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"Long, Feng"
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The Angelica dahurica: A Review of Traditional Uses, Phytochemistry and Pharmacology
Angelica dahurica ( A. dahurica ) root is a famous edible medicinal herb that has been used in China for thousands of years. To date, more than 300 chemical constituents have been discovered from A. dahurica . Among these ingredients, coumarins and volatile oils are the major active compounds. Moreover, a few other compounds have also been isolated from the root of A. dahurica , such as alkaloids, phenols, sterols, benzofurans, polyacetylenes and polysaccharides. Modern pharmacological studies demonstrated that the root of A. dahurica and its active components displayed various bioactivities such as anti-inflammation, anti-tumor, anti-oxidation, analgesic activity, antiviral and anti-microbial effects, effects on the cardiovascular system, neuroprotective function, hepatoprotective activity, effects on skin diseases and so on. Based on these studies, this review focused on the research publications of A. dahurica and aimed to summarize the advances in the traditional uses, phytochemistry and pharmacology which will provide reference for the further studies and applications of A. dahurica .
Journal Article
MicroRNA-21 (miR-21) Regulates Cellular Proliferation, Invasion, Migration, and Apoptosis by Targeting PTEN, RECK and Bcl-2 in Lung Squamous Carcinoma, Gejiu City, China
In South China (Gejiu City, Yunnan Province), lung cancer incidence and associated mortality rate is the most prevalent and observed forms of cancer. Lung cancer in this area is called Gejiu squamous cell lung carcinoma (GSQCLC). Research has demonstrated that overexpression of miR-21 occurs in many cancers. However, the unique relationship between miR-21 and its target genes in GSQCLC has never been investigated. The molecular mechanism involved in GSQCLC must be compared to other non-small cell lung cancers in order to establish a relation and identify potential therapeutic targets.
In the current study, we initially found overexpression of miR-21 occurring in non-small cell lung cancer (NSCLC) cell lines when compared to the immortalized lung epithelial cell line BEAS-2B. We also demonstrated that high expression of miR-21 could increase tumor cell proliferation, invasion, viability, and migration in GSQCLC cell line (YTMLC-90) and NSCLC cell line (NCI-H157). Additionally, our results revealed that miR-21 could suppress YTMLC-90 and NCI-H157 cell apoptosis through arresting cell-cycle at G2/M phase. Furthermore, we demonstrated that PTEN, RECK and Bcl-2 are common target genes of miR-21 in NSCLC. Finally, our studies showed that down-regulation of miR-21 could lead to a significant increase in PTEN and RECK and decrease in Bcl-2 at the mRNA and protein level in YTMLC-90 and NCI-H157 cell lines. However, we have not observed any remarkable difference in the levels of miR-21 and its targets in YTMLC-90 cells when compared with NCI-H157 cells.
miR-21 simultaneously regulates multiple programs that enhance cell proliferation, apoptosis and tumor invasiveness by targeting PTEN, RECK and Bcl-2 in GSQCLC. Our results demonstrated that miR-21 may play a vital role in tumorigenesis and progression of lung squamous cell carcinoma and suppression of miR-21 may be a novel approach for the treatment of lung squamous cell carcinoma.
Journal Article
Accelerating the integration of ChatGPT and other large‐scale AI models into biomedical research and healthcare
Large‐scale artificial intelligence (AI) models such as ChatGPT have the potential to improve performance on many benchmarks and real‐world tasks. However, it is difficult to develop and maintain these models because of their complexity and resource requirements. As a result, they are still inaccessible to healthcare industries and clinicians. This situation might soon be changed because of advancements in graphics processing unit (GPU) programming and parallel computing. More importantly, leveraging existing large‐scale AIs such as GPT‐4 and Med‐PaLM and integrating them into multiagent models (e.g., Visual‐ChatGPT) will facilitate real‐world implementations. This review aims to raise awareness of the potential applications of these models in healthcare. We provide a general overview of several advanced large‐scale AI models, including language models, vision‐language models, graph learning models, language‐conditioned multiagent models, and multimodal embodied models. We discuss their potential medical applications in addition to the challenges and future directions. Importantly, we stress the need to align these models with human values and goals, such as using reinforcement learning from human feedback, to ensure that they provide accurate and personalized insights that support human decision‐making and improve healthcare outcomes. This review provides an overview of large‐scale AI models, including language models (e.g., ChatGPT), vision‐language models, and language‐conditioned multiagent models, and discusses their potential applications in medicine, as well as their limitations and future trends. We also propose how large‐scale AI models can be integrated into various scenarios of clinical applications.
