The COVID-19 pandemic poses an ongoing global challenge, and several risk factors make people with multiple sclerosis (pwMS) particularly susceptible to running a severe disease course. Although the literature does report numerous articles on the risk factors for severe COVID-19 and vaccination response in pwMS, there is a scarcity of reviews integrating both these aspects into strategies aimed at minimizing risks. The aim of this review is to describe the risk of vulnerable pwMS exposed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the issues related to the SARS-CoV-2 vaccine and to evidence possible future strategies in the clinical management of pwMS. The authors searched for papers on severe COVID-19 risk factors, SARS-CoV-2 vaccination and people with multiple sclerosis in support of this narrative literature review. We propose a multilevel strategy aimed at: the evaluation of risk factors for severe COVID-19 in people with multiple sclerosis, identifying the most appropriate vaccination schedule that is safe for people on disease-modifying drugs (DMDs) and a strict follow-up of high-risk people with multiple sclerosis to allow for the prompt administration of monoclonal antibodies to manage COVID-19 risks in this patient population. Multiple sclerosis (MS) is a demyelinating disease of the central nervous system, characterized by inflammation and early axonal damage , and it MS is characterized by a wide range of motor, behavioral and cognitive symptoms , reflectThe disease-modifying drugs (DMDs) used to treat MS comprise a range of molecules and monoclonal antibodies, which have a wide spectrum of efficacy and adverse events ,12,13. AThe coronavirus disease COVID-19) pandemic has put enormous strain on healthcare organizations, medical resources and emergency medical services . Primary pandemicEven neurologists had to face with the fears associated with COVID-19, which led to concerns that modified neurologists’ attitude toward prescribing DMDs . They muNumerous epidemiological studies have described the risk factors involved in severe COVID-19 in MS patients ,23,24,25Therefore, this narrative review aims at describing the risk vulnerable pwMS run when exposed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the issues related to SARS-CoV-2 vaccine and proposing possible future strategies for the clinical management of pwMS.The decision to write this narrative review was prompted by the aim of describing the different aspects of SARS-CoV-2 infection in pwMS. The authors searched for the terms “COVID-19 and Multiple Sclerosis” in PubMed from 2020 to 2022 and identified 756 papers. This is a narrative review and, for the purpose of the study, the authors selected the most relevant papers reporting on the risk pwMS run of developing a severe COVID-19 and the management of their SARS-CoV-2 vaccination. Preprints from MEDRxiv and guidelines published by regulatory agencies, scientific societies and the Italian Ministry of Health have also been included in this review. The papers selected included studies on different aspects of COVID-19 in pwMS targeted at reporting a synthesis of current knowledge and describing possible future strategies to reduce the risks related to SARS-CoV-2 infection in pwMS.-COVID-19 mortality and risk factors for severe COVID-19 in pwMS;-vaccine response  in pwMS;-breakthrough infection in pwMS;-the effect of SARS-CoV-2 vaccination on MS.The following fields of research were taken into consideration:Although this review included studies where the diagnosis of COVID-19 was based on the patients’ clinical characteristics and a positive swab test for SARS-CoV-2, data on swabs were not mandatory, as during the first wave of the pandemic, the incidence overwhelmed the testing capacity in many countries. The studies selected on vaccination included only those that reported on BNT162b2 and mRNA-1273 vaccines.A collective author decision, based on a consensus upon papers’ relevance and quality, was made on the final choice of the 23 most relevant papers, as a systematic review and a meta-analysis were beyond the scope of this narrative review. A synthesis of the most relevant findings was then created and discussed.COVID 19 mortality during the pandemic is estimated by the case fatality rate. This rate is highly variable  and depeVariants and mutations are linked to the virus and may determine different infectivity and mortality, depending on the variant. The factors linked to the pandemic phase are the COVID-19 incidence rate and the intensive care unit (ICU) hospitalization rate. There is an association between mortality and incidence  and an iAlthough the COVID-19 crude mortality rate (CDR) for pwMS and the general population did not differ greatly, pwMS are mostly female and young. Therefore, when mortality was corrected for gender and age, it was higher in pwMS, particularly when they had significant disability and a progressive disease course . FurtherThe most common COVID-19 symptoms in pwMS were asthenia, cough, fever, headache, anosmia, ageusia, dyspnea, digestive disorders and/or dizziness. Cough and dyspnea were more frequently observed in severe cases and headache and anosmia in outpatient cases . SARS-CoRisk factors for severe COVID-19 were older age, male gender, an Extended Disability Status Scale (EDSS) score of >3, comorbidities , having been administered methylprednisolone in the month before infection, anti-CD20 ,23,24,25DMDs can affect vaccine efficacy in pwMS, as reported by some papers. The first one was published by Achiron et al., who observed a reduced vaccine humoral response in patients on ocrelizumab and fingolimod . Then, tOther studies investigated the T-cell immune response and the humoral response together after vaccination, observing a reduction in both responses during fingolimod treatment and a reduction in humoral response along with an enhancement in T-cell response during anti-CD20 therapy ,27.The immune response to SARS-CoV-2 infection in pwMS treated with DMDs was similar to the vaccine response, i.e., the cellular response was still present at 13 months after COVID-19 in 59.5%, and the humoral response persisted up to 6 months in 81.1%. Patients on treatment with anti-CD20 had a lower antibody response, but severe COVID-19 and a longer time lapse since the last infusion was associated with a better humoral response. There was a cellular response in all the treatment groups . Immune Some authors reported a better vaccine response in pwMS on anti-CD20 treatment who did not have a complete B-cell depletion at vaccination, and there was a better response in pwMS treated with S1P modulators in the presence of a high lymphocyte count .Di Filippo and Achiron investigated the relapse risk after Pfizer/BioNTech BNT162b2 vaccine in an Italian and Israeli cohort, reporting no increased risk of relapse activity ,39. HoweResearch investigating the risk of COVID 19 in vaccinated pwMS treated with DMDs is ongoing, and the Italian CovaXiMS group published a pre-print reporting an association between low specific SARS-CoV-2 antibody titers and the risk of SARS-CoV-2 breakthrough infection . The Fre-SARS-CoV-2 vaccination is recommended in all pwMS, as they are very vulnerable patients and should be vaccinated as a priority;-pwMS treated with injectables, dimethyl-fumarate, teriflunomide, azathioprine, S1P-modulators, natalizumab, cladribine should be vaccinated, whatever their therapy, but should be advised about the possibility of a reduced vaccine efficacy in the presence of lymphopenia;-pwMS treated with mitoxantrone, cyclophosphamide, ocrelizumab, ofatumumab, rituximab, alemtuzumab should be given DMDs at least 4–6 weeks after the second vaccination and be vaccinated at least three months after the last infusion. With regard to the pandemic phase and disease activity, these time intervals are not mandatory.;-the humoral response to the vaccination could be weak in pwMS treated with anti-CD20 or S1P-modulators;-an interval of one month is recommended between an MS relapse and vaccination, as well as between steroid treatment and vaccination;-vaccination is recommended for caregivers and pwMS relatives;-3 at vaccination);an additional vaccine dose at least 28 days after the second dose is recommended in patients with immunodeficiency due to pharmacological treatment  was approved in 2004, there was a voluntary withdrawal from the market due to two cases of progressive multifocal leukoencephalopathy only four months later. The drug was reintroduced onto the market with a black-box warning and a multilevel intervention to manage the risk–benefit profile at an individual level. Firstly, a risk stratification strategy was implemented  to clarify the risks at an individual level . A surveThe favorable risk–benefit profile of natalizumab is an example of a successful therapy risk management plan.The fears involved with the COVID-19 outbreak could affect the neural pathways involved in the modulation of behavior. Indeed, it directly modulates amygdala activation  and negaAt the beginning of the pandemic, the neurological community had strong reservations about administering DMDs to pwMS due the risk of severe COVID-19, and many neurologists were reluctant to begin therapy or switch it due to the fear of COVID-19 infection . HoweverA suitable strategy would be a risk management plan with multilevel interventions aimed at obtaining a better understanding of the risks of severe COVID-19 at an individual patient and drug level so as to minimize these risks through an appropriate immunization strategy, as well as monitoring for COVID-19 symptoms to allow for early treatment with antiviral drugs or monoclonal antibodies. There is an increased COVID-19 mortality rate in pwMS ,32,49. TAny pwMS on anti-CD20 or S1P modulators are eligible for an adjunctive vaccination dose  , and stuData have promoted SARS-CoV-2 vaccination for pwMS, especially with Pfizer-BioNTech (BNT162b2) and Moderna mRNA (mRNA-1273), and no association was observed between vaccination and MS relapses in most studies ,39, evenLiterature data reported no relevant effect on vaccine response for DMDs other than anti-CD 20 and fingolimod ,34,37. ASo as to allow for an increase in the lymphocyte count, vaccination in a set time frame of two months with a reduced dose of S1P modulators should be considered for pwMS treated by S1P modulators with a low lymphocyte count. The appropriate dose of S1P modulators should then be administered 2 weeks after the second dose. Although this strategy would mean pwMS would have a couple of months of low dose treatment, it should enhance the vaccine response. We propose that pwMS on anti-CD20 be vaccinated between 4 to 6 months from the last anti-CD20 infusion, preferentially when B-cell depletion is recovering. Moreover, these pwMS should minimize their risk of infection by SARS-CoV-2 through the adoption of scrupulous hygiene practices, e.g., the use of PPE, such as facial KN95 masks and regular hand sanitizing.Progressive pwMS, with comorbidities, older age and/or high EDSS score run a higher risk of severe COVID-19. Therefore, a careful risk/benefit assessment should be performed in this particular group so as to reduce relapses when still present or slow disability worsening, weighing these issues against the risks of specific therapy. We are aware that our review does have some limitations, the main one being the study design. That is, this is a narrative review without a systematic study collection or a meta-analysis, which limits the strength of the evidence . HoweverBriefly, we are of the opinion that, in light of the findings reported in the literature, although it is necessary to adapt the management of pwMS, this does not mean it should be compromised if the challenges still posed by COVID-19 are to be met. We suggest that a careful individual assessment of the risk of a severe COVID-19 infection be made for pwMS so as to manage this risk with the most appropriate vaccination schedule, chosen taking into consideration the DMD and that the risks involved in avoiding the administration of that DMD should be weighed against its benefits.


