Clonal expansion of shared T cell receptors reveals the existence of immune commonality among different lesions of synchronous multiple primary lung cancer.

The increase in the detection rate of synchronous multiple primary lung cancer (MPLC) has posed remarkable clinical challenges due to the limited understanding of its pathogenesis and molecular features. Here, comprehensive comparisons of genomic and immunologic features between MPLC and solitary lung cancer nodule (SN), as well as different lesions of the same patient, were performed. Compared with SN, MPLC displayed a lower rate of EGFR mutation but higher rates of BRAF, MAP2K1, and MTOR mutation, which function exactly in the upstream and downstream of the same signaling pathway. Considerable heterogeneity in T cell receptor (TCR) repertoire exists among not only different patients but also among different lesions of the same patient. Invasive lesions of MPLC exhibited significantly higher TCR diversity and lower TCR expansion than those of SN. Intriguingly, different lesions of the same patient always shared a certain proportion of TCR clonotypes. Significant clonal expansion could be observed in shared TCR clonotypes, particularly in those existing in all lesions of the same patient. In conclusion, this study provided evidences of the distinctive mutational landscape, activation of oncogenic signaling pathways, and TCR repertoire in MPLC as compared with SN. The significant clonal expansion of shared TCR clonotypes demonstrated the existence of immune commonality among different lesions of the same patient and shed new light on the individually tailored precision therapy for MPLC.

Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.

Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improve ternary classification. Purpose To determine whether a strategy that includes an adjudication approach can enhance the performance of DL ternary classification models in predicting the invasiveness of adenocarcinoma at chest CT and maintain performance in classifying pGGNs. Materials and Methods In this retrospective study, six ternary models for classifying preinvasive, minimally invasive, and invasive adenocarcinoma were developed using a multicenter data set of lung nodules. The DL-based models were progressively modified through framework optimization, joint learning, and an adjudication strategy (simulating a multireader approach to resolving discordant nodule classifications), integrating two binary classification models with a ternary classification model to resolve discordant classifications sequentially. The six ternary models were then tested on an external data set of pGGNs imaged between December 2019 and January 2021. Diagnostic performance including accuracy, specificity, and sensitivity was assessed. The χ2 test was used to compare model performance in different subgroups stratified by clinical confounders. Results A total of 4929 nodules from 4483 patients (mean age, 50.1 years ± 9.5 [SD]; 2806 female) were divided into training (n = 3384), validation (n = 579), and internal (n = 966) test sets. A total of 361 pGGNs from 281 patients (mean age, 55.2 years ± 11.1 [SD]; 186 female) formed the external test set. The proposed strategy improved DL model performance in external testing (P < .001). For classifying minimally invasive adenocarcinoma, the accuracy was 85% and 79%, sensitivity was 75% and 63%, and specificity was 89% and 85% for the model with adjudication (model 6) and the model without (model 3), respectively. Model 6 showed a relatively narrow range (maximum minus minimum) across diagnostic indexes (accuracy, 1.7%; sensitivity, 7.3%; specificity, 0.9%) compared with the other models (accuracy, 0.6%-10.8%; sensitivity, 14%-39.1%; specificity, 5.5%-17.9%). Conclusion Combining framework optimization, joint learning, and an adjudication approach improved DL classification of adenocarcinoma invasiveness at chest CT. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Sohn and Fields in this issue.

New evidence for a role of DANCR in cancers: a comprehensive review.

Cancer remains a leading cause of mortality and poses a substantial threat to public health. Studies have revealed that Long noncoding RNA DANCR is a cytoplasmic lncRNA whose aberrant expression plays a pivotal role in various cancer types. Within tumour biology, DANCR exerts regulatory control over crucial processes such as proliferation, invasion, metastasis, angiogenesis, inflammatory responses, cellular energy metabolism reprogramming, and apoptosis. By acting as a competitive endogenous RNA for miRNAs and by interacting with proteins and mRNAs at the molecular level, DANCR contributes significantly to cancer progression. Elevated DANCR levels have also been linked to heightened resistance to anticancer drugs. Moreover, the detection of circulating DANCR holds promise as a valuable biomarker for aiding in the clinical differentiation of different cancer types. This article offers a comprehensive review and elucidation of the primary functions and molecular mechanisms through which DANCR influences tumours.

Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window.

OBJECTIVES: To explore the performance of low-dose computed tomography (LDCT) with deep learning reconstruction (DLR) for the improvement of image quality and assessment of lung parenchyma. METHODS: Sixty patients underwent chest regular-dose CT (RDCT) followed by LDCT during the same examination. RDCT images were reconstructed with hybrid iterative reconstruction (HIR) and LDCT images were reconstructed with HIR and DLR, both using lung algorithm. Radiation exposure was recorded. Image noise, signal-to-noise ratio, and subjective image quality of normal and abnormal CT features were evaluated and compared using the Kruskal-Wallis test with Bonferroni correction. RESULTS: The effective radiation dose of LDCT was significantly lower than that of RDCT (0.29 ± 0.03 vs 2.05 ± 0.65 mSv, p < 0.001). The mean image noise ± standard deviation was 33.9 ± 4.7, 39.6 ± 4.3, and 31.1 ± 3.2 HU in RDCT, LDCT HIR-Strong, and LDCT DLR-Strong, respectively (p < 0.001). The overall image quality of LDCT DLR-Strong was significantly better than that of LDCT HIR-Strong (p < 0.001) and comparable to that of RDCT (p > 0.05). LDCT DLR-Strong was comparable to RDCT in evaluating solid nodules, increased attenuation, linear opacity, and airway lesions (all p > 0.05). The visualization of subsolid nodules and decreased attenuation was better with DLR than with HIR in LDCT but inferior to RDCT (all p < 0.05). CONCLUSION: LDCT DLR can effectively reduce image noise and improve image quality. LDCT DLR provides good performance for evaluating pulmonary lesions, except for subsolid nodules and decreased lung attenuation, compared to RDCT-HIR. CLINICAL RELEVANCE STATEMENT: The study prospectively evaluated the contribution of DLR applied to chest low-dose CT for image quality improvement and lung parenchyma assessment. DLR can be used to reduce radiation dose and keep image quality for several indications. KEY POINTS: • DLR enables LDCT maintaining image quality even with very low radiation doses. • Chest LDCT with DLR can be used to evaluate lung parenchymal lesions except for subsolid nodules and decreased lung attenuation. • Diagnosis of pulmonary emphysema or subsolid nodules may require higher radiation doses.

Canagliflozin alleviates pulmonary hypertension by activating PPARγ and inhibiting its S225 phosphorylation.

Pulmonary hypertension (PH) is a progressive fatal disease with no cure. Canagliflozin (CANA), a novel medication for diabetes, has been found to have remarkable cardiovascular benefits. However, few studies have addressed the effect and pharmacological mechanism of CANA in the treatment of PH. Therefore, our study aimed to investigate the effect and pharmacological mechanism of CANA in treating PH. First, CANA suppressed increased pulmonary artery pressure, right ventricular hypertrophy, and vascular remodeling in both mouse and rat PH models. Network pharmacology, transcriptomics, and biological results suggested that CANA could ameliorate PH by suppressing excessive oxidative stress and pulmonary artery smooth muscle cell proliferation partially through the activation of PPARγ. Further studies demonstrated that CANA inhibited phosphorylation of PPARγ at Ser225 (a novel serine phosphorylation site in PPARγ), thereby promoting the nuclear translocation of PPARγ and increasing its ability to resist oxidative stress and proliferation. Taken together, our study not only highlighted the potential pharmacological effect of CANA on PH but also revealed that CANA-induced inhibition of PPARγ Ser225 phosphorylation increases its capacity to counteract oxidative stress and inhibits proliferation. These findings may stimulate further research and encourage future clinical trials exploring the therapeutic potential of CANA in PH treatment.

A comparative analysis of deep learning and hybrid iterative reconstruction algorithms with contrast-enhancement-boost post-processing on the image quality of indirect computed tomography venography of the lower extremities.

