Pulse measurements made using wearable devices can aid the monitoring of human physiological condition. Accurate estimation of waveforms is often difficult for nonexperts; motion artifacts may occur during tonometry measurements when the skin–sensor contact pressure is insufficient. An alternative approach is to extract only high-quality pulses for use in index calculations. The present study aimed to determine the effectiveness of using machine-learning analysis in discriminating between high-quality and low-quality pulse waveforms induced by applying different contact pressures. Radial blood pressure waveform (BPW) signals were measured noninvasively in healthy young subjects using a strain-gauge transducer. One-minute-long trains of pulse data were measured when applying the appropriate contact pressure (67.80 ± 1.55 mmHg) and a higher contact pressure (151.80 ± 3.19 mmHg). Eight machine-learning algorithms were employed to evaluate the following 40 harmonic pulse indices: amplitude proportions and their coefficients of variation and phase angles and their standard deviations. Significant differences were noted in BPW indices between applying appropriate and higher skin–surface contact pressures. The present appropriate contact pressure could not only provide a suitable holding force for the wearable device but also helped to maintain the physiological stability of the underlying tissues. Machine-learning analysis provides an effective method for distinguishing between the high-quality and low-quality pulses with excellent discrimination performance (leave-one-subject-out test: random-forest AUC = 0.96). This approach will aid the development of an automatic screening method for waveform quality and thereby improve the noninvasive acquisition reliability. Other possible interfering factors in practical applications can also be systematically studied using a similar procedure. Wearable systems monitoring health status provide significant benefits in healthcare applications ,2. The aSeveral diseases can induce changes in arterial stiffness and thereby affect the amplitude and speed of arterial pulse-wave transmission and thus change the arterial pulse waveform. Therefore, several indices have been used to capture changes in the pulse waveform related to disease-induced physiological changes . For exaThe pulse waveform can also be described by frequency-domain indices, with specific patterns having been noted in pulse indices in patients with cardiovascular diseases, such as cerebrovascular disease  and stroBoth mechanical and optical devices are currently used to detect arterial pulses on the skin surface . Pulse oWearable devices can be used for the low-cost, noninvasive, and continuous monitoring of the pulse waveform. However, in practical applications, it is often difficult to accurately estimate waveform indices, especially for nonexperts. Such artifacts may give rise to erroneous interpretations and false alarms of the pulse indices in clinical physiological measurements . For exaFacing these interferences that may especially occur outside a laboratory environment, an alternative approach is to assess the signal quality of each pulse waveform and then extract only high-quality pulses for use in further index calculations. The development of algorithms capable of removing—or at least identifying—artifacts in the recorded pulse data is therefore of critical importance . MachineWhen wearable sensors are attached to the human body to measure pulse signals, relative displacement between the sensor and the skin surface can occur. Taking PPG measurements as an example, the relative displacement may change the location and contacting condition of the optical sensor and hence change the illumination or receiving location. For tonometry measurements, motion artifacts may occur when the contact pressure between the sensor and the skin is insufficient. Numerous factors can distort the pulse waveform, and their interfering effects can differ. It is therefore necessary to consider the interfering effect corresponding to each factor separately in order to obtain a clearer understanding and also to develop the corresponding distortion-detection algorithm for the effects on the pulse waveform. Since the heartbeat-driven pulse signal is quasiperiodic, beat-to-beat artifact detection should be provided in the algorithm. Beat-to-beat analysis can not only help to increase the amount of acquired data  but also provide more-detailed information about the waveform quality of the acquired pulse data sequence.The aims of the present study were (1) to determine and compare the effects on the tonometry pulse waveform  of applying appropriate and higher contact pressures and (2) to determine the effectiveness of using machine-learning analysis in automatically discriminating between high-quality and low-quality pulse waveforms induced by applying different contact pressures. Since changes in the pulse waveforms are subtle and often difficult to detect, two strategies were adopted in order to enhance the discriminating ability: (1) frequency-domain analysis to minimize the possible distortion effects on time-domain pulse waveform caused by small perturbations induced by motion artifacts and other factors and (2) machine-learning techniques using frequency-domain waveform indices as features to detect the induced differences in the spectral patterns. The obtained knowledge may be useful in the development of a wearable device for automatically detecting the abnormal pulse waveforms induced by different contact pressures.Details of the present experimental setup and the signal processing methods are available elsewhere ,12,15 an(1)Perform measurements on healthy young subjects.(2)Calculate the harmonic indices and compare the pulse waveforms between applying appropriate and higher contact pressures on the same subject.(3)Extract the high-quality and low-quality pulses in measurements made using appropriate and higher contact pressures, respectively, according to the judgment of two experts.(4)Calculate the harmonic indices and compare the results between high-quality and low-quality pulse waveforms.(5)Use the harmonic indices as features to classify high-quality and low-quality pulse waveforms using machine-learning analysis.The study procedures were designed as follows: The BPW signal was noninvasively measured in the subjects  and then chose the length of the elastic belt as L to provide appropriate contact pressure between the sensor and the skin surface. We then shortened the elastic belt in steps of 0.5 mm to gradually increase the contact pressure in order to determine the belt length required for the higher contact pressure. The body structure, such as the bone size and weight, can be different between genders. In the present study, the belt lengths that resulted in a higher contact pressure were chosen as L minus 2.0 mm and L minus 1.5 mm for male and female subjects, respectively. The appropriate and higher contact pressures were measured to be 67.80 ± 1.55 mmHg (mean ± standard deviation) and 151.80 ± 3.19 mmHg, respectively.Before the measurements, the subjects sat rested in a chair for 10 min. We first measured the circumference of the wrist  were selected from the measurements when applying the appropriate contact pressure, whereas the low-quality pulses (Data B) were selected from measurements when applying the higher contact pressure. High-quality pulses constituted 98.0% of the measurements made when applying the appropriate contact pressure, whereas low-quality pulses constituted 78.2% of the measurements made when applying the higher contact pressure. The performance of the machine-learning algorithms in classifying the high-quality and low-quality pulses could then be estimated.n = 1–10: amplitude proportion (nC), coefficient of variation of nC (nCV), phase angle (nP), and standard deviation of nP (nP_SD). These 40 indices for each pulse (n = 1–10) were then used as the features of the ML analysis: nC, nCV, nP, and nP_SD.There are two parts in the present analysis procedure: signal processing and information processing. In signal processing, frequency-domain analysis was performed to derive the following 40 BPW harmonic indices for In information processing, ML analysis was used to discriminate between groups  or marginally (0.05 < p < 0.1) larger when applying higher-than-appropriate contact pressure. The characteristics of the study subjects are listed in nC, nCV, and nP_SD indices were significantly larger for Data B than for Data A. Regarding phase-angle indices, there were significant differences in P2, P5, and P7–P10.After selecting pulses according to the judgment of the experts, The results of the eight machine-learning analysis methods are listed in The results of the leave-one-subject-out analysis listed in For the BPW indices, we performed hold-out analysis to further evaluate the model accuracy . For RF as listed in The present study found significant differences in several BPW indices between applying appropriate and higher skin-surface contact pressures. By choosing high-quality and low-quality pulses from these two groups, the machine-learning analysis using frequency-domain pulse indices as features can aid the identification of the quality of pulse waveforms.Several previous studies have investigated the use of waveform analysis to discriminate between contaminated and noncontaminated pulses, and most of them focused on PPG signals ,19,20. FCompared with PPG signals, the BPW signals acquired around the radial artery travel a shorter distance to the main artery. This characteristic may facilitate the monitoring of the pulse propagation condition on the main artery and also help to reduce the interfering effects of the changes in the peripheral microcirculation at the extremity (which are often encountered in PPG measurements). However, for wearable-device designs, it can be more difficult to achieve stable pulse waveforms for BPW measurements, since the anatomic structure of the lower arm is more complex than that of the finger. Tonometric transducers exhibit excellent sensitivity in acquiring BPW signals, but they often need careful handling and exact positioning over the artery . TherefoCompared with the results in the above-described previous PPG studies, the present findings indicated similar performance in discriminating between high-quality and low-quality pulses. The threefold cross-validation results of the eight machine-learning methods indicated that MLP and RF had the highest AUCs, at 0.83 and 0.85, respectively. Meanwhile, in the leave-one-subject-out tests, RF and KNN had the highest AUCs, at 0.96 and 0.93, respectively. These values indicate the excellent performance of RF analysis in discriminating between high-quality and low-quality pulses. In practical wearable-device applications, recording for 0.5–2.0 min can be necessary to acquire sufficient numbers of pulses, and rapid judgments can be made using machine-learning analysis . This means that the user will not perceive a marked difference in the time required when using a wearable device.When contact pressure is exerted on the skin surface, it can be transmitted through the tissue onto the blood vessel walls of the peripheral vascular beds. Applying a contact pressure to the skin surface may therefore induce several types of physiological effects in the peripheral blood circulation ,25,26. IDifferent degrees of vessel compression may induce different degrees of deformation and thus different vascular regulatory responses. A vessel-pressing model (VPM) has previously been proposed for describing and explaining the relationship between the different effects induced by applying different contact pressures around the normal BP ,28,29. C2 detection was found to lie between 5 kPa and 15 kPa [Furthermore, for skin-surface pulse measurements, it was found that applying the sensor with a gentle contact pressure may improve the detection capacities for both PPG ,16 and wd 15 kPa . TensionThe present findings indicated that the contact pressure can markedly affect the reliability of pulse-wave measurements and that the present method of machine-learning analysis can aid the ability to discriminate between high-quality and low-quality pulses. The present studied condition of using different applied contact pressures is a commonly encountered factor in practical applications, which means that the present findings have meaning in the quality control of data acquired using wearable devices for measuring the pulse waveform. Furthermore, several previous studies used the pulse amplitude as the indicator of the waveform quality ,16,30. TThe present adopted mean value for the appropriate contact pressure (67.80 mmHg) was around the normal diastolic BP. According to the previous relevant studies discussed above, the used contact-pressure range was also around the normal BP; for example, 10 kPa corresponds to around 75 mmHg . This prWhen acquiring the heartbeat or pulse signals using wearable devices, the heartbeat interval of around 1 s makes it necessary to maintain a stable measurement condition for the duration required to accumulate sufficient pulses to fulfill the fundamental mathematical requirements of the analysis. For example, if 30 pulses are necessary for the analysis and the heart rate is 72 beats/s, the wearable device will need to record for about 30/72 × 60 = 25 s. Moreover, the wearable device needs to apply sufficient contact pressure to minimize the interfering effects of motion artifacts. Furthermore, to avoid the interference induced by different physiological conditions, it is also necessary to keep the physiological condition of the local blood supply stable while the wearable device is being applied. The present findings may be useful in practical applications of wearable devices. Ensuring that the skin-surface contact pressure remains within the appropriate range may satisfy both of the above-mentioned requirements: (1) providing a sufficient holding force for the wearable device and (2) helping to maintain the physiological stability for contacting the skin surface and its underlying tissues. We have further applied frequency-domain analysis and machine-learning analysis to develop an effective method for classifying the high-quality and low-quality pulses acquired by a wearable device, which yielded excellent discrimination performance (RF AUC = 0.96). This may aid the development of a method for the automatic screening of pulse-waveform quality and therefore help to improve the reliability of pulse-waveform measurements when using various types of wearable devices. Furthermore, similar to the present findings regarding the effects of applying different contact pressures, the effects of other possible interfering factors in practical wearable applications could also be systematically studied using the procedures similar to those employed in the present study. To enhance the discriminating effectiveness, adding more eigenvalues can be an important issue in further studies.


