Cavernous Sinus MRI Findings in Inflammatory and Ischemic Oculomotor Cranial Nerve Palsies.

BACKGROUND: The significance of asymmetric enhancement on cavernous sinus MRIs in the differential diagnosis of ischemic and inflammatory oculomotor cranial nerve (OCN) palsies remains controversial. This study explored the cavernous sinus MRI findings for cavernous sinus idiopathic inflammation (inflammation group), microvascular ischemic OCN palsy (ischemic group), and ocular myasthenia gravis (OMG group) patients. METHODS: A total of 66, 117, and 60 patients were included in the inflammation, ischemic, and OMG groups, respectively. Cavernous sinus MRIs were retrospectively analyzed. RESULTS: The abnormality rates of cavernous sinus MRIs for OMG and ischemic groups were 41.7% (25/60) and 61.5% (72/117), respectively. Inconsistency rates between clinical topical diagnosis and imaging findings for inflammation and ischemic groups were 3.0% (2/66) and 13.7% (16/117), respectively (P = 0.020). In the inflammation group, cavernous sinus thickness, thickening enhancement, and enhancing adjacent lesions were noted in 90.9% (60/66), 71.2% (47/66), and 25.8% (17/66) of the patients, whereas in the ischemic group, they were noted in 51.3% (60/117), 38.5% (45/117), and 0.9% (3/117) of the patients, respectively (P < 0.001). Among ischemic CN III palsy patients, 55.5% (15/27) and 16.7% (2/12) of the cases had CN III enlargement and enhancement in the diabetic and nondiabetic groups, respectively (P = 0.037). CONCLUSIONS: Cavernous sinus MRI abnormalities can be explained by specific pathologic mechanisms of the primary disease based on the complex neuroanatomy. However, suspicious inflammatory changes cannot exclude the possibility of ischemia and over reliance on these findings should be avoided.

Acute iron oxide nanoparticles exposure induced murine eosinophilic airway inflammation via TLR2 and TLR4 signaling.

Iron oxide nanoparticles (Fe2 O3 NPs) is the main component of air pollution particles in urban rail transit environment. People are more exposed to Fe2 O3 NPs, however, the studies on relationship between Fe2 O3 NPs and respiratory health are limited. In the present study, acute airway inflammation caused by Fe2 O3 NPs and its possible mechanism were investigated. BALB/c mice were intratracheally challenged with different concentrations of Fe2 O3 NPs. Fe2 O3 NPs induced bronchial epithelial barrier function damage, infiltration of neutrophils and lymphocytes into the airway submucosa, secretion of mucus in the airway epithelium and elevated expression of eosinophil major basic protein (EMBP) in lungs. Compared with the control group, Fe2 O3 NPs increased eosinophils by 20 times in bronchoalveolar lavage fluid (BALF), and markedly increased eosinophils related cytokines and chemokines, including interleukin (IL) -5, IL-33, thymic stromal lymphopoietin (TSLP), monocyte chemotactic protein (MCP)-3, eotaxin, tumor necrosis factor (TNF)-α, keratinocyte chemoattractant (KC). Furthermore, Fe2 O3 NPs up-regulated levels of IL-5, MCP-3, eotaxin, and KC in serum. In vitro studies showed that Fe2 O3 NPs increased the genes and proteins expression of Toll-like receptors (TLR)-2, TLR4, TNF receptor associated factor 6 (TRAF6), myeloid differentiation factor 88 (MyD88), nuclear factor (NF)-κB, and TNF-α in RAW267.4 cells. The downstream inflammatory cytokine protein expression and release such as TNF-α was significantly decreased after using TLR2/TLR4 inhibitor OxPAPC, but not MyD88 inhibitor ST2825. These results suggest that TLR2 and TLR4 played important role in Fe2 O3 NPs inducing acute eosinophilic airway inflammation in the murine lung.

Application of computer-aided detection for NCCN-based follow-up recommendation in subsolid nodules: Effect on inter-observer agreement.

RATIONALE AND OBJECTIVES: Computer-aided detection (CAD) of pulmonary nodules reduces the impact of observer variability, improving the reliability and reproducibility of nodule assessments in clinical practice. Therefore, this study aimed to assess the impact of CAD on inter-observer agreement in the follow-up management of subsolid nodules. MATERIALS AND METHODS: A dataset comprising 60 subsolid nodule cases was constructed based on the National Cancer Center lung cancer screening data. Five observers independently assessed all low-dose computed tomography scans and assigned follow-up management strategies to each case according to the National Comprehensive Cancer Network (NCCN) guidelines, using both manual measurements and CAD assistance. The linearly weighted Cohen's kappa test was used to measure agreement between paired observers. Agreement among multiple observers was evaluated using the Fleiss kappa statistic. RESULTS: The agreement of the five observers for NCCN follow-up management categorization was moderate when measured manually, with a Fleiss kappa score of 0.437. Utilizing CAD led to a notable enhancement in agreement, achieving a substantial consensus with a Fleiss kappa value of 0.623. After using CAD, the proportion of major and substantial management discrepancies decreased from 27.5% to 15.8% and 4.8% to 1.5%, respectively (p < 0.01). In 23 lung cancer cases presenting as part-solid nodules, CAD significantly elevates the average sensitivity in detecting lung cancer cases presenting as part-solid nodules (overall sensitivity, 82.6% vs. 92.2%; p < 0.05). CONCLUSION: The application of CAD significantly improves inter-observer agreement in the follow-up management strategy for subsolid nodules. It also demonstrates the potential to reduce substantial management discrepancies and increase detection sensitivity in lung cancer cases presenting as part-solid nodules.

