BioTextQuest v2.0: An evolved tool for biomedical literature mining and concept discovery.

The process of navigating through the landscape of biomedical literature and performing searches or combining them with bioinformatics analyses can be daunting, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related repositories. Herein, we present BioTextQuest v2.0, a tool for biomedical literature mining. BioTextQuest v2.0 is an open-source online web portal for document clustering based on sets of selected biomedical terms, offering efficient management of information derived from PubMed abstracts. Employing established machine learning algorithms, the tool facilitates document clustering while allowing users to customize the analysis by selecting terms of interest. BioTextQuest v2.0 streamlines the process of uncovering valuable insights from biomedical research articles, serving as an agent that connects the identification of key terms like genes/proteins, diseases, chemicals, Gene Ontology (GO) terms, functions, and others through named entity recognition, and their application in biological research. Instead of manually sifting through articles, researchers can enter their PubMed-like query and receive extracted information in two user-friendly formats, tables and word clouds, simplifying the comprehension of key findings. The latest update of BioTextQuest leverages the EXTRACT named entity recognition tagger, enhancing its ability to pinpoint various biological entities within text. BioTextQuest v2.0 acts as a research assistant, significantly reducing the time and effort required for researchers to identify and present relevant information from the biomedical literature.

scRNA-Explorer: An End-user Online Tool for Single Cell RNA-seq Data Analysis Featuring Gene Correlation and Data Filtering.

In the majority of downstream analysis pipelines for single-cell RNA sequencing (scRNA-seq), techniques like dimensionality reduction and feature selection are employed to address the problem of high-dimensional nature of the data. These approaches involve mapping the data onto a lower-dimensional space, eliminating less informative genes, and pinpointing the most pertinent features. This process ultimately leads to a reduction in the number of dimensions used for downstream analysis, which in turn speeds up the computation of large-scale scRNA-seq data. Most approaches are directed to isolate from biological background the genes characterizing different cells and or the condition under study by establishing lists of differentially expressed or coexpressed genes. Herein, we present scRNA-Explorer an open-source online tool for simplified and rapid scRNA-seq analysis designed with the end user in mind. scRNA-Explorer utilizes: (i) Filtering out uninformative cells in an interactive manner via a web interface, (ii) Gene correlation analysis coupled with an extra step of evaluating the biological importance of these correlations, and (iii) Gene enrichment analysis of correlated genes in order to find gene implication in specific functions. We developed a pipeline to address the above problem. The scRNA-Explorer pipeline allows users to interrogate in an interactive manner scRNA-sequencing data sets to explore via gene expression correlations possible function(s) of a gene of interest. scRNA-Explorer can be accessed at https://bioinformatics.med.uoc.gr/shinyapps/app/scrnaexplorer.

A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning.

Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention has been given to epigenetic biomarkers in SCZ. In this study, we introduce a three-step, automated machine learning (AutoML)-based, data-driven, biomarker discovery pipeline approach, using genome-wide DNA methylation datasets and laboratory validation, to deliver a highly performing, blood-based epigenetic biosignature of diagnostic clinical value in SCZ. Publicly available blood methylomes from SCZ patients and healthy individuals were analyzed via AutoML, to identify SCZ-specific biomarkers. The methylation of the identified genes was then analyzed by targeted qMSP assays in blood gDNA of 30 first-episode drug-naïve SCZ patients and 30 healthy controls (CTRL). Finally, AutoML was used to produce an optimized disease-specific biosignature based on patient methylation data combined with demographics. AutoML identified a SCZ-specific set of novel gene methylation biomarkers including IGF2BP1, CENPI, and PSME4. Functional analysis investigated correlations with SCZ pathology. Methylation levels of IGF2BP1 and PSME4, but not CENPI were found to differ, IGF2BP1 being higher and PSME4 lower in the SCZ group as compared to the CTRL group. Additional AutoML classification analysis of our experimental patient data led to a five-feature biosignature including all three genes, as well as age and sex, that discriminated SCZ patients from healthy individuals [AUC 0.755 (0.636, 0.862) and average precision 0.758 (0.690, 0.825)]. In conclusion, this three-step pipeline enabled the discovery of three novel genes and an epigenetic biosignature bearing potential value as promising SCZ blood-based diagnostics.

