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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]

Protein prenylation mediated by the Arabidopsis thaliana PLURIPETALA (AtPLP) gene plays a crucial role in plant growth, development, and environmental response by adding a 15-carbon farnesyl group or one to two 20-carbon geranylgeranyl groups onto one to two cysteine residues at the C-terminus of the target protein. However, the homologous genes and their functions of AtPLP in rapeseed are unclear. In this study, bioinformatics analysis and gene cloning demonstrated the existence of two homologous genes of AtPLP in the Brassica napus L. genome, namely, BnPLP1 and BnPLP2. Evolutionary analysis revealed that BnPLP1 originated from the B. rapa L. genome, while BnPLP2 originated from the B. oleracea L. genome. Genetic transformation analysis revealed that the overexpression of BnPLP1 in Arabidopsis plants exhibited earlier flowering initiation, a prolonged flowering period, increased plant height, and longer main inflorescence length compared to the wild type. Contrarily, the downregulation of BnPLP1 expression in B. napus plants led to delayed flowering initiation, shortened flowering period, decreased plant height, and reduced main inflorescence length compared to the wild type. These findings indicate that the BnPLP1 gene positively regulates flowering time, plant height, and main inflorescence length. This provides a new gene for the genetic improvement of flowering time and plant architecture in rapeseed. [ABSTRACT FROM AUTHOR]

We investigated the functional mechanism of long non-coding small nucleolar host gene 17 (SNHG17) in diffuse large B-cell lymphoma (DLBCL). lncRNAs related to the prognosis of patients with DLBCL were screened to analyze long non-coding small nucleolar host gene 17 (SNHG17) expression in DLBCL and normal tissues, and a nomogram established for predicting DLBCL prognosis. SNHG17 expression in B-cell lymphoma cells was detected using qPCR. The effects of SNHG17 with/without doxorubicin on the proliferation and apoptosis of DoHH2 and Daudi were detected. The effects of combined SNHG17 and doxorubicin were analyzed. The regulatory function of SNHG17 in DLBCL was investigated using a mouse tumor xenotransplantation model. RNA sequencing was used to analyze the signaling pathways involved in SNHG17 knockdown in B-cell lymphoma cell lines. The target relationships among SNHG17, microRNA, and downstream mRNA biomolecules were detected. A higher SNHG17 level predicted a lower survival rate. SNHG17 was highly expressed in DLBCL patient tissues and cell lines. We established a prognostic model containing SNHG17 expression, which could effectively predict the overall survival rate of DLBCL patients. SNHG17 knockdown inhibited the proliferation and induced the apoptosis of B-cell lymphoma cells, and the combination of SNHG17 and doxorubicin had a synergistic effect. SNHG17, miR-34a-5p, and ZESTE gene enhancer homolog 2 (EZH2) had common hypothetical binding sites, and the luciferase reporter assay verified that miR-34a-5p was the direct target of SNHG17, and EZH2 was the direct target of miR-34a-5p. The carcinogenic function of SNHG17 in the proliferation and apoptosis of DLBCL cells was partially reversed by a miR-34a-5p inhibitor. SNHG17 increases EZH2 levels by inhibiting miR-34a-5p. Our findings indicate SNHG17 as critical for promoting DLBCL progression by regulating the EZH2 signaling pathway and sponging miR-34a-5p. These findings provide a new prognostic marker and therapeutic target for the prognosis and treatment of DLBCL. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the three symptom indicators of scalp seborrheic dermatitis (SSD), namely scalp flaking, maximum erythema area, and pruritus, to develop a "16‐point scale," to explore its relationship with the severity of SSD, and verify the reliability of the 16‐point scale. Method: A dermatologist evaluated patients with SSD using a 16‐point scale, and statistically analyzed the collected data with the help of SPSS 26.0 software. The measurement data are expressed as (mean ± SD), and the intergroup comparison was done using a non‐parametric test. We performed the correlation analysis using the bivariate correlation analysis method, and the relationship among non‐normal distribution data variables were analyzed using Spearman's correlation coefficient. p < 0.05 indicated that the difference was statistically significant. Results: The total score of the "16‐point scale" strongly correlated with the severity of disease, where scalp flaking had the strongest correlation. As compared with a single score, the correlation of the total score with the severity of disease was higher. The scoring range for mild patients was (0, 5], that for moderate patients was (5, 9], and that for severe patients was (9, 16]. Conclusion: A "16‐point scale", consisting of items for adherent scalp flaking (0–10), maximum erythema area (0–3), and pruritus (0–3), was used to score the patients with SSD, and the total score was strongly correlated with and differentiated the severity of SSD. Recommended evaluation criteria: a total score of 0–5 points indicates mild SSD, 6–9 points indicates moderate SSD, 10–16 points indicates severe SSD. These criteria can help to standardize disease diagnosis and treatment, and efficacy assessment. [ABSTRACT FROM AUTHOR]

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.

