Log-transformation and its implications for data analysis

Machine learning-based delta check method for detecting misidentification errors in tumor marker tests

OBJECTIVES

Misidentification errors in tumor marker tests can lead to serious diagnostic and treatment errors. This study aims to develop a method for detecting these errors using a machine learning (ML)-based delta check approach, overcoming limitations of conventional methods.

METHODS

We analyzed five tumor marker test results: alpha-fetoprotein (AFP), cancer antigen 19-9 (CA19-9), cancer antigen 125 (CA125), carcinoembryonic antigen (CEA), and prostate-specific antigen (PSA). A total of 246,261 records were used in the analysis. Of these, 179,929 records were used for model training and 66,332 records for performance evaluation. We developed a misidentification error detection model based on the random forest (RF) and deep neural network (DNN) methods. We performed an in silico simulation with 1 % random sample shuffling. The performance of the developed models was evaluated and compared to conventional delta check methods such as delta percent change (DPC), absolute DPC (absDPC), and reference change values (RCV).

RESULTS

The DNN model outperformed the RF, DPC, absDPC, and RCV methods in detecting sample misidentification errors. It achieved balanced accuracies of 0.828, 0.842, 0.792, 0.818, and 0.833 for AFP, CA19-9, CA125, CEA, and PSA, respectively. Although the RF method performed better than DPC and absDPC, it showed similar or lower performance compared to RCV.

CONCLUSIONS

Our research results demonstrate that an ML-based delta check method can more effectively detect sample misidentification errors compared to conventional delta check methods. In particular, the DNN model demonstrated superior and stable detection performance compared to the RF, DPC, absDPC, and RCV methods.

Validation of Downloadable Mobile Snore Applications by Polysomnography (PSG)

Purpose

Obstructive sleep apnea (OSA) is a common breathing disorder during sleep that is associated with symptoms such as snoring, excessive daytime sleepiness, and breathing interruptions. Polysomnography (PSG) is the most reliable diagnostic test for OSA; however, its high cost and lengthy testing duration make it difficult to access for many patients. With the availability of free snore applications for home-monitoring, this study aimed to validate the top three ranked snore applications, namely SnoreLab (SL), Anti Snore Solution (ASS), and Sleep Cycle Alarm (SCA), using PSG.

Patients and Methods

Sixty participants underwent an overnight PSG while simultaneously using three identical smartphones with the tested apps to gather sleep and snoring data.

Results

The study discovered that all three applications were significantly correlated with the total recording time and snore counts of PSG, with ASS showing good agreement with snore counts. Furthermore, the Snore Score, Time Snoring of SL, and Sleep Quality of SCA had a significant correlation with the natural logarithm of apnea hypopnea index (lnAHI) of PSG. The Snore Score of SL and the Sleep Quality of SCA were shown to be useful for evaluating snore severity and for pre-diagnosing or predicting OSA above moderate levels.

Conclusion

These findings suggest that some parameters of free snore applications can be employed to monitor OSA progress, and future research could involve adjusted algorithms and larger-scale studies to further authenticate these downloadable snore and sleep applications.

Associations between the objective and perceived food environment and eating behavior in relation to socioeconomic status among adults in peri-urban settings: results from the CIVISANO study in Flanders, Belgium

Obesity, a significant public health concern, disproportionately affects people with lower socioeconomic status (SES). Food environments have been identified as part of the causal chain of this disparity. This study investigated variations in the food environment across groups with different SES profiles residing in peri-urban municipal settings. In addition, it examined the association of the perceived and objective food environments with eating behaviour and assessed if these associations were moderated by SES. Utilizing GIS and survey data (n = 497, aged 25-65), results showed differences in the objective and perceived food environments based on SES. Respondents with higher SES perceived their food environments as better but resided farther from all food outlets compared to respondents with lower SES. However, there was no difference in outlet density or mRFEI between SES groups. SES moderated associations between the objective and perceived food environments and most eating behavior outcomes except fast food consumption frequency. For fruits and vegetables, SES moderated the association between neighborhood availability and consumption frequency (β0.23,CI0.03;0.49). Stratified analysis revealed a positive association for both lower (β0.15, CI0.03;0.27) and higher (β0.37, CI 0.12;0.63) SES groups. For snack foods, SES moderated the association between healthy outlet density and consumption frequency (β-0.60, CI-0.94; -0.23), showing statistical significance only for respondents with higher SES (β0.36,CI 0.18;0.55). Similarly, for sugar-sweetened beverages, a statistically significant interaction was observed between unhealthy outlet density in the 1000m buffer and consumption frequency (β 0.06, CI 0.02; 0.11). However, this association was only statistically significant for respondents with higher SES (β-0.02,CI -0.05;-0.0002). These results emphasize the significance of SES as a crucial element in comprehending the connection between the food environment and eating behaviour. Indicating the need for policymakers to take SES into account when implementing food environment interventions, particularly when focusing on the neighborhood food environment without considering residents' SES and their perceptions.

Pretreating and normalizing metabolomics data for statistical analysis

Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples. Metabolomics is emerging as a powerful tool generally for precision medicine. Particularly, integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease. However, metabolomics data are very complicated. Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis. In this review article, we comprehensively review various methods that are used to preprocess and pretreat metabolomics data, including MS-based data and NMR -based data preprocessing, dealing with zero and/or missing values and detecting outliers, data normalization, data centering and scaling, data transformation. We discuss the advantages and limitations of each method. The choice for a suitable preprocessing method is determined by the biological hypothesis, the characteristics of the data set, and the selected statistical data analysis method. We then provide the perspective of their applications in the microbiome and metabolome research.

Cognitive-motor interference during walking with modified leg mechanics: a dual-task walking study

Background

The use of mobile exoskeletons as assistive walking devices has the potential to affect the biomechanics of the musculoskeletal system due to their weight and restricted range of motion. This may result in physical and cognitive load for the user. Understanding how lower extremity loading affects cognitive-motor interference is crucial for the design of wearable devices, including powered exoskeletons, and the development of effective training interventions.

Objective

This study aims to examine the effects of modified leg mechanics on cognitive-motor interference in dual-task walking. Gait variability, as an indicator of motor control, was analyzed to investigate its relation to cognitive task difficulty and to determine whether lower extremity loading modifies this relationship. Additionally, the impact on the gait pattern, as represented by the mean values of spatio-temporal gait parameters were investigated.

Method

Fifteen healthy young adults walked on a treadmill with and without weight cuffs bilaterally attached to their thighs and shanks while performing a visual-verbal Stroop test (simple task) and a serial subtraction task (difficult task). Dependent variables include mean values and variability (coefficients of variation) of step length, step width, stride time and double support time. Additionally, secondary task performance as correct response rates and perceived workload were assessed.

Results

Double support time variability decreased during dual-task walking, but not during walking with modified leg mechanics while performing the difficult secondary task. Walking with modified leg mechanics resulted in increased gait variability compared to normal walking, regardless of cognitive load. During walking with modified leg mechanics, step length, step width, and stride time increased, while double support time decreased. The secondary tasks did not affect the gait pattern.

Conclusion

The interplay between an external focus of attention and competition for attentional resources may influence the variability of double support time. The findings suggest that walking with modified leg mechanics could increase cognitive-motor interference for healthy young adults in demanding dual-task situations. Therefore, it is important to analyze the underlying mechanisms of cognitive-motor interference in the context of human-exoskeleton interaction.

Distinct Type 1 Immune Networks Underlie the Severity of Restrictive Lung Disease after COVID-19

The variable etiology of persistent breathlessness after COVID-19 have confounded efforts to decipher the immunopathology of lung sequelae. Here, we analyzed hundreds of cellular and molecular features in the context of discrete pulmonary phenotypes to define the systemic immune landscape of post-COVID lung disease. Cluster analysis of lung physiology measures highlighted two phenotypes of restrictive lung disease that differed by their impaired diffusion and severity of fibrosis. Machine learning revealed marked CCR5+CD95+ CD8+ T-cell perturbations in mild-to-moderate lung disease, but attenuated T-cell responses hallmarked by elevated CXCL13 in more severe disease. Distinct sets of cells, mediators, and autoantibodies distinguished each restrictive phenotype, and differed from those of patients without significant lung involvement. These differences were reflected in divergent T-cell-based type 1 networks according to severity of lung disease. Our findings, which provide an immunological basis for active lung injury versus advanced disease after COVID-19, might offer new targets for treatment.

Association of systemic immune-inflammation index and systemic inflammation response index with chronic kidney disease: observational study of 40,937 adults

BACKGROUND

Chronic kidney disease (CKD) is linked to immunity and inflammation. Systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) are novel measures for gauging an individual's systemic inflammatory activity. We aim to investigate the potential associations between them.

METHODS

This study encompassed a cohort of 40,937 adults from the National Health and Nutrition Examination Survey (NHANES) 1999-2018. SII and SIRI were log2-transformed before conducting regression analysis, considering that these inflammatory markers were right skewed distributed. Weighted logistic regression models assessed the association of log2-SII and log2-SIRI levels with CKD prevalence. Weighted Cox regression models were utilized to estimate the risk of death. Subgroup analyses were performed to further clarify the effects of other covariates on the associations. Sensitivity analyses were performed to assess the robustness of our results.

