Prognostic biomarkers associated with immune checkpoint inhibitors in hepatocellular carcinoma.

The treatment of hepatocellular carcinoma (HCC), particularly advanced HCC, has been a serious challenge. Immune checkpoint inhibitors (ICIs) are landmark drugs in the field of cancer therapy in recent years, which have changed the landscape of cancer treatment. In the field of HCC treatment, this class of drugs has shown good therapeutic prospects. For example, atezolizumab in combination with bevacizumab has been approved as first-line treatment for advanced HCC due to significant efficacy. However, sensitivity to ICI therapy varies widely among HCC patients. Therefore, there is an urgent need to search for determinants of resistance/sensitivity to ICIs and to screen biomarkers that can predict the efficacy of ICIs. This manuscript reviews the research progress of prognostic biomarkers associated with ICIs in HCC in order to provide a scientific basis for the development of clinically individualised precision medication regimens.

A non-invasive method to determine core temperature for cats and dogs using surface temperatures based on machine learning.

BACKGROUND: Rectal temperature (RT) is an important index of core temperature, which has guiding significance for the diagnosis and treatment of pet diseases. OBJECTIVES: Development and evaluation of an alternative method based on machine learning to determine the core temperatures of cats and dogs using surface temperatures. ANIMALS: 200 cats and 200 dogs treated between March 2022 and May 2022. METHODS: A group of cats and dogs were included in this study. The core temperatures and surface body temperatures were measured. Multiple machine learning methods were trained using a cross-validation approach and evaluated in one retrospective testing set and one prospective testing set. RESULTS: The machine learning models could achieve promising performance in predicting the core temperatures of cats and dogs using surface temperatures. The root mean square errors (RMSE) were 0.25 and 0.15 for cats and dogs in the retrospective testing set, and 0.15 and 0.14 in the prospective testing set. CONCLUSION: The machine learning model could accurately predict core temperatures for companion animals of cats and dogs using easily obtained body surface temperatures.

Sec62 promotes gastric cancer metastasis through mediating UPR-induced autophagy activation.

BACKGROUND AND AIMS: Sec62 is a membrane protein of the endoplasmic reticulum that facilitates protein transport. Its role in cancer is increasingly recognised, but remains largely unknown. We investigated the functional role of Sec62 in gastric cancer (GC) and its underlying mechanism. METHODS: Bioinformatics, tissue microarray, immunohistochemistry (IHC), western blotting (WB), quantitative polymerase chain reaction (qPCR), and immunofluorescence were used to examine the expression of target genes. Transwell, scratch healing assays, and xenograft models were used to evaluate cell migration and invasion. Transmission electron microscopy and mRFP-GFP-LC3 double-labeled adenoviruses were used to monitor autophagy. Co-immunoprecipitation (CO-IP) was performed to evaluate the binding activity between the proteins. RESULTS: Sec62 expression was upregulated in GC, and Sec62 upregulation was an independent predictor of poor prognosis. Sec62 overexpression promoted GC cell migration and invasion both in vitro and in vivo. Sec62 promoted migration and invasion by affecting TIMP-1 and MMP2/9 balance. Moreover, Sec62 could activate autophagy by upregulating PERK/ATF4 expression and binding to LC3II with concomitant FIP200/Beclin-1/Atg5 activation. Furthermore, autophagy blockage impaired the promotive effects of Sec62 on GC cell migration and invasion, whereas autophagy activation rescued the inhibitory effect of Sec62 knockdown on GC metastasis. Notably, Sec62 inhibition combined with autophagy blockage exerted a synergetic anti-metastatic effect in vitro and in vivo. CONCLUSION: Sec62 promotes GC metastasis by activating autophagy and subsequently regulating TIMP-1 and MMP2/9 balance. The activation of autophagy by Sec62 may involve the unfolded protein response (UPR)-related PERK/ATF4 pathway and binding of LC3II during UPR recovery involving FIP200/Beclin-1/Atg5 upregulation. Specifically, the dual inhibition of Sec62 and autophagy may provide a promising therapeutic strategy for GC metastasis.

