Properties of Stress Granule and P-Body Proteomes.

Stress granules and P-bodies are cytosolic biomolecular condensates that dynamically form by the phase separation of RNAs and proteins. They participate in translational control and buffer the proteome. Upon stress, global translation halts and mRNAs bound to the translational machinery and other proteins coalesce to form stress granules (SGs). Similarly, translationally stalled mRNAs devoid of translation initiation factors shuttle to P-bodies (PBs). Here, we review the cumulative progress made in defining the protein components that associate with mammalian SGs and PBs. We discuss the composition of SG and PB proteomes, supported by a new user-friendly database (http://rnagranuledb.lunenfeld.ca/) that curates current literature evidence for genes or proteins associated with SGs or PBs. As previously observed, the SG and PB proteomes are biased toward intrinsically disordered regions and have a high propensity to contain primary sequence features favoring phase separation. We also provide an outlook on how the various components of SGs and PBs may cooperate to organize and form membraneless organelles.

Literature-based predictions of Mendelian disease therapies.

In the effort to treat Mendelian disorders, correcting the underlying molecular imbalance may be more effective than symptomatic treatment. Identifying treatments that might accomplish this goal requires extensive and up-to-date knowledge of molecular pathways-including drug-gene and gene-gene relationships. To address this challenge, we present "parsing modifiers via article annotations" (PARMESAN), a computational tool that searches PubMed and PubMed Central for information to assemble these relationships into a central knowledge base. PARMESAN then predicts putatively novel drug-gene relationships, assigning an evidence-based score to each prediction. We compare PARMESAN's drug-gene predictions to all of the drug-gene relationships displayed by the Drug-Gene Interaction Database (DGIdb) and show that higher-scoring relationship predictions are more likely to match the directionality (up- versus down-regulation) indicated by this database. PARMESAN had more than 200,000 drug predictions scoring above 8 (as one example cutoff), for more than 3,700 genes. Among these predicted relationships, 210 were registered in DGIdb and 201 (96%) had matching directionality. This publicly available tool provides an automated way to prioritize drug screens to target the most-promising drugs to test, thereby saving time and resources in the development of therapeutics for genetic disorders.

OTTM: an automated classification tool for translational drug discovery from omics data.

Omics data from clinical samples are the predominant source of target discovery and drug development. Typically, hundreds or thousands of differentially expressed genes or proteins can be identified from omics data. This scale of possibilities is overwhelming for target discovery and validation using biochemical or cellular experiments. Most of these proteins and genes have no corresponding drugs or even active compounds. Moreover, a proportion of them may have been previously reported as being relevant to the disease of interest. To facilitate translational drug discovery from omics data, we have developed a new classification tool named Omics and Text driven Translational Medicine (OTTM). This tool can markedly narrow the range of proteins or genes that merit further validation via drug availability assessment and literature mining. For the 4489 candidate proteins identified in our previous proteomics study, OTTM recommended 40 FDA-approved or clinical trial drugs. Of these, 15 are available commercially and were tested on hepatocellular carcinoma Hep-G2 cells. Two drugs-tafenoquine succinate (an FDA-approved antimalarial drug targeting CYC1) and branaplam (a Phase 3 clinical drug targeting SMN1 for the treatment of spinal muscular atrophy)-showed potent inhibitory activity against Hep-G2 cell viability, suggesting that CYC1 and SMN1 may be potential therapeutic target proteins for hepatocellular carcinoma. In summary, OTTM is an efficient classification tool that can accelerate the discovery of effective drugs and targets using thousands of candidate proteins identified from omics data. The online and local versions of OTTM are available at http://otter-simm.com/ottm.html.

Incidence of RSV in Adults: A Comprehensive Review of Observational Studies and Critical Gaps in Information.

BACKGROUND: We conducted a global comprehensive literature review of observational studies reporting RSV incidence in adults and determined current evidence gaps. METHODS: PubMed and Embase were searched for English-language publications (2000-2022) and congress abstracts (2019-2021) reporting RSV incidence rates/cumulative incidence. Cross-sectional studies, case series, and other designs estimating only RSV frequency were excluded. The search included all geographic areas; data were extracted by age group and underlying condition where available. RESULTS: 528 potentially relevant records were identified, of which 37 primary studies were relevant to this review. Most evidence was from high-income regions. Approximately two-thirds of the studies reported RSV incidence in the hospital setting. Fifteen studies included or focused exclusively on RSV incidence in adult populations with underlying conditions. Studies varied in their measurement and presentation of incidence. RSV incidence estimates were highly variable within and between geographic regions. Overall, RSV incidence tended to increase with age and was highest in adults with underlying conditions. CONCLUSION: Estimates of RSV incidence are highly variable across populations and geographies. Further population-based studies with well-defined consistent case definitions and surveillance strategies are needed for accurate and comparable estimates of RSV incidence, particularly in the geographic regions identified by the gap analysis.

Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique.

BACKGROUND: Automated hypothesis generation (HG) focuses on uncovering hidden connections within the extensive information that is publicly available. This domain has become increasingly popular, thanks to modern machine learning algorithms. However, the automated evaluation of HG systems is still an open problem, especially on a larger scale. RESULTS: This paper presents a novel benchmarking framework Dyport for evaluating biomedical hypothesis generation systems. Utilizing curated datasets, our approach tests these systems under realistic conditions, enhancing the relevance of our evaluations. We integrate knowledge from the curated databases into a dynamic graph, accompanied by a method to quantify discovery importance. This not only assesses hypotheses accuracy but also their potential impact in biomedical research which significantly extends traditional link prediction benchmarks. Applicability of our benchmarking process is demonstrated on several link prediction systems applied on biomedical semantic knowledge graphs. Being flexible, our benchmarking system is designed for broad application in hypothesis generation quality verification, aiming to expand the scope of scientific discovery within the biomedical research community. CONCLUSIONS: Dyport is an open-source benchmarking framework designed for biomedical hypothesis generation systems evaluation, which takes into account knowledge dynamics, semantics and impact. All code and datasets are available at: https://github.com/IlyaTyagin/Dyport .

Robustness evaluations of pathway activity inference methods on gene expression data.

BACKGROUND: With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches. RESULTS: Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets. CONCLUSION: However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.

Parsing 20 Years of Public Data by AI Maps Trends in Proteomics and Forecasts Technology.

The trends of the last 20 years in biotechnology were revealed using artificial intelligence and natural language processing (NLP) of publicly available data. Implementing this "science-of-science" approach, we capture convergent trends in the field of proteomics in both technology development and application across the phylogenetic tree of life. With major gaps in our knowledge about protein composition, structure, and location over time, we report trends in persistent, popular approaches and emerging technologies across 94 ideas from a corpus of 29 journals in PubMed over two decades. New metrics for clusters of these ideas reveal the progression and popularity of emerging approaches like single-cell, spatial, compositional, and chemical proteomics designed to better capture protein-level chemistry and biology. This analysis of the proteomics literature with advanced analytic tools quantifies the Rate of Rise for a next generation of technologies to better define, quantify, and visualize the multiple dimensions of the proteome that will transform our ability to measure and understand proteins in the coming decade.

Hereditary spastic paraparesis type 46 (SPG46): new GBA2 variants in a large Italian case series and review of the literature.

Hereditary spastic paraparesis (HSP) is a group of central nervous system diseases primarily affecting the spinal upper motor neurons, with different inheritance patterns and phenotypes. SPG46 is a rare, early-onset and autosomal recessive HSP, linked to biallelic GBA2 mutations. About thirty families have been described worldwide, with different phenotypes like complicated HSP, recessive cerebellar ataxia or Marinesco-Sjögren Syndrome. Herein, we report five SPG46 patients harbouring five novel GBA2 mutations, the largest series described in Italy so far. Probands were enrolled in five different centres and underwent neurological examination, clinical cognitive assessment, column imaging for scoliosis assessment, ophthalmologic examination, brain imaging, GBA2 activity in peripheral blood cells and genetic testing. Their phenotype was consistent with HSP, with notable features like upper gaze palsy and movement disorders. We review demographic, genetic, biochemical and clinical information from all documented cases in the existing literature, focusing on the global distribution of cases, the features of the syndrome, its variable presentation, new potential identifying features and the significance of measuring GBA2 enzyme activity.

Analysis of three primary prostatic sarcoma cases and literature review.

