HPV-ferroptosis related genes as biomarkers to predict the prognosis of cervical cancer.

BACKGROUND: Ferroptosis can be used as a powerful predictor of cancer prognosis. HPV persistent infection is the main cause of cervical cancer, so it is very important to improve the prognosis of patients. Therefore, it is necessary to explore the value of HPV-ferroptosis related genes as prognostic biomarkers of cervical cancer patients. METHODS: In this study, differentially expressed HPV-ferroptosis related genes were obtained from GSE7410, HPV gene set crossed with iron death genes. Five HPV-ferroptosis related genes with prognostic features were finally identified: CYBB, VEGFA, CKB, EFNA1 and HELLS. Multifactorial Cox regression was applied to establish and validate the prognostic model, and drug susceptibility and immune infiltration analyses were also performed. RESULTS: The prognostic model was validated in the training set (TCGA) and validation set (GSE44001). Kaplan-Meier curves reveal significant differences in overall survival (OS) between high-risk and low-risk groups. Receiver operating characteristic (ROC) curve reflects the stability and accuracy of the prognostic model established in this study. In terms of immune function, T cell costimulation was better in the low-risk group than in the high-risk group (P < 0.01). The therapeutic effects of cisplatin, paclitaxel, docetaxel and cyclophosphamide, commonly used chemotherapy drugs for cervical cancer, are better in the high-risk group than in the low-risk group (P < 0.001). CONCLUSION: HPV-ferroptosis related gene prognostic model not only has good stability and accuracy in predicting the prognosis of cervical cancer patients, but also has certain guiding value for clinicians in terms of drug sensitivity and immune microenvironment.

Non-alkaloid components with inhibitory activity against LPS induced NO production in RAW 264.7 cells isolated from the roots of Sophora flavescens.

Sophora flavescens Aiton is a plant in the Leguminosae family. As a traditional Chinese medicine, it is used to treat eczema, bloody stool, skin pruritus, and so on. By studying non-alkaloid components in the roots of S. flavescens, we obtained a total of 49 compounds (1-49), including three undescribed flavonoids (13, 15 and 18), five undescribed isopentenyl flavonoids (32, 34, 38, 39 and 48), two known coumarins (1-2), three phenolic acids (3-5), one known isopentenyl flavonoids (19-31, 33, 35-37, 40-47 and 49). On the basis of chemical evidences and spectral data analysis (UV, ECD, Optical rotation data, 1D/2D-NMR and HR-ESI-MS), the structures of undescribed compounds were elucidated. The inhibitory effect of compounds 1-49 on LPS induced NO production in RAW 264.7 cells was detected. Compounds 11, 19, 21-24, and 28-30 showed significant inhibitory effects, and the IC50 values of compounds 11 and 22 even reached 4.58 ± 0.66 and 4.53 ± 0.66 µM. This study suggests that flavonoids may be the main component that exerts anti-inflammatory effects in the non-alkaloid extraction layer of the extract from the roots of S. flavescens.

Relationships of the adversity quotient subtypes of nursing interns with depression, coping styles, positive psychological capital, and professional adaptability: a cross-sectional study.

BACKGROUND: Nursing interns encounter numerous professional pressures during clinical practice. Assessing adversity quotient levels and understanding the influencing factors are crucial for supporting students' seamless transition to professional nurses. PURPOSE: This study examined the adversity quotient subtypes of nursing interns and explored the relationships between adversity quotient and depression, coping styles, positive psychological capital, and professional adaptability. METHODS: This study employed a cross-sectional research design, involving a survey of 287 nursing interns in five general hospitals in Shanghai, China. Latent profile analysis was conducted to explore the subtypes of adversity quotient with the four domains as input variables. Multinomial logistic regression models and multiple correspondence analysis were used for subsequent data analysis. RESULTS: The average adversity quotient score of the nursing interns was 116.63 ± 32.22. A three-profile solution was obtained based on the latent profile analysis results. Three distinct subtypes emerged: a high-adversity quotient subtype (n = 50, 17.4%), a medium-adversity quotient subtype (n = 189, 65.9%), and a relatively low-adversity quotient subtype (n = 48, 16.7%). Multinomial logistic regression revealed that nursing interns in the high-adversity quotient subtype tended to be male, and had higher scores for positive psychological capital, negative coping style, and professional adaptability (p < 0.05). CONCLUSION: Most of the nursing interns were in the medium adversity quotient subtype. Gender, positive psychological capital, negative coping style and professional adaptability were all significantly related to the adversity quotient. Boosting the positive psychological capital of nursing interns is an effective way to improve the adversity quotient.