RESULTS

6986 participants with CKD were recorded, and 2818 patients died during a mean follow-up time of 100 months. After adjusting for all covariates, the highest level of log2-SII increased the CKD incidence (odds ratio [OR]: 1.47, 95% confidence intervals [CI]: 1.32-1.65, P < 0.001), as well as log2-SIRI (OR: 1.79, 95% CI 1.60-2.01, P < 0.001) when compared with the lowest level reference group. The highest level of log2-SII significantly increased all-cause mortality (hazard risk [HR]: 1.29; 95% CI 1.13-1.48, P < 0.001), cardiovascular mortality (HR: 1.61, 95% CI 1.25-2.09, P < 0.001), and hypertension mortality (HR: 1.73, 95% CI 1.23-2.42, P = 0.001) in CKD patients. Additionally, the positive associations were also found between log2-SIRI and all cause (HR: 1.54, 95% CI 1.35-1.76, P < 0.001), cardiovascular (HR: 1.90, 95% CI 1.38-2.60, P < 0.001), and hypertension mortality (HR: 2.15, 95% CI 1.56-2.94, P < 0.001). Subgroup analyses unveiled variations in these effects among different populations.

CONCLUSION

There existed a substantial association of SII and SIRI levels with CKD prevalence, as well as mortality in patients with CKD in the U.S.

POPULATION

A genome-wide study of the lipoxygenase gene families in Medicago truncatula and Medicago sativa reveals that MtLOX24 participates in the methyl jasmonate response

BACKGROUND

Lipoxygenase (LOX) is a multifunctional enzyme that is primarily related to plant organ growth and development, biotic and abiotic stress responses, and production of flavor-associated metabolites. In higher plants, the LOX family encompasses several isozymes with varying expression patterns between tissues and developmental stages. These affect processes including seed germination, seed storage, seedling growth, fruit ripening, and leaf senescence. LOX family genes have multiple functions in response to hormones such as methyl jasmonate (MeJA) and salicylic acid.

RESULTS

In this study, we identified 30 and 95 LOX homologs in Medicago truncatula and Medicago sativa, respectively. These genes were characterized with analyses of their basic physical and chemical properties, structures, chromosomal distributions, and phylogenetic relationships to understand structural variations and their physical locations. Phylogenetic analysis was conducted for members of the three LOX subfamilies (9-LOX, type I 13-LOX, and type II 13-LOX) in Arabidopsis thaliana, Glycine max, M. truncatula, and M. sativa. Analysis of predicted promoter elements revealed several relevant cis-acting elements in MtLOX and MsLOX genes, including abscisic acid (ABA) response elements (ABREs), MeJA response elements (CGTCA-motifs), and antioxidant response elements (AREs). Cis-element data combined with transcriptomic data demonstrated that LOX gene family members in these species were most likely related to abiotic stress responses, hormone responses, and plant development. Gene expression patterns were confirmed via quantitative reverse transcription PCR. Several MtLOX genes (namely MtLOX15, MtLOX16, MtLOX20, and MtLOX24) belonging to the type I 13-LOX subfamily and other LOX genes (MtLOX7, MtLOX11, MsLOX23, MsLOX87, MsLOX90, and MsLOX94) showed significantly different expression levels in the flower tissue, suggesting roles in reproductive growth. Type I 13-LOXs (MtLOX16, MtLOX20, MtLOX21, MtLOX24, MsLOX57, MsLOX84, MsLOX85, and MsLOX94) and type II 13-LOXs (MtLOX5, MtLOX6, MtLOX9, MtLOX10, MsLOX18, MsLOX23, and MsLOX30) were MeJA-inducible and were predicted to function in the jasmonic acid signaling pathway. Furthermore, exogenous MtLOX24 expression in Arabidopsis verified that MtLOX24 was involved in MeJA responses, which may be related to insect-induced abiotic stress.

CONCLUSIONS

We identified six and four LOX genes specifically expressed in the flowers of M. truncatula and M. sativa, respectively. Eight and seven LOX genes were induced by MeJA in M. truncatula and M. sativa, and the LOX genes identified were mainly distributed in the type I and type II 13-LOX subfamilies. MtLOX24 was up-regulated at 8 h after MeJA induction, and exogenous expression in Arabidopsis demonstrated that MtLOX24 promoted resistance to MeJA-induced stress. This study provides valuable new information regarding the evolutionary history and functions of LOX genes in the genus Medicago.

Cytokine expression in subjects with Mycobacterium avium ssp. paratuberculosis positive blood cultures and a meta-analysis of cytokine expression in Crohn's disease

Objectives

1) Culture Mycobacterium avium ssp. paratuberculosis (MAP)from blood, 2) assess infection persistence, 3) determine Crohn's disease (CD) cytokine expression, 4) compare CD cytokine expression to tuberculosis, and 5) perform a meta-analysis of cytokine expression in CD.

Methods

The Temple University/Abilene Christian University (TU/ACU) study had a prospective case control design with 201 subjects including 61 CD patients and 140 non-CD controls. The culture methods included MGIT, TiKa and Pozzato broths, and were deemed MAP positive, if IS900 PCR positive. A phage amplification assay was also performed to detect MAP. Cytokine analysis of the TU/ACU samples was performed using Simple Plex cytokine reagents on the Ella ELISA system. Statistical analyses were done after log transformation using the R software package. The meta-analysis combined three studies.

Results

Most subjects had MAP positive blood cultures by one or more methods in 3 laboratories. In our cytokine study comparing CD to non-CD controls, IL-17, IFNγ and TNFα were significantly increased in CD, but IL-2, IL-5, IL-10 and GM-CSF were not increased. In the meta-analysis, IL-6, IL-8 and IL-12 were significantly increased in the CD patients.

Conclusion

Most subjects in our sample had MAP infection and 8 of 9 subjects remained MAP positive one year later indicating persistent infection. While not identical, cytokine expression patterns in MAP culture positive CD patients in the TU/ACU study showed similarities (increased IL-17, IFNγ and TNFα) to patterns of patients with Tuberculosis in other studies, indicating the possibilities of similar mechanisms of pathogen infection and potential strategies for treatment.

Time-dependent pharmacodynamics of amikacin on Mycobacterium abscessus growth and resistance emergence

Mycobacterium abscessus pulmonary disease is increasing in prevalence globally, particularly for individuals with cystic fibrosis. These infections are challenging to treat due to a high rate of resistance. Amikacin is critical to treatment, but the development of toxicity, amikacin resistance, and treatment failure are significant challenges. Amikacin has been characterized previously as peak-dependent and extended-interval dosing is commonly used. In our hollow fiber infection model of M. abscessus, amikacin exhibited time-dependent rather than the expected peak-dependent pharmacodynamics. Humanized amikacin exposures with more frequent, short-interval dosing (continuous infusion or every 12 hours) yielded improved microbiological response compared to extended-interval dosing (every 24 hours or 1-3 times per week). Short-interval dosing inhibited growth with a mean (SD) maximum Δlog10 colony forming units of -4.06 (0.52), significantly more than extended-interval dosing (P = 0.0013) every 24 hours, -2.40 (0.58), or 1-3 times per week, -2.39 (0.38). Growth recovery, an indicator of resistance emergence, occurred at 6.56 (0.70) days with short-interval dosing but was significantly earlier with extended-interval dosing (P = 0.0032) every 24 hours, 3.88 (0.85) days, and 1-3 times per week, 3.27 (1.72) days. Microbiological response correlated best with the pharmacodynamic index of %T > minimum inhibitory concentration (MIC), with an EC80 for growth inhibition of ~40%T > MIC. We used a previously published population model of amikacin to determine the probability of achieving 40%T > MIC and show that current dosing strategies are far below this target, which may partially explain why treatment failure remains so high for these infections. These data support a cautious approach to infrequent amikacin dosing for the treatment of M. abscessus.IMPORTANCEPulmonary disease caused by Mycobacterium abscessus complex (MABSC) is increasing worldwide, particularly in patients with cystic fibrosis. MABSC is challenging to treat due to high levels of antibiotic resistance. Treatment requires 2-4 antibiotics over more than 12 months and has a significant risk of toxicity but still fails to eradicate infection in over 50% of patients with cystic fibrosis. Antibiotic dosing strategies have been largely informed by common bacteria such as Pseudomonas aeruginosa. The "pharmacodynamic" effects of amikacin, a backbone of MABSC treatment, were thought to be related to maximum "peak" drug concentration, leading to daily or three times weekly dosing. However, we found that amikacin MABSC kill and growth recovery, an indicator of antibiotic resistance, are dependent on how long amikacin concentrations are above the minimum inhibitory concentration, not how high the peak concentration is. Therefore, we recommend a re-evaluation of amikacin dosing to determine if increased frequency can improve efficacy.

Label-Free Quantitation of Endogenous Peptides

Liquid chromatography-mass spectrometry (LC-MS)-based peptidomics methods allow for the detection and identification of many peptides in a complex biological mixture in an untargeted manner. Quantitative peptidomics approaches allow for comparisons of peptide abundance between different samples, allowing one to draw conclusions about peptide differences as a function of experimental treatment or physiology. While stable isotope labeling is a powerful approach for quantitative proteomics and peptidomics, advances in mass spectrometry instrumentation and analysis tools have allowed label-free methods to gain popularity in recent years. In a general label-free quantitative peptidomics experiment, peak intensity information for each peptide is compared across multiple LC-MS runs. Here, we outline a general approach for label-free quantitative peptidomics experiments, including steps for sample preparation, LC-MS data acquisition, data processing, and statistical analysis. Special attention is paid to address run-to-run variability, which can lead to several major problems in label-free experiments. Overall, our method provides researchers with a framework for the development of their own quantitative peptidomics workflows applicable to quantitation of peptides from a wide variety of different biological sources.

Statistical normalization methods in microbiome data with application to microbiome cancer research

Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.

