Fungal Coculture of Herpotrichia sp. and Trametes versicolor Induces Production of Diverse Metabolites with Anti-Parkinson's Neuroprotective Activity.

Co-cultivation of isopod-associated fungi Herpotrichia sp. SF09 and Trametes versicolor SF09A led to the reciprocal induction of thirteen new compounds (1-7 and 9-13) with diverse architectures. Importantly, compounds 1 and 2 are rare fungal sesquiterpene-saccharide hybrids incorporating a xylopyranose moiety, compound (±)-3 represents the first example of a natural linear sesquiterpene racemate, and compound 7 is a rare α-pyrone derivative with a xylopyranose motif. Their structures were elucidated by analysis of NMR and mass spectrometry data, and their absolute configurations were determined by Mosher's method, microscale derivatization, and single-crystal X-ray diffraction, as well as ECD calculations. All the isolated compounds ameliorated MPP+-induced oxidative damage in PC12 cells in a dose-dependent fashion. Among them, compounds 5 and 15 showed significant protective action against neuronal injury by MPP+ at 5 µM. Meanwhile, transcriptome sequencing was performed to evaluate the molecular mechanism of the neuroprotective activity for compound 5. Results indicated that compound 5 might mitigate MPP+-induced neuronal injury through the regulation of multiple signaling pathways, including the PI3K-Akt and MAPK pathways. Our findings suggested that compound 5 could be a promising neuroprotective agent for the treatment of Parkinson's disease.

Classification of Color Fundus Photographs Using Fusion Extracted Features and Customized CNN Models.

This study focuses on overcoming challenges in classifying eye diseases using color fundus photographs by leveraging deep learning techniques, aiming to enhance early detection and diagnosis accuracy. We utilized a dataset of 6392 color fundus photographs across eight disease categories, which was later augmented to 17,766 images. Five well-known convolutional neural networks (CNNs)-efficientnetb0, mobilenetv2, shufflenet, resnet50, and resnet101-and a custom-built CNN were integrated and trained on this dataset. Image sizes were standardized, and model performance was evaluated via accuracy, Kappa coefficient, and precision metrics. Shufflenet and efficientnetb0demonstrated strong performances, while our custom 17-layer CNN outperformed all with an accuracy of 0.930 and a Kappa coefficient of 0.920. Furthermore, we found that the fusion of image features with classical machine learning classifiers increased the performance, with Logistic Regression showcasing the best results. Our study highlights the potential of AI and deep learning models in accurately classifying eye diseases and demonstrates the efficacy of custom-built models and the fusion of deep learning and classical methods. Future work should focus on validating these methods across larger datasets and assessing their real-world applicability.

Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors.

BACKGROUND: To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors. METHODS: We enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set. RESULTS:  Among the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists. CONCLUSIONS:  We developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.

Clinical use of color Doppler ultrasonography to predict and evaluate the collateral development of two common revascularizations in patients with moyamoya disease.

Objective: To explore the value of color Doppler ultrasonography (CDU) to predict preoperatively and evaluate postoperatively the collateral development of two common revascularizations in patients with moyamoya disease (MMD). Methods: We prospectively enrolled 49 patients with MMD who underwent unilateral superficial temporal artery (STA) -middle cerebral artery (MCA) anastomosis or encephalo-duro-arterio-synangiosis (EDAS). The parameters of the extracranial arteries, including STA, internal carotid artery (ICA), external carotid artery (ECA), and vertebral artery (VA), were performed before and at 3-6 months after surgery. DSA results were used to assess surgical collateral development. Results: To predict good collateral development before STA-MCA anastomosis, the preoperative D > 1.75 mm in the STA had the highest area under the Receiver Operating Characteristic curve (AUC). To predict good collateral development before EDAS, the preoperative EDV > 12.00 cm/s in the STA had the highest AUC. To evaluate the good collateral development after STA-MCA anastomosis, the postoperative EDV > 16.50 cm/s in the STA had the highest AUC. To evaluate the good collateral development after EDAS, an increase of D of 0.15 mm in the STA had the highest AUC. Logistic regression analysis showed that the preoperative RI and EDV in the STA were highly correlated with collateral development. Besides, the preoperative RI was an independent risk factor for collateral development. Conclusion: CDU could predict preoperatively and evaluate postoperatively the collateral development of STA-MCA anastomosis and EDAS surgery postoperatively by detecting ultrasound parameters of the STA.

Dinuclear Nickel-Oxygen Cluster-Based Metal-Organic Frameworks with Octahedral Cages for Efficient Xe/Kr Separation.

