ABSTRACT Background: Musculoskeletal inflammatory lesions in chronic Chikungunya virus (CHIKV) infection have not been thoroughly assessed using whole-body magnetic resonance imaging (WBMRI). This study aimed to determine the prevalence of these lesions in such patients. Methods: From September 2018 to February 2019, patients with positive Chikungunya-specific serology (Immunoglobulin M/Immunoglobulin G anti-CHIKV), with a history of polyarthralgia for > 6 months prior to MRI with no pre-existing rheumatic disorders, underwent 3T WBMRI and localized MRI. The evaluation focused on musculoskeletal inflammatory lesions correlated with chronic CHIKV infection. Pain levels were assessed using a visual analogue scale on the same day as WBMRI. Results: The study included 86 patients of whom 26 met the inclusion criteria. All patients reported pain and most (92.3%) categorized it as moderate or severe. The most common finding across joints was effusion, particularly in the tibiotalar joint (57.7%) and bursitis, with the retrocalcaneal bursa most affected (48.0%). Tenosynovitis was prevalent in the flexor compartment of the hands (44.2%), while Kager fat pad and soleus edema were also observed. Bone marrow edema-like signals were frequently seen in the sacroiliac joints (19.2%). Most WBMRI findings were classified as mild. Conclusions: This study represents the first utilization of 3T WBMRI to assess musculoskeletal inflammatory disorders in chronic CHIKV infection. The aim was to identify the most affected joints and prevalent lesions, providing valuable insights for future research and clinical management of this condition regarding understanding disease pathophysiology, developing targeted treatment strategies, and using advanced imaging techniques in the assessment of musculoskeletal manifestations. Background (CHIKV wholebody whole body . (WBMRI) Methods 201 2019 Chikungunyaspecific specific Immunoglobulin MImmunoglobulin M antiCHIKV, antiCHIKV anti , anti-CHIKV) preexisting pre existing T Results 8 2 criteria 92.3% 923 92 3 (92.3% severe effusion 57.7% 577 57 7 (57.7% bursitis 48.0%. 480 48.0% 48 0 (48.0%) 44.2%, 442 44.2% 44 (44.2%) observed edemalike like 19.2%. 192 19.2% 19 (19.2%) mild Conclusions pathophysiology strategies manifestations (WBMRI 20 anti-CHIKV 92.3 9 (92.3 57.7 5 (57.7 48.0 4 (48.0% 44.2 (44.2% 19.2 1 (19.2% 92. (92. 57. (57. 48. (48.0 44. (44.2 19. (19.2 (92 (57 (48. (44. (19. (9 (5 (48 (44 (19 ( (4 (1

Abstract Endemic systemic mycoses are prevalent in specific geographic areas of the world and are responsible for high rates of morbidity and mortality in the populations of such areas, as well as in immigrants and travelers returning from endemic regions. Pulmonary histoplasmosis is an infection caused by Histoplasma capsulatum, a dimorphic fungus. This infection has a worldwide distribution, being endemic in Brazil. Histoplasmosis can affect the lungs, and its diagnosis and management remain challenging, especially in non-endemic areas. Therefore, recognition of the various radiological manifestations of pulmonary histoplasmosis, together with the clinical and epidemiological history of the patient, is essential to narrowing the differential diagnosis. This essay discusses the main computed tomography findings of pulmonary histoplasmosis. regions capsulatum fungus distribution Brazil lungs challenging nonendemic non Therefore patient

Resumo As micoses sistêmicas endêmicas são prevalentes em áreas geográficas específicas do mundo e são responsáveis por altas taxas de morbidade e mortalidade nessas populações e em imigrantes e viajantes que retornam de regiões endêmicas. A histoplasmose pulmonar é uma infecção causada pelo Histoplasma capsulatum, um fungo dimórfico. Essa infecção tem distribuição mundial, apresentando-se de forma endêmica no Brasil. A histoplasmose pode afetar os pulmões de pacientes, e seu diagnóstico e manejo permanecem desafiadores, especialmente em áreas não endêmicas. Portanto, o reconhecimento das várias manifestações radiológicas da histoplasmose pulmonar associadas a história clínica e epidemiológica dos pacientes é fundamental para estreitar o diagnóstico diferencial. Este ensaio discute os principais achados tomográficos da histoplasmose pulmonar. capsulatum dimórfico mundial apresentandose apresentando se Brasil desafiadores Portanto diferencial

