Optical brain-computer interface (OBCI) represents an emerging class of neural interaction technologies that use “light” as an information carrier to enable the acquisition, decoding, and modulation of neural signals. Compared with conventional electrical brain-computer interface (BCI), OBCI demonstrates distinct advantages in spatial resolution, cell-type specificity, and the capacity for simultaneous multiparametric monitoring. Driven by rapid advances in functional near-infrared spectroscopy, optical neuroimaging, and optogenetics, optical approaches have progressively extended across the full “read, decode, write” continuum of neural activity, providing a novel technological framework for the development of high-precision closed-loop brain-computer systems. This review systematically summarizes the principal technological strategies and recent advances in OBCI, and further discusses the key challenges encountered during clinical translation, as well as future development direction.

ObjectiveTo establish a tumor prognostic risk assessment model related to target genes of the miR-34 family. MethodsTarget genes of the miR-34 family were screened， and the scores of miR-34 target genes were assessed in 16 tumor types. Univariate Cox regression analysis was used to identify the tumor type with the strongest correlation between miR-34 target gene scores and overall survival （OS）. Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） analyses were performed to elucidate the functional roles and signaling pathways of miR-34 target genes. A prognostic risk model based on the miR-34 target genes was constructed using univariate Cox and LASSO regression analyses. Quantitative real-time PCR （qPCR） and dual-luciferase reporter assays were conducted to validate whether the target genes bind to miR-34 and measure their RNA expression levels in the relevant tumors. Additionally， the risk score was integrated with other clinical indicators to develop a nomogram prediction model for patient survival. ResultsA total of 65 target genes of the miR-34 family were screened. The cancer type exhibiting stronger correlation between the target gene scores and OS was lung adenocarcinoma （P = 0.003， HR= 5.150）. Furthermore， miR-34 target genes were predominantly enriched in oxidative stress pathways and various tumor-related processes. Three genes， LDHA， GALNT7， and SATB2， were identified as core components of the prognostic analysis model for lung adenocarcinoma. Additionally， the constructed nomogram model demonstrated robust predictive performance. ConclusionThe risk model and prognosis model of lung adenocarcinoma constructed based on the key target genes of miR-34 have good predictive performance.

Background/Aims: MicroRNA (miRNA) isoforms (isomiRs) broaden the regulatory landscape of canonical miRNAs, but their role in metabolic dysfunction-associated steatotic liver disease (MASLD) remains unknown. We aimed to characterize the hepatic isomiR landscape in MASLD and define their association with disease activity and fibrosis. Methods: Small RNA (sRNA) sequencing was performed on liver biopsies from 79 patients across the histological spectrum of MASLD. IsomiRs were annotated and quantified. Their association to disease activity and fibrosis score was assessed by differential expression, ordinal regression, and machine learning. Parallel mRNA sequencing and pathway enrichment were used to map isomiR–mRNA interactions and regulatory networks, which were validated against an independent dataset. Results: MiRNAs accounted for 75% of sRNAs in liver tissue, of which 67% were isomiRs. Across MASLD severity, 173 isomiRs correlated with disease activity and 58 with fibrosis stage. Key findings included a miR-122 isomiR uniquely targeting INSIG1 (cholesterol metabolism) and a miR-21 isomiR targeting PPARA and HMGCS2 (lipid and fibrosis pathways). Integration with mRNA data revealed 33 dysregulated pathways, including PPAR signaling, insulin resistance, and TGF-β response. Several novel isomiRs from miR-26b, let-7c, and miR-32 families were also linked to lipid metabolism and fibrosis progression. Conclusions IsomiRs represent the majority of hepatic miRNAs and uncover novel regulatory networks masked by canonical miRNA analysis. These findings provide new insights into the molecular heterogeneity of MASLD, highlight candidate pathways driving disease progression, and identify potential biomarkers and therapeutic targets for precision hepatology.

Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

Objective To investigate the feasibility and safety of bedside transesophageal echocardiography(TEE)-guided atrial septostomy in patients undergoing extracorporeal membrane oxygenation(ECMO)support.Methods Eight patients(2 females and 6 males)who developed pulmonary edema during VA-ECMO treatment between January 2020 and October 2024 were enrolled.All patients underwent bedside TEE-guided atrial septal puncture,followed by gradual balloon dilation using an Inoue balloon(diameter:18-24 mm).The endpoint for dilation was defined as mean left atrial pressure＜15 mmHg.Post-procedure evaluation included assessment of the septal defect size,mean left atrial pressure,and improvement in bilateral pulmonary edema.Results The mean age of all 8 patients was(43.50±16.84)years old.Atrial septostomy was successfully performed in all patients.The mean balloon dilation diameter was(22.75±2.68)mm,resulting in a mean septal defect size of(6.31±0.37)mm.The mean left atrial pressure significantly decreased from(26.63±1.77)mmHg pre-procedure to(12.75±1.28)mmHg post-procedure(P＜0.001).All patients demonstrated marked improvement in bilateral pulmonary edema,and six patients also showed a significant reduction in the severity of mitral regurgitation following the procedure.No procedure-related complications were observed.Conclusions Bedside TEE-guided atrial septostomy is safe and effective for reducing left heart loading during ECMO support and improving pulmonary edema.

