Dexamethasone 6 mg daily for 10 days is the recommended treatment for patients with severe or critical coronavirus disease 2019 (COVID-19). The evidence on the benefit of high-dose dexamethasone is limited. The goal of this study was to assess the effects of 6 mg daily vs. 20 mg daily of dexamethasone in hospitalized patients with COVID-19 pneumonia.We conducted a single-center, randomized, clinical trial involving hospitalized patients with COVID-19 pneumonia. Participants were randomized 1:1 to dexamethasone 6 mg daily or dexamethasone 20 mg daily, and were stratified by the WHO-Ordinal Scale for Clinical Improvement (OSCI). The primary outcome was clinical improvement equal to or greater than 2 points by OSCI on day 28. Secondary outcomes were 28-day mortality, intensive care unit-free days, and ventilator-free days on day 28.Of the 107 patients who enrolled and completed the follow up, 55 patients enrolled in the low-dose group and 52 patients enrolled in the high-dose group. Treatment with dexamethasone 20 mg daily compared with dexamethasone 6 mg daily did not result in better clinical improvement based on OSCI on day 28 . For participants who required high-flow oxygen or noninvasive ventilation at randomization, the 6-mg group had better survival than the 20-mg group on day 28 . Although more participants in the 6-mg group received immune modulators , 50% of death cases in the 20-mg group who required high-flow oxygen or noninvasive ventilation at randomization received immune modulators.Dexamethasone 20 mg daily did not result in better clinical outcome improvement, and was probably associated with higher 28-day mortality in patients on high-flow oxygen or noninvasive ventilation, compared with dexamethasone 6 mg daily.NCT04707534, registered January 13, 2021Clinialtrials.gov number,  Dexamethasone is the recommended treatment for patients with severe or critical coronavirus disease 2019 (COVID-19) . In the Studies revealed that overwhelming inflammation is associated with severe and critical COVID-19 cases . A higheThis study was conducted at the University of Oklahoma Medical Center, Oklahoma, United States. Eligible participants were patients older than 18 years with PCR-confirmed COVID-19 infection on admission, needing supplemental oxygen administered via nasal cannula, face mask, high-flow nasal cannula, or positive pressure ventilation (noninvasive or invasive). Patients were excluded if they had been taking corticosteroids more than 48 hours prior to screening, had an underlying disease requiring chronic corticosteroids, had severe medical events before admission , had contraindication(s) for corticosteroids, or were recruited in other interventional clinical trials. Participation in registries was allowed. Patients were excluded if death was deemed imminent or inevitable during the subsequent 24 hours. Pregnant patients and patients on judicial protection were excluded.The study was registered on clinialtrials.gov  and approved by the University of Oklahoma Health Sciences Center Institutional Review Board (IRB #:12927). Informed consent was obtained from participants or legally authorized representatives.The study was a pragmatic randomized parallel-group controlled open-label trial conducted between January 2021 and December 2021. Participants were randomly assigned in a 1:1 ratio to a high-dose dexamethasone group  or a low-dose dexamethasone group (6 mg once daily for ten days). Participants were randomized with a block size of 2 and 4 and stratified by the WHO-Ordinal Scale for Clinical Improvement (WHO-OSCI) . The ranSubjects were screened and enrolled within 48 hours after the first dose of systemic corticosteroid. Randomization occurred shortly after the informed consent was signed.The pragmatic nature of the trial allowed for primary providers and multidisciplinary team to change the dexamethasone dose and treatment duration if needed after randomization. Participation in the study ended after day 28 or at discharge from the hospital, whichever was earlier.The primary outcome was the clinical improvement at day 28 after randomization. Sustained clinical improvement was defined as an improvement equal to or greater than 2 points using the WHO-OSCI scale, as follows : 1. Not REDCap was used to store clinical data. Data were transferred from REDCap to STATA 16 to perform statistical analyses. Before analyses, data were screened for normality, outliers, and all other statistical test assumptions.t-test or Mann-Whitney test  for two groups. Chi-squared tests and Fisher’s exact tests were used to evaluate the relationship between two categorical variables. Multivariable Cox regression was used to evaluate the overall survival. Prior studies indicated that COVID-19 clinical severity was associated with old age, male sex, and multiple chronic medical conditions [All analyses adhered to an intention-to-treat approach. Descriptive statistics were conducted to characterize the study population at baseline, the health outcomes, and adverse events. Between-group comparisons were made using the independent nditions –13. Clinnditions , 15. AgeClinical improvement was defined as an improvement equal to or greater than 2 points using the WHO-OSCI scale, and was examined on days 14 and 28. The actual days of hospital stay and WHO-OSCI were used for survival analysis.The sample size was calculated based on evaluating change in the primary outcome, the WHO-OSCI, between the high- and low-dose groups. According to the RECOVERY trial , at leasDue to the urgent need for effective treatment, an internal data and safety monitoring board was appointed for monitoring the trial progress. The board included all project investigators. The board members met once per three months, usually by teleconference, to review data from this trial. The data board review included interim analysis of primary and secondary outcome data and dexamethasone adverse events. A special responsibility of the board was to review serious adverse events, as defined by deaths, life-threatening conditions, or events requiring permanent discontinuation of the treatment. If the high-dose dexamethasone group had a statistically significant worse health outcome compared with the control group, it might trigger an immediate suspension of the study. At the end of each board meeting, the board voted whether to continue the study as planned or whether to recommend changes to the study. The board had the option to recommend that the study be stopped early if there was evidence that the risk benefit ratio did not warrant continuation of the trial.A total of 110 patients were consented and enrolled in the trial. Three patients were excluded from the final data analysis . One hundred and seven patients were included for final data analysis . BaselinThe primary outcome was the clinical improvement of greater than or equal to 2 points using the WHO-OSCI . There wMortality on day 28 was one of the secondary outcomes. Five participants (9.1%) in the low-dose group and 11 participants (21.2%) in the high-dose group died by day 28  in the low dose-group and 6 days  in the high-dose group (p = 0.538)The incidence of adverse events, including hyperglycemia (requiring insulin infusion), pulmonary and systemic infections, and gastrointestinal bleeding, was low . BactereOnly five participants had changes in dexamethasone dose or duration when deemed in their best interest by their providers . ParticiIn this randomized clinical trial involving 107 adults with hypoxemia or acute respiratory failure associated with COVID-19, treatment with dexamethasone 20 mg daily for five days followed by 10 mg daily for five days compared with dexamethasone 6 mg daily for ten days did not result in better clinical improvement based on the WHO-OSCI. For participants who required high-flow oxygen or noninvasive ventilation, the 20-mg group had higher mortality than did the 6-mg group. There was no difference between groups in ICU-free days or ventilator-free days for patients requiring ICU care. The longer median hospital length of stay in the 6-mg group compared with the 20-mg group for survivors could be due to more patients surviving and needing longer hospitalization in the 6-mg group. The incidence of adverse events was low and similar in frequency in both groups. The high mortality in the high-dose and OSCI of 5 subgroup is a new finding compared with several previous studies and is an important warning about the “more is better” approach for corticosteroids use in our clinical practice.Systemic corticosteroids are widely used for hospitalized patients with COVID-19 and hypoxemia. Several trials for assessing the therapeutic effects of different doses of systemic corticosteroids for COVID-19 showed various results. The COVID STEROID 2 trial evaluated the effect of dexamethasone 12 mg daily versus 6 mg daily in patients with COVID-19 and severe hypoxemia . This trTaboada et al. compared dexamethasone 20 mg daily versus 6 mg daily and found that the groups who received the higher dose had reduced clinical worsening within 11 days after randomization . An impoSome clinical trials showed a benefit of higher dose of dexamethasone therapy , 19, whiA systematic review and meta-analysis of high-dose versus low-dose corticosteroids in patients with COVID-19, including 12 observational studies and randomized controlled trials, demonstrated that high-dose corticosteroids did not reduce mortality . The autIn our study’s subgroup of individuals with a WHO-OSCI of 5, all six cases of death received dexamethasone 20 mg daily. There was no difference between death cases and survivors in terms of age, gender, medical comorbidities, or use of remdesivir or immune modulators. There was no difference in the incidence of corticosteroid adverse events. We suspect that the higher dose of corticosteroids may have over suppressed the normal antiviral responses in patients needing high-flow oxygen or noninvasive ventilation on admission. Caution must be taken when prescribing high dose systemic corticosteroids for this subgroup of patients in clinical practice.The strengths of this trial include that participants with differing severities of acute hypoxemia were enrolled and stratified by OSCI at randomization. The pragmatic protocol ensured that the multidisciplinary team had flexibility in participant management, enhancing enrollment and completion of the study protocol. Our trial’s limitations include the single-center location, follow-up limited to while participants were in the hospital until day 28, and lack of knowledge of long-term health outcomes. This trial stopped early because the interim data analysis revealed that one subgroup (OSCI 5) in the high-dose arm had significantly higher mortality, which led to the small sample size. We were not able to draw conclusions for other OSCI subgroups. There is a small chance that the higher mortality may be a false-positive due to the small sample size. However, it would not have been ethical to continue enrolling more participants. There were more participants with heart disease in the low-dose group. More participants in the low-dose group received immune modulator therapy. These imbalances may affect the health outcomes. Although the multivariable regression to estimate the survival did not show a statistically significant difference after adjustment for heart disease and immune modulator use, it is well known that immune modulator therapy may improve survival. Half of the deaths in the high-dose group with OSCI = 5 at randomization received immune modulators. The other half did not. It was the same for those surviving. It is unlikely that not receiving immune modulators is the cause of the high mortality in this particular subgroup.Dexamethasone 20 mg once daily compared with dexamethasone 6 mg once daily did not result in significantly better clinical improvement in hospitalized patients with COVID-19-related acute hypoxemia at Day 28 after randomization. Dexamethasone 20 mg daily was probably associated with higher 28-day mortality in patients who needed high-flow oxygen or noninvasive ventilation. Caution must be taken when we prescribe high dose corticosteroids to this subgroup of patients.S1 Checklist(PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S1 File(PDF)Click here for additional data file.S1 Dataset(DTA)Click here for additional data file.


