Antibacterial effect of phage cocktails and phage-antibiotic synergy against pathogenic Klebsiella pneumoniae.

The global rise of antibiotic resistance has renewed interest in phage therapy, as an alternative to antibiotics to eliminate multidrug-resistant (MDR) bacterial pathogens. However, optimizing the broad-spectrum efficacy of phage therapy remains a challenge. In this study, we addressed this issue by employing strategies to improve antimicrobial efficacy of phage therapy against MDR Klebsiella pneumoniae strains, which are notorious for their resistance to conventional antibiotics. This includes the selection of broad host range phages, optimization of phage formulation, and combinations with last-resort antibiotics. Our findings unveil that having a broad host range was a dominant trait of isolated phages, and increasing phage numbers in combination with antibiotics significantly enhanced the suppression of bacterial growth. The decreased incidence of bacterial infection was explained by a reduction in pathogen density and emergence of bacterial resistance. Furthermore, phage-antibiotic synergy (PAS) demonstrated considerable broad-spectrum antibacterial potential against different clades of clinical MDR K. pneumoniae pathogens. The improved treatment outcomes of optimized PAS were also evident in a murine model, where mice receiving optimized PAS therapy demonstrated a reduced bacterial burden in mouse tissues. Taken together, these findings offer an important development in optimizing PAS therapy and its efficacy in the elimination of MDR K. pneumoniae pathogens. IMPORTANCE: The worldwide spread of antimicrobial resistance (AMR) has posed a great challenge to global public health. Phage therapy has become a promising alternative against difficult-to-treat pathogens. One important goal of this study was to optimize the therapeutic efficiency of phage-antibiotic combinations, known as phage-antibiotic synergy (PAS). Through comprehensive analysis of the phenotypic and genotypic characteristics of a large number of CRKp-specific phages, we developed a systematic model for phage cocktail combinations. Crucially, our finding demonstrated that PAS treatments not only enhance the bactericidal effects of colistin and tigecycline against multidrug-resistant (MDR) K. pneumoniae strains in in vitro and in vivo context but also provide a robust response when antibiotics fail. Overall, the optimized PAS therapy demonstrates considerable potential in combating diverse K. pneumoniae pathogens, highlighting its relevance as a strategy to mitigate antibiotic resistance threats effectively.

Evaluation of preoperative magnetic resonance imaging features and diagnostic effectiveness of grades II and III intracranial solitary fibroma.

OBJECTIVES: To explore the value of preoperative magnetic resonance imaging (MRI) characterization of intracranial solitary fibrous tumors (ISFT) and to evaluate the effectiveness of preoperative MRI features in predicting pathological grading. MATERIALS AND METHODS: This retrospective analysis comprised the clinical and preoperative MRI characterization of 55 patients with ISFT in our hospital, including 27 grade II cases and 28 grade III cases confirmed by postoperative pathology. Variables included age, sex, tumor location, cross-midline status, signal characteristics of T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), T2-fluid-attenuated inversion recovery (T2-FLAIR), and diffusion­weighted imaging (DWI), peritumoral edema, intralesional hemorrhage, focal necrosis/cystic degeneration, tumor empty vessel, maximum tumor diameter, maximum, minimum, and average values of apparent diffusion coefficient (ADCmax, ADCmin, and ADCmean), tumors enhancement mode, meningeal tail sign, skull invasion, cerebral parenchymal invasion, and venous sinus involvement. The independent samples t test or Mann-Whitney U test was performed to compare continuous data between the two groups, and the Pearson chi-squared test or Fisher's exact test was used to compare categorical data. In addition, bivariate logistic regression was performed to construct a comprehensive model, and receiver operating characteristic (ROC) curves were generated to calculate the areas under the curve (AUCs), thereby determining the value of each parameter in the differential diagnosis of grades II and III ISFT. RESULTS: The mean age at onset was similar between patients with grades II and III ISFT (46.77 ± 14.66 years and 45.82 ± 12.07 years, respectively). The proportions of men among patients with grades II and III ISFT were slightly higher than those of female patients (male/female: 1.25 [15/12] and 1.33 [16/12], respectively). There were significant differences between grades II and III ISFT in the T2-FLAIR and DWI signal characteristics, maximum, minimum, and average values of the apparent diffusion coefficient (ADCmax, ADCmin, and ADCmean), tumor location, and skull invasion (P = 0.001, P = 0.018, P = 0.000, P = 0.000, P = 0.000, P = 0.010, and P = 0.032, respectively). However, no significant differences were noted between grades II and III ISFT in age, sex, cross-midline status, T1WI and T2WI signal characteristics, peritumoral edema, intralesional hemorrhage, focal necrosis/cystic degeneration, tumor empty vessel shadow, enhancement mode, meningeal tail sign, maximum tumor diameter, brain parenchyma invasion, or venous sinus involvement (all P > 0.05). Moreover, binary logistic regression analysis showed that the model accuracy was 89.1% when ADCmin was included in the regression equation. Moreover, ROC curve analysis showed that the AUC of ADCmin was 0.805 (0.688, 0.922), sensitivity was 74.1%, specificity was 75.0%, and the cutoff value was 672 mm2/s. CONCLUSIONS: Grade III ISFT patients displayed more mixed T2-FLAIR signal characteristics and DWI signal characteristics than grade II patients, as shown by higher skull invasion and tumor mass collapse midline distribution and lower ADCmax, ADCmean, and ADCmin values. The ADCmin value was significant in the preoperative assignment of grades II and III ISFT, thereby contributing to enhanced accuracy in the imaging grading diagnosis of the disease.

