Communications medicine最新文献

Background: Peru recorded one of the world's highest COVID-19 mortality rates, with nearly 4.5 million reported cases and 220,000 deaths by March 2024. Understanding the emergence and spread of SARS-CoV-2 variants in this context is key to informing effective public health responses. This study describes the genomic diversity, transmission dynamics, and geographic spread of SARS-CoV-2 in Peru from 2020 to 2024.

Methods: We analyzed nearly 50,000 high-quality public SARS-CoV-2 genome sequences collected nationwide between March 2020 and March 2024. Phylogeographic and mutational analyses were performed to identify variant lineages, trace their origins, and map viral movements within and beyond Peru.

Results: We show that Peru's epidemic waves were shaped by the emergence of locally evolved variants, including Lambda (C.37), Gamma (P.1.12), and Omicron (XBB.2.6 and DJ.1) sub-lineages. The city of Lima acted as the primary hub for inter-regional spread, accounting for 47.3% of inferred viral movements to other departments, notably Ancash, Cusco, and Piura. Peru was the source of various lineages that spread internationally, primarily to Chile, the USA, and Europe. Mutational analysis highlighted critical mutations in the spike protein, including L452Q and F490S in Lambda, associated with immune evasion and increased transmissibility.

Conclusions: This work demonstrates the capacity of genomic surveillance in Peru to detect and track emerging SARS-CoV-2 variants, providing insights into regional and global transmission dynamics in a high-transmission, middle-income country setting. Sustained, cost-effective genomic monitoring, combined with strengthened bioinformatics and laboratory capacity, is essential for pandemic preparedness in resource-limited settings.

Background: Heart diseases in children and teenagers, including congenital and non-congenital cardiovascular diseases remain major causes of illness and death worldwide. Yet, long-term patterns and recent changes are poorly understood. This study explores global trends and inequalities in their burden.

Methods: We used data from the Global Burden of Disease 2021 study to examine incidence, prevalence, mortality, and disability-adjusted life years among people younger than 20 years across 204 countries from 1992 to 2021. We examined trends over time and used statistical models to see how age, historical period, and year of birth influence the risk of pediatric heart diseases.

Results: Here we show that in 2021, there are 2.64 billion people under 20 years of age, mainly in countries with lower income and education levels. Heart diseases cause 309,000 deaths and 28.7 million years of healthy life lost. Congenital heart disease accounts for most deaths, while non-congenital heart disease is more common overall and rises by over one-third since 1992, with sharper increases after 2019. Death rates in poorer regions are up to eight times higher than in richer regions. Although the overall death rate declines by 55 %, adolescents and individuals born more recently face increasing risk for non-congenital conditions.

Conclusions: The burden of heart disease in the young remains heavy, with significant regional and socioeconomic inequalities. Stronger prevention, earlier detection, and better health care are urgently needed, especially in disadvantaged regions and in the years after the coronavirus pandemic.

Background: There is growing interest in using biosignals from wearable devices to assess anxiety disorders. Among these, electrocardiography is the most widely used due to its ability to monitor cardiovascular activity. Other signals, such as respiratory, electrodermal activity, and photoplethysmography, also show promise. This review aims to evaluate how these signals, individually and in combination, have been used for anxiety detection.

Methods: We systematically reviewed 26 studies published between 2014 and 2024 that used wearable devices to collect signals for anxiety detection. Extracted information included study design, signal types, features, classification methods, and accuracy outcomes. Pooled accuracies were calculated to compare single-signal and multi-signal approaches.

Results: Here we show that approaches combining multiple signals outperform those using a single signal, with a pooled accuracy of 81.94% compared to 76.85%. Electrocardiography was the most reliable individual signal, with a pooled accuracy of 80.34% across 12 studies. However, the limited number of single-sensor studies and methodological variability limit conclusions about the superiority of any one modality. The most common features included mean heart rate and heart rate variability for electrocardiography, the mean inspiratory-to-expiratory time ratio for respiratory signals, mean skin conductance for electrodermal activity, and the mean heart rate for photoplethysmography. Support vector machine was the predominant classifier.

Conclusions: This review underscores the clinical potential of wearable devices for anxiety detection, emphasizing the value of multimodal approaches. Future research should focus on refining algorithms, expanding sample sizes, and exploring diverse contexts to improve the accuracy and generalizability of these methods.

Background: Applying large language models to medicine faces critical trust challenges in diagnostic reasoning. Existing approaches often fail to generalize across different models and datasets, particularly those covering a wide range of diseases and diverse patient records. This study aims to develop a universal model-based clinical framework that improves diagnostic performance while providing explainable reasoning.

