OBJECTIVE To study the protective effect and potential mechanism of chikusetsu saponin Ⅳ a （chsⅣ） on renal function in diabetic nephropathy （DN） model rats. METHODS DN rat model was established by high-fat diet combined with streptozotocin injection. Thirty-six model rats were randomly divided into model group （i.g. administration of normal saline， high-fat diet）， chsⅣ low-dose and high-dose groups （i.g. administration of 90， 180 mg/kg chsⅣ， high-fat diet）， with 12 rats in each group. Additionally， 10 normal rats were set as the control group （i.g. administration of normal saline， regular diet）. From the 5th to the 12th week after streptozotocin injection， they were given intragastric administration of relevant drug or normal saline， once a day. After the last medication， the levels of fasting blood glucose， fasting insulin， blood urea nitrogen， serum creatinine and urine protein as well as the levels of reduced glutathione （GSH）， superoxide dismutase （SOD） and malondialdehyde （MDA） in renal tissues were measured. Additionally， the insulin resistance index was calculated. Hematoxylin-eosin， periodic acid-Schiff， and Masson staining techniques were employed to examine the histopathological alterations in the renal tissue. The expressions of Notch signaling pathway-related proteins in renal tissue were detected by immunohistochemical staining and Western blot methods. RESULTS Compared with model group， the histomorphological of renal tissues in the chsⅣ low- and high-dose groups were significantly improved， with significant decreases in renal histological scores， mesangial expansion index， and glomerulosclerosis scores （ P ＜0.05）； the levels of fasting blood glucose， fasting insulin， blood urea nitrogen， serum creatinine， urine protein and homeostasis model assessment for insulin resistance， as well as MDA content， the expression levels of Notch1， Notch intracellular domain， hairy and enhancer of Split 1 and Delta-like protein 1 in renal tissue were all significantly decreased （ P ＜0.05）. The levels of GSH and SOD in renal tissue were significantly elevated （ P ＜0.05）. Moreover， the improvement in these indicators was significantly more pronounced in the chsⅣ high-dose group compared to the chsⅣ low-dose group （ P ＜0.05）. CONCLUSIONS ChsⅣ can ameliorate renal pathological damage and functional impairment in DN rats. Its underlying mechanisms include restoration of glucose homeostasis and insulin sensitivity， attenuation of renal oxidative stress， and suppression of aberrant Notch signaling pathway activation.

ObjectiveIn the context of the digital transformation of education, this study aims to construct a triadic interactive teaching model for surgical animal surgery in clinical medicine using modern information technology. It explores the effectiveness of different teaching methods in improving students' practical skills, aseptic awareness, and teamwork abilities, providing a reference for the reform of clinical practice education. MethodsA quasi-experimental research design was adopted. A total of 80 students from the eight-year clinical medicine program at Nanjing University were selected, including the Class of 2020 (control group, n=40) and the Class of 2021 (experimental group, n=40). The control group received traditional teaching methods, while the experimental group implemented the "Teacher-Student-AI" triadic interactive teaching model. This model utilized a smart teaching platform for personalized pre-class preparation , as well as data-driven post-class review and feedback throughout the entire teaching process. The "assessment indicators and scoring criteria for the surgical animal surgery course" were used to evaluate teaching effectiveness, with independent samples t-tests used for statistical analysis. ResultsPre-course assessments revealed no statistically significant differences in baseline theoretical knowledge or practical skills between the two groups (P>0.05). Upon completion of the course, the experimental group achieved higher scores than the control group across three key dimensions: practical skills (47.98±1.34 vs 46.92±2.51, P=0.022), aseptic awareness (17.84±1.16 vs 16.94±2.29, P=0.029), and teamwork (16.82±1.44 vs 15.95±1.22, P=0.004). However, no statistically significant difference was observed in the scores for humane care awareness between the two groups (8.24±0.70 vs 8.16±0.53, P=0.589). ConclusionThe AI-based triadic interactive teaching model can, to some extent, address the limitations of traditional surgical animal surgery education. It plays a positive role in enhancing medical students' surgical skills, aseptic awareness, and collaborative abilities. This model facilitates the transition from traditional to personalized teaching and offers a practical framework for the digital reform of clinical practice education.

