Objective To explore the pro-inflammation resolution and protective effects of reactive oxygen species (ROS)-responsive liposomes modified with a cardiac-targeted peptide and loaded with resolvin D1 (RvD1, C-LP-RvD1) on myocardial ischemia-reperfusion (MI/R) injury. Methods The C-LP-RvD1 nanoliposomes were constructed, characterized physically and chemically, and evaluated for in vitro release. Non-targeting peptide-modified drug-loaded liposomes (LP-RvD1) were served as controls. Apoptotic adult mouse cardiomyocytes (AMCMs) were used to verify in vitro targeted binding capacity of C-LP-RvD1. In MI/R mice models, the in vivo distribution and cardiac enrichment of C-LP-RvD1 were assessed. Levels of specialized pro-resolving mediator (SPM) and inflammatory factors in cardiac tissue homogenates and cell culture supernatants were measured using enzyme-linked immunosorbent assay (ELISA). Cardiac function and fibrosis remodeling were evaluated via echocardiography and Masson staining four weeks after treatment. Biosafety was evaluated in healthy mice injected by C-LP-RvD1. Results The C-LP-RvD1 exhibited good nanoscale uniformity and stability, with ROS-triggered accelerated release characteristics. In vitro experiments showed that C-LP-RvD1 had higher binding capacity to apoptotic AMCMs than LP-RvD1, with significantly higher SPM levels (P＜0.01) and lower inflammatory factor levels (P＜0.05). In vivo experiments indicated enhanced cardiac enrichment of C-LP-RvD1 in MI/R injured hearts, with higher local myocardial SPM levels and lower inflammatory factor levels compared to LP-RvD1 (P＜0.05). Four weeks after treatment, compared with LP-RvD1, the C-LP-RvD1 mice group showed improved cardiac function indicators and reduced ventricular fibrosis remodeling ratio (P＜0.05). Safety evaluation revealed no significant systemic inflammation, immunogenicity, or coagulation abnormalities in healthy mice, with liver and kidney function and major organ histology showing no notable damage. Conclusions C-LP-RvD1 improves effective delivery of RvD1 to MI/R injured hearts through injury-targeted enrichment and ROS-responsive release, promoting inflammation resolution and suppressing excessive inflammation, thereby improving cardiac function and reducing adverse remodeling, with favorable biosafety.

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 analyze the epidemiological patterns and temporal trends of gastric cancer incidence and mortality in China and globally, and to formulate evidence-based prevention strategies.Methods:Based on the GLOBOCAN 2022 database, we evaluated gastric cancer incidence and mortality patterns stratified by sex, age group, geographic region and human development index (HDI). Simple linear regression and Spearman's correlation analysis assessed associations between HDI and age-standardized incidence rate (ASIR) or age-standardized mortality rates (ASMR). Temporal trends from 2002 to 2020 were described in selected regions, and projections of global gastric cancer burden by 2050 were estimated.Results:In 2022, there were estimated 969 000 new gastric cases and 660 000 deaths worldwide. The burden was higher in men than in women, with incidence peaking at ages 65-69 and mortality at 70-74. ASIR was weakly correlated with HDI ( r=0.261, P＜0.001), while no significant association was found between HDI and ASMR ( r=-0.005, P=0.947). China accounted for 359 000 new cases and 260 000 deaths, representing 37.0% and 39.4% of the global totals, respectively. Both ASIR (13.7/10 5) and ASMR (9.4/10 5) in China exceeded the global averages (9.2/10 5 and 6.1/10 5, respectively). Although the overall global burden is decreasing, absolute case numbers are projected to increase by 84.1% (1.78 million cases) and deaths by 91.2% (1.26 million cases) by 2050. High-HDI regions will bear greater absolute burdens, whereas low-HDI regions face steeper relative increases. In China, new cases and deaths are projected to reach 607 000 and 504 000 by 2050, rising by 69.1% and 93.8%, respectively. Conclusions:Despite a declining global trend, the burden of gastric cancer remains substantial, with notable disparities across regions, sex and age groups. Targeted strategies are urgently needed, particularly in East Asia, among males, and older populations, to mitigate the future burden.

