This systematic review of prospective longitudinal primary studies sought to determine whether electronic cigarette (e-cigarette) use by teenagers who had never smoked conventional tobacco cigarettes (tobacco cigarettes) at baseline was associated with subsequently commencing tobacco cigarette smoking.The review followed the principles of a systematic review and meta-analysis. A key word search identified peer-reviewed articles published between 1 January 2005 and 2 October 2019 from seven bibliographic databases and one search engine. Using pre-prepared inclusion/exclusion criteria two researchers independently screened abstracts, and subsequently, full text papers. Selected articles were quality assessed in duplicate. Data on study participants characteristics, exposure and outcome measures were recorded in an adapted Cochrane Data Extraction Form. Feasibility assessment was done to detect clinical heterogeneity and choose an approach to meta-analysis. Analysis comprised pairwise random effects meta-analyses, and sensitivity and subgroup analyses.2 68%, 9 primary studies) times higher odds of commencing tobacco cigarette smoking for teenagers who had ever used e-cigarettes at baseline, though the odds ratio were marginally lower  when only the four high-quality studies were analysed.From the 6619 studies identified, 14 one-off primary studies in 21 articles were suitable for inclusion. The participants ages ranged from 13 to 19 years and comprised teenagers based in Europe and North America. Nine of the 14 one-off studies, with follow-up periods between 4 and 24 months, met the criteria for inclusion in a meta-analysis of the association between ever use of e-cigarettes and subsequent initiation of tobacco cigarette use. Based on primary study adjusted odds ratios, our meta-analysis calculated a 4.06 : 3.00–5.48, IThe systematic review found that e-cigarette use was associated with commencement of tobacco cigarette smoking among teenagers in Europe and North America, identifying an important health-related harm. Given the availability and usage of e-cigarettes, this study provides added support for urgent response by policymakers to stop their use by teenagers to decrease direct harms in this susceptible population group, as well as to conserve achievements in diminishing tobacco cigarette initiation.The online version contains supplementary material available at 10.1186/s12889-021-10935-1. E-cigarettes were first sold into Europe in 2006 and into the United States of America (USA) in 2007 and as of 2017, 433 brands of e-cigarettes and 15,586 flavours had been documented [The 2011–2018 USA-based National Youth Tobacco Surveys reported that current e-cigarette use among high-school students, increased from 1.5% in 2011 to 20.8% in 2018 . SurveysThe emergence of e-cigarettes is a disruptive change challenging tobacco control globally –10, and In addition to the direct harm from e-cigarettes, one of the main concerns for this vulnerable group is potential for e-cigarette use to lead to tobacco cigarette use with its associated lifelong harms, and thereby undermining tobacco control initiatives. This is a critical area where evidence is required to inform public health policy .2 88%) [Four published systematic reviews investigated whether e-cigarette use to led to tobacco cigarette use. Soneji et al., combining seven primary studies in their synthesis, reported that e-cigarette use is associated with an increased risk of subsequent cigarette smoking initiation and current cigarette smoking in young people aged 14–30 years, even after adjusting for potential confounding by demographic, psychosocial, and behavioural risk factors . Glasser2 88%) . Aladeok2 88%) .Given the significance of teenage years in establishing future smoking behaviour internationally , the aimThis study adhered to the tenets of a systematic review and meta-analysis.. Our keywords were based on different English words for e-cigarette, for example, e-cig*, e-liquid, vape, vaping, cigalike, electronic nicotine delivery system (ENDS), and electronic non-nicotine delivery. Non-English words for these concepts were also used, for example, e-sigaret*, E-zigarette, and e-papieros. Using predefined inclusion/exclusion criteria  14-item quality assessment tool for observational studies . We performed two sensitivity analyses: one including studies rated as high-quality and another including studies that controlled for three domains of confounding . Subgroup analyses were also completed by year of data collection (baseline data collection pre 2014 compared to baseline data collection 2014 to date), geographical region (North America versus Europe) and length of follow-up (less than 12 months compared to 12 months or more). A level of evidence [A meta-analysis feasibility assessment including the 14 studies was done to decide whether to do meta-analysis and to choose the most suitable meta-analysis method –26. The evidence  and a GRevidence  were ascevidence .The searches retrieved 6619 studies . Two researchers selected 21 papers for inclusion in the study  times higher odds of initiating smoking tobacco cigarettes for those who had ever used e-cigarettes at baseline, although this combined odds ratio decreased marginally to 3.71 times  when only the four high-quality studies [Our ever e-cigarette-use meta-analysis was based on the primary study adjusted odds ratios and calculated a combined 4.06  were like the results for the high-quality studies sensitivity analysis, but the level of heterogeneity remained high ; compared to 2014 onwards . However, the confidence intervals overlap indicating that they are not statistically significantly different.Three subgroup analysis were done  [2 65%) [Finally, considering the significance of the geographical, regulatory, and cultural context of these studies, we compared European studies with those from the USA. The combined OR was higher in the European studies  , 51, 52  [2 65%) , 44, 47.2 0%)  while examining the association between using e-cigarettes and initiating smoking tobacco cigarettes, and had a similar, significant estimate of effect in meta-analysis. The four high-quality studies also had a similar estimate of effect but lower statistical heterogeneity. The remainder of the studies were judged to be moderate quality because of their small sample sizes and/or high loss to follow-up.The longitudinal prospective study design used to evaluate the relationship between e-cigarette use and initiation of tobacco cigarette smoking permits researchers to start the process of establishing a causal relationship. Across all primary studies included in the meta-analysis, the adjusted odds ratios and confidence intervals are consistent, and the strength of association is statistically significant. Moreover, the use of e-cigarettes which occurred before initiating smoking, fulfils the criteria for a temporal relationship. Finally, two studies in this review have illustrated a dose-response relationship. We have moderate confidence that the true effect is probably close to the estimated effect for initiating smoking at follow-up for those who had ever used e-cigarettes at baseline, as all meta-analyses indicate that there is a significant positive association between using e-cigarettes at baseline and smoking tobacco cigarettes at follow-up, and this effect size is quite large; the findings are statistically significant, consistent, and the exposure occurred before the outcome. We also found a significant two-fold positive association between past-30-day e-cigarette use at baseline and subsequent cigarette smoking initiation at follow-up. This is a more restricted measure of exposure with a shorter duration during which the non-user can take up vaping, but shows the same positive association among smoking initiators demonstrating that e-cigarette vaping in non-smokers in the 30 days prior to data collection increases the odds of taking up tobacco cigarette smoking.Given that there were 433 brands of e-cigarettes and 15,586 flavours documented by 2017 , 53 of tNone of the cohort studies did biochemical verification of outcomes as they relied on the tried and tested questions about ever use, recent or last year use, and current or last 30 days use and these measures are accepted the world over for surveying the use of tobacco products, licit drugs, and illicit drugs . The mosThe results of this up-to-date and comprehensive systematic review are in line with three published meta-analyses , 18, 19,An important question still to be answered relates to the principal catalysts in the relationship between e-cigarette and tobacco cigarette use. Researchers have tried to clarify the move from using e-cigarettes to smoking tobacco cigarettes through three theories which are the gateway theory , the comIn terms of the most appropriate study design for assessing causality, Etter recommended large longitudinal epidemiological studies which measure smoking onset, control for confounders, and include a propensity score measure of liability to smoking . In addiMost research has taken place in Europe and North America: there is a need for research for cross-country research including low- and middle-income countries where the burden of tobacco cigarette use is the highest.No study provided information on e-cigarette type, generation or liquid. It is important to note that e-cigarettes and their e-liquids were not a standard commodity or exposure but an umbrella term for a device that delivers nicotine and other products including flavourings . IndepenGiven over six million deaths attributable to smoking worldwide each year , tacklinTo find a way forward through these competing viewpoints, policymakers must carefully appraise evidence on risk, benefits, and trade-offs while understanding the framing and wider context of the debate , 73–75. Against a backdrop of often clashing harm reduction and precautionary viewpoints on e-cigarettes, mobilising evidence, while necessary, is difficult for policymakers This difficulty is exacerbated when the relationship between evidence and policy is seen as a linear “know-do-gap”, instead of recognising the “muddling through” of the policy process . The harIn the case of any non-smokers, be they children, young people, or adults, e-cigarettes offer no benefits and present potential for harm from nicotine dependence and exposure to known toxins. The mandate for policy action to maximise protection of children and adolescents is further strengthened by the systematic review presented in this paper, in which we found that e-cigarettes were associated with initiation of tobacco cigarette smoking among adolescents. This is a identifying an important public health harm which undermines hard-won progress in tobacco control, that have been largely delivered through preventing smoking initiation in youth. Other researchers have noted that two-thirds of these adolescents may go on to smoke tobacco cigarettes for a period . ChildreThe systematic review and meta-analysis found that e-cigarette use was associated with initiation of tobacco cigarette smoking among teenagers in Europe and North America, identifying an important public health harm. The meta-analysis presents a more precise and generalisable estimate of the odds of tobacco cigarette smoking initiation following ever use of e-cigarettes in teenagers. Given the widespread availability and use of e-cigarettes, this study further supports urgent action by policymakers to prevent their use by adolescents to reduce direct harms in this vulnerable population group as well as to protect gains in reducing tobacco cigarette initiation.Additional file 1: We present all additional information in one excel file (supplementary excel file) and within the file each tab is a single appendix. Appendix 1. (or Table 1) presents the Medline literature search strategy. Appendix 2. presents the study PRISMA flow chart. Appendix 3. presents the quality assessment tool and results. Appendix 4. presents characteristics of included studies. Appendix 5. presents primary research AOR for ever e-cigarette and cigarette use among adolescents in the selected longitudinal cohort studies. Appendix 6. presents primary studies ever use of e-cigarettes and cigarettes among adolescents in the selected longitudinal cohort studies using statistical measures such as adjusted relative risk, estimates, or standardised coefficient. Appendix 7. presents AORs for different frequencies of e-cigarette and cigarette use among adolescents in the selected longitudinal cohort studies. Appendix 8. presents adjusted relative risk for different frequencies of e-cigarette and cigarette use. Appendix 9. presents the feasibility assessment for meta-analysis and Appendix 10. presents the results of the meta-analysis, sensitivity analysis, and subgroup analysis.


