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Guiding ethical principles in transplant candidate selection committees: A scoping review protocol
Objective The goal of this scoping review is to examine the published literature regarding ethical principles that guide solid-organ transplant candidate selection committees. The review will identify the available research and provide recommendations to selection committees. Introduction Transplantation is a field where ethics is involved with almost every juncture of treatment. Organ selection, pathways to donation, organ allocation, and recipient selection are just a few of the myriads of processes that require ethical thoughtfulness. However, the ethical insights involved in the decision-making during these processes are not well elucidated in the literature. An application of an ethical framework(s) to this process could substantially improve the equality of the process. Inclusion criteria This review will include studies focused on transplant candidate selection committees and/or the transplant candidate selection process. It will include full papers and abstracts published in English. Methods PubMED, EMBASE, APA PsycInfo, SCOPUS, and CINAHL databases will be used for the literature search. Only articles published in English between the years 1960 and 2024 will be considered. The review will consist of title/abstract screening followed by full-text screening to determine which articles meet inclusion criteria. Each article will be reviewed by two independent reviewers, with a third stepping in to resolve conflicts in decision. After all articles have been screened, data will be extracted in duplicate from the selected included articles using a template in Covidence created by the study team. Results will then be summarized and presented in narrative summary. Discussion An examination of the transplant selection process is due and warranted as selection carries significant weight in determining who receives an organ and, indeed, the stakes are life and death. Yet few studies exist on transplant selection. Ultimately, the problem with selection is how committees can further improve their promise to patients: are they making the right decision and how they define “the right decision.” Thus, it is critical to review selection committees for systematic bias and discrimination, especially in a process that depends heavily on individuals utilizing judgment-based reasoning, in order to improve selection and rid it of historic biases. This way, the transplant candidate selection process can foster fairness, thereby instilling increased public trust.
Cost-effectiveness analysis of MASLD screening using FIB-4 based two-step algorithm in the medical check-up
A study of gene expression programming algorithm for dynamically adjusting the parameters of genetic operators
The fast developments in artificial intelligence together with evolutionary algorithms have not solved all the difficulties that Gene Expression Programming (GEP) encounters when maintaining population diversity and preventing premature convergence. Its restrictions block GEP from successfully handling high-dimensional along with complex optimization problems. This research develops Dynamic Gene Expression Programming (DGEP) as an algorithm to control genetic operators dynamically thus achieving improved global search with increased population diversity. The approach operates with two unique operators which include Adaptive Regeneration Operator (DGEP-R) and Dynamically Adjusted Mutation Operator (DGEP-M) to preserve diversity while maintaining exploration-exploitation balance during evolutionary search. An extensive evaluation of DGEP occurred through symbolic regression problem tests. The study employed traditional benchmark functions and conducted evaluations versus baselines Standard GEP, NMO-SARA, and MS-GEP-A to assess fitness outcomes, R² values, population diversification, and the avoidance of local optima. All key metric evaluations showed that DGEP beat standard GEP along with alternative improved variants. DGEP produced the optimal results for 8 benchmark functions that produced 15.7% better R² scores along with 2.3 × larger population diversity. The escape rate from local optima within DGEP reached 35% higher than what standard GEP could achieve. The DGEP model serves to enhance GEP performance through the effective maintenance of diversity and improved global search functions. The results indicate that adaptive genetic methods strengthen evolutionary procedures for solving complex problems effectively.
An optimized deterministic concurrency control approach for geo-distributed transaction processing on permissioned blockchains
The relationship between person-vocation fit and burnout among resident physicians: A single-center cross-sectional survey
Background Burnout among resident physicians affects their physical and mental health as well as the quality of healthcare. Person-Vocation Fit (P-V Fit) may play a key role in preventing burnout. This study aims to explore the relationship between P-V Fit and burnout among resident physicians. Method A cross-sectional survey was conducted, including demographic information, work factors, burnout assessment using the Maslach Burnout Inventory-Human Services Survey (MBI-HSS), and the Person-Vocation Fit (P-V Fit) scale. Data were analyzed using logistic and linear regression with SPSS-27. Results 636 resident physicians (response rate 80%) participated in the survey. The mean age was 25.6 ± 2.3 years, 52.5% were male, 90.6% were unmarried, and 55.7% had a bachelor’s degree. The overall burnout rate was 46.07%, with mean scores for emotional exhaustion, depersonalization, and personal accomplishment of 30.5 ± 11.9, 12.9 ± 7.4, and 28.5 ± 9.6, respectively. For each one-point increase in the P-V Fit score, the overall likelihood of burnout decreased by 19% (OR, 0.81;95%CI, 0.76–0.87;P < 0.001), emotional exhaustion decreased by 0.85 points (95%CI, -1.10 to -0.59), depersonalization decreased by 0.55 points (95%CI, -0.73 to -0.38), and personal accomplishment increased by 1.34 points (95%CI, 1.12–1.56)(all P < 0.001). For each additional hour of work, the risk of burnout increased by 33% (OR, 1.33;95%CI, 1.12–1.59;P = 0.001). Conclusion Improving Person-Vocation Fit is an effective strategy for preventing burnout among resident physicians. Residency training should focus on vocational compatibility assessment, develop differentiated support strategies, and arrange reasonable workloads.
