Personalized, Precision Medicine to Cure Alzheimer's Dementia: Approach #1.

The goal of the treatment for Alzheimer's dementia (AD) is the cure of dementia. A literature review revealed 18 major elements causing AD and 29 separate medications that address them. For any individual with AD, one is unlikely to discern which major causal elements produced dementia. Thus, for personalized, precision medicine, all causal elements must be treated so that each individual patient will have her or his causal elements addressed. Twenty-nine drugs cannot concomitantly be administered, so triple combinations of drugs taken from that list are suggested, and each triple combination can be administered sequentially, in any order. Ten combinations given over 13 weeks require 2.5 years, or if given over 26 weeks, they require 5.0 years. Such sequential treatment addresses all 18 elements and should cure dementia. In addition, any comorbid risk factors for AD whose first presence or worsening was within ±1 year of when AD first appeared should receive appropriate, standard treatment together with the sequential combinations. The article outlines a randomized clinical trial that is necessary to assess the safety and efficacy of the proposed treatments; it includes a triple-drug Rx for equipoise. Clinical trials should have durations of both 2.5 and 5.0 years unless the data safety monitoring board (DSMB) determines earlier success or futility since it is uncertain whether three or six months of treatment will be curative in humans, although studies in animals suggest that the briefer duration of treatment might be effective and restore defective neural tracts.

Intolerance of uncertainty affects the behavioral and neural mechanisms of higher generalization.

Intolerance of uncertainty (IU) is associated with several anxiety disorders. In this study, we employed rewards and losses as unconditioned positive and negative stimuli, respectively, to explore the effects of an individual's IU level on positive and negative generalizations using magnetic resonance imaging technology. Following instrumental learning, 48 participants (24 high IU; 24 low IU) were invited to complete positive and negative generalization tasks; their behavioral responses and neural activities were recorded by functional magnetic resonance imaging. The behavior results demonstrated that participants with high IUs exhibited higher generalizations to both positive and negative cues as compared with participants having low IUs. Neuroimaging results demonstrated that they exhibited higher activation levels in the right anterior insula and the default mode network (i.e. precuneus and posterior cingulate gyrus), as well as related reward circuits (i.e. caudate and right putamen). Therefore, higher generalization scores and the related abnormal brain activation may be key markers of IU as a vulnerability factor for anxiety disorders.

The mediating role of hope in the relation between uncertainty and social support with self-management among patients with ESKD undergoing hemodialysis.

BACKGROUND: Self-management behaviours are critical for patients requiring regular hemodialysis (HD) therapy. This study aimed to test the relationship between social support, uncertainty and self-management among HD patients and to explore whether hope plays a mediating role. METHODS: In a cross-sectional study, a convenience sample of 212 HD patients from two hospitals completed the Perceived Social Support Scale (PSSS), Herth Hope Index (HHI), Short form Mishel Uncertainty in Illness Scale (SF-MUIS), and hemodialysis Self-Management Instrument (HD-SMI). Data were analysed using structural equation modelling. RESULTS: The main finding indicated that social support positively affected self-management (ß = 0.50, t = 4.97, p < 0.001), and uncertainty negatively affected self-management (ß =-0.37, t=-4.12, p = < 0.001). In mediational model analysis, the effect of social support on self-management was fully mediated [(ß = 0.12; 95% BC CI (0.047, 0.228)] by hope. Also, the effect of uncertainty on self-management was fully mediated [(ß=- 0.014; 95% BC CI (-0.114, -0.003)] by hope. CONCLUSIONS: "Considering factors influencing self-management in HD patients is crucial for improving quality of life. Receiving support and informational resources can not only foster hope but also reduce their uncertainty, thus aiding in enhancing clinical outcomes, quality of life, and reducing complications. "Health care providers, especially nurses were advised to accept the existence of uncertainty, help patients make optimal use of support resources, and give more importance to disambiguation to reassure them. Therefore, well-designed interventions that enhance social support and hope and reduce uncertainty may help improve self-management behaviour in HD patients.

