This review described the potential health threats to the cardiovascular system from micro-nano plastics (MNPs) and their multifaceted toxicity mechanisms. The article reviewed the environmental distribution of MNPs, exposure pathways, and their toxic effects on the cardiovascular system, and summarized the specific mechanisms of MNPs involving oxidative stress, inflammatory response, mitochondrial damage, apoptosis, pyroptosis, and autophagy dysregulation. Meanwhile, the combined toxic effects of MNPs with other environmental pollutants (e.g., heavy metals and polycyclic aromatic hydrocarbons), including synergistic, antagonistic, and dual effects, were analyzed, and the potential risks of MNPs as carriers of microorganisms and toxic chemicals were pointed out. The widespread presence of MNPs and their complex toxicity mechanisms may make them important triggers for cardiovascular diseases, but current research still suffers from unbalanced studies across environmental systems, incomplete understanding of plastic properties, and limited knowledge of long-term biological effects. Future research should focus on the long-term effects of MNPs, the joint toxicity mechanisms with other pollutants, and the differential effects across population subgroups. It is suggested to accelerate plastic recycling technology innovation, promote biodegradable materials, and optimize waste treatment process to mitigate the potential threat of MNPs pollution to human health. Through multidisciplinary collaboration and in-depth research, combining innovative concepts from toxicology, public health policy, and environmental science, it is expected to provide new methods and approaches for the prevention and treatment of cardiovascular diseases associated with MNPs.

Glioma is a common primary intracra-nial tumor.Malignant glioma has a high mortality rate and an inferior prognosis.Despite various ther-apeutic interventions,the overall survival rate is still meager.Sesquiterpene lactone is a kind of natu-ral product containing α-methylene-γ-lactone,which has strong anti-tumor activity.In recent years,there have been many reports on the anti-gli-oma effect of sesquiterpene lactone compounds,such as ACT001,which is a structural modification of sesquiterpene lactone(Parthenolide)and has en-tered the clinical trial stage as a potential antican-cer drug.This paper reviews the activity and mecha-nism of sesquiterpene lactones with anti-glioma ef-fects,which have been studied in recent.years.

Objective To construct models of viral transfection and hypoxia/reoxygenation induced cellular injury in adult mouse cardiomyocytes(AMCMs)isolated using a non-Langendorff method.Methods AMCMs were isolated,extracted,sedimented,and plated using a non-Langendorff method.The morphology and survival rate of the isolated cells were evaluated 2,24,48 and 72 h after plating,and their integrity was observed by immunofluorescence staining for α-actinin.The isolated AMCMs were infected with adenoviruses carrying an RFP-expressing vector and fluorescence images were obtained at 36 and 48 h post-infection and used to calculate transfection efficiency.The cells were cultured under hypoxic conditions for 45 min,reoxygenated for 24 h,and then stained with propidium iodide(PI)to verify establishment of the hypoxia/reoxygenation injury model.Results The survival rates of AMCMs at 2,24 and 48 h after plating were comparable,but survival was significantly reduced at 72 h.The integrity of the AMCMs was good and＞80%of the cells were transfected with adenovirus at 48 h.After hypoxia/reoxygenation treatment,42%of cells were stained by PI,suggesting successful establishment of the AMCM injury model.Conclusions In this study,we developed a non-Langendorff method for the fast and easy isolation of AMCMs with high cell viability.The isolated cells can be efficiently infected with adenovirus and respond to hypoxia/reoxygenation injury.These findings provide a systematic method for isolating AMCMs and for applying gene modification and hypoxia/reoxygenation injury in these cells.

