Mesenchymal stem cells （MSCs）, the major stem cells for cell therapy, have been used in the clinic for approximately 10 years. From animal models to clinical trials, MSCs have afforded promise in the ...treatment of numerous diseases, mainly tissue injury and immune disorders. In this review, we summarize the recent opinions on methods, timing and cell sources for MSC administration in clinical applications, and provide an overview of mechanisms that are significant in MSC-mediated therapies. Although MSCs for cell therapy have been shown to be safe and effective, there are still challenges that need to be tackled before their wide application in the clinic.
Oral squamous cell carcinoma is an increasingly prevalent cancer type characterized by high incidence and mortality rates. Its early detection is challenging, primarily because of the absence of ...early molecular markers. Cuproptosis is a novel regulatory mechanism of cell death with implications in various cancers. In this study, we aimed to study cuproptosis-related genes in oral squamous cell carcinoma to identify their prognostic value.
By analyzing genomic, bulk RNA-seq, and single-cell RNA-seq data, we investigated 13 cuproptosis-related genes in The Cancer Genome Atlas-Oral Squamous Cell Carcinoma dataset and Gene Expression Omnibus repository (GSE172577).
ATP7A, ATP7B, and DLST were the most frequently mutated genes, with nine of our studied genes associated with overall survival. Single-cell analysis was conducted to identify cuproptosis-related tumor cells in oral squamous cell carcinoma, which revealed two distinct patterns based on the expression of cuproptosis-related genes. These patterns exhibit differences in genetic alterations and tumor immune microenvironment. Finally, we developed a cuproptosis index using a random forest algorithm based on cuproptosis pattern-related genes in which higher levels were linked to poorer prognosis.
Our findings provide valuable insights into the mechanisms underlying oral squamous cell carcinoma-associated cuproptosis.
Lung cancer is one of the leading causes of cancer-related death worldwide. Cytology plays an important role in the initial evaluation and diagnosis of patients with lung cancer. However, due to the ...subjectivity of cytopathologists and the region-dependent diagnostic levels, the low consistency of liquid-based cytological diagnosis results in certain proportions of misdiagnoses and missed diagnoses. In this study, we performed a weakly supervised deep learning method for the classification of benign and malignant cells in lung cytological images through a deep convolutional neural network (DCNN). A total of 404 cases of lung cancer cells in effusion cytology specimens from Shanghai Pulmonary Hospital were investigated, in which 266, 78, and 60 cases were used as the training, validation and test sets, respectively. The proposed method was evaluated on 60 whole-slide images (WSIs) of lung cancer pleural effusion specimens. This study showed that the method had an accuracy, sensitivity, and specificity respectively of 91.67%, 87.50% and 94.44% in classifying malignant and benign lesions (or normal). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.9526 (95% confidence interval (CI): 0.9019-9.9909). In contrast, the average accuracies of senior and junior cytopathologists were 98.34% and 83.34%, respectively. The proposed deep learning method will be useful and may assist pathologists with different levels of experience in the diagnosis of cancer cells on cytological pleural effusion images in the future.
Porous clay heterostructures (PCHs) are capable of adsorbing volatile organic compounds (VOCs). In this study, PCH was synthesized by modifying bentonite (Bent) with cetyltrimethylammonium bromide ...(CTMAB) and dodecylamine (DDA). Adsorption of six volatile organic compounds (VOCs) including acetone, toluene, ethylbenzene,
o-xylene,
m-xylene and
p-xylene by PCH was investigated. It was observed that adsorption capacities of VOCs were strongly dependent on their properties including cross-sectional area, polarizability, enthalpy of vaporization and critical volume by the multiple linear regression (MLR) approach. Furthermore, PCH had higher adsorption affinity for the aliphatic hydrocarbon compound (acetone) than that for aromatic compounds, which could be attributed to the HOMO energy effects of VOCs. Therefore, PCH could be attractive candidate adsorbents for VOC removal.
Urban commercial districts (UCDs) are the concentrated areas for commercial activities in a city, which provide shopping, leisure, business, and other functions. Urban planners usually face problems ...in how to plan and design UCDs. The layout of UCDs should not only be appropriately concentrated to realize economic benefits, but should also be properly dispersed to accommodate the distribution of the population. Using Beijing as a case study, this study conducted research into UCDs from a microscopic perspective by utilizing open source big data. A recognition and classification method of UCDs was proposed based on the data of POI and road networks. The proposed model combines Huff’s model and the Voronoi method to analyze how various UCDs should be distributed within a city according to the spatial pattern of the population. The results showed that different kinds of UCDs had different spatial distribution features. Problems were also found, for example, UCDs on the urban outskirts served a large population; there were limitations to the spatial distribution of UCDs in the downtown area; and there was incongruity between the UCD types and the population layout. Based on these findings, suggestions regarding the optimization of the urban commercial spatial structure were also put forward.
Based on the spillover index and an improved spillover asymmetric measure method, this paper studies the volatility spillover and its asymmetric effect between crude oil and agricultural commodity ...futures in pre- and post-outbreak of COVID-19. We find that the total volatility spillover is higher with pre-outbreak of COVID-19. In addition, the volatility spillover caused by China’s crude oil is more prominent than international crude oil around the COVID-19, which highlights the necessity of risk control through the establishment of an energy financial market in China. Finally, although the asymmetric effect of volatility spillover has always existed, crude oil was less impacted by good news post-outbreak of COVID-19, indicating that the outbreak of COVID-19 makes assets dominated by commodity attributes more sensitive to bad news. These findings are beneficial for investors to establish a cross-sector risk hedging portfolio, and provide empirical evidence for policymakers to ensure energy and food security.