Objective:To analyze the current status and trends of lung cancer incidence and mortality in China and selected global regions, providing evidence for lung cancer prevention strategies in China.Methods:We extracted data from the GLOBOCAN 2022 database. Age-standardized Incidence rate (ASIR) and Age-standardized Mortality rate (ASMR) were calculated using Segi's world standard population. Epidemiological patterns were analyzed by region, age, sex, and human development index (HDI). Simple linear regression and Spearman's rank correlation coefficient were used to examine associations between HDI and ASIR/ASMR.Results:In 2022, global lung cancer incidence and mortality reached 2.48 million and 1.82 million cases respectively, with age-standardized rates of 23.6 per 100 000 (ASIR) and 16.8 per 100 000 (ASMR). Gender disparities were prominent, with male ASIR and ASMR being 2.0-fold and 2.5-fold higher than females. Elderly populations showed 11.6-fold higher ASIR and 14.4-fold higher ASMR compared to working-age adults. HDI demonstrated strong positive correlations with both ASIR ( r=0.79, P＜0.001) and ASMR ( r=0.74, P＜0.001). China accounted for 1.06 million new cases and 0.73 million deaths, with ASIR (40.8 per 100 000) and ASMR (26.7 per 100 000) exceeding global averages by 1.7-fold and 1.6-fold respectively. Chinese males showed 1.7-fold higher ASIR and 2.7-fold higher ASMR than females. Trend analysis revealed persistently high male incidence in China whereas rapidly increasing female rates, narrowing gender disparities. Projections estimate 1.80 million incident cases and 1.41 million deaths by 2050, representing 69.3% and 92.0% increases from 2022 levels. Conclusions:Significant heterogeneity exists in lung cancer burden across demographics and development levels, with strong HDI correlations. China bears disproportionate disease burden, necessitating intensified prevention efforts. These findings underscore the urgency of targeted interventions in high-risk populations.

Objective:To analyze the epidemiological patterns and temporal trends of gastric cancer incidence and mortality in China and globally, and to formulate evidence-based prevention strategies.Methods:Based on the GLOBOCAN 2022 database, we evaluated gastric cancer incidence and mortality patterns stratified by sex, age group, geographic region and human development index (HDI). Simple linear regression and Spearman's correlation analysis assessed associations between HDI and age-standardized incidence rate (ASIR) or age-standardized mortality rates (ASMR). Temporal trends from 2002 to 2020 were described in selected regions, and projections of global gastric cancer burden by 2050 were estimated.Results:In 2022, there were estimated 969 000 new gastric cases and 660 000 deaths worldwide. The burden was higher in men than in women, with incidence peaking at ages 65-69 and mortality at 70-74. ASIR was weakly correlated with HDI ( r=0.261, P＜0.001), while no significant association was found between HDI and ASMR ( r=-0.005, P=0.947). China accounted for 359 000 new cases and 260 000 deaths, representing 37.0% and 39.4% of the global totals, respectively. Both ASIR (13.7/10 5) and ASMR (9.4/10 5) in China exceeded the global averages (9.2/10 5 and 6.1/10 5, respectively). Although the overall global burden is decreasing, absolute case numbers are projected to increase by 84.1% (1.78 million cases) and deaths by 91.2% (1.26 million cases) by 2050. High-HDI regions will bear greater absolute burdens, whereas low-HDI regions face steeper relative increases. In China, new cases and deaths are projected to reach 607 000 and 504 000 by 2050, rising by 69.1% and 93.8%, respectively. Conclusions:Despite a declining global trend, the burden of gastric cancer remains substantial, with notable disparities across regions, sex and age groups. Targeted strategies are urgently needed, particularly in East Asia, among males, and older populations, to mitigate the future burden.

