Urban Residential Greenness and Cancer Mortality: Evaluating the Causal Mediation Role of Air Pollution in a Large Cohort.

Evidence linking greenness to all-site and site-specific cancers remains limited, and the complex role of air pollution in this pathway is unclear. We aimed to fill these gaps by using a large cohort in southern China. A total of 654,115 individuals were recruited from 2009 to 2015 and followed-up until December 2020. We calculated the normalized difference vegetation index (NDVI) in a 500-meter buffer around the participants' residences to represent the greenness exposure. Cox proportional-hazards models were used to evaluate the impact of greenness on the risk of all-site and site-specific cancer mortality. Additionally, we assessed both the mediation and interaction roles of air pollution (i.e., PM2.5, PM10, and NO2) in the greenness-cancer association through a causal mediation analysis using a four-way decomposition method. Among the 577,643 participants, 10,088 cancer deaths were recorded. We found a 10% (95% CI: 5-16%) reduction in all-site cancer mortality when the NDVI increased from the lowest to the highest quartile. When stratified by cancer type, our estimates suggested 18% (95% CI: 8-27%) and 51% (95% CI: 16-71%) reductions in mortality due to respiratory system cancer and brain and nervous system cancer, respectively. For the above protective effect, a large proportion could be explained by the mediation (all-site cancer: 1.0-27.7%; respiratory system cancer: 1.2-32.3%; brain and nervous system cancer: 3.6-109.1%) and negative interaction (all-site cancer: 2.1-25.7%; respiratory system cancer: 2.0-25.7%; brain and nervous system cancer: not significant) effects of air pollution. We found that particulate matter (i.e., PM2.5 and PM10) had a stronger causal mediation effect (25.0-109.1%) than NO2 (1.0-3.6%), while NO2 had a stronger interaction effect (25.7%) than particulate matter (2.0-2.8%). In summary, greenness was significantly beneficial in reducing the mortality of all-site, respiratory system, and brain and nervous system cancer in southern China, with the impact being modulated and mediated by air pollution.

The effect of residential greenness on cardiovascular mortality from a large cohort in South China: An in-depth analysis of effect modification by multiple demographic and lifestyle characteristics.

BACKGROUND: The potential for residential greenness to improve cardiovascular health through both physical and psychological mechanisms is well recognized. However, evidence from rapidly urbanizing developing countries and cohort-based causal inference approaches, remains limited. We aim to examine the effect of residential greenness and time to cardiovascular mortality in South China. METHODS: We utilized data from a community-based population survey involving 748,209 participants at baseline from 2009 to 2015, followed up until 2020. Residential greenness exposure was assessed by the annual Normalized Difference Vegetation Index (NDVI) in the 500 m radius of each participant's residence. We used time-varying proportional hazard Cox models coupled with inverse probability weighting to fit marginal structural models and obtain hazard ratios (HRs) for cardiovascular disease (CVD) mortality after adjusting for confounders. Multiple effect modifiers on both additive and multiplicative scales were further explored. RESULTS: A total of 15,139 CVD-related deaths were identified during a median of 7.9 years of follow-up. A protective effect was found between higher greenness exposure and reduced CVD mortality, with a 9.3 % lower rate of total CVD mortality (HR 0.907, 95 % CI 0.859-0.957) based on a 0.1 increase in annual average NDVI. Demographic (age, marital status) and lifestyle factors (smoking, drinking status) were found to modify the association between residential greenness and CVD mortality (all P interaction values < 0.05 or 95 %CI for RERI excluded the value 0). Notably, this effect was more pronounced among older adults, married, and individuals having healthier lifestyles, indicating a greater benefit from greenness for these subgroups. CONCLUSIONS: Our findings support a causal link between increased residential greenness exposure and a reduced risk of CVD mortality in South China with marked heterogenous effects, which has public health implications for cultivating greener urban environments to mitigate the impact of CVD within the context of rapid urbanization.

Association of long-term exposure to PM2.5 and its chemical components with the reduced quality of sleep.

