A novel approach to study multi-domain motions in JAK1's activation mechanism based on energy landscape.

The family of Janus Kinases (JAKs) associated with the JAK-signal transducers and activators of transcription signaling pathway plays a vital role in the regulation of various cellular processes. The conformational change of JAKs is the fundamental steps for activation, affecting multiple intracellular signaling pathways. However, the transitional process from inactive to active kinase is still a mystery. This study is aimed at investigating the electrostatic properties and transitional states of JAK1 to a fully activation to a catalytically active enzyme. To achieve this goal, structures of the inhibited/activated full-length JAK1 were modelled and the energies of JAK1 with Tyrosine Kinase (TK) domain at different positions were calculated, and Dijkstra's method was applied to find the energetically smoothest path. Through a comparison of the energetically smoothest paths of kinase inactivating P733L and S703I mutations, an evaluation of the reasons why these mutations lead to negative or positive regulation of JAK1 are provided. Our energy analysis suggests that activation of JAK1 is thermodynamically spontaneous, with the inhibition resulting from an energy barrier at the initial steps of activation, specifically the release of the TK domain from the inhibited Four-point-one, Ezrin, Radixin, Moesin-PK cavity. Overall, this work provides insights into the potential pathway for TK translocation and the activation mechanism of JAK1.

Magnetic Properties of Self-Assemble Naphthalene Diimide Radical Aggregates.

The concept of creating room-temperature ferromagnets from organic radicals proposed nearly sixty years ago, has recently experienced a resurgence due to advances in organic radical chemistry and materials. However, the lack of definitive design paradigms for achieving stable long-range ferromagnetic coupling between organic radicals presents an uncertain future for this research. Here, an innovative strategy is presented to achieve room-temperature ferromagnets by assembling π-conjugated radicals into π-π stacking aggregates. These aggregates, with ultra-close π-π distances and optimal π-π overlap, provide a platform for strong ferromagnetic (FM) interaction. The planar aromatic naphthalene diimide (NDI) anion radicals form nanorod aggregates with a π-π distance of just 3.26 Å, shorter than typical van der Waals distances. The suppressed electron paramagnetic resonance (EPR) signal and emergent near-infrared (NIR) absorption of the aggregates confirm strong interactions between the radicals. Magnetic measurements of NDI anion radical aggregates demonstrate room-temperature ferromagnetism with a saturated magnetization of 1.1 emu g-1, the highest among pure organic ferromagnets. Theoretical calculations reveal that π-stacks of NDI anion radicals with specific interlayer translational slippage favor ferromagnetic coupling over antiferromagnetic coupling.

Difference in Rumor Dissemination and Debunking Before and After the Relaxation of COVID-19 Prevention and Control Measures in China: Infodemiology Study.

BACKGROUND: The information epidemic emerged along with the COVID-19 pandemic. While controlling the spread of COVID-19, the secondary harm of epidemic rumors to social order cannot be ignored. OBJECTIVE: The objective of this paper was to understand the characteristics of rumor dissemination before and after the pandemic and the corresponding rumor management and debunking mechanisms. This study aimed to provide a theoretical basis and effective methods for relevant departments to establish a sound mechanism for managing network rumors related to public health emergencies such as COVID-19. METHODS: This study collected data sets of epidemic rumors before and after the relaxation of the epidemic prevention and control measures, focusing on large-scale network rumors. Starting from 3 dimensions of rumor content construction, rumor propagation, and rumor-refuting response, the epidemic rumors were subdivided into 7 categories, namely, involved subjects, communication content, emotional expression, communication channels, communication forms, rumor-refuting subjects, and verification sources. Based on this framework, content coding and statistical analysis of epidemic rumors were carried out. RESULTS: The study found that the rumor information was primarily directed at a clear target audience. The main themes of rumor dissemination were related to the public's immediate interests in the COVID-19 field, with significant differences in emotional expression and mostly negative emotions. Rumors mostly spread through social media interactions, community dissemination, and circle dissemination, with text content as the main form, but they lack factual evidence. The preferences of debunking subjects showed differences, and the frequent occurrence of rumors reflected the unsmooth channels of debunking. The χ2 test of data before and after the pandemic showed that the P value was less than .05, indicating that the difference in rumor content before and after the pandemic had statistical significance. CONCLUSIONS: This study's results showed that the themes of rumors during the pandemic are closely related to the immediate interests of the public, and the emotions of the public accelerate the spread of these rumors, which are mostly disseminated through social networks. Therefore, to more effectively prevent and control the spread of rumors during the pandemic and to enhance the capability to respond to public health crises, relevant authorities should strengthen communication with the public, conduct emotional risk assessments, and establish a joint mechanism for debunking rumors.

