Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

The therapeutic concept of amblyopia has gradually shifted from improving the visual acuity of the amblyopic eye to restoring binocular visual function.Conventional occlusion therapy and routine visual training have issues such as long treatment cycles, insufficient entertainment value and poor patient compliance, which limits the stability of therapeutic effects. Virtual reality(VR)technology, characterized by immersion, interactivity, and adjustable stimulus parameters, has been increasingly applied to visual training for amblyopia.Current evidence suggests that VR-based training can improve visual acuity and binocular visual function in amblyopia patients to a certain extent.Moreover, its gamified interactions and feedback-driven design may also enhance patient motivation and treatment adherence. This review begins with the mechanisms and theoretical basis of VR training for amblyopia, proceeds to summarizeits advantages over conventional methods, current systems, training modes, clinical efficacy, commonly used outcome measures, as well as safety and acceptability, aiming to provide a reference for its standardization, clinical translation, and widespread adoption.

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that severely affects the health of the elderly, marked by its incurability, high prevalence, and extended latency period. The current approach to AD prevention and treatment emphasizes early detection and intervention, particularly during the pre-AD stage of mild cognitive impairment (MCI), which provides an optimal “window of opportunity” for intervention. Clinical detection methods for MCI, such as cerebrospinal fluid monitoring, genetic testing, and imaging diagnostics, are invasive and costly, limiting their broad clinical application. Speech, as a vital cognitive output, offers a new perspective and tool for computer-assisted analysis and screening of cognitive decline. This is because elderly individuals with cognitive decline exhibit distinct characteristics in semantic and audio information, such as reduced lexical richness, decreased speech coherence and conciseness, and declines in speech rate, voice rhythm, and hesitation rates. The objective presence of these semantic and audio characteristics lays the groundwork for computer-based screening of cognitive decline. Speech information is primarily sourced from databases or collected through tasks involving spontaneous speech, semantic fluency, and reading, followed by analysis using computer models. Spontaneous language tasks include dialogues/interviews, event descriptions, narrative recall, and picture descriptions. Semantic fluency tasks assess controlled retrieval of vocabulary items, requiring participants to extract information at the word level during lexical search. Reading tasks involve participants reading a passage aloud. Summarizing past research, the speech characteristics of the elderly can be divided into two major categories: semantic information and audio information. Semantic information focuses on the meaning of speech across different tasks, highlighting differences in vocabulary and text content in cognitive impairment. Overall, discourse pragmatic disorders in AD can be studied along three dimensions: cohesion, coherence, and conciseness. Cohesion mainly examines the use of vocabulary by participants, with a reduction in the use of nouns, pronouns, verbs, and adjectives in AD patients. Coherence assesses the ability of participants to maintain topics, with a decrease in the number of subordinate clauses in AD patients. Conciseness evaluates the information density of participants, with AD patients producing shorter texts with less information compared to normal elderly individuals. Audio information focuses on acoustic features that are difficult for the human ear to detect. There is a significant degradation in temporal parameters in the later stages of cognitive impairment; AD patients require more time to read the same paragraph, have longer vocalization times, and produce more pauses or silent parts in their spontaneous speech signals compared to normal individuals. Researchers have extracted audio and speech features, developing independent systems for each set of features, achieving an accuracy rate of 82% for both, which increases to 86% when both types of features are combined, demonstrating the advantage of integrating audio and speech information. Currently, deep learning and machine learning are the main methods used for information analysis. The overall diagnostic accuracy rate for AD exceeds 80%, and the diagnostic accuracy rate for MCI also exceeds 80%, indicating significant potential. Deep learning techniques require substantial data support, necessitating future expansion of database scale and continuous algorithm upgrades to transition from laboratory research to practical product implementation.

