MPS-Net: Multi-Point Supervised Network for CT Image Segmentation of COVID-19.

The new coronavirus, which has become a global pandemic, has confirmed more than 88 million cases worldwide since the first case was recorded in December 2019, causing over 1.9 million deaths. Since COIVD-19 lesions have clear imaging features on CT images, it is suitable for the auxiliary diagnosis and treatment of COVID-19. Deep learning can be used to segment the lesions areas of COVID-19 in CT images to help monitor the epidemic situation. In this paper, we propose a multi-point supervision network (MPS-Net) for segmentation of COVID-19 lung infection CT image lesions to solve the problem of a variety of lesion shapes and areas. A multi-scale feature extraction structure, a sieve connection structure (SC), a multi-scale input structure and a multi-point supervised training structure were implemented into MPS-Net. In order to increase the ability to segment various lesion areas of different sizes, the multi-scale feature extraction structure and the sieve connection structure will use different sizes of receptive fields to extract feature maps of various scales. The multi-scale input structure is used to minimize the edge loss caused by the convolution process. In order to improve the accuracy of segmentation, we propose a multi-point supervision training structure to extract supervision signals from different up-sampling points on the network. Experimental results showed that the dice similarity coefficient (DSC), sensitivity, specificity and IOU of the segmentation results of our model are 0.8325, 0.8406, 09988 and 0.742, respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment COVID-19 infection on CT images. It can be used to assist the diagnosis and treatment of new coronary pneumonia.

A Multi-Scale Feature Fusion Method Based on U-Net for Retinal Vessel Segmentation.

Computer-aided automatic segmentation of retinal blood vessels plays an important role in the diagnosis of diseases such as diabetes, glaucoma, and macular degeneration. In this paper, we propose a multi-scale feature fusion retinal vessel segmentation model based on U-Net, named MSFFU-Net. The model introduces the inception structure into the multi-scale feature extraction encoder part, and the max-pooling index is applied during the upsampling process in the feature fusion decoder of an improved network. The skip layer connection is used to transfer each set of feature maps generated on the encoder path to the corresponding feature maps on the decoder path. Moreover, a cost-sensitive loss function based on the Dice coefficient and cross-entropy is designed. Four transformations-rotating, mirroring, shifting and cropping-are used as data augmentation strategies, and the CLAHE algorithm is applied to image preprocessing. The proposed framework is tested and trained on DRIVE and STARE, and sensitivity (Sen), specificity (Spe), accuracy (Acc), and area under curve (AUC) are adopted as the evaluation metrics. Detailed comparisons with U-Net model, at last, it verifies the effectiveness and robustness of the proposed model. The Sen of 0.7762 and 0.7721, Spe of 0.9835 and 0.9885, Acc of 0.9694 and 0.9537 and AUC value of 0.9790 and 0.9680 were achieved on DRIVE and STARE databases, respectively. Results are also compared to other state-of-the-art methods, demonstrating that the performance of the proposed method is superior to that of other methods and showing its competitive results.

Identification of characteristic heroin metabolites in urine based on data-mining technology and multivariate statistics analysis combined with a targeted verification approach for distinguishing heroin abusers.

A common phenomenon shows that ingestion of opium poppy shell-containing drugs can result in a "false-positive" urinalysis test result for mandatory or workplace heroin abuse screening. Owing to the short detection window (8 h in urine) of the characteristic heroin metabolite 6-monoacetylmorphine (6-MAM) confirmation or exclusion of heroin abusers still presents major challenges for toxicologists. In this work, we developed an ultra-performance liquid chromatography-time-of-flight mass spectrometry method (UPLC-TOF-MS) with online data acquisition and multiple post-data-mining technologies combined with a multivariate statistical and batch validation analysis workflow to assess the characteristic urine metabolites of heroin abusers. Based on the proposed methods, 28 characteristic metabolites were structurally identified, and their fragmentation patterns and metabolite pathways were also summarized. Correlation analysis was used to investigate the internal relationship and similarities among the identified metabolites, and seven representative metabolites were selected as "Target-metabolites". Multi-batch urine of samples of heroin abusers were certified based on the UPLC-MS/MS method for further validation of the practicability of using this method for routine analysis. Overall, the target-metabolites can be utilized as assistant "biomarkers" in workplace or mandatory drug screenings. This approach encourages further studies on the development of the "false-positive" identification system.

Simultaneous Determination of 18 Chemical Constituents in Traditional Chinese Medicine of Antitussive by UPLC-MS-MS.

