HMGB1 mediates synaptic loss and cognitive impairment in an animal model of sepsis-associated encephalopathy.

BACKGROUND: Microglial activation-mediated neuroinflammation is one of the essential pathogenic mechanisms of sepsis-associated encephalopathy (SAE). Mounting evidence suggests that high mobility group box-1 protein (HMGB1) plays a pivotal role in neuroinflammation and SAE, yet the mechanism by which HMGB1 induces cognitive impairment in SAE remains unclear. Therefore, this study aimed to investigate the mechanism of HMGB1 underlying cognitive impairment in SAE. METHODS: An SAE model was established by cecal ligation and puncture (CLP); animals in the sham group underwent cecum exposure alone without ligation and perforation. Mice in the inflachromene (ICM) group were continuously injected with ICM intraperitoneally at a daily dose of 10 mg/kg for 9 days starting 1 h before the CLP operation. The open field, novel object recognition, and Y maze tests were performed on days 14-18 after surgery to assess locomotor activity and cognitive function. HMGB1 secretion, the state of microglia, and neuronal activity were measured by immunofluorescence. Golgi staining was performed to detect changes in neuronal morphology and dendritic spine density. In vitro electrophysiology was performed to detect changes in long-term potentiation (LTP) in the CA1 of the hippocampus. In vivo electrophysiology was performed to detect the changes in neural oscillation of the hippocampus. RESULTS: CLP-induced cognitive impairment was accompanied by increased HMGB1 secretion and microglial activation. The phagocytic capacity of microglia was enhanced, resulting in aberrant pruning of excitatory synapses in the hippocampus. The loss of excitatory synapses reduced neuronal activity, impaired LTP, and decreased theta oscillation in the hippocampus. Inhibiting HMGB1 secretion by ICM treatment reversed these changes. CONCLUSIONS: HMGB1 induces microglial activation, aberrant synaptic pruning, and neuron dysfunction in an animal model of SAE, leading to cognitive impairment. These results suggest that HMGB1 might be a target for SAE treatment.

Convolutional neural network based anatomical site identification for laryngoscopy quality control: A multicenter study.

OBJECTIVES: Video laryngoscopy is an important diagnostic tool for head and neck cancers. The artificial intelligence (AI) system has been shown to monitor blind spots during esophagogastroduodenoscopy. This study aimed to test the performance of AI-driven intelligent laryngoscopy monitoring assistant (ILMA) for landmark anatomical sites identification on laryngoscopic images and videos based on a convolutional neural network (CNN). MATERIALS AND METHODS: The laryngoscopic images taken from January to December 2018 were retrospectively collected, and ILMA was developed using the CNN model of Inception-ResNet-v2 + Squeeze-and-Excitation Networks (SENet). A total of 16,000 laryngoscopic images were used for training. These were assigned to 20 landmark anatomical sites covering six major head and neck regions. In addition, the performance of ILMA in identifying anatomical sites was validated using 4000 laryngoscopic images and 25 videos provided by five other tertiary hospitals. RESULTS: ILMA identified the 20 anatomical sites on the laryngoscopic images with a total accuracy of 97.60 %, and the average sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 100 %, 99.87 %, 97.65 %, and 99.87 %, respectively. In addition, multicenter clinical verification displayed that the accuracy of ILMA in identifying the 20 targeted anatomical sites in 25 laryngoscopic videos from five hospitals was ≥95 %. CONCLUSION: The proposed CNN-based ILMA model can rapidly and accurately identify the anatomical sites on laryngoscopic images. The model can reflect the coverage of anatomical regions of the head and neck by laryngoscopy, showing application potential in improving the quality of laryngoscopy.

Predictors of Occult Lymph Node Metastasis and Prognosis in Patients with cN0 T1-T2 Supraglottic Laryngeal Carcinoma: A Retrospective Study.

