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The extensive usage of metal oxide nanoparticles has aided in the spread and accumulation of these nanoparticles in the environment, potentially endangering both human health and the agroecological system. This research describes in detail the hazardous and advantageous impacts of common metal oxide nanomaterials, such as iron oxide, copper oxide, and zinc oxide, on the life cycle of rice. In-depth analyses are conducted on the transport patterns of nanoparticles in rice, the plant's reaction to stress, the reduction of heavy metal stress, and the improvement of rice quality by metal oxide nanoparticles, all of which are of significant interest in this subject. It is emphasized that from the perspective of advancing the field of nanoagriculture, the next stage of research should focus more on the molecular mechanisms of the effects of metal oxide nanoparticles on rice and the effects of combined use with other biological media. The limitations of the lack of existing studies on the effects of metal oxide nanomaterials on the entire life cycle of rice have been clearly pointed out. [ABSTRACT FROM AUTHOR]

The liner is an important part of shaped charge. In this paper, the spherical cone composite structure liner composed of a spherical missing body and truncated cone (hereinafter referred to as the SCS liner) is studied. The SCS liner is made of copper. Based on this, a shaped charge structure based on the explosion pressure-coupling constraint principle is designed, filling an 8701 explosive (RDX-based explosive). Through pulse X-ray tests, numerical simulation, and static explosion tests, the significance of the detonation pressure-coupling constraint principle, as well as the forming law and penetration efficiency of the SCS liner are studied. The results show that in the pulsed X-ray test, a split jet with high velocity is formed in the SCS liner. The explosion pressure-coupling constraint principle delays the attenuation of the internal explosion pressure and improves the shape of jet. After the SCS liner is selected, the penetration depth is increased by 70.38%. The average head velocity of the explosive charge jet is 7594.81 m/s. The diameter of the hole formed by the jet of the explosive charge is 20.33 mm. The hole expands inside, and the perforation depth is 178.87 mm. The numerical simulation is in good agreement with the test. [ABSTRACT FROM AUTHOR]

The topological organization underlying brain networks has been extensively investigated using resting-state fMRI, focusing on the low frequency band from 0.01 to 0.1 Hz. However, the frequency specificities regarding the corresponding brain networks remain largely unclear. In the current study, a data-driven method named complementary ensemble empirical mode decomposition (CEEMD) was introduced to separate the time series of each voxel into several intrinsic oscillation rhythms with distinct frequency bands. Our data indicated that the whole brain BOLD signals could be automatically divided into five specific frequency bands. After applying the CEEMD method, the topological patterns of these five temporally correlated networks were analyzed. The results showed that global topological properties, including the network weighted degree, network efficiency, mean characteristic path length and clustering coefficient, were observed to be most prominent in the ultra-low frequency bands from 0 to 0.015 Hz. Moreover, the saliency of small-world architecture demonstrated frequency-density dependency. Compared to the empirical mode decomposition method (EMD), CEEMD could effectively eliminate the mode-mixing effects. Additionally, the robustness of CEEMD was validated by the similar results derived from a split-half analysis and a conventional frequency division method using the rectangular window band-pass filter. Our findings suggest that CEEMD is a more effective method for extracting the intrinsic oscillation rhythms embedded in the BOLD signals than EMD. The application of CEEMD in fMRI data analysis will provide in-depth insight in investigations of frequency specific topological patterns of the dynamic brain networks. [ABSTRACT FROM AUTHOR]

