Clinical efficacy of precision liver resection for primary liver cancer.

AIM: Precision liver resection is considered the gold standard in liver surgery. Therefore, optimizing the resection of lesions and minimizing unnecessary time of liver ischemia and hypoxia have become focal points. METHODS: A total of 96 patients with primary liver cancer admitted to Cangzhou People's Hospital from January 2017 and December 2019 were included in this retrospective study, and divided into two groups according to the different surgical treatment, with 50 cases in the control group (conventional hepatic resection) and 46 cases in the observation group (precision liver resection). The surgical indicators, liver function, alpha-fetoprotein (AFP), complications, and three-year follow-up results were analyzed in the two groups. RESULTS: The operation time, intraoperative bleeding, hospital stay, and time of anal venting in the observation group were shorter than those in the control group (P<0.05). One week after surgery, AST, TBiL, ALT, and γ-GT levels decreased in both groups, with more significant decreases in the observation group than those in the control group (P<0.05). PCT and hs-CRP levels in the observation group were significantly lower than those in the control group (P<0.05) observation. The incidences of pleural effusion, bile leak, abdominal infection, pulmonary infection, as well as the total complication rates in the observation group were lower in the observation group than those in the control group (P<0.05). The follow-up data revealed that the observation group exhibited a lower recurrence rate observationand higher survival rate than the control group within 3 years, but these differences were not significant (P>0.05). CONCLUSION: Precision liver resection can effectively treat primary liver cancer, reduce the incidence of complications, and promote patient recovery after surgery.

Systematic Review and Critical Appraisal of Prediction Models for Readmission in Coronary Artery Disease Patients: Assessing Current Efficacy and Future Directions.

Purpose: Coronary artery disease (CAD) patients frequently face readmissions due to suboptimal disease management. Prediction models are pivotal for detecting early unplanned readmissions. This review offers a unified assessment, aiming to lay the groundwork for enhancing prediction models and informing prevention strategies. Methods: A search through five databases (PubMed, Web of Science, EBSCOhost, Embase, China National Knowledge Infrastructure) up to September 2023 identified studies on prediction models for coronary artery disease patient readmissions for this review. Two independent reviewers used the CHARMS checklist for data extraction and the PROBAST tool for bias assessment. Results: From 12,457 records, 15 studies were selected, contributing 30 models targeting various CAD patient groups (AMI, CABG, ACS) from primarily China, the USA, and Canada. Models utilized varied methods such as logistic regression and machine learning, with performance predominantly measured by the c-index. Key predictors included age, gender, and hospital stay duration. Readmission rates in the studies varied from 4.8% to 45.1%. Despite high bias risk across models, several showed notable accuracy and calibration. Conclusion: The study highlights the need for thorough external validation and the use of the PROBAST tool to reduce bias in models predicting readmission for CAD patients.

Increasing Sufu gene dosage reveals its unorthodox role in promoting polydactyly and medulloblastoma tumorigenesis.

Suppressor of fused (SUFU) is widely regarded as a key negative regulator of the sonic hedgehog (SHH) morphogenic pathway and a known tumor suppressor of medulloblastoma (MB). However, we report here that SUFU expression was markedly increased in 75% of specimens compiled in a tissue array comprising 49 unstratified MBs. The SUFU and GLI1 expression levels in this MB array showed strong positive correlation, which was also identified in a large public data set containing 736 MBs. We further report that increasing Sufu gene dosage in mice caused preaxial polydactyly, which was associated with the expansion of the Gli3 domain in the anterior limb bud and heightened Shh signaling responses during embryonic development. Increasing Sufu gene dosage also led to accelerated cerebellar development and, when combined with ablation of the Shh receptor encoded by Patched1 (Ptch1), promoted MB tumorigenesis. These data reveal multifaceted roles of SUFU in promoting MB tumorigenesis by enhancing SHH signaling. This revelation clarifies potentially counterintuitive clinical observation of high SUFU expression in MBs and may pave way for novel strategies to reduce or reverse MB progression.

Locally Strained 2D Materials: Preparation, Properties, and Applications.

2D materials are promising for strain engineering due to their atomic thickness and exceptional mechanical properties. In particular, non-uniform and localized strain can be induced in 2D materials by generating out-of-plane deformations, resulting in novel phenomena and properties, as witnessed in recent years. Therefore, the locally strained 2D materials are of great value for both fundamental studies and practical applications. This review discusses techniques for introducing local strains to 2D materials, and their feasibility, advantages, and challenges. Then, the unique effects and properties that arise from local strain are explored. The representative applications based on locally strained 2D materials are illustrated, including memristor, single photon emitter, and photodetector. Finally, concluding remarks on the challenges and opportunities in the emerging field of locally strained 2D materials are provided.