Background: Social cognitive impairments, specifically in facial emotion processing and mental state attribution, are common in post-traumatic stress disorder. However few studies so far have examined whether social cognitive ability impacts on PTSD recovery.Objective: To examine whether baseline social cognitive abilities are associated with treatment outcomes following trauma-focused therapy for PTSD.Method: This is a cohort study that will relate treatment outcomes post-discharge to baseline measures of social cognition (five tasks: Emotion Odd-One-Out Task (Oddity), Reading the Mind in the Eyes Task (RMET), Social Shapes Test (SST), Spontaneous Theory of Mind Protocol (STOMP), and Reflective Functioning Questionnaire (RFQ-8)) in people starting a course of psychological therapy for PTSD (target N = 60). The primary outcome will be pre- to post-treatment change in PTSD symptom severity (assessed using the PTSD Checklist for DSM-5). Secondary outcomes include functional impairment , drop-out rate, and analyses differentiating participants with DSM-5 PTSD and ICD-11 PTSD and CPTSD. Regression models will be used to examine associations between baseline social cognitive performance and outcome measures while adjusting for potential confounders. Two pilot studies informed the development of our study protocol. The first involved qualitative analysis of interviews with nine participants with lived experience of mental health problems to inform our research questions and study protocol. The second involved trialling social cognitive tasks on 20 non-clinical participants to refine our test battery.Discussion: This study will address a gap in the literature about whether abilities in social cognition in people living with PTSD are associated with treatment-related recovery.Impairments in social cognition are recognised in people with PTSD.Few studies have examined whether social cognitive ability is associated with recovery from PTSD.We present a study protocol, developed after pilot testing, to address this question. Cohort Cognitive models aim to explain the development and maintenance of PTSD as the inability to recover from psychological trauma due to maladaptive processing of the traumatic memory. Fragmented and disorganised trauma memories are hypothesised to occur because of cognitive avoidance and emotion suppression. These core mechanisms are maintained by unhelpful negative self-attributions about the trauma, the self, and its sequelae (e.g. ‘The trauma happened because I am weak.’) ) from established PPI (Patient and Public Involvement) groups based at the universities of Cardiff, Exeter and Bristol. Convenience sampling was used by a researcher contacting the groups and inviting members into this study. Between March and August of 2019, we conducted two focus groups (one with three participants and one with four) and separately interviewed two individuals (one with CPTSD and one with EUPD). Both the focus groups and interviews were semi-structured using the same topic list, which explored participants’ understanding of social cognition, trauma, and PTSD, as well as asking for feedback on our initial protocol design. Interviews were transcribed and thematic analysis was used  Social cognition is variable within an individual across time and context. (2) Impaired social cognitive ability could increase the risk of PTSD. (3) Trauma exposure and PTSD can affect social cognition. More detailed information is supplied in the 2.2.We recruited 20 non-clinical participants from the University of Bristol and piloted a battery of pre-existing social cognitive tasks during August 2019. The aim of this study was to ensure that the tasks and duration of testing were acceptable, while selecting several types of measures that were likely to produce individual differences, rather than testing a specific a priori model of social cognition. The measures used were: The Emotion Odd-One-Out Task (Oddity), The Reading the Mind in the Eyes Task (RMET), The Social Shapes Test (SST), Spontaneous Theory of Mind Task (STOMP), and the Reflective Functioning Questionnaire (RFQ-8). They are described in greater detail below. We also included a verbal IQ measure (Spot the Word Test (STW)), a measure of attachment style (The Adult Attachment Questionnaire (AAQ)) and a feedback questionnaire. Participants received a £10 gift voucher.The battery of tasks took participants between 40 minutes to 1 hour to complete. Feedback was positive, and participants found the tasks interesting and varied (mean scores 9/10); see 3.3.1.Ethical approval was granted by the Health Research Authority and the Oxford B Regional Ethics Committee in June 2019.3.2.This is a cohort study of patients with DSM-5 PTSD and ICD-11 PTSD or Complex PTSD who will be assessed before and after a course of psychological therapy.3.3.Participants will be recruited from two specialist PTSD treatment services in the South-West of England and South Wales. Patients with a clinical diagnosis of either DSM-5 PTSD or ICD-11 PTSD or CPTSD who are at the top of the waiting list for a trauma-focused therapy (either EMDR or TF-CBT) will be contacted by letter or telephone and invited into the study. Participants will undergo baseline data collection as close to commencement of therapy as possible, and no later than four weeks after their initial appointment. All patients complete the PCL-5 and ITQ at the time of referral and at their initial therapy appointment. Exclusion criteria are age <18 years, lack of English fluency, and a primary diagnosis other than PTSD.3.4.d = 0.4 for a standardised score change in PCL-5 per SD change in social cognition score. We, therefore, aim to recruit 60 individuals to allow for 20% dropout  judgement task. This is a perceptual odd-one-out discrimination task where participants must identify which of three faces is displaying a different ‘basic’ emotion Ekman,  from the3.6.1.2.Originally designed as a test of mentalising, the RMET is now considered an explicit emotion labelling task , as well as measures of verbal IQ, attachment style, personality dysfunction and trauma history as potential confounders to adjust for in our analyses.3.7.1.Verbal ability and education are correlated with social cognitive performance . Secondary outcomes are change in functional ability pre- to post-therapy (assessed using the WSAS), and treatment drop-out, defined as therapy ending without therapist agreement.3.8.1.This symptom checklist enquires about the four clusters of PTSD symptoms according to the DSM-5  that have occurred over the previous month  online. Participants receive a £20 gift voucher for participation.3.12.Participants are assigned random study identification numbers, which are used on the study platform Qualtrics . Personal information from consent forms is securely stored separately according to the University of Bristol's secure data policy.3.13.Data analysis will be completed using STATA statistical software STATA, . SST, RMThe primary outcome measure will be treatment response measured as the change in PCL-5 score pre- to post-therapy. As secondary outcomes, we will also examine change in DSM-5 PTSD symptom-cluster scores, change in the proportion meeting criteria for DSM-5 PTSD, ICD-11 PTSD, and ICD-11 CPTSD post-therapy, drop-out from therapy and change in functional ability measured as change in WSAS scores pre- and post-therapy. The impact of confounding in our analyses will be examined by comparing unadjusted models to models adjusted for age, gender, personality dysfunction, other psychiatric conditions, attachment style, verbal IQ, severity of trauma history and type of PTSD (ICD-11 PTSD versus CPTSD). As an exploratory analysis, we will also examine results separately in those with ICD-11 PTSD and CPTSD, although statistical power comparing these subgroups will be limited, and evidence suggests outcomes might be similar in these groups  and for an ICD-11 diagnosis of PTSD or CPTSD (using the ITQ) we hope to be able to assess the impact of baseline social cognition on treatment outcome in these three different diagnostic groups.We have developed a testing battery to measure social cognition that includes previously validated tasks that map to several aspects of social cognition, including basic and complex face emotion processing and mental state attribution . We use both forced-choice and open-ended response tasks, as well as a self-report measure. The measures were selected to broadly cover the domain of social cognition with varying levels of naturalism and be sensitive to individual differences, rather than testing a specific a priori model of social cognition (Schaafsma et al., We acknowledge there will be challenges with our data collection and study design. Defining and measuring social cognition is difficult, given the lack of a formal ontology of social cognition (Happé et al., One issue we foresee is the potential high degree of correlation among the different social cognition measures (Schaafsma et al., As far as we are aware, this is the first study to examine the association between a comprehensive battery of social cognitive measures and response to trauma-focused therapy in an adult sample. The results of this study may help us to better understand why patients vary in their response to evidence-based therapies for PTSD.Ethical approval was granted by the Health Research Authority and the Oxford B Regional Ethics Committee in June 2019 for Pilot Study 2 and for the main study, IRAS number 263222. Pilot Study 1 was discussed with the Health Research Authority and University of Bristol Ethics Committee and deemed to be Patient and Public Involvement in Healthcare, and therefore did not require ethical approval. Informed consent and consent to publish research findings, including quotations, was obtained and copies of relevant correspondence are available on request.


