In eukaryotes, the exosome plays a central role in RNA maturation, turnover, and quality control. In Saccharomyces cerevisiae, the core exosome is composed of nine catalytically inactive subunits constituting a ring structure and the active nuclease Rrp44, also known as Dis3. Rrp44 is a member of the ribonuclease II superfamily of exoribonucleases which include RNase R, Dis3L1 and Dis3L2. In this work we have functionally characterized three residues located in the highly conserved RNB catalytic domain of Rrp44: Y595, Q892 and G895. To address their precise role in Rrp44 activity, we have constructed Rrp44 mutants and compared their activity to the wild-type Rrp44. When we mutated residue Q892 and tested its activity in vitro, the enzyme became slightly more active. We also showed that when we mutated Y595, the final degradation product of Rrp44 changed from 4 to 5 nucleotides. This result confirms that this residue is responsible for the stacking of the RNA substrate in the catalytic cavity, as was predicted from the structure of Rrp44. Furthermore, we also show that a strain with a mutation in this residue has a growth defect and affects RNA processing and degradation. These results lead us to hypothesize that this residue has an important biological role. Molecular dynamics modeling of these Rrp44 mutants and the wild-type enzyme showed changes that extended beyond the mutated residues and helped to explain these results.

Mobile health apps are used to improve the quality of health care. These apps are changing the current scenario in health care, and their numbers are increasing.

Incomplete O-glycosylation is a feature associated with malignancy resulting in the expression of truncated glycans such as the sialyl-Tn (STn) antigen. Despite all the progress in the development of potential anti-cancer antibodies, their application is frequently hindered by low specificities and cross-reactivity. In this study, a novel anti-STn monoclonal antibody named L2A5 was developed by hybridoma technology. Flow cytometry analysis showed that L2A5 specifically binds to sialylated structures on the cell surface of STn-expressing breast and bladder cancer cell lines. Moreover, immunoblotting assays demonstrated reactivity to tumour-associated O-glycosylated proteins, such as MUC1. Tumour recognition was further observed using immunohistochemistry assays, which demonstrated a high sensitivity and specificity of L2A5 mAb towards cancer tissue, using bladder and colorectal cancer tissues. L2A5 staining was exclusively tumoural, with a remarkable reactivity in invasive and metastasis sites, not detectable by other anti-STn mAbs. Additionally, it stained 20% of cases of triple-negative breast cancers, suggesting application in diseases with unmet clinical needs. Finally, the fine specificity was assessed using glycan microarrays, demonstrating a highly specific binding of L2A5 to core STn antigens and additional ability to bind 2-6-linked sialyl core-1 probes. In conclusion, this study describes a novel anti-STn antibody with a unique binding specificity that can be applied for cancer diagnostic and future development of new antibody-based therapeutic applications.

Notch signalling is an evolutionary conserved cell-to-cell communication pathway crucial for development and tissue homeostasis. Abnormal Notch signalling by mutations or deregulated expression of its receptors and/or ligands can lead to cancer making it a potential therapeutic target. Delta-like1 (DLL1) is a ligand of the Notch pathway implicated in different types of cancer, including breast cancer. Herein, we produced rhDLL1-DE3, a novel soluble form of DLL1 protein, which contains the DSL domain and EGF1-3 repeats critical for Notch pathway activation. cDNA fragments of human DLL1, encoding truncated versions of DLL1 with regions required to activate Notch receptors, were cloned and expressed as histidine-fused proteins in bacterial and mammalian cells. Expression tests in mammalian cells showed almost exclusively expression of the rhDLL1-DE3 protein form comprising the minimal binding regions DSL to EGF3 to Notch receptors. The highest yield of rhDLL1-DE3 was obtained from E. coli inclusion bodies. The produced protein, with purity higher than 95% bound to human Notch1 recombinant protein, by both Biolayer interferometry and ELISA assays. Cellular assays revealed rhDLL1-DE3 was biologically active as it increased expression of Notch-dependent genes in inducible pluripotent and breast cancer cells. Moreover, rhDLL1-DE3 allowed the generation of polyclonal antibodies by immunization that efficiently recognized DLL1 proteins by immunoblot, and caused a significant decrease of Notch1 expression in MCF7 breast cancer cells. The rhDLL1-DE3 protein might thus be used for Notch pathway activation and to generate anti-DLL1 monoclonal antibodies by immunization or phage display technology to unveil the effect of DLL1 in breast cancer.

Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.

Notch signalling is a well-established oncogenic pathway, and its ligand Delta-like 1 (DLL1) is overexpressed in estrogen receptor-positive (ER+) breast cancers and associated with poor patient prognosis. Hence, DLL1 has become an interesting therapeutic target for breast cancer. Here, the development of specific functional blocking anti-DLL1 antibodies with potential activity against ER+ breast cancer cells is reported. Human DLL1 proteins, containing the essential regions for binding to the Notch receptor and Notch signalling activation, were produced and used to select specific scFv antibody fragments by phage display. Fifteen unique scFvs were identified and reformatted into full IgGs. Characterization of these antibodies by ELISA, surface plasmon resonance and flow cytometry enabled selection of three specific anti-DLL1 IgGs, sharing identical VH regions, with nM affinities. Cellular assays on ER+ breast cancer MCF-7 cells showed that one of the IgGs (IgG-69) was able to partially impair DLL1-mediated activation of the Notch pathway, as determined by Notch reporter and RT-qPCR assays, and to attenuate cell growth. Treatment of MCF-7 cells with IgG-69 reduced mammosphere formation, suggesting that it decreases the breast cancer stem cell subpopulation. These results support the use of this strategy to develop and identify potential anti-DLL1 antibodies candidates against breast cancer.

The Notch signaling ligand JAG1 is overexpressed in various aggressive tumors and is associated with poor clinical prognosis. Hence, therapies targeting oncogenic JAG1 hold great potential for the treatment of certain tumors. Here, we report the identification of specific anti-JAG1 single-chain variable fragments (scFvs), one of them endowing chimeric antigen receptor (CAR) T cells with cytotoxicity against JAG1-positive cells. Anti-JAG1 scFvs were identified from human phage display libraries, reformatted into full-length monoclonal antibodies (Abs), and produced in mammalian cells. The characterization of these Abs identified two specific anti-JAG1 Abs (J1.B5 and J1.F1) with nanomolar affinities. Cloning the respective scFv sequences in our second- and third-generation CAR backbones resulted in six anti-JAG1 CAR constructs, which were screened for JAG1-mediated T-cell activation in Jurkat T cells in coculture assays with JAG1-positive cell lines. Studies in primary T cells demonstrated that one CAR harboring the J1.B5 scFv significantly induced effective T-cell activation in the presence of JAG1-positive, but not in JAG1-knockout, cancer cells, and enabled specific killing of JAG1-positive cells. Thus, this new anti-JAG1 scFv represents a promising candidate for the development of cell therapies against JAG1-positive tumors.

This paper presents a real-time air quality monitoring system based on Internet of Things. Air quality is particularly relevant for enhanced living environments and well-being. The Environmental Protection Agency and the World Health Organization have acknowledged the material impact of air quality on public health and defined standards and policies to regulate and improve air quality. However, there is a significant need for cost-effective methods to monitor and control air quality which provide modularity, scalability, portability, easy installation and configuration features, and mobile computing technologies integration. The proposed method allows the measuring and mapping of air quality levels considering the spatial-temporal information. This system incorporates a cyber-physical system for data collection and mobile computing software for data consulting. Moreover, this method provides a cost-effective and efficient solution for air quality supervision and can be installed in vehicles to monitor air quality while travelling. The results obtained confirm the implementation of the system and present a relevant contribution to enhanced living environments in smart cities. This supervision solution provides real-time identification of unhealthy behaviours and supports the planning of possible interventions to increase air quality.

