Communications medicine最新文献

Background: Eribulin plus gemcitabine improves progression-free survival in chemotherapy-naive metastatic breast cancer patients, but its efficacy in second-line or later treatment remains unclear.

Methods: This single-arm, phase II study was conducted at 13 Chinese medical centers. Eligible patients had histologically confirmed human epidermal growth factor receptor 2 negative metastatic breast cancer and had received at least one prior taxane-containing chemotherapy regimen and anthracycline-containing regimens in the adjuvant setting. Patients received intravenous infusions of eribulin (1.4 mg/m²) and gemcitabine (1.0 g/m²) on days 1 and 8 of a 21-day cycle. Progression-free survival, objective response rate and disease control rate were assessed. Adverse events were also recorded.

Results: Here we show 70 patients took part in this study (71.4% hormone receptor positive/HER2 negative). Patients had received a median of three prior lines of systemic treatment. Overall, 48.6% have significant tumor shrinkage, 92.9% have tumor control, and median time without disease progression is 7.2 months (95% confidence interval, 5.5-10.9). Median time without disease progression is 8.4 months (hormone receptor positive) vs. 6.3 months (triple-negative, p = 0.1849). Grade 3-4 side effects mostly affect blood cells and are manageable.

Conclusions: Eribulin plus gemcitabine is effective and well-tolerated in patients with HER2-negative metastatic breast cancer needing second-line or later treatment, providing a valuable treatment option.

Background: Social isolation, an objective lack of social connections, and loneliness, the subjective distress from perceived social deficits, are established risk factors for poor cancer prognosis. However, their associations with cancer incidence remain unclear. We investigated these associations using UK Biobank data.

Methods: We analyzed data from 354,537 UK Biobank participants aged 38-73. Participants linked to national health registries, without cancer within one year post-baseline, and with complete exposure and covariate data were included. The primary outcome was cancer incidence. Covariates were classified into demographic, physiological, socioeconomic, lifestyle, and health-related indicators. Cox proportional hazards models were used, with subgroup interaction analysis and mediation analyses performed.

Results: Here we show that 20,767(5.8%) of participants are isolated and 15,942(4.5%) of participants are lonely. During a median 11.60 years (IQR8.40-12.72) of follow-up, 38,103 participants are diagnosed with cancer. After adjusting for covariates, social isolation is associated with an 8% higher cancer risk(CSHR1.087 95% CI 1.043-1.133; sHR1.073 95% CI 1.029-1.120), while loneliness is not. Social isolation shows a strong interaction by sex (P-interaction<0.01), with isolated females at higher risk than males. Social isolation increases the risk of breast, lung, uterine, ovarian, bladder, and stomach cancers in females, and bladder cancer in males. Socioeconomic factors, health behaviours, and inflammation status largely explain these associations.

Conclusions: Social isolation is a risk factor for cancer with significant sex and organ-specific effects. Addressing socioeconomic challenges, unhealthy lifestyles, and poor mental well-being through health policies could help reduce cancer risk in isolated populations.

Background: Pancreatic cancer is one of the most aggressive malignancies with a 5-year relative survival rate of only 13%. Its poor prognosis is largely attributed to the lack of reliable tools for early detection. Current diagnostic standards rely on imaging methods that are invasive, costly, and often inadequate to detect early-stage disease. A noninvasive blood-based test with high sensitivity and specificity could substantially improve patient outcomes.

Methods: Lipid concentrations in plasma and serum samples were determined by ultrahigh-performance supercritical fluid chromatography-mass spectrometry, and multivariate statistical modeling was used to analyze lipid profiles and differentiate between groups.

Results: Here, we present results from a pilot study evaluating lipidomic profiling of prospectively collected plasma and serum samples from patients with pancreatic ductal adenocarcinoma (PDAC, n = 177), healthy controls (n = 218), and high-risk individuals for pancreatic cancer (n = 93). The lipidomic test distinguishes PDAC patients from healthy controls with an accuracy exceeding 95%, including robust detection of early-stage cases and even individuals with low CA 19-9 secretion. The sensitivity is approximately 30% higher than that of CA 19-9. In high-risk individuals, the method achieves a specificity of over 96% (95% CI, 89-99%), comparable to established imaging-based approaches.

