Automatic diagnosis of depression based on speech can complement mental health treatment methods in the future. Previous studies have reported that acoustic properties can be used to identify depression. However, few studies have attempted a large-scale differential diagnosis of patients with depressive disorders using acoustic characteristics of non-English speakers.This study proposes a framework for automatic depression detection using large-scale acoustic characteristics based on the Korean language.We recruited 153 patients who met the criteria for major depressive disorder and 165 healthy controls without current or past mental illness. Participants' voices were recorded on a smartphone while performing the task of reading predefined text-based sentences. Three approaches were evaluated and compared to detect depression using data sets with text-dependent read speech tasks: conventional machine learning models based on acoustic features, a proposed model that trains and classifies log-Mel spectrograms by applying a deep convolutional neural network (CNN) with a relatively small number of parameters, and models that train and classify log-Mel spectrograms by applying well-known pretrained networks.The acoustic characteristics of the predefined text-based sentence reading automatically detected depression using the proposed CNN model. The highest accuracy achieved with the proposed CNN on the speech data was 78.14%. Our results show that the deep-learned acoustic characteristics lead to better performance than those obtained using the conventional approach and pretrained models.Checking the mood of patients with major depressive disorder and detecting the consistency of objective descriptions are very important research topics. This study suggests that the analysis of speech data recorded while reading text-dependent sentences could help predict depression status automatically by capturing the characteristics of depression. Our method is smartphone based, is easily accessible, and can contribute to the automatic identification of depressive states. Depression is a serious psychiatric illness affecting >300 million people worldwide. It leads to a variety of negative health outcomes in individuals ,2. When A promising approach to address the abovementioned problems is to identify depression markers and advanced machine learning (ML) techniques using real-world accessible sensors . These approaches may make it easier for nonspecialists to effectively identify symptoms in patients with depression and accordingly direct them toward appropriate treatment or management. Previous studies have explored a spectrum of behavioral signal approaches, such as speech ,8, text Automatic depression detection (ADD) has gained popularity with the advent of publicly available data sets  and the Text-dependent read speech helps to reduce acoustic variability, as reading language content can be designed to express behavior in a controlled manner, such as the same length and content . TherefoTo address these issues, we used audio samples based on text-dependent reading modes to determine whether depressive symptoms could be detected based on differences in acoustic characteristics. We believe that an autosensing approach using deep learning to describe text-dependent reading modes can significantly contribute to this field of research. Therefore, in this study, a Korean-based speech cohort was used to diagnose depression using text-dependent read speech. We then applied a convolutional neural network (CNN) layer and a dense layer to model the depressive state. Finally, the trained model was used to predict depression among unknown data samples. The performance of the model was compared with that of other approaches, ML, and pretrained models. The results show that our approach is effective in easily and automatically assessing depressive states in speech.The participants were 153 patients with major depressive disorder (MDD) and 165 healthy controls. All participants were from South Korea. The inclusion criterion was that study participants should be aged ≥19 years. All patients with MDD were evaluated by board-certified psychiatrists according to the Diagnostic and Statistical Manual of Mental Disorder criteria to identify their current mood states. The severity of depressive symptoms was assessed using the Hamilton Depression Rating Scale (HAM-D) and Patient Health Questionnaire-9 (PHQ-9) . PatientThis study was reviewed and approved by the Institutional Review Boards of Inje University Ilsan Paik Hospital (number 2019-12-011-015) and Chungnam National University Hospital (number 2019-10-101-018), which specializes in mental health in South Korea.The experimental protocol in this study was designed to assess speech responses to identify depression using predefined text-dependent speech tasks . The proThe reading tasks of each participant were recorded using a smartphone . Speech samples on the microphone were saved as mono-PCM  .wav files, sampled at 44.1 kHz. The speech samples were recorded in a quiet room, and the microphone was positioned at a distance of approximately 30 cm from the participants to ensure the quality of the recordings. We created slides with scripts for each task and placed them on a table where the participants could easily see them. The participants were instructed to read the script through the slides in a comfortable and self-selected pitch and volume. The collected audio data set was used for the experiments in this study.Noisereduce [The recorded audio signal included the speaker’s speech and nonspeech parts, such as silence and background noise caused by environmental factors. These nonspeech parts can be an obstacle to training acoustic features for depression recognition ,36. Thersereduce , which isereduce  to obtaiPretrained CNN Models section.The goal of the proposed approach is to capture a series of acoustic features from audio samples using text-dependent speech tasks and map them into a similar representation space to determine the presence of depression. As CNN is a powerful framework for learning a feature hierarchy, it can provide a representation space capable of detecting depression. Thus, we chose the CNN architecture to learn spatially invariant features of audio samples. We trained a deep CNN from scratch and evaluated its classification performance. The audio signal must be properly converted to enable deep CNN input, and deep CNN training requires a large amount of labeled data to use deep feature extraction using a CNN for audio samples. To address this issue, we segmented the audio signal into an optimal length to enable depression recognition in each segment, as shown in We adapted augmentation techniques to increase the size of the training set to improve generalization. An augmentation method that generates new additional training data is a successful method for reducing model overfitting on sparse data and improving generalization performance. SpecAugment is the latest augmentation method developed by Google for spectrograms of input audio . SpecAugWe designed a CNN architecture to extract features from an LM spectrogram. The detailed model structure of the proposed CNN is shown in In addition to the proposed CNN architecture, pretrained CNN models were used as feature extractors to evaluate depression detection in this study. We tested the following additional pretrained CNNs designed to extract audio representations: 4 state-of-the-art image CNN models, namely, VGG16, VGG19 , ResNet5The image CNN models Inception-v3, VGG16, VGG19, ResNet50, and MobileNet-v2 were also used with LM spectrogram images as the 2D input. The LM spectrograms were resized to 224×224 images with 3 channels  because of their suitability for deep feature extraction. For the classification of the image CNN models, we applied global average pooling instead of using a flattening layer after the feature extractor. For all these models, the final fully connected layers were eliminated and redesigned in the new classifier.VGGish is a deep audio embedding method for training a modified visual geometry group architecture to predict video tags from the Youtube-8M data set . It extrFor the proposed CNN and pretraining models, the classifier uses 128 and 64 fully connected dense layers, respectively, and rectified linear unit activation. We use a 0.5 dropout probability between dense layers to prevent overfitting of the training data. The output layer consisted of 1 hidden unit to classify an image into 2 classes: MDD and control. For the output layer, we used a sigmoid activation function for binary classification.The training process was set up to run 20 epochs for all the CNN models. For model training, the Adam optimizer was selected with a fixed learning rate of 1e-3. In this study, the learning rate was based on the validation accuracy of the model, and early stopping was performed. All the parameters of the testing stage were applied similar to the training stage. The deep CNN was implemented using Python (version 3.7.11) and TensorFlow (version 2.5.0) on a Quadro RTX 8000 48GB GPU (NVIDIA).The baseline audio signals extracted 3 widely used acoustic feature representations—Mel-frequency cepstral coefficients (MFCCs), extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) , and IntThe 318 audio data sets were randomly split into 254 training sets, 32 validation sets, and 32 testing sets in a balanced ratio between patients with MDD and healthy controls during the experiments. Participant-independent splits were used for training, validation, and testing.F1-score, by using the confusion matrix, as the evaluation criteria. The area under receiver operating characteristic curve (AUC) was also calculated.A 10-fold cross-validation method was used to split the data and evaluate the performance of each classifier. We compared the predicted results with test data to evaluate their performance. In our study, we adopted the accuracy, precision, recall, receiver operating characteristic curve, and U test. Sex, alcohol history, and smoking history were compared using the chi-square test.The demographic and clinical characteristics of the age, education, HAM-D, and PHQ-9 factors were not normally distributed; these factors were compared between the control and MDD groups using the Mann-Whitney P<.001). In addition, the MDD group had a significantly higher HAM-D (P<.001) and PHQ-9 than the control group.The descriptive demographic and clinical characteristics of the participants in this are summarized in In this section, we analyze the classification capability of the proposed approach, including the conventional ML models and the CNN method, to recognize depression in unknown audio samples (test set) obtained from each text-based speech task.F1-score from the SVM, LDA, kNN, and RF classifiers. The results in F1-score, respectively, compared with other classifiers when applying the COMPARE feature set on the vowel data set. As shown in First, we investigated the ability of the conventional ML methods to classify the samples as MDD or non-MDD for each data set of the vowel, digit, and passage tasks. We further analyzed the variability of MFCCs between the 2 groups in each task to explore the differences in MFCCs between the MDD and control groups. As a result of analyzing the difference between the 2 groups among the 12 MFCCs, MFCC3 showed the greatest difference. F1-score of 78.14%, 76.83%, 77.90%, and 77.27%, respectively. These results achieved a performance improvement of approximately 8% accuracy compared with the best performance for the conventional ML approach, as reported in Second, we compared the performance of depression detection for each CNN-based representation model using our data sets. The best performances were obtained with a batch size of 32 in our experiment; thus, all results provided below refer to the models trained with this batch size. The receiver operating characteristic curves and AUCs based on the CNN models in the passage task are shown in In addition, benchmark experiments were performed to compare the performance of depression detection and PHQ-9 score prediction in the benchmark model and the proposed CNN model on audio data sets in a text-dependent setting (read-dependent speech mode) and a text-independent setting (spontaneous mode). A previously reported model  that perF1-score and the CCC or Root Mean Square Error, respectively [We first trained both the benchmark and proposed models in a text-dependent setting and then compared the mean performance of 10-folds for depression detection and PHQ-9 score prediction. Here, the performance of depression detection and PHQ-9 score prediction were adopted as the accuracy or ectively . As showThese results prove that the model proposed in this study can classify depression better than other models in the dependent setting.Our results suggest that the speech characteristics obtained through text-dependent speech can be a promising biomarker for MDD. In this study, we recorded speech data using a mobile phone with predefined text-based reading speech tasks and confirmed the potential for automatically detecting depression based on the recorded data and a deep learning approach. As previous studies have suggested the possibility of detecting depression in speech data ,30,57,63In the passage task, the approach that adopted the CNN model improved performance by approximately ≥7% compared with the conventional ML approach. Compared with conventional ML methods for evaluating handcraft-based extracted feature sets, the acoustic feature extraction approach of CNNs generally exhibits features in a broader spectrum and shows higher performance, as shown in terms of classification accuracy. When the performances of the 3 text-dependent reading tasks were compared , the CNNThe features of the audio samples were extracted from well-known pretrained models , such as features extracted through a customized CNN model, and the classification performance was compared. We found that the feature set extracted through pretrained models from all data sets of text-based speech tasks did not significantly affect the classification performance, as shown in Audio samples in the passage task achieved 78% accuracy in depression detection , and we Previous studies have focused on developing depression detection models based on open cohort data ,21. HoweOur study had some limitations. First, we used a small number of voice samples in the experiments; thus, we are currently recruiting additional participants to collect more data to run deep learning. With further research in this cohort, we plan to report the outcomes of developing ML techniques for disease diagnosis and severity prediction. Second, all the audio samples were recorded in a quiet environment. Extended studies are needed to apply this approach to other recording environments . In addition, we plan to explore different strategies for combining our speech-based systems with various information, such as video or physiological signals. Approaching the multimodal detection will present a robust framework that operates under more precise and natural conditions. We believe that these efforts will help to build more robust predictors of MDD for daily life in the future.This study has opened new opportunities to identify speech markers related to the assessment of depression through readily obtainable speech patterns. This study also presented an approach to automatically detect whether a person has depression by analyzing their speech. We acquired audio samples from 318 participants with depression and healthy controls based on the Korean text-dependent read speech tasks using a smartphone and analyzed their association with depression. We found that there are many benefits of learning audio acoustic patterns and detecting depression using deep learning. This approach has the potential to reduce depression and shows that it is powerful and effective in ADD via speech.