We describe a risk profile reconstruction model for cancer-specific survival estimation for continuous time points after urologic cancer diagnosis. We used artificial intelligence (AI)-based algorithms, a national cancer registry data, and accessible clinical parameters for the risk-profile reconstruction. We derived a risk stratification model and estimated the minimum follow-up duration and the likelihood for risk stability in prostate, kidney, and testicular cancers. The estimated follow-up duration was in alignment with recognized clinical guidelines for these cancers. Moreover, the estimated follow-up duration was differed by the cancer origin and the disease dissemination status. Overall, the reconstruction of the population’s risk profile for the cancer-specific prognostic score estimation is feasible using AI and has potential application in clinical settings to improve risk stratification and surveillance management.Background: Prognostication is essential to determine the risk profile of patients with urologic cancers. Methods: We utilized the SEER national cancer registry database with approximately 2 million patients diagnosed with urologic cancers . The cohort was randomly divided into the development set (90%) and the out-held test set (10%). Modeling algorithms and clinically relevant parameters were utilized for cancer-specific mortality prognosis. The model fitness for the survival estimation was assessed using the differences between the predicted and observed Kaplan–Meier estimates on the out-held test set. The overall concordance index (c-index) score estimated the discriminative accuracy of the survival model on the test set. A simulation study assessed the estimated minimum follow-up duration and time points with the risk stability. Results: We achieved a well-calibrated prognostic model with an overall c-index score of 0.800 (95% CI: 0.795–0.805) on the representative out-held test set. The simulation study revealed that the suggestions for the follow-up duration covered the minimum duration and differed by the tumor dissemination stages and affected organs. Time points with a high likelihood for risk stability were identifiable. Conclusions: A personalized temporal survival estimation is feasible using artificial intelligence and has potential application in clinical settings, including surveillance management. Genitourinary malignancies are amongst the most common cancers  and theiDifferent ML approaches have been introduced for survival modeling; for instance, Random Survival Forests is a popular non-parametric ML approach adapted to censored survival data . AnotherTo address the limitations of current methods, we propose a survival model that reconstructs the cancer-specific mortality risk profile of a representative population over an extended period. Our hypothesis is that memorable machine learning (ML) facilitates a time series of cancer-specific survival estimations based on information obtained around the time of cancer diagnosis. This hypothesis is supported by prior studies demonstrating the potential of ML for survival analysis ,24,25. WThe longitudinal risk profile for cancer-specific mortality was constructed based on clinicopathological information obtained around the time of diagnosis. The term longitudinal refers to the time elapsed since diagnosis. Using the Surveillance, Epidemiology and End-Results (SEER) database (version 18), we identified patients diagnosed with genitourinary cancers between 1975 and 2017 . The SEE(1)Standard-of-care treatment is accessible and utilized;(2)Cancer-specific survival is not impacted by the definition of the standard-of-care treatment, as shown by previous studies ,29,30,31(3)Cancer-specific survival in the representative populations indirectly incorporate the risk of any significant oncologic event  or a consequence of an oncologic condition ;(4)Cancer-specific survival estimates reflect the cancer-specific risk profile of a population with urologic cancers, given that exact life tables are not readily available ;(5)Change in cancer-specific mortality risk over time (risk velocity) defines cancer progression risk, while differences in time to progression do not predict cancer-specific survival .The reconstruction of risk profiles from the representative national cancer registry necessitates assumptions about the population; thus, five assumptions regarding population-based risk profiles for cancer-specific mortality were employed to ease the complexity of survival modeling:PROGRESS recommendations for model development and validation were adhered . The SEET) in the feature input of the model in addition to other clinicopathological covariates (X). As part of data preprocessing, the development set was sorted by date of diagnosis to account for changes in data patterns and risk profiles with diagnosis time. The final steps of training included the most recent cases to weight towards recent cohorts in every epoch, thus reflecting contemporary treatment strategies and population risk profiles.We applied a simple recurrent neural network (RNN), a memorable machine learning model , to recoFor all patients, the true endpoint was cancer-related death status, and the model output was the prediction score for cancer-specific mortality. We expected the prediction scores not to reflect the survival probability due to overconfidence bias  and the p < 0.05.We measured the discriminatory accuracy by Harrell’s Concordance Index . The utiS) is defined as The collection of the survival probabilities (P) for survival probabilities; we estimated the years elapsed  from the age at diagnosis (a) to the median U.S. life expectancy  of a time interval from I.viiAfter generating the intervals (ps1) and the survival probability of the fifth time point (ps5). After the velocity calculation for all intervals, a quantitative assessment of the dynamic of the risk profile can be performed.For instance, the risk velocity for the first interval with 5 time points (6 months) would be the absolute difference between the survival probability of the first point ((1)Recommender for the minimum follow-up durationWe conducted a simulation study with the assumption that patients were receiving standard of care therapy. The simulation covered 10,606,996 random and unique clinical conditions based on the TNM stage, biomarker status, gender, and age at diagnosis for three urologic cancers . For all cases, the time lapse from diagnosis was set to 12 years. We explored two utilization scenarios of the novel model, which are:(2)Determine intervals with unchanged risk profilesWe developed the risk velocity-based follow-up duration assessment as the following; the definition of the period for close follow-up is based on the grade of velocities; time intervals with score velocities exceeding the threshold of 0.5% were marked for a close follow-up. This threshold was used because the overall lowest 5-year cancer-specific death probability was 0.5% . For eacp-value estimation was omitted for frequency comparison.Building on the previous scenario, we determined risk stabilization based on the risk velocity. A risk velocity of 0 indicates that the risk profile remained unchanged or stable for a given time interval, while a risk velocity deviating from 0 implies that the risk condition is changing. A high velocity suggests a rapid change in the risk profile over time, and a low velocity suggests that the risk profile changes slowly. After calculating the velocities during 12 years after diagnosis, as described previously, we estimated the frequency of nil velocity in each year for all simulation cases or after stratification by the tumor dissemination status. The results were plotted as a bar graph over 12 years. Years where a nil risk velocity was found in more than 30% of the year cases were then highlighted. The threshed of 30% was aimed to visually distinguish the years according to the frequency of nil velocity. Given the large simulation cohort, the Using the SEER database, complete follow-up information was available for 1,941,893 cases. Here, 14.34% of patients died due to one of the urologic cancers, with an overall median follow-up of 12 years (range 1–44 years). When patients were stratified by age at diagnosis, the age group of 30–64-year-olds had a median follow up of 14 years, and the age group of 65–69-year-olds had a median follow up of 13 years . By stran = 1,747,703 cases, from which 250,555 cases died because of urologic cancers); during model training, the last 10% of the training set  was excluded from the model training and used for in-training validation. The test set had 194,190 cancer patients, from which 27,840 patients died due to urologic cancers. The overall concordance index on the test set was 0.80 (95% CI: 0.795–0.805). The Kaplan–Meier (KM) curve revealed a well-fitted model for cancer-specific survival rates over a follow-up period of 35 years on the test set  and RMSprop for model designs. The number of units of RNN was set to 16. The grid search analysis did not recommend the inclusion of the dropout function. The model was trained on a training set (test set . The ovep < 0.001). When compared to the tumor dissemination status used by SEER for survival estimation Recommender for the minimum follow-up durationOur solution suggested at least a four year follow-up after diagnosis for prostate cancer or five years for kidney and testicular cancers. In the upper quartile (75%) of the whole simulation cohorts, the follow-up durations can reach 11 years for prostate cancers, 8 years for kidney cancers or 7 years for testicular cancers.When we evaluated the minimum duration of the follow-up suggested by our algorithms after stratifying by the tumor dissemination status, we found that the suggested minimum follow-up periods also differed between localized cancers (4 years) and distant metastases (9 years) for kidney cancers . In cont(2)Determine intervals with unchanged risk profilesAs shown in The distribution of stabilized cancer-specific mortality risk in the years after diagnosis was mostly comparable between localized cancers and distant metastases in prostate cancer. The cancer-specific mortality risk was more likely stabilized in the third year for local cancers or in the third and fourth years for distant metastases of kidney cancers when compared to other follow-up years. Finally, the cancer-specific mortality risk for localized testicular cancers was more likely stabilized during the 3rd, 4th, and 6th years in comparison to the remaining years, whereas the highest likelihood for stabilized risk profile was found during the period from 5th to 7th year in testicular cancers with distant metastases.This study introduces and validates the risk-profile reconstruction concept of a population using advanced machine learning to estimate the cancer-specific survival probability. Our prognostic model can accurately estimate the cancer-specific mortality risk and provide a discriminative risk stratification. Furthermore, we delivered a well-calibrated model for cancer-specific survival estimation over a long period, while providing stability and good fitness . Our canProstate, kidney, and testicular cancers are amongst the most common cancers seen in urologic oncology. Additionally, kidney cancers affect both genders and testicular cancers are amongst the most frequent cancer diseases in young males , facilitRecently, studies on survival modeling using deep learning have emerged ,52,53,54The current study has limitations that warrant mention. First, this is a proof of concept for the development of a sophisticated model with potential utility in surveillance management. Future studies will focus on evaluating the cost-effectiveness of surveillance planning and further adoption of this concept to clinical needs before conducting a prospective study. Although the SEER database does not include detailed treatment information, the version of the SEER database (1975–2017) considered by the current study very likely covered patient groups who received standard-of-care treatment that evolved over time. Diagnostic and therapeutic improvements have helped to detect cancers at earlier stages, resulting in longer survival, as reflected in the SEER database . Most reIn summary, the current work introduces, as proof-of-concept, a dynamic risk profile model using advanced machine learning and the risk-profile reconstruction from a population-based database; the novel survival model provides a foundation for a time series of risk estimations and velocity calculation that have potential application in shaping the follow-up plans for cancer survivors.This study reveals a feasible data-driven artificial intelligence solution for cancer-specific survival estimation and potentially the follow-up management of urologic cancers.


