Subjective Variables Research Articles

Optimizing Recovery Strategies in Elite Speedskating: A Comparative Analysis of Different Modalities.

Background/Objectives: As short-track speed skaters have to race multiple races to achieve success during competition, optimizing the recovery between efforts is a noteworthy performance determinant. Therefore, we compared three different recovery modalities (active cycling recovery, pneumatic compression boots, and isocapnic breathing protocol) in the context of perceived subjective pain and recovery variables, multiple biochemical and biomechanical indices, CMJ height and power, as well as repeated efforts on the ice track. Methods: Fifteen elite short-track speed skaters (eight males and seven females; age 18.3 ± 1.0 years, height 175.6 ± 7.5 cm, weight 73.7 ± 7.7 kg, 23.8 kg/m2, VO2max 55.5 mL·kg-1·min-1: ♂ 58 20 ± 3.6 mL·kg-1·min-1; and ♀ 53 ± 4.5 mL·kg-1·min-1) completed the study experiment and were included in the analyses. Repeated measures ANOVA with optional post hoc Bonferroni correction was used to assess the association magnitude of changes in variables across the recovery methods. Results: All the investigated protocols were associated with significant changes in multiple recovery indices observed within all the investigated protocols (p ≤ 0.05). However, for this sample, they resulted in analogous effects on subjective variables, hormonal response, creatine kinase, CMJ parameters, and on-ice performance (between-protocol effect: p ≥ 0.002). Changes in creatine kinase were generally higher in males than females (p = 0.05), which might suggest that optimal recovery protocols in short-track are gender-dependent. Conclusions: Since compression and active cycling remain gold standard recovery protocols, a similar response from isocapnic breathing suggests it may be a modality particularly useful in real-world settings.

In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models Against Variability.

Deep learning (DL)-based Human Activity Recognition (HAR) using wearable inertial measurement unit (IMU) sensors can revolutionize continuous health monitoring and early disease prediction. However, most DL HAR models are untested in their robustness to real-world variability, as they are trained on limited lab-controlled data. In this study, we isolated and analyzed the effects of the subject, device, position, and orientation variabilities on DL HAR models using the HARVAR and REALDISP datasets. The Maximum Mean Discrepancy (MMD) was used to quantify shifts in the data distribution caused by these variabilities, and the relationship between the distribution shifts and model performance was drawn. Our HARVAR results show that different types of variability significantly degraded the DL model performance, with an inverse relationship between the data distribution shifts and performance. The compounding effect of multiple variabilities studied using REALDISP further underscores the challenges of generalizing DL HAR models to real-world conditions. Analyzing these impacts highlights the need for more robust models that generalize effectively to real-world settings. The MMD proved valuable for explaining the performance drops, emphasizing its utility in evaluating distribution shifts in HAR data.

Influence of Menstrual Phase and Symptoms on Sleep Before and After Matches for Professional Footballers.

This study investigated the association of menstrual cycle phase and symptoms with objective and subjective sleep measures from professional footballers before and after matches. Twenty-three non-hormonal contraceptive-using professional footballers (from four clubs) were monitored for up to four menstrual cycles during a domestic league season. Menstrual phases (menstruation, mid-late follicular, luteal) were determined using calendar counting and urinary hormone tests (luteinizing hormone and pregnandiol-3-glucuronide). Players rated the severity of 18 symptoms on the evenings of matches and the following two evenings. Individual daily summed menstrual symptom severity (MSS) scores were calculated. Subjective sleep quality was rated the morning of matches and the following two mornings. Objective sleep (bedtime, waketime, total sleep time [TST], sleep onset latency, wake after sleep onset, sleep efficiency) obtained from actigraphy was measured the night prior to (MN-1), of (MN), and following (MN+1) matches. Linear mixed models were performed for each sleep measure to examine the effects of menstrual phase and symptoms. Bedtime was significantly later for MN (p < 0.001), waketime was significantly earlier for MN+1 (p < 0.001), and TST was significantly longer for MN-1 (p < 0.001). Menstrual phase did not have a significant effect on any sleep variable (p > 0.05). Increased MSS score was associated with increased TST (p = 0.03) and increased waketime (p = 0.03). Increased lower back pain severity and mood changes/anxiety severity were associated with increased waketime (p = 0.048) and TST (p = 0.009), respectively. Overall, bedtime and waketime were affected by the night related to matches, with increased TST the night before a match. Menstrual phase was not related to any objective or subjective sleep variables, whilst increasing menstrual symptom severity was related to later waketime and longer TST.

