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Objective: To establish and verify a diagnostic model for distinguishing multiple sclerosis (MS) from other neurological diseases with similar symptoms by usingcerebrospinal fluid oligoclonal band (CSF-OCB)combined with IgG intrathecal synthesis indicators and biochemical markers. Methods: Multiple sclerosis (MS) patients admitted to the Neurology Department of Beijing Tiantan Hospital affiliated with Capital Medical University from January 2014 to December 2022 were selected as the case group, while patients with similar neurological symptoms were selected as the control group. Using the case-control study design, a retrospective analysis was conducted on the detection of age, gender, oligoclonal bands in cerebrospinal fluid, IgG intrathecal synthesis indicators and biochemical indicators for all study subjects. The differential diagnosis model was determined by the multiple logistic regression analysis, and the receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficiency of the differential diagnosis model for neurological diseases with similar symptoms to MS and other conditions. Results: This study included 167 patients in the case group and 335 patients in the control group, of which 128 patients in the case group and 265 patients in the control group were used to construct the model, and 39 patients in the case group and 70 patients in the control group were used for model validation. The differential diagnostic model constructed by a multivariate logistic regression model was Y=0.871×CSF-OCB-0.051×CSFprotein-0.231×CSFchloride+1.183×gender-0.036×LDH+35.770. The model showed that the area under the curve, sensitivity and specificity were respectively 0.916, 87.3% and 87.6%. The Delong test results showed that the diagnostic efficacy of the model was significantly different from OCB, IgG intrathecal synthesis indicators, and OCB combined with IgG intrathecal synthesis indicators ( P <0.05). The new model validation showed that the actual diagnostic consistency rate for the MS group was 84.6%, while the actual diagnostic consistency rate for the control group was 90.0%. Conclusion: This study combines OCB, IgG intrathecal synthesis indicators, and biochemical indicators to establish a diagnostic prediction model for neurological diseases with similar clinical symptoms in MS. This model may have good differential diagnostic value and can better assist clinical diagnosis in the early stages of disease progression in MS patients.

Single-molecule immunoassay technology represents an ultrasensitive immunoassay method that enables the resolution and detection of individual biomolecules at the nanoscale. This article highlights various representative techniques and clinical applications of single-molecule immunoassay technology, while also discussing the current challenges and future development directions. Through multiple optimizations at both the technical and commercial levels, single-molecule immunoassay technology exhibits unique advantages in real-time detection, disease diagnosis and treatment, and medical research. This technology is poised to contribute to the advancement of precision medicine by integrating individualized detection methods into clinical practice.

Objective: To investigate the risk factors for lymph node metastasis in patients with early gastric cancer and establish a model for prediction of risk. Methods: The cohort of this retrospective observational study comprised 1096 patients who had undergone radical gastric cancer surgery combined with standard D1 lymphadenectomy and been diagnosed with early gastric cancer by postoperative pathology in Zhongshan Hospital affiliated with Fudan University from January 2016 to July 2022. The patients were allocated to groups with and without lymph node metastases. Clinicopathological characteristics were compared between the two groups and multi-factor logistic regression analysis used to identify independent risk factors for lymph node metastasis in patients with early gastric cancer. Indications for endoscopic resection in the Japanese Gastric Cancer Association (JGCA) guideline were also incorporated into construction of the model. The patient cohort was divided into training and validation sets in a 6:4 ratio. The identified independent risk factors were used to construct a predictive nomogram. Receiver operating characteristic curves were plotted separately and the difference between them in predictive efficacy was compared using the area under the curve (AUC). Results: A total of 1,096 patients with early gastric cancer were included, with 750 males and 346 females. Their average age was (61.4±10.9) years old, and the mean tumor diameter was (23.8±11.4) mm. Among them, 188 patients (17.2%) had positive lymph node metastasis, with 109 cases in N1 stage, 42 cases in N2 stage, and 37 cases in N3 stage. Additionally, 462 patients were in T1a stage, while 634 patients were in T1b stage. Univariate analysis showed that tumor diameter, location, Lauren classification, gross morphology, histological type, intravascular invasion, ulceration, differentiation type and tumor T stage were associated with lymph node metastasis after radical gastrectomy for early gastric cancer (all P <0.05). Multifactorial analysis showed that the presence of intravascular invasion (OR=14.822, 95%CI: 9.323-23.572, P <0.001), undifferentiated type (OR=3.095, 95%CI: 1.649-5.811, P <0.001), tumor T1b (OR=1.798, 95%CI: 1.053-3.079, P =0.032), and tumor diameter ≥2 cm (OR=1.229, 95%CI: 1.031-1.469, P =0.022) were independent risk factors for lymph node metastasis. The baseline data of the training set and validation set were consistent in terms of balance (all P >0.05). We used the above variables to establish a predictive nomogram for lymph node metastasis in patients with early gastric cancer. The AUC values obtained from the validation of the model in the training and validation sets were 0.880 (95%CI: 0.849-0.911) and 0.881 (95%CI: 0.841-0.921), respectively, and were significantly better than the predictive efficacy based on the JGCA guideline (AUC=0.777, 95%CI: 0.746-0.809, P <0.001). Conclusions: Patients with early gastric cancer and intravascular invasion, undifferentiated tumors, tumor T1b, and diameter ≥2 cm are at higher risk of postoperative lymph node metastasis than other patients. The predictive model developed in this study more accurately predicts lymph node metastasis in patients with early gastric cancer than previously proposed methods.