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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.

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]

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]

This study introduces a novel methodology for the dynamic extraction of information on cotton growth in terms of height utilizing the DJI Zenmuse L1 LiDAR sensor mounted onto a DJI Matrice 300 RTK Unmanned Aerial Vehicle (UAV), aimed at enhancing the precision and efficiency of growth monitoring within the realm of precision agriculture. Employing the Progressive TIN Densification (PTD) and Cloth Simulation Filter (CSF) algorithms, combined with Kriging interpolation, we generated Canopy Height Models (CHMs) to extract the cotton heights at two key agricultural sites: Zengcheng and Tumxuk. Our analysis reveals that the PTD algorithm significantly outperforms the CSF method in terms of accuracy, with its R2 values indicating a superior model fit for height extraction across different growth stages (Zengcheng: 0.71, Tumxuk: 0.82). Through meticulous data processing and cluster analysis, this study not only identifies the most effective algorithm for accurate height extraction but also provides detailed insights into the dynamic growth patterns of cotton varieties across different geographical regions. The findings highlight the critical role of UAV remote sensing in enabling large-scale, high-precision monitoring of crop growth, which is essential for the optimization of agricultural practices such as precision fertilization and irrigation. Furthermore, the study demonstrates the potential of UAV technology to select superior cotton varieties by analyzing their growth dynamics, offering valuable guidance for cotton breeding and cultivation. [ABSTRACT FROM AUTHOR]

Drug resistance substantially compromises antibiotic therapy and poses a serious threat to public health. Fusidic acid (FA) is commonly used to treat staphylococcal infections, such as pneumonia, osteomyelitis and skin infections. However, Gram-negative bacteria have natural resistance to FA, which is almost restrained in cell membranes due to the strong interactions between FA and phospholipids. Herein, we aim to utilize the strong FA–phospholipid interaction to pre-form a complex of FA with the exogenous phospholipid. The FA, in the form of an FA–phospholipid complex (FA-PC), no longer interacts with the endogenous membrane phospholipids and thus can be delivered into bacteria cells successfully. We found that the water solubility of FA (5 µg/mL) was improved to 133 µg/mL by forming the FA-PC (molar ratio 1:1). Furthermore, upon incubation for 6 h, the FA-PC (20 µg/mL) caused a 99.9% viability loss of E. coli and 99.1% loss of P. aeruginosa, while free FA did not work. The morphology of the elongated bacteria cells after treatment with the FA-PC was demonstrated by SEM. The successful intracellular delivery was shown by confocal laser scanning microscopy in the form of coumarin 6-PC (C6-PC), where C6 served as a fluorescent probe. Interestingly, the antibacterial effect of the FA-PC was significantly compromised by adding extra phospholipid in the medium, indicating that there may be a phospholipid-based transmembrane transport mechanism underlying the intracellular delivery of the FA-PC. This is the first report regarding FA-PC formation and its successful reversing of Gram-negative bacteria resistance to FA, and it provides a platform to reverse transmembrane delivery-related drug resistance. The ready availability of phospholipid and the simple preparation allow it to have great potential for clinical use. [ABSTRACT FROM AUTHOR]