Curcumin (Cur), a natural polyphenolic compound extracted from the rhizomes of plants belonging to the Zingiberaceae and Araceae families, has attracted considerable interest for its remarkable antioxidant, anti-inflammatory, antibacterial, and anticancer activities. Extensive studies have focused on its potential in treating various diseases, including cancers, neurodegenerative diseases, inflammation, and autoimmune disorders. However, its clinical application has been hampered by inherent physicochemical challenges, such as poor aqueous solubility, short half-life, and low bioavailability. To address these challenges, nanodrug delivery systems have been developed to improve the pharmacokinetic and pharmacodynamic profiles of curcumin, thereby enhancing its therapeutic efficacy. This review summarizes the pharmacological activities and underlying mechanisms of curcumin, encompassing its antioxidant, anti-inflammatory, anticancer, antiviral, and antibacterial effects. Furthermore, it highlights the significant advancements achieved through the development of curcumin-based nanomedicines. By synthesizing current findings, this review aims to provide insights that may inspire innovative strategies for the future design and broader applications of curcumin nanomedicines.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

To identify factors associated with the recurrence of polymyalgia rheumatica(PMR) within one year.

Objective A serum proteomic approach was used to explore the targets of action of Peitu Qingxin Granules(composed of Rhizoma Atractylodis Macrocephalae,Forsythiae Fructus,Imperatae Rhizoma,Pseudostellariae Radix,etc.)in the treatment of atopic dermatitis.Methods Five patients with atopic dermatitis were selected and treated with Peitu Qingxin Granules for 12 weeks,and five healthy volunteers were used as controls.The clinical core evaluation indexes of atopic dermatitis patients after treatment,including Eczema Area and Severity Index/Scoring Atopic Dermatitis(EASI/SCORAD),Pruritus Score,Patient-Oriented Eczema Measure(POEM),and quality of life index,were assessed.Serum samples were examined using data-independent acquisition-mass spectrometry(DIA-MS)technology,and serum differential proteins between atopic dermatitis patients and healthy people,as well as serum differential proteins in atopic dermatitis patients before and after treatment with Peitu Qingxin Granules were screened according to P＜0.05 and Fold Change>1.2.GO function enrichment analysis and KEGG pathway enrichment analysis were performed on the differential proteins.Results(1)Compared with the pre-treatment period,the clinical core evaluation indexes of patients with atopic dermatitis,including the EASI/SCORAD,Pruritus Score,POEM,and quality-of-life index,were significantly improved after treatment,and the differences were all statistically significant(P＜0.05,P＜0.01).(2)A total of 28 differential proteins were analyzed in the healthy control group and atopic dermatitis group,of which 12 proteins expressions were increased and 16 proteins were decreased,including ALAD(δ-aminolevulinic acid dehydrogenase),LTA4H(leukotriene A-4 hydrolase),CA1(carbonic anhydrase 1),F11(coagulation factor XI),and LCP1(lymphocyte cytoplasmic protein 1),etc..The main signaling pathways involved are PI3K-AKT signaling pathway,lipids and atherosclerosis,ECM-receptor interaction,platelet activation,NF-κB signaling pathway,and neutrophil extracellular trap formation.(3)A total of 12 different proteins were analyzed in atopic dermatitis patients before and after treatment with Peitu Qingxin Granules,of which 8 proteins were increased and 4 proteins were decreased,including ALAD,FGA(fibrinogen α-chain),IGHV3-64D,and IGHV3-38.They were mainly involved in signaling pathways such as lipids and atherosclerosis,complement pathway,Staphylococcus aureus infection,NF-κB signaling pathway,fluid shear stress and atherosclerosis.(4)The expressions of three protein targets including ALAD,FGA and IGHV3-64D,were significantly down-regulated in patients with atopic dermatitis and significantly up-regulated after treatment with Peitu Qingxin Granules.Conclusion The differentially expressed proteins ALAD,FGA and IGHV3-64D may be the action targets of Peitu Qingxin Granules in the treatment of atopic dermatitis,which lays the foundation for further experimental validation.