Does obesity Kuznets curve exist in developing economies? Evidence from 38 African countries based on heterogeneous panel data analysis on income-level classification

Introduction

The global pandemic disease known as the obesity epidemic has spread throughout the planet. Particularly, Africa is facing a growing problem of obesity, and the trend is rising. This is a result of a ticking time bomb. Given the claim that multiple socio-economic factors significantly affect the diversity in obesity rates between nations, economic development can be seen as a key contributor to this variation.

Methods

Relying on the aforementioned avowal, this extant research examines the relationship between obesity and economic growth using urbanization, trade openness, and unemployment as intermittent variables within the Obesity Kuznets Curve (OKC) framework. Using panel data from 1990 to 2020, a panel of 38 African countries subdivided into income levels (Low income, Lower-middle income, and Upper-middle income) were analyzed. With the presence of residual cross-sectional reliance and slope heterogeneity, the Augmented Mean Group (AMG) econometric approach is employed.

Results

Key outcomes from the mentioned estimation method unveiled that economic growth positively impacts obesity among all the study panels. Variably, unemployment was evidenced to have a palpable positive impact on obesity concerning Low-income economies whereas on the side of the Lower-middle income panel together with Upper-middle income economies and the aggregated panel, a significant negative relationship is observed with obesity. Further, urbanization enhanced obesity in the Low-income panel and the aggregated panel of African nations, whereas an adverse effect is identified in both the Lower-middle and Upper-middle economies in Africa. Moreover, except for Low-income African economies, all the other panels of African nations in terms of income levels were noted to have a significant negative effect on obesity from trade openness.

Discussion

Finally, the long-run coefficients indicated that the OKC is valid among all panels of African countries. The study thus preferably suggests in African economies that addressing the inverted U-shape relationship between obesity and economic growth requires a multifaceted approach that considers the evolving dynamics of both factors. Policy makers should, therefore, aim to balance promoting economic growth and safeguarding public health through targeted interventions and long-term strategies.

Visual search in infantile nystagmus syndrome

CLINICAL RELEVANCE

Research on infantile nystagmus syndrome (INS) and visual search is limited. Conducting this research could assist practitioners in understanding how INS affects the real-life visual activities of patients and aid in developing new clinical visual function assessments for INS.

BACKGROUND

The aim of this work is to investigate how subjects with INS perform visual search tasks, and, particularly, to assess how INS subjects perform when targets are located at their null position or away from it, and when under additional cognitive demands.

METHODS

INS subjects (N = 15) and controls (N = 20) performed conjunction and feature search tasks, both with and without mental arithmetic. Search performance was assessed using log-transformed total search time, gaze-dependent search time, and accuracy. Cognitive demand was quantified by pupil size and the NASA task-load index score.

RESULTS

INS subjects showed longer search times compared to controls in conjunction search (P < 0.01), but not in feature search. Within INS and control subjects, the total search times were significantly increased by the addition of mental arithmetic (P < 0.0001). There was no difference in gaze-dependent search times between null target position and 15° away from null target position of subjects in conjunction search (P > 0.05). Accuracies were 100% for both control and INS subjects in both conjunction and feature search.

CONCLUSION

Conjunction visual search was impaired in adult INS subjects, and further worsened under increased cognitive demand. The null position did not affect the visual search performance in INS.

Modelling of intensive care unit (ICU) length of stay as a quality measure: a problematic exercise

BACKGROUND

Intensive care unit (ICU) length of stay (LOS) and the risk adjusted equivalent (RALOS) have been used as quality metrics. The latter measures entail either ratio or difference formulations or ICU random effects (RE), which have not been previously compared.

METHODS

From calendar year 2016 data of an adult ICU registry-database (Australia & New Zealand Intensive Care Society (ANZICS) CORE), LOS predictive models were established using linear (LMM) and generalised linear (GLMM) mixed models. Model fixed effects quality-metric formulations were estimated as RALOSR for LMM (geometric mean derived from log(ICU LOS)) and GLMM (day) and observed minus expected ICU LOS (OMELOS from GLMM). Metric confidence intervals (95%CI) were estimated by bootstrapping; random effects (RE) were predicted for LMM and GLMM. Forest-plot displays of ranked quality-metric point-estimates (95%CI) were generated for ICU hospital classifications (metropolitan, private, rural/regional, and tertiary). Robust rank confidence sets (point estimate and 95%CI), both marginal (pertaining to a singular ICU) and simultaneous (pertaining to all ICU differences), were established.

RESULTS

The ICU cohort was of 94,361 patients from 125 ICUs (metropolitan 16.9%, private 32.8%, rural/regional 6.4%, tertiary 43.8%). Age (mean, SD) was 61.7 (17.5) years; 58.3% were male; APACHE III severity-of-illness score 54.6 (25.7); ICU annual patient volume 1192 (702) and ICU LOS 3.2 (4.9). There was no concordance of ICU ranked model predictions, GLMM versus LMM, nor for the quality metrics used, RALOSR, OMELOS and site-specific RE for each of the ICU hospital classifications. Furthermore, there was no concordance between ICU ranking confidence sets, marginal and simultaneous for models or quality metrics.

CONCLUSIONS

Inference regarding adjusted ICU LOS was dependent upon the statistical estimator and the quality index used to quantify any LOS differences across ICUs. That is, there was no "one best model"; thus, ICU "performance" is determined by model choice and any rankings thereupon should be circumspect.

Effects of lifestyle physical activity and sedentary behaviour interventions on disease activity and patient- and clinician- important health outcomes in rheumatoid arthritis: a systematic review with meta-analysis

BACKGROUND

Lifestyle physical activity (PA) is defined as any type of PA undertaken as part of daily life. It can include engagement in activities of daily living (i.e., household chores, gardening, walking to work), incidental PA, walking and/or reducing sedentary or sitting behaviours (SB). Regular PA is recommended for people with Rheumatoid Arthritis (RA) to reduce disease activity and systemic inflammation, as well as to improve patient- and clinician-important health outcomes. However, there is no summarised evidence of the effectiveness of interventions specifically targeting lifestyle PA and SB in this population. The aims of this systematic review with meta-analysis were to evaluate interventions targeting lifestyle PA and/or SB on 1) disease activity; 2) PA, SB and 3) patient- and clinician-important outcomes in people with RA.

METHODS

Eight databases [Medline, Cochrane Library CENTRAL, Web of Science, PsychINFO, Cumulative Index to Nursing & Allied Health Literature, Scopus, Excerpta Medica database and Physiotherapy Evidence Database] were searched from inception-August 2022. Inclusion criteria required interventions to target lifestyle PA and/or SB, conducted in adults with RA, assessing patient- and/or clinician-important outcomes.

RESULTS

Of 880 relevant articles, 16 interventions met the inclusion criteria. Meta-analyses showed statistically significant effects of interventions on disease activity (standardised mean difference = -0.12 (95% confidence interval = -0.23 to -0.01, I2 = 6%, z = 2.19, p = .03), moderate-to-vigorous PA, light/leisure PA, steps, functional ability, and fatigue. Whereas, no intervention effects were visualised for total PA, pain, anxiety or quality of life.

CONCLUSIONS

Lifestyle PA interventions led to increased PA, reductions in SB and improvements in disease activity and other patient- and/or clinician-important health outcomes in people with RA. Future interventions should be less heterogenous in content, structure, focus and outcome measures used to aid understanding of the most effective intervention components for improving health. More SB interventions are needed to determine their effectiveness at producing clinical benefits.

Cerebrospinal fluid levels of neuroinflammatory biomarkers are increased in athletes with persistent post-concussive symptoms following sports-related concussion

A sports-related concussion (SRC) is often caused by rapid head rotation at impact, leading to shearing and stretching of axons in the white matter and initiation of secondary inflammatory processes that may exacerbate the initial injury. We hypothesized that athletes with persistent post-concussive symptoms (PPCS) display signs of ongoing neuroinflammation, as reflected by altered profiles of cerebrospinal fluid (CSF) biomarkers, in turn relating to symptom severity. We recruited athletes with PPCS preventing sports participation as well as limiting work, school and/or social activities for ≥ 6 months for symptom rating using the Sport Concussion Assessment Tool, version 5 (SCAT-5) and for cognitive assessment using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Following a spinal tap, we analysed 27 CSF inflammatory biomarkers (pro-inflammatory chemokines and cytokine panels) by a multiplex immunoassay using antibodies as electrochemiluminescent labels to quantify concentrations in PPCS athletes, and in healthy age- and sex-matched controls exercising ≤ 2 times/week at low-to-moderate intensity. Thirty-six subjects were included, 24 athletes with PPCS and 12 controls. The SRC athletes had sustained a median of five concussions, the most recent at a median of 17 months prior to the investigation. CSF cytokines and chemokines levels were significantly increased in eight (IL-2, TNF-α, IL-15, TNF-β, VEGF, Eotaxin, IP-10, and TARC), significantly decreased in one (Eotaxin-3), and unaltered in 16 in SRC athletes when compared to controls, and two were un-detectable. The SRC athletes reported many and severe post-concussive symptoms on SCAT5, and 10 out of 24 athletes performed in the impaired range (Z < - 1.5) on cognitive testing. Individual biomarker concentrations did not strongly correlate with symptom rating or cognitive function. Limitations include evaluation at a single post-injury time point in relatively small cohorts, and no control group of concussed athletes without persisting symptoms was included. Based on CSF inflammatory marker profiling we find signs of ongoing neuroinflammation persisting months to years after the last SRC in athletes with persistent post-concussive symptoms. Since an ongoing inflammatory response may exacerbate the brain injury these results encourage studies of treatments targeting the post-injury inflammatory response in sports-related concussion.

