Modulation of gamma oscillations recorded from the human motor cortex and basal ganglia appears to play a key role in movement execution. However, there are still major questions to be answered about the specific role of cortical gamma activity in both the planning and execution of movement features such as the scaling of peak velocity and movement time. In this study, we characterized movement-related gamma oscillatory dynamics and its relationship with kinematic parameters based on 256-channels EEG recordings in 64 healthy subjects while performing fast and uncorrected reaching movements to targets located at three distances. In keeping with previous studies, we found that movement-related gamma synchronization occurred during movement execution. As a new finding, we showed that gamma synchronization occurred also before movement onset, with planning and execution phases involving different gamma peak frequencies and topographies. Importantly, the amplitude of gamma synchronization in both planning and execution increased with target distance and predicted peak velocity and movement time. Additional analysis of phase coherence revealed a gamma-coordinated long-range network involving occipital, frontal and central regions during movement execution that was positively related to kinematic features. This is the first evidence in humans supporting the notion that gamma synchronization amplitude and phase coherence pattern can reliably predict peak velocity amplitude and movement time. Therefore, these findings suggest that cortical gamma oscillations have a crucial role for the selection, implementation and control of the appropriate kinematic parameters of goal-directed reaching movements. Voluntary movements are accompanied by modulation of oscillatory activity of both the beta (13.5–25 Hz) and gamma frequency ranges (25.5–90 Hz) that have been consistently observed with EEG, MEG and ECoG over the sensorimotor cortex and with electrode recordings in basal ganglia structures .Movement-related beta oscillatory event-related desynchronization (ERD) and synchronization (ERS) have been characterized to a great extent in both healthy and clinical populations . On the For instance, the results of some ECoG studies of gamma ERS in the human primary somatosensory cortex  have sugThe notion of a prokinetic nature of gamma oscillations is also supported by correlations of abnormal gamma oscillations with motor symptoms of Parkinson’s disease, essential tremor and dystonia , 2021. AIn recent studies, we have extensively used a reaching task that allows for the parameterization of peak velocity and acceleration. In fact, in that task, the fast and uncorrected movements to different target distances result from a more prominent scaling of peak velocity rather than of movement duration , 2021. UWe enrolled 64 right-handed healthy subjects  with normal or corrected vision, and without known disorders of the nervous system. This investigation was approved by the CUNY University Integrated Institutional Review Board (UI-IRB) and performed in accordance with the ethical principles of the Declaration of Helsinki and its subsequent amendments. Each participant signed an IRB-approved informed consent form before completing the experiment.mov test).High-density EEG was recorded with the 256-channel HydroCel Geodesic Sensor Net  while the participants performed planar reaching movements toward target placed at three distances  in eight directions (45° separation) . The cenad hoc MATLAB-based pipeline. First, we filtered the coordinates with a Butterworth filter and then computed the first, second, and third derivative of the trajectory to obtain velocity, acceleration, and jerk for all the movements.The  coordinates of each trajectory were recorded with a custom-designed software and analyzed using an As detailed in previous publications , we compp < 0.05) were followed by Bonferroni-corrected pairwise comparisons. Further, we computed linear mixed-effect regression models using the MATLAB function fitlme to unveil the specific contribution of peak velocity and movement time on movement extent. Linear mixed-effects regression analysis is a versatile extension of simple linear regression models that allows the estimate of both fixed and random effects, thus controlling some expected variation on the independent variable . Importantly, as mixed-effect models fit an intercept and/or a slope for each random-effect, they address the problem of the non-independency of the data , while respecting between-subject variability.Following Kolmogorov-Smirnov and Shapiro-Wilk normality tests on standardized residuals of each kinematic parameter, two participants were excluded from the analyses. Therefore, analyses of kinematic data were performed on the movements of 62 participants. Repeated measures ANOVA were run on reaction time, movement time, peak velocity amplitude and timing, and movement extent, with target distance  as within-subjects factor. Violations of sphericity assumption were Greenhouse-Geisser-corrected and significant main effects , and the Theoretical Likelihood Ratio Test (TLRT), which is commonly used to compare the goodness of fit of two statistical models. Specifically, the TLRT compared the model with random intercept and slope and the one with random intercept by computing their likelihood ratio test under the Chi-square distribution.For each subject, we included all the available trials and set peak velocity and movement time as fixed-effect factors; the 62 participants were instead included as a random-effect factor. To best account for between-subjects variability, we tested the fit of a model including either an individual intercept or intercept and slope for each participant. To ascertain whether and which fitted model provided the best fit to the data, the following metrics were assessed: adjusted Importantly, even if visual inspection of residual plots did not reveal heteroscedasticity and deviations from normality, and Kolmogorov-Smirnov and Shapiro-Wilk tests on the residuals were not significant, all the included values were z-transformed to obtain standardized estimates from the regression models.High-density (HD) EEG data were acquired using a 256-channel HydroCel Geodesic Sensor Net  with a Net Amp 300 amplifier  and Net Station software (version 5.0). Sampling frequency was 250 Hz and channel impedances were maintained below 50 kΩ throughout the recording to preserve a good signal-to-noise ratio.All recorded data were preprocessed using the public Matlab toolbox EEGLAB version 13.6.5b (v.2016b) . The conIndependent Component Analysis (ICA) with Principal Component Analysis (PCA)-based dimension reduction (max 108 components) was run to delete stereotypical artifacts, such as eye blinks, muscular activity and heartbeat. After a visual inspection of the power spectral density, topographical maps and time course of each estimated component, we retained an average of 16.05 ± 6.27 components per subject. Channels previously removed due to bad signal quality were reconstructed using spherical spline interpolation, whereas those located on the cheeks and neck were removed. Re-reference to overall signal average was finally applied on the resulting 180 channels.All the subsequent analyses were carried out using custom data analysis scripts with the MATLAB-based Fieldtrip Toolbox .Importantly, to avoid ambiguous effects from improperly executed movements, after the preprocessing, we discarded epochs representing movements whose kinematic parameters exceeded two SD.SD, with a similar number of trials for each target distance . Data were then time-locked to movement onset (–1 to 2.5 s). Time-frequency representations (1–80 Hz) were computed by convolving the signal using Complex Morlet Wavelets at linearly spaced frequencies  and a constant time-window (–1 to 2.5 s). The number of wavelet cycles and length were increased as a function of frequency . We applied this approach to obtain a good balance between frequency and temporal resolution .After trial rejection, the average number of trials per subject was 76.2 ± 13.2 Each trial was baseline corrected by subtracting and dividing the average signal of the entire time-window of all trials. In keeping with our previous works , 2021 onEEG spectral and time-frequency analyses were run using the non-parametric cluster-based permutation procedure implemented in Fieldtrip . Brieflypost-hoc tests.In order to characterize gamma oscillatory activity (25.5–80 Hz) during the planning and execution phases of the reaching movements, we first ran non-parametric permutation statistics and compared the broad gamma band time-course activity with the average spectral power . Significant consecutive time-windows were then used to run second-level analyses against the average spectral power to summarize the topography of different sub-bands of gamma . With the resulting clusters of electrodes (ROIs), time-windows and gamma frequency bands, we then explored whether target distance affected gamma amplitude. Thus, cluster-based permutation statistics were run with target-distance as within-subjects factor , along with the correspondent permutation-based p < 0.05). Therefore, non-parametric related-samples Friedman’s test was run to check for peak frequency differences in the three time-windows. Post-hoc pairwise comparisons were obtained with Dunn’s test and Bonferroni correction for multiple-comparisons .To investigate whether the different phases of the reaching movements would be also characterized by spectral differences within the gamma range, we extracted for each subject the peak frequency value during the two significant time windows  and a post-movement control time-window , when movements were certainly completed. Due to the distribution of the independent variable, normality assumption could not be satisfied , a measure of phase-synchronization implemented in Fieldtrip as the debiased wPLI .The wPLI derives from Stam’s Phase Lag Index , as it iE denotes the expected value operator and ℑ{X} the imaginary component of the cross-spectrum.where x 180 adjacency matrix C = [cij], where each node is represented by a given electrode and each edge as the node-wise functional connectivity estimated by the wPLI. The functional connectivity matrices  for two gamma frequency-bands  and then estimated the debiased wPLI across all channel-pairs. The result of this process was a weighted network (for each time-window and each frequency band) represented as a 180 matrices  were expmatrices .The Network Based Statistics (NBS) method was applied to detect eventual differences among the movement, planning and post-movement windows in their wPLI-based functional connectivity subnetworks . This ap-value is computed for each component using the null distribution of maximal connected component size, which is empirically derived via a nonparametric permutation method. Here, a test with 5,000 random permutations was performed to compute statistical significance for the identified network component. Finally, the hypothesis test is performed for the empirically determined components by comparing their extent with the proportion of permutations yielding a component with equal or greater size, correcting for the family-wise error rate at cluster level with p < 0.05. Importantly, here we defined the subnetworks in terms of connected graph components, including nodes and edges, associated with statistical effects above a predefined threshold t-score of 4.8 .The results of the NBS procedure are presented as three-dimensional graph visualizations, which represented N = 59).Finally, in order to unveil a possible relationship between the observed changes in gamma oscillatory activity and the peak velocity amplitude and movement time, we fitted linear regression models to predict the kinematic parameters based on the gamma power during the planning and execution time-windows and the network connectivity values. Specifically, for the regression models with gamma amplitude as the predictor, we entered the average gamma power for the significant time-window (planning and execution), gamma sub-band and electrodes as independent variable and peak velocity and movement time as dependent