First Known Human Death After Infection With the Avian Influenza A/H3N8 Virus: Guangdong Province, China, March 2023.

Here, we report on a case of human infection with the H3N8 avian influenza virus. The patient had multiple myeloma and died of severe infection. Genome analysis showed multiple gene mutations and reassortments without mammalian-adaptive mutations. This suggests that avian influenza (A/H3N8) virus infection could be lethal for immunocompromised persons.

Trustworthy Breast Ultrasound Image Semantic Segmentation Based on Fuzzy Uncertainty Reduction.

Medical image semantic segmentation is essential in computer-aided diagnosis systems. It can separate tissues and lesions in the image and provide valuable information to radiologists and doctors. The breast ultrasound (BUS) images have advantages: no radiation, low cost, portable, etc. However, there are two unfavorable characteristics: (1) the dataset size is often small due to the difficulty in obtaining the ground truths, and (2) BUS images are usually in poor quality. Trustworthy BUS image segmentation is urgent in breast cancer computer-aided diagnosis systems, especially for fully understanding the BUS images and segmenting the breast anatomy, which supports breast cancer risk assessment. The main challenge for this task is uncertainty in both pixels and channels of the BUS images. In this paper, we propose a Spatial and Channel-wise Fuzzy Uncertainty Reduction Network (SCFURNet) for BUS image semantic segmentation. The proposed architecture can reduce the uncertainty in the original segmentation frameworks. We apply the proposed method to four datasets: (1) a five-category BUS image dataset with 325 images, and (2) three BUS image datasets with only tumor category (1830 images in total). The proposed approach compares state-of-the-art methods such as U-Net with VGG-16, ResNet-50/ResNet-101, Deeplab, FCN-8s, PSPNet, U-Net with information extension, attention U-Net, and U-Net with the self-attention mechanism. It achieves 2.03%, 1.84%, and 2.88% improvements in the Jaccard index on three public BUS datasets, and 6.72% improvement in the tumor category and 4.32% improvement in the overall performance on the five-category dataset compared with that of the original U-shape network with ResNet-101 since it can handle the uncertainty effectively and efficiently.

BUSIS: A Benchmark for Breast Ultrasound Image Segmentation.

Breast ultrasound (BUS) image segmentation is challenging and critical for BUS computer-aided diagnosis (CAD) systems. Many BUS segmentation approaches have been studied in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which results in a discrepancy in performance comparison. Therefore, there is a pressing need for building a benchmark to compare existing methods using a public dataset objectively, to determine the performance of the best breast tumor segmentation algorithm available today, and to investigate what segmentation strategies are valuable in clinical practice and theoretical study. In this work, a benchmark for B-mode breast ultrasound image segmentation is presented. In the benchmark, (1) we collected 562 breast ultrasound images and proposed standardized procedures to obtain accurate annotations using four radiologists; (2) we extensively compared the performance of 16 state-of-the-art segmentation methods and demonstrated that most deep learning-based approaches achieved high dice similarity coefficient values (DSC ≥ 0.90) and outperformed conventional approaches; (3) we proposed the losses-based approach to evaluate the sensitivity of semi-automatic segmentation to user interactions; and (4) the successful segmentation strategies and possible future improvements were discussed in details.

Constituents promoting osteogenesis from the fruits of Psoralea corylifolia and their structure-activity relationship study.

The mature fruit of Psoralea corylifolia L. is a common traditional Chinese medicine used to tonify the kidney and yang, and as well as to treat osteoporosis. Systematic phytochemical investigations have established the most comprehensive constituent library to date, covering over 180 compounds. In this study, 109 chemical constituents containing 37 undescribed compounds were reported and incorrect structures of four known coumarins were corrected. The structures of these undescribed compounds were elucidated using spectroscopic methods, single-crystal X-ray diffraction, Rh2(OCOCF3)4 and Mo2(OAc)4-induced circular dichroism spectra. To identify potentially active compounds and investigate their structure-activity relationship (SAR), 89 constituents in the library were evaluated for their osteogenic differentiation and mineralisation activities in MC3T3-E1 cells. We found that coumarins, isoflavones, flavonones, and meroterpenoids were the material basis for Psoralea corylifolia-based treatment of osteoporosis, with some compounds exhibiting excellent activities. These compounds function via the estrogen receptor (ER) pathway and were natural phytoestrogen. Further SAR analysis showed that compounds with an intact isopentenyl replacement possessed superior activities, which was explained by their improved affinity with the ER.

