Comparative analysis of integrative classification methods for multi-omics data.

Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple 'omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications.

Characterization of humoral and cellular immunologic responses to an mRNA-based human cytomegalovirus vaccine from a phase 1 trial of healthy adults.

mRNA-1647 is an investigational mRNA-based vaccine against cytomegalovirus (CMV) that contains sequences encoding the CMV proteins glycoprotein B and pentamer. Humoral and cellular immune responses were evaluated in blood samples collected from healthy CMV-seropositive and CMV-seronegative adults who participated in a phase 1 trial of a three-dose series of mRNA-1647 (NCT03382405). Neutralizing antibody (nAb) titers against fibroblast and epithelial cell infection in sera from CMV-seronegative mRNA-1647 recipients were higher than those in sera from control CMV-seropositive samples and remained elevated up to 12 months after dose 3. nAb responses elicited by mRNA-1647 were comparable across 14 human CMV (HCMV) strains. Frequencies of antigen-specific memory B cells increased in CMV-seropositive and CMV-seronegative participants after each mRNA-1647 dose and remained elevated for up to 6 months after dose 3. mRNA-1647 elicited robust increases in frequencies and polyfunctionality of CD4+ T helper type 1 and effector CD8+ T cells in samples from CMV-seronegative and CMV-seropositive participants after stimulation with HCMV-specific peptides. The administration of three doses of mRNA-1647 to healthy adults elicited high nAb titers with wide-breadth, long-lasting memory B cells, and strong polyfunctional T-cell responses. These findings support further clinical development of the mRNA-1647 vaccine against CMV.IMPORTANCECytomegalovirus (CMV), a common virus that can infect people of all ages, may lead to serious health problems in unborn babies and those with a weakened immune system. Currently, there is no approved vaccine available to prevent CMV infection; however, the investigational messenger RNA (mRNA)-based CMV vaccine, mRNA-1647, is undergoing evaluation in clinical trials. The current analysis examined samples from a phase 1 trial of mRNA-1647 in healthy adults to better understand how the immune system reacts to vaccination. Three doses of mRNA-1647 produced a long-lasting immune response, thus supporting further investigation of the vaccine in the prevention of CMV infection.CLINICAL TRIALSRegistered at ClinicalTrials.gov (NCT03382405).

Ionic Hyperbranched Poly(amido-amine)-Incorporated Nanofiltration Membranes for High-Efficiency Dye Desalination.

High-efficiency dye desalination is crucial in the textile industry, considering its importance for human health, safe aquatic ecological systems, and resource recovery. In order to solve the problem of effective separation of univalent salt and ionic dye under the condition of high salt, ionic hyperbranched poly(amido-amine) (HBPs) were synthesized based on a simple and scalable one-step polycondensation method and then incorporated into the polyamide (PA) selective layers to construct charged nanochannels through interfacial polymerization (IP) on the surface of a polyvinyl chloride ultrafiltration (PVC-UF) hollow fiber membrane. Both the internal nanopores of HBPs (internal nanochannels) and the interfacial voids between HBPs and the PA matrix (external nanochannels) can be regarded as a fast water molecule transport pathway, while the terminal ionic groups of ionic HBPs endow the nanochannels with charge characteristics for improving ionic dye/salt selectivities. The permeate fluxes and dye/salt selectivities of HBP-TAC/PIP (57.3 L m-2 h-1 and rhodamine B (RB)/NaCl selectivity of 224.0) and HBP-PS/PIP (63.7 L m-2 h-1 and lemon yellow (LY)/NaCl selectivity of 664.0) membranes under 0.4 MPa operation pressure are much higher than PIP-only and HBP-NH2/PIP membranes. At the same time, this project also studied the membrane desalination process in a simulated high-salinity dye/salt mixture system to provide a theoretical basis and technical support for the actual dye desalination process.