Incident diabetes in adolescents using antidepressant: a systematic review and meta-analysis.

BACKGROUND: The use of antidepressants has been on the rise among adolescents and young adults, populations also increasingly at risk for type 2 diabetes. However, the relationship between antidepressant uses and diabetes incidence in these age groups remains poorly understood. METHODS: Adhering to PRISMA guidelines and the Cochrane Handbook, we conducted a comprehensive search in PubMed, Scopus, Embase, and Web of Science up to 21 February 2024, registering our protocol on PROSPERO (CRD42024516272). RESULTS: Six studies, ranging from 16, 470 to 1, 582, 914 participants and spanning 2010 to 2023 across North America, Europe, and Asia, were included. The meta-analysis revealed a significant association between antidepressant use and diabetes onset, with 10 cases per 1, 000 observations (p < 0.01; I2 = 100%). Adolescents using high doses of antidepressants showed a 62% increased risk of developing diabetes compared to non-users or those on low doses (Risk ratio = 1.67; 95% CI 1.19-2.35; I2 = 87%; p < 0.01). The overall quality of the studies was high, with an average Newcastle-Ottawa Scale score of 7.66. Sensitivity analysis highlighted the robustness of these findings, except when removing specific studies, indicating potential sources of heterogeneity. CONCLUSION: Antidepressant use in adolescents is associated with a significantly increased risk of diabetes onset, particularly at higher doses. This finding underscores the necessity for vigilant monitoring of glucose levels in this population and warrants further investigation into the underlying mechanisms and long-term outcomes.

The global prevalence of depression, anxiety, and sleep disorder among patients coping with Post COVID-19 syndrome (long COVID): a systematic review and meta-analysis.

BACKGROUND: Post COVID-19 syndrome, also known as "Long COVID," is a complex and multifaceted condition that affects individuals who have recovered from SARS-CoV-2 infection. This systematic review and meta-analysis aim to comprehensively assess the global prevalence of depression, anxiety, and sleep disorder in individuals coping with Post COVID-19 syndrome. METHODS: A rigorous search of electronic databases was conducted to identify original studies until 24 January 2023. The inclusion criteria comprised studies employing previously validated assessment tools for depression, anxiety, and sleep disorders, reporting prevalence rates, and encompassing patients of all age groups and geographical regions for subgroup analysis Random effects model was utilized for the meta-analysis. Meta-regression analysis was done. RESULTS: The pooled prevalence of depression and anxiety among patients coping with Post COVID-19 syndrome was estimated to be 23% (95% CI: 20%-26%; I2 = 99.9%) based on data from 143 studies with 7,782,124 participants and 132 studies with 9,320,687 participants, respectively. The pooled prevalence of sleep disorder among these patients, derived from 27 studies with 15,362 participants, was estimated to be 45% (95% CI: 37%-53%; I2 = 98.7%). Subgroup analyses based on geographical regions and assessment scales revealed significant variations in prevalence rates. Meta-regression analysis showed significant correlations between the prevalence and total sample size of studies, the age of participants, and the percentage of male participants. Publication bias was assessed using Doi plot visualization and the Peters test, revealing a potential source of publication bias for depression (p = 0.0085) and sleep disorder (p = 0.02). However, no evidence of publication bias was found for anxiety (p = 0.11). CONCLUSION: This systematic review and meta-analysis demonstrate a considerable burden of mental health issues, including depression, anxiety, and sleep disorders, among individuals recovering from COVID-19. The findings emphasize the need for comprehensive mental health support and tailored interventions for patients experiencing persistent symptoms after COVID-19 recovery.

Levels of brain-derived neurotrophic factor (BDNF) among patients with COVID-19: a systematic review and meta-analysis.

Many individuals have been suffering from consistent neurological and neuropsychiatric manifestations even after the remission of coronavirus disease (COVID-19). Brain-derived neurotrophic factor (BDNF) is a protein involved in the regulation of several processes, including neuroplasticity, neurogenesis, and neuronal differentiation, and has been linked to a range of neurological and psychiatric disorders. In this study, we aimed to synthesize the available evidence on the profile of BDNF in COVID-19. A comprehensive search was done in the Web of Science core collection, Scopus, and MEDLINE (PubMed), and Embase to identify relevant studies reporting the level of BDNF in patients with COVID-19 or those suffering from long COVID. We used the NEWCASTLE-OTTAWA tool for quality assessment. We pooled the effect sizes of individual studies using the random effect model for our meta-analysis. Fifteen articles were included in the systematic review. The sample sizes ranged from 16 to 183 participants. Six studies compared the level of BDNF in COVID-19 patients with healthy controls. The pooled estimate of the standardized mean difference in BDNF level between patients with COVID-19 and healthy individuals was - 0.84 (95% CI - 1.49 to - 0.18, p = 0.01, I2 = 81%) indicating a significantly lower BDNF level in patients with COVID-19. Seven studies assessed BDNF in different severity statuses of patients with COVID-19. The pooled estimate of the standardized mean difference in BDNF level was - 0.53 (95% CI - 0.85 to - 0.21, p = 0.001, I2 = 46%), indicating a significantly lower BDNF level in patients with more severe COVID-19. Three studies evaluated BDNF levels in COVID-19 patients through different follow-up periods. Only one study assessed the BDNF levels in long COVID patients. Sensitivity analyses did not alter the significance of the association. In this study, we showed a significant dysregulation of BDNF following COVID-19 infection. These findings may support the pathogenesis behind the long-lasting effects of this disease among infected patients. PROSPERO: CRD42023413536.

