In March 2019, a pertussis outbreak occurred in children in a junior school (7–11 years) in England who had been offered pertussis-containing booster vaccine at 40 months of age. In a case–control investigation, we assessed the extent of transmission and any difference in protection afforded to those who had previously received a booster 3- or 5-component acellular pertussis vaccine (aP). We took oral fluid specimens from the students to determine IgG antibodies against pertussis toxin (anti-PT). Parents of students attending the school were sent a questionnaire on pertussis symptoms and vaccination status was retrieved from general practitioner records for all students. Of 381 students, 134 (35.2%) were classified as pertussis cases, 133 by demonstration of significant anti-PT IgG titres and one clinically. There was no significant difference in the risk of pertussis between students receiving 3-component (33.7%) or 5-component (32.3%) aP boosters. However, pertussis infection differed significantly in school year 4, with 22.9%, 50.0%, 23.7% and 38.1% pertussis cases in years 3, 4, 5 and 6, respectively. The proportion of students with incomplete vaccinations recorded was higher than the proportion of those not covered according to the national reported coverage, possibly contributing to sustained transmission within the school. Bordetella pertussis that is transmitted by aerosol droplets. Pertussis symptoms typically last up to 3 months and include low-grade fever, coughing with ‘whooping’ sound in some infants and vomiting [Whooping cough (pertussis) is a highly contagious bacterial infection caused by a Gram-negative bacterium vomiting ,2. Globavomiting . Vaccinevomiting -6.In England, as in the rest of the United Kingdom (UK), the routine immunisation programme consists of three primary infant doses of a pertussis-containing vaccine at 8, 12 and 16 weeks of age, and one preschool booster dose at 40 months (3 years and 4 months) of age ,8. VacciIn 1990, an accelerated diphtheria, tetanus toxoids and whole-cell pertussis (DTwP) schedule was introduced to improve protection earlier in infancy, where the risk of severe disease is highest. The primary infant schedule changed from a whole-cell pertussis vaccine (wP) to a 5-component acellular pertussis vaccine (aP), Pediacel (manufactured by Sanofi Pasteur MSD and distributed by Movianto UK Ltd), in October 2004 . From JuIt is well recognised that not all pertussis vaccines are the same. Differences in efficacy and effectiveness have been demonstrated between the licensed wP vaccines as well as between aP vaccines . In 2008The aim of the 2001 preschool booster programme was to increase herd immunity and reduce the transmission of pertussis to young infants, given the evidence that older siblings in the household were an important source of infection for these infants . An econIn England, a national pertussis outbreak was declared in 2012 which led to the introduction of the maternal vaccination programme . Since tIn early March 2019, a single pertussis case was confirmed  in a junior school  in South East England. The index case had a symptom onset date in late February and their sibling, attending the infant school (ages 4–7 years), also tested positive for pertussis.The local Public Health England (PHE) Health Protection Team (HPT) was notified of the pertussis case in the junior school. In response to the initial case, a letter was sent in early March to the parents of the students attending the junior school raising awareness of pertussis, which was followed by several reports of students at the school being absent with coughs. Within the same week of sending the letter, four additional students at the school were notified as possible cases with onset dates varying between late January 2019 and late February 2019, 26 days apart. By end of April 2019, 18 students had presented to their GP with clinical symptoms and were tested for pertussis by the GP. As a result, an incident management team meeting was convened, as this was the first pertussis outbreak in a junior or primary school notified to PHE since the introduction of the preschool booster. It was agreed to offer oral fluids tests to all students in the junior school and undertake an extensive investigation to better understand the reasons for this outbreak and the potential implications for the wider school-aged population in the UK. Of note, most students had received the same vaccine product for their primary series although the preschool booster vaccine product varied.The aim of this analysis was to evaluate the extent of the pertussis outbreak in a junior school in South East England and determine whether the odds of pertussis among the students in this outbreak varied with the two preschool booster vaccines.In light of an increasing number of pertussis cases in the school, a decision was taken to conduct enhanced case finding by asking parents of all students in the junior school to complete a clinical questionnaire and consenting for their child to have an oral fluid sample taken, to better understand the extent of transmission. The oral fluid assay determines the pertussis toxin IgG titre which when raised can be used as a marker of recent pertussis infection .The clinical questionnaire asked about onset and duration of any symptoms and number of days absent from school. Detailed information on the pertussis-containing vaccines received, including vaccination date, manufacturer/batch number for the primary series and booster vaccines, were requested for each child from the GP practice where they were registered.B. pertussis toxin IgG antibody) – equivalent to a serum threshold > 70 IU/mL  of anti-PT IgG titre – are reported as consistent with recent pertussis infection; a titre of 60–70 aU is reported as elevated close to the diagnostic threshold for a single sample and a titre < 60 aU as no evidence of recent pertussis infection [Oral fluid samples were offered to all students in the junior school and were collected by the local HPT and sent to the pertussis National Reference Laboratory to test for IgG antibodies against pertussis toxin (anti-PT) at PHE’s National Infection Service in Colindale, London. Oral fluid testing is offered as part of PHE’s national surveillance of notified pertussis cases in 2–16-year-olds . In the nfection ,21. Howe481 or IS481 and ptxP detected, or oral fluid anti-PT IgG titres. Cases attending the junior school were defined as:We conducted a case–control study assessing the students in the junior school at the time of the pertussis outbreak. Controls were students attending the junior school in South East England who did not have elevated serum anti-PT IgG titre, positive PCR IS481, or IS481 and ptxP detected [Symptomatic with a clinically compatible illness and an oral fluid anti-PT IgG titre of ≥ 60 aU, positive PCR (ISdetected ), or serAsymptomatic with a high or elevated oral fluid anti-PT IgG titre ≥ 60 aU;Symptomatic with a clinically compatible illness and treated with antibiotics without laboratory evidence of pertussis (based on a review by a clinician at PHE with vaccination status blinded) to clinically agree that the cases had pertussis.Students were included in the analysis if they met the following criteria based on the routine immunisation schedule :First dose of pertussis vaccine from 42 days-old (6 weeks);All three primary doses administered by the time the infant turned 1 year;All three primary doses were offered a minimum of 28 days apart, but one interval could be a minimum of 21 days (3 weeks);The booster dose was a minimum of 3 years after the final primary dose;The booster vaccine was no later than the child’s fifth birthday.We assessed the total number of students at the junior school and distribution of pertussis cases by sex and school year. We used univariable logistic regression and multivariable logistic regression adjusting for vaccine, type, school year and sex to assess whether there was an association between Repevax, Infanrix-IPV, no booster vaccine and unknown vaccine on all confirmed cases in this outbreak . Statistically significant variables  were added to the multivariable model. A test for interaction of school year and vaccine type was completed to check for a linear effect as a proxy for time since booster vaccination . The model and test for interaction was rerun as a sensitivity analysis without the asymptomatic cases for comparison. We conducted a secondary logistic regression analysis  including the students that had received their booster dose early  and later (after age 5 years) but still met the inclusion criteria based on the routine immunisation schedule. All analyses were conducted in Stata 15 (StataCorp LLC).http://www.legislation.gov.uk/uksi/2002/1438/regulation/3/made). This includes PHE’s responsibility to monitor the safety and effectiveness of vaccines. Individual patient consent is therefore not required by PHE for pertussis cases.PHE has legal permission, provided by Regulation 3 of The Health Service (Control of Patient Information) Regulations 2002, to process patient confidential information for national surveillance of communicable diseases . Thirteen of these 18 students were retested in the school. Of the 13, nine retested positive at the school swabbing, while four tested positive at the school after previously testing negative at the GP. The remaining five students tested positive at their GP prior to the school swabbing and chose not retest in the school. Oral fluid tests were offered at the school on 1 and 2 May 2019. Of the 384 students, 379 had an oral fluid test during the school swabbing. A total of 18 students were tested at their GP prior to the school swabbing, where 11 were laboratory-confirmed for pertussis with high or elevated anti-PT IgG titres or ISThree of the 384 students with complete data were excluded from the analysis , leaving 381 students for the analyses. A total of 134 of 381 (35.2%) students at the school were classified as pertussis cases during the outbreak . Thirty-nine (29.1%) of the confirmed cases were asymptomatic and did not report any coughing. All students attending the junior school were born before the introduction of maternal pertussis immunisation in England (which began in October 2012). In addition, all students attending the junior school were born between 2008 and 2012 and during that period, all infants received Pediacel vaccine for the primary series, as this was the only vaccine offered at the time. Students were offered Repevax or Infanrix-IPV for their booster dose .Students who had been immunised according to the routine immunisation schedule were included in the statistical analyses. We excluded 98 of 381 (25.7%) students from the outbreak investigation who did not meet the investigation criteria because they had an incomplete immunisation schedule, additional doses, or the doses did not adhere to the schedule . Of 381 The index case of the pertussis outbreak was a female student. Among the students that met the criteria based on the routine immunisation schedule, the total number of boys and girls in the school was approximately even . However, 56 (40.6%) girls were positive for pertussis as opposed to 41 (28.3%) boys. The number of students in each year group was also evenly distributed, with a higher proportion of positive pertussis cases in school year 4, the year group where the first case was confirmed .A total of 186 of 283 (65.7%) students had no laboratory evidence of pertussis infection, while 96 (33.9%) tested positive for pertussis and one (0.4%) was clinically diagnosed with pertussis. Of those who tested positive, 26 were asymptomatic. Of the two booster vaccines offered to the students before school entry, Repevax was offered to more children than Infanrix-IPV, 167 vs 86, respectively. Eight students did not have a booster vaccine and 22 had unknown vaccination status. A total of 283 students were included in the main logistic regression analyses. The multivariable logistic regression, adjusted for vaccine type, school year and sex, indicated no statistically significant difference between Infanrix-IPV and Repevax vaccines. Compared with year 3 (reference age group), the odds of pertussis were significantly increased for children in school year 4 (odds ratio (OR) = 3.86; 95% CI: 1.80–8.30) and in year 6 , but there was no significant difference in year 5 . The priThe secondary logistic regression analysis comprised 326 students, including children who had received the booster dose early  or late (later than age 5 years). There was less difference in the odds of pertussis between the two products and the OR remained insignificant (OR for Repevax changed to 0.83 (95% CI: 0.45–1.51) compared with the primary logistic regression