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Background: There are many risk factors for severe acute pancreatitis (SAP) complicated with acute gastrointestinal injury (AGI), but few reports on the interaction between these risk factors., Aim: To analyze the risk factors for SAP complicated with AGI and their interactive effects., Methods: We selected 168 SAP patients admitted to our hospital between December 2019 and June 2022. They were divided into AGI group and non-AGI group according to whether AGI was present. Demographic data and laboratory test data were compared between the two groups. The risk factors for SAP with concomitant AGI were analyzed using multifactorial logistic regression, and an analysis of the interaction of the risk factors was performed., Results: The percentage of patients with multiple organ dysfunction syndrome, acute physiological and chronic health scoring system II (APACHE II) score, white blood cell count and creatinine (CRE) level was higher in the AGI group than in the non-AGI group. There was a statistically significant difference between the two groups ( P < 0.05). Logistic regression analysis indicated that an APACHE II score > 15 and CRE > 100 µmol/L were risk factors for SAP complicating AGI. The interaction index of APACHE II score and CRE level was 3.123., Conclusion: An APACHE II score > 15 and CRE level > 100 µmol/L are independent risk factors for SAP complicated with AGI, and there is a positive interaction between them., Competing Interests: Conflict-of-interest statement: The authors declare no conflicts of interest for this article., (©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.)

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.

Secoisolariciresinol (SECO) is one of the major lignans occurring in various grains, seeds, fruits, and vegetables. The gut microbiota plays an important role in the biotransformation of dietary lignans into enterolignans, which might exhibit more potent bioactivities than the precursor lignans. This study aimed to identify, synthesize, and evaluate the microbial metabolites of SECO and to develop efficient lead compounds from the metabolites for the treatment of osteoporosis. SECO was fermented with human gut microbiota in anaerobic or micro-aerobic environments at different time points. Samples derived from microbial transformation were analyzed using an untargeted metabolomics approach for metabolite identification. Nine metabolites were identified and synthesized. Their effects on cell viability, osteoblastic differentiation, and gene expression were examined. The results showed that five of the microbial metabolites exerted potential osteogenic effects similar to those of SECO or better. The results suggested that the enterolignans might account for the osteoporotic effects of SECO in vivo. Thus, the presence of the gut microbiota could offer a good way to form diverse enterolignans with bone-protective effects. The current study improves our understanding of the microbial transformation products of SECO and provides new approaches for new candidate identification in the treatment of osteoporosis.

Positron emission tomography (PET) can provide functional images and identify abnormal metabolic regions of the whole-body to effectively detect tumor presence and distribution. The filtered back-projection (FBP) algorithm is one of the most common images reconstruction methods. However, it will generate strike artifacts on the reconstructed image and affect the clinical diagnosis of lesions. Past studies have shown reduction in strike artifacts and improvement in quality of images by two-dimensional morphological structure operators (2D-MSO). The morphological structure method merely processes the noise distribution of 2D space and never considers the noise distribution of 3D space. This study was designed to develop three-dimensional-morphological structure operators (3D MSO) for nuclear medicine imaging and effectively eliminating strike artifacts without reducing image quality. A parallel operation was also used to calculate the minimum background standard deviation of the images for three-dimensional morphological structure operators with the optimal response curve (3D-MSO/ORC). As a result of Jaszczak phantom and rat verification, 3D-MSO/ORC showed better denoising performance and image quality than the 2D-MSO method. Thus, 3D MSO/ORC with a 3 × 3 × 3 mask can reduce noise efficiently and provide stability in FBP images.

Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG) signals to assess the patient's condition and give appropriate medications. However, it is not easy to interpret the ECG signals. Even physicians with more than 10 years of clinical experience may still misjudge. Therefore, this study uses convolutional neural networks to classify ECG image types to assist in anesthesia assessment. The research uses Internet of Things (IoT) technology to develop ECG signal measurement prototypes. At the same time, it classifies signal types through deep neural networks, divided into QRS widening, sinus rhythm, ST depression, and ST elevation. Three models, ResNet, AlexNet, and SqueezeNet, are developed with 50% of the training set and test set. Finally, the accuracy and kappa statistics of ResNet, AlexNet, and SqueezeNet in ECG waveform classification were (0.97, 0.96), (0.96, 0.95), and (0.75, 0.67), respectively. This research shows that it is feasible to measure ECG in real time through IoT and then distinguish four types through deep neural network models. In the future, more types of ECG images will be added, which can improve the real-time classification practicality of the deep model.

Single photon emission computed tomography (SPECT) has been employed to detect Parkinson's disease (PD). However, analysis of the SPECT PD images was mostly based on the region of interest (ROI) approach. Due to limited size of the ROI, especially in the multi-stage classification of PD, this study utilizes deep learning methods to establish a multiple stages classification model of PD. In the retrospective study, the 99mTc-TRODAT-1 was used for brain SPECT imaging. A total of 202 cases were collected, and five slices were selected for analysis from each subject. The total number of images was thus 1010. According to the Hoehn and Yahr Scale standards, all the cases were divided into healthy, early, middle, late four stages, and HYS I~V six stages. Deep learning is compared with five convolutional neural networks (CNNs). The input images included grayscale and pseudo color of two types. The training and validation sets were 70% and 30%. The accuracy, recall, precision, F-score, and Kappa values were used to evaluate the models' performance. The best accuracy of the models based on grayscale and color images in four and six stages were 0.83 (AlexNet), 0.85 (VGG), 0.78 (DenseNet) and 0.78 (DenseNet).

The B-mode ultrasound usually contains scattering speckle noise which reduces the detailed resolution of the target and is regarded as an intrinsic noise that interferes with diagnostic precision. The aim of this study was to classify hepatic steatosis through applying attenuation correction with a phantom to reduce speckle noise in liver ultrasound tomography in patients. This retrospective study applied three randomized groups signifying different liver statuses. A total of 114 patients' effective liver ultrasound images-30 normal, 44 fatty, and 40 cancerous-were included. The proposed depth attenuation correction method was first applied to images. Three regions of interest were manually drawn on the images. Next, five feature values for the regions of interest were calculated. Finally, the hybrid method of logistic regression and support vector machine was employed to classify the ultrasound images with 10-fold cross-validation. The accuracy, kappa statistic, and mean absolute error of the proposed hybrid method were 87.5%, 0.812, and 0.119, respectively, which were higher than those of the logistic regression method-75.0%, 0.548, and 0.280-or those of the support vector machine method-75.7%, 0.637, and 0.293-respectively. Therefore, the hybrid method has been proven to be more accurate and have better performance and less error than either single method. The hybrid method provided acceptable accuracy of classification in three liver ultrasound image groups after depth attenuation correction. In the future, the deep learning approaches may be considered for the application in classifying liver ultrasound images.

Increasing CO 2 levels are speculated to change the effects of engineered nanomaterials in soil and on plant growth. How plants will respond to a combination of elevated CO 2 and nanomaterials stress has rarely been investigated, and the underlying mechanism remains largely unknown. Here, we conducted a field experiment to investigate the rice ( Oryza sativa L. cv. IIyou) response to TiO 2 nanoparticles (nano-TiO 2 , 0 and 200 mg kg -1 ) using a free-air CO 2 enrichment system with different CO 2 levels (ambient ∼370 μmol mol -1 and elevated ∼570 μmol mol -1 ). The results showed that elevated CO 2 or nano-TiO 2 alone did not significantly affect rice chlorophyll content and antioxidant enzyme activities. However, in the presence of nano-TiO 2 , elevated CO 2 significantly enhanced the rice height, shoot biomass, and panicle biomass (by 9.4%, 12.8%, and 15.8%, respectively). Furthermore, the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that genes involved in photosynthesis were up-regulated while most genes associated with secondary metabolite biosynthesis were down-regulated in combination-treated rice. This indicated that elevated CO 2 and nano-TiO 2 might stimulate rice growth by adjusting resource allocation between photosynthesis and metabolism. This study provides novel insights into rice responses to increasing contamination under climate change.