Observational comparison of fatigue and sleep disturbance after traumatic injury, in those with and without concomitant mild traumatic brain injury.

Fatigue and subjective sleep disturbance are elevated after mild traumatic brain injury (mTBI), raising the question of whether mTBI-specific factors contribute to the experience of fatigue and subjective sleep disturbance after mTBI. 110 premorbidly healthy individuals who had suffered a traumatic injury during an accident approximately 8 weeks prior were examined and assessed with subjective measures of fatigue and sleep disturbance, psychological distress and pain. Individuals with a traumatic injury reported significant elevations in most fatigue and all subjective sleep disruption components compared to community-based control participants (n = 45). After controlling for pain, psychological status, sex and premorbid intellectual functioning, those individuals experiencing a mTBI at time of traumatic injury (n = 64) had equivalent levels of fatigue and subjective sleep disruption as those who had experienced no head injury at time of traumatic injury (n = 46). Trauma group membership did not significantly predict fatigue or subjective sleep disturbance. In contrast, psychological distress consistently predicted fatigue and subjective sleep disturbance, with pain also inconsistently predicting some fatigue and subjective sleep disturbance variables. This study suggests it is the experience of suffering traumatic injury, rather than mTBI-specific factors, that is associated with elevations in fatigue and subjective sleep disturbance, approximately 8 weeks after injury.

Measuring The Level of Cybersecurity Awareness of Social Media Users Among Students

The role of social media is increasingly important and easily accessible through mobile phones, slowly replacing conventional mass media. The development of Internet technology has made many users forget the importance of cybersecurity awareness, which impacts social media activities. As a result, cyber-attacks on social networks are now more frequent because many users still need help understanding cybersecurity well. Based on data BSSN, cyberattacks in Indonesia increased significantly from 290.3 million in 2019 to 495.3 million in 2020, representing a 41% rise. Social media users need to understand cybersecurity, a technology that protects data, networks, and programs from illegal access or digital attacks, known as cybercrime. Eighty-four respondents participated in the study. This study aims to measure the level of cybersecurity awareness and contribute to the existing literature by providing empirical insights into the level of cybersecurity awareness among university students. This study can also help raise awareness among the public, particularly young users, about the importance of protecting privacy and security while engaging in the digital world. This behaviour is measured using the five TPB variables: attitude, subjective form, perceived behaviour control, intention, and behaviour. Variables from TPB are then processed using SEM-PLS tools with the help of SmartPLS. Based on the study's results, it is concluded that the attitude and subjective form variables do not significantly affect the intention variable. In contrast, the perceived behaviour control variable significantly affects the intention variable, and the behaviour variable significantly affects intention.

An analytical framework for corpus-based translation studies

Corpus-based translation studies (CBTS) have undergone significant evolution, transitioning from descriptive methodologies to theoretical and applied approaches in recent years. However, the analysis of corpus-based research outcomes is crucial, and the absence of a unified framework often leads to less experienced researchers overlooking critical factors. This, in turn, results in varied interpretations of the same data, substantially compromising the objectivity and scientific rigor of the approach. Inspired by House’s (2014) model of translation quality assessment, Berman (2009)’s view on translation criticism, and De Sutter and Lefer (2020)’s multi-methodological, multifactorial, and interdisciplinary approach to CBTS, this study proposes a tripartite empirical-analytical framework to help researchers identify the potential factors influencing translator decision-making: textual characteristics, translator’s personal attributes, and the sociocultural context of the target language. To evaluate its utility, utilizing the mixed-effects logistic regression method, a case study is conducted to examine significant factors conditioning the reporting verb say and its Chinese translations in an English-Chinese parallel corpus of news texts, employing Appraisal Theory as the basis to determine equivalences and non-equivalences between the source language and target language. The case study shows that the framework facilitates a comprehensive analysis of the corpus findings by encompassing diverse perspectives within this scaffold. As digital technology, studies in multimodal discourse, and CBTS continue to intersect, the framework can also incorporate non-linguistic elements and AI translation tools, provided there are explicit criteria for examining translation phenomena. This framework equips researchers with a comprehensive set of perspectives, enabling them to consider as many factors as possible, thus bolstering the objectivity and scientific rigor of CBTS. The combined use of the structured framework and the multivariate analysis technique offers a holistic approach and stands as a critical advancement in CBTS by standardizing the analysis process and mitigate the subjective variability inherent in explaining translation phenomena.

