Synthesis and preclinical evaluation of a novel probe [18F]AlF-NOTA-IPB-GPC3P for PET imaging of GPC3 positive tumor.

Glypican-3 (GPC3) is markedly overexpressed in hepatocellular carcinoma (HCC) and not expressed in normal liver tissues. In this study, a novel peptide PET imaging agent ([18F]AlF-NOTA-IPB-GPC3P) was developed to target GPC3 expressed in tumors. The overall radiochemical yield of [18F]AlF-NOTA-IPB-GPC3P was 10-15 %, and its lipophilicity, expressed as the logD value at a pH of 7.4, was -1.18 ± 0.06 (n = 3). Compared to the previously reported tracer [18F]AlF-GP2633, [18F]AlF-NOTA-IPB-GPC3P exhibited higher cellular uptake (15.13 vs 5.96) and internalized rate (80.63 % vs 35.93 %) in Huh7 cells at 120 min. Micro-PET/CT and biodistribution studies further demonstrated that [18F]AlF-NOTA-IPB-GPC3P exhibited significantly increased tumor uptake and prolonged tumor residence in Huh7 tumors compared to [18F]AlF-GP2633 (4.66 ± 0.22 % ID/g vs 0.72 ± 0.09 % ID/g at 60 min, p < 0.001; 5.05 ± 0.23 % ID/g vs 0.35 ± 0.08 % ID/g at 120 min, p < 0.001, respectively). Furthermore, the tumor-to-organ ratios of [18F]AlF-NOTA-IPB-GPC3P surpassed those of [18F]AlF-GP2633. Our results support the utilization of [18F]AlF-NOTA-IPB-GPC3P as a PET imaging agent targeting the GPC3 receptor for tumor detection.

Synthesis and preclinical evaluation of a novel molecular probe [18F]AlF-NOTA-PEG2-Asp2-PDL1P for PET imaging of PD-L1 positive tumor.

Immunotherapy has brought great benefits to cancer patients, but only some patients benefit from it. Noninvasive, real-time and dynamic monitoring of the effectiveness of immunotherapy through PET imaging may provide assistance for the treatment plan of immunotherapy. In this study, we designed and synthesized a new targeted PD-L1 peptide NOTA-PEG2-Asp2-PDL1P, which was labeled with nuclide 18F to obtain a new imaging agent [18F]AlF-NOTA-PEG2-Asp2-PDL1P. The total radiochemical yield of [18F]AlF-NOTA-PEG2-Asp2-PDL1P was 13.7 % (Uncorrected radiochemical yield, n > 5). [18F]AlF-NOTA-PEG2-Asp2-PDL1P achieved high radiochemical purity (>95 %) with a molar activity more than 51.2 GBq/µmol. [18F]AlF-NOTA-PEG2-Asp2-PDL1P exhibited good hydrophilicity and had good stability both in vivo and in vitro, it can specifically targets B16F10 tumor with PD-L1 expression, and had a relatively high retention in tumor, a relatively fast clearance in vivo and a higher tumor-to-non-target ratio, all of which could make [18F]AlF-NOTA-PEG2-Asp2-PDL1P a potential tracer for PD-L1 prediction before clinical immunotherapy.

PET/CT imaging fibroblast activation protein in initial colorectal cancer: compared to 18 F-FDG PET/CT.

