Efficient and rapid identification of active SARS-CoV-2 infections has been key to monitoring and mitigating the spread of the virus. The implementation of nucleic acid testing  was broadly adopted by most public health organizations at the national and community levels across the globe, which was followed by more accessible means of home testing including lateral flow immunochromatographic assay (LFA), also known as a rapid antigen test. Here we report the case of an adult female who repeatedly and consecutively tested positive by RAT (BTNX inc). This sustained false positive was not linked with an active SARS-CoV-2 infection, which was ruled out by RT-PCR and serological analyses. SARS-CoV-2 serology revealed no detectable levels of antibodies against the nucleocapsid suggesting no recent prior infection by SARS-CoV-2. This continuous false positive was limited to BTNX testing devices. This case report aims to describe that such continuous false positives can occur and describes alternative testing approaches that can be performed to confirm RAT results. In addition, broader awareness of such occurrences is warranted in the healthcare and public health community to avoid unnecessary negative impacts on individual’s day to day life. The ability to rapidly identify SARS-CoV-2 infected individuals has been central to most public health measures across the globe and is a major component to contact tracing initiatives. Population-level testing requirements prompted the establishment of testing centers that conducted viral nucleic acid detection  , 2. DespSeveral studies have investigated RAT performances in real life settings. In a cohort of 72,382 paired RT-PCR and RAT, Eyre et al. reported an overall sensitivity of 63.2%  100%  on December 28th 2021. At that time, she remained positive for over 2 weeks until she stopped self-testing. Following Canadian public health recommendations, she tested herself regularly by RAT after a high-risk contact, or when experiencing COVID-19 associated symptoms. Repeated self-testing with RAT yielded a constant positive result. After each positive RAT result, she followed recommendations from public health to self-isolate. She was careful about masking and wore N95 in public spaces. Given that RATs were repeatedly positive, she contacted public health authorities for guidance and they recommended she stop testing. On the other hand, her family physician told her to keep testing, thereby suggesting that she was still contagious. These conflicting recommendations have led to confusion, distress and uncertainty for the subject. The numerous isolation periods have impacted her day-to-day life, impacted her family, and prevented her from visiting friends and relatives.th 2021, and a fourth dose of (Pfizer) on August 9th 2022.We were contacted in early fall 2022 to offer guidance on the continuous SARS-CoV-2 positive results on RAT given the mixed recommendations from her physician and local public health. Her medical history included an allergy to dust mites and mild asthma that did not require medication. Her SARS-CoV-2 vaccination history included a first dose of (Pfizer) on May 1st 2021, a second dose of (Moderna) on June 19th 2021, a third dose of (Moderna) on November 19We collected a blood sample to investigate SARS-CoV-2 associated antibodies in serum. We also requested that she provide a saliva sample every week for a month for molecular testing. At the time of providing the saliva sample, we also asked that she perform a set of RAT tests, which included 3 BTNX tests. One test was performed following the manufacturer’s instructions , the other was a swab of the oral cavity, and the third comprised of combination swabbing techniques of both sites. For the first week, a RAT from another supplier was also performed simultaneously .All four weekly RATs that were performed following manufacturer recommended swabbing techniques or the combination of oral and nasal swabbing techniques yielded unambiguous positive results. All four weekly RATs performed by oral swabbing were negative . InteresWhile false positives occur with any diagnostic tests, including RATs, here we report the case of a continuous false positive. To document the events related to the positive RAT and to investigate potential sources of cross-reactivity, a series of RATs were performed. One weekly RAT was performed for four weeks by anterior and deep nasal swabbing as per manufacturer’s instructions. Each of these RATs were positive, confirming the subject’s testimony . Given tWhile the subject did not display any COVID-19 related symptoms at the time of taking the RATs, we still collected saliva samples to perform nucleic acid testing given that subclinical asymptomatic SARS-CoV-2 infections have been documented . The mulSARS-CoV-2 antigen deposition in various tissue following infection have been documented previously , 20. As The source driving the false positive signal on BTNX devices remains unclear. In principle, endogenous peptides could cross-react with BTNX RAT antibodies within the devices. It is also possible that cross-reactivity be driven by exogenous peptides from the microbiome. Given the proprietary nature of the antibodies used in each RATs by the various manufacturers, further investigation on how the antibody selection contributes to false positives is difficult. Anecdotally, the subject’s children and family direct members did not generate a false positive result on RATs.Continuous false positives can occur with RATs despite their overall high positive predictive value. A confirmatory RAT from another supplier could be used as confirmation. Molecular testing remains the gold standard to confirm active infections. Serological testing can rule out chronic or previous SARS-CoV-2 infections in the vast majority of cases. Increased awareness by the medical and public health community of the existence of individuals that continuously test positive is warranted to avoid unnecessary isolation periods and subsequent impact on an individual’s mental health, family, professional activities, social and day-to-day life.Detailed descriptions of the methods are available as The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Ethical approval was obtained from the University of Ottawa REB (H-11-20-6172) for the studies involving humans. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.YG and M-AL wrote the manuscript. YG performed the serology experiments and created the figures. AX performed the qPCR experiments. AD provided participant coordination and sample collection. CC offered clinical perspective and insights. All authors contributed to the article and approved the submitted version.


Background: The incidence and prevalence of heart failure (HF) in the United States has steadily increased in the past few decades. Similarly, the United States has experienced an increase in HF-related hospitalizations which has added to the burden of a resource-stretched healthcare system. With the emergence of the coronavirus disease 2019 (COVID-19) pandemic in 2020, hospitalizations due to the COVID-19 infection sky-rocketed further exacerbating the burden on both patient health and the healthcare system. The focus of this study is to examine how a secondary COVID-19 diagnosis affects the outcome of HF patients, and how a pre-existing diagnosis of heart failure impacts the outcomes of patients hospitalized with COVID-19 infection.Methods: This was a retrospective observational study of adult patients hospitalized with heart failure and COVID-19 infection in the United States in the years 2019 and 2020. Analysis was conducted using the National Inpatient Sample (NIS) database of the Healthcare Utilization Project (HCUP). The total number of patients included in this study from the NIS database 2020 was 94,745. Of those, 93,798 had heart failure without a secondary diagnosis of COVID-19; 947 had heart failure along with a secondary diagnosis of COVID-19. The primary outcome of our study was in-hospital mortality, length of stay, total hospital charges and time from admission to right heart catheterization, which were compared between the two cohorts. Results: Our main study findings are that mortality in HF patients with secondary diagnosis of COVID-19 infection was not statistically different compared to those who were without a secondary diagnosis of COVID-19. Our study findings also showed that length of stay (LOS) and hospital costs in HF patients who had a secondary diagnosis of COVID-19 were not statistically different compared to those who did not have the secondary diagnosis. Time from admission to right heart catheterization (RHC) in HF patients who had a secondary diagnosis of COVID-19 was shorter in heart failure with reduced ejection fraction (HFrEF) but not in heart failure preserved ejection fraction (HFpEF) compared to those without secondary diagnoses of COVID-19. Finally, when evaluating hospital outcomes for patients admitted with COVID-19 infection, we found that inpatient mortality increased significantly when they had a pre-existing diagnosis of heart failure.Conclusion: The COVID-19 pandemic significantly impacted hospitalization outcomes for patients admitted with heart failure. The time from admission to right heart catheterization was significantly shorter in patients admitted with heart failure reduced ejection fraction who also had a secondary diagnosis of COVID-19 infection. When evaluating hospital outcomes for patients admitted with COVID-19 infection, we found that inpatient mortality increased significantly when they had a pre-existing diagnosis of heart failure. Length of hospital stay and hospital charges also were higher for patients with COVID-19 infection who had pre-existing heart failure. Further studies should focus not just on how medical comorbidities like COVID-19 infection, affect outcomes of heart failure but also on how overall strains on the healthcare system, such as pandemics, may affect the management of conditions such as heart failure. Heart failure (HF) was designated as an epidemic in 1997 due to an observed exponential increase in hospitalization rate . From 20The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly by the end of 2019, creating a global health emergency. The spread of this virus further exacerbated the public health burden of HF . StudiesPatients living with HF were particularly vulnerable to infection