Selection of appropriate high-risk smokers is one of the major challenges for implementing low-dose computed tomography screening for lung cancer. Many lung cancer risk prediction models have been proposed to supplement lung cancer screening. This study evaluated the potential of inflammatory protein markers to enhance lung cancer risk stratification beyond lung cancer risk models. The addition of inflammatory protein markers to existing lung cancer risk models improved risk prediction. The inflammatory protein markers may enhance current risk stratification and may be useful to identify high-risk populations for lung cancer screening.Randomized trials have demonstrated a substantial reduction in lung cancer (LC) mortality by screening heavy smokers with low-dose computed tomography (LDCT). The aim of this study was to assess if and to what extent blood-based inflammatory protein biomarkers might enhance selection of those at highest risk for LC screening. Ever smoking participants were chosen from 9940 participants, aged 50–75 years, who were followed up with respect to LC incidence for 17 years in a prospective population-based cohort study conducted in Saarland, Germany. Using proximity extension assay, 92 inflammation protein biomarkers were measured in baseline plasma samples of ever smoking participants, including 172 incident LC cases and 285 randomly selected participants free of LC. Smoothly clipped absolute deviation (SCAD) penalized regression with 0.632+ bootstrap for correction of overoptimism was applied to derive an inflammation protein biomarker score (INS) and a combined INS-pack-years score in a training set, and algorithms were further evaluated in an independent validation set. Furthermore, the performances of nine LC risk prediction models individually and in combination with inflammatory plasma protein biomarkers for predicting LC incidence were comparatively evaluated. The combined INS-pack-years score predicted LC incidence with area under the curves (AUCs) of 0.811 and 0.782 in the training and the validation sets, respectively. The addition of inflammatory plasma protein biomarkers to established nine LC risk models increased the AUCs up to 0.121 and 0.070 among ever smoking participants from training and validation sets, respectively. Our results suggest that inflammatory protein biomarkers may have potential to improve the selection of people for LC screening and thereby enhance screening efficiency. With 2.2 million incident cases and 1.8 million deaths in 2020, lung cancer (LC) is the second most common cancer and leading cause of cancer mortality globally ,10,11 caThe protein biomarkers were measured in ever smoking participants from ESTHER, an ongoing population-based cohort study . Details of the ESTHER study have been published elsewhere ,31. In bPlasma protein concentrations in the samples were measured utilizing the proximity extension assays (PEA) offered by Olink . The full protocol of the PEA has been reported previously . BrieflyThe demographic and smoking characteristics of incident LC cases and control participants without LC diagnosis were assessed, and the differences were tested for statistical significance by chi square test. The linear protein values were log transformed to produce normalized protein expression (NPX) and one NPX represents two-fold change in protein concentration. Inflammation biomarkers with >1% of the values below limit of detection (LOD) were excluded from all analyses. The NPX values of each individual protein were compared between LC cases and control participants without LC diagnosis during follow-up using Wilcoxon rank-sum test with adjustment for multiple testing by the Benjamini and Hochberg method . A logisIn order to derive multi-marker algorithms for prediction of incidence of LC, a split-sample approach was used. First, 65% of participants was randomly selected in the training set and the remaining 35% of participants was included in validation set. In the training set, comprising 107 incident LC cases and 190 participants free of LC, smoothly clipped absolute deviation (SCAD) . The perFurthermore, we assessed if, and to what extent, the combination of inflammatory protein signatures with LC risk models (“LC risk model-Inf”) could enhance LC prediction by the risk models alone. Nine established LC risk models that are based on slightly different variables as shown in https://dceg.cancer.gov/tools/risk-assessment/lcmodels, accessed on 24 January 2022) was used. All statistical analyses were performed with statistical software R language and environment  [p values of 0.05 or less were considered to be statistically significant.To calculate risk prediction of all the nine models, the publicly available R-package (re team)  using R The flow diagram displaying the selection of study participants is provided in The quality control criteria (QCC) of the biomarker measurements were considered good with 97% of the samples meeting QCC, and the intra-assay and inter-assay coefficient of variances (CV) were 7% and 12%, respectively. When checked for expression, 33 inflammation protein biomarkers that had >1% of the measured values below LOD were excluded from all analyses  for 11 proteins; however, after adjustment for multiple testing, only three protein biomarkers displayed statistically significant differences in protein levels . Three biomarkers, CUB domain-containing protein 1 (CDCP1), eotaxin (CCL11) and interleukin 12 subunit beta (IL12B), were identified with AUCs ≥ 0.60.Mean plasma concentrations of the 59 inflammation proteins in LC cases and participants free of LC are presented in To evaluate the performance of multi-marker prediction signatures for comparing LC cases to LC-free controls, SCAD and 0.632+ bootstrap were applied to the 59 inflammation protein biomarkers in participants from the training set. As shown in When SCAD-penalized regression was applied to the 59 protein biomarkers and to the self-reported pack-years of smoking in the training set, an INS-pack-years score was obtained with improved AUC* and AUC of 0.796 and 0.811 , respectively. The INS-pack-years score included self-reported pack-years of smoking and the following ten proteins: CASP8, CCL11, C-C motif chemokine 25 (CCL25), CDCP1, CD8A, CD244, CXCL10, C-X-C motif chemokine 9 (CXCL9), FGF19 and matrix metalloproteinase-1 (MMP1). In the validation set, AUCs of 0.742  and 0.782  were observed for INS and INS-pack-years scores, respectively.The predictive performance of derived scores by LC types in both training and validation sets is reported in p value < 0.05 for the combined LLP-INf and PLCOM2012-INf models. Since a separate algorithm was developed for each LC risk model, different sets and numbers of inflammatory protein biomarkers were selected in each combined LC risk model-INf signature. However, several protein biomarkers, such as CDCP1, CD244, CXCL10 and IL8 were included in almost all of the combined algorithms. The INS-pack-years score presented with an AUC of 0.782 in the validation set and the only other LC risk model-INf score performing better was Pittsburg predictor-INf with an AUC of 0.794. For the cases diagnosed within the first 10 years, the Bach-INf outperformed all other models, and at a cutoff yielding 80% specificity, sensitivities of 73% and 60% were observed in the training and validation sets, respectively.The predictive performances of derived algorithms for each LC risk model alone and in combination with inflammatory protein biomarkers are presented in This exploratory study evaluated the predictive performance of blood-based protein markers alone and in combination with pack-years, in a prospective cohort with up to 17 years of follow-up. In the current study, the signature of ten inflammatory protein biomarkers and pack-years of smoking (INS-pack-years) predicted incident lung cancer cases with AUCs of 0.811 and 0.782 in the training and validation sets, respectively. The addition of inflammatory protein biomarkers to the established LC risk models showed improved prediction potential as compared to the LC risk models alone.M2012, Pittsburgh Predictor, LCRAT and LCDRAT, were developed exclusively for ever smokers. The addition of inflammatory protein biomarkers improved the predictive ability of all LC risk models assessed in our study, although the increase in AUCs in the validation set was statistically significant for two of the LC models only, given the small sample size of the validation set. Further validation in larger studies is therefore warranted. Nevertheless, this exploratory study suggests the combination of LC risk models with inflammatory protein scores to be a promising approach for enhanced selection of participants for lung cancer screening programs. The gain in predictive performance would have to be weighed against the additional complexity and costs of risk assessment by the need of blood sampling and analysis. However, such laboratory analyses could be easily embedded in other routine blood sampling commonly employed among older adults in primary care, and blood tests customized to measure these inflammatory proteins could most likely be developed and offered at low cost. Further research should also evaluate the gain in predictive ability and the associated complexity and costs compared to alternative approaches, such as combination of the LC risk models with alternative signatures of proteins [The human blood proteome, metabolome, and genome carry great potential for novel approaches to cancer risk prediction and cancer early detection. With emerging technologies for sensitive protein detection even in small sample volumes, standardized multiplex protein detection and quantitation methods are a particularly promising approach in this context. Some of the recent protein detection methods such as PEA are straightforward for routine clinical application as standardized laboratory and statistical data processing procedures have been established. The proximity extension assays used in the current study utilize a pair of oligonucleotide-labeled antibodies or probes that have to be in close proximity for the detection of each protein. PEAs require an exceptionally low sample volume of 1 µL and can detect and quantify protein concentrations with good reproducibility CV < 15%). The technical assay sensitivity for the PEA assays is in the picogram/ml range, and they can quantify across five logs of abundance. Good assay sensitivity with high target specificity because of dual recognition and requirements of low volume of sample make PEA an efficient method. The inflammation panel used in our study allows for simultaneous detection of 92 circulatory inflammation biomarkers. Inflammation has been associated with both carcinogenesis and tumor progression ,40. Chro%. The teproteins ,29, autoproteins , DNA metproteins ,49, or mproteins  biomarkeA major strength of the current study is that the ever smoking participants were selected from a large population-based cohort of older adults who were recruited in the relevant age range for LC screening and followed up with respect to LC incidence over 17 years. However, despite the overall large size of the ESTHER cohort (N = 9940) and the long follow-up, the number of ever smoking participants with incident LC was still rather limited. Utilizing state-of-the-art technology of PEA, 92 circulating inflammatory markers were assessed in 1 µl plasma per sample. Establishment of standardized laboratory procedures for reliable multiplex measurements of proteins even in such small sample volumes should facilitate their implementation in routine medical practice including screening programs. Applying cutting-edge statistical machine learning algorithms, thorough control for overoptimism and internal validation, the markers were evaluated for possible combinations and comparisons with a wide range of established LC risk models. To the best of our knowledge, the INS, INS-pack-years and combined LC risk model-INf scores were evaluated for the first time for long-term prediction of LC in our study. Major limitations include the limited sample size of LC patients, leading to rather wide confidence intervals of the derived indicators of predictive performance. Potential misreporting and recall bias of the smoking variables need careful consideration, although previous biomarker-based validation suggests high accuracy of self-reported smoking in the ESTHER cohort . As the This study highlights the potential of inflammatory protein biomarkers for enhancing smoking-based prediction of lung cancer risk. We have identified inflammatory protein biomarkers that in combination with LC risk models enabled improved prediction of LC incidence. Models incorporating the inflammatory protein biomarkers along with established LC risk models may have important clinical implications for screening and preventive strategies. Further research should aim for further optimization of risk stratification algorithms and their validation in independent cohorts.


