Pleurocinus ostreatus Polysaccharide Alleviates Cyclophosphamide-Induced Immunosuppression through the Gut Microbiome, Metabolome, and JAK/STAT1 Signaling Pathway.

This study investigated the structure of Pleurocinus ostreatus polysaccharide (POP-1) and its effect on immunocompromised mice induced by cyclophosphamide (CY). Novel POP-1 was α- and ß-glucopyranose, its molecular weight was 4.78 × 104 Da, it was mainly composed of glucose (88.9%), and it also contained galactose (2.97%), mannose (5.02%), fucose (0.3%), arabinose (0.21%), ribose (0.04%), galactose acid (0.17%), and glucose acid (1.45%). After POP-1 was administered to immunosuppressed mice, results showed that POP-1 increased the body weight, spleen, and thymus index and enhanced T lymphocyte proliferation in mice. POP-1 up-regulated the expression of CD3+, CD4+, and CD8+ lymphocytes and the ratio of CD4+/CD8+ in the mouse spleen to increase immunoglobulin (IgM, IgG, and IgA) and secrete cytokines (IL-2, IL-6, TNF-α, and IFN-γ) through activation of the JAK/STAT1 signaling pathway. Moreover, POP-1 remarkably reversed the gut-microbiota dysbiosis in immunosuppressed mice by increasing the abundance of Muribaculaceae, Lactobacillaceae, Blautia, and Ligilactobacillus and altered the fecal metabolites by increasing hexahomomethionine, DG(8:0/20:4(5Z, 8Z, 11Z, 14Z)-OH(20)/0:0, 2-((3-aminopyridin-2-yl)methylene)hydrazinecarbothioamide, Ginkgoic acid, and carboxy-ethyl-hydroxychroman, which is closely related to the immunity function. This study indicates that P. ostreatus polysaccharide effectively restores immunosuppressive activity and can be a functional ingredient in food and pharmaceutical products.

Effect of flaxseed oil cyclolinopeptides on lipid oxidation, protein oxidation, and lipid profile during in vitro digestion of high-fat beef.

This study investigated the influence of flaxseed oil cyclolinopeptides (CLs) on lipid and protein oxidation during high-fat meat digestion. Fourteen CLs were identified in flaxseed oil through UHPLC-ESI-QTOF-MS/MS, with dominant CLA, CLB, CLE, and CLM. During in vitro digestion, CLs inhibited lipid oxidation products (lipid hydroperoxide, Malondialdehyde, and 4-hydroxynonenal) and protein carbonylation. Compared to other groups, the lipid (16.28 ± 0.35) and protein (17.5 ± 0.6) oxidation was significantly inhibited, and antioxidant activity was remarkably increased when the CLs content reached 200 mg/kg. Through untargeted lipidomic analysis using Q-Exactive, it was observed that CLs mitigated the formation of oxidized triglycerides (OxTG) products and enhanced the hydrolysis of lipids to generate sphingolipid and polyunsaturated fatty-acids enriched glycerophospholipids imparting nutritional value to meat. Electron spin-resonance and fluorescence quenching showed that primary radicals such as alkyl and alkoxy radicals during high-fat meat digestion with flaxseed oil CLs significantly mitigate their formation. These findings collectively indicate that consuming CLs enriched flaxseed oil could reduce lipid oxidation and enhance the nutritional value of high-fat meat during digestion.

Generation of Triplet States by Host-Stabilized Through-Space Conjugation for the Construction of Efficient Supramolecular Photocatalysts.

