Chemical exchange saturation transfer (CEST) is a novel imaging modality in clinical practice and scientific research. Angiopep-2 is an artificial peptide that can penetrate blood-brain barrier. The aim of this study was to explore the feasibility of Angiopep-2 serving as an exogenous CEST contrast.  Phantoms of Angiopep-2 with different concentrations were prepared and then scanned using the 7.0T small animal MRI scanner. Different parameters including saturation powers and saturation duration were used to achieve the optimal CEST effect, and the optimal parameters were finally selected based on Z-spectra, asymmetric spectra, and phantom CEST imaging. CEST scanning of dimethyl sulfoxide (DMSO), the substance helping Angiopep-2 to be dissolved in water, was performed to exclude its contribution for the CEST effect. M–3.2ppm − M+3.2ppm)/M–3.2ppm. The CEST effect of Angiopep-2 was concentration dependent; the effect increased as the concentration increased. In addition, the CEST effect was more obvious as the saturation power increased and peaked at 5.5 µT, and the CEST effect increased as the saturation duration increased. DMSO showed nearly 0% of the CEST effect at 3.2 ppm.  A broad dip was observed from 2.5 to 3.5 ppm in the Z-spectra of Angiopep-2 phantoms. The most robust CEST was generated at 3.2 ppm when using formula (β protein, amyloid-β targeting CEST, with Angiopep-2 as an exogenous contrast agent, can be potentially used as a novel imaging modality for early diagnosis of Alzheimer's disease. Collectively, Angiopep-2 may play a critical role in early diagnosis of Alzheimer's disease. Our results demonstrate that Angiopep-2 can act as an excellent exogenous CEST contrast. As it can penetrate blood-brain barrier and bind amyloid- It is characterized with progressive cognitive and functional decline in late adult life . Its halcificity , but itsar level .Chemical exchange saturation transfer (CEST) is a relatively new MRI contrast approach in which exogenous or endogenous compounds containing exchangeable protons are selectively irradiated and then indirectly detected with enhanced sensitivity by measuring the decrease of the bulk water signal . A varieWith the development of molecular imaging technology, CEST has been the focus of both animal and clinical experiment for its capability of revealing the pathophysiological process at the molecular level. It has been successfully applied in the AD mouse model , 22. CESDerived from the Kunitz domains of aprotinin and other human proteins, Angiopeps have been found to cross the blood-brain barrier (BBB) . It is wβ deposits to detect early AD [Interestingly, Angiopep-2, as we described, can not only cross the BBB but also bind to amyloid-early AD . AdditioAngiopep-2 was synthesized using the solid-phase synthesis method which was assisted by GL Biochem Ltd. . The steps were demonstrated as follows. In step 1, Fmoc-Tyr (tub)-wang resin was treated with DMF/piperidine. In step 2, the reaction was monitored using 2,2-dihydroxyindane-1, 3-dione. Both step 1 and step 2 were repeated multiple times by adding other amine acids . In stepμl of DMSO can dissolve 10 mg of Angiopep-2. So, the DMSO/water ratio of the concentration of 8 mmol/L (mM) Angiopep-2 was 1 : 100. Another set of phantoms with only DMSO and Angiopep-2 (with DMSO) was prepared.Angiopep-2 of different concentrations  was prepared with same pH (7.0) at 37°C. To be noticed, Angiopep-2 must be supplemented with dimethyl sulfoxide (DMSO) to be dissolved in water, and approximately 5 B0 field was shimmed, while B1 filed was calibrated prior to scanning. To obtain Z-spectra, different radiofrequency saturation powers  were used with duration of saturation of 4 seconds. In addition, different saturation durations were used  to optimize the parameters. The parameters for obtained Z-spectrum of Angiopep-2 were as follows: B1 = 2.5 µT, duration of saturation = 7 seconds, echo time = 20 ms, repetition time = 14 seconds, slice thickness = 2 mm, field of view = 25 × 25 mm, and matrix size = 64 × 64. The CEST imaging and Z-spectra were acquired, which ranged from 5 to −5 ppm. A saturation pulse was applied at 51 frequency offsets that cover the range of ±5 ppm and step of 0.2 ppm to contain around ±3.2 ppm of Angiopep-2 saturation peaks. The total time for acquisition was 5 minutes and 24 seconds.The scanning was carried out on a 7.0 Tesla horizontal bore (bore size 160 mm) small animal MRI scanner  with a surface coil  for transmission and reception. An echoplanar imaging sequence with continuous wave presaturating field (CW-EPI) was used for CEST scanning. All the scan parameters were as follows: for T2W, field of view = 30 × 30, slice number = 6, slice thickness = 2 mm, matrix size = 256 × 256, echo time = 20 ms, and repetition time = 5,000 ms. B0 field was corrected using the water saturation shift referencing method [B1 maps generated from the same brain slice were used to correct the B1 field [MATLAB  was used for CEST imaging processing. The regions of interest (ROIs) were drawn manually based on the T2-weighted images . B0 fielg method . B1 mapsB1 field . The CESThe CEST imaging, Z-spectra, and asymmetric spectra of Angiopep-2 of different concentrations are shown in Figures µT and then decreased as the saturation power exceeded 5.5 µT.The Z-spectra and asymmetric spectra of Angiopep-2 scanning with different saturation powers are shown in Figures For different saturation time, the dip was deeper as the saturation increased Figures . The CESTo explore whether the CEST effect was partially contributed by DMSO which was used to help the dissolve of Angiopep-2 in water, phantom of DMSO and phantom of Angiopep-2 (with DMSO) were scanned. The Z-spectrum of DMSO revealed no dip around 3.2 ppm, and the phantom showed the CEST effect around 0%, while Angiopep-2 phantom had an obvious CEST effect Figures . The resB1 power would not only augment the magnetization transfer signal but also increase the specific absorption rate [µT, the increase of the CEST effect was not significant. Therefore, 2.5 µT was chosen as the optimal saturation power. As for the saturation duration, we noticed that the CEST effect was more obvious as the duration increased. However, the total scanning time prolonged significantly as the saturation duration increased (from 3 minutes 51 seconds when the saturation duration was 4 seconds to 6 minutes 42 seconds). Increased saturation duration is acceptable for scanning phantoms, but for scanning animals or patients, saturation duration of 4 seconds would be superior.The results of our study provided the very first evidence for the feasibility of using Angiopep-2 as an exogenous CEST contrast in vitro. By using Angiopep-2 phantoms, we found the optimal parameters to achieve the best CEST effect under high magnetic field. Eventhough the CEST effect increased as the saturation power increased, the use of high ion rate . We noti1H-magnetic resonance spectroscopy has been applied extensively to clinical practice and research studies [β protein [β protein deposition already occurs. In the animal model, it has been revealed that amyloid-β plaques appear at three months old APP/PS1 mice with AD, and the number and distribution of the plaques increase as the condition progresses [As an noninvasive imaging modality to detect molecules both in vitro and in vivo,  studies . However studies . CEST is studies . With de studies  has been studies . Multipl studies , 38. The protein , making ogresses . AngiopeThis study only explored the capability of Angiopep-2 as a CEST contrast agent in vitro, so further study to assess the feasibility of applying it in clinical research is still warranted. Even so, this was the very first study reporting Angiopep-2, an artificial peptide with promising treatment and diagnostic potential for clinical practice, served as a CEST contrast agent, and providing a solid foundation for its future use. In addition, Angiopep-2 exchange rates should be determined using the QUEST or QUESP technique, but this was not achieved due to the technical limitations of our lab. We are now still working on the research and application of the QUEST technology.β for early diagnosis of AD.Our study demonstrates for the very first time that Angiopep-2 is a potential exogenous CEST contrast agent, which has a promising value for quantitatively detecting amyloid-


Temporal-action proposal generation (TAPG) is a well-known pre-processing of temporal-action localization and mainly affects localization performance on untrimmed videos. In recent years, there has been growing interest in proposal generation. Researchers have recently focused on anchor- and boundary-based methods for generating action proposals. The main purpose of this paper is to provide a comprehensive review of temporal-action proposal generation with network architectures and empirical results. The pre-processing step for input data is also discussed for network construction. The content of this paper was obtained from the research literature related to temporal-action proposal generation from 2012 to 2022 for performance evaluation and comparison. From several well-known databases, we used specific keywords to select 71 related studies according to their contributions and evaluation criteria. The contributions and methodologies are summarized and analyzed in a tabular form for each category. The result from state-of-the-art research was further analyzed to show its limitations and challenges for action proposal generation. TAPG performance in average recall ranges from 60% up to 78% in two TAPG benchmarks. In addition, several future potential research directions in this field are suggested based on the current limitations of the related studies. Recent developments in computer vision have led to video analysis and understandings focusing on video categorization or captioning in academic and industrial fields. Since video content is generally based on motion information of objects and humans, their actions are the main key feature in video analysis and understanding. However, real-world videos  are usually untrimmed. Therefore, temporal-action localization is one of the most important research topics in video understanding. Temporal-action localization focuses on action instances or proposals presented in untrimmed videos and categorizes their action classes and video content. Similar to object localization in an image, there are two fundamental processes in