Risk for stillbirth among pregnant individuals with SARS-CoV-2 infection varied by gestational age.

BACKGROUND: Despite previous research findings on higher risks of stillbirth among pregnant individuals with SARS-CoV-2 infection, it is unclear whether the gestational timing of viral infection modulates this risk. OBJECTIVE: This study aimed to examine the association between timing of SARS-CoV-2 infection during pregnancy and risk of stillbirth. STUDY DESIGN: This retrospective cohort study used multilevel logistic regression analyses of nationwide electronic health records in the United States. Data were from 75 healthcare systems and institutes across 50 states. A total of 191,403 pregnancies of 190,738 individuals of reproductive age (15-49 years) who had childbirth between March 1, 2020 and May 31, 2021 were identified and included. The main outcome was stillbirth at ≥20 weeks of gestation. Exposures were the timing of SARS-CoV-2 infection: early pregnancy (<20 weeks), midpregnancy (21-27 weeks), the third trimester (28-43 weeks), any time before delivery, and never infected (reference). RESULTS: We identified 2342 (1.3%) pregnancies with COVID-19 in early pregnancy, 2075 (1.2%) in midpregnancy, and 12,697 (6.9%) in the third trimester. After adjusting for maternal and clinical characteristics, increased odds of stillbirth were observed among pregnant individuals with SARS-CoV-2 infection only in early pregnancy (odds ratio, 1.75, 95% confidence interval, 1.25-2.46) and midpregnancy (odds ratio, 2.09; 95% confidence interval, 1.49-2.93), as opposed to pregnant individuals who were never infected. Older age, Black race, hypertension, acute respiratory distress syndrome or acute respiratory failure, and placental abruption were found to be consistently associated with stillbirth across different trimesters. CONCLUSION: Increased risk of stillbirth was associated with COVID-19 only when pregnant individuals were infected during early and midpregnancy, and not at any time before the delivery or during the third trimester, suggesting the potential vulnerability of the fetus to SARS-CoV-2 infection in early pregnancy. Our findings underscore the importance of proactive COVID-19 prevention and timely medical intervention for individuals infected with SARS-CoV-2 during early and midpregnancy.

Federal funding allocation on HIV/AIDS research in the United States (2008-2018): an exploratory study using Big Data.

Literature suggests that federal funding allocation for HIV-related research in the US may not align with HIV disease burden but is influenced by structural disparities. This study sought to examine how federal funding allocation is associated with HIV disease burden and research capacity of states by applying Big Data integration, text mining, and statistics. Using text mining, we identified 20,678 HIV-related federal projects from 2008 to 2018 in NIH ExPORTER, which were then integrated with data from AtlasPlus and US Census Bureau. We developed Gini coefficients to assess the inequality of funding and the Generalized Estimating Equations model to examine the associations between funding allocation and (1) state HIV disease burden, (2) state research capacity, and (3) geographic regions, respectively. The Gini coefficients (0.60 to 0.80) suggest a highly skewed funding distribution. Funding allocation was not associated with state HIV disease burden (p = 0.269) but HIV research capacity (p = 0.000). The South (with the heaviest HIV disease burden) did not receive significantly more federal funding. Our findings for the first time identified disparities of federal funding allocation, suggesting that federal agencies favor states of high research capacity over heavy disease burden, which may reinforce the HIV-related health disparities.

Awareness and prevalence of e-cigarette use among Chinese adults: policy implications.

