Database Access Research Articles

Challenges in assessing the effects of environmental governance systems on conservation outcomes.

Effective governance is crucial for the success of conservation projects aimed at protecting wildlife populations and supporting human well-being. However, few large-scale, comprehensive syntheses have been conducted on the effects of different environmental governance types on conservation outcomes (i.e., biological and ecological effectiveness or effects of conservation on human well-being), and clarity on the quantity and quality of evidence remains dispersed and ambiguous. We attempted a systematic map of the evidence on the effectiveness of different governance types to meet desired conservation outcomes in Africa, Asia, and Latin America. However, early in this effort, we observed a general lack of empirical research on the links between governance and conservation outcomes. To fill observed data gaps in the evidence base, we tried triangulating governance data from alternative sources (Protected Planet database) and pooling evidence from research conducted within the same conservation areas. Limited data were contained in the Protected Planet database, and governance types in conservation areas and landscapes were complex, making it difficult to use these approaches to assign governance types to conservation areas. To illustrate our observations from the failed systematic map attempt, we prepared a rapid evidence map that outlines a subset of the evidence base of articles linking governance types and governance principles with conservation outcomes. Only 3.2% (34 of 1067) of the articles we screened directly related conservation outcomes to governance type, and even fewer related governance principles to conservation outcomes. Based on our findings, we recommend improving the evidence base by supporting empirical research and increasing the availability and quality of governance data in freely accessible databases. These recommendations are critical for enhancing understanding of the role of governance in conservation projects and improving conservation outcomes.

Rainfall Erosivity over Brazil: A Large National Database

Rainfall erosivity (RE) represents the potential of rainfall to cause soil erosion, and understanding its impact is essential for the adoption of soil and water conservation practices. Although several studies have estimated RE for Brazil, currently, no single reliable and easily accessible database exists for the country. To fill this gap, this work aimed to review the research and generate a rainfall erosivity database for Brazil. Data were gathered from studies that determined rainfall erosivity from observed rainfall records and synthetic rainfall series. Monthly and annual rainfall erosivity values were organized on a spreadsheet and in the shapefile format. In total, 54 studies from 1990 to 2023 were analyzed, resulting in the compilation of 5516 erosivity values for Brazil, of which 6.3% were pluviographic, and 93.7% were synthetic. The regions with the highest availability of information were the Northeast (35.6%), Southeast (30.1%), South (19.9%), Central-West (7.7%), and North (6.7%). The database, which can be accessed on the Mendeley Data platform, can aid professionals and researchers in adopting public policies and carrying out studies aimed at environmental conservation and management basin development.

Who owns reefer vessels? Uncovering the ecosystem of transshipment in fisheries.

A central barrier to effective governance and accountability in fisheries is the limited transparency of corporate ownership. Transshipment-the transfer of catches, fuel, parts, or crew between fishing and cargo vessels known as reefers-is often criticized for its opacity and poor governance. Better insight into the beneficial ownership of vessels involved in transshipment and their operational patterns could lead to more effective management. Our study presents a publicly accessible database of reefers' owners, operators, and flags. We identified 569 individual reefers and found that Russian and Chinese owners control 26 and 20% of the global reefer fleet, respectively. Results also show that 65% of all reefer vessels fly the flags of Russia, Panama, or China. This high level of consolidation suggests considerable leverage for enhancing transparency and governance. Our findings highlight the potential for reforming existing transshipment practices through collaboration among owners, flag states, fishery regulators, and scientists.

Everything, everywhere, and all at once: A blueprint for supra-organization of core facilities.

