Inhibition of Hmbox1 Promotes Cardiomyocyte Survival and Glucose Metabolism Through Gck Activation in Ischemia/Reperfusion Injury.

BACKGROUND: Exercise-induced physiological cardiac growth regulators may protect the heart from ischemia/reperfusion (I/R) injury. Homeobox-containing 1 (Hmbox1), a homeobox family member, has been identified as a putative transcriptional repressor and is downregulated in the exercised heart. However, its roles in exercise-induced physiological cardiac growth and its potential protective effects against cardiac I/R injury remain largely unexplored. METHODS: We studied the function of Hmbox1 in exercise-induced physiological cardiac growth in mice after 4 weeks of swimming exercise. Hmbox1 expression was then evaluated in human heart samples from deceased patients with myocardial infarction and in the animal cardiac I/R injury model. Its role in cardiac I/R injury was examined in mice with adeno-associated virus 9 (AAV9) vector-mediated Hmbox1 knockdown and in those with cardiac myocyte-specific Hmbox1 ablation. We performed RNA sequencing, promoter prediction, and binding assays and identified glucokinase (Gck) as a downstream effector of Hmbox1. The effects of Hmbox1 together with Gck were examined in cardiomyocytes to evaluate their cell size, proliferation, apoptosis, mitochondrial respiration, and glycolysis. The function of upstream regulator of Hmbox1, ETS1, was investigated through ETS1 overexpression in cardiac I/R mice in vivo. RESULTS: We demonstrated that Hmbox1 downregulation was required for exercise-induced physiological cardiac growth. Inhibition of Hmbox1 increased cardiomyocyte size in isolated neonatal rat cardiomyocytes and human embryonic stem cell-derived cardiomyocytes but did not affect cardiomyocyte proliferation. Under pathological conditions, Hmbox1 was upregulated in both human and animal postinfarct cardiac tissues. Furthermore, both cardiac myocyte-specific Hmbox1 knockout and AAV9-mediated Hmbox1 knockdown protected against cardiac I/R injury and heart failure. Therapeutic effects were observed when sh-Hmbox1 AAV9 was administered after I/R injury. Inhibition of Hmbox1 activated the Akt/mTOR/P70S6K pathway and transcriptionally upregulated Gck, leading to reduced apoptosis and improved mitochondrial respiration and glycolysis in cardiomyocytes. ETS1 functioned as an upstream negative regulator of Hmbox1 transcription, and its overexpression was protective against cardiac I/R injury. CONCLUSIONS: Our studies unravel a new role for the transcriptional repressor Hmbox1 in exercise-induced physiological cardiac growth. They also highlight the therapeutic potential of targeting Hmbox1 to improve myocardial survival and glucose metabolism after I/R injury.

Integrative analysis of genetics, epigenetics and RNA expression data reveal three susceptibility loci for smoking behavior in Chinese Han population.

Despite numerous studies demonstrate that genetics and epigenetics factors play important roles on smoking behavior, our understanding of their functional relevance and coordinated regulation remains largely unknown. Here we present a multiomics study on smoking behavior for Chinese smoker population with the goal of not only identifying smoking-associated functional variants but also deciphering the pathogenesis and mechanism underlying smoking behavior in this under-studied ethnic population. After whole-genome sequencing analysis of 1329 Chinese Han male samples in discovery phase and OpenArray analysis of 3744 samples in replication phase, we discovered that three novel variants located near FOXP1 (rs7635815), and between DGCR6 and PRODH (rs796774020), and in ARVCF (rs148582811) were significantly associated with smoking behavior. Subsequently cis-mQTL and cis-eQTL analysis indicated that these variants correlated significantly with the differential methylation regions (DMRs) or differential expressed genes (DEGs) located in the regions where these variants present. Finally, our in silico multiomics analysis revealed several hub genes, like DRD2, PTPRD, FOXP1, COMT, CTNNAP2, to be synergistic regulated each other in the etiology of smoking.

A real-time augmented reality system integrated with artificial intelligence for skin tumor surgery: experimental study and case series.

