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At least 19 recordsLinked to original sources
PubMed2026

Striping artifact removal in VisiumHD data through nuclear counts modeling.

MOTIVATION: 10x Genomics VisiumHD enables spatial transcriptomics at 2 µm × 2 µm resolution but exhibits slide-specific, non-periodic striping artifacts due to lane-width variability. These multiplicative row/column effects distort bin total counts and can bias downstream analyses. The state-of-the-art destriping approach is the normalization procedure used as a preprocessing step in bin2cell; it applies sequential high-quantile row- then column-wise normalization, which is asymmetric and can introduce edge effects/macro-stripes and distortions of large-scale total-count structure. RESULTS: We propose a statistical destriping approach that leverages nuclei segmentation from the co-registered H&E image. Assuming transcript abundance is constant within each nucleus, we model bin counts with a negative binomial distribution whose mean is a product of a nucleus-specific concentration and row- and column-specific stripe-factors reflecting lane-width variation. We fit all parameters in a generalized linear modeling framework with cross-validated regularization on stripe-factors and iterative dispersion estimation, and use the fitted parameters to correct the observed counts into a destriped image. On synthetic data with known ground truth, our method improves stripe-factor estimation accuracy and reduces error in corrected counts relative to bin2cell and bin2cell-derived baselines. Across four public VisiumHD slides, it consistently lowers striping intensity while substantially better preserving biological signal present in the large-scale global count structure and avoiding the artifacts introduced by other methods. AVAILABILITY AND IMPLEMENTATION: All source code and links to publicly available data used for this study are available at https://github.com/paolamalsot/destriping-GLM.

Artifacts↗
PubMed1976

Electrical responses of isolated Nitella protoplasm--excitations or artifacts?

Isolated protoplasmic droplets of the alga Nitella were investigated with microelectrodes under current clamp conditions. The following observations were made: 1. Long pulses of either polarity yielded almost symmetric current-voltage relations. Near the rather small resting potential (inside negative) the measuring points lay on a straight line corresponding to an apparent surface membrane resistance of 1.7 +/- 0.45 komega (mean +/- S.E.M., n=5). 2. Experiments with various pulse programs revealed no mechanism comparable to Na inactivation but stressed the electrical symmetry of the droplet with respect to the resting potential. 3. Changes of the [Ca2+] in the bathing medium between 0 to 10 mM as well as of the pH between 5 and 9 did not influence the responses. Replacing K+ by Na+ (or vice versa) or exchanging NO3 for acetylglycine or Cl- was also ineffective. These observations are not consistent with a normal excitable surface membrane. Similar responses are obtained with a RC network which is described and which may have its substrate in histological peculiarities of the protoplasmic droplet.

Action Potentials↗
PubMed2026

Identification and masking of artifactual and misleading within-host variants in deep-sequencing SARS-CoV-2 data.

Deep-sequencing data are increasingly used to study within-host viral diversity and to inform evolutionary inference. For SARS-CoV-2, analyses based on intra-host single-nucleotide variants (iSNVs) have been widely applied to quantify within-host diversity and infer transmission dynamics. However, these applications critically depend on the reliable identification of low-frequency variants, which remain vulnerable to systematic and technical artifacts. In this study, we show that recurrent artifactual iSNVs are common in large-scale SARS-CoV-2 sequencing data and can persist even under conservative minor allele frequency thresholds. Using data from the UK's Office for National Statistics COVID-19 Infection Survey, we demonstrate that such artifacts are predominantly sequencing center-specific rather than primer-specific. Each center exhibits a modest, distinct set of recurrent artifactual variants showing little overlap with sites routinely masked at the consensus level. To address this, we developed a systematic, dataset-aware framework that uses recurrence within sequencing datasets to identify small, noise-adapted sets of artifactual iSNVs to mask. Applying this framework reduces spurious sharing of low-frequency variants between samples and qualitatively alters downstream inferences, including estimates of within-host diversity and transmission bottleneck sizes. Although this study focused on SARS-CoV-2, it is likely that recurrent artifactual iSNVs will be problematic for other viruses as mass-sequencing becomes increasingly routine. Together, these findings highlight the importance of explicit, dataset-aware artifact control for robust inference from within-host variation, particularly as genomic studies increasingly seek to exploit sub-consensus diversity in rapidly evolving pathogens.