The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP3Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP3Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters. [ABSTRACT FROM AUTHOR]

Rapeseed is a globally important economic crop that can be severely impacted by aphids. However, our understanding of rapeseed resistance to aphid stress is very limited. In this study, we analyzed the resistance characteristics of the low aphid-susceptible variety APL01 and the highly aphid-susceptible variety Holly in response to aphid stress. APL01 had a more significant inhibitory effect on aphid proliferation compared with Holly during the early stage of inoculation, whereas Holly showed stronger tolerance to aphid stress compared with APL01 during the later stage of inoculation. Through transcriptome, physiological, and gene expression analyses, it was revealed that chitinase activity, catalase activity, calcium signal transduction, and activation of systemic acquired resistance might be involved in aphid resistance in B. napus. The degree of inhibition of photosynthesis in plants under aphid stress directly determines the tolerance of B. napus to aphid stress. Furthermore, four promising candidate genes were screened from eight genes related to rapeseed response to biotic stress through RT-qPCR analysis of gene expression levels. These research findings represent an important step forward in understanding the resistance of rapeseed to aphid stress and provide a solid foundation for the cloning of genes responsible for this resistance. [ABSTRACT FROM AUTHOR]

To enhance the efficiency of mechanical automatic picking of cherry tomatoes in a precision agriculture environment, this study proposes an improved target detection algorithm based on YOLOv5n. The improvement steps are as follows: First, the K-means++ clustering algorithm is utilized to update the scale and aspect ratio of the anchor box, adapting it to the shape characteristics of cherry tomatoes. Secondly, the coordinate attention (CA) mechanism is introduced to expand the receptive field range and reduce interference from branches, dead leaves, and other backgrounds in the recognition of cherry tomato maturity. Next, the traditional loss function is replaced by the bounding box regression loss with dynamic focusing mechanism (WIoU) loss function. The outlier degree and dynamic nonmonotonic focusing mechanism are introduced to address the boundary box regression balance problem between high-quality and low-quality data. This research employs a self-built cherry tomato dataset to train the target detection algorithms before and after the improvements. Comparative experiments are conducted with YOLO series algorithms. The experimental results indicate that the improved model has achieved a 1.4% increase in both precision and recall compared to the previous model. It achieves an average accuracy mAP of 95.2%, an average detection time of 5.3 ms, and a weight file size of only 4.4 MB. These results demonstrate that the model fulfills the requirements for real-time detection and lightweight applications. It is highly suitable for deployment in embedded systems and mobile devices. The improved model presented in this paper enables real-time target recognition and maturity detection for cherry tomatoes. It provides rapid and accurate target recognition guidance for achieving mechanical automatic picking of cherry tomatoes. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is a prevalent chronic respiratory disease that poses a significant health risk to individuals. Patients with COPD are predisposed to a higher incidence of coronary artery disease (CAD) than the general population. This study aims to investigate the correlation between COPD and the incidence of in-stent restenosis (ISR) after percutaneous coronary intervention (PCI).Methods: This study retrospectively analyzed the clinical data and laboratory test results of patients who underwent PCI at our hospital between January 2018 and December 2021 to investigate the relationship between COPD and drug-Eluting Stents (DES) postoperative ISR. We employed the best subset method to select the most suitable combination of predictive factors, utilizing the data, and verified the precision of the model by means of internal validation. We ultimately assessed the performance of the prediction model using an ROC curve.Results: The research indicates that COPD is an independent risk factor for ISR after PCI (OR=2.437, 95% CI [1.336, 4.495], P=0.004). The analysis revealed an area under the receiver operating characteristic (ROC) curve of 0.783 for the training group and 0.705 for the testing group, indicating a model fitting for both groups (both > 0.5).Conclusion: COPD history is a dependable predictor of stent restenosis post percutaneous coronary intervention. [ABSTRACT FROM AUTHOR]

Unknown objects in agricultural soil can be important because they may impact the health and productivity of the soil and the crops that grow in it. Challenges in collecting soil samples present opportunities to utilize Ground Penetrating Radar (GPR) image processing and artificial intelligence techniques to identify and locate unidentified objects in agricultural soil, which are important for agriculture. In this study, we used finite-difference time-domain (FDTD) simulated models to gather training data and predict actual soil conditions. Additionally, we propose a multi-class support vector machine (MSVM) that employs a semi-supervised algorithm to classify buried object materials and locate their position in soil. Then, we extract echo signals from the electromagnetic features of the FDTD simulation model, including soil type, parabolic shape, location, and energy magnitude changes. Lastly, we compare the performance of various MSVM models with different kernel functions (linear, polynomial, and radial basis function). The results indicate that the FDTD-Yee method enhances the accuracy of simulating real agricultural soils. The average recognition rate of the hyperbola position formed by the GPR echo signal is 91.13%, which can be utilized to detect the position and material of unknown and underground objects. For material identification, the directed acyclic graph support vector machine (DAG-SVM) model attains the highest classification accuracy among all soil layers when using an RBF kernel. Overall, our study demonstrates that an artificial intelligence model trained with the FDTD forward simulation model can effectively detect objects in farmland soil. [ABSTRACT FROM AUTHOR]