BACKGROUND: Skin tumors affect many people worldwide, and surgery is the first treatment choice. Achieving precise preoperative planning and navigation of intraoperative sampling remains a problem and is excessively reliant on the experience of surgeons, especially for Mohs surgery for malignant tumors. MATERIALS AND METHODS: To achieve precise preoperative planning and navigation of intraoperative sampling, we developed a real-time augmented reality (AR) surgical system integrated with artificial intelligence (AI) to enhance three functions: AI-assisted tumor boundary segmentation, surgical margin design, and navigation in intraoperative tissue sampling. Non-randomized controlled trials were conducted on manikin, tumor-simulated rabbits, and human volunteers in Hunan Engineering Research Center of Skin Health and Disease Laboratory to evaluate the surgical system. RESULTS: The results showed that the accuracy of the benign and malignant tumor segmentation was 0.9556 and 0.9548, respectively, and the average AR navigation mapping error was 0.644 mm. The proposed surgical system was applied in 106 skin tumor surgeries, including intraoperative navigation of sampling in 16 Mohs surgery cases. Surgeons who have used this system highly recognize it. CONCLUSIONS: The surgical system highlighted the potential to achieve accurate treatment of skin tumors and to fill the gap in global research on skin tumor surgery systems.

scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding.

Single-cell proteomics sequencing technology sheds light on protein-protein interactions, posttranslational modifications and proteoform dynamics in the cell. However, the uncertainty estimation for peptide quantification, data missingness, batch effects and high noise hinder the analysis of single-cell proteomic data. It is important to solve this set of tangled problems together, but the existing methods tailored for single-cell transcriptomes cannot fully address this task. Here we propose a versatile framework designed for single-cell proteomics data analysis called scPROTEIN, which consists of peptide uncertainty estimation based on a multitask heteroscedastic regression model and cell embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings in a unified framework. We demonstrate that scPROTEIN is efficient for cell clustering, batch correction, cell type annotation, clinical analysis and spatially resolved proteomic data exploration.

Determination of genetic correlation between tobacco smoking and coronary artery disease.

Backgrounds: Tobacco smoking is an important risk factor for coronary artery disease (CAD), but the genetic mechanisms linking smoking to CAD remain largely unknown. Methods: We analyzed summary data from the genome-wide association study (GWAS) of the UK Biobank for CAD, plasma lipid concentrations (n = 184,305), and smoking (n = 337,030) using different biostatistical methods, which included LD score regression and Mendelian randomization (MR). Results: We identified SNPs shared by CAD and at least one smoking behavior, the genes where these SNPs are located were found to be significantly enriched in the processes related to lipoprotein metabolic, chylomicron-mediated lipid transport, lipid digestion, mobilization, and transport. The MR analysis revealed a positive correlation between smoking cessation and decreased risk for CAD when smoking cessation was considered as exposure (p = 0.001), and a negative correlation between the increased risk for CAD and smoking cessation when CAD was considered as exposure (p = 2.95E-08). This analysis further indicated that genetic liability for smoking cessation increased the risk of CAD. Conclusion: These findings inform the concomitant conditions of CAD and smoking and support the idea that genetic liabilities for smoking behaviors are strongly associated with the risk of CAD.

Effect of chitosan coating, carbon dots and ultrasound treatment on microorganisms and physicochemical quality of fresh-cut lettuce.

The effect of chitosan (CH) coating, carbon dots (CDs) and ultrasound (US) treatment on microorganisms and the physicochemical quality of fresh-cut (FC) lettuce was investigated. FC lettuces were treated by US and dipped into CD/CH coating, then packed and stored for 15 d at 4°C. Results presented that CD/CH coating exhibited a superior effect on the depressing growth of aerobic plate count, mould and yeast, the decrease of respiratory rate, the inhibition of peroxidase and polyphenol oxidase activities, the maintenance of ascorbic acid and chlorophyll contents, the reduction of mass loss, the restriction of water distribution in US-treated FC lettuce. This exhibited that CD/CH coating effectively kept the microbial and physicochemical quality of FC lettuce.

DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis.

Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson's correlation of 0.813 in predicting the binding affinity of TCR, and a median area under the receiver-operating characteristic curve (AUC) of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma and inflammatory bowel disease in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity.

Surformer: An interpretable pattern-perceptive survival transformer for cancer survival prediction from histopathology whole slide images.