Promoting the generation of triplet states is essential for developing efficient photocatalytic systems. This research presents a novel approach of host-stabilized through-space conjugation via the combination of covalent and non-covalent methods. The designed building block, 4,4'-(1,4(1,4)-dibenzene cyclohexaphane-1,4-diyl)bis(1-phenylpyridinium) chloride, features inherently stable through-space conjugation. When this block forms a 1:1 host-guest complex with cucurbit[8]uril, the through-space conjugation is further stabilized within the confined cavity. Both the generation and lifetime of triplet state are significantly increased, resulting from the host-stabilized through-space conjugation. Additionally, the ultrahigh binding constant of 6.58 × 1014 M-1 ensures the persistence of host-stabilization effect. As a result, the host-guest complex acts as a highly efficient catalyst in the photocatalytic oxidation of thioether and aromatic alcohol. In the photodegradation of lignin, a complex natural product, the host-guest complex also exhibits high efficiency, demonstrating its robustness. This line of research is anticipated to enrich the toolbox of supramolecular photochemistry and provide a strategy for fabricating efficient supramolecular photocatalysts.

Steroid hormone-deprived sex reversal in cyp11a1 mutant XX tilapia experiences an ovary-like stage at molecular level.

Fish sex is largely influenced by steroid hormones, especially sex hormones. Here, we established a steroid hormone-free genetic model by mutation of cyp11a1 in Nile tilapia, which was confirmed by EIA assay. Gonadal phenotype and transcriptome analyses showed that the XX mutants displayed sex reversal from female to male but with defective spermatogenesis. Despite the sex reversal, the aromatase encoding gene cyp19a1a was continuously expressed in the gonads of the XX mutants, which might be caused by androgen deficiency. Whole-mount fluorescence in situ hybridization and transcriptome analysis showed that the gonads of the XX mutants firstly developed towards ovary but shifted to testis between 10 to 15 days after hatching. Detailed expression analysis of key sex differentiation pathway genes foxl3 and dmrt1 combined with apoptosis analysis revealed transdifferentiation of germ cells from female to male during sex reversal. Rescue experiments showed that both P5 and E2 treatment rescued the sex reversal of cyp11a1 mutant XX fish. Overall, our results revealed a transient ovary-like stage and transdifferentiation of germ cells from female to male in the early gonads of the steroid hormone-deprived cyp11a1 mutant XX fish.

Wild rodents in three provinces of China exhibit a wide range of Enterocytozoon bieneusi diversity.

Introduction: Enterocytozoon bieneusi is one of the most important zoonotic pathogens, responsible for nearly 90% of human infections. Its host spectrum is broad in China, encompassing humans, non-human primates, domestic animals, wildlife, and wastewater. Wild rodents have the potential to act as carriers of E. bieneusi, facilitating the parasite's transmission to humans and domestic animals. Methods: The present study involved the collection of 344 wild rodents, representing nine species, from three provinces in China. The prevalence and genotypes of E. bieneusi were determined through amplification of the ITS gene. Evolutionary analysis was conducted using Mega 5.0 with the neighbor-joining method (Kimura 2-parameter model, 1,000 replicates). Results: Among the sampled wild rodents, 41 (11.92%) were tested positive for E. bieneusi. Rattus flavipectus exhibited the highest prevalence (11/39), while Bandicota indica and Rattus rattus sladeni showed no infections (0/39 and 0/5, respectively), highlighting significant differences. Environmental factors strongly influenced E. bieneusi infection; rodents residing in lake beaches (10.27%, 15/146) and fields (19.95%, 18/95) were more susceptible compared to those in mountainous areas (7.77%, 8/103). The study identified four known genotypes (D, Type IV, SDD5, PigEBITS7) and five novel genotypes (HNRV-1 to HNRV-3, GXRL-1, GXRL-2) in the investigated wild rodents, with Genotype D exhibiting the highest prevalence. Discussion: Remarkably, this study reports the presence of E. bieneusi, R. flavipectus, M. fortis, A. agrarius, R. losea, and N. lotipes for the first time. These findings underscore the common occurrence of E. bieneusi infection in wild rodents in China, highlighting its diverse nature and significant potential for zoonotic transmission. Hence, it is imperative to conduct a comprehensive epidemiological investigation of rodent infection with E. bieneusi, particularly focusing on wild rodents that are closely associated with humans. Additionally, developing appropriate measures and monitoring strategies to minimize the risk of infection is essential.