Impact of Nanoparticles as an Air Pollutant on Angulin-1/Lipolysis-Stimulated Lipoprotein Receptor in Asthma

Background: The tricellular tight junction protein angulin-1/lipolysis-stimulated lipoprotein receptor (LSR) is linked to numerous signal transduction pathways that govern gene expression, epithelial cell function, and morphogenesis. The effect of titanium dioxide (TiO2) on LSR and asthma remains unknown. The objective of the present study was to evaluate the impact of TiO2 on LSR expression in asthma. Methods: A TiO2-induced animal model of asthma was established using BALB/c mice and cell lines using normal human bronchial epithelial (NHBE) lung cells and we examined LSR, RAGE, and TGFβ expression using this model. Additionally, we analyzed plasma-LSR concentrations and their correlation with clinical variables in asthma patients and control subjects. Results: The LSR concentrations in patients with asthma were lower compared to controls, and were correlated with lung function and inflammatory cell ratio. In NHBE cells treated with Derp1, LSR protein expression was reduced and changed by exposure to TiO2, whereas TGFβ expression was increased and changed. In mouse lungs, LSR expression was significantly reduced in OVA mice and changed in OVA/TiO2 mice. Conclusion: Circulating LSR levels were decreased and correlated with clinical variables in patients with asthma, and they were influenced by TiO2 exposure in mice, suggesting the potential involvement of LSR in asthma pathogenesis.

Placebo effect in the treatment with antidepressants : Implications for the scientific evaluation and clinical use of pharmaceutical treatments of depression

Blinded placebo treatment arms in clinical trials often achieve up to 80% of the clinical improvements of the verum groups. Apparently, the majority of the effects found in the antidepressant groups in clinical trials are due to factors that are not specific to antidepressant treatment. This article reviews the factors that contribute to the high effectiveness of placebo interventions for antidepressants. Anarrative literature review is presented with particular emphasis on placebo effects in antidepressant clinical trials. There is aparticularly strong placebo effect in depression compared to other mental disorders. The magnitude of this effect can be modulated by helpful versus nonhelpful instructions, by patient participation in the decision-making process but also by personal attention, especially if the clinician is perceived as competent and warmhearted. The occurrence of subtle side effects can also reinforce placebo effects. Placebo effects are not only reflected in subjective patient-reported outcome variables but are also evident in neurochemical changes in the body. Clinicians essentially contribute to the effectiveness of antidepressant treatment through their own behavior. At the same time, the relatively small overall difference between verum and placebo treatments for depression leads to the necessity of acritical evaluation of the cost-benefit ratio, adapted to the individual case. Study designs for the evaluation of antidepressants are required that better reflect the complex interactions between the genuine drug effects and placebo effects.

The Impact of Chronic Hypertension, Kidney Disorders, and Employment Status on Preeclampsia in Makassar

Preeclampsia is a serious pregnancy complication characterized by hypertension and organ dysfunction, especially the kidneys, which usually appear after 20 weeks of gestation. In Indonesia, preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. The high maternal mortality rate in this country requires a deeper understanding of the risk factors for preeclampsia in order to formulate more effective and targeted prevention strategies. This study aims to identify the main risk factors for preeclampsia in pregnant women in the Pampang Health Center working area, Makassar City, and to evaluate the influence of these factors on the incidence of preeclampsia. This study used a cross-sectional design involving 200 pregnant women, consisting of 100 women with preeclampsia and 100 without preeclampsia. Taken systematically from medical records for the period 2021 – 2023. Data were collected from medical records and confirmed through interviews if necessary. Data analysis used the Chi-square test with p &lt;0.05. The main findings show that a history of preeclampsia, chronic hypertension, renal impairment, autoimmune conditions, and maternal employment status are significant risk factors for preeclampsia. Working mothers with chronic hypertension had a higher risk of preeclampsia (81.8%) compared to non-working mothers (30.1%). This study highlights the importance of intensive monitoring for mothers with high-risk factors. Working mothers are more susceptible to stress and hypertension, increasing the risk of preeclampsia. A history of autoimmune and renal impairment also shows a strong association with preeclampsia. History of preeclampsia, chronic hypertension, renal impairment, autoimmune conditions, and maternal employment status are significant risk factors for preeclampsia. Recommendations include intensive monitoring, education and support for pregnant women, especially those who work, and further longitudinal studies for a clearer causal relationship. Objective medical validation is needed for subjective variables to improve data accuracy.

