Acceptance rates of COVID-19 vaccines have still not reached the required threshold to achieve herd immunity. Understanding why some people are willing to be vaccinated and others are not is a critical step to develop efficient implementation strategies to promote COVID-19 vaccines.We conducted a theory-based content analysis based on the capability, opportunity, motivation–behavior (COM-B) model to characterize the factors influencing behavioral intentions toward COVID-19 vaccines mentioned on the Twitter platform.We collected tweets posted in English from November 1-22, 2020, using a combination of relevant keywords and hashtags. After excluding retweets, we randomly selected 5000 tweets for manual coding and content analysis. We performed a content analysis informed by the adapted COM-B model.P<.001), motivation (P<.001), and opportunity (P=.03) factors were more likely to indicate negative behavioral intentions.Of the 5000 COVID-19 vaccine–related tweets that were coded, 4796 (95.9%) were posted by unique users. A total of 97 tweets carried positive behavioral intent, while 182 tweets contained negative behavioral intent. Of these, 28 tweets were mapped to capability factors, 155 tweets were related to motivation, 23 tweets were related to opportunities, and 74 tweets did not contain any useful information about the reasons for their behavioral intentions (κ=0.73). Some tweets mentioned two or more constructs at the same time. Tweets that were mapped to capability (Most behavioral intentions regarding COVID-19 vaccines were related to the motivation construct. The themes identified in this study could be used to inform theory-based and evidence-based interventions to improve acceptance of COVID-19 vaccines. The global COVID-19 pandemic is affecting 219 countries worldwide . FortunaPrevious studies have described potential impediments to COVID-19 vaccines, including questioning the need for vaccines and preferring to benefit from the immunity conferred by surviving COVID-19 ; safety Compared with surveys, Twitter can gather timely information regarding behavioral intentions toward COVID-19 vaccines, especially to understand “anti-vaxxers” and those influenced by misinformation who are inclined not to get the vaccine. These types of users could be the most vulnerable population for COVID-19 vaccine outreach interventions. On the Twitter platform, there are more than 330 million users, and the median number of posts per person each month is 2 ,13. A reTo better characterize the factors that influence behavioral intentions on the COVID-19 vaccines mentioned in the tweets, we conducted a theory-based content analysis based on the capability, opportunity, motivation–behavior (COM-B) model. The COM-B model was proposed by Michie et al  in 2011,The adapted COM-B model has three theoretical constructs: (1) capability, (2) motivation, and (3) opportunity. The WHO Regional Office for Europe also provided examples for each construct see . CapabilWe collected English tweets posted from November 1-22, 2020, using a combination of relevant keywords and hashtags: (#covid OR covid OR #covid19 OR covid19) AND (#vaccine OR vaccine OR #vacine OR vacine OR vaccinate OR immunization OR immune OR vax). After excluding retweets, we randomly selected 5000 tweets for manual coding and content analysis. The random numbers were generated through the NumPy package in Python. Then, we mapped the random numbers with the index of collected tweets.We performed a content analysis informed by the adapted COM-B model. The coding schema was developed iteratively. First, we developed the coding schema based on the definitions of constructs in the adapted COM-B model. Two reviewers (SL and JL) independently coded 1000 tweets in each round. After completing one round of coding, the two reviewers met with a third reviewer to discuss disagreements and update the coding schema until consensus was reached. We calculated the interrater reliability for the last round. If a tweet mentioned ≥2 constructs simultaneously, we coded it with multiple labels. We conducted chi-square tests to explore the relationship between theoretical constructs with the positive/negative behavioral intention. The statistical significance threshold was .05.21=17.286, P<.001), motivation , and opportunity  were more likely to have negative behavioral intentions .In the physical opportunities category, some users said they would not accept the vaccine if they had to pay for it. Many users said vaccination should be a free choice and that they would refuse to receive the vaccine if it became mandatory. Some users wanted more regulation of the COVID-19 vaccine, and some were concerned about the availability of information.Among the social opportunities, we found some factors influencing the COVID-19 vaccination related to social and cultural demands and support, such as going against religion, defying culture, health workers not recommending vaccination, and family members actively discouraging vaccination. Others intended to be vaccinated because of the fear of the social consequences and reactions to vaccination, such as fear of affecting their work and requiring proof of vaccination for many activities in the future.Most of the reasons for behavioral intentions were categorized into the motivation construct. Many users expressed attitudes and opinions about the COVID-19 vaccine or the disease itself, such as not considering the disease to be severe or life-threatening and not considering the vaccine to be effective (because of low efficiency and mutation of the virus). Others assessed the risks and considered the rushed vaccine to be more harmful than COVID-19. Some of those with positive behavioral intentions stated that they chose vaccination because they did not want to be infected with COVID-19.Some users expressed strong emotions and feelings about COVID-19 vaccines, such as fear of the vaccine and concern about its side effects because it is new. Notably, others felt that vaccination was selfish or unfair, as they would not be exposing themselves to the same risk as others who survived COVID-19; thus, they were reluctant to be vaccinated.Other themes were values and beliefs. Some users expressed a belief that the body’s natural immune system is better than any vaccine, or they believed more in alternative medicine. On the other hand, others who wanted to be vaccinated emphasized the positive values of vaccination, such as saving more lives, reopening the economy, and returning to normal life.Confidence and trust were dominant themes. Users expressed distrust in many areas: the quality of the vaccines , health authorities, science, companies, and government. For Twitter users in the United States, we found that unlike with other vaccines, part of the reason people do not trust the government is because of their past handling of the COVID-19 pandemic. In addition, the COVID-19 vaccine rollout came during a time when a new president was being elected in the United States, and some people lacked confidence in the newly elected president’s party.In this study, we conducted a theory-based content analysis using a dataset of 5000 tweets posted from November 1-22, 2020. We identified 279 tweets that contained behavioral intentions regarding COVID-19 vaccines and mapped them to constructs in the adapted COM-B model. We generated nine themes that influence Twitter users’ intentions to receive COVID-19 vaccines. The constructs in the COM-B model could be applied systematically to characterize factors that influence behavioral intentions toward COVID-19 vaccines. In addition, we found that among tweets that simply stated behavioral intentions without including any reason, the number of positive intention tweets was higher than that of negative intention tweets. The results also implied that more than half of the tweets expressing the decision-making process were negative intention tweets. This finding aligns with our expectation of understanding the factors that contribute to vaccine hesitancy to better develop tailored vaccine promotion programs.The novelty of COVID-19 vaccines and the current social context have created further difficulties in vaccine rollout. Identified barriers of influenza vaccination intention and behavior include lack of confidence , inconvenience , calculation , and complacency  . Our resI'm not against every vaccine in history. Just this specific COVID-19 vaccine. There are many reasons why, for example the nonsensical social regulations.The above differences contribute to the fact that the rollout of COVID-19 vaccines could be more complicated than that of other vaccines. Researchers need to develop interventions specific to the COVID-19 vaccine to improve acceptance rates. This also provides an opportunity for future studies to comprehensively analyze why behavioral intentions toward COVID-19 vaccines are different from those toward other vaccines.In several studies, Twitter content analyses have been conducted of health care behaviors other than COVID-19 vaccination. For example, Chew and Eysenbach  collecteThis study has several limitations. First, we only analyzed the behavioral intentions of users on Twitter. Previous studies have shown that health care providers are the primary advocates for vaccination and largely influence vaccination acceptance rates -38. In tIn future research, a literature review could be conducted to summarize current implementation strategies for COVID-19 vaccine promotion and map them to the themes identified in this study to determine gaps in recent research. The inner mechanism of the adapted COM-B model—the behavior change wheel—could inform evidence-based and theoretical implementation strategies to improve the effectiveness of COVID-19 vaccine promotion programs.The study demonstrates the capability of applying the COM-B model to characterize behavioral intentions toward COVID-19 vaccines on the Twitter platform. We successfully generated nine themes of factors that affect behavioral intentions. Positive behavioral intentions were affected by the positive values of vaccination . In contrast, negative behavioral intentions were associated with attitudes and perceptions about COVID-19 vaccines or the disease itself , values and beliefs , confidence and trust , and lack of knowledge. The generated themes could be used to create theory-based and evidence-based implementation strategies to promote COVID-19 vaccines.