Poor sleep quality is a widespread concern. While the influence of particle exposure on sleep disturbances has received considerable attention, research exploring other dimensions of sleep quality and the chemical components of the particles remains limited. We employed a marginal structural model to explore the association of long-term exposure to PM2.5 and its chemical components with poor sleep quality. The odds ratio (95 % CI) for poor sleep quality was 1.335 (1.292-1.378), 1.097 (1.080-1.113), 1.137 (1.100-1.174), 1.197 (1.156-1.240), and 1.124 (1.107-1.140) per IQR increase in the concentration of PM2.5, SO42-, NO3-, NH4+, and BC, respectively. The score (and 95 % CI) of sleep latency, use of sleep medication, habitual sleep efficiency, subjective sleep quality, and daytime dysfunction were affected by PM2.5, with an increase of 0.059 (0.050-0.069), 0.054 (0.049-0.059), 0.011 (0.008-0.014), 0.011 (0.005-0.018), and 0.026 (0.018-0.034) per IQR increase in PM2.5 concentrations, respectively. This study supports the association of long-term exposure to PM2.5 and its chemical components with poor sleep quality.

Time series analysis of the interaction between ambient temperature and air pollution on hospitalizations for AECOPD in Ganzhou, China.

This study aimed to investigate the univariate and bivariate effects of ambient temperature and air pollutants on 57,251 inpatients with AECOPD (Acute Exacerbation of Chronic Obstructive Pulmonary Disease) in Ganzhou from January 1, 2016, to December 31, 2019. We categorized the daily mean temperature and air pollutant variables based on the exposure-response curve of the Distributed Lag Non-Linear Model. Poisson regression model was used for interaction and stratification analysis. The Relative Excess Risk due to Interaction (RERI) with 95% confidence intervals (95% CI) between daily mean temperature (Tmean) and air pollutants including NO2, PM2.5, and PM10 were - 0.428 (95% CI - 0.637, - 0.218), -- 0.227 (95% CI - 0.293, - 0.161), and - 0.119 (95% CI - 0.159, - 0.079). Further stratification analysis showed the relative risk (RR) (95% CI) of high NO2 (> 33 µg/m3) at low Tmean (≤ 28 °C) was 1.119 (95% CI 1.096, 1.142). Low temperatures with high PM10 in men and high PM2.5 in women were associated with a higher risk of AECOPD hospitalization. The results indicate a higher risk of hospitalization for AECOPD when there is with high concentrations of air pollution at low temperatures.

Assessing the modification impact of vaccination on the relationship of the Discomfort Index with hand, foot, and mouth disease in Guizhou: A multicounty study.

BACKGROUND: Hand, foot, and mouth disease (HFMD) is a major public health issue in China while temperature and humidity are well-documented predictors. However, evidence on the combined effect of temperature and humidity is still limited. It also remains unclear whether such an effect could be modified by the enterovirus 71 (EV71) vaccination. METHODS: Based on 320,042 reported HFMD cases during the summer months between 2012 and 2019, we conducted a study utilizing Distributed Lag Non-Linear Models (DLNM) and time-varying DLNM to examine how China's HFMD EV71 vaccine strategy would affect the correlation between meteorological conditions and HFMD risk. RESULTS: The incidence of HFMD changed with the Discomfort Index in an arm-shaped form. The 14-day cumulative risk of HFMD exhibited a statistically significant increase during the period of 2017-2019 (following the implementation of the EV71 vaccine policy) compared to 2012-2016 (prior to the vaccine implementation). For the total population, the range of relative risk (RR) values for HFMD at the 75th, 90th, and 99th percentiles increased from 1.082-1.303 in 2012-2016 to 1.836-2.022 in 2017-2019. In the stratified analyses, Han Chinese areas show stronger relative growth, with RR values at the 75th, 90th, and 99th percentiles increased by 14.3%, 39.1%, and 134.4% post-vaccination, compared to increases of 22.7%, 41.6%, and 38.8% in minority areas. Similarly, boys showed greater increases (24.4%, 47.7%, 121.5%) compared to girls (8.1%, 28.1%, 58.3%). Additionally, the central Guizhou urban agglomeration displayed a tendency for stronger relative growth compared to other counties. CONCLUSIONS: Although the EV71 vaccine policy has been implemented, it hasn't effectively controlled the overall risk of HFMD. There's been a shift in the main viral subtypes, potentially altering population susceptibility and influencing HFMD occurrences. The modulating effects of vaccine intervention may also be influenced by factors such as race, sex, and economic level.

