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Abstract In rotating machinery, the condition of rolling bearings is paramount, directly influencing operational integrity. However, the literature on the fault evolution of rolling bearings in their nascent stages is notably limited. Addressing this gap, our study establishes an innovative nonlinear dynamic model for early fault evolution of rolling bearings based on collision impact. Firstly, considering the fault evolution characteristics, the influence of the rolling element and fault structure, the dynamic model of early fault evolution between the rolling element and the local fault is established. Secondly, according to the Hertzian contact deformation theory, a nonlinear dynamic model of rolling bearings expressed as mass-spring is established. Thirdly, the energy contribution method is used to integrate the fault evolution model and the nonlinear dynamic model of the rolling bearing. A nonlinear dynamic model of early fault evolution of the rolling bearing is proposed by using the Lagrangian equation. Comparing the simulation results of the nonlinear dynamic with the experimental results, it can be seen that the numerical model can effectively predict the evolution process and vibration characteristics of the fault evolution of rolling bearings in the early stage.

Abstract Research impact is an important manifestation of research competence and the focus of medical education. This study is dedicated to exploring the relationships between coauthorship networks and the research impact of Chinese medical doctoral students from a social capital perspective. A total of 16291 scientific papers from 237 doctoral students and 126 mentors at Chinese universities were selected from databases, and a study dataset including 19 variables was constructed. Nine independent variables were defined and obtained through coauthorship network analysis, and the doctoral students’ research impact, as the only dependent variable, was used to test the hypothesized relationships among the variables. The results show that the betweenness centrality, student-mentor coauthorship count and the partnership ability index significantly affect the h-index. Specifically, the coauthorship unit count plays the most important role in developing centrality, which, in turn, produces a higher h-index. In addition, betweenness centrality, student–mentor coauthorship count and the partnership ability index are good predictors of the likelihood of doctoral students entering the greater research impact group and especially improving their betweenness centrality. Specifically, doctoral students whose partnership ability index is greater than 2, student-mentor coauthorship count is greater than 4, coauthorship unit count is greater than 6, and betweenness centrality is greater than 0.02 are considered to have greater research impact. These findings suggest that the important roles of cooperation in the development of research competence and good mentorship in the acquisition of social capital by doctoral students should be emphasized. Several strategies are advised for harnessing social capital rooted in doctoral students’ coauthorship network for relevant organizations, mentors and doctoral students who want to increase medical doctoral students’ research impact.

Abstract Multi-view subspace clustering (MVC) intends to separate out samples via integrating the complementary information from diverse views. In MVC, since the structural information in the graph is crucial to the graph learning, most of the existing algorithms construct the superficial graph from the original data by directly measuring the similarity between the first-order complementary nearest neighbors. However, the information provided by the superficial graph structure would be influenced by contaminated or absent samples. To address this problem, in the proposed method, the higher-order complementary neighbor graphs are exploited to discover the latent structural information between the samples, and fusing the latent structural information across different orders to achieve the MVC. Specifically, the higher-order neighbor graphs under different views are leveraged to estimate the missing samples. Then, to integrate the neighbor graphs of different orders, the multi-order neighbor diffusion fusion is proposed. Nevertheless, the above problem of diffusion fusion is an intractable non-convex issue. Thus, to address it, the multi-order neighbor diffusion fusion is considered as a combination problem of the solution under different order, and the heuristic algorithm is leveraged to solve it. In this way, not only the data representation under different view and also the neighbor structure under different order can be diffused under a joint optimization framework, thus the consistency and integral information among various perspectives and orders can be utilized effectively and simultaneously. Experiments on both incomplete and complete multi-view dataset demonstrate the convincingness of the high-order neighborhood structure based subspace clustering scheme by comparing with the existing approaches.

Background:Ticks represent a significant vector for the transmission of infectious diseases, with the prevalence of tick-borne diseases becoming a prominent global health concern in recent decades. Anaplasma spp., Rickettsia spp., and Piroplasma have been identified as significant pathogens with the potential to impact human and animal health. However, there is a dearth of data concerning the prevalence of these pathogens in the eastern Tibetan Plateau, China.MethodsIn this study, a total of 643 Dermacentor silvarum and 314 Haemaphysalis longicornis were identified through the application of morphological and molecular identification techniques on 957 ticks collected from yaks in Zoige County. The assessed of Anaplasma spp., Rickettsia spp., Theileria spp., and Babesia spp. was assessed in 957 ticks and 96 blood samples collected from yaks.ResultsSignificant discrepancies were observed in the positivity rates for the four pathogens among the tick species and sampling sites. The identification of different species within the four pathogens was based on the analysis of the 16S rRNA of Anaplasma spp., the ompA and ompB genes of Rickettsia spp., and the 18S rRNA of Theileria spp. and Babesia spp. The prevalence ranges of the four pathogens are 9.9-50.2%, 29.5-100%, 16.2-46.4%, and 14.5-58.4%, respectively.ConclusionIn view of the growing zoonotic risks, further investigations into the prevalence of additional pathogens in ticks and animals, including livestock, in the eastern Tibetan Plateau, China, are essential.

Background: Real-world data on C-MET protein overexpression in non-small cell lung cancer (NSCLC) patients, particularly among the Asian Chinese population, are limited. Objectives: This study aimed to evaluate the clinicomolecular characteristics and prognosis of C-MET overexpression in Chinese NSCLC patients, focusing on those with positive C-MET overexpression (immunohistochemistry (IHC) 3+). Design: A retrospective and observational study. Methods: Data were collected from NSCLC patients diagnosed at the First Affiliated Hospital of Guangzhou Medical University between November 2006 and April 2021. We identified C-MET overexpression using IHC and C-MET overexpression positivity was defined as IHC 3+ with ⩾50% tumor cells. Additionally, patient genotypes were collected for subgroup analysis. Results: Data from 9785 NSCLC patients were collected. C-MET (−) accounted for 5% (503/9785), C-MET (+) for 27% (2654/9785), C-MET (++) for 36% (3464/9785), and C-MET (+++) for 32% (3164/9785). Genetic testing was available for 4326 patients. Wild-type was observed in 37% (1591 cases), with epidermal growth factor receptor ( EGFR ) abnormalities being the most common at 49% (2127 cases). Positive C-MET overexpression correlated significantly with women ( p

Abstract The continuous production of mature blood cell lineages is maintained by hematopoietic stem cells but they are highly susceptible to damage by ionizing radiation (IR) that induces death. Thus, devising therapeutic strategies that can mitigate hematopoietic toxicity caused by IR would benefit acute radiation syndrome (ARS) victims and patients receiving radiotherapy. Herein, we describe the preparation of an injectable hydrogel formulation based on Arg‐Gly‐Asp‐alginate (RGD‐Alg) and Laponite using a simple mixing method that ensured a slow and sustained release of interleukin‐12 (IL‐12) (RGD‐Alg/Laponite@IL‐12). The local administration of RGD‐Alg/Laponite@IL‐12 increased survival rates and promoted the hematopoietic recovery of mice who had received sublethal‐dose irradiation. Local intra‐bone marrow (intra‐BM) injection of RGD‐Alg/Laponite@IL‐12 hydrogel effectively stimulated IL12 receptor‐phosphoinositide 3‐kinase/protein kinase B (IL‐12R‐PI3K/AKT) signaling axis, which promoted proliferation and hematopoietic growth factors secretion of BM mesenchymal stem/stromal cells. This signaling axis facilitates the repair of the hematopoietic microenvironment and plays a pivotal role in hematopoietic reconstitution. In conclusion, we describe a biomaterial‐sustained release of IL‐12 for the treatment of irradiated hematopoietic injury and provide a new therapeutic strategy for hematopoietic ARS.