A 65-year-old man presented to the dermatology clinic with a 6-month history of severe itching in the inguinal area on February 25, 2022. This patient worked at a construction site and reported that he had had no recent sexual contact. He had treated himself with diesel oil, but there was no improvement in his condition. Physical examination was notable for multiple black dots on the base of the pubic hair shaft and whitish-appearing concretions attached to the shafts of pubic hairs Figure , microscPediculus humanus and Phthirus pubis (pubic lice).There are more than 3,000 species of lice. Among these, humans constitute the preferred host for only two species:


What are the potential health outcomes and costs of SARS-CoV-2 monoclonal antibody postexposure prophylaxis (PEP) for household contacts of people with COVID-19 in the US?In this decision analytical model study, for a month with transmission intensity similar to that of May 2021, a monoclonal antibody PEP program reaching 50% of exposed, unvaccinated household members aged 50 years and older was estimated to avert 528 hospitalizations and 84 deaths in a low-transmission scenario and 1404 hospitalizations and 223 deaths in a high-transmission scenario. The program was also estimated to be cost saving to payers in the high-transmission scenario as a result of averted hospitalizations.These findings suggest that COVID-19 PEP with monoclonal antibodies may be associated with reduced costs and improved population health. This decision analytical model study estimates the health outcomes and net cost of implementing postexposure prophylaxis with monoclonal antibodies (mAbs) against household exposure to COVID-19. The COVID-19 pandemic has led to more than 900 000 deaths in the US and continues to disrupt lives even as effective vaccines are available.To estimate the health outcomes and net cost of implementing postexposure prophylaxis (PEP) with monoclonal antibodies (mAbs) against household exposure to COVID-19.This study is a decision analytical model of results from a randomized clinical trial of casirivimab with imdevimab administered as subcutaneous injections to unvaccinated, SARS-CoV-2–negative household contacts of people with confirmed COVID-19 with complementary data on household demographic structure, vaccine coverage, and confirmed COVID-19 case counts. The study used US data from May 2021 for a simulated population of US individuals of all ages within low-transmission or high-transmission scenarios.Age, sex, race, ethnicity, and COVID-19 vaccination status.Symptomatic infection, hospitalization, death, and net payer cost of monoclonal antibody PEP for COVID-19.In a month of transmission intensity similar to that of May 2021, a mAb PEP program reaching 50% of exposed, unvaccinated household members aged 50 years and older was estimated to avert 1820 symptomatic infections , 528 hospitalizations , and 84 deaths  in a low-transmission scenario and 4834 symptomatic infections , 1404 hospitalizations , and 223 deaths  in a high-transmission scenario. Without mAb PEP, the estimated cost of hospitalizations due to COVID-19 infections from household exposure in the lower transmission scenario was $149 million , whereas the estimated hospitalization cost in the higher transmission scenario was $400 million . In the lower transmission scenario, mAb PEP administered to 50% of eligible contacts aged 80 years and older was estimated to have 82% probability of saving costs, but was not associated with cost savings at age thresholds of 50 years and older or 20 years and older. In contrast, in the high-transmission scenario, mAb PEP administered to 50% of eligible household contacts had estimated cost savings in 100% of simulations at the 80-year age threshold, 96% of simulations at the 50-year threshold, and 2% of simulations at the 20-year thresholds.In this modeling study of a simulated US population, a mAb PEP for COVID-19 program was estimated to improve health outcomes and reduce costs. In the setting of a susceptible variant of SARS-CoV-2, health system and public health actors would have an opportunity to improve health and reduce net payer costs through COVID-19 PEP with mAbs. The findings supported an emergency use authorization for use as postexposure prophylaxis (PEP) among people who are unvaccinated or unlikely to mount a protective response following vaccination and who are at high risk for progression to severe COVID-19.15 However, with continued viral evolution, REGEN-COV is not effective against the Omicron variant of SARS-CoV-2 that became dominant in early 2022, leading the US Food and Drug Administration to limit its use and exclude geographic regions where SARS-CoV-2 infections are likely due to nonsusceptible variants, although development of additional mAb therapies continues.18 In this decision analytical model study, we estimated the health outcomes and costs of a hypothetical PEP program in which unvaccinated household members who have been exposed to COVID-19 are given mAb PEP, to understand the potential public health significance of an approach that was shown to have strong clinical benefit against susceptible variants of SARS-CoV-2.A randomized clinical trial19 Our focus was on the general US population to provide evidence for public health policy. The key decision points in our model were the coverage level and age targeting of the intervention.We used a decision analytical model to combine results from the REGEN-COV randomized clinical trial with population data on household demographic structure, confirmed COVID-19 case counts and demographics, and vaccination coverage to estimate the number of symptomatic infections, hospitalizations, deaths, and net costs for mAb PEP programs of varying intensities.CHEERS) reporting guideline.20 We published a replication archive for this analysis, which was coded in Python programming language version 3.8.8 (Python Software Foundation).21 These research activities used no identifiable private information and were therefore exempt from institutional board review, in accordance with 45 CFR §46.22We report estimates according to the Consolidated Health Economic Evaluation Reporting Standards  and 2 scenarios of secondary attack rates. We used a time horizon corresponding to 1 wave of SARS-CoV-2 transmission, roughly 1 month, to evaluate the costs and outcomes associated with the program. We used a payer perspective to estimate net costs of the mAb PEP intervention and the (offset) costs of COVID-19 hospitalizations. We did not use time-discounting for costs or outcomes because of the short time horizon.11 of the combination of mAbs casirivimab and indevimab administered as subcutaneous injections to unvaccinated, SARS-CoV-2–negative household contacts of people with confirmed COVID-19. The primary outcome of symptomatic SARS-CoV-2 infection occurred in 59 of 752 placebo recipients (7.8%) and 11 of 753 mAb PEP recipients (1.5%), indicating an 81% risk reduction.11 From this trial, we incorporated a household secondary attack rate of 7.8% in the low-transmission scenario. We developed a high-transmission scenario in which symptomatic SARS-CoV-2 infection developed in 21.1% of contacts who did not receive mAb PEP and 3.9% of mAb PEP recipients (81% risk reduction) using a meta-analysis of SARS-CoV-2 transmission in households.23 To quantify uncertainty, we sampled 100 attack rate values with and without mAb for each scenario.Efficacy data come from the randomized clinical trial19 For example, to estimate the number of Black female individuals aged 80 years and older who would be exposed to a Black male individual aged 30 to 39 years with COVID-19, we identified all households in the American Community Survey data with a Black male individual aged 30 to 39 and counted the number of Black female individuals aged 80 years and older in each household. Then, we used the arithmetic mean of these counts as the estimate of the number of people exposed. We excluded individuals living in group quarters. To quantify uncertainty, we used nonparametric bootstrap resampling of 100 households with replacement for an index case in each demographic stratum.24We combined the effect size and secondary attack rates with data on the demographic structure of US households, national data on confirmed cases of COVID-19, and COVID-19 vaccine coverage and clustering by household. We used data from the American Community Survey to group individuals into households by age group, sex, and race and ethnicity and estimated the average number of individuals who would have a household exposure to COVID-19 from an index case in any demographic stratum.25 We calculated the fraction of cases in each demographic stratum using a complete-case analysis that dropped rows with missing data on age, sex, or race and ethnicity and then scaled these fractions to match the total count of cases including those with missing demographic data. We used nonparametric bootstrap resampling to quantify uncertainty.24To find the age-specific, sex-specific, and race and ethnicity–specific rates of COVID-19 detection, we used confirmed COVID-19 case data from the Centers for Disease Control and Prevention for the month of May 2021.8 In this nationally representative survey of US adults, 77% of vaccinated respondents indicated that everyone in their household also was vaccinated against COVID-19, and 69% of unvaccinated respondents reported that everyone in their household was unvaccinated. We used Bayes law to derive the fraction of confirmed cases who are unvaccinated from the population coverage and efficacy data and combined this with survey data on the percentage of unvaccinated people living in a household where everyone is unvaccinated  is the same as the fraction of confirmed cases progressing to hospitalization (observed). The average cost of hospitalization did not vary by individual characteristics. Also, after stratifying by age group, sex, and race and ethnicity, the household structure of people with confirmed cases COVID-19 was assumed to match that of the general population.15 included 1555 participants (753 in the treatment group and 752 in the placebo group), which we combined with confirmed cases data on 154 136 individuals, vaccine coverage survey data from 1888 individuals, and household structure data derived from 3 088 232 individuals in 1 276 716 households. We estimated that the 154 136 confirmed cases of COVID-19 in May 2021 in the US would result in at least 256 832 unvaccinated individuals  with household exposure to COVID-19. Using the racial and ethnic characteristics of households and COVID-19 cases described above, we estimated unvaccinated household contacts to include 43 219 (16.8%) non-Hispanic Black individuals, 50 380 (19.6%) Hispanic individuals, 135 800 (52.9%) non-Hispanic White individuals, and 27 433 (10.7%) members of other non-Hispanic racial groups . However, the race and ethnicity characteristics were missing for 32% of confirmed cases , 24% of hospitalizations , and 20% of deaths , and our estimates rely on calculating the fraction of each demographic stratum with complete-case analysis.The REGEN-COV PEP trial15 providing PEP to 50% of unvaccinated household contacts aged 50 years and older was estimated to result in treatment of 28 309 individuals , with fewer individuals treated at higher age thresholds , whereas estimated hospitalization costs in the higher transmission scenario would be $400 million . The estimated costs of mAb PEP and hospitalization for different treatment age thresholds are shown in In this decision analytical model study, we estimated that providing mAb PEP to unvaccinated household contacts of persons with COVID-19 aged 50 years and older in the setting of a susceptible SARS-CoV-2 variant would prevent COVID-19 disease and deaths and be cost-saving in the higher transmission