Conclusions: This pilot study demonstrates the promising potential of lipidomic profiling as a noninvasive, blood-based screening tool for pancreatic cancer. The method outperforms current biomarkers, maintains high diagnostic accuracy in early-stage disease, and performs reliably in high-risk populations. These findings support the initiation of a clinical trial aimed at validating the lipidomic test for the early detection of PDAC.

Background: Postpartum depression is a common and disabling condition that differs from major depressive disorder and shows marked variation in symptoms and outcomes. Identifying distinct biological subtypes could improve diagnosis and treatment. The present study aims to uncover neurophysiological subtypes of postpartum depression and explore their underlying neural and molecular features.

Methods: We analyzed structural brain images from a cohort of postpartum women recruited at the West China Second Hospital, Sichuan University, including 76 patients with postpartum depression (age range: 24-39 years) and 62 healthy postpartum women (age range: 23-40 years). An unsupervised clustering approach was applied to gray matter volume patterns to identify neurobiological subtypes. Individualized structural covariance networks were then constructed to compare subtype-specific connectivity. Transcriptomic profiles and neurotransmitter density maps were further integrated to examine molecular mechanisms underlying the structural alterations.

Results: Here we show that postpartum depression can be divided into two neurobiological subtypes. Subtype 1 displays reduced gray matter volume in the dorsal attention network, consistent with cognitive impairments. Subtype 2 shows increased gray matter volume in the default mode network, reflecting emotional dysregulation. Subtype 2 also exhibits weaker structural connectivity between the middle temporal gyrus, parahippocampus, and amygdala. Molecular analysis indicates that Subtype 1 is related to energy metabolism and the neurotransmitter receptor mGluR₅, whereas Subtype 2 is associated with synaptic regulation, neuroplasticity, and neurotransmitter receptors such as 5-HT_1B, dopamine D₂, cholinergic M₁ and μ-opioid receptor (MOR).

Conclusions: These findings suggest that postpartum depression comprises two biologically distinct forms with different cognitive and emotional characteristics. Recognizing these subtypes may enhance our understanding of its neuropathology and support the development of personalized therapeutic strategies.

Background: Basic public health service (BPHS) in China is the most important policy to provide equal access to essential healthcare for all residents, but it lacks a core competency framework for healthcare workers (HCWs) to provide BPHS, which fails to provide theoretical support for the competency assessment and training. This study aims to establish a core competency framework for HCWs to deliver BPHS in China.

Methods: BPHS functions are first identified as the goal of competency enhancement for HCWs, literature research and job analysis are conducted to define the competencies needed to fulfill the functions, then a core competency framework is initially proposed and improved using two rounds of Delphi.

Results: After several iterations among experts, a consensus was reached on the indicator framework for the functions of BPHS and core competencies of HCWs providing BPHS. The core function of BPHS in China consists of one overarching function (To achieve the equalization of BPHS and promote health equality) and eight sub-functions ("Residents' health literacy improvement" among others). The finalized core competency framework for HCWs consists of 3 areas (specialized knowledge, skills, and conception and values), 19 domains ("Public health and preventive medicine knowledge" among others), 60 subdomains ("Epidemiology and health statistics" among others), and 116 competencies.

Conclusions: The core functions of BPHS and the core competency framework for HCWs to provide BPHS are constructed. This framework could serve as a guide for structured training on HCWs to provide high quality of BPHS and to develop criteria for those HCWs' competencies assessment.

Background: Public health resources are often allocated based on reported disease cases. However, for under-recognized infectious diseases such as tick-borne viruses, risk assessments should also account for ecological and socioeconomic factors that influence disease case reporting. This study identifies country-level predictors of tick-borne virus reporting and evaluates whether wealthier nations are more likely to report resource-intensive cases.

Methods: We applied boosted regression trees, a robust machine learning algorithm, to a comprehensive global database of tick-borne viruses and 24 environmental and socioeconomic variables.

Results: Countries with lower income inequality and greater expertise in verterinary, agricultural, or forestry sectors are more likely to report tick-borne virus cases. Wealthier nations with stronger institutional and professional capacity exhibit higher reporting rates, whereas countries affected by conflict or limited health infrastructure show underreporting. Climatic factors, particularly subartic environments, also contribute to reporting likelihood, complementing the effects of socioeconomic drivers.