temporal-action localization consisting of temporal-action proposal generation (TAPG) and action recognition for localization and classification. As far as we are concerned, action recognition has shown significant progress and promising classification performance. In contrast, TAPG still has much room for improvement, especially in action proposal quality. Hence, several recent studies have aimed to improve TAPG to generate high-quality proposals to be utilized as input data for action recognition.In general, TAPG techniques are utilized to classify whether the temporal proposals from untrimmed videos are background or action, and negative or positive results, respectively. There are two main techniques: anchor- and boundary-based techniques. The first technique generates candidate proposals with a pre-defined or fixed duration in multiple temporal scales. Then, a confidence score can be evaluated from each candidate. However, they are inflexible to various action proposal durations and have imprecise boundaries due to the fixed length of candidate proposals. Recently, a boundary-based technique was introduced to solve this problem in anchor-based methods. This technique was concerned with boundary prediction by utilizing the frame-level context information around the boundaries. Therefore, they could generate action proposals with a flexible duration and high accuracy of boundary prediction. Meanwhile, it was sensitive to noise and rich confidence scores from each candidate could not be extracted compared to anchor-based techniques. In addition, other researchers focused on how to optimize or train existing TAPG models to increase the accuracy of action boundary prediction. Semi- and weakly supervised learning ,2 were dHigh computation: as well as a spatial feature, TAPG also includes a temporal feature , resulting in high computational cost.Unclear boundary: Since there is no standard definition to distinguish between background and action proposals ,7, propoAction class variation: There is the large number of action classes for handling several categories in action recognition. Since each action class has a specific characteristic , it is unreliable to design a network for handling every action category.Temporal length variation: Action proposals in untrimmed videos also have large variation in the temporal length. For example, shaking hands can only take a few seconds while a cooking process can take about 5 min. This causes the long-tail problem from imbalanced data. In addition, there are spatial variations similar to object localization .Although the TAPG model is similar to object localization  ) for genThe related studies were initially searched using the keyword; ‘Temporal action proposal’, from 2012 to 2022. The three well-known databases—Scopus, Web of Science, and IEEE Xplore—are within the scope of this screening procedure. Most of the articles were selected from top-tier conferences in computer vision . The scope of this review article is primarily determined by ‘temporal action proposal generation’.The article selection process is shown in This paper aimed to give a survey summarizing deep-learning-based techniques from recent studies on TAPG. Unlike a survey on temporal action localization , the relAs shown in This section reviews the input data for video analysis from recent research related to TAPG. The input data could be categorized into two components: video data and descriptor. The video data can be downloaded from public datasets along with their action classes and temporal annotations. On the other hand, a video descriptor contains temporal and spatial features from an input video for feeding into the TAPG network. Therefore, the video dataset and the video-descriptor extraction can be summarized as follows:As stated in the Introduction, the dataset for evaluating TAPG should have a high variation in video conditions  and provide untrimmed video annotation for temporal proposal localization. According to the Introduction, TAPG is one of the important components in temporal-action localization. The dataset related to temporal-action localization could be considered and evaluated in TAPG. The datasets’ videos were annotated with specific purposes for action localization and classification. The datasets were listed as follows:THUMOS 2014  was intrActivityNet v1.3  is a larMEXaction2  is a temMultiTHUMOS  presentsCharades  focuses AVA  is one oAs far as we concerned, there are only two datasets (THUMOS 2014 and ActivityNet v1.3) utilized for TAPG evaluation in related studies due to a reasonable number of data annotations and high variation in action classes. In general, their temporal annotations were assigned as temporal boundaries (starting and ending time) for each action proposal, as shown in Since input data from temporal-action proposal datasets are videos or frame sequences having their characteristics or video descriptors, their features should not contain only spatial features but also temporal features. Similar to image classification and object localization, deep-learning-based techniques were recently applied to this research. In recent research, the networks were designed for action recognition in untrimmed videos. Most TAPG researchers tended to adapt their architectures by utilizing the output scores of the top FC-layer. This network would later be trained by an action recognition network. Several models for extracting video descriptors were inspired by convolutional neural networks (CNNs) according to the following designs:The image-classification network had a high performance for classifying spatial information. CNN could be modified for video classification that also had temporal information. The video features were extracted independently from each frame across the whole video . To dealSince temporal information was considered as the third dimension of input data, 3D convolution ,20,21,22By utilizing LSTM or 3D convolution, actions with low-level motion might not be captured, while this motion was critical information in several cases. In addition, their networks required much time complexity from large models and ran through frame sequences. To solve these problems, the two-stream network  was desiThe Inflated 3D ConvNets (I3D) network  was intrThe structure of 3D convolution and the two-stream network were designed for extracting the spatio-temporal features of action recognition from datasets focusing on trimmed videos. In this case, it used only visual features from action temporal proposals, and background proposals were not considered in this case. Therefore, temporally sensitive pretraining (TSP)  was propTemporal-action proposal generation could be categorized into several categories depending on their purpose or scope of the study. In terms of network design, the TAPG network is categorized into anchor-based and boundary-based techniques, where their differences are temporal boundary precision and the confidence score on each proposal. In this section, the deep-learning-based methods for TPAG are reviewed by focusing on these two techniques. To fulfill the advantages and improve prediction performance, complementary techniques and additional techniques are also discussed. Hence, the related studies are categorized into four aspects, as follows.This technique refers to the temporal refinements of pre-defined anchors with a fixed size of temporal length. In recent studies, the top-down approach was focused on enriching proposal features to generate their confidence scores. The whole scheme was similar to action recognition from trimmed videos, except the pre-processing was included to localize the proposal as the input for recognition. Therefore, there were two main stages, as shown in TURN, 2017: In the first technique, a sliding-window-based technique was deployed for proposal localization. Temporal Unit Regression Network (TURN) [k (TURN)  was the k (TURN) ) was utiSST, 2017: Since a sliding-window technique might have dis-connectivity within the proposal or video snippet features and required high time complexity, Single-Stream Temporal (SST) action proposal [proposal  was intrproposal  from theSAP, 2018: As a top-down approach, generating and searching action proposals through whole-video data generally requires a large time complexity and huge computational cost. Instead of focusing on every temporal position or video snippet in video data, the self-adaptive proposal (SAP) model [P) model  techniquP) model . Each viSRG, 2019: Untrimmed video characteristics usually contain several action proposals with similar action classes from each video snippet. It is well-known that temporal proposals with high recall and accurate boundaries could be generated as the overall performance. However, the proposal with specific action classes provided an unsatisfactory performance. This problem could be caused by the ignorance of relations among action proposals within untrimmed videos. To overcome this problem, Snippet Relatedness-based Generator (SRG) [or (SRG)  presenteCMSN, 2019: Action proposals from video snippets are usually continuous events and do not suddenly start or end. Therefore, the temporal stages close to the starting and ending time should be defined. The Continuous Multi-Stage Network (CMSN) [k (CMSN)  introduck (CMSN)  to encodRapNet, 2020: Even though boundary-based techniques were effective in locating temporal boundaries, long-range contextual information was not taken into account, and an action with various temporal lengths was omitted. Relation-Aware Pyramid Network (RapNet) [(RapNet)  presente(RapNet) . To extrRTD-Net, 2021: Transformers were recently utilized in object detection with simpler architectures, smaller networks (in terms of the number of parameters), more throughputs, and quicker training. Since TAPG is similar to object detection, the transformer was able to predict temporal boundaries or proposals. Relaxed Transformer Decoders Network (RTD-Net) [RTD-Net)  firstly RTD-Net) , as showAs mentioned in the introduction, an anchor-based technique has a limitation on action proposals with various temporal lengths due to the fixed sizes of sliding windows. Even though pyramid-based or u-shaped architectures was utilized, the boundary precision from some action proposals, especially very short temporal length (less than 30 s), provided insufficient performance. Instead of predicting temporal positions, temporal boundaries (starting and ending time) were the focus of this technique