Medicine Discovery Research Articles

Regulation of iodine-glucose flip-flop in SW1736 anaplastic thyroid cancer cell line.

The alternative manner of iodide and glucose uptake found in different types of thyroid cancer, referred to flip-flop. ATC cells indicate low iodide uptake and high glucose uptake, which lack the morphology and genetic characteristics of well-differentiated tumors and become increasingly invasive. Importance placed on the discovery of innovative multi-targeted medicines to suppress the dysregulated signaling in cancer. In this research, we aimed to clarify molecular mechanism of Rutin as a phytomedicine on anaplastic thyroid cancer cell line based on iodide and glucose uptake. The MTT test was employed to test cell viability. Iodide uptake assay was performed using a spectrophotometric assay to determine iodide uptake in SW1736 cells based on Sandell-Kolthoff reaction. For glucose uptake detection, ''GOD-PAP'' enzymatic colorimetric assay was applied to measure the direct glucose levels inside of the cells. Determination of NIS, GLUT1 and 3 mRNA expression in SW1736 cells was performed by qRT-PCR. Determination of NIS, GLUT1 and 3 protein levels in SW1736 cells was performed by western blotting. According to our results, Rutin inhibited the viability of SW1736 cells in a time- and dose-dependent manner. Quantitative Real-time RT-PCR analysis exposed that NIS mRNA levels were increased in Rutin treated group compared to the control group. Accordingly, western blot showed high expression of NIS protein and low expression of GLUT 1 and 3 in Rutin treated SW1736 cell line. Rutin increased iodide uptake and decreased glucose uptake in thyroid cancer cell line SW1736 compared to control group. Multiple mechanisms point to Rutin's role as a major stimulator of iodide uptake and inhibitor of glucose uptake, including effects at the mRNA and protein levels for both NIS and GLUTs, respectively. Here in, we described the flip-flop phenomenon as a possible therapeutic target for ATC. Moreover, Rutin is first documented here as a NIS expression inducer capable of restoring cell differentiation in SW1736 cell line. It also be concluded that GLUTs as metabolic targets can be blocked specifically by Rutin for thyroid cancer prevention and treatment.

Multimodal decoding of human liver regeneration.

The liver has a unique ability to regenerate1,2; however, in the setting of acute liver failure (ALF), this regenerative capacity is often overwhelmed, leaving emergency liver transplantation as the only curative option3-5. Here, to advance understanding of human liver regeneration, we use paired single-nucleus RNA sequencing combined with spatial profiling of healthy and ALF explant human livers to generate a single-cell, pan-lineage atlas of human liver regeneration. We uncover a novel ANXA2+ migratory hepatocyte subpopulation, which emerges during human liver regeneration, and a corollary subpopulation in a mouse model of acetaminophen (APAP)-induced liver regeneration. Interrogation of necrotic wound closure and hepatocyte proliferation across multiple timepoints following APAP-induced liver injury in mice demonstrates that wound closure precedes hepatocyte proliferation. Four-dimensional intravital imaging of APAP-induced mouse liver injury identifies motile hepatocytes at the edge of the necrotic area, enabling collective migration of the hepatocyte sheet to effect wound closure. Depletion of hepatocyte ANXA2 reduces hepatocyte growth factor-induced human and mouse hepatocyte migration in vitro, and abrogates necrotic wound closure following APAP-induced mouse liver injury. Together, our work dissects unanticipated aspects of liver regeneration, demonstrating an uncoupling of wound closure and hepatocyte proliferation and uncovering a novel migratory hepatocyte subpopulation that mediates wound closure following liver injury. Therapies designed to promote rapid reconstitution of normal hepatic microarchitecture and reparation of the gut-liver barrier may advance new areas of therapeutic discovery in regenerative medicine.

