Cognition and social cognition anomalies in patients with bipolar disorder (BD) and schizophrenia (SCZ) have been largely documented, but the degree of overlap between the two disorders remains unclear in this regard. We used machine learning to generate and combine two classifiers based on cognitive and socio-cognitive variables, thus delivering unimodal and multimodal signatures aimed at discriminating BD and SCZ from two independent groups of Healthy Controls (HC1 and HC2 respectively). Multimodal signatures discriminated well between patients and controls in both the HC1-BD and HC2-SCZ cohorts. Although specific disease-related deficits were characterized, the HC1 vs. BD signature successfully discriminated HC2 from SCZ, and vice-versa. Such combined signatures allowed to identify also individuals at First Episode of Psychosis (FEP), but not subjects at Clinical High Risk (CHR), which were classified neither as patients nor as HC. These findings suggest that both trans-diagnostic and disease-specific cognitive and socio-cognitive deficits characterize SCZ and BD. Anomalous patterns in these domains are also relevant to early stages of disease and offer novel insights for personalized rehabilitative programs. Despite their traditional nosological discrimination3, cross-domain evidence suggests a quite large degree of clinical overlap. Indeed, patients often do not fit completely within the boundaries of a single disorder and show a mixture of psychopathological features traditionally associated with both these illnesses5. Therefore, rather than considering schizophrenia and bipolar disorder as two separate diagnostic entities, recent views have hypothesized that they lie on a psychopathological continuum6. This view is further supported by growing evidence highlighting partially shared genetic risk between the two diseases8. Thus, partially-shared phenotypes9 may characterize these brain disorders, under the influence of partially-shared genetic risk factors.Schizophrenia and bipolar disorder are two severe brain diseases that heavily compromise quality of life and personal functioning11 play a key role. Indeed, such correlates are crucially associated with both the diseases12. Univariate literature14 is relatively consistent in showing more quantitative  than qualitative  cognitive differences between bipolar disorder and schizophrenia. Typically, SCZ exhibit more severe and pervasive core cognitive deficits, which affect multiple domains including processing speed15, verbal fluency16, episodic and working memory17, and cognitive flexibility18. These alterations usually emerge in the context of a global intellectual impairment19 and are tightly associated with negative symptoms and disorganization, crucially contributing to the typical increased functional disability in SCZ20.Among phenotypes in common between patients with schizophrenia (SCZ) and with bipolar disorder (BD), those related to cognitive and socio-cognitive impairments21, but to a lesser extent and within a framework of relatively preserved general intelligence, likely reflecting better premorbid functioning2. This pattern of milder cognitive impairment in bipolar disorder emerges across a broad range of domains, including attention, verbal memory, working memory, and executive functioning22. This body of evidence suggests that BD and SCZ could be differentiated at the cognitive level only quantitatively, and not qualitatively. Interestingly, this seems to be the case also for social cognition. In this regard, meta-analytic findings reveal a stable pattern of socio-cognitive deficits across domains, including recognition and perception of basic socio-emotional cues, social inference23 as well as theory of mind11, with SCZ showing the same socio-cognitive abnormalities of BD, but to a greater extent23. Taken together, this literature suggests that SCZ and BD are difficult to distinguish based on cognitive and socio-cognitive characteristics. Indeed, these impairments define a dimensional phenotype occurring among psychosis spectrum disorders24. Furthermore, subclinical signs identifying risk conditions for psychosis spectrum disorders are paralleled by both cognitive and socio-cognitive deficits22. In this regard, previous studies have highlighted that individuals at clinical high risk for psychosis 25 and bipolar disorder 26 show a large extent of quantitative and qualitative similarities in the occurrence of early cognitive27 and socio-cognitive impairments28.Also BD experience specific alterations affecting different cognitive domains29, techniques based on multivariate approaches, like Machine learning (ML), use information from multiple data sources to classify groups of subjects30 based on parsimonious sets of variables . This approach allows classification of individuals at the single-subject level, avoiding a group vs. group perspective32. Thus, it may contribute to successfully identify individualized and fine-grained patterns of cognitive and socio-cognitive anomalies in SCZ and BD, as well as to investigate their diagnosis-related or trans-diagnostic relevance.However, all this literature is based on univariate statistical approaches. Compared with these methods, which may leverage knowledge only at the group-levelThis study aimed to identify at the single-subject level data-driven cognitive and socio-cognitive signatures of BD and SCZ, and to investigate their trans-diagnostic or diagnosis-specific relevance. Another aim was to investigate if such signatures are also relevant to risk conditions for these diseases or their early stages. With this purpose, we devised a “reversal discovery-validation” ML strategy  (Table p < 0.001) (Table p < 0.05) (Table T-test and χ1) Table . On the 1) Table . Further29. The first algorithm was based on performance at cognitive tests, whereas the second incorporated socio-cognitive measures. The third was a multimodal SVM, i.e., stacking-based, which allows to combine the unimodal classifiers33, integrating their multiple learned decision values and then learning again from this meta-information . All the generated classifiers correctly discriminated BD from HC1 better than chance with a permuted significance of p < 0.05. In particular, we used balanced accuracy (BAC) as a measure of model performance, which refers to accuracy calculated in terms of true positive and negative cases, balanced by the sample size of each positive and negative group. BAC is used to optimize performance in models with unbalanced sample sizes29. According to this measure, the stacking-based model had the best classification performance, assigning membership of each individual to BD and HC1 with 80.0% BAC (p = 0.02). The cognitive classifier revealed a cross-validated BAC of 79.8% (p = <0.001), while BAC of the socio-cognitive classifier was 74.4% (p = <0.001)  or mania as assessed by Young Mania Rating Scale (YMRS)34  for BD.In order to classify HC1 vs. BD, we built three separate within-cohort Support Vector Machine (SVM) algorithms. SVM is a margin-based statistical technique of supervised learning able to discriminate individuals into two (or more) groups by establishing a linear space of classification  on the basis of specific cases called support vectors35, and the global memory IQ score36 38, and the total reaction time at the FEIT  between loads at the N-back working memory taskp = <0.001), while cross-validated BAC was 83.4% (p = <0.001) for the cognitive classifier, and 78.6% (p = <0.001) for the socio-cognitive classifier  or psychosis-related psychopathology as assessed by Positive And Negative Symptoms Scale (PANSS)39 total score  for SCZ.We used the same method described for HC1 vs. BD discrimination to build two unimodal and one stacking-based SVM algorithms for HC2 vs. SCZ classification. Here, all the generated models correctly classified each individual as SCZ or HC2 better than chance with a permuted significance <0.001. As in the HC1 vs. BD analysis, the stacking-based model performed better than the two unimodal classifiers with 84.9% BAC , followed by the stacking-based model (BAC: 77.4%) and the socio-cognitive classifier (BAC: 66.2%)  were disease-specific or able to correctly categorize individuals of the other cohort, we employed an OOCV to apply HC1 vs. BD discriminative models to the HC2-SCZ cohort, and vice-versa , as already done previouslyApplying the models discriminating HC2 vs. SCZ to the HC1-BD cohort indicated that both unimodal and multimodal classifiers assigned group memberships within the HC1-BD cohort better than chance, with the stacking-based model as the best-performing classifier (BAC: 83.1%), followed by the cognitive (BAC: 82.4%) and the socio-cognitive classifier (BAC: 77.9%) , but not from those for BD (p = 0.97), while CHR decision scores differed from both those for HC1 (p < 0.001) and for BD (p = 0.02) , but not from those for SCZ (p = 0.95). Differently, CHR decision scores differed from both those for SCZ (p < 0.001) and for HC2 (p < 0.001)  to earlier stages of disease and psychosis risk. Specifically, we first applied both the disease-related stacking-based models to CHR and FEP cognitive and socio-cognitive data via OOCV. Then, we used decision scores based on such classifiers as the dependent variable in an ANOVA model  . The application of the HC1 vs. BD model revealed that decision scores for FEP significantly differed from those for HC1 (02) Fig. . When th01) Fig. . In summOur results suggest that cognitive and socio-cognitive deficits are relevant to the multimodal categorization of SCZ or BD vs. HC, and that anomalies contributing to such discrimination are mostly disease-specific, with some overlap. On the other hand, multivariate models taking into account the entire set of these cognitive and socio-cognitive impairments are not predictive for differentiation between patients with these two brain disorders. Furthermore, the present findings also suggest a generalization of cognitive and socio-cognitive classifiers categorizing SCZ and BD vs. HC only to first episode of the illness, but not to conditions of clinical liability.23.We found that unimodal models based on either cognitive or socio-cognitive characteristics significantly and accurately assigned membership of BD and SCZ to the respective diagnostic groups. Indeed, the combination of these unimodal classifiers in a single stacking model led to the highest discriminative power between classes. These results suggest that cognitive and socio-cognitive profiles of alterations contribute to the phenotypical characterization of schizophrenia and bipolar disorder19 and mainly related to basic information processing and verbal-executive components of cognition, emerged for schizophrenia. Taken together, these results suggest that cognitive abnormalities differentiating either BD or SCZ from HC are generally qualitatively different, but commonalities are also present. Interestingly, findings from univariate literature primarily implicate quantitative rather than qualitative differences in cognitive anomalies between BD and SCZ43, which may be consistent with the view that a data-driven, multivariate approach is more sensitive to a fine-grained disentanglement of the cognitive asset of these two disorders.Analysis of the cognitive and socio-cognitive features contributing the most to the discrimination between