SPAST gene that encodes the microtubule-severing enzyme called spastin are the chief cause of Hereditary Spastic Paraplegia. Growing evidence indicates that pathogenic mutations functionally compromise the spastin protein and endow it with toxic gain-of-function properties. With each of these two factors potentially relevant to disease etiology, the present article discusses possible therapeutic strategies that may ameliorate symptoms in patients suffering from SPAST-based Hereditary Spastic Paraplegia, which is usually termed SPG4-HSP.Mutations of the  SPAST gene  account for the plurality of cases of autosomal dominant Hereditary Spastic Paraplegia (HSP), a heritable neurodegenerative disease involving progressive weakness and spasticity of lower limbs [SPG4-HSP is usually but not always adult-onset, with symptoms mainly resulting from degeneration of the corticospinal tracts. Corticospinal degeneration is characterized by pathological axonal swellings and dieback  degeneration of axons and associated synapses [Mutations of the er limbs . HSP patsynapses .SPAST encodes spastin, which is a microtubule-severing enzyme that also has functions relevant to the organization of membranous organelles [SPG4-HSP.ganelles ,4. An eaganelles ,10,11,12ganelles ,14,15. WSpast knockout mice with reduced spastin levels do not recapitulate most features of the disease [A loss-of-function  etiology for the disease was originally proposed on the basis of the genetics, but also on reports that failed to detect mutant spastin proteins in cells and tissues from human patients outside of the central nervous system ,17. The  disease ,20, wher disease . CuriousSPAST gene have two start codons that produce a longer and a shorter isoform called M1 and M87, respectively [SPG4-HSP patients remains to be explored.Transcripts produced from the ectively ,22. In mectively . Studiesectively ,14,23. Mectively . The ideSPG4-HSP phenotype, the loss-of-function component exacerbates the gain-of-function component [We have reported experimental data suggesting that while the gain-of-function component drives the omponent ,15. GiveA common gain-of-function pathway for many neurodegenerative diseases is the aberrant activation of kinases and associated inhibition of fast axonal transport (FAT) by neuropathogenic proteins ,25,26,27SPG4-HSP in Australia, with pre-clinical studies ongoing [We have observed in different experimental systems that mutant M1 causes a decrease in microtubule acetylation as well as a decrease in microtubule stability . It may  ongoing ,36.SPG4-HSP in humans or animal models. As previously done for other mouse models [SPG4-HSP patients. Rapamycin and other drugs targeting the autophagy activator mTOR are being tested in preclinical studies of other diseases, but effects of these drugs go beyond autophagy enhancement, so the results must be interpreted with caution. Finally, another therapeutic strategy targeting gain-of-function mechanisms could involve the development of peptides or antibodies targeting specific regions of mutant M1 spastin conferring toxicity. The precise mapping of such regions is an active line of research in our laboratories.It remains to be seen whether the correction of either of these two gain-of-function effects ameliorates and/or prevents the behavioral deficits of e models , genetice models ,40,41,42There is evidence that spastin dosage is crucial for the health of the axon, at least when confronted with challenges such as regeneration after injury . TherefoSPG4-HSP [Spastin preferentially severs axonal microtubules in the stable domain of the microtubule, as opposed to the labile/dynamic domain . This reSPG4-HSP ,53.It may be that reduced microtubule mobility underlies the vulnerability of corticospinal axons associated with lower spastin microtubule-severing activity. Reduced microtubule mobility imposes a burden on the axon because fewer tubulin subunits are supplied for the proper maintenance of the MT array and because microtubule mobility is the primary means by which incorrectly oriented microtubules are cleared from the axon . MicrotuSPG4-HSP would be to introduce into the patient’s motor cortex a viral vector that includes micro-RNA (miRNA) or short hairpin RNA (shRNA) to deplete the patient’s endogenous spastin, as well as the cDNA that expresses wild-type spastin under its endogenous promoter. Theoretically, any loss-of-function and gain-of-function defects would thereby be resolved. However, this strategy is not without potential snags. The level of spastin expression cannot be too high because too much spastin could tear down the microtubules of the cells to a detrimental level. Because the mutant M1 already accumulated in the corticospinal tracts may be long-lived, there is uncertainty as to how long it would take for that mutant M1 to be degraded after turning off the expression of new mutant protein. There may be a window of time past which this strategy may stop degeneration and promote regeneration, but to the detriment of patients should regenerating axons become misdirected. Additionally, there are potential issues of non-specificity or other unexpected side effects in viral-driven gene therapy approaches. Another idea is to specifically target the mutant gene for silencing while allowing the wild-type gene to continue to express normally, with the hope that haploinsufficiency would be tolerable if the toxic mutant protein can be sufficiently reduced.Gene therapy is achieving success in the treatment of genetic diseases. For example, the FDA has approved innovative gene therapy to treat pediatric patients with spinal muscular atrophy, a rare neurological disorder and the leading genetic cause of infant mortality . One ideThe most contemporary approach, which circumvents some of these potential snags, is gene editing via CRISPR-Cas9. This technology, which is currently undergoing testing for the treatment of other neurodegenerative disorders ,59,60, dSPG4-HSP may be coupling gene therapy to one or more of the other approaches discussed earlier to ameliorate the ill effects of the accumulated mutant M1 due to its resistance to degradation. For these various reasons, it may be that gene therapy is best suited for staving off the corticospinal degeneration of patients who are early in the display of symptoms, or asymptomatic patients. The rare pediatric cases of the disease may also be especially amenable to gene therapy because the nervous system of a child is still plastic/developing, because not as much mutant M1 would have accumulated in a young person, and because autophagy machinery is relatively more active at younger ages.The very best approach for treating patients with SPG4-HSP is an autosomal dominant monogenic disorder that is theoretically far more straightforward to understand mechanistically than multifactorial disorders such as Alzheimer’s disease. SPG4-HSP qualifies as a rare disorder and yet there are hundreds of thousands of affected patients worldwide. Developing effective therapies based on an understanding of disease etiology (see de novo mutations do appear, most SPG4-HSP cases are hereditary and hence can be identified with genetic testing prior to the appearance of symptoms. Thus, treatment can begin early to stave off progression of the disease long before the symptoms become debilitating and irreversible. With a rapidly growing understanding of the disease and the development of powerful animal and experimental models, there is every reason to predict that effective therapeutics for SPG4-HSP are within our grasp. In the present article, we have outlined various avenues toward treatment that we hope to pursue either alone or in combination for the effective amelioration of the symptoms of the disease.logy see  will not


A significant proportion of individuals experience lingering and debilitating symptoms following acute COVID-19 infection. The National Institute for Health and Care Excellence (NICE) have coined the persistent cluster of symptoms as post-COVID syndrome. This has been further sub-categorised into acute post-COVID syndrome for symptoms persisting three weeks beyond initial infection and chronic post-COVID syndrome for symptoms persisting beyond twelve weeks. The aim of this review was to detail the prevalence of clinical features and identify potential predictors for acute and chronic post-COVID syndrome.A systematic literature search, with no language restrictions, was performed to identify studies detailing characteristics and outcomes related to survivorship of post-COVID syndrome. The last search was performed on 6 March 2021 and all pre-dating published articles included. A means of proportion meta-analysis was performed to quantify characteristics of acute and chronic post-COVID syndrome. Study quality was assessed with a specific risk of bias tool. PROSPERO Registration: CRD42020222855A total of 43 studies met the eligibility criteria; of which, 38 allowed for meta-analysis. Fatigue and dyspnoea were the most prevalent symptoms in acute post-COVID (0·37 and 0·35) and fatigue and sleep disturbance in chronic post-COVID syndrome (0·48 and 0·44), respectively. The available evidence is generally of poor quality, with considerable risk of bias, and are of observational design.In conclusion, this review highlights that flaws in data capture and interpretation, noted in the uncertainty within our meta-analysis, affect the applicability of current knowledge. Policy makers and researchers must focus on understanding the impact of this condition on individuals and society with appropriate funding initiatives and global collaborative research. The emergence of post-COVID syndrome, in which recovering SARS-CoV-2 patients suffer from persistent symptoms extending several months beyond their initial diagnosis is gaining increasing recognition. However, there is a need for a greater understanding for diagnosis and management strategies. We searched Ovid in Medline, EMBASE, health management information consortium (HMIC), and PsycINFO databases without language restriction. The search was conducted in March 2021 using a list of terms relating to COVID-19 and persistent symptoms. Studies were included if they focussed on describing ‘long-COVID’ or ‘post-COVID syndrome’, the incidence of reported symptoms or predictors. Studies detailing a follow-up period shorter than 21 days; case series and articles focussing on other non-COVID-19 related conditions were excluded.To the best of our knowledge, this is the first systematic review and meta-analysis describing symptom prevalence and predictors for acute and chronic subtypes of post-COVID syndrome. However, during the process, several limitations of current literature surfaced. The significant heterogeneity in the field limits clinical applicability and high-quality evidence are urgently needed.This review highlights that flaws in data capture and interpretation, noted in the uncertainty within our meta-analysis, affect the applicability of current knowledge. Moreover, the majority of studies displayed significant risk of bias, were typically of observational design, based within a limited number of countries and of inconsistent methodologies. There is an urgent need for global collaboration and recruitment into COVID-19 trials to tackle this.Alt-text: Unlabelled box1th February 2021, SARS-CoV-2 has infected over 70 million individuals globally and has directly attributed to over 1.6 million deaths As of 4In contrast to the scientific community's rapidly developing understanding of acute SARS-CoV-2 infection, characterisation of post-COVID syndrome remains sparse. It is suggested that upwards of 20% of SARS-CoV-2 positive individuals go on to develop post-COVID syndrome Of note, there is a particular lack of understanding as to whether post-COVID syndrome constitutes a singular disease process. It has been suggested that the post-COVID syndrome may be characterised into either an acute or chronic subtype, depending on whether symptoms extend beyond 12 weeks following initial diagnosis [As such, the primary aim for this study aims to characterise the clinical features between acute and chronic post-COVID syndrome. The secondary aim is to identify predictors for post-COVID syndrome, irrespective of subtype, in order to understand the risk factors and the acute clinical course that is associated with syndrome development.22.1This systematic review was conducted in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines 2.2(1)What are the clinical features associated with acute and chronic post-COVID syndrome?