BACKGROUND AND OBJECTIVE: High-resolution histopathology whole slide images (WSIs) contain abundant valuable information for cancer prognosis. However, most computational pathology methods for survival prediction have weak interpretability and cannot explain the decision-making processes reasonably. To address this issue, we propose a highly interpretable neural network termed pattern-perceptive survival transformer (Surformer) for cancer survival prediction from WSIs. METHODS: Notably, Surformer can quantify specific histological patterns through bag-level labels without any patch/cell-level auxiliary information. Specifically, the proposed ratio-reserved cross-attention module (RRCA) generates global and local features with the learnable prototypes (pglobal, plocals) as detectors and quantifies the patches correlative to each plocal in the form of ratio factors (rfs). Afterward, multi-head self&cross-attention modules proceed with the computation for feature enhancement against noise. Eventually, the designed disentangling loss function guides multiple local features to focus on distinct patterns, thereby assisting rfs from RRCA in achieving more explicit histological feature quantification. RESULTS: Extensive experiments on five TCGA datasets illustrate that Surformer outperforms existing state-of-the-art methods. In addition, we highlight its interpretation by visualizing rfs distribution across high-risk and low-risk cohorts and retrieving and analyzing critical histological patterns contributing to the survival prediction. CONCLUSIONS: Surformer is expected to be exploited as a useful tool for performing histopathology image data-driven analysis and gaining new insights for interpreting the associations between such images and patient survival states.

Near-Infrared II Hyperspectral Imaging Improves the Accuracy of Pathological Sampling of Multiple Cancer Types.

Pathological histology is the "gold standard" for clinical diagnosis of cancer. Incomplete or excessive sampling of the formalin-fixed excised cancer specimen will result in inaccurate histologic assessment or excessive workload. Conventionally, pathologists perform specimen sampling relying on naked-eye observation, which is subjective and limited by human perception. Precise identification of cancer tissue, size, and margin is challenging, especially for lesions with inconspicuous tumors. To overcome the limits of human eye perception (visible: 400-700 nm) and improve the sampling efficiency, in this study, we propose using a second near-infrared window (NIR-II: 900-1700 nm) hyperspectral imaging (HSI) system to assist specimen sampling on the strength of the verified deep anatomical penetration and low scattering characteristics of the NIR-II optical window. We used selected NIR-II HSI narrow bands to synthesize color images for human eye observation and also applied a machine learning-based algorithm on the complete NIR-II HSI data for automatic tissue classification to assist pathologists in specimen sampling. A total of 92 tumor samples were collected, including 7 types. Sixty-two (62/92) samples were used as the validation set. Five experienced pathologists marked the contour of the cancer tissue on conventional color images by using different methods, and compared it with the "gold standard," showing that NIR-II HSI-assisted methods had significant improvements in determining cancer tissue compared with conventional methods (conventional color image with or without X-ray). The proposed system can be easily integrated into the current workflow, with high imaging efficiency and no ionizing radiation. It may also find applications in intraoperative detection of residual lesions and identification of different tissues.

Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning.

The objective of our study is to develop a deep learning model based on clinicopathological data and digital pathological image of core needle biopsy specimens for predicting breast cancer lymph node metastasis. We collected 3701 patients from the Fourth Hospital of Hebei Medical University and 190 patients from four medical centers in Hebei Province. Integrating clinicopathological data and image features build multi-modal and multi-instance (MMMI) deep learning model to obtain the final prediction. For predicting with or without lymph node metastasis, the AUC was 0.770, 0.709, 0.809 based on the clinicopathological features, WSI and MMMI, respectively. For predicting four classification of lymph node status (no metastasis, isolated tumor cells (ITCs), micrometastasis, and macrometastasis), the prediction based on clinicopathological features, WSI and MMMI were compared. The AUC for no metastasis was 0.770, 0.709, 0.809, respectively; ITCs were 0.619, 0.531, 0.634, respectively; micrometastasis were 0.636, 0.617, 0.691, respectively; and macrometastasis were 0.748, 0.691, 0.758, respectively. The MMMI model achieved the highest prediction accuracy. For prediction of different molecular types of breast cancer, MMMI demonstrated a better prediction accuracy for any type of lymph node status, especially in the molecular type of triple negative breast cancer (TNBC). In the external validation sets, MMMI also showed better prediction accuracy in the four classification, with AUC of 0.725, 0.757, 0.525, and 0.708, respectively. Finally, we developed a breast cancer lymph node metastasis prediction model based on a MMMI model. Through all cases tests, the results showed that the overall prediction ability was high.

Harnessing computational spatial omics to explore the spatial biology intricacies.

Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.

Mendelian randomization analyses of smoking and Alzheimer's disease in Chinese and Japanese populations.