Predicting postoperative delirium after hip arthroplasty for elderly patients using machine learning.

BACKGROUND: Postoperative delirium (POD) is a common and severe complication in elderly hip-arthroplasty patients. AIM: This study aims to develop and validate a machine learning (ML) model that determines essential features related to POD and predicts POD for elderly hip-arthroplasty patients. METHODS: The electronic record data of elderly patients who received hip-arthroplasty surgery between January 2017 and April 2021 were enrolled as the dataset. The Confusion Assessment Method (CAM) was administered to the patients during their perioperative period. The feature section method was employed as a filter to determine leading features. The classical machine learning algorithms were trained in cross-validation processing, and the model with the best performance was built in predicting the POD. Metrics of the area under the curve (AUC), accuracy (ACC), sensitivity, specificity, and F1-score were calculated to evaluate the predictive performance. RESULTS: 476 Arthroplasty elderly patients with general anesthesia were included in this study, and the final model combined feature selection method mutual information (MI) and linear binary classifier using logistic regression (LR) achieved an encouraging performance (AUC = 0.94, ACC = 0.88, sensitivity = 0.85, specificity = 0.90, F1-score = 0.87) on a balanced test dataset. CONCLUSION: The model could predict POD with satisfying accuracy and reveal important features of suffering POD such as age, Cystatin C, GFR, CHE, CRP, LDH, monocyte count, history of mental illness or psychotropic drug use and intraoperative blood loss. Proper preoperative interventions for these factors could reduce the incidence of POD among elderly patients.

The efficacy of transarterial chemoembolization in downstaging unresectable hepatocellular carcinoma to curative therapy: a predicted regression model.

Patients with hepatocellular carcinoma (HCC) outside Milan criteria (MC) may be candidates for curative therapy after successful downstaging. We aimed to identify the predictors of successful downstaging of unresectable HCC in patient by transarterial chemoembolization (TACE) outside MC. We performed a retrospective study on patients with unresectable HCC outside MC who received downstaging with TACE. Clinical and laboratory variables were recorded. We identified 101 patients with unresectable HCC who underwent initial TACE, who formed the derivation set of this study. Thirty patients who treated by TACE with the same selection criteria served as an external validation set. We performed univariate and multivariate logistic regression analyses to identify variables associated with successful downstaging. Then we did the predictive model to predict the efficiency of TACE. Of the 101 patients in the study, 26 patients (25.7%) were successfully downstaging and 75 patients (74.3%) failed downstaging. Multivariate analysis of factors to predict successful downstaging of HCC outside MC the number of tumor (P = 0.01), portal vein tumor thrombosis (PVTT)(p < 0.01), the size of tumor (P = 0.02), hepatitis B surface antigen (HBsAg) (P = 0.01), α-fetoprotein (AFP) (P = 0.02) as significant predictors of successful downstaging. Then we constructed the predictive model. The area under the ROC curve (AUROC) of the predictive equation was 0.90 (95% confidence interval, 0.83-0.95). We found in our study that the number and size of tumors, PVTT, HBsAg, and AFP are good predictors of successful downstaging of unresectable HCC in patients by TACE outside the MC.

Primary tuberculous granuloma in the falciform ligament of liver.

Isolated tuberculous granuloma in the falciform ligament of the liver has never been reported. Here, we report a case of tuberculous granuloma in the falciform ligament of the liver of a 45-year-old woman who complained only of persistent right upper abdominal pain. On abdominal contrast-enhanced CT and MRI, it is difficult to distinguish benign from malignant masses. Imaging on 18 F-FDG PET/CT showed high uptake of 18 F-FDG in the lesion, which was thus considered more likely to be malignant. However, postoperative pathological examination showed granulomatous inflammation with caseous necrosis, which was diagnosed as tuberculosis.

Automated Detection of Epiretinal Membranes in OCT Images Using Deep Learning.