scenario as a result of averted costs of hospitalization over 1 month of implementation in the US. In contrast, the lower transmission scenario observed in the REGEN-COV trial was unlikely to be cost-saving at an age threshold of 50 years and older, but was estimated to have an 82% probability of cost-savings at a threshold of age 80 years and older. We did not estimate COVID-19 cases and costs that could occur from a subsequent round of transmission from household contacts to other individuals. Despite this conservative assumption, the use of mAb PEP was estimated to avert morbidity and mortality and to be an efficient use of resources in the higher transmission scenario.17 Although there are many unknowns about the future course of the COVID-19 pandemic, mAb PEP could have a beneficial role in COVID-19 combination prevention for several reasons. First, less-than-full vaccination coverage among the US population has left persistent protection gaps, and household clustering of vaccination status means that unvaccinated people who are at greater risk for developing SARS-CoV-2 are more likely to share a household with other unvaccinated people. Second, mAb PEP may be acceptable to an unvaccinated person with a high-risk household exposure because it offers rapid protection compared with initiating a multiweek vaccine series and could be offered with follow-up vaccination. Third, promising advances in efforts to isolate and develop mAbs with activity against a broad range of sarbecoviruses could lessen the risk of loss of activity with future SARS-CoV-2 genotypes/variants.18 Finally, beyond households, mAb PEP may be used in other settings with high-risk exposures, such as long-term care facilities or among immunocompromised people who are less likely to mount an immune response to vaccination.30Early in 2022, the Omicron variant of SARS-CoV-2 became the dominant strain in the US, and administration of REGEN-COV was largely halted for lack of efficacy against this variant.31 Methodological strengths of our analysis include the use of large, publicly available data sets on household composition, COVID-19 cases, and vaccine coverage; inclusion of high-quality efficacy data observed in a randomized clinical trial; the assumption that the health economic profile is insensitive to epidemic size because costs and outcomes scale with cases; and incorporation of uncertainty measures throughout the model.Demonstrating the potential population health benefit and economic value of mAb PEP is crucial to overcoming the logistical challenges of implementing a novel intervention and advocating for production of sufficient antibody supply. Shortly following the emergency use authorization for REGEN-COV, the National Institutes of Health published guidelines prioritizing the use of mAb therapies for treatment of COVID-19 ahead of PEP when necessary to triage the use of mAb because of logistical or supply constraints.This analysis also has limitations. First, the analysis took a payer perspective in focusing on the cost of the medication and administration without including the cost of building up the public health infrastructure needed to identify and notify eligible contacts. In addition, we did not identify data to compare the actual cost to health facilities of administering mAb PEP with the $450 per infusion that Centers for Medicare & Medicaid Services reimburses. Second, population structure may have changed as a result of social distancing. Third, there were missing data for confirmed cases, especially about race and ethnicity, hospitalizations, and deaths. Fourth, we modeled the whole US population without differentiating regionally for vaccine coverage, household composition, and COVID-19 case activity. Fifth, we did not analyze mAb PEP for people who were vaccinated but unlikely to mount a response as a result of immunosuppression because of a lack of data. Similarly, we did not distinguish within age groups for people with comorbidities that are associated with increased risk of COVID-19 progression or costs of hospitalization. Sixth, we assumed that unvaccinated individuals are susceptible to SARS-CoV-2 infection without accounting for partial immunity from prior SARS-CoV-2 infection.In this modeling study of a simulated US population, a mAb PEP for COVID-19 program was estimated to improve health outcomes and reduce costs. In the scenario of a susceptible SARS-CoV-2 variant, health system and public health actors in the US may have an opportunity to improve health and reduce costs through COVID-19 PEP with mAbs.


Industries and services are undergoing an Internet of Things centric transformation globally, giving rise to an explosion of multi-modal data generated each second. This, with the requirement of low-latency result delivery, has led to the ubiquitous adoption of edge and cloud computing paradigms. Edge computing follows the data gravity principle, wherein the computational devices move closer to the end-users to minimize data transfer and communication times. However, large-scale computation has exacerbated the problem of efficient resource management in hybrid edge-cloud platforms. In this regard, data-driven models such as deep neural networks (DNNs) have gained popularity to give rise to the notion of edge intelligence. However, DNNs face significant problems of data saturation when fed volatile data. Data saturation is when providing more data does not translate to improvements in performance. To address this issue, prior work has leveraged coupled simulators that, akin to digital twins, generate out-of-distribution training data alleviating the data-saturation problem. However, simulators face the reality-gap problem, which is the inaccuracy in the emulation of real computational infrastructure due to the abstractions in such simulators. To combat this, we develop a framework, SimTune, that tackles this challenge by leveraging a low-fidelity surrogate model of the high-fidelity simulator to update the parameters of the latter, so to increase the simulation accuracy. This further helps co-simulated methods to generalize to edge-cloud configurations for which human encoded parameters are not known apriori. Experiments comparing SimTune against state-of-the-art data-driven resource management solutions on a real edge-cloud platform demonstrate that simulator tuning can improve quality of service metrics such as energy consumption and response time by up to 14.7% and 7.6% respectively. The fundamental driving factor for this transformation has been the increase in computational capacity as well the decrease in costs of computational devices1. As computation has become more affordable and accessible, the enormous amounts of data generated from IoT sensors and actuators have fueled the growth of paradigms such as edge and cloud computing2. To eschew sending all data to cloud backends that have high communication-latencies, recent solutions leverage compute resources at the edge of the network3. Having resources closer to the users facilitates the reduction of data processing times and improves the user-perceived Quality of Service (QoS)3. However, as the number of computing resources grows and the application workloads exhibit non-stationary fluctuations at small timescales, managing such resources becomes even more complex4. Static heuristic-based solutions are ineffective in such settings, and contemporary solutions rely on data-driven methods, such as Deep Learning (DL). One such DL approach, namely deep neural networks (DNNs), has become increasingly popular in managing hybrid edge-cloud platforms. DNN based methods have shown promise in effectively handling large-scale computational infrastructures, thanks to their high modeling accuracy and the ability to adapt in volatile settings if supplied enough data4. In this work, we leverage DNNs for effective resource management in edge-cloud computing platforms to revolutionize the computing landscape and optimize service delivery.In recent years, the technological landscape has seen a swift integration of the Internet of Things (IoT) driven infrastructures5. This is exacerbated by the non-stationary characteristic of most contemporary applications6. Even with modern neural network based solutions, most methods are unable to effectively adapt to non-stationary scenarios. In particular, as neural networks are trained on a set of pre-collected data, they tend to learn data patterns within the given data. Such data could be in the form of application execution traces in an edge-cloud environment, including the resource utilization characteristics, such as the CPU, Memory, Disk and Network bandwidth consumption of the running workloads on the computing devices. The trained DNNs can then predict, for instance, the utilization characteristics in a future timestep, facilitating online resource management. This also allows us to use neural networks as surrogates to QoS scores and as an aid to optimization7. However, DNNs tend to face the problem of data saturation, which is when giving more traces to the model does not improve its predictive performance8. This has been identified as a common issue in the past8, specifically due to the exposure bias at training time that is characteristic of the specific edge-cloud configurations used to generate the training data6.The problem of efficient resource management in edge-cloud platforms is challenging. This is prominent in the case of task scheduling, which refers to the placement of incoming tasks on available resources to optimize QoS. As the number of incoming tasks and edge-cloud devices increases, effectively scheduling tasks becomes challenging6. Other methods combine analytical methods with neural networks to simulate a physical environment10. However, such methods typically model a small set of specific aspects of a simulator, such as energy consumption or response time, using a low-fidelity DNN surrogate. Typically, such methods are not robust enough to be applicable to a generic simulator. Unlike the low-fidelity surrogates that directly predict QoS estimates for a future timestep, simulators have encoded information regarding the behavior characteristics of edge-cloud devices11, allowing them to perform a high-fidelity estimation of QoS scores. However, such information is typically encoded in simulators as system parameters by human experts13. These parameters include the typical communication latencies, the power consumption profiles, and task allocation overheads in edge and cloud machines14. In co-simulation driven methods, akin to a digital twin, an event-driven simulator is used to virtually execute multiple decisions and observe how they affect the environment and QoS scores. This enables us to explore out-of-distribution data, enabling the neural model to generalize to settings previously unseen during training and alleviate data saturation15. Additionally, a simulator could also facilitate injecting information regarding the system behavior within the surrogate optimization methods. Further, volatile scenarios can undergo significant shifts in the data trends, such as the resource utilization time series of running workloads. This is referred to as high-dynamism in edge-cloud setups16. Such shifts in trends can also be very frequent, referred to as high-volatility in edge-cloud environments17. Co-simulators can also facilitate obtaining additional data online without executing resource management decisions in the real infrastructure, facilitating the neural models to adapt to dynamism and volatility. However, co-simulation based methods face two critical drawbacks. First, it has been observed that the simplifications and assumptions in setting the human-encoded simulator parameter give rise to a reality gap between the estimates generated by the simulators and those from actual execution in the real