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.

ObjectiveThe frequency difference limen (FDL) serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system. However, traditional measurement methods rely heavily on the active behavioral responses of subjects and are consequently highly susceptible to the negative influence of confounding subjective factors. Furthermore, existing research paradigms frequently employ uniform stimulus configurations that overlook critical individual perceptual differences. Based on brain-computer interface (BCI) technology, this comprehensive study aims to establish an objective and quantitative evaluation method for auditory frequency discrimination by systematically analyzing and decoding the specific neural responses elicited at the exact threshold state. MethodsWe designed a personalized rapid serial auditory presentation (RSAP) paradigm customized based on each individual’s precise FDL. A cohort of eleven healthy participants was recruited to evaluate the paradigm using pure-tone sequences at a baseline frequency of 4 000 Hz. This experimental paradigm simulates a realistic auditory perception environment through the continuous presentation of acoustic stimuli, thereby allowing for an in-depth investigation into the specific neural representations evoked by weak frequency deviations at the threshold state. Given that auditory stimulus-evoked response features exhibit complex and differentiated spatiotemporal distribution patterns across multiple frequency domains, this study further deeply integrates the cross-scale feature interaction module with the dynamic spatiotemporal attention allocation strategy, innovatively proposing the Multi-Scale Spatial-Temporal Dual Attention Network (MS-STAMNet). Specifically, the network constructs parallel processing branches with multiple receptive fields and introduces a dynamic adaptive weighting strategy to precisely localize core neural activity signals, further deeply integrating multi-scale information through cross-branch feature information interaction to achieve robust single-trial decoding of weak auditory evoked responses. ResultsThe comprehensive electrophysiological data analysis demonstrated that subtle auditory frequency deviation stimuli presented at the threshold level successfully elicited pronounced N2 and P3 event-related potential features, reflecting pre-attentive mismatch detection and subsequent cognitive evaluation, which were prominently distributed over the frontal, central, and temporal regions of the scalp. In the complex time-frequency domain, the extracted neural response characteristics exhibited distinct, statistically significant event-related synchronization within both the low-frequency δ and θ frequency bands, which was simultaneously accompanied by a widespread, prominent event-related desynchronization within the higher α band. A comparative analysis of model performance demonstrated that MS-STAMNet achieved an average unweighted average recall (UAR) of (69.67±6.12)% and area under the curve (AUC) of 0.761 8±0.07, significantly outperforming the established baseline models such as EEGNet and PLNet. Furthermore, a distinct dissociation phenomenon was verified between neural decoding and behavioral performance through regression analysis (R²=0.016, P=0.709), indicating that this model can effectively capture the implicit features of subtle frequency deviations, even when they fail to trigger explicit conscious responses. Additionally, attention weight visualization analysis further reveals the highly accurate focus of the network on key features concentrated over the bilateral temporal and fronto-parietal regions. ConclusionThis study systematically and comprehensively uncovers the multi-dimensional spatiotemporal evolutionary patterns of complex neural responses processing subtle acoustic variations under long-sequence threshold auditory stimulation. Concurrently, it verifies the efficacy and robustness of the proposed MS-STAMNet architecture in accurately deciphering weak, single-trial electroencephalogram signals amidst complex background noise. Ultimately, these neurophysiological and algorithmic findings lay a solid theoretical and methodological foundation for the objective and quantitative evaluation of individual auditory cognitive capabilities in clinical applications, transcending the fundamental limitations of traditional behavioral paradigms and providing robust technical support for future auditory research and related clinical assessments.

