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Spectrometers with ever-smaller footprints are sought after for a wide range of applications in which minimized size and weight are paramount, including emerging in situ characterization techniques. We report on an ultracompact microspectrometer design based on a single compositionally engineered nanowire. This platform is independent of the complex optical components or cavities that tend to constrain further miniaturization of current systems. We show that incident spectra can be computationally reconstructed from the different spectral response functions and measured photocurrents along the length of the nanowire. Our devices are capable of accurate, visible-range monochromatic and broadband light reconstruction, as well as spectral imaging from centimeter-scale focal planes down to lensless, single-cell–scale in situ mapping.

Breast cancer (BC) is the most common malignancy among women. Nicotinamide (NAM) metabolism regulates the development of multiple tumors. Herein, we sought to develop a NAM metabolism-related signature (NMRS) to make predictions of survival, tumor microenvironment (TME) and treatment efficacy in BC patients. Transcriptional profiles and clinical data from The Cancer Genome Atlas (TCGA) were analyzed. NAM metabolism-related genes (NMRGs) were retrieved from the Molecular Signatures Database. Consensus clustering was performed on the NMRGs and the differentially expressed genes between different clusters were identified. Univariate Cox, Lasso, and multivariate Cox regression analyses were sequentially conducted to develop the NAM metabolism-related signature (NMRS), which was then validated in the International Cancer Genome Consortium (ICGC) database and Gene Expression Omnibus (GEO) single-cell RNA-seq data. Further studies, such as gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, SubMap, and Immunophenoscore (IPS) algorithm, cancer-immunity cycle (CIC), tumor mutation burden (TMB), and drug sensitivity were performed to assess the TME and treatment response. We identified a 6-gene NMRS that was significantly associated with BC prognosis as an independent indicator. We performed risk stratification according to the NMRS and the low-risk group showed preferable clinical outcomes ( < 0.001). A comprehensive nomogram was developed and showed excellent predictive value for prognosis. GSEA demonstrated that the low-risk group was predominantly enriched in immune-associated pathways, whereas the high-risk group was enriched in cancer-related pathways. The ESTIMATE and CIBERSORT algorithms revealed that the low-risk group had a higher abundance of anti-tumor immunocyte infiltration ( < 0.05). Results of Submap, IPS, CIC, TMB, and external immunotherapy cohort (iMvigor210) analyses showed that the low-risk group were indicative of better immunotherapy response ( < 0.05). The novel signature offers a promising way to evaluate the prognosis and treatment efficacy in BC patients, which may facilitate clinical practice and management.

Miniaturized spectrometers have significantly advanced real-time analytical capabilities in fields such as environmental monitoring, healthcare diagnostics, and industrial quality control by enabling precise on-site spectral analysis. However, achieving high sensitivity and spectral resolution within compact devices remains a significant challenge, particularly when detecting low-concentration analytes or subtle spectral variations critical for chemical and molecular analysis. This study introduces an innovative approach employing guided-mode resonance filters (GMRFs) to address these limitations. Functioning similarly to notch filters, GMRFs selectively block specific spectral bands while allowing others to pass, maximizing energy extraction from incident light and enhancing spectral encoding. Our design incorporates narrow band-stop filters, which are essential for accurate spectrum reconstruction, resulting in improved resolution and sensitivity. Our spectrometer delivers a spectral resolution of 0.8 nm over a range of 370–810 nm. It achieves sensitivity values that are more than ten times greater than those of conventional grating spectrometers during fluorescence spectroscopy of mouse jejunum. This enhanced sensitivity and resolution are particularly beneficial for chemical and biological applications, facilitating the detection of trace analytes in complex matrices. Furthermore, the spectrometer’s compatibility with complementary metal oxide semiconductor (CMOS) technology enables scalable and cost-effective production, fostering broader adoption in chemical analysis, materials science, and biomedical research. This study underscores the transformative potential of the GMRF-based spectrometer as an innovative tool for advancing chemical and interdisciplinary analytical applications.