INTRODUCTION: Development and validation of a deep learning algorithm to automatedly identify and locate ERM regions in OCT images. METHODS: OCT images of 468 eyes were retrospectively collected from a total of 404 ERM patients. One expert manually annotated the ERM regions for all images. A total of 422 images (90%) and the rest 46 images (10%) were used as the training dataset and validation dataset for deep learning algorithm training and validation, respectively. One senior and one junior clinician read the images. The diagnostic results were compared. RESULTS: The algorithm accurately segmented and located the ERM regions in OCT images. The image-level accuracy was 95.65%, and the ERM region-level accuracy was 90.14%, respectively. In comparison experiments, the accuracies of the junior clinician improved from 85.00% and 61.29% without the assistance of the algorithm to 100.00% and 90.32% with the assistance of the algorithm. The corresponding results of the senior clinician were 96.15%, 95.00% without the assistance of the algorithm, and 96.15%, 97.50% with the assistance of the algorithm. CONCLUSIONS: The developed deep learning algorithm can accurately segmenting ERM regions in OCT images. This deep learning approach may help clinicians in clinical diagnosis with better accuracy and efficiency.

Workshop Safety Helmet Wearing Detection Model Based on SCM-YOLO.

In order to overcome the problems of object detection in complex scenes based on the YOLOv4-tiny algorithm, such as insufficient feature extraction, low accuracy, and low recall rate, an improved YOLOv4-tiny safety helmet-wearing detection algorithm SCM-YOLO is proposed. Firstly, the Spatial Pyramid Pooling (SPP) structure is added after the backbone network of the YOLOv4-tiny model to improve its adaptability of different scale features and increase its effective features extraction capability. Secondly, Convolutional Block Attention Module (CBAM), Mish activation function, K-Means++ clustering algorithm, label smoothing, and Mosaic data enhancement are introduced to improve the detection accuracy of small objects while ensuring the detection speed. After a large number of experiments, the proposed SCM-YOLO algorithm achieves a mAP of 93.19%, which is 4.76% higher than the YOLOv4-tiny algorithm. Its inference speed reaches 22.9FPS (GeForce GTX 1050Ti), which meets the needs of the real-time and accurate detection of safety helmets in complex scenes.

From gratitude to injustice: Neurocomputational mechanisms of gratitude-induced injustice.

Gratitude shapes individuals' behaviours and impacts the harmony of society. Many previous studies focused on its association with prosocial behaviours. A possibility that gratitude can lead to moral violation has been overlooked until recently. Nevertheless, the neurocognitive mechanisms of gratitude-induced moral violation are still unclear. On the other hand, though neural correlates of the gratitude's formation have been examined, the neural underpinnings of gratitude-induced behaviour remain unknown. For addressing these two overlapped research gaps, we developed novel tasks to investigate how participants who had received voluntary (Gratitude group) or involuntary help (Control group) punished their benefactors' unfairness with functional magnetic resonance imaging (fMRI). The Gratitude group punished their benefactors less than the Control group. The self-report and computational modelling results demonstrated a crucial role of the boosted protection tendency on behalf of benefactors in the gratitude-induced injustice. The fMRI results showed that activities in the regions associated with mentalizing (temporoparietal junction) and reward processing (ventral medial prefrontal cortex) differed between the groups and were related to the gratitude-induced injustice. They suggest that grateful individuals concern for benefactors' benefits, value chances to interact with benefactors, and refrain from action that perturbs relationship-building (i.e., exert less punishment on benefactors' unfairness), which reveal a dark side of gratitude and enrich the gratitude theory (i.e., the find-bind-remind theory). Our findings provide psychological, computational, and neural accounts of the gratitude-induced behaviour and further the understanding of the nature of gratitude.

Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma.