Visualizing metagenomic and metatranscriptomic data: A comprehensive review.

The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.

Retinal capillary plexus in Parkinson's disease using optical coherence tomography angiography.

AIM: To evaluate the alterations of the retinal microvasculature and foveal avascular zone in patients with Parkinson's disease (PD) using optical coherence tomography angiography (OCT-A). METHODS: A retrospective study of PD patients examined in the Ophthalmology Department of the General Hospital of Athens, "Georgios Gennimatas" from March 2021 to March 2022 was conducted. Totally 44 patients with PD were included and 18 healthy controls were examined, hence a total of 124 eyes were enrolled in the study. The foveal and parafoveal superficial and deep capillary plexus vascular density (fSCP-VD, fDCP-VD, pSCP-VD, pDCP-CD) and foveal avascular zone (FAZ) were quantified with OCTA. Optical coherence tomography (OCT) was used to measure macular thickness. Our statistical analysis was conducted by using a mixed effect linear regression model. RESULTS: After adjustment for age and gender, the mean parafoveal superficial capillary plexus vascular density (pSCP-VD) and mean parafoveal deep capillary plexus vascular density (pDCP-VD) were significantly decreased in individuals with PD (P<0.001 in both) by -2.35 (95%CI -3.3, -1.45) and -7.5 (95%CI -10.4, -4.6) respectively. fSCP-VD and fDCP-VD didn't approach statistical significance. The FAZ area and perimeter were significantly decreased (P<0.001 in both) by -0.1 mm2 (95%CI -0.13, -0.07) and -0.49 mm2 (95%CI -0.66, -0.32) respectively. Circularity didn't approach statistical significance. Central retinal thickness (CRT) was significantly decreased in individuals with PD (P<0.001) by -23.1 µm (95%CI -30.2, -16) and temporal retinal thickness (TRT) was decreased (P=0.025) by -11 µm (95%CI -22, -1.5) while nasal retinal thickness (NRT) only approached statistical significance (P=0.066). CONCLUSION: The mean pSCP-VD, pDCP-VD, CRT and TRT are significantly decreased and FAZ is altered in individuals with PD. These findings can be potentially used as biomarkers for the diagnosis and evaluation of early PD.

CGG toolkit: Software components for computational genomics.

Public-domain availability for bioinformatics software resources is a key requirement that ensures long-term permanence and methodological reproducibility for research and development across the life sciences. These issues are particularly critical for widely used, efficient, and well-proven methods, especially those developed in research settings that often face funding discontinuities. We re-launch a range of established software components for computational genomics, as legacy version 1.0.1, suitable for sequence matching, masking, searching, clustering and visualization for protein family discovery, annotation and functional characterization on a genome scale. These applications are made available online as open source and include MagicMatch, GeneCAST, support scripts for CoGenT-like sequence collections, GeneRAGE and DifFuse, supported by centrally administered bioinformatics infrastructure funding. The toolkit may also be conceived as a flexible genome comparison software pipeline that supports research in this domain. We illustrate basic use by examples and pictorial representations of the registered tools, which are further described with appropriate documentation files in the corresponding GitHub release.

Unraveling the Role of Molecular Profiling in Predicting Treatment Response in Stage III Colorectal Cancer Patients: Insights from the IDEA International Study.

BACKGROUND: This study aimed to investigate the molecular profiles of 237 stage III CRC patients from the international IDEA study. It also sought to correlate these profiles with Toll-like and vitamin D receptor polymorphisms, clinicopathological and epidemiological characteristics, and patient outcomes. METHODS: Whole Exome Sequencing and PCR-RFLP on surgical specimens and blood samples, respectively, were performed to identify molecular profiling and the presence of Toll-like and vitamin D polymorphisms. Bioinformatic analysis revealed mutational status. RESULTS: Among the enrolled patients, 63.7% were male, 66.7% had left-sided tumors, and 55.7% received CAPOX as adjuvant chemotherapy. Whole exome sequencing identified 59 mutated genes in 11 different signaling pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) CRC panel. On average, patients had 8 mutated genes (range, 2-21 genes). Mutations in ARAF and MAPK10 emerged as independent prognostic factors for reduced DFS (p = 0.027 and p < 0.001, respectively), while RAC3 and RHOA genes emerged as independent prognostic factors for reduced OS (p = 0.029 and p = 0.006, respectively). Right-sided tumors were also identified as independent prognostic factors for reduced DFS (p = 0.019) and OS (p = 0.043). Additionally, patients with tumors in the transverse colon had mutations in genes related to apoptosis, PIK3-Akt, Wnt, and MAPK signaling pathways. CONCLUSIONS: Molecular characterization of tumor cells can enhance our understanding of the disease course. Mutations may serve as promising prognostic biomarkers, offering improved treatment options. Confirming these findings will require larger patient cohorts and international collaborations to establish correlations between molecular profiling, clinicopathological and epidemiological characteristics and clinical outcomes.