Humans↗
PubMed2026

Beyond blacklists: a critical assessment of exclusion set generation strategies and alternative approaches.

MOTIVATION: Short-read sequencing data can be affected by alignment artifacts in certain genomic regions. Removing reads overlapping these exclusion regions, previously known as Blacklists, help to potentially improve biological signal. Alternatively, "sponge" or decoy sequences have been proposed to reduce alignment artifacts. RESULTS: We examined the widely used Blacklist software and found that pre-generated exclusion sets were difficult to reproduce due to sensitivity to input data, aligner choice, and read length. We further explored the use of "sponge" sequences-unassembled genomic regions such as satellite DNA, ribosomal DNA, and mitochondrial DNA-as an alternative approach. We additionally investigated the effect of the T2T-CHM13 genome assembly on improving biological signals. Aligning reads to a genome that includes sponge sequences reduced signal correlation in ChIP-seq data comparably to Blacklist-derived exclusion sets while preserving biological signal. Sponge-based alignment also had minimal impact on RNA-seq gene counts, suggesting broader applicability beyond chromatin profiling. These results highlight the limitations of fixed exclusion sets, and recommend the use of the T2T-CHM13 assembly or, for the hg38 genome assembly, "sponge" sequences as an alignment-guided strategy for reducing artifacts and improving functional genomics analyses.

Software↗
PubMed2026

Longitudinal characterization of mixed-genotype SARS-CoV-2 infections in a military cohort reveals compartmentalized viral populations.

UNLABELLED: Mixed-genotype severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections are a concern due to the potential generation of novel recombinants that give rise to new variants. To better understand intra-host viral dynamics, we analyzed specimens from 24 participants from the U.S. Military Health System's Epidemiology, Immunology, and Clinical Characteristics of Emerging Infectious Diseases with Pandemic Potential COVID-19 cohort with suspected mixed-genotype SARS-CoV-2 infections. From an initial 24 suspected cases, we confirmed 17 as genuine coinfections and graded them by evidence: 7 were "strong"; 4 were "moderate"; 6 were "weak"; and 7 were deemed unlikely to be true mixed-genotype infections. Access to swabs from multiple body sites across the course of infection allowed us to observe compartmentalization and shifts in variant dominance that would have been missed by a single-timepoint analysis, as well as one recombinant Omicron BA.1/BA.2 genome. By using an evidence-based bioinformatic framework to assess sequencing data from well-characterized clinical cases, we distinguished genuine coinfections from bioinformatic artifacts. Our findings emphasize the importance of both extensive specimen collection and careful bioinformatic approaches in ascertaining dual genotype infections. IMPORTANCE: Novel recombinants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) arise from coinfections with different lineages, but mixed infections are not screened for despite risk to public health, and most surveillance relies on single swabs. We analyzed a longitudinal data set with specimens from multiple body sites, providing an opportunity to assess intra-host dynamics. To distinguish true coinfection from bioinformatic artifacts with confidence, we applied a framework that grades evidence for mixed genotypes by incorporating lineage and clade with manually validated variant calls. This allowed investigation beyond abundance levels of mixed genotypes within a single specimen, including observations of compartmentalization and a recombinant virus. This work enables further study of evolutionary, immunological, and clinical implications of mixed SARS-CoV-2 genotypes. Detecting dual-genotype infections and discriminating between true dual-genotype infection vs potential bioinformatics-based artifacts support public health and military readiness. These efforts provide evidence to bolster decision-making in molecular epidemiological studies to track transmission and for the choice of effective countermeasures.

SARS-CoV-2↗
PubMed2025

Whole Genome Methylation Sequencing via Enzymatic Conversion (EM-seq): Protocol, Data Processing, and Analysis.

