Introduction: A significant proportion of severe asthma patients require oral corticosteroids (OCS) to maintain symptom control. Prolonged OCS predisposes patients to hypothalamicpituitary-adrenal axis suppression which can be lifethreatening. Biologic therapies are effective steroid-sparing options suitable for select severe asthma patients. We evaluated steroid tapering outcomes and adrenal function during biologic therapy in a tertiary severe asthma clinic. Results: A total of 43 patients were included. Median age was 66 years (interquartile range [IQR] 60–74), and 65% were male. Mepolizumab was the most used (17 patients), followed by benralizumab (15), tezepelumab (6), dupilumab (4), and omalizumab (1). Median baseline prednisolone dose was 10 mg (IQR 5–10). Median lowest dose achieved was 3 mg (IQR 0–3), sustained for at least 4 weeks in 90.7% of patients. Median reduction in prednisolone dose was 5 mg (IQR 5–7.25). In all, 42% of patients successfully discontinued maintenance steroids. Adrenal function was assessed using morning serum cortisol (n = 37) and/or SST (n = 28). Evidence of adrenal insufficiency was identified in 19 patients (44%), including 15 patients with failed SST and four with low morning cortisol suggestive of adrenal suppression. Morning serum cortisol was indeterminate in four patients. A total of 17 patients were referred to endocrinology. During steroid tapering, 11 asthma-related hospital admissions and eight emergency department presentations occurred, involving 10 patients. Conclusion Biologics facilitate the reduction of the OCS burden in severe asthma. Tertiary adrenal suppression is common during steroid tapering and requires prompt biochemical assessment and endocrinology referral. Structured monitoring of adrenal function is essential during biologicfacilitated steroid tapering in routine clinical practice.
Biological Products ; Steroids ; Asthma

OBJECTIVES: Injury to human cardiac microvascular endothelial cells (HCMECs) compromises myocardial microcirculation and may contribute to major cardiovascular events such as coronary heart disease, posing a serious health threat. Understanding the mechanisms of hypoxia-induced HCMEC damage is thus of great clinical relevance. This study aims to investigate the protective effects and underlying mechanisms of Li Qi Huo Xue Di Wan against hypoxia-induced HCMEC injury. METHODS: HCMECs were cultured under hypoxic conditions for 24 hours to establish a cellular model of hypoxic injury. Cells were divided into six groups: normal control, hypoxia, hypoxia + low-dose Li Qi Huo Xue Di Wan, hypoxia + medium-dose, hypoxia + high-dose, and hypoxia + salvianolic acid B (positive control). Cell viability was assessed using the MTS assay. Lactate dehydrogenase (LDH) release and malondialdehyde (MDA) content were measured to evaluate cytotoxicity and oxidative stress. Activities of superoxide dismutase (SOD), catalase (CAT), caspase-3, and caspase-8 were determined with corresponding assay kits. Apoptosis was analyzed by flow cytometry, and expression of necroptosis-related proteins, receptor-interacting protein kinase 1 (RIPK1) and its phosphorylated form (p-RIPK1), receptor-interacting protein kinase 3 (RIPK3) and its phosphorylated form (p-RIPK3), mixed lineage kinase domain-like protein (MLKL) and its phosphorylated form (p-MLKL), was examined via Western blotting. RESULTS: Compared with the control group, hypoxia significantly decreased cell viability (P<0.01), increased MDA levels (P<0.05), and reduced CAT and SOD activity (P<0.05), accompanied by elevated apoptosis (P<0.01) and increased levels of p-RIPK1, p-RIPK3, and p-MLKL (P<0.05). High-dose Li Qi Huo Xue Di Wan significantly improved cell viability (P<0.01), reduced MDA content (P<0.05), increased CAT activity (P<0.05), and suppressed necroptosis-related protein expression (P<0.05) compared with the hypoxia group. CONCLUSIONS Li Qi Huo Xue Di Wan exerts a protective effect against hypoxia-induced injury in HCMECs. This effect is mediated by attenuation of oxidative stress, thereby reducing both apoptosis and necroptosis.
Humans ; Apoptosis/drug effects* ; Necroptosis/drug effects* ; Drugs, Chinese Herbal/pharmacology* ; Cell Hypoxia/drug effects* ; Endothelial Cells/pathology* ; Oxidative Stress/drug effects* ; Cells, Cultured ; Cell Survival/drug effects* ; Receptor-Interacting Protein Serine-Threonine Kinases/metabolism*

