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.

Metabolic reprogramming， as one of the core hallmarks of malignant tumors， plays a key role in the occurrence， development and treatment of breast cancer （BC）. Abnormal changes in glucose metabolism， amino acid metabolism， lipid metabolism， as well as the tricarboxylic acid （TCA） cycle and oxidative phosphorylation （OXPHOS） pathways significantly influence the pathogenesis and progression of BC. Studies have shown that various active components of traditional Chinese medicine （TCM） （such as berberine， matrine， quercetin， curcumin， etc.） and their compound formulations （e.g. Xihuang pill， Danzhi xiaoyao powder， Yanghe decoction， etc.） can inhibit the proliferation and migration of BC cells and induce apoptosis by regulating key metabolic pathways such as glycolysis， lipid synthesis， and amino acid metabolism. TCM demonstrates multi-target and holistic regulatory advantages in intervening in BC metabolic reprogramming， showing significant potential in modulating key molecules like hypoxia inducible factor-1α， hexokinase-2， pyruvate kinase M2， lactate dehydrogenase A， glucose transporter-1， fatty acid synthase， and signaling pathways such as AKT/mTOR. However， current researches still focus predominantly on glucose metabolism， with insufficient mechanistic studies on lipid metabolism， amino acid metabolism， the TCA cycle， and OXPHOS. The precise targets， molecular mechanisms， and clinical translation value of these interventions require further validation and clarification through more high-quality experimental studies and clinical trials.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

ObjectiveTo develop a standardized classification model for traditional Chinese medicine （TCM） syndrome patterns of mixed hemorrhoids using multi-center real-world data， and unveil their distribution patterns and core risk factors， thereby providing evidence-based support for standardizing TCM syndrome differentiation and implementing precision interventions. MethodsA multi-center cross-sectional study was conducted， enrolling 13 283 mixed hemorrhoid patients from eight hospitals in Jiangsu Province between September 1st， 2023 and December 31st， 2024. DeepSeek-R1-Distill-Qwen-7B and LLaMA-3.3 large language models （LLM） were integrated with latent class analysis （LCA） to perform unsupervised learning and latent class modeling of TCM symptomatology. Potential risk factors were screened via univariate analysis， followed by logistic regression to identify independent risk factors for each syndrome pattern. ResultsThe model's performance indicators were stable and reliable across different clinical data types，i.e. in the outpatient records， past medical history （F1=99.7%）， current medical history （F1=94.9%）， and specialist examination （F1=90.7%）； in inpatient records， past medical history （F1=98.2%）， current medical history （F1=91.2%）， specialist examination （F1=90.3%）， and discharge status （F1=90.6%）. Latent class mode-ling identified four core TCM syndrome patterns including spleen deficiency and qi sinking syndrome （915 cases， 6.89%）， damp-heat pouring downward syndrome （10 820 cases， 81.46%）， qi stagnation and blood stasis syndrome （1252 cases， 9.43%）， and wind injuring intestinal collaterals syndrome （296 cases， 2.22%）， with respective latent class probabilities of 0.069， 0.815， 0.094， and 0.022. Logistic regression demonstrated that gender， age， disease duration， hypertension， diabetes， hyperlipidemia， constipation， smoking history， and alcohol consumption were independent risk factors for pattern differentiation （P＜0.05）. The efficacy validation evaluation revealed that the cure rates for patients with spleen deficiency and qi sinking syndrome and qi stagnation and blood stasis syndrome were higher than those for patients with damp-heat pouring downward syndrome （adjusted P＜0.05）， with no statistically significant differences among other syndrome patterns. ConclusionDamp-heat pouring downward syndrome is the predominant syndrome in mixed hemorrhoids. Gender， age， disease duration， hypertension， diabetes， hyperlipi-demia， constipation， smoking history， and alcohol consumption are independent risk factors for the differentiation of syndrome types.

Background: The association of changes in metabolic syndrome (MetS) with cognitive function remains unclear. We explored this association using prospective and Mendelian randomization (MR) studies. Methods: MetS components including high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), waist circumference (WC), fasting plasma glucose (FPG), and triglycerides were measured at baseline and two follow-ups, constructing a MetS index. Immediate, delayed memory recall, and cognitive function along with its dimensions were assessed by immediate 10- word recall test (IWRT) and delayed 10-word recall test (DWRT), and mini-mental state examination (MMSE), respectively, at baseline and follow-ups. Linear mixed-effect model was used. Additionally, the genome-wide association study (GWAS) of MetS was conducted and one-sample MR was performed to assess the causality between MetS and cognitive function. Results: Elevated MetS index was associated with decreasing annual change rates (decrease) in DWRT and MMSE scores, and with decreases in attention, calculation and recall dimensions. HDL-C was positively associated with an increase in DWRT scores, while SBP and FPG were negatively associated. HDL-C showed a positive association, whereas WC was negatively associated with increases in MMSE scores, including attention, calculation and recall dimensions. Interaction analysis indicated that the association of MetS index on cognitive decline was predominantly observed in low family income group. The GWAS of MetS identified some genetic variants. MR results showed a non-significant causality between MetS and decrease in DWRT, IWRT, nor MMSE scores. Conclusion Our study indicated a significant association of MetS and its components with declines in memory and cognitive function, especially in delayed memory recall.

