Objective: To characterize perioperative dynamic changes in immune-cell phenotypes and inflammatory cytokines in patients undergoing CPB (cardiopulmonary bypass) cardiac surgery, and to explore their associations with postoperative outcomes. Methods: In this prospective cohort study, 120 adult patients who underwent elective cardiac surgery under CPB at West China Hospital from May 2022 to March 2023 were enrolled. Perioperative immune-cell phenotypes and concentrations of 40 inflammation-related cytokines were measured. The primary outcomes were the sequential organ failure assessment (SOFA) score at 24 h after surgery and ΔSOFA (the peak SOFA score within 48 h after surgery minus the preoperative SOFA score). Secondary outcomes included major adverse cardiovascular events (MACE), acute kidney injury (AKI), respiratory failure, severe liver injury, and infection. Results: The mean age of enrolled patients was 57±10 years. Of these, 52% (62/120) were male and 90% (108/120) underwent valve surgery. During the rewarming to the end of CPB, neutrophil counts rapidly increased (7.39×10 /L vs preoperative 3.07×10 /L, P<0.001), with significant upregulation of CD11b (7.30×10 /L vs preoperative 3.05×10 /L, P<0.001) and CD54 (7.15×10 /L vs preoperative 2.99×10 /L, P<0.001). Lymphocyte counts increased at the end of CPB (1.75×10 /L vs preoperative 1.12×10 /L, P<0.001) but decreased significantly at 24 h after surgery (0.59×10 /L vs preoperative 1.12×10 /L, P<0.001). Plasma analysis showed that multiple pro-inflammatory cytokines increased during CPB and remained elevated up to 24 h after surgery; five chemokines and the anti-inflammatory cytokine IL-10 peaked at the end of CPB. The SOFA score increased from 1 (1, 2) preoperatively to 7 (5, 10) at 24 h after surgery, with a ΔSOFA of 6 (4, 8). Within 30 days after surgery, 48 patients (40.0%) developed AKI, 17 (14.2%) developed infection, 4 (3.3%) developed severe liver injury, 3 (2.5%) developed respiratory failure, and 3 (2.5%) experienced MACE. During the 2-year follow-up, 8 patients (6.7%) experienced MACE and 5 (4.2%) died. Conclusion: Multi-organ dysfunction is common after cardiac surgery under CPB (median ΔSOFA, 6), accompanied by perioperative activation of multiple immune-cell subsets and upregulation of pro-inflammatory, anti-inflammatory, and chemotactic mediators. This study provides data-driven evidence and research clues for further investigation of the associations between CPB-related immune perturbations and postoperative organ dysfunction and clinical outcomes.

BACKGROUND:Eucommia ulmoides has a certain osteogenic effect,which can promote the proliferation and differentiation of osteoblasts.However,it is unclear whether Eucommia ulmoides has effects on alveolar bone formation and Wnt/β-Catenin signaling pathway. OBJECTIVE:To investigate the mechanism by which Eucommia ulmoides promotes alveolar bone formation in ovariectomized rats based on the Wnt/β-Catenin signaling pathway. METHODS:Sixty female Sprague-Dawley rats were selected and randomly divided into five groups:blank control group,sham-operation group,model group,low-dose group Eucommia ulmoides group,and high-dose Eucommia ulmoides group,with twelve rats in each group.Osteoporosis animal models were constructed by bilateral oophorectomy in the model group and the low-dose and high-dose Eucommia ulmoides groups.The sham-operation group underwent the same method to remove adipose tissue of equal mass around the bilateral ovaries.Three months after surgery,the low-and high-dose Eucommia ulmoides groups were given 2.1 g/kg/d and 4.2 g/kg/d Eucommia ulmoides by gavage,respectively.The sham-operation group and model group were given the same amount of physiological saline by gavage.After 12 weeks of drug intervention,the changes in alveolar bone mass of rats in each group were observed through Micro-CT;hematoxylin-eosin staining was used to observe the pathological structural changes of alveolar bone in rats;enzyme linked immunosorbent assay was used to detect the expression levels of alkaline phosphatase and osteocalcin in the serum of rats;western blot was used to detect the expression levels of β-Catenin and Frizzled9 receptor proteins in the alveolar bone of rats;and real-time fluorescence quantitative PCR was used to detect the expression of osteocalcin,Runt-related transcription factor 2(Runx2),alkaline phosphatase,β-catenin,and frizzled9 mRNAs in alveolar bone tissues of rats. RESULTS AND CONCLUSION:Compared with the blank control group,bone volume fraction,trabecular number,trabecular thickness,and bone mineral density were reduced in the model group(P＜0.05),and trabecular separation was elevated(P＜0.05).Pathological observation showed that the arrangement of trabeculae was disordered and irregular,the trabeculae were thinned or broken,and the marrow cavity was enlarged in the model group,with a significant reduction in bone volume;the level of alkaline phosphatase in the serum was increased(P＜0.05),and the level of osteocalcin was decreased(P＜0.05);mRNA expression of alkaline phosphatase,osteocalcin,Runx2,β-catenin,and frizzled9 were decreased(P＜0.05);protein expression of β-Catenin and Frizzled9 was decreased(P＜0.05).Compared with the model group,the low-and high-dose Eucommia ulmoides groups showed an increase in bone volume fraction,trabecular number,trabecular thickness,and bone mineral density(P＜0.05)and a decrease in trabecular separation(P＜0.05).In the low-and high-dose Eucommia ulmoides groups,bone trabeculae were slightly aligned and thickened,with a significant increase in bone mass.Compared with the model group,the serum level of alkaline phosphatase was reduced(P＜0.05)and the serum level of osteocalcin was elevated(P＜0.05)in the low-and high-dose Eucommia ulmoides groups.Compared with the model group,the mRNA expression of alkaline phosphatase,osteocalcin,Runx2,β-catenin,and frizzled9 were increased in the low-and high-dose Eucommia ulmoides groups(P＜0.05).Compared with the model group,the protein expression of Frizzled9 was increased in the low-dose Eucommia ulmoides group(P＜0.05),while the protein expression of β-Catenin and Frizzled9 was increased in the high-dose Eucommia ulmoides group(P＜0.05).Compared with the low-dose Eucommia ulmoides group,the high-dose Eucommia ulmoides group had a more significant improvement in the above indexes.To conclude,Eucommia ulmoides can effectively promote the alveolar bone formation,and its mechanism of action might be related to the activation of the Wnt/β-catenin signaling pathway.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

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

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*

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

In recent years, with the improvement of people's awareness of physical examination and the more accurate detection equipment, the detection rate of pulmonary nodules is getting higher and higher. Surgical resection is the first choice for the treatment of malignant pulmonary nodules, but multiple pulmonary nodules, nodules in complex areas and those with surgical contraindications are not suitable for surgery. As an effective, less invasive and low-cost treatment, ablation has developed rapidly in the treatment of multiple pulmonary nodules. This article introduces the progress of several common ablation techniques (radiofrequency ablation, microwave ablation, cryoablation) in the treatment of multiple pulmonary nodules, the indications and contraindications of ablation techniques, the efficacy evaluation and complications after ablation therapy, and the prospects of ablation techniques in the treatment of multiple pulmonary nodules.