Photo-controllable Luminescence from Radicals Leading to Ratiometric Emission Switching via Dynamic Intermolecular Coupling.

The development of photoinduced luminescent radicals with dynamic emission color is still challenging. Herein we report a novel molecular radical system (TBIQ) that shows photo-controllable luminescence, leading to a wide range of ratiometric color changes via light excitation. The conjugated skeleton of TBIQ is decorated with steric-demanding tertiary butyl groups that enable appropriate intermolecular interaction to make dynamic intermolecular coupling possible for controllable behaviors. We reveal that the helicenic pseudo-planar conformation of TBIQ experiences a planarization process after light excitation, leading to more compactly stacked supermolecules and thus generating radicals via intermolecular charge transfer. The photo-controllable luminescent radical system is employed for a high-level information encryption application. This study may offer unique insight into molecular dynamic motion for optical manufacturing and broaden the scope of smart-responsive materials for advanced applications.

Optimizing Intermolecular Interactions and Energy Level Alignments of Red TADF Emitters for High-Performance Organic Light-Emitting Diodes.

Adequately harvesting all excitons in a single molecule and inhibiting exciton losses caused by intermolecular interactions are two important factors for achieving high efficiencies thermally activated delayed fluorescence (TADF). One potential approach for optimizing these is to tune alignment of various excited state energy levels by using different doping concentrations. Unfortunately, emission efficiencies of most TADF emitters decrease rapidly with concentrations which limits the window for energy level tunning. In this work, by introducing a spiro group to increase steric hindrance of a TADF emitter (BPPXZ) with a phenoxazine and a dibenzo[a,c]phenazine, emission efficiency of the resulting molecule (BPSPXZ) is much less affected by concentration increase. This enables exploitation of the concentration effects to tune energy levels of its excited states for obtaining simultaneously small singlet-triplet energy offset and large spin-orbital coupling, leading to high-efficiency reverse intersystem crossing. With these merits, organic light-emitting diodes (OLEDs) using the BPSPXZ emitter from 5 to 60 wt% doping can all deliver EQE of over 20%. More importantly, record-high EQEs of 33.4% and 15.8% are respectively achieved in the optimized and nondoped conditions. This work proposes a strategy for developing red TADF emitters by optimizing the intermolecular interaction and energy level alignments to facilitate exciton utilization over wide doping concentrations.

Recent progress in hot exciton materials for organic light-emitting diodes.

According to Kasha's rule, high-lying excited states usually have little effect on fluorescence. However, in some molecular systems, the high-lying excited states partly or even mainly contribute to the photophysical properties, especially in the process of harvesting triplet excitons in organic electroluminescent devices. In the current review, we focus on a type of organic light-emitting diode (OLED) materials called "hot exciton" materials, which can effectively harness the non-radiative triplet excitons via reverse intersystem crossing (RISC) from high-lying triplet states to singlet states (Tn→ Sm; n≥ 2, m≥ 1). Since Ma and Yang proposed the hot exciton mechanism for OLED material design in 2012, there have been many reports aiming at the design and synthesis of novel hot exciton luminogens. Herein, we present a comprehensive review of the recent progress in hot exciton materials. The developments of the hot exciton mechanism are reviewed, the fundamental principles regarding molecular design are discussed, and representative reported hot exciton luminogens are summarized and analyzed, along with their structure-property relationships and OLED applications.

