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Background and aimsMetabolic reprogramming has been found to be a typical feature of tumors. Hepatocellular carcinoma (HCC), a cancer with high morbidity and mortality, has been extensively studied for its metabolic reprogramming-related mechanisms. Our study aims to identify the hotspots and frontiers of metabolic reprogramming research in HCC and to provide guidance for future scientific research and decision-making in HCC metabolism.MethodsRelevant studies on the metabolic reprogramming of HCC were derived from the Web of Science Core Collection (WoSCC) database up until November 2023. The bibliometrix tools in R were used for scientometric analysis and visualization.ResultsFrom 2011 to 2023, a total of 575 publications were obtained from WoSCC that met the established criteria. These publications involved 3,904 researchers and 948 organizations in 37 countries, with an average annual growth rate of 39.11% in research. These studies were published in 233 journals, with Cancers (n = 29) ranking first, followed by Frontiers in Oncology (n = 20) and International Journal of Molecular Sciences (n = 19). The top ten journals accounted for 26% of the 575 studies. The most prolific authors were Wang J (n = 14), Li Y (n = 12), and Liu J (n = 12). The country with the most publications is China, followed by the United States, Italy, and France. Fudan University had the largest percentage of research results with 15.48% (n = 89). Ally A’s paper in Cell has the most citations. A total of 1,204 keywords were analyzed, with the trend themes such as “glycolysis,” “tumor microenvironment,” “Warburg effect,” “mitochondria,” “hypoxia ,” etc. Co-occurrence network and cluster analysis revealed the relationships between keywords, authors, publications, and journals. Moreover, the close collaboration between countries in this field was elucidated.ConclusionThis bibliometric and visual analysis delves into studies related to metabolic reprogramming in HCC between 2012 and 2023, elucidating the characteristics of research in this field, which has gradually moved away from single glycolipid metabolism studies to the integration of overall metabolism in the body, pointing out the trend of research topics, and the dynamics of the interaction between the tumor microenvironment and metabolic reprogramming will be the future direction of research, which provides blueprints and inspirations for HCC prevention and treatment programs to the researchers in this field.Systematic Review Registration: [https://www.bibliometrix.org].

Small and imbalanced fault samples have a profound impact on the diagnostic performance of a model in the process of locating and quantifying the rolling bearing damage of aeroengines in practice. Therefore, a Siamese Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model was proposed in this paper. Random selection and cross combination methods were used to augment and balance sample sizes at first. Then, two weight-sharing CNN-BiLSTM models were used for adaptive extraction and distance measurement of weak fault features. Finally, the fault classification was performed based on feature distance. Model performance was verified using simulated fault test data of rolling bearings. The results showed that the Siamese CNN-BiLSTM model could achieve an accuracy of up to 96.0% for quantitative diagnosis and 98.0% for location diagnosis. This model was also capable of solving the imbalanced classification of samples and made it possible to transfer between different rotating speeds and working conditions.

Objective: This retrospective study was aimed to develop a predictive model for assessing the necessity of tracheostomy (TT) in patients admitted to the neurosurgery intensive care unit (NSICU). Method: We analyzed data from 1626 NSICU patients with severe acute brain injury (SABI) who were admitted to the Department of NSICU at the Affiliated People's Hospital of Jiangsu University between January 2021 and December 2022. Data of the patients were retrospectively obtained from the clinical research data platform. The patients were randomly divided into training (70%) and testing (30%) cohorts. The least absolute shrinkage and selection operator (LASSO) regression identified the optimal predictive features. A multivariate logistic regression model was then constructed and represented by a nomogram. The efficacy of the model was evaluated based on discrimination, calibration, and clinical utility. Results: The model highlighted six predictive variables, including the duration of NSICU stay, neurosurgery, orotracheal intubation time, Glasgow Coma Scale (GCS) score, systolic pressure, and respiration rate. Receiver operating characteristic (ROC) analysis of the nomogram yielded area under the curve (AUC) values of 0.854 (95% confidence interval [CI]: 0.822–0.886) for the training cohort and 0.865 (95% CI: 0.817–0.913) for the testing cohort, suggesting commendable differential performance. The predictions closely aligned with actual observations in both cohorts. Decision curve analysis demonstrated that the numerical model offered a favorable net clinical benefit. Conclusion: We developed a novel predictive model to identify risk factors for TT in SABI patients within the NSICU. This model holds the potential to assist clinicians in making timely surgical decisions concerning TT.