Numerous brain oscillations are well organized into several brain rhythms to support complex brain activities within distinct frequency bands. These rhythms temporally coexist in the same or different brain areas and may interact with each other with specific properties and physiological functions. However, the identification and evaluation of these various brain rhythms derived from BOLD-fMRI signals are obscure. To address this issue, we introduced a data-driven method named Complementary Ensemble Empirical Mode Decomposition (CEEMD) to automatically decompose the BOLD oscillations into several brain rhythms within distinct frequency bands. Thereafter, in order to evaluate the performance of CEEMD in the detection of subtle BOLD signals, a novel CEEMD-based high-dimensional pattern classification framework was proposed to accurately identify mild cognitive impairment individuals from the healthy controls. Our results showed CEEMD is a stable frequency decomposition method. Furthermore, CEEMD-based frequency specific topological profiles provided a classification accuracy of 93.33%, which was saliently higher than that of the conventional frequency separation based scheme. Importantly, our findings demonstrated that CEEMD could provide an effective means for brain oscillation separation, by which a more meaningful frequency bins could be used to detect the subtle changes embedded in the BOLD signals. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the differentiation efficiency of texture analysis of T1WI, T2WI and contrasted-enhanced T1WI MRI sequences in gliomas with and without IDH1 mutation based on entire tumor region.Materials and Methods: A total of 42 patients with histopathologically confirmed gliomas, including 21 patients carrying IDH1 mutation (IDH1mutation group) and 21 with wild-type IDH1 (IDH1wt group) were included in this study. The preoperative MRI and clinical data were collected. The regions of interest (ROIs) covering the entire tumor and edema were manually delineated on axial slices using O.K. (Omni Kinetics, GE Healthcare, China) software; and the histogram and GLCM features based on T1WI, T2WI and contrasted-enhanced T1WI sequences were automatically generated.Results: Based on contrasted-enhanced T1WI features, the inertia resulted as the best feature for diagnosis, with the AUC of 0.844. Furthermore, the AUC for gliomas prediction with IDH1mutation was 0.800 for cluster prominence. IDH1-mutation was differentiated on T2WI with the highest AUC of 0.848, which corresponded to GLCM Entropy. After modeling, the accuracy of the contrasted-enhanced T1WI, T1WI, and T2WI features model was 0.952, 0.857, and 0.738, respectively. The AUC of Joint VariableT1WI+C for predicting IDH1mutation was 0.984, while the AUC of Joint VariableT1WI for predicting the same mutation was 0.927. The diagnostic efficiency of Joint VariableT2WI was also desirable.Conclusion: MRI texture analysis could be used as a new noninvasive method for identification of gliomas with IDH1 mutation. The present results show that the Joint Variable derived from conventional MR imaging histogram and GLCM features is suitable for precise detection of IDH1-mutated gliomas. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the application of conventional MRI histogram analysis based on the whole tumor measurement on assessing meningioma grading.Materials and Methods: This retrospective study was approved by the institutional review board. A total amount of 90 patients with meningioma were enrolled and the preoperative MRI of them were analyzed. To be specific, the patient group were consisted of 45 patients with grade I, 38 with grade II, and 7 with grade III meningioma. Grade I meningioma is classified as low grade meningioma (LGM), whereas Grade II and III meningioma were combined and classified as high grade meningioma (HGM). ROIs were drawn along the edge of the tumor on each section of T1WI, T2WI, and contrasted T1WI. 3D ROI signal intensity histogram and all its parameters were obtained. Independent t-test and Kruskal-Wallis test were used for comparison between two groups. Univariate logistic regression analysis and Spearman's correlation analysis were used to screen for the parameters with high predictive efficiency, while multivariate logistic regression analysis was used to determine the optimal model for the classification of meningioma.Results: There were significant differences observed between HGM and LGM groups regarding to histogram volume count, uniformity of three sequences, range of T1WI and T2WI, kurtosis, standard deviation, variance, max intensity of T2WI, skewness, mean deviation, minimum intensity, mean value, the 5th percentile, the 10th percentile, the 25th percentile, the 50th percentile, the 75th percentile, and the 90th percentile of contrasted T1WI. Volume count and uniformity were high predictive parameters in distinguishing HGM from LGM. Logistic regression model included contrasted T1WI histogram parameters (i.e. minimum intensity, volume count, skewness, uniformity, and the 75th percentile) showed the best diagnostic efficiency for meningioma grade, with a sensitivity and specificity of 83.9% and 77.4% (AUC = 0.834, cutoff value = 0.413), respectively. The optimal model was achieved with a sensitivity of 71.4% and a specificity of 78.6% in the test set (AUC = 0.791, cutoff value = 0.413).Conclusions: Histogram analysis of conventional MRI based on 3D tumor measurement can be applied in the assessment of meningioma grading in clinical. [ABSTRACT FROM AUTHOR]

Purpose: This prospective study aimed to investigate the value of kinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating uterine endometrioid adenocarcinoma (EAC) from adenocarcinoma of cervix (AdC).Methods: Seventy-five newly diagnosed patients with distinctive pathology underwent DCE-MRI. Observers independently calculated the tumor diameters and DCE-MRI parameters using both population and individual-based arterial input function (AIF). Inter-observer consistency was evaluated, and a comparative analysis between EAC (n = 47) and AdC (n = 28) was performed. Regression analysis was used to select parameters that best distinguished EAC from AdC, and to generate predictive models. Receiver operating characteristic curve (ROC) was applied to calculate the diagnostic efficiency of single parameter and the predictive models.Results: Inter-observer consistency was excellent (intra-class correlation [ICC] = 0.902-0.981), especially when calculated via population AIF with relatively higher ICC and smaller SD on Bland-Altman plot. Tumor diameters were not correlated with tumor types. All the DCE-MRI parameters were lower in EAC compared to AdC, except Kep by population AIF and TTP by both sets of AIFs. The statistical parameters were Ve, Maxslop, and Maxconc by population AIF, and Maxslop and Ktrans by individual AIF included in the predictive models, respectively. The two predictive models with combined parameters showed improved diagnostic efficiency in differentiating these two diseases compared with a single parameter.Conclusion: DCE-MRI can quantitatively evaluate the perfusion difference between EAC and AdC, thus improving the identification of uterine adenocarcinoma with uncertain biopsy pathology. [ABSTRACT FROM AUTHOR]

