Addictive smartphone use in the elderly: relationship with depression, anxiety and sleep quality.

BACKGROUND: The aim of the present study was to explore the relationship between addictive smartphone use (ASU) and depressive symptoms, anxiety and sleep quality in elderly adults. METHODS: The study sample included smartphone users over the age of 65 years. The research data were obtained from social networking sites via a Google survey link. In addition to filling out a sociodemographic data form, the participants were also assessed with Smartphone Addiction Scale (SAS), Geriatric Depression Scale, Beck Anxiety Inventory and Pittsburgh Sleep Quality Index tools. RESULTS: The correlation analysis revealed the SAS score to be positively correlated with depression and anxiety, and negatively correlated with sleep quality. In the regression analysis, depressive symptoms, anxiety level and sleep quality were all found to have an effect on the SAS total score. Furthermore, the SAS score was found to have an effect on depressive symptoms, anxiety and sleep quality. CONCLUSIONS: Our findings reveal a bidirectional relationship between ASU and depressive, anxiety symptoms and impaired sleep quality in elderly adults. It is important to question smartphone use patterns in people with sleep problems, symptoms of depression or anxiety.

Associations of depressive symptoms and perceived social support with addictive use of social media among elderly people in Turkey.

BACKGROUND: The aim of this study was to investigate the relationship of addictive use of social media (AUSM) with depressive symptoms, perceived social support and demographic variables among people aged 65 years and older. METHODS: People aged 65 years and older who use social media constituted the study sample. The data were obtained from social networking sites via Google survey link. Bergen social media addiction scale (BSMAS) for determining AUSM, Multidimensional Scale of Social Support for determining social support, Geriatric Depression Scale to identify depressive symptoms and sociodemographic data form were administered to the participants. RESULTS: The mean age of the sample was 68.86 ± 2.0 years. AUSM showed significant differences depending on gender, marital status, economic status, educational level, settlement, occupation, and time spent in social media (P = 0.00). AUSM had correlations with both sub-dimensions of perceived social support and depressive symptoms (P < 0.01). In the regression analysis, it was found that the depressive symptoms, social support from family (P = 0.00) and from a significant other (P = 0.001) had significant effects on AUSM. CONCLUSIONS: When evaluating elderly individuals with depressive symptoms, it is important to evaluate these individuals in terms of social media addiction. Interventions to improve social support systems, especially for older people with little perceived social support can help prevent the development of AUSM.

An in silico approach to the identification of diagnostic and prognostic markers in low-grade gliomas.

Low-grade gliomas (LGG) are central nervous system Grade I tumors, and as they progress they are becoming one of the deadliest brain tumors. There is still great need for timely and accurate diagnosis and prognosis of LGG. Herein, we aimed to identify diagnostic and prognostic biomarkers associated with LGG, by employing diverse computational approaches. For this purpose, differential gene expression analysis on high-throughput transcriptomics data of LGG versus corresponding healthy brain tissue, derived from TCGA and GTEx, respectively, was performed. Weighted gene co-expression network analysis of the detected differentially expressed genes was carried out in order to identify modules of co-expressed genes significantly correlated with LGG clinical traits. The genes comprising these modules were further used to construct gene co-expression and protein-protein interaction networks. Based on the network analyses, we derived a consensus of eighteen hub genes, namely, CD74, CD86, CDC25A, CYBB, HLA-DMA, ITGB2, KIF11, KIFC1, LAPTM5, LMNB1, MKI67, NCKAP1L, NUSAP1, SLC7A7, TBXAS1, TOP2A, TYROBP, and WDFY4. All detected hub genes were up-regulated in LGG, and were also associated with unfavorable prognosis in LGG patients. The findings of this study could be applicable in the clinical setting for diagnosing and monitoring LGG.

A Counterintuitive Neutrophil-Mediated Pattern in COVID-19 Patients Revealed through Transcriptomics Analysis.

The COVID-19 pandemic has persisted for almost three years. However, the mechanisms linked to the SARS-CoV-2 effect on tissues and disease severity have not been fully elucidated. Since the onset of the pandemic, a plethora of high-throughput data related to the host transcriptional response to SARS-CoV-2 infections has been generated. To this end, the aim of this study was to assess the effect of SARS-CoV-2 infections on circulating and organ tissue immune responses. We profited from the publicly accessible gene expression data of the blood and soft tissues by employing an integrated computational methodology, including bioinformatics, machine learning, and natural language processing in the relevant transcriptomics data. COVID-19 pathophysiology and severity have mainly been associated with macrophage-elicited responses and a characteristic "cytokine storm". Our counterintuitive findings suggested that the COVID-19 pathogenesis could also be mediated through neutrophil abundance and an exacerbated suppression of the immune system, leading eventually to uncontrolled viral dissemination and host cytotoxicity. The findings of this study elucidated new physiological functions of neutrophils, as well as tentative pathways to be explored in asymptomatic-, ethnicity- and locality-, or staging-associated studies.