These findings provide further evidence that DLL1 exerts carcinogenic effects in BC cells. The dissimilar effects of DLL1 downregulation observed amongst MCF-7, BT474, and MDA-MB-231 cells is likely due to their distinctive genetic and biologic characteristics, suggesting that DLL1 contributes to BC through various mechanisms.

Traumatic brain injuries are complex situations in which the emergency medical services must quickly determine the risk of deterioration using minimal diagnostic methods. The aim of this study is to analyze whether the use of early warning scores can help with decision-making in these dynamic situations by determining the patients who need the intensive care unit. A prospective, multicentric cohort study without intervention was carried out on traumatic brain injury patients aged over 18 given advanced life support and taken to the hospital. Our study included a total of 209 cases. The total number of intensive-care unit admissions was 50 cases (23.9%). Of the scores analyzed, the National Early Warning Score2 was the best result presented with an area under the curve of 0.888 (0.81-0.94; p < 0.001) and an odds ratio of 25.4 (95% confidence interval (CI):11.2-57.5). The use of early warning scores (and specifically National Early Warning Score2) can help the emergency medical services to differentiate traumatic brain injury patients with a high risk of deterioration. The emergency medical services should use the early warning scores routinely in all cases for the early detection of high-risk situations.

The Notch-signaling ligand DLL1 has emerged as an important player and promising therapeutic target in breast cancer (BC). DLL1-induced Notch activation promotes tumor cell proliferation, survival, migration, angiogenesis and BC stem cell maintenance. In BC, DLL1 overexpression is associated with poor prognosis, particularly in estrogen receptor-positive (ER+) subtypes. Directed therapy in early and advanced BC has dramatically changed the natural course of ER+ BC; however, relapse is a major clinical issue, and new therapeutic strategies are needed. Here, we report the development and characterization of a novel monoclonal antibody specific to DLL1. Using phage display technology, we selected an anti-DLL1 antibody fragment, which was converted into a full human IgG1 (Dl1.72). The Dl1.72 antibody exhibited DLL1 specificity and affinity in the low nanomolar range and significantly impaired DLL1-Notch signaling and expression of Notch target genes in ER+ BC cells. Functionally, in vitro treatment with Dl1.72 reduced MCF-7 cell proliferation, migration, mammosphere formation and endothelial tube formation. In vivo, Dl1.72 significantly inhibited tumor growth, reducing both tumor cell proliferation and liver metastases in a xenograft mouse model, without apparent toxicity. These findings suggest that anti-DLL1 Dl1.72 could be an attractive agent against ER+ BC, warranting further preclinical investigation.

According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. Consequently, the objectives, data collection techniques, development process and the validation metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using the methodology proposed by Arksey and O'Malley et al. and the PRISMA protocol was adopted to select the relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75% of the analyzed studies implement machine learning-based models using Python.

Despite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.

Indoor air quality has been a matter of concern for the international scientific community. Public health experts, environmental governances, and industry experts are working to improve the overall health, comfort, and well-being of building occupants. Repeated exposure to pollutants in indoor environments is reported as one of the potential causes of several chronic health problems such as lung cancer, cardiovascular disease, and respiratory infections. Moreover, smart cities projects are promoting the use of real-time monitoring systems to detect unfavorable scenarios for enhanced living environments. The main objective of this work is to present a systematic review of the current state of the art on indoor air quality monitoring systems based on the Internet of Things. The document highlights design aspects for monitoring systems, including sensor types, microcontrollers, architecture, and connectivity along with implementation issues of the studies published in the previous five years (2015-2020). The main contribution of this paper is to present the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. The results show that 70%, 65%, and 27.5% of studies focused on monitoring thermal comfort parameters, CO2, and PM levels, respectively. Additionally, there are 37.5% and 35% of systems based on Arduino and Raspberry Pi controllers. Only 22.5% of studies followed the calibration approach before system implementation, and 72.5% of systems claim energy efficiency.

Anthropometric measures show high heritability, and genetic correlations have been found between obesity-related traits. However, we lack a comprehensive analysis of the genetic background of human body morphology using detailed anthropometric measures.