Objective To investigate the perioperative safety of patients with myasthenia gravis who take high doses of oral glucocorticoids. Methods A retrospective analysis was conducted on the clinical data of patients with myasthenia gravis who received oral glucocorticoids and underwent thoracoscopic thymectomy at the Department of Thoracic Surgery, the University of Hong Kong-Shenzhen Hospital from April 2013 to October 2019. Patients were divided into a high-dose steroid group and a medium-to-low dose steroid group based on the dosage of oral steroids, and the clinical data of the two groups were compared. Results A total of 102 patients were included, including 19 (18.62%) males and 83 (81.37%) females, with an average age of (32.25±9.83) years. There were 75 patients in the medium-to-low dose steroid group and 27 patients in the high-dose steroid group. All patients in both groups successfully completed the surgery without major intraoperative bleeding, conversion to open chest surgery, delayed extubation, severe infection, or perioperative death. The daily oral steroid dose for the high-dose steroid group was (35.81±4.29) mg, and for the medium-to-low dose steroid group it was (15.29±2.17) mg. There was no statistical difference in the operation time [(124.69±23.51) min vs. (117.89±21.46) min, P=0.172] and intraoperative blood loss [(21.19±3.48) mL vs. (20.56±3.41) mL, P=0.419] between the two groups. Postoperatively, 12 (11.76%) patients developed complications: one patient of myasthenic crisis (the medium-to-low dose steroid group), which was improved after short-term respiratory support and intravenous immunoglobulin treatment; 11 patients of respiratory/swallowing difficulties (9 in the medium-to-low dose steroid group and 2 in the high-dose steroid group), which were improved after anticholinergic treatment to reduce oral secretions and sputum suction, and the patients were discharged smoothly. There was no statistical difference in the incidence of postoperative complications between the two groups (P=0.637). Conclusion On the basis of good perioperative management, it is safe and feasible for patients with myasthenia gravis who take high dose of oral steroids to undergo thymectomy, and they have the same perioperative safety as patients with medium-to-low dose steroids.

This paper discussed the differentiation and treatment of chemotherapy-induced nausea and vomiting （CINV） in traditional Chinese medicine， based on the theory of "yin qi descends， while all yang qi ascends” from Inner Canon of Yellow Emperor （《黄帝内经》）. The core pathogenesis of CINV is attributed to chemotherapy-induced injury to the middle jiao （焦）， resulting in the separation of yin and yang and loss of their mutual communication. Acute and explosive CINV is often characterized by "all yang qi ascends"， with stomach yang constraint transforming into fire， and qi counterflow leading to vomiting， which can be treated by unblocking stomach yang， draining fire and scattering the counterflow， with Huoxiang Zhengqi Powder （藿香正气散） and medicinals of clearing function. For delayed cases， which are often due to "yin qi descends"， and the accumulation of turbid yin obstructs and constrains the deficient yang， the treatment should focus on promoting qi flow to eliminate turbidity， unblocking and boosting yang qi， and Chengqi-series Formulas （承气类方） or Xiao Banxia Decoction （小半夏汤） can be considered. Refractory and anticipated cases usually involve stomach-kidney deficiency and zang-fu （脏腑） organs disharmony， making yin and yang difficult to restore balance， for which the treatment should focus on nourishing both the stomach and kidneys and harmonize the zang-fu organs， with formulas such as Fuzi Lizhong Decoction （附子理中汤） or Xiaoyao Powder （逍遥散） modified as appropriate.

