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Rhinoplasty can achieve both aesthetic and functional improvements by altering the internal and external structures of the nose. Given the complex anatomy and significant individual variability of the nasal structure, rhinoplasty requires high precision. 3D printing technology, with its advantages of personalization and rapid prototyping, is increasingly being applied in the field of nasal reconstruction. This technology allows for the creation of three-dimensional models from patient imaging data, enhancing preoperative planning and simulation, enabling custom prosthetic design, and providing intraoperative guidance. Additionally, it aids in postoperative evaluation and care, while providing preoperative training models for surgeons. Clinical applications demonstrate that 3D printing can shorten surgical time, reduce complications, and improve patient satisfaction. However, 3D printing still faces several challenges, including insufficient imaging accuracy, high costs, and technical limitations in bioprinting. Future advancements are needed in imaging and modeling technologies, the development of advanced biomaterials, and cost reduction to further enhance and expand the application of 3D printing in rhinoplasty.
With the increasing aging population, the health management of older people has become an increasingly important issue. The author aims to meet the health needs of older people by designing a wearable device and a vital sign monitoring system based on optical sensing technology. The author analyzed the system's requirements and functions, provided the overall design architecture, and logically divided the system into the following functional modules: wireless environment monitoring, elderly physiological parameter collection, indoor area positioning, network node management, and the cloud platform. The functions that each module needs to implement were analyzed. Using the STM32F103C8T6 microcontroller as the core control device, combined with DS18B20 and MAX30102 sensors, it collects wrist temperature, heart rate, and blood oxygen data. The MPU6050 gyroscope is used to detect changes in human body deflection angle and acceleration, serving as the basis for fall detection. LCD screens and buttons are used to achieve human-computer interaction. The test results show that the average error between wrist temperature and forehead temperature is about 0.2 °C, and the mistake with armpit temperature is about 0.6 °C; In terms of heart rate measurement, the photodegradation method can obtain reliable human heart rate and blood oxygen signals, and the system measurement values match well with the heart rate image output by PGG; In terms of fall detection, the fusion of deflection angle and acceleration changes can improve the accuracy of fall detection to 95.2%. This system has the characteristics of small size, high precision, low power consumption, and ease of wear, and offers a wide range of application prospects.
The porcine epidemic diarrhea virus (PEDV) causes a gastrointestinal disease generating mortality rates approaching 100% in piglets worldwide. The S glycoprotein of PEDV is the main target for the development of vaccines. Two vaccines approved by the Ministry of Agriculture and Rural Development are used in Mexico: the first vaccine is based on an inactivated virus isolated more than a decade ago, whereas the second vaccine is based on mRNA technology. The most important tool for controlling PEDV outbreaks is vaccination; however, coronaviruses are characterized by the accumulation of multiple mutations, which compromise the immune response elicited by outdated vaccines. In this work, we classified the Mexican strains of PEDV reported so far in GenBank, according to their genotypes. Subsequently, we searched for B and T cell epitopes conserved in Mexican PEDV strains using bioinformatic tools. In addition, we explored whether these epitopes can induce allergies, autoimmunity, and/or toxic effects. Next, we determined the localization of B cell epitopes in the S glycoprotein using the protein crystal and protein modeling of several S glycoproteins. Finally, we carried out molecular docking analysis to assess whether these T cell epitopes could interact with the peptide-binding groove of the Swine Leukocyte Antigens (SLAs). Five conserved B cell epitopes were found to be exposed on the surface of the S glycoprotein, whereas several promiscuous CTL and HTL epitopes were bound, with low free energy, to the peptide-binding grooves of SLA-I and SLA-II, respectively. The best epitopes were used to generate a plasmid carrying the sequence to produce a recombinant protein. This plasmid was used for transfection experiments in PK-15 cell culture. The B cell epitopes reported here were recognized by the sera from pigs infected with PEDV but not by the sera from uninfected animals. These results justify future evaluations of the ability of these epitopes to stimulate cytokine production by T cells, antibody generation, and their neutralizing activity.