variables. Furthermore, to establish whether the average gamma power during the significant movement could be a good predictor, we also extracted the amplitude of gamma oscillations at the timing of the peak velocity for both the out and back movements. Specifically, for each trial, we computed the timing of the peak velocity for both the out-going and return movements and extracted the corresponding broad and high-gamma amplitude in the electrodes that showed significant gamma ERS in the movement window (52–500 ms). These values were averaged across trials and used as linear predictors of the peak velocity amplitude. For these analyses, we had to exclude additional 3 participants due to technical problems in their raw kinematic data files , and the Theoretical Likelihood Ratio Test  . Following the indication provided by these indices, the model with random intercept and slope showed the best fitting of the data  = 46.83; p < 0.0001], while the effect of movement time was less prominent .Thus, we first assessed: adjusted the data . The resAltogether, these findings show that movement extent resulted mainly from the scaling of the force to the appropriate target distance with a lesser contribution of movement duration.t = 907.27, p = 0.0003, CI: 0.0004) and the other from 52 to 500 ms after moment onset, that is, during the movement execution time . As shown in We next investigated the progression and the topographical distribution of gamma oscillatory activity (25.5–80 Hz) during movement planning and execution. We thus compared gamma oscillatory activity of each 24 ms-time bins to the average spectral power of the entire epoch  with non-parametric Monte Carlo permutation testing. This analysis revealed significant electrode clusters in two time-windows: one from 152 to 52 ms before movement onset, that is, during the reaction time period (Cluster We then averaged the topography of gamma activity in the time bins of the reaction time period (from 152 to 52 ms before movement) and that in the time bins from 52 to 500 ms after moment onset, during the movement execution time. Cluster-based permutation analyses on four gamma bands  revealed for both time windows significant cluster of electrodes in the broad, medium and high gamma bands . In all These results prompt some considerations about the role of gamma activity in both movement planning and execution. In line with a few reports , the occ2 (2) =  93.23, p < 0.0001]. During the planning window, gamma ERS was maximally expressed in the medium gamma range  whereas during movement execution the peak shifted toward higher frequencies (65.8 Hz ± 10.72). Importantly, after movement completion, cortical activity synchronized back to the beta/low gamma frequency range . These findings demonstrate that gamma ERS occurring during movement planning and execution is maximally expressed in two distinctive frequency ranges, thus suggesting possible different functional properties of gamma ERS.We then determined whether the observed gamma ERS during planning and execution differed in their peak frequency ranges . We used non-parametric related-samples Friedman’s ANOVA to test possible differences of gamma ERS frequency between the planning, execution and post-movement time windows. As displayed in p < 0.001, corrected for multiple comparison) in almost the whole brain. Post-hoc network-based statistics (NBS) analyses revealed a subnetwork of increased functional connectivity during movement execution compared to movement planning in the broad gamma frequency. The subnetwork consisted of 244 edges connecting 92 different electrodes . Interestingly, apart from a few pathways linking frontal to occipital regions, these patterns of increased connectivity mainly involved a left fronto-temporal-parietal network . These patterns of increased connectivity mainly involved bilateral fronto-temporal and fronto-central electrodes , a functional connectivity metric that minimizes the impact of volume conduction effects . The wPLI was computed across all channel-pairs to provide a whole-brain mapping of functional EEG networks and to ascertain possible differences of connectivity between the planning, execution and post-movement windows. In the broad gamma frequency, rmANOVA revealed significant functional connectivity differences between planning, execution and post-movement windows . Post-hoc NBS analysis highlighted a subnetwork of increased connectivity during movement execution compared to planning  that consisted of 571 edges connecting 122 different electrodes. In addition to patterns of connectivity mainly involving the fronto-temporal-parietal areas bilaterally, this subnetwork also involved fronto-occipital connections , that mainly involved the right fronto-temporal network . In this frequency range, rm-ANOVA revealed significant widespread functional connectivity differences between planning, execution, and post-movement windows  and movement execution (7.9 ± 13.0%), post-hoc tests did not reveal any significant differences between medium and long-distance targets. Similar results were obtained for medium gamma and high gamma frequency bands, except for medium and short movement trials during planning in the medium gamma frequency band  and high (55.5–80 Hz) frequency ranges, was visible immediately before and after movement onset, albeit with different topographies that may reflect different functions carried out during movement planning and execution. Interestingly, during movement planning, the first burst of gamma ERS was maximally expressed over the centro-parietal region, whereas the two gamma ERS bursts corresponding to the out- and back-movements were mostly visible over the parieto-occipital region.The occurrence of gamma synchronization during the planning phase has been reported in previous studies  and suppAlthough the observed scaling of the pre-movement gamma ERS supports our interpretation of its role in movement planning, we cannot completely exclude that gamma ERS prior to movement onset might arise from subtle muscle contractions occurring before the motor act that can be captured only by comprehensive electromyography (EMG) recordings .Therefore, future analyses should combine EEG, EMG and kinematic recordings in order to clarify the relationship between pre-movement cortical gamma oscillations and muscle activation.After the initial gamma ERS, gamma oscillatory activity visibly increased after movement onset and strikingly tracked the temporal profile of movements’ velocity. Compared to the initial burst that peaked around 54 Hz, this movement-related synchronous activity was most prominent in the high-gamma range, in agreement with different studies . AlthougAltogether, we may speculate that movement-related ERS in the high gamma range during movement execution could reflect the specific activity of the dorso-medial stream, integrating online afferent sensorimotor and visual input and sending real-time feedback to premotor and motor regions to control the ongoing movement. Moreover, the observed positive linear relation between the functional connectivity and ERS amplitude during the movement and peak velocity might reflect either the amount of neural assemblies recruited to produce more force to increase speed  or the aInterestingly, we also found increased connectivity (mostly in the high gamma range) in a subnetwork that mainly involved the right fronto-temporal areas after movement termination. Differently from gamma connectivity during the movement, this subnetwork was not linked to movement kinematics, thus it might reflect a different function. A possible interpretation of this result is that it might reflect working-memory processes occurring after movement termination and the update of the activated motor representations. Alternatively, this finding may represent re-orienting attentional processes to unattended locations that precede the appearance of the next target. Indeed, re-orienting attentional processes engage the ventral attentional network that is lateralized to the right hemisphere and includes the temporo-parietal junction as well as the frontal cortex at different levels . AlthougIn conclusion, this study provides an extensive characterization of scalp recorded movement-related gamma synchronization during planar reaching movements in a large sample of healthy participants. Gamma ERS amplitude during movement planning and execution, as well as increased parieto-occipital and fronto-central connectivity pattern reliably predict the specific movement features.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by CUNY University Integrated Institutional Review Board (UI-IRB). The patients/participants provided their written informed consent to participate in this study.MFG, ET, and AQ: study conception and design. ET, MFG, FF, AC, and CC: data collection and analyses. ET, MFG, AC, AQ, and FF: interpretation of the results and manuscript drafting. ET, FF, MFG, and AQ: manuscript revising. All authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Emergency department (ED) use is a concern for surgery patients, physicians and health administrators particularly during a pandemic. The objective of this study was to assess the impact of the pandemic on ED use following cancer-directed surgeries. This is a retrospective cohort study of patients undergoing cancer-directed surgeries comparing ED use from 7 January 2018 to 14 March 2020 (pre-pandemic) and 15 March 2020 to 27 June 2020 (pandemic) in Ontario, Canada. Logistic regression models were used to (1) determine the association between pandemic vs. pre-pandemic periods and the odds of an ED visit within 30 days after discharge from hospital for surgery and (2) to assess the odds of an ED visit being of high acuity . Of our cohort of 499,008 cancer-directed surgeries, 468,879 occurred during the pre-pandemic period and 30,129 occurred during the pandemic period. Even though there was a substantial decrease in the general population ED rates, after covariate adjustment, there was no significant decrease in ED use among surgical patients . However, the adjusted odds of an ED visit being of high acuity was 23% higher among surgeries occurring during the pandemic . Although ED visits in the general population decreased substantially during the pandemic, the rate of ED visits did not decrease among those receiving cancer-directed surgery. Moreover, those presenting in the ED post-operatively during the pandemic had significantly higher levels of acuity. The COVID-19 pandemic has disrupted cancer care. Cancer patients are a vulnerable population with increased risk of morbidity and mortality from COVID-19 . To helpED use is an important quality metric and outcome of surgical care and a unique concern for patients, physicians and health administrators given the unique challenges in navigating healthcare during a pandemic. Cancer surgery patients are at particularly high-risk of requiring acute assessment or interventions and seeing their care disrupted by pandemic-related changes. Indeed, delays and cancellations in cancer surgery associated with the ramp down of surgical services to create capacity for COVID-19 care during the pandemic may lead to advanced presentations with high symptom burden or complications requiring urgent care. In addition, increased use of virtual care and lack of access to physical clinics for post-surgical follow-up may translate into increased reliance on the ED for management of post-operative events . This isThe objective of this study was to assess the impact of the COVID-19 pandemic on ED use following cancer-directed surgeries, with a view to better understand healthcare utilization in a resource limited environment and to inform innovative structures of care for future waves of the pandemic. We hypothesized that there would be lower use of the ED in the general population but increased use in patients who had undergone cancer-directed surgeries.We conducted a retrospective cohort study using population-wide administrative databases from the province of Ontario, Canada. All patients undergoing cancer-directed surgeries from 7 January 2018 to 27 June 2020 were identified using the Canadian Institute for Health Information’s