bims-curels 2026-05-31 papers

bims-curels

Biomed News

on Leigh syndrome

Issue of 2026–05–31
fourteen papers selected by
Cure Mito Foundation

MyD88 deficiency modestly attenuates disease in a Leigh syndrome mouse model while enrofloxacin accelerates disease.
Cholesterol in Mitochondrial Diseases-Friend or Foe?
Mito_Plot: open-source pipeline for quantification and visualization of mitochondrial DNA heteroplasmy.
External controls for rare disease drug development: Lessons for emerging and advanced therapeutic modalities.
Mitochondrial transfer: A comprehensive analysis of mechanistic insights, preclinical applications, and technological innovations.
Artificial Intelligence Across the Drug Development Lifecycle.
Communicating in Palliative Care for Neurodegenerative Diseases: A Qualitative Study on Professional-Family Interactions.
Turning failure into success: how artificial intelligence can help personalize therapies and re-use patient data.
Mitochondrial myopathy biomarkers.
What Do We Know About How Generative AI is Being Used for Patient-Centered Communication? A Scoping Review.
Beyond readability: Advancing patient-centered evaluation of AI-generated clinical reports.
Toward equitable access to cell and gene therapies: rethinking co-payments.
MitoFREQ: A novel approach for mitogenome frequency estimation from top-level haplogroups and single nucleotide variants.
Battling the feed: How social media has overtaken professional medical advice.

bioRxiv. 2026 May 17. pii: 2026.05.13.724988. [Epub ahead of print]

MyD88 deficiency modestly attenuates disease in a Leigh syndrome mouse model while enrofloxacin accelerates disease.

Allison R Hanaford, Elizaveta A Olkhova, Ryan Liao, Ashley Ching, Alvin Huang, Erin Shien Hsieh, Kino Watanabe, Yihan Chen, Megan Wichman, Noelle Hwang, Katerina James, Michael Mulholland, Vivian Truong, Holly Coulson, Kerry Gibbins, Owen Cairns, Anastasia Dimitriou, Bernhard Kayser, Brittany M Johnson, Surojit Sarkar, Vandana Kalia, Simon C Johnson.

Primary genetic mitochondrial diseases (GMDs) are a clinically and genetically diverse group of diseases estimated to impact over 1 in 4,000 individuals. Leigh syndrome (LS) is the most common pediatric presentation of GMD. LS typically presents within the first years of life and is a severe progressive multi-system disorder. Symmetric progressive inflammatory brain lesions are a defining feature of the disease. Patients can also present with seizures, metabolic dysfunction, muscle weakness, and other symptoms. No effective clinical treatments currently exist. Recent data from the Ndufs4 (-/-) mouse model shows that peripheral macrophages contribute to brain lesions in LS, that disease is causally driven by innate immune populations, and that depletion of innate immune cells prevents LS disease. However, the precise mechanisms underlying immune activation remain unknown. Certain mitochondrial macromolecules retain bacterial signatures and can act as potent agonists for innate immune pathways. For example, cytoplasmic mitochondrial RNA and mitochondrial DNA are detected by Toll-like receptors (TLRs) 7 and 9, respectively, at the endosome. Accordingly, these are considered strong candidates for mediating innate immune activation in LS. Here, we generated TLR signaling deficient Ndufs4 (-/-)/ MyD88 (-/-) animals to assess whether TLR signaling plays a role in disease onset or progression in LS. Loss of MyD88 in Ndufs4 (-/-) animals statistically significantly increased survival and delayed the onset of some symptoms, but the benefits were modest compared to CSF1R inhibition from prior work. We conclude that Myd88 -mediated immune signaling is not a primary driver of LS. Notably, prophylactic enrofloxacin treatment, which was necessary for production of innate immune deficient MyD88 (-/-) animals, modestly decreased survival and accelerated disease. The impact of enrofloxacin and similar drugs in the context of mitochondrial disease warrants further investigation.

DOI: https://doi.org/10.64898/2026.05.13.724988
Int J Mol Sci. 2026 May 13. pii: 4353. [Epub ahead of print]27(10):

Cholesterol in Mitochondrial Diseases-Friend or Foe?