Background: Previous epidemiological studies have reported controversial results on the relationship between smoking and Alzheimer's disease (AD). Therefore, we sought to assess the association using Mendelian randomization (MR) analysis. Methods: We used single nucleotide polymorphisms (SNPs) associated with smoking quantity (cigarettes per day, CPD) from genome-wide association studies (GWAS) of Japanese population as instrumental variables, then we performed two-sample MR analysis to investigate the association between smoking and AD in a Chinese cohort (1,000 AD cases and 500 controls) and a Japanese cohort (3,962 AD cases and 4,074 controls), respectively. Results: Genetically higher smoking quantity showed no statistical causal association with AD risk (the inverse variance weighted (IVW) estimate in the Chinese cohort: odds ratio (OR) = 0.510, 95% confidence interval (CI) = 0.149-1.744, p = 0.284; IVW estimate in the Japanese cohort: OR = 1.170, 95% confidence interval CI = 0.790-1.734, p = 0.434). Conclusion: This MR study, for the first time in Chinese and Japanese populations, found no significant association between smoking and AD.

Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification.

MOTIVATION: Multiple instance learning (MIL) is a powerful technique to classify whole slide images (WSIs) for diagnostic pathology. The key challenge of MIL on WSI classification is to discover the critical instances that trigger the bag label. However, tumor heterogeneity significantly hinders the algorithm's performance. RESULTS: Here, we propose a novel multiplex-detection-based multiple instance learning (MDMIL) which targets tumor heterogeneity by multiplex detection strategy and feature constraints among samples. Specifically, the internal query generated after the probability distribution analysis and the variational query optimized throughout the training process are utilized to detect potential instances in the form of internal and external assistance, respectively. The multiplex detection strategy significantly improves the instance-mining capacity of the deep neural network. Meanwhile, a memory-based contrastive loss is proposed to reach consistency on various phenotypes in the feature space. The novel network and loss function jointly achieve high robustness towards tumor heterogeneity. We conduct experiments on three computational pathology datasets, e.g. CAMELYON16, TCGA-NSCLC, and TCGA-RCC. Benchmarking experiments on the three datasets illustrate that our proposed MDMIL approach achieves superior performance over several existing state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: MDMIL is available for academic purposes at https://github.com/ZacharyWang-007/MDMIL.

A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer.

Background: The current study aimed to develop a deep learning (DL) model for prediction of lymph node metastasis (LNM) based on hematoxylin and eosin (HE)-stained histopathological images of endometrial cancer (EC). The model was validated using external data. Methods: A total of 2,104 whole slide image (WSI) from 564 patients with pathologically confirmed LNM status were collated from West China Second University Hospital. An artificial intelligence (AI) model was built on the multiple instance-learning (MIL) framework for automatic prediction of the probability of LNM and its performance compared with "Mayo criteria". An additional external data source comprising 533 WSI was collected from two independent medical institutions to validate the model's robustness. Heatmaps were generated to demonstrate regions of the WSI that made the greatest contributions to the DL network output to improve understanding of these processes. Results: The proposed MIL model achieved an area under the curve (AUC) of 0.938, a sensitivity of 0.830 and a specificity of 0.911 for LNM prediction to EC. The AUC according to Mayo criteria was 0.666 for the same test dataset. For types I, II and mixed EC, AUCs were 0.927, 0.979 and 0.929, respectively. The predictive performance of the MIL model also achieved an AUC of 0.921 for early staging. In external validation data, the proposed model achieved an AUC of 0.770, a sensitivity of 0.814 and a specificity of 0.520 for LNM prediction. AUCs were 0.783 for type I and 0.818 for early stage EC. Conclusions: The proposed MIL model generated from histopathological images of EC has a much better LNM predictive performance than that of Mayo criteria. A novel DL-based biomarker trained on different histological subtypes of EC slides was revealed to predict metastatic status with improved accuracy, especially for early staging patients. The current study proves the concept of MIL-based prediction of LNM in EC for the first time, and brought a new sight to improve the accuracy of LNM prediction. Multicenter prospective validation data is required to further confirm the clinical utility.

High Dynamic Range Dual-Modal White Light Imaging Improves the Accuracy of Tumor Bed Sampling After Neoadjuvant Therapy for Breast Cancer.