Exploring the Pharmacological Potential of Acacia auriculiformis: A Comprehensive Review of Medicinal Properties and Therapeutic Applications

This review explores the pharmacological potential of Acacia auriculiformis, highlighting its diverse medicinal properties and therapeutic applications. Through a detailed analysis of experimental evidence, the study showcases the plant's efficacy in various health concerns. The plant exhibits significant wound healing, antifungal, antibacterial, antifilarial, antioxidant, antimalarial, hepatoprotective, chemoprotective, antimutagenic, CNS depressant, spermicidal, and cestocidal activities. The global interest in herbal medicine and the increasing utilization of A. auriculiformis underscore its importance in alternative healthcare approaches. The review emphasizes the need for further research to elucidate the mechanisms of action underlying the plant's pharmacological activities and to explore its potential clinical benefits. The findings suggest that A. auriculiformis holds promise as a source of therapeutic agents for a wide range of health conditions. Continued investigation into the bioactive compounds derived from the plant, along with clinical trials to assess efficacy and safety, could pave the way for the development of novel treatments. Overall, this review sheds light on the multifaceted medicinal properties of A. auriculiformis and its potential contribution to the field of herbal medicine and drug discovery.

An innovative development of a whole-body diagnostic device led to the ingenious discovery of comprehensive digital medicine that has potential to lead to health promotion of humankind and consequential rapid economic growth of the world

After working for a few decades, gas-discharge-visualization (GDV) device, alternatively called electro-photonic-imaging (EPI) device, was innovatively developed by Prof. K.G. Korotkov in 1995. The sophisticated GDV diagnostic device additionally is being used as an important tool for generating comprehensive-digital-medicine by feeding an electromagnetic signal from the GDV device to the comprehensive-digital-medicine preparation device, involving very low cost. This medicine helps to achieve near zero disease-levels in the various organs as well as the immune, endocrine and nervous systems of all health-conscious persons, who want to continue sincerely with the optimal combination of the preventive medicines of digital-homeopathy and evolved-homeopathy, and occasional GDV (once in a month) check-up. When this condition is achieved, about 20 years age-reduction is felt along with improvement of physical and mental stamina and increment in longevity. To begin with, the global organizations and insurance companies will have to provide for the first few years fund and offer suitable incentives (may be, appropriate annual cash-incentives after remaining under coverage of comprehensive-digital-medicine prevention scheme), since vast majority of the apparently healthy part of humankind believe that they’re healthy, although all of their organs are having imperceptible disease level between 0.8 to 1.0, and such disease level in immune system (including Spleen) cannot prevent tumors/ cancer and many other severe diseases. After a span of few years, global GDP can increase by 5% over a year (in contrast with the present 3%), when this increased GDP (due to better health and increase in retirement age) can fund such schemes.

Synthesis of 18F‐labeled Aryl Trifluoromethyl Sulfones, ‐Sulfoxides, and ‐Sulfides for Positron Emission Tomography

Positron emission tomography (PET) is becoming increasingly important in nuclear medicine and drug discovery. To date, the development of many potential PET tracers is hampered by the lack of suitable synthetic pathways for their preparation. This is particularly true for the highly desired radiolabeling of compounds bearing [18F]CF3‐groups. For instance, S(O)nCF3‐groups (n = 0, 1, 2) serve as structural motif in a range of biologically active compounds, but their radiosynthesis remains largely unprecedented (for n = 1, 2). Herein, we describe general methods for the radiosynthesis of 18F‐labeled aryl trifluoromethyl sulfones, ‐sulfoxides, and –sulfides. All three methods are operationally straightforward, start from widely available precursors, i.e., sulfonyl fluorides and thiophenols, and make use of the recently established [18F]Ruppert‐Prakash reagent. Further, the syntheses display good functional group tolerance as demonstrated by the 18F‐labeling of more than 40 compounds. The applicability of the new method is demonstrated by the radiolabeling of three bioactive molecules, optionally to be used as PET tracers. In a broader context, this work presents a substantial expansion of the chemical space of radiofluorinated structural motifs to be used for the development of new PET tracers.

Synthesis of 18F-labeled Aryl Trifluoromethyl Sulfones, -Sulfoxides, and -Sulfides forPositron Emission Tomography.

Positron emission tomography (PET) is becoming increasingly important in nuclear medicine and drug discovery. To date, the development of many potential PET tracers is hampered by the lack of suitable synthetic pathways for their preparation. This is particularly true for the highly desired radiolabeling of compounds bearing [18F]CF3-groups. For instance, S(O)nCF3-groups (n = 0, 1, 2) serve as structural motif in a range of biologically active compounds, but their radiosynthesis remains largely unprecedented (for n = 1, 2). Herein, we describe general methods for the radiosynthesis of 18F-labeled aryl trifluoromethyl sulfones, -sulfoxides, and -sulfides. All three methods are operationally straightforward, start from widely available precursors, i.e., sulfonyl fluorides and thiophenols, and make use of the recently established [18F]Ruppert-Prakash reagent. Further, the syntheses display good functional group tolerance as demonstrated by the 18F-labeling of more than 40 compounds. The applicability of the new method is demonstrated by the radiolabeling of three bioactive molecules, optionally to be used as PET tracers. In a broader context, this work presents a substantial expansion of the chemical space of radiofluorinated structural motifs to be used for the development of new PET tracers.