Acoustic ejection mass spectrometry has emerged as a powerful bioanalytical tool by combining the precision and accuracy of mass spectrometry with the speed provided by acoustic dispensing technology. Based on the variety of analytes that can be quantified with this method, we asked whether it could cover the range of pharmacologically relevant molecules transported by members of the solute carrier protein family and potentially become a universal assay principle for this class of drug targets. Here, we report the establishment of a high-throughput compatible assay platform based on acoustic ejection mass spectrometry that enables quantification of a variety of SLC substrates. As proof-of-concept, we set up cell-based assays for transporters belonging to several SLC sub-families, and used key optimization parameters, to assess method development steps deemed critical for successful application of the technology. Finally, we demonstrate its scalability for hit identification purposes, by performing a pilot HTS campaign for SGLT2, a prominent drug target among the members of the SLC family. Our study redefines the landscape of enabling technologies for SLCs, offering a powerful and versatile toolbox for the identification and mechanistic characterization of SLC modulators.
Myocarditis, as an important cardiovascular disease, involves complex pathogenic mechanisms including immune dysregulation and metabolic disorders. The Traditional Chinese Medicine (TCM) theory of "simultaneous treatment of heart and spleen" has accumulated substantial clinical experience in treating myocarditis, yet its underlying mechanisms remain insufficiently elucidated. The development of drug nanodelivery systems has revolutionized targeted therapy by enhancing drug bioavailability, prolonging circulation time, and enabling precise delivery to diseased tissues. This study aims to systematically analyze the molecular mechanisms by which Xin-Pi simultaneous treatment formula, when formulated into nanocarriers, regulates myocarditis through machine learning approaches, with particular emphasis on its regulation of macrophage polarization and the TEAD2/PKM2 signaling pathway via enhanced nanodelivery-mediated targeting. This study employed a multi-dimensional integration strategy combining network pharmacology, machine learning algorithms, and multi-omics technologies. Active compounds and targets of representative Xin-Pi simultaneous treatment formulae were retrieved from TCMSP, with core targets screened using machine learning algorithms such as Random Forest, Support Vector Machine, and XGBoost. A myocarditis mouse model was established, and single-cell RNA sequencing technology was utilized to analyze dynamic changes and polarization characteristics of macrophage subpopulations. Spatial transcriptomics technology was employed to map the spatial distribution patterns of key molecules in myocardial tissue. Western blot, immunofluorescence, and qRT-PCR were used to validate expression changes in the TEAD2/PKM2 signaling pathway. Metabolomics and proteomics technologies were applied to comprehensively analyze the multi-target regulatory network of the Xin-Pi simultaneous treatment formula. The drug nanodelivery system demonstrated superior pharmacokinetic profiles with 3.5-fold increased cardiac tissue accumulation compared to free drug formulation (P < 0.001). Nanoparticles showed preferential uptake by inflammatory macrophages in the myocardial infarct border zone, achieving 4.2-fold higher intracellular drug concentration than non-targeted nanoparticles. Machine learning models successfully identified 68 core targets, with TEAD2, PKM2, TNF-α, and IL-1β ranking at the forefront, achieving a prediction accuracy of 92.3%. Single-cell sequencing analysis revealed seven macrophage subpopulations in myocarditis tissue. Following nano-formulated Xin-Pi formula intervention, the proportion of M1-type pro-inflammatory macrophages significantly decreased (from 42.6% to 18.3%, P < 0.001), while M2-type anti-inflammatory macrophages significantly increased (from 15.7% to 38.9%, P < 0.001). Spatial transcriptomics analysis identified six functional regions in myocardial tissue. Molecular mechanism studies demonstrated that the nano-formulated Xin-Pi formula significantly upregulated TEAD2 expression (3.6-fold, P < 0.001) and downregulated PKM2 expression (0.28-fold, P < 0.001). Metabolomics analysis identified 32 differential metabolites and proteomics identified 156 differentially expressed proteins. This study provides the first systematic elucidation of the molecular mechanisms by which Xin-Pi simultaneous treatment formula, delivered via advanced nanocarrier systems, repairs myocarditis injury through regulating macrophage M1/M2 polarization balance and the TEAD2/PKM2 metabolic reprogramming network. The findings provide preclinical mechanistic evidence supporting future translational investigation; validation in human cohorts and clinical trials is required before clinical translation can be claimed.