Objective:To analyze the current status and trends of lung cancer incidence and mortality in China and selected global regions, providing evidence for lung cancer prevention strategies in China.Methods:We extracted data from the GLOBOCAN 2022 database. Age-standardized Incidence rate (ASIR) and Age-standardized Mortality rate (ASMR) were calculated using Segi's world standard population. Epidemiological patterns were analyzed by region, age, sex, and human development index (HDI). Simple linear regression and Spearman's rank correlation coefficient were used to examine associations between HDI and ASIR/ASMR.Results:In 2022, global lung cancer incidence and mortality reached 2.48 million and 1.82 million cases respectively, with age-standardized rates of 23.6 per 100 000 (ASIR) and 16.8 per 100 000 (ASMR). Gender disparities were prominent, with male ASIR and ASMR being 2.0-fold and 2.5-fold higher than females. Elderly populations showed 11.6-fold higher ASIR and 14.4-fold higher ASMR compared to working-age adults. HDI demonstrated strong positive correlations with both ASIR ( r=0.79, P＜0.001) and ASMR ( r=0.74, P＜0.001). China accounted for 1.06 million new cases and 0.73 million deaths, with ASIR (40.8 per 100 000) and ASMR (26.7 per 100 000) exceeding global averages by 1.7-fold and 1.6-fold respectively. Chinese males showed 1.7-fold higher ASIR and 2.7-fold higher ASMR than females. Trend analysis revealed persistently high male incidence in China whereas rapidly increasing female rates, narrowing gender disparities. Projections estimate 1.80 million incident cases and 1.41 million deaths by 2050, representing 69.3% and 92.0% increases from 2022 levels. Conclusions:Significant heterogeneity exists in lung cancer burden across demographics and development levels, with strong HDI correlations. China bears disproportionate disease burden, necessitating intensified prevention efforts. These findings underscore the urgency of targeted interventions in high-risk populations.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective To evaluate the practical application of the Media Fill Test(MFT)in assessing aseptic compounding competency of personnel in Pharmacy Intravenous Admixture Services(PIVAS).Methods A multicenter controlled study was conducted across six tertiary hospitals(center ①-⑥)in China.Participants were divided into an inexperienced group(Group A,n=118)and an experienced group(Group B,n=118),each performing five MFT operations.Positive controls validated medium efficacy.Contamination rates and pass rates were analyzed using chi-square and Fisher's exact tests.Results Valid samples included 584 for Group A and 588 for Group B.The sample pass rate was 66.78%(390/584)in Group A and 91.67%(539/588)in Group B,while personnel pass rates were 46.15%(54/117)and 80.51%(95/118),respectively,with significant intergroup differences(both P<0.01).All centers except Center ⑥ showed significantly higher pass rates in Group B(all P<0.05).Conclusion MFT effectively differentiates technical proficiency levels and is suitable for training evaluation of novice PIVAS staff.

Objective To evaluate the practical application of the Media Fill Test(MFT)in assessing aseptic compounding competency of personnel in Pharmacy Intravenous Admixture Services(PIVAS).Methods A multicenter controlled study was conducted across six tertiary hospitals(center ①-⑥)in China.Participants were divided into an inexperienced group(Group A,n=118)and an experienced group(Group B,n=118),each performing five MFT operations.Positive controls validated medium efficacy.Contamination rates and pass rates were analyzed using chi-square and Fisher's exact tests.Results Valid samples included 584 for Group A and 588 for Group B.The sample pass rate was 66.78%(390/584)in Group A and 91.67%(539/588)in Group B,while personnel pass rates were 46.15%(54/117)and 80.51%(95/118),respectively,with significant intergroup differences(both P<0.01).All centers except Center ⑥ showed significantly higher pass rates in Group B(all P<0.05).Conclusion MFT effectively differentiates technical proficiency levels and is suitable for training evaluation of novice PIVAS staff.