Decoding the Structural Diversity: A New Horizon in Antimicrobial Prospecting and Mechanistic Investigation.

The escalating crisis of antimicrobial resistance (AMR) underscores the urgent need for novel antimicrobials. One promising strategy is the exploration of structural diversity, as diverse structures can lead to diverse biological activities and mechanisms of action. This review delves into the role of structural diversity in antimicrobial discovery, highlighting its influence on factors such as target selectivity, binding affinity, pharmacokinetic properties, and the ability to overcome resistance mechanisms. We discuss various approaches for exploring structural diversity, including combinatorial chemistry, diversity-oriented synthesis, and natural product screening, and provide an overview of the common mechanisms of action of antimicrobials. We also describe techniques for investigating these mechanisms, such as genomics, proteomics, and structural biology. Despite significant progress, several challenges remain, including the synthesis of diverse compound libraries, the identification of active compounds, the elucidation of complex mechanisms of action, the emergence of AMR, and the translation of laboratory discoveries to clinical applications. However, emerging trends and technologies, such as artificial intelligence, high-throughput screening, next-generation sequencing, and open-source drug discovery, offer new avenues to overcome these challenges. Looking ahead, we envisage an exciting future for structural diversity-oriented antimicrobial discovery, with opportunities for expanding the chemical space, harnessing the power of nature, deepening our understanding of mechanisms of action, and moving toward personalized medicine and collaborative drug discovery. As we face the continued challenge of AMR, the exploration of structural diversity will be crucial in our search for new and effective antimicrobials.

Sophoridine exerts anti-arthritic effects on fibroblast-like synoviocytes and collagen-induced arthritis in rats.

The discovery of alternative medicines with fewer adverse effects is urgently needed for rheumatoid arthritis (RA). Sophoridine (SR), the naturally occurring quinolizidine alkaloid isolated from the leguminous sophora species, has been demonstrated to possess a wide range of pharmacological activities. However, the effect of SR on RA remains unknown. In this study, the collagen-induced arthritis (CIA) rat model and tumor necrosis factor alpha (TNFα)-induced fibroblast-like synoviocytes (FLSs) were utilized to investigate the inhibitory effect of SR on RA. The anti-arthritic effect of SR was evaluated using the CIA rat model in vivo and TNFα-stimulated FLSs in vitro. Mechanistically, potential therapeutic targets and pathways of SR in RA were analyzed through drug target databases and disease databases, and validation was carried out through immunofluorescence, immunohistochemistry, and Western blot. The in vivo results revealed that SR treatment effectively ameliorated synovial inflammation and bone erosion in rats with CIA. The in vitro studies showed that SR could significantly suppress the proliferation and migration in TNFα-induced arthritic FLSs. Mechanistically, SR treatment efficiently inhibited the activation of MAPKs (JNK and p38) and NF-κB pathways in TNFα-induced arthritic FLSs. These findings were further substantiated by Immunohistochemistry results in the CIA rat. SR exerts an anti-arthritic effect in CIA rats through inhibition of the pathogenic characteristic of arthritic FLSs via suppressing NF-κB and MAPKs (JNK and p38) signaling pathways. SR may have a great potential for development as a novel therapeutic agent for RA treatment.

Intervention of IT in Rog Nidan Evum Vikriti Vigyan - An Explorative Review

Introduction: Going by the trend, the Indian traditional medicine system Ayurveda is observing a paradigm shift in its growth, and in the era of technology AI (Artificial Intelligence) is one of the main factors behind it. Be it the discovery of new medicines, implementation of a new drug discovery model, getting its global acceptance through patents, or the delivery of the final product through AI equipped supply chain model system, the intervention of technology cannot be ruled out. Methodology: The literature survey was done through Google Scholar database, PubMed, and Web of Science. The dig out information about the use of IT in Medical fields and, use of technology in Ayurveda were screened for relevant studies synthesis. Result: At last, we have sorted out the areas of Rog-Nidan that need to be taken care of from a research point of view with the help of information technology. Discussion:  In this research paper, an effort has been made to review the areas where technology can play its role, especially in Rog-Nidan. Additionally, the approaches that are being used in Ayurveda where it is being presented as wellness therapy and preventive care have been studied along with identifying the gaps that can be addressed with efficient use of IT-based technologies.

