A self-sustaining effect induced by iron sulfide generation and reuse in pyrite-woodchip mixotrophic bioretention systems: An experimental and modeling study.

Dual electron donor bioretention systems have emerged as a popular strategy to enhance dissolved nitrogen removal from stormwater runoff. Pyrite-woodchip mixotrophic bioretention systems showed a promoted and stabilized removal of dissolved nutrients under complex rainfall conditions, but the sulfate reduction process that can induce iron sulfide generation and reuse was overlooked. In this study, experiments and models were applied to investigate the effects of filler configuration and dissolved organic carbon (DOC) dissolution rate on treatment performance and iron sulfide generation in pyrite-woodchip bioretention systems. Key parameters govern that DOC dissolution and microbe-mediated processes were obtained by experiments. The water quality models that integrate one-dimensional constant flow, sorption and microbial transformation kinetics were used to predict the performance of bioretention systems. Results showed that the mixotrophic bioretention system with woodchip mixed in the vadose zone and pyrite in the saturated zone achieves a better performance in both nitrogen removal efficiency and by-product control. Comparably, woodchip and pyrite mixed in the saturated zone could encounter a high secondary pollution risk. The sensitivity coefficients of oxic/anoxic DOC dissolution rates to total nitrogen removal are 0.36 and -2.43 respectively. Iron sulfide generation was affected by DOC distribution and the competition between heterotrophic denitrifiers, autotrophic denitrifiers, and sulfate-reducing bacteria (SRB). DOC accumulation has an antagonistic effect on iron production and sulfate reduction. Extra DOC accumulation favors sulfate reduction while high DOC concentration inhibits pyrite-based denitrification and reduces Fe(III) production. The recycling of iron sulfide can improve the robustness and sustainability of bioretention systems.

A quantitative analysis of the improvement provided by comprehensive annotation on CT lesion detection using deep learning.

BACKGROUND: Data collected from hospitals are usually partially annotated by radiologists due to time constraints. Developing and evaluating deep learning models on these data may result in over or under estimation PURPOSE: We aimed to quantitatively investigate how the percentage of annotated lesions in CT images will influence the performance of universal lesion detection (ULD) algorithms. METHODS: We trained a multi-view feature pyramid network with position-aware attention (MVP-Net) to perform ULD. Three versions of the DeepLesion dataset were created for training MVP-Net. Original DeepLesion Dataset (OriginalDL) is the publicly available, widely studied DeepLesion dataset that includes 32 735 lesions in 4427 patients which were partially labeled during routine clinical practice. Enriched DeepLesion Dataset (EnrichedDL) is an enhanced dataset that features fully labeled at one or more time points for 4145 patients with 34 317 lesions. UnionDL is the union of the OriginalDL and EnrichedDL with 54 510 labeled lesions in 4427 patients. Each dataset was used separately to train MVP-Net, resulting in the following models: OriginalCNN (replicating the original result), EnrichedCNN (testing the effect of increased annotation), and UnionCNN (featuring the greatest number of annotations). RESULTS: Although the reported mean sensitivity of OriginalCNN was 84.3% using the OriginalDL testing set, the performance fell sharply when tested on the EnrichedDL testing set, yielding mean sensitivities of 56.1%, 66.0%, and 67.8% for OriginalCNN, EnrichedCNN, and UnionCNN, respectively. We also found that increasing the percentage of annotated lesions in the training set increased sensitivity, but the margin of increase in performance gradually diminished according to the power law. CONCLUSIONS: We expanded and improved the existing DeepLesion dataset by annotating additional 21 775 lesions, and we demonstrated that using fully labeled CT images avoided overestimation of MVP-Net's performance while increasing the algorithm's sensitivity, which may have a huge impact to the future CT lesion detection research. The annotated lesions are at https://github.com/ComputationalImageAnalysisLab/DeepLesionData.

Diverse metabolism drives comammox in continental-scale agricultural streams: Important ammonia oxidation but low N2O production.