OBJECTIVES: Accurate evaluation of residual cancer burden remains challenging because of the lack of appropriate techniques for tumor bed sampling. This study evaluated the application of a white light imaging system to help pathologists differentiate the components and location of tumor bed in specimens. METHODS: The high dynamic range dual-mode white light imaging (HDR-DWI) system was developed to capture antiglare reflection and multiexposure HDR transmission images. It was tested in 60 specimens of modified radical mastectomy after neoadjuvant therapy. We observed the differential transmittance among tumor tissue, fibrosis tissue, and adipose tissue. RESULTS: The sensitivity and specificity of HDR-DWI were compared with x-ray or visual examination to determine whether HDR-DWI was superior in identifying tumor beds. We found that tumor tissue had lower transmittance (0.12 ± 0.03) than fibers (0.15 ± 0.04) and fats (0.27 ± 0.07) (P < .01). CONCLUSIONS: HDR-DWI was more sensitive in identifying fiber and tumor tissues than cabinet x-ray and visual observation (P < .01). In addition, HDR-DWI could identify more fibrosis areas than the currently used whole slide imaging did in 12 samples (12/60). We have determined that HDR-DWI can provide more in-depth tumor bed information than x-ray and visual examination do, which will help prevent diagnostic errors in tumor bed sampling.

Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire.

Underlying medical conditions, such as cancer, kidney disease and heart failure, are associated with a higher risk for severe COVID-19. Accurate classification of COVID-19 patients with underlying medical conditions is critical for personalized treatment decision and prognosis estimation. In this study, we propose an interpretable artificial intelligence model termed VDJMiner to mine the underlying medical conditions and predict the prognosis of COVID-19 patients according to their immune repertoires. In a cohort of more than 1400 COVID-19 patients, VDJMiner accurately identifies multiple underlying medical conditions, including cancers, chronic kidney disease, autoimmune disease, diabetes, congestive heart failure, coronary artery disease, asthma and chronic obstructive pulmonary disease, with an average area under the receiver operating characteristic curve (AUC) of 0.961. Meanwhile, in this same cohort, VDJMiner achieves an AUC of 0.922 in predicting severe COVID-19. Moreover, VDJMiner achieves an accuracy of 0.857 in predicting the response of COVID-19 patients to tocilizumab treatment on the leave-one-out test. Additionally, VDJMiner interpretively mines and scores V(D)J gene segments of the T-cell receptors that are associated with the disease. The identified associations between single-cell V(D)J gene segments and COVID-19 are highly consistent with previous studies. The source code of VDJMiner is publicly accessible at https://github.com/TencentAILabHealthcare/VDJMiner. The web server of VDJMiner is available at https://gene.ai.tencent.com/VDJMiner/.

SOTIP is a versatile method for microenvironment modeling with spatial omics data.

The rapidly developing spatial omics generated datasets with diverse scales and modalities. However, most existing methods focus on modeling dynamics of single cells while ignore microenvironments (MEs). Here we present SOTIP (Spatial Omics mulTIPle-task analysis), a versatile method incorporating MEs and their interrelationships into a unified graph. Based on this graph, spatial heterogeneity quantification, spatial domain identification, differential microenvironment analysis, and other downstream tasks can be performed. We validate each module's accuracy, robustness, scalability and interpretability on various spatial omics datasets. In two independent mouse cerebral cortex spatial transcriptomics datasets, we reveal a gradient spatial heterogeneity pattern strongly correlated with the cortical depth. In human triple-negative breast cancer spatial proteomics datasets, we identify molecular polarizations and MEs associated with different patient survivals. Overall, by modeling biologically explainable MEs, SOTIP outperforms state-of-art methods and provides some perspectives for spatial omics data exploration and interpretation.

Computer grading of lung disease severity in patients with lymphangioleiomyomatosis referred for transplantation.

