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Although patient-derived xenografts (PDX) are commonly used for preclinical modeling in cancer research, a standard approach to in vivo tumor growth analysis and assessment of antitumor activity is lacking, complicating the comparison of different studies and determination of whether a PDX experiment has produced evidence needed to consider a new therapy promising. We present consensus recommendations for assessment of PDX growth and antitumor activity, providing public access to a suite of tools for in vivo growth analyses. We expect that harmonizing PDX study design and analysis and assessing a suite of analytical tools will enhance information exchange and facilitate identification of promising novel therapies and biomarkers for guiding cancer therapy.

Abstract Background The leftover material from the heat-pressing of IPS e.max Press ceramic is often discarded, despite some laboratories exploring its potential for reuse. However, there is a lack of data on the performance of IPS e.max Press ceramic when combined with the button portions. This study investigated the impact of repeated heat-pressing on the crystal structure and flexural strength of lithium disilicate glass-ceramic (LDGC). Methods Specimens (N = 30, n = 10 per group) were categorized based on the number of heat-pressing cycles: G0 (control group, no heat-pressing), G1 (one cycle of heat-pressing), and G2 (two cycles of heat-pressing). The crystal structure of LDGC bars was characterized using X-ray diffraction (XRD). Flexural strength was tested, and microstructures were analyzed via scanning electron microscopy (SEM) and the ImageJ processing program. Data were analyzed using one-way analysis of variance (ANOVA), and multiple pairwise comparisons of means were performed with Tukey’s post-hoc test. Results G2 exhibited significantly lower flexural strength and crystallinity, as well as larger crystal size, compared to G1 and G0 (p

Abstract Stem cell-derived exosomes exert comparable therapeutic effects to those of their parental stem cells without causing immunogenic, tumorigenic, and ethical disadvantages. Their therapeutic advantages are manifested in the management of a broad spectrum of diseases, and their dosing versatility are exemplified by systemic administration and local delivery. Furthermore, the activation and regulation of various signaling cascades have provided foundation for the claimed curative effects of exosomal therapy. Unlike other relevant reviews focusing on the upstream aspects (e.g., yield, isolation, modification), and downstream aspects (e.g. phenotypic changes, tissue response, cellular behavior) of stem cell-derived exosome therapy, this unique review endeavors to focus on various affected signaling pathways. After meticulous dissection of relevant literature from the past five years, we present this comprehensive, up-to-date, disease-specific, and pathway-oriented review. Exosomes sourced from various types of stem cells can regulate major signaling pathways (e.g., the PTEN/PI3K/Akt/mTOR, NF-κB, TGF-β, HIF-1α, Wnt, MAPK, JAK-STAT, Hippo, and Notch signaling cascades) and minor pathways during the treatment of numerous diseases encountered in orthopedic surgery, neurosurgery, cardiothoracic surgery, plastic surgery, general surgery, and other specialties. We provide a novel perspective in future exosome research through bridging the gap between signaling pathways and surgical indications when designing further preclinical studies and clinical trials.

Abstract EMT dysfunction is a dominant mechanisms of hypospadias. Thus, identification of EMT‐related lncRNAs based on transcriptome sequencing data of hypospadias might provide novel molecular markers and therapeutic targets for hypospadias. First, the microarray data related to hypospadias were downloaded from Gene Expression Omnibus (GEO). Besides, the differentially expressed lncRNAs and messenger RNAs (mRNAs) related to EMT were screened to construct lncRNA‐mRNA co‐expression interaction pairs. In addition, the microRNA (miRNA) prediction analysis was performed through bioinformatics methods to construct a ceRNA network. Moreover, function prediction and function enrichment and pathway analyses were also performed. Finally, the core EMT‐related lncRNAs were verified based on mRNA expression changes and cell functions. A total of 6 EMT‐related lncRNAs were identified and 123 mRNA‐lncRNA co‐expression interaction pairs were screened in this study. Additionally, a ceRNA regulatory network comprising 17 mRNAs, 4 lncRNAs, and 28 miRNAs was constructed based on the prediction of hypospadias‐related miRNAs. The validation results of the dataset GSE121712 revealed that only BEX1 was positively correlated with the expression of the lncRNA GNAS‐AS1 (r = 0.874, P < 0.01), both of which had high expression. The cell experiment results demonstrated that interfering with the expression of GNAS‐AS1 significantly promoted the proliferation, migration, and EMT of cells. Importantly, it was confirmed that GNAS‐AS1 can serve as a ceRNA and play an important role in the EMT of hypospadias. Hence, it may be considered as a potential target in the treatment of this disease.

