Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

BACKGROUND: Non-invasive computed tomography angiography (CTA)-based fractional flow reserve (CT-FFR) could become a gatekeeper to invasive coronary angiography. Deep learning (DL)-based CT-FFR has shown promise when compared to invasive FFR. To evaluate the performance of a DL-based CT-FFR technique, DeepVessel FFR (DVFFR). METHODS: This retrospective study was designed for iScheMia Assessment based on a Retrospective, single-center Trial of CT-FFR (SMART). Patients suspected of stable coronary artery disease (CAD) and undergoing both CTA and invasive FFR examinations were consecutively selected from the Beijing Anzhen Hospital between January 1, 2016 to December 30, 2018. FFR obtained during invasive coronary angiography was used as the reference standard. DVFFR was calculated blindly using a DL-based CT-FFR approach that utilized the complete tree structure of the coronary arteries. RESULTS: Three hundred and thirty nine patients (60.5 ±10.0 years and 209 men) and 414 vessels with direct invasive FFR were included in the analysis. At per-vessel level, sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of DVFFR were 94.7%, 88.6%, 90.8%, 82.7%, and 96.7%, respectively. The area under the receiver operating characteristics curve (AUC) was 0.95 for DVFFR and 0.56 for CTA-based assessment with a significant difference (P < 0.0001). At patient level, sensitivity, specificity, accuracy, PPV and NPV of DVFFR were 93.8%, 88.0%, 90.3%, 83.0%, and 95.8%, respectively. The computation for DVFFR was fast with the average time of 22.5 ± 1.9 s. CONCLUSIONS The results demonstrate that DVFFR was able to evaluate lesion hemodynamic significance accurately and effectively with improved diagnostic performance over CTA alone. Coronary artery disease (CAD) is a critical disease in which coronary artery luminal narrowing may result in myocardial ischemia. Early and effective assessment of myocardial ischemia is essential for optimal treatment planning so as to improve the quality of life and reduce medical costs.

Specific learning disorder (SLD) is a common neurodevelopmental disorder in children that significantly affects academic performance and quality of life. At present, diagnosis mainly relies on standardized tests and professional evaluations, a process that is complex and time-consuming. Multiple studies have shown that machine learning can analyze diverse data, including test scores, handwriting samples, eye movement data, neuroimaging data, and genetic data, to automatically learn the relationships between input features and output labels and achieve efficient prediction. It shows great potential for early screening, auxiliary diagnosis, and research on underlying mechanisms in SLD. This article reviews the applications of machine learning in the auxiliary diagnosis of SLD and discusses its performance when handling different data types.
Humans ; Machine Learning ; Specific Learning Disorder/diagnosis* ; Child

To compare and analyze the clinical manifestations,laboratory characteristics,imaging findings,and treatment outcomes of patients with acute and chronic brucellosis,a retrospective analysis was conducted on 258 patients with brucellosis(202 in the acute group and 56 in the chronic group)hospitalized in Xinkang Hospital in Dalad Banner,Ordos City,Inner Mongolia Autonomous Region,from November 2023 to November 2024.General data,epidemiological characteristics,clinical presentations,laboratory test results,imaging findings,treatment outcomes,and prognosis were collected.The incidences of fever(51.5%vs 7.1%),fatigue(30.2%vs 12.5%),joint pain(42.9%vs 16.1%),and muscle pain(9.9%vs.1.8%)were significantly higher in the acute phase group(all P<0.05).The incidence of osteoarthritis complications was higher in the chronic brucellosis group(51.8%vs 8.9%,χ2=75.697,P<0.01).Univariate ANOVA analysisshowed that the Serum Agglutination Tests(SAT),alanine aminotransferase(ALT),aspartate aminotransferase(AST),total bilirubin(TBIL),creatinine(CRE),C-reactive protein(CRP),erythrocyte sedimentation rate(ESR),and bone destructionexhibited statistically significant differences between the acute and chronic phases of brucellosis(all P<0.05).Multivariate logistic regression analysis indicated that abnormal ALT(OR=14.18,95%CI:1.11-181.72;P=0.041)and bone destruction(OR=0.16,95%CI:0.04-0.63;P=0.009)were associated with chronic brucellosis.After treatment,all patients experienced have symptom relief in varying degrees,with 157 patients(60.9%)cured and 101 patients(39.1%)symptomatic improved(P<0.01).In conclusion,the incidences of fever,fatigue,and joint pain in patients during the acute phase is significantly higher than that those in patients during the chronic phase,while the incidence of osteoarthritis complications is higher in chronic phase patients.The incidences of abnormal SAT,ALT,AST,TBIL,CRE,CRP,and ESR,and bone destruction varies at different stages of brucellosis.Of those,abnormal ALT and bone destruction show a stronger association with,which can assist the clinical staging of brucellosis.