Whole genome bisulfite sequencing (WGBS) has been the gold standard technique for base resolution analysis of DNA methylation for the last 15 years. It has been, however, associated with technical biases, which lead to overall overestimation of global and regional methylation values, and significant artifacts in extreme cytosine-rich DNA sequence contexts. Enzymatic conversion of cytosine is the newest approach, set to replace entirely the use of the damaging bisulfite conversion of DNA. The EM-seq technique utilizes TET2, T4-BGT, and APOBEC in a two-step conversion process, where the modified cytosines are first protected by oxidation and glucosylation, followed by deamination of all unmodified cytosines to uracil. As a result, EM-seq is degradation-free and bias-free, requires low DNA input, and produces high library yields with longer reads, little batch variation, less duplication, uniform genomic coverage, accurate methylation over a larger number of captured CpGs, and no sequence-specific artifacts.

DNA Methylation↗
PubMed1976

Changes in the electric dipole vector of human serum albumin due to complexing with fatty acids.

The magnitude of the electric dipole vector of human serum albumin, as measured by the dielectric increment of the isoionic solution, is found to be a sensitive, monotonic indicator of the number of moles (up to at least 5) of long chain fatty acid complexed. The sensitivity is about three times as great as it is in bovine albumin. New methods of analysis of the frequency dispersion of the dielectric constant were developed to ascertain if molecular shape changes also accompany the complexing with fatty acid. Direct two-component rotary diffusion constant analysis is found to be too strongly affected by cross modulation between small systematic errors and physically significant data components to be a reliable measure of structural modification. Multicomponent relaxation profiles are more useful as recognition patterns for structural comparisons, but the equations involved are ill-conditioned and solutions based on standard least-squares regression contain mathematical artifacts which mask the physically significant spectrum. By constraining the solution to non-negative coefficients, the magnitude of the artifacts is reduced to well below the magnitudes of the spectral components. Profiles calculated in this way show no evidence of significant dipole direction or molecular shape change as the albumin is complexed with 1 mol of fatty acid. In these experiments albumin was defatted by incubation with adipose tissue at physiological pH, which avoids passing the protein through the pH of the N-F transition usually required in defatting. Addition of fatty acid from soluion in small amounts of ethanol appears to form a complex indistinguishable from the "native" complex.

Binding Sites↗
PubMed2025

Beyond Blacklists: A Critical Assessment of Exclusion Set Generation Strategies and Alternative Approaches.

Short-read sequencing data can be affected by alignment artifacts in certain genomic regions. Removing reads overlapping these exclusion regions, previously known as Blacklists, help to potentially improve biological signal. Tools like the widely used Blacklist software facilitate this process, but their algorithmic details and parameter choices are not always clearly documented, affecting reproducibility and biological relevance. We examined the Blacklist software and found that pre-generated exclusion sets were difficult to reproduce due to variability in input data, aligner choice, and read length. We also identified and addressed a coding issue that led to over-annotation of high-signal regions. We further explored the use of "sponge" sequences-unassembled genomic regions such as satellite DNA, ribosomal DNA, and mitochondrial DNA-as an alternative approach. Aligning reads to a genome that includes sponge sequences reduced signal correlation in ChIP-seq data comparably to Blacklist-derived exclusion sets while preserving biological signal. Sponge-based alignment also had minimal impact on RNA-seq gene counts, suggesting broader applicability beyond chromatin profiling. These results highlight the limitations of fixed exclusion sets and suggest that sponge sequences offer a flexible, alignment-guided strategy for reducing artifacts and improving functional genomics analyses.

Journal Article↗
PubMed2026

Dataset Readiness Assessment With Large Language Model (DRAFT-LLM): A Multi-Axis Audit Guided by LLM.

