To facilitate the early intelligent screening of pediatric genu valgum, this study develops a deep learning-based gait recognition model tailored for clinical application. The model is constructed upon a three-dimensional residual network architecture and incorporates a triplet attention module alongside a spatial hierarchical pooling module, jointly enhancing feature interaction across temporal, spatial, and channel dimensions. This design ensures an optimal balance between representational capacity and computational efficiency. Evaluated on a self-constructed dataset, the model achieves precision of 98.0%, 97.1%, and 96.5%, recall rates of 97.5%, 97.0%, and 95.0%, and F ₁-scores of 0.98, 0.97, and 0.96 on the training, validation, and test sets, respectively, demonstrating excellent recognition performance and strong generalization ability. Ablation experiments confirm the importance of the proposed model's core components in improving performance, and comparative experiments further highlight its significant advantages in recognition accuracy and robustness. Visualization experiments reveal that the model effectively focuses on key regions of gait images, with attention regions aligning closely with clinical anatomical landmarks, thereby enhancing the interpretability of the model's decision-making in clinical applications. In summary, the proposed model not only offers an efficient and reliable technical solution for early intelligent screening of genu valgum in children, but also provides a practical pathway for applying gait recognition technology in medical diagnosis.
Humans ; Gait ; Deep Learning ; Genu Valgum/physiopathology* ; Child ; Neural Networks, Computer ; Algorithms

As the most common type of protein glycosylation, N-glycosylation begins with the synthesis of the dolichol-linked oligosaccharide (DLO) precursor in the endoplasmic reticulum. The mannosyltransferase Alg1 catalyzes the addition of the first mannose molecule to DLO, serving as a key enzyme in this biochemical pathway. The defect of human ALG1 gene can lead to the congenital disorders of glycosylation (CDG), i.e., ALG1-CDG. Therefore, it is of great significance to establish the expression and activity assay system of Homo sapiens Alg1 (HsAlg1) in vitro. In this study, full-length plasmid pET28a-His6-HsAlg1 and transmembrane domain-lacking plasmid pET28a-His6-HsAlg1_23-464 were constructed and expressed in Escherichia coli, and the activity of recombinant HsAlg1 and HsAlg1_23-464 was measured by liquid chromatography tandem mass spectrometry (LC-MS) with dolichyl-pyrophosphate GlcNAc2 (DPGn2) as the substrate. The results showed that HsAlg1 had transglycosylation activity, while the activity decreased after protein purification, which was partially restored upon re-addition of membrane components. However, HsAlg1_23-464 was unable to catalyze glycosylation. The results indicate that the N-terminal transmembrane domain (TMD) of HsAlg1 plays an important role in the catalytic reaction. This study lays a foundation for further expression and activity analysis of ALG1-CDG-related mutants.
Humans ; Escherichia coli/metabolism* ; Mannosyltransferases/biosynthesis* ; Glycosylation ; Recombinant Proteins/metabolism* ; Protein Domains

The gut microbiota is closely related to human health, and various gut microbiota health indices have been developed to assist in evaluating the health of the gut microbiota and even the overall health of the human body. Diets are one of the main factors that regulate the gut microbiota, while there is still no good method for evaluating the regulatory effects of dietary factors. To assess the regulatory effects of dietary factors on the gut microbiota of overweight individuals, we conducted an in vitro fermentation experiment based on 17 dietary factors, and developed an evaluation method for the regulatory effects of dietary factors based on the health index with principal component analysis (hiPCA). The results showed that most dietary factors had positive regulatory effects on the gut microbiota of overweight individuals. Galactooligosaccharides (GOS) and puerarin were the most significant dietary factors in regulating the gut microbiota of overweight individuals. The analysis of the contribution of species to the hiPCA indicated that GOS and puerarin might inhibit the activities of bacteria associated with overweight by regulating Eubacterium dolichum, Lactobacillus salivarius, Clostridium clostridioforme, Clostridium citroniae, and Lachnospiraceae bacterium 9_1_43BFAA. In addition, GOS may further enhance the inhibition of these activities by regulating Lachnospiraceae bacterium 6_1_63FAA, thereby reducing the gut health risks in overweight individuals. In summary, this study evaluated the health effects of dietary factors based on the hiPCA and specifically analyzed the role of different dietary factors in regulating the gut microbiota of overweight individuals. This provides new ideas and methods for improving gut microbiota health and has potential applications in the field of precision nutrition.
Humans ; Gastrointestinal Microbiome/physiology* ; Isoflavones/pharmacology* ; Overweight/microbiology* ; Diet ; Fermentation ; Oligosaccharides/pharmacology* ; Principal Component Analysis