Introduction: Improving glycemic control while minimizing unnecessary insulin exposure is an important goal in diabetes management. The Rapid Optimization Clinic (ROC) was established as a structured multidisciplinary service to support therapy individualization, close follow-up, and timely insulin deintensification. This audit evaluated early changes in glycated hemoglobin A1c (HbA1c) and insulin treatment burden following ROC care over 3 months. Methodology: We conducted a retrospective audit of routine clinical data from patients with diabetes managed in the ROC at a district hospital. Baseline and 3-month HbA1c and insulin data were extracted from non-electronic clinic records. Insulin dose was standardized as total daily dose (TDD) in units/kg/day. Insulin deintensification was evaluated primarily by change in TDD from baseline to 3 months and by the proportion of patients who discontinued insulin during follow-up. Paired analyses were performed for patients with complete baseline and follow-up data for each outcome. Continuous variables are presented as mean ± standard deviation or median with interquartile range, as appropriate. Exploratory analyses were undertaken to assess whether available patient factors were associated with HbA1c improvement. Results: Twenty-three patients were included. Paired HbA1c data were available for 12 patients, whereas paired TDD data were available for 21 patients. Mean HbA1c decreased from 10.78 ± 2.59% at baseline to 8.42 ± 2.29% at 3 months, representing a mean reduction of 2.36 percentage points (95% confidence interval [CI] 0.09–4.62; p = 0.043). Mean TDD decreased from 0.434 ± 0.248 to 0.286 ± 0.313 units/kg/day, corresponding to a mean reduction of 0.148 units/kg/day (95% CI 0.077–0.219; p <0.001). Insulin was discontinued in 9 of 21 patients (42.9%). No clear association was observed between HbA1c improvement and age, sex, or number of visits. Interpretation is limited by the small sample size, reflecting the early phase of a newly established clinic. Conclusion In this early audit, ROC care was associated with clinically meaningful improvement in glycemic control and significant insulin deintensification over 3 months. These findings support the potential role of a structured multidisciplinary optimization clinic in delivering individualized diabetes care and facilitating safe reduction of insulin burden.
Humans ; Glycemic Control ; Retrospective Studies ; Diabetes Mellitus ; Insulins

ObjectiveTo investigate the safety and efficacy of transparent cap-assisted peroral cholangiopancreatoscopy-guided electrohydraulic lithotripsy （EHL）. MethodsA retrospective analysis was performed for the clinical data of 60 patients with large common bile duct stones who were admitted to Department of Gastroenterology， Jilin People’s Hospital， from May 1， 2023 to January 31， 2026， and according to the treatment method， the patients were divided into transparent cap-assisted peroral cholangiopancreatoscopy-guided electrohydraulic lithotripsy group and peroral cholangiopancreatoscopy-guided electrohydraulic lithotripsy group， with 30 patients in each group. The two groups were compared in terms of the rate of successful single-session stone fragmentation and removal， procedural time， duration of electrohydraulic lithotripsy， clinical success rate， and complications. The independent-samples t test or the Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. ResultsCompared with the peroral cholangiopancreatoscopy-guided electrohydraulic lithotripsy group， the transparent cap-assisted peroral cholangiopancreatoscopy-guided electrohydraulic lithotripsy group had a significantly higher technical success rate （83.33% vs 60.00%， χ²=4.022， P=0.045） and significantly shorter procedural time ［46.00 （42.25 — 49.00） min vs 54.50 （42.00 — 65.00） min， Z=2.012， P=0.044］ and duration of electrohydraulic lithotripsy ［8.00 （6.25 — 11.75） min vs 13.50 （9.25 — 17.75） min， Z=2.749， P=0.006］. Both groups achieved a clinical success rate of 100%， with no significant difference in the incidence rate of complications （P>0.05）. ConclusionTransparent cap-assisted peroral cholangiopancreatoscopy-guided electrohydraulic lithotripsy has a high rate of successful single-session stone fragmentation and removal and a short procedural time， with good safety and efficacy， and therefore， it holds promise for clinical application.

AIM: To investigate the predictive value of serum histone deacetylase 1(HDAC1)and endothelial nitric oxide synthase(eNOS)for infectious endophthalmitis after cataract surgery.METHODS: A total of 362 cataract patients(362 eyes)admitted to our hospital from January 2020 to January 2023 were selected as the research objects. According to the occurrence of postoperative infectious endophthalmitis, they were divided into infection group(15 cases, 15 eyes)and non-infection group(347 cases, 347 eyes). Enzyme linked immunosorbent assay(ELISA)was applied to detect the levels of serum HDAC1 and eNOS in all subjects, and the levels of serum HDAC1 and eNOS in both groups were compared; the influencing factors of infectious endophthalmitis were analyzed by multivariate Logistic regression; the receiver operative curve(ROC)was applied to analyze the predictive value of serum HDAC1 and eNOS levels for postoperative infectious endophthalmitis in cataract patients.RESULTS: The levels of serum HDAC1 and eNOS in the infected group were obviously higher than those in the uninfected group(all P<0.01). Surgical time, vitreous overflow, HDAC1, and eNOS were all risk factors for postoperative infectious endophthalmitis(all P<0.05). ROC results showed that the AUC of HDAC1 and eNOS in predicting postoperative infectious endophthalmitis in cataract patients was 0.878 and 0.877, respectively, with sensitivity of 88.7% and 87.7%, specificity of 70.4% and 7.8%, respectively, while the AUC of the two combination in predicting postoperative infectious endophthalmitis in cataract patients was 0.978, with a sensitivity of 86.7% and a specificity of 85.3%.CONCLUSION:The serum levels of HDAC1 and eNOS in patients with infectious endophthalmitis after cataract surgery are obviously increased, and the combined detection of serum HDAC1 and eNOS can improve the predictive efficacy of infectious endophthalmitis in cataract patients after surgery. Both can provide reference for clinical diagnosis and treatment.

