Communications medicine最新文献

Background: Cell free DNA (cfDNA) analysis has a large potential for cancer patient diagnosis. Given the evidence that cfDNA partially preserves chromatin architecture of tumor cells, it potentially allows the detection of tumor responses after therapeutic treatment. This study aimed to detect the responses of abemaciclib, a selective inhibitor of cell cycle regulators CDK4 and CDK6, in metastatic recurrent breast cancer patients through cfDNA analysis.

Methods: Serum-derived cfDNA was purified from breast cancer patients before and after abemaciclib treatment and from healthy donors. cfDNA concentrations were measured, and sequencing analysis was performed to assess enrichment patterns at open chromatin regions.

Results: Here we show that cfDNA can be used as a valuable marker to monitor cancer burden and responses to drug treatment. cfDNA concentrations are significantly higher in patients with breast cancer than in healthy individuals and decline following abemaciclib therapy. Sequencing analysis reveals distinct cfDNA signatures between treated and untreated samples, particularly at chromatin regions linked to cell death and pathways regulated by CDK4 and CDK6. Chromatin features also differ between long-term responders and short-term progression cases. Pathway analysis identifies the Polycomb complex as a potential modulator of treatment efficacy, and inhibition of its enzyme EZH2 enhanced the anti-proliferative effect of abemaciclib in luminal breast cancer cells.

Conclusion: Our findings suggest that cfDNA profiling captures molecular changes induced by targeted therapy and may serve as a practical biomarker for treatment monitoring and prediction of therapeutic response. This research provides insight into the mechanisms of CDK4/6 inhibitor action in metastatic breast cancer.

Background: Glioblastoma (GBM) is the most common high-grade primary malignant brain tumor, characterized by a notably poor prognosis. Current treatments for GBM have shown limited effectiveness in improving patient survival, highlighting the urgent need for effective therapeutic strategies. Combination therapy offers significant potential in overcoming resistance by targeting multiple signaling pathways; however, it often comes with increased toxicity compared to monotherapy. Co-encapsulating multiple therapeutic agents into a tumor-targeted drug delivery platform holds promise for overcoming these limitations and improving treatment outcomes.

Methods: We developed a tumor-targeted liposomal nanoformulation (TTL) using phospholipids, cholesterol, DSPE-(PEG)2000-OMe, and a proprietary tumor-targeting peptide (TTP). The TTL was loaded with everolimus (TTL-E), vinorelbine (TTL-V), rapamycin (TTL-R), a combination (TTL-EV), or (TTL-RV). These formulations were tested in vivo on orthotopic GBM mice, combined with temozolomide and radiation. RNA sequencing was performed to identify molecular and transcriptome changes post-treatment.

Results: TTL demonstrated tumor-specific uptake, effectively delivering drugs to GBM tumors. TTL-EV and TTL-RV outperformed single-drug formulations. Radiation combined with TTL-EV/RV improved tumor growth inhibition and survival, while temozolomide provided minimal benefit. Transcriptome analysis revealed differentially expressed genes (DEGs) linked to DNA damage repair, cell cycle, metabolism, and extracellular matrix pathways.

Conclusions: TTL crossed the blood-brain barrier, targeting tumors effectively. Radiation plus TTL-EV/RV enhanced tumor suppression and survival in GBM models. Gene expression analysis identified DEGs related to DNA damage and cell death. Mechanistic studies suggest TTL-EV plus radiation inhibits mTOR/MAPK pathways and sensitizes tumors to radiation. These findings offer a potential approach for improving GBM treatment.

Background: Functional constipation is common in children and often responds poorly to standard treatments. This study evaluated the efficacy and mechanisms of multi-strain Bacillus spore probiotics, which tolerate gastrointestinal conditions, in paediatric functional constipation.