due to reduced immunity and hemodynamic ability to cope with the stress of severe infection . FurtherAccordingly, since the onset of the pandemic, many studies have investigated the relationship between a COVID-19 diagnosis and HF. In a meta-analysis and systemic review that looked at a 21,640-patient sample from 18 studies, it was shown that patients with pre-existing HF that developed COVID-19 had a higher mortality rate than COVID-19 patients with no prior HF diagnosis . Here weStudy designThis was a retrospective observational study of adult patients hospitalized with a primary diagnosis of heart failure and a secondary diagnosis of COVID-19 infection in the United States in the year 2020. The study aims to estimate the impact of the COVID-19 pandemic on the outcomes of patients admitted with heart failure. This study also aimed to assess the outcomes of patients admitted with COVID-19 infection who had a pre-existing diagnosis of heart failure.Data source and sampleFor data analysis, we utilized one of the largest inpatient databases in the United States, the National Inpatient Sample (NIS) which contains discharge data from a 20% stratified sample of United States hospitalizations nationwide. The NIS uses the tenth revision of the International Classification of Diseases (ICD10-CM) for codes for diagnosis. We included codes for primary diagnosis of congestive heart failure (ICD10-CM I1504), Heart failure reduced ejection fraction (ICD10-CM I1502), heart failure preserved ejection fraction (ICD10-CM I1503) and a code for co-morbid COVID-19 infection (ICD10-CM U071). We excluded patients with diagnosis of shock  and patients requiring mechanical ventilation .Variables of interest and study outcomesThe primary outcome of our study was in-hospital mortality, length of stay, total hospital charges and time from admission to right heart catheterization (RHC), which were compared between the two cohorts which is with or without secondary diagnosis of COVID-19. The secondary outcome of our study was to evaluate the impact of preexisting heart failure on COVID-19 patients in terms of inpatient mortality, length of stay and total hospital charges. The exposure of interest was COVID-19 infection. Potential confounders that were adjusted for included age in years, female sex, race, median income in patient’s zip code, patient comorbidities measured using Charlson Comorbidity Index (CCI) for administrative data, hospital region, hospital location and teaching status, hospital bed size and primary expected payer.Statistical analysisIn accordance with the Healthcare Cost and Utilization Project data use agreement, we did not report variables containing a small number of observations (≤10) that could pose risk of patient identification or data privacy violation.Statistical analysis was performed using STATA version 17.0 software  . Data waPopulation demographicsThe total number of admissions included in this study from the NIS database 2020 was 94,745. Of those, 93,798 had heart failure without a secondary diagnosis of COVID-19; 947 had heart failure along with a secondary diagnosis of COVID-19. Table Inpatient mortality outcomePatients who had heart failure with reduced ejection fraction (HFrEF) with a secondary diagnosis of COVID-19 had a higher inpatient mortality than patients without a secondary diagnosis of COVID-19 . Patients who had heart failure preserved ejection fraction (HFpEF) with a secondary diagnosis of COVID-19 had lower inpatient mortality than patients without a secondary diagnosis of COVID-19 infection . Mortality outcomes for both HFrEF and HFpEF and COVID-19 were not statistically significant with p-values greater than (0.05). Age, gender, race, Charlson comorbidity Index (CCI) were kept constant. These results are shown in Table Length of stay (LOS)We compared length of stay for patients hospitalized with HF and with or without a secondary diagnosis of COVID-19. Length of stay comparisons were not statistically significant with p-values greater than 0.05. For patients with HFrEF and HFpEF, the mean length of stay was lower for patients with a secondary diagnosis of COVID-19, versus those without . These results are shown in Table Hospital cost and resource utilizationWhen assessing costs, there were lower costs associated with hospital stays for patients with heart failure and a secondary diagnosis of COVID-19 infection when compared to those without a secondary diagnosis of COVID-19 infection, however, hospital charge comparisons were not statistically significant with p-values greater than 0.05 . These results are shown in Table Time from admission to right heart catheterizationWe compared the time from admission to right heart catheterization (RHC) for our patient populations with heart failure with and without a secondary diagnosis of COVID-19 infection. Patients with heart failure (either HFrEF or HFpEF) and with a secondary diagnosis of COVID-19 had a lower mean time from admission to RHC . When we compare the data for the year 2019, the pre-pandemic period, to the year 2020, the time from admission to RHC was higher pre-pandemic, with an average time of 3.66 days in 2019 for HFrEF compared to 3.5 days in 2020, and 4.04 days in 2019 for HFpEF compared to 3.9 in 2020. These results are shown in Table Impact of the COVID-19 pandemic on HFrEF and HFpEF compared to pre-pandemicTable Impact of pre-existing HF on a patient admitted with a primary diagnosis of COVID-19 infectionFor patients who were admitted with a primary diagnosis of COVID-19 infection in the year 2020, we assessed the effect of having a pre-existing heart failure diagnosis. We compared inpatient mortality; length of hospital stays and hospital charges between those who were admitted with COVID-19 infection who also had heart failure and those without a history of heart failure. Results are shown in Table In this large nationwide observational study, and to the best of our knowledge this is the largest analysis to date, we explored the outcomes of HF patients in this patient population during the first year of the COVID-19 pandemic. Our main study findings are that mortality in HF patients with secondary diagnosis of COVID-19 was not statistically different compared to those who were without a secondary diagnosis of COVID-19. Our study findings also showed that length of stay (LOS) and hospital costs in HF patients who had a secondary diagnosis of COVID-19 were not statistically different compared to those who did not have the secondary diagnosis. Time from admission to right heart catheterization (RHC) in HF patients who had a secondary diagnosis of COVID-19 was shorter in HFrEF but not in HFpEF compared to those without secondary diagnoses of COVID-19. Lastly, when evaluating hospital outcomes for patients admitted with COVID-19 infection, patients that had a pre-existing diagnosis of heart failure had higher inpatient mortality rates and longer lengths of stay than those without pre-existing heart failure.The relation between heart failure and COVID-19 is bidirectionally interrelated in both direct and indirect ways. Bhatt et al. observed that in hospitalized patients with HF, having COVID-19 was associated with greater odds of in-hospital mortality (24.2%) as compared to those hospitalized only for acute HF exacerbation (2.6%) . AlvarezA large observational study conducted by Fox et al., analyzed 27,427 emergency department visits or hospitalizations from a 12-hospital health system to examine changes in treatment (right and left-heart catheterization), and outcomes for patients with heart failure (HF) during the COVID-19 pandemic compared with pre-COVID-19. They reported that there was no difference in the rate of RHC, length of stay, or mortality .On the other hand, a meta-analysis by Yonas et al. observed that seven studies reported a statistically significant difference in mortality between patients with COVID-19 with and without pre-existing HF . This isCOVID-19 infection may alter the course of heart failure and other cardiac conditions through activation of the inflammatory cascade, among other numerous potential pathways involved in the pathogenesis of the infection and the complications resulting from it. The stress of the infection may result in new-onset heart failure, or an acute exacerbation of pre-existing heart failure and a number of studies have observed the effects of COVID-19 on outcomes of either pre-existing or new-onset heart failure ,17.LimitationsThere are some limitations in this study. First, we used a database to select patients who had specific diagnoses. Coding errors and documentation errors may be present. We anticipate this to be small in number and insignificant. Second, patient-specific data, such as individual lab results, oncologic status, medications, imaging results, that would be expected to influence outcomes were not accounted for. Third, while inpatient mortality was accounted for in this study, mortality after discharge from the hospital and mortality without hospitalization were not accounted for and were not available in the database. Finally, there may be selection bias or unmeasured bias in any observational study including this meta-analysis. We completed a multivariable analysis to reduce allocation bias.The COVID-19 pandemic significantly impacted hospitalization outcomes for patients admitted with heart failure. This study provides a nationwide retrospective study of a large number of patients with heart failure during the COVID-19 pandemic to evaluate the effect of the pandemic on hospital outcomes. The time from admission to right heart catheterization was significantly shorter in patients admitted with heart failure reduced ejection fraction who also had a secondary diagnosis of COVID-19 infection. When evaluating hospital outcomes for patients admitted with COVID-19 infection, we found that inpatient mortality increased significantly when they had a pre-existing diagnosis of heart failure. Length of hospital stay and hospital charges also were higher for patients with COVID-19 infection who had pre-existing heart failure than for those without pre-existing heart failure. Further studies should focus not just on how medical conditions, such as COVID-19 infection, affect outcomes of heart failure but also on how overall strains on the healthcare system, such as pandemics, may affect the management of conditions such as heart failure.


