The global expansion of the Visual Internet of Things (VIoT)’s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput. Over the last two decades, the development of the Internet of Things (IoT) has demonstrated its excellent conditions, and it has the potential to be outfitted with “smart” things enriched with modern digitalization, allowing for flawless internet collaboration. This Internet of Things concept has progressed from modest and personal implementations to scale-integrated operations worldwide. In the late 1990s, Radio Frequency Identification (RFID) devices were popular for supporting various Internet-oriented applications. The worldwide flourishing of the Internet of Things (IoT) in the past decade has enabled numerous new applications through the internetworking of various devices and sensors. More recently, visual sensors have been deployed widely for various IoT applications because these sensors can provide more affluent and versatile information. The Internet of Video Things (IoVT) has been considered a large-scale visual sensor. Moreover, the IoVT has unique sensing, transmission, storage, and analysis characteristics that fundamentally differ from the conventional IoT. These new characteristics of IoVT are expected to impose significant challenges to existing technical infrastructures. This article investigates the depth of understanding of recent advances in the various fronts of IoVT and addresses a broad range of challenges [Presently, internet traffic has increased due to multimedia streaming at home or in the workplace after the global pandemic threat. Due to this traffic, there is tremendous pressure on the network infrastructure. Massive bandwidth is required to manage this internet traffic, which is also needed to support the multicasting technique with the support of IoT. The authors proposed software-defined NG-EPON for adaptive video data streaming using 5G technology. This video-streaming quality of service can be achieved at a high level by the incorporation of a Dynamic Bandwidth and Wavelength Allocation (DWBA) scheme .Visual sensor networks (VSNs) are widely used in smart homes, object recognition, and wildlife surveillance. Scalar sensors produce much less data than visual sensors do. These data are difficult to store and send. One popular video compression standard is high-efficiency video coding (HEVC/H.265). HEVC, which can compress the visual data with a high compression ratio but leads to high computational complexity, reduces the bit rate by about 50% compared to H.264/AVC while maintaining the same level of video quality. This paper suggests an H.265/HEVC accelerating method with excellent efficiency and hardware friendliness to overcome this complexity for visual sensor networks. The recommended method uses texture direction and complexity to accelerate intra-prediction for intra-frame encoding and skip redundant processing in the CU partition. Experimental findings showed that the suggested approach could decrease .The effective transmission of video streams is essential because video has overtaken other types of traffic as a significant kind of traffic through wired and wireless networks. Therefore, maximizing available bandwidth for wireless networks while maintaining user requirements for Quality of Service and Quality of Experience is vital. Accurately predicting future video frame sizes can be very helpful in achieving this goal. This research aims to produce such a precise prediction for videos encoded with H.264 and H.265, the two leading modern standards according to market share. Unlike other studies, we model the changing bit rate video traces using neural networks and employ single-step and multi-step methods to capture the long-range and short-range dependent features. We assess the convolutional long short-term memory’s accuracy [Real-time multimedia applications today demand good video quality at acceptable bitrates. Compared to other coding schemes, the H.264 coding provided low bitrate costs while maintaining excellent video quality, but it was limited to higher rates. Later, by offering higher video quality at an effective bitrate, High-Efficiency Video Coding (HEVC) improved on H.264. This enhancement requires increased processing costs due to improved methods such as quad-tree for CTU partitioning. The framework for CTU partitioning proposed in this paper, CtuNet, is approximated using deep learning methods. To forecast the CTU partition of the HEVC standard, a ResNet18-CNN model is used. We used cutting-edge techniques to establish a baseline for our recommendation. The outcomes show how superior the suggested CtuNet is to the competing strategies .The video has become crucial for monitoring, identification, and knowledge sharing. Videos encoded using particular protocols are transferred to intelligent gateways for industrial applications, namely the Industrial Internet of Things (IIoT). In a typical IIoT scenario, the video protocol is initially identified to set up future video tasks. It is difficult for IIoT because of the limited resources in such systems; the video quality can deteriorate during encoding and compression procedures. Deep neural networks (DNNs) have recently been the focus of substantial studies concentrating on protocol identification (PI) and video quality enhancement (VQE) activities on IIoT edge devices. DNNs frequently need a lot of computing power; sophisticated networks are hardly ever deployable on edge devices .To meet the demand for multimedia services, video transmission in the IoT system must ensure video quality while lowering the packet loss rate and latency with limited resources. This research suggests an energy-efficient IoT video transmission technique that guards against interference based on reinforcement learning. The encoding rate, modulation and coding system, and transmit power of the IoT device are all under the control of the base station. Without knowledge of the transmission channel model at the transmitter and receiver, the state-action-reward-state-action reinforcement learning algorithm selects the transmission action based on the observed state. The authors suggested an energy-efficient IoT video transmission technique based on deep reinforcement learning that uses a deep neural network to approximate the Q value to speed up the learning process for selecting the best transmission action and to enhance video transmission performance. A summary of the literature is shown in A 5G network can offer high throughput with low latency by connecting many devices using a IoT-based network infrastructure. Nowadays, the 5G-enabled IoT gives service to smart cities, healthcare, defense, education, etc. One of the biggest tasks of the 5G-enabled IoT is to mitigate the service response time with computational techniques. The authors proposed the edge computing technique with the IoT for smart city users .Compared to 5G, 6G networking is considered to show much better performance with respect to reliability parameters, low delay and high bandwidth. Furthermore, 6G technology is developed by migrating space, cellular, and underwater networks .From the literature, it is suggested that video quality transmission is more important in the case of telemedicine and education sectors. Due to the bandwidth constraints, large volumes of data transmission and the mobility of the user vehicles, propagation path loss, etc., are reasons for the reduced video quality. The challenges for the transmission of quality video data are bandwidth limitation, compression and expansion of data speed, and latency in data transmission from the transmitter to the receiver. The existing H.264 and H.265 protocols in the 5G network video signal transmission give less accuracy and efficient data transmission. Suppose additional compression techniques are used at the transmitter and receiver devices. In that case, additionally, there is a big challenge to balance the quality of the video, the compression levels, and the latency in the video data display. To provide video compression without losing any video quality, the authors propose a novel concept of the LVT model with VIoT incorporation into the KNN merged H.265 protocol and H.264 protocols. The main objective of this proposed research is to improve video quality with less latency in the sensor network. The authors identified a significant improvement between the proposed KNN classifier-based H.265 protocol and the existing typical H.265 and H.264 protocols using performance metrics such as PSNR, Packet loss rate, SSIM, VMAF, latency, and throughput.One of the main challenges in the sensing phase is the development of VIoT-specific video data compression and LVT to the raw data shown in The VIoT has already progressed with a set of global standards for MPEG and HEVC. Due to fundamental differences in video streams, adopting video compression in VIoT is an open research issue. In general, while compressing entertainment films, the high-definition reproduction of virtually all images is essential for comfortably watching video content. The ultimate goal of VIoT applications is to preserve the relevant information rather than the pixel value. As a result, keeping the spatial contents and settings of VIoT data is the essential quality for VIoT video compression and sensor data processing. Novel video compression algorithms fundamentally differ from current MPEG and HEVC standards that should maintain the best interpretation and background of the sensor data throughout the compression process. Recently, the development of a new video coding standard for machine communications produced enormous amounts of information for various applications. Because of the unique features of visual sensor data, VIoT systems can reveal more insights than those typical of IoT systems. These unique features enable VIoT systems to reach a wide range of new application industries by adding new aspects to existing IoT applications. The obtained visual sensor data can be pooled, analyzed, and interpreted using modern data modeling, machine learning, and supervised learning techniques. The resulting knowledge, which includes the recognition of behaviors and trends, reveals new perspectives that can affect each aspect of our lives, from better congestion control to crime prevention and from primary prevention to environmental protection ,12,13.Lossy Video Transmission (LVT) simulator is a framework for analyzing the impact of network congestion on the segmentation of video frames (at WVSNs) (on the decoder side). The main goal of WSNs in system simulators is scalability  capability to manage large groups of nodes. Because LVT focuses solely on image quality assessments, it can work enormous sets of simulations with multiple images, procedures, and loss patterns. Simply put, LVT simulation consists of five major components or stages ,15.(1) Forward Feature Extraction: Using an image processing algorithm on an input image. This method yields a customized rendition of the original image .(2) Packetization: The use of a packetization system. This step connects processed image data to packets.(3) Packet loss Modeling: This stage simulates data loss during network transmission. The losses are created randomly or by inputting a loss pattern file.(4) Depacketization: The packetization scheme’s inverse application.(5) Reverse Image Acquisition: The process of running the inverse image processing method. As a result, a version of the source image is created with some lost sequences.(6) Error Hiding: An error concealment approach might be used to fill the gaps.(A) LVT simulated modelI is an  matrix, I = {Ir,c}, with r, c ϵ Χ ˄ 0 ≤ r ˂ L ˄ 0 ≤ c ˂ B; each Ir,c pixel has its b bits in each pixel, where b ϵ R+ considers the communication system Ґ by transmitting I in ⌊(L × B × b)/m⌋ with packets P, and where m states the number of bits allocated for video data transfer in a packet. During communication, each packet pl has a chance of being lost. Various loss models can be employed to accomplish this. Because packet drops are expected to occur over a wireless channel with one or more intermediary nodes, the path characteristics should be irrelevant to the simulation . Averaging the well-received neighboring pixels yield an estimate of lost data for error concealing.(i) Proposed simulation model: The models used are fundamental. An incoming video frame i,jF, where fL and fB are the length and breadth of the block frame, and i,j = {Irf,cf}F, where, j + 1).(ii) Encoding of Frames: As previously noted, the earliest form of LVT included a frame coding error-resilient computation technique. In a typical single block-based transmission, the image is first divided into ith block to the packet in a sequential transmission, with I, where i,jF can be kept in position  Video Quality AssessmentLVT aims to support the measurement of the quality of produced image frames in WVSNs. Both subjective and objective assessment indicators for quality evaluation are conducted. Direct visualization of the rebuilt frames provides subjective judgment.