analysis. A test for interaction between school year and vaccine type indicated no evidence of differential waning in this outbreak investigation (p = 0.095).A total of 427 students in school years 3–6 were enrolled at the junior school at the time of the outbreak. In response to the outbreak, all students in the school were offered a booster dose of pertussis vaccine and 363 of 427 students (85%) were vaccinated. No students were hospitalised with pertussis and only one pertussis case was reported 9 months after the outbreak.Pertussis activity has remained at heightened levels across England since 2012 and while infant disease has been at low levels following the introduction of the maternal programme, rates of disease in school-age children appear to be increasing . In thisEarly accurate diagnosis is important to control transmission of pertussis . Most peIn this outbreak, a total of 39 students  with laboratory evidence of recent pertussis infection were not recorded as having a cough. Given the nature of the study, where parents completed the questionnaire retrospectively in late April 2019 (based on their child’s symptoms since January 2019), there was a risk of recall bias. However, it is also possible that these students were protected from vaccine doses that they had received earlier before the outbreak. The sensitivity analysis indicated that even when excluding those without reported symptoms, there remained no difference in the risk of pertussis among those who received either the 3 or 5 component vaccine as their preschool booster. It is worth noting that all symptomatic cases linked to this outbreak reported mild symptoms with no hospitalisations. One further pertussis case  occurred in the junior school 9 months later; this case is not included in this study.This outbreak provided a unique opportunity to evaluate the comparative protection afforded by the two available products for the preschool booster programme. The two vaccines differ in two pertussis antigenic components and in the amount of toxoid or antigen in some of the shared components. Infanrix-IPV is licensed for both primary and booster immunisation, whereas Repevax is licensed as a booster following primary immunisation. Of the three shared pertussis antigens, Repevax has greater amounts of all three with 10 × more pertussis toxid than Infanrix (25 vs 2.5 µg). Our study did not see a difference in the odds of pertussis between the two different preschool booster vaccines offered in the UK (Infanrix-IPV and Repevax). The OR of pertussis was higher among girls, which may be due to the social interactions of the index case, who was a girl.The routine immunisation schedule recommends that children receive their booster dose at the age of 3 years and 4 months. A total of 12.3% of the students at the junior school received the preschool booster after their fifth birthday or had no booster dose at all. This is similar to the national DTaP booster coverage at 5 years: In the period 2013 to 2016 when the students in this outbreak investigation would have been eligible for the preschool booster vaccine, 11.1–13.7% of UK children were unvaccinated . Informa


Depression and anxiety are common problems among breast cancer survivors. Carer support is one of the most important determinants of women's psychological wellbeing. Survivors' distress can be alleviated by giving carers access to survivors' evidence-based treatment, which will help carers understand what survivors have been going through and help survivors feel more supported. Given the limited access to evidence-based treatments, an adapted internet-delivered cognitive behavioural therapy (iCBT) intervention for breast cancer survivors, but also open for carers' access, has the potential to decrease survivors' depression and anxiety symptoms and improve cancer-related communication and relationship quality between survivors and carers.This study evaluates (1) the effectiveness of a guided iCBT intervention for depression and/or anxiety symptoms among breast cancer survivors with and without main carer access, and (2) the acceptability and satisfaction with the iCBT programme.n = 72) or (2) treatment-as-usual control group (n = 36) with a 2:1 ratio. The participants in the treatment group will be assigned to either iCBT alone or iCBT with the main carer also having access to the same content based on their preference. The primary outcome measure is the Hospital Anxiety and Depression Scale, and alongside secondary measures such as Cancer-Related Quality of Life, Breast Cancer Worry Scale, Brief COPE, and Medical Outcomes Study Social Support Survey will be completed by the survivors at baseline, post-treatment, and 2-month follow-up. Survivors who have carers will also complete Survivor-Carer Cancer Communication and Relationship Quality measures to provide insights into the effects of carer access. To assess the acceptability and satisfaction with the programme, survivors and their main carers will fill out the Helpful Aspects of Therapy Form (HAT) and Satisfaction with Online Treatment (SAT). Programme effectiveness and the effects of carer access on primary and secondary outcome measures will be evaluated on intention-to-treat and per-protocol basis using Linear-Mixed-Models.In this pilot study comparing the effectiveness of an adapted 7-week iCBT without main carer access against the iCBT with main carer access and treatment-as-usual control, 108 breast cancer survivors will be recruited and then randomised to either (1) treatment (This is the first trial comparing the effectiveness and acceptability of iCBT with and without carer access for depression and anxiety among breast cancer survivors. The findings of this study will provide novel data on the acceptability of iCBT programmes for breast cancer survivors and the impact of carer access on them and their carers. •Depression and anxiety are common among breast cancer survivors, and treating them is critically important.•Carer support is an important determinant of breast cancer survivors’ psychological distress and well-being.•Effects of carer access to the survivors’ treatment programme will be explored.•iCBT alone will be compared with iCBT with the main carer access and Treatment-As-Usual.•A primary outcome of depression and anxiety symptoms will be assessed. ResearcWomen with breast cancer often deal with this stressful experience by turning to their partners or spouses , or theiSeveral meta-analyses have established Cognitive Behavioural Therapy (CBT) as clinically effective in the treatment of depression and anxiety among breast cancer patients and survivors . Its lonHowever, access to evidence-based CBT treatments remains challenging  due to tAn internet-delivered cognitive behavioural therapy (iCBT) intervention focusing mainly on alleviating survivors' depression and anxiety symptoms, but also open for carers  to access may overcome these aforementioned challenges. It would allow breast cancer survivors and their carers access to the programme at a time and place convenient to them regardless of their location; furthermore, it may reduce concerns about stigma and privacy . Giving 1.1iCBT is a promising approach that can be used to provide information and support to cancer survivors on managing unhelpful thinking and behaviours, normalising feelings, and thereby relieving distress . ResearcEffectiveness studies of iCBT interventions for depression and anxiety among cancer patients and survivors are only recently emerging, and most studies have included people with all cancer types rather than focusing only on one type of cancer e.g. . A randoIn qualitative research, iCBT has also been demonstrated as acceptable among cancer patients who used the programme. Early-stage cancer patients and survivors with depression and/or anxiety found the iCBT programme acceptable in terms of the format of internet-delivery and the user-friendly nature of the material; overall, participants had good engagement with the programme . Both reInternet interventions designed specifically for people living with breast cancer have so far focused on insomnia , sexual Despite the critical influence of the social environment, specifically the perceptions of partner and carer support, on breast cancer patients' psychological adjustment , only on1.2Space from Depression and Anxiety is an 8-week CBT based online intervention programme, designed originally for the treatment of depression and anxiety symptoms in the general population and adapted for the breast cancer context for this study. The original Space from Depression programme (without the anxiety module) has been found effective for adults with depressive symptoms in the general population, with a large effect size at post-treatment . Based on their feedback on the original programme and the findings in the literature, the programme has been adapted for women with breast cancer and their main carers by the principal researcher (SA). After the adaptation of the content, one woman living with breast cancer, who completed the treatment, evaluated all the personal stories, and stories were tailored accordingly. Then, a clinical psychologist, who is an expert in CBT and psycho-oncology reviewed the first three modules of the adapted programme and gave feedback. Next, the primary researcher reviewed the changes and edited the whole programme content based on the received feedback. Afterward, another clinical psychologist who is an expert in CBT reviewed the adapted content including all eight modules and the programme content was updated accordingly.For the adaptation of the programme, The adaptation included combining the first two modules, updating and checking the relevance of research-based information in the breast cancer context, adding information on (i) breast cancer , (ii) the manifestation of depression and anxiety symptoms in breast cancer, (iii) the effectiveness and use of CBT for breast cancer, (iv) adapting physical activities for patients who have finished their treatment, and (v) how to support as a carer. Furthermore, the quiz questions and answers were revised to make them focused on the module content.The personal stories in the programme were rewritten for breast cancer survivors, which were mostly inspired by the experiences of the interview participants and the clinical knowledge of the primary researcher. This was done to help survivors and carers relate to personal stories. In total 6 personal stories were prepared: 2 representing single women living with breast cancer, 2 for informal carers of the survivors, and 2 for survivor-carer cancer-related relationship problems. Careful attention was paid to ensure that the stories accurately portrayed different experiences. As the fear of cancer returning and progressing in the same organ or another part of the body is one of the most common and distressing concerns among cancer survivors , the feaMoreover, all the examples were checked and replaced with breast-cancer specific examples in sections such as thinking errors, thoughts-feelings-behaviour (TFB) cycles, and worry cycles. For example, the TFB cycle examples did not address the common negative thoughts experienced by breast cancer patients; thus, they were changed with the ones such as “I can't cope with breast cancer” or “My cancer will come back”. Furthermore, there were other examples in the programme that did not fit the new context. For example, an example of catastrophising in the thinking errors section was “Stumbling over a few words when giving a presentation and then thinking the whole thing was a mess”, which did not match the new context; therefore, these were modified, for example, with “although your doctor says your prognosis is good, you still believe that you can't recover from cancer” to reflect common thinking patterns in the context of breast cancer.Information on fatigue was added as suggested by the patients in Alberts et al.'s study (2018). Information on emotional expression and effective listening was also added as it was the main difficulty mentioned by most of the participants during the interviews and was not included in the original version of the programme. In addition, distraction techniques for coping with difficult situations were introduced in the Challenging Thoughts module as some of the negative thoughts that patients with advanced breast cancer experience  may indeed have a realistic basis . Content1.4a)To evaluate the effectiveness of an adapted iCBT on the treatment of breast cancer survivors' depression and anxiety symptoms in response to the treatmentb)To determine any changes in breast cancer survivors' quality of life, coping, fear of cancer recurrence in response to the treatmentc)To evaluate any changes in breast cancer