A prediction model based on deep learning and radiomics features of DWI for the assessment of microsatellite instability in endometrial cancer.

BACKGROUND: To explore the efficacy of a prediction model based on diffusion-weighted imaging (DWI) features extracted from deep learning (DL) and radiomics combined with clinical parameters and apparent diffusion coefficient (ADC) values to identify microsatellite instability (MSI) in endometrial cancer (EC). METHODS: This study included a cohort of 116 patients with EC, who were subsequently divided into training (n = 81) and test (n = 35) sets. From DWI, conventional radiomics features and convolutional neural network-based DL features were extracted. Random forest (RF) and logistic regression were adopted as classifiers. DL features, radiomics features, clinical variables, ADC values, and their combinations were applied to establish DL, radiomics, clinical, ADC, and combined models, respectively. The predictive performance was evaluated through the area under the receiver operating characteristic curve (AUC), total integrated discrimination index (IDI), net reclassification index (NRI), calibration curves, and decision curve analysis (DCA). RESULTS: The optimal predictive model, based on an RF classifier, comprised four DL features, three radiomics features, two clinical variables, and an ADC value. In the training and test sets, this model exhibited AUC values of 0.989 (95% CI: 0.935-1.000) and 0.885 (95% CI: 0.731-0.967), respectively, demonstrating different degrees of improvement compared with the clinical, DL, radiomics, and ADC models (AUC-training = 0.671, 0.873, 0.833, and 0.814, AUC-test = 0.685, 0.783, 0.708, and 0.713, respectively). The NRI and IDI analyses revealed that the combined model resulted in improved risk reclassification of the MSI status compared to the clinical, radiomics, DL, and ADC models. The calibration curves and DCA indicated good consistency and clinical utility of this model, respectively. CONCLUSIONS: The predictive model based on DWI features extracted from DL and radiomics combined with clinical parameters and ADC values could effectively assess the MSI status in EC.

Abundance, characteristics and ecological risk assessment of microplastics in ship ballast water in ports around Liaodong Peninsula, China.

The development of the shipping industry has led to a large volume of ballast water discharge annually. This accelerates pollutants' transfer and dispersion, such as microplastics. Currently, empirical data on microplastics in ballast water are rarely available. This study innovatively investigated the abundance, morphological characteristics (particle size, shape, and color), and polymer composition of microplastics in ballast water from ports surrounding the Liaodong Peninsula. The results revealed that the average abundance of microplastics in 13 ships' ballast water was 6071.30 ± 1313.85 items/m3. Notably, the small microplastics (0.06-2.50 mm) were most abundant, accounting for 94.52 % of the total microplastics. Transparent, fiber, and polyethylene glycol terephthalate were the most prevalent color, shape, and polymer composition of microplastics detected in the ballast water. The risk assessment indicated that these microplastics present ecological risks to organisms. These findings suggest that ship ballast water is the potential "hotspot" for marine microplastics transport.

Impaired glymphatic function as a biomarker for subjective cognitive decline: An exploratory dual cohort study.