OBJECTIVE: To assess the awareness and prevalence of electronic cigarettes (e-cigarettes) and associated factors among Chinese adults (15 years and older). METHOD: This study examined data from Global Adults Tobacco Survey China Project, which was nationally representative and used stratified multiphase cluster randomised sampling design. Data were collected in 2018 through a household survey with in-person interviews using tablet computers. Complex sampling weighted analysis method was used. RESULTS: 48.5% of Chinese adults had heard of e-cigarettes. The proportions of Chinese adults who had ever used, had used in the last 12 months, and currently used e-cigarettes were 5.0%, 2.2% and 0.9%, respectively; people in the 15-24 years group showed the highest rates of ever use, last 12-month use and current use at 7.6%, 4.4%, and 1.5%, respectively. Among males, higher e-cigarette use was associated with 15-24 years age group, college/university or above education, and daily use of combustible cigarettes. Among all e-cigarette users, 90.6% also used combustible cigarettes. The most common reason for e-cigarette use was smoking cessation (46.2%) while among ever smokers, 9.5% of ever e-cigarette users had quit smoking and 21.8% of never e-cigarette users had quit smoking (adjusted OR 0.454, 95% CI 0.290 to 0.712). CONCLUSION: Prevalence of e-cigarettes among Chinese adults had increased since 2015, especially among young people aged 15-24. The high level of dual use and lower quit rate among e-cigarette users indicated e-cigarettes had not shown cessation utility at the population level in China. Regulation of e-cigarettes is needed to protect youth and minimise health risks.

Assessing Patient Experience and Healthcare Quality of Dental Care Using Patient Online Reviews in the United States: Mixed Methods Study.

BACKGROUND: Over the last two decades, patient review websites have emerged as an essential online platform for doctor ratings and reviews. Recent studies suggested the significance of such websites as a data source for patients to choose doctors for healthcare providers to learn and improve from patient feedback and to foster a culture of trust and transparency between patients and healthcare providers. However, as compared to other medical specialties, studies of online patient reviews that focus on dentists in the United States remain absent. OBJECTIVE: This study sought to understand to what extent online patient reviews can provide performance feedbacks that reflect dental care quality and patient experience. METHODS: Using mixed informatics methods incorporating statistics, natural language processing, and domain expert evaluation, we analyzed the online patient reviews of 204,751 dentists extracted from HealthGrades with two specific aims. First, we examined the associations between patient ratings and a variety of dentist characteristics. Second, we identified topics from patient reviews that can be mapped to the national assessment of dental patient experience measured by the Patient Experience Measures from the Consumer Assessment of Healthcare Providers and Systems (CAHPS) Dental Plan Survey. RESULTS: Higher ratings were associated with female dentists (t71881=2.45, P<.01, g=0.01), dentists at a younger age (F7, 107128=246.97, P<.001, g=0.11), and those whose patients experienced a short wait time (F4, 150055=10417.77, P<0.001, g=0.18). We also identified several topics that corresponded to CAHPS measures, including discomfort (eg, painful/painless root canal or deep cleaning), and ethics (eg, high-pressure sales, and unnecessary dental work). CONCLUSIONS: These findings suggest that online patient reviews could be used as a data source for understanding the patient experience and healthcare quality in dentistry.

18F-FDG PET Biomarkers Help Detect Early Metabolic Response to Irreversible Electroporation and Predict Therapeutic Outcomes in a Rat Liver Tumor Model.

Purpose To test the hypothesis that biomarkers of fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET) can be used for the early detection of therapeutic response to irreversible electroporation (IRE) of liver tumor in a rodent liver tumor model. Materials and Methods The institutional animal care and use committee approved this study. Rats were inoculated with McA-RH7777 liver tumor cells in the left median and left lateral lobes. Tumors were allowed to grow for 7 days to reach a size typically at least 5 mm in longest diameter, as verified with magnetic resonance (MR) imaging. IRE electrodes were inserted, and eight 100-µsec, 2000-V pulses were applied to ablate the tumor tissue in the left median lobe. Tumor in the left lateral lobe served as a control in each animal. PET/computed tomography (CT) and MR imaging measurements were performed at baseline and 3 days after IRE for each animal. Additional MR imaging measurements were obtained 14 days after IRE. After 14-day follow-up MR imaging, rats were euthanized and tumors harvested for hematoxylin-eosin, CD34, and caspase-3 staining. Change in the maximum standardized uptake value (ΔSUVmax) was calculated 3 days after IRE. The maximum lesion diameter change (ΔDmax) was measured 14 days after IRE by using axial T2-weighted imaging. ΔSUVmax and ΔDmax were compared. The apoptosis index was calculated by using caspase-3-stained slices of apoptotic tumor cells. Pearson correlation coefficients were calculated to assess the relationship between ΔSUVmax at 3 days and ΔDmax (or apoptosis index) at 14 days after IRE treatment. Results ΔSUVmax, ΔDmax, and apoptosis index significantly differed between treated and untreated tumors (P < .001 for all). In treated tumors, there was a strong correlation between ΔSUVmax 3 days after IRE and ΔDmax 14 days after IRE (R = 0.66, P = .01) and between ΔSUVmax 3 days after IRE and apoptosis index 14 days after IRE (R = 0.57, P = .04). Conclusion 18F-FDG PET imaging biomarkers can be used for the early detection of therapeutic response to IRE treatment of liver tumors in a rodent model. © RSNA, 2017.

