Abstract Direct mapping of protein positions along long, stretched DNA molecules requires simultaneous visualization of the DNA backbone and associated protein signals. We recently established a heavy-metal- and polymer-assisted scanning electron microscopy (SEM) approach for imaging stretched DNA molecules and associated protein signals on silicon wafers. Here, we present a practical protocol for implementing this workflow using λ DNA and streptavidin–fluorescent protein-labeled λ DNA as model DNA samples. The protocol encompasses silicon wafer preparation, microchannel-guided DNA deposition, heavy-metal staining with UranyLess, polyvinylpyrrolidone (PVP) treatment of the deposited DNA sample, SEM imaging, and image analysis. UranyLess provides heavy-metal staining to enhance SEM contrast, whereas the PVP concentration modulates the relative visibility of DNA backbones and protein-associated signals. Low-PVP conditions facilitate protein-signal visualization, while high-PVP conditions enhance DNA-backbone imaging. We also describe optional in-channel and droplet-based methods for delivering UranyLess and PVP. Finally, the protocol covers DNA backbone tracing, intensity-profile analysis, and machine-learning-based determination of protein positions from SEM images. Representative dCas9-bound DNA samples further demonstrate the applicability of the workflow to different protein-binding patterns, including closely spaced binding across a repetitive target array and localized binding on genomic DNA. This workflow provides a practical framework for preparing stretched DNA samples on silicon wafers and analyzing DNA backbones and associated protein signals by SEM.
Abstract Vascular hyperpermeability is conventionally evaluated by the Miles Evans blue assay. The assay is based on the intravenous injection of Evans blue dye which binds non-covalently to albumin and remains within the blood vasculature. As a consequence of the vascular hyperpermeability associated with inflammation, the dye bound to albumin, extravasates from blood vessels along with macromolecules to accumulate in interstitial tissues at sites of inflammation. In the mouse model, Evans blue dye is injected intravenously through the lateral tail vein, a procedure that has a significant failure rate, even when performed by highly trained personnel and requires extensive practice. We developed a simpler and easier assay that quantifies endogenous plasma immunoglobulin G antibodies instead of Evans blue dye bound to albumin, as a measure of vascular hyperpermeability to macromolecules; the assay does not require intravenous injection and uses tissue homogenates as samples that can be used in conjunction with other assays, thereby reducing the number of experimental animals needed. With increased vascular permeability during inflammation or other diseased states, immunoglobulin G, like albumin, also leaks from the vasculature, extravasates into tissues, and accumulates at inflammatory foci. In this work, we compared the Miles Evans blue assay with the immunoglobulin G assay to evaluate the increase in vascular permeability associated with inflammation due to cutaneous infection with Bacillus anthracis and to topical application of the pro-inflammatory agent, phorbol 12-myristate 13-acetate. We found the immunoglobulin G assay easier to perform and requires fewer animals when additional tissue samples are required.
Abstract Nanoscale protein dynamics are closely associated with molecular function and pathological structure formation, yet their measurement within animal tissues remains technically challenging. In this study, we developed a diffracted X-ray blinking (DXB) method to evaluate amyloid-β (Aβ)-associated nanoscale fluctuations in fixed and permeabilised whole-mount Caenorhabditis elegans. Intracellular Aβ expressed in neurons (nAβ) or body-wall muscle cells (mAβ) was labelled with gold nanoparticles using an antibody-mediated strategy, without dissection or sectioning. The labelled worms produced detectable Au(111) diffraction signals, and the resulting time-resolved diffraction intensity fluctuations were analysed by autocorrelation function (ACF) analysis. A comparison of the ACF decay constants obtained from nAβ and mAβ worms revealed that compared with nAβ, mAβ contained a greater fraction of fast relaxation components. Furthermore, in vitro DXB measurements of Aβ revealed local fast relaxation components that changed over time in association with changes in Aβ assemblies, partly resembling the Aβ-associated motional features observed in worms. These findings suggest that the detected relaxation components may reflect structurally diverse Aβ states arising during aggregation. These results demonstrate the feasibility of applying DXB to detect Aβ-associated nanoscale fluctuation signatures in fixed whole-mount C. elegans and provide a methodological framework for analysing protein-associated dynamic states in animal tissue samples.
