An mRNA transcript contains many potential antisense oligodeoxynucleotide target sites. Identification of the most efficacious targets remains an important and challenging problem. Building on separate work that revealed a strong correlation between the inclusion of short sequence motifs and the activity level of an oligo, we have developed a predictive artificial neural network system for mapping tetranucleotide motif content to antisense oligo activity. Trained for high-specificity prediction, the system has been cross-validated against a database of 348 oligos from the literature and a larger proprietary database of 908 oligos. In cross- validation tests the system identified effective oligos (i.e. oligos capable of reducing target mRNA expression to <25% that of the control) with 53% accuracy, in contrast to the <10% success rates commonly reported for trial-and-error oligo selection, suggesting a possible 5-fold reduction in the in vivo screening required to find an active oligo. We have implemented a web interface to a trained neural network. Given an RNA transcript as input, the system identifies the most likely oligo targets and provides estimates of the probabilities that oligos targeted against these sites will be effective.

Histological slices of skin samples with the subcutaneous adipose tissue after photothermal/photodynamic treatment are analyzed. In the case of subcutaneous indocyanine green injection and 808-nm diode laser exposure of the rat skin site in vivo, the greatest changes in tissue condition were observed. Processes were characterized by dystrophy, necrosis, and desquamation of the epithelial cells, swelling and necrosis of the connective tissue, and widespread necrosis of the subcutaneous adipose tissue. The obtained data are useful for safe layer-by-layer dosimetry of laser illumination of ICG-stained adipose tissue for treatment of obesity and cellulite.

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Our review summarizes the evidence that COVID-19 can be complicated by SARS-CoV-2 infection of immune cells. This evidence is widespread and accumulating at an increasing rate. Research teams from around the world, studying primary and established cell cultures, animal models, and analyzing autopsy material from COVID-19 deceased patients, are seeing the same thing, namely that some immune cells are infected or capable of being infected with the virus. Human cells most vulnerable to infection include both professional phagocytes, such as monocytes, macrophages, and dendritic cells, as well as nonprofessional phagocytes, such as B-cells. Convincing evidence has accumulated to suggest that the virus can infect monocytes and macrophages, while data on infection of dendritic cells and B-cells are still scarce. Viral infection of immune cells can occur directly through cell receptors, but it can also be mediated or enhanced by antibodies through the Fc gamma receptors of phagocytic cells. Antibody-dependent enhancement (ADE) most likely occurs during the primary encounter with the pathogen through the first COVID-19 infection rather than during the second encounter, which is characteristic of ADE caused by other viruses. Highly fucosylated antibodies of vaccinees seems to be incapable of causing ADE, whereas afucosylated antibodies of persons with acute primary infection or convalescents are capable. SARS-CoV-2 entry into immune cells can lead to an abortive infection followed by host cell pyroptosis, and a massive inflammatory cascade. This scenario has the most experimental evidence. Other scenarios are also possible, for which the evidence base is not yet as extensive, namely productive infection of immune cells or trans-infection of other non-immune permissive cells. The chance of a latent infection cannot be ruled out either.

Background: Computer programs for the generation of multiple sequence alignments such as "Clustal W" allow detection of regions that are most conserved among many sequence variants. However, even for regions that are equally conserved, their potential utility as hybridization targets varies. Mismatches in sequence variants are more disruptive in some duplexes than in others. Additionally, the propensity for self-interactions amongst oligonucleotides targeting conserved regions differs and the structure of target regions themselves can also influence hybridization efficiency. There is a need to develop software that will employ thermodynamic selection criteria for finding optimal hybridization targets in related sequences.