Abstract Our aim was to evaluate the effects of cisplatin and dexamethasone alone and combined on gastric contractility and histomorphometry of BALB/c and C57BL/6 mice. BALB/c and C57BL/6 male mice (8-week-old) were randomly separated into: Control; Cisplatin (7.5 mg/Kg); Dexamethasone (2.0 mg/Kg); and Dexamethasone plus Cisplatin (2.0 mg/Kg of dexamethasone 1-hour prior to 7.5 mg/Kg of cisplatin). Drugs were administered intraperitoneally for three days. Body weight and food intake were evaluated on 2nd day. Alternating Current Biosusceptometry technique was employed to measure gastric contractions on 3rd day. Afterward, mice were killed for gastric histomorphometric analysis. Cisplatin decreased food intake and caused bradygastria in BALB/c mice; however, the amplitude of gastric contractions decreased in both BALB/c and C57BL/6. Dexamethasone and cisplatin combined restored the gastric frequency and food intake only in BALB/c, but drug combination reduced the gastric amplitude of contractions in both strains. Dexamethasone alone increased gastric mucosa thickness in C57BL/6 and decreased muscular thickness in BALB/c. In conclusion, the mouse strains presented differences in acute effects of cisplatin and dexamethasone alone and combined on gastric function. This reinforces the importance of choosing the appropriate mouse strain for studying the acute effects of drugs on the gastrointestinal tract.

Abstract Background: Neuroimaging strategies are essential to locate, to elucidate the etiology, and to the follow up of brain disease patients. Magnetic resonance imaging (MRI) provides good cerebral soft-tissue contrast detection and diagnostic sensitivity. Inflammatory lesions and tumors are common brain diseases that may present a similar pattern of a cerebral ring enhancing lesion on MRI, and non-enhancing core (which may reflect cystic components or necrosis) leading to misdiagnosis. Texture analysis (TA) and machine learning approaches are computer-aided diagnostic tools that can be used to assist radiologists in such decisions. Methods: In this study, we combined texture features with machine learning (ML) methods aiming to differentiate brain tumors from inflammatory lesions in magnetic resonance imaging. Retrospective examination of 67 patients, with a pattern of a cerebral ring enhancing lesion, 30 with inflammatory, and 37 with tumoral lesions were selected. Three different MRI sequences and textural features were extracted using gray level co-occurrence matrix and gray level run length. All diagnoses were confirmed by histopathology, laboratorial analysis or MRI. Results: The features extracted were processed for the application of ML methods that performed the classification. T1-weighted images proved to be the best sequence for classification, in which the differentiation between inflammatory and tumoral lesions presented high accuracy (0.827), area under ROC curve (0.906), precision (0.837), and recall (0.912). Conclusion: The algorithm obtained textures capable of differentiating brain tumors from inflammatory lesions, on T1-weghted images without contrast medium using the Random Forest machine learning classifier.

RESUMO Objetivo: O objetivo deste estudo foi desenvolver e validar uma ferramenta computacional para auxiliar as decisões radiológicas na enterocolite necrotizante. Metodologia: Pacientes que exibiam sinais clínicos e evidências radiográficas do estágio 2 ou superior de Bell foram incluídos no estudo, que resultou em 64 exames. A ferramenta foi usada para classificar o aumento localizado da espessura da parede intestinal e a pneumatose intestinal com medidas de largura total a meia altura e análises de textura baseadas na decomposição da energia wavelet. Os achados radiológicos de aumento suspeito da espessura da parede intestinal e das alças na pneumatose intestinal foram confirmados pela cirurgia e análise histopatológica do paciente. Dois radiologistas experientes selecionaram um intestino afetado e um intestino normal na mesma radiografia. A largura total a meia altura e a característica da textura baseada em wavelet foram então calculadas e comparadas com o uso do teste U de Mann-Whitney. Foram calculados a especificidade, sensibilidade, valores preditivos positivos e negativos. Resultados: Os resultados da largura total a meia altura foram significativamente diferentes entre a alça normal e a distendida (mediana de 10,30 e 15,13, respectivamente). Medidas de energia wavelet horizontal, vertical e diagonal foram avaliadas em oito níveis de decomposição. Os níveis 7 e 8 na direção horizontal apresentaram diferenças significativas. Para o nível 7, as medianas foram 0,034 e 0,088 para os grupos normal e com pneumatose intestinal, respectivamente, e para o nível 8, as medianas foram 0,19 e 0,34, respectivamente. Conclusões: A ferramenta desenvolvida pode detectar diferenças nos achados radiográficos do aumento da espessura da parede intestinal e PI de difícil diagnóstico, demonstra seu potencial na rotina clínica. A ferramenta desenvolvida no presente estudo pode ajudar os médicos a investigar alças intestinais suspeitas e melhorar consideravelmente o diagnóstico e as decisões clínicas.