After IRB approval, clinical testing was performed for the safety and feasibility of early ABI detection. (3) Results: No deflection force was measured with a 7.5 French Maquet Linear IABP within the 0.064 T field. Three adult ECMO patients  supported with IABP completed four POC brain MRI exams (median exam time: 30 min). Multiple signal abnormalities were detected on the POC brain MRI, corresponding to HCT results. (4) Conclusions: Our preliminary results suggest that adult VA-ECMO patients with IABP support can be safely imaged with low-field POC brain MRI in the intensive care unit, allowing for the early and bedside imaging of patients.(1) Background: Fifty percent of patients supported on veno-arterial extracorporeal membrane oxygenation (VA-ECMO) are concurrently supported with an intra-aortic balloon pump (IABP). Acute brain injury (ABI) is a devastating complication related to ECMO and IABP use. The standard of care for ABI diagnosis requires transport to a head CT (HCT) scanner. Recent data suggest that point-of-care (POC) magnetic resonance imaging (MRI) is safe and may be effective in diagnosing ABI in ECMO patients; however, no data exist in patients supported on ECMO with an IABP. We report pre-clinical safety data and a case series to evaluate the safety and feasibility of POC brain MRI in ECMO patients supported with IABP. (2) Methods: Prior to patient use, ex vivo testing with an IABP catheter within the Swoop The use of veno-arterial extracorporeal membrane oxygenation (VA-ECMO) has rapidly expanded over the past decade for patients in cardiogenic shock . AlthougAlthough MCS devices can provide circulatory support with potential outcome benefits, neurologic complications are common in patients with MCS ,5. AdditRecent advances in low-field 0.064 T), portable MRI technology enable the acquisition of clinically meaningful imaging in the presence of ferromagnetic materials. We have previously demonstrated the safety and feasibility of low-field point-of-care (POC) MRI to obtain brain imaging in ECMO patients 4 T, port. HoweverThe purpose of the study is to evaluate the safety and feasibility of low-field POC brain MRI in ECMO patients supported on IABP with both pre-clinical and human research.Clinical data were acquired from patients enrolled in an ongoing prospective observational study of POC MRI in adults with veno-arterial or veno-venous (VA- or VV-) ECMO that was approved by the institutional review board of Johns Hopkins Hospital (IRB00285716). Written consent was obtained and documented from a legally authorized representative as ECMO patients were unable to provide consent.® Portable MR Imaging System™ . Preliminary testing was performed to evaluate whether the catheter was deflected due to the magnetic field of the Swoop system. This was performed by constructing a nonmagnetic mechanical fixture, which included a spring gauge to measure the displacement of the catheter. The spring gauge was connected to the tip of the catheter via a spring-and-pulley system. The mechanical fixture and catheter were placed on the patient bridge and slowly moved into the MRI coil. At this point, the spring gauge was used to measure the deflection of the magnetic force on the catheter.Prior to clinical implementation, a preliminary risk assessment was performed using a 7.5 French Maquet Linear IABP catheter with the SwoopThe primary outcome for the pre-clinical testing was to determine the deflection force induced by the magnetic field on the IABP catheter. According to the ASTM F2052-15 standards, a device is MR-compatible if the magnetically induced deflection force is less than the force on the device due to gravity. If the deflection force exerted on the IABP catheter was greater than the force of gravity, the study would not progress to the clinical phase.® devices, and any metallic foreign bodies within the chest.Exclusion criteria included weight over 200 kg, pregnant patients, and contraindications to 1.5 T MRI other than ECMO and IABP. Incompatible implants included left ventricular assist devices, pacemakers, Impella® MR imaging system . The MR system was wheeled into the patient’s room with all ICU equipment outside the magnet’s 5-Gauss line. Once the patient’s head of bed was aligned with the head coil, 4 trained individuals, including a perfusionist, respiratory therapist, intensivist, and nurse, slid the patient into position using a lift-and-slide maneuver. The patient was maintained as flat as possible while positioning at the direction of the team leader. Pads were placed around the patient’s head to prevent motion (2) of 10% from baseline. Resultant images were read by a neuroradiologist (H.I.S.) who was blinded to clinical information.The study procedures were previously reported in our prior study . A POC bt motion . A physiPrimary outcomes for the clinical study were safety and feasibility, defined as completion of the POC MRI exam without SAEs. The secondary outcome was the clinical result of MR images compared to HCT images.Zero deflection force was measured when a 7.5 French Maquet Linear IABP catheter was placed within the 0.064 T field .We report three patients  that underwent a total of four POC brain MRI exams while on concomitant VA-ECMO and IABP support. Two patients (67%) had post-cardiotomy shock, and one (33%) had acute cardiogenic shock secondary to COVID-19 myocarditis . Two  and was found to have global hypoxic ischemic brain injury and cerebral edema. A second HCT performed on day 4 of IABP (day 3 ECMO) had no changes from the prior HCT. POC MRI exams were performed immediately after each HCT, and both showed T2 signal abnormalities within the occipital lobes and left temporal and parietal lobes. For each POC MRI, the IABP was placed on a 1:2 rate, triggered with EKG waveform.Patient 2: A 37-year-old female with a past medical history of asthma, who was cannulated on VA-ECMO via the femoral vein and artery with IABP support for cardiogenic shock secondary to COVID-19 myocarditis. She underwent a POC brain MRI on day 2 of IABP (day 1 of ECMO), which showed focal encephalomalacia of the left cerebellar hemisphere. She did not undergo HCT imaging while on VA-ECMO and IABP support. During the POC MRI, the IABP was placed on a 1:1 rate, triggered with arterial pressure.Patient 3: A 71-year-old female with a past medical history of hypertension, diabetes, and chronic kidney disease who was cannulated on VA-ECMO via femoral vein and artery and IABP for post-cardiotomy shock. She underwent an HCT on day 1 of IABP (day 1 of ECMO), which was significant for age-indeterminate infarcts in the right thalamus and the right parietal and left occipital lobes. A subsequent POC brain MRI on day 4 of IABP (day 4 of ECMO) was without abnormalities. During the POC MRI, the IABP was paced on a 1:1 rate, triggered with EKG waveform.We previously reported that adult patients supported on VA- and VV-ECMO can be safely imaged with a low-field POC brain MRI at the bedside in an ICU setting . HoweverWe report here the first use of POC brain MRI for patients supported on both IABP and VA-ECMO and for a patient centrally cannulated on VA-ECMO. We successfully conducted POC brain MRI without SAEs, demonstrating the safety and feasibility of a POC brain MRI for ECMO patients supported with IABP. Additionally, no ABI were detected on POC brain MRI imaging, corresponding to clinical HCT results. In our previous report, we collected four MR sequences: T1-weighted, T2-weighted, fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted imaging with an apparent diffusion coefficient map (DWI ADC), which resulted in an average exam time of 39 minutes . In thisAs part of the preparation for MRI, the IABP was switched from an EKG trigger to a pressure trigger. If the patient was unable to pressure-trigger the IABP, EKG leads were moved to the patient’s hips to be outside the 5-Gauss line, and proper EKG triggering was confirmed after positioning into the MRI. There was minor EKG interference during the DWI and ADC sequences that was not sustained and had no effects on he0modynamics, ECMO flow, or IABP inflation.As this is the first ever series of MR images acquired on ECMO patients with IABP, there were several challenges encountered in this early experience. In order to have ideal MR brain images, the head needed to be within 4 cm of the MRI coil. Patients 1 and 3 had a large body habitus  that prevented their heads from being placed within four centimeters of the MRI coil, resulting in partial brain coverage. In Patient 2, MR images were collected while she contained metallic pins within her hair, resulting in significant artifacts on the MR images. Lastly, Patient 3 had significant use of accessory muscles for breathing during the scan, resulting in significant head motion. While pads were placed along the side of the head to minimize the head motion, the MRI had to recalibrate motion sensing, in addition to the DWI ADC sequence requiring a second run to collect images. A larger head coil to accommodate obese patients would improve image acquisition in all patients. Further investigation with the adjustments above is needed to determine if the image quality can be improved to the point that ABI can be reliably detected.Overall, it is notable that POC MRI had no impact on the hemodynamic status (ECMO flow and MAP) and oxygenation in all patients with IABP support. Therefore, we demonstrated that POC brain MRI in an ECMO patient supported with IABP is safe and offers logistic advantages over conventional neuroimaging. Accessible POC brain MRI has the potential to markedly improve our ability to diagnose subclinical ABI and to immediately alter clinical management with the goal of mitigating injury in a patient population that had previously been excluded.Adult ECMO patients with IABP support can be safely imaged with a low-field POC brain MR at the bedside in an ICU setting, which may allow the early detection and timely intervention of ABI.