BACKGROUND: Radiomics may provide more objective and accurate predictions for extrahepatic cholangiocarcinoma (ECC). In this study, we developed radiomics models based on magnetic resonance imaging (MRI) and machine learning to preoperatively predict differentiation degree (DD) and lymph node metastasis (LNM) of ECC. METHODS: A group of 100 patients diagnosed with ECC was included. The ECC status of all patients was confirmed by pathology. A total of 1200 radiomics features were extracted from axial T1 weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), and apparent diffusion coefficient (ADC) images. A systematical framework considering combinations of five feature selection methods and ten machine learning classification algorithms (classifiers) was developed and investigated. The predictive capabilities for DD and LNM were evaluated in terms of area under precision recall curve (AUPRC), area under the receiver operating characteristic (ROC) curve (AUC), negative predictive value (NPV), accuracy (ACC), sensitivity, and specificity. The prediction performance among models was statistically compared using DeLong test. RESULTS: For DD prediction, the feature selection method joint mutual information (JMI) and Bagging Classifier achieved the best performance (AUPRC = 0.65, AUC = 0.90 (95% CI 0.75-1.00), ACC = 0.85 (95% CI 0.69-1.00), sensitivity = 0.75 (95% CI 0.30-0.95), and specificity = 0.88 (95% CI 0.64-0.97)), and the radiomics signature was composed of 5 selected features. For LNM prediction, the feature selection method minimum redundancy maximum relevance and classifier eXtreme Gradient Boosting achieved the best performance (AUPRC = 0.95, AUC = 0.98 (95% CI 0.94-1.00), ACC = 0.90 (95% CI 0.77-1.00), sensitivity = 0.75 (95% CI 0.30-0.95), and specificity = 0.94 (95% CI 0.72-0.99)), and the radiomics signature was composed of 30 selected features. However, these two chosen models were not significantly different to other models of higher AUC values in DeLong test, though they were significantly different to most of all models. CONCLUSION: MRI radiomics analysis based on machine learning demonstrated good predictive accuracies for DD and LNM of ECC. This shed new light on the noninvasive diagnosis of ECC.

Identification of Barrett's esophagus in endoscopic images using deep learning.

BACKGROUND: Development of a deep learning method to identify Barrett's esophagus (BE) scopes in endoscopic images. METHODS: 443 endoscopic images from 187 patients of BE were included in this study. The gastroesophageal junction (GEJ) and squamous-columnar junction (SCJ) of BE were manually annotated in endoscopic images by experts. Fully convolutional neural networks (FCN) were developed to automatically identify the BE scopes in endoscopic images. The networks were trained and evaluated in two separate image sets. The performance of segmentation was evaluated by intersection over union (IOU). RESULTS: The deep learning method was proved to be satisfying in the automated identification of BE in endoscopic images. The values of the IOU were 0.56 (GEJ) and 0.82 (SCJ), respectively. CONCLUSIONS: Deep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the BE scope in endoscopic images. This automated recognition method helps clinicians to locate and recognize the scopes of BE in endoscopic examinations.

Application of Near-Infrared Fluorescence Imaging Technology in Liver Cancer Surgery.

Background. Hepatocellular carcinoma, among the most common malignant digestive system tumorsworldwide, is most effectively treated with precise surgical resection. Near-infrared fluorescence imaging technology is being increasingly used clinically and has achieved great initial results in the navigation of liver cancer surgery. Methods. This review describes the application of indocyanine green fluorescence (ICG) imaging technology with near-infrared window I in the navigation of liver cancer surgery, explores novel fluorescent probes and near-infrared window II fluorescence imaging technology, and discusses the development status of the 2 emerging tools. Results. ICG fluorescence imaging technology can precisely localize the tumor, reveal the boundary of liver cancer or liver segment, and identify the bile leakage. The novel fluorescent probe is more targeted than ICG, which makes the detection of cancer more accurate. Near-infrared window II fluorescence imaging technology can lead to outstanding gains in deeper detection, higher resolution, and fidelity. But, due to the shortcomings of machine and probe, it is not widely used in clinical. Conclusion. Near-infrared fluorescence imaging has great development potential. With the advent of precision medicine and the progress of various biotechnology studies, fluorescence imaging technology will be better developed and applied in the diagnosis, surgical navigation, and treatment of liver cancer.