OBJECTIVE: In this study, the potential advantage of FAPI over 18 F-labelled deoxyglucose ( 18 F-FDG) in evaluation of the initial staging colorectal cancer (CRC) was investigated. MATERIALS AND METHODS: Thirty-two patients with histopathologically confirmed primary CRC were included in our study. They all underwent both 18 F-FDG and FAPI PET/CT. Lesion detectability and tracer uptakes, mainly quantified by maximum standardized uptake value (SUVmax) and target-to-background ratio (TBR), were compared for paired lesions between both modalities using the Wilcoxon signed-rank test and paired t-test. RESULTS: Thirty-five CRC lesions in 32 patients were diagnosed. The sensitivity of FAPI PET/CT in diagnosis of the CRC lesions was 100% while 93.8% of 18 F-FDG PET/CT. FAPI and 18 F-FDG had a similar uptake in CRC lesion (mean SUVmax: 14.3 ±â€…8.6 vs. 15.4 ±â€…9.8, P  = 0.604), but lesions contained mucus and/or signet-ring cell carcinoma seemed to have a trend of higher FAPI uptake although there was no statistical difference (mean SUVmax: 12.7 ±â€…5.6 vs. 8.5 ±â€…4.1, P  = 0.152) and higher TBR (13.4 ±â€…6.2 vs. 4.9 ±â€…2.2, P  = 0.004) than those of 18 F-FDG. For regional lymph node metastases, both FAPI and FDG PET/CTs showed high sensitivity (7/8 vs. 7/8), specificity (7/8 vs. 6/8) and accuracy (14/16 vs. 13/16) (all P  > 0.05). For distant metastasis, FAPI PET/CT depicted more positive lesions in distant lymph node (46 vs. 26), liver (13 vs. 7) and peritoneum (107 vs. 45) than 18 F-FDG PET/CT. FAPI PET/CT also had a higher peritoneal cancer index score (median 11 vs 4; P  < 0.001) than 18 F-FDG PET/CT in evaluation of peritoneal metastases. CONCLUSION: FAPI PET/CT showed high sensitivity in detection of primary CRC and superiority to 18 F-FDG PET/CT in detection of metastases to distant lymph node, liver and peritoneum.

Predicting the risk of mortality in ICU patients based on dynamic graph attention network of patient similarity.

Predicting the risk of mortality of hospitalized patients in the ICU is essential for timely identification of high-risk patients and formulate and adjustment of treatment strategies when patients are hospitalized. Traditional machine learning methods usually ignore the similarity between patients and make it difficult to uncover the hidden relationships between patients, resulting in poor accuracy of prediction models. In this paper, we propose a new model named PS-DGAT to solve the above problem. First, we construct a patient-weighted similarity network by calculating the similarity of patient clinical data to represent the similarity relationship between patients; second, we fill in the missing features and reconstruct the patient similarity network based on the data of neighboring patients in the network; finally, from the reconstructed patient similarity network after feature completion, we use the dynamic attention mechanism to extract and learn the structural features of the nodes to obtain a vector representation of each patient node in the low-dimensional embedding The vector representation of each patient node in the low-dimensional embedding space is used to achieve patient mortality risk prediction. The experimental results show that the accuracy is improved by about 1.8% compared with the basic GAT and about 8% compared with the traditional machine learning methods.

Radiosynthesis, preclinical evaluation and pilot clinical PET imaging study of a 18F-labeled tracer targeting fibroblast activation protein.

Fibroblast activation protein (FAP) is a promising molecular target for imaging in various types of cancers. Several 18F-labeled FAP inhibitor (FAPI) tracers have been evaluated in clinical study. However, these tracers display high physiological uptake in gallbladder and bile duct system. To overcome the limitation, we herein designed a novel radiotracer named 18F-FAPTG. 18F-FAPTG was produced with a non-decay-corrected radiochemical yield of 24.0 ± 6.0% and 22.0 ± 7.0% for manual and automatic synthesis, respectively. 18F-FAPTG exhibited high hydrophilicity and stability in vitro. The studies of cellular uptake, internalization, efflux properties and competitive binding to FAP of 18F-FAPTG indicated that the tracer showed high specificity, rapid internalization and low cellular efflux in FAP-positive cells. Biodistribution studies and microPET in mice bearing FAP-positive xenografts demonstrated extremely low uptake in the majority of other organs and main excretion of 18F-FAPTG through the urinary system. Furthermore, compared to 18F-FAPI-42, 18F-FAPTG showed significantly lower uptake in gallbladder, higher tumor uptake and longer tumor retention. In the pilot clinical study, 18F-FAPTG PET/CT demonstrated favorable tumor-to-background ratios in most organs and clearly displayed the malignant lesions. Our findings indicated that 18F-FAPTG had an advantage over 18F-FAPI-42 in PET imaging for cancers located in gallbladder the bile duct system. Thus, 18F-FAPTG could be an alternative to the currently available FAPI tracers.

ConvLSTM coordinated longitudinal transformer under spatio-temporal features for tumor growth prediction.

