Promoting H2 generation of BiFeO3 photocathodes by a catalyst-sensitizer dyad linked with Sn(dipicolinate)2.

An innovative method for the fabrication of a catalyst-sensitizer dyad-based photoelectrode was developed by using the coordinated interaction between the pyridine-2,6-dicarboxylic group and Sn4+. A dyad (C1 + PDI) was loaded on the mesoporous BiFeO3 (BFO) photocathode for light-driven H2 generation. The dyad could expand the light absorption range and promote the surface charge separation of BFO, resulting in an enhanced photocurrent.

Computer-aided cognitive training combined with tDCS can improve post-stroke cognitive impairment and cerebral vasomotor function: a randomized controlled trial.

BACKGROUND: Post-stroke cognitive impairment (PSCI) is the focus and difficulty of poststroke rehabilitation intervention with an incidence of up to 61%, which may be related to the deterioration of cerebrovascular function. Computer-aided cognitive training (CACT) can improve cognitive function through scientific training targeting activated brain regions, becoming a popular training method in recent years. Transcranial direct current stimulation (tDCS), a non-invasive brain stimulation technique, can regulate the cerebral vascular nerve function, and has an effect on the rehabilitation of cognitive dysfunction after stroke. This study examined the effectiveness of both CACT and tDCS on cognitive and cerebrovascular function after stroke, and explored whether CACT combined with tDCS was more effective. METHODS: A total of 72 patients with PSCI were randomly divided into the conventional cognitive training (CCT) group (n = 18), tDCS group (n = 18), CACT group (n = 18), and CACT combined with tDCS group (n = 18). Patients in each group received corresponding 20-minute treatment 15 times a week for 3 consecutive weeks. Montreal Cognitive Assessment (MoCA) and the Instrumental Activities of Daily Living Scale (IADL) were used to assess patients' cognitive function and the activities of daily living ability. Transcranial Doppler ultrasound (TCD) was used to assess cerebrovascular function, including cerebral blood flow velocity (CBFV), pulse index (PI), and breath holding index (BHI). These outcome measures were measured before and after treatment. RESULTS: Compared with those at baseline, both the MoCA and IADL scores significantly increased after treatment (P < 0.01) in each group. There was no significantly difference in efficacy among CCT, CACT and tDCS groups. The CACT combined with tDCS group showed greater improvement in MoCA scores compared with the other three groups (P < 0.05), especially in the terms of visuospatial and executive. BHI significantly improved only in CACT combined with tDCS group after treatment (p ≤ 0.05) but not in the other groups. Besides, no significant difference in CBFV or PI was found before and after the treatments in all groups. CONCLUSION: Both CACT and tDCS could be used as an alternative to CCT therapy to improve cognitive function and activities of daily living ability after stroke. CACT combined with tDCS may be more effective improving cognitive function and activities of daily living ability in PSCI patients, especially visuospatial and executive abilities, which may be related to improved cerebral vasomotor function reflected by the BHI. TRIAL REGISTRATION NUMBER: The study was registered in the Chinese Registry of Clinical Trials (ChiCTR2100054063). Registration date: 12/08/2021.

MMGAT: a graph attention network framework for ATAC-seq motifs finding.

BACKGROUND: Motif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of transcription factor binding sites (TFBSs) and their pivotal roles in gene regulation. Deep learning technologies including convolutional neural networks (CNNs) and graph neural networks (GNNs), have achieved success in finding ATAC-seq motifs. However, CNN-based methods are limited by the fixed width of the convolutional kernel, which makes it difficult to find multiple transcription factor binding sites with different lengths. GNN-based methods has the limitation of using the edge weight information directly, makes it difficult to aggregate the neighboring nodes' information more efficiently when representing node embedding. RESULTS: To address this challenge, we developed a novel graph attention network framework named MMGAT, which employs an attention mechanism to adjust the attention coefficients among different nodes. And then MMGAT finds multiple ATAC-seq motifs based on the attention coefficients of sequence nodes and k-mer nodes as well as the coexisting probability of k-mers. Our approach achieved better performance on the human ATAC-seq datasets compared to existing tools, as evidenced the highest scores on the precision, recall, F1_score, ACC, AUC, and PRC metrics, as well as finding 389 higher quality motifs. To validate the performance of MMGAT in predicting TFBSs and finding motifs on more datasets, we enlarged the number of the human ATAC-seq datasets to 180 and newly integrated 80 mouse ATAC-seq datasets for multi-species experimental validation. Specifically on the mouse ATAC-seq dataset, MMGAT also achieved the highest scores on six metrics and found 356 higher-quality motifs. To facilitate researchers in utilizing MMGAT, we have also developed a user-friendly web server named MMGAT-S that hosts the MMGAT method and ATAC-seq motif finding results. CONCLUSIONS: The advanced methodology MMGAT provides a robust tool for finding ATAC-seq motifs, and the comprehensive server MMGAT-S makes a significant contribution to genomics research. The open-source code of MMGAT can be found at https://github.com/xiaotianr/MMGAT , and MMGAT-S is freely available at https://www.mmgraphws.com/MMGAT-S/ .