BackgroundSevere burns can lead to systemic inflammatory response syndrome (SIRS) and multiple organ dysfunction syndrome (MODS) due to inflammation-immunity dysregulation. This study aimed to identify key immune-related molecules and potential drugs for immune regulation in severe burn treatment.MethodMicroarray datasets GSE77791 and GSE37069 were analyzed to identify immune-related differentially expressed genes (DEGs), enriched pathways and prognosis-related genes. The DGIdb database was used to identify potentially clinically relevant small molecular drugs for hub DEGs. Hub DEGs were validated by total RNA from clinical blood samples through qPCR. The efficacy of drug candidates was tested in a severe burn mouse model. Pathologic staining was used to observe organ damage. Enzyme Linked Immunosorbent Assay (ELISA) was used to detect the serum IL-1b, IL-6, TNF-a and MCP-1 contents. Activation of the NF-κB inflammatory pathway was detected by western blotting. Transcriptome sequencing was used to observe inflammatory-immune responses in the lung.ResultsA total of 113 immune-related DEGs were identified, and the presence of immune overactivation was confirmed in severe burns. S100A8 was not only significantly upregulated and identified to be prognosis-related among the hub DEGs but also exhibited an increasing trend in clinical blood samples. Methotrexate, which targets S100A8, as predicted by the DGIdb, significantly reduces transcription level of S100A8 and inflammatory cytokine content in blood, organ damage (lungs, liver, spleen, and kidneys) and mortality in severely burned mice when combined with fluid resuscitation. The inflammatory-immune response was suppressed in the lungs.ConclusionS100A8 with high transcription level in blood is a potential biomarker for poor severe burn prognosis. It suggested that methotrexate has a potential application in severe burn immunotherapy. Besides, it should be emphasized that fluid resuscitation is necessary for the function of methotrexate.

Event causality identification is an important task of relationship extraction, which has received much attention recent years.Most of the existing methods separate syntactic structure from the background knowledge information.The early causality extraction methods focus on the analysis of syntactic structure level.With the development of deep learning, the methods that use the pre-training model combined with background knowledge has become the mainstream.However, neither of the above two kinds of methods fully integrates the sentence information and external knowledge, resulting in different degrees of information loss.To address this problem, we proposed a novel model of event causality identification combining syntactic structure and background knowledge.Our model parses sentences into knowledge syntactic graph structures that contain both syntax and knowledge, and uses the graph convolution network for information fusion.It considers both syntax and knowledge information, which further enriches the event representation and performs effectively.In experiments on the widely-used dataset EventStoryLine, the F1 score of our model achieves 0.445, a 2.3% improvement over existing methods.

Abstract Backgrounds Intraplacental choriocarcinoma (IC) is an extremely rare subtype of gestational choriocarcinoma. The long-term follow-up and reproductive outcomes of IC patients remain unclear. Here, we report a series of 14 cases and conduct a literature review to assess the fertility and recurrence results of this rare disease. Results Fourteen patients with pathologically confirmed IC treated in Peking Union Medical College Hospital between January 2002 and July 2022 were included in this study. Half of them had metastatic IC and were treated by chemotherapy with or without surgery. Only 1 patient had chemoresistant disease, but she achieved complete remission after immunotherapy. The median follow-up time was 45.5 months (range 4-192), and no recurrence occurred. One metastatic IC patient who achieved remission after chemotherapy had a full-term delivery. Among the 5 patients with fertility demands, 3 abandoned their pursuit of pregnancy because of “fear and worry about choriocarcinoma recurrence”. We reviewed a total of 89 cases of IC in English and Chinese literature from 1963 to 2022, and only 5 cases with subsequent pregnancy were reported, all of them were nonmetastatic IC cases. Conclusions IC is sensitive to chemotherapy and has good long-term remission and a low recurrence rate. Patients with metastatic or nonmetastatic IC can have good pregnancy results after treatment. Doctors should pay more attention to the psychology of these patients. Clinical trial registration N/A.

Abstract Objective To describe the characteristics of children and adolescents with borderline ovarian tumors (BOTs) and evaluate the efficacy and safety of fertility-sparing surgery (FSS) in these patients. Methods Patients with BOTs younger than 20 years who underwent FSS were included in this study. Results A total of 34 patients were included, with a median patient age of 17 (range, 3–19) years; 97.1% (33/34) of cases occurred after menarche. Of the patients, 82.4% had mucinous borderline tumors (MBOTs), 14.7% had serous borderline tumors (SBOTs), and 2.9% had seromucinous borderline tumor (SMBOT). The median tumor size was 20.4 (range, 8–40)cm. All patients were at International Federation of Gynecology and Obstetrics stage I and all underwent FSS: cystectomy (unilateral ovarian cystectomy, UC, 14/34, 41.2% and bilateral ovarian cystectomy, BC, 1/34, 2.9%), unilateral salpingo-oophorectomy (USO; 18/34; 52.9%), or USO + contralateral ovarian cystectomy (1/34; 2.9%). The median follow-up time was 65 (range, 10–148) months. Recurrence was experienced by 10 of the 34 patients (29.4%). One patient with SBOT experienced progression to low-grade serous carcinoma after the third relapse. Two patients had a total of four pregnancies, resulting in three live births. The recurrence rate of UC was significantly higher in MBOTs than in USO (p = 0.005). The 5-year disease-free survival rate was 67.1%, and the 5-year overall survival rate was 100%. Conclusions Fertility-sparing surgery is feasible and safe for children and adolescents with BOTs. For patients with MBOTs, USO is recommended to lower the risk of recurrence.

Least risk bomb location(LRBL) structure is a device for placing suspicious explosives on the aircraft.Exploring the influence of structure size and explosive location on its anti-explosion performance can provide a reference for improving the design. Based on the method of combining orthogonal experimental design with numerical simulation, the maximum strain and deformation of each dangerous part of LRBL structure are used as evaluation indicators. The range analysis method and variance analysis method are respectively used to carry out parameter sensitivity analysis on the five influencing factors of the tank wall thickness, bottom cover thickness, connection boss thickness, shear pin diameter and explosive position of LRBL structure. The results show that when the explosive equivalent is determined as 200 g, the significant degree of influence of each factor on the antiknock performance of the LRBL structure from large to small is as follows: explosive position, tank wall thickness, bottom cover thickness, boss thickness and shear pin diameter; Based on comprehensive analysis of evaluation indicators, the LRBL structural design scheme at the optimal level of each factor is obtained.

Abstract SARS-CoV-2 provokes devastating tissue damage by cytokine release syndrome and leads to multi-organ failure. Modeling the process of immune cell activation and subsequent tissue damage is a significant task. Organoids from human tissues advanced our understanding of SARS-CoV-2 infection mechanisms though, they are missing crucial components: immune cells and endothelial cells. This study aims to generate organoids with these components. We established vascular immune organoids from human pluripotent stem cells and examined the effect of SARS-CoV-2 infection. We demonstrated that infections activated inflammatory macrophages. Notably, the upregulation of interferon signaling supports macrophages’ role in cytokine release syndrome. We propose vascular immune organoids are a useful platform to model and discover factors that ameliorate SARS-CoV-2-mediated cytokine release syndrome.