3D Convolutional Neural Networks for Predicting Protein Structure for Improved Drug Recommendation

INTRODUCTION: Protein structure prediction is critical for recommendation personalized medicine and drug discovery. This paper introduces a robust approach using 3D Convolution Neural Networks (3D CNN’s) to improve the accuracy of the structure of protein structure thus contributing for the drug recommendation system.
 OBJECTIVES: In contrast to conventional techniques, 3D CNNs are able to identify complicated folding patterns and comprehend the subtle interactions between amino acids because they are able to capture spatial dependencies inside protein structures.
 METHODS: Data sets are collected from Protein Data Bank, including experimental protein structures and the drugs that interact with them, are used to train the model. With the efficient processing of three-dimensional data, the 3D CNNs exhibit enhanced capability in identifying minute structural details that are crucial for drug binding. This drug recommendation system novel method makes it easier to find potential drugs that interact well with particular protein structures.
 RESULTS: The performance of the proposed classifier is compared with the existing baseline methods with various parameters accuracy, precision, recall, F1 score, mean squared error (MSE)  and area under the receiver operating characteristic curve (AUC-ROC).
 CONCLUSION: Deep learning and 3D structural insights work together to create a new generation of tailored and focused therapeutic interventions by speeding up the drug development process and improving the accuracy of pharmacological recommendations.

Abstract LB413: A newly developed patient-derived culture panel identifies precision therapies and pan-effective agents for head and neck squamous cell carcinoma

Abstract Head and neck squamous carcinoma (HNSCC) is an aggressive cancer with few treatment options. Its well-known genetic heterogeneity presents a major challenge for precision medicine development. We report a newly developed panel of 20 HNSCC patient-derived cultures (HNSCC-PDCs) from advanced or recurrent HNSCC, with comprehensive somatic and germline characterization for precision medicine (PM) discovery. The PDCs retain genetic heterogeneity of the original patient tumors, and xenografts of which also phenocopied HNSCC patient tumors. Importantly, somatic and germline events that were never or seldom captured by long-established HNSCC cell lines are represented by the PDC panel, including HRAS-CASP8 co-mutations, EGFR-AS1 germline mutation, exonic germline PIK3CA mutation, sonic hedgehog (SHH) pathway and FGFR pathway mutations. We hypothesize that drug screening with this newly developed HNSCC-PDC panel that recapitulates HNSCC patient genetics may identify new gene-drug sensitivity relationships for precision medicine development in advanced HNSCC. Here, data from our pilot drug screen of 38 selected FDA approved/Phase I-II clinical trial agents first reveal: 1) HNSCC-PDC with the hotspot HRASp.G13R, together with CASP8p.L183S, is sensitive to tipifarnib vs. tipifarnib-resistance in a long-established HNSCC cell line, SNU899 with the same HRAS mutation. Subsequent high-content imaging cell death assay further demonstrates a dose-dependent tipifarnib-induced cell death in HRASp.G13R-mutated PDC, but not in SNU-899; 2) TP53-EP300 co-mutated HNSCC-PDCs are most sensitive to cisplatin with ~340 nM sensitivity (mean IC50) among all PDCs tested, which is 22 times more sensitive than that reported in Genomics of Drug Sensitivity in Cancer (GDSC) HNSCC cell lines; 3) EGFR-AS1 (c.2361G>A)-germline mutated PDCs, and MAPK1-mutated PDCs, are both hypersensitive to EGFR inhibitors (EGFRi: erlotinib and mobocertinib), which independently credentialed gene-drug sensitivity relationships reported in exceptional responders in HNSCC clinical trials. Further signaling investigation in PDC models demonstrates phospho-MAPK activation as a reliable surrogate biomarker for EGFRi sensitivity in HNSCC; 4) FRS3-mutated PDCs are sensitive to FDA-approved FGFR pathway inhibitors; and 5) TP53-wildtype HNSCC-PDCs are 150-fold more sensitive to novel p53-MDM2 interaction inhibitors vs. TP53-mutated PDCs, a phenomenon that cannot be captured in long-established TP53-wildtype HNSCC cell lines. Lastly, 4 pan-PDC effective agents are identified with nM sensitivities and high drug activities (i.e. drug-induced growth inhibition) in more than half of our PDC models. These include 3 FDA-approved agents, TAK-981, Selinexor (a novel XPO1 inhibitor), and trametinib, as well as the preclinical agent JQ1. In conclusion, our pilot HNSCC-PDC drug screen identifies new gene-drug sensitivity relationships for HNSCC PM development and new pan-effective agents for advanced HNSCC. Citation Format: Yuen Keng Ng, Hui Li, Wenying Piao, Cecilia Pik Yuk Lau, Peony Hiu Yan Poon, Tin Yu Samuel Law, Jason Ying Kuen Chan, Yuxiong Su, Chin Wang Lau, Zigui Chen, Laura Ren Huey Ip, Thomas Chung-Kit Yeung, Sai Fung Yeung, Jason Hoi-Lam Ngan, Zoey Wing Yee Liu, Noah R. Wiese, Vivie Vo, Zoey Patel, Jeremia Onyekachi, Laili Afzali, Jasmine Zohal Afzali, Jack Nimitz Smith, Vivian Wai Yan Lui. A newly developed patient-derived culture panel identifies precision therapies and pan-effective agents for head and neck squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB413.