Core facilities are crucial for cutting-edge scientific research in academic institutions, yet they place a significant financial burden on budgets. The viability of these facilities can be improved through cross-institutional collaborations, although initiating and sustaining such partnerships poses challenges. Insights from Israel's recent nationwide organization of core facilities could offer valuable lessons for fostering similar cooperation elsewhere. Despite the chronic shortfall in public research funding, Israeli research institutions were slow to fully embrace infrastructure sharing. This gap led to the creation of the Israel Research Core Facilities (IRCF) in 2022, which linked core facilities across the country through a bottom-up approach. IRCF facilitated the formation of numerous specialized nation-wide networks for intellectual exchange, and supported training workshops and meetings aimed at core technology providers. These initiatives serve dual purposes: they ensure the ongoing advancement of technological capabilities across facilities, regardless of their size or location, and they strengthen the commitment to the IRCF mission by motivating the maintenance of the IRCF database. As a result, a model of "capacity sharing" emerged, connecting all of Israel's core facility centers. This model enhances infrastructure use, supports strategic planning, and fosters growth. With over 450 core experts offering over 1100 scientific services consolidated into a publicly accessible database, IRCF supports research in universities, hospitals, government, and industry. This strategy could act as a model for creating regional core facility organizations to elevate research quality and ensure efficient infrastructure development.

Establishing a tailings storage facility database in Gauteng

As an initial step of zero harm, an understanding of the scale and magnitude of Tailings Storage Facilities (TSFs) is necessary for risk assessments. There is no publicly available TSF database in each South African province with details on TSF distribution of risks and characteristics. TSF-related cases such as the Jagersfontein Tailings Dam failure and the crime challenges by illegal miners at the North Sands Dump are significant motivations to establish a publicly accessible TSF database, especially in Gauteng. The objectives of the study were to establish a TSF database comprising the spatial distribution of TSFs and to classify each TSF by its operational activity status. The Department of Mineral Resources and Energy publishes data on TSFs, and it was used to create the database. Due to coordinate data limitations, only 94 TSFs could be presented on ArcGIS Pro. The study finds that there is almost an equal split of active and inactive TSFs in Gauteng. Most active TSFs are situated in the East Rand of Gauteng. Furthermore, companies that own some of the TSFs in Gauteng include Durban Roodepoort Deep Gold, which owns 70 % of TSFs, followed by AfriSam (6 %) and Blyvoor Gold (4 %). Companies need to disclose all their TSF data to strengthen the database.

Molecular mechanisms underlying the effects of hypo- and hyper-prolactinemia on spermatogenesis and fertility in male rats.

Hypo- and hyper-prolactinemia have deleterious effects on male reproduction, yet there is a dearth of information regarding the underlying mechanisms. The aim of this study was to delineate the molecular mechanisms by which hypo- and hyper-prolactinemia affects spermatogenesis and fertility in male rats. In vivo male rat models for hypo- and hyper-prolactinemia were established using dopamine receptor agonist, Bromocriptine (Brm), and antagonist, Fluphenazine (Flu), respectively. Effects on fertility and spermatogenesis were assessed by studying pre- and post-implantation loss, litter size, sperm parameters, hormonal profile, testicular histology, testicular cell population, and testicular transcriptome in rats. Treatment with Brm and Flu for 60 days led to subfertility, which was indicated by an increase in pre- and post-implantation loss and decrease in litter size, when mated with control female rats. Decreased sperm count was observed after both treatments, whereas reduced sperm motility was noted in Flu group. Serum FSH was unaffected, and LH was decreased by Flu treatment. Testosterone was decreased in both the groups, whereas estradiol was decreased in the Flu group. An arrest in spermatogenic cycle beyond round spermatids was observed in the Flu group. Additionally, testicular apoptosis in germ cells, mostly spermatocytes of Stage IX-XIV was noted in both the groups. Further, testicular RNA-Seq analysis revealed a total of 1539 and 824 differentially expressed genes/DEGs in Brm and Flu, respectively (Sequence Read Archive/SRA Database accession number: PRJNA1150513). Gene ontology and pathway analysis of DEGs highlighted enrichment of steroid metabolic pathway and ribosomal biogenesis pathway. Hub genes identified from the DEGs were validated by qPCR and the results showed that Uba52, Rps27a, Rpl23, Rps5, Rps16 were significantly down-regulated by Brm, whereas Rps27a, Rps29, Rps15, Rps27, Faul1 were significantly down-regulated by Flu. Hypo- and hyper-prolactinemia leads to subfertility and decreased sperm parameters possibly through an effect on steroid metabolism and ribosomal biogenesis pathway. Therefore, maintaining prolactin levels in physiological range is crucial.

