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Protein arginine methyltransferase 5 (PRMT5) has emerged as a promising cancer drug target, and three PRMT5 inhibitors are currently in clinical trials for multiple malignancies. In this study, we investigated the role of PRMT5 in human acute myeloid leukemia (AML). Using an enzymatic dead version of PRMT5 and a PRMT5-specific inhibitor, we demonstrated the requirement of the catalytic activity of PRMT5 for the survival of AML cells. We then identified PRMT5 substrates using multiplexed quantitative proteomics and investigated their role in the survival of AML cells. We found that the function of the splicing regulator SRSF1 relies on its methylation by PRMT5 and that loss of PRMT5 leads to changes in alternative splicing of multiple essential genes. Our study proposes a mechanism for the requirement of PRMT5 for leukemia cell survival and provides potential biomarkers for the treatment response to PRMT5 inhibitors.

Background Salivary cell secretion (SCS) plays a critical role in blood feeding by medicinal leeches, making them of use for certain medical purposes even today. Results We annotated the Hirudo medicinalis genome and performed RNA-seq on salivary cells isolated from three closely related leech species, H. medicinalis, Hirudo orientalis , and Hirudo verbana . Differential expression analysis verified by proteomics identified salivary cell-specific gene expression, many of which encode previously unknown salivary components. However, the genes encoding known anticoagulants have been found to be expressed not only in salivary cells. The function-related analysis of the unique salivary cell genes enabled an update of the concept of interactions between salivary proteins and components of haemostasis. Conclusions Here we report a genome draft of Hirudo medicinalis and describe identification of novel salivary proteins and new homologs of genes encoding known anticoagulants in transcriptomes of three medicinal leech species. Our data provide new insights in genetics of blood-feeding lifestyle in leeches.

Temporal molecular changes in ageing mammalian organs are of relevance to disease aetiology because many age-related diseases are linked to changes in the transcriptional and epigenetic machinery that regulate gene expression. We performed quantitative proteome analysis of chromatin-enriched protein extracts to investigate the dynamics of the chromatin proteomes of the mouse brain, heart, lung, kidney, liver, and spleen at 3, 5, 10, and 15 months of age. Each organ exhibited a distinct chromatin proteome and sets of unique proteins. The brain and spleen chromatin proteomes were the most extensive, diverse, and heterogenous among the six organs. The spleen chromatin proteome appeared static during the lifespan, presenting a young phenotype that reflects the permanent alertness state and important role of this organ in physiological defence and immunity. We identified a total of 5928 proteins, including 2472 nuclear or chromatin-associated proteins across the six mouse organs. Up to 3125 proteins were quantified in each organ, demonstrating distinct and organ-specific temporal protein expression timelines and regulation at the post-translational level. Bioinformatics meta-analysis of these chromatin proteomes revealed distinct physiological and ageing-related features for each organ. Our results demonstrate the efficiency of organelle-specific proteomics for in vivo studies of a model organism and consolidate the hypothesis that chromatin-associated proteins are involved in distinct and specific physiological functions in ageing organs.

The interpretability requirement is one of the largest obstacles when deploying machine learning models in various practical fields. Methods of eXplainable Artificial Intelligence (XAI) address those issues. However, the growing number of different solutions in this field creates a demand to assess the quality of explanations and compare them. In recent years, several attempts have been made to consolidate scattered XAI quality assessment methods into a single benchmark. Those attempts usually suffered from a focus on feature importance only, a lack of customization, and the absence of an evaluation framework. In this work, the eXplainable Artificial Intelligence Benchmark (XAIB) is proposed. Compared to existing benchmarks, XAIB is more universal, extensible, and has a complete evaluation ontology in the form of the Co-12 Framework. Due to its special modular design, it is easy to add new datasets, models, explainers, and quality metrics. Furthermore, an additional abstraction layer built with an inversion of control principle makes them easier to use. The benchmark will contribute to artificial intelligence research by providing a platform for evaluation experiments and, at the same time, will contribute to engineering by providing a way to compare explainers using custom datasets and machine learning models, which brings evaluation closer to practice.

