Simultaneous Nucleophilic-Substituted and Electrostatic Interactions for Thermal Switching of Spiropyran: A New Approach for Rapid and Selective Colorimetric Detection of Thiol-Containing Amino Acids

Complementary electrostatic interaction between the zwitterionic merocyanine and dipolar molecules has emerged as a common strategy for reversibly structural conversion of spiropyrans. Herein, we report a concept-new approach for thermal switching of a spiropyran that is based on simultaneous nucleophilic-substitution reaction and electrostatic interaction. The nucleophilic-substitution at spiro-carbon atom of a spiropyran is promoted due to electron-deficient interaction induced by 6- and 8-nitro groups, which is responsible for the isomerization of the spiropyran by interacting with thiol-containing amino acids. Further, the electrostatic interaction between the zwitterionic merocyanine and the amino acids would accelerate the structural conversion. As proof-of-principle, we outline the route to glutathione (GSH)-induced ring-opening of 6,8-dinitro-1′,3′,3′-trimethylspiro [2H-1-benzopyran-2,2′-indoline] (<b>1</b>) and its application for rapid and sensitive colorimetric detection of GSH. In ethanol–water (1:99, v/v) solution at pH 8.0, the free <b>1</b> exhibited slight-yellow color, but the color changed clearly from slight-yellow to orange-yellow when GSH was introduced into the solution. Ring-opening rate of <b>1</b> upon accession of GSH in the dark is 0.45 s^–1, which is 4 orders of magnitude faster in comparison with the rate of the spontaneous thermal isomerization. The absorbance enhancement of <b>1</b> at 480 nm was in proportion to the GSH concentration of 2.5 × 10^–8–5.0 × 10^–6 M with a detection limit of 1.0 × 10^–8 M. Furthermore, due to the specific chemical reaction between the probe and target, color change of <b>1</b> is highly selective for thiol-containing amino acids; interferences from other biologically active amino acids or anions are minimal

Realization of Thin Film Encapsulation by Atomic Layer Deposition of Al₂O₃ at Low Temperature

We investigated Al₂O₃ atomic layer deposition growth layer for encapsulation of organic light emitting diodes (OLEDs). It was found that surface properties of these O₃-based Al₂O₃ were superior to those of H₂O based Al₂O₃ grown at relative higher temperatures. Therefore, the water vapor transmission rate of ∼60 nm thick Al₂O₃ films can be reduced from 4.9 × 10^–4g/(m² day) (80 °C–H₂O) to 8.7 × 10^–6 g/(m² day) (80 °C–O₃) under a controlled environment of 20 °C and relative humidity of 60%. Besides, the OLEDs integrated with 80 °C–O₃ based Al₂O₃ film were undamaged, and their luminance decay time was altered to a considerable extent

γ‑Aminobutyric Acid (GABA) Accumulation in Tea (Camellia sinensis L.) through the GABA Shunt and Polyamine Degradation Pathways under Anoxia

γ-Aminobutyric acid (GABA) is an important bioactive component of tea (Camellia sinensis) providing various health benefits. We studied GABA accumulation via the GABA shunt and polyamine degradation pathways under anoxia in tea leaves. Anoxia caused a ∼20-fold increment in GABA concentration, relative to fresh tea leaves. This increment was due to the increase of glutamate decarboxylase and diamine oxidase activities. Genes involved in GABA formation, such as <i>CsGAD1</i> and <i>CsGAD2</i>, were significantly up-regulated by anoxia. The concentrations of putrescine and spermine, two substrates for GABA production, were also increased by anoxia. Treating tea leaves with aminoguanidine completely inhibited diamine oxidase activity during anoxia, but the concentration of GABA decreased by only ∼25%. We infer that about one-fourth of GABA formed in tea leaves under anoxia comes from the polyamine degradation pathway, opening the possibility of producing GABA tea based through the regulation of metabolism

Self-Assembly of Graphene Oxide with a Silyl-Appended Spiropyran Dye for Rapid and Sensitive Colorimetric Detection of Fluoride Ions

Fluoride ion (F^–), the smallest anion, exhibits considerable significance in a wide range of environmental and biochemical processes. To address the two fundamental and unsolved issues of current F^– sensors based on the specific chemical reaction (i.e., the long response time and low sensitivity) and as a part of our ongoing interest in the spiropyran sensor design, we reported here a new F^– sensing approach that, via assembly of a F^–-specific silyl-appended spiropyran dye with graphene oxide (GO), allows rapid and sensitive detection of F^– in aqueous solution. 6-(<i>tert</i>-Butyldimethylsilyloxy)-1′,3′,3′-trimethylspiro [chromene- 2,2′-indoline] (SPS), a spiropyran-based silylated dye with a unique reaction activity for F^–, was designed and synthesized. The nucleophilic substitution reaction between SPS and F^– triggers cleavage of the Si–O bond to promote the closed spiropyran to convert to its opened merocyanine form, leading to the color changing from colorless to orange-yellow with good selectivity over other anions. With the aid of GO, the response time of SPS for F^– was shortened from 180 to 30 min, and the detection limit was lowered more than 1 order of magnitude compared to the free SPS. Furthermore, due to the protective effect of nanomaterials, the SPS/GO nanocomposite can function in a complex biological environment. The SPS/GO nanocomposite was characterized by XPS and AFM, etc., and the mechanism for sensing F^– was studied by ¹H NMR and ESI-MS. Finally, this SPS/GO nanocomposite was successfully applied to monitoring F^– in the serum

