Infections complicate the acute phase of stroke in one third of patients and especially pneumonia is associated with increased risk of death or dependency. In randomized trials of stroke patients, preventive antibiotics reduced overall infections, but did not reduce pneumonia or improve outcome. This may be explained by broad selection criteria, including many patients with a low risk of pneumonia. To assess the potential of selection of patients at high risk of pneumonia, we performed a post-hoc analysis in the Preventive Antibiotics in Stroke Study (PASS).PASS was a multicentre phase 3 trial in acute stroke patients who were randomized to preventive ceftriaxone for four days within 24 hours or standard care. For this analysis patients were divided based on the ISAN risk score for pneumonia as follows: low (0–6), medium (7–14) and high (15–21). Primary outcomes were pneumonia rate during admission as judged by the treating physician, and by an independent committee; secondary outcomes were overall infections and unfavorable outcome . We adjusted with multivariable regression for possible confounders: age, stroke subtype and severity, pre-stroke dependency and diabetes.Pneumonia occurred more frequently in higher risk groups (25.7% (high), 9.0% (medium) 1.5%, (low)). The absolute difference in pneumonia rate between patients treated with ceftriaxone or standard care increased with the ISAN score . After adjustment ceftriaxone reduced overall infections in the low and medium groups, not in the high-risk group. There was a trend towards reduction of pneumonia as judged by the committee  in the high-risk group.This post-hoc analysis of PASS confirmed higher rates of pneumonia with higher ISAN scores, and suggests that in acute stroke patients with an ISAN score of ≥15, preventive ceftriaxone for four days may reduce pneumonia rate. Infections complicate the acute phase of stroke in about one third of patients and are associated with an increased risk of death or dependency . This asThe ongoing randomized PREvention of Complications to Improve Outcome in elderly patients with acute Stroke (PRECIOUS) trial, recruits only patients aged 66 years or older with moderately-severe to severe stroke. Patients are randomized to preventive treatment with ceftriaxone, metoclopramide, paracetamol, any combination of these, or to standard care alone .To assess the potential of ceftriaxone to prevent pneumonia in these patients, we performed a post-hoc analysis of patients with a high risk of pneumonia based on the integer-based pneumonia risk score (ISAN)  in the PWe performed a post-hoc analysis of PASS ISRCTN66140176), of which the methods and results have been reported previously [6140176, In PASS, patients were eligible for inclusion if they were aged 18 years or older and had ischemic stroke or intracerebral hemorrhage in the previous 24 hours with a score of 1 or more on the National Institutes of Health Stroke Scale (NIHSS). Exclusion criteria were clinical signs of infection on hospital admission requiring antibiotic therapy, use of antibiotics less than 24 hours before admission, pregnancy, hypersensitivity for any cephalosporin, previous anaphylaxis for penicillin derivatives, and imminent death on hospital admission. All patients or their legal representatives provided written informed consent. For the current study, baseline demographics, comorbidities, stroke type and data on dysphagia assessment were collected. In the Case Record Form of PASS researchers were asked to provide details on whether a swallow screening was performed and the results of the screening. Dysphagia was scored as present when patients had an abnormal swallow test, as absentin case of a normal swallow test or if no swallow test was performed because of excellent recovery. To investigate the potential of preventive ceftriaxone to reduce the occurrence of pneumonia in the PRECIOUS population we divided the ISAN score as follows: low (0–6), medium (7–14) or high risk (15–21). Patients recruited into PRECIOUS will by definition have a ISAN score of 7 or higher and fall in to the medium- or high-risk group. A sensitivity analysis with the original 4 categories  will be performed.For the present post-hoc analysis, patients were categorised in subgroups based on the ISAN risk score . The ISAThe current study has a co-primary outcome: pneumonia rate (as assessed by the treating physician) and pneumonia rate as assessed by an independent adjudication committee . Secondary outcomes were infection rate (as assessed by the treating physician and the adjudication committee) and an unfavorable outcome at 90 ± 14 days, defined as a score on the mRS of 3 or higher. Infections were categorised as diagnosed by the clinician, and as judged by an independent adjudication committee  according to modified Centers for Disease Control and Prevention criteria . To asseTable 2 for each ISAN group. Patients randomized to ceftriaxone or standard care were comparable for age, sex, comorbidities, pre-stroke mRS, stroke severity, stroke subtype and dysphagia in the low, medium and high ISAN groups. In the group of patients who were randomized to standard care, 193 patients eventually received antibiotics (15.2%). The proportion of patients with standard care in the higher ISAN group was 44.1% (n = 26).We used data from all 2538 patients included in the intention-to-treat analysis of PASS . One tho. After adjustment for confounders there was a reduction in the rate of pneumonia as judged by the adjudication panel in patients with a high ISAN score randomized to ceftriaxone (2/54 (3.7%)) as compared to standard care (8/59 (13.6%) aOR 0.164, p = 0.063), this was not statistically significant. This was not seen in patients with a low or medium ISAN score or in pneumonia incidence as judged by the treating physician.  had a pneumonia as diagnosed by the treating physician and 57 (2.2%) as diagnosed by the adjudication panel. Pneumonia was more frequently diagnosed in higher risk groups (low: 17/1166 (1.5%); medium: 13/1259 (9.0%); high 29/113 (25.7%)) and the difference in pneumonia incidence as judged by the physician between patients who were randomized to ceftriaxone or standard care was greater in the highest ISAN group in favour of ceftriaxone (absolute risk reduction 0.5% (95% CI -0.9 to 1.9) for low risk, 1.2%  for medium risk, and 10.1%  for high risk). This was comparable for pneumonia incidence as judged by the adjudication panel  for low, 0.6%  for medium and 9.9%  for high risk) , causing a generally low ISAN score, median NIHSS scores of patients in the active treatment arms of other randomized trials of preventive antibiotics ranged from 14 to 17 –11. In PNot surprisingly, patients with the highest ISAN score not only had the highest rate of pneumonia, but also the greatest risk of a poor functional outcome. The higher age and higher NIHSS scores which drive the ISAN score are also established predictors of a poor functional outcome and death –15. In nGiven the apparent large absolute reduction in the risk of pneumonia with ceftriaxone in the subgroup of patients with a high ISAN score, an evaluation in a larger population of patients with acute stroke and a high ISAN score is warranted. The ongoing PRECIOUS trial currently assesses whether prevention of aspiration, infection, or fever with metoclopramide, ceftriaxone, paracetamol, or any combination of these in the first 4 days after stroke onset can prevent these complications and improve outcome in elderly patients with moderately severe to severe stroke [This post-hoc analysis of PASS confirmed higher rates of pneumonia with higher ISAN scores and suggests that patients with acute stroke who are at a high risk of stroke-associated pneumonia are a worthwhile target for studies on preventive antibiotics.S1 Table(DOCX)Click here for additional data file.