Agriculture receives approximately 25 % of the annual global nitrogen input, 37 % of which subsequently runs off into adjacent low-order streams and surface water, where it may contribute to high nitrification and nitrous oxide (N2O). However, the mechanisms of nitrification and the pathways controlling N2O production in agricultural streams remain unknown. Here, we report that the third microbial ammonia oxidation process, complete ammonia oxidation (comammox), is widespread and contributes to important ammonia oxidation with low ammonia-N2O conversion in both basin- and continental-scale agricultural streams. The contribution of comammox to ammonia oxidation (21.5 ± 2.3 %) was between that of bacterial (68.6 ± 2.7 %) and archaeal (9.9 ± 1.8 %) ammonia oxidation. Interestingly, N2O production by comammox (18.5 ± 2.1 %) was higher than archaeal (10.5 ± 1.9 %) but significantly lower than bacterial (70.2 ± 2.6 %) ammonia oxidation. The first metagenome-assembled genome (MAG) of comammox bacteria from agricultural streams further revealed their potential extensive diverse and specific metabolism. Their wide habitats might be attributed to the diverse metabolism, i.e. harboring the functional gene of nitrate reduction to ammonia, while the lower N2O would be attributed to their lacking biological function to produce N2O. Our results highlight the importance of widespread comammox in agricultural streams, both for the fate of ammonia fertilizer and for climate change. However, it has not yet been routinely included in Earth system models and IPCC global assessments. Synopsis Widespread but overlooked comammox contributes to important ammonia oxidation but low N2O production, which were proved by the first comammox MAG found in agricultural streams.

Ammonium-derived nitrous oxide is a global source in streams.

Global riverine nitrous oxide (N2O) emissions have increased more than 4-fold in the last century. It has been estimated that the hyporheic zones in small streams alone may contribute approximately 85% of these N2O emissions. However, the mechanisms and pathways controlling hyporheic N2O production in stream ecosystems remain unknown. Here, we report that ammonia-derived pathways, rather than the nitrate-derived pathways, are the dominant hyporheic N2O sources (69.6 ± 2.1%) in agricultural streams around the world. The N2O fluxes are mainly in positive correlation with ammonia. The potential N2O metabolic pathways of metagenome-assembled genomes (MAGs) provides evidence that nitrifying bacteria contain greater abundances of N2O production-related genes than denitrifying bacteria. Taken together, this study highlights the importance of mitigating agriculturally derived ammonium in low-order agricultural streams in controlling N2O emissions. Global models of riverine ecosystems need to better represent ammonia-derived pathways for accurately estimating and predicting riverine N2O emissions.

Machine learning-based identification of contrast-enhancement phase of computed tomography scans.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew's correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.

Aptamer-based nanointerferometer enables amplification-free ultrasensitive detection and differentiation of SARS-CoV-2 variants.

The state-of-the-art SARS-CoV-2 detection methods include qRT-PCR and antibody-based lateral flow assay (LFA) point-of-care tests. Despite the high sensitivity and selectivity, qRT-PCR is slow, expensive and needs well-trained operators. On the other extreme, LFA suffers from low sensitivity albeit its fast detection speed, low detection cost and ease of use. Therefore, the continuing COVID-19 pandemic calls for a SARS-CoV-2 detection method that is rapid, convenient and cost-effective without compromise in sensitivity. Here we provide a proof-of-principle demonstration of an optimized aptamer-based nanointerferometer that enables rapid and amplification-free detection of SARS-CoV-2 spike protein-coated pseudovirus directly from human saliva with the limit of detection (LOD) of about 400 copies per mL. This LOD is on par with that of qRT-PCR, making it 1000 to 100,000-fold more sensitive than commercial LFA tests. Using various combinations of negative selections during the screens for the aptamer targeting the receptor binding domain of the spike protein of SARS-CoV-2, we isolated two aptamers that can distinguish the Omicron and Delta variants. Integrating these two aptamers with LFA strips or the nanointerferometer sensors allows both detection and differentiation of the Omicron and Delta variants which has the potential to realize rapid triage of patients infected different SARS-CoV-2 variants.