Journal Article
PCSK9 promotes tumor growth by inhibiting tumor cell apoptosis in hepatocellular carcinoma
Background
Proprotein convertase subtilisin/kexin type 9 (PCSK9), one of the key enzymes in the process of lipid transport, is involved in the disease progression of various types of tumors. This article is to study the role of PCSK9 in the progression of hepatocellular carcinoma (HCC).
Methods
Immunohistochemistry was used to assess the expression of PCSK9 in tumor specimens from 105 HCC patients who underwent curative resection. Western blotting and quantitative real-time PCR were used to test the protein and mRNA expression levels in HCC cell lines. Cell Counting Kit-8 (CCK-8) and clone formation assays were performed to evaluate the proliferation ability of different kinds of cells in vitro. Flow cytometry was used to analyze cell cycle distribution and apoptosis rate. A xenograft model was established to study the effect of PCSK9 on HCC growth in vivo. TUNEL and immunofluorescence assays were used to detect cell apoptosis.
Results
High expression of PCSK9 in tumor tissues was related to microvascular invasion (
p
= 0.036) and large tumor size (
p
= 0.001) in HCC patients. Overall survival and disease-free survival after surgery were poor in patients with high expression of PCSK9 (
p
= 0.035 and
p
= 0.007, respectively). In vivo and in vitro experiments showed that PCSK9 promoted the growth of HCC by inhibiting cell apoptosis. A mechanistic study revealed that PCSK9 increases FASN expression, thereby inhibiting apoptosis of HCC cells via the Bax/Bcl-2/Caspase9/Caspase3 pathway.
Conclusions
PCSK9 expression level in HCC is an indicator of poor prognosis for patients with HCC. FASN-mediated anti-apoptosis plays an important role in PCSK9-induced HCC progression.
Journal Article
Microbiome–metabolomics reveals gut microbiota associated with glycine-conjugated metabolites and polyamine metabolism in chronic kidney disease
Dysbiosis of the gut microbiome and related metabolites in chronic kidney disease (CKD) have been intimately associated with the prevalence of cardiovascular diseases. Unfortunately, thus far, there is a paucity of sufficient knowledge of gut microbiome and related metabolites on CKD progression partly due to the severely limited investigations. Using a 5/6 nephrectomized (NX) rat model, we carried out 16S rRNA sequence and untargeted metabolomic analyses to explore the relationship between colon’s microbiota and serum metabolites. Marked decline in microbial diversity and richness was accompanied by significant changes in 291 serum metabolites, which were mediated by altered enzymatic activities and dysregulations of lipids, amino acids, bile acids and polyamines metabolisms. Interestingly, CCr was directly associated with some microbial genera and polyamine metabolism. However, SBP was directly related to certain microbial genera and glycine-conjugated metabolites in CKD rats. Administration of poricoic acid A (PAA) and Poria cocos (PC) ameliorated microbial dysbiosis as well as attenuated hypertension and renal fibrosis. In addition, treatments with PAA and PC lowered serum levels of microbial-derived products including glycine-conjugated compounds and polyamine metabolites. Collectively, the present study confirmed the CKD-associated gut microbial dysbiosis and identified a novel dietary and therapeutic strategy to improve the gut microbial dysbiosis and the associated metabolomic disorders and retarded the progression of kidney disease in the rat model of CKD.
Journal Article
The predictive value of heparin-binding protein for bacterial infections in patients with severe polytrauma
Heparin-binding protein is an inflammatory factor with predictive value for sepsis and participates in the inflammatory response through antibacterial effects, chemotaxis, and increased vascular permeability. The role of heparin-binding protein in sepsis has been progressively demonstrated, but few studies have been conducted in the context of polytrauma combined with bacterial infections. This study aims to investigate the predictive value of heparin-binding protein for bacterial infections in patients with severe polytrauma.
