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Complex nanophotonic structures hold the potential to deliver exquisitely tailored optical responses for a range of applications. Metal–insulator–metal (MIM) metasurfaces arranged in supercells, for instance, can be tailored by geometry and material choice to exhibit a variety of absorption properties and resonant wavelengths. With this flexibility, however, comes a vast space of design possibilities that classical design paradigms struggle to effectively navigate. To overcome this challenge, here, we demonstrate a tandem residual network approach to efficiently generate multiplexed supercells through inverse design. By using a training dataset with several thousand full-wave electromagnetic simulations in a design space of over three trillion possible designs, the deep learning model can accurately generate a wide range of complex supercell designs given a spectral target. Beyond inverse design, the presented approach can also be used to explore the structure–property relationships of broadband absorption and emission in such supercell configurations. Thus, this study demonstrates the feasibility of high-dimensional supercell inverse design with deep neural networks, which is applicable to complex nanophotonic structures composed of multiple subunit elements that exhibit coupling.

Bladder urothelial carcinoma (BLC) is one of the most common cancers in men, and its heterogeneity challenges the treatment to cure this disease. Recently, microRNAs (miRNAs) gained promising attention as biomarkers due to their potential roles in cancer biology. Identifying survival-associated miRNAs may help identify targets for therapeutic interventions in BLC. This work aims to identify a miRNA signature that could estimate the survival in patients with BLC. We developed a survival estimation method called BLC-SVR based on support vector regression incorporated with an optimal feature selection algorithm to select a robust set of miRNAs as a signature to estimate the survival in patients with BLC. BLC-SVR identified a miRNA signature consisting of 29 miRNAs and obtained a mean squared correlation coefficient and mean absolute error of 0.79 ± 0.02 and 0.52 ± 0.32 year between actual and estimated survival times, respectively. The prediction performance of BLC-SVR had a better estimation capability than other standard regression methods. In the identified miRNA signature, 14 miRNAs, hsa-miR-432-5p, hsa-let-7e-3p, hsa-miR-652-3p, hsa-miR-629-5p, and hsa-miR-203a-3p, hsa-miR-129-5p, hsa-miR-769-3p, hsa-miR-570-3p, hsa-miR-320c, hsa-miR-642a-5p, hsa-miR-496, hsa-miR-5480-3p, hsa-miR-221-5p, and hsa-miR-7-1-3p, were found to be good biomarkers for BLC diagnosis; and the six miRNAs, hsa-miR-652-5p, hsa-miR-193b-5p, hsa-miR-129-5p, hsa-miR-143-5p, hsa-miR-496, and hsa-miR-7-1-3p, were found to be good biomarkers of prognosis. Further bioinformatics analysis of this miRNA signature demonstrated its importance in various biological pathways and gene ontology annotation. The identified miRNA signature would further help in understanding of BLC diagnosis and prognosis in the development of novel miRNA-target based therapeutics in BLC.

Gefitinib and erlotinib are the first-line tyrosine kinase inhibitors (TKI) for advanced non-small-cell lung cancer. However, co-administration of either drug with proton pump inhibitors (PPI) or histamine-2 receptor antagonists (H2RA) may reduce TKI’s bioavailability. Therefore, we aimed to investigate the effects of these drug–drug interactions. We surveyed nationwide population-based databases between Jan 1, 2010, and Dec 30, 2018. Newly diagnosed patients with advanced lung adenocarcinoma who received first-line gefitinib or erlotinib were identified. Effects on overall survival (OS) and time to next treatment (TTNT) association between PPIs or H2RAs and co-administrated gefitinib or erlotinib were evaluated. PPIs or H2RAs users were defined if the period overlapped with TKIs by ≥ 20%. A total of 4340 gefitinib and 1635 erlotinib users were included. PPI group had the shortest median OS and TTNT compared to the H2RA and non-user groups (in gefitinib cohort: OS: 14.35 vs. 17.67 vs. 21.87 months; P  < 0.0001, TTNT: 8.47 vs. 10.78 vs. 10.33 months; P  < 0.0001); (in erlotinib cohort: OS: 16.97 vs. 20.07 vs. 23.92 months; P  < 0.0001, TTNT: 9.06 vs. 11.85 vs. 10.90 months; P  = 0.0808). Compared with the non-user group, the adjusted hazard ratio (aHR) of the PPI group in the gefitinib was 1.58 on OS (95% CI 1.42–1.76), 1.37 on TTNT (95% CI 1.24–1.52); in the erlotinib was 1.54 on OS (95% CI 1.30–1.82) and 1.19 on TTNT (95% CI 1.01–1.39). Concurrent use of PPIs with first-line gefitinib or erlotinib therapy was associated with a worse OS and TTNT in patients with lung adenocarcinoma harboring EGFR mutations.

