Machine learning algorithms for identifying contralateral central lymph node metastasis in unilateral cN0 papillary thyroid cancer.

Purpose: The incidence of thyroid cancer is growing fast and surgery is the most significant treatment of it. For patients with unilateral cN0 papillary thyroid cancer whether to dissect contralateral central lymph node is still under debating. Here, we aim to provide a machine learning based prediction model of contralateral central lymph node metastasis using demographic and clinical data. Methods: 2225 patients with unilateral cN0 papillary thyroid cancer from Wuhan Union Hospital were retrospectively studied. Clinical and pathological features were compared between patients with contralateral central lymph node metastasis and without. Six machine learning models were constructed based on these patients and compared using accuracy, sensitivity, specificity, area under the receiver operating characteristic and decision curve analysis. The selected models were then verified using data from Differentiated Thyroid Cancer in China study. All statistical analysis and model construction were performed by R software. Results: Male, maximum diameter larger than 1cm, multifocality, ipsilateral central lymph node metastasis and younger than 50 years were independent risk factors of contralateral central lymph node metastasis. Random forest model performed better than others, and were verified in external validation cohort. A web calculator was constructed. Conclusions: Gender, maximum diameter, multifocality, ipsilateral central lymph node metastasis and age should be considered for contralateral central lymph node dissection. The web calculator based on random forest model may be helpful in clinical decision.

Signaling networks in B cell development and related therapeutic strategies.

B cells are essential for Ab production during humoral immune responses. From decades of B cell research, there is now a detailed understanding of B cell subsets, development, functions, and most importantly, signaling pathways. The complicated pathways in B cells and their interactions with each other are stage-dependent, varying with surface marker expression during B cell development. With the increasing understanding of B cell development and signaling pathways, the mechanisms underlying B cell related diseases are being unraveled as well, making it possible to provide more precise and effective treatments. In this review, we describe several essential and recently discovered signaling pathways in B cell development and take a look at newly developed therapeutic strategies targeted at B cell signaling.

Novel Discoveries in Immune Dysregulation in Inborn Errors of Immunity.

With the expansion of our knowledge on inborn errors of immunity (IEI), it gradually becomes clear that immune dysregulation plays an important part. In some cases, autoimmunity, hyperinflammation and lymphoproliferation are far more serious than infections. Thus, immune dysregulation has become significant in disease monitoring and treatment. In recent years, the wide application of whole-exome sequencing/whole-genome sequencing has tremendously promoted the discovery and further studies of new IEI. The number of discovered IEI is growing rapidly, followed by numerous studies of their pathogenesis and therapy. In this review, we focus on novel discovered primary immune dysregulation diseases, including deficiency of SLC7A7, CD122, DEF6, FERMT1, TGFB1, RIPK1, CD137, TET2 and SOCS1. We discuss their genetic mutation, symptoms and current therapeutic methods, and point out the gaps in this field.