between risk score and immune cell infiltration was finally assessed using TIMER and CIBERSORT. Results Three hundred and fifty‐six immune‐related lncRNAs were obtained. Among them, eight immune‐related lncRNAs were identified to build a prognostic risk signature model. The model's performance was then confirmed using the Kaplan–Meier curves, risk plots, and time‐dependent receiver‐operating characteristic curves in the training cohort. The risk score was identified and confirmed as an independent prognostic factor through univariate and multivariate Cox regression analyses. These results were further verified in the testing and whole cohorts. CIBERSORT algorithm showed that the infiltration levels of T cells CD8, M1 macrophages, plasma cells, T cells CD4 memory activated, T cells gamma delta, and mast cells activated were significantly lower in the high‐risk group while the infiltration level of macrophages M0 was significantly lower in the low‐risk group. Conclusion The immune‐related lncRNA signature offers prognostic markers and potential immunotherapeutic targets for melanoma.

Significant advances in sensor technology and virtual reality (VR) offer new possibilities for early and effective detection of mild cognitive impairment (MCI), and this wealth of data can improve the early detection and monitoring of patients. In this study, we proposed a non-invasive and effective MCI detection protocol based on electroencephalogram (EEG), speech, and digitized cognitive parameters. The EEG data, speech data, and digitized cognitive parameters of 86 participants (44 MCI patients and 42 healthy individuals) were monitored using a wearable EEG device and a VR device during the resting state and task (the VR-based language task we designed). Regarding the features selected under different modality combinations for all language tasks, we performed leave-one-out cross-validation for them using four different classifiers. We then compared the classification performance under multimodal data fusion using features from a single language task, features from all tasks, and using a weighted voting strategy, respectively. The experimental results showed that the collaborative screening of multimodal data yielded the highest classification performance compared to single-modal features. Among them, the SVM classifier using the RBF kernel obtained the best classification results with an accuracy of 87%. The overall classification performance was further improved using a weighted voting strategy with an accuracy of 89.8%, indicating that our proposed method can tap into the cognitive changes of MCI patients. The MCI detection scheme based on EEG, speech, and digital cognitive parameters proposed in this study provides a new direction and support for effective MCI detection, and suggests that VR and wearable devices will be a promising direction for easy-to-perform and effective MCI detection, offering new possibilities for the exploration of VR technology in the field of language cognition.

Background: The GINS complex, composed of GINS1/2/3/4 subunits, is an essential structure of Cdc45-MCM-GINS (CMG) helicase and plays a vital role in establishing the DNA replication fork and chromosome replication. Meanwhile, GINS genes have been associated with the poor prognosis of various malignancies. However, the abnormal expression of GINS genes and their diagnostic and prognostic value in sarcomas (SARC) remain unclear.Methods: Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier Plotter, Cancer cell line encyclopedia (CCLE), The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN), R studio, and Tumor Immune Estimation Resource (TIMER) were used to analyze the expression profiles, prognostic value, biological function, ceRNA, and immune infiltration associated with GINS genes in sarcomas.Results: We found that GINS1/2/3/4 genes exhibited significantly upregulated transcription levels in SARC samples compared to non-tumor tissues and exhibited high expression levels in sarcoma cell lines. In addition, SARC patients with increased expression levels of GINS1/2/3/4 showed poorer survival rates. Immune infiltration analysis showed that GINS subunits were closely associated with the infiltration of immune cells in sarcomas.Conclusion: Our research identified GINS subunits as potential diagnostic and prognostic biological targets in SARC and elucidated their underlying effects in the genesis and progression of SARC. These results may provide new opportunities and research directions for targeted sarcoma therapy.

Background: Osteoarthritis (OA) is a degenerative disease which serious affects patients. Ligusticum chuanxiong (CX) has been shown to have a certain curative effect on osteoarthritis in traditional Chinese medicine therapy. This study is based on network pharmacology and molecular docking technology to explore the potential mechanism of CX.Methods: Components of CX to treat osteoarthritis were screened in the TCMSP database and targets were predicted by the PharmMapper database, the osteoarthritis targets were collected from the GeneCards database, and intersection genes were found to be the possible targets of CX anti-OA. The STRING database and Cytoscape software were utilized for protein-protein interaction analysis and further screening of core targets. The Metascape database was used for KEGG and GO enrichment analyses. Then, the top 10 pathways were selected to construct “drug-compound-target-pathway-disease” network analysis. Finally, molecular docking was used to analyze the binding affinity of seven compounds with core targets and TNF-α.Results: Seven compounds with 253 non-repetitive targets of CX were screened from the TCMSP database and 60 potential intersection targets of CX anti-OA were found. PPI network analysis showed that the core targets were ALB, AKT1, IGF1, CASP3, MAPK1, ANXA5, and MAPK14, while GO and KEGG pathway enrichment analyses showed that the relevant biological processes involved in the treatment of osteoarthritis by CX might include the MAPK cascade and reactive oxygen species metabolic process. The KEGG pathway analysis result was mainly associated with the MAPK signaling pathway and PI3K-AKT signaling pathway. We further docked seven ingredients with MAPK1 and MAPK14 enriched in the MAPK pathway, and TNF-α as the typical inflammatory cytokine. The results also showed good binding affinity, especially FA, which may be the most important component of CX anti-OA.Conclusion: Our research revealed the potential mechanism of CX in the treatment of OA, and our findings can also pave the way for subsequent basic experimental verification and a new research direction.