(i) Peak Signal-to-Noise RatioAs shown below, the peak signal-to-noise ratio (PSNR) is employed to evaluate the restoration quality of the suggested image restoration by SR, wherein MSE denotes the mean squared error.(ii) Mean absolute deviation (MAD)ix = pixel values; n = number of pixels value;The mean absolute deviation IQA model is formulated as a deviation of spatial regions from its average values in Equations (2) and (3):(iii) Structural similarity index (SSIM)µ) and contrast (σ) as follows and as shown in Equations (4) and (5)C1 and C2 are constants, the picture structure is determined by normalizing, as illustrated in Equation (6):The structural similarity index (SSIM) is used to compare the resemblance of low input resolution and high-resolution images using orthogonal quantitative measures such as luminance . The concept of Macroblocks is used in H.265. The typical size of each macroblock can be different; 16 × 16 or 8 × 8 luma/chroma channels define the best size. Contemporary to macroblock is Coding Tree Unit; both can be combined to form a slice. The slice can be decoded separately even if the same frame is unavailable [The encoding standard used is H.265. It consists of a video coding layer that encodes and transmits the video over the network. The video is broken up into three frames. vailable ,18,19,20The generated encoded video from the VCL is converted into bits by NAL, which further improves the efficiency of the video transmission. For example, RTP can be converted into mp3 or mp4 for video storage, and NAL can be classified into two types—VCL NAL and Non-VCL NAL units. The former carries the data for the video requirements, and the latter has additional overhead information. H.265 defines different types of NAL standards ,22.The authors applied the proposed LVT model with VIoT sensors to the modified H.265 protocol. The above figure shows the machine learning-based H.265 protocol containing the KNN Classifier for the feature extractions by training and testing the samples. In general, H.265 protocol has video compression and extractors, but it contains low computational speed. Due to this, the latency will be increased, which causes the efficiency decrement. To improve the latency by reducing the computational rate, the authors proposed merging the KNN classifier in the H.265 protocol, as shown in The LVT and VIoT sensors are placed at the transmitter and receiver to process the video. The KNN-based H.265 protocol is applied to compress and extract faster in the network devices. The video transmission takes care of the LVT using compression and extraction, and the VIoT sensors are for measuring the video quality. These two are computed based on the KNN-H.265 protocol. The main objective is to balance the process between the LVT and VIoT without any latency, as well as to provide excellent video quality.A simple architecture was developed with three modules: a transmitter, a packet failure model, and a receiver. The proposed architecture comprises compressed video data using a modified H.265 protocol to realize the packet loss and improve video streaming . The comThe KNN-based H.265 model was used as a reference for the encoder at the transmitter section. Video data such as live streaming or any recorded video can be transmitted from base station transmitter to user receiver device. The primary parameters used while transmitting the video are listed in To determine the errors in the packet sensing and transmission, four types of packet errors—propagation, sensing, busy receiver, and collision errors—were analyzed in ,29,30,31The main objective of this design is to ensure data transfer without packet delays. The decoding of the received data should be conducted one at a time in the receiver section so as to avoid the impact on QoE metrics. The second criterion is that the loss sequence should match the parameter set, e.g., if there is a 10% increase in the PLR, then the same 10% increase must also be seen with packet loss. This requirement is satisfied using a random number generator of the packet loss model with a number of seeds fixed. Next, by keeping payloads fixed, RTP packets are transmitted by H.265, and the loss sequence should not depend on cross-traffic from any other application ,31,32,33This section discusses results concerning the packet loss environment and the delay in investigating the behavior of prospective metrics in the presence of packet delay in various IoT scenarios. The proposed protocol was assessed and compared with existing state-of-the-art works using MATLAB software. The assumptions for the simulation environment are shown in We employed a learning curve to analyze the KNN model with the H.265 protocol and with LVT model detection. The learning curve of the KNN model shows that the training and test converges together, as we observe in The authors of the research in  proposedThe new Internet of Video Things refers to an emerging type of IoT system incorporating wireless visual sensors at the front end (VoIT). The H.265 coder with a LVT programmable framework for simulating video communications in Wireless Visual Sensor Networks has been experimented on with error-tolerant methodologies such as block connecting. The KNN classifier-based machine learning techniques with LVT and VoIT were applied to the H.265 protocol for the fast computation and transmission of video data packets from transmitter to receiver. In this context, the proposed protocol’s performance was compared and analyzed with the existing H.264 and H.265 protocols. The parameters of throughput, delay, PSNR, Packet loss ratio, and SSIM were used to estimate the protocol’s performance. The proposed protocol achieves improved performance by reducing latency by up to 190 ms compared to the existing state-of-the-art H.264 and H.265 protocols; in terms of throughput, the proposed protocol achieves improved capacity compared with the H.264 and H.265 protocols, with a nominal 300 ms of delay. Therefore, the performance of the proposed protocol can achieve better results when compared with the existing protocols with regard to lower latency and higher throughput. The proposed work was compared with the existing MS-KNN single buffer model. The proposed work gives 16%, 5% and 15% better improvement in terms of collision error, propagation error and sensing error, respectively. There is a plan to extend this work in the future by applying LSTM-based machine learning algorithms using the KNN-H.265 protocol.


Purpose: O3® Regional Oximetry  is validated for cerebral oximetry. We aimed to assess agreement of somatic and renal near-infrared spectroscopy with reference blood samples. Methods: O3 sensors were placed bilaterally on the quadriceps and flank of 26 healthy volunteers. A stepped, controlled hypoxia sequence was performed by adding a mixture of nitrogen and room air to the breathing circuit. O3-derived oxygen saturation values were obtained at baseline and at six decremental saturation levels (5% steps). Blood samples  and renal vein) were obtained at each step. Reference values were calculated as: 0.7 × venous saturation + 0.3 × arterial saturation. The agreement between O3-derived values with blood reference values was assessed by calculating root-mean-square error accuracy and Bland-Altman plots. Results: The root-mean-square error accuracy was 6.0% between quadriceps oxygen saturation and somatic reference values. The mean bias was 0.8%, with limits of agreement from -7.7 to 9.3%. These were 5.1% and 0.6% (-8.3 to 9.5%) for flank oxygen saturation and somatic reference values, respectively, and 7.7% and -4.9% (-15.0 to 5.2%) for flank oxygen saturation and renal reference values. The kidney depth was 3.1 ± 0.9 cm below the skin. Conclusion: O3 regional oximetry can be used on the quadriceps and flank to monitor somatic saturation, yet has a saturation-level dependent bias. O3-derived values obtained at the flank underestimated renal reference values. Additionally, it is unlikely that the flank sensors did directly measure renal tissue. Trial registration: Clinicaltrials.gov (NCT04584788): registered October 6th, 2020. Oxygen deficit is a determinant of postoperative morbidity and mortality , 2. Near® Regional Oximetry  was validated for cerebral oximetry in healthy volunteers by comparing cerebral oxygen saturation values with invasive reference oxygen saturation values . The accuracy of the sensors was determined by comparing somatic regional oxygen saturation readings from the O3 sensor and blood reference oxygen saturation values and calculating the root-mean-square error accuracy. Additionally, the agreement was evaluated by Bland-Altman plots, allowing the calculation of bias and limits of agreement between the O3-derived oxygen saturation values and invasive reference values. To account for repeated measures and missing values, we used the Parker method to calculate the weighted mean bias and the total SD of the bias  . The 95%cerebral tissue oxygenation agreement studies. Generally, in such studies a minimal detectable difference in regional oximetry values between 3% and 5% (SD between 2% and 8%) is used, which requires the inclusion of about 20 subjects when a power of 0.8 and an alpha of 0.05 is set together with a  effect size of 0.1 [We estimated 25 subjects would provide adequate power for our primary endpoint, based on assumptions derived from previous studies in e of 0.1 .2 plateau, leading to 6 missing data points. The available data were included in the analysis since they were all obtained from dark-skinned volunteers. Another dark-skinned volunteer was included after approval of the Ethics committee, to ensure that an adequate proportion of our samples represented this population. This resulted in a total of 26 subjects  and blood sampling (n = 1). One subject was excluded being an outlier in terms of body mass index . One subject did not start the hypoxia procedure due to an adverse event  during femoral venous sheath placement. In three subjects the hypoxia procedure was prematurely terminated due to a decrease in mean arterial pressure of more than 30% of the baseline value after the 90% (n = 1) and 75% (n = 2) SpOThe root-mean-square error accuracy between quadriceps oxygen saturation and somatic reference oxygen saturation was 6.0%. The mean bias and SD between quadriceps oxygen saturation and somatic reference was 0.8 ± 4.3% with limits of agreement from − 7.7 to 9.3% Fig.  showed tThe root-mean-square error accuracy was 7.7% for flank oxygen saturation and renal reference. The mean bias was − 4.9 ± 5.2%, resulting in limits of agreement from − 15.0 to 5.2% (Fig. somatic reference values obtained from the radial artery and iliac vein during a controlled hypoxia sequence in healthy volunteers, however a saturation-level dependent bias was present. Additionally, flank oximetry underestimated invasive renal reference values obtained from the radial artery and renal vein.Quadriceps and flank oximetry had a good agreement with invasive In a previous study, in which O3 cerebral oximetry monitoring was validated, the authors found a bias of 0.4% between cerebral oxygen saturation values and blood reference values of cerebral oxygen saturation . In our Flank oxygen saturation underestimated renal reference values. It is imperative to investigate whether these flank sensors actually reflect renal oxygen saturation, as has been done previously , since iThis is one of the very few studies that incorporates invasive organ oxygen saturation monitoring in a tightly controlled experimental study in healthy volunteers , 7. Moresomatic reference values, with bias and limits of agreement that appear to be comparable to a previous study validating O3 cerebral oximetry. However, a saturation-level dependent bias was present. Additionally, flank oximetry underestimated invasive renal reference values and should not be considered a surrogate for renal tissue oxygen saturation. We believe it is unlikely that the renal tissue was within reach of the flank sensor.In conclusion, O3-derived quadriceps and flank oximetry had a good agreement with invasive