Accurate quantification of tumor growth patterns can indicate the development process of the disease. According to the important features of tumor growth rate and expansion, physicians can intervene and diagnose patients more efficiently to improve the cure rate. However, the existing longitudinal growth model can not well analyze the dependence between tumor growth pixels in the long space-time, and fail to effectively fit the nonlinear growth law of tumors. So, we propose the ConvLSTM coordinated longitudinal Transformer (LCTformer) under spatiotemporal features for tumor growth prediction. We design the Adaptive Edge Enhancement Module (AEEM) to learn static spatial features of different size tumors under time series and make the depth model more focused on tumor edge regions. In addition, we propose the Growth Prediction Module (GPM) to characterize the future growth trend of tumors. It consists of a Longitudinal Transformer and ConvLSTM. Based on the adaptive abstract features of current tumors, Longitudinal Transformer explores the dynamic growth patterns between spatiotemporal CT sequences and learns the future morphological features of tumors under the dual views of residual information and sequence motion relationship in parallel. ConvLSTM can better learn the location information of target tumors, and it complements Longitudinal Transformer to jointly predict future imaging of tumors to reduce the loss of growth information. Finally, Channel Enhancement Fusion Module (CEFM) performs the dense fusion of the generated tumor feature images in the channel and spatial dimensions and realizes accurate quantification of the whole tumor growth process. Our model has been strictly trained and tested on the NLST dataset. The average prediction accuracy can reach 88.52% (Dice score), 89.64% (Recall), and 11.06 (RMSE), which can improve the work efficiency of doctors.

CNN-LSTM Neural Network for Identification of Pre-Cooked Pasta Products in Different Physical States Using Infrared Spectroscopy.

Infrared (IR) spectroscopy is nondestructive, fast, and straightforward. Recently, a growing number of pasta companies have been using IR spectroscopy combined with chemometrics to quickly determine sample parameters. However, fewer models have used deep learning models to classify cooked wheat food products and even fewer have used deep learning models to classify Italian pasta. To solve these problems, an improved CNN-LSTM neural network is proposed to identify pasta in different physical states (frozen vs. thawed) using IR spectroscopy. A one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) were constructed to extract the local abstraction and sequence position information from the spectra, respectively. The results showed that the accuracy of the CNN-LSTM model reached 100% after using principal component analysis (PCA) on the Italian pasta spectral data in the thawed state and 99.44% after using PCA on the Italian pasta spectral data in the frozen form, verifying that the method has high analytical accuracy and generalization. Therefore, the CNN-LSTM neural network combined with IR spectroscopy helps to identify different pasta products.

A CNN-LSTM-att hybrid model for classification and evaluation of growth status under drought and heat stress in chinese fir (Cunninghamia lanceolata).

BACKGROUND: Cunninghamia lanceolata (Chinese fir), is one of the most important timber trees in China. With the global warming, to develop new resistant varieties to drought or heat stress has become an essential task for breeders of Chinese fir. However, classification and evaluation of growth status of Chinese fir under drought or heat stress are still labor-intensive and time-consuming. RESULTS: In this study, we proposed a CNN-LSTM-att hybrid model for classification of growth status of Chinese fir seedlings under drought and heat stress, respectively. Two RGB image datasets of Chinese fir seedling under drought and heat stress were generated for the first time, and utilized in this study. By comparing four base CNN models with LSTM, the Resnet50-LSTM was identified as the best model in classification of growth status, and LSTM would dramatically improve the classification performance. Moreover, attention mechanism further enhanced performance of Resnet50-LSTM, which was verified by Grad-CAM. By applying the established Resnet50-LSTM-att model, the accuracy rate and recall rate of classification was up to 96.91% and 96.79% for dataset of heat stress, and 96.05% and 95.88% for dataset of drought, respectively. Accordingly, the R2 value and RMSE value for evaluation on growth status under heat stress were 0.957 and 0.067, respectively. And, the R2 value and RMSE value for evaluation on growth status under drought were 0.944 and 0.076, respectively. CONCLUSION: In summary, our proposed model provides an important tool for stress phenotyping in Chinese fir, which will be a great help for selection and breeding new resistant varieties in future.

Deep Learning Algorithm Enables Cerebral Venous Thrombosis Detection With Routine Brain Magnetic Resonance Imaging.

