In Silico Pharmacol. 2025 Oct 17;13(3):154. doi: 10.1007/s40203-025-00440-3. eCollection 2025.ABSTRACTBreast, ovarian, lung, cervical, and colorectal cancers are among the most prevalent malignancies affecting women worldwide. This study aimed to elucidate the common molecular mechanisms of tumorigenesis and identify potential biomarkers using an integrative bioinformatics and network-based approach. Integrative profiling of five microarray datasets identified 66 differentially expressed genes (
In Silico Pharmacol. 2025 Oct 17;13(3):154. doi: 10.1007/s40203-025-00440-3. eCollection 2025.
ABSTRACT
Breast, ovarian, lung, cervical, and colorectal cancers are among the most prevalent malignancies affecting women worldwide. This study aimed to elucidate the common molecular mechanisms of tumorigenesis and identify potential biomarkers using an integrative bioinformatics and network-based approach. Integrative profiling of five microarray datasets identified 66 differentially expressed genes (DEGs) that are common across five cancer types. Gene ontology and KEGG pathway analyses of common DEGs were performed using the DAVID database. The cell cycle processes were the most enriched functions, and oocyte meiosis, oocyte maturation, the p53 signaling pathway, cancer pathways, and cellular senescence were the most important pathways identified. Protein-protein interaction (PPI) networks for the DEGs were constructed using the STRING database, and the resulting networks were visualized in Cytoscape. Through PPI network analysis, ten hub genes were identified, and subsequent survival analysis confirmed that CHEK1, DLGAP5, CCNB2, and CCNA2 are significantly associated with poor patient survivability, establishing them as common biomarkers across multiple cancer types. Subsequently, ten transcription factors (TFs) and ten post-transcriptional regulators were identified through the assessment of regulatory networks involving TFs-DEGs and miRNAs-DEGs. Finally, drug-gene association analysis from the GSCA library was used to anticipate drug-like compounds using the drug repurposing approach. Overall, this comprehensive investigation holds promise for future in vitro and in vivo studies, offering a molecular foundation for the diagnosis, prognosis, and treatment of malignant cancers.
SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-025-00440-3.
Nature, Published online: 20 October 2025; doi:10.1038/d41586-025-03344-6Biomedical research techniques that don’t involve the use of animals are gaining momentum, but those using innovative approaches still face resistance from some quarters.
Biomedical research techniques that don’t involve the use of animals are gaining momentum, but those using innovative approaches still face resistance from some quarters.
Oral Oncol. 2025 Nov;170:107760. doi: 10.1016/j.oraloncology.2025.107760. Epub 2025 Oct 17.ABSTRACTNon-viral head and neck squamous cell carcinoma (HNSCC) has poor survival and high recurrence rates. Circulating tumor DNA (ctDNA) is a promising biomarker for understanding tumor biology, assessing treatment response, and monitoring disease progression. While extensively studied in virally mediated HNSCC, its role in non-viral HNSCC remains underexplored. This systematic review and meta-analysis c
Oral Oncol. 2025 Nov;170:107760. doi: 10.1016/j.oraloncology.2025.107760. Epub 2025 Oct 17.
ABSTRACT
Non-viral head and neck squamous cell carcinoma (HNSCC) has poor survival and high recurrence rates. Circulating tumor DNA (ctDNA) is a promising biomarker for understanding tumor biology, assessing treatment response, and monitoring disease progression. While extensively studied in virally mediated HNSCC, its role in non-viral HNSCC remains underexplored. This systematic review and meta-analysis consolidates evidence on the diagnostic, prognostic, and therapeutic value of ctDNA in non-viral HNSCC. A systematic search across Medline, PubMed, Embase, and the Cochrane Library identified 1,915 records, of which 47 were included. Data extraction followed PRISMA guidelines, with overall survival (OS), progression-free survival (PFS), and recurrence-free survival (RFS), pooled as hazard ratios (HRs) with 95% confidence intervals (CIs) using a fixed-effect model. Among 3,574 patients, the most common tumor sites were the oral cavity (35 %) and oropharynx (22 %), with the majority presenting with stage IVA/IVB disease (29 %). Pre-treatment ctDNA detection rates ranged from 50 % to 100 % (median: 83 %), while post-treatment detection rates varied between 28 % and 100 % (median: 48 %). ctDNA detected recurrence in 80 % of patients, with a median lead time of 4.6 months. ctDNA detection was significantly associated with worse OS (HR 10.26, 95 % CI 3.58-29.40; P < 0.0001). Residual ctDNA was strongly correlated with worse PFS (HR 7.32, 95 % CI 4.17-12.86; P < 0.00001) and RFS (HR 7.33, 95 % CI 2.75-19.58; P < 0.0001). ctDNA holds potential for improving diagnostic accuracy, monitoring progression, and predicting survival outcomes in non-viral HNSCC. However, further large-scale studies and standardized guidelines are needed for validation and clinical implementation.
