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Hepatocellular carcinoma (HCC) is a prevalent type of tumor. Given the controversy surrounding atorvastatin and HCC, we conducted this study to determine whether atorvastatin has anticancer activity against HCC. The impact of varying concentrations of atorvastatin (ATO) on the biological function of HCC cells was studied in vitro, high-throughput mRNA assays on cells and tumor tissue. Finally, an examination was conducted to assess the correlation between the ATO and the prognosis of HCC. ATO significantly inhibited the proliferation, migration, and invasiveness of HCC cells. Furthermore, in vivo, animal experiments showed that a high-fat diet facilitated the progression of HCC and that ATO did not effectively counteract these detrimental consequences. Tumor sequencing of cells and normal diet mice revealed the disparities were primarily concentrated in the MAPK signaling pathway. Western blot demonstrated ATO reduced the expression of levels of p-MEK, p-RAF1, p-P38, p-ERK, and p-JNK proteins in HCC cells compared to controls. Clinical data showed that HCC patients with ATO exhibited improved recurrence-free survival (RFS) and overall survival (OS). Following the utilization of propensity score, HCC patients with ATO were found to have better OS, whereas there was no substantial difference in RFS. Atorvastatin effectively inhibits the proliferation, invasion, and migration of HCC cells in vitro through the MAPK pathway. Additionally, ATO may help improve the prognosis of some individuals with HCC.

The Daxing’anling region in Inner Mongolia has always been the most active area of tectonic magmatic activity in the Xingmeng orogenic belt. This study investigated the rock geochemistry of trachyandesite and rhyolite tuff of the Late Carboniferous Gegen Aobao Formation in eastern Inner Mongolia. This study presents new petrography, zircon U-Pb age, and whole-rock geochemical data for the Late Carboniferous Gegenaobao Formation in volcanic rocks in order to constrain their petrogenesis and geodynamic setting. The results indicate that the aluminum content of trachyandesite is relatively high, and the calcium and magnesium content is higher than that of rhyolite tuff, showing a sodium-rich characteristic. It is a quasi-aluminum peraluminous rock, and the europium anomaly is not obvious. The formation age is 304.4 ± 2.3 Ma. The calcium and magnesium content of the rhyolite tuff is relatively low, exhibiting characteristics of calcium alkali and weak peraluminous rocks. It has more obvious characteristics of light and heavy rare earth fractionation and negative europium anomalies, with a formation age of 307.6 ± 2.0 Ma. Comprehensive analysis shows that the magma of Late Carboniferous volcanic rocks in eastern Inner Mongolia mainly originates from the crust, with a deeper source of andesite and partial melting of the mantle material. Both are tectonic environments of continental margin arc volcanic rocks. The Xing’an Block and the Songnen Block completed collision assembly in the Early Carboniferous and were in a post-orogenic extension environment in the Late Carboniferous. The ancient Asian Ocean in the northern part of the Erlian Hegenshan Zhalantun Heihe tectonic belt had already closed in the Late Carboniferous, and the Xingmeng orogenic belt began to enter the orogenic extension stage.

In this paper, we propose a generic boosting framework for multiple object tracking (MOT). Unlike other works tracking objects from zero, our framework uses their results (tracklets) and makes further optimizations. The motivation of us derives from the observation that most modern MOT trackers have been acceptable performance and can yield relatively reliable tracklets; accordingly, we straight focus on the tracklet-level re-identification, which is the most challenging issue in this case. To achieve that goal, we simultaneously utilize the techniques of single object tracking, tracking fragment (tracklets) and re-identification mechanism through casting them into a multi-label energy optimization and then innovatively solving it using the α-expansion with label costs algorithm. All these techniques inspire recent MOT a lot to mitigate the occlusion problem, but to our knowledge, by far few works explore to reasonably combine them all like us. Furthermore, we introduce a spatial attention to improve the appearance model and a hierarchical clustering as post-process to progressively improve the tracking consistency. Finally, testing results on the most used benchmarks demonstrate the significant effectiveness and generality of our framework, and the importance of each contribution is also verified through ablative studies.