How long-term PM exposure may affect all-site cancer mortality: Evidence from a large cohort in southern China.

BACKGROUND: Evidence of a potential causal link between long-term exposure to particulate matter (PM) and all-site cancer mortality from large population cohorts remained limited and suffered from residual confounding issues with traditional statistical methods. AIMS: We aimed to examine the potential causal relationship between long-term PM exposure and all-site cancer mortality in South China using causal inference methods. METHODS: We used a cohort in southern China that recruited 580,757 participants from 2009 through 2015 and tracked until 2020. Annual averages of PM1, PM2.5, and PM10 concentrations were generated with validated spatiotemporal models. We employed a causal inference approach, the Marginal Structural Cox model, based on observational data to evaluate the association between long-term exposure to PM and all-site cancer mortality. RESULTS: With an increase of 1 µg/m³ in PM1, PM2.5, and PM10, the hazard ratios (HRs) and 95% confidence interval (CI) for all-site cancer were 1.033 (95% CI: 1.025-1.041), 1.032 (95% CI: 1.027-1.038), and 1.020 (95% CI: 1.016-1.025), respectively. The HRs (95% CI) for digestive system and respiratory system cancer mortality associated with each 1 µg/m³ increase in PM1 were 1.022 (1.009-1.035) and 1.053 (1.038-1.068), respectively. In addition, inactive participants, who never smoked, or who lived in areas of low surrounding greenness were more susceptible to the effects of PM exposure, the HRs (95% CI) for all-site cancer mortality were 1.042 (1.031-1.053), 1.041 (1.032-1.050), and 1.0473 (1.025-1.070) for every 1 µg/m³ increase in PM1, respectively. The effect of PM1 tended to be more pronounced in the low-exposure group than in the general population, and multiple sensitivity analyses confirmed the robustness of the results. CONCLUSION: This study provided evidence that long-term exposure to PM may elevate the risk of all-site cancer mortality, emphasizing the potential health benefits of improving air quality for cancer prevention.

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning.

We report a fast, easy-to-implement, highly sensitive, sequence-specific, and point-of-care (POC) DNA virus detection system, which combines recombinase polymerase amplification (RPA) and CRISPR/Cas12a system for trace detection of DNA viruses. Target DNA is amplified and recognized by RPA and CRISPR/Cas12a separately, which triggers the collateral cleavage activity of Cas12a that cleaves a fluorophore-quencher labeled DNA reporter and generalizes fluorescence. For POC detection, portable smartphone microscopy is built to take fluorescent images. Besides, deep learning models for binary classification of positive or negative samples, achieving high accuracy, are deployed within the system. Frog virus 3 (FV3, genera Ranavirus, family Iridoviridae) was tested as an example for this DNA virus POC detection system, and the limits of detection (LoD) can achieve 10 aM within 40 min. Without skilled operators and bulky instruments, the portable and miniature RPA-CRISPR/Cas12a-SPM with artificial intelligence (AI) assisted classification shows great potential for POC DNA virus detection and can help prevent the spread of such viruses.

Residential greenness and chronic obstructive pulmonary disease in a large cohort in southern China: Potential causal links, risk trajectories, and mediation pathways.