The original idea that providing higher doses of cytotoxic agents will result in higher rates of tumor cell killing was proposed in the 1980s. Preclinical data supported clinical testing. Advancements in bone marrow and peripheral stem-cell support technologies during the 1980s and 1990s allowed for clinical developments that permitted testing the higher dose hypothesis in oncology patients. The results of almost 20 years of clinical trials proved the linear relationship between dosing and clinical outcome to be mostly inaccurate. As a consequence, the adult oncology field abandoned high-dose chemotherapy strategies by the turn of the 21st century. Neuroblastoma is the only pediatric extracranial solid tumor where high-dose chemotherapy has remained part of the standard management for high-risk cases. This systematic review aims to understand the historical reason for such an exception and analyzes data challenging the benefit of high-dose chemotherapy and autologous stem-cell transplants in the era of anti-GD2 immunotherapy.Curing high-risk neuroblastoma (HR-NB) is a challenging endeavor, which involves the optimal application of several therapeutic modalities. Treatment intensity for cancer became highly appealing in the 1990s. Investigative trials assumed that tumor response correlated with the dosage or intensity of drug(s) administered, and that this response would translate into improved survival. It was postulated that, if myelotoxicity could be reversed by stem-cell rescue, cure might be possible by increasing the dose intensity of treatment. The principle supported autologous stem-cell transplant (ASCT) strategies. High-dose therapy transformed clinical practice, legislation, and public health policy, and it drove a two-decade period of entrepreneurial oncology. However, today, no ASCT strategies remain for any solid tumor indication in adults. As with most solid malignancies, higher dosing of cytotoxic agents has not resulted in a clear benefit in survival for HR-NB patients, whereas the long-term toxicity has been well defined. Fortunately, novel approaches such as anti-GD2 immunotherapy have demonstrated a significant survival benefit with a much less adverse impact on the patient’s wellbeing. On the basis of extensive experience, persisting with administering myeloablative chemotherapy as the standard to treat children with HR-NB is not consistent with the overall aim in pediatric oncology of curing with as little toxicity as possible. The era of hematopoietic stem-cell transplantation began after the first atomic bomb explosion, with the pioneering observations by Leon Jacobson et al. in 1949. Mice were protected from the lethal effects of ionizing radiation on bone marrow (BM) by protecting their spleens with lead . The disThe second half of the 1960s provided a significant evolution of high-dose myeloablative conditioning regimens. These consisted of maximally tolerated doses of TBI and/or chemotherapeutic agents such as cyclophosphamide, which not only served to kill cancer cells, but also suppressed the immune system of the host so that BM grafts would not be rejected . In 1990With the improvement of supportive therapy and sophistication in transplant-related techniques, treatment intensity for cancer became highly regarded in the 1990s partly because of the lessons from Hodgkin’s lymphoma. Higher cumulative dosage or higher dose intensity (DI) could be applied since myelotoxicity was no longer limiting in the face of stem-cell rescue. Investigative trials assumed that tumor response correlated positively with the dosage or intensity of drug(s) administered, and that this response would translate into improved overall survival (OS). Among the many preclinical studies, a steep log–linear dose–response curve was consistently documented for some cytotoxic agents  8]. Inc. Inc8]. Chemoresistance, be it intrinsic or acquired, is a key factor impacting response and the ability to achieve a cure. In chemotherapy-sensitive solid cancers, a high initial response rate to chemotherapy is commonly followed by subsequent relapse and resistance to further treatment. Tumors fitting this model usually show a dose–response behavior to drugs given in the conventional dose range. In principle, the drugs causing this effect should be appropriate for further dose escalation, with dose-limiting toxicity (DLT) primarily affecting the hematopoietic organ rescuable with hematopoietic stem cells. Ideally, their nonhematologic effects should be limited, and morbidity and mortality should be minimal. Inherent to this approach is the assumption that resistance can be overcome with the increase in dose, which can be achieved with stem-cell support . Alkylat2/week). A dose-intensive regimen may or may not yield high peak concentrations (HDT). Continuous administration of cyclophosphamide might be quite dose-intensive, but it could be associated with lower peak concentrations and less acute toxicity than a similarly dose-intensive, pulsed, high-dose regimen. Different trials have shown the importance of dose intensity in the treatment of small round cell tumors including sarcomas [The concept of HDT refers to pulsed chemotherapy, which achieves high peak drug concentrations that theoretically increase the overall exposure of tumor cells to supratherapeutic levels of cytotoxic drugs . The consarcomas ,14 and nsarcomas .Myelosuppression associated with cytotoxic therapy often limited the amount of drug that could be administered. This limitation was overcome to some degree with the use of hematopoietic cytokines such as granulocyte colony-stimulating factor (G-CSF) and techniques to support hematopoiesis such as autologous peripheral blood stem cells and/or BM. Prospective studies in adults with solid tumors showed that dose escalation with cytokine support did not increase cure rates in patients with chemotherapy-responsive solid tumors , advanced ovarian cancer, non-Hodgkin’s lymphoma, or bladder cancer ). The maTesticular germ cell tumors are among the most chemotherapy-sensitive solid tumors, and cisplatin appears to be the best single agent for this disease. If doubling the dose of cisplatin does not result in significant benefit, it would be unlikely that lesser increases in dosage above standard dose levels in other less chemotherapy-sensitive solid tumors would be beneficial. Indeed, preclinical studies suggested that 5–10× higher drug concentrations may be necessary to overcome drug resistance for most carcinomas . By the What about myeloablative therapies with BM support? Many clinical trials involving a variety of adult solid tumors were performed in the 1990s testing myeloablative therapy with BM transplant . For theAs for neuroblastoma, similar controversies dismissed in adult cancer 25 years ago remain and are analyzed in the next section.2R for correlation with dose intensity was EFS, greater than that for objective response and OS. There was no indication that EFS or OS curves were asymptotic with increasing DI, raising a serious question of whether cure was within reach. The publication from 1991 led to the formal test of the role of DI in neuroblastoma in the form of sequential trials . This initial report from 1999 prompted the worldwide adoption of myeloablative therapy (regardless of the conditioning regimen) with ASCT as the standard of care for all patients with HR-NB in first complete remission (CR).From January 1991 until April 1996, the CCG-3891 study enrolled patients with HR-NB in a randomized trial, becoming a landmark study published in 1999 . A largecis-retinoic acid (cis-RA) given after intensive therapy resulted in a significant improvement in 5 year OS rates, regardless of the type of consolidation. Surprisingly, all the statistical analyses initially published were corrected 5 years later [cis-RA given after consolidation therapy significantly improved OS. The amended results confirmed the predictions from the 1991 meta-analysis where maximal dose intensification of selective drugs over a short duration (HDT and ASCT) improved the outcome of patients with HR-NB with superior EFS but not OS [Long-term results of the CCG-3891 study were reported 10 years later . In the rs later . The amet not OS .Over three decades, three phase III randomized trials were completed ,41,42 teAnother independent observation that reduced the likelihood of HDT to ablate NB that survived exposure during induction was the study from the COG documenting no advantage of ex vivo purging . NB contIn conclusion, the accumulated data demonstrate that HDT and ASCT strategies improve EFS (delays relapse) but not OS (life expectation) of HR-NB patients. In light of these results, should ASCT remain as part of the standard management of patients with HR-NB? What does it mean for patients to undergo a highly toxic (and costly) treatment in order to increase EFS but not OS?Overall survival (OS) is the gold standard for evaluation of treatment efficacy. It is the critical test for drug approval since it takes into account both safety and efficacy. In contrast, EFS is a surrogate endpoint and, thus, requires validation . Unless After the CCG-3981 landmark study, there have been only two other reports on randomized ASCT trials for HR-NB ,42. Each123I-metaiodobenzylguanidine scintigraphy and molecular quantitation of BM minimal residual disease). An important final consideration is that the term “transplant” was widely invoked in these studies (and protocols), but the variability of the cytoreductive agents used was extreme, ranging from a single agent  to multiple agents plus TBI, which is no longer used for any solid tumor treatment including NB.In the CCG-3981 landmark study and all randomized studies of HR-NB testing ASCT, OS failed to show significance. The only explanation of this discrepancy is that relapse after ASCT was invariably fatal. Taking into account the revised results of the landmark study and the critical importance of OS with very long follow-up, a reevaluation of ASCT for HR-NB should be undertaken. This is even more the case when considering that randomized ASCT studies carried out in 1982–1985 , 1991–19The high prevalence of late effects including second malignant neoplasms (SMN) following myeloablative therapy further undermines the use of ASCT . It is iNot only one but two high-dose chemotherapy and sequential transplants have been proposed as a consolidative strategy for HR-NB. In the sole reported randomized trial involving tandem transplant , the comFortunately, chemo-resistant NB is highly responsive to treatments far less toxic than myeloablative therapy, namely, anti-GD2 antibody + granulocyte–macrophage colony-stimulating factor (GM-CSF)  ± low-doImmunotherapy targeting GD2 emerged with the aim to eliminate chemotherapy-refractory disease, similar to HDT strategies, in the 1990s and early 2000s. The murine monoclonal antibody (mAb) 3F8 underwent extensive preclinical testing and, when administered with GM-CSF, led to significant responses in patients with refractory NB . Clinicacis-RA after ASCT (ANBL0032) [cis-RA only (p = 0.042). The 5 year OS was 73.2% versus 56.6% for triple immunotherapy versus cis-RA only patients, respectively (p = 0.045). The role of anti-GD2 immunotherapy after HDT and ASCT was clearly shown to be relevant for the survival of patients with HR-NB, and anti-GD2 immunotherapy was then adopted worldwide as the standard of care for all HR-NB patients in remission post ASCT. These results, however, question even further the role of HDT and ASCT in the era of anti-GD2 immunotherapy given that the landmark CCG-3891 study, as described earlier [The only randomized controlled trial involved HR-NB patients who received either anti-GD2 mAb dinutuximab-containing immunotherapy or NBL0032) . The priNBL0032) . The sur earlier , did not earlier .n = 54) adding to that from MSKCC, increasing the population of HR-NB patients managed in the current era without ASCT and showing no difference in survival [Initial evidence of the superiority of anti-GD2 immunotherapy over HDT and ASCT for the management of patients with HR-NB came from the MSKCC group. This group had a great interest in and experience with transplant during the HDT era ,36,37,38survival . Recent survival , the lessurvival , and theCuring high-risk neuroblastoma is a challenging endeavor which involves optimal application of several therapeutic modalities. Unfortunately, these may not be available in many centers and regions of the world. The first goal, as always in medicine, is primum non nocere (“first do no harm”); that is, if possible, avoid treatment that is worse than the disease itself. The balance between efficacy and toxicity should be carefully evaluated, especially in young children given their heightened susceptibility to long-term and all-too-frequent debilitating side-effects from chemotherapy and radiotherapy, which contrast strikingly with the absence of delayed toxicity from anti-GD2 mAbs. As with most solid malignancies, higher dosing of cytotoxic agents has not resulted in a clear benefit in survival of HR-NB patients, whereas the long-term toxicity has been extensively described. Fortunately, novel approaches such as those based on anti-GD2 immunotherapy have demonstrated a significant survival benefit with a much less adverse impact on a patient’s wellbeing. On the basis of extensive experience in recent years, most notably with mAbs, persisting with administering myeloablative chemotherapy as the standard to treat children with HR-NB is not consistent with the overall aim in pediatric oncology of achieving cure with as little toxicity as possible.