Circadian rhythms and objective measures of physical activity among middle-aged and older adults in Taiwan

BackgroundTo identify the association between daytime activity with objectively monitor and subjective variables at a different time-zone of day as well as the sex differences. MethodsParticipants aged 50 years and older living in the community in Taiwan were recruited. The activity was measured by wearable actigraphy devices, diaries, and self-reported questionnaires. The study used nonparametric analysis to examine the association between mean activity level and demographic and subjective measurement variables. ResultsAmong the 55 participants enrolled in the study, data of 34 (62 %) participants who meet the criteria that wore a wearable actigraphy device for at least 7 completed days were analyzed. There is no significant relationship between each demographics and subjective measurement variables. However, actigraphy counts are significantly related to female's nutrition (Z = -2.367, p = 0.017*), and male's retirement status (Z = -2.132, p = 0.033*). ConclusionsThe activity in morning is highest of the day with significant correlation to evening activities. Moreover, actigraphy counts that indicating objective measure of physical activity in female is significantly related to physiological variables (nutritional status), while male are predicted by social variables (retired status).

Examining Brain Structures and Cognitive Functions in Patients with Recovered COVID-19 Infection: A Multicenter Study Using 7T MRI.

Emerging evidence suggests that severe acute respiratory syndrome, COVID-19, negatively impacts brain health, with clinical magnetic resonance imaging (MRI) showing a wide range of neurologic manifestations but no consistent pattern. Compared with 3 Tesla (3T) MRI, 7 Tesla (7T) MRI can detect more subtle injuries, including hippocampal subfield volume differences and additional standard biomarkers such as white matter lesions. 7T MRI could help with the interpretation of the various persistent post-acute and distal onset sequelae of COVID-19 infection. To investigate the differences in white matter hyperintensity (WMH), hippocampal subfields volumes, and cognition between patients hospitalized with COVID-19 and non-hospitalized participants in a multi-site/multi-national cohort. Original investigation of patients hospitalized with COVID-19 between 5/2020 and 10/2022 in 3 USA and 1 UK medical centers with follow-up at hospital discharge. A total of 179 participants without a history of dementia completed cognitive, mood and other assessments and MRI scans. COVID-19 severity, as measured by hospitalization vs no hospitalization. 7T MRI scans were acquired. All WMH and hippocampal subfield volumes were corrected for intracranial volumes to account for subject variability. Cognition was assessed using a comprehensive battery of tests. Pearson correlations and unpaired t-tests were performed to assess correlations and differences between hospitalized and non-hospitalized groups. We found similar WMH volume (4112 vs 3144mm³, p=0.2131), smaller hippocampal volume (11856 vs 12227mm³, p=0.0497) and lower cognitive and memory performance, especially the MoCA score (24.9 vs 26.4 pts, p=0.0084), duration completing trail making test B (97.6 vs 79.4 seconds, p=0.0285), Craft immediate recall (12.6 vs 16.4 pts, p<0.0001), Craft delay recall (12.0 vs 15.6 pts, p=0.0001), and Benson figure copy (15.2 vs 16.1 pts, p=0.0078) in 52 patients hospitalized for COVID-19 (19[37%] female; mean[SD] age, 61.1[7.4] years) compared with 111 age-matched non-hospitalized participants (66[59%] female; mean[SD] age, 61.5[8.4] years). Our results indicate that hospitalized COVID-19 cases show lower hippocampal volume when compared to non-hospitalized participants. We also show that WMH and hippocampal volumes correlate with worse cognitive scores in hospitalized patients compared with non-hospitalized participants, potentially indicating recent lesions and atrophy. Question: Do white matter hyperintensity burden, hippocampal whole and subfield volumes, and cognition differ between patients hospitalized with COVID-19 versus participants without hospitalization?Findings: We found no significant difference in white matter hyperintensity volume, but hippocampal volume was reduced, and cognitive and memory performance were worse in those hospitalized for COVID-19 compared with age-matched non-hospitalized group (either mild COVID-19 or no COVID-19 reported). In the hospitalized group, increased white matter hyperintensity and reduced hippocampal volumes are significantly higher correlated with worse cognitive and memory scores.Meaning: Adults hospitalized for COVID-19 had lower hippocampal volumes and worse cognitive performance than adults with COVID-19 that did not lead to hospitalization or without reported COVID-19 infection.

