Our matching scheme can enhance the between-group comparability in prevalent new-user cohort designs to minimize time-related bias, improve confounder adjustment, and ensure the reliability and validity of study findings.We developed a three-step matching algorithm to enhance the between-group comparability for comparative drug effect studies involving prevalent new-users of the newer study drug versus older comparator drug(s). The three-step matching scheme is to match on: (1) index date of initiating the newer study drug to align the cohort entry time between study groups, (2) medication possession ratio measures that consider prior exposure to all older comparator drugs, and (3) propensity scores estimated from potential confounders. Our approach is illustrated with a comparative cardiovascular safety study of glucagon-like peptide-1 receptor agonist (GLP-1ra) versus sulfonylurea (SU) in type 2 diabetes patients using Taiwan’s National Health Insurance Research Database 2003–2015. 66% of 3195 GLP-1ra users had previously exposed to SU. The between-group comparability was well-achieved after implementing the matching algorithm . Compared to SU, the use of GLP-1ra yielded a significantly reduced risk of the primary composite cardiovascular events (hazard ratio [95% confidence interval]: 0.71 [0.54–0.95],  However, when a study involves a comparison between a study (newer) drug and a comparator (older) drug that has been on the market for a long time, there will be many prevalent new-users (PNUs) among the newer study drug group . These PNUs are likely to account for a significant portion of real-life patients using the newer drug, but typically they are all excluded when incident new-user cohort designs (where only “treatment naïve” users of a newer drug can be included) are applied4 have recognized the importance of including PNUs in analyses, there are two main challenges. First, past exposure to the older drug(s) can be very complicated  and difficult for adjustment in analyses. Second, the between-group comparability of baseline patient characteristics and treatment patterns before the initiation of the newer drug could be difficult to achieve. Compared to incident new-users, PNUs are likely to have a wider variety of disease conditions, disease severities, and past drug exposure patterns. Suissa et al.5 therefore proposed approaches for baseline characteristics adjustment to achieve the comparability between the newer and older drug groups. They constructed either time- or prescription-based exposure sets to compute the propensity score (PS) of initiating the newer drug and to identify matched individuals from those who received the older drug.Although an increasing number of comparative drug effect studies5. In real-world settings, however, the newer drug users may have already exposed to multiple older comparator drugs in addition to that specific older comparator drug of interest. In particular, when several older comparator drugs are available on the market before a newer drug is introduced, the patterns of older drug exposure history would be too complex to be measured using the prescription-based exposure set approach proposed by Suissa et al.5 Moreover, the prescription-based exposure set, which only measures the past pattern regarding the number of older drugs being used5 but does not consider the treatment duration of the older drugs, would raise a concern because the duration of drug exposure is an important indicator for disease progression or severity and can affect study outcomes.Suissa et al.’s methods only considered past exposure of one specific older drug  and adjusted it in the matching processAgainst this background, we developed a matching scheme with applying the medication possession ratios (MPRs) measure to quantify the complexity of past exposure patterns of all possible older comparator drugs to achieve the baseline characteristics comparability between study groups in PNU cohort designs. In the following, we describe the detailed procedures and illustrate them with a comparative safety study of glucagon-like peptide-1 receptor agonist  versus sulfonylurea  for cardiovascular diseases (CVDs) in a population-based cohort with type 2 diabetes (T2D).The Institutional Review Board of National Cheng Kung University Hospital approved the study before commencement (A-EX-106-013). The informed consents to individual study patients were waived by the Institutional review board of National Cheng Kung University Hospital because the data were de-identified. The study was carried out in accordance with relevant guidelines and regulations.To achieve the between-group comparability of baseline characteristics, we developed a three-step matching algorithm: (1) matching based on the index date at which the study drug was initiated, (2) matching based on MPRs, which quantified past exposure of all potential older comparator drugs before the index date, and (3) matching based on PS estimated from a variety of confounder patient characteristics measured before or at the index date. Three steps are detailed below.The index date refers to the initiation date of either the newer study drug or the older comparator drug. We identify the index date of the study drug user along with a pre-defined time interval to match the index date of the comparator drug user to align the study cohort entry time between study groups Fig. a. Consid7 for measuring the use of all potential comparator drugs prior to the index dates and match these past utilization patterns between study groups  prior to the index dates between study groups. We propose the utilization of MPRsups Fig. b. Matchi10. Given a set of covariates identified at the initiation of the study or comparator drugs, the PS summarizes the confounder patient characteristics  and reconstructs the distribution under the overlapping area between study groups to ensure comparability  use, including SU (about 66% of 3195 GLP-1ra users). If using an incident new-user design considering only GLP-1ra users without previous exposure to SU, only 1086 new (naïve) GLP-1ra users can be in the study. This would greatly affect the study generalizability. However, using the PNU cohort design allows us to consider the entire 3195 GLP-1ra users in the study. The study patient selection was illustrated in Supplementary Fig. 1.An illustrative study for our proposed matching algorithm in a prevalent new-user cohort study for comparative CVD safety of GLP-1ra versus SU in T2D patients is provided in greater detail in Supplementary Method. Briefly, Taiwan’s National Health Insurance Research Database (NHIRD) 2003–2015 was utilizedThere were three matching steps Fig. . First, As a result, a lower crude incidence rate of each study outcome was found in GLP-1ra group compared to SU group Table . GLP-1ra5, where only the two-step matching process is constructed, our proposed three-step matching algorithm provides more comprehensive and precise adjustment to enhance the between-group comparability.With increasing comparative drug effect studies aimed to include PNUs for enhancing the generalizability of study results to real-world settings, the proposed matching algorithm can be a feasible approach to achieve the cohort comparability between study groups for assessing the drug’s effects on study outcomes. Our proposed matching algorithm considers all aspects of potential confounding and bias that may be introduced in analyzing PNUs to enhance the confounder adjustment and ensure the study validity. Specifically, matching based on the index date is essential to align the study cohort entry time between study groups, matching based on MPRs is important to balance past utilization patterns of all possible older comparator drugs between study groups, and matching based on PS is used to adjust for all other potential confounders  that could possibly influence study outcomes. Compared to the PNU cohort design by Suissa et al.19. Specifically, immortal time and time-window biases can be reduced by aligning the study cohort entry time between study comparison groups . Also, the time-lag bias to study estimate is lowered when study comparison groups possess comparable disease stage/severity, which can be achieved by balancing both prior use patterns of all possible older comparator drugs  and confounder patient characteristics between study groups .In particular, our algorithm minimizes potential time-related biases , which are common issues in observational studies and could exaggerate the benefits of a study drug of interest5, only the past exposure to the specific comparator drug of interest  was considered in the analysis. Different from it, we further introduce the MPR measure to quantify the past exposure to all comparator drugs and match them between study groups. However, considering the prior exposure to all comparator drugs rather than only a specific one for the matching would downsize the study population. For example, our illustration study identified a final study cohort of 1573 GLP-1ra users from 3195 GLP-1ra users, where most of them were dropped from the MPR matching process. This is because in the real world, the GLP-1ra users usually have had exposure to multiple GLAs before using GLP-1ra, while the SU users generally have not. When we adjusted the past utilization of all different GLAs instead of only the past SU use, some GLP-1ra users who had the complicated GLA use history could not be matched to the SU users with the comparable past GLA exposure and then were unable to be included in the analyses. This trade-off between the study precision by comprehensively adjusting for the past drug use and sample size could be a consideration of applying our matching algorithm.The application of the MPR in our matching algorithm yields the following advantages. First, in real-world practice settings where multiple comparator drugs that could have an impact on study outcomes are available before the study drug is introduced, adjustment based on the prior exposure patterns of all potential comparator drugs is important. In our illustration, there were several GLAs available  before GLP-1ra, and some may have potential CVD effects. We found that about 87% of the GLP-1ra users were previously exposed to at least two different GLAs. However, in the study of Suissa et al.Second, the pattern  of the past history of all potential comparator drugs could be very complex, and is thus difficult to quantify and adjust. In this circumstance, the MPR can be a convenient measure to sum the total days of supply within a specific time period  for a given drug, regardless of changes in utilization pattern . Therefore, matching can then be feasibly done according to the MPR values of individual prior drug uses between study drug groups.5. In our illustration, matching based on MPR ensured that the patterns of prior exposure to different GLAs, in terms of their numbers, types, and exposure durations, were balanced between the GLP-1ra and SU groups.Third, the MPR approach considers not only the number and type of drugs but also the length/duration of drug exposure, whereas the original prescription-based exposure set approach only considers the number of drugs being exposed previously21. For example, patients who have high persistence or adherence to medication refills  may tend to engage in healthy behaviors  compared to those that do not . With this regard, the comparative effectiveness of study drugs  on study outcomes  might be confounded by this healthy user effect. Hence, the adjustment based on MPR measures might allow researchers to control for potential healthy user bias/effect.Lastly, the MPR is calculated based on medication refills, which might be a proxy for patients’ health behaviors23. Three cohort studies based on the incident new-user cohort design also showed a statistically non-significant lower risk of the composite CVDs with GLP-1ra compared to SU26. The findings of favorable cardiovascular outcomes of GLP-1ra versus SU observed in our illustration study are generally consistent with those in previous studies including several cardiovascular outcome trials of GLP-1ra versus placebo30, which supports the validity of our approach and may suggest that cardiovascular benefits reported in clinical trials could be extended to clinical practice settings. However, unlike previous studies considering either highly selective trial populations or only incident new users of GLP-1ra, including a broader spectrum of GLP-1ra users and applying more rigorous matching methodology in this study may guarantee us to reveal valid and generalizable results. Nonetheless, more real-world studies are warranted to corroborate the comparative effectiveness and safety of GLP-1ra versus other GLAs to inform T2D management. Our proposed approach would therefore be handy for this task.The proposed matching algorithm was illustrated in a study of the comparative safety of GLP-1ra versus SU for CVDs and mortality, which has been previously investigated. Recent meta-analyses of existing trials indicate either statistically significant or non-significant lower risks for all-cause mortality and fatal CVDs associated with GLP-1ra versus SU use17 in the analyses to ensure accurate assessment and implications when relating medication treatment to health outcomes. Second, our matching algorithm might be computationally demanding because it is operated on the basis of matching drug use sets of the comparator drug . However, this issue could be overcome by random sampling as shown in our illustration. Also, if the sample size of the comparator drug group is much larger than that of the study drug group, computing the PS based on the logistic regression analysis or Cox model would be problematic due to such a large disparity in sample size between the two drug groups32. As shown in our illustration, we recommend performing a random sampling procedure  on the comparator drug group to inspect the fitness of the distribution of PS  between the two groups. Third, the influence of reusing comparator drug use sets remains to have future research, in particular the potential impact of non-independence in the estimation of CIs, which could further require techniques based on bootstrap sampling5. Fourth, a number of medication use measures exists in the literature. For efficient computation, MPR was chosen in this study. The impact of adopting different medication utilization measures  on study results deserves future research. Lastly, some pre-defined time periods were applied in our illustration , which could be further examined for their appropriateness.There are several limitations inherent to the developed approach. First, in real-world practice, several factors such as physicians’ prescribing tendency, drug adverse effects, or financial considerations may lead to patients’ early discontinuation or non-adherence of medications. However, the validity of effectiveness estimates in the real-world drug studies could be affected by the bias or confounding introduced from these accidental use or non-adherence issues. Therefore, our illustration only considered the stable study drug usersFor comparative drug effectiveness studies involving PNUs, we developed a feasible and precise three-step matching algorithm that can be used to enhance the between-group comparability, minimize time-related bias issues, and improve confounder adjustment. The illustrative study with the application of the three-step matching algorithm revealed that compared to a SU, GLP-1ra use was associated with a significant lower cardiovascular disease risk among a real-world T2D population.Supplementary Information.