In this study, matrix-assisted pulsed laser evaporation (MAPLE) was used to deposit graphene-like materials (GL), a new class of biocompatible graphene-related materials (GRMs) obtained from a controlled top-down demolition of a carbon black, on silicone slices to test their potential use as functional coating on invasive medical devices as indwelling urinary catheters. Results indicate that the relevant chemical-physical features of the deposit (controlled by FTIR and AFM) were maintained after MAPLE deposition. After deposition, GL films underwent a biological survey toward target cellular lines . Results indicate that the GL films did not lead to any perturbations in the different biological parameters evaluated. The presented results and the possibility to further functionalize the GL or combine them with other functional materials in a hybrid fashion to assure a tighter adhesion onto the substrate for use in harsh conditions open the door to practical applications of these new-concept medical devices  paving the way to the prevention of nosocomial infections driven by catheterization through antibiotics-free approaches. The health emergency linked to the pandemic outbreak has posed the critical issue of long-term patients’ hospitalization in intensive care units (ICUs) requiring the use of invasive medical devices. Intravascular catheters are indispensable in current medical practice as well as urinary catheters, although their use exposes patients to risk of healthcare-associated infections (HAI) as local and systemic infectious complications  and catheter-associated urinary tract infections (CAUTIs) which have significant morbidity and mortality . The resCarbon-based and in particular graphene-based materials are also emerging as possible coatings for catheters and biomedical devices ,10. The The control of coating thickness and film uniformity is a relevant general issue related to the surface coating. Former items, indeed, represent key elements to determine the overall properties of innovative materials and for circumventing issues that do not allow the development of new solutions for various research fields. For example, single layer materials growth requires substrates having an atomically flat surface properly treated ,12 as reTo date, there are many options to finely tune the surface chemistry and the overall characteristics of the catheter coating including chemical deposition, dip-coating, spin-coating, photo–chemical deposition, physical vapor deposition and sputtering deposition techniques . The majIn the last few years, advanced carbon-based materials found applications in many technological fields ,34,35, bGRMs including few-layer and multi-layered structures can be produced from C-based sources different from graphite such as carbon black, coke, carbonized/pyrolyzed wastes and biomasses through many approaches ,48,49,50S. aureus cells culture media, one of the most prevalent Gram-positive bacteria associated with CAUTIs, induces a severe decrease of colony forming units  test , in ordeThe murine fibroblasts (NIH3T3), the human keratinocytes (HaCAT) and the human cervical adenocarcinoma epithelial-like (HeLa) cells were used to evaluate the response of different cell types following exposure. In particular, NIH3T3 cell line have been selected since a time-dependence and size-dependence cytotoxicity behavior towards graphene oxide (GO) have been found, differently from other adherent  and semi-adherent and suspension cells  .Before the MTT test, we verify the absence of side effects on cell growth and viability ascribable to neat PDMS slices. To this aim, the most sensitive cell line, NIH3T3, was exposed to direct contact with the PDMS slices for up to 72 hrs. As shown by the growth curves reported in the supporting material section panel A, , PDMS diPreliminary tests were conducted on all the three cells lines on GL water suspension at concentration ranging from 0.05 to 50 μg/mL for 48 h.The MTT assay showed that GL did not result in any significant effect on the viability of the three cell lines  when it To further explore cytotoxicity of GL, we examined cultured cells by phase-contrast microscopy. The analysis did not reveal differences between the morphology of GL-treated cells and the corresponding control cells. The vast majority of the cells were visually viable and remained spread on the surface of the wells; The HaCAT and HeLa cell populations include a low percentage of rounded cells, which is characteristic of these cultures. Furthermore, poorly attached or detached cells were not observed but the density of cells was lower than that of the control, indicating that GL did not have toxic effects even at high concentration, but it rather affected the cell proliferation rate.Before proceed with cytotoxicity testing on GL-MAPLE films, the stability of GL-MAPLE deposits in aqueous medium was probed. To this aim, GL-MAPLE films were incubated in the cell culture medium (DMEM) for 24, 48 and 96 h at 37 °C with shacking. The same slice was photographed before (0 h) and after 24, 48 and 96 h of incubation in the cell culture medium. Digital photos and phase–contrast light microscopy images of PDMS slices coated with GL-MAPLE before and after the incubation are reported in This result is particularly relevant as the GL-MAPLE films are expected to be used in the bladder at the contact with urine medium . GL-MAPLE films on PDMS were tested for in vitro cytotoxicity by direct contact with cell monolayers. For MTT assay, GL-MAPLE slices were cut into small pieces covering about 30% of the surface area of cell monolayers and then cells were incubated for 24–48 and 72 h. The assay results reported in The cell viability, indeed, was above 90% up to 72 h of incubation in HeLa and HaCAT cells and did not decrease under 75% in NIH3T3 fibroblasts.Observation of the slices removed from the cell cultures with phase-contrast light microscopy showed the progressive detachment of small amounts of GL deposits from the PDMS support that were released in the culture medium . DespiteThe progress in the MAPLE of graphenic materials obtained from CB has been investigated. The testing was performed on GL as a promising candidate for in vivo applications. GL deposited on PDMS substrates from water suspension via MAPLE were inspected by a biological survey toward target cellular lines  demonstrating no perturbations in the different biological parameters evaluated, including the cytotoxic potential. GL, differently from most GRMs, are confirmed to be promising bio safe nanomaterials, stable in water suspensions and suitable to be firmly deposited by MAPLE approach in thin film on soft substrates. The GL films deposited by MAPLE exhibit a good resistance over time in cell culture medium. Specific tests to probe the resistance under different pH are planned in the future with the aim to expand the exploitation of the GL-MAPLE films in other market segments.These results and the possibility to further functionalize the GL with suitable target molecules  or combine them in hybrid materials  to assur


The coronavirus disease 2019 (COVID-19) has become a significant public health problem worldwide. In this context, CT-scan automatic analysis has emerged as a COVID-19 complementary diagnosis tool allowing for radiological finding characterization, patient categorization, and disease follow-up. However, this analysis depends on the radiologist’s expertise, which may result in subjective evaluations.To explore deep learning representations, trained from thoracic CT-slices, to automatically distinguish COVID-19 disease from control samples.Two datasets were used: SARS-CoV-2 CT Scan (Set-1) and FOSCAL clinic’s dataset (Set-2). The deep representations took advantage of supervised learning models previously trained on the natural image domain, which were adjusted following a transfer learning scheme. The deep classification was carried out: (a) via an end-to-end deep learning approach and (b) via random forest and support vector machine classifiers by feeding the deep representation embedding vectors into these classifiers.The end-to-end classification achieved an average accuracy of 92.33% (89.70% precision) for Set-1 and 96.99% (96.62% precision) for Set-2. The deep feature embedding with a support vector machine achieved an average accuracy of 91.40% (95.77% precision) and 96.00% (94.74% precision) for Set-1 and Set-2, respectively.Deep representations have achieved outstanding performance in the identification of COVID-19 cases on CT scans demonstrating good characterization of the COVID-19 radiological patterns. These representations could potentially support the COVID-19 diagnosis in clinical settings. Some artificial intelligence strategies have been developed to accurately diagnose lung diseases such as pneumonia, pulmonary nodules, chronic obstructive pulmonary disease (COPD), and diffuse pulmonary fibrosis using radiologic studies We explored and analyzed here convolutional deep learning representations to support the automatic classification of COVID-19 and non-COVID-19 samples in clinically relevant CT slices previously selected by radiologists. From a supervised scheme, a set of architectures originally trained on the natural image domain were adjusted to implicitly identify radiological visual patterns associated with COVID-19. After, the learned deep representations were used to classify new samples using an end-to-end scheme, as well as high-level embedding vectors with classical machine learning classifiers. Such representations were validated on two different datasets separately showing remarkable results to support radiological analyses. The best performance of the proposed strategies yielded scores of 90% accuracy, 91% sensitivity, and 94% specificity on the mentioned datasets.,,Thoracic CT is useful to analyze the transverse area, the anatomical structure, and the density of the lungs. Over such images, it is possible to characterize pneumonia from a set of radiological findings as described by the Fleischner Society glossary ,,The localization of the radiological findings on thoracic CT slices is specific for each patient and varies depending on the stage of the disease Here the evaluation of deep learning representations was considered on two different sets aimed at determining the generalization capability of the classification, as well as the effectiveness in retrospective studies including demographic information on patients. In both cases, only axial CT volumes were considered. The datasets are described as follows:SARS-CoV-2 CT Scan dataset. This public collection contains a total of 210 cases comprising 4,173 thoracic CT slices. A subset of 80 cases corresponds to patients infected by SARS-CoV-2  and 50 to non-infected patients (757 CT slices). The remaining 80 cases are patients with other pulmonary diseases that were not taken into account in this work. For each CT volume, the most relevant CT slice in terms of radiological findings was manually selected as input for deep learning. This dataset was collected in hospitals of Sao Paulo, Brazil, and all patients were confirmed as positive or negative for SARS-CoV-2 by RT-PCR test (22). Therefore, the automated categorization of patients by the deep learning models may be biased to detect those cases also detected by the RT-PCR test. In our study, this bias was mitigated by the fact that the radiological findings identified by the experts were previously selected on the most significant CT slice for diagnosis.FOSCAL dataset. This dataset comes from a retrospective study of CT scans collected at Clínica FOSCAL in Santander, Colombia, from March 1 to August 19, 2020. The dataset is composed of thoracic CT scans from 355 patients. A subset of 175 patients were positive for COVID-19 infection by RT-PCR  while the remaining 180 patients were negative for COVID-19  but could have had other pulmonary diseases. Each patient underwent CT and RT-PCR testing for SARS-COV-2. The dataset contains information on 1,846 slices from non-SARS patients and 416 slices from SARS patients. Clinically relevant slices were selected by two radiologists with 3 and 4 years of experience. The number of selected slices per CT scan varies between 4 to 15 from among the tomographies. Every single slice had a spatial size of 512 x 512 pixels. The demographic information and comorbidities distribution are shown in Universidad Industrial de Santander and of the FOSCAL clinical center in Bucaramanga, Colombia.Our study was retrospective using human subjects’ data and it was approved by the Ethics Committees at We used deep convolutional representations to automatically classify COVID-19 cases from thoracic CT slices. These representations aimed to recover and learn distinctive