The timing of 30-day pediatric readmissions is skewed with approximately 40% of the incidents occurring within the first week of hospital discharges. The skewed readmission time distribution coupled with delay in health information exchange among healthcare providers might offer a limited time to devise a comprehensive intervention plan. However, pediatric readmission studies are thus far limited to the development of the prediction model after hospital discharges. In this study, we proposed a novel pediatric readmission prediction model at the time of hospital admission which can improve the high-risk patient selection process. We also compared proposed models with the standard at-discharge readmission prediction model. Using the Hospital Cost and Utilization Project database, this prognostic study included pediatric hospital discharges in Florida from January 2016 through September 2017. Four machine learning algorithms—logistic regression with backward stepwise selection, decision tree, Support Vector machines (SVM) with the polynomial kernel, and Gradient Boosting—were developed for at-admission and at-discharge models using a recursive feature elimination technique with a repeated cross-validation process. The performance of the at-admission and at-discharge model was measured by the area under the curve. The performance of the at-admission model was comparable with the at-discharge model for all four algorithms. SVM with Polynomial Kernel algorithms outperformed all other algorithms for at-admission and at-discharge models. Important features associated with increased readmission risk varied widely across the type of prediction model and were mostly related to patients’ demographics, social determinates, clinical factors, and hospital characteristics. Proposed at-admission readmission risk decision support model could help hospitals and providers with additional time for intervention planning, particularly for those targeting social determinants of children’s overall health. Unplanned hospital readmissions disrupt the daily routine lives of patient and families, and expose patient to the risk of hospital-acquired infections and other potentially harmful conditions ,2,3. TheIn the last decade, with the wide adoption of electronic medical record systems across hospitals and other provider systems, researchers have focused on predictive analytics to identify patient at greater risk of being readmitted and finding ways to prevent unplanned hospital visits ,16,17. AOur study hence aimed to develop prediction models that can better identify those children that are at high risk of unplanned hospital readmission visits. In this study, we propose a novel pediatric readmission prediction model at the time of hospital admission, which we hypothesized would have provided physicians and care providers with additional time for intervention planning, particularly for those targeting social determinants of children’s overall health. In addition, we looked into the predictability performance of our proposed model compared with the existing at discharge model, investigated the performance by diagnosis groups and the timing of implementing pediatric readmission models.Using the Hospital Cost and Utilization Project (HCUP) State inpatient database, this retrospective study included all pediatric admissions from 1 January 2016 to 30 September 2017, across all Florida’s hospitals. Developed by the AHRQ, the HCUP SID is an all-payer dataset that includes the uninsured database of hospital inpatient stays across all non-federal hospitals . The datThe primary outcome was all-cause readmission to any Florida hospital within 30 days following discharge of an acute care hospitalization. We used the previously validated all-cause Pediatric All-Condition Readmission algorithm by the Boston Children Hospital to identify pediatric readmissions . ConsistIn this study, we evaluated the pediatric readmissions prediction model using patient information and available data for three major time points: (1) prediction model that uses data available at the time of hospital admission or transferring to another acute care hospital, (2) standard readmission prediction model that uses all available information during discharge time, and (3) hospital admission prediction model that uses only available patient information and social determinants of health before any hospitalization event occurs. Predictors for the three models—at-admission prediction model (AD-PDR), at-discharge prediction model (DS-PDR), and prior to the hospital admission prediction model (PT-PDR)—were included based on the availability of the information at that certain time point . PT-PDR PT-PDR models included available variables before hospital admission including demographics, socioeconomic status, provider density, prior hospital visit, and community-level social determinants of health . DemograThe AD-PDR models included admitting hospitals characteristics, patient travel information, individual-level social determinants of health, diagnosis presented at the time of hospital admission, and the variables included in the PT-PDR model. Hospital-level covariates are children’s hospital status, location , ownership status (for-profit and non-profit/government), and hospital bed size . Travel distances between patients’ residences and discharge hospitals were calculated by geocoding using geographical information software . The individual-level SDH variable was a binary variable indicating potential health hazards related to children’s family conditions . The ICD-10-CM admitting diagnosis codes used to characterize hospital visits by patient disease complexity for at admission, AD-PDR models. Each of the admitting diagnosis ICD-10-CM codes is included as a binary (yes/no) variable included in the building AD-PDR models.The DS-PDR models included patient discharge information, ICD-10 CM diagnosis, and procedures along with variables used in the PT-PDR and AD-PDR models. The ICD-10 CM diagnosis and procedures codes during hospitalization were coded into individual binary codes (yes/no) and included in the model. The other patient-level factors included only in the DS-PDR model were inpatient LOS , and discharge planning . The total number of variables included in building the prediction model for PT-PDR, AD-PDR, and DS-PDR was 18, 3721, and 6324, respectively.The overall model training and validation process we followed is shown in For the repeated cross-validation process, each cohort  entire dataset was divided into 5 equal cross-validation folds. For each cross-validation repetition, each fold is alternatively used as the test dataset while training our predictors on the other remaining folds. The hyperparameter of each technique was optimized through a grid search with 10 repeated 5-fold cross-validation iterations. While training, we also explored the issues with class imbalanced problems by using the Synthetic Minority Over-sampling Technique (SMOTE) on the training dataset ,44. We rp-value less than 0.05 was considered statistically significant.We developed and investigated several established machine learning algorithms for each model cohort to identify children at high risk for 30-day unplanned readmission. The target variables for the three patient cohorts based on timing was binary variable (yes/no) if the children have been readmitted within 30 days of hospital discharge. To assesses the performance of the learning model, we applied logistic regression (LR) with backward stepwise selection, Decision tree (C4.5), Support Vector machines (SVM) with the polynomial kernel, and Gradient Boosting (GB) algorithms for each disease cohort. The area under the receiver operating characteristics (ROC) curve (AUC) was used to evaluate the performance of each prediction model. The average AUC values from traditional DS-PDR models were considered as baselines for the performance comparison of the machine-learning algorithms. All statistical analyses were performed using R studio, and a two-sided The analysis included 87,865 index hospital visits by 64,597 children with a mean age of 7.8 years with 50.04% females from 1 January 2016 to 30 September 2017. Among these index visits, we identified 7288 (8.29%) pediatric hospital 30-day readmission visits. The baseline patient demographics and hospital characteristics for the hospital visits were provided in We also extracted important features from the Random Forest algorithms for all three prediction models. The top 10 important features are shown in The presence of comorbidity and complex procedures were also important predictors of readmission for AD-PDR and DS-PDR. Disruptive mood disorder was the most important feature for both AD-PDR and DS-PDR models. The other important clinical features for the AD-PDR model were Dehydration and Abdominal pain. For the DS-PDR model, Pneumonia and Major Depressive Disorder-recurrent were other top ten clinical diagnoses related to high-risk readmission. The Drainage of the Spinal Canal and Resection of the Appendix procedure was also found important for predicting readmission at the DS-PDR model. Children’s hospital status was only found important for the AD-PDR model and longer hospital stay was found within the top ten features for DS-PDR models. Children living with challenging family conditions and in poor neighborhoods were found as important features for both PT-PDR and AD-PDR models. Similarly, children living in communities with fewer high school diplomas and a higher percentage of unemployed persons were found important for the PT-PDR models.In summary, we developed and compared several variants of machine learning-based predictive models for three different care timepoints that can improve the prediction of pediatric readmission, with the possibility of at-admission pediatric readmission risk prediction. To our knowledge, this is the first study to develop an at-admission pediatric readmission model and compared prediction performance with the traditional at-discharge readmission prediction model. Our proposed at-admission all-causes readmission prediction model showed similar prediction