Side-Chain-Tuned Molecular Packing Allows Concurrently Boosted Photoacoustic Imaging and NIR-II Fluorescence.

It is generally considered that photoacoustic imaging (PAI) and fluorescence imaging (FLI) cannot be enhanced concurrently, as they are dependent on competitive photophysical processes at the single-molecule level. Herein, we reveal that BDTR9-OC8 and BDTR9-C8, which have identical π-conjugated backbones but are substituted by side chains of different rigidity, show distinct phototheranostic properties in the aggregated state. The NIR-II FLI and PAI brightness of BDTR9-C8 nanoparticles are enhanced by 4.6 and 1.4 times compared with BDTR9-OC8 nanoparticles. Theoretical calculations and GIWAXS analysis revealed that BDTR9-C8 with rigid side chains shows a relative amorphous condensed state, which will benefit the efficient transportation of photo-generated excitons and phonons, subsequently enhancing the FLI and PAI signals. Besides, both nanoparticles exhibit excellent photothermal conversion efficiency due to their strong light-harvesting capability and are considered effective photothermal therapy materials. This work provides an illuminating strategy for material design in the future.

Fluorescence Resonance Energy Transfer-Mediated Immunosensor Based on Design and Synthesis of the Substrate of Amp Cephalosporinase for Biosensing.

The traditional enzyme-linked immunosorbent assay (ELISA) has some disadvantages, such as insufficient sensitivity and low stability of the labeled enzyme, which limit its further applications. In this study, a more stable enzyme, Amp cephalosporinase (AmpC), was selected as the labeled enzyme, and its substrate was designed and synthesized. This substrate contained the cephalosporin ring core as the enzymatic recognition section and the structural motif of the 3-hydroxyflavone (3-HF) as the reporter molecule. AmpC can specifically catalyze the substrate and release 3-HF, which can enter the cavity of ß-cyclodextrin (ß-CD) on the surface of ZnS quantum dots and form a fluorescence resonance energy transfer (FRET) signal amplification system. An AmpC-catalyzed, FRET-mediated ultrasensitive immunosensor (ACF immunosensor) for procalcitonin (PCT) was developed by combining the signal amplification system of the polystyrene microspheres and effective immune-based magnetic separation. The ACF immunosensor has high sensitivity and specificity for the detection of PCT: its linear range is from 0.1 ng mL-1 to 70 ng mL-1, and the limit of detection can reach 0.03 ng mL-1. The spiking recoveries of PCT in human serum samples range from 98.3% to 107%, with relative standard deviations ranging from 2.14% to 12.0%. This approach was applied to detect PCT in real patient serum samples, and the results are consistent with those obtained with a commercial ELISA kit.

Phytic acid functionalized Fe3O4 nanoparticles loaded with Ti(IV) ions for phosphopeptide enrichment in mass spectrometric analysis.

A novel magnetic nanomaterial for use in metal ion based affinity chromatography is described. It is based on the chelation between the phosphate groups of phytic acid (PA) and Ti(IV) ions. Due to the large number (6) of phosphate groups of PA, it has a large capacity for Ti(IV) ions. PA was first immobilized on magnetite nanoparticles (PA-MNPs) and then loaded with Ti(IV) ions to obtain the sorbent (Ti-PA-MNPs). The fraction of Ti(IV) ions on the surface of PA-MNPs that is exposed to the solution binds the phosphate groups of phosphopeptides. The bound phosphopeptides can then be magnetically separated. The method was applied to the enrichment of the phosphopeptides in a ß-casein tryptic digest. A tryptic digest of bovine serum albumin (BSA) was added at a molar ratio (ß-casein to BSA) of 1:2000 to study selectivity. The phosphopeptides were quantified by mass spectrometry. The limit of detection can be as low as 8 × 10-10 mol L-1. This sorbent has a high absorption capacity (53.5 µg mg-1) and shows good recoveries (90%). As many as 2145 phosphopeptides were isolated from 500 µg tryptic digest of a rat liver lysate after enrichment by Ti-PA-MNPs. This is superior to that (1568 phosphopeptides) of commercial TiO2 kit. Graphical abstract Schematic presentation of fabrication for a novel modified magnetic nanomaterial (Ti-PA-MNPs) based on the chelation of phytic acid (PA) with Ti(IV) ions. Ti-PA-MNPs were successfully applied to enriching low abundance phosphopeptides from biosamples in mass spectrometric analysis.

