Health self-management is important for healthcare undergraduates who are at the late adolescent or early adulthood stage, and will play an important part in health promotion for the general population. Previous research has shown that perceived health status affects health self-management. However, few studies have uncovered the mechanism between self-rated health and health self-management among healthcare undergraduates. Based on social ecology theory and Pender’s health promotion model, this study aimed to explore the associations between health self-management ability, self-rated health, eHealth literacy and resistance to peer influence of healthcare undergraduates, with a focus on identifying the mediating effects of eHealth literacy and resistance to peer influence. A cross-sectional study was conducted among 515 healthcare undergraduates in Eastern China between July and September 2021. Serial mediation analysis was performed using Haye’s Model 6 PROCESS macro to examine the mediating effects. The study found that health self-management ability was significantly and positively correlated with eHealth literacy, resistance to peer influence and self-rated health. Self-rated health had a direct and positive predictive effect on health self-management, with a direct effect value of 0.654. eHealth literacy and resistance to peer influence played both an independent mediating and a chain-mediating role in the mechanism of self-rated health affecting health self-management among healthcare undergraduates, with indirect effect values of 0.085, 0.101, and 0.013, respectively. The results suggest that eHealth literacy and resistance to peer influence could be intervention targets in programs for improving these students’ health self-management ability. Undergraduate students are at the stage of late adolescence or early adulthood, during which their establishment of healthy habits and health-related behaviors shape their current and future health ,2. MeanwStudies have reported that health self-management was suboptimal among college healthcare students, which can manifest as various physical, mental and social health problems such as sedentary behaviors, binge drinking, eating disorders ,6, stresPender’s health promotion model and Bronfenbrenner’s social ecology theory were used as the theoretical background of this study.Pender’s health promotion model (HPM) posits that situational influences are predictors of an individual’s health-promoting behaviors . SituatiThe social ecology theory states that individuals’ health is affected by multiple and interacting factors situated in multi-layered systems . For insThe social ecology theory also suggests that digital technologies located in the exosystem is a proximal factor influencing health behaviors of young people . DigitaleHealth literacy was found to be a major determinant of health self-management among patients with chronic diseases ,26. For For current healthcare undergraduates, digital technologies and peers are two significant sources of influence on health self-management. The social ecology theory highlights the interaction of factors influencing individuals’ health . Hence, Based on social ecological theory, Pender’s HPM, and extant literature, we hypothesized that self-rated health would be correlated with health self-management among undergraduate healthcare students. Further, we hypothesized that eHealth literacy and resistance to peer influence would mediate the relationship between self-rated health and health self-management. The present study will expand prior research on the relationship between self-rated health and health self-management by examining potential mechanisms underlying this relationship among healthcare undergraduates.A descriptive quantitative research design was adopted by using a self-administered survey to investigate health self-management ability, self-rated health, eHealth literacy and resistance to peer influence of undergraduate healthcare students, and explore their relationships. Convenient sampling was adopted to recruit participants from the Faculty of Medicine at a public and comprehensive university located in an east coastal city of Shandong Province. Eligible students included those aged 18 and above, being full-time undergraduates, enrolled in a healthcare program, and who agreed to participate in the study. All undergraduates in this university are full-time and generally reside on campus in a dormitory. Students in the Faculty of Medicine major in seven disciplines, namely, clinical medicine, dentistry, preventive medicine, nursing, pharmacy, medical imaging, and medical laboratory science. The study did not recruit students in the last year of their programs because they engaged in practicing in different clinical settings.2 of 0.15, power (1 − β) of 0.95, and number of predictors of 13, the resulting sample size was 189. For a multiple mediator model, research suggested that a sample size of 500 was required to detect significant mediating effect if the indirect effect was small ). The total direct effect and indirect effect of self-rated health on health self-management was 0.654 , and 0.199 , respectively. The indirect effects followed three paths: self-rated health→eHealth literacy→health self-management (estimated effect = 0.085); self-rated health →resistance to peer influence→health self-management (estimated effect = 0.101); and self-rated health→eHealth literacy→resistance to peer influence→health self-management (estimated effect = 0.013). As displayed in The results of bias-corrected percentile bootstrap analysis revealed significant indirect effects of eHealth literacy and resistance to peer influence on the relationship between self-rated health on health self-management . The totHealth self-management is especially important for healthcare undergraduates, as these individuals are at the early adulthood stage and will play an important part in health promotion for the general population. This study identified the associations between self-rated health, eHealth literacy, resistance to peer influence and health self-management, as well as the mediating effects by eHealth literacy and resistance to peer influence. The findings verified the hypothesis that self-rated health had both direct and indirect effects on health self-management. In particular, eHealth literacy and resistance to peer influence played both an independent mediating and serial-multiple mediating roles in the association between self-rated health and health self-management.In the present study, healthcare undergraduates reported a medium level of health self-management, with the lowest score in the health self-management behavior subscale and highest score in the health self-management environment subscale. This finding indicates that healthcare undergraduates could not adequately translate resources and knowledge into healthy behaviors, which casts light on a gap between cognition and behavior. Pender’s HPM pinpoints that a set of variables of behavioral specific knowledge and affect are of motivational significance for developing health-promoting behaviors . TherefoThe present study evinced that self-rated health, eHealth literacy, and resistance to peer influence were positively correlated with health self-management among healthcare undergraduates. Simultaneously, the three variables were the major significant predictors of health self-management in the multivariate regression analysis. This is consistent with Pender’s HPM and some previous research. Self-rated health represents an individual’s subjective perception of their own state of health. Yang et al.  found thNotably, the current study uncovered the mechanism of how self-rated health influenced health self-management among healthcare undergraduates. Specifically, eHealth literacy mediated the positive effect of self-rated health on health self-management. The integrative model of eHealth use indicates that intrinsic interest in health is a factor influencing eHealth literacy, and people with eHealth literacy are more inclined to use the internet to find, understand, evaluate and apply health-related information to promote health behaviors . OverallIn addition, resistance to peer influence played an independent mediating role, similar to eHealth literacy. For undergraduates at the late adolescence or early adulthood stage, peers serve as a significant source of socialization and exert impact on their attitudes, cognition and health behaviors on account of homophily  and for seeking social influence . PreviouNotably, eHealth literacy and resistance to peer influence played a chain-mediating role in the mechanism of self-rated health affecting health self-management. Namely, self-rated health promoted students’ resistance to peer influence by increasing their eHealth literacy, which, in turn, influenced health self-management. The underlying mechanism could be that students with better ability to search and acquire online information were more capable of utilizing the information to resist peer influence. According to the social ecology theory  and healThe study results provide important implications for educators and policymakers in medical institutions. Firstly, the health self-management ability of healthcare undergraduates needs to be enhanced, particularly in the domain of health self-management behavior. In an era of network information, the use of modern technology has shown promise in promoting healthcare professionals’ skills of supporting patients’ self-management . InterneIn view of the mediating effects of eHealth literacy and resistance to peer influence, interventions such as educational programs designed to increase eHealth literacy and resistance to peer influence are essential to boost health self-management among healthcare undergraduates. A study revealed a positive correlation between eHealth literacy and leaning experiences of the six domains  of eHealth literacy among Japanese undergraduate nursing students . TherefoIn the meantime, strengthening healthcare undergraduates’ levels of confidence in resisting peer influence is a worthwhile intervention strategy, which can be designed and tailored by incorporating aspects of social skills training to resist negative peer pressure . Some potential limitations of the study should be considered during interpretation of the findings. First, the sample did not include senior undergraduates and the findings, thus, may not be generalizable to the diverse healthcare undergraduates in China. Second, all variables in the study were measured using self-reporting scales, which may lead to some potential social desirability response bias when estimating the associations. Additionally, this study only tested two mediators while other psychosocial variables such as self-efficacy and social support may exert mediating effects, as demonstrated by previous research on different populations ,46,47. MSelf-rated health directly and indirectly affects healthcare undergraduates’ health self-management through the mediating effect of eHealth literacy and resistance to peer influence. The findings contribute to a better understanding of the mechanism of how self-rated health influences the health self-management of healthcare undergraduates. Interventions targeting eHealth literacy and resistance to peer influence could be implemented to promote students’ health self-management and further improve their ability of supporting clients for optimal health self-management in their future practice.