Separation of lignin derivatives from hemp fiber using supercritical CO2, ethanol, and water at different temperatures.

The process of lignin extraction often involves intricate chemical transformations, influencing its potential for high-value utilization. By investigating the process of lignin derivatives extraction from hemp fibers using supercritical CO2, ethanol, and water, we identified the relationship between the chemical structure of lignin derivatives and temperature. This discovery contributes to controlling the chemical structure of lignin derivatives through temperature modulation. We observed that lignin derivatives extracted within the temperature range of 100-120 °C exhibited the lowest average molecular weight and polydispersity index, presenting a disordered microstructure with the highest hydroxyl content. Lignin derivatives extracted between 140 and 160 °C showed an increase in average molecular weight and polydispersity index, decreased hydroxyl content, and a gradual transformation of microstructure into spherical particles. At 180 °C, the average molecular weight and polydispersity index of lignin derivatives decreased, the microstructure of lignin derivatives showed fewer spherical particles, while its hydroxyl content exhibited a partial recovery. Chemical analysis revealed a lower degree of condensation in lignin derivatives at 100-120 °C. Between 120 and 160 °C, the degree of condensation increased. At 180 °C, extensive degradation occurred in lignin derivatives. This research advances innovative techniques for lignin derivative separation, contributing to their utilization in higher-value applications.

Removal of nitrate by FeSiBC metallic glasses: high efficiency and superior reusability.

The development of sustainable technologies for efficient nitrate removal has attracted increasing attention, because excessive nitrate emissions can result in serious environmental, economic, and health effects. Herein, we propose to utilize FeSiBC metallic glass (MG) powders as a potential solution for nitrate removal. In terms of removal efficiency and reusability, our results show that the MG powders, as special zero-valent iron carriers, are 2-3 orders of magnitude more efficient in nitrate removal than the previous studies, while maintaining more than 50% nitrate removal efficiency after 9 cycles of reaction. Moreover, the optimal FeSiBC MG dosage, pH value, and temperature for nitrate removal are determined. The mechanism of nitrate removal is also revealed. The present study offers a promising approach to remediate nitrate, one of the world's most widespread water pollutants.

3DCNN predicting brain age using diffusion tensor imaging.

Neuroimaging-based brain age prediction using deep learning is gaining popularity. However, few studies have attempted to leverage diffusion tensor imaging (DTI) to predict brain age. In this study, we proposed a 3D convolutional neural network model (3DCNN) and trained it on fractional anisotropy (FA) data from six publicly available datasets (n = 2406, age = 17-60) to estimate brain age. Implementing a two-loop nested cross-validation scheme with a tenfold cross-validation procedure, we achieved a robust prediction performance of a mean absolute error (MAE) of 2.785 and a correlation coefficient of 0.932. We also employed Grad-Cam++ to visualize the salient features of the proposed model. We identified a few highly salient fiber tracts, including the genu of corpus callosum and the left cerebellar peduncle, among others that play a pivotal role in our model. In sum, our model reliably predicted brain age and provided novel insight into age-related changes in brains' axonal structure.

Harnessing microcomb-based parallel chaos for random number generation and optical decision making.

Optical chaos is vital for various applications such as private communication, encryption, anti-interference sensing, and reinforcement learning. Chaotic microcombs have emerged as promising sources for generating massive optical chaos. However, their inter-channel correlation behavior remains elusive, limiting their potential for on-chip parallel chaotic systems with high throughput. In this study, we present massively parallel chaos based on chaotic microcombs and high-nonlinearity AlGaAsOI platforms. We demonstrate the feasibility of generating parallel chaotic signals with inter-channel correlation <0.04 and a high random number generation rate of 3.84 Tbps. We further show the application of our approach by demonstrating a 15-channel integrated random bit generator with a 20 Gbps channel rate using silicon photonic chips. Additionally, we achieved a scalable decision-making accelerator for up to 256-armed bandit problems. Our work opens new possibilities for chaos-based information processing systems using integrated photonics, and potentially can revolutionize the current architecture of communication, sensing and computations.

Understanding and combatting misinformation across 16 countries on six continents.