BACKGROUND: Cerebral venous thrombosis (CVT) is a rare cerebrovascular disease. Routine brain magnetic resonance imaging is commonly used to diagnose CVT. This study aimed to develop and evaluate a novel deep learning (DL) algorithm for detecting CVT using routine brain magnetic resonance imaging. METHODS: Routine brain magnetic resonance imaging, including T1-weighted, T2-weighted, and fluid-attenuated inversion recovery images of patients suspected of CVT from April 2014 through December 2019 who were enrolled from a CVT registry, were collected. The images were divided into 2 data sets: a development set and a test set. Different DL algorithms were constructed in the development set using 5-fold cross-validation. Four radiologists with various levels of expertise independently read the images and performed diagnosis within the test set. The diagnostic performance on per-patient and per-segment diagnosis levels of the DL algorithms and radiologist's assessment were evaluated and compared. RESULTS: A total of 392 patients, including 294 patients with CVT (37±14 years, 151 women) and 98 patients without CVT (42±15 years, 65 women), were enrolled. Of these, 100 patients (50 CVT and 50 non-CVT) were randomly assigned to the test set, and the other 292 patients comprised the development set. In the test set, the optimal DL algorithm (multisequence multitask deep learning algorithm) achieved an area under the curve of 0.96, with a sensitivity of 96% (48/50) and a specificity of 88% (44/50) on per-patient diagnosis level, as well as a sensitivity of 88% (129/146) and a specificity of 80% (521/654) on per-segment diagnosis level. Compared with 4 radiologists, multisequence multitask deep learning algorithm showed higher sensitivity both on per-patient (all P<0.05) and per-segment diagnosis levels (all P<0.001). CONCLUSIONS: The CVT-detected DL algorithm herein improved diagnostic performance of routine brain magnetic resonance imaging, with high sensitivity and specificity, which provides a promising approach for detecting CVT.

Pharmacokinetic analysis of 6-O-[18F]FEE for PET imaging of EGFR mutation.

6-O-[18F]Fluoroethylerlotinib (6-O-[18F]FEE), with a suitable half-life for commercial distribution, may be a good replacement for [11C]erlotinib to identify epidermal growth factor receptor (EGFR) positive tumors with activating mutations to tyrosine kinase inhibitors therapy. In this study, we explored the fully automated synthesis of 6-O-[18F]FEE and investigated its pharmacokinetics in tumor-bearing mice. 6-O-[18F]FEE with high specific activity (28-100 GBq/µmol) and radiochemistry purity (over 99 %) was obtained by two-step reaction and Radio-HPLC separation in PET-MF-2 V-IT-1 automated synthesizer. PET imaging of 6-O-[18F]FEE in HCC827, A431, and U87 tumor-bearing mice with different EGFR expression and mutation was performed. Uptake and blocking of PET imaging indicated that the probe specifically targeted exon 19 deleted EGFR (the quantitative analysis of tumor-to-mouse ratio for HCC827, HCC827 blocking, U87, A431 was 2.58 ± 0.24, 1.20 ± 0.15, 1.18 ± 0.19, and 1.05 ± 0.13 respectively). Dynamic imaging was used to study the pharmacokinetics of the probe in tumor-bearing mice. Logan plot graphical analysis demonstrated late linearity and a high fitting correlation coefficient (0.998), supporting reversible kinetics. According to the Akaike Information Criterion (AIC) rule, the 2-compartment reversible model was more consistent with the metabolic properties of 6-O-[18F]FEE. The automated radiosynthesis and pharmacokinetic analysis will promote clinically transformation of 6-O-[18F]FEE.

CT-based radiomics signature for differentiating pyelocaliceal upper urinary tract urothelial carcinoma from infiltrative renal cell carcinoma.