Both pulmonary and mental health are affected following hospitalization for COVID-19 pneumonia. Pulmonary rehabilitation therapy has demonstrated benefits in improving mental health, but no validated combined programs that include mental health have been proposed.This article presents the design of a trial that aimed to assess whether the participation in a combined rehabilitation program that includes home-based respiratory physiotherapy and telephone-based psychological support is associated with a greater improvement of pulmonary and mental health outcomes 7-12 weeks after COVID-19 hospitalization discharge compared with posthospital usual care provided by a public Peruvian hospital.WAYRA (the word for air in the Quechua language) was an open-label, unblinded, two-arm randomized controlled trial. We recruited 108 participants aged 18-75 years who were discharged from the hospital after COVID-19 pneumonia that required >6 liters/minute of supplemental oxygen during treatment. Participants were randomly assigned at a 1:1 ratio to receive the combined rehabilitation program or usual posthospital care provided by a public Peruvian hospital. The intervention consisted of 12 at-home respiratory rehabilitation sessions and 6 telephone-based psychological sessions. The primary outcome was the 6-minute walk distance. Secondary outcomes included lung function, mental health status , and quality of life. Outcomes were assessed at baseline (before randomization) and at 7 and 12 weeks after hospital discharge to assess the difference between arms.This study was funded by the Peruvian National Council of Science Technology and Technology Innovation in July 2020. Ethics approval was obtained on September 2, 2020. Recruitment and data collection occurred between October 2020 and June 2021. Results are expected to be published by the end of 2022.WAYRA was the first randomized controlled trial evaluating combined pulmonary-mental health rehabilitation for hospitalized COVID-19 survivors in resource-limited settings, potentially providing a foundation for the cost-effective scale-up of similar multidisciplinary rehabilitation programs.ClinicalTrials.gov NCT04649736; https://clinicaltrials.gov/ct2/show/NCT04649736RR1-10.2196/36001 The COVID-19 pandemic is resulting in millions of survivors with long-term complications ,3. The fAlthough recommendations for post-COVID-19 rehabilitation already exist -9, the eConsidering that COVID-19 is a disease that results in short-, medium-, and long-term physical and mental impairment, and that limited rehabilitation strategies have been evaluated, this protocol describes the design and methodology of a randomized controlled trial that aimed to evaluate the effects of integrated home-based respiratory physiotherapy combined with a telephone-based psychological intervention on pulmonary and mental health–related outcomes at 7 and 12 weeks after hospital discharge in patients with COVID-19 pneumonia. The primary objective of the trial was to compare 6-minute walking distances at 7 and 12 weeks after hospital discharge among participants in a combined program versus those who received usual posthospital care provided by a Peruvian public hospital.WAYRA (the word for air in the Quechua language) was a randomized, controlled, open-label, two-arm clinical trial that evaluated the efficacy of a 6-week home-based respiratory physiotherapy and telephone-based psychological program compared to usual posthospital care provided by a public Peruvian hospital, aimed at improving exercise tolerance, lung function, mental health, and quality of life (QoL) outcomes. A baseline assessment was conducted at hospital discharge, with two follow-ups at week 7 and 12 . Due to This study took place in the COVID-19 ward at Hospital Nacional Cayetano Heredia (HNCH), a public tertiary-care hospital and a major referral center that serves approximately 3 million people from underserved neighborhoods in the northern metropolitan area of Lima, the capital of Peru. HNCH has been one of the main COVID-19 national referral centers throughout the pandemic, with 241 hospitalization beds and 23 intensive care unit beds . During The study population consisted of confirmed cases of COVID-19, diagnosed by serological or molecular tests, including members of both sexes, between 18 and 75 years of age, discharged from hospitalization, who received high-flow oxygen (>6 liters/minute) treatment at any point during hospitalization. Participants with prior respiratory pathology or psychiatric diagnoses were excluded. The complete eligibility criteria are provided in Inclusion criteriaAge between 18 and 75 yearsDischarged from hospitalization with a confirmed COVID-19 diagnosisCapable of understanding study proceduresCapable of providing informed consentRequired oxygen flow >6 liters/minute or through a high-flow device at any time during hospitalizationEvaluated by the rehabilitation service at least once during hospitalizationExclusion criteriaContraindications to the 6-minute walk testContraindications to spirometryComplications during the baseline 6-minute walk testNeurological pathology, neuropathy, limb dysfunction, or other underlying physical disability that makes physical exercise impossiblePregnant or breastfeedingNo access to the internet or a telephone lineHistory or existing lung diseases such as asthma, chronic obstructive pulmonary disease, fibrosis, or tuberculosisModerate or severe heart disease Diagnosed with another severe disease in the last 6 monthsSevere depression or suicidal ideationTaking any medication for depression and/or anxiety, or another medication prescribed by a psychiatrist before the onset of COVID-19Cognitive impairment or sensory disturbanceA trained nurse made the initial contact and prescreening, starting with a daily review of the list of hospitalized patients during the enrollment period, to identify potentially eligible participants among newly discharged patients or those close to discharge in the following days. Potential participants were informed about the study and its procedures and were invited to participate. Those who accepted received a detailed explanation of the nature of the study, randomization, study procedures, their potential risks and benefits, their rights as participants, and the study timelines. Potential subjects were explicitly told that participation was not mandatory, that there was no penalty for refusing to participate, and that their clinical treatment at the hospital would not be compromised in any way if they refuse to participate or opt out of the study at any time. All interested patients received the screening informed consent form  for theiAfter obtaining participant consent and signature, a trained nurse performed the screening assessments, including review of eligibility criteria compliance and baseline measurements. These included cognitive status , sociodralloc command (StataCorp). Finally, all participants were scheduled for follow-up visits as described in We used a blinded list preuploaded in REDcap , which wThe intervention consisted of 12 sessions of home-based respiratory rehabilitation and 6 sessions of weekly psychological support delivered through telephone home calls throughout a period of 6 weeks. Participants began the rehabilitation program no later than 1 week after hospital discharge.Respiratory rehabilitation consisted of 12 sessions (1-3 per week depending on the participant’s availability) performed at home under trained physiotherapist guidance. Each respiratory rehabilitation session lasted approximately 1 hour. In the first rehabilitation session, the physiotherapist brought the necessary equipment to perform the exercises . In each session, the participant performed the following exercises: respiratory muscle training by breathing through pursed lips and breathing education exercises, coughing exercises to produce effective coughs, diaphragm training by teaching diaphragmatic contractions in the supine position with a light weight placed on the anterior abdominal wall, and stretching exercises whereby the participant was placed in a supine or lateral position with the knees bent to correct for lumbar lordosis. In addition, participants performed arm movements in flexion, horizontal extension, abduction, and external rotation. Participants were instructed to perform pursed-lip breathing and effective coughing on the days they did not receive assisted therapy, with a frequency of 30 times per day. The day of nonattendance at respiratory therapy was recorded. Given the risk of exposure to COVID-19, physiotherapists followed a strict biosafety protocol involving the use of personal protective equipment and a thorough disinfection process at each visit. In addition, rehabilitation was ideally performed in a well-ventilated room and the participant wore a surgical mask during visits as an extra protective measure.The psychological support intervention consisted of an introductory session (Session 0) and 6 1-hour structured sessions based on emotion-centered problem-solving therapy (EC-PST). A trained licensed psychologist provided these sessions once a week by telephone home calls following the guidelines of the Inter-Agency Standing Committee (IASC) on Mental Health and Psychosocial Support in Humanitarian and Disaster Emergencies of the World Health Organization . The EC-Participants assigned to the control arm received posthospital usual care delivered by a Peruvian public hospital consisting of discharge recommendations and a telephone follow-up for 14 days provided by the same hospital. Although there are no national guidelines for standardized discharge indications in Peru, many hospitals recommended patients to perform home breathing exercises and symptomatic respiratory treatment. There are currently no diagnostic or follow-up plans for patients that experience or develop mental illness or a strategy for respiratory or psychological rehabilitation in most hospitals in Peru. We collected data about any additional nonstudy therapies that participants may have received from any internal or external health care provider.The primary health outcome was the change in 6-minute walk distance (6MWD) at 7 and 12 weeks after discharge compared to baseline. The 6MWT is a submaximal effort test of cardiorespiratory functional capacity with correlation to QoL and independence in activities of daily living . The 6MWA trained physician measured forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), and the FEV1/FVC ratio using a portable spirometer (Easy-On-PC). These spirometers are frequently used in pulmonary research because their