BMAL2 promotes eCIRP-induced macrophage endotoxin tolerance.

Background: The disruption of the circadian clock is associated with inflammatory and immunological disorders. BMAL2, a critical circadian protein, forms a dimer with CLOCK, activating transcription. Extracellular cold-inducible RNA-binding protein (eCIRP), released during sepsis, can induce macrophage endotoxin tolerance. We hypothesized that eCIRP induces BMAL2 expression and promotes macrophage endotoxin tolerance through triggering receptor expressed on myeloid cells-1 (TREM-1). Methods: C57BL/6 wild-type (WT) male mice were subjected to sepsis by cecal ligation and puncture (CLP). Serum levels of eCIRP 20 h post-CLP were assessed by ELISA. Peritoneal macrophages (PerM) were treated with recombinant mouse (rm) CIRP (eCIRP) at various doses for 24 h. The cells were then stimulated with LPS for 5 h. The levels of TNF-α and IL-6 in the culture supernatants were assessed by ELISA. PerM were treated with eCIRP for 24 h, and the expression of PD-L1, IL-10, STAT3, TREM-1 and circadian genes such as BMAL2, CRY1, and PER2 was assessed by qPCR. Effect of TREM-1 on eCIRP-induced PerM endotoxin tolerance and PD-L1, IL-10, and STAT3 expression was determined by qPCR using PerM from TREM-1-/- mice. Circadian gene expression profiles in eCIRP-treated macrophages were determined by PCR array and confirmed by qPCR. Induction of BMAL2 activation in bone marrow-derived macrophages was performed by transfection of BMAL2 CRISPR activation plasmid. The interaction of BMAL2 in the PD-L1 promoter was determined by computational modeling and confirmed by the BIAcore assay. Results: Serum levels of eCIRP were increased in septic mice compared to sham mice. Macrophages pre-treated with eCIRP exhibited reduced TNFα and IL-6 release upon LPS challenge, indicating macrophage endotoxin tolerance. Additionally, eCIRP increased the expression of PD-L1, IL-10, and STAT3, markers of immune tolerance. Interestingly, TREM-1 deficiency reversed eCIRP-induced macrophage endotoxin tolerance and significantly decreased PD-L1, IL-10, and STAT3 expression. PCR array screening of circadian clock genes in peritoneal macrophages treated with eCIRP revealed the elevated expression of BMAL2, CRY1, and PER2. In eCIRP-treated macrophages, TREM-1 deficiency prevented the upregulation of these circadian genes. In macrophages, inducible BMAL2 expression correlated with increased PD-L1 expression. In septic human patients, blood monocytes exhibited increased expression of BMAL2 and PD-L1 in comparison to healthy subjects. Computational modeling and BIAcore assay identified a putative binding region of BMAL2 in the PD-L1 promoter, suggesting BMAL2 positively regulates PD-L1 expression in macrophages. Conclusion: eCIRP upregulates BMAL2 expression via TREM-1, leading to macrophage endotoxin tolerance in sepsis. Targeting eCIRP to maintain circadian rhythm may correct endotoxin tolerance and enhance host resistance to bacterial infection.

Harnessing eCIRP by PS-OMe miR130: A promising shield against hemorrhage-induced lung injury.

INTRODUCTION: Hemorrhagic shock (HS) poses a life-threatening condition with the lungs being one of the most susceptible organs to its deleterious effects. Extracellular cold-inducible RNA binding protein (eCIRP) has emerged as a pivotal mediator of inflammation, and its release has been observed as a case of HS-induced tissue injury. Previous studies unveiled a promising engineered microRNA, designated PS-OMe miR130, which inhibits eCIRP, thereby safeguarding vital organs. In this study, we hypothesized that PS-OMe miR130 serves as a protective shield against HS-induced lung injury by curtailing the overzealous inflammatory immune response. METHODS: Hemorrhagic shock was induced in male C57BL6 mice by withdrawing blood via a femoral artery cannula to a mean arterial pressure of 30 mm Hg for 90 min. The mice were resuscitated with twice the shed blood volume with Ringer's Lactate solution. They were then treated intravenously with either PBS (vehicle) or 62.5 nmol PS-OMe miR130. At 4 h later, blood and lungs were harvested. RESULTS: Following PS-OMe miR130 treatment in HS mice, a substantial decrease was observed in serum injury markers including aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH), and blood urea nitrogen (BUN). Serum IL-6 exhibited a similar reduction. In lung tissues, PS-OMe miR130 led to a significant decrease in the mRNA expressions of pro-inflammatory cytokines (IL-6, IL-1ß, and TNF-α), chemokines (KC and MIP-2), and an endothelial injury marker, E-selectin. PS-OMe miR130 also produced substantial inhibition of lung MPO activity and resulted in a marked reduction in lung injury as evidenced by histological evaluation. This was further confirmed by the observation that PS-OMe miR130 significantly reduced the presence of Ly6G-positive neutrophils and TUNEL-positive apoptotic cells. CONCLUSION: PS-OMe miR130 emerges as a potent safeguard against HS-induced lung injury by effectively inhibiting proinflammation and injuries, offering a promising therapeutic strategy in such critical clinical condition.

