Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

Objective:To retrospectively analyze the results of auditory examination,vestibular function examination and laboratory examination of 63 patients diagnosed as vestibular neuritis.Methods:A total of 63 patients diagnosed with vestibular neuritis hospitalized in the Department of Otolaryngology, Head and Neck Surgery of the Third Affiliated Hospital of Sun Yat-sen University, from October 2012 to December 2022 were recruited. All patients met the diagnostic criteria for the 2022 Bárány association vestibular neuritis. Clinical data and the results of pure tone audiometry, electrocochleogram, video electronystagmogram, caloric test, cervical vestibuloevoked myogenic potential（cVEMP）, ocular vestibuloevoked myogenic potential（oVEMP）, video head impulse test（vHIT） was collected.A total of 63 age-and sex-matched healthy subjects in the physical examination center were randomly selected as the control group. The differences of blood indexs and lipid metabolism indexes between the two groups were compared. Results:In patients with vestibular neuritis, 50 out of 63 patients presented normal threshold in pure tone audiometry, 8 out of 63 patients had bilateral high-frequency sensorineural hearing loss and 5 out of 63 patients had unilateral mild high-frequency sensorineural hearing loss, 56 out of 63 cases completed the electrocochleogram, of which 3 cases had a binaural-SP/AP amplitude ratio≥0.4, 5 cases had monaural amplitude ratio ≥0.4. Fifty-five out of 63 patients completed the caloric test with CP values greater than 30% in all. The ratio of patients completed cVEMP, oVEMP and vHIT were 46 cases, 22 cases and 30 cases, respectively. 17 out of 63 cases completed all the four vestibular function tests. According to these tests, 49 patients could determine the extent of injury，including 27 cases with unilateral superior vestibular nerve injury, 21 cases with unilateral superior and inferior vestibular nerve injury and 1 case with unilateral inferior vestibular nerve injury. There were significant differences in neutrophil value（P<0.001）, lymphocyte value（P<0.005）, neutrophil/lymphocyte ratio（P<0.001） and apolipoprotein A1（P<0.001） between patient group and control group. Inflammatory markers were risk factors for patients with vestibular neuritis. The OR values of neutrophil value and blood neutrophil/lymphocyte ratio were 1.81（1.38-2.37, P<0.001） and 2.11（1.41-3.16, P<0.001）, respectively. Apolipoprotein A1 was a protective factor for patients with vestibular neuritis, and the OR value was 0.004（0.001-0.042, P<0.001）. Conclusion:Electrocochleogram could be used in vestibular neuritis patients with normal pure tone threshold to test whether there is hidden hearing loss in these patients. Neutrophil value, lymphocyte value, neutrophil/lymphocyte ratio and apolipoprotein A1 were correlated with vestibular neuritis. The Neutrophil value and neutrophil/lymphocyte ratio were risk factors for morbidity.
Humans ; Vestibular Neuronitis/physiopathology* ; Retrospective Studies ; Female ; Male ; Audiometry, Pure-Tone ; Hearing Loss, Sensorineural/physiopathology* ; Middle Aged ; Adult ; Vestibular Function Tests ; Vestibular Evoked Myogenic Potentials ; Aged

Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

Purpose To investigate the value of peritumoral expansion-based radiomics from tumor regions of interest(ROIs)for predicting epidermal growth factor receptor(EGFR)mutation status and to identify the optimal peritumoral expansion margin.Materials and Methods This retrospective study included 390 patients with pathologically confirmed non-small cell lung cancer(NSCLC)and definitive EGFR genotyping results from the Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University(December 2021 to September 2023).Patients were randomly divided into training and validation sets(8∶2 ratio,310 vs.80 patients;CT slice thickness:5-7 mm).An additional independent test set of 100 patients undergoing thin-section CT(1-2 mm)was included to assess model generalizability across slice thicknesses.Clinical characteristics and CT semantic features were analyzed.After automated tumor segmentation with manual refinement,ROIs were sequentially expanded outward by 1,2,3,4 and 5 mm.A total of 2 264 radiomic features were extracted from each ROI.Feature selection using minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms was performed in the training set to calculate radiomics scores.Logistic regression was used to develop prediction models.A combined model integrating optimal peritumoral radiomics score with clinical and CT features was established,with performance compared across models.Results Gender(χ2=24.922,P<0.001),smoking history(χ2=11.199,P=0.001),emphysema(χ2=40.802,P<0.001),and lymph node metastasis(χ2=5.674,P=0.017)were associated with EGFR mutation status.In the validation set,the area under the curve for the tumor ROI-based radiomics model was 0.676,while the five expansion models achieved area under the curve of 0.723,0.720,0.734,0.681,and 0.598,respectively.The combined model based on 3 mm expansion demonstrated superior performance to individual radiomics and clinical models,with area under the curve of 0.826,0.734,0.711 in the validation set,and 0.809,0.760,0.702 in the independent test set.Conclusion Peritumoral expansion-based CT radiomics demonstrates value for predicting EGFR mutations in NSCLC,with 3 mm identified as the optimal expansion margin.Integration with clinical information further enhances predictive accuracy.