Genome-wide analysis provides insight into the genetic diversity and adaptability of Kazakhstan local goats
Abstract To understand the genetic structure of local goat populations in Kazakhstan, we present the first comprehensive genomic analysis using a 70 K SNP array, examining 120 samples from six regions. The populations (Kundyzdy, Darbaza, Shokpar, Ushterek, Kenes and Kosseit) are named based on their breeding regions and are referred to ecotypes. Our results present a high genetic diversity, with observed heterozygosity (Ho) ranging from 0.389 to 0.444. Shokpar and Kundyzdy showed excess heterozygosity, while Kosseit and Darbaza had high inbreeding levels. Most populations recorded weak genetic differentiation, except for Ushterek goats, which were more distinct. For adaptability, Runs of Homozygosity (ROH) and Integrated Haplotype Score (iHS) identified 71 genes under selection, with gene ontology enrichment indicating localization as the most represented biological pathway important for cell response. Additionally, over 60% of these genes are co-expressed, suggesting their significant role in survival of these goats in their local environments. Some identified genes, such as NLRC4, HCLS1, IL17D, IL17RE, and IL17RC, were linked to immune response, an important adaptation trait in the context of climate change. This study not only emphasizes on high genetic variability of local goats in Kazakhstan but also lays the groundwork for developing conservation and breeding programs for local goats in Kazakhstan and other similar agroecological regions for adaptability and resilience under changing environmental conditions.
Multi-view clustering via global-view graph learning
Multiview clustering aims to improve clustering performance by exploring multiple representations of data and has become an important research direction. Meanwhile, graph-based methods have been extensively studied and have shown promising performance in multiview clustering tasks. However, most existing graph-based multiview clustering methods rely on assigning appropriate weights to each view based on its importance, with the clustering results depending on these weight assignments. In this paper, we propose an a novel multiview spectral clustering framework with reduced computational complexity that captures complementary information across views by optimizing a global-view graph using adaptive weight learning. Additionally, in our method, once the Global-view Graph is obtained, cluster labels can be directly assigned to each data point without the need for any post-processing, such as the K-means required in standard spectral clustering. Our method not only improves clustering performance but also reduces computational resource consumption. Experimental results on real-world datasets demonstrate the effectiveness of our approach.
Polylepis wood acclimation strategies to ENSO events
A new audio cue to object weight resembles a naturalistic weight cue during movement planning but not during weight illusions
When a person picks up an object, naturalistic cues inform fine motor planning that is reflected in early spikes in force rate changes. Naturalistic cues to weight can also create an illusion whereby a signal to being heavier leads to the object being perceived as lighter – for example, the size-weight illusion. The present study asked to what extent an arbitrary new auditory cue, one that signals object weight, participates in these effects. In Experiment 1, participants used the new signal to adjust both their peak grip force rates and peak load force rates while lifting an object, consistent with using it for efficient motor planning. This matched how they used a naturalistic visual size cue. In Experiment 2, a new audio cue to heavier weight led to a heavier reported weight – the opposite of a size-weight illusion, and opposite to how the same participants used a naturalistic visual size cue. Thus, while the newly learned audio-weight mapping had similar functional properties to its more familiar perceptual counterpart, it did not show the same signature of automatic processing. These results have implications for understanding the flexible use of new cues and for targeting the underlying mechanisms in order to augment human abilities.