Glutathione peroxidases (Gpx) are a group of antioxidant enzymes that protect cells or tissues against damage from reactive oxygen species (ROS). The Gpx proteins identified in mammals exhibit high catalytic activity toward glutathione (GSH). In contrast, a variety of non-mammalian Gpx proteins from diverse organisms, including fungi, plants, insects, and rodent parasites, show specificity for thioredoxin (TRX) rather than GSH and are designated as TRX-dependent peroxiredoxins. However, the study of the properties of Gpx in the environmental microbiome or isolated bacteria is limited. In this study, we analyzed the Gpx sequences, identified the characteristics of sequences and structures, and found that the environmental microbiome Gpx proteins should be classified as TRX-dependent, Gpx-like peroxiredoxins. This classification is based on the following three items of evidence: i) the conservation of the peroxidatic Cys residue; ii) the existence and conservation of the resolving Cys residue that forms the disulfide bond with the peroxidatic cysteine; and iii) the absence of dimeric and tetrameric interface domains. The conservation/divergence pattern of all known bacterial Gpx-like proteins in public databases shows that they share common characteristics with that from the environmental microbiome and are also TRX-dependent. Moreover, phylogenetic analysis shows that the bacterial Gpx-like proteins exhibit a star-like radiating phylogenetic structure forming a highly diverse genetic pool of TRX-dependent, Gpx-like peroxidases. The antioxidant enzymes include superoxide dismutase (E.C. 1.15.1.1), catalase (E.C. 1.11.1.6), peroxiredoxin (E.C. 1.11.1.7), and glutathione peroxidase (E.C. 1.11.1.9). Peroxiredoxin (Prx) and glutathione peroxidase (Gpx) are the mostly widely studied due to their primary role in protecting cells from oxidative damage caused by ROS.Reactive oxygen species (ROS), such as free radicals and peroxides, are produced in the cells of organisms under oxidative stress, causing the cells extensive damage. This demonstrates that organisms have developed diverse antioxidant mechanisms for protection against cellular damage. The antioxidant system includes the small molecular nonenzymatic antioxidants  ts family .The presence of the Gpx-like proteins in bacteria has been regarded as widespread and even ancient . HoweverIn the present study, we leverage this potential of the soil microbiome by mining these data from our in-house dataset (PRJNA237577 and SRP036853) and the NCBI environmental sequence dataset (env_nr) . We invewww.rcsb.org), which includes yeast Gpx3 [Drosophila DmGPx [The Gpx-like protein sequences with known biochemical properties or structure information were derived from UniProt  or the Past Gpx3  (PDB ID:ast Gpx3  (PDB ID:la DmGPx  (UniProtla DmGPx (PDB ID: la DmGPx  and adjula DmGPx . The seqwww.ncbi.nih.gov). The metagenomic sequences from the depository SAMN02630628 were assembled using velvet (version 1.0.18) [The microbiome Gpx-like proteins were obtained from the metagenomes of the data depository SAMN02630628 and NCBI env_nr database  ( 1.0.18)  and the www.uniprot.org; (ii) by sequence comparison using BLAST against a set of previously prepared Gpx proteins. The protein entries derived from the two methods were combined and de-duplicated. The extracted proteins were processed by discarding those sequences that were too short or too long and trimming the divergent N-terminus and C-terminus in the multiple sequence alignment. The non-redundant protein set was derived by performing cd-hit on the processed proteins [The bacterial Gpx-like proteins were collected from the UniProt database using two methods : (i) by proteins . The mulproteins  and the proteins .The three-dimensional structure of MtGpx0 was modeled using the crystal structure of Schistosome Gpx4 2V1M as the template (with similarity ~ 43%) on the Phyre server (version 2.0) . The strThe phylogenetic tree was constructed for the multiple aligned sequences using MEGA (version 6)  with booA total of 1,319 Gpx-like proteins were identified from the environmental microbiome metagenomes in the combined NCBI env_nr dataset and our in-house dataset . Using this set of proteins, we created a non-redundant set of 392 proteins (averaged to 186 amino acids in length) by merging the proteins with pair-wise similarities greater than 80%. Multiple sequence alignment of the 392 proteins exhibits a well-conserved pattern . ConstruDrosophila, Schistosomes and humans (Drosophila DmGpx) or phospholipid peroxidases (Schistosomes SmGpx and human Gpx4) with average similarities of ~42% compared to the ~30%with the canonical human Gpx proteins (Gpx1-Gpx3 and Gpx5-8). The relationship is also supported by their phylogenetic structure, where the microbiome-encoded Gpx-like proteins are clustered together with yeast Hyr1, poplar PtGpx5, Drosophila DmGpx, Schistosomes mansoni SmGpx, and human Gpx4  (framed in orange), but not in the canonical human Gpx proteins. This second Cys residue in the “Cys block” was identified as the “resolving Cys” by forming a disulfide bridge with the peroxidatic Cys in the non-mammalian Gpx [We performed comparative analysis of the primary sequences and secondary structures of the Gpx-like proteins from the environmental microbiome dataset and non-microbial species to examine the characteristics of the environmental microbiome-encoded Gpx-like proteins. We found that the four active site tetrad residues  and the key functional domains are conserved, we used the longest sequence  as a representative basis for building the structural model using homology modeling with Phyre2  and deepterminus .In the modeled structure of MtGpx0, the resolving Cys107 on the helix α1a faces the peroxidatic Cys61 in the loop preceding α1. This conformation enables the two Cys to form the intramolecular disulfide bond. The formation of the disulfide bond was shown to be required for reacting with TRX . We obseThe TRX-binding specific residues are localized around the extra helix, α1a, including Pro97, Phe106, and Tyr111  and 3B. Schistosomes SmGpx and 34% for poplar PtGPX5. The active tetrad was surrounded by several non-charged residues, i.e., Ser59, Gly62, Phe63, Thr64, Phe92, Gly93, and Ser169, and one positively charged residue, Lys60  form a cleft on the structure surface with a relative solvent-accessible area of 52.7%, making it accessible to the solvent  and 4B. e, Lys60 . The dise, Lys60 , in contTo explore the oligomerization state of MtGpx0, the modeled structure was aligned to the subunit of tetrameric human Gpx3 and monomeric human Gpx4 . MtGpx0 i.e., the conformation of the two Cys residues, the substrate binding sites, the surface charge distribution, and the oligomeric state, clearly point to an explanation as to the functional homology of MtGpx0 with the TRX-dependent Prx in spite of the high sequence similarity with the canonical Gpx proteins.Taken together, the structural properties presented here for MtGpx0, Using sequence alignment and structural modeling, we have shown that Gpx-like proteins from environmental microbiome metagenomes harbor the key functional motifs required for the TRX-dependent catalysis. To determine whether the functional motifs related with the TRX-dependence could be extended to other bacterial Gpx-like proteins, we collected the known bacterial proteins homologous to Gpx-like proteins from the UniProt database and performed preprocessing to improve the sequence quality  . The finBased on the primary sequence and secondary structure alignments, we established the conservation pattern as shown in gradient colors . The proTaken together, the Gpx-like proteins from isolated bacterial species, similar to those from environmental microbiome, plants and fungi, share common structural elements and functional motifs specific for TRX-dependent catalysis.To investigate the sequence diversity and phylogenetic structures of the bacterial Gpx-like proteins, we created a non-redundant protein set by merging the protein sequences with similarities greater than 70%, thus obtaining 376 representative sequences for phylogeny construction. Interestingly, the phylogeny exhibits a star-like structure and most of the branches have a bootstrap support value lower than 70, with the exception of some external branches  and glutathione peroxidases (Gpx), usually using TRX and GSH as reducing agents, respectively, have attracted extensive research interest due to their primary roles in protecting cells from oxidative damage. The rapidly accumulated research findings revealed the surprisingly high versatility and complexity of these two families of proteins regarding their catalytic mechanisms, sequence classifications, and evolution. In addition to the heterogeneous subtypes with subtle differences within each family, the two families of proteins also exhibit interconnections with respect to function and evolution. Previous studies have identified multiple Gpx-like peroxidases in diverse organisms, including fungi , 9, plan13In this study, we addressed these issues for the first time by comprehensively analyzing the Gpx-like proteins from environmental microbiome metagenomes using public databases and our own dataset . We alsoOf particular interest, a small proportion of sequences from the environmental microbiome 4.2%) and isolated bacteria species 17%) contain the oligomerization loop domain but lack the dimerization loop domain . Based o% and iso% containRegardless, the external environments played key roles in shaping the evolution of the two groups of proteins. The TRX-dependent catalysis of Gpx-like proteins in bacteria and GSH-dependent catalysis of Gpx in mammals might simply reflect the availability of TRX or GSH in their cellular environments. In bacteria, GSH is very limited or absent, especially in gram-positive bacteria . TherefoThe shaping effects of the environments were also manifested by the phylogenetic structure of the Gpx-like proteins of bacteria. The availability of the large amount of Gpx sequences allowed us to build a phylogeny capturing a sufficient diversity of bacterial taxonomy. The phylogeny of the bacterial Gpx-like proteins exhibits a star-like radiating structure, where the proteins are unable to form confident branch splitting. This structure is different from the previous report in , where tIn conclusion, we have comprehensively analyzed the Gpx-like proteins encoded by the environmental microbiome and isolated bacterial species, and identified the characteristics of the proteins in terms of sequences, structures, and phylogeny. We showed that the Gpx-like proteins from bacteria should be classified as TRX-dependent peroxiredoxins using TRX as the reductant rather than GSH. The high diversity of this group of proteins may provide a genetic pool to be used as a resource for antioxidant applications or targets of antibacterial agents. In the meantime, the huge diversity of the proteins poses challenges for future functional and application studies. Novel methods of high-throughput screening are needed to identify the sequences with high antioxidant capability under specific oxidative stresses. Also, the physiological relationship between the Gpx-like proteins and the Prx proteins, and the regulatory network within bacteria, remain to be delineated and that will help to enhance the understanding of the molecular mechanisms of bacteria in protecting cells from diverse oxidative damage.http://jmb.or.kr.Supplementary data for this paper are available on-line only at


To quantify population health risks for domiciliary care workers (DCWs) in Wales, UK, working during the COVID-19 pandemic.A population-level retrospective study linking occupational registration data to anonymised electronic health records maintained by the Secure Anonymised Information Linkage Databank in a privacy-protecting trusted research environment.Registered DCW population in Wales.Records for all linked DCWs from 1 March 2020 to 30 November 2021.Our primary outcome was confirmed COVID-19 infection; secondary outcomes included contacts for suspected COVID-19, mental health including self-harm, fit notes, respiratory infections not necessarily recorded as COVID-19, deaths involving COVID-19 and all-cause mortality.Confirmed and suspected COVID-19 infection rates increased over the study period to 24% by 30 November 2021. Confirmed COVID-19 varied by sex  and age (>55 years: 19% vs <35 years: 26%) and were higher for care workers employed by local authority social services departments compared with the private sector . 