Background
Misdiagnosis of malignant musculoskeletal tumors may lead to the delay of intervention, resulting in amputation or death.
Purpose
To improve the diagnostic efficacy of musculoskeletal ...tumors by developing deep learning (DL) models based on contrast‐enhanced magnetic resonance imaging and to quantify the improvement in diagnostic performance obtained by using these models.
Study type
Retrospective.
Population
Three hundreds and four musculoskeletal tumors, including 212 malignant and 92 benign lesions, were randomized into the training (n = 180), validation (n = 62) and testing cohort (n = 62).
Field strength/sequence
A 3 T/T1‐weighted (T1‐w), T2‐weighted (T2‐w), diffusion‐weighted imaging (DWI), and contrast‐enhanced T1‐weighted (CET1‐w) images.
Assessment
Three DL models based, respectively, on the sagittal, coronal, and axial MR images were constructed to predict the malignancy of tumors. Blinded to the prediction results, a group of specialists made independent initial diagnoses for each patient by reading all image sequences. One month after the initial diagnoses, the same group of doctors made another round of diagnoses knowing the malignancy of each tumor predicted by the three models. The reference standard was the pathological diagnosis of malignancy.
Statistical tests
Sensitivity, specificity, and accuracy (all with 95% confidential intervals CI) corresponding to each diagnostic test were computed. Chi‐square tests were used to assess the differences in those parameters with and without DL models. A P value < 0.05 was considered statistically significant.
Results
The developed models significantly improved the diagnostic sensitivities of two oncologists by 0.15 (95% CI: 0.06–0.24) and 0.36 (95% CI: 0.24–0.28), one radiologist by 0.12 (95% CI: 0.04–0.20), and three of the four orthopedists, respectively, by 0.12 (95% CI: 0.04–0.20), 0.29 (95% CI: 0.18–0.40), and 0.23 (95% CI: 0.13–0.33), without impairing any of their diagnostic specificities (all P > 0.128).
Data conclusion
The DL models developed can significantly improve the performance of doctors with different training and experience in diagnosing musculoskeletal tumors.
Evidence Level
3
Technical Efficacy
Stage 2
•In order to better realize artificial intelligence-enabled smart vocational education, the study addresses the problem of dynamic changes in learners' learning interests.•A course recommendation ...model based on the contribution of short-term preference reconstruction behaviour is proposed.•The results show that the proposed model improves by more than 5 % over the NAIS model in terms of HR and NDCG indicators.•The model can improve the effectiveness and generalisation of the course recommendation model to a certain extent, and has some application potential.
With the swift advancement of online teaching in vocational education, an increasing number of web-based course materials are being made available to students, granting them the freedom to select resources that suit their personal needs. To optimize the effectiveness of artificial intelligence-enabled smart vocational education, this study presents a course recommendation model centered on learning behaviors and interests. The model utilizes short-term preferences reconstruction behavior contribution to identify fluctuations in learners' interests in real-time. A model for recommending courses is proposed based on short-term preferences and enhancements to learning behavior. Its purpose is to tackle the issue of generalization arising from sparsity and weak correlation in learning behavior. The outcomes demonstrated the model put forth in the study achieved higher Hit Rate (HR) and Normalized Discounted Cumulative Gain (NDCG) values in comparison experiments with multiple models. Hence, this suggested that creating a novel component of historical learning behavior, powered by dynamic interest factors, could resolve the issue of changing learning interests and enhance the efficacy of course recommendation models. Furthermore, the introduction of a correlation mapping network enables the forward mapping transformation from weak to strong learning behavior, thus improving and optimizing input for the agent strategy, reducing data sparsity, and enhancing the performance and generalization of the course recommendation model.
Background
The standardization of quantification data is critical for ensuring the reliability and measurement traceability in the screening of neonatal inherited metabolic disorders. However, the ...availability of national certified reference materials is limited in China.
Methods
In this study, we developed a series of dried blood spot (DBS) reference materials containing 9 amino acids (AA) and 10 acylcarnitines (AC) for neonatal screening. Four levels of the reference materials were measured with tandem mass spectrometry (MS/MS) by seven laboratories using different commercial In Vitro Diagnostic Device (IVD) kits. Then, 100 clinical samples were measured using both derivatization and non‐derivatization methods by the same laboratory.
Results
We found high homogeneity and stability at all levels of the reference materials, with the coefficient of variation (CV) of the analytes less than 15%. These reference materials can be used to assess the testing capabilities of different laboratories. Our test also revealed that the correction factors (CF) calculated by the reference materials, along with clinical samples, could increase the consistency for different kits.
Conclusion
The DBS reference materials proposed in this study provide reliability for the harmonization in multi‐center analysis for the screening of neonatal inherited metabolic disorders. And applying our correction method for the screening could improve the data consistency of the DBS samples prepared by different methods.
The flowchart outlines our research process. A range of evaluations were carried out after the development of DBS reference materials, which are applicable to correct the results of diverse laboratories, kits and platforms. We anticipate our products will ensure dependable harmonization and standardization for multi‐center metabolism analysis in the future.
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