Human Data Research Articles

Are Δ9-Tetrahydrocannabinol and Its Major Metabolites Substrates or Inhibitors of Placental or Human Hepatic Drug Solute-Carrier Transporters?

Δ9-Tetrahydrocannabinol (THC) is the primary psychoactive component of cannabis which is being increasingly consumed by pregnant people. In humans, THC is sequentially metabolized in the liver to its circulating metabolites 11-hydroxy-THC (11-OH-THC, psychoactive) and 11-nor-9-carboxy-THC (THC-COOH, non-psychoactive). Human and macaque data show that fetal exposure to THC is considerably lower than the corresponding maternal exposure. Through perfused human placenta studies, we showed that this is due to the active efflux of THC (fetal-to-maternal) by a placental transporter(s) other than P-glycoprotein or breast cancer resistance protein. The identity of this placental transporter(s) as well as whether THC or its metabolites are substrates or inhibitors of hepatic solute carrier transporters is unknown. Therefore, we investigated whether 5 μM THC, 0.3 μM 11-OH-THC, and 2.5 μM THC-COOH are substrates and/or inhibitors of placental or hepatic solute carrier transporters at their pharmacologically relevant concentrations. Using HEK cells overexpressing human OATP1B1, OATP1B3, OATP2B1, OCT1, OCT3, OAT2, OAT4, or NTCP, and prototypic substrates/inhibitors of these transporters, we found that THC and THC-COOH were substrates but not inhibitors of OCT1. THC-COOH was a weak substrate of OCT3 and a weak inhibitor of OAT4. THC, 11-OH-THC, and THC-COOH were found not to be substrates/inhibitors of the remaining transporters investigated.

Demonstrative Learning for Human-Agent Knowledge Transfer

Demonstrative learning in virtual reality (VR) is a pivotal learning strategy for knowledge transfer for embodied agents. While existing studies have extensively explored agents’ knowledge transfer through self-demonstrative learning (SDL) or teacher-demonstrative learning (TDL), there has been limited focus on a system that integrates both paradigms. This paper proposes a comprehensive system that combines the SDL paradigm with the TDL paradigm in VR from a top-down perspective. The system involves using directed probabilistic graphs (DPGs) for knowledge representation, constructing detectors and actuators based on object fluents and atomic actions, representing knowledge acquired from both learning paradigms on the DPGs, and incorporating knowledge integration and visualization. Through system evaluation, we show the advantages of integrating two demonstrative learning paradigms, including increased learning efficiency, mitigating demonstrator's deficiencies, and more logical task execution. The study also reveals that a dynamically decreasing fusion factor with the learning progresses, performs well across different correct percentages of teacher demonstrations. Additionally, we show a more decentralized demonstration in the middle of the agent's learning progress maximizing learning efficiency when demonstrating only a few atomic actions. Finally, we assess the system's generalizability and delineate its boundaries. With the ongoing development and the increasing availability of human data in VR, we anticipate that our system can be applied to future scenarios of efficient human-agent knowledge transfer in human-agent symbiosis.

Performance Comparison between Deep Neural Network and Machine Learning based Classifiers for Huntington Disease Prediction from Human DNA Sequence.

Huntington Disease (HD) is a type of neurodegenerative disorder which causes problems like psychiatric disturbances, movement problem, weight loss and problem in sleep. It needs to be addressed in earlier stage of human life. Nowadays Deep Learning (DL) based system could help physicians provide second opinion in treating patient's disease. In this work, human Deoxyribo Nucleic Acid (DNA) sequence is analyzed using Deep Neural Network (DNN) algorithm to predict the HD disease. The main objective of this work is to identify whether the human DNA is affected by HD or not. Human DNA sequences are collected from National Center for Biotechnology Information (NCBI) and synthetic human DNA data are also constructed for process. Then numerical conversion of human DNA sequence data is done by Chaos Game Representation (CGR) method. After that, numerical values of DNA data are used for feature extraction. Mean, median, standard deviation, entropy, contrast, correlation, energy and homogeneity are extracted. Additionally, the following features such as counts of adenine, thymine, guanine and cytosine are extracted from the DNA sequence data itself. The extracted features are used as input to the DNN classifier and other machine learning based classifiers such as NN (Neural Network), Support Vector Machine (SVM), Random Forest (RF) and Classification Tree with Forward Pruning (CTWFP). Six performance measures are used such as Accuracy, Sensitivity, Specificity, Precision, F1 score and Mathew Correlation Co-efficient (MCC). The study concludes DNN, NN, SVM, RF achieve 100% accuracy and CTWFP achieves accuracy of 87%.

