Prognositic value of anoikis and tumor immune microenvironment-related gene in the treatment of osteosarcoma. / Anoikiså’Œè‚¿ç˜¤å ç–«å¾®çŽ¯å¢ƒç›¸å ³åŸºå› åœ¨éª¨è‚‰ç˜¤æ²»ç–—ä¸­çš„é¢„åŽä»·å€¼ï¼ˆè‹±æ–‡ï¼‰.

OBJECTIVES: Osteosarcoma is a highly aggressive primary malignant bone tumor commonly seen in children and adolescents, with a poor prognosis. Anchorage-dependent cell death (anoikis) has been proven to be indispensable in tumor metastasis, regulating the migration and adhesion of tumor cells at the primary site. However, as a type of programmed cell death, anoikis is rarely studied in osteosarcoma, especially in the tumor immune microenvironment. This study aims to clarify prognostic value of anoikis and tumor immune microenvironment-related gene in the treatment of osteosarcoma. METHODS: Anoikis-related genes (ANRGs) were obtained from GeneCards. Clinical information and ANRGs expression profiles of osteosarcoma patients were sourced from the therapeutically applicable research to generate effective therapies and Gene Expression Omnibus (GEO) databases. ANRGs highly associated with tumor immune microenvironment were identified by the estimate package and the weighted gene coexpression network analysis (WGCNA) algorithm. Machine learning algorithms were performed to construct long-term survival predictive strategy, each sample was divided into high-risk and low-risk subgroups, which was further verified in the GEO cohort. Finally, based on single-cell RNA-seq from the GEO database, analysis was done on the function of signature genes in the osteosarcoma tumor microenvironment. RESULTS: A total of 51 hub ANRGs closely associated with the tumor microenvironment were identified, from which 3 genes (MERTK, BNIP3, S100A8) were selected to construct the prognostic model. Significant differences in immune cell activation and immune-related signaling pathways were observed between the high-risk and low-risk groups based on tumor microenvironment analysis (all P<0.05). Additionally, characteristic genes within the osteosarcoma microenvironment were identified in regulation of intercellular crosstalk through the GAS6-MERTK signaling pathway. CONCLUSIONS: The prognostic model based on ANRGs and tumor microenvironment demonstrate good predictive power and provide more personalized treatment options for patients with osteosarcoma.

SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening.

The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.

Differences in the Quantitative HRCT Characteristics of Patients with Asthma, COPD and Asthma-COPD Overlap and Their Relationships with Pulmonary Function.

Purpose: We compared pulmonary function indices and quantitative CT parameters of airway remodeling, air trapping, and emphysema in asthmatic patients and patients with COPD and asthma-COPD overlap (ACO) and explored their relationships with airflow limitation. Patients and Methods: Patients with asthma (n=48), COPD (n=52), and ACO (n=30) and controls (n=54) who completed pulmonary function tests and HRCT scans were retrospectively enrolled in our study. Quantitative CT analysis software was used to assess emphysema (LAA%), airway wall dimensions (wall area (WA), luminal area (LA), and wall area percentage (WA%)), and air trapping ((relative volume change of -860 HU to -950 HU (RVC-860 to-950) and the expiration-to-inspiration ratio of the mean lung density (MLDE/I))). Differences in pulmonary function and HRCT parameters were compared among the groups. Spearman correlation analysis and regression analysis were utilized to explore structure‒function relationships. Results: The LAA% in COPD and ACO patients was significantly greater than that in asthmatic patients and controls. The WA% and WA in COPD and ACO patients were greater than those in controls, whereas the WA% and LA between asthmatic patients and controls reached statistical significance. The RVC-860 to -950 levels decreased in the following order: ACO, COPD, and asthma. RVC-860 to -950 independently predicted FEV1% in asthmatic patients; LAA% and MLDE/I in COPD patients; and LAA%, WA% and RVC-860 to -950 in ACO patients. Conclusion: Comparable emphysema was observed in patients with COPD and ACO but not in asthmatic patients. All patients exhibited proximal airway remodeling. The bronchi were thickened outward in COPD and ACO patients but are thickened inward in asthmatic patients. Furthermore, air trapping in ACO patients was the most severe among all the groups. Indirect lung densitometry measurements might be more predictive of the degree of airflow limitation than direct airway measurements in obstructive airway diseases.

