Search Results (72)

Introduction: Burnout syndrome, long described as an “occupational phenomenon”, now affects 15–20% of the general workforce and more than 50% of clinicians, teachers, social-care staff and first responders. Its precise nosological standing remains disputed. We conducted a mechanistic review of electroencephalography (EEG) studies to determine whether burnout is accompanied by reproducible brain-function alterations that justify disease-level classification. Methods: Following PRISMA-adapted guidelines, two independent reviewers searched PubMed/MEDLINE, Scopus, Google Scholar, Cochrane Library and reference lists (January 1980–May 2025) using combinations of “burnout,” “EEG”, “electroencephalography” and “event-related potential.” Only English-language clinical investigations were eligible. Eighteen studies (n = 2194 participants) met the inclusion criteria. Data were synthesised across three domains: resting-state spectra/connectivity, event-related potentials (ERPs) and longitudinal change. Results: Resting EEG consistently showed (i) a 0.4–0.6 Hz slowing of individual-alpha frequency, (ii) 20–35% global alpha-power reduction and (iii) fragmentation of high-alpha (11–13 Hz) fronto-parietal coherence, with stage- and sex-dependent modulation. ERP paradigms revealed a distinctive “alarm-heavy/evaluation-poor” profile; enlarged N2 and ERN components signalled hyper-reactive conflict and error detection, whereas P3b, Pe, reward-P3 and late CNV amplitudes were attenuated by 25–50%, indicating depleted evaluative and preparatory resources. Feedback processing showed intact or heightened FRN but blunted FRP, and affective tasks demonstrated threat-biassed P3a latency shifts alongside dampened VPP/EPN to positive cues. These alterations persisted in longitudinal cohorts yet normalised after recovery, supporting trait-plus-state dynamics. The electrophysiological fingerprint differed from major depression (no frontal-alpha asymmetry, opposite connectivity pattern). Conclusions: Across paradigms, burnout exhibits a coherent neurophysiological signature comparable in magnitude to established psychiatric disorders, refuting its current classification as a non-disease. Objective EEG markers can complement symptom scales for earlier diagnosis, treatment monitoring and public-health surveillance. Recognising burnout as a clinical disorder—and funding prevention and care accordingly—is medically justified and economically imperative. Full article

Background: Breast cancer, the most prevalent cancer among women globally, develops primarily in the breast’s ducts or lobules. Drug resistance is a significant challenge in treating advanced cases, contributing to over 685,000 breast cancer-related deaths annually, and identifying novel compounds that inhibit key proteins is crucial for developing effective therapies. Methods: In this study, five transferase proteins with PDB IDs were selected due to their involvement in breast cancer: 1A52, 3PP0, 4EJN, 4I23, and 7R9V. Multitargeted docking studies were conducted using three different docking strategies and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) to calculate the binding affinities against the ZINC Natural compound library. Isoetin (ZINC000006523948), found mainly in Isoetaceae, was identified, and the results were compared with the Food and Drug Administration (FDA)-approved drug Tucatinib. In addition, molecular interaction fingerprints and pharmacokinetic profiling were evaluated. We also performed 5 ns WaterMap simulations to identify hydration sites and interactions, followed by 100 ns molecular dynamics (MD) simulations and MM/GBSA to assess the stability of the Isoetin–protein complexes. Results: The docking results indicated that Isoetin demonstrated superior binding and docking scores ranging from −9.901 to −13.903 kcal/mol compared to Tucatinib, which showed values between −4.875 and −10.948 kcal/mol, suggesting Isoetin’s potential efficacy as a therapeutic agent for breast cancer. Interaction fingerprints revealed significant interactions between Isoetin and key residues, including 28LEU, 12MET, 9PHE, 7ASP, 6ASN, and 6THR. The pharmacokinetics and DFT analysis of Isoetin supported its potential as a viable drug candidate. Furthermore, the 5 ns WaterMap simulations identified various hydration sites, and the 100 ns MD simulations showed that the Isoetin–protein complexes exhibited minimal deviations and fluctuations, indicating better stability than Tucatinib, and MM/GBSA confirmed Isoetin’s superior binding stability. Conclusions: Isoetin, a natural compound identified through in silico screening, demonstrates significant promise as a potential therapeutic agent for breast cancer as it outperforms the FDA-approved drug Tucatinib, the respective native and FDA-approved drug. However, experimental validation is necessary before considering Isoetin for clinical use. Full article