Alzheimer's disease (AD) develops over years, creating an opportunity for interventions that target modifiable risk factors before the onset of clinical dementia. Here we applied a federated electronic health record (EHR) system encompassing over 29 million de-identified patients toward a target-trial emulation framework involving 153,412 adults aged 50 years or older with at least one documented AD risk factor. New users of glucagon-like peptide-1 (GLP-1)-based incretin therapy were compared with new users of non-GLP-1 antidiabetic medications after a 12-month washout, with 1:1 propensity-score matching on 30 baseline variables and additional exact matching on index year of therapy initiation and 5-year age band. In the primary matched cohort, 28,901 patients per arm, incretin initiation was associated with lower first recorded AD diagnosis (hazard ratio [HR] 0.46, 95% confidence interval [CI] 0.29-0.73, q = 0.003), all-cause dementia (HR 0.66 [0.55-0.79], q < 0.001), and all-cause mortality (HR 0.46 [0.37-0.58], q < 0.001), with directionally-lower Mild Cognitive Impairment (HR 0.76 [0.61-0.94], q = 0.135) and Alzheimer's-related medication initiation (HR 0.82 [0.65-1.03], q = 0.217). The AD diagnosis mitigation signal was independently reproduced for semaglutide (HR 0.56; N = 23,675 per arm; q = 0.02), with the directionally consistent tirzepatide point estimate (HR 0.60) not reaching significance. GLP-1 RA initiation was also associated with substantially lower (all q < 0.001) incidence of heart failure (HR 0.50 [0.45-0.55]), cardiomyopathy (HR 0.38 [0.31-0.47]), major adverse cardiovascular events (HR 0.74 [0.67-0.82]), acute kidney injury (HR 0.67 [0.67-0.74]), and chronic kidney disease (HR 0.68 [0.62-0.75]). Negative-control outcomes showed no significant separation (all q > 0.45), including allergic rhinitis (HR 0.96 [0.87-1.07]), hemorrhoids (HR 1.07 [0.95-1.21]), and inguinal hernia (HR 1.39 [0.89-2.18]). Stricter two-code ICD definitions supported lower first recorded AD diagnosis in the incretin arm (HR 0.51 [0.29-0.89], q = 0.018) and lower first recorded all-cause dementia diagnosis (HR 0.68 [0.54-0.85], q = 0.003). In 12-month landmark analyses among semaglutide initiators, ≥5% weight-loss responders had lower 3-year AD cumulative incidence than non-responders after matching (0.07% vs. 0.40%; incidence ratio 5.76; P = .036), although this was not significant in the hazard-ratio model (HR 0.54, P = .39). Sustained-dose stratification showed no comparable gradient (high-dose vs. low-dose 0.30% vs. 0.14%; HR 2.77; P = .38), with ≤12 AD events per group. The weight-loss-specific pattern did not extend to all-cause dementia: weight-loss responders and non-responders had similar 3-year cumulative incidence (1.54% vs. 1.70%; HR 0.87, P = .53). Initiation of GLP-1 receptor agonist therapy in adults with documented AD risk factors was associated with lower recorded incidence of AD, dementia, mortality, and multiple cardiovascular and renal outcomes in this observational target-trial emulation. These findings support the hypothesis that earlier incretin therapy may contribute to AD risk modification through upstream cardiometabolic pathways, while prospective randomized prevention studies will be required to determine causality, underlying biological mechanisms, and optimal treatment timing.
