ObjectiveProtein-protein interactions (PPIs) are fundamental to the execution of biological functions within living cells. However, traditional biochemical methods, such as co-immunoprecipitation (Co-IP), often fail to capture transient, weak, or membrane-associated interactions due to the stringent detergent requirements for cell lysis. Proximity labeling (PL) has emerged in recent years as a transformative technology for mapping the proteomes of specific subcellular compartments and identifying dynamic interactomes in situ. Golgi protein 73 (GP73, also known as GOLPH2), a resident type II Golgi transmembrane protein, is a well-recognized clinical biomarker for liver diseases, including hepatocellular carcinoma (HCC). Despite its clinical significance, the comprehensive physiological and pathological functions of GP73 remain partially understood. This study aims to establish an APEX2-mediated proximity labeling system specifically targeting GP73 to map its interactome in a living cellular environment, thereby providing new insights into its molecular roles and regulatory mechanisms. MethodsTo achieve spatial specificity, we first constructed a stable cell line expressing a fusion protein consisting of GP73 and the engineered soybean peroxidase APEX2. The localization of the GP73-APEX2 fusion protein was validated to ensure it correctly targeted the Golgi apparatus. The proximity labeling reaction was initiated by incubating the cells with biotin-phenol (BP) for 30 min, followed by a brief (1 min) treatment with1 mmol/L hydrogen peroxide (H₂O₂). This catalytic reaction converts BP into highly reactive, short-lived biotin-phenoxyl radicals that covalently attach to endogenous proteins within a small labeling radius of the GP73-APEX2 enzyme. Subsequently, the cells were quenched, and biotinylated proteins were enriched using high-affinity streptavidin-coated magnetic beads. The captured “neighbor” proteins were subjected to on-bead digestion and analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS) for high-throughput identification. Rigorous bioinformatics analysis, including Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction network mapping, was performed to interpret the biological significance of the identified candidates. ResultsOur results demonstrate the successful establishment of a robust and sensitive APEX2-based proximity labeling system for GP73. We identified a total of 95 high-confidence interacting proteins that were significantly enriched in the GP73 proximity proteome compared to control groups. Bioinformatics analysis revealed that these interactors were predominantly associated with biological processes such as vesicular transport, protein localization, and, most notably, molecular functions related to “ribosome binding” and “translation regulation”. This suggested an unexpected role for the Golgi-resident GP73 in the cellular translation machinery. To validate these findings, we performed targeted biochemical assays which confirmed a direct interaction between GP73 and the subunits of the eukaryotic translation initiation factor 3 (eIF3) complex, specifically EIF3G and EIF3I. Furthermore, functional validation using the surface sensing of translation (SUnSET) assay—a non-radioactive method to monitor protein synthesis—revealed that the overexpression of GP73 significantly promoted global protein translation levels in the cell, whereas its depletion or inhibition resulted in reduced translation efficiency. ConclusionThis study successfully utilized APEX2-mediated proximity labeling to provide the first systematic map of GP73 interactome in living cells. Our findings uncover a novel, unconventional function of GP73 as a regulator of cellular protein translation, likely mediated through its interaction with the eIF3 complex. This discovery significantly broadens our understanding of the biological roles of GP73 beyond its traditional function in the Golgi apparatus and suggests that it may act as a bridge between Golgi-related trafficking and the protein synthesis machinery. Furthermore, the technical framework established in this study provides a valuable template for investigating other complex organelle-associated protein networks and resolving transient macromolecular interactions in various physiological and pathological contexts.

Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.

Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

This study aims to explore the scientific connotation of sinking Rehmanniae Radix has the best quality and compare the quality between floating and sinking fresh Rehmanniae Radix samples. Ultra-performance liquid chromatography tandem quadrupole electrostatic field Orbitrap high-resolution mass spectrometry(UHPLC-Q-Orbitrap HRMS) was employed to detect the chemical components in floating and sinking fresh Rehmanniae Radix samples. The fingerprint of fresh Rehmanniae Radix was established by high performance liquid chromatography(HPLC), and four index components were determined simultaneously. The cluster analysis, principal component analysis(PCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA) were conducted to compare the quality of floating and sinking fresh Rehmanniae Radix samples. An evaporative light-scattering detector was used to compare the content of five sugars. The extract yield and drying rate were determined, and the quality connotation of sinking Rehmanniae Radix has the best quality was explained by multiple indicators. A total of 41 components were preliminarily identified from fresh Rehmanniae Radix by UHPLC-Q-Orbitrap HRMS, including 7 iridoid glycosides, 9 phenylethanol glycosides, 6 amino acids, 4 sugars, 3 phenolic acids, 5 nucleosides, 3 organic acids, 1 ionone, 1 furan, 1 coumarin, and 1 phenylpropanoid. The results showed that the main chemical components were consistent between floating and sinking fresh Rehmanniae Radix. Nine common peaks were identified in the fingerprints of 15 batches of floating and sinking fresh Rehmanniae Radix samples, and the similarity of fingerprints was greater than 0.9. The cluster analysis, PCA, and OPLS-DA classified floating and sinking fresh Rehmanniae Radix sasmples into two categories, indicating differences in the quality between them. The total content of catalpol, rehmannioside D, ajugol, and verbascoside in sinking fresh Rehmanniae Radix samples was higher than that in floating samples of the same batch and specification, and the main differential component was catalpol. The total content of fructose, glucose, sucrose, raffinose, and stachyose in sinking fresh Rehmanniae Radix samples was higher than that in floating samples of the same batch and specification, and the main differential component was stachyose. The extract yield and drying rate of the sinking samples were higher than those of floating samples. This study preliminarily showed that floating and sinking fresh Rehmanniae Radix samples had the same components but great differences in the content of medicinal substance basis. The total content of four glycosides and five sugars, extract yield, and drying rate of sinking fresh Rehmanniae Radix samples is higher than that of floating samples of the same batch and specification. These findings, to a certain extent, explains the scientificity of sinking Rehmanniae Radix has the best quality recorded in ancient books and provide a reference for the quality control and clinical application of fresh Rehmanniae Radix.
Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Rehmannia/chemistry* ; Chemometrics ; Mass Spectrometry/methods* ; Quality Control ; Principal Component Analysis ; Plant Extracts

Poor water solubility is the primary obstacle preventing the development of many pharmacologically active compounds into oral preparations. Self-nanoemulsifying drug delivery systems(SNEDDS) have become a widely used strategy to enhance the oral bioavailability of poorly soluble drugs by inducing a supersaturated state, thereby improving their apparent solubility and dissolution rate. However, the supersaturated solutions formed in SNEDDS are thermodynamically unstable systems with solubility levels exceeding the crystalline equilibrium solubility, making them prone to drug precipitation in the gastrointestinal tract and ultimately hindering drug absorption. Therefore, maintaining a stable supersaturated state is crucial for the effective delivery of poorly soluble drugs. Incorporating polymers as precipitation inhibitors(PPIs) into the formulation of supersaturated self-nanoemulsifying drug delivery systems(S-SNEDDS) can inhibit drug aggregation and crystallization, thus maintaining a stable supersaturated state. This has emerged as a novel preparation strategy and a key focus in SNEDDS research. This review explores the preparation design of SNEDDS and the technical challenges involved, with a particular focus on polymer-based S-SNEDDS for enhancing the solubility and oral bioavailability of poorly soluble drugs. It further elucidates the mechanisms by which polymers participate in transmembrane transport, summarizes the principles by which polymers sustain a supersaturated state, and discusses strategies for enhancing drug absorption. Altogether, this review provides a structured framework for the development of S-SNEDDS preparations with stable quality and reduced development risk, and offers a theoretical reference for the application of S-SNEDDS technology in improving the oral bioavailability of poorly soluble drugs.
Solubility ; Administration, Oral ; Polymers/chemistry* ; Drug Delivery Systems/methods* ; Humans ; Emulsions/chemistry* ; Biological Availability ; Animals ; Pharmaceutical Preparations/administration & dosage*