survivors' perceived social support, cancer communication, and relationship quality in response to the carer access to survivors' treatment contentd)To evaluate any changes in carers' cancer communication and relationship quality in response to their access to survivors' treatment contente)To compare the effects of an adapted iCBT with and without main carer access on breast cancer survivors' depression and anxiety symptomsf)To evaluate the helpful aspects of the iCBT intervention and satisfaction with the programme among breast cancer survivors and their main carersThe present pilot study has six aims:Therefore, breast cancer survivors will be randomly assigned to either iCBT intervention or treatment-as-usual control. The survivors assigned to the iCBT intervention group and who have a carer will be asked their preference to give their carers access to the intervention programme content. The iCBT group will be allocated to either iCBT alone or iCBT with carer access based on their preference. Therefore, there will be 3 groups in total.Group 1: iCBT aloneGroup 2: iCBT with carer accessGroup 3: Treatment-as-usual control1.5H1Survivors in both treatment groups (Group 1 and Group 2) are expected to show a greater reduction in depression and anxiety symptoms than survivors in the TAU (Group 3) at post-treatment and 2-month follow-up.Hypothesis about the primary outcomes:H2Survivors in both treatment groups (Group 1 and Group 2) are expected to have greater improvements in cancer-related quality of life, coping, and fear of recurrence compared to survivors in the TAU (Group 3) at post-treatment and 2-month follow-up.H3Survivors in both treatment groups (Group 1 and Group 2) are expected to use more active coping strategies than survivors in the TAU (Group 3) at post-treatment and 2-month follow-up.H4A reduction in depression and anxiety symptoms of survivors in both treatment groups (Group 1 and Group 2) is expected to be mediated by the change in coping strategies after controlling for depression and anxiety levels at baseline.H5An improvement in cancer-related quality of life in both treatment groups (Group 1 and Group 2) will be mediated by the change in coping strategies after controlling for cancer-related quality of life at baseline.Hypotheses about the secondary outcomes:The effects of carer access will be explored using the following research questions:RQ1. Is there a difference in depression and anxiety scores between iCBT alone (Group 1) and iCBT with the carer access (Group 2) at post-treatment after controlling for baseline depression and anxiety scores?RQ2. Is there a difference in perceived social support between iCBT alone (Group 1) and iCBT with the carer access (Group 2) at post-treatment after controlling for baseline perceived social support?RQ3. Is there a difference in survivor-carer relationship outcomes  of survivors in iCBT with carer access between baseline and post-treatment?RQ4. Is there a difference in survivor-carer relationship outcomes  of carers between baseline and post-treatment?22.1This study is comparing 7-module iCBT with guidance against iCBT with main carer access and treatment-as-usual control among women living with breast cancer who completed their treatment. Both immediate treatment groups will be followed up until 2 months post-treatment. This is a pragmatic pilot trial as survivors' allocation to the iCBT alone or iCBT with the carer access group will be based on survivors' preference. Therefore, the effects of being in the iCBT with the carer access arm are examined in an exploratory manner using research questions rather than confirmatory hypotheses testing. 2.2Participants will be recruited online from across Ireland and will be screened for eligibility. Inclusion criteria for survivors are (1) being female, (2) having completed the active breast cancer treatments such as chemotherapy, radiotherapy, and surgery (participants could be on hormone therapy) and being cancer-free, (3) being at least mildly confident with using the internet, and (4) being at least mildly confident in reading and writing in English.Inclusion criteria for main carers are (1) currently caring or have cared for a woman living with breast cancer , (2) being at least mildly confident with using the internet, and (3) being at least mildly confident in reading and writing in English.Exclusion criteria for breast cancer survivors and carers are (1) current suicidal ideation or intent, (2) current alcohol or drug misuse, (3) enduring mental health disorders such as schizophrenia, psychosis, and bipolar disorder, and (4) currently being in psychological treatment for depression or anxiety. As it is a pilot study, no limit has been set for the time since the treatment completion or cancer stage as an exclusion criterion for survivors.2.3Participants will be recruited through online cancer support groups, cancer research email listings, flyers, posters, and internet social media advertising (Facebook and Twitter). A Qualtrics link will be provided on the advertisement and via email. All survivors who show an interest in this trial will be able to read the Information Leaflet and sign the informed consent through the provided Qualtrics survey link.2.4f = 0.30) for the pre, post, follow-up comparisons between iCBT alone, iCBT with carer access, and TAU with a power of 0.80 and significance level of α = 0.05, the sample size is calculated to be 27 breast cancer survivors in each group and 81 in total (54 in the iCBT group and 27 in the TAU group using 2:1 ratio). Considering the 25% attrition (n = 72 for iCBT and = 36 for TAU) will be recruited.The G-Power software programme was used to carry out power analysis and determine the sample size needed for three-group, pre-test, post-test, and follow-up design. The data from a randomised controlled trial of an iCBT programme for depression and anxiety among cancer survivors revealed a large within-between interaction effect size for the HADS-Total after comparing the pre-treatment and post-treatment scores of the iCBT and the TAU . To calcttrition , 108 bre2.5n = 36) with a 2:1 ratio. Those who are assigned to iCBT group and have carers will be asked whether they will participate with or without their main carer. Participants will be assigned to iCBT alone or iCBT with carer access based on their preference and informed immediately about their group assignment. Survivors in the iCBT alone group (Group 1) will be using the iCBT programme by themselves, and survivors in the iCBT with the main carer access group (Group 2) will be using the same programme, but their main carers will also have access to the programme content using a separate username and password. Participants in the treatment-as-usual control group (Group 3) will continue their usual care and will not receive the iCBT programme during this study. They will fill out the same measures as the other two groups at pre-treatment, post-treatment, and 2-month follow-up.Eligible participants will be randomly assigned to either iCBT intervention (n = 72) or treatment-as-usual control , and current psychotherapy for depression or anxiety. Potential participants who responded yes to the questions regarding current suicidal ideation and intent will see a message providing contact details for recommended support services on the screen. Eligible survivors will be able to continue with baseline questionnaires (Time 1) assessing depression and anxiety symptoms, quality of life, fear of recurrence, coping, perceived social support, relationship quality, and cancer-related communication. After filling out the questionnaires, eligible participants will be automatically randomised and those who prefer to participate with their carer (iCBT with the main carer access) will be asked to provide their carers' email address on the Qualtrics page. Carers will be contacted through the email address provided by breast cancer survivors and asked to complete the informed consent, screening questionnaire, and survivor-carer cancer communication, and relationship quality measures through a separate Qualtrics link. All survivors and carers will be reassessed online at the end of the intervention , and at 2-month follow-up (Time 3). At the end of the intervention, survivors and their main carers in both treatment groups will be asked about their perspectives on the helpful aspects of the programme and their satisfaction with the programme.2.7Space in Breast Cancer from Depression and Anxiety is a seven module CBT-based intervention for depression and anxiety adapted specifically for women breast cancer survivors and their informal main carers. The programme was designed to alleviate depression and anxiety symptoms through the use of CBT skills such as activity planning , understanding the role of feelings, identification of unhelpful or irrational thinking patterns, generating more realistic and helpful thoughts by changing their perspective (cognitive restructuring), setting up a worry time, and differentiating practical and hypothetical worries.It consists of 7 modules completed ideally over 7 weeks and each takes approximately an hour to complete. A brief description of the programme content in each module can be seen in 2.8As this is a guided iCBT intervention, each survivor will be assigned a supporter, who provides weekly feedback on their progress on the programme. The supporters are masters' level students in applied psychology who have completed an undergraduate degree in Psychology. They were given an online health supporter training by an experienced psychologist from SilverCloud Health. The training took 1.5 h and the aim was to make supporters to become familiar with the evidence base of iCBT, to provide them a working knowledge of the platform being used, and help them communicate effectively with the users through demonstrating how to write reviews. The supporters were also given psycho-education training on breast cancer and its psychological impacts by the principal researcher (SA) before starting their role as a supporter. Supporters will be provided weekly supervision by the researchers (SA and DH).Each supporter will provide asynchronous weekly feedback on the platform to their assigned users. Feedback will take 10 to 15 min per participant for each session, which will consist of motivating users to keep using the programme, providing feedback on their progress, and suggesting the use of specific helpful tools in the programme. Supporters will schedule a specific time for feedback each week for a period of seven weeks so that survivors will know when they will get the feedback.2.92.9.12.9.1.1The Sociodemographic and Clinical History Questionnaire is an instrument developed to be used as a screening measure for an online CBT intervention . It incl2.9.22.9.2.1HADS is a 14-item self-report questionnaire, which is designed to measure anxiety and depression symptoms in medical settings  will be 2.9.32.9.3.1To assess the overall health-related life quality of cancer survivors, a single item from the European Organization for Research and Treatment of Cancer Quality of Life Core Questionnaire (EORTC) will be used . It is a2.9.3.2The six-item version of the Cancer Worry Scale (CWS)  will be 2.9.3.3The Brief COPE is a 28-item measure, which assesses strategies used for coping with a stressful life event . This in2.9.3.4Survivors' perceived support will be measured using the Medical Outcomes Study social support survey MOS; . The sca2.9.3.5Cancer communication difficulties between survivors and carers will be measured by 6-items drawn  from the 30-item measure of Patient-Family Discord . This me2.9.3.6Survivors and carers will be asked to rate the quality of their relationship with each other on a scale ranging between 0 and 10. A score of 10 represents the excellent quality of relationship and 0 represents the very poor quality of the relationship. This single item scale measuring the relationship quality between survivors and carers will allow them to base their judgments on aspects of their relationship that are most important for them based on .2.9.42.9.4.1The HAT assesses the most helpful and hindering effects for survivors during the therapy . It asks2.9.4.2The SAT assesses satisfaction with online treatment . The fir2.102.10.1t-tests for continuous variables.All data will be organised and prepared in the Statistical Package for the Social Sciences (SPSS). Demographic information and characteristics of the participants  will be analysed using descriptive statistics. Group differences in demographic and clinical characteristics at baseline will be analysed with the Chi-squared test for categorical variables, and analysis of variance (ANOVA) or 2.10.2g) and within-group (Cohen's d) effect sizes will be calculated