Objective Investigation of explainable deep learning methods for graph neural networks to predict HIV infections with social network information and performing domain adaptation to evaluate model transferability across different datasets.Methods Network data from two cohorts of younger sexual minority men (SMM) from two U.S. cities (Chicago, IL, and Houston, TX) were collected between 2014 and 2016. Feature importance from graph attention network (GAT) models were determined using GNNExplainer. Domain adaptation was performed to examine model transferability from one city dataset to the other dataset, training with 100% of the source dataset with 30% of the target dataset and prediction on the remaining 70% from the target dataset.Results Domain adaptation showed the ability of GAT to improve prediction over training with single city datasets. Feature importance analysis with GAT models in single city training indicated similar features across different cities, reinforcing potential application of GAT models in predicting HIV infections through domain adaptation.Conclusion GAT models can be used to address the data sparsity issue in HIV study populations. They are powerful tools for predicting individual risk of HIV that can be further explored for better understanding of HIV transmission.

The high prevalence of acute kidney injury (AKI) in ICU patients emphasizes the need to understand factors influencing continuous renal replacement therapy (CRRT) circuit lifespan for optimal outcomes. This review examines key pharmacological interventions—citrate (especially in regional citrate anticoagulation), unfractionated heparin (UFH), low molecular weight heparin (LMWH), and nafamostat mesylate (NM)—and their effects on filter longevity. Citrate shows efficacy with lower bleeding risks, while UFH remains cost-effective, particularly in COVID-19 cases. LMWH is effective but associated with higher bleeding risks. NM is promising for high-bleeding risk scenarios. The review advocates for non-tunneled, non-cuffed temporary catheters, especially bedside-inserted ones, and discusses the advantages of surface-modified dual-lumen catheters. Material composition, such as polysulfone membranes, impacts filter lifespan. The choice of treatment modality, such as Continuous Veno-Venous Hemodialysis (CVVHD) or Continuous Veno-Venous Hemofiltration with Dialysis (CVVHDF), along with the management of effluent volume, blood flow rates, and downtime, are critical in prolonging filter longevity in CRRT. Patient-specific conditions, particularly the type of underlying disease, and the implementation of early mobilization strategies during CRRT are identified as influential factors that can extend the lifespan of CRRT filters. In conclusion, this review offers insights into factors influencing CRRT circuit longevity, supporting evidence-based practices and suggesting further multicenter studies to guide ICU clinical decisions.

Objective: Osteosarcoma (OS) is the most common primary bone sarcoma with a high propensity for local invasion and metastasis. Although the antitumor effect of apatinib has been well confirmed in advanced OS, the synergistic effect of apatinib and immunotherapies has not yet been elucidated. Methods: In this study, we established tumour-bearing mice and observed tumour size with low and high doses of apatinib treatments. The expression of 17 cytokines, including vascular endothelial growth factor (VEGF), was detected by protein microarray analysis. Moreover, we designed apatinib and antigen-specific dendritic cell (DC)-T combination treatment for tumour-bearing mice. Tumour growth was detected by statistical analysis of tumour size and microvessel density (MVD) counting, the protein expression of VEGF by western blotting, the cytokines interleukin 6 (IL-6), IL-17 and interferon-gamma (IFN-γ) by enzyme-linked immunosorbent assay (ELISA), and the numbers of myeloid-derived suppressor cells (MDSCs) and tumour-infiltration macrophages (TAMs) by flow cytometry. Results: The results showed that apatinib efficiently suppressed tumour growth, and high-dose apatinib achieved a stronger effect. The same was true for DC-T immunotherapy. However, their combination treatment revealed a better oncolytic effect. Meanwhile, apatinib or DC-T treatment inhibited the expression of VEGF and the proangiogenic mediators IL-6 and IL-17 but increased IFN-γ production. Combination therapy further reduced/increased these effects. In addition, the combination treatment reduced MDSC but enhanced TAM-M1 ratios in the OS microenvironment. These findings indicated that apatinib and antigen-specific DC-T combination therapy was more efficient in oncolysis by regulating pro-/anti-angiogenic inducers and improving the immune state in the OS microenvironment. Conclusion: This study proved that it was feasible to employ immunotherapy with therapeutic agents in OS treatment, which may provide a new approach in addition to the combination of surgery with chemotherapy in tumour treatment.

In invasive lung adenocarcinoma (LUAD), patients with micropapillary (MIP) or solid (SOL) components had a significantly poorer prognosis than those with only lepidic (LEP), acinar (ACI) or papillary (PAP) components. It is interesting to explore the genetic features of different histologic subtypes, especially the highly aggressive components.Based on a cohort of 5,933 patients, this study observed that in different tumor size groups, LUAD with MIP/SOL components showed a different prevalence, and patients with ALK alteration or TP53 mutations had a higher probability of developing MIP/SOL components. To control individual differences, this research used spatial whole-exome sequencing (WES) via laser-capture microdissection of five patients harboring these five coexistent components and identified genetic features among different histologic components of the same tumor. In tracing the evolution of components, we found that titin (TTN) mutation might serve as a crucial intratumor potential driver for MIP/SOL components, which was validated by a cohort of 146 LUAD patients undergoing bulk WES. Functional analysis revealed that TTN mutations enriched the complement and coagulation cascades, which correlated with the pathway of cell adhesion, migration, and proliferation.Collectively, the histologic subtypes of invasive LUAD were genetically different, and certain trunk genotypes might synergize with branching TTN mutation to develop highly aggressive components.

The large-scale integration of renewable energy sources and power electronic devices has led to increasingly dispersed and networked characteristics of power quality disturbances in distribution systems. Traditional control devices, limited by their fixed-point control, no longer meet the development needs of modern distribution systems. Considering the distribution and structural characteristics of the current new-type sources and loads, a multifunctional inverter power quality coordinated control strategy based on comprehensive evaluation is proposed. This strategy aims to achieve power quality coordinated control by utilizing optimal compensation capacity while the grid-connected inverter provides active power output. Firstly, the traditional ipiq power quality detection method is analyzed and improved to enable detection of harmonics, reactive power, and three-phase imbalance currents in single-phase systems or three-phase four-wire systems. Secondly, in the power quality assessment, a balanced algorithm is employed to obtain the comprehensive evaluation index of power quality, thereby improving the deficiencies of single-weight evaluation. Next, to ensure the grid-connected inverter achieves optimal power quality coordinated control with minimal compensation capacity, an optimization compensation function is established between compensation capacity and comprehensive power quality index. This function is optimized using non-dominated sorting genetic algorithm to enhance equipment utilization and system economy. Finally, the feasibility and effectiveness of the proposed method are validated through simulations, demonstrating the achievement of optimal capacity configuration for power quality coordinated control while ensuring active power output by the grid-connected inverter.

