Hearing loss has become increasingly prevalent and causes considerable disability, thus gravely burdening the global economy. Irreversible loss of hair cells is a main cause of sensorineural hearing loss, and currently, the only relatively effective clinical treatments are limited to digital hearing equipment like cochlear implants and hearing aids, but these are of limited benefit in patients. It is therefore urgent to understand the mechanisms of damage repair in order to develop new neuroprotective strategies. At present, how to promote the regeneration of functional hair cells is a key scientific question in the field of hearing research. Multiple signaling pathways and transcriptional factors trigger the activation of hair cell progenitors and ensure the maturation of newborn hair cells, and in this article, we first review the principal mechanisms underlying hair cell reproduction. We then further discuss therapeutic strategies involving the co-regulation of multiple signaling pathways in order to induce effective functional hair cell regeneration after degeneration, and we summarize current achievements in hair cell regeneration. Lastly, we discuss potential future approaches, such as small molecule drugs and gene therapy, which might be applied for regenerating functional hair cells in the clinic.
Infant, Newborn ; Humans ; Hair Cells, Auditory, Inner/physiology* ; Ear, Inner/physiology* ; Hair Cells, Auditory/physiology* ; Regeneration/genetics* ; Stem Cells

As a common clinical Chinese medicine, Prunellae Spica has the effects of clearing liver-fire, improving eyesight, resolving massesand detumescence, and has strong anti-tumor effects against thyroid cancer, breast cancer, liver cancer and other cancers. Extracts of Prunellae Spica and its active components can play an anti-tumor role in a variety of ways, including cell apoptosis, inhibiting cell invasion and metastasis, inhibiting cell proliferation, inducing autophagy, anti tumor angiogenesis, reversing tumor multidrug resistance and regulating immune function, by regulating miRNA and Wnt/β-catenin, PI3/AKT, AMPK/mTOR/ULK1, RANKL/RANK/OPG and other signal pathways . In this paper, the anti-tumor mechanism of Prunellae Spica extract was reviewed, in order to provide reference for further research and application.

OBJECTIVE To establish high performance liquid chromatography （HPLC） fingerprint of Xiaohe syrup and determine the contents of 10 effective ingredients in them. METHODS With 12 batches of Xiaohe syrup as samples， ultra-high performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） was adopted with Athena C18 （250 mm×4.6 mm， 5 μm） as the chromatographic column， acetonitrile-0.1% phosphoric acid aqueous solution as mobile phase for gradient elution. The flow rate was 1.0 mL/min， and the detection wavelength was 210 nm. Similarity Evaluation System for Traditional Chinese Medicine Chromatographic Fingerprint （2012A version） was imported to establish the fingerprint of Xiaohe syrup and evaluate the similarity. The content determination was performed on ACQUITY UPLC BEH C18（ 100 mm×2.1 mm， 1.7 μm） chromatographic column， with 0.01% formic acid acetonitrile-0.01% formic acid water as mobile phase for gradient elution at a flow rate of 0.4 mL/min； combined with high-resolution mass spectrometer， positive and negative ions were scanned with an electric spray ion source to determine the content of each main component in 12 batches of Xiaohe syrup. RESULTS A total of 33 common peaks were calibrated in 12 batches of samples， with similarities greater than 0.97； 10 chromatographic peaks were confirmed， namely flavonoid glycosides， paeoniflorin， ferulic acid， naringin， rosmarinic acid， neohesperidin， salvianolic acid B， tetrahydropalmatine， saikosaponin A， and saikosaponin D. The results of content determination showed that the above 10 components had good linear relationships within their respective mass concentration ranges （all R 2＞0.999）， with contents ranging from 0.35 to 0.64， 3.15 to 5.61， 0.11 to 0.17， 1.68 to 3.17， 1.59 to 1.90， 1.15 to 1.64， 0.78 to 1.48， 0.11 to 0.26， 0.06 to 0.13， and 0.33 to 0.61 mg/mL， respectively. CONCLUSIONS The main components of 12 batches of Xiaohe syrup are similar， but the contents vary； HPLC fingerprint and UPLC-MS/MS content determination method established in this study can be used for comprehensive quality evaluation of Xiaohe syrup.

