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The kynurenine pathway (KP) constitutes the primary route of tryptophan catabolism, generating a spectrum of neuroactive metabolites that profoundly influence central nervous system function. Dysregulation of the KP is increasingly recognized as a critical pathogenic mechanism underlying diverse neuropathological conditions. This review critically evaluates the most widely cited mammalian cellular models currently utilized to delineate the causal role of KP alterations in neurological disease. Specifically, this article examines primary cell cultures, immortalized and tumor-derived cell lines, stem cell-derived systems, and ex vivo organotypic brain slices and tissues, highlighting their distinct methodological advantages, translational limitations, and specific enzymatic profiles. Across the described cellular systems, a recurring mechanistic theme emerges: quinolinic acid-driven mitochondrial dysfunction, oxidative stress, and NAD+ depletion converge in neurodegenerative conditions such as Alzheimer's disease, Huntington's disease, and amyotrophic lateral sclerosis. Conversely, kynurenic acid exhibits disorder-dependent-and at times opposing-roles, attenuating dopaminergic neurotoxicity in Parkinson's disease models while contributing to synaptic pruning deficits in schizophrenia models. Furthermore, cellular models demonstrate that IDO1/TDO induction and downstream metabolite shifts are frequently cell type- and species-dependent, complicating direct extrapolation to human pathology. Because no single experimental system achieves complete physiological fidelity, elucidating the complex dynamics of the KP and identifying novel therapeutic targets requires the integration of data across complementary platforms.

Pregnancy concurrent with incretin-based medications is contraindicated due to unknown risk of teratogenicity, as is breastfeeding. The aim of this systematic scoping review was to investigate potential risks and benefits of incretin-based medications in relation to preconception, pregnancy, and postnatal health, and to propose expert guidelines for clinical practice. An international expert multidisciplinary group was formed. Research questions relevant to women's reproductive health and incretin-based medications were refined collaboratively, utilizing a lifecourse approach. A systematic search was undertaken on July 23, 2025, across several databases and grey literature sources. Primary data from human studies were prioritized above animal studies. Titles, abstracts, and full text articles were screened independently by two authors. Data were extracted by two people independently using a pre-defined proforma and synthesized narratively. Consensus recommendations were made. Thirty-four articles were included in the evidence synthesis: 11 randomized trials, nine observational studies, two pharmacovigilance reviews, nine case reports/series, two animal studies, and one ex vivo study. No qualitative studies were identified. Evidence was found for 18/32 (56.3%) research questions. Most studies related to preconception or pregnancy usage; two addressed contraception, and one was about lactation. The sample size of exposed pregnancies ranged from 1 to 4267. Three studies reported exposure throughout pregnancy. Three studies investigated postpartum usage. One (animal) study had offspring data beyond birth. No studies reported an increase in congenital anomalies. Clinical practice and research recommendations were made based on current available evidence and evidence gaps, encompassing contraception, preconception, nutritional, pregnancy monitoring, lactation, and longer-term outcomes.