Clinical Efficacy of Perioperative Immunonutrition Containing Omega-3-Fatty Acids in Patients Undergoing Hepatectomy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: The effect of immunonutrition in patients undergoing hepatectomy remains unclear. This meta-analysis aimed to assess the impact of immunonutrition on postoperative clinical outcomes in patients undergoing hepatectomy. METHODS: A literature search of PubMed, Cochrane Library, Web of Science, and Embase databases was performed to identify all randomized controlled trials (RCTs) exploring the effect of perioperative immunonutrition in patients undergoing hepatectomy until the end of March 10, 2020. Quality assessment and data extraction of RCTs were conducted independently by 3 reviewers. Mean difference (MD) and odds ratio (OR) with 95% confidence interval (CI) were calculated using a fixed-effects or random-effects model. The meta-analysis was performed with RevMan 5.3 software. RESULTS: Nine RCTs involving a total of 966 patients were finally included. This meta-analysis showed that immunonutrition significantly reduced the incidences of overall postoperative complications (OR = 0.57, 95% CI: 0.34-0.95; p = 0.03), overall postoperative infectious complications (OR = 0.53, 95% CI: 0.37-0.75; p = 0.0003), and incision infection (OR = 0.50, 95% CI: 0.28-0.89; p = 0.02), and it shortened the length of hospital stay (MD = -3.80, 95% CI: -6.59 to -1.02; p = 0.007). There were no significant differences in the incidences of pulmonary infection (OR = 0.60, 95% CI: 0.32-1.12; p = 0.11), urinary tract infection (OR = 1.30, 95% CI: 0.55-3.08; p = 0.55), liver failure (OR = 0.54, 95% CI: 0.23-1.24; p = 0.15), and postoperative mortality (OR = 0.69, 95% CI: 0.26-1.83; p = 0.46). CONCLUSION: Given its positive impact on postoperative complications and the tendency to shorten the length of hospital stay, perioperative immunonutrition should be encouraged in patients undergoing hepatectomy.

Reduction of Cold Ischemic Injury with the Addition of Compound Glycyrrhizin in HTK Solution in a Mouse Heart Transplantation Model.

Cold ischemic injury in heart storage is an important issue pertaining to heart transplantation. This study aims to evaluate the addition of compound glycyrrhizin (CG) in histidine-tryptophan-ketoglutarate (HTK) solution on chronic isograft injury in comparison to traditional HTK solution.Hearts of mouse were stored for 8 h in 4°C cold preservation solution and then transplanted heterotopically into mouse. Five groups were evaluated: HTK, low dose of CG solution (LCG), medium dose of CG solution (MCG), high dose of CG solution (HCG), and hearts without cold ischemia (sham). Survival was assessed. Time to restoration of heartbeat and strength of the heartbeat was measured. Lactate dehydrogenase (LDH) and creatine kinase (CK) levels in the preservation solution were determined. The myocardial damage and interstitial fibrosis of transplanted hearts were evaluated. TGF-ß1 expression in the transplanted hearts was assessed.Addition of CG to HTK solution significantly attenuated cold ischemic injury during cold storage, as evidenced by the lower time to restoration of heartbeat, higher strength of the heartbeat, lower LDH, and CK leakage. After transplantation, hearts stored in HTK solution containing CG had decreased the myocardial damage and interstitial fibrosis, compared with those stored without CG. The percentage of TGF-ß1-positive cells and TGF-ß1 level in the transplanted hearts were also decreased when stored in CG-containing HTK solution.The addition of CG to HTK solution attenuates cold ischemic injury during cold storage.

Aplastic anemia associated with Crohn's disease: a tertiary center retrospective study.