Development and validation of a sepsis risk index supporting early identification of ICU-acquired sepsis: an observational study

BackgroundSepsis is a threat to global health, and domestically is the major cause of in-hospital mortality. Due to increases in inpatient morbidity and mortality resulting from sepsis, healthcare providers (HCPs) would accrue significant benefits from identifying the syndrome early and treating it promptly and effectively. Prompt and effective detection, diagnosis, and treatment of sepsis requires frequent monitoring and assessment of patient vital signs and other relevant data present in the electronic health record. MethodsThis study explored the development of machine learning-based models to generate a novel sepsis risk index (SRI) which is an intuitive 0–100 marker that reflects the risk of a patient acquiring sepsis or septic shock and assists in timely diagnosis. Machine learning models were developed and validated using openly accessible critical care databases. The model was developed using a single database (from one institution) and validated on a separate database consisting of patient data collected across multiple ICUs. ResultsThe developed model achieved an area under the receiver operating characteristic curve of 0.82 and 0.84 for the diagnosis of sepsis and septic shock, respectively, with a sensitivity and specificity of 79.1% [75.1, 82.7] and 73.3% [72.8, 73.8] for a sepsis diagnosis and 83.8% [80.8, 86.5] and 73.3% [72.8, 73.8] for a septic shock diagnosis. ConclusionThe SRI provides critical care HCPs with an intuitive quantitative measure related to the risk of a patient having or acquiring a life-threatening infection. Evaluation of the SRI over time may provide HCPs the ability to initiate protective interventions (e.g., targeted antibiotic therapy).

Copy Number Variants in 30 Saudi Pediatric Patients with Neurodevelopmental Disorders: From Unknown Significance to Diagnosis.

Structural variants (SVs), such as copy number variants (CNVs), insertions, deletions, inversions, and translocations, contribute significantly to genetic diversity and disease etiology. CNVs, which involve the duplication or deletion of DNA segments, are particularly impactful on genes crucial for biological functions and disease processes. To reassess unclassified SVs that may be underlying unresolved neurodevelopmental disorders among Saudi patients. In this retrospective study conducted at King Saud Medical City, Riyadh, Saudi Arabia, 30 probands with neurodevelopmental disorders and congenital malformations were examined using next-generation sequencing methods-exome sequencing, gene panels, or SNP arrays (the Illumina platform). Reclassification was aided by online tools such as VarSome and ClinVar, with pathogenicity assessments using the ClinGen CNV Pathogenicity Calculator based on American College of Medical Genetics and Genomics criteria for CNV loss and gain, and dosage sensitivity. A total of 31 CNVs were analyzed, of which 2 were reclassified: one as benign and the other as pathogenic. The pathogenic CNV, [3p13p12.3 (70411134_75249376) x1], included a deletion of the FOXP1 gene and was associated with an intellectual developmental disorder, language impairment, possible autistic features, psychomotor impairment, developmental regression, and epilepsy. This study underscores the importance of continuously documenting and revisiting unclassified CNVs in accessible databases to enhance the diagnosis and understanding of complex genotype-phenotype relationships. Reclassifying these CNVs not only accelerates diagnostic processes but also enriches our insight into their significant roles in health and disease.

Comprehensive Herpesviruses Antiviral drug Resistance Mutation Database (CHARMD)

A comprehensive and accessible Herpesvirus drug resistance database was designed to serve as an international reference for diagnosis and clinical studies. This database available at https://www.unilim.fr/cnr-herpesvirus/outils/codexmv/includes both resistance-related mutations and natural polymorphisms. Initially designed for human cytomegalovirus, it will be expanded to include herpes simplex and varicella-zoster viruses. Newly published mutations and new mutations reported by users or collaborating expert laboratories will be reviewed by an international committee of reference laboratories before inclusion in the database. Coupled with the Herpesvirus Sequence Analysis tool (HSA) mutation reports from NGS or Sanger sequences, it will be an open source for researchers in the field of Herpesviruses. We hope to fill this unmet need for the development and standardization of resistance genotyping.

Bulk and selective autophagy cooperate to remodel a fungal proteome in response to changing nutrient availability.