Discharge Abstract Database (CIHI-DAD) and CIHI-Same Day Surgery (CIHI-SDS) database, as previously published by our group . CIHI inWe linked health administrative databases held at ICES . The Canadian Institute for Health Information’s National Ambulatory Care Reporting System (CIHI-NACRS) database was used to capture emergency department visits and corresponding information on acuity scores from the Canadian Triage and Acuity Scale (CTAS). Date of death and demographic characteristics were obtained from the Registered Persons Database (RPDB) . ImmigraThe primary exposure was the period during which the cancer-directed surgery occurred: pre-pandemic (on or prior to 14 March 2020) vs. pandemic . The primary outcome was the odds of experiencing an ED visit within 30 days following discharge from surgery. Characteristics measured at the time of cancer-directed surgery included: patient factors—age, sex, rurality, neighborhood income quintile, region of residence based on Local Health Integrated Networks , immigration status (immigrant vs. not), marginalization quintiles, Elixhauser comorbidity level  based on a 5 year look back window prior to the time of surgery ,13; surgp-values) were used to establish whether covariate distributions were balanced between periods; a standardized difference <0.1 indicated balance.Analyses were conducted at the level of cancer-directed surgeries. The distributions of baseline characteristics were assessed among individuals undergoing surgery in the pre-pandemic period compared to those in the pandemic period. Due to the large cohort size, standardized differences  and the odds of an ED visit within 30 days after discharge. A generalized estimating equations approach was incorporated to account for possible correlation of surgeries undergone in the same institution. The regression models adjusted for all covariates listed above based on an a priori analysis plan. In addition to the main effects model, 2-way interactions between period of cancer-directed surgery and various covariates were also explored. This was performed to assess if the impact of the pandemic on the odds of ED varied by levels of socioeconomic status, comorbidity and other covariates.Lastly, among all cancer-directed surgeries that had an ED visit within 30 days following discharge, we examined the association between the period of cancer-directed surgery (pandemic vs. pre-pandemic) and the odds of the ED visit being of high acuity. Acuity was determined using CTAS, a 5-level scale where each level is determined based on a patient’s need for medical interventions with level 1 being resuscitation for conditions that are life or limb threatening, while level 5 is non-urgent, typically chronic, problems . High acThis study involved secondary data analyses only and was thus exempt from requiring REB approval because ICES is a designated “45.1 entity” under the Personal Health Information Protection Act (PHIPA) enabling the use of personal health information.Our population-based cohort consisted of 499,008 cancer-directed surgeries, of which 468,879 occurred during the pre-pandemic period and 30,129 occurred during the pandemic period. Surgery by cancer type did not differ in the two cohorts for central nervous system tumors, endocrine, esophagus, genitourinary (not including prostrate), prostate, gynecological, head and neck, hepatobiliary, lung, gastric, sarcoma and non-melanoma skin. It did however increase for colorectal , breast  and genitourinary  and decrease for melanoma . There were no differences by region  of residence.The variation in ED rates following cancer-directed surgeries in the pandemic period compared to the same corresponding weeks in the prior year is seen by the funnel plot in p-value 0.93). One of the strongest associations was between urgency of surgery and odds of ED. Surgeries considered urgent had 38% higher odds of experiencing an ED visit compared to non-urgent surgeries . There was a clear gradient in the association between comorbidity and ED visit, where those with a larger number of comorbidities had higher odds of ED visit. There were no significant interactions found between period of cancer-directed surgery and other characteristics (data not shown).The associations between the period of cancer-directed surgery (pandemic vs. pre-pandemic) and odds of 30-day ED visits after cancer-directed surgery are illustrated by the forest plot in The association between period of cancer-directed surgery (pandemic vs. pre-pandemic) and the odds of the ED visit being of high acuity in the sub-group of patients who had an ED visit after surgery are illustrated in This population-based retrospective cohort study demonstrated a drop in ED visits from the pre- to the pandemic period for the general population, with absolute differences varying from 5.0 to 21.1 per 1000. After adjustment, the surgical cohort ED rates post-operatively were stable but with higher acuity in the pandemic period. During the pandemic period, higher percentages of surgeries related to breast, colorectal and genitourinary cancers took place, with a lower percentage of melanoma-related surgeries. Urgent procedures comprised 27.9% in the pandemic period, whereas 14.8% were considered urgent in the pre-pandemic period.In a publicly funded healthcare system, we have previously demonstrated equally equitable access to surgical care during the pandemic compared to pre-pandemic, particularly early on during the first wave . That inWe observed an increase in unadjusted 30-day ED rates post discharge during the pandemic period compared to the pre-pandemic period amongst surgical patients despite a drop in the monthly ED rate (for any reason) among the entire population for the same period, which was consistent across regions. One of the strongest associations was between urgency of surgery and odds of ED visit; surgeries considered urgent had 38% higher odds of experiencing an ED visit compared to non-urgent surgeries. Rural patients were more likely to use the ED peri-operatively ; howeverThe drop in ED use in the general population observed in our study is in keeping with other jurisdictions . This isThese data must be interpreted in the context of the study design. This study did not account for the COVID-19 status of the patients as this data was not readily available for all patients in our cohort at the time of study completion. This makes it hard to ascertain whether ED use was related to COVID-19 related complications. Nonetheless, COVID-19 testing for surgical patients was routine and patients who were COVID-19 positive pre-operatively were delayed. It is therefore unlikely that nosocomial or delayed COVID-19 diagnoses is what accounted for the stable ED use. Furthermore, the exact cause of admission was not extracted. This could provide further insight into patterns of preventable causes and may lead to multifaceted interventions to help curb emergency department use. Nonetheless, this is the first and largest study to assess this question at a population-based level. The study is strengthened by its large sample size, a focus on cancer-directed surgeries and a high capture of post-operative events.In conclusion, although the rate of overall ED in the general population decreased substantially during the COVID-19 pandemic compared to the pre-pandemic period, the rate of ED visits did not significantly decrease among those receiving cancer-directed surgery. Moreover, those presenting in ED post-operatively during the COVID-19 pandemic period had significantly higher levels of acuity compared to those in the pre-pandemic period. Further work is required to investigate the possible causes for these findings and identify potential gaps in care for patients during a pandemic whilst accounting for COVID-19 status during the observation trajectory.


Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay. Discovery and design of effective treatments against COVID-19 is actually an active research field. Tremendous efforts have been deployed worldwide to find new molecules with therapeutic potential against its pathogenic agent SARS-CoV-2 . The mosComputational approaches may be split into two subcategories: Structure-Based Drug Discovery (SBDD) and Ligand-Based Drug Discovery (LBDD). For SBDD, the structure of a molecular target is used to perform virtual screenings of large chemical libraries. The most popular targets are the Spike protein, known as the S protein, the 3-Chymotrypsin-Like cysteine protease (3CLpro), also called the main protease (Mpro), and the Papain-Like protease (PLpro) . These aOn the other hand, LBDD is more likely dependent on the availability of data on the biological activity of molecules. Machine learning (ML) approaches demonstrated their ability to predict the activity of novel molecules based on such data . The undvia backpropagation on an undirected graph representation of the molecule  were applied to a variety of DD projects. Applications can be split into two types: 1) activity prediction through regression and 2) classification of molecules into classes, mostly active vs. inactive . ML algomolecule . The autmolecule .Here, we present a dataset of molecules validated for their effects on SARS-CoV-2 and/or SARS-CoV through viral growth inhibition, cell-based, or enzymatic experiments. Data were collected through an extensive search of the literature and databases, curated and formatted for cheminformatics simulations toward LBDD against COVID-19. Chemical structures of the molecules were then encoded through multiple systems to be readily useful as input to conventional ML algorithms or for GCN. We run an extensive set of simulations under different splitting and formatting conditions of the data to identify the ML and DL algorithms that could achieve satisfactory results. Most promising models were then optimized, and their performances were validated through cross-validation. An external validation step was performed to assess the potential of these algorithms to achieve drug repurposing using experimental data on the FDA-approved drugs collection.in vitro IC50 values, cellulo IC50 values, and any other valuable biological data . The chemical structure of the molecules was encoded using the Simplified Molecular Input Line Entry System (SMILES). For compounds with a graphical description of their structure in the literature, we used the Optical Structure Recognition Application (OSRA) tool  and SARS-Cov-2 . The second approach consisted in retrieving data on molecules deposited in the RCSB PDB as cocrystals with SARS-CoV and SARS-CoV-2 proteins, mainly the 3CL-protease and the Papain-Like protease. When available, activity data on these cocrystallized inhibitors were fetched from corresponding scientific publications. The third approach consisted in retrieving data from bioassays deposited in the PubChem database . PrioritRA) tool  to correThe benchmark datasets used herein were split using two different approaches. First, a random split with no consideration for chemical equilibration among the training, validation, and test sets was applied. Then, a scaffold split  was applIn addition, we tested how the size of the validation and test sets would affect the algorithms’ performances. Thus, we tested two scenarios: 80/10/10 and 60/20/20 split. An additional splitting method of the original dataset that permitted the generation of category-specific subsets for validation purposes was applied. Undersampling and oversampling were applied in order to obtain equilibrated datasets in each case. Undersampling consisted in reducing the inactive molecules subset to achieve equilibrated classes. Oversampling consisted in artificially generating additional SMILES of the active molecules in order to reach the inactive subset size.