Sensitive monitoring tools are needed to track progression in neurodegenerative diseases and assess interventions before overt brain damage occurs. We propose speech as a non-invasive, easily collected biomarker to capture disease-related variation over time. We developed and validated Neurodegenerative Disease Speech Network (NDSNet), an automated deep learning model that generates individual speech-derived estimates of contemporaneous clinical scores at each visit in people with Huntington's disease, from presymptomatic stages (Huntington's Disease Integrated Staging System [HD-ISS] stages 0-1) to symptomatic stages (HD-ISS stages 2-3). We included data from people with Huntington's disease and healthy controls from three prospective longitudinal studies (Bio-HD, REPAIR-HD, and MIG-HD) with speech recordings and Unified Huntington's Disease Rating Scale (UHDRS) scores. NDSNet combines a pre-trained wav2vec 2.0 model and a recurrent attentive network in a contrastive learning framework for processing audio waveforms of speech. We trained and cross-validated (10-fold) NDSNet to predict the observed UHDRS scores in people with Huntington's disease across visits, with external validation in a replication cohort (the TPMH study). We then compared NDSNet predictions with striatal atrophy on MRI, the best-established marker of Huntington's disease progression. Our developmental cohort included 191 people with Huntington's disease and 58 healthy controls, with speech data collected between 2001 and 2025 (MIG-HD data collected in 2001-13 and REPAIR-HD and Bio-HD data collected in 2018-25). The replication cohort included 110 people with Huntington's disease, of whom 78 were not included in the developmental cohort, with speech data collected between Oct 10, 2022, and Feb 5, 2024. In the developmental cohort, we analysed speech recordings obtained from 146 people with Huntington's disease (62 with brain MRI). Relative error between NDSNet predictions and observed clinical scores was 11·4% (95% CI 9·7-12·5) overall. The intraclass correlation coefficient (ICC) between NDSNet-predicted and observed motor scores (ICC 0·87 [95% CI 0·83-0·91]) was similar to that for clinician ratings (ICC 0·847). Predictions showed strong temporal association and responsiveness to observed clinical change at the individual level. Performance remained consistent in 67 people with Huntington's disease in the replication cohort (after excluding 32 participants already included in the developmental cohort; relative error 15·0% [13·0-19·0]; ICC 0·67 [0·38-0·71]). Predicted scores showed MRI associations similar to those of observed clinical scores across symptomatic stages, and stronger associations with striatal atrophy at HD-ISS stages 0-1. NDSNet predictions captured clinically relevant progression from speech in people with Huntington's disease, showed neuroanatomical grounding across disease symptomatic stages, and estimated the subclinical state at HD-ISS stages 0-1. These findings support the use of NDSNet as a scalable tool to complement standard assessments for longitudinal monitoring in Huntington's disease. Neuratris and Centre de Référence Maladies Rares-Maladie de Huntington.

The blood-brain barrier (BBB) blocks most drugs from entering the brain. Over 98% of small-molecule drugs and nearly all biologics fail to cross this barrier. Nanoparticles (NPs) provide multiple ways to bypass the BBB. These include receptor-mediated transcytosis, adsorptive-mediated transport, and intranasal delivery. NPs can also modify disease-related pathways. For example, they promote amyloid-β clearance, reduce tau phosphorylation, and reprogram neuroimmune responses. Many preclinical studies have shown promising results in Alzheimer's, Parkinson's, and Huntington's diseases. However, no NP-based therapy has moved beyond early-stage clinical trials. Several issues remain unresolved. Direct comparisons between different NP platforms are lacking. The long-term toxicity of NPs in the brain is not well understood. Animal models also do not accurately reflect human disease. We suggest that future work should focus on standardized characterization, better predictive models, and clinical trial designs that address NP diversity. Researchers should also compare NP therapies with existing treatments in a rigorous manner.

R-loops are three-stranded nucleic acid structures formed by a DNA-RNA hybrid and a displaced single-stranded DNA. They regulate transcription, replication, and DNA repair, but their dysregulation causes genomic instability and inflammation, contributing to brain diseases. The nervous system exhibits selective vulnerability to R-loop stress due to ultra-long gene transcription, post-mitotic longevity, and high metabolic demands. This review synthesizes current literature from PubMed, Scopus, Web of Science, and Embase (2010-2026) on R-loop biology, with a focus on brain-specific mechanisms, regulatory factors (SETX, ZPR1, METTL3, TDP-43/FUS), and disease models. In neurodegeneration, R-loop accumulation drives repeat expansion disorders (Fragile X, Huntington's disease) and loss-of-function SETX mutations (AOA2), whereas gain-of-function SETX (L389S) causes pathological R-loop depletion in ALS4, disrupting TGF-β signaling. TDP-43/FUS and SMN are integral to R-loop resolution, unifying ALS/FTD and SMA. In brain cancers, METTL3-mediated m6A modification of TERRA stabilizes telomeric R-loops in ALT-positive neuroblastoma, creating a therapeutic vulnerability to METTL3 inhibitors (STM2457, STC-15). Glioma stem cells rely on m6A-modified circPOLR2B to regulate R-loop formation and malignancy. Clinical-stage agents (EP102, TUG1ASO, ATX-559) and R-loop-derived prognostic signatures (RLPI) are emerging, but translation is hindered by a lack of non-invasive biomarkers and the dual physiological/pathological roles of R-loops. R-loops are central to brain disease pathogenesis, offering promising therapeutic targets. Future research should prioritize precision R-loop modulators, non-invasive biomarkers, and combinatorial strategies.