The human milk microbiome is minimally associated with breastfeeding practices
Abstract The human milk microbiome is dominated by typical oral and skin bacteria, suggesting that bacterial communities from the infant mouth and maternal skin contribute to the development of the human milk microbiome. It is postulated that breastfeeding characteristics, such as breastfeeding frequency and duration, could lead to different levels of exposure to oral and skin bacteria, and subsequently, altered bacterial profiles in human milk. To investigate the associations between breastfeeding characteristics and the human milk microbiome, this study analysed milk samples collected from 56 participants at 3 months postpartum by full-length 16 S rRNA gene sequencing. Breastfeeding characteristics (breastfeeding frequency, total 24 h breastfeeding duration, and 24 h milk removal volume) were recorded by the participants using the 24 h test weighing protocol. The milk microbiome was largely robust to breastfeeding practices, with only one association detected between breastfeeding characteristics and the milk microbiome. Duration of breastfeeding from the sampled breast (per 24 h) was weakly positively associated with the relative abundance of Streptococcus salivarius (P = 0.035). No associations with alpha nor beta diversity were detected. In conclusion, variations in breastfeeding characteristics do not have a major impact on the composition or diversity of the human milk microbiome.
Effects of a forefoot strengthening protocol on explosive tasks performance and propulsion kinetics in athletes: a single-blind randomized controlled trial
Purpose To investigate the effects of an 8-week “periodized high-load” forefoot strengthening protocol on athlete’s metatarsophalangeal joints (MTPj) flexion torque, MTPj flexors volume, sprint acceleration, cutting, and jumping overall performance and kinetics. Methods Twenty-height highly-trained athletes were randomized into a TRAINING or control group. Following a 4-week control period, TRAINING performed an 8-week forefoot strengthening protocol (2 sessions per week) followed by a 4-week detraining period. CONTROL group athletes were asked to continue their usual activities. During weeks 1, 5, 14 and 18, we assessed MTPj flexion torque, MTPj flexors volume, maximal sprint acceleration, 90-degree cutting, vertical and horizontal jumps, and foot-ankle hops. A linear mixed model was used along with individual statistical analyses using the minimal detectable change (MDC). Results TRAINING significantly and substantially increased MTPj flexion torque and MTPj flexors volume (effect size [ES]: 1.36–1.96; p < 0.001) with 92% of athletes exceeding the MDC. Subsequently, TRAINING induced significant improvements in cutting and horizontal jumping performance (ES: 0.53–1.14; p < 0.01) with 42–67% of athletes exceeding the MDC. These gains were partly attributed to enhanced medio-lateral ground reaction force transmission during cutting and increased propulsive horizontal force production and transmission during jumping (ES: 0.38–0.57; p < 0.05). Despite no effects on overall sprint acceleration performance, vertical propulsion kinetics at maximal speed improved in TRAINING after intervention (ES: 0.87–1.19; p < 0.01). No significant differences were found between the results of the interventional and detraining period demonstrating potential long-lasting effects. Conclusion An 8-week “periodized high-load” forefoot strengthening protocol allowed to improve MTPj maximal torque and MTPj flexors volume. This strength gains led also to cutting, horizontal jump overall performance and kinetics improvement as well as greater maximal speed propulsion kinetics. MTPj strength capacity may exert a more substantial impact on performance and kinetics on horizontally and medio-lateral-oriented explosive movements than on vertically-oriented ones.
Design of PRS enabled monopole slotted-antenna sensor for breast tumor detection
Abstract This article introduces a near-field microwave sensor based on a monopole antenna for breast tumor detection. The designed antenna operates at 2 GHz and is miniaturized by 37% through the etching of two pairs of rectangular slots along its edges, achieving a compact footprint of 0.28λg × 0.42λg (where λg is the guided wavelength at 2 GHz). To enhance gain, a partially reflective surface (PRS) is positioned behind the radiating monopole, increasing the antenna gain from 2.15 to 7 dBi. The monopole antenna is fabricated using Rogers RT5880 (0.787 mm thickness), while the PRS is made from Rogers TMM13i (3.8 mm thickness). The proposed antenna exhibits high radiation efficiency (95–99%) across the 1.9–2.1 GHz bandwidth. A 3D artificial female breast equivalent phantom is developed to evaluate the sensor’s performance. The PRS-enabled antenna is analyzed in terms of reflected and transmitted power variations at different distances from the phantom. Simulation and experimental results confirm that integrating PRS improves the sensor’s sensitivity and its ability to differentiate between healthy and malignant tissues. Furthermore, specific absorption rate (SAR) analysis indicates that an input power of 50 mW meets SAR safety standards. The proposed antenna is compact, simple, and a safer alternative to X-rays, providing a portable solution for effective breast tumor detection.