performance compared with the at-discharge model. In terms of predictive power, the models we developed showed comparable results with other published works ,30,45,46The pre-admission model in our study showed lower discrimination compared with at admission and at-discharge readmission models mainly due to lack of patient-level clinical information. Therefore, a single pre-admission model may not be sufficient in a clinical setting for identifying high readmission risk children. The similar performance of the AD-PDR models and DS-PDR models reported in this study suggests that pediatric readmission risk prediction during both at-discharge and at-hospital admission can be used by hospital providers to design and implement appropriate intervention programs. This additional time provided by the at-admission readmission risk prediction (AD-PDR) model could allow comprehensive care transition and discharge planning particularly for high-risk returning patients ,47. AlthIn our study, we found a variation of extracted important features across three readmission prediction models. History of prior hospital admissions, medical complexity, and non-acute post-discharge were found important features in predicting readmission, which is consistent with the previous investigations ,24,28,49This study has several common limitations, most of which are related to a retrospective analysis of administrative claim databases. First, the HCUP database is an administrative claim dataset that uses ICD codes to classify patients’ medical diagnoses, procedures, and outcomes. The possibility of coding inaccuracy or incorrect information cannot be dismissed. Second, although our study makes a significant contribution of presenting an at-admission readmission prediction model across Florida hospitals, the findings of this study may not be generalizable to other U.S. states or international countries’ patient populations. Third, our research did not include information regarding laboratory tests, patient detailed vitals, parent health literacy, and post-acute care quality, thus, including these factors may have improved prediction performance. Fourth, the dataset does not include data from federal hospitals  or out-of-state hospital deaths or readmissions after the initial index admission. However, pediatric admissions in federal hospitals are few compared to non-Federal hospitals. In addition, out-of-state pediatric readmissions are expected to have minimal impact on our results, due to the unique geographical location of Florida State. Finally, we include community-level SDHs in the ZTCAs level, and more precise census tract level data or patient-provided information may have improved accuracy in capturing community-level variables.Although pediatric readmissions are costly and rates have increased over time, accurate prediction of high readmission risk children and timely intervention implementation remains challenging for the care providers. To explore the possibility of improving pediatric readmission risk prediction, a novel at-admission readmission prediction model was proposed and compared with the traditional at-discharge models. Similar performance achieved by the proposed at-admission models suggests potential in early identification of high-readmission risk children without trading off the prediction performance over traditional at-discharge approaches. This additional intervention planning time could allow hospitals and payers  to devise comprehensive care transition , discharge planning, and non-clinical interventions  particularly across the vulnerable population. In addition, the findings of non-clinical important factors such as living in a disadvantaged community and longer travel distance other than the clinical risk factors imply the existence of rural–urban and socioeconomic disparities in pediatric care access. Therefore, these findings reinforce the need to develop a tailored interventions/programs by policy-makers particularly, for these underserved areas.


Socioeconomic disadvantage is associated with mental illness, yet its relationship with mental well-being is unclear. Mental well-being is defined as feeling good and functioning well. Benefits of mental well-being include reduced mortality, improved immune functioning and pain tolerance, and increased physical function, pro-social behaviour, and academic and job performance. This study aims to explore the relationship between individual socioeconomic position (SEP), neighbourhood disadvantage and mental well-being in mid-age adults.Multilevel modelling was used to analyse data collected from 7866 participants from the second (2009) wave of HABITAT , a longitudinal study (2007–2018) of adults aged 40–65 years living in Brisbane, Australia. Mental well-being was measured using the Warwick Edinburgh Mental Well-Being Scale (WEMWBS). Exposure measures were education, occupation, household income, and neighbourhood socioeconomic disadvantage.The lowest MWB scores were observed for the least educated , those permanently unable to work , the unemployed , and members of low-income households . Residents of the most disadvantaged neighbourhoods had lower MWB scores than those living in the least disadvantaged neighbourhoods, after adjustment for individual-level SEP .Both individual-level SEP and neighbourhood disadvantage are associated with mental well-being although the association is stronger for individual-level SEP. This research highlights the need to address individual and neighbourhood-level socioeconomic determinants of mental well-being.The online version contains supplementary material available at 10.1186/s12889-022-12905-7. During the last decade, a growing number of studies have examined the relationship between individual-level socioeconomic position (SEP), neighbourhood disadvantage and mental health. With few exceptions,  most of MWB comprises two dimensions, namely how we feel (the subjective experience of positive emotions) and how we function  . CharactTo date, few studies have examined associations between individual-level SEP, neighbourhood disadvantage, and MWB. Studies that have investigated associations between individual-level socioeconomic factors and MWB, measured with the Warwick-Edinburgh Mental Well-being Scale (WEMWBS), have shown that people with low SEP have increased risk of low MWB, regardless of the SEP measure used , 15. TheResearch spanning the last 25 years consistently shows that neighbourhood disadvantage—characterised by poverty, deprivation, and social and economic disadvantage brought about by an area’s social, cultural, and economic factors and its physical and environmental infrastructure —adverselBeyond examining the main effects of neighbourhood disadvantage and MWB, testing interactions between the same level of SEP and different levels of neighbourhood disadvantage can highlight ‘deprivation amplification’ effects  and helpGiven the accumulating evidence on the protective effects of MWB on physical health and the adverse mental health outcomes of low SEP, there is reason to further explore associations between socioeconomic characteristics and MWB at the individual- and neighbourhood-levels. The aims of this study were to investigate: (1) the relationships between individual-level SEP, neighbourhood disadvantage and MWB, and (2) whether the relationship between SEP and MWB differed across levels of neighbourhood disadvantage. We hypothesised that residing in disadvantaged neighbourhoods would be associated with lower MWB, independent of individual-level socioeconomic factors (which themselves would be related to MWB). If this reasoning is supported, policies and programs aimed at reducing socioeconomic inequality in MWB should target both individuals and their neighbourhood contexts.This study used data from the second (2009) wave of HABITAT , a multilevel longitudinal study (2007–2018) of mid-age adults living in Brisbane, Australia. Consent to participate was obtained via return of the participants’ completed survey. Data for this study were analysed from June 2020 to January 2021. Methods were carried out in accordance with relevant guidelines and regulations; HABITAT received ethics approval from the Queensland University of Technology Human Research Ethics Committee .n = 200) of Census Collector’s Districts (CCD) in 2007 [HABITAT’s sampling design has been published elsewhere . Briefly in 2007 . CCDs weEligible study participants were mailed a self-administered survey between May and July in the years 2007, 2009, 2011, 2013 and 2016 using the method by Dillman . Of 16,1Mental well-being, the outcome, was measured using the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS), which comprises items on subjective well-being, psychological functioning, and relationships . The 200Socioeconomic predictor variables included education, occupation, household income and neighbourhood disadvantage.Participants selected their highest level of education attainment from nine response categories. These were recoded to bachelor’s degree or higher , diploma or associate degree, certificate (trade or business) and no post-secondary school qualification.Employed respondents provided the full title of their occupation. This information was subsequently coded to the Australian and New Zealand Classification of Occupations (ANZCO) . The oriParticipants selected their pre-tax household income from 13 categories. These were recoded into six categories: ≥A$130,000, A$129,999- A$72,800, A$72,799-A$52,000, A$51,999-A$26,000, ≤A$25,999, and ‘don’t know’/ ‘don’t want to answer’.n = 40) least disadvantaged and Q5 the most disadvantaged 20% (n = 40) areas, relative to the whole of Brisbane.A neighbourhood socioeconomic disadvantage measure was derived using scores from the Australian Bureau of Statistics (ABS) Index of Relative Socioeconomic Disadvantage (IRSD) . IRSD scCovariates were age and sex. Age was derived from self-reported date of birth and categorised into five groups: 42–46, 47–51, 52–56, 57–61, 62–67 years.n = 138). After excluding these respondents, 6721 individuals were available for analyses.Of 7866 residents who completed the 