Cutaneous immunosurveillance and regulation of inflammation by group 2 innate lymphoid cells.

Type 2 immunity is critical for defense against cutaneous infections but also underlies the development of allergic skin diseases. We report the identification in normal mouse dermis of an abundant, phenotypically unique group 2 innate lymphoid cell (ILC2) subset that depended on interleukin 7 (IL-7) and constitutively produced IL-13. Intravital multiphoton microscopy showed that dermal ILC2 cells specifically interacted with mast cells, whose function was suppressed by IL-13. Treatment of mice deficient in recombination-activating gene 1 (Rag1(-/-)) with IL-2 resulted in the population expansion of activated, IL-5-producing dermal ILC2 cells, which led to spontaneous dermatitis characterized by eosinophil infiltrates and activated mast cells. Our data show that ILC2 cells have both pro- and anti-inflammatory properties and identify a previously unknown interactive pathway between two innate populations of cells of the immune system linked to type 2 immunity and allergic diseases.

Development of convolutional neural network models that recognize normal anatomic structures during real-time radial-array and linear-array EUS (with videos).

BACKGROUND AND AIMS: EUS is a high-skill technique that requires numerous procedures to achieve competence. However, training facilities are limited worldwide. Convolutional neural network (CNN) models have been previously implemented for object detection. We developed 2 EUS-based CNN models for normal anatomic structure recognition during real-time linear- and radial-array EUS evaluations. METHODS: The study was performed from February 2020 to June 2022. Consecutive patient videos of linear- and radial-array EUS videos were recorded. Expert endosonographers identified and labeled 20 normal anatomic structures within the videos for training and validation of the CNN models. Initial CNN models (CNNv1) were developed from 45 videos and the improved models (CNNv2) from an additional 102 videos. CNN model performance was compared with that of 2 expert endosonographers. RESULTS: CNNv1 used 45,034 linear-array EUS frames and 21,063 radial-array EUS frames. CNNv2 used 148,980 linear-array EUS frames and 128,871 radial-array EUS frames. Linear-array CNNv1 and radial-array CNNv1 achieved a 75.65% and 71.36% mean average precision (mAP) with a total loss of .19 and .18, respectively. Linear-array CNNv2 obtained an 88.7% mAP with a .06 total loss, whereas radial-array CNNv2 achieved an 83.5% mAP with a .07 total loss. CNNv2 accurately detected all studied normal anatomic structures with a >98% observed agreement during clinical validation. CONCLUSIONS: The proposed CNN models accurately recognize the normal anatomic structures in prerecorded videos and real-time EUS. Prospective trials are needed to evaluate the impact of these models on the learning curves of EUS trainees.

Guidelines for the Use of Generative Artificial Intelligence Tools for Biomedical Journal Authors and Reviewers.

Authors are permitted to use generative artificial intelligence (AI) large language models (LLM) to improve the readability of their own writing. However, authors must review and edit the output resulting from generative AI and are accountable for the accuracy of their publications. AI may not be listed, or cited, as an author. Authors who use AI in the scientific writing process must disclose the use of AI LLM in their manuscript including a description of the tool and reason for use. Authors are not permitted to use AI to create or alter images or videos, (unless this is part of the research design in which case a statement is required explaining what was created or altered, with what tools, how, and for what reason). Finally, AI use by reviewers and editors is not permitted and violates confidentiality and proprietary rights and may breach data privacy rights. In conclusion, scientific writing and peer review is the responsibility of humans.

KARATECH: A Practice Support System Using an Accelerometer to Reduce the Preliminary Actions of Karate.