Abortion-related complications are one of the five main causes of maternal mortality. However, research about abortion is very limited in fragile and conflict-affected settings. Our study aims to describe the magnitude and severity of abortion-related complications in two referral hospitals supported by Médecins Sans Frontières and located in such settings in northern Nigeria and Central African Republic (CAR).We used a methodology similar to the World Health Organization (WHO) near-miss approach adapted in the WHO multi-country study on abortion (WHO-MCS-A). We conducted a cross-sectional study in the two hospitals providing comprehensive emergency obstetric care. We used prospective medical records’ reviews of women presenting with abortion-related complications between November 2019 and July 2021. We used descriptive analysis and categorized complications into four mutually exclusive categories of increasing severity.We analyzed data from 520 and 548 women respectively in Nigerian and CAR hospitals. Abortion complications represented 4.2%  and 19.9%  of all pregnancy-related admissions. The severity of abortion complications was high: 103 (19.8%) and 34 (6.2%) women were classified as having severe maternal outcomes (near-miss cases and deaths), 245 (47.1%) and 244 (44.5%) potentially life-threatening, 39 (7.5%) and 93 (17.0%) moderate, and 133 (25.6%) and 177 (32.3%) mild complications, respectively in Nigerian and CAR hospitals. Severe bleeding/hemorrhage was the main type of complication in both settings , followed by infection . Among the 146 women  and 231 women  who did not report severe bleeding or hemorrhage before or during admission, anemia was more frequent in the Nigerian hospital (66.7%) compared to the CAR hospital (37.6%).Our data suggests high severity of abortion-related complications in these two referral facilities of fragile and conflict-affected settings. Factors that could contribute to this high severity in these contexts include greater delays in accessing post-abortion care, decreased access to contraceptive and safe abortion care that result in increased unsafe abortions; as well as increased food insecurity leading to iron-deficiencies and chronic anaemia. The results highlight the need for better access to safe abortion care, contraception, and high quality postabortion care to prevent and manage complications of abortion in fragile and conflict-affected settings.The online version contains supplementary material available at 10.1186/s12884-023-05427-6. Exposure to conflict and contextual fragility significantly increases the risk of maternal deaths . MaternaAccess to comprehensive abortion care, which consists of safe abortion care, post-abortion care and contraception, was identified as a priority intervention in the 2010 Inter-Agency Field Manual on Sexual and Reproductive Health (SRH) in Humanitarian Settings . HoweverEvidence on abortion complications in stable limited-resource settings is increasingly available –18. A WHAMoCo  study in two such contexts. They were: Bangui in the Central African Republic (CAR); and Jigawa State in northern Nigeria. The objective of this paper is to describe the severity, magnitude, and types of abortion complications in two referral hospitals, one in each setting.To inform abortion-related policies, guidelines, and interventions in fragile and conflict-affected settings, we conducted the AMoCo study: a quantitative cross-sectional study conducted using a prospective medical record review among women with pregnancy losses before fetal viability. Additional file AMoCo study. This study is registered with ClinicalTrials.gov, NCT04331847.This article focuses on one component of the For this study, we used a methodology similar to the WHO Multi-Country Study on Abortion (WHO-MCS-A)  to generWe selected two referral hospitals supported by Medecins Sans Frontieres  in two different types of fragile and conflict-affected settings: an urban setting in CAR (Bangui); and a rural setting in northern Nigeria (Jigawa State). Each setting had to meet the following criteria: hospitals were in areas where the security of participants and researchers could be guaranteed; hospitals had wide catchment areas ; provided post-abortion care to at least 500 women per year; conducted > 1000 deliveries per year; and were capable of providing all CEmOC signal functions. Signal functions are key medical interventions needed to provide emergency obstetric care including: the capacity to remove retained products; provide blood transfusion; and conduct abdominal surgery . MSF supCAR is a country of 5.4 million inhabitants, with 71% of the population living below the international poverty line in 2020  and wherNigeria had an estimated population of 206 million inhabitants in 2020  and had While both countries have recently developed national safe abortion care guidelines , 50, traAll women presenting to the study hospitals with any signs or symptoms of pregnancy-loss-related complications or deaths at discharge were eligible for inclusion. Pregnancy loss included spontaneous/induced abortion, ectopic pregnancy, or molar pregnancy before fetal viability (28 weeks of gestation). Women with threatened abortion  were excluded. In accordance with the Council for International Organizations of Medical Sciences guidelines , an infoThe sample size was computed to estimate with precision the proportion of severe maternal outcome, which includes near-miss cases and deaths, among all women presenting with abortion complications in each study hospital. The minimum target sample size was 430 women with abortion complications.The included participants presented between February 2020 and July 2021 at the Nigerian hospital (with an interruption between April and July 2020 due to COVID-19) and between November 2019 and January 2020 at the CAR hospital.Using a list of standard key words about symptoms and diagnoses , trained study clinicians screened the triage, gynecology/obstetric, and intensive care wards’ registers daily to identify potentially eligible women. They reviewed medical records of these women with a standardized eligibility form and included them in the study the same day as their presentation if they were eligible and did not opt out. Thereafter, they extracted data from their MSF standardized PAC medical records daily till their admission ended, consulting the clinician in charge of their medical management. They recorded sociodemographic data, reproductive history, obstetric characteristics, clinical signs and symptoms, laboratory markers, medical management, clinical outcomes, and status at discharge. We also collected aggregate data on the weekly number of live births and pregnancy-related admissions from each hospital’s health information system to calculate the magnitude endpoints. Both hospitals use the same MSF maternal health information system including standardized PAC medical records, variable definitions, and monitoring systems.Quality assurance procedures were implemented to ensure the collection of high-quality data. In summary, all standardized data collection tools, and procedures were piloted and revised. Standardized detailed definitions of each severity criteria and types of complications were used to collect consistent and comparable data in both study sites  adapted WHO-MCS-A classification” for the rest of the paper. To classify a woman in the SMO category, we used a cut-off of two or more units of blood transfused instead of five for the hematologic/coagulation dysfunction criteria as recommended by Tura et al.  and otheWe calculated the facility-based abortion-related mortality ratio, near-miss ratio, and mortality index for each facility as defined in the WHO near-miss approach guidelines . SensitiFor each type/underlying condition of abortion complication reported , the near-miss risk and the case fatality risk (CFR) were computed. The near-miss risk is the number of near-miss cases per 100 cases of each abortion complication type/underlying condition. And the CFR is the number of deaths per 100 cases of each complication type/underlying condition.Gestational age at presentation was categorized as first trimester (fewer than 13 weeks), second trimester (13 weeks or more) and was estimated from weeks of gestation using the ultra-sound assessment as the reference assessment method. For those missing this information, we used the last menstrual period date or, if missing, the uterine size assessed by the provider, or if missing, the provider’s estimation of gestational age. Marital status was categorized as currently married or in union (married/living with a partner) or not (single/separated/divorced/widowed).We performed descriptive analysis using Stata 16.0 software . Sociodemographic, reproductive, obstetrics characteristics of the sample as well as the percentage of women in each severity category, the mortality, near-miss and magnitude indicators were described using summary statistics. We calculated 95% confidence interval (95% CI) using exact methods.In each study hospital, a local steering committee involving members from the Ministries of Health and Social Affairs, local researchers, local administrative and religious leaders as well as women’s and civil society organizations participated in the study conduct oversee, interpretation of the results, and dissemination of the findings.Figure Table Table Using the SSA adapted WHO-MCS-A classification, Fig. Applying the original WHO-MCS-A classification, the sensitivity analysis showed that the percentage of women with severe complications remained unchanged but the distribution of the women in the SMO and PLTC categories differed. In particular, lower percentages of women were classified in the SMO category  and higher percentages of women were in the PLTC category   than in the CAR hospital (27.0%). In both sites, women with infection were more likely to experience a near-miss complication  or die  than women with hemorrhage . In both sites, women with severe anemia had the highest near-miss risks .Our results suggest that women who accessed post-abortion services in two referral hospitals in fragile and conflict-affected settings experienced a higher frequency of severe abortion complications than women who used similar referral hospitals in more stable African settings. When applying the original WHO-MCS-A classification, the percentage experiencing severe complications (PLTC and SMO) in our study hospitals was 5.4 to 7.2 times that reported in the WHO-MCS-A African hospitals (9.4%) . PLTC apP = 0.56) [Our results differ from results of a secondary analysis of the WHO-MCS-A data performed among 304 women of the Democratic Republic of Congo (DRC) showing no evidence that women in insecure areas were more likely to have a severe complication compared to women in secure areas  . Authors = 0.56) , 61–65 cWe have several hypotheses that can be raised to explain the high severity in our study.Evidence suggests that maternal health indicators deteriorate in fragile and conflict-affected areas as well as in their surrounding regions , 47, 64 Although Bangui appears to be more directly affected by conflict and insecurity than Jigawa, the severity of abortion complications is higher in the Jigawa hospital than the Bangui one. We believe that in this case, the combination of being a rural, poorer community  with expExpanding the provision