Hearing loss is one of the most prevalent sensory disorders affecting the human nervous system. Liquid-liquid phase separation (LLPS) is a physiological process that facilitates the reversible and dynamic assembly of biomolecular condensates. Increasing evidence suggests that LLPS plays a significant role in the pathogenesis of hereditary hearing loss. Nevertheless, there is a conspicuous lack of systematic investigations exploring the impact of LLPS abnormalities on the etiology of hereditary hearing loss. In this review, we examine the mechanisms by which dysfunctions in LLPS contribute to hereditary hearing loss, specifically focusing on its effects on mechanoelectrical transduction in hair bundles, transcriptional regulation, post-transcriptional modifications, the actin cytoskeleton, ion homeostasis within the inner ear, and energy and redox homeostasis. Furthermore, we evaluate the considerable potential of targeting LLPS as a therapeutic approach for hearing loss and propose innovative perspectives on LLPS that may guide future research initiatives in the field of auditory disorders.
Humans ; Animals ; Hearing Loss/physiopathology* ; Phase Separation

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

Objective:To investigate the contents of natural radionuclides in agricultural soils in some of Gansu Hexi area to accumulate the relevant basic data.Methods:A stratified sampling method was used to collect 146 soil samples in the area. ORTEC P-type HPGE gamma spectrometry system was used to measure radionuclides. The measurement data were collated and analyzed.Results:The activity concentrations measured were 232Th 18.94-108.39 Bq/kg, 226Ra 14.37-79.20 Bq/kg and 40K 440.03-1 358.18 Bq/kg, in turn with the mean values of (68.22±16.32), (47.90±11.12) and (763.90±133.93) Bq/kg, respectively. The difference in activity concentrations of 232Th, 226Ra and 40K in agricultural soils in different areas was statistically significant( H=50.87, 45.14, 40.28, P<0.05). Conclusions:The study on the activity concentrations of natural radionuclides in agricultural soils provides basic information for the transfer of radionuclides to crops, which needs further investigation, monitoring and analysis.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.

Objective:To analyze the iodine nutrition status of pregnant women in Weihai City before and after (2022, 2023) the iodine deficiency disorders (IDD) intervention, evaluate the effectiveness of intervention measures, and provide a scientific basis for adjusting IDD prevention strategies in the city.Methods:In May 2023, the intervention action of IDD was carried out for pregnent women in Weihai City. In April 2022 and December 2023, respectively, a cross-sectional survey method was conducted in four districts (cities) of Weihai City. Each district (city) was divided into five areas (east, west, south, north, and central) each year, and one township (street) was selected from each areas. At least 20 pregnant women were selected from each township (street) as survey subjects, and their household salt samples and random urine samples were collected to measure salt iodine and urinary iodine concentrations, and to compare the test results.Results:A total of 922 household salt samples were tested, including 530 iodized salt samples and 469 qualified iodized salt samples. The median salt iodine was 24.00 mg/kg. The coverage rate of iodized salt, the qualified rate of iodized salt, and the consumption rate of qualified iodized salt were 57.48%, 88.49%, and 50.87%, respectively. No significant difference was observed in medians salt iodine between 2022 (24.26 mg/kg) and 2023 (24.00 mg/kg, Z = - 1.58, P = 0.113). However, the coverage rate of iodized salt and the consumption rate of qualified iodized salt in 2023 werehigher than those in 2022 [73.72% (373/506) vs 37.74% (157/416), 65.22% (330/506) vs 33.41% (139/416), χ 2 = 120.90, 92.40, P < 0.001]. A total of 922 urine samples were collected from pregnant women, with a median urinary iodine of 135.90 μg/L. The median urinary iodine of pregnant women in 2022 was 113.55 μg/L. There was a statistically significant difference in medians urinary iodine among different regions ( H = 27.91, P < 0.001). The median urinary iodine of pregnant women in 2023 was 153.00 μg/L. There was no statistically significant difference in medians urinary iodine among different regions ( H = 3.33, P = 0.343). The medians urinary iodine of pregnant women in Huancui District, Wendeng District, and Rushan City in 2023 (156.60, 155.00, 140.85 μg/L) were higher than those in 2022 (93.60, 110.00, 110.70 μg/L), and the differences were statistically significant ( Z = - 5.44, - 4.92, - 5.99, P < 0.001). The median urinary iodine of pregnant women in Weihai City in 2023 showed a statistically significant difference compared to 2022 ( Z = - 7.62, P < 0.001). Conclusions:The IDD intervention measures in Weihai City have achieved good results, and the coverage of iodized salt, the consumption rate of qualified iodized salt, and the iodine nutrition level of pregnant women have improved. We should continue to implement intervention measures, improve the iodine nutrition level of key populations, and maintain a sustained elimination of IDD.