Virus-neutralizing peptides (VNPs) emerged as promising antiviral drug candidates with unprecedented specificity and cost-effectiveness during the recent COVID-19 pandemic. However, limited avidity, lack of effector functions, short circulatory half-life, and restricted administration routes make them inferior compared to neutralizing antibodies. To address these constraints, a potent VNP that targets the SARS-CoV-2 S protein is combined with Barnase, a highly active RNA-cleaving enzyme from Bacillus amyloliquefaciens . The resulting LCB1-Barnase (LCB1-Bn) chimera retains strong binding affinity for the SARS-CoV-2 S protein and demonstrates a fourfold reduction in IC 50 compared to the LCB1 peptide alone in competitive ELISA and in in vitro neutralization tests. In transgenic CAG-hACE2 mice infected with wild-type SARS-CoV-2, intranasal administration of LCB1-Bn significantly improves survival and reduces viral load by 29-fold. To extend circulation life and allow systemic intravenous administration, an albumin-binding domain (ABD) from Streptococcus protein G is added to LCB1-Bn, producing LCB1-ABD-Bn fusion protein which displays a 95-fold increase in serum half-life. LCB1-ABD-Bn exhibits good tolerability at doses below 10 mg/kg and provides protection of SARS-CoV-2-infected CAG-hACE2 animals in 24-hour post-infection intraperitoneal treatment. Cryo-EM reveals the LCB1-ABD-Bn’s tight interaction with S protein RBD domains, highlighting its potential as a promising drug candidate against SARS-CoV-2.

The primary goal of this paper is to develop an approach for predicting important clinical indicators, which can be used to improve treatment. Using mathematical predictive modeling algorithms, we examined the course of COVID-19-based pneumonia (CP) with inpatient treatment. Algorithms used include dynamic and ordinary Bayesian networks (OBN and DBN), popular ML algorithms, the state-of-the-art auto ML approach and our new hybrid method based on DBN and auto ML approaches. Predictive targets include treatment outcomes, length of stay, dynamics of disease severity indicators, and facts of prescribed drugs for different time intervals of observation. Models are validated using expert knowledge, current clinical recommendations, preceding research and classic predictive metrics. The characteristics of the best models are as follows: MAE of 3.6 days of predicting LOS (DBN plus FEDOT auto ML framework), 0.87 accuracy of predicting treatment outcome (OBN); 0.98 F1 score for predicting facts of prescribed drug (DBN). Moreover, the advantage of the proposed approach is Bayesian network-based interpretability, which is very important in the medical field. After the validation of other CP datasets for other hospitals, the proposed models can be used as part of the decision support systems for improving COVID-19-based pneumonia treatment. Another important finding is the significant differences between COVID-19 and non-COVID-19 pneumonia.

The ideology behind process modelling is to visualise lengthy event logs into simple representations interpretable to the end user. Classifying process models as simple or complex is based on criteria that evaluate attributes of models and quantify them on a scale. These metrics measure various characteristics of process models and describe their qualities. Over the years, vast amounts of metrics have been proposed in the community, making it difficult to find and select the appropriate ones for implementation. This paper presents a state-of-the-art meta-review that lists and summarises all the evaluation metrics proposed to date. We have studied the behaviour of the four most widely used metrics in process mining with an experiment. Further, we have used seven healthcare domain datasets of varying natures to analyse the behaviour of these metrics under different threshold conditions. Our work aims to propose and demonstrate the capabilities to use our selected metrics as a standard of measurement for the process mining domain.

A system of hospitals in large cities can be considered a large and diverse but interconnected system. Widely applied in hospitals, electronic health records (EHR) are crucially different from each other because of the use of different health information systems, internal hospital rules, and individual behavior of physicians. The unstructured (textual) data of EHR is rarely used to assess the citywide quality of healthcare. Within the study, we analyze EHR data, particularly textual unstructured data, as a reflection of the complex multi-agent system of healthcare in the city of Saint Petersburg, Russia. Through analyzing the data collected by the Medical Information and Analytical Center, a method was proposed and evaluated for identifying a common structure, understanding the diversity, and assessing information quality in EHR data through the application of natural language processing techniques.