Environment-wide association study of elevated liver enzymes: results from the Korean National Environmental Health Survey 2018-2022

Background

Environmental exposure is characterized by low concentration, chronic, and complex exposure. Traditional epidemiological studies show limitations in reflecting these characteristics since they usually focus on a single or very limited number of exposure factors at a time. In this study, we adopted the methodology of environment-wide association study (EWAS) to figure out the association of human liver function with various environmentally hazardous substances.

Methods

We analyzed 2,961 participants from the Korean National Environmental Health Survey Cycle 4 (2018-2020). Using generalized linear model (GLM) analysis, we analyzed the association of 72 variables with 3 liver function indices (aspartate aminotransferase [AST], alanine aminotransferase [ALT], and gamma glutamyl transferase [GGT]). Finally, we visualized our results with Manhattan plot.

Results

In GLM analysis, perfluorooctanesulfonate were positively associated with ALT (odds ratio [OR]: 2.2; 95% confidence interval [CI]: 1.39-3.46; p adjusted = 0.0147) and perfluorodecanoic acid showed positive association with GGT (OR: 2.73; 95% CI: 1.36-5.5; p adjusted = 0.0256). Plasma mercury showed positive association with GGT (OR: 1.45; 95% CI: 1.14-1.84; p adjusted = 0.0315). Using a plastic container while keeping food in the refrigerator was associated with elevated GGT compared to using a glass container (OR: 1.51; 95% CI: 1.16-1.95; p adjusted = 0.0153). 2-ethyl-5-oxohexyl phthalate, showed a negative trend with all 3 indices, with AST (OR: 0.54; 95% CI: 0.39-0.73; p adjusted = 0.00357), ALT (OR: 0.5; 95% CI: 0.34-0.75; p adjusted = 0.036), GGT (OR: 0.55; 95% CI: 0.4-0.76; p adjusted = 0.00697). Bisphenol S and frequent use of sunblock cream showed negative association with ALT (OR: 0.77; 95% CI: 0.66-0.89), and GGT (OR: 0.25; 95% CI: 0.11-0.55), respectively.

Conclusions

We conducted an exploratory study on environmental exposure and human liver function. By using EWAS methodology, we identified 7 factors that could have potential association with liver function.

The Association between Neighborhood Walkability and Physical Activity in a Behavioral Weight Loss Trial Testing the Addition of Remotely Delivered Feedback Messages to Self-Monitoring

Adding feedback messages (FB) to self-monitoring (SM) may reinforce behavior change. However, socio-environmental conditions (e.g., limited access to parks or low walkability) may limit one's ability to respond to FB focused on physical activity (PA). In this analysis, we hypothesized that high neighborhood walkability will positively modify the treatment effect of FB on PA, and residents of high walkability neighborhoods will achieve higher PA levels at 12 months than those in low walkability neighborhoods. The study is a secondary analysis of a 12-month behavioral weight-loss trial. Adults with overweight/obesity were randomized to SM + FB (n = 251) or SM alone (n = 251). SM + FB group received smartphone pop-up messages thrice/week tailored to their PA SM data. The assessment included neighborhood walkability via Walk Score (low [<50] vs. high [≥50]), moderate to vigorous PA (MVPA) and step count via Fitbit Charge 2™, and weight via smart scale. We report adjusted linear regression coefficients (b) with standard errors (SE). The analysis included participants who were primarily white, female, and with obesity. In adjusted models, neighborhood walkability did not moderate the effect of treatment assignment on log-transformed (ln) MVPA or steps count over 12 months. The SM + FB group had greater lnMVPA than the SM group, but lnMVPA and steps were similar between walkability groups. There were no significant interactions for group and time or group, time, and walkability. These findings suggest that adding FB to SM had a small but significant positive impact on PA over 12 months, but neighborhood walkability did not moderate the treatment effect of FB on PA.

Associations of fatty acids composition and estimated desaturase activities in erythrocyte phospholipids with biochemical and clinical indicators of cardiometabolic risk in non-diabetic Serbian women: the role of level of adiposity

Introduction

Fatty acids (FAs) composition and desaturase activities can be altered in different metabolic conditions, but the adiposity-independent associations with clinical and biochemical indicators of cardiometabolic risk are still unclear. This study aimed to analyze the associations of FAs composition and estimated desaturase activities with anthropometric, clinical, and biochemical cardiometabolic risk indicators in non-diabetic Serbian women, and to investigate if these associations were independent of the level of adiposity and other confounders.

Methods

In 76 non-diabetic, otherwise healthy Serbian women, aged 24-68 years, with or without metabolic syndrome or obesity (BMI=23.6±5.6 kg/m2), FA composition in erythrocyte phospholipids was measured by gas-liquid chromatography. Desaturase activities were estimated from product/precursor FAs ratios (D9D:16:1n-7/16:0; D6D:20:3n-6/18:2n-6; D5D:20:4n-6/20:3n-6). Correlations were made with anthropometric, biochemical (serum glucose, triacylglycerols, LDL-C, HDL-C, ALT, AST, and their ratios) and clinical (blood pressure) indicators of cardiometabolic risk. Linear regression models were performed to test the independence of these associations.

Results

Estimated desaturase activities and certain FAs were associated with anthropometric, clinical and biochemical indicators of cardiometabolic risk: D9D, D6D, 16:1n-7 and 20:3n-6 were directly associated, while D5D and 18:0 were inversely associated. However, the associations with clinical and biochemical indicators were not independent of the associations with the level of adiposity, since they were lost after controlling for anthropometric indices. After controlling for multiple confounders (age, postmenopausal status, education, smoking, physical activity, dietary macronutrient intakes, use of supplements, alcohol consumption), the level of adiposity was the most significant predictor of desaturase activities and aforementioned FAs levels, and mediated their association with biochemical/clinical indicators. Vice versa, desaturase activities predicted the level of adiposity, but not other components of cardiometabolic risk (if the level of adiposity was accounted). While the associations of anthropometric indices with 16:1n-7, 20:3n-6, 18:0 and D9D and D6D activities were linear, the associations with D5D activity were the inverse U-shaped. The only adiposity-independent association of FAs profiles with the indicators of cardiometabolic risk was a positive association of 20:5n-3 with ALT/AST ratio, which requires further exploration.

Discussion

Additional studies are needed to explore the mechanisms of the observed associations.

Association of heavy metal complex exposure and neurobehavioral function of children

Background

Exposure to heavy metals is a public health concern worldwide. Previous studies on the association between heavy metal exposure and neurobehavioral functions in children have focused on single exposures and clinical manifestations. However, the present study evaluated the effects of heavy metal complex exposure on subclinical neurobehavioral function using a Korean Computerized Neurobehavior Test (KCNT).

Methods

Urinary mercury, lead, cadmium analyses as well as symbol digit substitution (SDS) and choice reaction time (CRT) tests of the KCNT were conducted in children aged between 10 and 12 years. Reaction time and urinary heavy metal levels were analyzed using partial correlation, linear regression, Bayesian kernel machine regression (BKMR), the weighted quantile sum (WQS) regression and quantile G-computation analysis.

Results

Participants of 203 SDS tests and 198 CRT tests were analyzed, excluding poor cooperation and inappropriate urine sample. Partial correlation analysis revealed no association between neurobehavioral function and exposure to individual heavy metals. The result of multiple linear regression shows significant positive association between urinary lead, mercury, and CRT. BMKR, WQS regression and quantile G-computation analysis showed a statistically significant positive association between complex urinary heavy metal concentrations, especially lead and mercury, and reaction time.

Conclusions

Assuming complex exposures, urinary heavy metal concentrations showed a statistically significant positive association with CRT. These results suggest that heavy metal complex exposure during childhood should be evaluated and managed strictly.

Lemming winter habitat: the quest for warm and soft snow

During the cold arctic winter, small mammals like lemmings seek refuge inside the snowpack to keep warm and they dig tunnels in the basal snow layer, usually formed of a soft depth hoar, to find vegetation on which they feed. The snowpack, however, is a heterogenous medium and lemmings should use habitats where snow properties favor their survival and winter reproduction. We determined the impact of snow physical properties on lemming habitat use and reproduction in winter by sampling their winter nests for 13 years and snow properties for 6 years across 4 different habitats (mesic, riparian, shrubland, and wetland) on Bylot Island in the Canadian High Arctic. We found that lemmings use riparian habitat most intensively because snow accumulates more rapidly, the snowpack is the deepest and temperature of the basal snow layer is the highest in this habitat. However, in the deepest snowpacks, the basal depth hoar layer was denser and less developed than in habitats with shallower snowpacks, and those conditions were negatively related to lemming reproduction in winter. Shrubland appeared a habitat of moderate quality for lemmings as it favored a soft basal snow layer and a deep snowpack compared with mesic and wetland, but snow conditions in this habitat critically depend on weather conditions at the beginning of the winter. With climate change, a hardening of the basal layer of the snowpack and a delay in snow accumulation are expected, which could negatively affect the winter habitat of lemmings and be detrimental to their populations.