Parkinson’s Disease (PD) is a neurodegenerative disorder characterised by motor and non-motor features. Sleep disruption, including REM-sleep behaviour disorder and circadian rhythm dysfunction, is a well-recognised prodromal aspect of PD, although other features such as restless leg syndrome and other physical factors may contribute. Treatment of PD remains challenging due to the multifactorial nature of the disorder and requires a holistic approach. Improved understanding of central causes may be key to developing novel, effective and targeted treatments.In this month’s journal club, we first review the association between alpha-synuclein in CSF and sleep disorders in prodromal and established PD. Secondly, we explore the role of Diffusion Tensor Imaging in assessing the glymphatic system in a similar cohort comparison. Finally, we will appraise the efficacy of Nabilone as a possible new therapeutic option for sleep disturbance in patients with established PD.Alpha-synuclein aggregation in brain tissue is a core pathological hallmark of PD. Routine testing of this protein in cerebrospinal fluid (CSF) is not currently established as a useful or necessary biomarker for diagnosis or monitoring of disease progression. However, recent studies have demonstrated lower CSF levels of alpha-synuclein in individuals with PD as compared to healthy controls. This study focusses on the relationship between sleep disorders and alpha-synuclein levels in CSF, comparing individuals with established PD, prodromal PD, and healthy controls. Sleep disturbance was measured by the REM-sleep Behaviour Disorder Screening Questionnaire (RBDSQ) and Epworth Sleepiness Scale (ESS).575 patients were included in the study  with follow-up over a 3-year period. Individuals with prodromal PD were those with hyposmia (15/46) or REM-sleep behaviour disorder (RBD) (31/46). One-third of the prodromal PD group were included on the basis of hyposmia. This is a sensitive but not specific marker which may have biased results in this group.At baseline, sleep disturbance by both measures was highest in the prodromal PD group, followed by the PD group, and healthy controls respectively. In keeping with previous research, CSF alpha-synuclein at baseline was lowest in the PD group, followed by the prodromal PD group, and then the control group. Participants with both PD and RBD-specific behaviours had significantly lower levels of CSF alpha-synuclein than those with only PD.At follow-up, significantly decreased levels of CSF alpha-synuclein as compared to baseline were only seen in a subset of the patients .Comment: Sleep disturbance is a prominent feature of PD and can predate the diagnosis by many years. RBD correlation with lower levels of CSF alpha-synuclein, as seen here in cases of prodromal and established, PD may provide an important window for treatment with future disease-modifying therapies targeted at halting or preventing alpha-synuclein aggregation.Wang X. et al. (2022), Journal of Neurology, 269(5) 2469–2478. 10.1007/s00415-021-10812-2The glymphatic system has become a key focus of interest for neurodegenerative disorders. However, establishing a reliable imaging modality for assessment has proven challenging. Recently Diffusion Tensor Imaging (DTI) has been trialled in Alzheimer’s Disease, Normal Pressure Hydrocephalus and PD. This study used DTI along the perivascular space (DTI-ALPS) to evaluate glymphatic system activity in individuals with PD, RBD, and healthy controls. In limited studies of this technology to date, higher ALPS scores have been associated with an improved glymphatic activity.A total of 416 patients were enrolled including 168 with PD, 119 with RBD, and 129 healthy controls at a single centre. Exclusion criteria of note included those with significant cerebral parenchymal or cerebrovascular abnormalities, very low cognitive scores, and subjects with poor-quality imaging. Age and gender were matched across the groups. However, significant differences were noted in multiple demographics including education, smoking, hypertension, cognitive and depression scores, and sleep assessment scores. Anti-parkinsonian medication was temporarily withheld prior to the MRI where relevant, though the effect of this on results is unclear. The Hoehn and Yahr scale identified a mean score of 2.34 within the PD cohort suggesting the exclusion of those with more advanced disease.The ALPS index was lowest in individuals with PD, followed by RBD, and healthy controls respectively. ALPS scores also correlated with disease severity within the PD and RBD groups. A follow-up group of 50 patients with established PD did not show any significant difference in ALPS from their baseline scan, suggesting it may have limited value as a modality for monitoring disease progression, although the timeframe of expected change will require further investigation.Comment: The novel application of this emerging technology, together with issues in cohort matching, limits applicability. However, results suggest that glymphatic system dysfunction is prominent in PD and RBD versus healthy controls and is detectable via DTI. Sleep disruption in PD may be one mechanism contributing to glymphatic dysfunction with sleep thought to enhance glymphatic activity. This aberrant process may result in alpha-synuclein accumulation and have relevance for disease progression.Si X. et al. (2022), npj Parkinson's Disease, 8(1) 1–9. 10.1038/s41531-022-00316-9Cannabinoids are gaining increasing attention for their possible therapeutic benefits. Nabilone is a synthetic cannabinoid currently used as an anti-emetic for symptoms related to cytotoxic chemotherapy. The NMS-Nab trial investigated the effects of Nabilone on non-motor symptoms (NMS) of Parkinson’s disease (PD). This paper describes the post-hoc analysis of the trial investigating the effects on sleep outcomes in those known to experience sleep problems at baseline.This double-blind, placebo-controlled study was undertaken in a single centre in Austria. Inclusion criteria included age > 30 years, with stable motor symptoms and medication for > 30 days. Exclusion criteria included those with impulse control disorders, orthostatic hypotension, major psychiatric disorder, history of drug or alcohol abuse, and those with impaired liver function. Nabilone was given as an escalating dose regime up to a maximum of 2 mg, until NMS were much improved on the Clinical Global Impression of Improvement (CGI-I) scale. In phase 2, patients were randomised to continue on their optimal dose or placebo for 4 weeks. The medication was well tolerated with side effects consistent with its known profile.In this post-hoc analysis, 31/38 patients randomised  had sleep disturbance at baseline as measured by components of two analogous scales (NMSS and MDS-UPDRS-D2) and were compared. Following the initial treatment period, scores in both the MDS-UPDRS-1.7 and the NMSS-D2 improved. Of note, 96.8% of patients showed some improvement in MDS-UPDRS (≥ 1 point). A deterioration in score was seen in phase 2 in both groups. However, this was less in the Nabilone group. 29.7% of patients in the Nabilone group worsened by at least one point on the MDS-UPDRS suggesting a wearing-off effect may occur and a longer follow-up would be useful to explore this.Comment: Nabilone demonstrated positive sleep effects in the majority of patients in this small study. Its widespread effects via the endocannabinoid system makes it challenging to determine in these individuals which symptoms  improved leading to this result. Caution is also needed in patient selection, in particular those with psychiatric disturbance or orthostatic dizziness and may limit its use in later stages of the disease.https://doi.org/10.1002/mdc3.13471Peball M. et al. (2022), Movement Disorders Clinical Practice, 9(6), 751–758. These papers explore the important role of sleep in the progression and management of Parkinson’s disease. Levels of alpha-synuclein in CSF seem to correlate with REM-sleep behaviour disorder and this could suggest a time for therapeutic intervention. Glymphatic dysfunction leading to alpha-synuclein aggregation is an emerging area of interest but the role of imaging needs more validation. Nabilone demonstrated a positive short-term response in a majority of a small cohort of patients with PD and may prove a promising option in select patients.


A wide range of disorders are thought to arise from dysfunction in brain circuitry  a lack of native (i.e. patient-specific) functional imaging, (2) limited test-retest reliability in intra-individual acquisitions (meaning that single acquisitions are inherently less reliable than aggregates)  approval (REB #14-8255) and informed consent was acquired for the addition of rs-fMRI acquisitions during the preoperative MRI. The work described was performed in accordance with the Declaration of Helsinki. Included patients had a clinical diagnosis of idiopathic PD and were awaiting DBS implantation of either the subthalamic nucleus (STN) or internal globus pallidus (GPi). Patients with confounding neurological comorbidity  were excluded from the study. Demographic and clinical information for these patients can be seen in Included patients underwent MRI prior to their DBS surgery. For each patient, a T1-weighted 3D spoiled gradient echo recall (SPGR) sequence and a gradient echo (GRE) echo planar-imaging (EPI) rs-fMRI sequence (duration = 6.5 min) were acquired. MRI acquisition parameters for each sequence can be obtained from https://dataverse.harvard.edu/dataverse/GSP)    data (ov) Cox,  library.http://lead-dbs.org/) . The structural data (i.e. T1-weighted scan) was non-linearly transformed to standard space, after which the ensuing transform was applied to the rs-fMRI data. This was performed with the “Effective: low variance” preset, which has been shown to be the most accurate spatial normalization technique amongst other commonly used normalization algorithms  for each volume in the time series (n = 175)–was computed using the program Lead Connectome (https://www.lead-dbs.org)  space using Lead-DBS  from their analysis (https://doi.org/10.5281/zenodo.4310183) and Lead-DBS (https://www.lead-dbs.org/) for public access and use. A visual user-guide for investigators can be seen in The resultant 77 BOLD signal time-series matrices–corresponding to the 77 rs-fMRI acquisitions–were collated into a single dataset, which we have named the Toronto-Parkinson's Disease (Tor-PD) connectome. In addition, we also created a mask corresponding to the voxels in standard space where BOLD signal could be calculated in at least 80% of the 77 matrices analysis . The Torhttps://dataverse.harvard.edu/dataverse/GSP)   and RMINC (https://github.com/Mouse-Imaging-Centre/RMINC).To investigate its overall reliability and utility within the connectomic field, we performed three exemplar connectivity analyses using the Tor-PD dataset. First, we assessed whether we were able to identify the default mode network (DMN)–a canonical brain network that shows increased BOLD response during wakeful rest–in the Tor-PD connectome. Second, to assess for gross differences in PD DBS target connectivity, we compared the whole-brain functional connectivity of the subthalamic nucleus (STN), internal globus pallidus (GPi), and ventral intermediate nucleus (VIM) of the thalamus when computed using the Tor-PD connectome and a commonly used connectome composed of 1,000 rs-fMRI acquisitions acquired within the Brain Genomics Superstruct Project (r-) map. All raw r-maps described herein were computed using the Lead Connectome Mapper software; https://www.lead-dbs.org) . Finally, we compared the similarity of our thresholded and binarised t-maps, and a publicly available mask of the DMN  is a canonical large-scale brain network consisting of distributed nodes that show increased and correlated BOLD response during wakeful rest  . This maIn which |X| and |Y| are the cardinalities of the two sets .t > 2.8; p = 0.001, uncorrected) and binarised t-map derived from seeding the dorsal medial prefrontal cortex in the Tor-PD connectome included the posterior cingulate cortex and the angular gyrus, comprising the remaining nodes of the DMN . To compare the raw r-maps computed by either connectome, we performed mass-univariate testing using false discovery rate to correct for multiple comparisons. We also examined the effect of different MRI hardware, performing the same comparison but with a subset (n = 58) of the Tor-PD connectome using 3T rs-fMRI scans only .We compared the whole-brain functional connectivity of PD-DBS targets, namely the (i) STN  voxels across the whole brain  between raw r-maps computed using either connectome . Moreover, when we examined the effect of different MRI hardware, the number of significantly different voxels between raw r-maps from either connectome was the same when computed with the full Tor-PD connectome or a subset of the Tor-PD connectome using 3T rs-fMRI acquisitions (58 scans) only.When