A multi-omics method for breast cancer diagnosis based on metabolites in exhaled breath, ultrasound imaging, and basic clinical information.

Background and aims: Through a nested cohort study, we evaluated the diagnostic performance of breath-omics in differentiating between benign and malignant breast lesions, and assessed the diagnostic performance of a multi-omics approach that combines breath-omics, ultrasound radiomics, and clinic-omics in distinguishing between benign and malignant breast lesions. Materials and methods: We recruited 1,723 consecutive patients who underwent an automated breast volume scanner (ABVS) examination. Breath samples were collected and analyzed by high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOF-MS) to obtain breath-omics features. 238 of 1,723 enrolled participants have received pathological confirmation of breast nodules finally. The breast lesions of the 238 participants were contoured manually based on ABVS images for ultrasound radiomics feature calculation. Then, single- and multi-omics models were constructed and evaluated for breast nodules diagnosis via five-fold cross-validation. Results: The area under the curve (AUC) of the breath-omics model was 0.855. In comparison, the multi-omics model demonstrated superior diagnostic performance for breast cancer, with sensitivity, specificity, and AUC of 84.1 %, 89.9 %, and 0.946, respectively. The multi-omics performance was comparable to that of the Breast Imaging Reporting and Data System (BI-RADS) classification via senior ultrasound physician evaluation. Conclusion: The multi-omics approach combining metabolites in exhaled breath, ultrasound imaging, and basic clinical information exhibits superior diagnostic performance and promises to be a non-invasive and reliable tool for breast cancer diagnosis.

Reappraisal of the diagnostic value of alpha-fetoprotein for surveillance of HBV-related hepatocellular carcinoma in the era of antiviral therapy.

This study was designed to explore if antiviral treatment influences the performance of serum alpha-fetoprotein (AFP) for hepatocellular carcinoma (HCC) among the high-risk chronic HBV-infected patients. A total of 5936 patients who had evidence of chronic HBV infection were enrolled from four independent centres in this retrospective study, including 1721 chronic hepatitis B (CHB), 2286 liver cirrhosis (LC), 798 HCC within Milan criteria and 1131 HCC beyond Milan criteria patients. Stratified by whether they received treatment or not, the patients were further divided into antiviral and non-antiviral groups. Then, the performance of AFP for discriminating HCC was evaluated. Patients receiving antivirals had significantly lower median levels of AFP compared with the non-antiviral patients (P < .001), and there were significantly less patients with abnormal AFP levels in antiviral groups (P < .001). Antiviral therapy improved the AUROCs of AFP for discriminating HCC within Milan criteria. When setting the cut-off values at 20 ng/mL and 100 ng/mL as surveillance and confirmatory tests respectively for HCC among patients receiving antiviral treatment, AFP exhibited a significantly higher sensitivity than those of 200 ng/mL and 400 ng/mL, which are currently recommended by some guidelines, without compromising specificity. Further analysis in antiviral patients revealed that serum AFP had better performance for discriminating HCC within Milan criteria in ALT ≤ 1ULN patients than that in ALT > 1ULN patients. In conclusion, in the era of antiviral therapy, serum AFP's surveillance performance was substantially improved for HCC within Milan criteria among the high-risk population of CHB and LC patients.

Estimation of Crop Growth Parameters Using UAV-Based Hyperspectral Remote Sensing Data.

Above-ground biomass (AGB) and the leaf area index (LAI) are important indicators for the assessment of crop growth, and are therefore important for agricultural management. Although improvements have been made in the monitoring of crop growth parameters using ground- and satellite-based sensors, the application of these technologies is limited by imaging difficulties, complex data processing, and low spatial resolution. Therefore, this study evaluated the use of hyperspectral indices, red-edge parameters, and their combination to estimate and map the distributions of AGB and LAI for various growth stages of winter wheat. A hyperspectral sensor mounted on an unmanned aerial vehicle was used to obtain vegetation indices and red-edge parameters, and stepwise regression (SWR) and partial least squares regression (PLSR) methods were used to accurately estimate the AGB and LAI based on these vegetation indices, red-edge parameters, and their combination. The results show that: (i) most of the studied vegetation indices and red-edge parameters are significantly highly correlated with AGB and LAI; (ii) overall, the correlations between vegetation indices and AGB and LAI, respectively, are stronger than those between red-edge parameters and AGB and LAI, respectively; (iii) Compared with the estimations using only vegetation indices or red-edge parameters, the estimation of AGB and LAI using a combination of vegetation indices and red-edge parameters is more accurate; and (iv) The estimations of AGB and LAI obtained using the PLSR method are superior to those obtained using the SWR method. Therefore, combining vegetation indices with red-edge parameters and using the PLSR method can improve the estimation of AGB and LAI.