Image_4_Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome.tif

ObjectiveAs a common yet intractable complication of severe sepsis, acute respiratory distress syndrome (ARDS) is closely associated with poor clinical outcomes and elevated medical expenses. The aim of the current study is to generate a model combining transcriptional biomarkers and clinical parameters to alarm the development of ARDS in septic patients.MethodsGene expression profile (GSE66890) was downloaded from the Gene Expression Omnibus database and clinical data were extracted. Differentially expressed genes (DEGs) from whole blood leukocytes were identified between patients with sepsis alone and septic patients who develop ARDS. ARDS prediction model was constructed using backward stepwise regression and Akaike Information Criterion (AIC). Meanwhile, a nomogram based on this model was established, with subsequent internal validation.ResultsA total of 57 severe septic patients were enrolled in this study, and 28 (49.1%) developed ARDS. Based on the differential expression analysis, six DEGs (BPI, OLFM4, LCN2, CD24, MMP8 and MME) were screened. According to the outcome prediction model, six valuable risk factors (direct lung injury, shock, tumor, BPI, MME and MMP8) were incorporated into a nomogram, which was used to predict the onset of ARDS in septic patients. The calibration curves of the nomogram showed good consistency between the probabilities and observed values. The decision curve analysis also revealed the potential clinical usefulness of the nomogram. The area under the receiver operating characteristic (AUROC) for the prediction of ARDS occurrence in septic patients by the nomogram was 0.86 (95% CI = 0.767-0.952). A sensitivity analysis showed that the AUROC for the prediction of ARDS development in septic patients without direct lung injury was 0.967 (95% CI = 0.896-1.0).ConclusionsThe nomogram based on transcriptional biomarkers and clinical parameters showed a good performance for the prediction of ARDS occurrence in septic patients.</p

Image_3_Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome.tif

ObjectiveAs a common yet intractable complication of severe sepsis, acute respiratory distress syndrome (ARDS) is closely associated with poor clinical outcomes and elevated medical expenses. The aim of the current study is to generate a model combining transcriptional biomarkers and clinical parameters to alarm the development of ARDS in septic patients.MethodsGene expression profile (GSE66890) was downloaded from the Gene Expression Omnibus database and clinical data were extracted. Differentially expressed genes (DEGs) from whole blood leukocytes were identified between patients with sepsis alone and septic patients who develop ARDS. ARDS prediction model was constructed using backward stepwise regression and Akaike Information Criterion (AIC). Meanwhile, a nomogram based on this model was established, with subsequent internal validation.ResultsA total of 57 severe septic patients were enrolled in this study, and 28 (49.1%) developed ARDS. Based on the differential expression analysis, six DEGs (BPI, OLFM4, LCN2, CD24, MMP8 and MME) were screened. According to the outcome prediction model, six valuable risk factors (direct lung injury, shock, tumor, BPI, MME and MMP8) were incorporated into a nomogram, which was used to predict the onset of ARDS in septic patients. The calibration curves of the nomogram showed good consistency between the probabilities and observed values. The decision curve analysis also revealed the potential clinical usefulness of the nomogram. The area under the receiver operating characteristic (AUROC) for the prediction of ARDS occurrence in septic patients by the nomogram was 0.86 (95% CI = 0.767-0.952). A sensitivity analysis showed that the AUROC for the prediction of ARDS development in septic patients without direct lung injury was 0.967 (95% CI = 0.896-1.0).ConclusionsThe nomogram based on transcriptional biomarkers and clinical parameters showed a good performance for the prediction of ARDS occurrence in septic patients.</p

Image_2_Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome.tif

ObjectiveAs a common yet intractable complication of severe sepsis, acute respiratory distress syndrome (ARDS) is closely associated with poor clinical outcomes and elevated medical expenses. The aim of the current study is to generate a model combining transcriptional biomarkers and clinical parameters to alarm the development of ARDS in septic patients.MethodsGene expression profile (GSE66890) was downloaded from the Gene Expression Omnibus database and clinical data were extracted. Differentially expressed genes (DEGs) from whole blood leukocytes were identified between patients with sepsis alone and septic patients who develop ARDS. ARDS prediction model was constructed using backward stepwise regression and Akaike Information Criterion (AIC). Meanwhile, a nomogram based on this model was established, with subsequent internal validation.ResultsA total of 57 severe septic patients were enrolled in this study, and 28 (49.1%) developed ARDS. Based on the differential expression analysis, six DEGs (BPI, OLFM4, LCN2, CD24, MMP8 and MME) were screened. According to the outcome prediction model, six valuable risk factors (direct lung injury, shock, tumor, BPI, MME and MMP8) were incorporated into a nomogram, which was used to predict the onset of ARDS in septic patients. The calibration curves of the nomogram showed good consistency between the probabilities and observed values. The decision curve analysis also revealed the potential clinical usefulness of the nomogram. The area under the receiver operating characteristic (AUROC) for the prediction of ARDS occurrence in septic patients by the nomogram was 0.86 (95% CI = 0.767-0.952). A sensitivity analysis showed that the AUROC for the prediction of ARDS development in septic patients without direct lung injury was 0.967 (95% CI = 0.896-1.0).ConclusionsThe nomogram based on transcriptional biomarkers and clinical parameters showed a good performance for the prediction of ARDS occurrence in septic patients.</p