Abstract A simple and flexible mass balance approach was applied to observations of XCH4 from TROPOMI to estimate CH4 emissions over Shanxi Province, including the impacts of advective transport, pressure transport, and atmospheric diffusion. High-frequency eddy-covariance flux observations were used to constrain the driving terms of the mass balance equation. This equation was then used to calculate day-to-day and 5 km × 5 km grided CH4 emissions from May 2018 to July 2022 based on TROPOMI RPRO column CH4 observations. The Shanxi-wide emissions of CH4, 126 ± 58.8 ug/m2/s, shows a fat tail distribution and high variability on a daily time scale (the 90th percentile is 2.14 times the mean and 2.74 times the median). As the number of days in the rolling average increases, the change in the variation decreases to 128 ± 35.7 ug/m2/s at 10-day, 128 ± 19.8 ug/m2/s at 30-day and 127 ± 13.9 ug/m2/s at 90-day. The range of values of the annual mean emissions on coal mine grids within Shanxi for the years 2018 to 2022 was 122 ± 58.2, 131 ± 71.2, 111 ± 63.6, 129 ± 87.1, and 138 ± 63.4 ug/m2/s, respectively. The 5-year average emissions from TROPOMI are 131 ± 68.0 ug/m2/s versus 125 ± 94.6 ug/m2/s on the grids where the EDGAR bottom-up database also has data, indicating that those pixels with mines dominate the overall emissions in terms of both magnitude and variability. The results show that high-frequency observation-based campaigns can produce a less biased result in terms of both the spatial and temporal distribution of CH4 emissions as compared with approaches using either low-frequency data or bottom-up databases, that coal mines dominate the sources of CH4 in Shanxi, and that the observed fat tail distribution can be accounted for using this approach.

In order to objectively and accurately assess the airborne flight posture in the trajectory-based operation (TBO) mode, a flight posture assessment method based on complex network and MFIM-TOPSIS is proposed. Firstly, the precise protection zone of different aircraft types is established by analyzing the risk of lateral collision between aircraft, and the flight conflict network is constructed by using the three-dimensional velocity barrier method, which is close to the TBO air operation environment, based on which six network topology indexes, such as total node degree, average point strength and network density, are selected to build the flight attitude assessment index system. To weaken the influence of the assessment subjectivity, an interaction matrix that can reflect subjective factors-objective factors is proposed. The maximum information coefficient is used to improve the coding of interaction matrix. Finally the flight attitude level status is assessed by using the superiority-disadvantage solution distance method. The simulation analysis and the example analysis of Takasaki Airport show that the evaluation model can accurately assess the air traffic situation and provide auxiliary information for controllers' decision making and command.

Abstract Background Preoperative discrimination between non-muscle-invasive bladder cancer (NMIBC) and the muscle invasive bladder cancer (MIBC) is a determinant of management. The purpose of this research is to employ radiomics to evaluate the diagnostic value in determining muscle invasiveness of compressed sensing (CS) accelerated 3D T2-weighted-SPACE sequence with high resolution and short acquisition time. Methods This prospective study involved 108 participants who underwent preoperative 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted sequences. The cohort was divided into training and validation cohorts in a 7:3 ratio. In the training cohort, a Rad-score was constructed based on radiomic features selected by intraclass correlation coefficients, pearson correlation coefficient and least absolute shrinkage and selection operator . Multivariate logistic regression was used to develop a nomogram combined radiomics and clinical indices. In the validation cohort, the performances of the models were evaluated by ROC, calibration, and decision curves. Results In the validation cohort, the area under ROC curve of 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted models were 0.87(95% confidence interval (CI):0.73-1.00), 0.79(95%CI:0.63–0.96) and 0.77(95%CI:0.60–0.93), respectively. The differences in signal-to-noise ratio and contrast-to-noise ratio between 3D-CS-T2-weighted-SPACE and 3D-T2-weighted-SPACE sequences were not statistically significant(p > 0.05). While the clinical model composed of three clinical indices was 0.74(95%CI:0.55–0.94) and the radiomics-clinical nomogram model was 0.88(95%CI:0.75-1.00). The calibration curves confirmed high goodness of fit, and the decision curve also showed that the radiomics model and combined nomogram model yielded higher net benefits than the clinical model. Conclusion The radiomics model based on compressed sensing 3D T2WI sequence, which was acquired within a shorter acquisition time, showed superior diagnostic efficacy in muscle invasion of bladder cancer. Additionally, the nomogram model could enhance the diagnostic performance.