A Novel Machine Learning Model and a Web Portal for Predicting the Human Skin Sensitization Effects of Chemical Agents

Skin sensitization is a significant concern for chemical safety assessments. Traditional animal assays often fail to predict human responses accurately, and ethical constraints limit the collection of human data, necessitating a need for reliable in silico models of skin sensitization prediction. This study introduces HuSSPred, an in silico tool based on the Human Predictive Patch Test (HPPT). HuSSPred aims to enhance the reliability of predicting human skin sensitization effects for chemical agents to support their regulatory assessment. We have curated an extensive HPPT database and performed chemical space analysis and grouping. Binary and multiclass QSAR models were developed with Bayesian hyperparameter optimization. Model performance was evaluated via five-fold cross-validation. We performed model validation with reference data from the Defined Approaches for Skin Sensitization (DASS) app. HuSSPred models demonstrated strong predictive performance with CCR ranging from 55 to 88%, sensitivity between 48 and 89%, and specificity between 37 and 92%. The positive predictive value (PPV) ranged from 84 to 97%, versus negative predictive value (NPV) from 22 to 65%, and coverage was between 75 and 93%. Our models exhibited comparable or improved performance compared to existing tools, and the external validation showed the high accuracy and sensitivity of the developed models. HuSSPred provides a reliable, open-access, and ethical alternative to traditional testing for skin sensitization. Its high accuracy and reasonable coverage make it a valuable resource for regulatory assessments, aligning with the 3Rs principles. The publicly accessible HuSSPred web tool offers a user-friendly interface for predicting skin sensitization based on chemical structure.

Repeatability of quantitative MR fingerprinting for T1 and T2 measurements of metastatic bone in prostate cancer patients.

MR fingerprinting (MRF) has the potential to quantify treatment response. This study evaluated the repeatability of MRF-derived T1 and T2 relaxation times in bone metastasis, bone, and muscle in patients with metastatic prostate cancer. This prospective single-centre study included same-day repeated MRF acquisitions from 20 patients (August 2019-October 2020). Phantom and human data were acquired on a 1.5-T MR scanner using a research MRF sequence outputting T1 and T2 maps. Regions of interest (ROIs) across three tissue types (bone metastasis, bone, muscle) were drawn on two separate acquisitions. Repeatability of T1 and T2 was assessed using Bland-Altman plots, together with repeatability (r) and intraclass correlation (ICC) coefficients. Mean T1 and T2 were reported per tissue type. Twenty patients with metastatic prostate cancer (mean age, 70 years ± 8 (standard deviation)) were evaluated and bone metastasis (n = 44), normal-appearing bone (n = 14), and muscle (n = 20) ROIs were delineated. Relative repeatability of T1 measurements was 6.9% (bone metastasis), 32.6% (bone), 5.8% (muscle) and 21.8%, 32.2%, 16.1% for T2 measurements. The ICC of T1 was 0.97 (bone metastasis), 0.94 (bone), 0.96 (muscle); ICC of T2 was 0.94 (bone metastasis), 0.94 (bone), 0.91 (muscle). T1 values in bone metastasis were higher than in bone (p < 0.001). T2 values showed no difference between bone metastasis and bone (p = 0.5), but could separate active versus treated metastasis (p < 0.001). MRF allows repeatable T1 and T2 measurements in bone metastasis, bone, and muscle in patients with primary prostate cancer. Such measurements may help quantify the treatment response of bone metastasis. Question MR fingerprinting has the potential to characterise bone metastasis and its response to treatment. Findings Repeatability of MRF-based T1 measurements in bone metastasis and muscle was better than for T2. Clinical relevance MR fingerprinting allows repeatable T1 and T2 quantitative measurements in bone metastasis, bone, and muscle in patients with primary prostate cancer, which makes it potentially applicable for disease characterisation and assessment of treatment response.

