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Machine learning derived retinal pigment score from ophthalmic imaging shows ethnicity is not biology
AbstractFew metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as a surrogate marker for biological variability. We derived a continuous, measured metric, the retinal pigment score (RPS), that quantifies the degree of pigmentation from a colour fundus photograph of the eye. RPS was validated using two large epidemiological studies with demographic and genetic data (UK Biobank and EPIC-Norfolk Study) and reproduced in a Tanzanian, an Australian, and a Chinese dataset. A genome-wide association study (GWAS) of RPS from UK Biobank identified 20 loci with known associations with skin, iris and hair pigmentation, of which eight were replicated in the EPIC-Norfolk cohort. There was a strong association between RPS and ethnicity, however, there was substantial overlap between each ethnicity and the respective distributions of RPS scores. RPS decouples traditional demographic variables from clinical imaging characteristics. RPS may serve as a useful metric to quantify the diversity of the training, validation, and testing datasets used in the development of AI algorithms to ensure adequate inclusion and explainability of the model performance, critical in evaluating all currently deployed AI models. The code to derive RPS is publicly available at: https://github.com/uw-biomedical-ml/retinal-pigmentation-score.
Predicting prosthetic gait and the effects of induced stiff-knee gait
Prosthetic gait differs considerably from the unimpaired gait. Studying alterations in the gait patterns could help to understand different adaptation mechanisms adopted by these populations. This study investigated the effects of induced stiff-knee gait (SKG) on prosthetic and healthy gait patterns and the capabilities of predictive simulation. Self-selected speed gait of two participants was measured: one healthy subject and one knee disarticulation subject using a variable-damping microprocessor controlled knee prosthesis. Both performed unperturbed gait and gait with restricted knee flexion. Experimental joint angles and moments were computed using OpenSim and muscle activity was measured using surface electromyography (EMG). The differences between the conditions were analyzed using statistical parametric mapping (SPM). Predictive models based on optimal control were created to represent the participants. Additionally, a hypothetical unimpaired predictive model with the same anthropometric characteristics as the amputee was created. Some patterns observed in the experimental prosthetic gait were predicted by the models, including increased knee flexion moment on the contralateral side caused by SKG in both participants, which was statistically significant according to SPM. With the exception of the rectus femoris muscle, we also found overall good agreement between measured EMG and predicted muscle activation. We predicted more alterations in activation of the hip flexors than other muscle groups due to the amputation and in the activation of the biceps femoris short head, quadratus femoris, and tibialis anterior due to SKG. In summary, we demonstrated that the method applied in this study could predict gait alterations due to amputation of the lower limb or due to imposed SKG.
Chemotherapeutic hormesis induced by the tumor microenvironment in refractory ovarian cancer
Ironing out differences in attenuation and blooming artifact in acute stroke thrombi
AbstractThis study aims to improve our understanding of acute ischemic stroke clot imaging by integrating CT attenuation information with MRI susceptibility signal of thrombi. For this proof-of-principle experimental study, fifty-seven clot analogs were produced using ovine venous blood with a broad histological spectrum. Each clot analog was analyzed to determine its RBC content and chemical composition, including water, Fe III, sodium, pH, and pO2. Non-contrast CT and a susceptibility-weighted MRI sequence were used for imaging. The study found that RBC content correlated more accurately than iron content with clot attenuation on CT. There was a strong correlation between Fe III content and RBC percentage in clots. Specifically, changes in RBC content accounted for 64% of the variance in Fe III content (R2 = 0.640; p < .0001). Thrombi with blooming artifacts (BA) displayed higher attenuation on non-contrast CT than those without (73.4 vs. 40 HU, p < .0001) and had the highest RBC and iron contents. The cut-off value of 1242 µg/g of iron predicted blooming artifacts with high sensitivity and specificity. The pH level strongly affected the appearance of blooming artifacts, particularly for negative clots with high RBC content. These findings provide significant insights into the imaging behavior of acute ischemic stroke clots across both imaging modalities and could potentially improve the diagnosis and treatment of acute stroke patients. Furthermore, these results open the possibility for future research aimed at developing pH-modulated therapeutic strategies based on the acid-base state of thrombi.