Insights into the Effects of Violating Statistical Assumptions for Dimensionality Reduction for Chemical "-omics" Data with Multiple Explanatory Variables

Biological volatilome analysis is inherently complex due to the considerable number of compounds (i.e., dimensions) and differences in peak areas by orders of magnitude, between and within compounds found within datasets. Traditional volatilome analysis relies on dimensionality reduction techniques which aid in the selection of compounds that are considered relevant to respective research questions prior to further analysis. Currently, compounds of interest are identified using either supervised or unsupervised statistical methods which assume the data residuals are normally distributed and exhibit linearity. However, biological data often violate the statistical assumptions of these models related to normality and the presence of multiple explanatory variables which are innate to biological samples. In an attempt to address deviations from normality, volatilome data can be log transformed. However, whether the effects of each assessed variable are additive or multiplicative should be considered prior to transformation, as this will impact the effect of each variable on the data. If assumptions of normality and variable effects are not investigated prior to dimensionality reduction, ineffective or erroneous compound dimensionality reduction can impact downstream analyses. It is the aim of this manuscript to assess the impact of single and multivariable statistical models with and without the log transformation to volatilome dimensionality reduction prior to any supervised or unsupervised classification analysis. As a proof of concept, Shingleback lizard (Tiliqua rugosa) volatilomes were collected across their species distribution and from captivity and were assessed. Shingleback volatilomes are suspected to be influenced by multiple explanatory variables related to habitat (Bioregion), sex, parasite presence, total body volume, and captive status. This work determined that the exclusion of relevant multiple explanatory variables from analysis overestimates the effect of Bioregion and the identification of significant compounds. The log transformation increased the number of compounds that were identified as significant, as did analyses that assumed that residuals were normally distributed. Among the methods considered in this work, the most conservative form of dimensionality reduction was achieved through analyzing untransformed data using Monte Carlo tests with multiple explanatory variables.

Modelling count, bounded and skewed continuous outcomes in physical activity research: beyond linear regression models

BACKGROUND

Inference using standard linear regression models (LMs) relies on assumptions that are rarely satisfied in practice. Substantial departures, if not addressed, have serious impacts on any inference and conclusions; potentially rendering them invalid and misleading. Count, bounded and skewed outcomes, common in physical activity research, can substantially violate LM assumptions. A common approach to handle these is to transform the outcome and apply a LM. However, a transformation may not suffice.

METHODS

In this paper, we introduce the generalized linear model (GLM), a generalization of the LM, as an approach for the appropriate modelling of count and non-normally distributed (i.e., bounded and skewed) outcomes. Using data from a study of physical activity among older adults, we demonstrate appropriate methods to analyse count, bounded and skewed outcomes.

RESULTS

We show how fitting an LM when inappropriate, especially for the type of outcomes commonly encountered in physical activity research, substantially impacts the analysis, inference, and conclusions compared to a GLM.

CONCLUSIONS

GLMs which more appropriately model non-normally distributed response variables should be considered as more suitable approaches for managing count, bounded and skewed outcomes rather than simply relying on transformations. We recommend that physical activity researchers add the GLM to their statistical toolboxes and become aware of situations when GLMs are a better method than traditional approaches for modeling count, bounded and skewed outcomes.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

A method to improve the reproducibility of findings from epigenome- and transcriptome-wide association studies

Reproducibility is a cornerstone of scientific progress. In epigenome- and transcriptome-wide association studies (E/TWAS) failure to reproduce may be the result of false discoveries. Whereas multiple methods exist to control false discoveries due to sampling error, minimizing false discoveries due to outliers and other data artefacts remains challenging. We propose a robust E/TWAS approach that outperforms alternative methods to improve reproducibility such as split-half replication. Furthermore, robust E/TWAS results in only a minor loss of power if there are no outliers and can in the presence of outliers, likely a more realistic scenario, even be more powerful than regular E/TWAS.

Association of burnout with depression in pharmacists: A network analysis

Background

Burnout and depression have overlapping symptoms, but the extent of overlap remains unclear, and the complex relationship between burnout and depression in pharmacists is rarely explored.

Methods

We investigated burnout and depression in 1,322 frontline pharmacists, and explored the complex relationship between burnout and depression in those pharmacists using network analysis.

Results

Network analysis showed that there were 5 communities. A partial overlap was found between burnout and depressive symptoms in pharmacists. The nodes MBI-6 (I have become more callous toward work since I took this job), D18 (My life is meaningless), and D10 (I get tired for no reason) had the highest expected influence value. D1 (I feel down-hearted and blue) and D14 (I have no hope for the future) were bridge symptoms connected with emotional exhaustion and reduced professional efficacy, respectively.

Conclusion

A partial overlap exists between burnout and depressive symptoms in pharmacists, mainly in the connection between the emotional exhaustion and reduced professional efficacy and the depressive symptoms. Potential core targets identified in this study may inform future prevention and intervention.

Examining the Effectiveness of the 2012 Canadian Graphic Warning Label Policy Change by Sex, Income, and Education

INTRODUCTION

We examined the differential impact of the 2012 Canadian GWL policy changes on key indicators of warning label impact and quit intentions using national cohorts of Canadian and U.S. adults who smoke.

AIMS AND METHODS

We used data from all waves of the International Tobacco Control surveys (2002-2020) in Canada and the United States. Our key measures were quit intentions and an index of warning label effectiveness (salience, cognitive and behavioral reactions). We estimated overall policy impact by comparing Canada (treatment group) with the United States (control group) using controlled interrupted time series (CITS) regression models, with interactions to examine whether policy impact varied by sex, education, and income.

RESULTS

The CITS model showed a statistically significant increase in the warning label effectiveness in Canada post-policy, compared to the United States (β = 0.84, 95% CI 0.35,1.33). Similarly, the odds of quit intentions were relatively higher among adults who smoked in Canada compared to the United States (OR = 1.89, 95% CI 1.51,2.36) post-policy. The three-way interaction model showed that these associations were greater among adults from low socioeconomic status (SES) groups than in high SES groups.

CONCLUSIONS

The 2012 change in the Canadian GWL policy was associated with stronger cognitive and behavioral responses to GWLs and higher odds of quit intentions among adults who smoked in Canada when compared to the United States, specifically among individuals from low SES groups, suggesting a positive equity impact. Our findings affirm the need for countries to implement or enhance GWLs, in line with the WHO Framework Convention on Tobacco Control (FCTC).

IMPLICATIONS

The evidence on the potential health equity benefit of GWL policies is mixed. To further understand the influence of GWL policies on tobacco use disparities, more systematic research using pre/post-policy designs with control groups is needed. Using a CITS model, we aimed to strengthen the available evidence on the causal influence of this tobacco control approach. Our findings show that the 2012 GWL policy change had a greater impact on adults who smoked from low SES groups than it did on adults who smoked from high SES groups, indicating a potentially positive equity impact and confirming the need for countries to implement or maximize the size of GWLs, as recommended by the WHO FCTC.

Predicting the severity of COVID-19 patients using the CD24-CSF1R index in whole blood samples

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has become one of the most serious public health crises worldwide. Most infected people are asymptomatic but are still able to spread the virus. People with mild or moderate illnesses are likely to recover without hospitalization, while critically ill patients face a higher risk of organ injury or even death. In this study, we aimed to identify a novel biomarker that can predict the severity of COVID-19 patients. Clinical information and RNA-seq data of leukocytes from whole blood samples with and without a COVID-19 diagnosis (n = 100 and 26, respectively) were retrieved from the National Center for Biotechnology Information Gene Expression Omnibus database. Raw data were processed using the Transcripts Per Million (TPM) method and then transformed using log₂ (TPM+1) for normalization. The CD24-CSF1R index was established. Violin plots, Kaplan-Meier curves, ROC curves, and multivariate Cox proportional hazards regression analyses were performed to evaluate the prognostic value of the established index. The CD24-CSF1R index was significantly associated with ICU admission (n = 50 ICU, 50 non-ICU) and ventilatory status (n = 42 ventilation, 58 non-ventilation) with p = 4.186e-11 and p = 1.278e-07, respectively. The ROC curve produced a relatively accurate prediction of ICU admission with an AUC of 0.8524. Additionally, patients with a high index had significantly fewer mechanical ventilation-free days than patients with a low index (p = 6.07e-07). Furthermore, the established index showed a strong prognostic ability for the risk of using a ventilator in the multivariate Cox regression model (p < 0.001). The CD24-CSF1R index was significantly associated with COVID-19 severity. The established index could have potential implications for prognosis, disease severity stratification, and clinical management.

Ultrasound-Guided Percutaneous Needle Electrolysis Combined With Therapeutic Exercise May Add Benefit in the Management of Soleus Injury in Female Soccer Players: A Pilot Study

CONTEXT

The performance of sprints during male soccer matches usually is slow medium paced, where the soleus and gastrocnemius (ankle plantar flexors) play a very important role. As in male soccer, soleus injuries should be considered in female soccer; but the scientific evidence is very limited in this case.

DESIGN

Pilot clinical trial study.

OBJECTIVE

To determine whether adding an ultrasound-guided percutaneous needle electrolysis (US-guided PNE) technique to a specific exercise program improved perceived pain at stretching and at palpation, ankle dorsiflexion range of motion, muscle fatigue, and sport performance in women soccer players with soleus injury.

METHODS

This pilot study recruited 20 female players with chronic soleus injury (type 1, characterized by hypoechoic image) who were assigned to one of 2 groups: an experimental group (exercise program + US-guided PNE; n = 10) or a control group (exercise program + sham stimulation; n = 10). Pain intensity, dorsiflexion range of motion, knee-flexion heel raise test, curve sprint test, and the global rating of change scale were analyzed at baseline and after treatment (4 wk) and there was no further follow-up.

RESULTS

Pain intensity at palpation and at stretching, dorsiflexion range of motion, and heel raise test values showed significant improvements (P < .05) between pretreatment and posttreatment for both groups, however, no significant differences were observed between groups. Curve sprint tests did not show significant differences between pretreatment and posttreatment for either group or between groups. However, the percentage of changes always revealed better values in favor of the PNE group. Both groups showed good player satisfaction with the therapies.

CONCLUSION

The application of the US-guided PNE combined with a specific exercise program may cause clinical benefits in the treatment of female soccer players with soleus injury.