Abstract Objective: To determinate the accuracy of computed tomography (CT) imaging assessed by deep neural networks for predicting the need for mechanical ventilation (MV) in patients hospitalized with severe acute respiratory syndrome due to coronavirus disease 2019 (COVID-19). Materials and Methods: This was a retrospective cohort study carried out at two hospitals in Brazil. We included CT scans from patients who were hospitalized due to severe acute respiratory syndrome and had COVID-19 confirmed by reverse transcriptionpolymerase chain reaction (RT-PCR). The training set consisted of chest CT examinations from 823 patients with COVID-19, of whom 93 required MV during hospitalization. We developed an artificial intelligence (AI) model based on convolutional neural networks. The performance of the AI model was evaluated by calculating its accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve. Results: For predicting the need for MV, the AI model had a sensitivity of 0.417 and a specificity of 0.860. The corresponding area under the ROC curve for the test set was 0.68. Conclusion: The high specificity of our AI model makes it able to reliably predict which patients will and will not need invasive ventilation. That makes this approach ideal for identifying high-risk patients and predicting the minimum number of ventilators and critical care beds that will be required. Objective (CT (MV 201 COVID19. COVID19 COVID 19 . (COVID-19) Methods Brazil COVID-1 RTPCR. RTPCR RT PCR (RT-PCR) 82 COVID19, 19, 9 hospitalization (AI (ROC Results 0417 0 417 0.41 0860 860 0.860 068 68 0.68 Conclusion highrisk risk 20 COVID1 1 (COVID-19 COVID- (RT-PCR 8 041 41 0.4 086 86 0.86 06 6 0.6 2 (COVID-1 04 4 0. 08 0.8 (COVID- (COVID

Resumo Objetivo: Determinar a acurácia da tomografia computadorizada (TC), avaliada por redes neurais profundas, na ventilação mecânica, de pacientes hospitalizados por síndrome respiratória aguda grave por COVID-19. Materiais e Métodos: Trata-se de estudo de coorte retrospectivo, realizado em dois hospitais brasileiros. Foram incluídas TCs de pacientes hospitalizados por síndrome respiratória aguda grave e COVID-19 confirmada por RT-PCR. O treinamento consistiu em TC de tórax de 823 pacientes com COVID-19, dos quais 93 foram submetidos a ventilação mecânica na hospitalização. Nós desenvolvemos um modelo de inteligência artificial baseado em redes de convoluções neurais. A avaliação do desempenho do uso da inteligência artificial foi baseada no cálculo de acurácia, sensibilidade, especificidade e área sob a curva ROC. Resultados: A sensibilidade do modelo foi de 0,417 e a especificidade foi de 0,860. A área sob a curva ROC para o conjunto de teste foi de 0,68. Conclusão: Criamos um modelo de aprendizado de máquina com elevada especificidade, capaz de prever de forma confiável pacientes que não precisarão de ventilação mecânica. Isso significa que essa abordagem é ideal para prever com antecedência pacientes de alto risco e um número mínimo de equipamentos de ventilação e de leitos críticos. Objetivo TC, , (TC) profundas COVID19. COVID19 COVID 19. 19 Métodos Tratase Trata se retrospectivo brasileiros COVID-1 RTPCR. RTPCR RT PCR. PCR RT-PCR 82 COVID19, 19, 9 hospitalização Resultados 0417 0 417 0,41 0860 860 0,860 068 68 0,68 Conclusão críticos (TC COVID1 1 COVID- 8 041 41 0,4 086 86 0,86 06 6 0,6 04 4 0, 08 0,8