Objective A porcine vocal cord injury model was established to analyze and compare the damage range of porcine vocal cords and the repair process after vocal cord injury caused by CO2 laser surgery and radiofre-quency ablation with plasma spot excitation technology.Methods Fifteen pigs,30 sides of vocal cords,were used in this study.The bilateral vocal cords of the same pig were divided into CO2 laser and radiofrequency coblation(RFC)groups respectively.CO2 laser and radiofrequency ablation with plasma spot excitation technology were used to perform ubligamental cordectomy.Three pigs were sacrificed after anesthesia on postoperative day 1 and postop-erative 3,6,9,and 15 weeks.The vocal cords were harvested for HE staining.The depth of edema degeneration zone and the fibrous tissue thickness were measured vertically in the two groups,and the postoperative vocal cord damage range and vocal cord repair process were compared between the two groups.Results The depth of edema degeneration zone in the CO2 laser group was deeper than that in the RFC group at 1 day postoperatively(P＜0.01).There was no significant difference in the depth of the edema degeneration zone between the two groups at the 3rd,6th,and 9th weeks after operation(P＞0.05).The depth of the edema degeneration zone at 3rd weeks postoperatively was less than that at 1 day postoperatively,and at 6th weeks postoperatively was less than that at 3rd weeks postoperatively for both operation modes(P＜0.05).There was no statistically significant difference be-tween 9th weeks postoperatively and 6th weeks postoperatively(P＞0.05).At the 15th week after operation,the fibrous tissue thickness of the vocal fold lamina propria in the RFC group was thinner than that in the CO2 laser group(P＜0.01).Conclusion Radiofrequency ablation with plasma spot excitation technology causes less damage to porcine vocal fold than CO2 laser.There is no significant difference between the two operation modes in terms of postoperative repair process,while the fibrous tissue in the lamina propria of the vocal fold is thinner in the RFC group when a vocal fold scar is formed.

Objective:To identify the relationship between the CT arterial radiomics score and the treatment response to neoadjuvant therapy for pancreatic cancer.Methods:The clinical data of 243 pancreatic cancer patients who received surgical resection after neo-adjuvant therapy in the First Affiliated Hospital of Naval Medical University from March 2017 to March 2023 were retrospectively analyzed. Based on the tumor regression grade (TRG), the patients were divided into good response group (TRG 0-1, n=30) and non-good response group (TRG 2-3, n=213). The clinical, radiological and pathological features were compared between two groups. Fully-automated segmentation tool was used for segmenting the arterial CT scan of pancreatic tumor before and after treatment. Python package was applied to extract the radiomics features of tumors after segmentation and the extracted features were reduced and chosen using the least absolute shrinkage and selection operator (Lasso) logistic regression algorithm. Lasso logistic regression formula was applied to calculate the arterial radiomics score. Univariate and multivariate logistic regression models were used to analyze the association between arterial radiomics score and treatment response to neoadjucant therapy. Receiver operating-characteristics (ROC) curve was drawn and area under curve (AUC), specificity, sensitivity and accuracy for evaluating the treatment response were calculated. The clinical usefulness of arterial radiomics score for diagnosing the response of neoadjuvant treatment for pancreatic cancer were determined by decision curve analysis (DCA) . Results:A total of 330 arterial radiomics CT features were obtained, and 9-selected arterial phase features associated with treatment response were determined after being reduced by the Lasso logistic regression algorithm. Univariate analysis showed that the arterial radiomics score, three-dimensional diameter after neoadjuvant therapy, pancreatic contour, T stage, N stage, Peri-pancreatic nerve invasion, lymph-vascular space invasion (LVSI) and invasion of duodenum were all associated with treatment response (all P value <0.05). Multivariate logistic regression analyses confirmed that arterial radiomics score was obviously associated with the neoadjuvant treatment response ( P<0.001). At the cut-off value of 1.93, AUC of the arterial radiomics score for diagnosing neoadjuvant treatment response was 0.92, and the specificity, sensitivity and accuracy was 86.7%, 84.5% and 84.8%. DCA demonstrated that when the percentage for predicting the treatment response by using the arterial radiomics score was >0.2, the patients could benefit from the application of arterial radiomics score for evaluating neoadjuvant therapy response. Conclusions:The arterial radiomics score was strongly correlated with the neoadjuvant treatment response of pancreatic cancer, and can accurately predict neoadjuant treatment efficacy.