Mixed cumulative probit: a multivariate generalization of transition analysis that accommodates variation in the shape, spread and structure of data

Biological data are frequently nonlinear, heteroscedastic and conditionally dependent, and often researchers deal with missing data. To account for characteristics common in biological data in one algorithm, we developed the mixed cumulative probit (MCP), a novel latent trait model that is a formal generalization of the cumulative probit model usually used in transition analysis. Specifically, the MCP accommodates heteroscedasticity, mixtures of ordinal and continuous variables, missing values, conditional dependence and alternative specifications of the mean response and noise response. Cross-validation selects the best model parameters (mean response and the noise response for simple models, as well as conditional dependence for multivariate models), and the Kullback-Leibler divergence evaluates information gain during posterior inference to quantify mis-specified models (conditionally dependent versus conditionally independent). Two continuous and four ordinal skeletal and dental variables collected from 1296 individuals (aged birth to 22 years) from the Subadult Virtual Anthropology Database are used to introduce and demonstrate the algorithm. In addition to describing the features of the MCP, we provide material to help fit novel datasets using the MCP. The flexible, general formulation with model selection provides a process to robustly identify the modelling assumptions that are best suited for the data at hand.

A logarithmic model for hormone receptor-positive and breast cancer patients treated with neoadjuvant chemotherapy

OBJECTIVE

The aim of this study was to investigate the predictive importance of the previously validated log(ER)*log(PgR)/Ki-67 predictive model in a larger patient population.

METHODS

Patients with hormone receptor positive/HER-2 negative and clinical node positive before chemotherapy were included. Log(ER)*log(PgR)/Ki-67 values of the patients were determined, and the ideal cutoff value was calculated using a receiver operating characteristic curve analysis. It was analyzed with a logistic regression model along with other clinical and pathological characteristics.

RESULTS

A total of 181 patients were included in the study. The ideal cutoff value for pathological response was 0.12 (area under the curve=0.585, p=0.032). In the univariate analysis, no statistical correlation was observed between luminal subtype (p=0.294), histological type (p=0.238), clinical t-stage (p=0.927), progesterone receptor level (p=0.261), Ki-67 cutoff value (p=0.425), and pathological complete response. There was a positive relationship between numerical increase in age and residual disease. As the grade of the patients increased, the probability of residual disease decreased. Patients with log(ER)*log(PgR)/Ki-67 above 0.12 had an approximately threefold increased risk of residual disease when compared to patients with 0.12 and below (odds ratio: 3.17, 95% confidence interval: 1.48-6.75, p=0.003). When age, grade, and logarithmic formula were assessed together, the logarithmic formula maintained its statistical significance (odds ratio: 2.47, 95% confidence interval: 1.07-5.69, p=0.034).

CONCLUSION

In hormone receptor-positive breast cancer patients receiving neoadjuvant chemotherapy, the logarithmic model has been shown in a larger patient population to be an inexpensive, easy, and rapidly applicable predictive marker that can be used to predict response.

When do contrast sensitivity deficits (or enhancements) depend on spatial frequency? Two ways to avoid spurious interactions

Studies across a broad range of disciplines-from psychiatry to cognitive science to behavioural neuroscience-have reported on whether the magnitude of contrast sensitivity alterations in one group or condition varies with spatial frequency. Significant interactions have often gone unexplained or have been used to argue for impairments in specific processing streams. Here, we show that interactions with spatial frequency may need to be re-evaluated if the inherent skew/heteroscedasticity was not taken into account or if visual acuity could plausibly differ across groups or conditions. By re-analysing a publicly available data set, we show that-when using raw contrast sensitivity data-schizophrenia patients exhibit an apparent contrast sensitivity impairment that lessens with spatial frequency, but that when using log-transformed data or when using generalized estimating equations, this interaction reversed. The reversed interaction, but not the overall contrast sensitivity deficit, disappeared when groups were matched on visual acuity. An analysis of the contrast threshold data yielded similar results. A caveat is that matching groups on acuity is probably only defensible if acuity differences arise from non-neural factors such as optical blur. Taken together, these analyses reconcile seemingly discrepant findings in the literature and demonstrate that reporting contrast sensitivity interactions with spatial frequency requires properly accounting for visual acuity and skew/heteroscedasticity.

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG-force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle's coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs.

Adverse childhood experiences, recent negative life events, and non-suicidal self-injury among Chinese college students: the protective role of self-efficacy

BACKGROUND

Non-suicidal self-injury (NSSI) is a severe health problem closely related to adverse childhood experiences (ACEs). However, the underlying mechanisms by which ACEs may affect NSSI are largely unknown. Self-efficacy (NSSI-SE) and recent negative life events (RNLEs) may play important roles in this relationship. This study aimed to clarify the relationship between ACEs and NSSI among college students by examining the role of self-efficacy (NSSI-SE) and RNLEs in this process.

METHOD

Relevant self-report questionnaires were used to evaluate ACEs, RNLEs, NSSI-SE, and NSSI. A questionnaire of 1036 Chinese undergraduates (M_age = 19.65, 28.9% males, 71.1% females) was collected in a cross-sectional manner. The associations between ACEs, RNLEs, NSSI-SE and NSSI were assessed using Pearson correlation analyses. Then, hierarchical multiple linear regressions were used to analyze the effects of ACEs and RNLEs on NSSI, as well as the protective effect of NSSI-SE on the above relations.

RESULTS

NSSI was associated with both ACEs and RNLEs. ACEs and RNLEs could directly increase the risks of participating in NSSI, and the effects of ACEs and RNLEs on NSSI were independent without an interactive effect. NSSI-SE buffered the relationship between ACEs and NSSI, as well as between RNLEs and NSSI. Compared to individuals with a low level of NSSI-SE, ACEs and RNLEs were not significantly associated with NSSI in persons with a high level of NSSI-SE.

CONCLUSION

NSSI-SE may buffer the effect of ACEs and RNLEs on NSSI, indicating that future interventions can be enhanced by targeting NSSI-SE among college students with ACEs or RNLEs to prevent their engagement in NSSI.

Adjusting intervention strategies for mental health of COVID-19 patients: A network analysis based on a survey in Omicron-infected patients

Background

The COVID-19 pandemic had a major impact on people's mental health. As the SAS-Cov-2 evolves to become less virulent, the number of asymptomatic patients increases. It remains unclear if the mild symptoms are associated with mild perceived stress and mental illness, and the interventions to improve the mental health of the patients are rarely reported.

Methods

This cross-sectional study investigated the level of depression, anxiety and perceived stress of 1,305 COVID-19 patients who received treatment in the Fangcang shelter hospitals in Shanghai, China. Network analysis was used to explore the relationship among depression, anxiety and perceived stress.

Results

The prevalence of depression, anxiety and perceived stress in the patients with Omicron infection were 9.03, 4.60, and 17.03%, respectively, lower than the prevalence reported during the initial outbreak of COVID-19. "Restlessness (A5)," "Uncontrollable worry (A2)," "Trouble relaxing (A4)" and "Fatigue (D4)" had the highest expected influence values. "Irritability (A6)" and "Uncontrollable (S1)" were bridge symptoms in the network. Comparative analysis of the network identified differences in the network structures between symptomatic and asymptomatic patients.

Conclusion

This study investigated the prevalence of depression, anxiety and perceived stress and the correlation among them in Omicron-infected patients in Fangcang shelter hospital, in Shanghai, China. The core symptoms identified in the study provide insight into targeted clinical prevention and intervention of mental health in non-severe Omicron-infected patients.

Moderate ethanol exposure during early ontogeny of the rat alters respiratory plasticity, ultrasonic distress vocalizations, increases brain catalase activity, and acetaldehyde-mediated ethanol intake

Early ontogeny of the rat (late gestation and postnatal first week) is a sensitive period to ethanol’s positive reinforcing effects and its detrimental effects on respiratory plasticity. Recent studies show that acetaldehyde, the first ethanol metabolite, plays a key role in the modulation of ethanol motivational effects. Ethanol brain metabolization into acetaldehyde via the catalase system appears critical in modulating ethanol positive reinforcing consequences. Catalase system activity peak levels occur early in the ontogeny. Yet, the role of ethanol-derived acetaldehyde during the late gestational period on respiration response, ultrasonic vocalizations (USVs), and ethanol intake during the first week of the rat remains poorly explored. In the present study, pregnant rats were given a subcutaneous injection of an acetaldehyde-sequestering agent (D-penicillamine, 50 mg/kg) or saline (0.9% NaCl), 30 min prior to an intragastric administration of ethanol (2.0 g/kg) or water (vehicle) on gestational days 17–20. Respiration rates (breaths/min) and apneic episodes in a whole-body plethysmograph were registered on postnatal days (PDs) 2 and 4, while simultaneously pups received milk or ethanol infusions for 40-min in an artificial lactation test. Each intake test was followed by a 5-min long USVs emission record. On PD 8, immediately after pups completed a 15-min ethanol intake test, brain samples were collected and kept frozen for catalase activity determination. Results indicated that a moderate experience with ethanol during the late gestational period disrupted breathing plasticity, increased ethanol intake, as well brain catalase activity. Animals postnatally exposed to ethanol increased their ethanol intake and exerted differential affective reactions on USVs and apneic episodes depending on whether the experience with ethanol occur prenatal or postnatally. Under the present experimental conditions, we failed to observe, a clear role of acetaldehyde mediating ethanol’s effects on respiratory plasticity or affective states, nevertheless gestational acetaldehyde was of crucial importance in determining subsequent ethanol intake affinity. As a whole, results emphasize the importance of considering the participation of acetaldehyde in fetal programming processes derived from a brief moderate ethanol experience early in development, which in turn, argues against “safe or harmless” ethanol levels of exposure.

