Metagenomic next-generation sequencing-assisted diagnosis of a rare case of primary cutaneous acanthamoebiasis in an HIV patient: a case report.

Pathogenic and free-living Acanthamoeba are widely distributed in the environment and have been reported to cause keratitis and universally fatal encephalitis. Primary cutaneous acanthamoebiasis caused by Acanthamoeba is exceedingly rare and presents as isolated necrotic cutaneous lesions without involvement of the cornea or central nervous system. Cutaneous acanthamoebiasis often occurs in immunocompromised patients and is likely overlooked or even misdiagnosed only by cutaneous biopsy tissue histopathological analysis. Here, we report a HIV-infected 63-year-old female with oral leukoplakia for 4 months and scattered large skin ulcers all over the body for 2 months. The cause of the cutaneous lesions was unclear through cutaneous specimens histopathological analysis, and subsequently Acanthamoeba were detected by metagenomic next-generation sequencing (mNGS), which may be the cause of cutaneous lesions. Based on the mNGS results, a pathologist subsequently reviewed the previous pathological slides and found trophozoites of Acanthamoeba so that the cause was identified, and the skin ulcers improved significantly after treatment with multi-drug combination therapy. Acanthamoeba is also a host of pathogenic microorganisms. The presence of endosymbionts enhances the pathogenicity of Acanthamoeba, and no other pathogens were reported in this case. mNGS is helpful for rapidly diagnosing the etiology of rare skin diseases and can indicate the presence or absence of commensal microorganisms.

GETdb: A comprehensive database for genetic and evolutionary features of drug targets.

The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.

GABNet: global attention block for retinal OCT disease classification.

Introduction: The retina represents a critical ocular structure. Of the various ophthalmic afflictions, retinal pathologies have garnered considerable scientific interest, owing to their elevated prevalence and propensity to induce blindness. Among clinical evaluation techniques employed in ophthalmology, optical coherence tomography (OCT) is the most commonly utilized, as it permits non-invasive, rapid acquisition of high-resolution, cross-sectional images of the retina. Timely detection and intervention can significantly abate the risk of blindness and effectively mitigate the national incidence rate of visual impairments. Methods: This study introduces a novel, efficient global attention block (GAB) for feed forward convolutional neural networks (CNNs). The GAB generates an attention map along three dimensions (height, width, and channel) for any intermediate feature map, which it then uses to compute adaptive feature weights by multiplying it with the input feature map. This GAB is a versatile module that can seamlessly integrate with any CNN, significantly improving its classification performance. Based on the GAB, we propose a lightweight classification network model, GABNet, which we develop on a UCSD general retinal OCT dataset comprising 108,312 OCT images from 4686 patients, including choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal cases. Results: Notably, our approach improves the classification accuracy by 3.7% over the EfficientNetV2B3 network model. We further employ gradient-weighted class activation mapping (Grad-CAM) to highlight regions of interest on retinal OCT images for each class, enabling doctors to easily interpret model predictions and improve their efficiency in evaluating relevant models. Discussion: With the increasing use and application of OCT technology in the clinical diagnosis of retinal images, our approach offers an additional diagnostic tool to enhance the diagnostic efficiency of clinical OCT retinal images.

Development of a Cancer-Associated Fibroblast-Related Prognostic Model in Breast Cancer via Bulk and Single-Cell RNA Sequencing.

Background: The most numerous cells in the tumor microenvironment, cancer-associated fibroblasts (CAFs) play a crucial role in cancer development. Our objective was to develop a cancer-associated fibroblast breast cancer predictive model. Methods: We acquire breast cancer (BC) scRNA-seq data from Gene Expression Omnibus (GEO), and "Seurat" was used for data processing, including quality control, filtering, principal component analysis, and t-SNE. Afterward, "singleR" software was used to annotate cells. Seurat's "FindAllMarkers" program is used to locate particular CAF markers. clusterProfiler was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The Cancer Genome Atlas (TCGA) database was utilized to provide univariate Cox regression, least absolute shrinkage operator (LASSO) analysis using bulk RNA-seq data. For model development, multivariate Cox regression studies are used. Utilizing pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms, chemosensitivity and immunotherapy response were predicted. The "rms" software was used to facilitate and simplify modeling. Results: Integrating the scRNA-seq (GSE176078) dataset yielded 28 cell clusters. In addition, well-known cell types helped identify 12 cell types. We found 193 marker genes that are elevated in CAFs. In addition, a five-gene predictive model associated to CAF was created in the training set. In the training set, the validation set, and the external validation set, greater risk scores were associated with a worse prognosis. And individuals with a higher risk score were more susceptible to immunotherapy and conventional chemotherapy medicines. Conclusion: In conclusion, we establish a strong prognostic model comprised of 5 genes related with CAF that might serve as a potent prognostic indicator and aid clinicians in making more rational medication choices.

Subchronic perfluorooctanesulfonate (PFOS) exposure induces elevated mutant frequency in an in vivo λ transgenic medaka mutation assay.

Perfluorooctanesulfonate (PFOS) has been widely detected in the environment, wildlife and humans, but few studies have ever examined its mutagenic effect in vivo. In the present study, we use a transgenic fish model, the λ transgenic medaka, to evaluate the potential mutagenicity of PFOS in vivo following a subchronic exposure of 30 days. The mutant frequency of cII target gene was 3.46 × 10-5 in liver tissue from control fish, which increased by 1.4-fold to 4.86 × 10-5 in fish exposed to 6.7 µg/L PFOS, 1.55-fold to 5.36 × 10-5 in fish exposed to 27.6 µg/L PFOS, and 2.02-fold to 6.99 × 10-5 in fish exposed to 87.6 µg/L PFOS. This dose-dependent increase of mutant frequency was also accompanied with mutational spectrum changes associated with PFOS exposure. In particular, PFOS-induced mutation was characterized by +1 frameshift mutations, which increased from 0% in control fish to 13.2% in fish exposed to 27.6 µg/L PFOS and 14.6% in fish exposed to 87.6 µg/L PFOS. Our findings provide the first evidence of PFOS's mutagenicity in an aquatic model system. Given the fact that most conventional mutagenic assays were negative for PFOS, we propose that PFOS-induced mutation in liver tissue of λ transgenic medaka may be mediated through compromised liver function.