Respiratory viral infections continue to impose a large burden of hospitalization, organ failure and death, particularly among older adults and people with cardiometabolic disease. Whether prior semaglutide exposure is associated with lower post-infection severity across distinct respiratory viruses remains uncertain. We analyzed de-identified longitudinal electronic health record data from a federated U.S. network spanning more than 29 million patients. Adults with documented COVID-19 or influenza infection were classified by pre-infection semaglutide use versus metformin use without prior GLP-1 receptor agonist exposure and one-to-one propensity matched on demographics, BMI, HbA1c, vaccination status, prior infection status, antiviral use, healthcare utilization extent, and baseline comorbidities, yielding matched cohorts of N = 7092 for COVID-19 and N = 1920 for influenza (flu). In the COVID-19 cohort, semaglutide exposure was associated with significantly lower 30-day mortality (0.3% vs 0.8%, RR 0.39, P < .001), hospitalization (5.3% vs 7.4%, RR 0.72, P < .001), and the composite disease severity outcome (8.2% vs 10.6%, RR 0.77, P < .001). Differences in rates of intensive care admission (1.4% vs 1.8%, RR 0.79, P = .07) and mechanical ventilation (0.7% vs 0.9%, RR 0.76, P = .14) were not statistically significant. In the flu cohort, semaglutide use was also associated with significantly lower mortality (0.2% vs 0.7%, RR 0.29, P = .02), hospitalization (6.5% vs 9.6%, RR 0.68, P < .001), and the composite outcome (10.3% vs 14.6%, RR 0.70, P < .001); ICU admissions (1.9% vs 2.7%, RR 0.71, P = .11) and mechanical ventilation (0.7% vs 0.9%, RR 0.78, P = .48) were not statistically significant. Among organ-specific outcomes, for which P values were adjusted for multiple comparisons, a significant risk reduction was observed for acute kidney injury (COVID-19 RR 0.73, P = .02; flu RR 0.58, P = .03) in the 30-day observation period, whereas reductions in acute coronary syndrome (COVID-19 RR 0.67, P = .14; flu RR 1.00, P = 1.00) and acute respiratory failure (COVID-19 RR 0.84, P = .17; flu RR 0.74, P = .27) were not statistically significant against the metformin comparator. Within the COVID-19 cohort, prior semaglutide exposure was associated with lower composite COVID-19 severity across the full study period, reaching significance in both vaccinated (RR 0.81, P = .04) and unvaccinated (RR 0.76, P < .001) patients, and during the pandemic period ending May 11, 2023, reaching significance in unvaccinated patients (RR 0.76, P = .001) but not vaccinated patients (RR 0.85, P = .20). When restricting to those treated with standard-of-care therapeutics during the COVID-19 pandemic era, semaglutide users had significantly lower 30-day composite risk among those receiving Paxlovid within ±7 days of infection (9.6% vs 12.7%; RR 0.76, P = .02). Among those initiating corticosteroids ±7 days of infection, the composite outcome remained significantly lower (27.3% vs 36.3%; RR 0.75, P = .007). Among the influenza cohort, semaglutide was associated with a reduced risk of the composite outcome among unvaccinated patients (RR 0.69, P < .001), but this trend was not statistically significant among vaccinated patients (RR 0.74, P = .06). Notably, semaglutide use was associated with significantly lower composite risk among Medicare-eligible adults aged ≥65 years for both COVID-19 (11.2% vs 12.9%; RR 0.86, P = .04) and influenza (17.0% vs 23.7%; RR 0.72, P = .003). Finally, composite risk did not vary significantly across pre-infection weight loss strata of <5%, 5%-15%, and ≥15% for COVID (P = .89) or flu (P = .54), or across dosage strata of 0.25-0.5, 1.0, and 1.7-2.4 mg/week for COVID (P = .50) or flu (P = .32), suggesting semaglutide-associated lower COVID and influenza severity may not be related to substantial weight loss or maximal dosing. Taken together, this study motivates prospective evaluation of low-dose semaglutide to blunt the severity of respiratory viral infections.
Bacterial conjugation is an important means of horizontal gene transfer in which DNA is transferred from a donor to a recipient cell by direct cell-to-cell contact. Dissemination of antibiotic resistance in bacteria is very often driven by conjugative plasmids harboring antibiotic resistance genes. Recent research highlighted the temporal cascade of DNA reactions that are critical for the establishment of a plasmid in the new host, which include prompt and robust induction of anti-defense genes as well as re-formation of double-stranded circular form of the plasmid after transfer as single-stranded linear DNA. The nature of conjugation which occurs in a mixture of cell populations, constrained the research on plasmid establishment. Since plasmid molecules before and after transfer are indistinguishable, distinction between donor and recipient/transconjugant cells is required, and physical separation is particularly necessary for the genomic approaches. We established a novel method exploiting a mutant donor which can be expeditiously eliminated from the conjugation, mixture with simple manipulation. This method, dubbed ED-TA for elimination of donor cells for transconjugant analysis, was shown to be a powerful tool to unveil plasmid gene expression profile at the early stages of conjugation (Wen et al. 2025, Nucleic Acids Res, doi: 10.1093/nar/gkaf1299). Here we present an optimized protocol for the ED-TA method which will help our study on plasmid actions during establishment in new host cell, including emerging interests between host defense and plasmid anti-defense systems.
