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Systematic mapping of climate and environmental framing experiments and re-analysis with computational methods points to omitted interaction bias
Get PDFChronic Liver Enzyme Elevation and Use of Contemporary ARVs Among People With HIV.
Get PDFBACKGROUND
While use of some older antiretroviral drugs (ARVs) is associated with chronic liver enzyme elevation (cLEE), the impact of newer ARVs remains unknown.
METHODS
People with HIV enrolled in the RESPOND cohort who started an ARV after January 1, 2012 were included (baseline). The primary outcome was first cLEE individuals were censored at first of cLEE, last visit, death, or December 31, 2021. Incidence rates (IRs; events/1000 person-years) were calculated for each ARV overall and by ARV exposure (6-12 months, 1-2 years, and 2+ years). Poisson regression was used to estimate the incidence rate ratio (IRR) of cLEE and its association with individual ARVs and ARV class.
RESULTS
Of 17 106 individuals included contributing 87 924 person-years of follow-up, 1932 (11.3%) experienced cLEE (incidence rate [IR], 22.0; 95% CI, 21.0-23.0). There was no evidence of a cumulative ARV effect on cLEE incidence, (6-12 months: IR, 45.8; 95% CI, 41.4-50.19; 1-2 years: IR, 34.3; 95% CI, 31.5-37.4; and 2+ years: IR, 18.5; 95% CI, 17.4-19.7). Any use (vs no prior use) of non-nucleoside reverse transcriptase inhibitors (NNRTIs) as a class and tenofovir disoproxil fumarate (TDF) was independently associated with an increased IRR of cLEE, and any use of darunavir (DRV) was associated with a decreased risk of cLEE.
CONCLUSIONS
cLEE is common and more frequent during the first year after initiating new ARVs. With a >5-year median follow-up, we found no short-term liver safety concerns with the use of INSTIs. Use of NNRTIs and TDF was associated with an increased cLEE risk, while DRV was associated with lower risk
Neuroinflammatory reactive astrocyte formation correlates with adverse outcomes in perinatal white matter injury.
Get PDFPerinatal white matter injury (WMI) is the leading cause of long-term neurological morbidity in infants born preterm. Neuroinflammation during a critical window of early brain development plays a key role in WMI disease pathogenesis. The mechanisms linking inflammation with the long-term myelination failure that characterizes WMI, however, remain unknown. Here, we investigate the role of astrocyte reactivity in WMI. In an experimental mouse model of WMI, we demonstrate that WMI disease outcomes are improved in mutant mice lacking secretion of inflammatory molecules TNF-α, IL-1α, and C1q known, in addition to other roles, to induce the formation of a neuroinflammatory reactive astrocyte substate. We show that astrocytes express molecular signatures of the neuroinflammatory reactive astrocyte substate in both our WMI mouse model and human tissue affected by WMI, and that this gene expression pattern is dampened in injured mutant mice. Our data provide evidence that a neuroinflammatory reactive astrocyte substate correlates with adverse WMI disease outcomes, thus highlighting the need for further investigation of these cells as potential causal players in WMI pathology
A highly potent, orally bioavailable pyrazole-derived cannabinoid CB2 receptor-selective full agonist for in vivo studies.
Get PDFThe cannabinoid CB2 receptor (CB2R) is a potential therapeutic target for distinct forms of tissue injury and inflammatory diseases. To thoroughly investigate the role of CB2R in pathophysiological conditions and for target validation in vivo, optimal pharmacological tool compounds are essential. Despite the sizable progress in the generation of potent and selective CB2R ligands, pharmacokinetic parameters are often neglected for in vivo studies. Here, we report the generation and characterization of a tetra-substituted pyrazole CB2R full agonist named RNB-61 with high potency (K i 0.13-1.81 nM, depending on species) and a peripherally restricted action due to P-glycoprotein mediated efflux from the brain. 3H and 14C labelled RNB-61 showed apparent K d values 6000-fold selectivity over CB1 receptors and negligible off-targets in vitro, combined with high oral bioavailability and suitable systemic pharmacokinetic (PK) properties, prompted the assessment of RNB-61 in a mouse ischemia-reperfusion model of acute kidney injury (AKI) and in a rat model of chronic kidney injury/inflammation and fibrosis (CKI) induced by unilateral ureteral obstruction. RNB-61 exerted dose-dependent nephroprotective and/or antifibrotic effects in the AKI/CKI models. Thus, RNB-61 is an optimal CB2R tool compound for preclinical in vivo studies with superior biophysical and PK properties over generally used CB2R ligands
Storage-induced mechanical changes of porcine lenses assessed with optical coherence elastography and inverse finite element modeling.