Purpose To investigate the value of peritumoral expansion-based radiomics from tumor regions of interest(ROIs)for predicting epidermal growth factor receptor(EGFR)mutation status and to identify the optimal peritumoral expansion margin.Materials and Methods This retrospective study included 390 patients with pathologically confirmed non-small cell lung cancer(NSCLC)and definitive EGFR genotyping results from the Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University(December 2021 to September 2023).Patients were randomly divided into training and validation sets(8∶2 ratio,310 vs.80 patients;CT slice thickness:5-7 mm).An additional independent test set of 100 patients undergoing thin-section CT(1-2 mm)was included to assess model generalizability across slice thicknesses.Clinical characteristics and CT semantic features were analyzed.After automated tumor segmentation with manual refinement,ROIs were sequentially expanded outward by 1,2,3,4 and 5 mm.A total of 2 264 radiomic features were extracted from each ROI.Feature selection using minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms was performed in the training set to calculate radiomics scores.Logistic regression was used to develop prediction models.A combined model integrating optimal peritumoral radiomics score with clinical and CT features was established,with performance compared across models.Results Gender(χ2=24.922,P<0.001),smoking history(χ2=11.199,P=0.001),emphysema(χ2=40.802,P<0.001),and lymph node metastasis(χ2=5.674,P=0.017)were associated with EGFR mutation status.In the validation set,the area under the curve for the tumor ROI-based radiomics model was 0.676,while the five expansion models achieved area under the curve of 0.723,0.720,0.734,0.681,and 0.598,respectively.The combined model based on 3 mm expansion demonstrated superior performance to individual radiomics and clinical models,with area under the curve of 0.826,0.734,0.711 in the validation set,and 0.809,0.760,0.702 in the independent test set.Conclusion Peritumoral expansion-based CT radiomics demonstrates value for predicting EGFR mutations in NSCLC,with 3 mm identified as the optimal expansion margin.Integration with clinical information further enhances predictive accuracy.

Objective:To explore the feasibility of constructing an objective tinnitus subtype model based on peripheral blood differentially expressed genes (DEGs) using a combination of Weighted Gene Co-expression Network Analysis (WGCNA) and Random Forest algorithm (RF).Methods:From October 2019 to June 2020, peripheral blood DEGs were obtained from 37 patients (from the Third Affiliated Hospital of Sun Yat-sen University)with chronic subjective high-frequency tinnitus (21 unbothersome type, 16 bothersome type) and 20 healthy volunteers through high-throughput sequencing. WGCNA was used to construct gene modules with different expression patterns and analyze their relationships with tinnitus characteristics. Subsequently, RF was employed to build subtype models, which were evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, and F1-score.Results:A total of 12 351 intergroup DEGs were divided into 9 gene modules. Among them, MEblue, MEgreen, and MEbrown showed significant negative correlations with the healthy volunteer group, while MEpink showed a significant positive correlation with the tinnitus distress group. The "Tinnitus vs. Normal" and "Compensatory vs. Decompensatory" subtype models, based on MEblue and MEpink respectively, both had AUCs greater than 0.80, accuracies above 90%, and F1-scores above 0.90, indicating good performance.Conclusions:Peripheral blood DEGs are potential biological indicators for objective classification of subjective tinnitus. The combined application of WGCNA and the Random Forest algorithm should be a viable approach to constructing an objective tinnitus subtype model. However, further exploration and refinement are needed to validate the model′s generalizability, cross-dataset performance, and algorithm optimization.

