Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. Enzymes, as biological catalysts, play a key role in biosynthetic pathways, significantly enhancing the rate and selectivity of biochemical reactions. However, the catalytic efficiency, stability, substrate specificity, and tolerance of natural enzymes often fall short of industrial production requirements. Therefore, exploring and modifying enzymes to suit specific biomanufacturing processes has become crucial. In recent years, artificial intelligence (AI) has played an increasingly important role in the discovery, evaluation, engineering, and de novo design of proteins. AI can accelerate the discovery and optimization of proteins by analyzing large amounts of bioinformatics data and predicting protein functions and characteristics by machine learning and deep learning algorithms. Moreover, AI can assist researchers in designing new protein structures by simulating and predicting their performance under different conditions, providing guidance for protein design. This paper reviews the latest research advances in protein discovery, evaluation, engineering, and de novo design for biomanufacturing and explores the hot topics, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for researchers in related fields.
Protein Engineering/methods* ; Artificial Intelligence ; Proteins/genetics* ; Computational Biology ; Machine Learning ; Data Mining ; Algorithms ; Deep Learning

Objective:To evaluate the efficacy of bevacizumab in reducing dyspnea, avoiding tracheostomy, and assessing the overall safety and effectiveness of the treatment in patients with juvenile-onset recurrent respiratory papillomatosis (JORRP).Methods:This study included 19 patients with JORRP treated with Bevacizumab at the Department of Otolaryngology-Head and Neck Surgery, BenQ Medical Center, from March 2022 to June 2024. The age of patients ranged from 1.0 to 27.0 years (10.47±8.45 years), with age at onset ranging from 0.5 to 15.0 years (3.66±3.70 years). The cohort included 11 males and 8 females. Bevacizumab was administered intravenously at a dose of 10 mg/kg every three weeks for three sessions. Efficacy was evaluated by comparing the standardized lesion volume pre-and post-treatment, with statistical analysis performed using R software (4.3.1).Results:Among the 19 patients, 11 presented with dyspnea before treatment. All patients experienced varying degrees of dyspnea relief within 72 hours following the initial treatment, and only one patient had mild dyspnea by the second treatment session three weeks later. The average reduction rates at 24 and 48 hours post-initia treatment were 25.75% and 47.16%, respectively. Following three treatment cycles, the average cumulative reduction rate was 67.47%, significantly higher than after the first treatment ( Z=3.38, P=0.002). Throughout the treatment period, no adverse events that of grade 2 or higher were noted. Conclusions:Bevacizumab can rapidly alleviate dyspnea symptoms and significantly reduce lesion volume in JORRP patients, exhibiting satisfactory overall safety and effectiveness. However additional large-scale prospective studies are warranted to validate its long-term safety and efficacy.

BackgroundDementia seriously affects the quality of life and lifespan of elderly people， with Alzheimer's disease （AD） being the most common type of dementia. Previous studies have suggested that gout may reduce the risk of developing AD， but the causal relationship between the two still requires further research. ObjectiveTo investigate the potential causal relationship between gout and AD through a two-sample Mendelian randomization （MR） analysis， so as to provide references for the prevention and treatment of AD. MethodsData from Genome-Wide Association Studies （GWAS） extracted in 2024 were analyzed， using pooled data on gout （6 810 cases in the case group and 477 788 cases in the control group） published by UK Biobank in 2021 as the exposure variable， and data on AD （3 899 cases in the case group and 214 893 cases in the control group） published by FinnGen in the same year as the outcome variable. The inverse-variance weighted， MR-Egger regression， weighted median estimation， simple model and weighted model were used to analyze the potential causal relationship between gout and AD. Pleiotropic effects were assessed using MR-Egger regression. Heterogeneity assessment was conducted using Cochran's Q test. The leave-one-out analysis was carried out for sensitivity analysis. And a funnel plot was drawn to detect potential publication bias. ResultsThe inverse-variance weighted analysis demonstrated a negative causal relationship between gout and AD （OR=0.004， 95% CI： 0~0.700， P<0.05）. The plot resembled a symmetrical inversed funnel， indicating the absence of publication bias. No heterogeneity was detected by Cochran's Q test. The MR-Egger regression indicated no significant horizontal pleiotropy. Concerning the reverse directions， no significant associations between AD and gout were noted. ConclusionThere is a negative causal relationship between gout and AD， with gout potentially reducing the risk of developing AD. ［Funded by The Third Batch of Social Welfare and Basic Research Projects （Medical and Health） of Zhongshan City in 2022 （number， 2022B3017）］