Enzyme-Free Amplification Strategy for Biosensing Using Fe3+-Poly(glutamic acid) Coordination Chemistry.

In this work, we outline a signal amplification strategy using the coordination chemistry between Fe3+ and poly(glutamic acid) (PGA) for biosensing applications. The theoretical calculation based on density functional theory shows that PGA has a much higher binding affinity with Fe3+ than the other metal ions. Guided by this rationale, we prepare a PGA-mediated signal probe through conjugating PGA onto polystyrene (PS) nanoparticles to form a brushlike nanostructure for Fe3+ coordination. This PGA-PS brush (PPB) has a large loading capacity of Fe3+ with a number of 1.92 × 108 Fe atoms per nanoparticle that greatly amplifies the signals for assays in an enzyme-free way. Combined with ferrozine coloration-based readout, this PPB-mediated amplification is further applied for the enzyme-free immunoassay that shows an ultrahigh sensitivity for detection of microcystins-LR (12 pg/mL), a 5-fold enhancement compared with that of traditional enzyme-linked immunosorbent assay (ELISA) (60 pg/mL). In addition, the good stability, rapid response, and long shelf life make this enzyme-free amplification strategy a promising platform for point-of-care biosensing applications.

Reverse intersystem crossing from upper triplet levels to excited singlet: a 'hot excition' path for organic light-emitting diodes.

Since researches on the fate of highly excited triplet states demonstrated the existence of reverse intersystem crossing (RISC) from upper triplet levels to singlet manifold in naphthalene, quinoline, isoquinoline, etc. in the 1960s, this unique photophysical process was then found and identified in some other aromatic materials. However, the early investigations mainly focus on exploring the mechanism of this photophysical process; no incorporation of specific application was implemented. Until recently, our group innovatively used this 'sleeping' photophysical process to enhance the efficiency of fluorescent organic light-emitting diodes by simultaneously harvesting singlet and triplet excitons. Efforts are devoted to developing materials with high photoluminescence efficiency and effective RISC through appropriate molecular design in a series of donor-acceptor material systems. The experimental and theoretical results indicate that these materials exhibit hybridized local and charge-transfer excited state, which achieve a combination of the high radiation from local excited state and the high T(m)→S(n) (m≥2, n≥1) conversion along charge-transfer excited state. As expected, the devices exhibited favourable external quantum efficiency and low roll-off, and especially an exciton utilization efficiency exceeding the limit of 25%. Considering the significant progress made in organic light-emitting diodes with this photophysical process, we review the relevant mechanism and material systems, as well as our design principle in materials and device application.

Separation of electrical and optical energy gaps: selectively adjusting the electrical and optical properties for a highly efficient blue emitter.

The design concept of separation of optical and electrical bandgap for wide bandgap materials is further developed in DCzSiPI. The HOMO/LUMO levels can be tuned by incorporation of PI and DCz substituents. The tetraphenylsilane core avoids the intramolecular charge transfer from DCz to PI (DCz = dimer carbazole, PI = phenanthro[9,10-d]imidazole). The allowed transitions are found to be from HOMO-1 to LUMO providing DCzSiPI with sufficient bandgap.

Surface polarization enhanced Seebeck effects in vertical multi-layer metal-polymer-metal thin-film devices.