Physicochemical properties of different crystal forms of manganese dioxide prepared by a liquid phase method and their quantitative evaluation in capacitor and battery materials.

Although there are many studies on the preparation and electrochemical properties of the different crystal forms of manganese dioxide, there are few studies on their preparation by a liquid phase method and the influence of their physical and chemical properties on their electrochemical performance. In this paper, five crystal forms of manganese dioxide were prepared by using manganese sulfate as a manganese source and the difference of their physical and chemical properties was studied by phase morphology, specific surface area, pore size, pore volume, particle size and surface structure. The different crystal forms of manganese dioxide were prepared as electrode materials, and their specific capacitance composition was obtained by performing CV and EIS in a three-electrode system, introducing kinetic calculation and analyzing the principle of electrolyte ions in the electrode reaction process. The results show that δ-MnO2 has the largest specific capacitance due to its layered crystal structure, large specific surface area, abundant structural oxygen vacancies and interlayer bound water, and its capacity is mainly controlled by capacitance. Although the tunnel of the γ-MnO2 crystal structure is small, its large specific surface area, large pore volume and small particle size make it have a specific capacitance that is only inferior to δ-MnO2, and the diffusion contribution in the capacity accounts for nearly half, indicating it also has the characteristics of battery materials. α-MnO2 has a larger crystal tunnel structure, but its capacity is lower due to the smaller specific surface area and less structural oxygen vacancies. ε-MnO2 has a lower specific capacitance is not only the same disadvantage as α-MnO2, but also the disorder of its crystal structure. The tunnel size of ß-MnO2 is not conducive to the interpenetration of electrolyte ions, but its high oxygen vacancy concentration makes its contribution of capacitance control obvious. EIS data shows that δ-MnO2 has the smallest charge transfer impedance and bulk diffusion impedance, while the two impedances of γ-MnO2 were the largest, which shows that its capacity performance has great potential for improvement. Combined with the calculation of electrode reaction kinetics and the performance test of five crystal capacitors and batteries, it is shown that δ-MnO2 is more suitable for capacitors and γ-MnO2 is more suitable for batteries.

Comprehensive analysis of PLKs expression and prognosis in breast cancer.

OBJECTIVE: A thorough examination of PLKs in breast cancer, including their expression and prognosis. METHODS: With the help of the Oncomine database, the transcript levels of PLKs in breast cancer were examined. The changes in PLKs expression with tumor stage and indeed the relationship between PLKs expression and stage of cancer in women with breast cancer were scrutinized by using the GEPIA database. Based on Kaplan-Meier plots, breast cancer patients were assessed for their prognosis. Breast cancer gene expression and mutations were analyzed within the cBioPortal database. RESULTS: According to Oncomine data, PLK1 and PLK4 mRNA expression levels were dramatically elevated in breast cancer patients while PLK2 and PLK5P levels were significantly downregulated. PLK1 and PLK4 expression were discovered to be greater in breast cancer tissues than in healthy tissues following analysis of the GEPIA database (P < 0.05). High levels of PLK1 and PLK4 transcripts have been linked to poor relapse-free survival rates across all patients with breast cancer according to the Kaplan-Meier Plotter database. The high levels of PLK2, PLK3, and PLK5 were associated with a higher recurrence-free survival rate. In the cBioPortal database, PLK was altered in 9.6% of breast cancer samples. Genetic alterations occurred in 15.07% of clinically counted invasive breast cancers, with mutations in 4.11%, gene amplifications in 9.59%, and gene deletion mutations in 1.37%. Additionally, the KEGG database demonstrates that PLKs are crucial for the cell cycle. The findings imply that elevated PLK1 and PLK4 expression in tissues of breast cancer might contribute significantly to the carcinogenesis of breast cancer. Moreover, PLK1 and PLK4 are highly expressed in breast cancer, and their use as molecular markers to identify high-risk subsets from patients with breast cancer is potentially possible. CONCLUSIONS: For the precise therapy of breast cancers, PLK1 and PLK4 are potential targets, while PLK2, PLK3, and PLK5 are brand-new biomarkers for predicting the prognosis of breast cancer.

Novel fabrication of hydrophobic/oleophilic human hair fiber for efficient oil/water separation through one-pot dip-coating synthesis route.