Forest fuel type classification: Review of remote sensing techniques, constraints and future trends.

Improved forest management plans require a better understanding of wildfire risk and behavior to enhance the conservation of biodiversity and plan effective risk mitigation activities across the landscape. More particularly, for spatial fire hazard and risk assessing as well as fire intensity and growth modeling across a landscape, an adequate knowledge of the spatial distribution of key forest fuels attributes is required. Mapping fuel attributes is a challenging and complicated procedure because fuels are highly variable and complex. To simplify, classification schemes are used to summarize the large number of fuel attributes (e.g., height, density, continuity, arrangement, size, form, etc.) into fuel types which groups vegetation classes with a similar predicted fire behavior. Remote sensing is a cost-effective and objective technology that have been used to regularly map fuel types and have demonstrated greater success compared to traditional field surveys, especially with recent advancements in remote sensing data acquisition and fusion techniques. Thus, the main goal of this manuscript is to provide a comprehensive review of the recent remote sensing approaches used for fuel type classification. We build on findings from previous review manuscripts and focus on identifying the key challenges of different mapping approaches and the research gaps that still need to be filled in. To improve classification outcomes, more research into developing state-of-the-art deep learning algorithms with integrated remote sensing data sources is encouraged for future research. This review can be used as a guideline for practitioners, researchers, and decision-makers in the domain of fire management service.

Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model.

One of the worst environmental catastrophes that endanger the Australian community is wildfire. To lessen potential fire threats, it is helpful to recognize fire occurrence patterns and identify fire susceptibility in wildfire-prone regions. The use of machine learning (ML) algorithms is acknowledged as one of the most well-known methods for addressing non-linear issues like wildfire hazards. It has always been difficult to analyze these multivariate environmental disasters because modeling can be influenced by a variety of sources of uncertainty, including the quantity and quality of training procedures and input variables. Moreover, although ML techniques show promise in this field, they are unstable for a number of reasons, including the usage of irrelevant descriptor characteristics when developing the models. Explainable AI (XAI) can assist us in acquiring insights into these constraints and, consequently, modifying the modeling approach and training data necessary. In this research, we describe how a Shapley additive explanations (SHAP) model can be utilized to interpret the results of a deep learning (DL) model that is developed for wildfire susceptibility prediction. Different contributing factors such as topographical, landcover/vegetation, and meteorological factors are fed into the model and various SHAP plots are used to identify which parameters are impacting the prediction model, their relative importance, and the reasoning behind specific decisions. The findings drawn from SHAP plots show the significant contributions made by factors such as humidity, wind speed, rainfall, elevation, slope, and normalized difference moisture index (NDMI) to the suggested model's output for wildfire susceptibility mapping. We infer that developing an explainable model would aid in comprehending the model's decision to map wildfire susceptibility, pinpoint high-contributing components in the prediction model, and consequently control fire hazards effectively.

Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI).

Urban vegetation mapping is critical in many applications, i.e., preserving biodiversity, maintaining ecological balance, and minimizing the urban heat island effect. It is still challenging to extract accurate vegetation covers from aerial imagery using traditional classification approaches, because urban vegetation categories have complex spatial structures and similar spectral properties. Deep neural networks (DNNs) have shown a significant improvement in remote sensing image classification outcomes during the last few years. These methods are promising in this domain, yet unreliable for various reasons, such as the use of irrelevant descriptor features in the building of the models and lack of quality in the labeled image. Explainable AI (XAI) can help us gain insight into these limits and, as a result, adjust the training dataset and model as needed. Thus, in this work, we explain how an explanation model called Shapley additive explanations (SHAP) can be utilized for interpreting the output of the DNN model that is designed for classifying vegetation covers. We want to not only produce high-quality vegetation maps, but also rank the input parameters and select appropriate features for classification. Therefore, we test our method on vegetation mapping from aerial imagery based on spectral and textural features. Texture features can help overcome the limitations of poor spectral resolution in aerial imagery for vegetation mapping. The model was capable of obtaining an overall accuracy (OA) of 94.44% for vegetation cover mapping. The conclusions derived from SHAP plots demonstrate the high contribution of features, such as Hue, Brightness, GLCM_Dissimilarity, GLCM_Homogeneity, and GLCM_Mean to the output of the proposed model for vegetation mapping. Therefore, the study indicates that existing vegetation mapping strategies based only on spectral characteristics are insufficient to appropriately classify vegetation covers.