We explore a new mechanism to develop Seebeck effects by using temperature-dependent surface polarization based on vertical multi-layer Al-P3HT:PCBM-Al thin-film devices. Here, the temperature-dependent surface polarization functions as an additional driving force, as compared with the traditional driving force from the entropy difference, to diffuse the charge carriers under a temperature gradient towards the development of Seebeck effects. The temperature-dependent surface polarization is essentially generated by both the thermally dependent polarization through the charge-phonon coupling mechanism and the thermally modulated interface dipoles by Fermi electrons. It is noted that the entropy difference often causes an inverse relationship between the Seebeck coefficient and electrical conductivity in thermoelectric developments. However, this temperature-dependent surface polarization provides a mechanism allowing a co-operative relationship between the Seebeck coefficient and electrical conductivity. We demonstrate simultaneously the enhanced Seebeck coefficient and electrical conductivity by using the dielectric interface through the temperature-dependent surface polarization to diffuse charge carriers in the Al-MoO3-P3HT:PCBM-Al thin-film device.

Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network.

BACKGROUND: Acute heart failure (AHF) in the intensive care unit (ICU) is characterized by its criticality, rapid progression, complex and changeable condition, and its pathophysiological process involves the interaction of multiple organs and systems. This makes it difficult to predict in-hospital mortality events comprehensively and accurately. Traditional analysis methods based on statistics and machine learning suffer from insufficient model performance, poor accuracy caused by prior dependence, and difficulty in adequately considering the complex relationships between multiple risk factors. Therefore, the application of deep neural network (DNN) techniques to the specific scenario, predicting mortality events of patients with AHF under intensive care, has become a research frontier. METHODS: This research utilized the MIMIC-IV critical care database as the primary data source and employed the synthetic minority over-sampling technique (SMOTE) to balance the dataset. Deep neural network models-backpropagation neural network (BPNN) and recurrent neural network (RNN), which are based on electronic medical record data mining, were employed to investigate the in-hospital death event judgment task of patients with AHF under intensive care. Additionally, multiple single machine learning models and ensemble learning models were constructed for comparative experiments. Moreover, we achieved various optimal performance combinations by modifying the classification threshold of deep neural network models to address the diverse real-world requirements in the ICU. Finally, we conducted an interpretable deep model using SHapley Additive exPlanations (SHAP) to uncover the most influential medical record features for each patient from the aspects of global and local interpretation. RESULTS: In terms of model performance in this scenario, deep neural network models outperform both single machine learning models and ensemble learning models, achieving the highest Accuracy, Precision, Recall, F1 value, and Area under the ROC curve, which can reach 0.949, 0.925, 0.983, 0.953, and 0.987 respectively. SHAP value analysis revealed that the ICU scores (APSIII, OASIS, SOFA) are significantly correlated with the occurrence of in-hospital fatal events. CONCLUSIONS: Our study underscores that DNN-based mortality event classifier offers a novel intelligent approach for forecasting and assessing the prognosis of AHF patients in the ICU. Additionally, the ICU scores stand out as the most predictive features, which implies that in the decision-making process of the models, ICU scores can provide the most crucial information, making the greatest positive or negative contribution to influence the incidence of in-hospital mortality among patients with acute heart failure.

Scientific misconduct responsibility attribution: An empirical study on byline position and team identity in Chinese medical papers.

OBJECTIVE: The primary objective of this inquiry was to explore the nexus between authorship attribution in medical literature and accountability for scientific impropriety while assessing the influence of authorial multiplicity on the severity of sanctions imposed. METHODS: Probit regression models were employed to scrutinize the impact of authorship on assuming accountability for scientific misconduct, and unordered multinomial logistic regression models were used to examine the influence of authorship and the number of bylines on the severity of punitive measures. RESULTS: First authors and corresponding authors were significantly more likely to be liable for scientific misconduct than other authors and were more likely to be penalized particularly severely. Furthermore, a negative correlation was observed between the number of authors' affiliations and the severity of punitive measures. CONCLUSION: Authorship exerts a pronounced influence on the attribution of accountability in scientific research misconduct, particularly evident in the heightened risk of severe penalties confronting first and corresponding authors owing to their principal roles. Hence, scientific research institutions and journals must delineate authorship specifications meticulously, ascertain authors' contributions judiciously, bolster initiatives aimed at fostering scientific research integrity, and uphold an environment conducive for robust scientific inquiry.

A study on the factors influencing the intention to receive booster shots of the COVID-19 vaccine in China based on the information frame effect.