Background Environmental metal exposure is closely associated with the onset and progression of mild cognitive impairment (MCI) in the elderly. Effectively identifying hazardous metal exposure and assessing their interaction effects have significant public health implications. Objective To explore the relationship between urinary single metal and metal mixture exposure and MCI in elderly compound residents. Methods This study included 391 elderly individuals aged 60 and above from residential compounds in Zhongshan City, Guangdong Province. Concentrations of iron (Fe), copper (Cu), selenium (Se), arsenic (As), cadmium (Cd), manganese (Mn), chromium (Cr), nickel (Ni), vanadium (V), cobalt (Co), antimony (Sb), thallium (Tl), zinc (Zn), calcium (Ca), and magnesium (Mg) in urine were measured using inductively coupled plasma mass spectrometry. Cognitive function in the elderly was assessed using the Chinese version of the Mini-Mental State Examination (MMSE). Logistic regression was used to explore the relationship between single metal exposure level and MCI. LASSO regression and multi-metal logistic regression models were used to identify key metal ions associated with MCI. Bayesian kernel machine regression (BKMR) was employed to analyze the relationship between key metal ion mixtures and MCI, as well as the interactions between metals. Age, gender, education level, occupation, and body mass index were adjusted as covariates. Results A total of 78 among the 391 elderly individuals surveyed (19.94%) were diagnosed with MCI (MCI group), and the other 313 individuals were controls. The levels of Se, Cd, Mn, and As in the urine of the MCI group were significantly higher than those in the control group (P < 0.05). In the single-metal model, after adjusting for covariates and using the first quartile (Q₁) of each metal concentration as the reference, the OR for MCI in the elderly in the Q₄ group of Se was 2.190 (95%CI: 1.017, 4.716); for Cd, the OR was 2.345 (95%CI: 1.041, 5.283) in the Q₃ group and 2.371 (95%CI: 1.043, 5.393) in the Q₄ group; for Mn, the OR was 2.355 (95%CI: 1.038, 5.344) in the Q₂ group; for As, the OR was 3.377 (95%CI: 1.442, 7.908) in the Q₃ group and 2.886 (95%CI: 1.227, 6.788) in the Q₄ group; for Sb, the OR was 2.779 (95%CI: 1.234, 6.257) in the Q₂ group. When urinary metal concentrations were ln-transformed and included as continuous variables in the single-metal model, Cd concentration was positively correlated with MCI (OR=1.377; 95%CI: 1.008, 1.882; P=0.044). Cd, Se, Mg, Ca, Mn, As, Cr, Co, Tl, and Sb were selected by the LASSO regression model and included in the multi-metal model. In the multi-metal model, compared with Q₁, the OR for MCI in the elderly was 0.395 (95%CI: 0.164, 0.953) in the Q₂ group of Co and 0.390(95%CI: 0.167，0.911) in the Q₃ group of Co; for Mn, the OR in the Q₂ group was 2.636 (95%CI: 1.053, 6.596); for Sb, the OR in the Q₂ group was 2.640 (95%CI: 1.047, 6.658). As continuous variables, Mg (OR=0.472; 95%CI: 0.248, 0.899; P=0.022) and Co (OR=0.857; 95%CI: 0.737, 0.996; P=0.044) concentrations were negatively correlated with MCI. The BKMR mixture analysis suggested that Mg and Co exhibited a synergistic negative correlation with MCI, while Mn and Sb exhibited a synergistic positive correlation with MCI. Mg and Co attenuated the positive correlation of Mn and Sb with MCI, whereas Mn weakened the protective effects of Mg and Co. Conclusion Elevated levels of Se, Cd, As, Mn, and Sb in urine may increase the risk of MCI in the elderly, while Mg and Co have protective effects. Potential synergistic or antagonistic interactions may be found among Mn, Sb, Mg, and Co, which should not be overlooked in terms of their impact on the cognitive function of the elderly.