Since the beginning of the Covid-19 pandemic, efforts have been made to shield older adults from exposure to the virus due to an age-related higher risk for severe health outcomes. While a reduction of in-person contacts was necessary in particular during the first months of the pandemic, concerns about the immediate and longer-term secondary effects of these measures on subjective well-being were raised. In the present study, we focused on self-reported resilience of older people in a longitudinal design to examine risk and protective factors in dealing with the restrictions. Data from independently living people aged 60+ in Luxembourg were collected via a telephone/online survey after the first lockdown in June (N = 611) and September/October 2020 (N = 523), just before the second pandemic wave made restrictions necessary again. Overall, results showed an increase in life-satisfaction from T1 to T2, although life-satisfaction was still rated slightly lower than before the crisis. Also, about a fifth of participants indicated at T2 difficulties to recover from the crisis. Participants who reported higher resilience to deal with the Covid-19 crisis at T2 showed higher self-efficacy, agreed more strongly with measures taken by the country and felt better informed about the virus. In contrast, participants who reported more difficulties in dealing with the pandemic, indicated reduced social contacts to family and friends at T2, and also felt lonelier. Results will be discussed applying a life-span developmental and systemic perspective on risk and protective factors in dealing with the secondary impacts of the pandemic.


The proper assembly and dissemination of progeny virions is a fundamental step in virus replication. As a whole, viruses have evolved a myriad of strategies to exploit cellular compartments and mechanisms to ensure a successful round of infection. For enveloped viruses such as retroviruses and herpesviruses, acquisition and incorporation of cellular membrane is an essential process during the formation of infectious viral particles. To do this, these viruses have evolved to hijack the host Endosomal Sorting Complexes Required for Transport  to coordinate the sculpting of cellular membrane at virus assembly and dissemination sites, in seemingly different, yet fundamentally similar ways. For instance, at the plasma membrane, ESCRT-I recruitment is essential for HIV-1 assembly and budding, while it is dispensable for the release of HSV-1. Further, HSV-1 was shown to recruit ESCRT-III for nuclear particle assembly and egress, a process not used by retroviruses during replication. Although the cooption of ESCRTs occurs in two separate subcellular compartments and at two distinct steps for these viral lifecycles, the role fulfilled by ESCRTs at these sites appears to be conserved. This review discusses recent findings that shed some light on the potential parallels between retroviral budding and nuclear egress and proposes a model where HSV-1 nuclear egress may occur through an ESCRT-dependent mechanism. One of the most important trafficking pathways in the cell is the sorting of cargo once it has been internalized at the plasma membrane . Cargo dDespite the conservation of L-Domains seen in HIV-1 and host factors, there are notable exceptions to the consensus motifs commonly associated with ESCRT recruitment, namely in Simian Immunodeficiency Viruses (SIVs), or HIV-1-progenitor viruses, that encode divergent ALIX recruitment domains as determined by X-ray crystallography . SIVs enFlaviviruses, another class of RNA virus that recruit ESCRT proteins, are classified on the basis of their tripartite use of a single stranded RNA genome as an mRNA, a genomic copy, and as a parental strand for transcription . This isUnlike RNA viruses, particle formation of the DNA virus HSV-1 begins in the nucleus b 31]. T. T31]. THIV-1 particle assembly at the membrane has been the focus of intensive study for more than two decades . The retRetroviral budding is a highly organized process in which the formation of the Gag lattice is critical for the proper recruitment and function of ESCRT in virus release ,53. AlthThe immature hexagonal lattice exhibited by HIV-1 Gag during assembly is such an essential component of the particle maturation pathway that it has even become the target of novel antiretroviral drugs . BevirimUnlike in retroviral budding where infectious particles are released from the plasma membrane, HSV-1 particle assembly and egress occurs first at the inner nuclear envelope and then again at the plasma membrane, for the release of infectious particles . This fi8YPGHPGDAFEGL19. However, despite the predicted flexibility of this sequence, it sterically clashed with UL31, suggesting that it would not likely be accessible for binding to ALIX, or could be engaged in interactions with UL31. The second sequence just downstream was 41YSPSSL46. However, this motif was shown to embed its tyrosine residue within secondary structure elements of UL34 where it makes hydrophobic contacts with neighboring phenylalanine and tryptophan residues in the context of the UL31-UL34 heterodimer, making it inaccessible for binding to ALIX [202YGAEAGL208. However, this region of UL34 has not been previously characterized structurally, likely due to its intrinsic flexibility and location proximal to the C-terminal transmembrane domain. Therefore, a molecular model of this region of the HSV-1 NEC was generated using the resolved residues from the HSV-1 NEC crystal structure (PDB:4ZXS), combined with structural predictions from Phyre2 and MODELLER in order to structurally analyze the missing regions of the protein [202YGAEAGL208. Curiously, sequence alignments revealed little conservation of putative L-Domains across viruses, suggesting that the model for ALIX recruitment may only occur in select herpesvirus nuclear egress pathways. Perhaps in cases of ESCRT-III dependent nuclear egress, an edge of the NEC containing the L-Domain sequence is exposed to the nucleoplasm to be recognized for ALIX-mediated membrane budding and scission, much like how HIV-1 Gag recruits ALIX to the plasma membrane [In analyzing the NEC for potential non-canonical L-domains, like those found in the RNA viruses mentioned above that may promote its association with ALIX, sequence alignments and structural modelling were used to uncover such potential motifs. The sequence of HSV-1 UL34 was compared to that of several herpesviruses across three herpesvirus families  a. Sequen protein ,70 (Figumembrane . Althougmembrane ,73. ThisThe parallels between retroviral budding and viral nuclear egress described here point toward a remarkable replication mechanism reached by divergent virus families. These viruses operate via a particle assembly pathway that is topologically equivalent to cellular mechanisms ,75,76. UOne example of a scenario in which the nuclear envelope is recycled rather than repaired is in the membrane damage induced by defects in Lamin A in cases of Progeria . ProgeriDrosophila cells in a similar manner to HSV-1 capsids [Drosophila germline cells, it plausible that ALIX could also serve as a major recruitment factor of Shrub for large RNP nuclear egress [In a phenotypically normal scenario, large RNPs required for neuronal plasticity can be seen budding across the inner nuclear membrane of  capsids . Like HS capsids . However capsids ,89. Furt capsids . Yet ther egress  as well.Despite the tremendous diversity seen across their genomes and morphologies, viruses are bound by the molecular machinery of their cellular hosts. The outcome is the evolution of several independent viral mechanisms that converge on cooption of conserved host mechanisms . One of


Mitochondria are critical subcellular organelles that produce most of the adenosine triphosphate (ATP) as the energy source for most eukaryotic cells. Moreover, recent findings show that mitochondria are not only the “powerhouse” inside cells, but also excellent targets for inducing cell death via apoptosis that is mitochondria-centered. For several decades, cancer nanotherapeutics have been designed to specifically target mitochondria with several targeting moieties, and cause mitochondrial dysfunction via photodynamic, photothermal, or/and chemo therapies. These strategies have been shown to augment the killing of cancer cells in a tumor while reducing damage to its surrounding healthy tissues. Furthermore, mitochondria-targeting nanotechnologies have been demonstrated to be highly efficacious compared to non-mitochondria-targeting platforms both in vitro and in vivo for cancer therapies. Moreover, mitochondria-targeting nanotechnologies have been intelligently designed and tailored to the hypoxic and slightly acidic tumor microenvironment for improved cancer therapies. Collectively, mitochondria-targeting may be a promising strategy for the engineering of nanoparticles for drug delivery to combat cancer. Mitochondria are essential subcellular organelles for the life and death of cells ,2. SinceThe era of chemotherapy began in the 1940s with the first uses of nitrogen mustards and antifolate drugs . AlthougRecent studies show that engineered nanoparticles hold great promise to reduce side effects while improving the therapeutic efficacy of chemotherapy, particularly when it is combined with photodynamic and/or photothermal therapies. Various nanoparticle drug delivery systems (DDSs) have been developed in a broad range of clinical applications, and demonstrated tremendous potential for overcoming the limitations of free therapeutics ,33,34,35Mitochondria are essential subcellular organelles for drug targeting due to their important roles in cell proliferation and death. They perform a variety of important cellular functions including the production of the majority of cellular ATP needed for endergonic processes, which enables the pumping action of the transmembrane drug efflux pumps in drug-resistant cancer cells ,12. TheyThe idea of mitochondria-targeting was proposed in the 1950s after determining the mitochondrial structure and some molecules with high mitochondria affinity . The strThe most successful case of using lipophilic cations for mitochondria-targeting is the discovery of TPP . TPP has2 that is needed for type II PDT in the hypoxic tumor microenvironment, catalase was incorporated into the nanoparticles to promote the conversion of H2O2 into O2 to enhance the therapeutic efficacy of PDT for destroying tumors. Finally, TPP was used to target the mitochondria since intracellular ROS could cause cell necrosis by damaging the mitochondria and DNA. In vitro studies showed that the mitochondria-targeting nanoparticles were able to induce apoptosis more effectively than the non-mitochondria-targeting nanoparticles. This suggests that the mitochondria-targeting capability enhances the ROS generation-based cell killing in PDT. In their in vivo study, the greatest tumor inhibition was observed in the treatment group with mitochondria-targeting ability, suggesting that mitochondria-targeting can enhance PDT even in vivo.Photodynamic therapy (PDT) is based on the generation of ROS by photosensitizers (PSs) under the irradiation of light to destroy cancer cells. Unfortunately, as