Mila Taylor, Michal Halicki, Paul Chazot.

  Serving as central signalling organelles and hubs of metabolism, mitochondria are essential for cellular homeostasis. Mitochondrial disease can arise from mutations to nuclear or mitochondrial DNA, which result in disruptions to normal mitochondrial function. This generates a suite of rare disorders which are multi-system and often fatal. Variable tissue distribution of mitochondria, alongside a high degree of heterogeneity in associated phenotype, has resulted in an inadequate understanding and characterisation of mitochondrial disease. Addressing this issue is therefore crucial for better clinical management and patient outcomes. Cholesterol dyshomeostasis is a potential pathological hallmark of numerous mitochondrial diseases. Cholesterol is an essential lipid and bioactive compound involved in numerous mitochondrial and cellular processes. A growing number of studies have reported perturbations to cholesterol biosynthesis, cholesterol import, and cholesterol ratios in cell and animal models and individuals with mitochondrial disease, suggesting it could be a unifying feature of these disparate and variable disorders. This review summarises the current experimental evidence for the role of cholesterol dyshomeostasis in mitochondrial disease. It will further discuss reports of statin intolerance, generally attributed to off-target action on mitochondrial structures, in the context of this evidence. Ultimately, the necessity of further integrative clinical and experimental studies exploring the potential of cholesterol dyshomeostasis as a pathological hallmark of mitochondrial disease will be highlighted.

Keywords:  cholesterol; dyshomeostasis; lipid; mitochondria; statin

DOI:  https://doi.org/10.3390/ijms27104353
BMC Bioinformatics. 2026 May 25.

Mito_Plot: open-source pipeline for quantification and visualization of mitochondrial DNA heteroplasmy.

Kohta Nakamura, Naoyuki Matsumoto, Yasushi Okazaki.

   BACKGROUND: Mitochondrial DNA heteroplasmy plays a crucial role in mitochondrial function, aging, and a wide range of human diseases. Recent advances in high-throughput sequencing have enabled large-scale detection of heteroplasmic variants; however, effective cohort-level integration, comparison, and visualization of Mutant Allele Frequency (MAF) values remain challenging. Existing tools often focus on single-sample visualization or require substantial manual preprocessing, limiting their scalability and usability for large cohorts. To address these challenges, we developed Mito_Plot, an open-source computational pipeline designed for standardized quantification and intuitive visualization of Mitochondrial DNA (mtDNA) heteroplasmy across multiple samples.
RESULTS: Mito_Plot accepts standard mitochondrial VCF files and automatically calculates MAF based on allelic depth information. MAF data from multiple samples are aggregated into a unified matrix aligned by genomic position, enabling direct cross-sample comparison. The pipeline provides interactive two-dimensional circular plots that map MAF onto the mitochondrial genome with gene-level annotations, facilitating rapid identification of mutation hotspots and sample-specific patterns. In addition, Mito_Plot offers optional three-dimensional visualizations that enhance exploration of large cohorts by separating variant distributions across samples and genomic regions. Application of Mito_Plot to multi-sample mitochondrial sequencing datasets demonstrated robust handling of both variants with low and high MAF values, efficient processing of large cohorts, and improved interpretability compared with static or single-sample visualizations.
CONCLUSIONS: Mito_Plot is a scalable, user-friendly software pipeline for cohort-scale quantification and visualization of mtDNA MAF. By integrating standardized MAF calculation with interactive 2D and 3D visualizations, Mito_Plot facilitates comprehensive exploration of mitochondrial variant landscapes across large datasets. The open-source and modular design of the software supports reproducible research and flexible integration into existing analysis workflows, making Mito_Plot a practical resource for mitochondrial genomics research and clinical investigations.

Keywords:  Circular genome; Cohort-scale analysis; Data visualization; Mitochondrial DNA; Mitochondrial heteroplasmy; Variant analysis

DOI:  https://doi.org/10.1186/s12859-026-06476-2
Mol Ther Adv. 2026 Jun 11. 34(2): 201754

External controls for rare disease drug development: Lessons for emerging and advanced therapeutic modalities.

