Laparoscopic-assisted microwave ablation in treatment of small hepatocellular carcinoma: safety and efficacy in comparison with laparoscopic hepatectomy.

Laparoscopic-assisted microwave ablation (LAMWA), as one of the locoregional therapies, has been employed to treat hepatocellular carcinoma (HCC). This study aims to compare the efficacy and safety of LAMWA and laparoscopic hepatectomy in the treatment of small HCC.This study included 140 patients who met the inclusion criteria. Among them, 68 patients received LAMWA and 72 patients underwent laparoscopic hepatectomy. The perioperative condition, liver function recovery, the alpha fetoprotein (AFP) level, morbidities, hospitalization time, overall survival (OS), disease-free survival (DFS) and recurrence rate between the two groups were compared. The rate of complete elimination of tumor tissue was 100% and the AFP level was returned to normal within 3 months after surgery in both groups (P > 0.05). The mean alanine transaminase (ALT) and aspartate transaminase (AST) peak in the LAMWA group was lower than that in the laparoscopic hepatectomy group (259.51 ± 188.75 VS 388.9 ± 173.65, P = 0.000) and (267.34 ± 190.65 VS 393.1 ± 185.67, P = 0.000), respectively. The mean operation time in the LAMWA group was shorter than that in the laparoscopic hepatectomy group (89 ± 31 min VS 259 ± 48 min, P = 0.000). The blood loss in the LAMWA group was less than that in the laparoscopic hepatectomy group (58.4 ± 64.0 ml VS 213.0 ± 108.2 ml, P = 0.000). Compared with the laparoscopic hepatectomy group, patients in the LAMWA group had lower mean hospital stay (4.8 ± 1.2d VS 11.5 ± 2.9d, P = 0.000). The morbidities of the LAMWA group and the hepatectomy group were 14.7%(10/68) and 34.7%(25/72), respectively (P = 0.006). The one-, three-, and five-year OS rates were 88.2%, 69.9%, 45.6% for the LAMWA group and 86.1%, 72.9%, 51.4% for the laparoscopic hepatectomy group (P = 0.693). The corresponding DFS rates for the two groups were 76.3%, 48.1%, 27.9% and 73.2%, 56.7%, 32.0% (P = 0.958). Laparoscopic-assisted microwave ablation is a safe and effective therapeutic option for selected small HCC.

Versatile Structural Engineering of Metal-Organic Frameworks Enabling Switchable Catalytic Selectivity.

The structure engineering of metal-organic frameworks (MOFs) forms the cornerstone of their applications. Nonetheless, realizing the simultaneous versatile structure engineering of MOFs remains a significant challenge. Herein, a dynamically mediated synthesis strategy to simultaneously engineer the crystal structure, defect structure, and nanostructure of MOFs is proposed. These include amorphous Zr-ODB nanoparticles, crystalline Zr-ODB-hz (ODB = 4,4'-oxalyldibenzoate, hz = hydrazine) nanosheets, and defective d-Zr-ODB-hz nanosheets. Aberration-corrected scanning transmission electron microscopy combined with low-dose high-angle annular dark-field imaging technique vividly portrays these engineered structures. Concurrently, the introduced hydrazine moieties confer self-reduction properties to the respective MOF structures, allowing the in situ installation of catalytic Pd nanoparticles. Remarkably, in the hydrogenation of vanillin-like biomass derivatives, Pd/Zr-ODB-hz yields partially hydrogenated alcohols as the primary products, whereas Pd/d-Zr-ODB-hz exclusively produces fully hydrogenated alkanes. Density functional theory calculations, coupled with experimental evidence, uncover the catalytic selectivity switch triggered by the change in structure type. The proposed strategy of versatile structure engineering of MOFs introduces an innovative pathway for the development of high-performance MOF-based catalysts for various reactions.

SIIR: Symmetrical Information Interaction Modeling for News Recommendation.

