BACKGROUND:Patellar tendonitis can present as tendon degeneration that fails to heal due to tissue overload and incomplete recovery.Patellar tendonitis is a predisposition to high jumping and its pathogenesis has not been clearly defined.OBJECTIVE:To explore the stress-strain relationship of patellar tendon in the take-off technique of high jump through the finite element model with accurate human anatomical structure,so as to provide ideas for the prevention and rehabilitation of patellar tendinitis.METHODS:Based on the CT and MRI imaging data of the lower extremity(including the knee and ankle)of one subject(22 years old,183 cm height,70 kg body mass),a three-dimensional finite element model of the lower extremity was reconstructed using medical imaging software,reverse engineering software and modeling software.The plantar pressure of the take-off leg was collected in eight subjects by gait testing system,and the technical action of high jump take-off was collected by motion capture system.The captured data were imported into human sports biomechanics software for analysis,and kinematic and kinetic data were obtained as the boundary conditions of finite element model for finite element simulation analysis.RESULTS AND CONCLUSION:The force borne by the patellar tendon reached 3.29 times of its own body mass when the subjects took off.In the take-off stage,the peak values of normal equivalent stress,strain and shear stress of the patellar tendon were 127.76 MPa,0.81 and 37.69 MPa,respectively,which were in the nonlinear region of the stress-strain curve,and the peak values were distributed in the proximal and posterior parts of patellar tendon.To conclude,the high patellar tendon force,strain and shear stress caused by the load of 3.29 times its own body mass during take-off are related to the induction of patellar tendinitis.

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.

BACKGROUND:Patellar tendonitis can present as tendon degeneration that fails to heal due to tissue overload and incomplete recovery.Patellar tendonitis is a predisposition to high jumping and its pathogenesis has not been clearly defined.OBJECTIVE:To explore the stress-strain relationship of patellar tendon in the take-off technique of high jump through the finite element model with accurate human anatomical structure,so as to provide ideas for the prevention and rehabilitation of patellar tendinitis.METHODS:Based on the CT and MRI imaging data of the lower extremity(including the knee and ankle)of one subject(22 years old,183 cm height,70 kg body mass),a three-dimensional finite element model of the lower extremity was reconstructed using medical imaging software,reverse engineering software and modeling software.The plantar pressure of the take-off leg was collected in eight subjects by gait testing system,and the technical action of high jump take-off was collected by motion capture system.The captured data were imported into human sports biomechanics software for analysis,and kinematic and kinetic data were obtained as the boundary conditions of finite element model for finite element simulation analysis.RESULTS AND CONCLUSION:The force borne by the patellar tendon reached 3.29 times of its own body mass when the subjects took off.In the take-off stage,the peak values of normal equivalent stress,strain and shear stress of the patellar tendon were 127.76 MPa,0.81 and 37.69 MPa,respectively,which were in the nonlinear region of the stress-strain curve,and the peak values were distributed in the proximal and posterior parts of patellar tendon.To conclude,the high patellar tendon force,strain and shear stress caused by the load of 3.29 times its own body mass during take-off are related to the induction of patellar tendinitis.

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.

Background: and Purpose Vertebrobasilar fusiform aneurysms (VBFAs) carry substantial morbidity and mortality, but optimal management for unruptured VBFAs remains unclear. We compared the safety and efficacy of conservative management (CM), stent-assisted coiling (SAC), and flow diverters (FDs) in patients with unruptured VBFAs, focusing on long-term prognosis. Methods: This study included data from a nationwide Chinese cohort of patients with vertebrobasilar dissecting aneurysms. Inverse probability of treatment weighting (IPTW) balanced confounders across groups. The primary outcome was poor prognosis (modified Rankin Scale score >2). Secondary outcomes included aneurysm rupture, ischemic stroke, compression symptoms, and VBFA-related deaths. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed. Results: Among 1,115 patients with unruptured VBFAs, 838 (median age, 54 years; 655 men) were included. After IPTW, baseline characteristics were balanced. Median follow-up was 54 months. FD was associated with a lower risk of poor prognosis than CM (OR, 0.48 [95% CI, 0.30 to 0.77]; p=0.002), with no difference between CM and SAC. FD also reduced aneurysm rupture (OR, 0.20 [95% CI, 0.07 to 0.60]; p=0.004) and compression symptoms (OR, 0.30 [95% CI, 0.13 to 0.68]; p=0.004) versus CM. Time-to-event analyses further revealed significant differences in vertebral artery lesions and Type I–II VBFAs, whereas no significant differences were observed in basilar or vertebrobasilar junction lesions or in Type III–IV VBFAs. Conclusions Compared with CM, FD was associated with improved long-term outcomes in unruptured VBFAs, particularly in vertebral artery lesions and Type I–II VBFAs, although residual confounding cannot be excluded.