Ischemic heart disease is the main cause of death in developed countries, and full recovery remains unachievable. A potential cure can be based on utilizing miRNAs capable of triggering cardiac regeneration by stimulating cardiomyocyte proliferation. However, to deliver miRNAs efficiently, nanocarriers are required for protection from rapid cleavage in the extracellular milieu and successful uptake by cardiomyocytes. Here, we present biocompatible chitosan nanoparticles, formulated via a green polyelectrolyte complexation process, and efficiently loaded with miR199a-3p. Their safety and therapeutic potential were evaluated in vitro and in vivo. To enhance cardiac accumulation, we further explored the functionalization of these nanoparticles with tannic acid, a polyphenolic compound that exhibits favored cardiac targeting and sustained retention. Thus, we have optimized the miR-loaded chitosan-based formulation to achieve tunable sizes (100-300 nm) while maintaining high biocompatibility with HL-1 cardiomyocytes and primary murine or rat cardiomyocytes. Notably, miRNA-loaded nanoparticles boosted cardiomyocyte proliferation by up to 75%. Confocal microscopy confirmed successful uptake by cardiomyocytes. In vivo, no mortality or adverse effects were observed during the 4-day observation period, with miRNA expression increasing up to sixfold and target gene downregulation reaching 50%. These findings establish a proof of concept that chitosan-based polyelectrolyte complexes can serve as a safe and effective nanoplatform for delivering regenerative miRNAs to the heart, paving the way for next-generation cardiac therapies.

Huntington disease (HD) is a progressive neurodegenerative disease caused by an expanded CAG repeat in the HTT (huntingtin) gene, leading to the accumulation of mutant HTT (mHTT). IL17A (interleukin 17A), a proinflammatory cytokine primarily secreted by Th17 and γδ T cells, has been implicated in immune-mediated neurodegeneration. However, the role of IL17A in the pathogenesis of HD remains poorly understood. Here, we identify IL17A as a critical pathogenic factor in HD that promotes neuroinflammation, mHTT aggregation, and autophagy-lysosomal dysfunction. IL17A disrupts autophagic flux by downregulating CTSB and CTSD, inducing SQSTM1/p62 and MAP1LC3B-II/LC3-II accumulation, and impairing lysosomal reformation. Mechanistically, IL17A suppresses lysosomal biogenesis by inhibiting the nuclear translocation of TFE3. This regulation occurs via a novel GSK3B/GSK-3β-TFE3 signaling pathway. Therapeutic neutralization of IL17A with a monoclonal antibody (IL17A mAb) ameliorates disease phenotypes in R6/2 HD mice, improving motor performance, extending survival, and reducing gliosis. IL17A mAb also attenuates mHTT aggregation and enhances neuroprotective signaling, as evidenced by increased expression of DLG4/PSD-95, phosphorylated CREB1, and BDNF. Moreover, IL17A mAb restores autophagy-lysosomal function by facilitating the clearance of protein aggregates and upregulating lysosomal enzymes and biogenesis markers, including CTSB, CTSD, PIP5K1A, and LAMP2. These findings establish IL17A as a key modulator of HD pathophysiology and highlight IL17A inhibition as a promising therapeutic strategy for targeting autophagy-lysosomal dysfunction in HD.

