Failure of enhanced recovery after surgery in liver surgery: a systematic review and meta analysis

PurposeThis study aimed to conduct a systematic review of the literature to identify and summarize the existing evidence regarding ERAS failure and related risk factors after hepatic surgery. The objective was to provide physicians with a better understanding of these factors so that they can take appropriate action to minimize ERAS failure and improve patient outcomes.MethodA literature search of the PubMed MEDLINE, OVID, EMBASE, Cochrane Library, and Web of Science was performed. The search strategy involved terms related to ERAS, failure, and hepatectomy.ResultA meta-analysis was conducted on four studies encompassing a total of 1,535 patients, resulting in the identification of 20 risk factors associated with ERAS failure after hepatic surgery. Four of these risk factors were selected for pooling, including major resection, ASA classification of ≥3, advanced age, and male gender. Major resection and ASA ≥ 3 were identified as statistically significant factors of ERAS failure.ConclusionThe comprehensive literature review results indicated that the frequently identified risk factors for ERAS failure after hepatic surgery are linked to operative and anesthesia factors, including substantial resection and an American Society of Anesthesiologists score of 3 or higher. These insights will assist healthcare practitioners in taking prompt remedial measures. Nevertheless, there is a requirement for future high-quality randomized controlled trials with standardized evaluation frameworks for ERAS programs

A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix Factorization

The emergence of hyperspectral imagery paved a new way for rapid mineral mapping. As a classical hyperspectral classification method, spectral matching (SM) can automatically map the spatial distribution of minerals without the need for selecting training samples. However, due to the influence of noise, the mapping accuracy of SM is usually poor, and its per-pixel matching method is inefficient to some extent. To solve these problems, we propose an unsupervised clustering-matching mapping method, using a combination of k-means and SM (KSM). First, nonnegative matrix factorization (NMF) is used and combined with a simple and effective NMF initialization method (SMNMF) for feature extraction. Then, k-means is implemented to get the cluster centers of the extracted features and band depth, which are used for clustering and matching, respectively. Finally, dimensionless matching methods, including spectral angle mapper (SAM), spectral correlation angle (SCA), spectral gradient angle (SGA), and a combined matching method (SCGA) are used to match the cluster centers of band depth with a spectral library to obtain the mineral mapping results. A case study on the airborne hyperspectral image of Cuprite, Nevada, USA, demonstrated that the average overall accuracies of KSM based on SAM, SCA, SGA, and SCGA are approximately 22%, 22%, 35%, and 33% higher than those of SM, respectively, and KSM can save more than 95% of the mapping time. Moreover, the mapping accuracy and efficiency of SMNMF are about 15% and 38% higher than those of the widely used NMF initialization method. In addition, the proposed SCGA could achieve promising mapping results at both high and low signal-to-noise ratios compared with other matching methods. The mapping method proposed in this study provides a new solution for the rapid and autonomous identification of minerals and other fine objects

Surgical resection or radiofrequency ablation for small hepatocellular carcinoma

not require

Spectral clustering eigenvector selection of hyperspectral image based on the coincidence degree of data distribution

Spectral clustering is a well-regarded subspace clustering algorithm that exhibits outstanding performance in hyperspectral image classification through eigenvalue decomposition of the Laplacian matrix. However, its classification accuracy is severely limited by the selected eigenvectors, and the commonly used eigenvectors not only fail to guarantee the inclusion of detailed discriminative information, but also have high computational complexity. To address these challenges, we proposed an intuitive eigenvector selection method based on the coincidence degree of data distribution (CDES). First, the clustering result of improved k-means, which can well reflect the spatial distribution of various types was used as the reference map. Then, the adjusted Rand index and adjusted mutual information were calculated to assess the data distribution consistency between each eigenvector and the reference map. Finally, the eigenvectors with high coincidence degrees were selected for clustering. A case study on hyperspectral mineral mapping demonstrated that the mapping accuracies of CDES are approximately 56.3%, 15.5%, and 10.5% higher than those of the commonly used top, high entropy, and high relevance eigenvectors, and CDES can save more than 99% of the eigenvector selection time. Especially, due to the unsupervised nature of k-means, CDES provides a novel solution for autonomous feature selection of hyperspectral images