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson's disease behavioral analysis.

Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson's patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson's dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson's disease analysis.

[Causes of death: the uncertainty behind the numbers]. / Doodsoorzaken: de onzekerheid achter de cijfers.

When a person dies in the Netherlands it is legally required to report the cause of death. In most cases however, there is uncertainty when classifying causes of death. Additional postmortem diagnostics such as a CT scan or autopsy do not always provide absolute certainty. Data on causes of death can be used to determine what are, on a population level, relevant health problems. One must be cautious to fully rely on these data for making policy or financing healthcare and research. Firstly, incorrectly classifying the cause of death can give a distorted view of the underlying causes. Secondly, relevant health problems, such as obesity, might be overlooked in the statistics when they are not clearly a cause of death.

Uncertainty limits the use of power analysis.

The calculation of statistical power has been taken up as a simple yet informative tool to assist in designing an experiment, particularly in justifying sample size. A difficulty with using power for this purpose is that the classical power formula does not incorporate sources of uncertainty (e.g., sampling variability) that can impact the computed power value, leading to a false sense of precision and confidence in design choices. We use simulations to demonstrate the consequences of adding two common sources of uncertainty to the calculation of power. Sampling variability in the estimated effect size (Cohen's d) can introduce a large amount of uncertainty (e.g., sometimes producing rather flat distributions) in power and sample-size determination. The addition of random fluctuations in the population effect size can cause values of its estimates to take on a sign opposite the population value, making calculated power values meaningless. These results suggest that calculated power values or use of such values to justify sample size add little to planning a study. As a result, researchers should put little confidence in power-based choices when planning future studies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The role of uncertainty in regulating associative change.

Rescorla (2000, 2001) interpreted his compound test results to show that both common and individual error terms regulate associative change such that the element of a conditioned compound with the greater prediction error undergoes greater associative change than the one with the smaller prediction error. However, it has recently been suggested that uncertainty, not prediction error, is the primary determinant of associative change in people (Spicer et al., 2020, 2022). The current experiments use the compound test in a continuous outcome allergist task to assess the role of uncertainty in associative change, using two different manipulations of uncertainty: outcome uncertainty (where participants are uncertain of the level of the outcome on a particular trial) and causal uncertainty (where participants are uncertain of the contribution of the cue to the level of the outcome). We replicate Rescorla's compound test results in the case of both associative gains (Experiment 1) and associative losses (Experiment 3) and then provide evidence for greater change to more uncertain cues in the case of associative gains (Experiments 2 and 4), but not associative losses (Experiments 3 and 5). We discuss the findings in terms of the notion of theory protection advanced by Spicer et al., and other ways of thinking about the compound test procedure, such as that proposed by Holmes et al. (2019). (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Uncertainty quantification of the impact of peripheral arterial disease on abdominal aortic aneurysms in blood flow simulations.

Peripheral arterial disease (PAD) and abdominal aortic aneurysms (AAAs) often coexist and pose significant risks of mortality, yet their mutual interactions remain largely unexplored. Here, we introduce a fluid mechanics model designed to simulate the haemodynamic impact of PAD on AAA-associated risk factors. Our focus lies on quantifying the uncertainty inherent in controlling the flow rates within PAD-affected vessels and predicting AAA risk factors derived from wall shear stress. We perform a sensitivity analysis on nine critical model parameters through simulations of three-dimensional blood flow within a comprehensive arterial geometry. Our results show effective control of the flow rates using two-element Windkessel models, although specific outlets need attention. Quantities of interest like endothelial cell activation potential (ECAP) and relative residence time are instructive for identifying high-risk regions, with ECAP showing greater reliability and adaptability. Our analysis reveals that the uncertainty in the quantities of interest is 187% of that of the input parameters. Notably, parameters governing the amplitude and frequency of the inlet velocity exert the strongest influence on the risk factors' variability and warrant precise determination. This study forms the foundation for patient-specific simulations involving PAD and AAAs which should ultimately improve patient outcomes and reduce associated mortality rates.