Enhancing intention prediction and interpretability in service robots with LLM and KG

The rapid advancement of artificial intelligence has significantly expanded the role of service robots in everyday life. This expansion necessitates the accurate recognition and prediction of human intentions to provide timely and appropriate services. However, existing methods often struggle to perform effectively in complex and unstructured environments. To address this challenge, we propose the Large language model and Knowledge graph based Intention Recognition Framework (LKIRF), which combines large language model (LLM) with knowledge graphs (KG) to enhance the intention recognition capabilities of service robots. Our approach constructs an offline KG from human motion and environmental data and builds an online reasoning graph through real-time interaction, utilizing LLM for interpretation. Experimental results indicate that compared to traditional methods, LKIRF not only improves prediction accuracy across various scenarios but also enhances the transparency and interpretability of the intention reasoning process.

General feature selection technique supporting sex-debiasing in chronic illness algorithms validated using wearable device data

In tasks involving human health condition data, feature selection is heavily affected by data types, the complexity of the condition manifestation, and the variability in physiological presentation. One type of variability often overlooked or oversimplified is the effect of biological sex. As females have been chronically underrepresented in clinical research, we know less about how conditions manifest in females. Innovations in wearable technology have enabled individuals to generate high temporal resolution data for extended periods of time. With millions of days of data now available, additional feature selection pipelines should be developed to systematically identify sex-dependent variability in data, along with the effects of how many per-person data are included in analysis. Here we present a set of statistical approaches as a technique for identifying sex-dependent physiological and behavioral manifestations of complex diseases starting from longitudinal data, which are evaluated on diabetes, hypertension, and their comorbidity.

Improvement of Optimal Human Resource Allocation Algorithm Based on Deep Latent Semantic Model

With the advances of technology, the banking industry faces the challenge of processing large-scale, heterogeneous human resource data. Traditional methods have difficulties in providing efficient and accurate analysis and prediction. This paper proposes an optimized human resource allocation algorithm based on deep latent Semantic Model (DLSM), which improves the accuracy and efficiency of data analysis by integrating deep neural network (DNN) and transfer learning technology. The experimental results show that the DLSM model performs well in text classification and information retrieval tasks, with the accuracy, accuracy, recall and F1 scores improving by 7.5%, 5.5%, 6.0% and 5.8%, respectively. This study confirms the excellent performance of DLSM model in HR data processing, provides an efficient and scientifically based solution for recruitment optimization in the banking industry, and enhances the competitiveness of enterprises.

Cannabinoids and General Anesthetics: Revisiting Molecular Mechanisms of Their Pharmacological Interactions.