PUNDIT: Pulmonary nodule detection with image category transformation.

BACKGROUND: Convolutional neural networks (CNNs) have achieved great success in pulmonary nodules detection, which plays an important role in lung cancer screening. PURPOSE: In this paper, we proposed a novel strategy for pulmonary nodule detection by learning it from a harder task, which was to transform nodule images into normal images. We named this strategy as pulmonary nodule detection with image category transformation (PUNDIT). METHODS: There were two steps for nodules detection, nodule candidate detection and false positive (FP) reduction. In nodule candidate detection step, a segmentation-based framework was built for detection. We designed an image category transformation (ICT) task to translate nodule images into pixel-to-pixel normal images and share the information of detection and transformation tasks by multitask learning. As for references of transformation tasks, we proposed background consistency losses into standard cycle-consistent adversarial networks, which can solve the problem of background uncontrolled changing. A three-dimensional network was used in FP reduction step. RESULTS: PUNDIT was evaluated in two datasets, cancer screening dataset (CSD) with 1186 nodules for cross-validation and (CTD) with 3668 nodules for external test. Results were mainly evaluated by competition performance metric (CPM), the average sensitivity at seven predefined FP rates. The CPM was improved from 0.906 to 0.931 in CSD, and from 0.835 to 0.848 in CTD. CONCLUSIONS: Experimental results showed that PUNDIT can improve the performance of pulmonary nodules detection effectively.

Corncob-pyrite bioretention system for enhanced dissolved nutrient treatment: Carbon source release and mixotrophic denitrification.

Solid biomass waste amendment and substrates modification in bioretention systems have been increasingly used to achieve effective dissolved nutrients pollution control in stormwater runoff. However, the risk of excess chemical oxygen demand (COD) leaching from organic carbon sources is often overlooked on most occasions. Pyrite is an efficient electron donor for autotrophic denitrification, but little is known about the efficacy of autotrophic-heterotrophic synergistic effect between additional carbon source and pyrite in bioretention. Here, four bioretention columns (i.e., corncob column (C), pyrite column (P), the corncob-pyrite layered column (L-CP), and the corncob-pyrite mixed column (M-CP)) were designed and filled with soil, quartz sand, and modified media to reveal the synergistic effects. The results showed that the corncob-pyrite layered bioretention could maintain low COD effluent concentration with high stability and efficiency in treating dissolved nutrients. When the influent nitrogen and phosphorus concentrations were 8.46 mg/L and 0.94 mg/L, the average removal rates of ammonia nitrogen, total inorganic nitrogen, and phosphate were 83.6%, 70.52%, and 76.35%, respectively. The scouring experiment showed that placing the corncob in the mulch layer was beneficial to the sustained release of dissolved organic carbon (DOC). Erosion pits were found in the SEM images of used pyrite, indicating that autotrophic denitrifying bacteria in the bioretention could react with pyrite as an electron donor. The relative abundance of Thiobacillus in the submerged zone of the corncob-pyrite layered bioretention reached 38.39%, indicating that the carbon source in the mulch layer increased the relative abundance of Thiobacillus. Coexisting heterotrophic and autotrophic denitrification in this bioretention created a more abundant microbial community structure in the submerged zone. Overall, the corncob-pyrite layered bioretention is highly promising for stormwater runoff treatment, with effective pollution removal and minimal COD emission.

Distinguishing benign and malignant lesions on contrast-enhanced breast cone-beam CT with deep learning neural architecture search.