Multi-Quantifying Maxillofacial Traits via a Demographic Parity-Based AI Model.

Objective and Impact Statement: The multi-quantification of the distinct individualized maxillofacial traits, that is, quantifying multiple indices, is vital for diagnosis, decision-making, and prognosis of the maxillofacial surgery. Introduction: While the discrete and demographically disproportionate distributions of the multiple indices restrict the generalization ability of artificial intelligence (AI)-based automatic analysis, this study presents a demographic-parity strategy for AI-based multi-quantification. Methods: In the aesthetic-concerning maxillary alveolar basal bone, which requires quantifying a total of 9 indices from length and width dimensional, this study collected a total of 4,000 cone-beam computed tomography (CBCT) sagittal images, and developed a deep learning model composed of a backbone and multiple regression heads with fully shared parameters to intelligently predict these quantitative metrics. Through auditing of the primary generalization result, the sensitive attribute was identified and the dataset was subdivided to train new submodels. Then, submodels trained from respective subsets were ensembled for final generalization. Results: The primary generalization result showed that the AI model underperformed in quantifying major basal bone indices. The sex factor was proved to be the sensitive attribute. The final model was ensembled by the male and female submodels, which yielded equal performance between genders, low error, high consistency, satisfying correlation coefficient, and highly focused attention. The ensemble model exhibited high similarity to clinicians with minor processing time. Conclusion: This work validates that the demographic parity strategy enables the AI algorithm with greater model generalization ability, even for the highly variable traits, which benefits for the appearance-concerning maxillofacial surgery.

Ultrasound Elastography Assessment of Knee Intra-Articular Adhesions at Varying Knee Angles.

We aimed to verify the feasibility of using shear wave elastography (SWE) to quantify knee scars and the elastic modulus of scar tissues. Overall, 16 participants underwent SWE assessments and range-of-motion measurement and completed the Knee Injury and Osteoarthritis Outcome Score. The inter-rater reliability for SWE in the suprapatellar bursa, below the patellar tendon, and in the medial and lateral trochlear groove remained within 0.861-0.907. The SWE values in the four regions increased with increasing knee angle, and significant differences were observed between the values for below the patellar tendon and the suprapatellar bursa at knee flexion angles of 60° and 90°. The SWE values of the medial and lateral trochlear groove at 30°, 60°, and 90° knee flexion were higher on the affected side. A negative correlation was observed between the SWE values for the lateral trochlear groove at 0°, 30°, and 60° and those for below the patellar tendon at 0° and the suprapatellar bursa at 30° with both active and passive knee extension. The suprapatellar bursa value at 60° exhibited a positive correlation with both knee flexion and passive knee flexion, whereas that of the suprapatellar bursa at 90° exhibited a positive correlation with both the range of motion and passive range of motion. SWE is a replicable and effective method for detecting scar strength in the knee joint.

A multi-scale feature selection module based architecture for the diagnosis of Alzheimer's disease on [18F]FDG PET.

OBJECTIVE: Alzheimer's disease (AD) is a prevalent form of dementia worldwide as a cryptic neurodegenerative disease. The symptoms of AD will last for several years, which brings great mental and economic burden to patients and their families. Unfortunately, the complete cure of AD still faces great challenges. Therefore, it is crucial to diagnose the disease in the early stage. MATERIALS AND METHODS: The Visual Geometry Group (VGG) network serves as the backbone for feature extraction, which could reduce the time cost of network training to a certain extent. In order to better extract image information and pay attention to the association information in the images, the group convolutional module and the multi-scale RNN-based feature selection module are proposed. The dataset employed in the study are drawn from [18F]FDG-PET images within the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. RESULTS: Comprehensive experimental results show that the proposed model outperforms several competing approaches in AD-related diagnostic tasks. In addition, the model reduces the number of parameters of the model compared to the backbone model, from 134.27 M to 17.36 M. Furthermore, the ablation reaserch is conducted to confirm the effectiveness of the proposed module. CONCLUSIONS: The paper introduces a lightweight network architecture for the early diagnosis of AD. In contrast to analogous methodologies, the proposed method yields acceptable results.

Efficacy and safety of metronomic oral vinorelbine and its combination therapy as second- and later-line regimens for advanced non-small-cell lung cancer: a retrospective analysis.