Aplastic anemia (AA) has been reported to be associated with inflammatory bowel disease (IBD), but mostly with ulcerative colitis (UC). Little is known about the associations between AA and Crohn's disease (CD). We aim to determine the portraits of patients with AA-CD. Among a total of 657 patients with CD registered in Xijing Hospital of Digestive Diseases IBD center from January 2008 to October 2018, the patients diagnosed with concurrent AA were reviewed. Clinical presentation, medical history, endoscopic features, response to treatment, and prognosis in this set of patients were collected. Six male patients confirmed as CD associated with AA were identified. The incidence rate was 0.91% for CD associated with AA in our series. Average age at diagnosis of CD and AA was 41.5 and 39.2 years old, respectively. Abdominal pain and hyperpyrexia were the most common symptoms. Endoscopic findings showed discontinued severe inflammation, and all these patients presented with deformed ileocecal valve. Conventional pharmacotherapy failed to achieve a favorable effect. Four of six patients died from CD progression and its complications. None of these patients received bone marrow transplantation treatment because of poverty. Concurrence of AA and CD is a relatively rare condition. Immunologic impairment may play an important pathogenic role and deserves further attention. Males are more susceptible to this condition. Patients with AA-CD are prone to a severe clinical course and poor prognosis. Conventional therapy achieves no potent effect, and allogeneic stem cell transplantation may be a potentially efficient therapy.

Combination of CEUS and MRI for the diagnosis of periampullary space-occupying lesions: a retrospective analysis.

BACKGROUND: The value of magnetic resonance imaging (MRI), contrast-enhanced ultrasound (CEUS), and the combination of CEUS and MRI (CCWM) for the diagnosis of periampullary space-occupying lesions (PSOL) was investigated. METHODS: A total of 102 patients diagnosed with PSOLs by surgery or biopsy were recruited retrospectively. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of MRI, CEUS, and CCWM were analyzed. RESULTS: MRI, CEUS, and CCWM allowed for the accurate detection of 91.17, 92.15, and 99.01% of PSOLs, respectively. The specificity, PPV, and accuracy of CCWM were significantly different from MRI and CEUS (p < 0.05). However, there the sensitivity and NPV were not significantly different among the three diagnostic technologies. In addition, the specificity, PPV, and accuracy were not significantly different between MRI and CEUS (all p > 0.05). CONCLUSIONS: CCWM is valuable for differentiating benign and malignant PSOL, which provides important guiding significances for the clinic.

Incidence and risk factors for post-ERCP pancreatitis in chronic pancreatitis.

BACKGROUND AND AIMS: Almost all studies on post-ERCP pancreatitis (PEP) have mainly involved patients with biliary diseases rather than chronic pancreatitis (CP), and the concept that CP seems to be a protective factor associated with PEP has not been studied in detail. The aim of this study was to determine the incidence of PEP in patients with CP at different clinical stages and to identify the predictive and protective factors of PEP in a large cohort. METHODS: In this observational cohort study, medical records of patients with CP (CP group) and biliary diseases (BD group) in a tertiary hospital from January 2011 to May 2015 were examined. The difference in the incidence of PEP between CP group and BD group and the risk of PEP at different clinical stages of CP were calculated by the χ2 test or the Fisher exact test. The predictive and protective factors for PEP were investigated by univariate and multivariate analysis. RESULTS: In total, 2028 ERCP procedures were performed in 1301 patients with CP and 2000 procedures in 1655 patients with BD. The overall incidence of PEP in CP group (4.5%) was similar to that in the BD group (4.8%; P = .747). However, CP patients had significantly lower rates of moderate and severe attacks (0% vs 1.3%, P < .01). According to the M-ANNHEIM classification, the PEP incidences of CP at stages 0, I, II, III, and IV were 4.4%, 5.1%, 3.8%, 2.0%, and 2.0%, respectively. CP patients at stage Ia had the highest PEP incidence (8.0%) among all CP patients, significantly higher than that at stages Ib + Ic (3.9%) and II (3.8%). Female gender, history of acute pancreatitis, and prior PEP were independent risk factors of PEP, whereas extracorporeal shock wave lithotripsy was a protective factor. CONCLUSIONS: Compared with BD patients, CP patients had similar incidence of PEP overall but lower grades of severity. The incidence of PEP in CP patients decreased significantly with disease progression. (Clinical trial registration number: NCT02781987.).