Short videos on social media are playing an increasingly important role in cancer health education today. It is important to explore how the actual communication effect of health videos and the knowledge absorption of users are influenced by different factors of the video creation process.The objective of our study is to access the factors influencing breast cancer health education through short videos on efficiency and quality.t-test was used to analyze within-group change scores. RM-ANOVA was used to assess the relationship between the pretest, posttest, and three variables.Three pairs of videos about breast health were created and participants completed questionnaires before and after watching the videos. A paired p < 0.05). The viewers’ concentration level while watching was significantly higher for the video with background music (BGM) than for the video without BGM (p = 0.006). The viewers’ willingness to share was significantly higher for the video with a progress bar than for the video without a progress bar (p = 0.02). Using an interpreter wearing a doctor’s uniform instead of casual wear and setting a progress bar can significantly improve the efficiency of knowledge absorption (p < 0.05).Watching short videos can significantly increase viewers’ knowledge of related health topics (A uniformed interpreter, BGM and a progress bar are factors influencing the efficiency of short health videos. They can be applied in video making to explore better ways of promoting cancer health education in the new mobile Internet environment. In the past decade, the importance of social media as a means of disseminating health information has grown , 2. AccoBreast cancer is the most prevalent cancer among women and a worldwide public health problem. Global Cancer Statistics 2020 pointed out that breast cancer has surpassed lung cancer as the cancer with the highest incidence rate in the world . VariatiA study showed that short health videos on social media platforms are extremely popular among older women in China. Guidance through short health videos can help women establish new health awareness and lifestyle dynamics . With thPrevious research on the impact of popular science videos also discovered that seductive details refer to interesting content that is not necessarily related to the teaching objectives  and can According to prior research on health science popularization and the perception of doctors, the attire of the characters in the video, the presentation of the video directory structure, and the background music (BGM) may be the three main variables that affect the impact of science popularization video transmission. (1) A survey found that 69.9% of the 153 patients surveyed preferred doctors to wear white coats, because it made them feel more at ease and confident in their doctors . Zou et This study examines the factors that affect the transmission of health science short videos and discusses the impact of visual and auditory factors on the transmission effect of these videos. In the form of a questionnaire, we investigated the different effects on patients before and after watching videos to verify users’ information absorption and conversion degree of short video content. The aim was to reflect the communication effect from multiple perspectives.Based on the above discussion, in addition to the content of short videos, some auditory and visual forms of expression, such as background music, interpreter’s dress, program progress bar, and other factors, may affect the attention of viewers and ultimately affect the absorption efficiency of popular science knowledge. Therefore, this study proposes the following three hypotheses:H1: A interpreter wearing the uniform is a factor influencing the efficiency of short health videos.H2: BGM is a factor influencing the efficiency of short health videos.H3: A progress bar is a factor influencing the efficiency of short health videos.The three hypotheses put forward 3 variables, so we conducted a pretest-posttest control group trial on participants’ performance in their breast-health literacy under the 3 variables.We used a pretest-posttest control group design, which we developed on the basis of literature on the effect of online short videos. This study was conducted to assess the influence of three variables on the quality of breast health public education : (1) HowTo present the influence of the three variables mentioned above, the video production team, consisting of proficient short video producers and science popularization workers with medical backgrounds, created three groups of videos on three topics: (1) hyperplasia of mammary glands (2) Familial aggregation of breast cancer ; (3) marriage, childbirth, and breast cancer . AE (Adobe after Effects) and AI (Adobe Premiere) were used to clip the video and design the background, mask, and text layers to form the progress bar. The pink mask gradually expands over time to cover the background layer, allowing viewers to visually see the progress of the video. The work was completed under the direction of a Ph.D. in sociology and a breast surgeon, who acted as gatekeepers for the video design and scientific rationality, respectively. The duration of the video was between 3 and 4 min. Except for the variables we studied, all control variables in every pair of videos were completely consistent .To measure the effectiveness of the three variables mentioned above, we developed 2 questionnaires for the participants to complete before and after watching the three pairs of videos . The preThe questionnaires passed the verification of Credamo, which evaluated their negative effect. Eligible Credamo users could access the surveys from the platform.The participants’ performance in both the pretest and posttest reflected changes in their breast-health literacy, thus reflecting the effect of three variables and testing our hypotheses. Questions on both the pretest and posttest consisted of single and multiple choices. To ensure that the behavior of watching the video is related to breast health knowledge rather than changes in awareness, we designed the pretest and posttest based on the knowledge points mentioned in the video. Participants can only improve their accuracy by watching the video carefully and understanding the corresponding knowledge points, which cannot be achieved only by strengthening their awareness. The options in the two tests roughly corresponded. For a couple of options distributed in the pretest and posttest, if a participant answered the former correctly and the latter wrongly, −1 was marked, meaning that his/her breast health literacy had decreased after watching the video; if a participant answered the former incorrectly and the latter correctly, 1 was marked, meaning that his/her breast health literacy had increased after watching the video; and if a participant’s answer to both the former and the latter were correct or incorrect, 0 was marked, meaning that his/her breast health literacy had not changed significantly after watching the video.For each of the videos, participants were recruited through the Credamo platform to watch the video and fill out the questionnaires. The survey lasted from April 24 to April 29, 2022, and involved 780 participants from 31 provincial-level administrative regions . Of the 780 respondents, 27 respondents were excluded for poor quality of the questionnaires or an abnormal amount of time spent on the questionnaires (too long or too short).During the trial, there were 780 visits to the questionnaire website, and 753 (96.5%) participants were found to be eligible for the study. No significant differences in age, sex, education, or employment were found between the two groups for each video pair .t-test was used to analyse within-group scores changes. RM-ANOVAs were used to assess the relationship between the pretest, posttest, and three variables, with a significance level of p < 0.05. Analyses were performed using SPSS software version 24.0.Descriptive statistics were calculated for the participants’ demographic data. A paired p = 0.006). The viewers’ willingness to share was significantly higher for the video with a progress bar than for the video without a progress bar (p = 0.02).A 5-point scale was used to evaluate the public attitude toward the videos. A score of 3 points indicated a neutral attitude. The average score was calculated and is displayed in t-test was applied to estimate the difference between the pretest and posttest.To assess the effectiveness of the videos, the paired p < 0.05), meaning that the three groups of videos significantly improved the participants’ breast healthy literacy  and posttest (10.055 ± 1.594) was higher in the group in which the interpreter wore a doctor’s white coat  than in the group in which the interpreter wore casual wear. In the group in which BGM was present or absent, the score difference between the pretest (8.691 ± 1.953) and posttest (10.802 ± 2.558) was higher in the group in which BGM was present  than in the group in which BGM was absent. In the group in which the progress bar was present or absent, the score difference between the pretest (8.215 ± 1.343) and posttest (8.868 ± 0.875) was higher in the group in which the progress bar was present  than in the group in which the progress bar was absent.These experiments, which were designed to verify the influence of clothing, the progress bar, and BGM on the video science popularization effect, all adopted a mixed design, and each experiment included an intrasubject variable and an intersubject variable. Among them, the test time (pretest/posttest) was the intrasubject variable, and the intersubject variable was the clothing worn by the characters in the video , whether the video had a progress bar, and whether the video had BGM. Repeated measures ANOVA was used to test the interaction of these two factors. The simple effect test was used when the interaction was significant to show which factor influenced the effect of video science popularization.F = 5.196, p = 0.023). In the pretest stage, there was no significant difference between the test scores of the two kinds of clothing . In the posttest stage, the score of the white coat group was significantly higher than that of the casual wear group . This shows that compared with casual wear, a white coat can significantly improve subjects’ cognition of video science popularization knowledge points. In the progress bar experiment, the interaction of two factors (test time*progress bar factor) had a significant edge . In the pretest stage, there was no significant difference between the test scores of the two progress bar types . In the posttest stage, the score of the group with a progress bar was significantly higher than that of the group without a progress bar . This shows that the presence of a progress bar can significantly improve subjects’ cognition of video science popularization knowledge points. In the BGM experiment, the interaction of the two factors (test time*BGM factor) was significant . In the pretest stage, there was no significant difference between the test scores of the video with and without music . In the posttest results, the score of the group with BGM was higher than that of the group without BGM, but the difference was only marginally significant and did not reach a significant level . The results show that the progress bar had a lesser effect than the other two factors on improving the subjects’ cognition of video science popularization knowledge points.The simple effect analysis results are shown in In general, the clothing of the characters in the video and BGM had a significant impact on the cognitive improvement of the subjects’ popular science knowledge, and the progress bar had a significant edge effect.Previous studies of influencing factors of health video creation have focused on three main aspects: (1) conception of the script ; 2]; and , Philosophy and Social Science Planning Project of Guangdong Province in China (GD21YXW01), Research Start-up Funding Project of Shantou University (STF19026), the National Undergraduate Training Program for Innovation and Entrepreneurship (202110560036), Natural Science Foundation Committee of China (No. 82102948), Natural Science Foundation of Guangdong Province in China , Interdisciplinary project of Li-Ka-Shing Foundation (No. 2020LKSFG05C) and Guangdong Science and technology Special Fund project (210715106900933). The Medical Science and Technology Research Foundation of Guangdong Province (Grants No. B2020138); Shantou Technology Bureau Science Foundation (Grants No. [2019] 106).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We also prefer to use “crowdsourcing” over AI to investigate the type of questions asked in this paper because it can give the researcher the opinion of a very large number of evaluators, like AI, but with the benefit of allowing the gathering of information on the demographics of the evaluators, leading to a much clearer picture of how different groups judge the level of attractiveness.In this Video Commentary, we agree with the authors’ desire to quantify the improvement in “emotion” of varying facelift techniques through artificial intelligence (AI), as a “proof of concept” approach to objectively judging surgical results (Video).ojad049_Supplementary_DataClick here for additional data file.