The surge of multi-billion-dollar investments in AI has sparked growing debate over whether the industry is heading for a bubble similar to the dot-com boom.
Investors are watching closely for signs that enthusiasm might be fading or that the heavy spending on infrastructure and chips is failing to deliver expected returns. A recent survey by BofA Global Research found that 54% of fund managers believe AI stocks are already in bubble territory, while 38% disagree.
Echoes of the dot-com era
The surge of multi-billion-dollar investments in AI has sparked growing debate over whether the industry is heading for a bubble similar to the dot-com boom.
Investors are watching closely for signs that enthusiasm might be fading or that the heavy spending on infrastructure and chips is failing to deliver expected returns. A recent survey by BofA Global Research found that 54% of fund managers believe AI stocks are already in bubble territory, while 38% disagree.
Echoes of the dot-com era
Despite the optimism surrounding AI, sceptics remain unconvinced of its real-world impact. Some even call it a bluff or a bubble waiting to burst.
Speaking during Cisco’s recent Virtual Media Roundtable — AI Readiness Index 2025: Readiness Leads to Value, Ben Dawson, Senior Vice President and President for Asia Pacific, Japan, and Greater China (APJC), compared the current wave of AI hype to the early days of the internet. He said technological shifts of this scale often follow a familiar pattern — early excitement, heavy investment, and eventual market correction before long-term value takes hold.
Dawson noted that while some AI projects or business models may not last, the overall transformation is real and lasting. He added that, much like the internet revolution, AI will permanently reshape business and society, and organisations that ignore it do so at their own risk.
The role of governments and global policy
Public policy is also shaping how the AI cycle unfolds — and how governments might cushion the risks of a potential AI bubble. As Harvard Business Review pointed out, in the US, government involvement has helped define past technology eras — often through incentives and early investments that encourage private innovation. The same pattern is now visible in AI. Both the Trump and Biden administrations have positioned AI as a matter of economic strength and national security, sending a clear message that speed matters.
China has taken a state-led approach, directing capital toward local AI firms to reduce reliance on US technology. In Europe, efforts have focused more on regulation, though fears of overregulation have led to new programs — such as the AI Continent Action Plan and a €1 billion Apply AI fund — to boost adoption and competitiveness.
Meanwhile, venture capital and sovereign wealth funds are investing heavily, even before widespread AI demand exists. These early bets assume that adoption will eventually justify the buildout. But if that demand slows, some investors could be left with stranded assets, much like the unused fibre networks that followed the dot-com bubble.
For businesses, the challenge is different. Instead of financing the next infrastructure wave, they face the question of how to use AI to strengthen their operations. The companies that survived the dot-com downturn — such as Amazon — succeeded by aligning technology with real business value rather than market hype.
Market warnings over a possible AI bubble
The Bank of England recently warned that markets could suffer a sharp correction if confidence in AI falters, calling the potential impact on the UK’s financial system “material.” The warning reflects growing caution among policymakers about how quickly AI-related valuations have climbed.
This concern is shared by some investors and economists who believe the rapid pace of AI spending may outstrip short-term returns. Others, however, argue that building AI infrastructure now is essential groundwork for future innovation.
Building long-term AI infrastructure amid bubble fears
When asked whether companies are worried about AI infrastructure costs and energy demand, Simon Miceli, Managing Director of Cloud and AI Infrastructure for APJC at Cisco, said he views the issue from the opposite angle.
Rather than fearing overcapacity, he said what’s happening now is a large-scale buildout to support the industrialisation of AI. The question, he said, isn’t whether AI demand exists today, but whether the world is preparing fast enough for what’s coming.
Miceli acknowledged that some correction in the AI market is likely, but he believes the long-term need for AI computing power justifies current investment levels. “There’s a race to develop AI and build the capability behind it,” he said, adding that demand will eventually meet supply as applications mature.