Frequent oil spill accidents and industrial wastewater discharge has always been one of the most severe worldwide environmental problems. To cope with this problem, many fluorine-containing and high-cost materials with superwettability have been extensively applied for oil-water separation, which hinders its large-scale application. In this work, a novel human hair fiber (HHF)-polymerized octadecylsiloxane (PODS) fiber was fabricated with a facile one-pot dip-coating synthesis approach, inspired by the self-assembly performance and hydrophobicity of OTS modification. The benefits of prominent hydrophobic/lipophilic behavior lie in the low surface energy, and a rough PODS coating was rationally adhered on the surface of HHF. Driven solely by gravity and capillary force, the HHF-PODS showed excellent oil/water separation efficiency (> 99.0%) for a wide range of heavy and light oil/water mixtures. In addition, HHF-PODS demonstrated durability toward different harsh environments like alkaline, acid, and salty solutions.

Chlorophyll content for millet leaf using hyperspectral imaging and an attention-convolutional neural network / Estimativa do teor de clorofila para folhas de milheto usando imagens hiperespectrais e uma rede neural atenção-convolucional

ABSTRACT: Chlorophyll is a major factor affecting photosynthesis; and consequently, crop growth and yield. In this study, we devised a chlorophyll-content detection model for millet leaves in different stages of growth based on hyperspectral data. The hyperspectral images of millet leaves were obtained under a wavelength range of 380-1000 nm using a hyperspectral imager. Threshold segmentation was performed with near-infrared (NIR) reflectance and normalized difference vegetation index (NDVI) to intelligently acquire the regions of interest (ROI). Furthermore, raw spectral data were preprocessed using multivariate scatter correction (MSC). A correlation coefficient-successive projections algorithm (CC-SPA) was used to extract the characteristic wavelengths, and the characteristic parameters were extracted based on the spectral and image information. A partial least squares regression (PLSR) prediction model was established based on the single characteristic parameter and multi-characteristic parameter fusion. The determination coefficient (Rv 2) and the root-mean-square error (RMSEv) of the validation set for the multi-characteristic parameter fusion model were reported to be 0.813 and 1.766, respectively, which are higher than those obtained by the single characteristic parameter model. Based on the multi-characteristic parameter fusion, an attention-convolutional neural network (attention-CNN) (Rv 2 = 0.839, RMSEv = 1.451, RPD = 2.355) was established, which is more effective than the PLSR (Rv 2 = 0.813, RMSEv = 1.766, RPD = 2.167) and least squares support vector machine (LS-SVM) models (Rv 2 = 0.806, RMSEv = 1.576, RPD = 2.061). These results indicated that the combination of hyperspectral imaging and attention-CNN is beneficial to the application of nutrient element monitoring of crops.

RESUMO: A clorofila é um fator importante que afeta a fotossíntese e, consequentemente, o crescimento e o rendimento das culturas. Neste estudo, um modelo de detecção de conteúdo de clorofila é construído para folhas de milheto em diferentes estágios de crescimento, com base em dados hiperespectrais. As imagens hiperespectrais dos diferentes estágios de crescimento das folhas de milheto foram obtidas para 380-1000 nm, utilizando um gerador de imagens hiperespectrais. Uma segmentação de limiar foi realizada com refletância no infravermelho próximo (NIR) e índice de vegetação com diferença normalizada (NDVI) para adquirir de forma inteligente as regiões de interesse (ROI). Além disso, os dados espectrais brutos foram pré-processados usando o método de correção de dispersão multivariada (MSC). Um algoritmo de projeção de coeficiente de correlação sucessivo (CC-SPA) foi utilizado para extrair os comprimentos de onda característicos, e os parâmetros característicos foram extraídos com base nas informações espectrais e de imagem. O modelo de previsão de regressão parcial dos mínimos quadrados (PLSR) foi estabelecido com base nos parâmetros de característica única e na fusão de parâmetros de característica múltipla. O coeficiente de determinação (Rv2) e o erro quadrático médio da raiz (RMSEv) do conjunto de validação para o modelo de fusão de parâmetros com várias características foram obtidos como 0,813 e 1,766, sendo melhores do que os do modelo de parâmetro de característica única. Com base na fusão de parâmetros com várias características, foi estabelecida uma rede neural atenção-convolucional (atenção-CNN) (Rv2 = 0,839, RMSEv = 1,451, RPD = 2,355) mais eficaz que o PLSR (Rv2 = 0,813, RMSEv = 1,766, RPD = 2,167) e mínimos quadrados que suportam modelos de máquina de vetores (LS-SVM) (Rv2 = 0,806, RMSEv = 1,576, RPD = 2,061). Estes resultados indicam que o modelo atenção-CNN atinge uma previsão efetiva do teor de clorofila nas folhas de milheto usando os dados hiperespectrais. Além disso, esta pesquisa demonstra que a combinação de imagens hiperespectrais e a atenção-CNN se mostra benéfica para a aplicação do monitoramento dos elementos nutricionais das culturas.