using the pooled standard deviation and adjusted for sample size. For Hedges' g and Cohen's d an effect size 0.20 is considered small, 0.50 moderate, and 0.80 large.Linear Mixed Modelling (LMM) will be used to compare pre-treatment, post-treatment, and 2-month follow-up scores of the survivors in three groups  on the main outcome measure of depression and anxiety as well as secondary outcome measures including cancer-related quality of life, coping, and fear of cancer recurrence. The intention-to-treat (ITT) and per-protocol (PP) approach will be used to test the effectiveness of the treatment groups over the TAU control group. According to the ITT approach, participants who leave the study early or do not comply with the protocol are considered as belonging to the treatment arm they were originally randomised to. On the other hand, PP analytical approach does not include patients who violate the protocol, switch the allocation arm throughout the study, do not adhere to the assigned treatment or do not undergo to the scheduled assessments over time. Thus, it helps capture the “true” exposure to treatment. As suggested, the ITT will be used as the main method of analysis to determine effectiveness as it provides an unbiased estimate to treatment effect, and the PP will be used as a secondary and supportive analysis  to provin = 5000 bootstrap re-samples) and a 95% confidence interval . Inductive analysis is a process of data coding without relying on a pre-existing coding frame or the researcher's theoretical commitments or analytic preconceptions . To prov3Space in Breast Cancer from Depression and Anxiety, adapted specifically to alleviate depression and anxiety symptoms of women who completed breast cancer treatment in Ireland. There are some important gaps in the literature this study aims to fill. First of all, no study has targeted depressive and anxiety symptoms in iCBT interventions specifically with breast cancer survivors with all cancer stages. Although Murphy et al.'s study (2019) evaluated the effects of iCBT on clinical depression and/or anxiety among cancer survivors, they only included survivors with early-stage cancer and their findings may not be generalisable for those living with advanced cancer. Therefore, the current trial is the first randomised controlled trial of an online intervention for depression and/or anxiety in breast cancer survivors with all cancer stages. If proven effective and acceptable for breast cancer survivors and their main carers, this programme might be a good evidence-based alternative for women who do not have access to existing face-to-face evidence-based psychological treatments, given the lack of access to evidence-based psychological treatments  access to survivors' iCBT treatment makes a difference on survivors' anxiety and depression symptoms, social support perceptions, and cancer-related communication quality, and relationship quality with their carers as well as carers' cancer-related communication quality, and relationship quality with survivors. This study will provide novel insight regarding the feasibility of carer access based on the number of survivors who prefers to give their carers access to the iCBT programme. The trial is sufficiently powered for testing the primary and secondary hypotheses, and analyses regarding the effects of the iCBT with the carer access are more exploratory. As the iCBT alone and iCBT with the carer access group assignment is based on survivors' preference due to its exploratory nature, the study may be underpowered for detecting those effects in the exploratory analyses. Therefore, the results of the exploratory analyses will be interpreted with caution.Another notable strength of this study is that it uses a mixed-methods approach by combining quantitative methods to measure the programme effectiveness with qualitative measures through HAT and SAT to assess the user experiences and satisfaction, which will provide rich information and help us to better understand the nature of the existing relationship between outcomes. If the programme is found feasible and a clinically significant difference is found in depression and anxiety symptoms of survivors, the iCBT programme may be used as an alternative within stepped care of breast cancer patients and survivors, where guided low-intensity interventions are an attractive option .The trial recruitment started on 30 October 2020.psych.ethics@tcd.ie.Approved 15/07/2020, Trinity College Dublin School of Psychology Research Ethics Committee 1 896 1886; Ms. Selin Akkol-Solakoglu and Dr. David Hevey declare no conflicts of interest. Dr. Derek Richards is an employee of SilverCloud Health, developers of computerised psychological interventions for depression, anxiety, stress, and comorbid long-term conditions.


Cell motility and its regulation attract enormous interest. This is particularly evident in immunology with a literature explosion on T cell motility in search for antigen, surveillance of the organism for infected cells and cancer cells but also pathological infiltration of T cells in inflammatory diseases. The prevailing concept is that cell motility, including ameboid-like motility in T cells, is driven by integrin and chemokine receptor signaling and reorganization of actin –7. ThereContemporary motility research is focusing on outside-in signaling through chemokine receptor and integrin transmembrane signaling to the cytomusculature –14 or onLRP1 and TSP-1 are uniquely equipped for crosslinking interactions due to their size and numerous binding sites for other molecules , 21. LRPThe formation of motile active cell edges requires actin nucleation and polymerization by preformed actin filaments and nucleators, including the Arp2/3 complex, WASP and formins and linkage to the plasma membrane through ezrin-moesin-radixin. Cell surface receptors depend on this association to the actomyosin system for their expression, localization and function. Actomyosin contractility has a central role for cell motility and is generated by myosin-exerted force on actin filaments.A key question is how the actomyosin machinery in cells with ameboid-like motility and with a key role to search and surveil is regulated. In ameboid three-dimensional migration myosin is thought to start the motile process. However, there is substantial evidence that actin rearrangements and motility are directed at the level of the plasma membrane through crosslinking of cell surface receptors –25. CytoIn support of the concept that motility is directed at the plasma membrane level, early work showed that T cell motility is associated with surface expression of a complex of endogenous high-molecular weight proteins and a 55 kDa protein maintained by contact with type 1 collagen matrices and other cells but lost by contact with plastic , 27. Subcis via TSP-1. It is also worthy to note that MMP-9 stimulates three-dimensional T cell motility in an extracellular matrix model independent of proteolysis (LRP1 and associated calreticulin trigger a polarized motile morphology and migration through the NH-terminal region of TSP-1 and interaction of its distal COOH-terminal region with CD47, but TSP-1 may also crosslink other cell surface receptors Figure 1teolysis , which mteolysis  and hencT cell motility depends on the LRP1-regulated motility mechanism –34 and iT cell motility and extravasation, as well as motility of ameba, depend on protein synthesis although the driving of motility by actin reorganization and signaling does not –18. A cldoes not give T cells ability to enter 3D matrices but enhances existing capacity to enter such matrices (It is important to define the influence on T cell motility of the LRP1-directed motility mechanism and signaling through integrins and chemokine receptors. This issue is well illustrated by experiments studying T cells in a 3D environment. T cell motility into a collagen matrix hence depends on the LRP1-directed motility mechanism –34. In cmatrices . Howevermatrices . T cellsmatrices , 41. It matrices . Howevermatrices .set to merely counterbalance adhesion and prioritize motility.The LRP1-regulated motility mechanism promotes integrin-dependent adhesive contacts . HoweverThe chemokines CXCL12 and CCL5, and contact with integrin ligands, stimulate transport of TSP‐1 and LRP1 to the cell surface , 32. CXCThe LRP1-regulated motility mechanism hence combines an ability to sense environmental structures independent of conventional receptors and adapt to this sensing. Sensing and motility are hence tightly coupled indicating that this mechanism is important for T cell search and surveillance. Shedding of LRP1 may further be critical to promote search for antigen by preventing persistent adhesive contacts and may prevent this large molecule from interference with TCR-pMHC interactions. The ability to integrate and respond to integrin and chemokine receptor signals further endows this mechanism with a global sensing capacity consistent with a key role for T cell search and surveillance. Integrin and chemokine receptor signals stimulate this mechanism through enhanced TSP-1 transport to the cell surface antagonizing shedding of LRP1 , 32, 34.TCR-induced arrest upon antigen recognition, cytoplasmic spreading and actin polymerization are considered central in T cell biology and thought to represent decision to enter an activation program . CollaboThe cell-intrinsic motility mechanism represents a therapeutic target for interference with T cell activity, for example in inflammatory diseases. It is interesting in this context that the cornerstone therapy for inflammatory diseases, methotrexate, stimulates this motility mechanism. This is consistent with an immunoregulatory function of this mechanism and suggests that methotrexate, at the relatively low concentrations used to treat inflammatory diseases, is immunoregulatory rather than inhibitory .The author confirms being the sole contributor of this work and has approved it for publication.This work was supported by the Swedish Cancer Foundation and the Swedish Research Council.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Border controls and strong social distancing measures, particularly when targeted at superspreading, play a significant role in reducing the spread of COVID-19.New Zealand had 1499 cases of COVID-19 before eliminating transmission of the virus. Extensive contract tracing during the outbreak has resulted in a dataset of epidemiologically linked cases. This data contains useful information about the transmission dynamics of the virus, its dependence on factors such as age, and its response to different control measures. We use Monte-Carlo network construction techniques to provide an estimate of the number of secondary cases for every individual infected during the outbreak. We then apply standard statistical techniques to quantify differences between groups of individuals. Children under 10 years old are significantly under-represented in the case data. Children infected fewer people on average and had a lower probability of transmitting the disease in comparison to adults and the elderly. Imported cases infected fewer people on average and also had a lower probability of transmitting than domestically acquired cases. Superspreading is a significant contributor to the epidemic dynamics, with 20% of cases among adults responsible for 65–85% of transmission. Subclinical cases infected fewer individuals than clinical cases. After controlling for outliers serial intervals were approximated with a normal distribution ( Between 26 February and 22 May 2020, New Zealand recorded 1499 confirmed and probable cases of COVID-19 . Of thesProgressively stricter border measures were introduced from mid-March to mid-April. From 16 March, all international arrivals to New Zealand were required to go into 14 days self-isolation. On 20 March, the border was closed to non-citizens and non-residents. From 10 April, all international arrivals were required to go into 14 day mandatory government-managed quarantine.On 19 March, gatherings of more than 100 people were banned and, on 21 March, a four-level alert level system was introduced. Alert level 1 was effectively business as usual but with border restrictions. New Zealand entered alert level 2 on 21 March, level 3 on 23 March, and level 4 on 26 March. Alert level 4 included strict stay-at-home measures, and all non-essential business and schools were closed. The country remained at alert level 4 until 28 April, when it moved to level 3 allowing partial school openings and non-essential businesses to operate under strict non-contact guidelines. On 14 May, New Zealand moved to alert level 2 and most schools and businesses reopened but large gatherings were still banned. On 9 June, the