Multicenter propensity score-matched analysis to compare perioperative morbidity after laparoscopic or robotic complex hepatectomy for solitary hepatocellular carcinoma.

BACKGROUND: Postoperative complications are vital factors affecting the prognosis of patients with hepatocellular carcinoma (HCC), especially for complex hepatectomy. The present study aimed to compare perioperative complications between laparoscopic and robotic complex hepatectomy (LCH vs. RCH). METHODS: Patients with solitary HCC after complex hepatectomy were collected from a multicenter database. Propensity score-matched (PSM) analysis was adopted to control confounding bias. Multivariable analysis was performed to determine the prognostic factors. RESULTS: 436 patients were included. After PSM, 43 patients were included in both the LCH and RCH groups. The results showed that compared to LCH, RCH had lower rates of blood loss and transfusion, and lower postoperative 30-day and major morbidity, and post-hepatectomy liver failure (PHLF) (all P < 0.05). Additionally, the length of hospital stay was shorter in the RCH group (P = 0.026). Multivariable analysis showed RCH is an independent protective factor for reducing the 30-day morbidity, major morbidity and PHLF. CONCLUSION: RCH has advantages over LCH in the minimally invasive treatment of complex HCC, as it can reduce the incidence of postoperative morbidity. Therefore, RCH should be considered for patients with HCC who require complex hepatectomy.

VesselBoost: A Python Toolbox for Small Blood Vessel Segmentation in Human Magnetic Resonance Angiography Data.

Magnetic resonance angiography (MRA) performed at ultra-high magnetic field provides a unique opportunity to study the arteries of the living human brain at the mesoscopic level. From this, we can gain new insights into the brain's blood supply and vascular disease affecting small vessels. However, for quantitative characterization and precise representation of human angioarchitecture to, for example, inform blood-flow simulations, detailed segmentations of the smallest vessels are required. Given the success of deep learning-based methods in many segmentation tasks, we here explore their application to high-resolution MRA data, and address the difficulty of obtaining large data sets of correctly and comprehensively labelled data. We introduce VesselBoost, a vessel segmentation package, which utilizes deep learning and imperfect training labels for accurate vasculature segmentation. Combined with an innovative data augmentation technique, which leverages the resemblance of vascular structures, VesselBoost enables detailed vascular segmentations.

Efficient C(sp3)-H bond oxidation on perovskite quantum dots based on Ce-oxygen affinity.

Perovskite quantum dots (QDs) have shown attractive prospects in the field of visible photocatalysis, especially in the synthesis of high value-added chemicals. However, under aerobic conditions, the stable operation of QD catalysts has been limited by the reactive oxygen species (ROS) generated by photoexcitation, especially superoxide species. Here, we propose a strategy of Ce3+ doping in perovskite QDs to guide superoxide species for photocatalytic oxidation reactions. In C(sp3)-H bond oxidation of hydrocarbons, superoxide species were rapidly generated and efficiently utilized on the surface of perovskite QDs, which achieves the stable operation of the catalytic system and obtains a high product conversion rate (15.3 mmol/g/h for benzaldehydes). The mechanism studies show that the strong Ce-oxygen affinity accelerates the relaxation process of photoinduced exciton transfer to superoxide species and inhibits the radiative recombination pathway. This work provides a new idea of utilizing oxygen species on perovskite surface and broadens the design strategy of high-performance QD photocatalysts.

A High Nucleus Cu-Incorporated Giant Phosphotungstate with Photocatalytic Oxidation C-H of Toluene.