Different shades of caution
Across the industry, opinions vary on whether AI’s momentum represents hype or healthy growth.
According to Reuters, at the Milken Institute Asia Summit 2025, Singapore’s GIC Chief Investment Officer Bryan Yeo said valuations in early-stage AI ventures appear inflated, with many startups commanding “huge multiples” despite modest revenues. He suggested that while some firms may justify their valuations, others are unlikely to deliver returns that match investor expectations.
Jeff Bezos, Amazon’s founder, said that during periods of excitement like this, investors often struggle to separate good ideas from bad ones — though he also noted that innovation-driven bubbles often leave behind real progress once the market settles.
At Goldman Sachs, economist Joseph Briggs argued that the current surge in AI infrastructure spending remains economically sustainable. He said the long-term case for AI investment is strong, but the ultimate winners are still uncertain given how quickly technology changes and how easily companies can switch providers.
Meanwhile, ABB CEO Morten Wierod told Reuters that while he doesn’t see an AI bubble, supply chain and construction limits could slow the rollout of new data centres. IMF Chief Economist Pierre-Olivier Gourinchas added that even if there’s a downturn, it’s unlikely to cause a systemic financial crisis since AI investments aren’t debt-driven.
OpenAI CEO Sam Altman also acknowledged market overexcitement, predicting that some investors will lose large sums while others will profit heavily — an outcome that mirrors past technology bubbles.
Despite growing talk of an AI bubble, many investors remain committed to the sector. UBS equity strategists said that about 90% of investors who think the market is overheated are still holding AI-related assets, suggesting most believe the industry has not yet peaked.
A cycle, not a collapse
While concerns about an AI bubble are valid, most experts agree that the technology’s long-term impact is undeniable. As Cisco’s Ben Dawson put it, every major technological transition goes through a cycle of hype, correction, and consolidation — but what remains afterward reshapes industries for decades.
For now, the question isn’t whether AI will endure, but how well businesses and investors can navigate the growing pains that come with every market bubble.
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npj Digital Medicine, Published online: 17 October 2025; doi:10.1038/s41746-025-02008-zWhen helpfulness backfires: LLMs and the risk of false medical information due to sycophantic behavior
Background: Artificial intelligence-assisted conversational agents have been applied and developed in outpatient departments to improve health services in China. However, there has been little research that evaluates the effect of artificial intelligence-assisted conversational agents on the patient experience related to physicians during outpatient visits. Objective: This aim of this study was to examine whether the use of artificial intelligence-assisted conversational agents improves the pati
Background: Artificial intelligence-assisted conversational agents have been applied and developed in outpatient departments to improve health services in China. However, there has been little research that evaluates the effect of artificial intelligence-assisted conversational agents on the patient experience related to physicians during outpatient visits. Objective: This aim of this study was to examine whether the use of artificial intelligence-assisted conversational agents improves the patient experience related to physicians during outpatient visits and to further find out the difference in the patient experience between conversational agent users and nonusers. Methods: We used the Chinese Outpatient Experience Questionnaire to survey the patient experience related to physicians during outpatient visits. A sample of 392 adult residents who sought outpatient services from tertiary public hospitals in China was selected by random sampling. The t tests were used to test the mean difference in the patient experience scores between conversational agent users and nonusers. Multiple linear regression analysis was further performed to determine whether the use of artificial intelligence-assisted conversational agents during outpatient visits was associated with a better patient experience related to physicians. Results: Conversational agent user reported significantly higher scores than nonusers in the total patient experience scores (t392=5.589, P<.001 the items and dimensions of physician-patient communication p=".006)," health information short-term outcome general satisfaction multiple linear regression results further showed that after controlling for other factors on participant characteristics use artificial intelligence-assisted conversational agents during outpatient visits significantly influenced total patient experience scores related to physicians averagely increased by conclusions: could improve especially in terms making better accessing more targeted ameliorating outcomes increasing satisfaction. therefore we suggest public hospitals should consider benefits actively deploy departments so as continuously visits.>
Nature Biotechnology, Published online: 17 October 2025; doi:10.1038/s41587-025-02845-zThe interactions of CAR-T cells and solid tumors are modeled on a chip.