Nanometer-scale localization of specific proteins within cells is essential for understanding their roles in diverse physiological and pathological cellular processes. Such high-resolution localization of protein epitopes can be achieved by combining transmission electron microscopy (TEM) with immunogold labeling. Several immuno-TEM approaches are available, including pre-embedding and post-embedding techniques, each offering distinct advantages and limitations when applied to cultured cells. One of the major challenges is preparation of cell culture samples embedded in resin in a manner that enables reliable ultrathin sectioning and efficient collection of sections. Here, we describe an optimized post-embedding immunogold labeling procedure for the localization of one or more proteins in both adherent and non-adherent cells cultured on plastic coverslips. This approach has been successfully applied in our previous studies and proved suitable for routine ultrastructural immunolabeling of cultured cells. Although the protocol is designed to achieve an optimal balance between the preservation of cellular ultrastructure and antigenicity, optimal results may still depend on the specific characteristics of the proteins under investigation and their localization within particular cellular compartments. Therefore, we also present a complementary procedure for the ultrastructural analysis of cultured cells that can support and extend immuno-TEM investigations.
Cell membranes are not mere platforms for signalling proteins; they can shape how receptor inputs are assembled into local responses. In membrane-rich microdomains, receptor identification and pathway mapping do not reveal the logic of a measured effect. That effect may arise from independent receptor activity, pairwise crosstalk or higher-order integration governed by membrane state. The membrane-encoded chemosensory system (MECS) is introduced as a conceptual framework for addressing the inferential gap between receptor co-expression mapping and mechanistic crosstalk claims in territories with cannabinoid GPCRs, ectopic olfactory GPCRs and TRP channels. Its operational method, MECS baseline-edit-rescue (MECS-BER), fixes one membrane prior, one locked proximal outcome and one three-arm candidate assembly. Eligibility gates test arm engagement and outcome competence. Combinatorial responses are analysed with κ, the third-order interaction under a pairwise-only null within a complete three-factor perturbation design, to distinguish lower-order explanation from higher-order interpretation and test edit-rescue reversibility. Deterministic matrices and simulations establish classification logic and tolerance handling; biological adjudication awaits fully compliant MECS-BER datasets. The workflow provides a prespecified and formalized methodological route, not biological proof of any specific receptor triad. It keeps nomination separate from adjudication and requires co-localization, distal phenotypes, shared downstream signals and nonlinear mixtures to be tested against a locked proximal outcome before biological interpretation. Renal micro-niches specify prospective deployment across renin, transport, barrier and flow-sensitive calcium control without claiming validated cannabinoid-olfactory-TRP assemblies. Membrane lipids may shape both the signalling vocabulary of individual receptors and the local language through which receptors communicate.
Systematic reviews (SRs) are essential for evidence-based practice but remain labor-intensive, especially during abstract screening. This study evaluates whether multiple large language model (multi-LLM) collaboration can improve the efficiency and reduce costs for abstract screening. Abstract screening was framed as a question-answering (QA) task using cost-effective LLMs. Three multi-LLM collaboration strategies were evaluated, including majority voting by averaging opinions of peers, multi-agent debate (MAD) for answer refinement, and LLM-based adjudication against answers of individual QA baselines. These strategies were evaluated on 28 SRs of the CLEF eHealth 2019 Technology-Assisted Review benchmark using standard performance metrics such as Mean Average Precision (MAP) and Work Saved over Sampling at 95% recall (WSS@95%). Multi-LLM collaboration significantly outperformed QA baselines. Majority voting was overall the best strategy, achieving the highest MAP 0.462 and 0.341 on subsets of SRs about clinical intervention and diagnostic technology assessment, respectively, with WSS@95% 0.606 and 0.680, enabling in theory up to 68% workload reduction at 95% recall of all relevant studies. MAD improved weaker models most. Our own adjudicator-as-a-ranker method was the second strongest approach, surpassing adjudicator-as-a-judge, but at a significantly higher cost than majority voting and debating. Multi-LLM collaboration substantially improves abstract screening efficiency, and the success lies in model diversity. Making the best use of diversity, majority voting stands out in terms of both excellent performance and low cost compared to adjudication. Despite context-dependent gains and diminishing model diversity, MAD is still a cost-effective strategy and a potential direction of further research.