OBJECTIVE: To explore the genotype-phenotype correlation in a Chinese family with structural brain abnormalities due to variant of the TUBB gene. METHODS: A family undergoing prenatal diagnosis at Beijing Obstetrics and Gynecology Hospital in October 2024 was selected as the study subject. Clinical data were collected. Amniotic fluid sample was subjected to chromosomal copy number variation sequencing (CNV-seq). Trio whole-exome sequencing (Trio-WES) was carried out on the amniotic fluid and parental blood samples, and candidate variant was verified by Sanger sequencing. This study was approved by the Medical Ethics Committee of the hospital (Ethics No.: 2023-KY-076-01). RESULTS: Both prenatal ultrasound and fetal MRI showed deviation of brain midline, unilateral lateral ventriculomegaly, and bilateral gyral asymmetry. Trio-WES revealed that the fetus has harbored a maternally derived heterozygous missense variant of the TUBB gene [NM_178014.4: c.155A>G (p.N52S)]. Sanger sequencing confirmed that the woman and a previously terminated fetus both harbored the same variant. Both the proband and two fetuses exhibited similar neuroimaging abnormalities including midline deviation and asymmetrical gyri. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the variant was classified as likely pathogenic (PM2_Supporting+PS2_Moderate+PS3). CONCLUSION The heterozygous c.155A>G (p.N52S) variant was the TUBB gene probably underlay the pathogenesis of the structural brain abnormalities in this family. Above findings have expanded the phenotypic spectrum associated with the variant and facilitated the prenatal diagnosis for this family.
Humans ; Female ; Pregnancy ; Prenatal Diagnosis ; Tubulin/genetics* ; Adult ; Brain/diagnostic imaging* ; Male ; Pedigree ; DNA Copy Number Variations/genetics* ; Exome Sequencing ; Genetic Association Studies ; Magnetic Resonance Imaging

ObjectiveTo explore the effects of serine hydroxymethyltransferase 1 （SHMT1） rs1979277 polymorphisms on pharmacokinetic characteristics and clinical prognosis of methotrexate （MTX） in children with acute lymphoblastic leukemia （ALL）. MethodsMatrix-assisted laser desorption/ionization time of flight mass spectrometry was used for SHMT1 rs1979277 genotyping in children with ALL . Clinical data including serum MTX concentrations， incidences of adverse events， and ALL relapse after chemotherapy with MTX were collected. The associations of SHMT1 rs1979277 G>A genotypes with dose-adjusted serum concentrations （C/D ratios）， adverse events of MTX， and relapse were analyzed. The associations between rs1979277 genotypes and SHMT1 expression were explored based on Bioinformatics methods. ResultsAmong the 146 children with ALL included， the rs1979277 GG homozygous genotype accounted for 85.62% （125/146）， while the GA heterozygous genotype accounted for 14.38% （21/146）. The frequency of the G allele was 92.81% （271/292）， while the A allele was only 7.19% （21/292）. Children with the GG homozygous genotype had higher median C/D ratios of MTX in 24 h ［12.06 （μmol·m²）/（L·g）］ and higher relapse rates （12.80%） than those in GA heterozygous genotype carriers ［10.96 （μmol·m²）/（L·g）， and 9.52%， respectively］. However， none of the above differences were statistically significant （all P>0.05）. The incidences of respiratory （19.05%） and liver disorders （33.33%） in children with the GA heterozygous genotype were significantly higher than those in GG homozygous genotype carriers （4.00% and 12.00%， respectively， P<0.05）. There were no statistically significant differences in the incidences of other adverse events. Bioinformatics analysis showed that the rs1979277 A allele was significantly associated with higher SHMT1 expression in multiple tissues， such as the tibial artery， pancreas， and adrenal gland （P<0.05）. ConclusionSHMT1 rs1979277 GA genotype may be a risk factor for respiratory and liver disorders in ALL children treated with MTX.