• The ConvMolFeat featurizer  to gener• The MolGraphConvFeat  to generBased on the SMILES, we calculated either molecular fingerprints or graph convolution-based features that consist in binary or float values vectors to be used as input to the ML and DL algorithms, respectively. As fingerprints, we chose the extended-connectivity fingerprints with a radius of two atoms (ECFP4), also known as the circular Morgan fingerprints , to encoGraphical convolutional models map molecules as undirected graphs whose vertices and edges represent individual atoms and bonds, respectively. Graphical convolutions extract meaningful patterns from basic descriptions of graph structure (atom and bond properties and graph distances) to form molecule-level representations. They are considered fully integrated approaches to virtual screening. The output of the model is invariant to the order in which the atom and bond information is encoded in the input. The graph represents class similarity information and is fed into DL classification models.We implemented multiple artificial intelligence (AI) algorithms to develop classification models: ML, ensemble learning methods (EL), and DL. We implemented seven ML algorithms, out of which two are simple ML algorithms, namely, Logistic Regression (LR) and Support Vector Machine (SVM). Five additional EL algorithms were implemented, namely, Random Forests (RF), Multitask Classifier (MTC), IRV-MTC, Robust MTC, and Gradient Boosting (XGBoost). EL are learning algorithms that construct the first set of classifiers and then construct a new one by taking a weighted vote of data predictions from the previous classifiers . These aThen, four DL architectures were implemented under the DeepChem library : the GraUnder the DeepChem library, both the GraphConv Model and the DAG model were implemented to learn from MolConv featurizer  that corκ). Then, we performed a cross evaluation of the model performances when trained and tested on stratified subsets of the data based on the different categories of targets. Accuracy, F1-score, Recall, and specificity were used as evaluation metrics for these simulations.We performed the first comparison of all models’ performances with hyperparameters set to the optimal values obtained through the MoleculeNet benchmarks . To bettFor the metric definitions, the following abbreviations are used: the number of true positives (TP), the number of false positives (FP), the number of true negatives (TN), and the number of false negatives (FN). Specificity, also called the False Positive Rate (FPR), is the model’s ability to correctly reject an inactive molecule. Specificity of a test is the proportion of molecules that are truly inactive, which are classified as is. It is defined as follows:Model Recall can be thought of as the percentage of true class labels correctly identified by the model as true. It is equal to the model sensitivity in binary classification and is also called the True Positive Rate (TPR). It is defined as follows:The F1-score is the harmonic mean of the Recall and precision:Accuracy is the percentage of correctly identified labels out of the entire population.The ROC-AUC score tells how much the model is capable of distinguishing between classes. It varies between 0 and 1, where 1 means a perfect prediction. The MMC is a correlation coefficient between the observed and predicted binary classifications. It is between −1 and +1, where +1 indicates a perfect prediction, 0 indicates no better than random, and −1 indicates prediction and observation are totally different.Cohen’s Kappa method measures interclassifier agreement in qualitative classification tasks. It evaluates the agreement between two classifiers and takes into account the random occurrence of the agreement. A value close to one denotes better agreement between the results and ground truth.The best performers were then selected for hyperparameter optimization on the particular anticoronavirus dataset collected through the present study. Their performances were mainly assessed through ROC-AUC, F1-score, Recall, Accuracy, MCC, and Cohen’s Kappa scores, which are a set of popular metrics in evaluating ML algorithms in a variety of applications . ML algoTenfold cross-validation was performed, and the mean ROC-AUC, F1-score, and Recall values were reported. A stratified validation was also applied in order to assess the ability of the algorithms trained on the heterogeneous dataset to correctly predict active molecules from different categories of experiments. The sensitivity  and specificity were herein used as performance indicators. The optimized models were then subject to an external validation using an unseen set of molecules. We used a PubChem bioassay that consisted in a primary screen of 1,518 FDA-approved molecules against SARS-CoV-2-infected cells (AID_1409594). A total number of 17 hits were retained as potentially active molecules, and their antiviral efficacy was further confirmed through a second assay (AID_1409595). We performed a prediction of these 1,518 FDA-approved drugs as anti-SARS-CoV-2 inhibitors using the best performing algorithms.We collected data on molecules with anticoronavirus effects, out of which 533 were retrieved from literature. All remaining compounds were collected from 14 PubChem bioassays. Since activity types were different from one source to the other, we considered the activity as a binary variable. Initially, four classes of activity status were listed: active, inactive, unspecified, and inconclusive. Only molecules within the first two classes were retained in the frame of the present work. The combined set of active and inactive molecules was subject to redundancy check, and duplicates were removed. The number of active molecules was equal to 1,305 at this stage. We then looked to obtain an equal number  of inactive molecules, which were in larger numbers, namely, within large bioassays. Thus, from some SARS-CoV bioassays, only a subset of inactive molecules was randomly selected see . UltimatAt this stage, we disposed of 2,610 anticoronavirus molecules. We used a random and a scaffold split of the dataset using two splitting proportions of the training, validation, and test sets as follows: 80/10/10 and 60/20/20. We seek to identify which scenario is overall optimal. The final datasets, ready for the upcoming experiments, are available on GitHub.We first run preliminary simulations of seven ML algorithms and four DL algorithms using the hyperparameter values released by the MoleculeNet authors . These oTo better understand to which extent the heterogeneity of our dataset may be influential, we considered a subset of homogeneous data from the largest PubChem bioassay on SARS-CoV within our dataset: AID_1706. It is a biochemical assay targeting the enzymatic activity of the 3CLpro of SARS-CoV, through which 290,893 compounds were tested. A total of 405 molecules showed an inhibitory effect on the 3CLpro-mediated peptide cleavage. Based on this bioassay, we generated one undersampled (810 molecules) and one oversampled  homogeneous datasets. On randomly split data, ROC-AUC scores on the heterogeneous dataset were the most stable across the different algorithms. The best results were exhibited by RF and SVM on the oversampled homogeneous dataset. For the DL algorithms, GraphConv model, DAG, and GCN demonstrated satisfying performances  curves exhibited smooth exponential-like shapes for all models, indicating satisfying classification power. The Precision-Recall (PR) curves also presented fair shapes for a balanced dataset . At lastThe last validation step was performed on the three optimized algorithms in order to assess their predictive power in identifying lead compounds against coronaviruses in general and SARS-CoV-2 in particular. Considering the heterogeneity of our dataset in terms of experiments and targets, it is important to assess the ability of the AI algorithms to generalize when tested on unseen datasets. To this end, we split our dataset into category-based subsets. Only categories 3CLpro_Cov and PLpro_Cov presented sufficient data points  to be usHomogeneous training denotes all experiments where models were trained and tested on one category subset. Heterogeneous training denotes all experiments where models were trained on the mixed dataset and tested on one category subset. Finally, we called mixed training the experiments where models were trained and tested on the dataset consisting of a mix of categories. Performances in terms of accuracy, F1-score, Recall/sensitivity, and specificity were reported in Algorithms’ performances on the 3CLpro_cov category presented comparable values with the mixed training results. On the other hand, low Recall values were obtained with the PLpro_cov category trained on homogeneous and heterogeneous data . It is nNoticeably, RF and GCN models could achieve comparable Recall scores through the homogeneous and heterogeneous training experiments. This means that these algorithms exhibited a similar ability to correctly predict active molecules if trained either on the mixed dataset or on the subset of the 3CLpro_cov category and then tested on the 3CLpro_cov test set. In addition, the GCN scores were maintained close to those obtained on the mixed dataset and in comparison with cross-validation results . This reIn order to confirm such findings, we performed an external validation of the three algorithms’ ability to predict potential inhibitors targeting SARS-CoV-2 out of the FDA-approved drugs collection. We used a PubChem bioassay that consisted in a primary screen of 1,518 FDA-approved molecules against SARS-CoV-2-infected cells, out of which 17 molecules were retained as potentially active. Out of our mixed dataset, we removed all molecules included within this external validation set. We retrained all three models on our mixed dataset using its full content. Then, we predicted for all FDA-approved molecules from the validation set their activity class. We assessed the classification outcome in comparison with the experimental data and calculated the confusion matrix elements  for each model under two scenarios . First, For each algorithm, we first observed the TP and FN counts out of the 17 active molecules. Overall, the GCN model achieved the highest TP count of 8/17 and the lowest FN count of 9/17. The next best performer was the DAG model with TP counts of 7/17, while RF demonstrated the lowest TP count of 4/17 . InteresAI, precisely ML and DL, have now demonstrated high potential of delivering successful research outcomes in the field of DD . The appIn the present work, we assessed the performances of seven ML algorithms and four DL algorithms in predicting the activity of molecules against the COVID-19 viral agent. The training data is a unique collection of 2,610 data points integrated from different sources. It includes molecules presenting inhibiting actions against SARS-CoV and SARS-CoV-2 through multiple and heterogeneous experiments. Our results demonstrated the usefulness of such a dataset in building ML algorithms for activity prediction tasks toward DD against COVID-19. Best performing algorithms, namely, GCN and RF, demonstrated stable performances across different training/testing simulations on stratified subsets of the data. Through external validation on unseen data, the GCN model demonstrated the highest predictive power overall. The MoleculeNet authors performed an extensive benchmarking of multiple ML/DL algorithms, including those studied herein on different tasks and datasets . RF and Data have always been a determinant factor in delivering robust ML. In the field of DD, it is a constant challenge to overcome. Many groups made considerable efforts in constituting dedicated datasets for DD . The intIt seems important to engage further efforts to integrate more information in our dataset toward its use for a quantitative prediction of molecules activity. Moreover, a deeper analysis of the dataset content may reveal important knowledge for DD projects. Further tuning of the dataset will aim to integrate valuable knowledge on what to expect from effective anti-SARS-CoV-2 molecules . In factIn the present study, we collected and curated a dedicated dataset of 2,610 molecules having anticoronavirus effects. This valuable resource was formatted and used to perform different simulations and optimization of eleven ML and DL algorithms toward the classification of molecules into active and inactive classes. We were able to obtain three highly accurate classifiers that were validated through cross-validation and on an external set of data. The DL algorithms demonstrated the best performances.