34% of DCWs required support for a mental health condition, with mental health-related prescribing increasing in frequency when compared with the prepandemic period. Events for self-harm increased from 0.2% to 0.4% over the study period as did the issuing of fit notes. There was no evidence to suggest a miscoding of COVID-19 infection with non-COVID-19 respiratory conditions. COVID-19-related and all-cause mortality were no greater than for the general population aged 15–64 years in Wales . A comparable DCW workforce in Scotland and England would result in a comparable rate of COVID-19 infection, while the younger workforce in Northern Ireland may result in a greater infection rate.While initial concerns about excess mortality are alleviated, the substantial pre-existing and increased mental health burden for DCWs will require investment to provide long-term support to the sector’s workforce. Linking registration data to electronic health records in Wales provided a unique opportunity to explore health outcomes for domiciliary care workers (DCWs) working during the pandemic.No DCWs dissented for their registration data to be made available for linkage, allowing a comprehensive population-level analysis.Demographic data available by linked registration and healthcare datasets allowed assessment by key DCW subgroups.Our study provides a broad range of health outcome estimates for DCWs over an extended period of the COVID-19 pandemic.The recording of some data could have introduced bias, for example, coding for COVID-19 biased towards individuals presenting in primary or secondary healthcare.During 2018–20, >950 000 people in the UK received domiciliary care from nearly 823 000 domiciliary care workers (DCWs) from over 10 100 home care providers and the demand is predicted to increase significantly over the next 20 years.These close working conditions with clinically vulnerable people, and the varied functions that DCWs perform in a non-institutional setting in a workforce with a large number of employers, may increase exposure to COVID-19. A cross-sectional survey led by Ulster University found that during the early period of the COVID-19 pandemic (between May–July 2020 and November 2020–January 2021), both well-being and quality of working life deteriorated over all occupations including social care.From 1 April 2020, it became mandatory for DCWs in the active Welsh workforce to be registered with Social Care Wales.The Outcomes for Social Carers: an Analysis using Routine data (OSCAR) study aimed to use the registration data collected by Social Care Wales, individually linked to secure anonymised electronic health records (EHRs) to quantify population health risks for DCWs in Wales working during the COVID-19 pandemic. Specifically, we aimed to quantify rates of confirmed and suspected COVID-19 infection and, to fully establish the range of adverse health outcomes potentially affecting DCWs such as mental health including self-harm, issuing of fit notes, non-COVID-19 hospital admissions for respiratory conditions and COVID-19-related and all-cause mortality, as a consequence of the pandemic. We compared mental health outcomes  within the pandemic period with the prepandemic situation to assess the effect of the pandemic. Similarly, we examined admissions over time for respiratory infection in the early pandemic to identify the potential miscoding of COVID-19 infection. Outcome variation was explored by demographics, work-related factors, lifestyle and comorbidities. To develop timely public health policy messages that could be extended to DCWs in other nations of the UK, using up-to-date DCW workforce data from Scotland, Northern Ireland (NI) and England, we compared these populations with the Welsh DCW workforce and assessed the generalisability of prevalence findings to each nation.This study is a population-level mixed-methods retrospective routine data linkage study with analysis guided by exploratory qualitative interviews. Qualitative findings are reported separately.10.1136/bmjopen-2022-070637.supp1Supplementary dataThe study population was all registered DCWs resident in Wales on 1 March 2020 who did not subsequently opt-out to their data being transferred to the SAIL Databank and linked for research, either when DSCW data were added to the SAIL Databank or via their general practice (GP). Any DCWs not linked into the SAIL Databank with sufficient confidence were excluded. Additionally, individuals included in the dataset but registered as domiciliary care managers, adult care home managers or residential child care workers were excluded.The primary outcome was confirmed COVID-19 infection, defined as the earliest of the following events: a positive PCR test (Pathology Test Results Dataset (PATD)),11Secondary outcomes were:Suspected COVID-19 infection .Contacts for mental health and diagnoses, psychotropic medication and admissions (including self-harm) (data sources: WLGP and PEDW).Fit notes as a general marker of medically confirmed illness (data source: WLGP).Non-COVID-19 hospital admissions for respiratory conditions (data source: PEDW).Deaths involving COVID-19 and all-cause mortality (data source: ADDE).In brief, we examined depression and anxiety symptoms and diagnoses,19DCWs entered the cohort on 1 March 2020 and were followed up until 30 November 2021. We examined outcomes by waves of the pandemic; short-term outcomes from 1 March 2020 to 31 August 2020  and from 1 September 2020 to 28 February 2021 (wave 2). Long-term outcomes were examined to 30 November 2021. DCWs were followed up until the earliest of occurrence of death, first migration out of Wales, the outcome of interest or the end of the follow-up.We chose age, sex, ethnicity, health board, deprivation quintile and rurality as relevant characteristics for the DCWs using a number of data sources . We alsohttps://osf.io/9w6pe/) alongside codes for the comorbidities.A full coding list for outcomes is in The linkage of DCWs EHR were described and the study population characterised using frequencies and percentages. The presence of key health outcomes were described by the short-term and long-term periods; prevalence estimates presented alongside 95% confidence intervals (CIs). This study is primarily designed to describe variation in outcomes in the DCW population, by key subgroups, rather than for prediction or causal models. Using multilevel Cox regression to model time to first confirmed COVID-19 infection, we estimated hazard ratios (HRs) with 95% CIs, to quantify the risk of confirmed COVID-19 by comorbidity status adjusting for age, sex and deprivation, and also by their employing sector adjusting for health board, rurality and qualification type. DCWs were censored at the time of the event, migration, death or at the study end.itsa command in Stata. A Cumby-Huizinga test was used to determine the autocorrelation and was adjusted using Newey-West AC estimators. Using the same approach and as a post hoc analysis, the number of mental health GP contacts and prescriptions, and hospital admissions were also examined.To explore changes over time and the effect of the COVID-19 pandemic period, an interrupted time series approach was used to test the hypothesis that hospital admissions for non-COVID-19 respiratory infections would increase in comparison to the rates seen pre-March 2020, if there was misclassification of infections. Monthly data were divided into prepandemic (1 March 2016 to 28 February 2020) and the pandemic period (1 March 2020 to 31 October 2022). The changes in levels  and trends (the rate of change) of the prepandemic segment and pandemic segment were analysed using the Equivalent up-to-date DCW workforce data from Scotland, NI and England were obtained and compared with the Welsh DCW cohort allowing us to generalise the estimate of confirmed COVID-19 cases in other nations.To comply with disclosure control process and approvals, and protect the privacy of anonymised individuals, numbers <5 are suppressed. Percentages associated with small frequencies are rounded to the nearest 1%. Study findings were reported in accordance with applicable reporting guidelines for observational studies using administrative data  and REporting of studies Conducted using Observational Routinely-collected Data (RECORD)) .21 22 DaTwo stakeholder groups provided input to the project, an ongoing Study Advisory Group and an Implementation Reference Group. Both groups included membership drawn from the domiciliary care sector and both contributed to interpretation of emerging results.Data from 15 931 DCWs resident and working in Wales on 1 March 2020 were linked to EHR data (85% of them had GP records in SAIL). Records from 206 DCWs were excluded due to matching errors, leaving 15 725 (98.7%) DCWs included in the final analysis. The majority of DCWs were female (84%), of white ethnic background (95%), 43% had at least one comorbidity of which 22% had been recorded with mental health-related illness .Confirmed COVID-19 infection was 1.2% on 31 August 2020, rose to 14% by the end of February 2021 and reached 24% by the end of November 2021 . There wSuspected COVID-19 infections increased over the pandemic from 2% in early pandemic to 15% by at the end of February 2021, rising to 24% by the end of November 2021 . ExaminiContacts and diagnosis for mental health, psychotropic medication and admissions were 23% in August 2020, rising to 28% and 34% by the end of February and November 2021, respectively . IncreasVariations in mental health contacts were observed across many of the key demographics , and consistently higher in care workers with a disability reported at registration, with a comorbidity or shielding . There wThe issuing of fit notes as a general marker of medically confirmed illness increased over the time period from 5% in August 2020 to 10% by at the end of February and 15% by the end of November 2021 . Fit notBoth all-cause and COVID-19-related mortality involved 0.1% and 0.034%, respectively, of the care workers over the long-term study period (1 March 2020 and 30 November 2021) .Up-to-date DCW workforce from ScotlandConfirmed COVID-19 infection rates increased from 14% during the first two pandemic waves (1 March 2020 to 28 February 2021) to 24% by the end of November 2021. Infection rates varied by personal characteristics of care workers , by where care workers lived  and were higher for care workers employed by local authority social services departments compared with staff employed in the private sector. Suspected COVID-19 infections similarly increased to 24% over the study period. One-third (34%) of all care workers required support for a mental health condition, with rates of attending a GP or receiving a relevant prescription higher compared with the 4 years preceding pandemic onset. Rates in mental health contacts varied by key demographics and were consistently higher in care workers with a comorbidity, disability or who were shielding. Local authority care workers had a slightly elevated risk of mental health problems compared with those employed by the private sector. Events for self-harm also increased, from 0.2% to 0.4%. The issuing of fit notes as a general marker of medically confirmed illness increased by 10% over the COVID-19 pandemic and were more prevalent in female DCWs, those with an underlying comorbidity or at a higher risk of getting seriously ill from COVID-19. Unlike confirmed COVID-19 infection, fit notes were more likely in those that were older (aged 45 years and over). There was no evidence to suggest a miscoding of COVID-19 infection in the early pandemic. The mortality rate among care workers was no greater than that observed among the general population aged 15–64 years in Wales (which was 0.034%). Data from DCW workforces in England and Scotland showed a similar demographic with respect to age, sex and ethnicity. The impact of COVID-19 infection would therefore be similar to that observed in Wales. The NI workforce was younger and therefore we may expect a high prevalence rate.A principal strength of the study was the comprehensive and contemporaneous coverage of the DCW population in Wales through linkage to registration data. This contrasts with approaches reliant on census data which may be incomplete, at risk of being out of date or on sample surveys with varying levels of non-response.31 31The registration process was introduced by Social Care Wales to meet regulatory rules that came into force from 1 April 2020.Administrative records do not usually capture subjective patient-reported outcomes or well-being concerns. This may be a greater consideration if care workers felt reluctant or less able to present concerns to healthcare professionals during the pandemic, as has been observed in other settings.Outcome rates varied considerably based on characteristics and circumstances of care workers, such as age and geographical location. Such differences may not be directly attributable to the working conditions of care workers or even directly addressable. Nevertheless, it highlights that risks for care workers remain a vital consideration for employers.Some methodological caveats include that we could not distinguish between DCWs who did not consult with their GP or where their GP had not contributed data to SAIL. This will result in downward biased GP outcome rates . Second, if missing GP data are not at random , it is more likely that we would have missed mental health diagnosis or fit notes from DCWs with certain characteristics, resulting in bias in our parameter estimates (ORs/HRs) for these outcomes. Third, DCWs were followed up for different durations . While the time to event analyses (and corresponding HRs) account for this by censoring, summary statistics and ORs do not. Lastly, cohort ageing and differences in cohort entry or exit dates would likely lead to upward bias in the overall trends in the time series analyses.In contrast to our findings, ONS data from the initial 3-month period of the pandemic suggested that mortality rates for individuals employed as care workers and home carers were higher than those found in the general population.Differing study methodologies may reduce the value of direct comparisons, while also offering different levels of insight. Clarity of the study population is one such parameter. At the commencement of the pandemic consideration of work-place characteristics, opportunities to mitigate risk and rapidly emerging evidence of infection and associated mortality drove interest in social care and in our study, the work of DCWs. Subsequently, a semi-quantitative job exposure matrix (COVID-19-JEM) was developed to estimate the likelihood of workers becoming infected with SARS-CoV-2 in an occupational setting.Comparing rates across studies may not always be helpful due to the differences in methods used and the time intervals covered. For example, while the ONS survey used RT-PCR testing, other studies have used other methods ).et al used US national Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance Survey data to explore outcomes of home healthcare workers employed by home care agencies.Previous research on care worker health outcomes has often focused on musculoskeletal problems and dealing with challenging behaviour.This study presents evidence of the direct and indirect impact of the COVID-19 pandemic on DCWs in Wales. Higher rates of confirmed COVID-19 infection among DCWs did not translate into mortality rates greater than for the broader Welsh population. High baseline rates of mental ill-health further increased over time, a burden that fell unevenly across the workforce. It remains to be determined whether any facet of the employment role, such as staff training, occupational risk assessment or testing procedures, may have contributed to these differences or provided an opportunity to intervene. Evidence from our own work and that of others supports the value of co-produced solutions which draw on the direct experiences of care workers to support occupational related well-being.