Whole-tumor histogram analysis of multiple non-Gaussian diffusion models at high b values for assessing cervical cancer.

PURPOSE: To assess the diagnostic potential of whole-tumor histogram analysis of multiple non-Gaussian diffusion models for differentiating cervical cancer (CC) aggressive status regarding of pathological types, differentiation degree, stage, and p16 expression. METHODS: Patients were enrolled in this prospective single-center study from March 2022 to July 2023. Diffusion-weighted images (DWI) were obtained including 15 b-values (0 ~ 4000 s/mm2). Diffusion parameters derived from four non-Gaussian diffusion models including continuous-time random-walk (CTRW), diffusion-kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM) were calculated, and their histogram features were analyzed. To select the most significant features and establish predictive models, univariate analysis and multivariate logistic regression were performed. Finally, we evaluated the diagnostic performance of our models by using receiver operating characteristic (ROC) analyses. RESULTS: 89 women (mean age, 55 ± 11 years) with CC were enrolled in our study. The combined model, which incorporated the CTRW, DKI, FROC, and IVIM diffusion models, offered a significantly higher AUC than that from any individual models (0.836 vs. 0.664, 0.642, 0.651, 0.649, respectively; p < 0.05) in distinguishing cervical squamous cell cancer from cervical adenocarcinoma. To distinguish tumor differentiation degree, except the combined model showed a better predictive performance compared to the DKI model (AUC, 0.839 vs. 0.697, respectively; p < 0.05), no significant differences in AUCs were found among other individual models and combined model. To predict the International Federation of Gynecology and Obstetrics (FIGO) stage, only DKI and FROC model were established and there was no significant difference in predictive performance among different models. In terms of predicting p16 expression, the predictive ability of DKI model is significantly lower than that of FROC and combined model (AUC, 0.693 vs. 0.850, 0.859, respectively; p < 0.05). CONCLUSION: Multiple non-Gaussian diffusion models with whole-tumor histogram analysis show great promise to assess the aggressive status of CC.

Oxidative stress in the alveolar lavage fluid of children with Mycoplasma pneumoniae pneumonia.

