The identification and optimization of mutations in nanobodies are crucial for enhancing their thera-peutic potential in disease prevention and control.However,this process is often complex and time-consuming,which limit its widespread application in practice.In this study,we developed a work-flow,named Evolutionary-Nanobody(EvoNB),to predict key mutation sites of nanobodies by combining protein language models(PLMs)and molecular dynamic(MD)simulations.By fine-tuning the ESM2 model on a large-scale nanobody dataset,the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced.The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies.Additionally,we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations.MD simulations analyzed the energy changes caused by these mu-tations to predict their impact on binding affinity to the targets.The results showed that multiple mu-tations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target,further validating the potential of this workflow for designing and optimizing nanobody mutations.Additionally,sequence-based predictions are generally less dependent on structural absence,allowing them to be more easily integrated with tools for structural predictions,such as AlphaFold 3.Through mutation prediction and systematic analysis of key sites,we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes.The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.

Lung transplantation is an important means to treat end-stage lung disease and improve the survival rate and quality of life of patients. However, many postoperative complications seriously affect the prognosis of recipients. Accurate identification of key prognostic factors and construction of individualized and accurate prediction models are of great significance for postoperative prognosis evaluation, treatment strategy formulation and clinical decision-making. In recent years, the clinical prediction model of lung transplantation has gradually changed from traditional statistical methods to machine learning-driven. Compared with traditional models such as Cox regression and Logistic regression, machine learning models such as random forest, support vector machine and artificial neural network have certain advantages in postoperative survival rate prediction, early warning of complications and pulmonary function evaluation. However, their application is also affected by insufficient sample size and poor interpretability of models. Under the condition of small samples, the traditional model still has important value in prediction accuracy. The appropriate prediction model should be selected according to the clinical status of lung transplantation in China, considering the factors such as sample size, variable complexity and model interpretability. In the future, a multi-center, large-sample lung transplantation database should be constructed to further optimize and tap the potential of machine learning algorithms to improve the robustness and clinical applicability of the model.

Objective:To investigate the efficacy and safety of metallic stent implantation for the treatment of central airway malacia following lung transplantation.Methods:Clinical data of 14 recipients who underwent metallic stent implantation for central airway malacia after lung transplantation at Wuxi People's Hospital Affiliated to Nanjing Medical University from January 2023 to June 2024 were retrospectively analyzed. Clinical symptoms, modified Medical Research Council (mMRC) dyspnea scale scores, Karnofsky Performance Status (KPS) scores, and pulmonary function were evaluated before and 2 weeks after stent implantation. The incidence of post-implantation complications was recorded during follow-up.Results:Among the 14 recipients, 13 were male and 1 was female, with a mean age of (57.29±10.93) years (range: 27-71 years). All patients successfully underwent metallic stent placement in a single attempt, with no serious intraoperative complications. Clinical symptoms such as dyspnea improved significantly after stent implantation. Compared with pre-treatment values, mMRC scores decreased [3.00 (2.00, 4.00) vs 1.00 (1.00, 2.00), P<0.001], KPS scores increased [30.00 (10.00, 32.50) vs 80.00 (60.00, 90.00), P=0.001], and forced expiratory volume in one second (FEV 1) improved [(1.25±0.42) L vs (1.74±0.38) L, P=0.006]. Postoperative complications included granulation tissue formation (7 cases), epithelialization within the stent (3 cases), and mucus plugging (4 cases). As of the last follow-up in September 2024, eight patients had their stents successfully removed, and one patient had died due to cytomegalovirus infection. Conclusions:Metallic stent implantation can significantly relieve clinical symptoms of central airway malacia after lung transplantation, improve quality of life, and shows a favorable safety profile.

From July 23, 2018, to December 31, 2019, a total of 299 219 children in Shandong Province received the fifth dose of the diphtheria, tetanus, and acellular pertussis combined vaccine (DTaP). Among these recipients, the distribution by age was as follows: 20 children under 2 years old (0.01%), 273 996 children aged 2 years (91.57%), 20 242 children aged 3 years (6.76%), 3 932 children aged 4 years (1.31%), 963 children aged 5 years (0.32%), and 66 children aged 6 years and above (0.02%). In total, 1 972 cases of adverse events following immunization (AEFI) were reported after the administration of the fifth dose of DTaP, resulting in an incidence rate of 659.05 per 100 000 doses. Among these, 1 718 cases were classified as common vaccine reactions, with an incidence rate of 574.16 per 100 000 doses, while 247 cases were identified as rare reactions, yielding an incidence rate of 82.55 per 100 000 doses. The incidence of AEFIs, as well as the rates of common and rare reactions, exhibited a significant increasing trend with the number of doses administered (all P<0.001). Among the rare reactions, there were 10 cases classified as severe, resulting in a reported incidence of 3.34 per 100 000 doses.

