Communications medicine最新文献

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: 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: 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: 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.

Background: The association between radiation dose from Computed Tomography (CT) and subsequent cancer risk in adults remains poorly defined.

Methods: We conducted a statewide cohort study to examine the relationship between CT-related radiation exposure - measured by dose-length-product (DLP) - and cancer outcomes among adult trauma patients in Western Australia from 2004 to 2020. Patients with a documented cancer diagnosis within five years prior to trauma were excluded.

Results: After excluding patients with missing smoking data (n = 12,690), 2662 patients (17.3%) are included in the primary analysis. The cohort is predominantly male (75.8%), with a median age of 41 years (IQR: 27-58) and a median Injury Severity Score (ISS) of 17 (IQR: 16-22). Over a median follow-up of 5.9 years (IQR: 4.0-7.9), 374 patients (14.0%) died, including 21 cancer-related deaths (0.8%), accounting for 5.6% of all deaths. During the index trauma admission, patients underwent a median of 6 X-rays (IQR: 3-12) and 3 CT scans (IQR:1-5) with a median DLP of 1,941 mGy*cm (IQR: 637-3,388). DLP and absorbed radiation dose are significantly correlated with injury severity (Pearson r = 0.209 and 0.265, respectively; both p = 0.001). Radiation exposure is significantly associated with increased risk of new-onset cancer (adjusted hazard ratio [aHR]: 1.08 per 1,000 mGy*cm increment in DLP; 95%CI: 1.01-1.16; p = 0.042) and cancer-related mortality (aHR 3.35 for those exposed to >5000 mGy*cm; 95%CI: 1.20-9.38; p = 0.021). These findings are consistent in a larger cohort of 15,352 patients after multiple imputation for missing smoking data.

Conclusions: CT-related radiation exposure during trauma hospitalizations is associated with a dose-dependent increase in the risk of subsequent cancer incidence and mortality.

Background: Abdominal wall defects, such as incisional hernias, are a common source of pain and discomfort and often require repeated surgical interventions. Traditional mesh repair techniques typically rely on fixed overlap based on defect size, without considering important biomechanical factors like muscle activity, internal pressure, and tissue elasticity. This study aims to introduce a biomechanical approach to incisional hernia repair that accounts for abdominal wall instability and to evaluate a visualisation tool designed to support surgical planning.

Methods: We developed HEDI, a tool that uses computed tomography with Valsalva manoeuvre to automatically assess hernia size, volume, and abdominal wall instability. This tool was applied in the preoperative evaluation of 31 patients undergoing incisional hernia repair. Surgeries were performed concurrently with the development of the tool, and patient outcomes were monitored over a three-year period.

Results: Here we show that all 31 patients remain free of pain and hernia recurrence three years after surgery. The tool provides valuable visual insights into abdominal wall dynamics, supporting surgical decision-making. However, it should be used as an adjunct rather than a standalone guide.

Conclusions: This study presents a biomechanical strategy for hernia repair and introduces a visualisation tool that enhances preoperative assessment. While early results are promising, the tool's evolving nature and its role as a visual aid should be considered when interpreting outcomes. Further research is needed to validate its broader clinical utility.

Background: The independent and interactive associations of abdominal obesity and fatty acids with the risk of microvascular diseases (MVDs) are still unclear.

Methods: We conducted a prospective cohort study of 88,571 participants aged 40-69 years from the UK Biobank. Plasma fatty acids were quantified at baseline using high-throughput nuclear magnetic resonance spectroscopy and were analyzed in quartiles, with the lowest quartile of each fatty acid subtype as the reference. Cox regression models were employed to assess the associations between fatty acid levels and incident MVDs, with adjustment for relevant covariates.

Results: Over a median follow-up of 13.7 years, higher levels of total polyunsaturated fatty acids (PUFAs), n-3 PUFAs, and n-6 PUFAs are associated with a significantly lower risk of MVDs. The hazard ratios (HRs) for the highest versus lowest quartile (Q4 vs. Q1) are 0.81 (95% CI: 0.75-0.87), 0.89 (95% CI: 0.83-0.96), and 0.85 (95% CI: 0.79-0.91), respectively. Conversely, higher levels of saturated and monounsaturated fatty acids are associated with a higher risk of MVDs. Furthermore, an antagonistic additive interaction is observed between n-3 PUFAs and abdominal obesity (RERI: -0.14, 95% CI: -0.25- -0.03).

Conclusion: Higher plasma PUFAs are associated with a lower risk of MVDs. Furthermore, the association between n-3 PUFAs and a lower risk of MVDs is more pronounced among individuals with abdominal obesity. These findings contribute to the limited prospective evidence on the associations between plasma-specific fatty acids and MVDs.

Artificial intelligence is transforming breast cancer management through various machine learning applications. Artificial intelligence supports precision medicine by enhancing detection, diagnosis, prognosis, and treatment response prediction. It achieves this by analysing data from medical imaging, histopathology, genomics and multi-omics sources to improve patient recovery. This review summarises AI-driven advancements across the entire continuum of breast cancer management, spanning detection, diagnosis, prognosis, treatment and recovery. It evaluates their efficacy and limitations, explores their impact on healthcare costs and clinical practice, and addresses key challenges including generalisability, reproducibility and regulatory barriers. Evidence from recent studies highlights AI's role in improving breast cancer detection, molecular subtyping and prognostic accuracy. It also facilitates more patient-tailored therapeutic strategies and supports quality of life interventions. Nonetheless, the translation of these benefits into clinical practice requires rigorous validation, transparent model development, and equitable implementation.

Background: The post-acute sequelae of SARS-CoV-2 (PASC), also known as long COVID, remain a significant health issue that is incompletely understood. Predicting which acutely infected individuals will develop long COVID is challenging due to the absence of established biomarkers, clear disease mechanisms, or well-defined sub-phenotypes. Machine learning (ML) models may address this gap by leveraging clinical data to enhance diagnostic precision.

Methods: Clinical data, including antibody titers and viral load measurements collected at the time of hospital admission, are used to predict the likelihood of acute COVID-19 progressing to long COVID. Machine learning models are trained and evaluated for predictive performance. Feature importance analysis is performed to identify the most influential predictors.

Results: The machine learning models achieve median AUROC values ranging from 0.64 to 0.66 and AUPRC values between 0.51 and 0.54, demonstrating predictive capabilities. Low antibody titers and high viral loads at hospital admission emerge as the strongest predictors of long COVID outcomes. Comorbidities-such as chronic respiratory, cardiac, and neurologic diseases-and female sex are also identified as significant risk factors.

Conclusions: Machine learning models identify patients at risk for developing long COVID based on baseline clinical characteristics. These models guide early interventions, improve patient outcomes, and mitigate the long-term public health impacts of SARS-CoV-2.