BACKGROUND: Molecular subtyping is an essential complementarity after pathological analyses for targeted therapy. This study aimed to investigate the consistency of next-generation sequencing (NGS) results between circulating tumor DNA (ctDNA)-based and tissue-based in non-small cell lung cancer (NSCLC) and identify the patient characteristics that favor ctDNA testing. METHODS: Patients who diagnosed with NSCLC and received both ctDNA- and cancer tissue-based NGS before surgery or systemic treatment in Lung Cancer Center, Sichuan University West China Hospital between December 2017 and August 2022 were enrolled. A 425-cancer panel with a HiSeq 4000 NGS platform was used for NGS. The unweighted Cohen's kappa coefficient was employed to discriminate the high-concordance group from the low-concordance group with a cutoff value of 0.6. Six machine learning models were used to identify patient characteristics that relate to high concordance between ctDNA-based and tissue-based NGS. RESULTS: A total of 85 patients were enrolled, of which 22.4% (19/85) had stage III disease and 56.5% (48/85) had stage IV disease. Forty-four patients (51.8%) showed consistent gene mutation types between ctDNA-based and tissue-based NGS, while one patient (1.2%) tested negative in both approaches. Patients with advanced diseases and metastases to other organs would be suitable for the ctDNA-based NGS, and the generalized linear model showed that T stage, M stage, and tumor mutation burden were the critical discriminators to predict the consistency of results between ctDNA-based and tissue-based NGS. CONCLUSION ctDNA-based NGS showed comparable detection performance in the targeted gene mutations compared with tissue-based NGS, and it could be considered in advanced or metastatic NSCLC.
Humans ; Carcinoma, Non-Small-Cell Lung/pathology* ; Circulating Tumor DNA/blood* ; High-Throughput Nucleotide Sequencing/methods* ; Female ; Male ; Lung Neoplasms/pathology* ; Middle Aged ; Mutation/genetics* ; Aged ; Adult ; Aged, 80 and over

Medical record homepage is a core basis for healthcare quality management, medical insurance payment, and public hospital performance evaluation.The completeness and accuracy of its data directly affect the medical quality and economic benefits of hospitals. Since July 2022, a tertiary hospital had built an auxiliary decision-making system for diagnosis omissions based on artificial intelligence technology, which was officially launched in January 2023. The system aimed to improve the quality of data on the first page of medical records and ensure reasonable payment by medical insurance. This system was built on the hospital′s electronic medical records, and integrated natural language processing, medical knowledge graphs and deep learning technologies to create three engines: diagnosis omission recognition, ICD coding and DRG grouping. The diagnosis omission recognition engine identified both explicit and implicit omitted diagnoses by using a context semantic analysis model and a contrastive learning framework for dual judgment. It also interacted with the ICD coding and DRG grouping engines to enhance the accuracy of DRG grouping. Since its launched, the system has achieved remarkable results. A comparative analysis revealed that the rate of missing diagnoses on hospital medical record homepages has decreased from 31.31% during January to September 2022 to 12.34% during the same period in 2023, and the quality control time for a single medical record had been reduced from 20 minutes to 5 minutes. Additionally, a simulation calculation showed that the system-assisted DRG grouping can increase the hospital′s medical insurance surplus. The system could provide reference and guidance for public hospitals in China to improve the quality of the homepage of medical records and better adapt to medical insurance payment reform.

Objective To understand the relationship between sleep status and cognitive function in middle-aged and elderly peo-ple in China,as well as the mediating effect of depression.Methods Selecting middle-aged and elderly people aged 45 years and above from the 2020 China Health and Retirement Longitudinal Survey(CDHARLS),K-means clustering method was used to divide cognitive function into high score group and low score group.Logistic regression analysis was used to analyze the influencing factors of cognitive function,and a restricted cubic spline model was used to explore the dose-response relationship between sleep and cognitive function.A structural equation model was constructed to analyze the mediating effect of depression between sleep status and cognitive function.Results Among 7964 middle-aged and elderly people,the cognitive function score was 13.49±2.99 points,the depression detection rate was 30.8％.After controlling for other confounding factors,the probability of cognitive decline in middle-aged and elderly people with a nighttime sleep duration of＞8hours was 1.4 times that of a nighttime sleep duration of 6-8hours,and the probability of cognitive decline in middle-aged and elderly people with a nap duration of 0-30minutes and≥90minutes was 1.20 times and 1.39 times that of a nap du-ration of 30-60minutes.The probability of cognitive decline in middle-aged and elderly people with depression was 1.52 times that of the non-depression group.The restricted cubic spline model showed that there was a U-shaped and J-shaped correlation between nighttime and afternoon sleep duration and the risk of cognitive decline.The results of the structural equation model showed that sleep sta-tus had a direct effect on cognitive function(β=-0.278,95％CI:-0.376--0.186),while depression has an indirect effect be-tween sleep status and cognitive function(β=0.152,95％CI:0.111-0.201).Conclusion The sleep status,depression,and cogni-tive function of middle-aged and elderly people in China are closely related,and depression mediates the relationship between sleep sta-tus and cognitive decline a certain extent.