Cells remodel their proteomes in response to changing environments by coordinating changes in protein synthesis and degradation. In yeast, such degradation involves both proteasomal and vacuolar activity, with a mixture of bulk and selective autophagy delivering many of the vacuolar substrates. Although these pathways are known to be generally important for such remodeling, their relative contributions have not been reported on a proteome-wide basis. To assess this, we developed a method to pulse-label the methylotrophic yeast Komagataella phaffii (i.e. Pichia pastoris) with isotopically labeled nutrients, which, when coupled to quantitative proteomics, allowed us to globally monitor protein degradation on a protein-by-protein basis following an environmental perturbation. Using genetic ablations, we found that a targeted combination of bulk and selective autophagy drove the vast majority of the observed proteome remodeling activity, with minimal non-autophagic contributions. Cytosolic proteins and protein complexes, including ribosomes, were degraded via Atg11-independent bulk autophagy, whereas proteins targeted to the peroxisome and mitochondria were primarily degraded in an Atg11-dependent manner. Notably, these degradative pathways were independently regulated by environmental cues. Taken together, our new approach greatly increases the range of known autophagic substrates and highlights the outsized impact of autophagy on proteome remodeling. Moreover, the resulting datasets, which we have packaged in an accessible online database, constitute a rich resource for identifying proteins and pathways involved in fungal proteome remodeling.

Mechanistic insights into Suanzaoren Decoction's improvement of cardiac contractile function in anxiety-induced cardiac insufficiency

Ethnopharmacological relevanceAccording to traditional Chinese medicine, Anxiety-induced cardiac blood insufficiency leads to palpitations and restlessness. Suanzaoren Decoction (SD) is effective in replenishing blood and promoting blood circulation. Clinical practice has shown that it has a better therapeutic effect on cardiac insufficiency. However, its mechanism of action is still unclear. Aim of the studyThe study aims to determine the mechanism by which SD treats chronic restraint stress (CRS)-induced anxiety-induced cardiac insufficiency (ACI). Materials and methodsSD was orally administered to mice with CRS-induced ACI. Firstly, we constructed an anxiety model in mice by CRS. Subsequently, SD was investigated to assess cardiac function and pathological changes through echocardiography, H&E staining, and Masson staining. Thirdly, the function of sympathetic and parasympathetic nerves was evaluated using enzyme-linked immunosorbent assay (ELISA) and enzyme activity assays. Network pharmacology and molecular docking were employed to predict potential targets for SD treatment of cardiac insufficiency. CaMKII expression was scrutinized utilizing publicly accessible databases. CaMKII was identified as a target through immunohistochemistry and Western Blot analysis in mouse hearts. Finally, the therapeutic mechanism of SD was confirmed in injured cardiomyocytes via Western Blot and quantitative PCR. ResultsSD exerted anxiolytic effects by increasing the frequency of entries into and the duration spent in open arms while reducing the time spent in the light chamber and increasing the number of transitions between light and dark chambers. Additionally, it mitigated cardiac insufficiency, as evidenced by the enhancement of left ventricular ejection fraction (LVEF) and attenuation of cardiomyocyte damage and inflammatory infiltration.However, SD did not alleviate the elevated norepinephrine (NE) and decreased Acetylcholine (Ach) in anxiety states. To investigate the mechanism of action of SD, we constructed a Drug-Component-Target-Disease network, identifying 13 potential active compounds. Additionally, leveraging bioinformatics analysis and molecular docking targeting heart diseases characterized by clinical left ventricular ejection fraction (LVEF), we focused on the CaMKII target. The ability of SD to modulate CaMKII expression and phosphorylation in the mouse heart was investigated using immunohistochemistry and Western blotting. SD was found to alleviate NE-injured cardiomyocytes by modulating the Ca2+/CaMKII/MEF2 and GATA4 pathways. ConclusionSD is a potential formula for the treatment of chronic restraint stress (CRS)-induced ACI that ameliorates cardiomyocyte injury and improves cardiac function. Its efficacy is associated with the inhibition of the Ca2+/CaMKII/MEF2 and GATA4 signaling pathways.