(2)Which features predict the development of post-COVID syndrome?This review sought to answer the following questions:2.3A systematic search, with expert librarian support, was performed using electronic databases through Ovid in Medline, EMBASE, health management information consortium (HMIC), and PsycINFO databases without language restriction. The search was conducted using a list of terms relating to COVID-19 and persistent symptoms; the complete search strategy is available in Appendix 1. Further studies not captured by the search were identified through bibliometric cross-referencing. Grey literature was additionally searched.All identified studies were uploaded to Covidence , a Cochrane supported systematic review package tool 2.4The inclusion criteria for study selection were focussed on studies describing ‘long-COVID’ or ‘post-COVID syndrome’, the incidence of reported symptoms and predictors. The last search was performed in March 2021. No language restrictions were placed.Given the rapidly expanding literature surrounding COVID-19, a wide range of publications were included, . Studies detailing a follow-up period shorter than 21 days; case series and articles focussing on other non-COVID-19 related conditions were excluded.2.5Outcome measures were the prevalence of symptoms indicative of acute and chronic post-COVID syndrome.All included study characteristics and outcome measures were independently extracted by two investigators (FI and KL) with consensus achieved. All full text reports of studies identified as potentially eligible after title and abstract review were obtained for further review.2.6Risk of bias was assessed using a validated quality assessment checklist for prevalence studies 2.7We characterised studies describing symptom clusters with a follow-up period of 12 weeks or more into chronic post-COVID syndrome and studies detailing a follow-up period shorter than 12 weeks as acute post-COVID, in keeping with the definitions by the National Institute for Health and Care Excellence (NICE) 2 statistic. We considered a value less than 30% as low heterogeneity, between 30-60% moderate, and over 60% as high.A meta-analysis of proportions was performed in RStudio version 3.6.3  Chest pain and chest tightness were grouped into one variable, given their close clinical relationship 2.8No funding was received for this study; all authors had access to the data and decided to submit for publication.33.1A total of 623 citations were retrieved through literature searches. An additional 18 articles were found from bibliography cross-referencing. Full text review was performed for 89 articles with 43 meeting the inclusion criteria for analysis, of which, 30 allowed for meta-analysis. Studies were conducted in 18 countries most of which are deemed as high-income. Included studies were observational in design with a mixture of previously hospitalised and non-hospitalised individuals recruited into the trials; the characteristics are shown in 3.2All studies reporting the prevalence of clinical features for acute and chronic post-COVID syndrome are shown in To meta-analyse the acute post-COVID cohort, seven studies were removed. Two studies study failed to include demographics or characteristics of the included individuals appropriately and was excluded from the sub-group analysis ,21. Addi2 = 98%), dyspnoea  and anxiety .In the acute post-COVID phase, studies reported 13 predominant symptoms allowing for pooled analysis . The mos2 = 100%), sleep disturbance , and dyspnoea  were reported as most prevalent symptoms.The lack of standardisation between enrolment and assessments into the trial precluded one study for the chronic post-COVID syndrome sub-group analysis 3.3Studies detailing predictors of post-COVID syndrome were limited to five studies. Carvalho-Schneider et al. reported that hospitalisation during the acute infection  and an age between 40-49 years  were deemed the most significant predictors of developing post-COVID syndrome. The presence of initial symptoms , gender or number of comorbidities did not predict post-COVID syndrome Moreover, the severity of initial infection  was associated with patient-reported impairment, although no relationships between age or pre-existing comorbidities and the persistence of post-COVID symptoms were observed 3.4Twenty-two included studies were deemed to be at high risk of bias; 17 studies were deemed as moderate, and the remaining were considered low risk . Frequen3.5Overall, the pooled analyses display significant heterogeneity urging for cautious interpretation of our results. The finding of heterogeneity is partly expected given the pragmatic choice of studies from a range of settings with different study populations  with differing co-morbidity demographics ; differiIn order to explore the sources of substantial heterogeneity, stratification by sampling methodology  was conducted for symptoms displaying an I-squared of over 95% (Appendix 3). Overall, the heterogeneity was lower although, values remained high, particularly in the mixed cohorts (Appendix 3). Further stratification for other factors which we hypothesised to be contributory to the heterogeneity could not be further explored given the insufficient number of papers available for sub-group analysis.4This study suggests that there is a broad range of symptoms that persist beyond the acute phase of COVID-19 in patients with post-COVID syndrome. Fatigue and sleep disturbance were reported to be most common symptoms in acute post-COVID syndrome and fatigue, anxiety and dyspnoea were the most common in chronic post-COVID syndrome. The sizeable prevalence of extra-respiratory  and functional  symptoms illustrates the multi-system burden that post-COVID syndrome imposes upon individuals. Moreover, we also noted that the number of symptoms associated with chronic post-COVID is lower in comparison to the acute post-COVID experience. Lastly, although studies do comment upon specific predictors of post-COVID development, they report varied results, thus hindering clinical application of this knowledge. It seems, however, that severity of initial infection or symptom load during the acute phase of illness is associated with a greater likelihood of continued post-COVID symptomatology.Given the prevalence of respiratory symptoms in acute COVID infection, the persistence of respiratory symptoms in post-COVID syndrome can be expected. Furthermore, persistent respiratory symptoms are in keeping with previous outbreaks of SARS-CoV which have demonstrated a restrictive pattern of lung function metrics consistent with the resultant muscle weakness six to eight weeks following hospital discharge To combat post-COVID syndrome effectively, a multi-faceted approach will be required ,35. CurrWe highlight the limited literature predicting post-COVID syndrome, indicating the need for enhanced surveillance programmes to be employed. Comparisons can be drawn from cancer survivorship in which the development of evidence-based frameworks  are deemed essential for the provision of personalised care Policymakers should aim to educate the public and clinicians concerning post-COVID syndrome, thus recognising it as a legitimate health condition Despite the importance of our work, a series of limitations are to be mentioned. Whilst the broad inclusion of studies, including those deemed at high risk of bias, in our analysis resulted in significant heterogeneity , it allowed for a broad insight into the prevalence and predictors of post-COVID syndrome based on the current literature in a condition with a limited but growing evidence base. Studies reported a mixture of cohort sampling , follow-up timepoints, and data collection protocols which likely contributed to the existing heterogeneity. Further sub-group analyses assisted in providing insight into this heterogeneity, with mixed cohorts displaying large I-squared values (Appendix 3). However, given the pragmatic nature of study inclusion, this was expected. Calls to incorporate post-COVID sufferers’ perceptions within its evolving definition suggests the grouping of these cohorts within our analysis may assist in depicting the overall disease burden. Furthermore, the majority of published literature excludes low-income countries, an important omission in the midst of a global pandemic given available resources distinctly tailor potential available strategies for surveillance and treatment. Moreover, included cross-sectional studies consisted of small sample sizes; were single-centre; and involved questionnaires requiring retrospective recall of symptoms resulting in potential for recall bias and subjective assessment . Indeed,Varied terminology  have contributed to heterogenous research; given the adoption of the latter by NICE guidelines, widespread adoption of the term ‘post-COVID syndrome’ is required to aid homogenisation of future symptom data, allowing predictors to be accurately described In conclusion, the applicability of current knowledge on post-COVID syndrome is limited by the quality of available data, a result of the flaws in data capture and interpretation, as demonstrated in the uncertainty of our meta-analysis and there is need for global collaboration to further understand the prevalence, clinical characteristics, and prognosis of this novel disease. Clinicians, policy makers, and researchers must focus on understanding the impact of this condition on individuals and society.None.