Despite the widespread adoption of indoor positioning technology, the existing solutions still face significant challenges. On one hand, Wi-Fi-based positioning struggles to balance accuracy and efficiency in complex indoor environments and architectural layouts formed by pre-existing access points (APs). On the other hand, vision-based methods, while offering high-precision potential, are hindered by prohibitive costs associated with binocular camera systems required for depth image acquisition, limiting their large-scale deployment. Additionally, channel state information (CSI), containing multi-subcarrier data, maintains amplitude symmetry in ideal free-space conditions but becomes susceptible to periodic positioning errors in real environments due to multipath interference. Meanwhile, image-based positioning often suffers from spatial ambiguity in texture-repeated areas. To address these challenges, we propose a novel hybrid indoor positioning method that integrates multi-granularity and multi-modal features. By fusing CSI data with visual information, the system leverages spatial consistency constraints from images to mitigate CSI error fluctuations while utilizing CSI’s global stability to correct local ambiguities in image-based positioning. In the initial coarse-grained positioning phase, a neural network model is trained using image data to roughly localize indoor scenes. This model adeptly captures the geometric relationships within images, providing a foundation for more precise localization in subsequent stages. In the fine-grained positioning stage, CSI features from Wi-Fi signals and Scale-Invariant Feature Transform (SIFT) features from image data are fused, creating a rich feature fusion fingerprint library that enables high-precision positioning. The experimental results show that our proposed method synergistically combines the strengths of Wi-Fi fingerprints and visual positioning, resulting in a substantial enhancement in positioning accuracy. Specifically, our approach achieves an accuracy of 0.4 m for 45% of positioning points and 0.8 m for 67% of points. Overall, this approach charts a promising path forward for advancing indoor positioning technology. Full article

Background/Objectives: Montelukast (MLK), a leukotriene receptor antagonist, has been associated with neuropsychiatric side effects. This study aimed to rationally modify MLK’s structure to reduce these risks by optimizing its interactions with dopamine D2 (DRD2) and serotonin 5-HT1A receptors using computational molecular simulation techniques. Methods: A library of MLK derivatives was designed and screened using structural similarity analysis, molecular docking, molecular dynamics (MD) simulations, MM/PBSA binding free energy calculations, and ADME-Tox predictions. Structural similarity analysis, based on Tanimoto coefficient fingerprinting, compared MLK derivatives to known neuropsychiatric drugs. Docking was performed to assess initial receptor binding, followed by 100 ns MD simulations to evaluate binding stability. MM/PBSA calculations quantified binding affinities, while ADME-Tox profiling predicted pharmacokinetic and toxicity risks. Results: Several MLK derivatives showed enhanced DRD2 and 5-HT1A binding. MLK_MOD-42 and MLK_MOD-43 emerged as the most promising candidates, exhibiting MM/PBSA binding free energies of −31.92 ± 2.54 kcal/mol and −27.37 ± 2.22 kcal/mol for DRD2 and −30.22 ± 2.29 kcal/mol and −28.19 ± 2.14 kcal/mol for 5-HT1A, respectively. Structural similarity analysis confirmed that these derivatives share key pharmacophoric features with atypical antipsychotics and anxiolytics. However, off-target interactions were not assessed, which may influence their overall safety profile. ADME-Tox analysis predicted improved oral bioavailability and lower neurotoxicity risks. Conclusions: MLK_MOD-42 and MLK_MOD-43 exhibit optimized receptor interactions and enhanced pharmacokinetics, suggesting potential neuropsychiatric applications. However, their safety and efficacy remain to be validated through in vitro and in vivo studies. Until such validation is performed, these derivatives should be considered as promising candidates with optimized receptor binding rather than confirmed safer alternatives. Full article