Objectives: To develop a CT-based radiomics model and a combined model for preoperatively discriminating infiltrative renal cell carcinoma (RCC) and pyelocaliceal upper urinary tract urothelial carcinoma (UTUC), which invades the renal parenchyma. Materials and methods: Eighty patients (37 pathologically proven infiltrative RCCs and 43 pathologically proven pyelocaliceal UTUCs) were retrospectively enrolled and randomly divided into a training set (n = 56) and a testing set (n = 24) at a ratio of 7:3. Traditional CT imaging characteristics in the portal venous phase were collected by two radiologists (SPH and ZXL, who have 4 and 30 years of experience in abdominal radiology, respectively). Patient demographics and traditional CT imaging characteristics were used to construct the clinical model. The radiomics score was calculated based on the radiomics features extracted from the portal venous CT images and the random forest (RF) algorithm to construct the radiomics model. The combined model was constructed using the radiomics score and significant clinical factors according to the multivariate logistic regression. The diagnostic efficacy of the models was evaluated using receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Results: The RF score based on the eight validated features extracted from the portal venous CT images was used to build the radiomics model. Painless hematuria as an independent risk factor was used to build the clinical model. The combined model was constructed using the RF score and the selected clinical factor. Both the radiomics model and combined model showed higher efficacy in differentiating infiltrative RCC and pyelocaliceal UTUC in the training and testing cohorts with AUC values of 0.95 and 0.90, respectively, for the radiomics model and 0.99 and 0.90, respectively, for the combined model. The decision curves of the combined model as well as the radiomics model indicated an overall net benefit over the clinical model. Both the radiomics model and the combined model achieved a notable reduction in false-positive and false-negativerates, resulting in significantly higher accuracy compared to the visual assessments in both the training and testing cohorts. Conclusion: The radiomics model and combined model had the potential to accurately differentiate infiltrative RCC and pyelocaliceal UTUC, which invades the renal parenchyma, and provide a new potentially non-invasive method to guide surgery strategies.

Displacement sensing in a multimode SNAP microcavity by an artificial neural network.

Benefiting from the coupling between the Surface Nanoscale Axial Photonics (SNAP) microcavity and the waveguide, i.e., influenced by their abrupt field overlap, multiple axial modes in the transmission spectrum form a functional relationship with the coupling position, thus enabling displacement sensing. However, this functional relationship is complex and nonlinear, which is difficult to be fitted using analytical methods. We introduce a back-propagation neural network (BPNN) to model this functional relationship. The numerical results show that the multimode sensing scheme has great potential for practical large-range, high-precision displacement sensing platforms compared with the single-mode sensing based on the whispering gallery mode (WGM) resonators.

Haze optical-model-based nighttime image dehazing by modifying attenuation and atmospheric light.

Scene imaging is often affected by artificial light sources within a hazy environment at night, causing degraded images with low brightness, color distortion, and glow. These problems render the traditional atmospheric scattering optical model obsolete and incompatible. To address this issue, we established an optical imaging model suitable for nighttime dehazing, and an illumination component is incorporated into the attenuation term. We also introduced the near-light source coefficient to redefine the glow. Based on this model, we propose a new nighttime dehazing method. First, the rough atmospheric light is estimated using its low-frequency characteristics. Then, the glow is calculated by the near-light source coefficient. Finally, we remove the haze and illumination to get a clear image. Extensive experiments prove that our method exhibits a better color recovery effect, which effectively improves the visibility and detail. Furthermore, we believe our method outperforms other methods, both qualitatively and quantitatively.

Using carbon dioxide-added microalgal-bacterial granular sludge for carbon-neutral municipal wastewater treatment under outdoor conditions: Performance, granule characteristics and environmental sustainability.

Microalgal-bacterial granular sludge (MBGS) process has a gorgeous prospect for municipal wastewater treatment, but the research on the treatment of complex organic wastewater by MBGS process with CO2 addition under outdoor conditions is not enough. Therefore, this paper evaluated the feasibility of CO2-added MBGS process for complex organic wastewater disposal under natural day-night cycles. The results showed that the addition of CO2 overall improved the removal efficiency of pollutants. Typically, the removal efficiency of total phosphorus increased averagely from 88.5 % to 95.0 % in 12-h day cycle and from 26.2 % to 45.3 % in 12-h night cycle. The addition of CO2 increased the size of MBGS from 1.0 mm to 16.5 mm within 30 days due to extracellular polymeric substances secretion and the dominant filamentous microalgae on granules. The decrease of catalase activity and malondialdehyde content indicated that CO2 reduced oxidative damage and maintained the normal growth of MBGS. Further estimates of the collected gas showed that CO2-added MBGS process could reduce global CO2 emissions by one hundred million tons per year. This study is expected to contribute to the goal of carbon neutrality in the area of wastewater treatment by MBGS process.