Neural Stem Cell-based Regenerative Therapy: A New Approach to Diabetes Treatment.

Diabetes mellitus (DM) is the most common metabolic disorder that occurs due to the loss, or impaired function of insulin-secreting pancreatic beta cells, which are of two types - type 1 (T1D) and type 2 (T2D). To cure DM, the replacement of the destroyed pancreatic beta cells of islet of Langerhans is the most widely practiced treatment. For this, isolating neuronal stem cells and cultivating them as a source of renewable beta cells is a significant breakthrough in medicine. The functions, growth, and gene expression of insulin-producing pancreatic beta cells and neurons are very similar in many ways. A diabetic patient's neural stem cells (obtained from the hippocampus and olfactory bulb) can be used as a replacement source of beta cells for regenerative therapy to treat diabetes. The same protocol used to create functional neurons from progenitor cells can be used to create beta cells. Recent research suggests that replacing lost pancreatic beta cells with autologous transplantation of insulin-producing neural progenitor cells may be a perfect therapeutic strategy for diabetes, allowing for a safe and normal restoration of function and a reduction in potential risks and a long-term cure.

Revolutionizing Biomedical Innovation: AI-Driven Advancements in Drug Discovery

Abstract: Using AI to Drive Advances in Drug Development: Transforming Biomedical Innovation. Medicine discovery and development have been transformed recently by the intersection of biomedicine and artificial intelligence. Because of the exponential growth in data and AI algorithm capabilities, we are witnessing hitherto unseen opportunities to expedite the creation of new medications, boost the efficacy of current treatments, and ultimately save lives. How AI is changing drug discovery in many ways. In order to identify new drug targets and forecast the efficacy of proposed treatments, we will explore how machine learning algorithms can assess intricate biological datasets. We will also go through the ways in which AI-driven methods are being applied to simplify clinical trials, improve drug development procedures, and tailor specific patient therapies.

The Future of ChatGPT in Medicinal Chemistry: Harnessing AI for Accelerated Drug Discovery

AbstractArtificial Intelligence (AI) has emerged as a transformative tool in various fields, including medicinal chemistry and drug discovery. The integration of artificial intelligence (AI) and machine learning (ML) technologies in medicinal chemistry has revolutionized drug discovery processes. Among AI models, ChatGPT (Generative Pre‐trained Transformer) has shown remarkable capabilities in natural language understanding and generation. This review article delves into the potential applications and prospects of ChatGPT in the domain of medicinal chemistry, focusing on its contributions to drug discovery, compound design, virtual screening, target identification, and optimization of pharmacokinetic properties. ChatGPT′s ability to generate contextually relevant text makes it a valuable tool for data mining and analysis of vast chemical and biological information repositories. In this review article, the authors have discussed the challenges and ethical considerations associated with the implementation of ChatGPT 3.5 in medicinal chemistry research. Authors also extend their acknowledgment to ChatGPT (version 3.5) for its contribution in furnishing responses to the prompt, thereby shedding light on the applications of ChatGPT in the field of medicinal chemistry. The authors have analyzed the responses of ChatGPT.