Chronic obstructive pulmonary disease (COPD) currently lacks effective treatments to halt disease progression, making the search for preventive and therapeutic drugs a pressing issue. Natural products, with their accessibility, affordability, and low toxicity, offer promising avenues. Investigating the pharmacological effects and related signaling mechanisms of active components from natural products on COPD animal models induced by various triggers has become an important focus. In animal models induced by cigarette smoke, cigarette smoke combined with lipopolysaccharide (LPS), air pollution, elastase, bacterial or viral infections, the active compounds of natural products, such as flavonoids, terpenoids, and phenolics, can exert anti-inflammatory, antioxidant, mucus-regulating, and airway remodeling-inhibiting effects through key signaling pathways including nuclear factor-erythroid 2-related factor 2 (Nrf2)/heme oxygenase-1 (HO-1), nuclear factor-kappa B (NF-κB), and mitogen-activated protein kinase (MAPK). These findings not only provide a theoretical basis for the clinical diagnosis and treatment of COPD but also point to new directions for future scientific research.
Pulmonary Disease, Chronic Obstructive/etiology* ; Animals ; Disease Models, Animal ; Biological Products/pharmacology* ; Humans ; NF-kappa B/metabolism* ; Flavonoids/pharmacology* ; Signal Transduction/drug effects* ; Anti-Inflammatory Agents/pharmacology* ; Heme Oxygenase-1/metabolism* ; Terpenes/pharmacology* ; Antioxidants/pharmacology* ; NF-E2-Related Factor 2/metabolism* ; Smoke/adverse effects* ; Phenols/therapeutic use*

OBJECTIVES: Chronic obstructive pulmonary disease (COPD) is a major chronic respiratory condition with high morbidity and mortality, imposing a serious economic and public health burden. The World Health Organization ranks COPD among the top 4 chronic diseases worldwide. Zangsiwei Qingfei Mixture (ZSWQF), a novel Tibetan herbal formulation independently developed by our research team, has shown therapeutic potential for chronic respiratory diseases. This study aims to evaluate the effects of aerosolized ZSWQF on cigarette smoke-induced COPD in mice and explore its underlying mechanisms. METHODS: Thirty C57 mice were randomly divided into a Control group, a COPD group, and a ZSWQF group. The Control group received saline aerosol inhalation without cigarette smoke exposure; both the COPD group and the ZSWQF group were exposed to cigarette smoke, with the former receiving saline inhalation and the latter treated with ZSWQF aerosol. White blood cell (WBC) count was performed using a fully automatic blood cell analyzer. Serum, alanine transaminase (ALT), and serum creatinine (SCr), as well as interleukin (IL)-6, IL-8, and tumor necrosis factor (TNF)-α levels in serum and bronchoalveolar lavage fluid (BALF) were measured by enzyme-linked immunosorbent assay (ELISA). BALF cell classification was determined using a hematology analyzer. Lung function was assessed with a small animal pulmonary function system, including airway resistance (RI) and cyclic dynamic compliance (CyDN). Lung tissues were stained with hematoxylin and eosin (HE), and mean linear intercept (MLI) and destruction index (DI) were calculated to evaluate morphological changes. Network pharmacology was applied to identify disease-related and ZSWQF-related targets, followed by intersection and protein-protein interaction (PPI) network analysis, and enrichment analysis of biological functions and pathways. Primary type II alveolar epithelial cell (AEC II) from SD rats were isolated and divided into a Control group, a lipopolysaccharide (LPS) group, a normal serum group, a water extract of ZSWQF (W-ZSWQF) group, a ZSWQF containing serum group, and a MLN-4760 [angiotensin-converting enzyme (ACE) 2 inhibitor]. Western blotting was performed to assess protein expression of ACE, p38 [a mitogen-activated protein kinase (MAPK)], phospho (p)-p38, extracellular signal-regulated kinases 1 and 2 (ERK1/2), p-ERK1/2, c-Jun N-terminal kinase (JNK), p-JNK, inhibitor of nuclear factor-kappa B alpha (IκBα), p-IκBα, and p-p65 subunit of nuclear factor-kappa B (NF-κBp65). RESULTS: WBC counts were significantly higher in the COPD group than in controls (P<0.01) and decreased following ZSWQF treatment (P<0.05). No significant intergroup differences were found in organ weights, ALT, or SCr (all P>0.05). Serum and BALF levels of IL-6, IL-8, and TNF-α, as well as total BALF cells, neutrophils, and macrophages, were elevated in the COPD group compared with controls and reduced by ZSWQF treatment (P<0.05). COPD mice exhibited increased RI, decreased CyDN, marked alveolar congestion, inflammatory infiltration, thickened septa, and higher MLI and DI values versus controls (P<0.05); ZSWQF treatment significantly reduced MLI and DI (P<0.05). Network pharmacology identified 151 potential therapeutic targets for ZSWQF against COPD, with key nodes including TNF, IL-6, protein kinase B (Akt) 1, albumin (ALB), tumor protein p53 (TP53), non-receptor tyrosine kinase (SRC), epidermal growth factor receptor (EGFR), signal transducer and activator of transcription 3 (STAT) 3, matrix metalloproteinase (MMP)-9, and beta-catenin (CTNNB1). Enrichment analysis indicates involvement of cancer-related, phosphatidylinositol 3-kinase (PI3K)/Akt, hypoxia-inducible factor (HIF)-1, calcium, and MAPK signaling pathways. Western blotting results showed that compared with the LPS group, AEC II treated with ZSWQF-containing serum exhibited decreased expression of ACE, p-p38/p38, p-ERK1/2/ERK1/2, p-JNK/JNK, p-IκBα/IκBα, and p-NF-κBp65, while ACE2 expression was upregulated, consistent with the MAPK/nuclear factor-kappa B (NF-κB) pathway regulation predicted by network pharmacology. CONCLUSIONS Aerosolized ZSWQF provides protective effects in COPD mice by reducing airway inflammation and remodeling.
Animals ; Pulmonary Disease, Chronic Obstructive/etiology* ; Drugs, Chinese Herbal/therapeutic use* ; Mice ; Mice, Inbred C57BL ; Male ; Network Pharmacology ; Smoke/adverse effects* ; Bronchoalveolar Lavage Fluid ; Administration, Inhalation ; Inflammation/drug therapy* ; Tumor Necrosis Factor-alpha ; Lung/drug effects* ; Interleukin-6/blood*

