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.

Objective To investigate the prevalence of soil-transmitted nematode human infections in Hubei Province from 2016 to 2023, so as to provide insights into formulation of soil-transmitted nematodiasis control strategies and evaluation of soil-transmitted nematodiasis control effectiveness. Methods There were 10 surveillance points established in Hubei Province for soil-transmitted nematodiasis each year from 2016 to 2023. The surveillance points were divided into eastern, western, southern, northern and central regions according to geographical locations, and one administrative village was sampled from a township in each region as a surveillance point. Then, at least 200 permanent residents at ages of over 3 years were randomly sampled at each point, with at least 1 000 individuals sampled. Soil-transmitted nematode infection was detected using a modified Kato-Katz thick smear method (two slides for one stool sample), and Enterobius vermicularis infection was additionally detected with a cellophane tape test among children at ages of 3 to 9 years. The prevalence of soil-transmitted nematode human infections was descriptively analyzed. Results A total of 80 547 individuals underwent surveillance for soil-transmitted nematodiasis in Hubei Province from 2016 to 2023, and a total of 262 individuals were detected with soil-transmitted nematode infections, including two cases with mixed infections, yielding an overall prevalence rate of 0.33% (262/80 547). The prevalence rates of hookworm, Ascaris lumbricoides, Trichuris trichiura, and E. vermicularis infections were 0.02% (20/80 547), 0.07% (55/80 547), 0.02% (15/80 547) and 0.22% (174/80 547), respectively, appearing a parasite species-specific prevalence rate (χ² = 250.235, P < 0.05), and the prevalence rates of soil-transmitted nematode infections were 0.50%(51/10271),0.37%(34/9120),0.39%(39/10099),0.40%(41/10168), 0.36% (37/10 234), 0.45% (46/10 211), 0.03% (3/10 238) and 0.11% (11/10 206) in Hubei Province from 2016 to 2023, respectively, showing a year-specific prevalence rate (χ² = 60.589, P < 0.05). Soil-transmitted nematode infections were detected across 36 surveillance points, and there was a significant difference in the prevalence of soil-transmitted nematode infections among surveillance points (0.02% to 3.30%, P < 0.001). In addition, the differences in prevalence rates of human soil-transmitted nematode infection among different age groups (χ² = 531.091, P < 0.05), occupations (P < 0.001), and education levels (P < 0.001) were all statistically significant. Among these, the children at ages of 3 to 9 years old (1.40%, 174/12 461), nursery-school children (1.69%, 90/5 312) and preschool-aged children (1.56%, 93/5 969) were relatively high. Conclusions The overall prevalence of soil-transmitted nematodiasis was low in Hubei Province from 2016 to 2023; however, the prevalence varied across regions and among populations, with E. vermicularis as the predominant species of soil-transmitted nematode. Soil-transmitted nematodiasis control should be given a high priority in key areas in Hubei Province, and preschool children, primary school students, and individuals with a low educational level are recommended as key target populations for health education and interventions.

Traditional Chinese medicine(TCM)is an ancient medical system distinctive and effective in treating cancer,depression,coronavirus disease 2019(COVID-19),and other diseases.However,the relatively abstract diagnostic methods of TCM lack objective measurement,and the complex mechanisms of action are difficult to comprehend,which hinders the application and internationalization of TCM.Recently,while breakthroughs have been made in utilizing methods such as network pharmacology and virtual screening for TCM research,the rise of machine learning(ML)has significantly enhanced their inte-gration with TCM.This article introduces representative methodological cases in quality control,mechanism research,diagnosis,and treatment processes of TCM,revealing the potential applications of ML technology in TCM.Furthermore,the challenges faced by ML in TCM applications are summarized,and future directions are discussed.

