Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

This review provides a comprehensive summary of the latest advancements in high-throughput protein structural bioinformatics, a field that has undergone a revolutionary transformation with the advent of deep learning-based protein structure prediction systems like AlphaFold2. These systems have significantly increased the accuracy, speed, and scale of protein structure prediction, resulting in an exponential growth in the number of protein structures available for analysis. Notably, the AlphaFold Protein Structure Database (AFDB) has amassed over 214 million protein structures, surpassing the PDB’s 50-year cumulative data by over 1 000-fold within several months. Big data is driving the comprehensive upgrade of protein structural bioinformatics. This review focuses on three main areas: structure data management, tool development, and structure data mining. In the realm of structure data management, the review spotlights the optimization strategy of AlphaFold-like systems, which significantly reduces the resource requirements for protein folding, enabling more researchers to make custom structure predictions and further enlarging the data scale. The resulting “data explosion” has exerted increased pressure on storage and bandwidth, prompting the development of cutting-edge tools such as Foldcomp, PDC, and ProteStAr for compressing PDB files. Moreover, the review underscores the critical role of public repositories like ModelArchive and PDB-Dev in archiving and sharing third-party AlphaFold models. It also highlights the utilization of independent services like MineProt and 3D-Beacons to create more interactive and accessible data portals. In terms of tool development, the review spotlights recent breakthroughs in structure alignment algorithms, represented by Foldseek, which enable ultra-fast searching of large protein structure databases. It also covers tools for functional annotation of proteins based on their structures, including AlphaFill for ligand annotation, DeepFRI for Gene Ontology (GO) annotation, TT3D for protein-protein interaction (PPI) prediction, among others. It is proposed that 3Di sequences born concurrently with Foldseek can enhance many sequence-based deep learning models developed in the pre-AlphaFold era, enabling them to be applied to structure-based function prediction. The challenges on traditional molecular docking methods in the high-throughput era are mentioned at last, in a gesture to arouse the attention of researchers. Finally, the review explores the burgeoning field of structure data mining. Whole proteome structuring has become feasible in recent years, and scientists are processing large structure datasets from an omics viewpoint, continuously identifying analyzable elements and optimizing methodologies, as well as utilizing newly developed tools to push the boundaries. Notable examples include the identification of new protein families, the development of protein structure clustering, and the integration of AlphaFold with conventional experimental techniques to solve large structures. These advancements are paving the way for a deeper understanding of protein structure and function and have the potential to unlock new discoveries in the life sciences. However, the review also acknowledges the challenges and limitations that persist in the field, including the lack of diversity in high-throughput software for protein structural bioinformatics and the existing bottleneck in rapidly predicting protein complex structures. Overall, structural bioinformatics is expected to play an even more crucial role in the life sciences with the development of high-throughput methodology.

The third-generation epidermal growth factor receptor (EGFR) inhibitor osimertinib (OSI) has been approved as the first-line treatment for EGFR-mutant non-small cell lung cancer (NSCLC). This study aims to explore a rational combination strategy for enhancing the OSI efficacy. In this study, OSI induced higher CD47 expression, an important anti-phagocytic immune checkpoint, via the NF-κB pathway in EGFR-mutant NSCLC HCC827 and NCI-H1975 cells. The combination treatment of OSI and the anti-CD47 antibody exhibited dramatically increasing phagocytosis in HCC827 and NCI-H1975 cells, which highly relied on the antibody-dependent cellular phagocytosis effect. Consistently, the enhanced phagocytosis index from combination treatment was reversed in CD47 knockout HCC827 cells. Meanwhile, combining the anti-CD47 antibody significantly augmented the anticancer effect of OSI in HCC827 xenograft mice model. Notably, OSI induced the surface exposure of "eat me" signal calreticulin and reduced the expression of immune-inhibitory receptor PD-L1 in cancer cells, which might contribute to the increased phagocytosis on cancer cells pretreated with OSI. In summary, these findings suggest the multidimensional regulation by OSI and encourage the further exploration of combining anti-CD47 antibody with OSI as a new strategy to enhance the anticancer efficacy in EGFR-mutant NSCLC with CD47 activation induced by OSI.
Humans ; Mice ; Animals ; Carcinoma, Non-Small-Cell Lung/metabolism* ; Lung Neoplasms/metabolism* ; Acrylamides/pharmacology* ; ErbB Receptors/metabolism* ; Cell Line, Tumor ; CD47 Antigen/therapeutic use*