PURPOSE: To utilize a neural architecture search (NAS) approach to develop a convolutional neural network (CNN) method for distinguishing benign and malignant lesions on breast cone-beam CT (BCBCT). METHOD: 165 patients with 114 malignant and 86 benign lesions were collected by two institutions from May 2012 to August 2014. The NAS method autonomously generated a CNN model using one institution's dataset for training (patients/lesions: 71/91) and validation (patients/lesions: 20/23). The model was externally tested on another institution's dataset (patients/lesions: 74/87), and its performance was compared with fine-tuned ResNet-50 models and two breast radiologists who independently read the lesions in the testing dataset without knowing lesion diagnosis. RESULTS: The lesion diameters (mean ± SD) were 18.8 ± 12.9 mm, 22.7 ± 10.5 mm, and 20.0 ± 11.8 mm in the training, validation, and external testing set, respectively. Compared to the best ResNet-50 model, the NAS-generated CNN model performed three times faster and, in the external testing set, achieved a higher (though not statistically different) AUC, with sensitivity (95% CI) and specificity (95% CI) of 0.727, 80% (66-90%), and 60% (42-75%), respectively. Meanwhile, the performances of the NAS-generated CNN and the two radiologists' visual ratings were not statistically different. CONCLUSIONS: Our preliminary results demonstrated that a CNN autonomously generated by NAS performed comparably to pre-trained ResNet models and radiologists in predicting malignant breast lesions on contrast-enhanced BCBCT. In comparison to ResNet, which must be designed by an expert, the NAS approach may be used to automatically generate a deep learning architecture for medical image analysis.

Burden and etiology of moderate and severe diarrhea in children less than 5 years of age living in north and south of China: Prospective, population-based surveillance.

BACKGROUND: Diarrhea remains the leading cause of childhood illness in China. Better understanding of burden and etiology of diarrheal diseases is important for development of effective prevention measures. METHODS: Population-based diarrhea surveillance was conducted in Sanjiang (southern China) year-round and Zhengding (northern China) in autumn/winter. Stool specimens were collected from children < 5 years of age experiencing diarrhea. The TaqMan Array Card (TAC), based on multiplex real-time PCR, was applied to detect multiple enteric microbial agents simultaneously. Results using these methods were compared to those derived from conventional PCR assays. RESULTS: During the study period, 6,380 children in Zhengding and 3,581 children in Sanjiang < 5 years of age participated. Three hundred and forty (31.2%) and 279 (22.9%) diarrhea episodes were identified as moderate-to-severe in the two counties, with incidence of 60.4 and 88.3 cases per 1,000 child-years in Zhengding and Sanjiang, respectively. The five most frequently detected bacterial and viral agents in Sanjiang were adenovirus, enterovirus, enteroaggregative Escherichia coli (EAEC), rotavirus, and sapovirus all the year round, while the most common viral agents in Zhengding were rotavirus, followed by astrovirus and adenovirus during the cool season. Compared to conventional PCR assay, the average incremental detection via the TAC method was twofold. CONCLUSION: Our study demonstrated high diversity and prevalence of multiple major bacterial and viral agents, including rotavirus and calicivirus, among children in China. Further studies are needed to define the public health significance of neglected but frequently detected pathogens such as EAEC, enterotoxigenic E. coli, Campylobacter, adenovirus, and enterovirus.

Genetic susceptibility to rotavirus infection in Chinese children: a population-based case-control study.

Rotaviruses (RVs) are the leading cause of acute gastroenteritis in children, while histo-blood group antigens (HBGAs) are believed to be host attachment and susceptibility factors of RVs. A large case-control study nested in a population-based diarrhea surveillance targeting children <5 y of age was performed in rural Hebei province, north China. Saliva and serum samples were collected from all participants to determine HBGA phenotyping, FUT2 mutations, and RV IgG antibody titers. A logistic model was employed to assess the association between host HBGA secretor status and risk of RV infection. Among 235 RV cases and 680 non-diarrhea controls studied, 82.4% of participants were IgG positive by an average age of 77 months. Out of the 235 RV cases, 216 (91.9%) were secretors, whereas the secretor rate was 76.3% in the non-diarrhea controls, resulted in an adjusted OR of 3.0 (95%CI: 1.9-4.7, P < .0001) between the two groups. Our population-based case-control study indicated a strong association between host HBGA secretor status and risk of RV infection in Chinese children. The high prevalence of Lewis-positive secretor status strongly suggests that Chinese children may be genetically susceptible to current co-circulating RV strains, and thus, a universal childhood immunization program against RV disease should be successful in China.