Samuel H Hughes, Lauren A Beretich, Matthew Fuller, Kimberly Goodspeed, Melissa Penn, Leonard A Valentino, Caitlin McCombs.

External controls, particularly natural history studies, play an increasingly important role in rare disease therapeutic development where traditional randomized trials are often infeasible. This review examines regulatory acceptance patterns for gene and cell therapies approved between 2019 and 2025, analyzing successful cases like onasemnogene abeparvovec (Zolgensma) for spinal muscular atrophy and elivaldogene autotemcel (Skysona) for cerebral adrenoleukodystrophy alongside unsuccessful applications. Key success factors include systematic data collection, clinically meaningful endpoints, appropriate patient matching, and disease characteristics that preclude randomization. Recent FDA initiatives, including the Rare Disease Evidence Principles program, signal growing regulatory flexibility, although acceptance remains context-dependent and requires robust data quality standards.

DOI: https://doi.org/10.1016/j.omta.2026.201754
Neural Regen Res. 2026 May 14.

Mitochondrial transfer: A comprehensive analysis of mechanistic insights, preclinical applications, and technological innovations.

Yao Wang, Zitong Wang, Jie Zhao, Yimin Zhou, Dong Wei, Jingjing Zhao, Zhongqing Sun, Wen Jiang.

  Mitochondrial transfer, the intercellular exchange of functional mitochondria, is crucial for maintaining cellular homeostasis and promoting tissue repair, particularly in neurological disorders associated with mitochondrial dysfunction. This review addresses the mechanisms through which mitochondrial transfer occurs, including tunneling nanotubes, extracellular vesicles, gap junction channels, and cell fusion. Mitochondrial transfer and transplantation have demonstrated positive therapeutic effects in various disease models, such as cerebral hemorrhage, ischemic stroke, Alzheimer's disease, and multiple sclerosis. Exogenous mitochondria can integrate into recipient cells, enhancing adenosine triphosphate production, restoring redox balance, and improving cellular survival under stress conditions. However, clinical translation faces significant hurdles, including immune rejection, limited recipient cell uptake capacity, a lack of standardized manufacturing protocols, and unresolved ethical concerns regarding mitochondrial sourcing. To address these challenges, cutting-edge biotechnological strategies, such as mitochondrial surface modification, nanocarrier-based delivery, biomaterial-assisted transplantation, and the use of engineered vesicles, are being developed to enhance the precision, stability, and biocompatibility of mitochondrial delivery. Furthermore, innovative approaches, including CRISPR-based genome editing, 3D-bioprinted tissue models, and artificial intelligence-assisted predictive platforms, are being explored to enhance mitochondrial function and delivery efficiency. Current strategies to harness mitochondrial transfer include pharmacological agents that enhance mitochondrial dynamics, stem cell-based delivery of healthy mitochondria, and the aforementioned bioengineered platforms. In conclusion, the integration of mitochondrial transfer as a groundbreaking treatment option for neurological disorders relies on addressing two to three fundamental challenges. These include the establishment of standardized and scalable protocols for production and quality control, formulating approaches to minimize immune reactions and improve the efficiency of mitochondrial integration, and creating a well-defined ethical and regulatory framework for sourcing and utilizing mitochondria. The primary contribution of this work lies in its integrated analysis of mechanistic insights, preclinical applications, and technological innovations, providing a consolidated roadmap for advancing mitochondrial transplantation from bench to bedside.

Keywords:  artificial cells; biomaterial-assisted transplantation; extracellular vesicles; mesenchymal stem cells; mitochondrial dysfunction; mitochondrial surface modification; mitochondrial transfer; mitochondrial transplantation; neurological disorders; tunneling nanotubes

DOI:  https://doi.org/10.4103/NRR.NRR-D-25-01156
Med Sci (Basel). 2026 May 10. pii: 248. [Epub ahead of print]14(2):

Artificial Intelligence Across the Drug Development Lifecycle.

