Development and validation of a deep learning-based framework for automated lung CT segmentation and acute respiratory distress syndrome prediction: a multicenter cohort study.

Background: Acute respiratory distress syndrome (ARDS) is a life-threatening condition with a high incidence and mortality rate in intensive care unit (ICU) admissions. Early identification of patients at high risk for developing ARDS is crucial for timely intervention and improved clinical outcomes. However, the complex pathophysiology of ARDS makes early prediction challenging. This study aimed to develop an artificial intelligence (AI) model for automated lung lesion segmentation and early prediction of ARDS to facilitate timely intervention in the intensive care unit. Methods: A total of 928 ICU patients with chest computed tomography (CT) scans were included from November 2018 to November 2021 at three centers in China. Patients were divided into a retrospective cohort for model development and internal validation, and three independent cohorts for external validation. A deep learning-based framework using the UNet Transformer (UNETR) model was developed to perform the segmentation of lung lesions and early prediction of ARDS. We employed various data augmentation techniques using the Medical Open Network for AI (MONAI) framework, enhancing the training sample diversity and improving the model's generalization capabilities. The performance of the deep learning-based framework was compared with a Densenet-based image classification network and evaluated in external and prospective validation cohorts. The segmentation performance was assessed using the Dice coefficient (DC), and the prediction performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The contributions of different features to ARDS prediction were visualized using Shapley Explanation Plots. This study was registered with the China Clinical Trial Registration Centre (ChiCTR2200058700). Findings: The segmentation task using the deep learning framework achieved a DC of 0.734 ± 0.137 in the validation set. For the prediction task, the deep learning-based framework achieved AUCs of 0.916 [0.858-0.961], 0.865 [0.774-0.945], 0.901 [0.835-0.955], and 0.876 [0.804-0.936] in the internal validation cohort, external validation cohort I, external validation cohort II, and prospective validation cohort, respectively. It outperformed the Densenet-based image classification network in terms of prediction accuracy. Moreover, the ARDS prediction model identified lung lesion features and clinical parameters such as C-reactive protein, albumin, bilirubin, platelet count, and age as significant contributors to ARDS prediction. Interpretation: The deep learning-based framework using the UNETR model demonstrated high accuracy and robustness in lung lesion segmentation and early ARDS prediction, and had good generalization ability and clinical applicability. Funding: This study was supported by grants from the Shanghai Renji Hospital Clinical Research Innovation and Cultivation Fund (RJPY-DZX-008) and Shanghai Science and Technology Development Funds (22YF1423300).

Male hermaphroditism with rare malignant transformation of gonadal organs: a case report.

A 27-year-old man was admitted to the hospital after a year of marriage due to infertility. During laparoscopic exploratory surgery, the presence of a retrovesical uterus was clearly observed, and the gonadal organs were visible on both sides. However, the testicles or ovaries were not identifiable, nor were the spermatic vessels and fallopian tubes at the joint. Intraoperative bilateral gonad biopsy was performed. Cryopreservation of the right gonadal gland revealed gonadoblastoma and malignant germinoma (asexual tumor/seminoma) with sclerosis and atrophy of testicular tissue. No proliferation of germ cells and sertoli cells was observed in spermatic tubule. The left gonad was diagnosed as a gonadoblastoma. Finally, total hysterectomy and bilateral gonadal tumor organectomy were performed to seal the vaginal stump. Local radiotherapy was administered after surgery. In general, tumors were found on both sides of the gonads, especially gonadoblastoma and malignant germinoma on the right side and gonadoblastoma on the left side.

LPS-induced monocarboxylate transporter-1 inhibition facilitates lactate accumulation triggering epithelial-mesenchymal transformation and pulmonary fibrosis.