Objective To explore the targeted regulatory relationship of miR-330-5p on OY-TES-1 in glioblastoma and the effect of miR-330-5p/OY-TES-1 axis on the migration ability of glioblastoma.Methods Bioinformatics analysis was performed to analyze the expression level of miR-330-5p in patients with glioblastoma and its influence on prognosis and survival of patients.The glioblastoma cells U251 were divided into miR-330-5p minics group,minics-NC group,and miR-330-5p+OY-TES-1 overexpression group(miR-330-5p minics+pcDNA3.1-OY-TES-1).The effect of miR-330-5p on the activity of OY-TES-1 3'UTR region was detected by double luciferase reporter gene experiment.The expression of OY-TES-1 mRNA was detected by qRT-PCR.The effect of miR-330-5p/OY-TES-1 axis on the migration ability of glioblastoma cells was detected by Transwell migration assay.Results The expression of miR-330-5p in glioblastoma tissue was significantly lower than those in non-tumor brain tissue and low-grade glioma tissue(P<0.05).The survival time of glioblastoma patients with high expression of miR-330-5p was significantly longer than that of patients with low expression of miR-330-5p(P<0.05).After overexpression of miR-330-5p,the activity of OY-TES-1 3'UTR region was decreased(P<0.05).Compared with minics-NC group,the expression levels of OY-TES-1 mRNA of U251 and U87MG cells in miR-330-5p minics group were significantly decreased(P<0.01).Compared with minics-NC group,the numbers of migrating cells in miR-330-5p minics group and miR-330-5p+OY-TES-1 overexpression group were significantly decreased(P<0.05).Compared with miR-330-5p minics group,the number of migrating cells in miR-330-5p+OY-TES-1 overexpression group was significantly increased(P<0.01).Conclusion MiR-330-5p targets OY-TES-1 to inhibit the migration of glioblastoma.

Objective:To investigate the anti-heart failure mechanism of N-acetylcysteine(NAC)in diabetic cardiomyop-athy independent from coronary artery factors.Methods:A total of 40 diabetic mice after heart failure model construction were randomly divided into two groups,NAC group(n=20,NAC 100mg·kg-1·d-1)and control group(n=20,Saline 100 mg·kg-1·d-1).Echocardiography was performed to detect left ventricular end-diastolic volume(LVEDV),left ventricular end-systolic volume(LVESV),left ventricular ejection fraction(LVEF),mitral left ventricular early-dias-tolic peak flow velocity/left ventricular late-diastolic peak flow velocity(E/A),isovolumic relaxation time(IVRT)and cardiac output(CO)after 4 weeks.Terminal uridine nick-end labeling(TUNEL)was performed to detect apoptosis in-dex,and Western Blot was performed to detect the expression of extracellular regulated protein kinases(ERK)1/2 after 6 weeks in two groups.Results:Compared to those in control group,mice in NAC group had significant higher LVEF[(40.5±3.4)％vs.(36.9±3.2)％],E/A[(1.5±0.1)vs.(1.4±0.1)]and CO[(10.3±0.6)ml/min vs.(9.9±0.5)ml/min](P＜0.05 or＜0.01);and significant lower LVESV[(23.1±1.3)μl vs.(24.7±1.5)μl],apoptosis index[(31.2±0.5)％vs.(45.1±0.9)％]and the expression of ERK1/2[(2.2±0.2)vs.(3.9±0.1)](P＜0.001 all).Conclusion:NAC exerts anti-heart failure effect by attenuating apoptosis of cardiomyocytes via regulating ERK1/2 pathway.