Neglect is a severe neuropsychological/neurological deficit that usually develops due to lesions of the posterior inferior parietal area of the right hemisphere and is characterized by a lack of attention to the left side. Our case is a proven right-handed, 30-year-old female patient with a low-grade glioma, which was located in the temporo-opercular region and also in the superior temporal gyrus of the right hemisphere. Upon presurgical planning, the motor, language, and visuospatial functions were mapped. In order to achieve this, the protocol for routine magnetic resonance imaging and navigated transcranial magnetic stimulation has been expanded, accordingly.The online version contains supplementary material available at 10.1007/s00701-021-04822-2. Recent clinical studies suggest the important role of supramaximal safe tumor resection in increasing life expectancy of patients with low-grade gliomas. During extended resective surgery, it is of utmost importance to spare functionally eloquent areas. This goal is best achieved by awake surgery, aided by intraoperative neuromonitoring and cognitive assessment. Neglect is a severe neuropsychological/neurological deficit characterized by the lack of attention for the left side of the space. It develops as a result of a lesion at the posterior section of the inferior-parietal region mostly of the right hemisphere , 6, 8.A 30-year-old right-handed female has been diagnosed with a suspected low-grade glioma of the temporo-opercular region and superior temporal gyrus of the right hemisphere, affecting both cortical and subcortical areas. The patient has provided an informed consent acknowledging purported defect in the visual field.Preoperative imaging workup included structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI) to evaluate white matter tracts and functional MRI (fMRI) to investigate neural correlates of the sensory language area.Cortical mapping was performed with nTMS using Navigated Brain Stimulation system combined with the NEXSPEECH® module to locate the motor cortex, areas of language dominance, and those regions susceptible to neglect.Although the patient was right-handed, language testing was necessary because the tumor was located in a Wernicke-homologue area, which (in a minor region) showed activation during comprehension of speech targeted fMRI testing. Mapping was performed on both hemispheres according to the previously published protocol  using trThe examination of visuospatial capabilities was especially important because the tumor affected the neglect circle—although a less sensitive and less extensive region of it, while, in general, it was close to those structures which were widely associated with severe neglect syndrome .During testing for neglect, 44 stimulation target points on the parietal region of the right hemisphere were defined, where a total of 140 stimuli were delivered while the patient was asked to evaluate the length of 80 differently mid-transected line . The pictures have been presented randomly. To aid the inhibitory effect a train of 10 Hz/5 pulses was used, with 100% of resting motor threshold.Pre- and intraoperative mapping of neglect-eloquent areas were performed by a line bisection judgment task, referred to as the landmark task according to descriptions –4.Our own image library was built by the adaptation of NBS system. Horizontal black lines were split with a vertical line on a white background Fig. .Fig. 1LWrong judgment of length or the lack of answer were considered errors (neglect and non-neglect specific). Neglect-specific errors were categorized as follows: (1) longer left lines were named as equal; (2) longer left lines were named as longer right lines; (3) equal length lines were named as longer right. Non-neglect specific errors were categorized similarly: (1) longer right lines were named as equal; (2) longer right lines were named as longer left lines; (3) equal length lines were named as longer left.Mapping of neurocognitive and other psychological functions was performed 2 days prior to, as well as 4 and 5 days after surgery..A variety of neuropsychological tests were performed for evaluating global neurocognitive functioning.  and Bells Test  were included in our battery. These tests have been performed multiple times following surgery to monitor peripersonal visual neglect symptoms more closely.On fMRI, a right-sided subdominant activity appeared by the posterior-inferior margin of the lesion, besides the left-sided dominant Wernicke activation, which was not confirmed with nTMS Fig. .Fig.2a Superior longitudinal fasciculus (FLS) and pyramidal tracts were intact by using DTI and fMRI—a finding confirmed by nTMS examination, too Fig. . A clustSignificant neurological and neuropsychological disorder, which refers to contraindication in relationship with the examinations, could not be detected at the patient. Neglect-like symptoms have not been observed and motor functions seemed intact.The asleep-awake-asleep operation was planned based on tractography and navigational MRI sequences. During the operation, frameless navigation-guided temporal craniotomy was performed; next functional areas were identified by bipolar fork probe using continuous cortical and subcortical stimulation. During intraoperative stimulation, we were not able to provoke speech error at the location  according to nTMS results.Initially, we barely found symptoms of neglect (1%) during intraoperative cortical mapping, while the rate of non-neglect specific errors was 22%. Contrarily, during the resection of the rear pole of the tumor, number of neglect specific errors increased significantly (+ 46%), while the number of non-neglect specific errors changed slightly + 3%). However, this location was further away from the area identified with nTMS as susceptible to neglect . Due to the transient manner of errors during the stimulation of this area, the resection was carried on. Using the acquired information, maximal safety was achieved during the resection. Postoperative MRI scan at 24 h confirmed total resection of the tumor  and also in the recorded tests . The patient lies on the right side of the bed, despite the fact that the surgical scar was also located on the right side of her head. Nevertheless, personal and/or extrapersonal neglect syndrome was not perceived by the clinical observation.w” value = 415/severe; total simultaneous: point = 0, “w” value = 429/severe; total right: point = 24, “w” value = 488/normal zone).On the fourth day following the operation (more than 72 h later), visuospatial capabilities show a significant improvement ; however, vision problems are still indicated by the patient (“Something is weird”); therefore, a confrontational visual field exam was completed based on the Dean-Woodcock Neuropsychological Battery. Complete right and left side, as well as simultaneous  testing of the upper, middle, and lower visual fields shows partial left homonymous hemianopia in the patient  referred to the neglect completely disappeared. The Bells test has already performed correctly, with similar search strategy as before the surgery, and no significant difference was observed between the length of horizontal lines that have been halved in the line-bisection task.Regarding the other neuropsychological functions, the pre- and postoperative results were summarized in Table In this work, we have identified a complex pre-, intra-, and postoperative protocol for the assessment of neglect in conjunction with awake surgery upon gross total removal of a low-grade glioma. Navigated—MRI-based—transcortical magnetic stimulation has been included as integrated part of the preoperative workup and planning.According to the pre- and intraoperative assessments and the results presented in this case, the applied paradigm was useful for identifying eloquent areas of visuospatial attention and had predictive value for the development of postoperative neglect-like symptoms. In addition to neuromonitoring, this was further improved by the surgical strategy followed in the intraoperative. Our case report supports the literature data that lesions of the superior temporal gyrus play less role in the appearance of permanent neglect-like symptoms, but rather can result in transient changes. The paradigms established here should aid supramaximal safe resection of tumors aiding prolonged survival and better quality of life of our patients.ESM 1(PDF 306 kb)


An unexpected high prevalence of enterococcal bloodstream infection (BSI) has been observed in critically ill patients with COVID-19 in the intensive care unit (ICU).The primary objective was to describe the characteristics of ICU-acquired enterococcal BSI in critically ill patients with COVID-19. A secondary objective was to exploratorily assess the predictors of 30-day mortality in critically ill COVID-19 patients with ICU-acquired enterococcal BSI.Enterococcus faecalis and Enterococcus faecium, respectively. The cumulative incidence of ICU-acquired enterococcal BSI was of 229 episodes per 1000 ICU admissions . Most patients received an empirical therapy with at least one agent showing in vitro activity against the blood isolate . The crude 30-day mortality was 42% (18/43) and 57% (4/7) in the entire series and in patients with vancomycin-resistant E. faecium BSI, respectively. The sequential organ failure assessment (SOFA) score showed an independent association with increased mortality .During the study period, 223 patients with COVID-19 were admitted to COVID-19-dedicated ICUs in our centre. Overall, 51 episodes of enterococcal BSI, occurring in 43 patients, were registered. 