visual patterns associated with the disease and properly distinguish between COVID-19 and non-COVID-19 CT images. A transfer-learning scheme was implemented to train and adjust the deep representations in an end-to-end classification setup. As an alternative for evaluating deep representations, we took the last fully connected layers as embedding representations, which were then used on classical machine learning classifiers such as random forests or support vector machines. The pipeline of this strategy is shown in The CT slice characterization by CNNs is based on a hierarchical representation of the visual patterns distinctive of COVID-19 disease and healthy regions. In general, the first layers of the CNN perform a decomposition of the input images into basic visual primitives, which is achieved through a set of kernels learned for the specific task of the convolutional network. Subsequently, more complex patterns are modeled in the upper layers such as relevant texture patterns or regional distributions. For doing so, the activations from the previous decomposition are then convolved with another set of learned filters which extract patterns of a higher degree of non-linear correlation. Finally, such complex patterns are transformed until reaching a semantic level used as a set of features that represent radiological studies with the presence or absence of COVID-19.Thanks to the success of CNNs on different domains, today there is a wide range of CNN architectures with specific deep properties and learning specifications VGG16. The Visual Geometry Group (VGG) developed a relatively deep network composed of 13 convolutional and 3 fully connected layers that account for a total of 138 million parameters. This network is highly uniform around its layers using multiple stacked small-size filters (2 x 2 and 3 x 3) that allow for the learning of more complex features. It was accomplished on the ILSVRC 2014 challenge training and testing with the ImageNet dataset of natural images ResNet-152. The Residual Networks consists of a CNN architecture that incorporates identity shortcut connections which reduce the vanishing gradient problem by creating the so-called residual block. Such connections in the image domain improved the classification performance by training deeper networks than the conventional CNN architecture. This network, with 60 million parameters, won the ImageNet Large Scale Visual Recognition Competition (ILSVRC) in 2015 InceptionV3. This net is nowadays one of the most representative architectures. Its main proposal centers on reducing the computational cost of deeper networks without affecting generalization through a dimensionality reduction with stacked asymmetric convolutions. First, a 1 x 1 convolution is applied to decrease drastically large filters input dimensions. Then, such large filters are factorized (an N x N filter is the combination of 1 x N and N x 1 filters) and these multiple asymmetric filters are ordered to operate on the same level achieving a progressively wider network instead of a deeper one. With 23 million parameters, InceptionV3 shared first place with the VGG16 on the ILSVRC 2015 challenge Mk= , where F is the feature space and P(F) the marginal probability distribution. In this case, F= [Fi(Fi-1)] represented a hierarchical representation of the general image domain regarding a particular task, T= . Thus, the task Tt covered the set of classes Dt=  defined in the original problem (ImageNet) Tt into a new radiological task Ts as Tt=  → TsThe CNN architectures used in this work were selected for their effectiveness in the natural image domain. However, they may be unsuited to represent and differentiate patterns from the radiological domain. Therefore, we obtained an adjusted representation for the radiological domain by using transfer learning. Transfer learning (TL) is a widely known technique that deals with learned weights from large general image representations by adjusting several layers to a specific domain, in our case, CT radiological images. Formally, the learned image representation in the Mk model  was defined as Tt→Ts ) is an adaptive iterative process learning through several epochs that uses a relatively low learning rate and batches from a new domain, in this case, trained CT slices. Finally, we obtained a deep representation for each CNN architecture with the capacity of capturing COVID-19 patterns on thoracic CT slices.Transfer learning  and a random forest (RF).Random forest. An RF defines boundaries in the feature space between the COVID-19 and non-COVID- 19 classes. The RF is comprised of a set of independent decision tree (DT) algorithms. Each DT was trained over different parts of the embedding feature space to reduce prediction variability. A bootstrap aggregating strategy, which consists in randomly selecting a set of training embedding features, was used to build each DT. The final prediction was made by averaging the predictions of the individual trees Support vector machine: The SVM selects a hyperplane that separates the embedding features of the two classes. The selection is performed by maximizing the distance between the decision limit and the feature vectors from both classes k=(1+yFiTFj)d where Fi and Fj are the i-th and j-th embedded in deep features, d is the degree of the polynomial kernel, and gamma is 1/NCNNs were previously trained with images from the public ImageNet The trained models yielded the probability per radiological study of the presence or absence of the disease. A 0.5 probability threshold was used to assign the predicted label 1 (COVID-19) or 0 (non-COVID-19). Here, true positives corresponded to patients effectively classified with COVID-19. It should be noted that the gold standard for COVID-19 diagnosis is the RT- PCR, which has limited sensitivity for its detection. To mitigate the potential bias associated with the sensitivity of the RT-PCR test, for each considered CT volume, CT slice selection was done by an expert radiologist based on observed radiological findings. The evaluation of this classification task was measured by first computing the true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). The typical metrics for classification tasks such as accuracy (Acc), sensibility (Sens), precision (Pre), and F1 score were used . The areIn this section, we present the performance of the proposed approach separately for each considered dataset and with respect to the two classification schemes.et al. ,et al. suggest As for the proposed classification method using the deep features (embedding), we conducted a fine-tuning for the RF and SVM classifiers as shown in et al. Previous results  showed tUsing the FOSCAL dataset, we evaluated two methods and computed the performance metrics as follows: a) end-to-end classification from transfer learning approach using VGG16, ResNet-152, and InceptionV3 architectures and the classification from deep features using SVM and RF methods. A similar fine-tuning procedure was performed for the embedding method over the FOSCAL dataset shown in The evaluation in both datasets showed a remarkable performance of deep representations, which could be key to reducing radiologists’ subjectivity in the analysis and diagnosis of CT scans. Also, the evaluation over both datasets suggested that the best boundaries separation was obtained from embedding vectors with an additional optimization over an SVM hyperparametric space. These embedding vectors recovered a high semantic level of knowledge in deep representations and the additional non-linear kernel separation could induce a better boundary separation among defined diseases.The main public health problem today in the world is the COVID-19 disease, therefore, it is fundamental to join forces and establish synergies for innovation and propose alternative and complementary methods to characterize, diagnose, and follow up the disease. Such efforts would contribute to early diagnosis, mitigate the collapse of health services, and help with proper analysis and treatment of more patients. Here we presented a deep learning representation for COVID-19 detection in thoracic CT slices. From each CT scan, an expert selected a set of relevant slides exhibiting the most distinctive radiological patterns of COVID-19 patients and healthy lungs. Deep feature extraction was performed to represent the complex visual patterns of the disease exploring different convolution neural networks and then, an end-to-end learning approach and an embedding classification strategy were evaluated to differentiate COVID-19 and non-COVID-19 cases from such deep features. The three networks used  achieved an outstanding performance characterizing radiological patterns to detect COVID-19 cases in CT scans. Such deep features were also used to feed two binary classification frameworks: a) end-to-end learning using different CNN architectures, and b) a machine learning approach using SVM and RF models. Finally, these models evaluated new thoracic CT slices to determine whether such image visually corresponded to a lung infected by COVID-19.The best performance in the open SARS-CoV-2 CT Scan dataset was achieved by the embedding strategy outperforming even state-of-the-art methods evaluated in that dataset. On the other hand, the methods also showed to be capable of identifying positive COVID-19 cases in the FOSCAL dataset. The results also point to their potential implementation in the clinical routine to support the diagnosis. It should be noted that in both datasets, the positive reference was based on the RT-PCR test, which may introduce a bias related to its false positives rate. In both datasets, the CT slice with the most information related to radiological findings was selected to mitigate the potential bias induced by false negatives. Besides, the computational approach was based on a statistical representation that captures visual patterns from a significant amount of data. Hence, it is expected that trained representations would deal with some of the outliers resulting from false negatives annotations.,,et al. et al.Currently, several computational strategies have been proposed to detect COVID-19 cases on thoracic CT slices et al. et al. et al. et al.’s work corresponded to a smaller network (EfficientNetB0 with 5 million parameters) while we used deeper networks (VGG16 and ResNet-152 both have over 60 million parameters). Besides, although the end-to-end learning approach achieved competitive results, the embedding approach exhibited a better boundary to separate the classes thus obtaining the highest performance.Silva, et al.et al.’s study and ours, Ragab, et al. used the validation scheme they proposed for the SARS-CoV-2 CT Scan dataset On the other hand, Ragab, To demonstrate that the method was generalizable to different populations and acquisition devices, we performed an additional evaluation process with data collected locally. The FOSCAL dataset is collected in Santander (Colombia) from different hospitals with diverse CT acquisition devices. The best results were obtained with the end-to-end classification strategy. The strategy yielded a 95.57% accuracy, a 95.74% precision, and a 95.79% sensibility in the FOSCAL testing set. This strategy seems to benefit from the increased number of patients and CT slices available in the FOSCAL dataset. The results show that the method herein proposed was able to accurately detect COVID-19 cases using thoracic CT slices from two different populations and competed well with those from other studies on the state of the art. The proposed strategy is nonetheless dependent on the selection of a significant CT slice, which may limit the automatic detection framework. Moreover, some additional slices with complementary information about radiological findings were discarded and this may be a limitation for including additional information to better discriminate COVID-19 patterns as compared to other classes.In future studies, we are including the training and evaluation of this method in a cross-dataset setup to ensure the proper COVID-19 disease detection in a larger set of images acquired via CT imaging. In addition, an automatic selection procedure for the clinically relevant CT slices might be a useful tool to facilitate the integration of the proposed strategy in the clinical routine practice. In this sense, the use of an additional stratification related to the stage of the disease could be useful to build and re-train models with more discriminative information. Moreover, the exploration of new deep alternatives may be useful to process the complete CT volumes. In fact, the literature today includes 3D convolutional nets that could be considered in future perspectives to try the problem of automatic COVID-19 diagnosis.