environments18. This diminishes the credibility of using simulators to avoid data saturation as now the bias leaks into the co-simulation pipeline due to the assumptions made by human experts while settings the simulator parameters. Second, in the case of highly volatile workloads, executing several simulations may be time inefficient or computationally heavy, making it ineffective in large-scale setups with resource-limited edge devices that run such simulations.The problem of data saturation in neural network based resource management methods has been addressed to some extent using coupled simulators, also referred to as co-simulators in the literatureSimTune, which aims to tune the simulator parameters to bridge the reality gap and improve simulated estimates. The key insight that we use is to leverage a DNN based surrogate model that acts as a low-fidelity twin of the simulator. This surrogate model uses the hand-encoded simulator parameters, environment and workload characteristics to generate QoS estimates. It is trained to mimic a high-fidelity simulator in terms of how close it can match the QoS estimates. Considering a trace generated from a real environment, this surrogate is then used to update the simulator parameters, both offline and online, such that the reality gap between the simulated estimates and ground-truth values is minimized. The tuned simulator parameters are then used to perform data augmentation to support data-driven schedulers. Experiments with multiple state-of-the-art schedulers on a real edge-cloud platform demonstrate that the tuned simulator can improve QoS scores compared to hand-encoded parameters and baseline tuning methods. Specifically, SimTune reduces energy consumption and response time by up to 14.7% and 7.6% respectively compared to baselines in an edge-cloud platform with DL based workloads.In this work, we aim to address both drawbacks of co-simulation based methods highlighted above. We develop a novel framework, called The rest of the paper presents a brief background with motivation and related work in “Many data-driven scheduling methods have been proposed to effectively manage resources in edge-cloud computing environments. Such methods typically rely on data-driven DNNs to generate QoS estimates and run gradient-free or gradient-based optimization in the decision space to optimize objective scores.19. For instance, a line of work uses evolutionary search approaches such as particle swarm optimization (PSO) using a trained DNN based QoS surrogate22. Other methods utilize genetic algorithms for QoS aware decision optimization24. Typically, such approaches run a gradient-free search scheme with non-local jumps, using cross-over and mutation-like operations to converge towards an optimum. However, gradient-free methods are known to take much longer to converge25 and are not as scalable26 as gradient-based methods. This problem is alleviated by gradient-free optimization in schedulers such as GOBI6 and GOSH7. Such methods take the scheduling decision and state of the edge-cloud system as resource utilization characteristics of workloads and Fog nodes and output a QoS estimate. Using backpropagation to input6, i.e., fixing the neural network parameters and updating the scheduling decision based on the gradient of DNN output, these methods find the optimal scheduling decisions. However, continuous approximation of a discrete optimization problem is known to give sub-optimal decisions in some cases27 and thus, we consider both gradient-free and gradient-based schedulers in this work. Another category of methods is the ones that leverage reinforcement learning, popular in discrete-time control optimization settings such as distributed computing30. Such methods rely on deep neural networks that directly predict decisions instead of QoS estimates. For instance, some methods model the scheduling problem as a Markov Decision Process (MDP) and use a deep-reinforcement learning strategy, namely deep Q-Learning (DQL) to schedule workloads in a heterogeneous computing environment30. Policy gradient methods, such as31, train a DNN to predict the optimal scheduling decision instead of Q values directly. A recent method, Asynchronous Advantage Actor-Critic (A3C), is a policy gradient method that schedules workloads using an actor-critic pair of DNN agents32. These methods rely on data traces to train their models and make resource management decisions in edge-cloud environments. We consider these methods in our evaluation to test the efficacy of a tuned simulator on QoS for offline training and online fine-tuning of the neural networks used in such methods.Most state-of-the-art scheduling methods utilize DNNs or search strategies to find optimum scheduling decisions18. The root cause of simulations being far from realism is the improper tuning of the simulation parameters for diverse scenarios that need to be simulated. For instance, in an edge simulator that simulates the energy consumption of edge devices for a given set of workloads, the power profile is often set by human experts using existing profiling data33. However, this profile, i.e., power consumption for varying utilization of the CPU may change based on the temperature of the ambient environment, cooling solutions as well as device characteristics. Thus, having a preset profile curve may not be helpful in unseen configurations. Such simplifying assumptions give rise to the discrepancies between simulated and true metric values. Considering the growing complexity of modern simulators with millions of parameters, each with millions of possible values, running a brute-force approach is intractable. To address this, prior work has leveraged evolutionary optimization strategies such as Sim2Real18. Sim2Real iteratively updates the simulator parameters, performs simulations and evaluates the deviation between the simulated and true values. However, for complex simulators, running a high-fidelity simulation each time has a high computational overhead, thus, limiting the number of iterations that can be performed to update the parameters. Another method, DiffTune13, uses a differentiable surrogate model that can quickly generate simulated scores and can be used to update the simulation parameters. However, DiffTune can only be used to tune simulator parameters offline, where we cannot use the surrogate to perform online data augmentation and fine-tuning of the scheduling methods. DiffTune assumes a differentiable surrogate and a continuous approximation of the discrete optimization problem; this may not be ideal in most settings as many simulation parameters typically take categorical values. We solve this issue in our work by developing a deep neural network based low-fidelity simulator and using gradient based parameter update. These assumptions constrain us to utilizing a restrictive set of neural or analytical models to optimize simulation parameters. Forgoing these assumptions, and using more accurate neural models (such as with non-linear activation functions such as ReLU or sigmoid) with sub-gradients and rounding, limits the performance of such methods35. We empirically demonstrate this in "The concern of the reality gap in simulators has been highlighted in the past, albeit in domains other than resource management24. An overview of the system model is presented in Fig. 4. Each task also has expected QoS metrics such as service deadline associated. Such deadlines are also referred to as Service Level Objectives (SLOs). The tasks are realized in the form of virtualized Docker container applications to allow ease of management and secure computing36. The container applications and SLOs are relayed to the edge-cloud broker, which takes all resource management decisions. It monitors the edge and cloud nodes and generates traces of system characteristics such as resource utilization and QoS metrics. The broker also leverages a discrete event-driven high-fidelity simulation in tandem with a low-fidelity surrogate model to run the SimTune approach. The tuned simulator is used by the scheduler to decide optimal scheduling decisions. The decision is enacted in the physical environment in the form of container allocation or migration to respective edge or cloud nodes. Further, we assume a bounded execution timeline, which we divide into fixed-size scheduling intervals. We consider a bag-of-task workload model wherein a set of new tasks is created at the start of each interval and all tasks can be scheduled independently.In this work, we assume a typical edge-cloud computing environment with multiple heterogeneous edge and cloud nodes in a broker-worker setupt-th interval by state of the edge-cloud environment as the collection of resource utilization metrics including CPU, RAM and Disk of the host machines. This also includes the network topology of the system as a graph with each edge consisting of parameters such as latency and network bandwidth. We denote the state at the start of interval measured QoS metrics at the end of simulated QoS trace by following equation . Note that we utilize the QoS estimates of the surrogate We now describe how the SimTune framework aids in informed decision making. An overview is presented in Algorithm 1. Having a trained scheduler i.e., B2s with a dual-core CPU and 4GB RAM, B4ms with a quad-core CPU and 16GB RAM and B8ms with an octa-core CPU and 32GB RAM. We consider a geographically distributed cloud environment. Our environment consists of 20 VMs in the UK-South Azure datacenter and 14 in the East-US datacenter. The UK-South cluster consist of 10 B2s and 10 B4ms nodes, while our East-US cluster consists of 7 B4ms and 7 B8ms nodes. Our resource management policies are run on a cloud broker node in the UK-South location and is a D16asv4 node with a 16 core CPU and 64 GB RAM. The execution costs are taken from Azure pricing calculator42. The power consumption values of increments of 10% CPU utilization of Azure VM types are taken from the COSCO simulator43, which includes the power consumption characteristics of B2s, B4ms and B8ms Azure VMs derived from the Standard Performance Evaluation Corporation (SPEC) benchmark repository44. We ignore the power consumption characteristics of the cooling infrastructure as in prior work14. As these power characteristics may be on out-of-distribution workloads and we use the fraction of incoming workload requests as a proxy of CPU utilization, the absolute values of the reported energy consumption in this paper are rough estimates.We consider a hybrid edge-cloud computing setup with 16 Raspberry Pi 4B nodes, 8 with 4GB RAM and another 8 with 8GB RAM each. This allows the setup to have heterogeneous nodes with different memory capacities. Our cloud environment consists of 34 virtual machines provisioned from Microsoft Azure cloud platform. We use diverse VM types in our cloud infrastructure, 45. This is a widely used AI-based computing benchmark suite that consists of various real-world computer vision application instances46. The seven specific application types correspond to the CNN neural networks for image classification. These include three typical heavy-weight networks: ResNet18, ResNet34, ResNext32x4d, as well as four light-weight networks: SqueezeNet1-0, GoogleNet, MobileNetV2, MnasNet. These neural models are from various industry applications, showing that this benchmark captures real-world workloads. In terms of the data to process, we use 50 images from the COCO dataset47. We use the interval duration in our experiments, i.e., 48. To evaluate the proposed method in a controlled environment, we abstract out the users and IoT layers described in "6. The distribution is a natural choice for a bag-of-tasks workload model, common in edge environments50. Each task has an associated SLO deadline generated from prior work6. When the response time exceeds