GCN-Based Risk Prediction for Necrosis Slide of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world which ranks fourth in cancer deaths. Primary pathological necrosis is an effective prognostic indicator for hepatocellular carcinoma. We propose a GCN-based approach that mimics the pathologist's perspective for global assessment of necrosis tissue distribution to analyze patient survival. Specifically, we introduced a graph convolutional neural network to construct a spatial map with necrotic tissue and tumor tissue as graph nodes, aiming to mine the contextual information between necrotic tissue in pathological sections. We used 1381 slides from 303 patients from the First Affiliated Hospital of Zhejiang University School to train the model and used TCGA-LIHC for external validation. The C-index of our method outperforms the baseline by about 4.45%, which proves that the information about the spatial distribution of necrosis learned by GCN is meaningful for guiding patient prognosis.

A NOVEL OLIGONUCLEOTIDE MRNA MIMIC ATTENUATES HEMORRHAGE-INDUCED ACUTE LUNG INJURY.

ABSTRACT: Hemorrhagic shock (HS) is accompanied by a pronounced activation of the inflammatory response in which acute lung injury (ALI) is one of the most frequent consequences. Among the pivotal orchestrators of this inflammatory cascade, extracellular cold-inducible RNA-binding protein (eCIRP) emerges as a noteworthy focal point, rendering it as a promising target for the management of inflammation and tissue injury. Recently, we have reported that oligonucleotide poly(A) mRNA mimic termed A 12 selectively binds to the RNA binding region of eCIRP and inhibits eCIRP binding to its receptor TLR4. Furthermore, in vivo administration of eCIRP induces lung injury in healthy mice and that mouse deficient in CIRP showed protection from inflammation-associated lung injury. We hypothesize that A 12 inhibits systemic inflammation and ALI in HS. To test the impacts of A 12 on systemic and lung inflammation, extent of inflammatory cellular infiltration and resultant lung damage were evaluated in a mouse model of HS. Male mice were subjected to controlled hemorrhage with a mean arterial pressure of 30 mm Hg for 90 min and then resuscitated with Ringer's lactate solution containing phosphate-buffered saline (vehicle) or A 12 at a dose of 4 nmol/g body weight (treatment). The infusion volume was twice that of the shed blood. At 4 h after resuscitation, mice were euthanized, and blood and lung tissues were harvested. Blood and tissue markers of inflammation and injury were evaluated. Serum markers of injury (lactate dehydrogenase, alanine transaminase, and blood urea nitrogen) and inflammation (TNF-α, IL-6) were increased after HS and A 12 treatment significantly decreased their levels. A 12 treatment also decreased lung levels of TNF-α, MIP-2, and KC mRNA expressions. Lung histological injury score, neutrophil infiltration (Ly6G staining and myeloperoxidase activity), and lung apoptosis were significantly attenuated after A 12 treatment. Our study suggests that the capacity of A 12 in attenuating HS-induced ALI and may provide novel perspectives in developing efficacious pharmaceutics for improving hemorrhage prognosis.

MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggregation for Lesion Detection in Multi-Phase CT Images.