Medical record homepage is a core basis for healthcare quality management, medical insurance payment, and public hospital performance evaluation.The completeness and accuracy of its data directly affect the medical quality and economic benefits of hospitals. Since July 2022, a tertiary hospital had built an auxiliary decision-making system for diagnosis omissions based on artificial intelligence technology, which was officially launched in January 2023. The system aimed to improve the quality of data on the first page of medical records and ensure reasonable payment by medical insurance. This system was built on the hospital′s electronic medical records, and integrated natural language processing, medical knowledge graphs and deep learning technologies to create three engines: diagnosis omission recognition, ICD coding and DRG grouping. The diagnosis omission recognition engine identified both explicit and implicit omitted diagnoses by using a context semantic analysis model and a contrastive learning framework for dual judgment. It also interacted with the ICD coding and DRG grouping engines to enhance the accuracy of DRG grouping. Since its launched, the system has achieved remarkable results. A comparative analysis revealed that the rate of missing diagnoses on hospital medical record homepages has decreased from 31.31% during January to September 2022 to 12.34% during the same period in 2023, and the quality control time for a single medical record had been reduced from 20 minutes to 5 minutes. Additionally, a simulation calculation showed that the system-assisted DRG grouping can increase the hospital′s medical insurance surplus. The system could provide reference and guidance for public hospitals in China to improve the quality of the homepage of medical records and better adapt to medical insurance payment reform.

Objective:To evaluate the efficacy of bevacizumab in reducing dyspnea, avoiding tracheostomy, and assessing the overall safety and effectiveness of the treatment in patients with juvenile-onset recurrent respiratory papillomatosis (JORRP).Methods:This study included 19 patients with JORRP treated with Bevacizumab at the Department of Otolaryngology-Head and Neck Surgery, BenQ Medical Center, from March 2022 to June 2024. The age of patients ranged from 1.0 to 27.0 years (10.47±8.45 years), with age at onset ranging from 0.5 to 15.0 years (3.66±3.70 years). The cohort included 11 males and 8 females. Bevacizumab was administered intravenously at a dose of 10 mg/kg every three weeks for three sessions. Efficacy was evaluated by comparing the standardized lesion volume pre-and post-treatment, with statistical analysis performed using R software (4.3.1).Results:Among the 19 patients, 11 presented with dyspnea before treatment. All patients experienced varying degrees of dyspnea relief within 72 hours following the initial treatment, and only one patient had mild dyspnea by the second treatment session three weeks later. The average reduction rates at 24 and 48 hours post-initia treatment were 25.75% and 47.16%, respectively. Following three treatment cycles, the average cumulative reduction rate was 67.47%, significantly higher than after the first treatment ( Z=3.38, P=0.002). Throughout the treatment period, no adverse events that of grade 2 or higher were noted. Conclusions:Bevacizumab can rapidly alleviate dyspnea symptoms and significantly reduce lesion volume in JORRP patients, exhibiting satisfactory overall safety and effectiveness. However additional large-scale prospective studies are warranted to validate its long-term safety and efficacy.

Objective To understand the relationship between sleep status and cognitive function in middle-aged and elderly peo-ple in China,as well as the mediating effect of depression.Methods Selecting middle-aged and elderly people aged 45 years and above from the 2020 China Health and Retirement Longitudinal Survey(CDHARLS),K-means clustering method was used to divide cognitive function into high score group and low score group.Logistic regression analysis was used to analyze the influencing factors of cognitive function,and a restricted cubic spline model was used to explore the dose-response relationship between sleep and cognitive function.A structural equation model was constructed to analyze the mediating effect of depression between sleep status and cognitive function.Results Among 7964 middle-aged and elderly people,the cognitive function score was 13.49±2.99 points,the depression detection rate was 30.8％.After controlling for other confounding factors,the probability of cognitive decline in middle-aged and elderly people with a nighttime sleep duration of＞8hours was 1.4 times that of a nighttime sleep duration of 6-8hours,and the probability of cognitive decline in middle-aged and elderly people with a nap duration of 0-30minutes and≥90minutes was 1.20 times and 1.39 times that of a nap du-ration of 30-60minutes.The probability of cognitive decline in middle-aged and elderly people with depression was 1.52 times that of the non-depression group.The restricted cubic spline model showed that there was a U-shaped and J-shaped correlation between nighttime and afternoon sleep duration and the risk of cognitive decline.The results of the structural equation model showed that sleep sta-tus had a direct effect on cognitive function(β=-0.278,95％CI:-0.376--0.186),while depression has an indirect effect be-tween sleep status and cognitive function(β=0.152,95％CI:0.111-0.201).Conclusion The sleep status,depression,and cogni-tive function of middle-aged and elderly people in China are closely related,and depression mediates the relationship between sleep sta-tus and cognitive decline a certain extent.