Objective:To assess the efficacy and safety of Tacrolimus（TAC）in combination with glucocorticosteroid（GC）for treating IgA vasculitis nephritis（IgAVN）in children.Methods:A retrospective analysis was conducted on pediatric patients who were diagnosed with IgAVN from January 2015 to January 2022 in Children's Hospital of Hebei Province.The patients presented with nephrotic-range proteinuria or persistent urine protein（>0.5g/24 h）despite adequate glucocorticoid and other treatments in patients who do not reach massive proteinuria levels.They were treated with TAC combined with GC. The following laboratory parameters were obtained for outcome assessment: 24-hour urinary protein excretion, serum albumin, serum creatinine levels, and fasting blood glucose measurements. The efficacy and adverse reactions of TAC were summarized.Results:A total of 97 children (55 males and 42 females) were included. The average age of diagnosis of IgA vasculitis was (8.65±2.46) years, and 95.9% of the children developed renal involvement within 30 days after diagnosis. Pathological examination of renal puncture: 5 cases of grade Ⅱa, 2 cases of grade Ⅱb, 31 cases of grade Ⅲa, 57 cases of grade Ⅲb, and 2 cases of grade Ⅳb.Remission rate at 3 months was 96.9%（94/97）.Three patients failed to achieve clinical remission who were treaed with other immunosuppressants.After 1, 3, 6 and 12 months of TAC treatment, the urine protein levels of 94 children were lower than those before treatment, and the differences were statistically significant ( P < 0.05), showing a gradual downward trend. Serum albumin levels were higher than those before treatment, and the differences were statistically significant ( P < 0.05), showing a gradual upward trend.After 3 months and 6 months of TAC treatment, the serum creatinine and fasting blood glucose of the children increased. With the remission of the disease, TAC dosage decreased, the mean values of serum creatinine and fasting blood glucose decreased after 12 months of treatment.The average treatment time of TAC was (10.8±2.6) months, the average follow-up time was (3.33±1.56) years, and the longest follow-up time was 8 years. During the follow-up period, there were no serious adverse reactions such as gastrointestinal discomfort, liver function damage and severe infection. After stopping GC and TAC treatment, 80 children got sustained remission. Conclusion:The combination of TAC and GC has been proved to be effective in treating IgAVN in children.The overall effective rate is high,and clinical remission can be achieved quickly with relatively mild adverse reactions.