OBJECTIVE: To investigate the correlation between oxidative stress in the bronchoalveolar lavage fluid (BALF) of children with Mycoplasma pneumoniae pneumonia (MPP) and the clinical characteristics of severe MPP (SMPP) and refractory MPP (RMPP). METHODS: Clinical and BALF-related data were collected from 83 patients with MPP, of which 29 had SMPP and 54 had general MPP (GMPP); 37 patients were in the RMPP group and 46 in the non-RMPP group. The levels of malondialdehyde (MDA) and advanced oxidation protein products (AOPP) as well as the activity levels of superoxide dismutase (SOD) and glutathione peroxidase (GSH-PX) in BALF were detected. Logistic regression analyses were performed on MDA, AOPP, SOD, GSH-PX, gender, heat peak, neutrophil percentage, C-reactive protein, lactate dehydrogenase, d-dimer, lung consolidation, sputum embolus, and pleural effusion. RESULTS: The levels of MDA and AOPP in the BALF of the MPP group were significantly higher than those in the control group (p < .05), whereas SOD and GSH-PX levels were lower than those in the control group (p < .05). The BALF AOPP levels in the RMPP group were higher than those in the non-RMPP group, and the SOD and GSH-PX levels in the BALF were lower than those in the non-RMPP group; the difference was statistically significant (p < .05). The levels of MDA and AOPP in the BALF of children in the SMPP group were higher than those in the GMPP group, and the levels of SOD and GSH-PX were lower than those in the GMPP group, with statistically significant differences (p < .05). The C-index of the logistic regression model was 0.960 (95% confidence interval 0.958-0.963), which indicates that the model has good predictive ability. CONCLUSION: Advanced oxidation protein products may be a marker for predicting the conditions of SMPP and RMPP, and the prediction model can assess the risk of progression in children to RMPP, which is conducive to clinical diagnosis and treatment.

Extraocular muscle volume on time-of-flight magnetic resonance angiography in patients with myasthenia gravis.

INTRODUCTION/AIMS: Despite being a prominent feature of myasthenia gravis (MG), extraocular muscle (EOM) has received little attention in clinical research. The aim of this study was to examine EOM volume in patients with MG and controls using time-of-flight magnetic resonance angiography (TOF-MRA). METHODS: EOM volumes (overall and individual rectus muscles) were calculated using TOF-MRA images and compared between MG patients (including subgroups) and controls. The correlation between EOM volume and disease duration was examined. Predictive equations for the selected parameters were developed using multiple linear regression analysis. RESULTS: EOM volume was lower in MG patients than controls, especially in MG patients with ophthalmoparesis (MG-O). MG-O exhibited a moderate negative correlation between EOM volume and disease duration. Multiple linear regression showed that disease duration and EOM status (ophthalmoparesis or not) account for 48.4% of EOM volume. DISCUSSION: Patients with MG show atrophy of the EOMs, especially those with ophthalmoparesis and long disease duration.

Multiple diffusion models for predicting pathologic response of esophageal squamous cell carcinoma to neoadjuvant chemotherapy.

BACKGROUND: To assess the feasibility and diagnostic performance of the fractional order calculus (FROC), continuous-time random-walk (CTRW), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), mono-exponential (MEM) and stretched exponential models (SEM) for predicting response to neoadjuvant chemotherapy (NACT) in patients with esophageal squamous cell carcinoma (ESCC). MATERIALS AND METHODS: This study prospectively included consecutive ESCC patients with baseline and follow up MR imaging and pathologically confirmed cT1-4aN + M0 or T3-4aN0M0 and underwent radical resection after neoadjuvant chemotherapy (NACT) between July 2019 and January 2023. Patients were divided into pCR (TRG 0) and non-pCR (TRG1 + 2 + 3) groups according to tumor regression grading (TRG). The Pre-, Post- and Delta-treatment models were built. 18 predictive models were generated according to different feature categories, based on six models by five-fold cross-validation. Areas under the curve (AUCs) of the models were compared by using DeLong method. RESULTS: Overall, 90 patients (71 men, 19 women; mean age, 64 years ± 6 [SD]) received NACT and underwent baseline and Post-NACT esophageal MRI, with 29 patients in the pCR group and 61 patients in the non-pCR group. Among 18 predictive models, The Pre-, Post-, and Delta-CTRW model showed good predictive efficacy (AUC = 0.722, 0.833 and 0.790). Additionally, the Post-FROC model (AUC = 0.907) also exhibited good diagnostic performance. CONCLUSIONS: Our study indicates that the CTRW model, along with the Post-FROC model, holds significant promise for the future of NACT efficacy prediction in ESCC patients.