Performance on hold-out videos remained high for nest attendance (F1 = 0.990), active nursing (F1 = 0.828), and licking and grooming (F1 = 0.766) but was lower for eating, drinking, and self-directed grooming (F1 = 0.534–0.554). A set of 242 videos was used with AMBER and produced behavior measures in the expected range from postnatal 1–10 home-cage videos. This pipeline is a major advancement in assessing home-cage dam-pup interactions in a way that reduces experimenter burden while increasing reproducibility, reliability, and detail of data for use in developmental studies without the need for special housing systems or proprietary software.Mother-infant interactions during the early postnatal period are critical for infant survival and the scaffolding of infant development. Rodent models are used extensively to understand how these early social experiences influence neurobiology across the lifespan. However, methods for measuring postnatal dam-pup interactions typically involve time-consuming manual scoring, vary widely between research groups, and produce low density data that limits downstream analytical applications. To address these methodological issues, we developed the Automated Maternal Behavior during Early life in Rodents (AMBER) pipeline for quantifying home-cage maternal and mother–pup interactions using open-source machine learning tools. DeepLabCut was used to track key points on rat dams (32 points) and individual pups (9 points per pup) in postnatal day 1–10 video recordings. Pose estimation models reached key point test errors of approximately 4.1–10 mm (14.39 pixels) and 3.44–7.87 mm (11.81 pixels) depending on depth of animal in the frame averaged across all key points for dam and pups respectively. Pose estimation data and human-annotated behavior labels from 38 videos were used with Simple Behavioral Analysis (SimBA) to generate behavior classifiers for dam active nursing, passive nursing, nest attendance, licking and grooming, self-directed grooming, eating, and drinking using random forest algorithms. All classifiers had excellent performance on test frames, with F Intergenerational patterns of maternal behavior are observed in several mammalian species, thus effects of these early social experiences can be perpetuated across generations4.Maternal behavior during early life in mammals ensures offspring survival by supporting the physical needs of the offspring including transfer of nutrients and warmth to young and protection from predators and the environment. Beyond ensuring survival, mother–offspring interactions also guide emotional, social, and cognitive development of offspring. Maternal behaviors provide stimulation to offspring through vestibular, auditory, tactile, and visual modalities during sensitive periods of elevated plasticity early in life. This somatosensory input directs development of pup physiology to have lasting effects on offspring brain and behavior across the lifespan6. Maternal behavior can moderate environmental effects on offspring by buffering the effects of the environment, or increase susceptibility when disruptions to maternal care occur. Maternal effects signal to the infant the conditions of the distal environment and may modify offspring physiology to prepare the offspring for the environment they will encounter later in life8. Early environmental exposures are also potential contributors to vulnerability for neurodevelopmental disorders, which can be moderated by maternal-offspring interactions9.Factors in the broader ecological environment, such as chemical exposure, distal stressors, resource availability, and social context, can affect offspring development in early life through alterations to maternal offspring-directed behavior14. Like humans, rodent maternal behavior is influenced by the external environment and by offspring physiological and behavioral cues17. Measures of rodent pup-directed maternal behaviors typically include nest attendance, anogenital and body licking and grooming of pups, nursing , nest building, and retrieval of pups when they are displaced from the nest18. Natural variations of home-cage maternal behaviors in Long-Evans rats are well characterized and variation in these maternal behaviors leads to shifts in offspring developmental trajectories24.The extensive, enduring effects of mother–offspring interactions and potential for disruption of these interactions by external influences make maternal behavior an important measure for all developmental studies. Laboratory rodent models are a preferred choice for studying the impact of early life exposures on offspring development because of the ability to precisely control environmental conditions, their relatively short lifespan, overlap in the hormonal and neural mechanisms governing maternal behavior with humans, and ability to examine the molecular physiological outcomes following early life exposures not possible or ethical in human research25. However, between-study methodological variation in the acquisition and coding of this data limits comparisons between studies. Time-sampling methods, where point observations about maternal behavior are made throughout the day, permit analyses of behavior throughout the circadian cycle, but this approach has poor temporal resolution relative to focal observations and prohibits accurate quantification of behavior durations27. Frequency, duration, and bout length of maternal behaviors are increasingly used in analyses, particularly measures of “entropy” that are used to capture consistency of dam behavior patterns28. However, these measurements are notoriously time-intensive to score from videos and involve substantial training on coding maternal behavior. To address these issues, more sophisticated tools are needed to quantify maternal behavior over longer periods of time.Scoring of home-cage maternal behavior live or through video recordings provides detailed information on dam-pup interactions guiding pup development29. Using open-source tools, we developed the Automated Maternal Behavior during Early life in Rodents (AMBER) pipeline for scoring home-cage maternal behavior. AMBER uses DeepLabCut31 to extract pose estimation data from rat dam and pups from home-cage recordings within standard cages. Side-view recordings permit tracking of key body points on dams and pups, unlike top-down recordings where the dam occludes pups during nest attendance. AMBER then uses Simple Behavior Analysis32 (SimBA) to run behavior classifiers for seven maternal behaviors: nest attendance, licking and grooming, active nursing, passive nursing, self-directed grooming, dam eating, and dam drinking. We applied the AMBER pipeline to a set of 242 videos from postnatal day (P) 1–10 to evaluate maternal phenotype. All scripts and models used in the pipeline are publicly available .Recent advances in machine learning tools for behavior analysis have the capacity to build standardized behavior pipelines for video data to produce high-quality and highly-reproducible measurementsHome cage behavior was recorded from Long-Evans dams and litters on postnatal day (P) 1–14 with Raspberry Pi 3B + minicomputers equipped with Raspberry Pi Module 1 NoIR cameras. One hour and 24-h recordings captured video during the light and dark phase under infrared LED lights.30. 4,710 frames were extracted from 255 videos. Thirty-two dam body points were labeled providing adequate coverage of the dam regardless of partial body occlusion or body orientation relative to the camera  across frames in the second half of the multi-animal DeepLabCut workflow. Although pup detections achieved good performance, we observed substantial loss of pup points after individual assembly, where only pups with a majority of tracked body points visible had reliable tracking. A primary goal of tracking pups for this pipeline is to identify nest location, so the litter can be treated as a unit rather than identify individual pups. Therefore, all pup key point detections, at the midpoint of the multi-animal workflow by running the DeepLabCut function analyze_videos with auto_track = false, rather than individual pup tracks obtained at the end of the workflow, were used for pup pose estimation. This ensured that all pup detected key point coordinates are kept for the entire litter, with the drawback of not know which points belong to specific pups. A custom python script (pheno_pickle_raw.py) was used to convert the pup detections pickle file to a csv file containing pup key point detections. Raw detection key points assigned to individual pups after conversion to csv do not necessarily belong to the assigned pup.Next, unfiltered dam and pup pose estimation files were joined by frame number using a custom script (join_dam_pup.py). Column headers were reformatted to match the expected input by SimBA.https://github.com/sgoldenlab/simba)32. We used a single animal SimBA project configuration with user-defined body points consisting of all dam and pup key points. Width of the wire cage top at the lowest point  was used to define pixels per mm during the Video Settings step  was used to generate seven behavior classifiers from pose estimation data from a subset of videos using the standard workflow except where noted below  from 28 one-hour videos and 10 additional shorter clips (1.5–22 m each) were annotated for nest attendance, active nursing, passive nursing, licking and grooming, self-directed grooming, eating, and drinking. Behavior definitions are provided in Fig. https://github.com/lapphe/AMBER-pipeline17. Because of the large number of frame annotations, the frames used for training classifiers was reduced by taking every other frame from each video. Adjacent frames are likely to have similar features and behavior annotations, so this allowed for a reduction in data set size while maintaining diversity of the training set within and across videos. 