The end-of-life power battery recycling & remanufacturing center location-adjustment problem considering battery capacity and quantity uncertainty.

The booming electric vehicle market has led to an increasing number of end-of-life power batteries. In order to reduce environmental pollution and promote the realization of circular economy, how to fully and effectively recycle the end-of-life power batteries has become an urgent challenge to be solved today. The recycling & remanufacturing center is an extremely important and key facility in the recycling process of used batteries, which ensures that the recycled batteries can be handled in a standardized manner under the conditions of professional facilities. In reality, different adjustment options for existing recycling & remanufacturing centers have a huge impact on the planning of new sites. This paper proposes a mixed-integer linear programming model for the siting problem of battery recycling & remanufacturing centers considering site location-adjustment. The model allows for demolition, renewal, and new construction options in planning for recycling & remanufacturing centers. By adjusting existing sites, this paper provides an efficient allocation of resources under the condition of meeting the demand for recycling of used batteries. Next, under the new model proposed in this paper, the uncertainty of the quantity and capacity of recycled used batteries is considered. By establishing different capacity conditions of batteries under multiple scenarios, a robust model was developed to determine the number and location of recycling & remanufacturing centers, which promotes sustainable development, reduces environmental pollution and effectively copes with the risk of the future quantity of used batteries exceeding expectations. In the final results of the case analysis, our proposed model considering the existing sites adjustment reduces the cost by 3.14% compared to the traditional model, and the average site utilization rate is 15.38% higher than the traditional model. The results show that the model has an effective effect in reducing costs, allocating resources, and improving efficiency, which could provide important support for decision-making in the recycling of used power batteries.

A novel framework for high resolution air quality index prediction with interpretable artificial intelligence and uncertainties estimation.

Accurate air quality index (AQI) prediction is essential in environmental monitoring and management. Given that previous studies neglect the importance of uncertainty estimation and the necessity of constraining the output during prediction, we proposed a new hybrid model, namely TMSSICX, to forecast the AQI of multiple cities. Firstly, time-varying filtered based empirical mode decomposition (TVFEMD) was adopted to decompose the AQI sequence into multiple internal mode functions (IMF) components. Secondly, multi-scale fuzzy entropy (MFE) was applied to evaluate the complexity of each IMF component and clustered them into high and low-frequency portions. In addition, the high-frequency portion was secondarily decomposed by successive variational mode decomposition (SVMD) to reduce volatility. Then, six air pollutant concentrations, namely CO, SO2, PM2.5, PM10, O3, and NO2, were used as inputs. The secondary decomposition and preliminary portion were employed as the outputs for the bidirectional long short-term memory network optimized by the snake optimization algorithm (SOABiLSTM) and improved Catboost (ICatboost), respectively. Furthermore, extreme gradient boosting (XGBoost) was applied to ensemble each predicted sub-model to acquire the consequence. Ultimately, we introduced adaptive kernel density estimation (AKDE) for interval estimation. The empirical outcome indicated the TMSSICX model achieved the best performance among the other 23 models across all datasets. Moreover, implementing the XGBoost to ensemble each predicted sub-model led to an 8.73%, 8.94%, and 0.19% reduction in RMSE, compared to SVM. Additionally, by utilizing SHapley Additive exPlanations (SHAP) to assess the impact of the six pollutant concentrations on AQI, the results reveal that PM2.5 and PM10 had the most notable positive effects on the long-term trend of AQI. We hope this model can provide guidance for air quality management.