Digital health technology measurements show promise as objective biomarkers in Parkinson's disease. However, their sensitivity and consistency across early disease stages remain poorly defined, which currently limits their utility in clinical trial design. The study aims to evaluate the effectiveness of various DHT measures within tremor, bradykinesia, and axial symptom domains, in differentiating early stage PD from healthy controls and in monitoring short-term disease progression. In this study, we examined a range of tremor, bradykinesia, and axial symptom measures across age-matched healthy volunteers (n = 45), newly diagnosed Parkinson's disease patients (n = 54), and participants after 12 months of follow-up (n = 40) from WATCH-PD, a multicenter, observational study. Our findings reveal distinct categories of functional measures: some effectively differentiate healthy controls from patients with recent Parkinson's diagnosis but show limited sensitivity to early progression, while others are insensitive to initial diagnosis yet capture longitudinal change. Models trained on disease-stage specific feature sets were most effective for their intended task, with the performance gain over combined features being more pronounced for progression detection (∆AUC = 0.15, ∆Cohen's d = 0.72). These findings underscore the need to align composite digital biomarker design and feature selection to both disease stage and clinical objective, and suggest that adaptive, symptom- and side specific DHT measures may enhance sensitivity in trial population selection and short-term progression monitoring.

Neurodegeneration results from the convergence of several molecular processes, including inflammation in the brain (i.e., neuroinflammation), elevated levels of free radicals that damage cells, mitochondrial dysfunction, and the inability to remove damaged proteins from the brain. Even though many agents provide neuroprotection in research models, their clinical use is limited because they cannot effectively cross the blood-brain barrier to reach the areas of the brain where they are needed. Limitations include the inability to cross the blood-brain barrier, poor bioavailability, rapid metabolism and clearance, non-specific targeting, efflux by transport proteins, toxicity, and low solubility and stability. The classification of micronutrients (e.g., vitamins, polyphenols, minerals), which are naturally present antioxidants and anti-inflammatory substances, plays a role in modulating the most important signaling pathways in the body, including those mediating the inflammatory response (i.e., NF-κB and NLRP3) and the process that causes glial cell death (i.e., JAK/STAT). Micronutrients have a significant drawback for therapeutic use because they are rapidly metabolized and cannot cross the blood-brain barrier. Developments in synthetic biomaterials and nanotechnology offer a potential avenue for addressing the challenges of delivering micronutrients to the brain by targeting them to specific areas and releasing them over a sustained period. This study presents current information on the mechanisms by which micronutrients modulate molecular pathways and their potential application in emerging biomaterials to develop a new class of neuroprotective therapeutic agents that may ultimately be used to treat patients with degenerative diseases (e.g., Alzheimer's, Parkinson's, and Huntington's). Additionally, clinical challenges are addressed to translate these products from the laboratory to the clinic. The idea presented in this review connects molecular neuromodulation via micronutrients and bioactive nutraceuticals with a new strategy for pharmacological delivery using biomaterials. Instead of considering nutrition and those biomaterials as separate therapeutic areas, an integrated mechanistic model is presented that shows how micronutrients can act as endogenous pathway regulators and how biomaterials can enhance pharmacokinetics and targeting.

Polygenic risk scores (PRSs), which quantify inherited susceptibility to complex traits and diseases, have emerged as valuable tools for risk stratification and precision medicine. Despite their promise, PRS developed on European cohorts often demonstrate substantially reduced predictive accuracy in non-European populations, due to differences in genetic architecture. The disproportionate representation of European ancestry cohorts in genome-wide association studies (GWAS) leads to inequitable deployment of PRS technologies across diverse populations. Here, we introduce PRANA (Polygenic Risk Adaptation via Neural-network Architecture), a deep learning framework that adapts an existing PRS developed on one population to other ancestries. Unlike methods that require large-scale GWAS in the target population, PRANA leverages pre-trained PRS models derived from European cohorts and adapts them using modestly sized cohorts from the target population. We evaluated PRANA on seven complex traits in South Asian, East Asian and Ashkenazi Jewish populations, as well as in selected smaller East Asian subpopulations where the scarcity of training data poses a particular challenge. PRANA mostly improved predictive performance of the baseline PRS models by 5%-20% in terms of effect size (β) and Nagelkerke's R2, and, in most cases, outperformed existing cross-ancestry multi-PRS approaches. These results highlight PRANA as a scalable and practical strategy to reduce disparities in genomic risk prediction and advance the equitable application of PRS in diverse populations.