OBJECTIVE: This retrospective analysis aimed to evaluate the efficacy and adverse reactions of metronomic oral vinorelbine and its combination therapy as second- and later-line regimens for advanced non-small-cell lung cancer (NSCLC). METHODS: NSCLC patients undergoing metronomic oral vinorelbine as second- and later-line regimens in Fujian Cancer Hospital from October 2018 to October 2022 were enrolled, and patients' demographic and clinical characteristics were collected. The efficacy and safety of metronomic oral vinorelbine monotherapy and its combination therapy regimens were compared. RESULTS: Of 57 study subjects, 63.2% received third- and later-line therapy, with median progression-free survival (mPFS) of 4 months, overall response rate (ORR) of 10.5%, and disease control rate (DCR) of 80.7%. The incidence of therapy-related adverse events was 42.1%, and there was only one case presenting grades 3 and 4 adverse events (1.8%). Among driver gene-negative participants, vinorelbine combination therapy regimens achieved longer mPFS (4.6 vs. 1.2 months, hazards ratio = 0.11, P < 0.0001) and comparable toxicity in relative to metronomic oral vinorelbine, and metronomic oral vinorelbine combined with immune checkpoint inhibitors showed the highest response, with mPFS of 5.6 months (95% CI 4.8 to 6.4 months), ORR of 25%, and DCR of 81.3%. Among participants with gradual resistance to osimertinib, continuing osimertinib in combination with metronomic oral vinorelbine achieved mPFS of 6.3 months (95% CI 0.1 to 12.5 months) and DCR of 86.7%. CONCLUSION: Metronomic oral vinorelbine and its combination therapy regimens are favorable options as second- and later-line therapy for advanced NSCLC patients, with acceptable efficacy and tolerable toxicity. Vinorelbine combination therapy regimens show higher efficacy and comparable toxicity in relative to metronomic oral vinorelbine, and metronomic oral vinorelbine may have a synergistic effect with immunotherapy and EGFR-TKI targeted therapy.

Cracking the Code: Enhancing Molecular Tools for Progress in Nanobiotechnology.

Nature continually refines its processes for optimal efficiency, especially within biological systems. This article explores the collaborative efforts of researchers worldwide, aiming to mimic nature's efficiency by developing smarter and more effective nanoscale technologies and biomaterials. Recent advancements highlight progress and prospects in leveraging engineered nucleic acids and proteins for specific tasks, drawing inspiration from natural functions. The focus is developing improved methods for characterizing, understanding, and reprogramming these materials to perform user-defined functions, including personalized therapeutics, targeted drug delivery approaches, engineered scaffolds, and reconfigurable nanodevices. Contributions from academia, government agencies, biotech, and medical settings offer diverse perspectives, promising a comprehensive approach to broad nanobiotechnology objectives. Encompassing topics from mRNA vaccine design to programmable protein-based nanocomputing agents, this work provides insightful perspectives on the trajectory of nanobiotechnology toward a future of enhanced biomimicry and technological innovation.

Modeling the reactive oxygen species (ROS) wave in Chlamydomonas reinhardtii colonies.

Reactive oxygen species (ROS) play a crucial role as signaling molecules in both plant and animal cells, enabling rapid responses to various stimuli. Among the many cellular mechanisms used to generate and transduce ROS signals, ROS-induced-ROS release (RIRR) is emerging as an important pathway involved in the responses of various multicellular and unicellular organisms to environmental stresses. In RIRR, one cellular compartment, organelle, or cell generates or releases ROS, triggering an increased ROS production and release by another compartment, organelle, or cell, thereby giving rise to a fast propagating ROS wave. This RIRR-based signal relay has been demonstrated to facilitate mitochondria-to-mitochondria communication in animal cells and long-distance systemic signaling in plants in response to biotic and abiotic stresses. More recently, it has been discovered that different unicellular microorganism communities also exhibit a RIRR cell-to-cell signaling process triggered by a localized stress treatment. However, the precise mechanism underlying the propagation of the ROS signal among cells within these unicellular communities remained elusive. In this study, we employed a reaction-diffusion model incorporating the RIRR mechanism to analyze the propagation of ROS-mediated signals. By effectively balancing production and scavenging processes, our model successfully reproduces the experimental ROS signal velocities observed in unicellular green algae (Chlamydomonas reinhardtii) colonies grown on agar plates, furthering our understanding of intercellular ROS communication.