The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 99m Tc-TRODAT-1 have been employed to detect the stages of Parkinson's disease (PD). In this retrospective study, a total of 202 99m Tc-TRODAT-1 SPECT imaging were collected. All of the PD patient cases were separated into mild (HYS Stage 1 to Stage 3) and severe (HYS Stage 4 and Stage 5) PD, according to the Hoehn and Yahr Scale (HYS) standard. A three-dimensional method was used to estimate six features of activity distribution and striatal activity volume in the images. These features were skewness, kurtosis, Cyhelsky's skewness coefficient, Pearson's median skewness, dopamine transporter activity volume, and dopamine transporter activity maximum. Finally, the data were modeled using logistic regression (LR) and support vector machine (SVM) for PD classification. The results showed that SVM classifier method produced a higher accuracy than LR. The sensitivity, specificity, PPV, NPV, accuracy, and AUC with SVM method were 0.82, 1.00, 0.84, 0.67, 0.83, and 0.85, respectively. Additionally, the Kappa value was shown to reach 0.68. This claimed that the SVM-based model could provide further reference for PD stage classification in medical diagnosis. In the future, more healthy cases will be expected to clarify the false positive rate in this classification model.

With the increase of extreme weather events, the frequency and severity of urban flood events in the world are increasing drastically. Therefore, this study develops ARMT (automatic combined ground weather radar and CCTV (Closed Circuit Television System) images for real-time flood monitoring), which integrates real-time ground radar echo images and automatically estimates a rainfall hotspot according to the cloud intensity. Furthermore, ARMT combines CCTV image capturing, analysis, and Fourier processing, identification, water level estimation, and data transmission to provide real-time warning information. Furthermore, the hydrograph data can serve as references for relevant disaster prevention, and response personnel may take advantage of them and make judgements based on them. The ARMT was tested through historical data input, which showed its reliability to be between 83% to 92%. In addition, when applied to real-time monitoring and analysis (e.g., typhoon), it had a reliability of 79% to 93%. With the technology providing information about both images and quantified water levels in flood monitoring, decision makers can quickly better understand the on-site situation so as to make an evacuation decision before the flood disaster occurs as well as discuss appropriate mitigation measures after the disaster to reduce the adverse effects that flooding poses on urban areas.

High-energy rechargeable Li metal batteries are hindered by dendrite growth due to the use of a liquid electrolyte. Solid polymer electrolytes, as promising candidates to solve the above issue, are expected to own high Li ion conductivity without sacrificing mechanical strength, which is still a big challenge to realize. In this study, a bifunctional solid polymer electrolyte exactly having these two merits is proposed with an interpenetrating network of poly(ether-acrylate) (ipn-PEA) and realized via photopolymerization of ion-conductive poly(ethylene oxide) and branched acrylate. The ipn-PEA electrolyte with facile processing capability integrates high mechanical strength (ca. 12 GPa) with high room-temperature ionic conductance (0.22 mS cm -1 ), and significantly promotes uniform Li plating/stripping. Li metal full cells assembled with ipn-PEA electrolyte and cathodes within 4.5 V vs Li + /Li operate effectively at a rate of 5 C and cycle stably at a rate of 1 C at room temperature. Because of its fabrication simplicity and compelling characteristics, the bifunctional ipn-PEA electrolyte reshapes the feasibility of room-temperature solid-state Li metal batteries.