Objective:To explore the reoperation cause and molecular classification of patients reoperated for papillary thyroid carcinoma (PTC).Methods:This is a retrospective case series study. Clinical data from 102 PTC patients who underwent reoperation at the Department of Head and Neck Surgery, Sun Yat-sen University Cancer Center were collected between February 2019 and December 2024. The cohort comprised 26 males (25.5%) and 76 females (74.5%), with initial age of (33.1±12.2) years (range: 9 to 67 years). At initial surgery, 25.5% (26/102) exhibited extrathyroidal extension, 52.0% (53/102) had multifocal tumors, and 19.6% (20/102) had metastatic lymph nodes with extranodal extension. AJCC staging classified 95.1% (97/102) as stage Ⅰ, 2.9% (3/102) as stage Ⅱ, and 2.0% (2/102) as stage Ⅲ. Standardized primary tumor resection was performed in 81.4% (83/102), prophylactic central compartment lymph node dissection (LND) in 89.2% (91/102), and therapeutic lateral LND in 47.1% (48/102). Data on recurrence, genetic alterations, reoperation intervals, and clinical features of multiple recurrent PTC cases were analyzed.Results:Among 102 patients, 81.4% (83/102) presented with lateral neck metastases, 48.0% (49/102) with central compartment metastases, and 22.6% (23/102) with residual thyroid lobe recurrence at reoperation. Reoperation occurred within 6 months postoperatively in 18.6% (19/102) and after 6 months in 81.4% (83/102). Genetic detection revealed BRAF mutation in 63.7% (65/102), RET fusions in 19.6% (20/102), and TERT promoter mutations in 8.8% (9/102). During reoperation, 88.2% (90/102) underwent therapeutic lateral LND, and 39.2% (40/102) required residual gland resection. Twelve patients received multiple surgeries, including 4 cases with BRAF+TERT mutations, 4 with RET fusions, and 4 with BRAF mutation alone. Conclusions:The reasons for the reoperation of PTC mainly include recurrence and complementary surgery. Genetic alterations such as BRAF mutation and RET fusion are common in PTC patients requiring reoperation.

Objective: This study aimed to investigate the efficacy and safety of 3-dimensional printing noncoplanar template (3D-PNCT)-assisted computed tomography (CT)-guided high-doserate interstitial brachytherapy (HDR-ISBT) for reirradiation of pelvic recurrent cervical carcinoma after external beam radiotherapy. Methods: From January 2019 to August 2023, 45 eligible patients were enrolled in this prospective cohort. All patients underwent 3D-PNCT-assisted CT-guided HDR-ISBT with a prescribed dose of 4–7 Gy/fraction to the high-risk clinical target volume (HR-CTV) over 3–8 fractions, either for curative or palliative purposes. The primary endpoints were local progression-free survival (LPFS) and tumor response rate (TRR). The secondary outcome measures included overall survival (OS), toxicities, and symptom resolution. Results: Forty-five patients received 261 fractions of 3D-PNCT-assisted HDR-ISBT. Twentynine patients had isolated pelvic recurrence, and 16 patients had simultaneous extra-pelvic or distant recurrences. The TRR was 66.7%. The 2- and 5-year LPFS rates were 30.0% and 25.7%, respectively. The median OS was 23.2 months, and 2- and 5-year OS rates were 49.5% and 34.0%, respectively. The multivariate analysis indicated that squamous cell carcinoma, radical surgery, recurrence-free interval≥12 months, tumor diameter, pelvic recurrence type, and HR-CTV D 90 ≥45 Gy were independent factors influencing LPFS (all p<0.05). D 100 ≥21 Gy, V 100 ≥83%, and V50≥45% were associated with better LPFS (all p<0.05). Tumor diameter and metastasis were independent predictive factors for OS (all p<0.05). The pain relief rate was 66.7% (10/15). Grade 3–4 toxicities occurred in 20.0% of patients. Conclusion 3D-PNCT-assisted HDR-ISBT for reirradiation of recurrent cervical cancer proved to be an effective and safe alternative to radical surgery.

Objective To investigate the value of fragmented QRS complex(fQRS)in the electro-cardiogram,heart rate variability(HRV)and LVEF in evaluating the prognosis in patients with dilated cardiomyopathy(DCM).Methods A retrospective analysis was conducted on 59 DCM pa-tients admitted in the Second Affiliated Hospital of Wannan Medical College from January 2020 to December 2023.According to the occurrence of MACE during 6-month follow-up period,they were classified into a poor prognosis group(26 cases)and a good prognosis group(33 cases).Clin-ical baseline data,positive rate of fQRS,HRV and LVEF were compared between the two groups.Time-domain measurements of HRV included standard deviation of normal NN intervals(SDNN),standard deviation of the averages of NN intervals in all 5 min segments of the entire recording(SDANN),mean of the standard deviation of NN intervals for all 5 min segments of the entire recording(SDNN index),root mean square of standard deviation of NN intervals(r-MSSD)and HRV triangular index.Spearman/Pearson correlation analysis was performed to analyze the correlation of prognosis of DCM with positive rate of fQRS,HRV and LVEF.ROC curve was drawn to analyze the efficiency of fQRS,HRV and LVEF in predicting the prognosis of DCM.Results The poor prognosis group exhibited significantly higher positive rate of fQRS and obvi-ously reduced SDNN,SDANN,SDNN index,r-MSSD,triangular index and LVEF when compared with the good prognosis group(P＜0.01).Correlation analysis suggested that poor prognosis of DCM was positively correlated with the positive rate of fQRS(P＜0.01),and negatively with SDNN,SDANN,SDNN index,r-MSSD,triangular index and LVEF(P＜0.05,P＜0.01).ROC curve analysis revealed that the AUC value of above indicators in turn in predicting the prognosis of DCM was 0.718,0.7 56,0.7 62,0.807,0.858,0.805 and 0.747,respectively,and the AUC value of their combination was 0.980(P＜0.01).Conclusion fQRS,HRV and LVEF have important cor-relation with poor prognosis of DCM patients.Their combination can be used as an effective mark-er for clinical evaluation and prediction of poor prognosis of DCM.