Cannabis has been used for recreation and medical purposes for more than a millennium across the world; however, its use's consequences remain poorly understood. Although a growing number of surgical patients are regular cannabis consumers, little is known regarding the pharmacological interactions between cannabis and general anesthetics; consequently, there is not a solid consensus among anesthesiologists on the perioperative management of these patients. The existing evidence about the molecular mechanisms underlying pharmacological interactions between cannabinoids and anesthetic agents, both in animal models and in humans, shows divergent results. While some animal studies have demonstrated that phytocannabinoids (tetrahydrocannabinol [THC], cannabidiol [CBD], and cannabinol [CBN]) potentiate the anesthetic effects of inhalation and intravenous anesthetics, while others have found effects comparable with what has been described in humans so far. Clinical studies and case reports have consistently shown increased requirements of GABAergic anesthetic drugs (isoflurane, sevoflurane, propofol, midazolam) to achieve adequate levels of clinical anesthesia. Several potential molecular mechanisms have been proposed to explain the effects of these interactions. However, it is interesting to mention that in humans, it has been observed that the ingestion of THC enhances the hypnotic effect of ketamine. Animal studies have reported that cannabinoids enhance the analgesic effect of opioids due to a synergistic interaction of the endogenous cannabinoid system (ECS) with the endogenous opioid system (EOS) at the spinal cord level and in the central nervous system. However, human data reveals that cannabis users show higher scores of postoperative pain intensity as well as increased requirements of opioid medication for analgesia. This review aims to improve understanding of the molecular mechanisms and pharmacological interactions between cannabis and anesthetic drugs and the clinical outcomes that occur when these substances are used together.

Ventricular arrhythmias in association with athletic cardiac remodelling.

Athletes are predisposed to atrial arrhythmias but the association between intense endurance exercise training, ventricular arrhythmias (VAs) and sudden cardiac death is less well established. Thus, it is unclear whether the 'athlete's heart' promotes specific arrhythmias or whether it represents a more general pro-arrhythmogenic phenotype. Whilst direct causality has not been established, it appears possible that repeated exposure to high-intensity endurance exercise in some athletes contributes to formation of pro-arrhythmic cardiac phenotypes that underlie VAs. Theories regarding potential mechanisms for exercise-induced VAs include repeated bouts of myocardial inflammation and stretch-induced cellular remodelling. Small animal models provide some insights, but larger animal and human data are sparse. The current clinical approach to VAs in athletes is to differentiate those with and without structural or electrical heart disease. However, if the athlete's heart involves a degree of pro-arrhythmogenic remodelling, then this may not be such a simple dichotomy. Questions are posed by athletes with VAs in combination with extreme remodelling. Some markers, such as scar on magnetic resonance imaging, may point toward a less benign phenotype but are also quite common in ostensibly healthy athletes. Other clinical and invasive electrophysiology features may be helpful in identifying the at-risk athlete. This review seeks to discuss the association between athletic training and VAs. We will discuss the potential mechanisms, clinical significance and approach to the management of athletes with VAs.

A Field-Programmable Gate Array-Based Adaptive Sleep Posture Analysis Accelerator for Real-Time Monitoring

This research presents a sleep posture monitoring system designed to assist the elderly and patient attendees. Monitoring sleep posture in real time is challenging, and this approach introduces hardware-based edge computation methods. Initially, we detected the postures using minimally optimized sensing modules and fusion techniques. This was achieved based on subject (human) data at standard and adaptive levels using posture-learning processing elements (PEs). Intermittent posture evaluation was performed with respect to static and adaptive PEs. The final stage was accomplished using the learned subject posture data versus the real-time posture data using posture classification. An FPGA-based Hierarchical Binary Classifier (HBC) algorithm was developed to learn and evaluate sleep posture in real time. The IoT and display devices were used to communicate the monitored posture to attendant/support services. Posture learning and analysis were developed using customized, reconfigurable VLSI architectures for sensor fusion, control, and communication modules in static and adaptive scenarios. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed Board-based field-programmable gate array (FPGA) Xilinx board was used for experimental validation.

Network medicine informed multiomics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. We developed a network medicine methodology via integrating human brain multi-omics data to prioritize drug targets and repurposable treatments for ALS. We leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on human brain expression quantitative trait loci (QTL) (eQTL), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Using a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in known ALS pathobiological pathways. Applying network proximity analysis of predicted ALS-associated genes and drug-target networks under the human protein-protein interactome (PPI) model, we identified potential repurposable drugs (i.e., Diazoxide and Gefitinib) for ALS. Subsequent validation established preclinical evidence for top-prioritized drugs. In summary, we presented a network-based multi-omics framework to identify drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Evolucollateral dynamics in stroke: Evolutionary pathophysiology, remodelling and emerging therapeutic strategies.

