The current outbreak of Covid-19 infection due to SARS-CoV-2, a virus from the coronavirus family, has become a major threat to human healthcare. The virus has already infected more than 44 M people and the number of deaths reported has reached more than 1.1 M which may be attributed to lack of medicine. The traditional drug discovery approach involves many years of rigorous research and development and demands for a huge investment which cannot be adopted for the ongoing pandemic infection. Rather we need a swift and cost-effective approach to inhibit and control the viral infection. With the help of computational screening approaches and by choosing appropriate chemical space, it is possible to identify lead drug-like compounds for Covid-19. In this study, we have used the Drugbank database to screen compounds against the most important viral targets namely 3C-like protease (3CLpro), papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp) and the spike (S) protein. These targets play a major role in the replication/transcription and host cell recognition, therefore, are vital for the viral reproduction and spread of infection. As the structure based computational screening approaches are more reliable, we used the crystal structures for 3C-like main protease and spike protein. For the remaining targets, we used the structures based on homology modeling. Further, we employed two scoring methods based on binding free energies implemented in AutoDock Vina and molecular mechanics—generalized Born surface area approach. Based on these results, we propose drug cocktails active against the three viral targets namely 3CLpro, PLpro and RdRp. Interestingly, one of the identified compounds in this study i.e. Baloxavir marboxil has been under clinical trial for the treatment of Covid-19 infection. In addition, we have identified a few compounds such as Phthalocyanine, Tadalafil, Lonafarnib, Nilotinib, Dihydroergotamine, R-428 which can bind to all three targets simultaneously and can serve as multi-targeting drugs. Our study also included calculation of binding energies for various compounds currently under drug trials. Among these compounds, it is found that Remdesivir binds to targets, 3CLpro and RdRp with high binding affinity. Moreover, Baricitinib and Umifenovir were found to have superior target-specific binding while Darunavir is found to be a potential multi-targeting drug. As far as we know this is the first study where the compounds from the Drugbank database are screened against four vital targets of SARS-CoV-2 and illustrates that the computational screening using a double scoring approach can yield potential drug-like compounds against Covid-19 infection. It appears like the ongoing pandemic due to SARS-CoV-2 is the one of the most adverse infections observed during recent decades. Even though the mortality rate appears to be quite lower for this compared to previous outbreaks due to SARS and MERS viruses, the severity, disruption to the healthcare and damage to the economy have become very high due to its more aggressive human-to-human spread2. After its first report in December, 2019 within 6 months time, it has spread to almost all countries, infecting more than 44 M people. Moreover, it has been lethal to more than 1.1 M people. It is a challenging time for all researchers in medicine and pharmacology to develop a vaccine, small molecular drug or an epitope to circumvent the current case of outbreak3. There exist standard protocols to rationally develop such medicine from scratch for any such infections starting from gene mining which involves targets discovery and identification of lead drug-like compounds from structure based design. Another approach is through the high throughput experimental screening4 of compounds from chemical space which is rather shooting for something in the darkness. Our aim here is to present a rational approach involving computational screening for identification of lead drug-like compounds for Covid-19 associated coronavirus5.The viral infection affects all forms of life including birds, mammals and humans causing severe effects on the healthcare system and economy. Most of the viral infections are contagious and spread through air, water, food or exchange of body fluids. The viral infection can lead to outbreak, or can become epidemic or pandemic based on the spreading mechanism and strength of virus-host cell interaction. There are many factors that dictate the severity of a viral infection such as intrinsic pathogenicity, mortality rate, basic reproduction number6. Therefore, in order to design drugs against viral pathogens, we need to start with the data mining of viral genome. More than 10,000 genomics data are already deposited for SARS-CoV-2 in GISAID, an open source online platform (https://www.gisaid.org)7. It is a wealth of information which can be used to find the routes followed by the virus to spread the infection8. The SARS-CoV-2 genome is made of less than 30000 nucleotides and contains genes for 29 different proteins9. The ORF1ab alone encodes as many as 16 non-structural proteins10. Some of the key proteins encoded by this gene are PLpro (NSP2), 3CLpro (NSP5), RdRp (NSP12), and helicase (NSP13) which play a vital role in the replication and transcription12. The ORF2-10 encodes various structural proteins such as membrane protein (M), envelope protein (E), spike protein (S), nucleocapsid protein (N) and other auxiliary proteins13. The M, E and S make the viral coat while the RNA gene is packaged within the N protein12. Further, the spike protein is involved in the host cell recognition and in particular binds to Angiotensin-converting enzyme 2 (ACE2) mammalian receptor14. Based on their involvement in different biological processes, many SARS-CoV-2 proteins  can be considered as potential targets for therapy. More specifically, for therapeutic purpose, it is essential to target enzymes involved in viral replication as well as transcription. In addition, we need to target those catalytic sites involved in the key enzymatic reaction. In SARS-CoV-2, the main role of 3CLpro and PLpro proteases is to cleave the polyprotein into smaller functional units to facilitate replication/transcription process and thus are potential targets for the therapeutics15.The genomics data encode the biomolecular machineries necessary for the life processes of any organism including pathogens and can be used to obtain information regarding potential targets relevant for therapy or diagnostics16 against the selected viral drug targets using a molecular docking approach18. The Drugbank database is a chemical space of compounds approved by FDA and molecules under drug trials . Since these compounds are already under clinical trials or in the market, the synthesizability19 is not a problem which is often a major problem when the compounds are designed using de novo design20 or those designed from fragments based drug discovery approaches21. Further since many of the compounds are approved, if they are found to be active against viral targets during the computational screening they can be straight-away repurposed for treating Covid-1922. Since these “approved” compounds are already verified for safety, the time associated with the clinical trials can be significantly reduced. The structure based screening of compounds requires 3 dimensional structure for the viral proteins. As of now, the structures for 3CLpro, spike glycoprotein, ADP ribose phosphatase, RNA binding domain of nucleocapsid phosphoprotein, Endoribonuclease have been reported in the protein databank (PDB)23. The spike protein structure is based on cryogenic electron microscopy and has been reported in two different conformations namely when it is bound to ACE-2 mammalian receptor and in free state25. In case of 3CLpro, the crystal structures are reported for both apo form and for the co-crystallized form with certain inhibitors26. For computational screening, we have used the crystal structures for the two targets 3CLpro and spike protein. For the remaining two targets PLpro and RdRp, we have carried out homology modeling using template structures from SARS-CoV-1 which was reported to have a highly homologous genome as SARS-CoV-2. The models were developed using SWISS-MODEL, a web server for doing homology modeling27.Designing and screening of small molecules or peptides targeting these vital proteins can help in developing therapeutics against the infection. High-throughput experimental screening on viral particles or specific target could be performed to test the activity of such compounds, however, this is expensive and time taking. In addition, extreme care needs to be taken as it may require handling of live and potentially pathogenic viral strains. In this study, we used an alternative approach by computational screening of compounds from the Drugbank database28. Even though the molecular docking approaches as implemented in Autodock, AutoDock Vina and Glide are quite fast, they were shown to fail in ranking complexes in many occasions29. Among many force-field based scoring functions, the MM-GBSA based ranking of protein-ligand complexes has been reported to be reliable often31. This has been the reason for choosing a double-scoring approach based on AutoDock Vina and MM-GBSA based binding free energies in this study. In addition to identifying the lead drug-like molecules for various targets, the study proposes the drug molecules which can target multiple targets simultaneously32. The study also involves the computational validation of many drug compounds currently considered for drug trials33 in various countries by estimating binding free energies using MM-GBSA approach. The trial compounds included in this study are Remdesivir, Chloroquine, Lopinavir, Oseltamivir, Ritonavir, Favipiravir, Baricitinib, Darunavir and Umifenovir36. We also would like to recall that many researchers have screened compounds from different chemical-spaces such as natural products database37, FDA-approved compounds38, phytochemicals41, marine natural products42 ChEMBL45 and pubchem44 databases against different viral targets by using different scoring functions46. The main difference of the current study is that we have considered four different viral targets and so we are able to propose drug cocktails effective for Covid-19 along with suggestions of drugs which have the potential as multi-targeting drugs. Further we have also discussed on the computational scoring of trial compounds in a way to argue whether we need to validate the compounds before considering for drug trials by employing cost-effective screening approaches.For each of the selected target protein, we identified top five compounds from a list of “approved” drugs and another top five from the subset of “investigational” drugs. Further the stability and binding affinity of these screened compounds have been validated using molecular dynamics and molecular mechanics-generalized Born surface area (MM-GBSA) approach47, binding of a drug molecule to a single or multiple targets can be of significance depending upon the different stages of the viral infection. For instance, during the host cell recognition only the spike protein of SARS-CoV-2 plays a key role and can be targeted. However, once the infection occurs, other proteins associated with transcription and