Summary: Aiming at the current SPI (solder paste inspection) system for printing solder paste similar defects detection accuracy is not high, the system intelligence degree is low and so on, design a for the solder paste similar defects and combined with phase modulation profile measurement technique and improve the YOLOX intelligent detection system. The core of the system is the improved YOLOX depth model based on s-mosica and kt-iou algorithms proposed in this paper. The experimental results show that the proposed s-mosica and kt-iou algorithms can effectively improve the detection accuracy of printed solder paste, and when combined with the YOLOX model, the best 90.33% detection accuracy is obtained, which is better than the detection performance of the existing algorithms in the same scenario, and it provides an effective and feasible reference program for the design of the SPI high-precision intelligent detection system.

Medical artificial intelligence (AI) and big data technology have rapidly advanced in recent years, and they are now routinely used for image-based diagnosis. China has a massive amount of medical data. However, a uniform criteria for medical data quality have yet to be established. Therefore, this review aimed to develop a standardized and detailed set of quality criteria for medical data collection, storage, annotation, and management related to medical AI. This would greatly improve the process of medical data resource sharing and the use of AI in clinical medicine.

This study used a nickel-based filler metal to fabricate a surfacing layer on a low-carbon steel substrate by a hybrid welding process with a rotating laser and arc. The surfacing layer's weld formation was studied using optical microscopic observation (OM). The microstructure of the layers was studied by using the scanning electron microscope (SEM), energy dispersive spectrum (EDS) and X-ray diffraction (XRD). The arc, metal transfer, molten pool flow behaviors, and U–I characteristics during hybrid laser arc welding (HLAW) were analyzed using the arc analyzer and high-speed camera. The results show that a rotating laser improves the weld formation of gas metal arc welding (GMAW). The microstructure of the two surfacing layers consists of dendritic γ phase and a small amount of Laves and NbC phases. The high dilution rate of the HLAW surfacing layer results in relatively more Laves and fewer NbC phases. The arc length of HLAW is dynamically deflected with the rotation of the laser, which expands the arc action and heating area of the molten pool. The droplet transfer mode changed from globular transfer dominant in GMAW to the projected transfer dominant in HLAW. The location where the droplet transfer into the molten pool is dynamic changed, forming a multi-point impact on the molten pool. Combined with the deflected arc with the laser rotating laser, the HLAW has a broad and deep molten pool. Understanding the metal transfer behavior of Ni-based alloys in rotating laser-induced arc hybrid surfacing is of great significance for improving surfacing forming.

Abstract In contrast to the well-established genomic 5-methylcytosine (5mC), the existence of N6-methyladenine (6 mA) in eukaryotic genomes was discovered only recently. Initial studies found that it was actively regulated in cancer cells, suggesting its involvement in the process of carcinogenesis. However, the contribution of 6 mA in tongue squamous cell carcinoma (TSCC) still remains uncharacterized. In this study, a pan-cancer type analysis was first performed, which revealed enhanced 6 mA metabolism in diverse cancer types. The study was then focused on the regulation of 6 mA metabolism, as well as its effects on TSCC cells. To these aspects, genome 6 mA level was found greatly increased in TSCC tissues and cultured cells. By knocking down 6 mA methylases N6AMT1 and METTL4, the level of genomic 6 mA was decreased in TSCC cells. This led to suppressed colony formation and cell migration. By contrast, knockdown of 6 mA demethylase ALKBH1 resulted in an increased 6 mA level, enhanced colony formation, and cell migration. Further study suggested that regulation of the NF-κB pathway might contribute to the enhanced migration of TSCC cells. Therefore, in the case of TSCC, we have shown that genomic 6 mA modification is involved in the proliferation and migration of cancer cells.

