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The chronic inflammatory disease ankylosing spondylitis (AS) is marked by back discomfort, spinal ankylosis, and extra-articular symptoms. In AS, inflammation is responsible for both pain and spinal ankylosis. However, the processes that sustain chronic inflammation remain unknown. Despite the years of research conducted to decipher the intricacy of AS, little progress has been made in identifying the signaling events that lead to the development of this disease. T cells, an immune cell type that initiates and regulates the body’s response to infection, have been established to substantially impact the development of AS. T lymphocytes are regarded as a crucial part of adaptive immunity for the control of the immune system. A highly coordinated interaction involving antigen-presenting cells (APCs) and T cells that regulate T cell activation constitutes an immunological synapse (IS). This first phase leads to the controlled trafficking of receptors and signaling mediators involved in folding endosomes to the cellular interface, which allows the transfer of information from T cells to APCs through IS formation. Discrimination of self and nonself antigen is somatically learned in adaptive immunity. In an autoimmune condition such as AS, there is a disturbance of self/nonself antigen discrimination; available findings imply that the IS plays a preeminent role in the adaptive immune response. In this paper, we provide insights into the genesis of AS by evaluating recent developments in the function of vesicular trafficking in IS formation and the targeted release of exosomes enriched microRNAs (miRNA) at the synaptic region in T cells.

Objective: OsteoDIP aims to collect and provide, in a simple searchable format, curated high throughput RNA expression data related to osteoarthritis. Design: Datasets are collected annually by searching “osteoarthritis gene expression profile” in PubMed. Only publications containing patient data and a list of differentially expressed genes are considered. From 2020, the search has expanded to include non-coding RNAs. Moreover, a search in GEO for “osteoarthritis” datasets has been performed using ‘Homo sapiens' and ‘Expression profiling by array’ filters. Annotations for genes linked to osteoarthritis have been downloaded from external databases. Results: Out of 1204 curated papers, 63 have been included in OsteoDIP, while GEO curation led to the collection of 28 datasets. Literature data provides a snapshot of osteoarthritis research derived from 1924 human samples, while GEO datasets provide expression for additional 1012 patients. Similar to osteoarthritis literature, OsteoDIP data has been created mostly from studies focused on knee, and the tissue most frequently investigated is cartilage. GEO data sets were fully integrated with associated clinical data. We showcase examples and use cases applicable for translational research in osteoarthritis. Conclusions: OsteoDIP is publicly available at http://ophid.utoronto.ca/OsteoDIP. The website is easy to navigate and all the data is available for download. Data consolidation allows researchers to perform comparisons across studies and to combine data from different datasets. Our examples show how OsteoDIP can integrate with and improve osteoarthritis researchers’ pipelines.

Pedestrian detection is an important research domain due to its relevance for autonomous and assisted driving, as well as its applications in security and industrial automation. Often, more than one type of sensor is used to cover a broader range of operating conditions than a single-sensor system would allow. However, it remains difficult to make pedestrian detection systems perform well in highly dynamic environments, often requiring extensive retraining of the algorithms for specific conditions to reach satisfactory accuracy, which, in turn, requires large, annotated datasets captured in these conditions. In this paper, we propose a probabilistic decision-level sensor fusion method based on naive Bayes to improve the efficiency of the system by combining the output of available pedestrian detectors for colour and thermal images without retraining. The results in this paper, obtained through long-term experiments, demonstrate the efficacy of our technique, its ability to work with non-registered images, and its adaptability to cope with situations when one of the sensors fails. The results also show that our proposed technique improves the overall accuracy of the system and could be very useful in several applications.

Sedimentary Rocks composing the Thar Lignite basin Pakistan are of clastic origin. These rocks have comparatively low uniaxial compressive strength (UCS) values, and hence recognized as ‘soft sedimentary rocks’ in this study. UCS is a fundamental property of rocks, used by mine design engineers in designing the surface and underground excavations. The purpose of this study is to investigate the relationship between UCS with Brazilian tensile strength (BTS), and index properties of soft sedimentary rock formations at Thar Lignite basin. Various correlations between mechanical and physical properties of rocks have been developed previously. However, no significant correlation has been developed on UCS with BTS and index properties for soft sedimentary rocks. Numerous Rock samples from Two complete geotechnical drillholes at Block-IX Thar Coalfield were selected. Standard test procedures were implemented to determine the UCS, indirect Tensile and index properties such as point load strength, and shore Scleroscope hardness. The correlations between rock properties were established using simple and multiple regression techniques, and empirical equations were obtained. These equations can be used to predict the UCS and tensile strength of soft sedimentary rocks by performing simple index tests; which are quick, economical, and easier to be performed on the site. [ABSTRACT FROM AUTHOR]

Pedestrian detection is an important research domain due to its relevance for autonomous and assisted driving, as well as its applications in security and industrial automation. Often, more than one type of sensor is used to cover a broader range of operating conditions than a single-sensor system would allow. However, it remains difficult to make pedestrian detection systems perform well in highly dynamic environments, often requiring extensive retraining of the algorithms for specific conditions to reach satisfactory accuracy, which, in turn, requires large, annotated datasets captured in these conditions. In this paper, we propose a probabilistic decision-level sensor fusion method based on naive Bayes to improve the efficiency of the system by combining the output of available pedestrian detectors for colour and thermal images without retraining. The results in this paper, obtained through long-term experiments, demonstrate the efficacy of our technique, its ability to work with non-registered images, and its adaptability to cope with situations when one of the sensors fails. The results also show that our proposed technique improves the overall accuracy of the system and could be very useful in several applications. [ABSTRACT FROM AUTHOR]

Sepsis and septic shock are major healthcare problems, affecting millions of people around the world each year. The speed and appropriateness of therapy administered in the initial hours of treatment are likely to influence the outcome. We conducted a study to validate the clinical assessment score named 'quick sequential organ failure assessment' (qSOFA) score for use in the early identification of sepsis patients in the emergency department. Our primary objective was to see the sensitivity and specificity of the qSOFA-score for diagnosing sepsis in the emergency department and our secondary objective was to compare the sensitivity of the qSOFA score with the National Early Warning (NEW) score in patients with sepsis. A prospective observational study was conducted at Max Super Speciality Hospital Saket, New Delhi, from July 2016 to January 2017. Adult patients presenting to the emergency department with clinical signs and symptoms suggestive of infection were enrolled as per the eligibility criteria and divided into two groups on the basis of their qSOFA score at presentation. Out of 120 patients who had a positive qSOFA score 30 were subsequently confirmed as having sepsis whereas in qSOFA negative group 14 patients were subsequently diagnosed as having sepsis. This leads to the fact that although the test has near-acceptable specificity, the sensitivity is quite low. Calculations of the secondary outcome, that is 28-day mortality, revealed that 17 patients out of 120 who had a positive qSOFA score died within 28 days of first presentation whereas in the control group, nine patients had died. This means it successfully predicted mortality in only 17 patients and failed to predict mortality in nine patients out of 26 patients that died. The p-value is 0.097 which indicates both poor sensitivity as well as specificity for predicting mortality. We also compared qSOFA with the NEW score and found the latter to have a better sensitivity for detecting sepsis. This study shows that the qSOFA score, which has been specifically designed for early detection of sepsis patients in the emergency department or a pre-hospital setting in whom infection is suspected on a clinical basis, does not seem to be a good screening tool for early detection of sepsis patients in the emergency department., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Wani et al.)