summarized in 2 for type II PDT [Another tool that can be used efficaciously in mitochondria-targeting is 2D nanomaterial sheets. The direct use of 2D metal-free semiconductor nanomaterials is limited by the inefficient photocatalytic activity and visible light-responsive properties . A 2D hee II PDT . Using JTo overcome the poor endosomal escape and delivery efficacy of many nanoparticle systems, a pH-activable nanoparticle  was designed for efficient endosomal escape and targeting of the mitochondria for fluorescence imaging and enhanced PDT efficacy , as show3O4 nanoparticles  [2O2 in tumor cells into O2 via Mn3O4. After the decomposition of the Mn3O4 nanoparticles with the conversion of H2O2 into O2, the IR780 can be released into the cytosol to efficiently target the mitochondria via the intrinsic lipophilic cationic properties of the molecule. Afterwards, 808 nm laser irradiation was used to generate ROS in the mitochondria, cause their dysfunction, and significantly induce apoptosis of the cell. This work demonstrated that targeting of the mitochondria leads to mitochondria respiratory inhibition, which can reverse the hypoxia in the tumor, in contrast to conventional PDT that consumes oxygen in the tumor microenvironment to exacerbate the hypoxic environment in a tumor. Both the in vitro and in vivo data suggested that the nanoparticles can efficiently kill cancer cells and ameliorate the hypoxic tumor microenvironment. The nanoparticles with laser treatment resulted in the highest levels of oxygenated hemoglobin and lowest levels of non-oxygenated hemoglobin in the tumor tissue 24 h after intravenous (IV) injection. Moreover, there was no detectable increase in the HIF-1α levels of the nanoparticles with laser treatment compared to the control groups, which suggests that the treatment inhibits the hypoxia signaling pathway of the cancer cells. Moreover, the nanoparticle treatment with laser irradiation led to a significantly increased inhibition of tumor growth, compared to the other treatments, suggesting that the mitochondria-targeting and suppression of the tumor hypoxia augment tumor killing.Another study, as shown in tential) . These n3+-sensitive upconversion of the MOFs with TPP-surface functionalization for mitochondria-targeting. Their imaging studies with confocal microscopy highlighted that there is increased overlap between the mitochondria and nanoparticles with the TPP modification, which validates that the MOFs are being driven into the mitochondria. Additionally, much enhanced killing of the cancer cells was observed for the mitochondria-targeted MOFs. Cytochrome C and caspase-3, two proteins in the apoptotic cascade, showed much higher expression in the cells treated with the mitochondria-targeted MOFs than in the cells treated with MOFs without any mitochondria-targeting capability. In the in vivo study with mice bearing 4T1 tumors, the mitochondria-targeted MOF treatment resulted in the greatest inhibition of tumor growth during the 14-day period of observation, suggesting that mitochondria-targeting can greatly enhance the PDT efficacy. The authors emphasized that they were able to overcome the overheating problem of many upconversion nanoparticle-based PDT systems quite robustly. Compared with the 980 nm laser that increased the temperature of the skin by 6.8 °C after irradiation for 5 min, the 808 nm laser did not cause obvious change in skin temperature even after 10 min of irradiation given other conditions being the same. Overall, this work demonstrated a sophisticated PDT that can overcome major barriers to PDT using a shifted irradiation wavelength and mitochondria-targeting.One major challenge to PDT can be an overheating effect from the laser. This happens with many upconversion nanoparticle-based PDT systems as a result of their superior heating effect . Many upTo overcome the aggregation-induced quenching phenomenon that reduces ROS generation of fluorescent photosensitizers and the efficiency of PDT, the development of fluorescent photosensitizers with AIE (aggregation-induced emission) characteristics has led to much interest because they show highly efficient ROS production in the aggregated state ,71,72. ZPhotothermal therapy (PTT) kills cancer cells/tumors by converting the electromagnetic energy of light into thermal energy via a photosensitizer to heat the cells/tumors A. AlthouNanomaterials for phototherapy can be divided into three types: organic, inorganic, and hybridized with both organic and inorganic components ,84. In aRecently, a group  used an Additionally, both PTT and photodynamic therapy (PDT) can be used concurrently to elicit an antitumor effect . Black p2O4 nanoparticles  for a magnetic hyperthermia treatment (MHT) and chemodynamic therapy against cancer [2O2 into cytotoxic OH free radicals via the Fenton reaction. This in situ generation of OH helps to overcome the limited penetration depth of PDT. Moreover, the iridium (III) complexes have excellent two-photon absorption and are superior to one-photon imaging in terms of red-shifted absorption, enhanced tissue penetration, reduced photobleaching, prolonged observation time, and improved spatial resolution. These results suggest that the nanoparticles have excellent magnetothermal properties and can be used to achieve non-invasive and highly localized therapy. The magnetothermogenic nanozyme nanoparticles were injected in vivo into mice bearing xenografted HeLa tumors. The nanoparticles with an alternating magnetic field are the only treatment to cause a decrease in tumor volume. A statistically significant increased expression of HSP70 was also observed for this treatment compared to the other control treatments. These results demonstrate that the nanoparticles increase tumor temperature with the alternating magnetic field, leading to the increased HSP70 expression. Magnetothermogenic nanomaterials are a tremendous tool that can overcome several barriers of photothermal therapies and can be modified for mitochondria-targeting for localized heating and increased killing of cancer cells.Mitochondria-targeting has also been applied to magnetothermogenic nanomaterials to elicit an augmented therapeutic effect ,112. A mt cancer . After eDue to the success of mitochondria-targeting in chemotherapy applications, there has been exploration into some novel targeting ligands that can also boost mitochondrial permeability and enhance the cargo uptake into mitochondria ,121,122.Interestingly, Xu et al. designed and synthesized several dual-targeting (both cellular and subcellular targets) DDSs, aiming to enhance the accumulation of anticancer agents into mitochondria of cancer cells to amplify their anticancer effect ,50,126. Superparamagnetic iron oxide nanoparticles (SPIONs) are highly desired for use in the human body compared with other metal oxide nanoparticles due to their high durability, biocompatibility, biodegradability, and low toxicity. The low toxicity of SPIONs to healthy/normal cells offers a critical advantage, compared to other inorganic nanoparticles. SPIONs are also able to cross the nuclear membrane  and geneMoreover, besides the aforementioned inorganic materials (excluding GA), organic materials such as polymers and lipids have been used in mitochondria-targeted nanotechnologies. A TPP-modified polymer was used in the work of Wang et al. in which PF127 (poly(ethylene oxide)-block-poly(propylene oxide)-block-poly(ethylene oxide), PEO-PPO-PEO triblock copolymer) was modified with TPP and HA to neutralize the positive charges of the TPP in order to avoid rapid clearance and achieve a long-term circulation . The HA In Kuznetsova et al. , cationiMitochondria have critical roles in tumor progression because they not only produce ATP to supply the energy needed for rapidly proliferating cancer cells, but also provide the complex molecules for anabolism, their capacity to produce ROS, their resistance to adverse microenvironmental conditions, interaction with tumor stroma, and metastatic potential, which all rely on optimal mitochondrial biogenesis and oxidative phosphorylation . Mitocho


Recognition of the potential of the conductionband bottom allows one to classify the electronic states as deep orshallow electron traps or conduction band states, which play differentroles in photocatalysis. The comparative analysis shows that bothmethods provide very useful information which can be used in understandingand predicting the photo(electro)catalytic reactivity of semiconductors.The diffuse reflectancespectroelectrochemistry (SE-DRS) and reverseddouble-beam photoacoustic spectroscopy (RDB-PAS) provide unique, complementaryinformation on the density of electronic states (DOS) in the vicinityof the conduction band bottom. The measurements are performed underquite different conditions, representing the solid/liquid and solid/gasinterfaces in SE-DRS and RDB-PAS, respectively. DOS profiles obtainedfrom both types of measurements can be considered as unique “fingerprints”of the tested materials. The analysis of DOS profiles recorded for16 different TiO For instance, Wang et al. showed that the separationefficiency of photogenerated electrons and holes could be significantlyimproved by adjusting the bulk/surface defects ratio.12 Particularly surface defects seem to play a pivotal rolesince the surface is a scene of the most important photocatalyticprocesses. Defects  cannot only facilitatebinding of substrates and act as active sites but also affect theelectronic structure of semiconductors.13 They can act as traps of photogenerated holes and electrons. Inthis way, they can influence the availability and recombination efficiencyof charges as well as determine the effective semiconductor redoxpotential.15 Therefore, the ability to determinethe energy and density of electronic states (DOS) relative to theconduction band and a reference potential  is of paramountimportance.The activity of heterogeneousphotocatalysts strongly depends on their physicochemical properties,such as band gap energy, potential of the conduction and valence bandedges, phase composition,17 The principle is thatvacant electronic states in the bandgap, electron traps (ETs), arefilled by direct energy (wavelength)-selective photoexcitation ofelectrons in the valence band (VB) from the deeper (longer wavelength)side to the shallower side by wavelength-scanned continuous monochromaticlight irradiation, and an increase in photoabsorption by accumulationof electrons in ETs is measured by photoacoustic spectroscopy usingmodulated  LED-light. Then the obtained spectrum is differentiatedfrom the longer-wavelength (lower energy) side to the shorter-wavelength(higher energy) side to obtain the energy-resolved distribution ofETs (ERDT), i.e., the DOS profile for vacant electronic states inthe bandgap. The obtained density recorded in arbitrary units is convertedin the unit of μmol g–1 by comparing the reportedresults of chemical titration of ETs.18 The energy of ETs, determined from the wavelength of the excitationlight, is presented as energy measured from the VB top (VBT) of eachsample. Since the density of ETs in samples is, in general, very lowor almost negligible, the actual photoexcitation of VB electrons toET must occur from the high density-of-states (DOS) part of VB, notVB top, where the DOS is almost zero and the energy of ERDT is overestimated.19 For titania samples, this overestimation wassuggested to be 0.1–0.2 eV.17 This is thereason why a part of ETs are found in a conduction band (CB) in ERDT,since the plotted CB bottom position is estimated by measuring theabsorption edge wavelength, corresponding to the bandgap energy andshown in the same energy scale, energy with reference to VB top (sameas ERDT). At present we have no possible way to calibrate the differencein energy between VB top and a high-DOS part. In this sense, if thisenergy difference varies depending on the bulk crystalline (amorphous)structure, discussion on the difference in ERDT of different samplesis not straightforward. The influence of this problem will be discussedlater.Reversed double-beam-photoacoustic spectroscopy(RDB-PAS) has beendeveloped as a unique spectroscopic-analysis method enabling identificationand detailed characterization of semiconducting materials, not limitedto photocatalysts, by getting a fingerprint of metal oxides and theother semiconducting materials.