Background In diagnosing type 1 autoimmune pancreatitis (AIP), serum IgG4 (sIgG4) can be false-negative. EUS-guided fine-needle aspiration/biopsy (EUS-FNA/FNB) pathology is key for diagnosis, but clinical features’ impact on pathologic confirmation is unclear. This study analyzed their link and factors improve diagnostic accuracy. Methods We analyzed data from a single-center retrospective study at Changhai Hospital (Jan 2009-Jan 2024). Type 1 AIP was diagnosed per International Consensus Diagnostic Criteria (ICDC). Patients with surgical diagnosis, no EUS, or incomplete biopsy data were excluded; eligible cases were grouped into “Confirmed”/“Unconfirmed” per ICDC. Baseline data, laboratory indicators, imaging, and EUS-FNA/FNB data were collected. Statistical analyses (ROC, χ² tests, multivariate logistic regression) were done with R 4.4.0. Results A total of 182 suspected type 1 AIP patients were enrolled; 84.07% were male, 88.46% middle-aged/elderly. Common symptoms: abdominal discomfort (65.93%), obstructive jaundice (43.41%). sIgG4 > 2×ULN (twice upper normal limit) occurred in 64.84%. Multivariate analysis: pathological confirmation rate 65.12% (EUS-FNB) vs. 18.75% (EUS-FNA) ( P  < .001, former higher). For IgG4-positive cells: 82.56% confirmation rate (> 10 cells/high-power field[HPF]) vs. 16.67% (< 10 cells/HPF) ( P  < .001). EUS-FNB (OR = 3.56, 95% CI: 1.55–8.18, P  = .003) and IgG4-positive cell count (> 10 cells/HPF) (OR = 15.71, 95% CI: 6.96–35.46, P  < .001) were independent confirmation predictors. Gender, age, sIgG4 had limited value; renal involvement, retroperitoneal fibrosis were auxiliary indicators. Conclusions Following systematic multi-dimensional factor screening, pathological confirmation of type 1 AIP relies on two key factors: EUS-FNB and histopathological detection of IgG4-positive cells (> 10 cells/HPF). Integrating these core diagnostic modalities with additional indicators—such as auxiliary markers of extrapancreatic involvement (e.g., renal involvement)—further enhances diagnostic precision, which facilitates the refinement of clinical diagnostic workflows for type 1 AIP.

Nanodrug delivery systems (NDDS) have demonstrated broad application prospects in disease treatment, prevention, and diagnosis due to several advantages, including functionalization capability, high drug‐loading capacity, drug stability protection, and the enhanced permeability and retention (EPR) effect. However, their clinical translation still faces multiple challenges, including rapid clearance by the reticuloendothelial system (RES), poor targeting specificity, and insufficient efficiency in crossing biological barriers. To address these limitations, researchers have developed the biological carrier‐mediated nanomedicine hitchhiking strategy (BCM‐NHS), which leverages circulating cells, proteins, or bacteria as natural “mobile carriers” to enhance drug delivery. This approach enables nanocarriers to inherit the intrinsic biological properties, endowing them with immune evasion, prolonged circulation, dynamic targeting, biocompatibility, biodegradability, and naturally optimized biological interfaces. Here, a systematic overview of the BCM‐NHS is provided. First, the review delves into the methods of nanoparticles (NPs) binding and immobilization, encompassing both the surface‐attachment‐mediated “backpack” strategy and the encapsulation‐based “Trojan horse” strategy. Second, the classification of biological carriers, including both cell‐based and non‐cell‐based carriers, is elucidated. Third, the physical properties and release mechanisms of these nanomaterials are thoroughly described. Finally, the latest applications of BCM‐NHS in therapeutic and diagnostic contexts across various disease models including tumor, ischemic stroke, and pneumonia are highlighted. Diagram illustrating the binding methods of NPs and biological barriers. The biological carrier‐mediated nanomedicine hitchhiking strategy (BCM‐NHS) utilizes two distinct techniques: the surface‐based “Backpack” method, which relies on ligand‐receptor binding, covalent conjugation, and non‐covalent binding, and the encapsulated “Trojan horse” approach, which employs neutrophils and monocytes/macrophages as natural delivery vehicles. Created with Biorender (Agreement number: GW2854RTK7).