calibration remains stable over time . We aimeFor mental health and QoL assessments, participants responded to questionnaires administered by a trained health worker. We used PHQ-9, GAD-7, and IES-R questionnaires to assess mental health, including depression , anxietyWe will perform an intention-to-treat analysis of primary and secondary outcomes at 7 and 12 weeks after hospital discharge. The final analysis will be performed when all 108 participants completed the trial. We will compare the 6MWD (meters), FVC (liters), FEV1 (liters), and questionnaire scores at 7 and 12 weeks after hospitalization discharge between the intervention and control arms. In the secondary analyses, we will compare the proportion of participants with respect to restrictive spirometry pattern, depression, anxiety, and posttraumatic stress disorder at 7 and 12 weeks after hospital discharge between the intervention and control arms. In addition, following the recommendation by Cocks and Torgerson , we willWe used STATA Version 16 (StataCorp) to calculate the ideal sample size. We attempted to enroll 108 participants in total, allocating 54 to each treatment arm. We estimate that a sample size of 86 participants would be sufficient to detect a mean difference between arms of 55.1 meters in the 6MWT according to the study by Liu et al  considerEthical approval of the study protocol and informed consent form was obtained from the Institutional Review Board at Universidad Peruana Cayetano Heredia (#202852) and HNCH (#085-2020). Likewise, any changes in the protocol were reported to the mentioned ethics committees. Written informed consent was requested from all participants prior to any data collection. To protect participants’ confidentiality, all collected data of clinical assessments were linked to a unique ID code. Data were registered in paper form and electronically. The electronic information was stored, backed-up, and secured by password protection in the RedCap server. Paper forms were archived in secure locked cabinets in the HNCH. All confidential information, including participants’ contact details or other sensitive data, was only accessible to authorized staff from the project. Long-term data management complied with Universidad Peruana Cayetano Heredia research policy. The Peruvian National Health Institute reviewed the protocol and determined that it was exempt from review by the Ethics Committee due to the nonpharmacological nature of the intervention.Both control- and intervention-arm participants benefited from the study. These benefits included education and counseling on respiratory disease risk factors, reporting of pulmonary function test results by a specialist physician, and referral to trained personnel if necessary.Potential risks of respiratory rehabilitation were oxygen desaturation, palpitations, sweating, arrhythmias, chest tightness, shortness of breath, and muscle aches. Potential risks of psychological support were symptom substitution, dependence on the physiotherapist, stigmatization, problems in social relationships or even separation, as well as alcohol or drug abuse, deliberate self-harm, and suicidal ideation or attempts. Adverse events of the interventions were collected after participants provided informed consent and were duly enrolled in the study. If adverse events were detected, the field staff immediately notified the study coordinator and the management protocol was activated.The trial, which obtained funding in July 2020, was approved by the Institutional Review Board on September 2, 2020. Data collection began in October 2020. Enrollment of participants began in October 2020 and was completed in June 2021. Results of the study are expected to be published by the end of 2022.WAYRA is the first randomized controlled trial evaluating integrated pulmonary and mental health rehabilitation for hospitalized COVID-19 survivors in a low- and middle-income country setting. Through this trial, we gained experience in implementing a remote rehabilitation program in a resource-limited country and will generate evidence on pulmonary, mental health, and QoL alterations following COVID-19 the first 3 months after hospitalization. We hypothesize that participation in the combined program will improve pulmonary, physical, and mental health outcomes. We hope that our study will provide a reliable basis for further clinical trials focused on comprehensive rehabilitation in patients hospitalized for severe COVID-19 and other lung diseases.The burden of long-term complications of COVID-19 will increase because, although several countries have achieved broad vaccination coverage, newly emerging variants may be resistant to vaccines . The mosThe main strength of this study is that the calculated sample size has the power to detect a clinically significant difference between the groups. Nevertheless, this study has some limitations to be considered. First, mental health and QoL outcomes will be assessed with screening tools. Second, this trial has an open-label design due the nature of the intervention.In conclusion, the COVID-19 pandemic will result in millions of people prone to develop respiratory and mental health sequelae worldwide, and health systems are not prepared to respond adequately to this situation. Little evidence exists on rehabilitation strategies, and most of the existing evidence does not come from low- and middle-income countries that face additional challenges compared with developed countries. The clinical trial described in this protocol will generate useful evidence to develop effective interventions to benefit patients after COVID-19, and will serve as a basis to help plan future studies evaluating pulmonary rehabilitation strategies and mental psychological support.


Short-term prediction of COVID-19 epidemics is crucial to decision making. We aimed to develop supervised machine-learning algorithms on multiple digital metrics including symptom search trends, population mobility, and vaccination coverage to predict local-level COVID-19 growth rates in the UK.Using dynamic supervised machine-learning algorithms based on log-linear regression, we explored optimal models for 1-week, 2-week, and 3-week ahead prediction of COVID-19 growth rate at lower tier local authority level over time. Model performance was assessed by calculating mean squared error (MSE) of prospective prediction, and naïve model and fixed-predictors model were used as reference models. We assessed real-time model performance for eight five-weeks-apart checkpoints between 1st March and 14th November 2021. We developed an online application (COVIDPredLTLA) that visualised the real-time predictions for the present week, and the next one and two weeks.Here we show that the median MSEs of the optimal models for 1-week, 2-week, and 3-week ahead prediction are 0.12 (IQR: 0.08–0.22), 0.29 (0.19–0.38), and 0.37 (0.25–0.47), respectively. Compared with naïve models, the optimal models maintain increased accuracy (reducing MSE by a range of 21–35%), including May–June 2021 when the delta variant spread across the UK. Compared with the fixed-predictors model, the advantage of dynamic models is observed after several iterations of update.With flexible data-driven predictors selection process, our dynamic modelling framework shows promises in predicting short-term changes in COVID-19 cases. The online application (COVIDPredLTLA) could assist decision-making for control measures and planning of healthcare capacity in future epidemic growths. Understanding how COVID-19 case numbers will change in the short term in local contexts is helpful for planning public health measures. Here, using population data on Google searches on COVID-19 symptoms, mobility , and vaccination coverage, we develop statistical methods that forecast changes in COVID-19 case numbers for the next one to three weeks in different local authorities in the UK. We further develop an online application that provides visualisations of these forecasts. We show that our programmes achieve better predictive accuracy compared with two existing models. This highlights the promise of our methods in forecasting future local COVID-19 outbreaks. Wang et al. predict local COVID-19 incidence one to three weeks ahead based on symptom search trends, population mobility, and vaccination coverage. Their dynamic model is more accurate than naïve and fixed-predictor models. Given the continuous spread of SARS-CoV-2, short-term predictions are important to assist effective decision making for control measures and planning of healthcare capacity4.The COVID-19 pandemic has caused a substantial health and economic burden in the UK and globally. In the UK, SARS-CoV-2 has caused about 22 million cases, 960 thousand hospital admissions, and 201 thousand deaths as of 12th August 20225, Apple COVID-19 Mobility Trends6, and Facebook Data for Good7, and have been increasingly used to understand changes in public physical contacts during the COVID-19 pandemic9. Between the sources, data are collected and provided differently. Our earlier work found that mobility at different types of locations are associated with varying degrees of changes in transmission of SARS-CoV-2, and visits to retail and recreation areas, workplaces, and transit stations are key drivers of COVID-19 epidemic in the UK10. Moreover, online search queries on Google have been used to detect and predict influenza and other infectious diseases before the COVID-19 pandemic13. The search trends of infection-related symptoms reflect infected people searching their symptoms in real time, thus can also be used as an early warning of the confirmed cases that are subject to the testing capacity and delays15.Population aggregated digital big data based on personal digital devices and applications have been widely used to predict COVID-19 epidemics as they capture well the changes in population behaviours without disclosing personal information, in a near-real-time manner. Population-level mobility and internet searches are two of the most important digital data metrics that are commonly used in modelling infectious disease outbreaks. Population-level mobility data are publicly accessible from several sources, including Google COVID-19 Community Mobility Reports16. Kogan and colleagues16 developed an early warning approach to monitor COVID-19 activity with multiple digital databases among three states of the US, which showed that the predictions of COVID-19 cases and deaths were improved by integrating multiple digital traces. However, no dynamic models were applied in that study that allowed for data-driven adjustment over time , and the predictions were made for March–September 2020, before the emergence of variants of concern and the mass roll-out of the COVID-19 vaccination. The Zoe COVID study17 found that there was addition of new symptoms over time, e.g., after the vaccination or potentially associated with the emergence of new variants. Moreover, the COVID-19 vaccination coverage may be also relevant to the prediction given that the mass rollout of COVID-19 vaccines could have reduced COVID-19 transmission18. As the COVID-19 situation changes rapidly, it is essential for COVID-19 prediction modelling studies to develop flexible algorithms adaptive to the most up-to-date data.Integrating the two metrics above that capture different types of population behaviours may improve the prediction from the perspective of COVID-19 early warningIn this study, we predicted the short-term changes in the COVID-19 epidemics at finer geographical scale in the UK, through a dynamic supervised machine learning algorithm that could reflect the best real-time prediction informed by data on internet searches on COVID-19 symptoms, mobility, and vaccination coverage. The programmes achieved better predictive accuracy compared with two reference models, showing promises in forecasting future local COVID-19 outbreaks. Furthermore, we developed a publicly accessible web application to present the predictions.Our primary objective was to develop data-driven machine-learning models for 1-, 2- and 3-week ahead predictions of growth rates in the COVID-19 cases  at lower-tier local authority (LTLA) level in the UK. In the UK, COVID-19 cases are reported by publication date  and by the date of collection of specimen. Therefore, there were six prediction targets in our study, 1-, 2- and 3-week growth rates by publication date and those by the date of collection of specimen  for modelling, given that case reporting was relatively consistent and reliable at LTLA level after 1st June 2020. The modelling work initially began on 15th May 2021 and was continuously updated using the latest available data since then; when models were fit, only the versions of the data that were available in real time were used. In this study, we used 14th November 2021 as the time cut-off for reporting  although our model continues to update regularly.We analysed the Google Search Trends symptoms dataset21. We used the percentage change in the symptom searches for each week during the pandemic compared to the pre-pandemic period (the three-year average for the same week during 2017–2019). We considered 173 symptoms for which the search trends had a high-level completeness in the analyses. These search trends were provided by upper-tier local authorities, and were extrapolated to each LTLA. The Google mobility dataset records daily population mobility relative to a baseline level for six specific areas, namely workplaces, residential areas, parks, retail and recreational areas, grocery and pharmacy, and transit stations22. The weekly averages of each of the six mobility metrics for each LTLA were the model inputs. The mobility in LTLAs of Hackney and City of London were averaged, given that they were grouped into one LTLA in other datasets. Cornwall and Isles of Scilly were combined likewise. The COVID-19 vaccination coverage dataset records the cumulative percentage of population vaccinated with the first dose of vaccine and that for the second dose on each day. Before the start of the vaccination rollout (7th December 2020 for first dose and 28th December 2020 for second dose), the coverage was deemed to be zero. We used the weekly maximum cumulative percentage of people vaccinated for the first dose and second dose for each LTLA in our models. Missing values on symptom search trends, mobility, and vaccination coverage were imputed using linear interpolation for each LTLA23. Thirteen LTLAs were excluded as data were insufficient to allow for linear interpolation.The Google symptom search trends show the relative popularity of symptoms in searches within a geographical area over timeWe developed a dynamic supervised machine learning algorithm based on log-linear regression. The algorithm could allow the optimal prediction models to vary over time given the best available data to date, and therefore reflected the best real-time prediction given all available data.24 that included LTLA (as dummy variables), the six Google mobility metrics, vaccination coverage for the first and second doses, and eight base symptoms from the Google symptom search trends, including cough, fever, fatigue, diarrhoea, vomiting, shortness of breath, confusion, and chest pain, which were most relevant to COVID-19 symptoms based on existing evidence25. Dysgeusia and anosmia as the two other main symptoms of COVID-1926 were not included as base symptoms because Google symptom search data on the two symptoms were only sufficient to allow for modelling in about 56% of the LTLAs . We then selected and assessed the optimal lag combination28 between each predictor and growth rate. Next, starting from the eight base symptoms, we applied a forward data-driven method for including additional symptoms in the model. This would allow the inclusion of other symptoms that could improve model predictability. Lastly, we assessed the different predictor combinations  of the predictions over the previous four weeks, i.e., 4-week MSE, with the MSE for each week being evaluated separately by fitting the same candidate model  carried forward the last available observation for each of the outcomes as the prediction. Similar to the full models , we considered a time lag between zero and three weeks, and used the 4-week MSE for naïve models , referred to as prospective MSE  as the first reference model, to understand how much the models driven by covariates could outperform models that assume status quo. As the second reference model, to further demonstrate the advantages of our dynamic model selection approach over the conventional model with a fixed list of predictors, we used the optimal model for the first checkpoint  and fixed its covariates (referred to as fixed-predictors model); then we compared its prospective MSEs for the next seven checkpoints , allowing the model coefficients to vary.17 to assess how the predictive accuracy was influenced.As sensitivity analysis, the base symptoms were expanded to further include dysgeusia and anosmia, as well as headache, nasal congestion, and sore throat that have been recently reported as common symptoms of COVID-19https://leoly2017.github.io/COVIDPredLTLA/), officially launched on 1st December 2021, uses real-time data from the above sources and currently updates twice per week. The application presents the predicted percentage changes (and uncertainties where applicable) in the COVID-19 cases in the present week (nowcasts) and the one and two weeks ahead (forecasts) compared with the previous week, using the optimal models , by two forms (publication date and the collection date of specimen) for each LTLA.We developed a web application COVIDPredLTLA using R ShinyApp, presenting our best prediction results at local level of the UK given all available data to date. COVIDPredLTLA . We followed the STROBE guidelines for the reporting of observational studies as well as the EPIFORGE guidelines for the reporting of epidemic forecasting and prediction research. All the data included in the analyses were population-aggregated data available in the public domain and therefore, ethical approval was not required.Further information on research design is available in the We included 367 LTLAs with complete data. The time series of COVID-19 growth rates, mobility, the eight base symptoms and COVID-19 vaccination coverage are in Fig. For the full models, application of time lags and inclusion of additional Google search symptoms (besides the eight base symptoms) improved the predictive accuracy considerably based on the retrospective 4-week MSE , by the timeframe of prediction and between LTLAs. The median MSEs for 1-week, 2-week, and 3-week ahead prediction were 0.12 (IQR: 0.08–0.22), 0.29 (0.19–0.38), and 0.37 0.25–0.47), respectively, indicating that the prediction accuracy declined with longer prediction timeframe , the optimal model outperformed the fixed-predictors model consistently across the three prediction timeframes (the reduction in MSE ranged 28–58% across the three prediction timeframes)  over time. Compared with the naïve model, the predictions, optimised on the digital metrics, were more accurate on most occasions and managed to reduce MSE by 21–35%. During the times when the COVID-19 cases increased rapidly (around week 2/3), the models with predictors maintained increased predictive accuracy compared with the naïve models. In addition, we present a publicly accessible web application COVIDPredLTLA developed to update the predictions of COVID-19 growth rates by LTLA using near real-time data.4. Fitting, prediction and assessment are addressed at LTLA resolution; LTLA-specific predictions could be used to inform control strategies at fine geographical level. We highlight the consistently better performance of the optimal models compared with naïve models, including the time period (around week 2/3) when there were substantial rise in the COVID-19 growth rates, likely due to the rapid circulation of the delta variant in the UK31. The model performance suggests that our model shows promise in predicting rapid changes in the COVID-19 cases. The model could be helpful when there are epidemic growths in future that are associated with the emergence and circulation of new variants and lineages, e.g., the omicron variant with high potential for evading or eroding existing infection- and vaccine-derived immunity, which became the predominant circulating variant at the time of writing32. As we applied a flexible algorithm for selection of symptoms, our model has the potential for adapting itself to the changes in the spectrum of symptoms over time17, e.g., due to the circulation of new variants and the mass rollout of the vaccination programme. Compared with the fixed-predictors model, our dynamic model is more flexible in nature and showed advantages in predictive accuracy after a few months of updating from the original model.We focused on predicting the growth rate of COVID-19 cases in our models. We consider that the COVID-19 growth rate could better inform the implementation and relaxation of the control strategies compared with predicted COVID-19 cases33. This highlights the challenge in forecasting respiratory infectious disease epidemics such as COVID-19 that are highly