Correction: Missed opportunities for vaccination among children aged 0–23 months visiting health facilities in a southwest State of Nigeria, December 2019
Heatmap clustering and performance analysis of cotton genotypes in response to environmental conditions
Hydroethanolic extract of Schinus terebinthifolia as a promising source of anti-influenza agents: Phytochemical profiling, cheminformatics, molecular docking and dynamics simulations
Although Schinus terebinthifolia (commonly known as Brazilian peppertree) has been documented to possess various biological activities, such as anticancer, antibacterial, and antioxidant properties, its anti-influenza activity has not yet been documented. Here, an aqueous-ethanolic extract (30% v/v ethanol solution), prepared from its aerial parts (leaves and stalks), was established to determine whether it is a rich source of antiviral agents. The hydroethanolic plant extract, with a TPC value of 264.11 mg (GAE)/g DE, exhibits a promising IC50 value of 16.33 μg/mL, similar to that of authentic quercetin (IC50 = 12.72 μg/mL), and approximately 5.34 times higher than that of gallic acid (IC50 = 3.06 μg/mL) as determined by the DPPH assay. This extract contains 1.71 mg of gallic acid (representative marker) per gram of dried plant material, according to HPLC analysis. Using untargeted metabolomics analysis coupled with a series of cheminformatics tools (MetFrag, SIRIUS, CSI:FingerID, and CANOPUS), we ultimately proved that the S. terebinthifolia hydroethanolic extract contains simple phenolics (e.g., methyl gallate, ethyl gallate, and chlorogenic acid), flavonoids (afzelin and myricitrin), dicarboxylic acids, and germacrone. As anticipated, the plant extract exhibited anti-influenza activity with an IC50 of 2.21 μg/mL (CC50 > 50 μg/mL) and did not exert hemolytic activity at the concentration of 2000 μg/mL, underscoring its efficacy as a safe antiviral solution. In silico molecular docking and dynamic simulations suggest that neuraminidase and the cap-binding domain of influenza RNA polymerase (PB2) are preferentially targeted for inhibition by the detected metabolites. Owing to the diverse therapeutic effects of secondary metabolites, the anti-H5N1 activity of the newly developed plant extract is currently under investigation.
Effects of external diaphragmatic pacing with neurally adjusted ventilatory assist on diaphragm function in AECOPD patients
Resilience factors and mechanisms in the coal power supply chain: A quantitative analysis using fuzzy DEMATEL, ISM, and ANP methods
Under the influence of global energy market fluctuations, policy changes, and natural disasters, the coal power supply chain faces significant risks. However, existing research on supply chain resilience management, particularly regarding the identification and quantitative analysis of resilience factors in the coal power supply chain, remains insufficient. Therefore, this study aims to identify the key factors influencing the resilience of the coal power supply chain and analyze the mechanisms of interaction between these factors. This study first conducted a literature review to identify 20 factors classified into three dimensions: adaptive capacity, restorative capacity, and absorptive capacity. Secondly, an innovative approach integrating fuzzy DEMATEL, ISM, and ANP was introduced to systematically identify the key influencing factors of coal power supply chain resilience and analyze their mechanisms. The results reveal that electricity demand forecasting capability, government intervention and coordination capacity, and technological level of the coal industry are the primary causal factors affecting supply chain resilience, playing critical roles in ensuring supply chain stability and adaptability. Additionally, factors such as risk prevention and maintenance level and information transmission efficiency also serve as important supporting elements. This research constructs a cause-degree-centeredness diagram and a hierarchical model for coal power supply chain resilience, providing theoretical support and practical recommendations for optimizing the supply chain structure and enhancing the capacity to cope with uncertainties in the coal power industry.
Tribological study of amorphous polyethylene under different rough surfaces of chromium based on molecular dynamics simulation
A high-efficiency palmprint recognition model integrating ROI and Gabor filtering
Palmprint recognition, as a biometric recognition technology, has unique individual recognition and high accuracy, and is broadly utilized in fields such as identity verification and security monitoring. Therefore, a palm print recognition model that integrates regions of interest and Gabor filters has been proposed to solve the problem of difficulty in feature extraction caused by factors such as noise, lighting changes, and acquisition angles that often affect palm print images during the acquisition process. This model extracts standardized feature regions of palmprint images through the region of interest method, enhances texture features through multi-scale Gabor filters, and finally uses support vector machines for classification. The experiment findings denote that the region of interest model performs better than other methods in terms of signal-to-noise ratio and root mean square error, with a signal-to-noise ratio of 0.89 on the GPDS dataset and 0.97 on the CASIA dataset. The proposed model performs the best in recognition accuracy and error convergence speed, with a final accuracy of 95%. The proposed model has the shortest running time, less than 0.4 seconds in all groups, especially less than 0.3 seconds in Group 4, demonstrating high recognition efficiency. The research conclusion shows that the palmprint recognition method combining regions of interest and Gabor filters has high efficiency and performance, and can effectively improve recognition accuracy.