PURPOSE: To examine whether there is a significant difference in image quality between the deep learning reconstruction (DLR [AiCE, Advanced Intelligent Clear-IQ Engine]) and hybrid iterative reconstruction (HIR [AIDR 3D, adaptive iterative dose reduction three dimensional]) algorithms on the conventional enhanced and CE-boost (contrast-enhancement-boost) images of indirect computed tomography venography (CTV) of lower extremities. MATERIALS AND METHODS: In this retrospective study, seventy patients who underwent CTV from June 2021 to October 2022 to assess deep vein thrombosis and varicose veins were included. Unenhanced and enhanced images were reconstructed for AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images were obtained using subtraction software. Objective and subjective image qualities were assessed, and radiation doses were recorded. RESULTS: The CT values of the inferior vena cava (IVC), femoral vein ( FV), and popliteal vein (PV) in the CE-boost images were approximately 1.3 (1.31-1.36) times higher than in those of the enhanced images. There were no significant differences in mean CT values of IVC, FV, and PV between AIDR 3D and AiCE, AIDR 3D-boost and AiCE-boost images. Noise in AiCE, AiCE-boost images was significantly lower than in AIDR 3D and AIDR 3D-boost images ( P < 0.05). The SNR (signal-to-noise ratio), CNR (contrast-to-noise ratio), and subjective scores of AiCE-boost images were the highest among 4 groups, surpassing AiCE, AIDR 3D, and AIDR 3D-boost images (all P < 0.05). CONCLUSION: In indirect CTV of the lower extremities images, DLR with the CE-boost technique could decrease the image noise and improve the CT values, SNR, CNR, and subjective image scores. AiCE-boost images received the highest subjective image quality score and were more readily accepted by radiologists.

Runoff nitrogen losses under confluence and diverging drainage systems in the sloped plot scale: A comparative study.

The drainage system is a key measure for regulating runoff nutrient losses on sloping farmlands. Confluence and diverging drainage systems are two drainage layouts representing natural water network systems and are widely distributed in sloping farmlands; however, the effects of these drainage systems on runoff nutrient losses in the sloped plots remain unclear. This study investigated the effects of different drainage systems on the characteristics of runoff nitrogen (N) losses in sloped plots using laboratory rainfall simulations. Three treatments, including bare slope (without drainage system, CK), confluence drainage system (T1), and diverging drainage system (T2), were used to compare the changes in concentrations and losses of total nitrogen (TN), dissolved nitrogen (DN), and particulate nitrogen (PN), and the DN:TN ratio in runoff under a combination of 1.8 mm min-1 rainfall intensity and three slope gradients (5°, 10°, and 15°). The results showed that the time to runoff was significantly delayed in T2 compared with that in CK and T1 across all slopes (p < 0.05). Accumulated runoff depth was considerably lower in T1 and T2 than in CK across all slopes (p < 0.05). The TN and PN concentrations in T1 were markedly lower than those in T2 on the 10° and 15° slopes (p < 0.05). The DN concentration in T1 was lowest at the 5° slope (p < 0.05). TN loss in T1 was 14.7-33.9% and 17.9-30.3% lower than those in CK and T2 across all slopes, respectively (p < 0.05). The PN loss in T1 was 56.7% and 53.3% lower than that in T2 on the 10° and 15° slopes, respectively (p < 0.05). DN loss in T1 was 39.3-72.5% lower than that in CK for all slopes (p < 0.05). DN:TN in T2 was lower than that in CK and T1 at the 10° and 15° slopes (p < 0.05). Our results confirm the effectiveness of drainage systems in reducing runoff nutrient losses in a sloped plot and demonstrate that the confluence drainage system is better at reducing N losses in runoff than diverging drainage systems.

[Intraspinal Metastasis of Thymic Carcinoma:Report of One Case].

Intraspinal metastasis from malignant carcinomas in other body parts is rarely reported.Intraspinal metastases are often epidural,with primary tumors mostly from the lung and prostate.The extramedullary subdural metastasis of thymic carcinoma is particularly rare and prone to misdiagnosis due to overlapping imaging features with primary intraspinal tumors.This article reports one case of intraspinal metastasis of thymic carcinoma,with the main diagnostic clues including a history of thymic carcinoma,fast growth rate,and irregular shape.

[Exploring the mechanism of IgA vasculitis pathogenesis through the interaction of thrombin and inflammatory factors using urinary proteomics]. / è¿ç”¨å°¿è›‹ç™½ç»„å­¦æŠ€æœ¯æŽ¢ç´¢å‡è¡€é ¶ä¸Žç‚Žç—‡å› å­ç›¸äº’ä½œç”¨å‚ä¸ŽIgAè¡€ç®¡ç‚Žå‘ç— çš„æœºåˆ¶.