POSTN promotes nucleus pulposus cell senescence and extracellular matrix metabolism via activing Wnt/ß-catenin and NF-κB signal pathway in intervertebral disc degeneration.

BACKGROUND: Intervertebral disc (IVD) degeneration (IVDD) is a prevalent condition contributing to back pain and disability. Periostin (POSTN) has emerged as a potential molecular marker and therapeutic target in IVDD, prompting further investigation into its role and mechanisms. METHODS: This study employs bioinformatics analysis combined with experimental validation to explore the role of POSTN in IVDD. Gene expression datasets from the GEO database were analyzed to identify genes associated with IVDD, and the effects of POSTN on rat nucleus pulposus (NP) cells senescence and extracellular matrix (ECM) metabolism were assessed both in vitro and in vivo. RESULTS: Elevated POSTN expression was observed in degenerated discs from IVDD patients, correlating with disease severity. In vitro experiments demonstrated that POSTN promotes NP cells senescence and ECM metabolism in a dose- and time-dependent manner. In vivo studies confirmed that POSTN inhibition can ameliorate the progression of IVDD. Further mechanistic insights revealed that POSTN may exert its effects by activating the NF-κB and Wnt/ß-catenin signaling pathways. CONCLUSION: POSTN plays a significant role in the pathogenesis of IVDD, with its upregulated expression closely linked to NP cells senescence and ECM metabolism. Targeting POSTN could offer a novel therapeutic strategy for IVDD. Additionally, the study predicts small molecules that may inhibit POSTN expression, providing potential candidates for the development of new drug treatments.

Description and outcomes of a staff-assisted peritoneal dialysis program in the United States.

BACKGROUND: Staff-assisted peritoneal dialysis (PD) can help overcome barriers to self-care but is not yet available in the United States (US). We developed and implemented a staff-assisted PD program that fits within current regulatory and cost restraints in the US healthcare environment. METHODS: Patient care technicians (PCTs) were trained on PD procedures and troubleshooting common problems. The program expanded from two centers in August 2020 to sixteen by October 2022. We described the logistic elements of program delivery, and patient and treatment outcomes for patients discharged by end of April 2023, with a cohort follow up until October 2023. RESULTS: A total of 121 patients were referred to the program. The most common indications for referral were physical function limitations, cognitive impairment, and psychosocial challenges. Staff assistance was provided for 73 patients. Mean age was 72 (standard deviation 14) years. A total of 604 visits were delivered, with a median 5 (interquartile range [IQR] 3-10, range: 1-49) visits per patient. Median duration of assistance was 8 (IQR: 2-21, range: 1-84) days. Assistance was most frequently needed for PD treatment setup and for observing and directing the technique. No peritonitis events or exit-site infections were reported. Sixty-eight patients (93%) were discharged on PD without staff assistance. The 6- and 12-month survival of PD without assistance was 71% and 57%, respectively. CONCLUSIONS: Staff-assisted PD for limited time periods is operationally feasible with PCTs in the US and can support transitioning and maintaining patients on PD.ClinicalTrials.gov Identifier: NCT04319185.

Machine learning-inferred and energy landscape-guided analyses reveal kinetic determinants of CRISPR/Cas9 gene editing.

The CRISPR/Cas nucleases system is widely considered the most important tool in genome engineering. However, current methods for predicting on/off-target effects and designing guide RNA (gRNA) rely on purely data-driven approaches or focus solely on the system's thermal equilibrium properties. Nonetheless, experimental evidence suggests that the process is kinetically controlled rather than being in equilibrium. In this study, we utilized a vast amount of available data and combined random forest, a supervised ensemble learning algorithm, and free energy landscape analysis to investigate the kinetic pathways of R-loop formation in the CRISPR/Cas9 system and the intricate molecular interactions between DNA and the Cas9 RuvC and HNH domains. The study revealed (a) a novel three-state kinetic mechanism, (b) the unfolding of the activation state of the R-loop being the most crucial kinetic determinant and the key predictor for on- and off-target cleavage efficiencies, and (c) the nucleotides from positions +13 to +16 being the kinetically critical nucleotides. The results provide a biophysical rationale for the design of a kinetic strategy for enhancing CRISPR/Cas9 gene editing accuracy and efficiency.