replication processes are expressed. In this case, it is advantageous to use a drug which targets multiple proteins or cocktail of drugs with each drug having significant binding affinity towards a specific target. Due to the complexity involved in the development of infection, it is desirable to target multiple targets with many low affinity ligands instead of targeting a single target with high affinity ligand47. This also has an advantage that even when a specific target mutates rapidly, the other targets can be inhibited by the drug cocktails which eventually makes the treatment effective. Such combination of drugs subscription is already in practice for viral infections48. In case of HIV treatment, a combination of drugs belonging to categories such as nucleoside reverse transcriptase inhibitor, non-nucleoside reverse transcriptase inhibitor, protease inhibitor and integrase inhibitor has been successfully tested49. For example, a FDA-approved drug Combivir is a mixture of AZT and 3TC and targets enzymes which appear in the early and later stage of HIV replication48.In this study, we have identified several compounds from the Drugbank that are predicted to bind to individual target proteins with high affinity Tables , 2 and 3Based on the larger binding affinity towards each of the selected Covid-19 targets i.e. 3CLpro, PLpro, RdRp, spike-protein, top five FDA-approved compounds were selected from the Drugbank database . The compound CP-609754 is under phase-I trial for treating advanced malignant tumours and exhibits its activity as farnesyltransferase inhibitor54. Among approved compounds, Tadalafil, Metocurine, Lorlatinib and Lumacaftor are respectively used for treating erectile dysfunction, muscular relaxant, ALK-positive metastatic non-small cell lung cancer and cystic fibrosis. Only Natamycin is an antibiotic and inhibits fungal growth by binding to sterols (ergosterol in particular)55.The top five FDA-approved compounds showing high affinity towards the protein PLpro, are Tadalafil, Metocurine, Lorlatinib, Lumacaftor, Natamycin while the compounds identified from the “investigational” category are Zoliflodacin, JE-2147, Phthalocyanine, Quarfloxin and CP-609754. The binding free energy range for the former set of compounds is between The PLpro also has well defined binding site as shown in the Fig.  16. The pharmacological action of the other two selected compounds RU85053 and Golvatinib is not documented elaborately in the literature. The approved list of high affinity compounds for RdRp protein includes Lumacaftor, Ergotamine, Natamycin, Dihydroergotamine and Imatinib. We have already discussed the pharmacological properties of the compounds Lumacafor and Natamycin in the previous section as they were also predicted to interact with PLpro . The compounds, Sonidegib and Enasidenib are again anticancer compounds used in the treatment of basal cell carcinoma and acute myeloid leukemia respectively while Regorafenib is active against metastatic colorectal cancer and advanced gastrointestinal stromal tumours. The compound, Capmatinib is used in the treatment of melanoma, gliosarcoma, solid tumors, colorectal cancer. The remaining compound in the subset, Lifitegrast is active against dry eye syndrome. The top most high affinity compounds in the subset of experimental drugs include Lifirafenib, Resiniferatoxin, JTK-853, Tegobuvir and PCO-371 and the binding affinities are in the range 56. The compound PCO-371 was an investigational compound for treating hypoparathyroidism and works as a parathyroid 1 receptor agonist.The subset of approved compounds which bind to interfacial region located between the viral spike protein and ACE-2 mammalian receptor with high binding affinity are Dexamethasone metasulfobenzoate (DMS), Nilotinib, Sonidegib, Enasidenib, Regorafenib, Lifitegrast and Capmatinib. The binding affinities for this set of compounds are in the range As mentioned earlier, the viral spike protein recognizes the mammalian ACE-2 receptor and mediates the binding to the host cell membrane. The drug-like compounds should weaken the protein–protein interaction through binding to interfacial region between the spike protein and ACE-2. Indeed, the compounds studied here bind to a site which is located in the interfacial region between the two biomolecules  in four different viral targets Fig. . Most ofmentclass2pt{minimmentclass2pt{minimThe AutoDock Vina based scoring uses a single rigid protein conformation for ranking the compounds. However, inclusion of the conformational flexibility of protein might help to remove strains for certain ligands in the binding site and so a rescoring with the inclusion of protein-ligand dynamics will result in an improved ranking of the ligands. Therefore, we have further ranked the 10 compounds  in Table 59. Nevertheless, it is considered to be a better approach in ranking complexes when compared to AutoDock or AutoDock Vina59. For ranking of complexes, relative difference in free energies is more significant compared to the absolute binding free energies and this makes the MM-GBSA based approach promising. Furthermore, we also checked various contributions to the total binding free energies obtained using MM-GBSA approach for the top 2 compounds in each target  but also gives confidence for the use of computational approach-based screening as a starting step for drug repurposing/discovery. Moreover, from the list of the identified compounds we also proposed drug(s) which can be either used individually or in combination, against the virus. We also report the multi-targeting capacity of a few drugs like Phthalocyanine, Tadalafil, Lonafarnib, Nilotinib, Dihydroergotamine and R-428 which have the potential to simultaneously inhibit three viral targets such as 3CLpro, PLpro and RdRp. Further, the study also included the binding energy estimation for various compounds which are currently under drug trials. It is shown that Remdesivir binds to RdRp and 3CLpro with high binding affinity indicating that it can be categorised as a multi-targeting drug. Baricitinib and Umifenovir were found to be compounds with superior target-specific binding while Darunavir is found again to be a multi-targeting drug. It is shown that Hydroxy chloroquine and Oseltamivir are not very active against any of the viral targets and in particular, the former compound has only moderate binding affinity towards RdRp.The study demonstrates how to repurpose drugs from different chemical spaces such as Drugbank database to swiftly develop therapeutics against Covid-19 like viral infection. The compounds identified can be subjected to experimental (through binding assay studies) and clinical (by administering the lead compounds to patients having infection) validations before subscribing to a wider population. Screening the compounds from Drugbank gives as another advantage that these compounds are already validated for favourable ADMET properties and so clinical validation can be quickly performed. The reliability of the lead compounds generated from computational studies is highly dependent on the accuracy of the scoring functions employed. So, we need to employ accurate scoring methods as the one employed in the current study. We are also currently working on developing triple scoring functions by incorporating QM/MM and QM fragmentation based methods to further improving the ranking of the compounds following a force-field based preliminary ranking.In this section, firstly we have described methods used for proposing the three-dimensional structure of the viral targets like PLpro and RdRp. This is followed by the description of the computational screening approach to identify potential compounds against the targets for which the structures are obtained both from the homology modeling and experimental structure elucidation approaches. For a selected set of compounds, we also carried molecular dynamics and free energy calculations using molecular mechanics- generalized Born surface area (MM-GBSA) approach.67. We identified the following sequences decoding key targets for virus which are responsible for host cell receptor binding, replication and transcription: spike-protein (YP_009724390.1_3), 3L-main protease(QHD43415_5 ), PLpro (QHD43415_3) and RdRp (YP_009725307.1).The full genome sequence of severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1 (Covid-19) (NC_045512.2) was retrieved from the National Center for Biotechnology Information (NCBI) database27 with default parameters using automated mode for these two targets. For both targets templates were chosen from SARS-CoV-1. The structure for PLpro is based on the PDB id 5Y3E and for RdRp the PDB structure from 6NUR was used as a template. In the former case, the sequence identity was 83% while in the latter case it was 96.4%. Usually, the models based on templates having above 30% sequence identity are considered reliable. So, high sequence identities of the templates used to build models make the obtained structures very reliable.PDB structures of PLpro and RdRp of Covid-19 were not available when we started the work. Therefore, homology modelling was performed using the SWISS-MODEL server16. and with the use of openbabel software, the three dimensional structure for these compounds have been generated. Further, using the autodocktools, the Gasteiger type charges were generated for all these compounds and for the targets. The compounds were screened against the four targets mentioned above using AutoDock Vina software68.The compounds from the Drugbank database were screened against the four key viral targets: 3CLpro, PLpro, RdRP and spike protein. For the 3CLpro and spike protein, the three dimensional structures were taken from PDB database. For the remaining two targets, we used the structures from homology models which is described in the previous section. The current version of the Drugbank database (v5.1.5) contains 13,529 drug entries including 2,630 approved small molecule drugs, 1,371 approved biologics, 131 nutraceuticals and over 6,354 experimental drugs. In particular, we only retrieved the subset of the Drugbank database compounds having three dimensional structure and there are about 8773 such compounds. The structure data file (SDF) format of compounds were retrieved from the Drugbank database(v5.1.5)25. Since the open state of the RBD domain is responsible for binding to ACE-2 receptors, we have used this conformational state for developing therapeutics. Also the binding site has been restricted to be in the interfacial region of the RBD domain and ACE-2 as the drug is eventually supposed to modulate the protein–protein interaction between these two proteins. Similarly in the case of 3CLpro, the structures are reported for both apo state and for the inhibitor bound state. We have considered apo form as it has certain advantages. In particular, in the bound state, the conformational states of the residues around the inhibitor might have reorganised to maximize interaction with the inhibitor. Therefore, this target structure may not be binding effectively to any other ligands with different molecular volumes. The best approach is to adopt a flexible docking but this is computationally very demanding. In this situation, the apo form of the 3CLpro corresponds to its structure in solution and therefore, can serve as a reliable