Raman spectroscopy enables fast, label-free, qualitative, and quantitative observation of the physical and chemical properties of various substances. Here, we present a 785 nm custom-built Raman spectroscopy instrument designed for sensing applications in the 400–1700 cm⁻¹ spectral range. We demonstrate the performance of the instrument by fingerprinting 14 pesticide reference samples with over twenty technical repeats per sample. We present molecular Raman fingerprints of the pesticides comprehensively and distinguish similarities and differences among them using multivariate analysis and machine learning techniques. The same pesticides were additionally investigated using a commercial 532 nm Raman instrument to see the potential variations in peak shifts and intensities. We developed a unique Raman fingerprint library for 14 reference pesticides, which is comprehensively documented in this study for the first time. The comparison shows the importance of selecting an appropriate excitation wavelength based on the target analyte. While 532 nm may be advantageous for certain compounds due to resonance enhancement, 785 nm is generally more effective for reducing fluorescence and achieving clearer Raman spectra. By employing machine learning techniques like the Random Forest Classifier, the study automates the classification of 14 different pesticides, streamlining data interpretation for non-experts. Applying such combined techniques to a wider range of agricultural chemicals, clinical biomarkers, or pollutants could provide an impetus to develop monitoring technologies in food safety, diagnostics, and cross-industry quality control applications. Full article

Background/Objectives: Liquid chromatography coupled with mass spectrometry (LC-MS) is a commonly used platform for many metabolomics studies. However, metabolite annotation has been a major bottleneck in these studies in part due to the limited publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known compounds. Application of deep learning methods is increasingly reported as an alternative to spectral matching due to their ability to map complex relationships between molecular fingerprints and mass spectrometric measurements. The objectives of this study are to investigate deep learning methods for molecular fingerprint based on MS/MS spectra and to rank putative metabolite IDs according to similarity of their known and predicted molecular fingerprints. Methods: We trained three types of deep learning methods to model the relationships between molecular fingerprints and MS/MS spectra. Prior to training, various data processing steps, including scaling, binning, and filtering, were performed on MS/MS spectra obtained from National Institute of Standards and Technology (NIST), MassBank of North America (MoNA), and Human Metabolome Database (HMDB). Furthermore, selection of the most relevant m/z bins and molecular fingerprints was conducted. The trained deep learning models were evaluated on ranking putative metabolite IDs obtained from a compound database for the challenges in Critical Assessment of Small Molecule Identification (CASMI) 2016, CASMI 2017, and CASMI 2022 benchmark datasets. Results: Feature selection methods effectively reduced redundant molecular and spectral features prior to model training. Deep learning methods trained with the truncated features have shown comparable performances against CSI:FingerID on ranking putative metabolite IDs. Conclusion: The results demonstrate a promising potential of deep learning methods for metabolite annotation. Full article

Background: Malaria remains a global health crisis, with the World Health Organization (WHO) reporting 241 million cases and 627,000 deaths worldwide in 2020, predominantly affecting Sub-Saharan Africa. The region accounted for 95% of cases and 96% of deaths, reflecting the immense challenges in malaria prevention and treatment. Plasmodium falciparum Schizont Egress Antigen-1 (PfSEA-1) is crucial in facilitating immune evasion and promoting the sequestration of infected red blood cells (RBCs), contributing to severe malaria symptoms, including cerebral malaria, and necessitates the urgent identification of novel or repurposed drugs targeting PfSEA1. Methods: The protein structure of PfSEA-1 (UniProt ID: A0A143ZXM2) was modelled in three dimensions, prepared, and subjected to a 50 ns molecular dynamics (MD) simulation to achieve a stable structure. The equilibrated structure was minimised for molecular docking against the DrugBank compound library. Docking analysis identified potential inhibitors, including Alparabinos, Dihycid, Ambenzyne, Amiflupipquamine, Ametchomine, and Chlobenethyzenol, with docking scores ranging from −8.107 to −4.481 kcal/mol. Advanced analyses such as interaction fingerprints, density functional theory (DFT), and pharmacokinetics evaluations were conducted. Finally, a 100 ns MD simulation in the NPT ensemble was performed to assess the stability of protein–ligand complexes, with binding free energy and total energy calculations derived from the simulation trajectories. Results and Discussion: The identified compounds exhibited satisfactory pharmacokinetic profiles and binding interactions with PfSEA-1. The MD simulations demonstrated overall stability, with minor fluctuations in some instances. Key intermolecular interactions were observed, supporting the binding stability of the identified compounds. Binding free energy calculations confirmed favourable interactions, underscoring their potential as therapeutic agents against Plasmodium falciparum. While the in silico results are promising, experimental validation is essential to confirm their efficacy and safety for clinical use. Conclusion: These findings highlight PfSEA-1 as a promising antimalarial target and identify potential inhibitors with strong binding affinities and favourable pharmacokinetics. While the computational results are encouraging, further in vitro and in vivo validation is necessary to confirm their therapeutic potential and facilitate future drug development. Full article

