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Clinical Kidney Journal Sep 2023Glomerular filtration rate (GFR) is estimated in clinical practice from equations based on the serum concentration of endogenous biomarkers and demographic data. The... (Review)
Review
Glomerular filtration rate (GFR) is estimated in clinical practice from equations based on the serum concentration of endogenous biomarkers and demographic data. The 2009 creatinine-based Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPI) was recommended worldwide until 2021, when it was recalibrated to remove the African-American race factor. The CKD-EPI and CKD-EPI equations overestimate GFR of adults aged 18-30 years, with a strong overestimation in estimated GFR (eGFR) at age 18 years. CKD-EPI does not perform better than CKD-EPI in US population, overestimating GFR in non-Black subjects, and underestimating it in Black subjects with the same magnitude. CKD-EPI performed worse than the CKD-EPI in White Europeans, and provides no or limited performance gains in Black European and Black African populations. The European Kidney Function Consortium (EKFC) equation, which incorporates median normal value of serum creatinine in healthy population, overcomes the limitations of the CKD-EPI equations: it provides a continuity of eGFR at the transition between pediatric and adult care, and performs reasonably well in diverse populations, assuming dedicated scaling of serum creatinine (Q) values is used. The new EKFC equation based on cystatin C (EKFC) shares the same mathematical construction, namely, it incorporates the median cystatin C value in the general population, which is independent of sex and ethnicity. EKFC is therefore a sex-free and race-free equation, which performs better than the CKD-EPI equation based on cystatin C. Despite advances in the field of GFR estimation, no equation is perfectly accurate, and GFR measurement by exogenous tracer clearance is still required in specific populations and/or specific clinical situations.
PubMed: 37664574
DOI: 10.1093/ckj/sfad039 -
Memory & Cognition Apr 2024To acquire and process information, performers can frequently rely on both internal and extended cognitive strategies. However, after becoming acquainted with two...
To acquire and process information, performers can frequently rely on both internal and extended cognitive strategies. However, after becoming acquainted with two strategies, performers in previous studies exhibited a pronounced behavioral preference for just one strategy, which we refer to as perseveration. What is the origin of such perseveration? Previous research suggests that a prime reason for cognitive strategy choice is performance: Perseveration could reflect the preference for a superior strategy as determined by accurately monitoring each strategy's performance. However, following our preregistered hypotheses, we conjectured that perseveration persisted even if the available strategies featured similar performances. Such persisting perseveration could be reasonable if costs related to decision making, performance monitoring, and strategy switching would be additionally taken into account on top of isolated strategy performances. Here, we used a calibration procedure to equalize performances of strategies as far as possible and tested whether perseveration persisted. In Experiment 1, performance adjustment of strategies succeeded in equating accuracy but not speed. Many participants perseverated on the faster strategy. In Experiment 2, calibration succeeded regarding both accuracy and speed. No substantial perseveration was detected, and residual perseveration was conceivably related to metacognitive performance evaluations. We conclude that perseveration on cognitive strategies is frequently rooted in performance: Performers willingly use multiple strategies for the same task if performance differences appear sufficiently small. Surprisingly, other possible reasons for perseveration like effort or switch cost avoidance, mental challenge seeking, satisficing, or episodic retrieval of previous stimulus-strategy-bindings, were less relevant in the present study.
Topics: Humans; Cognition
PubMed: 37874485
DOI: 10.3758/s13421-023-01475-7 -
Biomolecules Aug 2023Computational prediction of cell-cell interactions (CCIs) is becoming increasingly important for understanding disease development and progression. We present a... (Comparative Study)
Comparative Study
Computational prediction of cell-cell interactions (CCIs) is becoming increasingly important for understanding disease development and progression. We present a benchmark study of available CCI prediction tools based on single-cell RNA sequencing (scRNA-seq) data. By comparing prediction outputs with a manually curated gold standard for idiopathic pulmonary fibrosis (IPF), we evaluated prediction performance and processing time of several CCI prediction tools, including CCInx, CellChat, CellPhoneDB, iTALK, NATMI, scMLnet, SingleCellSignalR, and an ensemble of tools. According to our results, CellPhoneDB and NATMI are the best performer CCI prediction tools, among the ones analyzed, when we define a CCI as a source-target-ligand-receptor tetrad. In addition, we recommend specific tools according to different types of research projects and discuss the possible future paths in the field.
