PurposeTo develop CT-based machine learning radiomics models used for the diagnosis of dysthyroid optic neuropathy (DON).Materials and MethodsThis is a retrospective study included 57 patients (114 orbits) diagnosed with thyroid-associated ophthalmopathy (TAO) at the Beijing Tongren Hospital between December 2019 and June 2023. CT scans, medical history, examination results, and clinical data of the participants were collected. DON was diagnosed based on clinical manifestations and examinations. The DON orbits and non-DON orbits were then divided into a training set and a test set at a ratio of approximately 7:3. The 3D slicer software was used to identify the volumes of interest (VOI). Radiomics features were extracted using the Pyradiomics and selected by t-test and least absolute shrinkage and selection operator (LASSO) regression algorithm with 10-fold cross-validation. Machine-learning models, including random forest (RF) model, support vector machine (SVM) model, and logistic regression (LR) model were built and validated by receiver operating characteristic (ROC) curves, area under the curves (AUC) and confusion matrix-related data. The net benefit of the models is shown by the decision curve analysis (DCA).ResultsWe extracted 107 features from the imaging data, representing various image information of the optic nerve and surrounding orbital tissues. Using the LASSO method, we identified the five most informative features. The AUC ranged from 0.77 to 0.80 in the training set and the AUC of the RF, SVM and LR models based on the features were 0.86, 0.80 and 0.83 in the test set, respectively. The DeLong test showed there was no significant difference between the three models (RF model vs SVM model: p = .92; RF model vs LR model: p = .94; SVM model vs LR model: p = .98) and the models showed optimal clinical efficacy in DCA.ConclusionsThe CT-based machine learning radiomics analysis exhibited excellent ability to diagnose DON and may enhance diagnostic convenience.

BACKGROUND:Patients with self-limited epilepsy with centrotemporal spikes (SeLECTS) and electrical status epilepticus in sleep (ESES) lead to cognitive impairment. However, therapeutic options are limited. OBJECTIVES:The sleep spindle is a biomarker of cognitive dysfunction in patients with SeLECTS. This study aimed to explore whether the sleep spindle is linked to the outcome of repetitive transcranial magnetic stimulation (rTMS) in patients with SeLECTS with ESES. METHODS:Nine patients with SeLECTS with ESES underwent low-frequency rTMS (≤ 1 Hz) or continuous theta burst stimulation (cTBS) for 10 days. To assess the clinical efficacy and alteration in the sleep spindle, EEG recordings were performed both before and after rTMS. A machine learning algorithm YASA was used to calculate the coupling of the sleep spindle and slow waves. RESULTS:75% of patients remained seizure-free for 6 months after rTMS. The spike-wave index (SWI) decreased significantly after rTMS compared with the baseline. The sleep spindle significantly increased in all patients at 3 months and 6 months after rTMS (p = 0.002). Both IQ and MQ improved significantly at 6 months after rTMS. Improvement of IQ and increase of the sleep spindle was significantly positively correlated (p = 0.035). The mean probability of the sleep spindle coupling in the slow wave "up" state increased from 28% to 55%. CONCLUSION(S):Increase of the sleep spindle and the mean probability of the sleep spindle coupling in the slow wave "up" state might be a potential mechanism for cognition improvement in patients with SeLECTS with ESES.

AIM: To conduct a bibliometric analysis of studies on microphthalmos and anophthalmos (M/A), explore research hotspots, and provide information on future research interests in this field to benefit clinicians and researchers. METHODS: Totally 751 publications related to M/A from the year 2004 to 2023 were collected from the Web of Science Core Collection database. These publications consist of both original and review articles, that are composed in English. The contributions of different countries, institutions, journals, and authors were analyzed, and network analysis was conducted by using Microsoft Excel 2021, VOSviewer, and R Studio to visualize research hotspots. RESULTS: Among all publications included, the highest number of publications came from USA (218, 29.03%). China followed with 99 publications (13.18%), and England with 86 publications (11.45%). The publications from the USA had the highest frequency of citations, with 16 699 citations, and the highest H-index of 49. The American Journal of Medical Genetics Part A (43, 5.73%) published the largest number of papers, and the University of London had the most publications (41, 5.46%). The genetic and molecular mechanisms of M/A were still unclear and the clinical intervention for M/A had gained a lot of attention as an emerging area of interest. CONCLUSION: Data have been gathered on the yearly count of published materials and citations, as well as the rise in publication trends, the efficiency of regions or countries, authors, journals, and organizations, along with the high-cited publications in M/A. The recent trend of research has shifted from genetic mechanisms to different clinical phenotypes and corresponding clinical interventions, which can give direction to future research.

