Automatic recognition of white blood cell images with memory efficient superpixel metric GNN: SMGNN

An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of many diseases, where accuracy and efficiency are paramount for computer-based systems. In this paper, we presented a new image processing system to recognize the five types of white blood cells in peripheral blood with marked improvement in efficiency when juxtaposed against mainstream methods. The prevailing deep learning segmentation solutions often utilize millions of parameters to extract high-level image features and neglect the incorporation of prior domain knowledge, which consequently consumes substantial computational resources and increases the risk of overfitting, especially when limited medical image samples are available for training. To address these challenges, we proposed a novel memory-efficient strategy that exploits graph structures derived from the images. Specifically, we introduced a lightweight superpixel-based graph neural network (GNN) and broke new ground by introducing superpixel metric learning to segment nucleus and cytoplasm. Remarkably, our proposed segmentation model superpixel metric graph neural network (SMGNN) achieved state of the art segmentation performance while utilizing at most 10000X less than the parameters compared to existing approaches. The subsequent segmentation-based cell type classification processes showed satisfactory results that such automatic recognizing algorithms are accurate and efficient to execeute in hematological laboratories. Our code is publicly available at https://github.com/jyh6681/SPXL-GNN

High Prevalence of Extended-Spectrum Beta Lactamases among Salmonella enterica Typhimurium Isolates from Pediatric Patients with Diarrhea in China

We investigated the extended-spectrum beta lactamases among 62 Salmonella enterica Typhimurium isolates recovered from children with diarrhea in a Chinese pediatric hospital. A large proportion of S. enterica Typhimurium isolates were resistant to multiple antimicrobial agents, including ampicillin (90.3%), tetracycline (80.6%), trimethoprim/sulfamethoxazole (74.2%), chloramphenicol (66.1%), cefotaxime (27.4%). Forty-nine (79.0%) of S. enterica Typhimurium isolates were positive for blaTEM-1b and resistant to ampicillin. Thirteen S. enterica Typhimurium isolates (21.0%) were positive for blaCTX-M-1-group and blaCTX-M-9-group, and all isolates harboring blaCTX-M genes were positive for ISEcp1. Two main clones (PFGE type A and D) accounted for nearly 70% of S. enterica Typhimurium isolates, and 7 CTX-M-producing isolates belonged to PFGE type D. Collectively, our data reveal multi-drug resistance and a high prevalence of extended spectrum beta lactamases among S. enterica Typhimurium isolates from children in China. In addition, we report the first identification of blaCTX-M-55 within Salmonella spp. Our data also suggest that clonal spread is responsible for the dissemination of S. enterica Typhimurium isolates

Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction

Low-dose CT (LDCT) imaging attracted a considerable interest for the reduction of the object's exposure to X-ray radiation. In recent years, supervised deep learning has been extensively studied for LDCT image reconstruction, which trains a network over a dataset containing many pairs of normal-dose and low-dose images. However, the challenge on collecting many such pairs in the clinical setup limits the application of such supervised-learning-based methods for LDCT image reconstruction in practice. Aiming at addressing the challenges raised by the collection of training dataset, this paper proposed a unsupervised deep learning method for LDCT image reconstruction, which does not require any external training data. The proposed method is built on a re-parametrization technique for Bayesian inference via deep network with random weights, combined with additional total variational (TV) regularization. The experiments show that the proposed method noticeably outperforms existing dataset-free image reconstruction methods on the test data

Combined measurement of serum zinc with PSA ameliorates prostate cancer screening efficiency via support vector machine algorithms

Background: Early screening of prostate cancer (PCa) is pivotal but challenging in the clinical scenario due to the phenomena of false positivity or false negativity of some serological evaluations, e.g. PSA testing. Decline of serum Zn2+ levels in PCa patients reportedly plays a crucial role in early screening of PCa. Accordingly, we combined 4 indices comprising the serum levels of total PSA (tPSA), free PSA (fPSA), Zn2+ and demographic information (especially age) in order to ameliorate the efficacies of PCa screening with support vector machine (SVM) algorithms. Methods: A total of 858 male patients with prostate disorders and 345 healthy male controls were enrolled. Patients’ data included 4 variables and serum Zn2+ was quantified via a self-invented Zn2+ responsive AIE-based fluorescent probe as previously published. tPSA and fPSA were routinely determined by a chemiluminescent method. Mathematical simulations were conducted to establish a SVM model for the combined diagnostics with the four variables. Moreover, ROC and its characteristic AUC were also employed to evaluate the classification efficacy of the model. Sigmoid function was utilized to estimate corresponding probabilities of classifying the clinical subjects as per 5 grades, which were incorporated into our established prostate index (PI) stratification system. Results: In SVM model, the mean AUC of the ROC with the quartet of variables was approximately 84% for PCa diagnosis, whereas the mean AUC of the ROCs with tPSA, fPSA, [Zn2+] or age alone was 64%, 62%, 55% and 59%, respectively. We further established an integrated prostate index (PI) stratification system with 5 grades and a software package to support clinicians in predicting PCa, with the accuracy of our risk stratification system being 83.3%, 91.6% and 83.3% in predicting normal, benign and PCa cases in corresponding groups. Follow-up findings especially MRI results and PI-RADS scores supported the reliability of this stratification platform as well. Conclusion: Findings from our present study demonstrated that index combination via SVM algorithms may well facilitate clinicians in early differential screening of PCa. Meanwhile, our established PI stratification system based on SVM model and Sigmoid function provided substantial accuracy in preclinical risk prediction of developing prostate cancer

Bisamides and rhamnosides from mangrove actinomycete <i>Streptomyces</i> sp. SZ-A15

<p>Four new bisamides <b>1</b>–<b>4</b>, and two new rhamnosides (<b>5</b>, <b>6</b>), along with four known compounds <b>7</b>–<b>10</b>, were isolated from a scale culture of the mangrove-derived actinomycete <i>Streptomyces</i> sp. SZ-A15. All structures were determined through analysis of the UV, IR, HRESIMS, 1D and 2D NMR spectra as well as by comparison with literature data. BRD4 inhibition of all isolated compounds was evaluated. As for the ability to inhibit protein BRD4, compound <b>9</b> exhibited moderate activity with the value of 78.4 ± 2.2% at 10 <i>μ</i>M.</p