ObjectiveTo investigate the incidence and influencing factors of posttraumatic stress disorder （PTSD） three months after a traffic accident， and to explore the role of social support and coping strategies. MethodsA total of 117 individuals exposed to trauma following road traffic accidents were recruited. General demographic and clinical information was collected within one week， and the hamilton anxiety rating scale （HAMA）， the hamilton depression rating scale-24 （HAMD-24）， the social support rating scale （SSRS）， and the simplified coping style questionnaire （SCSQ） were administered. A 3-month follow-up was subsequently conducted， during which PTSD symptoms were assessed using the post-traumatic stress disorder checklist for DSM-5 （PCL-5）. Participants were divided into a PTSD group and a non-PTSD group according to whether PTSD occurred. Between-group comparisons were performed using the Mann-Whitney U non-parametric test or the χ² test， as appropriate. Spearman correlation analysis was used to examine the associations between general characteristics and PCL-5 scores. Binary Logistic regression was applied to identify factors influencing PTSD， and receiver operating characteristic （ROC） curve analysis was conducted to evaluate the diagnostic value of the SCSQ and SSRS. ResultsDuring the 3-month follow-up of the 117 trauma-exposed individuals， 17 cases developed PTSD， with a higher proportion of females （70.59%）. Between-group comparisons showed that， compared with the PTSD group， the non-PTSD group had higher scores for positive coping， objective support， and subjective support （P<0.05）， and lower scores for negative coping， HAMA， HAMD， and PCL-5 （P<0.05）. Correlation analysis indicated that female gender， negative coping， and higher HAMA and HAMD scores were associated with greater PTSD severity. Logistic regression analysis demonstrated that educational level （OR=1.715， 95% CI： 1.020-2.883， P=0.042） and negative coping （OR=1.590， 95% CI： 1.003-2.522， P=0.048） were risk factors for PTSD， whereas objective support （OR=0.646， 95% CI： 0.451-0.925， P=0.017） was a protective factor. The ROC analysis showed that the total SCSQ score and its negative and positive coping dimensions， the total SSRS score and its subjective and objective support dimensions， as well as their combined use， all demonstrated good discriminative ability in distinguishing between the PTSD and non-PTSD groups. ConclusionThe results suggest that individuals who are female， with higher HAMA and HAMD scores after a motor vehicle accident， and those with lower social support and negative coping strategies， should be given particular attention. Early interventions for these individuals may reduce the incidence of PTSD.

Objective To construct an index system for assessment of schistosomiasis transmission risk following natural disasters such as rainstorms, floods, earthquakes, mudslides, and landslides, so as to provide insights into rapid identification of schistosomiasis transmission risk post-disasters and formulation of targeted schistosomiasis control strategies. Methods An initial framework for the index system for assessment of schistosomiasis transmission risk following natural disasters was drafted through literature review, brainstorming, and focus group discussions. Two rounds of expert correspondence consultations were conducted using the Delphi method to refine and finalize the system, and the degrees of expert activeness, authority and endorse ment, and consensus were evaluated. In addition, the weights of each index were calculated using the analytic hierarchy process. Results A total of 18 experts participated in the consultation. The expert positive coefficients were 100.00% and 94.44% for two rounds of consultations, with authority coefficients of 0.92 and 0.94, respectively. The coefficients of coordination on the index importance, rationality and operability were 0.209, 0.185, 0.222 and 0.407, 0.214, 0.257 for two rounds of consultations, respectively, and all consistency tests were statistically significant (χ² = 246.771 to 505.278, all P values < 0.001). Following two rounds of expert consultations, an index system consisting of 6 first-level indicators, 15 second-level indicators, and 49 third-level indicators was ultimately constructed. In terms of first-level indicators, “disaster situation”, “previous epidemics”, “healthcare guarantee”, “response capacity” and “emergency recovery” had the highest weights, each at 18.18%. Regarding second-level indicators, “Schistosoma japonicum infections in animals”, “S. japonicum infections in snails” and “medical treatment” had the highest weights, each at 7.35%. In terms of third-level indicators, ten items had the highest weights, including “identification of schistosomiasis cases”, “detection of S. japonicum infections in wild feces”, “detection of S. japonicum infections in snails”, “reserves of schistosomiasis diagnostic/testing reagents and consumables”, “reserves of chemotherapy agents for human and animal schistosomiasis”, “reserves of cercariacides”, “periodical surveillance on schistosomiasis”, “identification of schistosomiasis transmission risk and timely response”, “normal provision of diagnosis and treatment services” and “post-disaster schistosomiasis surveillance”, each at 2.40%. Conclusion A scientific, systematic, and practical index system has been constructed for assessment of schistosomiasis transmission risk following natural disasters, which may provide insights into rapid post-disaster identification of schistosomiasis transmission risk, formulation of targeted schistosomiasis control strategies and optimization of resource allocation.