Mechanisms of concentration control alkali activated fly ash stabilized saline soil in seasonally frozen regions
Limitations of ice cores in reconstructing temperature seasonality
MCM2-7 ring closure involves the Mcm5 C-terminus and triggers Mcm4 ATP hydrolysis
AbstractThe eukaryotic helicase MCM2-7, is loaded by ORC, Cdc6 and Cdt1 as a double-hexamer onto replication origins. The insertion of DNA into the helicase leads to partial MCM2-7 ring closure, while ATP hydrolysis is essential for consecutive steps in pre-replicative complex (pre-RC) assembly. Currently it is unknown how MCM2-7 ring closure and ATP-hydrolysis are controlled. A cryo-EM structure of an ORC-Cdc6-Cdt1-MCM2-7 intermediate shows a remodelled, fully-closed Mcm2/Mcm5 interface. The Mcm5 C-terminus (C5) contacts Orc3 and specifically recognises this closed ring. Interestingly, we found that normal helicase loading triggers Mcm4 ATP-hydrolysis, which in turn leads to reorganisation of the MCM2-7 complex and Cdt1 release. However, defective MCM2-7 ring closure, due to mutations at the Mcm2/Mcm5 interface, leads to MCM2-7 ring splitting and complex disassembly. As such we identify Mcm4 as the key ATPase in regulating pre-RC formation. Crucially, a stable Mcm2/Mcm5 interface is essential for productive ATP-hydrolysis-dependent remodelling of the helicase.
ATLAS-seq: a microfluidic single-cell TCR screen for antigen-reactive TCRs
Gut microbiota dysbiosis, sarcopenia, osteoporosis and osteosarcopenia in older people: A systematic review protocol
Introduction Sarcopenia and Osteoporosis are two prevalent conditions in the older population and are defined by low strength, muscle quality/volume and low Bone Mineral Density, respectively. When there is a concomitant presence of both, there is a novel musculoskeletal condition called Osteosarcopenia. These conditions adversely affect quality of life and elevate the risk of fractures, disability, and mortality among older individuals. Dysbiosis of the gut microbiota is the impairment of the mutualistic relationship between microorganisms, metabolic products and the host’s immune system. Gut microbiota dysbiosis could be intricately linked to sarcopenia and osteoporosis, shedding light on the complex microbiota-gut-bone-muscle axis. Furthermore, the intestinal microbiota experiences a notable decline in beneficial microorganisms as part of the aging process. The relationship between dysbiosis of the intestinal microbiota in older people and sarcopenia, osteoporosis or osteosarcopenia is still unclear. This review protocol aims to systematically review the literature and compile evidence on the influence of gut microbiota dysbiosis on musculoskeletal function in older people with sarcopenia, osteoporosis or osteosarcopenia. Methods/Analysis This systematic review will analyze observational studies that have investigated the relationship between the effects of gut microbiota dysbiosis and sarcopenia, osteoporosis and osteosarcopenia in older people aged 65 and over. Studies will be retrieved from PubMed/MEDLINE, EMBASE, Scopus, Web of Science and the Cochrane Library. Outcome measures will include body composition for diagnosing osteoporosis and screening for sarcopenia/osteosarcopenia by any criteria. Data synthesis will involve quantitative analysis using summary measures. If sufficient studies, homogeneity and heterogeneity analysis will be performed to conduct Meta-analysis and pooled OR, RR and HR measures will be provided.