This Research Topic brings together articles that deal with many aspects of redox regulation in plants, with special emphasis on reactions taking place in chloroplasts. It contains 4 papers, of which 3 are original research and one is a review.Redox reactions, a type of reaction involving a transfer of electron between two species, are fundamental for life on Earth. Universal carriers of reducing power such as pyridine nucleotides (NAD+/NADPH and thioredoxins (TRXs). In the light, reducing power is transferred from the photosynthetic electron transport system to TRXs, which then activate their target enzymes in the Calvin-Benson-Bassham (CBB) cycle. The TRX system has recently gained considerable attention as it is involved in maintaining the redox balance and regulating signal transduction.Chloroplasts of higher plants are a site of the intensive turnover of NADPZhou et al. characterize the typical TRX gene family in the wheat genome in regard to their distribution, duplication events, and phylogenic relationships. Several methodological approaches were employed to document the important role of Triticum aestivum TRX genes in the maintenance of redox homeostasis in control conditions and under stress. Among them were the functional annotation analyses of typical TaTRX genes by Gene Ontology and characterization of cis-elements in their promoters. On the example of the chosen TRXs gene (TaTRX24) response to osmotic stress was followed with WT and overexpressing line. Further on, publicly available RNA-seq samples from five tissues  were explored with a focus on the typical expression patterns of TaTRX genes in a broad spectrum of stress situations .In the first article, García-Cañas et al. focuses the attention on two CBB enzymes, namely fructose-1,6-bisphosphatase and sedoheptulose-1,7-bisphosphatase , which are validated targets of light-dependent regulation mediated by TRXs. To assess the relative importance of their activity in ensuring photoautotrophism, the authors followed an interesting approach by complementing the cyanobacterial SBPase/FBPase mutant with orthologs from higher plants. The expression of both plant enzymes restored photoautotrophic growth, but at reduced rates and only under low light conditions. Improved growth of the complemented strain was achieved when plant f-type TRX was co-expressed with CBB enzymes, indicating that an efficient modulation of the redox state of plant enzymes is crucial to allow proper functioning under light conditions. Overall, this study demonstrates that plant proteins can be reduced by the cyanobacterial TRX system, and opens the possibility to use this complementation approach as a reliable tool to analyze in vivo the specificity of the TRX system toward redox-regulated plant proteins.A study by Meloni et al. provides a comprehensive survey of three CBB cycle enzymes involved in the regeneration of the substrate of the ribulose-1-5-bisphosphate carboxylase/oxygenase (Rubisco). These enzymes are ribose-5-phosphate isomerase (RPI), ribulose-5-phosphate epimerase (RPE), and phosphoribulokinase (PRK). The authors retrieve from the literature the current knowledge on the structural and catalytic properties of these photosynthetic enzymes, highlighting the molecular basis of enzyme functioning in the context of the chloroplast stroma. In addition, the authors provide information on regulatory mechanisms involving metabolites or post-translational modifications such as oxido-reduction of protein cysteines and phosphorylation at serine and threonine residues. These redox and phospho-dependent regulations are of particular interest since the three enzymes have been identified as potential targets by proteomic-based approaches. Modifications of specific residues can thus alter the function of the protein in response to both physiological and stress conditions, with consequences for metabolic flux and thus for Rubisco substrate regeneration. As future perspectives, the authors discuss possible strategies to be applied as future research lines aimed at improving photosynthetic efficiency and plant productivity.The review by The reducing power carriers are also necessary for the action of non-photosynthetic plastids. Noteworthy, plastids in non-photosynthetic tissue are also capable of producing NADPH from glucose via the oxidative pentose phosphate pathway (OPPP). This reducing power is necessary for nitrite reduction, antioxidant function and several other pathways.Née et al. demonstrates that the entry point of OPPP in non-photosynthetic plastids, namely glucose-6-phosphate dehydrogenase (G6PDH) reaction, primarily involves G6PDH2 and G6PDH3 isoforms in the model plant Arabidopsis thaliana. In vitro analysis revealed the activity of both enzymes undergoes a dithiol/disulfide regulatory interchange with m-type TRX being found to be the most efficient. This regulation is crucial for root growth, as shown with the trxm1trxm2trxm4 triple mutant. Such redox activation is important, for example, for stimulation of G6PDH activity under salinity, in which the G6PDH2 contributes predominantly.The paper by In conclusion, we believe that this Research Topic indeed provides new information about a number of proteins involved in redox regulatory networks in the chloroplast, either thioredoxins or carbon-related metabolic enzymes. It is our hope that in the near future, it will be possible to cover additional topics, in particular, in the field of light-dependent reactions of photosynthesis, as components of the photosynthetic apparatus along with regulatory proteins are likely regulated through redox mechanisms.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


Necrotizing enterocolitis (NEC) is one of the most fatal diseases in premature infants. Damage to the intestinal epithelial barrier (IEB) is an important event in the development of intestinal inflammation and the evolution of NEC. The intestinal epithelial monolayer formed by the tight arrangement of intestinal epithelial cells (IECs) constitutes the functional IEB between the organism and the extra-intestinal environment. Programmed death and regenerative repair of IECs are important physiological processes to maintain the integrity of IEB function in response to microbial invasion. However, excessive programmed death of IECs leads to increased intestinal permeability and IEB dysfunction. Therefore, one of the most fundamental questions in the field of NEC research is to reveal the pathological death process of IECs, which is essential to clarify the pathogenesis of NEC. This review focuses on the currently known death modes of IECs in NEC mainly including apoptosis, necroptosis, pyroptosis, ferroptosis, and abnormal autophagy. Furthermore, we elaborate on the prospect of targeting IECs death as a treatment for NEC based on exciting animal and clinical studies. The The anti-permeability of IEB is mainly maintained and regulated by intestinal epithelial cells (IECs) and the tight junction (TJ) formed between IECs in the apical region . The int2.The definition of NEC is derived from the fact that IECs die in this disease . Therefo2.1.Intestinal epithelial cells have an extremely high rate of self-renewal, and their self-clearance at the end of the life cycle in an apoptotic manner is essential. However, excessive apoptosis may exacerbate the disruption of the intestinal barrier, allowing more microorganisms to enter the submucosa and producing large amounts of cytokines, and further inducing IECs apoptosis . For exaκB signaling pathway and leads to increased apoptosis of intestinal cells  , 33, whi2-fucosyl lactose isolated from breast milk can effectively prevent the activation of necroptosis-characteristic genes in the ileum tissue of premature infants with NEC . This re2.3.Cronobacter sakazakii has been reported to induce NEC. The underlying mechanism is to promote the activation of NLRP3 inflammasome by activating TLR4/MyD88/NF-kB signaling pathway, resulting in the up-regulation of downstream caspase-1 expression and the increase of IL-1β release, and ultimately inducing pyroptosis of intestinal cells  treatment inhibits excessive autophagy in IECs may through the CXCR4 signaling pathway. Meanwhile, hBD3 significantly reduces the expression of autophagy-related proteins  and inflammatory cytokines, leading to improved migration of IECs and intestinal mucosal integrity, as well as a reduction in mortality in NEC rats (β-carotene also attenuates LPS-induced apoptosis and autophagy in IECs by activating the PI3K/AKT/mTOR signaling pathway (Autophagy is a process of lysosome-dependent degradation of proteins and organelles and interacts with apoptosis to synergistically eliminate aging, redundant or damaged cells . Excessiithelium . Human βNEC rats . SimilarNEC rats . EpidermNEC rats , 75. β-c pathway . All the3.How does the pathological IECs death mechanism interact and induce increased intestinal permeability, leading to intestinal barrier dysfunction? Can targeting one of these cell death mechanisms as a therapeutic strategy provide effective treatment for infants with NEC? Although these problems remain questionable, understanding the mechanisms of IECs' death has brought new insights to explore new treatments . In this