driven by most recent population behaviours.As expected, owing to the absence of input digital metrics in the most recent timeframe, the predictive accuracy dropped for longer prediction timeframe for all models in our analysis, which is similar to a recent COVID-19 forecasting study in Germany and Poland that utilised different a modelling approach1. We choose the COVID-19 cases by publication date rather than the date of collection of specimen as the main outcome. When using date of collection of specimen as the input data for analysis, historical data may have the strength of accounting for testing delays, but the real-time data are usually incomplete with some cases awaiting to be added in the next few days. This type of reporting delays causes an artificial decrease in the real-time COVID-19 cases. Google mobility and symptom searches are further delayed for several days (4–5 days) relative to the COVID-19 cases. Secondly, the real trend of the cases could have been blurred by changes in the testing practice and this impact is difficult to quantify. In the UK, the testing programmes were scaled up in the first few weeks of the study period (June–July 2020)34. Since then the daily cases include results from a wider population in addition to those with clinical needs and healthcare workers. In England, the cases include positive polymerase chain reaction (PCR) test results, positive rapid lateral flow test results confirmed with PCR tests taken within 72 h, and positive loop-mediated isothermal amplification test results; the other nations only include positive PCR results34. Thirdly, certain age subgroups  may be underrepresented in the Google mobility data and symptom search trends due to their limited access to the internet. Additionally, we noticed a spike in Google searches for fever in the first quarter of the study period. The spike could be a signal of subsequent increase in cases. Since then the subsequent peaks in fever were notably lower; it could be because public were less concerned in fever or less had fever after vaccination17. We did not include dysgeusia and anosmia in the main model due to incompleteness of Google search data on the two symptoms; among the LTLAs with complete data on dysgeusia and anosmia, the MSEs were similar when including them (Supplementary Table 35, which might also have influenced the COVID-19 epidemic. Due to data scarcity on the proportion of different SARS-CoV-2 variants by LTLA, we were unable to assess the model predictability specifically by variant.We acknowledge several potential limitations. The accuracy of real-time nowcasts and forecasts are challenged by delayed dataral days –5 days rWe developed and validated a dynamic supervised machine-learning modelling framework that predicted local weekly growth rates in the COVID-19 cases in the UK by integrating digital metrics of Google mobility and search trends, and the COVID-19 vaccination coverage. Our models were validated against reference models in the time period when there were rapid increases associated with the emergence and circulation of the delta variant, and thus showed promises in predicting future rapid changes in the COVID-19 cases. We will continue monitoring the model performance and updating all the predictions regularly using new data on a publically accessible web application, which could assist decision-making at local authorities during the ongoing pandemic.Supplmentary InformationDescription of Additional Supplementary FilesSupplmentary Data 1Supplmentary Data 2Supplmentary Data 3Supplmentary Data 4Supplmentary Data 5Supplmentary Data 6Supplmentary Data 7Supplmentary Data 8Supplmentary Data 9Reporting Summary


To achieve longevity, IgA plasma cells require a sophisticated anatomical microenvironment that provides cytokines, cell-cell contacts, and nutrients as well as metabolites. The intestinal epithelium harbors cells with distinct functions and represents an important defense line. Anti-microbial peptide-producing paneth cells, mucus-secreting goblet cells and antigen-transporting microfold (M) cells cooperate to build a protective barrier against pathogens. In addition, intestinal epithelial cells are instrumental in the transcytosis of IgA to the gut lumen, and support plasma cell survival by producing the cytokines APRIL and BAFF. Moreover, nutrients are sensed through specialized receptors such as the aryl hydrocarbon receptor (AhR) by both, intestinal epithelial cells and immune cells. However, the intestinal epithelium is highly dynamic with a high cellular turn-over rate and exposure to changing microbiota and nutritional factors. In this review, we discuss the spatial interplay of the intestinal epithelium with plasma cells and its potential contribution to IgA plasma cell generation, homing, and longevity. Moreover, we describe the impact of nutritional AhR ligands on intestinal epithelial cell-IgA plasma cell interaction. Finally, we introduce spatial transcriptomics as a new technology to address open questions in intestinal IgA plasma cell biology. The intestine consists of the small and the large intestine. The small intestine starts at the pylorus and is subdivided into three main parts: the duodenum, the jejunum, and the ileum. The large intestine consists of the caecum, the proximal colon, the transverse colon, the distal colon, the rectum, and ends at the anus. The cellular composition of the epithelium as well as that of the lamina propria (LP) differs along the segments of the intestine, concomitant with the different physiological functions and the different bacterial densities of the small intestine and the colon. The primary function of the small intestine is digestion and the absorption of nutrients. To increase the surface for food absorption, the small intestine is characterized by the presence of villi, whereas villi cannot be found in the caecum and the colon. The main function of the colon is water reabsorption and removal of undigested food. The colon contains the highest density of commensal bacteria , 2. DespMucosal surfaces are the most critical entry sites for pathogens into our body. Therefore, a sophisticated mucosal defense system evolved that combines chemical, physical, and cellular barriers. The mucosal immune system in the intestine consists of immune cells and the intestinal epithelium that orchestrates innate as well as adaptive immune responses. The epithelium constitutes the interface between the gut lumen and the LP. Its functions include the uptake of nutrients and antigens on the one hand, as well as microbial sensing and exclusion of pathogens on the other hand. The intestinal epithelium consists of an epithelial cell monolayer, the LP and the muscularis mucosae. It can be subdivided into the crypt area where stem cells are located and the villus area Figure 1+ stem cells in the crypts and differentiate into specialized epithelial cells  2 and natural killer T (NKT) cells, and contributes to anti-helminth immune responses . Enteroe+ dendritic cells (DCs) are recruited to the epithelium and are instrumental in antigen sampling by forming protrusions through the epithelium to capture antigen from the gut lumen. Antigens are subsequently processed, and peptide fragments derived from the antigen are presented on MHC II molecules on the cell surface. Antigen-presenting DCs migrate to the PPs and mLNs to activate antigen-specific T cells  and immune cells within the LP are involved in sensing and transporting antigens to the Peyer`s patches (PPs) and the mesenteric lymph nodes (mLNs) and maintaining epithelial integrity . The maj T cells .+ DCs migrate to the PP or mLNs to prime antigen-specific T cells. PPs as well as mLNs are secondary lymphoid organs and are structurally divided into a B cell and a T cell zone. B cells residing in PPs and mLNs bind antigen via their B cell receptor (BCR) and are activated upon cognate interaction with antigen-specific T cells. As a result, activated B cells proliferate, undergo class switch recombination (CSR) to Immunoglobulin (Ig)A and somatic hypermutation (SHM) induced by Activation-induced cytidine deaminase (AID) within the germinal center (GC) reaction in PPs and mLNs (FH) cells, DCs, eosinophils, and also B cells  . M cellsand mLNs Figure 1and mLNs –24. IgA and mLNs , 26. Cla B cells , 27–29.  B cells . MoreoveR to IgA , 31. GraR to IgA .+ tissue-resident T cells (TRM)  . In respls (TRM) –45. Morels (TRM) , 46–54. ls (TRM) –57. In tls (TRM) .Immune cell homing to the intestinal LP is controlled by the timely and spatially coordinated action of specific selectins, chemokine receptors and integrins . Gut hom+ T cells in the LP due to impaired homing (We have identified the transcription factor Krüppel-like factor 2 (KLF2) as a key regulator of integrin α4β7 and CCR9 expression and consequently as an essential regulator of IgA plasmablast homing to the intestinal LP. B cell-specific deletion of KLF2 resulted in reduced integrin β7 and CCR9 expression and subsequently in a severe reduction of IgA plasmablasts/plasma cells in the LP concomitant with reduced IgA in the serum and in the gut lumen , 78. Besd homing . Moreoved homing , a modeld homing .via E-Cadherin (via the poly-IgR (p-IgR) on IECs to the gut lumen -mediated migration to blood vessels and lymph. Tissue-resident cells upregula te CD69, which binds to the S1P-receptor 1 (S1PR1) resulting in S1PR1 inhibition and degradation ; a mechaThus, the β7 integrin chain plays a central role in IgA plasma cells homeostasis in the gut by controlling the migration from IgA plasmablasts from the blood to the LP when bound to the α4 integrin chain, and by enabling the direct interaction of IgA plasma cells with the epithelium when paired with the αE integrin chain Figure 2via integrin αEβ7; an interaction that might support the persistence of IgA plasma cells and the long-lasting secretion of protective IgA. However, due to the high turn-over rate of IECs, the constant process of epithelial cell death, and their dynamic replacement, it remains unclear how IgA plasma cells in the proximity or in direct contact with the epithelium achieve longevity. Furthermore, it remains elusive how the plasma cell survival niche in this highly dynamic environment is preserved. Hence, kinetic studies combined with spatial analyses of IgA plasma cells in their intestinal survival niches and in different sections of the intestine  and within different areas along the crypt-villus axis need to be performed.In addition to the supportive function of dimeric IgA transport to the gut lumen, IECs also promote the survival of plasma cells. IECs and smooth muscle cells in