Osteoporosis is a complex multifactorial disorder linked to various risk factors and medical conditions. Bone marrow-derived mesenchymal stem cell (BMSC) dysfunction potentially plays a critical role in osteoporosis pathogenesis. Herein, the study identified that miR-4739 was upregulated in BMSC cultures harvested from osteoporotic subjects. BMSCs were isolated from normal and osteoporotic bone marrow tissues and identified for their osteogenic differentiation potential. In osteoporotic BMSCs, miR-4739 overexpression significantly inhibited cell viability, osteoblast differentiation, mineralized nodule formation, and heterotopic bone formation, whereas miR-4739 inhibition exerted opposite effects. Through direct binding, miR-4739 inhibited distal-less homeobox 3 (DLX3) expression. In osteoporotic BMSCs, DLX3 knockdown also inhibited BMSC viability and osteogenic differentiation. Moreover, DLX3 knockdown partially attenuated the effects of miR-4739 inhibition upon BMSCs. Altogether, the miR-4739/DLX3 axis modulates the capacity of BMSCs to differentiate into osteoblasts, which potentially plays a role in osteoporosis pathogenesis. The in vivo and clinical functions of the miR-4739/DLX3 axis require further investigation.

Background: Osteosarcoma is the most general bone malignancy that mostly affects children and adolescents. Numerous stem cell-related genes have been founded in distinct forms of cancer. This study aimed at identifying a stem cell-related gene model for the expected assessment of the prognosis of osteosarcoma patients.Methods: We obtained the genes expression data and relevant clinical materials from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We identified differentially expressed genes (DEGs) from the GEO dataset, whereas prognostic stem cell-related genes were obtained from the TARGET database. Subsequently, univariate, LASSO and multivariate Cox regression analyses were applied to establish the stem cell-related signature. Finally, the prognostic value of the signature was validated in the GEO dataset.Results: Twenty-five genes were prognostic ferroptosis-related DEGs. Consequently, we identified eight stem cell-related genes as a signature of prognosis of osteosarcoma patients. Then, the Kaplan–Meier (K-M) curve, the AUC value of ROC, and Cox regression analysis verified that the eight stem cell-related gene model were a new and substantial prognostic marker independent of other clinical traits. Moreover, the nomogram on the foundation of risk score and other clinical traits was established for predicting the survival rate of osteosarcoma patients. Biological function analyses displayed that tumor related pathways were affluent.Conclusion: The expression level of stem cell-related genes offers novel prognostic markers as well as underlying therapeutic targets for the therapy and prevention of osteosarcoma.

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial–mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

Osteosarcoma is the most common malignant bone tumor in children and adolescence. Multiple immune-related genes have been reported in different cancers. The aim is to identify an immune-related gene signature for the prospective evaluation of prognosis for osteosarcoma patients. In this study, we evaluated the infiltration of immune cells in 101 osteosarcoma patients downloaded from TARGET using the ssGSEA to the RNA-sequencing of these patients, thus, high immune cell infiltration cluster, middle immune cell infiltration cluster and low immune cell infiltration cluster were generated. On the foundation of high immune cell infiltration cluster vs. low immune cell infiltration cluster and normal vs. osteosarcoma, we found 108 common differentially expressed genes which were sequentially submitted to univariate Cox and LASSO regression analysis. Furthermore, GSEA indicated some pathways with notable enrichment in the high- and low-immune cell infiltration cluster that may be helpful in understanding the potential mechanisms. Finally, we identified seven immune-related genes as prognostic signature for osteosarcoma. Kaplan-Meier analysis, ROC curve, univariate and multivariate Cox regression further confirmed that the seven immune-related genes signature was an innovative and significant prognostic factor independent of clinical features. These results of this study offer a means to predict the prognosis and survival of osteosarcoma patients with uncovered seven-gene signature as potential biomarkers.

