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.

Objective To explore the value of support vector machine(SVM)model based on gray matter volume(GMV)for identifying amyotrophic lateral sclerosis(ALS),also to analyze the relevant brain regions.Methods MR 3D T1WI data of 60 ALS patients(ALS group)and 60 healthy volunteers(control group)were retrospectively analyzed.Taken GMV of each brain region obtained by voxel-based morphometry as the input features.F-score analysis was used to select feature with the highest classification accuracy to construct SVM model.Receiver operating characteristic curve was drawn to evaluate the efficacy of SVM model for identifying ALS,and top 10％was used as the weight threshold to obtain gray matter brain regions contributed the most to this model.Results SVM model constructed based on the top 40％GMV features had the highest classification accuracy(82.50％),with sensitivity,specificity and area under the curve(AUG)of 85.05％,80.40％and 0.890,respectively.The left precentral gyrus,left anterior cingulate gyrus and paracingulate gyrus,right middle temporal gyrus,opercular part of left inferior frontal gyrus,right dorsolateral superior frontal gyrus,left temporal pole:middle temporal gyrus,right superior occipital gyrus,orbital part of right middle frontal gyrus,right calcarine fissure and surrounding cortex,right fusiform gyrus were the top 1-10 gray matter brain regions contributed to this model.Conclusion ALS had specific GMV change pattern.SVM model based on GMV could be used to effectively identify ALS,while the left precentral gyrus was the most contributive brain region to this model.

AIM To investigate the effects of Gan Jiang-Huang Qin-Huang Lian-Ren Shen Decoction(GJHQHLRSD)on the pyroptosis,pathway of colonic epithelial cells in mouse models of ulcerative colitis(UC).METHODS Among the 63 C57BL/6J mice,13 were randomly selected and assigned to the model group,and the others were divided into the control group,the positive Sulfasalazine Enteric-Coated Tablets group(0.6 g/kg),and low,medium,and high dose GJHQHLRSD groups(3.9,7.8,15.6 g/kg),with 10 mice in each group.The UC mouse model was established using DSS,and the corresponding drugs were administered by gavage.The mice had their general condition observed;their disease activity index(DAI)score assessed;their colon length measured;their histopathological damage of the colon analyzed using HE staining;their colonic IL-1β,IL-8,and TNF-α levels measured by ELISA method;their colonic NLRP3,GSDMD,pro-IL-1β,pro-caspase-1,and IL-1βprotein expression detected by Western blot method;and their cell pyroptosis detected by TUNEL and GSDMD fluorescence double staining.RESULTS Compared with the control group,the model group exhibited significant decrease in body weight and a shortened colon length(P＜0.01);increases in DAI score,levels of IL-1β,IL-8,TNF-α,as well as the protein expressions of NLRP3,GSDMD,and active-caspase-1(P＜0.05,P＜0.01);significant increase of colonic GSDMD and TUNEL positivity;indicating increased tissue damage and inflammatory response.Compared with the model group,the groups intervened with GJHQHLRSD showed a significant increase in body weight and colonic elongation(P＜0.05,P＜0.01);decreases in DAI score,levels of IL-1β,IL-8,TNF-α,as well as the protein expressions of NLRP3,GSDMD,and active-caspase-1(P＜0.05,P＜0.01);a gradient decrease in positivity of GSDMD and TUNEL;indicating a significantly reduced colonic pathological damage.CONCLUSION GJHQHLRSD can improve the DSS-induced inflammatory reaction of colonic mucosa in UC mice,and its mechanism mainly involves the NLRP3/caspase-1,thereby the regulation of the cell pyroptosis process.

Tuberculosis(TB),a chronic infectious disease caused by infection with the Mycobacterium tuberculosis complex(MTBC),has re-emerged as the leading cause of death from a single infectious agent worldwide.Because of widespread use and mis-use of anti-tuberculosis drugs,the emergence of multidrug-resistant TB(MDR-TB)and extensively drug-resistant TB(XDR-TB)is increasing,thus posing a serious threat to global health.The current problem of drug resistance is a major prevention and treatment challenge;therefore,the search for new drug targets is urgently needed.In recent years,substantial progress has been made in re-search on anti-tuberculosis drug targets and novel therapeutic strategies.Herein,we summarize recent research progress in anti-tuberculosis drug targets,primarily cell wall synthesis,nucleic acid replication and transcription,and energy metabolism.We also provide an overview of research progress regarding two novel therapeutic strategies,to provide a theoretical basis and research ideas for the development of new clinical drugs.