target for the drug design.Often protein contains multiple binding sites, therefore, recognition of the optimal binding site in appropriate domain is significant to study the interaction between the protein and the inhibitory compound(s). The spike glycoprotein contains receptor binding domain (RBD) responsible for host cell recognition and binding to the Angiotensin Converting Enzyme 2 (ACE2) mammalian receptors. Moreover, this domain undergoes significant conformational change before binding to ACE-2 receptors. The structures for the spike protein in its free state  and in its prefusion state (open state) have been solved using cryo-EM experiments51. Similar, the binding site for PLpro has been chosen based on the location of GRM inhibitor as in PDB structure 3MJ5. We used Lamarckian algorithm to identify the best binding modes and poses for the ligands within the binding sites. The molecular docking includes the solvation energy which accounts for the change in free energies due to aqueous environment in which protein-ligand complex is embedded. The scoring function also includes the entropic contributions due to conformational degrees of freedom in the ligand. For each rotational bond, 0.2–0.3 kcal/mol entropic contributions are added to binding free energies; the positive value means a more flexible ligand will bind less strongly with the protein (based on the entropic contribution). We have used the routine protocol for molecular docking where the protein framework is fixed while the ligand is flexible. The ligand flexibility is included through the motion along the rotatable bonds. Other intramolecular structural parameters such as bond lengths and bond angles are not altered during the docking. This procedure is efficient but does not account for the effect due to protein flexibility to the binding affinity. So, for the selected ligands, we have carried out all atom molecular dynamics and the free energies of binding were estimated as an average over a few hundreds configurations.During the molecular docking, the grid box size was chosen so that the binding sites in the domain responsible for key processes for viral life are targeted. The grid box dimension was chosen sufficiently enough so that even larger molecules in the chemical space can be identified during the screening. For example in the case of RdRp, we used a grid box dimension of 40X40X40 (with a grid size of 0.375 Å ). The binding site for 3CLpro has been identified based upon its co-crystallized structure with inhibitor, N369. For this set of calculations, we have used B3LYP functional and 6-31+G* basis sets. Thus obtained ESP charges and GAFF70 force-field, together describe the interaction of ligand with protein and solvents. For the protein targets, we used FF99SB force-field and the water solvent has been described using TIP3P force-field. The protein-ligand complexes were solvated and neutralized by adding sufficient numbers of counter-ions. Among the four different targets studied, the spike protein is the largest one. Setting-up MD for whole spike protein (including both S1 and S2 domains and the three chains) with a solvent box with 8 Å  cut-off yielded, approximately 400000 atoms. Therefore, the calculations were done for the RBD binding domain of spike protein and ACE-2 receptor. In remaining cases, the whole target proteins were included in the MD simulations. Firstly, a minimization run was carried out followed by MD simulations in an isothermal-isobaric ensemble. The temperature and pressure were set to correspond to ambient condition (300 K and 1 atm pressure). The time step for integration of the equation of motion was 2 fs and initially an equilibration was carried out for 5 ps. Followed by this, production runs for a total time scale of 40 ns were carried out. The convergence of energetics and other structural properties (such as densities) during the production runs has been verified  and RMSF for the protein alone using the molecular dynamics trajectories of selected protein-ligand complexes and the results are shown in Figures 71. The 500 configurations selected from the last 5ns were used for the subsequent MM-GBSA calculations. The ligand binding induced structural changes need to be accounted for in the proteins for reliably computing the binding free energies and so we have followed this procedure. The MM-GBSA approach employs an implicit solvent model for computing the solvation energies. The polar solvation free energies were obtained using generalized Born approach while the nonpolar solvation free energies are obtained from solvent accessible surface area. The protein-ligand energies were obtained as the sum of van der Waals and electrostatic energies in this scheme. The free energies of binding which are the sum of these four contributions were computed using the MMPBSA.py script72 as implemented in the Amber16 software.Molecular dynamics (MD) simulations require the force-fields (charges and Lennard-Jones parameters and parameters to describe intramolecular structure) to describe the subsystem interactions and initial structures for the protein-ligand complex systems. The most stable protein-ligand complex structure obtained from molecular docking study has been used as the starting configuration for MD simulations. The ligand structure in its bound state to the target protein has been used for computing the electrostatic potential fitted charges by employing Gaussian16 softwareSupplementary Information.


With an increase in consumer demand of video gaming entertainment, the game industry is exploring novel ways of game interaction such as providing direct interfaces between the game and the gamers’ cognitive or affective responses. In this work, gamer’s brain activity has been imaged using functional near infrared spectroscopy (fNIRS) whilst they watch video of a video game (League of Legends) they play. A video of the face of the participants is also recorded for each of a total of 15 trials where a trial is defined as watching a gameplay video. From the data collected, i.e., gamer’s fNIRS data in combination with emotional state estimation from gamer’s facial expressions, the expertise level of the gamers has been decoded per trial in a multi-modal framework comprising of unsupervised deep feature learning and classification by state-of-the-art models. The best tri-class classification accuracy is obtained using a cascade of random convolutional kernel transform (ROCKET) feature extraction method and deep classifier at 91.44%. This is the first work that aims at decoding expertise level of gamers using non-restrictive and portable technologies for brain imaging, and emotional state recognition derived from gamers’ facial expressions. This work has profound implications for novel designs of future human interactions with video games and brain-controlled games. Electronic sports (or eSports) is fast gaining acceptance as both at par with traditional sports, and the virtual athletes being celebrated as real-life sport athletes . eSportsAlthough there is a consensus to investigate the effect of the ever increasing usage of eSports, it is a rather complex task to quantify the eSports effect since there are varied genres, and sub-genres of video games, and also because how it affects one individual may not necessarily be the same for another individual. It has been established in previous studies that brain responses of individuals to a determined visual stimulus can vary significantly depending on their exposure to violent games . FurtherIndeed, games are able to elicit a myriad of different cognitive processes and affective responses in players ,9. The sConverging from all these evidences, the present novel study aims to automatically recognise gaming expertise of participants by capturing elicited neural responses from prefrontal cortex (PFC) using functional Near-Infra red Spectroscopy (fNIRS). In addition, the effect of integrating a measure of affective state, like emotion decoded from participant’s facial expressions, in such a recognition system would also be investigated. In the present study, we have bench-marked several classification methods and managed a satisfactory recognition of expertise in several episodes of the game LoL. The episodes of the game LoL were identified by other players, external to the participants’ sample, as pertaining to different emotional context .In particular, the present study aims at answering the following research question: to what extent is it possible to recognise gamer’s expertise from their brain responses to gameplay videos, what methods provide a better accuracy and to evaluate the effect that emotions recognition through participant’s facial expression analysis can have in bolstering expertise recognition. This research question is of special interest for the game industry .: It is not possible to recognise gamer’s expertise level from fNIRS neural responses from the PFC with recognition performance  better than a 95% confidence interval difference to the results from a baseline (rule 0) classifier.: It is possible to recognise gamer’s expertise level from fNIRS neural responses from the PFC with recognition performance better than a 95% confidence interval difference to the results from a baseline (rule0) classifier.: Assert does not bolster the recognition of expertise with statistical difference of : Assert bolsters the recognition of expertise with statistical difference of : Assert there are not statistical differences of performance  using brain signals acquired from a portable neuroimaging modality (fNIRS).This is also a first study that integrates brain signals with the emotional states of the participants, derived from the visual cues provided by their facial expressions, in the classification paradigm to investigate the interlink between the emotional state of a participant and their expertise level.This work also found that unsupervised deep feature extraction for time-series boosted recognition performance specially when applied to fNIRS signals.The main contributions and findings of this novel study are:The findings of this work pave the way for new avenues of interacting with games, such as affective brain-to-game interfacing for entertainment or serious games. This novel way of profiling of gamers can enable mind-to-game interlinked experience allowing a higher coupling between the real expertise of the user, and the level reached in the game.The paper is organised as follows: In With the increased consumption of eSports and the availability of sophisticated technology, there is an increasing trend of modifying game themes in real time to enhance a user’s experience. In this section, we discuss how varying feedback from reading users physical and mental state, for example fNIRS for reading brain activity and emotional state of the gamers perceived from their facial expressions, is being used to modify the game content to enhance their overall gaming experience.Traditional video gaming involve playing games in a simulated environment with users interacting with peripheral devices such as mouse, keyboard, and/or joysticks. However, with the availability of more sophisticated technology, now users are also able to interact in real-time with video games using their facial expressions, body movements, and even physiological signals like heart