IntroductionPatients with multiple sclerosis (MS) commonly present musculoskeletal disorders characterized by lower bone mineral density (BMD) and muscle weakness. However, the underlying etiology remains unclear. Our objective is to identify shared pleiotropic genetic effects and estimate the causal relationship between MS and musculoskeletal disorders.Materials and MethodsWe conducted linkage disequilibrium score regression (LDSR), colocalization, and Mendelian randomization (MR) analyses using summary statistics from recent large-scale genome-wide association studies (GWAS), encompassing MS, falls, fractures, and frailty. Additional MR analyses explored the causal relationship with musculoskeletal risk factors, such as BMD, lean mass, grip strength, and vitamin D.ResultsWe observed a moderate genetic correlation between MS and falls (RG = 0.10, P-value = 0.01) but not between MS with fracture or frailty in the LDSR analyses. MR revealed MS had no causal association with fracture and frailty but a moderate association with falls (OR: 1.004, FDR q-value = 0.018). We further performed colocalization analyses using nine SNPs that exhibited significant associations with both MS and falls in MR. Two SNPs (rs7731626 on ANKRD55 and rs701006 on OS9 gene) showed higher posterior probability of colocalization (PP.H4 = 0.927), suggesting potential pleiotropic effects between MS and falls. The nine genes are associated with central nervous system development and inflammation signaling pathways.ConclusionWe found potential pleiotropic genetic effects between MS and falls. However, our analysis did not reveal a causal relationship between MS and increased risks of falls, fractures, or frailty. This suggests that the musculoskeletal disorders frequently reported in MS patients in clinical studies are more likely attributed to secondary factors associated with disease progression and treatment, rather than being directly caused by MS itself.

To the Editor: Shah et al. (Sept. 6 issue) 1 provided a comprehensive review of the role of extracellular vesicles in human disease and of their clinical implications. In the article, the discussion of the nucleic acids contained in extracellular vesicles focused only on RNAs (e.g., messenger RNAs [mRNAs] and small noncoding RNAs). However, the DNA in extracellular vesicles is also important, and a large proportion of circulating cell-free DNA is localized in exosomes. 2 One study of pancreatic cancer has shown that the circulating level of exosomal DNA may be a better outcome predictor than circulating tumor-cell DNA. 3 The double-stranded DNA . . .

Asthma and chronic obstructive pulmonary disease (COPD) are chronic airway inflammatory diseases that share some common features, although these diseases are somewhat different in etiologies, clinical features, and treatment policies. The aim of this study is to investigate the common microRNA-mediated changes in bronchial epithelial cells of asthma and COPD. The microRNA profiles in primary bronchial epithelial cells from asthma (AHBE) and COPD (CHBE) patients and healthy subjects (NHBE) were analyzed with next-generation sequencing (NGS) and the significant microRNA changes common in AHBE and CHBE were extracted. The upregulation of hsa-miR-10a-5p and hsa-miR-146a-5p in both AHBE and CHBE was confirmed with quantitative polymerase chain reaction (qPCR). Using bioinformatic methods, we further identified putative targets of these microRNAs, which were downregulated in both AHBE and CHBE: miR-10a-5p might suppress BCL2, FGFR3, FOXO3, PDE4A, PDE4C, and PDE7A; miR-146a-5p might suppress BCL2, INSR, PDE4D, PDE7A, PDE7B, and PDE11A. We further validated significantly decreased expression levels of FOXO3 and PDE7A in AHBE and CHBE than in NHBE with qPCR. Increased serum miR-146a-5p level was also noted in patients with asthma and COPD as compared with normal control subjects. In summary, our study revealed possible mechanisms mediated by miR-10a-5p and miR-146a-5p in the pathogenesis of both asthma and COPD. The findings might provide a scientific basis for developing novel diagnostic and therapeutic strategies.