Abstract Lead (Pb) and cadmium (Cd) are toxic heavy metals commonly found in aqueous environments. Biochar as a green adsorbent generated from biomass feedstock may be used for effective removal of these heavy metals. This study investigated the adsorption kinetics and isotherms of Pb2+ and Cd2+ in aqueous solutions at different pH by biochar prepared from banana stem and leaf (BSL-BC) at 400 °C. Characterizations using scanning electron microscope, X-ray diffraction, and Fourier-transform infrared spectroscopy showed that the synthesized BSL-BC had rough surface, porous structure, and oxygen-containing functional groups. The adsorption of Pb2+ and Cd2+ onto BSL-BC reached equilibrium in 8 h and 200 min, respectively, with faster adsorption attained at higher pH and the optimum pH occurred at 5 (Pb2+) and 8 (Cd2+). All adsorption kinetic data followed the pseudo-second-order rate model. The adsorption isotherm data of Pb2+ and Cd2+ could be well-described by the Langmuir and Freundlich models, respectively, whereas neither the Temkin or Dubinin–Radushkevich models provided satisfactory fitting results. The maximum adsorption capacities for Pb2+ and Cd2+ were 302.20 and 32.03 mg/g, respectively. The calculated mechanism contributions showed that complexation with oxygen-containing functional groups, ion exchange, mineral precipitation, and Pb2+/Cd2+-π coordination accounted for 0.1%, 8.4%, 88.8%, and 2.6% to Pb2+ adsorption, and 0.4%, 6.3%, 83.0%, and 10.4% to Cd2+ adsorption, respectively. Therefore, mineral precipitation was likely the major mechanism responsible for adsorption of both Pb2+ and Cd2+ by BSL-BC. The results suggest that the synthesized BSL-BC has great potential for adsorption of Pb2+ and Cd2+ from aqueous solutions.

Aiming at the problem of remaining useful life prediction of rolling bearing in aero engine, a data-driven prediction method based on deep learning and particle filter is proposed. Initially, only the vibration data of rolling bearing in normal stage are trained by the deep convolution neural network. According to the feature distance between normal and degraded samples, the evolution features during the whole lifetime are extracted adaptively, and the health index of rolling bearing is constructed. Then, the alarm and failure threshold are determined by unsupervised clustering algorithm. Combined with the extracted feature, remaining useful life of rolling bearing is tracked and predicted by particle filter algorithm based on four parameter exponential model. Finally, the effectiveness of the proposed method is verified by three groups of whole lifetime test data of rolling bearings. Results show that the degradation feature extracted by deep learning method has higher prediction accuracy of 2.19%, 0.93%, and 1.43% respectively than RMS values, and has more stable performance and less influenced by the number of particles or resampling methods, which can better reflect the evolution trend of rolling bearing than the traditional feature.

The ratio of tumour area to metastatic lymph node area (T/MLN) is a clinical indicator that can improve prognosis prediction of gastric cancer. Here, the authors use machine learning on whole slide images to generate a method that can predict metastatic lymph nodes and obtain T/MLN.

Abstract RT-LAMP detection of SARS-CoV-2 has been shown to be a valuable approach to scale up COVID-19 diagnostics and thus contribute to limiting the spread of the disease. Here we present the optimization of highly cost-effective in-house produced enzymes, and we benchmark their performance against commercial alternatives. We explore the compatibility between multiple DNA polymerases with high strand-displacement activity and thermostable reverse transcriptases required for RT-LAMP. We optimize reaction conditions and demonstrate their applicability using both synthetic RNA and clinical patient samples. Finally, we validate the optimized RT-LAMP assay for the detection of SARS-CoV-2 in unextracted heat-inactivated nasopharyngeal samples from 184 patients. We anticipate that optimized and affordable reagents for RT-LAMP will facilitate the expansion of SARS-CoV-2 testing globally, especially in sites and settings where the need for large scale testing cannot be met by commercial alternatives.