ObjectiveMigraine is a leading neurological disorder and the fourth most common cause of years lived with disability worldwide, affecting nearly 116 million individuals. Although pharmacological treatments are available, their efficacy is often limited by side effects and variable response rates. Repetitive transcranial magnetic stimulation (rTMS) over the dorsolateral prefrontal cortex (DLPFC) offers a safe, non-invasive alternative for migraine management. However, the neurophysiological mechanisms, particularly how rTMS modulates local cortical excitability and distributed pain-related circuits, remain poorly understood. Elucidating these mechanisms is essential for optimizing treatment protocols and improving clinical outcomes. MethodsThis study employed concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) to investigate neuroplastic and neurocircuitry mechanisms of DLPFC-rTMS in migraine. Study 1 compared 30 migraineurs and 28 healthy controls to identify abnormalities in TMS-evoked potentials (TEPs) and significant current density (SCD) within sensory-discriminative regions including the primary somatosensory cortex (S1) and posterior insula (pINS), cognitive-affective regions including the anterior insula (aINS) and midcingulate cortex (MCC), and a descending modulatory region, the periaqueductal gray (PAG). Study 2 used a single-blind, crossover, sham-controlled design in 34 healthy participants. Each participant received both active (10 Hz, 80% RMT, 1 500 pulses) and sham DLPFC-rTMS in counterbalanced order. TMS-EEG and cold pain tolerance were assessed before and after each session. ResultsIn Study 1, migraineurs showed a significantly less negative N120 amplitude compared to healthy controls (P=0.027, Cohen’s d=0.60), indicating local intracortical disinhibition. No group differences were observed for N40, P60, or P180 components. At the source level, migraineurs exhibited significantly higher SCD in the S1, pINS, aINS, and MCC (allQ<0.05), but not in the ventroposterior thalamus (vpTHAL), mediodorsal thalamus (mdTHAL), or PAG. In Study 2, active rTMS significantly reduced SCD from pre- to post-stimulation in the S1, aINS, and MCC (all Q<0.05). Sham stimulation also reduced SCD in the S1 (Q<0.05) but not in the aINS or MCC. Although no significant group-level analgesic effect was observed between active and sham conditions (P=0.107), correlation analyses revealed that greater SCD reductions in the S1 and MCC were significantly associated with higher post-rTMS pain tolerance (R=-0.487 and -0.495, both Q<0.01) and larger improvements in pain tolerance(R=-0.487 and -0.451, both Q<0.05). No such correlations were found following sham stimulation, suggesting that the behavioural relevance of neural changes is specific to active rTMS. ConclusionThis study provides novel evidence that migraineurs exhibit both local neuroplastic abnormalities (reduced N120 amplitude) and hyperactivity in key pain-processing regions (S1, pINS, aINS, MCC). A single session of DLPFC-rTMS reduced hyperactivity in the aINS, MCC, and S1. Notably, greater reductions in the S1 and MCC were associated with improved pain tolerance. These findings identify distinct cortical circuitries, particularly within the cognitive-affective pain network, that may serve as potential biomarkers for optimizing rTMS treatment in migraine and other chronic pain conditions. Future studies should validate these results in patient populations experiencing spontaneous migraine attacks and explore multi-session or accelerated rTMS protocols.

Plant-derived exosome-like nanovesicles refer to spherical lipid bilayer vesicles isolated from plants that contain lipids,proteins,RNAs,and various small molecules.These nanovesicles exhibit di-verse biological activities,including anti-inflammatory,anti-tumor,antioxidant,and drug delivery prop-erties.However,the functional characteristics of nanovesicles derived from rhizoma polygonati remain un-explored.In this study,exosome-like nanovesicles derived from rhizoma polygonati(referred to as RP-EVs)were successfully isolated using ultracentrifugation and density gradient centrifugation.Their physi-cochemical properties and anti-inflammatory functions were systematically characterized.Our results show that RP-EVs are predominantly negatively charged,with an average particle size of 166.5±3.3 nm,and are spherical lipid vesicles.Cellular uptake assays demonstrated that RP-EVs can be phagocytized by macrophages.qPCR and ELISA experiments revealed that RP-EVs can inhibit the elevation of interleukin 6(IL-6),interleukin 1 β(IL-1 β),and tumor necrosis factor-alpha(TNF-α)induced by lipopolysac-charide(LPS)stimulation(****P＜0.0001).Additionally,reactive oxygen species(ROS)and 2,2-diphenyl-1-picrylhydrazyl(DPPH)scavenging assays confirmed that RP-EVs exhibit antioxidant proper-ties(*P＜0.05).Further investigation of the underlying mechanisms through immunofluorescence and Western blotting revealed that RP-EVs inhibit the nuclear translocation(**P＜0.01)and phosphoryla-tion(***P＜0.001)of nuclear factor kappa-B p65(NF-κB p65)via the IκBα/NF-κB signaling path-way,thereby regulating the expression of inflammatory mediators.In animal experiments,intraperitoneal injection of RP-EVs into mice for 48 hours showed predominant localization in the liver and spleen.Fi-nally,an acute inflammatory mouse model was established via intraperitoneal injection of LPS.qPCR and ELISA analyses demonstrated that RP-EVs alleviated the expression of inflammatory factors in both the serum and spleen of LPS-treated mice(*P＜0.05).In conclusion,this study isolated RP-EVs and elu-cidated their anti-inflammatory properties and potential mechanisms.These findings provide valuable in-sights into the functional exploration of nanoparticle vesicles derived from traditional Chinese medicine and suggest a novel therapeutic strategy for the treatment of inflammation-related diseases.