Unraveling the functional dark matter through global metagenomics.

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.

From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning.

As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. Despite the available stroke treatment options, patients frequently suffer from significant disability after a stroke. Initial stroke severity is a significant predictor of functional dependence and mortality following an acute stroke. The current study aims to collect and analyze data from the hyperacute and acute phases of stroke, as well as from the medical history of the patients, in order to develop an explainable machine learning model for predicting stroke-related neurological deficits at discharge, as measured by the National Institutes of Health Stroke Scale (NIHSS). More specifically, we approached the data as a binary task problem: improvement of NIHSS progression vs. worsening of NIHSS progression at discharge, using baseline data within the first 72 h. For feature selection, a genetic algorithm was applied. Using various classifiers, we found that the best scores were achieved from the Random Forest (RF) classifier at the 15 most informative biomarkers and parameters for the binary task of the prediction of NIHSS score progression. RF achieved 91.13% accuracy, 91.13% recall, 90.89% precision, 91.00% f1-score, 8.87% FNrate and 4.59% FPrate. Those biomarkers are: age, gender, NIHSS upon admission, intubation, history of hypertension and smoking, the initial diagnosis of hypertension, diabetes, dyslipidemia and atrial fibrillation, high-density lipoprotein (HDL) levels, stroke localization, systolic blood pressure levels, as well as erythrocyte sedimentation rate (ESR) levels upon admission and the onset of respiratory infection. The SHapley Additive exPlanations (SHAP) model interpreted the impact of the selected features on the model output. Our findings suggest that the aforementioned variables may play a significant role in determining stroke patients' NIHSS progression from the time of admission until their discharge.

Social Cognition Impairments in Association to Clinical, Cognitive, Mood, and Fatigue Features in Multiple Sclerosis: A Study Protocol.

Multiple sclerosis (MS) is a chronic immune-mediated disease of the central nervous system (CNS), characterized by the diffuse grey and white matter damage. Cognitive impairment (CI) is a frequent clinical feature in patients with MS (PwMS) that can be prevalent even in early disease stages, affecting the physical activity and active social participation of PwMS. Limited information is available regarding the influence of MS in social cognition (SC), which may occur independently from the overall neurocognitive dysfunction. In addition, the available information regarding the factors that influence SC in PwMS is limited, e.g., factors such as a patient's physical disability, different cognitive phenotypes, mood status, fatigue. Considering that SC is an important domain of CI in MS and may contribute to subjects' social participation and quality of life, we herein conceptualize and present the methodological design of a cross-sectional study in 100 PwMS of different disease subtypes. The study aims (a) to characterize SC impairment in PwMS in the Greek population and (b) to unveil the relationship between clinical symptoms, phenotypes of CI, mood status and fatigue in PwMS and the potential underlying impairment on tasks of SC.

LIRcentral: a manually curated online database of experimentally validated functional LIR motifs.