Background: Maturity-onset diabetes of the young (MODY) due to variants of hepatocyte nuclear factor 1-beta (HNF1β) (MODY5) has not been well studied in the Chinese population. This study aimed to estimate its prevalence and evaluate the application of a clinical screening method (Faguer score) in Chinese early-onset diabetes (EOD) patients. Methods: Among 679 EOD patients clinically diagnosed with type 2 diabetes mellitus (age at diagnosis ≤40 years), the exons of HNF1β were sequenced. Functional impact of rare variants was evaluated using a dual-luciferase reporter system. Faguer scores ≥8 prompted multiplex ligation-dependent probe amplification (MLPA) for large deletions. Pathogenicity of HNF1β variants was assessed following the American College of Medical Genetics and Genomics (ACMG) guidelines. Results: Two rare HNF1β missense mutations (E105K and G454R) were identified by sequencing in five patients, showing functional impact in vitro. Another patient was found to have a whole-gene deletion by MLPA in 22 patients with the Faguer score above 8. Following ACMG guidelines, six patients carrying pathogenic or likely pathogenic variant were diagnosed with MODY5. The estimated prevalence of MODY5 in Chinese EOD patients was approximately 0.9% or higher. Conclusion MODY5 is not uncommon in China. The Faguer score is helpful in deciding whether to perform MLPA analysis on patients with negative sequencing results.

Background: The association of changes in metabolic syndrome (MetS) with cognitive function remains unclear. We explored this association using prospective and Mendelian randomization (MR) studies. Methods: MetS components including high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), waist circumference (WC), fasting plasma glucose (FPG), and triglycerides were measured at baseline and two follow-ups, constructing a MetS index. Immediate, delayed memory recall, and cognitive function along with its dimensions were assessed by immediate 10- word recall test (IWRT) and delayed 10-word recall test (DWRT), and mini-mental state examination (MMSE), respectively, at baseline and follow-ups. Linear mixed-effect model was used. Additionally, the genome-wide association study (GWAS) of MetS was conducted and one-sample MR was performed to assess the causality between MetS and cognitive function. Results: Elevated MetS index was associated with decreasing annual change rates (decrease) in DWRT and MMSE scores, and with decreases in attention, calculation and recall dimensions. HDL-C was positively associated with an increase in DWRT scores, while SBP and FPG were negatively associated. HDL-C showed a positive association, whereas WC was negatively associated with increases in MMSE scores, including attention, calculation and recall dimensions. Interaction analysis indicated that the association of MetS index on cognitive decline was predominantly observed in low family income group. The GWAS of MetS identified some genetic variants. MR results showed a non-significant causality between MetS and decrease in DWRT, IWRT, nor MMSE scores. Conclusion Our study indicated a significant association of MetS and its components with declines in memory and cognitive function, especially in delayed memory recall.

Background: Maturity-onset diabetes of the young (MODY) due to variants of hepatocyte nuclear factor 1-beta (HNF1β) (MODY5) has not been well studied in the Chinese population. This study aimed to estimate its prevalence and evaluate the application of a clinical screening method (Faguer score) in Chinese early-onset diabetes (EOD) patients. Methods: Among 679 EOD patients clinically diagnosed with type 2 diabetes mellitus (age at diagnosis ≤40 years), the exons of HNF1β were sequenced. Functional impact of rare variants was evaluated using a dual-luciferase reporter system. Faguer scores ≥8 prompted multiplex ligation-dependent probe amplification (MLPA) for large deletions. Pathogenicity of HNF1β variants was assessed following the American College of Medical Genetics and Genomics (ACMG) guidelines. Results: Two rare HNF1β missense mutations (E105K and G454R) were identified by sequencing in five patients, showing functional impact in vitro. Another patient was found to have a whole-gene deletion by MLPA in 22 patients with the Faguer score above 8. Following ACMG guidelines, six patients carrying pathogenic or likely pathogenic variant were diagnosed with MODY5. The estimated prevalence of MODY5 in Chinese EOD patients was approximately 0.9% or higher. Conclusion MODY5 is not uncommon in China. The Faguer score is helpful in deciding whether to perform MLPA analysis on patients with negative sequencing results.