OBJECTIVE: The objective of this study is to evaluate the clinical presentations, diagnostic approaches, and treatment modalities for primary prostate sarcoma postradical prostatectomy, aiming to enhance its diagnosis and management. METHODS: We retrospectively reviewed the clinical records of three male patients diagnosed with primary prostate sarcoma at Beijing Chaoyang Hospital, affiliated with Capital Medical University, from February 2014 to February 2024. All patients underwent transrectal prostate biopsies, which informed the decision to proceed with laparoscopic radical prostatectomies. After surgery, one patient received a combination of epirubicin and ifosfamide as immunotherapy, along with external beam radiotherapy. After comprehensive discussions regarding potential benefits and risks, the remaining two patients decided against undergoing radiotherapy and chemotherapy. RESULTS: Based on the pathological examination results, two patients were diagnosed with stromal sarcoma and one with spindle cell sarcoma, all classified as high-grade sarcomas. Immunohistochemical analysis showed that all three cases were positive for VIMENTIN, but other results did not show significant specificity. During the follow-up period, one patient died within 12 months, and two patients were lost to follow-up after 6 months. However, there were no evident signs of recurrence observed during the follow-up period. CONCLUSIONS: Primary prostate sarcoma is extremely rare and typically has a poor prognosis once diagnosed. Early diagnosis should be based on pathological and immunohistochemical testing results, followed by prompt surgical treatment and adjuvant radiotherapy and chemotherapy. Despite these measures, recurrence is common, underscoring the need for a detailed and appropriate treatment plan and systematic therapy for affected patients.

Exploring undiscovered public knowledge in neuroscience.

In this essay, I argue that the combination of research synthesis and philosophical methods can fill an important methodological gap in neuroscience. While experimental research and formal modelling have seen their methods progressively increase in rigour and sophistication over the years, the task of analysing and synthesizing the vast literature reporting new results and models has lagged behind. The problem is aggravated because neuroscience has grown and expanded into a vast mosaic of related but partially independent subfields, each with their own literatures. This fragmentation not only makes it difficult to see the full picture emerging from neuroscience research but also limits progress in individual subfields. The current neuroscience literature has the perfect conditions to create what the information scientist Don Swanson called "undiscovered public knowledge"-knowledge that exists in the mutual implications of different published pieces of information but that is nonetheless undiscovered because those pieces have not been explicitly connected. Current methods for rigorous research synthesis, such as systematic reviews and meta-analyses, mostly focus on combining similar studies and are not suited for exploring undiscovered public knowledge. To that aim, they need to be adapted and supplemented. I argue that successful exploration of the hidden implications in the neuroscience literature will require the combination of these adapted research synthesis methods with philosophical methods for rigorous (and creative) analysis and synthesis.

Against "silent" retractions in neuroscience.

When an academic paper is published in a journal that assigns a digital object identifier (DOI) to papers, this is a de facto fait accompli. Corrections or retractions are supposed to follow a specific protocol, especially in journals that claim to follow the Committee on Publication Ethics (COPE) guidelines. In this paper, we highlight a case of a new, fully open access neuroscience journal that claims to be COPE-compliant, yet has silently retracted two papers since all records, bibliometrics, and PDF files related to their existence have been deleted from the journal's website. Although this phenomenon does not seem to be common in the neurosciences, we consider that any opaque corrective measures in journals whose papers could be cited may negatively impact the wider neuroscience literature and community. Instead, we encourage transparency in retraction to promote truthfulness and trustworthiness.

Efficacy and Safety of Trifluridine/Tipiracil-Containing Combinations in Colorectal Cancer and Other Advanced Solid Tumors: A Systematic Review.

We performed a systematic literature review to identify and summarize data from studies reporting clinical efficacy and safety outcomes for trifluridine/tipiracil (FTD/TPI) combined with other antineoplastic agents in advanced cancers, including metastatic colorectal cancer (mCRC). We conducted a systematic search on May 29, 2021, for studies reporting one or more efficacy or safety outcome with FTD/TPI-containing combinations. Our search yielded 1378 publications, with 38 records meeting selection criteria: 35 studies of FTD/TPI-containing combinations in mCRC (31 studies second line or later) and 3 studies in other tumor types. FTD/TPI plus bevacizumab was extensively studied, including 19 studies in chemorefractory mCRC. Median overall survival ranged 8.6-14.4 months and median progression-free survival 3.7-6.8 months with FTD/TPI plus bevacizumab in refractory mCRC. Based on one randomized and several retrospective studies, FTD/TPI plus bevacizumab was associated with improved outcomes compared with FTD/TPI monotherapy. FTD/TPI combinations with chemotherapy or other targeted agents were reported in small early-phase studies; preliminary data indicated higher antitumor activity for certain combinations. Overall, no safety concerns existed with FTD/TPI combinations; most common grade ≥ 3 adverse event was neutropenia, ranging 5%-100% across all studies. In studies comparing FTD/TPI combinations with monotherapy, grade ≥ 3 neutropenia appeared more frequently with combinations (29%-67%) vs. monotherapy (5%-41%). Discontinuation rates due to adverse events ranged 0%-11% for FTD/TPI plus bevacizumab and 0%-17% with other combinations. This systematic review supports feasibility and safety of FTD/TPI plus bevacizumab in refractory mCRC. Data on non-bevacizumab FTD/TPI combinations remain preliminary and need further validation.