In this study, a simple, rapid and sensitive liquid chromatography-tandem mass spectrometry (LC-MS-MS) method was developed for the simultaneous determination of 18 compounds, namely, morphine, codeine, tuberostemonine, thebaine, papaverine, scopoletin, liquiritin, narcotine, gynaroside, hyperoside, hesperidin, isoliquiritin, liquiritigenin, luteolin, isoliquiritigenin, apigenin, formononetin and glycyrrhizic acid in traditional Chinese medicine of antitussive. Analytes were chromatographically separated on a Waters C18 column under gradient elution with a mobile phase of water containing 0.1‰ HOAc (A) and methanol (MeOH) containing 0.1‰ HOAc (B). Detection was accomplished by multiple-reaction-monitoring scanning with an electrospray ionization source under positive and negative ion switching modes. Samples were prepared with dilution and ultrasonic extraction. All components were detected within the linear regime (r2 ≥ 0.9924) at the concentration ranges tested. The precision ranged from 1.3 to 4.6%, the accuracy was between 85.6 and 116.7% and the results of method validation tests were all within the required limits. The validated quantification method was successfully applied to analyze 40 batches of commercial traditional Chinese medicine of antitussive. These results will provide a basis for quality control of production process and the further pharmacokinetic study of traditional Chinese medicine of antitussive in vivo.

[Simultaneous determination of 7 benzodiazepines in human plasma by UHPLC-MS/MS].

The study was designed to develop the method for determination of 7 benzodiazepines concentration in human plasma. UHPLC-MS/MS was adopted to analyze plasma with protein precipitated by acetonitrile. Citalopram was used as an internal standard. Plasma samples were separated on CORTECS UHPLC C18 column with the mobile phase of aqueous solution (0.01% formic acid) - methanol (0.01% formic acid) at a flow rate of 0.3 m L·min(-1). Multiple reaction monitoring (MRM) mode was performed in combiation with electrospray ionization source operating in the positive ionization mode. The liner calibration curve of midazolam, nitrazepam, estazolam, clonazepam, lorazepam, triazolam and diazepam were obtained in the concentration range of 1.05-840 (r = 0.999 4), 2.06-824 (r = 0.998 1), 2.02-1 616 (r = 0.994 7), 6.18-2 472 (r = 0.997 9), 6.12-2 448 (r = 0.997 4), 3.02-2 416 (r = 0.990 2), 1.02-816 (r = 0.998 8) ng·m L(-1), respectively. The lowest detection limit were 0.02, 0.52, 0.51, 1.55, 0.77, 0.76, 0.02 ng·m L(-1), respectively. The RSD of inter-day and intra-day were less than 10.81%. The relative recovery was 81.46%-106.53%. The method was successfully applied to clinical analysis of blood samples from patients.

Selaginellin M, a new selaginellin derivative from Selaginella pulvinata.

A new selaginellin derivative, selaginellin M (1), together with one known compound, selaginellin E (2), was isolated from Selaginella pulvinata. The structure of the new compound was elucidated and named as (R,S)-4-((4'-hydroxy-4-((2-hydroxyethoxy)methyl))-3-((4-hydroxyphenyl)ethynyl)biphenyl-2-yl)(4-hydroxyphenyl)methylene)cyclohexa-2,5-dienone on the basis of the spectroscopic data including UV, IR, 1D, and 2D NMR as well as HR-ESI-MS analysis.

A new flavonoid from Selaginella tamariscina (Beauv.) Spring.

A new flavonoid, 6-(2-hydroxy-5-carboxyphenyl)-apigenin (1), together with two new natural products, 3-(4-hydroxyphenyl)-6,7-dihydroxy coumarin (2), 1-methoxy-3-methylanthraquinone (3) and four known compounds, were isolated from Selaginella tamariscina (BEAUV.) SPRING. The structures of the new isolated compounds were elucidated on the basis of 1D and 2D NMR as well as ESI-HR-MS spectroscopic analysis.

[Determination of six active components in three species of genus Swertia by HPLC multiwavelength with detection].

OBJECTIVE: To develop an HPLC method for the quantification of six active components in three species (Swertia davidi, S. nervosa and S. mussotii) . METHOD: The determination was performed on a Hypersil BDS colunm (4. 6 mm x 200 mm, 5 microm). Acetonitrile and 0.5% phosphoric acid solution were used as the mobile phases with a gradient elution. The flow rate was 1.0 mL x min(-1). The UV detection wavelength was at 240, 274, 325 and 334 nm. The column oven temperature was at 25 degrees C. RESULT: Six components were separated commendably in 60 minutes. The calibration curves of swertiamarin, gentiopicroside, norswertianolin, swertianolin, demethylbellidifolin and bellidifolin were in good linearity over the range of 0.520-20.8, 0.202-8.06, 0.107-4.28, 0.097-3.86, 0.094-3.77, 0.101-4.02 microg, respectively (r = 0.999 9). The average recoveries were 98.7%, 98.1%, 98.3%, 98.8%, 98.1% and 98.6%, respectively, and the RSD were less than 3.0% (n = 6). CONCLUSION: The method is accurate,simple and reproducible, and can be used to control the quality of Swertia.