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Background: Tertiary lymphoid structures (TLSs), organizationally resemble lymph nodes, are frequently present in breast cancer (BCa). It is usually, but not always, associated with a positive prognosis or immunotherapy response in cancer patients. This meta-analysis was performed to assess the prognostic and clinical impact of TLSs in BCa. Methods: We conducted a systematic search in PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, and WanFang Database to obtain eligible research data up to May 30, 2021. This meta-analysis is focusing on the studies evaluated the prognostic value of TLSs and the associated clinicopathologic indicators, related gene expression and survival. STATA software 16.0 software was used to assess the prognostic significance and clinical impact of TLSs. Results: Nine studies involved with 2281 cases were incorporated in this meta-analysis, in which four of them evaluated the prognostic value of TLSs. There are 6 studies assessed the relationship of TLSs and 4 studies investigated the clinicopathologic parameters as well as the key gene expression, respectively. The results showed the presence of TLSs were predicting a better OS (HR = 0.61, 95% CI: 0.51–0.73, p < 0.001) and DFS (HR = 0.40, 95% CI: 0.17–0.93, p < 0.001) of BCa patients. It also revealed that the presence of TLSs was significantly correlated with tumor differentiation (p < 0.001), pTNM stage (p < 0.001), lymph node metastasis (p < 0.001), and TILs density (p < 0.001) of BCa, and the expression of Her2 (p < 0.001), ER (p < 0.001), PR (p < 0.001) and Ki67 (p = 0.009) of the tumor cell. Conclusion: Our results indicated that high levels of TLSs could predict a favorable prognosis for BCa. Moreover, the TLSs were significantly correlated with the clinicopathological indicators and the critical gene expression of BCa, indicating its potential clinical impact on BCa patients. [ABSTRACT FROM AUTHOR]

Protein structure prediction is a way to bridge the sequence-structure gap, one of the main challenges in computational biology and chemistry. Predicting any protein's accurate structure is of paramount importance for the scientific community, as these structures govern their function. Moreover, this is one of the complicated optimization problems that computational biologists have ever faced. Experimental protein structure determination methods include X-ray crystallography, Nuclear Magnetic Resonance Spectroscopy and Electron Microscopy. All of these are tedious and time-consuming procedures that require expertise. To make the process less cumbersome, scientists use predictive tools as part of computational methods, using data consolidated in the protein repositories. In recent years, machine learning approaches have raised the interest of the structure prediction community. Most of the machine learning approaches for protein structure prediction are centred on co-evolution based methods. The accuracy of these approaches depends on the number of homologous protein sequences available in the databases. The prediction problem becomes challenging for many proteins, especially those without enough sequence homologs. Deep learning methods allow for the extraction of intricate features from protein sequence data without making any intuitions. Accurately predicted protein structures are employed for drug discovery, antibody designs, understanding protein–protein interactions, and interactions with other molecules. This article provides a review of conventional and deep learning approaches in protein structure prediction. We conclude this review by outlining a few publicly available datasets and deep learning architectures currently employed for protein structure prediction tasks. [ABSTRACT FROM AUTHOR]

Mucosa-associated lymphoid tissue (MALT) of special senses is poorly described and can be confused with nonspecific mononuclear cell infiltrates and tertiary lymphoid structures (TLS). In the eye, MALT consists mostly of conjunctiva-associated lymphoid tissue (CALT) and lacrimal drainage-associated lymphoid tissue (LDALT). In humans, CALT and LDALT are important components of the normal eye-associated lymphoid tissue (EALT), but EALT is less frequently described in ocular tissues of animals. The EALT are acquired postnatally in preferential mucosal sites, expand with antigenic exposure, form well-developed lymphoid follicles, and are reported to senesce. Lymphoid follicles that are induced concurrently with chronic inflammation are more appropriately considered TLS but must be differentiated from inflammation in MALT. Less understood is the etiology for formation of lymphoid tissue aggregates in the ciliary body, limbus, or choroid of healthy eyes in animals and humans. In the healthy eustachian tube and middle ear of animals and humans, MALT may be present but is infrequently described. Concurrent with otitis media, lymphoid follicles in the eustachian tube are probably expanded MALT, but lymphoid follicles in the middle ear may be TLS. The purpose of this comparative review is to familiarize toxicologic pathologists with MALT in the special senses and to provide considerations for differentiating and reporting eye and ear MALT from immune or inflammatory cell infiltrates or inflammation in nonclinical studies, and the circumstances for reporting TLS in compartments of the eye and ear. [ABSTRACT FROM AUTHOR]

Long non-coding RNAs (lncRNAs) are recently-discovered transcripts that regulate vital cellular processes and are crucially connected to diseases. Despite their unprecedented molecular complexity, it is emerging that lncRNAs possess distinct structural motifs. Remarkably, the 3D shape and topology of full-length, native lncRNAs have been visualized for the first time in the last year. These studies reveal that lncRNA structures dictate lncRNA functions. Here, we review experimentally determined lncRNA structures and emphasize that lncRNA structural characterization requires synergistic integration of computational, biochemical and biophysical approaches. Based on these emerging paradigms, we discuss how to overcome the challenges posed by the complex molecular architecture of lncRNAs, with the goal of obtaining a detailed understanding of lncRNA functions and molecular mechanisms in the future. [ABSTRACT FROM AUTHOR]

Ganoderma lucidum , commonly known as " Lingzhi " in Chinese, are well-known medicinal mushrooms. Lingzhi has been used in traditional Chinese herbal medicines for more than two thousand years. G. lucidum polysaccharides (GLPs) are present at high levels in G. lucidum cells and GLPs have molecular weights ranging from thousands to millions. GLPs have been widely studied for their various biological activities, such as antioxidant, antitumor, anti-inflammatory, antiviral, anti-diabetes, and immunomodulatory activities. The methods for GLPs extraction and characterization are mature, but the comprehensive research on the relationship between GLPs structure (i.e. , molecular weight, tertiary structure, branching, substituents, and monosaccharide composition) and function is still quite limited. The aim of this review is to update and summarize the mechanisms of the various bioactive polysaccharides extracted from G. lucidum. The information presented on these bio-mechanisms should be valuable in the research and development of GLPs-derived therapeutics. • The relationships between structure and bioactivities of Ganoderma lucidum polysaccharides (GLPs) are summarized. • The bioactivities mechanisms and the development of GLPs-derived therapeutics are under discussion. • The anti-cancer drugs containing GLPs are also introduced. [ABSTRACT FROM AUTHOR]