Objective To investigate the protective effect of Liraglutide in rats with diabetic kidney disease(DKD)by regulating the nuclear factor E2-related factor 2(Nrf2)/glutathione peroxidase 4(GPX4)ferroptosis signaling pathway.Methods Twelve male Sprague-Dawley(SD)rats were randomly divided into normal control(NC)group,DKD group,and Liraglutide treatment(Lir)group,with 4 rats in each group.The 24 hUAlb,TC,TG,LDL-C,serum creatinine(Scr),BUN,ferrous ion(Fe2+),the activity of glutathione peroxidase(GSH-Px),and malondialdehyde(MDA)were detected in each group.Hematoxylin and eosin(HE),periodic acid-Schiff(PAS),and periodic acid-silver methenamine-Masson(PASM-Masson)staining were used to observe the pathological changes of the kidneys.Immunofluorescence was performed to detect the localization and expression of reactive oxygen species(ROS)in the renal tissue.The protein expressions of Nrf2 and GPX4 were detected by Western blot.Results Compared with the NC group,the levels of 24 hUAlb,Scr,BUN,TC,TG,LDL-C,MDA,ROS,and Fe2+were increased(P＜0.05 or P＜0.01),while the expressions of GSH-Px,Nrf2,and GPX4 proteins were decreased in the DKD group(P＜0.01).Compared with the DKD group,the levels of 24 hUAlb,BUN,TC,TG,LDL-C,MDA,ROS,and Fe2+were decreased(P＜0.05 or P＜0.01),and the expressions of GSH-Px,Nrf2,and GPX4 proteins were increased in the Lir group(P＜0.01).Conclusions Liraglutide may exert a protective effect in DKD by upregulating the Nrf2/GPX4 signaling pathway and inhibiting ferroptosis.

Objective To propose a liver ultraound image classification model based on bimodal fusion and deep residual network to enhance the diagnosis accuracy of space-occupying lesions(SOLs)of liver.Methods Firstly,a liver ultrasound dataset containing 164 lesions from 100 patients was constructed,including the ultrasound and ultrasonography videos of the patients.Secondly,the parallel residual block was introduced to improve the ResNet-18 network,and the simple attention module and coordinate attention mechanism were used to extract the image features of ultrasound and echography videos,respectively.Finally,a bimodal fusion network was developed with the image features of ultrasound and sonography videos,which was combined with the improved ResNet-18 network to form an ultrasound image classification model.The ultrasound image classification model proposed underwent performance verification by ablation experiment,application evaluation,diagnosis efficacy evaluation,significance evaluation for assisting surgical operation and comparison with the existing classification models in terms of image classification ability.Results The ablation experiment results showed that the proposed model performed the best in classification speed and accuracy when compared with the existing classification models,with a floating-point operation speed of 5.328×109/s,an average accuracy of 0.941 and a calculation speed of 245.266 frames/s.The application evaluation results indicated when compared with the existing classification models the proposed model had the best convergence performance of the loss function curve and the lowest misdiagnosis rate of 7.03%.The diagnosis efficacy evaluation results proved the proposed model gained advantages over other models in diagnostic efficacy,with a sensitivity of 89.38%,a specificity of 94.12%,an accuracy of 90.85%,a positive predictive value of 97.12%and a negative predictive value of 80.00%.The significance evaluation for assisting surgical operation found when compared with the existing classification models the proposed model had the shortest end-to-end delay of 723 ms;laparoscopic hepatectomy assisted with the proposed model had the blood loss reduced by 109.832 mL when compared with the traditional laparoscopic procedure,with the difference being statistically significant(P<0.05).Conclusion The proposed model enhances the diagnosis efficiency and accuracy of ultrasond SOLs of liver,providing support for clinical diagnosis and surgical assistance.[Chinese Medical Equipment Journal,2025,46(10):9-16]