country moved back to alert level 1 and a strict testing programme for international arrivals was established to complement the ongoing 14-day mandatory quarantine.New Zealand also has a widespread testing regime that quickly encouraged testing of anyone with even minor symptoms. Up to 5 June (2 weeks after the last reported domestic case on 22 May), 291,994 tests were performed (58 tests per 1000 people) with a positivity rate of around 0.4% . Since mid-June, all international arrivals are tested on day three and day twelve, and are not permitted to leave their 14-day mandatory quarantine before returning a negative test result.Reff decreased from around 1.8 prior to alert level 4 to around 0.35 during alert level 4 [Mathematical models of COVID-19 transmission and control measures have estimated that the effective reproduction number In this paper, we examine the epidemic transmission tree in New Zealand using detailed case data and contact tracing information. This is an unusual dataset because it describes a closed outbreak in which there are no ongoing transmission chains, and many cases are epidemiologically linked to a likely index case. Because not all cases have a potential index case identified and some cases have multiple potential index cases, the transmission tree is not completely or uniquely defined. We reconstruct multiple instances of the transmission tree using a Monte-Carlo technique. This approach assigns a randomly selected index case to each secondary case based on the dates of symptom onset, cluster identification, and the results of contact tracing investigations. We assume a fixed proportion of infections were subclinical and undetected because they were asymptomatic, paucisymptomatic, or for some other reason.Ri for each infected individual and explore the distribution of Ri and the secondary attack rate at different points in the epidemic timeline. We explore how these quantities vary for different age groups and types of case, imported or domestic, and investigate the effect of early case isolation. We also investigate the degree of superspreading in the transmission dynamics. Although some assumptions are required to allow for missing or noisy data, our results are not model-dependent, unlike most estimates of effective reproduction number for COVID-19 [From this suite of generated transmission trees, we calculate the individual reproduction number COVID-19 –4. Our sCOVID-19 , GanyaniCOVID-19 , Cheng, COVID-19 ). This aAnonymised data were supplied by the Ministry of Health, New Zealand. The dataset included the baseline demographic characteristics of age and ethnicity. The project was given ethics approval by the Ministry of Health, New Zealand who waived the requirement for informed consent. Of the 1499 confirmed and probable cases, 575 had a recent history of overseas travel and were considered to be imported cases. The remaining 924 cases were considered to be infected within New Zealand and are referred to as domestic cases. Following contact tracing interviews, 636 of the domestic cases were each assigned up to five potential index cases. The remaining domestic cases did not have a potential index case identified. 627 of the domestic cases were linked to one of 18 significant clusters. Of the cases linked to clusters, 146 had no potential index case identified. For 1444 cases, the number of close contacts, obtained via case recall at a contact tracing interview, was recorded. The number of close contacts varied widely between individuals, e.g. 753 cases had 2 or fewer contacts and 26 cases had more than 50. 31 cases were recorded as being asymptomatic at the time of testing. All cases recorded age.i cases using the date recorded by case recall, Di, (if available) with a random offset, Di = Di + Xi where Xi ~ N. For the 38 cases with no onset date recorded  we assigned a randomly generated onset date using the reported date and a gamma distribution fitted to the data on time from onset to reporting, Γ  days.We constructed a potential instance of the transmission tree by randomly assigning a symptom onset date to each of the pasymp = 0.33 on average. These additional cases were randomly sampled from the recorded cases with the symptom onset date randomly jittered Di = Di + Xi where Xi ~ N. Model-generated subclinical cases had the same cluster status and potential index cases as the sampled case.In addition to the 31 recorded asymptomatic cases, each generated transmission tree included additional subclinical cases so that the total proportion of subclinical cases was SI can be expressed asGT is the generation time (time from infection of index case to infection of secondary case), and I1 and I2 are the incubation periods of the index and secondary case respectively. We assume that the generation time GT~Weibull days [Ij~Γ days [f(SI). Note that although the generation time must be positive, there is a positive probability of a negative serial interval. When index cases were assigned randomly no attempt was made to infer missing cases as part of transmission chains.Of the domestic cases, 17 had a unique index case identified in the data. For the 288 cases with no index case, an index case was selected based on the implied serial interval, i.e. the time between symptom onset in the index case and in the secondary case. The serial interval n > 1 potential index cases, the serial interval SIj corresponding to each potential index case j = 1,…n was calculated. Potential index case j was selected as the index case with probability Pj defined above was assumed to be independent of whether the potential cases was clinical or subclinical.For the 619 domestic cases with M = 500 independent instances of the transmission tree. For each instance, we calculated the number of secondary cases resulting from each reported case  and the associated symptom onset date, Dij.Assigning an index case to each secondary case produces a single instance of the epidemic transmission tree. We then repeated this Monte Carlo process to produce The original case data, including symptomatic and asymptomatic cases, was split by imported status and time period. The reported statistic is the probability of being in each age bracket within that group and the 95% binomial confidence interval. NZ population statistics are shown as benchmarks.M transmission trees was fitted with a normal distribution .The resulting serial interval distribution across all j. The reported statistic is the median and 95% range over the M transmission trees.We calculated the mean (or case ) reprodufitdist) to the effective reproduction numbers in each transmission tree separately for clinical individuals of different groups (age and domestic/imported status) and with onset date Dij in different time periods for each transmission tree. This gave an estimate of the mean effective reproduction number jth transmission tree, and the associated dispersion or superspreading parameter kj [M transmission trees. If the variance of the sample was less than the mean a negative binomial could not be fitted so a Poisson distribution was used instead (i.e. k = ∞).We fitted a negative binomial distribution  for the domestic cases; and during three distinct periods of border control for imported cases: no restrictions (before 16 March), self-isolation (16 March to 9 April) and government-mandated quarantine (after 9 April).Children under 10 are significantly under-represented in the case data relative to the New Zealand population demographic of 12.7% . The domThe distribution of serial intervals  resultinReff = 1.47 (382 index cases) (Reff = 0.69 (521 index cases). In the period after alert level 4, the vast majority of cases were detected as part of contact tracing efforts and were isolated swiftly, often before symptom onset. The reproduction number for domestic cases showed two small peaks during the alert level 4 period. The first coincided with Easter weekend (10–13 April). The second coincides with a long weekend (25–27 April) immediately preceding the pre-announced easing of restrictions on 28 April.For domestic cases, the average number of secondary cases infected by a clinical case before New Zealand entered alert level 4 on 26 March was x cases) . During Reff = 0.70 (119 index cases). While self-isolation was in place from 17 March to 9 April, Reff = 0.42 (439 index cases). After mandatory government-managed quarantine was introduced on 10 April, Reff = 0 (7 cases).For imported cases , the numReff = 0.87 for the under 10 years age group, Reff = 1.49 for the 10 to 65 years age group, and Reff = 1.51 for the over 65 years age group. Although children under 10 were equally likely to infect at least one person, adults tended to infect more people than children under 10 did. Cases among adults and older people also had a significant chance of being a superspreader (which we defined as infecting more than 5 people): 6% in the 10 to 65 year age group, and 7% in the over 65 age group.Before the move to alert level 4, over half of all domestic cases resulted in at least one secondary case . The proReff dropped below 1 for the under 10 and the 10 to 65 age groups, but stayed above 1 in over 65 group. This may be due to the over-representation of cases in aged care facilities in the later stages of the epidemic, where it may be more difficult to limit interactions. The proportion of superspreaders decreased in adults during alert level 4 but not in the elderly. There were no recorded superspreaders under 10 years old at any stage in the epidemic.During alert level 4 , Table 1Overall, there were 29 superspreaders, of which 21 had symptom onset before alert level 4 began, and the remaining 8 had symptom onset during alert level 4. Of these, the 6 most recent events were all related to clusters of cases at aged care facilities.Reff than domestic cases , had the lowest likelihood of transmitting the disease even accounting for both these cases happening during the mandatory quarantine period. There were 5 superspreaders amongst the imported cases, all of which occurred during the self-isolation period between 9 and 19 March.As seen previously, imported cases overall had a lower ic cases . Before k of a negative binomial distribution fitted to the data [k, the more heavy-tailed the distribution of secondary cases, and therefore the more superspreading is driving the transmission process. The results show that older age groups tend to be have more superspreading than younger ones  recorded as asymptomatic, 30 were in adults and older people. Before the move to alert level 4, these cases had al cases . During In most cases results for subclinical individuals are not included. There were very few subclinical individuals in the dataset so statistics derived only from the recorded individuals are not reliable. Including the randomly generated, i.e. missing, subclinical individuals can give misleading information. The results either mirror the clinical data, e.g. Figs We analysed the transmission dynamics of COVID-19 using a comprehensive dataset for a completed outbreak. Our analysis investigated the role of age and type of case (imported or domestic) on reproduction number, secondary attack rate, likelihood of superspreading. We also investigated the effect of various control measures, including population-wide restrictions, and self-isolation or quarantine of international arrivals.We found that children under 10 years contribute less to the spread of COVID-19 than other age groups. Children under 10 infected fewer people on average, had a lower secondary attack rate, and were less likely to be superspreaders. This is consistent with existing evidence on the role of children in COVID-19 transmission , 17.The finding that superspreading is a significant contributor to transmission is consistent with existing empirical and modelling studies –20. Our In the dataset we used to reconstruct epidemic transmission tree, the index case is either missing or not uniquely identified in some cases. It is also likely there were subclinical cases that were undetected. We therefore used Monte Carlo methods to reconstruct missing cases and missing transmission routes (nodes and links respectively in the epidemic tree), though without inferring missing cases as part of transmission chains directly. It is possible that phylodynamic data on the genetic sequence of the virus recovered from positive test results could be used to reduce uncertainty in this step and improve the accuracy of the reconstructed tree.S1 FigThe black line shows the age distribution of the New Zealand population . The title of each panel describes the setting and location  associated with each cluster; n is the total number of recorded cases in the cluster.(DOCX)Click here for additional data file.