Objective To summarize the clinical and genetic characteristics of end-stage renal disease caused by PLCE1 gene mutations.Methods A retrospective analysis of the clinical and genetic features of three children from a family with PLCE1 gene mutations was conducted,along with a literature review of hereditary kidney disease cases caused by PLCE1 gene mutations.Results The proband was an 8-year-old male presenting with nephrotic syndrome stage 4 chronic kidney disease.Renal biopsy showed focal segmental glomerulosclerosis.Two years and five months after kidney transplantation,the patient had persistent negative proteinuria and normal renal function.Whole-exome sequencing identified two pathogenic heterozygous variants:c.961C>T and c.3255_3256delinsT,with c.3255_3256delinsT being a novel mutation.Family screening revealed no renal involvement in the parents,but among five siblings,one brother died at age of 4 years from end-stage renal disease.A 7-year-old sister presented with proteinuria and bilateral medullary sponge kidney,with proteinuria resolving after one year of follow-up.A 3-year-old brother died after kidney transplantation due to severe pneumonia.The literature review included 45 patients with hereditary kidney disease caused by PLCE1 gene mutations.The main clinical phenotype was nephrotic syndrome(87%,39/45),and renal pathology predominantly showed focal segmental glomerulosclerosis(57%,16/28).No mutation hotspots were identified.Conclusions Compound heterozygous mutations in the PLCE1 gene can lead to rapid progression of the disease to end-stage renal disease,with favorable outcomes following kidney transplantation.Family screening is crucial for early diagnosis,and medullary sponge kidney may be a novel phenotype associated with these gene mutations.Citaion:[Chinese Journal of Contemporary Pediatrics,2025,27(5):580-587]

Acute kidney injury (AKI) is a critical clinical condition characterized by rapid renal function decline, with high morbidity, mortality, and healthcare costs. Traditional Chinese medicine (TCM) has shown potential effects on mitigating oxidative stress and programmed cell death in AKI models. Scutellaria barbata D. Don (SB) and Scleromitrion diffusum (Willd.) R. J. Wang (SD), a classic TCM herbal pair exhibited anti-inflammatory and antioxidant activities. Using advanced chromatographic separation technology, we enriched the effective fractions of water extracts from SB-SD, obtaining self-assembled herbal nanoparticles (SB and SD nanoparticles, SSNPs) rich in flavonoids and terpenoids. These SSNPs demonstrated robust antioxidant properties in vitro and mitigated AKI progression in vivo by activating the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway. Oral administration of SSNPs in mice resulted in absorption into the bloodstream, formation of a protein corona, reduced macrophage phagocytosis, and enhanced bioavailability and renal targeting. Furthermore, we investigated the self-assembly principle of SSNPs using representative flavonoids and terpenoids. Kinetic studies and in situ transmission electron microscopy (in situ TEM) revealed that these compounds self-assemble via supramolecular forces like hydrogen bonding and π-π interactions, forming stable nanostructures. This study elucidates the renoprotective effects and mechanisms of SB and SD, and provides a novel approach for the development of TCM-based nanomedicines, highlighting the potential of nano-TCM in AKI treatment.