Leptomeningeal collaterals (LMCs) are crucial in mitigating the impact of acute ischemic stroke (AIS) by providing alternate blood flow routes when primary arteries are obstructed. This article explores the evolutionary pathophysiology of LMCs, highlighting their critical function in stroke and the genetic and molecular mechanisms governing their development and remodelling. We address the translational challenges of applying animal model findings to human clinical scenarios, emphasizing the need for further research to validate emerging therapies-such as pharmacological agents, gene therapy and mechanical interventions-in clinical settings, aimed at enhancing collateral perfusion. Computational modelling emerges as a promising method for integrating experimental data, which requires precise parameterization and empirical validation. We introduce the 'Evolucollateral Dynamics' hypothesis, proposing a novel framework that incorporates evolutionary biology principles into therapeutic strategies, offering new perspectives on enhancing collateral circulation. This hypothesis emphasizes the role of genetic predispositions and environmental influences on collateral circulation, which may impact therapeutic strategies and optimize treatment outcomes. Future research must incorporate human clinical data to create robust treatment protocols, thereby maximizing the therapeutic potential of LMCs and improving outcomes for stroke patients.

Center of mass kinematic reconstruction during steady-state walking using optimized template models.

Template models, such as the Bipedal Spring-Loaded Inverted Pendulum and the Virtual Pivot Point, have been widely used as low-dimensional representations of the complex dynamics in legged locomotion. Despite their ability to qualitatively match human walking characteristics like M-shaped ground reaction force (GRF) profiles, they often exhibit discrepancies when compared to experimental data, notably in overestimating vertical center of mass (CoM) displacement and underestimating gait event timings (touchdown/ liftoff). This paper hypothesizes that the constant leg stiffness of these models explains the majority of these discrepancies. The study systematically investigates the impact of stiffness variations on the fidelity of model fittings to human data, where an optimization framework is employed to identify optimal leg stiffness trajectories. The study also quantifies the effects of stiffness variations on salient characteristics of human walking (GRF profiles and gait event timing). The optimization framework was applied to 24 subjects walking at 40% to 145% preferred walking speed (PWS). The findings reveal that despite only modifying ground forces in one direction, variable leg stiffness models exhibited a >80% reduction in CoM error across both the B-SLIP and VPP models, while also improving prediction of human GRF profiles. However, the accuracy of gait event timing did not consistently show improvement across all conditions. The resulting stiffness profiles mimic walking characteristics of ankle push-off during double support and reduced CoM vaulting during single support.

Forecasting Africa’s fertility decline by female education groups

While female education has long been recognized as a key driver of fertility decline during the process of demographic transition and most population projection models consider it implicitly or explicitly in their forecasts of overall fertility, there still is need for a method to forecast education-specific fertility trends directly. Here we propose a method for projecting education-specific fertility declines for cohorts of women in Sub-Saharan Africa based on all available demographic and health surveys data for African countries (including 1.03Mio cases). We study at different levels of aggregation (sample clusters, strata, and national) the associations between ideal family size and completed cohort fertility for education groups, on the one hand, and the average level of education in those units, on the other. The consistently very strong empirical associations suggest a plausible narrative by which a higher prevalence of educated women in a spatial unit influences the fertility levels of women in all specific education categories. Empirical associations between education-specific cohort fertility trends at the national level and newly available quality-adjusted human capital data for these cohorts are then operationalized to produce education-specific population projections as they are needed for-among other uses-the shared socioeconomic pathways scenarios that are widely used in the climate change research community. Sensitivity analyses including out-of-sample projections support the validity of the proposed method which is then applied to 37 African countries.

Single-Cell Multiomics Profiling Reveals Heterogeneity of Müller Cells in the Oxygen-Induced Retinopathy Model.