A Case of Esophageal Squamous Cell Carcinoma Metastasized to the Colonic Anastomotic Site of Right Hemicolectomy.

A 73-year-old woman with a history of right hemicolectomy for advanced ascending colon cancer 14 years earlier was referred to our facility for a 2-month history of solid food dysphagia. An esophagogastroduodenoscopy revealed a 7-cm fungating and ulcerated mass in the middle to lower esophagus. The biopsy from the esophageal mass showed a moderately to poorly differentiated squamous cell carcinoma. A colonoscopy showed an end-to-end ileocolonic anastomosis with a 7-mm ulceration in the transverse colon. The biopsy of the ulceration at the anastomotic site showed a moderately to poorly differentiated squamous cell carcinoma with a morphology similar to that of the esophageal mass, rendering the diagnosis of metastatic esophageal squamous cell carcinoma. Colonic metastasis from esophageal squamous cell carcinoma, especially at the anastomotic site, is extremely rare. Although surgical trauma may not have contributed to the anastomotic site metastasis, given the distant timeline, its role in the pathogenesis of metastasis cannot be completely ruled out.

MSF-GAN: Multi-Scale Fuzzy Generative Adversarial Network for Breast Ultrasound Image Segmentation.

Automatic breast ultrasound image (BUS) segmentation is still a challenging task due to poor image quality and inherent speckle noise. In this paper, we propose a novel multi-scale fuzzy generative adversarial network (MSF-GAN) for breast ultrasound image segmentation. The proposed MSF-GAN consists of two networks: a generative network to generate segmentation maps for input BUS images, and a discriminative network that employs a multi-scale fuzzy (MSF) entropy module for discrimination. The major contribution of this paper is applying fuzzy logic and fuzzy entropy in the discriminative network which can distinguish the uncertainty of segmentation maps and groundtruth maps and forces the generative network to achieve better segmentation performance. We evaluate the performance of MSF-GAN on three BUS datasets and compare it with six state-of-the-art deep neural network-based methods in terms of five metrics. MSF-GAN achieves the highest mean IoU of 78.75%, 73.30%, and 71.12% on three datasets, respectively.

Tumor saliency estimation for breast ultrasound images via breast anatomy modeling.

Tumor saliency estimation aims to localize tumors by modeling the visual stimuli in medical images. However, it is a challenging task for breast ultrasound (BUS) image due to the complicated anatomic structure of the breast and poor image quality; and existing saliency estimation approaches only model the generic visual stimuli, e.g., local and global contrast, location, and feature correlation, and achieve poor performance for tumor saliency estimation. In this paper, we propose a novel optimization model to estimate tumor saliency by utilizing breast anatomy. First, we model breast anatomy and decompose breast ultrasound image into layers using Neutro-Connectedness; then utilize the layers to generate the foreground and background maps; and finally propose a novel objective function to estimate the tumor saliency by integrating the foreground map, background map, adaptive center bias, and region-based correlation cues. The extensive experiments demonstrate that the proposed approach obtains more accurate foreground and background maps with breast anatomy; especially, for the images having large or small tumors. Meanwhile, the new objective function can handle the images without tumors. The newly proposed method achieves state-of-the-art performance comparing to eight tumor saliency estimation approaches using two BUS datasets.

Improved Inception V3 method and its effect on radiologists' performance of tumor classification with automated breast ultrasound system.

BACKGROUND: The automated breast ultrasound system (ABUS) is recognized as a valuable detection tool in addition to mammography. The purpose of this study was to propose a novel computer-aided diagnosis (CAD) system by extracting the textural features from ABUS images and to investigate the efficiency of using this CAD for breast cancer detection. METHODS: This retrospective study involved 149 breast nodules [maximum diameter: mean size 18.89 mm, standard deviation (SD) 10.238, and range 5-59 mm] in 135. We assigned 3 novice readers (<3 years of experience and 3 experienced readers (≥10 years of experience to review the imaging data and stratify the 149 breast nodules as either malignant or benign. The Improved Inception V3 (II3) method was developed and used as an assistant tool to help the 6 readers to re-interpret the images. RESULTS: Our method (II3) achieved an accuracy of 88.6% for the final result. The 3 novice readers had an average accuracy of 71.37%±4.067% while the 3 experienced readers was 83.03%±3.371% on the first-reading. With the help of II3 on the second-reading, the average accuracy of the novice readers increased to 84.13%±1.662% and the experienced readers increased to 89.50%±0.346%.The areas under the curve (AUCs) were similar compared with linear algorithms. The mean AUC of the novice readers was improved from 0.7751 (without II3) to 0.8232 (with II3). The mean AUC of the experienced readers was improved from 0.8939 (without II3) to 0.9211 (with II3). The mean AUC for all readers improved in both the second-reading mode (from 0.8345 to 0.8722, P=0.0081<0.05). CONCLUSIONS: With the help of the II3, the diagnostic accuracy of the two groups were both improved, and II3 was more helpful for novice readers than for experienced readers. Our results showed that II3 is valuable in the differentiation of benign and malignant breast nodules and it also improves the experience and skill of some novice radiologists. The II3 cannot completely replace the influence of experience in the diagnostic process and will retain an auxiliary role in the clinic at present.