Methods: We conducted a randomized, double-blind, placebo-controlled trial (ClinicalTrials.gov NCT06154525, 4/12/2023) in preschool children (24-60 months) with functional constipation in Vietnam. A total of 111 participants were randomly assigned (1:1:1) to receive placebo or two multi-strain Bacillus spore probiotics (LiveSpo Kids or Preg-Mom, ≥3 billion CFU/5 mL registered; each tested at 3.7 billion CFU/5 mL) for 28 days. Primary outcomes were changes in functional constipation (main focus), anorexia, and underweight risk at day 28. Secondary outcomes included serum cytokines, stool IgA, and gut microbiota; stool samples from 10 healthy children provided a reference microbiota profile.

Results: Both probiotic groups show significant improvements at day 28. Percentages of children with constipation decrease 3.7-fold in Kids and 5.1-fold in PregMom (p < 0.0001). Absolute Risk Reductions (ARR) are 52.38% (95%CI: 35.45%-77.26%) and 59.97% (95%CI: 44.48%-84.68%) in Kids and PregMom vs Placebo. Kids and PregMom groups improve anorexia (ARR: 24.40% (95%CI: 3.09%-49.44%) and 25.98% (95%CI: 4.69%-51.25%)) and underweight risk (ARR: 7.87% (95%CI: 0%-23.80%) and 19.30% (95%CI: 1.70%-37.50%)) vs. Placebo. Probiotics reduce serum IL-6 and IL-23, increase IL-10 and stool IgA, and shift the gut microbiota toward a composition more closely resembling healthy children, enriching beneficial species while reducing harmful ones.

Conclusions: Multi-strain Bacillus spore probiotics alleviate functional constipation, improve immune markers, and modulate gut microbiota in children, supporting their potential as effective microbiome-targeted interventions.

Background: Type 2 Diabetes Mellitus (T2DM) involves patient-specific immune dysfunction not addressed by current diabetes management methods. Traditional methods to assess functional changes to PBMCs are too costly and time-intensive for routine use.

Methods: We studied 57 individuals across healthy, prediabetic, and diabetic stages, performing chromatin imaging of live peripheral blood mononuclear cells (PBMCs) using a microfluidic imaging assay.

Results: Here, we present an orthogonal and adjunct chromatin imaging-based assay to monitor alterations to PBMCs during T2DM progression. By applying representation learning on chromatin images, we identified distinct PBMC clusters, with specific subpopulations enriched at different T2DM stages. We found that the levels of certain PBMC subsets are predictive of T2DM. Additionally, we observed significant changes in nuclear and chromatin mechanics in diabetic individuals compared to prediabetic and healthy individuals, along with decreased Lamin A/C expression and increased cellular activation in diabetic PBMCs.

Conclusions: Collectively, this study demonstrated a cost-effective and scalable solution for the routine monitoring of T2DM using PBMC chromatin biomarkers.

Background: Oncolytic viruses are cancer therapies that selectively replicate in tumors, deliver therapeutic genes, and stimulate immune responses. Combining these viruses with immune-boosting agents could enhance their effectiveness. Thymopentin (TP5), an immune-modulating peptide, may enhance the antitumour efficacy of ADV.

Methods: We tested TP5 combined with adenovirus type 5 in tumor models, measuring tumor growth and immune changes using flow cytometry and antibody-based cell depletion. A modified oncolytic adenovirus producing TP5 (ADV-TP5) was created using gene editing and tested in mice and human immune cell-engrafted tumor models. TP5 was also combined with herpes simplex virus and vaccinia virus.

Results: Here we show that combining adenovirus (ADV) with thymopentin (TP5) reprograms the tumor microenvironment and enhances antitumor efficacy in xenograft models. ADV + TP5 increases proinflammatory macrophages and cytotoxic CD8⁺ T-cell infiltration while stimulating long-term immune memory. We further engineer an oncolytic adenovirus expressing TP5 (ADV-TP5), which demonstrates superior tumor suppression compared to unmodified ADV in mice and human immune cell-engrafted models. TP5 also amplifies responses when combined with other oncolytic viruses (e.g., herpes simplex virus, vaccinia virus), supporting its role as a broad-spectrum adjuvant for clinical virotherapies.