Introduction: In the response to and prevention and control of the Novel coronavirus pneumonia, the COVID-19 vaccine does not provide lifelong immunity, and it is therefore important to increase the rate of booster shots of the COVID-19 vaccine. In the field of information health science, research has found that information frames have an impact in changing individual attitudes and health behaviors. Objective: This study focuses on the effects of different influencing factors on the public's willingness to receive the booster shots of the COVID-19 vaccine under two information frameworks. Methods: An online questionnaire was conducted to explore the effects of demographic characteristics, personal awareness, social relationships, risk disclosure, perceived booster vaccination protection rate, and duration of protection under the assumption of an information framework. T test and one-way analysis were used to testing the effect of variables. Results: (1) The persuasion effect under the gain frame is higher than that under the loss frame (B = 0.863 vs. B = 0.746); (2) There was no significant difference in subjects' intention of booster vaccination in terms of gender, age, income, occupation, educational background and place of residence. Whether family members received booster vaccination was strongly correlated with their intention of vaccination under the loss framework (p = 0.017, M = 4.63, SD = 0.664). (3) The higher the understanding of COVID-19, the higher the degree of compliance with the government's COVID-19 prevention and control measures, and the higher the willingness to strengthen vaccination; (4) Risk disclosure has a significant impact on people's willingness to receive COVID-19 booster shots (M = 2.48, under the loss framework; M = 2.44, under the gain framework); (5) Vaccine protection rate and duration of protection have an impact on people's willingness to vaccinate. Increased willingness to vaccinate when the protection rate of booster vaccine approaches 90% (M = 4.76, under the loss framework; M = 4.68, under the gain framework). When the vaccine protection period is 2 years, people are more willing to receive a booster vaccine; and the willingness to receive a booster shot is stronger under the loss framework (M = 4.60, SD = 0.721, p = 0.879). Conclusion: The impact of the information framework on COVID-19 vaccination intentions is different, and the disclosure of relevant health information should focus on the impact of the information framework and content on the public's behavior toward strengthening vaccination. Therefore, in the face of public health emergencies, public health departments, healthcare institutions, and other sectors can consider adopting the Gainful Information Framework tool to disseminate health information to achieve better persuasion and promote public health behavior change enhancing public health awareness, and promoting universal vaccination.

Identifying Key Genes as Progression Indicators of Prostate Cancer with Castration Resistance Based on Dynamic Network Biomarker Algorithm and Weighted Gene Correlation Network Analysis.

Background: Androgen deprivation therapy (ADT) is the mainstay of treatment for prostate cancer, yet dynamic molecular changes from hormone-sensitive to castration-resistant states in patients treated with ADT remain unclear. Methods: In this study, we combined the dynamic network biomarker (DNB) method and the weighted gene co-expression network analysis (WGCNA) to identify key genes associated with the progression to a castration-resistant state in prostate cancer via the integration of single-cell and bulk RNA sequencing data. Based on the gene expression profiles of CRPC in the GEO dataset, the DNB method was used to clarify the condition of epithelial cells and find out the most significant transition signal DNB modules and genes included. Then, we calculated gene modules associated with the clinical phenotype stage based on the WGCNA. IHC was conducted to validate the expression of the key genes in CRPC and primary PCa patients Results:Nomograms, calibration plots, and ROC curves were applied to evaluate the good prognostic accuracy of the risk prediction model. Results: By combining single-cell RNA sequence data and bulk RNA sequence data, we identified a set of DNBs, whose roles involved in androgen-associated activities indicated the signals of a prostate cancer cell transition from an androgen-dependent state to a castration-resistant state. In addition, a risk prediction model including the risk score of four key genes (SCD, NARS2, ALDH1A1, and NFXL1) and other clinical-pathological characteristics was constructed and verified to be able to reasonably predict the prognosis of patients receiving ADT. Conclusions: In summary, four key genes from DNBs were identified as potential diagnostic markers for patients treated with ADT and a risk score-based nomogram will facilitate precise prognosis prediction and individualized therapeutic interventions of CRPC.

Factors influencing user's health information discernment abilities in online health communities: based on SEM and fsQCA.