–2,17 suggesting that ETs detected by RDB-PAS mightnot be point defects but buried in a reconstructed surface structureor on ridges (steps) of crystalline facets.Another feature of ETs detected by RDB-PAS is that theyare mainlylocated on the surface since a plot of total ET density versus specificsurface area of commercial titania samples shows the trend of almostproportional relation. The rough estimation of density of ETs in commercialtitania samples was ∼1 ET nm21 In the proposed method, the studied powdermaterial is casted on the surface of a platinum plate (working electrode).A progressive, negative bias of the electrode leads to the electrochemicalreduction of the material. This should be understood as a gradualfilling of unoccupied electronic states. For instance, electrochemicalreduction of TiO2 leads to the formation of titanium(III)centers, characterized by a broad absorption band with a maximum localizedat 780 nm.22 Both in the SE-DRS and RDB-PASmethods, reduction of Ti(IV) plays a crucial role. Simultaneous observationof the cathodic current and Kubelka–Munk function changes (ΔKM)allows an unambiguous determination of the potential of the electronstate. Furthermore, quantification of the Kubelka–Munk functionalterations between consecutive reduction sequences gives informationon the relative concentration of available states.Recently, we havedeveloped a spectroelectrochemical method whichcombines UV–vis diffuse reflectance spectroscopy and electrochemistry(SE-DRS).26 The electrochemical approach can be directly applied to real, alsonanocrystalline, samples, there is no need to neglect the existenceof electronic states other than bands or to assume that the studiedmaterial consists of almost ideal crystals.27 This advantage of the SE-DRS method allows one to compare, inter alia, the redox properties of different semiconductors,their polymorphs, or (surface) modifications.32Electrochemicalreduction of electronic states allows the measurementof the effective redox potential of electronic states. The possibilityof DOS determination as the function of redox potential makes thismethod independent of the precise determination of the band edgesenergy.etc. Meyer et al.33 have shown that the flat band potential maybe shifted in the presence of some cations, which may penetrate thecrystal lattice. The use of the photochemical reduction method (hereRDB-PAS) solves some of these problems. First of all, the use of electrolytesis not required, and the range of available energies depends in this case only on the energy of photons. However, a holescavenger has to be involved. The ability to reduce states dependson the reduction rate (activation energy) and the oxidation rate ofthe scavenger. The use of primary alcohol as a scavenger significantlyfacilitates the reduction of electronic states close to the conductionband due to the photocurrent doubling effect.Electrochemical measurements require the use of electrolytes,whichimpose an applicable potential range and can influence the surfaceproperties of the studied material. Redox properties of semiconductorsdepend to some extent on pH, solvent polarity, presence of variouscations and anions, SE-DRS and RDB-PASmethods together provide more information thanany of them applied alone. Both methods, due to the distinct differencesdescribed above, may give slightly different results. Therefore, itis important to understand these differences and their origins. Herewe present a comparison of data taken with SE-DRS and RDB-PAS methodsfor a series of commercially available titanium(IV) oxide materials.Comparative measurements were carried out for samples of titaniumdioxide differing, among others, in the phase composition, the specificsurface area, and bandgap energy. Selected physical properties aresummarized in The density of electronic states obtained with RDB-PASand SE-DRSare determined relative to different references, which are eitherthe energy of the top of the valence band or the electrochemical potentialscale, respectively. Therefore, comparison of these data is not straightforward.Here we propose to assume that the main (left) slopes of the graph,i.e., the increase of the density of the electronic states associatedwith the lower edge of the conduction band, should overlap. In thisway, the results collected with RDB-PAS and SE-DRS were compared in 2 film, only one intense maximum wasobserved. However, when a thinner film was deposited at the platinumplate, two distinct maxima were recorded. The second, higher maximumappeared in the energy range where for the thicker film almost nosignal was recorded. A similar effect was observed when the Kubelka–Munkfunction change was analyzed at different wavelengths (DOS profiles collected in elengths 2b. At loelengths 2c. The setc.). Inparticular, in aqueous electrolytes the equilibria between adsorbed/desorbedH+ and OH– ions are established, whichinfluence the flat band potential.27 Innonaqueous solutions, such as acetonitrile, the flat band potentialis shifted toward lower potential values compared to the aqueous electrolytes.35 This shift strongly depends on the ions presentin the solution. In particular, lithium cations have a similar effectto protons in protic electrolytes. Enright et al. demonstrated thatin a solution containing 0.1 M Li+, the flat band potentialof polycrystalline TiO2 is shifted to −0.90 V from−0.82 V vs SCE measured in the aqueous electrolyte (pH = 7).36 The influence of these ions on DOS determinedby SE-DRS manifests mainly in shifting the DOS function along thepotential axis, while the shape of the function remains nearly unchanged.20 The presence of water in the electrolyte causesa significant decrease in the recorded signals intensity, which makesit difficult to observe any other effects of this solvent on DOS.However, measurements of SE-DRS in the acetonitrile electrolyte containingsmall amounts of water  show that the DOS contours determinedin aqueous and nonaqueous electrolytes are not completely different species and does not remain neutral also to the wholematerial, including its stoichiometry, defects, and crystal structure.Recognition of the potential of the conduction band bottom allowsone to classify the electronic states as deep or shallow electrontraps or conduction band states.Despite the aforementioned reasons for which SE-DRS and RDB-PASmethods may give different DOS profiles, for the energy range fromthe top of the valence band up to energy equal to In photocatalysis, electronicstates localized within the bandgapand close to CB and VB edges play a key role. According to the Kasharule, electrons excited to higher energy states, within CB (or holesgenerated below the maximum of VB), usually will relax to the states(traps) close to the band edges before taking part in any interfacialelectron transfer process. Thus, some discrepancies between the DOSprofiles within the conduction band resulting from the applicationof different approaches (SE-DRS and RDB-PAS) do not exclude the applicabilityof any of them.E , was calculated. This valuecan be considered as the conduction band bottom for pure rutile andanatase materials. Then, three kinds of corrections were applied toΔE. (1) As stated in the early part of thispaper, ET energy is shown in reference to VBT, for convenience, butactual excitation of electron in VB to ETs may occur from the high-DOSpart of VB, which is lower than VBT. This overestimation of ET energywas assessed to be ∼−0.15 eV and applied to all thedata of ΔE. (2) For the mixture of anataseand rutile, the value of ΔE is calculated fromthe determined bandgap energy and is a resultant of the energies ofthe conduction band bottoms for pure rutile and anatase materials.Furthermore, it has been proposed that interfacial charge-transferexcitation (ICTE) from the high-DOS part of rutile VB to anatase ETsoccurs and the energy of high-DOS part of rutile is ∼0.20 eVhigher than that of anatase resulting in the shift of the anatasepeak to lower energies.19 This correctionis applied to anatase–rutile mixed samples. (3) It has beensuggested that samples of high specific surface area (>80 m2 g–1) may contain an amorphous surface layerwitha large band gap (shorter-wavelength absorption edge) to make theET energy slightly higher  was ranged at ∼1.9–2.1 V and the averagewas 2.02 V. The exceptions are P25, TIO-1, and ST-F5. For P25, theanatase–rutile composite, the energy diagram seems to be shiftedto lower potentials compared with the other anatase samples,37 and hence the correction (2) might be underestimated.It is worth mentioning that fitting of SE-DRS and RDB-PAS profilesmay be imperfect and should take into account the best overlappingof the slopes of profile onsets ) microphonemodule, a quartz window, and PVK (polyvinylcarbazol) bulbs. Then,methanol-saturated high-purity (4N) nitrogen gas was made to flowthrough the cell. Methanol was used as a scavenger of positive holesavoiding disappearance of once-trapped electrons by capturing positiveholes. A sample in the cell was irradiated from the upper side withwavelength-scanned monochromatic (pump) light from a grating monochromatorwith a xenon lamp to excite valence-band electrons directly to electrontraps (ETs), and photoabsorption of electrons accumulated in the ETswere monitored by photoacoustic signal with an intensity-modulated(35 or 80 Hz) 625 nm LED (probe) light. By scanning the pump-lightwavelength from longer wavelength (650 nm) to shorter wavelength (300or 350 nm) with a 5 nm step, the ETs in the sample were filled fromthe deeper (lower energy) side to the shallower (higher energy) side.The thus-obtained RDB-PA spectrum was then differentiated from thelower-energy side to obtain energy-resolved distribution of ETs withdensity in arbitrary units. Calibration of absolute ET density wasmade in reference to the previously reported chemical titration studyon various titania particles18 assumingall the detected ETs were originated from titania. CBB was determinedby ordinary single-beam PAS using the same instruments following theprocedure reported previously.17RDB-PAS analyses were operatedon laboratory-madesetups to obtain ERDT/CBB (conduction-band bottom) patterns with thefollowing reported procedure,+ electrode [AgNO3 (10 mmol dm−3) in0.1 mol dm−3 tetrabutylammonium perchlorate in acetonitrile]as the counter and reference electrodes, respectively. Studied TiO2 samples deposited onto platinum foil (∼1 × 3cm2) were used as working electrodes. The electrodes wereplaced in a cuvette with a quartz window filled with 0.1 mol dm−3 LiClO4 solution in acetonitrile. The cuvettewas placed in front of the integrating sphere (5 cm diameter), facingthe working electrode toward the light beam. Oxygen was thoroughlyremoved from the electrolyte by purging it with argon before (15 min)and during experiments. The electrode potential was controlled bythe electrochemical analyzer . The applied potentialwas lowered every 10 min by 50 mV. The relative reflectance changes(at 780 nm) were collected by PerkinElmer UV–vis Lambda 12spectrometer. The relative reflectance changes were converted to theKubelka–Munk function (ΔKM). Finally, the density ofstates (DOS) was calculated as a difference in the Kubelka–Munkfunction between two consecutive potentials. The detailed experimentalprocedure of SE-DRS has been described and discussed elsewhere.20The SE-DRS measurements were carried out inthe three-electrode setup with platinum wire and Ag/Ag