29 (56.9%) and 22 (43.1%) BSI were caused by The cumulative incidence of enterococcal BSI is high in critically ill patients with COVID-19.The crude 30-day mortality of enterococcal BSI in critically ill patients with COVID-19 may be higher than 40%.There could be a crucial role of the severity of the acute clinical conditions, to which both the underlying viral pneumonia and the enterococcal BSI may contribute, in majorly influencing the outcome.The cumulative incidence of enterococcal BSI is high in critically ill patients with COVID-19. Our results suggest a crucial role of the severity of the acute clinical conditions, to which both the underlying viral pneumonia and the enterococcal BSI may contribute, in majorly influencing the outcome. Critically ill patients with acute hypoxemic respiratory failure due to coronavirus disease 2019 (COVID-19) requiring mechanical ventilation in intensive care units (ICU) have been reported to possibly be at increased risk of developing bloodstream infection (BSI) compared with other non-COVID-19 critically ill patient populations . AlthougRegarding the aetiology of BSI in critically ill patients with COVID-19, an unexpectedly high prevalence of enterococcal BSI has been previously observed ,6,7. AgaThe present, descriptive cases series was aimed to better depict the characteristics of ICU-acquired enterococcal BSI in critically ill patients with COVID-19, especially in the terms of cumulative incidence, causative microorganisms, and outcome.This retrospective, single-centre study was conducted in two ICU wards  at San Martino Policlinico Hospital, a 1200-bed teaching hospital in Genoa, Italy. From 1 January 2020 to 31 December 2020, all patients with COVID-19 and ICU-acquired enterococcal BSI were included in the study.The primary objective was to describe the characteristics of ICU-acquired enterococcal BSI in critically ill patients with COVID-19, in terms of cumulative incidence, causative microorganisms, clinical characteristics, antimicrobial treatment, and 30-day mortality. A secondary objective was to exploratorily assess the predictors of 30-day mortality in critically ill COVID-19 patients with ICU-acquired enterococcal BSI.The collection of anonymized data for the present study was approved by the local Ethics Committee  and specific informed consent was waived due to the retrospective nature of the study.COVID-19 was defined as at least one real-time polymerase chain reaction assay positive for SARS-CoV-2 on a respiratory specimen. ICU-acquired enterococcal BSI was defined as at least one blood culture drawn at >48 h after ICU admission positive for enterococci .3); presence of a central venous catheter (CVC); Pitt bacteraemia score  58–69) and 77% were males (33/43). The first episode of ICU-enterococcal BSI mainly occurred late during ICU stay . Overall, a moderate burden of baseline comorbidities was observed, with a median Charlson score of 3 (IQR 2–5). Most patients received an empirical therapy with at least one agent showing in vitro activity against the blood isolate .The demographic and clinical characteristics of the study population are shown in E. faecalis BSI, E. faecium BSI, and vancomycin-resistant E. faecium BSI, respectively. The results of the univariable and multivariable analyses of predictors of 30-day mortality are shown in p = .021).The crude 30-day mortality in our series was 42% (18/43), as also shown in During 2020, we registered 51 episodes of ICU-acquired enterococcal BSI in 43 different COVID-19 patients, with a high cumulative incidence of 229 episodes per 1000 ICU admissions. A high crude 30-day mortality of 42% was observed, despite the lack of a heavy burden of baseline comorbidities and a high rate of appropriate empirical therapy.Enterococcus spp., together with coagulase-negative staphylococci, was the most frequently responsible for BSI among critically ill COVID-19 patients in four different previous series [E. faecium, although some in vitro activity against E. faecalis has been reported) as empirical agents in the suspicion of community-acquired bacterial pneumonia superimposed to the viral disease, since this could have exerted a selective pressure increasing the risk of enterococcal BSI [Enterococcus spp. due to possibly relaxed infection-control measures  was apparently not observed [Enterobacterales and the lack of a clear elucidation of the underlying causal pathway and mechanisms.The high cumulative incidence of enterococcal BSI we observed is in line with the results of Bonazzetti and colleagues, who previously registered 55 episodes of enterococcal BSI among 96 critically patients with COVID-19, equal to a cumulative incidence of 573 episodes per 1000 ICU admissions, which was even greater than the high cumulative incidence we observed in our centre [patients ), althouin vitro active empirical therapy since the onset of BSI, suggesting a possible role of the severity of the underlying viral disease in unfavourably influencing the outcome. Of note, two patients did not receive prompt empirical therapy, possibly due to initially unclear or unrecognized infections in presence of mitigated clinical signs and inflammatory markers at the onset of BSI after previous treatment with anti-inflammatory and immunomodulatory agents [Despite the above-discussed uncertainty in deciphering the true reasons for the high cumulative incidence of enterococcal BSI we and others observed in critically ill COVID-19 patients, the high number of such infections remains, and with the present series, we tried to better depict the characteristics and outcomes of enterococcal BSI in this peculiar population. Notably, despite the high median age of patients in our cohort (63 years), we ultimately registered only a moderate burden of baseline comorbidities , possibly connected to the fact that 3 of the 5 in vitro inactive empirical therapies in the entire series were administered to patients with VRE BSI. Finally, it should be noted that we detected a high number of CRBSI (72%). However, we are unsure whether the CVC was the primary site of infection since we cannot exclude that colonization and infection of the device may have occurred after an initial translocation into the bloodstream from another site (e.g. the gut). Another factor showing a non-statistically significant direction of effect towards increased mortality and deserving further investigation in larger cohorts is the Pitt bacteraemia score (reflecting the severity of BSI presentation), whereas in our cohort there was no apparent association between baseline comorbidities (summarized by means of the Charlson score) and an unfavorable prognosis. The absence of endocarditis diagnoses and the low values of the DENOVA score (reflecting the risk of endocarditis) did not allow us to explore their possible prognostic effect in our cohort.The prognostic impact of the patients’ critical conditions is in line with the independent association we observed between higher SOFA score and 30-day mortality in the multivariable logistic regression model, although it should be recognized that this secondary analysis was burdened by the small sample size and the consequent low power for detecting other possible clinically relevant associations. For example, the potential unfavourable impact of vancomycin resistance in Besides the low power of the secondary, exploratory analysis of predictors of 30-day mortality (discussed above), the present study has some other important limitations. First, this was a case series of COVID-19 patients with enterococcal BSI, thus no comparison was made with critically ill COVID-19 patients with no BSI or with BSI caused by other microorganisms. However, such a possible comparison was not the aim of our study, which was instead conceived to depict the characteristics and outcome of ICU-acquired enterococcal BSI, once recognized their high cumulative incidence in the target population. Second, owing to the observational nature of the study, which implies the existence of unmeasured confounding, and the small sample size, which precludes more sophisticated adjustment for measurable confounding, the present study was not structured to assess the impact of antibacterial therapy (and the type of employed drug/s) on mortality in a completely reliable way. Other not included data that we were eventually unable to reliably collect retrospectively from all patients were: (i) presence and type of recent chemotherapy; (ii) the time elapsed from the last CVC substitution and development of enterococcal BSI. In addition, we cannot exclude the incompleteness of the information on previous glycopeptide use and previous VRE isolation . Finally, the single-centre nature of the study may reduce the generalizability of our findings.In conclusion, our results are complementary to those of other series reporting a high cumulative incidence of enterococcal BSI in critically ill patients with COVID-19. Our hypothesis-generating findings suggest a crucial role of the severity of the acute clinical conditions, to which both the underlying viral pneumonia and the enterococcal BSI may contribute, in majorly influencing the outcome.


Antiphospholipid syndrome (APS) is a rare autoimmune disorder characterized by thromboembolic events, fetal loss during pregnancy, and evidence of antiphospholipid (aPL) antibodies such as beta-2-glycoprotein I (B2-GPI) and anticardiolipin (aCL). The diagnosis and treatment of this condition in the pediatric population have limited literature evidence due to the rarity of the condition in this age group. Guidelines have been adopted from the adult counterpart of the affected population, thereby giving rise to diagnostic and therapeutic challenges. In this report, we describe a rare case of a 15-year-old male who presented with lepromatous leprosy and developed deep vein thrombosis in his right leg. The laboratory evidence of positive aPL antibodies guided our diagnosis of APS and treatment with oral anticoagulants. This report highlights the importance of screening and timely diagnosis of APS in the pediatric population presenting with venous thrombosis in the backdrop of infection. Antiphospholipid syndrome (APS) is a rare clinical entity characterized by an autoimmune process leading to thromboembolic events and recurrent fetal loss during pregnancy . APS hasA 15-year-old male presented to our tertiary care hospital with a nine-month history of progressive bilateral upper limb and lower limb paresthesia. This was associated with weakness of the distal muscles of both the upper and lower limbs. On examination, the presence of multiple hypopigmented macules was noted distributed predominantly over the extensor surfaces of bilateral extremities, which the patient reported to have been persistent for five months Figure . ModifieWe commenced our patient on a regimen of rifampicin, dapsone, and clofazimine. During the course of the treatment, our patient developed dull-aching pain in his right leg. An arterial and venous Doppler was done, which revealed a thrombosed intramuscular vein in the anterior compartment of the right leg with minimal subcutaneous edema in the right ankle and foot. The bilateral lower limb arterial system appeared normal. Coagulation profiles revealed an elevated activated partial thromboplastin time (aPTT) of 42.1 seconds . The international normalized ratio (INR) was 1.13. Laboratory evaluation demonstrated high lupus anticoagulant titers and beta-2-glycoprotein I (B2-GPI) IgM titers. Complement levels (C3 and C4), ANA titers, RA factor, antistreptolysin O (ASO) titers, and anticardiolipin (aCL) antibodies were negative. Liver enzyme tests were unremarkable, with an aspartate aminotransferase (AST) and alanine aminotransferase (ALT) of 56 IU/L and 50 IU/L, respectively. Meeting the revised Sapporo criteria, we diagnosed our patient with APS.The patient was started on heparin with a pediatric-adjusted dose for bridging and transitioned to oral anticoagulation with acenocoumarol that was titrated to maintain an INR of 3. We observed a spontaneous resolution of the deep vein thrombosis over one week. The patient was continued on oral anticoagulation for three months duration with close follow-up. The patient was symptomatically better with the antileprotic medications and anticoagulation. No further episodes of venous thrombosis were observed during the follow-up period.APS is defined as a systemic autoimmune disorder characterized by arterial and/or venous thrombotic events with positive antiphospholipid (aPL) antibodies. This disorder has been extensively studied in the adult population, and guidelines for diagnosis and treatment have been well defined . APS, inPediatric APS is defined as the syndrome presenting before the age of 18 years. To diagnose APS, the presence of at least one proven clinical event  and laboratory evidence of at least one persistently positive aPL test (>12 weeks positivity