Gender differences in clinical characteristics and influencing factors of suicide attempts in first-episode and drug-naïve major depressive disorder patients with comorbid metabolic syndrome

BackgroundsPatients with major depressive disorder (MDD) have a high rate of metabolic syndrome (MetS), which could worsen disease progression. One of the most serious progressions in MDD is suicide attempts (SAs). Previous studies have found gender differences in MetS and SAs among MDD patients respectively. Therefore, we aimed to explore gender differences of SAs in first-episode and drug-naïve (FEDN) MDD patients with comorbid MetS.Methods1718 outpatients with FEDN MDD were recruited. Depression, anxiety and psychotic symptoms were evaluated using the Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA) and Positive and Negative Syndrome Scale (PANSS) positive subscale, respectively. Blood sugar, blood fat, blood pressure and body mass index (BMI) were measured to evaluate MetS.Results34.4% patients with FEDN MDD were diagnosed as MetS and those subjects with or without MetS differed in the distribution of SAs and gender. In MetS subgroup, 29.5% and 29.7% of male and female subjects had SAs respectively, without significant differences. However, compared with non-suicide attempters, suicide attempters had higher level of blood pressure in female subjects, while there are no differences in any clinical variables in male subjects. Additionally, the influencing factors for SAs differed by gender. The HAMA scores and BMI were variables associated with SAs in male patients while HAMA scores, marital status and systolic blood pressure (SBP) were associated with SAs in female patients. Furthermore, the receiver operating characteristics (ROC) curves, demonstrating the combination all influencing factors by gender, showed good performance and model accuracy.ConclusionsIn FEDN MDD patients with comorbid MetS, there were no gender differences in SAs. However, clinical characteristics and influencing factors of SAs differed in different gender groups.

Suicide risk among adult subjects hospitalized in an acute psychiatric ward: 6-year retrospective investigation

BackgroundThe phenomenon of suicide risk (SR) represents a psychiatric, social and environmental emergency. The acute psychiatric ward as the Italian Service for Psychiatric Diagnosis and Care (SPDC) represents the place where SR is high due to the acute and serious conditions of people hospitalized. The objective of this study was to evaluate the characteristics of subjects admitted to a SPDC over a 6-year period for SR represented by: suicidal ideation, attempted and failed suicide.MethodsWith a retrospective single-center observational design, we collected hospitalizations from 01/01/2017 to 31/12/2022 in the SPDC of AUSL-Modena for SR, analyzed the modality of SR and compared the demographic and clinical variables of subjects with SR with those hospitalized for other clinical reasons in the same period. Data were statistically analyzed.ResultsIn the 6-year of study period, we collected 2,930 hospitalizations in the SPDC of AUSL- Modena and among them, 68% (n = 528) were carried out due to SR, which represented the second leading cause of hospitalization (18%), in particular among females (Pearson Chi2 = 17.41, p < 0.001). Individuals with SR were more frequently voluntary admitted (Pearson Chi2 = 215.41, p < 0.001) for a shorter period (7.36 ± 8.16 vs 11.66 ± 15.93, t = 6.03, t-test, p < 0.001) and less frequently repeated the hospitalization during the study period for the same reason (Pearson Chi2 = 6.0, p = 0.014). The most frequent psychiatric disorders associated with SR were depressive, personality and adjustment disorders, which represented the most common factor associated with SR (68%), followed by family/relationship problems (12%) and alcohol/substance abuse (8%). We highlighted three kinds of suicidal risk: suicidal ideation (40%), often associated with personality disorders and substance use disorders, drug ingestion (30%) concomitant with conflicting family relationships and use of violent means (30%) associated with depressive disorders (Pearson Chi2 = 42.83, p = 0.002).ConclusionsOur study provides a real-world setting evaluation of subjects hospitalized for SR and, in accordance with literature, suggests that suicidal behavior is the product of many clinical and social factors’ interaction, that occurred in a crucial moment of life in vulnerable individuals. The identification of subjects at SR represents the first step of preventive multi-professional interventions.

Clustering for mitigating subject variability in driving fatigue classification using electroencephalography source-space functional connectivity features.

