Tuberculosis (TB) caused by Mycobacterium tuberculosis is one of the most lethal infectious diseases with estimates of approximately 1.4 million human deaths in 2018. M. tuberculosis has a well-established ability to circumvent the host immune system to ensure its intracellular survival and persistence in the host. Mechanisms include subversion of expression of key microRNAs (miRNAs) involved in the regulation of host innate and adaptive immune response against M. tuberculosis. Several studies have reported differential expression of miRNAs during active TB and latent tuberculosis infection (LTBI), suggesting their potential use as biomarkers of disease progression and response to anti-TB therapy. This review focused on the miRNAs involved in TB pathogenesis and on the mechanism through which miRNAs induced during TB modulate cell antimicrobial responses. An attentive study of the recent literature identifies a group of miRNAs, which are differentially expressed in active TB vs. LTBI or vs. treated TB and can be proposed as candidate biomarkers.

Targeted amplicon sequencing of the 16S ribosomal RNA gene is one of the key tools for studying microbial diversity. The accuracy of this approach strongly depends on the choice of primer pairs and, in particular, on the balance between efficiency, specificity and sensitivity in the amplification of the different bacterial 16S sequences contained in a sample. There is thus the need for computational methods to design optimal bacterial 16S primers able to take into account the knowledge provided by the new sequencing technologies. We propose here a computational method for optimizing the choice of primer sets, based on multi-objective optimization, which simultaneously: 1) maximizes efficiency and specificity of target amplification; 2) maximizes the number of different bacterial 16S sequences matched by at least one primer; 3) minimizes the differences in the number of primers matching each bacterial 16S sequence. Our algorithm can be applied to any desired amplicon length without affecting computational performance. The source code of the developed algorithm is released as the mopo16S software tool (Multi-Objective Primer Optimization for 16S experiments) under the GNU General Public License and is available at http://sysbiobig.dei.unipd.it/?q=Software#mopo16S . Results show that our strategy is able to find better primer pairs than the ones available in the literature according to all three optimization criteria. We also experimentally validated three of the primer pairs identified by our method on multiple bacterial species, belonging to different genera and phyla. Results confirm the predicted efficiency and the ability to maximize the number of different bacterial 16S sequences matched by primers.

Drug-loaded, PEGylated, organic-modified silica (ORMOSIL) nanoparticles prepared by microemulsion condensation of vinyltriethoxysilane (VTES) were investigated as potential nanovectors for cancer therapy. To target cancer stem cells, anti-CD44v6 antibody and hyaluronic acid (HA) were conjugated to amine-functionalized PEGylated ORMOSIL nanoparticles through thiol-maleimide and amide coupling chemistries, respectively. Specific binding and uptake of conjugated nanoparticles were studied on cells overexpressing the CD44v6 receptor. Cytotoxicity was subsequently evaluated in the same cells after the uptake of the nanoparticles. Internalization of nanocarriers loaded with the anticancer drug 3N-cyclopropylmethyl-7-phenyl-pyrrolo- quinolinone (MG2477) into cells resulted in a substantial increase of the cytotoxicity with respect to the free formulation. Targeting with anti-CD44v6 antibodies or HA yielded nanoparticles with similar effectiveness, in their optimized formulation.