The spread of misinformation online is a global problem that requires global solutions. To that end, we conducted an experiment in 16 countries across 6 continents (N = 34,286; 676,605 observations) to investigate predictors of susceptibility to misinformation about COVID-19, and interventions to combat the spread of this misinformation. In every country, participants with a more analytic cognitive style and stronger accuracy-related motivations were better at discerning truth from falsehood; valuing democracy was also associated with greater truth discernment, whereas endorsement of individual responsibility over government support was negatively associated with truth discernment in most countries. Subtly prompting people to think about accuracy had a generally positive effect on the veracity of news that people were willing to share across countries, as did minimal digital literacy tips. Finally, aggregating the ratings of our non-expert participants was able to differentiate true from false headlines with high accuracy in all countries via the 'wisdom of crowds'. The consistent patterns we observe suggest that the psychological factors underlying the misinformation challenge are similar across different regional settings, and that similar solutions may be broadly effective.

Non-invasive detection of haemoglobin, platelets, and total bilirubin using hyperspectral cameras.

Hyperspectral imaging has emerged as a promising high-resolution and real-time imaging technology with potential applications in medical diagnostics and surgical guidance. In this study, we developed a high-speed hyperspectral camera by integrating a Fabry-Perot cavity filter on each CMOS pixel. We used it to non-invasively detect three blood components (haemoglobin, platelet, and total bilirubin). Specifically, we acquired transmission images of the subject's fingers, extracted spectral signals at each wavelength, and used dynamic spectroscopy to obtain non-invasive blood absorption spectra. The prediction models were established using the PLSR method and were modelled and validated based on the standard clinical-biochemical test values. The experimental results demonstrated excellent performance. The best predictions were obtained for haemoglobin, with a high related coefficient (R) of 0.85 or more in both the calibration and prediction sets and a mean absolute percentage error (MAPE) of only 5.7%. The results for total bilirubin were also ideal, with R values exceeding 0.8 in both sets and a MAPE of 10.6%. Although the prediction results for platelets were slightly less satisfactory, the error was still less than 15%, indicating that the results were also acceptable. Overall, our study highlights the potential of hyperspectral imaging technology for the development of portable and affordable devices for blood analysis, which can be used in various settings.

Degradation behavior of ZE21C magnesium alloy suture anchors and their effect on ligament-bone junction repair.

Current materials comprising suture anchors used to reconstruct ligament-bone junctions still have limitation in biocompatibility, degradability or mechanical properties. Magnesium alloys are potential bone implant materials, and Mg2+ has been shown to promote ligament-bone healing. Here, we used Mg-2 wt.% Zn-0.5 wt.% Y-1 wt.% Nd-0.5 wt.% Zr (ZE21C) alloy and Ti6Al4V (TC4) alloy to prepare suture anchors to reconstruct the patellar ligament-tibia in SD rats. We studied the degradation behavior of the ZE21C suture anchor via in vitro and in vivo experiments and assessed its reparative effect on the ligament-bone junction. In vitro, the ZE21C suture anchor degraded gradually, and calcium and phosphorus products accumulated on its surface during degradation. In vivo, the ZE21C suture anchor could maintain its mechanical integrity within 12 weeks of implantation in rats. The tail of the ZE21C suture anchor in high stress concentration degraded rapidly during the early implantation stage (0-4weeks), while bone healing accelerated the degradation of the anchor head in the late implantation stage (4-12weeks). Radiological, histological, and biomechanical assays indicated that the ZE21C suture anchor promoted bone healing above the suture anchor and fibrocartilaginous interface regeneration in the ligament-bone junction, leading to better biomechanical strength than the TC4 group. Hence, this study provides a basis for further research on the clinical application of degradable magnesium alloy suture anchors.

Diatomite-like KFeS2 for Use in High-Performance Electrodes for Energy Storage and Oxygen Evolution.

Bifunctional materials possess remarkable properties that allow them to store and convert electrical energy easily. In this paper, diatomite-like potassium iron disulfide (KFeS2) was synthesized by a multistep sacrificial template method, and its morphological, electrochemical, and oxygen evolution reaction (OER) properties were investigated. KFeS2 was found to be porous, hollow, and cake-like, which suggests a high specific surface area (SSA) and abundant electrochemically active sites. A very high specific capacitance of 651 F g-1 at 1.0 A g-1 was also obtained due to the substance's unique structure and high porosity. Additionally, the diatomite-like KFeS2 possessed a very low overpotential ƞ10 of 254 mV at a current density of 10 mA cm-2 and a small Tafel slope of about 48.4 mV dec-1. Thus, the diatomite-like KFeS2 demonstrates broad application prospects for both energy storage and conversion.

A large dataset of semantic ratings and its computational extension.