Future Anxiety in Young Spanish Adults: Psychometric Properties of the Dark Future Scale / Ansiedad Futura en Adultos Jóvenes Españoles: Propiedades Psicométricas de la Dark Future Scale

Background/Objective: The Dark Future Scale (DFS) is a self-report instrument which assesses the tendency to think about the future with anxiety, fear, and uncertainty. Although it has been applied in different populations, instrumental studies are scarce, and there is no validated Spanish version. The aim was therefore to develop a Spanish version of the scale (DFS-S) and to analyze its psychometric properties in a sample of young adults. Method: Participants were 1,019 individuals aged from 18 to 24 years. They completed the DFS-S and the IPIP-BFM-20. Validity evidence based on the internal structure, including measurement invariance across gender, as well as on relationships with personality traits was obtained. Reliability and gender differences in DFS-S scores were also examined. Results: Results supported a single-factor structure, χ2(5) = 10.79, CFI = .999, RMSEA = .034, SRMR = .016, that was invariant across gender. Reliability of test scores was satisfactory (ω = .92). In the correlation analysis, future anxiety showed a strong positive correlation with neuroticism (.42) and a moderate negative correlation with extraversion (-.25). Females scored higher than males on future anxiety. Conclusions: The DFS-S has satisfactory psychometric properties and it is an adequate tool for measuring future anxiety among young adults.(AU)

Antecedentes/Objetivo: La Dark Future Scale (DFS) evalúa la ten-dencia a pensar en el futuro con ansiedad, miedo e incertidumbre. Aunque ha sido usadaen diferentes poblaciones, los estudios instrumentales son es-casos y no hay una versión adaptada al español. El objetivo del estudio fue adaptarla al español (DFS-S) y analizar sus propiedades psicométricas en una muestra de adultos jóvenes. Método:Participaron 1.019 jóvenes entre 18 y 24 años. Completaron la DFS-S y el IPIP-BFM-20. Se analizan evidencias de validez basadas en la estructura interna, incluyendo la invarianza de me-dida según el género, y basadas en las relaciones con rasgos de personali-dad, así como análisis de la fiabilidad y de las diferencias de género. Resulta-dos:Los resultados apoyaron una estructura de un solo factor, χ2(5) = 10.79, CFI = .999, RMSEA = .034, SRMR = .016, con invarianza respecto al gé-nero, y con coeficiente de fiabilidad satisfactorio (ω= .92). Se encontró co-rrelación positiva fuerte entre ansiedad futura y neuroticismo (.42) y una correlación negativa moderada con extraversión (-.25). Las puntuaciones en ansiedad futura fueron mayores en las mujeres. Conclusiones:Los resultados muestran propiedades psicométricas satisfactorias delaDFS-S, siendo un instrumento adecuado para medir la ansiedad futura en adultos jóvenes.(AU)

Uncertainty in the mating strategy of honeybees causes bias and unreliability in the estimates of genetic parameters.

BACKGROUND: Breeding queens may be mated with drones that are produced by a single drone-producing queen (DPQ), or a group of sister-DPQs, but often only the dam of the DPQ(s) is reported in the pedigree. Furthermore, datasets may include colony phenotypes from DPQs that were open-mated at different locations, and thus to a heterogeneous drone population. METHODS: Simulation was used to investigate the impact of the mating strategy and its modelling on the estimates of genetic parameters and genetic trends when the DPQs are treated in different ways in the statistical evaluation model. We quantified the bias and standard error of the estimates when breeding queens were mated to one DPQ or a group of DPQs, assuming that this information was known or not. We also investigated four alternative strategies to accommodate the phenotypes of open-mated DPQs in the genetic evaluation: excluding their phenotypes, adding a dummy pseudo-sire in the pedigree, or adding a non-genetic (fixed or random) effect to the statistical evaluation model to account for the origin of the mates. RESULTS: The most precise estimates of genetic parameters and genetic trends were obtained when breeding queens were mated with drones of single DPQs that are correctly assigned in the pedigree. However, when they were mated with drones from one or a group of DPQs, and this information was not known, erroneous assumptions led to considerable bias in these estimates. Furthermore, genetic variances were considerably overestimated when phenotypes of colonies from open-mated DPQs were adjusted for their mates by adding a dummy pseudo-sire in the pedigree for each subpopulation of open-mating drones. On the contrary, correcting for the heterogeneous drone population by adding a non-genetic effect in the evaluation model produced unbiased estimates. CONCLUSIONS: Knowing only the dam of the DPQ(s) used in each mating may lead to erroneous assumptions on how DPQs were used and severely bias the estimates of genetic parameters and trends. Thus, we recommend keeping track of DPQs in the pedigree, and not only of the dams of DPQ(s). Records from DPQ colonies with queens open-mated to a heterogeneous drone population can be integrated by adding non-genetic effects to the statistical evaluation model.