Chronic obstructive pulmonary disease (COPD) and its comorbidities impose substantial economic burdens on healthcare systems, but evidence remains scarce in Asian countries where patients exhibit distinct clinical and inflammatory phenotypes, as well as policy and health system differences. This study aimed to estimate direct medical costs of COPD multimorbidity, comparing to non-COPD patients in Singapore, and identify high-cost users. Using Singapore's health administrative data (2012-2019), we created a propensity score-matched COPD and non-COPD cohort and applied generalised linear models to estimate all-cause, index disease- and comorbidity-attributable costs. All costs were measured in patient-years (PYs) in 2023 Singaporean dollars (SGD$1=US$0.76=₤0.60=€0.69). Patient characteristics and comorbidity prevalence were compared across patients incurring top 10%, 11%-50%, and bottom 50% of average annualised costs. The study included 18,866 patients from each group (83% males, 17% females). Average annual direct medical costs were significantly higher among COPD patients ($5,290.9/PY; 95% confidence interval [CI]: 5,242.9-5,350.1) than non-COPD patients ($1,110.4/PY; 95% CI: 1,085.9-1,135.9). 33.8% of total costs were COPD-attributable, with major contributions from other respiratory (15.0%), circulatory (14.9%), metabolic (7.8%), and digestive (4.7%) diseases. From 2012 to 2019, hospitalisation costs declined (-$59.0/year), while primary care (polyclinic) costs increased sharply (+$148.8/year). Indian patients comprised 67% of the top 10 cost percentile and experienced frequent hospitalisations (≥2/year). In Singapore's multi-ethnic Asian context, COPD patients incurred substantial multimorbidity costs, particularly from respiratory, circulatory, and metabolic diseases, underscoring distinct Asian multimorbidity patterns and highlighting the need for integrated, multimorbidity-focused care models. Disproportionately high costs among Indian patients and low female prevalence warrant further investigation.

International Classification of Diseases codes are widely used to describe diagnosis information, but manual coding relies heavily on human interpretation, which can be expensive, time consuming, and prone to errors. With the transition from the International Classification of Diseases, Ninth Revision, to the International Classification of Diseases, Tenth Revision (ICD-10), the coding process has become more complex, highlighting the need for automated approaches to enhance coding efficiency and accuracy. Inaccurate coding can result in substantial financial losses for hospitals, and a precise assessment of outcomes generated by a natural language processing (NLP)-driven autocoding system thus assumes a critical role in safeguarding the accuracy of the Taiwan diagnosis related groups (Tw-DRGs). This study aims to evaluate the feasibility of applying an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), autocoding system that can automatically determine diagnoses and codes based on free-text discharge summaries to facilitate the assessment of Tw-DRGs, specifically principal diagnosis and major diagnostic categories (MDCs). By using the patient discharge summaries from Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUCHH) from April 2019 to December 2020 as a reference data set we developed artificial intelligence (AI)-assisted ICD-10-CM coding systems based on deep learning models. We constructed a web-based user interface for the AI-assisted coding system and deployed the system to the workflow of the certified coding specialists (CCSs) of KMUCHH. The data used for the assessment of Tw-DRGs were manually curated by a CCS with the principal diagnosis and MDC was determined from discharge summaries collected at KMUCHH from February 2023 to April 2023. Both the reference data set and real hospital data were used to assess performance in determining ICD-10-CM coding, principal diagnosis, and MDC for Tw-DRGs. Among all methods, the GPT-2 (OpenAI)-based model achieved the highest F -score, 0.667 (F -score 0.851 for the top 50 codes), on the KMUCHH test set and a slightly lower F -score, 0.621, in real hospital data. Cohen κ evaluation for the agreement of MDC between the models and the CCS revealed that the overall average κ value for GPT-2 (κ=0.714) was approximately 12.2 percentage points higher than that of the hierarchy attention network (κ=0.592). GPT-2 demonstrated superior agreement with the CCS across 6 categories of MDC, with an average κ value of approximately 0.869 (SD 0.033), underscoring the effectiveness of the developed AI-assisted coding system in supporting the work of CCSs. An NLP-driven AI-assisted coding system can assist CCSs in ICD-10-CM coding by offering coding references via a user interface, demonstrating the potential to reduce the manual workload and expedite Tw-DRG assessment. Consistency in performance affirmed the effectiveness of the system in supporting CCSs in ICD-10-CM coding and the judgment of Tw-DRGs.