A model fusion method based DAT-DenseNet for classification and diagnosis of aortic dissection.

In this paper, we proposed a complete study method to achieve accurate aortic dissection diagnosis at the patient level. Based on the CT angiography (CTA) images, a classification model named DAT-DenseNet, which combined the deep attention Transformer module with the DenseNet architecture is proposed. In the first phase, two DAT-DenseNet are combined in parallel. It is used to accurately achieve two classification task at the CTA images. In the second stage, we propose a feature fusion module. It concatenates and fuses the image features output from the two classification models on a patient by patient basis. In the comparison experiments of classification model performance, DAT-DenseNet obtained 92.41 % accuracy at the image level, which was 2.20 % higher than the commonly used model. In the comparison experiments of model fusion method, our method obtained 90.83 % accuracy at the patient level. The experiments showed that DAT-DenseNet model exhibits high performance at the image level. Our feature fusion module achieves the mapping from two classification image features to patient outcomes. It achieves accurate patient classification. The experiments' results in the Discussion section elaborate the details of the experiment and confirmed that the results were reliable.

Ultrasonic-Induced Surface Disordering Promotes Photocatalytic Hydrogen Evolution of TiO2.

Surface disordering has been considered an effective strategy for tailoring the charge separation and surface chemistry of semiconductor photocatalysts. A simple but reliable method to create surface disordering is, therefore, urgently needed for the development of high-performance semiconductor photocatalysts and their practical applications. Herein, we report that the ultrasonic processing, which is commonly employed in the dispersion of photocatalysts, can induce the surface disordering of TiO2 and significantly promote its performance for photocatalytic hydrogen evolution. A 40 min ultrasonic treatment of TiO2 (Degussa P25) enhances the photocatalytic hydrogen production by 42.7 times, achieving a hydrogen evolution rate of 1425.4 µmol g-1 h-1 without any cocatalyst. Comprehensive structural, spectral, and electrochemical analyses reveal that the ultrasonic treatment induces the surface disordering of TiO2, and consequently reduces the density of deep electron traps, extends the separation of photogenerated charges, and facilitates the hydrogen evolution reaction relative to oxygen reduction. The ultrasonic treatment manifests a more pronounced effect on disordering the surface of anatase than rutile, agreeing well with the enhanced photocatalysis of anatase rather than rutile. This study demonstrates that ultrasonic-induced surface disordering could be an effective strategy for the activation of photocatalysts and might hold significant implications for the applications in photocatalytic hydrogen evolution, small molecule activation, and biomass conversion.

Sustainable H2O2 production via solution plasma catalysis.

Clean production of hydrogen peroxide (H2O2) with water, oxygen, and renewable energy is considered an important green synthesis route, offering a valuable substitute for the traditional anthraquinone method. Currently, renewable energy-driven production of H2O2 mostly relies on soluble additives, such as electrolytes and sacrificial agents, inevitably compromising the purity and sustainability of H2O2. Herein, we develop a solution plasma catalysis technique that eliminates the need for soluble additives, enabling eco-friendly production of concentrated H2O2 directly from water and O2. Screening over 40 catalysts demonstrates the superior catalytic performance of carbon nitride interacting with discharge plasma in water. High-throughput density functional theory calculations for 68 models, along with machine learning using 29 descriptors, identify cyano carbon nitride (CCN) as the most efficient catalyst. Solution plasma catalysis with the CCN achieves concentrated H2O2 of 20 mmol L-1, two orders of magnitude higher than photocatalysis by the same catalyst. Plasma diagnostics, isotope labeling, and COMSOL simulations collectively validate that the interplay of solution plasma and the CCN accounts for the significantly increased production of singlet oxygen and H2O2 thereafter. Our findings offer an efficient and sustainable pathway for H2O2 production, promising wide-ranging applications across the chemical industry, public health, and environmental remediation.