INTRODUCTION: Residential greenness may influence COPD mortality, but the causal links, risk trajectories, and mediation pathways between them remain poorly understood. OBJECTIVES: We aim to comprehensively identify the potential causal links, characterize the dynamic progression of hospitalization or posthospital risk, and quantify mediation effects between greenness and COPD. METHODS: This study was conducted using a community-based cohort enrolling individuals aged ≥ 18 years in southern China from January 1, 2009 to December 31, 2015. Greenness was characterized by normalized difference vegetation index (NDVI) around participants' residential addresses. We applied doubly robust Cox proportional hazards model, multi-state model, and multiple mediation method, to investigate the potential causal links, risk trajectories among baseline, COPD hospitalization, first readmission due to COPD or COPD-related complications, and all-cause death, as well as the multiple mediation pathways (particulate matter [PM], temperature, body mass index [BMI] and physical activity) connecting greenness exposure to COPD mortality. RESULTS: Our final analysis included 581,785 participants (52.52% female; average age: 48.36 [Standard Deviation (SD): 17.56]). Each interquartile range (IQR: 0.06) increase in NDVI was associated with a reduced COPD mortality risk, yielding a hazard ratio (HR) of 0.88 (95 % CI: 0.81, 0.96). Furthermore, we observed per IQR (0.04) increase in NDVI was inversely associated with the risk of multiple transitions (baseline - COPD hospitalization, baseline - death, and readmission - death risks), especially a declined risk of all-cause death after readmission (HR = 0.66 [95 %CI: 0.44, 0.99]). Within the observed association between greenness and COPD mortality, three mediators were identified, namely PM, temperature, and BMI (HR for the total indirect effect: 0.773 [95 % CI: 0.703, 0.851]), with PM showing the highest mediating effect. CONCLUSIONS: Our findings revealed greenness may be a beneficial factor for COPD morbidity, prognosis, and mortality. This protective effect is primarily attributed to the reduction in PM concentration.

Relationships between long-term exposure to major PM2.5 constituents and outpatient visits and hospitalizations in Guangdong, China.

Ambient fine particulate matter (PM2.5) has attracted considerable attention due to its crucial role in the rising global disease burden. Evidence of health risks associated with exposure to PM2.5 and its major constituents is important for advancing hazard assessments and air pollution emission policies. We investigated the relationship between exposure to major constituents of PM2.5 and outpatient visits as well as hospitalizations in Guangdong Province, China, where 127 million residents live in a severe PM2.5 pollution environment. An approach that integrates the generalized weighted quantile sum (gWQS) regression with the difference-in-differences (DID) approach was used to assess the overall mixture effects and relative contributions of each constituent. We observed significant associations between long-term exposure to the mixture of PM2.5 constituents (WQS index) and outpatient visits (IR%, percentage increases in risk per unit WQS index increase:1.73, 95%CI: 1.72, 1.74) as well as hospitalizations (IR%:5.15, 95%CI: 5.11, 5.20). Black carbon (weight: 0.34) and nitrate (weight: 0.60) respectively exhibited the highest contributions to outpatient visits and hospitalizations. The overall mixture effects on outpatient visits and hospitalizations were higher with increased summer air temperatures (IR%: 7.54, 95%CI: 7.33, 7.74 and IR%: 9.55, 95%CI: 8.36, 10.75, respectively) or decreased winter air temperatures (IR%: 1.88, 95%CI: 1.68, 2.08 and IR%: 4.87, 95%CI: 3.73, 6.02, respectively). Furthermore, the overall mixture effects on outpatient visits and hospitalizations were significantly higher in populations with higher socioeconomic status (P < 0.01). It's crucial to address the primary sources of nitrate precursor substances and black carbon (mainly traffic-related and industrial-related air pollutants) and consider the complex interaction effects between air temperature and PM2.5 in the context of climate change. Of particular concern is the need to prioritize healthcare demands in economically disadvantaged regions and to address the health inequalities stemming from the uneven distribution of healthcare resources and PM2.5 pollution.

Long-term PM1 exposure and hypertension hospitalization: A causal inference study on a large community-based cohort in South China.

Limited evidence exists on the effect of submicronic particulate matter (PM1) on hypertension hospitalization. Evidence based on causal inference and large cohorts is even more scarce. In 2015, 36,271 participants were enrolled in South China and followed up through 2020. Each participant was assigned single-year, lag0-1, and lag0-2 moving average concentration of PM1 and fine inhalable particulate matter (PM2.5) simulated based on satellite data at a 1-km resolution. We used an inverse probability weighting approach to balance confounders and utilized a marginal structural Cox model to evaluate the underlying causal links between PM1 exposure and hypertension hospitalization, with PM2.5-hypertension association for comparison. Several sensitivity studies and the analyses of effect modification were also conducted. We found that a higher hospitalization risk from both overall (HR: 1.13, 95% CI: 1.05-1.22) and essential hypertension (HR: 1.15, 95% CI: 1.06-1.25) was linked to each 1 µg/m3 increase in the yearly average PM1 concentration. At lag0-1 and lag0-2, we observed a 17%-21% higher risk of hypertension associated with PM1. The effect of PM1 was 6%-11% higher compared with PM2.5. Linear concentration-exposure associations between PM1 exposure and hypertension were identified, without safety thresholds. Women and participants that engaged in physical exercise exhibited higher susceptibility, with 4%-22% greater risk than their counterparts. This large cohort study identified a detrimental relationship between chronic PM1 exposure and hypertension hospitalization, which was more pronounced compared with PM2.5 and among certain groups.