Do incidental positive emotions induce more optimistic expectations of decision outcomes? An empirical study from the perspective of event-related potential.

INTRODUCTION: Previous research has found that incidental emotions of different valences (positive/negative/neutral) influence risky decision-making. However, the mechanism of their influence on psychological expectations of decision outcomes remains unclear. METHODS: We explored the effects of different incidental emotions on the behavioral, psychological, and electrophysiological responses of individuals in risky decision-making through a money gambling task using a one-way (emotion type: positive, negative, neutral emotions) between-subjects experimental design. RESULTS: Individuals with positive emotions had significantly greater risk-seeking rates than those with negative emotions during the decision selection phase (p < .01). In the feedback stage of decision outcomes, individuals showed stronger perceptions of uncertainty in the decision environment under gain and loss feedback compared with neutral feedback, as evidenced by a more positive P2 component (i.e., the second positive component of an event-related potential). Positive emotions produced greater than expected outcome bias than neutral emotions, as evidenced by a more negative FRN component (i.e., the feedback-related negativity component). CONCLUSION: Our results suggest that positive emotions increase individuals' psychological expectations of decision outcomes. This study provides new empirical insights to understand the influence of incidental emotions on risky decision outcome expectations.

Partial label learning for automated classification of single-cell transcriptomic profiles.

Single-cell RNA sequencing (scRNASeq) data plays a major role in advancing our understanding of developmental biology. An important current question is how to classify transcriptomic profiles obtained from scRNASeq experiments into the various cell types and identify the lineage relationship for individual cells. Because of the fast accumulation of datasets and the high dimensionality of the data, it has become challenging to explore and annotate single-cell transcriptomic profiles by hand. To overcome this challenge, automated classification methods are needed. Classical approaches rely on supervised training datasets. However, due to the difficulty of obtaining data annotated at single-cell resolution, we propose instead to take advantage of partial annotations. The partial label learning framework assumes that we can obtain a set of candidate labels containing the correct one for each data point, a simpler setting than requiring a fully supervised training dataset. We study and extend when needed state-of-the-art multi-class classification methods, such as SVM, kNN, prototype-based, logistic regression and ensemble methods, to the partial label learning framework. Moreover, we study the effect of incorporating the structure of the label set into the methods. We focus particularly on the hierarchical structure of the labels, as commonly observed in developmental processes. We show, on simulated and real datasets, that these extensions enable to learn from partially labeled data, and perform predictions with high accuracy, particularly with a nonlinear prototype-based method. We demonstrate that the performances of our methods trained with partially annotated data reach the same performance as fully supervised data. Finally, we study the level of uncertainty present in the partially annotated data, and derive some prescriptive results on the effect of this uncertainty on the accuracy of the partial label learning methods. Overall our findings show how hierarchical and non-hierarchical partial label learning strategies can help solve the problem of automated classification of single-cell transcriptomic profiles, interestingly these methods rely on a much less stringent type of annotated datasets compared to fully supervised learning methods.

Uncertainty among patients with advanced-stage lung cancer.