which is considered a bottom-up approach to generate video descriptors before finding temporal information. Its general pipeline is categorized into three stages, as shown in TAG, 2017: The learning-based bottom-up scheme for TAPG was introduced in Temporal Actionness Grouping (TAG) [ng (TAG)  for formng (TAG)  to generng (TAG) , or a twBSN, 2018: This research work was proposed to handle action proposal characteristics . Even though the first bottom-up approach was introduced by TAG [d by TAG , the temd by TAG  was desiBMN, 2019: The current bottom-up proposal-generation methods utilize multiple stages to predict temporal boundary and confidence scores. However, they cannot efficiently generate adequately reliable confidence scores for retrieving proposals. Boundary-Matching Network (BMN) [rk (BMN)  was intrDPP, 2019: Even though the previous bottom-up approach succeeded in handling action proposals with various temporal lengths, their temporal proposal quality relied on action grouping or normalized video descriptors. In addition, their strategy was complex and time consuming. To solve this issue, Deep Point-wise Prediction (DPP) [on (DPP)  was intron (DPP) , the modDBG, 2020: The previous boundary-based techniques predicted temporal boundaries as 1D information, starting and ending probabilities, in each temporal position. This strategy lacked global features for action proposals with blurred boundaries and various temporal lengths. Dense Boundary Generator (DBG) [or (DBG)  was intror (DBG)  was utilBC-GNN, 2020: Boundary-based techniques were utilized for finding starting and ending times. Thus, the relationship between action contents and their temporal boundaries could be defined. Therefore, Boundary Content Graph Neural Network (BC-GNN) [(BC-GNN)  was propIntraC and InterC, 2020: This research work [rch work  introducATAG, 2021: Irrelevant frames from complicated action proposals were also an important issue in TAPG, where their frame-level proposal features had various characteristics and were hard to classify. However, only local proposal features were insufficient to deliver promising performance. To overcome this problem, Augmented Transformer with Adaptive Graph (ATAG) [h (ATAG)  utilizedh (ATAG)  was utilWith the merits of anchor- and boundary-based techniques, many researchers tended to propose complementary techniques between these two methods. A complementary technique is a combined module from the anchor- and boundary-based techniques to design the TAPG network. There was no standard network architecture for this technique. The contribution summary for complementary techniques are shown in CTAP, 2018: Even though a sliding window technique was able to cover all temporal segments in the videos, their predicted temporal proposals provided imprecise temporal boundaries. On the other hand, more precise temporal boundaries could be achieved by temporal and proposal modules in the boundary-based technique. However, temporal proposals with low confidence scores might be ignored. Therefore, Complementary Temporal Action Proposal (CTAP) [l (CTAP)  utilizedl (CTAP)  was utilMGG, 2019: This technique [echnique  introducTRAN, 2021: The TAPG with a transformer [nsformer  was the nsformer  to captuAs described above, many studies tended to focus on the modification of the TAPG network to improve classification performance. However, other studies aimed to propose techniques to solve issues related to data such as the amount of annotated data, imbalanced data, and the characteristics of the TAPG dataset. Since annotating video sequences requires a large amount of time and labor effort, semi-supervised learning was taken into account to reduce the amount of annotated data . In addiSupervised learning: To achieve high temporal-boundary prediction accuracy, supervised-learning-based techniques were utilized by recent TAPG networks. However, these techniques required a large amount of annotated temporal action intervals from untrimmed videos. On the other hand, unsupervised learning-based techniques [chniques   [rk (AEN)  developeR) and average recall IoU=quations  and 3)TP), falAccording to the TAPG evaluation metric, most related studies were concerned about the quantity of predicted temporal proposals. Therefore, As mentioned in Without data annotation in test video data from ActivityNet, the related studies evaluated their networks with validation video data as shown in This section described the performance evaluation of the network optimization techniques in the TAPG network. The related studies used additional techniques for the TAPG network to solve following problems: supervised learning ,2, imbalThe experimental results evaluating semi-supervised learning  and selfs) of their video duration consisting of short (0.0 ≤ s < 0.06), medium (0.06 ≤ s < 0.6), and long (0.6 ≤ s < 1.0). Since the imbalanced data of temporal action lengths was one of the main challenges , scale-invariant (SI) loss from  was evalThis section evaluated effects of an attention mechanism focusing on the agent or human regions proposed by AEN . The THUhttps://github.com/JJBOY/BMN-Boundary-Matching-Network) (accessed on 2 August 2021) on ActivityNet v1.3 [This section discusses the result of our evaluation. Even though there is a standard evaluation criterion mentioned in Net v1.3 . We founIn general, proposals with similar patterns  are classified as the same classes . If action and background proposals have similar patterns, it might cause false positives in predicted proposals and generate imprecise action boundaries as the result. To visualize this hypothesis, the confidence maps from examples with and without similar patterns between action and background proposals are shown in According to various temporal lengths, action proposals with long temporal lengths were effective in the TAPG model. However, the proposals with short temporal lengths were ignored. As mentioned in In practical applications, TAPG had the main role of extracting action proposals from input video data. As described in the Introduction, these action proposals were mainly utilized as the pre-processing step for trimming videos in various applications  ,59,60,61Different from TAPG, TAL was an extended work of TAPG including action recognition. TAL aimed to localize the temporal position of each action class , where TVideo retrieval is the approach for facilitating the video search of large public video collections by indexing their contents or categories. Without examining the video contents, video search relies on metadata such as captions or keywords. TAPG could be utilized in video retrieval for extracting relevant frames or proposals from their video captions and contents. The video retrieval is generally divided into two major modules (feature extraction and similarity model). The similarity model is the main part for checking the similarity of visual features from video contents in the database.Besides action detection and classification, video highlights are another important process for video categorization or video search with similar techniques. TAPG can be modified for detecting highlight proposals, capturing a user’s primary attention within an untrimmed video, especially in a sports video. This application has the potential to significantly ease video search. The highlight is generally cued by observing or extracting latent signals from their benchmarks, depending on user’s demands or perspectives. Weakly supervised learning  is consiVideo summarization is an application for generating a short synopsis and excluding unnecessary content. It helps to summarize untrimmed video data by selecting its informative or key frames. The output from a video summarization usually consists of a set of video frames (video-key-frames), ordered by time . As mentAs discussed in previous sections, limitations can be overcome for future directions in TAPG. This section discusses several interesting directions from the related experimental results. The future trends may be as follows:Vision attention mechanisms have received much attractive throughout the last decade. As mentioned in AEN , the actAudio information could be considered as a relevant feature in action recognition and classification. However, only image information was taken into account in recent studies on TAPG. By observing videos in the benchmarks, the characteristics between action and background proposals (Playing accordion) are ambiguous and hard to distinguish, as shown in A video descriptor is one of the most important factors for predicting action proposals. As far as we are concerned, TSP  was the This paper conducted a comprehensive review of deep-learning-based techniques for temporal-action proposal generation. Related studies were discussed including video descriptors and their TAPG networks, consisting of anchor-based, boundary-based, and complementary techniques. Besides the network architecture, additional techniques were also included. Recent TAPG studies, including its benchmark, were selected from specific keywords of TAPG and evaluation metrics during 2012 and 2022. The empirical results showed that the two-stream network yielded higher performances in average recall in both TAPG benchmarks (AcitvityNet v1.3 and THUMOS 2014). Based on our result analysis, there were two main weak points in recent work consisting of similar patterns among proposals and contiguous proposal merging. In addition, the various aspects of the predicted action proposals were discussed for a specific purpose. Over the past few years, the related studies on TAPG tended to focus on the attention mechanism to extract salient features via the attention module and self-attention from the vision transformer.Since TAPG is expected to play a significant part in temporal action localization and video analysis, TAPG networks should generate a promising performance (at least 90% in AR@100) for practical use. The TAPG network should be proposed and designed for specific action categories. Since untrimmed videos is the main input data for TAPG, video descriptor and attention modules should be redesigned instead of being adapting from other research . In addition, all information from a video, including audio, could be utilized to distinguish temporal proposals, especially on action proposals with sound such as playing musical instruments. Therefore, TAPG still has a gap for improvement on several complicated and challenging issues as future directions.


Livestock accounts for an estimated 80% of total agricultural greenhouse gas emissions, making abatement of greenhouse gas emissions from livestock a high-priority challenge facing animal nutritionists. Mitigating greenhouse gases in ruminants without reducing animal production is desirable both as a strategy to reduce global greenhouse gas emissions and as a way of improving dietary feed efficiency. The inclusion of feed additives in the diets of ruminants can reduce energy losses as methane, which typically reduces animal performance and contributes to greenhouse gas emissions. The present study evaluated the abatement potential of nine essential oil blends to mitigate greenhouse gas emissions. The inclusion of the blends resulted in a reduction in greenhouse gas emissions and in vitro apparent dry matter digestibility with higher values noted for the control treatment. A similar trend was noted for in vitro truly dry matter digestibility with higher values noted in the control treatment. The efficiency of microbial production was greater for the blends. The inclusion of the blends affected the total and molar proportion of volatile fatty acid concentrations. Overall, inclusion of the blends modified the rumen function resulting in improved efficiency of microbial production.p > 0.05) for total gas and greenhouse gas emissions. The inclusion of EOBs in the diets resulted in a reduction (p < 0.001) in GHG emissions, except for EOB1 and EOB8 in the high concentrate diet at 6 h and for EOB8 in the high forage diet at 24 h of incubation. Diet type had no effect on apparent IVDMD (IVADMD) whereas the inclusion of EOBs reduced (p < 0.05) IVADMD with higher values noted for the control treatment. The efficiency of microbial production was greater (p < 0.001) for EOB treatments except for EOB1 inclusion in the high forage diet. The inclusion of EOBs affected (p < 0.001) the total and molar proportion of volatile fatty acid concentrations. Overall, the inclusion of the EOBs modified the rumen function resulting in improved efficiency of microbial production. Both the apparent and truly degraded DM was reduced in the EOB treatments. The inclusion of EOBs also resulted in reduced GHG emissions in both diets, except for EOB8 in the high forage diet which was slightly higher than the control treatment.The current study evaluated nine essential oil blends (EOBs) for their effects on ruminal in vitro dry matter digestibility (IVDMD), efficiency of microbial production, total short-chain fatty acid concentration (SCFA), total gas, and greenhouse gas (GHG) emissions using two dietary substrates (high forage and high concentrate). The study was arranged as a 2 × 2 × 9 + 1 factorial design to evaluate the effects of the nine EOBs on the two dietary substrates at two time points (6 and 24 h). The inclusion levels of the EOBs were 0 µL (control) and 100 µL with three laboratory replicates. Substrate × EOBs × time interactions were not significant ( Essential oils (EOs) have been known to have varying chemical structures and bioactive compounds as they are extracted from different plants . EssentiThe cannulated cows used in the present study were maintained under an Institutional Animal Care and Use Committee approved protocol # 21-009.0.The two dietary substrates were collected from North Carolina A&T State University Farm. The dietary samples consisted of corn silage identified as high forage (HF) and a total mixed ration (TMR) identified as high concentrate HC; . The TMRA total of nine EOBs were used for the study. The EOs used in the study were purchased from commercial vendors. The EOBs are as follows: EOB1 , EOB2 , EOB3 , EOB4 , EOB5 , EOB6 , EOB7 , EOB8 , and EOB9 . The EOBs were formulated after an extensive review of previous literature and the percent of each oil in the blend was adjusted based on previous results in literature [The batch culture studies were done to evaluate the effect of the EOBs on in vitro dry matter digestibility (IVDMD), efficiency of microbial population , total and molar proportion of volatile fatty acid concentrations, total gas production, and greenhouse gas emissions (GHG). The studies were arranged as a 2 × 2 × 9 + 1 factorial design to evaluate the effects of the nine EOBs on the two dietary substrates (HF and HC) at two time points (6 and 24 h). The inclusion level of the blends was 100 µL and a control (0 µL). The in vitro incubation procedure was based on the methods described by [Methane, ammonia, carbon dioxide, and hydrogen sulfide concentrations were estimated using a portable gas analyzer . The gas analyzer was calibrated per manufacturer’s instruction. An aliquot of gas from the samples were introduced into the analyzer with the aid of a 22 mm gauge needle attached to the end of the inlet tygon tube. Between each sampling, the unit was purged to eliminate any residual gas from the previous sampling.After gas readings, the Ankom bags were removed from the bottles, rinsed, and dried in a 55 °C oven for 48 h. In vitro apparent degradable dry matter (IVADDM) and in vitro true degradable dry matter (IVTDDM) were estimated as described by Anele et al. . g at 4 °C for 15 min. Blanks were also centrifuged and used as a correction factor for residues from the buffered ruminal inoculum. Immediately afterwards, the supernatant was poured off and the samples were placed in a freezer for 24 h. The frozen samples were placed in a freeze dryer  for approximately 48 h. The tubes were then reweighed, and the microbial mass was calculated as: Microbial mass determination was based on Blümmel and Lebzien  with a sVolatile fatty acid concentration was based on the protocol of . The samThe dietary samples were analyzed for DM #930.15), N (#954.01), and ether extract  according to . Nitroge, N  in a 2 × 2 × 9 + 1 factorial arrangement. Treatment effects and interactions were examined by using the probability of difference (PDIFF) option of the least squares means statement in the MIXED procedure of SAS. The means were separated using Duncan’s multiple comparisons test at p < 0.001) influenced by EOBs and substrates. The values of the undegraded DM were higher for EOB treatments compared with the control treatment. There was a reduction (p < 0.05) in IVADDM with the inclusion of EOBs across the two diets. The IVTDDM values were affected (p < 0.001) by both EOBs inclusion and diet with higher values for the control treatment. The PF was influenced by both EOBs inclusion (p < 0.001) and the type of substrates (p = 0.005). The EOBs treatments recorded greater PF compared with the control treatments across the two substrates. The inclusion of EOBs had no effect (p > 0.05) on the microbial mass values across the two substrates.The effects of the EOBs and substrates on undegraded DM, IVADDM, IVTDDM, PF, and microbial mass are presented on p > 0.001) for total gas production, methane, carbon dioxide, ammonia, and hydrogen sulfide. At 24 h of incubation, the control treatment produced more (p < 0.001) gas than the EOB treatments. The GHG values were significantly (p < 0.001) reduced by most of the EOBs.The effects of EOBs and time on total gas production, methane, carbon dioxide, ammonia, and hydrogen sulfide are presented on p < 0.001) with EOBs inclusion and there was significant (p = 0.009) EOBs × time interaction. Interactions for EOBs inclusion × substrate × time were not significant for the NDF, ADF, and ADL degradability. Acid detergent fiber and ADL degradability were not affected with EOBs inclusion, but differences (p < 0.001) were noted in the substrates with lower values noted for HC diet. The effects of EOBs and time of incubation on NDF digestibility (NDFD), ADF digestibility (ADFD), and ADL digestibility (ADLD) are presented in The effects of EOBs, time, and substrates on the total and molar proportions of VFA production are presented on 4 production. For the EOBs, EOB1 was better than the other EOBs in terms of DM digestibility but about 5.6% lower than the control treatment. Higher DM digestibility noted for EOB1 for the HC diet also resulted in a lower undegraded portion when compared with other EOBs, which is an indication that EOB1 had a lower depressing effect on DM digestibility. The EOBs 3 and 6 had the highest undegraded value, which was 33.0% higher than the control treatment and 12.8% higher than EOB1. For the HF diet, the control, EOBs 1 and 8 had similar values for the undegraded material, which implied that these two EOBs did not suppress digestibility as noted for the other EOBs. Variations in the undegraded portions of the substrates reflect their chemical composition with lower values noted for the HC diet due to its lower fiber concentration. Consistent with our result on the effect of the EOBs on the HC diet, [The observed decrease in IVDDM in the present with the inclusion of EOBs was expected as lipids tend to reduce DM digestibility. The decline in digestibility with the inclusion of EOBs could also be associated with a reduction in protozoa population that help to engulf starch in the rumen . A reducHC diet,  reportedHC diet, ,19,20.Higher PF values noted for the EOBs with the exception of EOB1 for HC diet showed that the EOBs were able to partition more nutrients into microbial mass which indicates a better efficiency of microbial protein synthesis. The increase in PF in the present study with the inclusion of EOBs, especially EOB4, 5, and 6 for both substrates, could also be as a result of lower gas production and this observation is consistent with previous studies of ,22 who rMethane production followed a similar pattern noted for total gas production. Inclusion of EOB5 in the HC diet at 6 h of incubation reduced methane by 98.7 and 99.1% when compared with the control treatment and EOB8, respectively. For HC and HF diets, inclusion of EOBs 4, 5, and 6 reduced methane by 98.1 and 98.6%, 99.9 and 99.1%, and 97.9 and 97.1%, respectively, when compared with the control treatment. These numbers clearly showed that the EOBs vary in their abilities to inhibit methane production. Methane is linked to energy loss, and a decrease in production efficiency and EOs and EOBs could be effective in methane reduction and the modulation of rumen microbiome resulting in improved feed efficiency, and overall productivity in ruminants . AlthougResults in the current study showed that not all the EOBs suppressed NDFD as EOB1 and 9 had a similar NDFD value with the control treatment for the HC diet after 6 h of incubation. Most of the DM and fiber in a feed or ration are digested by ruminants. As a result, ruminal DM and fiber digestibility are useful indicators for assessing EOs effects ,30. In l2 competition [Consistent with the varied responses noted in the other variables in the current study, TVFA was reduced with EOBs inclusion for both diets and incubation time points with the exception of EOBs 1 and 9 in the HF diet after 6 h of incubation. Lower TVFA for the HC diet with EOBs inclusion is an indication of a reduction in DM digestibility (DMD) with an exception for EOBs 1, 8, and 9 where DMD was not affected at 6 h of incubation. Contrary to this assumption, the inclusion of EOB5 in the HF diet at 6 h of incubation did not affect DMD despite a reduction in TVFA. EOBs 1 and 9 in the HF diet had no effect on both TVFA and DMD. The lack of change in TVFA of the HF diet at 6 h of incubation with the inclusion of EOB1 could have been considered as positive if it had reduced ammonia and methane concentrations. On the contrary, EOB9 reduced both ammonia and methane concentrations. The lack of effect on TVFA concentration could be considered favorable if it was accompanied by other changes such as lower ammonia and methane concentrations, or a shift in VFA molar proportions . Van Soepetition . The effpetition . At 24 hOverall, the EOBs in the present study had different effects on in vitro fermentation characteristics. The inclusion of the EOBs modified the rumen function resulting in improved efficiency of microbial production. Both apparent and truly degraded DM was reduced in the EOB treatments. The inclusion of EOBs also resulted in reduced GHG emissions in both diets, except for EOB8 in the high forage diet which was slightly higher than the control treatment. The observed differences in their effects show that they could be used as feed additives to improve livestock production. Future directions include using different doses of the EOBs as well as examining their effects on microbial diversity.