Objective.While Electroencephalography (EEG)-based driver fatigue state classification models have demonstrated effectiveness, their real-world application remains uncertain. The substantial variability in EEG signals among individuals poses a challenge in developing a universal model, often necessitating retraining with the introduction of new subjects. However, obtaining sufficient data for retraining, especially fatigue data for new subjects, is impractical in real-world settings.Approach.In response to these challenges, this paper introduces a hybrid solution for fatigue detection that combines clustering with classification. Unsupervised clustering groups subjects based on their EEG functional connectivity (FC) in an alert state, and classification models are subsequently applied to each cluster for predicting alert and fatigue states.Main results. Results indicate that classification on clusters achieves higher accuracy than scenarios without clustering, suggesting successful grouping of subjects with similar FC characteristics through clustering, thereby enhancing the classification process.Significance.Furthermore, the proposed hybrid method ensures a practical and realistic retraining process, improving the adaptability and effectiveness of the fatigue detection system in real-world applications.

Brain morphometrics correlations with age among 352 participants imaged with both 3T and 7T MRI: 7T improves statistical power and reduces required sample size.

Magnetic resonance imaging (MRI) at 7 Telsa (7T) has superior signal-to-noise ratio to 3 Telsa (3T) but also presents higher signal inhomogeneities and geometric distortions. A key knowledge gap is to robustly investigate the sensitivity and accuracy of 3T and 7T MRI in assessing brain morphometrics. This study aims to (a) aggregate a large number of paired 3T and 7T scans to evaluate their differences in quantitative brain morphological assessment using a widely available brain segmentation tool, FreeSurfer, as well as to (b) examine the impact of normalization methods for subject variability and smaller sample sizes on data analysis. A total of 452 healthy participants aged 29 to 68 were imaged at both 3T and 7T. Structural T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) images were processed and segmented using FreeSurfer. To account for head size variability, the brain volumes underwent intracranial volume (ICV) correction using the Residual (regression model) and Proportional (simple division to ICV) methods. The resulting volumes and thicknesses were correlated with age using Pearson correlation and false discovery rate correction. The correlations were also calculated in increasing sample size from 3 to the whole sample to estimate the sample size required to detect aging-related brain variation. 352 subjects (210 females) passed the image quality control with 100 subjects excluded due to excessive motion artifacts on 3T, 7T, or both. 7T MRI showed an overall stronger correlation between morphometrics and age and a larger number of significantly correlated brain volumes and cortical thicknesses. While the ICV is consistent between both field strengths, the Residual normalization method shows markedly higher correlation with age for 3T when compared with the Proportional normalization method. The 7T results are consistent regardless of the normalization method used. In a large cohort of healthy participants with paired 3T and 7T scans, we compared the statistical performance in assessing age-related brain morphological changes. Our study reaffirmed the inverse correlation between brain volumes and cortical thicknesses and age and highlighted varying correlations in different brain regions and normalization methods at 3T and 7T. 7T imaging significantly improves statistical power and thus reduces required sample size.

Subjective variability of the “just-right feeling”: Effectiveness of social media advertising design

With the rapid advancement of AI and algorithmic technologies, social media platforms have gained the ability to identify consumer personality traits. However, developing compelling advertising strategies for individuals with different self-construals remains challenging. Based on construal level theory, this research investigates the interactions between shot scale, advertising appeals, and self-construal on social media. The results of the four empirical studies indicated that independent individuals prefer advertisements with long-shot images and desirability appeals. In contrast, interdependent individuals favor advertisements with close-up images and feasibility appeals. Furthermore, the findings reveal that long-shot images match with desirability appeals or close-up images paired with feasibility appeals significantly increase the click-through rate and foster more positive advertising attitudes. The above findings are central to the feeling right during information processing, which plays a crucial role in advertising acceptance. Therefore, this research constructs a new framework for personalized advertisement design in social media and offers a practical guide for businesses seeking to optimize their advertising strategies.