BACKGROUND: Subjective cognitive decline (SCD) has been recognized as a potential risk stage for progression to Alzheimer's disease (AD), while glymphatic dysfunction is considered an important characteristic of AD. We hypothesize that glymphatic dysfunction occurs during the SCD stage, aiming to discover potential biomarkers for SCD. METHODS: Participants from two independent studies, Sino Longitudinal Study on Cognitive Decline (SILCODE, n = 654) and the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 650), representing different ethnicities and disease stages, were included to assess glymphatic function using diffusion tensor image analysis along the perivascular space (DTI-ALPS). RESULTS: Abnormal glymphatic function occurs during the SCD stage, with the ALPS index demonstrating excellent classification performance for SCD and normal controls (area under the receiver operating characteristic curve [AUC]SILCODE = 0.816, AUCADNI = 0.797). Lower ALPS index indicates higher risk of cognitive progression, which is negatively correlated with Subjective Cognitive Decline Questionnaire 9 scores and amyloid positron emission tomography burden. DISSCUSION: Our study suggests the ALPS index has the potential to serve as a biomarker for SCD. HIGHLIGHTS: Glymphatic function characterized by the analysis along the perivascular space (ALPS) index becomes abnormal in subjective cognitive decline (SCD), the earliest symptomatic manifestation and preclinical stage of Alzheimer's disease (AD). The ALPS index demonstrates excellent classification performance for SCD and normal controls in the East Asian and Western cohorts. Participants with a lower ALPS index show a higher risk of clinical progression. The ALPS index is closely associated with serval cognitive scales and amyloid beta burden.

Cognitive function in Parkinson's disease: associations with perivascular space in basal ganglia.

BACKGROUND: Cognitive impairment is one of the most common symptoms of Parkinson's disease (PD), and may be detectable through changes in neural features visualized by magnetic resonance imaging (MRI). Mild cognitive impairment is a transitional state between normal aging and dementia, and early recognition of Parkinson's disease with mild cognitive impairment (PD-MCI) can help improve the quality of life and treatment for patients. This study investigated the association of enlarged perivascular space (EPVS) and white matter hyperintensity (WMH) with PD-MCI. AIMS: This study aimed to evaluate whether EPVS and WMH can be used as potential MRI markers for PD-MCI. METHODS: This retrospective study involved 200 patients with PD who underwent cranial MRI in our hospital from April 2021 to April 2022. Patients were divided into those with no cognitive impairment (PD-NCI) or mild cognitive impairment. Uni- and multivariate logistic regression analyzed associations of EPVS, WMH, and clinicodemographic characteristics with cognitive decline. RESULTS: Univariate regression identified severe EPVS in basal ganglia, severe WMH, older age, late-onset, male sex, low educational level, longer duration of disease, low triglycerides, low uric acid, and low scores on the Mini-mental State Exam as risk factors for PD-MCI. After adjusting for clinicodemographic risk factors in multivariate regression, low education level and EPVS in basal ganglia remained risk factors for cognitive impairment. CONCLUSIONS: Severe EPVS in basal ganglia and poor education, but not WMH, are independent risk factors of PD-MCI. Our findings suggest that non-invasive detection of EPVS in basal ganglia by MRI may be a valuable early indicator of cognitive decline in PD patients.

Dominating Alzheimer's disease diagnosis with deep learning on sMRI and DTI-MD.

Background: Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder that has become one of the major health concerns for the elderly. Computer-aided AD diagnosis can assist doctors in quickly and accurately determining patients' severity and affected regions. Methods: In this paper, we propose a method called MADNet for computer-aided AD diagnosis using multimodal datasets. The method selects ResNet-10 as the backbone network, with dual-branch parallel extraction of discriminative features for AD classification. It incorporates long-range dependencies modeling using attention scores in the decision-making layer and fuses the features based on their importance across modalities. To validate the effectiveness of our proposed multimodal classification method, we construct a multimodal dataset based on the publicly available ADNI dataset and a collected XWNI dataset, which includes examples of AD, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN). Results: On this dataset, we conduct binary classification experiments of AD vs. CN and MCI vs. CN, and demonstrate that our proposed method outperforms other traditional single-modal deep learning models. Furthermore, this conclusion also confirms the necessity of using multimodal sMRI and DTI data for computer-aided AD diagnosis, as these two modalities complement and convey information to each other. We visualize the feature maps extracted by MADNet using Grad-CAM, generating heatmaps that guide doctors' attention to important regions in patients' sMRI, which play a crucial role in the development of AD, establishing trust between human experts and machine learning models. Conclusion: We propose a simple yet effective multimodal deep convolutional neural network model MADNet that outperforms traditional deep learning methods that use a single-modality dataset for AD diagnosis.