Degradable Tumor-Responsive Iron-Doped Phosphate-Based Glass Nanozyme for H2O2 Self-Supplying Cancer Therapy.

Tumor microenvironment (TME)-responsive chemodynamic therapy (CDT) mediated by nanozymes has been extensively studied both experimentally and theoretically, but the low catalytic efficiency due to insufficient H2O2 in the TME and the poor biodegradability of the nanozymes are still main challenges for clinical translation of nanozymes. Herein, we designed a H2O2 self-supplying nanozyme bearing glucose oxidase (GOX) and polyethyleneimine based on a degradable iron-doped phosphate-based glass (FePBG) nanomimic (FePBG@GOX), which can convert endogenous glucose into toxic hydroxyl radicals. The GOX loaded on the nanozyme can effectively consume glucose in tumor cells to produce a large amount of H2O2 to make up for the lack of H2O2 in the TME. Thereafter, enormous hydroxyl radicals, based on a Fenton reaction of FePBG without any exogenous H2O2, are generated to induce severe apoptosis of tumor cells. The nanozyme exhibits enhanced in vitro cytotoxicity in a high-glucose medium than in a low-glucose medium, illustrating sufficient generation of H2O2 by GOX. The excellent in vivo antitumor efficacy is manifested by a high tumor growth inhibition ratio of 94.65% in model mice. Excellent intrinsic biodegradability owing to its phosphate-based glass nature is a remarkable advantage of the prepared FePBG nanozyme over most other reported nanozymes. Big concerns about side effects caused by long-time residence in living organisms are eliminated since it degrades not only in an acid medium but also in a neutral physiological environment. Therefore, this novel strategy of the TME-responsive H2O2 self-supplying nanozyme based on an endogenous cascade catalytic reaction opens up an avenue for designing degradable nanozymes in CDT.

Synthesis and biological evaluation of Al[18F]-NOTA-IPB-PDL1P as a molecular probe for PET imaging of PD-L1 positive tumors.

PD-L1 is widely expressed in a variety of tumors, including NSCLC, melanoma, renal cell carcinoma, gastric cancer, hepatocellular as well as cutaneous and various leukemias, multiple myeloma and so on. Herein, we designed a novel peptide imaging agent (Al[18F]-NOTA-IPB-PDL1P) that specifically targets PD-L1 expressed in tumors. The overall radiochemical yield of Al[18F]-NOTA-IPB-PDL1P from 18F- was 10-15% (corrected radiochemical yield) within 20 min and the radiochemical purity of Al[18F]-NOTA-IPB-PDL1P was > 95% with a molar activity of 44.4-64.8 GBq/µmol. The lipophilicity logP value of Al[18F]-NOTA-IPB-PDL1P at pH 7.4 was -1.768 ±â€¯0.007 (n = 3). In the cellular uptake experiment, both HCT116 and PC3 cells dispalyed high uptake to Al[18F]-NOTA-IPB-PDL1P. The results of biodistribution showed that the uptake of Al[18F]-NOTA-IPB-PDL1P was high in kidneys, gall bladder and lung, and low in muscle and brain. In vivo micro PET studies, both HCT116 and PC3 tumors displayed high uptake for Al[18F]-NOTA-IPB-PDL1P, the tumor/muscle (T/M) radio was 2.93 and 3.57 respectively at 120 min. All the results indicate that Al[18F]-NOTA-IPB-PDL1P may have potential to be a PET imaging agent of tumors with high PD-L1 expression.

LRP1B is a Potential Biomarker for Tumor Immunogenicity and Prognosis of HCC Patients Receiving ICI Treatment.