Objective:To develop and validate the models based on mixed enhanced computed tomography (CT) radiomics and deep learning features, and evaluate the efficacy for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC) before surgery.Methods:The clinical data of 201 patients with surgically resected and histopathologically confirmed PASC (PASC group) and 332 patients with surgically resected histopathologically confirmed PDAC (PDAC group) who underwent enhanced CT within 1 month before surgery in the First Affiliated Hospital of Naval Medical University from January 2011 to December 2020 were retrospectively collected. The patients were chronologically divided into a training set (treated between January 2011 and January 2018, 156 patients with PASC and 241 patients with PDAC) and a validation set (treated between February 2018 and December 2020, 45 patients with PASC and 91 patients with PDAC) according to the international consensus on the predictive model. The nnU-Net model was used for pancreatic tumor automatic segmentation, the clinical and CT images were evaluated, and radiomics features and deep learning features during portal vein phase were extracted; then the features were dimensionally reduced and screened. Binary logistic analysis was performed to develop the clinical, radiomics and deep learning models in the training set. The models' performances were determined by area under the ROC curve (AUC), sensitivity, specificity, accuracy, and decision curve analysis (DCA).Results:Significant differences were observed in tumor size, ring-enhancement, upstream pancreatic parenchymal atrophy and cystic degeneration of tumor both in PASC and PDAC group in the training and validation set (all P value <0.05). The multivariable logistic regression analysis showed the tumor size, ring-enhancement, dilation of the common bile duct and upstream pancreatic parenchymal atrophy were associated with PASC significantly in the clinical model. The ring-enhancement, dilation of the common bile duct, upstream pancreatic parenchymal atrophy and radiomics score were associated with PASC significantly in the radiomics model. The ring-enhancement, upstream pancreatic parenchymal atrophy and deep learning score were associated with PASC significantly in the deep learning model. The diagnostic efficacy of the deep learning model was highest, and the AUC, sensitivity, specificity, and accuracy of the deep learning model was 0.86 (95% CI 0.82-0.90), 75.00%, 84.23%, and 80.60% and those of clinical and radiomics models were 0.81 (95% CI 0.76-0.85), 62.18%, 85.89%, 76.57% and 0.84 (95% CI 0.80-0.88), 73.08%, 82.16%, 78.59% in the training set. In the validation set, the area AUC, sensitivity, specificity, and accuracy of deep learning model were 0.78 (95% CI 0.67-0.84), 68.89%, 78.02% and 75.00%, those of clinical and radiomics were 0.72 (95% CI 0.63-0.81), 77.78%, 59.34%, 65.44% and 0.75 (95% CI 0.66-0.84), 86.67%, 56.04%, 66.18%. The DCA in the training and validation sets showed that if the threshold probabilities were >0.05 and >0.1, respectively, using the deep learning model to distinguish PASC from PDAC was more beneficial for the patients than the treat-all-patients as having PDAC scheme or the treat-all-patients as having PASC scheme. Conclusions:The deep learning model based on CT automatic image segmentation of pancreatic neoplasm could effectively differentiate PASC from PDAC, and provide a new non-invasive method for confirming PASC before surgery.