Distribution characteristics and ecological risk analysis of microplastics in sediments and effluents related to offshore oil and gas activities in the Bohai Sea, China.

Oil and gas activities are sources of marine microplastics (MPs) but have received less attention globally. This study assessed the distribution characteristics and ecological risks of MPs in 31 sediment samples and effluent samples of 5 oil and gas platforms related to offshore oil and gas activities in the Bohai Sea. The results showed that the mean abundance of MPs in sediment, produced water, and domestic sewage was 205.7 ± 151.5 items/kg d.w., 18 ± 11 items/L, and 26 ± 39 items/L, respectively. The MPs in sediments and effluents were dominated by transparent, rayon, and fibers <1 mm. Oil and gas activities may influence the abundance of MPs in the sediments. The sediments in the area were at a low level of risk, but some samples exhibited indexes beyond low levels. The mass of MPs carried by the effluents from oil and gas platforms in the Bohai Sea was less than that of other sources.

Human CD56+CD39+ dNK cells support fetal survival through controlling trophoblastic cell fate: immune mechanisms of recurrent early pregnancy loss.

Decidual natural killer (dNK) cells are the most abundant immune cells at the maternal-fetal interface during early pregnancy in both mice and humans, and emerging single-cell transcriptomic studies have uncovered various human dNK subsets that are disrupted in patients experiencing recurrent early pregnancy loss (RPL) at early gestational stage, suggesting a connection between abnormal proportions or characteristics of dNK subsets and RPL pathogenesis. However, the functional mechanisms underlying this association remain unclear. Here, we established a mouse model by adoptively transferring human dNK cells into pregnant NOG (NOD/Shi-scid/IL-2Rγnull) mice, where human dNK cells predominantly homed into the uteri of recipients. Using this model, we observed a strong correlation between the properties of human dNK cells and pregnancy outcome. The transfer of dNK cells from RPL patients (dNK-RPL) remarkably worsened early pregnancy loss and impaired placental trophoblast cell differentiation in the recipients. These adverse effects were effectively reversed by transferring CD56+CD39+ dNK cells. Mechanistic studies revealed that CD56+CD39+ dNK subset facilitates early differentiation of mouse trophoblast stem cells (mTSCs) towards both invasive and syncytial pathways through secreting macrophage colony-stimulating factor (M-CSF). Administration of recombinant M-CSF to NOG mice transferred with dNK-RPL efficiently rescued the exacerbated pregnancy outcomes and fetal/placental development. Collectively, this study established a novel humanized mouse model featuring functional human dNK cells homing into the uteri of recipients and uncovered the pivotal role of M-CSF in fetal-supporting function of CD56+CD39+ dNK cells during early pregnancy, highlighting that M-CSF may be a previously unappreciated therapeutic target for intervening RPL.

A Flexible Capacitive Pressure Sensor with Adjustable Detection Range Based on the Inflatable Dielectric Layer for Human-Computer Interaction.

As an essential component in wearable electronic devices and intelligent robots, flexible pressure sensors have enormous application value in fields such as healthcare, human-computer interaction, and intelligent perception. However, due to the complex and ever-changing pressure loads borne by sensors in different application scenarios, this also puts great demands on the flexible response and adjustment ability of a sensor's detection range. Therefore, developing a flexible pressure sensor with a wide and adjustable detection range, which can be applied flexibly under different pressure loads, is also a major challenge in current research. In this paper, we propose a flexible pressure sensor with a wide and adjustable detection range based on an inflatable adjustable safety airbag as the dielectric layer. This sensor uses inflatable airbags prepared using 3D printing technology and silicone reverse molding technology as the dielectric layer and achieves high sensitivity (0.6 kPa-1 to 1.19 kPa-1), wide detection range (220-1500 kPa), and flexible performance applicability by adjusting the air pressure inside the dielectric layer. At the same time, its simple production process, convenient production, fast response time (100 ms), and good stability provide the possibility for the flexible application of sensors in different pressure detection. The experimental results indicate that the sensor has enormous potential for applications in wearable devices, healthcare, human-computer interaction, and intelligent perception recognition.