Robust and reliable health information systems (HISs) are foundational to equitable health care delivery in resource-constrained settings. Yet, HISs often exhibit significant fragmentation and complexity, which stem from many factors, including inadequate infrastructure, limited and unevenly allocated financial resources, expertise gaps, and a lack of integrated systems. At the same time, advances in modern HISs and digital technologies, such as electronic medical records (EMRs), present opportunities for addressing these limitations and supporting evidence-based health systems if well implemented and sustained. However, limited attention has been paid to how modern and resilient HISs can be effectively sustained in fragile, resource-constrained settings. This empirical study seeks to identify pathways for strengthening the resilience, adaptability, and contextual-fit of EMRs in fragile, resource-constrained settings such as Haiti. Using a qualitative research methodology, the study used semistructured interviews with purposive sampling of key informants, including frontline doctors, nurses, and IT or data specialists. Interview participants were selected for their expertise and capacity to offer insightful and varied viewpoints on the topic. Transcripts were analyzed using an inductive approach to identify key emerging themes. The findings of this investigation reveal that implementing resilient EMR in limited-resource settings, such as Haiti, requires a comprehensive strategy that accounts for ecosystemic challenges from interdependent systems (electricity, sociopolitical instability, internet connectivity), as well as the intrinsic complexities of legacy systems. Drawing on empirical evidence, the study identifies a set of protective strategies that, if effectively implemented, may enhance the resilience and adaptability of EMRs. These strategies include prioritizing integration and interoperability between systems, building redundancy to mitigate cascading failures derived from other interdependent systems, strengthening staffing to support system use, right-sizing the paper footprint (eg, improving handwriting-to-text scanning and digitization solutions), and embracing technological innovations.

Benefits from continuous glucose monitors (CGMs) may depend on how devices are used over time-not only how often they are used. We linked one year of device-generated CGM wear data from 2,351 U.S. Veterans to electronic health records (EHRs) to characterize real-world usage during the first year after CGM initiation. To compare longitudinal use patterns in the presence of unsynchronized sensor replacement-related gaps and intermittent interruptions, we aligned daily wear streams using a complexity-adjusted, time-adaptive optimal transport (TAOT) distance and applied spectral clustering to identify data-driven usage phenotypes. To estimate the adjusted association between CGM usage phenotypes and clinical outcomes, we used a double/debiased machine learning framework to quantify 12-month changes in time in range (ΔTIR) and mean glucose (ΔMG). We identified three reproducible patterns of CGM usage: Consistent, Fluctuating, and Low engagement. Relative to Consistent wear, Fluctuating usage was associated with worse glycemic change (ΔTIR = -3.56%, 95% CI: -4.76 to -2.36; ΔMG = +7.12 mg/dL, 95% CI: 4.91 to 9.32), and Low engagement showed larger deterioration (ΔTIR = -7.00%, 95% CI: -10.80 to -3.14; ΔMG = +14.09 mg/dL, 95% CI: 6.52 to 21.67). Notably, 73.2% of Fluctuating users still met a common "adherence" threshold (≥80% days worn), indicating that simple coverage metrics can miss clinically relevant instability. The differences in glycemic change (ΔTIR and ΔMG) between the Consistent and Fluctuating groups were most pronounced among subgroups with more intensive diabetes management needs, such as insulin pump or glucagon users, suggesting that sustained CGM usage may be particularly important when clinical management is more complex. Beyond CGM and diabetes, this work provides a generalizable framework for characterizing longitudinal usage patterns of intermittently used digital health technologies and linking derived usage phenotypes to clinical outcomes. The approach can support more precise evaluation, monitoring, and intervention design for a wide range of real-world digital health tools.

