We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
In major depressive disorder (MDD), only ~35% achieve remission after first-line antidepressant therapy. Using UK Biobank data, we identify sociodemographic, clinical, and genetic predictors of antidepressant response through self-reported outcomes, aiming to inform personalized treatment strategies.
Methods
In UK Biobank Mental Health Questionnaire 2, participants with MDD reported whether specific antidepressants helped them. We tested whether retrospective lifetime response to four selective serotonin reuptake inhibitors (SSRIs) (N = 19,516) – citalopram (N = 8335), fluoxetine (N = 8476), paroxetine (N = 2297) and sertraline (N = 5883) – was associated with sociodemographic (e.g. age, gender) and clinical factors (e.g. episode duration). Genetic analyses evaluated the association between CYP2C19 variation and self-reported response, while polygenic score (PGS) analysis assessed whether genetic predisposition to psychiatric disorders and antidepressant response predicted self-reported SSRI outcomes.
Results
71%–77% of participants reported positive responses to SSRIs. Non-response was significantly associated with alcohol and illicit drug use (OR = 1.59, p = 2.23 × 10−20), male gender (OR = 1.25, p = 8.29 × 10−08), and lower-income (OR = 1.35, p = 4.22 × 10−07). The worst episode lasting over 2 years (OR = 1.93, p = 3.87 × 10−16) and no mood improvement from positive events (OR = 1.35, p = 2.37 × 10−07) were also associated with non-response. CYP2C19 poor metabolizers had nominally higher non-response rates (OR = 1.31, p = 1.77 × 10−02). Higher PGS for depression (OR = 1.08, p = 3.37 × 10−05) predicted negative SSRI outcomes after multiple testing corrections.
Conclusions
Self-reported antidepressant response in the UK Biobank is influenced by sociodemographic, clinical, and genetic factors, mirroring clinical response measures. While positive outcomes are more frequent than remission reported in clinical trials, these self-reports replicate known treatment associations, suggesting they capture meaningful aspects of antidepressant effectiveness from the patient’s perspective.
According to International Union for the Conservation of Nature (IUCN) guidelines, all species must be assessed against all criteria during the Red Listing process. For organismal groups that are diverse and understudied, assessors face considerable challenges in assembling evidence due to difficulty in applying definitions of key terms used in the guidelines. Challenges also arise because of uncertainty in population sizes (Criteria A, C, D) and distributions (Criteria A2/3/4c, B). Lichens, which are often small, difficult to identify, or overlooked during biodiversity inventories, are one such group for which specific difficulties arise in applying Red List criteria. Here, we offer approaches and examples that address challenges in completing Red List assessments for lichens in a rapidly changing arena of data availability and analysis strategies. While assessors still contend with far from perfect information about individual species, we propose practical solutions for completing robust assessments given the currently available knowledge of individual lichen life-histories.
The ectoparasite fauna of two damselfishes, Stegastes nigricans and Dascyllus aruanus, from Moorea Island in French Polynesia was investigated. Gills of these damselfishes were infected with congeneric Monopisthocotylea Monogenea belonging to the genus Haliotrema. Stegastes nigricans were found to harbour a guild of three Haliotrema species whereas only one species inhabited D. aruanus. Microhabitat distribution, inter- and intraspecific competition and interspecific associations on the gill were studied. Observations on site preference revealed no spatial segregation between the three congeneric species inhabiting the gills of S. nigricans. Juvenile and adult monogeneans of that guild occurred on the same microhabitat. The dominant species Haliotrema sp. 1 did not expand on the microhabitat when the intensity of infection increased. Interspecific association tests revealed positive and negative associations. Haliotrema sp. 4 expanded its distribution on the gills of Dascyllus aruanus when the intensity of infection increased suggesting the likelihood of intraspecific competition. Juvenile and adult monogeneans of Haliotrema sp. 4 appeared to segregate as a result of intraspecific competition. This competition may exist to enhance resource availability when the gill habitat is limited. Overlaps between niche breadth and species microhabitat were revealed for monogenean species inhabiting S. nigricans. Interspecific competition did not appear to play an important role in the distribution of S. nigricans congeneric ectoparasites. Reinforcement of reproductive barriers may have led to the avoidance of hybridization.
A machine learning model was created to predict the electron spectrum generated by a GeV-class laser wakefield accelerator. The model was constructed from variational convolutional neural networks, which mapped the results of secondary laser and plasma diagnostics to the generated electron spectrum. An ensemble of trained networks was used to predict the electron spectrum and to provide an estimation of the uncertainty of that prediction. It is anticipated that this approach will be useful for inferring the electron spectrum prior to undergoing any process that can alter or destroy the beam. In addition, the model provides insight into the scaling of electron beam properties due to stochastic fluctuations in the laser energy and plasma electron density.
The aim of the present work was to evaluate the distribution of the different clones of the parasite prevailing after treatment with benznidazole (BZ) and clomipramine (CLO), in mice infected with Trypanosoma cruzi, Casibla isolate which consists of a mixture of two discrete typing units (DTUs). Albino Swiss mice were infected and treated with high and low concentrations of BZ (100 or 6.25 mg/kg), CLO (5 or 1.25 mg/kg), or the combination of both low doses (BZ6.25 + CLO1.25), during the acute phase of experimental infection. Treatment efficacy was evaluated by comparing parasitaemia, survival and tissular parasite presence. For DTUs genotyping, blood, skeletal and cardiac muscle samples were analysed by multiplex quantitative polymerase chain reaction. The combined treatment had similar outcomes to BZ6.25; BZ100 was the most effective treatment, but it failed to reach parasite clearance and produced greater histological alterations. Non-treated mice and the ones treated with monotherapies showed both DTUs while BZ6.25 + CLO1.25 treated mice showed only TcVI parasites in all the tissues studied. These findings suggest that the treatment may modify the distribution of infecting DTUs in host tissues. Coinfection with T. cruzi clones belonging to different DTUs reveals a complex scenario for the treatment of Chagas disease and search for new therapies.