Differences in gray matter atrophy and functional connectivity between motor subtypes of Parkinson's disease.

Parkinson's disease (PD) patients with postural gait abnormalities exhibit poorer motor function scores, more severe non-motor symptoms, faster cognitive function deterioration, and a less favorable response to drugs and surgery compared to PD patients with tremor. This discrepancy is believed to be associated with more pronounced gray matter atrophy and abnormal functional connectivity. To investigate the distinctive pathological mechanisms between PD subtypes, we examined gray matter volume (GMV) and functional connectivity in patients with Parkinson's disease presenting with postural instability/gait difficulty (PD-PIGD), patients with tremor-dominant Parkinson's disease (PD-TD), and healthy controls. Voxel-based morphometry (VBM) of T1-weighted images was conducted to compare GMV among 64 PD-PIGD patients, 44 PD-TD patients, and 32 controls. Subsequently, functional connectivity within regions showing reduced GMV was compared across the groups. We analyzed whether differences among the groups were associated with clinical characteristics and neuroimaging biomarkers using partial correlation and binary logistic regression. Our comparison between PD-PIGD and PD-TD patients revealed a link between PD-PIGD and more extensive frontotemporal atrophy, potentially indicating increased basal ganglia activity accompanied by decreased cerebellum activity. Furthermore, in addition to the smaller GMV in the left middle temporal gyrus, the increased functional connectivity between this brain region and the right caudate was also the independent risk factor for PD-PIGD. In addition, we compared brain network connectivity between the PIGD and TD subtypes, using an independent component analysis (ICA). We found that Compared to PD-TD, PD-PIGD patients showed an enhanced sensorimotor network (SMN) around the left supplementary motor area. These findings suggest that severe gray matter atrophy and abnormal functional connectivity and brain networks may serve as pathophysiological mechanisms distinguishing PD-PIGD patients from other subtypes.

Amyloid and SCD jointly predict cognitive decline across Chinese and German cohorts.

INTRODUCTION: Subjective cognitive decline (SCD) in amyloid-positive (Aß+) individuals was proposed as a clinical indicator of Stage 2 in the Alzheimer's disease (AD) continuum, but this requires further validation across cultures, measures, and recruitment strategies. METHODS: Eight hundred twenty-one participants from SILCODE and DELCODE cohorts, including normal controls (NC) and individuals with SCD recruited from the community or from memory clinics, underwent neuropsychological assessments over up to 6 years. Amyloid positivity was derived from positron emission tomography or plasma biomarkers. Global cognitive change was analyzed using linear mixed-effects models. RESULTS: In the combined and stratified cohorts, Aß+ participants with SCD showed steeper cognitive decline or diminished practice effects compared with NC or Aß- participants with SCD. These findings were confirmed using different operationalizations of SCD and amyloid positivity, and across different SCD recruitment settings. DISCUSSION: Aß+ individuals with SCD in German and Chinese populations showed greater global cognitive decline and could be targeted for interventional trials. HIGHLIGHTS: SCD in amyloid-positive (Aß+) participants predicts a steeper cognitive decline. This finding does not rely on specific SCD or amyloid operationalization. This finding is not specific to SCD patients recruited from memory clinics. This finding is valid in both German and Chinese populations. Aß+ older adults with SCD could be a target population for interventional trials.

Proton-Induced Reversible Spin-State Switching in Octanuclear FeIII Spin-Crossover Metal-Organic Cages.