of quality post-abortion care in these settings would reduce delays in access to care and prevent severe complications and deaths. Primary health centers should be enabled to provide post-abortion care using misoprostol, manual vacuum aspiration, and adequate antibiotic therapy as has been successfully implemented in other conflict-affected settings . In addi. Research evidence shows that in these settings, several factors increase the risk of using unsafe methods to induce abortion and further lead women to delay seeking care for complications when they occur [Multiple studies suggest that contexts with restrictive abortion laws and policies , 70, 71—ey occur . In partey occur , 74. Theey occur ) and in ey occur ) with uney occur  and 15% ey occur , supportey occur ). This hey occur .Our findings suggest that increasing access to services that provide contraception and safe abortion care would decrease severe abortion morbidity in these contexts. For care to be successful, we recommend that health providers are trained to provide non-judgemental and respectful care, and that contraception and safe abortion care is decentralized to primary level facilities to increase access. Contraceptive services should provide both short- and long-acting methods, and safe abortion services should offer at least the recommended medication abortion methods. Emphasis should also be placed on improving women's and communities' access to quality information on safe and effective contraceptive methods and on how to use medication abortion safely. These efforts may also consider facilitating access to self-injectable contraceptives and medication abortion in the community as a harm reduction approach to avoid unsafe abortions .Our results also highlight the possible critical impact of underlying chronic anemia on the prognosis of abortion-related complications in fragile and conflict-affected settings. The increased food insecurity leading to increased iron-deficiencies  in theseAlthough some studies suggest that there is an association between women’s sociodemographic characteristics and abortion-complications severity , 55, 79,Interestingly, the magnitude of abortion complications is much higher in the CAR hospital  compared to the Nigerian one (4.2%). Even though the health information systems in both hospitals use the same standardized MSF records, this difference between these estimates could also be linked to differences in the comprehensiveness of pregnancy-related admissions recording in each hospital. Nevertheless, our results are similar to previous studies, finding that abortion complications represented 4.4% of pregnancy-related admissions in 42 Nigerian tertiary hospitals  and 13.6Our results suggest that both study settings provide good quality of care for abortion-related complications as evidenced by the mortality index in both hospitals , which are substantially lower than the 18.3% in the facilities in the WHO-MCS-A . The lonDespite the challenges in conducting abortion-related research in fragile and conflict-affected contexts, our study collected high quality data on the severity and magnitude of abortion complications of women presenting to referral hospitals in fragile and conflict-affected settings. Maternal Health research studies in low-resource environments have shown that data from facility records reviews can be unreliable, incomplete, or inaccurate especially when they are extracted retrospectively . AlthougThat said, our study has several important limitations. Because the insecure contexts obliged us to collect data in only one referral hospital in each setting, our results can’t be generalized to all referral facilities in Bangui, Jigawa State, Nigeria, CAR, or other fragile and conflict-affected settings. This also means that our results are not completely comparable with the WHO-MCS-A results which collected data in 10 referral facilities per randomly sampled geographical area. The lower proportion of women in the moderate category compared to the PLTC category reported in our study, is atypical and difficult to explain. We hypothesize that this might be due to differences in some detailed definitions in our study compared to the WHO study, particularly between the SBP thresholds used by clinicians for defining hypotension in the PLTC category. Our study introduced a threshold to define hypotension (SBP < 100 mmHg), whereas the WHO study definition did not. Clinicians in the WHO study facilities may have used varying SBP thresholds lower than the 100 mmHg in our study. There has been less research done to generate standardized and validated definitions with clear benchmarks for clinical signs and symptoms in the PLTC and moderate severity categories. Lastly, our results are not representative of the underlying population. While it can be hypothesized that near-miss abortion cases are only found in hospitals because they could not survive in the community without hospital care, all women who died in the community or who did not seek care or had complications managed in lower-level health facilities are not included in our samples.Despite these limitations, we believe that our study highlights an important but neglected health challenge faced by women in such settings and showed it is feasible to conduct rigorous research on abortion in this context.Our data showed a high severity of abortion-related complications in these two referral hospitals in fragile and conflict-affected settings. Factors that may contribute to this high severity include disruption and dislocation of the health system; delays in accessing post-abortion care, non-prioritization of post-abortion care, safe abortion care and contraception as key sexual and reproductive health services; increased unsafe abortions; and increased food insecurity leading to iron-deficiencies and chronic anaemia. Our results highlight the need for greater access to contraception, safe abortion care, and high quality postabortion care to prevent and manage complications of abortion in these fragile and conflict-affected settings. In addition, preventing and managing underlying chronic health conditions like malnutrition and chronic anemia may reduce the lethality of abortion complications. Settings which are perhaps less directly exposed to conflicts, but which are in a less visible but significant state of chronic fragility or protracted emergency should not be forgotten. Humanitarian stakeholders and researchers should further quantify the burden of abortion complications in these settings and identify the specific barriers that prevent women and girls from accessing comprehensive abortion care to develop targeted actions that address their needs.Additional file 1. Full AMoCo study design.Additional file 2. Additional information on the methodology of the prospective medical record review.Additional file 3. WHO-MCS-A and adapted WHO-MCS-A severity classification.Additional file 4. Facility-based near-miss ratio, mortality ratio and mortality index.Additional file 5. Sensitivity analyses applying the original WHO-MCS-A severity classification.


The massive growth in demand and cost associated with experimental PPI studies calls for computational tools for automated prediction and understanding of PPIs. Despite recent progress, in silico methods remain inadequate in modeling the natural PPI hierarchy. Here we present a double-viewed hierarchical graph learning model, HIGH-PPI, to predict PPIs and extrapolate the molecular details involved. In this model, we create a hierarchical graph, in which a node in the PPI network (top outside-of-protein view) is a protein graph (bottom inside-of-protein view). In the bottom view, a group of chemically relevant descriptors, instead of the protein sequences, are used to better capture the structure-function relationship of the protein. HIGH-PPI examines both outside-of-protein and inside-of-protein of the human interactome to establish a robust machine understanding of PPIs. This model demonstrates high accuracy and robustness in predicting PPIs. Moreover, HIGH-PPI can interpret the modes of action of PPIs by identifying important binding and catalytic sites precisely. Overall, “HIGH-PPI [ Despite recent progress, machine learning methods remain inadequate in modeling the natural protein-protein interaction (PPI) hierarchy for PPI prediction. Here, the authors present a double-viewed hierarchical graph learning model, HIGH-PPI, to predict PPIs and extrapolate the molecular details involved. In biology, hierarchy information has been widely exploited to gain in-depth information about phenotypes of interest, for example, in disease biology5, proteomics8, and neurobiology11. Naturally, PPIs encapsulate a two-view hierarchy: on the top view, proteins interact with each other; on the bottom view, key amino acids or residues assemble to form important local domains. Following this logic, biologists often take hierarchical approaches to understand PPIs13. Experimentally, scientists often employ high-throughput mapping16 to pre-build the PPI network at scale, and use bioinformatics clustering methods to identify functional modules of the network (top view). On the individual protein level, isolation methods, such as co-immunoprecipitation17, pull-down18, and crosslinking19 are used to establish the structures of individual proteins, so that surficial ‘hotspots’ can be located and analyzed. In short, hierarchy knowledge of structure information is important to understand the molecular details of PPIs.Biological functions are accomplished by interactions and chemical reactions among biomolecules. Among them, protein–protein interactions (PPIs) are arguably one of the most important molecular events in the human body and are an important source of therapeutic interventions against diseases. A comprehensive dictionary of PPIs can help connect the dots in complicated biological pathways and expedite the development of therapeutic21 and inferring potential PPIs by link predictions23. The former focuses on extracting structural information using protein sequences. In particular, Convolutional Neural Networks (CNNs)25 and Recurrent Neural Networks (RNNs)28 have demonstrated high generalization and fast inference speed to capture key sequence fragments for PPIs29. 