Promoting TFEB nuclear localization with curcumin analog C1 attenuates sensory hair cell injury and delays age-related hearing loss in C57BL/6 mice.

Sensory hair cell (HC) injuries, especially outer hair cell (OHC) loss, are well-documented to be the primary pathology of age-related hearing loss (AHL). Recent studies have demonstrated that autophagy plays an important role in HC injury in the inner ear. In our previous works, a decline in autophagy levels and HC loss were found to occur simultaneously in the inner ears of aged C57BL/6 mice, and the administration of rapamycin promoted autophagy levels, which reduced OHC loss and delayed AHL, but the underlying mechanism of autophagy in AHL has not been well elucidated. Transcription factor EB (TFEB), an autophagy regulator and the downstream target of mammalian target of rapamycin (mTOR), is involved in the pathological development of neurodegenerative disease. This study would address the link between autophagy and TFEB in aged C57BL/6 mouse cochleae and clarify the effect of the TFEB activator curcumin analog C1 (C1) in aged cochleae. Decreased TFEB nuclear localization (p = 0.0371) and autophagy dysfunction (p = 0.0273) were observed in the cochleae of aged C57BL/6 mice that exhibited AHL, HCs loss and HCs senescence. Treatment with C1 promoted TFEB nuclear localization and restored autophagy, subsequently alleviating HC injury and delaying AHL. The protective effect of C1 on HEI-OC1 cells against autophagy disorder and aging induced by D-galactose was abolished by chloroquine, which is one of the commonly used autophagy inhibitors. Overall, our results demonstrated that the capacity to perform autophagy is mediated by the nuclear localization of TFEB in aged C57BL/6 mouse cochleae. C1 promotes the nuclear localization of TFEB, subsequently alleviating HC injury and delaying AHL by restoring the impaired autophagy function. TFEB may serve as a new therapeutic target for AHL treatment.

Microglia drive transient insult-induced brain injury by chemotactic recruitment of CD8+ T lymphocytes.

The crosstalk between the nervous and immune systems has gained increasing attention for its emerging role in neurological diseases. Radiation-induced brain injury (RIBI) remains the most common medical complication of cranial radiotherapy, and its pathological mechanisms have yet to be elucidated. Here, using single-cell RNA and T cell receptor sequencing, we found infiltration and clonal expansion of CD8+ T lymphocytes in the lesioned brain tissues of RIBI patients. Furthermore, by strategies of genetic or pharmacologic interruption, we identified a chemotactic action of microglia-derived CCL2/CCL8 chemokines in mediating the infiltration of CCR2+/CCR5+ CD8+ T cells and tissue damage in RIBI mice. Such a chemotactic axis also participated in the progression of cerebral infarction in the mouse model of ischemic injury. Our findings therefore highlight the critical role of microglia in mediating the dysregulation of adaptive immune responses and reveal a potential therapeutic strategy for non-infectious brain diseases.

A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images.

BACKGROUND: This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images. METHOD: A total of 6393 OME otoscopic images from three centers were used to develop and validate a DL model for detecting atelectasis and attic retraction pocket. A threefold random cross-validation procedure was adopted to divide the dataset into training validation sets on a patient level. A team of otologists was assigned to diagnose and characterize atelectasis and attic retraction pocket in otoscopic images. Receiver operating characteristic (ROC) curves, including area under the ROC curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the DL model. Class Activation Mapping (CAM) illustrated the discriminative regions in the otoscopic images. RESULTS: Among all OME otoscopic images, 3564 (55.74%) were identified with attic retraction pocket, and 2460 (38.48%) with atelectasis. The diagnostic DL model of attic retraction pocket and atelectasis achieved a threefold cross-validation accuracy of 89% and 79%, AUC of 0.89 and 0.87, a sensitivity of 0.93 and 0.71, and a specificity of 0.62 and 0.84, respectively. Larger and deeper cases of atelectasis and attic retraction pocket showed greater weight, based on the red color depicted in the heat map of CAM. CONCLUSION: The DL algorithm could be employed to identify atelectasis and attic retraction pocket in otoscopic images of OME, and as a tool to assist in the accurate diagnosis of OME.

[Research on three-dimensional skull repair by combining residual and informer attention].