The widespread use of Chat Generative Pre-trained Transformer (known as ChatGPT) and other emerging technology that is powered by generative artificial intelligence (GenAI) has drawn attention to the potential ethical issues they can cause, especially in high-stakes applications such as health care, but ethical discussions have not yet been translated into operationalisable solutions. Furthermore, ongoing ethical discussions often neglect other types of GenAI that have been used to synthesise data (eg, images) for research and practical purposes, which resolve some ethical issues and expose others. We did a scoping review of the ethical discussions on GenAI in health care to comprehensively analyse gaps in the research. To reduce the gaps, we have developed a checklist for comprehensive assessment and evaluation of ethical discussions in GenAI research. The checklist can be integrated into peer review and publication systems to enhance GenAI research and might be useful for ethics-related disclosures for GenAI-powered products and health-care applications of such products and beyond., Competing Interests: Declaration of interests NL reports funding from the Duke–NUS Signature Research Programme funded by the Ministry of Health, Singapore. JS reports funding from the Wellcome Trust and roles as a Bioethics Committee consultant for Bayer and as an advisory panel member for the Hevolution Foundation. DSWT reports funding from National Medical Research Council, Singapore, Duke–NUS Medical School, and Agency for Science, Technology, and Research; patents on deep learning systems for diabetic retinopathy, glaucoma, and age-related macular degeneration (co-inventor; 2017), a computer-implemented method for training an image classifier using weakly annotated training data (2019), and automatically extracting measurements from an image of a display of a measurement device (2020); has a leadership role (unpaid) as Chair of the AI and Digital Innovation Standing Committee and the Asia–Pacific Academy of Ophthalmology; and serves on the executive committees of the American Academy of Ophthalmology AI Committee, STARD-AI Steering Committee, Imperial College London, DECIDE-AI, and QUANDAS-AI. EJT reports funding from the National Institutes of Health (NIH) Grant and consulting fees as an adviser to Tempus Labs, Pheno.AI, and Abridge. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Introduction: Major abdominal emergency surgery (MAES) patients have a high risk of mortality and complications. The time-sensitive nature of MAES necessitates an easily calculable risk-scoring tool. Shock index (SI) is obtained by dividing heart rate (HR) by systolic blood pressure (SBP) and provides insight into a patient's haemodynamic status. We aimed to evaluate SI's usefulness in predicting postoperative mortality, acute kidney injury (AKI), requirements for intensive care unit (ICU) and high-dependency monitoring, and the ICU length of stay (LOS)., Method: We retrospectively reviewed 212,089 MAES patients from January 2013 to December 2020. The cohort was propensity matched, and 3960 patients were included. The first HR and SBP recorded in the anaesthesia chart were used to calculate SI. Regression models were used to investigate the association between SI and outcomes. The relationship between SI and survival was explored with Kaplan-Meier curves., Results: There were significant associations between SI and mortality at 1 month (odds ratio [OR] 2.40 [1.67-3.39], P<0.001), 3 months (OR 2.13 [1.56-2.88], P<0.001), and at 2 years (OR 1.77 [1.38-2.25], P<0.001). Multivariate analysis revealed significant relationships between SI and mortality at 1 month (OR 3.51 [1.20-10.3], P=0.021) and at 3 months (OR 3.05 [1.07-8.54], P=0.034). Univariate and multivariate analysis also revealed significant relationships between SI and AKI (P<0.001), postoperative ICU admission (P<0.005) and ICU LOS (P<0.001). SI does not significantly affect 2-year mortality., Conclusion: SI is useful in predicting postopera-tive mortality at 1 month, 3 months, AKI, postoperative ICU admission and ICU LOS., Competing Interests: There are no conflicts of interest.

Genome regulatory programs such as telomere functioning require extracellular signals to be transmitted from the microenvironment to the nucleus and chromatin. Although the cytoskeleton has been shown to directly transmit stresses, we show that the intrinsically dynamic nature of the actin cytoskeleton is important in relaying extracellular signals to telomeres. Interestingly, this mechanical pathway not only transmits physical stimuli but also chemical stimuli. The cytoskeletal network continuously reorganizes and applies dynamic forces on the nucleus and feeds into the regulation of telomere dynamics. We further found that distal telomeres are mechanically coupled in a length- and timescale-dependent manner and identified nesprin 2G as well as lamin A/C as being essential to regulate their translational dynamics. Finally, we demonstrated that such mechanotransduction events impinge on the binding dynamics of critical telomere binding proteins. Our results highlight an overarching physical pathway that regulates positional and molecular stability of telomeres., (Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.)