2009 survey, the 568 (15%) who changed their residential address after the 2007 data collection were excluded to reduce potential selection bias due to movers being influenced by unmeasured preferences related to both residential choice and MWB . AnotherTwo multilevel linear regression models were used to examine associations between individual-level SEP, neighbourhood disadvantage and MWB. MWB score was the outcome variable. The independent variables of interest in Model 1 were education, household income and occupation as measures of individual-level SEP, with adjustment for age and sex. Model 2 added to Model 1 neighbourhood disadvantage as an independent variable of interest. Preliminary analysis showed that results for Models 1 and 2 were similar for men and women; therefore, they were analysed together. A sensitivity analysis was conducted to determine if the results for Model 2 changed when a variable representing the years lived at the current address was added. The results did not change, and hence, the results of the sensitivity analysis are not presented. Cross-level interactions between individual SEP and neighbourhood disadvantage on MWB scores were also modelled (adjusted for the other SEP variables) to examine variation in the mean MWB score for education, occupation and household income, by level of neighbourhood disadvantage. Data were analysed using Stata 15.1 .The mean MWB score in the sample was 50.7, ranging from 14 to 70, with a standard deviation of 8.1. MWB scores were lowest for respondents aged 40–46 years, those with the least education, those who were permanently unable to work, members of the lowest income households, and residents of the most disadvantaged neighbourhoods , neighbourhood disadvantage and MWB, and their interactions. The results showed that both individual-level SEP and neighbourhood disadvantage, after adjusting for individual-level SEP, were associated with MWB. This finding suggests that the neighbourhood socioeconomic effects are important for MWB in addition to individual-level SEP effects.This study’s findings at the individual-level are consistent with single-level studies of the UK population showing that adults with a low SEP, measured as education and occupation  or incomEarlier research examining associations between neighbourhood disadvantage and MWB is limited to two studies. Gale et al.  reportedResults in this study are consistent with positive associations reported in the broader literature on the influences of neighbourhood on health-related outcomes . The modThis is the first known study to examine cross-level interactions between individual-level SEP, neighbourhood disadvantage and MWB. Despite the interaction showing a pattern of ‘deprivation amplification’ when individual-level SEP and neighbourhood disadvantage interacted, this finding was not statistically significant. This finding supports McAneney et al.’s  findingsSeveral methodological limitations need to be taken into consideration when interpreting and generalising our results. First, causal inferences are limited by the cross-sectional design of the study. Although the plausibility of reverse causation between MWB and SEP indicators are unlikely, it is more likely for participants categorised as permanently unable to work. These participants may be permanently unable to work due to poor MWB. Second, the non-response rate for baseline 2007 HABITAT survey was 31.5%, and higher among those of low SEP and residents of disadvantaged neighbourhoods, a finding corroborated by prior studies examining participation rates of low SEP in survey research , 40. TheRespondents of low SEP or residing in disadvantaged neighbourhoods had low MWB. The neighbourhood disadvantage-MWB association was weaker than the individual SEP-MWB association but still suggested a modest but important effect of neighbourhood disadvantage on MWB, supporting previous multilevel studies, which commonly find modest neighbourhood-level effects .Future research should focus on the underlying mechanisms driving the associations between individual- and neighbourhood-level disadvantage and MWB so that policies and programs can be developed. Moving from cross-sectional to multilevel longitudinal study designs would enable changes in psychosocial characteristics associated with individual- and neighbourhood-level disadvantage and low MWB to be examined. To address methodological challenges of observational studies, such as endogeneity, dependency, exchangeability and structural confounding,  researchUntil research establishes the mediating factors on the pathways between individual- and neighbourhood-level disadvantage and low MWB, knowledge created in this study has limited applied application for developing programs or interventions that improve MWB at a population level. However, this study does inform the development of policies designed to minimise inequalities in MWB at the individual- and neighbourhood-level in several ways: by providing population MWB scores for a normally distributed Australian population that can be used as a benchmark to evaluate interventions, policies and programs; contributing evidence to debates on health indicator selection and development that is used for population health and well-being monitoring and reporting; and identifying population groups with low MWB.Additional file 1: Appendix Table 1. Predicted Mental well-being (MWB) mean from individual-level socioeconomic position and neighbourhood disadvantage cross-level interactions.


Hodgkin lymphoma is a highly curable disease. Although most patients achieve complete response following frontline therapy, key unmet clinical needs remain including relapsed/refractory disease, treatment-related morbidity, impaired quality of life and poor outcome in patients older than 60 years. The incorporation of novel therapies, including check point inhibitors and antibody–drug conjugates, into the frontline setting, sequential approaches, and further individualized treatment intensity may address these needs. We summarize the current treatment options for patients with classical Hodgkin lymphoma from frontline therapy to allogeneic hematopoietic stem cell transplantation and describe novel trials in the field. All patients in the Haplo group received T-cell replete NMAC transplant with post-transplant cyclophosphamide (PT-Cy). After a median follow-up of 31 months, the OS and PFS in the Haplo group were 75% and 66%, respectively, with no difference with the two other donor groups. The CI of grade 3–4 acute graft-versus-host disease (aGvHD) and chronic (c) GvHD were 3% and 15%, respectively, both being significantly lower in the Haplo cohort compared to the other groups. The non-relapse mortality (NRM) for the whole cohort was 12%. A significantly higher probability of GvHD-free, relapse-free survival (GRFS) was observed in patients who received Haplo . The authors concluded that in the absence of an HLA-identical donor, a T-cell replete, NMAC Haplo with PT-Cy was associated with better outcomes compared to other alternative donors for patients with R/R cHL   . There a= 0.007] . Haverko= 0.007] , five pa= 0.007] . In concollow-up .n = 3, PR, n = 1) and, overall, ibrutinib was well-tolerated [Our choice of treatment for relapse after allo-HCT is to first try BV, either alone or in combination with bendamustine, plus donor lymphocyte infusion (DLI), depending on patient and disease history. In the case of failure, we will use anti-PD-1 mAb as it allows good disease control and, unfortunately, there are few other options for survival. One should definitely be cautious when using anti-PD1 mAb after BV and DLI because of the increased risk of acute GVHD. Badar et al. recently assessed the use of oral ibrutinib, a small molecule drug  inhibitor), in seven patients relapsing after allo-HCT, as an alternative for patients with a prior history of GvHD. Four (57%) patients achieved response , assessed by the Short Form 36 (SF-36), in 456 cHL survivors with 2214 norms from the general Norwegian population. They examined the effect of disease characteristics and treatment on differences in HRQoL and demonstrated reduced HRQoL 3–23 years  after curative treatment primarily in physical health including physical functioning, role limitations due to physical problems and general health perceptions, compared to the general Norwegian population. Statistically significant differences were found in general health, physical functioning, role limitations , social functioning and in vitality. Time since diagnosis, types of primary treatment, and relapse, were not associated with statistically significant differences in HRQoL .In the H8 randomized trial, HRQoL data from 935 patients after treatment for early stage cHL showed strain and limitations in all subdomains apart from cognitive functioning, and also reduced motivation. Differences in HRQoL improvement with time were linked to age and sex, but not the type of treatment. Fatigue status at the end of treatment seems to predict subsequent HRQoL .Persistent fatigue represents one of the greatest challenges in cHL and efforts should be made to identify the contributing factors and better describe the patterns of recovery within the various HRQoL domains. The effects of treatment on QoL can really jeopardise a patient’s return to normal life, with familial, social, and professional consequences. There are many guidelines to help with the treatment of cHL resulting from numerous randomized controlled trials, but the patient cure does not just depend on prescribing the right treatment. Emphasis on HRQoL following therapy may inform treatment decisions and long-term survivorship goals. To improve HRQoL, clinicians need to learn more about the impact of treatments on HRQoL. Future research should include prospective, longitudinal randomized trials across both treatments and time. They also need to gain further insight into the course of recovery after cure, considering all aspects of life, not only physical but also functional, social and psychological domains. HRQoL will also be improved by the development of new, more effective but