Propylene glycol alginate sodium sulfate suppressed lung metastasis by blocking P-selectin to recruit CD4 regulatory T cells.

P-selectin has been shown to enhance growth and metastasis of mouse tumors by promoting regulatory T cell (Treg) infiltration into the tumors. Theoretically, a P-selectin antagonist could suppress the process. Popylene glycol alginate sodium sulfate (PSS) is a heparin-like marine drug, which was originally approved to treat cardiovascular disease in China. Previously, we reported that PSS was an effective P-selectin antagonist in vitro. However, it is unknown whether PSS can regulate Treg infiltration and its effect on lung metastasis in vivo. Our results showed that PSS at 30 mg/kg significantly suppressed lung metastasis and improved overall survival, with potency comparable to the positive control LMWH. Mechanistic study indicated that PSS blocked tumor cells adhesion and activated platelets by directly binding with activated platelet's P-selectin. Compared to the model group, PSS decreased the percent of Tregs by 63 % in lungs after treating for 21 days while increasing CD8+ T cells (1.59-fold) and Granzyme B+ CD8 T cells (2.08-fold)' percentage for generating an adaptive response for systemic tumor suppression. The study indicated that the P-selectin antagonist, PSS, suppressed lung metastasis by inhibiting the infiltration of regulatory T cells (Treg) into the tumors.

Changes in urinary renal injury markers in children with Mycoplasma pneumoniae pneumonia and a prediction model for related early renal injury.

BACKGROUND: This study aims to analyse changes in urinary kidney injury markers in children with Mycoplasma pneumoniae pneumonia (MPP), investigate the risk factors for MPP-related acute kidney injury (AKI) and establish a model to predict MPP-related AKI. METHODS: Ninety-five children were enrolled based on the study's inclusion and exclusion criteria. They were divided into a severe MPP (SMPP) group and a non-SMPP group and then into an AKI group and a non-AKI group according to the presence of AKI. A univariate logistic regression analysis was performed to explore the early risk factors for AKI. Based on a multivariate logistic regression analysis and a least absolute shrinkage and selection operator regression analysis, appropriate variables were selected to establish a prediction model, and R 4.2.2 software was used to draw nomograms and generate a dynamic nomogram website. RESULTS: Seven urinary kidney injury markers were abnormally elevated in the SMPP group and the non-SMPP group: urinary N-acetyl-ß-D-glucosaminidase (NAG), ß2-microglobulin, α1-microglobulin, retinol-binding protein, urinary immunoglobulin G, urinary transferrin and urinary microalbumin. Sixteen children were identified with AKI during hospitalisation. The AKI group had higher levels of urinary NAG, α1-microglobulin, ß2-microglobulin, urinary microalbumin, urinary transferrin and retinol-binding protein than the non-AKI group (P < 0.05). The MPP-related AKI prediction model consists of four indicators (serum immunoglobulin M [IgM], C-reactive protein [CRP], urine NAG and sputum plug presence) and a dynamic nomogram. CONCLUSION: Urinary kidney injury markers are often elevated in children with MPP; urinary NAG is the marker most likely to be elevated, and it is especially evident in severe cases. The nomogram of the prediction model, comprising serum IgM, CRP, urinary NAG and sputum plug presence, can predict the probability of AKI in children with MPP.

[Spatio-temporal Evolution and Driving Factors of Ecological Environment Quality in the Huaihe River Basin Based on RSEI].