OBJECTIVES: To explore the evidence, urinary biomarkers, and partial mechanisms of hypercoagulability in the pathogenesis of IgA vasculitis (IgAV). METHODS: Differential expression of proteins in the urine of 10 healthy children and 10 children with IgAV was screened using high-performance liquid chromatography-tandem mass spectrometry, followed by Reactome pathway analysis. Protein-protein interaction (PPI) network analysis was conducted using STRING and Cytoscape software. In the validation cohort, 15 healthy children and 25 children with IgAV were included, and the expression levels of differential urinary proteins were verified using enzyme-linked immunosorbent assay. RESULTS: A total of 772 differential proteins were identified between the IgAV group and the control group, with 768 upregulated and 4 downregulated. Reactome pathway enrichment results showed that neutrophil degranulation, platelet activation, and hemostasis pathways were involved in the pathogenesis of IgAV. Among the differential proteins, macrophage migration inhibitory factor (MIF) played a significant role in neutrophil degranulation and hemostasis, while thrombin was a key protein in platelet activation and hemostasis pathways. PPI analysis indicated that thrombin directly interacted with several proteins involved in inflammatory responses, and these interactions involved MIF. Validation results showed that compared to healthy children, children with IgAV had significantly higher urine thrombin/creatinine and urine MIF/creatinine levels (P<0.05). CONCLUSIONS: Thrombin contributes to the pathogenesis of IgAV through interactions with inflammatory factors. Urinary thrombin and MIF can serve as biomarkers reflecting the hypercoagulable and inflammatory states in children with IgAV.

A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer.

OBJECTIVES: To develop a pre-treatment CT-based predictive model to anticipate inoperable lung cancer patients' progression-free survival (PFS) to immunotherapy. METHODS: This single-center retrospective study developed and cross-validated a radiomic model in 185 patients and tested it in 48 patients. The binary endpoint is the durable clinical benefit (DCB, PFS ≥ 6 months) and non-DCB (NDCB, PFS < 6 months). Radiomic features were extracted from multiple intrapulmonary lesions and weighted by an attention-based multiple-instance learning model. Aggregated features were then selected through L2-regularized ridge regression. Five machine-learning classifiers were conducted to build predictive models using radiomic and clinical features alone and then together. Lastly, the predictive value of the model with the best performance was validated by Kaplan-Meier survival analysis. RESULTS: The predictive models based on the weighted radiomic approach showed superior performance across all classifiers (AUCs: 0.75-0.82) compared with the largest lesion approach (AUCs: 0.70-0.78) and the average sum approach (AUCs: 0.64-0.80). Among them, the logistic regression model yielded the most balanced performance (AUC = 0.87 [95%CI 0.84-0.89], 0.75 [0.68-0.82], 0.80 [0.68-0.92] in the training, validation, and test cohort respectively). The addition of five clinical characteristics significantly enhanced the performance of radiomic-only model (train: AUC 0.91 [0.89-0.93], p = .042; validation: AUC 0.86 [0.80-0.91], p = .011; test: AUC 0.86 [0.76-0.96], p = .026). Kaplan-Meier analysis of the radiomic-based predictive models showed a clear stratification between classifier-predicted DCB versus NDCB for PFS (HR = 2.40-2.95, p < 0.05). CONCLUSIONS: The adoption of weighted radiomic features from multiple intrapulmonary lesions has the potential to predict long-term PFS benefits for patients who are candidates for PD-1/PD-L1 immunotherapies. KEY POINTS: • Weighted radiomic-based model derived from multiple intrapulmonary lesions on pre-treatment CT images has the potential to predict durable clinical benefits of immunotherapy in lung cancer. • Early line immunotherapy is associated with longer progression-free survival in advanced lung cancer.

Evaluating the spatial and temporal distribution of emerging contaminants in the Pearl River Basin for regulating purposes.