Objective:To investigate the relationship between the outcome of skin injury and urinary arsenic methylation metabolism levels in people exposed to arsenic through drinking water.Methods:Using cluster sampling method, permanent residents from drinking-water-borne endemic arsenic poisoning areas in Bayannur City, Inner Mongolia Autonomous Region were selected as survey subjects in 2004 (before water improvement). In 2017 (after water improvement), 74 survey subjects from 2004 were tracked and followed up. Urine samples were collected from survey subjects and high-performance liquid chromatography inductively coupled plasma mass spectrometry was used to detect the levels of arsenic methylation metabolites in urine. According to the "Diagnosis of Endemic Arsenic Poisoning" (WS/T 211-2015), the clinical grading (normal, suspicious, mild, moderate and severe) of skin injury of the survey subjects and the outcome of 2017 (improved, unchanged, aggravated) were assessed. A database was established and SPSS 25.0 software was used for statistical analysis.Results:The clinical grading ratios of skin injuries among survey subjects in 2004 and 2017 were compared, the differences were statistically significant (normal, suspicious, mild, moderate and severe: 38, 18, 4, 14 cases in 2004 and 27, 31, 3, 13 cases in 2017, χ 2 = 53.02, P < 0.001). Compared with 2004, in 2017, the levels of total arsenic (tAs), inorganic arsenic (iAs), monomethylarsenic (MMA), dimethylarsenic (DMA), percentage of inorganic arsenic (iAs%), and ratio of monomethylarsenic to dimethylarsenic (MMA/DMA) in the urine of survey subjects were low, and the differences were statistically significant ( Z = - 8.24, - 9.07, - 7.81, - 8.04, - 8.24, - 3.56, P < 0.001). The levels of dimethylarsenic percentage (DMA%), monomethylation rate (PMI) and dimethylation rate (SMI) were higher, and the differences were statistically significant ( Z = - 6.39, - 8.24, - 3.52, P < 0.001). In 2004, patients with different clinical grading of skin injuries had different outcomes in 2017 (χ 2 = 30.80, P < 0.001). There were statistically significant differences in tAs, iAs, MMA and DMA variation in urine among skin injury patients with different outcomes ( H = 10.62, 9.35, 8.80, 9.13, P < 0.05). Conclusions:Improving water can significantly reduce the levels of tAs, iAs, MMA, and DMA in the urine of arsenic exposed individuals. The outcome of skin injury in individuals exposed to arsenic through drinking water is related to the variation of urinary arsenic methylation metabolites tAs, iAs, MMA, and DMA.

Medical image is a powerful tool to assist doctors in the diagnosis and treatment planning.Nowadays,the segmentation of medical images is no longer limited to manual segmentation methods.Traditional methods and deep learning methods have been used to achieve more accurate results in medical image segmentation.Herein some innovative medical image segmentation methods in recent years are reviewed.By elaborating on the innovations of deep learning methods(SAM,SegNet,Mask R-CNN,and U-NET)and traditional methods(active contour model and threshold segmentation model),the differences and similarities between them are compared.The summary of medical image segmentation methods and the prospect is expected to help researchers better grasp and familiarize themselves with research status and development trend.

During radiotherapy of lung tumors, patients′respiration will lead to tumor displacement, making it difficult to accurately expose target volumes to radiation. This will cause damage to the physiological structures of surrounding healthy tissues and reduce the efficacy. Therefore, it is critical to accurately predict the motion trajectories of lung tumors and adjust the positions of electron beams in a real-time manner. Currently, primary methods to predict the motion trajectories of lung tumors include marker-based and marker-free predictions. This review explores the advances in research on both prediction methods and analyzes their basic principles, application scenarios, current challenges, and future trends. It is expected to provide comparatively comprehensive insights for researchers and clinicians in related fields to facilitate the improvement and optimization of radiotherapy for lung tumors.

Brachytherapy is a kind of radiation therapy corresponding to external radiation therapy, i. It has been widely used because it can achieve a higher radiation dose to the lesion area and better protect to the organs at risk. However, tThe workflow of brachytherapy is time-consuming and may lead to patient discomfort, displacement of the applicator or interstitial needle, and organ changes. In recent years, deep learning technology has achieved significant success in the medical field, offering new avenues for the automation of brachytherapy, improvement of radiotherapy precision, and ensuring the safety and effectiveness of radiotherapy plans. This review summarizes the research progress of deep learning in the context of brachytherapy segmentation, image registration, applicator reconstruction, dose prediction and planning optimization, and quality assurance for clinical research reference.