Abstract A challenge of chronic diseases that remains to be solved is how to liberate patients and medical resources from the burdens of long-term monitoring and periodic visits. Precise management based on artificial intelligence (AI) holds great promise; however, a clinical application that fully integrates prediction and telehealth computing has not been achieved, and further efforts are required to validate its real-world benefits. Taking congenital cataract as a representative, we used Bayesian and deep-learning algorithms to create CC-Guardian, an AI agent that incorporates individualized prediction and scheduling, and intelligent telehealth follow-up computing. Our agent exhibits high sensitivity and specificity in both internal and multi-resource validation. We integrate our agent with a web-based smartphone app and prototype a prediction-telehealth cloud platform to support our intelligent follow-up system. We then conduct a retrospective self-controlled test validating that our system not only accurately detects and addresses complications at earlier stages, but also reduces the socioeconomic burdens compared to conventional methods. This study represents a pioneering step in applying AI to achieve real medical benefits and demonstrates a novel strategy for the effective management of chronic diseases.

Recently, drug delivery vehicles based on nanotechnology have significantly attracted the attention of researchers in the field of nanomedicine since they can achieve ideal drug release and biodistribution. Among the various organic or inorganic materials that used to prepare drug delivery vehicles for effective cancer treatment, serum albumin-based nanovehicles have been widely developed and investigated due to their prominent superiorities, including good biocompatibility, high stability, nontoxicity, non-immunogenicity, easy preparation, and functionalization, allowing them to be promising candidates for cancer diagnosis and therapy. This article reviews the recent advances on the applications of serum albumin-based nanovehicles in cancer diagnosis and therapy. We first introduce the essential information of bovine serum albumin (BSA) and human serum albumin (HSA), and discuss their drug loading strategies. We then discuss the different types of serum albumin-based nanovehicles including albumin nanoparticles, surface-functionalized albumin nanoparticles, and albumin nanocomplexes. Moreover, after briefly discussing the application of serum albumin-based nanovehicles used as the nanoprobes in cancer diagnosis, we also describe the serum albumin-based nanovehicle-assisted cancer theranostics, involving gas therapy, chemodynamic therapy (CDT), phototherapy (PTT/PDT), sonodynamic therapy (SDT), and other therapies as well as cancer imaging. Numerous studies cited in our review show that serum albumin-based nanovehicles possess a great potential in cancer diagnostic and therapeutic applications.

With the increasingly strict regulations for the energy-saving and emission-reduction technology of ships, minimizing fuel cost and thus reducing the carbon intensity index (CII) is one of the most critical issues in the design and operation of merchant ships. More recently, many wind-assisted devices, such as rotors, wind sails, etc., have been investigated and designed to utilize renewable wind energy. With the equipment of wind-assisted rotors, the optimization of ship routes becomes more important because the effect of this wind-assisted device highly depends on the local wind field along the shipping route. In this paper, an improved ship weather routing framework based on the A* algorithm has been proposed to determine the optimal ship route and ship operations with wind-assisted rotors. The proposed framework effectively utilizes different sources of data, including ship design, weather forecasting and historical sailing information, to produce a better estimation of fuel consumption under the effect of sea states. Several improvements on the classic A* algorithm, including directed searching and three-dimensional extension, are proposed to improve the routing effect and efficiency. Finally, the proposed method was applied to test cases of a VLCC operating from China to the Middle East and the results show that the total fuel consumption could be reduced compared to the minimum distance route.

RT-LAMP detection of SARS-CoV-2 has been demonstrated to be a valuable diagnostic method for the diagnosis of COVID-191,2, which can rapidly screen carriers of the virus to effectively control the spread of the SARS-CoV-2. Here, we present a combination of dyes for isothermal detection of SARS-CoV-2 as a commercial alternative, with expanded colorimetric spectrum. We compared them with commercial reagents and proved their suitability and sensitivity through clinical RNA samples. In addition, together with commercial single dye indicators, we believe the expanded color spectrum developed here as an indicator of rapid detection will promote the diagnosis of COVID-19.