Nucleic Acids Res. 2025 Oct 14;53(19):gkaf990. doi: 10.1093/nar/gkaf990.ABSTRACTBRCA2 mutations contribute to the pathogenesis and treatment sensitivity of a subset of ovarian, breast, prostate, and pancreatic cancers. When these cancers become therapy resistant, secondary mutations that restore the BRCA2 open reading frame are found in half the cases, but other causes of resistance remain incompletely understood. Here, we identified translational readthrough of a premature termination codon (PT
Nucleic Acids Res. 2025 Oct 14;53(19):gkaf990. doi: 10.1093/nar/gkaf990.
ABSTRACT
BRCA2 mutations contribute to the pathogenesis and treatment sensitivity of a subset of ovarian, breast, prostate, and pancreatic cancers. When these cancers become therapy resistant, secondary mutations that restore the BRCA2 open reading frame are found in half the cases, but other causes of resistance remain incompletely understood. Here, we identified translational readthrough of a premature termination codon (PTC) as a cause of resistance to poly(ADP-ribose) polymerase inhibitors (PARPis) and cisplatin in cells derived from the BRCA2-mutated ovarian cancer line PEO1 by PARPi selection. Despite persistence of the signature 4965C > G (p.Y1655X) BRCA2 mutation, low-level expression of full-length BRCA2 protein was detectable in these cells by immunoblotting and tandem mass spectrometry. Either BRCA2 knockdown or gene interruption 5' or 3' to the PTC restored treatment sensitivity, implicating BRCA2 in the resistance. Reporter assays demonstrated UAG-selective readthrough in the resistant clones but not parental cells. Moreover, custom searching of global proteomic data indicated readthrough of stop codons, particularly UAGs, in additional proteins in the resistant clones. Finally, multi-omic analysis identified multiple changes in the nonsense-mediated decay and termination machineries that favor readthrough. Accordingly, the present results identify PTC readthrough as a potential mechanism of drug resistance in cells with BRCA2 nonsense mutations.
Diagnostics (Basel). 2025 Sep 23;15(19):2425. doi: 10.3390/diagnostics15192425.ABSTRACTBackground/Objectives: Next-Generation Sequencing (NGS) has transformed cancer diagnostics and treatment by enabling comprehensive genomic profiling of tumors. This review aims to summarize the current applications of NGS in oncology, highlighting its role in early detection, precision therapy, and disease monitoring. Methods: We conducted a comprehensive review of the recent literature, focusing on the applic
Background/Objectives: Next-Generation Sequencing (NGS) has transformed cancer diagnostics and treatment by enabling comprehensive genomic profiling of tumors. This review aims to summarize the current applications of NGS in oncology, highlighting its role in early detection, precision therapy, and disease monitoring. Methods: We conducted a comprehensive review of the recent literature, focusing on the application of NGS in cancer care. Results: NGS enables high-resolution genomic profiling, identifying actionable mutations (e.g., EGFR, KRAS, and ALK) and immunotherapy biomarkers (e.g., PD-L1, TMB, and MSI), guiding personalized treatment selection and improving outcomes in advanced malignancies. Liquid biopsy enhances diagnostic accessibility and enables real-time monitoring of minimal residual disease and treatment resistance. Despite these advances, widespread clinical adoption remains constrained by technical limitations (e.g., coverage uniformity and sample quality), economic challenges (high costs and complex reimbursement), and interpretative issues, including the management of variants of uncertain significance (VUSs). Conclusions: NGS is central to precision oncology, enabling molecularly driven cancer care. Integration with artificial intelligence, single-cell sequencing, spatial transcriptomics, multi-omics, and nanotechnology promises to overcome current limitations, advancing personalized treatment strategies. Standardization of workflows, cost reduction, and improved bioinformatics expertise are critical for its full clinical integration.
Single-cell metabolomics (SCM) can probe metabolic heterogeneity but is hindered by low sensitivity for small molecules, limited scalability, and the lack of standardized frameworks for data analysis. HT SpaceM is a high-throughput MALDI-imaging-based SCM method to robustly detect small-molecule metabolites in single cells. Applied to over 140,000 cells across diverse conditions and cancer cell lines, HT SpaceM enabled reproducible metabolic profiling of over 100 small-molecule metabolites, iden
Single-cell metabolomics (SCM) can probe metabolic heterogeneity but is hindered by low sensitivity for small molecules, limited scalability, and the lack of standardized frameworks for data analysis. HT SpaceM is a high-throughput MALDI-imaging-based SCM method to robustly detect small-molecule metabolites in single cells. Applied to over 140,000 cells across diverse conditions and cancer cell lines, HT SpaceM enabled reproducible metabolic profiling of over 100 small-molecule metabolites, identification of subpopulation-specific markers, and detection of heterogeneity and pathways coordination, thus facilitating scalable and reproducible SCM.