Hyperspectral imaging acquires spatially resolved spectral signatures, enabling a wide range of applications from scientific research to industrial processes. Traditional microelectron-mechanical systems (MEMS) Fabry–Pérot (FP) spectrometers offer a compact and simple design but are limited by single free spectral range (FSR) operation. This limitation introduces a fundamental trade-off: achieving high spectral resolution necessitates narrowing the operational bandwidth. Furthermore, maintaining such high resolution demands a larger number of sampling channels, which increases the acquisition time for a single hyperspectral image and thereby limits the frame rate. Here, we present a computational hyperspectral imaging framework that achieves broadband spectral coverage and high frame rate without sacrificing spectral resolution. By dynamically modulating the MEMS-FP cavity to span multiple FSRs, we generate a set of low-correlation spectral sampling patterns as spectral encoders. When combined with a tailored reconstruction algorithm, the system accurately decodes spectral information from a significantly reduced number of sampling channels. We experimentally validate the effectiveness of our system through LED array inspection, demonstrating its potential for high-throughput defect detection in LEDs or screen manufacturing lines. Our work presents a strategy that leverages rapidly advancing computational techniques to overcome the limitations of conventional hardware architectures in hyperspectral imaging. This compact and integrable solution is particularly well-suited for deployment in resource-constrained environments.

Background Breast cancer (BC) is the most common malignant tumor worldwide. Apoptosis and hypoxia are involved in the progression of BC, but reliable biomarkers for these have not been developed. We hope to explore a gene signature that combined apoptosis and hypoxia‐related genes (AHGs) to predict BC prognosis and immune infiltration. Methods We collected the mRNA expression profiles and clinical data information of BC patients from The Cancer Genome Atlas database. The gene signature based on AHGs was constructed using the univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analysis. The associations between risk scores, immune infiltration, and immune checkpoint gene expression were studied using single‐sample gene set enrichment analysis. Besides, gene signature and independent clinicopathological characteristics were combined to establish a nomogram. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the potential functions of AHGs. Results We identified a 16‐AHG signature (AGPAT1, BTBD6, EIF4EBP1, ERRFI1, FAM114A1, GRIP1, IRF2, JAK1, MAP2K6, MCTS1, NFKBIA, NFKBIZ, NUP43, PGK1, RCL1, and SGCE) that could independently predict BC prognosis. The median score of the risk model divided the patients into two subgroups. By contrast, patients in the high‐risk group had poorer prognosis, less abundance of immune cell infiltration, and expression of immune checkpoint genes. The gene signature and nomogram had good predictive effects on the overall survival of BC patients. GO and KEGG analyses revealed that the differential expression of AHGs may be closely related to tumor immunity. Conclusion We established and verified a 16‐AHG BC signature which may help predict prognosis, assess potential immunotherapy benefits, and provide inspiration for future research on the functions and mechanisms of AHGs in BC. We identified a 16‐AHG signature (AGPAT1, BTBD6, EIF4EBP1, ERRFI1, FAM114A1, GRIP1, IRF2, JAK1, MAP2K6, MCTS1, NFKBIA, NFKBIZ, NUP43, PGK1, RCL1, SGCE) that could independently predict BC prognosis. The median score of the risk model divided the patients into two subgroups. By contrast, patients in the high‐risk group had poorer prognosis, less abundance of immune cell infiltration, and expression of immune checkpoint genes. The gene signature and nomogram had good predictive effects on the overall survival of BC patients. GO and KEGG analyses revealed that the differential expression of AHGs may be may be closely related to tumor immunity.