The dawn of personalized multi-omics: Detecting disease before you know it

Recent advancements in omics techniques have enabled deep profiling of an individual’s molecular makeup. The wealth of data produced offers insights into genetic predispositions, early disease markers, and personalized treatment strategies. However, the full potential of omics data emerges when combined into longitudinal and personal multi-omics space. Another interesting venue is the inclusion of continuous monitoring of physiological parameters through wearable technology. Wearable health devices, including smartwatches and biosensors, provide real-time data on heart rate, oxygen saturation, sleep patterns, activity levels, and much more. By integrating with omics data, wearables offer a comprehensive view of an individual’s health, allowing for early detection of deviations from normalcy. This convergence allows for the prediction and prevention of diseases at the individual level and provides a powerful monitoring tool in clinical and drug developmental settings. This review explores the fusion of omics and wearable technology, envisioning their synergy as a catalyst for a transformative shift in modern healthcare. Their merging enables predictive and personalized medicine. As these technologies continue to evolve, their translation into routine clinical practice holds the promise of a healthier future for all. Provided herein is a step-by-step vision for how longitudinal personalized multi-omics, combined with wearable devices, will guide proactive healthcare and transform drug discovery in translational medicine.

Deep learning enhanced drug discovery for novel biomaterials in regenerative medicine utilizing graph neural network approach for predicting cellular responses

This study introduces a novel approach to drug discovery in regenerative medicine through the utilization of a graph neural network (GNN). The research methodology integrates the development and training of the GNN with a subsequent evaluation of its performance metrics. The first phase involves the generation of synthetic data simulating a biological network, employing networkX and NumPy libraries to construct a random graph with Erdos-Renyi topology. The data, representing cellular responses to biomaterials, is then converted into PyTorch tensors for compatibility with the GNN architecture. The GNN model, characterized by two fully connected layers with ReLU and log-softmax activations, captures intricate relationships within the graph-structured data. The second phase employs a stochastic gradient descent algorithm, specifically the Adam optimizer, to train the GNN over 100 epochs using the cross-entropy loss for multi-class classification. The research methodology extends to the evaluation phase, producing three distinct output graphs for analysis: Visualization of the graph structure, a comparison between predicted and true labels, and a plot illustrating training loss over epochs. Performance metrics, including accuracy, precision, recall, and F1-score, are computed to assess the model’s predictive capabilities quantitatively. The study concludes with a discussion on the nuances revealed by each graph and their implications for refining GNN models in the context of drug discovery for regenerative medicine.

Chemical characterization of different parts of Forsythia suspensa and α-glucosidase and pancreatic lipase inhibitors screening based on UPLC-QTOF-MS/MS and plant metabolomics analysis

Forsythia suspensa (Thunb.) Vahl (FS) is an important plant with high edible and medicinal values. The edible fruit of FS is a common traditional medicine in China, Japan and Korea. Compared to the research on the phytochemistry and pharmacology of the fruits, study on the other parts of FS, such as leaves and flowers is still limited. In this study, an integrated strategy based on ultra-performance liquid chromatography quadrupole time of flight mass spectrometry (UPLC-QTOF-MS/MS), plant metabolomics and correlation analysis was established for comprehensively chemical characterization of fruits, leaves and flowers of FS, and discovery of α-glucosidase and pancreatic lipase inhibitory metabolites. The plant metabolic profiling of fruits, leaves and flowers of FS was performed by UPLC-QTOF-MS/MS, and a total of 74 secondary metabolites, including 15 phenylethanoid glycosides, 20 lignans, 10 cyclohexanol derivatives, 11 organic acids, 9 flavonoids, 3 triterpenes, and 10 other compounds were identified. Then, 29 differential secondary metabolites that responsible to distinguish the fruits, leaves and flowers of FS were further screened out by multivariate statistical analysis. Meanwhile, the α-glucosidase and pancreatic lipase inhibition of different parts of FS were evaluated and compared by in vitro experiments. The results demonstrated that the leaves of FS showed the highest inhibition on α-glucosidase and pancreatic lipase with IC50 of 0.17 ± 0.04 mg/mL and 0.56 ± 0.33 mg/mL, respectively. Then, the correlation between differential metabolites and enzyme inhibitory activities were investigated by Pearson correlation analysis, and 12 potential α-glucosidase and pancreatic lipase inhibitors were screen out. Additionally, the α-glucosidase and pancreatic lipase inhibitory activities of five potential enzyme inhibitory components, including quercitrin, rutin, kaempferol-3-O-rutinoside, pinoresinol-4-O-glucoside and phillyrin were further validated by in vitro assays and molecular docking analysis. The results showed that all the five potential inhibitors showed good inhibitory effects on α-glucosidase and pancreatic lipase, and the binding of the five inhibitors to enzymes mainly through hydrogen bonding, hydrophobic force, and ionic bonding. This study provided a feasible strategy for comparison and discrimination of different parts of medicinal plant and discovery of bioactive components, and also provided useful information for future utilization of different parts of FS.