seeding common PD DBS targets in the Tor-PD connectome and Healthy connectome, there was a negligible number of significantly different  and the rest of the brain are predictive—across DBS cohorts and centers—of DBS outcome in PD patients  that have previously been implicated in motor improvement in PD  and PM  could significantly explain outcome when using the Tor-PD connectome, but not the Healthy connectome . Functional connectivity between VTAs and SMA or cerebellum could not significantly explain outcome when using either connectome (p = 0.04), but not the Healthy connectome .When investigating functional connectivity between VTAs and motor ROIs, we found that connectivity of VTAs to M1  and Healthy connectomes , when performing analyses with PD DBS patient data, functional connectivity of VTAs to certain motor ROIs computed with the Tor-PD connectome could significantly explain variance in clinical outcome, whereas connectivity computed with the Healthy connectome could not. The adoption of the Tor-PD in future studies, which we have shown can deliver comparable and potentially unique insights relative to healthy normative connectomes, may ultimately engender progress in the understanding and treatment of PD. For example, analyses with healthy connectomes have shown that targets for invasive and non-invasive neuromodulatory therapies for depression map to common brain networks  and an earlier iteration (n = 98) were nearly identical  had no areas of significant difference compared to those acquired with the full Tor-PD connectome (acquired with both 3T and 1.5T). Future versions of the Tor-PD connectome, or the advent of new disease-specific connectomes, should focus on increasing the number of rs-fMRI acquisitions and ensuring the homogeneity of the MRI hardware, sequence parameters, and preprocessing steps used.It is important to acknowledge certain limitations of the Tor-PD connectome. Firstly, the connectome is composed entirely of patients who were due to undergo deep brain stimulation. It is important to note that these patients are therefore not representative of the whole PD population. Secondly, while it is the largest disease-specific connectome of its kind, it is composed of considerably fewer rs-fMRI acquisitions than the Healthy connectome. Moreover, comparison with other existing connectomes is inherently limited by differences in MRI hardware, acquisition parameters, and preprocessing. Nonetheless, previous groups have investigated the influence of connectome size, showing that Overall, our findings demonstrate that the readily accessible Tor-PD connectome is a robust alternative to existing datasets, while underlining the value of using disease-specific connectomes when performing connectivity analyses in PD populations.https://doi.org/10.5281/zenodo.4310183.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The studies involving human participants were reviewed and approved by Research Ethics Board - University Health Network. The patients/participants provided their written informed consent to participate in this study.ALoh, AB, JG, and GE conceived the study. ALoz, MH, SK, AF, PA, MP, and RM informed and facilitated participation selection, recruitment, and consent. ALoh, AB, RG, GE, and CN performed data acquisition. JK, JC, BA-F, and AH guided image preprocessing. JG, BA-F, and ALoh performed the data analysis. RP and EW contributed to figures. ALoh and AB wrote the manuscript. ALoz supervised the project. All authors edited and approved the final version of the manuscript.This work was supported by the German Research Foundation  (CN), RR Tasker Chair in Functional Neurosurgery (ALoz), Doctoral Research Grant from the German Academic Exchange Service–DAAD (BA-F), and German Research Foundation  (AH).AF serves as a consultant for Medtronic, Abbott, Boston Scientific, Brainlab, Ceregate, and Medtronic and received research grants, personal fees and non-financial support from Boston Scientific, Brainlab and Medtronic and personal fees from Abbott and Ceregate, all outside the submitted work. SK reports honorarium and consulting fees from Medtronic. ALoz serves as a consultant for Medtronic, Abbott, Boston Scientific, and Functional Neuromodulation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Heart rate variability (HRV) is the standard method for assessing autonomic nervous system (ANS) activity and is considered a surrogate marker for sympathetic overactivity in obstructive sleep apnea (OSA). Although HRV features are usually obtained from the short-term segment method, it is impossible to evaluate rapid dynamic changes in ANS activity. Herein, we propose the ultra-short-term analysis to detect the balance of ANS activity in patients with OSA. In 1021 OSA patients, 10 min HRV target datasets were extracted from polysomnographic data and analyzed by shifting the 2 min (ultra-short-term) and 5 min (short-term) segments. We detected frequency-domain parameters, including total power (Ln TP), very low frequency (Ln VLF), low frequency (Ln LF), and high frequency (Ln HF). We found that overall HRV feature alterations indicated sympathetic overactivity dependent on OSA severity, and that this was more pronounced in the ultra-short-term methodology. The apnea-hypopnea index, oxygen desaturation index, and Epworth sleepiness scale correlated with increased sympathetic activity and decreased parasympathetic activity, regardless of the methodology. The Bland-Altman plot analyses also showed a higher agreement of HRV features between the two methodologies. This study suggests that ultra-short-term HRV analysis may be a useful method for detecting alterations in ANS function in OSA patients. Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder and is characterized by total or partial upper airway collapse. Patients with OSA usually experience frequent hypoxic events and sleep fragmentation, resulting in repeated exposure to hypoxemia and hypercapnia. Additionally, this phenomenon could induce sympathetic overactivity in patients ,2,3. TheTo date, linear and non-linear methods of HRV analysis have been used. There are several algorithms of the non-linear method, such as Hurst exponent and Higuchi fractal dimension. However, HRV analysis is usually performed as a function of the time or temporal frequencies from linear analysis methods: peaks below 0.04 Hz , between 0.04 and 0.15 Hz , and between 0.15 and 0.4 . Previously, the majority of these studies used a short-term window, which investigates the degree of fluctuations in the time and frequency domains, to evaluate the HRV analysis. Short-term segment HRV is typically calculated over 5 min, and the magnitude of the frequency components obtained by spectral analysis is useful for assessing ANS activity ,17,18. H2) to lower than 3% below baseline. The AHI was defined as the sum of the number of apneas and hypopneas per hour of sleep. In this study, we defined apnea as when the peak signal excursions, measured using an oronasal thermal sensor, dropped by ≥90% of baseline before the event, lasting for more than 10 s. Conversely, hypopnea was scored when the peak signal excursions using nasal pressure dropped by ≥30% of baseline before the event for at least 10 s, followed by a ≥ 3% decrease in oxygen desaturation or accompanied by arousal. Based on AHI, we classified the severity of OSA as follows: normal (AHI < 5), mild OSA (5 ≤ AHI < 15), moderate OSA (15 ≤ AHI < 30), and severe OSA (AHI ≥ 30).We consecutively enrolled patients diagnosed with OSA at the Sleep Center of Hallym Medical University Chuncheon Sacred Hospital and the informed consent was obtained from all subjects involved in the study. We excluded patients who had previously been diagnosed with a psychological disorder, a history of any medical condition that could influence sympathovagal activity, and serious comorbidities . We performed overnight full polysomnography (PSG) and obtained the Epworth Sleepiness Scale (ESS) scores for all enrolled patients to assess daytime sleepiness. In this study, sleep stage and respiratory events were scored according to the American Academy of Sleep Medicine . The folHRV is characterized by the variability in beat-to-beat intervals of the heart and is typically measured using RR intervals extracted from an ECG signal. Thus, in this study, we extracted the electrocardiography data from PSG recordings visually inspected for accuracy and quality. Specifically, we eliminated ectopic beats and artifacts and selected only normal-to-normal beats for the HRV analysis. Generally, time-domain HRV parameters showed variability in beat-to-beat intervals, which influenced both sympathetic and parasympathetic activities . Thus, tt-test was used to compare HRV parameters after the normality test. ANOVA with post hoc analysis was performed to compare the specific values according to the OSA severity group. Pearson’s coefficient test was used to identify the relationships between HRV and sleep parameters according to OSA severity. To compare the agreement of the HRV parameters between the ultra-short-term and short-term segments, we used Bland-Altman plots. All statistical analyses were conducted using R version 3.5.0 . Statistical significance was set at p < 0.05.Numerical variables are expressed as mean ± standard deviation, and Student’s n = 238), moderate OSA (n = 331), and severe OSA (n = 452). The demographics and PSG parameters are presented in p < 0.001), whereas there were no significant group differences in TST, sleep latency, and sleep efficiency.In total, 1021 patients with OSA were enrolled in this study. The patients were divided into three groups: mild OSA (A 10 min HRV target segment was divided into consecutive individual ultra-short-term and short-term segments every 2 s, and all frequency-domain HRV parameters were averaged according to OSA severity . In mildThe correlation between sleep parameters and HRV parameters is shown in Moreover, we used Bland-Altman plots to analyze the consistency of the HRV parameters. The Bland-Altman plots for HRV analyses based on ultra-short and short-term segments are shown in HRV is widely used as a marker related to the ANS and is classically divided into two categories based on the length of the data recording. One is short-term HRV, typically calculated over 5 min, and the magnitude of its frequency components obtained by spectral analysis is used for the assessment of ANS function ,22,23,24The frequency analysis of HRV typically uses three parameters: Ln VLF, Ln LF, and Ln HF. Additionally, Ln TP can be measured by the sum of the total spectral power, and it is thought to represent the global autonomic function. The Ln LF/HF ratio, as a major marker for sympathovagal balance, can also be calculated during HRV analysis. Among HRV studies of OSA, there is widespread evidence demonstrating that the alteration of ANS balance toward sympathetic predominance is well reflected in the Ln LF/HF ratio, Ln LF ,34,35,36It is well known that HRV features obtained from short-term segments show smoother spectral profiles than those obtained from ultra-short-term segments. Thus, short-term methodology may be easy to evaluate in an entirely autonomic state during sleep. However, short-term HRV analysis has inherent limitations in real-world settings. First, short-term HRV-based studies are highly susceptible to noise. Second their failure to detect dynamic ANS activity, especially the rapid changes in Ln LF and Ln HF. However, ultra-short-term HRV analysis could assess the immediate changes in RR intervals on electrocardiograms before and after the period of respiratory events. For these reasons, to investigate sophisticated sleep studies, short-term HRV analysis is not recommended. Although ultra-short-term HRV analyses have not yet been adopted as a standard tool in OSA, a recent study showed that higher ultra-short-term HRV was strongly related to longer respiratory event durations . In thisHowever, our study had several limitations. First, only time-domain HRV parameters were assessed for ANS function, because frequency-domain HRV parameters require a longer RR interval segment for the analysis. Second, this study did not include data on HRV parameters in control subjects. However, to include the control group, we should match the age, sex, BMI, and comorbidities; thus, it is very difficult in the real-world study. Third, in this study, we obtained HRV features from only the linear method, not using the non-linear method. Thus, our further study should include the comparison of HRV features between the linear and the non-linear methodologies. Fourth, several comorbidities could influence the inflammatory status of OSA patients and these conditions could be confounding factors in the HRV analysis ,39. FinaIn conclusion, ultra-short-term HRV features are valid surrogates of short-term HRV features for investigating ANS function in OSA patients. Therefore, this study provides valuable insights into the balance of ANS activity in OSA patients.