Objective To analyze the MRI artifact of the inner ear structure in patients with peripheral vertigo acquired by using 3D fast imaging employing steady state acquisition(3D FIESTA-C)and 3D fast spin echo(3D-Cube T2).Methods Data of 63 patients with peripheral vertigo treated in the Department of Otolaryngology,The First Affiliated Hospital of Xi'an Jiaotong University,from October 2023 to June 2024 were filtered for analysis.The patients consisted of 16 males and 47 females,aged 18 to 60 years old.Two senior radiologists independently evaluated the quality of the acquired images through the two MRI sequences.Kappa test was used to evaluate the consistency of the two radiologists' subjective judgments,and Wilcoxon signed-rank test was used to compare the image quality between each sequence.The accuracy of 3D FIESTA-C,3D-Cube T2 and combination of the two sequences was calculated in the presentation of inner ear structure.Results The overall image quality of 3D-Cube T2 was better than that of 3D FIESTA-C(Z=-11.670,P＜0.001),and the accuracy of 3D FIESTA-C combined with 3D-Cube T2 was superior to that of each sequence in demonstrating the semicircular canals.The demonstration accuracy of horizontal semicircular canal among three scan protocols was statistically different(P＜0.001).Conclusion 3D FIESTA-C has the advantage in detecting horizontal semicircular canal,and 3D-Cube T2 always provides high quality images of upper semicircular canal.Compared with each scanning sequence,3D FIESTA-C combined with 3D-Cube T2 can effectively avoid misdiagnosis or missed diagnosis of semicircular canal structures in the patients with peripheral vertigo.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

Objective To explore the value of support vector machine(SVM)model based on gray matter volume(GMV)for identifying amyotrophic lateral sclerosis(ALS),also to analyze the relevant brain regions.Methods MR 3D T1WI data of 60 ALS patients(ALS group)and 60 healthy volunteers(control group)were retrospectively analyzed.Taken GMV of each brain region obtained by voxel-based morphometry as the input features.F-score analysis was used to select feature with the highest classification accuracy to construct SVM model.Receiver operating characteristic curve was drawn to evaluate the efficacy of SVM model for identifying ALS,and top 10％was used as the weight threshold to obtain gray matter brain regions contributed the most to this model.Results SVM model constructed based on the top 40％GMV features had the highest classification accuracy(82.50％),with sensitivity,specificity and area under the curve(AUG)of 85.05％,80.40％and 0.890,respectively.The left precentral gyrus,left anterior cingulate gyrus and paracingulate gyrus,right middle temporal gyrus,opercular part of left inferior frontal gyrus,right dorsolateral superior frontal gyrus,left temporal pole:middle temporal gyrus,right superior occipital gyrus,orbital part of right middle frontal gyrus,right calcarine fissure and surrounding cortex,right fusiform gyrus were the top 1-10 gray matter brain regions contributed to this model.Conclusion ALS had specific GMV change pattern.SVM model based on GMV could be used to effectively identify ALS,while the left precentral gyrus was the most contributive brain region to this model.

AIM To investigate the effects of Gan Jiang-Huang Qin-Huang Lian-Ren Shen Decoction(GJHQHLRSD)on the pyroptosis,pathway of colonic epithelial cells in mouse models of ulcerative colitis(UC).METHODS Among the 63 C57BL/6J mice,13 were randomly selected and assigned to the model group,and the others were divided into the control group,the positive Sulfasalazine Enteric-Coated Tablets group(0.6 g/kg),and low,medium,and high dose GJHQHLRSD groups(3.9,7.8,15.6 g/kg),with 10 mice in each group.The UC mouse model was established using DSS,and the corresponding drugs were administered by gavage.The mice had their general condition observed;their disease activity index(DAI)score assessed;their colon length measured;their histopathological damage of the colon analyzed using HE staining;their colonic IL-1β,IL-8,and TNF-α levels measured by ELISA method;their colonic NLRP3,GSDMD,pro-IL-1β,pro-caspase-1,and IL-1βprotein expression detected by Western blot method;and their cell pyroptosis detected by TUNEL and GSDMD fluorescence double staining.RESULTS Compared with the control group,the model group exhibited significant decrease in body weight and a shortened colon length(P＜0.01);increases in DAI score,levels of IL-1β,IL-8,TNF-α,as well as the protein expressions of NLRP3,GSDMD,and active-caspase-1(P＜0.05,P＜0.01);significant increase of colonic GSDMD and TUNEL positivity;indicating increased tissue damage and inflammatory response.Compared with the model group,the groups intervened with GJHQHLRSD showed a significant increase in body weight and colonic elongation(P＜0.05,P＜0.01);decreases in DAI score,levels of IL-1β,IL-8,TNF-α,as well as the protein expressions of NLRP3,GSDMD,and active-caspase-1(P＜0.05,P＜0.01);a gradient decrease in positivity of GSDMD and TUNEL;indicating a significantly reduced colonic pathological damage.CONCLUSION GJHQHLRSD can improve the DSS-induced inflammatory reaction of colonic mucosa in UC mice,and its mechanism mainly involves the NLRP3/caspase-1,thereby the regulation of the cell pyroptosis process.