Topics: Humans; Benchmarking; Cell Communication; Idiopathic Pulmonary Fibrosis; Single-Cell Gene Expression Analysis
PubMed: 37627276
DOI: 10.3390/biom13081211 -
Journal of Environmental Management Jul 2023Resource depletion and environmental pollution are increasingly a matter of concern for their adverse effects on ecosystems, human health, and the economy. Circular... (Review)
Review
Resource depletion and environmental pollution are increasingly a matter of concern for their adverse effects on ecosystems, human health, and the economy. Circular Economy (CE) practices can help us address these challenges. This paper proposes a composite circularity index (CI) to assess the level of implementation of CE practices. The main advantage of the proposed index is its ability to combine multiple indicators of circularity for different units operating in a given sector (given as inputs), using a 'Benefit of the Doubt' model. This new model is innovative in the manner it deals with ordinal scales and also by considering both relative and absolute performance indices. These indices are computed using mathematical programming tools, building on ideas from Data Envelopment Analysis models. Although the model can be applied to any sector, this work addresses the hotel industry in particular. The selection of indicators for this CI was based on seven blocks of the Circular Economy Action Plan and a literature review of circular practices. An application of the proposed index is performed by using data from Portuguese and Spanish hotels. The proposed CI allows the identification of the organizations with the best and worst performance in implementing the CE practices and clarifying the benchmarks they could follow to improve their level of circularity. Moreover, the index analysis also provides specific targets for improvement, indicating which circular practices should be improved for the lower performers to reach the implementation levels of the best performers.
Topics: Humans; Ecosystem; Industry; Environmental Pollution
PubMed: 36966633
DOI: 10.1016/j.jenvman.2023.117752 -
Scientific Reports Oct 2023Declining physical performance with age and disease is an important indicator of declining health. Biomarkers that identify declining physical performance would be...
Declining physical performance with age and disease is an important indicator of declining health. Biomarkers that identify declining physical performance would be useful in predicting treatment outcomes and identifying potential therapeutics. γ-aminobutyric acid (GABA), a muscle autocrine factor, is a potent inhibitor of muscle function and works as a muscle relaxant. L-α-aminobutyric acid (L-AABA) is a biomarker for malnutrition, liver damage, and depression. We sought to determine if GABA and L-AABA may be useful for predicting physical performance. Serum levels of GABA and L-AABA were quantified in 120 individuals divided by age, sex, and physical capacity into low, average, and high performer groups. Analyses explored correlations between serum levels and physical performance. Both GABA and the ratio of GABA/AABA (G/A), but not AABA, were highly positively associated with age (Pearson correlations r = 0.35, p = 0.0001 for GABA, r = 0.31, p = 0.0007 for G/A, n = 120). GABA showed negative associations in the whole cohort with physical performance [fast gait speed, 6 min walk test (6MWT), PROMIS score, and SF36PFS raw score] and with subtotal and femoral neck bone mineral density. L-AABA was positively associated with usual gait speed, 6MWT, total SPPB score, and SF36PFS raw score in the total cohort of 120 human subjects, also with 6MWT and SF36PFS raw score in the 60 male subjects, but no associations were observed in the 60 females. As both GABA and L-AABA appear to be indicative of physical performance, but in opposite directions, we examined the G/A ratio. Unlike GABA, the G/A ratio showed a more distinct association with mobility tests such as total SPPB score, usual and fast gait speed, 6MWT, and SF36PFS raw score in the males, regardless of age and metabolic status. Serum G/A ratio could be potentially linked to physical performance in the male population. Our findings strongly suggest that GABA, L-AABA, and the G/A ratio in human serum may be useful markers for both age and physical function. These new biomarkers may significantly enhance the goal of identifying universal biomarkers to accurately predict physical performance and the beneficial effects of exercise training for older adults.
Topics: Female; Humans; Male; Aged; gamma-Aminobutyric Acid; Aminobutyrates; Physical Functional Performance; Aging; Biomarkers
PubMed: 37816783
DOI: 10.1038/s41598-023-41628-x -
PloS One 2023Haze is a typical weather phenomena that has a significant negative impact on transportation safety, particularly in the port, highways, and airport runway areas. A...
Haze is a typical weather phenomena that has a significant negative impact on transportation safety, particularly in the port, highways, and airport runway areas. A multi-scale U-shaped dehazing network is proposed in this research, which is based on our multi-channel feature fusion attention structure. With the help of the feature fusion attention techniques, the model can focus on the intriguing locations with higher haze concentration area. In conjunction with UNet, it can achieve multi-scale feature reuse and residual learning, allowing it to fully utilize the feature information of each layer for image restoration. Experimental resulsts show that our technique performs well on a variety of test datasets. On highway data sets, the PSNR / SSIM / L∞ error performance over the novel technique is increased by 0.52% / 0.5% / 30.84%, 4.68% / 0.78% / 26.19% and 13.84% / 9.05% / 55.57% respectively, when compared to DehazeFormer, MIRNetv2, and FSDGN methods. The findings suggest that our proposed method performs better on image dehazing, especially in terms of L∞ error performance.