Objective To describe the epidemiological characteristics, investigation and treatment process of the first monkeypox cluster outbreak in Wuhan, and to provide reference for the prevention and control of monkeypox cluster outbreak in the future. Methods Field epidemiological investigation was conducted on the cases, and throat swabs, anal swabs, shingles fluid, whole blood, and serum samples were collected from the cases. Subsequently, real-time fluorescence quantitative polymerase chain reaction was employed for the detection of monkeypox virus nucleic acid and subsequent gene sequencing. Results Two cases had a high-risk exposure behavior in a hotel in Wuhan on June 2, 2023. The first case exhibited the emergence of beige papules on June 5 and sought medical treatment at a tertiary hospital in Wuhan on June 11. The Chinese Center for Disease Control and Prevention conducted tests on herpes fluid samples, which yielded positive results for the presence of monkeypox virus nucleic acid. Genetic sequencing analysis revealed that the infecting strain of the monkeypox virus in this case belonged to the West African clade B.1. Conclusion Based on epidemiological investigation and laboratory results, this monkeypox cluster outbreak may be caused by latent men with men sexual transmission. Monkeypox has the risk of both overseas importation and local transmission in our country. It is very necessary to detect, report and deal with monkeypox outbreak early.

The Gastroesophageal reflux disease(GERD)is related to the dynamic disorder of the digestive tract caused by the imbalance of the viscera and meridians.The spleen and stomach are the hub of the qi machinery,transforming the essence of water and valley into energy and regulating the Yin and Yang of all bodies.The sympathetic balance between Ren-Du and Qi is the motivity of the spleen and stomach.Interstitial cells of Cajal(ICC)are the hub of digestive motility,which can maintain mitochondrial energy metabolism through mitochondrial autophagy and improve the digestive motility.Therefore,in this paper,the molecular biological basis of GERD was discussed based on the"regulating pivot with pivot"theory that the pivots of viscera and meridians drive ICC mitochondrial energy balance.It also explains the feasibility of Qi-Shi-Sheng-Jiang-Gui-Yuan Decoction in treating GERD based on"regulating pivot with pivot",which is helpful to realize the microscopization and concretization of"regulating pivot with pivot"theory and realize the modernization of TCM theory.

Objective:To estimate the incidence and risk factors of acute kidney injury (AKI) in preterm infants, and provide basis for better evaluation and treatment of renal function in preterm infants.Methods:All the hospitalized premature infants who were admitted to three research centers (Department of Neonatology at Beijing Children′s Hospital; Department of Neonatology at Beijing Obstetrics and Gynecology Hospital; Department of Neonatology at Shunyi Maternal and Children′s Hospital of Beijing Children′s Hospital)from January 1, 2017 to June 30, 2019 and had more than two serum creatinine values or urine output were included.The incidence of AKI in preterm infants was calculated and the difference among different gestational weeks was compared.Preterm infants were divided into AKI and non-AKI groups according to AKI diagnostic criteria, and the clinical characteristics between two groups were compared, and the risk factors of AKI in preterm infants were analyzed.Results:A total of 763 premature infants were included in the analysis.Twenty two cases were diagnosed with AKI.The incidence of AKI in premature infants was 2.9%.The incidence of AKI was 33.3% (3/9), 3.7% (5/134), and 2.3% (14/620) in the 24-27 + 6 weeks, 28-31 + 6 weeks, and 32-36 + 6 weeks gestational age, respectively, and the difference was statistically significant ( χ2=31.010, P<0.001). Preterm infants in AKI group had a higher proportion of males(77.3% vs. 53.3%), lower gestational weeks[29(27, 33) weeks vs.31(29, 33)weeks], higher proportions of infants with diabetic mothers(40.9% vs.19.4%), lower Apgar scores at 1 and 5 minutes[8(7, 10) vs.9(8, 10), 9(9, 10) vs.10(9, 10), respectively], higher proportions of invasive and noninvasive respiratory support(45.5% vs.11.3%, 63.6% vs.19.2%, respectively), longer duration of invasive respiratory support[260(136, 742)h vs.72(18, 160)h], longer hospital stays[66(19, 88)d vs.42(26, 58)d], and higher rates of sepsis (27.3% vs. 6.5%), respiratory distress syndrome(40.9% vs. 11.6%), and patent ductus arteriosus that requiring ibuprofen or surgical closure(13.6% vs. 3.0%), diuretic(27.3% vs. 3.9%), and vasoactive drug use (22.7% vs. 3.6%) than those in non-AKI group, and the differences were statistically significant(all P<0.05). Multivariate regression analysis showed that sepsis was an independent risk factor for AKI in preterm infants ( P=0.039, OR=3.498, 95% CI 1.065-11.490) after adjustment of gestational age and birth weight. Conclusion:The incidence of AKI is relatively high in preterm infants with gestational age<28 weeks.Compared with preterm infants without AKI, preterm infants with AKI have smaller gestational weeks and longer hospital stay.Sepsis is an independent risk factor for AKI in preterm infants.

