Nikolas Mateus Pereira de Souza

Objective: To generate hematocrit percentile charts for schoolchildren aged 6–18 years and determine the prevalence of anemia by socioeconomic status class in southern Brazil.Methods: This is a cross‐sectional study utilizing data collected between 2014 and 2017 from southern Brazil. The study's sample consists of 4802 schoolchildren, aged 6 to 18 years. The percentile charts for sex‐specific hematocrit were developed using the LMS (Lambda‐Mu‐Sigma) method. The simplified economic classification, based on ABEP criteria, was used to group individuals into A + B (high), C (middle), and D + E (low) income classes. Anemia was defined as hematocrit z‐score ≤ −1.96 for age and sex.Results: Among boys, 58 (2.86%) were anemic, 1955 (94.81%) had normal hematocrit levels, and 48 (2.33%) had high hematocrit. Girls showed a similar pattern, with 73 (2.73%) anemic, 2534 (94.90%) with normal hematocrit, and 63 (2.36%) with high hematocrit. For girls, a higher prevalence of non‐anemic hematocrit was observed in class A (39.33%) compared to anemic children (23.28%), with significant standardized residuals. For boys, significant residuals were observed for a higher prevalence of anemic children in the lower socioeconomic class DE (13.79%) compared to non‐anemic children (5.18%), and a higher prevalence of non‐anemic children in the upper socioeconomic class A (42.63%) compared to anemic children (22.41%).Conclusion: The percentile charts generated from hematocrit levels enabled the comparison of anemia prevalence across socioeconomic status classes. A higher prevalence of anemia was found among boys in lower socioeconomic classes, while girls in higher socioeconomic classes showed a lower prevalence of anemia.

This study proposed radiographic measurements of the lumbar vertebral bodies (L1-L6) of canine cadavers capable of predicting the length of insertion of bicortical pins in the vertebral bodies of dogs through a Multiple Linear Regression (MLR) mathematical model. Eleven lumbar spines (L1 to L6) of canine cadavers were selected, and radiographic measurements were performed at the cranial quarter of the vertebral body, width (LQCr), height (AQCr), and height of the cranial epiphysis (AECr), including the length of implant insertion in the cranial quarter of the vertebral body at the midpoint between the pedicle and the transverse process, at angles of 50º, 55º, and 60º. Seven MLR models were created (six individual models for vertebrae L1-L6 and one general model for lumbar spine L1-L6). The adjusted R² values for the individual models ranged from 0.935 to 0.978, and the mean square error (RMSE) from 0.503 to 0.885 mm. The general model showed an adjusted R² of 0.922 and an RMSE of 0.991. All individual MLR models exhibited raw residuals of < 2 mm. The radiographic measurements obtained from the bodies of the lumbar vertebrae (L1-L6) of canine cadavers are capable of predicting, through mathematical models, the depth of insertion of bicortical pins in the lumbar vertebral bodies using the midpoint between the pedicle and the transverse process at an angle of 55º in relation to the sagittal plane as a reference, with better results when using individual RLM models.

Paracoccidioidomycosis (PCM) is a systemic mycosis that is diagnosed by visualizing the fungus in clinical samples or by other methods, like serological techniques. However, all PCM diagnostic methods have limitations. The aim of this study was to develop a diagnostic tool for PCM based on Fourier transform infrared (FTIR) spectroscopy. A total of 224 serum samples were included: 132 from PCM patients and 92 constituting the control group (50 from healthy blood donors and 42 from patients with other systemic mycoses). Samples were analyzed by attenuated total reflection (ATR) and a t-test was performed to find differences in the spectra of the two groups. The wavenumbers that had p < 0.05 had their diagnostic potential evaluated using receiver operating characteristic (ROC) curves. The spectral region with the lowest p value was used for variable selection through principal component analysis (PCA). The selected variables were used in a linear discriminant analysis (LDA). In univariate analysis, the ROC curves with the best performance were obtained in the region 1551–1095 cm⁻¹. The wavenumber that had the highest AUC value was 1264 cm⁻¹, achieving a sensitivity of 97.73%, specificity of 76.01%, and accuracy of 94.22%. The total separation of groups was obtained in the PCA performed with a spectral range of 1551–1095 cm⁻¹. LDA performed with the eight wavenumbers with the greatest weight from the group discrimination in the PCA obtained 100% accuracy. The methodology proposed here is simple, fast, and highly accurate, proving its potential to be applied in the diagnosis of PCM. The proposed method is more accurate than the currently known diagnostic methods, which is particularly relevant for a neglected tropical mycosis such as paracoccidioidomycosis.