The quality of neonatal resuscitation after delivery needs to be improved to reach the Sustainable Development Goals 3.2  by the year 2030. Studies have emphasized the importance of correctly performing the basic steps of resuscitation including stimulation, heart rate assessment, ventilation, and thermal control. Recordings with video cameras have previously been shown to be one way to identify performance practices during neonatal resuscitation.A description of a low-cost delivery room set up for video recording of neonatal resuscitation. The technical setup includes rechargeable high-definition cameras with two-way audio, NeoBeat heart rate monitors, and the NeoTapAS data collection tools for iPad with direct data export of data for statistical analysis. The setup was field tested at Mulago National Referral Hospital, Kampala, Uganda, and Phu San Hanoi Hospital, Hanoi, Vietnam.The setup provided highly detailed resuscitation video footage including data on procedures and team performance, heart rate monitoring, and clinical assessment of the neonate. The data were analyzed with the free-of-charge NeoTapAS for iPad, which allowed fast and accurate registration of all resuscitative events. All events were automatically registered and exported to R statistical software for further analysis.Video analysis of neonatal resuscitation is an emerging quality assurance tool with the potential to improve neonatal resuscitation outcomes. Our methodology and technical setup are well adapted for low- and lower-middle-income countries settings where improving neonatal resuscitation outcomes is crucial. This delivery room video recording setup also included two-way audio communication that potentially could be implemented in day-to-day practice or used with remote teleconsultants. The quality of neonatal resuscitation after delivery needs to be improved. In 2020 approximately 6,700 newborns died globally every day, 47% of all child deaths before 5 years of age. Most of these deaths occur during delivery, and the first day of life, and 98% in low- and middle-income countries (LMIC) , 2.The Sustainable Development Goals SDG 3.2) includes a specific target to end preventable neonatal deaths and decrease neonatal mortality to under 12 per 1,000 live births in all countries by 2030 .2 includ, 5. BirtTo improve neonatal resuscitation, we need to recognize what happens during this crucial time in a newborn's life, so we can review and optimize neonatal resuscitation to reduce unnecessary morbidity and mortality. This is true globally, from low- to high-resource settings. Furthermore, this is crucial in day-to-day services and clinical studies. It is essential to study neonatal asphyxia and its management in clinical settings. The need for basic and advanced resuscitation training and feedback for healthcare professionals, including doctors, midwives, and nurses, as well as teams, has been raised . A well-We introduce a low-cost, still high-quality way of identifying performance practices during neonatal resuscitation using a video camera, heart rate meter , and high-performance analyzing equipment . The collected data can be used to improve resuscitation performance, including adherence to guidelines and closed-loop communication.Our research team has used video cameras in previous studies and trials in Uganda, , 20 and In this methodology article, we introduce a comprehensive technical framework for monitoring newborn resuscitation and explain how it was implemented in two different settings: Kampala, Uganda, a low-income setting, and Hanoi, Vietnam, a lower middle-income setting.a)•Cameras: HD 1080P Black Box AI-IP018 (Shenzhen Aishine Electronics) (Uganda).HD 1080P PIR Black Box Wi-Fi Security Camera AI-IP018 cameras (Shenzhen Aishine Electronics) (Vietnam). (First used in Vietnam but later taken away).•Memory space: SD card  128 GB with space for up to 3.5 days of recording.•Hard drives, one for raw footage storage and one for cut version, 4 TB .•Homemade heat-proofed camera cases.•Software for cutting and mending data: Adobe Premiere Pro 64-bit version .Video cameras and video storage:This low-cost video recording setup for quality improvement in the delivery room includes:b)Heart rate monitoring equipment: NeoBeat Newborn HR Meter .c)iPad platform with software NeoTapAS for video analysis.d)Statistical software R.e)Local resuscitation table.f)Hospital-based neonatal resuscitation guidelines.In the list above, we used specific brands for practical quality reasons. However, similar equipment can be used.We describe a low-cost monitoring setup for the resuscitation table in the delivery room. This method helps to identify the process and actions of staff procedures to improve the quality of care. The method has been used at Mulago National Referral Hospital, Uganda, and Phu San Hanoi Hospital, Vietnam.We reviewed existing procedures and equipment, the organization of the resuscitation team, and current guidelines. This included discussions with the administration and clinical staff of the hospitals about how to minimize interference of monitoring with current practices and information about the study and equipment aimed to be used.At both sites, ethical approval was sought from the local institutional review boards. The decision regarding the number of cameras and NeoBeat monitors needed for the studies was done according to where most neonatal resuscitations took place at the hospitals.The equipment was installed to identify potential technical problems before the training of the staff. Cameras were placed at each resuscitation table away from the high-temperature area of the heater to capture exact footage of the newborn and the hands of the providers, eliminating the risk of staff identification. Also, placement was optimized for easy SD card access needed for the data collection. Tables were adapted to fit the cameras within the recommended focus range, between 60 and 120 cm. Cables and NeoBeat chargers  were alsMidwives, doctors, nurses, and technical staff were introduced to the study setup and trained in equipment management. Hospital executives were informed about practice changes, and cleaning staff were trained to avoid tampering with the new setup.Video cameras HD 1080P Black Box AI-IP018 (Shenzhen Aishine Electronics) were installed at three out of four resuscitation tables at Mulago National Referral Hospital, Kampala, Uganda, and recorded data around the clock. The cameras were placed so that only the hands of the resuscitator and the neonates were recorded. The SD cards of each camera were swapped every day to ensure that all recordings were properly saved . In caseEach morning and afternoon, the camera data manager checked the cameras to ensure that they functioned well, including power control and the orientation of the camera. In case of problems, the study coordinator or trial investigator was notified for corrective measures. Power banks were used to keep the cameras running in case of a power shortage. The steps of retrieving and saving data are summarized in step six. After retrieving data, each resuscitation of the day was identified by the data managers and shown to one of the study doctors to ensure that it was a case of need of PPV and that the complete case was caught on video.Video cameras HD 1080P PIR Black Box Wi-Fi Security Camera AI-IP018 cameras (Shenzhen Aishine Electronics)  were ins•RAW: Date -> Camera number -> VideosEach camera’s footage was secured by swapping the SD card and checking that the camera was recording the correct angle. The SD card with the latest data was downloaded onto the first hard drive  and marked RAW, identifying the date, and camera. The data were stored in folders with formats as follows:•CUT: filename of videos (ORDERNO_DDMMYY_LOCATION_ CAMNUM_PATIENTID) Patient ID was a generated study number.The technical staff downloaded all video data including identified resuscitations. The time when the resuscitations took place was recorded by the attending midwife or doctor. The sections with important data were selected using Adobe Premiere 64-bit version and transferred to storage in a 4 TB hard drive. A final evaluation of the resuscitation recordings was performed by a study doctor, also checking for missing data. The videos were stored in folders with formats as follows:Data extracted from the video recordings as well as maternal and neonatal characteristics were collected using standardized hard-copy case report forms. Resuscitation data were collected by analysis of the videos, using the NeoTapAS  app for https://github.com/Global-child-health-SDGs-Team-KI/Neonatal-Resuscitation.We used the free R statistical software . ThAt both study sites, all study-related information obtained was handled confidentially. All participant information, including case report forms, lists, logbooks, and any other listings linking participant ID numbers, was stored in locked file cabinets in areas with limited access. Research teams could only identify participants using a locally stored key file linked to the study numbers. Raw files were stored on a primary drive, cutout videos on a secondary drive, and backup copies were stored in a remote location . Videos The protocol for the Ugandan study was approved by the Institutional Review Board of Mulago National Referral Hospital, Uganda (MHREC 1168), the Uganda National Council of Science and Technology; the Director-General from the Ministry of Health, Uganda (MREC 1168); and the Regional Committee for Medical and Health Research Ethics (REK South-East reference number 2017/989) in Norway. The protocol for the Vietnamese study was approved by the Institutional Review Board at Dinh Tien Hoang Institute of Medicine, the Ethical Review Board of Hanoi (No: IRB-2001 and IRB-2002), and the Swedish Ethical Review Authority (Dnr 2021-00064/2021-03-01).Video recording with ethical considerations is today the standard for data collection in neonatal resuscitation , 35–37. The technical setup focused on neonatal resuscitation in two LMICs and had parallel protocols. We anticipated that the setups could produce data about the procedures and team performance in both LMICs. Below we summarize our experiences in the two different study settings.via Wi-Fi. The high quality of the camera footage allowed us to zoom in during the review, using standard software to identify additional details. A hanging kit using steel wires and duct tapes was made to secure the cameras. However, these hanging kits were difficult to reuse and magnetic adjustable camera mounts improved operations.Cameras were susceptible to overheating, and two cameras had to be replaced because of battery swelling. This happened although the resuscitation table heaters at Mulago Hospital were not in use. Power shortage was challenging but solved by connecting extra power banks to the cameras. We had cameras offline to avoid unsafe transferring data A total of 17,505 eligible neonates were born in the hospital during the study period of which 16,781 provided oral consent and 1,439 needed PPV. None were excluded because of missing video or poor quality. In total, 268 neonates were excluded due to other reasons. Thanks to the video reviews eight neonates were withdrawn from the main trial  .For handling case report form data collected simultaneously as video data, we used a Fujitsu iX1500 scanner to help us export the data for inclusion in our electronic database .To avoid overheating the cameras, we used three different setups. Staff surrounding the tables were trained to address possible overheating and avoid moving the camera angle . In the We initially used an HD-DV018 1080P Black box PIR Security Camera, AI (Shenzhen Aishine Electronics) . HoweverMidwives were unfamiliar with the new NeoBeat heart rate monitors  and needA total of 18,107 eligible neonates were born in the hospital during the study period of which 75 needed PPV. Fifty-seven neonates who received PPV were captured on a video camera, of which all provided oral consent. Thirty-six neonates required endotracheal intubation of which 24 were captured on video camera. The reasons for the neonates that were not captured on video recordings were: resuscitation on location with no camera (main reason); a problem with the camera . No videos had poor quality. No neonates were withdrawn from the study after the video review.For handling case report form data collected simultaneously as video data, we used a Fujitsu iX1500 scanner to help us export the data for inclusion in our electronic database .The quality of the overall documentation of neonatal resuscitation in medical charts has been investigated and points to an urge to improve documentation to evaluate resuscitation practices . In UganIn summary, we collected detailed resuscitation video footage with data showing the procedures done by the staff hands at the resuscitation table, including stimulation, ventilation, intubation, and heart rate monitoring with NeoBeat. NeoTapAS allowed fast and accurate registration of all events during video review. All events were automatically summarized in Excel format in a report ready for export. The information could then be directly exported to other statistical programs, such as R, for further analysis. The video footage provided crucial information to improve the quality of care during neonatal resuscitation in the delivery room.Important lessons learned:– Staff should be informed to notify data managers early if problems occur.– Each resuscitation table should have a charging station or extra batteries.– High-definition cameras with a minimum storage of 3.5 days are essential to eliminate the risk of running out of data storage if for unexpected reasons not changed according to plan.– In both settings, it got very hot at the resuscitation tables and the cameras were sensitive to heat, so we needed to protect the video cameras. In both LMIC settings, we have done this by optimizing/changing the placement of the camera and putting different kinds of heat shields around the most sensitive parts of the cameras and internal battery, as shown in – NeoBeat charging routines and correct placement of the device on the baby’s abdomen are critical for accurate data collection.– Even if relatively low-cost and easy to install, selecting the resuscitation tables where the equipment should be installed is important. Each extra camera takes time and adds an extra burden.– Video analysis for data documentation during neonatal resuscitation is not a perfect alternative. It is sometimes difficult to assess the quality of ventilation and the depth of suctioning. It is never possible to see the endotracheal tube position. The heart rate and saturation monitor needs to be placed so that they can be viewed on the video recordings, and even if the NeoBeat heart rate monitor is used it can sometimes be difficult to see the digits due to light reflections.If ethical approval allows, the video recordings can be used for training purposes and staff debriefing.We describe a method for a low-cost video recording setup for quality improvement at the newborn resuscitation table. The technical framework, data collection, and analyses used in one low- and one LMIC for identifying resuscitation procedures can provide highly detailed resuscitation video footage including data on procedures and team performance, heart rate monitoring, and clinical assessment of the neonate.Video recording for data documentation during neonatal resuscitation is increasingly regarded as a golden standard of practice. Both our own , 29, 30 Video footage also provided objective feedback on staff practice and performance, helping them to improve practice regardless of the research outcome. Progress in clinical practice during data collection is a confounder from a research perspective but justified in the context of site-specific quality improvement programs. The technical setup also holds the potential to be used in telemedicine programs in the future.We encountered technical issues such as overheating of cameras and software problems, but these issues were swiftly addressed. In recent studies, we used cameras with improved heat control, updated software , and higThis low-cost video recording setup has been used to monitor neonatal resuscitation. However, this method has the potential to be applied within a range of different areas to improve upon skills, procedures, and learning from experience, e.g., emergency care and trauma care.Video recordings can support staff debriefing and be embedded in site-specific quality improvement programs. The recordings can be used when training close-loop communication, in debriefings after complicated resuscitations with unpredicted results, and in general quality improvement programs focusing on neonatal resuscitation team training.Video analysis of neonatal resuscitation is an emerging quality assurance tool with the potential to improve neonatal resuscitation outcomes. The setup is well adapted for low- and lower-middle-income settings where improvement of neonatal resuscitation outcomes is crucial. The cameras also support two-way audio communication and could potentially be used with remote tele leaders or teleconsultants in the future.


TikTok was an important channel for consumers to access and adopt health information. But the quality of health content in TikTok remains underinvestigated.Our study aimed to identify upload sources, contents, and feature information of gallstone disease videos on TikTok and further evaluated the factors related to video quality.We investigated the first 100 gallstone-related videos on TikTok and analyzed these videos’ upload sources, content, and characteristics. The quality of videos was evaluated using quantitative scoring tools such as DISCERN instrument, the Journal of American Medical Association (JAMA) benchmark criteria, and Global Quality Scores (GQS). Moreover, the correlation between video quality and video characteristics, including duration, likes, comments, and shares, was further investigated.According to video sources, 81% of the videos were posted by doctors. Furthermore, disease knowledge was the most dominant video content, accounting for 56% of all the videos. The mean DISCERN, JAMA, and GQS scores of all 100 videos are 39.61 (SD 11.36), 2.00 (SD 0.40), and 2.76 (SD 0.95), respectively. According to DISCERN and GQS, gallstone-related videos’ quality score on TikTok is not high, mainly at fair  and moderate . The total DISCERN scores of doctors were significantly higher than that of individuals and news agencies, surgery techniques were significantly higher than lifestyle and news, and disease knowledge was significantly higher than news, respectively. DISCERN scores and video duration were positively correlated. Negative correlations were found between DISCERN scores and likes and shares of videos. In GQS analysis, no significant differences were found between groups based on different sources or different contents. JAMA was excluded in the video quality and correlation analysis due to a lack of discrimination and inability to evaluate the video quality accurately.Although the videos of gallstones on TikTok are mainly provided by doctors and contain disease knowledge, they are of low quality. We found a positive correlation between video duration and video quality. High-quality videos received low attention, and popular videos were of low quality. Medical information on TikTok is currently not rigorous enough to guide patients to make accurate judgments. TikTok was not an appropriate source of knowledge to educate patients due to the low quality and reliability of the information. Gallstone disease is a common disease worldwide. It occurs in 10%-15% of adults in the United States and Europe . CurrentThe unprecedented growth of web-based medical information has significantly changed the way people obtain health information. Nowadays, more and more patients seek information online before seeing a doctor . With thAlthough health content has been widely studied on video sites such as YouTube, it remains underinvestigated in emerging short-video apps such as TikTok . TikTok No clinical data, human specimens, or laboratory animals were involved in this study. All information used in this study was obtained from publicly released TikTok videos, and none of the data involved personal privacy. In addition, the study did not involve any interaction with users; therefore, no ethics review was required.In this cross-sectional study, the Chinese version of TikTok was searched on July 18, 2020, using the term gallstone in Chinese. Videos were sorted by comprehensive ranking with release time set to unlimited, which is the TikTok default. Non-Chinese videos were excluded until the top 100 videos in Chinese were displayed. We limited our analysis to the top 100 videos because multiple studies have confirmed that videos beyond the top 100 had no significant impact on the analysis ,18. For Video sources were categorized as follows: (1) doctors, (2) individuals , (3) news agencies , and (4) organizations . The content was categorized as follows: (1) surgery technique, (2) disease knowledge, (3) lifestyle, and (4) news. This classification allows us to group videos with the same content as much as possible, while distinguishing videos with different content.The quality of the information in videos was assessed using the DISCERN instrument, the Journal of American Medical Association (JAMA) benchmark criteria, and Global Quality Scores (GQS). The DISCERN instrument was developed for judging the quality of health information on treatment choices . It inclAll authors were senior surgeons engaged in hepatobiliary and pancreatic surgery for years and were knowledgeable in diagnosing and treating gallstone diseases. On the process for screening and rating, authors FS and JW used the DISCERN instrument, JAMA benchmark criteria, and GQS to evaluate the videos contemporaneously. The scores were determined through discussions. An arbitrator (SZ) resolved inconsistent scores between viewers and gave the final scores. Subsequently, all authors agreed on all the ratings.P<.05 was considered statistically signiﬁcant. Statistical analyses were performed using GraphPad Prism version 9.0.0 for Windows (GraphPad Software).Means and SDs were used for descriptive statistics. The Kruskal-Wallis test assessed differences between groups, and the Dunn multiple comparisons test for two-way intergroup comparisons of quantitative variables with no normal distribution. Spearman correlation analysis was used to evaluate the relationships between quantitative variables. The initial 100 videos collected by the TikTok search had 520,805 likes, 17,947 comments, and 181,355 shares. The mean video duration was 49.43 (SD 24.63) seconds. Moreover, the average number of days after uploading (days since upload) was 152.0 (SD 146.4) days by the data collection date.The mean DISCERN, JAMA, and GQS scores of all 100 videos are 39.61 (SD 11.36), 2.00 (SD 0.40), and 2.76 (SD 0.95), respectively. We found that JAMA was unable to make a discriminating and accurate assessment of video quality; the majority of the videos  were rated as 2, and others were rated as 1 , 3 , and 4 , respectively. Therefore, JAMA was excluded in the subsequent video quality and correlation analysis.P=.04 and P=.002, respectively), and DISCERN scores for surgery technique and disease knowledge were significantly higher