Conclusions: TP5 strengthens oncolytic virotherapy by orchestrating macrophages and CD8⁺ T cells to attack tumors. Its dual use, as a combination drug or engineered into viruses, offers a practical strategy to improve clinical outcomes. This approach provides a low-cost method to amplify oncolytic virus efficacy and inspires designs where single therapies mimic combination effects, advancing accessible cancer immunotherapy.

Background: Menstrual hygiene products (MHPs) may offer a high-reach, frequent, long-lasting yet underutilized channel for health education, particularly in low-resource settings. This study aimed to explore women's breast health awareness (BHA) and their attitudes towards and preferences for BHA messages on MHPs.

Methods: Women aged 18-49 years in Ghana and Tanzania (n = 438; 216 Ghana, 222 Tanzania), stratified by age and urban/rural setting, completed a questionnaire assessing socio-demographics, BHA, and preferences for BHA messages with MHP. BHA was assessed with an adapted 31-item Breast Cancer Awareness Measure Tool covering 4 domains: breast cancer signs and symptoms, confidence, skills, and behaviour in noticing breast changes, anticipated help-seeking delays, and risk factors. Items were scored on a 10-point scale.

Results: Here we show that overall BHA is slightly higher in Ghana than Tanzania (urban/rural: 6.2/6.4 vs. 5.6/5.3). Awareness is highest for signs and symptoms (median score range hereafter: 5.5-7.8) and lowest for risk factors (2.0-3.8). While women show confidence in noticing breast changes (8.0-10.0) and seeking help (10-10), their skills are insufficient (1.0-5.0) and previous help-seeking behaviours were low (0-0). Women would overwhelmingly (91%) choose MHPs with BHA messages over MHPs without, preferring varied presenting formats, mostly between outside of the package, on individual pad covers, and a leaflet.

Conclusions: Findings highlight generally low BHA level in Ghana and Tanzania. The strong acceptability of having BHA messages on MHP presents a scalable and sustainable opportunity to improve BHA with the goal of reducing BC incidence and mortality.

Background: Diabetes Mellitus is a common, chronic metabolic disorder affecting the cardiovascular system, autonomic nervous system, and sleep quality. Diabetes affects diverse physiological data including heart rate variability, distal body temperature, and sleep duration. We hypothesized that biologically informed features from wearable device data, combined with appropriate application of longitudinal data, can capture physiological covariates of diabetes and support the noninvasive detection of diabetes.

Methods: We obtained 4 months and 7 days of wearables data (Oura Ring) from 389 individuals self-reporting diabetes and 10,820 people self-reporting no diabetes diagnosis from the TemPredict database. We selected 36 features of sleep, circadian disruption, and distal body temperature from literature and evaluated whether time windows of these features could be classified to be from individuals self-reporting diabetes (N = 236) or self-reporting no diabetes diagnosis (N = 282).

Results: Here we show longer time windows of input perform better, with the best algorithm (21-nights) achieving 0.88 Area under ROC (AUROC) and 0.80 Area under Precision Recall (AUPRC) (0.30 improvement over random). Feature analyses reveal the importance of further derived distal body temperature features (increase AUROC by 0.0724), especially to differentiate other chronic conditions from diabetes. The model achieves 0.80 AUROC and 0.28 improvement over random in AUPRC in an imbalanced cohort drawn from 6,658 individuals, emulating a general population.

Conclusions: These results indicate the value of biologically informed features and longitudinal data for identifying people with diabetes and further, suggest that these methods could make such separations possible for other chronic conditions that affect sleep and inflammation.

Background: Interactions between SARS-CoV-2, influenza virus, and respiratory syncytial virus (RSV) at the population level remain poorly understood. This study aimed to quantify potential interactions among these viruses and assess their influence on transmission dynamics.

Methods: We analyzed weekly surveillance data on SARS-CoV-2, influenza A and B viruses (IAV and IBV), and RSV from seven regions from October 2021 to May 2024. Distributed lag nonlinear models within a spatiotemporal Bayesian hierarchical framework were used to assess the exposure-lag-response associations among virus pairs. Additionally, we developed a two-pathogen, meta-population mechanistic transmission model to capture the co-epidemic dynamics of IAV and SARS-CoV-2, and to quantify the strength and duration of their bidirectional interactions.