Background: Breast cancer remains a significant global health concern, with approximately 2.3 million diagnosed cases and 670,000 deaths annually. Current targeted therapies face challenges such as resistance and adverse side effects. This study aimed to explore natural compounds as potential multitargeted breast cancer therapeutics, focusing on Lucidin, an anthraquinone derived from Rubia cordifolia, and comparing its efficacy with Lapatinib, an FDA-approved drug. Methods: We performed multitargeted molecular docking studies on key breast cancer proteins using a natural compound library from ZINC. Comparative analyses of Lucidin and Lapatinib included molecular interaction fingerprints, pharmacokinetics, WaterMap computations (5 ns) to assess water thermodynamics and binding interactions, and Molecular Dynamics Simulations (100 ns) in water to evaluate complex stability and dynamic behaviour. Results: Lucidin demonstrated significant binding affinity and interaction potential with multiple breast cancer targets, outperforming Lapatinib in stability and binding interactions. WaterMap analysis revealed favourable hydration site energetics for Lucidin, enhancing its efficacy. The multitargeted profile of Lucidin suggests a broader therapeutic approach with potential to overcome resistance and side effects associated with Lapatinib. Conclusions: Lucidin shows promise as a novel, multitargeted anti-breast cancer agent with improved efficacy over Lapatinib. These findings provide a foundation for further in vitro and in vivo validation to develop Lucidin as a potential therapeutic option for breast cancer treatment. Full article

Nucleic acid aptamers are single-stranded oligonucleotides that are selected through exponential enrichment (SELEX) technology from synthetic DNA/RNA libraries. These aptamers can specifically recognize and bind to target molecules, serving as specific recognition elements. Surface-enhanced Raman scattering (SERS) spectroscopy is an ultra-sensitive, non-destructive analytical technique that can rapidly acquire the “fingerprint information” of the measured molecules. It has been widely applied in qualitative and trace analysis across various fields, including food safety, environmental monitoring, and biomedical applications. Small molecules, such as toxins, antibiotics, and pesticides, have significant biological effects and are harmful to both human health and the environment. In this paper, we mainly introduced the application and the research progress of SERS detection with aptamers (aptamer-based SERS techniques) in the field of small-molecule detection, particularly in the analysis of pesticide (animal) residues, antibiotics, and toxins. And the progress and prospect of combining the two methods in detection were reviewed. Full article

Betalains, which contain nitrogen and are water soluble, are the pigments responsible for many traits of plants and biological activities in different organisms that do not produce them. To better annotate and identify betalains using a spectral library and fingerprint, a database catalog of 140 known betalains (112 betacyanins and 28 betaxanthins) was made in this work to simplify betalain identification in mass spectrometry analysis. Fragmented peaks obtained using MassFrontier, along with chemical structures and protonated precursor ions for each betalain, were added to the database. Product ions made in MS/MS and multistage MS analyses of betanin, beetroot extract, and red pitaya extract revealed the fingerprint of betalains, distinctive ions of betacyanin, betacyanin derivatives such as decarboxylated and dehydrogenated betacyanins, and betaxanthins. A distinctive ion with m/z 211.07 was found in betaxanthins. By using the fingerprint of betalains in the analysis of red pitaya extracts, the catalog of betalains in red pitaya was expanded to 86 (31 betacyanins, 36 betacyanin derivatives, and 19 betaxanthins). Four unknown betalains were detected to have the fingerprint of betalains, but further research will aid in revealing the complete structure. Taken together, we envisage that the further use of the fingerprint of betalains will increase the annotation coverage of identified molecules in studies related to revealing the biological function of betalains or making technologies based on these natural colorants. Full article

