Quantum computing led innovation for achieving a more sustainable Covid-19 healthcare industry

The connection between aging, cellular senescence and gut microbiome alterations: A comprehensive review

The intricate interplay between cellular senescence and alterations in the gut microbiome emerges as a pivotal axis in the aging process, increasingly recognized for its contribution to systemic inflammation, physiological decline, and predisposition to age-associated diseases. Cellular senescence, characterized by a cessation of cell division in response to various stressors, induces morphological and functional changes within tissues. The complexity and heterogeneity of senescent cells, alongside the secretion of senescence-associated secretory phenotype, exacerbate the aging process through pro-inflammatory pathways and influence the microenvironment and immune system. Concurrently, aging-associated changes in gut microbiome diversity and composition contribute to dysbiosis, further exacerbating systemic inflammation and undermining the integrity of various bodily functions. This review encapsulates the burgeoning research on the reciprocal relationship between cellular senescence and gut dysbiosis, highlighting their collective impact on age-related musculoskeletal diseases, including osteoporosis, sarcopenia, and osteoarthritis. It also explores the potential of modulating the gut microbiome and targeting cellular senescence as innovative strategies for healthy aging and mitigating the progression of aging-related conditions. By exploring targeted interventions, including the development of senotherapeutic drugs and probiotic therapies, this review aims to shed light on novel therapeutic avenues. These strategies leverage the connection between cellular senescence and gut microbiome alterations to advance aging research and development of interventions aimed at extending health span and improving the quality of life in the older population.

Oncological Applications of Quantum Machine Learning

Background: Cancer is a leading cause of death worldwide. Machine learning (ML) and quantum computers (QCs) have recently advanced significantly. Numerous studies have examined the application of quantum machine learning (QML) in healthcare and validated its superiority over classical ML algorithms. Objectives: This review investigates and reports the oncological applications of QML. Methods: In March 2023, an electronic investigation of PubMed, Scopus, Web of Science, IEEE, and Cochrane databases was performed. The articles were screened based on titles and abstracts, and their full texts were examined. Results: Initially, a total of 207 articles were retrieved. Thereafter, 9 articles were included in the study, most of which were published from 2020 onwards. The results indicated the implementation of various QML techniques in different aspects of oncology, such as reducing mammography image noise, edge detection of breast cancer, clinical decision support in radiotherapy treatment, and cancer classification. Conclusion: These studies revealed that integrating quantum science with ML can significantly improve patient care and clinical outcomes. Future studies should explore the integration of QC and ML and the development of novel algorithms to enhance cancer prognosis, diagnosis, and treatment planning.