Economic evaluation of NALIRIFOX vs. nab-paclitaxel and gemcitabine regimens for first-line treatment of metastatic pancreatic ductal adenocarcinoma from U.S. perspective

BackgroundThe cost-effectiveness of NALIRIFOX as a potential new standard of care for patients with metastatic pancreatic ductal adenocarcinoma (mPDAC) has yet to be established. Our objective was to evaluate the cost-effectiveness of NALIRIFOX vs. nab-paclitaxel and gemcitabine in this indication from the perspective of U.S. public payers.MethodsA partitioned survival model was constructed from the perspective of U.S. public payers, drawing on baseline patient characteristics and vital clinical data from the NAPOLI-3 trial. Costs and utilities were sourced from publicly accessible databases and literature. A lifetime horizon was applied, with an annual discount rate of 3%. We calculated and compared cumulative costs, life years, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios (ICER). To evaluate the model’s robustness, sensitivity analyses, scenario analyses, and subgroup analyses were carried out. Additionally, a price simulation for the costly liposomal irinotecan was conducted to inform the pricing strategy at the given willingness to pay (WTP) threshold.ResultsIn the base-case analysis, NALIRIFOX provided an additional 0.29 QALYs with an ICER of $206,340.69 /QALY compared to nab-paclitaxel and gemcitabine, indicating it is not cost-effective at a $150,000/QALY threshold. Sensitivity analysis showed the model was most sensitive to the costs of liposomal irinotecan, capecitabine, and post-progression care. Probabilistic sensitivity analysis indicated a 17.66% probability of NALIRIFOX being cost-effective at $150,000/QALY, rising to 47.48% at $200,000/QALY. Pricing simulations suggested NALIRIFOX could become cost-effective at $150,000/QALY if the price of irinotecan liposome drops to $53.24/mg (a 14.8% reduction).ConclusionsNALIRIFOX may not be cost-effective at its current price as a first-line treatment for patients with mPDAC in the long term. The cost of liposomal irinotecan has the greatest impact. It may become cost-effective only if its cost is reduced by 14.8%, with a WTP threshold of $150,000 /QALY.

Examining the reciprocal associations between symptoms of depression and anxiety and contact with the criminal justice system.

Taken together, prior publications on the association between symptoms of depression and anxiety and contact with the criminal justice system (CJS) suggest a bi-directional relationship, but all the studies only focus on one direction in this relationship. To examine, in longitudinally collected data, period-specific within-individual change in anxiety and depression measures preceding arrest measurement and, separately, following arrest measurement. Data were obtained from the National Longitudinal Study of Youth 1997, a nationally representative sample of people born between 1980 and 1984 and first interviewed between ages 12-17 and a publicly accessible database. Our focus was on data for the 11years 2000-2010. Using whole sample data, we tested for a reciprocal association between depression and anxiety during each 2-year period and arrests during the following year, and vice versa, allowing for relatively fixed characteristics such as sex, age and socio-economic indicators. We used period-specific change modelling to test relationships. We found that within-individual increases in depression and anxiety scores over short periods (2-year periods) of time was associated with an increase in the number of arrests subsequent over the following year, consistently throughout the whole of the 10years studies. The reciprocal association was also observed, albeit the magnitude of the effects was much smaller. This study adds to the literature on the association between mental health and CJS contact by showing that they may be reciprocally associated. This suggests that facilitating co-working or even formal partnerships between community mental health services and justice-related services could be beneficial.

TPLAD: Template-Parsed Log Anomaly Detection for Electrical Database Systems

In recent years, with the increasing complexity of software systems, logs have become crucial for system maintenance. Log-based anomaly detection plays a vital role in automatically detecting system anomalies through log analysis. However, current log-based anomaly detection approaches encounter significant practical challenges. Supervised methods often require a large amount of manually labeled training data, which can be time-consuming and costly to obtain. On the other hand, unsupervised and semi-supervised approaches may suffer from subpar performance, as they do not leverage historical anomalies to improve their detection capabilities. These challenges underscore the necessity for the development of more efficient and effective log-based anomaly detection methods. Database anomaly access detection is critical for ensuring the stability and security of database systems. We present a survey of existing log anomaly detection models and propose a novel approach, Template-Parsed Log Anomaly Detection (TPLAD) model, for automated anomaly detection in massive database log files. The proposed model combines the original log template with template parsing using code and text semantic representation. Experimental results demonstrate the effectiveness of the proposed approach in detecting abnormal database access patterns, including runtime errors, unauthorized access, and data leaks. The findings indicate that TPLAD model shows promise in enhancing database security and stability in business systems.