Image_1_Combination of transcriptional biomarkers and clinical parameters for early prediction of sepsis indued acute respiratory distress syndrome.tif

ObjectiveAs a common yet intractable complication of severe sepsis, acute respiratory distress syndrome (ARDS) is closely associated with poor clinical outcomes and elevated medical expenses. The aim of the current study is to generate a model combining transcriptional biomarkers and clinical parameters to alarm the development of ARDS in septic patients.MethodsGene expression profile (GSE66890) was downloaded from the Gene Expression Omnibus database and clinical data were extracted. Differentially expressed genes (DEGs) from whole blood leukocytes were identified between patients with sepsis alone and septic patients who develop ARDS. ARDS prediction model was constructed using backward stepwise regression and Akaike Information Criterion (AIC). Meanwhile, a nomogram based on this model was established, with subsequent internal validation.ResultsA total of 57 severe septic patients were enrolled in this study, and 28 (49.1%) developed ARDS. Based on the differential expression analysis, six DEGs (BPI, OLFM4, LCN2, CD24, MMP8 and MME) were screened. According to the outcome prediction model, six valuable risk factors (direct lung injury, shock, tumor, BPI, MME and MMP8) were incorporated into a nomogram, which was used to predict the onset of ARDS in septic patients. The calibration curves of the nomogram showed good consistency between the probabilities and observed values. The decision curve analysis also revealed the potential clinical usefulness of the nomogram. The area under the receiver operating characteristic (AUROC) for the prediction of ARDS occurrence in septic patients by the nomogram was 0.86 (95% CI = 0.767-0.952). A sensitivity analysis showed that the AUROC for the prediction of ARDS development in septic patients without direct lung injury was 0.967 (95% CI = 0.896-1.0).ConclusionsThe nomogram based on transcriptional biomarkers and clinical parameters showed a good performance for the prediction of ARDS occurrence in septic patients.</p

Synthesized complex-frequency excitation for ultrasensitive molecular sensing

Detecting trace molecules remains a significant challenge. Surface-enhanced infrared absorption (SEIRA) based on plasmonic nanostructures, particularly graphene, has emerged as a promising approach to enhance sensing sensitivity. While graphene-based SEIRA offers advantages such as ultrahigh sensitivity and active tunability, intrinsic molecular damping weakens the interaction between vibrational modes and plasmons. Here, we demonstrate ultrahigh-sensitive molecular sensing based on synthesized complex-frequency waves (CFW). Our experiment shows that CFW can amplify the molecular signals (~1.2-nm-thick silk protein layer) detected by graphene-based sensor by at least an order of magnitude and can be universally applied to molecular sensing in different phases. Our approach is highly scalable and can facilitate the investigation of light-matter interactions, enabling diverse potential applications in fields such as optical spectroscopy, metasurfaces, optoelectronics, biomedicine and pharmaceutics

Two-In-One Method for Graphene Transfer: Simplified Fabrication Process for Organic Light-Emitting Diodes

Graphene as one of the most promising transparent electrode materials has been successfully applied in organic light-emitting diodes (OLEDs). However, traditional poly­(methyl methacrylate) (PMMA) transfer method usually results in hardly removed polymeric residues on the graphene surface, which induces unwanted leakage current, poor diode behavior, and even device failure. In this work, we proposed a facile and efficient two-in-one method to obtain clean graphene and fabricate OLEDs, in which the poly­(9,9-di-<i>n</i>-octylfluorene-<i>alt</i>-(1,4-phenylene-(4-<i>sec</i>-butylphenyl)­imino)-1,4-phenylene) (TFB) layer was inserted between the graphene and PMMA film both as a protector during the graphene transfer and a hole-injection layer in OLEDs. Finally, green OLED devices were successfully fabricated on the PMMA-free graphene/TFB film, and the device luminous efficiency was increased from 64.8 to 74.5 cd/A by using the two-in-one method. Therefore, the proposed two-in-one graphene transfer method realizes a high-efficient graphene transfer and device fabrication process, which is also compatible with the roll-to-roll manufacturing. It is expected that this work can enlighten the design and fabrication of the graphene-based optoelectronic devices