Methods: We introduce a structured clinical approach that replicates real-world diagnostic workflows. Patient narratives are first transformed into labeled clinical components. A validation mechanism then checks model-generated diagnoses using a disease knowledge algorithm. Additionally, a stepwise decision-making model simulates consultations progressing from junior to senior clinicians to refine diagnostic reasoning. The framework is evaluated across multiple large language models and clinical reasoning datasets using standard diagnostic accuracy metrics.

Results: Here we show that our approach outperforms existing prompting methods across six large language models and two clinical datasets. One model achieves the highest diagnostic F1 scores (0.93 on NEJM, 0.95 on MedCaseReasoning) with minimal misclassification (1 false positive and 3 false negatives). It also attains the best text-based reasoning scores on NEJM, demonstrating effective, explainable clinical outputs. When validated on real-time electronic health record data, the method shows high diagnostic accuracy (0.91) and human-like rationales (4.5 out of 5), confirming its applicability in real-world clinical settings.

Conclusions: These findings confirm the robustness and generalizability of our framework, highlighting its potential for reliable, scalable, and explainable clinical decision support across diverse models and datasets.

Plasmodium malariae (P.m.) represents the least studied of the five human-malaria-causing Plasmodium species, despite its widespread global distribution. Control of P.m. is challenging due to the parasite's unique biological features, unavailability of P.m.-specific diagnostic methods, chronic low-grade parasitemia, and suboptimal clinical features. Emerging evidence suggests increasing antimalarial drug resistance and reduced susceptibility to first-line antimalarials. Its capacity for chronic infection, diagnostic challenges, and emerging drug resistance threaten malaria elimination efforts. Thus, it represents a significant yet underappreciated contributor to global malaria burden. Enhanced molecular diagnostics, targeted therapeutic strategies, and improved surveillance systems are urgently needed to address this neglected pathogen and prevent its resurgence when other malaria species are under control. Here, we synthesize current knowledge on P.m. biology, public health impact, immune paradigm, and clinical manifestations. We discuss the research gaps, outstanding questions, and novel approaches to study P.m. biology.

Background: Drug repurposing describes the approval of an already authorized medicine for a new therapeutic indication. Rising development costs, long clinical timelines and attrition in first-in-class discovery have renewed interest in this strategy as a way to extend pharmacological value using pre-validated mechanisms. This study evaluates how repurposing has contributed to pharmaceutical innovation over four decades, examining approval patterns, therapeutic redirection and industry behavior.

Methods: A longitudinal dataset of all new molecular entities and biologic products approved by the United States regulator between 1985 and 2024 was constructed. Repurposing was defined strictly as a new therapeutic indication distinct from the original approval. All cases were verified using regulatory documentation. Descriptive analyses quantified approval volumes, therapeutic transitions, applicant trajectories and development intervals. We compared the time to repurposing when development remained within the original company versus when rights transferred externally.

Results: Here we show that 451 drugs received subsequent approval for a new therapeutic use, representing a substantial fraction of authorized medicines. Oncology and neurological disorders act as major nodes of redirection, serving both as frequent endpoints and as mechanistic sources for cross-domain translation. The mean interval between first approval and repurposing is 7.2 years, shorter than typical development timelines for newly originated drugs. Repurposing occurs more rapidly when development rights remain with the original owner, and large firms account for most approvals.

Conclusions: Repurposing has become a durable component of pharmaceutical innovation, enabling faster clinical deployment of validated mechanisms across disease domains. These findings highlight its potential to expand treatment options while reducing R&D uncertainty.

Background: Immune checkpoint inhibitors (ICIs) have greatly improved advanced melanoma prognosis. However, the efficacy of ICIs in Japanese patients has been found to be lower than that in their white counterparts. We aimed to elucidate the genomic and transcriptomic features associated with response to ICIs in Japanese patients with melanoma.

Methods: A total of 129 tumor samples were collected from 78 patients with melanoma who received therapeutic regimens with or without ICI treatment. We performed exome and RNA sequencing and investigated the association between genomic and transcriptomic factors and the clinical efficacy of ICI.

Results: The number of somatic SNVs in Japanese patients with melanoma is lower than that in the TCGA white data owing to the biased distribution of WHO subtypes. The driver subtypes BRAF, NRAS, and NF1 are less prevalent, but the triple wildtype predominantly exists in this cohort. An exome-wide survey reveals no significant association of mutated genes with ICI response; however, transcriptomic analysis reveals inflammation-associated genes, including several chemokines and cytokines, that are highly expressed in clinically benefited patients. Follicular helper T cells, measured by immune cell composition analysis, are significantly enriched in clinically benefited patients (p = 0.0373). Through time-course transcriptome analysis, in addition to several cytotoxic T-cell genes, MARCO on tumor-associated macrophages is found to be induced by ICI treatment in clinically benefited patients (p = 0.0040). Protein expression of these genes is confirmed by immunohistochemical and multiplex immunofluorescence analyses.