Dual-phenotype hepatocellular carcinoma (DPHCC) is a new subtype of hepatocellular carcinoma (HCC). This study aimed to investigate the relationship between the computerized tomography scan (CT) imaging and clinicopathologic features of DPHCC. The CT imaging and clinicopathologic data of 97 HCC cases who underwent radical resection were collected retrospectively. The CT imaging feature was evaluated by the ratio of the average CT value of tumor to liver (TLR) in the plain scan, arterial, portal vein and delayed phases. The association between CT imaging and clinicopathologic features was analyzed using the t -test or chi-square test. Univariate and multivariate recurrence-free survival (RFS) analysis and overall survival (OS) were performed. The positive rates of cytokeratin 7 (CK7) and CK19 were 35.1% and 20.6% respectively. The positive rate of CK19 was significantly higher in cases with age < 47 years ( P  = 0.005), tumor diameter > 4 cm ( P  = 0.016) or AFP ≥ 400 ng/ml ( P  = 0.007). The TLR in the portal vein phase was significantly lower in CK19 positive group ( P  = 0.024). The recurrence risk was significantly higher in cases with CK19 positive (HR: 2.17, 95% CI 1.16 to 4.04, P  = 0.013), tumor diameter > 4 cm (HR: 2.05, 95% CI 1.11 to 3.78, P  = 0.019), AFP ≥ 400 ng/ml (HR: 2.50, 95% CI 1.37 to 4.54, P  = 0.002) or CA199 ≥ 37 U/ml (HR: 2.23, 95% CI 1.12 to 4.42, P  = 0.020). However, imaging features, pathological subtype, CK7 or CK19 expression were not significantly related to HCC OS in the univariate and multivariate analysis (all P  > 0.05). The expression of CK19 may be associated with the enhancement feature of the portal vein phase CT image, and CK19 positive may suggest a worse RFS.

Background and objective Post-hepatectomy liver failure (PHLF) is one of the major complications following hepatectomy for hepatocellular carcinoma (HCC). Early identification and precise prediction of PHLF are essential for effective management. This study aimed to evaluate the predictive value of intra-hepatectomy indocyanine green retention rate at 15 min (ICGR15) for the remnant liver for grade B/C PHLF in HCC patients undergoing hemi-hepatectomy. Methods This prospective study recruited 31 HCC patients who underwent hemi-hepatectomy. ICGR15 was measured at three time points: pre-hepatectomy, intra-hepatectomy (for the remnant liver), and post-hepatectomy. The primary endpoint was the occurrence of grade B/C PHLF according to ISGLS criteria. Logistic regression analysis was employed to evaluate the predictive performance of each parameter and to conduct risk assessment. The XGBoost algorithm was utilized to compare the predictive values of various parameters by calculating the mean Shap values. Results Among the study participants, 25.8% (8 patients) developed grade B/C PHLF. The intra-hepatectomy ICGR15 for remnant liver exhibited the highest predictive accuracy for grade B/C PHLF, with a ROC-AUC of 0.864 and a PR-AUC of 0.791. The optimal threshold for ICGR15-intra was established at 19.8%. Patients with ICGR15-intra value of 19.8% or higher were found at significantly increased risk of grade B/C PHLF (OR[95% CI] = 3.602[1.437–6.750], P value = 0.004), and experienced a higher incidence of severe post-hepatectomy complications. Conclusion Intra-hepatectomy ICGR15 for the remnant liver was an important predictor of grade B/C PHLF in patients undergoing hemi-hepatectomy for HCC. An intra-hepatectomy ICGR15 threshold of 19.8% might effectively identify patients at high risk of developing grade B/C PHLF and severe post-hepatectomy complications, helping surgeons’ final decision-making on the table.