Study on the Correlation Between the Expression of NF-Ƙb in the Alveolar Lavage Fluid of Children with Severe Mycoplasma Pneumoniae Pneumonia, Its Clinical Characteristics, and Cellular Immunity.

Objective: This study explored the level of nuclear factor-ƙB (NF-ƙB) in the bronchoalveolar lavage fluid (BALF) of children with severe Mycoplasma Pneumoniae pneumonia (SMPP) and the correlation between NF-ƙB, cellular immunity, and clinical characteristics. Methods: A total of 41 hospitalized children diagnosed with SMPP were selected and included in the SMPP group, and 13 bronchial foreign bodies (FB) without infection during the same period were included in the FB group. The NF-ƙB in the BALF of participants was detected by enzyme-linked immunosorbent assay. The correlation between NF-ƙB and laboratory findings, cellular immunity, and the clinical features in children with SMPP was analyzed. The differences in chest imaging and bronchoscopy in children with SMPP were observed. Results: The levels of NF-ƙB were significantly increased in the SMPP group compared with the FB group (P < 0.001). There were correlations between different NF-ƙB pairs in the SMPP group (P < 0.01). Nuclear factor-ƙB (NF-ƙB) correlated with IL-6, the mycoplasma load in BALF, fever peak, length of hospital stay, and sputum suppository (P < 0.05). The higher the intracellular NF-ƙB level in BALF, the lower the CD3+ CD4+ value in peripheral blood (P < 0.05). Intracellular NF-ƙB and total NF-ƙB correlated with pleural effusion, pericardial effusion, and extrapulmonary complications (P < 0.05). Conclusion: NF-ƙB is involved in airway inflammation changes in children with SMPP. The higher the level of NF-ƙB in the airway, the more severe the clinical manifestations, and the longer the length of hospital stay is likely to be.

Different Phenotypic, Photosynthetic, and Physiological Responses to Flooding between Q. nuttallii and Q. palustris.

Flooding stress is an increasingly serious problem in wetlands, often affecting large areas of crops and timber production areas. The current study aimed to explore the species differences in responses to flooding stress between Q. nuttallii and Q. palustris in an outdoor environment. All the tested plants survived after a 60-day flooding treatment that left 5 cm of water above the soil surface. This suggests that the two species are flood-tolerant, so they can be applied in the construction of riparian protection forests and wetland restoration. Compared with control conditions, flooding treatment significantly decreased seedling height and diameter and the Pn, Gs, Tr, Fv/Fm, ABS/CSm, TR0/CSm, ET0/CSm, RE0/CSm, IAA, and GA3 content and significantly increased the content of MDA, H2O2, soluble sugars, SOD, POD, ADH, ABA, and JA. Under control conditions, Q. nuttallii showed significantly greater growth and photosynthetic capability than Q. palustris. In contrast, Q. palustris exhibited less inhibition of growth and photosynthesis, oxidative stress levels, and antioxidant enzyme activities than Q. nuttallii under flooding conditions. The findings indicate that Q. palustris has better defense mechanisms against the damage caused by flooding stress than Q. nuttallii. Q. nuttallii was more sensitive and responsive to flooding than Q. palustris.

Integrative neurovascular coupling and neurotransmitter analyses in anisometropic and visual deprivation amblyopia children.

The association between visual abnormalities and impairments in cerebral blood flow and brain region potentially results in neural dysfunction of amblyopia. Nevertheless, the differences in the complex mechanisms of brain neural network coupling and its relationship with neurotransmitters remain unclear. Here, the neurovascular coupling mechanism and neurotransmitter activity in children with anisometropic amblyopia (AA) and visual deprivation amblyopia (VDA) was explored. The neurovascular coupling of 17 brain regions in amblyopia children was significantly abnormal than in normal controls. The classification abilities of coupling units in brain regions differed between two types of amblyopia. Correlations between different coupling effects and neurotransmitters were different. The findings of this study demonstrate a correlation between the neurovascular coupling and neurotransmitter in children with AA and VDA, implying their impaired neurovascular coupling function and potential molecular underpinnings. The neuroimaging evidence revealed herein offers potential for the development of neural therapies for amblyopia.