Objective To explore the effects of tripterygium glycosides(TGs)on adipocyte differentiation,inflam-mation,and fibrosis of orbital fibroblasts(OFs)in thyroid associated ophthalmopathy(TAO).Methods OFs isolated from 12 TAO patients were cultured and identified.CCK-8 experiment was used to detect the effect of different concentra-tions of TGs(0,12.5,25.0,50.0,100.0 mg·L-1)on the activity of OFs.Then,adipocyte differentiation phenotypes of OFs were induced and divided into a control group,an adipocyte differentiation group(DM),and TG groups(12.5,25.0,and 50.0 mg·L-1 TGs).Inflammation phenotypes of OFs were induced and divided into a control group,an IL-1 β group,and TG groups(12.5,25.0,and 50.0 mg·L-1 TGs).Fibrosis phenotypes of OFs were induced and divided into a control group,a TGF-βi group,and TG groups(12.5,25.0,and 50.0 mg·L-1 TGs).Oil red O staining was used to detect the formation of lipid droplets in OFs of adipocyte differentiation phenotype.ELISA was used to measure IL-6 and TNF-α levels in OFs of inflammation phenotype and HA levels in OFs of fibrosis phenotype.Transwell experiment was used to detect the cell migration rate of OFs of fibrosis phenotype.Real time quantitative polymerase chain reaction(RT-qPCR)and Western blot were used to detect the expression levels of PPAR-γ,c/EBP-α,FABP-4,perilipin-1,and adiponectin mRNAs and pro-teins in OFs of adipocyte differentiation phenotype,the expression levels of IL-6,TNF-α,COX-2,MCP-1 and ICAM-1 mR-NAs and proteins in OFs of inflammation phenotype,and the expression levels of Vimentin,Fibronectin,COL1A1,COL1A2 and ACTA2 mRNAs and proteins in OFs of fibrosis phenotype.Results The cells were identified as OFs.12.5,25.0,and 50.0 mg·L-1 of TGs had no significant effect on OF activity and these three concentrations were used in the subsequent ex-periment.In OFs of adipocyte differentiation phenotype,the number of orange red lipid droplets greatly decreased after TG treatment.The expression levels of PPAR-γ,c/EBP-α,FABP-4,perilipin-1,and adiponectin mRNAs and proteins in cells of TG groups were significantly lower than those of the DM group(all P＜0.05).In OFs of inflammation phenotype,IL-6 and TNF-α levels in supernatant and cells of TG groups were significantly decreased,compared with those of the IL-1βgroup(all P＜0.05).The expression levels of COX-2,MCP-1,and ICAM-1 mRNAs and proteins in cells of TG groups were significantly lower than those of the IL-1 β group(all P＜0.05).In OFs of fibrosis phenotype,the cell migration rates,HA levels in cell culture supernatant,and the expression levels of Vimentin,Fibronectin,COL1A1,COL1A2,and ACTA2 mR-NAs and proteins in cells of TG groups were significant lower than those of the TGF-β1 group(all P＜0.05).Conclusion TGs can inhibit the adipocyte differentiation,IL-1 β-induced inflammatory responses and TGF-β1-induced fibrosis of TAO-OFs.

Glycoengineering was carried out in the mammalian cell line CHO for the production of pro-tein-based drugs.Firstly,the genome sequence of the Rosa26 locus of CHO cells was determined,the gRNA sequences were designed,and the landing pad was integrated into the Rosa26 locus of CHO cells by CRISPR/Cas9 technology.Three targeting vectors co-expressed by glycosyltransferases,which are β-1,4 galactosyltransferase(B4GALT1),α-2,6-sialyltransferase 1(ST6GAL1)and N-acetaminoglycosyl-transferase Ⅲ(GnT Ⅲ),were constructed by overlapping PCR and seamless ligation technology,and the three glycosyltransferase genes were integrated into the CHO Rosa26 locus by Cre enzyme-mediated cassette exchange technology.PCR confirmed that three glycosyltransferases had been successfully site-directed integrated into the Rosa26 site.The mRNA expression levels of the three glycosyltransferases were more than 50 000-fold by qRT-PCR,and the protein expression levels of the three glycosyltrans-ferases were more than 4-fold via western blotting(P＜0.001).A CHO-engineered cell line with three glycosyltransferases integrated into Rosa26 site was successfully constructed.

Objective: To investigate the distribution of IgM anti-A/B titers among group O whole blood donors in Jiangsu, establish a low-titer threshold, and analyze the demographic characteristics of low-titer donors, so as to provide data for recruiting low-titer group O whole blood (LTOWB) donors. Methods: Plasma samples from 1 009 group O whole blood donors were tested for IgM anti-A and anti-B titers using the microplate technique. The distribution of antibody titers was analyzed to establish a low-titer threshold. The distribution trends of titers across different demographic groups were also analyzed. Results: The peak titer for anti-A, anti-B were 64 (31.5%), 4 (23.8%), respectively, The proportion of donors with both anti-A and anti-B titers below 64 was 97.3% (982/1 009). The mean anti-A titer was higher than anti-B titer. Anti-A titers were higher in female donors than in male donors (P<0.05). The anti-A titers differed significantly among different age groups (P<0.05). However, no significant difference in titers was observed based on the number of donations (P>0.05). Conclusion: A titer of 64 can be used as the reference threshold of LTOWB in Jiangsu. Male donors of appropriate age are more suitable than female donors for establishing an emergency panel of LTOWB mobile donors.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.