Responsive spin-crossover (SCO) metal-organic cages (MOCs) are emerging dynamic platforms with potential for advanced applications in magnetic sensing and molecular switching. Among these, FeIII-based MOCs are particularly noteworthy for their air stability, yet they remain largely unexplored. Herein, we report the synthesis of two novel FeIII MOCs using a bis-bidentate ligand approach, which exhibit SCO activity above room temperature. These represent the first SCO-active FeIII cages and feature an atypical {FeN6}-type coordination sphere, uncommon for FeIII SCO compounds. Our study reveals that these MOCs are sensitive to acid/base variations, enabling reversible magnetic switching in solution. The presence of multiple active proton sites within these SCO-MOCs facilitates multisite, multilevel proton-induced spin-state modulation. This behavior is observed at room temperature through 1H NMR spectroscopy, capturing the subtle proton-induced spin-state transitions triggered by pH changes. Further insights from extended X-ray absorption fine structure (EXAFS) and theoretical analyses indicate that these magnetic alterations primarily result from the protonation and deprotonation processes at the NH active sites on the ligands. These processes induce changes in the secondary coordination sphere, thereby modulating the magnetic properties of the cages. The capability of these FeIII MOCs to integrate magnetic responses with environmental stimuli underscores their potential as finely tunable magnetic sensors and highlights their versatility as molecular switches. This work paves the way for the development of SCO-active materials with tailored properties for applications in sensing and molecular switching.

Chromene meroterpenoids from Rhododendron dauricum L. and their anti-inflammatory effects.

Rhododendron dauricum L. is a perennial herb belonging to the genus Rhododendron, commonly utilized in formulations for treating coughs and bronchitis, as well as in herbal teas for enhancing immunity and preventing tracheitis. In this study, fifteen previously undescribed chromene meroterpenoids (1a/1b-4a/4b, 5-8, 9b, 10a, 11b), along with twenty-one known compounds were isolated from the dried twigs and leaves of Rhododendron dauricum L. Of these, (-)-rhodonoid E (9b), (+)-confluentin (10a), and (-)-rubiginosin D (11b) were separated for the first time by chiral HPLC separation. The elucidation of their structures, including absolute configurations, was achieved through a combination of techniques such as NMR, HRESIMS, modified Mosher's method and quantum-chemical calculation of electronic circular dichroism (ECD) spectra. Seven pairs of enantiomers, compounds 1a/1b-4a/4b and 9a/9b-11a/11b, were initially obtained in a racemic manner and were further separated by chiral HPLC preparation. The biological assessment of these compounds against NO production was conducted in the LPS-induced RAW264.7 macrophage cells model. Compounds 9a, 9b, and 11a displayed inhibitory rates exceeding 80%, with IC50 values ranging from 8.69 ± 0.94 to 13.01 ± 1.11 µM. A preliminary examination of the structure-activity relationship (SAR) for these isolates indicated that chromene meroterpenoids with α, ß-unsaturated ketone carbonyl and Δ12(13) double bond functionalities exhibited enhanced anti-inflammatory properties.

Enhancing mechanical and water barrier properties of starch film using chia mucilage.

Biodegradable packaging materials are increasingly being investigated due to rising concerns about food safety and environmental conservation. This study examines the incorporation of chia mucilage (CM) into starch-based films using the casting method, aiming to understand its effects on the structure and functionality of the films. CM, an anionic heteropolysaccharide, is hypothesized to enhance the mechanical and barrier properties of the films through polymer interactions and hydrogen bonding. Our findings confirm that CM incorporation results in films with uniformly smooth surfaces, indicating high compatibility and homogeneity within the starch matrix. Notably, CM improves film transparency and crystallinity. Mechanical assessments show a remarkable elevation in tensile strength, soaring from 5.21 MPa to 12.38 MPa, while elongation at break decreases from 61.73 % to 31.42 %, indicating a trade-off between strength and flexibility. Additionally, water solubility decreases from 57.97 % to 41.40 %, and water vapor permeability is reduced by 30 % with CM loading. These results highlight the role of CM in facilitating the formation of a dense, interconnected polymeric network within the starch matrix. Given the soluble dietary fiber nature of CM, the CS/CM (corn starch/chia mucilage) blended films are expected to be safe for food packaging and applicable as edible films with health benefits.