Myocardial amyloidosis (CA) differs from other etiological pathologies of left ventricular hypertrophy in that transthoracic echocardiography is challenging to assess the texture features based on human visual observation. There are few studies on myocardial texture based on echocardiography. Therefore, this paper proposes an adaptive machine learning method based on ultrasonic image texture features to identify CA. In this retrospective study, a total of 289 participants (50 cases of myocardial amyloidosis; Hypertrophic cardiomyopathy: 70 cases; Uremic cardiomyopathy: 92 cases; Hypertensive heart disease: 77 cases). We extracted the myocardial ultrasonic imaging features of these patients and screened the features, and four models of random forest (RF), support vector machine (SVM), logistic regression (LR) and gradient decision-making lifting tree (GBDT) were established to distinguish myocardial amyloidosis from other diseases. Finally, the diagnostic efficiency of the model was evaluated and compared with the traditional ultrasonic diagnostic methods. In the overall population, the four machine learning models we established could effectively distinguish CA from nonCA diseases, AUC (RF 0.77, SVM 0.81, LR 0.81, GBDT 0.71). The LR model had the best diagnostic efficiency with recall, F1-score, sensitivity and specificity of 0.21, 0.34, 0.21 and 1.0, respectively. Slightly better than the traditional ultrasonic diagnosis model. In further subgroup analysis, the myocardial amyloidosis group was compared one-by-one with the patients with hypertrophic cardiomyopathy, uremic cardiomyopathy, and hypertensive heart disease groups, and the same method was used for feature extraction and data modeling. The diagnostic efficiency of the model was further improved. Notably, in identifying of the CA group and HHD group, AUC values reached more than 0.92, accuracy reached more than 0.87, sensitivity equal to or greater than 0.81, specificity 0.91, and F1 score higher than 0.84. This novel method based on echocardiography combined with machine learning may have the potential to be used in the diagnosis of CA.

Transport of fast electrons in overdense plasmas is of key importance in high energy density physics. However, it is challenging to diagnose the fast electron transport in experiments. In this article, we study coherent transition radiation (CTR) generated by fast electrons on the back surface of the target by using 2D and 3D first-principle particle-in-cell (PIC) simulations. In our simulations, aluminum targets of 2.7 g cc −1 are simulated in two different situations by using a newly developed high order implicit PIC code. Comparing realistic simulations containing collision and ionization effects, artificial simulations without taking collision and ionization effects into account significantly underestimate the energy loss of electron beams when transporting in the target, which fail to describe the complete characteristics of CTR produced by electron beams on the back surface of the target. Realistic simulations indicate the diameter of CTR increases when the thickness of the target is increased. This is attributed to synergetic energy losses of high flux fast electrons due to Ohm heating and colliding drags, which appear quite significant even when the thickness of the solid target only differs by micrometers. Especially, when the diagnosing position is fixed, we find that the intensity distribution of the CTR is also a function of time, with the diameter increased with time. As the diameter of CTR is related to the speed of electrons passing through the back surface of the target, our finding may be used as a new tool to diagnose the electron energy spectra near the surface of solid density plasmas.

Patients with hepatocellular carcinoma (HCC) exhibit a high rate of recurrence and poor prognosis after surgery, and effective prognostic indicators and stratification strategies are currently lacking. Hence, this study proposes new prognostic markers to provide a theoretical basis for patients with HCC. We aim to build and evaluate a model estimating the effect of alpha-fetoprotein-tumor size ratio (ATR) on the prognosis of patients undergoing hepatectomy for HCC. We retrospectively reviewed hospital records to identify patients who underwent hepatectomy for HCC at the First Affiliated Hospital of Guangxi Medical University from January 2013 to December 2018. Outcomes (recurrence events and mortality) not available in the outpatient medical records were determined through telephone interviews until February 2022. The optimal cutoff value was determined using X-tile (Yale School of Medicine). Independent risk factors for prognosis were investigated by Cox regression modeling, and between-group differences were reduced through propensity score matching. A predictive model for HCC prognosis was constructed using a nomogram, and the predictive performance of the model was evaluated using the C-index. Of the 1628 eligible patients, 1204 patients were included in the analysis. Patients were stratified into low, medium, and high ATR groups with X-tile. Before propensity score matching, ATR was identified as an independent risk factor for overall survival (low vs medium: HR 1.41, 95% CI 1.03-1.94; P=.03; medium versus high: HR 1.59, 95% CI 1.02-2.47; P=.04) and relapse-free survival (low vs medium: HR 1.33, 95% CI 1.03-1.70; P=.03; medium versus high: HR 2.10, 95% CI 1.40-3.15; P<.001) of patients with HCC following hepatectomy. A nomogram incorporating ATR, China Clinic Liver Cancer staging, bleeding, and postoperative transcatheter arterial chemoembolization was developed to predict moderate predictive efficacy for overall survival (C-index: 0.73) and relapse-free survival (C-index: 0.73). ATR was found to be associated with microvascular, macroinvasion, and poor tumor differentiation. ATR is an independent prognostic risk factor in patients with HCC after hepatectomy and is associated with microvascular, macroinvasion, and poor tumor differentiation.