Several selective macroautophagy receptor and adaptor proteins bind members of the Atg8 (autophagy related 8) family using short linear motifs (SLiMs), most often referred to as Atg8-family interacting motifs (AIMs) or LC3-interacting regions (LIRs). AIM/LIR motifs have been extensively studied during the last fifteen years, since they can uncover the underlying biological mechanisms and possible substrates for this key catabolic process of eukaryotic cells. Prompted by the fact that experimental information regarding LIR motifs can be found scattered across heterogeneous literature resources, we have developed LIRcentral (https://lircentral.eu), a freely available online repository for user-friendly access to comprehensive, high-quality information regarding LIR motifs from manually curated publications. Herein, we describe the development of LIRcentral and showcase currently available data and features, along with our plans for the expansion of this resource. Information incorporated in LIRcentral is useful for accomplishing a variety of research tasks, including: (i) guiding wet biology researchers for the characterization of novel instances of LIR motifs, (ii) giving bioinformaticians/computational biologists access to high-quality LIR motifs for building novel prediction methods for LIR motifs and LIR containing proteins (LIRCPs) and (iii) performing analyses to better understand the biological importance/features of functional LIR motifs. We welcome feedback on the LIRcentral content and functionality by all interested researchers and anticipate this work to spearhead a community effort for sustaining this resource which will further promote progress in studying LIR motifs/LIRCPs.Abbreviations: AIM, Atg8-family interacting motif; Atg8, autophagy related 8; GABARAP, GABA type A receptor-associated protein; LIR, LC3-interacting region; LIRCP, LIR-containing protein; MAP1LC3/LC3, microtubule associated protein 1 light chain 3; PMID, PubMed identifier; PPI, protein-protein interaction; SLiM, short linear motif.

A Prospective, Observational Study Assessing Effectiveness, Safety, and QoL of Greek Patients with Multiple Sclerosis Under Treatment with Fingolimod.

INTRODUCTION: Fingolimod is the first approved oral therapy for relapsing-remitting multiple sclerosis (RRMS). The present study aimed to further characterize fingolimod's safety profile, and to assess the patient-reported treatment satisfaction and impact of fingolimod on the quality of life (QoL) of patients with multiple sclerosis (MS) treated in routine care in Greece. METHODS: This was a multicenter, prospective, observational, 24-month study conducted in Greece by hospital-based and private practice neurologists who specialize in MS. Eligible patients had initiated fingolimod within 15 days in accordance with the locally approved label. Safety outcomes included any adverse event (AE) observed during the study period and efficacy outcomes included both objective (disability progression and 2-year annualized relapse rate) and patient-reported assessments (Treatment Satisfaction Questionnaire for Medication (TSQM) v1.4 and the EuroQol (EQ)-5-dimension (5D) 3-level instruments). RESULTS: A total of 489 eligible patients (age 41.2 ± 9.8 years; 63.7% female; 4.2% treatment-naive) were exposed to fingolimod for a median of 23.7 months. During the observation period, 20.5% of the participants experienced 233 AEs. Lymphopenia (8.8%), leukopenia (4.2%), hepatic enzyme increased (3.4%), and infections (3.0%) were the most common. Most patients (89.3%) did not experience disability progression; the 2-year annualized relapse rate decreased by 94.7% compared to baseline. The median EQ-visual analogue scale (VAS) was 74.5 at month 24 vs. 65.0 at enrollment (p < 0.001) and the EQ-5D index score was 0.80 vs. 0.78, respectively. Significant improvements were noted in the TSQM global satisfaction and effectiveness domain scores between 6 and 24 months post enrollment (median scores at month 24, 71.4 and 66.7, respectively) (p < 0.001). Significant increases from enrollment to the 24th month were also noted in the patients' global satisfaction and effectiveness domain scores [mean change of 7.4 ± 17.7 (p = 0.005) and mean increase of 5.4 ± 16.2) (p = 0.043), respectively]. CONCLUSION: In the real-world setting of Greece, fingolimod demonstrates a clinical benefit and a predictable and manageable safety profile, which contribute towards high patient-reported treatment satisfaction and improvements in the QoL of patients with MS.

Genome-Wide Analysis of lncRNA-mRNA Co-Expression Networks in CD133+/CD44+ Stem-like PDAC Cells.

