BACKGROUND:In the initial stage of growth plate injury inflammation,platelet-derived growth factor BB promotes the repair of growth plate injury by promoting mesenchymal progenitor cell infiltration,chondrogenesis,osteogenic response,and regulating bone remodeling. OBJECTIVE:To elucidate the action mechanism of platelet-derived growth factor BB after growth plate injury. METHODS:PubMed,VIP,WanFang,and CNKI databases were used as the literature sources.The search terms were"growth plate injury,bone bridge,platelet-derived growth factor BB,repair"in English and Chinese.Finally,66 articles were screened for this review. RESULTS AND CONCLUSION:Growth plate injury experienced early inflammation,vascular reconstruction,fibroossification,structural remodeling and other pathological processes,accompanied by the crosstalk of chondrocytes,vascular endothelial cells,stem cells,osteoblasts,osteoclasts and other cells.Platelet-derived growth factor BB,as an important factor in the early inflammatory response of injury,regulates the injury repair process by mediating a variety of cellular inflammatory responses.Targeting the inflammatory stimulation mediated by platelet-derived growth factor BB may delay the bone bridge formation process by improving the functional activities of osteoclasts,osteoblasts,and chondrocytes,so as to achieve the injury repair of growth plate.Platelet-derived growth factor BB plays an important role in angiogenesis and bone repair tissue formation at the injured site of growth plate and intrachondral bone lengthening function of uninjured growth plate.Inhibition of the coupling effect between angiogenesis initiated by platelet-derived growth factor BB and intrachondral bone formation may achieve the repair of growth plate injury.

Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

OBJECTIVE To prepare a self-microemulsifying drug delivery system(SMEDDS) for the oral administration of nebivolol hydrochloride(NBH) and to conduct in vitro evaluation. METHODS The solubility of NBH was determined using various oil phases, surfactants, and co-surfactants. The composition of the blank self-microemulsifying formulation was determined using pseudo-ternary phase diagrams. A centralcomposite design-response surface method was employed to screen and optimize the formulation variables, and an excess amount of NBH raw material was incorporated to determine the drug loading capacity. RESULTS The optimized composition of the NBH-SMEDDS formulation consisted of medium-chain glycerides, capryl caproyl macrogol glycerides, and 2-(2-ethoxyethoxy) ethyl acetate at a ratio of 20∶48∶32, with a drug loading capacity of 20.05 mg. The particle size, self-emulsification time, and particle size distribution range of the formulation were in agreement with the predicted values. Dissolution testing demonstrated that the overall dissolution trend of NBH-SMEDDS in the medium was higher than that of NBH powder and NBH ordinary tablet. The stability of NBH-SMEDDS was found to be satisfactory under accelerated conditions for 1, 2, and 3 months. CONCLUSION The SMEDDS shows potential for enhancing the in vitro dissolution of NBH and demonstrates good stability.

OBJECTIVE To solve the problem of insolubility of itraconazole, improve its dissolution in vitro, and provide a reference for further industrial scale-up of the itraconazole multiparticle system. METHODS Itraconazole multiparticle system pellets were dissolved in an organic solvent and prepared in a fluidized bed by bottom spraying. Itraconazole and hydroxypropyl methylcellulose were sprayed onto the surface of the sucrose pellet core to form a uniform solid dispersion. The preparation parameters of the fluidized bed bottom spray coating were investigated by single factor method. The mass ratio of drug to carrier and core weight gain of the itraconazole multiparticle system were optimized by central composite design and response surface methodology with accumulative dissolution rate, application efficiency and adhesion rate as response values. Samples were prepared to verify the optimized prescription, the microscopic hierarchical structure of the itraconazole multiparticle system was observed by scanning electron microscope, and the solid dispersion in the itraconazole multiparticle system pellets was characterized by differential scanning calorimetry(DSC) and X-ray diffraction(XRD). The dissolution curves of itraconazole pellets and the physical mixture in 0.1 mol·L−1 HCl dissolution medium were compared to verify the solubilization effect. RESULTS Single factor method was used to determine the bottom spray coating parameters of the fluidized bed. The pumping speed was set as 3.0−5.0 mL·min−1, the atomization pressure was set as 1.5 bar, the inlet air volume was set as 110 m3·h−1, and the material temperature was set as 35 ℃. According to the central composite design and response surface methodology, the mass ratio of drug to carrier of the optimized prescription was 1∶1.5 and the core weight of the pill was 75%, and the response values reached the expected value. The result of scanning electron microscopy showed that the diameter of the itraconazole multiparticle system pellet was about 910 µm, the diameter of the sucrose pellet core was about 570 µm, the thickness of the drug loading layer was about 110 µm, and the thickness of encapsulation layer was about 11 µm. The results of DSC and XRD showed that itraconazole formed a uniform solid dispersion in the itraconazole multiparticle system pellets, which was amorphous. In the dissolution medium of 0.1 mol·L−1 HCl, the accumulative dissolution rate of the multiparticle system after 90 min was about 10 times that of the physical mixture, which showed that the solubilization effect was remarkable. CONCLUSION The dissolution of itraconazole in vitro can be significantly improved by processing itraconazole into pellets with multiparticle system and forming solid dispersion.