Grigory Demyashkin, Mikhail Parshenkov, Sergey Zyryanov, Alexander Yavorskiy, Petr Shegai, Andrey Kaprin.

  Artificial intelligence (AI) is becoming a central driver of change across the drug development lifecycle. However, its integration is evolving so rapidly that it remains essential to understand how these technologies are currently positioned within the field. Because reliable access to high-quality (effective and safe) drugs is essential to public health, the pharmaceutical product lifecycle (PPL) offers a coherent framework for evaluating how AI can enhance evidence and data creation across all stages. To understand where AI genuinely adds value, this review examines its contribution across the major stages of the PPL. Rather than treating drug discovery, nonclinical evaluation, clinical research, and post-marketing assessment as separate domains, we view them as a continuous chain of data, where digital technologies enhance different decision points in distinct ways. In early discovery, AI narrows the search space by integrating diverse datasets to prioritize candidates most likely to succeed. Nonclinical models increasingly rely on machine-learning systems designed to improve the human relevance of safety predictions. Within clinical trials, AI supports cohort formation, real-time monitoring, and new analytic strategies that supplement empirical evidence. Case studies from leading pharmaceutical companies illustrate that the most meaningful advances emerge when AI is embedded not as a standalone tool but as part of a broader data strategy that links information across stages. Taken together, current evidence suggests that AI is beginning to transform data generation and integration throughout the PPL. Given the accelerating pace of digital innovation, it is essential for the field to maintain continuous awareness of emerging methodologies and evolving regulatory frameworks to ensure that these technologies are implemented in a reliable, transparent, and scientifically grounded manner.

Keywords:  artificial intelligence; clinical trials; drug development; drug discovery; machine learning; nonclinical trials; precision medicine

DOI:  https://doi.org/10.3390/medsci14020248
Brain Sci. 2026 Apr 29. pii: 481. [Epub ahead of print]16(5):

Communicating in Palliative Care for Neurodegenerative Diseases: A Qualitative Study on Professional-Family Interactions.

Barbara Rizzi, Maria Chiara Gandini, Andreina Saba, Giada Lonati, Angela Recchia.

  Background/Objectives: In Palliative Care (PC), the communication is an essential aspect of care becoming particularly significant at the end-of-life. In neurodegenerative diseases, communication involves additional complexity due to prolonged disease trajectories, early cognitive decline, and frequent loss of decision-making capacity. The aim of this study was to explore PC healthcare professionals' experiences with communication process and relational dynamics involving families of patients with advanced and terminal neurogenerative disease. Methods: The study design was qualitative, using semi-structured interviews and reflexive thematic analysis. Participants were healthcare professionals directly involved in communication with the family. Results: Twenty PC professionals were interviewed, generating 792 coded excerpts. Four themes emerged: (1) Navigating PC in neurodegenerative diseases, highlighting shift from oncology-centred palliative models toward neuropalliative care, with distinctive relational challenges; (2) Navigating conversations between professionals and families, describing multidisciplinary communication, core clinical and emotional topics, and goal-oriented decision-making in contexts of impaired patient capacity; (3) Facing challenges in health care professional-family communication, including conspiracy of silence, absence of Advance Treatment Directives (ATD) or Shared Care Planning (SCP), and limited collaboration with neurologists; and (4) Envisioning methods for improvement, emphasizing the need for disease-specific competencies, advanced relational skills, interprofessional coordination, and support for professionals' emotional wellbeing. Conclusions: Communication in neurodegenerative palliative care is an ongoing relational and interpretative process requiring professionals to mediate uncertainty, surrogate decision-making, and caregiver burden. Strengthening disease-specific communication skills, early integrated PC, and structured interprofessional collaboration may enhance shared decision-making, caregiver support, and care continuity.

Keywords:  caregiver; communication; family; health care professional; neurodegenerative diseases; palliative care; qualitative research

DOI:  https://doi.org/10.3390/brainsci16050481
Purinergic Signal. 2026 May 29. pii: 54. [Epub ahead of print]22(3):

Turning failure into success: how artificial intelligence can help personalize therapies and re-use patient data.

