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The incidence of each of the rare disease is very low. The complexity and diagnosis difficulty of the rare disease lead to the difficulties in the clinical research and development (R&D) of drugs for rare diseases. There is an urgent clinical need for the drug development of rare diseases in China. Encouraging R&D of new drugs, particularly the innovative drugs with China's own independent intellectural property is the basis for solving the predicament in drug shortage in China.. In order to further improve the efficiency of clinical R&D of drugs for rare diseases, the National Medical Products Administration (NMPA), Center for Drug Evaluation (CDE) issued Technical Guidance for Clinical Research and Development of Drugs for Rare Diseases. This is the first guidance for rare diseases in China that is drafted from the standpoint of the clinical technology research and development.The guidance is the scientifitc thinking and framework for the drug developing enterprises to research and develop drugs for rare disease efficiently and appropriately by following drug developing protocols and relating to the special features of rare disease.This paper presents the concepts and rationale in the guidance for the appraisal of rare disease drug research and development.

Objective The purpose of this study was to compare the effects of different concentrations of green tea on salivary flow rate and pH value. Methods 20 healthy college students aged from 18 to 25 were included in this study, at State Key Laboratory of Oral Diseases, West China College of Stomatology, Sichuan University. Salivary flow rate and pH value were measured between 21: 00 and 22: 00 after drinking water, low, medium, and high concentration of green tea. Results Female salivary flow rates in low and medium concentration group were significant higher than that in high concentration group and water group (F＝5.20, P < 0.01). There was no significant difference in male sali⁃ vary flow rates among 4 groups (F＝1.810, P＝0.143). Salivary pH value in 3 green tea groups were lower than that in water group (F＝3.50, P＝0.02), with no significant difference among them. Conclusion Low and medium concentra⁃ tion green tea prevent decrease of salivary flow rate in healthy women but not in males. Salivary pH lowers after drink⁃ ing different concentrations of green tea.

Interleukin-33 (IL-33) plays a protective role in myocardial ischemia and reperfusion (I/R) injury, but the underlying mechanism was not fully elucidated. The present study was designed to investigate whether IL-33 protects against myocardial I/R injury by regulating both P38 mitogen-activated-protein kinase (P38 MAPK), which is involved in one of the downstream signaling pathways of IL-33, and high mobility group box protein 1 (HMGB1), a late pro-inflammatory cytokine. Myocardial I/R injury increased the level of IL-33 and its induced receptor (sST) in myocardial tissue. Compared with the I/R group, the IL-33 group had significantly lower cardiac injury (lower serum creatine kinase (CK), lactate dehydrogenase (LDH), and cTnI levels and myocardial infarct size), a suppressed inflammatory response in myocardial tissue (lower expression of HMGB1, IL-6, TNF-α and INF-γ) and less myocardial apoptosis (much higher Bcl-2/Bax ratio and lower cleaved caspase-3 expression). Moreover, IL-33 activated the P38 MAPK signaling pathway (up-regulating P-P38 expression) in myocardial tissue, and SB230580 partially attenuated the anti-inflammatory and anti-apoptosis effects of IL-33. These findings indicated that IL-33 protects against myocardial I/R injury by inhibiting inflammatory responses and myocardial apoptosis, which may be associated with the HMGB1 and P38 MAPK signaling pathways.

Based on CAN bus, this paper studies the system control and management system of locomotive in mine, analyzes the working principle of locomotive system, gives the CAN bus scheme, hardware circuit design and CAN communication protocol, and implements long-distance, high-reliability communication function and remote monitoring function. Experiments show that the auxiliary system based on CAN bus control easier, operation more secure, as well as improving the control performance and service life of the electric locomotive.

The existing forage harvester header cannot automatically adjust the height and inclination during operation, resulting in uneven stubble height of forage, which, in turn, affects the efficiency of harvesting and the quality of forage regeneration. To address this issue, this study conducted the design and experimentation of a 2-degrees-of-freedom (DOF) profiling header. Firstly, this study designed an adaptive profiling header with 2-DOF adjustment, which was realized by the height adjustment mechanism and the tilt angle adjustment mechanism. The relationship model between the profiling device and the attitude of the header was established so that the header can acquire ground undulation in real time through the angle sensor of the profiling device. In order to verify the rationality of the header design, a co-simulation model of ADAMS and MATLAB/Simulink was built, and the header attitude control system was designed based on the fuzzy PID algorithm. The co-simulation results show that the header height ( H ) is always kept around 150 mm during the forwarding process of the harvester, with a maximum error of 5.8 mm, and the average relative error ( R E H ) and root mean square error ( R M S E H ) were 1.4% and 2.6 mm, respectively, and the maximum error of the tilt angle ( γ ) of the header is 0.53° and the R M S E γ is 0.22°, which indicates that the header profiling mechanism can accurately reflect the undulation of the terrain and the header attitude control system has good robustness. Finally, the test platform was built and tested in a grassland. The test results show that the average height of the header is 149.8 mm, the maximum error is 7.5 mm, and the R E H and R M S E H are 3.4% and 5.3 mm, respectively. The average error of the header inclination is 0.34°, and the maximum error is 0.57°. The test results indicate that the header can realize the adaptive adjustment of height and inclination, and the control system has high precision, stability and reliability, meeting the demand of automatic regulation of header attitude of a forage harvester. [ABSTRACT FROM AUTHOR]