the LP were shown to produce IL-6 . The rolThe intestinal immune system can be influenced by various external factors, e.g., the diet . TherefoAmong the initially described toxic chemicals functioning as AhR ligands, a vast number of naturally occurring  and syntBrassicaceae is already metabolized in the oral cavity by myrosinases, resulting in the production of the AhR agonist precursor indole-3-carbinol (I3C) and indole-3-acetonitrile (I3ACN)  . In the  (I3ACN) –139.Diet-derived short-chain fatty acids (SCFAs) like butyrate can also induce AhR and AhR target gene expression in human IECs . This isEven though studies of human and murine blood plasma showed biologically active concentrations of diet-derived AhR ligands that can potentially control AhR activity in distinct organs –148, theAhR protein abundance has been identified in various cell types, including a wide range of immune cells as well as cells functioning at barrier sites like the lung, skin or intestine . UpholdiOf note, depending on the sensitivity of the used method, a gene expression analysis in whole organs does not always allow reliable distinguishing between different cell types. For this reason, more specific and detailed studies are required to identify the cell types in the intestinal tract that express AhR and to reveal how these cells are influenced by AhR activity. Therefore, advanced technologies like spatial transcriptomics will help to solve the remaining questions about the intestinal plasma cell niche and the functional relevance of AhR signaling therein.in vivo may not exclusively be attributed to the intrinsic role of AhR in IECs, as the activation of AhR also affects immune cells in the intestinal tissue (Citrobacter rodentium (Cyp1a1-mice) –induced murine colitis . The autl tissue . Supportodentium –156. Thia1-mice) . In thisC. rodentium infection may also be caused by a diminished intestinal mucus layer. This hypothesis is supported by the observation that AhR is required for the differentiation of secretory cells in the intestinal epithelium, thereby enhancing the resistance to enteropathogenic Escherichia coli , β-catenin and/or AXIN2 causes this pathogenicity . TherefoPCB 126, formerly produced as a lubricant in electronic equipment , is a veBraccicacea-originating AhR ligand indole-3-carboxaldehyde (ICA) promotes goblet cell differentiation and proliferation of IECs through IL-10 in an AhR-dependent manner -induced colitis showed significantly less damage in colonic tissue and reduced infiltration of inflammatory cells when treated with the AhR ligand TCDD prior to disease onset . Surprisand BAFF Figure 2Based on these data, it is out of the question that AhR signaling plays a vital role in IECs. However, if AhR-regulated processes in IECs are essential for the intestinal plasma cell niche and healthy IgA secretion remains unanswered.-/- mice (+MHCIIhigh DCs in the mLNs expressed less integrin αE (CD103), potentially affecting their migration behavior in the intestinal tissue and indicating that CD103 is a potential AhR target gene. As CD103 might be involved in IgA plasma cell homing to the intestinal tissue and their interaction with IECs, this mechanism is of potential interest when determining their intestinal survival niche. Of note, IgA levels did not differ between the feces of mice lacking AhR in DCs and of control animals.Mice with a CD11c-Cre-mediated specific deletion of AhR in DCs suffer as much from DSS-induced colitis as AhR-/- mice . Surprisin vivo (Culturing human monocytes in a specific differentiation culture system was shown to induce differentiation into monocyte-derived (mo-) macrophages and mo-DCs which resemble those found in vivo . Interesin vitro results in an increase of IL-6  when lacking AhR and butyrate feeding markedly reduced Blimp-1 expression independent of the genotype. This gene expression profile questions the nature of the analyzed cell population, especially as plasma cells can also secrete IL-10 in autoimmune inflammation  induces IL-10 secretion, thereby, dampening inflammatory Th1 and Th17 responses in a murine rheumatoid arthritis model . A more ammation  and exprammation , 197. OfDue to contradicting data, the role of AhR signaling in B cell activation and plasma cell differentiation is still unresolved. Especially the function of AhR in the intestinal ASC pool, and their migration behavior remains completely unclear.In addition to several studies that described a potential function of AhR signaling in B cells, there are also analyses addressing a connection between AhR activity and IgA production.Analyzing the effect of TCDD treatment during viral airway infection with Influenza A demonstrated an overall immunosuppressive phenotype . InteresResearch focusing on the formation of IgA-secreting cells in the intestine revealed that feeding wildtype C57Bl/6 mice with the AhR ligand TCDD alters fecal IgA concentrations independent of the circadian rhythm . HoweverIn support, AhR ligand 2,3,7,8-tetrachlorodibenzofuran (TCDF)-fed male wildtype mice showed increased fecal IgA abundance and elevated concentrations of inflammatory cytokines in the ileum . Surpris+ Tregs in the intestines -induced colitis, which was associated with increased numbers of Foxp3testines . Interes-/- mice and wildtype mice whose parents were already fed with a standard diet or an AhR ligand-free diet, which was continued after birth. Two-week-old AhR-/- mice showed no fecal IgA, while their serum IgA levels were comparable to wildtype mice fed with a standard diet. In contrast, an AhR ligand-free diet abolished serum IgA in wildtype mice but did not affect fecal IgA concentrations. Furthermore, the authors claimed that IgA abundance in the gastric content were comparable between animals during the nursing period, indicating no influence of maternal IgA on the observed phenotype. However, a direct diet-mediated effect on 2 week-old suckling mice is hard to imagine. Therefore, it has to be considered that other components in the maternal milk besides IgA itself may contribute to the observed differences in the IgA concentrations in the different groups of neonatal mice. Interestingly, depending on the available diet for nutrient intake, eight-week-old weaned mice did not show any differences in serum IgA concentrations. In contrast, only mice fed with an AhR ligand-free diet lacked fecal IgA, likely caused by reduced CD19+B220+ B cell numbers in the GALT. Even though the authors provided a unique set of data analyzing the influence of a diet or AhR signaling in neonatal mice, the results of this study are contradicting. As the observed alterations in IgA concentrations differed between AhR ligand-free diet-fed mice and AhR-/- mice, these results implicate an AhR-independent effect of the diet on the intestinal immune system. As obviously all cells of an organism are affected by the deficiency in AhR-/- mice or mice fed with a specific diet, their analysis does not allow conclusions about the B cell-intrinsic role of AhR in the formation of IgA-secreting cells or IgA-secretion itself. However, the differences between fecal and serum IgA concentrations in mice of the same genotype or diet-fed group imply a differential AhR function for secreted mucus-associated IgA and serum IgA, again.A unique study among AhR-focused research projects determined the influence of AhR activity on IgA production in neonatal mice . TherefoRegarding the significant number of studies addressing the AhR function, it was often neglected that TCDD is a toxic substance causing oxidative stress, DNA damage and endocrine disruption amongst other symptoms –207. UntNovel approaches utilizing spatial transcriptomics (ST) will help to understand how plasma cells differ in their phenotypes and functions in different anatomical locations. ST allows the comparative analysis of gene-expression profiles of IgA plasmablasts and plasma cells in their anatomical and physiological environment. In this context, comparing the transcriptomes of IgA plasma cells in the villus tips to those located next to the crypts in human and murine small intestines will be of interest. ST of intestinal stromal cells along the small intestine villi - crypt axis have already been successfully conducted and resulted in the identification of four spatially distinct mesenchymal cell populations . The cha1) How can IgA plasma cells persist for decades in the LP in close proximity to the epithelium while IECs are constantly replaced and renewed? The intestinal epithelium is characterized by a high turn-over rate of 3-5 days. Stem cells in the crypts continually divide and give rise to transit-amplifying cells which then differentiate into specialized IECs. During this process, the newly formed cells move from the crypt towards the villus tip. As soon as they reach the villus tip, they undergo cell death and are shed off. The processes of proliferation, differentiation and cell death are regulated by gradients of ligands of the Wnt, the BMP, the Notch, and the Ephrin signaling pathways , 8, some2) How does the interaction with the basement membrane and the ECM affect IgA plasma cell survival and functions? IECs reside on a basement membrane consisting of a dense network of ECM components. Therefore, the interaction of IgA plasma cells with ECM components might be crucial for retaining IgA plasma cells in close proximity to the epithelial layer. The basement membrane has been shown to contribute to cell migration and differentiation. In addition, the basement membrane exerts barrier functions . It cont3) How does compartmentalization affect IgA plasma cell and epithelial cell function? The small intestine consists of the duodenum, jejunum, and ileum. The duodenum is characterized by a high concentration of food ligands and a low bacterial content. Along the duodenum–ileum axis, the concentrations of food ligands decrease, the bacterial concentration, however, increases , 2. Concvia the IgA-BCR might also contribute to the localization of IgA plasma cells within the villus.4) How do signals mediated by the IgA surface BCR on plasma cells affect their localization, survival, and function? IgA plasmablasts and, more importantly, mature IgA plasma cells still express IgA-BCRs on their cell surface , 214–216Hence, new genetic models in combination with ST need to be developed to investigate the biological relevance of the surface IgA-BCR and its signals in the context of IgA plasma cell function and longevity.KP and WS conceptualized and wrote the manuscript. JW, FK, and H-MJ revised the manuscript. All authors contributed to the article and approved the submitted version.


