Chinese hamster ovary (CHO) cells are the most established and versatile mammalian expression system for the large-scale production of recombinant therapeutic proteins, owing to their genetic stability, adaptability to serum-free suspension culture, and ability to perform human-like post-translational modifications. More than 70% of biologics approved by the U.S. Food and Drug Administration rely on CHO-based production platforms, underscoring their central role in modern biopharmaceutical manufacturing. Despite these advantages, CHO systems continue to face three persistent bottlenecks that limit their potential for high-yield, reproducible, and cost-efficient production: excessive metabolic burden during high-density culture, heterogeneity of glycosylation patterns, and progressive loss of long-term expression stability. This review provides an integrated analysis of recent advances addressing these challenges and proposes a forward-looking framework for constructing intelligent and sustainable CHO cell factories. In terms of metabolic regulation, excessive lactate and ammonia accumulation disrupts energy balance and reduces recombinant protein synthesis efficiency. Optimization of culture parameters such as temperature, pH, dissolved oxygen, osmolarity, and glucose feeding can effectively alleviate metabolic stress, while supplementation with modulators including sodium butyrate, baicalein, and S-adenosylmethionine promotes specific productivity (qP) by modulating apoptosis and chromatin structure. Furthermore, genetic engineering strategies—such as overexpression of MPC1/2, HSP27, and SIRT6 or knockout of Bax, Apaf1, and IGF-1R—have demonstrated significant improvements in cell viability and product yield. The combination of multi-omics metabolic modeling with artificial intelligence (AI)-based prediction offers new opportunities for building self-regulating CHO systems capable of dynamic adaptation to environmental stress. Regarding glycosylation uniformity, which determines therapeutic efficacy and immunogenicity, gene editing-based glycoengineering (e.g., FUT8 knockdown or ST6Gal1 overexpression) has enabled the humanization of CHO glycan profiles, minimizing non-human sugar residues and enhancing drug stability. Process-level strategies such as galactose or manganese co-feeding and fine control of temperature or osmolarity further allow rational regulation of glycosyltransferase activity. Additionally, in vitro chemoenzymatic remodeling provides a complementary route to construct human-type glycans with defined structures, though industrial applications remain constrained by cost and scalability. The integration of model-driven process design and AI feedback control is expected to enable real-time prediction and correction of glycosylation deviations, ensuring batch-to-batch consistency in continuous biomanufacturing. Long-term expression stability, another critical challenge, is often impaired by promoter silencing, chromatin condensation, and random genomic integration. Molecular optimization—such as the use of improved promoters (CMV, EF-1α, or CHO endogenous promoters), Kozak and signal peptide refinement, and incorporation of chromatin-opening elements (UCOE, MAR, STAR)—helps maintain durable transcriptional activity, while site-specific integration systems including Cre/loxP, Flp/FRT, φC31, and CRISPR/Cas9 can enable single-copy, position-independent gene insertion at genomic safe-harbor loci, ensuring stable, predictable expression. Collectively, this review highlights a paradigm shift in CHO system optimization driven by the convergence of genome editing, synthetic biology, and artificial intelligence. The transition from empirical optimization to rational, data-driven design will facilitate the development of programmable CHO platforms capable of autonomous regulation of metabolic flux, glycosylation fidelity, and transcriptional activity. Such intelligent cell factories are expected to accelerate the transformation from laboratory-scale research to industrial-scale, high-consistency, and economically sustainable biopharmaceutical manufacturing, thereby supporting the next generation of efficient and customizable biologics manufacturing.