Chemoinformatic Characterization of NAPROC-13: A Database for Natural Product 13C NMR Dereplication.

Natural products (NPs) are secondary metabolites of natural origin with broad applications across various human activities, particularly the discovery of bioactive compounds. Structural elucidation of new NPs entails significant cost and effort. On the other hand, the dereplication of known compounds is crucial for the early exclusion of irrelevant compounds in contemporary pharmaceutical research. NAPROC-13 stands out as a publicly accessible database, providing structural and 13C NMR spectroscopic information for over 25 000 compounds, rendering it a pivotal resource in natural product (NP) research, favoring open science. This study seeks to quantitatively analyze the chemical content, structural diversity, and chemical space coverage of NPs within NAPROC-13, compared to FDA-approved drugs and a very diverse subset of NPs, UNPD-A. Findings indicated that NPs in NAPROC-13 exhibit properties comparable to those in UNPD-A, albeit showcasing a notably diverse array of structural content, scaffolds, ring systems of pharmaceutical interest, and molecular fragments. NAPROC-13 covers a specific region of the chemical multiverse (a generalization of the chemical space from different chemical representations) regarding physicochemical properties and a region as broad as UNPD-A in terms of the structural features represented by fingerprints.

Construction of a molecular diagnostic system for neurogenic rosacea by combining transcriptome sequencing and machine learning

Patients with neurogenic rosacea (NR) frequently demonstrate pronounced neurological manifestations, often unresponsive to conventional therapeutic approaches. A molecular-level understanding and diagnosis of this patient cohort could significantly guide clinical interventions. In this study, we amalgamated our sequencing data (n = 46) with a publicly accessible database (n = 38) to perform an unsupervised cluster analysis of the integrated dataset. The eighty-four rosacea patients were partitioned into two distinct clusters. Neurovascular biomarkers were found to be elevated in cluster 1 compared to cluster 2. Pathways in cluster 1 were predominantly involved in neurotransmitter synthesis, transmission, and functionality, whereas cluster 2 pathways were centered on inflammation-related processes. Differential gene expression analysis and WGCNA were employed to delineate the characteristic gene sets of the two clusters. Subsequently, a diagnostic model was constructed from the identified gene sets using linear regression methodologies. The model's C index, comprising genes PNPLA3, CUX2, PLIN2, and HMGCR, achieved a remarkable value of 0.9683, with an area under the curve (AUC) for the training cohort's nomogram of 0.9376. Clinical characteristics from our dataset (n = 46) were assessed by three seasoned dermatologists, forming the NR validation cohort (NR, n = 18; non-neurogenic rosacea, n = 28). Upon application of our model to NR diagnosis, the model's AUC value reached 0.9023. Finally, potential therapeutic candidates for both patient groups were predicted via the Connectivity Map. In summation, this study unveiled two clusters with unique molecular phenotypes within rosacea, leading to the development of a precise diagnostic model instrumental in NR diagnosis.

Causal relationship between immune cells and pulmonary arterial hypertension: Mendelian randomization analysis.

Immunity and inflammation in pulmonary arterial hypertension (PAH) has gained more attention. This research aimed to investigate the potential causal connections between 731 immunophenotypes and the likelihood of developing PAH. We obtained immunocyte data and PAH from openly accessible database and used Mendelian randomization (MR) analysis to evaluate the causal association between each immunophenotype and PAH. Various statistical methods were employed: the MR-Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode. In the study of 731 different types of immune cells, it was found that 9 showed a potential positive connection (IVW P < .05) with increased risk of PAH, while 19 had a possible negative link to decreased risk. Following false discovery rate (FDR) adjustment, the analysis using the IVW method demonstrated that 5 immune phenotypes were significantly associated with PAH (FDR < 0.05, OR > 1). Conversely, there was a negative correlation between PAH and 4 immune cell types (FDR < 0.05, OR < 1). Sensitivity analyses suggested the robustness of all MR findings. This research, for the first time, has revealed indicative evidence of a causal link between circulating immune cell phenotypes and PAH through genetic mechanisms. These results underscore the importance of immune cells in the pathogenesis of PAH.