ABSTRACT Objective: The objective of this study was to develop and validate a computational tool to assist radiological decisions on necrotizing enterocolitis. Methodology: Patients that exhibited clinical signs and radiographic evidence of Bell's stage 2 or higher were included in the study, resulting in 64 exams. The tool was used to classify localized bowel wall thickening and intestinal pneumatosis using full-width at half-maximum measurements and texture analyses based on wavelet energy decomposition. Radiological findings of suspicious bowel wall thickening and intestinal pneumatosis loops were confirmed by both patient surgery and histopathological analysis. Two experienced radiologists selected an involved bowel and a normal bowel in the same radiography. The full-width at half-maximum and wavelet-based texture feature were then calculated and compared using the Mann-Whitney U test. Specificity, sensibility, positive and negative predictive values were calculated. Results: The full-width at half-maximum results were significantly different between normal and distended loops (median of 10.30 and 15.13, respectively). Horizontal, vertical, and diagonal wavelet energy measurements were evaluated at eight levels of decomposition. Levels 7 and 8 in the horizontal direction presented significant differences. For level 7, median was 0.034 and 0.088 for normal and intestinal pneumatosis groups, respectively, and for level 8 median was 0.19 and 0.34, respectively. Conclusions: The developed tool could detect differences in radiographic findings of bowel wall thickening and IP that are difficult to diagnose, demonstrating the its potential in clinical routine. The tool that was developed in the present study may help physicians to investigate suspicious bowel loops, thereby considerably improving diagnosis and clinical decisions.

Abstract Background: Background: Tuberculosis (TB) is an infectious lung disease with high worldwide incidence that severely compromises the quality of life in affected individuals. Clinical tests are currently employed to monitor pulmonary status and treatment progression. The present study aimed to apply a three-dimensional (3D) reconstruction method based on chest radiography to quantify lung-involvement volume of TB acute-phase patients before and after treatment. In addition, these results were compared with indices from conventional clinical exams to show the coincidence level. Methods: A 3D lung reconstruction method using patient chest radiography was applied to quantify lung-involvement volume using retrospective examinations of 50 patients who were diagnosed with pulmonary TB and treated with two different drugs schemes. Twenty-five patients were treated with Scheme I (rifampicin, isoniazid, and pyrazinamide), whereas twenty-five patients were treated with Scheme II (rifampicin, isoniazid, pyrazinamide, and ethambutol). Acute-phase reaction: Serum exams included C-reactive protein levels, erythrocyte sedimentation rate, and albumin levels. Pulmonary function was tested posttreatment. Results: We found strong agreement between lung involvement and serum indices pre- and posttreatment. Comparison of the functional severity degree with lung involvement based on 3D image quantification for both treatment schemes found a high correlation. Conclusions: The present 3D reconstruction method produced a satisfactory agreement with the acute-phase reaction, most notably a higher significance level with the C-reactive protein. We also found a quite reasonable coincidence between the 3D reconstruction method and the degree of functional lung impairment posttreatment. The performance of the quantification method was satisfactory when comparing the two treatment schemes. Thus, the 3D reconstruction quantification method may be useful tools for monitoring TB treatment. The association with serum indices are not only inexpensive and sensitive but also may be incorporated into the assessment of patients during TB treatment.