Jellyfish stings are a common issue globally, particularly in coastal areas in the summer. Victims can suffer pain, itching, swelling, shock, and even death. Usually, hot water, vinegar, or alumen is used to treat the normal symptoms of a jellyfish sting. However, a specific antivenom may be an effective treatment to deal with severe jellyfish stings. Cyanea nozakii often reach a diameter of 60 cm and are responsible for hundreds of thousands of stings per year in coastal Chinese waters. However, there has been no specific C. nozakii antivenom until now, and so the development of this antivenom is very important. Herein, we collected C. nozakii antisera from tentacle extract venom immunized rabbits and purified the immunoglobulin (IgG) fraction antivenom (AntiCnTXs). Subsequently, two complete procedures to produce a refined F(ab’)2 type of antivenom (F(ab’)2-AntiCnTXs) and Fab type of antivenom (Fab-AntiCnTXs) by multiple optimizations and purification were established. The neutralization efficacy of these three types of antivenoms was compared and analyzed in vitro and in vivo, and the results showed that all types of antibodies displayed some neutralization effect on the lethality of C. nozakii venom toxins, with the neutralization efficacy as follows: F(ab’)2-AntiCnTXs ≥ AntiCnTXs > Fab-AntiCnTXs. This study describes the preparation of novel C. nozakii jellyfish antivenom preparations towards the goal of developing a new, effective treatment for jellyfish stings.

Jellyfish envenomations result in extensive dermatological symptoms, clinically named as jellyfish dermatitis, which can seriously affect the daily activities and physical health of people. Inflammatory response accompanies the whole process of jellyfish dermatitis and the complexity of jellyfish venom components makes it difficult to treat jellyfish dermatitis symptoms effectively. Moreover, inhibiting inflammation is essential for the treatment of jellyfish stings and exploring the main components of jellyfish venom that cause inflammation is an urgent research area. In this study, the inhibitory effects of matrix metalloproteinase (MMP) inhibitors for venom-induced inflammation were explored at a cellular level. The expression of the three inflammatory factors, IL-6, TNF-α and MCP-1 in two skin cell lines, human keratinocyte cells (HaCaT) and human embryonic skin fibroblasts cells (CCC-ESF-1), at the cellular level, after treatment with the inhibitors of jellyfish Nemopilema nomurai (N. nomurai) nematocyst venom (NnNV-I), were determined. The results showed that inhibitors of MMP can significantly reduce the toxic effects of jellyfish Nemopilema nomurai nematocyst venom (NnNV) to skin cells. The expression levels of the three inflammatory factors IL-6, MCP-1, and TNF-α in the cells were also significantly decreased, indicating that MMPs in jellyfish venom are probably vital factors leading to jellyfish dermatitis. This study is beneficial in the prevention and treatment of jellyfish stings.

Abstract Background Agrobacterium tumefaciens-mediated leaf disc genetic transformation is an important way to achieve transgenics or gene editing. Ensuring stable and efficient genetic transformation is still an important problem in modern biology. It is assumed that the difference in the development status of genetic transformation cells of receptor materials is the main reason for the difference and instability of genetic transformation efficiency; the stable and efficient genetic transformation rate can be obtained by defining the appropriate treatment period of the receptor material and applying genetic transformation in a timely manner. Results Based on these assumptions, we studied and established an efficient and stable Agrobacterium-mediated plant transformation system with hybrid poplar (Populus alba × Populus glandulosa, 84 K) leaves, stem segments and tobacco leaves as the research objects. There were differences in the development process of leaf bud primordial cells from different explants, and the genetic transformation efficiency was significantly related to the cell development stage of the in vitro cultured materials. Among them, the genetic transformation rate of poplar and tobacco leaves was the highest on the 3rd and 2nd day of culture, reaching 86.6% and 57.3%, respectively. The genetic transformation rate of poplar stem segments was the highest on the 4th day of culture, reaching 77.8%. The best treatment period was from the development of leaf bud primordial cells to the S phase of the cell cycle. The number of cells detected using flow cytometry and 5-ethynyl-2ʹ-deoxyuridine (EdU) staining, the expression of cell cycle-related protein CDKB1; 2, CDKD1; 1, CYCA3; 4, CYCD1; 1, CYCD3; 2, CYCD6; 1, and CYCH; 1 of explants, and morphological changes of explants can be used as indicators to determine the appropriate treatment period for genetic transformation. Conclusions Our study provides a new and universal set of methods and characteristics to identify the S phase of the cell cycle and apply genetic transformation treatments at the appropriate time. Our results are of great significance for improving the efficiency and stability of plant leaf disc genetic transformation.