20% of remaining frames were used as a test set to evaluate model performance. Frequency of behaviors in the training and test set ranged from 2.5% (passive nursing) to 47.8% of frames (nest attendance). Random forest models were run in SimBA with the following hyperparameters: 100–1500 trees, minimum node = 1 or 2, RF_criterion = gini, RF_max_features = sqrt, test size = 20%, and no sampling adjustment  were manually scored as a hold-out video validation set. Because no frames from the hold-out videos are used for model training, this allows for evaluation of model generalizability.1 = 0.990), active nursing (F1 = 0.828), and licking and grooming (F1 = 0.766). Self-directed grooming, eating, and drinking classifiers performance was substantially lower (F1 = 0.534-0.550). Lower performance for eating and drinking was partly due to overlap in false positive and false negatives between these classifiers. Considering eating and drinking as a single behavior improved performance . Passive nursing did not occur in the hold-out video set, precluding performance evaluation for that classifier.Behavior classifiers were evaluated by calculating the precision , recall , and F1 scores  for all models , licking and grooming , active nursing , and passive nursing  significantly decreased with litter age. Drinking  and eating  significantly increased with litter age and self-directed grooming  did not significantly change with litter age  to least important (rank 217) based on feature importance score for between-model comparison. The average rank for each feature category was calculated for heatmap visualization . Figure Shapely Additive Explanations (SHAP) is another explanability metric that uses a game theoretic approach that can be applied to tree-based machine learning models to allocate the contributions of individual features to the overall final behavior probability in a frame based on magnitude of feature attributions1 scores of 0.990 (nest attendance), 0.828 (active nursing), and 0.766 (licking and grooming). AMBER uses open-source software, standard rat housing equipment, and does not require any specialized recording hardware or animal identification markers. When paired with automated recording equipment, home-cage behavior can be collected from an entire cohort of animals simultaneously for long time periods while avoiding the effects of experimenter presence or bias on behavior. Maternal behavior affects a variety of developmental outcomes, and AMBER eliminates the reproducibility concerns, training, and inter-rater reliability drawbacks of manual scoring home cage maternal behavior, allowing assessment of maternal behavior regardless of behavior expertise.Continuous home-cage monitoring is an optimal approach to assess dam-pup interactions in a laboratory setting, but the burden of manual scoring limits the implementation of this approach. We present a pipeline that automates scoring of rodent dam-pup home-cage video recordings to produce frame-level annotations of seven maternal behaviors with high accuracy. Pup-directed maternal behaviors performed particularly well on the hold-out video set with F37. Licking and grooming, nest attendance, and self-directed grooming were relatively normally distributed, with the range of percent of time for licking and grooming similar to frequency observed in time-sampling studies for Long-Evans rats37. Active nursing was not normally distributed, although this difference may be attributed to the one-hour observation during early dark phase for the present study versus sample observations throughout the dark and light cycle used in previous work38. The number of bouts for behaviors was very high in a few videos  the convex hull areas of the dam will get larger as she moves toward the nest (closer to the camera) at the front of the cage; (2) dam-pup Euclidian distances will decrease when the dam is interacting with pups and will increase when the dam is off-nest; (3) dam movement features are informative for classifiers that can be operationally defined by specific body movements (e.g. licking and grooming); (4) the water bottle is located in the top of the cage, so the angle of the dam’s back can be informative in identifying drinking behavior.Model explanability metrics shed light on the “black-box” of behavioral classification by providing articulated descriptions of how features influence model performance that allows users to critique model construct validity and compare different models beyond model performance42. We are currently expanding these model training sets to include frames from videos of Sprague–Dawley rats, C57/Bl6 mice, and CD1 mice in different home cages to make the pose estimation models more robust and able to perform well for a wider variety of rodent developmental studies. These models will be made publicly available on the AMBER repositories. Despite these current limitations, the AMBER pipeline is a significant step forward for improving analysis of home cage dam-pup interactions to provide standardized, detailed behavioral data likely to yield new insights in developmental studies.AMBER is a substantial improvement over manual scoring methods for dam-pup home cage behavior, but also has some notable limitations. Classifier performance for pup-directed behaviors may be compromised in recordings where pups are occluded for the duration of the video since pup coordinate information and dam-pup distances are important features for several classifiers. This shortcoming could be circumvented by manually adding nest location information in place of pup tracking. Furthermore, the behavior classifiers presented here are trained on tracking information from side-view recordings and will not generalize to top view recordings. We chose side view recordings to allow for better pup tracking and to eliminate the need for any specialized home-cage equipment as many standard home cages contain food and water in the cage lid. In addition, F1 scores for self-directed grooming, eating, and drinking classifiers were lower on the hold-out video set compared to the test set. The improvement in performance when combining eating and drinking behaviors suggest that information about the location of the food and water could improve the models. Finally, the pose estimation models at present are optimized for detecting key points in Long-Evans rats and are unlikely to generalize well to other rodent species that are visually different without training on additional frames. Likewise, differences in camera angle, bedding material, enrichment objects, cage layouts, or lighting in recordings compared to the training videos may interfere model transferability, requiring some additional labeled frames and pose estimation model retrainingab libitum through bottles held at approximately a 45-degree angle in the wire tops. Eighty-eight adult P60–70 Long-Evans females and 35 adult Long-Evans males were purchased from Charles River Labs and acclimated to the vivarium for at least two weeks before breeding. During breeding, P75–85 females were screened daily for receptive behavior and housed with a breeder male overnight on the day lordosis was observed. All dams were socially housed throughout pregnancy until they were separated into individual cages a few days before giving birth. Day of birth was considered P0.All animal protocols were approved by the IACUC at the University of Texas at Austin, were performed in accordance with IACUC guidelines and regulations, and are reported in accordance with the ARRIVE guidelines. Animals were housed in polycarbonate cages (19″ × 10.5″ × 8″) with standard wire tops and were kept on a 12:12 h light cycle (lights off at 10 am EST). All dams were provided with Aspen shavings (Nepco) for bedding material, which can be manipulated by dams to construct nests. No additional bedding was provided. All animals were fed standard chow (Lab diet 5LL2) and water https://github.com/lapphe/raspberry_rat.Home cage behavior recording was conducted with Raspberry Pi 3B + minicomputers running Debian bullseye with the Raspberry Pi Desktop and equipped with Raspberry Pi Module 1 NoIR cameras. One Raspberry Pi was placed perpendicular to the short end of the cage and closest to the nest location for each cage Fig. . Raspber30. A total of 4,710 frames were extracted from 255 videos. Thirty-two dam body points were selected from 60 candidate body points for labeling based on the user ability to label key points consistently and to provide adequate coverage of the dam regardless of partial body occlusions or orientation relative to the camera  from 28 one-hour videos and 10 additional shorter video clips (1.7–22 min each) were annotated for nest attendance, active nursing, passive nursing, licking and grooming, self-directed grooming, eating, and drinking . These hold-out videos were of four different dams and pups of different ages (P2-9). Passive nursing is an infrequent behavior in Long-Evans rats provided with sufficient bedding material and unfortunately, passive nursing did not occur in any of the four videos precluding evaluation of the passive nursing classifiers in the hold-out video set.36.242 videos of one-hour home cage recordings taken beginning one hour after lights off on P1–10 from 49 dams were used in the AMBER pipeline workflow as shown in Fig. https://osf.io/e3dyc/.Feature importance permutations were calculated for each behavior classifier in SimBA. SHAP analysis was run in SimBA on 150 random frames with behavior present and 150 random frames with behavior absent for each model. Full results files for feature importance permutations and SHAP analysis are available on our OSF repository: All models were trained on a Dell Precision 7920 Tower with a Dual Intel Xeon Gold 5122 3.6 GHz processor, 64 GB RAM, Windows 10 operating system, and a NVIDIA Quadro P5000 video card. Pose estimation models were trained using DeepLabCut version 2.3. Behavioral classifiers were generated using SimBA version 1.65.5. Python was used for pickle file conversion and pose estimation data joining.Supplementary Information.