Antimicrobial functional divergence of the cecropin antibacterial peptide gene family in Musca domestica.

BACKGROUND: It has been reported that there are more than ten antimicrobial peptides (AMPs) belonging to the cecropin family in Musca domestica; however, few of them have been identified, and the functions of the other molecules are poorly understood. METHODS: Sequences of the M. domestica cecropin family of genes were cloned from cDNA template, which was reverse-transcribed from total mRNA isolated from third-instar larvae of M. domestica that were challenged with pathogens. Sequence analysis was performed using DNAMAN comprehensive analysis software, and a molecular phylogenetic tree of the cecropin family was constructed using the Neighbour-Joining method in MEGA v.5.0 according to the mature peptide sequences. Antibacterial activity of the synthetic M. domestica cecropin protein was detected and the minimum inhibitory concentration (MIC) values were determined using broth microdilution techniques. Time-killing assays were performed on the Gram-negative bacteria, Acinetobacter baumannii, at the logarithmic or stabilizing stages of growth, and its morphological changes when treated with Cec4 were assessed by scanning electron microscopy (SEM) and detection of leakage of 260 nm absorbing material. RESULTS: Eleven cecropin family genes, namely Cec01, Cec02 and Cec1-9, show homology to the Cec form in a multigene family on the Scaffold18749 of M. domestica. In comparing the encoded cecropin protein sequences, most of them have the basic characteristics of the cecropin family, containing 19 conservative amino acid residues. To our knowledge, this is the first experimental demonstration that most genes in the Cec family are functional. Cec02, Cec1, Cec2, Cec5 and Cec7 have similar antibacterial spectra and antibacterial effects against Gram-negative bacteria, while Cec4 displays a more broad-spectrum of antimicrobial activity and has a very strong effect on A. baumannii. Cec4 eliminated A. baumannii in a rapid and concentration-dependent manner, with antibacterial effects within 24 h at 1× MIC and 2× MIC. Furthermore, SEM analysis and the leakage of 260 nm absorbing material detection indicated that Cec4 sterilized the bacteria through the disruption of cell membrane integrity. CONCLUSIONS: Although there are more than ten cecropin genes related to M. domestica, some of them have no preferred antibacterial activity other than Cec4 against A. baumannii.

Antibacterial mechanism of peptide Cec4 against Acinetobacter baumannii.

BACKGROUND: A case of Acinetobacter baumannii (A. baumannii), known as gram-negative bacteria, causes a range of nosocomial infections. Due to the continuous detection of multi-drug resistant A. baumannii in the clinic, there is an urgent need to find alternative therapies, including broad-spectrum antibacterial peptides (AMP). Recently it has been found that the peptide Cec4 has good antibacterial activity against A. baumannii, but the antibacterial mechanism remains elusive. MATERIALS AND METHODS: The basic structure of Cec4 was analyzed by circular dichroism (CD) spectroscopy, and the potential antibacterial mechanism of Cec4 was detected by flow cytometry, transmission electron microscopy, fluorescence and confocal microscopy. The minimum inhibitory concentration (MIC) of antimicrobial peptides against various A. baumannii was determinated with broth microdilution techniques. The biofilm formation and the sensitivity detection of biofilms to antimicrobial peptides were detected by crystal violet staining. RESULTS: In this study, the main secondary structure of the antibacterial peptide Cec4 is α-helix (99.7%) in the hydrophobic environment. Furthermore, after the treatment with Cec4, an amount of leakage of A. baumannii and the destruction of its cell membrane were detected. Moreover, it was observed that FITC-Cec4 can enter the cell, and more cells were held in the G1 phase with peptide Cec4. However, the DNA binding assay of the peptide Cec4 indicates that the peptide does not target DNA. In addition, peptide Cec4 was superior in reducing adherent biofilms of A. baumannii compared to conventional antibiotics and has no cytotoxicity. CONCLUSION: It is apparent that the antibacterial peptide Cec4 may achieve rapid sterilization by multi-target interaction and presents an attractive therapeutic option for the prevention and control of A. baumannii infections.