Xe/Kr separation is industrially important but remains a daunting issue in chemical separations. Herein, a fluorinated metal-organic framework (MOF), [Ni2(µ2-O)(TFBPDC)(tpt)2]n (named JXNU-13-F), built from 3,3',5,5'-tetrakis(fluoro)biphenyl-4,4'-dicarboxylic (TFBPDC2-) and 2,4,6-tri(4-pyridinyl)-1,3,5-triazine (tpt) ligands is provided. JXNU-13-F displays a three-dimensional (3D) framework constructed from distorted octahedral cages and an impressive Xe capacity of 144 cm3 g-1 at 273 K and 1 bar, ranking among top MOFs. The high Xe uptake and moderate Xe/Kr adsorption selectivity endow JXNU-13-F with efficient Xe/Kr separation demonstrated by experimental column breakthrough tests. The comparative studies of gas adsorption between isostructural JXNU-13-F and JXNU-13 (the nonfluorinated analogue ([Ni2(µ2-O)(BPDC))(tpt)2]n with biphenyl-4,4'-dicarboxylic (BPDC2-)) revealed that the F groups serve as the innocent groups during the Xe and Kr adsorption in JXNU-13-F. Thus, a combination of highly hydrophobic and π-electron-rich pore surfaces made of aromatic rings with strong interactions with the Xe atom possessing large polarizability and appropriate pore sizes that match well Xe having a large atom diameter has resulted in high Xe uptake and effective Xe/Kr separation characteristics of JXNU-13-F.

Transcranial color Doppler sonography as an alternative tool for evaluation of terminal internal carotid artery steno-occlusion in moyamoya disease.

OBJECTIVE: To investigate color Doppler ultrasonography criteria and its value in assessing the steno-occlusion of the terminal internal carotid artery in moyamoya disease. METHODS: According to the digital subtraction angiography findings, patients were divided into three groups: occlusion of the terminal internal carotid artery, >50% stenosis, and ≤50% stenosis. We measured the terminal and proximal internal carotid artery parameters of each group using transcranial and cervical color Doppler sonography. RESULTS: Blood flow signals in 12 terminal internal carotid arteries were absent, which were verified as occlusion by digital subtraction angiography. Parameters were obtained in the remaining 159 terminal/proximal internal carotid arteries to predict cutoffs for >50% stenosis and occlusion. For >50% stenosis, mean flow velocity >88.50 cm/s in the terminal internal carotid artery could achieved the highest receiver operating characteristic curve area of 0.776 with 62.50% sensitivity, 88.15% specificity, 48.39% positive predictive values, 92.97% negative predictive value, and 84.27% overall accuracy. For the occlusion, mean flow velocity <49.50 cm/s in the terminal internal carotid artery achieve the highest receiver operating characteristic curve area of 0.734 with 55.56% sensitivity, 83.81% specificity, 63.83% positive predictive values, 78.57% negative predictive value, and 74.21% overall accuracy. The area under receiver operating characteristic curve cutoffs for other parameters were less than 0.7. CONCLUSION: Mean flow velocity of terminal internal carotid artery provides useful diagnostic information for detecting steno-occlusion in moyamoya disease. It may be used as an alternative tool to evaluate steno-occlusion of the terminal internal carotid artery in moyamoya disease.

[Spatiotemporal Evolution and Driving Mechanism of Aerosol Optical Depth in the Ebinur Lake Basin].

Aerosol optical depth (AOD) describes the attenuation of light by aerosols and reflects the degree of regional air pollution to some extent. This study was based on the data from the long-term sequence MOD09A1 from 2000 to 2015 and the generation of a lookup table using the deep blue algorithm (DB) to perform AOD remote sensing estimation on the Landsat TM/ETM+/OLI data from the Ebinur Lake Basin to analyze the temporal and spatial variation characteristics of AOD in the Ebinur Lake Basin and to perform an AOD prediction and factor contribution ranking using the random forest model (RF) combined with environmental variables. The results showed that:① AOD of Ebinur Lake Basin has significant seasonal variation characteristics, and the AOD values were spring (0.414) > summer (0.390) > autumn (0.287), with the largest variation in spring. ② The AOD average of the Ebinur Lake Basin was 0.374, and the interannual variation as a whole showed an upward trend. However, the AOD increased rapidly during 2010-2015, with an interannual increase of 32.32%, which indicated increasing air pollution in the basin over the past 15 years, especially the past five years. ③ The spatial distribution of AOD in the Ebinur Lake Basin was stepped up from the north to the south of Lake Ebinur. In this area, the pollution in Jinghe County was the most prominent, and the AOD value reached 0.483. ④ The RF model had a good predictive effect on AOD, R2=0.866, RMSE=0.042, and evapotranspiration had the most significant effect on AOD in the Ebinur Lake basin.

In vivo metabolism of 8,2'-diprenylquercetin 3-methyl ether and the distribution of its metabolites in rats by HPLC-ESI-IT-TOF-MSn.