Log-transformation of Independent Variables: Must We?

Epidemiologic studies often quantify exposure using biomarkers, which commonly have statistically skewed distributions. Although normality assumption is not required if the biomarker is used as an independent variable in linear regression, it has become common practice to log-transform the biomarker concentrations. This transformation can be motivated by concerns for nonlinear dose-response relationship or outliers; however, such transformation may not always reduce bias. In this study, we evaluated the validity of motivations underlying the decision to log-transform an independent variable using simulations, considering eight scenarios that can give rise to skewed X and normal Y. Our simulation study demonstrates that (1) if the skewness of exposure did not arise from a biasing factor (e.g., measurement error), the analytic approach with the best overall model fit best reflected the underlying outcome generating methods and was least biased, regardless of the skewness of X and (2) all estimates were biased if the skewness of exposure was a consequence of a biasing factor. We additionally illustrate a process to determine whether the transformation of an independent variable is needed using NHANES. Our study and suggestion to divorce the shape of the exposure distribution from the decision to log-transform it may aid researchers in planning for analysis using biomarkers or other skewed independent variables.

A Linear Decomposition Approach to Explain Excess Direct Healthcare Expenditures Associated with Pain Among Adults with Osteoarthritis

Objective:

Many patients with osteoarthritis experience pain which can lead to higher healthcare expenditures. It is important to understand the factors that drive the excess expenditures associated with pain in osteoarthritis.

Design:

Cross-sectional.

Study sample:

Our study sample consisted of adults (age ⩾ 18 years) from the Medical Expenditure Panel Survey (MEPS, 2018).

Methods:

Adults who were alive during the calendar year and had pain status were included in this study (N = 2804 weighted N = 32.03 million). Osteoarthritis was identified from the medical conditions file and household file. We used multivariable ordinary least squares regression to identify the statistically significant association of pain with direct healthcare expenditures. The Blinder-Oaxaca post-linear decomposition on log-transformed total direct healthcare expenditures was used to estimate the extent to which differences in characteristics contribute to the excess expenditures associated with pain.

Results:

Adults with osteoarthritis and pain had higher average expenditures ($21 814 vs $10 827, P < .001; 9.318 vs 8.538 in logtransformed expenditures) compared to those without pain. Pooled regression weights explained 62.9% of excess expenditures differences in characteristics between the 2 groups. The 2 main drivers of excess healthcare expenditures among adults with osteoarthritis and pain were (i) comorbidities (diabetes, asthma, chronic obstructive pulmonary disease, depression, heart diseases, cancer, and non-cancer pain conditions and (ii) prescription medications (NSAIDs, opioids, and polypharmacy).

Conclusion:

Need factors such as comorbid conditions, and prescription treatment explained the excess healthcare expenditures among adults with osteoarthritis and pain. The study findings suggest that reducing polypharmacy and appropriate management of comorbid conditions may be a pathway to reduce excess expenditures among adults with osteoarthritis and pain.

Tissue proteome analysis revealed an association between cancer, immune system response, and the idiopathic granulomatous mastitis

Idiopathic Granulomatous Mastitis (IGM) is a disease that clinically mimics breast cancers with symptoms of pain, edema, erythema, nipple discharge, nipple retraction, and fistula. Although IGM is considered to be formed by autoimmune responses or infections, the molecular mechanism behind formation and progress is unknown. Therefore, in this study, we aimed to investigate molecular mechanisms underlying IGM formation, progress, and recurrence by monitoring the changes at the proteome level. Protein extracts prepared from IGM (n = 15) and within-control tissues (n = 15) were subjected to nHPLC followed by LC-MS/MS proteomic analysis. Label-free quantitation analysis revealed that sixty differentially regulated between the two groups. Those proteins were classified based on their role in metabolic pathways using bioinformatics tools. Based on DAVID analysis, 16 of the differently regulated proteins were associated with the immune system, while 17 proteins were involved in cancer metabolism. STRING analysis showed that five of the differentially regulated proteins were associated with combined immune deficiency which were PNP, TAP1, ITGAL, PRKDC, and PTPRC while the other proteins were involved in insulin response and neutrophil degranulation. This study is one of the very few studies that investigated the changes in protein expressions of IGM tissues compared to controls. For the first time, we have shown the relationship of IGM with the immune system at the protein level and also underlined the cancer-like behavior of the disease. Furthermore, the proteins that were pointed out as combined immune deficiency-related proteins may have value as diagnostic markers for idiopathic granulomatous mastitis although further studies are needed to shed more light on the pathogenesis of the disease.

Deep learning-based quantitative estimation of lymphedema-induced fibrosis using three-dimensional computed tomography images

In lymphedema, proinflammatory cytokine-mediated progressive cascades always occur, leading to macroscopic fibrosis. However, no methods are practically available for measuring lymphedema-induced fibrosis before its deterioration. Technically, CT can visualize fibrosis in superficial and deep locations. For standardized measurement, verification of deep learning (DL)-based recognition was performed. A cross-sectional, observational cohort trial was conducted. After narrowing window width of the absorptive values in CT images, SegNet-based semantic segmentation model of every pixel into 5 classes (air, skin, muscle/water, fat, and fibrosis) was trained (65%), validated (15%), and tested (20%). Then, 4 indices were formulated and compared with the standardized circumference difference ratio (SCDR) and bioelectrical impedance (BEI) results. In total, 2138 CT images of 27 chronic unilateral lymphedema patients were analyzed. Regarding fibrosis segmentation, the mean boundary F1 score and accuracy were 0.868 and 0.776, respectively. Among 19 subindices of the 4 indices, 73.7% were correlated with the BEI (partial correlation coefficient: 0.420–0.875), and 13.2% were correlated with the SCDR (0.406–0.460). The mean subindex of Index 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left( {\frac{{P_{Fibrosis\, in\, Affected} - P_{Fibrosis\, in\, Unaffected} }}{{P_{Limb\, in\, Unaffected} }}} \right)$$\end{document}PFibrosisinAffected-PFibrosisinUnaffectedPLimbinUnaffected presented the highest correlation. DL has potential applications in CT image-based lymphedema-induced fibrosis recognition. The subtraction-type formula might be the most promising estimation method.

Genomic regions associated with tuber traits in tetraploid potatoes and identification of superior clones for breeding purposes

In potato breeding, morphological tuber traits are important selection targets to meet the demands of the fresh and processing markets. Understanding the genetic basis of tuber traits should guide selection and improve breeding efficiencies. However, this is challenging in potato due to the complexity of the traits and the polyploid nature of the potato genome. High-throughput affordable molecular markers and new software specific for polyploid species have the potential to unlock previously unattainable levels of understanding of the genetic basis of tuber traits in tetraploid potato. In this study, we genotyped a diversity panel of 214 advanced clones with the 22 K SNP potato array and phenotyped it in three field environments in Texas. We conducted a genome-wide association study using the GWASpoly software package to identify genomic regions associated with tuber morphological traits. Some of the QTLs discovered confirmed prior studies, whereas others were discovered for the first time. The main QTL for tuber shape was detected on chromosome 10 and explained 5.8% of the phenotypic variance. GWAS analysis of eye depth detected a significant QTL on chromosome 10 and explained 3.9% of the phenotypic variance. Another QTL peak for eye depth on chromosome 5 was located near the CDF1 gene, an important regulator of maturity in potato. Our study found that multiple QTLs govern russeting in potato. A major QTL for flesh color on chromosome 3 that explained 26% of the phenotypic variance likely represents the Y locus responsible for yellow flesh in potato tubers. Several QTLs were detected for purple skin color on chromosome 11. Furthermore, genomic estimated breeding values were obtained, which will aid in the early identification of superior parental clones that should increase the chances of producing progenies with higher frequencies of the desired tuber traits. These findings will contribute to a better understanding of the genetic basis of morphological traits in potato, as well as to identifying parents with the best breeding values to improve selection efficiency in our potato breeding program.

Playing coding games to learn computational thinking: What motivates students to use this tool at home?

Coding games are widely used to teach computational thinking (CT). Studies have broadly investigated the role of coding games in supporting CT learning in formal classroom contexts, but there has been limited exploration of their use in informal home-based settings. This study investigated the factors that motivated students to use a coding game called Coding Galaxy in a home-based setting. It explored the connections between the students' perceptions of and usage of the tool. An 11-day intervention was conducted at a primary school in Hong Kong with 104 participants. The students' perceptions of the game were collected via questionnaires and information on their use of the tool was extracted from log files. Results indicated that coding motivation and feeling of enjoyment were predictors of the actual use of the game, with coding motivation the dominant factor. Focus group interviews were also conducted to further explore the students' motivation to play the game. Through comparisons of active and inactive users, the qualitative findings supported the quantitative results, indicating that students who were more intrinsically motivated tended to be more active in using the game. The implications of the study for researchers and practitioners in CT education are discussed.

Concurrent urinary organophosphate metabolites and acetylcholinesterase activity in Ecuadorian adolescents

BACKGROUND

Organophosphates are insecticides that inhibit the enzymatic activity of acetylcholinesterase (AChE). Because of this, AChE is considered a physiological marker of organophosphate exposure in agricultural settings. However, limited research exists on the associations between urinary organophosphate metabolites and AChE activity in children.