Organ-on-chips (OoC) have the potential to revolutionize drug testing. However, the fragmented landscape of existing OoC systems leads to wasted resources and collaboration barriers, slowing broader adoption. To unite the ecosystem, there is an urgent need for generic OoC platforms based on interoperability and modularity. Technology platforms based on open designs would enable seamless integration of diverse OoC models and components, facilitating translation. Our study introduces a modular microfluidic platform that integrates swappable modules for pumping, sensing, and OoCs, all within the ANSI/SLAS microplate footprint. Sub-components operate as microfluidic building blocks (MFBBs) and can interface with the demonstrated fluidic circuit board (FCB) universally as long as the designs adhere to ISO standards. The platform architecture allows tube-less inter-module interactions via arbitrary and reconfigurable fluidic circuits. We demonstrate two possible fluidic configurations which include in-line sensors and furthermore demonstrate biological functionality by running both in vitro and ex vivo OoC models for multiple days. This platform is designed to support automated multi-organ experiments, independent of the OoC type or material. All designs shown are made open source to encourage broader compatibility and collaboration.
Plasma proteomics and metabolomics snapshots reveal a molecular signature in circulation delineating pathophysiology of major and minor neurocognitive disorder. To identify new cues to disease aetiology and diagnostic approach, we applied plasma proteomics and metabolomics profiling platforms to samples collected in a population-based study of the Singapore Longitudinal Ageing Studies Wave 2 (SLAS-2). In this longitudinal study, blood samples were analysed with standard clinical chemistry, plasma proteomics (Sengenics) and metabolomics (Nightingale) panels. Participants were followed up for the development of mild cognitive impairment (MCI) and dementia for 3-5 years. Of the total 1,892 molecules in all assay types, 463 demonstrated significant associations with baseline prevalent MCI and dementia. We trained an automatic linear modelling of predictors for follow-up new-onset MCI and dementia. The best model consists of 10 variables including ZSCAN18, PRKD3, SPANXN4, DDX43, saturated fatty acids, PPP3CA, NFATC4, IL-8, PAK6, and PDGFB. In terms of molecular function, these molecular markers are involved in immunological dysfunction and inflammatory reaction, protein coding, lipids, DNA-binding transcription factor activity, and nervous system development. In conclusion, our current research has identified an omics signature linked to new-onset mild cognitive disorder and dementia, which we hope can help enhance the accuracy of their diagnosis using circulating blood samples.
G protein-coupled receptors (GPCRs) represent one of the most important target classes in drug discovery, yet many remain pharmacologically underexplored due to limited biological knowledge and technical challenges associated with their characterisation. Here, we evaluate grating-coupled interferometry (GCI) as a versatile optical biosensor platform for GPCR research. Using the human adenosine A2A receptor (A2AR) as a model system, we demonstrate that GCI provides high-quality kinetic data that correlates with data obtained using well-established Biacore technology. When properly set up, the waveRAPID injection method enables fast and reliable determination of binding kinetics and affinities from single-concentration injections. We further extend this approach to determine thermodynamic parameters for diverse A2AR ligands using minimal protein consumption, by combining waveRAPID injections with the fast temperature changes available on the WAVEdelta system. Finally, we performed a kinetic fragment screening using a 704-member fragment library to identify specific A2AR binders. Nano differential scanning fluorimetry (nanoDSF) was subsequently applied to confirm binding for the identified hits. Collectively, this study establishes GCI-based analysis as a powerful and information-rich platform for kinetic, thermodynamic, and fragment-based discovery on GPCR targets, which is particularly valuable for early-stage drug discovery.