Exploring a novel photocatalyst for catalytic oxidation of toluene is a sustainable strategy for energy conversion in times of an energy crisis. However, designing an effective photocatalyst for the conversion of toluene remains challenging. Herein, a novel organic monophosphonate-modified high nucleus Cu-incorporated polyoxotungstate, K8H33[{Cu0.5(H2O)4}{Cu2(O3PCH2COO)(1,4,9-α-P2W15O56)}]4·Cl·60H2O (1), has been intentionally synthesized by a self-assembly process utilizing conventional aqueous method. It reveals that 1 contains a polyanion of [{Cu0.5(H2O)}4{Cu2(O3PCH2COO)(1,4,9-α-P2W15O56)}]440- composed of four Dawson-type {1,4,9-α-P2W15} subunits, forming an oval-shaped structure and further connecting into a three-dimensional (3D) framework by lateral {Cu(H2O)4}2+. Interestingly, the trivacant {1,4,9-α-P2W15} subunits were observed in the organophosphonate acid-functionalized polyoxometalates for the first time. Notably, 1 exhibits a wonderful performance in catalytic oxidation of the recalcitrant C(sp3)-H bond of toluene to benzoic acid with a conversion as high as 97% under visible light utilizing O2 as an oxidant.

A comprehensive survey on deep active learning in medical image analysis.

Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible. In this survey, we review the core methods of active learning, including the evaluation of informativeness and sampling strategy. For the first time, we provide a detailed summary of the integration of active learning with other label-efficient techniques, such as semi-supervised, self-supervised learning, and so on. We also summarize active learning works that are specifically tailored to medical image analysis. Additionally, we conduct a thorough comparative analysis of the performance of different AL methods in medical image analysis with experiments. In the end, we offer our perspectives on the future trends and challenges of active learning and its applications in medical image analysis. An accompanying paper list and code for the comparative analysis is available in https://github.com/LightersWang/Awesome-Active-Learning-for-Medical-Image-Analysis.

Mn(II)-Based Metal Halide with Near-Unity Quantum Yield for White LEDs and High-Resolution X-ray Imaging.

Metal halides have drawn great interest as luminescent materials and scintillators due to their outstanding optical properties. Exploring new types of phosphors with easy production processes, excellent photophysical properties, high light yields, and environmentally friendly compositions is crucial and quite challenging. Herein, a novel Mn(II)-based metal halide (4-BTP)2MnBr4 was produced using a facile solvent evaporation method, which exhibited a strong green emission peaking at 524 nm from the d-d transition of tetrahedral-coordinated Mn2+ ion and a near-unity quantum yield. The prepared white light-emitting diode device has a wide color gamut of 100.7% NTSC with CIE chromaticity coordinates of (0.32, 0.32). In addition, (4-BTP)2MnBr4 demonstrates excellent characteristics in X-ray scintillation, including a high light yield of 98 000 photons/MeV, a sensitive detection limit of 37.4 nGy/s, excellent resistance to radiation damage, and successful demonstration of X-ray imaging with high resolution at 21.3 lp/mm, revealing the potential for application in diagnostic X-ray medical imaging and industry radiation detection.

Sanxiapeptin is an ideal preservative with a dual effect of controlling green mold and inducing systemic defense in postharvest citrus.

Green mold is a common postharvest disease infected by Penicillium digitatum that causes citrus fruit decay, and severely affects fruit storage quality. This work aimed to investigate the antifungal activity of Sanxiapeptin against P. digitatum, and elucidate the possible mechanisms involved. Sanxiapeptin was capable of inhibiting spore germination, germ tube length and mycelial growth. The SYTOX green staining assay revealed that Sanxiapeptin targeted the fungal membrane, and changed the membrane permeability, leading to the leakage of cell constituents. Meanwhile, Sanxiapeptin could influence the cell wall permeability and integrity by increasing the activities of chitinase and glucanase, resulting in abnormal chitin consumption and the decrease of glucan. Intriguingly, Sanxiapeptin could effectively control postharvest decay in citrus fruits, and activate the host resistance responses by regulating the phenylpropanoid pathway. In conclusion, Sanxiapeptin exhibits multiphasic antifungal mechanisms of action to control green mold in citrus fruits, shows great potential as novel food preservatives.

Adrenomedullin improved endothelial dysfunction via receptor-Akt pathway in rats with obesity-related hypertension.