Maria P Abbracchio, Ernesto Damiani.

  Despite robust preclinical evidence, many clinical trials, including several that targeted the purinergic system, fail to demonstrate efficacy in humans. Failure may stem from inability to accurately identify patient subgroups responding similarly to treatments. Here, we explore the potential of artificial intelligence to revolutionize how we group and classify patients in clinical studies. We introduce a new framework using Large Language Models-generated embeddings of detailed patient data, to create a semantic-aware latent space, enabling us to identify truly meaningful patients' clusters. Large Language Models can provide explainable groupings, giving clear reasons why certain patients respond similarly to treatments. We present an example of successful application of this approach through the re-analysis of the AMARANTH clinical trial (NCT02245737, involving ~ 2200 patients and completed in 2018) testing Lanabecestat, a BACE1 inhibitor decreasing β-amyloid production in Alzheimer's disease, for which traditional analysis reported no efficacy. As in the original trial, our simulation showed no overall benefit. However, re-analysis per patients' clusters and subjects' re-stratification by semantic similarities (shared symptom profiles, progression patterns) identified a patients' subgroup in one of the clusters showing Lanabecestat-associated slower disease worsening, thus succeeding where the full trial had failed. By making a new therapy available to at least a subset of patients with a defined disease, this new approach may help maximize the return on drug development and reduce the burden on healthcare. Moreover, it will significantly improve the precision, efficiency and interpretability of clinical trials, paving the way for a new era of personalized medical treatments.

Keywords:  Artificial intelligence; Clinical trials; Large language models; Patient stratification; Precision medicine; Purinergic signalling

DOI:  https://doi.org/10.1007/s11302-026-10165-3
Adv Clin Chem. 2026 ;pii: S0065-2423(26)00021-1. [Epub ahead of print]133 161-216

Mitochondrial myopathy biomarkers.

Marco Refrigeri, Alessandra Tola, Rosangela Mogavero, Maria Michela Pietracupa, Giulia Gionta, Roberto Scatena.

  Mitochondrial myopathies comprise a heterogeneous group of disorders arising from structural or functional mitochondrial impairments that disrupt oxidative phosphorylation and cellular ATP production. The resulting energy deficit manifests not only in muscle but frequently leads to multi-systemic disease involving the brain, heart, kidneys, and endocrine system, creating a complex and often confounding clinical presentation. A critical, often overlooked aspect of their pathophysiology is that mitochondrial dysfunction extends far beyond bioenergetics. These organelles are vital hubs for biosynthetic pathways, calcium homeostasis, thermogenesis, apoptosis, and redox-sensitive signaling pathways that govern gene expression. The disruption of these integrated functions, whose molecular consequences are still being elucidated, is central to the disease's progression and heterogeneity. This clinical and molecular complexity contributes to significant diagnostic delay, with many remaining undiagnosed. Therefore, the development and strategic implementation of reliable biomarkers are essential. This review critically evaluates current and emerging biomarkers, proposing a diagnostic framework designed to improve diagnostic accuracy, limit unnecessary procedures, and ensure timely access to therapeutic interventions and genetic counseling.

Keywords:  Biomarkers; Cell-free circulating mtDNA; Creatine; Diagnosis; Exercise intolerance; FGF21; GDF-15; Genetics; Mitochondrial disease; Mitochondrial medicine; Mitochondrial myopathy; Neurofilaments; Oxidative phosphorylation

DOI:  https://doi.org/10.1016/bs.acc.2026.01.007
Health Commun. 2026 May 26. 1-21

What Do We Know About How Generative AI is Being Used for Patient-Centered Communication? A Scoping Review.

Ashley Barrett, Heidi Uhey.