Objective:To develop and validate clinical predictive models for identifying poor short-term response to recombinant human growth hormone(rhGH) treatment in children with short stature.Methods:A retrospective analysis was conducted on 118 children diagnosed with growth hormone deficiency or idiopathic short stature who were treated at the First Affiliated Hospital of Zhengzhou University and two other hospitals between January 1, 2020, and January 1, 2024. A poor response to rhGH was defined as a height increase of less than 0.2 standard deviation score(SDS) after 6 months of rhGH treatment. LASSO regression was used to identify predictive variables from baseline and follow-up data. Two logistic regression models were conducted: Model A(incorporating baseline variables only) and model B(incorporating both baseline and follow-up variables), and nomograms were created for visualization. External data and internal resampling were used for dual validation of the models, and their performance was compared.Results:A total of 118 children with short stature were included. Six baseline predictive variables(diagnosis, initial height SDS, bone age, bone age-chronological age difference, rhGH dose, and gender) and one follow-up variable(height SDS after 3 months of rhGH treatment) were identified. Area under the curve values for Model A and Model B were 0.753(95% CI 0.696-0.811) and 0.930(95% CI 0.891-0.975), respectively. Calibration curves, decision curve analysis, and other evaluation metrics demonstrated good discrimination and clinical utility for both models. Model B, incorporating the 3-month follow-up variable, showed superior predictive performance compared to Model A. Conclusions:The clinical prediction models developed in this study(Model A and Model B) are practical and reliable tools for quantitatively, conveniently, and intuitively identifying children with short stature at risk of poor response to rhGH treatment.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective To explore the time-varying characteristics and correlation degree of autonomous rehabili-tation behavior and related symptoms of middle-aged stroke patients,and to provide a basis for clinical transitional nursing and precise rehabilitation.Methods Ecological momentary assessment was used to select 57 middle-aged stroke patients who underwent rehabilitation treatment in the Rehabilitation Medicine Department of a tertiary com-prehensive hospital in Wuhan from March 15 to August 30,2024,using convenience sampling method.Their au-tonomous rehabilitation behavior and related symptoms(fatigue,pain,emotion,sensation)were continuously monitored for 2 weeks.A hierarchical linear model was used to analyze the correlation between behavior and symptoms.Results The autonomous rehabilitation behavior of middle-aged stroke patients showed a fluctuating increasing trend,and the symptom score showed a slow decreasing trend.The hierarchical linear model showed that compared with female patients,male patients have longer duration of autonomous rehabilitation behavior(P＜0.05).The number and duration of autonomous rehabilitation behaviors in patients without fatigue were higher(P＜0.05).The number of autonomous rehabilitation behaviors in patients without pain was higher than that in patients with pain(P＜0.05).The number of autonomous rehabilitation behavior items among patients who perceived more pleasure in rehabilitation was higher than that among patients with greater difficulty in rehabilitation challenges(P＜0.05).Patients with high rehabilitation confidence have higher numbers and duration of autonomous rehabilitation behaviors compared to pa-tients with low rehabilitation confidence(P＜0.05).Conclusion There are significant individual differences and dy-namic changes in the autonomous rehabilitation behavior and related symptoms of middle-aged stroke patients.Nurs-ing staff should accurately implement personalized rehabilitation services during the transition period,enhance pa-tients'autonomy and self-management ability in home-based rehabilitation,in order to improve the overall rehabilita-tion effect.

Objective To evaluate the changes of perivascular space(PVS)in patients with subacute ischemic stroke(IS),as well as the relationship between PVS and cognitive dysfunction in IS patients,and to analyze the relationship between PVS indicators and cognitive function in subacute IS patients of different ages.Methods A total of 44 healthy controls(HC)and 39 patients with subacute IS were prospectively included.Automated quantitative analysis of MRI data was conducted to determine pPVS in the white matter and subcortical regions.Differences in pPVS between patients with subacute IS and HC were compared.Correlations between age,pPVS,and cognitive dysfunction were analyzed and modeled.Results Compared with the HC,the white matter pPVS in patients with subacute IS was significantly reduced(t=-2.21,P=0.031).In patients with subacute IS,the white matter pPVS showed a significant positive correlation with lesion volume(r=0.42,P=0.030)and mini-mental state examination(MMSE)scores(r=0.44,P=0.018).Both white matter pPVS(r=-0.42,P=0.010)and MMSE scores(r=-3.82,P=0.000 7)were negatively correlated with age.When age was over 61.35 years,there was a positive correlation between the white matter pPVS and MMSE scores.Conclusion PVS are closely related to cognitive impairment in patients with subacute IS.Age is a significant modulating factor in the relationship between PVS and cognitive dysfunction.Early monitoring of PVS and targeting it for intervention may positively impact the improvement of cognitive dysfunction following IS.

A 21-year-old unmarried female visited Obstetrics and Gynecology Hospital,Fudan University with the chief complaint,memory decline for more than 20 days and discovery of a pelvic mass for more than 10 days.Through systematic examination,she was diagnosed with teratoma-associated anti-N-methyl-D-aspartate receptor(NMDAR)encephalitis.The patient underwent bilateral ovarian teratoma enucleation,and postoperative pathology revealed immature teratoma grade Ⅰ in the left ovary and mature teratoma in the right ovary.Perioperative combined immunotherapy significantly improved her neuropsychiatric symptoms.The diagnosis and treatment of ovarian teratoma-associated anti-NMDAR encephalitis require multidisciplinary collaboration,with accurate diagnosis,timely surgical resection within 1 month,and early initiation of immunotherapy being critical factors for improving prognosis.Clinicians should remain vigilant to avoid missed or misdiagnosed cases,as this condition can lead to severe outcomes.