Results: Among all virus pairs examined, a statistically significant association is identified only between IAV positivity and subsequent SARS-CoV-2 risk. When IAV positive rate percentile is between the 52nd and 88th percentiles, the relative risk (RR) of SARS-CoV-2 infection is significantly reduced. The lowest RR for SARS-CoV-2 (0.58, 95% CrI: 0.40-0.85) occurs at a 5-week lag when IAV positivity reaches the 70th percentile. The fitted mechanistic model using incidence data in Beijing shows that IAV infection substantially reduces infection to SARS-CoV-2 by 94.24% (95% CrI: 88.50%-99.24%), with the protective effect lasting 38.24 days (95% CrI: 35.50-41.29 days). Conversely, SARS-CoV-2 infection is associated with a slight increase in infection to IAV.

Conclusions: Our findings indicate that IAV circulation may transiently reduce population-level infection to SARS-CoV-2, potential through ecological or immunological mechanisms.

Background: Only a subset of individuals infected with SARS‑CoV‑2 develop severe COVID‑19. Improved tools for early diagnosis and prognostication are needed. We hypothesized that unsupervised analysis of detailed circulating proteomes could reveal biologically meaningful patient endotypes and help identify individuals at elevated risk of severe outcomes.

Methods: We performed unsupervised stratification of the circulating proteome in 731 SARS‑CoV‑2 PCR‑positive participants from the Biobanque québécoise de la COVID‑19 (BQC19), representing a range of disease severities. We also developed a prognostic model based solely on clinical laboratory measurements and applied it to 903 patients recruited across early pandemic waves (2020-2022) to generalize identified endotypes.

Results: Six proteomic endotypes (EPs) emerged. Endotype EP6 showed the highest frequencies of ICU admission, ARDS, and mortality. EP6 was marked by elevated C‑reactive protein, D‑dimer, interleukin‑6, ferritin, soluble fms‑like tyrosine kinase‑1, increased neutrophils, and reduced lymphocyte counts. SHC4 emerged as a protein quantitative trait locus associated with EP6. Among EP6 patients requiring mechanical ventilation, we observed alterations in lipoprotein metabolism, and alpha‑L‑iduronidase levels inversely correlated with duration of ventilation.

Conclusions: Unsupervised proteomic analysis identified biologically coherent endotypes that advance understanding of acute lung injury in COVID‑19 and support improved diagnostic and prognostic strategies.

Background: Early detection of metabolic dysfunction before diabetes onset remains a critical challenge in preventive medicine. Although glucose dynamics provide high-dimensional insights into metabolic states, it remains unclear which combination of glucose dynamics-derived measures most effectively summarises interindividual differences in glucose regulation.

Methods: We analysed continuous glucose monitoring (CGM) data from 8025 adults without diagnosed diabetes, using all recordings of at least seven days' duration, to derive a low-dimensional representation of glucose dynamics. Using exploratory factor analysis, we identified a small set of latent features that accounted for variation between individuals in CGM-derived metrics. We then trained and validated machine-learning models to predict postprandial glucose trajectories from these features in 863 participants who underwent standardised meal tests. We further assessed the generalisability of this representation in an independent oral glucose tolerance test dataset. Finally, we examined associations between the derived features and markers of vascular and liver health in 1784 non-diabetic adults.

Results: Three features, "mean", "variance", and "autocorrelation", explain more than 80% of the interindividual differences in CGM-derived measures. A three-dimensional representation based on these features reconstructs postprandial glucose trajectories with high accuracy and outperforms fasting, mean, and two-hour postprandial glucose values. Each feature shows independent associations with carotid artery intima-media thickness and with hepatic steatosis and stiffness.

Conclusions: By compressing high-dimensional glucose dynamics into three interpretable features with minimal loss of information, this framework provides a simple yet physiologically meaningful representation of glucose regulation that may facilitate a more precise and interpretable assessment of diabetes-related risk.