Objective To investigate the efficacy and significance of Rox index, lymphocyte count and blood glucose in predicting retracheal intubation after high-flow nasal cannula(HFNC) oxygen therapy for severe pneumonia. Methods A total of 180 patients with severe pneumonia were selected as the study subjects, and all patients were treated with invasive and high-flow oxygen therapy sequentially. According to whether mechanical ventilation with re-intubation occurred after HFNC oxygen therapy, the patients were divided into re-intubation group (n=31) and non-re-intubation group (n=149). The ROX index, lymphocyte index [lymphocyte (LYM) count, helper/inducer T lymphocyte (CD4⁺T) count and suppressor/cytotoxic T lymphocyte (CD8⁺T) count] and blood glucose levels between the two groups before and at 6 hours and 12 hours after high-flow nasal cannula (HFNC) oxygen therapy were compared. The random forest algorithm was used to sort and screen the characteristic variables affecting retracheal intubation. Receiver operating characteristic (ROC) curve was used to analyze the predictive value of ROX index, lymphocyte index and blood glucose for retracheal intubation. Results The ROX index of the reintubation group was significantly lower at 6 and 12 h after HFNC oxygen therapy, and the blood glucose was significantly higher than that of the non-reintubation group (P < 0.05). Random forest algorithm showed that the top 3 variables in importance ranking were ROX index, CD4⁺T count and blood glucose. ROX index, CD4⁺T count combined with blood glucose at 6 and 12 h after HFNC oxygen therapy predicted the area under the curve (AUC) of retracheal intubation was greater than that of the single index, and the AUC of the three combined to predict retracheal intubation at 12 h after HFNC oxygen therapy was greater than that at 6 h after HFNC oxygen therapy (P < 0.05). Conclusion The combined evaluation of ROX index, CD4⁺T count and blood glucose has certain predictive efficacy for retracheal intubation mechanical ventilation after HFNC oxygen therapy in patients with severe pneumonia.

Pre-rheumatoid arthritis （Pre-RA） is the asymptomatic state of rheumatoid arthritis （RA）， and its progression showed complexity， dynamics and significant individual differences. The dynamic prediction model is valuable in identifying individual risks and taking timely preventive and control measures. It is believed that applying the dynamic prediction model to traditional Chinese medicine （TCM） clinical studies of Pre-RA is expected to predict the critical state of Pre-RA to RA， reveal the evolution of Pre-RA， reflect the external authenticity of the TCM clinical studies， and complement the clinical efficacy evaluation method. Accordingly， it is envisioned to construct a dynamic prediction model for Pre-RA progression based on TCM clinical trial data through the dynamic prediction model algorithm， and to apply long-term follow-up cohort data for external validation， thereby providing a basis for scientific warning and clinical decision-making on the evolution of Pre-RA.

Alginate composite hydrogel is one of the research hotspots of tumor drug delivery system materials at present. Alginate hydrogel has good biocompatibility and regeneration properties. However, the natural alginate saline gel may not achieve the expected results in the body environment due to the slow degradation and instability of the gel. Alginate has found extensive application in cancer therapy through its combination with other materials and the utilization of ionic cross-linking, covalent cross-linking, and free radical polymerization to form hydrogels. Based on the composite system of alginate brine gel, this paper summarizes the structure and properties of alginate hydrogel, with a focus on recent research into alginate composite hydrogel in common cancer treatments application, summarizes the current research focus and discuss the existing problems of alginate composite hydrogel, so as to provide reference for further expanding the research of alginate composite hydrogel in clinical cancer treatment.