Clarifying the spatio-temporal evolution of the ecological environment quality of a watershed and its response to the natural environment and human factors are crucial for policy implementation in the ecological environment of the watershed. Using the Google earth engine（GEE） to establish a remote sensing ecological index （RSEI）, the spatio-temporal changes in the ecological environment quality of the Huaihe River Basin from 2002 to 2022 were evaluated combined with trend analysis, variation coefficient, and Hurst index. The main driving factors of spatial differentiation of RSEI were explored using the geographic detector. The results showed that： ① In the past 21 years, RSEI of the Huaihe River Basin had generally improved, but it showed a gradual upward-downward trend. Overall, the area of poor and less poor grades decreased, the area of medium grades increased, and the area of good and excellent grades increased. The improved area accounted for 55.93%, and the degraded area accounted for 22.01%. ② In terms of spatial distribution, RSEI gradually deteriorated from east to west （except in the northwest and southwest marginal mountainous areas）. The stability was better in the east and worse in the western and central areas. In the future, the ecological quality change in the basin was prone to be anti-sustainable and mainly improved. ③ Factor detection results showed that the spatial differentiation of RSEI in the basin was mainly driven by vegetation factors, followed by altitude. The interaction between two factors enhanced the driving force for RSEI spatial differentiation, in which the interaction between vegetation factor and elevation had the strongest driving force for RSEI spatial differentiation, reaching 86.3%.

Polysaccharides of Floccularia luteovirens regulate intestinal immune response, and oxidative stress activity through MAPK/Nrf2/Keap1 signaling pathway in immunosuppressive mice.

This study explores the novel immunomodulatory effects of polysaccharides from the rare Floccularia luteovirens, a fungus with significant potential yet unexplored bioactive components, traditionally used in Tibetan medicine. This study employs a wide array of analytical techniques, including HPGPC, HPLC, western blotting, ELISA, and 16S rRNA gene sequencing, to comprehensively investigate FLP1's effects. The main structure of FLP1 was characterized by IF-TR and NMR spectrometry. The structural backbone of FLP1 was →3,6)-ß-D-Glcp-(1 â†’ and →2,3)-α-D-Manp-(1→. After immunosuppressed mice treated with FLP1, the findings demonstrated that FLP1 stimulated the production of secretory sIgA and secretion of cytokines (IL-4, TNF-α, and IFN-γ) in the intestine of Cy-treated mice, resulting in the activation of the MAPK pathway. Additionally, FLP1 protected oxidative stress by triggering Nrf2/Keap1 pathways and antioxidation enzymes (SOD, MDA, T-AOC, CAT, and GSH-Px). It also enhanced the intestinal barrier function by regulating the villous height ratio and expression of tight-junction protein. Furthermore, FLP1 remarkably reversed the gut microbiota dysbiosis in immunosuppressed mice by increasing the abundance of Oscilliospiraceae, and Lachnospiraceae, and altered the fecal metabolites by increasing LysoPE (0:0/18:0); 0:0/16:0; 18:1(11Z)/0:0, LysoPG (16:0/0:0), LysoPG 18:1 (2n) PE (14:0/20:1), echinenone, 2-(2-Nitroimidazol-1-yl)-N-(2,2,3,3,3-pentafluoropropyl) acetamide, and suberic acid which is closely related to the immunity function. These results suggested that FLP1 may regulate the intestinal immune response by modulating the gut microbiota and fecal metabolites in immunosuppressed mice thereby activating the immune system.

Prevalence and subtypes of Blastocystis in wild rodents from three provinces in China.

Introduction: Blastocystis is one of the most critical intestinal protozoans in various hosts, including humans and mice. To determine the status of Blastocystis infection in wild rodents in China. Methods: A total of 344 faecal samples were collected from seven wild rodent species from three provinces, and the small subunit ribosomal RNA (SSU rRNA) genes of Blastocystis were amplified to determine their prevalence and subtypes. Results: Of the 344 samples, 54 (15.70%) were detected as Blastocystis-positive. The prevalence of Blastocystis was 26.14% (40/153), 7.95% (7/88), and 6.80% (7/103) in wild rodents from Hunan Province, Yunnan Province, and Guangxi Province, respectively. The prevalence of Blastocystis in different wild rodent species varied from 0.00% (0/13) in Mus musculus to 40.00% (2/5) in Rattus rattus sladeni. The prevalence of Blastocystis in samples from the lake beach area (27.40%, 40/146) was significantly higher than in those from the mountain (6.80%, 7/103) and field regions (7.37%, 7/95). The prevalence in different seasons was 26.14% in summer (40/153), 7.95% in autumn (7/88), and 6.80% in winter (7/103). Moreover, a total of two Blastocystis subtypes were identified in the investigated wild rodents, including ST4 and ST5. Discussion: The present study discovered the existence of Blastocystis infection in Rattus favipectus, Microtus fortis, Apodemus agrarius, Bandicota indica, Rattus rattus sladeni, and Rattus losea, expanding the host range of this parasite. The findings also demonstrate that wild rodents may be an important potential infection source for Blastocystis infection in humans and other animals.

