Nitric oxide and thyroid carcinoma: A review.

Thyroid carcinoma is the most common endocrine cancer in the world, and its incidence has been steadily increasing in recent years. Despite its relatively good prognosis, therapies have not improved greatly in recent years. Therefore, exploring new therapies for thyroid carcinoma represents an unmet need. Nitric oxide (NO) is a short-term endogenous signaling molecule that plays a vital role in various physiological and pathological processes and is synthesized by nitric oxide synthase (NOS). Many studies have been conducted over the past decades to explain its correlation to cancer. NO exerts a wide range of effects on cancer, involving angiogenesis, apoptosis, cell cycle, invasion, and metastasis. It also serves a dual function by promoting and halting tumor development simultaneously. The relationship between NO and thyroid carcinoma has been intensively studied and discussed. This paper reviews the role and molecular mechanism of NO in thyroid carcinoma and discusses potentials of prevention and treatment of thyroid carcinoma.

Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia.

BACKGROUND: Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP). METHODS: A total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5-63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0-78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves. RESULTS: Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <-500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5-10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs. 0.71) and achieved human specialist-level performance. CONCLUSIONS: Our study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission.

Determination of Trace Antimony (III) in Water Samples with Single Drop Microextraction Using BPHA-[C4mim][PF6] System Followed by Graphite Furnace Atomic Absorption Spectrometry.

A new sensitive method for antimony (III) determination by graphite furnace atomic absorption spectrometry (GFAAS) has been developed by using N-benzoyl-N-phenylhydroxylamine (BPHA) and 1-butyl-3-methylimidazolium hexafluorophosphate ([C4mim][PF6]) single drop microextraction. The single drop microextraction (SDMM) system is more competitive compared with other traditional extraction methods. Under the optimized conditions, the limit of detection (signal-to-noise ratio is 3) and the enrichment factor of antimony (III) are 0.01 µg·L-1 and 112, respectively. The relative standard deviation of the 0.5 µg·L-1 antimony (III) is 4.2% (n=6). The proposed method is rather sensitive to determinate trace antimony (III) in water.