Case report: Disseminated mucormycosis misdiagnosed as malignancy developed from allergic bronchopulmonary mycosis caused by Rhizopus microsporus following SARS-CoV-2 infection in a woman.

Mucormycosis has become more prevalent during the COVID-19 pandemic and is associated with a high mortality rate. However, concurrent host allergic reactions, invasive pulmonary mucormycosis, and disseminated mucormycosis are rarely reported. Herein, we describe a case of disseminated mucormycosis initially misdiagnosed as a malignancy that developed from allergic bronchopulmonary mycosis caused by Rhizopus microsporus in a woman with post-SARS-CoV-2 infection. The previously healthy patient presented with a sizeable mass in the right middle lobe and multiple lesions across the lungs, brain, spleen, kidneys, pancreas, and subcutaneous tissue 6 months after SARS-CoV-2 infection, mimicking an extensive metastatic malignancy. Eosinophilia, elevated total plasma immunoglobulin E, and significant eosinophilic lung tissue infiltration were observed. Rhizopus microsporus was isolated from subcutaneous tissue, and hyphae were detected in the lung tissue. Sequential amphotericin B liposomes followed by isavuconazole antifungal therapy combined with systemic corticosteroids improved symptoms, significantly reduced the sizes of pulmonary lesions, and reduced eosinophil count. However, it failed to halt the overall progression of the disease, and the patient died. The absence of asthma-like symptoms and delayed recognition of invasive fungal infection signs contributed to poorer outcomes, highlighting the need for a thorough post-COVID-19 follow-up.

Intelligent diagnosis of lung nodule images based on machine learning in the context of lung teaching.

The vast majority of intelligent diagnosis models have widespread problems, which seriously affect the medical staff judgment of patients' injuries. So depending on the situation, you need to use different algorithms, The study suggests a model for intelligent diagnosis of lung nodule images based on machine learning, and a support vector machine-based machine learning algorithm is selected. In order to improve the diagnostic accuracy of intelligent diagnosis of lung nodule images as well as the diagnostic model of lung nodule images. The objectives are broken down into algorithm determination and model construction, and the proposed optimized model is solved using machine learning techniques in order to achieve the original algorithm selected for intelligent diagnosis of lung nodule photos. The validation findings demonstrated that dimensionality reduction of the features produced 17 × 1120 and 17 × 2980 non-node matrices with 1216 nodes and 3407 non-nodes in 17 features. The support vector machine classification method has more benefits in terms of accuracy, sensitivity, and specificity when compared to other classification methods. Since there were some anomalies among both benign and malignant tumors and no discernible difference between them, the distribution of median values revealed that the data was symmetrical in terms of texture and gray scale. Non-small nodules can be identified from benign nodules, but more training is needed to separate them from the other 2 types. Pulmonary nodules are a common disease. MN are distinct from the other 2 types, non-small nodules and benign small nodules, which require further training to differentiate. This has great practical value in teaching practice. Therefore, building a machine learning-based intelligent diagnostic model for pulmonary nodules is of significant importance in helping to solve medical imaging diagnostic problems.

[Effect of Silencing FMOD on Proliferation and Migration of H322 Cells and Its Mechanism].

OBJECTIVE: To investigate the regulation of fibromodulin (FMOD) on proliferation, adhesion and migration of non-small cell lung cancer cell line H322, and discuss its action mechanism. METHODS: H322 cells were randomly divided into control group, small interfering RNA (siRNA) silencing FMOD ( FMOD siRNA) group and control siRNA (Con siRNA) group. FMOD siRNA and Con siRNA were transfected into H322 cells. The cell viability of each group was detected by CCK-8 method. The adhesion ability of cells was detected by fluorescein diacetate (FDA) fluorescent staining. The cell migration ability was detected by Transwell method. Real time-PCR was used to detect the mRNA expressions of Cyclin D1, intercellular adhesion molecule -1 （ICAM-1）, E-cadherin, FMOD, transforming growth factor-ß (TGF-ß), Smad2, Smad3, Smad4 and Smad7 in cells. The protein expressions of Cyclin D1, ICAM-1, E-cadherin, FMOD, TGF-ß1, Smad2, Smad3, Smad4 and Smad7 were detected by Western blot. RESULTS: Compared with the Con siRNA group, the cell viability, cell adhesion and migration ability of the FMOD siRNA group were decreased, and the difference was statistically significant ( P<0.01). There was no significant difference between the control group and the Con siRNA group. Real time-PCR and Western blot results showed that the mRNA and protein expression levels of Cyclin D1, ICAM-1, TGF-ß1, Smad2, Smad3 and Smad4 were decreased in FMOD siRNA group, compared with Con siRNA group, while the mRNA and protein expression levels of E-cadherin and Smad7 are elevated. CONCLUSION: Silencing of the FMOD gene significantly reduces the proliferation, adhesion and migration of H322 cells, which may be conducted by inhibiting the TGF-ß/Smad signaling pathway.

EIF2AK4 mutation in pulmonary veno-occlusive disease: A case report and review of the literature.

BACKGROUND: Pulmonary veno-occlusive disease (PVOD) is a rare and devastating cause of pulmonary arterial hypertension with a non-specific clinical presentation and a relatively specific presentation in high-resolution thoracic CT scan images. Definitive diagnosis is made by histological examination in previous. According to the 2015 ESC/ERS Guidelines, detection of a mutation in the eukaryotic translation initiation factor 2 alpha kinase 4 (EIF2AK4) without histological confirmation is recommended to validate the diagnosis of PVOD. METHODS: We report the case of a 27-year-old man who was admitted for persistent cough and dyspnea that had lasted for 5 months and had developed and experienced progressive dyspnea for the last 2 months. The echocardiogram and right heart catheterization without vasodilator challenge confirmed the diagnosis of pulmonary arterial hypertension. Other tests, such as high-resolution thoracic CT scan, V/Q scan, pulmonary function test with diffusion capacity, and blood tests, excluded other associated diseases which could have caused pulmonary hypertension. RESULTS: The initial diagnosis at admission was idiopathic pulmonary arterial hypertension and an oral vasodilator (sildenafil) was given. However, the dyspnea subsequently worsened, and the patient was transferred to a regional lung transplant center, where he died of heart failure 1 week later. Using exome sequencing, we found an EIF2AK4 mutation, which was sufficient to confirm the diagnosis of PVOD. CONCLUSION: This is the first reported case of EIF2AK4 mutation in PVOD in a Chinese patient population. We found the frameshift EIF2AK4 mutation c.1392delT (p.Arg465fs) in this case. Up to now, there has been a paucity of data on this rare disease, and the exact role of EIF2AK4 loss-of-function mutations in the pathogenesis of PVOD is still unknown. More investigations should be conducted in the future.