This is a prospective single-center study. Patients with polytrauma in the emergency intensive care unit were selected for the study, and plasma heparin-binding protein concentrations and other laboratory parameters were measured within 48 hours of admission to the hospital. A two-sample comparison and univariate logistic regression analysis investigated the relationship between heparin-binding protein and bacterial infection in polytrauma patients. A multifactor logistic regression model was constructed, and the ROC curve was plotted.
Ninety-seven patients with polytrauma were included in the study, 43 with bacterial infection and 54 without infection. Heparin-binding protein was higher in the infected group than in the control group [(32.00±3.20) ng/mL vs. (18.52±1.33) ng/mL, P = 0.001]. Univariate logistic regression analysis shows that heparin-binding protein is related to bacterial infection (OR = 1.10, Z = 3.91, 95%CI:1.05~1.15, P = 0.001). Multivariate logistic regression equations showed that patients were 1.12 times more likely to have bacterial infections for each value of heparin-binding protein increase, holding neutrophils and Procalcitonin (PCT) constant. ROC analysis shows that heparin-binding protein combined with neutrophils and PCT has better predictive value for bacterial infection [AUC = 0.935, 95%CI:0.870~0.977].
Heparin-binding protein may predict bacterial infection in patients with severe polytrauma. Combining heparin-binding protein, PCT, and neutrophils may improve bacterial infection prediction.
Journal Article
Integrating novel chemical weapons and evolutionarily increased competitive ability in success of a tropical invader
The evolution of increased competitive ability (EICA) hypothesis and the novel weapons hypothesis (NWH) are two non-mutually exclusive mechanisms for exotic plant invasions, but few studies have simultaneously tested these hypotheses. Here we aimed to integrate them in the context of Chromolaena odorata invasion.
We conducted two common garden experiments in order to test the EICA hypothesis, and two laboratory experiments in order to test the NWH.
In common conditions, C. odorata plants from the nonnative range were better competitors but not larger than plants from the native range, either with or without the experimental manipulation of consumers. Chromolaena odorata plants from the nonnative range were more poorly defended against aboveground herbivores but better defended against soilborne enemies. Chromolaena odorata plants from the nonnative range produced more odoratin (Eupatorium) (a unique compound of C. odorata with both allelopathic and defensive activities) and elicited stronger allelopathic effects on species native to China, the nonnative range of the invader, than on natives of Mexico, the native range of the invader.
Our results suggest that invasive plants may evolve increased competitive ability after being introduced by increasing the production of novel allelochemicals, potentially in response to naïve competitors and new enemy regimes.
Journal Article
Dual niche modeling with GEE and SHAP for predicting habitat shifts of Haloxylon ammodendron and Cistanche deserticola under climate change
Haloxylon ammodendron , a keystone woody species, and its parasitic plant, Cistanche deserticola , play critical roles in sustaining arid ecosystems and supporting regional economies. However, their distribution is increasingly threatened by global climate change. Here, we propose a dual niche modeling framework that integrates climate and soil suitability layers using a multi-model ensemble approach combined with interpretable machine-learning techniques, specifically SHapley Additive exPlanations (SHAP). Using CMIP6 scenarios (SSP126, SSP245, and SSP585), we predicted the current and future potential habitats for both species. The results demonstrated that the ensemble models delivered robust performance, surpassing the accuracy of single-model predictions. Currently, suitable habitats are concentrated in northwestern China as well as parts of Mongolia and Kazakhstan. Under SSP585 (2081–2100), H. ammodendron habitats are projected to shrink by 56.2%, whereas C. deserticola is expected to lose more than 97% of its habitat, nearly disappearing from Central Asia. Key climatic drivers include temperature seasonality and precipitation patterns, whereas the soil water-holding capacity and gravel content significantly affect local suitability. Niche overlap analysis revealed a strong host dependency for C. deserticola. However, the climate–soil niche congruence is projected to decrease under future scenarios, indicating the potential risks of ecological decoupling. This integrative and interpretable approach offers a scalable tool for biodiversity assessment and provides actionable insights for conservation planning in climate-sensitive, arid ecosystems.
Journal Article