There is compelling evidence that human epidermal growth factor receptor-2 (HER2) overexpression contributes to resistance to endocrine therapies in oestrogen receptor-positive (ER+) breast cancer. ER+/HER2+ tumours comprise about 10% of all breast cancer cases and about 60% of the whole set of HER2+ tumours. Most patients with primary ER+/HER2+ disease will receive antibody-based HER2-targeted therapy, but this is generally for no more than one year while endocrine treatment is usually for at least 5 years. A number of HER2-kinase inhibitors are also now in clinical use or in clinical trials, and the interaction of these with endocrine treatment may differ from that of antibody treatment. In this review article, we aim to summarise knowledge on molecular mechanisms of breast cancer resistance to endocrine therapies attributable to the impact of HER2 signalling on endocrine sensitivity, to discuss data from clinical trials addressing the role of HER2 in the development of endocrine resistance in the metastatic, neoadjuvant and adjuvant settings and to explore rational new therapeutic strategies.Endocrine therapies are the main treatment strategies for the clinical management of hormone-dependent breast cancer. Despite prolonged time to recurrence in the adjuvant setting and the initial clinical responses in the metastatic setting, many patients eventually encounter tumour relapse due to acquired resistance to these agents. Other patients experience a lack of tumour regression at the beginning of treatment indicating  Breast cancer development and progression are significantly affected by signalling pathways involving oestrogen receptor (ER) and growth factor receptors . Hormonede novo and acquired resistance to treatment occur in a large number of patients and this significantly limits its optimal use in the clinic  as e clinic . Evidence clinic , but cone clinic . Almost in situ hybridisation (FISH)  gene-amplification, such that approximately 10% of all breast cancers are ER+/HER2+ , indicating a significant association between HER2 overexpression and treatment failure. The test for heterogeneity (P = 0.27) showed that the differences among individual studies may be explained by chance and that combining data was an appropriate method. Interestingly, the interaction between HER2 overexpression and treatment failure was retained despite the endocrine therapy choice. The relative risk was 1.48  for studies pertaining to tamoxifen, and 1.43  for studies involving other endocrine drugs. The results suggested that patients with metastatic breast cancer and HER2 overexpression are relatively less responsive to endocrine therapies than patients in whom HER2 is not overexpressed. Importantly, however, because of the lack of a control arm, in which a therapeutic intervention was not administered, the more rapid progression may, at least to a certain degree, reflect a more aggressive inherent behaviour of the HER2-overexpressing tumours , while no significant difference was observed in ER+ and HER2− patients . Moreover, rather than letrozole’s effects being diminished in HER2+ cases, letrozole was significantly more effective in patients overexpressing HER2 and/or EGFR compared to those that were negative for both receptors  , suggesting that they are sensitive to short-term oestrogen deprivation therapy. When ER+ tumours with HER2 gene amplification were treated with tamoxifen, the point estimate for the clinical response was poorer in HER2+ disease than in HER2− tumours, but this difference did not approach statistical significance  (P = 0.18).Because of the difference in pharmacological activity among types of endocrine therapy, it has been suggested in a neoadjuvant study that the choice of therapeutic agent might affect the responsiveness of HER2-overexpressing tumours . OverallP = 0.0001), even when a clinical response was observed   . As suchOn the other hand, there are a few conflicting studies indicating no significant difference in disease-free and overall survival with adjuvant tamoxifen based on HER2 status (P = 0.0018). For tamoxifen, it was 18.8 and 9% for patients with HER2+ and HER2- tumours, respectively . The benefit of anastrozole did not differ from that of tamoxifen in the HER2+ cohort, but there were only 44 patients in the HER2+ group  1-98 trial assessed the effect of HER2 status on the benefit of tamoxifen and letrozole in early breast cancer patients . In patients with HER2- tumours, an improved outcome was observed following treatment with upfront AI compared to tamoxifen, while patients with HER2+ tumours showed no difference or slightly worse outcome in the first 2–3 years. Nevertheless, it should be noted that a large degree of heterogeneity in the HER2+ groups across all trials might be attributable to the small number of patients with HER2+ tumours, as well as subtle differences between AIs .All these studies were limited in terms of assessing the markers and/or determinants of treatment resistance because of the absence of accessible disease after surgery. To produce meaningful and statistically valid results, adjuvant studies need a sufficiently large number of patient samples. The inverse correlation between ER and HER2 expression leads to there being only a minority population, generally around 10% of the total, in studies that recruited ER+ patients irrespective of HER2 status . Direct Resistance of HR+/HER2+ breast cancer to endocrine therapy is heterogeneous and complex and may depend on the individual patient’s genetic background, the choice of endocrine therapy, and the type of resistance . There iP < 0.006)   Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and The Institute of Cancer Research, London. We also thank Breast Cancer Now for funding this work as part of Programme Funding to the Breast Cancer Now Toby Robins Research Centre. A A was supported by a grant from Le Cure Breast Cancer Research Fund. The funding bodies did not have a role in the design, analysis or interpretation of this review.Not applicable as no datasets were generated during this study.Not applicable as this review does not contain any studies with human patients or animals performed by any of the authors.A A searched and evaluated all relevant literature and wrote the manuscript. M D conceived the topic idea, supervised the search strategy, and reviewed and revised the manuscript drafts. All authors read and approved the final manuscript.


This cohort study examines changes in the proportion of mental health–associated hospitalizations among adolescents in the US and France during the first year of the COVID-19 pandemic vs before the pandemic. Was the onset of the COVID-19 pandemic associated with changes in the proportion of hospitalizations for mental health conditions among adolescents?In this cohort study of 9696 and 11 101 adolescents hospitalized with a mental health condition before and during the pandemic, respectively, at 8 children’s hospitals in the US and France, there was a significant increase in the monthly proportion of hospitalizations associated with mental health conditions following onset of the pandemic.This study showed that global disruption in the first year of the COVID-19 pandemic was associated with increased mental health–related hospitalizations among adolescents. The COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents.To estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic.This retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children’s hospitals in the US and France.Change in the monthly proportion of mental health condition–associated hospitalizations between the prepandemic  and pandemic  periods using interrupted time series analysis.There were 9696 adolescents hospitalized with a mental health condition during the prepandemic period  and 11 101 during the pandemic period . The mean (SD) age in the prepandemic cohort was 14.6 (1.9) years and in the pandemic cohort, 14.7 (1.8) years. The most prevalent diagnoses during the pandemic were anxiety (6066 [57.4%]), depression (5065 [48.0%]), and suicidality or self-injury (4673 [44.2%]). There was an increase in the proportions of monthly hospitalizations during the pandemic for anxiety , depression , and suicidality or self-injury . There was an estimated 0.60% increase  overall in the monthly proportion of mental health–associated hospitalizations following onset of the pandemic compared with the prepandemic period.In this cohort study, onset of the COVID-19 pandemic was associated with increased hospitalizations with mental health diagnoses among adolescents. These findings support the need for greater resources within children’s hospitals to care for adolescents with mental health conditions during the pandemic and beyond. Several studies have documented increases in depression, anxiety, and suicidality among adolescents since the start of the pandemic.9 It is unclear whether the proportion of hospitalizations for mental health conditions in pediatric hospitals has changed since the onset of the pandemic. One single-center study of a pediatric hospital in the US found that children and adolescents presenting to the emergency department with mental health conditions during the pandemic were more likely to require admission than in the prepandemic period, pointing to potential increased demand for inpatient mental health services.10Adolescents with severe mental health conditions may require hospitalization if they are at risk of self-harm and unable to be safely maintained at home. Inpatient psychiatric care for this population is provided in dedicated psychiatric institutions or units tailored to pediatric psychiatric patients. When there are shortages of psychiatric beds, adolescents are frequently boarded in nonpsychiatric units until definitive placement can be provided. Boarding should be minimized as adolescents are typically not able to receive all the mental health services required during this period.Assessing changes in the proportion of hospitalizations for mental health conditions among adolescents is critical to ensure the prioritization of public health programs targeting psychiatric health care needs that have emerged during the pandemic. It is also necessary to better understand the health care infrastructure and resources required to provide high-quality mental health care to these patients. Using data from a multisite cohort of children’s hospitals, we aimed to estimate changes in the monthly proportion of hospitalizations associated with mental health conditions among adolescents following onset of the pandemic.12 All health care systems with a dedicated children’s hospital able to provide data participated in the analysis (eTable 1 in STROBE) reporting guideline.In this cohort study, the study sample was drawn from sites participating in the Consortium for Clinical Characterization of COVID-19 (4CE), an international collaborative focused on facilitating clinical investigations of COVID-19 using federated informatics approaches for the analysis of aggregate and deidentified electronic health record data.13 Five pediatric hospitals in the US and 3 in France contributed patient-level data to the analyses. The contributing hospitals in the US were Boston Children’s Hospital, Children’s Hospital of Philadelphia, CS Mott Children’s Hospital, Cincinnati Children’s Hospital Medical Center, and Lurie Children’s Hospital of Chicago. The French contributing hospitals were Centre Hospitalier Universitaire de Bordeaux, Hôpital Necker–Enfants Malades, and Hôpital Armand Trousseau. The latter 2 hospitals in France are part of the health care system of Assistance Publique–Hôpitaux de Paris and were analyzed together as a single site.Patients were included in the analysis if they were 11 to 17 years of age and had a hospitalization associated with at least 1 diagnosis of a mental health condition between February 1, 2019, and