Retinal neovascularization poses heightened risks of vision loss and blindness. Despite its clinical significance, the molecular mechanisms underlying the pathogenesis of retinal neovascularization remain elusive. This study utilized single-cell multiomics profiling in an oxygen-induced retinopathy (OIR) model to comprehensively investigate the intricate molecular landscape of retinal neovascularization. Mice were exposed to hyperoxia to induce the OIR model, and retinas were isolated for nucleus isolation. The cellular landscape of the single-nucleus suspensions was extensively characterized through single-cell multiomics sequencing. Single-cell data were integrated with genome-wide association study (GWAS) data to identify correlations between ocular cell types and diabetic retinopathy. Cell communication analysis among cells was conducted to unravel crucial ligand-receptor signals. Trajectory analysis and dynamic characterization of Müller cells were performed, followed by integration with human retinal data for pathway analysis. The multiomics dataset revealed six major ocular cell classes, with Müller cells/astrocytes showing significant associations with proliferative diabetic retinopathy (PDR). Cell communication analysis highlighted pathways that are associated with vascular proliferation and neurodevelopment, such as Vegfa-Vegfr2, Igf1-Igf1r, Nrxn3-Nlgn1, and Efna5-Epha4. Trajectory analysis identified a subset of Müller cells expressing genes linked to photoreceptor degeneration. Multiomics data integration further unveiled positively regulated genes in OIR Müller cells/astrocytes associated with axon development and neurotransmitter transmission. This study significantly advances our understanding of the intricate cellular and molecular mechanisms underlying retinal neovascularization, emphasizing the pivotal role of Müller cells. The identified pathways provide valuable insights into potential therapeutic targets for PDR, offering promising directions for further research and clinical interventions.

Self-awareness from whole-body movements.

Humans can recognize their whole-body movements even when displayed as dynamic dot patterns. The sparse depiction of whole-body movements, coupled with a lack of visual experience watching ourselves in the world, has long implicated non-visual mechanisms to self-action recognition. We aimed to identify the neural systems for this ability. Using general linear modeling and multivariate analyses on human brain imaging data from male and female participants, we first found that cortical areas linked to motor processes, including frontoparietal and primary somatomotor cortices, exhibit greater engagement and functional connectivity when recognizing self-generated versus other-generated actions. Next, we show that these regions encode self-identity based on motor familiarity, even after regressing out idiosyncratic visual cues using multiple regression representational similarity analysis. Last, we found the reverse pattern for unfamiliar individuals: encoding localized to occipito-temporal visual regions. These findings suggest that self-awareness from actions emerges from the interplay of motor and visual processes.Significance Statement: We report for the first time that self-recognition from visual observation of our whole-body actions implicates brain regions associated with motor processes. On functional neuroimaging data, we found greater activity and unique representational patterns in brain areas and networks linked to motor processes when viewing our own actions relative to viewing the actions of others. These findings introduce an important role of motor mechanisms in distinguishing the self from others.

Accuracy Standards of Wearable Technologies for Assessment of Soccer Kicking: Protocol for a Systematic Literature Review.