Introduction Immediate postmastectomy reconstruction for breast cancer has been widely used due to its unique esthetic and psychological effects. However, no other population‐based study has investigated the effects of different reconstruction types on the survival in patients with triple negative breast cancer (TNBC). Methods We selected patients who met the eligibility criteria from the Surveillance, Epidemiology, and End Results cancer registry (N = 9760). We then assessed the effect of different reconstructive surgical approaches (implant, autologous, implant and autologous combined reconstruction) on the overall survival (OS) and breast cancer‐specific survival (BCSS) by using the Kaplan–Meier survival curve and Cox proportional hazard regression analyses. The nomograms were used to predict OS and BCSS. And the competitive risk model was used to assess breast cancer‐specific death (BCSD) and non‐breast cancer‐specific death (NBCSD). Results Statistical analysis suggested that the three reconstruction methods had better OS and BCSS with lower hazard than mastectomy alone (log‐rank test, p < 0.05). Multivariate stratified analysis showed that patients aged 40–60 years had the greatest improvement in OS (Adjusted hazard ratio [AHR], 0.646; 95% Confidence Interval [CI], 0.439–0.950; p = 0.026) with combined reconstruction. BCSS could be improved only by implant reconstruction (AHR, 0.672; 95% CI, 0.514–0.878; p = 0.004). In addition, autologous reconstruction (AHR, 0.570; 95% CI, 0.350–0.929; p = 0.024) and implant reconstruction (AHR, 0.538; 95% CI, 0.339–0.853; p = 0.008) improved OS in patients >60 years of age. The survival prediction model quantified the survival benefits of TNBC patients undergoing different surgeries. Moreover, the C‐indexes showed the good predictive ability of the nomograms. Conclusions Our results suggest that for TNBC patients, there is a survival benefit of immediate postmastectomy reconstruction compared with mastectomy alone. Among them, implant reconstruction has the most obvious advantage. Kaplan–Meier survival curves and log‐rank tests were used to evaluate the effect of different reconstruction methods (implant, autologous, implant and autologous combined reconstruction) on the overall survival (OS) of triple negative breast cancer patients. All three reconstruction methods had higher OS and lower risk than mastectomy alone (p < 0.0001). Among the three reconstruction methods, autologous reconstruction has the lowest survival rate and the highest risk.

We aimed to characterize the expression patterns of glycolysis and hypoxia genes in colon cancers as well as their value in prognosis and immune microenvironment. The expression profiles were acquired from the Cancer Genome Atlas database. Enrichment of hypoxia and glycolysis gene sets in colon cancer was identified by gene set enrichment analysis. Then, a prognostic signature was built up after Cox regression analyses, and overall survival analysis validated the predictive ability. Immune status and infiltration in cancer tissues were explored using the single sample gene set enrichment analysis and CIBERSORT algorithm. A nomogram model integrating clinical variables and the gene signature was established and assessed. Altogether, 378 cancer and 39 control cases were enrolled. Three glycolysis gene sets and two hypoxia gene sets were enriched in colon cancer (P < 0.05). Five independent genes ( , , , , and ) were significantly correlated with prognosis of colon cancer patients. Patients with higher risks had significantly better prognosis than those with lower risks (P = 0.002 and AUC = 0.750), which was also observed in the elderly, female and stage I-II subgroups (P < 0.05). In high-risk cases, proportion of NK cells resting increased (P < 0.05) while that of dendritic cells activated (P < 0.05), dendritic cells resting (P < 0.01) and monocytes (P < 0.01) decreased. Besides, expressions of 22 checkpoint genes were found abnormal in groups with different risks (P < 0.05). The predictive nomogram presented satisfactory performance with C-index of 0.771 (0.712-0.830). The area under ROC curve was 0.796 and 0.803 for 3- and 5-year survival prediction, respectively. A glycolysis and hypoxia combined gene signature was a promising method to evaluate the prognosis and immune infiltration of colon cancer patients, which may provide a new tool for cancer management.

Background Anaplastic thyroid cancer (ATC) is a highly aggressive malignancy with dismal prognosis. This study aimed to identify the independent risk factors and construct a readily-to-use nomogram to predict the probability of early death in ATC patients. Method Patients diagnosed with ATC between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled in this study for model development and internal validation. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for early death of ATC. Nomograms for predicting the probability of all-cause early death (ACED) and cancer-specific early death (CSED) of ATC were subsequently developed. The performance of the nomograms was comprehensively evaluated and validated in an internal cohort. Result A total of 696 ATC patients were included in this study, of which 488 patients in the training cohort and 208 patients in the validation cohort. The univariate and multivariate logistic regression analyses identified five independent factors (tumor size, M stage, surgery, radiotherapy and chemotherapy) in the ACED model and six variables in the CSED (gender, tumor size, M stage, surgery, radiotherapy and chemotherapy) model for the establishment of the nomograms. Calibration curves and receiver operating characteristic (ROC) curves showed satisfactory efficacy and consistency both in the training (ACED: AUC values: 0.814 (0.776–0.852); CSED: 0.778 (0.736–0.820)) and validation sets (ACED: 0.762 (0.696–0.827); CSED: 0.745 (0.678–0.812)). In addition, the decision curve analysis (DCA) demonstrated the favorable potential of the two nomograms in clinical application. Conclusion The two nomograms assist clinicians to identify risk factors and predict the early death probability among ATC patients, thus guide individualized treatment to improve the prognosis.