Objective:To analyze the epidemiological and clinical characteristics of respiratory pathogens in China.Methods:This study was a cross-sectional study, which encompassed 19 core units of the clinical pathogen network and established a three-tiered clinical pathogen surveillance system. Thirty respiratory samples were collected every two weeks from various units from January to December 2024, and the clinical and pathogen diagnostic information were gathered. A total of 11 864 samples were tested using this system. The tier-1 clinical pathogen surveillance system covered influenza A virus (Flu-A), influenza B virus (Flu-B), respiratory syncytial virus (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The tier-2 clinical pathogen surveillance system focused on 18 key respiratory pathogens. The tier-3 clinical pathogen surveillance system further clarified whether any emerging infectious diseases had occurred.Results:The tier-1 clinical pathogen surveillance system showed Flu-A predominated in December, Flu-B predominated in January, SARS-CoV-2 peaked in March and August, whereas RSV circulated sporadically throughout the year. Geographic trends were broadly consistent across the seven major regions, although Flu-A detection in December was notably higher in Northeast China (48.1%(111/231)) and East China (36.2%(148/409)), and RSV detection was concentrated in the Northwest and South China from January to March. Data from the tier-2 clinical pathogen surveillance system indicated that Streptococcus pneumoniae, Mycoplasma pneumoniae, rhinovirus, and adenovirus were detected year-round, of these, Streptococcus pneumoniae and rhinovirus showed elevated positive detection rates from August to September, while adenovirus peaked in January. Legionella pneumophila was not detected throughout the year, and other pathogens fluctuated throughout the year without a consistent pattern. The predominant etiologic agents of pediatric pneumonia were Mycoplasma pneumoniae (35.0%(105/300)), rhinovirus (25.7%(77/300)), and adenovirus (17.3%(52/300)), whereas adult pneumonia was mainly caused by Streptococcus pneumoniae (10.5%(29/277)), Staphylococcus aureus (6.9%(19/277)), Mycoplasma pneumoniae (6.9%(19/277)), and Flu-A (6.1%(17/277)). The tier-3 clinical pathogen surveillance system did not identify any emerging respiratory pathogens. Conclusion:Respiratory pathogens in China in 2024 exhibit distinct temporal and spatial distribution patterns and vary among different populations.

Objective Taking into account the physical and mental characteristics of patients with COPD,we develop and implement a lung rehabilitation education game software,aiming to explore more effective health education practices for COPD.Methods Drawing on the Health Belief Model theory,the game content was developed by semi-structured interviews,brainstorming sessions,and expert consultations,and it was implemented via a WeChat mini program.Patients with COPD from the respiratory department of a tertiary general hospital of Luzhou city between March and October 2023 were conveniently selected as the study subjects.Among them,40 patients from July to October 2023 were designated as an experimental group,and another 40 from March to June 2023 constituted a control group.The experimental group was provided with routine health education combined with pulmonary rehabilitation games,whereas the control group received standard health education.After the intervention,the researchers compared the COPD knowledge questionnaire scores and pulmonary rehabilitation adherence between the 2 groups,and assessed the satisfaction of both patients and nurses with the software.Results No sample detachment.The score of the COPD knowledge questionnaire in the experimental group was higher than that in the control group,and the difference was statistically significant(Z=5.361,P＜0.001).The proportion of patients in the experimental group with good adherence to pulmonary rehabilitation(85％)was significantly higher than it in the control group(25％),(x2=29.091,P＜0.001).The patients'overall satisfaction rate with the game was 95％,with operational effectiveness receiving the highest satisfaction rating at 97％.Conclusion The pulmonary rehabilitation education game for patients with COPD can improve their knowledge of COPD,enhance their compliance with pulmonary rehabilitation.Patients have high satisfaction with the software.The software enriches the clinical health education methods,which can be used in clinical practice.

OBJECTIVE To explore the effect of hydrogen peroxide disinfectant on terminal disinfection in wards of medical institutions.METHODS The surfaces of highly frequent contact objects of the wards of the First Affilia-ted Hospital of Nanchang University from which the public health center patients were discharged between Apr.2024 and Jun.2024 were respectively disinfected with 0.5％(low)and 5％(high)concentrations of hydrogen peroxide disinfecting wipes,totally 180 samples were randomly collected before and after the disinfection,and the pathogens were detected.The air of the wards from which the public health center patients were discharged be-tween Jul.2024 and Aug.2024 were disinfected with hydrogen peroxide dry mist disinfection device,and 90 sam-ples were respectively collected before and after the disinfection.Geobacillus stearothermophilus was used as the biological indicator and placed at various points within the air-disinfected wards to evaluate the disinfection level.The environmental sampling results and distribution of bacteria were observed and compared.RESULTS The qualified rates of disinfection of the object surfaces were 95.56％(86/90)and 98.89％(89/90)respectively for the low and high concentratioins of hydrogen peroxide disinfecting wipes,and there was no significant difference in the disinfection effect.Totally 120 strains of pathogens were isolated from unqualified samples before the disinfection,among which gram-negative bacteria(69.17％)were dominant,and the isolation rate of multidrug-resistant or-ganisms was 22.50％(27/120);only 1 strain of carbapenem-resistant Acinetobacter baumannii was isolated after the disinfection.The qualified rate of disinfection of air in the wards was 96.00％by 7.5％hydrogen peroxide dry mist disinfection device,the average bacterial colony counts in the air were 2 CFU/(5 min·vsl)after the dis-infection,and the killing rate of Geobacillus stearothermophilus was 100.00％by the air disinfection.CONCLUSION The hydrogen peroxide disinfectant can meet the requirement for terminal disinfection of the wards of the medical institutions,and it is portable and highly efficient.