Object detection using convolutional neural networks (CNNs) has achieved high performance and achieved state-of-the-art results with natural images. Compared to natural images, medical images present several challenges for lesion detection. First, the sizes of lesions vary tremendously, from several millimeters to several centimeters. Scale variations significantly affect lesion detection accuracy, especially for the detection of small lesions. Moreover, the effective extraction of temporal and spatial features from multi-phase CT images is also an important issue. In this paper, we propose a group-based deep layer aggregation method with multiphase attention for liver lesion detection in multi-phase CT images. The method, which is called MSPA-DLA++, is a backbone feature extraction network for anchor-free liver lesion detection in multi-phase CT images that addresses scale variations and extracts hidden features from such images. The effectiveness of the proposed method is demonstrated on public datasets (LiTS2017) and our private multiphase dataset. The results of the experiments show that MSPA-DLA++ can improve upon the performance of state-of-the-art networks by approximately 3.7%.

Dual-Attention Model Fusing CNN and Transformer for Pancreas Segmentation.

Pancreatic cancer is a highly malignant cancer of the digestive tract and is rapidly progressing and spreading clinically. Automatic and accurate pancreatic tissue segmentation in abdominal CT images is essential for the early diagnosis of pancreatic-related diseases. It is challenging that the pancreas is small in size and complex in morphology. To address this problem, we propose a dual-attention model fusing CNN and Transformer to effectively activate pancreas-related features expression. The CNN structure weights the importance of pancreas-related features at the channel level and weakens the background information. Transformer feature aggregation module constructs spatial correlations among long-distance pixels from a global perspective. This study is validated on the NIH-TCIA dataset and achieved a mean Dice Similarity Coefficient of 85.82%, which is outperforming than the state-of-the-art methods. The visualization of surface distance also demonstrates the effective segmentation of pancreas boundary details by the proposed model.

CMIR: A Unified Cross-Modality Framework for Preoperative Accurate Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

Microvascular invasion of HCC is an important factor affecting postoperative recurrence and prognosis of patients. Preoperative diagnosis of MVI is greatly significant to improve the prognosis of HCC. Currently, the diagnosis of MVI is mainly based on the histopathological examination after surgery, which is difficult to meet the requirement of preoperative diagnosis. Also, the sensitivity, specificity and accuracy of MVI diagnosis based on a single imaging feature are low. In this paper, a robust, high-precision cross-modality unified framework for clinical diagnosis is proposed for the prediction of microvascular invasion of hepatocellular carcinoma. It can effectively extract, fuse and locate multi-phase MR Images and clinical data, enrich the semantic context, and comprehensively improve the prediction indicators in different hospitals. The state-of-the-art performance of the approach was validated on a dataset of HCC patients with confirmed pathological types. Moreover, CMIR provides a possible solution for related multimodality tasks in the medical field.

Position Prior Attention Network for Pancreas Tumor Segmentation.

Segmentation of pancreatic tumors on CT images is essential for the diagnosis and treatment of pancreatic cancer. However, low contrast between the pancreas and the tumor, as well as variable tumor shape and position, makes segmentation challenging. To solve the problem, we propose a Position Prior Attention Network (PPANet) with a pseudo segmentation generation module (PSGM) and a position prior attention module (PPAM). PSGM and PPAM maps pancreatic and tumor pseudo segmentation to latent space to generate position prior attention map and supervises location classification. The proposed method is evaluated on pancreatic patient data collected from local hospital and the experimental results demonstrate that our method can significantly improve the tumor segmentation results by introducing the position information in the training phase.

Whole-Liver Based Deep Learning for Preoperatively Predicting Overall Survival in Patients with Hepatocellular Carcinoma.

Survival prediction is crucial for treatment decision making in hepatocellular carcinoma (HCC). We aimed to build a fully automated artificial intelligence system (FAIS) that mines whole-liver information to predict overall survival of HCC. We included 215 patients with preoperative contrast-enhance CT imaging and received curative resection from a hospital in China. The cohort was randomly split into developing and testing subcohorts. The FAIS was constructed with convolutional layers and full-connected layers. Cox regression loss was used for training. Models based on clinical and/or tumor-based radiomics features were built for comparison. The FAIS achieved C-indices of 0.81 and 0.72 for the developing and testing sets, outperforming all the other three models. In conclusion, our study suggest that more important information could be mined from whole liver instead of only the tumor. Our whole-liver based FAIS provides a non-invasive and efficient overall survival prediction tool for HCC before the surgery.