Immunomodulatory cancer therapy is witnessing the rise of viral immunotherapy. The oncolytic influenza A virus, although promising in preclinical investigations, remains to be implemented in clinical practice. Recent progress in genetic engineering, coupled with experiential insights, offers opportunities to enhance the therapeutic efficacy of the influenza A virus. This review explores the use of the influenza virus, its attenuated forms, and associated vaccines in cancer immunotherapy, highlighting their respective advantages and challenges. We further elucidate methods for engineering influenza viruses and innovative approaches to augment them with cytokines or immune checkpoint inhibitors, aiming to maximize their clinical impact. Our goal is to provide insights essential for refining influenza A virus-based viral tumor immunotherapies.
Humans ; Neoplasms/immunology* ; Immunotherapy/trends* ; Influenza A virus/immunology* ; Oncolytic Virotherapy/trends* ; Animals ; Cancer Vaccines/therapeutic use* ; Oncolytic Viruses ; Genetic Engineering ; Immune Checkpoint Inhibitors/therapeutic use*

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

Traditional Chinese medicine formula (TCMF) represents a fundamental component of Chinese medical practice, incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities, while providing comprehensive insights into health and disease. The foundation of TCMF lies in its holistic approach, manifested through herbal compatibility theory, which has emerged from extensive clinical experience and evolved into a highly refined knowledge system. Within this framework, Chinese herbal medicines exhibit intricated characteristics, including multi-component interactions, diverse target sites, and varied biological pathways. These complexities pose significant challenges for understanding their molecular mechanisms. Contemporary advances in artificial intelligence (AI) are reshaping research in traditional Chinese medicine (TCM), offering immense potential to transform our understanding of the molecular mechanisms underlying TCMFs. This review explores the application of AI in uncovering these mechanisms, highlighting its role in compound absorption, distribution, metabolism, and excretion (ADME) prediction, molecular target identification, compound and target synergy recognition, pharmacological mechanisms exploration, and herbal formula optimization. Furthermore, the review discusses the challenges and opportunities in AI-assisted research on TCMF molecular mechanisms, promoting the modernization and globalization of TCM.
Artificial Intelligence ; Drugs, Chinese Herbal/pharmacokinetics* ; Humans ; Medicine, Chinese Traditional ; Animals

Artificial intelligence (AI) has emerged as a transformative technology in accelerating drug discovery and development within natural medicines research. Natural medicines, characterized by their complex chemical compositions and multifaceted pharmacological mechanisms, demonstrate widespread application in treating diverse diseases. However, research and development face significant challenges, including component complexity, extraction difficulties, and efficacy validation. AI technology, particularly through deep learning (DL) and machine learning (ML) approaches, enables efficient analysis of extensive datasets, facilitating drug screening, component analysis, and pharmacological mechanism elucidation. The implementation of AI technology demonstrates considerable potential in virtual screening, compound optimization, and synthetic pathway design, thereby enhancing natural medicines' bioavailability and safety profiles. Nevertheless, current applications encounter limitations regarding data quality, model interpretability, and ethical considerations. As AI technologies continue to evolve, natural medicines research and development will achieve greater efficiency and precision, advancing both personalized medicine and contemporary drug development approaches.
Biological Products/pharmacology* ; Artificial Intelligence ; Humans ; Drug Discovery/methods* ; Machine Learning ; Deep Learning

Network pharmacology has gained widespread application in drug discovery, particularly in traditional Chinese medicine (TCM) research, which is characterized by its "multi-component, multi-target, and multi-pathway" nature. Through the integration of network biology, TCM network pharmacology enables systematic evaluation of therapeutic efficacy and detailed elucidation of action mechanisms, establishing a novel research paradigm for TCM modernization. The rapid advancement of machine learning, particularly revolutionary deep learning methods, has substantially enhanced artificial intelligence (AI) technology, offering significant potential to advance TCM network pharmacology research. This paper describes the methodology of TCM network pharmacology, encompassing ingredient identification, network construction, network analysis, and experimental validation. Furthermore, it summarizes key strategies for constructing various networks and analyzing constructed networks using AI methods. Finally, it addresses challenges and future directions regarding cell-cell communication (CCC)-based network construction, analysis, and validation, providing valuable insights for TCM network pharmacology.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Network Pharmacology/methods* ; Humans ; Drugs, Chinese Herbal/chemistry* ; Drug Discovery

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms