Mycobacterium tuberculosis and Mycobacterium bovis BCG. In our checkerboard experiments, NSC19723 displayed better profiles than TAC in combination with known first-line and recently approved drugs. Mechanistic studies revealed that NSC19723 inhibits mycolic acid biosynthesis by targeting the HadABC complex. Computational studies revealed that the binding pocket of HadAB is similarly occupied by NSC19723 and TAC. NSC19723 also improved the efficacy of isoniazid in macrophages and mouse models of infection. Cumulatively, we have identified a benzaldehyde thiosemicarbazone scaffold that improved the activity of TB drugs in liquid cultures, macrophages, and mice.The complexity and duration of tuberculosis (TB) treatment contributes to the emergence of drug resistant tuberculosis (DR-TB) and drug-associated side effects. Alternate chemotherapeutic agents are needed to shorten the time and improve efficacy of current treatment. In this study, we have assessed the antitubercular activity of NSC19723, a benzaldehyde thiosemicarbazone molecule. NSC19723 is structurally similar to thiacetazone (TAC), a second-line anti-TB drug used to treat individuals with DR-TB. NSC19723 displayed better MIC values than TAC against IMPORTANCEMycobacterium tuberculosis, the causative agent of TB is among the leading causes of death among infectious diseases in humans. This situation has worsened due to the failure of BCG vaccines and the increased number of cases with HIV-TB coinfections and drug-resistant strains. Another challenge in the field is the lengthy duration of therapy for drug-sensitive and -resistant TB. Here, we have deciphered the mechanism of action of NSC19723, benzaldehyde thiosemicarbazone. We show that NSC19723 targets HadABC complex and inhibits mycolic acid biosynthesis. We also show that NSC19723 enhances the activity of known drugs in liquid cultures, macrophages, and mice. We have also performed molecular docking studies to identify the interacting residues of HadAB with NSC19723. Taken together, we demonstrate that NSC19723, a benzaldehyde thiosemicarbazone, has better antitubercular activity than thiacetazone. Mycobacterium tuberculosis, the causative agent for tuberculosis (TB), is responsible for infecting ~10 million people and ~1.5 million deaths in 2020 (M. tuberculosis growth by inhibiting either ATP synthesis (BDQ) or mycolic acid biosynthesis (DLM or PTM)  58. These M. tuberculosisin vivo , and ethionamide (ETH) are three TB drugs that are modified by mycobacterial enzymes, and their activated forms interfere with mycolic acid biosynthesis. INH is converted to an active isonicotinoyl radical by mycobacterial catalase peroxidase , which binds to NAD+ resulting in the formation of an INH-NAD adduct (26–Mycolic acids (MA) are essential components of the rculosis . The MA-D adduct , 26. Theadduct 2628. TAC iadduct 26. Subsequadduct 26, 31. Thiadduct 26, 31. Furadduct 26–34.Mycobacterium bovis BCG and M. tuberculosis  pathogens, M. bovis BCG, and M. tuberculosis. As reported earlier, we observed that NSC19723 possessed a MIC value of 0.39 to 0.78 μM against both M. tuberculosis and M. bovis BCG. As shown in M. smegmatis or ESKAPE pathogens even at 50 μM, the highest concentration tested in the study. The control drugs inhibited the growth of mycobacterial strains and ESKAPE pathogens in the expected range  values of 0.5 for each drug . In comparison, in combination with INH or BDQ or PA-824, 0.391 μM TAC was required to inhibit the growth of M. tuberculosis . These findings also reveal that combining NSC19723 with TB drugs improved the MIC of RIF 2.0-fold and of INH/Levo/BDQ/PA-824 each by 4.0-fold against M. tuberculosis (Table S1).We next determined the antimycobacterial activity of NSC19723 on the growth of ach drug  to J. Weach drug  to H. InM. tuberculosis H37Rv. We observed that the bacterial counts after exposure to either NSC19723 or TAC at 10× MIC for 7 days were comparable to those of untreated cultures (day 0 readings) . As shown in M. tuberculosis in liquid cultures. We observed that MIC values of BDQ and PA-824 against M. tuberculosis were 0.4 μM. As shown in *, P < 0.05). We also observed that in combination with NSC19723, the in vitro activity of BDQ and PA-824 was increased by 4.0- and 2.0-fold, respectively .Early bactericidal activity EBA) has an important role in predicting the bacteriostatic/bactericidal nature of a combination therapy , 41. Theeadings)  and B. TA has an M. tuberculosis reaches the alveolar air sacs of the lungs and macrophages clear the intracellular pathogen through the process of phagocytosis. We also compared the ability of NSC19723 either alone or in combination with INH to inhibit the growth of intracellular bacteria. The concentrations of drug that reduced viability of macrophages by 50% compared to untreated control macrophages (TC50) of NSC19723 and INH against THP-1 macrophages were >50 μM in our cell viability experiments. In agreement with in vitro activity data, exposure to NSC19723 slightly inhibited the growth of intracellular M. tuberculosis by 5.0-fold compared to that of dimethyl sulfoxide (DMSO)-treated cells (P < 0.05). Surprisingly, we did not observe any growth inhibition of intracellular M. tuberculosis after exposure to TAC. As expected, INH treatment inhibited the growth of intracellular M. tuberculosis by ~820.0-fold (P < 0.05). This killing activity of INH increased to ~1,300.0-fold in combination with NSC19723 (P < 0.05). We also observed that INH-mediated killing of intracellular bacteria did not improve in combination with TAC  pathogen and easier to handle, we next attempted to generate spontaneous resistant strains against NSC19723 in M. bovis BCG. We were able to raise M. bovis BCG Pasteur strains that were resistant to NSC19723. We observed that the in vitro frequency of resistance against NSC19723 to be 2 × 10−7. This mutation frequency was similar to the frequency of resistance against TAC and INH (A→G) in the hadC gene. The amino acid was changed of Thr123 to Ala123 as a result of this missense mutation.Further, we performed experiments to gain further insights into the mechanism of action of NSC19723. We observed that MIC values of NSC19723 against ch other . The gen and INH , 43. As  and INH . In ordeM. tuberculosis fatty acid synthase type II (FAS-II) system   system . EarlieradCA151V , 42, 45.adCA151V . PreviouadCA151V . Since tadCA151V . Furthern strain . Next, w (MAMES) . Similar (MAMES) . The lev (MAMES)  and B. TFurther, molecular docking studies were performed to better understand the binding pattern and interactions of NSC19723 and TAC with the HadAB protein. Using AutoDock, the top three clusters of the ligands that are substantially populated with the number of conformations at the active site were selected for further analysis. The first cluster in the docking simulation exhibited the least energy and had the highest population of conformations for all three ligands . Based oM. tuberculosis Erdman . In agreement, treatment of animals with INH for 2 weeks reduced the splenic bacterial loads by 7.5-fold (P < 0.05). We did not observe any inhibition of M. tuberculosis growth in both lungs and spleen tissues upon exposure to NSC19723 after 2 weeks of treatment. Further, the activity of INH did not increase upon coadministration with NSC19723 after 2 weeks of treatment . Also, coadministration of NSC19723 along with INH further improved its activity in lung tissues by ~2.0-fold . In agreement, the splenic bacillary loads in INH-treated mice were reduced by 32.0-fold compared to those of untreated mice (P < 0.01). In a combination of INH with NSC19723, ~70.0-fold reduction in splenic bacillary load was observed in spleens at 4 weeks posttreatment. This ~2.0-fold reduction in the combination group relative to the INH-treated group was observed to be statistically significant (P < 0.05). We observed that daily administration of NSC19723 also reduced the bacterial growth by 3.5-fold in spleens of infected animals (P < 0.05). Taken together, these findings imply that NSC19723 treatment marginally inhibited M. tuberculosis growth in animal tissues but improved the activity of INH in chronic model of infection.Previously, Jagannath et al. demonstrated that administration of 60 mg/kg TAC treatment for 4 weeks resulted in an ~10.0-fold reduction in both lung and splenic bacillary load in C57B/6 mice infected with s Erdman . In thisB/c mice . We alsoreatment  and B. AM. tuberculosis inhibitors, which compromise cell wall architecture, not only kill but also improve the intrabacterial concentrations of other TB drugs. TAC and INH combination has been successful for treatment of individuals with MDR-TB . TX-100, Tween 80, and phorbol 12-myristate 13-acetate (PMA) were purchased from Sigma . Middlebrook growth media 7H9 broth, 7H10 and 7H11 agar, Mueller-Hinton broth, brain heart infusion broth, tryptic soy broth, and Luria-Bertani (LB) broth were procured from Becton, Dickinson .M. tuberculosis H37Rv ATCC-27294, M. bovis BCG Pasteur, and M. smegmatis mc2 155) were grown in Middlebrook 7H9 broth (MB broth) supplemented with Tween 80 (0.05%), glycerol (0.2%), and albumin-dextrose-saline (ADS) (1.0×) with shaking at 200 rpm and 37°C. ESKAPE pathogens were cultured in Mueller-Hinton broth , brain heart infusion broth (Enterococcus faecium ATCC-19434), tryptic soy broth (Acinetobacter baumannii ATCC-BAA-2800), and Luria-Bertani broth (Escherichia coli MSG1655) with shaking at 200 rpm and 37°C. For CFU enumeration, bacterial cultures were serially diluted in phosphate-buffered saline (PBS) and plated on Middlebrook 7H11 agar (MB agar) supplemented with glycerol (0.5%) and oleic-albumin-dextrose-saline (OADS) (1×) followed by incubation at 37°C for 3 to 4 weeks.Mycobacterial strains  and 13C-NMR data were collected to determine purity.The details of the synthesis of NSC19723 are described in the Supplementary Methods found in the supplemental material. The chemicals for the synthesis of NSC19723 were purchased from Sigma-Aldrich. The formation of products in various chemical reactions was monitored by thin-layer chromatography (TLC). The final compound was resuspended in DMSO-d600], ~0.2). The plates were incubated at 37°C for either 14 days (for M. tuberculosis), 2 days (for M. smegmatis), or 1 day (for ESKAPE pathogens). The MIC value for a particular drug was reported as a lowest concentration at which no visible growth was seen in the form of a pellet.MIC values of compounds against all strains were determined using the broth microdilution method as previously described . Briefly600 of ~0.2 of M. tuberculosis was diluted 1:1,000 times and added to the plates followed by incubation at 37°C for 14 days. Synergistic, indifferent, or antagonistic interactions for various drug combinations were determined by calculating fractional inhibitory concentration (FIC) and fractional inhibitory concentration index (FICI) using the below mentioned formula as described earlier (Drug combination experiments were performed by two-drug checkerboard assays in 96-well clear U bottom plates. The compounds (NSC19723 and TAC) were diluted horizontally 2.0-fold, and known TB drugs were diluted vertically 2.0-fold to prepare various drug combinations. Further, early-logarithmic culture of an OD earlier .