Abstract:This study investigates the impact of digital talent flow on industrial restructuring in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), considering the threshold effect of technological innovation and spatial dependence among cities. The study emphasizes that the benefits of talent mobility extend beyond inflow to include the broader dynamics of both inflow and outflow, along with the accompanying spill overs of knowledge, technology, and experience. Using panel data from cities in the GBA from 2003 to 2021, our results show: First, there is no significant spatial dependence in the rationality of industrial structure among cities in the GBA. However, negative spatial dependence is observed in the quantity of industrial structure upgrading, while positive spatial dependence is noted in the quality. Second, the flow of digital talent promotes the rationality and quality of industrial structure but suppresses the quantity of industrial structure upgrading. The robustness test confirms the importance of peripheral cities, and policy implications from these findings suggest tailored strategies for peripheral areas.
Abstract:Objective: This study aims to analyse the long-term trends and future projections of liver cancer burden in China from 1990 to 2035, focusing on incidence, prevalence, mortality, and disability-adjusted life years (DALYs), using data from the Global Burden of Disease Study and a Bayesian Age-Period-Cohort model. Methods: Age-standardized rates for incidence, prevalence, mortality, DALYs, and years lived with disability (YLDs) were used to assess the liver cancer burden from1990 to 2021. A Bayesian Age-Period-Cohort model was applied to project trends from2021to2035. Results: From 1990 to 2021, age-standardized liver cancer incidence slightly decreased, from 13.51 to 13.29 per 100,000, while rates for males rose from18.94to20.00 per 100,000. Mortality and DALYs significantly decreased, particularly in males, but prevalence declined modestly for both sexes. The liver cancer burden peaked in different age groups for males (50–54) and females (65–69). Future projections (2021–2035) suggest a 31.7% increase in incidence, from197,000 cases in 2021 to 259,000 in 2035, and a 31.2% rise in prevalence. DALYs and deaths are projected to increase by 30.5% and 35.9%, respectively. Conclusion: Liver cancer burden in China has decreased in terms of age-standardized incidence, mortality, and DALYs. However, absolute numbers are expected to rise by 2035, particularly among males and older populations. This highlights the need for strengthened prevention, early detection, and treatment strategies to address the growing liver cancer burden.
Abstract:Background: Depression in children and adolescents poses significant public health challenges, impacting individual and societal well-being. Traditional methods for diagnosing and predicting depression often rely on symptomatic assessments post-onset, delaying intervention. Recent advancements in machine learning (ML) provide new opportunities for early prediction and prevention. Methods: This study leveraged data from the U.S. National Health Interview Survey(NHIS) from 2004 to 2014, involving 27,642 participants aged 4-17. We employed machine learning models, including XGBoost, Random Forest, Decision Tree, and Bagging Classifier, to analyze a vast array of physical activity and health behavior data. These models were evaluated for their accuracy in predicting mental health indicators. Results: The XGBoost model demonstrated notable accuracy (77.5%) in forecasting mental health scores, closely followed by the Random Forest classifier (77.4%). The models were further assessed for their ability to classify varying degrees of mental anxiety, with the redefined categories of 'Mild', 'Moderate', and 'Severe'. The retrained XGBoost model showed enhanced accuracy across these categories (81.7%- 85.7%), with AUC values indicating reliable differentiation between mental health states. Discussion: This study expands the scope of ML in predicting depression, highlighting the intricate relationship between diverse health behaviors and mental health. The predictive accuracy of the models underscores the potential of M Linearly detection and intervention for depression. Future research should focus on refining these models for broader application and exploring their utility in real-world clinical settings.
Abstract:Pancreatic ductal adenocarcinoma (PAAD) is characterized by its highly aggressive nature, presenting substantial challenges in clinical management. Surgical resection remains a potential curative option; however, most patients receive a diagnosis at intermediate or advanced stages, where the tumor often invades nearby critical blood vessels, nerves, and organs, thus reducing the probability of successful resection. Additionally, the issues of postoperative recurrence and metastasis require urgent attention and resolution. The applicationof single-cell transcriptome sequencing technology holds promise for exploringtumor heterogeneity, mechanisms of drug resistance, the plasticity ofthe immune microenvironment, and independent prognostic markers in PAAD. This article aims to provide a comprehensive review of current treatment strategies for PAAD, critically evaluating their strengths and limitations, and to summarize the novel applications of transcriptomics in identifying tumor-targeted therapeutic and diagnostic targets, thereby offering theoretical support for its implementation inPAAD treatment.