Many non-natural amino acids can be incorporated by biological systems into coded functional peptides and proteins. For such incorporations to be effective, they must not only be compatible with the desired function but also evade various biochemical error-checking mechanisms. The underlying molecular mechanisms are complex, and this problem has been approached previously largely by expert perception of isomer compatibility, followed by empirical study. However, the number of amino acids that might be incorporable by the biological coding machinery may be too large to survey efficiently using such an intuitive approach. We introduce here a workflow for searching real and computed non-natural amino acid libraries for biosimilar amino acids which may be incorporable into coded proteins with minimal unintended disturbance of function. This workflow was also applied to molecules which have been previously benchmarked for their compatibility with the biological translation apparatus, as well as commercial catalogs. We report the results of scoring their contents based on fingerprint similarity via Tanimoto coefficients. These similarity scoring methods reveal candidate amino acids which could be substitutable into modern proteins. Our analysis discovers some already-implemented substitutions, but also suggests many novel ones. Full article

Bluetooth devices have been widely used for pedestrian positioning and navigation in complex indoor scenes. Bluetooth beacons are scattered throughout the entire indoor walkable area containing stairwells, and pedestrian positioning can be obtained by the received Bluetooth packets. However, the positioning performance is sharply deteriorated by the multipath effects originating from indoor clutter and walls. In this work, an ultra-wideband (UWB)-assisted Bluetooth acquisition of signal strength value method is proposed for the construction of a Bluetooth fingerprint library, and a multi-frame fusion particle filtering approach is proposed for indoor pedestrian localization for online matching. First, a polynomial regression model is developed to fit the relationship between signal strength and location. Then, particle filtering is utilized to continuously update the hypothetical location and combine the data from multiple frames before and after to attenuate the interference generated by the multipath. Finally, the position corresponding to the maximum likelihood probability of the multi-frame signal is used to obtain a more accurate position estimation with an average error as low as 70 cm. Full article

Synthetic biology has advanced in creating artificial microbial and algal communities, but technical and evolutionary complexities still pose significant challenges. Traditional methods, like microscopy and pigment analysis, are limited in throughput and resolution. In contrast, advancements in full-spectrum cytometry enabled high-throughput, multidimensional analysis of single cells based on size, complexity, and spectral fingerprints, offering more precision and flexibility than conventional flow cytometry. This study uses full-spectrum cytometry to analyze synthetic algal–microbial communities, enabling rapid species identification and enumeration. The workflow involves recording individual spectral signatures from monocultures, using autofluorescence to capture populations of interest, and creating a spectral library for further analysis. This spectral library was used for the analysis of the synthetic phytoplankton communities, revealing differences in spectral signatures. Moreover, the synthetic consortium experiment monitored algal growth, comparing results from different instruments, highlighting the advantages of the spectral virtual filter system for precise population separation and abundance tracking. By capturing the entire emission spectrum of each cell, this method enhances understanding of algal–microbial community dynamics and responses to environmental stressors. The development of standardized spectral libraries would improve the characterization of algal communities, further advancing synthetic biology and phytoplankton ecology research. Full article

With the rapid development of the Internet of Things (IoT) technology, the number and types of devices accessing the Internet are increasing, leading to increased network security problems such as hacker attacks and botnets. Usually, these attacks are related to the type of device, and the risk can be effectively reduced if the type of network device can be efficiently identified and controlled. The traditional network device identification method uses active detection technology to obtain information about the device and match it with a manually defined fingerprint database to achieve network device identification. This method impacts the smoothness of the network and requires the manual establishment of fingerprint libraries, which imposes a large labor cost but only achieves a low identification efficiency. The traditional machine learning method only considers the information of individual packets; it does not consider the timing relationship between packets, and the recognition effect is poor. Based on the above research, in this paper, we considered the packet temporal relationship, proposed the TCN model of the Inception structure, extracted the packet temporal relationship, and designed a multi-head self-attention mechanism to fuse the features to generate device fingerprints for device identification. Experiments were conducted on the publicly available UNSW dataset, and the results showed that this method achieved notable improvements compared to the traditional machine learning method, with F1 reaching 96.76%. Full article

Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active—while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules. Full article