Abstract Acute asthma exacerbation refers to the progressive deterioration of asthma symptoms that is always triggered by virus infection represented by respiratory syncytial virus (RSV). After RSV infection, exaggerated Th2‐mediated pulmonary inflammation is the critical pathological response of asthmatic patients with acute exacerbation. Significantly, airway epithelial cells, being the primary targets of RSV infection, play a crucial role in controlling the pulmonary inflammatory response by releasing airway epithelial cell‐derived exosomes (AEC‐Exos), which potentially influence the development of asthma. However, the specific role of AEC‐Exos in acute asthma exacerbation after RSV infection remains obscure. The purpose of this study was to determine the distinct function of AEC‐Exos in exacerbating acute asthma following RSV infection. Blockade of exosomes by GW reduce the enhanced pulmonary inflammation significantly. Specifically, the enhanced Th2 inflammation was induced by AEC‐Exos thorough transportation of hsa‐miR‐155‐5p–Sirtuin 1 (SIRT1) pathway during acute asthma exacerbation. Targeted inhibition of hsa‐miR‐155‐5p blocks the exaggerated Th2 inflammation effectively in mice with acute asthma exacerbation. In summary, our study showed that during acute asthma exacerbation after RSV infection, AEC‐Exos promote the enhanced Th2 inflammation through transportation of increased hsa‐miR‐155‐5p, which was mediated partly through SIRT1‐mediated pathway. hsa‐miR‐155‐5p is a potential biomarker for early prediction of acute asthma exacerbation.

IntroductionGreen pepper yield estimation is crucial for establishing harvest and storage strategies.MethodThis paper proposes an automatic counting method for green pepper fruits based on object detection and multi-object tracking algorithm. Green pepper fruits have colors similar to leaves and are often occluded by each other, posing challenges for detection. Based on the YOLOv5s, the CS_YOLOv5s model is specifically designed for green pepper fruit detection. In the CS_YOLOv5s model, a Slim-Nick combined with GSConv structure is utilized in the Neck to reduce model parameters while enhancing detection speed. Additionally, the CBAM attention mechanism is integrated into the Neck to enhance the feature perception of green peppers at various locations and enhance the feature extraction capabilities of the model.ResultAccording to the test results, the CS_YOLOv5s model of mAP, Precision and Recall, and Detection time of a single image are 98.96%, 95%, 97.3%, and 6.3 ms respectively. Compared to the YOLOv5s model, the Detection time of a single image is reduced by 34.4%, while Recall and mAP values are improved. Additionally, for green pepper fruit tracking, this paper combines appearance matching algorithms and track optimization algorithms from SportsTrack to optimize the DeepSort algorithm. Considering three different scenarios of tracking, the MOTA and MOTP are stable, but the ID switch is reduced by 29.41%. Based on the CS_YOLOv5s model, the counting performance before and after DeepSort optimization is compared. For green pepper counting in videos, the optimized DeepSort algorithm achieves ACP (Average Counting Precision), MAE (Mean Absolute Error), and RMSE (Root Mean Squared Error) values of 95.33%, 3.33, and 3.74, respectively. Compared to the original algorithm, ACP increases by 7.2%, while MAE and RMSE decrease by 6.67 and 6.94, respectively. Additionally, Based on the optimized DeepSort, the fruit counting results using YOLOv5s model and CS_YOLOv5s model were compared, and the results show that using the better object detector CS_YOLOv5s has better counting accuracy and robustness.

The epitranscriptomic mark N6-methyladenosine (m6A) is the most common type of messenger RNA (mRNA) post-transcriptional modification in eukaryotes. With the discovery of the demethylase FTO (FAT MASS AND OBESITY-ASSOCIATED PROTEIN) in Homo Sapiens, this modification has been proven to be dynamically reversible. With technological advances, research on m6A modification in plants also rapidly developed. m6A modification is widely distributed in plants, which is usually enriched near the stop codons and 3′-UTRs, and has conserved modification sequences. The related proteins of m6A modification mainly consist of three components: methyltransferases (writers), demethylases (erasers), and reading proteins (readers). m6A modification mainly regulates the growth and development of plants by modulating the RNA metabolic processes and playing an important role in their responses to environmental signals. In this review, we briefly outline the development of m6A modification detection techniques; comparatively analyze the distribution characteristics of m6A in plants; summarize the methyltransferases, demethylases, and binding proteins related to m6A; elaborate on how m6A modification functions in plant growth, development, and response to environmental signals; and provide a summary and outlook on the research of m6A in plants.

The transformation regulation of the radial finite Airy–Gaussian beam array (FAiGBA) in uniaxial crystals orthogonal to the optical axis has been explored analytically and numerically under the paraxial approximation. The analytical evolution solution of this beam array in the x- and y-directions has been derived, respectively. The intensity distribution and side view of FAiGBA propagating in three types of uniaxial crystals have been demonstrated in several instances. Furthermore, particular attention has been devoted to the linear effect when this beam array transmits through the electro-optic crystal. The influence of non-paraxial longitudinal component and temperature on FAiGBA evolution characteristics has also been examined to verify the validity of the paraxial mechanism of this beam array propagating in uniaxial crystals. It is anticipated that these research findings will contribute to the advancement of FAiGBA applications in micro- and nano-control, optical fiber communication, optical trapping, and manipulation fields.

The quality of meat can differ between grazing and feedlot yaks. The present study examined whether spectral fingerprints by visible and near-infrared (Vis-NIR) spectroscopy and chemo-metrics could be employed to identify the meat of grazing and feedlot yaks. Thirty-six 3.5−year−old castrated male yaks (164 ± 8.38 kg) were divided into grazing and feedlot yaks. After 5 months on treatment, liveweight, carcass weight, and dressing percentage were greater in the feedlot than in grazing yaks. The grazing yaks had greater protein content but lesser fat content than feedlot yaks. Principal component analysis (PCA) was able to identify the meat of the two groups to a great extent. Using either partial least squares discriminant analysis (PLS-DA) or the soft independent modeling of class analogies (SIMCA) classification, the meat could be differentiated between the groups. Both the original and processed spectral data had a high discrimination percentage, especially the PLS-DA classification algorithm, with 100% discrimination in the 400–2500 nm band. The spectral preprocessing methods can improve the discrimination percentage, especially for the SIMCA classification. It was concluded that the method can be employed to identify meat from grazing or feedlot yaks. The unerring consistency across different wavelengths and data treatments highlights the model's robustness and the potential use of NIR spectroscopy combined with chemometric techniques for meat classification. PLS-DA's accurate classification model is crucial for the unique evaluation of yak meat in the meat industry, ensuring product traceability and meeting consumer expectations for the authenticity and quality of yak meat raised in different ways.

Abstract Cigarette smoking is a major preventable cause of morbidity and mortality. While quitting smoking is the best option, switching from cigarettes to non-combustible alternatives (NCAs) such as e-vapor products is a viable harm reduction approach for smokers who would otherwise continue to smoke. A key challenge for the clinical assessment of NCAs is that self-reported product use can be unreliable, compromising the proper evaluation of their risk reduction potential. In this cross-sectional study of 205 healthy volunteers, we combined comprehensive exposure characterization with in-depth multi-omics profiling to compare effects across four study groups: cigarette smokers (CS), e-vapor users (EV), former smokers (FS), and never smokers (NS). Multi-omics analyses included metabolomics, transcriptomics, DNA methylomics, proteomics, and lipidomics. Comparison of the molecular effects between CS and NS recapitulated several previous observations, such as increased inflammatory markers in CS. Generally, FS and EV demonstrated intermediate molecular effects between the NS and CS groups. Stratification of the FS and EV by combustion exposure markers suggested that this position on the spectrum between CS and NS was partially driven by non-compliance/dual use. Overall, this study highlights the importance of in-depth exposure characterization before biological effect characterization for any NCA assessment study.