less toxic therapies.The management of cHL has a huge effect on patients’ QoL not only due to the treatment but also due to the strain and limitations experienced in most aspects of life. In frontline therapy, further individualized intensity with PET-guided therapy may reduce treatment-related morbidity and help prevent relapse by using escalating or de-escalating approaches with ABVD and BEACOPP regimens Another valid frontline option is the combination of BV with AVD, especially in patients who have a contraindication to bleomycin Fig. .Fig. 1SuBV-ICE Nivo-ICE PEM-GVD BV-Nivo BV-Bendamustine Future perspectives include sequential strategies and the use of anti-PD-1 mAb in combination with AVD. In elderly patients (>60 years), the use of PVAB or the sequential combination of BV-AVD are attractive approaches. Certainly, there is a need for randomized controlled trials to compare the new combination therapies. Regarding second-line treatment, the latest advances show that immunotherapies can safely be combined with chemotherapy followed by consolidation with ASCT in responders. The following treatments showed promising results as first-salvage therapy:For patients with a higher risk of relapse, post-ASCT BV maintenance should be given, regardless of remission status or prior BV exposure. Allo-HCT performed after anti-PD-1 mAb is a feasible strategy associated with an excellent PFS and a low incidence of relapse. PT-Cy should be considered for all cHL patients treated with anti-PD1 mAb to reduce the risk of GvHD. Finally, CAR T-cell therapy, not discussed here given the few clinical data to date, but hopefully , will al


Periodontitis is a chronic inflammatory disease that leads to the destruction of both soft and hard periodontal tissues. Complete periodontal regeneration in clinics using the currently available treatment approaches is still a challenge. Mesenchymal stem cells (MSCs) have shown promising potential to regenerate periodontal tissue in various preclinical and clinical studies. The poor survival rate of MSCs during in vivo transplantation and host immunogenic reaction towards MSCs are the main drawbacks of direct use of MSCs in periodontal tissue regeneration. Autologous MSCs have limited sources and possess patient morbidity during harvesting. Direct use of allogenic MSCs could induce host immune reaction. Therefore, the MSC-based indirect treatment approach could be beneficial for periodontal regeneration in clinics. MSC culture conditioned medium (CM) contains secretomes that had shown immunomodulatory and tissue regenerative potential in pre-clinical and clinical studies. MSC-CM contains a cocktail of growth factors, cytokines, chemokines, enzymes, and exosomes, extracellular vesicles, etc. MSC-CM-based indirect treatment has the potential to eliminate the drawbacks of direct use of MSCs for periodontal tissue regeneration. MSC-CM holds the tremendous potential of bench-to-bed translation in periodontal regeneration applications. This review focuses on the accumulating evidence indicating the therapeutic potential of the MSC-CM in periodontal regeneration-related pre-clinical and clinical studies. Recent advances on MSC-CM-based periodontal regeneration, existing challenges, and prospects are well summarized as guidance to improve the effectiveness of MSC-CM on periodontal regeneration in clinics. Periodontitis is a complicated chronic inflammatory oral disease, which is globally prevalent and has direct involvement of vast oral microbiome, oral tissues and immune cells , 2. PeriPeriodontal tissue regeneration involves the regeneration of the gingiva, alveolar bone, PDL and cementum. Among them, the regeneration and natural alignment of PDL is so far one of the most challenging tasks in the field of tissue engineering . To regeStem cell-based periodontal regeneration is currently at the center of attention . DiffereShreds of evidence suggest that MSCs enhance immune responses during early-stage inflammation through the paracrine and autocrine manners, and subsequent tissue regeneration by producing a spectrum of protective bioactive factors , 27. ThePeriodontitis results from oral microbial dysbiosis, which disrupts the ecologically balanced biofilm associated with periodontal tissue homeostasis and finally causes destruction of the tooth-attachment apparatus, including gingiva, alveolar bone, root cementum, and PDL . The dysThe true regeneration of periodontium includes alveolar bone, PDL, and cementum, which is characterized by newly formed alveolar bone and cementum connected by regenerated periodontal ligament fibers aligned in certain direction . It has The purpose of periodontal therapy is to control the infection and reconstruct the structure and function of periodontal tissues. The effectiveness of traditional treatments in periodontal tissue regeneration is still limited and unpredictable . Tissue Stem cells are at the forefront of new therapies because of their ability to self-renew and differentiate towards various cell lineages . Stem ceMSCs from different sources display tissue reparative potential , 66. MSCFor exogenous stem cells therapies, various techniques have been developed to achieve periodontal tooth-supportive tissue regeneration. Two review articles by Park CH et al. and Xu et al. had well summarized recent advances on exogenous stem cell-based therapies for periodontal tissue regeneration , 70. HowThe function of MSCs appears not to be mediated through engraftment in the injured tissues but a ‘hit and run’ mechanism, which indicated that MSCs mainly act through the bio-active factors , 74. TheCM from different sources of MSCs has been identified to have beneficial effects on the recipient, such as anti-inflammatory, anti-scarring, and immunomodulatory . In receMSC-CM could promote the regeneration of periodontal tissues. It has been reported that after transplantation of MSC-CM for 4 weeks, periodontal ligament like structures were seen between regenerated cementum-like structure and bone . PDLSC-CApical tooth germ cell-CM (APTG-CM) had been reported to promote cementogenic differentiation of PDLSCs . AnotherPeriodontal tissue regenerative potential of MSC-CM is mainly mediated by the cooperative effects of the cocktail of cytokines, growth factors, and enzymes such as insulin-like growth factor-1 (IGF-1), vascular endothelial growth factor (VEGF), transforming growth factor-β1 (TGF-β1) , 93, etcStudies have shown that the process of bone formation and tooth regeneration is coupled to angiogenesis , 97. OstImmune cells, including T cells, B cells, macrophages, and neutrophils play a vital role in the pathophysiology of periodontitis. Regulation of immune cells’ function to obtain the favorable immunomodulatory conditions for periodontal tissue regeneration is a challenging task. The immunomodulatory potential of MSC-CM can be utilized for periodontal tissue regeneration in clinics. MSC-CM had been reported to treat colitis by upregulating TGF-β, IL-10 and percentage of Treg cells, and downregulating IL-17 . MSC-CM MSC-CM had been reported to induce neutrophil apoptosis in inflammatory conditions . Human AThe cytokines and growth factors in CM also play a key role in the chemotaxis of endogenous precursor cells. Chemotaxis of osteogenic and angiogenic precursor cells is essential for effective periodontal regeneration. It has been reported that MSC-CM could stimulate migration and proliferation of dog PDLSCs, which may enhance periodontal tissue regeneration . MSC-CM MSCs could be harvested from different origins, such as bone marrow, adipose tissue, dental tissues, and umbilical cord. CM from different MSC sources have different effects on cells migration. BMSCs expresse highest mRNA levels of SDF1 and VCAM-1, and TNF-α. Priming of ASCs gained a significant increase in IDO1 and CCL5. And HUCMSCs release higher protein levels of IL-6, IL-8, MCP-1, ICAM1, HGF, MMP1 and CH3L1 . MSC-CM Recent studies have shown that stem cells are effective in tissue mainly via the paracrine effect . The secAlthough the applications of MSC-CM on periodontal regeneration have been proved useful in animal models from pre-clinical studies, much work needs to be done to apply it to clinics. The content of MSC-CM varies from cell type to culture condition and batch. It is impossible to get the MSC-CM containing similar secretomes in each treatment in clinics. Therefore MSC-CM cannot guarantee a similar effect in every treatment. The regenerative effect of MSC-CM is usually from the cumulative effect of a cocktail of cytokines and growth factors rather than a few factors present in elevated levels. Not having the worldwide consensus protocol for MSC-CM harvesting for tissue regeneration application is also one problem. So far, there is no data to illustrate that CM from which specific MSC origin is suitable for the specific tissue regeneration. This makes it difficult to choose the proper MSC origin for MSC-CM-based periodontal regeneration. Limited source and invasive procedures to harvest MSCs are key challenges of the use of autologous or allogenic MSC-CM for periodontal regeneration. CM from cell-sheet and co-culture of different cell types such as MSCs, ECs, monocytes, etc. could be more effective for periodontal regeneration compared to 2D expanded MSC-CM. Further in vitro, preclinical, and clinical studies are indispensable to improve the clinical efficacy of MSC-CM-based periodontal tissue regeneration.The role of stem cells in promoting tissue regeneration mainly depends on their paracrine function. The use of MSC-CM is safer and effective for periodontal tissue regeneration compared to MSC transplantation. The MSC-CM can be tailored as required using different drugs or culture conditions during in vitro culture of MSC. Moreover, the concentration of effective components and growth factors in MSC-CM can be optimized as required. MSC-CM-based periodontal tissue regeneration has the potential to eliminate the use of autologous and allogeneic stem cells. Based on the aforementioned facts, MSC-CM-based periodontal tissue regeneration has tremendous potential for bench-to-bed translation.