Immune response to different types of hepatitis B vaccine booster doses 2-32 years after the primary immunization schedule and its influencing factors.

OBJECTIVE: To assess the immune effect of different types of hepatitis B vaccine (HepB) booster doses 2-32 years after primary immunization, explore the influencing factors, and offer guidance regarding the necessity and timing of boosters. METHODS: In total, 1163 participants who were born from 1986 to 2015, received the HepB full-course primary vaccination, were hepatitis B surface antigen (HBsAg) and hepatitis B core antibody (anti-HBc) negative, and had hepatitis B surface antibody (anti-HBs) <10 mIU/mL were enrolled. Individuals were randomly divided into two groups and received a booster dose of HepB. Venous blood samples were collected 30 days later and tested for anti-HBs. RESULTS: In total, 595 and 568 individuals received a single dose of HepB (CHO) and HepB (SC), respectively. Venous blood samples were obtained from 1079 vaccinees (CHO: 554, SC: 525). The seroconversion rates were 93.68% (519/554) and 86.67% (455/525) (p < 0.05), with geometric mean concentrations (GMCs) of 426.58 mIU/ml and 223.8 mIU/ml, respectively. This result indicated that BMI, smoking status, vaccine types of booster and prebooster anti-HBs concentration significantly influenced anti-HBs levels. Only BMI, prebooster anti-HBs concentrations and booster types were different between the anti-HBs positive and negative groups. CONCLUSIONS: Participants boostered with HepB (CHO) had a relatively higher seroconversion rate than those boostered with HepB (SC). The high seroconversion rates in the two groups suggested that the subjects remained protected despite low circulating antibodies, so there is currently no urgent need for booster immunization. Factors including BMI ≥ 25 and prebooster anti-HBs concentration <2.5 mIU/mL, which contributed to lower responses to a booster dose, might indicate a greater risk of breakthrough infection.

Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine.

PURPOSE: The clinical adoption of quantitative imaging biomarkers (radiomics) has established the need for high quality contrast-enhancement in medical images. We aimed to develop a machine-learning algorithm for Quality Control of Contrast-Enhancement on CT-scan (CECT-QC). METHOD: Multicenter data from four independent cohorts [A, B, C, D] of patients with measurable liver lesions were analyzed retrospectively (patients:time-points; 503:3397): [A] dynamic CTs from primary liver cancer (60:2359); [B] triphasic CTs from primary liver cancer (31:93); [C] triphasic CTs from hepatocellular carcinoma (121:363); [D] portal venous phase CTs of liver metastasis from colorectal cancer (291:582). Patients from cohort A were randomized to training-set (48:1884) and test-set (12:475). A random forest classifier was trained and tested to identify five contrast-enhancement phases. The input was the mean intensity of the abdominal aorta and the portal vein measured on a single abdominal CT scan image at a single time-point. The output to be predicted was: non-contrast [NCP], early-arterial [E-AP], optimal-arterial [O-AP], optimal-portal [O-PVP], and late-portal [L-PVP]. Clinical utility was assessed in cohorts B, C, and D. RESULTS: The CECT-QC algorithm showed performances of 98 %, 90 %, and 84 % for predicting NCP, O-AP, and O-PVP, respectively. O-PVP was reached in half of patients and was associated with a peak in liver malignancy density. Contrast-enhancement quality significantly influenced radiomics features deciphering the phenotype of liver neoplasms. CONCLUSIONS: A single CT-image can be used to differentiate five contrast-enhancement phases for radiomics-based precision medicine in the most common liver neoplasms occurring in patients with or without liver cirrhosis.