Heat transport into the interior ocean induced by water-mass subduction

The subduction of oceanic water masses provides a crucial pathway for anthropogenic heat to enter the subsurface ocean, thereby shaping deep-reaching warming signatures. Analyzing data from eight ocean and atmosphere reanalysis datasets, we show that the average annual subduction rate of the global ocean (excluding 10° S–10° N) is 312.4 ± 27.9 Sv, resulting in a mean heat transport of 20.2 ± 2.1 PW towards the subsurface ocean. This subduction-driven heat transport has exhibited an increase of 0.09 ± 0.08 PW/decade since 1970. The increase predominantly stems from the overall enhancement of subduction within the latitudes of 30° S–50° S, dictated by intensified westerly winds that lead to the deepening of the local mixed layer depth. Our findings underscore the essence of wind-driven changes in the Southern Ocean subduction, which wield considerable influence over the global climate by regulating the vertical transport of heat and carbon from the sea surface to the deep waters

The Regulatory Roles of Polysaccharides and Ferroptosis-Related Phytochemicals in Liver Diseases

Liver disease is a global health burden with high morbidity and mortality worldwide. Liver injuries can develop into severe end-stage diseases, such as cirrhosis or hepatocellular carcinoma, without valid treatment. Therefore, identifying novel drugs may promote liver disease treatment. Phytochemicals, including polysaccharides, flavonoids, alkaloids, and terpenes, are abundant in foods and medicinal plants and have various bioactivities, such as antioxidation, immunoregulation, and tumor killing. Recent studies have shown that many natural polysaccharides play protective roles in liver disease models in vitro and in vivo, such as fatty liver disease, alcoholic liver disease, drug-induced liver injury, and liver cancer. The mechanisms of liver disease are complex. Notably, ferroptosis, a new type of cell death driven by iron and lipid peroxidation, is considered to be the key mechanism in many hepatic pathologies. Therefore, polysaccharides and other types of phytochemicals with activities in ferroptosis regulation provide novel therapeutic strategies for ferroptosis-related liver diseases. This review summarizes our current understanding of the mechanisms of ferroptosis and liver injury and compelling preclinical evidence of natural bioactive polysaccharides and phytochemicals in treating liver disease

Evaluating the Scale Effect of Soil Erosion Using Landscape Pattern Metrics and Information Entropy: A Case Study in the Danjiangkou Reservoir Area, China

The regular patterns of soil erosion tend to change at different scales of observation, affecting the mechanism of soil erosion and its evolution characteristics. This phenomenon has essential scientific significance for the rational allocation of land resources and for studies on sustainable ecosystems. As an important agricultural area in China, Danjiangkou reservoir is threatened by severe soil erosion. In this study, we selected four kinds of landscape pattern metrics, including patch density, fractal dimension, Shannon diversity index, and connectivity, to analyze soil erosion intensity in the Danjiangkou reservoir area at different scales based on landscape ecological principles. In addition, we determine the optimum research scale of the experimental area by calculating the information entropy value of soil patches at different scales. The findings suggest that: (1) the landscape pattern of soil erosion in the experimental area is obviously scale-dependent, and the responses to scale differ from index to index; (2) as the scale of observation increases, the fragmentation of soil patches is weakened, the stability of different landscape components is enhanced, and the soil becomes less vulnerable to erosion; and (3) based on information entropy theory, 60 m is confirmed to be the optimum scale of this study

A perception-aware decomposition and fusion framework for underwater image enhancement