Computational Phenotyping of OMOP CDM Normalized EHR for Prenatal and Postpartum Episodes: An Informatics Framework and Clinical Implementation on All of Us.

The use of Electronic Health Records (EHR) in pregnancy care and obstetrics-gynecology (OB/GYN) research has increased in recent years. In pregnancy, timing is important because clinical characteristics, risks, and patient management are different in each stage of pregnancy. However, the difficulty of accurately differentiating pregnancy episodes and temporal information of clinical events presents unique challenges for EHR phenotyping. In this work, we introduced the concept of time relativity and proposed a comprehensive framework of computational phenotyping for prenatal and postpartum episodes based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). We implemented it on the All of Us national EHR database and identified 6,280 pregnancies with accurate start and end dates among 5,399 female patients. With the ability to identify different episodes in pregnancy care, this framework provides new opportunities for phenotyping complex clinical events and gestational morbidities for pregnant women, thus improving maternal and infant health.

Early Prediction of COVID-19 Associated Hospitalization at the Time of CDC Contact Tracing using Machine Learning: Towards Pandemic Preparedness.

Objective: To develop and validate machine learning models for predicting COVID-19 related hospitalization as early as CDC contact tracing using integrated CDC contact tracing and South Carolina medical claims data. Methods: Using the dataset (n=82,073, 1/1/2018 - 3/1/2020), we identified 3,305 patients with COVID-19 and were captured by contact tracing. We developed and validated machine learning models (i.e., support vector machine, random forest, XGboost), followed by multi-level validations and pilot statewide implementation. Results: Using 10-cross validation, random forest outperformed other models (F1=0.872 for general hospitalization and 0.763 for COVID-19 related hospitalization), followed by XGBoost (F1=0.845 and 0.682) and support vector machine (F1=0.845 and 0.644). We identified new self-reported symptoms from contact tracing (e.g., fatigue, congestion, headache, loss of taste) that are highly predictive of hospitalization. Conclusions: Our study demonstrated the feasibility of identifying individuals at risk of hospitalization at the time of contact tracing for early intervention and prevention. Policy implications: Our findings demonstrate existing promise for leveraging CDC contact tracing for establishing a cost-effective statewide surveillance and generalizability for nationwide adoption for enhancing pandemic preparedness in the US.

Data Veracity of Patients and Health Consumers Reported Adverse Drug Reactions on Twitter: Key Linguistic Features, Twitter Variables, and Association Rules.

As Twitter emerged as an important data source for pharmacovigilance, heterogeneous data veracity becomes a major concern for extracted adverse drug reactions (ADRs). Our objective is to categorize different levels of data veracity and explore linguistic features of tweets and Twitter variables as they may be used for automatic screening high-veracity tweets that contain ADR-related information. We annotated a published Twitter corpus with linguistic features from existing studies and clinical experts. Multinomial logistic regression models found that first-person pronouns, expressing negative sentiment, ADR and drug name being in the same sentence were significantly associated with higher levels of data veracity (p<0.05), using medical terminology and fewer indications were associated with good data veracity (p<0.05), less drug numbers were marginally associated with good data veracity (p=0.053). These findings suggest opportunities for developing machine learning models for automatic screening of ADR-related tweets using key linguistic features, Twitter variables, and association rules.