Evidence from psychology and cognitive neuroscience indicates that the human brain's semantic system contains several specific subsystems, each representing a particular dimension of semantic information. Word ratings on these different semantic dimensions can help investigate the behavioral and neural impacts of semantic dimensions on language processes and build computational representations of language meaning according to the semantic space of the human cognitive system. Existing semantic rating databases provide ratings for hundreds to thousands of words, which can hardly support a comprehensive semantic analysis of natural texts or speech. This article reports a large database, the Six Semantic Dimension Database (SSDD), which contains subjective ratings for 17,940 commonly used Chinese words on six major semantic dimensions: vision, motor, socialness, emotion, time, and space. Furthermore, using computational models to learn the mapping relations between subjective ratings and word embeddings, we include the estimated semantic ratings for 1,427,992 Chinese and 1,515,633 English words in the SSDD. The SSDD will aid studies on natural language processing, text analysis, and semantic representation in the brain.

Repairing of rutin to the toxicity of combined F-53B and chromium pollution on the biofilm formed by Pseudomonas aeruginosa.

The wastewater discharge from the process of chrome plating, which contains 6:2 chlorinated polyfluorinated ether sulfonate (F-53B) and chromium (Cr), may be toxic to biofilm. In this study we found that the biofilm formed by Pseudomonas aeruginosa PAO1 was inhibited by exposure to a combination of F-53B and Cr(VI). The combined pollution damaged the cell membranes and the structure of the biofilm, and inhibited the production of the Pseudomonas quinolone-based signal, which affected biofilm formation. Moreover, the secretion of extracellular polymeric substances decreased as a result of this combined exposure. Exposure to F-53B and Cr(VI) individually or in combination could induce the excessive accumulation of intracellular reactive oxygen species (ROS), and the ROS positive rate of the bacteria increased under the treatment with 0.2 mmol/L of Cr(VI) and 250 nmol/L of F-53B, respectively. In addition, the activities of superoxide dismutase (SOD) and catalase (CAT) were enhanced for scavenging ROS in the bacteria that were exposed to Cr(VI) and F-53B. As an antioxidant, rutin was used to repair the toxicity of Cr(VI) and F-53B towards the biofilm formed by the bacteria. When rutin was added to the bacteria medium, with either Cr(VI) or F-53B as pollutant, or with the combined pollutants, the extracellular protein content of the bacteria recovered to 0.84, 0.94, and 0.85 times that of the control, respectively. Meanwhile, the accumulation of ROS and the activities of SOD and CAT decreased, which indicated that the addition of rutin can alleviate the oxidative stress and promote the antioxidant stress system.

An fMRI Dataset for Concept Representation with Semantic Feature Annotations.

The neural representation of concepts is a focus of many cognitive neuroscience studies. Prior works studying concept representation with neural imaging data have been largely limited to concrete concepts. The use of relatively small and constrained sets of stimuli leaves open the question of whether the findings can generalize other concepts. We share an fMRI dataset in which 11 participants thought of 672 individual concepts, including both concrete and abstract concepts. The concepts were probed using words paired with images in which the words were selected to cover a wide range of semantic categories. Furthermore, according to the componential theories of concept representation, we collected the 54 semantic features of the 672 concepts comprising sensory, motor, spatial, temporal, affective, social, and cognitive experiences by crowdsourcing annotations. The quality assessment results verify this as a high-quality neuroimaging dataset. Such a dataset is well suited to study how the brain represents different semantic features and concepts, creating the essential condition to investigate the neural representation of individual concepts.

A benchmark study of deep learning-based multi-omics data fusion methods for cancer.

BACKGROUND: A fused method using a combination of multi-omics data enables a comprehensive study of complex biological processes and highlights the interrelationship of relevant biomolecules and their functions. Driven by high-throughput sequencing technologies, several promising deep learning methods have been proposed for fusing multi-omics data generated from a large number of samples. RESULTS: In this study, 16 representative deep learning methods are comprehensively evaluated on simulated, single-cell, and cancer multi-omics datasets. For each of the datasets, two tasks are designed: classification and clustering. The classification performance is evaluated by using three benchmarking metrics including accuracy, F1 macro, and F1 weighted. Meanwhile, the clustering performance is evaluated by using four benchmarking metrics including the Jaccard index (JI), C-index, silhouette score, and Davies Bouldin score. For the cancer multi-omics datasets, the methods' strength in capturing the association of multi-omics dimensionality reduction results with survival and clinical annotations is further evaluated. The benchmarking results indicate that moGAT achieves the best classification performance. Meanwhile, efmmdVAE, efVAE, and lfmmdVAE show the most promising performance across all complementary contexts in clustering tasks. CONCLUSIONS: Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate deep learning-based multi-omics data fusion methods, but also suggest the future directions for the development of more effective multi-omics data fusion methods. The deep learning frameworks are available at https://github.com/zhenglinyi/DL-mo .