Communications medicine最新文献

Background: It is commonly believed that Africa largely evaded the worst of the COVID-19 pandemic, with fewer cases than other continents. However, regional comparisons that ignore differences in testing intensity may misrepresent dynamics. Studying the spread and case-fatality relationship during COVID-19 across WHO regions requires explicitly adjusting for time-varying test volumes.

Methods: We build a weekly panel dataset spanning May 2020 to December 2021 for the WHO regions: Africa, Eastern Mediterranean, South-East Asia, the Americas, Western Pacific, and Europe. Data on tests, confirmed cases, and COVID-19-attributed deaths were sourced from Our World in Data. We apply a novel metric that corrects for fluctuating test volumes to quantify week-to-week acceleration in infections and in mortality. We then compare the frequency, magnitude, and timing of these acceleration episodes across regions.

Results: Accounting for testing dynamics, we show that Africa exhibits multiple infection-acceleration episodes whose magnitude and frequency match those in other regions. Mortality accelerations in Africa closely follow infection surges, with an average lag of ten weeks. A positive correlation between infection acceleration in Africa and the Americas further indicates synchrony. These findings hold when using a larger secondary dataset of 140 countries.

Conclusions: Contrary to prevailing assumptions, Africa was not spared from the pandemic's severe dynamics. Infection surges were on par with those elsewhere and were followed by mortality accelerations. These results underscore that accounting for testing variability is essential to accurately assess pandemic progression, and they highlight the urgent need to strengthen surveillance and healthcare capacity across all regions.

Background: Before surgical resection of lung tumor, intraoperative biopsy is needed for cancer diagnosis, while current techniques that guide biopsy have limited performance in tumor identification and boundary determination. Remodeling of extracellular matrix (ECM), mainly collagen and elastin fibers, is an emerging hallmark of tumorigenesis.

Methods: Herein, we establish a quantitative multiphoton microscopy (MPM) imaging method for time-efficient, highly-sensitive lung cancer detection via characterization of ECM remodeling. From label-free images of collagen and elastin fibers acquired simultaneously, we construct a similarity coefficient (SC) metric to describe their interaction, and further develop an artificial intelligence (AI)-ECM framework by producing a fiber voxel dictionary via unsupervised learning of morpho-structural features for explainable and visible assessments of cancer risk.

Results: The application is demonstrated by ex vivo human lung cancer diagnosis with a sensitivity of 99.37%, and recognizing the tumor boundary. The translational potential is further revealed via in vivo imaging of a murine model harboring human lung cancer.

Conclusions: This technology can help surgeons perform more precise biopsies and surgeries by providing explainable visual cues, thus leading to better outcomes for lung cancer patients.

Background: In adolescents, identifying objective biomarkers for treatment response is crucial for the development of effective interventions. Voice-based biomarkers have recently shown potential to capture treatment-related changes in Major Depressive Disorder (MDD). While prior studies have been cross-sectional experiments with single speech sample, this study addresses a critical gap by evaluating intra-patient changes in speech over treatment period, providing insight into how these voice biomarkers evolve within individuals.

Methods: We collected pre- and post-treatment voice samples from 48 adolescent MDD patients. We hypothesized that deep learning models could detect clinically meaningful changes in depressive states during treatment. Therefore, we compared machine learning and deep learning models for depressive classification. Additionally, we introduced the Dual Voice-based Depressive State Analysis (DVDSA) method to categorize intra-patient depressive state changes as recovery, worsening, or unchanged, highlighting the deep learning models' ability to detect these variations.

Results: Among the acoustic features, only the fundamental frequency exhibits significant changes between pre- and post-treatment states after Holm-Bonferroni correction. Machine learning models demonstrate limited performance in distinguishing treatment states, with the best F1-score reaching 65.83%. In contrast, deep learning model, particularly WavLM, achieves remarkably higher performance in binary classification, with an F1-score of 78.05%. The WavLM maintains robust performance, when applied to the DVDSA method, achieves an F1-score of 70.58%.

Conclusions: These findings suggest that machine learning models and individual acoustic features may not sufficiently capture treatment-related changes in MDD patients. This study underscores the value of deep learning models using the DVDSA method, addressing the limitations of pre- and post-treatment classification and highlighting their potential to advance personalized treatment strategies for adolescent MDD.

Background: Low-dose computed tomography (LDCT) employed in lung cancer screening (LCS) programmes is increasing in uptake worldwide. LCS programmes herald a generational opportunity to simultaneously detect cancer and non-cancer-related early-stage lung disease, yet these efforts are hampered by a shortage of radiologists to interpret scans at scale. Here, we present TANGERINE, a computationally frugal, open-source vision foundation model for volumetric LDCT analysis.

Methods: Designed for broad accessibility and rapid adaptation, TANGERINE can be fine-tuned off the shelf for a wide range of disease-specific tasks with limited computational resources and training data. The model is pretrained using self-supervised learning on more than 98,000 thoracic LDCT scans, including the United Kingdom's largest LCS initiative to date and 27 public datasets. By extending a masked autoencoder framework to three-dimensional imaging, TANGERINE provides a scalable solution for LDCT analysis, combining architectural simplicity, public availability, and modest computational requirements.

Results: TANGERINE demonstrates superior computational and data efficiency in a retrospective multi-dataset analysis: it converges rapidly during fine-tuning, requiring significantly fewer graphics processing unit hours than models trained from scratch, and achieves comparable or superior performance using only a fraction of the fine-tuning data. The model achieves strong performance across 14 disease classification tasks, including lung cancer and multiple respiratory diseases, and generalises robustly across diverse clinical centres.

Conclusions: TANGERINE's accessible, open-source, lightweight design lays the foundation for rapid integration into next-generation medical imaging tools, enabling lung cancer screening programmes to pivot from a singular focus on lung cancer detection toward comprehensive respiratory disease management in high-risk populations.

Background: Forced displacement and migration are on the rise worldwide. Asylum seeking and refugee minors (ASRM) are particularly exposed to risk factors for mental health problems. Yet, there is a lack of comprehensive data on the prevalence of specific mental health problems as well as applied screening and follow-up care in Germany.

Methods: Using the online platform REDCap, we conducted the cross-sectional SAVE-KID survey among health and social care professionals (HSCP) working with ASRM in Germany (n = 201; 44% medical doctors, 38% social workers) to assess the estimated mental health burden among ASRM, the conducted screening measures, and provided mental health care as well as the extent to which communication problems affect care for ASRM.

Results: Here we show, that on average, 21% of ASRM are reported with one or more listed mental health problem. Only 37% receive follow-up. Less than 24% of participants conduct mental health screening by informal questions, interviews, trained staff or questionnaires. 84% of participants report frequent communication problems. Most used aids are online tools or relatives' translations.

Conclusions: SAVE-KID describes an imbalance between the occurrence of and screening for mental health problems among ASRM. Comprehensive, systematic detection of mental health problems remains challenging due to communication problems, lack of specialized staff and diagnostic tools as well as follow-up care structures.

Background: Multimorbidity, the co-occurrence of multiple long-term conditions (LTCs), is an increasingly important clinical problem, but little is known about the underlying causes. We investigate the role of a critical multimorbidity risk factor, obesity, as measured by body mass index (BMI), in explaining shared genetics amongst 71 common LTCs.

Methods: In a population of northern Europeans, we estimated genetic correlation, between LTCs and partial genetic correlations after adjustment for the genetics of BMI. We used multiple causal inference methods to confirm that BMI causally affects individual LTCs, and their co-occurrence. Finally, we quantified the population-level impact of intervening and lowering BMI on the prevalence of 15 key common multimorbid LTC pairs.

Results: BMI partially explains some of the shared genetics for 740 LTC pairs (30% of all pairs considered). For a further 161 LTC pairs, the genetic similarity between the LTCs was entirely accounted for by BMI genetics. This list included diabetes and osteoarthritis and gout and osteoarthritis: Causal inference methods confirmed that higher BMI acts as a common risk factor for a subset of these pairs, and therefore BMI-lowering interventions would likely reduce their prevalence. For example, we estimated that a 1 standard deviation or 4.5 unit decrease in BMI would result in 17 fewer people with both chronic kidney disease and osteoarthritis per 1000 who currently have both LTCs.

Conclusions: Our genetics-centred approach quantifies the contribution of obesity to multi-morbidity. Our method for calculating full and partial genetic correlations is published as an R package {partialLDSC}.

Background: Early life nutrition is associated with child behaviour; however, the interplay with genetic vulnerability is understudied. We hypothesised that psychiatric genetic risk interacted with early nutrition to predict behavioural problems in childhood and adolescence.

Methods: The Raine Study participants with genetic information aged 2-17 were repeatedly evaluated with the child behaviour checklist total problems score (CBCL_TOT). Breastfeeding duration was recalled at age 1, 2 and 3 follow-up, and toddler diet derived by an age-1 24-h maternal recall (EAT1, scale 0-70, SD 10, higher scores proxying healthy diet). We derived polygenic scores (PGS) impacting general psychopathology: attention-deficit hyperactivity disorder (ADHD), depression, chronic multisite pain (CMSP), total behaviour problems and birthweight. In confounder-adjusted mixed-effects models of CBCL_TOT throughout follow-up we examined nutrition-by-PGS interactions.

Results: In 1393 participants, a borderline signal suggests that 1 month longer breastfeeding reduces CBCL_TOT by -0.108 (95% CI [-0.184, -0.0289]) exclusively in individuals with a higher CMSP PGS (Interaction p = 0.03). In 1310 participants, a strong signal suggests that 1 EAT1 point increase results in a reduced CBCL_TOT by 0.121 points (95% CI [-0.171, -0.0704]) exclusively in individuals with a lower ADHD PGS (Interaction p = 0.0005). Post hoc analysis suggests that plant-based food consumption drives the favourable EAT1-CBCL_TOT association.