Temporal Events Detector for Pregnancy Care (TED-PC): A rule-based algorithm to infer gestational age and delivery date from electronic health records of pregnant women with and without COVID-19.

OBJECTIVE: Identifying the time of SARS-CoV-2 viral infection relative to specific gestational weeks is critical for delineating the role of viral infection timing in adverse pregnancy outcomes. However, this task is difficult when it comes to Electronic Health Records (EHR). In combating the COVID-19 pandemic for maternal health, we sought to develop and validate a clinical information extraction algorithm to detect the time of clinical events relative to gestational weeks. MATERIALS AND METHODS: We used EHR from the National COVID Cohort Collaborative (N3C), in which the EHR are normalized by the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). We performed EHR phenotyping, resulting in 270,897 pregnant women (June 1st, 2018 to May 31st, 2021). We developed a rule-based algorithm and performed a multi-level evaluation to test content validity and clinical validity, and extreme length of gestation (<150 or >300). RESULTS: The algorithm identified 296,194 pregnancies (16,659 COVID-19, 174,744 without COVID-19) in 270,897 pregnant women. For inferring gestational age, 95% cases (n = 40) have moderate-high accuracy (Cohen's Kappa = 0.62); 100% cases (n = 40) have moderate-high granularity of temporal information (Cohen's Kappa = 1). For inferring delivery dates, the accuracy is 100% (Cohen's Kappa = 1). The accuracy of gestational age detection for the extreme length of gestation is 93.3% (Cohen's Kappa = 1). Mothers with COVID-19 showed higher prevalence in obesity or overweight (35.1% vs. 29.5%), diabetes (17.8% vs. 17.0%), chronic obstructive pulmonary disease (0.2% vs. 0.1%), respiratory distress syndrome or acute respiratory failure (1.8% vs. 0.2%). DISCUSSION: We explored the characteristics of pregnant women by different gestational weeks of SARS-CoV-2 infection with our algorithm. TED-PC is the first to infer the exact gestational week linked with every clinical event from EHR and detect the timing of SARS-CoV-2 infection in pregnant women. CONCLUSION: The algorithm shows excellent clinical validity in inferring gestational age and delivery dates, which supports multiple EHR cohorts on N3C studying the impact of COVID-19 on pregnancy.

Emergence and evolution of big data science in HIV research: Bibliometric analysis of federally sponsored studies 2000-2019.

BACKGROUND: The rapid growth of inherently complex and heterogeneous data in HIV/AIDS research underscores the importance of Big Data Science. Recently, there have been increasing uptakes of Big Data techniques in basic, clinical, and public health fields of HIV/AIDS research. However, no studies have systematically elaborated on the evolving applications of Big Data in HIV/AIDS research. We sought to explore the emergence and evolution of Big Data Science in HIV/AIDS-related publications that were funded by the US federal agencies. METHODS: We identified HIV/AIDS and Big Data related publications that were funded by seven federal agencies from 2000 to 2019 by integrating data from National Institutes of Health (NIH) ExPORTER, MEDLINE, and MeSH. Building on bibliometrics and Natural Language Processing (NLP) methods, we constructed co-occurrence networks using bibliographic metadata (e.g., countries, institutes, MeSH terms, and keywords) of the retrieved publications. We then detected clusters among the networks as well as the temporal dynamics of clusters, followed by expert evaluation and clinical implications. RESULTS: We harnessed nearly 600 thousand publications related to HIV/AIDS, of which 19,528 publications relating to Big Data were included in bibliometric analysis. Results showed that (1) the number of Big Data publications has been increasing since 2000, (2) US institutes have been in close collaborations with China, Canada, and Germany, (3) some institutes (e.g., University of California system, MD Anderson Cancer Center, and Harvard Medical School) are among the most productive institutes and started using Big Data in HIV/AIDS research early, (4) Big Data research was not active in public health disciplines until 2015, (5) research topics such as genomics, HIV comorbidities, population-based studies, Electronic Health Records (EHR), social media, precision medicine, and methodologies such as machine learning, Deep Learning, radiomics, and data mining emerge quickly in recent years. CONCLUSIONS: We identified a rapid growth in the cross-disciplinary research of HIV/AIDS and Big Data over the past two decades. Our findings demonstrated patterns and trends of prevailing research topics and Big Data applications in HIV/AIDS research and suggested a number of fast-evolving areas of Big Data Science in HIV/AIDS research including secondary analysis of EHR, machine learning, Deep Learning, predictive analysis, and NLP.

Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina.

Disparities and their geospatial patterns exist in morbidity and mortality of COVID-19 patients. When it comes to the infection rate, there is a dearth of research with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs). This work aimed to assess the temporal-geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina. We used the spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR) as global models and the geographically weighted regression model (GWR) as a local model. The data were retrieved from multiple sources including USAFacts, U.S. Census Bureau, and the Population Estimates Program. The percentage of males and the unemployed population were positively associated with geodistributions of COVID-19 infection (p values < 0.05) in global models throughout the time. The percentage of the white population and the obesity rate showed divergent spatial correlations at different times of the pandemic. GWR models fit better than global models, suggesting nonstationary correlations between a region and its neighbors. Characterized by temporal-geospatial patterns, disparities in COVID-19 infection rate and their PIDRs are different from the mortality and morbidity of COVID-19 patients. Our findings suggest the importance of prioritizing different populations and developing tailored interventions at different times of the pandemic.

Yttrium-90 Radioembolization and Tumor Hypoxia: Gas-challenge BOLD Imaging in the VX2 Rabbit Model of Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: To use a rapid gas-challenge blood oxygen-level dependent magnetic resonance imaging exam to evaluate changes in tumor hypoxia after 90Y radioembolization (Y90) in the VX2 rabbit model. MATERIALS AND METHODS: White New Zealand rabbits (n = 11) provided a Y90 group (n = 6 rabbits) and untreated control group (n = 5 rabbits). R2* maps were generated with gas-challenges (O2/room air) at baseline, 1 week, and 2 weeks post-Y90. Laboratory toxicity was evaluated at baseline, 24 hours, 72 hours, 1 hours, and 2 weeks. Histology was used to evaluate tumor necrosis on hematoxylin and eosin and immunofluorescence imaging was used to assess microvessel density (CD31) and proliferative index (Ki67). RESULTS: At baseline, median tumor volumes and time to imaging were similar between groups (p = 1.000 and p = 0.4512, respectively). The median administered dose was 50.4 Gy (95% confidence interval:44.8-55.9). At week 2, mean tumor volumes were 5769.8 versus 643.7 mm3 for control versus Y90 rabbits, respectively (p = 0.0246). At two weeks, ΔR2* increased for control tumors to 12.37 ± 12.36sec-1 and decreased to 4.48 ± 9.00sec-1 after Y90. The Pearson correlation coefficient for ΔR2* at baseline and percent increase in tumor size by two weeks was 0.798 for the Y90 group (p = 0.002). There was no difference in mean microvessel density for control versus Y90 treated tumors (p = 0.6682). The mean proliferative index was reduced in Y90 treated tumors at 30.5% versus 47.5% for controls (p = 0.0071). CONCLUSION: The baseline ΔR2* of tumors prior to Y90 may be a predictive imaging biomarker of tumor response and treatment of these tumors with Y90 may influence tumor oxygenation over time.

Cigarette Package Warnings for Adult Smoking Cessation - China, 2018.