Herewith, it is apparent that professionals with a higher self-perception of emotional intelligence report stronger levels of engagement, thereby leading to greater performance overall. The present work evinces the necessity for proactively developing the emotional competencies of the health-care workforce, especially in high-emotional demand contexts.Upon the eruption of COVID-19, frontline health-care workers confronted substantial workload and stress along with braving additional difficulties when performing at work. The main aim of this research was to assess the mediating role of work engagement in the direct impact of emotional intelligence on health-care professionals’ work performance. A cross-sectional study was conducted in several Spanish hospitals during the second half of 2020. A total of 1549 health-care workers  filled the Wong and Law Emotional Intelligence Scale, the Utrecht Work Engagement Scale and the Individual Work Performance Questionnaire. Our findings demonstrated that work engagement plays a mediating effect between emotional intelligence and work performance, even when accounting for sociodemographic variables. Indeed, among the three constructs of engagement, vigor dimension (a1b1 = 0.09; CI: 0.06; 0.12;  Mankind is at present poised upon the menace of a newly arisen form of severe acute respiratory syndrome (SARS) specifically referred to as SARS-CoV-2 . The desHealth-care professionals confront a profound apprehension not only when exposed to stressors such as the consolidation and sustenance of the health condition of their patients, but also in terms of struggling with enormous stress and fear coupled with a sense of social stigma bound to their occupational position . Above tThe negative relationship between such an emotionally demanding COVID environment and health-care professionals’ outcomes could be explained by the health impairment process of the job demands-resources theory (JD-R) . AccordiUnder this scope of abduction, professional sectors are presently imperiled by a pervasive phenomenon that not only symbolizes a mental health crisis , but bewAt the heart of it, raising self-awareness of personal emotions along with actively providing their insights and first-hand stories with their patients may assist them in retaining centeredness amid this pandemic, both for the maintenance of the desired quality of their performance and to forth-propel it via the intensity of their subsequent engagement at work . In otheFollowing the preceding theoretical and empirical findings, the aim of the present investigation was to provide additional empirical support for the potential underlying mechanism in the relationship between EI and work performance of health-care professionals amid the COVID-19 pandemic. In particular, we propose three hypotheses: EI would be positively correlated with health-care professionals’ engagement (H1a) and work performance (H1b); work engagement  would be positively correlated with health-care professionals’ work performance (H2); and work engagement  would mediate the relationship between EI and work performance (H3). The hypothesized model is drawn in By means of utilization of a cross-sectional design, a descriptive study was conducted in several Spanish hospitals during the second half of 2020 in order to examine the mediating role of the three engagement dimensions in the relationship between EI and work performance in a sample of health-care professionals. The sample included 1549 people (62.1% were women). The mean age was 36.51 years , the overall work experience was 8.71 years, and the average organizational seniority was 6.1 years. As concerns the educational level of the participants, 2.6% had high school, 19.4% vocational education, 32.4% equal or more than 4 years of college and 45.6% more than 4 years of college. In relation to the marital status of the participants, 34.2% were married, 50.8% single or in a relationship, 13.1% separated/divorced and 1.9% widowed. Participants exclusively accounted for the health-care professional category constituted by doctors (26.9%), nurses (43.4%), porters (17.2%) and several minor professions in hospitals  which represented the remaining 12.5%.The Wong and Law Emotional Intelligence Scale (WLEIS) in its Spanish adaptation was utilized to address the perception of EI . This scThe Individual Work Performance Questionnaire (IWPQ) in its Spanish adaptation  was emplThe Utrecht Work Engagement Scale  was admiA self-administered socio-demographic questionnaire was devised to collect data of age, gender, marital status, educational level, professional sector, work experience and organizational seniority. In accordance with a convenience sampling approach and in line with former research, the sample was compiled with the collaboration of undergraduate students who were familiar with the administration of questionnaires. Upon the investigators’ guidelines, the students subsequently contacted several hospitals, querying the human resources area about the possibility of cooperating. Additionally, an e-mail invitation to the experiment was sent to 4322 people, 1588 of whom responded (response rate = 36.9%). Out of all the responses collected, 39 participants were discarded due to omission of the solicited data. This entire procedural design was performed under the terms of the criteria provided by Wheeler et al.  for the χ2), the comparative fit index (CFI), the Tucker-Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR), as proposed by Baron and Kenny [The analyzed data were processed using the SPSS software version 26.0 (IBM). The first analysis entailed descriptive statistics, encompassing the mean, standard deviation and reliability of the study variables. To test the hypotheses H1a, H1b and H2, Pearson correlations were conducted between EI, work engagement and work performance. These analyses were necessary before testing the mediator model. After that, H3 was tested by means of a structural equation modeling (SEM) approach using AMOS 26 (IBM). Goodness of fit was controlled through the Chi-square index . On the other hand, EI correlated with all work performance variables  as well as with the overall score named work performance (r = 0.48), thereby supporting H1b. As hypothesized (H2), all three engagement dimensions were significantly correlated with work performance, being this relationship positive-correlated with task and contextual performance, but negatively correlated with CWB. The Cronbach’s alpha values showed good reliability of the study variables (between 0.77 and 0.90).Regarding H1a, as shown in χ2 = 3.862, CFI = 0.995, TLI = 0.990, RMSEA = 0.012, SRMR = 0.019). In order to analyze the specific direct and indirect effects in the relationship between health-care professionals EI and work performance, an analysis of mediation was conducted. Moreover, this analysis offered the possibility of delineating the respective functions between the three domains of engagement. The confidence intervals (CIs) were computed utilizing a multiple mediation modeling approach. c = 0.26; p < 0.01). Upon computation of the indirect effects . Post-hoc analyses were subsequently completed so as to identify the similar model for every dimension of EI. The results revealed a resembling structure to that previously documented one in turn enabling to assert that each EI construct and its association with performance are mediated through each and every element of work engagement.Regarding H3, SEM analyses allowed to examine the association between EI and work performance through engagement. The results show an excellent fit , which may encourage the agency to adopt emotional responses to ameliorate their discomfort in turn achieving their occupational effectiveness . These aOn balance, work engagement may be benchmarked as a mediator insofar as the current research has been in conformity with that approach in its bid to integrate the pandemic into a prospective insight towards the relevance of engagement at work ,24. UponConclusively, this investigation helps empower experiential proof on the major mediating effect of work engagement in times when the COVID-19 pandemic entails a genuine hazard to all occupational streams within the health-care workforce and the subsequent job performance at it. Indeed, harnessing their labor and talents converts them into the most critical endowment for organizations to conserve and upgrade their status in the field . This apCertain caveats are indicative of potential thematic areas of concern for further exploration in this paper. At the outset, it is critical to remark that the employment of cross-sectional data hampered the operation of deciphering the extent of the associations between the variables as well as their orientation. Yet, the findings of the herein presented research are substantiated by a solid and robust scientific evidence baseline. Still, replicating these outcomes with longitudinal methods in this study might provide further insight into the mediating role of work engagement in the correlation between EI and work performance.An additional restricting factor stems from the evaluation approach adopted for the appraisal of EI. Namely, from the determination to be advantageous in terms of providing a speedier administration and thus the choice of the WLEIS, it is hereby borne in mind that this as a self-report questionnaire and it is recommended to employ both self-reports and performance tests when attempting to use it for the measurement of EI ,44. FurtUltimately, the unsettled emotional climate in those periods of severe conditions in the course of the current pandemic may have altered individuals’ perceived self-image as related to EI, work engagement, and job performance. Indeed, the present scenario depicts a time fraught with uncertain results if hypothesized to publish investigations such as the present one before the occurrence of the actual disease prior to driving human emotions to the borderline ,49. p < 0.01) as well as the mediating involvement of engagement in a sample of Spanish health-care professionals. Specifically, after controlling for sociodemographic variables, vigor  emerged over dedication  and absorption  as the most decisive engagement dimension. Apparently, professionals exhibiting elevated levels of emotional intelligence dispose of optimal internal resourcefulness when confronting work demanding issues and, as a corollary, do maximize their engagement, which in turn promotes the perception of sustained effectiveness at work and facilitates an improved work performance indicator. Such findings attest to the critical significance of work engagement in comprising work performance in health-care environments and emphasize the role of emotional intelligence as an empowering variable particularly valuable in demanding contexts such as the COVID-19 pandemic. Therefore, the current research reasserts the imperative to develop such constructs in health-care facilities in pursuit of enhancing emotional intelligence to build healthy workplaces that may assist workers in achieving their peak performance when immersed in those enthusiasm-inducing occupational stations.Precedent experience in catastrophes, pandemics and other high-trauma events might imply providing further assistance tools for health-care professionals so that they develop and acknowledge their own feelings. Furthermore, sharing their perceptions and lived history with patients can aid them in preserving their competence and awareness within the midst of these severe disruptive conditions. That is due to the reality that health-care is not a mere scientific endeavor, but a subject of empathy and thus communicative techniques are required to convey it. Accordingly, outreach efforts in this arena should prioritize enhancing self-confidence to competently perform clinical assignments, encompassing COVID-19 specific issues, advocating self-protection policies and encouraging health-care workers to be supported by the commitment of empowered leadership. Additionally, building a cooperative internal organizational culture is instrumental in order to ensure adequate levels of emotional intelligence and ultimately higher performance within a superior work engagement in such a day-after-day emotionally self-consuming occupation. As a final concluding statement, the results demonstrated in this investigation evidence the significant direct effect of emotional intelligence toward individual work performance (c = 0.26;