Revisiting the complex time-varying effect of non-pharmaceutical interventions on COVID-19 transmission in the United States.

Introduction: Although the global COVID-19 emergency ended, the real-world effects of multiple non-pharmaceutical interventions (NPIs) and the relative contribution of individual NPIs over time were poorly understood, limiting the mitigation of future potential epidemics. Methods: Based on four large-scale datasets including epidemic parameters, virus variants, vaccines, and meteorological factors across 51 states in the United States from August 2020 to July 2022, we established a Bayesian hierarchical model with a spike-and-slab prior to assessing the time-varying effect of NPIs and vaccination on mitigating COVID-19 transmission and identifying important NPIs in the context of different variants pandemic. Results: We found that (i) the empirical reduction in reproduction number attributable to integrated NPIs was 52.0% (95%CI: 44.4, 58.5%) by August and September 2020, whereas the reduction continuously decreased due to the relaxation of NPIs in following months; (ii) international travel restrictions, stay-at-home requirements, and restrictions on gathering size were important NPIs with the relative contribution higher than 12.5%; (iii) vaccination alone could not mitigate transmission when the fully vaccination coverage was less than 60%, but it could effectively synergize with NPIs; (iv) even with fully vaccination coverage >60%, combined use of NPIs and vaccination failed to reduce the reproduction number below 1 in many states by February 2022 because of elimination of above NPIs, following with a resurgence of COVID-19 after March 2022. Conclusion: Our results suggest that NPIs and vaccination had a high synergy effect and eliminating NPIs should consider their relative effectiveness, vaccination coverage, and emerging variants.

Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model.

BACKGROUND: Infectious diarrhea remains a major public health problem worldwide. This study used stacking ensemble to developed a predictive model for the incidence of infectious diarrhea, aiming to achieve better prediction performance. METHODS: Based on the surveillance data of infectious diarrhea cases, relevant symptoms and meteorological factors of Guangzhou from 2016 to 2021, we developed four base prediction models using artificial neural networks (ANN), Long Short-Term Memory networks (LSTM), support vector regression (SVR) and extreme gradient boosting regression trees (XGBoost), which were then ensembled using stacking to obtain the final prediction model. All the models were evaluated with three metrics: mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE). RESULTS: Base models that incorporated symptom surveillance data and weekly number of infectious diarrhea cases were able to achieve lower RMSEs, MAEs, and MAPEs than models that added meteorological data and weekly number of infectious diarrhea cases. The LSTM had the best prediction performance among the four base models, and its RMSE, MAE, and MAPE were: 84.85, 57.50 and 15.92%, respectively. The stacking ensembled model outperformed the four base models, whose RMSE, MAE, and MAPE were 75.82, 55.93, and 15.70%, respectively. CONCLUSIONS: The incorporation of symptom surveillance data could improve the predictive accuracy of infectious diarrhea prediction models, and symptom surveillance data was more effective than meteorological data in enhancing model performance. Using stacking to combine multiple prediction models were able to alleviate the difficulty in selecting the optimal model, and could obtain a model with better performance than base models.

Assessing the effectiveness of the expanded hepatitis A vaccination program in China: an interrupted time series design.