Mediating role of self-esteem on university students’ physical activity attitude and frequency

Background and Study Aim. The health benefits of physical activity are well-known. Despite this, there is an ongoing decline in total physical activity (PA), especially among adolescents and young women. This decline points to the need to find more effective solutions. This study aimed to determine the relationship between physical activity and attitudes toward physical activity, and also between self-esteem body image (as subjective variables) and body mass index (as an objective variable). Material and Methods. This cross-sectional study measured and investigated 152 female students from various faculties in their first and second years of study at the Bucharest University of Economic Studies. The research utilized direct anthropometrical measurements and several self-reported instruments. These included questionnaires on self-esteem body image, physical activity attitude, and a report on the frequency of physical activity. Self-esteem was assessed using the Rosenberg Self-Esteem Scale. Body dissatisfaction was evaluated using the Contour Drawing Rating Scale. Results. The mean Body Mass Index (BMI) value of 20.89 kg/m² indicates a healthy, normal BMI. The mean self-esteem score (31.48) falls within the normal range, but 60% of respondents could not match their actual body shape with their ideal or desired body image. 54% of respondents reported engaging in 30 minutes of physical activity, three times a week, outside the university program. As a result, the mean value of the attitude towards physical activity is slightly positive. 33% of young women's self-esteem is related to physical factors such as body image, frequency of physical activity, and attitude towards physical activity. Conclusions. The study indicates that targeted physical education programs and consistent, independent physical activity can mitigate the challenges associated with the investigated variables. Moreover, self-esteem significantly mediates the relationship between body image and physical activity frequency, as well as students’ attitudes toward physical activity.

Review of deep learning-based pathological image classification: From task-specific models to foundation models

Pathological diagnosis is considered the gold standard in cancer diagnosis, playing a crucial role in guiding treatment decisions and prognosis assessment for patients. However, achieving accurate diagnosis of pathology images poses several challenges, including the scarcity of pathologists and the inherent subjective variability in their interpretations. The advancements in whole-slide imaging technology and deep learning methods provide new opportunities for digital pathology, especially in low-resource settings, by enabling effective pathological image classification. In this article, we begin by introducing the datasets, which include both unimodal and multimodal types, as essential resources for advancing pathological image classification. We then provide a comprehensive overview of deep learning-based pathological image classification models, covering task-specific models such as supervised, unsupervised, weakly supervised, and semi-supervised learning methods, as well as unimodal and multimodal foundation models. Next, we review tumor-related indicators that can be predicted from pathological images, focusing on two main categories: indicators that can be recognized by pathologists, such as tumor classification, grading, and region recognition; and those that cannot be recognized by pathologists, including molecular subtype prediction, tumor origin prediction, biomarker prediction, and survival prediction. Finally, we summarize the key challenges in digital pathology and propose potential future directions.

IEHT vs. Regular Treatment - Are there Differences between the Two forms of Treatment in the Retrospective Course with regard to Individual Objective and Subjective Outcome Variables?

This retrospective follow-up study over one year (after discharge from the index treatment), compares the effects of inpatient-equivalent home treatment (IEHT) and regular psychiatric treatment using subjective and objective quality indicators. Using a 1:1 matching of cohorts (diagnosis, age, gender, number of previous treatments and severity of illness), 732 IEHT patients were assigned to a comparison group. Outcome data was taken from the routine data of the clinic database and the in-house basic documentation. In the subjective quality measure, patients and practitioners in regular psychiatric treatment reported a significantly higher improvement in condition. In terms of the objective quality measure, the number of further inpatient treatment days in the observation period, the two groups did not differ significantly, which can be interpreted as encouragement for the use of this new, increasingly established form of treatment.

Camera-Based Respiratory Imaging System for Monitoring Infant Thoracoabdominal Patterns of Respiration.

Existing respiratory monitoring techniques primarily focus on respiratory rate measurement, neglecting the potential of using thoracoabdominal patterns of respiration for infant lung health assessment. To bridge this gap, we exploit the unique advantage of spatial redundancy of a camera sensor to analyze the infant thoracoabdominal respiratory motion. Specifically, we propose a camera-based respiratory imaging (CRI) system that utilizes optical flow to construct a spatio-temporal respiratory imager for comparing the infant chest and abdominal respiratory motion, and employs deep learning algorithms to identify infant abdominal, thoracoabdominal synchronous, and thoracoabdominal asynchronous patterns of respiration. To alleviate the challenges posed by limited clinical training data and subject variability, we introduce a novel multiple-expert contrastive learning (MECL) strategy to CRI. It enriches training samples by reversing and pairing different-class data, and promotes the representation consistency of same-class data through multi-expert collaborative optimization. Clinical validation involving 44 infants shows that MECL achieves 70% in sensitivity and 80.21% in specificity, which validates the feasibility of CRI for respiratory pattern recognition. This work investigates a novel video-based approach for assessing the infant thoracoabdominal patterns of respiration, revealing a new value stream of video health monitoring in neonatal care.