Unusually high snowfall in western Washington State in February 2019 led to widespread school and workplace closures. We assessed the impact of social distancing caused by this extreme weather event on the transmission of respiratory viruses.Residual specimens from patients evaluated for acute respiratory illness at hospitals in the Seattle metropolitan area were screened for a panel of respiratory viruses. Transmission models were fit to each virus to estimate the magnitude reduction in transmission due to weather-related disruptions. Changes in contact rates and care-seeking were informed by data on local traffic volumes and hospital visits.Disruption in contact patterns reduced effective contact rates during the intervention period by 16 to 95%, and cumulative disease incidence through the remainder of the season by 3 to 9%. Incidence reductions were greatest for viruses that were peaking when the disruption occurred and least for viruses in an early epidemic phase.High-intensity, short-duration social distancing measures may substantially reduce total incidence in a respiratory virus epidemic if implemented near the epidemic peak. For SARS-CoV-2, this suggests that, even when SARS-CoV-2 spread is out of control, implementing short-term disruptions can prevent COVID-19 deaths.The online version contains supplementary material available at 10.1186/s12879-021-06028-4. Novel respiratory viruses emerge periodically to cause global pandemics –3. DurinGeneralizing to broader social distancing efforts from these studies is difficult, however, as school closures tend to have limited impacts on working-age and older adults, and school-aged children may recongregate outside of schools –11. In FThe Seattle Flu Study, initiated during the 2018/19 influenza season, is a regional surveillance project which aims to evaluate the transmission of influenza and other respiratory pathogens at a city-wide scale . The SeaNucleic acids extracted from respiratory swab specimens from study participants were screened for the presence of multiple respiratory pathogens by reverse-transcription and Taqman. Screened pathogens included influenza A/H1N1, A/H3N2, and B; respiratory syncytial virus (RSV) A and B; human coronavirus  (hCoV); human metapneumovirus (hMPV); rhinovirus (HRV), and adenovirus (AdV).Unusually heavy snowfall occurred in the greater Seattle metropolitan area between Feb 3 and Feb 112,019, totaling 20.2 in. at Seattle-Tacoma International Airport over this time. The majority of public schools in the region were closed for at least 5 days, with a weekend in between. City of Seattle public preschools were closed from Feb 9 through Feb 13, and King County public transportation was on emergency service from Feb 9 through Feb 12. We used Washington State Department of Transportation traffic data on regional interstate highways to quantify mobility during January–March 2019 )In contrast, the extreme snowfall occurred close to the predicted peak of the seasonal epidemics of several other viruses, particularly influenza A/H1N1 and RSV. For these viruses, weather-related social distancing had larger impacts, with final attack rates reduced by 7.6 to 9.2% and prevalence remaining below the pre-disruption level for the remainder of the season. This finding suggests that short-term social distancing is most effective at reducing overall attack rates when it occurs close to the peak of the epidemic, or equivalently when force of infection is greatest. There are important differences between the respiratory viruses we model and SARS-CoV-2, such as the burden and possible overwhelming of the healthcare system and the role of different age groups in spreading the virus. However, for localities that have lost control of the current pandemic, interventions can still reduce the expected burden of COVID-19. As the pandemic progresses even short measures can have significant effects.It is unlikely that traffic and school closure data fully capture the changes in population contacts due to extreme snowfall. For this reason, we did not attempt to parse the relative impacts of specific changes on routes of transmission. However, pathogens that tended to infect younger age groups also tended to have greater reductions in effective contact rates. This suggests that weather-related school closures may have had a greater effect on contact frequencies among children than commuting and workplace disruptions among adults.Several limitations of this study should be considered. First, the specimens from this study came from patients evaluated in hospitals. These were chosen to minimize changes in detection due to behavior (i.e. patients with mild illness may be less likely to seek care during the extreme weather). If respiratory virus illness in hospital settings does not fully represent underlying illness in the community, our results may be biased. Second, we assumed latent and infectious periods for each pathogen based on the existing literature, which is more comprehensive for some viruses (e.g. influenza) than for others, though we partly address this with the sensitivity analysis (see SI). Finally, our model is a simple mass-action model and does not attempt to account for potential heterogeneity in the population by age, risk status, or other factors.Simulation studies to estimate the impact of social distancing on an influenza pandemic –31 have Additional file 1.


This work aims to utilize selenium nanoparticles (Se-NPs) as a novel dyestuff, which endows wool fibers with an orange color because of their localized surface plasmon resonance. The color characteristics of dyed fibers were evaluated and analyzed. The color depth of the dyed fabrics under study was increased with the increase in Se content and dyeing temperature. The colored wool fabrics were characterized using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDX) and an X-ray diffraction (XRD) analysis. The results indicated that spherical Se-NPs with a spherical shape were consistently deposited onto the surface of wool fibers with good distribution. In addition, the influence of high temperature on the color characteristics and imparted functionalities of the dyed fabrics were also investigated. The obtained results showed that the proposed dyeing process is highly durable to washing after 10 cycles of washes, and the acquired functionalities, mainly antimicrobial activity and UV-blocking properties, were only marginally affected, maintaining an excellent fastness property. Recently, the usage of unique dyes and finishing agents has had a substantial influence on textiles’ functionality. The dyeing of textiles with multi-functional characteristics has attracted significant interest in recent years. Nanotechnologies and nanomaterials offer a wider application potential for preparing functional textiles, such as flame retardant, self-cleaning, wrinkle-resistant, antistatic, antimicrobial, and UV-protective, etc. ,12,13,14The coloration of fabric/fiber can be achieved through a combination with metal nanoparticles by means of the particular optical properties of plasmon nanomaterials, called localized surface plasmon resonance (LSPR), producing brilliant and vivid colors, which are different from traditional dyes in that they are not the chromophore of traditional dyes but the shape and size of nanoparticles that determine the colors ,32. ThisOwing to the localized Surface Plasmon Resonance (LSPR) property of selenium nanoparticles (Se-NPs), they exhibit adaptable colors depending on the synthesizing measures. Besides, Se-NPs have strong cytotoxicity towards a broad range of microorganisms, low toxicity to human cells, high selectivity, and long-term durability . Se-NPs In our previous study, cotton and polyester were colored with silver and gold NPs via a printing technique to render different functions of fibrous materials . SubsequLoba Chemie, India. In addition, the other chemicals used in this study were of commercial grade.The fabrics used here were 100% scoured and bleached wool. Sodium hydrogen selenite, vitamin C and polyvinylpyrrolidone (PVP) were purchased from Dyeing was carried out using an Infra Color Dyeing Machine, which consists of 12 beakers that are mounted in a rotating beaker-carrying wheel. Heating occurs through infrared radiation, cooling through air and automation through the microprocessor programmer DC4 F/R. The maximum temperature was up to 140 °C, a maximum heating rate was up to 5 °C/min and cooling had a maximum rate up to 3 °C/min.Se-NPs were prepared according to the method reported by Abou Elmaaty et al.  through Wool fabrics were dyed with different concentrations of Se-NP solution using a wet chemistry method through an immersion process. Briefly, the wool fabrics were immersed into the Se-NP solutions (dyeing solutions) without any chemical additives. Dyeing was performed in a laboratory-scale thermal HT dyeing machine with a liquor-to-goods ratio of 100:1. The solutions containing Se-NPs and wool fabrics were shaken at different temperatures, including room temperature, 40 °C, 70 °C and 100 °C for 60 min to obtain the coloration of fabrics. At the end of the dyeing process, the dyed wool samples were removed, rinsed with water and dried at room temperature.TEM images of the synthesized Se-NPs were obtained using a JEM-2100 Transmission Electron Microscope (TEM) with an acceleration voltage of 200 kV to determine the size and morphology of the NPs. Samples for the TEM analysis were prepared by dripping a drop of Se-NP solution onto a carbon-coated copper grid and drying at room temperature.The surface morphology of wool fabrics, either blank or dyed fabrics, was examined using high resolution scanning electron microscopy . The chemical structure of the dyed samples was analyzed using a surface energy dispersive spectroscopy (EDX) analysis unit  attached to the electron microscope.X-ray diffraction was analyzed for