Obesity-related hypertension (OH) is accompanied by obvious endothelial dysfunction, which contributes to increased peripheral vascular resistance and hypertension. Adrenomedullin (ADM), a multifunctional active peptide, is elevated in obese humans. The OH rats induced by high fat diet (HFD) for 28 weeks and the human umbilical vein endothelial cells (HUVECs)-treated by palmitic acid (PA) were used to investigate the effects of ADM on endothelial dysfunction and the underlying mechanisms. Vascular reactivity was assessed using mesenteric arteriole rings, and the protein expression levels were examined by Western blot analysis. Compared with the control rats, OH rats exhibited hypertension and endothelial dysfunction, along with reduced eNOS protein expression and Akt activation, and increased protein expression of proinflammatory cytokines and ROS levels. Four-week ADM administration improved hypertension and endothelial function, increased eNOS protein expression and Akt activation, and attenuated endothelial inflammation and oxidative stress in OH rats. In vitro experiment, the antagonism of ADM receptors with ADM22-52 and the suppression of Akt signaling with A6730 significantly blocked ADM-caused increase of NO content and activation of eNOS and Akt, and inhibited the anti-inflammatory and anti-oxidant effect of ADM in PA-stimulated HUVECs. These data indicate that endothelial dysfunction in OH rats is partially attributable to the decreased NO level, and the increased inflammation and oxidative stress. ADM improves endothelial function and exerts hypotensive effect depending on the increase of NO, and its anti-inflammatory and anti-oxidant effect via receptor-Akt pathway.

Survival benefit from adjuvant TACE combined with Lenvatinib for patients with hepatocellular carcinoma and microvascular invasion after curative hepatectomy.

BACKGROUND AND AIMS: The prognosis of patients with hepatocellular carcinoma (HCC) undergoing hepatectomy is unsatisfactory, especially for those with microvascular invasion (MVI). This study aimed to determine the impact of adjuvant transcatheter arterial chemoembolization (TACE) and Lenvatinib on the prognosis of patients with HCC and MVI after hepatectomy. METHODS: Patients diagnosed with HCC and MVI were reviewed, and stratified into four groups according to adjuvant TACE and/or Lenvatinib. Multivariate Cox regression analyses are used to determine independent risk factors. RESULTS: 346 patients were included, and divided into four groups (Group I, TACE+ Lenvatinib; Group II, Lenvatinib; Group III, TACE; Group IV, without adjuvant therapy). Multivariable analysis showed that compared to Group IV, Group I had the best effect on improving the overall survival (OS, HR 0.321, 95%CI 0.099-0.406, P = 0.001) and recurrence-free survival (RFS, HR 0.319, 95%CI 0.129-0.372, P = 0.001). Additionally, compared with Group II or Group III, Group I also can significantly improve the OS and RFS. There is no significant difference between Group II and Group III in OS and RFS. CONCLUSION: The combination of TACE and Lenvatinib should be considered for anti-recurrence therapy for patients with HCC and MVI after hepatectomy.

The treatment process of a giant phyllodes tumor of the breast: a case report and review of the literature.

Giant phyllodes tumors are rare fibroepithelial tumors that are usually larger than 10 cm in diameter, have rapid tumor growth, and are easily recurrent. They are frequently accompanied by skin necrosis and infection, particularly in malignant phyllodes tumors. This case report presents a 50-year-old woman who presented to the hospital with a huge left breast mass that was ruptured and infected. The patient received anti-infective treatment and underwent mastectomy and skin grafting, which indicated a malignant phyllodes tumor. The tumor was completely excised after a local recurrence in the chest wall 6 months post-surgery. Unfortunately, one year later, the patient pass away due to multiple organ failure. Giant phyllodes tumor management presents challenges to the surgeon. This case is being presented to enhance understanding and treatment of phyllodes tumors, specifically giant malignant phyllodes tumors, with the aim of improving patients' quality of life.

Preoperative Antiviral Therapy and Long-Term Outcomes for Hepatitis B Virus-Related Hepatocellular Carcinoma After Curative Liver Resection: A Multicenter Analysis.