Purpose: This study aims to analyze the literature on periocular basal cell carcinoma, identify research trends, and offer insights into future research areas in this field to assist clinicians and researchers. Methods: 903 publications on periocular basal cell carcinoma were collected from the Web of Science Core Collection database. We assessed the contributions from various countries, institutions, journals, and authors, and performed network analysis using Excel, VOSviewer, and R Studio to represent the prominent areas of research visually. Results: The country with the highest number of publications and citations in this study was the United States of America, with 250 publications, 5917 citations, and the highest H-index of 44. Ophthalmic Plastic and Reconstructive Surgery is the leading journal. The UTMD Anderson Cancer Center had the highest number of publications, accounting for 43, or 4.76% of the total. Selva D from the University of Adelaide, Australia, is the top author with 26 publications, and 751 citations. Targeted therapy for PBCC-related pathways has been a hot topic in recent years. Conclusions: This study using bibliometrics seeks to explore the patterns and focal points of research and analyzes publication patterns, key research areas, influential authors, and prominent journals in periocular basal cell carcinoma during the last 2 decades.

Introduction: Asthma is associated with upper airway diseases and allergic diseases; however, the causal effects need to be investigated further. Thus, we performed this two-sample Mendelian randomization (MR) analysis to explore and measure the causal effects of asthma on allergic rhinitis (AR), vasomotor rhinitis (VMR), allergic conjunctivitis (AC), atopic dermatitis (AD), and allergic urticaria (AU). Methods: The data for asthma, AR, VMR, AC, AD, and AU were obtained from large-scale genome-wide association studies summarized recently. We defined single-nucleotide polymorphisms satisfying the MR assumptions as instrumental variables. Inverse-variance weighted (IVW) approach under random-effects was applied as the dominant method for causal estimation. The weighted median approach, MR-Egger regression analysis, MR pleiotropy residual sum and outlier test, and leave-one-out sensitivity analysis were performed as sensitivity analysis. Horizontal pleiotropy was measured using MR-Egger regression analysis. Significant causal effects were attempted for replication and meta-analysis. Results: We revealed that asthma had causal effects on AR (IVW, odds ratio [OR] = 1.93; 95% confidence interval [CI], 1.74-2.14; p < 0.001), VMR (IVW, OR = 1.40; 95% CI, 1.15-1.71; p < 0.001), AC (IVW, OR = 1.65; 95% CI, 1.49-1.82; p < 0.001), and AD (IVW, OR = 2.13; 95% CI, 1.82-2.49; p < 0.001). No causal effect of asthma on AU was observed. Sensitivity analysis further assured the robustness of these results. The evaluation of the replication stage and meta-analysis further confirmed the causal effect of asthma on AR (IVW OR = 1.81, 95% CI 1.62-2.02, p < 0.001), AC (IVW OR = 1.44, 95% CI 1.11-1.87, p < 0.001), and AD (IVW OR = 1.85, 95% CI 1.42-2.41, p < 0.001). Conclusions: We revealed and quantified the causal effects of asthma on AR, VMR, AC, and AD. These findings can provide powerful causal evidence of asthma on upper airway diseases and allergic diseases, suggesting that the treatment of asthma should be a preventive and therapeutic strategy for AR, VMR, AC, and AD.