Traditional unsupervised domain adaptation (UDA) usually assumes that the source domain has labels and the target domain has no labels. In a real environment, labelled source domain data usually comes from multiple different distributions. To handle this problem, multi‐source unsupervised domain adaptation (MUDA) is proposed. Multi‐source unsupervised domain adaptation aims to adapt the model trained on multi‐labelled source domains to the unlabelled target domain. In this paper, a novel MUDA method by domain‐specific feature recalibration and alignment (FRA) is proposed. Specifically, to achieve feature recalibration, the authors leverage channel attention to pick out significant channels and spatial attention to focus on important features in different channels. Such integration of channel and spatial attention can lead to effective domain‐specific feature recalibration that may be of great importance to MUDA. In addition, to achieve better MUDA, the authors propose domain‐specific feature alignment which consists of Maximum Mean Discrepancy and JS‐divergence loss. Maximum Mean Discrepancy can reduce the difference between the source domain and target domain. Meanwhile, JS‐divergence loss may ensure the prediction consistency of different classifiers in the source domains. Four experiments have proved that FRA can achieve significantly better results in popular benchmarks for MUDA.

Reminiscence, a therapy that uses stimulating materials such as old photos and videos to stimulate long-term memory, can improve the emotional well-being and life satisfaction of older adults, including those who are cognitively intact. However, providing personalized reminiscence therapy can be challenging for caregivers and family members. This study aimed to achieve three objectives: (1) design and develop the GoodTimes app, an interactive multimodal photo album that uses artificial intelligence (AI) to engage users in personalized conversations and storytelling about their pictures, encompassing family, friends, and special moments; (2) examine the app's functionalities in various scenarios using use-case studies and assess the app's usability and user experience through the user study; and (3) investigate the app's potential as a supplementary tool for reminiscence therapy among cognitively intact older adults, aiming to enhance their psychological well-being by facilitating the recollection of past experiences. We used state-of-the-art AI technologies, including image recognition, natural language processing, knowledge graph, logic, and machine learning, to develop GoodTimes. First, we constructed a comprehensive knowledge graph that models the information required for effective communication, including photos, people, locations, time, and stories related to the photos. Next, we developed a voice assistant that interacts with users by leveraging the knowledge graph and machine learning techniques. Then, we created various use cases to examine the functions of the system in different scenarios. Finally, to evaluate GoodTimes' usability, we conducted a study with older adults (N=13; age range 58-84, mean 65.8 years). The study period started from January to March 2023. The use-case tests demonstrated the performance of GoodTimes in handling a variety of scenarios, highlighting its versatility and adaptability. For the user study, the feedback from our participants was highly positive, with 92% (12/13) reporting a positive experience conversing with GoodTimes. All participants mentioned that the app invoked pleasant memories and aided in recollecting loved ones, resulting in a sense of happiness for the majority (11/13, 85%). Additionally, a significant majority found GoodTimes to be helpful (11/13, 85%) and user-friendly (12/13, 92%). Most participants (9/13, 69%) expressed a desire to use the app frequently, although some (4/13, 31%) indicated a need for technical support to navigate the system effectively. Our AI-based interactive photo album, GoodTimes, was able to engage users in browsing their photos and conversing about them. Preliminary evidence supports GoodTimes' usability and benefits cognitively intact older adults. Future work is needed to explore its potential positive effects among older adults with cognitive impairment.