BD or SCZ from the respective HC cohorts allows to further characterize this contention. In particular, the most reliable cognitive features for the discrimination of BD were related to overall working memory accuracy and global memory quotient, suggesting a memory-related pattern of alterations consisting of load-independent working memory impairments and multi-domain memory disruptions at the core of the disorder. On the other hand, disruptions within the domain of memory, namely the global memory quotient and load-dependent working memory efficiency, contributed to discriminate SCZ from HC, together with other cognitive deficits related to information processing (0-back accuracy), incidental immediate learning, and semantic fluency. Therefore, a broader pattern of multi-domain cognitive anomalies, already reported by previous literature44 and decreased accuracy in identifying45 and distinguishing46 anger from other emotions44,47. Results in SCZ indicate that the most reliable features selected to classify between patients and controls were the ability to properly infer both lie and global sarcasm, the ability in identifying surprise facial expressions, and the average response time to correctly identify emotions on faces. Previous literature already reported deficits in enriched social inference in schizophrenia. In particular, a previous study indicated lower performance in SCZ at both lie and sarcasm TASIT subscales when compared with BD and HC individuals, with the latter two groups having similar performance23. Similarly, surprise was already reported as the most misinterpreted emotion by SCZ patients, although within a frame of disrupted emotion recognition48.Our results from the socio-cognitive domain may be inserted in a similar explanatory framework. We found that the socio-cognitive variables contributing the most to BD discrimination were the accuracy in identifying angry facial expressions and the global time needed to identify facial emotion expressions. These results appear to delineate a pattern of alterations mainly related to emotion identification processes and are partially consistent with previous univariate literature reporting slower reaction times in BD during emotion recognition11. Furthermore, as for the cognitive domain discussed above, our findings also suggest both qualitative differences and overlaps in discriminant socio-cognitive features in BD and SCZ vs. HC. Specifically, a broad spectrum of socio-cognitive anomalies, encompassing basic facial emotion identification and both minimal and enriched social inference, emerged for schizophrenia. Differently, BD were discriminated from HC by a prevalent occurrence of emotion identification deficits. A possible interpretation of these results is that SCZ are discriminated by alterations of complex social processes as well as by minimal social inference abilities, while discrimination of BD is mainly supported by the latter. However, it should be noted that also the ability to properly infer paradoxical sarcasm was highly discriminant for classifying both BD and SCZ. Sarcasm comprehension has been previously associated with ToM abilities49 and impairments in this social ability are reported for both disorders, although a greater deficit appears to be present in schizophrenia50. Thus, we may speculate that ToM deficits could trans-diagnostically affect the ability to make judgments on the meaning of conversational remarks both in schizophrenia and bipolar disorder. However, hierarchical models able to better specify the reciprocal relationships among paradoxical sarcasm and ToM abilities need to be developed in the future to validate this view.Overall, these findings suggest that socio-cognitive features reveal a key discriminative power in discriminating BD or SCZ from HC, which is mostly related to the emotion recognition and social inference broader domains, consistently with previous literature reporting their alteration in both these brain disorders11. However, within these deficits, those that are more at the core of each disease are different. Therefore, personalized programs targeted at chronic patients should be oriented to primarily manage disease-related “hub” alterations, but always within a broader framework of intervention aimed to monitor also to the “side”, less central, trans-diagnostic deficits. According to this interpretation, our findings might be considered as a starting evidence corroborating the existence of a psychopathological continuum between schizophrenia and bipolar disorder, emerging at the level of cognitive-behavioral phenotypes. However, as widely reported by previous literature conceptualizing bipolar disorder and schizophrenia as different brain diseases51, cognitive and socio-cognitive deficits in both the disorders might be underpinned by multiple structural and functional abnormalities53. Thus, delivering classification models based also on brain features is needed to more deeply investigate—and potentially corroborate—the bipolar disorder-schizophrenia continuum hypothesis. For instance, to build new knowledge useful to better refine current nosology, future machine learning studies generating models based on both behavioral and structural/functional brain data should aim at combining this multivariate information to grasp the possible between-domains latent interactions at the single-subject level.Results from the between-cohort application of within-cohort generated models indicate high performance of classification for the cognitive, the socio-cognitive, and the stacking-based classifiers, with high discriminative power of all the HC1 vs. BD generated models in the HC2 vs. SCZ comparison, and vice-versa. These results suggest that the overall signatures generated for each disorder are not disease-specific. On the other hand, we found that some of the cognitive and socio-cognitive features discriminating the most between BD vs. HC1 and SCZ vs. HC2 do not overlap, which seems at odds with the generalizability of the overall models between diagnoses. A possible explanation of these seemingly discrepant findings is that the variables selected as most reliably discriminating between HC1 and BD, and between HC2 and SCZ, respectively, were different because our ML pipeline identified deficits that are more “at the core” of each disease at the single-subject level, and therefore representing “hub” cognitive and socio-cognitive diagnosis-related deficits. Despite the identification of these “core”, diagnosis-related alterations, the high generalization of each model to unseen individuals with a different diagnosis proved the non-specificity of the overall bipolar and schizophrenia signatures, suggesting how, at the level of the entire pool of variables, our algorithm may have caught further cognitive and socio-cognitive deficits relevant for both the disorders. Based on this interpretation, our results may support the notion that schizophrenia and bipolar patients are characterized by similar cognitive and socio-cognitive deficits, consistently with previous univariate literatureAs a final step, we tested the generalization of each stacking-based signature  to earlier stages of diseases and to psychosis risk. Here, FEP decision scores did not differ from those of BD or SCZ when the respective predictive models were applied, while such scores diverged from those of HC individuals. These patterns indicate that, for both HC1 vs. BD and HC2 vs. SCZ models, the algorithm recognizes FEP more as “BD-like” or “SCZ-like”, rather than “HC-like”, suggesting that both BD-related and SCZ-related multimodal signatures generalized to early stages of disease irrespectively from diagnostic boundaries, thus showing trans-diagnostical prognostic relevance.A different pattern of results was present when both HC1 vs. BD and HC2 vs. SCZ models were separately applied to CHR. Here, CHR decision scores differed from both those of BD and SCZ as well as from those of HC individuals. In other words, for both HC1 vs. BD and HC2 vs. SCZ models, neither patient- nor control-like patterns emerged for CHR, suggesting that cognitive and socio-cognitive features of individuals at clinical risk diverge from those of HC, consistently with the notion that these individuals may be affected by anomalies related to these domains; however, such cognitive and socio-cognitive impairments may not be shaped as those present in full-blown schizophrenia or bipolar disorder yet.Our results should be interpreted considering several limitations. Although we carefully controlled for potential confounding factors including age and sex , their nonlinear interaction with other external confounds, like medication itself, onset of disease and duration of illness or premorbid intelligence quotient, cannot be ultimately ruled out. However, considering that no significant associations emerged between decision scores extracted from our best models and psychopharmacological treatment or symptoms, it is less likely that these confounders might have affected our results. Moreover, because of the lack of information about non-pharmacological interventions that patients may have undertaken, we could not explore their putative effects on cognitive and socio-cognitive performance. Future studies are warranted to shed light on this possibility.Although we have employed a stringent double cycle nested Cross-Validation (CV) strategy which should enforce unbiased estimations, further validations of our results in external wider groups of individuals with completely different demographic and geographic profiles are needed to fulfil requirements for generalizability. Indeed, this further evidence would strengthen the potential for translation of our findings into clinical practice and is particularly warranted for the at-risk and FEP cohorts, whereas validation results may have been affected by the small sample size.32. Nevertheless, future studies are warranted to further validate our findings and investigate more deeply the relationship between the identified cognitive and socio-cognitive alterations from a trans-diagnostic perspective as well as their potential associations with brain structural and functional underpinnings.We delivered ML algorithms highlighting that, despite “hub” cognitive and socio-cognitive alterations specifically related to each full-blown diagnosis can be identified, bipolar disorder and schizophrenia share in both domains an overall common pattern of impairments that should therefore be trans-diagnostically approached. Therefore, effective remediation strategies for BD or SCZ individuals should be tailored both on these specific cognitive and socio-cognitive deficits at the core of each disorder, and on less central behavioral alterations. Furthermore, our findings support the potential translation of such trans-diagnostic intervention strategies at earlier stages of diseases . In this framework, we think our results are potentially relevant from a clinical perspective, as they provide ready-to-use information to refine individualized intervention focused on cognitive and socio-cognitive impairments for both the earlier and the chronic phases of the diseases3. To obtain two cohorts with a proportion between healthy subjects and patients of about 2:1, 95 HC  randomly entered the first cohort with the BD group  and 29 were FEP individuals . A