April 30, 2021. We focused on adolescent patients since mental health conditions are more common in this age group than in children younger than 11 years, and we selected the age range based on standard definitions of adolescence.12 This initial harmonization step allowed a common script in the R programming language to be run at each site to generate results. The output of locally implemented analysis was shared centrally for additional aggregate analyses. The Structured Query Language scripts used for data harmonization and local analysis at each children’s hospital are publicly available on GitHub,14 as is the R code for the intrasite statistical and aggregate analyses.15Data extraction and analyses used a federated approach wherein data were stored and analyzed locally at each site to protect patient confidentiality. Each contributing children’s hospital formatted its data in accordance with a common data standard shared across the 4CE consortium.16 The end date of April 30, 2021, was selected to capture the first year in which social distancing measures were in place.To examine changes in the proportion of hospitalizations, we defined 2 periods: a prepandemic period from February 1, 2019, to March 31, 2020 (14 months), and a pandemic period spanning April 1, 2020, to April 30, 2021 (13 months). In keeping with prior studies, we chose April 1, 2020, as the cutoff date to reflect the initiation of mitigation strategies over the latter half of March 2020 following the World Health Organization declaration of the pandemic in mid-March.17 which provides International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnostic codes for 30 mental health disorder groups. Sixteen of these were selected as relevant to adolescent mental health. Modifications were made to add ICD-10 codes used in France following quality control analysis .18 Hospitalizations in which at least 1 of these diagnostic codes was assigned were defined as mental health condition–associated hospitalizations.The primary outcome was the change in the monthly proportion of hospitalizations associated with at least 1 mental health condition. Mental health conditions were defined using a modified version of the Child and Adolescent Mental Health Disorders Classification System,ICD-10 code for a condition in these condition-specific analyses. Information on patient sex was also collected to allow for stratified analyses.Secondary outcomes focused on the change in monthly proportions of hospitalizations for specific mental health condition groups. We included hospitalizations with at least 1 ICD-10 codes per mental health condition used at each health care site . We estimated 95% CIs for the proportion differences using parametric bootstrapping (10 000 iterations), assuming each proportion was drawn from a binomial distribution. Significance testing for within-period proportions and proportion differences was performed using z test parametric bootstrapped distributions.To measure the association between pandemic onset and the proportion of hospitalizations, we performed interrupted time-series (ITS) analyses using monthly proportions of hospitalizations for mental health conditions  at each contributing children’s hospital.20 Each site then stratified the analyses by sex.The same method was used to examine the change in the proportion of hospitalizations for specific mental health conditions . We focused on the 3 condition groups most prevalent in the study population and also examined eating disorders, which have been reported to have increased during the pandemic in prior studies.We used random-effects meta-analysis to estimate pooled proportion differences across hospitals and countries. The hospital and country effects were treated as random effects to account for heterogeneity in the effects of the pandemic across populations.P = .05 was used to determine statistical significance. Analyses were performed using R, version 4.0.0 .A 2-sided P < .001) and during the pandemic . The median duration of hospitalization was 7 days both before the pandemic and during the pandemic .We identified 9696 adolescents who had at least 1 mental health condition–associated hospitalization during the reference prepandemic period and 11 101 during the examined pandemic period. The mean (SD) age at hospitalization of patients in the prepandemic cohort was 14.6 (1.9) years and in the pandemic cohort, 14.7 (1.8) years . There wP < .001). The proportion of adolescents diagnosed with suicidality or self-injury also increased significantly between the prepandemic and pandemic periods , though there was no significant change observed for the proportion of adolescents diagnosed with depression .The 3 most prevalent mental health conditions in the study population were anxiety, depression, and suicidality or self-injury during both the prepandemic and the pandemic periods  were associated with mental health conditions; during the pandemic, 11 605 of 31 908 hospitalizations (36.4%) were associated with a mental health condition. The monthly proportion of hospitalizations associated with mental health conditions remained stable at each health care site during the prepandemic period , depressive disorders , and suicidality or self-injury . We did not observe a significant increase in hospitalizations for eating disorders  (eTable 3 in 8 and demonstrate the application of large-scale federated data approaches to increase our understanding of the burden of mental health conditions among adolescents and the rising need for adolescent mental health treatment resources.In this multisite retrospective cohort study, we found that adolescents had a significantly increased proportion of hospitalizations associated with mental health conditions during the first 13 months following onset of the COVID-19 pandemic compared with a 14-month prepandemic period. In this cohort, anxiety, depression, and suicidality or self-injury were the most prevalent conditions, and the proportion of hospitalizations for these conditions increased during the examined pandemic period. Adolescent females had a greater increase in the proportion of hospitalizations associated with mental health conditions during the pandemic compared with males. The temporal increase in mental health condition–associated hospitalization proportions was observed in 4 of the 5 US health care sites and 1 of the 2 health care sites in France, with a between-period proportion difference of 0.60% per month. Our findings extend those of prior studies focusing on the well-being of adolescents during the pandemic24 The greater illness burden observed among female adolescents is also in line with findings reported in other analyses.24 These patterns have been documented primarily within individual countries and especially in single-center studies; few robust data exist across multicenter and international settings. Importantly, specific mitigation strategies and timing of containment measures differed across countries throughout the pandemic. Thus, given the scale and global scope of the public health crisis, analyzing overall changes in proportions of hospitalizations across countries provides a measure of the consequences of the pandemic for adolescents that is specific to neither individual mitigation measures nor national settings. Quantifying the change in mental health burden provides data to guide future policies and public health programs globally to improve access to high-quality mental health care in the outpatient and inpatient settings, even after the COVID-19 pandemic. In the short term, these findings support efforts to improve screening and detection of several mental health conditions and their related complications and to inform programs seeking to ensure widespread availability of mental health access, including through telemedicine, school-based programs, and community mental health services and response teams.25Our results are consistent with prior studies, highlighting the potential association of pandemic-related disruptions with adolescent social, emotional, and behavioral well-being.28 Since 2010, rates of suicide have been rising in this population, and suicide represented the third leading cause of death among individuals 10 to 24 years of age in 2020.29 While the factors contributing to these conditions are not fully known, the social isolation and other disruptions related to the pandemic may have exacerbated underlying trends and led to the recent declaration by the American Academy of Pediatrics of a national emergency in child and adolescent mental health.25 This declaration was accompanied by a set of recommendations for strategies to support improved mental health delivery to this population, including approaches to reduce patient boarding in nonpsychiatric units and increase access to acute psychiatric treatment.It is important to note that prior to the onset of the COVID-19 pandemic, more than 40% of hospitalized adolescents in this study’s cohort had a diagnosis of anxiety, depression, or suicidality or self-injury. This is consistent with the high mental health burden observed among children and adolescents before 2020.11 While the focus to date has been on COVID-19, the contemporaneous efforts on methods development, ongoing addition of new hospital sites across health care systems, and open-source release of all code has produced a robust infrastructure that supports ongoing collaborative research studies on diverse topics.32 In particular, the 4CE infrastructure will be able to support continued monitoring of health care utilization for mental health conditions among adolescents, including assessing trends in specific conditions and identifying patient groups at greatest risk for mental health disorders.The 4CE network leverages a multisite research infrastructure based on routinely collected health care data to perform COVID-19–related clinical analyses expeditiously and at scale while preserving patient confidentiality.33 Second, variables selected for structured data extraction across sites were prespecified by the 4CE network, limiting our ability to explore additional hospital and patient characteristics. This is a trade-off for efficiently implementing multiple simultaneous analyses across the 4CE network. Third, our modeling relied on the assumption of linearity of underlying temporal trends. In particular, we did not account for seasonality, as this would have required fitting a model over several consecutive years, and we only had 1 year of data in the prepandemic period.35 However, such adjustment may not be required, as findings from studies examining the association between seasons and affective disorders remain inconclusive.36 In addition, the use of proportions rather than absolute counts served to mitigate potential patterns in overall hospital admission.There are several limitations to this study. First, we were unable to ascertain the primary reason for hospitalization among adolescents with a mental health condition and instead identified all hospitalizations associated with at least 1 mental health diagnosis to examine trends over time. This is a known limitation with the use of diagnostic codes, and ongoing 4CE efforts are focused on distinguishing specific admission diagnoses vs incidental ones.This large, multisite cohort study provides estimates of the increased proportion of adolescent hospitalizations associated with mental health conditions during the COVID-19 pandemic. Based on data from 8 children’s hospitals across 2 countries, we observed high proportions of hospitalizations associated with anxiety, depression, and suicidality or self-injury during the pandemic. The increase in hospitalizations was particularly notable for adolescent females. These findings support the need for ongoing prioritization of resources for children’s hospitals to provide care to adolescents with mental health conditions.