Design of a Transformer Oil Viscosity, Density, and Dielectric Constant Simultaneous Measurement System Based on a Quartz Tuning Fork.

Transformer oil, crucial for transformer and power system safety, demands effective monitoring. Aiming to address the problems of expensive and bulky equipment, poor real-time performance, and single parameter detection of traditional measurement methods, this study proposes a quartz tuning fork-based simultaneous measurement system for online monitoring of the density, viscosity, and dielectric constant of transformer oil. Based on the Butterworth-Van Dyke quartz tuning fork equivalent circuit model, a working mechanism of transformer oil density, viscosity, and dielectric constant was analyzed, and a measurement model for oil samples was obtained. A miniaturized simultaneous measurement system was designed based on a dedicated chip for vector current-voltage impedance analysis for data acquisition and a Savitzky-Golay filter for data filtering. A transformer oil test platform was built to verify the simultaneous measurement system. The results showed that the system has good repeatability, and the measurement errors of density, viscosity, and dielectric constant are lower than 2.00%, 5.50%, and 3.20%, respectively. The online and offline results showed that the system meets the requirements of the condition maintenance system for online monitoring accuracy and real-time detection.

Kinetic pathway of HIV-1 TAR cotranscriptional folding.

The Trans-Activator Receptor (TAR) RNA, located at the 5'-end untranslated region (5' UTR) of the human immunodeficiency virus type 1 (HIV-1), is pivotal in the virus's life cycle. As the initial functional domain, it folds during the transcription of viral mRNA. Although TAR's role in recruiting the Tat protein for trans-activation is established, the detailed kinetic mechanisms at play during early transcription, especially at points of temporary transcriptional pausing, remain elusive. Moreover, the precise physical processes of transcriptional pause and subsequent escape are not fully elucidated. This study focuses on the folding kinetics of TAR and the biological implications by integrating computer simulations of RNA folding during transcription with nuclear magnetic resonance (NMR) spectroscopy data. The findings reveal insights into the folding mechanism of a non-native intermediate that triggers transcriptional pause, along with different folding pathways leading to transcriptional pause and readthrough. The profiling of the cotranscriptional folding pathway and identification of kinetic structural intermediates reveal a novel mechanism for viral transcriptional regulation, which could pave the way for new antiviral drug designs targeting kinetic cotranscriptional folding pathways in viral RNAs.

Embracing exascale computing in nucleic acid simulations.

This mini-review reports the recent advances in biomolecular simulations, particularly for nucleic acids, and provides the potential effects of the emerging exascale computing on nucleic acid simulations, emphasizing the need for advanced computational strategies to fully exploit this technological frontier. Specifically, we introduce recent breakthroughs in computer architectures for large-scale biomolecular simulations and review the simulation protocols for nucleic acids regarding force fields, enhanced sampling methods, coarse-grained models, and interactions with ligands. We also explore the integration of machine learning methods into simulations, which promises to significantly enhance the predictive modeling of biomolecules and the analysis of complex data generated by the exascale simulations. Finally, we discuss the challenges and perspectives for biomolecular simulations as we enter the dawning exascale computing era.

EIF4A3 modulated circ_000999 promotes epithelial-mesenchymal transition in cadmium-induced malignant transformation through the miR-205-5p/ZEB1 axis.

Cadmium (Cd) is an accumulative toxic metal which poses a serious threat to human health, even in trace amounts. One of the most important steps in the pathophysiology of lung cancer (LC) is the epithelial-mesenchymal transition (EMT). In this investigation, a cell malignant transformation model was established by exposing human bronchial epithelial cells (16HBE) to a low dose of Cd for 30 weeks, after which a highly expressed circular RNA (circ_000999) was identified. Cd-induced EMT was clearly observed in rat lungs and 16HBE cells, which was further enhanced following circ_000999-overexpression. Furthermore, upregulated EIF4A3 interacted with the parental gene AGTPBP1 to promote high expression of circ_000999. Subsequent experiments confirmed that circ_000999 could regulate the EMT process by competitively binding miR-205-5p and inhibiting its activity, consequently upregulating expression of zinc finger E-box binding protein 1 (ZEB1). Importantly, the circ_000999 expression level in LC tissues was significantly increased, exhibiting a strong correlation with EMT indicators. Overall, these findings provide a new objective and research direction for reversing lung EMT and subsequent treatment and prevention of LC.

