ObjectiveTo develop an assessment tool for apraxia of speech (AOS) of Chinese Putonghua speakers and test its reliability and validity. MethodsThe Chinese Apraxia of Speech Assessment and Diagnostic Tool (CAADT) was developed based on the Apraxia of Speech Rating Scale 3.5, combined with the linguistic characteristics of Chinese and clinical experience. The tool consistsed of eleven items across three sections: articulation, prosody and alternating motion rates. Six experts evaluated the content validity. From November, 2024 to May, 2025, 51 patients with post-stroke AOS (experimental group) and ten patients with post-stroke aphasia without AOS (control group) were recruited from Anhui Wannan Rehabilitation Hospital (the Fifth People's Hospital of Wuhu), and tested with CAADT. Reliability was assessed using Cronbach's α coefficient, Kendall's coefficient of concordance W and Pearson correlation coefficient. Validity was evaluated using the content validity index (CVI) and Spearman correlation coefficient. Discriminative effect was analyzed using the receiver operating characteristic (ROC) curve. ResultsThe Cronbach's α coefficients for the articulation and prosody sections and the total scale were all > 0.9, while it was 0.454 for the alternating motion rates. Inter-rater reliability was good (W ≥ 0.598, P < 0.001). Test-retest reliability showed high positive correlations for the three sections and the total score between the two assessments (r ≥ 0.84, P < 0.001). The scale-level CVI was 0.95, and the item-level CVI ≥ 0.83. The Spearman correlation coefficients among the sections ranged from 0.30 to 0.70. ROC analysis revealed an area under the curve of 0.953, with a cut-off value of 11, yielding a sensitivity of 0.92 and a specificity of 0.90. ConclusionCAADT demonstrates good reliability, validity and discriminative effect, which can be used for clinical assessment and auxiliary diagnosis of Chinese Putonghua speaking patients with post-stroke AOS.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Multidrug-resistant (MDR) bacterial infections have emerged as a serious challenge of global public health crisis. The overuse and misuse of conventional antibiotics have dramatically accelerated the emergence, evolution and worldwide spread of drug-resistant bacterial strains, necessitating urgent exploration of novel antibacterial strategies. Bacteriophages serve as natural bacterial predators offering distinct advantages including high host specificity, autonomous self-replication capabilities and cost-effective large-scale production. However, wild-type phages present significant clinical limitations due to their narrow host ranges, susceptibility to rapid immune clearance and poor penetration of bacterial biofilms, which severely restrict their therapeutic applications. The convergence of synthetic biology, nanotechnology and advanced gene editing technologies has accelerated the development of engineered bacteriophage platforms, providing programmable, scalable and clinically translatable pathways to overcome these inherent biological constraints. Here, we systematically delineate four fundamental strategies for engineered bacteriophage development. Chemical modification utilizes reactive functional groups such as amino, carboxyl and thiol moieties on capsid proteins through esterification, amidation or click chemistry reactions to achieve precise drug conjugation and surface functionalization. In vivo editing encompasses ultraviolet or chemical mutagenesis for random mutation induction, homologous recombination for targeted genetic alterations, recombineering methodologies including electroporation-mediated bacteriophage recombination engineering, and CRISPR-Cas systems for precise genome editing to enable exact genetic reconstruction and host range reprogramming. In vitro synthesis leverages genome engineering platforms where intact phage genomes are transferred into yeast or host bacteria to facilitate highly efficient homologous recombination, enabling large DNA fragment assembly and cross-gene host range expansion without bacterial toxicity constraints. Directed evolution combines artificial selection through mutation library screening with rational design approaches involving chimeric receptor binding protein construction or site-specific mutagenesis, effectively balancing the discovery of unknown adaptive pathways with targeted host specificity modification. Moreover, we comprehensively discuss therapeutic applications across diverse clinical scenarios. Engineered bacteriophage effectively disrupt bacterial biofilms through sophisticated functionalized delivery platforms including nanozyme-conjugated phages, phage-liposome nanoconjugates and bio-responsive hydrogels, demonstrating significantly enhanced bactericidal efficiency compared to unmodified free phages. These bioengineered vectors attenuate bacterial virulence and resensitize pathogens to antibiotics by delivering CRISPR-Cas systems or base editors to disrupt critical virulence factors such as pili, capsule synthesis machineries and quorum sensing systems, or by inactivating antibiotic resistance determinants including beta-lactamase genes. As an intelligent nanomedicine delivery platform, engineered bacteriophage enable precise pathogen elimination an through photocatalytic reactive oxygen species generation, immunomodulatory interventions, or controlled release of antibacterial drugs. Furthermore, oral administration of engineered bacteriophage facilitates microbiota modulation, which selectively eliminate intestinal pathogens while preserve beneficial commensal microbiota, thereby restoring microbial community balance and preventing complications associated with dysbiosis. Finally, we critically analyze persistent challenges including host strain matching complexity, evolution of bacterial resistance mechanisms, pharmacokinetic optimization requirements, optimal administration route selection, large-scale production quality control standards and clinical dosing determination protocols. Through multidisciplinary integration of synthetic biology, infectious disease medicine and immunology, future translational medicine studies of bacteriophage should establish comprehensive technical platforms encompassing rapid phage screening, intelligent rational design, rigorous in vivo evaluation and standardized clinical validation processes, ultimately advancing engineered bacteriophage from laboratory innovations to clinically approved therapeutics for effectively combating MDR bacterial infections.