Chinese hamster ovary (CHO) cells are the most established and versatile mammalian expression system for the large-scale production of recombinant therapeutic proteins, owing to their genetic stability, adaptability to serum-free suspension culture, and ability to perform human-like post-translational modifications. More than 70% of biologics approved by the U.S. Food and Drug Administration rely on CHO-based production platforms, underscoring their central role in modern biopharmaceutical manufacturing. Despite these advantages, CHO systems continue to face three persistent bottlenecks that limit their potential for high-yield, reproducible, and cost-efficient production: excessive metabolic burden during high-density culture, heterogeneity of glycosylation patterns, and progressive loss of long-term expression stability. This review provides an integrated analysis of recent advances addressing these challenges and proposes a forward-looking framework for constructing intelligent and sustainable CHO cell factories. In terms of metabolic regulation, excessive lactate and ammonia accumulation disrupts energy balance and reduces recombinant protein synthesis efficiency. Optimization of culture parameters such as temperature, pH, dissolved oxygen, osmolarity, and glucose feeding can effectively alleviate metabolic stress, while supplementation with modulators including sodium butyrate, baicalein, and S-adenosylmethionine promotes specific productivity (qP) by modulating apoptosis and chromatin structure. Furthermore, genetic engineering strategies—such as overexpression of MPC1/2, HSP27, and SIRT6 or knockout of Bax, Apaf1, and IGF-1R—have demonstrated significant improvements in cell viability and product yield. The combination of multi-omics metabolic modeling with artificial intelligence (AI)-based prediction offers new opportunities for building self-regulating CHO systems capable of dynamic adaptation to environmental stress. Regarding glycosylation uniformity, which determines therapeutic efficacy and immunogenicity, gene editing-based glycoengineering (e.g., FUT8 knockdown or ST6Gal1 overexpression) has enabled the humanization of CHO glycan profiles, minimizing non-human sugar residues and enhancing drug stability. Process-level strategies such as galactose or manganese co-feeding and fine control of temperature or osmolarity further allow rational regulation of glycosyltransferase activity. Additionally, in vitro chemoenzymatic remodeling provides a complementary route to construct human-type glycans with defined structures, though industrial applications remain constrained by cost and scalability. The integration of model-driven process design and AI feedback control is expected to enable real-time prediction and correction of glycosylation deviations, ensuring batch-to-batch consistency in continuous biomanufacturing. Long-term expression stability, another critical challenge, is often impaired by promoter silencing, chromatin condensation, and random genomic integration. Molecular optimization—such as the use of improved promoters (CMV, EF-1α, or CHO endogenous promoters), Kozak and signal peptide refinement, and incorporation of chromatin-opening elements (UCOE, MAR, STAR)—helps maintain durable transcriptional activity, while site-specific integration systems including Cre/loxP, Flp/FRT, φC31, and CRISPR/Cas9 can enable single-copy, position-independent gene insertion at genomic safe-harbor loci, ensuring stable, predictable expression. Collectively, this review highlights a paradigm shift in CHO system optimization driven by the convergence of genome editing, synthetic biology, and artificial intelligence. The transition from empirical optimization to rational, data-driven design will facilitate the development of programmable CHO platforms capable of autonomous regulation of metabolic flux, glycosylation fidelity, and transcriptional activity. Such intelligent cell factories are expected to accelerate the transformation from laboratory-scale research to industrial-scale, high-consistency, and economically sustainable biopharmaceutical manufacturing, thereby supporting the next generation of efficient and customizable biologics manufacturing.

ObjectiveZheng’s San Qi San (ZSQS) power, a classic traditional Chinese medicine (TCM) formula, is used for treating soft tissue injuries involving muscles, tendons, and ligaments. However, its underlying therapeutic mechanisms remain unclear. This study aimed to screen and identify pharmaceutically active ingredients and their candidate biomolecule targets, and further elucidate the molecular mechanism of ZSQS in the treatment of skeletal muscle injury. MethodsNetwork pharmacology was employed to construct “ZSQS-component-target”, “protein-protein interaction (PPI)” and “active ingredient-core protein-pathway” networks to predict the key active ingredients and potential core targets of ZSQS for skeletal muscle injury. The predicted results were then validated via microarray data from the GEO database. Molecular docking was then performed to assess the binding ability between the screened active ingredients of ZSQS and the candidate core targets. Moreover, liquid chromatography-mass spectrometry (LC-MS) was used for qualitative and quantitative analysis to verify the active components of the drug and ZSQS serum. Finally, an animal model of eccentric exercise-induced skeletal muscle injury and a myotube cell model of oxidative stress-induced injury were established to validate the effects of ZSQS and its interventional effects on the biological functions of critical targets, thereby demonstrating the potential therapeutic mechanism of ZSQS. ResultsAmong the 111 active components identified in ZSQS and their corresponding 204 targets related to the skeletal muscle injury repair process, 14 core targets (including AKT1) and 4 core active components (quercetin, luteolin, kaempferol, and β‑sitosterol) were screened out, while the corresponding metabolites of quercetin, luteolin and kaempferol were detected in the ZSQS serum. Among these targets, 5 candidate genes (IL-6, CASP3, HIF1A, STAT3, and JUN) overlapped with the differential expression screening results with GEO data, and IL-6 was confirmed to be enriched in the PI3K/AKT pathway. Combined with the prediction results of the AKT expression levels, these findings suggest that the phosphorylation level of AKT1 plays a core role in the therapeutic mechanism of ZSQS. Molecular docking analysis further revealed that the PH domain of AKT1 had high binding energy with all 4 core active components, as verified by LC-MS. Finally, animal model studies have shown the promoting effect of ZSQS administration on skeletal muscle injury repair and its possible antioxidant damage mechanism. Cell model studies further demonstrated that ZSQS-containing serum, core active ingredient combination therapy, and quercetin monomer could increase the phosphorylation level of AKT, promote the nuclear translocation of Nrf2, upregulate the expression of downstream antioxidant enzymes (SOD, GPx, and GR), and inhibit the expression of inflammatory factors (IL-6 and TNF-α), thereby alleviating oxidative stress and the inflammatory response. ConclusionZSQS alleviates skeletal muscle injury mainly by activating the AKT/Nrf2 signaling pathway, enhancing cellular antioxidant and anti-inflammatory capabilities. The results of this study provide a scientific basis for the clinical application and modernized development of ZSQS.