Achieving Real-Time Prediction of Paroxysmal Atrial Fibrillation Onset by Convolutional Neural Network and Sliding Window on R-R Interval Sequences.

Early diagnosis of paroxysmal atrial fibrillation (PAF) could prompt patients to receive timely interventions in clinical practice. Various PAF onset prediction algorithms might benefit from accurate heart rate variability (HRV) analysis and machine learning classification but are challenged by real-time monitoring scenarios. The aim of this study is to present an end-to-end deep learning-based PAFNet model that integrates a sliding window technique on raw R-R intervals of electrocardiogram (ECG) segments to achieve a real-time prediction of PAF onset. This integration enables the deep convolutional neural network (CNN) to be customized as a light-weight architecture that accommodates the size of sliding windows simply by altering the input layer, and specifically its effectiveness in making a new prediction with each new heartbeat. Catering to the potential influence of input sizes, three CNN models were trained using 50, 100, and 200 R-R intervals, respectively. For each model, the performance of the automated algorithms was evaluated for PAF prediction using a ten-fold cross-validation. As a results, a total of 56,381 PAFN-type and 56,900 N-type R-R interval segments were collected from publicly accessible ECG databases, and a promising prediction performance of the automated algorithm with 100 R-R intervals was achieved, with a sensitivity of 97.12%, a specificity of 97.77%, and an accuracy of 97.45%, respectively. Importantly, the automated algorithm with a sliding window step of 1 could process one sample in only 23.1 milliseconds and identify the onset of PAF at least 45 min in advance. The present results suggest that the sliding window technique on raw R-R interval sequences, along with deep learning-based algorithms, may offer the possibility of providing an accurate, real-time, end-to-end clinical tool for mass monitoring of PAF.

Identifying microRNAs Possibly Implicated in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Fibromyalgia: A Review.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and fibromyalgia (FM) are chronic syndromes of unknown etiology, accompanied by numerous symptoms affecting neurological and physical conditions. Despite frequent revisions of the diagnostic criteria, clinical practice guidelines are often outdated, leading to underdiagnosis and ineffective treatment. Our aim was to identify microRNA (miRNA) biomarkers implicated in pathological mechanisms underlying these diseases. A comprehensive literature review using publicly accessible databases was conducted. Interesting miRNAs were extracted from relevant publications on ME/CFS and/or FM, and were then linked to pathophysiological processes possibly manifesting these chronic diseases. Dysregulated miRNAs in ME/CFS and FM may serve as promising biomarkers for these diseases. Key identified miRNAs, such as miR-29c, miR-99b, miR-128, miR-374b, and miR-766, were frequently mentioned for their roles in immune response, mitochondrial dysfunction, oxidative stress, and central sensitization, while miR-23a, miR-103, miR-152, and miR-320 were implicated in multiple crucial pathological processes for FM and/or ME/CFS. In summary, both ME/CFS and FM seem to share many dysregulated biological or molecular processes, which may contribute to their commonly shared symptoms. This miRNA-based approach offers new angles for discovering molecular markers urgently needed for early diagnosis or therapeutics to tackle the pathology of these medically unexplained chronic diseases.

Analysis and evaluation of the usefulness of open data for research projects—The case of the BrineRIS project

AbstractOpen research data refer to publicly available scientific information that can be accessed free of charge, usually provided by public data sources. Users must comply with specific requirements set by the institutions providing the data and always acknowledge the source of the data when processing, transmitting, storing or publishing it. One of the tasks of the BrineRIS project is the mapping of brine resources, requiring reliable data on the location of exploration facilities, environmental characteristics, brine exploitation parameters and formal and legal information. These data come from a review of various archives, databases and survey results. Initially, information on the location of the sources should be obtained, which may be available in publicly accessible databases. Next, geological and hydrogeological parameters, which can be obtained from scientific papers and reports, are useful. An important part of the project is also the analysis of legal regulations concerning water extraction and environmental protection. Therefore, data should be obtained from various sources, such as public administration, state institutions or research units. These will serve to develop the database needed to perform further analyses within the BrineRIS research project. It is therefore crucial to carefully collect, analyse and assess the usefulness of the data.