Background The optimal model method for estimation of benchmark dose (BMD) does not consider the uncertainty of model selection. There is a lack of studies on using Bayesian model averaging (BMA) to estimate BMD. Objective To apply BMA to the exposure assessment of cadmium pollution in China, discuss the role of BMA in estimating BMD based on dose-response models, and to provide methodological support for health risk assessment of hazardous substances. Methods The parameters of five dose-response models (Gamma, Log-logistic, Log-probit, Two-stage, and Weibull models) estimated from the data from a cadmium-contaminated area in Baiyin City of Gansu Province and the urinary cadmium ranges in five cadmium-contaminated areas in China were used to simulate the data of varied correct models with different numbers of dosage groups (5 and 8) and different sample sizes (50, 100, and 200), then the performance of BMA and traditional optimal model were compared. The case analysis used the cadmium exposure data in Baiyin, Gansu Province. All analyses set urinary cadmium as the indicator of cadmium exposure, the abnormal rate of β₂-microglobulin as the effect indicator, and the benchmark response to 10%. The correct model (the model used when simulating data), optimal model [the model with smallest Akaike information criterion (AIC)], and BMA were used to estimate BMD and lower confidence limit of benchmark dose (BMDL); the BMDs, BMDLs, and relative deviations from different methods were compared. Results In the simulation study, with increasing sample size or the number of dosage groups, the intervals of the 5th percentile and the 90th percentile of BMD tended to be narrower; when the correct model was a single model, the relative deviation of BMD estimation by BMA was greater than that of the traditional optimal model; when the correct model was an equal weight mixed model, the relative deviation of BMD estimation by BMA was less than that by the traditional optimal model. For the data of cadmium-contaminated areas, the optimal model was a Log-probit model (AIC=1814.46), followed by a Log-logistic model (AIC=1814.57); the BMDs (BMDLs) estimated by the Log-probit model, the Log-logistic model, and BMA were 3.46 (2.68), 3.16 (2.33), and 2.92 (2.07) μg·g⁻¹, respectively. Conclusion The traditional optimal model is still recommended when the correct model is known. However, when the dose-response relationship of a hazardous substance is uncertain or with different sources or exposure grouping, compared with the traditional optimal model, BMA theoretically provides more stable estimation of BMD and BMDL by considering multiple possible alternative models.

Objective:To explore the differences in clinical and imaging features between pancreatic adenosquamous carcinoma (PASC) and pancreatic ductal adenocarcinoma (PDAC).Methods:The clinical data, imaging and pathological data of 171 patients pathologically diagnosed with PASC after surgical resection (PASC group) (from February 2011 to October 2020, 148 patients from the First Affiliated Hospital of Naval Medical University and 23 patients from the Second Affiliated Hospital of Zhejiang University School of Medicine) and 100 patients pathologically diagnosed with PDAC after surgical resection (PDAC group) (from January to June, 2018, at the First Affiliated Hospital of Naval Medical University) were retrospectively analyzed. Computed tomography and magnetic resonance imaging features were analyzed by two associate chief physician of department of radiology. Independent sample t test, rank sum test, chi-square test or Fisher exact probability test were used for statistical analysis. Multivariate logistic regression analysis was used to analyze independent predictors of PASC. Results:The longest diameter of tumor of PASC group was larger than that of PDAC group (35.0 mm (28.0 mm to 45.0 mm) vs. 29.5 mm (23.0 mm to 36.0 mm)), and the rates of cystic necrosis, ring-enhancement, normal distal main pancreatic duct and normal pancreatic parenchyma of PASC group were higher than those of PDAC group (62.0%, 106/171 vs. 12.0%, 12/100; 66.1%, 113/171 vs. 25.0%, 25/100; 52.0%, 89/171 vs. 12.0%, 12/100; 70.2%, 120/171 vs. 29.0%, 29/100, respectively); and the differences were statistically significant ( Z=-4.001, χ2=72.183, 42.612, 43.284 and 43.221, all P<0.01). The results of multivariate logistic regression analysis showed that the cystic necrosis, ring-enhancement, normal distal main pancreatic duct and normal pancreatic parenchyma were indenpendent predictors of PASC (odds ratio=10.083, 2.361, 3.086 and 2.632, 95% confidence interval 8.736 to 11.639, 2.096 to 2.660, 2.605 to 3.656 and 2.267 to 3.057, all P<0.01); and the sensitivity for PASC diagnosis was 62.0%, 66.1%, 51.7% and 70.3%, respectively; the specificity was 88.0%, 75.0%, 88.0% and 71.0%, respectively; the positive predictive value was 89.3%, 81.9%, 88.1% and 80.5%, respectively. Conclusions:PASC and PDAC have similar clinical features. The imaging features of cystic necrosis, ring-enhancement, normal distal main pancreatic duct and normal pancreatic parenchyma are independent predictive factors of PASC.