Fully multi-target segmentation for breast ultrasound image based on fully convolutional network.

Ultrasound image segmentation plays an important role in computer-aided diagnosis of breast cancer. Existing approaches focused on extracting the tumor tissue to characterize the tumor class. However, other tissues are also helpful for providing the references. In this paper, a multi-target semantic segmentation approach is proposed based on the fully convolutional network for segmenting the breast ultrasound image into different target tissue regions. For handling the uncertain affiliation of pixels in blurry boundaries, the certain outputs of pixel characteristics in AlexNet are transformed into the fuzzy decision expression. For improving the image detail representation, the AlexNet network structure of fully convolutional network is optimized with fully connected skip structure. In addition, the output of net model is optimized with fully connected conditional random field to improve the characterization of spatial consistency and pixels' correlation of the image. Moreover, a data training optimization method is developed for improving the efficiency of network training. In the experiment, 325 ultrasound images and four error metrics are utilized for validating the segmentation performance. Comparing with existing methods, experimental results show that the proposed approach is effective for handling the breast ultrasound images accurately and reliably. Graphical abstract.

Strepimidazoles A-G from the Plant Endophytic Streptomyces sp. PKU-EA00015 with Inhibitory Activities against a Plant Pathogenic Fungus.

Seven new 4-acyl-2-aminoimidazoles, designated strepimidazoles A-G (1-7), were discovered from the endophytic Streptomyces sp. PKU-EA00015 isolated from Salvia miltiorrhiza Bunge, whose dry root "Danshen" is one of the most widely used traditional Chinese medicines. The resonance signals of the 2-aminoimidazole moiety in 1-7 were absent in the NMR spectra due to tautomerization, and the structures of 1-7 were identified after preparation of their acetylation products 1a-7a, respectively. Compounds 1-7 represent a new family of 2-aminoimidazole-containing natural products, enriching the structural diversity of natural products from endophytic origin. Compounds 1-7 showed different degrees of inhibitory activities against the plant pathogenic fungus Verticillium dahliae V991, revealing structure-activity relationships on the acyl moieties. The plant pathogenic fungus V. dahliae has been confirmed to cause serious chlorosis of cultivated S. miltiorrhiza Bunge in China. This study opens the door for further investigation of mutualistic relationships between S. miltiorrhiza Bunge and their endophytic actinomycetes and for possible antifungal agent development for biological control of V. dahliae in the future.

Oxytrodiflavanone A and Oxytrochalcoflavanones A,B: New Biflavonoids from Oxytropis chiliophylla.

Three previously undescribed biflavonoids, oxytrodiflavanone A (1), and oxytrochalcoflavanones A,B (2,3), were isolated from the aerial part of Oxytropis chiliophylla, together with their putative biosynthetic monomers, i.e., (2S)-5,7-dihydroxyflavanone (4), (2S)-7-hydroxyflavanone (5), and 2',4'-dihydroxychalcone (6). The structures of these compounds were elucidated by a combination analysis of spectroscopic data. The cytotoxic activities of all the isolated compounds against PC-3 human prostate cancer cell line are also presented.

Molecular Diagnostics in the Neoplasms of Small Intestine and Appendix: 2018 Update.

Neoplasms of the small intestine are rare in comparison with colorectal tumors. The most common tumor types arising in the small intestine are adenocarcinomas, well-differentiated neuroendocrine tumors, gastrointestinal stromal tumors, and lymphoma. Primary appendiceal neoplasms are rare and found in less than 2% of appendectomy specimens with an incidence of approximately 1.2 cases per 100,000 people per year in the United States. This article explores molecular diagnostics in the neoplasms of small intestine and appendix.