Can a machine learning model help medical dispatchers improve recognition of out-of-hospital cardiac arrest?In this randomized clinical trial of 5242 emergency calls, a machine learning model listening to calls could alert the medical dispatchers in cases of suspected cardiac arrest. There was no significant improvement in recognition of out-of-hospital cardiac arrest during calls on which the model alerted dispatchers vs those on which it did not; however, the machine learning model had higher sensitivity that dispatchers alone.These findings suggest that while a machine learning model recognized a significantly greater number of out-of-hospital cardiac arrests than dispatchers alone, this did not translate into improved cardiac arrest recognition by dispatchers. This randomized clinical trial examines how a machine learning model trained to identify out-of-hospital cardiac arrest and alert dispatchers during emergency calls affected the recognition of and response to out-of-hospital cardiac arrests. Emergency medical dispatchers fail to identify approximately 25% of cases of out-of-hospital cardiac arrest (OHCA), resulting in lost opportunities to save lives by initiating cardiopulmonary resuscitation.To examine how a machine learning model trained to identify OHCA and alert dispatchers during emergency calls affected OHCA recognition and response.This double-masked, 2-group, randomized clinical trial analyzed all calls to emergency number 112  in Denmark. Calls were processed by a machine learning model using speech recognition software. The machine learning model assessed ongoing calls, and calls in which the model identified OHCA were randomized. The trial was performed at Copenhagen Emergency Medical Services, Denmark, between September 1, 2018, and December 31, 2019.Dispatchers in the intervention group were alerted when the machine learning model identified out-of-hospital cardiac arrest, and those in the control group followed normal protocols without alert.The primary end point was the rate of dispatcher recognition of subsequently confirmed OHCA.P = .15). Machine learning alerts alone had a significantly higher sensitivity than dispatchers without alerts for confirmed OHCA  but lower specificity  and positive predictive value .A total of 169 049 emergency calls were examined, of which the machine learning model identified 5242 as suspected OHCA. Calls were randomized to control (2661 [50.8%]) or intervention (2581 [49.2%]) groups. Of these, 336 (12.6%) and 318 (12.3%), respectively, had confirmed OHCA. The mean (SD) age among of these 654 patients was 70 (16.1) years, and 419 of 627 patients (67.8%) with known gender were men. Dispatchers in the intervention group recognized 296 confirmed OHCA cases (93.1%) with machine learning assistance compared with 304 confirmed OHCA cases (90.5%) using standard protocols without machine learning assistance (This randomized clinical trial did not find any significant improvement in dispatchers’ ability to recognize cardiac arrest when supported by machine learning even though artificial intelligence did surpass human recognition.NCT04219306ClinicalTrials.gov Identifier:  In 2018, the overall rate of CPR by bystanders was 77% in Denmark,6 which corresponded to the rate of dispatcher-recognized OHCA at the Copenhagen Emergency Medical Services (EMS).7Survival after out-of-hospital cardiac arrest (OHCA) has increased in several countries following improvements in bystander interventions, the response of emergency medical services (EMS), and postresuscitation care. Of these, early bystander interventions, particularly cardiopulmonary resuscitation (CPR) with early defibrillation, can have the greatest potential impact on outcome.A rate-limiting step to initiating bystander CPR and expediting EMS response lies in the recognition of OHCA by dispatchers. A promising strategy for improving OHCA recognition is the use of artificial intelligence systems based on deep neural networks to provide real-time information to the dispatcher. Such systems estimate the likelihood of OHCA based on the patterns of the words spoken that might be missed by the dispatcher.7 Despite the potential for such clinical decision support tools to improve health care outcomes by offering data-driven insights, almost all decision support tools driven by artificial intelligence or machine learning have so far failed to do so in practice.8 Thus, while the translation of research techniques like machine learning into clinical practice presents a new frontier of opportunity, the real-world deployment of this modality remains rare and in need of further exploration.9A clinical decision support tool based on machine learning models was tested at the Copenhagen EMS and was able to identify OHCA with better sensitivity and only slightly lower specificity than medical dispatchers.In this randomized clinical trial, we investigated the effect of real-time information on dispatchers’ ability to recognize OHCA during emergency calls. Our primary aim was to examine whether the machine learning model affected the clinical practice of medical dispatchers. We hypothesized that the machine learning model would increase recognition of OHCA when dispatchers were augmented with machine learning compared with standard call procedures. Secondary outcomes were differences between the 2 approaches in time-to-recognition of OHCA, initiation of dispatcher-assisted CPR (DA-CPR), and time to its initiation.CONSORT) reporting guideline. The regional ethical committee waived the need for preregistration of this study. However, we retrospectively registered this trial with ClinicalTrials.gov in August 2019. We obtained written informed consent from all study participants, ie, medical dispatchers.We followed the General Data Protection Regulation and registered the study at the Danish Data Protection Agency. The study is approved by the Danish Patient Safety Authority. The research ethics committee in the Capital Region of Denmark waived the need for ethical approval. The study followed the Consolidated Standards of Reporting Trials  and paramedics (30%) who receive 6 weeks of training in communication, prioritization of emergency calls, and DA-CPR instructions. In cases of suspected OHCA, dispatchers instruct callers in CPR while simultaneously dispatching an ambulance and a physician-manned Mobile Critical Care Unit.The study was performed in Copenhagen, Denmark, with a population of 1.8 million persons and a size of 2563 km7 which listens to every call and can immediately alert the dispatcher when the model suspects OHCA. The machine learning model in this study is identical to that used previously7 and estimates OHCA with a 1-second resolution, meaning that for each second of conversation between caller and dispatcher, the machine learning model calculates the probability of whether there is an OHCA in the accumulated call information. If the probability of the machine learning model exceeds a prespecified threshold, the call is defined as a suspected OHCA and a warning can be issued to the dispatcher.Emergency calls to Copenhagen EMS are analyzed by the previously described machine learning model,This double-masked, 2-group, randomized clinical trial (RCT) evaluated a machine learning model that analyzes calls in real time and delivers decision support to medically trained dispatchers compared with conventional call-handling without decision support from the machine learning model. A more extensive description of the study is available in the trial protocol in 10: if the machine learning model erroneously identified an OHCA that was not subsequently confirmed by the Danish Cardiac Arrest Registry; calls with a broken chain of alert ; repeated calls on the same incident; calls from police requesting help from the EMS; and calls regarding EMS-witnessed OHCAs. Finally, calls for which CPR had been initiated prior to the call were excluded as an already confirmed OHCA.All emergency calls received between September 1, 2018, and December 31, 2019, were considered for trial enrollment. However, only calls that the machine learning model suspected as OHCA were eligible for randomization to the intervention or control group. Calls placed on hold while the dispatcher conferred with EMS physicians were excluded if the machine learning alert only appeared after the hold. In addition, calls were excluded according to predefined postrandomization exclusions12 Medical dispatchers were masked to their group assignment. Unless the machine learning model generated and disclosed an OHCA alert, neither group knew whether the machine learning model was operative during the call. Investigators involved in data analysis and outcome assessment were also masked to group assignment.Calls meeting the inclusion criteria were assigned to either the intervention or control group in a 1:1 ratio, using a random number generating program with no stratification factors.In a post hoc analysis, we examined compliance to the machine learning model and the consequences if the intervention group had heeded every machine learning alert. We compared the time of the machine learning alert in the intervention group with the dispatcher’s time of OHCA recognition  in the control group.All calls to EMS Copenhagen are transferred from the police and start with a conference between the police and medical dispatcher. In case of OHCAs that are recognized by the dispatcher, time to recognition is measured from the start of the call, which includes the conference time. After recognition of OHCA, the next event in the call is initiation of DA-CPR.13 When the alert appeared, the dispatcher had the option to ignore the alert, or if heeded, they were instructed to immediately dispatch an ambulance and a physician-staffed vehicle on the suspicion of OHCA. The precise time of the machine learning alert, both those heeded and ignored, was recorded and stored in a central database. The dispatcher’s reaction to the alert, whether heeded or ignored, did not influence the enrollment or subsequent analysis. For the control group, the alerts generated by the machine learning model were suppressed and not shown to the dispatchers. However, the time when the machine learning alert occurred, although not directed to the dispatcher, was recorded. The alert was designed as a conspicuous display on the dispatcher’s main screen while not otherwise interfering in the dispatcher’s