Kumite is a karate sparring competition in which two players face off and perform offensive and defensive techniques. Depending on the players, there may be preliminary actions (hereinafter referred to as "pre-actions"), such as pulling the arms or legs, lowering the shoulders, etc., just before a technique is performed. Since the presence of a pre-action allows the opponent to know the timing of the technique, it is important to reduce pre-actions in order to improve the kumite. However, it is difficult for beginners and intermediate players to accurately identify their pre-actions and to improve them through practice. Therefore, this study aims to construct a practice support system that enables beginners and intermediate players to understand their pre-actions. In this paper, we focus on the forefist punch, one of kumite's punching techniques. We propose a method to estimate the presence or absence of a pre-action based on the similarity between the acceleration data of an arbitrary forefist punch and a previously prepared dataset consisting of acceleration data of the forefist punch without a pre-action. We found that the proposed method can estimate the presence or absence of a pre-action in an arbitrary forefist punch with an accuracy of 86%. We also developed KARATECH as a system to support the practice of reducing pre-actions using the proposed method. KARATECH shows the presence or absence of pre-actions through videos and graphs. The evaluation results confirmed that the group using KARATECH had a lower pre-action rate.

Efficient Human Violence Recognition for Surveillance in Real Time.

Human violence recognition is an area of great interest in the scientific community due to its broad spectrum of applications, especially in video surveillance systems, because detecting violence in real time can prevent criminal acts and save lives. The majority of existing proposals and studies focus on result precision, neglecting efficiency and practical implementations. Thus, in this work, we propose a model that is effective and efficient in recognizing human violence in real time. The proposed model consists of three modules: the Spatial Motion Extractor (SME) module, which extracts regions of interest from a frame; the Short Temporal Extractor (STE) module, which extracts temporal characteristics of rapid movements; and the Global Temporal Extractor (GTE) module, which is responsible for identifying long-lasting temporal features and fine-tuning the model. The proposal was evaluated for its efficiency, effectiveness, and ability to operate in real time. The results obtained on the Hockey, Movies, and RWF-2000 datasets demonstrated that this approach is highly efficient compared to various alternatives. In addition, the VioPeru dataset was created, which contains violent and non-violent videos captured by real video surveillance cameras in Peru, to validate the real-time applicability of the model. When tested on this dataset, the effectiveness of our model was superior to the best existing models.

Remote Heart Rate Estimation Based on Transformer with Multi-Skip Connection Decoder: Method and Evaluation in the Wild.

Heart rate is an essential vital sign to evaluate human health. Remote heart monitoring using cheaply available devices has become a necessity in the twenty-first century to prevent any unfortunate situation caused by the hectic pace of life. In this paper, we propose a new method based on the transformer architecture with a multi-skip connection biLSTM decoder to estimate heart rate remotely from videos. Our method is based on the skin color variation caused by the change in blood volume in its surface. The presented heart rate estimation framework consists of three main steps: (1) the segmentation of the facial region of interest (ROI) based on the landmarks obtained by 3DDFA; (2) the extraction of the spatial and global features; and (3) the estimation of the heart rate value from the obtained features based on the proposed method. This paper investigates which feature extractor performs better by captioning the change in skin color related to the heart rate as well as the optimal number of frames needed to achieve better accuracy. Experiments were conducted using two publicly available datasets (LGI-PPGI and Vision for Vitals) and our own in-the-wild dataset (12 videos collected by four drivers). The experiments showed that our approach achieved better results than the previously published methods, making it the new state of the art on these datasets.

How Do Physicians Frame Medical Information in Talks With Their Patients? An Inductive Microanalysis.

During medical consultations, physicians need to share a substantial amount of information with their patients. How this information is framed can be crucial for patient understanding and outcomes, but little is known about the details of how physicians frame information in practice. Using an inductive microanalysis approach in the study of videotaped medical interactions, we aimed to identify the information frames (i.e., higher-level ways of organizing and structuring information to reach a particular purpose) and the information-framing devices (i.e., any dialogic mechanism used to present information in a particular way that shapes how the patient might perceive and interpret it) physicians use spontaneously and intuitively while sharing information with their patients. We identified 66 different information-framing devices acting within nine information frames conveying: (1) Do we agree that we share this knowledge?, (2) I don't like where I (or where you are) am going with this, (3) This may be tricky to understand, (4) You may need to think, (5) This is important, (6) This is not important, (7) This comes from me as a doctor, (8) This comes from me as a person, and (9) This is directed to you as a unique person. The kaleidoscope of information-framing devices described in this study reveals the near impossibility for neutrality and objectivity in the information-sharing practice of medical care. It also represents an inductively derived starting point for further research into aspects of physicians' information-sharing praxis.