ObjectiveTo investigate the association between insulin resistance （IR） and cardiovascular metabolic indicators， including blood pressure， blood lipids， and serum uric acid， in a middle-aged population. MethodsBased on the baseline survey of the Hong Sifang cohort of China National Salt Industry Group， a total of 575 participants were included. According to the homeostasis model assessment of insulin resistance， the participants were divided into an IR group （n=116） and a non-IR group （n=459）. Differences between the two groups were compared using the t-test and chi-square test. Multivariate logistic regression analysis was performed to evaluate the associations between IR and abnormalities in metabolic indicators. ResultsA total of 575 participants were included， including 390 men and 185 women， with a mean age of （48.58 ± 5.14） years and a mean body mass index （BMI） of （24.16 ± 2.97） kg/m². Blood pressure， serum uric acid， and blood lipid levels were significantly higher in the IR group than in the non-IR group （P<0.001）. After adjusting for potential confounders including age， sex， and BMI， IR was independently associated with hypertension， dyslipidemia， and hyperuricemia， with odds ratios （OR） and 95% confidence intervals （CI） of 2.57 （1.61-4.09）， 3.81 （2.36-6.17）， and 2.68 （1.53-4.71）， respectively （all P<0.001）. ConclusionInsulin resistance is significantly associated with multiple cardiovascular metabolic risk factors in the middle-aged population. Monitoring IR may contribute to the early prevention and control of cardiovascular diseases.

Under the current upsurge of cross-disciplinary research between artificial intelligence （AI） and traditional Chinese medicine （TCM）， the inheritance of academic thought in TCM is confronted with the deep-seated predicament of cognitive paradigm dislocation. The existing AI model is based on the classical reasoning mode of formal logic and statistical probability， which is difficult to adapt to the nonlinear and holistic cognitive logic of TCM with image-based thinking and syndrome-differentiated treatment as the core， resulting in systematic misreading and loss of the core essence in digital inheritance. Based on this， this paper proposes to construct a new cognitive model oriented towards the context of TCM. Its essence is not to have AI imitate the thinking of TCM， but to establish an auxiliary understanding framework based on semantic mapping and relational reasoning. It is advocated that the role of AI should be reconstructed from a knowledge substitute to a clinical interactor， and its application boundary in the inheritance of TCM wisdom should be clarified. By establishing the theoretical framework and practical path of human-computer collaboration， this paper aims to provide a scientific basis for realizing the deep integration of AI technology and the academic thought in TCM.

Under the current upsurge of cross-disciplinary research between artificial intelligence （AI） and traditional Chinese medicine （TCM）， the inheritance of academic thought in TCM is confronted with the deep-seated predicament of cognitive paradigm dislocation. The existing AI model is based on the classical reasoning mode of formal logic and statistical probability， which is difficult to adapt to the nonlinear and holistic cognitive logic of TCM with image-based thinking and syndrome-differentiated treatment as the core， resulting in systematic misreading and loss of the core essence in digital inheritance. Based on this， this paper proposes to construct a new cognitive model oriented towards the context of TCM. Its essence is not to have AI imitate the thinking of TCM， but to establish an auxiliary understanding framework based on semantic mapping and relational reasoning. It is advocated that the role of AI should be reconstructed from a knowledge substitute to a clinical interactor， and its application boundary in the inheritance of TCM wisdom should be clarified. By establishing the theoretical framework and practical path of human-computer collaboration， this paper aims to provide a scientific basis for realizing the deep integration of AI technology and the academic thought in TCM.