Accurate matching between user and candidate news plays a fundamental role in news recommendation. Most existing studies capture fine-grained user interests through effective user modeling. Nevertheless, user interest representations are often extracted from multiple history news items, while candidate news representations are learned from specific news items. The asymmetry of information density causes invalid matching of user interests and candidate news, which severely affects the click-through rate prediction for specific candidate news. To resolve the problems mentioned above, we propose a symmetrical information interaction modeling for news recommendation (SIIR) in this article. We first design a light interactive attention network for user (LIAU) modeling to extract user interests related to the candidate news and reduce interference of noise effectively. LIAU overcomes the shortcomings of complex structure and high training costs of conventional interaction-based models and makes full use of domain-specific interest tendencies of users. We then propose a novel heterogeneous graph neural network (HGNN) to enhance candidate news representation through the potential relations among news. HGNN builds a candidate news enhancement scheme without user interaction to further facilitate accurate matching with user interests, which mitigates the cold-start problem effectively. Experiments on two realistic news datasets, i.e., MIND and Adressa, demonstrate that SIIR outperforms the state-of-the-art (SOTA) single-model methods by a large margin.

Single Site Coordination Enabled Construction of Metal-Diketimine-Linked Covalent Organic Frameworks for Boosted Electrooxidation of Biomass Derivative.

Aromatic aldehydes are widely used for the construction of covalent organic frameworks (COFs). However, due to the high flexibility, high steric hindrance, and low reactivity, it remains challenging to synthesize COFs using ketones as building units, especially the highly flexible aliphatic ones. Here, the single nickel site coordination strategy is reported to lock the configurations of the highly flexible diketimine to transform discrete oligomers or amorphous polymers into highly crystalline nickel-diketimine-linked COFs (named as Ni-DKI-COFs). The strategy has been successfully extended to the synthesis of a series of Ni-DKI-COFs by the condensation of three flexible diketones with two tridentate amines. Thanks to the ABC stacking model with high amount and easily accessible single nickel (II) sites on their 1D channels, Ni-DKI-COFs are exploited as well-defined electrocatalyst platforms for efficiently electro-upgrading biomass-derived 5-Hydroxymethylfurfural (HMF) into value-added 2,5-furandicarboxylic acid (FDCA) with a 99.9% yield and a 99.5% faradaic efficiency as well as a high turnover frequency of 0.31 s-1 .

KFWC: A Knowledge-Driven Deep Learning Model for Fine-grained Classification of Wet-AMD.

BACKGROUND AND OBJECTIVES: Automated diagnosis using deep neural networks can help ophthalmologists detect the blinding eye disease wet Age-related Macular Degeneration (AMD). Wet-AMD has two similar subtypes, Neovascular AMD and Polypoidal Choroidal Vasculopathy (PCV). However, due to the difficulty in data collection and the similarity between images, most studies have only achieved the coarse-grained classification of wet-AMD rather than a fine-grained one of wet-AMD subtypes. Therefore, designing and building a deep learning model to diagnose neovascular AMD and PCV is a great challenge. METHODS: To solve this issue, in this paper, we propose a Knowledge-driven Fine-grained Wet-AMD Classification Model (KFWC) to enhance the model's accuracy in the fine-grained disease classification with insufficient data. We innovatively introduced a two-stage method. In the first stage, we present prior knowledge of 10 lesion signs through pre-training; in the second stage, the model implements the classification task with the help of human knowledge. With the pre-training of priori knowledge of 10 lesion signs from input images, KFWC locates the powerful image features in the fine-grained disease classification task and therefore achieves better classification. RESULTS: To demonstrate the effectiveness of KFWC, we conduct a series of experiments on a clinical dataset collected in cooperation with a Grade III Level A ophthalmology hospital in China. The AUC score of KFWC reaches 99.71%, with 6.69% over the best baseline and 4.14% over ophthalmologists. KFWC can also provide good interpretability and effectively alleviate the pressure of data collection and annotation in the field of fine-grained disease classification for wet-AMD. CONCLUSIONS: The model proposed in this paper effectively solves the difficulties of small data volume and high image similarity in the wet-AMD fine-grained classification task through a knowledge-driven approach. Besides, this method effectively relieves the pressure of data collection and annotation in the field of fine-grained classification. In the diagnosis of wet-AMD, KFWC is superior to previous work and human ophthalmologists.

Multiparametric MRI model with synthetic MRI, DWI multi-quantitative parameters, and differential sub-sampling with cartesian ordering enables BI-RADS 4 lesions diagnosis with high accuracy.