∑FICI=F2. PMA at a concentration of 20 ng/mL was added to differentiate the monocytes into macrophages. For cell viability assay, THP-1 cells (2 × 104/well) were seeded in 96-well flat-bottom plates in the presence of PMA. After 48 h of incubation, the monolayer was washed twice with 1× PBS and overlaid in RPMI medium (10% HI-FBS) for 24 h. The dilutions of various drugs were prepared in RPMI medium (10% HI-FBS) and added in triplicates. After 96 h of incubation, cell proliferation reagent (WST-1) was added to each well. After 30 min of incubation, the absorbance at 450 nm and 630 nm was measured. The percentage of cell viability in the presence of drugs (TC50) was calculated as per manufacturer’s recommendations. TC50 was defined as the concentration of drug that reduced viability of macrophages by 50% compared to untreated control macrophages.Roswell Park Memorial Institute (RPMI) medium supplemented with heat-inactivated fetal bovine serum (HI-FBS) (10%) was used to culture the THP-1 cell line at 37°C and 5% COin vitro killing assay, early-log-phase (~0.2 OD600) cultures of M. tuberculosis were exposed to 10× MIC of various drugs for 7 days. For intracellular killing experiments, THP-1 cells (2 × 105/well/mL) were seeded in a 48-well cell culture plate in the presence of PMA. Following 48 h of incubation, the monolayer was washed twice with 1× PBS and overlaid in RPMI medium (10% HI-FBS) for 24 h. Subsequently, macrophages were infected with single-cell M. tuberculosis suspension with a multiplicity of infection (MOI) of 1:10 (2 × 106 bacilli/well). After 4 h of infection, extracellular M. tuberculosis was removed by overlaying macrophages with RPMI containing 200 μg/mL of amikacin for 2 h. Further, macrophages were washed twice with 1× PBS and overlaid with RPMI containing compounds/drugs. The samples were collected at day 0, day 2, and day 4 by lysing macrophages in 1 mL 1× phosphate-buffered saline with triton X-100 (PBST) (0.1% Triton X-100), and bacterial enumeration was performed as mentioned above.For M. bovis BCG Pasteur  was plated on MB agar plates containing the NSC19723 at 10× MIC. The colonies that appeared on these drug-containing plates were subcultured, and MIC values were determined as described above. The colonies that displayed at least a 4.0-fold increase in MIC values were subjected to whole-genome sequencing. For identification of genes linked with resistance, genomic DNA was isolated from various strains and shipped to Clevergene, Biocorp Pvt Ltd, India for next-generation sequencing and subsequent analysis. Briefly, whole-genome sequencing libraries were prepared using NEBNext Ultra II FS DNA library prep kit for Illumina. The samples were purified using 0.8× AMPure XP beads, and the DNA was amplified using NEBNext Ultra II Q5 master mix for 6 cycles of PCR. The NEBNext Multiplex Oligos for Illumina kit was used for multiplexing. The Agilent HS D1000 ScreenTape System was used to assess the library’s quality. The Agilent 4150 TapeStation system was used to run the samples, and the Agilent TapeStation analysis software was used to analyze them. Quality trimmed reads were aligned to the reference genome of M. bovis BCG Pasteur using Bowtie 2 with default parameters. Freebayes v1.3.1 was used to call variants, and these were annotated using snpEff.NSC19723 spontaneous resistant mutants were generated as described earlier by Kidwai et al. with slight modifications . In briehadABC was PCR amplified using gene-specific primers  and cloned into the mycobacterial expression vector, pVV16. The construction of the recombinant plasmid was verified by sequencing. The overexpression strains were prepared by electroporation of pVV16-hadABC in M. bovis BCG Pasteur followed by the selection of transformants on 7H11 medium supplemented with kanamycin (25 μg/mL) at 37°C for 3 to 4 weeks.For overexpression studies, M. bovis BCG were grown to an OD600 of ~0.4 and then treated for 24 h at 37°C with compounds/drugs (TAC and NSC19723) at multiple concentrations . Cultures were then radiolabeled using  acetic acid  and incubated at 37°C for another 24 h. The culture was subsequently divided into two equal 5-mL aliquots. In order to isolate nonpolar and polar lipids, one aliquot was treated to organic extraction as reported earlier  were performed, and for the second direction, three developments of petroleum ether/diethyl ether (85:15 vol/vol) were performed. The TLC plate was processed as described above, and the results were compared to recognized standards.In order to isolate the combined fatty acid methyl esters (FAMEs) and mycolic acid methyl esters (MAMEs) , the second aliquot was treated with alkaline hydrolysis, followed by methylation as previously described , 53. The4RLJ) was obtained from the PDB database (https://www.rcsb.org/). Both of the chains HadA and HadB were selected for molecular docking experiments. The structures of the ligands, NSC19723, and TAC were optimized using the Biovia Discovery Studio 2020 for docking studies as previously described. The AutoDock tool 1.5.6 suite was used to simulate the docking of ligands with the HadAB protein  or six mice per group  were treated with either 75 mg/kg NSC19723, 10 mg/kg isoniazid, or 75 mg/kg NSC19723 plus 10 mg/kg isoniazid for either 2 weeks or 4 weeks. The drugs were administered daily via oral route in PEG 400 formulations for 6 days a week. Lungs and spleens were homogenized in saline, and 10-fold serial dilutions was plated on MB 7H11 agar plates at 37°C for 3 to 4 weeks for CFU enumeration.The animal experimental procedures were performed in an animal biosafety level III facility of the Translational Health Science and Technology Institute and approved by the Institutional Animal Ethics Committee. For drug efficacy experiments, mice were infected via aerosol route with approximately 100 CFU of P value of <0.05 between the indicated group using paired t test (two-tailed) was considered to be significant.Prism 8 (version 8.4.3) was used for preparation of graphs and statistical analysis. Statistical significance has been shown in figures and figure legends. A


Perianal fistulas of the perianal soft tissues are an important cause of morbidity and a significant portion of colorectal surgery. To our knowledge, there is no study evaluating YouTube videos pertaining perianal fistulas. In this study, we aimed to evaluate YouTube video contents on this topic.  Whether the videos contained real images, animations or presentations, video duration, number of views, comments and likes, uploading date, and daily view were recorded. Reliability of the videos was assessed using the Quality Criteria for Consumer Health Information (DISCERN) scale and quality of the videos using the Global Quality Score (GQS).  A total of 100 YouTube videos regarding perianal fistulas were included in the study. Sixty-four (64%) videos were uploaded by healthcare professionals and 36 (36%) videos by nonprofessionals. The mean video length was calculated as 6.70 ± 8.00 minutes for all videos. The mean DISCERN score of all videos was found as 3.92 ± 0.81 and the mean GQS score as 3.97 ± 0.83. According to the DISCERN score, most videos included (94%) were of good quality. There was an excellent agreement between the two surgeons in terms of the DISCERN and GQS scores.  Most of the videos included in the study were uploaded by health-care professionals. On the other hand, the majority of the videos contained surgical management of perianal fistulas as it is the definitive treatment. Healthcare related content should be audited and filtered by YouTube with new and effective policies. Perianal fistulas of the perianal soft tissues are an important cause of morbidity and a significant portion of colorectal surgery . PerianaIn today's digital world, the internet and especially social media has become an indispensable part of daily life. The Internet has become a popular source of health related information . PreviouYouTube is the most commonly used video sharing platform and the second most popular website in the world following Google search . Every mHowever there are concerns about the reliability and quality of online health related information. Eighty-six percent of health seekers are concerned about the reliability of information available on the internet, and 44% believe that only part of this information is accurate . In a reTherefore, the number of studies investigating the reliability and quality of YouTube video contents related to various fields of medicine is increasing –16. To oEthic committee approval for this study was not obtained, because the study did not include human or animal subjects, and YouTube videos were accessible to anyone, consistently with previous studies .This study was performed by two experienced general surgeons (10-year and 15-year experienced) as a reliability and quality analysis of YouTube videos pertaining perianal fistulas at 05-07/07/2022. Searching terms were determined by consensus of the two surgeons as “perianal fistula” and “perianal fistula treatment”. From filter options of YouTube, the videos were sorted by “relevance” which is the default searching option. Studies have reported that the users mostly review videos on the first three pages of the results , 18. AftWhether the videos contained real images, animations or presentations, video duration, number of views, comments and likes (dislike option was removed by YouTube), uploading date, and daily view were recorded. Daily view was calculated by the formula, . Video source was categorized as physician, technician, health channel, patient, and lay person. Physicians and technicians were considered as healthcare professionals and the others as lay persons.Reliability of the videos was assessed using the DISCERN scale and quality of the videos using the Global Quality Score (GQS). These two measurement tools have been widely used in the previous studies –16. In aDISCERN is a scoring measurement tool for assessment of the reliability of consumers' health information on treatment options. In this study, we used the modified DISCERN tool, which was created for the first time by Charnock et al. and shortened by Singh et al. , 21. TheGQS scale was developed for the first time by Bernard et al. in order to measure the quality of a video content based on the usefulness of the information offered in the video . This scThe videos included in the analysis were scored independently by the two surgeons. The Kappa coefficient was used for the determination of interobserver agreement.U test, while the categorical variables were compared with Chi-Square test. The DISCERN and GQS scores according to video sources were compared using the Kruskal–Wallis test. The continuous variables are expressed as mean ± standard deviation and categorical variables as frequency . Cronbach's alpha coefficients were used to calculate the agreement between the two researchers. p < 0.05 values were considered statistically significant.Statistical analysis was performed using the SPSS version 25.0  statistical software. Normal distribution of the variables was tested using the Kolmogorov-Smirnov method. The variables were found to be nonnormally distributed. The comparison of the continuous variables between the two groups was made with Mann–Whitney A total of 100 YouTube videos regarding perianal fistulas were included in the study. The total view count of all videos was 15,005,136, and the total duration was 11.17 hours. The most common video content was treatment followed by general information and imaging .Types of images were divided into three groups as real image, animation, and presentation. Accordingly, 82 (82%) videos contained real images; 12 (12%) videos contained presentations, and 6 (6%) videos contained animation.When qualification of the uploaders was examined, medical doctors uploaded 61 videos, health channels 15 (15%) videos, technicians 3 (3%) videos, patients 4 (4%) videos, and others 17 videos. Therefore, 64 (64%) videos were uploaded by healthcare professionals and 36 (36%) videos by nonprofessionals.p > 0.05). The oldest video was uploaded on October 11, 2011, and the newest video on November 22, 2021. The mean duration since the upload was 1330.50 ± 865.02 days. The mean daily view count was calculated as 104 ± 139. The mean daily view count was found as 104 ± 140 in the professional and 103 ± 139 in the nonprofessional group (p > 0.05). The mean number of daily views, comments, and like counts of the videos are given in The mean video length was calculated as 6.70 ± 8.00 minutes for all videos. The mean video length was found as 6.74 ± 8.03 in the videos uploaded by professionals and 6.74 ± 8.03 in the videos uploaded by nonprofessionals. There was no statistically significant difference between the professionals and nonprofessionals in terms of the video length  regarding perianal fistulas were useful, while the others were misleading (23%). This evaluation was made independently from the DISCERN and GQS scoring and regardless of the uploaders.Reliability of the videos was evaluated with DISCERN and quality of the videos with GQS scales. The mean DISCERN score of all videos was found as 3.92 ± 0.81 and the mean GQS score as 3.97 ± 0.83. Distribution of the DISCERN and GQS scores according to the uploaders is presented in α coefficient. There was an excellent agreement between the two surgeons in terms of the DISCERN and GQS scores . In another study by Krakowiak et al., the rate of animated videos was reported as 26% with higher DISCERN scores  and animations (6%). Similarly, in a study by Toprak and Tokat, investigating YouTube videos on nocturnal enuresis, 85.2% of the videos contained real images, and 14.8% contained animation .In our study, 61 videos were uploaded directly by physicians, 17 by lay persons, 15 by health channels, 4 by patients, and 3 by technicians. When examining the contents of the videos, we considered the videos uploaded by physicians and technicians as professional and the others nonprofessional videos. Accordingly, 64% were uploaded by professionals and 36 by nonprofessionals. The mean daily view count was found as 104 ± 140 in the professional and 103 ± 139 in the nonprofessional group (In a study by Chang and Park, investigating YouTube videos regarding exercises and compensated maneuvers for dysphagia, 20 videos were uploaded by universities/professional organizations, 14 videos by therapists, 10 videos by health related websites, and 4 videos by physicians . In the Interestingly, like and comment counts were significantly higher in the videos uploaded by nonprofessionals. This may be attributed to the fact that physician's narration during the video may contain medical terminology that everyone cannot understand exactly. On the other hand, Toprak and Tokat found that the number of likes and comments were not indicators of video quality .In the current study, we evaluated DISCERN and GCS scores according to uploaders and video contents. Accordingly, physicians had the highest DISCERN and GCS scores followed by health channels, while patients had the lowest scores. However, even in some videos directly uploaded by physicians, there was no narration or any explanation except for the real images during surgery. Considering video contents, education had the highest scores followed by treatment and general information. Almost all treatment-related videos contained surgical treatment for perianal fistulas with real images and narration, which is the definitive solution for perianal fistulas. In the literature, the highest scores for DISCERN and GQS were considered to indicate the highest reliability and quality, which was also the case in our study –16, 29.One of the major limitations was taking snapshots of information, but YouTube has a dynamic structure, and new contents are updated constantly. Another limitation was that we only analyzed English-language videos. However, English is accepted as the prevailing language for access to online information. Finally, the video assessment process was subjective, although interrater agreement was excellent.Most of the videos included in the study were uploaded by health-care professionals. And most of them were evaluated as useful. However, the mean reliability and quality scores were low even for some videos uploaded by physicians such as videos with music alone without any speech or explanation. On the other hand, the majority of the videos contained surgical management of perianal fistulas as it is the definitive treatment. Healthcare related content should be audited and filtered by YouTube with new and effective policies.


This project details the development of a Quality Improvement Project aiming to review and improve the management of behavioural and psychological symptoms of dementia (BPSD) on an old age psychiatry ward. BPSD refers to a constellation of non-cognitive symptoms and signs which arise in people with dementia, including disturbed perception, thought content, mood or behaviour. Examples include agitation, depression, apathy, repetitive questioning, psychosis, aggression, sleep problems, and socially inappropriate behaviours. BPSD arise in 5/6 of people with dementia over the course of their illness and are associated with a deterioration in cognition and progression in dementia plus secondary harms such as falls and hospitalisation. Pyrland Two ward is a mixed gender specialised organic old age psychiatry inpatient unit serving the county of Somerset. Most patients have a diagnosis of dementia, are being cared for using either MHA or MCA legislation and exhibit one or more BPSD. There was no structured or formalised approach to the management of BPSD at inception.A point-in-time audit was conducted to produce baseline measurements of BPSD management on the ward, measured against NICE criteria.Plan-Do-Study-Act (PDSA) methodology was employed to incorporate incremental quality improvement interventions such as a ward-round checklist and staff education.Baseline: (n = 14) 4/14 formally diagnosed with BPSD. 6/14 were prescribed antipsychotic medications, of which 1/6 fully met NICE standards. 2/14 had structured assessment tools used.Results following introduction of improvement methods: (n = 8) 8/8 formally diagnosed with BPSD. 7/8 were prescribed antipsychotic medications, of which 4/7 fully met NICE standards. 7/8 had structured assessment tools used.It was possible to see modest improvements in the ward-based management of BPSD using quality improvement methodology, including more favourable psychotropic prescribing. However, total patient numbers are small and further interventions, such as more PDSA cycles, may add value and encourage sustainability.