rate, skin conductivity etc. . An inteIn recent years, researchers have been focusing on finding the link to gamer’s experience in lieu with their physiological markers to drive the online game development content towards enhancing the overall gamers experience. The work by Drachen et al.  focus onAnother important consideration when creating an emotionally adaptive game is behavioural expressions. In the work by , the linThe role of functional Magnetic Resonance Imaging (fMRI) in establishing the functions of different brain regions before, and after learning, remains pivotal. However, for neuroergonomic studies in specific, which focus on understanding the human brain functions in everyday life settings, the confinement of a scanner environment is not ideal. In this regard, fNIRS, another optical neuroimaging modality, is fast emerging as a de-facto choice for imaging the human brain for neuroergonomic studies owing to its non-invasive, highly portable and wearable characteristics ,20. fNIRIn the study by Cakir et al.  fNIRS waAnother study which focused on gauging whether the effect of a decrease in oxyHb in dorsal PFC on playing video games for a long time is also exhibited by young children aged 7–14 years is reported here . They coIn this section we outline relevant works that investigated how playing video games impact gamers’ cognition, and further how measurements from brain, in particular using fNIRS, have been previously made use of for decoding manual dexterity of users. All of the research evidence provided in the following subsections helped us frame the research question of the present study.To gauge the effect on playing video games on cognition and behaviour, a notable review is presented in . They diSome of the possible effects of playing video games can be attributed to practise in the parallel of real-world games. The practise of paying attention, staying focused, for longer duration of times has proven to improve visual skills in individuals with amblyopia (lazy eye) .In the study by Hyun et al. , they esAnother notable study trying to draw the link between intelligence and expertise in playing video game is done by Kokkinakis et al. . They arAlthough in these notable works some behavioral and/or anatomical differences were found in participants on account of playing video games, these studies did not encompass a direct recognition of participants expertise from their functional bio-signals, as intended in this present study.The neuroimaging modalities most commonly utilised for measuring brain activity in BCI applications are fNIRS  and elecAnother study that investigated the relation between changes in mental workload, level of expertise, and task performance for aerospace application on brain activity recorded using fNIRS is by Ayaz et al. . They coIndeed, the success of decoding participant’s skill level directly from brain activity recorded using fNIRS, used in tandem with emotional state of the participants, has the potential to conduct objective assessment of particpants whilst they perform a task in more naturalistic settings. The feedback from such a decoding system may also be used to improve operator performance during technical skill training. Hence towards this end, in this work, we aim to decode gamers expertise level using their brain activity recorded with fNIRS as well as emotional state derived from facial expressions.In this work, a total of 30 participants, casual and professional gamer’s, of varying expertise levels were recruited to visualise 15 gameplay videos taken from the game LoL. This gives a total of 450 trials . This study was conducted following Declaration of Helsinki norms and approval by a local Ethics Committee on Human Research (CEIH) and The International School for Postgraduate Studies (Ref.: UGR24102017). Written consent was obtained from all participants. A representative image from all 15 gameplay videos which the participants watch in the experiment are shown in The motivation of the experiment is to be able to ascertain the expertise level of the participants in playing the game LoL using only their brain data, and emotions predicted scores derived from their facial expressions. In order to achieve this goal  a fNIRS sensor is placed on PFC of each participant as can also be seen in In order to also establish the link between the expertise level and the emotional state of participants while they are performing a given experiment, a video of their facial expressions is also recorded. To the best of the author’s information, there is no established correlation between the expertise level and the emotions of a gamer in the literature. This is why, in this work, a preliminary investigation is performed to see if the additional information from estimated emotions scores from the facial expressions would give further insights that would bolster the classification for expertise of gamers.novices (NVs), 9 intermediates (ITs), and 11 experts (EXs). The criterion used to classify a participant in one of the three given expertise categories (NV or IT or EX) is based on the average victory points accumulated by the participant, and the average number of hours per week the participant plays the game LoL, as outlined in The 30 participants are grouped in three main expertise groups: as 10 The fNIRS data is recorded using a commercially available research-grade fNIRS system . The 16 fNIRS channels on the fNIRS sensor have 8 channels on the right PFC, and 8 on the left PFC.The data from any channel that fails to meet the set pass criterion is eliminated from subsequent data analysis. The most common reasons for failing to meet the pass criterion were significant light leakage, low signal levels, and/or saturated signals.The data from the passed channels is low pass filtered (cut off frequency 0.14 Hz) and compared with baseline signal to compute change in optical density using modified Beer-Lambert law.Outlier data beyond the 3 standard deviations from the mean is removed before any features are calculated.The fNIRS data collected from the fNIRS sensor is pre-processed in 3 stages:Overall 12% of the fNIRS data failed to meet the quality checks listed above and was excluded from the study. For more details on the fNIRS data pre-processing stages, please see the earlier work here .A video of the face of the participants performing the experiments is also recorded in tandem whilst their brain activity is recorded using fNIRS. The reason the participants are recorded while performing the experiments is to record their facial expressions in order to subsequently use them in combination with the brain data to determine their expertise level group.All videos of the participants for each trial are split into 18 video frames. Each frame of the participant’s video is then categorized into one of the following seven emotions: Anger, Disgust, Fear, Happiness, Sadness, Surprise, Neutral. A pictorial compilation of all of the aforementioned facial expressions from emotions for one subject is shown in The participants’ video for each of the 15 trials, one trial for each of the 15 gameplay videos from LoL, is sampled twice per second. Each gameplay had a duration of about 30.26 ± 14.33 s. The total number of frames included from the video of each trial is 18 so that all trials have the same consistent number of frames. The facial emotions classifier used for categorising each video frame is VGG19 . The claThe participant’s expressions from the gamers’ experiment in this work are not used to train the emotions recognition model. The intent is to use an off-shelf model that is already trained using thousands of images of human facial expressions as a universally trained model to be used on any human facial expression dataset. Each of the 18 video frames for a given trial is then given emotion prediction score by FER-2013. For example, the prediction score DS1: Brain time-series data acquired from fNIRS alone.DS2: Brain time-series data acquired from fNIRS appended with predicted emotion scores, on participant’s expression data obtained from VGG19, on a per trial basis.In this section, supervised and unsupervised classification paradigms are explored to establish which combination of techniques allows for maximum classification accuracy for determining the expertise level of the gamers. The classification paradigms are investigated with two datasets (DSs):The motivation for using the two aforementioned DSs is to establish whether accuracy of expertise classification is improved when fNIRS data is used in tandem with predicted emotion scores data (DS2) in comparison to when only fNIRS data is used (DS1). In addition, the classification prowess of the supervised classifiers are also gauged when these classifiers are given statistical features in comparison to random convolutional kernel transform (ROCKET)  featuresA total of 8 statistical features are computed for individual fNIRS channels (1–16), ‘All’ channels , ‘Right’ channels (Average of passed channels located on the right PFC), ‘Left’ channels (Average of passed channels located on the left PFC). The 8 features computed per trial are: Mean, Standard Deviation, Maximum, Minimum, Range, Signal Slope, Skewness, and Zero-crossing. These features are computed for each of the total 450 trials. This results in dataset (DS1) of size 450  by 24  as also illustrated in In this work, both DSs time series feature learning is done using ROCKET . A set oA total of 7 supervised classifiers of varying genres are used with both DS1 and DS2 each separately with both statistical and ROCKET features as shown in Random Forest (RF): A RF is a ensemble learning method that incorporates multiple decision tree classifiers on varying sub-samples of the input data set. RF improve the predictive accuracy, and control over-fitting by averaging the decision of the multiple decision tree classifiers [Scikit-learn [ssifiers . In thisit-learn  library.Support Vector Machines (SVM): SVM is inherently a supervised discriminative classifier constructed by a separating hyper plane in a multi-dimensional space [radial basis function is used with gamma set at auto. We used the implementation in Scikit-learn [al space . SVM optit-learn  library.k-Nearest Neighbors (kNN): kNN capitalizes on the similarity idea that data points from a given class would have more similarity with each other in comparison to data points from different classes. The measure of similarity can be computed using different metrics such as distance, proximity, or closeness [Scikit-learn [loseness . The numit-learn  library.Gaussian Naive Bayes (GNB): GNB is an effective inductive learning algorithms based on applying Bayes’ theorem. The term ‘naive’ appears because of the underlying assumption of conditional independence between every pair of features used in the implementation of GNB [Scikit-learn [n of GNB . In thisit-learn  library.XGradient Boost (XGB): XGB provides parallel tree boosting machine learning algorithms using an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable [multiclass with fraction of columns to be subsampled as 0.3, learning rate as 0.1, maximum depth as 3, alpha as 5, nestimators as 15, and numclass as 3. We used the implementation in Scikit-learn [portable . In thisit-learn  library.Fully Connected Deep Neural Network (FCDNN): The FCDNN consists of 5 blocks. Each block consists of:-Fully connected (FC) layer.