3D CNNs31 have shown to be better at extracting 3D structural features of proteins and thus capturing the spatial-biological arrangements of residues32 that are important to PPI predictions. However, 3D CNN suffers from high computational burdens and limited resolution that is prone to quantization errors29. The latter aspect of DL in PPI predictions focuses on the PPI network structures, which involves developing link prediction methods to identify missing interactions within the known network topology. Link prediction methods based on common neighbor (CN)33 assign high probabilities of PPI to protein pairs that are known to share common PPI partners. CN can be generalized to consider neighbors from a greater path length (L3)22, which captures the structural and evolutionary forces that govern biological networks such as the interactome. Additionally, distance-based methods measure the possible distances between protein pairs, such as Euclidean commute time (ECT)34 and random walk with restart (RWR)35. Most methods of traditional link prediction focus on known interactions but tend to overlook important network properties such as node degrees and community partitions.More recently, the massive growth in the demand and the cost of experimentally validating PPIs make it impossible to characterize most unknown PPIs in wet laboratories. To map out the human interactome efficiently and inexpensively, computational methods are increasingly being used to predict PPIs automatically. Over the past decade, as one of the most revolutionary tools in computation, Deep Learning (DL) methods, have been applied to study PPIs. Development in this field has been mostly focused on two aspects, learning appropriate protein representations37 to represent protein and network structures, respectively. In this way, the limitations of 3D CNN and link prediction methods mentioned above can be circumvented. First, GNNs can learn the protein 3D structures on more efficient graph representations, even when facing high-resolution requirements for structure processing. Second, due to the propagation mechanism, GNNs are capable of recovering network properties such as node degrees and community partitions. In short, this hierarchical graph approach aims at modeling the natural PPI hierarchy with more effective and efficient structure perceptions.More importantly, these methods perceive only one of the two views of outside-of-protein and inside-of-protein. Few can model the natural PPI hierarchy by connecting both views. To address this issue, we present a hierarchical graph that applies two Graph Neural Networks (GNNs)38 and compare it with leading DL methods. We demonstrate the superiority of HIGH-PPI with higher prediction accuracy and better interpretability. We also show examples that HIGH-PPI can identify binding and catalytic sites with high precision automatically.Here we describe a generic DL platform tailored for predicting PPIs, Hierarchical Graph Neural Networks for Protein–Protein Interactions (HIGH-PPI). HIGH-PPI models the structural protein representations with the bottom inside-of-protein view GNNs (BGNN) and the PPI network with the top outside-of-protein view GNNs (TGNN). In the bottom view, HIGH-PPI constructs protein graphs by treating amino acid residues as nodes and physical adjacencies as edges. Thus, BGNN integrates the information of protein 3D structures and residue-level properties in a synergistic fashion. In the top view, HIGH-PPI constructs the PPI graph by taking protein graphs (the bottom view) as nodes and interactions as edges and learns protein–protein relationships with TGNN. In an end-to-end training paradigm, HIGH-PPI gains mutual benefits from both views. On the one hand, the bottom view feeds protein representations to the top view to learn accurate protein relationships. On the other hand, protein relationships learned by the top view provide insights to further optimize the bottom view to establish better protein representations. HIGH-PPI outputs the probabilities of interactions for given protein pairs and predicts key “contact” sites for such interactions by calculating residue importance. We show the effectiveness of HIGH-PPI on the human interactome from the STRING databaseAlthough deep learning (DL) models for Protein–Protein Interaction (PPI) prediction have been studied extensively, it has not yet been developed for simulating the natural PPI hierarchy. Here, we suggest HIGH-PPI, a hierarchical graph neural network, for accurate and interpretable PPI prediction. HIGH-PPI works like biologists in a hierarchical manner as it contains the bottom inside-of-protein view and top outside-of-protein view 37 layer, ReLU activation function and a BN layer. Node features of the PPI graph are updated with recursive neighborhood aggregations of three GIN blocks. Two arbitrary protein embeddings are combined by concatenation operations, and a Multi-Layer Perceptron (MLP) is then applied as a classifier for prediction. Moreover, we also consider graph attention (GAT) and arbitrarily deploy two of the three GNN layers  on BGNN and TGNN. The performance of HIGH-PPI with various GNN layers is shown in Supplementary Fig. In the proposed end-to-end model, the initial stage is to create protein graphs for learning appropriate protein representation. An adjacency matrix of a protein graph is derived from a contact map connecting physically close residues (See Section 4.1 in “Methods” for details). Node attributes are defined with residue-level features for expressing the physicochemical properties of proteins (See Section 4.1 in “Methods” for details). To produce a protein graph representation, Graph Convolutional Network (GCN)38, which contains a critical assessment and integration of PPIs. SHS27k26, a homo sapiens subset from STRING38 that comprises 1,690 proteins and 7,624 PPIs, is used to train and evaluate the HIGH-PPI unless otherwise noted. However, a small fraction of proteins (∼ 8%) sometimes need to be removed because of the lack of their native structures in the PDB database. While evaluating the prediction performance for multi-type PPIs, we consider the prediction for each PPI type as a one-vs-all binary classification problem, for which two metrics, F1 score and area under the precision-recall curve (AUPR) are used for predicting the presence or absence of the corresponding PPI class. The overall performance of micro-F1 and AUPR scores for multi-type PPI prediction is averaged across all PPI types.We train and evaluate HIGH-PPI on multi-type human PPIs from the STRING databaseTo validate the predictive power of our model, we compare HIGH-PPI with leading methods from four perspectives, including (1) the overall performance under a random data split, (2) the robustness of HIGH-PPI against random interaction perturbation, (3) model generalization for predicting PPI pairs containing unknown proteins, (4) evaluations in terms of AUPR on five separate PPI types. For each method, all the proposed modules and strategies are involved to get the best performance.24), one sequence-based method , one 2D CNN-based method  and one machine learning (ML) method based on random forest . GNN-PPI applies a GNN module to learn the PPI network topology and 1D CNN to learn protein representations by taking pre-trained residue embeddings as inputs. PIPR, an end-to-end framework based on recurrent neural networks (RNN), represents proteins with only pre-trained residue embeddings. DrugVQA applies a visual question-answering mode to learn from protein contact maps with a 2D CNN model and extract semantic features with a sequential model. Supplementary Data File First, we compare the overall performance of HIGH-PPI with leading baselines in Fig. Second, to evaluate the robustness of HIGH-PPI, we analyze the model tolerance against interaction data perturbation including random addition or removal of known interactions. This simulates scenarios where PPI datasets always omit undiscovered interactions and may introduce mislabeled ones. Based on the perturbated PPI network, we split the training and test sets at an 8:2 ratio. We observe in Fig. 24. For PIPR, DrugVQA and RF-PPI, we visualize their best performances among all OOD cases using dotted lines, to demonstrate the absolute dominance of HIGH-PPI and GNN-PPI. Furthermore, we observe that HIGH-PPI consistently outperforms GNN-PPI, the second-best method, with large margins in all five scenarios. BFS typically produces worse performance than DFS, because BFS creates a more challenging and realistic mode where unknown proteins exist in cluster forms. ML method (RF-PPI) exhibits poor generalization. Furthermore, we follow Park and Marcotte42 to explore the differences in model performance on 3 kinds of PPI pairs with different degrees of OOD. Specifically, 42. There is a noticeable difference in model test performance across the 3 distinct classes of test pairs. Particularly, on Class 1 test pairs, both models (HIGH-PPI and GNN-PPI) perform the best, on Class 2 test pairs they are the second best, and on Class 3 test pairs they are the poorest. Furthermore, we find that for each model, the class proportion  scenarios where unknown proteins arrive in the test sets with different probabilities see Fig. . For exaFinally, for each of the five PPI types, we offer a separate performance analysis in terms of AUPR. In all five types, HIGH-PPI consistently beats other baselines with high significance as shown in Fig. We investigate the role of the bottom inside-of-protein view from four perspectives, including (1) the effectiveness of graph representations and backbones with native protein structures, (2) the model tolerance with low-quality protein structures, (3) the capability to predict motifs  in a protein, (4) the overall and type-specific feature importance.21, a deep learning framework for identifying interfaces of PPIs. For GNN, we learn from protein graphs in which the adjacency matrix is determined by First, we explore the effectiveness of backbones including RF, RNN, CNN and GNN in Fig. 43). We evaluate the quality of the input protein structure by calculating the root-mean-square deviation (RMSD) of the native one and the input. Native protein structures (RMSD = 0) are retrieved from the PDB database at the highest resolutions. We compute the best-F1 scores (box plots) of our method on a set of AlphaFold structures with various RMSDs , and show the average result of second-best method (GNN-PPI) in a blue dotted line. As can be seen, our model performance is always better than GNN-PPI, even with RMSD up to 8. The comparison with 3D CNN model21 further proves the denoising ability of the hierarchical graph for protein structure errors  the importance of degree and community recovery for predicting network structures, (2) comparison results of TGNN and other leading link prediction methods, (3) a real-life example to show the shortcomings of the leading link prediction methods.Recently, various works have demonstrated the usefulness of structure properties  of networks for predicting missing links. HIGH-PPI is inspired to efficiently recover the degree and community partitions of the PPI network by utilizing the network topology. We show an empirical study in Fig. 33, Katz index (Katz)50, Adamic-Adar (AA)51, preferential attachment (PA)52, SimRank (SR)53 and paths of length three (L3)22. MLP_IP, a DL approach, learns node representations using a multilayer perceptron (MLP) and identifies the node similarity via inner product (IP) operation. We calculate the MAE and NMI values of recovered networks and highlight those with a high capacity for recovery (NMI ≥ 0.7 and MAE ≤ 0.35) in orange. Results show that link prediction methods that are more adept at recovering network properties typically perform better. This gain validates our findings in Fig. Second, we evaluate the performance of TGNN and leading link prediction methods using PPI network structure as input. Our method (TGNN) takes interactions as edges and node degrees as node features. We compare HIGH-PPI with six heuristic methods and one DL-based method. Heuristic methods, the simple yet effective ones utilizing the heuristic node similarities as the link likelihoods, include common neighbors (CN)54 to the recovered structure, it cannot produce an accurate community partition as the abundant inter-community interactions disrupt the original community structure. To examine degree recovery ability, we randomly select 50% of interactions as inputs and show