PurposeThis study aimed to explore seizure semiology and the effects of intracerebral electrical stimulation on the human posterior cingulate cortex (PCC) using Stereoelectroencephalography (SEEG) to deepen our comprehension of posterior cingulate epilepsy (PCE).MethodsThis study examined the characteristics of seizures through video documentation, by assessing the outcomes of intracranial electrical stimulation (iES) during SEEG. We further identified the connection between the observed semiology and precise anatomical locations within the PCC subregions where seizure onset zones (SOZ) were identified.ResultsAnalysis was conducted on 59 seizures from 15 patients recorded via SEEG. Behavioural arrest emerged as the predominant manifestation across the PCC subregions. Where ictal activity extended to both the mesial and lateral temporal cortex, automatism was predominantly observed in seizures originating from the ventral PCC (vPCC). The retrosplenial cortex (RSC) is associated with complex motor behaviour, with seizure discharges spreading to the temporal lobe. Seizures originating from the PCC include axial tonic and autonomic seizures. Only one case of positive clinical seizures was documented. High frequencies of iES within the PCC induced various clinical responses, categorised as vestibular, visual, psychological, and autonomic, with vestibular reactions primarily occurring in the dorsal PCC (dPCC) and RSC, visual responses in the left RSC, and autonomic reactions in the vPCC and RSC.ConclusionThe manifestations of seizures in PCE vary according to the SOZ and the patterns of seizure propagation. The occurrence of seizures induced by iES is exceedingly rare, indicating that mapping of the PCC could pinpoint the primary sector of PCC.

Purpose Dysthyroid optic neuropathy (DON) leads to vision loss. This study aimed to investigate a new method that can directly evaluate the change in muscle cone inner volume (MCIV) and distinguish DON orbits from non-DONs. Materials and methods This study included 54 patients (108 orbits) who were diagnosed with thyroid eye disease and treated at the Beijing Tongren Hospital between December 2019 and September 2021. The extraocular muscle volume (EOMV), orbital fat volume (OFV), and bony orbit volume (BOV) of the patients were measured using three-dimensional reconstruction. MCIV was measured using artificially defined boundaries. The associations between these volumes and clinical indicators were studied, and the diagnostic efficacy of these volumes for DON was described using receiver operating characteristic (ROC) curves. Results The ROC curve showed that the area under the curve of MCIV/BOV (%) combined with EOMV/BOV (%) reached 0.862 ( p < 0.001), with a sensitivity of 85.7% and a specificity of 76.1%. Conclusion The combination of MCIV/BOV (%) and EOMV/BOV (%) is a good indicator for the diagnosis of DON, which aids in the early detection and intervention of DON.

This study aims to establish a random forest model for detecting the severity of Graves Orbitopathy (GO) and identify significant classification factors. This is a hospital-based study of 199 patients with GO that were collected between December 2019 and February 2022. Clinical information was collected from medical records. The severity of GO can be categorized as mild, moderate-to-severe, and sight-threatening GO based on guidelines of the European Group on Graves' orbitopathy. A random forest model was constructed according to the risk factors of GO and the main ocular symptoms of patients to differentiate mild GO from severe GO and finally was compared with logistic regression analysis, Support Vector Machine (SVM), and Naive Bayes. A random forest model with 15 variables was constructed. Blurred vision, disease course, thyroid-stimulating hormone receptor antibodies, and age ranked high both in mini-decreased gini and mini decrease accuracy. The accuracy, positive predictive value, negative predictive value, and the F1 Score of the random forest model are 0.83, 0.82, 0.86, and 0.82, respectively. Compared to the three other models, our random forest model showed a more reliable performance based on AUC (0.85 vs. 0.83 vs. 0.80 vs. 0.76) and accuracy (0.83 vs. 0.78 vs. 0.77 vs. 0.70). In conclusion, this study shows the potential for applying a random forest model as a complementary tool to differentiate GO severity.

