Revisiting Supersaturation of a Biopharmaceutical Classification System IIB Drug: Evaluation via a Multi-Cup Dissolution Approach and Molecular Dynamic Simulation.

As a subclass of the biopharmaceutical classification system (BCS) class II, basic drugs (BCS IIB) exhibit pH-dependent solubility and tend to generate supersaturation in the gastrointestinal tract, leading to less qualified in vitro-in vivo correlation (IVIVC). This study aims to develop a physiologically based multi-cup dissolution approach to improve the evaluation of the supersaturation for a higher quality of IVIVC and preliminarily explores the molecular mechanism of supersaturation and precipitation of ketoconazole affected by Polyvinylpyrrolidone-vinyl acetate copolymer (PVPVA) and hydroxypropyl methyl-cellulose (HPMC). The concentration of ketoconazole in each cup of the dynamic gastrointestinal model (DGIM) was measured using fiber optical probes. Molecular interactions between ketoconazole and PVPVA or HPMC were simulated by Materials Studio. The results demonstrated that PVPVA and HPMC improved and maintained the supersaturation of ketoconazole. PVPVA exhibited superior precipitation inhibitory effect on ketoconazole molecule aggregation due to slightly stronger van der Waals forces as well as unique electrostatic forces, thereby further enhancing in vitro drug absorption, which correlated well with in vivo drug absorption. Compared with a conventional dissolution apparatus paddle method, the DGIM improved the mean prediction error through the IVIVC from 19.30% to 9.96%, reaching the qualification criteria. In conclusion, the physiologically based multi-cup dissolution approach enables improved evaluation of supersaturation in gastrointestinal transportation of BCS IIB drug ketoconazole, enabling screening screen precipitation inhibitors and achieving qualified IVIVC for drug formulation studies.

Network module analysis and molecular docking-based study on the mechanism of astragali radix against non-small cell lung cancer.

BACKGROUND: Most lung cancer patients worldwide (stage IV non-small cell lung cancer, NSCLC) have a poor survival: 25%-30% patients die < 3 months. Yet, of those surviving > 3 months, 10%-15% patients survive (very) long. Astragali radix (AR) is an effective traditional Chinese medicine widely used for non-small cell lung cancer (NSCLC). However, the pharmacological mechanisms of AR on NSCLC remain to be elucidated. METHODS: Ultra Performance Liquid Chromatography system coupled with Q-Orbitrap HRMS (UPLC-Q-Orbitrap HRMS) was performed for the qualitative analysis of AR components. Then, network module analysis and molecular docking-based approach was conducted to explore underlying mechanisms of AR on NSCLC. The target genes of AR were obtained from four databases including TCMSP (Traditional Chinese Medicine Systems Pharmacology) database, ETCM (The Encyclopedia of TCM) database, HERB (A high-throughput experiment- and reference-guided database of TCM) database and BATMAN-TCM (a Bioinformatics Analysis Tool for Molecular mechanism of TCM) database. NSCLC related genes were screened by GEO (Gene Expression Omnibus) database. The STRING database was used for protein interaction network construction (PIN) of AR-NSCLC shared target genes. The critical PIN were further constructed based on the topological properties of network nodes. Afterwards the hub genes and network modules were analyzed, and enrichment analysis were employed by the R package clusterProfiler. The Autodock Vina was utilized for molecular docking, and the Gromacs was utilized for molecular dynamics simulations Furthermore, the survival analysis was performed based on TCGA (The Cancer Genome Atlas) database. RESULTS: Seventy-seven AR components absorbed in blood were obtained. The critical network was constructed with 1447 nodes and 28,890 edges. Based on topological analysis, 6 hub target genes and 7 functional modules were gained. were obtained including TP53, SRC, UBC, CTNNB1, EP300, and RELA. After module analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that AR may exert therapeutic effects on NSCLC by regulating JAK-STAT signaling pathway, PI3K-AKT signaling pathway, ErbB signaling pathway, as well as NFkB signaling pathway. After the intersection calculation of the hub targets and the proteins participated in the above pathways, TP53, SRC, EP300, and RELA were obtained. These proteins had good docking affinity with astragaloside IV. Furthermore, RELA was associated with poor prognosis of NSCLC patients. CONCLUSIONS: This study could provide chemical component information references for further researches. The potential pharmacological mechanisms of AR on NSCLC were elucidated, promoting the clinical application of AR in treating NSCLC. RELA was selected as a promising candidate biomarker affecting the prognosis of NSCLC patients.