Spinocerebellar ataxia type 17 (SCA17) is an autosomal dominant repeat-expansion disorder with marked phenotypic heterogeneity. Cognitive and neuropsychiatric symptoms may dominate early recognition and initially suggest a primary dementia syndrome. We aimed to illustrate diagnostic redirection in dementia-first SCA17 through integrated clinical, imaging, familial, and molecular assessment. We describe the clinical course, neurological findings, cognitive and functional assessments, ancillary investigations, neuroimaging, pedigree information, and molecular genetic findings of a proband with SCA17 and one tested at-risk adult relative. To contextualize the family, we conducted a focused literature review of genetically confirmed SCA17 case and family reports identified through PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), and Wanfang up to April 16, 2026. Repeat-expansion testing established SCA17 in a proband who had initially presented through a dementia-first clinical pathway, with TATA-box binding protein (TBP) alleles of 37/51 repeats. Targeted presymptomatic cascade testing identified the same expanded 51-repeat allele in her asymptomatic adult daughter. Review of 25 published studies showed broad variation in age at onset, TBP repeat size, family context, presenting syndrome, and cumulative phenotype, including cognition-dominant, behavior-dominant, Huntington disease-like, parkinsonian, dystonic, choreic, seizure-associated, and atypical neuroimaging presentations. The present family illustrates a dementia-first route to SCA17 recognition, in which the initial syndrome-based dementia interpretation remained etiologically provisional as cerebellar signs, cerebellar-predominant atrophy, autosomal-dominant family context, and TBP expansion were integrated. This case-based perspective is intended to support etiological reconsideration in selected dementia-first presentations, rather than to serve as validated clinical criteria.

Sleep disorders are highly prevalent yet underdiagnosed across the general population, contributing significantly to physical and mental health burdens. General practitioners play a pivotal role in recognizing, screening, and initiating treatment of common sleep disturbances such as insomnia, obstructive sleep apnea, and comorbid insomnia and sleep apnea. Integrating sleep assessment into routine care through practical tools and lifestyle interventions can greatly enhance patient outcomes. This article provides a concise, clinically oriented overview to empower primary care providers with the knowledge and strategies to manage sleep complaints effectively across the lifespan.

Huntington's disease (HD) is a fatal neurodegenerative disorder caused by an expanded CAG repeat within exon 1 of the huntingtin (HTT) gene, resulting in a mutant protein that drives neuronal dysfunction and loss. A key event in the pathogenesis of HD is proteolytic cleavage of mutant HTT, which generates aggregation-prone N-terminal fragments that contribute to toxicity. Strategies that prevent this process thus hold therapeutic potential. Here we develop CRISPR base editors that generate proteolysis-resistant HTT isoforms by disrupting the splice acceptor of HTT exon 13, an exon that encodes critical proteolytic cleavage sites implicated in N-terminal fragment production. When delivered to the striatum of an HD rodent model, these editors reduced HTT fragment formation, decreased aggregation, improved functional deficits and attenuated brain atrophy. Collectively, these results demonstrate the potential of base editing and splice-site modulation to mitigate mutant HTT toxicity in HD.

Huntington disease (HD) is a debilitating, genetic disorder with a prevalence of 2.7 per 100,000 people. It is neurodegenerative, leading to cognitive, behavioral, and motor symptoms from neuronal loss within the striatum of the basal ganglia and cortex. Currently, the treatments involve symptomatic management, instead of treating the pathophysiology of the disease. Grape seed extract (GSE) is a complex mixture of polyphenols, proteins, and lipids with antioxidant and anti-inflammatory properties. This literature review examines the possibility of using GSE as a potential adjunctive therapy for HD. Preclinical studies have shown a neuroprotective effect through biologically plausible mechanisms. Clinical research has shown that GSE works on redox and inflammatory pathways related to the pathogenesis of HD. Although there are not many clinical trials on GSE in HD patients directly, the overlap of mechanisms behind both GSE and HD and the favorable side effect profile make GSE a potential adjunctive therapy. Targeted clinical investigation is warranted to determine the full therapeutic potential of GSE.