Introduction: Online health communities have become the main source for people to obtain health information. However, the existence of poor-quality health information, misinformation, and rumors in online health communities increases the challenges in governing information quality. It not only affects users' health decisions but also undermines social stability. It is of great significance to explore the factors that affect users' ability to discern information in online health communities. Methods: This study integrated the Stimulus-Organism-Response Theory, Information Ecology Theory and the Mindsponge Theory to constructed a model of factors influencing users' health information discernment abilities in online health communities. A questionnaire was designed based on the variables in the model, and data was collected. Utilizing Structural Equation Modeling (SEM) in conjunction with fuzzy-set Qualitative Comparative Analysis (fsQCA), the study analyzed the complex causal relationships among stimulus factors, user perception, and the health information discernment abilities. Results: The results revealed that the dimensions of information, information environment, information technology, and information people all positively influenced health information discernment abilities. Four distinct configurations were identified as triggers for users' health information discernment abilities. The core conditions included information source, informational support, technological security, technological facilitation, and perceived risk. It was also observed that information quality and emotional support can act as substitutes for one another, as can informational support and emotional support. Discussion: This study provides a new perspective to study the influencing factors of health information discernment abilities of online health community users. It can provide experiences and references for online health community information services, information resource construction and the development of users' health information discernment abilities.

Dual Sulfone-Bridged Triphenylamine Heteroaromatics for High-Performance Blue Organic Electroluminescence.

Polycyclic heteroaromatics (PHAs) are a highly versatile class of functional materials, especially applicable as efficient luminophores in organic light-emitting diodes (OLEDs). Those constructed by tethered phenyl surrounding the main group center attract extensive attention due to their excellent OLED device performance. However, the development of such a class of emitters is often limited to boron, nitrogen-doped π-conjugated heterocycles. Herein, we proposed a novel kind of blue emitter by constructing a donor-acceptor molecular configuration, utilizing a dual sulfone-bridged triphenylamine (BTPO) core and mono/di-diphenylamine (DPA) substituents. The twisted D-A molecular structures and appropriate donor strength facilitate the effective separation of natural transition orbitals, endowing the emitters with charge-transfer dominant hybridized local and charge-transfer characteristics for the excited states. Both BTPO-DPA and BTPO-2DPA own small S1-T1 splitting energy, thus demonstrating blue thermally activated delayed fluorescence. The more symmetrical structure and enhanced CT features brought by additional DPA moiety confer BTPO-2DPA with a shorter delayed fluorescence lifetime, a higher fluorescence quantum yield and narrower emission. Therefore, BTPO-2DPA based OLED devices exhibit superior blue electroluminescence performance, with external quantum efficiencies reaching 12.31 %.

Deep-qGFP: A Generalist Deep Learning Assisted Pipeline for Accurate Quantification of Green Fluorescent Protein Labeled Biological Samples in Microreactors.

Absolute quantification of biological samples provides precise numerical expression levels, enhancing accuracy, and performance for rare templates. Current methodologies, however, face challenges-flow cytometers are costly and complex, whereas fluorescence imaging, relying on software or manual counting, is time-consuming and error-prone. It is presented that Deep-qGFP, a deep learning-aided pipeline for the automated detection and classification of green fluorescent protein (GFP) labeled microreactors, enables real-time absolute quantification. This approach achieves an accuracy of 96.23% and accurately measures the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, providing precise template concentrations. Deep-qGFP demonstrates remarkable speed, quantifying over 2000 microreactors across ten images in just 2.5 seconds, with a dynamic range of 56.52-1569.43 copies µL-1 . The method demonstrates impressive generalization capabilities, successfully applied to various GFP-labeling scenarios, including droplet-based, microwell-based, and agarose-based applications. Notably, Deep-qGFP is the first all-in-one image analysis algorithm successfully implemented in droplet digital polymerase chain reaction (PCR), microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and bacterial quantification, without requiring transfer learning, modifications, or retraining. This makes Deep-qGFP readily applicable in biomedical laboratories and holds potential for broader clinical applications.