ATP-independent molecular chaperones are important for maintaining cellular fitness but the molecular determinants for preventing aggregation of partly unfolded protein substrates remain unclear, particularly regarding assembly state and basis for substrate recognition. The BRICHOS domain can perform small heat shock (sHSP)-like chaperone functions to widely different degrees depending on its assembly state and sequence. Here, we observed three hydrophobic sequence motifs in chaperone-active domains, and found that they get surface-exposed when the BRICHOS domain assembles into larger oligomers. Studies of loop-swap variants and site-specific mutants further revealed that the biological hydrophobicities of the three short motifs linearly correlate with the efficiency to prevent amorphous protein aggregation. At the same time, they do not at all correlate with the ability to prevent ordered amyloid fibril formation. The linear correlations also accurately predict activities of chimeras containing short hydrophobic sequence motifs from a sHSP that is unrelated to BRICHOS. Our data indicate that short, exposed hydrophobic motifs brought together by oligomerisation are sufficient and necessary for efficient chaperone activity against amorphous protein aggregation. BRICHOS domain oligomerization exposes three short hydrophobic motifs that are necessary for efficient chaperone activity against amorphous protein aggregation. Among them, ATP-independent molecular chaperones, like the small heat shock proteins (sHSPs) prevent toxic consequences of protein aggregation by binding to un- or misfolded substrates and keeping them in a soluble, refolding competent state4. Bri2 is a type II transmembrane protein, ubiquitously expressed, and its proteolytic processing releases a molecular chaperone domain—BRICHOS6. Interestingly, other molecular chaperones like HSP60, HSP70, and HSP90 are part of the Bri2 interactome in the brain and retina8. The isolated Bri2 BRICHOS domain modulates the aggregation pathways of several amyloid-forming substrates, averts amyloid-associated toxicity, and prevents non-fibrillar, amorphous protein aggregation, similar to ATP-independent molecular chaperones like crystallins or clusterin13. The ability of the Bri2 BRICHOS domain to interact with substrates involved in protein aggregation disorders like Alzheimer’s disease16, type II diabetes17, and Parkinson’s disease18 highlights its potential importance for cellular proteostasis.Molecular chaperones are central to the proteostasis machinery by assisting correct folding of polypeptides20. The human Bri2, and Bri3, BRICHOS domains assemble into large oligomers of about 20–30 subunits, which efficiently inhibit amorphous protein aggregation. In contrast, Bri2 BRICHOS monomers and dimers, as well as the BRICHOS domain from prosurfactant protein C (proSP-C), which mainly exists as trimers, are all inactive against amorphous protein aggregation20. The BRICHOS domains from different families have low pairwise sequence identities but homology models of the Bri2 BRICHOS monomer based on the proSP-C BRICHOS crystal structure show a conserved central five-stranded β-sheet that is flanked by two α-helices connected via a long flexible loop .The BRICHOS domain is about 100 amino acid residues in size and exists as monomers or multimers ranging from dimers to large polydisperse oligomers stabilized by covalent and non-covalent interactionsoop Fig. 19–22. To10. Data suggest that the structural plasticity of ATP-independent molecular chaperones mediates the exposure of individual binding regions for either amyloid or amorphous clients. While the binding sites for amyloid substrates might depend on the substrate and type of fibrillar aggregate25, the flexible and partially disordered N-terminal domains (NTDs) of sHSPs, enriched in hydrophobic residues, appear important for the formation of high-molecular-weight assemblies and interaction with amorphously aggregating model substrates29.Classical ATP-independent sHSP molecular chaperones, like αB-crystallin, assemble into large polydisperse and dynamic complexes and can bind to a diverse set of protein substratesIn this study, we aimed to understand the architecture of the Bri2 BRICHOS domain oligomers, and define the client binding site regarding the chaperone activity against amorphous protein aggregation. Our results show that three short hydrophobic motifs in an unstructured loop of the Bri2 BRICHOS domain constitute the key region for stabilizing partly unfolded substrates against amorphous protein aggregation, are important for oligomer plasticity but do not dictate the ability to retard amyloid formation.Ictidomys tridecemlineatus (NCBI accession number: XP_013216182) and Octodon degus (NCBI accession number: XP_004633182) lack the entire loop but are otherwise almost identical to human Bri2 BRICHOS and are here referred to as delta loop (ΔL) Bri2 BRICHOS  were produced. Strikingly, NT*-loop oligomers  are able to reduce CS aggregation by about 40%, while NT*-loop monomers are inactive  and rhodanese (Rho), which unfold and form amorphous aggregates at elevated temperatures, in the absence and the presence of oligomeric fractions of all BRICHOS loop variants. LS Bri2 and ΔL Bri2 BRICHOS have significantly reduced activities against both CS Fig.  and Rho ate Fig. . LS Bri2ate Fig.  but theyate Fig. . The rea31. ΔL Bri2 BRICHOS, independent of its assembly state, has similar efficiency against Aβ42 fibril formation as wt Bri2 BRICHOS monomers  was used to trace the kinetics of Aβ42 fibril formation11. These results suggest Bri2 BRICHOS oligomers can prevent amorphous protein aggregation by forming complex with partly denatured substrates. To probe the solvent accessibility of the Bri2 BRICHOS loop in active oligomers, we designed a reporter system based on Trp fluorescence , but still no correlation is found for the activity against Rho . No correlation was detected between the motif hydropathies and the number of constituent subunits of the chaperone oligomers, although all mutant oligomers had somewhat smaller average sizes than the wt Bri2 BRICHOS counterpart  and (i) the loop motif hydropathy or (ii) oligomer size  is partly disordered, has several hydrophobic motifs and has been shown to be important for binding of amorphous substratesins Fig. . The resins Fig.  form larins Fig. . Oligomeive Fig. . Intriguive Fig. , fit almive Fig. .37 points to that formation of larger oligomers is essential. We found that the hydrophobicity of the three short loop motifs ultimately determines the chaperone activity seemingly by defining the affinity of the substrate-binding site as well as the quaternary structural arrangement in BRICHOS oligomers. The essential importance of the loop hydrophobicity was perhaps most clearly demonstrated by loop-swaps from Bri2 BRICHOS or αB-crystallin, respectively, to proSP-C BRICHOS, which resulted in highly efficient oligomeric chaperones, in contrast to the essentially completely inefficient wt proSP-C BRICHOS trimers  using a Superdex 200 column  as an additional last step. The gene fragment of the human GKN2 BRICHOS domain, corresponding to residues 21 to 151 of human GKN2, was cloned into a modified pET32 vector containing the His6-NT* tag and expressed and purified according to the protocol used for Bri2 BRICHOS. Mutations were introduced via PCR using KAPA HiFi HotStart ReadyMix PCR Kit  or constructs were synthesized by GenScript biotech. All sequences were confirmed by DNA sequencing (Eurofins Genomics). BRICHOS protein concentrations were determined by measuring the absorbance at 280 nm using the respective extinction coefficients. All concentrations given are based on monomeric subunits.All variants of the recombinant human Bri2 BRICHOS domain (residues 113 to 231 of human Bri2 BRICHOS), LS Bri2 BRICHOS, ΔL Bri2 BRICHOS, the BRICHOS chimeras αB Bri2 BRICHOS and αB proSP-C BRICHOS that harbor residues 1–18 of human αB crystallin , and the isolated Bri2 BRICHOS loop (residues 187 to 216 of human Bri2 protein) were cloned into a modified pET32 vector containing the spider silk derived His6-NT* tag−1 protein samples were prepared in 20 mmol L−1 NaPi, 0.2 mmol L−1 EDTA, pH 8.0 in a quartz glass cuvette with a 1 mmol L−1 path length. Five consecutive scans per sample between 180 and 260 nm, with an increment of 0.5 nm, were acquired. Averaged and blank subtracted spectra were converted to the mean residue ellipticity .Circular dichroism (CD) spectra were recorded at 25 °C in a J-1500 CD spectrophotometer (JASCO) with a PTC-517 Peltier thermostat cell holder. Five or 10 µmol L−1 protein samples were prepared in 20 mmol L−1 NaPi, 0.2 mmol L−1 EDTA, pH 8.0, and the same buffer was used as SEC running buffer.Analytical SEC was performed using a Superdex 200 increase column using a 500 µL sample loop. 10 µmol LFor tryptophan fluorescence measurements, recombinant proteins were prepared in triplicates in 20 mM NaPi pH 8.0 with 0.2 mM EDTA with a volume of 150 uL in black polystyrene flat-bottom 96-well plates (Costar) and excited at 280 nm (5 mm bandwidth). Fluorescence emission from 300–400 nm  was recorded using a spectrofluorometer (Tecan Saphire 2). Data were corrected by subtracting the background fluorescence (buffer).−1 porcine heart citrate synthase (CS)  and 3 µmol L−1 bovine liver rhodanese (Rho)  were followed by measuring the absorbance at 360 nm in 90 s cycles during incubation at 45 °C using a POLARstar Omega plate reader . Samples were prepared in black, clear-bottom half-area polystyrene 96-well plates  and the reactant volume was 100 µL. Stock solutions of the model substrates were prepared in either 40 mmol L−1 HEPES/KOH, pH 7.5 (for CS), or PBS, pH 7.4 (for Rho) and diluted in buffer containing 40 mmol L−1 HEPES/KOH, pH 7.5 (for CS) or PBS, pH 7.4 (for Rho). Samples were complemented with 20 mmol L−1 NaPi, 0.2 mmol L−1 EDTA, pH 8.0 for equal buffering conditions from BRICHOS proteins. Measurements were performed in triplicates and aggregation kinetics analyzed by integrating the area under the curve, expressed as aggregation mass normalized to the aggregation of CS alone. Molar ratios indicated refer to monomers. For complex formation, 0.6 µmol L−1 CS  was incubated with 6 µmol L−1 rh Bri2 BRICHOS oligomers in 40 mmol L−1 HEPES/KOH pH 7.5 at 45 °C using a POLARstar Omega plate reader . The final product was injected into a superose 6 column (Cytiva) and eluted with 20 mmol L−1 NaPi pH 8.0 containing 0.2 mmol L−1 EDTA, and different fractions were collected for SDS-PAGE and native PAGE analysis. The BRICHOS-CS complex was applied to carbon-coated copper grids , negative-stained with 1% phosphotungstic acid (PTA) and observed under transmission electron microscopy .Thermo-induced aggregation of 0.6 µmol LE. coli cells and the fusion protein NT*- Aβ42 was purified by immobilized metal affinity chromatography (IMAC)47. Purified NT*- Aβ42 was cleaved by tobacco etch virus (TEV) protease overnight in cold room, lyophilized and re-dissolved in 7 mol L−1 Gdn-HCl. The Aβ42 monomers were isolated in 20 mmol L−1 sodium phosphate pH 8.0 with 0.2 mmol L−1 EDTA by a Superdex 30 column . The monomeric Aβ42 concentration was calculated using an extinction coefficient of 1 424 M−1 cm-1 for (A280−A300). For Aβ42 fibrillization, 20 µL solution containing 10 µmol L−1 ThT, 3 µmol L−1 Aβ42 monomer and different concentrations of the BRICHOS variants at molar ratios 0, 10, 50, and 100% (relative to Aβ42), were added in wells of half-area 384-well microplates with clear bottom , and incubated at 37 °C under quiescent conditions. The ThT fluorescence was recorded with a 440 nm excitation filter and a 480 nm emission filter using a microplate reader . For all the experiments, aggregation traces were normalized and averaged using four replicates, and data for one set of experiments were recorded from the same plate.Recombinant Aβ(1–42), here referred to as Aβ42, was produced fused to the NT* solubility tag in BL21*(DE3) 49, and the aggregation half time A is the amplitude and F0 the base value.Aggregation profiles of Aβ42 in the presence of different concentrations of BRICHOS variants were fitted to an empirical sigmoidal equation (Eq. )48,49, aM(t), is described by the following integrated rate law32:kn, k+, and k2 are the primary nucleation, elongation, and secondary nucleation rate constants, respectively, and the parameters nC and n2 are the reaction orders for primary and secondary nucleation, respectively.The aggregation traces of the total fibril mass concentration, kn, k+ or k2, is the sole fitting parameter49.We identified the microscopic events inhibited by BRICHOS variants by applying Eq.  to descrThe protein aggregation data for testing chaperone activities and the ThT fluorescence data are presented as means ± standard deviation, and the aggregation traces are averaged from 3–4 replicates.