Accurate identifying head and neck squamous cell cancer (HNSCC) patients at high risk of local persistence/recurrence (P/R) is of importance for personalized patient management. Here we developed a multi-objective, multi-classifier radiomics model for early HNSCC local P/R prediction based on post-treatment PET/CT scans and clinical data.We retrospectively identified 328 individuals  with HNSCC treated with definitive radiation therapy at our institution. The median follow-up from treatment completion to the first surveillance PET/CT imaging was 114 days (range: 82-159 days). Post-treatment PET/CT scans were reviewed and contoured for all patients. For each imaging modality, we extracted 257 radiomic features to build a multi-objective radiomics model with sensitivity, specificity, and feature sparsity as objectives for model training. Multiple representative classifiers were combined to construct the predictive model. The output probabilities of models built with features from various modalities were fused together to make the final prediction.We built and evaluated three single-modality models and two multi-modality models. The combination of PET, CT, and clinical data in the multi-objective, multi-classifier radiomics model trended towards the best prediction performance, with a sensitivity of 93%, specificity of 83%, accuracy of 85%, and AUC of 0.94.Our study demonstrates the feasibility of employing a multi-objective, multi-classifier radiomics model with PET/CT radiomic features and clinical data to predict outcomes for patients with HNSCC after radiation therapy. The proposed prediction model shows the potential to detect cancer local P/R early after radiation therapy. In the United States, head and neck squamous cell cancers (HNSCC) represent a substantial number of cancers, with an estimated 53,000 new cases per year and 10,800 deaths from the disease , 3. HoweFluorodeoxyglucose (FDG) PET/CT is routinely used for post-radiation treatment surveillance to detect cancer P/R in HNSCC, as it provides both anatomic and metabolic information , 8. HoweRadiomics involves advanced imaging analysis techniques that offer a possibility for differentiating intratumoral heterogeneity and observing a patient’s response to treatment by extracting quantitative features from radiological images such as CT, PET, MRI, or PET/CT , 14. TheAlthough radiomics is a hot topic for treatment outcome prediction of HNSCC, most of the works and datasets are focusing on pre-treatment outcome prediction. Here we hypothesize that a radiomic feature set extracted from PET/CT images of HNSCC primary tumors after radiation therapy correlates with patients’ local control and that a machine learning model can be trained by these features to predict patients’ local control after radiotherapy. The goal of this study was to develop and validate a multi-objective, multi-classifier radiomics model that can predict post-treatment local P/R in patients with HNSCC. As many clinical parameters such as patient age, tumor stage, primary site and HPV status have shown strong correlation to treatment outcome in different studies, we added several of these parameters as features for model training , 24, 25.All investigations in this study were carried out in accordance with the guidelines and regulations of institutional review board (IRB). We originally included 432 patients with HNSCC diagnosed at our institution from August 2005 to November 2018. This institutional database contained information about the baseline features, therapy, and follow-up data. Patients with distant metastases, those who did not receive a PET/CT scan from the treating institution within six months after treatment, and those who had a history of radiotherapy were excluded. Thus, 328 patients were included in this study: 69 with tumor persistence/local recurrence and 259 without locoregional recurrence. All 69 cases who have locoregional recurrence were confirmed with biopsy (cytologic or histologic). 328 patients were divided into a training cohort (262 patients) and a validation cohort (66 patients) using baseline characteristics-based case-control matching with a 4:1 fashion Figure 1Patients were treated with radiation therapy in different treatment modalities, including definitive radiation treatment (RT), concurrent chemoradiotherapy (CCRT), and surgery then RT alone or CCRT. Patients referred for re-irradiation were excluded. Cisplatin, paclitaxel, and cetuximab were combined with radiotherapy in different treatment regimens in CCRT. Each of these patients received a total radiation dose of 66-72 Gy in 2-2.2 Gy fractions to the gross tumor. Radiation treatments were performed according to a conventional (five fractions per week) schedule. And patients were followed up every 3-6 months for the first two years and every 6-12 months after the therapy, albeit the exact timing was subject to normal clinical variability. The follow-up practices included physical examination, blood test, PET/CT and considerable chest imaging, and a biopsy confirmation was commonly recommended if lesions were suspected of harboring malignancy. Recurrence was defined as re-emergence of the tumor after initial complete regression and was divided into local  and regional recurrence . Recurrence was distinguished from a second primary tumor by considering pathological features, clinical features, and the location of the tumor in relation to radiotherapy . The medDiagnostic post-treatment PET and CT scans were exported through the digital Picture Archiving Communication System . The median follow-up time from treatment completion to PET/CT imaging was 114 days (range: 82-159 days). Gross tumor volume (GTV) was defined as areas suspicious for cancer local P/R after radiation therapy on PET/CT images, which included pretreatment GTVs and areas with a standardized uptake value (SUV) ≥ 2.5. A radiation oncologist and a nuclear medicine radiologist with more than six years of clinical experience contoured the GTVs on CT in Velocity AI . Details about the PET/CT imaging protocol of the included patient scans are shown in 3 via bilinear interpolation to correct for the differences in voxel spacing and slice thicknesses between different scans.We extracted radiomic features from PET/CT images within the delineated GTV. We corrected GTV masks to cover soft-tissue-only areas by removing voxels whose Hounsfield units (HU) are outside of  on CT images. For PET images, we calculated the standardized uptake value (SUV) . For CT We extracted 257 radiomic features for each imaging modality by using a MATLAB based open source radiomics toolbox . These rIn addition to radiomic features, clinical characteristics such as patient age, tumor stage, primary site and HPV status may improve the performance of local P/R prediction models –33. SeveWe created a multi-classifier, multi-objective, and multi-modality radiomics model to identify HNSCC patients who have a high risk of local P/R by fusing the output probabilities from separate predictive models built with features from different modalities: CT, PET, and clinical data. The training and prediction pipeline for the proposed radiomics model is illustrated in β, and weights of classifiers ω for classifier fusion according to the predictive performance on the training cohort. Different values and combinations of ƒ, β and ω result in different solutions, e.g., sub-models, and solutions in the pareto-optimal solution set were kept during model updating with IMIA. Each solution in the pareto-optimal solution set has either higher feature sparsity, higher sensitivity, or higher specificity on the training data than the other solutions in the pareto-optimal set. During model training, the probability output of solutions in the pareto-optimal solution set were weight-fused by using the evidential reasoning (ER) method to evaluate the training performance of the pareto-optimal solution set  method . The pretion set . The weivia ER again to give a final prediction value. See the After the model performance on the training cohort converged or the number of model updating generations reached the maximum, which was set as 50 in our experiment, we calculated the weights of modalities for output probability fusion based on their training performances. Then, for a sample from the validation cohort, models built with clinical data, CT radiomic features, and PET radiomic features gave their prediction results separately. Finally, these prediction results were fused together To evaluate the benefit of adding feature sparsity to the objective during model training, we compared the number of selected features and model performance of the multi-classifier, multi-objective radiomics models trained with and without feature sparsity objective for PET and CT separately. To evaluate the robustness of the multi-classifier model over single-classifier ones, we compared the prediction performance of PET and CT multi-classifier radiomics to their single-classifier ones . To illustrate the added prediction power of using multi-modality fusion strategy, we constructed five different multi-objective, multi-classifier prediction models for comparison. Three of them were single-modality models: clinical features model, CT radiomic features model and PET radiomic features model. The other two were multi-modality models: fused CT and PET radiomics model and fused Clinical, CT and PET features (CT+PET+Clinic) model.We evaluated the final models by calculating the sensitivity, specificity, accuracy, and AUC of the prediction results on the validation cohort. We calculated the statistical differences between the receiver operating characteristic curves (ROCs) of different models by using the Delong test with a significance level of 0.05 . To evalThe radiomics feature selection results and the validation performance are shown in The performance comparison of radiomics models built with single classifier and multiple classifiers is shown in The prediction performance of the separate single-modality models and the fused multi-modality models is summarized in The Kaplan-Meier curves of local P/R free survival of differentiated low- and high-risk patient group using the CT+PET+Clinic model are shown in In our dataset, 33 local failure and 25 regional failure were recorded in the training cohort, while the validation cohort patients had 11 local and 5 regional failure recorded. When probability value of 0.5 was used as the