Recent world events have caused a dramatic rise in the use of video conferencing solutions such as Zoom and FaceTime. Although 3D capture and display technologies are becoming common in consumer products , 3D telecommunication has not yet seen any appreciable adoption. Researchers have made great progress in developing advanced 3D telepresence systems, but often with burdensome hardware and network requirements. In this work, we present HoloKinect, an open-source, user-friendly, and GPU-accelerated platform for enabling live, two-way 3D video conferencing on commodity hardware and a standard broadband internet connection. A Microsoft Azure Kinect serves as the capture device and a Looking Glass Portrait multiscopically displays the final reconstructed 3D mesh for a hologram-like effect. HoloKinect packs color and depth information into a single video stream, leveraging multiwavelength depth (MWD) encoding to store depth maps in standard RGB video frames. The video stream is compressed with highly optimized and hardware-accelerated video codecs such as H.264. A search of the depth and video encoding parameter space was performed to analyze the quantitative and qualitative losses resulting from HoloKinect’s lossy compression scheme. Visual results were acceptable at all tested bitrates (3–30 Mbps), while the best results were achieved with higher video bitrates and full 4:4:4 chroma sampling. RMSE values of the recovered depth measurements were low across all settings permutations. For decades, researchers have been envisioning and developing 3D telepresence systems ,6,7,8,9.Researchers and product developers now have access to a multitude of well-established 3D capture methods, including passive stereo, active stereo, structured light, and time-of-flight (ToF) techniques . State-oCurrent state-of-the-art systems ,8,9 yielThe COVID-19 pandemic has brought about a rapid transition to remote work , educatiIn this work, we present HoloKinect. HoloKinect is an end-to-end 3D video conferencing solution that leverages off-the-shelf hardware and software technologies, all streaming over a conventional internet connection at a user-configurable target bitrate. Our system is built entirely in the Unity game engine, resulting in a highly portable, self-contained executable project with a multitude of visualization options. The current implementation uses a Microsoft Azure Kinect for RGB-D capture and a Looking Glass Portrait multiscopic display for 3D visualization at each end of the two-way video conference, enabling a hologram-like effect. The implementation is highly modular and easily facilitates alternative RGB-D capture systems  or 3D display technologies . The system runs on Windows PCs for the best compatibility with the currently implemented 3D capture and display hardware, but the underlying compression, streaming, decompression, and postprocessing framework can be compiled to most of the device types supported by the Unity game engine.To enable the efficient streaming of high-resolution RGB-D data, we use the multiwavelength depth (MWD)  method tThe remainder of this paper describes the HoloKinect 3D video conferencing system. An overview of the HoloKinect pipeline can be seen in The Microsoft Azure Kinect is equipped with a 1-megapixel amplitude-modulated continuous wave (AMCW) time-of-flight (ToF) depth camera. It uses a pair of near-infrared laser diodes, allowing it to illuminate the scene using either a narrow or wide field of view mode. An indirect measure of the time of flight of the IR light received at each pixel location is used to generate a depth map. The Azure Kinect supports a variety of color and depth sensor capture settings , all of HoloKinect allows two main options for data preprocessing: transforming the depth map to the perspective of the color camera, or transforming the color map to the perspective of the depth camera. This work focuses on transforming the depth map to the perspective of the color camera since it provides the higher native sensor resolution. We further elected to take the Kinect’s 640 × 576 depth map and perform 2 × 2 binning to reduce the resolution to 320 × 288. Although this reduces the effective resolution, it also lessens the apparent sensor noise. The removal of high-frequency information via binning also assists in the eventual compression of the depth data, as is further discussed in Since our depth encoding method performs more precisely when encoding a more limited depth range, we filter depth values that fall outside of some user-specified [To achieve real-time 3D video communications at 30 FPS over a typical broadband connection, the 3D information acquired from the Kinect must be compressed. There have been a variety of techniques proposed for the compression of such information in 3D mesh representations . For thiZ, into a traditional 2D RGB image, the multiwavelength depth (MWD) encoding method [To encode a 1280 × 720, 16-bit depth map, g method  was utilZ refers to the 1280 × 720 depth map to be encoded; P refers to a user-defined fringe width. This fringe width ultimately determines the encoding frequency of the depth map, and it is often defined via its inverse relation to the number of periods  increases, so will the encoding frequency, leading toward a higher precision encoding. There is a trade-off, however, as an increased encoding frequency causes the resultant encodings to vary in intensity more rapidly, leading toward larger file sizes and the potential for compression artifacts . Practically, video compression artifacts can result in errors within each of the decoding operations listed above. These errors occur particularly in regions that contain sharp transitions, such as along the outside edges of the captured subject. For example, if the values in the received blue channel . Therefore, a depth discontinuity threshold is used to filter out any vertex that neighbors a depth discontinuity (determined by distance between neighboring vertices). This allows a single mesh to contain neighboring foreground and background objects, without unrealistically connecting them.Oftentimes the Azure Kinect depth sensor has small holes where it failed to reliably measure the depth. This can be caused by issues such as reflective surfaces. Since this work aims to facilitate two-way 3D video conferencing, it is advantageous to attempt to fill holes caused by obstructions such as eyeglasses. Similarly, the depth sensor also may capture small surface patches that are not connected to any larger objects, especially after depth discontinuity filtering. These small patches can be removed to further improve aesthetics. We employ hole filling and small surface patch removal via a series of dilations and erosions of the depth map to create appropriate masks.The Azure Kinect depth sensor exhibits sensor noise that often survives the encoding and decoding process. One of the best ways to improve the subjective visual appearance of this noisy depth map is to apply spatial filtering. A naive approach would be to apply a Gaussian blur to smooth the depth map. However, if iterated many times, a Gaussian blur will erode away sharper features in the depth map such as noses. Therefore, we also implemented a modified bilateral filter (similar to ) that taAlthough the Azure Kinect color camera can capture images at resolutions of up to 4 K, these images can often appear a bit soft. Sharpening the color map can enhance details and improve the subjective appearance of a remote participant. Both a standard sharpening kernel and unsharp masking are available to sharpen the color texture. After postprocessing, the 3D mesh data structure containing the 3D coordinates and color information is handed off to Unity’s rendering engine. The rendered mesh is formed by connecting neighboring vertices into a series of triangles. Undefined depth values (discarded by any one of the postprocessing steps) are coded with not a number (NaN), which causes Unity to skip the rendering of any triangle containing an undefined depth value. The mesh’s surface normals are recomputed and updated after the final depth map postprocessing step completes. An option to use either an unlit graphics shader or a lit graphics shader is provided. The unlit shader does not interact with the game lighting in any way besides creating shadows. The apparent visual details in this mode derive mostly from the color map. Conversely, if the lit shader is chosen, the 3D mesh can be dynamically relit with new light sources to create highlights and enhance the details of the underlying geometry.The Looking Glass Unity Plugin  providesTo explore the effects of various depth and video encoding parameters on the eventual reconstruction quality of the 3D mesh, an analysis of this parameter space was performed. A reference video, containing both depth and color maps, was recorded with the Azure Kinect at 30 FPS for a total of 19.4 s (582 frames). For this analysis, The preprocessed 1280 × 720 depth maps were encoded with MWD and then stored side-by-side with their respective 1280 × 720 color textures in standard RGB frames with total resolutions of 2560 × 720. We can use Agora with H.264 video encoding to send these frames, but Agora does not allow for fine control over the video compression parameters. Further, network inconsistencies would have added confounding factors to the quantitative analysis. In order to precisely and reproducibly control the video compression settings, we losslessly compressed each composite frame as a PNG image and then leveraged FFmpeg [Each permutation of the original reference video was decoded offline with FFmpeg into PNG image frames. These image frames were ingested by the HoloKinect Unity project and processed by the same MWD decoding and postprocessing modules used to perform the live two-way 3D video streaming. A black-level threshold of 0.2 was selected, and a depth discontinuity filtering threshold of 10 cm was used. Depth values that fall outside of [It can be seen that stepping artifacts present in the ground truth data (caused by 16-bit quantization) have largely been smoothed away by the video compression in all encoding qualities. Overall, despite the wide range in compression ratios, the decoded renderings all appear remarkably similar to one another. In comparison to the ground truth data in the top row, these renderings all suffer from losses around the edges of the valid depth measurement regions, likely due to ringing artifacts resulting from the sharp transitions to black in the MWD-encoded images. These errors often result in discontinuities greater than the 10 cm threshold, causing these vertices to be filtered out. The higher bitrate encodings suffer less severe ringing artifacts and thus demonstrate less edge erosion than the 3 Mbps encoding. Similar sharp transitions and subsequent vertex filtering occur in regions of steep depth changes, such as directly under the chin. Losses in this region are present in all encoding qualities and are most evident in renderings for frames (c) and (d).Lower bitrates result in more blocking artifacts and an overall lower quality in the decoded depth maps. These differences are easiest to observe on the subject’s right hand in frame (c). Here, the lower bitrate encoding clearly suffers from more noise and an unnaturally jagged skin texture. However, these degradations in the quality of the recovered depth map would be more difficult to perceive without strong lighting effects or off-axis viewing angles. Lower bitrate videos will suffer from additional losses during frames with higher levels of motion , it explicitly discards 75% of the chroma data (CbCr), disproportionately affecting the blue and red channels when converted back into the RGB color space. Effectively, 2 × 2 pixel neighborhoods have the same CbCr values when using 4:2:0 chroma subsampling, resulting in additional error in the reconstruction of 75% of the pixels. Fortunately, these errors will not be very substantial in areas of smoothly varying depth, since neighboring pixels will have very similar depth values. However, areas of rapid depth changes suffer from increased errors.Image-based depth encoding techniques have proved valuable as they are able to achieve large compression ratios for 3D range data by taking advantage of the more standardized fields of image and video compression. This work presents results that utilize the MWD encoding method to represent depth values within the color channels of a 2D image for lossy compression. Comparisons of MWD against previous image-based methods can be found in  with extP decreases, causing