work.All dispatchers participated in the trial and were instructed to follow a specified protocol when receiving an alert from the machine learning model.15 Case review allowed us to collect data for the secondary end points, ie, time to recognition and time to DA-CPR.All emergency dispatch calls were recorded. To verify confirmed OHCA, calls were linked to the Danish Cardiac Arrest Registry. Calls for which the machine learning model had identified a true OHCA were analyzed by a group of trained evaluators who were masked to the randomization assignment using a modified dispatch data survey catalog from the American Cardiac Arrest Registry to Enhance Survival.The primary outcome was the rate of dispatchers’ recognition of subsequently confirmed OHCA. Secondary outcomes were dispatchers’ time to recognition of OHCA and the rate of DA-CPR, defined as when the dispatcher instructed the caller to place hands on the patient’s chest or similar action directives.P values when appropriate. Time-to-event analyses were conducted with Kaplan-Meier failure curves to estimate time to recognition in each group and for the machine leaning model overall.The performance of the dispatchers in the control and intervention groups was compared using the Danish Cardiac Arrest Registry as the reference for a confirmed OHCA as was the overall performance of the machine learning model. Results are reported with 95% CIs and P < .05 was considered significant for all analyses. Data management and statistical analyses were performed using SAS version 9.4 (SAS Institute).In assessing the machine learning performance, calls classified by the model as OHCA and confirmed by the registry reference standard were considered true-positive findings. The Mann-Whitney test was used to compare calls classified as OHCA and non-OHCA by the machine learning model. A 2-tailed 7 the difference in recognition between machine learning model and dispatcher was 10%. Setting the significance level (α) at 5% and the power (1 − β) at 95%, 356 calls were needed in each group, resulting in a total study sample of 712 calls.Based on our previous study,During the intervention period, Copenhagen EMS received 226 130 calls, while the machine learning model processed only 169 049 calls (74.7%) due to downtime. The downtime was randomly spread across the day and across dispatchers, showing equal distribution between OHCA calls and non-OHCA calls and not affecting the randomization. Of the 169 049 calls processed by the machine learning model during the 16-month study period, the machine learning model identified 5847 calls as suspected OHCA. Of these, 5242 (89.7%) were eligible for randomization and were randomized to either control (2661 [50.8%]) or intervention (2581 [49.2%]) dispatcher groups; 4588 (87.5%) of the calls were excluded after randomization .The characteristics of the included patients are shown in P = .15). In the 304 calls in the intervention group, the mean (SD) time to dispatchers OHCA recognition was 1.72 (1.52) minutes vs 1.70 (1.63) minutes in 296 calls in the control group (P = .90)  in the intervention group and 304 of 336 (90.5%) in the control group, with no significant difference between the 2 groups (P = .90) .P = .60)In all 318 calls in the intervention group, the machine learning model recognized the OHCA and generated an alert in a mean (SD) of 1.39 (1.32) minutes from the emergency call’s onset, a difference of only 0.05 minutes compared with the suppressed alert not displayed in the 336 calls in the control group , with no significant difference in elapsed time from when the OHCA was recognized. Elapsed time without CPR instructions was 2 seconds longer in the intervention than control group  in the intervention group and 208 calls (61.9%) in the control group .P < .001) but lower specificity . The machine learning model had a significant lower positive predictive value than dispatchers . Put in pragmatic terms, if the dispatchers had heeded the machine learning model alert, 54 additional OHCAs that were not recognized by dispatchers would have been recognized.We examined the 169 049 calls processed by the machine learning model, of which 1110 (0.7%) were OHCAs unwitnessed by EMS and confirmed by the Danish Cardiac Registry. Compared with medical dispatchers, the machine learning model had a significantly higher sensitivity . Had all such alerts also been disclosed to and heeded by the control group, 2161 cases (81.2%) of false-positive OHCAs would have been produced. From the 4283 false alerts, 1519 (35.5%) received a response other than a full OHCA response, increasing the total number of ambulances dispatched from 56 449 to 57 968, a 2.7% increase.16 To our knowledge, this study is the only published study analyzing machine learning models processing speech in a real-time conversation between a medical dispatcher and patient or bystander, during which the system automatically extracts information from the conversation to be transformed into real-time decision support for the medical professional. There are only a few RCTs testing artificial intelligence and machine learning technologies, all of which either report negative or insignificant findings.18The current study was designed as an RCT that also addressed the paucity of RCTs investigating artificial intelligence.This study tested a novel approach to telephone triage, in which a machine learning model automatically extracted information in real-time and transformed this into decision support for the medical dispatcher recognizing OHCA. The machine learning model was found to correctly recognize more OHCAs and do this significantly faster than the dispatchers. However, the use of a machine learning model in the current setting did not result in an increased number of correct recognitions of OHCA by dispatchers, and the time to recognition of OHCA was unchanged.7 The specificity was also similar to the retrospective study, with 97.4% compared with 97.3%. Thus, the developed machine learning decision support tool performs in a live, prospective setting with similar results to the retrospective studies. It is through prospective studies, preferably RCTs, that we can understand the utility of machine learning models, given that performance is likely to be worse when encountering real-world data that differ from those encountered in algorithm training.9 The very similar results of the prospective and retrospective studies demonstrates the robustness of the model, validating the basic principles of the machine learning model.In 169 049 calls to Copenhagen EMS, including 1110 calls regarding OHCA, we found that the machine learning model could correctly recognize OHCA calls in more than 85.0% of OHCA calls, which is similar to a previous retrospective study, in which we found a sensitivity of 84.1%.We further examined the consequences of compliance to all alerts from the machine learning model. This would have resulted in 54 additional OHCAs being recognized. Conversely, in a scenario with 100% compliance, 1519 ambulances would have been dispatched as part of a full OHCA response, increasing the total number of dispatches by 2.7%, from 56 449 to 57 968 ambulances dispatched with lights and sirens.18There is a paucity of literature about the clinical performance of machine learning models or artificial intelligence analyzing calls to support medical dispatchers. Experience with machine learning models has been limited to retrospective studies, whereas the current study showed higher sensitivity and less time to decision on suspected OHCA compared with medically trained dispatchers. However, as shown in this trial, being alerted to a suspected OHCA did not significantly affect the behavior of the dispatchers who responded to the OHCA call. The results underpin the need for discussing implementation when adapting new technology. These results are similar to the results of the Computerised Interpretation of Fetal Heart Rate During Labor trial and other RCTs testing machine learning models or artificial intelligence in clinical practice, which found no evidence of computerized decision support reducing the likelihood of poor outcomes.20 Our findings are concordant with a review focusing on compliance with decision support tools, which reported that actual use rates remain low despite clinicians acknowledging these technologies as useful.21The human factor in the interaction with decision support tools is crucial, and several studies report hesitancy in the interaction with decision support tools.While the machine learning model delivers robust advice with high sensitivity, it appears that dispatchers did not comply with alerts. Further consideration is needed before implementing such a tool in clinical practice to determine whether further training of medical professionals can remedy the missing compliance.We found that creating a machine learning model with high sensitivity was not sufficient to improve recognition of OHCA. It would also be necessary to establish confidence in the machine learning model. We found that extreme obedience to the machine learning model would have resulted in more OHCAs being recognized and reduced the time to recognition, although with more frequent high-priority dispatch of ambulances.This study has limitations. A potential weakness is that the medical dispatchers can learn from prior exposure to the machine learning model when not passive and in turn improve outcomes in the control group when encountering an OHCA call during which the model remains passive. In hindsight, we should have provided more education to dispatchers to improve compliance with the machine learning model. Third, the servers analyzing the phone calls had downtime because the server was underdimensioned. To remediate this, the randomization period was prolonged.In this RCT, we did not find any significant improvement in dispatcher recognition of OHCA when supported by machine learning, even though artificial intelligence did surpass human recognition. This suggests that machine learning has the potential to positively affect the recognition rate of OHCA while also improving the rate of DA-CPR, but these findings did not lead to improved recognition of OHCA or improved rate of DA-CPR. Future studies are needed to improve human-computer interaction. However, efforts should be made to improve the specificity of the machine learning model to improve the relevance of alerts.