Pancreatic ductal adenocarcinoma (PDAC), the second most prevalent gastrointestinal malignancy and the most common type of pancreatic cancer is linked with poor prognosis and, eventually, with high mortality rates. Early detection is seldom, while tumor heterogeneity and microarchitectural alterations benefit PDAC resistance to conventional therapeutics. Although emerging evidence suggest the core role of cancer stem cells (CSCs) in PDAC aggressiveness, unique stem signatures are poorly available, thus limiting the efforts of anti-CSC-targeted therapy. Herein, we report the findings of the first genome-wide analyses of mRNA/lncRNA transcriptome profiling and co-expression networks in PDAC cell line-derived CD133+/CD44+ cells, which were shown to bear a CSC-like phenotype in vitro and in vivo. Compared to CD133-/CD44- cells, the CD133+/CD44+ population demonstrated significant expression differences in both transcript pools. Using emerging bioinformatic tools, we performed lncRNA target coding gene prediction analysis, which revealed significant Gene Ontology (GO), pathway, and network enrichments in many dyregulated lncRNA nearby (cis or trans) mRNAs, with reported involvement in the regulation of CSC phenotype and functions. In this context, the construction of lncRNA/mRNA networks by ingenuity platforms identified the lncRNAs ATF2, CHEK1, DCAF8, and PAX8 to interact with "hub" SC-associated mRNAs. In addition, the expressions of the above lncRNAs retrieved by TCGA-normalized RNAseq gene expression data of PAAD were significantly correlated with clinicopathological features of PDAC, including tumor grade and stage, nodal metastasis, and overall survival. Overall, our findings shed light on the identification of CSC-specific lncRNA signatures with potential prognostic and therapeutic significance in PDAC.

N1 versus N2 and PMN-MDSC: A critical appraisal of current concepts on tumor-associated neutrophils and new directions for human oncology.

Research on tumor-associated neutrophils (TAN) currently surges because of the well-documented strong clinical relevance of tumor-infiltrating neutrophils. This relevance is illustrated by strong correlations between high frequencies of intratumoral neutrophils and poor outcome in the majority of human cancers. Recent high-dimensional analysis of murine neutrophils provides evidence for unexpected plasticity of neutrophils in murine models of cancer and other inflammatory non-malignant diseases. New analysis tools enable deeper insight into the process of neutrophil differentiation and maturation. These technological and scientific developments led to the description of an ever-increasing number of distinct transcriptional states and associated phenotypes in murine models of disease and more recently also in humans. At present, functional validation of these different transcriptional states and potential phenotypes in cancer is lacking. Current functional concepts on neutrophils in cancer rely mainly on the myeloid-derived suppressor cell (MDSC) concept and the dichotomous and simple N1-N2 paradigm. In this manuscript, we review the historic development of those concepts, critically evaluate these concepts against the background of our own work and provide suggestions for a refinement of current concepts in order to facilitate the transition of TAN research from experimental insight to clinical translation.

Intravenous thrombolysis for acute ischemic stroke associated with known left ventricular thrombus: safe or not?

Safety and efficacy data on intravenous thrombolysis (IVT) for acute ischemic stroke (AIS) in case of known left ventricular thrombus (LVT) are lacking. We present the case of a 35-year-old male with disabling AIS and known LVT that was treated successfully with intravenous alteplase. Apart from neurological improvement, post-procedural full thrombus lysis was also evident. Even though performing IVT in similar instances constitutes a difficult decision for physicians, it may be reasonable in the context of acute disabling stroke.

IL-6 Signaling Attenuates TNF-α Production by Plasmacytoid Dendritic Cells in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is characterized by autoimmune joint destruction with debilitating consequences. Despite treatment advancements with biologic therapies, a significant proportion of RA patients show an inadequate clinical response, and restoration of immune self-tolerance represents an unmet therapeutic need. We have previously described a tolerogenic phenotype of plasmacytoid dendritic cells (pDCs) in RA patients responding to anti-TNF-α agents. However, the molecular mechanisms involved in tolerogenic reprogramming of pDCs in RA remain elusive. In this study, guided by transcriptomic analysis of CD303+CD123+ pDCs from RA patients in remission, we revealed enhanced expression of IL-6R and its downstream signaling compared with healthy pDCs. Functional assessment demonstrated that IL-6R engagement resulted in marked reduction of TNF-α secretion by pDCs whereas intracellular TNF-α was significantly increased. Accordingly, pharmacologic inhibition of IL-6R signaling restored TNF-α secretion levels by pDCs. Mechanistic analysis demonstrated impaired activity and decreased lysosomal degradation of ADAM17 (a disintegrin and metalloproteinase 17) sheddase in pDCs, which is essential for TNF-α cleavage. Importantly, reduction of TNF-α secretion by IL-6-treated pDCs attenuated the inflammatory potential of RA patient-derived synovial fibroblasts. Collectively, these findings position pDCs as an important source of TNF-α in RA pathogenesis and unravel an anti-inflammatory mechanism of IL-6 by limiting the pDC-derived TNF-α secretion.

