Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

Steroid-induced osteonecrosis of the femoral head (SONFH) is avascular necrosis of the femoral head caused by long-erm use of corticosteroids, and its pathogenesis is complex and affected by changes in the dynamic balance of the bone microenvironment. With the deepening of research, the role of bone microenvironment in the pathogenesis of SONFH has been gradually revealed. In the case of excessive use of glucocorticoids (GCs), the bone microenvironment changes significantly, causing imbalance in bone lipid metabolism, microcirculation disorders and disorders of immune regulation, which promotes the increase of the number and activity of osteoclasts, and interferes with the differentiation of osteoblasts and adipoblasts. Through the regulation of PI3K/AKT, OPG/RANKL/RANK, MAPK, JAK/STAT, Hedgehog and other signaling pathways, it eventually leads to osteocyte apoptosis, bone microvascular rupture and destruction of trabecular bone structure, which in turn leads to osteonecrosis, bone density reduction and bone microstructure destruction due to bone microcirculation ischemia, and finally leads to necrosis of the femoral head. This article reviews the role of bone microenvironment homeostasis in GCs-induced ONFH and the regulatory mechanism of bone microenvironment, which is helpful to reveal the pathogenesis of SONFH and provide a theoretical basis for exploring effective intervention strategies.
Humans ; Femur Head Necrosis/physiopathology* ; Animals ; Signal Transduction ; Bone and Bones/metabolism* ; Glucocorticoids/adverse effects* ; Cellular Microenvironment

CRM 197(cross-reacting material 197),a naturally occurring mutant of diphtheria toxin,is a safe and effective vaccine vector and extensively used on developing conjugate or combined vaccines.The mutant loses its enzymatic activity,but fully retains its receptor-binding ability and immunogenicity.In current work,the diphtheria toxin mutant CRM 197 and its fusion proteins with the receptor-binding do-main of botulinum neurotoxin serotype E(EHc)were developed using genetic engineering technology.These recombinant proteins were confirmed by Western blotting and SDS-PAGE.BALB/c mice were im-munized with the CRM197-EHc and EHc-CRM197 fusion proteins,and their immunogenicity was evalua-ted.These two fusion protein molecules,CRM197-EHc and EHc-CRM197,as subunit vaccines,elicited a robust humoral immune response targeting both CRM197 and EHc antigens in the immunized mice.Compared to the mixture of CRM197 and EHc,the mice vaccinated with the fusion proteins(CRM197-EHc and EHc-CRM197)induced higher levels of anti-CRM197 antibodies,and the mice vaccinated with EHc-CRM197 also generated strongest anti-EHc antibodies.Consequently,as a carrier molecule in the fusion protein vaccine,EHc enhances the immunogenicity of CRM197 molecules.Likewise,CRM197 boosts the immunogenicity of EHc in the EHc-CRM197 fusion protein.

CRM 197(cross-reacting material 197),a naturally occurring mutant of diphtheria toxin,is a safe and effective vaccine vector and extensively used on developing conjugate or combined vaccines.The mutant loses its enzymatic activity,but fully retains its receptor-binding ability and immunogenicity.In current work,the diphtheria toxin mutant CRM 197 and its fusion proteins with the receptor-binding do-main of botulinum neurotoxin serotype E(EHc)were developed using genetic engineering technology.These recombinant proteins were confirmed by Western blotting and SDS-PAGE.BALB/c mice were im-munized with the CRM197-EHc and EHc-CRM197 fusion proteins,and their immunogenicity was evalua-ted.These two fusion protein molecules,CRM197-EHc and EHc-CRM197,as subunit vaccines,elicited a robust humoral immune response targeting both CRM197 and EHc antigens in the immunized mice.Compared to the mixture of CRM197 and EHc,the mice vaccinated with the fusion proteins(CRM197-EHc and EHc-CRM197)induced higher levels of anti-CRM197 antibodies,and the mice vaccinated with EHc-CRM197 also generated strongest anti-EHc antibodies.Consequently,as a carrier molecule in the fusion protein vaccine,EHc enhances the immunogenicity of CRM197 molecules.Likewise,CRM197 boosts the immunogenicity of EHc in the EHc-CRM197 fusion protein.

Based on whole genome data, the identification and expression pattern analysis of the Atropa belladonna WRKY transcription factor family were conducted to provide a theoretical foundation for studying the biological functions and mechanisms of these transcription factors. In this study, bioinformatics methods were employed to identify members of the A. belladonna WRKY gene family and to predict their physicochemical properties, conserved motifs, promoter cis-acting elements, and chromosomal localization. Additionally, the expression patterns of the A. belladonna WRKY gene family under the regulation of environmental factors such as light quality and temperature were analyzed. The results revealed a total of 28 AbWRKY transcription factors, randomly distributed across 16 chromosomes, encoding 324-707 amino acids. Most AbWRKY proteins were acidic, unstable, and hydrophilic. Based on multiple sequence alignment and phylogenetic analysis, the WRKY gene family members were classified into two subfamilies. Conserved motif and domain analysis indicated that WRKY transcription factors in the same subfamily possessed conserved structural features. Promoter analysis predicted that the A. belladonna WRKY family contained light-responsive elements, hormone-responsive elements, and stress-responsive elements. Collinearity analysis showed that AbWRKY24 plays a crucial role in the expansion of the AbWRKY gene family. Then qRT-PCR results indicated that AbWRKY6, AbWRKY8, AbWRKY14, and AbWRKY24 responded to red light stress, while AbWRKY8, AbWRKY14, and AbWRKY24 responded to yellow light/low-temperature combined stress. AbWRKY6 and AbWRKY8 were significantly expressed in leaves and stems, AbWRKY27 and AbWRKY28 were significantly expressed in fibrous roots, and AbWRKY25 was significantly expressed in flowers. This study is the first to identify and analyze the WRKY gene family in A. belladonna and to examine its expression patterns under light and temperature regulation, laying a foundation for in-depth analysis and functional validation of the molecular mechanisms of A. belladonna WRKY transcription factors in responding to light quality and temperature environmental factors.
Transcription Factors/chemistry* ; Plant Proteins/metabolism* ; Phylogeny ; Gene Expression Regulation, Plant ; Light ; Temperature ; Atropa belladonna/metabolism* ; Multigene Family/genetics* ; Promoter Regions, Genetic/genetics* ; Sequence Alignment ; Amino Acid Sequence ; Genome, Plant/genetics*