Bifidobacterium. infantis EVC001) or synbiotic (B. infantis EVC001 + Lacto-N-neotetraose [LNnT]) or placebo (Lactose) for four weeks and were followed for four more weeks after supplementation. In multivariable linear regression model, the mean rate of weight gain in the probiotic arm compared to placebo was higher by 2.03 unit (P < 0.001), and 1.13 unit (P = 0.030) in the synbiotic arm. In linear mixed-effects model, mean WAZ was higher by 0.57 unit (P = 0.018) in probiotic arm compared to placebo. Although not statistically significant, delta length for age z score (LAZ) trended to be higher among children in probiotc (β = 0.25) and synbiotic (β = 0.26) arms compared to placebo in multivariable linear regression model. Our study describes that young SAM infants had a higher rate of weight gain when supplemented with probiotic alone, compared to their counterparts with either synbiotic or placebo.Severe acute malnutrition (SAM) is a major global public health problem. We aimed to assess the effects of probiotic and synbiotic supplementation on rate of weight gain and change in length in young SAM infants. This study was substudy of a single-blind randomized clinical trial (NCT0366657). During nutritional rehabilitation, 67 <6 months old SAM infants were enrolled and randomized to receive either probiotic ( Malnutrition is responsible for around 60% of childhood deaths under the age of 5 years and two-thirds of those occur during 1st year of life2. Despite introducing standardized treatment and re-feeding protocols, inpatient mortality reaches up to 30% in many hospitals6. Bangladesh also contributes to this high inpatient mortality rate owing to the high prevalence of malnutrition in the country7. The situation is further deteriorating as the COVID-19 pandemic is having a devastating impact on food security8. Infancy represents a critical window when the gastrointestinal system develops rapidly, a process that accompanies characteristic age-specific changes in the intestinal microbiota11. Recent reports suggest about four million infants aged under 6 months are suffering from SAM worldwide13. The mortality of children with SAM is primarily associated with diarrhea, intestinal inflammation, and systemic inflammation14. However, these associations are not solely mediated by the presence of enteropathogens15, and metagenomic and molecular approaches have revealed substantial loss of microbiome diversity in large gut of children with SAM16.Globally, 13.6 million children under the age of five are affected by severe acute malnutrition (SAM)17. Probiotics are live, commercially-produced bacterial taxa that are generally recognized as safe (GRAS) when administered in appropriate levels to the host18. Among the most common probiotics are various species and strains within the genera, Lactobacillus and Bifidobacterium24. Bifidobacterium sp. constitutes the most abundant member of the gut microbiota in young infants. The nutritional value of foods is influenced partially by the intestinal microbial community and the and the ability of that community to break down dietary oligosaccharides (fibre components) that are not digestible by the host25. Evidence suggests that children’s nutritional status is also connected to their gut microbial community26. Recent works indicate that children with SAM have been found to suffer from a condition known as gut dysbiosis or the imbalance in the microbial community, which subsequently mediates some of their nutritional deficiencies and pathological conditions31.The human digestive system possesses a diverse microbial community (the gut microbiome) which mediates various metabolic functions essential to the host physiology32. These undigested dietary carbohydrates are fermented by colonic bacteria in order to produce short-chain fatty acids (SCFA) as end products that can provide nourishment to the colonic cells33, lower the colonic pH, minimize enteropathogen growth, and support the development of the immune system. Human milk contains an abundance of unique prebiotic oligosaccharides, known as human milk oligosaccharides (HMOs) which are primarily metabolized by infant-specific gut microbiotia such as Bifidobacterium infantis34 producing acetate and lactate as end products. Infants who are not breast fed have a paucity of B. infantis in their gut microbiome which gives rise to a more diverse gut microbiota containing elevated levels of opportunistic pathogens due to the deficiency of HMO35. Ready-to-Use Therapeutic Foods (RUTF) are used today to facilitate weight gain in millions of children threatened by acute malnutrition in the world today. However, such foods do not contain any HMO for the support of infant-spedific gut bacteria and the effort and time required to feed an infant several times per day may not be feasible for mothers of low socioeconomic status as they are involved in different types of employment and activities36. To improve the rate of weight gain in severely malnourished children and prevent malnutrition, different interventions are being carried out in different countries, for example: l-carnitine supplementation37, Lipid-Based Nutrient Supplements38, ready to use therapeutic foods39 etc. Several researchers have reported a positive association of probiotic supplementation with child growth40. However, none of these studies were conducted in low-and-middle-income settings involving young infants aged less than 6 months40 and these studies vary widely in the probiotic taxa used, most of which do not carry HMO processing genes. Decreased breastfeeding practices have been reported among children with severe acute malnutrition41, implying reduced availability of prebiotic human milk oligosaccharides (HMOs), which are in turn needed to promote the growth of age-specific probiotic microbiota, such as B. infantis42. A recent systematic review has reported that probiotics have a heterogeneous effect on linear growth of healthy children in low and middle income countries43.Prebiotics generally refer to oligosaccharides that are indigestible to the host, but are consumed by gut microflora, including any added probioticsThis current study was conducted to explore the role of probiotic and synbiotic supplementation on the ponderal and linear growth of partially/non-breastfed infants of 2–6 months of age with SAM, recovering from acute illness in a hospital.It was a single-blind randomized clinical trial.The study was conducted at Dhaka Hospital . The institution has well-equipped clinical care set ups as well as laboratory facilities capable of performing all the analyses required. After acute phase management, children with severe acute malnutrition (SAM) were transferred to the Nutritional Rehabilitation Unit (NRU) for stabilization and preparation for discharge for home.44.Defined by weight for length Z score < − 3, according to WHO growth standard or presence of bilateral pedal edema, independent of anthropometric measurementsFrom the start of enrolment, 28 days supplementation period, and four weeks of post supplementation follow-up.The rate of weight gain (gm/kg/day) was measured using the following formula,Placebo (Lactose)B. infantis EVC001)Probiotic  + prebiotic (Lacto-N-neotetraose -LNnT)Synbiotic: Probiotic  to ensure equal participation of infants by age groups. Allocation was achieved by using random permuted blocks of the size of three prepared by a third party not related to the study.For this pilot trial, the sample size was 67 SAM infants. As it was a pilot trial, the minimum number of patients was included in each arm.https://clinicaltrials.gov/ct2/show/NCT03666572) and the research protocol was approved by the Institutional Review Board of icddr,b (Protocol reference no. PR-17112) which is an independent body that approves the research protocol, Secures the rights of the participants and investigators. Written informed consents were obtained from the legal guardians or parents of the participants prior to their inclusion in the study. All procedures followed in the present study were in accordance with the ethical standards of the responsible committee on human experimentation  and with the Helsinki Declaration of 1964. It is registered with the ClinicalTrials.gov and the registration number is NCT03666572. All clinical aspects of the study were supervised by the investigators and study physicians. All activities were conducted in accordance with the Helsinki Declaration of 1975, revised in 1983. Names of the participants were de-identified prior to analysis.The randomized controlled trial was registered at ClinicalTrials.gov (NCT03666572) on 12/09/2018 . In the NRU, F-75 was replaced by F-100 therapeutic milk (WHO protocol)46. The nutrient composition of both diets are included in the supplementary table. Both the feeds were prepared by the hospital dietician, and F-100 was provided with a frequency of every 2–3 h, and a daily volume of 150 ml/kg for non-edematous infants and 130 ml/kg for edematous infants. In addition to feeding as a routine practice, all caregivers at NRU received sessions on psychosocial stimulation, immunization, follow-up, etc. Breastfeeding was encouraged in between the feeds. The amount of breast milk consumed was measured by the test weighing method47. The contents of sachets of placebo or probiotic were mixed with a teaspoon of infant formula or breast milk and fed to the infant once a day. The prebiotic, LnNT, was mixed with F-100 (1.6 g in 200 ml of F-100) during the hospital stay.Eligible SAM infants were treated for the acute phase management in the Longer Stay Unit by the standardized icddr,b treatment protocol for children with SAM48. The caregivers had been trained by study personnel on the proper administration of the supplements and reconstitution of infant formula. After discharge from the hospital, the caregivers of the infants in the synbiotic arm were advised to mix one sachet of prebiotic (1.6 g) with 120 ml of infant formula to be given twice a day to complete the total duration of supplementation of 4 weeks.The infants were discharged upon being declared clinically well and, if possible, upon attainment of a WLZ ≥ − 2Infant formula provided to the study participant mothers who were not breastfeeding or patially breast feeding their infants did not contain any probiotics or prebiotics.Infants were followed up through home visits by Field Research Assistants (FRA) twice weekly throughout the entire study period of 8 weeks (4 weeks supplementation + 4 weeks post-supplementation follow-up). Caregivers were asked to keep empty sachets after administration of supplements. FRAs collected the information on daily administration of supplements. Missing doses were not repeated. Infants developing an infection during the follow-up period were re-admitted to the hospital.Change in rate of weight gain (g/kg per day) with supplementsChange in Length-for-age Z score with supplementsData were collected by study physician in the case record forms. Data were randomly checked by co-investigators. Data will be immediately copied on the hard disks of two computers as soon as data verification is completed.Data were analyzed through the statistical software Stata (15.0 version for windows) after cleaning with repeated checks. To explore demographic, clinical, and socioeconomic data, we used the median with an interquartile range (IQR) for asymmetric quantitative data and frequency with proportion for categorical data. To evaluate the difference in baseline characteristics Krusker wallis test was done in the continuous dataset of three supplements. Chi square test was done for categorical data set of baseline characteristics.As 47 infants had edema during enrolment, the rate of weight gain was calculated from the change of weight at hospital discharge to the end of study period. It was not possible to calculate WAZ during enrolment of patients with nutritional edema as it would not represent their actual Z score. To keep the uniformity of data, the change in LAZ was assessed from discharge to study completion.The effect of the supplements on the rate of weight gain of the infants as