detailed description of the clinical characteristics of these cohorts is reported in SI, Section 1.Moreover, individuals at risk for psychosis or at early stages of disease were also included Table . Specifi56 and the Intelligence Quotient (IQ) using the Wechsler Adult Intelligence Scale-Revised (WAIS-R)57. Furthermore, the Italian version of the Wide Reading Achievement Test (WRAT)58 was employed to measure premorbid IQ. In clinical populations, the Global Assessment of Functioning Scale (GAF)59, the PANSS, and the YMRS were administered to evaluate patients’ global functioning, positive and negative symptoms, global psychiatric symptoms, and manic symptoms.For all participants, we assessed socio-economic status with the Hollingshead scale2 tests, and ANOVA were used to investigate group differences in terms of demographic and clinical characteristics, both within , global memory (WMS), incidental (RAVLT) and episodic  auditory verbal learning, working memory (N-back), verbal fluency  and abstract reasoning (Wisconsin Card Sorting Test – WCST64) performance.a “cognitive” classifier based on variables from different target cognitive domains previously associated with bipolar disorder65. In particular, the algorithm was fed by 37 variables, which included domains measured with: (1) the FEIT, which allowed quantifying the individual ability to correctly identify and process emotions from facial expression; (2) the TASIT, which measures the higher-level ability to infer other people’s emotions and thoughts using contextual information during social interactions; the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT)66 which measured the ability to efficiently manage emotional reactions during fictional every-day situations.a “socio-cognitive” classifier based on variables from three social cognition domains of interest, i.e., identification of emotions, social inference, and emotion management, reported as frequently altered in both schizophrenia and bipolar disorderAll participants underwent an extensive assessment protocol, aimed at collecting data about cognitive and socio-cognitive abilities in adult healthy and clinical populations, through different standardized tools . Using these cognitive and socio-cognitive measures, we trained:SI reports detailed descriptions of the tools administered (Sections 3 and 4), a complete list of the variables which fed ML models (Supplementary Table-www.pronia.eu/neurominer/)30. In particular, to properly detect cognitive and socio-cognitive similarities and differences between schizophrenia and bipolar disorder, we employed a “reversal discovery-validation strategy”, as already done in previous publications42, consisting in two phases, i.e., Phases 1 and 2. In a third step (Phase 3), we investigated how discriminative models generated and validated in the previous phases applied to patients at early stages of disease and at-risk populations.Our overall ML analytic strategy consisted of three phases and was carried out using the NeuroMiner software, version 1.05  and Bipolar Disorder patients” and “Phase 1: classification models between Healthy Controls (group 2) and Schizophrenia patients”).67, to allow for unbiased estimation of the model’s generalizability, preventing information leaking throughout the strict separation between subjects used for training the models and independent individuals used for testing decisions68. According to this strategy, models trained at the inner CV cycle (CV1) of each unimodal classifier  that contributed most to the discriminative pattern between HC1 vs. BD and HC2 vs. SCZ, were then applied to independent test data at the outer CV cycle (CV2). We implemented a 10-fold CV cycle both in the inner CV1 and in the outer CV2 levels, employing a repeated nested CV29. In particular, we permuted the participants within their groups (number of permutations = 5) and repeated the CV cycle for each of these permutations. Thus, within our nested CV framework, we built an ensemble of 50 models (n = 5 repetition × k = 10 folds) for each CV2 partition31. All model training steps of parameter optimization, that use group-level statistical procedures for features’ preprocessing, occur only in CV1 training data. Instead, CV1 test data are used to pick hyperparameter combinations that provide potentially good model generalization capacity. Specifically, all the features entering our classifiers underwent three preprocessing steps within CV1:i.feature-wise scaling was performed to remove between-features differences effect from the training sample information;ii.scaled data entered a k-Nearest Neighbor imputation step, aimed at filling the missing values for each given subject in the data, using the feature-specific median value computed in the 7 more similar nearest neighbors on the basis of Euclidean distance;iii.70 and socio-cognitive72 alterations in schizophrenia and bipolar disorder (see SI-Section 4.3 for details).partial correlations were finally performed to regress out the variance associated with age and gender and control for their confounding effect, given the reported association between these variables and the heterogeneity and severity of cognitiveIn our ML pipeline  we implemented a double-cycle, repeated nested CV73. More specifically, after the data entering in a greedy forward search wrapper73, we computed the feature-related probability of being selected for classification purposes within the inner CV loop for each variable, providing maximum prognostic performance with the smallest amount of predictors. This way, we then applied the trained model to CV2 cycle, where generalization error estimation is performed68. Indeed, CV2 validation data, as derived from unseen study participants 68, serve exclusively the purpose to measure the models’ generalizability. With this procedure, we finally obtained parsimonious SVM decision models based on preprocessed cognitive and socio-cognitive features, determining each validation individual’s outcome class  through a majority voting procedure across all ensemble models. Classification performance was measured using BAC, sensitivity, specificity, Area-Under-the Curve, Positive Predictive Value, Negative Predictive Value, and Positive Likelihood Ratio. A stacking procedure was further implemented to build a third multimodal classifier74, aimed at combining the unimodal classifiers within the ML environment. Specifically, stacking used the decisions models from our lower-level cognitive and socio-cognitive unimodal classifiers to generate a new, higher-level algorithm that did not learn from domain-specific raw data , but from the decisions scores of the unimodal classifiers29. The stacking generalization was performed to investigate whether using all the information coming from both cognition and social cognition would have led to an increase in HC1 vs. BD and HC2 vs. SCZ classification accuracy, compared with the classification ability of single domains-related unimodal classifiers. Permutation analyses were performed to assign statistical significance to the observed classification performance of our models75. Specifically, for each permutation, we performed 1000 random permutations of the outcome labels and we retrained all linear SVM models in the repeated nested CV design, using the respective feature subsets resulted from the observed-label analyses. For each permutation, we collected the predictions of the random models into a permuted prediction ensemble for each outer cycle subject. Thus, we built a BAC-based null distribution of out-of-training classification performance. The final calculated significance of the observed out-of-training BAC consisted in the number of events with a permuted out-of-training BAC higher or equal to the observed BAC divided by the number of performed permutations. We set the model significance at α = 0.05. To explore a possible relationship between classification and medication or symptoms severity, we performed non-parametric correlation analyses between decision scores extracted from the best-performing classifiers in both the cohorts and drugs dosages, scores of mania (as assessed by YMRS) and psychosis-related psychopathology (as assessed by PANSS) .To assess the discriminative utility of the input variables within each unimodal classifier, we performed a feature selection procedure within the CV1 loop33, without any re-in-between training. Specifically, OOCV is usually required in an ML framework for a thorough generalizability assessment and usually involves the application of the trained models used to predict targets in the CV2 data folds  to a separate dataset that has been independently collected . Thus, this method allowed us to investigate whether the disease-related signatures built independently for each cohort were disease-specific or had discriminative power also for the other .We applied (i) HC1 vs. BD models to the HC2-SCZ cohort, and vice-versa, (ii) HC2 vs. SCZ models to the HC1-BD cohort using OOCVTo explore the generalization of the independently generated HC1 vs. BD and HC2 vs. SCZ ML signatures to earlier stages of diseases and to psychosis risk, we compared the performance of the best classifier in assigning membership to HC or patient groups with those obtained applying such classifier to CHR and FEP. With this aim, we performed an OOCV without any in-between retraining as done in Phase 2 . Then, we employed an ANOVA to compare the OOCV-based ML decision scores of each of these cohorts, with, respectively, the HC1 vs. BD and the HC2 vs. SCZ decision scores . Therefore, in terms of decision scores comparison, the presence of a significant difference between CHR or FEP and any other clinical group would suggest that the OOCV-ed algorithm did not classify them as patients. On the other hand, the presence of a significant difference between CHR or FEP and any control group would suggest that the algorithm did not classify them as controls. Finally, the presence of significant differences between CHR or FEP and both controls and clinical cohorts would suggest that CHR and FEP individuals were not comparable either to controls or to patients, based on the rule generated by the OOCV-ed algorithm.Supplementary Information