than those of the news . Note that the reliability of gallbladder stone videos from professional sources and content is stronger than that of videos from nonprofessional sources and content. In section 2, DISCERN score of doctors was significantly higher than that of individuals and news agencies , and DISCERN scores for surgery technique and disease knowledge were significantly higher than those of news agencies . It indicates that the gallbladder stone videos from professional sources and content are better than those from nonprofessional sources and content in providing treatment information. In section 3, DISCERN score for doctors was significantly higher than that of the news agencies (P<.001), and DISCERN scores for surgery technique and disease knowledge were significantly higher than those of the news . Overall, the total scores of doctors were significantly higher than those of individuals and news agencies , scores for surgery techniques were significantly higher than those of lifestyle and news , and scores of disease knowledge was significantly higher than those of news (P=.007). In general, the quality of gallbladder stone video information from professional content and professional sources is higher than that from nonprofessional sources and nonprofessional content  and moderate . We put these 5 levels in one-to-one correspondence, and the 5-level scores are reasonably consistent in DISCERN and GQS . Furtherr=0.25, P=.01), likes and shares , likes and days since upload , comments and shares , shares and days since upload . Negative correlations were found between DISCERN scores and likes and shares of videos  of gallstone videos on TikTok were posted by doctors, ensuring the professionalism of the content. However, according to DISCERN and GQS video quality ratings, the doctors’ videos were not as good as expected. It is worth noting that TikTok does not authenticate the bloggers’ identities who claim to be doctors, so there may be some possibility that the identities are fraudulent, such as impersonating doctors to gain extra video views.The number of likes, comments, and shares reflect, to some extent, the popularity of a video or post ,26. We fThe mean DISCERN, JAMA, and GQS scores of all 100 videos are 39.61 (SD 11.36), 2.00 (SD 0.40), and 2.76 (SD 0.95), respectively. Gallstone videos on TikTok were mainly evaluated at fair and poor grading ranges, consistent with previous studies about video quality on YouTube . HoweverA positive correlation between video duration and video quality was found. The longer the video duration, the higher the video quality, which is the same conclusion as some previous studies . Most TiWe found a negative correlation between likes and DISCERN scores, as shown in In addition, the results may be related to the user characteristics of TikTok. As TikTok is a lifestyle entertainment app, its users prefer entertaining videos. Videos with beautiful graphics are more attractive. However, videos with high credibility are not popular. Because professional content tends to be unentertaining or even dull and therefore challenging to attract an audience, it is harder for them to gain popularity.Other studies have found that viewers may be more likely to seek video content that deviates from conventional treatment regimens . NontradWe found that the JAMA score could not accurately assess the video information because there were only 4 indicators, and they were not precise enough. This result was consistent with previous reports . TherefoThe likes and comments of individuals and news agencies are significantly higher than those of doctors and organizations, indicating that individuals and news agencies have more significant influence than doctors and organizations on TikTok, but the quality of videos is lower than that of professionals. On the one hand, news and individuals need to improve the quality of their information. On the other hand, doctors need to increase their influence. Medical video creators need to avoid boring content on the premise of professional content and better attract audiences to further spread the correct medical information.Due to the complexity and professionalism of medical content, the length of medical-related videos should be extended accordingly to ensure sufficient information and content quality. TikTok should give professional certification to some video creators who publish medical professional content. A certification mark can be given, increasing the audience’s recognition of the information and helping the dissemination of professional information. At the same time, a professional review can improve the quality and reliability of medical professional videos. When searching and evaluating medical content on the internet, health searchers mostly choose the results that appear on the first page of the search engine and rarely view the results that appear outside the second search results page ,32. TherIn this study, no advertising content was found in the search results for gallstones. Since TikTok mainly features entertainment videos, medical videos are still in the process of increasing. The same goes for advertising videos.This study is the first to use multiple tools  to evaluate the quality and reliability of gallstone-related videos on TikTok. We further analyzed the correlation between video duration, likes, comments, and shares and video quality, and found that likes and shares were negatively correlated with video quality, and video duration was positively correlated with video quality. This study is also the first to analyze the quality of surgical disease-related videos on TikTok.However, our study still has some limitations. Because these 3 evaluation tools were initially designed to evaluate textual content, there remain some limitations to the content of the video, such as not being able to evaluate the sophisticated production level and audio effect of the video. Furthermore, although we conducted a thorough analysis of the number of comments, we did not conduct a deeper analysis of the content of the comments. Sometimes, viewers leave comments not as a praise, but due to dissatisfaction. These negative reviews also increase the number of comments. Of course, we found in our research that the number of comments and likes, as well as the number of comments and shares, are positively correlated, so the number of comments can to some extent reflect the popularity of the video. Therefore, conducting a more in-depth study of positive and negative reviews in comments to explore their relationship with video quality is something we need to further analyze in future research. The current videos on TikTok are mainly for entertainment, and medical videos account for only a tiny part; therefore, further observation is needed to study the evolution process of medical videos on TikTok.Although videos on gallstones on TikTok are mainly provided by doctors and contain disease knowledge, they are of low quality. We found a positive correlation between video duration and video quality. High-quality videos received low attention, and popular videos were of low quality. Medical information on TikTok is currently not rigorous enough to guide patients to make accurate judgments. TikTok was not an appropriate source of knowledge to educate patients due to the low quality and reliability of the information.


P = .015). However, the video popularity was not related to its usefulness. The reliability and quality (3.70 ± 0.82 vs 0.75 ± 0.50 and 2.95 ± 1.21 vs 1.50 ± 1.00) assessed by the modified DISCERN tool and GQS, respectively, were significantly higher in the video clips presented by healthcare professionals compared to those presented by others. There were misleading YouTube video clips on CMT. Video clips presented by healthcare professionals could be more useful, reliable, and of better quality. The popularity of the video clips does not indicate more usefulness, reliability, and better quality. YouTube viewers should be aware of these findings. We recommend that the viewers preferentially choose video clips on CMT presented by healthcare professionals, not by the video popularity.This study aimed to evaluate the usefulness, reliability, quality, and related characteristics of YouTube video clips on congenital muscular torticollis (CMT). This cross-sectional study analyzed 47 YouTube video clips on CMT. They were classified as either useful or misleading by 2 rehabilitation doctors. The modified DISCERN tool and the Global Quality Scale (GQS) were used to evaluate their reliability and quality. An analysis was conducted using the characteristics, such as presenters, ownership of YouTube channel accounts, countries, contents, and the video popularity. Of the 47 YouTube video clips, 8 (17%) were evaluated as misleading, which indicated that they included at least one scientifically unproven piece of information on CMT or more. They were less reliable and of lower quality than the useful video clips. The video clips presented by healthcare professionals were more useful compared to those presented by others ( Family members of patients with CMT frequently access medical information, including a home exercise program, on CMT online, such as on YouTube. YouTube is a popular video-sharing platform commonly visited by patients and healthcare professionals for medical information. However, the usefulness, reliability, and quality of YouTube video clips on CMT are not yet known. Therefore, which characteristics of these video clips indicate more useful, reliable, and better quality are also unknown. Many views and likes may not always guarantee useful, reliable, or better-quality video clips. Therefore, the purpose of this study was to evaluate the usefulness, reliability, quality, and related characteristics of YouTube video clips on CMT.According to YouTube’s algorithm, related video clips are displayed continuously according to the video’s popularity, such as the number of views and likes. and the reporting followed the guidelines for reporting observational studies.This was a cross-sectional study based on the strengthening the reporting of observational studies in epidemiology guidelinesSince this study did not include human participants, the requirement for obtaining informed consent was waived by the Institutional Review Board of Ajou University Medical Center as of July 22, 2021.https://www.youtube.com/) using the following 5 keywords in Ajou University School of Medicine, Suwon, South Korea on July 9, 2021: “congenital muscular torticollis,” “muscular torticollis,” “torticollis,” “wry neck,” and “tilted neck.”We performed a comprehensive electronic search on CMT on YouTube , irrelevant n = 137), non-English (n = 18), no voice commentary or narration (n = 8), or adult authentication required (n = 13) video clips were excluded , others, and unknowns, which meant that there were no specific details on the presenter. The ownership of YouTube channel accounts was classified by the owner as follows: healthcare information websites, hospitals, physicians, physical therapists, academic/professional organizations, or nonmedical individual YouTube creators. The countries of the owners were identified. The contents of the video clips were classified as follows: general information, physical therapy, diagnostic methods, surgery, treatment guidelines, and pathogenesis of CMT.The characteristics of the video clips were obtained regarding the exposure period since the upload (days), duration of the video (minutes), number of views and likes, video popularity (views/day), number of likes per view, and number of likes per dislike. Video popularity was defined as the ratio of the number of views/days of the exposure period since