Abstract Background Handelin is a bioactive compound from Chrysanthemum indicum L. that improves motor function and muscle integrity during aging in Caenorhabditis elegans. This study aimed to further evaluate the protective effects and molecular mechanisms of handelin in a mouse muscle atrophy model induced by cachexia and aging. Methods A tumour necrosis factor (TNF)‐α‐induced atrophy model was used to examine handelin activity in cultured C2C12 myotubes in vitro. Lipopolysaccharide (LPS)‐treated 8‐week‐old model mice and 23‐month‐old (aged) mice were used to examine the therapeutic effects of handelin on cachexia‐ and aging‐induced muscle atrophy, respectively, in vivo. Protein and mRNA expressions were analysed by Western blotting, ELISA and quantitative PCR, respectively. Skeletal muscle mass was measured by histological analysis. Results Handelin treatment resulted in an upregulation of protein levels of early (MyoD and myogenin) and late (myosin heavy chain, MyHC) differentiation markers in C2C12 myotubes (P

Abstract Introduction Gestational trophoblastic neoplasia (GTN) with intracardiac metastasis is rare, and here we reported a patient with intracardiac metastasis of high-risk and refractory gestational choriocarcinoma and reviewed relevant literatures. Case presentation A 37-year-old woman presented with vaginal bleeding and high level of β-human chorionic gonadotropin (β-hCG) at 199,060 (mIU/mL). It was clinically diagnosed with gestational choriocarcinoma. The patient initially received eight cycles of chemotherapy but unsatisfactory response was observed, and the level of β-hCG still ranged between 5000 and 10,000. Then there was found intracardiac masses in the right atrium (2.6*1.7 cm), anterior chordae tendineae of the tricuspid valve (1.4*0.7 cm) and the right ventricle (4.1*2.9 cm) by ultrasonic cardiogram (UCG). PET/CT highly suspected the intracardiac metastasis of choriocarcinoma (SUVmax = 9.3) and no disease was found in the lung and pelvis. The patient undertook complete intracardiac masses resection. The pathology confirmed the intracardiac metastasis of disease. After a week of operation, the UCG found a 5.4*4.2 cm mass in the right atrium again. Considering the poor prognosis, the patient received palliative care and eventually died of disease progression. Conclusion Intracardiac metastasis of GTN is an aggressive sign of disease. Patients can benefit from chemotherapy and surgery. Future investigation of PD-1 immunotherapy combines with chemotherapy are expected to improve the prognosis in this group of patients.

Abstract Background With more clinical trials are offering optional participation in the collection of bio-specimens for biobanking comes the increasing complexity of requirements of informed consent forms. The aim of this study is to develop an automatic natural language processing (NLP) tool to annotate informed consent documents to promote biorepository data regulation, sharing, and decision support. We collected informed consent documents from several publicly available sources, then manually annotated them, covering sentences containing permission information about the sharing of either bio-specimens or donor data, or conducting genetic research or future research using bio-specimens or donor data. Results We evaluated a variety of machine learning algorithms including random forest (RF) and support vector machine (SVM) for the automatic identification of these sentences. 120 informed consent documents containing 29,204 sentences were annotated, of which 1250 sentences (4.28%) provide answers to a permission question. A support vector machine (SVM) model achieved a F-1 score of 0.95 on classifying the sentences when using a gold standard, which is a prefiltered corpus containing all relevant sentences. Conclusions This study provides the feasibility of using machine learning tools to classify permission-related sentences in informed consent documents.

The regenerative capacity of muscle, which primarily relies on anabolic processes, diminishes with age, thereby reducing the effectiveness of therapeutic interventions aimed at treating age-related muscle atrophy. In this study, we observed a decline in the expression of methionine adenosine transferase 2A (MAT2A), which synthesizes S-adenosylmethionine (SAM), in the muscle tissues of both aged humans and mice. Considering MAT2A’s critical role in anabolism, we hypothesized that its reduced expression contributes to the impaired regenerative capacity of aging skeletal muscle. Mimicking this age-related reduction in the MAT2A level, either by reducing gene expression or inhibiting enzymatic activity, led to inhibiting their differentiation into myotubes. In vivo, inhibiting MAT2A activity aggravated BaCl2-induced skeletal muscle damage and decreased the number of satellite cells, whereas supplementation with SAM improved these effects. RNA-sequencing analysis further revealed that the Fas cell surface death receptor (Fas) gene was upregulated in Mat2a-knockdown C2C12 cells. Suppressing MAT2A expression or activity elevated Fas protein levels and increased the proportion of apoptotic cells. Additionally, inhibition of MAT2A expression or activity increased p53 expression. In conclusion, our findings demonstrated that impaired MAT2A expression or activity compromised the regeneration and repair capabilities of skeletal muscle, partially through p53–Fas-mediated apoptosis.

Rheumatoid arthritis (RA) is a chronic disease characterized by joint inflammation and severe disability. However, there is a lack of safe and effective drugs for treating RA. In our previous study, we discovered that myricetin (MC) and celecoxib have a synergistic effect in the treatment of RA. We conducted in vitro and in vivo experiments to further investigate the effects and mechanisms of action of MC. Our findings demonstrated that MC treatment effectively reduced the release of neutrophil extracellular traps (NETs) and alleviated the inflammatory response in RA. Mechanistic studies showed that MC prevents the entry of PADI4 and MPO into the cell nucleus, thereby protecting DNA from decondensation. In a rat arthritis model, MC improved histological changes in ankle joints and suppressed NET-related signaling factors. In conclusion, MC protects the ankle joints against arthritis by inhibiting MPO and PADI4, thereby reducing NET release. The pharmacological mechanism of MC in RA involves the inhibition of NET release.

Abstract BackgroundIndividuals from minoritized racial and ethnic backgrounds experience pernicious and pervasive health disparities that have emerged, in part, from clinician bias. ObjectiveWe used a natural language processing approach to examine whether linguistic markers in electronic health record (EHR) notes differ based on the race and ethnicity of the patient. To validate this methodological approach, we also assessed the extent to which clinicians perceive linguistic markers to be indicative of bias. MethodsIn this cross-sectional study, we extracted EHR notes for patients who were aged 18 years or older; had more than 5 years of diabetes diagnosis codes; and received care between 2006 and 2014 from family physicians, general internists, or endocrinologists practicing in an urban, academic network of clinics. The race and ethnicity of patients were defined as White non-HispanicBlack non-HispanicHispanic or Latino ResultsWe examined EHR notes (n=12,905) of Black non-Hispanic, White non-Hispanic, and Hispanic or Latino patients (n=1562), who were seen by 281 physicians. A total of 27 clinicians participated in the validation study. In terms of bias, participants rated negative adjectives as 8.63 (SD 2.06), fear and disgust words as 8.11 (SD 2.15), and positive adjectives as 7.93 (SD 2.46) on a scale of 1 to 10, with 10 being extremely indicative of bias. Notes for Black non-Hispanic patients contained significantly more negative adjectives (coefficient 0.07, SE 0.02) and significantly more fear and disgust words (coefficient 0.007, SE 0.002) than those for White non-Hispanic patients. The notes for Hispanic or Latino patients included significantly fewer positive adjectives (coefficient −0.02, SE 0.007), trust verbs (coefficient −0.009, SE 0.004), and joy words (coefficient −0.03, SE 0.01) than those for White non-Hispanic patients. ConclusionsThis approach may enable physicians and researchers to identify and mitigate bias in medical interactions, with the goal of reducing health disparities stemming from bias.