The increase in the rate of mRNA vaccination against coronavirus disease 2019 (COVID-19) worldwide has been accompanied by reports of an increase in the side effects of the vaccine. In the field of neurosurgery, several cases of venous thrombosis have been reported as possible complications after COVID-19 vaccination. However, no such side effects have been reported in patients with brain tumors, and COVID-19 vaccination is considered safe for this patient population. In this report, we present the cases of two patients with brain tumors who experienced intratumoral hemorrhage as a possible side effect of the COVID-19 vaccine. In the first case, a 54-year-old man who had received CyberKnife treatment for a vestibular schwannoma eight years prior presented with tongue discomfort, right-side facial numbness, and dizziness since the day after his COVID-19 vaccination. MRI revealed intratumoral hemorrhage of the vestibular schwannoma. The second patient was a 60-year-old woman who presented with a sudden-onset headache and vomiting that had started three days after her COVID-19 vaccination. CT revealed a meningioma with intratumoral hemorrhage. Both patients had undergone surgery prior to this presentation, and their symptoms had improved. They had no risk factors for intratumoral hemorrhage, suggesting that it may be a side effect of the mRNA vaccine against COVID-19. Although the causal relationship is unclear, acute inflammation with predominantly lymphocytic infiltration and thrombogenicity after COVID-19 vaccination may damage the fragile microcirculation of the tumor. The efficacy and safety of vaccines against coronavirus disease 2019 (COVID-19) have been vigorously debated. Although myocarditis and other side effects have occasionally been reported, reports on the vaccine's neurosurgical side effects have been scarce, except for venous thrombosis in some individuals [Case 1Ⓡ  stereotactic radiosurgery (SRS) for Koos grade 3 right acoustic neuroma eight years prior. MRI at his last follow-up six years after the CyberKnife treatment had shown a slight shrinkage of the tumor . He had undergone CyberKnifeThe patient's consciousness was clear, with right-sided deafness and grade II right-side facial palsy according to House-Brackmann (HB) grading, with occasional spasms of the orbicularis oculus muscle. He also complained of sensory disturbance in the right half of his tongue and altered taste. Cerebellar ataxia, such as gait disturbance or paralysis of the limbs, was not observed. Brain MRI showed an increase in tumor size. The intensity of the tumor had changed, showing high intensity on diffusion-weighted images and T1-weighted images, and heterogeneity on T2-weighted images, which suggested intratumoral hemorrhage. High-intensity signals were prominent in the brainstem and cerebellar peduncle on fluid-attenuated inversion recovery (FLAIR) images. T2-weighted images showed that the tumor was compressing the trigeminal nerve. The tumor showed a heterogeneous density on CT  score of 1. At a follow-up visit two months after the surgery, the facial palsy had subsided.The pathological findings were typical of a vestibular schwannoma, and there was no mitotic image suggestive of malignancy. Elastica van Gieson staining revealed no obvious thrombi in the specimen. The surrounding necrotic tissue with hemorrhage showed infiltration by foam cells and lymphocytes Figure .Case 2A 60-year-old woman with no significant medical history started experiencing a sudden-onset severe headache and vomiting three days after getting the first dose of COVID-19 vaccination . Because her symptoms persisted even after five days, she visited our clinic.She had a clear consciousness and no obvious neurological symptoms other than the headache. Brain CT showed a high-density mass in the right anterior cranial fossa and ipsilateral subdural hematoma, and contrast-enhanced CT showed a uniform contrast effect. There was no prominent high-intensity signal around the mass on FLAIR, and the lesion was enhanced homogeneously with gadolinium-diethylenetriaminepentaacetic acid (Gd-DTPA) Figure .The patient underwent craniotomy for tumor resection under general anesthesia. Both the tumor and its dural attachment site were resected, as the pathology indicated a meningioma. Postoperatively, the patient developed partial right oculomotor nerve palsy, probably due to the excision of the dura mater through the superior orbital fissure. The patient was discharged on postoperative day 19 with an mRS score of 2 Figure .The pathological diagnosis revealed World Health Organization (WHO) grade I meningothelial meningioma. The tumor cells had oval nuclei and eosinophilic spores, with indistinct cell borders. Partial hemorrhage, inflammatory cell infiltration, and granulation were observed; however, no necrosis or infiltration of the brain parenchyma was observed. Immunostaining showed epithelial membrane antigen (EMA)-positive, vimentin-positive, and CD34-negative staining. The dura mater of the tumor attachment showed negative margins Figure .We presented two cases of intratumoral hemorrhage immediately following mRNA vaccination for COVID-19. Various side effects of COVID-19 mRNA vaccines have been reported. Among these, there have been several cases of venous thrombosis . The perIn case 1, intratumoral hemorrhage of a vestibular schwannoma, which was stable for eight years after SRS, occurred on the day after vaccination. Intratumoral hemorrhage in vestibular schwannomas is rare, with a reported incidence of 0.4% in general  and 0.26In case 2, meningioma with intratumoral hemorrhage resulted in symptoms of headache and vomiting three days after the vaccination, and the tumor was EMA-positive and vimentin-positive pathologically, consistent with WHO grade I meningioma . MeningiIn both cases, COVID-19 vaccination appeared to have affected the tumor to cause bleeding. In case 1, the patient presented with various neurological symptoms following the third dose, while the female patient in case 2 had a headache after the first dose. One study has reported myocarditis complicated by the booster dose , but itsThe number of reports on the side effects of COVID-19 vaccines has increased with the increase in the number of vaccinations worldwide. AstraZeneca and Johnson & Johnson vaccines causing vaccine-induced thrombocytopenia caused by antiplatelet factor IV antibodies has been reported . ConsideWe discussed two cases of intratumoral hemorrhage in benign tumors following mRNA vaccination against COVID-19. Intratumoral hemorrhage can occur due to acute inflammation with predominantly lymphocytic infiltration or fragile microcirculation with thrombus formation. The causal relationship between mRNA vaccines and the occurrence of intratumoral hemorrhage requires further investigation. Further studies are needed to determine the frequency of intratumoral hemorrhage following mRNA vaccination.


Serial testing for SARS-CoV-2 is recommended to reduce spread of the virus; however, little is known about adherence to recommended testing schedules and reporting practices to health departments.The Self-Testing for Our Protection from COVID-19 (STOP COVID-19) study aims to examine adherence to a risk-based COVID-19 testing strategy using rapid antigen tests and reporting of test results to health departments.STOP COVID-19 is a 12-week digital study, facilitated using a smartphone app for testing assistance and reporting. We are recruiting 20,000 participants throughout the United States. Participants are stratified into high- and low-risk groups based on history of COVID-19 infection and vaccination status. High-risk participants are instructed to perform twice-weekly testing for COVID-19 using rapid antigen tests, while low-risk participants test only in the case of symptoms or exposure to COVID-19. All participants complete COVID-19 surveillance surveys, and rapid antigen results are recorded within the smartphone app. Primary outcomes include participant adherence to a risk-based serial testing protocol and percentage of rapid tests reported to health departments.As of February 2022, 3496 participants have enrolled, including 1083 high-risk participants. Out of 13,730 tests completed, participants have reported 13,480  results to state public health departments with full personal identifying information or anonymously. Among 622 high-risk participants who finished the study period, 35.9% showed high adherence to the study testing protocol. Participants with high adherence reported a higher percentage of test results to the state health department with full identifying information than those in the moderate- or low-adherence groups .Preliminary results from the STOP COVID-19 study provide important insights into rapid antigen test reporting and usage, and can thus inform the use of rapid testing interventions for COVID-19 surveillance. Despite relatively widespread vaccination for SARS-CoV-2 throughout the United States, in late February 2022, nearly 90,000 new cases of COVID-19 were reported daily in the United States among both unvaccinated and vaccinated individuals, and herd immunity remains uncertain .Antigen detection rapid diagnostic tests (Ag-RDTs) for COVID-19 pose great opportunity for community surveillance owing to their relative ease of use and quick turn-around time for results, making them amenable to testing outside traditional clinical environments . Serial To fill these knowledge gaps, we are performing a longitudinal study to examine adherence to a risk-based testing protocol supported by a digital infrastructure to allow recruitment and study engagement nationwide. The goals of this prospective, site-less digital study are to leverage strong partnerships with community organizations and local health departments to assess adherence to a risk-based SARS-CoV-2 testing strategy using over-the-counter tests, and to describe participants’ behavior for reporting test results to public health departments and factors associated with test reporting behavior.We are recruiting up to 20,000 participants throughout the United States who meet our predefined inclusion/exclusion criteria . Study eThe Self-Testing for Our Protection from COVID-19 (STOP COVID-19) study utilizes the Mstudy app, a custom smartphone app created within the MyDataHelps interface. The app is used to electronically collect survey data from all participants, as well as guide participants through rapid antigen testing and interpreting their results. Residents of the mainland United States with the MyDataHelps app receive an in-app notification inviting them to participate in the study. Community partners distribute emails and flyers to interested participants with instructions to download the Mstudy app and enter a join code for the study; participants are also able to autonomously sign up through the study website. The Mstudy app is free of charge and compatible with both Apple and Android smartphone devices. All participants consent and enroll in the study using the Mstudy app. Participants less than 18 years old are required to assent, as well as receive written consent from their parents/guardians. On enrollment, participants’ street address is also collected and verified against the United States Postal Service database through the digital platform to ensure that study staff can ship required testing materials directly to participants’ homes. Participants are eligible for a US $50 gift card on two separate occasions throughout the study, based on their completion of surveys and rapid antigen tests.Inclusion criteria≥8 years of ageAccess to a smartphoneSpeak English or SpanishAble to provide informed consent or assent with parental consent (for participants under 18 years old)Exclusion criteriaCurrent incarcerationLack of mailing addressLack smartphone internet accessLiving outside mainland United StatesAfter enrollment, participants are asked if they would like to securely and confidentially share information from their wearable activity-tracker device . If participants consent to sharing wearable data, data from their activity tracker are passively collected for the 3-month study period, with no additional requirements from the participant. Sharing wearable data is optional, and participants may decline to share wearable data and still participate fully in the study. The study platform is enabled to collect measures of physical activity , mobility , vitals , and sleep analysis, depending on the type of