Structurally Diverse Cytotoxic Dimeric Chalcones from Oxytropis chiliophylla.

Ten isomeric cyclobutane- and cyclohexene-containing chalcone dimers, oxyfadichalcones A-G, were isolated from the aerial parts of Oxytropis chiliophylla. These included six new compounds and three pairs of enantiomers that are being reported from natural sources for the first time. The relative configurations were elucidated by spectroscopic data analysis, while the absolute configurations were determined by comparing the experimental and calculated electronic circular dichroism spectra. Quantitative LC-MS analysis of the main dimers from different parts of the plant revealed their characteristic accumulation in the viscous secretion and provided supporting evidence for the hypothesized photochemical biosynthesis. In addition, the cytotoxic activities of all isolates against the PC-3 human prostate cancer cell line are reported.

Bacillibactin and Bacillomycin Analogues with Cytotoxicities against Human Cancer Cell Lines from Marine Bacillus sp. PKU-MA00093 and PKU-MA00092.

Nonribosomal peptides from marine Bacillus strains have received considerable attention for their complex structures and potent bioactivities. In this study, we carried out PCR-based genome mining for potential nonribosomal peptides producers from our marine bacterial library. Twenty-one "positive" strains were screened out from 180 marine bacterial strains, and subsequent small-scale fermentation, HPLC and phylogenetic analysis afforded Bacillus sp. PKU-MA00092 and PKU-MA00093 as two candidates for large-scale fermentation and isolation. Ten nonribosomal peptides, including four bacillibactin analogues (1-4) and six bacillomycin D analogues (5-10) were discovered from Bacillus sp. PKU-MA00093 and PKU-MA00092, respectively. Compounds 1 and 2 are two new compounds and the ¹H NMR and 13C NMR data of compounds 7 and 9 is first provided. All compounds 1-10 were assayed for their cytotoxicities against human cancer cell lines HepG2 and MCF7, and the bacillomycin D analogues 7-10 showed moderate cytotoxicities with IC50 values from 2.9 ± 0.1 to 8.2 ± 0.2 µM. The discovery of 5-10 with different fatty acid moieties gave us the opportunity to reveal the structure-activity relationships of bacillomycin analogues against these human cancer cell lines. These results enrich the structural diversity and bioactivity properties of nonribosomal peptides from marine Bacillus strains.

Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.

This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions.

Quantitative analysis of intracellular nucleoside triphosphates and other polar metabolites using ion pair reversed-phase liquid chromatography coupled with tandem mass spectrometry.

Simultaneous, quantitative determination of intracellular nucleoside triphosphates and other polar metabolites using liquid chromatography with electrospray ionization tandem mass spectrometry (LC-MS/MS) represents a bioanalytic challenge because of charged, highly hydrophilic analytes presented at a large concentration range in a complex matrix. In this study, an ion pair LC-MS/MS method using triethylamine (TEA)-hexafluoroisopropanol (HFIP) ion-pair mobile phase was optimized and validated for simultaneous and unambiguous determination of 8 nucleoside triphosphates (including ATP, CTP, GTP, UTP, dATP, dCTP, dGTP, and dTTP) in cellular samples. Compared to the the less volatile ion-pair reagent, triethylammonium acetate (100mM, pH 7.0), the combination of HFIP (100mM) and TEA (8.6mM) increased the MS signal intensity by about 50-fold, while retaining comparable chromatographic resolution. The isotope-labeled internal standard method was used for the quantitation. Lower limits of quantitation were determined at 0.5nM for CTP, UTP, dATP, dCTP, and dTTP, at 1nM for ATP, and at 5nM for GTP and dGTP. The intra- and inter-day precision and accuracy were within the generally accepted criteria for bioanalytical method validation (<15%). While the present method was validated for the quantitation of intracellular nucleoside triphosphates, it had a broad application potential for quantitative profiling of nucleoside mono- and bi-phosphates as well as other polar, ionic metabolic intermediates (including carbohydrate derivatives, carboxylic acid derivatives, co-acyl A derivatives, fatty acyls, and others) in biological samples.

Ubiquitination and degradation of ribonucleotide reductase M1 by the polycomb group proteins RNF2 and Bmi1 and cellular response to gemcitabine.