Abstract Objective: To evaluate the diagnostic performance of computed tomography (CT) fluoroscopy-guided percutaneous transthoracic needle biopsy (PTNB) in pulmonary nodules ≤ 10 mm during the coronavirus disease 2019 pandemic. Materials and Methods: Between January 1, 2020 and April 30, 2022, a total of 359 CT fluoroscopy-guided PTNBs were performed at an interventional radiology center. Lung lesions measured between 2 mm and 108 mm. Of the 359 PTNBs, 27 (7.5%) were performed with an 18G core needle on nodules ≤ 10 mm in diameter. Results: Among the 27 biopsies performed on nodules ≤ 10 mm, the lesions measured < 5 mm in four and 5-10 mm in 23. The sensitivity and overall diagnostic accuracy of PTNB were 100% and 92.3%, respectively. The mean dose of ionizing radiation during PTNB was 581.33 mGy*cm (range, 303-1,129 mGy*cm), and the mean biopsy procedure time was 6.6 min (range, 2-12 min). There were no major postprocedural complications. Conclusion: CT fluoroscopy-guided PTNB appears to provide a high diagnostic yield with low complication rates. Objective (CT fluoroscopyguided fluoroscopy guided (PTNB 1 201 pandemic Methods 202 30 2022 35 center 7.5% 75 7 (7.5% G diameter Results 510 5-1 23 100 923 92 3 92.3% respectively 58133 581 33 581.3 mGycm mGy cm range, range (range 3031,129 3031129 303 1,129 129 303-1,12 mGy*cm, , mGy*cm) 66 6 6. 212 12 2-1 min. . min) complications Conclusion rates 20 7.5 (7.5 51 5- 9 92.3 5813 58 581. 3031 3031,12 303112 1129 1,12 303-1,1 21 2- 7. (7. 92. 3031,1 30311 112 1,1 303-1, (7 3031, 11 303-1 ( 303-

Resumo Objetivo: Avaliar o desempenho diagnóstico da biópsia pulmonar percutânea transtorácica (BPPT) guiada por fluoroscopia associada a tomografia computadorizada (FTC) em nódulos pulmonares ≤ 10 mm no período de pandemia de COVID-19. Materiais e Métodos: No período de 1º de janeiro de 2020 a 30 de abril de 2022, 359 BPPTs guiadas por FTC foram realizadas em um centro terciário de radiologia intervencionista. As lesões pulmonares mediam entre 2 mm e 108 mm. Dessas 359 BPPTs, 27 (7,5%) foram realizadas com agulha 18G em nódulos de 2 mm a 10 mm. Resultados: Das 27 BPPTs realizadas nos nódulos ≤ 10 mm, quatro lesões tinham dimensões menores que 5 mm e 23 lesões mediam entre 5 e 10 mm. Sensibilidade e acurácia diagnóstica das BPPTs guiadas por FTC foram de 100% e 92,3%, respectivamente. A dose média de radiação ionizante para os pacientes durante o procedimento de BPPT guiada por FTC foi de 581,33 mGy*cm, variando de 303 a 1129 mGy*cm. A média de tempo dos procedimentos de biópsia foi de 6,6 minutos, variando de 2 a 12 minutos. Nas 27 BPPTs, nenhuma complicação maior foi descrita. Conclusão: A BBPT guiada por FTC resultou em alto rendimento diagnóstico e baixas taxas de complicações. Objetivo (BPPT (FTC 1 COVID19. COVID19 COVID 19. 19 COVID-19 Métodos º 202 3 2022 35 intervencionista 7,5% 75 7 (7,5% G Resultados 100 923 92 92,3% respectivamente 58133 581 33 581,3 mGycm mGy cm mGy*cm 112 66 6 6, minutos descrita Conclusão complicações COVID1 COVID-1 20 7,5 (7,5 9 92,3 5813 58 581, 11 COVID- 7, (7, 92, (7 (