Burden of viral gastroenteritis in children living in rural China: Population-based surveillance.

BACKGROUND: Despite the considerable disease burden caused by the disease, rotavirus vaccine has not been introduced into routine national immunization schedule, and norovirus vaccines are being developed without a comprehensive understanding of gastroenteritis epidemiology. To bridge this knowledge gap, we investigated the disease burden of viral gastroenteritis in rural China. METHODS: Between October 2011 and December 2013, population-based surveillance was conducted in Zhengding and Sanjiang counties in China. Stool samples were collected from children <5 years of age with diarrhea. All specimens were tested for rotaviruses, noroviruses, sapoviruses, enteric adenoviruses, and astroviruses. RESULTS: The most common pathogen causing diarrhea was rotavirus (54.7 vs 45.6 cases/1,000 children/year in Zhengding and Sanjiang, respectively), followed by norovirus (28.4 vs 19.3 cases/1,000 children/year in Zhengding and Sanjiang, respectively). The highest incidence of these viruses was observed in children 6-18 months of age. Among the 5 viral pathogens, rotaviruses caused the most severe illness, followed by noroviruses. CONCLUSION: Rotavirus and norovirus are the 2 most important viral pathogens causing childhood diarrhea in both northern and southern China; they should be the major targets for viral gastroenteritis prevention strategies among children in China.

Long-term protection at 20-31 years after primary vaccination with plasma-derived hepatitis B vaccine in a Chinese rural community.

Background: To assess the long-term protection conferred by plasma-derived hepatitis B vaccine at 20-31y after primary immunization during infancy in Chinese rural community.Method: Participants born between 1986 and 1996, who received a full course of primary vaccination with plasma-derived hepatitis B vaccine and had no experience with booster vaccination were enrolled. An epidemiological investigation was performed, and blood samples were collected to detect hepatitis B surface antigen (HBsAg), hepatitis B surface antibody (anti-HBs), and hepatitis B core antibody (anti-HBc). The positive rate of HBsAg, anti-HBs, and anti-HBc were calculated to evaluate the long-term protection of the plasma-derived hepatitis B vaccine.Results: A total of 949 participants were enrolled in the final analysis. Six subjects were detected to be HBsAg-positive, resulting in a HBsAg carrier rate of 0.63% (6/949). A total of 468 (52.41%) participants maintained a level of anti-HBs antibody ≥10 mIU/mL, with a GMC of 112.20 mIU/mL (95%CI: 97.72 ~ 128.82 mIU/mL). A significant downtrend was observed in the anti-HBs positive rate (P < .001). The average anti-HBc positive rate was 5.90% (56/949), increased with prolongation of immunization (P < .001).Conclusions: The plasma-derived hepatitis B vaccine maintained satisfactory protection at 20-31 y after primary immunization. These results indicate that a booster dose is not necessary. Further studies on the immune memory induced by the plasma-derived hepatitis B vaccine are needed.

Automated Identification of Optimal Portal Venous Phase Timing with Convolutional Neural Networks.

OBJECTIVES: To develop a deep learning-based algorithm to automatically identify optimal portal venous phase timing (PVP-timing) so that image analysis techniques can be accurately performed on post contrast studies. METHODS: 681 CT-scans (training: 479 CT-scans; validation: 202 CT-scans) from a multicenter clinical trial in patients with liver metastases from colorectal cancer were retrospectively analyzed for algorithm development and validation. An additional external validation was performed on a cohort of 228 CT-scans from gastroenteropancreatic neuroendocrine cancer patients. Image acquisition was performed according to each centers' standard CT protocol for single portal venous phase, portal venous acquisition. The reference gold standard for the classification of PVP-timing as either optimal or nonoptimal was based on experienced radiologists' consensus opinion. The algorithm performed automated localization (on axial slices) of the portal vein and aorta upon which a novel dual input Convolutional Neural Network calculated a probability of the optimal PVP-timing. RESULTS: The algorithm automatically computed a PVP-timing score in 3 seconds and reached area under the curve of 0.837 (95% CI: 0.765, 0.890) in validation set and 0.844 (95% CI: 0.786, 0.889) in external validation set. CONCLUSION: A fully automated, deep-learning derived PVP-timing algorithm was developed to classify scans' contrast-enhancement timing and identify scans with optimal PVP-timing. The rapid identification of such scans will aid in the analysis of quantitative (radiomics) features used to characterize tumors and changes in enhancement with treatment in a multitude of settings including quantitative response criteria such as Choi and MASS which rely on reproducible measurement of enhancement.