Little information is available on how the types, concentrations, and distribution of chemicals have evolved over the years. The objective of the present study is therefore to review the spatial and temporal distribution profile of emerging contaminants with limited toxicology data in the pearl river basin over the years to build up the emerging contaminants database in this region for risk assessment and regulatory purposes. The result revealed that seven groups of emerging contaminants were abundant in this region, and many emerging contaminants had been detected at much higher concentrations before 2011. Specifically, antibiotics, phenolic compounds, and acidic pharmaceuticals were the most abundant emerging contaminants detected in the aquatic compartment, while phenolic compounds were of the most profound concern in soil. Flame retardants and plastics were the most frequently studied chemicals in organisms. The abundance of the field concentrations and frequencies varied considerably over the years, and currently available data can hardly be used for regulation purposes. It is suggested that watershed management should establish a regular monitoring scheme and comprehensive database to monitor the distribution of emerging contaminants considering the highly condensed population in this region. The priority monitoring list should be formed in consideration of historical abundance, potential toxic effects of emerging contaminants as well as the distribution of heavily polluting industries in the region.

Simulation video: a tool to evaluate communications skills in radiologist residents.

BACKGROUND: Effective communication is a crucial component of radiology resident training, and many different aspects need to be explored when teaching and evaluating communication skills. To ensure that radiology residents' communication skill levels can be measured accurately, a standardized evaluation tool has been introduced. In twenty hospitals in Beijing, simulation videos have been developed as a way to assess the communication skills of radiology residents during their certification exams, to minimize evaluating biases. This study aims to assess the performance of a simulation video model in evaluating communications skills compared to the standard patient model. METHODS: This is a retrospective observational study. The performance of standard patient and simulation video models was evaluated through an eight-year examination of communication skills in radiology residents. From 2014 to 2021, communications skill tests were administered to 1003 radiology residents in 20 hospitals in Beijing. The standardized patient (SP) model was applied in 2014, and simulation videos were used from 2015 to 2021. The difficulty and discrimination radio of the tests were evaluated. The subjective survey for candidates on two models of communication skills evaluation was performed and analyzed. RESULTS: The simulation video model evaluation demonstrated stable difficulty (ranging from 0.92 to 0.98) and discrimination ratio (ranging from 0.37 to 0.49), except for minor exceptions of discrimination in 2019 (0.58) and 2020 (0.20). Furthermore, the Kruskal-Wallis H test revealed no significant differences in average scores between 2016 (93.9 ± 4.6) and 2018 (94.5 ± 4.2), 2016 and 2019 (97.3 ± 3.9), 2017 (97.0 ± 5.6) and 2019, 2017 and 2020 (97.7 ± 4.7), as well as 2019 and 2020 exams (all p ≥ 0.05). In addition, candidates who responded to the survey preferred the simulation video model (with a 77.2% response rate), with 62.7% choosing it over the SP model for communication skills evaluation. CONCLUSION: The simulation video demonstrated a stable and better acceptable construct for assessing radiology residents' communication skills.

Predicting the impact of the COVID-19 pandemic on globalization.

The COVID-19 pandemic has significantly influenced the global economy, international travel, global supply chains, and how people interact, and subsequently affect globalization in coming years. In order to understand the impact of COVID-19 on globalization and provide potential guidance to policymakers, the present study predicted the globalization level of the world average and 14 specific countries in scenarios with and without COVID-19 based on a new Composite Indicator method which contains 15 indicators. Our results revealed that the world average globalization level is expected to decrease from 2017 to 2025 under the scenario without COVID-19 by -5.99%, while the decrease of globalization under the COVID-19 scenario is predicted to reach -4.76% in 2025. This finding implies that the impact of COVID-19 on globalization will not be as severe as expected in 2025. Nevertheless, the downward trend of globalization without COVID-19 is due to the decline of the Environmental indicators, whereas the decline under the COVID-19 scenario is attributed to Economic aspects (almost -50%). The impact of COVID-19 on globalization varies across individual countries. Among the countries investigated, COVID-19 had a positive impact on the globalization of Japan, Australia, the United States, the Russian Federation, Brazil, India and Togo. In contrast, the globalization in the United Kingdom, Switzerland, Qatar, Egypt, China and Gabon are expected to decrease. The variation of impact induced by COVID-19 on those countries is attributed to the weighting of economic, environmental and political aspects of globalization is different across these countries. Our results can help governments take suitable measures to balance economic, environmental and political policies, which may better support their decision-making.

[CT-Based Weighted Radiomic Score Predicts Tumor Response to Immunotherapy in Non-Small Cell Lung Cancer].