Mining Whole-liver Information with Deep Learning for Preoperatively Predicting HCC Recurrence-free Survival.

Hepatocellular carcinoma (HCC) is globally a leading cause of cancer death. Non-invasive pre-operative prediction of HCC recurrence-free survival (RFS) after resection is essential but remains challenging. Previous models based on medical imaging focus only on tumor area while neglecting the whole liver condition. In fact, HCC patients usually suffer from chronic liver diseases which also hamper the patient survival. This work aims to develop a novel convolutional neural network (CNN) to mine whole-liver information from contrast-enhanced computed tomography (CECT) to predict RFS after hepatic resection in HCC. Our proposed RFSNet takes liver regions from CECT as input, and outputs a risk score for each patient. Cox proportional-hazards loss was applied for model training. A total of 215 patients with primary HCC and treated with hepatic resection were included for analysis. Patients were randomly split into developing subcohort and testing subcohort by 4:1. The developing subcohort was further split into the training subcohort and validation subcohort for model training. Baseline models were built with tumor region, radiomics features and/or clinical features the same as previous tumor-based approaches. Results showed that RFSNet achieved the best performance with concordance-indinces (CIs) of 0.88 and 0.65 for the developing and testing subcohorts, respectively. Adding clinical features did not improve RFSNet. Our findings suggest that the proposed RFSNet based on whole liver is able to extract more valuable information concerning RFS prognosis compared to features from only tumor and the clinical indicators.

MVI-Wise GAN: Synthetic MRI to Improve Microvascular Invasion Prediction in Hepatocellular Carcinoma.

MRI is crucial for the diagnosis of HCC patients, especially when combined with CT images for MVI prediction, richer complementary information can be learned. Many studies have shown that whether hepatocellular carcinoma is accompanied by vascular invasion can be evidenced by imaging examinations such as CT or MR, so they can be used as a multimodal joint prediction to improve the prediction accuracy of MVI. However, it is high-risk, time-consuming and expensive in current clinical diagnosis due to the use of gadolinium-based contrast agent (CA) injection. If MRI could be synthesized without CA injection, there is no doubt that it would greatly optimize the diagnosis. Based on this, this paper proposes a high-quality image synthesis network, MVI-Wise GAN, that can be used to improve the prediction of microvascular invasion in HCC. It starts from the underlying imaging perspective, introduces K-space and feature-level constraints, and combines three related networks (an attention-aware generator, a convolutional neural network-based discriminator and a region-based convolutional neural network detector) Together, precise tumor region detection by synthetic tumor-specific MRI. Accurate MRI synthesis is achieved through backpropagation, the feature representation and context learning of HCC MVI are enhanced, and the performance of loss convergence is improved through residual learning. The model was tested on a dataset of 256 subjects from Run Run Shaw Hospital of Zhejiang University. Experimental results and quantitative evaluation show that MVI-Wise GAN achieves high-quality MRI synthesis with a tumor detection accuracy of 92.3%, which is helpful for the clinical diagnosis of liver tumor MVI.

Siamese Delta Network with Multimodality Fusion for Systemic Treatment Response Prediction in Pancreatic Cancer.

Systemic treatment is a main way for pancreas cancer patients that are ineligible for surgery. A subgroup of patients showed good response to systemic treatment and the rest received limited benefits. CT images provide a non-invasive way to assess the treatment response. Alternative non-image methods include radiology analysis, tumor marker analysis and combination analysis. To combine the image and non-image data, we propose the Siamese Delta Network with Multimodality Fusion (SDN-MF) to predict systemic treatment response in an end-to-end way. First, a Siamese Delta Network (SDN) is designed to process pre-treatment and pre-surgery CT images and get the image feature changes to predict response. Then, patients' characteristics from EMR and alternative analysis results forms non-image data, which is incorporated into SDN with a multimodality fusion (MF) module. The proposed SDN-MF is evaluated on a private dataset and achieves average AUC value of 0.883 with five cross-validation. Comparison among image-only, non-image-only, and fusion models verifies the superior of multimodality model in predicting systemic treatment response of pancreas cancer patients.

Vision-Guided Attention-Enhanced Network for Predicting Microvascular Invasion in Hepatocellular Carcinoma.