its deadline, we call that a violation of the SLO. Our tasks are executed using Docker containers. We run all experiments for 100 scheduling intervals, with each interval being 300 seconds long, giving a total experiment time of 8 hours and 20 minutes. We average over five runs and use diverse workload types to ensure statistical significance in our experiments.In order to evaluate the performance of SimTune, we use the AIoTBench benchmarksFor training, we randomly split the dataset into 80% training and 20% testing data. We use a learning rate of equation . We use 6.Human Set uses the preset simulator parameters to generate the offline and online training data required by the scheduling methods.HGP+ES uses a heteroskedastic Gaussian Process (HGP)51 as a low-fidelity surrogate of the simulator and evolutionary search (ES) strategy18 to tune the simulator parameters.LSTM+SGD uses a Long-Short-Term-Memory (LSTM) neural network with differentiable activation functions motivated from DiffTune13. It also uses stochastic-gradient-descent (SGD) to perform gradient optimization of simulator parameters.LSTM+Sim2Real also uses an LSTM neural network to act as a low-fidelity surrogate of the simulator with a mutation-crossover based evolutionary search strategy to optimize simulator parameters as done in Sim2Real18. Akin to Sim2Real and DiffTune methods, HGP+ES, LSTM+SGD and LSTM+Sim2Real utilize the high-fidelity simulator to generate the on-the-fly data for online scheduler training. We also do not dynamically tune the simulator parameters in these methods18.We compare SimTune against four baselines as described below. Each of these methods is for tuning the simulator parameters, which we compare against SimTune across six state-of-the-art schedulers: PSO, GA, DQL, A3C, GOBI and GOSH . This also translates to lower average waiting times for the incoming tasks.Figure n in Eq. . SimTuneSimTune w/o HiFi. Second, we replace the low-fidelity surrogate model with the high-fidelity simulator to generate data for online training of the scheduler in SimTune. We call this approach as SimTune w/o LoFi. Table SimTune w/o HiFi), we observe a drop in the QoS metrics; for instance, SLO Violation rates increase by 5.4%. This is due to the lack of unexplored simulator configurations by the surrogate leading to poor offline training of the schedulers. Further, without the surrogate (SimTune w/o LoFi), the online data generation is more time-consuming, which gives rise to higher scheduling and wait times and poorer QoS scores. For instance, SLO violation rates increase by 13% when we do not use low-fidelity model. This demonstrates the effectiveness of the hybrid high and low fidelity approach of SimTune.To test the importance of the hybrid approach of SimTune that utilizes both a high-fidelity simulator and low-fidelity surrogate, we modify the approach as follows. First, we consider a model without the high-fidelity simulator to generate offline training data and utilize the surrogate itself. We refer to this approach by 52. We also compare the tuning time of the simulator parameters for the different number of nodes in the setup in Fig. Human Set, there is no tuning overhead. However, this limits its performance in non-stationary settings where dynamic parameter updates may be required. Within the parameter update based methods, SimTune has the lowest tuning time thanks to its Transformer based design that allows us to furnish all tuning data together in lieu of the auto-regressive inference style in recurrent models such as LSTM+SGD and LSTM+Sim2Real.To test the performance of the SimTune approach at different scales we generate QoS scores for diverse sizes of the edge-cloud testbed. We perform a controlled experiment with 1:2 ratio of edge and cloud nodes where we use equal number of 4GB and 8GB RAM Raspberry Pi 4B nodes in our edge environment and B2s cloud VM type in UK-South. We vary the number of edge devices from 2 to 16, with number of cloud nodes being 4 to 32. This gives the total number of nodes from 6 to 48. We keep the 53. We also aim to explore the application of SimTune in the domain of fault-tolerant computing.This paper proposes SimTune, a framework to bridge the reality gap between the simulated and ground-truth QoS traces. SimTune leverages a low-fidelity neural network based surrogate model to tune the parameters of a high-fidelity simulator. The SimTune approach trains the neural network surrogate to mimic the simulator and updates the simulator parameters using the surrogate reality gap as a proxy and updating parameters using a gradient-based Monte-Carlo search strategy. The updated parameters are then used to generate offline data using the simulator and online data using the surrogate to train a data-driven scheduler. Experiments with real-life AI-based benchmark applications on a heterogeneous edge-cloud testbed show that SimTune gives at least 14.7% lower energy consumption, 7.6% lower response times and 11.8% lower SLO violation rates compared to state-of-the-art baselines. This demonstrates the importance of simulator tuning for optimal QoS in the domain of edge intelligence. Future work would investigate the application of the SimTune approach to also include additional resource management decisions such as resource provisioning and autoscaling


At a propensity-score-matched analysis, the mAbs/antivirals group had a significantly lower occurrence of long COVID in comparison with untreated controls . In conclusion, mAbs and antivirals administered against the progression of COVID-19 were associated with a reduced risk of long COVID.Long COVID is a complex condition affecting quality of life, with limited therapeutic options. We investigated the occurrence of long COVID in subjects receiving early therapy with monoclonal antibodies (mAbs) or antivirals to reduce the risk of COVID-19 progression. In this retrospective study we enrolled 737 adult patients , who experienced COVID-19 between January 2021 and March 2022. Antiviral or mAbs were administered to symptomatic patients who did not require oxygen therapy or hospital admission for SARS-CoV-2 infection, and who were at high risk of progression to severe disease, as identified by age > 65 years or the presence of comorbidities. Long COVID, defined as newly or persistent long-term symptoms 4 weeks after the onset of the acute illness, was reported in 204 cases (28%). Age (OR 1.03;  The coronavirus disease 2019 (COVID-19) pandemic is a worldwide outbreak of infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with a broad clinical spectrum varying from asymptomatic manner to respiratory illness, multi-organ failure and fatal outcomes , severalDespite advances in acute phase management, increasing evidence suggest that COVID-19 is associated with long-term sequelae—identified as post-COVID syndrome or long COVID—that negatively affect quality of life and represent a new challenge in the coming years . Long COTo the current state of knowledge, pathophysiological mechanisms underlying the long COVID condition need to be elucidated and thus a therapeutic approach remains unclear. It is reasonable to speculate that preventing progression of SARS-CoV-2 infection by early therapy could be reduce the risk of long-term sequelae, but this hypothesis has never been tested. The aim of this retrospective research was to investigate the occurrence of long COVID in subjects at high risk of COVID-19 progression who received early therapy with mAbs or antivirals.In this single-center retrospective study, we enrolled 737 consecutive patients from a large cohort of subjects affected by COVID-19, entrusted to the Special Unit of Continuity Care between January 2021 and March 2022 in the city of Catanzaro . All patients underwent collection of medical history and physical examination. Diagnosis of SARS-CoV-2 infection was detected by real-time reverse transcriptase–polymerase chain reaction (RT-PCR) on nasopharyngeal swab specimens. Clinical course was reported and evaluated by electronical medical records. Patients aged <18 years and those without available clinical data or who died were excluded. Main clinical outcome was the occurrence of long COVID: first, we examined frequencies and determinants of long COVID in the study population; then, we evaluated the occurrence of long COVID in subjects who received mAbs or antivirals against SARS-CoV-2 infection in comparison with who did not receive this early therapy, in a propensity-score-matched case–control study. The local ethics committee approved the protocol and informed written consent was obtained from all participants. All the investigations were performed according with the principles of Declaration of Helsinki.Administration of mAbs or antivirals against SARS-CoV-2 was achieved in accordance with the criteria endorsed by Agenzia Italiana del Farmaco. Briefly, adult subjects were retained eligible to early therapy in the presence of the following criteria: (1) SARS-CoV-2 infection, confirmed by RT-PCR on nasopharyngeal swab specimens; (2) symptoms onset within 10 days for mAbs and 5 days for antivirals; (3) without hospital admission for COVID-19; (4) without oxygen therapy for COVID-19; (5) at high risk of progression to severe disease defined by the presence of at least one of the following factors: age >65 years, obesity, uncontrolled or complicated diabetes, chronic kidney disease or dialysis, primary or secondary immunodeficiency state, cardiovascular and cerebrovascular disease (including hypertension with organ damage), chronic respiratory disease or oxygen therapy for clinical reason other than COVID-19.Among mAbs, the associations Bamlanivimab/Etesevimab (700/1400 mg) and Casirivimab/Imdevimab (1200/1200 mg) and the monotherapy Sotrovimab (500 mg) were administered as a single intravenous dose in a hospital setting, under clinical observation during and after inoculation, according to the drugs’ data sheets and modalities, as previously validated ,9. AmongPatients were deemed ineligible for early therapy if any of the following criteria were present: refused administration; age < 18 years; ongoing pregnancy; history of allergic reactions to any component of the drugs; symptoms lasting more than 10 days and more than 5 days for mAbs and antivirals, respectively; clinical or radiological signs of COVID-19 pneumonia; oxygen therapy requirement due to COVID-19 progression; severe hepatic or renal impairment (filtrate < 30 mL/min) for antivirals.Follow-up was conducted firstly by phone survey 6 months after healing and then by visiting them at the dedicated “Long COVID” clinic referred to the Internal Medicine Unit of the “Pugliese-Ciaccio” Hospital of Catanzaro . All subjects were asked about clinical course, any hospitalizations, discharge summaries and diagnoses during and after SARS-CoV-2 infection. According to the literature , long COt-test for continuous variables and the chi-square test for categorical variables, as appropriate. Mean ± standard deviation for continuous variables, frequencies and percentages for categorical data were used to describe characteristics of the study population. To test for the differences among the observed clinical data, we used the unpaired Student’s p value. In order to identify factors associated with the occurrence of long COVID, a logistic regression analysis was performed, including in the multivariate model the variables that were significantly different at descriptive analysis or significantly associated with the outcome at univariate model, including age and gender. Data were expressed as odds ratio (OR), 95% confidence interval (CI) and p value < 0.05 was considered statistically significant. All analyses were performed using SPSS v. 25.0 .With the aim of minimizing bias due to potential confounding factors, the method of propensity score matching was used, assuming