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive loss of motor neurons. Accurate and accessible blood-based diagnostics for neurodegenerative diseases, including ALS, are being progressively required. Although blood cell gene expression profiles have potential clinical utility for distinguishing ALS, robust transcriptomic biomarkers for supportive diagnosis have not yet been established. Here, we analyzed publicly available peripheral blood mononuclear cell (PBMC) transcriptomic data from ALS patients using Maximum Mean Discrepancy, a kernel-based method that captures nonlinear distributional differences in a reproducing kernel Hilbert space and enables the extraction of informative gene combinations while minimizing multicollinearity, a common issue in multiple regression models. Using this approach, we identified a nonlinear three-gene combination-PRKAR1A, QPCT, and TMEM71-that distinguished ALS from healthy controls with an area under the curve (AUC) of 0.83 in a public PBMC dataset. This achievement was confirmed in laboratory PBMC samples with an AUC of 0.85, supporting the robustness of the identified gene signature in independent samples. Furthermore, these genes also enabled ALS classification in induced pluripotent stem cell-derived motor neurons with an AUC of 0.79. Knockdown of PRKAR1A, QPCT, or TMEM71 in motor neurons increased the TDP-43 expression levels, and PRKAR1A knockdown induced the mislocalization of TDP-43, accompanied by phosphorylation, suggesting a potential link to ALS-related pathophysiology. These findings suggest that nonlinear gene combinations may provide a useful strategy for identifying blood-based biomarkers and offer insights into ALS pathogenesis. This nonlinear, data-driven analytical framework enabled the transition from unbiased gene discovery to the identification of pathophysiology-associated molecules by in vitro functional validation.
Antimicrobial resistance (AMR) is a global health and environmental challenge, driven by complex interactions among microbial communities, resistance genes, and selective pressures in various ecological niches. Traditional surveillance procedures often fall short in capturing the full diversity and dynamics of resistance reservoirs in the environment. This review examines the integration of artificial intelligence (AI) and machine learning (ML) with next-generation sequencing (NGS) technologies for comprehensive resistome profiling. We discuss advances in multi-omics approaches, particularly metagenomics, microbiome-based analytics, and metatranscriptomics. We also highlight computational workflows that enable high-resolution mapping of resistance genes, their mobile genetic elements, and host associations. The role of AI/ML in resistome prediction, classification, and source tracking, as well as the incorporation of environmental metadata for contextual interpretation is discussed based on the selected literature. Moreover, we assess current challenges and propose future directions for developing standardized, scalable, and interpretable bioinformatic pipelines in AMR surveillance. This review primarily elucidates the potential of integrated AI-omics platforms to revolutionize aquatic environmental AMR monitoring and inform risk assessment and mitigation strategies.
Doxorubicin (DOX) is a widely used chemotherapeutic agent that induces apoptosis through DNA intercalation, topoisomerase II inhibition, and oxidative stress. However, its cardiotoxicity is a significant concern. Flow cytometry, particularly with Annexin V/propidium iodide (PI) or Annexin V/7AAD assays, is commonly used to assess DOX-induced cell death. A challenge arises from DOX's intrinsic fluorescence, which interferes with accurate detection. To address this, we successfully used DRAQ7, a far-red dye, in HeLa and other cancer cell lines. As our study demonstrates DRAQ7 eliminates auto-fluorescence interference, this approach offers precise differentiation of apoptotic cells and improving experimental and therapeutic reliability.
Artificial intelligence (AI) can analyse high-resolution CT lung scans (HRCT) in various interstitial lung diseases (ILDs), including systemic sclerosis (SSc). Older HRCT lung scans may have been saved as small DICOM file sets consisting of non-contiguous slices. These are not amenable to AI analyses. Our aim was to develop and test a method of rebuilding small non-contiguous sets of HRCT lung slices into larger sets of contiguous slices that could be analysed by AI programs. We deleted sets of DICOM files from 14 large DICOM file set scans from SSc patients and were left with a scan with about 30 equidistant non-contiguous slices. We then inserted copies of scans between each pair of slices to create a large DICOM file set similar in size to the original large file set scan. Both the original scan and the rebuilt large DICOM file set scan were analysed by Contextflow ADVANCE Chest CT. We recorded the values for honeycombing (HC), reticular pattern (RP), ground glass opacities (GGO), and total ILD. We analysed agreement between the original scan and the rebuilt large DICOM file set scan using intraclass correlation coefficient (ICC), Lin's concordance correlation coefficient (CCC), Bland-Altman limits-of-agreement (LOA) plots and the Bradley-Blackwood P-value. ICC, CCC, Bradly-Blackwood P-values and Bland-Altman plots showed excellent agreement between scans for HC, RC, GGO, and total ILD except for the Bradley-Blackwood P-value for RP. Non-contiguous HRCT small DICOM file set lung scans in SSc can be manipulated to allow analysis by AI.