Drug testing has many test elements. It aims to prevent unqualified drugs from entering the market and ensure drug safety. The existing artificial intelligence (AI) online monitoring system identifies active ingredients in the process of use. Owing to their openness, data are easy to be lost, failing to meet user needs and inducing a specific impact on the use of the monitoring system. With the continuous development of computer and measurement technologies, various biochemical data are increasing at an unprecedented speed, and numerous databases are emerging. Extracting patterns from considerable known data and experimental facts is an essential task for a wide range of biological and chemical workers. Pattern recognition is one of the essential technologies for data mining. It is widely used in industry, agriculture, national defense, biomedicine, meteorology, astronomy, and other fields. To improve the effect of the online drug ingredient recognition system, this study used AI to design an online drug ingredient recognition-embedded monitoring system and applied AI to the teaching field to improve teaching efficiency. First, this study constructed the framework of the AI online drug ingredient recognition-embedded monitoring system and introduced the process of online drug ingredient recognition. Then, it introduced the pattern recognition method, constructed the pattern recognition system, and presented the pattern recognition algorithm and the algorithm evaluation index. Afterward, it used pattern recognition to conduct a qualitative analysis of the infrared spectrum of drug components and introduced the overall process of the qualitative analysis. In addition, this study employed AI to implement changes to the embedded system instruction in colleges and universities, summarizing the current issues. The impact of drug component recognition and the educational impact of embedded systems were investigated in the experimental portion. The experimental findings demonstrated the excellent accuracy, sensitivity, specificity, and Matthew correlation coefficient of the online drug component recognition-integrated monitoring system in this work. Compared with that of other systems, its average drug component recognition accuracy was above 0.85. Students in five majors reported high levels of satisfaction with the embedded system teaching, which is better for delivering college instruction.

Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerability semantics. In this paper, we present an XAI-based framework to assess program code in a graph context as feature representations and their effect on code vulnerability classification into multiple Common Weakness Enumeration (CWE) types. Our XAI framework is deep-learning-model-agnostic and programming-language-neutral. We rank the feature importance of 40 syntactic constructs for each of the top 20 distributed CWE types from three datasets in Java and C++. By means of four metrics of information retrieval, we measure the similarity of human-understandable CWE types using each CWE type’s feature contribution ranking learned from XAI methods. We observe that the subtle semantic difference between CWE types occurs after the variation in neighboring features’ contribution rankings. Our study shows that the XAI explanation results have approximately 78% Top-1 to 89% Top-5 similarity hit rates and a mean average precision of 0.70 compared with the baseline of CWE similarity identified by the open community experts. Our framework allows for code vulnerability patterns to be learned and contributing factors to be assessed at the same stage.

Existing group recommendation models often have a monotonous approach when solving user representation, and only simple social relationships between users are utilized. This makes user representation inaccurate and most models do not consider the impact of user common intention and social interaction on group preferences. As a result, recommended items are not aligned with user needs. To address these issues, a new group recommendation model based on user common intention and social interaction (GR-UCISI) is proposed. Firstly, a user intention separation model that combines user-item interaction history with social interaction is constructed. Graph neural networks are utilized to collect user-item interaction and social interaction information, and to solve user intention and item representation. Secondly, by utilizing the social network random walk algorithm and the [K-means] clustering algorithm, users can be grouped. User group, user intention and group intention aggregation process are combined to obtain group common intention representation. Finally, group common intention representation and item representation are calculated to obtain the list of recommended items for the group. This method fully considers the impact of user individuality and commonality among group members on group preferences. It also utilizes social relationships to alleviate the problem of data sparsity and improve model performance. The experimental results show that compared with the model with the best recommendation effect of nine models, on the Gowalla dataset, the Precision and NDCG of the GR-UCISI model are increased by 3.01% and 5.26% respectively, on the Yelp-2018 dataset, the Precision and NDCG of the GR-UCISI model are increased by 2.96% and 1.12% respectively.