Visual antipredator effects of web flexing in an orb web spider, with special reference to web decorations.

Some visual antipredator strategies involve the rapid movement of highly contrasting body patterns to frighten or confuse the predator. Bright body colouration, however, can also be detected by potential predators and used as a cue. Among spiders, Argiope spp. are usually brightly coloured but they are not a common item in the diet of araneophagic wasps. When disturbed, Argiope executes a web-flexing behaviour in which they move rapidly and may be perceived as if they move backwards and towards an observer in front of the web. We studied the mechanisms underlying web-flexing behaviour as a defensive strategy. Using multispectral images and high-speed videos with deep-learning-based tracking techniques, we evaluated body colouration, body pattern, and spider kinematics from the perspective of a potential wasp predator. We show that the spider's abdomen is conspicuous, with a disruptive colouration pattern. We found that the body outline of spiders with web decorations was harder to detect when compared to spiders without decorations. The abdomen was also the body part that moved fastest, and its motion was composed mainly of translational (vertical) vectors in the potential predator's optical flow. In addition, with high contrast colouration, the spider's movement might be perceived as a sudden change in body size (looming effect) as perceived by the predator. These effects alongside the other visual cues may confuse potential wasp predators by breaking the spider body outline and affecting the wasp's flight manoeuvre, thereby deterring the wasp from executing the final attack.

Development of an artificial intelligence system for real-time intraoperative assessment of the Critical View of Safety in laparoscopic cholecystectomy.

BACKGROUND: The Critical View of Safety (CVS) was proposed in 1995 to prevent bile duct injury during laparoscopic cholecystectomy (LC). The achievement of CVS was evaluated subjectively. This study aimed to develop an artificial intelligence (AI) system to evaluate CVS scores in LC. MATERIALS AND METHODS: AI software was developed to evaluate the achievement of CVS using an algorithm for image classification based on a deep convolutional neural network. Short clips of hepatocystic triangle dissection were converted from 72 LC videos, and 23,793 images were labeled for training data. The learning models were examined using metrics commonly used in machine learning. RESULTS: The mean values of precision, recall, F-measure, specificity, and overall accuracy for all the criteria of the best model were 0.971, 0.737, 0.832, 0.966, and 0.834, respectively. It took approximately 6 fps to obtain scores for a single image. CONCLUSIONS: Using the AI system, we successfully evaluated the achievement of the CVS criteria using still images and videos of hepatocystic triangle dissection in LC. This encourages surgeons to be aware of CVS and is expected to improve surgical safety.

Global assessment of surgical skills (GASS): validation of a new instrument to measure global technical safety in surgical procedures.

BACKGROUND: Broad implementation of the American Board of Surgery's entrustable professional activities initiative will require assessment instruments that are reliable and easy to use. Existing assessment instruments of general laparoscopic surgical skills have limited reliability, efficiency, and validity across the spectrum of formative (low-stakes) and summative (high-stakes) assessments. A novel six-item global assessment of surgical skills (GASS) instrument was developed and evaluated with a focus upon safe versus unsafe surgical practice scoring rubric. METHODS: The GASS was developed by iterative engagement with expert laparoscopic surgeons and includes six items (economy of motion, tissue handling, appreciating operative anatomy, bimanual dexterity, achievement of hemostasis, overall performance) with a uniform three-point scoring rubric ("poor-unsafe", "adequate-safe", "good-safe"). To test inter-rater reliability, a cross-sectional study of four bariatric surgeons with experience ranging from 4 to 28 years applied the GASS and the global operative assessment of laparoscopic skills (GOALS) to 30 consecutive Roux-en-Y gastric bypass procedure operative videos. Inter-rater reliability was assessed for a simplified dichotomous "safe" versus "unsafe" scoring rubric using Gwet's AC2. RESULTS: The GASS inter-rater reliability was very high across all six domains (0.88-1.00). The GASS performed comparably to the GOALS inter-rater reliability scores (0.96-1.00). The economy of motion and bimanual dexterity items had the highest percentage of unsafe ratings (9.2% and 5.8%, respectively). CONCLUSION: The GASS, a novel six-item instrument of general laparoscopic surgical skills, was designed with a simple scoring rubric (poor-safe, adequate-safe, good-safe) to minimize rater burden and focus feedback to trainees and promotion evaluations on safe surgical performance. Initial evaluation of the GASS is promising, demonstrating high inter-rater reliability. Future research will seek to assess the GASS against a broader spectrum of laparoscopic procedures.