Ulcerative colitis （UC） is a chronic inflammatory bowel disease primarily affecting the colon and rectum. With a rising incidence， UC has aroused an increasingly serious public health concern. The etiology and pathogenesis of UC are complex， involving genetic susceptibility， intestinal barrier dysfunction， immune dysregulation， and environmental factors such as diet， smoking， and infection. Current UC therapies face limitations including persistent drug resistance， multiple side effects， and compromised immune function， necessitating the exploration of novel intervention strategies. In recent years， with the continuous advancement of research into UC mechanisms， the Toll-like receptor 4 （TLR4）/nuclear factor-κB （NF-κB） signaling pathway has been revealved as one of the classical pathways involved in inflammation. Being crucial for maintaining intestinal homeostasis， this pathway regulates inflammatory and immune responses by controlling gene transcription， thereby playing a significant role in the occurrence and progression of UC. However， systematic summarization within this field remains lacking. Traditional Chinese medicine （TCM） possesses unique advantages through its multi-target， multi-pathway， and multi-mechanism approaches， being capable of curbing the pathological progression of UC by targeting key molecules in the TLR4/NF-κB signaling pathway. This paper systematically summarizes the mechanisms by which TLR4/NF-κB-related pathways regulate UC， primarily encompassing inflammatory responses， oxidative stress， immune balance， and gut microbiota. It reviews single herbs （e.g.， Rosa roxburghii root and Saposhnikoviae Radix）， active components （e.g.， resveratrol， Tripterygium glycosides， and gypenosides）， herbal extracts （e.g.， Zingiberis Rhizoma extract and Taraxaci Herba extract）， and compound formulas for clearing heat and removing dampness， invigorating the spleen and warming the kidneys， clearing heat and detoxifying， harmonizing the liver and spleen， and treating cold and heat in complexity （e.g.， Huaihua Powder， Shenling Baizhu Powder， and Jiedu Huayu Formula）. The aim is to fully leverage the advantages of TCM in UC treatment and provide novel insights for clinical and pharmacological research on UC.

Ulcerative colitis （UC） is a chronic inflammatory bowel disease primarily affecting the colon and rectum. With a rising incidence， UC has aroused an increasingly serious public health concern. The etiology and pathogenesis of UC are complex， involving genetic susceptibility， intestinal barrier dysfunction， immune dysregulation， and environmental factors such as diet， smoking， and infection. Current UC therapies face limitations including persistent drug resistance， multiple side effects， and compromised immune function， necessitating the exploration of novel intervention strategies. In recent years， with the continuous advancement of research into UC mechanisms， the Toll-like receptor 4 （TLR4）/nuclear factor-κB （NF-κB） signaling pathway has been revealved as one of the classical pathways involved in inflammation. Being crucial for maintaining intestinal homeostasis， this pathway regulates inflammatory and immune responses by controlling gene transcription， thereby playing a significant role in the occurrence and progression of UC. However， systematic summarization within this field remains lacking. Traditional Chinese medicine （TCM） possesses unique advantages through its multi-target， multi-pathway， and multi-mechanism approaches， being capable of curbing the pathological progression of UC by targeting key molecules in the TLR4/NF-κB signaling pathway. This paper systematically summarizes the mechanisms by which TLR4/NF-κB-related pathways regulate UC， primarily encompassing inflammatory responses， oxidative stress， immune balance， and gut microbiota. It reviews single herbs （e.g.， Rosa roxburghii root and Saposhnikoviae Radix）， active components （e.g.， resveratrol， Tripterygium glycosides， and gypenosides）， herbal extracts （e.g.， Zingiberis Rhizoma extract and Taraxaci Herba extract）， and compound formulas for clearing heat and removing dampness， invigorating the spleen and warming the kidneys， clearing heat and detoxifying， harmonizing the liver and spleen， and treating cold and heat in complexity （e.g.， Huaihua Powder， Shenling Baizhu Powder， and Jiedu Huayu Formula）. The aim is to fully leverage the advantages of TCM in UC treatment and provide novel insights for clinical and pharmacological research on UC.