AIM: To explore the optimal concentration of endoplasmic reticulum stress immunohistochemical(IHC)staining antibody in mouse retinitis pigmentosa(RP)model, which provides the corresponding index detection method for studying the pathogenesis and intervention measures of RP.METHODS: Clean male C57BL/6J mice were intraperitoneally injected with N-methyl-N-nitrosourea(MNU, 60mg/kg)to prepare RP mouse model. Electroretinogram(ERG)and hematoxylin-eosin(HE)staining were performed on 7d after modeling to verify the successful modeling. The expression of endoplasmic reticulum stress-related proteins(IRE1, ATF6, PERK, GRP78, Caspase-12)was detected by IHC staining.RESULTS: The following proteins, including IRE1, ATF6, PERK, GRP78 and Caspase-12, were positively expressed in retina of RP mouse. The optimal concentrations of the above proteins were as follows: IRE1 antibody concentration was 1:1000, ATF6 antibody concentration was 1:500 and 1:1000(with no difference in positive expression, P>0.05), PERK antibody concentration was 1:1500, GRP78 antibody concentration was 1:200 and Caspase-12 antibody concentration was 1:100, the proteins were well expressed at the above concentrations, and the positive expressions of corresponding proteins were different from those of other antibody concentrations(P<0.05).CONCLUSION: The optimal concentrations for IHC staining in different proteins of mouse RP models were as follows: the concentrations of endoplasmic reticulum stress-related protein antibodies were 1:1000 in IRE1, 1:500 and 1:1000 in ATF6, 1:1500 in PERK, 1:200 in GRP78, and 1:100 in Caspase-12.

Parkinson's disease (PD) is the most common neurodegenerative movement disease. It is featured by abnormal alpha-synuclein (α-syn) aggregation in dopaminergic neurons in the substantia nigra. Macroautophagy (autophagy) is an evolutionarily conserved cellular process for degradation of cellular contents, including protein aggregates, to maintain cellular homeostasis. Corynoxine B (Cory B), a natural alkaloid isolated from Uncaria rhynchophylla (Miq.) Jacks., has been reported to promote the clearance of α-syn in cell models by inducing autophagy. However, the molecular mechanism by which Cory B induces autophagy is not known, and the α-syn-lowering activity of Cory B has not been verified in animal models. Here, we report that Cory B enhanced the activity of Beclin 1/VPS34 complex and increased autophagy by promoting the interaction between Beclin 1 and HMGB1/2. Depletion of HMGB1/2 impaired Cory B-induced autophagy. We showed for the first time that, similar to HMGB1, HMGB2 is also required for autophagy and depletion of HMGB2 decreased autophagy levels and phosphatidylinositol 3-kinase III activity both under basal and stimulated conditions. By applying cellular thermal shift assay, surface plasmon resonance, and molecular docking, we confirmed that Cory B directly binds to HMGB1/2 near the C106 site. Furthermore, in vivo studies with a wild-type α-syn transgenic drosophila model of PD and an A53T α-syn transgenic mouse model of PD, Cory B enhanced autophagy, promoted α-syn clearance and improved behavioral abnormalities. Taken together, the results of this study reveal that Cory B enhances phosphatidylinositol 3-kinase III activity/autophagy by binding to HMGB1/2 and that this enhancement is neuroprotective against PD.