well as the change in LAZ from discharge to study completion was examined using multivariable linear regression models. The rate of weight gain model was adjusted for sex, age (in months), and WAZ at the time of discharge. The change in LAZ model was adjusted for sex, age (in months), and LAZ at the time of discharge. For a sensitivity analysis, we examined the effects of probiotic and synbiotic supplementation on weekly WAZ scores from the first week following enrollment to study completion in a mixed-effects multiple linear regression model specifying a random effect at the child level to account for within-child correlations. The mixed-effects model was adjusted for sex, age (in months), and WAZ at enrollment. We had 8 data points per infant in our total 8-week study period for WAZ. Three infants had edema in the first week following enrollment, so they were excluded and analysis were carried out on 64 infants. The strength of association was expressed as β (95% CI), where P < 0.05 was considered significant.A total of 67 SAM infants were included in the analysis. Out of 67 SAM infants, 23 infants were in the placebo group, 21 infants were in the probiotic group, and 23 infants were in the synbiotic group. Table Table In the adjusted model, a significant increase in weight gain of 2.03 units was observed with the probiotic group compared to the placebo group. A lesser, but still significant increase in weight gain vs. placebo was also seen in the symbiotic group Table .Table 3IIn the linear mixed-effect model the probiotic was significantly associated with the weekly change in WAZ after for adjusting sex, age and WAZ during the first week Table B. infantis EVC001 (the probiotic group) demonstrated better weight gain in comparison to the other two groups who were supplemeted with either the synbiotic or placebo.There is paucity of studies assessing the impact of probiotic and synbiotic supplementation on rate of weight gain among young malnourished infants. In this respect, this is the first randomized clinical trial on probiotic and synbiotic supplementation in young infants with severe acute malnutrition. The most important finding of this study was that the infants supplemented with 49. All the infants included in the study received standardized management of malnutrition. Their diet was also similar, F-75 followed by F-100 and at home infant formula for those who were partially or non-breastfed. The surprisingly reduced growth rate of the synbiotic group relative to the probiotic group may be a consequence of the complete lack of breast milk nutrition in the in the symbiotic group vs. a median of 6.4% of calories from breast milk in the probiotic group  provided in the symbiotic group. synbiotic group. HMOs modulate health by serving as decoy receptors for many opportunistic infectious agents50, improves host defence mechanism by strengthening the gut barrier activities51, and improves immunity.The mean rate of weight gain for SAM infants  was 4.35 g/kg/day in placebo arm, 5.90 g/kg/day in probiotic arm and 4.77 g/kg/day in synbiotic arm, with the rate for the probiotic arm being graded as moderate by WHO52. B. infantis strains characterized to date can grow in vitro with HMOs as a sole carbon source; monosaccharides from HMOs are key components for the “bifid shunt” (fructose-6-phosphate phosphoketolase pathway), which produces ATP, acetate, and lactate as end products53. With this special metabolic process B. infantis is able to utilize LNT and LNnT (with Bifid shunt)54. In addition safety of LNnT was evaluated by EFSA (European Food Safety Authority) for infants55. For these reasons, we used LNnT as prebiotic in our study with B. infantis, a known consumer of prebiotic.More complex HMOs, LNT, and LNnT core can comprise up to 70% of total HMOs57. However, the rate of weight gain was not calculated in any of these studies. During recovery phase many studies have shown rapid weight gain58 and during this period proper energy intake is important. Recently, researchers have proposed at least three possibilities for this change in growth rate: (1) a change in total nutritional intake ; (2) more efficient utilization of protein and calories throughout the period of rapid growth; and (3) differences in the composition of the tissue being laid down59. In absence of any other supplementary food consumption relative to the placebo control group, we found a weight gain of 5.90 g/kg/day in the probiotic group after discharge from hospital to the end of study. The rate of weight gain observed in this trial by simply providing a specific infant-oriented probiotic (B. infantis EVC001) with no other nutritional changes was comparatively higher than that found in other studies with supplementary foods60.Follow-up after discharge from NRU can help to assess the growth and development of children. Previous studies have highlighted the mortality outcome after discharge from facility-based management of SAM62, and/or potentially decreasing the risk of anaemia63.The importance of the gut microbiome in child growth has been well recognized in previous studies. Some probiotics may help to improve child growth indirectly through the prevention of infections and micronutrient and vitamin deficiencies Pediococcus pentoseceus, Leuconostoc mesenteroides, Lactobacillus paracasei, and Lactobacillus plantarum on the growth of under-nourished children in a large RCT (PRONUT study) was studied by Kerac et al.64. In that trial, probiotics did not appear to give any benefits on the health or the nutritional condition of these children64. Importantly, the probiotics used in the PRONUT trial represent bacteria not commonly found in any large numbers the human gut and especially the infant human gut. Furthermore, the age range of the subjects in the PRONUT trial was from 5 months to 14 years, the children were HIV positive, and being treated with antibiotics65, which would have had a significant impact on the gut microbime community itself.The effect of a four-probiotic supplement (Synbiotic 2000 Forte) comprising 65. However, results were inconsistant as the studies varied in type and quantity of probiotic taxa given, duration of interventions, characteristics of participants, setting, and units of outcome measures. Several studies were conducted in different countries to determine the anthropometric changes with probiotic supplementation but these were limited to children less than 5 years of age67.The benefit of dietary intake of probiotics mixed with infant formula in terms of weight and height gain in malnourished children and possible benefits in terms of weight gain in well-nourished children in developing countries has been reviewed69. Use of specific prebiotics as dietary fibers has been shown to stimulate satiety hormones and improve appetite control, which may assist in body weight control70. Consequently, some researchers have focused on the synergistic effect of pre- and probiotic combinations and the modulation of the immune system by combining probiotics with LC-PUFA (long chain polyunsaturated fatty acids)71. Other studies in the review, also added either prebiotics or LC-PUFA did not explain any major benefits in developed country settings73.Dietary carbohydrates, especially the fibre that is not digested in the upper gastrointestinal system, are known to boost growth and functionality of the gut microbiota communityB. infantis EVC001 result in a better rate of weight gain in comparison to the synbiotic supplementation. In addition, the weekly change of WAZ also shows better result when predicted by the probiotic supplement. The trend in the change of LAZ, from hospital discharge to study completion is also comparately better in the symbiotic supplementation arm (0.26 unit). The small sample size and short duration of follow up, might have an impact on insignificant difference in LAZ.. However, a study in Kenya reported that no significant benefit in height for age z score74 could be achieved in spite of inpatient medical and nutritional management for complicated SAM followed by outpatient therapeutic feeding and active follow-up to 1 year with access to additional care and consultant services. In that study, a rapid weight gain was nevertheless observed. In the limited period of 1 month, we were unable to find any significant change in linear growth among the SAM infants supplemented with the probiotic/synbiotic.We have observed that probiotic supplements comprising 65. In our study we have focused on supplementation of the infants with only Bifidobacterium infantis which is the predominant bacteria during infancy and is subsequently depleted among SAM infants resulting to persistent immaturity of gut microbiota16. The recently published SYNERGIE trial observed that B. infantis EVC001 supplementation can improve weight gain and reduce intestinal inflammation in young severely malnourished infants75. The SYNERGIE trial also reported that a extant strain of Bifidobacterium infantis (B. infantis Bg_2D9) derived from a healthy Bangladeshi infant contained most of the genes associated with the metabolism of human milk oligosaccharides found in the EVC001 strain of this study, but was missing key HMO transporters located on the H5 gene cluster (Blon 2175–2177). However, when this B. infantis Bg_2D9 was introduced to mice models raised in gnotobiotic environment and colonized with fecal microbiota of SAM infants, and fed a Mirapur diet, increased weight gain over the EVC001 strain was observed75. This was likely because the Bg_2D9 strain also contained glycosyl hydrolases that were able to deconstruct the plant fiber found in the Mirapur diet but not found in breast milk suggesting the Bg_2D9 strain may be most effective for weaning or transitional diets whereas the EVC001 strain would be preferred for infants getting most of their nutrition from human milk. The gnotobiotic mouse model data with Bg_2D9 may or may not be predictive of weight gain, weekly change of WAZ, Delta LAZ of the infants with supplementations in this study.A systematic review has shown the effect of different probiotics in different studies76. Some studies demonstrated that relapse of SAM or poor weight gain in spite of protocolized management could be related to anthropometric indices on admission78.We have observed that approximately 15% of the study infants with SAM (n = 9) became sick and were shifted to the longer stay ward while on supplementation in NRU and then treated accordingly. When they became well and were discharged from hospital they continued with the study supplements at home. The reasons of transfer from the NRU were pneumonia (6 cases), diarrhea (2 cases) and sepsis (1 case). These infections were thought to be hospital acquired and the rate was similar to a previous study conducted in same settingOne limitation of the study was that the infants were only followed for a period of 8 weeks so longer term outcome effects of the supplements are unknown. We had no record on the volume of breast milk or infant formula during follow up period. We could not control the use of antibiotics during follow up period in the community as they may have visited local physicians or pharmaciess without informing the study staff. Although our research assistants collected data twice a week from SAM infants the actual intake of feed was not meticulously measured and we had to rely on the mothers’ information on diet and supplemetation. Daily visit of field staffs could minize these undue activities of caregivers. Finally, some of the infants started complementary feeding after 6 months and such feeding data were not included in the analysis.B. infantis in the post-SAM period without any further changes in their diet. A larger trial with a longer follow up period is required to assess the long term effects of probiotic supplementation on ponderal as well as linear growth of infants with severe malnutrition.The findings from the study support the possibility of establishing a moderate weight gain in infants hospitalized with SAM by using a probiotic supplementation protocol comprising Supplementary Information.