Exploring and Analyzing the Systemic Delivery Barriers for Nanoparticles.

Most nanomedicines require efficient in vivo delivery to elicit diagnostic and therapeutic effects. However, en route to their intended tissues, systemically administered nanoparticles often encounter delivery barriers. To describe these barriers, we propose the term "nanoparticle blood removal pathways" (NBRP), which summarizes the interactions between nanoparticles and the body's various cell-dependent and cell-independent blood clearance mechanisms. We reviewed nanoparticle design and biological modulation strategies to mitigate nanoparticle-NBRP interactions. As these interactions affect nanoparticle delivery, we studied the preclinical literature from 2011-2021 and analyzed nanoparticle blood circulation and organ biodistribution data. Our findings revealed that nanoparticle surface chemistry affected the in vivo behavior more than other nanoparticle design parameters. Combinatory biological-PEG surface modification improved the blood area under the curve by ~418%, with a decrease in liver accumulation of up to 47%. A greater understanding of nanoparticle-NBRP interactions and associated delivery trends will provide new nanoparticle design and biological modulation strategies for safer, more effective, and more efficient nanomedicines.

Title, abstract and keywords: a practical guide to maximize the visibility and impact of academic papers.

In a growing digital landscape, enhancing the discoverability and resonance of scientific articles is essential. Here, we offer 10 recommendations to amplify the discoverability of studies in search engines and databases. Particularly, we argue that the strategic use and placement of key terms in the title, abstract and keyword sections can boost indexing and appeal. By surveying 230 journals in ecology and evolutionary biology, we found that current author guidelines may unintentionally limit article findability. Our survey of 5323 studies revealed that authors frequently exhaust abstract word limits-particularly those capped under 250 words. This suggests that current guidelines may be overly restrictive and not optimized to increase the dissemination and discoverability of digital publications. Additionally, 92% of studies used redundant keywords in the title or abstract, undermining optimal indexing in databases. We encourage adopting structured abstracts to maximize the incorporation of key terms in titles, abstracts and keywords. In addition, we encourage the relaxation of abstract and keyword limitations in journals with strict guidelines, and the inclusion of multilingual abstracts to broaden global accessibility. These recommendations to editors are designed to improve article engagement and facilitate evidence synthesis, thereby aligning scientific publishing with the modern needs of academic research.

The changing landscape of text mining: a review of approaches for ecology and evolution.

In ecology and evolutionary biology, the synthesis and modelling of data from published literature are commonly used to generate insights and test theories across systems. However, the tasks of searching, screening, and extracting data from literature are often arduous. Researchers may manually process hundreds to thousands of articles for systematic reviews, meta-analyses, and compiling synthetic datasets. As relevant articles expand to tens or hundreds of thousands, computer-based approaches can increase the efficiency, transparency and reproducibility of literature-based research. Methods available for text mining are rapidly changing owing to developments in machine learning-based language models. We review the growing landscape of approaches, mapping them onto three broad paradigms (frequency-based approaches, traditional Natural Language Processing and deep learning-based language models). This serves as an entry point to learn foundational and cutting-edge concepts, vocabularies, and methods to foster integration of these tools into ecological and evolutionary research. We cover approaches for modelling ecological texts, generating training data, developing custom models and interacting with large language models and discuss challenges and possible solutions to implementing these methods in ecology and evolution.

BioGPT: generative pre-trained transformer for biomedical text generation and mining.

Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.

Quality of life of family carers of people living with dementia: review of systematic reviews of observational and intervention studies.

INTRODUCTION: Family members are the primary source of support for the growing number of people living with dementia (PLWD) worldwide. However, caring for a person living with dementia can have detrimental impacts on the carer quality of life (QoL). This review of systematic reviews explored the factors associated with the QoL of family carers of PLWD and interventions aimed at improving their QoL. SOURCES OF DATA: Several health-related databases (PUBMED, Psychinfo, Google Scholar and COCHRANE) were consulted in November 2022. Nineteen systematic reviews were included, and their methodological quality was assessed via AMSTAR-2. AREAS OF AGREEMENT: Better carer physical and mental health, provision of formal support, relationship quality between carers and PLWD, as well as positive psychological traits were associated with better carer QoL. There is no one-size-fits-all intervention that can improve the QoL of all carers, but promising results were found in most of the interventions. AREAS OF CONTROVERSY: There is inconsistency in evidence on the association between the carer age and QoL. The use of a wide range of QoL measures, particularly generic QoL scales, has contributed to inconsistencies when comparing the efficacy of interventions. GROWING POINTS: Evidence suggests the need for a person-centred approach to improving carer QoL, considering individual and contextual needs as well as the continuum and progressive nature of dementia care. TIMELY AREAS FOR DEVELOPING RESEARCH: Future research should be focused on understanding how to best implement and measure person-centred care approaches to carer QoL, including cost-effectiveness. More qualitative studies are necessary to explore carer negative and positive experiences of QoL.