Phenylpyruvic acid (PPA) is used as a food and feed additive and has a wide range of applications in the pharmaceutical, chemical and other fields. At present, PPA is mainly produced by chemical synthesis. With the green transformation of the manufacturing industry, biotransformation will be a good alternative for PPA production. The L-amino acid deaminase (PmiLAAD) from Proteus mirabilis has been widely studied for the production of PPA. However, the low yield limits its industrial production. To further enhance the production of PPA and better meet industrial demands, a more efficient synthesis method for PPA was established. In this study, PmiLAAD was heterologously expressed in Escherichia coli. Subsequently, a colorimetric reaction method was established to screen the strains with high PPA production. The semi-rational design of PmiLAAD was carried out, and the obtained triple-site mutant V18 (V437I/S93C/E417A) showed a 35% increase in catalytic activity compared with the wild type. Meanwhile, the effect of N-terminal truncation on the catalytic activity of the V18 mutant was investigated. After the optimization of the whole-cell conditions for the obtained mutant V18-N7, fed-batch conversion was carried out in a 5-L fermenter, and 44.13 g/L of PPA was synthesized with a conversion rate of 88%, which showed certain potential for industrial application. This study lays foundation for the industrial production of phenylpyruvic acid and also offers insights into the biosynthesis of other chemicals.
Escherichia coli/metabolism* ; Proteus mirabilis/genetics* ; Phenylpyruvic Acids/metabolism* ; Protein Engineering/methods* ; Recombinant Proteins/biosynthesis* ; Bacterial Proteins/metabolism*

L-tyrosine is one of the 20 amino acids that make up proteins and is an essential amino acid for mammals, often used as a nutritional supplement. The conventional methods for synthesizing L-tyrosine have some problems such as the production of many by-products, high requirements for production conditions, and environmental pollution. In this study, we designed and constructed a multi-enzyme cascade for the synthesis of L-tyrosine with alanine, glutamate, ammonium chloride, and phenol as substrates. Initially, the sources of glutamate oxidase, alanine aminotransferase, and tyrosine phenol lyase were screened and analyzed, which was followed by the identification of the rate-limiting enzyme in the reaction process. A colorimetric screening method was established, and the rate-limiting enzyme DbAlaA was engineered to enhance its activity by 40.0%. Subsequently, the reaction conditions, including temperature, pH, cell concentration, and surfactant and coenzyme dosages, were optimized. After optimization, the yield of L-tyrosine reached 9.93 g/L, with a alanine conversion rate of 54.90%. Finally, a feed-batch fermentation strategy was adopted, and the yield of L-tyrosine reached 56.07 g/L after 24 h, with a alanine conversion rate of 65.22%. This study provides a reference for the whole-cell catalytic synthesis of L-tyrosine and its industrialization.
Tyrosine/biosynthesis* ; Escherichia coli/metabolism* ; Tyrosine Phenol-Lyase/genetics* ; Multienzyme Complexes/metabolism* ; Fermentation