Combining self-organizing maps and biplot analysis to preselect maize phenotypic components based on UAV high-throughput phenotyping platform.

BACKGROUND: With environmental deterioration, natural resource scarcity, and rapid population growth, mankind is facing severe global food security problems. To meet future needs, it is necessary to accelerate progress in breeding for new varieties with high yield and strong resistance. However, the traditional phenotypic screening methods have some disadvantages, such as destructive, inefficient, low-dimensional, labor-intensive and cumbersome, which seriously hinder the development of field breeding. Breeders urgently need a high-throughput technique for acquiring and evaluating phenotypic data that can efficiently screen out excellent phenotypic traits from large-scale genotype populations. RESULTS: In the present study, we used an unmanned aerial vehicle (UAV) high-throughput phenotyping (HTP) platform to collect RGB and multispectral images for a breeding program and acquired multiple phenotypic components (or traits), such as plant height, normalized difference vegetation index, biomass accumulation, plant-height growth rate, lodging, and leaf color. By implementing self-organizing maps and principal components analysis biplots to establish phenotypic map and similarity, we proposed an UAV-assisted HTP framework for preselecting maize (Zee mays L.) phenotypic components (or traits). CONCLUSIONS: This framework gives breeders additional information to allow them to quickly identify and preselect plants that have genotypes conferring desirable phenotypic components out of thousands of field plots. The present study also demonstrates that remote sensing is a powerful tool with which to acquire abundant phenotypic components. By using these rich phenotypic components, breeders should be able to more effectively identify and select superior genotypes.

Liver Stiffness Measurement Can Reflect the Active Liver Necroinflammation in Population with Chronic Liver Disease: A Real-world Evidence Study.

Background and Aims: Non-invasive evaluation of liver necroinflammation in patients with chronic liver disease is an unmet need in clinical practice. The diagnostic accuracy of transient elastography-based liver stiffness measurement (LSM) for liver fibrosis could be affected by liver necroinflammation, the latter of which could intensify stiffness of the liver. Such results have prompted us to explore the diagnosis potential of LSM for liver inflammation. Methods: Three cross-sectional cohorts of liver biopsy-proven chronic liver disease patients were enrolled, including 1417 chronic hepatitis B (CHB) patients from 10 different medical centers, 106 non-alcoholic steatohepatitis patients, and 143 patients with autoimmune-related liver diseases. Another longitudinal cohort of 14 entecavir treatment patients was also included. The receiver operating characteristic (ROC) curve was employed to explore the diagnostic value of LSM. Results: In CHB patients, LSM value ascended with the increased severity of liver necroinflammation in patients with the same fibrosis stage. Such positive correlation between LSM and liver necroinflammation was also found in non-alcoholic steatohepatitis and autoimmune-related liver diseases populations. Furthermore, the ROC curve exhibited that LSM could identify moderate and severe inflammation in CHB patients (area under the ROC curve as 0.779 and 0.838) and in non-alcoholic steatohepatitis patients (area under the ROC curve as 0.826 and 0.871), respectively. Such moderate diagnostic value was also found in autoimmune-related liver diseases patients. In addition, in the longitudinal entecavir treated CHB cohort, a decline of LSM values was observed in parallel with the control of inflammatory activity in liver. Conclusions: Our study implicates a diagnostic potential of LSM to evaluate the severity of liver necroinflammation in chronic liver disease patients.

[Inversion of organic matter content of the north fluvo-aquic soil based on hyperspectral and multi-spectra].

The present study aims to assess the feasibility of multi-spectral data in monitoring soil organic matter content. The data source comes from hyperspectral measured under laboratory condition, and simulated multi-spectral data from the hyperspectral. According to the reflectance response functions of Landsat TM and HJ-CCD (the Environment and Disaster Reduction Small Satellites, HJ), the hyperspectra were resampled for the corresponding bands of multi-spectral sensors. The correlation between hyperspectral, simulated reflectance spectra and organic matter content was calculated, and used to extract the sensitive bands of the organic matter in the north fluvo-aquic soil. The partial least square regression (PLSR) method was used to establish experiential models to estimate soil organic matter content. Both root mean squared error (RMSE) and coefficient of the determination (R2) were introduced to test the precision and stability of the modes. Results demonstrate that compared with the hyperspectral data, the best model established by simulated multi-spectral data gives a good result for organic matter content, with R2=0.586, and RMSE=0.280. Therefore, using multi-spectral data to predict tide soil organic matter content is feasible.