Connectomics has emerged as a powerful tool in neuroimaging and has spurred recent advancements in statistical and machine learning methods for connectivity data. Despite connectomes inhabiting a matrix manifold, most analytical frameworks ignore the underlying data geometry. This is largely because simple operations, such as mean estimation, do not have easily computable closed-form solutions. We propose a geometrically aware neural framework for connectomes, i.e., the mSPD-NN, designed to estimate the geodesic mean of a collections of symmetric positive definite (SPD) matrices. The mSPD-NN is comprised of bilinear fully connected layers with tied weights and utilizes a novel loss function to optimize the matrix-normal equation arising from Fréchet mean estimation. Via experiments on synthetic data, we demonstrate the efficacy of our mSPD-NN against common alternatives for SPD mean estimation, providing competitive performance in terms of scalability and robustness to noise. We illustrate the real-world flexibility of the mSPD-NN in multiple experiments on rs-fMRI data and demonstrate that it uncovers stable biomarkers associated with subtle network differences among patients with ADHD-ASD comorbidities and healthy controls. The ‘connectome’ is a whole-brain map of these connections, often represented as a correlation or covariance matrix [Resting state functional MRI (rs-fMRI) measures steady state patterns of coactivation  , leverages a matrix autoencoder formulation  that per2Let matrices The matrix exponential and the matrix logarithm maps allow us to translate geodesics on the manifold back and forth to the local tangent space at a reference point. The matrix exponential mapping translates a vector expm(·) and logm(·) refer to the matrix exponential and logarithm respectively, each requiring an eigenvalue decomposition of the argument matrix, a point-wise transformation of the eigenvalues, and a matrix reconstruction.Here, Given two connectomes 2.1The geodesic mean of istances  to each ty GR(⋅) . To estity GR(⋅) .The geodesic mean Denotingthe matrix multiplications in the argument of theterm can be efficiently expressed within the feed-forward operations of a neural network with unknown parametersThe proof follows by computing the first order necessary  condition for optimality for 2.2The mSPD-NN uses the form above to perform geodesic mean estimation. The architecture is illustrated in We train mSPD-NN for a maximum of 100 epochs with an initial learning rate of 0.001 decayed by 0.8 every 50 epochs. The tolerance criteria for the training loss is set at 33.1Approximate Joint Diagonalization Log Euclidean (ALE) mean estimation [We evaluate the scalability, robustness, and fidelity of mSPD-NN using simulated data. We compare the mSPD-NN against two popular mean estimation algorithms, the first being the Riemannian gradient descent  on the otimation , which dtimation .Our synthetic experiments are built off the CPC model . In thisIn the absence of corrupting noise, the theoretically optimal geodesic mean of the examples 1ΓnB-TBT . We evalExperiment 1, we fix the data dimensionality at Experiment 2, we fix the dataset size at We randomly sample columns of the component matrix We score performance based on the correctness of the solution and the execution time in seconds. Correctness is measured in two ways. First is the final condition fit Going one step further, we evaluate the efficacy of the mSPD-NN framework when there is deviation from the ideal CPC generating process. In this case, we add rank-one structured noise to obtain the input data: 3.2To probe the efficacy of the mSPD-NN for representation learning on real world matrix manifold data, we experiment on several groupwise discrimination tasks  on the publicly available CNI 2019 Challenge dataset  consistiWe expect that FC differences between the ASD and ADHD cohorts are harder to tease apart than differences between ADHD and controls ,15. We t/90% validation/test split. We estimate the geodesic mean for each group across the training samples via 10 bootstrapped trials, in which we sub-sample 80% of the training subjects from the respective group. Permutation testing is performed on the mean estimates [Building on the observation that mSPD-NN provides reliable group-separability, we adopt this framework for classification. Using the AAL parcellation, we randomly sample 25 subjects from each class for training, and set aside the rest for evaluation with a 10%stimates , and funWe use classification accuracy and area under the receiver operating curve (AU-ROC) as metrics for evaluation. The mSPD-NN feature selection plus Random Forest approach provides an accuracy of 0.62 ± 0.031 and an AU-ROC of 0.60 ± 0.04 for ADHD-Control classification on the test samples. We note that this approach outperforms all but one method on the CNI challenge leaderboard . MoreoveTo better understand the group-level connectivity differences, we plot the most consistently selected features (top 10 percent) from the previous experiment (ADHD-control feature selection) in Expectation-Maximization (EM) algorithm. At each iteration of the mSPD-EM, the E-Step assigns cluster memberships to a given subject according to the geodesic distance  as an initialization and track the shift in the diagnostic assignments upon running an unsupervised distance  from the4We have proposed a novel mSPD-NN framework to reliably estimate the geodesic mean of a collection of functional connectivity matrices. Through extensive simulation studies, we demonstrate that the mSPD-NN scales well to high-dimensional data and can handle input noise when compared with current iterative methods. By conducting a series of experiments on group-wise discrimination, feature selection, classification, and clustering, we demonstrate that the mSPD-NN is a reliable framework for discovering consistent group differences between patients diagnosed with ADHD-Autism comorbidities and controls. The mSPD-NN makes minimal assumptions about the data and can potentially be a useful tool to advance data-scientific and clinical research.


CALR) are drivers of the disease resulting in constitutive activation of the thrombopoietin receptor (MPL). In the current study, we describe a healthy CALR mutated individual during a 12 year follow-up from initial identification of CALR clonal hematopoiesis of indeterminate potential (CHIP) to the diagnosis of pre-MF. The pre-diagnostic exponential development dynamics of the malignant clone demonstrated close correlation with the platelet counts, neutrophil-to-lymphocyte (NLR) ratio, and inversely correlated to hemoglobin and erythrocyte counts. Backward extrapolation of the growth rate indicated the potential for discovery of the malignant clone many years prior to presentation of overt disease, opening a window of opportunity for early treatment intervention. We did not find any additional mutations associated with MPNs and the current case report provides novel information regarding the development of a driver mutation and the association with blood cell counts prior to clinical manifestation of symptoms suggesting that pre-diagnostic dynamics may supplement future diagnostic criteria for early diagnosis and intervention in MPN patients.Initial diagnosis of overt myeloproliferative neoplasms (MPNs) represents the juncture during clonal evolution when symptoms or complications prompt an afflicted individual to seek medical attention. In 30-40% of the MPN subgroups essential thrombocythemia (ET) and myelofibrosis (MF), somatic mutations in the calreticulin gene ( Janus kinase (JAK2) mutation - JAK2V617F - is the ‘driver’ of more than 95% of cases with PV, in addition to 50-60% of cases with ET and MF  are present in 80-90% of the JAK2V617F-negative ET and MF patients  are clonally derived entities sub-grouped into essential thrombocythemia (ET), polycythemia vera (PV), and myelofibrosis (MF) according to their clinical features, genetic aberrations, and preferably - bone marrow morphology . The mosinase JAK mutationT and MF . Mutatiopatients , 11. Clopatients , 13. Alabelow 2% . Interesmutation . In MPNsnger age –17. The nger age . In MPNsThe discrepancy between the prevalence of driver mutations in the background population and MPN diagnoses might involve the expected long latency from acquisition of the mutation until diagnosis . IncreasCALR mutated individual with 12-year longitudinal follow-up until development of pre-fibrotic MPN, where early treatment could be initiated, demonstrating consistent exponential growth of the mutant clone independent of other somatic mutations associated with hematological neoplasms. The expansion rate of the clone suggests a narrow time window from diagnosis to full dominance of the mutated clone. Close correlation of pre-diagnostic mutant allele burden-and platelet count dynamics combined with backward extrapolation of the growth rate suggested the potential for discovery of the malignant clone many years prior to presentation of overt disease, opening a window of opportunity for early treatment intervention.In this case report, we present for the first time a healthy JAK2V617F and CALR mutations was performed on samples collected in 2010 for the Danish General Suburban Population Study (GESUS) – a cross sectional cohort of the Danish background population   results in CALR), and that the assay has the ability to detect 3 mutated copies, the detection limit will correspond to approximately 0.003% mutated alleles. To determine the ‘window of opportunity’ for detection of the mutation, we assume that the exponential fit also describes the growth of the mutated clone prior to the first measurement, and we extrapolate the exponential fit backwards in time to the detection limit of the assay (0.003%)  . Solvingem cells    and erythrocytes   (Pearson: R=0.0003) , 26.CALR mutation detected at a very low mutant allele burden in an individual longitudinally monitored over a 12-year period prior to developing pre-fibrotic myelofibrosis. Absence of known co-mutations involved in myeloid malignancies suggested that CALR was the only genetic cause of the expansion.To our knowledge, this is the first observation of a CALR growth rate of approximately 54%, in the current study, exceeds the rate of expansion for the fastest clones such as P95HSRSF2 and mutant U2AF1  for the publication of any potentially identifiable images or data included in this article.LK, VS, ML, TK, CS, MH, MA, and HH conceived and designed the study. HH and CE managed the patient. MK carried out the pathological analysis. VS carried out the NS analysis. TB, MA, TS, JG, JA and JO did the mathematical calculations. LK wrote the manuscript. All authors contributed to the article and approved the submitted version.