OBJECTIVES: To investigate the characteristics of auditory processing (AP) in preschool children with attention deficit hyperactivity disorder (ADHD) using Preschool Auditory Processing Assessment Scale (hereafter referred to as "auditory processing scale"). METHODS: A total of 41 children with ADHD and 41 typically developing (TD) children were assessed using the auditory processing scale, SNAP-IV rating scale, and Conners' Kiddie Continuous Performance Test (K-CPT). The auditory processing scale score was compared between the TD and ADHD groups. The correlations of the score with SNAP-IV and K-CPT scores were assessed. RESULTS: Compared with the TD group, the ADHD group had significantly higher total score of the auditory processing scale and scores of all dimensions except visual attention (P<0.05). In the children with ADHD, the attention deficit dimension score of the SNAP-IV rating scale was positively correlated with the total score of the auditory processing scale (r_s30=0.531, P<0.05; r_s27=0.627, P<0.05) as well as the scores of its subdimensions, including auditory decoding (r_s=0.628, P<0.05), auditory attention (r_s=0.492, P<0.05), and communication (r_s=0.399, P<0.05). The hyperactivity-impulsivity dimension score of the SNAP-IV rating scale was positively correlated with the hyperactivity-impulsivity dimension score of the auditory processing scale (r_s=0.429, P<0.05). In the children with ADHD, the attention deficit dimension score of the K-CPT was positively correlated with the total score (r_s30=0.574, P<0.05; r_s27=0.485, P<0.05) and the hyperactivity-impulsivity dimension score (r_s=0.602, P<0.05) of the auditory processing scale. CONCLUSIONS Preschool children with ADHD have the risk of AP abnormalities, and the auditory processing scale should be used early for the screening and evaluation of AP abnormalities in children.
Child, Preschool ; Humans ; Attention Deficit Disorder with Hyperactivity ; Schools ; Auditory Perception

Objective: To establish the norms and clinical application standards of mass spectrometry method to measure vitamin D in capillary blood. Methods: Following the "Province-City-Hospital" sampling procedure, a cross-sectional sample of 1 655 healthy children under 7 years of age were recruited from 12 provinces, autonomous regions, or municipalities in China from November 2020 to December 2021. Both venous and capillary blood samples from the same individual were collected, for which serum 25(OH)D levels were measured by high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) method. Pearson correlation analysis and linear regression analysis were used to detect the correlation and determine a correction algorithm. The agreement was analyzed using Bland-Altman plot and Kappa statistic. The sensitivity and specificity were evaluated using receiver operating characteristic (ROC) curve method. Results: Venous and capillary 25(OH)D levels of 1 655 healthy children under 7 years of age were 74.25 (59.50, 92.00) and 68.75 (54.44, 86.25) nmol/L, respectively, showed a significant difference(Z=22.14, P<0.001) as well as a highly significant correlation between venous and capillary 25(OH)D levels(r=0.95, P<0.001). Linear regression analysis was then performed to determine the correction algorithm: lg(corrected capillary 25(OH)D)=0.13+0.95×lg(capillary 25(OH)D)(R²=0.90,P<0.001). The deviation between venous and corrected capillary 25(OH)D levels was (0.50±17.50) nmol/L, a difference value that did not reach statistical significance (P>0.05). The cut-off values of capillary blood 25(OH)D values 30.00, 50.00, 75.00 nmol/L corresponding to venous blood 25(OH)D values were 26.59, 45.56, and 69.84 nmol/L, respectively. Good consistency was observed between venous and corrected capillary 25(OH)D levels in clinical diagnosis (Kappa value 0.68-0.81). Corrected capillary 25(OH)D showed a high clinically predictive value (area under curve 0.97-0.99,sensitivity 0.72-0.92,specificity 0.89-0.99). Conclusion: The standardized capillary HPLC-MS/MS method can be used to detect 25(OH)D levels in children clinically.
Child ; Humans ; Vitamin D ; Cross-Sectional Studies ; Tandem Mass Spectrometry ; Vitamins ; Reference Standards