Ferroptosis is a non-apoptotic mode of programmed cell death characterized by iron dependence and lipid peroxidation. Since the ferroptosis was proposed, researchers have revealed the mechanisms of its formation and continue to explore effective inhibitors of ferroptosis in disease. Recent studies have shown a correlation between ferroptosis and the pathological mechanisms of neurodegenerative diseases, as well as diseases involving tissue or organ damage. Acting on ferroptosis-related targets may provide new strategies for the treatment of ferroptosis-mediated diseases. This article specifically describes the metabolic pathways of ferroptosis and summarizes the reported mechanisms of action of natural and synthetic small molecule inhibitors of ferroptosis and their efficacy in disease. The paper also describes ferroptosis treatments such as gene therapy, cell therapy, and nanotechnology, and summarises the challenges encountered in the clinical translation of ferroptosis inhibitors. Finally, the relationship between ferroptosis and other modes of cell death is discussed, hopefully paving the way for future drug design and discovery.

Electrical impedance tomography (EIT) provides an indirect measure of the physiological state and growth of the maize ear by reconstructing the distribution of electrical impedance. However, the two-dimensional (2D) EIT within the electrode plane finds it challenging to comprehensively represent the spatial distribution of conductivity of the intact maize ear, including the husk, kernels, and cob. Therefore, an effective method for 3D conductivity reconstruction is necessary. In practical applications, fluctuations in the contact impedance of the maize ear occur, particularly with the increase in the number of grids and computational workload during the reconstruction of 3D spatial conductivity. These fluctuations may accentuate the ill-conditioning and nonlinearity of the EIT. To address these challenges, we introduce RFNetEIT, a novel computational framework specifically tailored for the absolute imaging of the three-dimensional electrical impedance of maize ear. This strategy transforms the reconstruction of 3D electrical conductivity into a regression process. Initially, a feature map is extracted from measured boundary voltage via a data reconstruction module, thereby enhancing the correlation among different dimensions. Subsequently, a nonlinear mapping model of the 3D spatial distribution of the boundary voltage and conductivity is established, utilizing the residual network. The performance of the proposed framework is assessed through numerical simulation experiments, acrylic model experiments, and maize ear experiments. Our experimental results indicate that our method yields superior reconstruction performance in terms of root-mean-square error (RMSE), correlation coefficient (CC), structural similarity index (SSIM), and inverse problem-solving time (IPST). Furthermore, the reconstruction experiments on maize ears demonstrate that the method can effectively reconstruct the 3D conductivity distribution.

The tissues or organs derived decellularized extracellular matrix carry immunogenicity and the risk of pathogen transmission, resulting in limited therapeutic effects. The cell derived dECM cultured in vitro can address these potential risks, but its impact on wound remodeling is still unclear. This study aimed to explore the role of decellularized extracellular matrix (dECM) extracted from adipose derived stem cells (ADSCs) in skin regeneration.Methods: ADSCs were extracted from human adipose tissue. Then we cultivated adipose-derived stem cell cells and decellularized ADSC-dECM for freeze-drying. Western blot (WB), enzyme-linked immunosorbent assay (ELISA) and mass spectrometry (MS) were conducted to analyzed the main protein components in ADSC-dECM. The cell counting assay (CCK-8) and scratch assay were used to explore the effects of different concentrations of ADSC-dECM on the proliferation and migration of human keratinocytes cells (HaCaT), human umbilical vein endothelia cells (HUVEC) and human fibroblasts (HFB), respectively. Moreover, we designed a novel ADSC-dECM-CMC patch which used carboxymethylcellulose (CMC) to load with ADSC-dECM; and we further investigated its effect on a mouse full thickness skin wound model.Results: ADSC-dECM was obtained after decellularization of in vitro cultured human ADSCs. Western blot, ELISA and mass spectrometry results showed that ADSC-dECM contained various bioactive molecules, including collagen, elastin, laminin, and various growth factors. CCK-8 and scratch assay showed that ADSC-dECM treatment could significantly promote the proliferation and migration of HaCaT, human umbilical vein endothelia cells, and human fibroblasts, respectively. To evaluate the therapeutic effect on wound healing in vivo, we developed a novel ADSC-dECM-CMC patch and transplanted it into a mouse full-thickness skin wound model. And we found that ADSC-dECM-CMC patch treatment significantly accelerated the wound closure with time. Further histology and immunohistochemistry indicated that ADSC-dECM-CMC patch could promote tissue regeneration, as confirmed via enhanced angiogenesis and high cell proliferative activity.Conclusion: In this study, we developed a novel ADSC-dECM-CMC patch containing multiple bioactive molecules and exhibiting good biocompatibility for skin reconstruction and regeneration. This patch provides a new approach for the use of adipose stem cells in skin tissue engineering.

Alzheimer's disease (AD) is the most common neurodegenerative disorder with the pathological hallmarks of amyloid beta (Aβ) plaques and neurofibrillary tangles (NFTs) in the brain. Although there is a hope that anti-amyloid monoclonal antibodies may emerge as a new therapy for AD, the high cost and side effect is a big concern. Non-drug therapy is attracting more attention and may provide a better resolution for the treatment of AD. Given the fact that hypoxia contributes to the pathogenesis of AD, hyperbaric oxygen therapy (HBOT) may be an effective intervention that can alleviate hypoxia and improve AD. However, it remains unclear whether long-term HBOT intervention in the early stage of AD can slow AD progression and ultimately prevent cognitive impairment in this disease. In this study we applied consecutive 3-month HBOT interventions on 3-month-old APPswe/PS1dE9 AD mice which represent the early stage of AD. When the APPswe/PS1dE9 mice at 9-month-old which represent the disease stage we measured cognitive function, 24-h blood oxygen saturation, Aβ and tau pathologies, vascular structure and function, and neuroinflammation in APPswe/PS1dE9 mice. Our results showed that long-term HBOT can attenuate the impairments in cognitive function observed in 9-month-old APPswe/PS1dE9 mice. Most importantly, HBOT effectively reduced the progression of Aβ plaques deposition, hyperphosphorylated tau protein aggregation, and neuronal and synaptic degeneration in the AD mice. Further, long-term HBOT was able to enhance blood oxygen saturation level. Besides, long-term HBOT can improve vascular structure and function, and reduce neuroinflammation in AD mice. This study is the first to demonstrate that long-term HBOT intervention in the early stage of AD can attenuate cognitive impairment and AD-like pathologies. Overall, these findings highlight the potential of long-term HBOT as a disease-modifying approach for AD treatment.

The development of high-energy continuous wave (CW) laser has enabled the generation of output powers ranging from 300 to 500 kW. Understanding the ablation mechanism of CW lasers at high laser fluence is crucial for the design of effective high-energy laser protection materials. We note that the particle-like impurities present in the protective coating material can induce defects on the ceramic-like target surface during high-energy CW laser ablation, which can alter the laser absorptivity and manifest a completely different ablation mechanism. Here, we systematically investigate the mechanism of high-energy CW laser ablation of alumina ceramic, with a specific focus on the roles of defect induction, energy transfer, and impact sputtering in the dynamic ablation of target. Specifically, the transient processes between high-energy CW laser and ceramic target will be investigated in terms of the evolution results of the ablation region, the microscopic characteristics of the ablated material, the high time-resolved on-line monitoring, and the multiphase hydrodynamics simulation. Finally, the transparent property of molten alumina ceramic to laser is proved to allow for penetrating damage to ceramic-metal composites.