Almost three decades ago it was reported that patients who lacked CD53 suffered from an increased susceptibility to pathogens resulting in the clinical manifestation of recurrent viral, bacterial, and fungal infections. This clearly suggests a vital and non-redundant role for CD53 in immune function. Yet, despite this striking finding, the specific functional roles of CD53 within the immune system have remained elusive. This review aims to provide a concise overview of the published literature concerning CD53 and reflect on the underappreciated role of this protein in immune cell regulation and function.Tetraspanins are membrane organizing proteins that play a role in organizing the cell surface through the formation of subcellular domains consisting of tetraspanins and their partner proteins. These complexes are referred to as tetraspanin enriched microdomains (TEMs) or the tetraspanin web. The formation of TEMs allows for the regulation of a variety of cellular processes such as adhesion, migration, signaling, and cell fusion. Tetraspanin CD53 is a member of the tetraspanin superfamily expressed exclusively within the immune compartment. Amongst others, B cells, CD4 There are 33 known tetraspanin family members expressed in human cells. Tetraspanins are integral membrane proteins containing four transmembrane domains, two extracellular domains, and two short intracellular tails . TetraspCD37 has been extensively studied and its loss has been linked to defects in numerous cellular and humoral immune functions such as migration, adhesion, proliferation, and antibody production –18. The In contrast, CD53, also known as OX44 or TSPAN25, has been comparatively understudied despite strong evidence that this tetraspanin has important and non-redundant functions within the immune system –25. CD53The aim of this review is to present a framework for the unique function of CD53 within the immune system based on the published literature. I will discuss the regulatory role of CD53 in different cellular processes with a focus on immune cell adhesion and signaling.The ability of cells to adhere to one another and to the extracellular matrix (ECM) is important for maintaining the architecture and integrity of tissues and enabling cells to create the forces required for movement. Cell adhesion depends on cell adhesion molecules (CAMs) expressed on the cell surface, which include integrins, selectins, and cadherins. Cell adhesion plays an especially important role in the immune system, where adhesion molecules are used to stabilize immunological synapses between immune cells and facilitate their migration to sites of inflammation or infection , 40.Many tetraspanins have been proposed to interact with CAMs, of which integrins in particular have been found to be key interaction partners , 41. IntCD53 has also been linked to integrins and implicated in the regulation of adhesion of multiple immune cell types. Traditionally, the role of CD53 in adhesion has been studied using antibody ligation of CD53. For example, Todros-Dawda et al. showed that ligation of CD53 induced activation of the β2 integrin LFA-1 (αLβ2) in natural killer (NK) cells resulting in homotypic adhesion . FurtherThe interaction between CD53 and LFA-1 is not restricted to NK cells as similar results have also been obtained in other immune cell types. In B and T cell lines the ligation of CD53, either via whole antibodies or via Fab’ fragments, also resulted in homotypic cell adhesion, which could be prevented through pre-treatment with antibodies against LFA-1 or its ligand ICAM-1 . Based oThese findings were contradicted by the work of Lazo et al. who found that blocking of LFA-1 did not relieve homotypic adhesion of rat B cells induced by CD53 ligation . SuggestWhile the abovementioned studies have made a good case for the involvement of CD53 in immune cell adhesion, they all suffered from the same shortcoming, namely, the exclusive use of antibodies to study CD53 function. While this method has been useful to identify potential partners of CD53, the results should be interpreted with caution as Fc receptor cross-linking may also play a role in the observed experimental outcomes. Modulation of the expression on CD53 through knockdowns, knockouts or overexpression are all viable alternatives to this method, as is illustrated by the following study.Cd53−/− mouse model, Demaria et al. have shown that the homing of Cd53−/− lymphocytes to the lymph node is defective, with B cells more severely affected than T cells [Cd53−/− B cells. The impaired L-selectin expression in the absence of CD53 was confirmed in two human CD53−/− B cell lines. Further experiments revealed that the phenotype could be partially rescued by the application of a metalloprotease inhibitor suggesting that metalloproteases are partly responsible for the decrease in L-selectin expression when CD53 is absent. This was confirmed in experiments showing that CD53 inhibits the L-selectin shedding via ADAM17-dependent and -independent mechanisms. Finally, the authors showed that this defect in lymph node homing caused by the absence of CD53 resulted in a delayed adaptive immune response upon in vivo challenge with model antigens, illustrating the importance of this tetraspanin in the adaptive immune response.Using an elegant  T cells . This waTaken together the work discussed here illustrates the vital and varied role CD53 plays in regulating immune cell adhesion and migration, controlling this process through multiple distinctive adhesion molecules in different cell types. In addition, the work of Demaria et al. specifically, highlights the negative consequences associated with the absence of CD53 which result in pronounced immune dysfunction associated with an adhesion-related migration defect.An overview of the most prominent interactions discussed in this section can be found in Fig. A healthy immune system depends on the intricate and reciprocal relationship that exists between immune cells and their environment. Central to this cellular interplay is the process of signaling, which involves receiving, transducing and responding to signals provided by neighboring cells or by the external surroundings. As the boundary between the intracellular and extracellular environment, the plasma membrane is vital to this process, providing a dynamic platform through which receptors activate signaling pathways.Though signaling is a ubiquitous cell function, it has a particularly important role within the immune system which is dependent on signaling to recognize and eliminate pathogens and transformed cells. Tetraspanins help to support this function by forming TEMs that can function as platforms to support intense signaling activity. Different interactions between tetraspanins and signaling proteins have been reported, including both kinases and phosphatases .CD53 has been found to interact with numerous membrane proteins involved in signaling. For example, CD2, which is expressed on the surface of NK and T cells, plays a role in cell adhesion and functions as a co-stimulatory molecule . CD2 wasCd53−/− B cells and an associated reduction in pre-B and immature B cell populations. Not only does this study serve to further cement the role of CD53 in signaling, it also supports the premise that the immune system is reliant on CD53 in important and non-redundant ways.More recently a role for CD53 has been recognized as a regulator of B cell development via interleukin 7 receptor (IL-7R) signaling . The autFinally, numerous tetraspanins, including CD53, have been reported to associate with MHC class I (MHC-I) and class II (MHC-II) proteins . CD53 waBeyond membrane protein interactions, tetraspanins also have the ability to interact directly with intracellular signaling molecules, forming membrane hotspots for intense signaling activity. The foremost example of this is the interaction between tetraspanins and protein kinase C (PKC), which is especially relevant to tetraspanin CD53. In jurkat T cells PKCα has been found to co-immunoprecipitate reciprocally with numerous tetraspanins . The autPKC is not the only kinase CD53 has been linked to. In B and T lymphoma cell lines the use of antibodies to ligate CD53 resulted in increased protein kinase B (Akt) phosphorylation which reduced the number of cells entering apoptosis upon serum deprivation . UnfortuLike Akt, c-Jun N-terminal kinase (JNK) has also been identified as an effector protein functioning downstream of CD53 . The antKinases are not the only signaling molecules associated with CD53, almost three decades ago Carmo et al. provided initial evidence of an interaction between CD53 and an unidentified tyrosine phosphatase in lymph node cells and in a thymoma cell line . This waAn overview of the most important interactions discussed in this section can be found in Fig. Despite the fact that we have known for over 25 years that CD53 plays an important and non-redundant role in the immune system, it is only recently that we have begun to unravel the actual function of this tetraspanin . Here, IThe literature examined in this review gives rise to a number of unresolved subjects and questions I will discuss here. For example, CD53 is expressed on all cells of the lymphoid-myeloid lineage, yet the majority of research has been focused exclusively on B and T cell lines Table . It woulFurthermore, the greater majority of research described here has relied on the use of antibody ligation to study CD53 function. Though this has no doubt been incredibly valuable in the past, we would be remiss not to take more advantage of the opportunities presented by novel techniques like CRISPR/Cas9 and the tetraspanin knockout mice established in the recent past to investigate CD53 function further. These tools allow for much more flexibility than the traditional antibody-based method and would permit us to begin designing studies to build on the fundamental knowledge we have gained in the last years.In line with this, a strong case has been made for the interaction between CD53 and PKCs, which presents a unique opportunity to target this ubiquitous signaling molecule through an accessible surface protein. For many years, PKC inhibitors have been a target for intense research as an anti-cancer therapy . These eBeyond the matters raised in the literature discussed here, there are also many fundamental open questions related to CD53 which have not been addressed. For example, the exact composition of CD53 TEMs is still largely undefined. Which other partner proteins and lipids are contained by these microdomains and how do these contribute to CD53 function? Specifically, in the case of signaling there are numerous downstream pathways that have been linked to CD53, but the partner molecules mediating these effects have remained undefined.Likewise, it would be interesting to further study what happens during activation of immune cells when CD53 is absent or impaired in its function. This is particularly interesting because cell activation is dependent on both the adhesive/migratory capacity of cells as well as their ability to signal, both of which have been linked to CD53 function. How CD53 would go about offsetting these functions in such a setting is therefore very interesting, particularly from the point of view of spatiotemporal dynamics. Finally, since CD53 has been proposed to interact with both kinases and phosphatases, it would be interesting to investigate how CD53 contributes to balancing signaling responses and whether this is dependent on factors such as timing, signal intensity, etc.In conclusion, it is time to embrace a novel view of the immune system which recognizes the importance of membrane organizing proteins like CD53 and the central roles they can play in immunological processes. As a consequence, questions concerning how these organizing proteins function become increasingly important to our understanding of pathologies and the development of novel therapeutic strategies.