Objective: To assess the feasibility and diagnostic performances of synthetic magnetic resonance imaging (SyMRI) combined with diffusion-weighted imaging (DWI) and differential subsampling with cartesian ordering (DISCO) in breast imaging reporting and data system (BI-RADS) 4 lesions. Methods: A total of 98 BI-RADS 4 patients, including 68 cases assigned to a malignant group and 33 cases assigned to a benign group, were prospectively enrolled, and their MRI and clinical information were collected. Two physicians jointly analyzed the characteristics of conventional MRI. T1, T2, proton density (PD), and ADC values were obtained from three different regions of interest (ROIs). Logistic regression analyses were used to select features and build models, and a nomogram was constructed with the best model. Results: Using the ROI delineation method at the most obvious enhancement to measure the ADC value revealed the best diagnostic performance in diagnosing BI-RADS type 4 mass lesions. The diagnostic efficiency of the maximum level drawing method of the quantitative relaxation model was better than that of the whole drawing method and the most obvious enhancement method. The best relaxation model (model A) was composed of two parameters: T2stand and ΔT1%stand (AUC=0.887), and the BI-RADS model (model B) was constructed by two MRI features of edge and TIC curve (AUC=0.793). Using the quantitative parameters of SyMRI and DWI of the best ROC method combined with DISCO enhanced MRI features to establish a joint diagnostic model (model C: edge, TIC curve type, ADClocal, T2stand, ΔT1%stand) showed the best diagnostic efficiency (AUC=0.953). The nomogram also had calibration curves with good overlap. Conclusions: The combined diagnosis model of SyMRI and DWI quantitative parameters combined with DISCO can improve the diagnostic efficiency of BI-RADS 4 types of mass lesions. Also, the line diagram based on this model can be used as an auxiliary diagnostic tool.

Identification of ocular refraction based on deep learning algorithm as a novel retinoscopy method.

BACKGROUND: The evaluation of refraction is indispensable in ophthalmic clinics, generally requiring a refractor or retinoscopy under cycloplegia. Retinal fundus photographs (RFPs) supply a wealth of information related to the human eye and might provide a promising approach that is more convenient and objective. Here, we aimed to develop and validate a fusion model-based deep learning system (FMDLS) to identify ocular refraction via RFPs and compare with the cycloplegic refraction. In this population-based comparative study, we retrospectively collected 11,973 RFPs from May 1, 2020 to November 20, 2021. The performance of the regression models for sphere and cylinder was evaluated using mean absolute error (MAE). The accuracy, sensitivity, specificity, area under the receiver operating characteristic curve, and F1-score were used to evaluate the classification model of the cylinder axis. RESULTS: Overall, 7873 RFPs were retained for analysis. For sphere and cylinder, the MAE values between the FMDLS and cycloplegic refraction were 0.50 D and 0.31 D, representing an increase of 29.41% and 26.67%, respectively, when compared with the single models. The correlation coefficients (r) were 0.949 and 0.807, respectively. For axis analysis, the accuracy, specificity, sensitivity, and area under the curve value of the classification model were 0.89, 0.941, 0.882, and 0.814, respectively, and the F1-score was 0.88. CONCLUSIONS: The FMDLS successfully identified the ocular refraction in sphere, cylinder, and axis, and showed good agreement with the cycloplegic refraction. The RFPs can provide not only comprehensive fundus information but also the refractive state of the eye, highlighting their potential clinical value.

Research on early warning of renal damage in hypertensive patients based on the stacking strategy.

BACKGROUND: Among the problems caused by hypertension, early renal damage is often ignored. It can not be diagnosed until the condition is severe and irreversible damage occurs. So we decided to screen and explore related risk factors for hypertensive patients with early renal damage and establish the early-warning model of renal damage based on the data-mining method to achieve an early diagnosis for hypertensive patients with renal damage. METHODS: With the aid of an electronic information management system for hypertensive out-patients, we collected 513 cases of original, untreated hypertensive patients. We recorded their demographic data, ambulatory blood pressure parameters, blood routine index, and blood biochemical index to establish the clinical database. Then we screen risk factors for early renal damage through feature engineering and use Random Forest, Extra-Trees, and XGBoost to build an early-warning model, respectively. Finally, we build a new model by model fusion based on the Stacking strategy. We use cross-validation to evaluate the stability and reliability of each model to determine the best risk assessment model. RESULTS: According to the degree of importance, the descending order of features selected by feature engineering is the drop rate of systolic blood pressure at night, the red blood cell distribution width, blood pressure circadian rhythm, the average diastolic blood pressure at daytime, body surface area, smoking, age, and HDL. The average precision of the two-dimensional fusion model with full features based on the Stacking strategy is 0.89685, and selected features are 0.93824, which is greatly improved. CONCLUSIONS: Through feature engineering and risk factor analysis, we select the drop rate of systolic blood pressure at night, the red blood cell distribution width, blood pressure circadian rhythm, and the average diastolic blood pressure at daytime as early-warning factors of early renal damage in patients with hypertension. On this basis, the two-dimensional fusion model based on the Stacking strategy has a better effect than the single model, which can be used for risk assessment of early renal damage in hypertensive patients.

