Exposure to air pollutants has a direct impact on human health, resulting in increased mortality rates. Airborne particulate matter (PM) has major adverse effects on health and can be classified as high-risk respiratory particles (fine/PM_2.5, aerodynamic diameter < 2.5 µm) or thoracic particles (coarse/PM₁₀, aerodynamic diameter < 10 µm). In addition, airborne PM can carry microbial communities that alter the commensal microbiota and lead to dysbiosis. Our aim was to synthesize the current research evidence describing the association between air pollution exposure and the microbiome composition of the upper respiratory tract (URT) of the adult population. In this work, a systematic search of the PubMed, EMBASE and Scopus databases was conducted. A total of 9 studies published from 2018 to 2023 were included. 66.5% of the participants were exposed to PM_2.5 concentrations higher than 40 µg/m³, and data showed that PM_2.5 atmospheric levels were positively correlated with PM₁₀ (r_s = 0.95, p < 0.001). All the reviewed studies performed 16S rRNA sequencing of the V3–V4 region from URT samples, using different methods. Overall, evidence of URT microbiome alterations after high PM exposure was observed, with seasonal and geographical influence. Discordant findings were found about bacterial diversity, with a predominant decrease after exposure to high PM levels. Regarding microbiome composition, the relative abundance of the Actinobacteria phylum declined following exposure to high levels of PM, but that of Bacteroidetes and Fusobacteria increased. The studies showed a low-middle risk of bias due to heterogeneity regarding sample processing, sequencing methods, and confounder control. To confirm the observed evidence of an association between PM levels and alterations in the URT microbiome, we strongly recommend that future research work be conducted in accordance with standard guidelines for reporting microbiome studies. In summary, the entry of fine and coarse particles into the URT is associated with microbial dysbiosis, increasing the risk of developing respiratory diseases and allergies. Prospero registration: This systematic review was registered on PROSPERO (#CRD42023416230) Graphical Abstract

Hepatitis C virus (HCV) is the primary etiologic agent of liver cirrhosis or hepatocellular carcinoma. HCV elevated infection rates are mostly due to the lack of an accurate and accessible screening and diagnosis, especially in low- and middle-income countries. Conventional HCV diagnostic algorithm consists of a serological test followed by a nucleic acid test. This sequence of tests is time consuming and not affordable for low-resource settings. Nanotechnology have introduced new promising tests for the diagnose of infectious diseases. Based on the employment of nanoparticles and other nanomaterials which lead to highly sensitive and specific nanoscale tests, most of them target pathogen genome. Implementation of nanoscale tests, which are affordable, portable and easy to use by non-specialized personal, would improve HCV diagnosis algorithm. In this review, we have summed up the current emerging nanotechnology tools, which will improve actual screening and treatment programs, and help to reach HCV elimination proposal.

Genome mining using standard bioinformatics tools has allowed for the uncovering of hidden biosynthesis gene clusters for specialized metabolites in Streptomyces genomes. In this work, we have used an alternative approach consisting in seeking “Streptomyces Antibiotic Regulatory Proteins” (SARP) encoding genes and analyzing their surrounding DNA region to unearth cryptic gene clusters that cannot be identified using standard bioinformatics tools. This strategy has allowed the unveiling of the new ahb cluster in Streptomyces argillaceus, which had not been retrieved before using antiSMASH. The ahb cluster is highly preserved in other Streptomyces strains, which suggests a role for their encoding compounds in specific environmental conditions. By combining overexpression of three regulatory genes and generation of different mutants, we were able to activate the ahb cluster, and to identify and chemically characterize the encoded compounds that we have named ahbamycins (AHBs). These constitute a new family of metabolites derived from 3-amino-4-hydroxybenzoate (3,4-AHBA) known for having antibiotic and antitumor activity. Additionally, by overexpressing three genes of the cluster (ahbH, ahbI, and ahbL2) for the synthesis and activation of 3,4-AHBA, a new hybrid compound, AHB18, was identified which had been produced from a metabolic crosstalk between the AHB and the argimycin P pathways. The identification of this new BGC opens the possibility to generate new compounds by combinatorial biosynthesis.

Coinfection with hepatitis C virus (HCV) influences HIV reservoir size. However, it is unknown whether this coinfection also induces a higher provirus transcription. Viral transcription is promoted by synergy between cellular factors such as NF-κB and the viral regulator Tat. The impact of HCV coinfection on HIV provirus transcription was analyzed in resting (r)CD4 T+ cells (CD3+CD4+CD25-CD69-HLADR-) and rCD4 T cells-depleted PBMCs (rCD4 T- PBMCs) from a multicenter cross-sectional study of 115 cART-treated HIV patients: 42 HIV+/HCV+ coinfected individuals (HIV+/HCV+), 34 HIV+ patients with HCV spontaneous clearance (HIV+/HCV−) and 39 HIV patients (HIV+). Viral transcription was assessed in total RNA through the quantification of unspliced, single spliced, and multiple spliced viral mRNAs by qPCR. Linear correlations between viral reservoir size and viral splicing were determined. A 3-fold increase of multiple spliced transcripts in rCD4 T+ cells of HIV+/HCV+ patients was found compared to HIV+ individuals (p < 0.05). As Tat is synthesized by multiple splicing, the levels of Tat were also quantified in these patients. Significant differences in single and multiple spliced transcripts were also observed in rCD4 T- PBMCs. Levels of multiple spliced mRNAs were increased in rCD4 T+ cells isolated from HIV+/HCV+ subjects, which could indicate a higher Tat activity in these cells despite their resting state.

Regular monitoring of the air pollutant nitrogen dioxide (NO₂), an indicator for traffic-related emissions, is a priority in urban environments. The health impacts associated with NO₂ exposure are the result of a combination of factors, including concentration, duration of exposure, and interactions with other pollutants. WHO has established air quality guidelines based on epidemiological studies. This study develops a new concept "Health Impact Pathways (HIPs)" using adversity as a probabilistic indicator of health effects. For this purpose, it integrates available toxicological and epidemiological information, using Adverse Outcome Pathways (AOPs), in order to understand chemical-biological interactions and their consequences on health. Literature review and meta-analysis of toxicological data supported by expert judgment were performed to establish: a) adversity pathways, b) quantitative criteria for scoring the observed toxicological effects (adversity indicators), c) NO₂ exposure - adversity relationship for both long-term (1-36 months) and shortterm (1-7 days). The NO₂ daily concentrations from January 2001 to December 2022, were obtained from Madrid city Air Quality network monitoring database. Adversity levels were compared with relative risk levels for all-cause and respiratory mortality estimated using linear equations from WHO 2021 guidelines. Non-linear relations were obtained for all long- and short-term NO₂ related adversity indicators; for long-term effects, the best fitting was obtained with a modified Haber's law model with an exponential coefficient for the exposure time of 0.25. Estimations are presented for a set of case studies for Madrid city, covering temporal and spatial variability. A clear improvement trend along the two decades was observed, as well as high inter- and intra-station variability; the adversity indicators provided integrated information on the temporal and spatial evolution of population level risk. The proposed HIP conceptual approach offers promising advances for integrating experimental and epidemiological data. The next step is linking the concentration-adversity relationship with population health impacts through probability estimations, the preliminary estimations confirm the need for assessing independently different population groups.