that an imbalance may exists in patients’ backgrounds between the group who received mAbs or antivirals and the group who did not. The propensity score for each patient was calculated as a probability using a logistic regression model, including all covariates that were considered clinically important and might have an impact on the outcome, with a one-to-one nearest-neighbor-matching algorithm at a caliper of 0.2 . VariablThe demographic and clinical characteristics of the whole study population and according to presence or absence of long COVID are reported in p < 0.001) and were mostly women  when compared to those without. In addition, they were more commonly affected by hypertension  and diabetes  and had a greater clinical complexity as suggested by the percentage of those with at least three comorbidities . Hospital admission was more represented in the long COVID group . Interestingly, we observed that subjects who developed long COVID have less commonly received vaccination  and early therapy against SARS-CoV-2 with mAbs or antivirals . Regarding safety concerns, neither withdrawal nor severe or major adverse effects were observed in those who received early therapy. With regard to the outcome of this study, we found 204 cases of post-COVID syndrome (28% of whole population). As reported in p < 0.001), female gender , hospital admission  and comorbidities . Conversely, vaccination  and early therapy with mAbs or antivirals  were inversely related with incidence of long COVID. Using the multivariate model including all the covariates previously tested, we observed that age, gender and at least three comorbidities remained significantly associated with long COVID occurrence; of interest, vaccination  and early therapy with mAbs/antivirals  were confirmed as variables independently associated with a reduced risk of long COVID.To identify the main determinants of long COVID occurrence we performed a logistic regression analysis . At univTo further investigate the role of early therapy with mAbs or antivirals in the incidence of post-COVID condition, we performed a propensity-score-matched analysis to eliminate the risk of potential bias and confounding factors . In the p = 0.001), supporting the hypothesis that early therapy could have a protective effect against post-COVID sequelae.As shown in p = 0.001). Taken together, these results are of significant clinical relevance because they could provide physicians with an additional treatment option for long COVID, based on the early administration of mAbs or antivirals, with the aim of reducing the risk of both disease progression and long-term sequelae.In this retrospective, propensity-score-matched case–control study, we have demonstrated for the first time that early therapy with mAbs or antivirals against acute SARS-CoV-2 infection is able to reduce the risk of occurrence of long COVID syndrome. Long COVID represents a broad and very heterogeneous clinical entity, with pathophysiological mechanisms which have not been fully elucidated and, consequently, have a lack of established therapies or prophylaxis ,14,15,16In the statistical inference analysis, we considered as dichotomous covariate together mAbs and antivirals (treated vs. untreated) to increase cases of subjects received early therapy, with the aim of testing the effect of early therapy on incidence of long COVID. We did not test mAbs or antivirals alone on the risk of long COVID due to reduced number of cases and possible low statistic power. Furthermore, qualitative and quantitative detection of patient-generated antibodies against SARS-CoV-2 was not available for all patients.Despite the retrospective design of the study, the propensity-score-matched case–control approach reduced the potential bias in the analysis and improved the statistical power and biological plausibility of the observed data. However, these results should be confirmed in further research with a broader sample size and randomized, prospective design.Early therapy with mAbs or antivirals in patients with SARS-CoV-2 infection at high risk for disease progression, in particular when affected by multiple comorbidities, was associated with reduced risk of long COVID.


N = 112; male:78%; 9–17 years) randomised to the control arm of the “ORBIT-Trial” . For this analysis, the control arm was split into two groups: one group was followed up to 12-months’ post-randomisation before the pandemic started ; the other group was impacted by the pandemic at the 12-month follow-up . Mixed effects linear regression modelling was conducted to explore differences in YGTSS at 6- and 12-months post-randomisation. There were no significant differences in tic symptom or severity between participants who were assessed before and during COVID-19. This finding was not influenced by age, gender, symptoms of anxiety or autism spectrum disorder. Thus, the COVID-19 pandemic did not significantly impact existing tic symptoms.To understand how children and young people with tic disorders were affected by COVID-19, we compared pre and during pandemic scores on the Yale Global Tic Severity Scale (YGTSS). Participants were young people ( The coronavirus COVID-19) pandemic created a global health crisis resulting in the United Kingdom (UK) government implementing unprecedented societal restrictions from March 2020 to April 2021, including practicing social distancing, lockdown restrictions consisting of home confinement for all but essential activity and wearing of personal protective equipment (PPE) when outside the home. Furthermore, from March-May 2020, school closures were enforced for all but vulnerable children and children of keyworkers, affecting an estimated 80–90% of children 9 pandemi, with feThe impact of COVID-19 is difficult to calculate being confounded by several key factors, including the evidence of existing decline in CYPs mental health pre-pandemic , restricEarly findings indicated that lockdown was associated with poor mental health , 7. ProbAlthough during early lockdown, it was intuitively believed that young people with an existing mental health condition would be particularly vulnerable to the negative mental health impact of COVID-19  and schoYoung people with tic disorders may be particularly affected by the pandemic. Tics are sudden, repeated, non-rhythmic motor or vocal movements. Tourette syndrome, a form of tic disorder, is often diagnosed if two or more motor tics  and at least one vocal tic  have been present for at least one year. The condition is highly comorbid with other neuropsychiatric disorders; 85% of young people with a tic disorder will also have obsessive-compulsive disorder, ADHD, depression, anxiety and/or ASD . StatistBeyond ad-hoc clinical reports, there has been limited research into the impact of COVID-19 on tics. A COVID survey of 178 adults with a tic disorder in Europe and North America found that approximately half (48%) of respondents reported a perceived worsening in their tic symptoms, 8% reported tic improvement, with the remainder (44%) reporting no change in tic symptoms. Adults reporting a worsening of symptoms were younger and with more severe existing tics, although no impact of comorbidity was found . To the during COVID-19 group), for the other half, the 12-month follow-up was in pre-pandemic times . Utilising YGTSS data from the control group only (to minimise the confounding impact of treatment on symptom course), we aimed to understand changes in tic symptoms in CYP with a pre-existing tic disorder during the COVID-19 pandemic compared to a group of CYP who had been followed-up pre-pandemic. Although the study was not originally designed to assess this, in this post-hoc analysis we investigated whether tic severity and impairment in CYP with a tic disorder was different between the pre-COVID-19 and during COVID-19 groups. We also investigated for any differences in tic complexity. Tic complexity was specifically assessed as a possible indicator of functional tics. Although similar to classical tics, functional tics tend to be more complex in presentation and do not typically follow the rostro-caudal gradient [Data from the Online Remote Behavioural Intervention for Tics (ORBIT) randomised controlled trial  is uniqugradient . We hypoN = 112 participants enrolled into the ORBIT Trial [The sample were IT Trial  and randEligible participants were children aged 9–17 years with a moderate/severe tic disorder, defined as scores on the YGTSS  Total TiParticipants were recruited at (i) one of 16 patient identification child and adolescent mental health services (CAMHS) or community paediatric clinics in England, (ii) the two study sites, or (iii) by self-referral via Tourettes Action (UK tic disorder charity) website or the study website. Informed consent was obtained from all participants. Where the child was under 16 years old, parents provided written consent for their child’s participation, verbal or written assent was also gained from the young person. Where the child was 16 years old or over, both the parent and young person provided written consent. Ethical and Health Research Authority (HRA) approval was granted by North West Greater Manchester Research Ethics Committee on 23 March 2018 (ref.:18/NW/0079). Details of the trial protocol and its primary outcome have been published , 30.no impairment) and 50 (severe impairment). The complexity item is scored as 0 to 5 points for motor and vocal tics separately, so a total of 0 to 10 points for both combined can be obtained. For all indices, higher scores mean greater presence/impact of tics. For this study, we used the YGTSS overall impairment score, the complexity score, and the YGTSS-TTSS.The YGTSS is an investigator-based semi-structured interview completed with the parent and the young person. The scale focusses on motor and vocal tic number, frequency, intensity, complexity, and interference to give an overall severity rating, and tic related impairment over the previous week. The YGTSS symptom checklist comprises 46 tics, including 12 simple motor tics , 19 complex motor tics , seven simple vocal tics , and eight complex vocal tics , with four of these items designated on the instrument as “other” symptoms. Five index scores can also be obtained: Total Motor Tic Score , Total Phonic Tic Score , YGTSS-TTSS , Overall Impairment Rating (0–50), and Global Severity Score (0-100). The Overall Impairment Rating is rated on a 50-point scale anchored by 0 . Agreement with the expert rater was established at the start of the trial and monitored every 6-months throughout the trial. The YGTSS was conducted face-to-face at baseline and via videoconferencing or telephone at each follow-up stage. The assessor was blind to the arm allocation within the trial.The social communication questionnaire (SCQ)  was usednever) to 3 .The Spence Child Anxiety Scale (SCAS)  was compIndex of Multiple Deprivation (IMD) is a relative measure of deprivation across seven different domains: income deprivation; employment deprivation; education, skills and training deprivation; health deprivation and disability; crime; barriers to housing and services and living environment deprivation . ParticiIn order to investigate the impact of COVID-19, we noted whether follow-up data were obtained prior to COVID-19 or during COVID-19. Only data up to 12 months’ post recruitment were included in the analysis, as by 18-months, the number unaffected by the COVID period was too small for meaningful analysis.Baseline statistics (demographics and the outcomes) were calculated overall and by COVID-19 timing at 12 months.Categorical variables were summarised as frequencies and percentages, and chi square tests were used to determine whether there was a difference between characteristics and timing of 12-month data collection. For continuous variables mean (SD) or median (IQR) were used