Ribonucleotide reductase M1 (RRM1) is required for mammalian deoxyribonucleotide (dNTP) metabolism. It is the primary target of the antimetabolite drug gemcitabine, which is among the most efficacious and most widely used cancer therapeutics. Gemcitabine directly binds to RRM1 and irreversibly inactivates ribonucleotide reductase. Intra-tumoral RRM1 levels are predictive of gemcitabine's therapeutic efficacy. The mechanisms that regulate intracellular RRM1 levels are largely unknown. Here, we identified the E3 ubiquitin-protein ligases RNF2 and Bmi1 to associate with RRM1 with subsequent poly-ubiquitination at either position 48 or 63 of ubiquitin. The lysine residues 224 and 548 of RRM1 were identified as major ubiquitination sites. We show that ubiquitinated RRM1 undergoes proteasome-mediated degradation and that targeted post-transcriptional silencing of RNF2 and Bmi1 results in increased RRM1 levels and resistance to gemcitabine. Immunohistochemical analyses of 187 early-stage lung cancer tumor specimens revealed a statistically significant co-expression of RRM1 and Bmi1. We were unable to identify suitable reagents for in situ quantification of RNF2. Our findings suggest that Bmi1 and possibly RNF2 may be attractive biomarkers of gemcitabine resistance in the context of RRM1 expression. They also provide novel information for the rational design of gemcitabine-proteasome inhibitor combination therapies, which so far have been unsuccessful if given to patients without taking the molecular context into account.

Biotransformation on the flavonolignan constituents of Silybi Fructus by an intestinal bacterial strain Eubacterium limosum ZL-II.

Eubacterium limosum ZL-II is an anaerobic bacterium with demethylated activity, which was isolated from human intestinal bacteria in our previous work. In this study, the flavonolignan constituents of Silybi Fructus were biotransformed by E. limosum(1) ZL-II, producing four new transformation products - demethylisosilybin B (T1), demethylisosilybin A (T2), demethylsilybin B (T3) and demethylsilybin A (T4), among which T1 and T2 were new compounds. Their chemical structures were identified by ESI-TOF/MS, (1)H NMR, (13)C NMR, HMBC and CD spectroscopic data. The bioassay results showed that the transformation products T1-T4 exhibited significant inhibitory activities on Alzheimer's amyloid-ß 42 (Aß42(2)) aggregation with IC50 values at 7.49 µM-10.46 µM, which were comparable with that of the positive control (epigallocatechin gallate, EGCG(3), at 9.01 µM) and much lower than those of their parent compounds (at not less than 145.10 µM). The method of biotransformation by E. limosum ZL-II explored a way to develop the new and active lead compounds in Alzheimer's disease from Silybi Fructus. However, the transformation products T1-T4 exhibited decreased inhibitory activities against human tumor cell lines comparing with their parent compounds.

A kinome screen identifies checkpoint kinase 1 (CHK1) as a sensitizer for RRM1-dependent gemcitabine efficacy.

Gemcitabine is among the most efficacious and widely used antimetabolite agents. Its molecular targets are ribonucleotide reductase M1 (RRM1) and elongating DNA. Acquired and de novo resistance as a result of RRM1 overexpression are major obstacles to therapeutic efficacy. We deployed a synthetic lethality screen to investigate if knockdown of 87 selected protein kinases by siRNA could overcome RRM1-dependent gemcitabine resistance in high and low RRM1-expressing model systems. The models included genetically RRM1-modified lung and breast cancer cell lines, cell lines with gemcitabine-induced RRM1 overexpression, and a series of naturally gemcitabine-resistant cell lines. Lead molecular targets were validated by determination of differential gemcitabine activity using cell lines with and without target knock down, and by assessing synergistic activity between gemcitabine and an inhibitor of the lead target. CHK1 was identified has the kinase with the most significant and robust interaction, and it was validated using AZD7762, a small-molecule ATP-competitive inhibitor of CHK1 activation. Synergism between CHK1 inhibition and RRM1-dependent gemcitabine efficacy was observed in cells with high RRM1 levels, while antagonism was observed in cells with low RRM1 levels. In addition, four cell lines with natural gemcitabine resistance demonstrated improved gemcitabine efficacy after CHK1 inhibition. In tumor specimens from 187 patients with non-small-cell lung cancer, total CHK1 and RRM1 in situ protein levels were significantly (p = 0.003) and inversely correlated. We conclude that inhibition of CHK1 may have its greatest clinical utility in malignancies where gemcitabine resistance is a result of elevated RRM1 levels. We also conclude that CHK1 inhibition in tumors with low RRM1 levels may be detrimental to gemcitabine efficacy.