2'-Hydroxycinnamaldehyde Alleviates Intestinal Inflammation by Attenuating Intestinal Mucosal Barrier Damage Via Directly Inhibiting STAT3.

BACKGROUND: The currently available clinical therapeutic drugs for ulcerative colitis (UC) are considered inadequate owing to certain limitations. There have been reports on the anti-inflammatory effects of 2'-hydroxycinnamaldehyde (HCA). However, whether HCA can improve UC is still unclear. Here, we aimed to investigate the pharmacological effects of HCA on UC and its underlying molecular mechanisms. METHODS: The pharmacological effects of HCA were comprehensively investigated in 2 experimental setups: mice with dextran sulfate sodium (DSS)-induced colitis and lipopolysaccharide (LPS)-treated fetal human colon (FHC) cells. Furthermore, the interaction between HCA and signal transducer and activator of transcription 3 (STAT3) was investigated using molecular docking. The FHC cells with STAT3 knockdown or overexpression and mice with intestinal epithelium-specific STAT3 deletion (STAT3ΔIEC) were used to evaluate whether STAT3 mediated the pharmacological effects of HCA. RESULTS: 2'-Hydroxycinnamaldehyde attenuated dysregulated expression of inflammatory cytokines in a dose-dependent manner while increasing the expression of tight junction proteins, reducing the apoptosis of intestinal epithelial cells, and effectively alleviating inflammation both in vivo and in vitro. 2'-Hydroxycinnamaldehyde bound directly to STAT3 and inhibited its activation. The modulation of STAT3 activation levels due to STAT3 knockdown or overexpression influenced the mitigating effects of HCA on colitis. Further analysis indicated that the remission effect of HCA was not observed in STAT3ΔIEC mice, indicating that STAT3 mediated the anti-inflammatory effects of HCA. CONCLUSIONS: We present a novel finding that HCA reduces colitis severity by attenuating intestinal mucosal barrier damage via STAT3. This discovery holds promise as a potential new strategy to alleviate UC.

The current clinical therapeutic drugs for ulcerative colitis (UC) remain inadequate owing to certain adverse events. Administration of 2ʹ-hydroxycinnamaldehyde (HCA) significantly reduces colitis severity via direct inhibition of STAT3 to attenuate intestinal mucosal barrier damage. Hence, HCA may be a potential new strategy in UC.

Bringing Artificial Intelligence to the operating room: edge computing for real-time surgical phase recognition.

BACKGROUND: Automation of surgical phase recognition is a key effort toward the development of Computer Vision (CV) algorithms, for workflow optimization and video-based assessment. CV is a form of Artificial Intelligence (AI) that allows interpretation of images through a deep learning (DL)-based algorithm. The improvements in Graphic Processing Unit (GPU) computing devices allow researchers to apply these algorithms for recognition of content in videos in real-time. Edge computing, where data is collected, analyzed, and acted upon in close proximity to the collection source, is essential meet the demands of workflow optimization by providing real-time algorithm application. We implemented a real-time phase recognition workflow and demonstrated its performance on 10 Robotic Inguinal Hernia Repairs (RIHR) to obtain phase predictions during the procedure. METHODS: Our phase recognition algorithm was developed with 211 videos of RIHR originally annotated into 14 surgical phases. Using these videos, a DL model with a ResNet-50 backbone was trained and validated to automatically recognize surgical phases. The model was deployed to a GPU, the Nvidia® Jetson Xavier™ NX edge computing device. RESULTS: This model was tested on 10 inguinal hernia repairs from four surgeons in real-time. The model was improved using post-recording processing methods such as phase merging into seven final phases (peritoneal scoring, mesh placement, preperitoneal dissection, reduction of hernia, out of body, peritoneal closure, and transitionary idle) and averaging of frames. Predictions were made once per second with a processing latency of approximately 250 ms. The accuracy of the real-time predictions ranged from 59.8 to 78.2% with an average accuracy of 68.7%. CONCLUSION: A real-time phase prediction of RIHR using a CV deep learning model was successfully implemented. This real-time CV phase segmentation system can be useful for monitoring surgical progress and be integrated into software to provide hospital workflow optimization.

Effect of Dietary Patterns on Inflammatory Bowel Disease: A Machine Learning Bibliometric and Visualization Analysis.

AIMS: This study aimed to analyze the related research on the influence of dietary patterns on IBD carried out over the past 30 years to obtain the context of the research field and to provide a scientific basis and guidance for the prevention and treatment of IBD. METHODS: The literature on the effects of dietary patterns on inflammatory bowel disease published over the past three decades was retrieved from the Web of Science Core Collection (WoSCC) database. CiteSpace, VOSviewer, the R software (version 4.3.0) bibliometrix package, the OALM platform, and other tools were used for the analyses. RESULTS: The growth of scientific papers related to this topic can be divided into two stages: before and after 2006. Overall, the growth of the relevant literature was in line with Price's literature growth curve. Subrata Ghosh and Antonio Gasbarrini are the authors with the highest academic influence in the field, and Lee D.'s research results are widely recognized by researchers in this field. Among the 72 countries involved in the study, the United States contributed the most, while China developed rapidly with regard to research being carried out in this area. From a regional perspective, countries and institutions in North America, Europe, and East Asia have made the most significant contributions to this field and have the closest cooperation. Among the 1074 articles included in the study, the most influential ones tended to consider the mechanism of the effect of dietary patterns on IBD from the perspective of the microbiome. Multiple tools were used for keyword analysis and mutual verification. The results showed that NF-κB, the Mediterranean diet, fatty acids, fecal microbiota, etc., are the focus and trends of current research. CONCLUSIONS: A Mediterranean-like dietary pattern may be a good dietary habit for IBD patients. Carbohydrates, fatty acids, and inulin-type fructans are closely related to IBD. Fatty acid, gut microbiota, NF-κB, oxidative stress, and endoplasmic reticulum stress are the hot topics in the study of the effects of dietary patterns on IBD and will be emerging research trends.