HunFlair, a NER tagger fulfilling these requirements. HunFlair is integrated into the widely used NLP framework Flair, recognizes five biomedical entity types, reaches or overcomes state-of-the-art performance on a wide set of evaluation corpora, and is trained in a cross-corpus setting to avoid corpus-specific bias. Technically, it uses a character-level language model pretrained on roughly 24 million biomedical abstracts and three million full texts. It outperforms other off-the-shelf biomedical NER tools with an average gain of 7.26 pp over the next best tool in a cross-corpus setting and achieves on-par results with state-of-the-art research prototypes in in-corpus experiments. HunFlair can be installed with a single command and is applied with only four lines of code. Furthermore, it is accompanied by harmonized versions of 23 biomedical NER corpora.Named entity recognition (NER) is an important step in biomedical information extraction pipelines. Tools for NER should be easy to use, cover multiple entity types, be highly accurate and be robust toward variations in text genre and style. We present HunFlair ist freely available through the Flair NLP framework (https://github.com/flairNLP/flair) under an MIT license and is compatible with all major operating systems.Bioinformatics online. HUNER tagger , although such models were the basis for many recent breakthroughs in NLP research  such as genes, chemicals or diseases in unstructured scientific text is a crucial step of all biomedical information extraction pipelines. The respective tools are typically trained and evaluated on rather small gold standard datasets. However, in any real application they are applied ‘in the wild’, i.e. to a large collection of texts often varying in focus, entity distribution, genre (e.g. patents versus scientific articles) and text type (e.g. abstract versus full text). This mismatch can lead to severely misleading evaluation results. To address this, we recently released the R tagger  that wasHunFlair, a redesigned and retrained version of HUNER integrated into the widely used Flair NLP framework. HunFlair builds upon a pretrained character-level language model. It recognizes five important biomedical entity types with high accuracy, namely Cell Lines, Chemicals, Diseases, Genes and Species. Through its shipping as a Flair component, it can be easily combined with other IE tools  or other language models and benefits from the experiences and future developments of the large user and developer base of Flair. Through its simple but extensible interface, it is easily accessible also for non-experts. Technically, HunFlair combines the insights from HunFlair, we specially trained a character-level in-domain LM on a large corpus of biomedical abstracts and full-texts and make it publicly available to facilitate further research.Here, we present HunFlair using a consistent format, which enables researchers and practitioners to rapidly train their own models and experiment with new approaches within Flair. Note that these are the same corpora that were already made available through HUNER. However, the integration into Flair has the additional benefits of more convenient automated downloading and flexible preprocessing. While HUNER’s corpora came preprocessed with a particular method, users of HunFlair may process the corpora along with their own choices, for instance by using different sentence resp. word segmentation methods.In addition, we integrate 23 biomedical NER corpora into HunFlair was created by implementing the approach behind HUNER into the Flair NLP framework, along with its improvement by integrating a pretrained language model. Flair is an NLP framework designed to allow intuitive training and distribution of sequence labeling, text classification and language models. Flair achieves state-of-the-art performance in several NLP research challenges (https://github.com/flairNLP/flair).allenges , allows HunFlair and illustrates how little coding is required to use it. At the core, it relies on a Flair character-level language model trained on roughly 24 million abstracts of biomedical articles from PubMed and 3 million full texts originating from PMC as well as fastText word embeddings  comes with pretrained prediction models (ease of use), and (ii) can be locally installed . In contrast, we classify a tool as research prototype when its application requires a retraining of models or when it is only usable as a web service.We compare the tagging accuracy of HunFlair also in this setting.Our primary comparisons to off-the-shelve tools are based on cross-corpus experiments, because these give insight into the generalization properties of a model across different text types (e.g. full text versus abstract) and scientific subdomains . Clearly, this comes at the price of introducing a bias against methods which were designed for specific annotation guidelines that differ from those of an evaluation corpus. Therefore, our comparisons to research prototypes are based on in-corpus experiments which evaluate the architecture of HunFlair in a cross-corpus setting to five other state-of-the-art biomedical NER tools using three gold standard corpora: CRAFT  against SciSpacy , the overall ranking of methods is not changed.HunFlair’s results in an in-corpus setting to those reported by four different research prototypes based on three different corpora: JNLPBA  and NCBI Disease, it reaches on-par results with the competitor methods. We also investigate the effect of pretraining on multiple gold standard corpora, by comparing HunFlair to a non-pretrained version on all 23 NER corpora. On average, finetuning improves results on all entity types with the improvements in F1 ranging from 0.8 pp for chemicals to 4.75 pp for cell lines. The full results per corpus are provided in The results can be found in HunFlair, a state-of-the-art biomedical NER tagger. Through its tight integration into the Flair NLP framework, it is easy to install, easy to use and easy to combine with other NLP modules. It comes comes along with 23 biomedical NER corpora in a single format while still enabling customized pre-processing. HunFlair is a redesign of HUNER, which it extends with pretrained domain-specific character-level language models. It outperforms a series of other off-the-shelf tools in a cross-corpus evaluation setting on different datasets, and achieves on-par results with current state-of-the-art research prototypes based on in-corpus experiments.We proposed L.W. and J.M. were funded by the Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS). M.H. was funded by the German Research Council [LE-1428/7-1].Conflict of Interest: none declared.btab042_Supplementary_DataClick here for additional data file.


The Swiss portal for immune cell analysis (SPICA) is a web resource dedicated to the exploration and analysis of single-cell RNA-seq data of immune cells. In contrast to other single-cell databases, SPICA hosts curated, cell type-specific reference atlases that describe immune cell states at high resolution, and published single-cell datasets analysed in the context of these atlases. Additionally, users can privately analyse their own data in the context of existing atlases and contribute to the SPICA database. SPICA is available at  Recent breakthroughs in single-cell technologies have enabled the exploration of immune responses across diseases, tissues and individuals. Importantly, we have now the opportunity to discriminate specific immune states at single cell resolution, and to associate these states to key clinical parameters, such as responsiveness to immunotherapy and disease prognosis ,2. HowevThe Swiss portal for immune cell analysis (SPICA) aims to provide researchers a reference web resource to explore immune cell states in mouse and human. SPICA provides easy exploration of cell type-specific reference atlases, and a database of published studies analysed in the context of these atlases. Users can also privately upload their single-cell datasets and project them onto one of the atlases, to obtain an automated analysis of immune cell state composition and gene expression profiles across samples or conditions.The SPICA portal consists of three main components Figure : (i) a dAccessing the ‘Explore Atlas’ functionality in SPICA, the user can interrogate one of the existing reference atlases in the database Figure . On the https://spica.unil.ch/home/api).SPICA hosts a collection of public scRNA-seq datasets of mouse and human immune cells from different tissues and diseases, pre-projected onto a relevant reference atlas (see next section). A search bar allows filtering the database selection by organism, by atlas type, or by a free text description Figure . Each prhttps://spica.unil.ch/projects/Carmona_2019). Within the analysis session, the user can also perform differential expression (DE) analysis between a pair of samples, for a specific cell subtype. DE analysis results are reported in an exportable tabular form, indicating log-change and p-values for differentially expressed genes, and displayed as interactive volcano plots. All plots can be exported in high-resolution PDF format. Full project sessions including plots and input and output data can also be downloaded. Note that user-uploaded datasets are kept confidential within a private session, and require explicit ‘publishing’ permission from the user to become publicly available on the portal.Besides pre-analysed datasets, users can apply SPICA to interpret their own scRNA-seq data by reference atlas projection. Raw or normalized gene expression matrices  can be uploaded in several formats , with the possibility to upload multiple samples at once as a compressed archive. After selecting a reference atlas, the user query data will be projected onto the atlas using ProjecTILs . This alIn the following section we present two case studies using publicly available scRNA-seq datasets to highlight the functionalities of the database.et al. (https://spica.unil.ch/projects/Bassez_2021). We can observe areas of high density of projected cells, such as in the CD8_EffectorMemory cell cluster in both groups. We can also observe some differences, such as the increased exhausted CD8 T cell population (CD8_Tex) in the responders group. Cell subtype composition can be examined more quantitatively in the ‘composition’ stacked barplots  infection is one of the best-studied model systems of viral infection, and has been instrumental in elucidating the biology of T cell responses. A recent study by Sandu et al.  used scRhttps://spica.unil.ch/projects/Sandu_2020). However, there was also a level of heterogeneity between tissues in terms of subtype composition. For example, lung, spleen and blood showed increased percentages of short-lived effector cells (SLEC), whereas