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Decomposing the effect of normal aging and Alzheimer’s disease in brain morphological changes via learned aging templates
Abstract Alzheimer’s disease (AD) subjects usually show more profound morphological changes with time compared to cognitively normal (CN) individuals. These changes are the combination of two major biological processes: normal aging and AD pathology. Investigating normal aging and residual morphological changes separately can increase our understanding of the disease. This paper proposes two scores, the aging score (AS) and the AD-specific score (ADS), whose purpose is to measure these two components of brain atrophy independently. For this, in the first step, we estimate the atrophy due to the normal aging of CN subjects by computing the expected deformation required to match imaging templates generated at different ages. We used a state-of-the-art generative deep learning model for generating such imaging templates. In the second step, we apply deep learning-based diffeomorphic registration to align the given image of a subject with a reference imaging template. Parametrization of this deformation field is then decomposed voxel-wise into their parallel and perpendicular components with respect to the parametrization of the expected atrophy of CN individuals in one year computed in the first step. AS and ADS are the normalized scores of these two components, respectively. We evaluated these two scores on the OASIS-3 dataset with 1,014 T1-weighted MRI scans. Of these, 326 scans were from CN subjects, and 688 scans were from subjects diagnosed with AD at various stages of clinical severity, as defined by clinical dementia rating (CDR) scores. Our results reveal that AD is marked by both disease-specific brain changes and an accelerated aging process. Such changes affect brain regions differently. Moreover, the proposed scores were sensitive to detect changes in the early stages of the disease, which is promising for its potential future use in clinical studies. Our code is freely available at https://github.com/Fjr9516/DBM_with_DL.
Burnout syndrome in resident physicians: A study after the third COVID-19 wave in two tertiary hospitals of southeastern Brazil
Background In recent years, there has been an increase in the concern for the mental health of resident physicians. The COVID-19 pandemic may have further contributed negatively to the mental health of this population. Objectives We aimed to evaluate the probability of Burnout Syndrome in resident physicians involved in COVID-19 care services during the Pandemic and possible factors associated with it. Methods A cross-sectional study was conducted in two tertiary hospitals in Belo Horizonte, Brazil, between the 5th of June and the 14th of September 2022. A survey including the instruments Oldenburg Burnout Inventory (OLBI), Depression, Anxiety and Stress Scale (DASS-21), Patient Health Questionnaire (PHQ-9), and the Brief Resilient Coping Scale (BRCS) was applied. Results From the 181 resident physicians invited to participate, 104 agreed. The mean age (SD) was 29.9 (3.3) years; 56.7% were female, and 67.3% were from a clinical residency program. The score of the OLBI was high. In the multivariate analysis, being single, using psychiatric medications, and taking direct care of COVID-19 were associated with increases in the OLBI scale scores. The frequencies of probable depression and anxiety assessed by DASS-21 were 15.3% and 5.7%, respectively. Notwithstanding, the frequency of probable stress was 61.5%. Depressive symptoms, as evaluated by the PHQ-9 questionnaire, were highly prevalent at 61.5%. Further, 29% of the resident physicians interviewed in our study had probable low resilience according to the BRCS score. Conclusion The frequency of Burnout, depression, and stress found in our study appears to be relevant in the resident physicians.
The effects of triclabendazole, combined tetramisole with Oxyclozanide, and albendazole against ovine fasciolosis
Epidemiological, serological, and viral genomic analysis of an outbreak of elephant hemorrhagic disease in Switzerland
Elephant hemorrhagic disease (EHD), caused by several Elephant endotheliotropic herpesviruses (EEHV), represents a frequently lethal syndrome, affecting both captive and free-living elephants. In the summer of 2022, three young Asian elephants (Elephas maximus) succumbed to EHD in a zoo in Switzerland, despite considerable preventive efforts and early detection of EEHV1A viremia. In this communication, we describe the extent of preventive measures in terms of prior virus detection, active survey of viremia, and antibody status. The results show that: (1) A previously undetected EEHV1A strain had remained unrecognized among these elephants. Probably, the virus re-emerged after almost 40 years of latency from one of the oldest elephants in the zoo. (2) While two of the three affected animals had prior immune responses against EEHV1, their strain-specific immunity proved insufficient to prevent EHD. The complete genomic DNA sequence of the EEHV1A strain involved was determined, and detailed comparisons with multiple EEHV1 strains were made, revealing a much greater extent of divergence and level of complexity among the encoded proteins than previously described. Overall, these data confirmed that all three EHD cases here had been infected by the same novel strain of EEHV subtype 1A.