wearable device activated.On enrollment in the study, participants are stratified into high- or low-risk groups based on SARS-CoV-2 vaccination and infection history . High-riHigh-risk participants are asked to test for COVID-19 twice per week for 12 consecutive weeks using an Ag-RDT (Quidel QuickVue) with the Mstudy app for testing assistance. High-risk participants receive a weekly surveillance questionnaire to assess if they are experiencing two or more new COVID-19–related symptoms  or have had close contact with someone who tested positive for SARS-CoV-2 in the past 7 days. In the case of two or more symptoms or exposure, participants are sent a home polymerase chain reaction (PCR) collection kit via Quest Diagnostics and told to continue using the Ag-RDT at home, as the PCR test is the current diagnostic gold standard. The home PCR kit should be utilized immediately upon receipt to collect a nasal specimen and returned to Quest in the preaddressed envelope within 24 hours. Participants receive all testing and survey reminders through the app.Low-risk participants are asked to complete weekly surveillance questionnaires to monitor for COVID-19–related symptoms or exposure to SARS-CoV-2. If a low-risk participant reports two or more COVID-19–related symptoms and/or a close exposure to someone with SARS-CoV-2, they are sent Ag-RDT kits within 48 hours and advised to test twice weekly for 2 weeks following the symptom or exposure. Additionally, participants are sent a home PCR collection kit and asked to self-collect a nasal specimen and ship it back to Quest Laboratories using the preaddressed envelope within 24 hours. After completing the 2-week testing period, low-risk participants resume completing weekly surveillance questionnaires.The Mstudy app contains detailed instructions for using the rapid tests for COVID-19 . ParticiStudy participants complete weekly surveillance questionnaires through the Mstudy app, as well as additional surveys during enrollment, at baseline, after each at-home test, and on conclusion of the 3-month study period  results to their respective state public health departments. Reporting differed by test result , with 3.7% (95% CI 2.0%-6.1%) of positive tests unreported in comparison to 1.8% (95% CI 1.5%-2.0%) of negative tests reported. Reporting choices did not differ significantly by risk category . Among tTwelve weeks of adherence to twice-weekly serial testing was assessed among 622 high-risk participants who completed the full study period. Of these participants, 223 (35.9%) were highly adherent to the testing protocol . The perThe STOP COVID-19 study is a novel longitudinal, digital study aimed at understanding adherence and public health reporting of rapid antigen testing throughout the United States, using a risk-based testing protocol. Here, we describe the study methodology, which utilizes the Mstudy app for data collection and study coordination purposes, and preliminary results from phase 1 of participant recruitment in Michigan. Phase 1 of enrollment began in August 2021, and 3496 participants in Michigan have enrolled to date, including more than 1000 high-risk participants. Digital site-less studies have many advantages in the age of COVID-19, especially to facilitate study recruitment and retention. The site-less study approach has allowed us to dynamically change the recruitment strategy throughout the study to prioritize communities with high prevalence of SARS-CoV-2, seasonal surges, or low vaccination rates in order to optimally sample communities with a high burden of COVID-19. Further, digital studies require less active study coordination than traditional site-based studies because of the ability of technology to facilitate certain tasks , which allowed us to implement a risk-based testing algorithm in participants’ homes nationwide while conserving the time and resources of study personnel. The data collected from this study will offer tremendous insight into COVID-19 at-home testing behaviors, and using the digital approach was highly effective.There has been significant interest in rapid antigen testing on a national level as a cost-effective solution to expand serial testing for COVID-19 for surveillance and asymptomatic case detection. However, little is known about perceptions of serial testing and how individuals use rapid test results in altering their COVID-19 risk behaviors. Among the study participants engaged in rapid serial testing, the COVID-19 positivity rate was the highest among high-risk, exposed, symptomatic participants, consistent with the published literature, and supporting our approach to risk stratification . Based oThroughout the COVID-19 pandemic, perceptions and trust of government have strongly influenced the adoption or rejection of public health initiatives, including masking, social distancing, and vaccination. Our preliminary results showed that 14,000 rapid tests have been completed by study participants, with a full or anonymized reporting rate to participants’ respective state department of public health of over 98%. We observed statistically significant differences in reporting status based on test result, with negative results being reported more than positive results.In a study of 1420 Australian adults, individuals with higher trust in government had 6-times greater odds of adopting recommended COVID-19 avoidance behaviors, including social distancing, self-quarantine, and hand washing, than individuals with low governmental trust . AdditioThe site-less digital study approach is still relatively new in the fields of medical and public health research, although it has garnered significant interest owing to the potential benefits to participant accessibility, engagement, longitudinal data volume, and study administrative costs . As the Additionally, during phase 1 enrollment, convenience sampling resulted in a sample of predominately highly educated, White, female participants. Further, only 1% of the phase 1 cohort is over 75 years old, despite older adults facing the most devastating outcomes due to SARS-CoV-2 . We recoThis is the first study to examine the reporting of COVID-19 results from Ag-RDTs and adherence to serial testing schedules without supervision, providing important data to guide the administration and use of Ag-RDTs in a real-world setting. The site-less study design offers great flexibility to recruit based on fluctuations in the pandemic, as well as to obtain a geographically diverse participant pool. Study strengths also include the use of the MyDataHelps app as a tool for reporting test results, as well as a risk-based approach to serial testing. Nevertheless, certain inclusion criteria such as the use of a smartphone may limit the generalizability of this study. Participation, nonresponse, and attrition biases are also of concern, as individuals choosing to enroll in and comply with a longitudinal study to test for COVID-19 may differ from nonparticipants in relation to COVID-19 perceptions, education, race, and health literacy.This report describes the study design and preliminary results of a longitudinal study aimed to understand individuals’ testing and reporting decisions. Site-less study designs are increasingly valuable in the era of COVID-19. The data collected in this study will provide important insights into how individuals navigate their testing decisions during the COVID-19 pandemic, as well as how we should understand the data reported from rapid antigen tests for COVID-19.


Non-respiratory long-coronavirus disease 2019 (COVID-19) symptoms are mainly related to a long-lasting endothelial dysfunction and microcirculation impairment. We hypothesized that Sulodexide, a purified glycosaminoglycan mixture with a beneficial endothelial effect in arterial and venous peripheral diseases, may be effective in a subset of patients with long COVID-19.p < 10−3), palpitations , and endothelial function . Endothelial function improvement was significantly correlated with chest pain and palpitations recovery .We conducted a multicenter prospective quasi-experimental study. A total of 290 patients from the TUN-EndCOV study with long-COVID-19 symptoms and endothelial dysfunction were included. The endothelial function was clinically assessed using a post-occlusive reactive hyperemia protocol with finger thermal monitoring device. Endothelial quality index (EQI) was assessed at inclusion and at 21 days later. The study population was assigned to a sulodexide group (144 patients) or a no-medical treatment group (146 patients). Clinical characteristics were similar at inclusion in the two groups. Fatigue, shortness of breath, and chest pain were the most common symptoms, respectively, 54.5, 53.8, and 28.3%. At 21 days, the sulodexide group improved significantly better than the no-medical treatment group in chest pain (83.7 vs. 43.6%, Sulodexide significantly improves long-lasting post-COVID-19 endothelial dysfunction and alleviates chest pain and palpitations. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was detected in China in December 2019 . The COVBeyond pulmonary injury, inflammation, and particularly cytokine storm, prothrombotic state and endothelial dysfunction are proved to be the main causes of detrimental outcome of patients with COVID-19 in the acute phase .“ESC group for atherosclerosis and vascular biology” stated that it seems relevant to follow the endothelial function in convalescent patients for early detection and prevention of long-term cardiovascular complications , and the Royal College of General Practitioners (RCGP) . These gSulodexide, a highly purified mixture of glycosaminoglycan that includes fast-moving heparin and dermatan sulfate, had beneficial effects on the fibrinolytic system, platelets, endothelial cells, and inflammation . SulodexSulodexide was not tested in the setting of long-COVID-19 syndrome. We hypothesized that it may be effective in the long-COVID-19 spectrum and particularly in the subset of symptoms linked to microcirculation impairment and endothelium dysfunction.This is a prospective multicenter quasi-experimental case–control study. The recruiting period extended from 20 January to 26 June 2021. The study protocol was recorded in the Pan African Clinical Trials Registries (PACTR) with trial ID PACTR202102867544936. The study had the local Ethics and Investigation Committee approval, being designated with approval number CPP SUD 0299/2020. All authors reviewed the manuscript for the accuracy and completeness of the data.Patients were recruited by local health authorities relying on COVID-19 registry. The standard protocol of endothelial function study was previously detailed . After iMain exclusion criteria were the patients recovered from COVID-19 with a good endothelial function (EQI ≥ 2), patients under anticoagulation or having experienced venous thromboembolism in the past 6 months, patients with chronic use of steroid medication, patients with life expectancy <1 year, according to clinical judgment, patient with symptoms related to an organic cause , pregnancy and breastfeeding and foreseen inability to attend scheduled visits. Patients who did not attend the scheduled 21-day visit were excluded from the data analysis.The long-COVID-19 symptoms were screened relying on long-COVID-19 NICE guidelines . ElectroAmbulatory 24-h cardiac monitoring