Mean Apparent Propagator MRI: Quantitative Assessment of Tumor-Stroma Ratio in Invasive Ductal Breast Carcinoma.

Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (P < .001 to P = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], P < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], P < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. Keywords: MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 Supplemental material is available for this article. © RSNA, 2024.

Risk factors and early prediction of cardiorenal syndrome type 3 among acute kidney injury patients: a cohort study.

BACKGROUND: Type 3 cardiorenal syndrome (CRS type 3) triggers acute cardiac injury from acute kidney injury (AKI), raising mortality in AKI patients. We aimed to identify risk factors for CRS type 3 and develop a predictive nomogram. METHODS: In this retrospective study, 805 AKI patients admitted at the Department of Nephrology, Second Hospital of Shanxi Medical University from 1 January 2017, to 31 December 2021, were categorized into a study cohort (406 patients from 2017.1.1-2021.6.30, with 63 CRS type 3 cases) and a validation cohort (126 patients from 1 July 2021 to 31 Dec 2021, with 22 CRS type 3 cases). Risk factors for CRS type 3, identified by logistic regression, informed the construction of a predictive nomogram. Its performance and accuracy were evaluated by the area under the curve (AUC), calibration curve and decision curve analysis, with further validation through a validation cohort. RESULTS: The nomogram included 6 risk factors: age (OR = 1.03; 95%CI = 1.009-1.052; p = 0.006), cardiovascular disease (CVD) history (OR = 2.802; 95%CI = 1.193-6.582; p = 0.018), mean artery pressure (MAP) (OR = 1.033; 95%CI = 1.012-1.054; p = 0.002), hemoglobin (OR = 0.973; 95%CI = 0.96--0.987; p < 0.001), homocysteine (OR = 1.05; 95%CI = 1.03-1.069; p < 0.001), AKI stage [(stage 1: reference), (stage 2: OR = 5.427; 95%CI = 1.781-16.534; p = 0.003), (stage 3: OR = 5.554; 95%CI = 2.234-13.805; p < 0.001)]. The nomogram exhibited excellent predictive performance with an AUC of 0.907 in the study cohort and 0.892 in the validation cohort. Calibration and decision curve analyses upheld its accuracy and clinical utility. CONCLUSIONS: We developed a nomogram predicting CRS type 3 in AKI patients, incorporating 6 risk factors: age, CVD history, MAP, hemoglobin, homocysteine, and AKI stage, enhancing early risk identification and patient management.

Smooth-Guided Implicit Data Augmentation for Domain Generalization.

The training process of a domain generalization (DG) model involves utilizing one or more interrelated source domains to attain optimal performance on an unseen target domain. Existing DG methods often use auxiliary networks or require high computational costs to improve the model's generalization ability by incorporating a diverse set of source domains. In contrast, this work proposes a method called Smooth-Guided Implicit Data Augmentation (SGIDA) that operates in the feature space to capture the diversity of source domains. To amplify the model's generalization capacity, a distance metric learning (DML) loss function is incorporated. Additionally, rather than depending on deep features, the suggested approach employs logits produced from cross entropy (CE) losses with infinite augmentations. A theoretical analysis shows that logits are effective in estimating distances defined on original features, and the proposed approach is thoroughly analyzed to provide a better understanding of why logits are beneficial for DG. Moreover, to increase the diversity of the source domain, a sampling-based method called smooth is introduced to obtain semantic directions from interclass relations. The effectiveness of the proposed approach is demonstrated through extensive experiments on widely used DG, object detection, and remote sensing datasets, where it achieves significant improvements over existing state-of-the-art methods across various backbone networks.

First Report of Bacterial Leaf Streak of Rice Caused by Pantoea ananatis in Guangdong Province, China.