Objective:To predict mechanical complications (MC) following spinal deformity surgery for adult spine deformity (ASD) using machine learning models, identify key risk factors, and develop a visualizable tool for individualized risk assessment.Methods:Clinical and radiological data from 525 patients with ASD who underwent surgery in our hospital between January 2017 and December 2021 were collected. Patients were randomly assigned to a training set (70%) and a test set (30%) for model development. The cohort included 88 males and 437 females, with a mean age of 42.2±18.1 years. Variables included demographic data, comorbidities, local and systemic radiological parameters, paraspinal muscle fat infiltration (FI), and vertebral bone quality (VBQ) scores. Multiple machine learning algorithms: Random Forest (RF), Gaussian Naive Bayes (GNB), Light GBM, Support Vector Machine (SVM), XGBoost (XGB), and Logistic Regression (LR) were trained and evaluated. Model performance was compared using the receiver operating characteristic curve (ROC) and precision-recall curve (PRC). SHAP (Shapley Additive Explanations) was used to rank risk factors, while LIME (Local Interpretable Model-Agnostic Explanations) was applied to visualize MC risk in individual cases.Results:Of the 525 patients, 135 (25.7%) developed postoperative MC. Among these, 80 (59.3%) experienced proximal junction kyphosis or failure (PJK/PJF), 7 (5.2%) had distal junction kyphosis or failure (DJK/DJF), 28 (20.7%) sustained rod fractures, and 29 (21.5%) showed significant loss of correction. In the validation cohort, the RF model achieved the highest area under the curve (AUC=0.80), followed by GNB (0.77), XGB (0.76), LR (0.74), LightGBM (0.73), and SVM (0.66). The RF model also demonstrated the best PRC value (0.58), highest sensitivity (0.65), and lowest Brier score (0.20). GNB, Light GBM, and LR models achieved the highest accuracy (0.78 each), while LightGBM exhibited the highest specificity (0.93). SHAP analysis identified higher preoperative VBQ scores, larger T 1 pelvic angle (TPA), and higher paraspinal muscle FI as the main risk factors for MC. Based on the RF model, a LIME-based tool was successfully constructed for individualized MC risk estimation. Conclusion:The RF model demonstrated the best overall predictive performance for MC. A machine learning-based prediction model has the potential to provide valuable guidance for surgical decision-making in ASD patients.

Objective To establish a method for the simultaneous determination of 16 cephalosporin antibiotics of the fourth generation in whole blood by ultra-high performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS),including representative drugs such as cefalexin,cefuroxime axetil,cefetamet pivoxil,ceftizoxime,cefodizime,cefteram pivoxil,cefpodoxime proxetil,cefditoren pivoxil,cefminox sodium,cefoperazone,cefpirome,cefoxitin,cefamandole nafate,cefquinome sulfate,cefpiramide,and ceftiofur.Methods Whole blood was pretreated with acetonitrile for protein precipitation and then determined by ultra-high performance liquid chromatography-triple quadrupole mass spectrometry.The liquid phase used a Hypersil GOLD? C18 column(2.1 mm ×100 mm,1.9 μm).The organic phase was 0.1％formic acid methanol solution,and the aqueous phase was 0.1％formic acid aqueous solution(containing 10 mmol/mL ammonium formate)for gradient elution.Detection was performed in electrospray positive ionization mode with selected reaction monitoring(SRM).Results The 16 drugs showed good linearity within their respective concentration ranges,with R2 values all greater than 0.99.Limits of detection for cefminox sodium and cefpiramide were 50 and 20 ng/mL,respectively,and for the remaining 14 drugs were all lower than 5 ng/mL.The relative standard deviations(RSDs)of intra-day and inter-day precisions at four spiked concentrations for the 16 drugs were all no higher than 10％(n=5).Accuracy ranged within±15％for mosg drugs,except for cefamandole nafate,ceftiofur,and cefetamet pivoxil at the lower limit of quantification,which showed accuracy within±20％.Extraction recoveries exceeded 80％for all compounds.Conclusion This method has high detection sensitivity,rapid speed,and good repeatability for the simultaneously determination of 16 cephalosporin antibiotics in whole blood.