This paper offers a scoping review of extant research examining how generative artificial intelligence (AI) is being designed, tested, and used to facilitate patient-centered communication. Using the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, we analyzed 221 publications from 2020 to 2025 across eight databases. Five themes emerged to depict previous research studying how AI healthcare applications have furthered patient-centered communication, including (1) the use of conversational agents and social robots to increase patient engagement, (2) using NLP to examine patient information texts to glean insights that can enhance patient communication, (3) employing LLMs to generate patient-friendly clinical reports and answer patient-specific medical questions, (4) integrating AI into digital health platforms to enhance patient reporting and self-management, and (5) integrating AI into decision support tools that facilitate shared decision-making and automate routine tasks so that physicians can spend more time communicating with patients. New AI tools show promise in increasing efficiency, care personalization, and emotional support, but research gaps remain. Specifically, a future agenda calls for scholars to further investigate how AI can be designed for equitable patient communication, how AI can enhance communication during health transitions, how AI tools can better involve family and caregivers in patient-centered communication, and longitudinal studies that evidence AI's impact on real-world outcomes over time, among others.

DOI: https://doi.org/10.1080/10410236.2026.2666572
Patient Educ Couns. 2026 May 22. pii: S0738-3991(26)00235-1. [Epub ahead of print]149 109702

Beyond readability: Advancing patient-centered evaluation of AI-generated clinical reports.

Michela Monaci, Elena Viviani, Federico Sottotetti, Sarah Bigi, Serena Barello.

  Large language models offer promising opportunities to simplify clinical documentation and improve the accessibility of medical information for patients. Commenting on the recent article by Fereydooni et al. (2026) on GPT-4-generated patient-centered prostate cancer pathology reports, this correspondence argues that readability, while necessary, is not sufficient to ensure genuinely patient-centered communication. Drawing on recent evidence on after-visit summaries, discharge summaries, medical reports, open notes, and patient-facing educational materials, we suggest that AI-generated clinical documents should be evaluated through broader multidimensional criteria, including comprehensibility, relevance, usability, emotional impact, and empowerment potential. We also emphasize that patients and caregivers should be directly involved in the design and evaluation of AI-generated clinical communication tools. Moving beyond readability is essential to ensure that these tools do not merely simplify medical terminology, but support patients in making sense of health information and participating meaningfully in their care.

Keywords:  Artificial intelligence; Clinical documentation; Health literacy; Patient engagement; Patient-centered communication; Shared decision-making

DOI:  https://doi.org/10.1016/j.pec.2026.109702
Am J Manag Care. 2026 May;32(5): 257-259

Toward equitable access to cell and gene therapies: rethinking co-payments.

William H Shrank, Ellen Kelsay, A Mark Fendrick.

Cell and gene therapies represent a transformative advance in the treatment of inherited disorders, hematologic malignancies, and progressive neuromuscular diseases, offering the potential for durable remission or cure. However, with onetime therapies now routinely priced above $3 million, their integration into the US health care system presents significant challenges related to affordability, access, and equity. Although patient cost sharing represents a small fraction of total cell and gene therapy spending, deductibles and coinsurance can expose commercially insured and Medicare beneficiaries to thousands of dollars in out-of-pocket costs, creating meaningful financial barriers for patients and families already burdened by illness-related economic strain. This article examines the role of co-payments in the context of high-cost, physician-administered, curative therapies and evaluates whether traditional cost-sharing rationales remain applicable. Drawing on emerging evidence from chimeric antigen receptor T-cell therapy utilization, we highlight persistent disparities in access by race, socioeconomic status, geography, and insurance type and discuss how patient cost sharing may exacerbate inequities in a category characterized by strict clinical eligibility, intensive oversight, and minimal risk of inappropriate use.

DOI: https://doi.org/10.37765/ajmc.2026.89933
Forensic Sci Int Genet. 2026 May 26. pii: S1872-4973(26)00107-9. [Epub ahead of print]85 103526

MitoFREQ: A novel approach for mitogenome frequency estimation from top-level haplogroups and single nucleotide variants.

Mikkel Meyer Andersen, Nicole Huber, Kimberly Sturk-Andreaggi, Tóra Oluffa Stenberg Olsen, Walther Parson, Charla Marshall.