The epithelial-mesenchymal transformation (EMT) process of alveolar epithelial cells is recognized as involved in the development of pulmonary fibrosis. Recent evidence has shown that lipopolysaccharide (LPS)-induced aerobic glycolysis of lung tissue and elevated lactate concentration are associated with the pathogenesis of sepsis-associated pulmonary fibrosis. However, it is uncertain whether LPS promotes the development of sepsis-associated pulmonary fibrosis by promoting lactate accumulation in lung tissue, thereby initiating EMT process. We hypothesized that monocarboxylate transporter-1 (MCT1), as the main protein for lactate transport, may be crucial in the pathogenic process of sepsis-associated pulmonary fibrosis. We found that high concentrations of lactate induced EMT while moderate concentrations did not. Besides, we demonstrated that MCT1 inhibition enhanced EMT process in MLE-12 cells, while MCT1 upregulation could reverse lactate-induced EMT. LPS could promote EMT in MLE-12 cells through MCT1 inhibition and lactate accumulation, while this could be alleviated by upregulating the expression of MCT1. In addition, the overexpression of MCT1 prevented LPS-induced EMT and pulmonary fibrosis in vivo. Altogether, this study revealed that LPS could inhibit the expression of MCT1 in mouse alveolar epithelial cells and cause lactate transport disorder, which leads to lactate accumulation, and ultimately promotes the process of EMT and lung fibrosis.

Pharmacological inhibition of the ACE/Ang-2/AT1 axis alleviates mechanical ventilation-induced pulmonary fibrosis.

Mechanical ventilation (MV) is an essential therapy for acute respiratory distress syndrome (ARDS) and pulmonary fibrosis. However, it can also induce mechanical ventilation-induced pulmonary fibrosis (MVPF) and the underlying mechanism remains unknown. Based on a mouse model of MVPF, the present study aimed to explore the role of the angiotensin-converting enzyme/angiotensin II/angiotensin type 1 receptor (ACE/Ang-2/AT1R) axis in the process of MVPF. In addition, recombinant angiotensin-converting enzyme 2(rACE2), AT1R inhibitor valsartan, AGTR1-directed shRNA and ACE inhibitor perindopril were applied to verify the effect of inhibiting ACE/Ang-2/AT1R axis in the treatment of MVPF. Our study found MV induced an inflammatory reaction and collagen deposition in mouse lung tissue accompanied by the activation of ACE in lung tissue, increased concentration of Ang-2 in bronchoalveolar lavage fluid (BALF), and upregulation of AT1R in alveolar epithelial cells. The process of pulmonary fibrosis could be alleviated by the application of the ACE inhibitor perindopril, ATIR inhibitor valsartan and AGTR1-directed shRNA. Meanwhile, rACE2 could also alleviate MVPF through the degradation of Ang-2. Our finding indicated the ACE/Ang-2/AT1R axis played an essential role in the pathogenesis of MVPF. Pharmacological inhibition of the ACE/Ang-2/AT1R axis might be a promising strategy for the treatment of MVPF.

Memory/Active T-Cell Activation Is Associated with Immunotherapeutic Response in Fumarate Hydratase-Deficient Renal Cell Carcinoma.

PURPOSE: Fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) is a rare and lethal subtype of kidney cancer. However, the optimal treatments and molecular correlates of benefits for FH-deficient RCC are currently lacking. EXPERIMENTAL DESIGN: A total of 91 patients with FH-deficient RCC from 15 medical centers between 2009 and 2022 were enrolled in this study. Genomic and bulk RNA-sequencing (RNA-seq) were performed on 88 and 45 untreated FH-deficient RCCs, respectively. Single-cell RNA-seq was performed to identify biomarkers for treatment response. Main outcomes included disease-free survival (DFS) for localized patients, objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) for patients with metastasis. RESULTS: In the localized setting, we found that a cell-cycle progression signature enabled to predict disease progression. In the metastatic setting, first-line immune checkpoint inhibitor plus tyrosine kinase inhibitor (ICI+TKI) combination therapy showed satisfactory safety and was associated with a higher ORR (43.2% vs. 5.6%), apparently superior PFS (median PFS, 17.3 vs. 9.6 months, P = 0.016) and OS (median OS, not reached vs. 25.7 months, P = 0.005) over TKI monotherapy. Bulk and single-cell RNA-seq data revealed an enrichment of memory and effect T cells in responders to ICI plus TKI combination therapy. Furthermore, we identified a signature of memory and effect T cells that was associated with the effectiveness of ICI plus TKI combination therapy. CONCLUSIONS: ICI plus TKI combination therapy may represent a promising treatment option for metastatic FH-deficient RCC. A memory/active T-cell-derived signature is associated with the efficacy of ICI+TKI but necessitates further validation.