Background & Aims: To examine the association of the history of preoperative antiviral therapy (AVT) with the tumor recurrence and overall survival in HBV-related HCC patients undergoing curative-intent hepatectomy. Methods: Patients who underwent curative-intent hepatectomy for HBV-related HCC between 2014 and 2019 at 4 Chinese hospitals were analyzed. Patients were categorized as having undergone preoperative antiviral therapy (AVT) > 1 year or without antiviral therapy (non-AVT). Patient clinical features, short-term outcomes, overall survival (OS), and time-to-recurrence (TTR) were also compared. Multivariate Cox regression analysis was performed to identify the impact of preoperative AVT on the OS and TTR. Results: Among the 565 patients, 190 (33.6%) underwent continuous AVT > 1 year before surgery. Patients in the non-AVT group were more likely to have worse liver function and more advanced tumor pathological characteristics than those in the AVT group. Postoperative morbidity and mortality rates were comparable between the two groups. Multivariate analyses revealed that a preoperative HBV viral level ≥ 2000 IU/mL was independently associated with poorer TTR (hazard ratio, 1.328; 95% CI, 1.049-1.682) and preoperative AVT was a protective factor for OS (hazard ratio, 0.691; 95% CI, 0.484-0.986). Conclusion: A high preoperative HBV DNA level was an independent risk factor for tumor recurrence. Preoperative AVT > 1 year was associated with better OS and a reduced incidence of tumor recurrence by inhibiting the preoperative level of HBV DNA.

Key developments and hotspots of exosomes in Alzheimer's disease: a bibliometric study spanning 2003 to 2023.

Background: Alzheimer's disease (AD) is a degenerative illness of the central nervous system that is irreversible and is characterized by gradual behavioral impairment and cognitive dysfunction. Researches on exosomes in AD have gradually gained the attention of scholars in recent years. However, the literatures in this research area do not yet have a comprehensive visualization analysis. The aim of this work is to use bibliometrics to identify the knowledge constructs and investigate the research frontiers and hotspots related to exosomes in AD. Methods: From January 2003 until June 2023, we searched the Web of Science Core Collection for literature on exosomes in AD. We found 585 papers total. The bibliometric study was completed using VOSviewer, the R package "bibliometrix," and CiteSpace. The analysis covered nations, institutions, authors, journals, and keywords. Results: Following 2019, the articles on exosomes in AD increased significantly year by year. The vast majority of publications came from China and the US. The University of California System, the National Institutes of Health, and the NIH National Institute on Aging in the US were the primary research institutions. Goetzl Edward J. was frequently co-cited, while Kapogiannis Dimitrios was the most prolific author in this discipline with the greatest number of articles. Lee Mijung et al. have been prominent in the last two years in exosomes in AD. The Journal of Alzheimer's Disease was the most widely read publication, and Alzheimers & Dementia had the highest impact factor. The Journal of Biological Chemistry, Proceedings of the National Academy of Sciences of the United States of America, and Journal of Alzheimer's Disease were the three journals with more than 1,000 citations. The primary emphasis of this field was Alzheimer's disease, exosomes, and extracellular vesicles; since 2017, the number of phrases pertaining to the role of exosomes in AD pathogenesis has increased annually. "Identification of preclinical Alzheimer's disease by a profile of pathogenic proteins in neurally derived blood exosomes: a case-control study" was the reference with the greatest citing power, indicating the future steered direction in this field. Conclusion: Using bibliometrics, we have compiled the research progress and tendencies on exosomes in Alzheimer's disease for the first time. This helps determine the objectives and paths for future study.

Physical activity attenuates the excess mortality risk from prolonged sitting time among adults with osteoporosis or osteopenia.