precursor exhausted (Tpex) T cells were enriched in lymph node and spleen compared to other tissues . By comparing transcriptional profiles of immune cells within the framework of a high-resolution reference atlas, SPICA allows streamlined exploration of tissue-specific adaptation signals.As expect in chronic infections, the majority of virus-specific T cells were predicted to be terminally exhausted  or the Single Cell Expression Atlas at EBI (https://www.ebi.ac.uk/gxa/sc) are very useful resources to share and explore published single cell datasets. However, these portals do not curate datasets with consistent cell type definitions, and do not allow mapping new data to entries of the database. Comparison of studies and interpretation of new datasets is therefore challenging. In an effort to automate single-cell data analysis, the Azimuth app (https://azimuth.hubmapconsortium.org/) allows interpreting query data by reference-based mapping. However, the app only hosts whole-tissue low-resolution atlases. We have previously shown that cell-type specific reference atlases are essential to provide enough resolution to distinguish biologically important subtypes within each cell type, and that ProjecTILs  outperforms Azimuth for T cell subtype identification  application (version 5.2.4) running on a Docker container. The backend relational database is implemented with PostgreSQL. Project search on the main page is powered by an indexation with Sunspot, a port of Solr for Ruby on Rails. The frontend uses several javascript libraries, including jQuery.js, datatables.js and Plotly.js, to generate and export plots in generic formats. User authentication is handled by Devise rails library. Rails natively implements security features against Cross-Site Request Forgery and SQL injection. Analysis runs are dispatched with DelayedJob rails library on two queues (i) a queue for slow analyses (main projection pipeline) and (ii) a queue for fast analyses . SPICA implements Websockets (ActionCable) to live monitor analysis progress and memory usage.https://spica.unil.ch. The code for the algorithms used in SPICA is publicly available at https://github.com/carmonalab: STACAS for batch-effect correction and atlas construction ; UCell for filtering cells by signature scores ; ProjecTILs for reference atlas projection .The SPICA web portal is freely available at


Intensity-modulated radiation therapy (IMRT) has been used for high-accurate physical dose distribution sculpture and employed to modulate different dose levels into Gross Tumor Volume (GTV), Clinical Target Volume (CTV) and Planning Target Volume (PTV). GTV, CTV and PTV can be prescribed at different dose levels, however, there is an emphasis that their dose distributions need to be uniform, despite the fact that most types of tumour are heterogeneous. With traditional radiomics and artificial intelligence (AI) techniques, we can identify biological target volume from functional images against conventional GTV derived from anatomical imaging. Functional imaging, such as multi parameter MRI and PET can be used to implement dose painting, which allows us to achieve dose escalation by increasing doses in certain areas that are therapy-resistant in the GTV and reducing doses in less aggressive areas. In this review, we firstly discuss several quantitative functional imaging techniques including PET-CT and multi-parameter MRI. Furthermore, theoretical and experimental comparisons for dose painting by contours (DPBC) and dose painting by numbers (DPBN), along with outcome analysis after dose painting are provided. The state-of-the-art AI-based biomarker diagnosis techniques is reviewed. Finally, we conclude major challenges and future directions in AI-based biomarkers to improve cancer diagnosis and radiotherapy treatment. This information can facilitate evaluating the severity of disease, improving tumour staging and thus the subsequent patient stratification and treatment  . Conventreatment . Thereforeatment . For exareatment .RT technology has been developed rapidly over the past decades and continues to be a standard treatment for malignant tumors, resulting in improved treatment outcomes over performing surgery or chemotherapy alone at certain cases. However, local recurrence after RT is one of the important modes of failure when dealing with most malignant tumors. The main reason may be that the planning target volume (PTV) of the tumor receives a uniform prescribed dose without considering the heterogeneity of the tumor itself in terms of time and space. In 1998, dose painting was originally proposed at ESTRO to address this challenge . In the Although current quantitative-imaging techniques are largely used for response management –11, therBecause of the plethora of functional imaging techniques, this article focuses on DW-MRI, MRS, Perfusion MRI including Dynamic susceptibility contrast (DSC) and dynamic contrast enhanced (DCE), DTI MRI and PET-CT. We present a review for these methods by surveying the state-of-the-art works. Diffusion-weighted magnetic resonance imaging (DW-MRI) is used to measure the mobility of water molecules in the microscopic environment of tissues. DW-MRI is very sensitive to cellular density, proliferation rate and cellular permeability , 9, and 2, ADC values are usually unpredictable because of the mixture of micronecrosis, normal tissues, high-cell tumors and oedema, as shown in 2) are used to eliminate the signal from edema (2) DWI can predict progression-free survival (PFS) (2) than the conventional b-value DW-MRI (b=800-1000s/mm2)  . About 4al (PFS) . Cao et 00s/mm2) .Hamstra et al.  and MoffMR-Spectroscopic Imaging (MRS) uses radio-labelled glucose and methionine to identify high-risk regions in large tumours. In MRS, active tumors exhibit at areas with high resonance in the choline spectral peak and a low NAA (N-acetylaspartate). Einstein et al. illustrated the procedure of Gamma Knife SRS incorporated with MRS functional imaging. Einstein et al. conducted the first prospective Phase II trial using MRS-targeted SRS for patients treated with GBM. In this work, the value of Cho/NAA> 2 was used as the area that is required to boost doses . HoweverPerfusion MRI is also a widely used method for tumour characterisation and diagnosis , which iIn particular for brain tumor, modelling the distribution of contrast agent in tissue allows us to quantify vascular leakage, cerebral blood volume (CBV), mean transit time and cerebral blood flow (CBF) –45. As tEven though DSC and DCE MRI can be used to estimate CBV, there are serval limitations of perfusion MRI. Similar with DW-MRI, DSC MRI has serious geometric distortion and signal loss because it is obtained by EPI. Moreover, CBV can be achieved from T1-weighted DCE MRI , howeverDiffusion tensor imaging (DTI)-MRI is one of the most popular technique to detect brain tumour and implement radiotherapy. Kelly et al.  and PricTrip et al. implemented the first phase 0 study to deline CTVs using the DTI in post-operative chemo-/radiotherapy for GBM . Their rin vivo imaging of biological processes. There are only five oncologic indications approved by the U.S. Food and Drug Administration, i.e. [18F]-fluoro deoxy-D-glucose (FDG), Na[18F], 18fluciclovine, [11C]-choline, and [68Ga]-DOTA-octreotate (DOTATATE). Many other candidates are being evaluated for clinical treatment.Positron emission tomography (PET)-CT has been a valuable technique for RT in staging  and accu18F]-FDG PET scan serves as a biomarker for adaptive dose painting. Kong et al. recently performed a phase II RT trial based on PET-CT for patients with NSCLC using interim [18F]-FDG PET to identify regions of poorly responding disease  and clinical target volume (CTV) were defined in ESTRO-ACROP . Standaretc. . These fetc. . In viewetc. . Dose paetc. . Dose paIn order to deliver a relatively higher proportion of dose to a more resistant part of tumor, dose escalation and dose redistribution have been proposed recently. There are two main strategies of dose painting, dose painting by contours (DPBC) based on threshold of biomarkers and dose painting by numbers (DPBN) based on voxels. In DPBC, a tumour’s sub-volumes are heterogeneous in the functional images needed to be treated in a differentiated dose level . In DPBNIn 2005, Ling et al. proposed the first DPBC technique . DPBC apIn particular, interobserver variability in target volumes is a well-known factor for radiotherapy. For PET-CT based dose painting, detailed contouring guidelines can be referred to the PET-Plan (NCT00697333) clinical trial protocol . As a pavia DPBC to the FDG avid region improves 2-year local-regional tumour control and overall survival rate (A commonly used treatment planning approach for DPBC is the simultaneous integrated boost (SIB) technique  to increval rate .The term ‘‘dose painting by numbers” describes prescription dose on a voxel-by-voxel level . DPBN isvia a conventional linear accelerator  patients’ treatment . In geneWe have investigated the potential of functional imaging on dose painting. With the help of the artificial intelligence (AI) techniques, we can define biological target volume by identifying biomarkers from functional images. Based on the degree of user interaction, methods used for the classification of various tumors can be classified as manual methods, semi-automatic methods and fully automatic methods. For the manual segmentation methods, radiologists use the multi-modality data, provided by the medical images along with information pertaining to both the anatomy and the physiology. This kind of segmentation needs the radiologist to manually draw the areas and regions of tumour carefully by going through slices of CT/MRI scans. Therefore, manual segmentation is a tedious, radiologist dependent task, resulting in variable outcomes. However, manual segmentations are useful in the evaluation process of the results obtained from semi-automatic and fully automatic methods.For semi-automatic methods for image segmentation-based cancer treatment, three processes are usually needed: initialization, intervention response or feedback response and evaluation of results . InitialUser interaction is not required in fully automatic tumor segmentation methods. The automatic segmentation is very challenging for some type of tumour, e.g., gliomas. Tumor bearing imaging data is a 3D data where tumor shapes, size and location vary greatly among patients . In addiCurrent quantitative imaging approaches have larger voxels and worse signal-to-noise-ratio (SNR) compared to other clinical imaging techniques, since quantitative imaging requires one or multiple quantitative parameters for each voxel . For dynAt present, the interest of imaging biomarkers is not only due to the state-of-the-art treatment development and personalized medicine