Research into super-resolution in medical imaging from 2000 to 2023: bibliometric analysis and visualization.

Background: Super-resolution (SR) refers to the use of hardware or software methods to enhance the resolution of low-resolution (LR) images and produce high-resolution (HR) images. SR is applied frequently across a variety of medical imaging contexts, particularly in the enhancement of neuroimaging, with specific techniques including SR microscopy-used for diagnostic biomarkers-and functional magnetic resonance imaging (fMRI)-a neuroimaging method for the measurement and mapping of brain activity. This bibliometric analysis of the literature related to SR in medical imaging was conducted to identify the global trends in this field, and visualization via graphs was completed to offer insights into future research prospects. Methods: In order to perform a bibliometric analysis of the SR literature, this study sourced all publications from the Web of Science Core Collection (WoSCC) database published from January 1, 2000, to October 11, 2023. A total of 3,262 articles on SR in medical imaging were evaluated. VOSviewer was used to perform co-occurrence and co-authorship analysis, and network visualization of the literature data, including author, journal, publication year, institution, and keywords, was completed. Results: From 2000 to 2023, the annual publication volume surged from 13 to 366. The top three journals in this field in terms of publication volume were as follows: (I) Scientific Reports (86 publications), (II) IEEE Transactions on Medical Imaging (74 publications), and (III) IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control (56 publications). The most prolific country, institution, and author were the United States (1,017 publications; 31,301 citations), the Chinese Academy of Sciences (124 publications; 2,758 citations), and Dinggang Shen (20 publications; 671 citations), respectively. A cluster analysis of the top 100 keywords was conducted, which revealed the presence of five co-occurrence clusters: (I) SR and artificial intelligence (AI) for medical image enhancement, (II) SR and inverse problem processing concepts for positron emission tomography (PET) image processing, (III) SR ultrasound through microbubbles, (IV) SR microscopy for Alzheimer and Parkinson diseases, and (V) SR in brain fMRI: rapid acquisition and precise imaging. The most recent high-frequency keywords were deep learning (DL), magnetic resonance imaging (MRI), and convolutional neural networks (CNNs). Conclusions: Over the past two decades, the output of publications by countries, institutions, and authors in the field of SR in medical imaging has steadily increased. Based on bibliometric analysis of international trends, the resurgence of SR in medical imaging has been facilitated by advancements in AI. The increasing need for multi-center and multi-modal medical images has further incentivized global collaboration, leading to the diverse research paths in SR medical imaging among prominent scientists.

Cervical cancer segmentation based on medical images: a literature review.