Objective:To observe the characteristics of brain functional activity in patients with chronic non-specific low back pain(CNLBP)and to explore the potential mechanism of brain functional remodeling in acupuncture treatment based on resting-state fMRI.Method:The 22 patients with CNLBP were included as the CNLBP group,and 22 healthy subjects(HCs)were included as the HCs group.The visual analogue scale(VAS)for pain,Oswestry disability index(ODI),Japanese Orthopedic Association(JOA)score for lumbar spine were collected before and after acu-puncture treatment in the CNLBP group.Resting-state fMRI data were acquired for all participants.The frac-tional amplitude of low frequency fluctuation(fALFF)values of the different brain regions in CNLBP patients before treatment compared with the HCs group were extracted and correlated with the differences in VAS,ODI,and JOA scores.Result:Acupuncture treatment significantly improved the VAS,ODI,and JOA scores in the CNLBP group(P＜0.001);Before treatment,the CNLBP group showed higher fALFF values in the right orbital middle frontal gy-rus,right anterior cingulate gyrus,left anterior cingulate gyrus and left precuneus compared to the HCs group,while lower fALFF values were observed in the right precentral gyrus/right postcentral gyrus,left precentral gy-rus/left postcentral gyrus,right thalamus,right postcentral gyrus/precentral gyrus,and left thalamus.After treat-ment,the CNLBP group exhibited increased fALFF values in the right precentral gyrus/postcentral gyrus,left precentral gyrus/postcentral gyrus,and left middle temporal gyrus,and decreased fALFF values in the left me-dial superior frontal gyrus,right precuneus/left precuneus compared to pre-treatment.Additionally,pre-treatment fALFF value in the right orbital part of the middle frontal gyrus,right anterior cingulate gyrus and left anteri-or cingulate gyrus in the CNLBP group was negatively correlated with the changes in VAS scores(r=-0.699,P=0.000).Conclusion:Acupuncture treatment can significantly improve pain intensity and lumbar spinal dysfunction in CNLBP.CNLBP has multiple abnormalities in brain functional activities related to pain regulation pathways and the default mode network(DMN),and acupuncture treatment can restore most of these abnormal brain ac-tivities;the characteristic of brain functional activity in the right orbital part of the middle frontal gyrus,right anterior cingulate gyrus and left anterior cingulate gyrus before CNLBP treatment is related to the difference in VAS score,providing a potential imaging biomarker for predicting treatment efficacy.

Thermal ablation (TA) is a widely applied minimally invasive treatment for benign thyroid nodules and low-risk papillary thyroid microcarcinoma. Compared to conventional surgery, TA offers advantages such as minimal trauma, rapid recovery, and no scarring. However, this procedure may lead to various complications, including intraoperative pain, nerve injury, hemorrhage, tracheal injury, skin burns, vasovagal reactions, nodule rupture, and thyroid dysfunction. Although TA demonstrates excellent safety and efficacy, further standardization of procedural protocols is necessary to minimize the incidence of complications.

Objective:To evaluate the predictive role of ultrasound, genetic testing, and clinical features in the malignancy risk of Bethesda Ⅲ thyroid nodules, and to explore strategies for optimizing treatment decisions.Methods:This retrospective study included 227 Bethesda Ⅲ thyroid nodules from patients who underwent surgical treatment at the Thyroid Surgery Department of Nanjing Drum Tower Hospital between Jan. 2020 and Dec. 2023. All patients underwent ultrasound evaluation and fine-needle aspiration. For nodules diagnosed as ultrasound, genetic testing, and clinical features were analyzed using univariate and multivariate regression to assess their association with malignancy.Results:Among the 227 nodules, 214 were malignant, resulting in a malignancy rate of 94.2%. The malignancy rate of thyroid nodules was 94.2%. In univariate analysis, age ( P=0.016), BRAF V600E gene mutation ( P<0.001), nodule size ( P=0.002), and TIRADS ( P<0.001) were significantly associated with malignancy in Bethesda Ⅲ thyroid nodules. Multivariate analysis confirmed that age ( OR=0.939, P=0.049) and BRAF V600E gene mutation ( OR=24.641, P<0.001) were significantly associated with thyroid nodule nature and served as independent predictive factors for malignancy. Conclusions:Genetic testing is an important method for predicting the malignancy of Bethesda Ⅲ thyroid nodules, and ultrasound also has high clinical value in assessing the malignancy risk of nodules. While some clinical features are highly correlated with nodule characteristics, they may not be practical in clinical application. For nodules classified as TIRADS 3 through ultrasound evaluation and negative for BRAF mutations, continued observation may be considered, whereas TIRADS 5 nodules or nodules with BRAF mutations should be prioritized for surgical treatment.