-Rectified linear unit (ReLU) activation.-Each FC layer also dropouts neurons to ensure FCDNN is not over fitting with any of the DSs.PyTorch 1.3.1 [ch 1.3.1  library.Deep Classifier Auto Encoder (DCAE): The architecture of the DCAE along with its hyper parameters i.e., the number of nodes for each layer, dropout layer rate, for both DS1, and DS2 are shown in PyTorch 1.3.1 [ch 1.3.1  library.The number of epochs the DCAE is trained on is 1000. The optimizer used is ADAM and the learning rate values for DS1 is Encoder: The number of inputs to the encoder are 3200 inputs for DS1 and 4600 inputs for DS2. The encoder consists of a fully connected layer (FC1), Rectifier Linear Unit (ReLU1) activation, and FC2.-Fully Connected Layer (FC1): A fully connected layer that performs a linear transformation  with ReLU1 activation. It has 517 hidden units that encode 3200 inputs for DS1, and 551 hidden units to encode 4600 inputs for DS2.-Fully Connected Layer (FC2): A fully connected layer that performs a linear transformation with 328 hidden units for DS1, and 306 units for DS2. The output of FC2 is the output of the encoder.Decoder: The number of inputs to the decoder are 328 inputs for DS1 and 306 inputs for DS2. The decoder consists of FC3, ReLU2 activation, and FC4.-Fully Connected Layer (FC3): A fully connected layer that performs a linear transformation with 517 hidden units for DS1, and 551 hidden units for DS2, with ReLU2 activation that decode from 328 hidden units for DS1, and 306 units for DS2.-Fully Connected Layer (FC4): A fully connected layer that performs a linear transformation which decode 517 encoded inputs to 3200 decoded inputs for DS1, and decode 551 encoded inputs to 4600 decoded inputs for DS2. The output of FC4 is the output of the decoder.The structure of the auto-encoder which consists of an encoder and a decoder is delineated as follows:The error between the decoded inputs, and the inputs from DS1 (or DS2) is computed using mean square error (MSE).Classifier: The input to the classifier is the output of FC2 i.e., 328 inputs for DS1 and 306 inputs for DS2. It consists of FC5, drop-out, hyperbolic tangent activation function (tanh1), FC6 and tanh2 layers, as outlined below:-Fully Connected Layer (FC5): A fully connected layer that performs a linear transformation with 328 inputs, and 104 outputs for DS1, and 306 inputs and 165 outputs for DS2.-Dropout: FC5 output undergoes 39.22% dropout for DS1, and 49.96% for DS2 followed by hyperbolic tangent activation function (tanh1).-Fully Connected Layer (FC6): A fully connected layer that performs a linear transformation with 104 inputs for DS1, and 165 inputs for DS2, and 3 outputs followed by hyperbolic tangent activation function (tanh2).The error between the predicted label of the expertise level of a participant (from FC6), and the true expertise level is computed using cross entropy (CE) loss.The hyper-parameters of FCDNN, and DCAE, which include number of hidden units for each FC layer, dropout rate, and learning rate, are optimized using a sequential model-based optimisation based on a tree-structured Parzen estimator search algorithm . In ordeThe expertise classification results are obtained both separately for each trial for DS1 and DS2. For each of the 7 classifiers, the classification results are reported for 10 repeated stratified k-fold cross-validation (CV) with 5 splits DS1, and DS2. The data was split with 20% as test and the remaining as training and validation. The mean and standard deviation of the statistical metrics of accuracy, precision, recall, and F1-scores for the classifiers are reported in Also, for both DSs, the classification performance of deep neural networks i.e., FCDNN and DCAE is considerably better than other machine learning algorithms i.e., RF, SVM etc. One possible explanation for this can be that the deep learning architecture further enables the discovery of relevant patterns automatically from the enhanced features provided by ROCKET for both DSs. Although there is minimal difference in accuracy metrics for FCDNN and DCAE, the convergence and optimisation happens earlier for DCAE because its range of search and number of hyper-parameters is smaller. Also, the inclusion of the autoencoder helps to reduce the unnecessary dimensions for the classifier. It is worth mentioning that the parameters for the autoencoder and classifier in DCAE are jointly learned as both loss functions are added and back-propagated through the layers.A comparison of which feature set to use, statistical features or ROCKET features, is best suited for the expertise recognition from bio-signal data is also drawn on Across all classifiers, the maximum increase in classification accuracy on inclusion of expression data  is for XGB with a percentage increase in classification accuracy by 40.87%. To appreciate the improvement in classification accuracy on inclusion of emotion scores, a bar chart for the F1 scores for all classifiers for both DS1 and DS2 is also plotted in In this section the test for the three hypotheses’ outlined in To test the it-learn  library)For all classifiers, except RF, In order to test In order to test With the ever-increasing consumption of eSports, it is pertinent to explore how it is affecting the gamers, and watchers alike. In particular, in this study, we explored two main aspects in which greater consumption of eSports can affect the gamers- mainly (i) the neuroplasticity of the brain in response to 460 gaining expertise in playing a game, and (ii) the emotional state of a gamer. Previous studies have demonstrated that the expertise level of participants can lead to a change in brain activity in response to experience, also known as neuroplasticity , and conThe best classification accuracy is obtained by DCAE classifier at 90.70 ± 7.84% for DS1 with ROCKET features. For DS2 the best classification accuracy is obtained by FCDNN at 91.44 ± 6.32% with ROCKET features. For six out of a total of seven classifiers, the  the PFC , and fro the PFC ,31.n = 30), and a selection of gameplay that evokes stronger emotional responses from the participants.A comparison of the F1-scores for all the classifiers for DS1, and DS2, also plotted in In addition our work has demonstrated that the performance of all classifiers, for both DS1 and DS2, improved markedly when recognising expertise level of gamers using ROCKET features in comparison to hand-crafted  features. The classification results from all classifiers are also tested for Overall, this work has demonstrated successful classification of gamer’s expertise level using their brain data, and emotions decoded from facial expressions. We expect that the findings of this work pave the way of new designs of affective and mind-controlled gaming. For example, integration of neural data in games, such as identification of gamers’ expertise based on their neural responses achieved after time played, rather than discrete counts of points achieved after game played.


Several neurological disorders have been linked to mutations in chaperonin genes and more specifically to the HSPD1 gene. In humans, HSPD1 encodes the mitochondrial Heat Shock Protein 60 (mtHsp60) chaperonin, which carries out essential protein folding reactions that help maintain mitochondrial and cellular homeostasis. It functions as a macromolecular complex that provides client proteins an environment that favors proper folding in an ATP-dependent manner. It has been established that mtHsp60 plays a crucial role in the proper folding of mitochondrial proteins involved in ATP producing pathways. Recently, various single-point mutations in the mtHsp60 encoding gene have been directly linked to neuropathies and paraplegias. Individuals who harbor mtHsp60 mutations that negatively impact its folding ability display phenotypes with highly compromised muscle and neuron cells. Carriers of these mutations usually develop neuropathies and paraplegias at different stages of their lives mainly characterized by leg stiffness and weakness as well as degeneration of spinal cord nerves. These phenotypes are likely due to hindered energy producing pathways involved in cellular respiration resulting in ATP deprived cells. Although the complete protein folding mechanism of mtHsp60 is not well understood, recent work suggests that several of these mutations act by destabilizing the oligomeric stability of mtHsp60. Here, we discuss recent studies that highlight key aspects of the mtHsp60 mechanism with a focus on some of the known disease-causing point mutations, D29G and V98I, and their effect on the protein folding reaction cycle. HSPD1 is a nuclear gene that is translated in the cytosol and imported into the mitochondria due to the presence of an N-terminal mitochondrial targeting sequence that is cleaved upon translocation across the mitochondrial membrane  or chaperonins . Acquired chaperonopathies are due to non-genetic chaperone defects such as defective post-translational modifications or mis regulated gene expression . A few eUnlike SPG13, MitCHAP-60 is an autosomal-recessive disease best described as a hypomyelinating leukodystrophy caused by the D29G missense mutation . Althougin vitro and in vivo. Independent groups have shown that both mutations are unable to support bacterial growth in GroEL knockout complementation assays. Furthermore, using malate-dehydrogenase refolding assays, it has been demonstrated that the ATPase activity of both mutant variants is substantially compromised in vitro when compared to wild-type mtHsp60. Additional refolding assays have been done using the ATP synthase F1 β-subunit where it was found that the D29G and V98I mutants result in impaired ATPase activity (Recently, mtHsp60 studies have highlighted some of the effects of the D29G and V98I missense mutations activity . It has activity . Structuactivity . The nucactivity . This maHSPD1 have been linked to the neurodegenerative diseases SPG13 and MitCHAP-60. Recently, in vitro studies have shown that mtHsp60 complex stability is affected by these mutations. Low protein concentrations and nucleotide binding resulted in complete oligomer dissociation when compared to wild-type mtHsp60.In summary, mtHsp60 is an indispensable chaperonin responsible for regulating mitochondrial protein homeostasis and mitochondrial function. Additional moonlighting roles, especially for cytosolic Hsp60, have been suggested to be important across many tissues and organs, but further work is required in order to establish which oligomeric state is responsible for its function. Presence or absence of Hsp60 is now thought to be a significant biomarker for several diseases . MtHsp60Although current research has elucidated some of the effects of these disease-causing mutations, further studies are needed to fully understand the effect of the mutations at a molecular level. High-resolution structural data of mtHsp60 complexes and intermediates are currently limited to the ATP “American football” conformation. It is imperative to have similar data of the ADP and nucleotide-free intermediates to be able to better understand the complete protein folding mechanism of wild-type mtHsp60. Structural information of stable D29G and V98I oligomers would then facilitate the comparison of these to their wild-type counterpart. Furthermore, current structures rely on mtHsp60 mutants that increase complex stability as well as non-hydrolyzable ATP analogs, both of which may produce off pathway intermediates. Studies using wild-type mtHsp60 as well as natural nucleotides may be necessary to obtain a more reliable model of its protein folding cycle.AR and RB contributed to the conception and preparation of the manuscript. AR, RB, DV, and BH contributed to the editing of the manuscript at all points of the preparation process. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The light damage (LD) model was mainly used to study some of the main aspects of age related macular degeneration (AMD), such as oxidative stress and photoreceptor death. Several protocols of light-induced retinal degeneration exist. Acute light damage is characterized by a brief exposure (24 hours) to high intensity light (1000 lux) and leads to focal degeneration of the retina which progresses over time. To date there are not experimental data that relate this model to neovascular events. Therefore, the purpose of this study was to characterize the retina after an acute light damage to assess whether the vascularization was affected. Functional, molecular and morphological investigations were carried out. The electroretinographic response was assessed at all recovery times . Starting from 7 days after light damage there was a significant decrease in the functional response, which remained low up to 120 days of recovery. At 7 days after light exposure, neo-vessels invaded the photoreceptor layer and retinal neovascularization occurred. Remarkably, neoangiogenesis was associated to the up-regulation of VEGF, bFGF and their respective receptors (VEGFR2 and FGFR1) with the progression of degeneration. These important results indicate that a brief exposure to bright light induces the up-regulation of pro-angiogenic pathways with subsequent neovascularization. The retinal vasculature is characterized by three parallel vascular plexuses, deriving from the central artery, that reaches the eye through the optic nerve and progressively branches along the retina3. The superficial plexus is located above the Ganglion Cell Layer (GCL); the inner plexus is located in the Inner Plexiform Layer (IPL), while the deep plexus is located in the Outer Plexiform Layer (OPL)4. The choroid lies behind the retinal pigment epithelium (RPE) and provides oxygen and nutrients by diffusion to the outer retina5. Any unbalance of the electrolytes alter retinal homeostatis and lead to vision impairment. The stimulus for retinal neovascularization is mainly due to local hypoxia or retinal ischemia and this causes blood vessel generation as an attempt to re-oxygenate the ocular tissue in several retinal pathologies8. In pathological conditions, the new vessels can infiltrate many ocular tissues, including vitreous, cornea, iris, lens, retina, optic disc, as well as the sub-retinal space. These vessels are leaky and are subject to bleeding, allowing potentially harmful factors to infiltrate the surrounding tissues9. Neovascularization often involves loss of vision, in fact it is known that abnormal ocular angiogenesis can occur in a wide spectrum of eye disorders, including AMD (age-related macular degeneration). AMD is a neurodegenerative disease and the leading cause of blindness worldwide. It consists of a progressive degeneration of the photoreceptors that are located in the macular region, an area of the retina that is responsible for acuity and colour vision10. It is important to note that AMD occurs in most cases as “atrophic AMD”, which is characterized by the deposition of drusen and the slow degeneration of RPE and subsequently of the photoreceptors, leading to a condition of extended retinal atrophy named “geographic AMD”. Another form of AMD is the “wet/exudative AMD”, also called “neovascular AMD” (nAMD)11. It is characterized by choroidal neovascularization (CNV) resulting in retinal detachment, photoreceptor degeneration and vision loss10. Patients affected by nAMD may also develop retinal atrophy after a few years from the beginning of the pathology11. The vascular endothelial growth factor (VEGF), also known as vascular permeability factor, is an angiogenic factor which has aroused particular interest in recent years, for its involvement in the angiogenic processes in many tissues. It has widely been shown that the isoform A of VEGF (VEGFA) plays a key role in nAMD pathogenesis. Additional angiogenic factors include insulin like growth factor 1 (IGF-1), basic fibroblast growth factor (bFGF), tumour necrosis factor-a (TNF-α), angiopoietin-2 and matrix metalloproteinases (MMPs)12. Also microglia cells play important roles in angiogenesis and maintenance of vascular homeostasis in the retina13. Activated microglia increase oxidative stress damage in vitro and promote apoptosis in pericytes. Moreover, microglia promote angiogenesis, migration, and proliferation of endothelial cells by destroying the tight junction and affecting the integrity of the vasculature14. Thus, microglia activation contribute to Blood-Retinal Barrier (BRB) breakdown and to neovascularization15. VEGF, in turn, exerts a feedback activity on microglia, enhancing their activation and migration.The retina is a high metabolic tissue and requires high concentrations of oxygen. The inner retina is supplied by the retinal vasculature, while the outer retina by the choroidin vivo16. The exposure to bright and continuous intense light induces in the end photoreceptor death and vision loss mainly through a mechanism that involves oxidative stress18. It is well known that an acute stress induced by 24 hours of light exposure leads to the photoreceptor degeneration in a specific region of the superior retina, that is particularly susceptible to damage, and which is identified in the literature as “hotspot”19. Moreover, the damaged area expands in size over time19. Recruitment of macrophages and their invasion in the photoreceptor layer have also been highlighted in LD models21. The features of the focal and progressive retinal damage, observed in this model, closely follow those observed during the progression of age-related macular degeneration, in the atrophic form of the human pathology19. On this basis, our goal was to assess whether this light damage model could also mimics some features of the exudative form of AMD. We demonstrated for the first time that acute light damage leads to the modulation of the most relevant pathways involved in nAMD, in particular the VEGF pathway, which is a fundamental feature in nAMD. This was associated with neovascularization, retinal degeneration and impairment of retinal function. To further deepen and characterize the degeneration processes due to acute light damage, we investigated its effects on retinal vascularization, which have been never investigated until now. We demonstrated for the first time that acute light damage leads to the modulation of the most relevant pathways involved in nAMD, in particular the VEGF pathway. This was associated with neovascularization, retinal degeneration and impairment of retinal function.Light damage (LD) is a common animal model used to study retinal degeneration 22. The OPs contribute to the rising slope necessary for the formation of the b-wave and they derive from the circuits of the inner retina23.We performed flash electroretinogram (fERG) recordings and analysed a-wave, b- wave and oscillatory potentials (OPs). The a-wave gives information about photoreceptor activity and in fact is the first wave occurring after a light stimulus. The b-wave is a positive potential which depends both on light stimulus and retinal adaptation and it is the overall response of the retina after light stimuli25. Accordingly, we observed a remarkable deflection in a-wave . Although IB4 is largely employed to identify endothelial cells, it also detects activated microglia both in rodents and humans39. Therefore, in order to discriminate endothelial cells from activated microglia, we performed the isolectin staining together with anti-IBA-1  immunostaining  confirming the presence of blood vessels. Moreover, consistently with previous results, some IB4 signals did not co-localize with vWF, due to the presence of activated microglia (green arrows). To further confirm neoangiogenesis in the ONL layer, we also excluded the signal deriving from lipofuscin, or retinal debris. In fact, it is well known that during retinal degeneration lipofuscin accumulates in the subretinal region. It is characterized by an autofluorescent emission which covers a wide spectrum of wavelengths and which therefore could create confusion in the identification of new vessels42. On this basis, we discriminated them from retinal debris by anti-vWF immunostaining and acquiring the images by 488 and 594 excitation wavelengths, as shown in Supplementary Fig. To prove more confidently the presence of vessels in the ONL, we also performed a double staining with IB4 and anti-Vwf Fig. , which a43. For this reason, that signal was excluded from the whole mount analysis. Activated microglia was highlighted around the retinal vessels starting 7 days after light exposure, supporting the evidence that microglia could be involved in vascular alterations in the light damaged model.Based on the observations on retinal cryosections, we moved on to study the vascular network present in the retinal plexuses. To investigate the retinal vasculature, we performed retinal whole mounts labelled with Isolectin B4. As shown in Supplementary Fig. 45.Starting from the superior plexus (corresponding to the ganglion cell layer) up to the deep retinal plexus (corresponding to the outer plexiform layer) the vessels percentage area and the number of tufts of neovascularization were analyzed. The analysis was focused on the central dorsal retina, that is the degenerating area after light damage. Tufts were counted considering the vessel bulges Fig. , which aIn the superior plexus changes of the vessels percentage area were not found Fig. . The numIn the inner plexus Fig. , which cInterestingly, in the deep plexus, corresponding to the outer plexiform layer (OPL), we found that the vessels percentage area Fig.  was signAcute light damage (LD) is a model of retinal degeneration used worldwide to study some of the main features of AMD. To better characterize the acute LD model, in this study we investigated for the first time the vascular events underlying light-induced retinal degeneration. The findings of this study can relate the acute LD model to fundamental aspects of patients suffering from neovascular AMD: neurodegeneration and impairment of retinal function, VEGFA up-regulation, neovascularization in the photoreceptor layer and in the end retinal atrophy. However, further studies need to be performed in order to investigate the origin of the vessels invading the photoreceptor layer and verify whether choroidal neovascularization occurs as well as in human nAMD.46. In fact, we demonstrated that the new vessels crossed the outer nuclear layer reaching the outer plexiform layer. Neovascularization occurred also within the vessel retinal plexuses as a consequence of light damage. Moreover, this study demonstrated for the first time that light damage induces VEGF (and other pro-angiogenic factors) up-regulation