Efficient Excitation and Tuning of Multi-Fano Resonances with High Q-Factor in All-Dielectric Metasurfaces.

Exciting Fano resonance can improve the quality factor (Q-factor) and enhance the light energy utilization rate of optical devices. However, due to the large inherent loss of metals and the limitation of phase matching, traditional optical devices based on surface plasmon resonance cannot obtain a larger Q-factor. In this study, a silicon square-hole nano disk (SHND) array device is proposed and studied numerically. The results show that, by breaking the symmetry of the SHND structure and transforming an ideal bound state in the continuum (BIC) with an infinite Q-factor into a quasi-BIC with a finite Q-factor, three Fano resonances can be realized. The calculation results also show that the three Fano resonances with narrow linewidth can produce significant local electric and magnetic field enhancements: the highest Q-factor value reaches 35,837, and the modulation depth of those Fano resonances can reach almost 100%. Considering these properties, the SHND structure realizes multi-Fano resonances with a high Q-factor, narrow line width, large modulation depth and high near-field enhancement, which could provide a new method for applications such as multi-wavelength communications, lasing, and nonlinear optical devices.

GKK1032B from endophytic Penicillium citrinum induces the apoptosis of human osteosarcoma MG63 cells through caspase pathway activation.

Chemical investigation of the culture extract of an endophytic Penicillium citrinum from Dendrobium officinale, afforded nine citrinin derivatives (1-9) and one peptide-polyketide hybrid GKK1032B (10). The structures of these compounds were determined by spectroscopic methods. The absolute configurations of 1 and 2 were determined for the first time by calculation of electronic circular dichroism (ECD) data. Among them, GKK1032B (10) showed significant cytotoxicity against human osteosarcoma cell line MG63 with an IC50 value of 3.49 µmol·L-1, and a primary mechanistic study revealed that it induced the apoptosis of MG63 cellsvia caspase pathway activation.

MBNM: Multi-branch network based on memory features for long-tailed medical image recognition.

BACKGROUND AND OBJECTIVES: Deep learning algorithms show revolutionary potential in computer-aided diagnosis. These computer-aided diagnosis techniques often rely on large-scale, balanced standard datasets. However, there are many rare diseases in real clinical scenarios, which makes the medical datasets present a highly imbalanced long-tailed distribution, leading to the poor generalization ability of deep learning models. Currently, most algorithms to solve this problem involve more complex modules and loss functions. But for complicated tasks in the medical domain, usually suffer from issues such as increased inference time and unstable performance. Therefore, it is a great challenge to develop a computer-aided diagnosis algorithm for long-tailed medical data. METHODS: We proposed the Multi-Branch Network based on Memory Features (MBNM) for Long-Tailed Medical Image Recognition. MBNM includes three branches, where each branch focuses on a different learning task: 1) the regular learning branch learns the generalizable feature representations; 2) the tail learning branch gains extra intra-class diversity for the tail classes through the feature memory module and the improved reverse sampler to improve the classification performance of the tail classes; 3) the fusion balance branch integrates various decision-making advantages and introduces an adaptive loss function to re-balance the classification performance of easy and difficult samples. RESULTS: We conducted experiments on the multi-disease Ophthalmic OCT datasets with imbalance factors of 98.48 and skin image datasets Skin-7 with imbalance factors of 58.3. The Accuracy, MCR, F1-Score, Precision, and AUC of our model were significantly improved over the strong baselines in the auxiliary diagnosis scenario where the clinical medical data is extremely imbalanced. Furthermore, we demonstrated that MBNM outperforms the state-of-the-art models on the publicly available natural image datasets (CIFAR-10 and CIFAR-100). CONCLUSIONS: The proposed algorithm can solve the problem of imbalanced data distribution with little added cost. In addition, the memory module does not act in the inference phase, thereby saving inference time. And it shows outstanding performance on medical images and natural images with a variety of imbalance factors.