Abstract:Frugality represents a fundamental aspect of traditional Chinesecultural values, consistently shaping individual behavior. Effectively stimulatingresidents' intrinsic motivation to adopt low-carbon consumption practices is crucial for reducing carbon emissions in China. This research utilizes aquestionnair survey to investigate the low-carbon consumption intentions of residents in Shanxi Province, utilizing a structural equation model for analysis andprediction with SPSS 26.0 and AMOS 26.0. Drawing on the Theory of PlannedBehavior (TPB), the study investigates the influence of attitude, subjective norms, and perceived behavioral control on low-carbon consumption intentions, withaparticular focus on the moderating role of frugality values. The findings not onlyreaffirm the moderating function of Confucian cultural values but also extend theTPB framework by incorporating frugality as a moderating factor. Consistent withprior research, the results indicate a positive relationship between low-carbonconsumption intentions and attitudes, subjective norms, and perceived behavioral control. Although frugality values did not exert a direct effect on low-carbon consumption intentions, they significantly moderated the relationships between attitude and perceived behavioural control with consumption intentions. However, no significant moderating effect was observed for subjective norms, adding complexity to the existing literature. These findings contribute to a deeper understanding of individual consumption behaviour and offer empirical insights into the role of cultural values in shaping low-carbon consumption intentions, providing novel strategies for enhancing public engagement in sustainable consumption practices in Shanxi Province.
Abstract:Timely and accurate identification of plant diseases is essential for increasing plant productivity. The widely used state-of-the-art CNN-based models still face challenges and limitations on leaf images under complex backgrounds due to a lack of a global receptive field and self-attention mechanism. This study proposes a lightweight transfer learning-based vision transformer architecture (TVITA) for the automatic identification of plant leaf diseases without using any convolution. Two popular PlantVillage datasets—the original dataset (OD) with 55,448 images and the augmented dataset (AD) with 61,486 images—were used for model training (70%), validation (20%), and testing (10%). The proposed TVITA model, leveraging transfer learning by fine-tuning the pre-trained VITSO model on the augmented dataset, outperformed both the VITSO and VITSA models, which were trained from scratch on the OD and AD, respectively. The TVITA achieved a recognition accuracy of 97.85%, precision of 96.58%, recall of 97.50%, F1 score of 96.95%, and AUC of 98.76% on the OD testing set, and 97.50%, 97.00%, 97.07%, 96.99%, and 98.74% on the AD testing set. The results indicate that the proposed TVITA model achieves stateof-the-art accuracy, greater robustness, and lower computational cost compared with popular stateof-the-art CNN-based architectures. This study highlights the efficacy of transfer learning in enhancing ViT models' performance for plant disease identification, suggesting potential applications in other domains requiring high classification accuracy.
Abstract:Purpose of the study. The aim of the study was to evaluate the differences in morphometric parameters of the kidneys in rats with longterm administration of nonsteroidal anti-inflammatory drugs and new derivatives of cyanothioacetamide to assess nephrotoxicity. Materials and methods. The new cyanothioacetamide derivatives synthesized at the “ChemEx” Research Laboratory were preliminarily subjected to virtual bioscreening. We selected 5 heterocyclic compounds of cyanothioacetamide derivatives with potential analgesic activity, which was confirmed in various pharmacological tests. To study the chronic effect of new cyanothioacetamide derivatives on the kidneys, rats were preliminarily administered the new compounds intragastrically at a dose of 5 mg/kg for 14 days. The comparison drugs were nimesulide (5 mg/kg), indomethacin (7 mg/kg), acetylsalicylic acid and paracetamol at a dose of 50 mg/kg. The stereometric indices of the kidneys were assessed in each group. Results. The study revealed that new cyanothioacetamide derivatives with laboratory codes d02–123, d02–133, d02–139, d02–172, d02–149 have lower nephrotoxicity compared to reference drugs. In particular, the compound with the code d02–149 showed minimal changes in the structure of the proximal and distal tubules and the absence of hemorrhages, indicating its low risk for the kidneys. Derivatives d02–123 and d02–133 showed adaptive changes, such as an increase in the area of the distal tubules and a decrease in the volume of normal glomeruli, which may be associated with compensatory mechanisms during long–term exposure. Conclusions. New cyanothioacetamide derivatives demonstrate potentially less nephrotoxic effects compared to existing NSAIDs. These results highlight the need for further study of new cyanothioacetamide derivatives to develop safer drugs with analgesic activity, especially given the ability of some of them to have less damaging effects on the kidneys.