Cranial defects may result from clinical brain tumor surgery or accidental trauma. The defect skulls require hand-designed skull implants to repair. The edge of the skull implant needs to be accurately matched to the boundary of the skull wound with various defects. For the manual design of cranial implants, it is time-consuming and technically demanding, and the accuracy is low. Therefore, an informer residual attention U-Net (IRA-Unet) for the automatic design of three-dimensional (3D) skull implants was proposed in this paper. Informer was applied from the field of natural language processing to the field of computer vision for attention extraction. Informer attention can extract attention and make the model focus more on the location of the skull defect. Informer attention can also reduce the computation and parameter count from N 2 to log( N). Furthermore,the informer residual attention is constructed. The informer attention and the residual are combined and placed in the position of the model close to the output layer. Thus, the model can select and synthesize the global receptive field and local information to improve the model accuracy and speed up the model convergence. In this paper, the open data set of the AutoImplant 2020 was used for training and testing, and the effects of direct and indirect acquisition of skull implants on the results were compared and analyzed in the experimental part. The experimental results show that the performance of the model is robust on the test set of 110 cases fromAutoImplant 2020. The Dice coefficient and Hausdorff distance are 0.940 4 and 3.686 6, respectively. The proposed model reduces the resources required to run the model while maintaining the accuracy of the cranial implant shape, and effectively assists the surgeon in automating the design of efficient cranial repair, thereby improving the quality of the patient's postoperative recovery.

Pregabalin mitigates microglial activation and neuronal injury by inhibiting HMGB1 signaling pathway in radiation-induced brain injury.

BACKGROUND: Radiation-induced brain injury (RIBI) is the most serious complication of radiotherapy in patients with head and neck tumors, which seriously affects the quality of life. Currently, there is no effective treatment for patients with RIBI, and identifying new treatment that targets the pathological mechanisms of RIBI is urgently needed. METHODS: Immunofluorescence staining, western blotting, quantitative real-time polymerase chain reaction (Q-PCR), co-culture of primary neurons and microglia, terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay, enzyme-linked immunosorbent assay (ELISA), and CRISPR-Cas9-mediated gene editing techniques were employed to investigate the protective effects and underlying mechanisms of pregabalin that ameliorate microglial activation and neuronal injury in the RIBI mouse model. RESULTS: Our findings showed that pregabalin effectively repressed microglial activation, thereby reducing neuronal damage in the RIBI mouse model. Pregabalin mitigated inflammatory responses by directly inhibiting cytoplasmic translocation of high-mobility group box 1 (HMGB1), a pivotal protein released by irradiated neurons which induced subsequent activation of microglia and inflammatory cytokine expression. Knocking out neuronal HMGB1 or microglial TLR2/TLR4/RAGE by CRISPR/Cas9 technique significantly inhibited radiation-induced NF-κB activation and pro-inflammatory transition of microglia. CONCLUSIONS: Our findings indicate the protective mechanism of pregabalin in mitigating microglial activation and neuronal injury in RIBI. It also provides a therapeutic strategy by targeting HMGB1-TLR2/TLR4/RAGE signaling pathway in the microglia for the treatment of RIBI.

A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images.

Importance: Otitis media with effusion (OME) is one of the most common causes of acquired conductive hearing loss (CHL). Persistent hearing loss is associated with poor childhood speech and language development and other adverse consequence. However, to obtain accurate and reliable hearing thresholds largely requires a high degree of cooperation from the patients. Objective: To predict CHL from otoscopic images using deep learning (DL) techniques and a logistic regression model based on tympanic membrane features. Design, Setting, and Participants: A retrospective diagnostic/prognostic study was conducted using 2790 otoscopic images obtained from multiple centers between January 2015 and November 2020. Participants were aged between 4 and 89 years. Of 1239 participants, there were 209 ears from children and adolescents (aged 4-18 years [16.87%]), 804 ears from adults (aged 18-60 years [64.89%]), and 226 ears from older people (aged >60 years, [18.24%]). Overall, 679 ears (54.8%) were from men. The 2790 otoscopic images were randomly assigned into a training set (2232 [80%]), and validation set (558 [20%]). The DL model was developed to predict an average air-bone gap greater than 10 dB. A logistic regression model was also developed based on otoscopic features. Main Outcomes and Measures: The performance of the DL model in predicting CHL was measured using the area under the receiver operating curve (AUC), accuracy, and F1 score (a measure of the quality of a classifier, which is the harmonic mean of precision and recall; a higher F1 score means better performance). In addition, these evaluation parameters were compared to results obtained from the logistic regression model and predictions made by three otologists. Results: The performance of the DL model in predicting CHL showed the AUC of 0.74, accuracy of 81%, and F1 score of 0.89. This was better than the results from the logistic regression model (ie, AUC of 0.60, accuracy of 76%, and F1 score of 0.82), and much improved on the performance of the 3 otologists; accuracy of 16%, 30%, 39%, and F1 scores of 0.09, 0.18, and 0.25, respectively. Furthermore, the DL model took 2.5 seconds to predict from 205 otoscopic images, whereas the 3 otologists spent 633 seconds, 645 seconds, and 692 seconds, respectively. Conclusions and Relevance: The model in this diagnostic/prognostic study provided greater accuracy in prediction of CHL in ears with OME than those obtained from the logistic regression model and otologists. This indicates great potential for the use of artificial intelligence tools to facilitate CHL evaluation when CHL is unable to be measured.

Investigating the use of a two-stage attention-aware convolutional neural network for the automated diagnosis of otitis media from tympanic membrane images: a prediction model development and validation study.