classification threshold, the three-modality-fused model predicted 23 patients in the validation cohort to have locoregional failure, in which 11 out of 11 local failure and 4 out of 5 regional failure was identified, however 9 patients were falsely predicted to be positive for locoregional failure. The corresponding sensitivities for local and regional failure are 100% and 80% respectively, while the overall specificity is 83%.As radiation therapy is an important and potentially curative modality for HNSCC, a better local control is linked to improved disease-free survival and quality of life after treatment. In the present study, we developed a multi-objective, multi-classifier radiomics model using post-treatment PET/CT radiomics features and clinical data from HNSCC patients who had undergone radiotherapy, which could provide important information for predicting locoregional recurrence after radiotherapy. By adding feature sparsity as an additional objective to our previous published method, the median selected radiomics feature number decreased a half and better validation performance was achieved. The multi-classifier strategy produced more robust prediction performance than single classifier models. The fused post-treatment PET/CT radiomics model achieved promising performance (AUC=0.93) at predicting local P/R. The model that achieved the highest AUC value fused the output probabilities from all the single-modality models (AUC=0.94).local = 100% and sensitivityregional = 80%) than radiologists’ diagnosis performance reported from a mate-analysis of 24 studies which included 2627 patients , but lower specificity (specificitymodel = 83% and specificityradiologist = 93% to 95%)  , 39. TheBecause PET/CT is a preferred surveillance modality for HNSCC patients after radiotherapy, many efforts have been made to improve the accuracy, sensitivity, and specificity of PET/CT in predicting clinical outcomes or distinguishing locoregional recurrence from radiation-induced inflammation –43. MancOur multi-objective, multi-classifier, and multi-modality model not only demonstrates the feasibility of predicting local P/R from PET/CT, but also indicates the potential to distinguish tumor sites from local inflammation. In clinical practice, physicians were generally unable to detect recurrence within 12 weeks. Because FDG uptake is not limited to cancer sites and may occur in a variety of inflammatory cells, sites of tumor recurrence and inflammation could be mistaken for each other due to their similar FDG levels. Therefore, it is recommended that PET/CT be conducted at least 12 weeks following the treatment completion to reduce the false-positive effects of inflammation. By optimizing training methods and including clinical inputs of PET/CT from before 12 weeks, the radiomics model may separate recurrence from local inflammation at earlier stages, which would shed light on the early prediction of tumor P/R from PET/CT imaging.This study has some limitations that could be addressed in our future research. First, although the three-modality fused model trended towards better performance than the radiomics-only models, we could not prove that the fusion of clinical features and PET/CT radiomic features provides an additive benefit over PET and/or CT radiomic features. The limited patient population in this study may have inhibited our ability to detect the difference, but the three-modality fusion model is still promising and worth investigating prospectively in a larger cohort. Second, this prediction model was trained and validated on data from a single institution: all the patients included were consistent in treatment planning, follow-up management, and tumor recurrence/persistence assessment. Although our results are encouraging, a large multicenter cohort analysis to further validate the clinical applicability of this prediction model would be worthwhile, especially given the concern of robustness, reproducibility, and standardization to radiomics related study , 45. ExtIn conclusion, we developed and validated a multi-objective, multi-classifier radiomics model for predicting local P/R after radiotherapy in patients with HNSCC by using post-treatment PET/CT and patients’ clinical data. This model was effective at predicting outcomes, which could provide clinicians with more information about potential recurrence for post-treatment tumors. The proposed strategy is worthy of further investigation in larger HNSCC cohorts and integrating features from other modalities.jing.wang@utsouthwestern.edu.The datasets presented in this article are not readily available because of institution regulations. Requests to access the datasets should be directed to QZ and KW are co-first authors for this manuscript, and they made equal contributions to this study. All authors contributed to the article and approved the submitted version.This work was supported in part by the US National Institutes of Health R01CA251792 (JW and DS).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Mindfulness training has been associated with improved attention and affect regulation in preadolescent children with anxiety related attention impairments, however little is known about the underlying neurobiology. This study sought to investigate the impact of mindfulness training on functional connectivity of attention and limbic brain networks in pre-adolescents. A total of 47 children with anxiety and/or attention issues (aged 9-11 years) participated in a 10-week mindfulness intervention. Anxiety and attention measures and resting-state fMRI were completed at pre- and post-intervention. Sustained attention was measured using the Conners Continuous Performance Test, while the anxiety levels were measured using the Spence Children’s Anxiety Scale. Functional networks were estimated using independent-component analysis, and voxel-based analysis was used to determine the difference between the time-points to identify the effect of the intervention on the functional connectivity. There was a significant decrease in anxiety symptoms and improvement in attention scores following the intervention. From a network perspective, the results showed increased functional connectivity post intervention in the salience and fronto-parietal networks as well as the medial-inferior temporal component of the default mode network. Positive correlations were identified in the fronto-parietal network with Hit Response Time and the Spence Children’s Anxiety Scale total and between the default mode network and Hit Response Time. A 10-week mindfulness intervention in children was associated with a reduction in anxiety related attention impairments, which corresponded with concomitant changes in functional connectivity. Attention, involving the concentration of one’s awareness on a given task to the exclusion of distracting stimuli, is often compromised in anxiety states   , intellectual disability, major medical illness, or who had sustained head injury (with loss of consciousness exceeding 10 minutes) were also excluded. Eligibility for the study was defined by parent-report of no disorder diagnosis and not taking any psychotropic medication during a telephone interview. The children were recruited through the University of Sunshine Coast’s social media platform and advertisements in various school newsletters. Individual children in years 4-6 (not class groups), from a variety of state and private schools across the Sunshine Coast participated in the pre- and post- assessments and intervention, which were conducted at the Thompson Institute. The one hour intervention was offered three afternoons per week, two sessions per afternoon, with participants selecting one session to attend per week. The group size ranged from 8-12 participants and groups were mixed ages. Of Participants undertook the following assessments with a total duration of 60 minutes: (i) the Spence Children’s Anxiety Scale (SCAS), (ii) the Conners Continuous Performance Test 3rd edition (CPT 3) and (iii) magnetic resonance imaging (MRI) scanning. Assessments were conducted at two time-points: (i) pre-intervention (‘pre’); up to two weeks before the 10-week mindfulness intervention; and (ii) post-intervention (‘post’); up to two weeks following the mindfulness intervention. The study was conducted at the Thompson Institute, University of the Sunshine Coast, between July 2019 and January 2020.never) to 3 , with a possible score ranging from 0 - 114. There is empirical support for test-retest reliability and internal consistency across child and adolescent populations  using a 64-channel head and neck coil at the Nola Thompson Centre of Advanced Imaging, Thompson Institute. The T1-weighted images were collected using magnetization prepared-rapid gradient echo sequence with TR/TE/flip angle =2000ms/1.7ms/7Data were pre-processed and analyzed using FMRIB's Software Library (FSL 6.0.4) and SPM12 . Following normalization, the fMRI data were smoothed using a 6mm Gaussian kernel of full width at half maximum.The rsfMRI scans were corrected for slice timing (FSL-slice timer) and head motion (FSL-mcflirt). Any fMRI datasets with head displacement > 3mm were excluded  at pre- and post- intervention for: (i) change in anxiety scores ; and (ii) change in attention scores . All statistical tests were conducted using SPSS Corp, . StatistTo explicate the effect of the mindfulness intervention on FC, voxel-based analysis of the different brain networks was performed to estimate differences between pre- and post-intervention FC. Differences were estimated by entering individual maps into a general linear model; paired t-tests were performed with permutation testing   at either time-point, hence, the final sample analyzed was N=42 . Participants had different ethnicities including Caucasian 94%, Asian 4%, Aboriginal and Torres Strait Islander 2%. In addition, participants were equally diverse in terms of their school type with 47% of the participants attending a state school, 51% attending a private school and 1% home-schooled. For more information about demographics, see Table For the behavioral data, Results showed a significant difference between median pre- and post- intervention scores for total anxiety score, separation anxiety score and obsessive-compulsive anxiety score Table . There wParticipants performed better after the intervention, with results indicating a significant decrease in commission errors from pre- to post- intervention