Objective:To investigate the arsenic metabolism pattern and possible influencing factors in the population in drinking-water-borne endemic arsenic poisoning (drinking-water-borne arsenic poisoning for short) areas.Methods:In December 2004, a cluster sampling method was used to select arsenic poisoning population (arsenic poisoning group) and healthy population (control group) in drinking-water-borne arsenic poisoning area of Bayannur City, Inner Mongolia Autonomous Region as the survey subjects. A questionnaire survey was conducted. Arsenic content in drinking water at home of survey subjects, the levels of urinary arsenic and its metabolites, including [trivalent arsenic (As Ⅲ), inorganic arsenic (iAs), monomethylarsenic acid (pentavalent, MMA V), dimethylarsenic acid (pentavalent, DMA V), total arsenic (tAs), percentage of inorganic arsenic (iAs%), percentage of monomethylarsenic acid (MMA%), percentage of dimethylarsenic acid (DMA%), primary methylation index (PMI), secondary methylation index (SMI)] were tested using high performance liquid chromatography-inductively coupled plasma mass spectrometry; nail arsenic and nail selenium levels were tested using atomic fluorescence spectrometer. The influencing factors of arsenic metabolism pattern were analyzed by multiple linear regression. Results:A total of 536 survey subjects were included, including 155 individuals in the arsenic poisoning group and 381 in the control group. The water arsenic level ranged from 0.0 to 825.7 μg/L. Compared with the control group, there was no significant difference in the distribution of gender, education level and dental fluorosis in the arsenic poisoning group ( P > 0.05), but there were significant differences in the distribution of age, marital status, smoking, drinking and water arsenic ( P < 0.05). Compared with the control group, the levels of urinary As Ⅲ, iAs, MMA V, DMA V, tAs, MMA%, MMA/DMA and nail arsenic in the arsenic poisoning group were higher ( P < 0.05), while the levels of urinary DMA%, SMI and nail selenium were lower ( P < 0.05); but there was no statistically significant difference in the levels of urinary iAs% and PMI ( P > 0.05). Gender, education level, depth of wells, water arsenic, total number of wells and nail arsenic were the influencing factors of urinary As Ⅲ (β = - 19.82, - 23.83, 0.61, 0.21, 7.26, 2.98, P < 0.05). Age, depth of wells, water arsenic and nail arsenic were the influencing factors of urinary tAs (β = 3.18, 3.25, 1.31, 15.59, P < 0.05). Gender, education level, depth of wells, water arsenic, total number of wells and nail arsenic were the influencing factors of urinary iAs (β = - 20.47, - 25.90, 0.64, 0.25, 7.87, 3.11, P < 0.05). Age, gender, education level, water arsenic and nail arsenic were the influencing factors of urinary MMA V (β = 0.52, - 17.07, - 21.84, 0.22, 2.77, P < 0.05). Age, depth of wells, water arsenic and nail arsenic were the influencing factors of urinary DMA V (β = 2.35, 2.47, 0.85, 9.22, P < 0.05). Conclusions:Compared with healthy individuals, there are differences in arsenic metabolism pattern among individuals with drinking-water-borne arsenic poisoning. Age, gender, education level, depth of wells, water arsenic, total number of wells and nail arsenic may be influencing factors of different arsenic metabolism patterns.

A dose reconstruction algorithm for electrionic portal imaging device(EPID)based on calibration and calculation is developed.The raw data of EPID in continuous acquisition mode are corrected for dark field and gain,and the gray level features of bright field are used to determine the field boundary.Subsequently,MU calibration,off-axis calibration and field size calibration are performed on the EPID data,and dose reconstruction is carried out based on the calibrated superimposed flux and the Monte Carlo model of the linac head.Nine cases of IMRT plans are selected for verification and measurement using EPID and MapCheck separately,and the passing rates between the two tools are compared under different gamma criteria(3%/3 mm and 2%/2 mm).For a planned case,the average passing rates of multiple cases verified by MapCheck under the two criteria were 99.02%±1.28%and 90.84%±4.49%,and the average passing rates of the EPID reconstruction models were 98.86%±1.19%and 91.39%±4.80%.Compared with MapCheck,the EPID reconstruction algorithm based on calibration and calculation has no significant difference in the passing rate of IMRT plan verification(P＞0.05),which meets the clinical requirements of dose verification.

Objective:To investigate the prediction of the tumor proliferation antigen(Ki-67) expression status in breast cancer patients based on ultrasound radiomics combined with clinicopathologic features.Methods:Breast cancer patients who underwent 2D ultrasound and Ki-67 examination from January 2018 to February 2022 in Changzhou Second People′s Hospital, Nanjing Medical University were retrospectively analyzed. Among them, 427 patients from Chengzhong campus were randomly divided into training and validation sets in the ratio of 8∶2, and 229 patients from Yanghu campus were used as an independent external test set. Radiomics features were extracted from the region of interest of 2D ultrasound images, and the Mann-Whitney U test, recursive feature elimination, and minimum absolute shrinkage and selection operators were used to perform feature dimensionality reduction and to establish a radiomics score(Rad-score). Subsequently, single/multifactor logistic regression regression analyses were used to construct a joint prediction model based on Rad-score and clinicopathological features. Model performance and utility were assessed using the subject operating characteristic area under the curve (AUC), calibration curve, and decision curve analyses. Results:The AUCs of the joint model for predicting Ki-67 expression status in breast cancer in the training, validation, and test sets were 0.858, 0.797, and 0.802, respectively, which were superior to those of the radiomics (0.772, 0.731, and 0.713) and clinical models (0.738, 0.750, and 0.707). Calibration curve and decision curve analyses indicated that the joint model had good calibration and clinical value.Conclusions:A joint model based on ultrasound radiomics and clinicopathological features can effectively predict the Ki-67 expression status of breast cancer, which is expected to become a non-invasive tool for Ki-67 detection and provide clinicians with an important auxiliary diagnostic and therapeutic decision-making basis.