the upload.,16,17 Useful video clips meant they were thought to provide medically and scientifically accurate and useful information on CMT. Misleading video clips included those thought to contain at least one scientifically unproven piece of information on CMT or more. If the judgment of the 2 reviewers was different, the discrepancy was resolved by discussion between them.The usefulness of the video clips was assessed by 2 board-certified rehabilitation doctors (KYJ and HJL) with multiple years of experience related to pediatric and musculoskeletal diseases. Before the assessment, the 3 authors had consensus meetings. The video clips were grouped into useful or misleading video clips.,18 The modified DISCERN tool was based on the DISCERN tool funded by the British Library.All video clips were independently assessed by 2 independent reviewers (KYJ and HJL). The reliability of the video clips was assessed using the modified DISCERN tool.It included 5 items and each question was answered with either “yes” (one point) or “no” (zero points):Are the aims clear and achieved?Are reliable sources of information used? .Is the information provided balanced and unbiased?Are additional sources of information listed for patient reference?Does the video address areas of controversy/uncertainty?In the modified DISCERN tool, zero indicated the poorest reliability, and 5 indicated the highest reliability.,19 by 2 independent reviewers (KYJ and HJL). The GQS, a subjective measure of the quality of video clips, was rated on a 5-point Likert scale.The quality of the video clips was assessed using the Global Quality Scale (GQS)Each video clip was rated with a score of 1, 2, 3, 4, and 5. Each score was expressed as follows:Score 1:Poor quality, poor flow of the video, most information missing, and not at all useful for patients.Score 2:In general, poor quality and flow, while some information was provided, many important topics were missing, and very limited use for patients.Score 3:Moderate quality, suboptimal flow, some important information was adequately discussed, however, others were poorly discussed, and somewhat useful for patients.Score 4:Good quality and generally good flow, most of the relevant information was listed, however, some topics were not covered, and useful for patients.Score 5:Excellent quality and flow, and very useful for patients.Therefore, score 1 indicated poor quality and score 5 indicated excellent quality.P < .05. Statistical analyses were performed using R version 4.1.2 .The intraclass correlation coefficient (ICC) was determined for the usefulness, reliability, and quality of the video clips on CMT as a measure of agreement between the 2 rehabilitation doctors who assessed them. Descriptive statistics, which included the presenters, ownership of YouTube channel accounts, countries, and contents, were presented as numbers and percentages for categorical variables. Variables of the video clips, such as exposure period since upload, playtime, and number of views, likes, and dislikes, were presented as means and standard deviations. Comparisons of the characteristics were performed using Fisher exact test and Kruskal-Wallis test for categorical and continuous data, respectively. Differences between groups were evaluated by subsequent post hoc analysis using multiple pairwise comparison tests with Bonferroni adjustments. The statistical significance was set at The demographic characteristics of the video clips on CMT are presented in Table The characteristics of the video clips on CMT are shown in Table  Furthermore, the agreement of the GQS was excellent with an ICC of 0.938 .There was no disagreement on the assessment of the usefulness of 47 video clips with an ICC of 1. The agreement of the modified DISCERN score was good with an ICC of 0.898 .Of the 47 YouTube video clips on CMT, 39 (83%) were assessed as useful. Hence, 8 (17%) were assessed as misleading, which indicated that they included at least one scientifically unproven piece of information on the CMT or more.The reliability score evaluated by the modified DISCERN was 2.91 ± 1.18 Table , where 0Regarding each item of the modified DISCERN, 43 video clips (91.5%) were clear and achieved (DISCERN 1). Reliable sources with 40 (85.1%) (DISCERN 2) and balanced and unbiased information with 38 (80.9%) (DISCERN 3) were used among the total video clips. However, the number of additional sources of information listed for patient reference (DISCERN 4) was only 9 (19.1%), and video clips that mentioned areas of uncertainty (DISCERN 5) were 7 (14.9%).The quality of the video clips evaluated by the GQS score was 3.98 ± 0.87 . The GQS was also significantly higher for the useful video clips compared to the misleading ones . The modified DISCERN scores ranged from 0 to 3 and 1 to 5 for the misleading and useful video clips, respectively . The subsequent post hoc analysis showed that the video clips presented by healthcare professionals were significantly more useful compared to those presented by others (P = .036).Table P = .723) and more views a day (P = .729), were not more useful than the less popular ones.Table P = .005 for modified DISCERN, P = .027 for GQS). In addition, the modified DISCERN score of the video clips uploaded by physicians was significantly higher compared to that of the video clips uploaded by nonmedical individual creators (P = .043). Otherwise, there were no significant differences among physical therapists, physicians, and occupational therapists. Lastly, the GQS score of the video clips uploaded by physical therapists was significantly higher compared to that of the video clips uploaded by nonmedical individual creators (P = .029). There was no other significant difference among the YouTube channel account owners.Table –23 Most patients with CMT have a favorable prognosis after comprehensive physical therapy. However, patients with neglected CMT may have chronic pain, limited range of motion of the neck, and secondary musculoskeletal deformities, such as craniofacial asymmetry and scoliosis.–27This is the first study to evaluate the usefulness, reliability, quality, and related characteristics of YouTube video clips on CMT. CMT is one of the most common musculoskeletal diseases in newborns. Its prevalence ranges from 0.3% to 2.0% and can be as high as 3.92% in neonates.Since most patients with CMT are newborns, the patient’s family caregivers need to understand the disease and pay attention to the appropriate treatment to improve the patient’s prognosis. Even though treatments by physical therapists may be more effective than a home program alone, parent or caregiver education and support to provide a daily home program are also useful for improving the patient’s prognosis. However, previous research has shown that not all YouTube video clips are useful.–17,29–31 To the best of our knowledge, the usefulness, reliability, and quality of YouTube video clips on CMT are still unknown. Therefore, we evaluated the usefulness, reliability, and quality of the video clips of CMT and analyzed which characteristics of video clips indicated more useful, reliable, and better quality.Recently, face-to-face treatment may be limited due to the COVID-19 pandemic. Hence, the obligations of family caregivers and the necessity of home programs are further emphasized. As YouTube becomes more and more accessible, family caregivers often use YouTube video clips for easy access to medical information regarding certain diseases.According to the results of this study, 39 out of the 47 identified video clips (83%) were assessed as useful. Nevertheless, 8 (17%) were assessed as misleading and showed relatively low modified DISCERN and GQS scores, which meant they contained unproven or inaccurate information on CMT which could lead to potential harm. Therefore, in this study, we performed characteristic analyses to find a way to choose more useful, reliable, and better-quality video clips.P = .005) and quality (P = .024) of the video clips presented by healthcare professionals were also higher compared to those presented by others.The most significantly different characteristic among video clips according to usefulness, reliability, and quality was the presenter among the demographic characteristics. Furthermore, as a result of pairwise multiple comparison tests between the presenters, video clips presented by healthcare professionals such as physical therapists, physicians, and occupational therapists were significantly more useful compared to those presented by others, which consisted of broadcasters, personal bloggers, and students. The reliability  were considered misleading even though they had more views compared to useful video clips.,34 Second, a comprehensive and quantitative evaluation was conducted on the characteristics of YouTube video clips on CMT. According to the results, it could be suggested to YouTube viewers to check the presenter as a criterion for preferentially choosing video clips, not video popularity. Third, as a cross-sectional study, we complied with the strengthening the reporting of observational studies in epidemiology guidelines.The strengths of this study are as follows. First, the inter-rater agreements between the 2 rehabilitation doctors on the usefulness, reliability, and quality assessment of the video clips on CMT were good and excellent. This was thought to be because CMT is a disease for which a relatively clear diagnosis and treatment are known. Second, the number of YouTube video clips on CMT was relatively small compared to video clips on other diseases with higher prevalence, such as rheumatoid arthritis or gout.,29 Third, not only a usefulness assessment of video clips, the DISCERN and GQS evaluation tools, which were widely used to evaluate the reliability or quality of YouTube video clips, could be affected by reviewer bias. However, as mentioned above, the inter-rater agreements on the assessment of the video clips on CMT were good and excellent. Fourth, as a cross-sectional study, a causal relationship could not be determined.This study also had some limitations. First, this study evaluated video clips on YouTube CMT only spoken in English. In the non-English-speaking population, there may be differences in YouTube’s characteristics due to differences in medical systems or accessibility to the YouTube platform.Approximately 83% of YouTube video clips on CMT were useful. However, this study found some inaccuracies in the medical information provided. Hence, 17% were misleading video clips on CMT. Video clips on CMT presented by healthcare professionals could be more useful, reliable, and of better quality. The popularity of video clips does not indicate more usefulness, reliability, and better quality. YouTube viewers should be aware of these findings. We recommend that viewers preferentially choose YouTube video clips on CMT presented by healthcare professionals, not by video popularity. Furthermore, healthcare professionals are recommended to actively participate in the development of YouTube video clips on CMT.Conceptualization: Shin-Young Yim.Data curation: Kil-Yong Jeong, Hyun Jung Lee.Formal analysis: Kil-Yong Jeong, Hyun Jung Lee, Shin-Young Yim.Investigation: Kil-Yong Jeong, Hyun Jung Lee, Shin-Young Yim.Methodology: Kil-Yong Jeong, Hyun Jung Lee, Shin-Young Yim.Project administration: Shin-Young Yim.Resources: Kil-Yong Jeong, Hyun Jung Lee, Shin-Young Yim.Software: Kil-Yong Jeong, Hyun Jung Lee.Supervision: Shin-Young Yim.Validation: Kil-Yong Jeong, Hyun Jung Lee, Shin-Young Yim.Visualization: Kil-Yong Jeong, Hyun Jung Lee.Writing – original draft: Kil-Yong Jeong, Hyun Jung Lee, Shin-Young Yim.Writing – review & editing: Kil-Yong Jeong, Hyun Jung Lee, Shin-Young Yim.