This article details the Dataset Readiness Assessment for Training (DRAFT), a systematic method for determining whether a high-dimensional biological dataset is suitable for developing reliable, equitable (i.e., the extent to which model performance, error patterns, and potential benefits or harms are evaluated and found to be acceptably distributed across relevant demographic, biological, clinical, and contextual subgroups), and scientifically meaningful machine-learning models, and DRAFT Large Language Model (DRAFT-LLM), its optional human-in-the-loop extension for calibrating study-specific audits through structured, critically reviewed LLM guidance. Standard model validation often fails to detect when apparent performance is driven by spurious correlations, technical artifacts, or hidden stratification, leading to irreproducible and inequitable findings. DRAFT-LLM addresses this gap by shifting the focus from model tuning to structured dataset auditing, organized around Support Protocols 1 to 4 that capture the scientific intent, data structure, and governance constraints of a given study. These Support Protocols: (1) elicit and formalize investigator input into a study intake and dataset card; (2) compute standardized dataset statistics and structural summaries suitable for downstream analysis and LLM context; (3) configure the language model using form-based responses, safety guardrails, and governance rules; and (4) generate personalized instructions, prompts, and code templates for running DRAFT audits. Basic Protocols 1 to 3 are instantiated from this support layer for generalization, equity, and stability: they are reusable execution patterns whose concrete behavior is determined by the cards, statistics, and configurations defined in the Support Protocols. DRAFT-LLM and DRAFT are demonstrated in this article through an end-to-end case study on The Cancer Genome Atlas (TCGA). © 2026 Wiley Periodicals LLC. Support Protocol 1: Study intake and dataset card construction Support Protocol 2: Dataset structure and advanced summary statistics for LLM context Support Protocol 3: LLM configuration using structured form responses Support Protocol 4: Generation of personalized instructions for DRAFT audits Basic Protocol 1: Generalization audit Basic Protocol 2: Equity audit Basic Protocol 3: Stability audit.

Large Language Models↗
PubMed2026

Precision ID mtDNA Whole Genome Panel and sequencing of telogen hairs - perspectives for validation and implementation in casework.

Shed hair is a commonly encountered type of forensic evidence. Shed telogen hairs generally contain insufficient or highly degraded nuclear DNA for STR profiling; however, mtDNA analysis of telogen hair and hair shafts remains possible. We validated whole mitochondrial genome (mtGenome) sequencing using the Precision ID mtDNA Whole Genome Panel (Thermo Fisher Scientific) and subsequently implemented the panel for the analysis of telogen hair, buccal, and casework samples. We analysed 90 diluted DNA samples containing 3-3,600 mtDNA copies, shed telogen hairs and their corresponding mtDNA from buccal swabs from 91 individuals, and 11 archived DNA extracts from hair samples in criminal cases. Complete mtGenome sequences were consistently recovered in 99% of samples across DNA dilution series at DNA input levels as low as 47 mtDNA copies, demonstrating the assay's robustness under low-template conditions. We obtained complete and reproducible mtGenome sequences with ≥ 327 mtDNA copies/µL from telogen hair samples. After applying ISFG recommendations and excluding low-confidence discrepancies associated with high-strand bias, heteroplasmic variants and sequencing artifacts, mtGenome sequence concordance increased from 93.4% to 100%. None of the 16 negative controls produced complete mtDNA sequences. Six negative controls showed low-level mtDNA signal (2-8 variants), consisting predominantly of common polymorphisms. These samples did not yield complete mtGenome sequences and showed no correspondence to any of the analysed samples. Finally, archived telogen hair samples from criminal cases presented complete mtGenome sequences with an average read depth of 1,037x.Our findings highlight the reliability of mtDNA analysis of telogen hairs using the Precision ID mtDNA Whole Genome Panel for implementation in forensic casework.

Forensic casework↗
PubMed2026

pSTRminer: integrated bioinformatic software for genome-wide identification and population-scale evaluation of polymorphic short tandem repeats.

Animal forensic genetics plays a critical role in criminal investigations by providing crucial evidence through domestic animal individualization and wildlife species identification. While human forensic genetics benefits from standardized short tandem repeats (STR) genotyping systems, animal forensic applications encounter significant challenges, including the limited availability of validated STR markers, the prevalence of error-prone dinucleotide STRs (di-STRs), and insufficient integration of population data. To address these challenges, we developed pSTRminer, an integrated bioinformatic tool that automates genome-wide STR mining and polymorphism evaluation. By applying pSTRminer to domestic cattle (Bos taurus), we identified 775,444 STRs de novo from the reference genome and genotyped them using whole-genome sequencing data from 60 Chinese and 111 African cattle to evaluate polymorphism across diverse genetic backgrounds. This led to the development of the cattle STR database (CSDB), comprising loci with a genotyping success rate&#x2009;&#x2265;&#x2009;40% and polymorphism information content (PIC)&#x2009;&#x2265;&#x2009;0.5. Experimental validation of 30 randomly selected tetranucleotide STRs (tetra-STRs) and 33 di-STRs via next-generation sequencing in a local Chinese cattle population (n&#x2009;=&#x2009;145) confirmed marker reliability. Although tetra-STRs had lower average polymorphism levels, they exhibited significantly lower stutter ratios (p&#x2009;<&#x2009;0.05), providing a viable path for identifying discriminative markers with fewer artifacts. Systematic screening revealed that certain tetra-STRs could surpass di-STRs in polymorphism. In conclusion, pSTRminer provides a scalable framework for developing standardized STR panels, facilitating the identification of robust and informative markers in forensic applications.