both the prepared Se-NPs and Se-dyed wool fabrics using an X-ray diffractometer system (XRD) . The Se-NP solution was dried at 130 °C until completely dried.The functionalities of wool fabrics dyed with Se-NPs were evaluated in terms of the UV protection factor (UPF) and antimicrobial activity.The biological activity of the Se-dyed wool samples was evaluated qualitatively against G+ve bacteria (Staphylococcus aureus and Bacillus cereus), G-ve bacteria (Escherichia coli) and yeast (Candida utilis). The antimicrobial test was performed according to the AATCC Test Method (147–1988). The antimicrobial activity was expressed as the growth inhibition zone (mm).The ultraviolet protection factor expressed as UPF and transmission of ultraviolet (UV) radiation through wool fabrics were evaluated based on the AS/NZS 4399:1996 test method. The UV protection was rated as good, very good or excellent if the UPF values were between 15–24, 25–39 or above 40, respectively.The color coordinates of CIE lab  and color strength (K/S) for both blank and Se-dyed wool fabrics were measured in the wavelength range of 360–720 nm using a Konica Minolta spectrophotometer . All samples were measured in three different areas, bearing in mind both sides of the fabrics, and the mean values were recorded.The color fastness properties were evaluated for all Se-dyed wool fabrics in accordance with the standard test method. Washing, rubbing and light fastness were measured according to the method of AATCC test methods (61–1972), (8–1972) and (16A–1972), respectively.Durability to washing of the imparted functionalities  as well as color strength (K/S) of Se-dyed wool fabrics were evaluated according to AATCC Test Method 61(2A)-1996 after 10 laundering cycles.The tensile strength (maximum load) and elongation at break (maximum strain) of blank and Se-dyed wool fabrics at high temperature were evaluated using the strip test method on a multi-tester machine according to ASTM-D4850 with a load cell 500 N, preload 0.01 N, speed of 100 mm/min and gauge length of 100 mm.Antimicrobial, UV-protection, and mechanical tests were performed by taking the average of three readings (samples). The standard error of the mean was calculated according to the equation given below and was found to be + (−) 0.1 SEX = S/√nWhere S = sample standard deviation and n = the number of observations of the samples.The dyeing process of wool fabrics using selenium nanoparticles (Se-NPs) as a new functional colorant was investigated to obtain multifunctional dyed fabrics based on colloidal solutions of nanomaterials. The influences of different dyeing temperatures on the adsorption of Se-NPs onto wool fabrics and alteration of the color characteristics of wool samples were observed. The imparted functionalities were also determined. The results obtained and appropriate discussions are presented below.The Se-NPs were well-dispersed and mostly spherical in shape when no agglomeration or deformation of Se-NPs were observed. The Se-NPs prepared at a concentration of 12.5 mM showed the largest size up to 115 nm, resulting in the appearance of a block structure, as shown in In The surface chemical elements of the Se-dyed wool fabrics were determined by EDX spectroscopy, as shown in An XRD analysis was performed for further confirmation of the Se-NP formation. This analytical method aids in the determination of crystallite materials and provides details of the unit cell dimensions. As shown in S. aureus and & Bacillus cereus), G-ve (E. coli) and yeast (Candida utilis). The results are reported in The large surface area of natural textile fabrics and their ability to retain moisture provide an excellent environment for microorganism growth. Therefore, imparting the antimicrobial properties for natural fabrics is of great interest. Thus, the antimicrobial activity of the unwashed and washed Se-dyed wool fabrics was evaluated qualitatively against G+ve , which severely damaged and inactivated the essential biomolecules, including proteins, DNA and lipids ,58Moreover, subjecting the Se-dyed samples to up to 10 cycles of consecutive laundry cycles according to AATCC Test Method 61(2A)-1996 led to a non-sense decrement in their antimicrobial properties. Colored wool fabrics with low amounts of Se-NPs were enough to achieve excellent antimicrobial activity.Staphylococcus aureus, Bacillus cereus, Escherichia coli and Candida albicans [On the other hand, Adomaviciute et al. (2016) revealed that no antimicrobial activity was observed for the PVP solution (8%) against albicans . HoweverThese results demonstrated the activity of Se-NPs for ingrain dyed wool fabrics against the microbial pathogens and confirmed the high efficiency of the current processing in the acquisition of wool fabrics’ bio-functionality, in addition to the ingrain coloration of fabrics.Skin diseases, including allergies, premature skin aging, sunburn, and skin cancer, can occur due to skin exposure to UV irradiation. A great number of approaches were done to protect human skin against the harmful effects of UV irradiation using nanoparticles ,61,62. TThe UPF refers to the fabric efficiency to block out UV irradiation from passing through and reaching the skin. The UPF value of the undyed wool fabrics was also measured to be 33.46 . From thAdditionally, the influence of dyeing temperature on the UV protection properties of wool fabrics was examined. The UPF value of the wool fabrics were increased dramatically, as shown in Furthermore, the results in At the end of the dyeing process, the color of the wool fabrics was changed, and the fabric color reflected the color of the impregnated Se-NPs, as shown in Red/green values (a *) were found to be raised in a positive direction by the increment in Se content, which might have been attributed to acquiring the red color by ingrain clustering of Se-NPs, and the yellow/blue ratio (b *) was slightly increased by increasing the content of Se, reflecting the redness/yellowness color of the fabrics, as shown in By increasing the dyeing temperature from 40 °C to 70 °C and 100 °C, the wool fabrics became red or brown, as indicated by the positive a* and b* values. The color of the wool fabrics darkened and changed to brown as the Se content and dyeing temperature increased, as shown in From the observations, it could be concluded that the dyeing process of wool fabric using synthesized Se-NPs resulted in the change of the fabric’s color from the original creamy-white color to a yellowish red (orange) color, varying to a brownish color, according to the increment in dyeing temperature and the concentration of Se-NPs. The significant increase in color depth with increasing Se content confirmed the main responsibility of Se-NPs in the ingrain coloration of wool fabrics due to the SPR effects of Se-NPs.Moreover, The washing, rubbing and light color fastness of dyed wool fabrics with Se-NPs at different dyeing temperatures were evaluated according to AATCC standard test methods (61–1972), (8–1972) and (16A–1972), respectively.The results represented in On the other hand, wool samples dyed at high temperatures (70 and 100 °C) exhibited lower color fastness properties. While washing fastness had a very good rating in both color alteration and staining, the rubbing fastness showed good results, and the wet one had a rate of 4 at 70 °C to 3/4 at 100 °C. In addition, the light fastness exhibited a very slight color fading rate of 4. It is well known that the larger the colorant (in size), the poorer the fastness for the colored fabrics. Therefore, the moderate fastness properties of dyed wool fabrics at a high temperature could be attributed to the enlarged size of Se-NPs onto the fabric surface, caused by heat treatment and, consequently, easy leaching ,58Moreover, a washing durability test was conducted according to AATCC Test Method 61(2A)-1996, and the results indicated that the fastness properties of all Se-dyed wool fabrics were still high, even after 10 cycles of consecutive laundry cycles. These results demonstrated that the wool fabrics dyed with synthesized Se-NPs have very good color fastness properties.2) and elongation at break (maximum strain %) were both evaluated for blank and dyed wool fabrics to give an indication for the change in mechanical properties of wool fabrics after dyeing with Se-NPs at a high temperature (100 °C). The results are reported in Tensile strength  and other waste materials, with a careful optimization of their response surface methodology, as a suitable and quite valuable wastewater treatment [All experimental findings clearly show the great efficiency of the proposed dyeing process in terms of color fastness combined with improved functionalities, such as antimicrobial activity and UV-blocking properties. These results lay the groundwork for a durable dyeing of different kinds of fabrics by use of the simple impregnation with colloidal solutions of metallic nanoparticles prepared with a sustainable method. This represents an important result, considering that, today, the main worldwide pollution of white waters is produced by textile treatments and dyes. Furthermore, the pollution produced by the textile industry, employing a large consumption of water and the use of harmful dyes (often azo dyes), is harmful for the environment and for human health, with a huge negative impact on the planet. Often, wastewater is not adequately treated to remove pollutants before being discharged into the environment. Very recently, specific, and targeted treatments have been developed as a suitable method for the removal of azo dyes from textile effluents. Many recent studies refer to the use of photocatalytic degradation under visible light, the employment of non-living cells of marine microalgae . The obtained colored wool fabrics had a stable bright color ranging from light orange to a dark orange color according to the concentration of the prepared Se-NPs as well as the dyeing temperatures. Moreover, the wool fabrics with Se-NPs exhibited significant antimicrobial activity and excellent UV protection properties with excellent washing durability up to 10 laundering cycles. XRD, SEM and EDX analyses revealed that the Se-NP solution was effectively absorbed on wool fabric surfaces. Furthermore, a high dyeing temperature led to a change in the color characteristics from a dark orange color to dark brownish color, especially at 100 °C. However, the UV-protection properties were not affected by high temperature dyeing; the antimicrobial properties slightly decreased, and the mechanical properties of the dyed wool fabrics also decreased when the dyeing temperature was increased. The study concluded that the Se-wool dyeing process at a low temperature (at room temperature or 40 °C) was better than that of high temperature dyeing. The proposed dyeing process of wool fabrics with Se-NPs demonstrated that the dyeing of fibers based on nanoparticles may facilitate the functionalization of fibrous materials better than the traditional dyestuffs that require energy and an increased temperature for dyeing. This work will open the way to the use of opportune nanoparticles for the functionalization of different kinds of surfaces, to obtain implemented mechanical properties .


