Pulmonary Giant Cavitary Coccidioides With Fungal Ball and Hemoptysis.

Coccidioidomycosis (CM) is a fungal disease that results from inhalation of spores of Coccidioides immitis and C posadasii. If symptomatic, disease primarily manifests as community-acquired pneumonia; however, additional pulmonary manifestations such as pleural effusion, empyema, and cavitation may occur. Diabetic patients have an increased risk of severe and cavitary CM. Cavitary disease may erode vasculature and pulmonary parenchyma leading to further complications. Furthermore, chronic cavities can become colonized as well and develop superimposed infections. This is a case of cavitary CM in uncontrolled diabetic nonadherent to treatment presenting with hemoptysis and mycetoma.

[CT Characteristics of Consolidation Type of Pulmonary Cryptococcosis in Immunocompetent Patients].

Objective To analyze the CT characteristics of consolidation type of pulmonary cryptococcosis in immunocompetent patients,and thus improve the diagnosis of this disease. Methods A total of 20 cases with consolidation-type pulmonary cryptococcosis confirmed by pathological examinations were studied.Each patient underwent breath-hold multislice spiral CT,and 10 patients underwent contrast enhanced CT.The data including lesion number,lesion distribution,lesion density,performance of enhanced CT scan,accompanying signs,and prognosis were analyzed. Results The occurrence rates of single and multiple lesions were 80.0%(n=16)and 20.0%(n=4),respectively.In all the 16 multiple-lesion patients,the occurrence rate of unilateral lobar distribution was 56.0%(n=9).The 76 measurable lesions mainly presented subpleural distribution(71.1%,n=54)and lower pulmonary distribution(75.0%,n=57).A total of 39 lesions were detected in the 10 patients received contrast enhanced CT,in which 31 lesions(79.5%)showed homogeneous enhancement,34 lesions(87.2%)showed moderate enhancement,and all the lesions manifested angiogram sign.Consolidation lesions were accompanied by many CT signs,of which air bronchogram sign had the occurrence rate of 63.2%(n=48),including types Ⅲ(n =37)and Ⅳ(n=11).Other signs included halo signs(43/76,56.6%),vacuoles or cavities(9/76,11.8%),pleural thickening(14/20,70.0%),and pleural effusion(2/20,10.0%).After treatment,the lesions of 7 patients were basically absorbed and eventually existed in the form of fibrosis. Conclusions The lesions in the immunocompetent patients with consolidation type of pulmonary cryptococcosis usually occur in the lower lobe and close to the pleura,mainly presenting unilateral distribution.The CT angiogram signs,proximal air bronchogram signs,and halo signs are the main features of this disease,which contribute to the diagnosis.

Fatal disseminated aspergillosis in an immunocompetent patient with COVID-19 due to Aspergillus ochraceus.

Aspergillus infection is a well-known complication of severe influenza and severe acute respiratory syndrome coronavirus (SARS-CoV), and these infections have been related with significant morbidity and mortality even when appropriately diagnosed and treated. Recent studies have indicated that SARS-CoV-2 might increase the risk of invasive pulmonary aspergillosis (IPA). Here, we report the first case of Aspergillus ochraceus in a SARS-CoV-2 positive immunocompetent patient, which is complicated by pulmonary and brain infections. Proven IPA is supported by the positive Galactomannan test, culture-positive, and histopathological evidence. The patient did not respond to voriconazole, and liposomal amphotericin B was added to his anti-fungal regimen. Further studies are needed to evaluate the prevalence of IPA in immunocompetent patients infected with SARS-CoV-2. Consequently, testing for the incidence of Aspergillus species in lower respiratory secretions and Galactomannan test of COVID-19 patients with appropriate therapy and targeted anti-fungal therapy based on the primary clinical suspicion of IPA are highly recommended.

A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis.

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19 and finding high-risk patients with worse prognosis for early prevention and medical resource optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from seven cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the deep learning system, making it learn lung features. Following this, 1266 patients (924 with COVID-19 (471 had follow-up for >5 days) and 342 with other pneumonia) from six cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the four external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC 0.87 and 0.88, respectively) and viral pneumonia (AUC 0.86). Moreover, the deep learning system succeeded to stratify patients into high- and low-risk groups whose hospital-stay time had significant difference (p=0.013 and p=0.014, respectively). Without human assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening of COVID-19 and identifying potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.