Microspace-confined sulfur molecules as cathodes for lithium-sulfur (Li-S) batteries have shown great significance in both scientific and technical aspects. A study of different microspace-confined sulfur will not only promote the advancement of Li-S batteries but also arouse a wide interest in sulfur chemistry and related applications. Herein, we choose two-dimensional (2D) graphene interlayer as host and construct 2D space-confined sulfur model systems by simple intercalation chemistry of graphite oxide. Two routes, including solvothermal method and interlamellar reaction approach, are developed, and sulfur can be easily intercalated into sub-nanometer-sized graphene interlayers, forming a graphene confined sulfur structure. The 2D space-confined sulfur can work well in a carbonate-based electrolyte and show similar electrochemical behaviors of small sulfur molecules, indicating the special molecular form of sulfur in graphene layers. The 2D space-confined sulfur concept will be helpful for further understanding the electrochemical character of confined sulfur molecules and designing a high-performance sulfur cathode., (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Soil bacteria play a key role in the ecological and evolutionary responses of agricultural ecosystems. Domestic herbivore grazing is known to influence soil bacterial community. However, the effects of grazing and its major driving factors on soil bacterial community remain unknown for different plant community compositions under increasing grazing intensity. Thus, to investigate soil bacterial community diversity under five plant community compositions (Grass; Leymus chinensis; Forb; L. chinensis & Forb; and Legume), we performed a four-year field experiment with different grazing intensity treatments (no grazing; light grazing, 4 sheep·ha-1; and heavy grazing, 6 sheep·ha-1) in a grassland in China. Total DNA was obtained from soil samples collected from the plots in August, and polymerase chain reaction (PCR) analysis and denaturing gradient gel electrophoresis (DGGE) fingerprinting were used to investigate soil bacterial community. The results showed that light grazing significantly increased indices of soil bacterial community diversity for the Forb and Legume groups but not the Grass and L. chinensis groups. Heavy grazing significantly reduced these soil bacterial diversity indices, except for the Pielou evenness index in the Legume group. Further analyses revealed that the soil N/P ratio, electrical conductivity (EC), total nitrogen (TN) and pH were the major environmental factors affecting the soil bacterial community. Our study suggests that the soil bacterial community diversity was influenced by grazing intensity and plant community composition in a meadow steppe. The present study provides a baseline assessment of the soil bacterial community diversity in a temperate meadow steppe.

An optimized nanocarbon-sulfur cathode material with ultrahigh sulfur loading of up to 90 wt % is realized in the form of sulfur nanolayer-coated three-dimensional (3D) conducting network. This 3D nanocarbon-sulfur network combines three different nanocarbons, as follows: zero-dimensional carbon nanoparticle, one-dimensional carbon nanotube, and two-dimensional graphene. This 3D nanocarbon-sulfur network is synthesized by using a method based on soluble chemistry of elemental sulfur and three types of nanocarbons in well-chosen solvents. The resultant sulfur-carbon material shows a high specific capacity of 1115 mA h g(-1) at 0.02C and good rate performance of 551 mA h g(-1) at 1C based on the mass of sulfur-carbon composite. Good battery performance can be attributed to the homogeneous compositing of sulfur with the 3D hierarchical hybrid nanocarbon networks at nanometer scale, which provides efficient multidimensional transport pathways for electrons and ions. Wet chemical method developed here provides an easy and cost-effective way to prepare sulfur-carbon cathode materials with high sulfur loading for application in high-energy Li-S batteries.

This paper proposes a three-dimensional (3D) human posture estimation system that locates 3D significant body points based on 2D body contours extracted from two cameras without using any depth sensors. The 3D significant body points that are located by this system include the head, the center of the body, the tips of the feet, the tips of the hands, the elbows, and the knees. First, a linear support vector machine- (SVM-) based segmentation method is proposed to distinguish the human body from the background in red, green, and blue (RGB) color space. The SVM-based segmentation method uses not only normalized color differences but also included angle between pixels in the current frame and the background in order to reduce shadow influence. After segmentation, 2D significant points in each of the two extracted images are located. A significant point volume matching (SPVM) method is then proposed to reconstruct the 3D significant body point locations by using 2D posture estimation results. Experimental results show that the proposed SVM-based segmentation method shows better performance than other gray level- and RGB-based segmentation approaches. This paper also shows the effectiveness of the 3D posture estimation results in different postures.