Distinct chromosomal "niches" in the genome of Saccharomyces cerevisiae provide the background for genomic innovation and shape the fate of gene duplicates.

Nearly one third of Saccharomyces cerevisiae protein coding sequences correspond to duplicate genes, equally split between small-scale duplicates (SSD) and whole-genome duplicates (WGD). While duplicate genes have distinct properties compared to singletons, to date, there has been no systematic analysis of their positional preferences. In this work, we show that SSD and WGD genes are organized in distinct gene clusters that occupy different genomic regions, with SSD being more peripheral and WGD more centrally positioned close to centromeric chromatin. Duplicate gene clusters differ from the rest of the genome in terms of gene size and spacing, gene expression variability and regulatory complexity, properties that are also shared by singleton genes residing within them. Singletons within duplicate gene clusters have longer promoters, more complex structure and a higher number of protein-protein interactions. Particular chromatin architectures appear to be important for gene evolution, as we find SSD gene-pair co-expression to be strongly associated with the similarity of nucleosome positioning patterns. We propose that specific regions of the yeast genome provide a favourable environment for the generation and maintenance of small-scale gene duplicates, segregating them from WGD-enriched genomic domains. Our findings provide a valuable framework linking genomic innovation with positional genomic preferences.

Acute-onset chronic inflammatory demyelinating polyneuropathy complicating SARS-CoV-2 infection and Ad26.COV2.S vaccination: report of two cases.

Background: The spectrum of reported neurological sequelae associated with SARS-CoV-2 is continuously expanding, immune mediated neuropathies like Guillain-Barre syndrome (GBS) and exacerbations of preexisting chronic inflammatory demyelinating polyneuropathy (CIDP) being among them. However, respective cases of acute onset CIDP (A-CIDP) are rare. Case presentation: We hereby report two cases of A-CIDP after COVID-19 infection and Ad26.COV2.S vaccination that presented with flaccid paraparesis and acroparesthesias (Case presentation 1; female, 52) and facial diplegia accompanied by acroparesthesias (Case presentation 2; male, 62), respectively. In both instances clinical, neurophysiological and CSF findings were indicative of acute inflammatory demyelinating polyneuropathy, thus both patients were initially treated with intravenous immunoglobulins resulting in clinical improvement. Nevertheless, the first patient relapsed 5 weeks after the initial episode, thus was diagnosed with GBS with treatment related fluctuations (GBS-TRF) and treated successfully with seven plasma exchange (PLEX) sessions. However, 11 weeks from symptom onset she relapsed again. Taking into account that the second relapse occurred more than 8 weeks after the first episode, the potential diagnosis of A-CIDP was reached and oral dexamethasone 40 mg/d for 4 consecutive days every 4 weeks was administered. With regards to the second patient, he relapsed > 8 weeks after the initial episode, thus was also diagnosed with A-CIDP and treated with 7 PLEX sessions followed by similar to the aforementioned corticosteroid therapy. On 2 month follow-up both patients exhibited remarkable clinical improvement. Conclusions: Close surveillance of patients presenting with immune neuropathies in the context of SARS-CoV-2 infection or immunization is crucial for timely implementation of appropriate treatment. Prompt A-CIDP distinction from GBS-TRF is of paramount importance as treatment approach and prognosis between these two entities differ. Supplementary Information: The online version contains supplementary material available at 10.1186/s41983-022-00515-4.

Prediction and Ranking of Biomarkers Using multiple UniReD.

Protein-protein interactions (PPIs) are of key importance for understanding how cells and organisms function. Thus, in recent decades, many approaches have been developed for the identification and discovery of such interactions. These approaches addressed the problem of PPI identification either by an experimental point of view or by a computational one. Here, we present an updated version of UniReD, a computational prediction tool which takes advantage of biomedical literature aiming to extract documented, already published protein associations and predict undocumented ones. The usefulness of this computational tool has been previously evaluated by experimentally validating predicted interactions and by benchmarking it against public databases of experimentally validated PPIs. In its updated form, UniReD allows the user to provide a list of proteins of known implication in, e.g., a particular disease, as well as another list of proteins that are potentially associated with the proteins of the first list. UniReD then automatically analyzes both lists and ranks the proteins of the second list by their association with the proteins of the first list, thus serving as a potential biomarker discovery/validation tool.