Bioinformatic software↗
PubMed2026

Multimodal intervention benefits: Responder analysis of J-MINT PRIME Kanagawa trial.

INTRODUCTION: The J-MINT PRIME Kanagawa trial was an 18-month multimodal intervention (incorporating exercise, nutrition, and metabolic management) for dementia prevention. Because the primary analysis showed no significant benefits, we performed an exploratory responder analysis to identify responsive subpopulations. METHODS: We analyzed the Full Analysis Set comprising 188 participants. Classification and regression tree (CART) analysis, applied to the intervention arm, identified baseline predictors of cognitive improvement. These rules were then applied to the entire cohort to evaluate treatment effects on the Mini-Mental State Examination (MMSE) using fully adjusted mixed-effects models for repeated measures (MMRM). RESULTS: CART identified a "Target Group" (N = 108) characterized by baseline profiles such as an MMSE score < 28 or specific metabolic ranges (e.g., LDL-C < 135 mg/dL). Within this target group, the intervention significantly preserved MMSE trajectories compared with the control group (group &#xd7; time interaction, P = 0.022). In contrast, the Non-Target Group (N = 80), consisting of high-functioning individuals (MMSE &#x2265; 28), exhibited no significant group &#xd7; time interaction. DISCUSSION: Multimodal interventions may effectively preserve global cognition in older adults with sub-threshold cognitive decline. Careful targeting of appropriate populations, while considering potential longitudinal measurement artifacts (e.g., practice effects), is essential. These findings provide a hypothesis-generating framework that warrants external validation in future prevention trials.

Humans↗
PubMed2026

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

Humans↗
PubMed2026

Metax enables accurate cross-domain taxonomic profiling of metagenomes.

Taxonomic profiling is fundamental to microbiome research, yet achieving high species-level accuracy remains challenging for complex communities that span bacteria, viruses, eukaryotes, and archaea, and these limitations are exacerbated in low-biomass, host-dominated samples. We introduce Metax, a cross-domain taxonomic profiler that integrates coverage-based probabilistic modeling with an expectation-maximization framework to distinguish true microbial signals from artifacts. Across >600 samples from host-associated, environmental, wastewater, and low-biomass clinical settings, including benchmarks with limited reference representation, Metax improved profiling accuracy, achieving on average 55% higher F1 scores and 45% lower Bray-Curtis dissimilarity than other methods. Moreover, this broad evaluation demonstrated that Metax resolved bacterial and viral signatures of peri-implantitis in oral microbiomes and revealed signals suggestive of reagent-borne contaminants and reference misassemblies in plasma-cell-free DNA. By leveraging genome-wide coverage evidence, Metax enables robust cross-domain profiling across diverse sample types and sequencing depths, including settings where reference databases are highly incomplete.

abundance estimation↗
PubMed2026

Strategies for mosaic variant calling in brain disorders.