Venoms of most Russian viper species are poorly characterized. Here, by quantitative chromato-mass-spectrometry, we analyzed protein and peptide compositions of venoms from four Vipera species (V. kaznakovi, V. renardi, V. orlovi and V. nikolskii) inhabiting different regions of Russia. In all these species, the main components were phospholipases A2, their content ranging from 24% in V. orlovi to 65% in V. nikolskii. Altogether, enzyme content in venom of V. nikolskii reached ~85%. Among the non-enzymatic proteins, the most abundant were disintegrins (14%) in the V. renardi venom, C-type lectin like (12.5%) in V. kaznakovi, cysteine-rich venom proteins (12%) in V. orlovi and venom endothelial growth factors (8%) in V. nikolskii. In total, 210 proteins and 512 endogenous peptides were identified in the four viper venoms. They represented 14 snake venom protein families, most of which were found in the venoms of Vipera snakes previously. However, phospholipase B and nucleotide degrading enzymes were reported here for the first time. Compositions of V. kaznakovi and V. orlovi venoms were described for the first time and showed the greatest similarity among the four venoms studied, which probably reflected close relationship between these species within the “kaznakovi” complex.

Mesenchymal stromal cells (MSC) are widely recognized as potential effectors in neuroprotective therapy. The protective properties of MSC were considered to be associated with the secretion of extracellular vesicles (MSC-EV). We explored the effects of MSC-EV in vivo on models of traumatic and hypoxia-ischemia (HI) brain injury. Neuroprotective mechanisms triggered by MSC-EV were also studied in vitro using a primary neuroglial culture. Intranasal administration of MSC-EV reduced the volume of traumatic brain damage, correlating with a recovery of sensorimotor functions. Neonatal HI-induced brain damage was mitigated by the MSC-EV administration. This therapy also promoted the recovery of sensorimotor functions, implying enhanced neuroplasticity, and MSC-EV-induced growth of neurites in vitro supports this. In the in vitro ischemic model, MSC-EV prevented cell calcium (Ca2+) overload and subsequent cell death. In mixed neuroglial culture, MSC-EV induced inositol trisphosphate (IP3) receptor-related Ca2+ oscillations in astrocytes were associated with resistance to calcium overload not only in astrocytes but also in co-cultured neurons, demonstrating intercellular positive crosstalk between neural cells. This implies that phosphatidylinositol 3-Kinase/AKT signaling is one of the main pathways in MSC-EV-mediated protection of neural cells exposed to ischemic challenge. Components of this pathway were identified among the most enriched categories in the MSC-EV proteome.Mesenchymal stromal cells (MSC) are widely recognized as potential effectors in neuroprotective therapy. The protective properties of MSC were considered to be associated with the secretion of extracellular vesicles (MSC-EV). We explored the effects of MSC-EV in vivo on models of traumatic and hypoxia-ischemia (HI) brain injury. Neuroprotective mechanisms triggered by MSC-EV were also studied in vitro using a primary neuroglial culture. Intranasal administration of MSC-EV reduced the volume of traumatic brain damage, correlating with a recovery of sensorimotor functions. Neonatal HI-induced brain damage was mitigated by the MSC-EV administration. This therapy also promoted the recovery of sensorimotor functions, implying enhanced neuroplasticity, and MSC-EV-induced growth of neurites in vitro supports this. In the in vitro ischemic model, MSC-EV prevented cell calcium (Ca2+) overload and subsequent cell death. In mixed neuroglial culture, MSC-EV induced inositol trisphosphate (IP3) receptor-related Ca2+ oscillations in astrocytes were associated with resistance to calcium overload not only in astrocytes but also in co-cultured neurons, demonstrating intercellular positive crosstalk between neural cells. This implies that phosphatidylinositol 3-Kinase/AKT signaling is one of the main pathways in MSC-EV-mediated protection of neural cells exposed to ischemic challenge. Components of this pathway were identified among the most enriched categories in the MSC-EV proteome.