For more than a year now, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been causing the coronavirus disease (COVID-19) pandemic with high mortality and detrimental effects on society, economy, and individual lives. Great hopes are being placed on vaccination as one of the most potent escape strategies from the pandemic and multiple vaccines are already in clinical use. However, there is still a lot of insecurity about the safety and efficacy of vaccines in patients with autoimmune diseases like multiple sclerosis (MS), especially under treatment with immunomodulatory or immunosuppressive drugs. We propose strategic approaches to SARS-CoV-2 vaccination management in MS patients and encourage fellow physicians to measure the immune response in their patients. Notably, both humoral and cellular responses should be considered since the immunological equivalent for protection from SARS-CoV-2 after infection or vaccination still remains undefined and will most likely involve antiviral cellular immunity. It is important to gain insights into the vaccine response of immunocompromised patients in order to be able to deduce sensible strategies for vaccination in the future. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the viral pathogen that causes coronavirus disease 2019 (COVID-19). Typical clinical manifestations range from mild upper respiratory tract symptoms to respiratory failure, severe COVID-19 disease can even lead to septic shock and multi-organ failure. Neurological complications encompass cerebrovascular disease, encephalopathy, seizures, and Guillain-Barré syndrome, amongst others , 4. The The rapid development of several vaccines against SARS-CoV-2 is a major achievement of the scientific community . As of AMultiple sclerosis (MS) is an autoimmune-mediated disorder defined by a timely and locally disseminated inflammation of the central nervous system . 2.8 milVaccination has been relieving the world of infections with multiple pathogens for more than a century . While vVaccines against SARS-CoV-2 currently authorized in Europe and the United States are based on mRNA and viral vector technology and hence belong to the group of non-live vaccines. As they are the first of their type in clinical use, representative data on their application in MS patients are still limited. However, deducing from studies on the safety of other non-live vaccines in patients with MS and from the first reported data on the application of BNT162b2 in MS patients , it is vBesides the discussed safety issues concerning SARS-CoV-2 immunization in MS patients, the question of vaccine efficacy needs to be addressed. Multiple studies evaluating the effectiveness of different vaccines in MS patients with and without DMT have been conducted and several reviews on this topic are available , 48–50:For MS patients without DMT, no difference was found in the virus-specific humoral and cellular response to the seasonal influenza vaccine as compared to healthy controls , 51–53.via different cascades downstream of the interferon receptor. For treatment with these substances, several studies were able to show that the humoral response to multiple vaccine types is not adversely affected -dependent and –independent mechanisms. Regarding dimethyl fumarate, responses to multiple vaccine types were not significantly affected compared to patients taking beta-interferons but only one published study is available for this agent (Dimethyl fumarate reduces inflammation is agent .Sphingosine-1-phosphate (S1P) receptor modulators prevent the exit of lymphocytes from lymph nodes. Fingolimod and siponimod caused a reduced humoral and cellular immune response to different vaccines according to evidence from several studies , 60–62. Natalizumab blocks the alpha-4 chain of the very late antigen 4 (VLA-4) on different inflammatory cells hindering them from entering the central nervous system. In natalizumab treatment, two studies did not show significant differences concerning vaccine-specific antibody responses to several types of immunization. Three other studies, however, found lower protection rates after influenza vaccination for natalizumab-treated patients compared to healthy controls , 63, 64.Alemtuzumab is a monoclonal anti-CD52 antibody leading to depletion of mature lymphocytes. With regard to vaccination in alemtuzumab-treated patients, only one study is available. Here, responses to multiple vaccine types were maintained, but the responding proportion was lower when immunization took place within 6 months of treatment .Cladribine is a purine analog which causes lymphocyte depletion by interfering with DNA synthesis, repair and cell metabolism. For cladribine, no conclusive data on vaccination are available yet, but multiple studies are currently being conducted , 66.Ocrelizumab, rituximab, and ofatumumab are anti-CD20 monoclonal antibodies leading to depletion of B cells in the circulation. Concerning treatment with humanized anti-CD20 antibody ocrelizumab, humoral responses to multiple vaccines were attenuated as compared to MS patients with beta-interferon or without treatment. However, immunization was able to elicit seroprotection or a considerable increase in antigen-specific antibody levels in ocrelizumab-treated patients when administered with an interval of 12 weeks to the time point of treatment initiation . As for High-dose corticosteroids are often employed for relapse treatment in MS. However, no studies on vaccine efficacy in this treatment regimen are available. Data about the impact of long-term low-dose glucocorticoid treatment on vaccine efficacy in patients with pulmonary or rheumatological diseases show small or no effects on humoral immune responses to non-live vaccines , 76, 77.In summary, most of the agents in use for the treatment of MS allow for the mounting of a protective immune response to vaccines but responses can be reduced under some of them. Timing may be a crucial factor for the purpose of enhancing vaccine efficacy.We would like to encourage fellow physicians and scientists to measure both humoral and cellular immune responses to SARS-CoV-2 vaccination in MS patients for a number of reasons Figure 1First, the available evidence for vaccination under some of the DMT is still limited. For a few of them, only one or no published study at all is available . Other therapies have only been investigated with regard to one type of vaccine . Based on the data available until now, vaccination is most likely efficacious under treatment with beta-interferons, glatiramer acetate, teriflunomide, dimethyl fumarate, and natalizumab. By contrast, vaccine responses under S1P receptor modulators, B cell depletion, and high-dose corticosteroids are expected to be attenuated. Especially for the latter three types of immunomodulation, it is necessary to gather more evidence on the efficacy of immunization and the most suitable time point of vaccination in relation to the treatment cycle as the acquired data may have implications for future disease management and vaccination schedules.Second, all of the studies cited above were conducted with inactivated, polysaccharide, conjugate, toxoid vaccines, or neoantigens. However, currently authorized vaccines against SARS-CoV-2 in Europe and the United States are based on mRNA and viral vector technology. Vaccines from these platforms have never been examined in MS patients before so that not all of the available data concerning the efficacy of other vaccine types may be transferable .Third, we need to collect information on the durability of protective immune responses. As for the vaccine trials, immunological data concerning the adaptive humoral response are available for a maximum of 119 days after first vaccination with mRNA-1273 by Moderna –82, day Fourth, it will be necessary to monitor the evolution of SARS-CoV-2 mutations possibly escaping the vaccination-elicited immune response. There have been reports about circulating viral mutants that subvert the neutralizing capacity of antibodies elicited by natural infection or SARS-CoV-2 vaccination , 88. LesIn most studies on vaccination efficacy in the past, humoral responses were measured as correlate for a protective immune response . FrequenThe adaptive immune response after SARS-CoV-2 infection is characterized by heterogenous humoral and cellular responses which are furthermore interdependent Figure 2via other mechanisms  for the measurement of binding antibodies have exhibited limited precision , 113. A The measurement of cellular responses poses certain problems as well. Frequently measured outcome parameters for the quantification of the amount and functionality of the T cell response include proliferation and cytokine secretion of T cells after stimulation with antigen peptide pools. Besides the composition and length of the peptides themselves, peptide concentration and number of peptides in the respective pool may significantly influence the results , 116.via a SARS-CoV-2 QuantiFERON® test (QIAGEN®) measuring the IFN-µ secretion of lymphocytes stimulated with two SARS-CoV-2 antigen pools will be compared to the ELISpot data. By adding an ELISA for the detection of nucleocapsid-specific antibodies, we will gather information on whether patients may have been infected with SARS-CoV-2 before or after vaccination. The aforementioned will be conducted as an observational clinical study in our center. In parallel, we participate in prospective, multicenter, open-label, low-intervention phase IV clinical trials evaluating the use of SARS-CoV-2 mRNA vaccines in MS patients treated with siponimod or ofatumumab .Most studies addressing the immunogenicity of SARS-CoV-2 vaccines employed ELISA for the detection of spike protein- and receptor binding domain (RBD)-specific binding antibody, different forms of neutralization assays, interferon-gamma (IFN-µ) enzyme-linked immunospot (ELISpot) assays of peripheral blood mononuclear cells (PBMC), and intracellular cytokine staining of CD4+ and CD8+ T cells. SARS-CoV-2 spike protein- and RBD-binding antibody titers correlated tightly with neutralizing antibody titers in several vaccination studies , 86, 117MS patients should be vaccinated against SARS-CoV-2 as soon as possible in order to reduce their infection and associated relapse risk. Extrapolating from other studies with non-live vaccines, application of currently authorized SARS-CoV-2 vaccines is expected to be safe in patients with MS. However, vaccination may induce an attenuated immune response in patients under certain DMT and evidence on sensible time points for vaccination, especially in B cell-depleting therapy, is currently lacking. Therefore, we propose to closely survey MS patients after vaccination and to measure their humoral and cellular responses to SARS-CoV-2 vaccination.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.CW wrote the manuscript and created the figures. TZ designed and revised the manuscript. KA and KS provided critical feedback and are in charge of conducting the described studies in our center. All authors contributed to the article and approved the submitted version.TZ received personal compensation from Biogen Idec, Bayer, Novartis, Sanofi, Teva, and Synthon for consulting services. TZ received additional financial support for research activities from Bayer, Biogen Idec, Novartis, Teva, and Sanofi Aventis. TZ is principal investigator of the AMA-VAC and KYRIOS study. KA received personal compensation from Roche, Novartis, Sanofi, and Celgene for consulting services.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The increasing antimicrobial-resistant prevalence has become a severe health problem. It has led to the invention of a new antimicrobial agent such as antimicrobial peptides. Heteroscorpine-1 is an antimicrobial peptide that has the ability to kill many bacterial strains. It consists of 76 amino acid residues with a cecropin-like region in N-terminal and a defensin-like region in the C-terminal. The cecropin-like region from heteroscorpine-1 (CeHS-1) is similar to cecropin B, but it lost its glycine-proline hinge region. The bioinformatics prediction was used to help the designing of mutant peptides. The addition of glycine-proline hinge and positively charged amino acids, the deletion of negatively charged amino acids, and the optimization of the hydrophobicity of the peptide resulted in two mutant peptides, namely, CeHS-1 GP and CeHS-1 GPK. The new mutant peptide showed higher antimicrobial activity than the native peptide without increasing toxicity. The interaction of the peptides with the membrane showed that the peptides were capable of disrupting both the inner and outer bacterial cell membrane. Furthermore, the SEM analysis showed that the peptides created the pore in the bacterial cell membrane resulted in cell membrane disruption. In conclusion, the mutants of CeHS-1 had the potential to develop as novel antimicrobial peptides. Antimicrobial resistance is one of the greatest threats to the global health problem that has become a challenge for infectious disease treatments . ApproxiAntimicrobial peptides are some promising novel antibiotics and have great potential to battle bacteria and other microbial . They haAmong antimicrobial peptides that have been established, most of them consist of short amino acid chains, containing approximately 5 to 40 amino acid residues . AlthougMost of the potent antimicrobial peptides have a range of net charges from +2 to +9 . The posMoreover, hydrophobicity and amphipathicity are important peptide properties that contribute to the interaction potential between the peptides and the composition of the bacterial cell membrane . Upon thApproximately 5000 recorded antimicrobial peptides have been investigated until now . They vaHeterometrus laoticus. It is categorized as a scorpine peptide that originally was purified from Pandinus imperator venom  (deg cm2/dmol) using the following equation -2,5 diphenyltetrazolium bromide (MTT)-based cytotoxicity assay . The celAs a note, the peptide solvent was used as a negative control comparison.In conclusion, the researcher investigated whether the mutation of CeHS-1 through the addition of glycine-proline hinge region increased the antimicrobial activity of the peptides without the significant increase in toxicity. This study confirmed that CeHS-1 GP and CeHS-1 GPK showed potent activity to kill Gram-positive and Gram-negative bacteria through their ability to disrupt bacterial membrane cells. Furthermore, the mutant peptides also showed low toxicity against normal mammalian cells.