of lupus anticoagulant (LA), anticardiolipin (aCL) or anti-beta-2-glycoprotein I (anti-B2-GPI) IgG or IgM) are necessary. Contrasting to the female preponderance in the adult population, pediatric APS has been found to occur almost equally in males and females . The PedMultiple hypotheses have been made trying to understand the pathophysiology of thrombosis in APS. The binding of membrane phospholipids of cells with defective apoptotic mechanisms to circulating plasma proteins, such as beta-2-glycoprotein 1, have been found to result in autoantibody production to the formed neoepitopes. Other mechanisms include the production of antibodies to coagulation factors such as prothrombin, protein C, protein S, and annexins, activation of platelets to endothelial cells, and complement activation ,6. An unThe risk of thrombosis in APS is the highest in triple positivity  . ObservaMycobacterium leprae antigen and self-antigens. Interesting to note was the close association researchers found between anti-B2-GPI IgG and venous thrombosis, which was largely greater than that observed with patients with positive IgM titers of the same antibody [The association between leprosy and APS received attention in the late 20th century. It was found that patients with leprosy showed a wide spectrum of antibodies in their sera, such as rheumatoid factor, anti-DNA, antinuclear, and anti-mitochondrial antibodies, due to the polyclonal activation of antibody response. Some authors suggested that autoantibodies in leprosy originated not because of an autoimmune phenomenon but because of the molecular mimicry due to the cross-reactivity between antibody . In the Early initiation of treatment is vital to reduce the morbidity associated with venous thrombosis in pediatric APS. Due to the limited available data regarding pediatric APS and its management, treatment guidelines have been adapted from literature evidence of adult-onset disease. The Single Hub and Access Point for Pediatric Rheumatology in Europe (SHARE) initiative in 2017 published treatment guidelines for any child with positive aPL antibodies and evidence of venous thrombosis. Vitamin K antagonist such as warfarin was described as the primary modality of treatment of thrombosis in this population. INR was to be targeted to 3.0-4.0, and in the case of persisting aPL antibodies, long-term anticoagulation was recommended. In the case of arterial thrombosis, it was also recommended to include antiplatelet agents such as aspirin and clopidogrel alongside long-term anticoagulation . Data isThis case highlights the rare occurrence of pediatric APS secondary to leprosy. Leprosy, an endemic disease in certain parts of the world, must involve careful evaluation and consideration of the potential venous thrombosis secondary to APS. Timely interventions through radiological diagnostics such as Doppler scans and thrombophilia workup are warranted in such patients. Increased aPTT during the workup of these patients necessitates further workup of potential APS. Vitamin K antagonists remain the mainstay of deep vein thrombosis in secondary pediatric APS, and larger randomized control trials are required to ascertain the use of NOACs and heparin in such patients.


Background: Accurate and fast diagnosis of COVID-19 is very important to manage the medical conditions of affected persons. The task is challenging owing to shortage and ineffectiveness of clinical testing kits. However, the existing problems can be improved by employing computational intelligent techniques on radiological images like CT-Scans (Computed Tomography) of lungs. Extensive research has been reported using deep learning models to diagnose the severity of COVID-19 from CT images. This has undoubtedly minimized the manual involvement in abnormality identification but reported detection accuracy is limited. Methods: The present work proposes an expert model based on deep features and Parameter Free BAT (PF-BAT) optimized Fuzzy K-nearest neighbor (PF-FKNN) classifier to diagnose novel coronavirus. In this proposed model, features are extracted from the fully connected layer of transfer learned MobileNetv2 followed by FKNN training. The hyperparameters of FKNN are fine-tuned using PF-BAT. Results: The experimental results on the benchmark COVID CT scan data reveal that the proposed algorithm attains a validation accuracy of 99.38% which is better than the existing state-of-the-art methods proposed in past. Conclusion: The proposed model will help in timely and accurate identification of the coronavirus at the various phases. Such kind of rapid diagnosis will assist clinicians to manage the healthcare condition of patients well and will help in speedy recovery from the diseases. Clinical and Translational Impact Statement— The proposed automated system can provide accurate and fast detection of COVID-19 signature from lung radiographs. Also, the usage of lighter MobileNetv2 architecture makes it practical for deployment in real-time. I.https://www.worldometers.info/coronavirus/). The medical experts and the researchers are working together for a better understanding of COVID-19 etiology. Novel strategies are being underway that can better control its spread. The conventional testing procedure is based on reverse transcription-polymerase chain reaction (RT-PCR) and nucleic acid sequencing from the virus. Although, RT-PCR being the gold standard, the procedure is time-consuming, needs to be re-iterated, and has considerable false-negative results. In such scenarios, CT-Scans of affected person plays an important role in better management of health condition. The variations like ground-glass opacities and pulmonary consolidation in CT images are an important biomarker for COVID-19 detection which can help in prompt identification of suspicious cases thereby saving crucial time and readily isolating the patient et alve and Normal categories. The researchers attained an accuracy value of 84.7% and an F1 score of 85.3% on a database encompassing 195 Normal and 275 COVID + ve scans. Kaur and Gandhi ve and 246 normal scans. The authors attained an accuracy of 98.35%. Loey et al.et al.et alve and normal. The hyperparameters of the DBM model were tuned using the Memetic Adaptive Differential Evolution (MADE)algorithm. The authors attained an accuracy of 98.37% and an F1-score of 98.14% under the train test ratios of 60: 40 over a dataset having 1252 + ve and 1230 − ve COVID scans. Surveying the literature reveals that the usage of the CT scans in diagnosing COVID-19 is drawing attention due to the inefficiency of the medical testing kits. Also manually inspecting each CT image in the entire volume is tiresome and challenging for the medical experts. As the number of cases are increasing by multi-folds daily, automatic Computer-aided diagnosis (CAD) systems is the need of the hour. Although CAD based on ML and DL are being developed and that have aided in speedy detection of the virus, however, not all the reported works are reproducible as the employed datasets are not available for public usage. Also, the DL requires large annotated training datasets for good detection results. To address this limitation, several works have employed transfer learned models on the benchmark dataset provided by Soares et al.new_fc’) layer of the transfer learned MobileNetv2 which are then fed to the FKNN whose hyper-parameters are fine-tuned via the PF-BAT. Summarizing the key contributions are as follows:•A novel diagnosis model is proposed based on the integration of deep features and one of the recent metaheuristic optimization algorithms.•The proposed model overcomes the drawback of the manual hyperparameter tuning via employing the PF-BAT optimization algorithm.•The proposed model attains an accuracy of 99.38% on the COVID-19 CT scan dataset that is better than the existing state of the art models.An outbreak of coronavirus infection (SARS-CoV-2) emerged in December 2019, and by the beginning of the year 2020, the World Health Organization (WHO) announced it as a global pandemic II.A.et al.ve and 1230 − ve for the virus ve and − ve COVID scans from the dataset are shown in In this proposed work, we have considered the SARS-CoV-2 CT image database provided by Soares B.This section provides the mathematical background for the blocks used in the proposed system.1)The structural design of MobileNetv2 is motivated by the MobileNetv1 The general architecture of the model is summarized in Depthwise separable convolution replaces conventional convolution via two processes. The first process works by applying a different convolution to every feature map which is known as feature map-wise convolution. The resultant feature maps are stacked together to be treated by the second process that is pointwise convolution. In the second process, all the feature maps undergo convolution using a kernel of size In the above equation 2)FKNN classifier improves upon the conventional KNN classifier by adding the concept of fuzzy logic to the KNN. FKNN algorithm operates by allocating the membership as a mathematical relation of the exemplar distance vector from its Also, for binary class scenario Depending upon the value of The motivation behind using FKNN is its excellent performance over a wide variety of disease diagnosis problems like Thyroid 3)PF-BAT is an improvement over the BAT algorithm developed by Yang The exploration and exploitation abilities of the BAT are impecunious et al.Here, 4)In the present sub-section, the hyper-parameter tuning issue of the FKNN classifier is overcome via using the PF-BAT optimization algorithm as manual tuning is a time-intensive process. The proposed methodology is shown in As illustrated, the designed methodology is separated into four phases; 1) data preparation, 2) transfer learning, 3) feature extraction, and 4) classification. In the data preparation part, the database is distributed in 8:2 proportion . For better generalization and prevention of overfitting, data augmentation is employed over training scan images. Augmentation involves the following processes, i.e., random translation in pixel range , random shear in the range from , and random rotation in the range .fc’) layer, a softmax layer, and a classification output layer. The size of the ‘fc’ layer is equal to the number of classes  in the new classification task Model = { MobileNetv2} be the pre-trained architecture. Let  of the learned model.In the feature extraction phase, attributes are extricated from the learned MobileNetv2 model by taking activations onto the last fully connected layer  The step-by-step procedure for hyper-parameter tuning is elicited in the form a simple illustrative example given as III.A.F-score is defined in In the above equation B.new_fc’ layer) for the classification of COVID CT images from Non-COVID images. Firstly, the MobileNetv2 is trained on the COVID CT scan image database and thereafter discriminative features are extracted from the ‘new_fc’ layer. The deep features from the ‘new_fc’ layer are used to train an FKNN whose hyper-parameters are fine-tuned using the PF-BAT. new_fc’ layer performs the best by achieving ceiling level validation accuracy of 99.38%, precision of 99.20%, recall of 99.60%, F1-score of 99.40%, and AUC of 99.58%.We have presented the experimental results obtained using transfer learned MobileNetv2 model and proposed PF-BAT enhanced FKNN model , the evaluation over larger multiclass data has also been carried out. The experimentations were done on multiclass dataset having 4173 CT scans from 210 patients V.ve signature from the lung radiographs. In the proposed model, transfer learned MobileNetv2 is used as a feature extractor. The discriminative features extracted from the fully connected layer of the learned model are fed to the PF-BAT enhanced FKNN classifier. The hyperparameters of the FKNN have been optimized using the PF-BAT algorithm. Thereafter, the proposed model has been extensively validated on publicly available CT scan image datasets. The analysis on the datasets reveals that with the scheme of hyper-parameter optimization, an increase in the validation accuracy is obtained. The accuracy improves by 0.617%, 1.189%, and 3.851% respectively with optimization. The comparative analysis shows that the proposed model outperforms the existing state-of-the-art models. Consequently, the proposed model can work as a fast automated intelligent tool for assisting healthcare professionals in decision making.In this present work, a novel model is proposed that automatically identifies the COVID +