Developing artificial intelligence models for medical student suturing and knot-tying video-based assessment and coaching.

BACKGROUND: Early introduction and distributed learning have been shown to improve student comfort with basic requisite suturing skills. The need for more frequent and directed feedback, however, remains an enduring concern for both remote and in-person training. A previous in-person curriculum for our second-year medical students transitioning to clerkships was adapted to an at-home video-based assessment model due to the social distancing implications of COVID-19. We aimed to develop an Artificial Intelligence (AI) model to perform video-based assessment. METHODS: Second-year medical students were asked to submit a video of a simple interrupted knot on a penrose drain with instrument tying technique after self-training to proficiency. Proficiency was defined as performing the task under two minutes with no critical errors. All the videos were first manually rated with a pass-fail rating and then subsequently underwent task segmentation. We developed and trained two AI models based on convolutional neural networks to identify errors (instrument holding and knot-tying) and provide automated ratings. RESULTS: A total of 229 medical student videos were reviewed (150 pass, 79 fail). Of those who failed, the critical error distribution was 15 knot-tying, 47 instrument-holding, and 17 multiple. A total of 216 videos were used to train the models after excluding the low-quality videos. A k-fold cross-validation (k = 10) was used. The accuracy of the instrument holding model was 89% with an F-1 score of 74%. For the knot-tying model, the accuracy was 91% with an F-1 score of 54%. CONCLUSIONS: Medical students require assessment and directed feedback to better acquire surgical skill, but this is often time-consuming and inadequately done. AI techniques can instead be employed to perform automated surgical video analysis. Future work will optimize the current model to identify discrete errors in order to supplement video-based rating with specific feedback.

A single session of action observation therapy versus observing a natural landscape in adults with chronic neck pain - a randomized controlled trial.

BACKGROUND: Action observation (AO) has emerged as a potential neurorehabilitation therapy for patients with neck pain (NP), but evidence of its effectiveness is scarce. This study aims to assess the effect of a single session of AO when compared to observing a natural landscape on NP intensity, fear of movement, fear-avoidance beliefs, neck muscles' strength, pressure pain threshold, and tactile acuity. METHODS: Sixty participants with NP were randomly allocated to the AO group (n = 30) or control group (n = 30). Both groups watched an 11-minute video: the AO group watched a video of a person matched for age and sex performing neck exercises, while the control group watched a video of natural landscapes. Neck pain intensity, fear of movement, fear-avoidance beliefs, tactile acuity, pressure pain thresholds, and neck muscle strength were assessed both at baseline and post-intervention. General linear models of repeated measures (ANCOVA of two factors) were used to explore between-group differences at post-intervention. RESULTS: There was a significant main effect of time for pain intensity (p = 0.02; η2p = 0.09; within-group mean change and 95% CI: AO=-1.44 (-2.28, -0.59); control=-1.90 (-2.74, -1.06), but no time versus group interaction (p = 0.46). A time versus group significant interaction was found for one out of the six measurement sites of two-point discrimination and the neck flexors strength (p < 0.05) favoring the control group. No other statistically significant differences were found for the remaining variables). CONCLUSIONS: Results suggest a similar acute benefit for both a single session of AO and observing natural landscapes for promoting hypoalgesia, but no impact on kinesiophobia, fear-avoidance beliefs, or pressure pain thresholds. Also, AO had no positive effect on two-point discrimination and muscle strength. Further research is needed, with longer interventions. TRIAL REGISTRATION: Clinialtrials.gov (NCT05078489).

What Do Social Media Influencers Say About Birth Control? A Content Analysis of YouTube Vlogs About Birth Control.

