Potential of Mycobacterium tuberculosis resuscitation-promoting factors as antigens in novel tuberculosis sub-unit vaccines

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Anxiety Detection Leveraging Mobile Passive Sensing

Anxiety disorders are the most common class of psychiatric problems affecting both children and adults. However, tools to effectively monitor and manage anxiety are lacking, and comparatively limited research has been applied to addressing the unique challenges around anxiety. Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods, allowing for real-time mental health surveillance and disease management. This paper presents eWellness, an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner. We report on an initial pilot study tracking ten people over the course of a month that showed a nearly 76% success rate at predicting daily anxiety and depression levels based solely on the passively monitored features

Impact of pollen on throughfall biochemistry in European temperate and boreal forests

Pollen is known to affect forest throughfall biochemistry, but underlying mechanisms are not fully understood. We used generalized additive mixed modelling to study the relationship between long-term series of measured throughfall fluxes in spring (April–June) at forest plots and corresponding airborne pollen concentrations (Seasonal Pollen Integral, SPIn) from nearby aerobiological monitoring stations. The forest plots were part of the intensive long term monitoring (Level II) network of the UNECE International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) with dominant tree genera Fagus, Quercus, Pinus and Picea, and were distributed all across Europe. We also conducted a 7-day laboratory dissolution experiment with bud scales and flower stalks of European beech (Fagus sylvatica L.), pollen of beech, common oak (Quercus robur L.), silver birch (Betula pendula L.), Scots pine (Pinus sylvestris L.), Corsican pine (Pinus nigra Arnold ssp. laricio (Poiret) Maire), Norway spruce (Picea abies (L.) Karst.) and sterilized pollen of silver birch in a nitrate (NO3--N) solution (11.3 mg N L-1). Throughfall fluxes of potassium (K+), ammonium (NH4+-N), dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) showed a positive relationship with SPIn whereas NO3--N fluxes showed a negative relationship with SPIn. In years with massive seed production of beech and oak SPIn and throughfall fluxes of K+ and DOC were higher, but fluxes of NO3--N were lower. The experiment broadly confirmed the findings based on field data. Within two hours, pollen released large quantities of K+, phosphate, DOC and DON, and lesser amounts of sulphate, sodium and calcium. After 24-48 hours, NO3--N started to disappear, predominantly in the treatments with broadleaved pollen, while concentrations of nitrite and NH4+-N increased. At the end of the experiment, the inorganic nitrogen (DIN) was reduced, presumably because it was lost as gaseous nitric oxide (NO). There was no difference for sterilized pollen, indicating that the involvement of microbial activity was limited in above N transformations. Our results show that pollen dispersal might be an overlooked factor in forest nutrient cycling and might induce complex canopy N transformations, although the net-impact on N throughfall fluxes is rather lo

Guided internet-based transdiagnostic individually tailored Cognitive Behavioral Therapy for symptoms of depression and/or anxiety in college students: A randomized controlled trial

Pathways through Adolescenc

Including information about co-morbidity in estimates of disease burden: results from the World Health Organization World Mental Health Surveys

Background The methodology commonly used to estimate disease burden, featuring ratings of severity of individual conditions, has been criticized for ignoring co-morbidity. A methodology that addresses this problem is proposed and illustrated here with data from the World Health Organization World Mental Health Surveys. Although the analysis is based on self-reports about one's own conditions in a community survey, the logic applies equally well to analysis of hypothetical vignettes describing co-morbid condition profiles. Method Face-to-face interviews in 13 countries (six developing, nine developed; n=31 067; response rate=69.6%) assessed 10 classes of chronic physical and nine of mental conditions. A visual analog scale (VAS) was used to assess overall perceived health. Multiple regression analysis with interactions for co-morbidity was used to estimate associations of conditions with VAS. Simulation was used to estimate condition-specific effects. Results The best-fitting model included condition main effects and interactions of types by numbers of conditions. Neurological conditions, insomnia and major depression were rated most severe. Adjustment for co-morbidity reduced condition-specific estimates with substantial between-condition variation (0.24-0.70 ratios of condition-specific estimates with and without adjustment for co-morbidity). The societal-level burden rankings were quite different from the individual-level rankings, with the highest societal-level rankings associated with conditions having high prevalence rather than high individual-level severity. Conclusions Plausible estimates of disorder-specific effects on VAS can be obtained using methods that adjust for co-morbidity. These adjustments substantially influence condition-specific rating

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model

Background: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. Results: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. Conclusions: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Predicting the severity of the grass pollen season and the effect of climate change in Northwest Europe

Allergic rhinitis is an inflammation in the nose caused by overreaction of the immune system to allergens in the air. Managing allergic rhinitis symptoms is challenging and requires timely intervention. The following are major questions often posed by those with allergic rhinitis: How should I prepare for the forthcoming season? How will the season's severity develop over the years? No country yet provides clear guidance addressing these questions. We propose two previously unexplored approaches for forecasting the severity of the grass pollen season on the basis of statistical and mechanistic models. The results suggest annual severity is largely governed by preseasonal meteorological conditions. The mechanistic model suggests climate change will increase the season severity by up to 60%, in line with experimental chamber studies. These models can be used as forecasting tools for advising individuals with hay fever and health care professionals how to prepare for the grass pollen season

A World Mental Health Surveys report

Funding: The Portuguese Mental Health Study was carried out by the Department of Mental Health, Faculty of Medical Sciences, NOVA University of Lisbon, with collaboration of the Portuguese Catholic University, and was funded by Champalimaud Foundation, Gulbenkian Foundation, Foundation for Science and Technology (FCT) and Ministry of HealthBackground Depressive and anxiety disorders are highly comorbid, which has been theorized to be due to an underlying internalizing vulnerability. We aimed to identify groups of participants with differing vulnerabilities by examining the course of internalizing psychopathology up to age 45. Methods We used data from 24158 participants (aged 45+) in 23 population-based cross-sectional World Mental Health Surveys. Internalizing disorders were assessed with the Composite International Diagnostic Interview (CIDI). We applied latent class growth analysis (LCGA) and investigated the characteristics of identified classes using logistic or linear regression. Results The best-fitting LCGA solution identified eight classes: A healthy class (81.9%), three childhood-onset classes with mild (3.7%), moderate (2.0%), or severe (1.1%) internalizing comorbidity, two puberty-onset classes with mild (4.0%) or moderate (1.4%) comorbidity, and two adult-onset classes with mild comorbidity (2.7% and 3.2%). The childhood-onset severe class had particularly unfavorable sociodemographic outcomes compared to the healthy class, with increased risks of being never or previously married (OR = 2.2 and 2.0, p < 0.001), not being employed (OR = 3.5, p < 0.001), and having a low/low-Average income (OR = 2.2, p < 0.001). Moderate or severe (v. mild) comorbidity was associated with 12-month internalizing disorders (OR = 1.9 and 4.8, p < 0.001), disability (B = 1.1-2.3, p < 0.001), and suicidal ideation (OR = 4.2, p < 0.001 for severe comorbidity only). Adult (v. childhood) onset was associated with lower rates of 12-month internalizing disorders (OR = 0.2, p < 0.001). Conclusions We identified eight transdiagnostic trajectories of internalizing psychopathology. Unfavorable outcomes were concentrated in the 1% of participants with childhood onset and severe comorbidity. Early identification of this group may offer opportunities for preventive interventions.publishersversionepub_ahead_of_prin