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Purpose: Lung adenocarcinoma (LUAD) significantly contributes to cancer-related mortality worldwide. The heterogeneity of the tumor immune microenvironment in LUAD results in varied prognoses and responses to immunotherapy among patients. Consequently, a clinical stratification algorithm is necessary and inevitable to effectively differentiate molecular features and tumor microenvironments, facilitating personalized treatment approaches. Methods: We constructed a comprehensive single-cell transcriptional atlas using single-cell RNA sequencing data to reveal the cellular diversity of malignant epithelial cells of LUAD and identified a novel signature through a computational framework coupled with 10 machine learning algorithms. Our study further investigates the immunological characteristics and therapeutic responses associated with this prognostic signature and validates the predictive efficacy of the model across multiple independent cohorts. Results: We developed a six-gene prognostic model (MYO1E, FEN1, NMI, ZNF506, ALDOA, and MLLT6) using the TCGA-LUAD dataset, categorizing patients into high- and low-risk groups. This model demonstrates robust performance in predicting survival across various LUAD cohorts. We observed distinct molecular patterns and biological processes in different risk groups. Additionally, analysis of two immunotherapy cohorts (N = 317) showed that patients with a high-risk signature responded more favorably to immunotherapy compared to those in the low-risk group. Experimental validation further confirmed that MYO1E enhances the proliferation and migration of LUAD cells. Conclusion: We have identified malignant cell-associated ligand–receptor subtypes in LUAD cells and developed a robust prognostic signature by thoroughly analyzing genomic, transcriptomic, and immunologic data. This study presents a novel method to assess the prognosis of patients with LUAD and provides insights into developing more effective immunotherapies. [ABSTRACT FROM AUTHOR]

Urine analysis stands as a critical diagnostic tool, offering insights into health and disease. However, current techniques demand sophisticated equipment or significant sample processing for urine examination, reducing their suitability for regular point‐of‐care assessments. This study introduces a novel multi‐component sensing platform to address these constraints. The proposed sensor array can detect sodium (Na+), potassium (K+), ammonium (NH4+), calcium (Ca++), chloride (Cl−), and pH levels, thus, enabling real‐time urine analysis. This sensing platform utilizes an extended gate (EG)‐field effect transistor (FET) design employing EG electrodes made of LASER engraved graphene on flexible Kapton substrates. These experimental findings from individual sensors demonstrate consistent linear responses to ion levels, discrimination of specific ions among interferences, and operational stability over time. Additionally, the six‐channel sensor array exhibits notable sensitivity and selectivity in a urine environment, effectively discerning various ions and pH, illustrating its efficacy for urine analysis and validating its potential for reliable point‐of‐care diagnostics. [ABSTRACT FROM AUTHOR]

High-performance alloy thin films and large-sized thin film wafers for infrared applications are the focus of international researchers. In this study, doped Ge1−xSnx and Ge1−yBiy semiconductor alloy films were grown on a 5-in. silicon (Si) wafer using high-quality Ge films as buffer layers. An efficient technique is presented to reduce the dark current density of near-infrared photoelectric devices. By using boron for p-type doping in Ge1−xSnx films and bismuth (Bi) for n-type doping in Ge1−yBiy films, an all-thin film planar nano-p-i-n optoelectronic device with the structure n-Ge1−yBiy/i-GeSn/p-Ge1−xSnx/Ge buffer/Si substrate has been successfully fabricated. The photoelectric performance of the device was tested, and it was found that the insertion of p-Ge1−xSnx/Ge films reduced the dark current density by 1–2 orders of magnitude. The maximum photoresponsivity reached up to 0.8 A/W, and the infrared photocurrent density ranged from 904 to 935 μA/cm2 under a +1 V bias voltage. Furthermore, the device is capable of modulating a terahertz wave using a voltage signal with a modulation bandwidth of 1.2 THz and a modulation depth of ∼83%, while the modulation rate is 0.5 MHz. This not only provides a clear demonstration of how doped alloy films and the development of nano-p-i-n heterojunctions will improve photoelectric devices' performance in the near-infrared and terahertz bands, but it also raises the possibility of optoelectronic interconnection applications being achieved through a single device. [ABSTRACT FROM AUTHOR]