Wearable technology is widely applied in performance monitoring, an integral part of sports and exercise sciences. The kick movement in soccer exemplifies a sports technique that could benefit from appropriate biomechanics assessment methodologies. However, the accuracy of wearables in quantifying soccer kick mechanics, particularly under field conditions, remains unclear. This study aims to present a protocol for a systematic review to discuss the measurement properties (validity, reliability, and/or accuracy aspects) of wearable technology systems explicitly used to measure ball-kicking features in soccer. This review protocol was preregistered in the Open Science Framework. A total of 2 authors will perform searches in major electronic databases using specific keyword combinations in PubMed, Physical Therapy and Sports Medicine, Web of Science, ProQuest, IEEE Xplore, EBSCOHost, and Scopus. Following a specific population, intervention, comparison, outcome framework (population: soccer players and/or collected human data in a football-related environment; intervention: at least 1 wearable used; comparator: criterion measures, repeated testing sessions and/or actual values; outcome: ball kicking data), studies will be screened based on predetermined inclusion and exclusion criteria. The methodological quality of the included studies will be assessed using the "consensus-based standards for the selection of health measurement instruments" checklist (in studies concerning validity or reliability) or the "quality assessment of diagnostic accuracy studies" tool (in studies concerning accuracy). Data extraction will be conducted to determine the level of evidence according to the "best evidence synthesis method," and an evidence gap map will be constructed. The Cohen κ coefficient will be used to estimate the interevaluator agreement. This ongoing systematic review has completed database searches and is currently in the screening phase. Depending on the number and consistency of studies, results may be presented by meta-analysis or qualitative synthesis, with subgroup analyses considering factors such as gender, age, and playing level. The final results are expected by July 2024, with manuscript submission anticipated by November 2024. Our study will provide a comprehensive summary of the highest level of evidence available on the use of wearables for the assessment of soccer kick mechanics, providing practical guidance for athletes and sports sciences professionals regarding the validity and reliability aspects of using wearable technology to measure ball-kicking features in soccer. OSF registries https://osf.io/zm3j6. DERR1-10.2196/57433.

Research on the sentiment recognition and application of allusive words based on text semantic enhancement.

In the era of digital intelligence empowerment, the data-driven approach to the mining and organization of humanistic knowledge has ushered in new development opportunities. However, current research on allusions, an important type of humanities data, mainly focuses on the adoption of a traditional paradigm of humanities research. Conversely, little attention is paid to the application of auto-computing techniques to allusive resources. In light of this research gap, this work proposes a model of allusive word sentiment recognition and application based on text semantic enhancement. First, explanatory texts of 36,080 allusive words are introduced for text semantic enhancement. Subsequently, the performances of different deep learning-based approaches are compared, including three baselines and two optimized models. The best model, ERNIE-RCNN, which exhibits a 6.35% improvement in accuracy, is chosen for the sentiment prediction of allusive words based on text semantic enhancement. Next, according to the binary relationships between allusive words and their source text, explanatory text, and sentiments, the overall and time-based distribution regularities of allusive word sentiments are explored. In addition, the sentiments of the source text are inferred according to the allusive word sentiments. Finally, the LDA model is utilized for the topic extraction of allusive words, and the sentiments and topics are fused to construct an allusive word-sentiment theme relationship database, which provides two modes for the semantic association and organization of allusive resources. The empirical results show that the proposed model can achieve the discovery and association of allusion-related humanities knowledge.

Intestinal transport of organic food compounds and drugs: A scoping review on the alterations observed in chronic kidney disease

Background and aimsAround 850 million people worldwide are affected by chronic kidney disease (CKD). Patients with CKD often develop malnutrition and sarcopenia and changes in the pharmacokinetics of drugs. A reduced kidney function partially explains the prolonged half-life of certain drugs due to decreased renal clearance, which leads to an increased risk of adverse effects. While the intestine plays a fundamental role in this context, a systematic review of the effects of CKD on intestinal transport is lacking. We aimed to systematically summarize all the available evidence on intestinal transport of organic food components (carbohydrates/sugar, proteins/amino acids, fats, vitamins) and drugs (including drug transporters) in chronic kidney disease. MethodsWe conducted a systematic search of all the articles published until the 1st of April 2024, on five databases i.e. Embase, PubMed, Web of Science Core Collection, Cochrane Library, and Scopus. This systematic review was registered on the Open Science Framework (OSF) (osf.io/5e6wb) and was carried out according to the PRISMA 2020 guidelines. ResultsFrom 9205 articles identified, 68 met the inclusion criteria. Absorption of organic food compounds seems to be altered, in general, and reduced for vitamins. The expression of intestinal efflux drug transporters may be altered in CKD. ConclusionsDespite alterations in intestinal transport is suggested to be altered in CKD, the lack of recent studies, the paucity of human data and the heterogeneity of the methodologies used underscore the need for more research on the effect of CKD and uremia on intestinal transport.