Get PDFINTRODUCTION
In an effort of gaining a better understanding of the lens mechanics, ex vivo lenses samples are often used. Yet, ex vivo tissue might undergo important postmortem changes depending on the unavoidable preservation method employed. The purpose of this study was to assess how various storage conditions and the removal of the lens capsule affect the mechanical properties of ex vivo porcine lens samples.
METHODS
A total of 81 freshly enucleated porcine eyes were obtained and divided into six groups and preserved differently. In the first three groups, the lens within the intact eye was preserved for 24 h by: (i) freezing at -80°C (n = 12), (ii) freezing at -20°C (n = 12), and (iii) refrigeration at +8°C (n = 12). In the remaining groups, the lenses were immediately extracted and treated as follows: (iv) kept intact, no storage (n = 12), (v) decapsulated, no storage (n = 21), and (vi) immersed in Minimum Essential Medium (MEM) at +8°C (n = 12) for 24 h. Frozen lenses were thawed at room temperature. Each lens was compressed between two glass lamella and subjected, first to a period of relaxation during which the compression force was recorded and second to an oscillating micro-compression while the deformation was recorded with a total of 256 subsequent B-scans via optical coherence tomography. The corresponding axial strain was retrieved via phase-sensitive image processing and subsequently used as input for an inverse finite element analysis (iFEA) to retrieve the visco-hyperelastic material properties of the lenses.
RESULTS
After freezing at temperatures of -80°C and -20°C, the cortical strains increased by 14% (p = 0.01) and 34% (p < 0.001), and the nuclear strains decreased by 17% (p = 0.014) and 36% (p < 0.001), compared to the lenses tested immediately after postmortem, respectively. According to iFEA, this resulted from an increased ratio of the nuclear: cortical E-modulus (4.06 and 7.06) in -80°C and -20°C frozen lenses compared to fresh lenses (3.3). Decapsulation had the largest effect on the material constant C10, showing an increase both in the nucleus and cortex. Preservation of the intact eye in the refrigerator induced the least mechanical alterations in the lens, compared to the intact fresh condition.
DISCUSSION
Combining iFEA with optical coherence elastography allowed us to identify important changes in the lens mechanics induced after different preserving ex vivo methods
Automatic quantification of scapular and glenoid morphology from CT scans using deep learning.
Get PDFOBJECTIVES
To develop and validate an open-source deep learning model for automatically quantifying scapular and glenoid morphology using CT images of normal subjects and patients with glenohumeral osteoarthritis.
MATERIALS AND METHODS
First, we used deep learning to segment the scapula from CT images and then to identify the location of 13 landmarks on the scapula, 9 of them to establish a coordinate system unaffected by osteoarthritis-related changes, and the remaining 4 landmarks on the glenoid cavity to determine the glenoid size and orientation in this scapular coordinate system. The glenoid version, glenoid inclination, critical shoulder angle, glenopolar angle, glenoid height, and glenoid width were subsequently measured in this coordinate system. A 5-fold cross-validation was performed to evaluate the performance of this approach on 60 normal/non-osteoarthritic and 56 pathological/osteoarthritic scapulae.
RESULTS
The Dice similarity coefficient between manual and automatic scapular segmentations exceeded 0.97 in both normal and pathological cases. The average error in automatic scapular and glenoid landmark positioning ranged between 1 and 2.5 mm and was comparable between the automatic method and human raters. The automatic method provided acceptable estimates of glenoid version (R2 = 0.95), glenoid inclination (R2 = 0.93), critical shoulder angle (R2 = 0.95), glenopolar angle (R2 = 0.90), glenoid height (R2 = 0.88) and width (R2 = 0.94). However, a significant difference was found for glenoid inclination between manual and automatic measurements (p < 0.001).
CONCLUSIONS
This open-source deep learning model enables the automatic quantification of scapular and glenoid morphology from CT scans of patients with glenohumeral osteoarthritis, with sufficient accuracy for clinical use
Comparison of baseline correction algorithms for in vivo 1H-MRS.