Objective To develop a CT-based weighted radiomic model that predicts tumor response to programmed death-1(PD-1)/PD-ligand 1(PD-L1)immunotherapy in patients with non-small cell lung cancer.Methods The patients with non-small cell lung cancer treated by PD-1/PD-L1 immune checkpoint inhibitors in the Peking Union Medical College Hospital from June 2015 to February 2022 were retrospectively studied and classified as responders(partial or complete response)and non-responders(stable or progressive disease).Original radiomic features were extracted from multiple intrapulmonary lesions in the contrast-enhanced CT scans of the arterial phase,and then weighted and summed by an attention-based multiple instances learning algorithm.Logistic regression was employed to build a weighted radiomic scoring model and the radiomic score was then calculated.The area under the receiver operating characteristic curve(AUC)was used to compare the weighted radiomic scoring model,PD-L1 model,clinical model,weighted radiomic scoring + PD-L1 model,and comprehensive prediction model.Results A total of 237 patients were included in the study and randomized into a training set(n=165)and a test set(n=72),with the mean ages of(64±9)and(62±8)years,respectively.The AUC of the weighted radiomic scoring model reached 0.85 and 0.80 in the training set and test set,respectively,which was higher than that of the PD-L1-1 model(Z=37.30,P<0.001 and Z=5.69,P=0.017),PD-L1-50 model(Z=38.36,P<0.001 and Z=17.99,P<0.001),and clinical model(Z=11.40,P<0.001 and Z=5.76,P=0.016).The AUC of the weighted scoring model was not different from that of the weighted radiomic scoring + PD-L1 model and the comprehensive prediction model(both P>0.05).Conclusion The weighted radiomic scores based on pre-treatment enhanced CT images can predict tumor responses to immunotherapy in patients with non-small cell lung cancer.

Sulfur-driven autotrophic denitrification of nitric oxide for efficient nitrous oxide recovery.

Nitrous oxide (N2 O) was previously deemed as a potent greenhouse gas but is actually an untapped energy source, which can accumulate during the microbial denitrification of nitric oxide (NO). Compared with the organic electron donor required in heterotrophic denitrification, elemental sulfur (S0 ) is a promising electron donor alternative due to its cheap cost and low biomass yield in sulfur-driven autotrophic denitrification. However, no effort has been made to test N2 O recovery from sulfur-driven denitrification of NO so far. Therefore, in this study, batch and continuous experiments were carried out to investigate the NO removal performance and N2 O recovery potential via sulfur-driven NO-based denitrification under various Fe(II)EDTA-NO concentrations. Efficient energy recovery was achieved, as up to 35.5%-40.9% of NO was converted to N2 O under various NO concentrations. N2 O recovery from Fe(II)EDTA-NO could be enhanced by the low bioavailability of sulfur and the acid environment caused by sulfur oxidation. The NO reductase (NOR) and N2 O reductase (N2 OR) were inhibited distinctively at relatively low NO levels, leading to efficient N2 O accumulation, but were suppressed irreversibly at NO level beyond 15 mM in continuous experiments. Such results indicated that the regulation of NO at a relatively low level would benefit the system stability and NO removal capacity during long-term system operation. The continuous operation of the sulfur-driven Fe(II)EDTA-NO-based denitrification reduced the overall microbial diversity but enriched several key microbial community. Thauera, Thermomonas, and Arenimonas that are able to carry out sulfur-driven autotrophic denitrification became the dominant organisms with their relative abundance increased from 25.8% to 68.3%, collectively.

Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease.