the encoding frequency to increase  and andnstaiquations ). As theacross the image. MWD, conversely, operates by encoding through the depth range  is a parameter that can be used in the H.264 and H.265 codecs to target a video quality level without specifying a target bitrate. Generally, with all other factors being equal, higher levels of motion (or other inter-frame variations) will increase the bitrate required to achieve a particular CRF. To investigate the effects of binning from the perspective of depth map decoding, the same pair of MWD-encoded recordings were video encoded to a target average bitrate of 3 Mbps. The 3-Mbps encoding target for this still scene roughly corresponds to the CRF values used in the 30 Mbps encodings used in While we clearly show that depth pixel binning improves compressibility when using the Microsoft Azure Kinect, this configuration is not a requirement. Primarily, this decision was made to increase the compressibility and to improve the qualitative results by preserving less visual noise. In use cases that are neither bandwidth-constrained nor concerned with sensor noise, it may be desirable to use the unbinned sensor data. This setting is easily toggled within HoloKinect.HoloKinect is designed to keep pace with the 30 FPS capture rate of the Microsoft Azure Kinect. However, much higher frame rates are achievable in practice. Although The core elements of HoloKinect are even able to run well on mobile devices. In HoloKinect presents a bandwidth-efficient, simple-to-use solution for two-way 3D video conferencing that uses affordable, off-the-shelf hardware. The integration with the Looking Glass Portrait provides an accessible and encumbrance-free method of enjoying the 3D video conferencing. The MWD encoding allows it to leverage mature, hardware-accelerated video compression codecs such as H.264. This allows for very modest bandwidth requirements, with high-quality reconstructions at 30 Mbps and very usable results at 3 Mbps. Although depth map reconstruction errors are more prevalent at lower bitrates, RMSE values of the recovered regions remain acceptably low. The software is highly configurable and open-source, so it can be adapted for many different use cases. The Unity game engine grants an advanced slew of lighting and rendering technologies, along with a very user-friendly interface. This Unity backbone also enables many components of HoloKinect to be highly portable and easily adapted for use on other supported platforms. For instance, While successful in providing an end-to-end 3D video conferencing solution, the current HoloKinect implementation is not without its limitations. For instance, it focuses on using only a single depth camera, so it only transmits a single mesh. Although this was an intentional choice for the sake of accessibility, it does limit the quality and completeness of the 3D data available for transmission. Additionally, the MWD encoding and lossy video compression inevitably degrade the depth and color data to some degree, although these losses are not always visually obvious. These data losses are exacerbated by the 4:2:0 chroma subsampling of the current Agora implementation. However, Agora makes the project more platform-agnostic than it would otherwise be, with video streaming support on Windows, macOS, iOS, and Android. HoloKinect currently processes data on a frame-by-frame basis, but temporal smoothing of the depth data would likely be helpful in reducing the impact of sensor noise and compression artifacts on the final visualization. Finally, although HoloKinect allows the final mesh to be affected by a virtual light source, this does not change the real lighting baked into the original color texture. Therefore, further work would need to be implemented to more accurately re-light the mesh. In , techniqThe initial use case for HoloKinect is to facilitate 3D video conferencing between two individuals using a pair of Looking Glass Portrait displays. However, this system is easily adapted to other application areas. Virtual and augmented reality has traditionally resorted to simplified digital avatars, but ever-growing system capabilities make true 3D capture and transmission a tantalizing possibility. With some very simple modifications, HoloKinect can facilitate 3D telepresence in a virtual reality headset. In robotics, depth information is critical to mapping and interacting with the world. HoloKinect’s depth streaming solution provides an efficient, low-bandwidth platform to stream this information for remote control and data analysis. The Azure Kinect is already widely used in robotics applications and is fully supported in HoloKinect, but the open-source and modular nature of HoloKinect make it easy to adapt for other depth sensing solutions such as structured-light 3D cameras and lidar sensors. A hybrid RGB-D streaming solution was explored in , intendeCurrently, HoloKinect performs only basic data preprocessing prior to transmission, with the option to perform more computationally burdensome postprocessing steps on the receiving end, if desired. However, it may be beneficial to perform additional preprocessing if the receiver is more computationally limited than the sender, or if a more powerful intermediate server is used to process the data. Depth data processing steps similar to those used in the postprocessing pipeline of HoloKinect could be implemented as a preprocessing step. This would lessen the demand on the final receiving device and allow the processing to be tailored specifically for the depth sensor noise. Additionally, since losses are inevitable in the depth and video encoding process, we may be able to lessen these losses by pre-distorting the inputs. One could envision a lightweight neural network implementation constructed to minimize end-to-end errors in the depth maps with prior knowledge of the encoding and compression methodology, similar to the work done in .Additionally, as components of HoloKinect are adapted for mobile devices such as VR and AR headsets, it may be beneficial to decouple the depth and color resolutions. In the current work, the depth map and color texture resolutions were forced to be equal in order to make processing on the receiving end more practical to implement. However, if the end device is very limited and is not able to perform substantial postprocessing, then this decision may be counterproductive. Computationally limited devices will likely utilize an unlit mesh shader, and therefore derive almost all of the mesh detail from the color texture. Therefore, instead of embedding the color texture into the vertices of the mesh, it would be beneficial to allow a higher resolution color texture to be used in conjunction with a lower resolution depth map.This paper has presented HoloKinect, an open-source and accessible GPU-accelerated platform for live two-way 3D video conferencing with hologram-like visualizations. HoloKinect’s depth encoding and postprocessing schemes enable the use of lossy, hardware-accelerated 2D video compression algorithms such as H.264. By leveraging the Microsoft Azure Kinect, ubiquitous 2D video compression, and the Looking Glass Portrait multiscopic display, HoloKinect achieves 3D video conferencing with commodity hardware over a standard broadband internet connection. A search of the depth and video encoding parameter space with 2240 permutations was performed to provide both qualitative and quantitative analysis. Both visual 3D reconstructions and numerical error rates were reported for a reference 3D video compressed from 3 to 30 Mbps. Overall, HoloKinect is a bandwidth-efficient, simple-to-use solution for two-way 3D video conferencing that utilizes affordable, off-the-shelf hardware. Further, given its open-source and modular design, HoloKinect can easily be adapted to provide efficient 3D video streaming to any number of applications.


Traditional video recommendation provides the viewers with customized media content according to their historical records . However, such systems tend to generate terrible results if the data is insufficient, which leads to a cold-start problem. An affective video recommender system (AVRS) is a multidiscipline and multimodal human-robot interaction (HRI) system, and it incorporates physical, physiological, neuroscience, and computer science subjects and multimedia resources, including text, audio, and video. As a promising research domain, AVRS employs advanced affective analysis technologies in video resources; therefore, it can solve the cold-start problem. In AVRS, the viewers’ emotional responses can be obtained from various techniques, including physical signals  and internal signals . The changes in these signals can be detected when the viewers face specific situations. The physiological signals are a response to central and autonomic nervous systems and are mostly involuntarily activated, which cannot be easily controlled. Therefore, it is suitable for reliable emotion analysis. The physical signals can be recorded by a webcam or recorder. In contrast, the physiological signals can be collected by various equipment, e.g., psychophysiological heart rate (HR) signals calculated by echocardiogram (ECG), electro-dermal activity (EDA), and brain activity (GA) from electroencephalography (EEG) signals, skin conductance response (SCR) by a galvanic skin response (GSR), and photoplethysmography (PPG) estimating users’ pulse. This survey aims to provide a comprehensive overview of the AVRS domain. To analyze the recent efforts in the field of affective video recommendation, we collected 92 relevant published articles from Google Scholar and summarized the articles and their key findings. In this survey, we feature these articles concerning AVRS from different perspectives, including various traditional recommendation algorithms and advanced deep learning-based algorithms, the commonly used affective video recommendation databases, audience response categories, and evaluation methods. Finally, we conclude the challenge of AVRS and provide the potential future research directions. Emotion or affection is a mental state which is unconscious and spontaneously arises accompanied by physiological and psychological status changes in human organs and tissues, e.g., heart rate, facial expression, brain, etc. . EmotionRecent research employs techniques closely related to neuroscience and human-robot interaction (HRI). The viewers’ emotional states are obtained from analyzing their physical and internal signal parameters with the help of various equipment. For example, researchers apply photoplethysmography (PPG) to estimate users’ pulse by using the fluctuations in skin color related to blood volume and the proportion of reflected light . Dabas eThe affective computing technology promotes the rapid development of the affective video recommender systems (AVRSs). An AVRS is a new trending research direction of recommender families in recent years. Unlike text, image, and speech emotion recognition , AVRS maDefinition 1: AVRS: is a multidiscipline and multimodal HRI system that videos are recommended based on the reviewers’ emotional responses (implicit or explicit), e.g., physical, physiological signals, comments, etc.The physical data reflect communicative signals, e.g., facial expressions, speech detection, and eye-tracking while viewing the video . In contAn AVRS is a relatively new recommender family branch that has begun to develop in recent years. At present, there are few comprehensive reviews related to affective video recommendations. Most works mainly focus on different domains of recommender systems, including recommender systems , the appAs shown in The relevant publications in this survey are obtained from Google scholar and published by Science Direct, Springer, IEEE, ACM, etc. The collected publications are from 2009 to 2022; filters are applied to the search engine by subject . We collected 92 non-repeated publications related to AVRS. Most of the articles are from IEEE, accounting for 38.04%, more than three times that of ACM and Elsevier. The distribution of publications from ACM, Elsevier, and Springer is similar, accounting for about 11–17%. The remaining publications are from various published websites. It can be seen from The distribution of AVRS publications is shown in This survey provides a concise, comprehensive understanding of the latest AVRS research and gives dynamic guidelines in AVRS for scientific researchers, practitioners, and developers interested in video recommendations. We define the internal logic and operating mechanism of various models and algorithms, the classification of existing technologies and their characteristics, the databases for affective computing, the types of audience responses, and the evaluation metrics. The main contributions of this survey are summarized in the following three aspects:(1)We systematically summarize and overview the current techniques in the affective video recommendation field.