Abstract:Fruit yogurt is consumed fondly as a healthy snack. The combination of both fruit and yogurt brings together the benefits of both ingredients. Proteins are essential for a healthy life. There is limited data on the protein content of fruit yogurts. This study described the total protein and casein fraction in different fruit yogurts, mostly produced and consumed: strawberry, forest fruit, fig-walnut, mango, blueberry, peach, raspberry, and pineapple. We also provided detailed information about fruit yogurts. We employed a nanodrop spectrophotometer to determine total protein content and SDS-PAGE, a widely recognized and effective protein identification method, though it has not yet been applied to fruit yogurt. Statistical evaluation was performed with an analysis of variance. The highest total protein amount was observed in pineapple (3.51 mg/mL) and fig-walnut yogurts (3.25 mg/mL), and the lowest in forest fruit (1.21 mg/mL) and strawberry yogurts (1.27 mg/mL). The highest casein density was determined in pineapple (41.05%) and fig-walnut yogurts (36.56%) and the lowest in mango (12.94%) yogurts. The remaining fruit yogurts had OD (Optical Density) values of 27.61% for strawberry, 22.40% for peach, 19.85% for raspberry, and 19.04% for blueberry, respectively. In addition, the casein of the fruit yogurts was determined around 25 kDa. The statistical differences were significant between fruit yogurts (P<0.001). As a result, yogurt producers must carefully consider their choice of starter culture to optimize both sensory attributes and health benefits, ensuring that consumers receive a product that aligns with their expectations for quality and nourishment.
Abstract:The integration of blockchain technology in the retail industry has been a growing area of interest, yet traditional research has often overlooked the intricate relationship between user sentiment and loyalty. This study addresses this gap by exploring the correlation between user acceptance, sentiment experience, and loyalty within the context of blockchain-supported retail environments. Utilizing both quantitative and qualitative methods, sentiment data from retail users was collected and analyzed using the Natural Language Toolkit (NLTK) for sentiment analysis, while a modified Technology Acceptance Model (TAM) was employed to assess user acceptance. The model was extended to incorporate user sentiment and loyalty as critical factors. The results indicate a significant positive correlation between user sentiment and loyalty, with correlation coefficients ranging from 0.68 to 0.82. These findings suggest that user sentiment, when adequately managed, can enhance user loyalty, thereby providing strategic insights for the retail industry. The study contributes to the literature by offering a more holistic understanding of user behaviour in the context of emerging technologies, with practical implications for enhancing user experience and guiding retail strategies.
Abstract:Purpose of the study. We aimed to find and study in silico and in vivo novel thienopyridine and dihydropyridine derivatives with hypoglycemic activity. Materials and methods. Three derivatives of thienopyridine and dihydropyridine with potential hypoglycemic effect were selected from 350 novel cyanothioacetamide derivatives synthesized by the authors in the “ChemEx” Research Laboratory using virtual bioscreening based on SwissTargetPrediction software, and their oral administration safety was assessed. During the experimental study conducted on 72 mature male Wistar rats, we modeled hyperglycemia in the animals by means of high-fat diet and dexamethasone load, then we performed pharmacological correction of metabolic disorders with the novel compounds and comparison drugs - Metformin, Vildagliptin. Upon withdrawal of animals from the experiment, blood was sampled for further biochemical investigation and determination of glucose concentration, ALT, AST, total bilirubin, triglycerides, and total blood cholesterol levels. Results. The results of molecular docking showed that the novel compounds with the codes AZ-383, AZ-257, and AZ-020 have putative effects on biotargets with potential for the correction of hyperglycemia and positive impact on lipid metabolism and liver function. In addition, these compounds have a good safety profile when administered orally. In the in vivo study, compounds AZ-383, AZ-257, and AZ-020 were found to have hypoglycemic activity and associated hepatoprotective and hypolipidemic properties. Conclusions. Novel derivatives of thienopyridine and dihydropyridine showed hypoglycemic activity in the experiment following metabolic disorder modeling in animals. This corresponds fully with the biotargets identified in silico for these compounds. Pharmacological activity of compounds with the codes AZ-383, AZ-257, and AZ-020, as well as their oral administration safety confirm the high relevance of further study.