Adaptive hip exoskeleton control using heart rate feedback reduces oxygen cost during ecological locomotion
Abstract Despite their potential, exoskeletons have not reached widespread adoption in daily life, partly due to the challenge of seamlessly adapting assistance across various tasks and environments. Task-specific designs, reliance on complex sensing and extensive data-driven training often limit the practicality of the existing control strategies. To address this challenge, we introduce an adaptive control strategy for hip exoskeletons, emphasizing minimal sensing and ease of implementation. Using only insole pressure and heart rate (HR) sensing, the controller modulates assistance across various locomotor tasks. We evaluated this strategy with twelve able-bodied participants in a real-world scenario including level walking, stairs, and inclines. The controller successfully adapted assistance timing and amplitude to different activities. This resulted in effort intensity reductions (measured by oxygen uptake) of up to 12.6% compared to walking with no exoskeleton, and up to 25.5% compared to walking with the exoskeleton in zero-torque mode. Cardiodynamic response of HR, although delayed, proved sufficient for adaptation in tasks lasting longer than around 45 s, and delay-induced limitations primarily affected brief bouts of abrupt change in intensity. However, we found discernible patterns in HR shortly after the onset of such changes that can be exploited to improve responsiveness. Our findings underscore the potential of HR as a promising measure of user effort intensity, encouraging future research to explore its integration into advanced adaptive algorithms.
Two-hit mouse model of heart failure with preserved ejection fraction combining diet-induced obesity and renin-mediated hypertension
Prediction of heavy metal spatial distribution in soils of typical industrial zones utilizing 3D convolutional neural networks
E4F1 coordinates pyruvate metabolism and the activity of the elongator complex to ensure translation fidelity during brain development
AbstractPyruvate metabolism defects lead to severe neuropathies such as the Leigh syndrome (LS) but the molecular mechanisms underlying neuronal cell death remain poorly understood. Here, we unravel a connection between pyruvate metabolism and the regulation of the epitranscriptome that plays an essential role during brain development. Using genetically engineered mouse model and primary neuronal cells, we identify the transcription factor E4F1 as a key coordinator of AcetylCoenzyme A (AcCoA) production by the pyruvate dehydrogenase complex (PDC) and its utilization as an essential co-factor by the Elongator complex to acetylate tRNAs at the wobble position uridine 34 (U34). E4F1-mediated direct transcriptional regulation of Dlat and Elp3, two genes encoding key subunits of the PDC and of the Elongator complex, respectively, ensures proper translation fidelity and cell survival in the central nervous system (CNS) during mouse embryonic development. Furthermore, analysis of PDH-deficient cells highlight a crosstalk linking the PDC to ELP3 expression that is perturbed in LS patients.
Sticky organisms create underwater biological adhesives driven by interactions between EGF- and GlcNAc- containing polysaccharides
Block selection in multiblock partial least squares for modeling genotype-phenotype relations in Saccharomyces
In data-based modeling, correlations between explanatory variables often lead to the formation of distinct gene blocks. This study focuses on identifying influential gene blocks and key variables within these blocks, with a particular application in mind: genotype-phenotype mapping in Saccharomyces. To overcome the challenges of a limited sample size, we use partial least squares (PLS). These gene blocks, which consist of combinations of genes, play a critical role in explaining phenotypic variations. Using partial least squares with multiple blocks, we propose a novel approach, weighted block importance on projection in partial least squares (BwIP-mbPLS), to identify influential gene blocks. Variable importance on projection is used to select significant genes within these blocks. Our study models copper chloride at 0.375mM and melibiose at 2% efficiency and rate in Saccharomyces cerevisiae yeast. Analysis based on silhouette index and total distance within clusters using k-means shows the classification of 5629 genes into 18 gene blocks. Remarkably, BwIP-mbPLS identifies 4 gene blocks on average and significantly improves the prediction of efficiency-based phenotypes. In contrast, traditional block importance in partial least squares projection identifies 6 gene blocks on average and shows comparable or better performance than BIP-mbPLS for rate-based phenotypes. Remarkably, most gene blocks contain fewer than 10 influential genes. Both proposed variants consistently outperform conventional approaches such as partial least squares and multi-block partial least squares in predicting phenotypes. These results highlight the potential of our methods for advancing data-based modeling and genotype-phenotype mapping.