Rice (Oryza sativa L.) is a crucial staple crop worldwide, and bacterial diseases are among the primary factors affecting rice yield. In late October 2022, bacterial leaf streak disease was observed on the leaves of the rice variety Meixiangzhan 2 across multiple fields (approximately 130 hm2) in Leizhou City, Guangdong Province, China. The incidence rate was up to 30% in each field. Infected rice leaves exhibited distinctive symptoms at the boundary between diseased and healthy tissue, featuring dark green to yellow-brown streaks, while most of the leaf margin exhibited symptoms of either leaf edge or sheath rot. Disease progression from the leaf tip inwards revealed gray-white or dehydrated lesions with a bluish-gray color. Some leaves exhibited wrinkling at the edges, and severe symptoms at the leaf tip resembled those of bacterial leaf blight in rice. Ten leaves were collected from 10 infected rice plants in three distinct fields, and leaf pieces at the border of diseased and healthy areas were surface disinfected with 75% anhydrous ethanol for 60 seconds, rinsed three times with sterile water, and then soaked in sterile water for 8 hours. The obtained bacterial suspension was diluted at a ratio of 1: 106, and 100 µL of the diluted samples were plated on Potato Dextrose Agar (PDA) plates. After incubation at 28°C for 48 hours, the yellow bacterial colonies that appeared, were purified on PDA plates. To confirm the bacterial species, the amplification of genes gyrB, leuS, rpoB, and 16S rDNA was performed on six randomly selected isolates from the three different fields using the primers 27F/1492R, gyrB-F/R, leuS-F/R and rpoB-F/R, as reported by Yu et al (2022), respectively. PCR products were sequenced. All six isolates had identical sequences for all genes sequenced.The gene sequences of 16S rDNA (960 bp), gyrB (953 bp), leuS (733 bp), and rpoB (877 bp) for LZ1, were deposited in the NCBI database under accession numbers PP048830 , PP068625 , PP068626, and PP068627, respectively. These sequences were subsequently compared using BLASTn tool against the NCBI nr/nt database. The 16S rDNA, gyrB, leuS, and rpoB of LZ1 showed similarities of 99.90%, 99.16%, 99.73%, and 99.89%, with the corresponding sequences of P. ananatis TZ39 (GenBank accession numbers MZ800600.1 for 16S rDNA, and CP081342.1 for gyrB, leuS and rpoB ). MLSA analysis using concatenated sequences of gyrB, leuS, and rpoB genes indicated that the isolated strain LZ1 belongs to P. ananatis. In the tillering stage of rice varieties Meixiangzhan 2 and Huahangyuzhan, P. ananatis LZ1 was inoculated at a concentration of 108 CFU/mL using the leaf-cutting method, with sterile water used as a control (Toh et al., 2019). After 14 days of bacterial inoculation, the inoculated leaves gradually became necrotic, changing from light green to brown showing identical symptoms as those in the field, while the control plants remained symptom-free. Subsequent 16S rDNA, gyrB, leuS and rpoB gene sequencing results further confirmed the identity of the pathogen as P. ananatis, thereby fulfilling Koch's postulates. Previous reports have already identified P. ananatis as the pathogen causing rice bacterial leaf streak (Kini et al., 2017; Arayaskul et al., 2019; Yu et al., 2022; Lu et al., 2022; Luna et al., 2023; Yuan et al., 2023). This is the first report of rice bacterial leaf streak caused by P. ananatis in Guangdong Province, China, laying the foundation for future research to establish strategies for the prevention and control of this disease.

HCL: A Hierarchical Contrastive Learning Framework for Zero-Shot Relation Extraction.

Zero-shot relation extraction (ZSRE) is shown to become more significant in the current information extraction system, which aims at predicting relation classes that lack annotations or have just never appeared during training. Previous works focus on projecting sentences with their corresponding relation descriptions to an intermediate semantic space and searching the nearest semantic for predicting unseen classes. Though these methods can achieve sound performance, they only obtain inferior semantic information via a trivial distance metric and neglect the interaction in the instance representations. We are thus motivated to tackle these issues and propose a hierarchical contrastive learning (HCL) framework for ZSRE including projection-level and instance-level modules. Specifically, the projection-level component replaces the distance score function by contrastive loss to connect the input sentence with the relation semantic space. And the instance-level component integrates the external knowledge from sentence entities to establish new contrastive pairs for efficiently learning representations from mutual information. The experimental results on three well-known datasets demonstrate that our model surpasses the existing SOTA by at most 18.97% improvement on the F1 score when unseen classes are 15 . Moreover, our model can achieve more competitive performance alone with the increasing number of unseen classes.