Objective:To investigate the effects of toosendanin(TSN)on the proliferation,apoptosis,migration and invasion of esophageal squamous cell carcinoma(ESCC)KYSE150 cells,and to elucidate its underlying molecular mechanisms.Methods:CCK-8 assay,colony formation assay,and EdU assay were used to assess the effects of varying TSN concentrations(0.062 5,0.125,and 0.25 μmol/L)on KYSE150 cell proliferation.The impacts of TSN on the apoptosis,migration,and invasion of KYSE150 cells were evaluated using flow cytometry,wound healing assay,and Transwell chamber assay,respectively.The expression of hypoxia-inducible factor 1 alpha(HIF1A)in esophageal cancer tissues was analyzed using the GEPIA database.qPCR was used to detect the expression level of HIF1A mRNA in human esophageal epithelial Het-1A and KYSE150 cells,and in TSN-treated KYSE150 cells.Western blot(WB)was performed to detect the effects of TSN on the upstream signaling pathway AKT/mTOR of HIF1A and the expression of downstream proteins related to cell migration,invasion,and apoptosis.Results:TSN of varying concentrations significantly inhibited proliferation,migration,and invasion of KYSE150 cells and promoted apoptosis in a dose-dependent manner(P<0.05 or P<0.01).HIF1A mRNA was highly expressed in KYSE150 cells,and its expression was significantly downregulated after TSN treatment(P<0.05 or P<0.01).TSN markedly downregulated the expression of HIF1A and key upstream signaling proteins p-AKT and p-mTOR.In addition,TSN significantly suppressed the expression of downstream proteins associated with cell migration,invasion,and apoptosis,including N-cadherin,vimentin,Bcl-2,and caspase-3,while upregulating the expression of E-cadherin(P<0.05 or P<0.01).Conclusion:TSN inhibits the proliferation,migration,and invasion,and induces apoptosis in ESCC KYSE150 cells by down-regulating HIF1A expression through suppression of the AKT/mTOR signaling pathway.

Objective To investigate the application value of the subject-achieving quality control circle theory in enhan-cing medication adherence among newly diagnosed tuberculosis patients through via popular science intervention.Methods From September 2023 to April 2024,100 patients with newly diagnosed tuberculosis treated at the outpatient department were ran-domly divided into control and intervention groups,each comprising 50 cases.The control group received only routine medication education;whereas the intervention group,in addition to this,implemented popular science intervention measures based on the quality control circle theory.The medication adherence,full course completion rate,non-disease interruption rate,and self-re-ported adverse reaction rate were compared.Results After treatment,the intervention group showed higher medication adher-ence(6.76±1.02 vs.6.15±1.36),better adherence rates(78％vs.62％),and higher cure rates(76％vs.42％)than the control group,all with statistical significance.The non-disease interruption rate was also significantly lower in the intervention group(2％vs.14％).Adverse reaction reports were 6％in control and 18％in intervention group.Conclusion The subject-achieving quality control circle effectively improves medication adherence in newly diagnosed tuberculosis patients.

Aim To explore the effect of the classical famous prescription Dahuang Zhechong pill(DHZCP)on relieving liver cirrhosis by regulating the stress fiber remodeling mediated by mechanistic signaling pathway and to explore the underlying mechanism.Methods Mice were randomly divided into the control group,model group,DHZCP low-dose group,DHZCP high-dose group,and Colchicine-positive control group.The liver cirrhosis mouse model was constructed by intrap-eritoneal injection of olive oil-solubilized CCl4.HE staining and serologic markers were used to reflect liver injury.Masson staining was used to evaluate collagen deposition in liver tissue.ELISA was applied to detect vasoactive molecules and cancer indicators.Atomic force microscopy was employed to detect liver tissue stiffness.Color Doppler diagnostic instrument was used to assess portal blood flow velocity.Western blot was utilized to detect ROCK2 expression and phosphoryla-tion of YAP,Cofilin,and MLC.Results The liver tis-sues in the model group had obvious inflammatory cell infiltration and collagen deposition,accompanied by significant elevation of serum transaminases and fibrosis indexes.Similarly,vasoactive molecules and cancer in-dicators were elevated,and the mechanoregulatory pro-tein ROCK2 expression and phosphorylation of Cofilin and MLC were elevated,with YAP being strongly de-phosphorylated.Both low and high doses of DHZCP re-versed the pathological changes,serological indices,and inhibited the activation of the stress fiber(SF)re-modeling mechanistic signaling pathway.Conclusion DHZCP effectively ameliorates liver tissue lesions in mice with liver cirrhosis,and its mechanism may be re-lated to the inhibition of SF remodeling mechanistic signaling pathway.