UHPLC-Q-TOF/MS metabonomics technology was used to clarify the metabolic regulation pathways by which Platycodon total saponins (PTS) exert antitussive and expectorant effects in a mouse cough model, in which coughing is induced by concentrated ammonia, and in a phenol red excretion model. After approval by the Experimental Animal Ethics Committee of Jiangxi University of Chinese Medicine (Approval No. JZLLSC-20190235), the mice were randomly divided into a normal group, a model group, a positive drug group and a PTS group. Endogenous metabolites in mouse serum were identified by UHPLC-Q-TOF/MS. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used for multivariate analysis. Metabolic pathways were analyzed by the Metaboanalyst platform. The results show that PTS can significantly prolong the cough latent period and cough frequency of mice, and significantly increase phenol red excretion. UHPLC-Q-TOF/MS identified 19 metabolites related to cough, and PTS significantly decreased 16 of them; 17 metabolites related to expectoration were identified, and PTS decreased the levels of all. Metabolic pathway analysis showed that linoleic acid metabolism, arachidonic acid metabolism and glycerophospholipid metabolism were the main pathways involved in serum metabolite changes in this mouse cough model. Linoleic acid metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, arachidonic acid metabolism, phenylalanine metabolism and α-linolenic acid metabolism were the main pathways involved in serum metabolite changes in the phenol red excretion model. This study is the first to elucidate the regulation of antitussive and expectorant metabolic pathways and the effect of PTS on these pathways.

After more than 20 years of multidisciplinary integration of medical science and technology,as well as research and practice in innovative diagnosis and treatment,digital medicine 4.0 has made a profound and important impact on the development of traditional surgery. To combine traditional surgery with digital medicine 4.0 technology is the direction of surgery development in the future.New technologies represented by digital intelligent navigation surgery have been deeply explored and widely applied in the diagnosis and treatment of many surgical diseases. With the innovative development and application of artificial intelligence,Big Data and mixed reality technology,the surgery will develop in ways similar to aerospace automatic and intelligent navigation,leading to the advent of digital medicine 5.0.
Artificial Intelligence ; Humans ; Medicine ; Surgery, Computer-Assisted ; Technology

Objective: To investigate the application effect of augmented reality and mixed reality navigation technology in three-dimensional(3D) laparoscopic narrow right hepatectomy(LRH). Methods: A retrospective analysis was performed on the clinical data of 5 patients with hepatic malignancy admitted to the First Department of Hepatobiliary Surgery,Zhujiang Hospital,Southern Medical University from September 2020 to June 2021,all of whom were males,aged from 42 to 74 years.Preoperative evaluation was performed using the self-developed 3D abdominal medical image visualization system; if all the 5 patients were to receive right hemihepatectomy,the remnant liver volume would be insufficient,so LRH were planned.During the operation,the independently developed 3D laparoscopic augmented reality and mixed reality surgical navigation system was used to perform real-time multi-modal image fusion and interaction between the preoperative 3D model and 3D laparoscopic scene.Meanwhile,intraoperative ultrasound assisted indocyanine green fluorescence was used to determine the surgical path.In this way,the LRH under the guidance of augmented reality and mixed reality navigation was completed.The predicted liver resection volume was evaluated before surgery,actual resected liver volume,surgical indicators and postoperative complications were analyzed. Results: All the 5 patients completed LRH under the guidance of augmented reality and mixed reality navigation technology,with no conversion to laparotomy.The median operative time was 300 minutes(range:270 to 360 minutes),no intraoperative blood transfusion was performed,and the median postoperative hospital stay was 8 days(range:7 to 9 days).There were no perioperative deaths,or postoperative complications such as liver failure,bleeding,or biliary fistula. Conclusion: For patients who need to undergo LRH,the use of augmented and mixed reality navigation technology can safely and effectively guide the implementation of surgery,retain more functional liver volume,improve surgical safety,and reduce postoperative complications.
Adult ; Aged ; Augmented Reality ; Hepatectomy/methods* ; Humans ; Imaging, Three-Dimensional ; Laparoscopy/methods* ; Liver Neoplasms/surgery* ; Male ; Middle Aged ; Retrospective Studies ; Technology