As the most common malignant tumor worldwide, hepatocellular carcinoma (HCC) has a high rate of death and recurrence, and microvascular invasion (MVI) is considered to be an independent risk factor affecting its early recurrence and poor survival rate. Accurate preoperative prediction of MVI is of great significance for the formulation of individualized treatment plans and long-term prognosis assessment for HCC patients. However, as the mechanism of MVI is still unclear, existing studies use deep learning methods to directly train CT or MR images, with limited predictive performance and lack of explanation. We map the pathological "7-point" baseline sampling method used to confirm the diagnosis of MVI onto MR images, propose a vision-guided attention-enhanced network to improve the prediction performance of MVI, and validate the prediction on the corresponding pathological images reliability of the results. Specifically, we design a learnable online class activation map (CAM) to guide the network to focus on high-incidence regions of MVI guided by an extended tumor mask. Further, an attention-enhanced module is proposed to force the network to learn image regions that can explain the MVI results. The generated attention maps capture long-distance dependencies and can be used as spatial priors for MVI to promote the learning of vision-guided module. The experimental results on the constructed multi-center dataset show that the proposed algorithm achieves the state-of-the-art compared to other models.

Contrastive Learning for Preoperative Early Recurrence Prediction of Hepatocellular Carcinoma with Liver CT Image and Tumor Mask.

High early recurrence (ER) rate is the main factor leading to the poor outcome of patients with hepatocellular carcinoma (HCC). Accurate preoperative prediction of ER is thus highly desired for HCC treatment. Many radiomics solutions have been proposed for the preoperative prediction of HCC using CT images based on machine learning and deep learning methods. Nevertheless, most current radiomics approaches extract features only from segmented tumor regions that neglect the liver tissue information which is useful for HCC prognosis. In this work, we propose a deep prediction network based on CT images of full liver combined with tumor mask that provides tumor location information for better feature extraction to predict the ER of HCC. While, due to the complex imaging characteristics of HCC, the image-based ER prediction methods suffer from limited capability. Therefore, on the one hand, we propose to employ supervised contrastive loss to jointly train the deep prediction model with cross-entropy loss to alleviate the problem of intra-class variation and inter-class similarity of HCC. On the other hand, we incorporate the clinical data to further improve the prediction ability of the model. Experiments are extensively conducted to verify the effectiveness of our proposed deep prediction model and the contribution of liver tissue for prognosis assessment of HCC.

A joint method of coherence factor and nonlinear beamforming for synthetic aperture imaging with a ring array.

Ultrasound computed tomography (USCT) with a ring array is an emerging diagnostic method for breast cancer. In the literature, synthetic aperture (SA) imaging has employed the delay-and-sum (DAS) beamforming technique for ring-array USCT to obtain isotropic resolution reflection images. However, the images obtained by the conventional DAS beamformer suffer from off-axis clutter and low resolution due to inhomogeneity of the medium and phase distortion. To address these issues, researchers have developed adaptive beamforming methods, such as coherence factor (CF) and convolutional beamforming algorithm (COBA), that improve image quality. In this study, we propose a joint method that combines CF with short-lag COBA (SLCOBA). First, we estimate the average sound speed using CF to address tissue inhomogeneity. Based on the corrected sound speed map, SLCOBA effectively suppresses side lobes and enhances image quality. Numerical results show that the proposed method reduces clutter and noise, improving resolution performance. These findings suggest that the proposed method may be a practical option for breast imaging in inhomogeneous mediums in the future.

Improving an Electronic Health Record-Based Clinical Prediction Model Under Label Deficiency: Network-Based Generative Adversarial Semisupervised Approach.