(2)We classified the works of literature related to different models and algorithms, the possible database resources for video recommendation, the types of audience responses, and the evaluation metrics.(3)We show the current challenges in the video recommendation field and envision possible future research directions.The structure of this survey is arranged in the following: Section 2 introduces currently-used algorithms and models of video recommender systems; Section 3 shows the database resources commonly used in the research of AVRS; Section 4 classifies the ways to obtain user responses in publications; Section 5 summarizes the evaluation metrics of recommendation effect in different publications; Section 6 analyzes the challenges in the current research and discusses future research directions.Video recommendation is based on video features and the viewers’ profiles. According to video clips, the viewers’ emotions are challenging to be captured simultaneously. Therefore, an AVRS is a more complex domain in recommender systems. Several researchers tend to solve the AVRS problem by various methods, traditional models, or algorithms, including support vector machine/support vector regression (SVM/SVR) , clusterIn this section, we classify the publications according to the adopted algorithms or models. We first introduce several commonly-used traditional video recommendation algorithms, then describe the application of prevalent deep learning algorithms in AVRS, and analyze the advantages and disadvantages of both conventional recommendation algorithms and deep learning algorithms.The fundamental idea of implementing SVM and SVR is classifying the mixed input features to predict the users’ emotional states during their interaction with the robots. An SVM/SVR is one of the most widely-used techniques in the affective video recommendation domain. Researchers devoted valuable efforts to promoting the performances of video recommendations based on SVM/SVR. In In Although these SVM-based algorithms have made significant progress in affective video recommendation, they are facing the problem of ignoring the temporal video factor and seriously affecting the recommendation quality. To solve this problem, In The basic idea of video recommendations using a clustering algorithm is to cluster viewers or videos into groups based on the emotional similarity of viewers or the similarity of video features. The former recommends videos to users with similar emotional states, and the latter recommends unseen videos in the same cluster. In The core idea of adopting AdaBoost learning algorithms is selecting discriminative features to construct a facial expression classifier. Unlike the original AdaBoost algorithms selecting the best features in several rounds and generating a weak classifier, the AdaBoost algorithms used in facial expression tend to develop a mid-strong classifier based on a compositional feature. In The main idea of the matrix-based algorithms is to compile the multidimensional attributes in the data into a neural decision matrix  and then conduct multiple standard neural analyses based on the neural decision matrix. To solve the recommendation problem of real estate advertising, video  considerThe general idea of AVRS research based on CF is mainly realized by measuring similarity, either recommending videos with similar emotions according to users’ emotional states or adding affective analysis factors when measuring users’ similarity. The collaborative filtering-based algorithm was one of recommender families’ most extensively used methods. In Traditional CF algorithms recommend users based on their historical behavior similarity. However, new users face the cold-start problem. Instead of using historical behavior records , the chaThe dominant thought behind the CBF of AVRS is to incorporate affective video metadata, explicit feedback information, and user mood as part of an item or user attributes. In Video data on the Internet does not exist alone but co-exist. For example, multimedia resources can contain video, audio, images, and other forms of existence at the same time . The affThe central idea behind KG of AVRS is to look for a particular emotion by KG, which has a similar emotion state extracted from user movie reviews. In The GA is often used to solve the optimization problem of multiple objectives with conflicts. In AVRS, the critical idea of GAs is to balance the imbalance between users’ emotional preferences and actual business objectives. In The dominant thought of employing HRS in AVRS is that combining multiple algorithms involving the viewers’ emotional states can promote recommendation efficiency. The effect of video recommendation by a single algorithm is limited, so researchers turned to HRS. In To summarize, in the last few years, researchers have made great efforts to video retrieve and recommendation domains by various traditional recommendation algorithms, including SVM/SVR, clustering, AdaBoost, MA, CF, CBF, KG, GA, and HRS. Some of their research works have achieved remarkable success, promoted the progress of AVRS, and improved the efficiency and quality of viewers’ access to video information. However, these algorithms still face the following problems:1)Although the algorithm is simple and easy to implement, it cannot make accurate judgments on complex scenarios, and the recommendation effect is minimal. For example, 2)The experiment databases are relatively small and not diverse. The portability of the recommendation strategy generated based on such a database is low, significant-good results on one database, while probably inferior on other databases. For example, Traditional recommendation algorithms, such as matrix factorization algorithms, are linear models, and the recommended effect is limited. Compared with conventional linear recommendation models, deep learning (DL)  can obtaSt, at, and rt demonstrate the state, action, and the reward of time t, whereas rt+1 represents the reward gained by performing action in the state of st. The learning process continued until state st+n.The core idea of adopting RL in AVRS is that DL can continuously and dynamically learn strategies through the real-time state changes caused by the impact of users on the surrounding environment to maximize the cumulative reward. In The basic idea of CNN in affective video analysis is that the CNNs can be employed for feature extraction from various types of signals and information. In Unlike the above method of detecting the viewer’s emotion change through the device, The dominant thought of adopting LSTM models in emotional video classification is that LSTMs can consider temporal, spatial, and frequency characteristics of various signals and information. In In In the input embedding layer, two significant parts are included: users’ text data comments and the images of video frames. This layer reduces the dimension of the input information, VGG processes the video information, and the Transformer processes the text information. The Transformer then converts the text representation into embedding vectors.Since text information and comments are synchronized, the Context Enhancement Layer mixes video information and text data through the attention mechanism.The purpose of the Emotion Attention Layer is to mine the emotional semantics of the comment text to obtain a good text representation. Due to the short length of the text, Bi-directional Long Short-Term Memory (BiLSTM) is adopted for mining the text data.The Classification Layer realizes the classification of users’ emotions throughout the whole connection layer. This is a multi-classification classification problem, including glad, dismissed, sad, amazed, and afraid.The VECERM architecture is shown in The central idea behind the MLP of AVRS is to extract features from multimodal data to classify emotional expressions, e.g., visual, audio, and textual information. To predict the emotional state of users when watching a stereoscopic 3D video, The fundamental idea of implementing DHM is combining different DL models . The fusing mode of multiple DL models can be either the output of one or several models is used as the input of another model, or several models simultaneously extract the features of video or multimodal data or signals. The combination of several models improves the limited non-linear performance of a single model . In To summarize, deep learning-based AVRS algorithms can learn the potential characteristics of audio, text, video, and other multimedia and obtain representations and abstraction from multiple levels, resulting in its significant advantages in dealing with emotional analysis. For example, the CNN can capture the global and local features and analyze spatial information changes during short time periods of video clips, remarkably enhancing efficiency . The RNNIn this section, we introduce the existing 31 valuable databases which play a vital role in AVRS research. These databases are composed of multiple modes, including comments, ratings, videos, films, audio, images, etc. There are various methods to obtain these data, such as capturing the changes in the viewer’s facial expressions through webcams, getting the user’s physiological signals through EEG, questionnaires, or a combination of these methods. Most of these databases are manually collected by researchers, which is time-consuming and error-prone. In The audience response to a video can be obtained in various ways, mainly including two categories: explicit acquisition and implicit acquisition. Standard methods for explicit acquisition include user interactions , questionnaires, surveys, and quizzes. The questionnaires can be achieved through self-assessment manikin (SAM) . There it-test to analyze the emotion variance and the recommender system’s performance. The commonly used performance indicators include mean accuracy, precision/recall/F1, mean absolute error (MAE), mean square error (MSE)/root mean square error (RMSE), confusion matrix, and valence, arousal, and dominance. However, viewers do not need perfect prediction accuracy but need wise recommendation strategies. Therefore, in addition to the former metrics, several researchers also began to pay attention to the quality of perceived recommendations to evaluate their models and algorithms. For example, In this survey, an overview of traditional recommendation methods  and deep learning-based technologies  adopted in AVRS has been depicted. The research of AVRS is challenging since a tremendous effort involving a multidisciplinary understanding of human behavior and perception and multimodal approaches integrating different modalities are required, such as text, audio, image, and video. Although many scholars have begun to pay attention to the field of AVRS in recent years and have made valuable contributions from the perspective of data, models, and algorithms, AVRS is still in its infancy. The challenges of the AVRS domain mainly come from the following three aspects:(1)Insufficient data and data analysis is highly sophisticated.Much of the existing facial data exists a lot of unnatural and exaggerated expressions . More in(2)Combining existing models and algorithms with deep learning-based techniques is insufficient.The exploration of affective video recommendation algorithms based on deep learning is currently limited. It only involves several deep models, such as RL, CNN, RNN, LSTM, MLP, and hybrid algorithms of several models. More advanced works and better performance are needed based on emotional analysis recommendations. The state-of-the-art technologies emerging in recent years may also be combined with the AVRS domain, e.g., the self-attention-based transformer model in sentiment changes detection , and the(3)The research direction is monotonous.The current focus is limited to the accuracy of prediction on video recommendations, and the main problem to be solved is the cold-start or long-tail effect . HoweverDW: conceptualization, methodology, formal analysis, resources, data curation, writing—original draft preparation, and visualization. XZ: validation and supervision. Both authors contributed to the writing—review and editing and approved the submitted version.