Objective:To develop an artificial intelligence-based model for automated recognition of serum indices using machine vision and deep learning.Methods:This study was a cross-sectional study.Serum sample images were collected fromWest China Hospital of Sichuan University from September 21, 2020 to January 20, 2023 using the imaging device of the fully automated sample pre-processing system. A computer random number generator was used to randomly select one whole hour each day, and all serum sample images processed within that hour were included. After excluding samples with unqualified images and missing serum index results, a total of 5, 534 samples were included. These were divided into a training set and a test set in an 8∶2 ratio using Python random shuffle function, and 4, 458 samples were in the training set and 1, 076 samples were in the test set. After manual inspection, the serum regions were annotated using the MATLAB Image Labeler tool and converted into YOLO format, and a YOLO v5-based model was constructed for automatic serum region extraction. The actual values of lipemia index (L-index), hemolysis index (H-index), and icterus index (I-index) were measured using the automatic biochemical analyzerwith matched reagent kits. A serum index regression model was constructed based on the MobileNet v2 network using the PyTorch 1.10.0 framework. The grading performance of the model was evaluated using accuracy, Kappa coefficient, sensitivity, specificity, positive predictive value, and negative predictive value. Regression performance was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and Bland-Altman analysis.Results:The overall accuracy rates for grading L-index, H-index, and I-index were 98.88%, 95.26%, and 92.47%, respectively, with Kappa coefficients of 0.72, 0.72, and 0.59. For L-index, MAE was 5.11, RMSE was 9.77, and R2 was 0.78. For H-index, MAE was 5.18, RMSE was 8.99, and R2 was 0.89. For I-index, MAE was 1.13, RMSE was 3.01, and R2 was 0.71. Bland-Altman analysis showed that 95.5%, 95.1%, and 95.7% of the data points fell within the consistency intervals for L-index, H-index, and I-index, respectively.Conclusion:The study developed an artificial intelligence-based serum index regression modelto estimate serum indices with high efficiency and accuracy. It shows great potential for reducing laboratory costs, improving clinical testing efficiency, and promoting intelligent development in laboratory medicine.

Organophosphorus nerve agents are the most threatening chemical warfare agents and terrorist agents.The number of nerve agents and their related chemicals involved in the verification of Chemical Weapon Convention(CWC)exceeds ten million,with the majority being isomers.Accurate structural identification of these chemicals has always been one of the challenges in CWC related verification analysis.In this work,a total of 17 kinds of alkyl phosphonate isomers and structural analogs from 5 groups were designed and synthesized,and then analyzed by gas chromatography-mass spectrometry(GC-MS)and gas chromatography-infrared spectroscopy(GC-FTIR).The spectra of isomers or structural analogs obtained from two techniques as well as the structural information provided therein were compared and analyzed.The results showed that for isomers or structural analogs with similar MS spectra,FTIR spectra could provided more structural fingerprint information of compounds and had advantages in confirming structures.Combined with the excellent separation ability of GC,GC-FTIR can be used to assist GC-MS in the structural confirmation of alkyl phosphates,achieving rapid and accurate identification of isomers or structural analogues.

Objective To evaluate the predictive value of dose-surface histogram(DSH)for radiation proctitis(RP)in prostate cancer(PCa)patients undergoing radiotherapy.Methods This prospective randomized controlled clinical trial included 380 PCa patients who underwent image-guided radiotherapy in the First Affiliated Hospital of Hebei Northern University from January 2018 to January 2023.Patients were randomly divided into observation group(n=200)and control group(n=180).The rectal dose distribution of patients in the two groups was evaluated by using DSH and dose-volume histogram(DVH),respectively.The receiver operating characteristic(ROC)curve was utilized to evaluate the predictive value of DSH for acute RP,with DVH serving as a reference.Results The difference was not statistically significant in clinical information such as age,KPS score,and body mass index(BMI)between the observation and control groups(P＞0.05),as well as in acute RP incidence(P＞0.05).There were significant differences in S40 and V40,S50 and V50,S60 and V60,S70 and V70,and S78 and V78 between the two groups(P＜0.05).S40,S50,V40,and V50 showed low efficacy(P＜0.001)in predicting acute RP at each level,with AUC≤0.700.S60 and V60 showed moderate efficacy(P＜0.001)in predicting acute RP at each level,with AUC 0.700-0.900.S70,S78,V70 and V78 showed high efficacy(P＜0.001)in predicting acute RP at each level,with AUC＞0.900.Conclusions The predictive value of DSH for rectal toxicity in patients with PCa is basically consistent with that of DVH.It is expected to become a novel and valuable tool for evaluating radiotherapy plans in the future.