Regulation of TNF-Induced Osteoclast Differentiation

Increased osteoclast (OC) differentiation and activity is the critical event that results in bone loss and joint destruction in common pathological bone conditions, such as osteoporosis and rheumatoid arthritis (RA). RANKL and its decoy receptor, osteoprotegerin (OPG), control OC differentiation and activity. However, there is a specific concern of a rebound effect of denosumab discontinuation in treating osteoporosis. TNFα can induce OC differentiation that is independent of the RANKL/RANK system. In this review, we discuss the factors that negatively and positively regulate TNFα induction of OC formation, and the mechanisms involved to inform the design of new anti-resorptive agents for the treatment of bone conditions with enhanced OC formation. Similar to, and being independent of, RANKL, TNFα recruits TNF receptor-associated factors (TRAFs) to sequentially activate transcriptional factors NF-κB p50 and p52, followed by c-Fos, and then NFATc1 to induce OC differentiation. However, induction of OC formation by TNFα alone is very limited, since it also induces many inhibitory proteins, such as TRAF3, p100, IRF8, and RBP-j. TNFα induction of OC differentiation is, however, versatile, and Interleukin-1 or TGFβ1 can enhance TNFα-induced OC formation through a mechanism which is independent of RANKL, TRAF6, and/or NF-κB. However, TNFα polarized macrophages also produce anabolic factors, including insulin such as 6 peptide and Jagged1, to slow down bone loss in the pathological conditions. Thus, the development of novel approaches targeting TNFα signaling should focus on its downstream molecules that do not affect its anabolic effect.

The Potential Mechanism of Exercise Combined with Natural Extracts to Prevent and Treat Postmenopausal Osteoporosis

Postmenopausal osteoporosis (PMOP) is a systemic chronic bone metabolic disease caused by the imbalance between bone formation and bone resorption mediated by estrogen deficiency. Both exercise and natural extracts are safe and effective means to prevent and control PMOP. The additive effect of exercise synergy extract against PMOP may be no less than that of traditional medicine. However, the mechanism of action of this method has not been clarified in detail. A large number of studies have shown that the pathogenesis of PMOP mainly involves the OPG-RANKL-RANK system, inflammation, and oxidative stress. Based on the abovementioned approaches, the present study reviews the anti-PMOP effects and mechanisms of exercise and natural extracts. Finally, it aims to explore the possibility of the target of the two combined anti-PMOP through this approach, thereby providing a new perspective for joint intervention research and providing a new direction for the treatment strategy of PMOP.

An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning

Benign prostatic hyperplasia (BPH) is the main cause of lower urinary tract symptoms (LUTS) in aging males. Transurethral resection of the prostate (TURP) surgery is performed by a cystoscope passing through the urethra and scraping off the prostrate piece by piece through a cutting loop. Although TURP is a minimally invasive procedure, bleeding is still the most common complication. Therefore, the evaluation, monitoring, and prevention of interop bleeding during TURP are very important issues. The main idea of this study is to rank bleeding levels during TURP surgery from videos. Generally, to judge bleeding level by human eyes from surgery videos is a difficult task, which requires sufficient experienced urologists. In this study, machine learning-based ranking algorithms are proposed to efficiently evaluate the ranking of blood levels. Based on the visual clarity of the surgical field, the four ranking of blood levels, including score 0: excellent; score 1: acceptable; score 2: slightly bad; and 3: bad, were identified by urologists who have sufficient experience in TURP surgery. The results of extensive experiments show that the revised accuracy can achieve 90, 89, 90, and 91%, respectively. Particularly, the results reveal that the proposed methods were capable of classifying the ranking of bleeding level accurately and efficiently reducing the burden of urologists.

Mechanisms of Bone Fragility: From Osteogenesis Imperfecta to Secondary Osteoporosis

Bone material strength is determined by several factors, such as bone mass, matrix composition, mineralization, architecture and shape. From a clinical perspective, bone fragility is classified as primary (i.e., genetic and rare) or secondary (i.e., acquired and common) osteoporosis. Understanding the mechanism of rare genetic bone fragility disorders not only advances medical knowledge on rare diseases, it may open doors for drug development for more common disorders (i.e., postmenopausal osteoporosis). In this review, we highlight the main disease mechanisms underlying the development of human bone fragility associated with low bone mass known to date. The pathways we focus on are type I collagen processing, WNT-signaling, TGF-ß signaling, the RANKL-RANK system and the osteocyte mechanosensing pathway. We demonstrate how the discovery of most of these pathways has led to targeted, pathway-specific treatments.

Unified Medical Language System resources improve sieve-based generation and Bidirectional Encoder Representations from Transformers (BERT)–based ranking for concept normalization

Objective Concept normalization, the task of linking phrases in text to concepts in an ontology, is useful for many downstream tasks including relation extraction, information retrieval, etc. We present a generate-and-rank concept normalization system based on our participation in the 2019 National NLP Clinical Challenges Shared Task Track 3 Concept Normalization. Materials and Methods The shared task provided 13 609 concept mentions drawn from 100 discharge summaries. We first design a sieve-based system that uses Lucene indices over the training data, Unified Medical Language System (UMLS) preferred terms, and UMLS synonyms to generate a list of possible concepts for each mention. We then design a listwise classifier based on the BERT (Bidirectional Encoder Representations from Transformers) neural network to rank the candidate concepts, integrating UMLS semantic types through a regularizer. Results Our generate-and-rank system was third of 33 in the competition, outperforming the candidate generator alone (81.66% vs 79.44%) and the previous state of the art (76.35%). During postevaluation, the model’s accuracy was increased to 83.56% via improvements to how training data are generated from UMLS and incorporation of our UMLS semantic type regularizer. Discussion Analysis of the model shows that prioritizing UMLS preferred terms yields better performance, that the UMLS semantic type regularizer results in qualitatively better concept predictions, and that the model performs well even on concepts not seen during training. Conclusions Our generate-and-rank framework for UMLS concept normalization integrates key UMLS features like preferred terms and semantic types with a neural network–based ranking model to accurately link phrases in text to UMLS concepts.

"Headmaster rank system" Helps Educators Run Schools

In the process of promoting the principal level system, there are often practical dilemmas such as the dislocation of modern school system construction, the dislocation of school values, the absence of assessment and evaluation mechanisms, and the lack of endogenous development of subjects. Therefore, it is necessary to actively establish a modern school system, stimulate the vitality of educators in running schools, adhere to the conscience of educating people, call on the professional awareness of educators in running schools, optimize the evaluation and evaluation mechanism, enhance the growth momentum of educators in running schools, enhance the connotative construction of the subject, and lead The continuous development of educators is the exploration of the main path.