Silver diamine fluoride (SDF) is a non-surgical treatment for the arrest and prevention of dental caries that results in irreversible black staining of dental decay. The objective of this study was to evaluate the short-term impact of SDF treatment on oral health-related quality of life (OHRQoL) relative to a standard package of glass ionomer sealants and atraumatic restorative treatment (ART) in children aged 5–13 years.t tests.CariedAway is a pragmatic, longitudinal, cluster-randomized non-inferiority trial of non-surgical interventions for caries. Secondary study outcomes included OHRQoL and academic performance. Oral health-related quality of life was measured at each study visit using the Child Oral Health Impact Profile. Change in OHRQoL was assessed using linear regression and non-inferiority was determined using 160 children with an average age of 8.7 years completed quality of life assessments. Untreated decay at baseline (approximately 25%) was associated with significantly worse OHRQoL and treatment in both groups resulted in incremental improvement: children receiving SDF improved their OHRQoL scores from 16.44 (SD = 11.12) to 14.62 (SD = 11.90), and those receiving traditional sealants and atraumatic restorations slightly improved from 16.65 (SD = 10.56) to 16.47 (SD = 11.09). Quality of life in children receiving silver diamine fluoride was non-inferior to those receiving sealants and ART at least 6 months post-treatment , and change in OHRQoL did not depend on the severity of baseline decay.OHRQoL is related to untreated dental caries, and observed changes following SDF treatment were non-inferior relative to standard preventive therapies. Dental caries is the most prevalent childhood disease in the world , found aOral health-related quality of life (OHRQoL) is a multidimensional construct consisting of subjective evaluations of oral health, functional well-being, emotional well-being, satisfaction with care, and sense of self . Caries CariedAway is a randomized controlled trial of non-surgical interventions for the prevention and treatment of caries in children aged 5–13 years , specifiEthical approval for the CariedAway clinical trial was obtained from the New York University School of Medicine Institutional Review Board (i17-00578). A previously published trial protocol contains additional study-related information  and the CariedAway is a longitudinal, cluster-randomized, single-blind, pragmatic trial with the primary objective of evaluating the non-inferiority of non-surgical treatments for dental caries. Any school in New York City with a student population of at least 80% receiving free or reduced lunch and at least 50% Hispanic/Latino or black was eligible to participate in the study. School-level exclusion beyond race/ethnicity and free/reduced lunch criteria included those with a preexisting school oral health program. All children in enrolled schools were provided informed consent, there were no inclusion criteria for child-level enrollment. Any subject with parental informed consent and child assent was randomly assigned to treatment and received care. Exclusion criteria for individual subjects included any child whose first language was anything other than English and children enrolled in special education classrooms. Treatment was provided in scheduled 6-month intervals. A total of 48 schools were enrolled in the CariedAway study at the time of this report.Interventions included two separate packages of non-surgical treatments for dental caries: a “simple” combination of fluoride varnish  applied to all teeth and silver diamine fluoride  applied to all pits and fissures and asymptomatic cavitated lesions of bicuspids and molars, and a “complex” combination consisting of the same application of fluoride varnish, glass ionomer sealants applied to pits and fissures of bicuspids and molars, and use of atraumatic restorative treatment on all frank asymptomatic cavitated lesions (GC Fuji IX). For SDF application, tooth surfaces were cleaned and dried and a microbrush was used to transfer the solution to all pits and fissures on bicuspids or molar teeth and to all posterior, asymptomatic carious lesions for a minimum of 30 s, followed by air drying for a minimum of 60 s. Excess material was removed from teeth with a 2 escribed .Enrolled schools were block randomized in a 1:1 allocation ratio.At each observation, study clinicians performed a full visual-tactile examination using a disposable mirror, disposable explorer, and head lamp in a portable dental chair or while using a lapboard. Teeth were assessed as being present or missing intraorally. Individual tooth surfaces were assessed as being intact/sound (ICDAS II codes 0–4), sealed, restored, decayed (ICDAS II code 5–6), or arrested [caries]. Clinicians were standardized prior to observing subjects . Following the oral examination and application of treatments, children were asked to complete the oral health-related quality of life survey. The clinician would then read each question aloud to subjects who would then note their answers on a provided tablet computer.Oral health-related quality of life was assessed using the Child Oral Health Impact Profile Short Form (COHIP-SF), consisting of 19 questions assessing oral health, functional well-being, socio-emotional well-being, school environment, and self-image domains , 30. LowDemographic information including age, sex, and race/ethnicity were obtained from informed consent documents or school records. A unique identification number maintained by the Office of School Health at the New York City Department Health and Mental Hygiene and New York City Department of Education was similarly used as the patient record number for this study.t tests. Post-treatment analysis of the caries-QoL association was assessed using linear regression, adjusting for treatment group, baseline COHIP scores, and demographic variables. The intraclass correlation coefficient for subjects nested within schools was estimated using an intercept-only mixed effects multilevel model. All analyses adjusted for the clustering effect of schools. The non-inferiority of SDF therapy compared to sealants/ART on oral health-related quality of life was determined by calculating the confidence interval for the difference in mean COHIP scores across treatment group. As per the original study protocol, a non-inferiority margin of ten was used.Analysis was restricted to only those subjects between the ages of 5 and 13 years at time of observation. Subjects were analyzed using intent to treat: any child who may have switched schools that was randomized to a different treatment arm was analyzed according to his or her original assignment. Baseline descriptive statistics for sociodemographic variables and COHIP scores were computed. The association between dental caries at baseline and initial quality of life was assessed using linear regression. Following baseline analyses, subjects were ordered sequentially by visit date and any child without two completed visits was removed from the analytic sample. Within-group differences for treatment groups used paired samples Although all study subjects randomly selected to complete oral health-related quality of life assessments at baseline were expected to complete follow-up data collection at each successive measurement period, the impact of COVID-19 required suspension of study procedures for approximately 18 months due to the shutdown of local school systems and subsequent prohibition of any school-based health programs from operating. As a result, analysis was restricted to those subjects who had completed their first 6 month follow-up assessment just prior to shutdown.1323 subjects completed the quality of life assessment at baseline, 160 of which (12%) completed a second-follow up at least 6 months post-treatment with either silver diamine fluoride or sealants/ART, prior to study suspension due to the impact of SARS-CoV-2. The analytic sample (N = 160) was spread across 17 schools. The baseline sample was approximately 53% female and consisted of 39% Hispanic/Latino, 15% black, 4% white, and 1.4% Asian  than those with no untreated decay. Adjusting for differences in race and ethnicity, each decayed tooth was associated with 1.34  point increase in COHIP scores, where higher scores indicate worse OHRQoL. There were no baseline differences in OHRQoL by treatment group . Across both SDF and sealant/ART groups, average OHRQoL improved following treatment . The point estimate favors silver diamine fluoride however the confidence interval is below the non-inferiority margin. Additionally, there was no significant interaction as no treatment differences were found among only subjects with baseline caries.As arresting and preventive agents for dental caries, certain materials used in atraumatic restorative treatments and glass ionomer sealants can be visually imperceptible under casual observation. In contrast, application of silver diamine fluoride results in permanent black staining of dental decay and superficial staining of the oral mucosa. Notably, perceptions of self are affected by facial aesthetics, being previously observed in adolescents seeking orthodontic treatment and in children with preexisting orofacial anomalies , 32. As Oral health-related quality of life slightly improved following treatment with either silver diamine fluoride or sealants/ART, and children receiving SDF exceeded the minimally important difference threshold of the COHIP-SF necessary for patient-centered clinically meaningful change . ResultsClinical application of silver diamine fluoride in the CariedAway trial does not include anterior teeth; SDF is often applied to posterior teeth in order to mitigate impacts on facial aesthetics. In children aged 5–9 years, the global prevalence rate of caries in deciduous teeth exceeds 40% . HoweverThe focus of this analysis was on the potential short-term impact of SDF application on oral health-related quality of life, relative to more traditional non-surgical caries treatments. It may be the case that the staining effect of SDF, even when confined to posterior teeth, becomes more appreciated with longer rates of follow-up or when children progress into adolescence where facial aesthetics may be of greater concern. Additionally, as overall oral health-related quality of life has been shown to be responsive to the severity of dental caries , the lonThe early suspension of the CariedAway trial due to the impact of the COVID-19 pandemic meant that we were unable to obtain 6-month follow-up data for a substantial proportion of the baseline sample, and longitudinal observation beyond 6 months was not viable. As a result, only a subset of those initially enrolled and completing OHRQoL assessments were able to be analyzed. While our initial power calculations used a total enrollment of 396, which assumed an ICC of 0.10, our results show that the actual cluster correlation is considerably smaller. Compared to the full baseline sample, the analytic sample was similar in sex, the prevalence of untreated decay, and the average number of decayed teeth, however there was a greater proportion of subjects of Hispanic or black race/ethnicity. As our primary objective was on the associations between oral health, non-surgical treatment, and OHRQoL, we do not expect the racial/ethnic differences to be a concern. Regardless, this selection bias is still a risk to internal validity, and the reported associations should be interpreted with caution. Future research in CariedAway will seek to explore more moderate- to long-term change in OHRQoL, which would not be as negatively affected by the COVID-19 pandemic.Silver diamine fluoride can be applied in significantly less time than atraumatic restorative treatments  and does