New oxylipins from Siegesbeckia glabrescens as potential antibacterial agents.

Seven rare oxylipins, siegesbeckins A-G (1-7) representing further bioactive constituents different from the general terpenyl compounds found in Siegesbeckia species, have been obtained from the aerial parts of Siegesbeckia glabrescens. These isolates were identified to be a series of methyl 4-methylpentanoates incorporating fatty acid moieties of different chain lengths, based on spectroscopic techniques, and their absolute configurations were determined via chemical degradation and comparison of experimental and theoretically calculated ECD spectra. With respect to bioactivity, antibacterial, anti-inflammatory and cytotoxic properties of selected compounds were evaluated. Compounds 1 and 5 showed moderate antibacterial activity against two Gram-positive bacteria with MIC values of 4.3 µg/mL, while 3 showed no pronounced activity in these assays.

Eu-based coordination polymer microrods for low-loss optical waveguiding application.

Lanthanide-based coordination polymers (CPs) have received great attention due to their tuneable structures and excellent luminescence properties. However, limited by the stability and micro/nanoscale morphology, a very small number of lanthanide-based CPs have been used for photonic applications. Herein, we present the synthesis of Eu-based CPs (compound 2) with highly regular one-dimensional (1D) microrod morphology by in situ structure transformation from compound 1. Moreover, the Eu-based CP microrods have an excellent stability and a high photoluminescence quantum yield (PLQY), and the distance-dependent PL spectra also exhibited typical characteristics of photoactive waveguides with a low optical-loss coefficient (0.00894 dB µm-1) during propagation. These intriguing behaviors not only extend the research field of optical waveguides through lanthanide-based CPs, but also provide an opportunity for further application in optical communication systems.

Germacrane-type sesquiterpenoids with cytotoxic activity from Sigesbeckia orientalis.

Eleven new highly oxygenated germacrane-type sesquiterpenoids (1-11) and 16 known analogues (12-27) were isolated from the aerial parts of Sigesbeckia orientalis. Their structures, including absolute configurations, were determined by comprehensive spectroscopic methods especially NMR and ECD analyses. Compounds 13, 21 and 23 possessing an 8-methacryloxy group showed stronger in vitro cytotoxicity against human A549 and MDA-MB-231 cancer cell lines than other co-metabolites, with IC50 values ranging from 6.02 to 10.77 µM comparable to the positive control adriamycin.

Clinical Trial Registration and Reporting: Drug Therapy and Prevention of Cardiac-Related Infections.

Objective: Clinical trials are the source of evidence. ClinicalTrials.gov is valuable for analyzing current conditions. Until now, the state of drug interventions for heart infections is unknown. The purpose of this study was to comprehensively assess the characteristics of trials on cardiac-related infections and the status of drug interventions. Methods: The website ClinicalTrials.gov was used to obtain all registered clinical trials on drug interventions for cardiac-related infections as of February 16, 2019. All registration studies were collected, regardless of their recruitment status, research results, and research type. Registration information, results, and weblink-publications of those trials were analyzed. Results: A total of 45 eligible trials were evaluated and 86.7% of them began from or after 2008 while 91.1% of them adopted interventional study design. Of all trials, 35.6% were completed and 15.6% terminated. Besides, 62.2% of interventional clinical trials recruited more than 100 subjects. Meanwhile, 86.7% of the eligible trials included adult subjects only. Of intervention trials, 65.8% were in the third or fourth phase; 78.1% adopted randomized parallel assignment, containing two groups; 53.6% were masking, and 61.0% described treatment. Moreover, 41.5% of the trials were conducted in North America while 29.3% in Europe. Sponsors for 40.0% of the studies were from the industry. Furthermore, 48.9% of the trials mentioned information on monitoring committees, 24.4% have been published online, and 13.3% have uploaded their results. Drugs for treatments mainly contained antibiotics, among which glycopeptides, ß-lactams, and lipopeptides were the most commonly studied ones in experimental group, with the former ones more common. Additionally, 16.2% of the trials evaluated new antimicrobials. Conclusions: Most clinical trials on cardiac-related infections registered at ClinicalTrials.gov were interventional randomized controlled trials (RCTs) for treatment. Most drugs focused in trials were old antibiotics, and few trials reported valid results. It is necessary to strengthen supervision over improvements in results, and to combine antibacterial activity with drug delivery regimens to achieve optimal clinical outcomes.