AIM: To investigate the predictive value of serum histone deacetylase 1(HDAC1)and endothelial nitric oxide synthase(eNOS)for infectious endophthalmitis after cataract surgery.METHODS: A total of 362 cataract patients(362 eyes)admitted to our hospital from January 2020 to January 2023 were selected as the research objects. According to the occurrence of postoperative infectious endophthalmitis, they were divided into infection group(15 cases, 15 eyes)and non-infection group(347 cases, 347 eyes). Enzyme linked immunosorbent assay(ELISA)was applied to detect the levels of serum HDAC1 and eNOS in all subjects, and the levels of serum HDAC1 and eNOS in both groups were compared; the influencing factors of infectious endophthalmitis were analyzed by multivariate Logistic regression; the receiver operative curve(ROC)was applied to analyze the predictive value of serum HDAC1 and eNOS levels for postoperative infectious endophthalmitis in cataract patients.RESULTS: The levels of serum HDAC1 and eNOS in the infected group were obviously higher than those in the uninfected group(all P<0.01). Surgical time, vitreous overflow, HDAC1, and eNOS were all risk factors for postoperative infectious endophthalmitis(all P<0.05). ROC results showed that the AUC of HDAC1 and eNOS in predicting postoperative infectious endophthalmitis in cataract patients was 0.878 and 0.877, respectively, with sensitivity of 88.7% and 87.7%, specificity of 70.4% and 7.8%, respectively, while the AUC of the two combination in predicting postoperative infectious endophthalmitis in cataract patients was 0.978, with a sensitivity of 86.7% and a specificity of 85.3%.CONCLUSION:The serum levels of HDAC1 and eNOS in patients with infectious endophthalmitis after cataract surgery are obviously increased, and the combined detection of serum HDAC1 and eNOS can improve the predictive efficacy of infectious endophthalmitis in cataract patients after surgery. Both can provide reference for clinical diagnosis and treatment.

ObjectiveTomatoes are one of the highest-yielding and most widely cultivated economic crops globally, playing a crucial role in agricultural production and providing significant economic benefits to farmers and related industries. However, early blight in tomatoes is known for its rapid infection, widespread transmission, and severe destructiveness, which significantly impacts both the yield and quality of tomatoes, leading to substantial economic losses for farmers. Therefore, accurately identifying early symptoms of tomato early blight is essential for the scientific prevention and control of this disease. Additionally, visualizing affected areas can provide precise guidance for farmers, effectively reducing economic losses. This study combines hyperspectral imaging technology with machine learning algorithms to develop a model for the early identification of symptoms of tomato early blight, facilitating early detection of the disease and visual localization of affected areas. MethodsTo address noise interference present in hyperspectral images, robust principal component analysis (RPCA) is employed for effective denoising, enhancing the accuracy of subsequent analyses. To avoid insufficient information representation caused by the subjective selection of regions of interest, the Otsu’s thresholding method is utilized to extract tomato leaves effectively from the background, with the average spectrum of the entire leaf taken as the primary object of study. Furthermore, a comprehensive spectral preprocessing workflow is established by integrating multivariate scatter correction (MSC) and standardization methods, ensuring the reliability and effectiveness of the data. Based on the processed spectral data, a discriminant model utilizing a linear kernel function support vector machine (SVM) is constructed, focusing on characteristic wavelengths to improve the model's discriminative capability. ResultsCompared to full-spectrum modeling, this approach results in an 8.33% increase in accuracy on the test set. After optimizing the parameters of the SVM model, when C=1.64, the accuracies of the training set and test set reach 91.67% and 94.44%, respectively, demonstrating a 1.19% increase in training set accuracy compared to the unoptimized model, while maintaining the same accuracy on the test set, effectively alleviating issues of underfitting. ConclusionThis study successfully establishes an early discriminant model for tomato early blight using hyperspectral imaging and achieves visualization of early symptoms. Experimental results indicate that the SVM discriminant model based on characteristic wavelengths and a linear kernel function can effectively identify early symptoms of tomato early blight. Visualization of these symptoms in terms of disease probability allows for a more intuitive detection of early diseases and timely implementation of corresponding control measures. This visual analysis not only enhances the efficiency of disease identification but also provides farmers with more straightforward and practical information, aiding them in formulating more reasonable prevention strategies. These research findings provide valuable references for the early identification and visualization of plant diseases, holding significant practical implications for monitoring, identifying, and scientifically preventing crop diseases. Future research could further explore how to apply this model to disease detection in other crops and how to integrate IoT technology to create intelligent disease monitoring systems, enhancing the scientific and efficient management of crops.