depending on distribution and accompanied by one-way ANOVA, two sample t-test or Kruskal-Wallis test as appropriate.For the statistical modelling, first, mixed effects linear regression modelling was carried out for the three outcomes: YGTSS-TTSS, impairment score and complexity score using data at 6 and 12 months in the outcome . The SCQ was dichotomised to ≥ 15 versus < 15. The models included a time variable and a time varying variable to indicate whether the data were collected pre or during COVID-19. Data from 3 months were not included as there was no variation in COVID-19 timing between the two groups at this follow-up . The objective of this longitudinal analysis was to explore any changes in tic symptoms over time and between the pre and during COVID groups.Four models were calculated: (1) including time, COVID timing and baseline of the outcome, (2) fully adjusted with the variables in model 1 plus age, sex, SCAS, SCQ. Models 3 and 4 were the same as 1 and 2, except they also included interactions between time and COVID timing.Following this, cross-sectional analyses at 12 months only were calculated using multiple linear regression. The objective of this analysis was to further test the findings of the longitudinal analysis and gain further insight into how the pre and during COVID groups may have differed at the 12-month time point. For this, two models were conducted: (1) including COVID timing and baseline score of the outcome, (2) fully adjusted including the same variables as in model 1, plus age, sex, SCAS score and dichotomised SCQ. The statistical analysis plan for the study was made publicly available ahead of conducting the analysis. ).Additionally, we conducted a further unplanned sensitivity analysis, excluding 12-month assessments in March 2020 to investigate whether the patterns remained consistent removing the time-period when COVID-19 lockdown was first instigated in the UK. All analyses were carried out using Stata version 16.In total, 112 participants were consented and randomised to the control arm of the ORBIT Trial . Table 1N = 112, 44 (39%) were in the pre-COVID-19 group (their 12-month follow-up was not during the COVID-19 time period), and 47 (41%) were in the during COVID-19 group (their 12-month follow-up was during COVID-19). Data from the remaining 21 (18%) participants were missing at the 12-month follow-up. The timing for the data collection of the 12-month data in the pre-COVID group was 17th May 2019 to 19th March 2020. The timing for the 12 month during-COVID group data was 23rd March 2020 to 29th October 2020.Of the total Table The results of the mixed model analyses are presented in Table The findings from the cross-sectional analysis at 12 months are presented in Table n = 8/44 in the pre-COVID group and n = 4/46 in the during-COVID group) revealed no statistically significant changes in the findings for either the longitudinal analysis . To our knowledge, this is the first evidence using pre-pandemic baseline and follow-up scores to investigate the impact of COVID-19 on CYP with a tic disorder. However, it is important to note that these analyses were post-hoc and the study was not specifically designed to address the question.The data neither indicated an improvement nor worsening of tics in either of the two groups. It is possible that the school closures and the attenuation of associated school anxiety for young people with tics may have acted as mitigating action offsetting any lockdown or disease-related anxiety . HoweverIt is interesting to note that our findings are not consistent with clinician reports of a perceived increase in the number of tics, both in children and young people with existing tics, as well as new cases . It is pOur study was not specifically designed to address the research question but offers some considerable strengths over previous studies. Tic symptoms were rated by trained, independent professionals. These data also provide a unique opportunity to follow a cohort of children, recruited prior to COVID-19, approximately half of whom had and had not been affected by the COVID-19 pandemic at a 12-month follow-up. The participants were recruited nationally across England and are broadly representative of the typical population usually referred for tics, in that they were pre-teens, predominately male and had moderate-severe tics. Thus, it is likely that our sample is reflective of a typical population seen in child mental health services with higher proportion of males, and high levels of co-occurring neurodevelopmental conditions. One limitation of the study is that, by definition, the two groups did not participate in the study at the same time  meaning that other factors, not measured in the study, could have influenced the results.The participants in our study did not receive any behavioural therapy for tics or start medication for tics during the first six months of the study. However, they were involved in a tic-focused control intervention which may have offered them some benefit beyond tic reduction. Participants were also able to start therapy or tic medication outside of the trial after six months; however, given the difficulty accessing services during COVID-19 , combineThe findings from this post-hoc analysis of the ORBIT trial suggest that COVID-19 may have had little impact on clinician-rated tic symptoms and impairment in children and young people with pre-existing tics in England. Whilst there may be patients with tic disorders who did experience an increase in symptoms, for which they will require appropriate assessment, treatment and care, the current study was not designed to address these individual factors or outcomes. This study shows that an increase in tic symptoms is not ‘a whole group phenomenon’ and may be related to different or more sophisticated factors than we measured in this research.Previous research exploring the impact of COVID-19 on tics has been limited to anecdotal account and has not compared independent rated tic scores pre and during the pandemic. Using data from a randomised controlled trial , this study aimed to explore the impact of COVID-19 on tics. The study compared changes to scores on the YGTSS for two groups of participants, one group was followed up to 12 months’ post randomisation with no impact of COVID, and the other group was impacted by COVID-19 at the 12-month follow-up (during COVID group). The study found no differences in tic symptom or severity between those participants who were assessed before and during COVID-19. Further analysis revealed this was not influenced by age or gender, or baseline symptoms of anxiety or symptoms of ASD. Therefore, using pre and during pandemic scores we found no evidence that the COVID-19 pandemic had a significant impact on the symptoms of a group of young people with existing tic disorders. The sample was predominately male and thus more research in this area, particularly with females, is needed in order to better understand the impact and best support young people with tics.


Coronary heart disease has the highest rate of death and morbidity in the Western world. Atherosclerosis is an asymptomatic condition that is considered the primary cause of cardiovascular diseases. The accumulation of low-density lipoprotein triggers an inflammatory process in focal areas of arteries, which leads to the formation of plaques. Lipid-laden plaques containing a necrotic core may eventually rupture, causing heart attack and stroke. Lately, intravascular optical coherence tomography (IV-OCT) imaging has been used for plaque assessment. The interpretation of the IV-OCT images is performed visually, which is burdensome and requires highly trained physicians for accurate plaque identification.in vivo imaging by offering faster screening and guided decision making during percutaneous coronary interventions.Our study aims to provide high throughput lipid-laden plaque identification that can assist An A-line-wise classification methodology based on time-series deep learning is presented to fulfill this aim. The classifier was trained and validated with a database consisting of IV-OCT images of 98 artery sections. A trained physician with expertise in the analysis of IV-OCT imaging provided the visual evaluation of the database that was used as ground truth for training and validation.This method showed an accuracy, sensitivity, and specificity of 89.6%, 83.6%, and 91.1%, respectively. This deep learning methodology has the potential to increase the speed of lipid-laden plaques identification to provide a high throughput of more than 100 B-scans/s.in vivo imaging by providing faster screening to assist in guided decision making during percutaneous coronary interventions.These encouraging results suggest that this method will allow for high throughput video-rate atherosclerotic plaque assessment through automated tissue characterization for  In the United States, heart disease is linked to 1 of 5 deaths, for which the medications and healthcare procedures result in an economic impact of hundreds of billions of dollars each year.–,,,Intravascular optical coherence tomography (IV-OCT) provides detailed high-resolution imaging of the microstructure information of coronary plaques,–,in vivo clinical interventions. The same research group developed A-line plaque classification approaches that reported similar sensitivity and the need for extensive preprocessing, feature extraction, classification, and classification noise-cleaning tasks.,,in vivo imaging of large OCT pullbacks.The use of convolutional neural networks (CNNs) in the medical imaging field has increased significantly. Multiple studies have focused on automatically identifying different tissue categories in OCT images using CNN, mainly for retinal imaging.In recent years, time-series forecasting using CNNs has gained popularity for scientific and industrial applications that require analysis of continuous data such as climate, biological sciences, and finance.22.1–The database consists of a collection of 98 IV-OCT images from 10 coronary arteries of five cadaveric human hearts. The imaging protocol was previously described in Ref. h B-scan . Then thh B-scan  and revih B-scan . The exph B-scan . The resh B-scan .,The formation of lipid plaques involves monocytes infiltration in the intima. These monocytes differentiate into macrophages to ingest lipids.2.2,An advantage of the proposed approach is the minimalistic image processing required. The linear OCT images are converted to their log-compressed version and downsampled to a half  performs the convolution along the A-line or depth dimension. Using Conv1D layers, the model extracts the features from sequential data regardless of where the feature is located in depth. The Maxpooling operation (MaxPooling1D) is used after Conv1D to downsample the input representation by keeping only the maximum value over the window, which reduces the computational cost by reducing the number of parameters to fit. Likewise, a dropout operation is used to reduce overfitting and refine the generalization of the CNN. In the second block, the flattening operation creates a single feature vector that serves as input for the fully connected dense layers. Each dense layer is followed by batch normalization and dropout operations, which dramatically accelerate the training process of the neural network.3The CNN model was implemented in Python using the Keras library with TensorFlow as the backend running on a computer with Windows 7 Enterprise 64-bit on an Intel i7 CPU with 6 cores at 3.2 GHz and with 48 GB RAM.3.1A fivefold cross-validation strategy was followed to optimize the hyperparameters. The training set was split into five chunks of 20% of the data, where one chunk was used for validation and the rest of the data was used for training; this process was repeated 5 times. At