Tuberculosis (TB) persists as a formidable global health challenge, particularly due to the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) variants that limit the effectiveness of existing therapies. These variants limit therapeutic options, prolong treatment duration and increase the risk of treatment failure. Additionally, the antitubercular drug discovery remains relatively limited; moreover, the rise in drug-resistant strains necessitates the need to identify and develop novel drug candidates that can overcome these resistance patterns. This study aims to design novel InhA inhibitors that can bind to and inhibit InhA and circumvent the resistance pathway in the KatG enzyme of Mycobacterium tuberculosis. We sourced an extensive library of 276 518 natural products from the LOTUS database and screened the compounds through a series of rigorous computational approaches such as drug-likeness filtration, molecular docking, MM-GBSA analysis, and ADMET prediction. We developed a machine learning model using a Message Passing Neural Network (MPNN). The MPNN was trained to predict bioactivity profiles of 31 597 natural products against the InhA enzyme. Molecular dynamics simulations further confirmed the stability of these interactions over 100-nanoseconds. Out of the screened compounds, four novel drug candidates exhibited strong binding affinities with binding energies of -12.204 kcal/mol, -11.926 kcal/mol, -11.624 kcal/mol, and -11.548 kcal/mol, respectively, surpassing the co-crystallized ligand (-8.895 kcal/mol), and the standard drug, Isoniazid (-12.204 kcal/mol). The top hit molecules demonstrated high and considerable structural stability during molecular dynamics simulations. Additionally, the pharmacokinetic profile of LTS0161715 exhibited low toxicity. positioning LTS0161715 for further investigation. These research findings elucidate the potential of direct InhA inhibitors, particularly LTS0161715, as a promising drug candidate for anti-tubercular drug development, while also highlighting the need for further optimization to enhance safety and efficacy.
Delimitation of morphotypes in cryptic lineages remains challenging because classical linear statistics and visual inspection may fail to capture complex non-linear phenotypic variations. To address this, we introduced an unsupervised machine learning framework that couples a Graph Autoencoders (GAE) with community detection algorithms to map high-dimensional morphospaces. Unlike many traditional models that treat biological specimens as independent points in feature space, our approach represents multidimensional morphological data as an interconnected network. Using a k-nearest neighbor graph, we captured local topological relationships and embedded them into an optimized latent space. As an empirical case study, we applied this pipeline to 484 specimens of the morphologically conservative Neotropical pitviper genus Porthidium, using 21 linear and pholidosis (scale counts) traits. The GAE revealed a morphological structure that showed limited concordance with current taxonomic boundaries, identifying 12 distinct morphotypes with high structural modularity (Q = 0.6973) but low agreement with taxonomy (normalized mutual information, NMI = 0.2812). This framework provides an objective and scalable approach for exploring phenotypic structure in complex dataset. Because it does not require prior taxonomic assignments, it may be particularly useful for investigating cryptic lineages where morphological boundaries are ambiguous. More broadly, this approach can serve as a complementary exploratory step in integrative taxonomy and evolutionary studies.
Standard Micro-C protocols typically require millions of cells, limiting their application to rare cell populations. Here, we present an optimized low-input Micro-C workflow that requires only 100 000 cells. By downsampling both our low-input dataset and a control dataset from 5 million cells to 120 million raw read pairs, we demonstrate that all key architectural features-Compartments, Topologically associating domains (TADs), and Chromatin loops-are reliably detected from as few as 100 000 cells. The low-input protocol achieved a high cis interaction ratio (96.1%) and low PCR duplication rate (3.0%), indicating high library complexity and low background noise. Applying this method to investigate acute CTCF (CCCTC-binding factor) degradation, we observed the loss of loops and TAD boundaries in CTCF-degraded samples, consistent with previous reports. Our optimized protocol enables nucleosome-resolution 3D genome mapping for sample-limited studies.