Background: Peer support (sharing experiences/support with others with the same condition) improves health outcomes among people with cardiovascular disease (CVD), including self-management behaviours and self-efficacy. However, current peer support interventions are diverse. Evidence is lacking on peer support attenders perceptions of benefits and the elements that are considered priorities, especially for digital interventions. Objective: The study objectives were to 1) describe perceived bene
Background: Peer support (sharing experiences/support with others with the same condition) improves health outcomes among people with cardiovascular disease (CVD), including self-management behaviours and self-efficacy. However, current peer support interventions are diverse. Evidence is lacking on peer support attenders perceptions of benefits and the elements that are considered priorities, especially for digital interventions. Objective: The study objectives were to 1) describe perceived benefits and recommendations for CVD peer support programs from people attending in-person peer support, 2) identify priorities for digital peer support from consumers and clinicians testing a peer support app prototype, and 3) develop a framework to inform future peer support intervention development. Methods: Qualitative methodology was used across two components to address the objectives of this study. In Component 1, semi-structured focus groups were conducted with attenders of established in-person CVD peer support groups, exploring the perceived benefits of peer support and recommendations for future programs. In Component 2, semi-structured interactive workshops with consumers with CVD and semi-structured online interviews with CVD clinicians/researchers were undertaken seeking feedback and recommendations for digital peer support using an exploratory digital CVD peer support application prototype. Data were recorded digitally, transcribed verbatim, and analysed thematically. Findings from both components were iteratively synthesised to inform a digital peer support development framework. Results: In Component 1, 22 participants (age range 29-84 years, male 45%) took part in focus groups. The overarching theme was that peer support provides benefits through sharing experiences. Five themes were refined and defined; (i) peer support provides a way of coping, (ii) peers learn from each other, (iii) peers understand what each other are going through, (iv) the peer community uplifts mood and build confidence, and (v) awareness, flexibility and resources are important for engagement. In Component 2, five participants (age range 55-74 years, male 60%) attended two workshops and eight clinicians/researchers (age range 30-65 years, male 10%) were interviewed. Three themes were refined and defined: (i) autonomy is essential to promote engagement, (ii) safeguarding is important to both users and clinicians, and (iii) interfaces that are simple, easy to use and visually attractive enable use. Priorities identified from both components included greater peer support awareness and uptake, flexibility with timing and family participation, healthcare professional involvement, provision of resources, autonomous features enabling choice, checklists and clinician moderation for safeguarding, and simple to use interfaces. Conclusions: Participants in peer support programs derive benefit from sharing their experience of living with CVD which enable coping, learning, feeling understood and a sense of community. Priorities were synthesised to create a framework for digital peer support development for future peer support with recommendations to focus on six key areas: uptake, flexibility, resources, autonomy, safeguarding and interface.
Cancers (Basel). 2025 Sep 29;17(19):3165. doi: 10.3390/cancers17193165.ABSTRACTBACKGROUND: Lung cancer remains the leading cause of cancer-related mortality globally, largely due to delayed diagnosis in its early stages. While conventional diagnostic tools like low-dose CT and tissue biopsy are routinely used, they suffer from limitations including invasiveness, radiation exposure, cost, and limited sensitivity for early-stage detection. Liquid biopsy, a minimally invasive alternative that captu
BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality globally, largely due to delayed diagnosis in its early stages. While conventional diagnostic tools like low-dose CT and tissue biopsy are routinely used, they suffer from limitations including invasiveness, radiation exposure, cost, and limited sensitivity for early-stage detection. Liquid biopsy, a minimally invasive alternative that captures circulating tumor-derived biomarkers such as ctDNA, cfRNA, and exosomes from body fluids, offers promising diagnostic potential-yet its sensitivity in early disease remains suboptimal. Recent advances in Artificial Intelligence (AI) and radiomics are poised to bridge this gap.