Objective To understand the research status of osteoimmunology by a bibliometric study and provide reference for potential research hotspots in the future. Methods The articles and reviews related to osteoimmunology were retrieved from the Web of Science Core Collection with the time interval from 2002 to 2022.VOSviewer,CiteSpace,and the Bibliometrix package in R were used to analyze the contributions and co-citation relationships of countries/regions,institutions,journals,authors,references,and keywords,and identify research hotspots. Results A total of 812 English-language articles published between 2002 and 2022 were collected,and the annual number of articles was increasing year by year.China had the most articles (n=233,28.69%),followed by the United States and Japan.Sichuan University had the highest number of articles (n=35,4.27%).Takayanagi H ranked first among both publishing authors and co-cited authors.Froniters in Immunology was the journal publishing the highest number of articles (n=45,impact factor of 7.3 in 2023) in this field.The clustering of key nodes and identification of keywords in co-cited references indicated that the research of osteoimmunology mainly focused on signal transduction mechanisms of bone immunity,bone immunity-mediated diseases,and drug treatment.In recent years,the research hotspots of osteoimmunology included macrophage polarization,bone biomaterials,bone regeneration,and therapy. Conclusion This study employed bibliometric methods to comprehensively analyze the countries,institutions,authors,keywords,and references of articles in osteoimmunology,providing guidance and reference for researchers engaged in this field.
Humans ; Bibliometrics ; China ; Allergy and Immunology ; Osteology

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Human-animal interaction has a long-standing tradition dating back to ancient times. With the rapid advancements in intelligent chips, wearable devices, and machine algorithms, the intelligent interaction between animals and electronic technology, facilitated by electronic devices and systems for communication, perception, and control, has become a reality. These electronic devices aim to implement an animal-centric working mode to enhance human understanding of animals and promote the development of animal intelligence and creativity. This article takes medium-sized and large animals as research objects, with the goal of developing their ability enhancement, and introduces the concept of “intelligent animal augmentation system (IAAS)”. This concept is used to describe the characteristics of such devices and provides a comprehensive overview of existing animal and computer interface solutions. In general, IAAS can be divided into implantable and non-implantable types, each composed of interface platforms, perception and interpretation, control and instruction components. Through various levels of enhancement systems and architectural patterns, intelligent interaction between humans and animals can be realized. Although existing IAAS still lack a complete independent interaction system architecture, they hold great promise and development space in the future. Not only can they be applied as substitutes for cutting-edge devices and transportation equipment, but they are also expected to achieve cross-species information interaction through intelligent interconnection. Additionally, IAAS can promote bidirectional interaction between humans and animals, playing a significant role in advancing animal ethics and ecological protection. Furthermore, the development of interaction models based on animal subjects can provide insightful research experiences for the design of human-computer interaction systems, thereby contributing to the more efficient realization of the ambitious goal of human-machine integration.

Microplastics(MPs)are emerging contaminants in aquatic environments characterized by their polar structure,small particle size(Typically less than 5 mm),large surface area,good stability,and resistance to biodegradation.They pose adverse effects on the normal physiological activities of aquatic organisms and can accumulate in biota,including humans.Therefore,there is an urgent need for rapid and accurate quantitative analysis of MPs in water environments.In this study,Raman spectroscopy combined with partial least squares(PLS)was employed for rapid and accurate quantitative analysis of polyethylene(PE)and polystyrene(PS)MPs in real water samples.Initially,33 simulated water samples containing different concentrations of MPs were prepared,and their Raman spectra were collected.Six spectral preprocessing methods(Normalization,multiplicative scatter correction,standard normal variate transformation,first derivative,second derivative,and wavelet transform)were investigated for their impact on the predictive performance of PLS calibration models.Subsequently,three variable selection methods including synergy interval partial least squares(SiPLS),variable importance in projection(VIP)and mutual information(MI)were employed to optimize the input variables of the PLS calibration model.The predictive capability of the PLS calibration model was evaluated and validated using leave-one-out cross-validation.Under the optimal conditions of spectral preprocessing,variable selection,input variables and latent variables,the wavelet transform-partial least squares(WT-PLS)calibration model based on distilled water was established,and the contents of PE and PS in real water samples were predicted with prediction correlation coefficients(R2p)of 0.9540 and 0.8472 for PE and PS,respectively,and prediction errors(Errorp)of 0.0690 and 0.1126,respectively.Furthermore,a mixed sample MI-PLS calibration model was developed,demonstrating the best predictive performance in real water samples(With R2p values of 0.9776 and 0.9755 for PE and PS,respectively,and Errorp values of 0.0360 and 0.0392,respectively).This method provided a novel approach and new methodology for quantitative analysis of MPs and other organic pollutants in real water samples.