Panax ginseng as a perennial herb of Araliaceae, exhibits pharmacological effects such as central nervous system stimulation, anti-tumor properties, and cardiovascular and cerebrovascular protection. The B3 gene family plays a crucial role in growth and development, antioxidant activity, stress resistance, and secondary metabolism regulation of plants and has been extensively studied in various plants. However, the identification and analysis of the B3 gene family in P. ginseng have not been reported. In this study, a total of 145 B3 genes(PgB3s) with complete open reading frames(ORF) were identified from P. ginseng and classified into five subfamilies based on domain types. Through correlation analysis with ginsenoside content, SNP/InDels analysis, and interaction analysis with key enzyme genes, 15 PgB3 transcripts were found to be significantly correlated with ginsenoside content and exhibited a close interaction network with key enzyme genes involved in ginsenoside biosynthesis, which indicated that these genes may participate in the regulation of ginsenoside biosynthesis. Additionally, this study found that PgB3 genes exhibited induced expression in response to methyl jasmonate(MeJA) stress, which aligned with the presence of abundant stress response elements in their promoters, confirming the important role of the B3 gene family in P. ginseng in stress resistance. The results of this study revealed the potential functions of PgB3 genes in ginsenoside biosynthesis and stress response, providing a significant theoretical basis for further research on the functions of PgB3 genes and their regulatory mechanisms.
Panax/metabolism* ; Gene Expression Regulation, Plant ; Plant Proteins/metabolism* ; Ginsenosides/biosynthesis* ; Multigene Family ; Phylogeny

OBJECTIVE: To establish digital model of Ideberg typeⅡregional glenoid fracture anatomical plate with 3D metal printing technology. METHODS: The scapular imaging data of a 34-year-old healthy male volunteer were retrospectively selected. Mimics 15.01, NX 12.0 and other software were used to design Ideberg typeⅡ regional scapular fracture guide plate system. STL data were input into a metal 3D printer to print 1∶1 scapular model and anatomical plate of scapular pelvis with guide sleeve. The fit of the plate was tested in vitro and the accuracy of the screw position was evaluated by imaging. The printing time of scapular model, design of the nail path and making time of the anatomic guided plate were recorded. RESULTS: 3D metal-printed Ideberg typeⅡ guide plate for scapular fracture fitted well to 3D printed scapular model, the locking screw was oriented accurately, and X-ray and CT showed good screw position. The printing time of scapula model, time of nail path design and special-shaped anatomical guide plate production were 52.0, 15.0 and 320 min, respectively. CONCLUSION Anatomical plates based on 3D metal printing technology could achieve good adhesion of Ideberg typeⅡ regional fractures and precise screw placement, providing a new and accurate surgical method for the treatment of Ideberg typeⅡ glenoid fractures.
Humans ; Printing, Three-Dimensional ; Male ; Adult ; Bone Plates ; Fractures, Bone/diagnostic imaging* ; Fracture Fixation, Internal/methods* ; Pelvic Bones/surgery* ; Metals ; Scapula/surgery* ; Models, Anatomic