DMS 300-4L was performed only in patients with palpitations. At ECG analysis, elevated heart rate (HR) was defined by an HR > 100 beats at rest, and supraventricular premature beats were defined by ectopic beats with narrow QRS QRS ≤ 120 ms) and abnormal P wave and ventricular premature beats by ectopic beats with large QRS (QRS > 120 ms) and not preceded by P waves .Patient outcomes were assessed at 21 days after inclusion. Primary end points were: long-COVID-19 symptoms evolution from T0 to T1 and endothelial function outcome (delta EQI) defined as the difference between EQI at 21-day follow-up and EQI at inclusion. Delta EQI = EQI(T1)–EQI (T0).Statistical analyses were performed using SPSS, Chicago  version 23.0. The complete database is maintained by the study team.Since we included a large long-COVID-19 population, we calculated a mean EQI amelioration without medical intervention of 0.2 (0.6) in an initial sample of patients. We then anticipated a 60% power of EQI after medical intervention, and we calculated a sample size of 142 subjects in each group.Categorical variables were expressed as frequencies and percentages. For quantitative variables, we checked the normality of the distribution by the Kolmogorov–Smirnov test and the Shapiro–Wilk test. An estimate of the means with their standard deviations (SDs) and of the median with interquartile range (IQR) was thus carried out.The comparison between two categorical variables was carried out by the Pearson's chi-squared test test or the Fisher's exact test if the conditions were not verified.t-test was used for the comparison of two means when the distribution was Gaussian and by the non-parametric U test of Mann–Whitney or Kruskal–Wallis when the distribution was not Gaussian.The Student's The McNemar's test was used to determine if there were differences in paired data during the follow-up in the different study groups.The area under the curve (AUC) using the receiver operating characteristic curves determined the correlation between the delta EQI and long-COVID-19 symptoms recovery.p < 0.05 was considered statistically significant.Estimates of risk ratios were presented with 95% confidence intervals (CIs). The value of p < 10−3) and palpitations  than controls (p = 0.07) and neurocognitive disorders . The sulodexide group presented also a significant endothelial function amelioration compared to control group    [28–180] days. They were assigned to a sulodexide group (144 patients) or a control group (146 patients). The demographics, clinical characteristics, and the number of chronic medications used by the patients were similar in the two study groups . The medcontrols . There w < 10−3] . Endothectively) .Our findings provide evidence that sulodexide significantly improves long-lasting post-COVID-19 endothelial dysfunction and alleviates chest pain and palpitations.In our study, we found that fatigue, shortness of breath, and chest pain were the most reported post-COVID-19 sequalae. Long-COVID-19 syndrome was characterized by a combination of non-specific symptoms. Cardiovascular (CV) symptoms especially chest pain and palpitations were frequently reported , 21–23.The underlying pathophysiology of CV symptoms is not yet well defined. Several explanations have been proposed, for example, a microvascular and endothelial dysfunction –26, vascin-vitro trials were available and there were no published in-vivo trials in the long-COVID 19 setting.To date, only limited series of p = 0.019) . In 3 matched series, flow-mediated dilation of peripheral arteries was substantially lower in patients with post-COVID-19 when compared to acute infection and controls  of patients still have microcirculation impairment monitored by a non-invasive PORH finger thermal device. Endothelial dysfunction (EQI < 2) was significantly related to non-respiratory long-COVID 19 symptoms  in multivariate analysis (OR = 1.522 (1.072–2.160), = 0.019) . These f.7% of paThese findings support the evidence of increased rates of vascular complications in patients with post-COVID-19 compared to general population and the necessity of effective treatment to avoid such complications . The incIn a review about the pathogenesis of COVID-19 through the lens of an undersulfated and degraded epithelial and endothelial glycocalyx, the prevention and treatment protocols proposed were to preserve and repair epithelial and endothelial glycocalyx integrity . This obSince endothelial dysfunction seems to be a serious driving cause of non-respiratory long-COVID-19 symptoms, we hypothesized that targeting microvascular impairment could be an effective solution. In this quasi-experimental TUN-EndCOV study, we have selected sulodexide  250 LSU two times a day during 21 days for symptomatic patients with proven endothelial dysfunction (EQI < 2). A clinical assessment and endothelial function test were performed in all patients at inclusion and at 21 days later.Sulodexide, an orally administered highly purified mixture of glycosaminoglycan that includes fast-moving heparin and dermatan sulfate, had beneficial effects on the fibrinolytic system, platelets, endothelial cells, and inflammation . Sulodexp < 10−3) and palpitations . There was also a trend of a significant decrease in fatigue  and neurocognitive disorders .Our results support evidence that sulodexide significantly improves long-lasting post-COVID-19 endothelial dysfunction and alleviates related chest pain  were not under represented as it could be in a randomized study. Our findings support the emphasized patients' hope in regaining their wellbeing and accelerating their return to work. The synergic activity of sulodexide's components and its pleiotropic effects may be essential to reach such results.TUN-EndCOV trial is not only the largest The main limitations of the study:- This was a quasi-experimental study with a control group. The need of placebo-randomized study is warranted to confirm such results.- Further studies with larger population are needed to confirm the significant recovery of the other “non-respiratory” symptoms such as neurocognitive difficulties and fatigue.- Limited follow-up of 3 weeks. Long-term follow-up is needed for major events  analysis.- Only sulodexide with one-dose regimen has been tested. Further studies with other protocols and drugs targeting endothelial dysfunction must be conducted.- Only symptomatic patients with proven endothelial dysfunction were included which raises the concern of need and availability of such tests in clinical practice.in-vitro and in-vivo studies in convalescent patients with COVID-19. Non-respiratory symptoms may be related to microcirculation impairment. Our results support that targeting this cause with sulodexide accelerates patients' recovery in parallel with vascular endothelium restoration. Other clinical trials are warranted.Long-lasting microcirculation and endothelial dysfunction have been proved The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the study had the local Ethics and Investigation Committee approval, being designated with approval number CPP SUD 0299/2020. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.LA, SA, SC, HI, and JJ conceived and designed the study, had full access to all of the data in the study, and take responsibility for the integrity of the data and the accuracy of the data analysis. SC, SA, JJ, and LA drafted the paper and performed the analysis. ST, SK, AG, ZM, and YT contributed to data collection and interpretation. All authors critically revised the manuscript for relevant intellectual content and gave final approval for the version to be published. All authors agree to be accountable for all aspects of the work and will answer any questions related to the accuracy or integrity of the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Statistically significant declines in mean EQ-5D index scores were observed for all socio-demographic and behavioural variables considered. Most of these changes were due to increased reporting of problems in the pain/discomfort and anxiety/depression health domains. Older age, being unemployed or economically non-active and having financial difficulties were significantly associated with lower HRQoL in both baseline and follow-up measurements. In the follow-up data, women had significantly lower HRQoL compared to men, whereas higher education proved to be the only protective factor regarding HRQoL. Unhealthy dietary habits and low physical activity had a negative impact on the HRQoL score in the follow-up data. These results indicate that the COVID-19 pandemic has had a substantial impact on HRQoL in the Estonian population.The aim of the study was to analyse health-related quality of life (HRQoL) among the Estonian general population and its socio-demographic and behavioural correlates during the COVID-19 pandemic. Longitudinal data on 1781 individuals from an Estonian rapid-assessment survey on COVID-19 were used. HRQoL was assessed with the EQ-5D-3L in June 2020 (baseline) and in May 2021 (follow-up). The HRQoL index score and its socio-demographic and behavioural variations were analysed using paired  The ongoing coronavirus disease COVID-19) pandemic has had a drastic impact on population health outcomes due to increased morbidity and mortality. Two and a half years since the first cases were identified in China 9 pandemi. As a reThe pandemic has disrupted the daily lives of the majority of people. In addition to the direct health effects due to COVID-19 infection, the potential indirect pathways to deteriorating health include changes in employment or work practices , loss orWhile these are just a few mechanisms that explain the pandemic’s health implications, they provide an extensive yet relevant list of indicators that could be used to capture these effects. Generic health-related quality of life (HRQoL) instruments offer an analytic solution to this problem as they provide a multidimensional summary of health status that can be compared across a variety of diseases/conditions and used for different populations . One of This study contributes to the field by providing comparable longitudinal data on HRQoL during the pandemic in Estonia. The first COVID-19 cases were registered in Estonia (population 1.3 million) in late February 2020, with the 14-day incidence rate during the first wave reaching 56.6 cases per 100,000 in April 2020. Its epidemiological impact was modest compared to the second and third wave of the pandemic when the incidence rate peaked at >1500 cases per 100,000  and the Consequently, the overall aim of the paper is to study HRQoL among the Estonian general population and to analyse socio-demographic and behavioural correlates during the COVID-19 pandemic. More specifically, we will focus on the following research questions: (a) which HRQoL scores are reported at two points in time ten months apart during the pandemic in Estonia? (b) which socio-demographic and health behaviour indicators had an impact on HRQoL score at the individual level and did this pattern change during the follow-up?n = 4606; adjusted response rate 40.3%), the survey design was altered to longitudinal with individually linkable data collections planned for two additional waves. As HRQoL was included in the survey starting from the second wave, this paper is based on the data from the second and third