Differentiation of confluent hepatic fibrosis and infiltrative hepatocellular carcinoma on MR imaging.

BACKGROUND: To identify reliable magnetic resonance imaging (MRI) features that can differentiate confluent fibrosis (CF) from infiltrative hepatocellular carcinoma (HCC). METHODS: A retrospective analysis was conducted on Twenty CF patients and 28 infiltrative HCC patients who underwent upper abdomen MRI scans. The imaging features of lesions were analyzed, and the apparent diffusion coefficient (ADC) of lesions were measured. Accuracy, sensitivity and specificity for the diagnosis of CF were calculated for each category individually and combined. RESULTS: Compared to infiltrative HCC, hepatic capsular retraction at the site of lesion, hepatic volume loss at the site of lesion and "nodular surround sign" were more common in patients with CF (all P < 0.001). Hepatic volume loss at the site of lesion, no or mild enhancement in arterial phase, and hyper-enhancing in delayed phase to the background parenchyma showed superior diagnostic accuracy (83.3%, 85.4%, 97.9%, respectively). When the lesion exhibited hepatic volume loss at the site of lesion or no or mild enhancement in arterial phase or hyper-enhancing in delayed phase, a sensitivity of 100.0% for the diagnosis of CF was achieved. When the lesion was positive for any two of three categories, or positive for all three categories, a specificity of 100.0% was achieved. The ADC values of CF were higher than those of infiltrative HCC (P < 0.001). CONCLUSION: The combination of the hepatic volume loss at the site of lesion, no or mild enhancement in arterial phase, and hyper-enhancing in delayed phase to the background parenchyma can be considered reliable MR features for the diagnosis of CF, as they allow differentiation from infiltrative HCC.

Stabilization of all-natural water-in-oil high internal phase pickering emulsion by using diosgenin/soybean phosphatidylethanolamine complex: Characterization and application in 3D printing.

This study aimed to prepare an all-natural water-in-oil high internal phase Pickering emulsion (W/O-HIPPE) using diosgenin/soybean phosphatidylethanolamine complex (DGSP) and investigate the 3D printing performance. Results suggested that the self-assembly of diosgenin crystal was modified by SP in DGSP (diosgenin-SP ratios at 3:1 and 1:1), revealing a variation from large-size outward radiating needle-like to small-size granular-like shape, which facilitated closely packing at the interface. Hydrophilicity of DGSP was also increased (contact angle varying from 133.3 o to 106.4 o), ensuring more adequate interfacial adsorption to reduce interfacial tension more largely (6.5 mN/m). Thus, the W/O-HIPPE made by DGSP with diosgenin-SP = 1:1, exhibited smaller droplets and better freeze/thawing stability. The W/O-HIPPE was also measured improved rheological properties for 3D printing: satisfied shear-thinning behavior, higher recovery and self-supporting (viscoelasticity and deformation resistance). Consequently, the W/O-HIPPE allowed for printing more delicate patterns. This work provided guidance to prepare W/O-HIPPE for 3D printing.

Iron Oxide Nanoparticles Engineered Macrophage-Derived Exosomes for Targeted Pathological Angiogenesis Therapy.

