StM171, a Stenotrophomonas maltophilia Bacteriophage That Affects Sensitivity to Antibiotics in Host Bacteria and Their Biofilm Formation.

Stenotrophomonas maltophilia mainly causes respiratory infections that are associated with a high mortality rate among immunocompromised patients. S. maltophilia exhibits a high level of antibiotic resistance and can form biofilms, which complicates the treatment of patients infected with this bacterium. Phages combined with antibiotics could be a promising treatment option. Currently, ~60 S. maltophilia phages are known, and their effects on biofilm formation and antibiotic sensitivity require further examination. Bacteriophage StM171, which was isolated from hospital wastewater, showed a medium host range, low burst size, and low lytic activity. StM171 has a 44kbp dsDNA genome that encodes 59 open-reading frames. A comparative genomic analysis indicated that StM171, along with the Stenotrophomonas phage Suso (MZ326866) and Xanthomonas phage HXX_Dennis (ON711490), are members of a new putative Nordvirus genus. S. maltophilia strains that developed resistance to StM171 (bacterial-insensitive mutants) showed a changed sensitivity to antibiotics compared to the originally susceptible strains. Some bacterial-insensitive mutants restored sensitivity to cephalosporin and penicillin-like antibiotics and became resistant to erythromycin. StM171 shows strain- and antibiotic-dependent effects on the biofilm formation of S. maltophilia strains.

PsychoAge and SubjAge: development of deep markers of psychological and subjective age using artificial intelligence.

Aging clocks that accurately predict human age based on various biodata types are among the most important recent advances in biogerontology. Since 2016 multiple deep learning solutions have been created to interpret facial photos, omics data, and clinical blood parameters in the context of aging. Some of them have been patented to be used in commercial settings. However, psychological changes occurring throughout the human lifespan have been overlooked in the field of "deep aging clocks". In this paper, we present two deep learning predictors trained on social and behavioral data from Midlife in the United States (MIDUS) study: (a) PsychoAge, which predicts chronological age, and (b) SubjAge, which describes personal aging rate perception. Using 50 distinct features from the MIDUS dataset these models have achieved a mean absolute error of 6.7 years for chronological age and 7.3 years for subjective age. We also show that both PsychoAge and SubjAge are predictive of all-cause mortality risk, with SubjAge being a more significant risk factor. Both clocks contain actionable features that can be modified using social and behavioral interventions, which enables a variety of aging-related psychology experiment designs. The features used in these clocks are interpretable by human experts and may prove to be useful in shifting personal perception of aging towards a mindset that promotes productive and healthy behaviors.

Psychological aging, depression, and well-being.

Aging is a multifactorial process, which affects the human body on every level and results in both biological and psychological changes. Multiple studies have demonstrated that a lower subjective age is associated with better mental and physical health, cognitive functions, well-being and satisfaction with life. In this work we propose a list of non-modifiable and modifiable factors that may possibly be influenced by subjective age and its changes across an individual's lifespan. These factors can be used for a future development of individual psychological aging clocks, which may be utilized as a sensitive measure for health status and overall life satisfaction. Furthermore, recent progress in artificial intelligence and biomarkers of biological aging have enabled scientists to discover and evaluate the efficacy of potential aging- and disease-modifying drugs and interventions. We propose that biomarkers of psychological age, which are just as important as those for biological age, may likewise be used for these purposes. Indeed, these two types of markers complement one another. We foresee the development of a broad range of parametric and deep psychological and biopsychological aging clocks, which may have implications for drug development and therapeutic interventions, and thus healthcare and other industries.