Tryptophanase (TnaA; EC 4.1.99.1) is a pyridoxal 5'-phosphate-dependent enzyme that catalyzes the β-elimination of L-tryptophan into indole, pyruvate, and ammonia. It plays a key role in bacterial signaling and biofilm formation and is industrially relevant for L-tryptophan biosynthesis. Existing assay methods for TnaA activity measurement, including Kovács reagent colorimetry, HPLC, and NADH-coupled spectrophotometry, each carry limitations in cost, complexity, or analytical sensitivity that restrict their routine use. The current study aims to develop and validate a simple, cost-effective spectrophotometric method for quantifying TnaA activity using 2,4-dinitrophenylhydrazine (2,4-DNPH) as a chromogenic reagent, targeting the pyruvate product of the enzymatic reaction. The DNPH-TnaA assay uses a stable hydrazone complex formed between pyruvate and 2,4-DNPH, which is measurable at 432 nm. It was applied to Escherichia coli lysates (ATCC 8739 and five clinical isolates). Performance was evaluated for linearity (5-500 µM), detection limits, intra- and inter-assay precision (CV%), spike recovery, selectivity against nine interference classes, and signal stability over 120 h. Validation included comparisons with Kovács colorimetric and HPLC methods, using Passing-Bablok and Bland-Altman analyses. The limits of detection (LOD) and quantification (LOQ) were 2.25 U/l and 6.7 U/l, respectively. Intra-assay coefficient of variation (CV) was 1.19% (mean 270.83 ± 3.23 U/l), inter-assay CV was 2.65% (mean 273.55 ± 7.25 U/l), both acceptable. Selectivity errors were within ±3.33%. The colored hydrazone was stable at 25°C for 48 h. Passing-Bablok regression showed r = 0.99 with near-unity slopes and negligible intercepts, and Bland-Altman analysis indicated minimal bias. ANOVA found no significant differences among the methods at the .05 level. The DNPH-TnaA assay is a precise, accurate, and robust method for measuring TnaA activity, comparable to Kovács and HPLC methods. Its simplicity, low cost, and compatibility with common lab equipment make it ideal for routine diagnostics and research applications.
The study focuses on seizure detection using EEG data from Mendeley. An early-alert IoT-BCI system is designed to simulate real-time support for patients during seizures. The proposed Multi-Dimensional CNN-Bi-GRU (MDCBG) outperforms hybrid deep learning models, achieving 97.43% accuracy, surpassing baseline EEGNet (92.17%) and CTNET (85.11%), along with models evaluated through ablation studies on seizure vs. non-seizure prediction. The proposed model, along with other models like Bi-GRU with attention, Bi-LSTM-GRU, and XGBoost, also performs well on classifying various types of seizures. SHAP analysis shows Channel 5 contributes most to predictions. An IoT-based automation system is simulated on seizure detection for triggering micro devices near the patient's environment. This approach supports early seizure warning and guides home-automation strategies to assist patients.
The global biosensors market is rapidly growing, driven by increasing demand for quick, affordable, and portable diagnostic tools across sectors, including healthcare, environmental monitoring, food safety, and biomedical research. The key to biosensor function is the biological recognition element (BRE), which determines the specificity, sensitivity, and reliability of the device. Traditional BREs, such as antibodies and enzymes, face significant limitations, including instability, high costs, and variability. Phage display technology offers a strong alternative, providing durable, stable, and highly specific peptides as BREs. However, it requires effective amplification and purification methods to produce high-quality peptide libraries. This study examines key factors affecting the amplification of filamentous M13 bacteriophage, highlighting the negative impact of high multiplicity of infection (MOI) caused by superinfection exclusion and reduced phage adsorption efficiency. Our findings indicate that the bacterial growth phase is the most important determinant of M13 amplification efficiency. Furthermore, post-infection PEG/NaCl precipitation followed by high-speed centrifugation significantly outperforms traditional filtration methods in purifying phages, maximizing recovery and viability. These findings present an optimized, reproducible, and scalable approach to M13 phage amplification, improving the effectiveness of phage display for developing advanced biorecognition elements. Ultimately, this research provides a foundational framework for more efficient biosensing and therapeutic applications, filling critical gaps in the current biosensor development landscape.