As a third major discipline on par with internal medicine and surgery,interventional radiology is one of the key branches of modern medicine.The unique practicality and innovation of interventional radiology make it play an irreplaceable role in the diagnosis and treatment of diseases.In recent years,with the rapid development of technology and the updating of educational concepts,the teaching mode of interventional radiology is facing new challenges and opportunities.This article combines the clinical training methods adopted by interventional physicians in Europe and Germany,integrates imaging,anatomy and pathology teaching,strengthens the foundation,uses virtual reality(VR),augmented reality(AR)technology,and simulation training to enhance the practical ability of medical students.At the same time,with the help of online education platforms,the innovative methods and optimization strategies such as personalized learning program are implemented,which can greatly improve the teaching effectiveness of interventional radiology and provide useful references for teaching reform in this field.

This article reports a first case of combined infection with severe fever with thrombocytopenia syndrome(SFTS)and scrub typhus in Dalian City.The patient was admitted to the hospital due to recurrent fever for 7 days and loss of consciousness for 1 day.Pathogen metagenomic sequencing(mNGS),SFTSV quantitative PCR,and enzyme-linked immunosorbent assay(ELISA)IgM tests were performed,showing positive results for Orientia tsutsugamushi and SFTSV nucleic acids.Based on clinical manifestations and epidemiological history,the patient was diagnosed with combined infections.

ObjectiveEarly detection of crop diseases is crucial for effective agricultural management and yield protection. While visible light imaging has been widely applied for disease detection due to its accessibility and non-destructive nature, most existing methods primarily focus on identifying diseases during the symptomatic phase, when visual symptoms are already prominent. However, detecting plant diseases during the incubation period—when symptoms are still subtle or invisible—remains a major challenge due to the lack of distinctive visual cues and limited research methodologies. This study aims to address this gap by proposing a novel three-channel recognition model to accurately identify early blight symptoms during the incubation stage in Solanaceae crops, particularly in chili and tomato, using only visible light images. MethodsWe established a controlled experimental setup in which healthy leaves and leaves inoculated with early blight pathogens were photographed continuously over time. A total of 1 258 visible light images were collected, capturing various stages of disease progression. From these images, lesion regions were manually annotated. To quantitatively characterize early and subtle color changes within the lesion areas, we extracted color moments—first-order (mean), second-order (standard deviation), and third-order (skewness)—from multiple color spaces, including Lab and HSV. By analyzing the temporal variation of these color moments across disease progression stages, we identified the first-order moment of the saturation (S) channel in the HSV color space as the most sensitive indicator of lesion development on inoculated leaves. Using this insight, we defined four disease categories: healthy, incubation stage, early stage, and late stage. Subsequently, a three-channel classification model was constructed by integrating features from three color channels that provided complementary information. Three-channel models were constructed based on R-G-B, L-a-b, and H-S-V color spaces, respectively, to evaluate performance across different crops and to determine which color representation provides the most discriminative power for identifying disease symptoms during the incubation period. ResultsThe proposed models demonstrated strong classification performance. The three-channel model built using the Lab color space achieved a 94.44% accuracy in recognizing the incubation stage of early blight in pepper, effectively distinguishing subtle pre-symptomatic features from healthy tissue. The model based on the HSV color space achieved 100% accuracy in detecting incubation-stage symptoms in tomato, underscoring the discriminative power of S-channel variations in this context. These results confirm the model’s capability to identify early blight before visible lesions become pronounced, which is essential for timely disease intervention. ConclusionThis study presents a new technical pathway for early-stage disease detection using visible light images by focusing on subtle color feature changes during the incubation period. The proposed three-channel recognition model effectively identifies early blight in both chili and tomato, offering a non-destructive, low-cost, and easily deployable solution for early warning and precision agriculture. Furthermore, this framework can be generalized to other crops and diseases where early detection plays a critical role in minimizing yield losses and ensuring sustainable production. The method lays a solid foundation for future research in pre-symptomatic plant disease recognition and provides valuable tools for intelligent crop monitoring and precision management systems.