Conclusions: To our knowledge, this is the first and largest genomic cohort study in Japanese patients with melanoma in which tumor samples were prospectively analyzed. Genomic and transcriptomic analyses reveal candidate biomarkers for ICI in Japan.

Background: Most women experience hot flashes (hot flushes) during the menopause transition. Menopausal hot flashes typically persist for years, and for a sizeable minority of women, are substantially impairing. Genetic investigations can improve understanding of hot flash etiology.

Methods: We conducted a trans-ancestry genome-wide association study (GWAS) of hot flashes (N = 149,560) among post-menopausal women age 35-88. The outcome variable was self-reported hot flashes in four samples (total n = 42,489) and menopausal hormone therapy as a proxy in one sample (n = 107,071). We estimated the heritability of hot flashes and genetic correlations with psychiatric phenotypes using linkage disequilibrium score regression.

Results: In our trans-ancestry meta-analysis, the top locus lies on chromosome 4 in the neurokinin 3 receptor gene (TACR3, p = 7.2×10^-41). We also identify another locus on chromosome 4 with top SNP rs13107507 (p = 3.5×10^-8). Gene results implicate TACR3, GRID1, NUDT4, and PHF21B. SNP heritability is estimated to be 8% (h²_liab = .08, h²_SNP = .04, se = .02). Genetic correlations are statistically significant between hot flashes and PTSD (rg = 0.25, p = 0.01), schizophrenia (rg = 0.17, p = 0.02), depression (rg = 0.21, p = 0.01), and ADHD (rg = .22, p = 0.03).

Conclusions: These genomic findings are consistent with independent, robust basic science research which led to a recently developed treatment for hot flashes, namely, a neurokinin 3 receptor antagonist. This non-hormonal class of hot flash drugs blocks the receptor coded for by the top locus reported here (TACR3, the neurokinin 3 receptor gene). Hot flash GWAS results provide an example of how GWAS findings can point to potent treatment targets for complex brain phenotypes.

Background: Mpox denotes a viral zoonosis caused by the Orthopoxvirus monkeypox (MPXV), which is endemic in West and Central Africa. In spring 2022, notable outbreaks of MPXV clade IIb were recorded in several high-income countries, predominantly affecting men who have sex with men (MSM). At the peak of the outbreak, over 200 new mpox cases per week were reported in Berlin, which constitutes one of the largest MSM population in Europe. Within the same year, the outbreak significantly declined, and it is unclear which factors contributed to this rapid decrease.

Methods: To investigate the concomitant effects of sexual contact networks, transient contact reductions and the effect of infection- vs. vaccine-derived immunity on the 2022 mpox outbreak, we calibrated an agent-based model with epidemic, vaccination, contact- and behavioral data.

Results: Our results indicate that vaccination has a marginal effect on the epidemic decline. Rather, a combination of infection-induced immunity of high-contact individuals, as well as transient behavior changes reduce the number of susceptible individuals below the epidemic threshold. However, the 2022 mpox vaccination campaign, together with infection-derived immunity may contribute to herd-immunity in the Berlin MSM population against ongoing clade I mpox outbreaks. Demographic changes and immune waning may deteriorate this herd immunity over time.

Conclusions: These findings highlight that, in addition to vaccination, timely and clear communication of transmission routes may trigger spontaneous protective behavior within key populations; underscoring the importance of targeted sexual health education as a core component of outbreak response.

Background: The screening tools for respiratory diseases typically involve spirometry (for asthma and COPD), CT scans (for interstitial lung disease), chest X-rays (for pneumonia and tuberculosis), and sputum analysis (for tuberculosis).

Methods: This work examines a diagnostic approach whereby a subject's chest is radio-exposed to non-ionizing 6G/WiFi multi-carrier radio signals at a frequency of 5.23 GHz. The fact that each respiratory disease modulates the amplitude, frequency, and phase of each radio frequency differently allows us to screen for five respiratory diseases: asthma, chronic obstructive pulmonary disease, interstitial lung disease, pneumonia, and tuberculosis. We collect a new dataset (OFDM-Breathe) from 220 individuals in a hospital setting, including 190 patients and 30 healthy controls. The dataset contains over 26,000 s of radio signal recordings across 64 frequencies. Several machine learning and deep learning models are evaluated to classify disease type based on the discriminatory signatures of radio signals.

Results: We learn that a vanilla convolutional neural network achieves 98% accuracy in differentiating between the five respiratory diseases, along with strong performance in precision, recall, and F1-score. An ablation study demonstrates that reliable screening with up to 96% accuracy is possible using only eight frequencies, representing just 12.5% of the total bandwidth and leaving 87.5% available for 6G/WiFi data communication.

Conclusions: The proposed method could enable real-time respiratory disease screening, could help realize the health equity in developing countries, and lays the groundwork for 6G/WiFi-enabled integrated sensing and communication platforms for healthcare systems of the future.