The Coronavirus has spread across the world and infected millions of people, causing devastating damage to the public health and global economies. To mitigate the impact of the coronavirus a reliable, fast, and accurate diagnostic system should be promptly implemented. In this study, we propose EpistoNet, a decision tree-based ensemble model using two mixtures of discriminative experts to classify COVID-19 lung infection from chest X-ray images. To optimize the architecture and hyper-parameters of the designed neural networks, we employed Epistocracy algorithm, a recently proposed hyper-heuristic evolutionary method. Using 2500 chest X-ray images consisting of 1250 COVID-19 and 1250 non-COVID-19 cases, we left out 500 images for testing and partitioned the remaining 2000 images into 5 different clusters using K-means clustering algorithm. We trained multiple deep convolutional neural networks on each cluster to help build a mixture of strong discriminative experts from the top-performing models supervised by a gating network. The final ensemble model obtained 95% accuracy on COVID-19 images and 93% accuracy on non-COVID-19. The experimental results show that EpistoNet can accurately, and reliably be used to detect COVID-19 infection in the chest X-ray images, and Epistocracy algorithm can be effectively used to optimize the hyper-parameters of the proposed models. COVID-19 can infect people of all ages including children and adolescents, resulting in serious complications. According to the World Health Organization, as of January 28, 2021, there have been more than 100 million confirmed cases of COVID-19, including 2,166,440 deaths. This virus can spread in the form of direct contact or by droplets expelled by coughing or sneezing. In some serious cases, COVID-19 can affect the respiratory system and cause severe pneumonia and ultimately death2. Pneumonia is a type of infection that causes inflammation in one or both lungs. Currently, a real-time reverse transcription polymerase chain reaction test (rRT-PCR) is required to detect the nucleic acid from SARS-CoV-2 in the respiratory specimens. However, this test is relatively time-consuming, complicated, and generally shows less consistent results4.The COVID-19 disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) first appeared in Wuhan, China in late December 20196, physical examination of X-ray images by experts provides 70%-80% accuracy. Therefore, a rapid and more accurate diagnosis system that helps physicians screen patients and detect COVID-19 symptoms seems more necessary for an effective and urgent treatment.Radiography examination is an alternative screening method used by experienced radiologists to visually diagnose SARS-CoV-2 viral infection. However, the diagnosis of COVID-19 from radiograph images is a massive challenge that require high expertise and dedicated knowledge. According to several studies7. Diagnosis of breast cancer8, epilepsy9, cardiovascular disease10, lung cancer11, and pneumonia12 via deep learning models has become a popular technique in the medical field.The abundant advances in deep learning and digital image processing, specifically in convolution neural networks (CNN), in recent years have opened new possibilities previously thought untenable. Over the past few years, researches developed various computer-aided diagnosis systems (CAD) based on artificial intelligence and machine learning algorithms to detect abnormalities in radiological images such as computed tomography (CT) and X-rays achieving promising results. CT and chest X-ray images (CXR) are generally considered to be fast and effective way for making clinical decisionsIn this paper, we propose a new approach for detecting COVID-19 infection on chest X-ray images using a decision tree-based ensemble model consisting of two mixtures of discriminative experts (MoE) called EpistoNet. The Epistocracy algorithm, a recently proposed hyper-heuristic evolutionary method, has been recruited to build and optimize the neural networks used in this work. The main motivation of developing EpistoNet is to employ it as a diagnostic tool that can help healthcare providers to detect COVID-19 faster, cheaper, and more accurately and accelerate the treatment of those who need it the most. Due to several key differences in other proposed approaches such as the size of the dataset used, the pre-processing steps of the data, statistical noise, hyper-parameter tuning, etc., the highest accuracy we achieved on our testing dataset using other approaches was less than 70%. We decided to develop our own model/algorithms to improve this accuracy. To the best of the authors’ knowledge, there is no similar study that proposed such a model for detecting COVID-19 in chest X-ray images.A new ensemble model called EpistoNet is proposed. EpistoNet is a decision tree-based ensemble model using two mixtures of discriminative experts to classify COVID-19 lung infection from chest X-ray images.A new dataset of 2500 X-ray images is created. All collected images belong to the Henry Ford Health System in Michigan where this research was conducted. These images have been individually reviewed, interpreted, and labeled by experienced radiologists.In order to accurately classify COVID-19 and non-COVID-19 X-ray images, we created a mixture of experts trained on k clusters of visually similar images.We also recruited the Epistocracy algorithm, a recently developed, multi-population, and self-adaptive optimization method to optimize the architecture and hyper-parameters of the designed neural networks.The main contributions of this study can be summarized as follows:13. However, clinical diagnosis from X-rays compared to other imaging modalities is much more difficult14 and requires significant training and expertise.Many researches have been recently proposed methods to detect COVID-19 positive cases from CXR and CT imaging using artificial intelligence (AI) and machine learning (ML) techniques. X-ray images are widely used in the diagnosis and evaluation of various diseases including COVID-19 infections by clinical experts. X-ray radiography is typically less expensive and exposes the patients to much less radiation compared to CT scans15 conducted a comparative study using various deep learning models  to detect and classify COVID-19.The experiments were performed using 6087 chest X-ray & CT images cases of COVID-19. The dataset was randomly split with 80% of the images for training and 20% for validation. The highest accuracy was achieved by InceptionResNetV2 with 92.18% of overall accuracy and 82.80% accuracy for detecting patients with Coronavirus.El Asnaoui et al.16 to predict COVID-19 from X-ray images. COVID19XrayNet comprises of two-step transfer learning pipeline based on ResNet32 with two newly integrated layers: smoothing layer (FSL) and feature extraction layer. COVID19XrayNet achieved 91.92% overall accuracy outperforming the original version of ResNet32.A deep learning-based method called COVID19XrayNet was proposed by Zhang et al.17 suggested COVIDX-Net, a deep learning framework based on seven convolutional neural network models namely MobileNetV2, VGG19, InceptionV3, DenseNet201, InceptionResNetV2, ResNetV2 and Xception to detect COVID-19 from chest X-ray images. COVIDX-Net was validated on 50 images comprised of 25 COVID-19 positive cases and 25 normal cases. In their review, VGG19 and DenseNet showed the best results of classification with f1-scores of 91% and 89% for COVID-19 and normal, respectively.Hemdan et al.18 proposed a multimodal classification network based on optimized VGG19 architecture. Before training their model, they applied histogram equalization to images followed by enhancement to textures and contrasts using OpenCV library. Their proposed network achieved 86% accuracy on X-ray images, 84% for CT scans, and 100% for Ultrasound.In order to identify COVID-19 from normal or other pneumonia cases, Horry et al.3, the authors presented COVID-Net, a deep convolutional neural network consisting of a heterogeneous mix of convolution layers with variation of kernel sizes for the detection of COVID-19 cases from chest X-rays. COVID-Net was trained and tested on COVIDx dataset comprised of 13,975 chest X-ray images. The proposed model was able to achieve an overall test accuracy of 93.3% and 91% accuracy specifically for COVID-19 cases.In Wang et al.19 proposed deep convolution network based on the concatenation of Xception and ReNet50V2. They evaluated their model on 11,302 chest X-ray images, consisting of only 31 cases of COVID-19 and 11,271 cases from the other two classes. Their proposed model achieved an average accuracy of 99.50%, and 80.53% sensitivity for the COVID-19, and an overall accuracy of 91.4%.Rahimzadeh et al.20 proposed a metaheuristic-based deep COVID-19 screening model using modified AlexNet architecture for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) was used to tune the hyper-parameters of modified AlexNet. The proposed model achieved a validation accuracy of 99.26%.Kaur et al.21 to diagnose COVID-19 infection from the X-ray images. The COVID-CheXNet system combines the results obtained from two different pre-trained deep learning models based on ResNet34 and HRNet (high-resolution network model) trained using a large-scale dataset. By enhancing the contrast of the X-ray images and reducing the noise level using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, the proposed COVID-CheXNet system has managed to diagnose the COVID-19 patients with a detection accuracy rate of 99.99%.COVID-CheXNet, is another hybrid deep learning framework developed by Al-Waisy et al.22 did a comprehensive investigation of ML based classification methods for automated diagnosis of COVID-19. Based on the results obtained from different experiments, ResNet50 model had achieved the optimum accuracy of 98.8% while the traditional techniques such as SVM demonstrated the best result for an accuracy of 95% and RBF  an accuracy of 94% for the prediction of coronavirus disease.Moreover, Mohammed et al.23 reported another deep learning approach that allows detection of COVID-19 patients. Authors used pretrained deep CNN models  for feature extraction, and the Support Vector Machines (SVM) for classification. Their dataset contained 180 COVID-19 and 200 normal chest X-ray images. The deep features extracted from the ResNet50 model and SVM classifier achieved an accuracy of 94.7%.Finally, Ismael et al.These approaches lack the generalizability for unseen data due to various pre-processing steps performed and assumptions involved in the model development and hyper-parameter fine tuning conducted specific to their own dataset. In this paper we describe the development and evaluation of a new approach for detection of COVID-19 from chest X-ray images using a minimal pre-processing pipeline and automatic optimization of the hyper-parameters of various models using a recently proposed algorithm.In this section, we will discuss the architecture design methodology and the key components of EpistoNet, motivated by the need of developing a feasible solution to help combat COVID-19.As depicted in Fig. The dataset utilized in this research is comprised of 2500 X-ray images consisting of 1250 COVID-19 and 1250 non-COVID-19 images provided by Henry Ford Health System (HFHS) of Michigan in Detroit , Detroit, MI. There were no potentially identifying marks/features and no patient identifiers in the images. This study was approved by the IRB committee of HFHS (No. 14030).Secondly, the IRB committee of HFHS waived the need for obtaining the informed consent for this study.Thirdly, all methods were performed in accordance with the relevant guidelines and regulations, including those of the Declaration of Helsinki.To build an efficient classification model, we propose a method using mixture of deep CNN experts to detect COVID-19 from chest X-ray images. The identification