BACKGROUND: Observational biomedical studies facilitate a new strategy for large-scale electronic health record (EHR) utilization to support precision medicine. However, data label inaccessibility is an increasingly important issue in clinical prediction, despite the use of synthetic and semisupervised learning from data. Little research has aimed to uncover the underlying graphical structure of EHRs. OBJECTIVE: A network-based generative adversarial semisupervised method is proposed. The objective is to train clinical prediction models on label-deficient EHRs to achieve comparable learning performance to supervised methods. METHODS: Three public data sets and one colorectal cancer data set gathered from the Second Affiliated Hospital of Zhejiang University were selected as benchmarks. The proposed models were trained on 5% to 25% labeled data and evaluated on classification metrics against conventional semisupervised and supervised methods. The data quality, model security, and memory scalability were also evaluated. RESULTS: The proposed method for semisupervised classification outperforms related semisupervised methods under the same setup, with the average area under the receiver operating characteristics curve (AUC) reaching 0.945, 0.673, 0.611, and 0.588 for the four data sets, respectively, followed by graph-based semisupervised learning (0.450, 0.454, 0.425, and 0.5676, respectively) and label propagation (0.475,0.344, 0.440, and 0.477, respectively). The average classification AUCs with 10% labeled data were 0.929, 0.719, 0.652, and 0.650, respectively, comparable to that of the supervised learning methods logistic regression (0.601, 0.670, 0.731, and 0.710, respectively), support vector machines (0.733, 0.720, 0.720, and 0.721, respectively), and random forests (0.982, 0.750, 0.758, and 0.740, respectively). The concerns regarding the secondary use of data and data security are alleviated by realistic data synthesis and robust privacy preservation. CONCLUSIONS: Training clinical prediction models on label-deficient EHRs is indispensable in data-driven research. The proposed method has great potential to exploit the intrinsic structure of EHRs and achieve comparable learning performance to supervised methods.

Micro-Quartz Crystal Tuning Fork-Based Photodetector Array for Trace Gas Detection.

In this paper, a micro-quartz crystal tuning fork (M-QCTF) was first demonstrated for developing a low-cost, highly sensitive quartz tuning fork photodetector array for spectroscopic applications. A gas sensing system based on the M-QCTF photodetector and highly sensitive wavelength modulation spectroscopy was developed. Typically, an atmospheric greenhouse gas methane (CH4) molecule was selected as the target analyte for evaluating the M-QCTF and standard commercial QCTF detectivity. The results indicate that the M-QCTF photodetector exhibits ∼3.3 times sensitivity enhancement compared to the standard commercial QCTF. The long-term stability was evaluated by using the Allan deviation analysis method; a minimum detection limit of 1.2 ppm was achieved with an optimal integration time of 85 s, and the corresponding normalized noise equivalent absorption coefficient was calculated to be 4.45 × 10-10 cm-1 W/√Hz. Finally, a two-M-QCTF array detection scheme was experimentally demonstrated, and a signal-to-noise ratio enhancement factor of more than 1.7 times compared to that achieved using a single M-QCTF photodetector was realized, which proves a great potential for developing ultra-sensitive quartz tuning fork photodetector arrays for various applications.

Tumor micronecrosis predicts poor prognosis of patients with hepatocellular carcinoma after liver transplantation.

BACKGROUND: Tumor micronecrosis is a histopathological feature predicting poor prognosis in patients with hepatocellular carcinoma (HCC) who underwent liver resection. However, the role of tumor micronecrosis in liver transplantation remains unclear. METHODS: We retrospectively reviewed patients with HCC who underwent liver transplantation between January 2015 and December 2021 at our center. We then classified them into micronecrosis(-) and micronecrosis(+) groups and compared their recurrence-free survival (RFS) and overall survival (OS). We identified independent prognostic factors using Cox regression analysis and calculated the area under the receiver operating characteristic curve (AUC) to evaluate the predictive value of RFS for patients with HCC after liver transplantation. RESULTS: A total of 370 cases with evaluable histological sections were included. Patients of the micronecrosis(+) group had a significantly shorter RFS than those of the micronecrosis(-) group (P = 0.037). Shorter RFS and OS were observed in micronecrosis(+) patients without bridging treatments before liver transplantation (P = 0.002 and P = 0.007), while no differences were detected in those with preoperative antitumor therapies that could cause iatrogenic tumor necrosis. Tumor micronecrosis improved the AUC of Milan criteria (0.77-0.79), the model for end-stage liver disease score (0.70-0.76), and serum alpha-fetoprotein (0.63-0.71) for the prediction of prognosis after liver transplantation. CONCLUSION: Patients with HCC with tumor micronecrosis suffer from a worse prognosis than those without this feature. Tumor micronecrosis can help predict RFS after liver transplantation. Therefore, patients with HCC with tumor micronecrosis should be treated with adjuvant therapy and closely followed after liver transplantation. CLINICAL TRIALS REGISTRATION: Not Applicable.