IrisPlex system represents the most popular model for eye colour prediction. Based on six polymorphisms this model provides very accurate predictions that strongly depend on the definition of eye colour phenotypes. The aim of the present study was to introduce a new approach to improve eye colour prediction using the well-validated IrisPlex system. A sample of 238 individuals from a Southern Italian population was collected and for each of them a high-resolution image of eye was obtained. By quantifying eye colour variation into CIELAB space several clustering algorithms were applied for eye colour classification. Predictions with the IrisPlex model were obtained using eye colour categories defined by both visual inspection and clustering algorithms. IrisPlex system predicted blue and brown eye colour with high accuracy while it was inefficient in the prediction of intermediate eye colour. Clustering-based eye colour resulted in a significantly increased accuracy of the model especially for brown eyes. Our results confirm the validity of the IrisPlex system for forensic purposes. Although the quantitative approach here proposed for eye colour definition slightly improves its prediction accuracy, further research is still required to improve the model particularly for the intermediate eye colour prediction. While forensic genetic research is searching for additional phenotypic characteristics for predicting human appearance, those related to the pigmentations  are today among the ones best characterized and validated4. In this context, eye colour is the best investigated phenotype for forensic genetic applications. In fact, a lot of genetic variants have been successfully identified in relation with iris pigmentation9. Some of these variants constitute the so-called IrisPlex system that to date represents the most popular model for eye colour prediction10. This system is based on the analysis of six Single Nucleotide Polymorphisms (SNP) located in six different genes: rs12913832 (HERC2), rs1800407 (OCA2), rs12896399 (SLC24A4), rs16891982 (SLC45A2), rs1393350 (TYR) and rs12203592 (IRF4). The IrisPlex model is based on a multinomial logistic regression model by which each individual is classified as being brown, blue or intermediate11. The parameters of such a model were initially estimated using phenotype and genotype data from 3804 Dutch individuals. In particular, genetic data are modelled in an additive fashion  and the highest probability of all 3 categories was taken as the predicted iris colour of that individual. Using this model, very accurate prediction values were obtained for brown and blue eyes, while the prediction of intermediate colour is less precise. There have been several attempts to refine the IrisPlex system to improve its predictive value. These were based on both an increased number of analysed genetic variants and a different statistical modelling strategy14. However, despite these precautions, these alternative systems did not obtain the desired effects since recent data showed that the IrisPlex system still was the best performing model for eye colour prediction15. Eye colour is usually described qualitatively using subjective and visually defined phenotype categories. This discretization approach oversimplifies the quantitative nature of the trait causing an inevitably loss of information2. For this reason, several authors proposed quantitative measurements of iris colour19. This strategy not only allowed in the past years the identification of new genetic variants, but also the determination of a genetic model able to explain about 50% of quantitative eye colour variation17. Anyhow, the introduction of these measurements requires a methodology able to capture eye/hair colour in its fully continuous spectrum as accurately as possible2 since current models for eye colour prediction, such as the IrisPlex system, are not able to handle this kind of data.Forensic DNA Phenotyping (FDP) is an emerging field of forensic genetics aimed at prediction of externally visible characteristics (EVC) of unknown sample donors directly from biological materials found at the crime scene. This approach is expected to provide clues helping investigators reduce/prioritize their list of suspects and make police investigations more rapid, efficient and less expensiveThe aim of this present study is to introduce a new quantitative approach for eye colour prediction using the well-validated IrisPlex system and high-resolution digital images and genotype data from 238 individuals from a Southern Italian population. To this purpose, several alternative iris colour categorizations were evaluated and inserted within the frame of the IrisPlex model for improving its classification accuracy.HERC2 gene.Table The visual inspection produced the following eye colour distribution in the analysed sample: 29 blue (3 blue-grey and 26 sky-blue), 55 intermediate (34 chestnut-green and 21 green), and 154 brown (52 light brown and 102 dark brown).In order to obtain an objective eye colour classification, several clustering algorithms were applied on the CIELAB parameters. Table We select the best clustering model based on a Pareto-optimal criterion; solutions that were top-ranked in either silhouette or adjusted rand index were deemed the optimal ones see Fig. . AccordiAlluvial plots Fig.  and 2 shWe then labelled each cluster according to the prevalence of the colour flows into the cluster itself. For all clustering solutions, cluster 1 was labelled as blue, cluster 3 as intermediate and both cluster 0 and 2 as brown. In general, all clustering results allowed to distinguish between a light and a dark intermediate colour .Table Since the best-performing clustering solution was the k-means on the original  data, all subsequent analyses were performed based on eye-colour defined on the basis of such an algorithm.Figure In order to investigate this increase in accuracy, Fig. Regarding the effect of thresholding, it can be observed that increasing the threshold to 0.7 redefined as undefined the brown eyes that are incorrectly predicted as blue. Brown eyes became inconclusive by 3.2% (5 out of 154) for eye colour defined by visual inspection and 7.1% (13 out of 183) for the clustering-based approach, respectively. Blue eyes predicted as brown were reduced by 40% (2 out of 5) for eye colour defined by visual inspection and 66.7% (2 out of 3) for the clustering approach. Intermediate eyes were never predicted as intermediate but incorrect predictions as brown decreased by 26.9% (14 out of 52) for eye-colour defined by visual inspection; for eye-colour defined by clustering analysis, applying a 0.7 threshold reduces the number of incorrect predictions by 22.2% (8 out of 36 samples becomes undefined).In Table The ternary plots in Fig. These plots also clearly underline the samples deemed as intermediate by visual inspection that become brown according to the clustering analysis (points switching from green to brown in the bottom left corner of the panel b) as well as the blue samples  turning into intermediate  in the top corner of the two triangles.25. Indeed, the accuracy values for blue and brown eye colour categories in our sample were very high and equal to 0.972 and 0.809, respectively, while no one intermediate eye colour was correctly predicted as previously reported in another Italian sample15.In the present study the efficacy of the IrisPlex model for eye colour prediction was analyzed in 238 individuals of Italian ancestry to evaluate their possible applicability as a tool of DNA intelligence in forensic investigations. Our results confirm the previous findings from several different populations showing once again that the IrisPlex system predicts blue and brown eye colour with high accuracy while it is inefficient in the prediction of intermediate eye colour19. Clustering algorithms applied on the CIELAB parameters allowed us to obtain a standardized and objective measurement of eye colour, as well as, a better and more precise definition of the phenotype under study. Slightly improved results were obtained when this clustering-based approach was used for eye colour classification. In particular, using several clustering algorithms applied on quantitative measurements of iris colour, we obtained an improved classification performance especially for the clustering-based brown category.Here, we quantified continuous eye colour variation into CIELAB colour space using high-resolution digital full-eye photographs following the procedure reported in EdwarsThe clustering-based approach here proposed, likewise other similar quantitative approaches for eye colour definition, may also be exploited as a standardized and objective measurement of eye colour useful also because it makes possible to directly compare results from different studies. In fact, one of the most important limitations affecting the development of a genetic model for eye colour prediction is the definition of the phenotype. Subjective interpretations of eye colour, by oversimplifying the quantitative nature of the trait and causing an inevitably loss of information, makes it difficult to compare and validate the results obtained in different populations and this also affects the classification performance of the adopted model.14. This approach did not obtain the desired effects since the IrisPlex system still represents the best performing model for eye colour prediction. Within this context, another very promising approach seems to be the inclusion of epigenetic markers. In fact, several authors observed that the hect domain and RCC1-like domain 2 (HERC2) rs12913832 variation, the marker of the IrisPlex system with the highest discrimination power, is located in an enhancer element that regulates the expression of OCA2 gene7. In addition, it was also shown that OCA2 expression was reduced in lightly pigmented melanocytes with the rs12913832-G variant with respect to darkly pigmented melanocytes with the A allele26. In agreement with this observation, the inclusion of epigenetic markers in the IrisPlex model might be useful to improve its prediction accuracy and in particular for the non-blue and non-brown eye colours.There have been several attempts to refine the IrisPlex system to improve its predictive value mainly focused on the increase in the number of genetic variants23. In line with these results, the Section of Forensic Genetics in Denmark recently began offering eye colour prediction to the police using two categories of eye colour (blue and brown) through the analysis of rs12913832 variability. All these lines of evidence, together with our results, suggest that the current definition of eye colour based on visual inspection should either be re-defined on the basis of more quantitative criteria or should be dropped all together in favour or a two-colour definition.Although the quantitative approach here proposed for eye colour definition improves the prediction accuracy of IrisPlex system, further research is still required to improve the model performance particularly for the non-blue and non-brown eye colour prediction.The aim of this work was to test the predictive capabilities of the IrisPlex system, using eye colour definitions based both on visual inspection and on quantitative approach (clustering). Consequently, we based our attention to the clustering solutions in which three or more groups were identified, discarding clustering solutions identifying only two eye colours, since testing the IrisPlex predictions on these solutions would have been problematic. However, an interesting study carried out by Meyer et al. clearly showed that the perception of intermediate eye colour varies greatly among individuals, and this represents the main reason why using only two categories of eye colour (blue and brown) provides better results than a three-category system The present study was carried out at the Department Biology, Ecology and Earth Sciences of the University of Calabria within a recruitment campaign focused on students and staff of the University between November 2018 and October 2019. 