but also due to the recent application of artificial intelligence (AI) algorithms in image processing .AI techniques can help construct image-based biomarkers which can provide a comprehensive view of the entire extent of the tumor and can capture regional tumor heterogeneity –143. SinHowever, use of AI to analyse deep radiomics features has several concerns. Firstly, the theoretical analysis of deep radiomic features extraction are not easily interpretable due to the complexity of deep learning models. This is because a deep neural network requires a large number of complex but weak regularities in the data, which has highly complex and non-linear interactions between multiple network nodes and layers . In addiImage registration is the process of geometrically aligning two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors .Since current radiotherapy treatment planning systems have to create a treatment plan on the images produced by a radiation simulator. Most of the simulators are CT-based, therefore, they can only produce anatomical imaging. Properties of functional imaging as mentioned in this article can be employed by image registration to the anatomical imaging. Thereafter, the dose painting can be implemented to deliver more doses to the high-risk area of tumour defined by the functional imaging. As mentioned in Sections 2 and 3, the high quality of registration is important when using functional imaging to perform the dose painting. For DPBC, the uncertainties of registration can be reduced by adding margins to the high-risk area. However, DPBN features the voxel-level analysis and is more sensitive to uncertainty arising in image registration . Most trMedical image registration is a broad topic which can be grouped from various perspectives. From an input image point of view, registration methods can be divided into unimodal, multimodal, interpatient, intra-patient  registration. From a deformation model point of view, registration methods can be divided in to rigid, affine and deformable methods. From a region of interest (ROI) perspective, registration methods can be grouped according to anatomical sites such as brain, lung registration. From an image pair dimension perspective, registration methods can be divided into 3D to 3D, 3D to 2D and 2D to 2D/3D. There was a survey summarizing algorithms of conventional image registration . The TheMost RT plans remain “unchanged”. Once the plans are initialized, they are carried out until the end of treatment, along with minimal modifications on top of the original plans . RT treaAdaptive radiotherapy is a process to control for anatomical and functional variation over the treatment course . Figurevia biomarkers. The interpretation of such data inevitably introduces some uncertainties. Therefore, it is foreseen that potential mathematical tools to solve uncertainties, e.g., partially observable Markov decision processes (POMDP), imperfect state information (ISI) and adjustable robust optimization (ARO) might be necessitated.The field of radiation oncology has been used to address different sources of uncertainties during patient planning, including range and setup uncertainties , organ mIn this article, we reviewed the state-of-the-art functional imaging techniques which facilitates the development of dose painting. Dose painting by contour and dose painting by numbers are discussed in detail, respectively, along with a summary of their advantages and disadvantages. Finally, we conclude some existing challenges and provide corresponding possible research directions. Efforts to develop image biomarkers for tumor characterization have been ongoing for decades. Using DPBC and DPBN could better consider the heterology than the current uniform dose distribution in tumour area. Therefore, they are beneficial for circumventing local recurrence and get better tumour control probability. It is necessary to establish strict pipelines and reproducible workflow, which make DPBC and DPBN feasible and practical in the clinic. In the future, combing the state-of-the-art AI techniques with advanced functional imaging approaches can open up possibilities to conduct new avenues of research in biomarkers discovery to improve cancer diagnosis and treatment.HL: study design. HW and YP: data collection. YP: data interpretation and writing. All authors contributed to the article and approved the submitted version.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.


Photoacoustic (PA) imaging is a promising non-invasive and non-ionizing biomedical imaging modality that emerged in recent years. The articles presented in this special issue describe some of newest progress in this field. We are extremely grateful to all contributing authors.The first part of the issue covers new laser source development, including fiber lasers and laser diodes. The second part is dedicated to improving the image resolution through chronic cranial window techniques, virtual-point concept, fast polygon scanning, and Fabry Perot sensing. The third part shows the basic principles of photoacoustic/ultrasound imaging and its applications.Jin and Liang describe recent developments in fiber-laser-based photoacoustic microscopy (PAM). Fiber lasers can be used to pump multiple wavelengths via different nonlinear effects. This approach is cost-effective and lightweight, and thus has been applied in handheld and head-mount PAM. Besides, fiber lasers have been used in all-fiber PA endoscopy for early cancer detection.In vivo microvasculature imaging is demonstrated with high signal-to-noise ratio.Li, Tsang, Kang, et al. present another cost-effective and compact light source, laser diodes (LD). Using a continuous-wave LD as the excitation source, they develop a high-speed high-resolution PAM system. A hybrid scanning mechanism that synchronizes a one-dimensional galvanometer mirror and a two-dimensional motorized stage is used for fast imaging. The PAM system has a high lateral resolution of 4.8 μm. in vivo imaging. Different chronic cranial windows have been fabricated to transmit more light through the skull in longitudinal imaging. The limitations and expectations for the PA cranial window are discussed in terms of lateral resolution, axial resolution, imaging depth, and stability.Wang and Xi describe a chronic cranial window technique as an effective tool for long-term Bai, Li, Ma, et al. introduce a virtual-point concept to fiber-sensor-based photoacoustic tomography to improve the image resolution. They demonstrate a fiber-laser virtual-point detector via bending the fiber into an arc. The curved fiber-laser sensor focuses on the arc center. A synthetic aperture focusing technique is used to significantly improve the elevational resolution beyond the focal zone. Via using a long cavity in the fiber laser sensor, a high numerical aperture and tight focus can be achieved to further increase the spatial resolution in the future. Compared with piezoelectric transducers, the fiber-laser detector is sensitive, immune to electromagnetism, lightweight, and flexible, making it attractive for high-resolution photoacoustic tomography.Pan, Chen, Liu, et al. propose an image correction method based on accurate ultrasound positioning to improve the image quality of polygon-scanning photoacoustic microscopy. The photoacoustic and ultrasonic data are simultaneously acquired. The angle position of the polygon mirror is extracted from the ultrasonic data to correct the photoacoustic images. Results show an effective reduction of dislocations in the reconstructed image and much-improved image quality.Yang, Zhang, Zeng, et al. describe a Fabry Perot sensor with improved signal-to-noise ratio and stability. Taking advantage of a double-cladding fiber, the sensing unit simultaneously couples the excitation and detection light for all-optical endoscopic detection. Furthermore, the microcavity-based sensor offers benefits of all-fiber integration, small size, and high sensitivity. The new miniaturized sensor can be used in non-destructive detection and biomedical applications.Wang, Zhao and Xu describe in detail the basic principles of photoacoustic/ultrasound (PA/US) imaging and their applications. Their attention is given to the relationship between photoacoustic and ultrasonic imaging. Multi-modal and real-time imaging has become a focus and an important trend for PA technology. In particular, the combination of PA and ultrasonic imaging can not only be used for acoustic signal detection, but also provide more complementary information, improved accuracy, and thus better meet the needs in preclinical research and clinical diagnosis.Chen and Tian describe the basic principles of PA technology for nondestructive testing and evaluation (NDT/E) and its applications. PAM, particularly OR-PAM, possesses a high spatial resolution to visualize fine structures and can achieve fast imaging speed. They show that the penetration depth of PA imaging can be extended via combining PA with US imaging. These technical advances can benefit NDT/E, structural health monitoring, and microscopy of metals.In brief, the reported results in this issue demonstrate the rapid development of PA technology and the accelerated translation to clinical and other applications.


Objectives: Sexual well-being (SWB) refers to the subjective emotional and cognitive evaluation of the quality of the individual's sexuality, it plays a relevant role in quality of life and health promotion on old age and has cross-cultural implications. The aim of this study is to analyse comparatively the perspectives of older adults on their SWB in Portugal and Mexico. Methods: Data were collected from 86 Portuguese and 80 Mexican community-dwelling participants aged 65 years and older, using a semi-structured interview protocol. Older adults were inquired about their perceptions on what contributes to their sexual well-being. Socio-demographic data were also enquired. Content analysis was used to identify key themes. Results: Outcomes indicated eight themes: eroticism, supportive relationship, positive self-concept, health and self-care, romance, active life, tenderness and care, and no pain and no pregnancy restrictions, for both samples. Eroticism was the most frequent theme reported by Portuguese participants (31.4%) and health and self-care were the most frequent theme reported by Mexican participants (26.5%). Conclusions: The empirical results of this study indicated that SWB is strongly influenced by socio-cultural and psychosocial values. This cross-cultural comparison between Portugal and Mexico contributes to understand this concept in old age with different perspectives and place a scenario for future culture-adapted interventions and comprehensive policies. Keywords: Mexican, older adults; Portuguese; qualitative study; sexual well-being