SRY-Box transcription factor 9 triggers YAP nuclear entry via direct interaction in tumors.

The translocation of YAP from the cytoplasm to the nucleus is critical for its activation and plays a key role in tumor progression. However, the precise molecular mechanisms governing the nuclear import of YAP are not fully understood. In this study, we have uncovered a crucial role of SOX9 in the activation of YAP. SOX9 promotes the nuclear translocation of YAP by direct interaction. Importantly, we have identified that the binding between Asp-125 of SOX9 and Arg-124 of YAP is essential for SOX9-YAP interaction and subsequent nuclear entry of YAP. Additionally, we have discovered a novel asymmetrical dimethylation of YAP at Arg-124 (YAP-R124me2a) catalyzed by PRMT1. YAP-R124me2a enhances the interaction between YAP and SOX9 and is associated with poor prognosis in multiple cancers. Furthermore, we disrupted the interaction between SOX9 and YAP using a competitive peptide, S-A1, which mimics an α-helix of SOX9 containing Asp-125. S-A1 significantly inhibits YAP nuclear translocation and effectively suppresses tumor growth. This study provides the first evidence of SOX9 as a pivotal regulator driving YAP nuclear translocation and presents a potential therapeutic strategy for YAP-driven human cancers by targeting SOX9-YAP interaction.

Nontarget Identification of Novel Organophosphorus Flame Retardants and Plasticizers in Indoor Air and Dust from Multiple Microenvironments in China.

The indoor environment is a typical source for organophosphorus flame retardants and plasticizers (OPFRs), yet the source characteristics of OPFRs in different microenvironments remain less clear. This study collected 109 indoor air samples and 34 paired indoor dust samples from 4 typical microenvironments within a university in Tianjin, China, including the dormitory, office, library, and information center. 29 target OPFRs were analyzed, and novel organophosphorus compounds (NOPs) were identified by fragment-based nontarget analysis. Target OPFRs exhibited the highest air and dust concentrations of 46.2-234 ng/m3 and 20.4-76.0 µg/g, respectively, in the information center, where chlorinated OPFRs were dominant. Triphenyl phosphate (TPHP) was the primary OPFR in office air, while tris(2-chloroethyl) phosphate dominated in the dust. TPHP was predominant in the library. Triethyl phosphate (TEP) was ubiquitous in the dormitory, and tris(2-butoxyethyl) phosphate was particularly high in the dust. 9 of 25 NOPs were identified for the first time, mainly from the information center and office, such as bis(chloropropyl) 2,3-dichloropropyl phosphate. Diphenyl phosphinic acid, two hydroxylated and methylated metabolites of tris(2,4-ditert-butylphenyl) phosphite (AO168), and a dimer phosphate were newly reported in the indoor environment. NOPs were widely associated with target OPFRs, and their human exposure risk and environmental behaviors warrant further study.

Broadband Room-Temperature Photodetection via InBiTe3 Nanosheet.

Broadband room-temperature photodetection has become a pressing need as application requirements for communication, imaging, spectroscopy, and sensing have evolved. Topological insulators (TIs) have narrow bandgap structures with a wide absorption spectral response range, which should meet the requirements of broadband detection. However, owing to their high carrier concentration and low carrier mobility resulting in poor noise equivalent power (NEP), they are generally considered unsuitable for photodetection. Here, InBiTe3 alloy nanosheet formed by doping In2Te3 into Bi2Te3(≈ 1:1) is utilized, effectively improving carrier mobility by over ten times while maintaining a narrow bandgap structure, to fabricate a broadband photodetector covering a wide response range from visible to millimeter wave (MMW). Under the synergistic multi-mechanism of the photoelectric effect in the visible-infrared region and the electromagnetic-induced potential well (EIW) effect in Terahertz band, the performance of NEP = 75 pW Hz-1/2 and response time τ ≈100 µs in visible to infrared band and the performance of NEP = 6.7 × 10-3 pW Hz-1/2, τ ≈8 µs in Terahertz region are achieved. The results demonstrate the promising prospects of topological insulator alloy (like InBiTe3) nanosheet in optoelectronic detection applications and provide a direction for the research into high-performance broadband photoelectric detectors via TIs.