BACKGROUND: Uncertainty is the inability to define the meaning of illness-related events, which may result in anxiety, depression, poor coping, the self-perception of being a burden and low quality of life. Uncertainty among Thai patients with advanced-stage lung cancer (ASLC) has not been well documented. AIMS: To assess uncertainty in patients with ASLC. METHODS: A cross-sectional survey design was adopted. Data were collected from 60 patients with ASLC at a university hospital. A demographic data form and the Mishel Uncertainty in Illness Scale (MUIS) were used to collect data. The data were analysed using descriptive statistics. RESULTS: The patients had moderate levels of uncertainty in illness (83.73±15.25). Ambiguity about the illness and unpredictability of the prognosis scored at a moderate level for patients, while complexity of treatment and the system of care and inconsistency or lack of information, about the diagnosis or severity of the illness were at a low level. CONCLUSION: The results of this study may help healthcare professionals better understand and manage uncertainty in patients with ASLC.

Is exercise therapy the right treatment for rotator cuff-related shoulder pain? Uncertainties, theory, and practice.

BACKGROUND: Exercise therapy is a popular non-surgical treatment to help manage individuals with rotator cuff-related shoulder pain (RCRSP) and is recommended in all clinical practice guidelines. Due to modest effect sizes, low quality evidence, uncertainty relating to efficacy, and mechanism(s) of benefit, exercise as a therapeutic intervention has been the subject of increasing scrutiny. AIMS: The aim of this critical review is to lay out where the purported uncertainties of exercise for RCRSP exist by exploring the relevant quantitative and qualitative literature. We conclude by offering theoretical and practical considerations to help reduce the uncertainty of delivering exercise therapy in a clinical environment. RESULTS AND DISCUSSION: Uncertainty underpins much of the theory and practice of delivering exercise therapy for individuals with RCRSP. Nonetheless, exercise is an often-valued treatment by individuals with RCRSP, when provided within an appropriate clinical context. We encourage clinicians to use a shared decision-making paradigm and embrace a pluralistic model when prescribing therapeutic exercise. This may take the form of using exercise experiments to trial different exercise approaches, adjusting, and adapting the exercise type, load, and context based on the individual's symptom irritability, preferences, and goals. CONCLUSION: We contend that providing exercise therapy should remain a principal treatment option for helping individuals with RCRSP. Limitations notwithstanding, exercise therapy is relatively low cost, accessible, and often valued by individuals with RCRSP. The uncertainty surrounding exercise therapy requires ongoing research and emphasis could be directed towards investigating causal mechanisms to better understand how exercise may benefit an individual with RCRSP.

Fear, Perceived Threat, and Anxiety During COVID-19 Pandemic: The Mediating Role of Cognitive Control/Flexibility and Intolerance of Uncertainty. / COVID-19 Pandemisinde Korku, Algilanan Tehdit ve Kaygi: Bilissel Kontrol/Esneklik ve Belirsizlige Tahammülsüzlügün Aracilik Rolü.

OBJECTIVE: In this study, we aimed to evaluate the relationship between fear of COVID-19, perceived threat of COVID-19, anxiety, cognitive control/flexibility, and intolerance to uncertainty. In addition, the mediating role of cognitive control/flexibility and intolerance to uncertainty were investigated. METHOD: 224 volunteers aged between 18-55 years were included in the study. Cognitive Control and Felxibility Scale, Intolerance of Uncertainty Scale, Fear of COVID-19 Scale, Perceived COVID-19 Threat Form and Beck Anxiety Scales were administered to all participants via an online environment. In this context, Pearson correlation, linear regression, and mediation analyzes were performed. RESULTS: There were significant relationships among Cognitive Control and Flexibility, Intolerance of Uncertainty, Beck Anxiety, fear of COVID-19, perceived COVID-19 threat (p<0,01). Linear regression analysis showed that the Beck Anxiety Scale, Intolerance of Uncertainty and Cognitive Control/ Flexibility Scale scores significantly predicted fear of COVID-19 and the perceived threat of COVID-19 (p<0,001). In addition, mediation analysis revealed that intolerance to uncertainty and cognitive control/flexibility are mediating factors between anxiety and the perceived threat of COVID-19 (p<0,01). CONCLUSION: There is a relationship between fear of COVID-19 and perception of threat, anxiety, intolerance of uncertainty, and cognitive control mechanisms. In accordance with these findings, psychosocial support and therapy programs can be created based on cognitive control/flexibility and intolerance of uncertainty in order to increase the mental health well-being of individuals.