The human brain is a genomic mosaic, where postzygotic mutations arising from embryogenesis to senescence drive diverse neurodevelopmental and neurodegenerative diseases. Because of numerous sequencing artifacts at ultralow variant allele frequencies (VAFs), detecting these variants remains a significant analytical challenge. This review focuses on single-nucleotide variants and small indels, summarizing current strategies for aligning sampling methods, including bulk, laser capture microdissection, and single-cell genomics, with the expected clonal architecture of the brain. It emphasizes that mosaic detection sensitivity is fundamentally constrained by sequencing depth, since even the most advanced algorithms cannot identify variants not physically represented in the sequencing library. The review further recommends the selection of variant calling algorithms based on validated VAF detection performance, matching tools like MuTect2 and MosaicForecast to their optimal performance ranges. Furthermore, we discuss how multitissue sampling, as emphasized by the SMaHT project, addresses the matched-control dilemma and supports accurate variant classification via cross-tissue VAF gradients. Integrating these established pipelines with multiomics modalities, including transcriptomic and epigenetic data, could advance the field toward a functional understanding of how the somatic genome impacts human brain health and disease.

Humans↗
PubMed2026

Mitigating pH-induced instability in deruxtecan-based ADCs: an onboard-mixing icIEF approach for robust charge heterogeneity characterization.

Accurate charge variant analysis of antibody-drug conjugates (ADCs) is essential for understanding product heterogeneity and ensuring quality control. However, Deruxtecan (DXd)-based ADCs present a unique analytical challenge due to the intrinsic instability of the payload, where the lactone ring readily undergoes hydrolysis under alkaline conditions, resulting in time-dependent shifts in charge distribution during imaged capillary isoelectric focusing (icIEF). In this study, we describe the development of an onboard-mixing icIEF method designed to minimize pH-induced degradation during sample preparation. By separating ADC samples from carrier ampholytes (CAs) prior to injection and enabling real-time mixing within the instrument, this approach effectively suppresses premature lactone ring opening and stabilizes charge variant profiles. Comparative studies between conventional premixing and onboard-mixing approach demonstrated that the latter significantly enhances reproducibility, particularly for acidic variants that are highly sensitive to structural conversion. Comprehensive method validation confirmed excellent precision, linearity, and sensitivity, with consistent performance across run-to-run and intra-day analyses. The results underscore the importance of controlling microenvironmental pH exposure in the analysis of chemically instable ADCs. The proposed onboard-mixing strategy provides a robust and efficient solution for icIEF-based characterization, reducing analytical artifacts while simplifying method development. This approach is broadly applicable to ADCs and other biotherapeutics containing pH-sensitive functional groups.

Hydrogen-Ion Concentration↗
PubMed2026

Fundamentals of pacemakers ECG interpretation - part 2.

BACKGROUND: Modern pacemakers incorporate arrhythmia-response algorithms, ventricular pacing minimization protocols, and safety mechanisms that generate ECG patterns indistinguishable from pathological AV block, sensing malfunction, or device-mediated tachycardia. Failure to recognize these algorithm-driven signatures leads to unnecessary interventions, misdiagnosis, and inappropriate device reprogramming. This manuscript is the second in a two-part series on pacemaker ECG interpretation. METHODS: We conducted a narrative review of peer-reviewed literature and device-specific documentation on algorithm-driven ECG behavior, synthesizing evidence across arrhythmia recognition, upper rate physiology, ventricular pacing minimization, mode switching, safety mechanisms, and hysteresis algorithms. RESULTS: Pacemaker-mediated tachycardia produces regular paced wide-complex tachycardia locked at the upper tracking rate, initiated by any event with retrograde VA conduction. Ventricular tachycardia is identified by QRS morphology diverging from the known paced pattern, absent pacing spikes, and AV dissociation. Upper rate Wenckebach behavior mimics Mobitz type I AV block; 2:1 upper rate response mimics second-degree AV block. Ventricular pacing minimization algorithms produce isolated nonconducted P waves and prolonged AV intervals that simulate pathological conduction disease. Mode switching causes abrupt rate drops misidentified as output failure. Ventricular safety pacing generates a conspicuously short, fixed AV interval. Three discrete pacing artifacts reflect AV-sequential cardiac resynchronization therapy (CRT), ventricular safety pacing in CRT, or His-bundle pacing with backup RV output. Rate and AV hysteresis produce pauses and wandering AV intervals mimicking oversensing or Wenckebach periodicity. CONCLUSIONS: Recognizing algorithm-driven ECG patterns requires knowledge of device timing intervals and refractory periods, which lets clinicians distinguish programmed behavior from true malfunction or cardiac arrhythmia.

Humans↗