What is already known about this topic? After the framework convention on tobacco control (FCTC) came into force, the health warnings on cigarette packaging in the mainland of China had been changed in three rounds. But the warning label is still only pure text and without descriptions of specific health consequences caused by tobacco use. What is added by this report? Although there were two rounds of changes from 2010 to 2018, current health warning labels did not work in increasing Chinese smokers' smoking cessation intention compared with the previous one. Large pictorial warning labels were more effective than the current health warning label in stimulating Chinese smoker's willingness to quit. What are the implications for public health practice? Pictorial health warnings could play a great role in promoting the realization of the tobacco control goals of Healthy China 2030. Pictorial health warnings should be printed on cigarette packs in the mainland of China as soon as possible.

Feasibility of Combination Intra-arterial Yttrium-90 and Irinotecan Microspheres in the VX2 Rabbit Model.

PURPOSE: To evaluate the combination of 90Y radioembolization (Y90) and drug-eluting bead irinotecan (DEBIRI) microspheres in the VX2 rabbit model. MATERIALS AND METHODS: An initial dose finding study was performed in 6 White New Zealand rabbits to identify a therapeutic but subcurative dose of Y90. In total, 29 rabbits were used in four groups: Y90 treatment (n = 8), DEBIRI treatment (n = 6), Y90 + DEBIRI treatment (n = 7), and an untreated control group (n = 8). Hepatic toxicity was evaluated at baseline, 24 h, 72 h, 1 week, and 2 weeks. MRI tumor volume (TV) and enhancing tumor volume were assessed baseline and 2 weeks. Tumor area and necrosis were evaluated on H&E for pathology. RESULTS: Infused activities of 84.0-94.4 MBq (corresponding to 55.1-72.7 Gy) were selected based on the initial dose finding study. Infusion of DEBIRI after Y90 was technically feasible in all cases (7/7). Overall, 21/29 animals survived to 2 weeks, and the remaining animals had extrahepatic disease on necropsy. Liver transaminases were elevated with Y90, DEBIRI, and Y90 + DEBIRI compared to control at 24 h, 72 h, and 1 week post-treatment and returned to baseline by 2 weeks. By TV, Y90 + DEBIRI was the only treatment to show statistically significant reduction at 2 weeks compared to the control group (p = 0.012). The change in tumor volume (week 2-baseline) for both Y90 + DEBIRI versus control (p = 0.002) and Y90 versus control (p = 0.014) was significantly decreased. There were no statistically significant differences among groups on pathology. CONCLUSION: Intra-arterial Y90 + DEBIRI was safe and demonstrated enhanced antitumor activity in rabbit VX2 tumors. This combined approach warrants further investigation.

Diffusion MRI biomarkers predict the outcome of irreversible electroporation in a pancreatic tumor mouse model.

The purpose of this work is to explore the potential contribution of diffusion MRI to predict the effects of irreversible electroporation (IRE) in a pancreatic ductal adenocarcinoma (PDAC) mouse model. Thirteen mice were injected with Panc-02 PDAC cells in both flanks. One tumor was treated with IRE when it reached a diameter of about 5 mm. T2- and diffusion-weighted MRI sequences were acquired before IRE treatment and 1, 3 and 7 days later. The mice were euthanized 1 day (n = 6) or 2 weeks (n = 7) after treatment. The tumors were excised and stained with H&E, caspase-3, CD-3, F4/80. The volume and the mean and standard deviation of the apparent diffusion coefficient (ADC) were compared between treated and untreated lesions and correlated with histology-derived measures. At 1-day post-treatment, a dramatic ADC increase (+50.81%, P < 0.05) was found in ablated lesions, strongly correlated with apoptosis (τ = 0.90). At later time points the ADC returned to pre-treatment values, though histopathology showed a quite different scenario compared to the untreated controls. The ADC standard deviation measured within the treated tumors 1 day after IRE treatment had a strong negative correlation with the number of tumor cells found 14 days later (τ = 0.80). There was also a strong correlation between 1-day ADC and 14-day apoptosis in untreated tumors (τ = 0.95). In conclusion, diffusion MRI is sensitive to the short-term effects of IRE in PDAC tumors, and can help predict the long-term treatment outcome.