The technology of food geographical origin traceability is essential for the country to effectively promote the implementation of food origin traceability and protect famous and unique products. To preserve the uniqueness and authenticity of food products in the market, maintain the market order, and safeguard the rights of producers and consumers, there is an urgent need to provide rapid, non-destructive testing for food geographical origin traceability. Compared with other detection techniques, spectroscopy, one of the most promising detection techniques for industrial application, is widely used for origin traceability due to its wide range of applications, fast analysis, simple operation, non-destructive, non-pollution, and online analysis. Herein, the principles of three standard spectroscopic techniques, namely Near-infrared spectroscopy (NIR), Mid-infrared spectroscopy (MIR), and Raman spectroscopy (RS), and their application status in geographical origin traceability of plant and animal foods are introduced in detail, and comparison is made among them. Besides, the application of multispectral data fusion in food geographical origin identification is also systematically discussed. The future development trend is prospected, providing a measure of technical references for applying spectroscopic techniques in food geographical origin traceability.

Abstract Globally, studying the impact of coal mining on groundwater remains challenging. This is because the exploitation of coal resources and the sustainable development of groundwater resources involve economic, social, and environmental aspects. Over the last few decades, the number of publications on groundwater-related studies in coal mining areas has increased. However, they are not currently reviewed in a widely visible manner through bibliometric analyses. This study investigated groundwater research in coal mining areas worldwide using scientometric analysis based on 1196 articles from the Web of Science database to provide a global perspective and gain quantitative insight into research frontiers and trends in the field by mapping existing knowledge. We analyzed the key contributors and development processes of coal mine groundwater research and identified four research frontiers based on scientometric mapping results with an understanding of the research field: numerical modeling, conceptual modeling and mechanisms, feedback mechanisms between anthropogenic-environmental systems and groundwater systems, ground subsidence management, groundwater quality evaluation and risk assessment, and groundwater resource management in coal mines. Finally, we summarize the current challenges and propose methods to promote the green mining of coal resources and the sustainable development and management of groundwater resources.

Abstract Multisource localization occupies an important position in the field of acoustic signal processing and is widely applied in scenarios, such as human‐machine interaction and spatial acoustic parameter acquisition. The direction‐of‐arrival (DOA) of a sound source is convenient to render spatial sound in the audio metaverse. A multisource localization method in a reverberation environment is proposed based on the angle distribution of time–frequency (TF) points using a first‐order ambisonics (FOA) microphone. The method is implemented in three steps. 1) By exploring the angle distribution of TF points, a single‐source zone (SSZ) detection method is proposed by using a standard deviation‐based measure, which reveals the degree of convergence of TF point angles in a zone. 2) To reduce the effect of outliers on localization, an outlier removal method is designed to remove the TF points whose angles are far from the real DOAs, where the median angle of each detected zone is adopted to construct the outlier set. 3) DOA estimates of multiple sources are obtained by postprocessing of the angle histogram. Experimental results in both the simulated and real scenarios verify the effectiveness of the proposed method in a reverberation environment, which also show that the proposed method outperforms reference methods.

With the increasing shortage of water resources and the aggravation of water pollution, solar-driven interfacial steam generation (SISG) technology has garnered considerable attention because of its low energy consumption, simple operation, and environmental friendliness. The popular multi-layer SISG evaporator is composed of two basic structures: a photothermal layer and a support layer. Herein, the support layer underlies the photothermal layer and carries out thermal management, supports the photothermal layer, and transports water to the evaporation interface to improve the stability of the evaporator. While most research focuses on the photothermal layer, the support layer is typically viewed as a supporting object for the photothermal layer. This review focuses on the support layer, which is relatively neglected in evaporator development. It summarizes existing progress in the field of multi-layer interface evaporators, based on various polymers and biomaterials, along with their advantages and disadvantages. Specifically, mainly polymer-based support layers are reviewed, including polymer foams, gels, and their corresponding functional materials, while biomaterial support layers, including natural plants, carbonized biomaterials, and other innovation biomaterials are not. Additionally, the corresponding structure design strategies for the support layer were also involved. It was found that the selection and optimal design of the substrate also played an important role in the efficient operation of the whole steam generation system. Their evolution and refinement are vital for advancing the sustainability and effectiveness of interfacial evaporation technology. The corresponding potential future research direction and application prospects of support layer materials are carefully presented to enable effective responses to global water challenges.

Optimizing the operation of photovoltaic (PV) storage systems is crucial for meeting the load demands of parks while minimizing curtailment and enhancing economic efficiency. This paper proposes a multi-scenario collaborative optimization strategy for PV storage systems based on a master–slave game model. Three types of energy storage system (ESS) application scenarios are designed to comprehensively stabilize PV fluctuations, compensate for load transfers, and participate in the frequency regulation (FR) market, thereby optimizing the overall operational strategy of PV storage systems in parks. The upper-level objective is to maximize the park operators’ profit, while the lower-level objective is to minimize the user’s power supply costs. Case studies demonstrate that this strategy can significantly increase the economic benefits for park operators by 25.8%, reduce user electricity expenditures by 5.27%, and lower curtailment through a load response mechanism, thereby promoting the development and construction of PV storage parks.

To achieve a hierarchical and quantitative evaluation of grid-wide power quality in the distribution network, reflecting the overall power quality level of the distribution network, a comprehensive evaluation method for power quality in a grid-wide system based on complex network theory is proposed. Firstly, based on the propagation characteristics of power quality disturbances, a power quality evaluation index system is constructed. Secondly, to reflect the constraint effect of the local power quality level of nodes on the overall power quality level of the distribution system, corresponding indices such as improved node degree, improved node electrical betweenness, and node self-healing capability are proposed based on complex network theory, and the power quality influence degree of nodes is calculated. Then, the GRA-ANP (Grey Relational Analysis–Analytic Network Process) subjective weight calculation method is improved by introducing grey relational analysis to address the impact of differences in different decision-making results. Based on power quality monitoring data, the entropy weight method is used for objective weighting. To avoid the partiality of a single weight evaluation result, the game equilibrium algorithm is employed to calculate the comprehensive weight of each power quality index. Subsequently, considering the correlation and dependency among indices, the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method is used to obtain the power quality grade of each node. Combining this with the calculation of the power quality influence degree of nodes, the overall power quality grade of the distribution network is determined, achieving a hierarchical and quantitative evaluation of power quality in the entire distribution system. Finally, through a case study analysis of an improved 13-node distribution network, it is verified that the proposed method can fully extract data information and produce comprehensive and accurate power quality assessment results by comparing it with other methods. This provides strong support for the safe and stable operation of the distribution system and the subsequent optimization and management of power quality.