OBJECTIVES: This study investigated the usefulness and performance of a two-stage attention-aware convolutional neural network (CNN) for the automated diagnosis of otitis media from tympanic membrane (TM) images. DESIGN: A classification model development and validation study in ears with otitis media based on otoscopic TM images. Two commonly used CNNs were trained and evaluated on the dataset. On the basis of a Class Activation Map (CAM), a two-stage classification pipeline was developed to improve accuracy and reliability, and simulate an expert reading the TM images. SETTING AND PARTICIPANTS: This is a retrospective study using otoendoscopic images obtained from the Department of Otorhinolaryngology in China. A dataset was generated with 6066 otoscopic images from 2022 participants comprising four kinds of TM images, that is, normal eardrum, otitis media with effusion (OME) and two stages of chronic suppurative otitis media (CSOM). RESULTS: The proposed method achieved an overall accuracy of 93.4% using ResNet50 as the backbone network in a threefold cross-validation. The F1 Score of classification for normal images was 94.3%, and 96.8% for OME. There was a small difference between the active and inactive status of CSOM, achieving 91.7% and 82.4% F1 scores, respectively. The results demonstrate a classification performance equivalent to the diagnosis level of an associate professor in otolaryngology. CONCLUSIONS: CNNs provide a useful and effective tool for the automated classification of TM images. In addition, having a weakly supervised method such as CAM can help the network focus on discriminative parts of the image and improve performance with a relatively small database. This two-stage method is beneficial to improve the accuracy of diagnosis of otitis media for junior otolaryngologists and physicians in other disciplines.

Comparison of KRAS mutation status between primary tumor and metastasis in Chinese colorectal cancer patients.

Detection of KRAS mutation status is a routine clinical procedure for predicting response to anti-EGFR therapy in colorectal cancer (CRC) patients. Previous studies showed high concordance of KRAS mutation status in primary lesion and corresponding metastatic sites in CRC. However, the data were mostly from Caucasians. The aim of this study is to compare KRAS mutation and other molecules mutation status between primary tumor and corresponding metastatic lesion in Chinese patients with CRC. In this retrospective study, Chinese CRC patients with paired samples of primary tumor and metastatic site were detected for KRAS codon 12 and 13 with quantitative real-time PCR, or detected for OncoCarta™ panel of 19 genes with MassARRAY(®) technique, including KRAS, BRAF, NRAS and PIK3CA et al. Forty-eight paired CRC samples were analyzed for KRAS codon 12 and 13 using quantitative real-time PCR. Ten paired samples were analyzed by 19 genes OncoCarta™ Panel with MassARRAY(®) technique. KRAS mutation was found in 15 (25.9 %) primary tumors and 18 (31.0 %) metastases. The discordance of KRAS was observed in 11 (19.0 %) patients. Alteration of mutation points in primary site with mutant KRAS was not observed. In the 10 patients with multiple gene detection, PIK3CA mutation showed concordant mutation status in primary tumor and metastatic site, whereas discordance in BRAF, NRAS and AKT1 was detected. A concordance rate of 81.0 % was detected in KRAS mutation between primary tumor and metastatic lesion in Chinese patients with CRC. Discordance of BRAF, NRAS and AKT1 mutation status in primary tumor and metastases was observed.

Melatonin overcomes gemcitabine resistance in pancreatic ductal adenocarcinoma by abrogating nuclear factor-κB activation.

Constitutive activation and gemcitabine induction of nuclear factor-κB (NF-κB) contribute to the aggressive behavior and chemotherapeutic resistance of pancreatic ductal adenocarcinoma (PDAC). Thus, targeting the NF-κB pathway has proven an insurmountable challenge for PDAC therapy. In this study, we investigated whether the inhibition of NF-κB signaling pathway by melatonin might lead to tumor suppression and overcome gemcitabine resistance in pancreatic tumors. Our results showed that melatonin inhibited activities of NF-κB by suppressing IκBα phosphorylation and decreased the expression of NF-κB response genes in MiaPaCa-2, AsPc-1, Panc-28 cells and gemcitabine resistance MiaPaCa-2/GR cells. Moreover, melatonin not only inhibited cell proliferation and invasion in a receptor-independent manner, but also enhanced gemcitabine cytotoxicity at pharmacologic concentrations in these PDAC cells. In vivo, the mice treated with both agents experienced a larger reduction in tumor burden than the single drug-treated groups in an orthotopic xenograft mouse model. Taken together, these results indicate that melatonin inhibits proliferation and invasion of PDAC cells and overcomes gemcitabine resistance of pancreatic tumors through NF-κB inhibition. Our findings therefore provide novel preclinical knowledge about melatonin inhibition of NF-κB in PDAC and suggest that melatonin should be investigated clinically, alone or in combination with gemcitabine for PDAC treatment.