In this paper, we propose a multi-scale feature extraction with novel attention-based convolutional learning using the U-SegNet architecture to achieve segmentation of brain tissue from a magnetic resonance image (MRI). Although convolutional neural networks (CNNs) show enormous growth in medical image segmentation, there are some drawbacks with the conventional CNN models. In particular, the conventional use of encoder-decoder approaches leads to the extraction of similar low-level features multiple times, causing redundant use of information. Moreover, due to inefficient modeling of long-range dependencies, each semantic class is likely to be associated with non-accurate discriminative feature representations, resulting in low accuracy of segmentation. The proposed global attention module refines the feature extraction and improves the representational power of the convolutional neural network. Moreover, the attention-based multi-scale fusion strategy can integrate local features with their corresponding global dependencies. The integration of fire modules in both the encoder and decoder paths can significantly reduce the computational complexity owing to fewer model parameters. The proposed method was evaluated on publicly accessible datasets for brain tissue segmentation. The experimental results show that our proposed model achieves segmentation accuracies of 94.81% for cerebrospinal fluid (CSF), 95.54% for gray matter (GM), and 96.33% for white matter (WM) with a noticeably reduced number of learnable parameters. Our study shows better segmentation performance, improving the prediction accuracy by 2.5% in terms of dice similarity index while achieving a 4.5 times reduction in the number of learnable parameters compared to previously developed U-SegNet based segmentation approaches. This demonstrates that the proposed approach can achieve reliable and precise automatic segmentation of brain MRI images.  The segmentation of medical images plays a vital role in the study and treatment of many diseases. In particular, magnetic resonance imaging (MRI) is typically favored for structural analysis as it generates images with high soft-tissue contrast and higher spatial resolution and does not entail any health hazards. Brain MRI scans are quantitatively examined to diagnose various brain disorders such as epilepsy, schizophrenia, Alzheimer’s disease, and other degenerative disorders . MRI is The attention mechanism allows the system to concentrate on the most salient features without additional monitoring. This limits the use of multiple identical feature maps and highlights the prominent features that are beneficial for a given task. It has been reported that attention modules benefit segmentation methods by creating an enhanced network for pixel-wise labeling ,7,8,9. CTo achieve higher segmentation accuracy with lower computation complexity, we propose deeper attention mechanisms, which are combined with multi-scale feature fusion that can enhance the performance of CNNs for the segmentation of brain MRI images. In particular, we propose a novel global attention module (GAM) at both the encoder and decoder sides with a multi-scale guided input. The multi-scale data extracted using 1 × 1 and 3 × 3 convolutional kernels jointly encode the complementary low-level information and high-level semantics from the extracted input patches. The feature maps from a previous network layer are provided together as input for the GAM on the encoder side with this multi-scale information. This proposed multi-scale strategy, along with the attention module, helps the encoder extract different semantic information. The GAM at the decoder helps capture discriminative information and focuses on relevant features while performing up-sampling operations. As a result, each network-layer contains two independent attention modules, which focus on extracting enhanced feature representations and generating an accurate segmentation network. The convolution blocks at the encoder and decoder layers are replaced with fire modules  that redOur main contributions are summarized as follows:We propose a modified U-SegNet architecture integrated with a novel global attention module. Attention is applied at both contracting and expansive paths, creating a multi-attention network. The key element in GAM is global average pooling, which provides the global context of high-level features as assistance to low-level features to obtain class-category localization.We propose a multi-scale input feature fusion strategy to extract the context information of high-level features at different scales, thus incorporating neighbor-scale feature information more precisely.The fire modules are used to replace the convolution layer to significantly reduce the number of model parameters, which results in a reduction in the model size and computation complexity; this consequently leads to a more efficient segmentation model.Contributions:The remainder of this paper proceeds as follows. Extensive research has been conducted on medical image segmentation in the past ref. ,14,15, wSegNet and U-net models, a new hybrid model is explored, namely U-SegNet . The U-SThe attention mechanisms highlight key local regions in the feature maps and discard unrelated data carried by the generated feature maps. The attention modules act as crucial components of a network that wants to gather global information. The inclusion of the attention blocks demonstrated very successful outcomes in various vision problems, such as image classification , image cMost of the deep learning-related studies have considered increasing network accuracy as their main objective. However, the computational burden of a significant number of parameters and deep architecture becomes a crucial issue. Recent studies have shown that most deep neural networks are over-parametrized, resulting in deep learning network redundancy, which leads to inefficient use of memory and computing resources. In these large parameter spaces, various compression techniques, such as shrinking, factorizing, or compressing pre-trained networks, are applied to minimize redundancy and obtain smaller models ,44,45,46It is known that the SegNet  and U-neAlthough U-SegNet shows better segmentation performance, the segmented outputs are still blurry, and the network is insensitive to the fine details of the image . To achiH × W × S), we pad zeros to the H × W of each slice and resize to a dimension of 256 × 256. Then, 48 slices are extracted starting from the 10-th slice with an interval of 3 slices [3 slices . Furtherl represents the index of the network layer. The convolution output for the squeeze block is computed as (1).f (∙).The squeeze output is fed into the expanding module. The expanding module consists of two parallel convolutions with kernel sizes of 3 × 3 and 1 × 1. Furthermore, the output from these parallel convolutions is concatenated to form the fire module output, and is expressed as (2).The max-pooled input is followed by a convolution of 1 × 1 and 3 × 3 filters separately. These convolved outputs are concatenated to form multi-scale feature maps, as shown in (4). The multi-scale information and the fire module output and are fed as input to the GAM at each encoding layer, as in (5).The output from GAM is given to the max-pooling layer to reduce the dimensionality and focus on the fine details of the feature map, as expressed in (6). Pooling indices are stored at each encoder layer so that the decoder uses the information to up-sample the feature maps. The output at each encoder layer is referred to as the encoding unit (down-sampling unit) and is obtained using (6).Similar to the encoding path, the decoder path in the proposed method uses transposed fire modules to reduce the number of model parameters. The main component of the decoder path is the up-sampling unit. Each up-sampling unit consists of a transposed fire module. The transposed fire module consists of a 1 × 1 transposed convolution. The output from the 1 × 1 transposed convolution is fed into two 3 × 3 and 1 × 1 kernel-sized parallel transposed convolutions that are concatenated to form the output transpose fire module, as in the down-sampling unit. The decoder is integrated with attention gates, which can highlight the salient features. The feature maps extracted from the The output from each decoder layer can be obtained using (7), where A distinctive brain signal processing system for human vision is the visual attention process. This is a tool for a human to pick relevant information instantly from a large amount of information using sources of limited attention. In deep learning, the attention process is similar to that of human visual attention. Its main objective is to determine the most relevant data from a vast amount of knowledge to accomplish its goal (tissue segmentation). By suppressing function activations that are not important to the task, the attention mechanism improves the network performance. To do this, we propose a novel global attention module with self-attention in an efficient manner. As a guide from low-level features to assess class localization, the GAM on each encoder and decoder layer enables global context details.b is the bias term, and GAP is a function of the global average pooling. The feature maps obtained from the attention module In the case of multiple semantic groups, learning multidimensional coefficients of attention is suggested. As guidance for low-level features to incorporate local features into the global context, global average pooling provides global context information. The global information produced from the high-level feature is fed to a 1 × 1 convolution with the ReLU activation function. To obtain weighted low-level characteristics, it is multiplied by 1 × 1 convoluted low-level features. To obtain the tensor coefficient of attention, we used multiplicative attention. To extract pixel localization specific to the class of the high-level feature index, the tensor coefficient