time-points Table . There wN=42 participants completed scans at both pre- and post-intervention time-points. Post-intervention, the group showed changes in FC at several networks including the SLN, FPN and the Medial-Inferior Temporal component (MTG) of the DMN and were corrected for multiple comparisons (p≤ 0.05). However, no significant differences were observed in anterior or posterior components of the DMN.Five participants were excluded from the rsfMRI analysis due to incomplete data acquisition resulting from image distortion artefact, thus For the SLN, compared to pre-intervention, there was increased FC post-intervention in multiple locations, as shown in Fig. Similarly, the FPN showed increased FC post-intervention in the dlPFC, dmPFC, vmPFC, insula, middle frontal gyrus (MFG), inferior frontal gyrus (IFG), middle cingulate cortex (MCC), primary sensory and motor cortices and supplementary motor area as shown in Fig. N=38 individuals with complete behavioral and MRI data at both time-points, hence, correlational analyses were conducted at the pre-intervention time-point. The HRT had a positive correlation with right FPN connectivity to left amygdala , right hippocampus , caudate head , left superior temporal gyrus , and precuneus . In addition, SCAS total score was positively correlated with left FPN connectivity to right caudate head . Furthermore, the posterior DMN connectivity to right PCC  and left PCC , and precuneus  were positively correlated with the attention variable HRT. No correlations were observed between the behavioral measures and the salience FC. In contrast to the pre-intervention time-point, there were no significant correlations at the post-intervention time-point for any of the functional networks.There were Additionally, we investigated the correlation between the change in the functional connectivity and the change in the behavioral outcomes at the regions where we observed functional connectivity changes. The results showed improved behavioral measures are correlated with the functional connectivity increases as shown in Table This is the first study to investigate effects of a mindfulness intervention on FC in resting-state networks in a preadolescent cohort with sub-clinical anxiety-related attention impairments. Understanding mindfulness-induced neural and associated behavioral changes is crucial to support the efficacy of early interventions  are to be playing a role in the recovery of anxiety levels towards normal levels. This is also supported by the previous research suggesting the involvement of the PCC in different functions including emotion regulation, attention control (Fox et al., However, the increased FPN to thalamus connectivity was correlated with the increase in the attention variable detectability which means increased connectivity was observed with reduced performance. This is the opposite to what we expected to see of increased functional connectivity in association with better performance in attention (Hart et al., Our study has several limitations that require mentioning. Firstly, this is not a randomised control study as a control group was not included in the design, as sub-clinical anxiety is pervasive in preadolescent children, with approximately up to 70% of children reporting they worry “every now and then” (Cartwright-Hatton, Our study has shown that mindfulness was associated with improved anxiety and attention behavioural scores as well as altered FC in several interconnected resting-state networks. The mindfulness induced behavioural and FC findings should be considered in the context of developmental changes occuring in pre-adolescent children. Furthemore, the findings of positive correlations at pre-intervention in the FPN and DMN networks to several brain regions with the attention variable HRT, and the anxiety variable SCAS total score, suggests that excessive FC in these networks may be a marker of anxiety-related attention impairments in sub-clinical populations. Our findings suggest mindfulness strategies may be an effective intervention for sub-clinical anxiety-related attention impairments in pre-adolescent children, however need to be considered in the context of developing functional connectivity.


Developing prediction tools for immunotherapy approaches is a clinically important and rapidly emerging field. The routinely used prediction biomarker is inaccurate and may not adequately utilize large amounts of medical data. Machine learning is a promising way to predict the benefit of immunotherapy from individual data by individuating the most important features from genomic data and clinical characteristics.Machine learning was applied to identify a list of candidate genes that may predict immunotherapy benefits using data from the published cohort of 853 patients with NSCLC. We used XGBoost to capture nonlinear relations among many mutation genes and ICI benefits. The value of the derived machine learning-based mutation signature (ML-signature) on immunotherapy efficacy was evaluated and compared with the tumor mutational burden (TMB) and other clinical characteristics. The predictive power of ML-signature was also evaluated in independent cohorts of patients with NSCLC treated with ICI.We constructed the ML-signature based on 429  patients who received immunotherapy and extracted 88 eligible predictive genes. Additionally, we conducted internal and external validation with the utility of the OAK+POPLAR dataset and independent cohorts, respectively. This ML-signature showed the enrichment in immune-related signaling pathways and compared to TMB, ML-signature was equipped with favorable predictive value and stratification.Previous studies proposed no predictive difference between original TMB and modified TMB, and original TMB contains some genes with no predictive value. To demonstrate that fewer genetic tests are sufficient to predict immunotherapy efficacy, we used machine learning to screen out gene panels, which are used to calculate TMB. Therefore, we obtained the 88-gene panel, which showed the favorable prediction performance and stratification effect compared to the original TMB. Immune checkpoint inhibitors (ICI) are an effective treatment for patients with advanced non-small cell lung cancer (NSCLC) , 2. The With the advent of “era of precision medicine”, the analyses of large-scale molecular data are beneficial for many aspects of oncology research, including the classification of possible subtypes, stages, and treatment of cancer . AccuratIn this study, we exploited the potential of machine learning methods to address the issue of identifying NSCLC patients with ICI benefits. With the hypothesis that mutations in certain genes may better predict NSCLC response to ICB treatment, we aimed to develop a machine learning based-mutation signature (ML-signature) to predict ICI clinical benefits effectively.This retrospective study was approved by our institutional review board (IRB No.202070). The genomic alterations and clinical data for POPLAR (NCT01903993) and OAK (NCT02008227) trials were downloaded from publicly accessible data reported by Gandara et al. . Both POMutations were measured by FoundationOne CDx NGS assay , which tThe ICI dataset was used as a development dataset to determine the gene signature of ICI benefits and consisted of 429 patients treated with ICI from the prospective POPLAR and OAK trials. In the research, we estimated the importance of features (mutation genes) for the ICI-benefit predictive modeling problem using the XGBoost method, which is a gradient boosting decision tree.Using XGBoost, we developed immunotherapy benefit prediction models based on mutation features (the ML-signature) , 19. Grahttps://www.genome.jp/), and KEGG results was visualized by R package ggplot2.For functional enrichment analysis, all genes in ML-signature were mapped to terms in the Kyoto Encyclopedia of Genes and Genomes database (KEGG) and P < 0.05 as the threshold. KEGG links genomic information with biological functions based on the online platform .To further validate the performance of ML-signature, we evaluated its predictive power on another NSCLC dataset from external cohorts, which can be downloaded from the cBioPortal database  and SAS (version 9.4). A Cox proportional hazards model was used to determine the HR for survival. Kaplan–Meier methodology was used to construct the survival curves and the significances of subgroups were estimated using the log-rank test. A two-sided P <0.05 was considered a statistically significant difference.To derive the optimal cut-offs of the candidate gene inclusion for ML-signature in the prediction of ICI-benefit/non-benefit stratification, we performed Cox regression analysis to assess the effect of inclusion criteria changes for ML-signature  on PFS in the immunotherapy treatment arm. The curves of Wald statistic of PFS HR of different ML-signature stratification, which was determined at numbers of mutations in ML-signature, were fitted using a locally estimated scatterplot smoothing (LOESS) with a span of 0.80 ; and strThis study contained two sections : 391 genes known to be involved in cancer development.The ML-signature development is shown in We examined the biological functions and signal transduction pathway associated with the ICI-benefits related ML-signature. Functional enrichment  and OS  in patients receiving immunotherapy; while the tumor mutational burden (TMB) score was not . In patients receiving chemotherapy, the ICI-benefit score was associated with poor PFS  and OS . These results confirmed that the ICI-benefit score based on the ML-signature was a specific predictor for immunotherapy. These data-driven results suggested an “elbow” region between≥1 and ≥6, and the cut-point analysis demonstrated that ICI-benefit score≥2 was the break point with better survival stratification in patients treated with immunotherapy  and OS HR: 0.61 ], compared with those of TMB-high vs. TMB-low stratification. In patients receiving chemotherapy , compared with those for TMB-high subgroup . The absolute difference in median PFS and OS of immunotherapy vs. chemotherapy in the ICI-benefit subgroup was relatively greater (median PFS: 8.2 vs. 2.9 months and median OS: 20.1 vs. 6.9 months) compared with those of the TMB-high subgroup (median PFS: 2.9 vs. 2.9 months and median OS: 13.5 vs. 6.9 months).Efficacy comparison evaluating immunotherapy Additionally, when classified as non-benefit candidates