INTRODUCTION: China initialised the expanded hepatitis A vaccination programme (EHAP) in 2008. However, the effectiveness of the programme remains unclear. We aimed to comprehensively evaluate the effectiveness of EHAP in the country. METHODS: Based on the provincial data on the incidence of hepatitis A (HepA), the population and meteorological variables in China, we developed interrupted time series (ITS) models to estimate the effectiveness of EHAP with the autocorrelation, seasonality and the meteorological confounders being controlled. Results were also stratified by economic zones, age groups and provinces. RESULTS: We found a 0.9% reduction (RR=0.991, 95% CI: 0.990 to 0.991) in monthly HepA incidence after EHAP, which was 0.3% greater than the reduction rate before EHAP in China. Across the three economic regions, we found a 1.1% reduction in HepA incidence in both central and western regions after EHAP, which were 0.3% and 1.2% greater than the reduction rates before EHAP, respectively. We found a decreased reduction rate for the eastern region. In addition, we found generally increased reduction rate after EHAP for age groups of 0-4, 5-14 and 15-24 years. However, we found decreased reduction rate among the 25-64 and ≥65 years groups. We found a slight increased rate after EHAP in Shanxi Province but not elsewhere. CONCLUSION: Our finding provides comprehensive evidence on the effectiveness of EHAP in China, particularly in the central and western regions, and among the population aged 0-24 years old. This study has important implications for the adjustment of vaccination strategies for other regions and populations.

Potential causal links and mediation pathway between urban greenness and lung cancer mortality: Result from a large cohort (2009 to 2020).

Urban greenness, as a vital component of the urban environment, plays a critical role in mitigating the adverse effects of rapid urbanization and supporting urban sustainability. However, the causal links between urban greenness and lung cancer mortality and its potential causal pathway remain poorly understood. Based on a prospective community-based cohort with 581,785 adult participants in southern China, we applied a doubly robust Cox proportional hazard model to estimate the causal associations between urban greenness exposure and lung cancer mortality. A general multiple mediation analysis method was utilized to further assess the potential mediating roles of various factors including particulate matter (PM1, PM2.5-1, and PM10-2.5), temperature, physical activity, and body mass index (BMI). We observed that each interquartile range (IQR: 0.06) increment in greenness exposure was inversely associated with lung cancer mortality, with a hazard ratio (HR) of 0.89 (95 % CI: 0.83, 0.96). The relationship between greenness and lung cancer mortality might be partially mediated by particulate matter, temperature, and physical activity, yielding a total indirect effect of 0.826 (95 % CI: 0.769, 0.887) for each IQR increase in greenness exposure. Notably, the protective effect of greenness against lung cancer mortality could be achieved primarily by reducing the particulate matter concentration.

Progression of cognitive dysfunction in NPC survivors with radiation-induced brain necrosis: A prospective cohort.

BACKGROUND AND PURPOSE: The evidence of longitudinal changes in cognition in nasopharyngeal carcinoma (NPC) survivors with radiation-induced brain necrosis (RIBN) after radiotherapy (RT) remained insufficient. We aimed to estimate the clinical progression rate of cognitive decline and identify patients with differential decline rates. MATERIALS AND METHODS: Based on an ongoing prospective cohort study, NPC patients aged ≥18 years old and diagnosed with RIBN were included in this current analysis if they finished the time frame of 3-year follow-up and had at least twice cognition assessments. The Chinese version of the Montreal Cognitive Assessment (MoCA) was used to assess the cognitive state. Linear mixed-effect models were used to analyze the annual progression rates of MoCA total and seven sub-items scores. RESULTS: Among 134 patients in this study, the transition probability from normal to mild/moderate cognitive dysfunction were 14.2 % (19/134) and 1.49 % (2/134) respectively during the median follow-up time of 2.35 years. The total MoCA score declined by -0.569 (SE 0.208) points annually (p = 0.008). Patients with ≤6 years of duration from RT to RIBN have higher annual progression rate of total scores [-0.851 (SE 0.321), p = 0.013; p for interaction = 0.041]. CONCLUSION: Our findings of the annual decline rate of cognition in NPC patients with RIBN from a 3-year longitudinal data, particularly for those who developed RIBN rapidly after RT, have important implications for the upcoming clinical trials designed to prevent or decrease cognitive decline in NPC patients with RIBN, regarding the selection of study patients and the calculation of sample size.

Feeding artery: a valuable feature for differentiation of regenerative nodule, dysplastic nodules and small hepatocellular carcinoma in CEUS LI-RADS.