waves of the survey.This study uses longitudinal data from an Estonian rapid-assessment survey on COVID-19 . The surn = 3464; adjusted response rate 31.1%) was carried out from 11 June to 20 July 2020  was conducted from 13 April to 5 May 2021 and coincided with the second pandemic wave in Estonia  of respondents also participated in the follow-up survey. Of those who participated in both the baseline and the follow-up, HRQoL questions for measuring the EQ-5D score were answered by 1824 unique respondents. However, 21 respondents answered the HRQoL questions in the baseline but not in the follow-up, and 22 respondents did not answer the HRQoL questions in the baseline but did in the follow-up. Therefore, 43 unique respondents were excluded from the analytic sample to avoid individual variation in comparing results from two time points. In this study, only those respondents who answered the HRQoL questions in both survey waves  were included in the analysis.From the baseline survey, 61.1%  18–29; (2) 30–44; (3) 45–54; (4) 55–64; and (5) ≥65-years. Self-reported ethnicity was grouped as: (1) Estonians; and (2) other ethnic groups. Educational level refers to the highest level of education obtained and was aggregated into three groups: (1) primary or lower; (2) secondary or vocational; and (3) tertiary or higher education. Current employment status was reported in both baseline and follow-up and divided into categories: (1) employed; and (2) unemployed/non-active, referring to various economically non-active groups. Similarly, financial status was reported twice; it captured a subjective assessment of a household’s financial wellbeing during the past month and was categorized as: (1) comfortable; (2) sufficient; and (3) difficulty coping.Health behaviour indicators in the analysis included self-estimated alcohol consumption, smoking status, physical activity and dietary habits. The frequency of alcohol consumption during the past 12 months was aggregated into a binary variable: (1) consumed alcohol ≥ 4 times a week (high risk consumption); and (2) consumed alcohol ≤ 3 times a week (low risk consumption). The indicator of smoking status included both traditional tobacco and smoke-free tobacco/nicotine products and was categorized as: (1) non-smoker; and (2) smoker . Physical activity was captured by the frequency of engaging in recreational sports activities requiring moderate physical effort (for a minimum of 30 min) and aggregated into a binary variable: (1) active ≥ 4 times a week (high activity); and (2) active ≤ 3 times a week (low activity). Self-assessed dietary habits were also dichotomized into a binary variable: (1) healthy ; and (2) unhealthy .The analytic sample for this study consisted of 1781 individuals  who responded in both baseline and follow-up waves . Additiot-tests were used. Additionally, the prevalence of health problems by health domain in both measurements was presented to describe the change in health states.HRQoL, measured by EQ-5D index scores, in the baseline and follow-up surveys was described by means and standard deviations (SD). Changes in HRQoL by socio-demographic and health behaviour indicators were presented as means within categories; by counts and proportions for increased and decreased EQ-5D index scores within categories of predictor variables. To indicate whether EQ-5D index score differences between variable categories were statistically significant, p-values. The beta coefficient indicates the mean change in the reference value within the variable. The threshold for statistical significance was set at p < 0.05 throughout the analyses. All statistical analyses were performed using the STATA 14 software [Tobit regression modelling was used to study which variables had an impact on EQ-5D index scores in both baseline and follow-up surveys. As the distribution of EQ-5D index scores was positively skewed , Tobit models were considered suitable for such censored or bounded data . Mutuallsoftware .n = 1781) in both baseline and follow-up measurements are presented in p < 0.05), no statistically significant changes were observed. The baseline and follow-up HRQoL by socio-demographic and health behaviour indicators are shown in p < 0.05) by age (18–29 vs. 55 and older), employment and financial status, physical activity and dietary habits. No statistically significant differences in HRQoL were found in sex, ethnicity, education, smoking or alcohol consumption.The characteristics of the study sample (p < 0.05) to 0.811 ± 0.188. Statistically significant declines in EQ-5D index score means were observed in all categories of the socio-demographic and behavioural variables considered. In addition, the existing socio-demographic health gradients from the baseline increased with statistically significant differences in HRQoL. The most notable declines were seen among respondents with primary or lower education (mean difference −0.072) and among those with financial difficulties (mean difference −0.062). In total, HRQoL increased for 13.6% and decreased for 39.4% of individuals. The largest decline in HRQoL was observed among respondents who consumed alcohol 4 or more times per week (50.8%), followed by those with primary or lower education (50.5%), unemployed or non-active individuals (43.7%) and those with financial difficulties (43.7%).In the follow-up data, the overall mean EQ-5D index score declined by 0.048 (n = 855) of respondents at baseline and 33.4% (n = 595) at follow-up. The proportions  of reported (some or severe) problems by EQ-5D domain at baseline and follow-up are presented in Changes in HRQoL during the pandemic can also be characterised by prevalence of reported health problems in health domains. No health problems in any of the five domains were reported by 48.0%  proved to be a protective factor regarding HRQoL. In addition to dietary habits, low physical activity had a negative effect on the HRQoL score in the follow-up data.The results of this longitudinal study indicate that HRQoL among Estonian adults in the study population deteriorated substantially during the COVID-19 pandemic between June 2020 and April 2021. Statistically significant declines in mean EQ-5D index scores were observed for all socio-demographic and behavioural variables considered. Most of this was due to the increased reporting of problems in the pain/discomfort and anxiety/depression health domains. Mutually adjusted regression models revealed that older age, being unemployed/economically non-active and having financial difficulties were the socio-demographic factors significantly associated with lower HRQoL at both baseline and follow-up measurements. In the follow-up data, women had significantly lower HRQoL compared to men, whereas having tertiary or higher education proved to be the only protective factor regarding HRQoL. Unhealthy dietary habits and low physical activity had a negative impact on HRQoL scores in the follow-up data.Before discussing these findings in detail, several potential limitations of the study and the data need to be addressed. First, the pandemic’s impact on HRQoL can only be partially explained by the current study as the baseline measurements were captured in the middle of the pandemic . Second, the data represent a subset of respondents in the baseline survey. While the baseline survey had 3464 respondents in total, only 1781 (51%) met the inclusion criteria. Due to attrition rate and non-response bias , the results cannot be generalized directly to the whole population; however, data within subgroups are representative. The third methodological consideration relates to the survey mode. As a self-administered online questionnaire was the only feasible option for a rapid assessment survey, the nationally representative sample (by sex and age distribution) was only able to include individuals with valid e-mail addresses in the population registry database. However, as an earlier study  has showOur results correspond to findings from previous longitudinal surveys from Den-mark  and JapaAlthough the proportion of reported problems rose in all health domains, the decline in HRQoL can mostly be explained by the pain/discomfort and anxiety/depression health domains, as their relative increase between two measurements was larger compared to other domains. Comparable results reporting an increase of problems in the pain/discomfort and anxiety/depression health domains were found among the Chinese population in March 2020 . CompareIn this study, the mean values of HRQoL score varied significantly by age, sex, education, employment status and financial situation of respondents. However, these socio-demographic patterns changed when baseline and follow-up data were compared. At the baseline, HRQoL differed significantly by age, employment and financial status, whereas additional sex and educational differences emerged in the follow-up. The age variation in the HRQoL where the mean EQ-5D index score decreased with increasing age was largely expected and has been reported in several other studies covering the pandemic period ,33,36. TUnhealthy dietary habits were the only behavioural indicator that had a significant effect on HRQoL in both baseline and follow-up surveys. The association remained significant in an additional analysis focusing on the change in health behaviour indicators during the pandemic. This could partly be explained by the possible change in eating patterns and meal preparation during the confinement and the wider application of remote work and home-schooling, which has been noted elsewhere as well ,38,39. TNo statistically significant differences in HRQoL were found for smoking or alcohol consumption. However, the mean HRQoL score was lower among high-risk alcohol consumers compared with low-risk consumers. In addition, the largest proportion of respondents with a decline in HRQoL score were among high-risk alcohol consumers. A previous study among suspected COVID-19 patients showed that the HRQoL score was significantly higher in people who did not consume alcohol . In lineDuring the COVID-19 pandemic, HRQoL declined substantially among the study population of Estonian adults. The difference in EQ-5D-3L mean scores between the baseline and the follow-up was significant within all demographic, socio-economic and health behaviour categories considered. Two EQ-5D domains that affected HRQoL decline were pain/discomfort and anxiety/depression. Moreover, those with lower education or financial difficulties seemed to be most affected, whereas the impact of health behaviour on HRQoL during the pandemic was relatively limited. Thus, the study demonstrated that the COVID-19 pandemic had an overall negative effect on HRQoL and its impact was stronger in vulnerable population groups. Age, dietary habits and employment and financial status had a statistically significant effect on HRQoL at baseline, and sex, education and physical activity additionally at follow-up. Consequently, the relative health inequalities in general likely increased during and as a result of the pandemic. Further longitudinal studies with extended follow-up periods are needed to assess whether these socio-demographic health discrepancies are present in other countries within the European region and whether these changes affect individual health and its trajectories for longer periods.