Printed Circuit Board (PCB) surface tiny defect detection is a difficult task in the integrated circuit industry, especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks. To improve the performance of PCB surface tiny defects detection, a PCB tiny defects detection model based on an improved attention residual network (YOLOX-AttResNet) is proposed. First, the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet (Squeeze and Excitation Network) attention network; then the improved K-means-SENet network is fused with the directly mapped edges of the traditional ResNet network to form an augmented residual network (AttResNet); and finally, the AttResNet module is substituted for the traditional ResNet structure in the backbone feature extraction network of mainstream excellent detection models, thus improving the ability to extract small features from the backbone of the target detection network. The results of ablation experiments on a PCB surface defect dataset show that AttResNet is a reliable and efficient module. In Torify the performance of AttResNet for detecting small defects in large-size complex circuit images, a series of comparison experiments are further performed. The results show that the AttResNet module combines well with the five best existing target detection frameworks (YOLOv3, YOLOX, Faster R-CNN, TDD-Net, Cascade R-CNN), and all the combined new models have improved detection accuracy compared to the original model, which suggests that the AttResNet module proposed in this paper can help the detection model to extract target features. Among them, the YOLOX-AttResNet model proposed in this paper performs the best, with the highest accuracy of 98.45% and the detection speed of 36 FPS (Frames Per Second),which meets the accuracy and real-time requirements for the detection of tiny defects on PCB surfaces. This study can provide some new ideas for other real-time online detection tasks of tiny targets with high-resolution images. [ABSTRACT FROM AUTHOR]

Abstract Background The common treatment for pediatric cataracts is to replace the cloudy lens with an artificial one. However, patients may suffer complications (severe lens proliferation into the visual axis and abnormal high intraocular pressure; SLPVA and AHIP) within 1 year after surgery and factors causing these complications are unknown. Methods Apriori algorithm is employed to find association rules related to complications. We use random forest (RF) and Naïve Bayesian (NB) to predict the complications with datasets preprocessed by SMOTE (synthetic minority oversampling technique). Genetic feature selection is exploited to find real features related to complications. Results Average classification accuracies in three binary classification problems are over 75%. Second, the relationship between the classification performance and the number of random forest tree is studied. Results show except for gender and age at surgery (AS); other attributes are related to complications. Except for the secondary IOL placement, operation mode, AS and area of cataracts; other attributes are related to SLPVA. Except for the gender, operation mode, and laterality; other attributes are related to the AHIP. Next, the association rules related to the complications are mined out. Then additional 50 data were used to test the performance of RF and NB, both of then obtained the accuracies of over 65% for three classification problems. Finally, we developed a webserver to assist doctors. Conclusions The postoperative complications of pediatric cataracts patients can be predicted. Then the factors related to the complications are found. Finally, the association rules that is about the complications can provide reference to doctors.

Summary: SIRT7 is a member of the mammalian sirtuins and functions as an NAD+-dependent deacylase. Here we show that SIRT7 deficiency leads to a lowered histone acetyltransferase 1 (HAT1) activity and therefore decreased histone H4K5 and H4K12 acetylation. This in turn causes CENP-A dislocation at the centromere, which further affects chromatin assembly. SIRT7 ablation results in aneuploidy and aging phenotypes, including senescence and nucleolar expansion. Moreover, SIRT7 knockout mice are susceptible to DSS-induced colitis and alcohol-derived epithelial disturbance, revealing a disrupted intestinal epithelial homeostasis. Notably, absence of SIRT7 aggravates the susceptibility of colorectal cancer incidence in APCMin/+ mouse model and elicits further the Wnt signaling. Our findings indicate a tumor suppressive role of SIRT7 in the case of colorectal cancer. Together with the activities in maintaining genome integrity and intestinal homeostasis, activating SIRT7 may serve as a strategy to treat bowel diseases and colorectal cancer.

Abstract Background Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial. Methods In this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient. Results Qualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method. Conclusion Our study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.