Conclusions: Nutrition in early life and psychiatric genetic risk may interact to determine lasting child behaviour. Contrary to our hypothesis, we find dietary benefits in individuals with lower ADHD PGS, necessitating replication. We also highlight the possibility of including genetics in early nutrition intervention trials for causal inference.

Background: Many transfeminine people (assigned male at birth with feminine gender identities) undergo vaginoplasty, a surgical procedure constructing a neovagina, typically using penile and scrotal tissue. In cisgender females, gynecological symptoms (pain, discharge, malodor) are often attributed to bacterial vaginosis, which can be diagnosed using Nugent scoring of gram-stained vaginal smears. The Nugent score assesses the abundance of large gram-positive rods versus small or curved gram-variable rods, traditionally for the detection of Lactobacillus, Gardnerella vaginalis, and Mobiluncus, respectively. Although unvalidated, this method is frequently applied to neovaginal samples to diagnose gynecological symptoms and dysbiosis. This study assessed the Nugent score's utility for diagnosing neovaginal dysbiosis in transfeminine people.

Methods: As a part of the TransBiota study, n = 39 transfeminine participants self-collected neovaginal smears. Smears were Gram stained and Nugent scored, and scores were correlated with data on neovaginal bacterial composition (16S rRNA gene sequencing), neovaginal cytokines (Luminex multiplex immunoassay), and self-reported symptoms.

Results: We show more than 70% of neovaginal smears fell in the 7-10 Nugent score range, indicative of Bacterial Vaginosis in cisgender women. However, scores fail to correlate with the abundance of Nugent-targeted bacteria. Bacteria with similar morphotypes, but not belonging to Lactobacillus, Gardnerella, or Mobiluncus, are highly abundant and prevalent in the neovagina. Nugent score also fails to predict local inflammation or clinical symptoms.

Conclusions: The Nugent score is not an effective tool to identify neovaginal dysbiosis or indicators of health in transfeminine individuals. Clinicians need the development of accurate, evidence-based diagnostic tools for the neovagina.

Background: Traumatic joint dislocations of the hip, knee, and shoulder (DOH, DOK, and DOS) significantly impact global healthcare. This study assesses the global burden of joint dislocations using the Global Burden of Disease (GBD) 2021 database, focusing on their association with the socio-demographic index (SDI).

Methods: Data from the GBD 2021 are analyzed to determine the age-standardized rates (ASR) of incidence, prevalence, and years lived with disability (YLDs) for dislocations. We integrate the SDI with the concentration index, assessing disparities in the burden of these joint dislocations. Frontier analysis is performed to identify potential improvement areas and disparities among countries by development status. The age-period-cohort (APC) model projects the disease burden to 2045, with a focus on age and gender distributions and primary causes.

Results: From 1990 to 2021, the incidence, prevalence, and YLDs of DOH/DOK/DOS all increase, while ASRs decline, suggesting a deceleration in growth. YLDs of DOH, DOK, and DOS rise by 57.21%, 28.38%, and 15.48%, respectively. Men exhibit a higher burden, yet women show a steeper rise. Significant geographical variation exists, with lower SDI countries facing higher burdens. Falls and road injuries remain the main contributors to the burden, and lower-development countries demonstrate potential for reduction. Temporal trends vary by age, sex, and SDI, with projections indicating continued disparities to 2045.

Conclusions: Traumatic joint dislocations show marked heterogeneity in age, sex, and SDI, with the most significant differences in low-income regions. Research should prioritize policy development and targeted prevention and treatment strategies for groups at high-risk for joint dislocation to effectively mitigate the disease burden.

Background: Cardiovascular disease remains a major source of morbidity and mortality, and population imaging studies have yielded insights into disease etiology and risk.

Methods: In this study, we segment the heart, aorta, and vena cava from abdominal magnetic resonance imaging (MRI) scans using deep learning. We generate six image-derived phenotypes (IDP): heart volume, four aortic and one vena cava cross-sectional areas (CSA), from 44,541 UK Biobank participants, and explore their associations with disease outcomes, as well as genetic and environmental factors.

Results: Here we show concordance between our IDPs and related IDPs from cardiac magnetic resonance imaging, the current gold standard, and replicate previous findings related to sex differences and age-related changes in heart and vessel dimensions. We identify a significant association between infrarenal descending aorta CSA and incident abdominal aortic aneurysm, and between heart volume and several cardiovascular disorders. In a genome-wide association study, we identify 72 associations at 59 loci (15 novel). We derive a polygenic risk score for each trait and demonstrated an association with thoracic aneurysm diagnosis, pointing to a potential screening opportunity. We demonstrate substantial genetic correlation with cardiovascular traits including aneurysms, varicose veins, dysrhythmia, and cardiac failure. Finally, heritability enrichment analysis implicates vascular tissue in the heritability of these traits.

Conclusions: This study illustrates the value of non-specific abdominal MRI for exploring cardiovascular disease risk in cohort studies, and identifies novel genetic associations with vascular dimensions.