Objectives Health-seeking behavior through social media including orthodontics treatment has become popular among community in Indonesia. However, the characteristics of uploaded video in term of quality, reliability, and usefulness are unknown. This study aims to analyze the characteristics of Indonesian-language orthodontic-related YouTube videos uploaded.Materials and Methods This study adopted cross-sectional design and analyzed 300 videos as the sample. A final 100 related videos were included for analysis of the quality, usefulness, and reliability of the video uploaded as well as viewers' interaction in term of popularity and visibility. Mann–Whitney's test was used for the statistical analysis.Results The majority of the videos were uploaded by individual users (60%) with moderate quality, usefulness, and reliability. Statistical analysis showed that orthodontics-related YouTube videos uploaded by individuals have lower popularity and reliability compared with health professional (p < 0.05).Conclusion Most of the Indonesian-language orthodontic-related YouTube videos have moderate quality, usefulness, and reliability. There is a need from health professional to contribute more related video as main source of health information for the general to make healthy health-seeking behavior. Internet is useful for learning tools and also spreading health-related information and disease prevention campaign.Social media usage, such as video, provides chances to gain and assess contents that are relevant and could be trusted by the users.Nowadays, esthetics is a popular topic in many fields, both in dentistry and in modern society.Previous studies have been conducted to learn the use of video in providing health-related information.However, the majority of these studies assessed videos in English language, while the study on Indonesian-language videos is scarce. As Indonesia is the fourth most populated country in the world after China, India, and the United States, with Indonesian being used by more than 94% of the population,www.youtube.com) on September 21, 2020, by using “sort by relevance” filter. The search term used was “merapikan gigi,” a general term for orthodontic treatment in Indonesian language. We took the first 300 videos from the search results. Video selection for analysis followed the flow diagram (Orthodontics-related video in Indonesian language uploaded on YouTube in the last 12 months were searched and screened (diagram . VideosVideos were sorted according to inclusion and exclusion criteria as shown inTwo researchers (A.R. and D.A.M.) were independently assessed videos. Information extracted included the video's uploader, date of upload, the number of views, likes, dislikes, and duration of each video. The uploaders were categorized as individual users and health care professionals. Viewers' interactions with videos were evaluated based on the viewer's interaction index (popularity) and the viewing rate (visibility) formulas, as presented inThe quality, uniqueness, and reliability of the video were assessed using the Global Quality Scale,The quality of the videos was classified according to criteria proposed by Sorensen et alThe evaluation of the video's usefulness score was based on the content presence in eight nonmutually exclusive domains of information on orthodontics. Those domains include definition, indications, contraindications, advantages, procedures involved, complications, prognosis and survival, and cost.In analyzing the reliability of the videos, a questionnaire by Singh et al was used.p < 0.05.The research data of the video characteristics were collected in Microsoft Excel, then processed and analyzed using IBM SPSS 22.0. The inter- and intraobserver reliability tests were conducted with the intraclass correlation coefficient. Descriptive analysis using mean, median, and frequency was conducted to give general information related to the characteristics of video uploaded. The normality test and the Mann–Whitney's test were performed to determine the differences between video categories. The statistical significance was evaluated atThe first 300 videos by relevance were added to a YouTube playlist. Videos were screened by its title and there were 176 videos excluded because of irrelevant titles and duplication. There were 124 videos assessed and 24 videos were further excluded. The exclusion was because the videos were irrelevant (the title and content do not match), uploaded by health companies, without sound, and less than 1 minute in duration, had no information on likes and dislikes, and had duplicate contents with different titles. Therefore, the remaining 100 videos were included .n = 60, 60%) with the average duration of 493 seconds . The least viewed video had 71 views and the most viewed video had 847,395 views. The most popular video had 21,891 likes and 325 dislikes. The videos analyzed averagely had moderate quality and moderate usefulness.The reliability tests of 20 videos by two observers indicated excellent inter- and intra-observer agreement (>0.80).p = 0.010 andp = 0.001, respectively). On the other hand, a significant higher difference was found for likes (p = 0.027), popularity (p = 0.001), quality (p = 0.001), and usefulness (p = 0.001) in video more than 6 minutes than less than 6 minutes length.The characteristics of Indonesian-language orthodontic-related YouTube video based on its uploader and duration are presented inp = 0.001), in which videos with higher quality showed higher popularity. Videos with higher quality have a higher number of likes . However, videos with lower quality have a higher number of views . In terms of video's usefulness, there were significant differences in visibility between videos with low and high usefulness . Video with low usefulness have higher views and likes but lower popularity than high usefulness video .Visibility and popularity of the videos were not different according to their reliability.Further analysis is presented inYouTube, one of the social media platforms, has gained its popularity from people in seeking health-related information including orthodontics-related videos.In this study, the majority of the orthodontic-related videos have moderate quality. Videos with a moderate quality show a below ideal flow, have some important information discussed, and are quite useful to the viewers. The majority of Indonesian-language orthodontic-related videos still have insufficient information but can be used as information that is quite useful to the audience. Our finding supported previous research about orthodontic-related videos in other language.This study found that popularity and reliability were higher in videos uploaded by health professionals, supporting previous studies.Although duration has significant effect on viewing numbers,From the aspect of video reliability, it was found that there was no difference in visibility and popularity between videos with high or low reliability. This contradicts previous research showing that high-reliability videos have higher visibility and popularity.The video with the highest number of views and likes in this study is a video that discusses the orthodontic treatment process. The video is of excellent quality and a good usefulness score. The quality, flow, and completeness of the video information are excellent and very useful to the audience. Besides, the information on the video covers important topics. This is similar to previous findings, stating that the video with the most views and likes is a video with excellent quality.This study has several limitations. YouTube is a dynamic platform that allows videos to be uploaded and deleted at any time, resulting in different search results at different times.Most of the Indonesian-language orthodontics-related videos posted on YouTube were of moderate quality, moderate usefulness, and moderate reliability. Although most of the videos in this study uploaded by individual, significant popularity and reliability were marked among health professionals. Moreover, popularity and usefulness were significant for video more than 6 minutes. Health professional should contribute more quality, usefulness, and reliable health-related videos in social media platform for public seeking information in oral health.


We describe a novel, practical, and inexpensive method to add video recording during tilt table testing (TTT): Open-Access-Video-TTT.The Open-Access-Video-TTT set-up uses a personal computer (PC) to capture screen video data from a non-invasive-beat-to-beat (NIBTB) haemodynamic blood pressure (BP) device, combined with video recording of a patient, using Open Broadcaster Software (OBS®). The new Open-Access-Video-TTT set up was tested with both the Finometer  and the Task Force® Touch Cardio monitor . For this, the Finapres Nova® was enabled in ‘remote’ mode and Real Video Network Computing  was installed on the PC/laptop. The Task Force® has a DisplayPort (DP) port, for which a DP/ high-definition multimedia interface (HDMI) cable and a video capture card is used to merge the signals to the PC/laptop. With this method the combined images are stored as a new video signal. TTT can be performed with any routine protocol.Open Access-Video-TTT worked well for both the Finapres NOVA® and the Task Force Monitor ®. This novel method can be used easily by all physicians who wish to add video recording during TTT who do not have access to an electroencephalogram machine. Novel implementation of video monitoring for tilt table testing.Implementation requires a personal computer, a video camera, and open access software.All physicians can now add video to tilt table testing.1 various TTT protocols have been reported with variation in the duration of the resting and tilted phases, tilt angle, type of support, and pharmacological provocation. In experienced hands, TTT is a valuable asset that improves syncope care through evoking witnessed events with recognition of complaints by the patient.3 Recent guidelines on syncope and on TTT recommended adding video recording to TTT, integrating video with non-invasive beat-to-beat (NIBTB) monitoring.4 Video recording during TTT allows an objective and repeatable review of clinical signs in relation to hemodynamic patterns, which increases the reliability of clinical observation of induced events. For example, in reflex syncope video recording of the actual moment of syncope helped distinguish between vasodepressor and cardioinhibitory mechanisms.5 Video recording also helped to reveal new pathophysiological insights in reflex syncope, such as that myoclonic jerks in syncope probably have a cortical origin.6 Video recording during TTT has also proved to aid diagnostic and therapeutic decision,8 when asystole starts after patients had already lost consciousness, asystole cannot be the primary cause of syncope, casting doubt of utility of pacing.10 Witnessed documentation of an attack, either with video recording during TTT, or with home video recording, is the gold standard to diagnose psychogenic pseudosyncope (PPS).12 Video recording of PPS during TTT ensures that apparent transient loss of consciousness occurs while blood pressure (BP) and heart rate (HR) are not low7 and thereby ensures the diagnosis. Video recording during TTT has helped confirm the combined presence of vasovagal syncope (VVS) and PPS, helping to distinguish VVS from PPS.8 A final advantage of video recording during TTT is that it allows physicians to review the semiology of tilt-evoked syncope at will, without having to be present during the entire TTT. See also Table 1 for potential differences of TTT with video recording vs. without video recording.Since the introduction of tilt table testing (TTT) in 1986,Obtaining these advantages of video recording during TTT requires synchronization of the video signal with physiological signals such as HR and BP, acquired from an NIBTB monitor. Electroencephalogram (EEG) machines have been used for this purpose, as they offer synchronized video as standard, bringing combined haemodynamic and video-EEG monitoring within easy reach of neurologists. Unfortunately, the use of EEG machines is often impractical for other specialties.Open-Access-Video-TTT. We applied the method to 50 TTT to test for technical problems and to see whether standard operation procedures (SOPs) needed to be adjusted.We here describe a novel, practical and moreover not expensive method for video recording during TTT requiring a personal computer (PC), a video camera and open-source software that we call The novel approach consists of capturing screen video data of the NIBTB device, combining this with a patient video, and storing the combined images as a new video signal. The new Open-Access-Video-TTT was installed and BP was recorded continuously, either with a Finometer  or with a Task Force® Touch Cardio monitor . TTT was performed according to the routine protocol available in either institute. Both