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application.

Tongue diagnosis is an essential technique of traditional Chinese medicine (TCM) diagnosis, and the need for objectifying tongue images through image processing technology is growing. The present study provides an overview of the progress made in tongue objectification over the past decade and compares segmentation models. Various deep learning models are constructed to verify and compare algorithms using real tongue image sets. The strengths and weaknesses of each model are analyzed. The findings indicate that the U-Net algorithm outperforms other models regarding precision accuracy (PA), recall, and mean intersection over union (MIoU) metrics. However, despite the significant progress in tongue image acquisition and processing, a uniform standard for objectifying tongue diagnosis has yet to be established. To facilitate the widespread application of tongue images captured using mobile devices in tongue diagnosis objectification, further research could address the challenges posed by tongue images captured in complex environments.

HSPA5, as a ferroptosis regulator, may serve as a potential therapeutic for head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a ferroptosis sensitive tumor type with high mortality rate. However, it remains largely unknown whether ferroptosis influences the tumor cell in HNSCC. MATERIALS AND METHODS: To investigate how ferroptosis regulators were differentially expressed between normal and tumor tissue, data related to HNSCC was downloaded from The Cancer Genome Atlas. The expression levels of key factors in HNSCC and the relationship between key factors and ferroptosis in HNSCC were conducted in vitro, and then analyzed to correlate with the differences in prognosis and survival. This was then combined with TNM staging data, and the migration effects of key factors in HNSCC were verified by scratch test and transwell test. RESULTS: In this study, gene expression analysis and correlation studies between genes showed that HSPA5 was a potentially key associated ferroptosis regulator in HNSCC. Bioinformatics analysis showed that high expression of HSPA5 in HNSCC was positively correlated with poor prognosis and distal metastasis of HNSCC. In vitro immunohistochemistry and western blot tests confirmed that HSPA5 was highly expressed in HNSCC tissues and cell lines. In vitro inhibition of HSPA5 reduced the viability of HNSCC cells and increased ferroptosis. The results of scratch, transwell, and immunofluorescence tests showed that HSPA5 was related to the migration of HNSCC. In addition, a pan-cancer analysis showed that HSPA5 was also overexpressed in many types of cancer with poor prognoses. CONCLUSION: In total, our study demonstrates the critical role of ferroptosis regulators in HNSCC and that HSPA5, as a ferroptosis regulator, can be regarded as a key molecular target for designing new therapeutic regimens to control HNSCC metastasis and progression.

Research and application of tongue and face diagnosis based on deep learning.

Objective: To explore the technical research and application characteristics of deep learning in tongue-facial diagnosis. Methods: Through summarizing the merits and demerits of current image processing techniques used in the traditional medical tongue and face diagnosis, the research status of deep learning in tongue image preprocessing, segmentation, and classification was analyzed and reviewed, and the algorithm was compared and verified with the real tongue and face image. Images of the face and tongue used for diagnosis in conventional medicine were systematically reviewed, from acquisition and pre-processing to segmentation, classification, algorithm comparison, result from analysis, and application. Results: Deep learning improved the speed and accuracy of tongue and face diagnostic image data processing. Among them, the average intersection ratio of U-net and Seg-net models exceeded 0.98, and the segmentation speed ranged from 54 to 58 ms. Conclusion: There is no unified standard for lingual-facial diagnosis objectification in terms of image acquisition conditions and image processing methods, thus further research is indispensable. It is feasible to use the images acquired by mobile in the field of medical image analysis by reducing the influence of environmental and other factors on the quality of lingual-facial diagnosis images and improving the efficiency of image processing.

Herceptin-Conjugated DOX-Fe3O4/P(NIPAM-AA-MAPEG) Nanogel System for HER2-Targeted Breast Cancer Treatment and Magnetic Resonance Imaging.