Background: Immune checkpoint inhibitor (ICI) therapy has been proved to be a promising therapy to many types of solid tumors. However, effective biomarker for estimating the response to ICI therapy and prognosis of hepatocellular carcinoma (HCC) patients remains underexplored. The aim of this study is to build a novel immune-related prognostic index based on transcriptomic profiles.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to identify immune-related hub genes that are differentially expressed in HCC cohorts. Next, univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to detect hub genes associated to overall survival (OS). To validate the immune-related prognostic index, univariate and multivariate Cox regression analysis were performed. CIBERSORT and ESTIMATE were used to explore the tumor microenvironment and immune infiltration level.Results: The differential expression analysis detected a total of 148 immune-related genes, among which 25 genes were identified to be markedly related to overall survival in HCC patients. LASSO analysis yielded 10 genes used to construct the immune-related gene prognostic index (IRGPI), by which a risk score is computed to estimate low vs. high risk indicating the response to ICI therapy and prognosis. Further analysis confirmed that this immune-related prognostic index is an effective indicator to immune infiltration level, response to ICI treatment and OS. The IRGPI low-risk patients had better overall survival (OS) than IRGPI high-risk patients on two independent cohorts. Moreover, we found that IRGPI high-risk group was correlated with high TP53 mutation rate, immune-suppressing tumor microenvironment, and these patients acquired less benefit from ICI therapy. In contrast, IRGPI-low risk group was associated with low TP53 and PIK3CA mutation rate, high infiltration of naive B cells and T cells, and these patients gained relatively more benefit from ICI therapy. Liver cancer remains a global health challenge, with an estimated incidence of more than 1 million cases by 2025  of HCC, by exploiting weighted gene co-expression network analysis (WGCNA) , which includes 424 samples (374 cancer samples and 50 para-cancer samples). We utilized the UCSC Xena browser  and transcriptional factors (TFs), miRNA and lncRNA were downloaded from TRRUST v2   was performed to identify hub genes. First, the similarity matrix was constructed by using the expression data by calculating the Pearson correlation coefficient between gene pairs. Next, the similarity matrix was transformed into an adjacency matrix with a network type of signed and then transformed into a topological matrix with the topological overlap measure (TOM) describing the degree of association between genes. 1-TOM was used as the distance to cluster the genes, and then the dynamic pruning tree was built to identify the modules. Finally, we identified the modules by setting the merging threshold function at 0.25.clusterProfiler R package.The modules with significantly different expression patterns between tumor and normal tissues were chosen for downstream analysis. We took the intersection between the modules and differentially expressed immune-related genes. To assess the potential biologic functions and involved pathways of the differentially expressed immune-related genes, gene set enrichment analysis (GSEA) was conducted using survival R package  were conducted to assess the associations between immune-related genes expressions and overall survival (OS). Cox and LASSO regression were carried out using survival R package. To validate the independent prognostic value of IRGPI, univariate and multivariate Cox regression analysis was performed on IRGPI risk score and other clinicopathologic feature.The IRGPI risk score was computed using each gene expression level multiplied by its linear regression coefficient obtained from the univariate Cox regression. According to the median risk score, patients were assigned to high-risk and low-risk groups. The Kaplan-Meier survival analysis was performed to compare overall survival between high-risk and low-risk groups. The receiver operating characteristic (ROC) curves were plotted by First, we identified the differentially expressed long non-coding RNAs (DElnRNAs) and miRNAs (DEmiRNAs) by setting the standards that log2 fold change>1 and FDR <0.05. Next, multiple miRNA target databases, including TargetScan, miRTarBase, and miRDB, were used to seek DEmiRNAs targeting the hub immune-related DEGs. The miRcode database was used to find lncRNA-miRNA associations. The TRRUST database version 2 was used to search the TFs regulating the hub immune-related DEGs. Finally, Cytoscape was used to visualize the regulatory network containing the miRNA-mRNA, lncRNA-miRNA, and TFs-mRNA associations.To explore the immune microenvironment of HCC, the transcriptomic data were imported into CIBERSORT  scores , in which 5,391 genes were up-regulated and 818 genes were down-regulated. By intersecting DEGs with the immune-related genes collected from ImmPort and InnateDB databases, 655 differentially expressed immune-related genes were obtained, in which 460 genes were up-regulated and 195 were down-regulated in tumor samples compared with normal samples.k) of the node with connectivity k was negatively correlated with the logarithm log[P(k)] of the probability of the node, and the correlation coefficient was >0.85. The optimal soft-thresholding power was set to 10. Eight modules were identified based on the average linkage hierarchical clustering and the optimal soft-thresholding power  were obtained, of which 122 genes were up-regulated and 26 were down-regulated.To uncover the role of the immune-related hub genes in the pathogenesis of HCC, GO, and KEGG enrichment analysis was carried out. The top 10 significantly enriched GO terms and KEGG pathways for IRGs are shown in The screened 148 immune-related hub genes were further evaluated by univariate Cox regression. As a result, 25 genes showed statistical significance to overall survival on TCGA cohort. Next, LASSO regression was performed to further narrow the scope of OS-related hub genes, and finally 10 genes were selected for the establishment of prognostic index. Specifically, the IRGPI risk score was computed using gene expression level multiplied by the weights of the 10 genes. The weights are shown in The univariate Cox regression analysis was performed on IRGPI risk score and other clinicopathologic feature , and verp = 0.001, log-rank test)  . The ROCnk test) .n = 221). As showed in Furthermore, to evaluate the generalization of IRGPI in prognosis, we conducted survival analysis on validation cohort . To be specific, the regulatory network included 5 miRNAs, 6 lncRNAs, and 4 TFs associated to eight out of the immune-related hub genes, as shown in We have conducted extensive literature study to verify these regulatory relationships. For example, the lncRNA FAM99A has been found to be up-regulated in HCC and closely related to clinical prognosis , which is consistent with previous study.Also, we assessed the immune infiltrating and tumor purity using ESTIMATE tool. The stromal score was considered to be correlated with the fraction of stromal cells, and the immune score reflected the infiltration of immune cells in solid tumor. As in previous studies, immune score has been confirmed to be correlated with prognosis in patients with several tumors . The p-values of all the correlations were smaller than 0.05, suggesting that the IRGPI score is significantly correlated with immune biomarkers.We explored the relationship between the IRGPI scores and these biomarkers. As shown in p = 0.001, chi-square test).The immune subtypes describe the immune landscape of tumors according to the tumor and stromal compartments. A consensus immune subtyping summarized four subtypes: wound healing (C1), IFN-gamma dominant(C2), inflammatory(C3), lymphocyte depleted(C4) . There were more samples in iCluster2 and fewer samples in iCluster1 and iCluster3 of the IRGPI low-risk group than in the IRGPI high-risk group, which is consistent with the prognosis of the molecular subtypes   . Accordi= 0.004) , while t= 0.004) . All then = 24). As showed in We also tested the predictive performance of the IRGPI score on the efficacy of ICB treatment on another HCC cohort . TP53 is one of the most mutated genes in human cancers than any other gene, and linked with more aggressive disease and poorer patient outcomes in many cancers  and the prevention of T cell infiltration in tumors with low CTL levels .