Engineering exosomes with nanomaterials usually leads to the damage of exosomal membrane and bioactive molecules. Here, pathological angiogenesis targeting exosomes with magnetic imaging, ferroptosis inducing, and immunotherapeutic properties is fabricated using a simple coincubation method with macrophages being the bioreactor. Extremely small iron oxide nanoparticle (ESIONPs) incorporated exosomes (ESIONPs@EXO) are acquired by sorting the secreted exosomes from M1-polarized macrophages induced by ESIONPs. ESIONPs@EXO suppress pathological angiogenesis in vitro and in vivo without toxicity. Furthermore, ESIONPs@EXO target pathological angiogenesis and exhibit an excellent T1-weighted contrast property for magnetic resonance imaging. Mechanistically, ESIONPs@EXO induce ferroptosis and exhibit immunotherapeutic ability toward pathological angiogenesis. These findings demonstrate that a pure biological method engineered ESIONPs@EXO using macrophages shows potential for targeted pathological angiogenesis therapy.

Copper oxide nanoparticles suppress retinal angiogenesis via inducing endothelial cell cuproptosis.

Background: Copper oxide nanoparticles (CuO NPs) exhibit antitumor activity; however, their potential as an antiangiogenesis agent is unknown. Materials & methods: The antiangiogenesis properties of CuO NPs were evaluated in vitro and in vivo and the underlying mechanism was examined using RNA sequencing and metabolomic analyses. Results: CuO NPs inhibited endothelial cell function in vitro. They also mitigated retinal vasculature development and alleviated pathological retinal angiogenesis in vivo. RNA sequencing and metabolomic analyses revealed that CuO NPs disrupt the tricarboxylic acid cycle and induce cuproptosis, which was further supported by evaluating cuproptosis-related metabolites and proteins. Conclusion: CuO NPs may be an effective antiangiogenic agent for the treatment of retinal angiogenesis.

A novel MRI-based diagnostic model for predicting placenta accreta spectrum.

Objective To develop and evaluate a diagnostic model based on MRI signs for predicting placenta accreta spectrum. Materials and Methods A total of 155 pregnant women were included in this study, randomly divided into 104 cases in the training set and 51 cases in the validation set. There were 93 Non-PAS cases, and 62 cases in the PAS group. The training set included 62 Non-PAS cases and 42 PAS cases. Clinical factors and MRI signs were collected for univariate analysis. Then, binary logistic regression analysis was used to develop independent diagnostic models with clinical relevant risk factors or MRI signs, as well as those combining clinical risk factors and MRI signs. The ROC curve analysis was used to evaluate the diagnostic performance of each diagnostic model. Finally, the validation was performed with the validation set. Results In the training set, four clinical factors (gestity, parity, uterine surgery history, placental position) and 11 MRI features (T2-dark bands, placental bulge, T2 hypointense interface loss, myometrial thinning, bladder wall interruption, focal exophytic mass, abnormal placental bed vascularization, placental heterogeneity, asymmetric placental thickening/shape, placental ischemic infarction, abnormal intraplacental vascularity) were considered as risk factors for PAS. The AUC of the clinical diagnostic model, MRI diagnostic model, and clinical + MRI model of PAS were 0.779, 0.854, and 0.874, respectively. In the validation set, the AUC of the clinical diagnostic model, MRI diagnostic model, and clinical + MRI model of PAS were 0.655, 0.728, and 0.735, respectively. Conclusion Diagnosis model based on MRI features in this study can well predict placenta accreta spectrum.

Inter-Class and Inter-Domain Semantic Augmentation for Domain Generalization.

The domain generalization approach seeks to develop a universal model that performs well on unknown target domains with the aid of diverse source domains. Data augmentation has proven to be an effective method to enhance domain generalization in computer vision. Recently, semantic-level based data augmentation has yielded remarkable results. However, these methods focus on sampling semantic directions on feature space from intra-class and intra-domain, limiting the diversity of the source domain. To address this issue, we propose a novel approach called Inter-Class and Inter-Domain Semantic Augmentation (CDSA) for domain generalization. We first introduce a sampling-based method called CrossSmooth to obtain semantic directions from inter-class. Then, CrossVariance obtains the styles of different domains by sampling semantic directions. Our experiments on four well-known domain generalization benchmark datasets (Digits-DG, PACS, Office-Home, and DomainNet) demonstrate the effectiveness of our approach. We also validate our approach on commonly-used semantic segmentation datasets, namely GTAV, SYNTHIA, Cityscapes, Mapillary, and BDDS which also show significant improvements.