ObjectiveTo explore the efficacy and predictive indicators of stellate ganglion block （SGB） as an adjunctive intervention for chronic subjective tinnitus and accumulate experience for the application of SGB in the clinical treatment of tinnitus. MethodsA retrospective review was conducted on the data of chronic subjective tinnitus patients who received SGB intervention， with unsatisfactory outcomes otherwise. Pure tone audiometry （PTA）， tinnitus loudness evaluation and Pittsburgh sleep quality index （PSQI） were used. The tinnitus handicap inventory （THI） scores were compared before and after SGB intervention. Correlation analysis and linear regression equations were employed to identify the potential indicators predicting the effectiveness of SGB intervention. Statistical analysis was performed by SPSS 24.0 software. ResultsBy April 2023， a total of 107 patients with chronic subjective tinnitus had undergone SGB intervention， including 67 male and 40 female， with a mean age of （45.32±11.40） years old and an average tinnitus history of （20.32±24.64） months ［16 （12~20）］. Only 7 patients （6.54%） quitted the intervention for personal reasons， which demonstrated good compliance with the intervention. No patients experienced adverse reactions such as infection at the injection site， hematoma， nerve injury， local anesthetic intoxication and so on， which revealed good safety. After SGB intervention， THI scores decreased to below 36 points in 77 patients and decrease by 10 points or more in 12 of the remaining patients， with a total effective rate of 89%. A paired sample t-test showed a significant difference in THI scores before and after SGB intervention （t=15.575， P<0.001）， indicating good improvement. Pearson correlation analysis suggested that pre-intervention THI scores and subjective tinnitus loudness were significantly positively correlated with the improvement level of THI scores （P<0.05）. Further stepwise linear regression analysis found that "pre-intervention THI scores" had statistical significance （P<0.001）， with a regression coefficient of 0.308， predicting a 17.4% improvement level in THI scores. ConclusionsDue to its good and safe short-term effects， SGB intervention can be used as a supplementary option for chronic subjective tinnitus when other interventions are not ideal， especially for patients with higher THI scores. However， further research is needed to clarify the long-term efficacy and underlying mechanisms， in order to establish a more solid theoretical basis for SGB intervention in the treatment of subjective tinnitus.

Acute carbon monoxide poisoning can cause hypoxic injury to multiple organs. Neurological impairment and cardiac dysfunction are common manifestations of severe poisoning patients, but hemorrhagic complications are rare in clinic. The clinical diagnosis and treatment of a case of massive intrathecal hematoma of the rectus abdominis secondary to acute severe carbon monoxide poisoning was reported. The pathophysiological mechanism and treatment strategy of rectus sheath hematoma secondary to acute severe carbon monoxide poisoning was analyzed, in order to improve the understanding of hemorrhagic complications of carbon monoxide poisoning. This case suggests that for patients with a history of cardiovascular disease and taking anticoagulants, clinicians should be alert for the risk of bleeding when making medical decisions.

Acute carbon monoxide poisoning can cause hypoxic injury to multiple organs. Neurological impairment and cardiac dysfunction are common manifestations of severe poisoning patients, but hemorrhagic complications are rare in clinic. The clinical diagnosis and treatment of a case of massive intrathecal hematoma of the rectus abdominis secondary to acute severe carbon monoxide poisoning was reported. The pathophysiological mechanism and treatment strategy of rectus sheath hematoma secondary to acute severe carbon monoxide poisoning was analyzed, in order to improve the understanding of hemorrhagic complications of carbon monoxide poisoning. This case suggests that for patients with a history of cardiovascular disease and taking anticoagulants, clinicians should be alert for the risk of bleeding when making medical decisions.

Combined with teaching practice, this study summarizes the teaching contents, methods and effect evaluation of pathological technology for professional postgraduates majoring in pathology. According to the basic conditions of postgraduates, the pathological technology training program has been formulated, student-centered heuristic teaching is carried out by using diversified teaching methods such as flipped classroom, interactive theoretical teaching is carried out by using the intelligent teaching platform, and practical teaching is carried out by using the problem-based learning mode, aiming to improve the theoretical literacy and practical level of pathological technology of professional postgraduates majoring in pathology, improve their clinical research thinking, and lay a foundation for clinical pathological diagnosis and scientific research in the future.