PURPOSE: Osteoporosis is a common generalized skeletal disorder characterized by compromised bone strength predisposing a person to an increased risk of fracture. This study aims to crystallize associations of physical activity (PA) and sedentary behaviour with the survival of adults with osteoporosis or osteopenia. METHODS: A total of 3103 participants aged 50 years or older from the National Health and Nutrition Examination Survey (NHANES) were included in the study. All participants were diagnosed with osteopenia or osteoporosis. Multivariable Cox proportional hazards regression models were used to assess the association of PA and sedentary behaviour with overall mortality, cancer-related mortality, and cardiovascular disease (CVD)-related mortality. RESULTS: During 21349 person-years of follow-up, 675 deaths were documented. Highly active participants had a lower risk of all-cause (hazard ratios [HR] = 0.61; 95% confidence interval [CI], 0.42-0.87; P for trend = 0.004), cancer-specific (HR = 0.64; 95%CI, 0.35-1.17; P for trend = 0.132), CVD-specific (HR = 0.75; 95%CI, 0.45-1.25; P for trend = 0.452), and other (HR, 0.51; 95%CI, 0.29-0.88; P for trend = 0.005) mortality than inactive participants. And sitting time was not associated with mortality among physically active participants; while among those who were insufficiently active or inactive, longer sitting time was associated with increased risks of all-cause (HR per 1-h increase = 1.05; 95% CI, 1.01-1.09), cancer-specific (HR per 1 h increase = 0.98; 95% CI, 0.90-1.07), CVD-specific (HR per 1-h increase = 1.11; 95% CI = 1.04-1.18), and other (HR per 1-h increase = 1.05; 95% CI, 0.98-1.13) mortality in a dose-response manner. CONCLUSIONS: PA can attenuate the excess mortality risk from prolonged sitting for individuals with osteoporosis and/or osteopenia. The combination of prolonged sedentary behaviour with inactive (participants without any PA during a week) PA was associated with an increased risk of mortality. The all-cause mortality risk of individuals who engage in less than 150 min/wk PA and sit more than 8 h/d is 2.02 (95% CI, 1.37-2.99) times higher than that of individuals who engage in more than 150 min/wk PA and sit less than 4 h/d.

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review.

Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.

Brain-Controlled Hand Exoskeleton Based on Augmented Reality-Fused Stimulus Paradigm.

Advancements in brain-machine interfaces (BMIs) have led to the development of novel rehabilitation training methods for people with impaired hand function. However, contemporary hand exoskeleton systems predominantly adopt passive control methods, leading to low system performance. In this work, an active brain-controlled hand exoskeleton system is proposed that uses a novel augmented reality-fused stimulus (AR-FS) paradigm as a human-machine interface, which enables users to actively control their fingers to move. Considering that the proposed AR-FS paradigm generates movement artifacts during hand movements, an enhanced decoding algorithm is designed to improve the decoding accuracy and robustness of the system. In online experiments, participants performed online control tasks using the proposed system, with an average task time cost of 16.27 s, an average output latency of 1.54 s, and an average correlation instantaneous rate (CIR) of 0.0321. The proposed system shows 35.37% better efficiency, 8.03% reduced system delay, and 35.28% better stability than the traditional system. This study not only provides an efficient rehabilitation solution for people with impaired hand function but also expands the application prospects of brain-control technology in areas such as human augmentation, patient monitoring, and remote robotic interaction. The video in Graphical Abstract Video demonstrates the user's process of operating the proposed brain-controlled hand exoskeleton system.

Mixture-of-experts and semantic-guided network for brain tumor segmentation with missing MRI modalities.

