Fetal Therapy for Severe Drug-Resisted Tachyarrhythmia With Progressive Hydrops by Fetoscopic Transesophageal Pacing: A Successful Attempt in Single Chinese Fetal Medicine Center.

Objective: To describe the outcome of a case of severe drug-resistant fetal tachyarrhythmia with progressive hydrops treated with fetoscopic transesophageal pacing (FTEP).

Method: A case of fetal tachyarrhythmia complicated by progressive hydrops is presented. The fetus, diagnosed at 26 weeks of gestation, had supraventricular tachycardia with a mechanism suggestive of atrial reentry.

Predicting survival in bladder cancer with a novel apoptotic gene-related prognostic model.

Background: Apoptosis and apoptotic genes play a critical role in the carcinogenesis and progression of bladder cancer. However, there is no prognostic model established by apoptotic genes.

Methods: Messenger RNA (mRNA), Expression data, and related clinical data were obtained from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database.

Exploring new mechanisms in cancer molecular pathways and pathogenic cell transformation: PIP4K2A as a prognostic marker and therapeutic target in cutaneous malignant melanoma.

Background: Cutaneous malignant melanoma is a very aggressive and metastatic form of skin cancer, typically linked with poor outcomes. Advances in genomic analysis have underscored the crucial role of T cells in tumor immunity. Immune checkpoint inhibitors have notably transformed melanoma treatment by boosting T cell activity.

[Independent component analysis for spectral unmixing in hyperspectral remote sensing image].

Hyperspectral remote sensing plays an important role in earth observation on land, ocean and atmosphere. A key issue in hyperspectral data exploitation is to extract the spectra of the constituent materials (endmembers) as well as their proportions (fractional abundances) from each measured spectrum of mixed pixel in hyperspectral remote sensing image, called spectral un-mixing. Linear spectral mixture model (LSMM) provides an effective analytical model for spectral unmixing, which assumes that there is a linear relationship among the fractional abundances of the substances within a mixed pixel.