is up-sampled and added with low-level features. The tensor coefficient of attention is obtained as follows:The fire module was initially introduced in SqueezeNet  to identioned in , the proThe brain MRI scan of each subject constitutes the dimensions H × W × S. Some of the starting and ending slices of the brain MRI scan do not provide much useful information, as analyzed in the previous research , and thei, the ground truth and predicted distribution score are y and The final decoder layer consists of a 1 × 1 convolutional layer with softmax activation to predict a reconstructed segmentation map. The output contains four predicted classes: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and background. The proposed model accepts the input image and produces the corresponding learned representation. Based on this feature representation, the input image is classified into any of the four output classes. The cross-entropy loss is used to measure the proposed model losses, as in (11). The softmax layer learns representations from the decoder and interprets them in the output class. The probability score The proposed method was evaluated using two sets of brain MRI images. The first sample included 416 T1 weighted brain MRI scans from the Open Access Series of Imaging Studies (OASIS) database , where iThe second dataset contains MRI from the Internet Brain Segmentation Repository (IBSR)  dataset.The training and testing were performed on an NVIDIA GeForce RTX 3090 GPU to build the proposed network and use stochastic gradient descent to optimize the loss function. For training, we set the learning rate to 0.001, a high momentum rate of 0.99, and the number of epochs to 10. The Keras framework for implementation of the proposed work was used.We evaluated the performance of the proposed method using quantitative metrics. We also assessed the segmentation performance in terms of the mean square error (MSE), which is the average square difference between the original and predicted Y values. The Hausdorff distance (HD)  was usedh of training time, requiring fewer resources among the existing methods in our proposed method. SegNet tends to miss several fine details because when performing up-sampling from low-resolution feature maps, it loses adjacent information. On the other hand, U-net uses skip connections as the core of the architecture, which blends deep, coarse information with shallow, fine semantic information. A drawback of U-net is its significant memory requirement because lower-level features in the up-sampling process must be stored for further concatenation. Because U-net uses low-level feature maps for up-sampling, translation invariance is often compromised. Moreover, U-SegNet [The quantitative analysis of the proposed method is performed in comparison with SegNet , U-net , U-SegNeU-SegNet  tends toOur proposed method presents a multi-scale feature fusion scheme combined with GAM as a potential solution to the problems discussed above and produces improved segmentation accuracy. The max-pooled output was filtered with 1 × 1, 3 × 3 kernels. Then, they are concatenated together and can extract the global context without losing the resolution of the segmentation map. In this way, global information can be exchanged between layers without reducing the resolution, leading to lowered blurring in the segmentation maps. In addition, the GAM at the encoder enables the presentation of global context information as a guide for low-level features to extract the original resolutions for segmentation. The GAM at the decoder shows that the combination of global features and local features is essential to discriminate brain tissues and is consistent with the results from previous studies. Furthermore, uniform input patches allow the network to concentrate better on local information. As a result of the selective integration of spatial information through uniform patches, feature maps followed by multi-scale guided multiple GAMs help in capturing context information and can efficiently encode complementary information to segment the brain MRI accurately.As mentioned above, we propose the use of a fire module for fewer learnable parameters while maintaining equivalent accuracy. We conducted an ablation study on the three simplified versions of the proposed modules to investigate the influence of each selection on the segmentation performance as follows: (i) Squeeze U-SegNet, (ii) Squeeze U-SegNet with multi-scale input, (iii) Squeeze U-SegNet with multi-global attention and (iv) multi-scale Squeeze U-SegNet with multi-global attention (proposed method). The Squeeze U-SegNet was obtained by replacing each convolution block by fire module in the conventional U-SegNet. The second network proposes that the encoder of the Squeeze U-SegNet includes a multi-scale input layer. This is achieved by max-pooling the input and performing parallel convolution with 1 × 1, 3 × 3 kernels, and concatenating these multi-scale features. These fused multi-scale features are concatenated with the corresponding fire module output and fed as input for further max-pooling operations. This process is repeated for all encoding layers. The multi-scale feature module extracts neighbor scale information of global features more precisely while filtering out irrelevant information. The impact of the attention mechanism is explored in the third network, where GAMs are integrated at both the encoder and decoder, forming a multi-attention network. Finally, the multi-scale squeeze U-SegNet with multi-global attention referred to as the proposed method incorporates semantic guidance by combining all the proposed modules. We also investigated the effects of patch size in terms of training time and segmentation performance. The experiments were performed on the OASIS dataset for three distinct patch sizes . This paper proposes multi-scale feature extraction with novel global attention-based learning based on the U-SegNet architecture for brain MRI segmentation. The multi-scale data provide rich spatial information and improve the robustness of feature extraction. The global attention module provides the global context as guidance for low-level features to select category localization details. Squeeze and expand layers lead to the generation of one million parameters, which are 3, 5, 4.5, 3, and 28 times smaller than SegNet, U-net, U-SegNet, U-net++, and CE-net networks, respectively. Our proposed network obtains the best DSC value of 96%. The training time for the proposed method for the OASIS dataset is 50% of that of the U-net++ and CE-net methods. Our validation proves that the network operating on patch-wise input, integrated with multi-scale global attention and fire modules, will yield an efficient brain MRI segmentation model. The proposed model can be easily extended to complex network architectures owing to flexibility and adaptability with faster computation. Hence, a three-dimensional (3D) segmentation model can be devised using the extended model of the proposed architecture as future works.


Despite the modeled estimations of the burden of asymptomatic spread, the duration of viral positivity and infectiousness of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains understudied. The objective of the present study was to estimate and compare the time till viral clearance of SARS-CoV-2 in asymptomatic and non-critical symptomatic individuals. We studied 184 SARS-CoV-2-positive participants, of whom 145 were asymptomatic. Our analysis uncovered that time till viral negativity is similar for subclinical  and overt infections  after controlling for age and sex. This has implications in understanding the period of infectivity for SARS-CoV-2 in order to plan adequate public health measures to control the community spread. Asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections remain highly under-diagnosed  and are Study population consisted of individuals who arrived in Bahrain from Iran and Egypt between 25 February 2020 and 14 March 2020 and tested positive for SARS-CoV-2 upon mandatory COVID-19 screening prior to entering the country.Information on reported COVID-19 symptoms was gathered from the study subjects who underwent nasopharyngeal swab testing for SARS-CoV-2 using reverse transcriptase–polymerase chain reaction (RT-PCR) test. After screening, all of the study subjects served mandatory quarantine in isolation wards , until viral clearance occurred—defined as two consecutive negative RT-PCR test results 24 h apart. We used Thermo Fisher Scientific  TaqPath 1-Step RT-qPCR Master Mix, CG on the Applied Biosystems  7500 Fast Dx RealTime PCR Instrument for conducting the PCR assay. The E gene was used and targeted for the assay, and when detected, the test was confirmed by RdRP and N genes. E gene Ct values > 40 were considered negative. For quality control, both positive and negative controls were used. Time until viral clearance was ascertained by testing the individuals every 2 days till viral clearance occurred. Any individuals who developed symptoms while undergoing quarantine were counted as symptomatic.Age, sex, and presence of symptoms in the participants have been reported as proportions. Odds of being symptomatic were estimated using a logistic regression model, which was adjusted for categories of age and sex. Time till viral clearance has been reported as medians and interquartile ranges (IQRs) , and difference across categories of covariates was tested using a log rank test. Hazards of viral clearance were estimated using a Cox proportional hazards model.n = 145, 78.8%) and were females . The number of subjects progressively increased across increasing age categories, with 22.3% (n = 41) of the subjects being in the ≥60 years category.The total number of participants in the study was 184, which included 132 subjects who tested positive at screening and 52 out of 2,526 people who tested negative upon screening and went on to test positive at the end of quarantine. Most of the subjects had asymptomatic infection  after adjusting for differences in sex .p = 0.491) days. To adjust for effects of age and sex, we computed hazards of viral clearance among the study subjects (Median time until viral clearance was similar for males and females  and showsubjects  and did These results indicate that after age and sex were controlled for, time till viral negativity of subclinical infection is similar to that of a non-critically overt one.To our knowledge, this is the first study reporting time till viral clearance of SARS-CoV-2 RNA in asymptomatic individuals and is the first of its kind analysis to uncover the finding that duration of viral positivity is similar for subclinical and overt infections after controlling for age and sex. Chang et al.  have repThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Bahrain National Covid-19 Taskforce Ethics Committee. The patients/participants provided their written informed consent to participate in this study.NK: data analysis, interpretation, and preparation of the manuscript. AA and SA: conception and design, data collection, and manuscript review. SO and SLA: writing and manuscript review. MA: conception and design of the study and manuscript review. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.