Objective:To investigate the relationship between the perineural invasion score based on multidetector computed tomography (MDCT) and extrapancreatic perineural invasion (EPNI) in pancreatic ductal adenocarcinoma (PDAC).Methods:The clinical, radiological, and pathological data of 374 patients pathologically diagnosed as pancreatic cancer who underwent radical resection in the First Affiliated Hospital of Naval Medical University from March 2018 to May 2020 were analyzed retrospectively. Patients were divided into EPNI negative group ( n=111) and EPNI positive group (n=263) based on the pathological presence of EPNI. The perineural invasion score was performed for each patient based on radiological images. Univariate and multivariate logistic regression models were used to analyze the association between the perineural invasion score based on MDCT and EPNI in PDAC. Results:There were significant statistical differences between EPNI negative group and positive group on both pathological characteristics (T stage, N stage, invasion of common bile duct, and positive surgical margin) and radiological characteristics (tumor size, vascular invasion, lymph node metastasis, perineural invasion score based on MDCT, pancreatic border, parenchymal atrophy, invasion of duodenum, invasion of spleen and splenic vein and invasion of common bile duct) (all P value <0.05). Univariate analysis revealed that the tumor size, vascular invasion, lymph node metastasis, perineural invasion score based on MDCT, pancreatic border, pancreatic atrophy, invasion of duodenum, invasion of spleen and splenic vein and invasion of common bile duct were independently associated with EPNI. Multivariate analyses revealed that the perineural invasion based on MDCT was an independent risk factor for EPNI in pancreatic cancer (score=1, OR=2.93, 95% CI 1.61-5.32, P<0.001; score=2, OR=5.92, 95% CI 2.68-13.10, P<0.001). Conclusions:The perineural invasion score based on MDCT was an independent risk factor for EPNI in pancreatic cancer and can be used as an evaluation indicator for preoperative prediction of EPNI in PDAC.

Objective:To investigate the phenotype, genotype and clinical course of centronuclear myopathy(CNM) in children.Methods:Clinical data of patients with CNM in the Department of Pediatrics, Peking University First Hospital from October 2008 to December 2018 were collected.The clinical, pathological and genetic data of 9 children with CNM were retrospectively analyzed.The patients were followed up from 8 months to 8.6 years [(4.4±3.1) years].Results:(1)Clinical phenotype: there were 6 males and 3 females with onset age ranging from 1 d to 10 years.Generalized muscle weakness or motor retardation was the main complaint in 8 cases, while elevated muscle enzymes presented in 1 case.Varying degrees of skeletal muscle weakness were noted on examination in all patients, with facial muscle involvement in 4 cases.Six patients were followed up.No deterioration in motor function was noted, while 2 patients had improvement.There was no significant cardiac involvement in all 6 patients.Scoliosis occurred in 4 patients.Restrictive ventilator disorder developed in 2 out of the 5 patients who underwent pulmonary function tests.(2)Genotype: 8 out of 9 patients underwent gene test, confirmed gene diagnosis in 4 patients including: DNM2 gene (c.1856C>T, c.1893+ 1G>A was novel) de novo heterozygous mutation in 2 cases, RYR1 gene (c.2044C>G, c.6823G>A, both were novel) compound heterozygous mutation in 1 case, and TTN gene (c.107377+ 1G>A, c.2106_2107 insAAGCTGTA was novel) compound heterozygous mutation in 1 case. Conclusions:The course of centronuclear myopathy is relatively static, with more frequent involvement of facial muscles than myocardium.This study enriched the gene mutation spectrum of centronuclear myopathy (4 novel mutations).