The study tested the hypothesis that transplantation of human neurotrophin-3 (hNT-3) over-expressing neural stem cells (NSCs) into rat striatum after a severe focal ischemia would promote functional recovery. Rat NSCs, transduced by Flag-tagged hNT-3 gene mediated by lentiviral vector (LV), were transplanted into the striatum ipsilateral to the injury of adult rats 7 days after 2-h occlusion of the middle cerebral artery (MCAO). From 3 days to 2 weeks after transplantation, the modified cells (NSCs-hNT3, as defined by Flag immunofluorencence staining) that survived the transplantation procedures could secrete significantly higher levels of neurotrophin-3 protein in the graft sites than controls (P<0.001). Furthermore, the rats that accepted NSCs-hNT3 exhibited enhanced functional recovery on neurological and behavioral tests, compared with controlled animals transplanted with saline or untransduced NSCs. This study suggests: (1) LV is an ideal vector to transduce foreign gene into the NSCs; (2) modified NSCs could carry therapeutic genes to disease tissues and express effectively; (3) modified cells could survive in the ischemic brains and continue to secrete neurotrophin-3 abundantly for over 2 weeks, which might have values for enhancing functional recovery after stroke.

Hesperidin is an abundant flavanone glycoside in citrus fruits and has been reported to possess a wide range of biological activities. However, hesperidin has poor bioavailability. Here, we tested the hypothesis that hesperetin found in chenpi will have a better bioavailability than hesperidin and that treatment of hesperidin with the glucosidase-like yeast Bg1A protein will increase its bioavailability. The results indicate that hesperidin in pure or extract form is hydrolyzed by BglA protein extracted from Sporobolomyces singularis or expressed in Escherichia coli BL21 (DE3). This biotransformation affected the plasma pharmacokinetics of total hesperetin in rats, in that the plasma T max was significantly shorter after administration of BglA protein-treated hesperidin than after administration of hesperidin extract. In addition, the area under the curve values for total hesperetin after administration of Bg1A-treated hesperidin were approximately 4-fold higher by oral administration and 3-fold higher by intravenous administration, respectively. In contrast, the plasma clearance value and volume of distribution after administration of Bg1A-treated hesperidin extract or pure hesperetin were significantly smaller than after administration of untreated hesperidin extract or pure hesperidin. This is the first study that systemically determines the absolute bioavailability of hesperidin and hesperetin simultaneously, shows clearly that hesperetin is more bioavailable than hesperidin regardless of the route of administration, and shows that prior transformation of hesperidin to hesperetin via fermentation should significantly increase its bioavailability because of the action of the yeast glycosidase-like protein BglA.

Chemical analyses and biological measurements were investigated in leaves of Vicia faba seedlings exposed to extraneous lead (Pb) at 0 to 2,000 mg/kg of soil for a month. The results showed that superoxide radical (O*(2)(-)) production, increased along with total Pb in leaves and available Pb in soil, resulted in enhancement of malondialdehyde and carbonyl groups. Antioxidant enzymes, including corresponding isoenzymes and heat shock protein 70 (hsp 70), were also enhanced to some extent. Significant changes were detected in the patterns and intensities of guaiacol peroxidase isoenzymes, while superoxide dismutase, catalase, and ascorbate peroxidase isoenzymes only changed intensities. Superoxide dismutase activities increased with the increase of extraneous Pb at 0 to 500 mg/kg of soil and tended to decline thereafter, which might be responsible for the decrease of hydrogen peroxide and accumulation of O*(2)(-). Guaiacol peroxidase and ascorbate peroxidase enzymes were upregulated to become major scavengers of excess hydrogen peroxide on the condition of decreased catalase activities. Levels of hsp 70 were well correlated with Pb contents in leaves (r=0.777), O*(2)(-) accumulation (r = 0.985, p<0.01), and carbonyl groups (r=0.920, p<0.01) under extraneous Pb at 0 to 250 mg/kg of soil, suggesting that hsp 70 induced by O*(2)(-) was possibly involved in disposal of denatured proteins. The results showed that O*(2)(-), hsp 70, and guaiacol peroxidase isoenzymes had the most sensitive responses in the seedlings and these parameters could be potential early biomarkers of soil Pb contamination.