While there is absolutely no evidence to ensure recovered patients are either likely or unlikely to get reinfected. But studies in non-human primates indicate that reinfection of recovered patients is highly unlikely. It is also clear that primary immune responses or induced immunity to severe acute respiratory syndrome coronavirus 2 remain in circulation for several months and at least temporarily confer immunity to protect from reinfection. In addition, negative virus culture analysis of re-positive suggests that positive reverse transcriptase-polymerase chain reactions in recovered patients are more likely to be false-positive, or detection of genetic remnants of virus discharged from lesions of lungs or better sampling at the time of repeat analysis. However, emerging severe acute respiratory syndrome coronavirus 2 variants are likely to be causing the infections observed in some of the recovered patients. Yet, the immunity of this novel virus is not defined, and there is rising global speculation about the risk of reinfection. Few recent reports from china have confirmed the positive reverse transcriptase-polymerase chain reaction (RT-PCR) in numerous patients after complete recovery of clinical symptoms or with previous negative test results.6 Conversely, another study indicated that repeat analysis of RT-PCR 48 hours apart is necessary to ensure virus clearance.3 Besides, laboratory tests, including D-dimer, absolute lymphocyte count, and antibody test in combination with RT-PCR negative testing, are essential to ensure that the virus is no longer transmitted by discharged or recovered patients.3 Few recent reports have shown that asymptomatic or moderately symptomatic or in close contact with patients recovered is also a means of transmission or infection in the population.8 Furthermore, persistent viral shedding has been recorded for an extended period of 60 days from the onset of symptoms, and even for 36 days after the recovery of symptoms.9 This suggests that patients with COVID-19 or those recovered may require prolonged isolation. Also, more precautions are obviously needed to be certain that a virus has not been reactivated after recovery, but the patient has fully recovered. Although reactivation may be rare or very unlikely, the possible risks are too high to ignore, warranting an urgent call for more in-depth research.Despite the fact that there is limited evidence, KCDC thus postulates the new hypothesis about the probability of COVID-1 9 viral reactivation instead of reinfection. Taking this into consideration, they issued testing guidelines that patients are only considered fully recovered if RT-PCR is tested negative twice within 24 hours.10 A similar study from US groups demonstrated that 5log10 reductions in median viral load and neither led to respiratory failure or mortality.11 Another group developed six candidates deoxyribonucleic acid (DNA) vaccines formulated using variants of the critical viral protein. These investigational vaccines were immunized in adult rhesus macaques to investigate the immunological response against the virus. The study revealed that most of the vaccinated animals showed no detectable virus, while few showed dramatically lower viral loads compared to that of control group.12 Those studies on non-human primates (rhesus macaques) suggest that immunity (antibody- and cell-mediated immune responses) acquired following primary infection provided immunity against SARS-CoV-2 relapse , which protects against infection . Such immunity remains in the body, which provides protective efficacy against secondary challenges (large peak-orange). During the infection phase, RT-PCR may be used to detect viral nucleic acid in the early infection phase (blue line) followed by detection of IgM antibodies (red line), while IgG antibodies may be detected in the later infection phase which serves as a long-term immunity (green line).It is therefore clear that higher antibody levels are directly proportional to lower levels of the virus that indicates neutralizing antibodies are a reliable marker of protection. Such findings raise the probability that immunological interventions will ultimately be effective for prevention and treatment. But it is critical to stress that notable variations exist between viral infection in macaques and humans. This suggests that rigorous clinical studies are required to evaluate immunity against SARS-CoV-2 reinfection in humans.2 RT-PCR will once again test absolutely negative upon full recovery of lesions in the lungs and clinical symptoms.2 Interestingly, KCDC has demonstrated negative results of virus isolation in cell culture on further analysis of respiratory samples from re-positive RT-PCR. Also, they showed that 96% positive for viral neutralizing antibodies in a serum sample of re-positive RT-PCR cases.4 In a row, experts in South Korea have confirmed that reactivation or reinfection is possibly due to the results of a false-positive test that is detected from the remains of dead viruses.13 According to new research, immunity to SARS-CoV-2 induced by infection or vaccination appears to be long-lasting. During an infection, cells that recognize the virus survive in the bone marrow and produce antibodies as needed.14 As a result, COVID-19 recovered individuals who were later immunized will not require boosters. While vaccinated people who have never been infected, or who have been infected but have not produced a strong immune response, will certainly need boosters. Vaccine breakthrough infection reported in some Nepalese might result from emerging variants circulating in Nepal. It is also possible that SARS-CoV-2 variants are less susceptible to vaccines than the wild-type virus.Besides, re-positive RT-PCR in recovered patients are unlikely to be caused by the reinfection from the residual virus, as described in Figure 1 . Actually, it emerges from the residual virus transferred to the upper respiratory tract (throat or nose) with a cough from the lower respiratory tract.Infection observed in some of the recovered patients is more likely as a result of SARS-CoV-2 emerging variants than reinfection with wild-type SARS-CoV-2. More research is required to assess if recovering patients are at risk of reinfection, and further precautions tend to be required to ensure that a patient has fully recovered from COVID-19 or is unable to reactivate or patients are fully vaccinated. Furthermore, genomic surveillance and vaccinations are only a key to drive evidence-based policies and implement effective control strategies in Nepal.


N = 6419) acted as controls. In phase 2, participants (N = 5031) were exposed to the COVID-19 pandemic between the baseline and follow-up assessments providing a natural experimental design. The primary analysis used a random intercept linear multivariable regression model with phase (exposure to the COVID-19 pandemic) included as the key predictor while controlling for baseline scores and individual and school-level covariates. Depressive symptoms were higher and life satisfaction scores lower in the group exposed to the COVID-19 pandemic. Had the COVID-19 pandemic not occurred, we estimate that there would be 6% fewer adolescents with high depressive symptoms. No effect of exposure to the pandemic on externalizing difficulties was found. Exploratory analyses to examine subgroup differences in impacts suggest that the negative impact of the COVID-19 pandemic on adolescent mental health may have been greater for females than males. Given the widespread concern over rising adolescent mental health difficulties prior to the pandemic, this paper quantifies the additional impacts of the pandemic. A properly resourced, multi-level, multi-sector public health approach for improving adolescent mental health is necessary. Following in-principle acceptance, the approved Stage 1 version of this manuscript was preregistered on the OSF at https://doi.org/10.17605/OSF.IO/B25DH. This preregistration was performed prior to data analysis.Despite widespread concern about the impact of COVID-19 on adolescent mental health, there remains limited empirical evidence that can causally attribute changes to the pandemic. The current study aimed to overcome existing methodological limitations by exploiting a serendipitously occurring natural experiment within two ongoing, multi-phase cluster randomized controlled trials. Depressive symptoms (primary outcome), externalizing difficulties and life satisfaction (secondary outcomes) were assessed at baseline  and 1-year follow-up . Participants in phase 1 ( In 2017, between 14 and 17% of adolescents aged 11–19 were found to meet diagnostic criteria for at least one mental health disorder in England . It is iDespite widespread concern and media coverage about the impact of COVID-19 and related school closures on adolescent mental health , there rMuch of the existing evidence is based on cross-sectional studies with no pre-pandemic assessments of mental health. Longitudinal data for this population are limited and in this age group pose the unique challenge of differentiating between developmental change and COVID-19 impact. A living systematic literature review investigating the changes in mental health symptoms within the same individuals from pre-COVID-19 and across distinct phases of the pandemic identified only four studies with child and adolescent samples (by June 2021 when this was written), none of which were from the United Kingdom (UK) . FindingIn the UK, data from an ongoing regional cohort  revealed stark increases in young adolescents' depressive symptoms, post-traumatic stress disorder and externalizing difficulties during the COVID-19 pandemic . At a na. 1.2To address some of these methodological challenges, this study exploited the serendipitous design of two large, ongoing multi-phase intervention trials. Using two cohorts of students , we are There has been some evidence from UK studies tracking families throughout the pandemic, that children with special educational needs (SEN) and from low-income homes were particularly impacted by COVID-19 related school closures and lockdown . To inve. 2. 2.1Details of the trials from which these data are drawn are described below. The Education for Wellbeing Programme (EfW) is an evaluation of five school-based, mental health and wellbeing interventions which are organized into two parallel-group cluster randomized controlled trials (RCTs) ,22. InteDue to the size of the two trials, schools were recruited in two phases . All participants who completed some items of the survey at baseline or 1-year follow up were considered as part of the primary analysis sample. A total of 11 450 pupils from 178 schools were included in the main analytic sample with 6419 pupils from 90 schools in phase 1 (pre-COVID-19 group) and 5031 pupils from 88 schools in phase 2 (COVID-19 group). In phase 1, there was an average of 89.8 pupils per school ; in phase 2, there were 78.9 pupils per school on average .. 2.3. 2.3.1We examined group differences and controlled for a range of individual pupil-level characteristics. These included age group , child gender , socio-economic position assessed using eligibility for free school meals (FSM eligible or not), ethnicity (white or ethnic minority) and special education needs status . Year group and gender data were provided directly by the schools and all other information on pupil-level socio-demographic characteristics were available via linkage to the NPD .Table 1. 