Worldwide productivity and research trend of publications concerning extracellular vesicles role in fibrosis: A bibliometric study from 2013 to 2022.

Background: Fibrosis is a heavy burden on the global healthcare system. Recently, an increasing number of studies have demonstrated that Extracellular vesicles play an important role in intercellular communication under both physiological and pathological conditions. This study aimed to explore the role of extracellular vesicles' in fibrosis using bibliometric methods. Methods: Original articles and reviews related to extracellular vesicles and fibrosis were obtained from the Web of Science Core Collection database on November 9, 2022. VOSviewer was used to obtain general information, including co-institution, co-authorship, and co-occurrence visualization maps. The CiteSpace software was used to analyze citation bursts of keywords and references, a timeline view of the top clusters of keywords and cited articles, and the dual map. R package "bibliometrix" was used to analyze annual production, citation per year, collaboration network between countries/regions, thematic evolution map, and historiography network. Results: In total, 3376 articles related to extracellular vesicles and fibrosis published from 2013 to 2022 were included in this study, with China and the United States being the top contributors. Shanghai Jiao Tong University has the highest number of publications. The main collaborators were Giovanni Camussi, Stefania Bruno, Marta Tepparo, and Cristina Grange. Journals related to molecular, biology, genetics, health, immunology, and medicine tended to publish literature on extracellular vesicles and fibrosis. "Recovery," "heterogeneity," "degradation," "inflammation," and "mesenchymal stem cells" are the keywords in this research field. Literature on extracellular vesicles and fibrosis associated with several diseases, including "kidney disease," "rheumatoid arthritis," and "skin regeneration" may be the latest hot research field. Conclusions: This study provides a comprehensive perspective on extracellular vesicles and fibrosis through a bibliometric analysis of articles published between 2013 and 2022. We identified the most influential countries, institutions, authors, and journals. We provide information on recent research frontiers and trends for scholars interested in the field of extracellular vesicles and fibrosis. Their role in biological processes has great potential to initiate a new upsurge in future research.

Spatial-temporal distribution of pulmonary tuberculosis registered in Fuling District of Chongqing during the 13th Five-Year Plan period / 中国热带医学

@#Abstract: Objective To analyze the spatial-temporal characteristics of the active pulmonary tuberculosis (PTB) and the pathogenic positive PTB in Fuling District of Chongqing during the 13th Five-Year Plan period, so as to explore the clustering areas, and provide scientific basis for the precise prevention and control of tuberculosis in Fuling District. Methods The PTB registration data of 27 townships in Fuling District from 2016 to 2020 were collected. The descriptive analysis were used to describe the temporal and spatial distribution characteristics of patients, SaTScan9.0 and ArcGis10.6 was used for spatial-temporal scanning analysis and local auto-correlation analysis. The results were visualized by ArcGis10.6. Results A total of 4 038 case of active PTB patients were registered and a downward trend was observed in PTB during the 13th Five-Year Plan period in Fuling District. The average annual registration rate of PTB was 70.17/100 000, and the annual PTB registration rate declined by 8.21%. The peak of active PTB and etiological positive PTB were mainly concentrated in March and June respectively. The top five streets of cumulative active PTB patients registered were Lizhi street, Dunren street, Chongyi street, Ma 'an street and Jiangdong street, accounting for 60.18% of the total registered PTB patients during the 13th Five-Year Plan period. The top three average annual registration rates were Dunren street (101.35/100 000), Chongyi street (101.34/100 000) and Wulingshan Township (99.21/100 000). The registered PTB from 2016 to 2020 showed a global auto-correlation (Moran's I=0.64, P<0.0001). The "high-high" area of active PTB and the etiological positive PTB all covered Lizhi street, Jiangdong street and Longqiao street. By scanning analysis of spatial-temporal, the primary cluster of active PTB concentrated in the main urban area south of the Yangtze River in Fuling during January 2016 to December 2017, and the primary cluster of pathogenic positive PTB concentrated in the main urban area south of the Yangtze River in Fuling and Jiangdong street during January 2019 to December 2020. Conclusions During the 13th Five-Year Plan period, there was the spatial-temporal clustering of PTB in Fuling District, which mainly gathered in the main urban area south of the Yangtze River in Fuling district and surrounding streets centered on Lizhi street.