each method’s degree recovery result for node KIF22 in Fig. We provide an example on an SHS27k sub-network. As can be seen, there exist two distinct communities connected by two inter-community edges. We use the original sub-network as inputs and find that non-TGNN link prediction methods  tend to give high scores for intercommunity interactions. As an interesting observation, when we apply the Louvain community detection algorithmTypically, functional sites are spatially clustered sets of residues. They control protein functions and are thus important for PPI prediction. As our proposed model has the capacity to capture spatial-biological arrangements of residues in the bottom view, this characteristic can be used to explain the model’s decision. It is meaningful to notice that HIGH-PPI can automatically learn the residue importance without any residue-level annotations. In this section, we provide (1) a case study of predicting residue importance for the binding surface, (2) two cases of estimating residue importance for catalytic sites, and (3) an explainable ability comparison of precision in predicting binding sites.55, which is colored in red in Fig. First, a binding example between the query protein (PDB id: 2B6H-A) and its partner (PDB id: 2REY-A) is investigated. The ground truth binding surface is retrieved from the PDBePISA database56 (CSA), a database for catalytic residue annotation for enzymes. We calculate the residue importance of catalytic sites for query proteins . As seen in Fig. Second, in order to evaluate the prediction of catalytic sites for PPIs, we utilize the same GNN explanation approach in our model. The ground truth catalytic site is retrieved from the Catalytic Site Atlas24 and 3D CNN module in DeepRank21, respectively. We apply grad-CAM57 and 3Dgrad-CAM44 approaches to determine residue importance for CNN and 3D CNN models, correspondingly. We use the binding type PPIs from the STRING dataset as the training set, and randomly select 20 binding type PPIs as the test set. We use the ground truth from PDBePISA for each query protein and treat its residues with importance >0 as surface compositions. To gauge the precision of the surface prediction, intersection over union (IoU) is used, and the box plots of the IoU score distributions are shown in Fig. Additionally, we compare the model interpretability of the CNN, 3D CNN and HIGI-PPI models. We employ the CNN module in GNN-PPIProtein functional site prediction sheds light on the model decisions and how to carry out additional experimental validations for PPI investigation. Excellent model interpretability also shows that our approach can accurately describe biological evidence for proteins.In this paper, we study the PPI problem from a hierarchical graph perspective and develop a hierarchical graph learning model named HIGH-PPI to predict PPIs. Empirically, HIGH-PPI for PPI prediction outperforms leading methods by a significant margin. The hierarchical graph exhibits high generalization for recognizing unknown proteins and robustness against protein structure errors and PPI network perturbations.Even without explicit supervision from binding site information, HIGH-PPI demonstrates its ability to capture residue importance for PPI with the aid of a hierarchical graph, which is a good indicator of excellent interpretability. Suppose HIGH-PPI predicts the presence of a catalytic interaction for a protein pair but identifies important sites unrelated to catalysis, we will hardly trust the model’s decision. Moreover, interpretability provides trusted guides for subsequent wet experimental validations. For example, if HIGH-PPI thinks a catalytic site is important, experiments may be designed by targeting the specific site for validation.In conclusion, interpretable, end-to-end learning with a hierarchical graph revealing the PPI nature can pave the way to map out human interactome and deepen our understanding of PPI mechanisms.58. Due to huge semantic gaps between hierarchical views, humans always use a multi-view learning strategy to deepen the understanding of one view from the other one. Given rich hierarchical information, recent machine intelligence methods can effectively learn knowledge in each separate view but are not experts in gaining mutual benefits from both views. This is the challenge that our hierarchical world presents to machine intelligence. Here we connect both views by employing the forward and backward propagation of DL models. The forward propagation benefits the learning for the PPI network in the top view. In turn, the backward propagation optimizes the PPI-appropriate protein representations in the bottom view.We describe our intuitions in the hierarchical graph learning for PPIs. The world is hierarchical. Humans tend to solve problems or learn knowledge by conceptualizing the world from a hierarchical view59) and computational methods29 for protein function prediction. Some annotations may speed up learning PPIs. For example, two proteins with low scores of the “protein binding” function term hardly interact with each other. We suggest that future work may consider leveraging function annotations to enhance the expressiveness of protein representations. Inspired by the contrastive learning principle, a potentially feasible solution is to enhance the consistency in protein representations and functions. (2) Protein domain information may be beneficial for hierarchical models. We clarify the core ideas here and provide a detailed description in Supplementary Method We describe two main limitations of HIGH-PPI and outline potential solutions in future work. (1) We did not explore in depth how to use protein-level annotations. Annotations for protein functions are becoming more available due to the recent growth of protein function databases  The PPI network structure determines the adjacency matrix (1) The adjacency matrix We use the bottom view graph neural networks (BGNN) to learn protein representations. Graph convolutional networks (GCNs) have shown great effectiveness for relational data and are suitable for learning graph-structured protein representations. Thus, we propose BGNN based on GCNs.Given the adjacency matrix 36:Formally, we update residue representations with the neighbor aggregations based on the work of Kipf and WellingWith the learnable weight matrix 39 and average aggregation to obtain the entire graph representation of a fixed size, Finally, we perform the readout operation with a self-attention graph pooling layer37), which has the superb expressive power to capture graph structures. Formally, we are given the PPI graph We use the top view graph neural networks (TGNN) to learn PPI network information. We are inspired by graph isomorphism network (GINAfter three GIN blocks, TGNN produces representations for all proteins. For an arbitrary query pair containing the Given a training set We determine all the hyper-parameters through a grid search based on a 5-fold cross-validation. For BGNN, we set the output dimension 62 to generate explanations for HIGH-PPI. By taking the well-trained GNN model and its predictions as inputs, GNNExplainer returns the most important subgraph by maximizing the mutual information We employ the method called GNNExplainerGiven protein graphs z-scores of final residue importance for standardization:z-score importance after standardization.Assume that all nodes in the subgraph t-test with P-value < 0.05 considered significant.As indicated in figure legends, data in bar charts are represented as mean Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Supplementary DataSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


Degenerative diseases in orthopaedics have become a significant global public health issue with the aging of the population worldwide. The traditional medical interventions, including physical therapy, pharmacological therapy and even surgery, hardly work to modify degenerative progression. Stem cell–based therapy is widely accepted to treat degenerative orthopaedic disease effectively but possesses several limitations, such as the need for strict monitoring of production and storage and the potential risks of tumorigenicity and immune rejection in clinical translation. Furthermore, the ethical issues surrounding the acquisition of embryonic stem cells are also broadly concerned. Exosome-based therapy has rapidly grown in popularity in recent years and is regarded as an ideal alternative to stem cell–based therapy, offering a promise to achieve ‘cell-free’ tissue regeneration.Traditionally, the native exosomes extracted from stem cells are directly injected into the injured site to promote tissue regeneration. Recently, several modified exosome–based strategies were developed to overcome the limitations of native exosomes, which include mainly exogenous molecule loading and exosome delivery through scaffolds. In this paper, a systematic review of the exosome-based strategy for degenerative disease in orthopaedics is presented.Treatment strategies based on the native exosomes are effective but with several disadvantages such as rapid diffusion and insufficient and fluctuating functional contents. The modified exosome–based strategies can better match the requirements of the regeneration in some complex healing processes.Exosome-based strategies hold promise to manage degenerative disease in orthopaedics prior to patients reaching the advanced stage of disease in the future. The timely summary and highlights offered herein could provide a research perspective to promote the development of exosome-based therapy, facilitating the clinical translation of exosomes in orthopaedics.Exosome-based therapy is superior in anti-senescence and anti-inflammatory effects and possesses lower risks of tumorigenicity and immune rejection relative to stem cell–based therapy. Exosome-based therapy is regarded as an ideal alternative to stem cell–based therapy, offering a promise to achieve ‘cell-free’ tissue regeneration. Osteoarthritis (OA) and intervertebral disc degeneration (IVDD) are the most common degenerative orthopaedic disorders, which cause pain and disability, leading to increased social burden among the expanding and aging population . A substWith the progression of research, extracellular vesicles (EVs) were recently demonstrated to be the primary functional molecules involved in tissue regeneration mediated by stem cells ,15. It wIn addition to the intrinsic effect exerted by endogenous substances, exosomes are a promising vehicle with diameters around 30–150 nm, enabling the delivery of exogenous bioactive molecules like microRNAs (miRNAs), proteins and small molecules to specific cells ,23. WithSmall molecule drugs ,26. Curr22.12.1.1in vitro and in vivo . T. T34]. T in vivo . Remarka in vivo . The exo in vivo   could also attenuate inflammation and promote the proliferation of chondrocytes, their inhibition of chondrocyte maturation restricted the capacity for cartilage restoration in OA. To overcome the shortage of native SMSC-Exos, Tao et al. overexpressed miR-140-5p in SMSCs and developed modified exosomes with an abundancy of miR-140-5p, an miRNA able to effectively promote chondrogenic differentiation of MSCs . Comparepatients . In thei effects .in vitro study, the exosomal miR-361-5p targeting Asp-Glu-Ala-Asp-box polypeptide 20, the critical factor up-regulated in OA, showed superior