Objective:To assess the effectiveness of kinetic parameters of 18F-prostate specific membrane antigen (PSMA)-1007 PET/MR in distinguishing tumor recurrence (TR) from radiation necrosis (RN) in glioma patients. Methods:From January 2023 to June 2023, imaging data of 10 patients (6 males, 4 females; media age of 39.5 years) with gliomas who were suspected of recurrence and were referred for 18F-PSMA-1007 PET/MR scans at Sun Yat-Sen University Cancer Center were retrospectively analyzed. Static parameters from 18F-PSMA-1007 PET scans, including SUV max, SUV mean, metabolic tumor volume (MTV), and total lesion′s PSMA (TLP), as well as dynamic parameters including K 1, k 2, k 3, k 4, net influx rate (K i), and volume of distribution ( Vt) were obtained by using compartmental and multigraphical models. Additionally, parameters from dynamic contrast-enhanced MRI (DCE-MRI) were collected. Mann-Whitney U test was used to compare parameter differences between TR and RN groups. Spearman rank correlation analysis was used to explore the correlation between K i and DCE-MRI parameters. Results:Finally, 8 cases were diagnosed as TR and 2 cases were diagnosed as RN. The kinetic compartmental model-based evaluation determined that irreversible 2-tissue model (2T3K) provided the best-fitting results. The differences in SUV mean (median: 2.48 vs 0.89; Z=-2.09, P=0.044), SUV max (median: 4.04 vs 1.40; Z=-2.09, P=0.044), and K i (median: 1.33×10 -2vs 3.87×10 -3;Z=-2.10, P=0.044) between TR and RN groups were statistically significant. Some parameters of DCE-MRI also showed differences between the two groups ( Z=-2.09, P=0.044 for all). The K i yielded moderate correlation with DCE-MRI parameter Ve ( rs=0.650, P=0.042), while correlations between K i and other DCE-MRI parameters were not significant ( rs values: from -0.207 to 0.632, all P>0.05). Conclusion:Dynamic and multi-parametric 18F-PSMA-1007 PET/MR system holds promise for accurately distinguishing TR from RN in treated glioma patients.

Objective:To explore the genetic and clinical characteristics of Guizhou patients with enlarged vestibular aqueduct（EVA） syndrome through combined SLC26A4 variant analysis and clinical phenotype analysis. Methods:Seventy-two EVA patients underwent comprehensive genetic testing using a multiplex PCR-based deafness gene panel and next-generation sequencing（NGS）. The audiological and temporal bone imaging characteristics were compared across mutation subtypes. Results:A total of 27 pathogenic loci of SLC26A4 were detected in 72 patients, including c.919-2A>G in 79.2%（57/72）. A novel deletion（c.1703_1707+6del） was discovered. Among 65 cases, truncated mutations were 89.2%（58/65）, 52.3%（34/65）, 28（43.1%） and 7（10.8%）. No significant differences were observed in the midpoint diameter of the vestibular aqueduct and the incidence of incomplete partitioning typeⅡ（IP-Ⅱ） of the cochlea among the three groups of patients. Moreover, there was no difference in the midpoint diameter of different vestibular pipes or the combination with IP-Ⅱ. Conclusion:The most common mutation site of SLC26A4 in EVA patients in Guizhou is c.919-2A>G, though genotype-phenotype correlations remain elusive. The detection of 27 mutation sites and the discovery of new mutation sites suggested the precise diagnostic significance of NGS technology in EVA patients in Guizhou.
Humans ; Sulfate Transporters ; Vestibular Aqueduct/abnormalities* ; Mutation ; Membrane Transport Proteins/genetics* ; Hearing Loss, Sensorineural/genetics* ; Male ; Female ; Child ; Adolescent ; Child, Preschool ; Adult ; Young Adult ; Phenotype ; High-Throughput Nucleotide Sequencing

The identification and optimization of mutations in nanobodies are crucial for enhancing their therapeutic potential in disease prevention and control. However, this process is often complex and time-consuming, which limit its widespread application in practice. In this study, we developed a workflow, named Evolutionary-Nanobody (EvoNB), to predict key mutation sites of nanobodies by combining protein language models (PLMs) and molecular dynamic (MD) simulations. By fine-tuning the ESM2 model on a large-scale nanobody dataset, the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced. The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies. Additionally, we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations. MD simulations analyzed the energy changes caused by these mutations to predict their impact on binding affinity to the targets. The results showed that multiple mutations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target, further validating the potential of this workflow for designing and optimizing nanobody mutations. Additionally, sequence-based predictions are generally less dependent on structural absence, allowing them to be more easily integrated with tools for structural predictions, such as AlphaFold 3. Through mutation prediction and systematic analysis of key sites, we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes. The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.