Background and Objective: Cervical cancer clinical target volume (CTV) outlining and organs at risk segmentation are crucial steps in the diagnosis and treatment of cervical cancer. Manual segmentation is inefficient and subjective, leading to the development of automated or semi-automated methods. However, limitation of image quality, organ motion, and individual differences still pose significant challenges. Apart from numbers of studies on the medical images' segmentation, a comprehensive review within the field is lacking. The purpose of this paper is to comprehensively review the literatures on different types of medical image segmentation regarding cervical cancer and discuss the current level and challenges in segmentation process. Methods: As of May 31, 2023, we conducted a comprehensive literature search on Google Scholar, PubMed, and Web of Science using the following term combinations: "cervical cancer images", "segmentation", and "outline". The included studies focused on the segmentation of cervical cancer utilizing computed tomography (CT), magnetic resonance (MR), and positron emission tomography (PET) images, with screening for eligibility by two independent investigators. Key Content and Findings: This paper reviews representative papers on CTV and organs at risk segmentation in cervical cancer and classifies the methods into three categories based on image modalities. The traditional or deep learning methods are comprehensively described. The similarities and differences of related methods are analyzed, and their advantages and limitations are discussed in-depth. We have also included experimental results by using our private datasets to verify the performance of selected methods. The results indicate that the residual module and squeeze-and-excitation blocks module can significantly improve the performance of the model. Additionally, the segmentation method based on improved level set demonstrates better segmentation accuracy than other methods. Conclusions: The paper provides valuable insights into the current state-of-the-art in cervical cancer CTV outlining and organs at risk segmentation, highlighting areas for future research.

Solution plasma-cobalt hydroxide-enabled nitrogen fixation.

Solution plasma-Co(OH)2 interaction significantly boosts nitrogen fixation and achieves a high concentration of NOx- at 9.42 mmol L-1. This surpasses the nitrogen content requirement of 7.67 mmol L-1 for commercial nutrient solutions, offering a sustainable approach for nitrogen fixation from nitrogen, water and electricity.

Protective effects of antibiotic resistant bacteria on susceptibles in biofilm: Influential factors, mechanism, and modeling.

In environmental biofilms, antibiotic-resistant bacteria facilitate the persistence of susceptible counterparts under antibiotic stresses, contributing to increased community-level resistance. However, there is a lack of quantitative understanding of this protective effect and its influential factors, hindering accurate risk assessment of biofilm resistance in diverse environment. This study isolated an opportunistic Escherichia coli pathogen from soil, and engineered it with plasmids conferring antibiotic resistance. Protective effects of the ampicillin resistant strain (AmpR) on their susceptible counterparts (AmpS) were observed in ampicillin-stress colony biofilms. The concentration of ampicillin delineated protective effects into 3 zones: continuous protection (<1 MIC of AmpS), initial AmpS/R dependent (1-8 MIC of AmpS), and ineffective (>8 MIC of AmpS). Intriguingly, Zone 2 exhibited a surprising "less is more" phenomenon tuned by the initial AmpS/R ratio, where biofilm with an initially lower AmpR (1:50 vs 50:1) harbored 30-90 % more AmpR after 24 h growth under antibiotic stress. Compared to AmpS, AmpR displayed superiority in adhesion, antibiotic degradation, motility, and quorum sensing, allowing them to preferentially colonize biofilm edge and areas with higher ampicillin. An agent-based model incorporating protective effects successfully simulated tempo-spatial dynamics of AmpR and AmpS influenced by antibiotic stress and initial AmpS/R. This study provides a holistic view on the pervasive but poorly understood protective effects in biofilm, enabling development of better risk assessment and precisely targeted control strategies of biofilm resistance in diverse environment.

Benign and malignant classification of breast tumor ultrasound images using conventional radiomics and transfer learning features: A multicenter retrospective study.

This study aims to establish an effective benign and malignant classification model for breast tumor ultrasound images by using conventional radiomics and transfer learning features. We collaborated with a local hospital and collected a base dataset (Dataset A) consisting of 1050 cases of single lesion 2D ultrasound images from patients, with a total of 593 benign and 357 malignant tumor cases. The experimental approach comprises three main parts: conventional radiomics, transfer learning, and feature fusion. Furthermore, we assessed the model's generalizability by utilizing multicenter data obtained from Datasets B and C. The results from conventional radiomics indicated that the SVM classifier achieved the highest balanced accuracy of 0.791, while XGBoost obtained the highest AUC of 0.854. For transfer learning, we extracted deep features from ResNet50, Inception-v3, DenseNet121, MNASNet, and MobileNet. Among these models, MNASNet, with 640-dimensional deep features, yielded the optimal performance, with a balanced accuracy of 0.866, AUC of 0.937, sensitivity of 0.819, and specificity of 0.913. In the feature fusion phase, we trained SVM, ExtraTrees, XGBoost, and LightGBM with early fusion features and evaluated them with weighted voting. This approach achieved the highest balanced accuracy of 0.964 and AUC of 0.981. Combining conventional radiomics and transfer learning features demonstrated clear advantages over using individual features for breast tumor ultrasound image classification. This automated diagnostic model can ease patient burden and provide additional diagnostic support to radiologists. The performance of this model encourages future prospective research in this domain.