performance in alleviating chondrocyte damage and inflammation induced by IL-1β. Furthermore, in an OA mouse model, reduced inflammation and synovial tissue hyperplasia were observed with the administration of exosomes with exogenous miR-361-5p [in vitro study employing MSCs, significantly higher cell-adhesion and -proliferation rates were revealed in the EX-miRNA-140 group relative to using miR-140 or exosomes individually. Enhanced cartilage matrix production and expression of chondrogenic genes like SOX9 and aggrecan were also observed, indicating that EX-miRNA-140 effectively guided stem cells towards a chondrogenic lineage [In addition to their extraction from engineered MSCs, miRNA-enrich exosomes could also be produced by directly loading the native exosomes with functional exogenous miRNAs. For example, Tao et al. incorporated miR-361-5p into MSC-Exos through electroporation. In an R-361-5p . In addi lineage .2.2.2Although several miRNAs carried by exosomes have been shown to prevent or slow the progression of OA, their shortages of insufficient stability and their rapid clearance have reduced the enthusiasm for their further application . Circula2.2.3in vitro. The superior KNG delivery to MSCs could be attributed to the lipid membrane of exosomes, which contributed to the preservation and release of KGN. In an established rat OA model, KGN-E7-Exos showed a superior cartilage-regeneration effect with a possible anti-inflammatory ability, promising a potential new approach to cartilage reversion in OA [Exosomes are also able to provide an ideal platform for small molecular protein delivery to promote cartilage regeneration in OA . The pooon in OA .in vivo [WNT3a, a strong activator of the WNT/β-catenin pathway essential in cartilage repair, has been incorporated into exosomes to penetrate dense cartilage and deliver molecules deep into chondrocytes. However, WNT3a is an insoluble molecule and easily degraded at the injury site. As WNT3a has a high affinity with exosomes, exosomes from L-cells  were employed to deliver WNT3a to prolong the effect and were injected into mouse joints to repair large osteochondral defects. After 8 weeks, WNT3a-loaded MSC-Exos had successfully improved osteochondral defect repair via WNT signalling activation, while WNT3a without vehicle failed to activate WNT signalling to promote cartilage regeneration . Overallin vivo ,58. Furtin vivo .2.3in vivo. Additionally, reticuloendothelial organs or macrophages might metabolise the exosomes before they are taken by chondrocytes or MSCs [in vitro study employing SM-MSCs and chondrocytes, more fluorescent-labelled KGN was found in SM-MSCs, indicating that E7-Exos could recognise SM-MSCs and increase the efficiency of KGN delivery. Compared to KGN-loaded native exosomes, the delivery of KGN via E7-Exos also provoked better cartilage regeneration in vivo. Exosomes targeting chondrocytes are also critical to delivering cargos to chondrocytes precisely and effectively as the avascularity and high density of cartilage hinder transport efficiency. In order to increase the uptake of exosomes with functional miRNA by chondrocytes, Liang et al. introduced a chondrocyte-affinity peptide on the surface of exosomes and loaded the purified exosomes with miRNA-140 via electroporation [in vitro was revealed, which effectively attenuated the degeneration induced by inflammation. During an in vivo study, chondrocyte-targeted exosomes were injected into joint spaces and achieved a longer retention and deeper diffusion of miR-140 in a mouse model compared to non-targeted exosome vesicles [Although exosomes provide an ideal platform for cargo delivery, the non-selectivity of native exosomes decreases the efficacy of cargo delivery to the target cells  or MSCs ,61. To p or MSCs . In an iporation . Increasvesicles . Briefly2.2.4in vivo application, Hu et al. developed a gelatin methacrylate/nanoclay hydrogel (Gel-nano) with outstanding biocompatibility, and mechanical and injectable properties for loading human umbilical cord MSC-Exos (hUC-MSCs-Exos) [in vitro through exosome retention, and enhance cartilage formation in a rabbit model [in vitro study using BMSCs, an adhesive hydrogel with encapsulated exosomes effectively recruited BMSCs, supported BMSC proliferation and guided them toward chondrocytes. Furthermore, the exosomes released from the novel adhesive hydrogel could promote the migration of endogenous BMSCs into the defect, which may be attributed to chemokine signalling pathways. Magnetic resonance imaging and histological analysis also indicated that the surface of regenerative cartilage was smoother and more similar to the native one with additional exosomes [l-lactide)-b-poly(ethylene glycol)-b-poly triblock copolymer as a vector. The novel hydrogel exhibited good performance in slowly releasing exosomes, effectively delivering circRNA3503-enriched exosomes to the chondrocytes and consequently protecting OA from progression in vivo [Although the benefits of exosomes in cartilage regeneration are well established, the rapid diffusion and degradation of exosomes after injection into the cartilage defect can weaken the regenerative ability, placing restrictions on their extensive application . DeliverCs-Exos) . ExosomeCs-Exos) . To adapit model . Recentlit model . In an iexosomes . To prol in vivo .In addition to hydrogels, decellularized cartilage matrix scaffolds also hold promise to be containers of exosomes. For instance, a recent study that implanted an acellular cartilage ECM (ACECM) scaffold into a cartilage defect found it could better retain the injected human Wharton’s jelly–derived MSC-derived exosomes (hWJMSC-Exos), aiming to promote the repair of rabbit osteochondral defect through the synergistic effect of decellularized scaffolds and exosomes . The resThe biomimetic ACECM scaffold provides a suitable environment for endogenous MSC attachment, proliferation and chondrogenesis, improving cartilage regeneration with hWJMSC-Exos synergistically . In addiExosomes have also been injected with hyaluronic acid (HA), which can reduce inflammation and relieve joint pain, expecting to improve the microenvironment to restore cartilage defects. For instance, Wong et al. combined HA with MSC-Exos to repair osteochondral defects in rabbits. After 12 weeks, the defects treated by HA and MSC-Exos were restored by hyaline cartilage with superior mechanical properties, while those treated by HA alone showed deteriorated cartilage formation . A recenCollectively, biomimetic materials are outstanding holders to preserve and protect exosomes at the cartilage defect, enabling exosomes to display long-term regenerative efficacy. The combination of exosomes and bioactive materials can efficiently regenerate cartilage defects in OA by recruiting endogenous chondrocytes, promoting their proliferation by exosomes and providing a favourable microenvironment through bioactive materials. Moreover, exosomes also hold promise to attract endogenous MSCs and guide them toward a chondrogenic lineage, effectively renewing aged chondrocytes with a degenerative status.33.13.1.1A gradual reduction in functional nucleus pulposus cells (NPCs) is considered to be the main pathological change in IVDD, resulting in an imbalance between ECM synthesis and degradation . One of 3.1.2in vivo experiment, not only NPCs but also endplate chondrocytes were better preserved after the administration of MSC-Exos. The exosomal miR-31-5p targeting apoptosis-related ATF6 is regarded as the underlying mechanism for the favourable effect of MSC-Exos in reversing cartilage endplate degeneration [As the degeneration of the cartilage endplate has been proved to exacerbate IVDD by disturbing nutrient transportation, priority should be given to restoring supplementation from the cartilage endplate to NPCs . One of neration .3.1.3Similar to OA, inflammation and oxidative stress induced by accumulated advanced glycation end-products with aging are partly responsible for IVDD progression ,75. The 3.1.4In normal intervertebral disc, blood vessels are limited in the outer surface of the annulus fibrosus, while ingrowth of blood vessels was observed in the context of IVDD, disrupting the maintenance of normal disc homeostasis and function . Because3.2Currently, the use of modified exosome–based therapy in IVDD is relatively rare. Exosomes are most commonly used to deliver exogenous miRNA to attenuate the progression of IVDD. A reduction in the amount of NPCs is considered to be responsible for the initiation of IVDD ,89. Althin vivo. Decellularized scaffolds are currently regarded as one of the most suitable bioactive materials to load with exosomes to achieve sustained release because of their appropriate degradation, allowing exosomes to remain in the disc for a long time [in situ gelation. The designed acellular hydrogel with exosomes effectively protected NPCs from pyroptosis in vitro and delayed IVDD in a mouse model, which was attributed to the co-operation of acellular scaffolds and exosomes [Given the rapid clearance and disruption of exosomes in IVDD therapy, it is necessary to find a vehicle that enables long-term exosome release ong time ,93. Moreong time . Recentlong time  (Table 2exosomes .Table 2S4Exosome therapy has progressed significantly in recent years and is widely accepted as one of the most potent strategies for treating degenerative diseases in their early stage. Previously, exosomes were directly injected at the degenerative site in their native status after being extracted from various stem cells, such as MSCs and chondrocytes. Nevertheless, the injection of native exosomes showed several disadvantages, including rapid diffusion, disruption and fluctuating functional contents, discouraging their application in some complex healing processes. In order to better match the requirements of the regenerative process, various modified exosome–based strategies have been developed. Given the fluctuating and insufficient functional contents of native exosomes, several exogenous small molecules, including miRNAs, circRNAs and proteins, were integrated into exosomes to further improve the anti-senility capacity in degenerative orthopaedic diseases. With regard to the rapid clearance and disruption of exosomes, researchers have developed bioactive scaffolds to deliver exosomes, which could not only release exosomes in a sustained fashion but also repair the degenerative tissue synergistically. Furthermore, exosomes with specific sequences targeting certain cells were also designed to achieve high transport efficacy and long-term maintenance in the degenerative tissue.Aging is inevitable, and senility is often accompanied by degenerative disease, badly affecting life quality. Currently, no approved therapeutic agents are available to reverse or counteract the progression of degenerative disease in orthopaedics. Conservative therapies, including self-management and education, physiotherapy and pain control, are the first-line treatment strategy for OA and IVDD . When a Exosome-based therapy is currently a hot research topic promising tissue restoration in the context of degenerative conditions. Exosomes enable tissue regeneration not only in degenerative diseases of orthopaedics but also retinal and even nervous system degeneration ,103. HowThe authors have no conflicts of interest to disclose in relation to this article.