Function-oriented mechanism discovery of coumarins from Psoralea corylifolia L. in the treatment of ovariectomy-induced osteoporosis based on multi-omics analysis

Ethnopharmacological relevancePsoraleae Fructus (Bu Gu Zhi) is the fruit of Psoralea corylifolia L. (PCL) and has been used for centuries in traditional Chinese medicine formulas to treat osteoporosis (OP). A new drug called "BX" has been developed from PCL, but its mechanism for treating OP is not yet fully understood. Aim of the studyTo explore the mechanism of action of BX in the treatment of ovariectomy-induced OP based function-oriented multi-omics analysis of gut microbiota (GM) and metabolites. Materials and methodsC57BL/6 mice were bilaterally ovariectomized to replicate the OP model. The therapeutic efficacy of BX was evaluated by bone parameters (BMD, BV/TV, Tb.N, Tb.Sp), hematoxylin and eosin (H&E) staining results, and determination of bone formation markers procollagen type Ⅰ amino-terminal peptide (PⅠNP) and bone-specific alkaline phosphatase (BALP). Serum and fecal metabolomics and high-throughput 16S rDNA sequencing were performed to evaluate effects on endogenous metabolites and GM. In addition, an enzyme-based functional correlation algorithm (EBFC) algorithm was used to investigate functional correlations between GM and metabolites. ResultsBX improved OP in OVX mice by increasing BMD, BV/TV, serum PⅠNP, BALP, and improving Tb.N and Tb.Sp. A total of 59 differential metabolites were identified, and 9 metabolic pathways, including arachidonic acid metabolism, glycerophospholipid metabolism, purine metabolism, and tryptophan metabolism, were found to be involved in the progression of OP. EBFC analysis results revealed that the enzymes related to purine and tryptophan metabolism, which are from Lachnospiraceae_NK4A136_group, Blautia, Rs-E47_termite_group, UCG-009, and Clostridia_UCG-014, were identified as the intrinsic link between GM and metabolites. ConclusionsThe regulation of GM and restoration of metabolic disorders may be the mechanisms of action of BX in alleviating OP. This research provides insights into the function-oriented mechanism discovery of traditional Chinese medicine in the treatment of OP.

Extracorporeal cryoimmunology. new scientific discovery in biomedical science and medicine

Extracorporeal cryoimmunology. new scientific discovery in biomedical science and medicine

Advances in the differentiation of pluripotent stem cells into vascular cells.

Blood vessels constitute a closed pipe system distributed throughout the body, transporting blood from the heart to other organs and delivering metabolic waste products back to the lungs and kidneys. Changes in blood vessels are related to many disorders like stroke, myocardial infarction, aneurysm, and diabetes, which are important causes of death worldwide. Translational research for new approaches to disease modeling and effective treatment is needed due to the huge socio-economic burden on healthcare systems. Although mice or rats have been widely used, applying data from animal studies to human-specific vascular physiology and pathology is difficult. The rise of induced pluripotent stem cells (iPSCs) provides a reliable in vitro resource for disease modeling, regenerative medicine, and drug discovery because they carry all human genetic information and have the ability to directionally differentiate into any type of human cells. This review summarizes the latest progress from the establishment of iPSCs, the strategies for differentiating iPSCs into vascular cells, and the in vivo transplantation of these vascular derivatives. It also introduces the application of these technologies in disease modeling, drug screening, and regenerative medicine. Additionally, the application of high-tech tools, such as omics analysis and high-throughput sequencing, in this field is reviewed.