Published studies in comparing pre and post the COVID-19 pandemic depression and anxiety levels among children and adolescents yielded incongruent results. Therefore, there is a necessity to perform a timely meta-analysis to synthesize existing evidence.A total of 10 digital databases  were fully searched for eligible studies published before November 6, 2021. Based on quality assessment results, relevant data were extracted for eligible studies of higher quality. We combined standardized mean difference (SMD) or prevalence ratio (RR) for anxiety and depression pre and post the COVID-19 pandemic by using random-effects models. Sensitivity analysis was further performed to evaluate heterogeneity of included studies.14,508 articles were preliminarily identified, and after stepwise screening process, 8 articles were included eventually. The results showed that the SMD for post COVID-19 anxiety score measured by GAD-7 was 0.12 , an significant increase compared with pre COVID-19 period; the SMDs and 95% CIs for post COVID-18 depression scores measured by PHQ-9, PHQ-8, and MFQ were 0.17 , 0.23 , and 0.11 , respectively, also significantly increased compared with pre COVID-19 period. The RR for depression was 2.54  in post COVID-19 period when compared with pre-pandemic.Children and adolescents reported deteriorated anxiety and depression levels after the COVID-19 pandemic. More attention should be paid to this vulnerable group. Effective, expedient, and practical intervention measures which are compatible with COVID-19 prevention and control policies should be developed and implemented to maintain mental health wellbeing of the youths. Corona Virus Disease 2019 (COVID-19), caused by SARS-CoV-2, is a sudden, rapidly evolving pandemic , 4.COVID-19 pandemic also imposes great influence on mental health of the public . Kang etCompared with other age groups, children and adolescent populations are observed increased risk of a variety of mood problems, typically depression and anxiety, and mood changes are associated with personality weaknesses that can flare up in the event of environmental triggers . During To better elucidate the influence of COVID-19 on depression and anxiety of children and adolescents, some scholars measured and compared pre and post COVID-19 depression and anxiety prevalence rates, with incongruent findings. For instance, Li et al. found that both anxiety and depressive symptoms were less severe in post-pandemic period; another Canadian study of 146 adolescents aged 12-13 showed reduced anxiety symptoms and increased depressive symptoms after the COVID-19 pandemic . TherefoThis meta-analysis was implemented and organized according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines . We focu*” OR “mental disorder*” OR “psychological illness*” OR “Psychiatric Disorder*” OR anxiety OR angst OR nervousness OR hypervigilance OR social anxiety OR “anxiety disorder*” OR anxious OR “overanxious disorder*” OR depress* OR “depressive disorder*” OR “Depressive Symptom*” OR “Emotional Depression” OR “affective disorder*” OR “mood disorder*”) AND (adolescent* OR teenager* OR child* OR student* OR juvenile* OR school* OR teen* OR young OR youth* OR minors) AND (COVID-19 OR coronavirus OR SARS-CoV-2 OR cov-19 OR 2019-nCoV OR severe acute respiratory syndrome Coronavirus2). Two authors (SW and LC) independently searched all the 10 databases with the same search term to ensure relevant studies were not missed.We systematically searched seven English electronic databases  and three Chinese databases . We searched studies published before November 6, 2021, the date that we performed literature searching. In the searching process, there is no need to put restrictions on the publication year, as nearly all articles related to COVID-19 were published from 2020 onward. According to the theme of our study, the keywords to be searched were “anxiety,” “depression,” “COVID-19,” “children” and “adolescents.” Every single keyword was searched separately. In reviews or other meta-analysis papers containing this keyword, we checked whether there are words with the same meaning but different expressions, and added these words into the search formula to expand the search scope. The following search term was used:  screened the titles and abstracts of the database records, retrieved full-text for qualification assessment, and independently qualified full-text records. If there is a disagreement between the two researchers, a third party  will participate in discussion and resolve the inconsistencies. Some articles reported results that from the same study, in this case, we selected the articles which reported outcome variables that best match the inclusion criteria of the current study. References of the included studies were also carefully screened for potentially eligible studies.Studies were deemed eligible and included into the meta-analysis if they: (1) Quantitatively assessed and compared anxiety or depression in children and adolescents pre and post COVID-19; (2) Were of longitudinal design or two-wave (pre and post COVID-19) cross-sectional design; (3) Written in English or Chinese; (4) Reported depression or anxiety prevalence rates/reported means and standard deviations for depression or anxiety scores. Studies were further excluded if: (1) Were not original studies, like reviews, case reports, correspondences, comments; (2) Study subjects were not children or adolescents; (3) Analytical results provided were not suitable for synthesis; (4) Study subjects were clinical populations (patients with COVID-19 or another disease); (5) Contain small sample size subjects (<100).The Newcastle-Ottawa Quality Assessment Scale (NOS) is specifically designed to evaluate the quality of longitudinal studies in systematic reviews. NOS is consisted of 8 entries in 3 sections: (i) Selection: a. Representativeness of the exposed cohort; b. Selection of the non-exposed cohort; c. Ascertainment of exposure; d. Demonstration that outcome of interest was not present at start of study. (ii) Comparability: e. Comparability of cohorts on the basis of the design or analysis. (iii) Outcome: f. Assessment of outcome; g. Was follow-up long enough for outcomes to occur; h. Adequacy of follow-up of cohorts . The NOSTwo authors (JP and HS) independently extracted data from the included studies. A standard information extraction table was designed in advance, which mainly includes the following information: author, publication year, origin of study, study period, study population, mean age of the participants, sample size, measuring scales used, reported prevalence or mean scores of anxiety or depression in pre and post COVID-19 periods. The information extracted by the two authors will be compared, if any disagreement exists, a senior researcher (YX) will perform data extraction again.Results were analyzed separately according to different outcome variables. For continuous outcome, measured by means and standard deviations of depression or anxiety scores, standardized mean difference (SMD) together with its 95% confidence interval (CI) was used; whereas for categorical outcome, measured by prevalence of depression or anxiety before and after COVID-19, relative risk (RR) with its 95% CI was used. Considering the included studies may use different depression or anxiety instruments, only studies used the same scale and reported the same outcome variable were combined. Therefore, the results were organized in subgroups.I2 statistic, with a I2> 50% considered substantial heterogeneity , therefore random-effects model was adopted. The combined SMD value was 0.12 , suggesting a statistically higher anxiety level after COVID-19 pandemic.A meta-analysis was performed on the 3 included articles that measured anxiety by using the GAD-7. The means together with their standard deviations for GAD-7 scores pre and post the COVID-19 pandemic were compared, as shown in As only 3 eligible studies were included for meta-analysis, regular statistical methods in detecting publication bias, such as Funnel plot and Begg's test, could not be applied. Therefore, we further performed a sensitivity analysis to evaluate the influence of individual studies. The results were presented in I2= 99.1%, p < 0.05)  for Giannopoulou et al. , 2.56 9. The com < 0.05) .Mean and standard deviation for depression scores were reported in 6 articles using PHQ-9, PHQ-8, and MFQ, respectively. The results for subgroup analysis by different measuring scales were collectively shown in As for each depression instrument, only two eligible studies were included, sensitivity analysis for depression would be unnecessary.Although a plenty of studies had been published in the past several years on the negative psychological impact of the COVID-19 in children and adolescents, only a few of them compared the difference in depression or anxiety levels pre and post the pandemic within the same or comparable populations, and with incongruent conclusions. The need to synthesize existing evidence on this topic is urgent, in the purpose of generating stronger evidence for intervention. In this meta-analysis, we systematically searched, screened, evaluated, and combined published studies of higher quality, and found that for children and adolescents, their post COVID-19 anxiety and depression levels were significantly higher than pre-pandemic period. This major finding suggests the necessity of targeted intervention measures for this psychologically vulnerable population in post-pandemic era.The COVID-19 pandemic has brought huge changes in lifestyle of children and adolescents, and these changes may act as environmental stressors for mood fluctuation. For instance, considering that novel coronavirus transmission is mainly through respiratory droplets and close contact, young people may actively avoid social activities in the fear of contracting the virus. Besides, due to the COVID-19 lockdown, children and adolescents in many areas were confined to their homes for a long time. It has been wide recognized that social isolation is associated with higher risk of depression and anxiety in children and adolescents , 43. In Some important mechanisms might be involved in explaining deteriorated anxiety and depression status for children and adolescents in post COVID-19 period. At first, numerous environmental stressors generated by the pandemic can trigger neuroinflammation, a process intimately associated with dysfunctional hypothalamic-pituitary-adrenal (HPA) axis, that can lead to increased risk of mood disorders such as depression and anxiety , 56. BesThe mental health conditions triggered by the COVID-19 pandemic, if left untreated, can compromise mental or even physical health of children and adolescents, and the adverse effect can extend into adulthood , 61. TheThis is the first comparative meta-analysis on depression and anxiety of children and adolescents pre and post COVID-19. Major findings of this study can provide important evidence for depression and anxiety intervention in children and adolescents during the COVID-19 pandemic. However, the current study has some limitations. First, as only a small number of eligible studies had been found and synthesized, our major findings should be further corroborated by future longitudinal studies of large samples and different origins. Second, for some of the included studies, pre COVID-19 depression and anxiety were measured by recall, therefore the possibility of information bias exists. Third, considering that for nearly all included studies, the time interval between pre and post COVID-19 measurements surpassed 6 months, it is possible that the change in symptoms of depression or anxiety was not a consequence of the pandemic. Finally, conspicuous heterogeneity existed between included studies, as suggested by sensitivity analysis, therefore, cautions should be taken when interpreting the combined results.In this meta-analysis, by synthesizing timely evidence, we found that for children and adolescents, their anxiety and depression status significantly deteriorated in post COVID-19 period. This major finding suggests unneglectable psychological impact of the COVID-19 on youths. More attention should be paid to this vulnerable population during the pandemic. Effective, expedient and practical intervention measures which are compatible with COVID-19 prevention and control policies should be designed, evaluated, and implemented to maintain mental health wellbeing of children and adolescents in post COVID-19 era.The original contributions presented in the study are included in the article/YX conceived and designed this study. HR and YC were responsible for literature search and study selection. DF and XL were responsible for quality assessment. JP and HS were responsible for data extraction. SW and LC performed analyses and wrote the original manuscript. YX critically reviewed and revised the manuscript. All authors contributed to the final version of the manuscript. All authors contributed to the article and approved the submitted version.This study was funded by National Natural Science Foundation of China (82060601), Top Young Talents of Yunnan Ten Thousand Talents Plan (YNWR-QNBJ-2018-286), Innovative Research Team of Yunnan Province (202005AE160002).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.