238 individuals (72 men and 166 women) were recruited. Trained staff members administered a brief and standardized questionnaire in order to obtain information regarding the socio-demographic data. During the interview, eye images using a professional camera were obtained and buccal swabs were collected as source of DNA. Written informed consent was obtained from all recruited individuals. The study was approved by the Ethics Committee of University of Calabria (Prot. NP-5942018) and met the criteria of the Helsinki declaration.Photographs were taken at a distance of approximately 10 cm of each individual’s left iris under similar light conditions with a Nikon P300 with 100 mm f/1.8 NIKKOR Optical Zoom Lens, ISO 800. A coaxial biometric illuminator was used to deliver a constant and uniform source of light to each iris at 5,500 K (D55 illuminant).25. Briefly, each eye image was graded independently by 2 different observers who classified eye colours into four categories: blue (including blue-grey and sky-blue), green , chestnut-green  and brown (including light brown and dark brown). In order to keep the three-category classification of the IrisPlex model and to ensure consistency across studies, we mapped green and chestnut-green categories to intermediate category. Note that these two categories correspond to light intermediate and dark intermediate classes described in other studies25. A third observer was consulted to resolve inconsistencies through majority-voting and to assess the final eye colour of each volunteer. Overall, 91% (217/238) of the classifications showed complete agreement between the 2 observers. Of the 21 remaining discrepancies, 18 were between light brown and chestnut-green, finally classified 17 as light brown, one as chestnut-green; the remaining discrepancy was between sky-blue and green, finally classified as green.Iris colour was classified qualitatively by human visual identification as already described in other studies19. In brief, after the scleral, pupillary and collarette boundaries are defined, the application automatically extracts a measurement of average eye colour starting from a 60° angle wedge taken from the left side of the iris. The web application also isolates the portion of the wedge that represents the ciliary zone and the portion of the wedge that represents the pupillary zone. At the end of this procedure, for each iris image, the average RGB value of the entire wedge, the ciliary and the pupillary zones are obtained. The obtained RGB values are then converted into in CIE 1976 L*a*b* (CIELAB) colour space. In this colour space, the L* coordinate represents the lightness dimension and ranges from 0 to 100, with 0 being black and 100 being white. The red/green colours are represented along the a* coordinate, with green at negative a* values and red at positive a* values. The yellow/blue colours are represented along the b* coordinate, with blue at negative b* values and yellow at positive b* values.Image processing was based on the procedure reported in Edwards and colleagues using the dedicated webtool25, the method here adopted as reported in Edwards et al19, appears to be superior as it allows to manually define the boundaries of the iris and to separate the eye into different regions. Since the left quadrant of the iris was least likely to be obstructed by eyelashes and eyelids, it would bias the colour of the iris towards the pupillary region, we selected a wedge to represent iris colour instead of the entire iris.Although several automated methods have been developed to facilitate the isolation of the iris from photographs of the eye27, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH)28, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)29, hierarchical clustering (hclust)30, k-means31, k-medoids32, k-modes33, mean-shift34, Ordering Points To Identify the Clustering Structure (OPTICS)35, and Spectral Clustering (SC)36. The settings adopted for each of the algorithms is indicated in Supplementary Table 37, produced results comparable with the ones obtained with the Euclidian distance. Thus, we decided to only use the latter, simpler metric rather than CIEDE2000. The optimal clustering solution was chosen according to the silhouette criterion38, while the agreement of each clustering solution with the categorization obtained through visual inspection was assessed through the adjusted rand index39. It should be noticed that among the clustering solutions identified by cluster analysis, since the IrisPlex model was developed for the prediction of three eye colour categories, we evaluated only the solutions providing at least three groups. In particular, solutions with four groups were taken into account only because we condensed two clusters in a single intermediate category.In order to make eye colour categorization process more objective, a cluster analysis approach based on the coordinates in CIELAB space was carried out. To this purpose, several clustering algorithms were experimented, including Affinity Propagation (AP)10. Genotyping was performed using TaqMan genotyping assays following manufacture’s instruction and 10 ng of DNA mixed with the TaqMan Genotyping Master Mix (Thermo Fisher Scientific).Genetic profiling was carried out on the DNA extracted from buccal swab samples by analysing the genetic polymorphisms included in the IrisPlex10. The parameters of such a model were estimated using phenotype and genotype data modeled in an additive fashion . Prediction with the IrisPlex model were obtained using the dedicated webtool (https://hirisplex.erasmusmc.nl/). As suggested by the authors, the predicted colour was the one with a probability higher than the threshold of 0.7. Individuals with all the colour probabilities under 0.7 were marked as “undefined”. Additionally, we also applied a threshold of 0.5. When no threshold was applied, the predictions were assigned to the colour with the absolute highest probability. In this last case, individuals that obtained equal probabilities for multiple (two or three) colour categories were classified as intermediate.From a statistical point of view the IrisPlex system exploits a multinomial logistic regression model by which each individual is classified as being brown, blue or intermediate based on the three obtained prediction probabilitiesSupplementary Information.


In March 2020, WHO announced the COVID-19 a pandemic and a major global public health emergency. Mortality from COVID-19 is rapidly increasing globally, with acute respiratory failure as the predominant cause of death. Many patients experience severe hypoxia and life-threatening respiratory failure often requiring mechanical ventilation. To increase safety margins during emergency anaesthesia and rapid sequence intubation (RSI), patients are preoxygenated with a closed facemask with high-flow oxygen and positive end-expiratory pressure (PEEP). Due to the high shunt fraction of deoxygenated blood through the lungs frequently described in COVID-19 however, these measures may be insufficient to avoid harmful hypoxemia. Preoxygenation with inhaled nitric oxide (iNO) potentially reduces the shunt fraction and may thus allow for the necessary margins of safety during RSI.2), and the null hypothesis is that there is no difference in the change in SpO2 following initiation of iNO.The INOCOV protocol describes a phase II pharmacological trial of inhaled nitric oxide (iNO) as an adjunct to standard of care with medical oxygen in initial airway and ventilation management of patients with known or suspected COVID-19 in acute respiratory failure. The trial is parallel two-arm, randomized, controlled, blinded trial. The primary outcome measure is the change in oxygen saturation . The SARS-CoV-2 virus may cause a viral pneumonia with serious respiratory failure characterized by hypoxia, hypocapnia and elevated respiratory rate, defined as the Corona virus disease COVID-19) [9 [1]. So2).We aim to perform a randomised trial to evaluate the effect of preoxygenation with 100% oxygen alone or in combination with iNO in hypoxic patients with suspected COVID-19, on the level of blood oxygen saturation  used as an adjunct to standard care in patients with known or suspected COVID-19 in acute respiratory failure.2 in the first five minutes after administration, and the null hypothesis is that there is no difference in the change in SpO2 following initiation of iNO and 100% oxygen compared to 100% oxygen alone. The trial is initiated as a single-centre study but is open for inclusion from other centres that have iNO delivered by INOblender®  as a treatment option.The primary objective of the present trial is to evaluate the clinical efficacy of iNO to increase oxygen saturation prior to, during and after rapid sequence induction (RSI) emergency anaesthesia in patients with suspected or confirmed COVID-19 respiratory failure. Primary outcome measure is the change in SpOth of May, 2020 (REK-128904).The INOCOV protocol was designed using the Norwegian clinical research infrastructure network templates  and willParticipants are recruited by emergency medical teams on-call. OUH, Air ambulance department crew consist of a consultant anaesthesiologist and helicopter emergency medical service (HEMS) crew or paramedic dispatched either by helicopter or rapid response car. To include patients in the present trial, the team members have to undergo formal training in pre-hospital iNO administration and the present study protocol.≥18 years of ageSuspected or probable cases of COVID-19 based on WHO definitions 2 <90% despite three minutes preoxygenation with 10cm H2O positive end-expiratory pressure (PEEP) and high flow 100% oxygenPersistent SpO2O PEEP + 100% O2 i.e. at time of allocation (t0) have a SpO2 <50%, or a reliable SpO2 is impossible to obtainPatients who after 3 mins of ventilation with 10cm HPatients in severe kidney failure or dialysisKnown or suspected pregnancy based on information on the time of inclusion2)Hypersensitivity to the active substance (NO) or to the excipient  or the next of kin will be informed with written information (see patient consent form) and asked to participateThe consent will be for storing, analyzing and publish data collated in the CRF If the consent is given by next of kind of a deceased patient, or a patient deemed to not recover to consent competency, the consent from next of kin is considered to be finalIf the consent is given by next of kin of a patient suspected to recover from incompetency, the consent from the patient will be obtained after recoveryInformation about the study and consent is given both oral (study personnel) and written (patient consent form)Consent is written (patient consent form). Oral consent can be obtained but require signature from two health care personnel (of which one study personnel/ investigator) stating that the patient has consentedPatients will be randomized in a 1:1 ratio between the two treatment arms, by a computer-generated block randomization with varying block sizes. Should more than one center be included, the randomization will be stratified by center. The institutional pharmacy at OUH will produce sealed envelopes with randomization, to be opened by the attending doctor at the time of inclusion.The INOmax gas blender and cylinder is contained in a bag, and the cylinder valve with the pressure gauge will not be visible to the study doctor, only to the study assistant. The assistant will either open or mimic opening the INOmax cylinder according to allocation, and is instructed not to reveal the allocation, unless explicitly asked to do so in the case of emergency unblinding. The allocation will be unblinded after arrival in hospital, after the patient is intubated or a decision is made not to perform intubation.2O by sealed face mask prior to any screening for inclusion. If SpO2 remains <90% at this point, patient will be assessed and randomized to either adding