Objective:To study the whole-brain distribution of vasoactive intestinal peptide(VIP)-positive neurons in the mouse brain and provide assistance for anatomical and functional studies of VIP neurons.Methods:VIP-Cre::Ai47 mice were used to label VIP neurons in the whole brain.Then,fluorescent imaging of the whole brain slices from VIP-Cre::Ai47 mice was calibrated using the standard brain map.Finally,the distribution density of VIP-positive neurons in different regions of the whole brain was statistically analyzed.Results:The overall distribution densities of VIP neurons in the cortex,olfactory bulb,and hippocampus were all greater than 5 neurons/mm2,and the distribution densities varied greatly in different subregions.The overall distribution density of VIP in the amygdala in the subcortical region was 4 neurons/mm2,and the distribution densities of VIP in the thalamus,hypothalamus,midbrain,and hind-brain regions were less than 2 neurons/mm2 on average,except for that in the supraoptic nucleus of the hypothalamus,where the density of VIP neuron distribution reached 20 neurons/mm2.Conclusion:VIP-positive neurons are mainly distributed in the cortex,hippocampus,and amygdala,which are highly associated with cognition and affection,and are rarely distributed in the thalamus and midbrain.These results suggest that VIP neurons play an essential role in emo-tional and cognitive functions.

OBJECTIVE To explore and establish the working mechanism for prevention and control of hospital-as-sociated infections in regional oral medical institutions so as to standardize the prevention and control of the hospi-tal-associated infections in the regional oral medical institutions.METHODS Taking an administrative division of Chongqing as example,the matrix evaluation was carried out based on the quality control mode for management of hospital-associated infections in oral medical institutions' action planning,training guidance,quality control super-vision,summary review' organically in combination with'quality control plus law enforcement',a color-co-ded management of the oral medical institutions in the region was implemented,and the effectiveness of improved work in infection control was examined.RESULTS From the perspective of the grade of medical institution,the qualified rates of hospital infection management system construction,architectural layout and process,cleaning,disinfection and sterilization of oral instruments,environmental cleaning and disinfection,isolation,safe injection,use of occupational protection supplies and disposal of medical waste of the primary and unrated medical institu-tions were respectively 26.51％,49.40％,24.10％,37.35％,31.33％,46.99％,67.47％and 51.81％before the improvement and were respectively increased to 67.47％,63.86％,45.78％,66.27％,63.86％,73.49％,84.34％and 66.27％after the improvement,and there were significant differences(P＜0.05).From the perspec-tive of the property of the medical institution,the qualified rates of the above items of the private medical institu-tions were respectively 24.66％,47.95％,21.92％,34.25％,31.51％,45.21％,69.86％and 50.68％before the improvement and were respectively increased to 65.75％,61.64％,42.47％,64.38％,63.01％,71.23％,84.93％and 63.01％after the improvement,and there were significant differences(P＜0.05).CONCLUSION The working mechanism on prevention and control of hospital-associated infections in regional oral medical institu-tions that is established based on'quality control plus law enforcement'with the introduction of social credit can effectively raise the qualified rates of the infection prevention and control measures,which achieves more remarka-ble improvement effectiveness in grass-roots oral medical institutions such as the private,primary and unrat-ed medical institutions.

Objective:To study the whole-brain distribution of vasoactive intestinal peptide(VIP)-positive neurons in the mouse brain and provide assistance for anatomical and functional studies of VIP neurons.Methods:VIP-Cre::Ai47 mice were used to label VIP neurons in the whole brain.Then,fluorescent imaging of the whole brain slices from VIP-Cre::Ai47 mice was calibrated using the standard brain map.Finally,the distribution density of VIP-positive neurons in different regions of the whole brain was statistically analyzed.Results:The overall distribution densities of VIP neurons in the cortex,olfactory bulb,and hippocampus were all greater than 5 neurons/mm2,and the distribution densities varied greatly in different subregions.The overall distribution density of VIP in the amygdala in the subcortical region was 4 neurons/mm2,and the distribution densities of VIP in the thalamus,hypothalamus,midbrain,and hind-brain regions were less than 2 neurons/mm2 on average,except for that in the supraoptic nucleus of the hypothalamus,where the density of VIP neuron distribution reached 20 neurons/mm2.Conclusion:VIP-positive neurons are mainly distributed in the cortex,hippocampus,and amygdala,which are highly associated with cognition and affection,and are rarely distributed in the thalamus and midbrain.These results suggest that VIP neurons play an essential role in emo-tional and cognitive functions.