Epidemiology: Gray immunity model gives qualitatively different predictions.

Compartmental models that dynamically divide the host population into categories such as susceptible, infected, and immune constitute the mainstream of epidemiological modelling. Effectively, such models treat infection and immunity as binary variables. We constructed an individual-based stochastic model that considers immunity as a continuous variable and incorporates factors that bring about small changes in immunity. The small immunity effects (SIE) comprise cross-immunity by other infections, small increments in immunity by subclinical exposures, and slow decay in the absence of repeated exposure. The model makes qualitatively different epidemiological predictions, including repeated waves without the need for new variants, dwarf peaks (peak and decline of a wave much before reaching herd immunity threshold), symmetry in upward and downward slopes of a wave, endemic state, new surges after variable and unpredictable gaps, and new surges after vaccinating majority of the population. In effect, the SIE model raises alternative possible causes of universally observed dwarf and symmetric peaks and repeated surges, observed particularly well during the COVID-19 pandemic. We also suggest testable predictions to differentiate between the alternative causes for repeated waves. The model further shows complex interactions of different interventions that can be synergistic as well as antagonistic. It also suggests that interventions that are beneficial in the short run could also be hazardous in the long run.

Gut microbiome influences incidence and outcomes of breast cancer by regulating levels and activity of steroid hormones in women.

BACKGROUND: Breast cancer, the leading cancer type in women worldwide, is affected by reproductive and nonreproductive factors. Estrogen and progesterone influence the incidence and progression of breast cancer. The microbiome of the gut, a complex organ that plays a vital role in digestion and homeostasis, enhances availability of estrogen and progesterone in the host. Thus, an altered gut microbiome may influence the hormone-induced breast cancer incidence. This review describes the current understanding of the roles of gut microbiome in influencing the incidence and progression of breast cancer, with an emphasis on the microbiome-induced metabolism of estrogen and progesterone. RECENT FINDINGS: Microbiome has been recognized as a promising hallmark of cancer. Next-generation sequencing technologies have aided in rapid identification of components of the gut microbiome that are capable of metabolizing estrogen and progesterone. Moreover, studies have indicated a wider role of the gut microbiome in metabolizing chemotherapeutic and hormonal therapy agents and reducing their efficacy in patients with breast cancer, with a predominant effect in postmenopausal women. CONCLUSION: The gut microbiome and variations in its composition significantly alter the incidence and therapy outcomes of patients with breast cancer. Thus, a healthy and diverse microbiome is required for better response to anticancer therapies. Finally, the review emphasizes the requirement of studies to elucidate mechanisms that may aid in improving the gut microbiome composition, and hence, survival outcomes of patients with breast cancer.