BACKGROUND: Lung cancer is the most common malignancy world-wide. There are a variety of immune infiltrating cells in tumor microenvironment, which is an important component of tumor immunity and has clinical significance for the prognosis of patients. CD45RO is a surface marker of memory T cells. The expression of CD45RO⁺ tumor infiltrating lymphocytes (TILs) is associated with the prognosis of many tumors. The purpose of this study was to evaluate the relationship between the density of CD45RO⁺ TILs in tumor and stromal area and the clinical characteristics of patients with non-small cell lung cancer (NSCLC) and its impact on the prognosis of patients. We aimed to explore the clinical value of CD45RO⁺ TILs and programmed cell death ligand 1 (PD-L1) as prognostic markers. METHODS: Multiple fluorescent immunohistochemical staining was used to stain the tissue microarray chips of 167 patients with NSCLC, marking CD45RO, cytokeratin (CK) and PD-L1. Using artificial intelligence image recognition technology and tumor cell-specific CK staining, divide the tumor and stromal area in the tissue, evaluate the density of CD45RO⁺ TILs in the tumor and stromal area, and the expression level of PD-L1 in tumor cells. The non-parametric test was used to analyze the relationship between CD45RO⁺ TILs and the clinical characteristics of patients, and the Kaplan-Meier method and Cox risk ratio model were used to analyze the relationship between CD45RO⁺ TILs independently or in combination with PD-L1 and tumor prognosis. RESULTS: The density of CD45RO⁺ TILs was significantly associated with patient age, smoking, tumor stage, and pathological type. Single-factor survival analysis showed that NSCLC (P=0.007) stromal region and lung adenocarcinoma (LUAD) (P<0.001) with CD45RO⁺ TILs high density had better OS. Multivariate survival analysis showed that the high density of CD45RO⁺ TILs in the stromal region of NSCLC (HR=0.559, 95%CI: 0.377-0.829, P=0.004) and lung adenocarcinoma (HR=0.352, 95%CI: 0.193-0.641, P=0.001) were independent prognostic factors for overall survival time (OS). Combined with PD-L1 score of tumor cells in tumor tissues and infiltration score of CD45RO⁺ TILs in all tumor tissues, the patients were divided into 4 groups: patients with PD-L1⁺/CD45RO⁺ had the longest disease-free survival (DFS) time, and patients with PD-L1⁺/CD45RO- had the shortest DFS time. Multivariate Cox regression analysis showed that PD-L1⁺/CD45RO- was an independent prognostic factor for DFS and had a higher risk of poor prognosis compared to the other three groups (HR=2.221, 95%CI: 1.258-3.919, P=0.006). CONCLUSIONS In tumor tissues, the density of CD45RO⁺ TILs, as well as the combination of CD45RO⁺ TILs and PD-L1 in tumor areas, significantly correlated with clinicopathological features and prognosis of NSCLC, which can be used as a new prognosis marker.

Lung cancer is the malignant tumor with the highest mortality rate in the world. Heterogeneity of lung cancer, usually studied by sequencing technology, is considered to have important clinical significance in current studies. However, general sequencing technology can only explain the differences between samples integrally and its resolution is not enough to describe the differences between the individual cells. Therefore, people urgently hope to understand the cell type, state, subgroup distribution in the tumor microenvironment and the communication behavior between cells in the single cell level. Single-cell sequencing technology solves this problem. Using this technique will contribute to further understanding the mechanism of the occurrence and development of lung cancer, discovering new diagnostic markers and therapeutic targets, and providing theoretical references for the precise treatment of lung cancer patients in the future. This article reviews the progress of single-cell sequencing technology and focuses on its research on lung cancer tumor heterogeneity, tumor microenvironment, invasion and metastasis, treatment response, and drug resistance.
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