Comparison of literature mining tools for variant classification: Through the lens of 50 RYR1 variants.

PURPOSE: The American College of Medical Genetics and Genomics and the Association for Molecular Pathology have outlined a schema that allows for systematic classification of variant pathogenicity. Although gnomAD is generally accepted as a reliable source of population frequency data and ClinGen has provided guidance on the utility of specific bioinformatic predictors, there is no consensus source for identifying publications relevant to a variant. Multiple tools are available to aid in the identification of relevant variant literature, including manually curated databases and literature search engines. We set out to determine the utility of 4 literature mining tools used for ascertainment to inform the discussion of the use of these tools. METHODS: Four literature mining tools including the Human Gene Mutation Database, Mastermind, ClinVar, and LitVar 2.0 were used to identify relevant variant literature for 50 RYR1 variants. Sensitivity and precision were determined for each tool. RESULTS: Sensitivity among the 4 tools ranged from 0.332 to 0.687. Precision ranged from 0.389 to 0.906. No single tool retrieved all relevant publications. CONCLUSION: At the current time, the use of multiple tools is necessary to completely identify the literature relevant to curate a variant.

Instantaneous Q10 of night-time leaf respiratory CO2 efflux - measurement and analytical protocol considerations.

The temperature sensitivity (e.g. Q10) of night-time leaf respiratory CO2 efflux (RCO2) is a fundamental aspect of leaf physiology. The Q10 typically exhibits a dependence on measurement temperature, and it is speculated that this is due to temperature-dependent shifts in the relative control of leaf RCO2. Two decades ago, a review hypothesized that this mechanistically caused change in values of Q10 is predictable across plant taxa and biomes. Here, we discuss the most appropriate measuring protocol among existing data and for future data collection, to form the foundation of a future mechanistic understanding of Q10 of leaf RCO2 at different temperature ranges. We do this primarily via a review of existing literature on Q10 of night-time RCO2 and only supplement this to a lesser degree with our own original data. Based on mechanistic considerations, we encourage that instantaneous Q10 of leaf RCO2 to represent night-time should be measured: only at night-time; only in response to short-term narrow temperature variation (e.g. max. 10°C) to represent a given midpoint temperature at a time; in response to as many temperatures as possible within the chosen temperature range; and on still attached leaves.

Performance of clinical, laboratory and imaging features for diagnosing spondyloarthritis-a systematic literature review and meta-analysis.

OBJECTIVE: The Berlin algorithm was developed to help diagnosing axial spondyloarthritis (axSpA), but new studies suggest some features typical of SpA are less specific than previously assumed. Furthermore, evidence is lacking for other SpA subtypes (e.g. peripheral SpA). We aimed to review the evidence on the performance of SpA features for diagnosing each SpA subtype. METHODS: Systematic literature review of studies reporting the diagnostic performance of ≥ 1 SpA feature in patients with suspected SpA. The external reference was the rheumatologist's diagnosis of SpA. Meta-analysis was performed, separately for each SpA subtype, to estimate pooled sensitivity, specificity, positive (LR+) and negative (LR-) likelihood ratios. Meta-regression assessed the effect of covariates (e.g. feature's prevalence) on each feature's performance. RESULTS: Of 13 844 articles screened, 46 were included. Sacroiliitis on magnetic resonance imaging, damage on pelvic radiographs and elevated C-reactive protein (CRP) had the best balance between LR+ and LR- (LR + 3.9-17.0, LR- 0.5-0.7) for diagnosing axSpA. HLA-B27 had an LR+ lower than anticipated (LR + =3.1). Inflammatory back pain (IBP) had low LR + (LR+∼1), but substantially decreased the likelihood of axSpA when absent (LR-=0.3). Conversely, peripheral features and extra-musculoskeletal manifestations showed high LR + (LR+ 1.6-5.0), but were as common in axSpA as no-axSpA (LR-∼1). The specificity of most features was reduced in settings when these were highly prevalent. Limited data precluded a detailed analysis on diagnosing other SpA subtypes. CONCLUSION: Imaging features and CRP have good diagnostic value for axSpA. However, the specificity of other features, especially HLA-B27 and IBP, is lower than previously known.