with subsequent neovascularization, as observed starting 7 days after light damage. The infiltration of new vessels in the photoreceptor layer, together with the increase of vascularization in the neuroretina, are certainly involved in the progression of the degeneration of the light damage model. In fact, in our model the late stages after light damage lead to an extended retinal atrophy, although no differences in the retinal function were highlighted. The photoreceptor layer became increasingly thin and degeneration lesions, named “rosettes”, occurred in the inner retina18. The degeneration of the inner retina could explain also the disruption of the retinal vasculature observed after 60 days from LD in the deep plexus. A longer time after LD (120 days), a recovery in the retinal vessel network, in terms of structure, was observed. However, a higher number of tufts was still present and the choroid adhered with the inner retina due to massive photoreceptor degeneration. VEGFA, bFGF and their receptors were up-regulated after 120 days and the amount was even higher than in earlier times of recovery. It is possible that the increase of pro-angiogenic factors is an attempt to restore the physiological vessel network of the neuroretina.In particular, our data show that a brief light exposure (24 h) induces an acute stress in the retina of albino rats, leading to neovascularization and progressive retinal degeneration up to 4 months. Although further studies need to be performed, we can speculate that these new vessels may possibly connect with the vessels of the deep plexus thus leading to the formation of anastomosis, as previously demonstrated in another model of chronic light damage13. In fact, although the interaction between microglia and blood vessels is important for a healthy retinal environment, when pathophysiological conditions occur, this interface contributes to the progression of the diseases and it culminates in the breakdown of blood vessels and to the sprouting of new branches47. Under stress conditions, microglia up-regulate the expression of VEGF48. We therefore hypothesized that the neovascularization phenomena were also related to microglia activation, which it is known to migrate to the photoreceptor layer and to the subretinal space in the light damaged model19. We demonstrated that activated microglia cells are still present after a long time from a brief light exposure, consistently with the progressive degeneration events observed in the retina. Moreover, activated microglia were found surrounding the new vessels, supporting the hypothesis that they are involved in angiogenesis alterations in our experimental model. Nevertheless, further studies need to be performed in order to deepen the knowledge on the role of microglia in the progression of retinal neovascularization, as well as the origin of the vessels infiltrating the photoreceptor layer. This work also adds an important interpretation of the retinal functional response observed after light damage. The electrophysiological result in fact has shown that already after 7 days of recovery from light damage the functional response decreases significantly compared to the healthy animals and it does not impair over time. In fact, there is not a direct relationship between photoreceptor death and the impairment of ERG responses. An explanation of this has already been described in other works in reference to the upregulation of inflammatory and self-protection factors28. Here, we demonstrated that the electrophysiological impairment of the retina in the LD model is also due to the neoangiogenic phenomena in the ONL and OPL and to the up-regulation of pro-angiogenic factors.In retinal neoangiogenesis, a pivotal role is also played by the microgliaIn conclusion, in this work we added relevant findings into the understanding of the degeneration processes following the exposure to acute light damage. We demonstrated for the first time that an albino Sprague Dawley rat exposed for 24 hours at 1000 lux undergoes retinal damage and develops neovascularization together with the up-regulation of relevant pro-angiogenic pathways and long-term microglia activation. Neovascularization in the photoreceptor layer, as well as the modulation of the pro-angiogenic signalings, occurs already a week after the induction of damage. After few months, retinal atrophy is reproduced as well and the choroid gets in touch with the neuroretina. The deepening of the new vessels origin and the investigation of the possible anastomosis between the choroid and the retinal vasculature with further future experiments will allow us to define whether the acute LD model can be used specifically as a model for neovascular AMD.All animal experiments were conducted according to the ARVO statement for the use of animals in ophthalmic and vision research. All experiments were authorized by the Italian Ministry of Health, number 448/2016-PR of April 27, 2016.Sprague Dawley (SD) albino rats were born and raised in dim cyclic light condition  with an ambient light level of approximately 5 lux. The animals had free access to food and water.18.Animals selected for the experiments were placed in individual Plexiglas cages with food placed on the floor and water in plastic bottles. They were dark adapted overnight and exposed to 1000 lux at 9 a.m. for 24 h (LD24h). Thereafter, they were returned to dim cyclic light conditions for 7, 60 and 120 days respectively to recover from the acute stress due to light exposure. The LD24h group instead was sacrificed immediately after light exposureControl (CTRL);LD24h (LD);LD24h + 7 days of recovery (LD + 7rec);LD24h + 60 days of recovery (LD + 60rec);LD24h + 120 days of recovery (LD + 120rec).On this basis the study was performed on 5 different groups:2 range). At the end of each recording session, traces were bandpass filtered between 0.3 and 500 Hz. A- and b-waves amplitude (μV) for each luminance were measured. We also performed Oscillatory potentials (OPs) analysis, by calculating the sum of the amplitude of OP1, OP2, OP3 and OP418.In order to minimize variability among the five experimental groups, electrophysiological recordings  were performed before exposing animals to high intensity light. This functional evaluation was necessary to select animals with similar starting retinal electrophysiological responses. The fERG recordings were performed at 7, 60 and 120 days after light exposure respectively. The fERG was recorded in a dark-adapted condition in response to a single white light flash of increasing intensity (scotopic), delivered by a standard Ganzfeld Stimulator . Before performing fERG, the rats were anaesthetized with an intraperitoneal injection of Ketamine/Xylazine (10 mg/ 100 g–1.2 mg/100 g), mounted on a stereotaxic apparatus and the body temperature was maintained at 37.5 °C. Corneas were anaesthetized with a drop of novocaine and pupils were dilated with visumidriatic 1.0% tropicamide. The recordings were carried out for both eyes simultaneously with a gold electrode loop (2.0 mm in diameter) placed on the corneas. The reference electrodes were inserted subcutaneously in the proximity of the eyes and the ground electrode was inserted in the anterior scalp, between the eyes. The responses were recorded at increasing light intensities . The cornea and lens were removed, the eyes were cryoprotected by immersion in 10%, 20% and 30% sucrose overnight, and finally they were embedded in the Tissue Tek OCT  compound for proper freezing in liquid nitrogen. Cryosections of 20 μm thickness were made for each retina and collected in gelatine and poly-l-lysine-coated slides. The sections crossing the optic nerve were chosen for subsequent analysis for all the experimental groups. All sections were stained with Bisbenzimide, to make visible the cell nucleiTo block non-specific binding sites, 5% bovine serum albumine (BSA), 10% Goat Serum (GS) or 0.75% Horse Serum (HS) were used. Sections were incubated overnight at 4 °C with primary antibodies: polyclonal anti-VEGFA  (1:250 in 1% BSA), polyclonal anti-VEGFR2  (1:250 in 1% BSA), polyclonal anti IBA-1  (1:200 in 1% GS), monoclonal bFGF (Millipore 2718303) , monoclonal FGFR1 (OriGene TA324059) (1:250 in 1% GS) and plyclonal anti-vWF  (1:250 in 1% BSA). All the antibodies used in this paper are summarized in Supplementary Table 49, IB4 was also used together with anti-IBA1 immunostaining in order to detect any activation of the microglia cells, and together with anti-vWF to specifically detect the signal deriving from endothelial cells. Images were then acquired by using a Leica TCS SP5 confocal microscope.In order to detect the vessels, Isolectin B4 staining was performed on retinal cryosections. To block non-specific binding sites, 10% goat serum (GS) was used. Sections were incubated overnight at 4 °C with Isolectin B4 (IB4) Alexa Fluor dye conjugates (1:200 in 1% GS). Since it is known that IB4 marks also activated microgliaFor whole mount analysis retinas were isolated from the eye, fixed in 4% paraformaldehyde, and washed with 0,1 M TrisHCl (pH 7.4). To block non-specific binding sites, 10% goat serum (GS) was used. Whole mounts were then incubated with Isolectin B4 Alexa Flour dye conjugates (1:150 in 1% GS) for 36 hours at 4 °C. The whole mounts were also labelled with nuclear staining Bisbenzimide in order to identify the retinal layers. They were then collected on gelatine and poly-l-lysine-coated slides. Images of Isolectin were acquired by using a Nikon Eclipse 80i confocal microscope. Vessels analysis was performed by the AngioTool software in order to quantify the amount of vessels in terms of “vessels percentage area”. The number of tufts of neovascularization was also quantified by counting them on the acquired images. Tufts were identified looking at a well defined swelling morphology both at the terminals and in the middle of the vessels. The analysis was performed on the vessels of the following plexuses: the superficial plexus (corresponding to GCL layer), the inner plexus (corresponding to the IPL and INL) and the deep plexus (corresponding to the OPL). The three different plexuses were discriminated by looking at the nuclei of the nervous cells. The vessels of each retinal layer were acquired at three different fields of the dorsal retina of each animal and the mean was calculated and compared between all groups.Total proteins were extracted from eye cup samples by using a Dounce Homogenizer and a lysis buffer . Bradford Assay  was performed in order to quantify the protein content. 70 µg of the protein extracts were run on a Bolt 4-12% Bis-Tris Plus (Thermo Fisher Scientific) at 200 V for 20 minutes. The proteins were transferred to PVDF membrane  through a iBlot 2 Dry Blotting System (Invitrogen IB21001).50.After blocking at RT for 1 h with 5% nonfat dry milk in TBST, the membranes were washed briefly and incubated with primary antibodies directed either against VEGF (Santa Cruz sc-7269), VEGFR2 , bFGF , FGFR1  and Tubulin  diluted in 5% non fat dry milk in TBST. All the antibodies used in this paper are summarized in Table Statistical analysis was performed by one-way ANOVA test followed by Tukey test. First type error was set at 5%. The statistical analysis was conducted using the SigmaPlot 12.0 software. Data are shown as mean ± SE.Supplementary information.