each iteration, the Keras backend session was cleared and reinitialized to avoid clutter from the model of the previous iteration. Layers in the CNN are computationally represented as functions with none or multiple arguments or parameters. First, the CNN was evaluated with only default parameters. Then, for each layer with one or more parameters, each parameter was fine-tuned by assigning a range of values, and the average classification performance for the fivefolds was recorded. The result of the tuning task is summarized in 3.2After fine-tuning the hyperparameters, the initial split of 80% of the whole database was used to retrain the network with the optimized parameters. The remaining 20% of data that was reserved for testing was then fed into the retrained CNN model to predict the presence of lipids in the test A-lines.Because the neural network is initialized with random weights, it leads to different starting points per each simulation during the training phase and, therefore, a slightly different outcome at each training run. Consequently, the last training–testing task was repeated 10 times by randomly shuffling the database and then dividing it into 80% for training and 20% for testing at each iteration. The algorithm reported an accuracy, sensitivity, and specificity of in vivo image-guided interventions. In terms of prediction time, the CNN classification performance was quantified for different computation scenarios, as summarized in Image processing and classification times are critical for 4,in vivo IV-OCT images can still suffer from image degradation due to residual blood speckle artifacts. Thus a predictive model suitable for clinical use will have to be trained using clinical IV-OCT data that incorporate the expected typical noise sources and artifacts while imaging in the catheterization lab. It should be noted that neural network models can, in principle, learn relevant image features even when trained with noisy data. Thus it is expected that neural network models trained with clinical IV-OCT data will be more robust to blood speckle image degradation. In practice, IV-OCT imaging of atherosclerotic plaques requires pullbacks consisting of hundreds of B-scans, making visual plaque classification unfeasible in clinical settings. Therefore, we present a computationally efficient IV-OCT classification method that could offer rapid automated fast identification of lipids in coronary atherosclerotic plaques. This binary classification method makes use of the one-dimensional CNN architecture to discriminate A-lines within lipid-laden plaques.IV-OCT plaque characterization requires trained experts to visually evaluate each B-scan, which is cumbersome and complicated due to variation in the tissue attenuation and speckle effects.–The association between calcified plaques and plaque vulnerability is unclear. In most cases, coronary thrombosis is caused by plaque rupture or plaque erosion, whereas calcified nodules are rarely present.ex vivo (postmortem) imaging and limited in size. Therefore, developing prediction models for plaque classification using comprehensive datasets with histopathology ground truth is unfeasible. Nevertheless, the small IV-OCT dataset with both histopathology and visual assessment ground truth that was available for this study enabled exploring the use of neural network models for predicting lipid-rich plaques from IV-OCT images. Future efforts will focus on demonstrating the feasibility of developing prediction models for plaque classification using IV-OCT datasets with only visual assessment ground truth.The main limitation of this study is the limited size of the data set used. This is a common problem in the field, as IV-OCT databases with ground truth based on histopathology are restricted to in vivo IV-OCT imaging is practiced. Thus plaque assessment relies only on visual examination. We demonstrated that our method can mimic the visual interpretation of the IV-OCT images, which is imperfect due to the difficulty of resolving features due to speckle and attenuation effects. Nevertheless, the use of more accurate labeling provided by more than one observer might increase the outcome of this method.Another limitation of this study is the use of manually annotated images by only one trained expert in IV-OVT as ground truth. Additionally, labeling should come from a histological assessment, which would be the ideal gold standard. However, histological images are not available in clinical scenarios where A specific model trained with the limited IV-OCT data available does not provide depth classification; thus it will not enable distinguishing between thin versus thick fibrous caps. However, neural network models should be able to pick A-line features that might lead to the discrimination between thin-cap versus thick-cap fibroatheroma if these models can be trained with sufficiently large and comprehensive databases of IV-OCT scans.Most deep learning approaches use data augmentation to expand the size of a training dataset by creating altered versions of the data and increasing the volume of relevant data in the dataset. This study demonstrated a minimalistic deep learning approach that can detect lipids in IV-OCT images without data augmentation. Nevertheless, a larger database will help to increase the generalization and, therefore, the accuracy of the CNN model. Furthermore, a larger database with a higher generalization of plaques allows for the classification of calcific plaques and the differentiation between intimal-thickening and fibrous plaques.This approach may lead to real-time IV-OCT assessment of a complete pullback and thus the possibility of (near) real-time assessment of the entire dataset of a patient undergoing PCI. This would be feasible with current/future GPU capabilities for deep learning inference  and visualization. Complementing this classification methodology with advanced visualization and analysis techniques will certainly be relevant for longitudinal tracking of disease progression and/or treatment response. The expected computational speed of a fully trained, optimized, and validated model will enable near real-time visualization of lipids in both cross-sectional and 2D maps of full pullbacks, improving decision making during PCI.Click here for additional data file.Click here for additional data file.


This cross-sectional study uses time-series data to evaluate the administration of bamlanivimab-etesevimab and casirivimab-imdevimab monoclonal antibody treatments for SARS-CoV-2 infection after the US Food and Drug Administration deauthorized their use in early 2022. The Omicron variant was determined to not be susceptible to these treatments, leading the FDA to deauthorize their use on January 24, 2022.3 All US hospitals and health systems are required to report the number of therapeutic courses of bamlanivimab-etesevimab and casirivimab-imdevimab administered in the prior week and the number currently on hand. Because these data are publicly available and aggregated, this study was exempt from institutional review board approval and informed consent was waived in accordance with the Common Rule. The study followed the STROBE reporting guideline.We conducted a serial cross-sectional study using the US Department of Health and Human Services COVID-19 Reported Patient Impact and Hospital Capacity by State Timeseries data set.4 For each state, we identified the number of mAb doses administered after deauthorization and the proportion of mAb doses reported as on hand that were administered during this period. Calculations were performed with Microsoft Excel.We examined reporting from October 27, 2021, through June 29, 2022, to identify national trends in weekly use of each mAb product before and after FDA deauthorization. We report the total doses administered and the ratio of mAbs used to COVID-19 cases reported.Administration of bamlanivimab-etesevimab and casirivimab-imdevimab peaked during the week of December 22, 2021, with 91 036 total doses reported. Use of mAbs declined throughout the remainder of the study period. Both bamlanivimab-etesevimab and casirivimab-imdevimab continued to be administered after FDA deauthorization on January 24, 2022 A. OveralA total of 158 395 mAb doses were administered after deauthorization. There was wide variability in mAb administration by state, with Florida and New York accounting for 24% and 20%, respectively A. Fourte5 indicating that spending on these deauthorized treatments likely exceeds $71 million. Our findings suggest that the use of deauthorized mAb products was widespread, even though patients had a minimal likelihood of benefit. Whether deauthorized treatments will be covered by payers and whether the FDA will take regulatory action against entities violating its guidance remains unknown.According to the results of this serial cross-sectional study, hospitals and health systems administered more than 158 000 anti–SARS-CoV-2 mAb doses in early 2022, despite FDA deauthorization because of a lack of efficacy against the Omicron variant. Medicare payments for mAb administration range from $450 to $750 per dose,2 lack of hospital awareness of deauthorization, or other factors. Although the FDA announcements clearly stated that these mAbs were no longer authorized for use, the agency did not fully revoke their emergency use authorizations because of the possibility that future COVID-19 variants could retain susceptibility, which could have led to misinterpretation.1The continued use of deauthorized anti–SARS-CoV-2 mAb treatments may reflect conflicting state government guidance,Limitations of this study include a reliance on hospital reporting, which does not include other mAb administration sites . Reporting data are not yet publicly available for other COVID-19 therapeutics. All public data on mAb administration are aggregated to the state level; thus, we were unable to explore facility variation in mAb use. Efforts to improve transparency, equity, and value in the COVID-19 response should include public release of facility-level reporting for all therapeutics.


Trichotillomania is a disorder  with common onset in childhood, rarely seen in adulthood, characterized by the repetitive pulling out of one’s own hair leading to hair loss and functional impairment, associated with other comorbidities: major depression (39-65%), anxiety disorder (23-32%), SUDs (15-19%), OCD (13-27%).To present a case of late-onset trichotillomania in a 60-year-old woman.The present study is a case report of a patient visited in outpatient psychiatry for trichotillomania. We also searched previously case reports, series and systematic reviews of clinical trichotillomania using a pubmed query.This is a 60-year-old morbidly obese woman diagnosed with dysthymia, binge eating disorder and histrionic personality disorder. She explained a worsening of anxiety associated with work problems of one year of evolution and, for six months, the beginning of the plucking of eyebrow hairs and scabs to decrease this symptom, with inability to avoid the behaviour and without eating the hairs. The mental evaluation highlighted psychic anxiety, hypothymia, low self-esteem and feelings of failure and did not suggest a delirium. We started treatment with topiramate up to 150mg/day which was not successful. After that we switched to fluoxetine up to 60mg/day associated to psychotherapy observing a slight gradual improvement.The clinical presentation suggested the diagnosis of trichotillomania in the context of dysthymia. No particular medication demonstrates efficacy in the treatment of trichotillomania. Preliminary evidence suggests treatment effects of clomipramine, NAC and olanzapine based on individual trials with small sample sizes. Research findings also recommend psychotherapy based on habit reversal.No significant relationships.