OBJECTIVES: To investigate whether deep learning reconstruction (DLR) could keep image quality and reduce radiation dose in interstitial lung disease (ILD) patients compared with HRCT reconstructed with hybrid iterative reconstruction (hybrid-IR). METHODS: Seventy ILD patients were prospectively enrolled and underwent HRCT (120 kVp, automatic tube current) and LDCT (120 kVp, 30 mAs) scans. HRCT images were reconstructed with hybrid-IR (Adaptive Iterative Dose Reduction 3-Dimensional [AIDR3D], standard-setting); LDCT images were reconstructed with DLR (Advanced Intelligence Clear-IQ Engine [AiCE], lung/bone, mild/standard/strong setting). Image noise, streak artifact, overall image quality, and visualization of normal and abnormal features of ILD were evaluated. RESULTS: The mean radiation dose of LDCT was 38% of HRCT. Objective image noise of reconstructed LDCT images was 33.6 to 111.3% of HRCT, and signal-to-noise ratio (SNR) was 0.9 to 3.1 times of the latter (p < 0.001). LDCT-AiCE was not significantly different from or even better than HRCT in overall image quality and visualization of normal lung structures. LDCT-AiCE (lung, mild/standard/strong) showed progressively better recognition of ground glass opacity than HRCT-AIDR3D (p < 0.05, p < 0.01, p < 0.001), and LDCT-AiCE (lung, mild/standard/strong; bone, mild) was superior to HRCT-AIDR3D in visualization of architectural distortion (p < 0.01, p < 0.01, p < 0.01; p < 0.05). LDCT-AiCE (bone, strong) was better than HRCT-AIDR3D in the assessment of bronchiectasis and/or bronchiolectasis (p < 0.05). LDCT-AiCE (bone, mild/standard/strong) was significantly better at the visualization of honeycombing than HRCT-AIDR3D (p < 0.05, p < 0.05, p < 0.01). CONCLUSION: Deep learning reconstruction could effectively reduce radiation dose and keep image quality in ILD patients compared to HRCT with hybrid-IR. KEY POINTS: • Deep learning reconstruction was a novel image reconstruction algorithm based on deep convolutional neural networks. It was applied in chest CT studies and received auspicious results. • HRCT plays an essential role in the whole process of diagnosis, treatment efficacy evaluation, and follow-ups for interstitial lung disease patients. However, cumulative radiation exposure could increase the risks of cancer. • Deep learning reconstruction method could effectively reduce the radiation dose and keep the image quality compared with HRCT reconstructed with hybrid iterative reconstruction in patients with interstitial lung disease.

Evaluation of left atrial remodeling using cardiovascular magnetic resonance imaging in breast cancer patients treated with adjuvant trastuzumab.

OBJECTIVES: We evaluated left atrial (LA) remodeling using cardiac MRI (CMR) in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer during and after trastuzumab therapy. METHODS: In this prospective 2-center longitudinal study, 41 women with HER2-positive breast cancer received adjuvant trastuzumab for 12 months, in addition to standard chemotherapy. Serial CMRs were performed at baseline, 6, 12, and 18 months after initiation of trastuzumab. LA volumes were measured by a blinded reader. Linear mixed model was used to evaluate longitudinal changes. RESULTS: Of 41 women (mean age 52 ± 11 [SD] years; 56% received anthracycline), one patient experienced trastuzumab-induced cardiotoxicity (TIC) for which trastuzumab was interrupted for one cycle. Mean baseline left ventricular ejection fraction (LVEF) was 68.0 ± 5.9% and LA ejection fraction (LAEF) was 66.0 ± 6.6%. Compared to baseline, LAEF decreased significantly at 6 months (62.7 ± 5.7%, p = 0.027) and 12 months (62.2 ± 6.1%, p = 0.003), while indexed LA minimum volume (LAmin) significantly increased at 12 months (11.6 ± 4.9 ml/m2 vs 13.8 ± 4.5 ml/m2, p = 0.002). At 18 months, all changes from baseline were no longer significant. From baseline to 6 months, change in LAEF correlated with change in LVEF (Spearman's r = 0.41, p = 0.014). No significant interactions (all p > 0.10) were detected between time and anthracycline use for LA parameters. CONCLUSIONS: Among trastuzumab-treated patients with low incidence of TIC, we observed a small but significant decline in LAEF and increase in LAmin that persisted for the duration of therapy and recovered 6 months after therapy cessation. These findings suggest that trastuzumab has concurrent detrimental effects on atrial and ventricular remodeling. KEY POINTS: • In trastuzumab-treated breast cancer patients evaluated by cardiac MRI, left atrial ejection fraction declined and minimum volume increased during treatment and recovered to baseline after trastuzumab cessation. • Changes in left atrial ejection fraction correlated with changes in left ventricular ejection fraction in the first 6 months of trastuzumab treatment. • Trastuzumab therapy is associated with concurrent detrimental effects on left atrial and ventricular remodeling.

Proteomic analysis and identification reveal the anti-inflammatory mechanism of clofazimine on lipopolysaccharide-induced acute lung injury in mice.

