Parasite classification and identification are central to controlling parasitosis. Traditional methods for identifying parasite species are based on morphological features, but these are time-consuming and inaccurate, especially for cryptic species. The purpose of the present study was to select molecular markers to promote the development of molecular systematic for parasites. The internal transcribed spacers (ITS) of nuclear ribosomal DNA (rDNA) falls in between 18S, 5.8S, and 28S rDNA sequences, including ITS-1 and ITS-2 sequences. Previous studies have demonstrated that rDNA ITS sequences provide useful genetic markers for identifying parasitic nematodes. With the ultimate goal of controlling parasite transmission, we identified Kalicephalus belonging to three species using ITS rDNA genes. The ITS genes (750–797 bp) of 21 Kalicephalus belonging to 3 species were cloned and sequenced. Intra- and interspecific identities were 98.4% and 80%–89%, respectively. The phylogenetic tree reconstructed with the neighbour-joining (NJ) method revealed that congener Kalicephalus form the same branch, which is far apart from other branches of other nematodes. This is consistent with morphological classifications, demonstrating the accuracy of our molecular method. This is the first report stating that ITS genes can be used to classify Kalicephalus, and it lays the foundation for identification, molecular epidemiology, and phylogenetics of Kalicephalus and related parasitic nematodes.

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipe-line and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to gen-erate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.