and extraction of relevant features from X-ray images is a challenging task that requires multiple neural network architectures to directly operate on the given data and find patterns that help in detection and classification of the COVID-19 infection. For this purpose, we have designed an ensemble model which is able to exploit discriminative features and obtain higher accuracy than individual CNN models on the HFHS dataset.Mixture of experts is a type of ensemble based on the divide-and-conquer principle where each individual model is specialized in a given part of the input space, learning different aspect of the problem.k expert models which are supervised by a gating network. The gating network is a discriminator network trained together with the experts on the same input and decides which expert(s) to use for the final classification task.As shown in Fig. x is assigned to expert i (see Eq. (24.The output of the gating network can be interpreted as the probability that input (see Eq. ). The gak clusters and train explicitly localized expert networks on each cluster capable of differentiating between visually similar images. To this point, first we applied a cluster-based pre-processing step to our dataset of 2000 images and partitioned them into 5 clusters of variable size using K-means clustering method. K-means is a type of unsupervised machine learning technique commonly used for clustering unlabeled data into k clusters.In order to effectively discriminate COVID-19 from non-COVID-19 X-ray images, we decided to split our main dataset into 25. Epistocracy algorithm is a multi-population self-adaptive optimization method that uses different explorative and exploitative techniques to search the problem space and find the optimal solution. To avoid stagnation and to prevent a premature convergence, the algorithm employs multiple mechanisms such as dynamic population allocation and regression-based leadership adjustment. The algorithm uses a stratified sampling method called Latin Hypercube Sampling (LHS)26 to evenly distribute the initial population for an efficient exploration of the search space. Figure To further improve the accuracy of each convolutional neural network, we have used Epistocracy algorithmAs illustrated in Fig. The architecture of each expert model is made of a base and a head. The base model is a popular CNN model pre-trained on ImageNet for transfer learning. The head model which consists of fully connected layers, is automatically constructed using Epistocracy algorithm. By repeatedly evolving each architecture and optimizing their corresponding hyper-parameters, Epistocracy algorithm can effectively produce the optimal model fine-tuned for classification.Using Epistocracy algorithm, the architecture of each neural network in the initial population is generated on a modular basis, in which each module consists of 1 dense layer and 1 dropout layer see Fig. . The MAXIn addition to generating the architecture of neural networks, the hyperparameters of the fully connected layers are also individually randomized. These hyperparameters are randomly selected to increase the diversity of the population and the possibility of finding an optimal one. The number of neurons, the activation method, and the dropout rate are randomly selected from Table The Epistocracy algorithm strives to find the optimal CNN architecture in an efficient amount of time.A model is first created by calling a unique function specific to the desired CNN architecture such as VGG16. This function takes the mixed list of hyper-parameter values and returns a model built with those values. Included within this function is functionality to map each hyper-parameter value to its respective place in the layer construction of the model. The new model is then trained on the input dataset using global variables for training parameters . Once training is complete, the validation accuracy score for the model is retrieved from the training history and returned by the function.When running Epistocracy, the population size  was set to 100. Mutation rate was 20%, and crossover rate  was 80%. 20 full generations of Epistocracy were run in full without any early stoppage. After Epistocracy fully runs and various architectures are generated the top performing one is finally returned.The proposed EpistoNet decision tree is then designed using MoE I and MoE II as shown in Fig. Extensive experiments were performed to evaluate the performance of the proposed model to classify COVID-19 from chest X-ray images. In our experiment, we set the training, and validation ratios to 80%, and 20% respectively. We hold out 25% of the entire dataset, namely 500 images out of 2500 for testing.To identify the best classification model, we employed deep convolutional neural networks of different depth and complexity and evaluated their performance using HFHS testing dataset. We applied transfer learning to initialize the data training and to facilitate the feature extraction from the input data using ImageNet weights. As it is shown in Table k, and plotted the number of clusters k versus the inertia which is the average of the squared distances from the cluster centers of the respective clusters. As it is shown in Fig. To determine the optimal number of clusters we employed the elbow method. In fact, we tried different numbers of clusters To design our classifier, we employed eight state-of-the-art CNN models on each cluster  was used. Grad-CAM is a class-discriminative localization map that displays the most relevant and significant regions of an image upon which the classification decision was made by the model. The localization maps are shown as a heatmap, where the intense red color represents the most significant area considered by the classification model. This visualization technique can be used by radiologists and pathologists to localize COVID-19 manifestations in the chest X-rays. In Fig. To understand which areas of the image were highlighted and utilized by the model to detect COVID-19 infection, Gradient-weighted Class Activation MappingFrom Fig. Compared to the performance of the individual models using the HFHS testing dataset, EpistoNet exhibits an excellent classification performance. During the initial testing on the Henry Ford dataset before any optimization, the highest accuracy achieved was 86% by VGG16. After partitioning the training-validation dataset into 5 clusters using K-means clustering algorithm and applying the Epistocracy optimization technique and building two mixtures of discriminative experts out of 5 best individual Convolutional Neural Network models the classification accuracy was significantly increased.Based on experimental results, it is demonstrated that EpistoNet can accurately, and reliably detect COVID-19 infection from X-ray images. The accuracy of the proposed model compared to the related work is quite encouraging, given the limited amount of labeled data and differences in the quality and quantity of samples used for training and testing. Using EpistoNet, the diagnosis of the Coronavirus disease can be done automatically at a low cost, rapidly, and with high accuracy. With isolation of suspicious cases and treatment of infected patients, the spread of the disease can be significantly reduced.In this study we proposed EpistoNet, an ensemble of Epistocracy-optimized mixture of discriminative experts for automatic detection of COVID-19 infection from chest X-rays. Each mixture of expert consists of 5 deep convolutional neural networks and a gating network. We evaluated the performance of various state-of-the-art convolutional neural networks using HFHS dataset. Transfer learning was utilized to get a better initialization state for classification of COVID-19 disease. Epistocracy algorithm was also employed to build and optimize the head models composed of neural networks of variable length. The experimental results show that EpistoNet can effectively classify COVID-19 vs. non-COVID-19 infections, even with a limited data set. The accuracy rates achieved by EpistoNet for the classification of COVID-19 were found to be higher than that of stand-alone VGG16 or similar models trained on HFHS dataset. Other approaches lack the generalizability of our method for unseen data due to various pre-processing steps performed and hyper-parameter fine tuning conducted specific to their own dataset. In EpistoNet pipeline, a minimal pre-processing step is required, and Epistocracy algorithm is recruited to systematically optimize the models’ hyper-parameters without any human intervention. EpistoNet can be effectively leveraged as a fast, cheap and portable tool to provide excellent diagnostic aid to healthcare professionals such as physicians and radiologists for the early detection and urgent treatment of patients with COVID-19, mitigating the devastating impact of COVID-19 on lives and livelihoods.


Background and aims. SARS-CoV-2-infected patients can experience long-lasting symptoms even after the resolution of the acute infection. This condition, defined as Long COVID, is now recognized as a public health priority and its negative impact on the quality of life of the patients could be more relevant in individuals with debilitating pathologies. We here evaluated the frequency of Long COVID in patients with inflammatory bowel diseases (IBD). Methods. IBD patients afferent for scheduled visits to our tertiary referral center at the Tor Vergata University Hospital, Rome, were recruited from 7 September to 22 October 2021. During the visits, patients were investigated about previous COVID-19 infection and the possible development of Long COVID. Results. Fifty-three out of 528 IBD patients (10%) have had a SARS-CoV-2 infection. Of these, 21 patients (40%) developed Long COVID, and asthenia was the more frequent symptom as it occurred in nearly two-thirds of patients. Patients with Long COVID were more frequently females, while other clinical and demographic characteristics did not differ between patients with Long COVID and those without Long COVID. In particular, the IBD relapses occurred with the same frequency in the two groups. Conclusions. Long COVID appears to be common in IBD patients even though it does not influence the IBD course. In the last two years, our knowledge about biological and clinical features of COVID-19, the disease caused by SARS-CoV-2, has increased . Patients’ characteristics were compared using the χ p = 0.041) while IBD duration appeared as a protective factor . However, multivariate analysis with multinomial logistic regression did not confirm such associations (From 7 September to 22 October 2021, we collected information of 528 IBD patients afferent to our center for scheduled follow-up visits . COVID-1ciations . A variety of cohorts and electronic medical record-based studies indicate that approximately 10–40% of SARS-CoV-2-infected patients can experience several physical and psychological symptoms that continue after the resolution of the acute phase. This condition, defined as Long COVID, is now recognized as a public health priority and its negative impact on the quality of life of the patients could be even more relevant in individuals with coexisting and debilitating pathologies ,9. Our sOur data indicate that Long COVID occurs in 40% of IBD population with no significant difference between CD and UC. As pointed out above, these data are consistent with the frequency of Long COVID in the general population, thus reinforcing the concept that IBD by itself does not represent a condition facilitating SARS-CoV-2 infection ,17,18. ATo our knowledge, this is the first study documenting the frequency of Long COVID in IBD. Although such a frequency resembles that seen in the general population and does not seem to affect IBD course, we advise IBD patients to adhere to the vaccination campaign in order to reduce the risk of acute COVID-19 and its long-term complications.