Given that traditional patent value evaluation methods are easily influenced by subjective factors and struggle to comprehensively and objectively reflect the value of patents, this study proposed a patent value evaluation method to identify high-value patents.

This study employs information gain and variance analysis methods to perform feature selection on patent data from medical universities, optimizing input features and reducing redundant information. In terms of model construction, a three-layer BP neural network structure was designed, incorporating ReLU activation functions and Dropout techniques to mitigate overfitting issues and enhance the model's ability to handle nonlinear relationships, thereby improving the model's stability and generalization.

The experimental results showed that the model achieved a prediction accuracy of 96%. Feature selection effectively improved the model's performance to a certain extent, demonstrating good results across multiple evaluation metrics and verifying the feasibility of the proposed method.

This study provides a feasible and effective practical method for patent value evaluation in medical universities, offering valuable reference and application significance, particularly in the identification and prediction of high-value patents.

To systematically elucidate the pivotal role of computational pharmaceutics in unveiling the structure-function relationships of nanodiamond (ND), addressing the challenges posed by their microscopic complexity in experimental research.

This review focused on multi-scale computational approaches, including molecular dynamics (MD) simulation, density functional theory (DFT), and machine learning, summarizing their application strategies and progress in ND research.

Computational simulations successfully revealed the pH-responsive drug release mechanisms governed by ND surface modifications, the influence of shape and charge on transmembrane behavior, and the optical stability of vacancy centers (e.g., NV/SiV). The simulation results showed strong agreement with experimental observations and provided atomic-scale insights into ND dispersion properties and interfacial interactions.

Computational pharmaceutics serves as a core driver for the rational design and application development of ND. Advancing high-precision machine learning potentials and deep multi-scale coupling will accelerate innovative applications of ND in cutting-edge fields such as precision medicine and biosensing.

To provide a reference for the compliance management of sample testing cooperation between pharmaceutical companies and third-party testing institutions in drug clinical trials.

Based on the actual practice of sample testing cooperation in drug clinical trials, this paper analyzes the core points and potential risks in the cooperation from the perspectives of cooperation key points and risk prevention and control, and proposes targeted prevention and control strategies.

Overall, non-compliant behaviors in the cooperation may expose both parties to civil liability, administrative liability, or even criminal liability. From the perspective of cooperation practice, pharmaceutical companies and third-party testing institutions generally face problems such as vague definitions of service content, unclear division of rights and obligations, and insufficient quality control. From the perspective of the industry environment, drug clinical trials regulation is becoming increasingly stringent, with continuously rising requirements for the standardization of sample testing and data authenticity.

In sample testing cooperation between pharmaceutical companies and third-party testing institutions, both parties should clarify the key points of cooperation, including refining the scope of services, standardizing expense settlement, defining the ownership of technological achievements, fulfilling confidentiality obligations, establishing a communication mechanism and adopting standardized entrustment agreements. They should also strengthen their awareness of risk prevention and control and improve their compliance management systems. At the same time, targeted prevention and control measures should be constructed against risks such as approval deficiencies, subcontracting, quality control failures, expense disputes, and intellectual property infringement, so as to ensure the quality of clinical trial data and the safety of subjects, safeguard the legitimate rights and interests of both parties, and promote the stable and orderly development of cooperation.

To prepare a borneol-low molecular organic gelator xerogel and evaluate its effect on borneol volatility.

4-Hydroxybenzaldehyde was used as the starting material, and the target low molecular organic gelator was synthesized through Williamson etherification reaction and condensation reaction. The gel was prepared by dissolving borneol and low molecular organic gelator in anhydrous ethanol and subsequently undergoing vacuum freeze-drying to remove the solvent, resulting in the borneol-low molecular organic gelator xerogel. The microscopic morphology of the xerogel was observed by scanning electron microscope (SEM). The phase transition temperature was determined by differential scanning calorimeter (DSC). The driving force for the gel formation was analyzed by Fourier transform infrared spectroscopy (FT-IR). The loss of borneol from borneol sample, borneol-low molecular organic gelator xerogel, and the borneol-low molecular organic gelator-glycerol xerogel were compared by accelerated evaporation experiment over 10 h.