Postnatal heart maturation is the basis of normal cardiac function and provides critical insights into heart repair and regenerative medicine. While static snapshots of the maturing heart have provided much insight into its molecular signatures, few key events during postnatal cardiomyocyte maturation have been uncovered. Here, we report that cardiomyocytes (CMs) experience epigenetic and transcriptional decline of cardiac gene expression immediately after birth, leading to a transition state of CMs at postnatal day 7 (P7) that was essential for CM subtype specification during heart maturation. Large-scale single-cell analysis and genetic lineage tracing confirm the presence of transition state CMs at P7 bridging immature state and mature states. Silencing of key transcription factor JUN in P1-hearts significantly repressed CM transition, resulting in perturbed CM subtype proportions and reduced cardiac function in mature hearts. In addition, transplantation of P7-CMs into infarcted hearts exhibited cardiac repair potential superior to P1-CMs. Collectively, our data uncover CM state transition as a key event in postnatal heart maturation, which not only provides insights into molecular foundations of heart maturation, but also opens an avenue for manipulation of cardiomyocyte fate in disease and regenerative medicine.
Gene Expression Regulation ; Heart ; Myocytes, Cardiac/metabolism* ; Single-Cell Analysis ; Transcription Factors/metabolism*

Background:In the clinical setting, the effect of intra-abdominal hypertension on the human body is dependent on time, but its role is not yet clear.Objective:To investigate the effect of the duration of intra-abdominal hypertension (IAH) on the prognosis of critically ill patients.Methods:This prospective cohort study enrolled 256 IAH patients who were admitted to the Surgical ICU of 10 Grade A hospitals in Fujian Province from January 2018 to December 2020. The duration of IAH (DIAH) was obtained after monitoring IAP, and ICU length of stay, duration of mechanical ventilation, duration of continuous renal replacement therapy (CRRT) and average daily energy intake from enteral nutrition during ICU stay were observed and recorded. The correlation was analyzed by Spearman rank correlation. The patients were divided into the survival group and the death group according to their survival state at 60 days after enrollment. Thereafter, clinical characteristics between the two groups were compared. Multivariable logistic regression was used to study and validate the relationship between DIAH and 60-day mortality. The receiver operating characteristics (ROC) curve was established to evaluate the predictive abilities of DIAH on the mortality risk.Results:In critically ill patients, DIAH was positively correlated with duration of mechanical ventilation ( r=0.679, P<0.001), duration of CRRT ( r=0.541, P<0.001) and ICU length of stay ( r=0.794, P<0.001), respectively. In addition, there was a negative correlation between DIAH and average daily energy intake from enteral nutrition ( r=-0.669, P<0.001). After multivariable adjustment, DIAH was an independent risk factor for 60-day mortality in critically patients with IAH ( OR=1.05, 95% CI: 1.01-1.12; P = 0.012), and exhibited a linearity change trend relationship with mortality risk. The ROC curve analysis of DIAH showed that the area under ROC curve (AUC) was 0.825 (95% CI: 0.763~0.886, P<0.01). When the cut-off value was 16.5 days, the sensitivity was 78.4% and the specificity was 75.4%. Conclusions:DIAH is an important risk factor for prognosis in critically ill patients. Early identification and rapid intervention for the etiology of IAH should be performed to shorten DIAH.

Objective To evaluate the application of the standard manual labeling on identification of retinopathy of prematurity ( ROP) images in deep learning. Methods According to the International Classification of ROP,different periods of ROP were classified into stage disease and plus disease in this study. From Joint Shantou International Eye Center from August 2009 to July 2018, a total of 1464 labeled fundus retinal photographs were divided randomly by stratified sampling into 3 groups:stage disease group(subgroup 1:173,subgroup 2:117) was used to train for labeling stage disease,whereas plus disease group(subgroup 1:163,subgroup 2:116) was used to train for labeling plus disease,and consistent labels group consisted of 895 consistent labeled images on both disease. Graders consisted of senior experts,3 senior ophthalmologists and 2 interns,and received training for classification and labeling on ROP fundus images. The results were compared among the doctors and doctors with deep learning,and the agreement between non-experts doctors and the reference standards, and deep learning and the reference standards were tested. Results After the first training,the overall agreement rate of the senior ophthalmologist group and the intern group were lower than 90％ for both two disease labeling. After two to three times of training, in image of consistent labels group,overall agreement rates of senior ophthalmologists and intern doctor's were 98. 99％ ( Kappa=0. 979),99. 22％ (Kappa=0. 984) on stage disease,and 97. 43％ (Kappa=0. 914),98. 11％ (Kappa=0. 935) on plus disease,respectively. The agreement on stage disease using deep learning based on human-machine combination was 94. 08％,Kappa value was 0. 880,which achieved good degree. Conclusions Standardized manual labeling can improve the intelligentization of deep learning on identification of ROP images,and be considered as an innovative method of homogenization and standardized training for doctors in ophthalmology.