Although depression is highly prevalent among cancer patients, it is often underdiagnosed and poorly managed particularly in developing nations. These shortcomings can have substantial adverse effects not only on the disease prognosis but also on patients’ quality of life. The Patient Health Questionnaire-9 is a widely used depression screening tool but it has not been validated among patients with chronic illnesses such as cancer in Ethiopia. We aim to validate the PHQ-9 among Ethiopian cancer patients in an outpatient setting.A cross-sectional study was conducted among cancer patients attending the oncology clinic at Tikur Anbessa Specialized Hospital (TASH). We assessed criterion validity and performance of the PHQ-9 test against the gold standard Mini-International Neuropsychiatric Interview (MINI) diagnostic tool among patients with cancer. The MINI was administered by psychiatric nurses who were blind to the initial PHQ-9 screening tool.A total of 163 patients completed the 2 stages of a diagnostic interview in the study. The majority (64%) of the participants were women, the mean age was 46 (13.5) years. Using the gold standard MINI test the prevalence of Major Depressive Episode (MDE) was 15%. The internal consistency (Cronbach’s α) for PHQ-9 was 0.78 suggesting good (acceptable) internal consistency for the reliability of the test scores. When the total PHQ-9 score was used to identify cases of MDE, the Area under the Curve (AUC) was 0.93  on Receiver Operating Characteristic (ROC) analysis. This shows evidence for the excellent discriminating power of the PHQ-9 between cases and non-cases of MDE. At cutoff point ≥4, the PHQ-9 had a sensitivity of 88% and specificity of 78.1% on the ROC curve to detect MDE.PHQ-9 is a reliable and valid instrument to detect MDE among individuals with chronic conditions such as cancer patients in outpatient settings and it can be used in resource-limited settings for early diagnosis and proper therapy of such patients. Cancer is a chronic medical illness with 18.1 million new cases and 9.6 million deaths only in 2018. Cancer is also an emerging public health issue in Africa, with estimates of 811,200 new cases and 533,800 cancer deaths in the same year. In Ethiopia, the annual incidence and mortality from all cancer types were 67,500 and 50,000 cases respectively  This co-Detecting depression is often difficult in cancer patients because of overlapping symptoms such as fatigue, loss of appetite, sleep disturbance, and also the effects of cancer treatment have been thought to have a confounding effect on the assessment of depression , 8. In aIn Ethiopia, two prior studies concluded that PHQ-9 was a valid and reliable instrument for detection of major depressive disorders among patients in outpatient settings and rural communities, while the latter study also emphasized the need for further study in the utility of the screening tool in clinical settings , 16. StrThe objective of this study is to evaluate the criterion validity of the PHQ-9 for detecting depression among patients with cancer attending the outpatient oncology clinic at Tikur Anbessa Specialized Hospital.Tikur Anbessa Specialized Hospital (TASH) is the largest and oldest referral hospital in Ethiopia which provides comprehensive multidisciplinary medical service for the nation including oncology service. The oncology care in TASH is comprised of inpatient and outpatient services including the only radiotherapy service in the country. It provides services for patients referred from different parts of Ethiopia. The data were collected between August and September 2016.The study population was all adult patients with a diagnosis of cancer attending the outpatient oncology services in TASH.Using a convenient sampling technique, we enrolled all consecutive patients who came for follow up during the study period. The sample size was determined using a formula for calculating sensitivity and specificity for single tests . With seWe used a hospital-based cross-sectional study design. Participants’ socio-demographic characteristics including age, educational level, marital status, occupation, and residential place were documented. Types of cancer, time of diagnosis, stage of cancer, treatment history, and duration of the illness were retrieved from the chart and the participants. The study questioners including the PHQ-9 tool were administered by two oncology nurses. The MINI was administered by two psychiatric nurses. All data collectors received in-depth training on the study instruments, ethical conduct of research, and data collection techniques for 3 days by a qualified independent mental health researcher.The PHQ-9 comprises nine items that can be scored from 0  to 3 (nearly every day) and the total score ranges from 0 to 27 to measure depression severity , 20. So The MINI is a gold standard brief assessment tool that allows the diagnosis of depression according to DSM-IV and ICD-10 criteria . It is mAfter getting a written informed consent, socio-demographic and clinical data together with the test assessment (PHQ-9) score for each participant were collected by two trained oncology nurses. Later on the same day, each patient was again re-assessed using the gold standard assessment MINI by two qualified psychiatric nurses. The data collectors readout and elaborated all the questions for the illiterate participants. The psychiatric nurses who were conducting the criterion assessment interviews were blinded to the results of the PHQ-9 and vice versa.Data were analyzed using the Statistical Package for Social Sciences version 20.0 software package . Initially, one case was excluded due to a missing value in the PHQ-9 data and then a sample of 162 cases were categorized into cases of MDE and non-cases based on the MINI assessment to determine the validity of the instrument.The sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were calculated to determine different cutoff scores for PHQ-9. Receiver Operating Characteristic (ROC) curve was used to identify optimal balance between sensitivity and specificity for the determination of the best PHQ-9 cutoff score for the diagnosis of major depression. Youden index (sensitivity+ specificity-1) was converted into a percentage and was used as an additional metric for cutoff determination, where measure above 50% was considered as acceptable values of diagnostic accuracy .The area under the curve (AUC) was used to address the performance of the test. Reliability related to internal consistency was measured by Cronbach’s alpha coefficient (Cronbach’s α).+ 13.5) years and 64% of them were females. More than two-thirds (73%) of our participants were educated from which 37% earned a college degree. Two-thirds (66.3%) of the participants were married. One in three (31.3%) described themselves as housewives and 70% were from rural areas  (Table The most frequent type of cancer identified was breast cancer 28.7% followed by skin cancer (16%) and gastrointestinal cancer (12%). According to TNM classification more than half (53%) of the patients were at stage II and followed by stage III at 27% and stage I at 18%. At the time of screening, almost half (47%) were receiving chemotherapy only, while 29% were receiving both treatments. The majority of the participants (81%) got their diagnosis between 1 and 5 years while those diagnosed in the past 1 year were 5.5% (n = 25). A quarter of cervical cancer patients were diagnosed with MDE which is the highest compared to other types of cancer. Nineteen percent (n = 15) of patients with stage II cancer and 24% (n = 8) of patients receiving only radiotherapy treatment were diagnosed with MDE  on analysis. According to this result, PHQ-9 showed an excellent discriminating power to differentiate between cases and non-cases of MDE. The detailed description of the ROC curve for PHQ-9 against the gold standard (MINI) is shown in Fig. In patients with chronic conditions recognizing and managing depression is important due to its potential in improving medication adherence, reducing the progression of the disease, and improving quality of life . In our The choice of the optimal cutoff score is always a tradeoff between sensitivity and specificity. A lower cutoff score makes the questionnaire very sensitive and inclusive, whereas a higher cutoff score will make it more specific at the cost of missing some cases . Meta-anThe strength of this study is that it is one of a handful of studies to consider the validity of the PHQ-9 in patients with the diagnosis of cancer in sub-Saharan Africa, the first in Ethiopia, and also one the few studies to provide the prevalence of depression among cancer patients in the country. We also used instruments that were previously translated into the Amharic language and validated in different settings. Data collectors and psychiatric nurses were blinded to the results of criterion assessment and screening instruments. Limitations of the study include the relatively small sample size, inability to test psychometric properties, and the factorial structure of the PHQ-9. We did not perform a regression analysis for the assessment of expected mean scores to evaluate the PHQ-9 mean values for different cancer types and we were also unable to explain the low rates of surgery among patients with cancer.The Amharic version of PHQ-9 appears to be a reliable and valid instrument to identify Major Depressive Episode among patients with chronic conditions such as cancer. Since it is free, brief, and easy to administer, this instrument can be used in resource-limited countries for depression screening.