Immune persistence 17 to 20 years after primary vaccination with recombination hepatitis B vaccine (CHO) and the effect of booster dose vaccination.

BACKGROUND: To assess the immune persistence conferred by a Chinese hamster ovary (CHO)-derived hepatitis B vaccine (HepB) 17 to 20 years after primary immunization during early life. METHODS: Participants born between 1997 and 1999 who received a full course of primary vaccination with HepB (CHO) and who had no experience with booster vaccination were enrolled. Blood samples were required from each participant for measurement of hepatitis B surface antibody (anti-HBs), surface antigen and core antibody levels. For those who possessed an anti-HBs antibody < 10 mIU/mL, a single dose of HepB was administered, and 30 days later, serum specimens were collected to assess the booster effects. RESULTS: A total of 1352 participants were included in this study. Of these, 1007 (74.5%) participants could retain an anti-HBs antibody ≥10 mIU/mL, with a geometric mean concentration (GMC) of 57.4 mIU/mL. HBsAg was detected in six participants, resulting in a HBsAg carrier rate of 0.4% (6/1352). Of those participants with anti-HBs antibodies < 10 mIU/mL, after a challenge dose, 231 (93.1%) presented an anti-HBs antibody ≥10 mIU/mL, with a GMC of 368.7 mIU/mL. A significant increase in the anti-HBs positive rate (≥ 10 mIU/mL) after challenge was observed in participants with anti-HBs antibodies between 2.5 and 10 mIU/mL and participants boosted with HepB (CHO), rather than those with anti-HBs antibodies < 2.5 mIU/mL and those boosted with HepB (SC). CONCLUSION: Since satisfactory immune protection against HBV infection conferred by primary vaccination administered 17-20 years ago was demonstrated, there is currently no urgent need for booster immunization.

Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling.

OBJECTIVES: The tyrosine kinase inhibitor (TKI)-sensitive mutations of the epidermal growth factor receptor (EGFR) gene is essential in the treatment of lung adenocarcinoma. To overcome the difficulty of EGFR gene test in situations where surgery and biopsy samples are too risky to obtain, we tried a noninvasive imaging method using radiomics features and random forest models. METHODS: Five hundred three lung adenocarcinoma patients who received surgery-based treatment were included in this study. The diagnosis and EGFR gene test were based on resections. TKI-sensitive mutations were found in 60.8% of the patients. CT scans before any invasive operation were gathered and analyzed to extract quantitative radiomics features and build random forest classifiers to identify EGFR mutants from wild types. Clinical features (sex and smoking history) were added to the image-based model. The model was trained on a set of 345 patients and validated on an independent test group (n = 158) using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: The performance of the random forest model with 94 radiomics features reached an AUC of 0.802. Its AUC was further improved to 0.828 by adding sex and smoking history. The sensitivity and specificity are 60.6% and 85.1% at the best diagnostic decision point. CONCLUSION: Our results showed that radiomics could not only reflect the genetic differences among tumors but also have diagnostic value and the potential to be a diagnostic tool. KEY POINTS: • Radiomics provides a potential noninvasive method for the prediction of EGFR mutation status. • In situations where surgeries and biopsy are not available, CT image-based radiomics models could help to make treatment decisions. • The accuracy, sensitivity, and specificity still need to be improved before the image-based EGFR identifier could be used in clinics.