The glomerulus is the filtering unit of the kidney, and the glomerular filtration barrier (GFB) is responsible for filtering waste, retaining plasma protein, and maintaining fluid balance. Drug-induced nephrotoxicity is characterized by dysfunction of the GFB and is a main obstacle in the new therapeutic screening process. This paper presents the simple and robust SLAS (Society of Laboratory Automation and Society) standard format-based microfluidic GFB-on-a-chip (GFBoC). We formed and cultured the GFB by aligning human glomerular mesangial cells (gMCs), podocytes, and glomerular endothelial cells (gECs) on each side of a conventional transwell membrane as the glomerular basement membrane (GBM). This provides facile loading/unloading of the GFB transwell into the microfluidic chip, enabling its cultivation under pump-/tubing-less perfusion flow and additional off-chip bioanalysis. It also allows various and multiple experiments in parallel in a conventional incubator at moderate operation complexity. Glomerular selective permeability of the GFB was characterized by filtration and leakage of the representative macromolecule, albumin, via the GFB, while the drug doxorubicin affected the GFB during cultivation in the GFBoC. This demonstrated that the GFBoC has potential as a simple, robust, and efficient platform for the multiple testing of nephrotoxicity and kidney disease drugs in parallel.
To explore the application value of DTI combined with DTT in brain function and volume in children with autism. A total of 616 children with autism diagnosed from January 2020 to December 2022 (ASD group) and 91 healthy children with age and gender matching (control group) were included in the study. All subjects underwent DTI and DTT examinations. The DTI-DTT examination was conducted to analyze the Fractional Anisotropy (FA) values of key brain regions such as the corpus callosum and internal capsule, and the correlation and diagnostic efficacy were analyzed with the scores of the Autism Behavior Checklist (ABC). The total scores of ABC and each factor in the ASD group were significantly higher than those in the control group (p<0.05). The FA values of the knee joint and compressed part of the corpus callosum as well as the anterior and posterior limbs of the internal capsule in the ASD group were significantly higher than those in the control group (p<0.05). Relevant analysis showed that the FA values of the anterior and posterior limbs of the capsule in the ASD group were moderately positively correlated with the scores of sensory and body movement factors in the ABC scale (p<0.01). The FA values of the knee and compression parts of the corpus callosum were also moderately positively correlated with the communication and connection factor scores (p<0.01). ROC curve analysis indicated that the FA values of the above-mentioned brain regions had a high diagnostic value for ASD (AUC values were all >0.64). The combination of DTI and DTT effectively reveals the microstructure abnormalities of the main white matter pathways (such as the corpus callosum and internal capsule) in children with autism. These abnormalities are significantly correlated with specific behavioral symptoms. This combined imaging technology provides important neuroimaging evidence for the early objective diagnosis and rehabilitation intervention of children with autism.
Gene therapy holds a great potential for treating a variety of human disorders by introducing foreign therapeutic genetic material into target cells using gene carriers (vectors). Most particularly, viral vectors show a notable effectiveness in newly available assays and alternative evaluation models developed to reduce the use of mammalian models in gene therapy research and across a broad spectrum of disease indications, showcasing encouraging results both in preclinical investigations and in clinical trials. This overview work aims at thoroughly exploring the pivotal role of viral vectors in gene therapy, comparing their efficacy across various alternative evaluation models and therapeutic applications that we investigate in our own research, with the exception of vaccination strategies. Challenges that need to be overcome to use viral vectors safely and across different fields are also discussed, as well as the advancements in viral vector technology, focusing on improving their stability and safety. Understanding the nuances of viral vector-mediated gene delivery is crucial to optimize the current gene therapy strategies and to establish effective clinical procedures. Incorporating the alternative evaluation models discussed in this review could advance the broader use of viral vectors in gene therapy by addressing current challenges while also reducing the reliance on experimental animals to adhere to the 3Rs principle (reduction, refinement, replacement).