RAS wild-type metastatic colorectal cancer (mCRC), an anti-epidermal growth factor receptor (EGFR) monoclonal antibody plus chemotherapy is a standard option for treatment in the first-line setting. Patients who progress while on treatment with anti-EGFR-based therapy can be resistant to further anti-EGFR treatment, but evidence suggests that the anti-EGFR-resistant clones decay, thereby opening the potential for rechallenge or reintroduction in later lines of treatment. Results from recent clinical studies have shown that some patients with mCRC who are rechallenged with anti-EGFR monoclonal antibodies exhibit durable responses. While other therapies have demonstrated improved overall survival in chemorefractory mCRC over the past decade, rechallenge with anti-EGFR monoclonal antibodies in later lines of treatment represents a new option that deserves further investigation in clinical trials. In this review, we summarize the molecular rationale for rechallenge or reintroduction in patients with mCRC who have progressed on earlier-line anti-EGFR treatment and examine the current evidence for using liquid biopsy as a method for selecting rechallenge as a therapeutic option. We also provide an overview of published trials and trials in progress in this field, and outline the potential role of rechallenge in the current clinical setting.In patients with  This pu therapy , 18.Different clinical scenarios can be considered based on the time interval between the last administration of the anti-EGFR agent in first-line treatment and the evidence of progressive disease (PD). True rechallenge occurs when a patient who is initially responding to anti-EGFR-based therapy  develops PD while on this treatment, switches to a non-anti-EGFR regimen until PD, and is then rechallenged with anti-EGFR-based therapy , 19. StuReintroduction occurs when a patient who is responding to an anti-EGFR-based regimen stops treatment for a reason other than PD  and is subsequently restarted on anti-EGFR-based therapy at a later time. This is not considered to be a rechallenge because the tumor did not become resistant to treatment during therapy . RetreatIn this review, we summarize the molecular rationale for rechallenge or reintroduction in patients with mCRC who have progressed on earlier-line anti-EGFR treatment and examine the current evidence for using liquid biopsy as a method for selecting rechallenge as a therapeutic option. In addition, we provide an overview of published and ongoing trials in this field and outline the potential role of rechallenge in the current clinical setting.2KRAS gene as well as mutations in exons 2, 3, and 4 of the NRAS gene (together considered RAS mutations) are negative predictive biomarkers for anti-EGFR-targeted therapy   , 32.EGFR-ECD can block the binding of anti-EGFR mAbs and lead to resistance  are higher when patients are rechallenged after increasing time intervals following previous treatment with anti-EGFR mAbs  may be effective  and less frequent markers such as BRAF, HER2, MET, MAP2K1, and others. A combination of liquid biopsies with mathematical modelling of tumor evolution may enable the design of optimal personalized treatments in the future , who had shown clinical benefit with cetuximab plus irinotecan (or FOLFIRI) in a prior line, developed disease progression, received intervening treatment with chemotherapy alone, and were rechallenged with cetuximab plus irinotecan-based therapy thereafter  and subsequent progression in response to first-line cetuximab plus chemotherapy as well as progression after second-line chemotherapy. The median cetuximab-free interval was 330 days (range: 56–1224 days). The ORR was 2.9% with a disease control rate (DCR) of 55.9%, the median PFS was 2.4 months, and the median OS was 8.2 months. In the JACCRO CC-08 study, neutropenia was the most frequent grade 3/4 AE, occurring in 28.6% of patients; the most common grade 1/2 skin toxicity (skin rash) occurred in 80% of all patients  with known RAS status by ctDNA at baseline, 8 weeks, and progression after initial therapy were enrolled. This post hoc analysis demonstrated that RAS status in ctDNA predicts survival of rechallenge treatment with an anti-EGFR agent. Patients with RAS mutations in ctDNA at baseline had a significantly shorter PFS and significantly poorer OS than those without mutations   .KRAS wt mCRC who progressed after initial cetuximab plus irinotecan treatment and progressed again after subsequent chemotherapy before receiving cetuximab plus irinotecan as a rechallenge therapy , second-line (1 patient), or third-line treatment (2 patients)  . During RAS wt mCRC can also be administered cetuximab as a second-line therapy , 6 PRs and 9 cases of SD were reported among 28 patients enrolled, thereby resulting in an ORR of 21%; the DCR was 54%, the median PFS was 3.4 months, and the median OS was 9.8 months. These data are comparable to the outcomes of a population of EGFR-positive patients unselected for RAS status and treated with cetuximab and irinotecan (RAS wt ctDNA had significantly longer PFS (4.0 months) compared with patients with RAS mt ctDNA at the start of rechallenge therapy , although no significant differences in median OS were observed. Furthermore, of 25 patients evaluable for a radiological response, 13 showed tumor shrinkage. Only patients who were ctDNA RAS and BRAF wt benefited from rechallenge in terms of response; no RAS mutations were detected in patients who achieved a confirmed PR, while 12 of 21 patients who did not achieve a response had RAS mutations detectable in ctDNA before starting retreatment. These results support the use of liquid biopsy before rechallenge, as well as the potential need to investigate other markers of resistance beyond RAS (such as EGFR-ECD or BRAF) to more accurately select patients who may benefit from cetuximab rechallenge. One patient with a low level of RAS mutation in ctDNA experienced a transient response to rechallenge, highlighting the validity of using mutational status as a threshold rather than as a yes/no indicator.The CRICKET study was a prospective phase II trial that examined rechallenge with cetuximab and irinotecan in patients with or XELOX . Of noteinotecan . In the inotecan , patientRAS and BRAF wt mCRC after acquiring resistance to first-line cetuximab plus irinotecan-based chemotherapy  with an anti-EGFR mAb regimen  plus chemotherapy after discontinuing the treatment in an earlier line; the median length of the anti-EGFR-free interval was 10.5 months . It shouRAS wt mCRC with the intent of rechallenging them with cetuximab plus avelumab in the third line after first-line use of anti-EGFR mAb plus chemotherapy (only for patients characterized by CR/PR/SD) and after subsequent second-line treatment (®)  and median PFS , compared with patients who experienced grade 0/1 skin toxicity (P=0.006). In the ctDNA RAS/BRAF wt population, patients with NLR <3 had a significant improvement in median OS compared with those with NLR ≥3 . These results suggest that NLR could be a potential biomarker of response to the combination of cetuximab plus avelumab  . In thisotherapy . A recentoxicity . In the toxicity . Median avelumab .KRAS wt mCRC, the rechallenge with panitumumab plus irinotecan resulted in a response rate of 8.3%; median PFS and median OS were 3.1 months and 8.9 months, respectively , in 25 patients with ectively . Severalectively Table 2,ectively . The VEL and ORR , 71.4RAS mutational status and identify patients who may benefit from rechallenge with anti-EGFR therapy, the technique is still not widely available internationally and is often used only in conjunction with research trials, so that the reliability of clinical factors in identifying patients who may benefit from rechallenge is investigated. Based on the published literature, a minimum of 6 months is frequently set as the time interval required between anti-EGFR treatment regimens; furthermore, prior responders who have a greater duration between anti-EGFR treatment intervals seem more likely to respond to rechallenge  and second- or later-line regorafenib , are also available , 73–75. Many patients still exhibit good disease control after chemotherapy and good long-term performance status while remaining suitable for additional therapy after progression beyond third- or fourth-line treatment; rechallenge with an anti-EGFR mAb may expand the continuum of care for these patients and may be considered multiple times in the therapeutic route of patients with EGFR-dependent tumors.5RAS mutations   can help define those patients who may benefit from the rechallenge strategy  or if onutations ] is suffhallenge . Further studies Table 2  studies , the CIT tumors) , the PARwt ctDNA  and the  wt mCRC . Future RAS/BRAF wt tumors is examining two sequence treatment strategies to determine the optimal anti-EGFR therapy based on cancer molecular evolution (RAS wt mCRC who had a PR/CR with first-line anti-EGFR therapy and subsequently received a second line of treatment (RAS/BRAF wt mCRC is currently underway (NCT05291156)  trial in Germany is the first trial to study cetuximab rechallenge with randomization between third-line rechallenge with cetuximab plus irinotecan or FOLFIRI or physician’s choice; this trial includes the largest number of patients  investigated for cetuximab rechallenge thus far . In Italvolution . Cetuximreatment . Further5291156) . Ongoing5291156) .6RAS wt mCRC and an appropriate (≥6 months) interval between anti-EGFR-based treatment regimens; in the rechallenge setting, the use of an anti-EGFR agent is not limited to a specific line. However, as these clinical factors are not reliable surrogate markers of anti-EGFR sensitivity, liquid biopsies are highly recommended for the selection of patients who are eligible for rechallenge (The continuum of care is ever evolving in the treatment of patients with mCRC. The results of the recently completed FRESCO-2 trial with fruquintinib in refractory mCRC show a significant and clinically meaningful improvement in OS . Regorafhallenge . CompletAll authors contributed equally to the conception of the intellectual content and writing of the manuscript. All authors also reviewed any revisions that were made and provided their final approval of the manuscript.