MRI-guided interventional natural killer cell delivery for liver tumor treatment.

While natural killer (NK) cell-based adoptive transfer immunotherapy (ATI) provides only modest clinical success in cancer patients. This study was hypothesized that MRI-guided transcatheter intra-hepatic arterial (IHA) infusion permits local delivery to liver tumors to improve outcomes during NK-based ATI in a rat model of hepatocellular carcinoma (HCC). Mouse NK cells were labeled with clinically applicable iron nanocomplexes. Twenty rat HCC models were assigned to three groups: transcatheter IHA saline infusion as the control group, transcatheter IHA NK infusion group, and intravenous (IV) NK infusion group. MRI studies were performed at baseline and at 24 h, 48 h, and 8 days postinfusion. There was a significant difference in tumor R2* values between baseline and 24 h following the selective transcatheter IHA NK delivery to the tumors (P = 0.039) when compared to IV NK infusion (P = 0.803). At 8 days postinfusion, there were significant differences in tumor volumes between the control, IV, and IHA NK infusion groups (control vs. IV, P = 0.196; control vs. IHA, P < 0.001; and IV vs. IHA, P = 0.001). Moreover, there was a strong correlation between tumor R2* value change (∆R2*) at 24 h postinfusion and tumor volume change (∆volume) at 8 days in IHA group (R2  = 0.704, P < 0.001). Clinically applicable labeled NK cells with 12-h labeling time can be tracked by MRI. Transcatheter IHA infusion improves NK cell homing efficacy and immunotherapeutic efficiency. The change in tumor R2* value 24 h postinfusion is an important early biomarker for prediction of longitudinal response.

Preclinical and clinical evaluation of the liver tumor irreversible electroporation by magnetic resonance imaging.

Irreversible electroporation (IRE) is a relatively new technique for tumor ablation. It has shown promising results in difficult cases where surgery is not recommended and delicate anatomic structures are present near or within the tumor. Currently, liver cancer is one of the most common targets for IRE treatment. Pre-operative and post-operative imaging has a key role in IRE procedures and research studies. Although ultrasound is usually the first choice, especially for intra-operative guidance, magnetic resonance imaging (MRI) plays an important role in the visualization and characterization of tumor before and after IRE in clinical and preclinical studies. However, the appearance of liver lesions after IRE with different MRI sequences has never been systematically investigated, and the most common practice is to limit the acquisition protocol to only contrast-enhanced T1-weighted images. In this work, the role of MRI in clinical and preclinical assessment of hepatic tumors treated with IRE is reviewed and discussed.

Irreversible electroporation in primary and metastatic hepatic malignancies: A review.

BACKGROUND: Liver cancer makes up a huge percentage of cancer mortality worldwide. Irreversible electroporation (IRE) is a relatively new minimally invasive nonthermal ablation technique for tumors that applies short pulses of high frequency electrical energy to irreversibly destabilize cell membrane to induce tumor cell apoptosis. METHODS: This review aims to investigate the studies regarding the use of IRE treatment in liver tumors and metastases to liver. We searched PubMed for all of IRE relevant English language articles published up to September 2016. They included clinical trials, experimental studies, observational studies, and reviews. This review manuscript is nothing with ethics issues and ethical approval is not provided. RESULTS: In recent years, increasingly more studies in both preclinical and clinical settings have been conducted to examine the safety and efficacy of this new technique, shedding light on the crucial advantages and disadvantages that IRE possesses. Unlike the current leading thermal ablation techniques, such as radiofrequency ablation (RFA), microwave ablation (MWA), and cryoablation, IRE requires shorter ablation time without damaging adjacent important vital structures. CONCLUSION: Although IRE has successfully claimed its valuable status in the field of hepatic cancer treatment both preclinical and clinical settings. In order to systemically test and establish its safety and efficacy for clinical applications, more studies still need to be conducted.