Mixtures of borneol and low molecular organic gelator with mass ratios of 2∶1, 3∶1 and 4∶1 formed a gel in anhydrous ethanol. SEM imaging revealed that the low molecular organic gelator showed a multilayered sheet-like structure. In contrast, the microstructures of the borneol-low molecular organic gelator xerogel with mass ratios of 2∶1, 3∶1 and 4∶1 showed a bulk, sheet-like and reticulate structure, respectively, which proved that the assemblies were formed. Furthermore, the addition of glycerol to the borneol-low molecular organic gelator system did not significantly alter the microstructure of the resulting xerogel. DSC analysis showed that the heat absorption peaks of the borneol-low molecular organic gelator xerogel varied with the mass ratios. FT-IR analysis revealed a shift in the hydroxyl group stretching vibration to lower wavenumbers for both borneol-low molecular organic gelator xerogel and borneol-low molecular organic gelator-glycerol xerogel, confirming that hydrogen bonding was involved in the formation of gel. After the 5 h accelerated volatilization test at 50 ℃, the borneol loss rate was 39.34% from borneol sample, while it was 17.40% from the 4∶1 borneol-low molecular organic gelator xerogel, and 9.98% after glycerol was added.

The borneol-low molecular organic gelator xerogel reduces the volatility of borneol, with a further reduction observed upon the addition of glycerol.

To establish the HPLC fingerprinting and quantitative method of eight characteristic components in water-decocted extract of Angelicae Dahuricae Radix, and investigate the similarities of 14 batches of Angelicae Dahuricae Radix samples.

SinoChrom ODS-BP column (250 mm×4.6 mm, 5 μm) was used for the fingerprinting and quantitative assay. The mobile phase was 0.025% phosphoric acid-acetonitrile under gradient elution mode. The column temperature was 35 ℃, the detection wavelength was 220 nm, the flow rates were 1.0 mL·min^-1 for fingerprinting and 1.2 mL·min^-1 for quantitative assay, respectively.

In the HPLC fingerprinting, there were 29 common peaks in 14 batches of Angelicae Dahuricae Radix, and eight characteristic components were identified (xanthotol, 5-hydroxy-8-methoxypsoralen, oxypeucedanin hydrate, byakangelicin, xanthotoxin, bergapten, isoimpinellin, imperatorin, and phellopterin). The fingerprints of the 14 batches of Angelicae Dahuricae Radix were similar and the similarities were greater than 0.980. The linear ranges of xanthotoxol, 5-hydroxy-8-methoxypsoralen, oxypeucedanin hydrate, byakangelicin, xanthotoxin, bergapten, imperatorin, and phellopterin were 3.24~162.00, 2.86~143.00, 3.10~155.00, 2.98~149.00, 3.12~156.00, 3.02~151.0, 3.16~158.00, and 2.30~115.00 μg·mg^-1, respectively. The content ranges of xanthotoxol, 5-hydroxy-8-methoxypsoralen, oxypeucedanin hydrate, byakangelicin, xanthotoxin, bergapten, imperatorin, and phellopterin were 0.14~0.37, 0.26~0.39, 2.31~3.48, 1.09~2.22, 0.04~0.26, 0.19~0.34, 0.03~0.14, and 0.11~0.23 μg·mg^-1, respectively for the 14 batches of Angelicae Dahuricae Radix.

The established methods of HPLC fingerprinting and quantitative analysis are reproducible, reliable, and stabile. In the future, these methods can be used for the quality control of original, processed materials and products of Angelicae Dahuricae Radix.

To investigate the pharmacological basis and potential mechanism of Chuanbei Qingfei Syrup in the treatment of viral pneumonia based on UPLC-Q-Exactive Orbitrap-HRMS and network pharmacology.

UPLC-Q-Exactive Orbitrap-HRMS was employed for qualitative characterization of the chemical constituents of Chuanbei Qingfei Syrup. Active compounds and their corresponding targets were identified using the TCMSP and BATMAN-TCM databases, while viral-pneumonia-related targets were retrieved from GeneCards and OMIM. The intersection of the two target sets was obtained with Venny 2.1.0. Protein-protein interaction networks were constructed via the STRING 11.0 database and visualized in Cytoscape. Finally, GO functional annotation and KEGG pathway enrichment analysis of the common targets were performed using the DAVID database.

A total of 256 constituents were identified in Chuanbei Qingfei Syrup. Network pharmacology revealed 113 targets overlapping with viral pneumonia, including 35 hub genes such as TNF, IL-6, IL-1β, and STAT3. GO enrichment indicated that the syrup modulates viral pneumonia mainly through biological processes related to gene expression, apoptosis, and inflammatory response. KEGG analysis further showed that the principal signaling pathways involved include AGE-RAGE, TNF, and IL-17 signaling.

Chuanbei Qingfei Syrup exerts therapeutic effects against viral pneumonia via multi-component, multi-target, and multi-pathway mechanisms. The putative active compounds are flavonoids, saccharides and glycosides, terpenoids, and phenylpropanoids, which act on targets such as TNF, AKT1, and IL-6 and regulate the TNF and IL-17 signaling pathways.