BACKGROUND:Evaluation of orbital pressure is crucial for monitoring various orbital disorders. However, there is currently no reliable technique to accurately measure direct orbital pressure (DOP). This study aimed to establish a new method for the DOP as well as to verify its repeatability and reproducibility in rabbits. METHODS:The study included 30 normal eyes from fifteen 3-month-old New Zealand white rabbits. After administering inhalation anesthesia, intraocular pressure (IOP) was determined by tonometry (Tonopen). For DOP manometry, a TSD104 pressure transducer was inserted between the disposable injection needle and the syringe, and the output results were displayed on a computer. Two observers independently participated in the experiment to verify its repeatability and reproducibility. RESULTS:The mean IOP of rabbits was significantly higher than the DOP in normal rabbits (11.67 ± 1.08 mm Hg versus 4.91 ± 0.86 mm Hg, P < 0.001). No significant interocular difference was detected for both IOP and DOP ( P > 0.05). A high correlation was found for intraobserver measurements of both IOP (intraclass correlation coefficient = 0.87, P < 0.001) and DOP (intraclass correlation coefficient = 0.89, P < 0.001). A high agreement was also presented for the interobserver reproducibility for the measurements of IOP [Pearson correlation coefficient ( R ) = 0.86, P < 0.001] and DOP ( R = 0.87, P < 0.001). Direct orbital pressure was positively correlated with IOP in both observers ( R 1 = 0.66, R 2 = 0.62, P < 0.001). The Bland-Altman plots revealed that 5.0% (3/60) of the IOP and DOP measurement points were outside of the 95% limits of agreement, respectively. CONCLUSIONS:The TSD104 pressure transducer-based manometry may serve as a reliable device for the measurement of DOP, providing real-time measuring results with acceptable reproducibility and repeatability.

ObjectiveMultiple system atrophy (MSA) is a degenerative disease. Immune dysfunction found to play a crucial role in the pathogenesis of this disease in the literature, while the characteristics of peripheral immune function remain unclear. This study aimed to investigate the characteristics and alterations of peripheral immune function in patients with MSA.MethodsA case–control study was conducted between January 2021 to December 2022 at SanBo Brain Hospital, Capital Medical University, Beijing, China. A total of 74 participants were recruited, including 47 MSA patients and 27 non-MSA participants. Peripheral blood samples were collected from each participant. A total of 29 types of immune cells were measured using the flow cytometry analysis technology. Single-factor analysis and multiple-factor analysis (multiple linear regression models) were performed to determine the differences and risk factors in immune cells between the MSA and non-MSA groups.ResultsAlterations of the count or percentage of CD19+ B lymphocytes and CD3−CD56+ B lymphocytes in MSA patients were found in this study. The reductions of the count and percentage of CD19+ B lymphocytes were still robust after adjusting for variables of age, gender, body mass index, albumin, and hemoglobin. Furthermore, the reductions in the count and percentage of CD19+ B lymphocytes in the MSA patients were more significant in women and individuals aged 60 years old or above than in the non-MSA participants.ConclusionOur findings suggested that MSA patients may be influenced by B lymphocytes, particularly CD19+ cells. Therefore, the reductions in immune cells should be considered in the diagnosis and treatment of MSA. Further studies are warranted to confirm and expand upon these findings.

Abstract Background This study aims to explore the relationship between psychiatric disorders and the risk of epilepsy using Mendelian randomization (MR) analysis. Methods We collected summary statistics of seven psychiatric traits from recent largest genome‐wide association study (GWAS), including major depressive disorder (MDD), anxiety disorder, autism spectrum disorder (ASD), bipolar disorder (BIP), attention deficit hyperactivity disorder (ADHD), schizophrenia (SCZ), and insomnia. Then, MR analysis estimates were performed based on International League Against Epilepsy (ILAE) consortium data (ncase = 15,212 and ncontrol = 29,677), the results of which were subsequently validated in FinnGen consortium (ncase = 6260 and ncontrol = 176,107). Finally, a meta‐analysis was conducted based on the ILAE and FinnGen data. Results We found significant causal effects of MDD and ADHD on epilepsy in the meta‐analysis of the ILAE and FinnGen, with corresponding odds ratios (OR) of 1.20 (95% CI 1.08–1.34, p = .001) and 1.08 (95% CI 1.01–1.16, p = .020) by the inverse‐variance weighted (IVW) method respectively. MDD increases the risk of focal epilepsy while ADHD has a risk effect on generalized epilepsy. No reliable evidence regarding causal effects of other psychiatric traits on epilepsy was identified. Conclusions This study suggests that major depressive disorder and attention deficit hyperactivity disorder may causally increase the risk of epilepsy.