BACKGROUND: This work aimed to explore the predictors of lymph node metastasis (LNM) and analyze the prognosis of patients with clinically node-negative (cN0) T1-T2 supraglottic laryngeal carcinoma (SGLC). METHODS: Data for 130 patients with cN0 T1-T2 SGLC who initially underwent surgery were retrospectively reviewed. Occult LNM incidence, relevant factors, and prognosis were analyzed. RESULTS: Of the 130 patients with cN0 T1-T2 SGLC, 21 (16.2%) had occult LNM. Based on univariate and multivariable regression analyses, male sex and poor tumor differentiation predicted the incidence of occult LNM. The incidence of occult LNM was 20.9% in males and 5.1% in females (p = 0.035). Patients with poorly differentiated tumors had a higher incidence of occult LNM (42.9%) than patients with well-differentiated (10.3%) and moderately differentiated tumors (14.3%; p < 0.05). Thirteen patients (10%) had cervical recurrence, and all had T2 tumors (p = 0.02). The 5-year disease-specific survival rates were 70 and 90% for patients with and without LNM, respectively (p = 0.000). CONCLUSIONS: Sex and tumor differentiation are potential predictors of occult nodal disease. Female patients with cN0 T1-T2 SGLC are less likely than male patients to have neck metastasis. Poorly differentiated tumors are associated with the frequency of neck metastasis, and selective neck dissection is strongly recommended for these tumors.

The Detection of Nasopharyngeal Carcinomas Using a Neural Network Based on Nasopharyngoscopic Images.

OBJECTIVE: To construct and validate a deep convolutional neural network (DCNN)-based artificial intelligence (AI) system for the detection of nasopharyngeal carcinoma (NPC) using archived nasopharyngoscopic images. METHODS: We retrospectively collected 14107 nasopharyngoscopic images (7108 NPCs and 6999 noncancers) to construct a DCNN model and prepared a validation dataset containing 3501 images (1744 NPCs and 1757 noncancers) from a single center between January 2009 and December 2020. The DCNN model was established using the You Only Look Once (YOLOv5) architecture. Four otolaryngologists were asked to review the images of the validation set to benchmark the DCNN model performance. RESULTS: The DCNN model analyzed the 3501 images in 69.35 s. For the validation dataset, the precision, recall, accuracy, and F1 score of the DCNN model in the detection of NPCs on white light imaging (WLI) and narrow band imaging (NBI) were 0.845 ± 0.038, 0.942 ± 0.021, 0.920 ± 0.024, and 0.890 ± 0.045, and 0.895 ± 0.045, 0.941 ± 0.018, and 0.975 ± 0.013, 0.918 ± 0.036, respectively. The diagnostic outcome of the DCNN model on WLI and NBI images was significantly higher than that of two junior otolaryngologists (p < 0.05). CONCLUSION: The DCNN model showed better diagnostic outcomes for NPCs than those of junior otolaryngologists. Therefore, it could assist them in improving their diagnostic level and reducing missed diagnoses. LEVEL OF EVIDENCE: 3 Laryngoscope, 134:127-135, 2024.

The response of anaerobic ammonium oxidation process to bisphenol-A: Linking reactor performance to microbial community and functional gene.

As a typical endocrine disruptor, bisphenol A (BPA) has been widely detected in various water bodies. Although the influence of BPA on traditional biological treatment system has been investigated, it is not clear whether it has potential impact on anaerobic ammonium oxidation (anammox) process. The short- and long-term influences of BPA on reactor operational performance, sludge characteristics and microbial community were investigated in this study. Results revealed that 1 and 3 mg L-1 BPA exhibited a limited adverse impact on granular sludge reactor performance. However, exposure of sludge under 10 mg L-1 BPA would cause an obvious inhibition on nitrogen removal rate from 10.3 ± 0.2 to 7.6 ± 0.4 kg N m-3 d-1. BPA would affect granular sludge metabolic substance excretion and lead to effluent dissolved organic content increase. Both the microbial community and redundancy analysis showed that BPA exhibited a negative influence on Ca. Kuenenia but a positive correlation with SBR1031. Low BPA concentration appeared a limited impact on functional genes while 10 mg L-1 BPA would cause decline of hzsA and hdh abundances. The results of this work might be valuable for in-depth understanding the potential influence of endocrine disruptor on anammox sludge.