  Forensic lineage markers pose a challenge in forensic genetics as their evidential value can be difficult to quantify. Lineage marker population frequencies can serve as one way to express evidential value. However, for some markers, e.g., high-quality whole mitochondrial DNA genome sequences (mitogenomes), population data remain limited. In this paper, we offer a new method, MitoFREQ, for estimating the population frequencies of mitogenomes. MitoFREQ uses the mitogenome resources HelixMTdb and gnomAD, harbouring information from 195,983 and 56,406 mitogenomes, respectively. Neither HelixMTdb nor gnomAD can be queried directly for individual mitogenome frequencies, but offers single nucleotide variant (SNV) allele frequencies for each of 30 "top-level" haplogroups (TLHG), which mainly correspond to the first letter of major mitochondrial DNA (mtDNA) haplogroups (e.g., A, B, C, D, E, etc.) except for the L0, L1, L2, L3, L4-6, HV, and R/B haplogroups. We propose using the HelixMTdb and gnomAD resources by classifying a given mitogenome within the TLHG scheme and subsequently using the frequency of its rarest SNV within that TLHG weighted by the TLHG frequency. We show that this method is guaranteed to provide a higher population frequency estimate than if a refined haplogroup and its SNV frequencies were used. Further, we show that top-level haplogrouping can be achieved by using only 227 specific positions for 99.9% of the tested mitogenomes, potentially making the method available for low-quality samples. The method was tested on two types of datasets: high-quality forensic reference datasets and a diverse collection of scrutinized mitogenomes from GenBank. This dual evaluation demonstrated that the approach is robust across both curated forensic data and broader population-level sequences. This method produced likelihood ratios in the range of 100-100,000, demonstrating its potential to strengthen the statistical evaluation of forensic mtDNA evidence. We have developed an open-source R package mitofreq that implements our method, including a Shiny app where custom TLHG frequencies can be supplied.

Keywords:  Evidential weight; Forensic genetics; Likelihood ratio; Match probability; mtDNA

DOI:  https://doi.org/10.1016/j.fsigen.2026.103526
Patient Educ Couns. 2026 May 23. pii: S0738-3991(26)00234-X. [Epub ahead of print]150 109701

Battling the feed: How social media has overtaken professional medical advice.

Mohammed Ayyad, Jared Walsh.

  Social media has become a dominant source of health information, often shaping patient beliefs before clinical encounters occur. Clinicians increasingly encounter situations in which brief, emotionally engaging online content competes with evidence-based explanations delivered in the clinical setting. This article examines why patients may find social media narratives more compelling than medical advice and outlines communication strategies that clinicians can use to address misinformation while preserving trust. We synthesize evidence from health communication and behavioral science to describe the psychological and structural mechanisms that contribute to susceptibility to misinformation, including the illusory truth effect, familiarity bias, emotional resonance, and the accessibility of simplified narratives delivered through algorithm-driven platforms. We also examine how digital environments amplify message visibility and credibility through repetition, visual presentation, and perceived authenticity, often shifting trust formation from credential-based authority toward relatability and presentation style. These dynamics challenge traditional models of patient education that rely primarily on data, guidelines, and technical explanations. Finally, we propose practical strategies for clinicians to mitigate misinformation during clinical encounters and in digital spaces. These include eliciting the source of a patient's belief, translating evidence into clear and relatable language, and participating responsibly in social media environments where health beliefs are increasingly formed. Emerging scholarship on expert influencers and knowledge influencers demonstrates that professionals who combine subject-matter expertise with platform-native communication styles can translate complex information into formats that are both credible and accessible to the public. Similarly, widely followed physician communicators illustrate how evidence-based messaging delivered through clear visuals, conversational language, and direct engagement can counter misinformation and build trust among digitally active audiences. Together, these examples highlight the importance of integrating scientific rigor with communication strategies that are accessible, credible, and responsive to contemporary patterns of information consumption. As digital platforms continue to shape health decision-making, clinicians should consider understanding and adopting communication practices that protect the integrity of medical counseling to support informed, collaborative care.

Keywords:  Digital health communication; Health literacy; Health misinformation; Patient education; Social media

DOI:  https://doi.org/10.1016/j.pec.2026.109701