2.3.2When investigating the effect of condition (pre-COVID-19 group versus COVID-19 group) on adolescent mental health outcomes, we also controlled for several school-level characteristics that were used for minimization for randomization following baseline data collection. This information was obtained from the Department for Education's Get Information About Schools (GIAS) service. School-level FSM eligibility (%) was included as an indicator of deprivation, as well as urban/rural status. The extent of existing mental health provision (prior interventions) reported by schools at baseline was also included as a covariate. See . 2.4. 2.4.1ω)) were calculated for all outcomes at baseline for the study sample and can be found in electronic supplementary material, table S1.At all timepoints and across both phases of the study, schools were instructed to administer the pupil questionnaires via a secure online survey in teacher-facilitated sessions during the normal school day. As described in the introduction, the primary outcome was depressive symptoms with externalizing difficulties and life satisfaction considered secondary outcomes. Reliability estimates  were conducted using the WLSMV estimator due to ordinal item responses. First, single-factor CFAs were conducted for outcomes at baseline and follow-up for both phases to confirm their unidimensional structure. Configural and scalar invariance were then assessed by first freely estimating factor loadings and thresholds and then fixing them across phases . Models . 2.4.2‘I felt miserable or unhappy’ and ‘I felt I was no good anymore’. Items are rated on a 3-point Likert scale . Possible total scores range from 0 to 26, with higher scores indicating greater depressive symptoms. The continuous depressive symptoms total score was the primary outcome measure. A binary score to assess impact on prevalence of caseness was also generated using the established cut-off score  indicating high levels of depressive symptoms , COVID-19 group n = 2776 [34.3%]). Greater drop-out could have resulted in a biased sample at follow-up, with schools and individuals with certain characteristics more likely to participate. However, predictors of non-response across the control and the COVID-19 group were mostly overlapping and controlling for the probability of drop-out in the analyses did not change the conclusions.Prior to analyses, we estimated an expected 185 schools in the analytic sample. The final figure was slightly lower with 173 schools across both phases. The smaller sample will have reduced our estimated statistical power and therefore the effect sizes would have needed to be larger for them to be detected. However, for externalizing difficulties, the only (secondary) outcome for which we did not detect a statistically significant effect, the effect size was very small and the reduction in sample size inconsequential for the reported findings. Although the two phases were well-balanced on several school and pupil-level characteristics, there was greater drop-out at 1-year follow-up in the COVID-19 group  and intervention schools across the two phases. However, the effectiveness of interventions could have been compromised during the pandemic. Assessments of measurement invariance across all outcomes showed that for the SMFQ, exposure to the COVID-19 pandemic did not appear to influence the way in which adolescents interpreted and responded to the measure at follow-up. However, for the behavioural difficulties subscale of the Me and My Feelings questionnaire (MMFQ – BD) and the LSS, violations of measurement invariance indicate that the pandemic may have influenced the interpretation and response to these measures. More psychometric analyses are needed to fully understand the impact of these violations on analyses that compare those exposed to the pandemic with the control group.. 4.2There was widespread concern prior to the pandemic regarding high rates of adolescent mental health difficulties in England. An extended period of austerity has left schools and Child and Adolescent Mental Health Services (CAMHS) with insufficient resources and capacity to support the increasing number of mental health difficulties experienced by adolescents. The current study provides evidence for increased adolescent depressive symptoms and decreased life satisfaction as a result of the COVID-19 pandemic. Schools and CAMHS services were forced to adapt to the challenging circumstances of the pandemic. If there had been clearer guidance and increased funding for schools, this may have enabled better systems for supporting pupils' mental health during this period. Given that the pandemic is ongoing and that these negative impacts are likely long lasting with other potential adverse lifelong consequences , the proClick here for additional data file.


Two years after the emergence of SARS-CoV-2, our understanding of COVID-19 disease pathogenesis is still incomplete. Despite unprecedented global collaborative scientific efforts and rapid vaccine development, an uneven vaccine roll-out and the emergence of novel variants of concern such as omicron underscore the critical importance of identifying the mechanisms that contribute to this disease. Overt inflammation and cell death have been proposed to be central drivers of severe pathology in COVID-19 patients and their pathways and molecular components therefore present promising targets for host-directed therapeutics. In our review, we summarize the current knowledge on the role and impact of diverse programmed cell death (PCD) pathways on COVID-19 disease. We dissect the complex connection of cell death and inflammatory signaling at the cellular and molecular level and identify a number of critical questions that remain to be addressed. We provide rationale for targeting of cell death as potential COVID-19 treatment and provide an overview of current therapeutics that could potentially enter clinical trials in the near future. On New Years eve 2019, media reports emerged ∼27 cases of viral pneumonia in the central city of Wuhan, China . Deciphering their role and regulation during COVID-19 might increase our understanding of how the detrimental cytokine cascades are initiated, sustained and eventually controlled. During infections, multiple pathways can be engaged creating a redundancy in the induction of cell death but importantly, different forms of cell death may have vastly different consequences in vivo . It is iTraditionally, activation of the NLRP3 inflammasone is regarded to be the main inducer of pathogenic pro-inflammatory cytokines IL-1β, IL-18 and IL-6 during COVID-19, but it has become clear that multiple non-conical pathways can contribute and indeed become the dominate inducers of IL-1β. Necroptotic signaling can lead to release of mature IL-1β, independent of GSDMD ,162,173.GSDMD, traditionally linked to pyroptotic death, was shown to cooperate in enhancing membrane disruption during other forms of cells death. During apoptosis, GSDMD has been linked to the disruption of mitochondrial membranes and the release of cytochrome C  while inCasp-8 gene. More recently, Caspase-8 has been identified as a master regulator of an extensive cross-talk between apoptosis and pyroptosis , even though it was traditionally linked to apoptosis only [Caspase-8 was primarily identified and characterized as the initiator Caspase of extrinsic apoptosis. It transduces signals from death receptors to drive potent cellular responses including inflammation, proliferation and/or death . Active sis only . When Casis only ,182.Multiple studies suggest the involvement of Caspase-8 in pathogenesis of COVID-19. Proteomic profiling revealed increased levels of Caspase-8 in blood of both ICU and non-ICU patients , while iin vivo models of SARS-CoV-2 infection. It is clear that COVID-19 is not due to disease in a single cell type nor through induction of a single cellular pathway. This complexity can only be dissected in vivo. Pharmaceutical targeting of one single pathway will likely not be sufficient to fully ameliorate the pro-inflammatory pathology, since lack of one pathway can usually be compensated by others. Even though a limited number of cytokines appear to be key drivers of severe COVID-19, the plethora of mechanisms and functional redundancies promoting their release and cellular responses need to be assessed in great detail. Additionally, the contribution of individual or combined cell death pathways in specific cell types  and their downstream effects on disease progression and on innate and adaptive immune responses warrant further investigation.Many PCD pathways appear to be activated in COVID-19 but deciphering which are dominant, which are redundant and which are causal of disease is complex. Many, if not all of these pathways are also inextricably linked to the production of cytokines characteristic of severe COVID-19 disease. This adds further to the complexity and confusion of whether the cell death pathways are the cause or effect of cytokine storms. Genetic and pharmacological targeting of cell death pathways may provide some clarity but these studies need to be performed using true Host directed therapies, together with effective antivirals, will be important weapons in the fight against COVID-19, particularly in those for whom an effective vaccination strategy is not possible (i.e. the immunocompromised). Given the relatively short time frame since the emergence of SARS-CoV-2 (∼2 years at the time of publication), a remarkable number of repurposed host-directed therapies have progressed to the clinic to treat severe COVID-19, including cytokine antagonists, recombinant cytokines, glucocorticoids, and cell-based therapies (reviewed ). These It is now well understood that host PCD and inflammation are intimately linked, providing rationale to target not only inflammatory effectors, but upstream cell death mediators, an approach thus-far largely unexplored in COVID-19. Pharmacologically curtailing inflammatory cell death or promoting immunologically silent cell death processes may be powerful approaches to ameliorate disease. Many therapeutic drugs that target host PCD pathways have been progressed into the clinic for the treatment of diverse cancers and inflammatory diseases and these can be readily repurposed to treat severe COVID-19 .Therapeutic induction of neutrophil apoptosis at the site of inflammation may act to reduce aberrant inflammatory processes mediated by neutrophils in severe COVID-19. Neutrophils rely heavily on pro-survival BCL-2 family members for survival  and may Local pathogen-induced inflammatory cytokine production such as TNF may provide an opportunity for preferential induction of apoptosis of infected lung epithelia and inflammatory immune cells such as macrophages. IAP inhibitor drugs, also known as SMAC mimetics, promote apoptotic cell death downstream of TNF receptor engagement. This approach could simultaneously facilitate rapid clearance of the pathogen while mitigating immune cell mediated inflammation.Caspases are cell death executioners, but they also function to orchestrate cytokine maturation and release and facilitate cross-talk between multiple cell death and inflammatory processes. This may make them prime targets for therapeutic interventions to ameliorate COVID-19  transgenic mice reproduces many aspects of human disease and allows testing of potential treatments , but creMultiple therapeutic approaches targeting cytokines and cytokine receptors have been evaluated in pre-clinical studies and have or are entering clinical trials to assess efficacy in managing COVID-19. Targeting the host cell death machinery should also be explored because it is likely a contributor or an effector in COVID-19 pathogenies. The pandemic has provided many lessons and one of them is that therapeutics targeting host cell pathways may have tremendous merit not just for the management of COVID-19 but future pandemics.