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.

The effects of prefrontal tDCS and hf-tRNS on the processing of positive and negative emotions evoked by video clips in first- and third-person.

The causal role of the cerebral hemispheres in positive and negative emotion processing remains uncertain. The Right Hemisphere Hypothesis proposes right hemispheric superiority for all emotions, while the Valence Hypothesis suggests the left/right hemisphere's primary involvement in positive/negative emotions, respectively. To address this, emotional video clips were presented during dorsolateral prefrontal cortex (DLPFC) electrical stimulation, incorporating a comparison of tDCS and high frequency tRNS stimulation techniques and manipulating perspective-taking (first-person vs third-person Point of View, POV). Four stimulation conditions were applied while participants were asked to rate emotional video valence: anodal/cathodal tDCS to the left/right DLPFC, reverse configuration (anodal/cathodal on the right/left DLPFC), bilateral hf-tRNS, and sham (control condition). Results revealed significant interactions between stimulation setup, emotional valence, and POV, implicating the DLPFC in emotions and perspective-taking. The right hemisphere played a crucial role in both positive and negative valence, supporting the Right Hemisphere Hypothesis. However, the complex interactions between the brain hemispheres and valence also supported the Valence Hypothesis. Both stimulation techniques (tDCS and tRNS) significantly modulated results. These findings support both hypotheses regarding hemispheric involvement in emotions, underscore the utility of video stimuli, and emphasize the importance of perspective-taking in this field, which is often overlooked.

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.

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).

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.

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.

[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.

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.

Palliative sedation - revised recommendations.

Palliative sedation is defined as the monitored use of medications intended to induce a state of decreased or absent awareness (unconsciousness) to relieve the burden of otherwise intractable suffering in a manner ethically acceptable to the patient, their family, and healthcare providers. In Switzerland, the prevalence of continuous deep sedation until death increased from 4.7% in 2001 to 17.5% of all deceased in 2013, depending on the research method used and on regional variations. Yet, these numbers may be overestimated due to a lack of understanding of the term "continuous deep sedation" by for example respondents of the questionnaire-based study. Inadequately trained and inexperienced healthcare professionals may incorrectly or inappropriately perform palliative sedation due to uncertainties regarding its definitions and practice. Therefore, the expert members of the Bigorio group and the authors of this manuscript believe that national recommendations should be published and made available to healthcare professionals to provide practical, terminological, and ethical guidance. The Bigorio group is the working group of the Swiss Palliative Care Society whose task is to publish clinical recommendations at a national level in Switzerland. These recommendations aim to provide guidance on the most critical questions and issues related to palliative sedation. The Swiss Society of Palliative Care (palliative.ch) mandated a writing board comprising four clinical experts (three physicians and one ethicist) and two national academic experts to revise the 2005 Bigorio guidelines. A first draft was created based on a narrative literature review, which was internally reviewed by five academic institutions (Lausanne, Geneva, Bern, Zürich, and Basel) and the heads of all working groups of the Swiss Society of Palliative Care before finalising the guidelines. The following themes are discussed regarding palliative sedation: (a) definitions and clinical aspects, (b) the decision-making process, (c) communication with patients and families, (d) patient monitoring, (e) pharmacological approaches, and (f) ethical and controversial issues. Palliative sedation must be practised with clinical and ethical accuracy and competence to avoid harm and ethically questionable use. Specialist palliative care teams should be consulted before initiating palliative sedation to avoid overlooking other potential treatment options for the patient's symptoms and suffering.

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.

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.

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.

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.

Value of Information for Clinical Trial Design: The Importance of Considering All Relevant Comparators.

Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.

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.

Dispositional factors in the explanation of symptoms of depression, anxiety, health anxiety and COVID-19 Phobia.

Maladaptive personality, the motivational systems, and intolerance of uncertainty play important roles in the statistical explanation of depression and anxiety. Here, we notably examined for the first time whether symptoms of depression, anxiety, health anxiety, and fear of COVID-19 share similar associations (e.g., variance explained) with these important dispositional dimensions. For this cross-sectional study, data from 1001 participants recruited in Germany (50% women; mean age = 47.26) were collected. In separate models, we examined the cross-sectional associations of the symptoms of depression, anxiety, health anxiety, and fear of COVID-19 with the Personality Inventory for DSM Short Form Plus scales, the Behavioral Inhibition System / Flight-Fight-Freeze System / Behavioral Activation System scales, and Intolerance of Uncertainty scales. Relative weight analyses were used to determine the within-model importance of the different scales in the prediction of the symptoms. All in all, our study showed that maladaptive personality and intolerance of uncertainty dimensions are more important sets of predictors of the studied outcomes (with which depressive and anxious symptomatology feature very similar associations) than are the motivational system dimensions. Within predictor sets, the scales with the most important predictors were: Negative Affectivity, the Behavioral Inhibition System, and Burden due to Intolerance of Uncertainty. Our findings highlight the relevance of focusing behavioral targets of psychotherapy on these within-set traits and identify potential research priorities (maladaptive personality and intolerance of uncertainty) in relation to the symptoms of interest.

Moderating role of political stability and economic policy uncertainty between country governance practice and stock market performance. A comparative analysis of Pakistan and Kurdistan Region of Iraq.

The objective of the study is to explore the relationship between country governance practices along with political stability and Economic policy uncertainty, and stock market performance of two different economies, Pakistan and Kurdistan region of Iraq. To meet our objectives, we used the 25 years past data from 1996 to 2021. Data is collected from the DataStream database. The regression analysis is used as the method of estimation for linear and moderation effect. Our results show that regulatory quality, rules of law and political stability has significant positive relationship with stock market performance of Pakistan, but all the governance indicators have significant positive relationship with stock market performance of the Kurdistan Region of Iraq. Moreover, political stability has significant moderating impact between the governance practices and the performance of the stock markets of both economies indicating that the governance practices perform well with the political stability that leads to rise in the stock market indices of selected countries. Economic policy uncertainty has significant negative moderation impact due to creating the risk in both economies that decrease the performance of the stock markets of the selected economies. Finally, our study advocated some implications for the investors to increase their confidence on the stock of high political stability and low economic policy uncertainty economies. Government can take significant measures to control the uncertainty of the policy and portfolio managers can adjust their risk on the ground of the political stability and efficient governance practices countries.

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.