This paper presents a perception-aware decomposition and fusion framework for underwater image enhancement (UIE). Specifically, a general structural patch decomposition and fusion (SPDF) approach is introduced. SPDF is built upon the fusion of two complementary pre-processed inputs in a perception-aware and conceptually independent image space. First, a raw underwater image is pre-processed to produce two complementary versions including a contrast-corrected image and a detail-sharpened image. Then, each of them is decomposed into three conceptually independent components, i.e., mean intensity, contrast, and structure, via structural patch decomposition (SPD). Afterwards, the corresponding components are fused using tailored strategies. The three components after fusion are finally integrated via inverting the decomposition to reconstruct a final enhanced underwater image. The main advantage of SPDF is that two complementary pre-processed images are fused in a perception-aware and conceptually independent image space and the fusions of different components can be performed separately without any interactions and information loss. Comprehensive comparisons on two benchmark datasets demonstrate that SPDF outperforms several state-of-the-art UIE algorithms qualitatively and quantitatively. Moreover, the effectiveness of SPDF is also verified on another two relevant tasks, i.e., low-light image enhancement and single image dehazing. The code will be made available soon

Increasing inhomogeneity of the global ocean

Author Posting. © American Geophysical Union, 2022. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 49(12), (2022): e2021GL097598, https://doi.org/10.1029/2021GL097598.The ocean is inhomogeneous in hydrographic properties with diverse water masses. Yet, how this inhomogeneity has evolved in a rapidly changing climate has not been investigated. Using multiple observational and reanalysis datasets, we show that the spatial standard deviation (SSD) of the global ocean has increased by 1.4 ± 0.1% in temperature and 1.5 ± 0.1% in salinity since 1960. A newly defined thermohaline inhomogeneity index, a holistic measure of both temperature and salinity changes, has increased by 2.4 ± 0.1%. Climate model simulations suggest that the observed ocean inhomogeneity increase is dominated by anthropogenic forcing and projected to accelerate by 200%–300% during 2015–2100. Geographically, the rapid upper-ocean warming at mid-to-low latitudes dominates the temperature inhomogeneity increase, while the increasing salinity inhomogeneity is mainly due to the amplified salinity contrast between the subtropical and subpolar latitudes.This work is supported by the Strategic Priority Research Program of Chinese Academy of Sciences (grant XDB42000000 and XDB40000000), the National Key R&D Program of China (2017YFA0603200), and the Shandong Provincial Natural Science Foundation (ZR2020JQ17), and the U.S. National Science Foundation Physical Oceanography Program (OCE- 2048336).2022-12-2

1D to 2D Growth of NaF Crystals in Photothermo-Refractive Glasses

International audienceThe precipitation of NaF crystals with a low refractive index only in the UV exposure region is essential to obtain high refractive index change in photothermo-refractive (PTR) glass for producing high-performance volume Bragg gratings (VBGs). However, the precision control of the growth of NaF crystals in the UV exposure region of PTR glass remains challenging. In this work, the effect of Al2O3 on the crystallization behavior of NaF crystals in PTR glass was investigated using the nonisothermal crystallization kinetic method. After photothermal-induced nucleation, the crystallization activation energy (Ea) of PTR glass decreased first and then increased with the rise in the Al2O3 content. The appropriate amount of Al2O3 (4 mol %) is helpful for reducing the Ea and promoting the formation of NaF crystals. The crystal growth index (n) and crystal growth dimension (m) suggest that the crystallization behavior of NaF crystals was photothermally induced nucleation and crystallization of 1D growth. The increasing trend of the m values indicates that the crystals tend to transit from 1D to 2D growth with the increase in Al2O3 content. When Al2O3 is 6 mol %, n equals m + 1, which implies the NaF crystals crystallize spontaneously rather than growing on the Ag nuclei. The crystals observed using scanning electron microscopy images showed that irregular precursors appeared first and then grew into needle-like crystals. When the NaF crystals grew sufficiently long, they transformed into lamellar crystals. This work elucidates the crystallization process of NaF crystals in PTR glass and provides guidance for the production of high-performance VBGs