OBJECTIVES:To establish a model in order to predict the functional outcomes of patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis and identify significant predictive factors using a random forest algorithm.METHODS:Seventy-nine patients with confirmed LGI1 antibodies were retrospectively reviewed between January 2015 and July 2020. Clinical information was obtained from medical records and functional outcomes were followed up in interviews with patients or their relatives. Neurological functional outcome was assessed using a modified Rankin Scale (mRS), the cutoff of which was 2. The prognostic model was established using the random forest algorithm, which was subsequently compared with logistic regression analysis, Naive Bayes and Support vector machine (SVM) metrics based on the area under the curve (AUC) and the accuracy.RESULTS:A total of 79 patients were included in the final analysis. After a median follow-up of 24 months (range, 8-60 months), 20 patients (25%) experienced poor functional outcomes. A random forest model consisting of 16 variables used to predict the poor functional outcomes of anti-LGI1 encephalitis was successfully constructed with an accuracy of 83% and an F1 score of 60%. In addition, the random forest algorithm demonstrated a more precise predictive performance for poor functional outcomes in patients with anti-LGI1 encephalitis compared with three other models (AUC, 0.90 vs 0.80 vs 0.70 vs 0.64).CONCLUSIONS:The random forest model can predict poor functional outcomes of patients with anti-LGI1 encephalitis. This model was more accurate and reliable than the logistic regression, Naive Bayes, and SVM algorithm.

Purpose:The aim of this study is to establish a random forest model to detect active and quiescent phases of patients with Graves Orbitopathy (GO). Methods:A total of 243 patients (486 eyes) diagnosed with GO in Beijing TongRen hospital were included in the study. The Clinical Activity Score of GO was regarded as the golden standard, whereas sex, age, smoking status, radioactive I131 treatment history, thyroid nodules, thyromegaly, thyroid hormone, and Thyroid-stimulating hormone receptor antibodies were chosen as predictive characteristic variables in the model. The random forest model was established and compared with logistic regression analysis, Naive Bayes, and Support vector machine metrics. Results:Our model has a sensitivity of 0.81, a specificity of 0.90, a positive predictive value of 0.87, a negative predictive value of 0.86, an F1 score of 0.85, and an out-of-bag error of 0.15. The random forest algorithm showed a more precise performance compared with 3 other models based on the area under receiver operating characteristic curve (0.92 versus 0.77 versus 0.76 versus 0.75) and accuracy (0.86 versus 0.71 versus 0.69 versus 0.66). Conclusions:By integrating these high-risk factors, the random forest algorithm may be used as a complementary method to determine the activity of GO, with accurate and reliable performance.

Objective: The aim of this study is to establish a random forest model to detect active and quiescent phases of patients with Thyroid-associated ophthalmopathy (TAO) and to evaluate its diagnostic performance. Methods： A total of 146 patients (292 eyes) who were diagnosed with TAO and were treated in the Ophthalmology Outpatient Clinic of Beijing TongRen hospital were retrospectively included in the study. We took the clinical activity score of TAO as the target; took gender, age, smoking status, I-131 treatment history, thyroid nodules, thyromegaly, thyroid hormone and TSH-receptor antibodies (TRAb) as predictive characteristic variables to establish a random forest model. The proportion of the training group to the testing group was 7:3. We analyzed the model’s accuracy, precision, sensitivity, specificity, positive predictive value (PPV), negative predictive value (PPV), F1 score and out-of-bag (OOB) error, with the accuracy, the brier loss and the area under the receiver operating characteristic curve compared with logistic regression model. Results： Our model has an accuracy of 0.93, a sensitivity of 0.88, a specificity of 0.96, a positive predictive value of 0.94, a negative predictive value of 0.93, an F1 score of 0.91 and an OOB error of 0.12. The accuracy of the random forest model and the logistic regression model were 0.93 and 0.79, respectively, the brier loss were 0.06 and 0.20, and the area under the receiver operating characteristic curve were 0.95 and 0.86. Conclusion： By integrating these high-risk factors, the random forest algorithm can be used as a complementary diagnostic method to determine the activity of TAO, showing prominent diagnostic performance.