BACKGROUND AND OBJECTIVE: Acute lung injury (ALI)/ acute respiratory distress syndrome (ARDS) was increasingly recognized as one of the most severe acute hyperimmune response of coronavirus disease 2019 (COVID-19). Clofazimine (CFZ) has attracted attention due to its anti-inflammatory property in immune diseases as well as infectious diseases. However, the role and potential molecular mechanism of CFZ in anti-inflammatory responses remain unclear. METHODS: We analyze the protein expression profiles of CFZ and LPS from Raw264.7 macrophages using quantitative proteomics. Next, the protective effect of CFZ on LPS-induced inflammatory model is assessed, and its underlying mechanism is validated by molecular biology analysis. RESULTS: LC-MS/MS-based shotgun proteomics analysis identified 4746 (LPS) and 4766 (CFZ) proteins with quantitative information. The key proteins and their critical signal transduction pathways including TLR4/NF-κB/HIF-1α signaling was highlighted, which was involved in multiple inflammatory processes. A further analysis of molecular biology revealed that CFZ could significantly inhibit the proliferation of Raw264.7 macrophages, decrease the levels of TNF-α and IL-1ß, alleviate lung histological changes and pulmonary edema, improve the survival rate, and down-regulate TLR4/NF-κB/HIF-1α signaling in LPS model. CONCLUSION: This study can provide significant insight into the proteomics-guided pharmacological mechanism study of CFZ and suggest potential therapeutic strategies for infectious disease.

A unique microbiome in a highly polluted and alkalic lake in a seasonally frozen area.

Highly polluted waters profoundly promoted microbial activities and vice versa. Although microbial community structures and bioprocesses in polluted environments have been fully investigated, they are rarely uncovered in abandoned and highly polluted aquatic environment, especially in a seasonally frozen area. East Lake had been polluted by surrounding paper mills and dairy plants from the 1960s to the early 2000s. Under low biodegradability (BOD5/COD ratio<0.2) and alkalic (pH > 9) conditions, a unique microbiome was discovered in East lake. Proteobacteria was the largest taxonomic group in the microbial community, covering almost half of the top-100 genera. Among them, seven genera are sulfate-reducing bacteria, including Desulfuromusa, Desulfuromonas, Desulfobulbus, Desulfocapsa, Desulfurivibrio, Desulfatiglans, and Desulfomicrobium. Functional analysis revealed abundant predicted functional genes for the respiration of sulfur compounds and sulfate respiration. Great vertical variations were found to both microbial community structures and predicted functions. This work provides unique evidence for future microbiome studies of nutrient-rich, alkalic, and seasonally frozen water habitats.

Dissolved organic matter heightens the toxicity of tetrabromobisphenol A to aquatic organisms.

Tetrabromobisphenol A (TBBPA) is a new type of persistent organic pollutant, which causes environmental pollution and health problems, and has attracted the attention of the international research community. Once released into the environment, TBBPA can interact with dissolved organic matter (DOM), which affects its behavior. However, the effect of DOM on the biological toxicity of TBBPA remains unclear. The toxic effects of TBBPA on three model aquatic organisms (Chlorella pyrenoidosa, Daphnia magna, and Danio rerio), in the absence and presence of DOM were investigated. The order of acute toxicity of TBBPA to the three aquatic organisms was D. magna > D. rerio > C. pyrenoidosa. In the presence of DOM the median effect/lethal concentrations values of TBBPA to the three aquatic organisms decreased by at least 32 (C. pyrenoidosa), 52 (D. magna), and 6.6% (D. rerio), implying that DOM enhanced the acute toxicity of TBBPA to all the organisms. Moreover, the higher the concentration of DOM, the higher the acute toxicity of TBBPA. Furthermore, the presence of DOM increased total reactive oxygen species (ROS) induced by TBBPA in a concentration-dependent manner. A tracking analysis of total ROS in the three aquatic organisms also showed that the presence of DOM aggravated the accumulation of total ROS induced by TBBPA, indicating that oxidative stress is a characteristic mechanism of toxicity of TBBPA to aquatic organisms when DOM is present. In addition, the evaluated risk quotient indicated that the ecological risk of TBBPA to aquatic organisms can increase in environments rich in DOM.