8,2'-Diprenylquercetin 3-methyl ether, a natural product with prominent anti-breast cancer activity, is the main active constituent of Sinopodophylli Fructus. A high-performance liquid chromatography with a diode array detector coupled with electrospray ionization ion trap time-of-flight multistage mass spectrometry (HPLC-DAD-ESI-IT-TOF-MSn) method was established and applied to profile and identify the metabolites of 8,2'-diprenylquercetin 3-methyl ether as well as study their distribution in rat organs for the first time. A total of 100 new metabolites were tentatively identified in rats. The metabolic reactions of 8,2'-diprenylquercetin 3-methyl ether in rats in vivo were hydroxylation, methylation, glucuronidation, dehydrogenation, sulfation, polymerization and cysteine conjugation as well as the specific reactions of leucine/isoleucine, proline, and vitamin C conjugation. The detected metabolites included 77 in faeces, 50 in urine, 11 in plasma, 50 in the small intestine, 32 in the stomach, 23 in the liver, 9 in the lungs, 9 in the spleen, 8 in the heart, and 6 in the kidneys. The results indicated that the small intestine, stomach, and liver were the major organs for the distribution of 8,2'-diprenylquercetin 3-methyl ether metabolites. Furthermore, 27 metabolites showed various bioactivities predicted by the analysis of "PharmMapper", among which 9 metabolites showed anti-cancer activity. These results are very useful for understanding the metabolism and pharmacological actions as well as the effective forms and toxic actions of 8,2'-diprenylquercetin 3-methyl ether in vivo; moreover, they will lay the foundation for further studies on the metabolism of prenylflavonoid compounds.

Changing Ischemic Lesion Patterns and Hemodynamics of the Posterior Cerebral Artery in Moyamoya Disease.

OBJECTIVE: The aim of this study was to determine how hemodynamics of the posterior cerebral artery (PCA) are associated with cerebral ischemic lesions in moyamoya disease (MMD). METHODS: Thirty-six patients with ischemic MMD (Suzuki grade IV-V) were retrospectively analyzed. Hemodynamic parameters of the PCA were measured by transcranial color-coded sonography. We classified the range of ischemic lesions into 3 grades and perfusion levels into 3 grades according to computed tomography (CT) results. PCA steno-occlusion and leptomeningeal collaterals were confirmed by digital subtraction angiography. Ultrasonographic parameters in the PCA were compared with these radiographic findings. RESULTS: The velocity in the involved PCA (mean flow velocity [MFV] median, 42.00 [range, 34.50-58.00] cm/s) was significantly lower than that in the normal PCA (MFV median, 95.00 [range, 76.50-119.50] cm/s) (P < .001). The velocity in the PCA increased significantly as the leptomeningeal collateral stage advanced (MFV stage 1: median, 38.50 [range, 29.75-63.50] cm/s; stage 2: median, 55.00 [range, 44.00-96.00] cm/s; stage 3: median, 94.00 [range, 54.00-118.25] cm/s; stage 4: median, 85.50 [range, 70.50-117.75] cm/s, respectively) (P < .05). Decreased PCA velocities were associated with a larger ischemic area on CT (P ≤ .001). PCA velocity had no correlation with CT perfusion level of the temporal and frontal lobes. PCA velocity had significant correlations with perfusion level in the occipital (P < .001) and parietal lobes (P < .05). CONCLUSIONS: Our results suggest ischemic lesion patterns (as demonstrated on CT imaging) are associated with PCA velocity measurements in the advanced stage of MMD. Thus, monitoring PCA velocity in patients with advanced MMD may provide additional information to assist in managing these patients.

Investigation of the in vivo metabolism of harpagoside and distribution of its metabolites in rats by HPLC-IT-TOF-MSn.

Harpagoside, an iridoid glycoside, is the major bioactive constituent of the traditional Chinese medicine Scrophulariae Radix. High-performance liquid chromatography with a diode array detector combined with electrospray ionization ion trap time-of-flight multistage mass spectrometry (HPLC-ESI-IT-TOF-MSn ) was used to profile and identify the metabolites of harpagoside in rats in vivo and to study the distribution of these metabolites in rats for the first time. A total of 45 metabolites were identified, 37 of which were postulated to be new compounds. The number of detected metabolites in the heart, liver, spleen, lung, kidney, stomach and small intestine was 2, 9, 6, 16, 4, 16 and 6, respectively, which indicated that the target organs of harpagoside should be spleen, lung and stomach. The main types of metabolic reactions of harpagoside in rats are hydrolysis, reduction, sulfuric acid addition, hydroxylation, methoxylation, sulfate substitution, methylation, glucose conjugation and amino acid conjugation. Furthermore, 23 metabolites were determined to have bioactivities based on the literature and 'PharmMapper' analysis. These findings are useful for better comprehension of the effective forms, target organs and pharmacological effects of harpagoside. Moreover, these findings provide a reference for studying the metabolism and distribution of iridoid compounds.