Polarization-independent dual-band quasi-bound states in the continuum based on graphene metasurface for tunable THz sensing application
The influence of exercise self-efficacy and gender on the relationship between exercise motivation and physical activity in college students
Meloxicam and levamisole administration ameliorates FMD vaccination stress on frozen semen attributes in Sahiwal bulls
The butyrate derived from probiotic Clostridium butyricum exhibits an inhibitory effect on multiple myeloma through cell death induction
Efficacy of kinesthetic motor imagery based brain computer interface combined with tDCS on upper limb function in subacute stroke
Speech emotion recognition with light weight deep neural ensemble model using hand crafted features
A novel hybrid layer-based encoder–decoder framework for 3D segmentation in congenital heart disease
Linguistic-visual based multimodal Yi character recognition
Cross-sectional associations between physical activity and sedentary time with cardiovascular health in children from the ALSPAC study using compositional data analysis
Abstract This study adopted a compositional framework to cross-sectionally examine the associations between physical activity (PA) and sedentary time (ST) with vascular structure and function and clustered cardiovascular disease (CVD) risk factors in 4277 children (2,226 girls), aged 10.6±0.2 years. Cardiovascular outcomes included flow mediated dilation, distensibility coefficient, pulse wave velocity and a clustered CVD risk factor score. Time spent in light PA (LPA) and moderate to vigorous PA (MVPA) and ST were determined using accelerometers. Multiple linear regression analyses were adjusted for key covariates with LPA, MVPA and ST entered as compositional exposure variables. Neither LPA, MVPA or ST were significantly associated with the vascular outcomes. The proportion of time spent in MVPA and ST were inversely (unstandardised b=−0.126; P=0.001) and positively (b=0.136; P=0.016) associated with clustered CVD risk in the whole group analysis, respectively. MVPA was negatively associated with clustered CVD risk in boys (b=−0.144; P=0.011) and girls (b=−0.110; P=0.032). Only girls had a positive association between ST and clustered CVD risk (b=0.199; P=0.005). Although no associations were observed for PA and ST with vascular outcomes, these data provide further support for interventions that promote MVPA and minimise ST for reducing risk factors for CVD in children.
Learner stimulus intent: a framework for eye tracking data collection and feature extraction in computer programming education
Geo-spatial pattern and determinants of composite index of anthropometric failure among children under five in Ethiopia
Dexamethasone: a double-edged sword in the treatment of osteoarthritis
Abstract Glucocorticoids are widely used to manage osteoarthritis (OA) symptoms, but long-term safety concerns exist. This study investigates the therapeutic potential of dexamethasone (DEX) and triamcinolone acetonide (TA) in chondrocytes, evaluating their anti-inflammatory effects and potential detrimental actions. This study evaluated the effects of DEX and TA on the expression of pro-inflammatory genes in inflamed chondrocytes. In addition, the effects of DEX treatment on chondrocytes were analyzed using next-generation sequencing, high-resolution mass spectrometry, proliferation and metabolic rate, wound healing capacity and senescence-associated B-galactosidase assays. A single therapeutic dose of DEX (40nM) effectively reduced the expression of inflammatory genes in chondrocytes, while TA showed no such effect. DEX significantly reduced inflammation but also ECM production in inflamed chondrocytes. At 24 h, DEX treatment led to 168 differentially expressed genes (DEGs) compared to untreated inflamed cells, decreasing to 5 DEGs by 48 h, indicating a rapidly diminishing anti-inflammatory effect. Conversely, the difference between DEX-treated and healthy cells increased over time, from 666 DEGs at 24 h to 1317 DEGs at 48 h. Pathway analysis revealed potential disruptions in cell cycle, mitosis, and ECM homeostasis in DEX-treated cells compared to both healthy and inflamed controls. Interestingly, repeated DEX administration at both a therapeutic (40nM) and a high dose (1µM) induced senescence in healthy cells but not in inflamed cells. In contrast, repeated high-dose DEX reduced apoptosis marker Caspase 3/7 in inflamed but not healthy cells. Despite the transient suppression of inflammation achieved with DEX treatment, the observed decrease in ECM production and induction of senescence in healthy chondrocytes at therapeutic doses, along with apoptosis in inflamed cells at higher doses, underscore the need for caution in its intra-articular administration.
Combination of antiangiogenic and systemic therapy in advanced non-small cell lung cancer: before and after progression to leptomeningeal metastasis
The MacqD deep-learning-based model for automatic detection of socially housed laboratory macaques
Abstract Despite advancements in video-based behaviour analysis and detection models for various species, existing methods are suboptimal to detect macaques in complex laboratory environments. To address this gap, we present MacqD, a modified Mask R-CNN model incorporating a SWIN transformer backbone for enhanced attention-based feature extraction. MacqD robustly detects macaques in their home-cage under challenging scenarios, including occlusions, glass reflections, and overexposure to light. To evaluate MacqD and compare its performance against pre-existing macaque detection models, we collected and analysed video frames from 20 caged rhesus macaques at Newcastle University, UK. Our results demonstrate MacqD’s superiority, achieving a median F1-score of 99% for frames with a single macaque in the focal cage (surpassing the next-best model by 21%) and 90% for frames with two macaques. Generalisation tests on frames from a different set of macaques from the same animal facility yielded median F1-scores of 95% for frames with a single macaque (surpassing the next-best model by 15%) and 81% for frames with two macaques (surpassing the alternative approach by 39% ). Finally, MacqD was applied to videos of paired macaques from another facility and resulted in F1-score of 90%, reflecting its strong generalisation capacity. This study highlights MacqD’s effectiveness in accurately detecting macaques across diverse settings.