Accurate brain tumor segmentation with multi-modal MRI images is crucial, but missing modalities in clinical practice often reduce accuracy. The aim of this study is to propose a mixture-of-experts and semantic-guided network to tackle the issue of missing modalities in brain tumor segmentation. We introduce a transformer-based encoder with novel mixture-of-experts blocks. In each block, four modality experts aim for modality-specific feature learning. Learnable modality embeddings are employed to alleviate the negative effect of missing modalities. We also introduce a decoder guided by semantic information, designed to pay higher attention to various tumor regions. Finally, we conduct extensive comparison experiments with other models as well as ablation experiments to validate the performance of the proposed model on the BraTS2018 dataset. The proposed model can accurately segment brain tumor sub-regions even with missing modalities. It achieves an average Dice score of 0.81 for the whole tumor, 0.66 for the tumor core, and 0.52 for the enhanced tumor across the 15 modality combinations, achieving top or near-top results in most cases, while also exhibiting a lower computational cost. Our mixture-of-experts and sematic-guided network achieves accurate and reliable brain tumor segmentation results with missing modalities, indicating its significant potential for clinical applications. Our source code is already available at https://github.com/MaggieLSY/MESG-Net .

Precise planning based on 3D-printed dry-laboratory models can reduce perioperative complications of laparoscopic surgery for complex hepatobiliary diseases: a preoperative cohort study.

BACKGROUND & AIMS: Complications after laparoscopic liver resection (LLR) are important factors affecting the prognosis of patients, especially for complex hepatobiliary diseases. The present study aimed to evaluate the value of a three-dimensional (3D) printed dry-laboratory model in the precise planning of LLR for complex hepatobiliary diseases. METHODS: Patients with complex hepatobiliary diseases who underwent LLR were preoperatively enrolled, and divided into two groups according to whether using a 3D-printed dry-laboratory model (3D vs. control group). Clinical variables were assessed and complications were graded by the Clavien-Dindo classification. The Comprehensive Complication Index (CCI) scores were calculated and compared for each patient. Multivariable analysis was performed to determine the risk factors of postoperative complications. RESULTS: Sixty-two patients with complex hepatobiliary diseases underwent the precise planning of LLR. Among them, thirty-one patients acquired the guidance of a 3D-printed dry-laboratory model, and others were only guided by traditional enhanced CT or MRI. The results showed no significant differences between the two groups in baseline characters. However, compared to the control group, the 3D group had a lower incidence of intraoperative blood loss, as well as postoperative 30-day and major complications, especially bile leakage (all P < 0.05). The median score on the CCI was 20.9 (range 8.7-51.8) in the control group and 8.7 (range 8.7-43.4) in the 3D group (mean difference, -12.2, P = 0.004). Multivariable analysis showed the 3D model was an independent protective factor in decreasing postoperative complications. Subgroup analysis also showed that a 3D model could decrease postoperative complications, especially for bile leakage in patients with intrahepatic cholelithiasis. CONCLUSION: The 3D-printed models can help reduce postoperative complications. The 3D-printed models should be recommended for patients with complex hepatobiliary diseases undergoing precise planning LLR.

New insights into the antibiofilm activity and mechanism of Mannosylerythritol Lipid-A against Listeria monocytogenes EGD-e.

Listeria monocytogenes is one of the leading causative agents of foodborne disease outbreaks worldwide. Herein, the antibiofilm effect and mechanism of Mannosylerythritol Lipid-A against L. monocytogenes EGD-e is reported for the first time. MEL-A effectively attenuated biofilm formation while reducing the viability and motility of bacteria within the biofilm in the early stage, and influenced bacterial adhesion by affecting the secretion of extracellular polysaccharides and eDNA. RT-qPCR revealed that MEL-A significantly suppressed the expression of genes involved in flagellar movement and virulence. Untargeted LC-MS metabolomics indicated that MEL-A affected the fluidity and permeability of cell membranes by significantly upregulating unsaturated fatty acids, lipids and glycoside metabolites, and affected protein biosynthesis, nucleotide metabolism and DNA synthesis and repair by significantly downregulating amino acid metabolism and nucleic acid metabolism. These pathways may constitute the key targets of biofilm formation inhibition by MEL-A. Furthermore, MEL-A showed good removal effects on mature biofilms under different temperatures, different materials and milk. Our data indicated that MEL-A could be used as a novel antibiofilm agent to improve food safety. Our study provides new insights into the possible inhibitory mechanism of MEL-A and the response of L. monocytogenes EGD-e to MEL-A.