METHODS

This study included 526 participants from 2 exams (April and July-October 2016) of ages 12-17 years living in agricultural communities in Ecuador. AChE activity was measured at both examinations, and organophosphate metabolites, including para-nitrophenol (PNP), 3,5,6-trichloro-2-pyridinol (TCPy), and malathion dicarboxylic acid (MDA) were measured in urine collected in July-October. We used generalized estimating equation generalized linear model (GEEGLM), adjusting for hemoglobin, creatinine, and other demographic and anthropometric covariates, to estimate associations of urinary metabolite concentrations with AChE activity (July-October) and AChE% change between April and July-October.

RESULTS

The mean (SD) of AChE and AChE% change (April vs July-October) were 3.67 U/mL (0.54) and -2.5% (15.4%), respectively. AChE activity was inversely associated with PNP concentration, whereas AChE% change was inversely associated with PNP and MDA. There was evidence of a threshold: difference was only significant above the 80th percentile of PNP concentration (AChE difference per SD increase of metabolite = -0.12 U/mL [95%CI: 0.20, -0.04]). Likewise, associations with AChE% change were significant only above the 80th percentile of TCPy (AChE % change per SD increase of metabolite = -1.38% [95%CI: 2.43%, -0.32%]) and PNP -2.47% [95%CI: 4.45%, -0.50%]). PNP concentration at ≥80th percentile was associated with elevated ORs for low AChE activity of 2.9 (95% CI: 1.5, 5.7) and for AChE inhibition of ≤ -10% of 3.7 (95% CI: 1.4, 9.8).

CONCLUSIONS

Urinary organophosphate metabolites, including PNP, TCPy and MDA, particularly at concentrations above the 80th percentile, were associated with lower AChE activity among adolescents. These findings bring attention to the value of using multiple constructs of pesticide exposure in epidemiologic studies.

The effects of locomotion on sensory-evoked haemodynamic responses in the cortex of awake mice

Investigating neurovascular coupling in awake rodents is becoming ever more popular due, in part, to our increasing knowledge of the profound impacts that anaesthesia can have upon brain physiology. Although awake imaging brings with it many advantages, we still do not fully understand how voluntary locomotion during imaging affects sensory-evoked haemodynamic responses. In this study we investigated how evoked haemodynamic responses can be affected by the amount and timing of locomotion. Using an awake imaging set up, we used 2D-Optical Imaging Spectroscopy (2D-OIS) to measure changes in cerebral haemodynamics within the sensory cortex of the brain during either 2 s whisker stimulation or spontaneous (no whisker stimulation) experiments, whilst animals could walk on a spherical treadmill. We show that locomotion alters haemodynamic responses. The amount and timing of locomotion relative to whisker stimulation is important, and can significantly impact sensory-evoked haemodynamic responses. If locomotion occurred before or during whisker stimulation, the amplitude of the stimulus-evoked haemodynamic response was significantly altered. Therefore, monitoring of locomotion during awake imaging is necessary to ensure that conclusions based on comparisons of evoked haemodynamic responses (e.g., between control and disease groups) are not confounded by the effects of locomotion.

Willingness-to-pay for COVID-19 vaccine in ten low-middle-income countries in Asia, Africa and South America: A cross-sectional study

Vaccine hesitancy is considered as one of the greatest challenges to control the ongoing coronavirus disease 2019 (COVID-19) pandemic. A related challenge is the unwillingness of the general public to pay for vaccination. The objective of this study was to determine willingness-to-pay (WTP) for COVID-19 vaccine among individuals from ten low- middle-income countries (LMICs) in Asia, Africa, and South America. Data were collected using an online questionnaire distributed during February - May 2021 in ten LMICs (Bangladesh, Brazil, Chile, Egypt, India, Iran, Nigeria, Pakistan, Sudan, and Tunisia). The major response variable of in this study was WTP for a COVID-19 vaccine. The assessment of COVID-19 vaccine hesitancy was based on items adopted from the World Health Organization (WHO) Strategic Advisory Group of Experts (SAGE) vaccine hesitancy scale constructs. In this study, 1337 respondents included in the final analysis where the highest number of respondents was from India, while the lowest number was from Egypt. A total of 88.9% (1188/1337) respondents were willing to pay for the COVID-19 vaccination, and 11.1% (149/1337) were not. The average WTP for COVID-19 vaccination was 87.9 US dollars ($), (range: $5-$200). The multivariate model analysis showed that the country, monthly household income, having a history of respiratory disease, the agreement that routine vaccines recommended by health workers are beneficial and having received the flu vaccination within the previous 12 months were strongly associated with the WTP. Based on the country of origin, the highest mean WTP for COVID-19 vaccine was reported in Chile, while the lowest mean WTP for the vaccine was seen among the respondents from Sudan. The availability of free COVID-19 vaccination services appears as a top priority in the LMICs for successful control of the ongoing pandemic. This is particularly important for individuals of a lower socio- economic status. The effects of complacency regarding COVID-19 extends beyond vaccine hesitancy to involve less willingness to pay for COVID-19 vaccine and a lower value of WTP for the vaccine.

Breathing Abnormalities During Sleep and Wakefulness in Rett Syndrome: Clinical Relevance and Paradoxical Relationship With Circulating Pro-oxidant Markers

Background

Breathing abnormalities are common in Rett syndrome (RTT), a pervasive neurodevelopmental disorder almost exclusively affecting females. RTT is linked to mutations in the methyl-CpG-binding protein 2 (MeCP2) gene. Our aim was to assess the clinical relevance of apneas during sleep-wakefulness cycle in a population with RTT and the possible impact of apneas on circulating oxidative stress markers.

Methods

Female patients with a clinical diagnosis of typical RTT (n = 66), MECP2 gene mutation, and apneas were enrolled (mean age: 12.5 years). Baseline clinical severity, arterial blood gas analysis, and red blood cell count were assessed. Breathing was monitored during the wakefulness and sleep states (average recording time: 13 ± 0.5 h) with a portable polygraphic screening device. According to prevalence of breath holdings, the population was categorized into the wakefulness apnea (WA) and sleep apnea (SA) groups, and apnea-hypopnea index (AHI) was calculated. The impact of respiratory events on oxidative stress was assessed by plasma and intra-erythrocyte non-protein-bound iron (P-NPBI and IE-NPBI, respectively), and plasma F2-isoprostane (F2-IsoP) assays.

Results

Significant prevalence of obstructive apneas with values of AHI &gt; 15 was present in 69.7% of the population with RTT. The group with SA showed significantly increased AHI values &gt; 15 (p = 0.0032), total breath holding episodes (p = 0.007), and average SpO2 (p = 0.0001) as well as lower nadir SpO2 (p = 0.0004) compared with the patients with WAs. The subgroups of patients with WA and SA showed no significant differences in arterial blood gas analysis variables (p &gt; 0.089). Decreased mean cell hemoglobin (MCH) (p = 0.038) was observed in the group with WAs. P-NPBI levels were significantly higher in the group with WA than in that with SAs (p = 0.0001). Stepwise multiple linear regression models showed WA being related to nadir SpO2, average SpO2, and P-NPBI (adjusted R2 = 0.613, multiple correlation coefficient = 0.795 p &lt; 0.0001), and P-NPBI being related to average SpO2, blood PaCO2, red blood cell mean corpuscular volume (MCV), age, and topiramate treatment (adjusted R2 = 0.551, multiple correlation coefficient = 0.765, p &lt; 0.0001).

Conclusion

Our findings indicate that the impact of apneas in RTT is uneven according to the sleep-wakefulness cycle, and that plasma redox active iron represents a potential novel therapeutic target.

Financial Institution Type and Firm-Related Attributes as Determinants of Loan Amounts

Access to formal credit remains critical for business operations, particularly for firms unable to generate sufficient funds internally. Using the World Bank’s Enterprise Survey dataset, 2018, we analyzed 230 Kenyan firms that applied for loans. These loans are sourced from banks (private, commercial, or state-owned) or non-banking financial institutions. Specifically, the paper explores the effect of financial institution type and firm-related characteristics on loan amounts advanced. The results show that the preferred credit provider matters, with the sensitivity level varying among the three institutional types. Additionally, the collateralization value, the owner’s equity proportion of fixed assets, and any existing credit facility correlate positively with the outcome variable. There is an inverse relationship between the largest shareholder’s ownership and the loan amount. The study uses the new product (service) launches to measure innovation. The findings suggest that firms in the innovation process access higher loan amounts than their non-innovative peers. Be that as it may, the difference in amount effect size between the two groups is small based on Cohen’s d rule. The paper highlights the theoretical and practical implications of these findings.

Socioeconomic Determinants of Universal Health Coverage in the Asian Region

The World Health Organization (WHO) states that examining medical financial systems is the most important process in evaluating universal health coverage (UHC). This study used the service coverage index (SCI) as a proxy of the progress toward UHC in eleven Asian countries. We employed a fixed-effects regression model to analyze panel data from 2015 to 2017, to explain the interrelationship between the SCI and major socioeconomic indicators. We also conducted a performance analysis (ratio of achieved SCI level to gross domestic product (GDP) or health expenditure displacement) to examine the balance between the degree of achievements related to UHC and a country’s economic level. The results showed that GDP and health expenditure were significantly positively correlated with the SCI (p < 0.01). The panel data analysis results showed that GDP per capita was a factor that greatly influenced the SCI as well as poverty (partial regression coefficient: 0.0017, 95% CI: 0.0013–0.0021). The results of the performance analysis showed that the Philippines had the highest scores (GDP: 1.84 SCI score/USD per capita, health expenditure: 1.04 SCI score/USD per capita) and South Korea the lowest. We conclude that socioeconomic factors, such as GDP, health expenditure, unemployment, poverty, and population influence the progress of UHC, regardless of system maturity or geographic characteristics.