Staphylococcus aureus bacteremia (SAB) causes significant morbidity and mortality, despite treatment with guideline-recommended antibiotics. A critical contributor to SAB treatment failure is the intracellular persistence of SA within liver sinusoidal macrophages (Kupffer cells [KCs]). Antibiotics have been shown to modulate immune cell function, which is governed by cellular metabolism; yet, the effect of antibiotics on host immunometabolic response relative to bacterial clearance is understudied. We developed a liver-on-chip (LoC) model of SAB to recapitulate the liver sinusoid during systemic infection using exclusively liver-derived cells and the GFP USA300 LAC SA strain and compared the effects of vancomycin and daptomycin on host immunometabolism by fluorescence lifetime imaging microscopy and related to intracellular antimicrobial efficacy. Utilizing both murine and human-based LoC infection models, we found that DAP was superior to VAN treatment in clearing intracellular SA from the liver microenvironment, which corresponded to differential host immunometabolic responses. DAP showed low NAD(P)H fluorescence lifetime and low mitochondrial ROS, suggestive of a redox-buffered state that balanced pathogen containment with hepatic metabolic homeostasis, while VAN treatment biased towards greater utilization of OXPHOS. Our findings using a novel in vitro microphysiological system mimicking the liver tissue microenvironment offer insights into antibiotic-mediated bacterial clearance that accounts for host immunometabolism beyond direct antimicrobial activity.

Oil spills represent a major disturbance for marine fungal communities, yet their response in coastal systems over time is uncharacterized. During the Orange County oil spill on October 1st, 2021, a pipeline leak released 93,484 L of crude oil in Huntington Beach, California, USA. We examined the effect of this disturbance on the richness and community composition of marine fungal communities over 2 months. Based on a qualitative shoreline survey, we collected surface seawater samples nearshore from very lightly or lightly oiled sites at 18, 34, 49, and 66 days after the spill and sequenced the ITS2 region. Unexpectedly, fungal richness remained similar across oiling degrees and time points. Furthermore, oil pollution had a limited effect on overall fungal community composition, with only marginal differences between oiling levels. Instead, changes occurred on a finer scale. Light oiling reduced the relative abundance of genera typical of coastal ecosystems, including Mycosphaerella, Papiliotrema and Rhodotorula. Moreover, indicators for light oiling included oil-tolerant and opportunistic species belonging to Aspergillus and Thermomyces, known for consuming oil-derived compounds. Despite these small shifts, fungal community composition in both oiling conditions did not become more similar over time. Collectively, our results indicate the oil spill had a subtle but persistent effect on marine fungal communities over two months, primarily by altering specific fungal taxa, rather than modifying overall composition and richness. Characterizing fungal responses to even limited oil exposure may be important for detecting, monitoring and understanding the ecological influence of oil contamination in coastal marine ecosystems.

Background/Objectives: Epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC) has undergone rapid therapeutic evolution. However, heterogeneous outcomes persist, driven by mutations, central nervous system (CNS) involvement, and dynamic tumor burden reflected in part by circulating tumor DNA (ctDNA). As such, this review aims to summarize the most recent risk stratification frameworks in treating EGFR-mutant NSCLC, evaluate evidence supporting treatment intensification strategies and managing adverse effects, and explore the evolving role of ctDNA in guiding personalized therapy. Methods: A comprehensive literature search was conducted using major medical databases with a focus on key relevant studies on the workup and management of EGFR-mutant NSCLC. All authors reviewed the literature, assessed study quality, and interpreted the results from each study. Results: Molecular co-alterations, such as TP53 and RB1, as well as central nervous system (CNS) involvement, are consistently associated with inferior outcomes, supporting consideration of upfront treatment intensification. Combination strategies, including osimertinib plus chemotherapy or amivantamab-based regimens, demonstrate improved progression-free survival and delayed CNS progression when compared against osimertinib monotherapy. Intensification, however, is associated with a higher risk of increased toxicity, including dermatologic adverse events and infusion-related reactions. Finally, the utilization of circulating tumor DNA (ctDNA) has emerged as a strong prognostic marker, with ongoing trials investigating its predictive role for both escalation and de-escalation of therapy. Conclusions: The treatment paradigm for EGFR-mutant NSCLC is gradually evolving beyond first-line osimertinib to include a more integrated approach that considers molecular features, CNS involvement, and early ctDNA response. Although intensified regimens offer meaningful efficacy gains for high-risk patients, proactive toxicity management is essential to preserving quality of life. ctDNA-guided strategies represent a new and promising frontier for escalation and de-escalation of therapy, with results from ongoing trials poised to further refine personalized treatment algorithms.