for immunotherapy (vs. chemotherapy (11.4%). The absolute ORR benefit of immunotherapy vs. chemotherapy increased from 13.1% (TMB-high) to 19.2% (ICI-benefit). In the immunotherapy arm, a greater difference was observed in ORR between ICI-benefit and ICI-non-benefit subgroups (30.6% vs. 8.8%) compared with those of TMB-high and TMB-low subgroups (22.3% vs. 12.0%). Additionally, in the chemotherapy arm, the difference in ORR between ICI-benefit and ICI-non-benefit subgroups was smaller (11.4% vs. 14.4%) compared with the ORR between TMB-high and TMB-low subgroups (9.2% vs. 15.1%). In patients treated with immunotherapy, the AUC for ML-signature distinguishing between responder and non-responder patients was 0.67 (95% CI: 0.59 to 0.75), which was higher than that of TMB  . Survival analysis of patients with different mutations in the 88-gene panel was performed. Results Figure 6We also explored the predictive performance of the ML-signature in a cohort of 350 NSCLC patients treated with ICI from the MSK-IMPACT cohort . There are 58 genes of the ML-signature (88 genes) that can be detected in the MSK-IMPACT panel (410 genes~468genes). Results also revealed that patients classified as ICI-benefit candidates by our ML-signature had better survival stratification, thus further validating the predictive power of the ML-signature Figure 7The heterogeneity of cancer patients results in various therapy efficacies and deciding whether to receive ICI is momentous . PreviouIn this study, we identified 88 ML-signature from almost 400 genes associated with cancer progression. These ICI-benefits related ML-signature primarily enriched in immune-related signaling pathways. Immune cells and immune cytokines in the tumor microenvironment are important during cancer development, and their biological functions change dynamically with tumor progression. In addition, these ML-signature also show decreasing transduction-related signaling pathways. Signal transduction pathways involve each stage of cancer cells, from stem cells to advanced cancer cells, including proliferation, metabolism, cell cycle, DNA repair, apoptosis, differentiation, tumor extracellular matrix remodeling, angiogenesis and metastasis , 29. NotRecent evidence has shown high TMB to be associated with improved clinical outcomes from ICI in multiple cancer types . HoweverOur ML-signature recognized the patients most likely to benefit  from immunotherapy. We demonstrated that immunotherapy is not suitable for all patients. Compared with immunotherapy, ICI-non-benefit patients can gain a longer survival time from chemotherapy, which can be interpreted as immune-related adverse events (irAEs). The detailed mechanisms of irAEs are still unclear. Presented mechanisms include activated T lymphocytes attacking health issues, increasing levels of autoantibodies, and inflammatory cytokines , 39. AltTo further validate the value of our ML-signature, we conducted survival analyses with two external NSCLC cohorts with WES data covering the overall 88 genes of ML-signature. This result validates the consistency of the internal cohort of our main result, notably the predictive effect of the ML-signature having better survival stratification than TMB, indicating that the prediction model was steady and accurate as the predictive tool for patient selection for immunotherapy. Additionally, we used the MSK-IMPACT dataset to authenticate the ML-signature, which involved more than four hundred genes and possessed the highest degree of dissemination . Only 58Gene mutations have also shown a crucial role in tumor heterogeneity, and the distribution of mutations represents sub-clonal status, which may compromise the efficacy of immunotherapy. The ratio of allele frequency to maximum somatic allele frequency (AF/MSAF) has also been used to represent allele frequency heterogeneity (AFH), and AFH was recognized as the negative factor of prognosis . To addrThere are also some limitations in our models. As a retrospective study, datasets are incomplete inevitably and information biased. Moreover, this model excluded routine clinical information, such as patients’ characteristics, pathological data, specific therapy options and surgery status. Furthermore, the partially detected genes also suggested that our model includes some genes with low predictive value and the number of eligible genes can be further refined. The ratios of eligible genes have not been tested in real-world patients. The enrichment of genes is generally shown in immune-related signaling pathways and needs further study according to their proportion in ML-signature.We provided an accurate ML-signature for NSCLC patients and demonstrated its feasibility with certain verified measurements. And our study supports the possibility and potential of using machine learning to screen for predictive molecular markers of immunotherapy efficacy or other treatments. In the ear of big data, further research can excavate novel biomarkers with the assistance of computing science and accelerate the process of translational medicine and precision medicine.https://doi.org/10.1038/s41591-018-0134-3.Publicly available datasets were analyzed in this study. This data can be found here: This study was reviewed and approved by the Research Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University, IRB No.202070. Written informed consent was obtained from all participants for their participation in this study.ZL and GL: writing. ZY: data curation. ZL, GL, LL, XW, and JS: data analysis. ZL and GL: visualization. JH, LZ, HL, and WW: supervision. All authors contributed to the article and approved the submitted version.Guangzhou Medical University Discipline Construction Funds (Basic Medicine) (No. JCXKJS2022A11).Author JS is employed by Tianpeng Technology Co. Ltd, Guangzhou, China.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This is followed by terminal erythropoiesis in which morphologically recognizable erythroblasts proliferate and differentiate to enucleated red blood cells.3 Each of these steps is coordinated by a highly complex regulatory network. Disruption of any component of this network leads to a variety of erythroid-related disorders, manifested mainly as anemia.4Erythropoiesis is the process in which hematopoietic stem cells (HSCs) differentiate, proliferate, and eventually form mature red blood cells.5 While primary anemia is usually caused by abnormalities in the hematopoietic system, secondary anemia can be induced by chronic diseases such as cancers.9 Cancer-related anemia is a common condition with prevalence of 30% to 90% depending on cancer types and the disease stage.10 Anemia significantly reduces the quality of life and shortens survival in cancer patients.8Anemia is a major public health problem worldwide.11 EPO and ESAs were believed to be safe and effective in these patients. However, studies in the past decade raised concerns on potential effects of EPO in promoting tumor cell proliferation and metastasis.7 Therefore, the development of new agents or therapies that are effective and safe is an unmet medical need to treat cancer-related anemia.The most common treatment options for cancer-related anemia include iron supplements, red cell transfusion, and erythropoietic-stimulating agents (ESAs) including erythropoietin (EPO) and its analogs. As an important cytokine for erythropoiesis, EPO is indispensable for erythropoiesis in proliferation, survival, and maturation; thus, it is commonly used for treating cancer-related anemia.12 In their study, distinctive biological CDs, derived from red dates (Jujube-derived CDs [J-CDs]), were synthesized. Further analyses showed that J-CDs had biocompatibility and membrane permeability that are suitable for the tests on their roles in erythropoiesis. To this end, J-CDs were first tested in an in vitro erythroid-differentiation system and found to be able to induce red blood cell production via promoting the self-renewal of BFU-E and CFU-E without affecting terminal erythropoiesis. This finding was confirmed by an in vivo assay. J-CDs treatment increases mouse bone marrow and spleen erythropoiesis similarly through promoting BFU-E and CFU-E self-renewal. Hematopoietic stem and progenitor cell (HSPC) populations and nonerythroid lineages were not affected by J-CDs. Importantly, J-CDs were compared with EPO in several cancer cell lines and a NOD/SCID xenograft tumor model. EPO treatment promoted proliferation of both red blood cells and cancer cell lines, leading to shortened survival in mice with grafted tumor cells. However, J-CDs only led to red blood cell production without induction of tumor growth or affecting animal survival , as a novel class of agents, to be able to promote red blood cell production and efficiently treat animal models of cancer-related anemia.14 In the studies from Xu et al,12 J-CDs were used to treat anemia since red dates are considered to be “blood tonic” in traditional Chinese medicine. An interesting finding is that red dates/jujube powders did not promote red blood cell production. The powders also failed to show any effect on the erythroid progenitor cells, indicating that J-CDs had unique properties and roles compared with red dates/jujubes. The finding that J-CDs only promote self-renewal of BFU-E and CFU-E without affecting other hematopoietic populations also indicates that J-CDs may have erythroid progenitor-specific mechanisms, which warrants detailed future investigation. Further studies including exploring the direct binding targets of J-CDs could provide a comprehensive understanding on their specificity to erythroid progenitor cells. Another major question is whether CDs in general, that is, not specifically derived from red dates, could also play similar functions in promoting red blood cell production.Interdisciplinary studies in drug development often provide more therapeutic choices. As new nanocomposite materials with suitable biocompatibility and specific physicochemical characteristics, CDs were proved to have therapeutic efficiency in Parkinson’s disease and cancer.The work from Xu et al successfully applied nanocomposite material in the biomedicine field. The development of J-CDs not only provides a new and promising choice to treat cancer-related anemia but also widens the application of nanocomposite materials. In addition, this study has important social and economic values in promoting the modernization of traditional Chinese medicine.