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.

Cancer remains a leading cause of global mortality, necessitating the development of advanced therapeutic strategies with enhanced efficacy and reduced systemic toxicity. Among promising bioactive agents, lactoferrin (LF)—a multifunctional iron-binding glycoprotein abundantly found in mammalian milk and exocrine secretions—has garnered significant interest for its potent and multifaceted anti-cancer properties. This review provides a comprehensive analysis of the current understanding of LF’s role in oncology, encompassing its structural biology, diverse mechanisms of action, and groundbreaking advancements in its application through nano-engineering. LF exerts anti-tumor effects through multiple pathways, including extracellular action, intracellular action, and immune regulation. It demonstrates a remarkable affinity for cancer cell membranes, binding to overexpressed anionic components such as glycosaminoglycans and sialic acids, as well as to specific receptors including the low-density lipoprotein receptor-related protein-1 (LRP-1). This selective binding facilitates targeted uptake. Upon internalization, LF orchestrates a direct assault by inducing cell-cycle arrest in phases such as G0/G1 or S phase through the modulation of key regulators including cyclins, CDKs, and p53. Furthermore, it promotes programmed cell death via apoptotic pathways, involving caspase activation and downregulation of anti-apoptotic proteins such as survivin. A more recently elucidated mechanism is the induction of ferroptosis, an iron-dependent form of cell death characterized by overwhelming lipid peroxidation. Beyond direct cytotoxicity, LF acts as a potent immunomodulator. It enhances natural killer (NK) cell activity, modulates T-lymphocyte populations, and crucially reprograms tumor-associated macrophages (TAMs) from a pro-tumor M2 state to an anti-tumor M1 state, thereby reversing the immunosuppressive tumor microenvironment (TME). The translation of LF’s potential has been significantly accelerated by nanotechnology. The inherent biocompatibility and natural tumor-targeting capabilities of LF make it an ideal platform for sophisticated drug-delivery systems. This review details various fabrication strategies for LF-based nanoparticles (NPs), including self-assembly, sol-in-oil emulsion, and electrostatic nanocomplexes, among others. Research demonstrates that nano-formulations not only protect LF from degradation but also enhance its bioactivity and anti-cancer potency. More importantly, LF NPs serve as versatile carriers for a wide array of therapeutic agents, including conventional chemotherapeutics, natural compounds, and imaging agents. These engineered systems enable synergistic therapy and facilitate site-specific delivery. Notably, the ability of LF to bind to receptors on the blood-brain barrier (BBB) has been leveraged to develop nano-systems for glioblastoma treatment. Other innovative designs utilize LF to modulate the TME—for instance, by alleviating tumor hypoxia to sensitize cells to radiotherapy and chemotherapy. Despite compelling pre-clinical evidence, the clinical translation of LF and its nano-formulations remains nascent. While early-phase trials have established a favorable safety profile for recombinant human LF, larger Phase III studies have yielded mixed results, underscoring the complexity of its action in humans. Key challenges include enhancing drug targeting, optimizing loading efficiency, ensuring batch-to-batch reproducibility, and achieving deep tumor penetration. Future research must focus on the rational design of next-generation LF-NPs. This entails developing standardized manufacturing protocols, engineering “smart” stimuli-responsive systems for targeted drug release in the TME, and constructing multi-targeting platforms. A concerted interdisciplinary effort is paramount to bridge the gap between bench and bedside. In conclusion, LF, particularly in its nano-engineered forms, represents a highly promising and versatile agent in the oncological arsenal, holding immense potential for precise and effective cancer therapy.