Exosomal-miR-129-2-3p derived from Fusobacterium nucleatum-infected intestinal epithelial cells promotes experimental colitis through regulating TIMELESS-mediated cellular senescence pathway.

Fusobacterium nucleatum (Fn) infection is known to exacerbate ulcerative colitis (UC). However, the link between Fn-infected intestinal epithelial cell (IEC)-derived exosomes (Fn-Exo) and UC progression has not been investigated. Differentially expressed miRNAs in Fn-Exo and non-infected IECs-derived exosomes (Con-Exo) were identified by miRNA sequencing. Then, the biological role and mechanism of Fn-Exo in UC development were determined in vitro and in vivo. We found that exosomes delivered miR-129-2-3p from Fn-infected IECs into non-infected IECs, exacerbating epithelial barrier dysfunction and experimental colitis. Mechanically, Fn-Exo induces DNA damage via the miR-129-2-3p/TIMELESS axis and subsequently activates the ATM/ATR/p53 pathway, ultimately promoting cellular senescence and colonic inflammation. In conclusion, Exo-miR-129-2-3p/TIMELESS/ATM/ATR/p53 pathway aggravates cellular senescence, barrier damage, and experimental colitis. The current study revealed a previously unknown regulatory pathway in the progression of Fn-infectious UC. Furthermore, Exosomal-miR-129-2-3p in serum and TIMELESS may function as novel potential diagnostic biomarkers for UC and Fn-high-UC.

Surgical Phase Recognition in Inguinal Hernia Repair-AI-Based Confirmatory Baseline and Exploration of Competitive Models.

Video-recorded robotic-assisted surgeries allow the use of automated computer vision and artificial intelligence/deep learning methods for quality assessment and workflow analysis in surgical phase recognition. We considered a dataset of 209 videos of robotic-assisted laparoscopic inguinal hernia repair (RALIHR) collected from 8 surgeons, defined rigorous ground-truth annotation rules, then pre-processed and annotated the videos. We deployed seven deep learning models to establish the baseline accuracy for surgical phase recognition and explored four advanced architectures. For rapid execution of the studies, we initially engaged three dozen MS-level engineering students in a competitive classroom setting, followed by focused research. We unified the data processing pipeline in a confirmatory study, and explored a number of scenarios which differ in how the DL networks were trained and evaluated. For the scenario with 21 validation videos of all surgeons, the Video Swin Transformer model achieved ~0.85 validation accuracy, and the Perceiver IO model achieved ~0.84. Our studies affirm the necessity of close collaborative research between medical experts and engineers for developing automated surgical phase recognition models deployable in clinical settings.

Fusobacterium nucleatum Extracellular Vesicles Promote Experimental Colitis by Modulating Autophagy via the miR-574-5p/CARD3 Axis.

BACKGROUND: Ulcerative colitis (UC) may be exacerbated by Fusobacterium nucleatum (Fn) infection. However, the mechanism underlying Fn-mediated progression of UC has yet to be established. Here, we aimed to establish whether and how Fn-derived extracellular vesicles (Fn-EVs) participate in the development of experimental colitis through microRNAs (miRNAs). METHODS: EVs were isolated and purified by ultracentrifugation from Fn and Escherichia coli culture supernatants. Differentially expressed miRNAs in control intestinal epithelial cells (IECs) and Fn-EV-treated IECs were identified by miRNA sequencing. EVs were cocultured with IECs or administered to CARD3wt/CARD3-/- mice by gavage to assess inflammatory responses to and the mechanism of action of Fn-EVs. RESULTS: Fn-EVs promoted upregulation of proinflammatory cytokines (interleukin [IL]-1ß, IL-6, tumor necrosis factor α), downregulation of anti-inflammatory IL-10 and intercellular tight junction proteins ZO-1 and occludin, and epithelial barrier dysfunction in IECs. Fn-EVs significantly aggravated experimental colitis in mice associated with Fn-EV-mediated downregulation of miR-574-5p expression and autophagy activation. Blockade of autophagy using chloroquine alleviates barrier damage exacerbated by Fn-EVs in vitro and in vivo. Inhibition of the miR-574-5p/CARD3 axis reduced the severity of colitis, epithelial barrier damage, and autophagy activation induced by Fn-EVs. CONCLUSIONS: Here, we describe a new mechanism by which Fn-EVs mediate experimental colitis severity through miR-574-5p/CARD3-dependent autophagy activation, providing a novel target for UC monitoring and targeted therapy.

Fusobacterium nucleatum­derived extracellular vesicles (Fn-EVs) alter the microRNA profile of intestinal epithelial cells and colitis tissues, especially the expression of miR-574-5p. Fn-EVs mediate experimental colitis severity through miR-574-5p/CARD3­dependent autophagy activation. Hence, inhibiting Fn-EV­activated autophagy or targeting the miR-574-5p/CARD3 axis may be potential new therapeutic strategies in ulcerative colitis.