RESUMO
Background and aims: Smoking cigarettes is a major global health problem that affects appetite and weight. The aim of this systematic review was to determine how smoking affected plasma leptin and ghrelin levels. Methods: A comprehensive search of PubMed, Scopus, Web of Science, and Ovid was conducted using a well-established methodology to gather all related publications. Results: A total of 40 studies were included in the analysis of 11,336 patients. The overall effect showed a with a mean difference (MD) of -1.92[95%CI; -2.63: -1.20] and p = 0.00001. Subgroup analysis by study design revealed significant differences as well, but with high heterogeneity within the subgroups (I2 of 82.3%). Subgroup by sex showed that there was a significant difference in mean difference between the smoking and non-smoking groups for males (MD = -5.75[95% CI; -8.73: -2.77], p = 0.0002) but not for females (MD = -3.04[95% CI; -6.6:0.54], p = 0.10). Healthy, pregnant, diabetic and CVD subgroups found significant differences in the healthy (MD = -1.74[95% CI; -03.13: -0.35], p = 0.01) and diabetic (MD = -7.69[95% CI, -1.64: -0.73], p = 0.03). subgroups, but not in the pregnant or cardiovascular disease subgroups. On the other hand, the meta-analysis found no statistically significant difference in Ghrelin serum concentration between smokers and non-smokers (MD = 0.52[95% CI, -0.60:1.63], p = 0.36) and observed heterogeneity in the studies (I2 = 68%). Conclusion: This study demonstrates a correlation between smoking and serum leptin/ghrelin levels, which explains smoking's effect on body weight. Systematic review registration: https://www.crd.york.ac.uk/ prospero/display_record.php, identifier (Record ID=326680).
RESUMO
BACKGROUND: Suicidal attempt is a significant risk factor for future attempts, with the highest risk during the first-year post-suicide. Telepsychiatry has shown promise by providing easy access to evidence-based interventions during mental health crises. AIMS: investigation the effectiveness of telehealth interventions in suicide prevention. METHODS: Four electronic databases (PubMed, Scopus, Web of Science, and Ovid) were systematically searched for studies on patients undergoing telepsychiatry intervention (TPI) up to June 2022. Following PRISMA guidelines, a systematic review and meta-analysis were conducted to investigate the effectiveness of telehealth interventions in suicide prevention. Continuous data were pooled as standardised mean difference (SMD), and dichotomous data were pooled as risk ratio using the random effects model with the corresponding 95% confidence intervals (CI). RESULTS: Sixteen studies were included in the review. Most studies were case-control and randomised controlled trials conducted in Europe and North America. The findings of the studies generally showed that TPIs are effective in reducing suicide rates (odds ratio = 0.68; 95% CI [-0.47, 0.98], p = .04) and suicidal reattempts. The interventions were also found to be well-accepted, with high retention rates. CONCLUSION: Our results suggest that TPIs are well-accepted and effective in reducing suicide rates and reattempts. It is recommended to maintain telephone follow-ups for at least 12 months. Further research is needed to understand the potential of telepsychiatry in suicide prevention fully.
RESUMO
OBJECTIVE: To perform a systematic literature review and meta-analysis of the two most common commercially available deep-learning algorithms for CT. METHODS: We used PubMed, Scopus, Embase, and Web of Science to conduct systematic searches for studies assessing the most common commercially available deep-learning CT reconstruction algorithms: True Fidelity (TF) and Advanced intelligent Clear-IQ Engine (AiCE) in the abdomen of human participants since only these two algorithms currently have adequate published data for robust systematic analysis. RESULTS: Forty-four articles fulfilled inclusion criteria. 32 studies evaluated TF and 12 studies assessed AiCE. DLR algorithmsâ¯produced images with significantly less noise (22-57.3% less than IR) but preserved a desirable noise texture with increased contrast-to-noise ratios and improved lesion detectability on conventional CT. These improvements with DLR were similarly noted in dual-energy CT which was only assessed for a single vendor. Reported radiation reduction potential was 35.1-78.5%. Nine studies assessed observer performance with the two dedicated liver lesion studies being performed on the same vendor reconstruction (TF). These two studies indicate preserved low contrast liver lesion detection (> 5 mm) at CTDIvol 6.8 mGy (BMI 23.5 kg/m2) to 12.2 mGy (BMI 29 kg/m2). If smaller lesion detection and improved lesion characterization is needed, a CTDIvol of 13.6-34.9 mGy is needed in a normal weight to obese population. Mild signal loss and blurring have been reported at high DLR reconstruction strengths. CONCLUSION: Deep learning reconstructions significantly improve image quality in CT of the abdomen. Assessment of other dose levels and clinical indications is needed. Careful choice of radiation dose levels is necessary, particularly for small liver lesion assessment.