Abstract Background Single-agent chemotherapy using methotrexate or actinomycin D is the first-line treatment for patients with low-risk gestational trophoblastic neoplasia. Various methotrexate-based and actinomycin D-based single-agent regimens can be used. However, there is insufficient evidence to determine the superior regimen. To guide doctors in selecting a single-agent chemotherapy regimen for patients with low-risk gestational trophoblastic neoplasia, we will compare two regimens. Methods We will conduct a multicentre, randomized, prospective clinical trial. Selected low-risk gestational trophoblastic neoplasia patients (FIGO score 0–4) will be randomized 1:1 to a biweekly single-dose actinomycin D group or a multiday methotrexate therapy group. The actinomycin D group will receive IV pulse actinomycin D (1.25 mg/m2) every 14 days, and the methotrexate group will receive methotrexate (50 mg) intramuscularly on days 1, 3, 5, and 7 (4 doses per cycle) and leucovorin (15 mg) intramuscularly on days 2, 4, 6, and 8. This process will be repeated every 14 days. The primary endpoints will include the complete remission rate by single-agent therapy and the overall complete remission rate. The secondary endpoints will include the duration needed to achieve complete remission after single-agent chemotherapy, number of courses needed to achieve complete remission after single-agent chemotherapy, incidence and severity of adverse effects, effects on menstrual conditions and ovarian function based on the anti-Mullerian hormone level, and patient-reported quality of life. Discussion Previous clinical trials comparing biweekly single-dose actinomycin D with multiday methotrexate therapy for treating low-risk gestational trophoblastic neoplasia patients failed to meet the expected case number. Through this multicentre study, the complete remission ratio and efficacy difference between biweekly single-dose actinomycin D and multiday methotrexate therapy will be obtained. This study will also provide the basis for formulating a preferred regimen for treating patients with low-risk gestational trophoblastic neoplasia. Trial registration ClinicalTrials.gov: NCT04562558, Registered on 13 September 2020 (Protocol version 2020–9-24, version 1.0).

Objective: To find the material basis of antioxidant activity of Sedum aizoon L. based on the study of spectrum-effect relationship, which provided a certain reference for its quality control. Method: The fringerprint of Sedum aizoon L. from different areas were established by high performance liquid chromatography-electrochemical detection (HPLC-ECD) and the similarity of the samples were evaluated. At the same time, the spectrum-effect relationship between HPLC-ECD fingerprint of Sedum aizoon L. and antioxidant activity was studied by partial least squares regression analysis and grey correlation analysis. Results: The HPLC-ECD fingerprint of Sedum aizoon L was established. The similarity of the samples was high and 17 common peaks were obtained. Among them, the 12 components were identified with the standard solution, including gallic acid, protocatechuic acid, tryptophan, methyl gallate, epigallocatechin, caffeic acid, epigallocatechin gallate, iriflophene, myricitrin, isoquercitrin, rosmarinic acid and quercitrin. The spectrum relationship showed that the variables important in projection (VIP) scores of gallic acid, protocatechuic acid, epigallocatechin, epigallocatechin gallate, rosmarinic acid and quercitrin were greater than 1. Among them, the grey correlation degree of gallic acid, rosmarinic acid and quercitrin was nearly 0.8. Furthermore, epigallocatechin gallate, myricitrin, isoquercitrin, rosmarinic acid and quercitrin had higher libdock score in molecular docking. Therefore, epigallocatechin gallate, myricitrin, isoquercitrin, rosmarinic acid and quercitrin were the primary components contributing to the antioxidant activity of Sedum aizoon L.. Conclusion: The basis of antioxidant activity of Sedum aizoon L. was determined by the study of spectrum effect relationship with chemometrics, which provides a certain scientific basis for the quality evaluation of Sedum aizoon L..

Oilseed rape (Brassica napus L.) is an allotetraploid (AACC, 2n ​= ​38) crop, valued for its edible oil and protein content. seed yield and nutritional composition of rapeseed are influenced by its yield and oil quality traits. However, the genetic basis of yield-related and oil-quality traits remain ambiguous. A panel of 266 diversified oilseed rape accessions was genotyped using 223 simple sequence repeat (SSR) markers covering all 19 chromosomes to identify significant markers associated with yield and quality traits. Twelve yield-related and six quality traits were investigated in two consecutive years (2014 and 2015), with three replications in two environments (Changshun, CS; and Qinghe, QH). Using the model GLM with population structure and kinship (Q ​+ ​K), a total of 25 significant SSR markers (P ​

BackgroundThe automatic generation of radiology reports, which seeks to create a free-text description from a clinical radiograph, is emerging as a pivotal intersection between clinical medicine and artificial intelligence. Leveraging natural language processing technologies can accelerate report creation, enhancing health care quality and standardization. However, most existing studies have not yet fully tapped into the combined potential of advanced language and vision models. ObjectiveThe purpose of this study was to explore the integration of pretrained vision-language models into radiology report generation. This would enable the vision-language model to automatically convert clinical images into high-quality textual reports. MethodsIn our research, we introduced a radiology report generation model named ClinicalBLIP, building upon the foundational InstructBLIP model and refining it using clinical image-to-text data sets. A multistage fine-tuning approach via low-rank adaptation was proposed to deepen the semantic comprehension of the visual encoder and the large language model for clinical imagery. Furthermore, prior knowledge was integrated through prompt learning to enhance the precision of the reports generated. Experiments were conducted on both the IU X-RAY and MIMIC-CXR data sets, with ClinicalBLIP compared to several leading methods. ResultsExperimental results revealed that ClinicalBLIP obtained superior scores of 0.570/0.365 and 0.534/0.313 on the IU X-RAY/MIMIC-CXR test sets for the Metric for Evaluation of Translation with Explicit Ordering (METEOR) and the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) evaluations, respectively. This performance notably surpasses that of existing state-of-the-art methods. Further evaluations confirmed the effectiveness of the multistage fine-tuning and the integration of prior information, leading to substantial improvements. ConclusionsThe proposed ClinicalBLIP model demonstrated robustness and effectiveness in enhancing clinical radiology report generation, suggesting significant promise for real-world clinical applications.

BackgroundMerkel cell carcinoma (MCC) is a rare type of invasive neuroendocrine skin malignancy with high mortality. However, with years of follow-up, what is the actual survival rate and how can we continually assess an individual’s prognosis? The purpose of this study was to estimate conditional survival (CS) for MCC patients and establish a novel CS-based nomogram model.MethodsThis study collected MCC patients from the Surveillance, Epidemiology, and End Results (SEER) database and divided these patients into training and validation groups at the ratio of 7:3. CS refers to the probability of survival for a specific timeframe (y years), based on the patient’s survival after the initial diagnosis (x years). Then, we attempted to describe the CS pattern of MCCs. The Least absolute shrinkage and selection operator (LASSO) regression was employed to screen predictive factors. The Multivariate Cox regression analysis was applied to demonstrate these predictors’ effect on overall survival and establish a novel CS-based nomogram.ResultsA total of 3,843 MCC patients were extracted from the SEER database. Analysis of the CS revealed that the 7-year survival rate of MCC patients progressively increased with each subsequent year of survival. The rates progressed from an initial 41–50%, 61, 70, 78, 85%, and finally to 93%. And the improvement of survival rate was nonlinear. The LASSO regression identified five predictors including patient age, sex, AJCC stage, surgery and radiotherapy as predictors for CS-nomogram development. And this novel survival prediction model was successfully validated with good predictive performance.ConclusionCS of MCC patients was dynamic and increased with time since the initial diagnosis. Our newly established CS-based nomogram can provide a dynamic estimate of survival, which has implications for follow-up guidelines and survivorship planning, enabling clinicians to guide treatment for these patients better.

Background The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug‐eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. Methods and Results We developed and validated a new AI‐based pipeline using retrospective data of drug‐eluting stent‐treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de‐identified Clinformatics Data Mart Database (n=9978). The 36 months following drug‐eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI‐DAPT model. The AI‐DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%–92%] for ischemia and 84% [95% CI, 82%–87%] for bleeding predictions. Conclusions Our AI‐DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.