Human action recognition has been actively explored over the past two decades to further advancements in video analytics domain. Numerous research studies have been conducted to investigate the complex sequential patterns of human actions in video streams. In this paper, we propose a knowledge distillation framework, which distills spatio-temporal knowledge from a large teacher model to a lightweight student model using an offline knowledge distillation technique. The proposed offline knowledge distillation framework takes two models: a large pre-trained 3DCNN  teacher model and a lightweight 3DCNN student model . During offline knowledge distillation training, the distillation algorithm trains only the student model to help enable the student model to achieve the same level of prediction accuracy as the teacher model. To evaluate the performance of the proposed method, we conduct extensive experiments on four benchmark human action datasets. The obtained quantitative results verify the efficiency and robustness of the proposed method over the state-of-the-art human action recognition methods by obtaining up to 35% improvement in accuracy over existing methods. Furthermore, we evaluate the inference time of the proposed method and compare the obtained results with the inference time of the state-of-the-art methods. Experimental results reveal that the proposed method attains an improvement of up to 50× in terms of frames per seconds (FPS) over the state-of-the-art methods. The short inference time and high accuracy make our proposed framework suitable for human activity recognition in real-time applications. Deep neural networks (DNNs) have immensely improved over the years and have shown remarkable success in various fields such as computer vision , naturaldistillation loss.To alleviate the challenge of a large labeled dataset requirement, computer vision researchers have introduced several solutions including semi-supervised learning , self-suteacher model and the target model refers to the student model, the pair of teacher-student model learns together during training and improves the task-specific performance by minimizing the mutual loss (distillation loss). For model compression, the student model is the smaller version of the teacher model, where the smaller student model tries to improve itself during training by capturing the same level of patterns from the data and the same level of prediction accuracy as the teacher model. After the training phase, the distilled student model performs same level of predictions as the teacher model despite having comparatively less parameters. Generally, knowledge from the teacher model can be transferred to the student model at different levels, that include response-based, features-based, and relational-based knowledge distillation. In response-based knowledge distillation, the student model learns the class distribution predicted by the teacher model (soft labels or probabilities) by minimizing the loss between the logits  of the student and the teacher model. The features-based knowledge distillation transfers the features representation level knowledge from the teacher to the student model by minimizing the features-level difference between the teacher and the student model. Generally, the teacher model has a deeper architecture and has better representation of features than the student model. Therefore, the feature-based knowledge distillation technique uses the intermediate layers of the teacher model to train the student model to extract the same level of features representation as the teacher model. Lastly, the relation-based knowledge distillation uses the relationship between the feature maps of teacher model to enhance the relationship between the feature maps of student model by minimizing the mutual distillation loss.For the first time, the concept of knowledge distillation was introduced by Hinton et al. , where tWe propose two 3DCNN models  to extract spatial and temporal features from video sequences. The network architecture of the proposed teacher model is based on the C3D  model haWe propose a response-based offline knowledge distillation framework for human action recognition, utilizing the knowledge of a large pre-trained teacher model to efficiently train a student model. During training, the offline knowledge distillation algorithm helps the lightweight student model to attain the same level of prediction accuracy as the large pre-trained teacher model by minimizing the distillation loss.By conducting extensive experiments, we obtain a computationally efficient knowledge distillation-driven 3DCNN student model, which has 10× less parameters than the teacher model. Further, the developed 3DCNN student model has a storage requirement of 28 MB, which is 11× less than the size of the 3DCNN teacher model having the model size of 321 MB, yet providing the same level of prediction accuracy as the teacher model.We evaluate and compare the run time efficiency of the proposed method with the state-of-the-art human action recognition methods. Experimental results demonstrate that the proposed method attains an improvement of up to 37× in terms of seconds per frame (SPF) metric and up to 50× in terms of frames per second (FPS) metric over the state-of-the-art methods.This paper mainly focuses on the concept of knowledge distillation for the task of human action recognition in videos. Considering the time-series nature of video data and the complexity of recognizing human actions in time series, where the position and movement of human and other objects vary across the video frames, we propose a 3D convolutional neural network (3DCNN)-driven knowledge distillation framework that consists of two 3DCNN networks namely 3DCNN teacher and 3DCNN student. For efficient knowledge transfer, we propose an offline knowledge distillation strategy, where the 3DCNN teacher model is pre-trained and the 3DCNN student model is untrained. It is worth mentioning here that, for knowledge distillation both models must share the same dataset, therefore, we first train the 3DCNN teacher model on a dataset and then use the knowledge distillation to transfer the knowledge from the trained 3DCNN teacher model to 3DCNN student model over the same dataset. To summarize, the main contributions of this work are as follows:The rest of this paper is organized as follows. The topic of human activity recognition has been actively studied over the last ten years, where numerous advanced methods have been presented to efficiently address the human action recognition problem. The existing literature of vision-based human action recognition reports several spatial-temporal approaches. Broadly, the existing approaches can be categorized into two-stream 2DCNN-based methods ,12,13,14In the work presented in , GirdharOn the other hand, RNN-based methods reported in the literature typically use single 2DCNN for spatial features extraction followed by the RNN network for temporal patterns learning. The literature reports that RNNs perform comparatively better than CNNs used for temporal features extraction due to their recurrent nature which can efficiently learn hidden temporal patterns. However, RNNs are shown vulnerable to vanishing gradient problem while dealing with long-term temporal patterns. To cope with this challenge, computer vision researchers have introduced gated RNN such as long short-term memory (LSTM)  and gateIn the work proposed in , Sharma Generally, the RNN-based methods are coupled with 2DCNN architectures, where 2DCNN transforms the input videos to latent represention and the RNNs operate on the extracted CNN features. Although, these methods have shown reasonable performance for human action recognition task, they can be replaced with unified network architectures that offers the same level of recognition performance.Beside the two-stream 2DCNN-based and RNN-based architectures, several studies have extended the 2DCNNs to 3DCNNs that have the ability to capture both spatial and temporal features from the input RGB video frames. For instance, Tran et al.  proposedIn general, the existing two-stream 2DCNN, RNN-based, and 3DCNN-based methods are very efficient in terms of modeling discriminative salient features from both spatial and temporal dimensions features. However, these methods are mostly based on computationally complex architectures (having tens of millions of parameters) which require gigantic amount of training data, large storage, and high computational power. To alleviate the computational burden of the aforementioned deep learning methods for human activity recognition task, numerous knowledge distillation-based methods are presented that compress the model size by transferring the knowledge from a large model (teacher) to a small model (student). For instance, Hao and Zhang  proposedsiamese neural network, which is sometimes also referred to as a twin neural network. A siamese neural network is an artificial neural network that comprises of two or more identical subnetworks, that is, the subnetworks have the same configuration with the same parameters and weights. Vu et al. [Continuing research efforts in the same directions, Vu et al.  proposedu et al.  method mu et al.  proposedu et al.  presenteu et al. , Vu et au et al.  presenteu et al.  proposedTo overcome the limitations of training a cumbersome 3DCNN model on large video data, in this work, we propose a knowledge distillation-driven lightweight 3DCNN architecture having a total of eight convolution layers. The proposed offline knowledge distillation algorithm facilitates both knowledge transfer and model compression in a single unified framework, which allows us to transfer response-based knowledge from a large pre-trained 3DCNN teacher model to a lightweight 3DCNN student model. Thus, the proposed 3DCNN architecture  provides the same recognition performance  as the 3DCNN teacher model, while having a far less network complexity than the 3DCNN teacher model.This section provides the detailed architecture and working of the proposed spatio-temporal knowledge distillation 3DCNN framework ant its major components. The proposed spatio-temporal knowledge distillation framework is based on three major components that include teacher-student 3DCNN architectures, offline knowledge distillation paradigm, and workflow of spatio-temporal knowledge distillation process. The first core component of the proposed framework is the pair of teacher-student 3DCNN networks, which are developed to capture spatio-temporal features from the input videos frames. The second major component of the proposed framework is the offline knowledge distillation paradigm, which uses a pre-trained model as a teacher or a source model and transfer the knowledge to a student or a target model. In this work, we have used offline knowledge distillation, where we use a pre-trained 3DCNN as a teacher model and an untrained 3DCNN with reduced computational complexity as a student model. The third and last component of the proposed framework is the spatio-temporal knowledge distillation process, which transfers both spatial and temporal knowledge from the 3DCNN teacher to the 3DCNN student model. Each major component is discussed in detail in subsequent subsections.H and W operate on spatial resolution of the input frames and the third dimension D performs temporal feature extraction from a series of frames as shown in In this section, we present the architectural details of the proposed teacher-student 3DCNNs. The architectural design of the proposed teacher-student models is inspired by the C3D model presented in . Originaonline knowledge distillation and offline knowledge distillation. In the online knowledge distillation, the teacher and the student model train and update simultaneously in the end-to-end training process. Here, both the teacher and the student model(s) learn collaboratively from each other on the same input data in a peer learning fashion. In this knowledge distillation paradigm, the teacher and the student(s) learn from the predictions of each other to improve their prediction accuracy. However, the predictions of the teacher and the student(s) can