Communications medicine最新文献

Background: There are known sex disparities in temperature perception with lower thermal detection thresholds found in people assigned female at birth compared to people assigned male at birth. However, underlying mechanisms of these differences and the influences of sex hormones are not yet sufficiently understood.

Methods: To assess the effects of sex hormones on temperature perception, we measured in a prospective observational cohort study temperature detection and pain thresholds with quantitative sensory testing and subjective temperature sensation in transgender patients undergoing gender-affirming hormone therapy (GAHT). We included 12 trans women (male-to-female transgender) and 17 trans men (female-to-male transgender) before and 3 and 6 months after start of GAHT. As a control group, we measured 13 cis women and 10 cis men without hormone treatment at the same timepoints.

Results: Here we show that temperature detection thresholds in persons assigned female at birth at baseline are lower than in persons assigned male at birth. Accordingly, in trans women, temperature detection thresholds decrease with GAHT. Pain detection thresholds do not differ between sexes assigned at birth and do not change with time.

Conclusions: We demonstrate that in trans women undergoing GAHT with estradiol and cyproterone acetate sensitivity to temperature changes increases, consistent with the greater temperature sensitivity observed in cis women compared to cis men. Future studies need to assess at which neurobiological processing stages the relevant changes occur and what molecular mechanisms play a role.

Trial registration: NCT04838249.

Background: Congenital heart disease (CHD) affects about 1% of births and is linked to differences in thinking and learning. Understanding how birth, genetic, clinical, and environmental factors together explain cognitive variability can inform monitoring and care. This study builds a multivariate model predicting cognition across multiple domains in adolescents and young adults with CHD.

Methods: We studied 89 adolescents and young adults (AYAs; mean age 16 years) with CHD who completed structural and diffusion MRI and fifteen neurocognitive tests across seven domains. Using an enhanced forward-inclusion and backward-elimination strategy with cross-validation, we built multivariate models incorporating biological, socioeconomic, clinical, genetic, and brain imaging features. Performance was evaluated using Pearson correlation ($r$) between observed and inferred scores, mean absolute error (MAE), and inverse inferability score (IIS).

Results: Here we show that models infer scores with moderate accuracy ($r$ = 0.245-0.648; MAE = 1.6-12.0 points; mean MAE = 6.3). Highest correlations include Digit Span ($r$ = 0.65; p < 0.001), Verbal Comprehension Index ($r$ = 0.594; p < 0.001), and Matrix Reasoning ($r$ = 0.574; p < 0.001). Domain ranking by IIS shows the best (lowest) scores for general intelligence (0.0886), followed by working memory (0.7100), and a higher (worse) score for perceptual reasoning (1.9199).

Conclusions: A multivariate approach combining brain imaging with genetic, clinical, and environmental factors provides clinically meaningful inference of individual cognitive performance in AYAs with CHD. These findings suggest complementary roles of brain, genetic, and contextual factors in shaping cognitive variability and motivate validation in larger cohorts.

Background: Epigenetic modulators may sensitize platinum-resistant ovarian cancer (PROC) to immune checkpoint inhibition by reprogramming the tumor microenvironment.

Methods: We report clinical and translational findings from a phase II non-randomized study of pembrolizumab and oral azacitidine in 34 women with PROC (NCT02900560). Key eligibility criteria included age 18 years or older, performance status of 0-1, measurable disease, platinum-resistant disease and histologically confirmed epithelial ovarian cancer, fallopian tube carcinoma or primary peritoneal carcinoma. Primary endpoints included safety, tolerability, overall response rate (ORR) and disease control rate (DCR). Secondary endpoints included CA-125 response. The effect of combined epigenetic and immunotherapy was evaluated by transcriptomic analyses of 72 serially biopsied tumors.

Results: We show that the combination is moderately well tolerated and most common grade 3-4 adverse events are gastrointestinal side effects and anemia. ORR is 2.9% and DCR is 50%; with 3 of the 27 evaluable patients attaining a CA-125 response. Differential gene expression analyses reveal an upregulation of inflammatory and cytolytic genes and co-inhibitory checkpoints 6 weeks on-therapy. Upregulation of interferon signaling, antigen presentation and immune cell adhesion and migration gene sets are prominent on-therapy, together with an increase in density of CD8 + T-cells. Patients with a CA-125 and/or clinical response show an enrichment of adaptive and conserved immune response gene sets on-therapy.

Conclusions: Our findings highlight the potential of epigenetic modulators to re-shape the tumor microenvironment of PROC toward a more inflammed phenotype and may point to approaches to augment immunotherapy response.

Background: Desmoglein-2 (DSG2) is an essential cardiac desmosomal cadherin, and its alteration underlies a broad spectrum of arrhythmogenic cardiomyopathy (ACM). Yet, the clinical significance of many DSG2 variants remains uncertain. This study aimed to systematically characterize the spectrum, structural impact, and clinical relevance of DSG2 variants by integrating large-scale genomic evidence, published data, and a deeply phenotyped validation cohort.

Methods: We conducted a systematic literature review (115 studies; 145 curated variants) and analyzed population-scale datasets (3570 variants in gnomAD; 1847 in ClinVar). All variants were uniformly reclassified following ACMG/ClinGen criteria. A validation cohort of 95 Italian DSG2-carriers underwent detailed phenotyping. Structural modeling via AlphaFold, supported protein modeling, calcium-binding site prediction, and DynaMut stability analysis were performed to evaluate the functional consequences of key variants.

Results: Literature and database integration reveal domain-specific variant clustering, with high-impact missense variants enriched in calcium-binding extracellular domains, the furin cleavage site, and the intracellular PKP2-binding region. In the validation cohort, penetrance among genotype-positive relatives is 42%, while 13% of definite ACM cases experience major ventricular arrhythmias; transplantation and mortality each occur in 3%. Biallelic and digenic variants are associated with earlier onset and more severe biventricular involvement. Structural modeling confirms that pathogenic missense substitutions destabilize DSG2 architecture or impair calcium-dependent adhesion.

Conclusions: This study refines the classification of DSG2 variants and highlights the importance of domain-level and multilocus interpretation in ACM. These findings support comprehensive genetic screening, structural modeling for variant assessment, and lifelong follow-up of DSG2 carriers to improve diagnosis and risk stratification.

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}.