types of NIBTB devices were set to depict the main hemodynamic parameters, i.e. BP and its determinants HR, stroke volume and total peripheral resistance, along with a one- or two-lead electrocardiogram (ECG).Figure 1 shows an example of the camera position mounted on the tilt table).A video camera was either mounted to the tilt table, so it moved up and down with patients while remaining aimed at patients’ head and shoulders or was ceiling-mounted, providing an overview of the entire tilt table. We have no preference for either camera position; one choice ensures continuous detailed views of the face but ignores limb movements; the other allows a whole body overview .A PC or laptop was used to capture screen data from both the NIBTB device and the video camera recording patient activity (https://obsproject.com/) on the PC/Laptop to add a live picture-in picture of the patient video to the captured screen data of the NIBTB device. OBS® allows to save and replay the recorded signal as picture in picture on the PC/laptop.We installed Open Broadcaster Software  and Open Broadcaster Software (OBS® available for download on: https://obsproject.com/) on the PC/laptop.Besides Open Broadcaster Software, we installed Real Video Network Computing .We then enabled the ‘remote’ setting on the Finapres NOVA® device, after which video computing network (VNC) could capture the Finapres NOVA® screen signal and show it on the PC .Once complete, OBS® was used to add a live picture-in picture of the patient video to the PC screen /high-definition multimedia interface (HDMI) cable is used to connect the DP port at the rear panel of the Task Force® monitor to the first HDMI channel of the video capture card.Then, the video camera is connected to the second HDMI port of the video capture card through an adapter [audio video (AV) composite Radio Corporation of America (RCA) to HDMI adapter] with an RCA connector input  and a HDMI output. The video capture card merges the two video signals, i.e. from the Task Force® monitor and the camera that are displayed on the PC monitor.Table 2).Finally, the output of the video capture card is connected to the PC/laptop through a USB 3.0 cable .OBS® was then used on the PC/Laptop as before . The first 50 TTT were carried out without technical problems: apart from connecting the device the TTT operating procedures were not affected.After installing the open-access software and changing the SOP, the novel method Open-Access-Video-TTT proved easy to operate in daily practice, both for technicians and physicians  video recording during TTT has been performed since about 2000, resulting in thousands of tests. EEG machines can store and replay synchronous continuous data consisting of the EEG, one or more ECG leads, any additional analogue or digital signal, and a video signal. Hence, adding an EEG machine to TTT offers more recording and reviewing opportunities than video-Open-Access-TTT, but at the obvious expense of acquiring and operating an EEG machine.3 or psychogenic non-epileptic seizures (PNES). When PNES is probable a priori, a test with video-EEG should preferentially be ordered. For PPS, the EEG may probably be supplanted by near-infrared spectrometry or Transcranial Doppler. If such events occur unexpectedly, the video may be shown to those with relevant expertise.We emphasize that a complete video-EEG registration is considered superior to video only when it comes to recognize PPS6 Recording EEG signals during TTT indicates when cortical function becomes severely impaired, teaching physicians which signs point at Loss of Consciousness, when to tilt back, and helps distinguish between syncope and PPS, as the EEG during PPS typically shows a completely normal ‘awake eyes closed’ pattern. The EEG is particularly useful in cases of syncope morphing into PPS or vice versa.8The advantage of an EEG during syncope does not lie in detecting the earliest signs of cerebral hypoperfusion, as the EEG only responds late during developing hypoperfusion. However, the EEG then does so quickly and impressively, with a loss of normal activity and the occurrence of coarse abnormal activity . The EEG is never normal during syncopal loss of consciousness, because the EEG mainly reflects cortical activity, the cortex is very susceptible to hypoperfusion, and because loss of consciousness in syncope is due to an overall profound loss of cortical function.An advantage of video-EEG reviewing is that EEG machines enable the user to change the time base of the recording, to zoom in on events lasting a few seconds, or to zoom out to obtain an overview of an entire syncopal period. In addition, most signals can be filtered digitally, which helps to reduce artefacts and allows scrutiny of specific frequency signals, such as EEG slowing.We believe that adding the novel video method to TTT will increase appreciation of the great utility of video recording to better understand paroxysmal events. In epilepsy, it is at present unthinkable to perform an EEG without video; the same situation may develop for TTT. Seeing patients with T-LOC in all its forms requires not only a structured approach to assess what happened during an event, but benefits from a multidisciplinary approach, with access to appropriate diagnostics and professionals. We therefore we hope that all syncope clinics applying this novel method will benefit from it.euac193_Supplementary_DataClick here for additional data file.


Posterior fossa high-grade arteriovenous malformations (AVMs) are challenging diseases. This video presents the treatment of a patient with a diffuse, high-flow AVM of the posterior fossa on the tonsil and cerebellopontine angle (4 cm in diameter) and deep draining veins. The patient had an intraventricular and cerebellar hemorrhage. After conservative treatment, total resection of the AVM was performed with embolization and surgery. The authors resected the nidus after the endovascular embolization, on the same day. The postoperative course was uneventful, and the patient was discharged with almost full recovery.https://youtu.be/logCCn3uKUcThe video can be found here:  Therefore, the treatment for posterior fossa AVM is recommended only for hemorrhage cases. However, most posterior fossa AVMs are associated with an increased hemorrhage rate.Posterior fossa high-grade arteriovenous malformations (AVMs) are challenging diseases.This video presents the treatment of a patient with a posterior fossa AVM on the tonsil and cerebellopontine angle, 4 cm in diameter , and deep draining veins. This AVM was Spetzler-Martin grade III and located on the tonsil and cerebellopontine angle.The patient had intraventricular and cerebellar hemorrhage. After conservative treatment, total resection of the AVM was performed with embolization and surgical resection. We resected the nidus on the same day after the endovascular embolization. Postoperative course was uneventful, and he was discharged almost fully recovered.0:21 Introduction. This video describes the surgical treatment of a large, high-flow, and diffuse AVM of the posterior fossa.0:31 Clinical History. The patient was a 37-year-old male. He presented with a loss of consciousness, headache, and dizziness. Neurological examination revealed ataxia. He was found to have intraventricular and cerebellar hemorrhage. After conservative treatment and observation, he was transferred to our institution.0:53 Preoperative Imaging. Computed tomography (CT) demonstrated left cerebellar and intraventricular hemorrhage. Magnetic resonance imaging (MRI) demonstrated the AVM at the tonsil and cerebellopontine angle. The nidus measured 4 cm in diameter.Supplemental Fig. 1).Angiography of the left vertebral artery (VA) demonstrated a large, high-flow, and diffuse AVM of the posterior fossa. 3D digital subtraction angiography demonstrated that the nidus was fed from the left anterior inferior cerebellar artery (AICA), left posterior inferior cerebellar artery (PICA), and several feeders around the occipital sinus. The deep draining veins enter the bilateral superior petrosal sinus (SPS), and the superficial draining veins enter the occipital sinus (In terms of the Spetzler-Martin grade, this AVM was grade III and located on the tonsil and cerebellopontine angle.1:48 Feeding Arteries and Draining Veins of the AVM. There are three major groups of arterial feeding arteries supplying the AVM: one group from the AICA, the second group from the PICA, and many feeders around the occipital sinus. All feeders are intradural feeding arteries. The AVM has three draining veins. Deep draining veins align to the bilateral SPS, and the superficial draining vein terminates to the occipital sinus.2:16 Operative Procedure. Preoperative embolization is useful to reduce the flow of the AVM. In this procedure, the AICA and PICA were embolized using endovascular treatment with Onyx. It is hard to identify the AICA on the operative field because it is derived from the ventral side of the nidus. After obliteration of the AICA and PICA, the AVM was operated on in the operating room on the same day.At first, the main feeder of the PICA was identified and the feeders around the superior surface of the nidus and the occipital sinus were dissected. Then, from the suboccipital surface to the petrosal surface was dissected. The drainer to the left SPS was identified. The main feeders of the AICA and PICA were separated. After all feeders were separated, the drainers were dissected and separated, and thereafter the nidus was removed.3:13 Endovascular Treatment. The patient underwent multiple embolization procedures in which the AICA and PICA were embolized before surgical resection. Onyx was injected through the microcatheter. The angiogram before surgery revealed continued slowing and thrombosis of the nidus.3:38 Positioning, Skin Incision, and Craniotomy. The patient was put in the park-bench position. The head was slightly turned with the contralateral side downward. A hockey-stick incision was made. The craniotomy was extended to the condylar fossa. The foramen magnum was opened and C1 laminectomy was carried out.3:57 Operative Video. The resection of the AVM was performed by the left transcondylar fossa approach on the same day of the endovascular treatment 35 days after the onset. The posterior fossa AVM resection is recommended 4–6 weeks after the onset due to mass effect.4:00 Pial and parenchymal dissection was carefully undertaken around the superior surface of the nidus.4:12 The main feeder of the PICA was identified.4:18 The feeders around the main draining vein to the occipital sinus and the superior surface of the nidus were individually dissected, coagulated, and separated. The arachnoid and pial dissection was carefully performed.4:46 The deepest part of the nidus was the hematoma cavity. We can see the hematoma cavity. The bleeding artery was coagulated and Surgicel cotton balls were used for hemostasis.5:01 The medial side of the main draining vein was dissected. A Weck clip was used when necessary to occlude and separate the feeders.5:15 The area from the occipital surface to the petrosal surface was also dissected. We can see the vertebral artery. The arachnoid around the lower cranial nerves was dissected, and the draining vein going to the SPS was exposed. We used the operative view from the transcondylar fossa to identify the vertebral artery and dissect the arachnoid around the lower cranial nerves.5:35 After separating the feeding arteries, the PICA was exposed and clipped. ICG angiography was used to ascertain blood flow. Having checked the flow of the AVM, the PICA was clipped, coagulated, and separated.5:58 Feeding arteries were separated on the ventral side of the nidus. We can see the AICA. The AICA was embolized by cast Onyx. The intravascular Onyx was then dissected.6:17 The draining vein going to SPS was separated, and the last feeder was separated.6:27 After the complete occlusion of all arteries, the nidus was movable. The draining vein going to the occipital sinus was separated.6:39 The AVM was totally resected.6:50 Postoperative Course. The postoperative course was uneventful. The patient experienced no other neurological deficit. He was discharged 3 weeks after surgery.6:58 Postoperative Imaging. Postoperative MRI demonstrated a minor cerebellar contusion but no cerebral ischemia. Postoperative angiography demonstrated total removal of the AVM and preservation of the passing artery.7:17 Patient Walking on Postoperative Day 10. The patient was able to move independently without any assistance. He could walk smoothly and returned to normal life.Primary surgeon: Torihashi. Assistant surgeon: Ogura, Kadowaki, Sakamoto, Kurosaki. Editing and drafting the video and abstract: Torihashi. Reviewed submitted version of the work: Torihashi. Approved the final version of the work on behalf of all authors: Torihashi. Supervision: Kurosaki. Endovascular surgeon: Sakamoto, Kadowaki.Supplemental Fig. 1