Development and psychometric evaluation of uncertainty about disease and treatment scale in hemodialysis patients: a sequential-exploratory mixed-method study.

BACKGROUND AND OBJECTIVE: The need for long-term treatment and frequent visits to treatment centers for hemodialysis can lead to psychological problems such as Uncertainty about Disease and Treatment (UC about D&T) in patients with chronic kidney failure. In order to understand uncertainty about disease and treatment and to plan for preventive measures and care interventions in various dimensions, there is a need for reliable and valid tools. The present study was conducted to design and psychometrically evaluate the Uncertainty about Disease and Treatment Scale (UC about D&TS) in patients undergoing hemodialysis. METHODS: This study is of a methodological type and conducted in two stages. The first stage included a deductive (literature review) and an inductive approach (face-to-face interviews). In the second stage, psychometric indices of the UC about D&TS, including face validity (qualitative-quantitative), content validity (qualitative-quantitative), construct validity (exploratory factor analysis), and reliability (using Cronbach's alpha and McDonald's omega) were examined. RESULTS: In the literature review stage, 66 items were extracted, and in the qualitative stage, 48 items were extracted. After merging similar items, 29 items were entered into the psychometric process. No items were removed in the face and content validity stages. In the construct validity stage, five factors were extracted, including self-uncertainty, uncertain situation, uncertain future, uncertainty of treatment outcomes, and information uncertainty, which constituted a total of 82.16% of the total variance. In this stage, five items were removed from the study due to a corrected item-total correlation below 0.32, and four items were removed due to cross-loading. The α and Ω were calculated as 0.828 and 0.818, respectively. The measurement stability and standard error of measurement were estimated at 0.977 and 2.019, respectively. CONCLUSION: The results showed that the UC about D&TS is a valid and reliable measure for patients undergoing hemodialysis. This scale is specifically designed to measure UC about D&T in hemodialysis patients, and it is recommended that healthcare providers (Hcps) use this scale in follow-up visits.

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

[Psychometric properties of the Hungarian version of the Intolerance of Uncertainty Scale - Short Version (IUS-12)]. / Az alkoholhasználat és az öndetermináció kapcsolata: a Kezelési Önszabályozási Kérdoív pszichometriai jellemzoi magyar mintán.

INTRODUCTION: Intolerance of uncertainty is the tendency to react negatively to an uncertain situation, regardless of the probability of the occurrence of the event and its consequences. Intolerance of uncertainty (IU) can also be conceptualized as a personality trait that is prominent in many anxiety and rumination-related pathologies. A growing body of research highlights its key role in understanding anxiety disorders. METHOD: The aim of present study was to investigate the dimensionality, validity and reliability of the Intolerance of Uncertainty Scale in a large non-clinical sample (N = 1747). Former was analysed by confirmatory factor analysis, the validity by correlation with the Perceived Stress Scale. Reliability was assessed using Cronbach's alpha coefficient and test-retest analysis. RESULTS: Confirmatory factor analysis failed to confirm the hypothesized two-factor structure (CFI = 0.907; TLI = 0.885; RMSEA = 0.103 [90% CI = 0.096-0.110]; SRMR = 0.071). However, the exploratory factor analysis identified the same two factors as in the original study: "Prospective" and "Inhibitory". The scale showed excellent internal reliability (α = 0.897) and test-retest reliability. There was moderate correlation with the Perceived Stress Scale (r = 0.438). CONCLUSION: Based on the results, the Hungarian version of the BTS-12 is a valid and reliable measurement tool. However, before its use in a Hungarian sample, its psychometric properties need to be confirmed by further studies on a large sample. In the future, the questionnaire will be useful in measuring intolerance of uncertainty and may be useful in identifying susceptibility to anxiety disorders.