Different biochemical assays yield different rates of false positives than others either due to the nature of the enzyme, the technology associated with the bioassay, or properties of the compounds being screened. Ensuring that the right counter-screens are in place to identify false positives without wasting time and resources on them is of great importance. Herein we describe the results of a high throughput screen (HTS) against non-structural protein 3 (nsp3) protease PLPro, which resulted exclusively in false-positive hits. By triaging hit compounds through purification of metal chelating resin, we identified contamination by either copper or palladium as the most likely source of false positives from the library screening campaign. We then performed a systematic assessment of the vulnerability of nsp3 protease screening to metal contamination and evaluated common additives to combat the inhibitory effects of different metal salts. We further conducted a thorough survey of the literature reports of nsp3 HTS campaigns with a focus on the presence of additives and what metal susceptibility was likely, given the results of our work. We conclude that the majority of reported nsp3 screens are susceptible to copper contamination with a smaller proportion also potentially susceptible to palladium contamination.
Membrane proteins represent more than half of therapeutic targets but remain underrepresented in quantitative ligand-binding datasets. Here we report Binder2030, a curated affinity selection-mass spectrometry (ASMS) dataset comprising 3384 small-molecule ligands across approximately 400 transmembrane proteins, including G protein-coupled receptors (GPCRs), solute carrier (SLC) transporters, and ion channels. Using Binder Selection Technology (BST) applied to membrane fractions, Binder2030 provides standardized dissociation constant (Kd) measurements with curated chemical identifiers and target annotations, enabling comparative analysis of affinity distributions and chemical space across target classes. To support external anchoring, we release a PubChem-overlap subset with matched activity annotations and Binder2030 Kd values. Finally, we demonstrate downstream integration in a structure-based modeling workflow by comparing Boltz-2 predicted potencies with experimental affinities for a GlyT-1 ligand set.
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This letter addresses the review by Liu and Xie on AI-empowered rehabilitation models, specifically regarding the integration of real-time assessments and adaptive interventions. While the authors present compelling figures about recovery time reduction and increased adherence in AI models, the basis for these figures remains unclear. The letter requests clarification on the sources and definitions of these metrics, such as recovery time and adherence, and how they relate to algorithm performance and clinical outcomes. A more precise mapping of the quantified claims to specific sources would improve the clarity and applicability of the proposed model for clinical use.
Periodontitis combined with orthodontic treatment creates a complex inflammatory and biomechanical microenvironment in gingival tissue, where macrophages play pivotal roles in tissue remodeling and immune regulation. However, the heterogeneity of macrophage subtypes and their polarization dynamics in this clinical context remain poorly understood. We performed single-cell RNA sequencing (scRNA-seq) on gingival tissue samples from periodontitis patients undergoing orthodontic treatment. Using integrated bioinformatics approaches including dimensionality reduction, pseudotime trajectory analysis, RNA velocity, cell-cell communication inference, and functional enrichment analysis, we comprehensively characterized macrophage subtypes, their polarization states, and intercellular signaling networks. We identified 11 distinct cell populations with significant macrophage heterogeneity, including M1-like pro-inflammatory and M2-like tissue-remodeling subtypes along with intermediate transitional states. Trajectory analysis revealed dynamic polarization pathways with multiple branching points, demonstrating remarkable phenotypic plasticity. Macrophages served as central communication hubs, engaging in extensive crosstalk with fibroblasts, epithelial cells, endothelial cells, and lymphocytes through cytokine, growth factor, chemokine, and extracellular matrix signaling pathways. Transcription factor analysis identified NF-κB, STAT1, and IRF family members driving M1 polarization, while PPARγ, C/EBPβ, and KLF4 promoted M2 phenotypes. Telomere maintenance gene profiling revealed differential cellular aging patterns across subtypes, with pro-inflammatory macrophages showing stress-induced senescence signatures. Functional enrichment demonstrated that macrophages integrate inflammatory responses with mechanotransduction pathways, responding to both periodontal pathogens and orthodontic mechanical forces. This single-cell transcriptomic atlas provides unprecedented insights into macrophage heterogeneity and functional specialization in periodontitis patients undergoing orthodontic treatment.