iNO or continuing 100% oxygen only. If SpO2 is >90% after the initial three minutes, patients will be treated as per local guidelines outside study protocol.The main intervention of this trial is to add 20 ppm nitric oxide 800 ppm mol/mol (INOmax®) delivered by the INOBlender® to the medical oxygen delivered by bag/ mask to patients in acute respiratory failure (Blinded treatment of 100% oxygen with or without 20 ppm of iNO will be administered for five minutes before any attempt to intubate (or decision not to) will be made.2/iNO before re- starting ventilation. This is to empty the bag of any toxic NO2 that may have been built up. Regardless, whether RSI is performed, or bag/mask ventilation is continued, a dose reduction of iNO should be attempted if SpO2 is ≥94% after 5 minutes (t1) Reduction is made in steps of 5 ppm every 5 minutes unless SpO2 drops by > 5 percentage points from the value at t1. After arrival in hospital, when patient is intubated or a decision not to intubate has been made, iNO administered by INOBlender should be weaned by 5 ppm every 5 minutes. In the unlikely event of a rebound effect (as assessed by the study doctor) iNO may be continued temporarily as necessary. iNO administrated by other devices (i.e.: not IMP) may be initiated as per local routines at the discretion of the attending doctor taking over the responsibility for the patient.RSI may be performed at the discretion of the study doctor in accordance with local guidelines, most commonly involving ketamine, fentanyl and rocuronium bromide. Following RSI or other reasons for the bag/mask system having been left idle, the ventilation bag is squeezed the 3–4 times with OAny additional medical intervention will be carried out as considered necessary by the attending doctor according to institutional guidelines and established clinical practice.Expected side effects such as systemic hypotension, bradycardia, pulmonary hypotension, kidney failure and methaemoglobinaemia, and platelet will be monitored and recorded as secondary endpoints. The dosage of 20 ppm is in accordance with previous trials, case reports and clinical experience and found to be an adequate balance between sufficient effect and potential side effects ,10–12,14This trial compares the effect of adding iNO at 20 ppm to preoxygenation in patients with COVID-19 and respiratory failure. The primary endpoint is the change in oxygen saturation in the first five minutes after adding iNO. Despite a short-term primary endpoint, the study is designed with a 28-day follow up for assessment of mortality, SARS-CoV-2 status and a range of exploratory outcomes relating to iNO treatment and intensive care management. Please consult 2 non-invasively by pulse oximetry before (baseline (t0)) and five minutes after start of IMP administration (t1). SpO2 serves as an approximated measure of arterial oxygen saturation. C.f. Primary endpoint is assessed by measuring SpO2 from baseline to 10 minutes after start of IMP administration (1.2.1) (1.2.4). Respiratory rate (1.2.5) reflects hypoxic drive and is expected to fall as SpO2 increases. Arterial blood gas analyses (1.2.6 and 1.2.7) are not easily obtainable in the pre-hospital environment but is considered a more accurate reflection of oxygen saturation. Where available, this measurement will be included in the analyses. On the extreme, cardiac arrest or severe bradycardia (1.2.8) are markers of hypoxemia with potentially lethal consequences and are included in the secondary analyses. Glasgow outcome score extended (GOS-E) may reflect brain damage following hypoxia (1.2.9). Mortality in 24 hours (1.2.9) may be direct result of hypoxemia in this patient group and a potential important marker for the efficacy of iNO. Mortality at 28 days is relevant as late complications following hypoxemia may manifest at this stage. Secondary endpoints also include known adverse effects of iNO. Although these are not described after short-term administration, they will be included in this study. Both bradycardia and hypotension are possible side effects of NO-gas. Heart rate and blood pressure are endpoints monitored every 5 minutes during the intervention phase. (2.1.1 and 2.1.2).Secondary endpoints include change in SpOAll other secondary outcome parameters are collected from the patient journal.Acute kidney injury is classified in three stages according to Kidney Disease Improving Global Outcome Guidelines (KDIGO) . (2.2.2)Source data in this trial includes study specific paper case report form. In addition, all patient records may be sources of specific information. Data will be collected manually on study specific paper CRF at the inclusion visit (the acute event) and by day 28 visit. For patients in this trial this includes:Study specific paper CRF inclusion visit and day 28 visitPrehospital paper records, computer notes in LABAS, records in AMIS and sound or in medical dispatch system.Records from medical monitors such as LP15 and CorPulseIn-hospital results in DIPS® or other EPJFurther details on source data hierarchy are outlined in the EUDRACT protocol. Members of the steering group will access the OUH EPJ system to obtain the necessary information, and make sure that patients that are transferred or discharged from hospital are not lost to follow-up.Data management will be performed by the data management unit at the Clinical trials unit (CTU), OUH . The datThe clinical study monitor of the study is the monitoring unit at CTU, OUH . The stuPatients will not be able to discontinue treatment prior to end of intervention in the present trial but can discontinue after end of intervention but before the 28-day follow-up.If SpO2 is < 50% at any point after allocation/randomization the patient case can be emergency unblinded and treatment continued as decided by study doctor. In case of cardiac arrest, iNO is continued at the discretion of the study doctor. In case a serious adverse reaction triggered by IMP is suspected, an emergency unblinding should be performed, and treatment with IMP discontinued or continued at the discretion of the study doctor.Definitions of adverse events follow the ICH GCP guidelines. The nature of the patient population studied, the pre-hospital environment, severity of illness included, and multitude of medical interventions makes the adverse events (AE) reporting challenging. Several events that meet the AE criteria set out in GCP are expected in the natural history of critical illness and in the treatment offered to patients included in this trial. On this basis we find support in the literature to prespecify outcomes that would be reported as AEs in other trials but are expected outcomes in the present ,22. The Post-trial care for included patients is equal to any patient not included in the study.The principal investigator will obtain insurance coverage for this study through membership of the Drug Liability Association and ensure proof of membership is sent Competent Authority and stored in the trial master file prior to inclusion of the first patient.The whole trial may be discontinued at the discretion of the principal investigator (PI) or the sponsor in the event of any of the following:Occurrence of AEs unknown to date in respect of their nature, severity and durationMedical or ethical reasons affecting the continued performance of the trialDifficulties in the recruitment of patientsAt sponsors discretionThe sponsor and principal investigator will inform all investigators, the relevant Competent Authorities and Ethics Committees of the termination of the trial along with the reasons for such action. If the study is terminated early on grounds of safety, the Competent Authorities and Ethics Committees will be informed within 15 days.Coordination of the study and training of personnel will be overseen by a steering group consisting of HJH , JH, MR, AS and FH. The steering group will ensure adequate training to ensure that recruiting clinicians have the proper knowledge to male correct inclusions/exclusions and skills to adhere to protocol. Heads of recruiting branches (HJH and AS) will monitor any occurrences of missed cases from the institution´s electronic patient journal system. Depending on the reasons for missing cases, measures such as additional individual training may be initiated by the steering committee. All investigators ascertain they will apply due diligence to avoid protocol deviations.OUH has complete ownership of all data and publishing rights of all results.The full protocol, Statistical Analysis Plan, information letter for consent and other trial documents will be published open access. The Clinical Study report and Statistical analysis report will also be made openly available, but may be altered to hide information that may lead to identification of individual study participants. These documents will be shared at Norwegian Centre for Research Data (NSD).All of the individual participant data collected during the trial, after de- identification will be made available to anyone who wishes to access the data. Data will be made indefinitely available through Norwegian Centre for Research Data (NSD). De- identified data can only be distributed in accordance with the data processor agreement entered into between the Sponsor and NSD.Data sharing with editors or peer- reviewers of scientific journals, conferences or the like will not require specific consent or data access agreement with Oslo University Hospital, in the understanding that the data will not be shared onward or used beyond reviewing this trial.We aim to publish the results of this trial in an international peer reviewed journal with authorships based on the Vancouver Convention. Eligible authors are the authors of this protocol. We have no intention of using professional writers.2 of >5 percentage points [2 (after minus before intervention) of 6.32%. A difference in improvement in SpO2 of 5 percentage points between the intervention groups is considered clinically significant. To identify a difference of improvement of SpO2 of 5 percentage points, with a power of 80%, we need to include 54 patients (27 in each arm) in this trial, using a significance level of 5%.One previous study in a different setting has shown that 71% of patients respond to the treatment with an absolute increase in SpOe points . We assu2 change, a simple blind re-estimation of the sample size based on the variance from a subsample of the 20 first patients will be done. The sample size will be adjusted if necessary, but the total sample will not be reduced (to less than 54). By using a blinded one-sample variance estimator for sample size recalculation, the effect of sample size adjusting on the type I error rate is negligible and no additional measures are needed to control the significance level [Because of the uncertainty related in the magnitude of the variance in the SpOce level .2 from before to after the intervention. The null hypothesis is that there is no difference in improved oxygen saturation between administering oxygen/iNO mixture to spontaneously breathing patients with COVID-19 related hypoxia, compared to oxygen alone. The alternative hypothesis is that there is a difference. The primary endpoint will be analyzed using a linear regression model where the intervention variable will be adjusted by the baseline SpO2. If more than one center is included, and used as a stratification variable in the randomization, this will also be adjusted for in the model. There will be one safety interim analysis in this trial, and one main efficacy analysis at the end of the trial.The primary efficacy endpoint is the difference in the change in SpOGiven the current pandemic development in our region, no patients have been recruited to this trial. Any significant protocol modifications prior to, or during the study will undergo formal application to national medical agency and published through the WHO/EUDRACT system.REK-128904 Click here for additional data file.S1 Study protocol(PDF)Click here for additional data file.