OBJECTIVE: To investigate whether the feeding artery (FA) feature can aid in discriminating small hepatocellular carcinoma (HCC) using the contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) from precancerous lesions. METHODS: Between June 2017 and May 2021, a total of 347 patients with 351 precancerous liver lesions or small HCCs who underwent CEUS were enrolled. Two independent radiologists assigned LI-RADS categories to all lesions and assessed the presence of the FA feature, which was used as an ancillary feature to either upgrade or downgrade the LI-RADS category. The diagnostic performance of CEUS LI-RADS, both with and without the FA feature, was evaluated based on accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: The FA feature was found to be more prevalent in HCC (85.54%, p < 0.001) than in regenerative nodules (RNs, 29.73%), low-grade dysplastic nodules (LGDNs, 33.33%), and high-grade dysplastic nodules (HGDNs, 55.26%). Furthermore, the presence of arterial phase hyperenhancement (APHE), washout (WO), and FA in liver nodules was associated with a higher expression of GPC-3 and Ki-67 compared to the group without these features (p < 0.001). After adjusting, the sensitivity and accuracy of LR-5 for HCC improved from 68.67% (95%CI: 62.46%, 74.30%) to 77.51% (95%CI: 71.72%, 82.44%) and from 69.23% (95%CI: 64.11%, 74.02%) to 73.79% (95%CI: 68.86%, 78.31%), respectively. CONCLUSION: The FA feature is a valuable feature for distinguishing small HCC and precancerous lesions and could be added as a possible ancillary feature in CEUS LI-RADS which was backed up by biomarkers. CLINICAL RELEVANCE STATEMENT: The presence of a feeding artery is a valuable imaging feature in the differentiation of HCC and precancerous lesions. Incorporating this characteristic in the CEUS LI-RADS can enhance the diagnostic ability. KEY POINTS: • Feeding artery is more frequent in HCC than in regenerative nodules, low-grade dysplastic nodules, and high-grade dysplastic nodules. • Feeding artery feature is a valuable ancillary feature for CEUS LI-RADS to differentiate regenerative nodules, low-grade dysplastic nodules, high-grade dysplastic nodules, and HCC. • The existence of feeding artery, arterial phase hyperenhancement, and washout is associated with more GPC-3 positive expression and higher Ki-67 expression than the group without these features.

A Nonviral piggyBac Transposon-Mediated Method to Generate Large-Scale CAR-NK Cells from Human Peripheral Blood Primary NK Cells.

With the inherent antitumor function and unique "off-the-shelf" potential, genetically engineered human natural killer (NK) cells with chimeric antigen receptors (CARs) bear great promise for the treatment of multiple hematological malignancies and solid tumors. Current methods of producing large-scale CAR-NK cells mainly rely on mRNA transfection and viral vector transduction. However, mRNA CAR-NK cells were not stable in CAR expression while viral vector transduction mostly ended up with low efficiency. In this chapter, we described an optimized protocol to generate CAR-NK cells by using the piggyBac transposon system via electroporation and to further expand these engineered CAR-NK cells in a large scale together with artificial antigen-presenting feeder cells. This method can stably engineer human primary NK cells with high efficiency and supply sufficient scale of engineered CAR-NK cells for the future possible clinical applications.

Prompt-enhanced hierarchical transformer elevating cardiopulmonary resuscitation instruction via temporal action segmentation.

The vast majority of people who suffer unexpected cardiac arrest are performed cardiopulmonary resuscitation (CPR) by passersby in a desperate attempt to restore life, but endeavors turn out to be fruitless on account of disqualification. Fortunately, many pieces of research manifest that disciplined training will help to elevate the success rate of resuscitation, which constantly desires a seamless combination of novel techniques to yield further advancement. To this end, we collect a specialized CPR video dataset in which trainees make efforts to behave resuscitation on mannequins independently in adherence to approved guidelines, promoting an auxiliary toolbox to assist supervision and rectification of intermediate potential issues via modern deep learning methodologies. Our research empirically views this problem as a temporal action segmentation (TAS) task in computer vision, which aims to segment an untrimmed video at a frame-wise level. Here, we propose a Prompt-enhanced hierarchical Transformer (PhiTrans) that integrates three indispensable modules, including a textual prompt-based Video Features Extractor (VFE), a transformer-based Action Segmentation Executor (ASE), and a regression-based Prediction Refinement Calibrator (PRC). The backbone preferentially derives from applications in three approved public datasets (GTEA, 50Salads, and Breakfast) collected for TAS tasks, which experimentally facilitates the model excavation on the CPR dataset. In general, we probe into a feasible pipeline that elevates the CPR instruction qualification via action segmentation equipped with novel deep learning techniques. Associated experiments on the CPR dataset advocate our resolution with surpassing 91.0% on Accuracy, Edit score, and F1 score.