An elastic single crystal composed of one-dimensional chiral coordination polymers.

A single crystal composed of one-dimensional coordinated polymers, [CdCl2(1-methyl-2-pyridone)]n, has been synthesized and characterized. This compound exhibits outstanding elastic bending due to the molecular spring nature of the CdCl2 coordination framework and weak intermolecular interactions between the coordination chains. Owing to the helical arrangement of organic ligands surrounding the coordination structure, the compound crystallizes in a chiral space group. As a result, it displays compelling circular dichroism spectra and second harmonic generation properties.

Comprehensive analysis of immune-related lncRNAs in AML patients uncovers potential therapeutic targets and prognostic biomarkers.

Purpose: The objective of this study was to provide theoretically feasible strategies by understanding the relationship between the immune microenvironment and the diagnosis and prognosis of AML patients. To this end, we built a ceRNA network with lncRNAs as the core and analyzed the related lncRNAs in the immune microenvironment by bioinformatics analysis. Methods: AML transcriptome expression data and immune-related gene sets were obtained from TCGA and ImmPort. Utilizing Pearson correlation analysis, differentially expressed immune-related lncRNAs were identified. Then, the LASSO-Cox regression analysis was performed to generate a risk signature consisting immune-related lncRNAs. Accuracy of signature in predicting patient survival was evaluated using univariate and multivariate analysis. Next, GO and KEGG gene enrichment and ssGSEA were carried out for pathway enrichment analysis of 183 differentially expressed genes, followed by drug sensitivity and immune infiltration analysis with pRRophetic and CIBERSORT, respectively. Cytoscape was used to construct the ceRNA network for these lncRNAs. Results: 816 common lncRNAs were selected to acquire the components related to prognosis. The final risk signature established by multivariate Cox and stepwise regression analysis contained 12 lncRNAs engaged in tumor apoptotic and metastatic processes: LINC02595, HCP5, AC020934.2, AC008770.3, LINC01770, AC092718.4, AL589863.1, AC131097.4, AC012368.1, C1RL-AS1, STARD4-AS1, and AC243960.1. Based on this predictive model, high-risk patients exhibited lower overall survival rates than low-risk patients. Signature lncRNAs showed significant correlation with tumor-infiltrating immune cells. In addition, significant differences in PD-1/PD-L1 expression and bleomycin/paclitaxel sensitivity were observed between risk groups. Conclusion: LncRNAs related to immune microenvironment were prospective prognostic and therapeutic options for AML.

Fluorescence Sensors for the Detection of L-Histidine Based on Silver Nanoclusters Modulated by Copper Ions.

In this study, Cu2+ modulated silver nanoclusters were constructed for the turn-on, label-free detection of L-histidine. Six Ag NCs protected by oligonucleotides (DNA-Ag NCs) were tested in a series of experiments. Finally, A-DAN-Ag NCs were chosen as the best candidate due to their excellent fluorescent properties. The fluorescence of A-DAN-Ag NCs was quenched using Cu2+ through energy or electron transfer. However, quenched fluorescence could be restored dramatically in the presence of L-histidine due to Cu2+ liberation from A-DAN-Ag NCs and because of the chelation between the imidazole group of L-histidine and Cu2+. The proposed sensor exhibited high selectivity towards L-histidine over other amino acids, with a limit of detection (LOD) of 0.096 µM ranging from 0 to 8 µM. The proposed sensor succeeded in detecting L-histidine in diluted human urine. Therefore, the sensor has promising practical applications in biological systems.

Tetraphenylene-based semiconductive metal-organic framework crystals for direct X-ray detection and imaging.

MOFs have good potential for X-ray detection, but direct X-ray detection in single crystal form is rarely reported. In this work, we successfully synthesized Pb-TCPE, and the single crystal achieves a low detection limit and high detection sensitivity of 4812.6 µC Gyair-1 cm-2, which exhibits great potential for X-ray detection and imaging.