Topics: Airports; Learning; Transportation; Weather
PubMed: 37578956
DOI: 10.1371/journal.pone.0286711 -
Frontiers in Neuroscience 2023A spiking neural network (SNN) is a bottom-up tool used to describe information processing in brain microcircuits. It is becoming a crucial neuromorphic computational...
A spiking neural network (SNN) is a bottom-up tool used to describe information processing in brain microcircuits. It is becoming a crucial neuromorphic computational model. Spike-timing-dependent plasticity (STDP) is an unsupervised brain-like learning rule implemented in many SNNs and neuromorphic chips. However, a significant performance gap exists between ideal model simulation and neuromorphic implementation. The performance of STDP learning in neuromorphic chips deteriorates because the resolution of synaptic efficacy in such chips is generally restricted to 6 bits or less, whereas simulations employ the entire 64-bit floating-point precision available on digital computers. Previously, we introduced a bio-inspired learning rule named adaptive STDP and demonstrated numerical simulation that adaptive STDP (using only 4-bit fixed-point synaptic efficacy) performs similarly to STDP learning (using 64-bit floating-point precision) in a noisy spike pattern detection model. Herein, we present the experimental results demonstrating the performance of adaptive STDP learning. To the best of our knowledge, this is the first study that demonstrates unsupervised noisy spatiotemporal spike pattern detection to perform well and maintain the simulation performance on a mixed-signal CMOS neuromorphic chip with low-resolution synaptic efficacy. The chip was designed in Taiwan Semiconductor Manufacturing Company (TSMC) 250 nm CMOS technology node and comprises a soma circuit and 256 synapse circuits along with their learning circuitry.
PubMed: 37521704
DOI: 10.3389/fnins.2023.1203956 -
The Hastings Center Report Sep 2023Building trust between academic medical centers and certain communities they depend on in the research process is hard, particularly when those communities consist of...
Building trust between academic medical centers and certain communities they depend on in the research process is hard, particularly when those communities consist of minoritized or historically marginalized populations. Some believe that engagement activities like the creation of advisory boards, town halls, or a research workforce that looks more like community members will establish or reestablish trust between academic medical centers and racialized communities. However, without systematic approaches to dismantle racism, those well-intended actions become public performativity, and trust building will fail. In this essay, we draw upon foundational ethical principles of trust, distrust, and trust building; apply the concept of bounded justice to performative trust acts; and center the works of Black and Indigenous feminist bioethicists to revisit some of the wisdom and valuable lessons they have contributed. Rebuilding trust is hard to do because people and institutions are often not honest about how hard it is and there is no simple box-checking task that can disentangle our society's injustices, but there are steps to take in this direction. Individuals and institutions can recognize valuable relevant work that has already been written, partake in critical reflection, and then apply insights gained to take both small and sustainable steps toward transformational change and deeper trust.
Topics: Humans; Trust; Racism; Ethicists
PubMed: 37963054
DOI: 10.1002/hast.1527 -
Proceedings. Biological Sciences Nov 2023Theories of innovation often balance contrasting views that either smart people create smart things or smartly constructed institutions create smart things. While...
Theories of innovation often balance contrasting views that either smart people create smart things or smartly constructed institutions create smart things. While population models have shown factors including population size, connectivity and agent behaviour as crucial for innovation, few have taken the individual-central approach seriously by examining the role individuals play within their groups. To explore how network structures influence not only population-level innovation but also performance among individuals, we studied an agent-based model of the Potions Task, a paradigm developed to test how structure affects a group's ability to solve a difficult exploration task. We explore how size, connectivity and rates of information sharing in a network influence innovation and how these have an impact on the emergence of inequality in terms of agent contributions. We find, in line with prior work, that population size has a positive effect on innovation, but also find that large and small populations perform similarly ; that many small groups outperform fewer large groups; that random changes to structure have few effects on innovation in the task; and that the highest performing agents tend to occupy more central positions in the network. Moreover, we show that every network factor which improves innovation leads to a proportional increase in inequality of performance in the network, creating 'genius effects' among otherwise 'dumb' agents in both idealized and real-world networks.
PubMed: 37989247
DOI: 10.1098/rspb.2023.2281