Conclusions: Disease reporting is shaped by both ecological context and socioeconomic capacity. Strengthening surveillance through targeted resource allocation and better integration of veterinary and public health expertise under the One Health framework could enhance global tick-borne disease mitigation. These findings provide valuable evidence to support the World Health Organization's Global Arbovirus Initiative and emphasize the need for equitable disease surveillance across regions.

Background: Esophageal squamous cell carcinoma (ESCC) has a poor prognosis and limited tools for early detection. Saliva is easily accessible and its microbiome composition can serve as a marker for upper gastrointestinal tract disease. This study aims to evaluate the potential of an oral microbiome signature for classifying ESCC.

Methods: In a cross-sectional study of 48 ESCC patients and 110 controls from South Africa, a region with high ESCC incidence, we studied the potential utility of an oral microbiome signature for the disease. We built models using nested cross-validation to evaluate whether this signature is generalizable to held-out samples and further evaluated generalizability in studies from China, a distinct geographic region.

Results: We find significant alterations in the oral microbiome in patients with ESCC including significantly reduced α diversity and increased abundance of Fusobacterium nucleatum. We also find that logistic regression models based on microbiome data can better classify ESCC in held-out samples (auROC=0.96) compared to clinical and demographic data (auROC = 0.69; DeLong p < 1 x 10^-8). Lastly, we find that microbiome-based models trained across multiple studies can generalize well to geographically distinct studies.

Conclusions: Our results show that the oral microbiome in individuals with ESCC is distinct from controls and that this signal can generalize across unseen samples, suggesting the potential of saliva to serve as a non-invasive screening tool for ESCC.

Background: Alzheimer's disease (AD) and Diabetes Mellitus Type II (DM2) share overlapping biological mechanisms, and diabetes increases the risk of developing AD. Treatments that modify the course of diabetes, such as metformin and semaglutide, have been proposed to protect the brain, but their effectiveness in preventing AD remains uncertain. This study aimed to systematically compare the potential of diabetes therapies to reduce the risk of AD.

Methods: We developed an integrative framework combining comparative network pharmacology to evaluate 39 diabetes therapies in relation to AD. The analysis examined how each treatment influenced shared molecular pathways between the two conditions and measured their comparative impact score and validated key findings using gene expression data.

Results: Here we show that metformin is the most promising therapy for protection against AD, while semaglutide ranks among the least effective, based on comparative analysis within the DM2-AD pathway-pathway comorbidity network. Metformin's effects are mediated through AMPK, insulin, and adipocytokine signaling, that influence key Alzheimer's-related processes. In contrast, semaglutide, despite its growing clinical prominence as a weight loss therapy, exhibits minimal engagement with core neurodegenerative pathways within the DM2-AD comorbidity network. Certain combination therapies, such as insulin glargine with lixisenatide and insulin degludec with liraglutide, display effects comparable to metformin.

Conclusions: These findings reveal that diabetes therapies differ in their ability to protect against AD. Metformin shows the strongest potential, supporting its prioritization for targeted studies in people with diabetes who are at high risk of AD, and highlighting the importance of precision medicine in future prevention trials.

Background: SARS-CoV-2 infection usually has a mild course in childhood, yet few children develop a Multisystem Inflammatory Syndrome (MIS-C). Several metabolic pathways have been found to be dysregulated in adults with COVID-19, yet data are lacking in children. Here we investigate serum metabolomic features of children with COVID-19 in relation to age, sex and both clinical and biochemical severity.

Methods: We carried out a prospective observational comparative cohort study enrolling 92 children (48 M, mean age 3.69 ± 5.1 years) with acute SARS-CoV-2 infection and 7 with MIS-C along with 41 age- and sex-matched controls. Sera collected at admission, acute phase, discharge and remission were analyzed by Gas Chromatography Mass Spectroscopy.

Results: Here we identify a distinct signature featuring inflammation, reactive oxygen species and glicerolipids pathways in children with acute SARS-CoV-2 infection compared to controls (permutation test p = 0.0015). Metabolomic profile changes are associated with age, disease status and disease severity, while a normalization of these changes is observed at disease resolution. MIS-C children showed a unique signature compared to age-/sex-matched COVID-19 patients or controls.

Conclusions: Pediatric COVID-19 has a characteristic metabolomic signature featuring glucose and aminoacid metabolism, that varies with age and disease phenotype. Our study supports the value of metabolomics to unveil pathways related to host-viral interaction that may also help identify early predictors of disease evolution.