OBJECTIVE: This review aims to explore how AI, in combination with radiomics, enhances the diagnostic capabilities of liquid biopsy for early detection of lung cancer and facilitates personalized monitoring strategies. Content Overview: We begin by outlining the molecular heterogeneity of lung cancer, emphasizing the need for earlier, more accurate detection strategies. The discussion then transitions into liquid biopsy and its key analytes, followed by an in-depth overview of AI techniques-including machine learning (e.g., SVMs, Random Forest) and deep learning models (e.g., CNNs, RNNs, GANs)-that enable robust pattern recognition across multi-omics datasets. The role of radiomics, which quantitatively extracts spatial and morphological features from imaging modalities such as CT and PET, is explored in conjunction with AI to provide an integrative, multimodal approach. This convergence supports the broader vision of precision medicine by integrating omics data, imaging, and electronic health records.
DISCUSSION: The synergy between AI, liquid biopsy, and radiomics signifies a shift from traditional diagnostics toward dynamic, patient-specific decision-making. Radiomics contributes spatial information, while AI improves pattern detection and predictive modeling. Despite these advancements, challenges remain-including data standardization, limited annotated datasets, the interpretability of deep learning models, and ethical considerations. A push toward rigorous validation and multimodal AI frameworks is necessary to facilitate clinical adoption.
CONCLUSION: The integration of AI with liquid biopsy and radiomics holds transformative potential for early lung cancer detection. This non-invasive, scalable, and individualized diagnostic paradigm could significantly reduce lung cancer mortality through timely and targeted interventions. As technology and regulatory pathways mature, collaborative research is crucial to standardize methodologies and translate this innovation into routine clinical practice.
Nature Biotechnology, Published online: 16 October 2025; doi:10.1038/s41587-025-02839-xSomatic small variants in cancer genomes are identified in both short-read and long-read data.
Ann Surg Oncol. 2025 Oct 14. doi: 10.1245/s10434-025-18556-3. Online ahead of print.ABSTRACTBACKGROUND: Familial pancreatic cancer (FPC) will be enriched for germline mutations (GLMs), particularly in homologous recombination repair (HRR) genes, but its distinction from sporadic pancreatic cancer (PC) remains unclear.METHODS: We retrospectively analyzed 111 resected PCs, including 13 patients with FPC (11.8%) and 98 with non-FPC (88.2%). Whole-exome sequencing targeted 151 cancer-related genes,
Ann Surg Oncol. 2025 Oct 14. doi: 10.1245/s10434-025-18556-3. Online ahead of print.
ABSTRACT
BACKGROUND: Familial pancreatic cancer (FPC) will be enriched for germline mutations (GLMs), particularly in homologous recombination repair (HRR) genes, but its distinction from sporadic pancreatic cancer (PC) remains unclear.
METHODS: We retrospectively analyzed 111 resected PCs, including 13 patients with FPC (11.8%) and 98 with non-FPC (88.2%). Whole-exome sequencing targeted 151 cancer-related genes, with parallel gene expression profiling. GLMs were assessed by ClinVar and in silico tools. Homologous recombination deficiency (HRD) scores, COSMIC signatures, immune deconvolution, and survival were compared.
RESULTS: Patients with FPC and non-FPC were comparable in age, sex, tumor stage, and receipt of adjuvant chemotherapy. ClinVar-annotated GLMs were found in 2/13 patients with FPC (15.4%) and 4/98 patients with non-FPC (4.1%). FPC cases more often carried pancreatitis-associated variants (SPINK1, CFTR), whereas non-FPC included HRR-related variants (PALB2, FANCG). When potentially pathogenic HRR-related variants were considered together, prevalence was similar (23.1% vs. 12.2%, p = 0.380). HRD scores did not differ (median 22 vs. 19, p = 0.591), and high HRD scores (≥ 42) were observed only in two non-FPC cases, including one with PALB2. Differential expression analysis revealed no significant differences after false discovery rate correction. Multivariate analysis indicated that FPC status was not an independent prognostic factor (hazard ratio 1.73, p = 0.084).
CONCLUSIONS: Transcriptomic profiles and HRD status were similar between patients with FPC and patients with non-FPC. A spectrum of GLMs was observed in both groups, suggesting that hereditary risk variants are not exclusive to FPC and underscoring the importance of germline testing in all patients with PC.
Nature, Published online: 15 October 2025; doi:10.1038/s41586-025-09616-5Mapping of spatial metabolic gradients in the mouse liver and intestine identifies fructose-induced focal derangements in liver metabolism.