ObjectiveHepatocyte nuclear factor 4-alpha (HNF4A) is a critical transcription factor in the liver and pancreas. Dysfunctions of HNF4A lead to maturity onset diabetes of the young 1 (MODY1). Notably, MODY1 patients with HNF4A pathogenic mutations exhibit decreased responses to arginine and reduced plasma triglyceride levels, but the mechanisms remain unclear. This study aims to investigate the potential target genes transcriptionally regulated by HNF4A and explore its role in these metabolic pathways. MethodsA stable 293T cell line expressing the HNF1A reporter was overexpressed with HNF4A. RNA sequencing (RNA-seq) was performed to analyze transcriptional differences. Transcription factor binding site prediction was then conducted to identify HNF4A binding motifs in the promoter regions of relevant target genes. ResultsRNA-seq results revealed a significant upregulation of transmembrane 4 L six family member 5 (TM4SF5) mRNA in HNF4A-overexpressing cells. Transcription factor binding predictions suggested the presence of five potential HNF4A binding motifs in the TM4SF5 promoter. Finally, we confirmed that the DR1 site in the -57 to -48 region of the TM4SF5 promoter is the key binding motif for HNF4A. ConclusionThis study identified TM4SF5 as a target gene of HNF4A and determined the key binding motif involved in its regulation. Given the role of TM4SF5 as an arginine sensor in mTOR signaling activation and triglyceride secretion, which closely aligns with phenotypes observed in MODY1 patients, our findings provide novel insights into the possible mechanisms by which HNF4A regulates triglyceride secretion in the liver and arginine-stimulated insulin secretion in the pancreas.

The faithful segregation of genetic material to daughter cells is a fundamental biological process and a prerequisite for autonomous construction of robustly self-replicating artificial cells.Artificial cells are mimic cell structures with part or whole functions of normal cells.The complexity of eukaryotic chromosome segregation machinery has limited its applications in the field of synthetic biology.In contrast,the plasmid segregation machineries employed by prokaryotes are relatively simple and can provide valuable approaches for the bottom-up construction of complex artificial cells.This review aimed to comprehensively analyze the origin species and working mechanism of three bacterial plasmid segregation systems,namely ParABS,ParMRC,and TubZRC systems.The current researches on plasmid segregation both in bacterial and artificial cellular systems were summarized,and the future directions of this field were also proposed.

Objective To construct an aptamer-functionalized carboxylated graphene oxide(CGO)fluo-rescent sensor to achieve highly sensitive and specific detection of ketamine(KET)and its metabolite norketamine(NK)using an aptamer capable of simultaneously recognizing KET and NK.Methods A specific aptamer for simultaneous recognition of KET and NK was screened using graphene oxide-sys-tematic evolution of ligand by exponential enrichment(GO-SELEX)and molecular docking tech-niques.The aptamer,labeled with Cy5 fluorescence,was chemically conjugated to CGO to construct an aptamer-functionalized CGO fluorescent sensor.By optimizing detection conditions,including the mass concentration of CGO,aptamer concentration,reaction temperature,and incubation time,quantita-tive analysis of the target analytes was achieved using the ratio of fluorescence intensity changes be-fore and after target addition.The stability of the sensor in biological matrices was evaluated by moni-toring fluorescence intensity changes over incubation time in blank blood and urine,in comparison with the traditional physical adsorption-based CGO fluorescent sensor.Spiked recovery experiments in blank blood and urine were conducted to compare performance with that of HPLC-MS/MS.Results A specific aptamer A5 was selected and chemically conjugated with CGO to construct the aptamer-functionalized CGO fluorescent sensor.Under optimized conditions,the proposed fluorescent sensor ex-hibited a linear detection range of 1.0-5.0 ng/mL for KET,with a limit of detection(LOD)of 0.86 ng/mL;while for NK,the linear detection range was 1.0-5.0 ng/mL,with an LOD of 0.70 ng/mL.Com-pared with the CGO fluorescent sensor constructed via physical adsorption,this sensor demonstrated greater stability in blood and urine.The spiked recovery rates of KET and NK in blank blood and urine ranged from 81.50%to 110.03%,exhibiting detection performance comparable to that of HPLC-MS/MS.Conclusion The aptamer screening method offers a novel approach for selecting aptamers tar-geting drugs and their metabolites.The constructed aptamer-functionalized CGO fluorescent sensor pro-vides an efficient and reliable strategy for the high-performance detection of KET and NK.

Objective To explore the factors predicting the reliability of platelet-impedance(PLT-I)re-sults through a retrospective analysis of non-aggregated samples with abnormal platelet histograms.Methods A total of 322 samples with abnormal platelet histograms were collected from the Sysmex XN9000 automatic hematology analyzer,all of which showed no platelet aggregation as determined by the DI-60 auto-matic digital image analysis system.Using the platelet count fluorescent(PLT-F)results as a standard,the absolute and relative deviations of PLT-I results were calculated,and the parameters of samples with devia-tions within and outside the allowable range were analyzed.Logistic regression analysis was used to identify potential factors associated with deviations of PLT-I from PLT-F beyond the allowable range.The receiver op-erating characteristic(ROC)curve was used to evaluate the predictive value of these potential factors for devi-ations beyond the allowable range.Results The deviations of PLT-I from PLT-F were within the allowable range in 279 cases(86.65％)and outside the allowable range in 43 cases(13.35％).Comparisons between the two groups revealed statistically significant differences in mean red blood cell valume(MCV),red blood cell distribution width-cuefficient of variation(RDW-SD),DI-60 red cell fragments,platelet distribution width(PDW),and DI-60 large platelet ratio(P＜0.05).Logistic regression indicated that DI-60 red cell fragments and DI-60 large platelet ratio were independent potential factors for deviations of PLT-I from PLT-F beyond the allowable range(P＜0.05).The cut off values for predicting deviations beyond the allowable range were 1.75 for DI-60 red cell fragments and 66％for DI-60 large platelet ratio.The area under the curve(AUC)for the combined diagnosis of the two factors was 0.813,with a sensitivity of 88.4％and a specificity of 66.3％,indicating a higher predictive value than individual factors(P＜0.05).Conclusion When platelet histograms are abnormal,DI-60 large platelet ratio and DI-60 red cell fragments are independent potential factors predic-ting the reliability of PLT-I results,and their combination has a high predictive value.