Background: Existing atlases for the human brainstem were generated from magnetic resonance images or traditional histologically stained slides, but both are insufficient for the identification of detailed brainstem structures at uniform intervals. Methods: A total of 319 sectioned images of the brainstem were selected from whole-body axial sectioned images, then coronal and sagittal sectioned images were reconstructed from the horizontal images. The fine and detailed structures were annotated in PowerPoint slides, then the volume model was produced and some white matter fibers were traced using MRIcroGL. Results: In this study, a novel brainstem atlas based on sectioned images was generated that shows the true color and shape, as well as the accurate location of the nuclei and tracts;it reveals the striking contrast between gray and white matter, as well as fine structures.In total, 212 structures, including nuclei and tracts, were annotated in axial, coronal, and sagittal plane views of sectioned images (48-bit true color; 0.2 mm intervals, 0.06 mm × 0.06 mm pixel size). To verify the accuracy of the annotations, a volume model of the brainstem was constructed for independent observations of the three planes. Conclusion In this paper, we describe several interesting structures included in the atlas. By depicting the fine structures of the human brainstem in detail, this atlas allows comprehensive understanding of the complicated topographies of the brainstem. As such, it will be of value for neuroanatomy education and research, in addition to enriching the literature on the human brain.

Objective:To analyze clinical characteristics and high-frequency ultrasound features of localized scleroderma, and to construct and validate a non-invasive prediction model for staging of skin lesions based on the high-frequency ultrasound features.Methods:Patients with localized scleroderma were retrospectively collected from the Department of Dermatology and Venereology, Second Xiangya Hospital of Central South University from February 1, 2021 to February 28, 2023, and clinical data as well as high-frequency ultrasound and pathologic features of 85 lesions from these patients were analyzed. Lesions were divided into modeling cohort and validation cohort according to the chronological order of patient enrollment. The univariate analysis and multivariable logistic regression models were used to analyze the independent influential factors in the staging of localized scleroderma lesions in the modeling cohort, construct the regression equation, and to build a nomogram prediction model. The Bootstrap validation method was used for internal validation, and the predictive performance of the nomogram model in the modeling cohort and validation cohort was further evaluated by the calibration curve and receiver operating characteristic (ROC) curve.Results:In the modeling cohort, 60 patients with localized scleroderma, including 16 males and 44 females, were enrolled, with the age [ M ( Q1, Q3) ] being 22.0 (10.0, 39.2) years, and there were 28 lesions in the oedematous phase and 32 lesions in the fibrotic and atrophic phase; in the validation cohort, 25 patients with localized scleroderma, including 8 males and 17 females, were enrolled, with the age being 18.0 (7.0, 30.0) years, and there were 9 lesions in the oedematous phase and 16 lesions in the fibrotic and atrophic phase. Univariate analysis in the modeling cohort showed no significant differences in the age and gender of patients or the location of lesions between the oedematous phase group and the fibrotic and atrophic phase group (all P > 0.05) ; compared with the oedematous phase group, the fibrotic and atrophic phase group showed an increased proportion of patients with disease duration ≥ 2 years (20/32 cases vs. 10/28 cases, χ2 = 4.29, P = 0.038), decreased thicknesses of the subcutaneous fat layer in skin lesions (1.4 [0.0, 26.0] mm vs. 1.8 [0.1, 14.3] mm, Z = -2.14, P = 0.032), increased decrements in the subcutaneous fat layer thickness in the lesional sites compared with non-lesional control sites (1.8 [0.5, 11.0] vs. 0.3 [-1.9, 8.0] mm, Z = -4.72, P < 0.001), increased ratios of the lesional elasticity values to control elasticity values (2.9 [1.8, 6.9] vs. 1.8 [1.1, 5.9], Z = -4.34, P < 0.001), and increased ultrasound-based lesional activity scores (5.0 [3.0, 8.0] points vs. 3.0 [0.0, 5.0] points, Z = -4.76, P < 0.001). Multivariable logistic stepwise regression analysis showed that the disease duration ≥ 2 years ( P = 0.032), increased ratios of the lesional elasticity values to control elasticity values ( P = 0.019), increased ultrasound-based lesional activity scores ( P = 0.013), and increased decrements in the subcutaneous fat layer thickness in the lesions compared with the controls ( P = 0.013) helped to confirm localized scleroderma lesions in the fibrotic and atrophic phase. Based on the results of regression analysis, a total of 4 factors were included in the nomogram prediction model, including the disease duration, the decrement in the subcutaneous fat layer thickness in lesions compared with controls, the ratio of the lesional elasticity values to control elasticity values, and the ultrasound-based lesional activity score; additionally, the constructed logistic regression model formula for predicting the probability (p) of skin lesions in fibrotic and atrophic phase was "ln (p/[1 - p]) = -9.595 + 2.204 × the disease duration + 0.784 × the decrement in the subcutaneous fat layer thickness in the lesions compared with the controls (mm) + 0.887 × the ratio of the lesional elasticity values to control elasticity values + 1.374 × the ultrasound-based lesional activity score". The calibration curve showed a good predictive performance of the model through the Bootstrap validation method, and the ROC curve demonstrated good discrimination and accuracy (modeling cohort: area under the curve = 0.936, 95% CI: 0.879 - 0.994; validation cohort: area under the curve = 0.889, 95% CI: 0.748 - 1.000) . Conclusions:High-frequency ultrasound could provide essential details for staging the localized scleroderma lesions. Based on the disease duration, subcutaneous fat layer thickness, skin elasticity values, and ultrasound-based lesional activity scores, the constructed prediction model could predict the stages of localized scleroderma lesions with excellent discrimination, accuracy, and predictive performance.