Very little is known about sickness absence among white-collars workers in the trade and retail industry, despite being a large and important group on the labour market. The aim was to investigate future sickness absence (SA) and disability pension (DP) in a cohort of privately employed white-collar employees in the trade and retail industry.A prospective population-based cohort study of all 192,077 such white-collar workers (44% women) in Sweden in 2012, using linked microdata from three nationwide registers covering 2012-2016. Prevalence and mean number of SA and/or DP net days/year in general and by diagnoses categories were calculated for all and also stratified by sex. Logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI) between sociodemographic and work-related factors and future SA/DP.The proportion who had SA and/or DP was higher in women  than men (4-6%) each year. Each studied year, women had more mean SA/DP net days than men in the entire cohort, however, among those who had SA and/or DP, there were no gender differences regarding the mean number of net days. The mean number of SA/DP net days increased for both women and men each year, especially SA due to mental diagnoses. SA in 2012 was the strongest factor associated with SA/DP in 2016 . Work-related factors were only weakly or insignificantly associated with future SA/DP. The ORs for most factors were stronger for men than for women.More knowledge about the mechanisms behind these results are needed. Some SA/DP measures showed large sex-differences, others not - it is important to use different measures to show the complexity of these phenomena. Several factors were more strongly associated with SA/DP among men than among women, indicating that there are other factors of importance for women.Most white-collar workers in the trade and retail industry had no SA/DP in any of the six studied years. SA due to mental diagnoses was the largest and fastest-growing SA/DP diagnostic group.Sickness absence in 2012, female sex, and low education were associated with future SA/DP. Most other sociodemographic and job-related factors were not, or only weakly, associated with future SA/DP.


Early detection of colorectal cancer (CRC) is crucial to the treatment and prognosis of patients. Traditional screening methods have disadvantages.231 blood samples were collected from 86 CRC, 56 colorectal adenoma (CRA), and 89 healthy individuals, from which extracellular vesicle long RNAs (exLRs) were isolated and sequenced. An CRC diagnostic signature (d-signature) was established, and prognosis-associated cell components were evaluated.The exLR d-signature for CRC was established based on 17 of the differentially expressed exLRs. The d-signature showed high diagnostic efficiency of CRC and control  samples with an area under the curve (AUC) of 0.938 in the training cohort, 0.943 in the validation cohort, and 0.947 in an independent cohort. The d-signature could effectively differentiate early-stage (stage I–II) CRC from healthy individuals (AUC 0.990), as well as differentiating CEA-negative CRC from healthy individuals (AUC 0.988). A CRA d-signature was also generated and could differentiate CRA from healthy individuals both in the training (AUC 0.993) and validation (AUC 0.978) cohorts. The enrichment of class-switched memory B-cells, B-cells, naive B-cells, and mast cells showed increasing trends between CRC, CRA, and healthy cohorts. Class-switched memory B-cells, mast cells, and basophils were positively associated with CRC prognosis while natural killer T-cells, naive B-cells, immature dendritic cells, and lymphatic endothelial cells were negatively associated with prognosis.Our study identified that the exLR d-signature could differentiate CRC from CRA and healthy individuals with high efficiency and exLR profiling also has potential in CRA screening and CRC prognosis prediction. Colorectal cancer (CRC) ranks the third common cancer in men and the second in women, as well as the second cause of cancer death worldwide, which remains an enormous socioeconomic burden on society , 2. MeanEarly detection of CRC is the key to reducing invasive treatment, morbidity, mortality, and treatment cost . CRC scrExtracellular vesicles (EVs) are extracellular membrane vesicles originated and released from endocytosis and exocytosis, containing proteins, DNA, RNA, and lipids . Due to In this study, a CRC diagnostic signature (d-signature) based on plasma exLR profiling was identified and validated, which could differentiate CRC from control  individuals efficiently. We also evaluated cell components and signaling pathways between CRC, CRA, and healthy groups, and associated prognostic significance were revealed.From February 2018 to January 2019, 194 blood samples were collected from 72 CRC patients, 42 CRA patients, and 80 age- and sex-matched healthy participants receiving routine medical examination. The diagnoses of all CRC and CRA patients were pathologically confirmed, and these participants did not have a history of other malignant tumors. All enrolled CRC patients underwent surgical treatment without preoperative chemotherapy or radiotherapy at the Colorectal Surgery Department of Fudan University Shanghai Cancer Center. 37 blood samples  were collected in an independent center from Fujian Medical University Union Hospital.The optimized strategy for plasma exLR-seq included several steps as follows: plasma sample collection, EV purification, transmission electron microscopy (TEM), size distribution measurement, RNA isolation, and RNA-seq library preparation . To be bThe qualified FASTQ files generated from RNA-seq were aligned to the human genome (hg38) using STAR v2.5.3 with default parameters . The mapiRC stands for the count of reads mapped to the gene and iL is the length of the gene. LR is the number of long RNA genes including protein coding and long non-coding genes.Where p values for multiple testing. A gene with a fold change (FC) bigger than 1.5 and adjusted p value smaller than 0.05 was defined as a DEG. To investigate the differential pathways between CRC and control samples, R package “clusterProfiler” was used for KEGG pathway enrichment analysis based on the DEGs  samples. To explore differentially expressed genes (DEGs) between these two cohorts, we applied R package “limma” on TPM expression profiles . The Benthe DEGs .http://home.penglab.com/proj/mRMR/) (https://www.csie.ntu.edu.tw/~cjlin/libsvm/ (The whole dataset was randomly divided into training cohort (48 CRC and 82 control) and validation cohort (24 CRC and 40 control). With respect to the training cohort, we firstly conducted DEG analysis. To elect informative and functional signature genes for effectively distinguishing CRC samples from control samples, we focused on these upregulated protein coding or long non-coding genes in CRC samples. Then, we employed the minimum redundancy maximum relevance (mRMR) algorithm to rank these candidate genes. This was implemented using the mRMR package with the “MIQ” feature selection scheme (j/mRMR/) . Next, wj/mRMR/) . The fir/libsvm/ . The CRATo infer the cell types of EV origins, we performed xCell analysis on TPM expression profiles using R package “xCell,” a gene signature-based method that integrates the advantages of gene set enrichment with deconvolution approaches . We obtaIn general, 194 participants were involved in our center, consisting of 72 CRC patients, 42 CRA patients, and 80 healthy individuals. The clinicopathological information is listed in The isolated EVs observed by TEM were round capsule bubbles. The scanning electron microscope images of EVs are shown in 2(FC) >0.59 and adjusted p value < 0.05). MRMR and SVM were used to select the optimal feature gene set among the training cohort. The top 17 genes of the ranked 66 genes were selected to build the SVM prediction model  is one of the most common cancer markers but limited by low diagnostic efficiency when used along for CRC diagnosis . The perIn addition to the diagnosis of CRC, detection of CRA is also a very important link in the management of CRC, considering CRA as precancerous lesions of CRC. In this part, we developed another exLR-based d-signature for CRA in the same way as building the CRC d-signature. Unsupervised hierarchical clustering revealed a clear separation of CRA and healthy samples Table 3.via KEGG analysis, showing that the differentially expressed exLRs were enriched in the intestinal immune network for the IgA production pathway and the circadian rhythm mammal pathway with a gradual rising trend between the three groups  CRC or CEA-negative CRC. In clinical practice, CRA patients need no additional surgery if the polyp has been completely endoscopically resected with favorable histologic features, while radical surgery plays a vital role in the treatment of most early-stage CRC patients , 28. DifThe 17 genes used to establish the d-signature comprised 16 protein-coding genes and one lncRNA gene, all of which were upregulated in CRC samples. The H2BFS expression level in lung cancer tissue has been reported to be higher than that in normal lung tissue . HoweverThis study was mainly designed to build a d-signature for CRC screening, and we were surprised to find that a similar model might be very efficient in CRA diagnosis. However, due to the limitation of CRA cohort size, we believe that the encouraging initial results need to be reconfirmed in further study with larger cohorts.In this study, statistical differences of 21 immune cell types estimated based on the gene expression profile were observed between CRC, CRA, and healthy cohorts. Actually, the relationship between systemic immune cells and CRC still remains poorly understood, even though some studies with a small sample size have yielded some preliminary conclusions , 39. In Furthermore, differentially expressed exLRs between CRC, CRA, and healthy cohorts were enriched in two pathways, the intestinal immune network for the IgA production pathway and the pathway of circadian rhythm of mammal. IgA deficiency is associated with a number of immune-mediated diseases, and it has also been proved to be associated with increased risk of gastrointestinal cancer in a nationwide population-based cohort study . CircadiLimitations and prospects of this study are listed as follows. First, the independent cohort size was limited and the diagnostic performance of the CRC d-signature needs to be validated in more independent centers. Second, we are continuing to recruit participants to identify the efficiency of the CRA d-signature. Third, the potential of EVs in predicting chemotherapy resistance is under study.In summary, our study evaluated the value of exLRs serving as markers in the detection of CRC. The d-signature we have established can differentiate CRC from control  cohorts efficiently, which is supposed to improve CRC early detection efficiency in clinical practice. The exLR profiling can also indicate immune cell distribution and associated prognostic significance. We believe that this d-signature can contribute to the early detection of CRC and improve CRC prognosis in the near future.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The studies involving human participants were reviewed and approved by the Ethics Committee of the Fudan University Shanghai Cancer Center. The patients/participants provided their written informed consent to participate in this study.T-AG, H-YL, Z-ZZ, H-BH, S-LH, and YX were responsible for the study concept and study design. T-AG, H-YL, and CL performed the data acquisition. H-YL, Y-TJ, YL, and Y-CL were responsible for the methodology, software, formal analysis, and visualization. T-AG and H-YL wrote the original draft. YX, S-LH, and Z-ZZ edited and revised the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the National Natural Science Foundation of China , the Shanghai Science and Technology Innovation Action Plan (20JC1419000), and the Shanghai Committee of Science and Technology .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.