Background: Anxiety is one of the most common mental health problems experienced by children worldwide. In the UK, many children experiencing anxiety do not receive adequate or timely help. Children living in economically-disadvantaged areas experience more mental health problems than those living in high income areas and are less able to engage in activities that can have a positive or protective impact on their mental health. The need for providing low-cost, accessible and engaging mental heal
Background: Anxiety is one of the most common mental health problems experienced by children worldwide. In the UK, many children experiencing anxiety do not receive adequate or timely help. Children living in economically-disadvantaged areas experience more mental health problems than those living in high income areas and are less able to engage in activities that can have a positive or protective impact on their mental health. The need for providing low-cost, accessible and engaging mental health interventions for children living in these areas is high. Objective: The study aimed to explore how a digital mental health therapeutic, ‘Lumi Nova: Tales of Courage’, could be used to support children living with anxiety in economically-disadvantaged areas. Methods: A mixed method study design was used to explore the implementation of Lumi Nova using a supported delivery model with mental health teams based in the North of England. Quantitative data collection on recruitment and engagement patterns were collected and analysed. Qualitative research explored children, parent and practitioner views and experiences with the Lumi Nova app. Results: 113 children were consented to use Lumi Nova and 98 (87%) accessed the intervention at least once. Qualitative semi-structured interviews found that children, their parents and practitioners viewed the Lumi Nova app positively. Quantitative analysis of the recruitment data suggested the feasibility of a future larger roll-out. Analysis of usage data demonstrated varied patterns of engagement with the intervention. The frequency and duration of usage varied across children, as did the activities completed within the game: almost half (49%) completed three in-game challenges indicating progression through the treatment pathway. Conclusions: The study demonstrated that a digital mental health intervention could be successfully deployed within economically-disadvantaged areas in the UK to support children experiencing anxiety. Expected barriers to the deployment of digital mental health interventions in economically-disadvantaged areas (e.g. lack of access to smartphones, data plans, lack of technical skills) were not reported. Digital mental health interventions have the potential to address current gaps in mental health provision for disadvantaged individuals and communities.
Front Med (Lausanne). 2025 Sep 26;12:1648612. doi: 10.3389/fmed.2025.1648612. eCollection 2025.ABSTRACTSmall airways-defined as bronchioles <2 mm in internal diameter that lack cartilaginous support-are frequently involved in the earliest stages of chronic obstructive pulmonary disease (COPD). While COPD is defined per GOLD by persistent post-bronchodilator airflow limitation, small-airway dysfunction can precede spirometric abnormality, motivating earlier, imaging- and physiology-based detec
Front Med (Lausanne). 2025 Sep 26;12:1648612. doi: 10.3389/fmed.2025.1648612. eCollection 2025.
ABSTRACT
Small airways-defined as bronchioles <2 mm in internal diameter that lack cartilaginous support-are frequently involved in the earliest stages of chronic obstructive pulmonary disease (COPD). While COPD is defined per GOLD by persistent post-bronchodilator airflow limitation, small-airway dysfunction can precede spirometric abnormality, motivating earlier, imaging- and physiology-based detection (Agustí et al., 2023). Pathological progression typically begins with loss and stenosis of terminal bronchioles, followed by mucus retention/plugging, fibrotic remodeling, chronic inflammation, microvascular abnormalities, and cellular senescence, ultimately resulting in irreversible impairment of gas exchange. Early diagnosis remains difficult, but a suite of advanced non-invasive modalities-including impulse oscillometry system/forced oscillation techniques (IOS/FOT), single- and multiple-breath washout tests, high-resolution CT with parametric response mapping (PRM), nuclear medicine approaches (e.g., SPECT), dynamic measurements of lung compliance, and Fluorine-19 (19F) MRI-combined with artificial intelligence markedly improve the sensitivity and specificity for detecting small-airway disease. Therapeutic strategies that target cellular senescence and fibrotic pathways-such as senolytics and antifibrotic interventions-are showing promise, particularly approaches that clear senescent cells or block pro-fibrotic signaling. The integration of single-cell omics, high-resolution microvascular imaging, and molecularly targeted therapies is expected to accelerate precision diagnostics and enable personalized early interventions. This review summarizes recent insights into small-airway physiology, key pathophysiological and molecular mechanisms, and current pharmacological strategies, and emphasizes the clinical principle of "early detection, early diagnosis, early intervention" for managing COPD-related small-airway disease.
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