Developing novel chemo-prevention techniques and advancing treatment are key elements to beating lung cancer, the most common cause of cancer mortality worldwide. Our previous cohort study showed that cysteinyl leukotriene receptor antagonists, mainly montelukast, decreased the lung cancer risk in asthma patients. In the current study, we conducted in vivo and in vitro experiments to demonstrate the inhibiting effect of montelukast on lung cancer and to investigate the underlying mechanisms. Using Lewis lung carcinoma-bearing mice, we showed that feeding montelukast significantly delayed the tumor growth in mice (p < 0.0001). Montelukast inhibited cell proliferation and colony formation and induced the cell death of lung cancer cells. Further investigation showed the down-regulation of B-cell lymphoma 2 (Bcl-2), up-regulation of Bcl-2 homologous antagonist/killer (Bak), and nuclear translocation of apoptosis-inducing factor (AIF) in montelukast-treated lung cancer cells. Montelukast also markedly decreased the phosphorylation of several proteins, such as with no lysine 1 (WNK1), protein kinase B (Akt), extracellular signal-regulated kinase 1/2 (Erk1/2), MAPK/Erk kinase (MEK), and proline-rich Akt substrate of 40-kDa (PRAS40), which might contribute to cell death. In conclusion, montelukast induced lung cancer cell death via the nuclear translocation of AIF. This study confirmed the chemo-preventive effect of montelukast shown in our previous cohort study. The utility of montelukast in cancer prevention and treatment thus deserves further studies.

Combining epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) with an anti‐ vascular endothelial growth factor (VEGF) agent, bevacizumab or ramucirumab, is indicated for advanced lung adenocarcinoma harboring EGFR mutation. This study aimed to show the real‐world data of combination therapy and compare the effectiveness between bevacizumab and ramucirumab in combination with an EGFR‐TKI. This retrospective study enrolled 47 patients diagnosed of stage IV lung adenocarcinoma with exon 19 deletion or L858R point mutation, receiving a first‐line EGFR‐TKI with anti‐VEGF agent, including 34 (72%) and 13 (28%) patients receiving bevacizumab and ramucirumab, respectively. The response rate was similar in both groups (p = 0.38). Patients receiving bevacizumab had similar progression free survival (PFS) as those receiving ramucirumab (median PFS: 21.9 vs. 24.2 months, p = 0.4871); similar finding was noted in overall survival (OS) (median OS: 33.5 months vs. not reached, p = 0.4618). Patients receiving ramucirumab experienced a significantly high‐grade hypertension compared to those receiving bevacizumab (p = 0.0351). Multivariable Cox regression analysis found independent risk factors for worse PFS included poorer ECOG performance status, multiple (≥3) metastatic sites, brain metastasis, and pleural metastasis/effusion, while the type of anti‐VEGF agent was not a risk factor. Pericardial metastasis/effusion was the only one independent risk factor for worse OS. In summary, ramucirumab may have similar effectiveness as bevacizumab in combination with an EGFR‐TKI as first line therapy for advanced lung adenocarcinoma harboring susceptible EGFR mutation. Further large‐scale registry‐based cohort studies may be needed to validate our findings.