Objective:To explore the predictive value of neutrophil-to-lymphocyte ratio (NLR) and tumor necrosis factor -α (TNF-α) level on the therapeutic effect of transcatheter arterial chemoembolization (TACE) combined with microwave ablation in patients with massive liver cancer.Methods:The medical records of 106 patients with massive liver cancer who underwent TACE combined with microwave ablation treatment in the Affiliated Hospital of Weifang Medical University from February 2020 to February 2023 were retrospectively analyzed. The efficacy was evaluated 6 weeks after surgery, and the patients were divided into remission group and non-remission group according to the therapeutic effect. The levels of NLR and TNF-α in the two groups were detected before surgery, 3 days after surgery and 7 days after surgery. Point two column correlation was used to analyze the relationship between the levels of NLR and TNF-α in different time periods and the therapeutic effect of TACE combined with microwave ablation in patients with massive liver cancer. The receiver operator characteristic (ROC) curve was drawn to analyze the predictive value of NLR and TNF-α levels in different time periods for the therapeutic effect of TACE combined with microwave ablation in patients with massive liver cancer.Results:Six weeks after surgery, out of 106 patients with massive liver cancer, 13 achieved complete remission, 48 achieved partial remission, 20 experienced disease progression, and 25 remained stable. The overall remission rate was 57.55% (61/106). Before surgery, the levels of NLR [ (2.26±0.13) vs. (2.43±0.12), t=6.87, P<0.001] and TNF-α [ (36.20±4.38) pg/ml vs. (42.74±5.74) pg/ml, t=6.66, P<0.001] in the remission group ( n=61) were lower than those in the non-remission group ( n=45), with statistically significant differences. At 3 days after surgery, there were no statistically significant difference in the levels of NLR [ (6.16±3.22) vs. (6.22±3.30), t=0.09, P=0.925] or TNF-α [ (48.84±7.22) pg/ml vs. (49.13±7.34) pg/ml, t=0.20, P=0.840] between the remission group and the non-remission group. At 7 days after surgery, the levels of NLR [ (2.60±0.18) vs. (2.82±0.26), t=5.15, P<0.001] and TNF-α [ (38.20±6.30) pg/ml vs. (45.57±5.79) pg/ml, t=6.16, P<0.001] in the remission group were lower than those in the non-remission group, with statistically significant differences. There were statistically significant differences in NLR and TNF-α levels before surgery, 3 days and 7 days after surgery between the remission group and the non-remission group ( F=82.43, P<0.001; F=54.45, P<0.001; F=76.23, P<0.001; F=15.61, P<0.001). Further pair-to-pair comparison showed that the levels of NLR and TNF-α were higher in both groups 3 and 7 days after surgery than before surgery, but the levels of NLR and TNF-α were lower in both groups 7 days after surgery than 3 days after surgery, with statistically significant differences (all P<0.005). Point two column correlation analysis showed that NLR level, TNF-α level and the efficacy of TACE combined with microwave ablation in patients with massive liver cancer were significantly positively correlated before and 7 days after surgery ( r=0.42, P<0.001; r=0.49, P<0.001; r=0.43, P<0.001; r=0.46, P<0.001). ROC curve showed that the area under the curve (AUC) of NLR and TNF-α alone in predicting the efficacy of TACE combined with microwave ablation in patients with massive liver cancer before and 7 days after surgery was 0.750 (95% CI: 0.656-0.844), 0.788 (95% CI: 0.699-0.877), 0.751 (95% CI: 0.652-0.850), 0.788 (95% CI: 0.700-0.876), respectively. The AUC of combined prediction of NLR and TNF-α before and 7 days after surgery were 0.818 (95% CI: 0.736-0.900) and 0.813 (95% CI: 0.730-0.897), respectively. There were no statistically significant differences in the AUC values of NLR and TNF-α alone or in combination for predicting the therapeutic effect of TACE combined with microwave ablation in patients with massive liver cancer before and 7 days after surgery (all P>0.05) . Conclusions:The levels of NLR and TNF-α before and 7 days after surgery are related to the effect of TACE combined with microwave ablation in patients with massive liver cancer, and the combination of NLR and TNF-α levels before and 7 days after surgery has certain value in predicting the effect of TACE combined with microwave ablation in patients with massive liver cancer.

Numerous research conducted in recent years has revealed that gut microbial dysbiosis, such as modifications in composition and activity, might influence lung tissue homeostasis through specific pathways, thereby promoting susceptibility to lung diseases. The development and progression of lung cancer, as well as the effectiveness of immunotherapy are closely associated with gut flora and metabolites, which influence immunological and inflammatory responses. During abnormal proliferation, non-small cell lung cancer cells acquire more substances and energy by altering their own metabolic pathways. Glucose and amino acid metabolism reprogramming provide tumor cells with abundant ATP, carbon, and nitrogen sources, respectively, providing optimal conditions for tumor cell proliferation, invasion, and immune escape. This article reviews the relationship of immune response with gut flora and metabolic reprogramming in non-small cell lung cancer, and discusses the potential mechanisms by which gut flora and metabolic reprogramming affect the occurrence, development, and immunotherapy of non-small cell lung cancer, in order to provide new ideas for precision treatment of lung cancer patients.
Humans ; Gastrointestinal Microbiome/immunology* ; Carcinoma, Non-Small-Cell Lung/therapy* ; Lung Neoplasms/therapy* ; Immunotherapy ; Metabolic Reprogramming