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Respiratory diseases contribute to a significant percentage of mortality and morbidity worldwide. The circadian rhythm is a natural biological process where our bodily functions align with the 24 h oscillation (sleep–wake cycle) process and are controlled by the circadian clock protein/gene. Disruption of the circadian rhythm could alter normal lung function. Chronotherapy is a type of therapy provided at specific time intervals based on an individual’s circadian rhythm. This would allow the drug to show optimum action, and thereby modulate its pharmacokinetics to lessen unwanted or unintended effects. In this review, we deliberated on the recent advances employed in chrono-targeted therapeutics for chronic respiratory diseases. The lungs are vital organs with a complex architecture, including the tracheobronchial airway tree, airway parenchyma and smooth muscles with a complex microvascular network that efficiently perform gaseous exchange and other essential respiratory functions. Aside from the gaseous exchange process, lungs are constantly exposed to various environmental, household, and occupational irritants that harm the lungs. Recurrent exposure to such irritants an toxins results in the alteration of the lung architecture and abnormal lung function, leading to the development of respiratory diseases ,2. ChronCLOCK) gene and the brain and muscle Arnt-like protein 1 (BMAL1) gene . CR. CR41]. Asthma is an inflammatory airway disease in which the obstruction of airflow occurs due to bronchoconstriction, enhanced mucus secretion leading to inflammation in the bronchial walls. . One of Sympathomimetic drugs, such as salbutamol, terbutaline, bambuterol, salmeterol and formoterol, cause bronchodilation, the inhibition of inflammatory responses and the relaxation of smooth muscles present in the airway. These drugs are available on the market in various dosage forms, such as oral, parenteral and as an inhaler. Inhalers produce a rapid response, whereas oral dosage forms are used for long-term treatment. Parenteral dosage forms are used for the treatment of acute asthma . The pha1) was enhanced up to 77% from 74% upon once-daily dosing of triamcinolone as compared to its conventional form, which was administered four times a day. The results suggested that triamcinolone administered at 3:00 PM did not enhance adverse systemic effects and produced complete relief in nocturnal asthma [Glucocorticoids cause anti-inflammatory action and minimize bronchial hyperreactivity. Glucocorticoids produce complete and sustained release action compared with other anti-asthmatic drugs. In one study, Pincus and their coworkers reported that 800 µg of inhaled triamcinolone administrated once daily at 3:00 PM produced a better response as compared to 200 µg of conventional treatment administered four times a day. After a four-week treatment period, the forced expiratory volume in 1 s  throughout the day in both groups in a trial with tiotropium as a single drug either in the morning or evening. Tiotropium caused sustained bronchodilation in the patients without any side effects in the CRs . A similCertain commercially available drugs which are used for the treatment of COPD include umeclidinium, a product of GlaxoSmithKline, and glycopyrronium, a product of Novartis Pharmaceuticals UK Ltd. They have become more common in clinical practice recently, and there are no other chronotherapy studies regarding these clinically utilized drugs . So, furThe association of circadian clocks in the pathogenesis of pulmonary fibrosis has been poorly explored. The event of pulmonary fibrosis effectively alters the circadian biology due to the achievement of asynchronous rhythmicity by the alveolar structures owing to the infiltration by fibroblasts. As such, the circadian core clock plays a crucial role in the lung pathophysiology  that valVandeleur et al. (2017) reported disruption in CRs in children with cystic fibrosis. The studies were carried out in children belonging to the age group of 7–18 years with cystic fibrosis free from pulmonary exacerbation. The obstructive sleep apnea-18 questionnaire, two-week actigraphy recordings and overnight oximetry formed a major part of the studies. Reportedly, cystic fibrosis contributed significantly towards the lower CRs, in addition to various other factors such as nocturnal cough . CunningPer1 and Per2 results in the deregulation of CycD1 expression, leading to shortening of the cell cycle and an increase in proliferation rate [As CR disruption, such as that caused by physiological perturbations , shift work, and mutation of the central core clock gene promotes lung cancer progression, chrono-targeted chemotherapy treatment could be a promising therapeutic model for lung cancer treatment . The cloion rate . In receion rate . Until 2ion rate ,90. The ion rate . The decion rate . Using mion rate The aim of chronotherapy is to deliver a therapeutic regimen when the biological clock of the normal/healthy cell is at rest so that these cells are protected from treatment-related unwanted effects or toxicity. As cancer cells undergo uncontrolled growth (cell division), they are more vulnerable to the chemotherapeutic regimen . Thus, cabcc2 gene expression show rhythmicity over the 24 h circadian oscillation, where gene expression ranges from 3-to-6-fold and protein expression from 2-to-3-fold [The identification of cancer invasion/metastasis-related core circadian rhythm genes and proteins is crucial for the therapeutic management of lung cancer. Chen et al., 2020 reported that circadian gene hepatic leukemia factor (HLF) expression was significantly decreased in early relapsed NSCLC tissues, and this decreased expression was correlated with early progression as well as metastasis in NSCLC patients. This was further supported by the study of lung colonization and metastasis of the bone, liver and brain where upregulating HLF inhibited and silencing HLF promoted all the processes of colonization and NSCLC metastasis. Furthermore, the downregulation of HLF expression promoted the proliferation of NSCLC cells by activating NF-κB/p65 signaling through interfering with the translocation of PPARα and PPARγ. Further studies found that both methylation and genetic deletion promote the downregulation of HLF in NSCLC tissues. This suggests that the circadian gene HLF may serve as a novel target to inhibit metastasis and may be useful in the management of NSCLC . A receno-3-fold . The meto-3-fold ,99. The o-3-fold .Various promising compounds have been studied for their potency against lung cancer through the modulation of circadian clock gene/proteins. Epigallocatechin-3-gallate (EGCG) is one of those potential compounds studied for its ability to inhibit self-renewing lung cancer stem-like cells through the inhibition of CLOCK in NSCLC cell line A549 and H1299. The expression of the CLOCK gene and protein levels were significantly increased in A549 and H1299 spheroids compared to their parental cells. Interestingly, the knockdown of CLOCK by siRNA resulted in a reduced ability for sphere formation and the inactivation of the Wnt/β-catenin signaling pathway, while treatment with EGCG repressed CLOCK expression in the spheroids. Moreover, in a xenograft model, EGCG suppressed the cancer stem-cell-like features  of A549 and H1299 cells by targeting CLOCK, suggesting EGCG is a compound with chronotherapeutic potential to inhibit the self-renewal capacity of lung cancer stem-like cells by modulating CLOCK . In anotVarious clinical trials have been carried out to evaluate the potential of drugs in the management of respiratory diseases such as asthma, lung cancer, COPD and cystic fibrosis . A randoMicrofluidics is the science and technology that can be utilized to mimic different tissues or organs by precisely patterning and co-culturing different cell types in a well-controlled microenvironment . MicroflRecently, organ-on-chips have also been tested to incorporate the factors that mimic circadian rhythms in organs, which is vital when the physiological impacts of different drugs are being tested . Organ-oThe COVID-19 pandemic means that the discovery of safe and effective therapies for SARS-CoV-2 is urgently needed. The understanding of chronobiology with regard to how COVID-19 infection affects circadian rhythms may provide opportunities for the development of chronotherapy for SARS-CoV-2. The host immune responses in COVID-19, particularly the severe inflammation that can possibly cause multi-organ failure, strongly modulate disease severity. The activity of all components of the immune system, including the inflammatory immune responses, follow robust CRs . It is pPer2 could be effectively regulated by administrating non-steroidal anti-inflammatory drugs during the active phase of the circadian rhythm [In COVID-19 cases, a group of patients experience cytokines release storm (CRS) that results in acute respiratory distress syndrome (ARDS). Since the intrinsic circadian clock of the lungs, together with the immune system, regulate the various component of CRS, the use of chrono-targeted therapy can be very effective in the management of ADRS during COVID-19 infection . As thern rhythm . InteresCircadian clocks regulate the pharmacokinetics, which includes absorption–distribution–metabolism–excretion and the potency of many medications, because various drug targets and genes or proteins that act as drug transporters or facilitate biotransformation show daily rhythmic oscillation . InvestiDespite the considerable advancement in the field of circadian rhythms and chronotherapy, significant information-related gaps still exist between patients receiving treatment, community pharmacists and physicians based on prescription-related specifics, awareness of circadian rhythms, application, and attitudes toward the application of chronotherapy in practice ,123. TheIn-depth understanding of how the disruption of circadian rhythm leads to the progression of respiratory diseases is essential for the selection of a chronotherapy for a specific individual. Moreover, defining the influence of the core circadian clock in the cell signaling pathways leading to lung disorders could unlock novel therapeutic avenues. The efficacy and side effects of a therapy may vary when administration occurs at 8 a.m. compared to 8 p.m.; therefore, elucidating the circadian oscillation (time of peak and trough) of various disease-related proteins, genes and enzymes will facilitate the selection of the timing of drug administration. For various diseases including respiratory diseases, chronotherapy could be superior over routine therapy in terms of efficacy and the control of side effects. However, more scientific studies that support chronotherapy as a choice of therapeutic management over conventional therapy are essential for the validation of its effectiveness.