The deficiency in immunogenicity and the presence of immunosuppression within the tumor microenvironment significantly hindered the efficacy of immunotherapy. Consequently, a nanoformulation containing metal sulfide of FeS and GSDMD plasmid (NP_FeS/GD) had been developed to effectively augment antitumor immune responses through dual activation of immunogenic PANoptosis and ferroptosis, as well as reprogramming immunosuppressive effects via H₂S amplification. The bioactive NP_FeS/GD exhibited controlled release of GSDMD plasmid, H₂S, and Fe²⁺ in response to the tumor microenvironment. Fe²⁺, H₂S, and the expression of GSDMD protein could effectively elicit highly immunogenic PANoptosis and ferroptosis. Furthermore, releasing H₂S could mitigate the overexpression of indoleamine 2,3-dioxygenase1 (IDO1) induced by immunogenic PANoptotic and ferroptotic cell death and disrupt the activity of IDO1. Consequently, NP_FeS/GD effectively triggered the antitumor innate and adaptive immune responses through induction of PANoptotic and ferroptotic cell death and reshaped the tumor immunosuppressive microenvironment to enhance antitumor immunotherapy for metastasis inhibition. This study unveiled the significant potential of immunogenic PANoptosis and ferroptosis in H₂S gas therapy for enhancing tumor immunotherapy, offering novel insights and ideas for the rational design of nanomedicine to enhance tumor immunogenicity while reprogramming the tumor immunosuppressive microenvironment.

G protein-coupled receptors (GPCRs) are significant drug targets, but their potential in cancer therapy remains underexplored. Conventional GPCR agonists or antagonists have shown limited effectiveness in cancer treatment, necessitating new GPCR-targeting strategies for more effective therapies. This study discovers that Yersinia pestis LcrV, a crucial linker protein for plague infection, acts as a biased agonist of a GPCR, the formyl peptide receptor 1 (FPR1). The LcrV protein induces unique conformational changes in FPR1, resulting in G proteins being activated in a distinctive state without subunit dissociation. This leads to a biased signaling profile characterized by cyclic adenosine monophosphate (cAMP) responses and β-arrestin2 recruitment, but not calcium mobilization. In FPR1-expressing triple-negative breast cancer (TNBC) cells, LcrV bi-directionally modulates intracellular signaling pathways, downregulating extracellular signal-regulated kinases (ERK1/2) and Akt pathways while upregulating Jun N-terminal kinase (JNK) and p38 pathways. This dual modulation results in cell cycle arrest and the inhibition of TNBC cell proliferation. In TNBC xenograft mouse models, long-term LcrV treatment inhibits tumor growth more effectively than a conventional FPR1 antagonist. Additionally, LcrV treatment reprograms tumor cells by reducing stemness-associated proteins OCT4 and c-MYC. Our findings highlight the potential of biased GPCR agonists as a novel GPCR-targeting strategy for cancer treatment.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore the feasibility of synthetic magnetic resonance imaging (syMRI) combined with clinicopathological characteristics for the pre-treatment prediction of chemoradiotherapy (CRT) response in advanced nasopharyngeal carcinoma (ANPC). Materials and Methods: Patients with ANPC treated with CRT between September 2020 and June 2022 were retrospectively enrolled and categorized into response group (RG, n = 95) and non RGs (NRG, n = 32) based on the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The quantitative parameters from pre-treatment syMRI (longitudinal [T1] and transverse [T2] relaxation times and proton density [PD]), diffusion-weighted imaging (apparent diffusion coefficient [ADC]), and clinicopathological characteristics were compared between RG and NRG. Logistic regression analysis was applied to identify parameters independently associated with CRT response and to construct a multivariable model. The areas under the receiveroperating characteristic curve (AUC) for various diagnostic approaches were compared using the DeLong test. Results: The T1, T2, and PD values in the NRG were significantly lower than those in the RG (all P < 0.05), whereas no significant difference was observed in the ADC values between these two groups. Clinicopathological characteristics (Epstein–Barr virus [EBV]-DNA level, lymph node extranodal extension, clinical stage, and Ki-67 expression) exhibited significant differences between the two groups. Logistic regression analysis showed that T1, PD, EBV-DNA level, clinical stage, and Ki-67 expression had significant independent relationships with CRT response (all P < 0.05). The multivariable model incorporating these five variables yielded AUC, sensitivity, and specificity values of 0.974, 93.8% (30/32), and 91.6% (87/95), respectively. Conclusion SyMRI may be used for the pretreatment prediction of CRT response in ANPC. The multivariable model incorporating syMRI quantitative parameters and clinicopathological characteristics, which were independently associated with CRT response, may be a new tool for the pretreatment prediction of CRT response.