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary DataReporting Summary


I2 tests were calculated; a forest plot was generated, and the pooled odds ratio (OR) with 95% confidence intervals (95%CI) were calculated using the random effects model. Results: Of the 90 studies identified, 12 were finally included in the meta-analysis. In the random effects model, malnutrition or increased malnutrition risk increased odds of in-hospital death by more than three-fold: OR 3.43 (95% CI 2.549–4.60), p < 0.001. The pooled prevalence estimate for malnutrition or increased malnutrition risk was 52.61% (95% CI 29.50–75.14%). Discussion and Conclusions: It is clear that malnutrition is an ominous prognostic sign in patients hospitalized with COVID. This meta-analysis, which included studies from nine countries on four continents with data from 354,332 patients, is generalizable.Background: Malnutrition and increased malnutrition risk are frequently identified in hospitalized adults. The increase in hospitalization rates during the COVID-19 pandemic was accompanied by the documentation of adverse hospitalization outcomes in the presence of certain co-morbidities, including obesity and type 2 diabetes. It was not clear whether the presence of malnutrition increased in-hospital death in patients hospitalized with COVID-19. Objectives: To estimate the effect of malnutrition on in-hospital mortality in adults hospitalized with COVID-19; and secondarily, to estimate the prevalence of malnutrition in adults hospitalized with malnutrition during the COVID-19 pandemic. Methods: EMBASE, MEDLINE, PubMed, Google Scholar, and Cochrane Collaboration databases were queried using the search terms malnutrition and COVID-19 and hospitalized adults and mortality. Studies were reviewed using the 14-question Quality Assessment Tool for Studies with Diverse Designs (QATSDD) (questions appropriate for quantitative studies). Author names; date of publication; country; sample size; malnutrition prevalence; malnutrition screening/diagnostic method; number of deaths in malnourished patients; and number of deaths in adequately nourished patients were extracted. Data were analyzed using MedCalc software v20.210 . The Q and  Malnutrition, or more specifically, undernutrition, can be defined as the adverse clinical, functional, metabolic, and anthropometric outcomes of nutrient deficiency . MalnutrMalnutrition prevalence estimates in older adults range from less than 5% among healthy community-dwelling seniors  to more Malnutrition has been associated with multimorbidity, frailty, sarcopenia, inflammation, and insulin resistance, leading to increased mortality . The impThe Global Leadership Initiative on Malnutrition (GLIM) acknowledged that while undernutrition is associated with inadequate nutrient intake and/or absorption, it is also associated with impaired health conditions such as illness, trauma and/or inflammatory states, which may lead to increased healthcare costs, functional decline, morbidity and death. Despite the universality of concern about malnutrition, a globally accepted definition of this condition, screening or diagnostic approach was not available. Thus, participants in the GLIM project endeavored to create a standardized, globally acceptable consensus on malnutrition diagnosis. GLIM has determined that malnutrition diagnosis is a two-step process which begins with screening for malnutrition using an accepted, valid malnutrition screening tool, followed by a nutrition assessment through which diagnosis and determination of severity are made . 3) to develop a patient score where a lower score indicates more severe nutrition risk [Screening tools thus represent the first step in diagnosing malnutrition. Frequently used among these are the Mini Nutrition Assessment (MNA); the Nutrition Risk Screening 2002 (NRS2002); the Malnutrition Universal Screening Tool (MUST); Controlling Nutritional status (CONUT); and Prognostic Nutrition Index (PNI). The MNA, often used in its short form (MNA-SF), screens for malnutrition in the elderly and includes queries on food intake, involuntary weight loss, mobility, psychological stress or acute disease, neurocognitive disorders, and current BMI . The NRSion risk .This plethora of diagnostic tools available gives great freedom to the clinician, but lack of between-method agreement has been documented . On the Worldwide, many governments managed the COVID-19 outbreak by mandating lockdowns. These curfews, quarantines, and school and business closures had a negative psychological impact on the populations subjected to them . SpecifiAssociations between emotional distress, including anxiety and depression and eating behaviors are well documented. A direct association between binge eating and anxiety has been reported , and pooGiven the worsening in diet quality during the pandemic, it is possible that adults were hospitalized for COVID-19 with greater malnutrition risk than they might have during other historical periods. The adverse impact of malnutrition on hospitalization outcomes is well established . Thus, tThe primary objective of the present study is to estimate the effect of malnutrition on in-hospital mortality in adults hospitalized with COVID; and secondarily, to estimate the prevalence of malnutrition in adults hospitalized with malnutrition. The present meta-analysis followed the PRISMA guidelines . The proStudies were eligible for inclusion in the present meta-analysis if they were observational, including cross-sectional, retrospective, or prospective designs. Study populations in the included studies were limited to adults aged 18 years or older hospitalized with COVID-19. All included articles were published in the period 2021–2023 to provide coverage of legacy and more recent COVID variants. All publications were published in the English language in peer-reviewed journals. Published and in-press articles were acceptable.For all included studies, malnutrition was the stated exposure and in-hospital morality was the stated outcome, though not necessarily the primary outcome of the study.In-hospital mortality was identified as an outcome (not necessarily a primary outcome).Studies were only eligible for inclusion in the present analysis if they provided the full information necessary for data extraction needed for the analysis. Additionally, studies in which malnutrition prevalence was not stated were not included. Finally, narrative reviews, systematic reviews, and meta-analyses were not eligible for inclusion in the present meta-analysis.The following databases were queried: EMBASE, MEDLINE, PubMed, Google Scholar, and Cochrane Collaboration.Search words used in each of the databases were: malnutrition and COVID-19 and hospitalized adults and mortality. Prior to inclusion, studies were reviewed using the 14-question Quality Assessment Tool for Studies with Diverse Designs (QATSDD) (questions appropriate for quantitative studies). Using this tool, a score on a given item can range from 0  to 3 (complete). Scores for each of the items were compared between the two reviewers , and agreement was defined as a score from one reviewer within one point of the other reviewer’s score. A 75% agreement was set as the minimum level of between-reviewer concordance. Disagreement beyond this was to be settled by discussion, but all articles reviewed met this criterion. Total scores were also compared between reviewers. According to protocol, an article with a total score less than 21 points  were to be omitted. None of the articles reviewed at the full manuscript level were omitted for this reason.The study examined all published and in-publication articles from 2020 to 2023, in order to capture hospitalizations for COVID. Only English-language publications were included. Search terms in each of the databases included malnutrition, COVID-19, mortality, and hospitalized adults. Data extracted from each included article were author names; date of publication; country; sample size; malnutrition prevalence; malnutrition screening/diagnostic method; number of deaths in malnourished patients; number of deaths in adequately nourished patients. Data were extracted to an Excel spreadsheet.I2 tests were calculated, indicating a great deal of heterogeneity between studies. Egger’s test and Begg’s test were used to assess publication bias. A forest plot was generated and the pooled odds ratio (OR) with 95% confidence intervals (95%CI) were calculated using the random effects model. Data were analyzed using MedCalc software v20.210 . The Q and The flow of study selection is presented in n = 5); the study was conducted in non-COVID patients (n = 6); and the study was conducted in non-hospitalized patients (n = 5). Another three manuscripts were omitted because the article was not a study, but rather a set of treatment guidelines or a case report.As can be seen, 90 studies were originally identified, and their abstracts reviewed. From these, 45 studies were eliminated due to title or abstract screening. A total of 26 studies were omitted because the exposure was not malnutrition, or the outcome did not include in-hospital mortality. Another 16 studies were omitted because the study population was not appropriate: specifically, the study was conducted in children ; three were retrospective; and four were cross sectional.The included studies were published between April 2021 and December 2022. Sample sizes ranged from 75 to 343,188. Of the 354,332 total participants, 34,437  were classed as malnourished or at increased risk of malnutrition.All studies were conducted