Youth get their sexual health information from social media, often from social media influencers (SMIs) or microcelebrities with large followings. Previous research suggests that SMIs have powerful persuasive effects on attitudes and behaviors. Thus, it is important to examine the ways in which sexual health information, such as birth control, is conveyed by SMIs. Using framing theory as a theoretical framework, this study examines characteristics of SMIs and their shared experiences pertaining to birth control. A content analysis of YouTube vlogs (n = 50) posted from December 2019-2021 was conducted on SMIs who talk about their experiences using hormonal and non-hormonal birth control. SMI status was determined based on the number of people subscribed to the YouTube channels. Results suggest that SMI YouTube videos are primarily about the discontinuation of hormonal birth control and may provide inaccurate sexual health information. Reasons for discontinuation of hormonal birth control provided by the SMIs are discussed. Future research should explore the effects of influencer sexual health messaging on beliefs, attitudes, and behaviors.

ChatGPT for assessment writing.

What is the educational challenge?Medical schools invest significant resources into the creation of multiple-choice items for assessments. This process is costly and requires faculty training. Recently ChatGPT has been used in various areas to improve content creation efficiency, and it has otherwise been used to answer USMLE-style assessment items.What are the proposed solutions?We proposed the use of ChatGPT to create initial drafts of multiple-choice items.What are the potential benefits to a wider global audience?The use of ChatGPT to generate assessment items can decrease resources required, allowing for the creation of more items, and freeing-up faculty time to perform higher level assessment activities. ChatGPT is also able to consistently produce items using a standard format while adhering to item writing guidelines, which can be very challenging for faculty teams.What are the next steps?We plan to pilot ChatGPT drafted questions and compare item statistics for those written by ChatGPT with those written by our content experts. We also plan to further identify the types of questions that ChatGPT is most appropriate for, and incorporate media into assessment items (e.g. images, videos).

Management reasoning and patient-clinician interactions: Insights from shared decision-making and simulated outpatient encounters.

PURPOSE: To expand understanding of patient-clinician interactions in management reasoning. METHODS: We reviewed 10 videos of simulated patient-clinician encounters to identify instances of problematic and successful communication, then reviewed the videos again through the lens of two models of shared decision-making (SDM): an 'involvement-focused' model and a 'problem-focused' model. Using constant comparative qualitative analysis we explored the connections between these patient-clinician interactions and management reasoning. RESULTS: Problems in patient-clinician interactions included failures to: encourage patient autonomy; invite the patient's involvement in decision-making; convey the health impact of the problem; explore and address concerns and questions; explore the context of decision-making (including patient preferences); meet the patient where they are; integrate situational preferences and priorities; offer >1 viable option; work with the patient to solve a problem of mutual concern; explicitly agree to a final care plan; and build the patient-clinician relationship. Clinicians' 'management scripts' varied along a continuum of prioritizing clinician vs patient needs. Patients also have their own cognitive scripts that guide their interactions with clinicians. The involvement-focused and problem-focused SDM models illuminated distinct, complementary issues. CONCLUSIONS: Management reasoning is a deliberative interaction occurring in the space between individuals. Juxtaposing management reasoning alongside SDM generated numerous insights.

Improving Small-Scale Human Action Recognition Performance Using a 3D Heatmap Volume.

In recent years, skeleton-based human action recognition has garnered significant research attention, with proposed recognition or segmentation methods typically validated on large-scale coarse-grained action datasets. However, there remains a lack of research on the recognition of small-scale fine-grained human actions using deep learning methods, which have greater practical significance. To address this gap, we propose a novel approach based on heatmap-based pseudo videos and a unified, general model applicable to all modality datasets. Leveraging anthropometric kinematics as prior information, we extract common human motion features among datasets through an ad hoc pre-trained model. To overcome joint mismatch issues, we partition the human skeleton into five parts, a simple yet effective technique for information sharing. Our approach is evaluated on two datasets, including the public Nursing Activities and our self-built Tai Chi Action dataset. Results from linear evaluation protocol and fine-tuned evaluation demonstrate that our pre-trained model effectively captures common motion features among human actions and achieves steady and precise accuracy across all training settings, while mitigating network overfitting. Notably, our model outperforms state-of-the-art models in recognition accuracy when fusing joint and limb modality features along the channel dimension.