No full textProton MRS is used clinically to collect localized, quantitative metabolic data from living tissues. However, the presence of baselines in the spectra complicates accurate MRS data quantification. The occurrence of baselines is not specific to short-echo-time MRS data. In short-echo-time MRS, the baseline consists typically of a dominating macromolecular (MM) part, and can, depending on B0 shimming, poor voxel placement, and/or localization sequences, also contain broad water and lipid resonance components, indicated by broad components (BCs). In long-echo-time MRS, the MM part is usually much smaller, but BCs may still be present. The sum of MM and BCs is denoted by the baseline. Many algorithms have been proposed over the years to tackle these artefacts. A first approach is to identify the baseline itself in a preprocessing step, and a second approach is to model the baseline in the quantification of the MRS data themselves. This paper gives an overview of baseline handling algorithms and also proposes a new algorithm for baseline correction. A subset of suitable baseline removal algorithms were tested on in vivo MRSI data (semi-LASER at TE = 40 ms) and compared with the new algorithm. The baselines in all datasets were removed using the different methods and subsequently fitted using spectrIm-QMRS with a TDFDFit fitting model that contained only a metabolite basis set and lacked a baseline model. The same spectra were also fitted using a spectrIm-QMRS model that explicitly models the metabolites and the baseline of the spectrum. The quantification results of the latter quantification were regarded as ground truth. The fit quality number (FQN) was used to assess baseline removal effectiveness, and correlations between metabolite peak areas and ground truth models were also examined. The results show a competitive performance of our new proposed algorithm, underscoring its automatic approach and efficiency. Nevertheless, none of the tested baseline correction methods achieved FQNs as good as the ground truth model. All separately applied baseline correction methods introduce a bias in the observed metabolite peak areas. We conclude that all baseline correction methods tested, when applied as a separate preprocessing step, yield poorer FQNs and biased quantification results. While they may enhance visual display, they are not advisable for use before spectral fitting
Performance of the MRI lesion pattern score in predicting neurological outcome after out of hospital cardiac arrest: a retrospective cohort analysis.
Get PDFBACKGROUND
Despite advances in resuscitation practice, patient survival following cardiac arrest remains poor. The utilization of MRI in neurological outcome prognostication post-cardiac arrest is growing and various classifications has been proposed; however a consensus has yet to be established. MRI, though valuable, is resource-intensive, time-consuming, costly, and not universally available. This study aims to validate a MRI lesion pattern score in a cohort of out of hospital cardiac arrest patients at a tertiary referral hospital in Switzerland.
METHODS
This cohort study spanned twelve months from February 2021 to January 2022, encompassing all unconscious patients aged ≥ 18 years who experienced out-of-hospital cardiac arrest of any cause and were admitted to the intensive care unit (ICU) at Inselspital, University Hospital Bern, Switzerland. We included patients who underwent the neuroprognostication process, assessing the performance and validation of a MRI scoring system.
RESULTS
Over the twelve-month period, 137 patients were admitted to the ICU, with 52 entering the neuroprognostication process and 47 undergoing MRI analysis. Among the 35 MRIs indicating severe hypoxic brain injury, 33 patients (94%) experienced an unfavourable outcome (UO), while ten (83%) of the twelve patients with no or minimal MRI lesions had a favourable outcome. This yielded a sensitivity of 0.94 and specificity of 0.83 for predicting UO with the proposed MRI scoring system. The positive and negative likelihood ratios were 5.53 and 0.07, respectively, resulting in an accuracy of 91.49%.
CONCLUSION
We demonstrated the effectiveness of the MLP scoring scheme in predicting neurological outcome in patients following cardiac arrest. However, to ensure a comprehensive neuroprognostication, MRI results need to be combined with other assessments. While neuroimaging is a promising objective tool for neuroprognostication, given the absence of sedation-related confounders-compared to electroencephalogram (EEG) and clinical examination-the current lack of a validated scoring system necessitates further studies. Incorporating standardized MRI techniques and grading systems is crucial for advancing the reliability of neuroimaging for neuroprognostication.
TRIAL REGISTRATION
Registry of all Projects in Switzerland (RAPS) 2020-01761
Carbon-phosphorus cycle models overestimate CO2 enrichment response in a mature Eucalyptus forest.
Get PDFThe importance of phosphorus (P) in regulating ecosystem responses to climate change has fostered P-cycle implementation in land surface models, but their CO2 effects predictions have not been evaluated against measurements. Here, we perform a data-driven model evaluation where simulations of eight widely used P-enabled models were confronted with observations from a long-term free-air CO2 enrichment experiment in a mature, P-limited Eucalyptus forest. We show that most models predicted the correct sign and magnitude of the CO2 effect on ecosystem carbon (C) sequestration, but they generally overestimated the effects on plant C uptake and growth. We identify leaf-to-canopy scaling of photosynthesis, plant tissue stoichiometry, plant belowground C allocation, and the subsequent consequences for plant-microbial interaction as key areas in which models of ecosystem C-P interaction can be improved. Together, this data-model intercomparison reveals data-driven insights into the performance and functionality of P-enabled models and adds to the existing evidence that the global CO2-driven carbon sink is overestimated by models
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