in hospitalized adults diagnosed with COVID-19; however, COVID-19 was not necessarily the reason for hospitalization. All of the studies reported malnutrition prevalence. n = 6 studies); modified NRS 2002 (n = 1 study); MNA-SF (n = 2 studies); MUST (n = 2 studies); SGA (n = 1 study); PNI (n = 1 study); anthropometric or biochemical measures (n = 2 studies); CONUT (n = 1); diagnostic code (n = 1); self-reported food reduction (n = 2). Of these, five studies incorporated more than one method of screening for/diagnosing malnutrition.Methods of diagnosing malnutrition risk or malnutrition itself included: NRS 2002 . The odds for in-hospital mortality in patients with malnutrition range from the non-significant OR 1.46 in the Kananen et al., study to OR 26.58 in the Nunes et al., study and OR 31.010 in the Zhang et al., study. The Kananen study was retrospective and conducted in geriatric hospitals in Sweden. The Zhang et al., study was prospective, while the Nunes et al., study was cross-sectional. Three studies did not have significant OR values in the random effects model: Shabanpur et al., ; Nicolau et al., . Both the Shabanpur et al., and the Nicolau et al., studies were cross-sectional, while the Kananen et al., study, as stated previously, was retrospective. Overall, in the random effects model, OR 3.43 (95% CI 2.55–5.40), p < 0.001, indicating that increased malnutrition risk or a diagnosis of malnutrition nearly quadruples the risk of in-hospital death. The forest plot of the association between malnutrition and in-hospital death is presented in The meta-analysis of the impact of malnutrition on risk of in-hospital death is summarized in p = 0.79; and Begg’s test: Kendall’s Tau = 0.24, p = 0.27.Publication bias was not present according to Egger’s test: intercept = 0.21, 95% CI −1.47–1.89, I2 statistic indicates a high degree of between-study heterogeneity, necessitating the use of the random effects model: I2 = 99.93%, 95% CI 99.92–99.94%, p < 0.001. The pooled prevalence estimate using the random effects model is 52.61% (95% CI 29.50–75.14%). Malnutrition prevalence is summarized in p = 0.002. Though Begg’s test was not significant: Kendall’s Tau −0.125, p = 0.58, the findings nevertheless suggest publication bias. Egger’s test indicated publication bias: intercept = 31.82, 95% CI 15.09–48.55, The purpose of the present study was to estimate the impact of malnutrition on in-hospital mortality in adults hospitalized with COVID-19. The pooled random effects model indicates that malnutrition almost quadruples the odds of in-hospital mortality in this population, supporting the study hypothesis.The analysis indicates that malnutrition increases odds of death by more than three-fold. This contrasts with the odds of death reported in the meta-analysis by Abate et al, who found that malnutrition increased odds of death by a factor of 10  , signifiThe influence of malnutrition on mortality in patients with COVID-19 was among the associations examined in a large, U.S. retrospective cohort study designed to identify risk factors for adverse outcomes of COVID-19, employing the National Inpatient Sample (NIS) 2020. Part of the Healthcare Cost and Utilization Project, the NIS 2020 includes data on patients hospitalized from all healthcare payers and includes unweighted data from approximately 7 million hospitalizations annually on a national level, providing data on more than 97% of the U.S. population. This study identified that malnutrition approximately doubled risk of mortality in patients with COVID-19, a risk increase similar to that of stroke and liver disease, and almost twice that of the increased risk conferred by obesity, uncomplicated diabetes and chronic obstructive pulmonary disease . This fiMalnutrition diagnosed using bioimpedance vector analysis (BIVA) was shown to more than quadruple the risk for adverse outcomes including mechanical ventilation and 60-day mortality in adults with COVID-19 pneumonia .p = 0.017. The MNA score also predicted incident geriatric syndrome, discharge location (home vs. institution) and functional status [Elevated malnutrition risk and diagnosed malnutrition have been shown to increase risk of in-hospital mortality and early post-discharge mortality in a number of disease states. For example, a retrospective study of elderly patients (mean age > 80 years) hospitalized in an acute care geriatric department defined malnutrition as MNA score < 17. At the three-month follow-up point, malnutrition was associated with increased risk of death: OR 3.519, 95% CI: 1.254–9.872, 2. Using this definition, investigators found that malnutrition more than doubled the risk of post-discharge mortality compared to patients with normal or elevated BMI. Among patients who died in-hospital, 3.2% were classed as having malnutrition compared to 8% of patients who were discharged home. This surprising finding was explained by the investigators as a reflection of the efficacy of the nutrition intervention provided to patients with malnutrition, improving survival [2 as the measure of malnutrition explains these findings. If individuals with BMI in the normal, overweight and obese ranges have elevated malnutrition risk or a diagnosis of malnutrition, they will be misclassified as adequately nourished or at minimal risk. Further, if overweight and obesity are associated with improved survival, as has been demonstrated in acutely and chronically ill patient populations [Malnutrition also predicted short term post-hospital discharge mortality. In a northern Italian cohort of 1451 consecutively enrolled adult patients (median age 80 years) admitted to internal medicine departments in a large tertiary care hospital, nearly 16% of participants died within four months of hospital discharge. Malnutrition was defined as BMI < 18.5 kg/msurvival . It is aulations ,54,55, tMalnutrition does not predict mortality in all patient populations. A study of the association between malnutrition risk screened using the NRS 2002 and in-hospital mortality was conducted in patients admitted to intensive care units following a cardiac arrest. This retrospective study found no difference in either the NRS 2002 score or BMI between patients who survived and those who expired; however, both serum albumin and total cholesterol levels, both markers of malnutrition, were significantly lower in patients who died. Nevertheless, the NRS 2002 score did not predict in-hospital mortality in patients after cardiac arrest .I2 values. The heterogeneity in malnutrition prevalence undoubtedly reflects the variability in malnutrition risk screening and malnutrition diagnosis methods. Further, this heterogeneity in prevalence likely contributes to the between-study heterogeneity in odds of in-hospital death. Other potential contributors to malnutrition prevalence estimate variability may be patient characteristics associated with malnutrition risk, such as advanced age and number of comorbidities.Included studies were characterized by a great deal of heterogeneity, evidenced by the very large Differences in odds estimates for death can arise from the diversity of malnutrition ascertainment methods employed in the included studies. Different screening and/or diagnostic tools can lead to differences in malnutrition prevalence estimates as well as differences in estimates of the impact of malnutrition on mortality . HoweverThe prevalence-estimate of 57.145% is somewhat greater than the prevalence estimate reported in the meta-analysis by Abate et al., which included studies of malnutrition published between 2019 and 2020 and estimated prevalence to be 49.11% (95% CI: 31.67 to 66.54) . While tIn the present meta-analysis, we sought to estimate the association between malnutrition and risk of in-hospital death in adults hospitalized with COVID-19. Our study only included those studies published between 2021 and 2023, permitting an estimation of this association in patients hospitalized with legacy as well as current variants of the virus. It is clear that malnutrition is an ominous prognostic sign in patients hospitalized with COVID. This meta-analysis, which includes studies from nine countries on four continents with data from 350,021 patients, seems generalizable.A potentially important study limitation was the identification of publication bias in the pooled prevalence estimates of malnutrition. While none were included in the present meta-analysis, an examination of unpublished but completed studies in study registries identified four such projects, all inClinicalTrials.gov: Nutritional Assessment of Hospitalized Patients With COVID-19 (DenutCOVID), which recruited ninety participants and did not post results, and for which malnutrition was the outcome variable (NCT04503525); Minimizing the Effects of COVID-19 Hospitalization With the COVID Rehabilitation Program for the Elderly (CORE) enrolled 124 participants in a randomized clinical trial in which oral nutrition supplements were included to prevent malnutrition, but in-hospital mortality was not a study endpoint (NCT04771052); an observational study in which 986 participants were enrolled, Investigating the Role of Vitamin D in the Morbidity of COVID-19 Patients (NCT04386044) examined the role of blood vitamin D levels  on several clinical outcomes; and Increased Risk of Severe Coronavirus Disease 2019 in Patients With Vitamin D Deficiency (COVIT-D), another observational study with 300 enrolled patients also looked at vitamin D nutriture  on COVID-19 severity (NCT04403932). Another important limitation is the high degree of heterogeneity in the included studies. This was remedied by using the random effects model; however, it cannot be assumed that the heterogeneity was fully neutralized. Other limitations include between-study variability in patient mix; between-study variability in COVID-19 variant (variant was not reported in any of the studies); and between-study variability in nutrition status screening/malnutrition diagnosis. This overall heterogeneity was remedied by using the random effects model, but residual variability may still influence findings.Findings of the present meta-analysis stress the importance in nutrition screening in adults hospitalized with COVID-19. While none of the included studies examined the impact of nutrition interventions, this is clearly an important future research objective. However, even without demonstrating efficacy, the successful use of nutrition interventions in a wide variety of clinical settings suggests that harm cannot be done to patients by improving their nutrition.Malnutrition is a common finding in patients hospitalized with COVID-19 and greatly increases the odds of in-hospital death.


Nature Communications 10.1038/s41467-023-38215-z, published online 16 May 2023Correction to: In this article the affiliation details for Beate Ritz were incorrectly given as ‘Division of Movement Disorders, Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA’ and ‘Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA’ but should have been ‘Department of Neurology, UCLA David Geffen School of Medicine, Los Angeles, CA, USA’ and ‘Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA, USA’. The original article has been corrected.