Non-Contact Monitoring of Fetal Movement Using Abdominal Video Recording.

Fetal movement (FM) is an important indicator of fetal health. However, the current methods of FM detection are unsuitable for ambulatory or long-term observation. This paper proposes a non-contact method for monitoring FM. We recorded abdominal videos from pregnant women and then detected the maternal abdominal region within each frame. FM signals were acquired by optical flow color-coding, ensemble empirical mode decomposition, energy ratio, and correlation analysis. FM spikes, indicating the occurrence of FMs, were recognized using the differential threshold method. FM parameters including number, interval, duration, and percentage were calculated, and good agreement was found with the manual labeling performed by the professionals, achieving true detection rate, positive predictive value, sensitivity, accuracy, and F1_score of 95.75%, 95.26%, 95.75%, 91.40%, and 95.50%, respectively. The changes in FM parameters with gestational week were consistent with pregnancy progress. In general, this study provides a novel contactless FM monitoring technology for use at home.

MSTA-SlowFast: A Student Behavior Detector for Classroom Environments.

Detecting students' classroom behaviors from instructional videos is important for instructional assessment, analyzing students' learning status, and improving teaching quality. To achieve effective detection of student classroom behavior based on videos, this paper proposes a classroom behavior detection model based on the improved SlowFast. First, a Multi-scale Spatial-Temporal Attention (MSTA) module is added to SlowFast to improve the ability of the model to extract multi-scale spatial and temporal information in the feature maps. Second, Efficient Temporal Attention (ETA) is introduced to make the model more focused on the salient features of the behavior in the temporal domain. Finally, a spatio-temporal-oriented student classroom behavior dataset is constructed. The experimental results show that, compared with SlowFast, our proposed MSTA-SlowFast has a better detection performance with mean average precision (mAP) improvement of 5.63% on the self-made classroom behavior detection dataset.

FRMDB: Face Recognition Using Multiple Points of View.

Although face recognition technology is currently integrated into industrial applications, it has open challenges, such as verification and identification from arbitrary poses. Specifically, there is a lack of research about face recognition in surveillance videos using, as reference images, mugshots taken from multiple Points of View (POVs) in addition to the frontal picture and the right profile traditionally collected by national police forces. To start filling this gap and tackling the scarcity of databases devoted to the study of this problem, we present the Face Recognition from Mugshots Database (FRMDB). It includes 28 mugshots and 5 surveillance videos taken from different angles for 39 distinct subjects. The FRMDB is intended to analyze the impact of using mugshots taken from multiple points of view on face recognition on the frames of the surveillance videos. To validate the FRMDB and provide a first benchmark on it, we ran accuracy tests using two CNNs, namely VGG16 and ResNet50, pre-trained on the VGGFace and VGGFace2 datasets for the extraction of face image features. We compared the results to those obtained from a dataset from the related literature, the Surveillance Cameras Face Database (SCFace). In addition to showing the features of the proposed database, the results highlight that the subset of mugshots composed of the frontal picture and the right profile scores the lowest accuracy result among those tested. Therefore, additional research is suggested to understand the ideal number of mugshots for face recognition on frames from surveillance videos.

Attention-Based Fusion of Ultrashort Voice Utterances and Depth Videos for Multimodal Person Identification.

Multimodal deep learning, in the context of biometrics, encounters significant challenges due to the dependence on long speech utterances and RGB images, which are often impractical in certain situations. This paper presents a novel solution addressing these issues by leveraging ultrashort voice utterances and depth videos of the lip for person identification. The proposed method utilizes an amalgamation of residual neural networks to encode depth videos and a Time Delay Neural Network architecture to encode voice signals. In an effort to fuse information from these different modalities, we integrate self-attention and engineer a noise-resistant model that effectively manages diverse types of noise. Through rigorous testing on a benchmark dataset, our approach exhibits superior performance over existing methods, resulting in an average improvement of 10%. This method is notably efficient for scenarios where extended utterances and RGB images are unfeasible or unattainable. Furthermore, its potential extends to various multimodal applications beyond just person identification.