Background: New predictors of the efficacy of hepatocellular carcinoma (HCC) immunotherapy are needed. The ability of a single gene mutation to predict the therapeutic effect of immune checkpoint inhibitors (ICI) in HCC remains unknown. Methods: The most frequently mutated genes in HCC were analyzed using the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Mutant genes that correlated with the tumor mutational burden (TMB) and prognosis were obtained. The mutation pattern and immunological function of one of the most frequently mutated genes, LRP1B, were determined. A pan-tumor analysis of LRP1B expression, association with cancer prognosis, and immunological role was also explored. A retrospective clinical study was conducted using 102 HCC patients who received ICI treatment to further verify whether gene mutations can predict the effectiveness of immunotherapy and prognosis of HCC. Results: LRP1B is among the most frequently mutated genes in HCC cohorts in TCGA and ICGC datasets. TCGA data showed that the LRP1B mutation activated immune signaling pathways and promoted mast cell activation. Patients with LRP1B mutations had significantly higher TMB than those with wild-type LRP1B. LRP1B expression correlated with the cancer-immunity cycle and immune cell infiltration. High LRP1B expression was also associated with poor survival among HCC patients. Results from the clinical study showed that HCC patients in the LRP1B mutation group had a poor response to ICI and worse prognosis than the wild-type group. The LRP1B mutation group had significantly higher TMB and mast cell infiltration in tumor tissues. Conclusion: This study is the first to report that a single gene LRP1B mutation is associated with a poor clinical response to ICI treatment and negative outcomes in HCC patients. HighLRP1B expression correlated with tumor immunity and HCC prognosis.

Efficacy and safety of lenvatinib monotreatment and lenvatinib-based combination therapy for patients with unresectable hepatocellular carcinoma: a retrospective, real-world study in China.

BACKGROUND: Lenvatinib and lenvatinib-based combination treatments are widely used in patients with unresectable hepatocellular carcinoma (uHCC) in clinical practice, but their curative effect and safety need further study in the real world. METHODS: This was a retrospective study involving patients with uHCC receiving lenvatinib monotherapy and lenvatinib-based combination treatment between Nov, 2018 and Sep, 2020 in Nanfang Hospital. Efficacy was evaluated with objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), time to tumor progression (TTP), and overall survival (OS). Treatment-related adverse events (TRAEs) were recorded and graded. Efficacy and safety of monotherapy and combination therapy were compared. Stratified analysis was performed according to systemic line of treatment and medication regimen for combination therapy. RESULTS: For lenvatinib monotherapy (n = 39), OS and PFS were 80 weeks and 24.3 weeks, respectively. For combination treatment (n = 72), median OS and PFS were 99 weeks and 45.6 weeks, respectively. OS, PFS, and TTP for patients in the combination treatment cohort were significantly longer compared to those of patients in the monotreatment cohort (OS: P = 0.04, PFS: P = 0.003; TTP, P = 0.005). The incidence of TRAEs could be controlled both in the monotherapy cohort and the combination treatment cohort. In the monotherapy cohort, OS and PFS were significantly decreased in the second-line treatment group compared with the first-line treatment group, while no differences were observed in the combination cohort. The efficacy of triple therapy (lenvatinib plus PD-1 antibody plus TACE or HAIF) was similar to lenvatinib plus PD-1 antibody or lenvatinib plus TACE or HAIF. CONCLUSIONS: Our real-world study showed that lenvatinib monotherapy and lenvatinib-based combination therapy were well tolerated, with encouraging efficacies in patients with uHCC. Lenvatinib-based combination therapy showed a better curative effect compared with lenvatinib single-agent therapy. In patients who have failed first-line TKI treatment, lenvatinib-based combination therapy may be a better choice than lenvatinib single-agent therapy. Lenvatinib-based triple therapy may not have an advantage over dual therapy.

Clinical Factors and Quantitative CT Parameters Associated With ICU Admission in Patients of COVID-19 Pneumonia: A Multicenter Study.

The clinical spectrum of COVID-19 pneumonia is varied. Thus, it is important to identify risk factors at an early stage for predicting deterioration that require transferring the patients to ICU. A retrospective multicenter study was conducted on COVID-19 patients admitted to designated hospitals in China from Jan 17, 2020, to Feb 17, 2020. Clinical presentation, laboratory data, and quantitative CT parameters were also collected. The result showed that increasing risks of ICU admission were associated with age > 60 years (odds ratio [OR], 12.72; 95% confidence interval [CI], 2.42-24.61; P = 0.032), coexisting conditions (OR, 5.55; 95% CI, 1.59-19.38; P = 0.007) and CT derived total opacity percentage (TOP) (OR, 8.0; 95% CI, 1.45-39.29; P = 0.016). In conclusion, older age, coexisting conditions, larger TOP at the time of hospital admission are associated with ICU admission in patients with COVID-19 pneumonia. Early monitoring the progression of the disease and implementing appropriate therapies are warranted.