Attention gate and dilation U-shaped network (GDUNet): an efficient breast ultrasound image segmentation network with multiscale information extraction.

Background: In recent years, computer-aided diagnosis (CAD) systems have played an important role in breast cancer screening and diagnosis. The image segmentation task is the key step in a CAD system for the rapid identification of lesions. Therefore, an efficient breast image segmentation network is necessary for improving the diagnostic accuracy in breast cancer screening. However, due to the characteristics of blurred boundaries, low contrast, and speckle noise in breast ultrasound images, breast lesion segmentation is challenging. In addition, many of the proposed breast tumor segmentation networks are too complex to be applied in practice. Methods: We developed the attention gate and dilation U-shaped network (GDUNet), a lightweight, breast lesion segmentation model. This model improves the inverted bottleneck, integrating it with tokenized multilayer perceptron (MLP) to construct the encoder. Additionally, we introduce the lightweight attention gate (AG) within the skip connection, which effectively filters noise in low-level semantic information across spatial and channel dimensions, thus attenuating irrelevant features. To further improve performance, we innovated the AG dilation (AGDT) block and embedded it between the encoder and decoder in order to capture critical multiscale contextual information. Results: We conducted experiments on two breast cancer datasets. The experiment's results show that compared to UNet, GDUNet could reduce the number of parameters by 10 times and the computational complexity by 58 times while providing a double of the inference speed. Moreover, the GDUNet achieved a better segmentation performance than did the state-of-the-art medical image segmentation architecture. Conclusions: Our proposed GDUNet method can achieve advanced segmentation performance on different breast ultrasound image datasets with high efficiency.

Constructing the Optimal Classification Model for Benign and Malignant Breast Tumors Based on Multifeature Analysis from Multimodal Images.

The purpose of this study was to fuse conventional radiomic and deep features from digital breast tomosynthesis craniocaudal projection (DBT-CC) and ultrasound (US) images to establish a multimodal benign-malignant classification model and evaluate its clinical value. Data were obtained from a total of 487 patients at three centers, each of whom underwent DBT-CC and US examinations. A total of 322 patients from dataset 1 were used to construct the model, while 165 patients from datasets 2 and 3 formed the prospective testing cohort. Two radiologists with 10-20 years of work experience and three sonographers with 12-20 years of work experience semiautomatically segmented the lesions using ITK-SNAP software while considering the surrounding tissue. For the experiments, we extracted conventional radiomic and deep features from tumors from DBT-CCs and US images using PyRadiomics and Inception-v3. Additionally, we extracted conventional radiomic features from four peritumoral layers around the tumors via DBT-CC and US images. Features were fused separately from the intratumoral and peritumoral regions. For the models, we tested the SVM, KNN, decision tree, RF, XGBoost, and LightGBM classifiers. Early fusion and late fusion (ensemble and stacking) strategies were employed for feature fusion. Using the SVM classifier, stacking fusion of deep features and three peritumoral radiomic features from tumors in DBT-CC and US images achieved the optimal performance, with an accuracy and AUC of 0.953 and 0.959 [CI: 0.886-0.996], a sensitivity and specificity of 0.952 [CI: 0.888-0.992] and 0.955 [0.868-0.985], and a precision of 0.976. The experimental results indicate that the fusion model of deep features and peritumoral radiomic features from tumors in DBT-CC and US images shows promise in differentiating benign and malignant breast tumors.