Objective:To detect adverse reaction risk signals of triazole antifungal agents and provide evidences for their safe use in clinic.Methods:Adverse reaction/event reports with fluconazole, itraconazole, voriconazole, posaconazole, or isavuconazonium as the primary suspect drug were collected from the data in National Adverse Drug Reaction Monitoring System of China reported by Shandong Province from January 2004 to June 2024 and the US Food and Drug Administration Adverse Event Reporting System (FAERS) database from the first quarter of 2004 to the second quarter of 2023. Adverse reaction/event terms were standardized using the preferred term (PT) and system organ class in Medical Dictionary for Regulatory Activities 24.0. Risk signals were detected using the reporting odds ratio (ROR) method and the Bayesian confidence propagation neural network (BCPNN) algorithm. A PT was defined as an adverse reaction risk signal if the number of reports was ≥3, the lower limit of the 95% confidence interval ( CI) for ROR was >2, and the lower limit of the 95% CI for the information component ( IC) was >0. Descriptive statistical analysis was performed. Results:A total of 3 988 reports with the above 5 antifungal drugs as the primary suspect drug were collected from data in National Adverse Drug Reaction Monitoring System of China reported by Shandong Province, 822 (20.6%) of which were serious cases. Voriconazole, fluconazole, itraconazole, posaconazole, and isavuconazonium was the primary suspect drug in 1 852, 1 395, 703, 27, and 11 cases among the 3 988 reports, and in 591 (31.9%), 149 (10.7%), 59 (8.4%), 18 (66.7%), and 5 (5/11) serious cases among the 822 serious case reports, respectively. A total of 20 066 reports with the above 5 drugs as the primary suspect drug were collected in FAERS database, 9 635 (48.0%) of which were serious cases. Voriconazole, fluconazole, itraconazole, posaconazole, and isavuconazonium was the primary suspect drug in 7 758, 6 180, 2 869, 1 796, and 1 463 cases among the 20 066 reports, and in 4 295 (55.4%), 2 806 (45.4%), 1 191 (41.5%), 828 (46.1%), and 515 (35.2%) serious cases among the 9 635 serious case reports, respectively. Based on the data reported by Shandong Province and in FAERS database, 18 and 207 risk signals of adverse reaction not mentioned in the labels were identified, respectively, and 5 of them were identified in both databases, including fluconazole-induced renal impairment and voriconazole-induced oliguria, delirium, psychiatric disorders, and rhabdomyolysis. In the data reported by Shandong Province and in FAERS database, 13 and 189 reports of muscle-related disorders (rhabdomyolysis, myopathy, and myositis) were identified respectively, involving voriconazole (in 8 and 62 cases), itraconazole (in 4 and 74 cases), and fluconazole (in 1 and 53 cases).Conclusions:Renal impairment induced by fluconazole and oliguria, delirium, psychiatric disorders, and rhabdomyolysis induced by voriconazole are risk signals of adverse reaction not mentioned in the labels for triazole antifungal agents. Voriconazole, itraconazole, and fluconazole may also cause muscle-related disorders, warranting vigilance in clinical practice.

Objective To develop a combined model integrating radiomics and 3D deep learning features for improving the predictive efficacy of overall survival in non-small cell lung cancer(NSCLC)patients undergoing radiotherapy,thereby providing a foundation for optimizing individualized radiotherapy strategies.Methods A retrospective analysis was conducted on 522 NSCLC patients from 3 centers.Radiomics features were extracted from the tumor region of interest on radiotherapy planning CT scans,and a 3D-SE-ResNet was constructed to extract deep learning features.Following feature extraction,features were selected via univariate Cox analysis and Lasso-Cox regression,and a combined model was established by fusing the two feature types through principal component analysis.The discriminative ability of the model was evaluated using the concordance index(C-index)and the area under the receiver operating characteristic curve(AUC),while the risk stratification efficacy was verified by Kaplan-Meier survival analysis.Results The predictive performance of deep learning features was significantly superior to that of radiomics features(C-index:0.73 vs 0.65).The combined model achieved the highest predictive performance in the training set,internal test set,and external test set(C-index:0.74,0.69,0.72 respectively),with higher AUC values for predicting 1-year,2-year,and 3-year OS than either single model.Kaplan-Meier analysis showed significant differences in survival between the high-and low-risk groups(Log-rank test,P<0.001),and calibration curves indicated good consistency between predicted and actual survival outcomes.Conclusion The combined model integrating radiomics and 3D deep learning features can accurately predict survival outcomes in NSCLC patients undergoing radiotherapy.The multi-center validation results support its potential application in prognosis stratification for individualized radiotherapy.