OBJECTIVE: To evaluate the effects of posterior cruciate ligament(PCL) resection on soft tissue elasticity and knee stability in total knee arthroplasty(TKA). METHODS: Six adult cadaveric knee specimens (involving 10 knees) were included in the study. With the assistance of the robotic system(TiRobot Recon, TINAVI, Beijing), total knee arthroplasty (TKA) was performed sequentially using cruciate retaining (CR) prostheses and posterior stabilizing (PS) prostheses. Between the two surgical procedures, the femoral and tibial osteotomy surfaces were not altered;only the posterior cruciate ligament (PCL) was resected and the intercondylar fossa was treated. After installing the femoral trial component, a soft tissue balance solver was used to apply tension ranging from 30 N to 90 N in 5 N increments at 0°, 10°, and 90° of knee flexion. Meanwhile, the medial and lateral joint gaps were measured synchronously. Based on the tension-gap coupling data, the equivalent elastic coefficients of the medial and lateral soft tissue sleeves at different knee flexion angles, as well as the range of the joint line convergence angle (JLCA) under fixed varus-valgus stress, were calculated. Additionally, the gap balance status under 80 N of tension was analyzed. Self-control comparisons of each indicator were conducted before and after PCL resection to analyze the change patterns. RESULTS: After PCL resection, in the fully extended position (knee flexion 0°). The medial equivalent elastic coefficient was 32.2 (25.7, 63.3) N·mm^-1 for the CR prosthesis and 27.7 (22.0, 51.9) N·mm^-1 for the PS prosthesis, and the statistically significant difference (P=0.013). The range of JLCA was 0.41°(0.26, 0.55)° for the CR prosthesis, which was smaller than 0.75° (0.40, 0.98)° for the PS prosthesis, and the difference was statistically significant(P=0.041). At 90° of knee flexion, the medial joint gap was 10.7(10.1, 11.7) mm for the CR prosthesis, which was smaller than 12.1(10.9, 15.1) mm for the PS prosthesis, with a statistically significant difference(P=0.011). No statistically significant differences were observed in other joint gaps. CONCLUSION PCL resection reduces the rigidity of the medial soft tissues in the fully extended knee and increases the medial joint gap in the flexed position, thereby affecting knee stability and balance. This finding suggests that PS and CR prostheses may require different morphological designs, and there should be differences in indications and osteotomy strategies between CR-TKA and PS-TKA. CR-TKA is more suitable for patients with preoperative medial soft tissue laxity.
Humans ; Posterior Cruciate Ligament/physiopathology* ; Knee Joint/physiopathology* ; Arthroplasty, Replacement, Knee ; Elasticity ; Male ; Female ; Middle Aged ; Aged ; Biomechanical Phenomena ; Adult

PURPOSE: The rate of complications among patients undergoing surgery has increased due to infection with SARS-CoV-2 and other variants of concern. However, Omicron has shown decreased pathogenicity, raising questions about the risk of postoperative complications among patients who are infected with this variant. This study aimed to investigate complications and related factors among patients with recent Omicron infection prior to undergoing orthopedic surgery. METHODS: A historical control study was conducted. Data were collected from all patients who underwent surgery during 2 distinct periods: (1) between Dec 12, 2022 and Jan 31, 2023 (COVID-19 positive group), (2) between Dec 12, 2021 and Jan 31, 2022 (COVID-19 negative control group). The patients were at least 18 years old. Patients who received conservative treatment after admission or had high-risk diseases or special circumstances (use of anticoagulants before surgery) were excluded from the study. The study outcomes were the total complication rate and related factors. Binary logistic regression analysis was used to identify related factors, and odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the impact of COVID-19 infection on complications. RESULTS: In the analysis, a total of 847 patients who underwent surgery were included, with 275 of these patients testing positive for COVID-19 and 572 testing negative. The COVID-19-positive group had a significantly higher rate of total complications (11.27%) than the control group (4.90%, p < 0.001). After adjusting for relevant factors, the OR was 3.08 (95% CI: 1.45-6.53). Patients who were diagnosed with COVID-19 at 3-4 weeks (OR = 0.20 (95% CI: 0.06-0.59), p = 0.005), 5-6 weeks (OR = 0.16 (95% CI: 0.04-0.59), p = 0.010), or ≥7 weeks (OR = 0.26 (95% CI: 0.06-1.02), p = 0.069) prior to surgery had a lower risk of complications than those who were diagnosed at 0-2 weeks prior to surgery. Seven factors (age, indications for surgery, time of operation, time of COVID-19 diagnosis prior to surgery, C-reactive protein levels, alanine transaminase levels, and aspartate aminotransferase levels) were found to be associated with complications; thus, these factors were used to create a nomogram. CONCLUSION Omicron continues to be a significant factor in the incidence of postoperative complications among patients undergoing orthopedic surgery. By identifying the factors associated with these complications, we can determine the optimal surgical timing, provide more accurate prognostic information, and offer appropriate consultation for orthopedic surgery patients who have been infected with Omicron.
Humans ; COVID-19/complications* ; Male ; Female ; Middle Aged ; Postoperative Complications/epidemiology* ; SARS-CoV-2 ; Orthopedic Procedures/adverse effects* ; Aged ; Nomograms ; Adult ; Retrospective Studies ; Risk Factors