OBJECTIVES: Lymphangioleiomyomatosis (LAM) patients with severe lung disease may be considered for lung transplantation. Clinical, physiologic, and quality of life data are usually employed for referral. The aim of this study was to determine whether computed tomographic measurement of lung volume occupied by cysts (cyst score) complemented clinical and physiologic data in supporting referral for transplantation. METHODS: Forty-one patients were studied. Pre-referral clinical data, pulmonary function tests, exercise testing, and high-resolution computed tomography (HRCT) scans were obtained. From HRCT, a computer-aided diagnostic program was employed to calculate cyst scores. These data were compared to those of 41 age-matched LAM patients not referred for lung transplantation. RESULTS: Cyst score, and % predicted FEV1 and DLCO were respectively, 48.1 ± 9.4%, 36.5 ± 9.1%, and 35.0 ± 10.7%. For the control group, cyst score, FEV1, and DLCO were respectively, 14.8 ± 8.3%, 77.2 ± 20.3%, and 66.7 ± 19.3%. Cyst score values showed a normal distribution. However, the frequency distribution of FEV1 was skewed to the right while the distribution of DLCO was bimodal. Correlations between cyst score and FEV1 and DLCO for the study group were respectively, r = - 0.319 and r = - 0.421. CONCLUSIONS: LAM patients referred for lung transplantation had nearly 50% of lungs occupied by cysts. Correlations between cyst score and FEV1 or DLCO were weak; as shown previously, DLCO was better related to cyst number while FEV1 had a better association with cyst size. Given its normal distribution, cyst score measurements may assist in evaluation of pre-transplant severity of lung disease before referral for transplantation.

Genetic variations in the bitter taste receptor gene TAS2R38 are related to cigarette smoking behavior in Han Chinese smokers.

BACKGROUND: Smoking behavior is influenced by multiple genes, including the bitter taste gene TAS2R38. It has been reported that the correlation between TAS2R38 and smoking behavior has ethnicity-based differences. However, the TAS2R38 status in Chinese smokers is still unclear. OBJECTIVE: This study aims to investigate the possible relationship between genetic variations in TAS2R38 (A49P, V262A and I296V) and smoking behaviors in the Han Chinese population. METHODS: The haplotype analyses were performed and smoking behavior questionnaire was completed by 1271 individuals. Genetic association analyses for smoking behavior were analyzed using chi-square test. Further, for investigating the molecular mechanism of TAS2R38 variants effect on smoking behavior, we conducted TAS2R38-PAV and TAS2R38-AVI expression plasmids and tested the cellular calcium assay by cigarette smoke compounds stimulus in HEK293. RESULTS: Significant associations of genetic variants within TAS2R38 were identified with smoking behavior. We found a higher PAV/PAV frequency than AVI/AVI in moderate and high nicotine dependence (FTND ≥ 4; X2 = 4.611, 1 df, p = 0.032) and strong cigarette smoke flavor intensity preference (X2 = 4.5383, 1 df, p = 0.033) in participants. Furthermore, in the in vitro cellular calcium assay, total particle matter (TPM), N-formylnornicotine and cotinine, existing in cigarette smoke, activated TAS2R38-PAV but not TAS2R38-AVI-transfected cells. CONCLUSION: Our data highlights that genetic variations in TAS2R38 are related to smoking behavior, especially nicotine dependence and cigarette smoke flavor intensity preference. Our findings may encourage further consideration of the taste process to identify individuals susceptible to nicotine dependence, particularly Han Chinese smokers.

A deep learning model with incorporation of microvascular invasion area as a factor in predicting prognosis of hepatocellular carcinoma after R0 hepatectomy.

INTRODUCTION: Microvascular invasion (MVI) is a known risk factor for prognosis after R0 liver resection for hepatocellular carcinoma (HCC). The aim of this study was to develop a deep learning prognostic prediction model by incorporating a new factor of MVI area to the other independent risk factors. METHODS: Consecutive patients with HCC who underwent R0 liver resection from January to December 2016 at the Eastern Hepatobiliary Surgery Hospital were included in this retrospective study. For patients with MVI detected on resected specimens, they were divided into two groups according to the size of the maximal MVI area: the small-MVI group and the large-MVI group. RESULTS: Of 193 patients who had MVI in the 337 HCC patients, 130 patients formed the training cohort and 63 patients formed the validation cohort. The large-MVI group of patients had worse overall survival (OS) when compared with the small-MVI group (p = 0.009). A deep learning model was developed based on the following independent risk factors found in this study: MVI stage, maximal MVI area, presence/absence of cirrhosis, and maximal tumor diameter. The areas under the receiver operating characteristic of the deep learning model for the 1-, 3-, and 5-year predictions of OS were 80.65, 74.04, and 79.44, respectively, which outperformed the traditional COX proportional hazards model. CONCLUSION: The deep learning model, by incorporating the maximal MVI area as an additional prognostic factor to the other previously known independent risk factors, predicted more accurately postoperative long-term OS for HCC patients with MVI after R0 liver resection.