Granulomatosis with polyangiitis (GPA) is a necrotizing granulomatous vasculitis classified as an autoimmune small-vessel vasculitis. Clinically, approximately 80% of affected organs in GPA involve the upper/lower respiratory tract and kidneys, with cardiovascular system involvement being rare. Here, we report a case of a 50-year-old female patient who presented with sudden-onset chest pain lasting for 1 hour. The patient had normal body temperature, and markers of infection such as C-reactive protein and erythrocyte sedimentation rate were within normal limits. Electrocardiography revealed ST-segment elevation in inferior, precordial, and posterior leads. Emergency coronary angiography showed no significant obstructive disease, prompting consideration of vasospastic angina given the patient's recurrent chest pain symptoms and findings on laboratory and imaging studies. The patient underwent treatment including coronary vasospasm antagonists and immunomodulation, resulting in clinical improvement and subsequent discharge. During a 7-month follow-up period, the patient did not experience any further adverse cardiovascular events. [ABSTRACT FROM AUTHOR]

This study aims to investigate their correlation and predictive utility for in-stent restenosis (ISR) in patients with acute coronary syndrome (ACS) following percutaneous coronary intervention (PCI). Methods: We collected medical records of 668 patients who underwent PCI treatment from January 2022 to December 2022. Based on follow-up results (ISR defined as luminal narrowing ≥ 50% on angiography), all participants were divided into ISR and non-ISR groups. The XGBoost machine learning (ML) model was employed to identify the optimal predictive variables from a set of 31 variables. Discriminatory ability was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), while calibration and performance of the prediction models were assessed using the Hosmer-Lemeshow (HL) test and calibration plots. Clinical utility of each model was evaluated using decision curve analysis (DCA). Results: In the XGBoost importance ranking of predictive factors, LMR and RC ranked first and fourth, respectively. The AUC of the entire XGBoost ML model was 0.8098, whereas the model using traditional stepwise backward regression, comprising five predictive factors, had an AUC of 0.706. The XGBoost model showed superior predictive performance with a higher AUC, indicating better discrimination and predictive accuracy for ISR compared to traditional methods. Conclusion: LMR and RC are identified as cost-effective and reliable biomarkers for predicting ISR risk in ACS patients following drug-eluting stent (DES) implantation. LMR and RC represent cost-effective and reliable biomarkers for predicting ISR risk in ACS patients following drug-eluting stent implantation. Enhances the accuracy and clinical utility of ISR prediction models, offering clinicians a robust tool for risk stratification and personalized patient management. [ABSTRACT FROM AUTHOR]

Obtaining consistent multi-view images of litchis is crucial for various litchi-related studies, such as data augmentation and 3D reconstruction. This paper proposes a two-stage model that integrates the Mask2Former semantic segmentation network with the Wonder3D multi-view generation network. This integration aims to accurately segment and extract litchis from complex backgrounds and generate consistent multi-view images of previously unseen litchis. In the first stage, the Mask2Former model is utilized to predict litchi masks, enabling the extraction of litchis from complex backgrounds. To further enhance the accuracy of litchi branch extraction, we propose a novel method that combines the predicted masks with morphological operations and the HSV color space. This approach ensures accurate extraction of litchi branches even when the semantic segmentation model's prediction accuracy is not high. In the second stage, the segmented and extracted litchi images are passed as input into the Wonder3D network to generate multi-view of the litchis. After comparing different semantic segmentation and multi-view synthesis networks, the Mask2Former and Wonder3D networks demonstrated the best performance. The Mask2Former network achieved a mean Intersection over Union (mIoU) of 79.79% and a mean pixel accuracy (mPA) of 85.82%. The Wonder3D network achieved a peak signal-to-noise ratio (PSNR) of 18.89 dB, a structural similarity index (SSIM) of 0.8199, and a learned perceptual image patch similarity (LPIPS) of 0.114. Combining the Mask2Former model with the Wonder3D network resulted in an increase in PSNR and SSIM scores by 0.21 dB and 0.0121, respectively, and a decrease in LPIPS by 0.064 compared to using the Wonder3D model alone. Therefore, the proposed two-stage model effectively achieves automatic extraction and multi-view generation of litchis with high accuracy. [ABSTRACT FROM AUTHOR]

Introduction: Coronary artery disease (CAD) remains a significant global health challenge, with percutaneous coronary intervention (PCI) being a primary revascularization method. In-stent restenosis (ISR) post-PCI, although reduced, continues to impact patient outcomes. Inflammation and platelet activation play key roles in ISR development, emphasizing the need for accurate risk assessment tools. The systemic inflammation aggregation index (AISI) has shown promise in predicting adverse outcomes in various conditions but has not been studied in relation to ISR. Methods: A retrospective observational study included 1712 patients post-drug-eluting stent (DES) implantation. Data collected encompassed demographics, medical history, medication use, laboratory parameters, and angiographic details. AISI, calculated from specific blood cell counts, was evaluated alongside other variables using machine learning models, including random forest, Xgboost, elastic networks, logistic regression, and multilayer perceptron. The optimal model was selected based on performance metrics and further interpreted using variable importance analysis and the SHAP method. Results: Our study revealed that ISR occurred in 25.8% of patients, with a range of demographic and clinical factors influencing the risk of its development. The random forest model emerged as the most adept in predicting ISR, and AISI featured prominently among the top variables affecting ISR prediction. Notably, higher AISI values were positively correlated with an elevated probability of ISR occurrence. Comparative evaluation and visual analysis of model performance, the random forest model demonstrates high reliability in predicting ISR, with specific metrics including an AUC of 0.9569, accuracy of 0.911, sensitivity of 0.855, PPV of 0.81, and NPV of 0.948. Conclusion: AISI demonstrated itself as a significant independent risk factor for ISR following DES implantation, with an escalation in AISI levels indicating a heightened risk of ISR occurrence. [ABSTRACT FROM AUTHOR]