Prokaryotes play a crucial role in marine ecosystem health and drive biogeochemical processes. The northern Ninety East Ridge (NER) of the Indian Ocean, a pivotal yet understudied area for these cycles, has been the focus of our study. We employed high-throughput 16S rRNA gene sequencing to analyze 35 water samples from five stations along the ridge, categorized into three depth- and dissolved oxygen-level-based groups. Our approach uncovered a clear stratification of microbial communities, with key bioindicators such as Prochlorococcus MIT9313, Sva0996 marine group, and Candidatus Actinomarina in the upper layer; Ketobacter, Pseudophaeobacter, Nitrospina, and SAR324 clade in the middle layer; and Methylobacterium-Methylorubrum, Sphingomonas, Sphingobium, and Erythrobacter in the deep layer. Methylobacterium-Methylorubrum emerged as the most abundant bacterial genus, while Nitrosopumilaceae predominated among archaeal communities. The spatial and depth-wise distribution patterns revealed that Ketobacter was unique to the northern NER, whereas Methylobacterium-Methylorubrum, UBA10353, SAR324 clade, SAR406, Sva0996_marine_group, Candidatus Actinomarina were ubiquitous across various marine regions, exhibiting niche differentiation at the OTU level. Environmental factors, especially dissolved oxygen (DO), silicate, nitrate, and salinity, significantly influence community structure. These findings not only reveal the novelty and adaptability of the microbial ecosystem in the northern NER but also contribute to the broader understanding of marine microbial diversity and its response to environmental heterogeneity.

Infectious bronchitis virus (IBV) is responsible for avian infectious bronchitis, a disease prevalent in countries with intensive poultry farming practices. Given the presence of multiple genotypic strains in China, identifying the regionally dominant genotypes is crucial for the implementation of effective prevention and control measures. This study focuses on the IBV strain CK/CH/WJ/215, isolated from a diseased commercial chicken flock in China in 2021. The CK/CH/WJ/215 isolate was genetically characterized through complete S1 sequence analysis. Phylogenetic comparisons were made with prevalent vaccine strains (H120, LDT3-A, and 4/91). Glycosylation patterns in the S1 protein were also analyzed. Pathogenicity was assessed in 7-day-old specific-pathogen-free chicks, monitoring morbidity, mortality, and tissue tropisms. Phylogenetic analysis clustered the CK/CH/WJ/215 isolate within the GI-19 lineage. Identity with the vaccination strains H120, LDT3-A, and 4/91 was low (75.7%, 78.6%, and 77.5% respectively). Novel glycosylation sites at positions 138 and 530 were identified compared to H120 and LDT-A. The isolate demonstrated nephropathogenic characteristics, causing 100% morbidity and 73.3% mortality in SPF chicks, with broader tropisms in tissues including trachea, lungs, kidneys, and bursa of Fabricius. Comprehensive genetic and pathological investigations revealed significant differences between the CK/CH/WJ/215 isolate and common vaccine strains, including novel glycosylation sites and a strong multiorgan infective capability. These findings are crucial for understanding the evolutionary dynamics of IBV and developing more effective prevention and control strategies.

This research investigates the dynamics of smoke dispersion in a subway train carriage with multiple lateral openings within a ventilated subway tunnel. Numerous extensive full-scale numerical simulations were conducted to analyze the thermal plume distribution within multiple lateral openings of a train carriage, and the longitudinal and transverse maximum excess ceiling temperature. The results reveal that the heat release rates have a greater impact on the airflow in the carriage than velocities ,even when the longitudinal ventilation velocity reaches 8 m/s or the heat release rate reaches 15 MW. The multiple lateral openings cause significant differences in smoke flow characteristics compared to tunnels with both ends open. Additionally, the transverse maximum ceiling excess temperature inclines towards the lateral opening side due to asymmetrical air entrainment. The analysis of longitudinal maximum ceiling excess temperature identified two regions based on the dimensionless heat release rate, showing a linear increase followed by a constant phase dependent on the heat release rate, with a maximum longitudinal excess gas temperature of 1242 K. Additionally, a relationship between the dimensionless heat release rate and the transverse dimensionless maximum ceiling excess temperature was established. This study can be used as a guide for smoke exhaust mitigation and fire safety measures in the event of subway train carriage fires.