It is essential to synthesize a "diagnosis and therapy" integration nanocarrier for magnetic resonance imaging-guided breast cancer-targeted chemotherapy. Here, we report Fe3O4/P(NIPAM-AA-MAPEG) nanogels (MNLs) based on in situ loading of doxorubicin (DOX) by miniemulsion polymerization. Especially, propyl acrylic acid (AA) moieties were introduced to absorb DOX by electrostatic interactions and conjugated with the antibody herceptin (HER) through the amino-carboxyl coupling reaction. The size and morphology of MNLs could be adjusted by varying the polymerization parameters, such as the monomer feeding ratio, ferrofluid content, and cross-linker content. The MNLs showed superior stability in a physiological environment, but their structures were destroyed in an acidic environment to accelerate DOX release. The dissociation of the HER-DOX-MNLs accelerated the delivery of DOX and enhanced the therapeutic effects. The studies exhibited that the HER-DOX-MNLs could inhibit the tumor growth. In addition, the MNLs with a high magnetic content had the potential advantages in magnetic resonance imaging (MRI) of breast cancer diagnosis. The dual-targeted pH-responsive nanogels were successfully designed as a multifunctional nanocarrier for realizing HER2-positive breast cancer chemotherapy and diagnostics.

Different Data Mining Approaches Based Medical Text Data.

The amount of medical text data is increasing dramatically. Medical text data record the progress of medicine and imply a large amount of medical knowledge. As a natural language, they are characterized by semistructured, high-dimensional, high data volume semantics and cannot participate in arithmetic operations. Therefore, how to extract useful knowledge or information from the total available data is very important task. Using various techniques of data mining can extract valuable knowledge or information from data. In the current study, we reviewed different approaches to apply for medical text data mining. The advantages and shortcomings for each technique compared to different processes of medical text data were analyzed. We also explored the applications of algorithms for providing insights to the users and enabling them to use the resources for the specific challenges in medical text data. Further, the main challenges in medical text data mining were discussed. Findings of this paper are benefit for helping the researchers to choose the reasonable techniques for mining medical text data and presenting the main challenges to them in medical text data mining.

Network pharmacology analysis of the therapeutic mechanisms of the traditional Chinese herbal formula Lian Hua Qing Wen in Corona virus disease 2019 (COVID-19), gives fundamental support to the clinical use of LHQW.

BACKGROUND: The traditional Chinese Medicine (TCM) herbal formula Lian Hua Qing Wen (LHQW) improves the results of COVID-19 treatment. Three very recent studies analyzed with network pharmacology some working mechanisms of LHQW. However, we used more techniques and also included Angiotensin converting enzyme 2 (ACE2) (a SARS-CoV receptor, possibly the viral entry point in alveolar lung cells) and the immune system, as cytokine storm is essential in the late phase. PURPOSE: Extensive detailed Network Pharmacology analysis of the LHQW- treatment mechanism in COVID-19. METHODS: TCM-herb-meridian and protein interaction network (PIN) of LHQW, based on LHQW herbs meridian information and the protein-protein interaction (PPI) information of the LHQW-component targets. Hub and topological property analyses to obtain crucial targets and construct the crucial LHQW-PIN. Functional modules determination using MCODE, GO and KEGG pathway analysis of biological processes and pathway enrichment. Intersection calculations between the LHQW-proteins and ACE2 co-expression-proteins. RESULTS: LHQW herbs have relationships to Stomach-, Heart-, Liver- and Spleen-systems, but most (10 of the 13 herbs) to the Lung system, indicating specific effects in lung diseases. The crucial LHQW PIN has the scale-free property, contains 2,480 targets, 160,266 PPIs and thirty functional modules. Six modules are enriched in leukocyte-mediated immunity, the interferon-gamma-mediated signaling pathway, immune response regulating signaling pathway, interleukin 23 mediated signaling pathway and Fc gamma receptor-mediated phagocytosis (GO analysis). These 6 are also enriched in cancer, immune system-, and viral infection diseases (KEGG). LHQW shared 189 proteins with ACE2 co-expression proteins. CONCLUSIONS: Detailed network analysis shows, that LHQW herbal TCM treatment modulates the inflammatory process, exerts antiviral effects and repairs lung injury. Moreover, it also relieves the "cytokine storm" and improves ACE2-expression-disorder-caused symptoms. These innovative findings give a rational pharmacological basis and support for treating COVID-19 and possibly other diseases with LHQW.