vary at any point during the training phase, where the output of the teacher and the student models can conflict with each other and even with the ground truth. In cases where the predictions of the teacher and the student vary over the training phase, the online knowledge distillation can greatly harm the performance of the distilled student model. On the other hand, in the offline knowledge distillation paradigm, the student model learns from a single pre-trained teacher model as shown in Generally, in terms of knowledge transfer, the knowledge distillation paradigm can be divided into two categories: The motivation behind using the offline knowledge distillation is that the knowledge from the cumbersome pre-trained teacher model can greatly help the student model to perform the predictions with a similar accuracy. During the training phase, the teacher model normally starts converging in the very early epochs due its deep architecture, whereas the student model takes time to land on the global minima which ensures the convergence of the model. As a result, the performance of the teacher model (in terms of predictions) enforce the student model to get better in predictions over the training period. Typically, at the end of each epoch in the forward pass, the distillation loss (cross-entropy loss) computes the difference between the teacher’s and the student’s predictions, which helps the student to adjust its weights in the backward pass and improve its predictions accuracy performance. Thus, using the offline knowledge distillation approach enables us to obtain a computationally efficient yet a robust model which offers the same level of performance as the teacher model.p) and the student’s predictions (q). The computed distillation loss enforces the student model to adjust its weight to minimize the distillation loss. It is worth mentioning here that for the distillation loss as given in Equation Distp and q in Equation . Therefore, we have considered different temperature T values in our experiments to observe its impact on the knowledge distillation performance. Thus, in our proposed framework, T is a hyperparameter which can be tuned to provide the best prediction accuracy. The student loss as given in Equation  and the variable y denotes the ground truth. The final loss function is the weighted sum of the distillation loss and the student loss as given in Equation FinalIn this section, we present the comprehensive experimental evaluation of our proposed framework on different datasets for human action recognition task. First, we briefly describe the implementation details and tools we have used to conduct our experiments. Next, the datasets used in this research are briefly discussed, followed by the comparative analysis of the results we have obtained with different settings of our proposed framework. Finally, we present the comparative analysis of our proposed framework with the state-of-the-art human action recognition methods.To implement our proposed framework, we have used Python language 3 and TensorFlow 2.0 Framework on a computing system with Intel(R) Xeon(R) CPU E5-2640 having processor frequency of 2.50 GHz and 32 GB of dedicated main memory . The utilized computing system is equipped with two Tesla T4 GPUs having compute capabilities of 7.5 with Nvidia CUDA library version 11.0. For training and validation, we have divided the datasets into subsets of 70% and 20% data, where the 70% data is used for training and 20% is used for validation. The remaining 10% data is used for testing the performance of the trained models. We set the epoch value 100 to train the models for 100 epochs with different different settings . To adjust the model weights, we have used Adam optimizer with a static learning rate of 0.0001 and categorical cross entropy loss. To learn spatio-temporal features, we set the input sequence length to 16 frames for the proposed 3DCNN framework. The 3DCNN learns the spatio-temporal features by sliding 3D kernels on the input sequence of 16 frames. Moreover, to analyze the performance of the proposed framework in a more generalized way, we have used two different performance evaluation metrics that include accuracy and run time analysis assessment. The accuracy metric is used to evaluate video-level prediction performance of the proposed frameworks, where the run time analysis assessment metric is used to analyze the run time of the proposed framework.To evaluate the effectiveness of the proposed methods, extensive experiments have been conducted on different human actions datasets that include UCF11 , HMDB51 In this research, we have explored the spatio-temporal knowledge distillation from a pre-trained 3DCNN teacher model to a 3DCNN student model with different settings. We have also evaluated the performance of a light-weight 3DCNN model (student) with and without knowledge distillation. To obtain a well-generalized yet an efficient model across each dataset, we have conducted extensive experiments with different settings in our knowledge distillation framework. As our proposed method is based on the offline knowledge distillation approach, which transfers spatio-temporal knowledge from a large 3DCNN teacher model to a lightweight 3DCNN student model. Therefore, first we have trained the 3DCNN teacher model and later we have used the pre-trained 3DCNN model in knowledge distillation training. It is worth mentioning here that we have trained the 3DCNN teacher model with two different settings that include training from the scratch, which we refer to as TFS, and the training using transfer learning from  using i on knowlWe have also compared the accuracy of the proposed framework with different knowledge distillation settings . The obtained quantitative results comparisons for UCF11, HMDB51, UCF50, and UCF101 are presented in In this section, we compare our proposed framework with the state-of-the-art human action recognition methods with and without knowledge distillation. The detailed quantitative comparative assessment of the proposed framework with non-distillation state-of-the-art human action recognition methods on UCF11, HMDB51, UCF50, and UCF101 datasets are presented in N method  attains N method  has the N method , BT-LSTMN method , deep auN method , two-strN method , weighteN method  achieve For the HMDB51 dataset, which is one of the most challenging video dataset, our proposed Studentg method  achievesg method  attains g method , opticalg method , TSN [55g method , deep aug method , TS-LSTMg method , HATNet g method , correlag method , STDAN [g method , DB-LSTMg method , DS-GRU g method , TCLC [6g method , and semg method  achievesFor the UCF50 dataset, the proposed StudentF method , which aF method  method aF method , deep auF method , ensemblF method , and DS-F method  achieve Finally, for the UCF101 dataset, the proposed StudentS method , which aS method  obtains S method , spatio-S method , long-teS method , CNN + BS method , OFF [69S method , attentiS method , VideolsS method , two strS method , mixed 3S method , TS-LSTMS method , TSN + TS method , STM [76S method  obtain aTo further validate the performance generalization of our method, we compute the confidence interval as in  of our pBeside comparing the proposed framework with the mainstream human action recognition methods, we also compare our proposed framework with the knowledge distillation-based human action recognition methods. The comparative analysis of the proposed method with the state-of-the-art knowledge distillation-based human action recognition methods on HMDB51 and UCF101 datasets are presented in We also evaluate the performance generalization of our proposed method in comparison with the state-of-the-art knowledge-distillation based methods using confidence interval (with a confidence level of 95%) on HMDB51 and UCF101 datasets. The obtained confidence interval values of the proposed method and the state-of-the-art methods are listed in Considering the overall comparative analysis across all datasets, the proposed framework outperforms all the comparative mainstream action recognition methods by obtaining an improvement in accuracy of 7%, 34.88%, 7.7%, and 8% on UCF11, HMDB51, UCF50, and UCF101 datasets, respectively. Furthermore, we compare our proposed framework with knowledge distillation-based human action recognition methods on HMDB51 and UCF101 datasets. Experimental results reveal that our proposed framework attains an improvement in accuracy of 56.46% and 6.39%, on average, on HMDB51 and UCF101 datasets, respectively, over the existing knowledge distillation-based human action recognition methods.To validate the effectiveness and suitability of the proposed methods for practical applications in real-time environment, we have computed the inference time of the proposed method for action recognition task in terms of seconds per frame (SPF) and frames per second (FPS) with and without GPU computational resources. The obtained run time results are then compared with the state-of-the-art human actions recognition methods and presented in To provide a fair comparison of the inference speed, we scaled  the run In this work, we have proposed a knowledge distillation-driven 3DCNN framework for vision-based human action recognition task. The proposed framework uses offline knowledge distillation approach to transfer the spatio-temporal knowledge from a large 3DCNN teacher model to a lightweight 3DCNN model. During the offline knowledge distillation training, the 3DCNN student model learns from predictions of the pre-trained 3DCNN teacher model and gradually improves it predictions performance. Using the offline knowledge distillation approach, we not only transfer the knowledge but also perform model compression, thereby, transferring the knowledge from a large teacher model to a small student mode, which offers a similar level of prediction accuracy as the teacher model for the human action recognition task. To evaluate the performance of the proposed framework, we have conducted extensive experiments with different settings on four benchmark human action recognition datasets that include UCF11, HMDB51, UCF50, and UCF101. We have compared our proposed framework with the mainstream human activity recognition methods as well as knowledge distillation-based human action recognition methods. The obtained experimental and comparative analysis validate the effectiveness of our proposed framework over state-of-the-art human action recognition methods. Experimental results show that our proposed framework obtains an accuracy improvement of 7%, 34.88%, 7.7%, and 8%, on average, for UCF11, HMDB51, UCF50, and UCF101 datasets, respectively, as compared to the state-of-the-art human action recognition methods. To further validate the performance generalization of our method, we have computed the confidence interval of our proposed method for each dataset used in this paper and compared it with the average confidence interval of the state-of-the-art methods. Our obtained confidence interval results indicate that our proposed method attains a higher confidence with small intervals as compare to the state-of-the-art methods. Besides, we have analyzed the run time performance of the proposed framework with other comparative methods in terms of SPF and FPS metrics for both GPU and CPU execution environments. The comparative results demonstrate that the proposed framework achieves a comparable inference speed on GPU while obtaining better accuracy whereas the proposed framework obtains a run time improvement of up to 37× in terms of SPF and 50× in terms of FPS on CPU over existing methods while also attaining better accuracy than existing methods. These experimental results demonstrate the suitability of our proposed framework for human action recognition task in real-time environments.The current version of our proposed framework uses plain 3DCNN teacher and student models with offline knowledge distillation mechanism. The obtained results validate the effectiveness of the current version of the proposed framework; however, in our future work, we plan to analyze residual 3DCNN and self-distillation instead of offline distillation.