Histological tumor micronecrosis in resected specimens after R0 hepatectomy for hepatocellular carcinomas is a factor in determining adjuvant TACE: A retrospective propensity score-matched study.

BACKGROUND: Tumor micronecrosis is a less investigated pathological feature of hepatocellular carcinoma (HCC). This study was aimed at evaluating the value of micronecrosis for guiding adjuvant transcatheter arterial chemoembolization (TACE) in HCC management. METHODS: We retrospectively reviewed the data of patients with HCC who underwent curative liver resection in our center from 2014 to 2018. The patients were divided into micronecrosis (+) and micronecrosis (-) groups. In each group, overall survival (OS) and disease-free survival (DFS) were compared between patients who underwent adjuvant TACE and those who did not. Propensity score matching (PSM) was conducted at a ratio of 1:1 to control selection bias. Univariate and multivariate analyses were performed to determine independent prognostic factors. Mass cytometry was applied to compare the immunological status of HCCs between the two groups. RESULTS: A total of 897 patients were included, with 417 and 480 patients in the micronecrosis (+) and micronecrosis (-) groups, respectively. No significant difference was detected in baseline parameters after PSM. In the micronecrosis (+) group, patients who underwent adjuvant TACE had significant longer OS than did those who did not (P = 0.004). However, patients in the micronecrosis (-) group did not benefit from adjuvant TACE. Although adjuvant TACE prolonged the DFS of patients with severe micronecrosis (P = 0.034), it may adversely affect the DFS of patients without micronecrosis (P = 0.131). Multivariate analysis showed that TACE was an independent prognostic factor for patients with micronecrosis but not for those without micronecrosis. The abundance of exhausted and regulatory T cells was significantly higher in patients with micronecrosis. CONCLUSIONS: For HCC patients with micronecrosis, adjuvant TACE after curative resection could improve the prognosis, while its survival benefits were limited in HCC patients without micronecrosis. TACE should be selectively performed in patients with micronecrosis, especially those with an Nscore = 2. The immunosuppressive status of HCC patients with micronecrosis may explain the effectiveness of adjuvant TACE in such scinario.

Comprehensive Clinical Analysis of Gallbladder Neuroendocrine Neoplasms: A Large-Volume Multicenter Study During One Decade.

BACKGROUND: This study aimed to comprehensively investigate the clinicopathologic characteristics and therapeutic situations of gallbladder neuroendocrine neoplasms (GB-NENs) in the real world via a multicenter, large-scale cohort study. METHODS: The study searched for patients in 143 hospitals in China and enrolled 154 patients with GB-NENs diagnosed in 40 hospitals between 2004 and 2021. Clinicopathologic characteristics and therapeutic approaches were analyzed retrospectively. RESULTS: The median age at the initial diagnosis of the patients with GB-NENs was 63 years (range 33-83 years), and 61.7% of the patients were women. Tumor-node-metastasis staging classified 92 patients as stage 3 or above. Based on the 2019 World Health Organization classification, 96 cases (62.3%) were confirmed pathologically as poorly differentiated neuroendocrine carcinomas, 13 cases (8.4%) as well-differentiated neuroendocrine tumors, and 45 cases as mixed neuroendocrine-non-neuroendocrine neoplasms. The liver was the most frequent metastatic site. Immunohistochemistry showed that synaptophysin was most frequently positive (80.4%), followed by chromogranin A (61.7%), and CD56 (58.4%). Computed tomography and magnetic resonance imaging showed more common clear boundaries (25/39 cases) and invasive growth features (27 cases). None of these cases had an accurate diagnosis before surgery, with a misdiagnosis rate of 100%. Surgical resection is the main treatment, and platinum-based chemotherapeutic regimens were preferred as adjuvant therapies for patients with GB-NENs. The available survival data for 74 patients showed an overall survival rate of 59% at 1 year, 33% at 3 years, and 29% at 5 years. No significant difference was found between the patients treated with and those treated without adjuvant chemotherapy. CONCLUSIONS: Gallbladder neuroendocrine neoplasms have high malignancy and a poor prognosis. Importantly, this large-scale cohort study significantly improves our understanding of GB-NENs and will benefit the exploration of its mechanism and treatment modes. Further investigation is necessary to explore the management of this disease.