Ringlike late gadolinium enhancement provides incremental prognostic value in non-classical arrhythmogenic cardiomyopathy.

BACKGROUND: The 2019 arrhythmogenic right ventricular cardiomyopathy (ARVC) risk model has proved insufficient in the capability of predicting ventricular arrhythmia (VA) risk in non-classical arrhythmogenic cardiomyopathy (ACM). Furthermore, the prognostic value of ringlike late gadolinium enhancement (LGE) of the left ventricle in non-classical ACM remains unknown. We aimed to assess the incremental value of ringlike LGE over the 2019 ARVC risk model in predicting sustained VA in patients with non-classical ACM. METHODS: In this retrospective study, consecutive patients with non-classical ACM who underwent CMR from January 2011 to January 2022 were included. The pattern of LGE was categorized as no, non-ringlike, and ringlike LGE. The primary outcome was defined as the occurrence of sustained VA. Univariable and multivariable Cox regression analysis was used to evaluate the impact of LGE patterns on sustained VA and area under curve (AUC) was calculated for the incremental value of ringlike LGE. RESULTS: A total of 73 patients were collected in the final cohort (mean age, 39.3 ± 14.4 years, 51 male), of whom 10 (13.7%) had no LGE, 33 (45.2%) had non-ringlike LGE, and 30 (41.1%) had ringlike LGE. There was no statistically significant difference in the 5-year risk score among the three groups (P = 0.190). During a median follow-up of 34 (13-56) months, 34 (46.6%) patients experienced sustained VA, including 1 (10.0%), 13 (39.4%) and 20 (66.7%) of patients with no, non-ringlike and ringlike LGE, respectively. After multivariable adjustment, ringlike LGE remained independently associated with the presence of sustained VA (adjusted hazard ratio: 6.91, 95% confidence intervals: 1.89-54.60; P = 0.036). Adding ringlike LGE to the 2019 ARVC risk model showed significantly incremental prognostic value for sustained VA (AUC: 0.80 vs. 0.67; P = 0.024). CONCLUSION: Ringlike LGE provides independent and incremental prognostic value over the 2019 ARVC risk model in patients with non-classical ACM.

Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma.

BACKGROUND: The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid component assessment still lacks a reference standard. METHODS: In this retrospective study, an AI algorithm was proposed for measuring the solid components ratio in SSNs, which was used to assess the diameter ratio (1D), area ratio (2D), and volume ratio (3D). The radiologist measured each SSN's consolidation to tumor ratio (CTR) twice, four weeks apart. The area under the receiver-operating characteristic (ROC) curve (AUC) was calculated for each method used to discriminate an Invasive Adenocarcinoma (IA) from a non-IA. The AUC and the time cost of each measurement were compared. Furthermore, we examined the consistency of measurements made by the radiologist on two separate occasions. RESULTS: A total of 379 patients (the primary dataset n = 278, the validation dataset n = 101) were included. In the primary dataset, compared to the manual approach (AUC: 0.697), the AI algorithm (AUC: 0.811) had better predictive performance (P =.0027) in measuring solid components ratio in 3D. Algorithm measurement in 3D had an AUC no inferior to 1D (AUC: 0.806) and 2D (AUC: 0.796). In the validation dataset, the AI 3D method also achieved superior diagnostic performance compared to the radiologist (AUC: 0.803 vs 0.682, P =.046). The two measurements of the CTR in the primary dataset, taken 4 weeks apart, have 7.9 % cases in poor consistency. The measurement time cost by the radiologist is about 60 times that of the AI algorithm (P <.001). CONCLUSION: The 3D measurement of solid components using AI, is an effective and objective approach to predict the pathological invasiveness of SSNs. It can be a preoperative interpretable indicator of pathological invasiveness in patients with lung adenocarcinoma.