Cardiac complications arising from diabetes mellitus manifest as structural and functional myocardial alterations independent of traditional cardiovascular risk factors. The intricate molecular underpinnings driving disease evolution and the spectrum of cellular diversity remain inadequately characterized. Our investigation sought to systematically elucidate the transcriptional architecture and intercellular signaling frameworks in diabetes-associated myocardial dysfunction through integrated omics methodologies. We executed parallel bulk and single-cell transcriptomic profiling of cardiac specimens from diabetic disease models. Gene expression disparities were determined via DESeq2 employing stringent thresholds (|log₂FC| > 1; FDR < 0.05). Quality control applied specific thresholds (200-6000 genes/cell, <20% mitochondrial content), with clustering resolution optimized at 0.8 for main cell types and 1.2 for fibroblast subclustering. Single-cell datasets underwent Seurat-based processing with t-distributed stochastic neighbor embedding for population delineation. WGCNA employed soft-thresholding (R² > 0.85), minimum module size of 30 genes, and merge cut height of 0.25. Co-expression module detection within fibroblast subsets was achieved through weighted correlation network construction. Functional annotation leveraged GO and KEGG repositories. CellChat analysis incorporated permutation-based significance testing (n = 100, p < 0.05) with CellPhoneDB validation. Intercellular signaling topology was reconstructed using CellChat, emphasizing macrophage migration inhibitory factor circuitry. Transcriptional profiling unveiled 2000 dysregulated transcripts against 28,840 stable genes, demonstrating substantial reprogramming during pathogenesis. Single-cell resolution exposed profound cellular heterogeneity encompassing myocytes, endothelium, fibroblasts, myeloid cells, and specialized populations including metabolic coordinators and stress-activated subsets. Granular fibroblast dissection revealed 21 molecularly distinct subtypes (designated M1-M21), underscoring remarkable intra-lineage diversity. Enrichment analyses highlighted perturbations in matrix architecture, inflammatory cascades, and proliferative control. Network analysis identified co-regulated gene clusters governing matrix remodeling, inflammation, and metabolic homeostasis. Communication mapping positioned MIF signaling as a pivotal intercellular coordination axis, with stress-responsive cells functioning as nodal integrators throughout disease progression. This integrative multi-platform investigation provides comprehensive molecular characterization of diabetes-induced cardiac pathology, revealing extensive cellular heterogeneity and intricate communication networks that extend previous single-cell cardiac studies.
Osteoarthritis is the predominant joint ailment. Multiple studies demonstrate that the dysregulation of catalytic regulators of ubiquitination and deubiquitination disrupts cartilage imbalance, consequently facilitating the advancement of osteoarthritis. Ubiquitination-related biomarkers for osteoarthritis were found by differential expression, weighted gene co-expression network analysis, Mendelian randomization, Receiver Operating Characteristic curves, and expression analyses. Subsequently, an examination of immune infiltration was conducted to evaluate the contrasting immunological circumstances between osteoarthritis and controls. Additionally, single-cell analysis was employed to screen key cell types, and analyze the expression of biomarkers during their differentiation. The expression of biomarkers was subsequently validated using real time quantitative polymerase chain reaction. CBLB and NQO2 were determined as biomarkers, having risk effects on osteoarthritis (odd ratio > 1). Analysis of immune infiltration indicated a significant disparity in the number of 15 immune cell types between osteoarthritis and control groups, such as type 2 T helper cells and macrophages, and two biomarkers showed opposite associations with these immune cells. Single-cell analysis annotated seven cell types, with prehypertrophic chondrocytes as the key cells. Notably, two biomarkers had expression early and late stages during prehypertrophic chondrocytes differentiation. Finally, experiments analysis indicated that CBLB decreased and NQO2 increased in osteoarthritis samples. CBLB and NQO2 were biomarkers associated with ubiquitination that exert causal effects on osteoarthritis. These findings provide potential therapeutic targets for clinical intervention and help to personalize treatment for osteoarthritis patients.