ObjectiveThe accuracy of Y-chromosome haplogroup assignment is crucial for tracing paternal lineage in male samples. With the advancement of high-throughput sequencing technologies, high-density Y-SNP genotyping from whole-genome or array-based data has become a standard method for determiningY-chromosome haplogroups. This study systematically evaluated the performance of 4 commonly used high-density SNP genotyping systems—namely, the Global Screening Array (GSA), Chinese Genotyping Array (CGA), Affymetrix array, and the 1240K capture panel—for haplogroup assignment. This work provides a reference for data comparison across different systems. MethodsWe extracted genotype data for the 4 Y-SNP panels from 30× whole-genome sequencing (WGS) data of 1 590 male samples from the 1000 Genomes Project. Additionally, GSA array genotype data from 384 relative pairs (spanning 1st- to 12th-degree relationships) from 109 Chinese Han families were collected. Haplogroup assignment was performed using Y-LineageTracker v1.3.0 software. We assessed the concordance and resolution of haplogroup assignments between the four Y-SNP panels and the WGS data. The consistency and resolution of haplogroup assignments were also evaluated for both the 1000 Genomes Project samples and the 109 family samples collected in this study. Furthermore, the impact of varying numbers of Y-SNPs on haplogroup assignment was examined. ResultsThe GSA and CGA panels demonstrated superior resolution and discrimination of haplogroup subclades compared with the other two panels. The haplogroup assignments from the GSA, CGA, and 1240K panels showed high concordance with WGS data, with consistency rates exceeding 88.70%, whereas the Affymetrix platform exhibited a significantly lower consistency rate of 61.89%. Specifically, the GSA and CGA panels consistently demonstrated superior performance compared with the other two panels in the assignment of haplogroups O-M175 and H-L901, achieving complete concordance (100%) for both haplogroups. In contrast, the Affymetrix panel erroneously assigned all individuals belonging to haplogroup O-M175 to haplogroup K2-M526. Furthermore, its accuracy for haplogroup H-L901 was exceedingly low, at merely 1.41%. This poor performance was characterized by the misassignment of 98.59% of H-L901 samples—specifically, 1.41% to J-M304 and a predominant 97.18% to F-M89. For haplogroup R-M207, all four panels exhibited uniformly high levels of consistency, with concordance values exceeding 94.00%. Notably, for haplogroup E-M96, the 1240K and Affymetrix panels outperformed the GSA and CGA panels in terms of concordance, representing the first instance in which these two panels surpassed the latter. Conversely, for haplogroups J-M304, Q-M242, and I-M170, all 4 panels showed relatively elevated misclassification rates, with the Affymetrix array demonstrating the poorest overall performance. None of the four panels showed any discordant haplogroup assignments among the familial relative pairs analyzed. A positive correlation was observed between the number of Y-SNPs (ranging from 1 000 to 10 000) and classification consistency; however, classification consistency plateaued when the number of Y-SNPs exceeded 10 000. Furthermore, a random sampling analysis conducted on the GSA and CGA panels demonstrated that the haplogroup misclassification rate exhibited negligible fluctuation across the Y-SNP range of 500 to 1 000. Conversely, a marked enhancement in classification consistency was observed as the number of markers increased from 1 000 to 5 000, ultimately reaching a plateau within the interval of 5 000 to 8 000 markers. ConclusionThese findings indicate that the GSA and CGA panels provide high resolution and concordance, delivering reliable Y-haplogroup assignment for forensic investigations.

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.