The mechanism of USP43 in the development of tumor: a literature review.

Ubiquitination of the proteins is crucial for governing protein degradation and regulating fundamental cellular processes. Deubiquitinases (DUBs) have emerged as significant regulators of multiple pathways associated with cancer and other diseases, owing to their capacity to remove ubiquitin from target substrates and modulate signaling. Consequently, they represent potential therapeutic targets for cancer and other life-threatening conditions. USP43 belongs to the DUBs family involved in cancer development and progression. This review aims to provide a comprehensive overview of the existing scientific evidence implicating USP43 in cancer development. Additionally, it will investigate potential small-molecule inhibitors that target DUBs that may have the capability to function as anti-cancer medicines.

J-Aggregate Promoting NIR-II Emission for Fluorescence/Photoacoustic Imaging-Guided Phototherapy.

J-aggregate is a promising strategy to enhance second near-infrared window (NIR-II) emission, while the controlled synthesis of J-aggregated NIR-II dyes is a huge challenge because of the lack of molecular design principle. Herein, bulk spiro[fluorene-9,9'-xanthene] functionalized benzobisthiadiazole-based NIR-II dyes (named BSFX-BBT and OSFX-BBT) are synthesized with different alkyl chains. The weak repulsion interaction between the donor and acceptor units and the S…N secondary interactions make the dyes to adopt a co-planar molecular conformation and display a peak absorption >880 nm in solution. Importantly, BSFX-BBT can form a desiring J-aggregate in the condensed state, and femtosecond transient absorption spectra reveal that the excited states of J-aggregate are the radiative states, and J-aggregate can facilitate stimulated emission. Consequently, the J-aggregated nanoparticles (NPs) display a peak emission at 1124 nm with a high relative quantum yield of 0.81%. The efficient NIR-II emission, good photothermal effect, and biocompatibility make the J-aggregated NPs demonstrate efficient antitumor efficacy via fluorescence/photoacoustic imaging-guided phototherapy. The paradigm illustrates that tuning the aggregate states of NIR-II dye via spiro-functionalized strategy is an effective approach to enhance photo-theranostic performance.

Planar-structured thiadiazoloquinoxaline-based NIR-II dye for tumor phototheranostics.

Second near-infrared (NIR-II) fluorescence imaging shows huge application prospects in clinical disease diagnosis and surgical navigation, while it is still a big challenge to exploit high performance NIR-II dyes with long-wavelength absorption and high fluorescence quantum yield. Herein, based on planar π-conjugated donor-acceptor-donor systems, three NIR-II dyes (TP-DBBT, TP-TQ1, and TP-TQ2) were synthesized with bulk steric hindrance, and the influence of acceptor engineering on absorption/emission wavelengths, fluorescence efficiency and photothermal properties was systematically investigated. Compared with TP-DBBT and TP-TQ2, the TP-TQ1 based on 6,7-diphenyl-[1,2,5]thiadiazoloquinoxaline can well balance absorption/emission wavelengths, NIR-II fluorescence brightness and photothermal effects. And the TP-TQ1 nanoparticles (NPs) possess high absorption ability at a peak absorption of 877 nm, with a high relative quantum yield of 0.69% for large steric hindrance hampering the close π-π stacking interactions. Furthermore, the TP-TQ1 NPs show a desirable photothermal conversion efficiency of 48% and good compatibility. In vivo experiments demonstrate that the TP-TQ1 NPs can serve as a versatile theranostic agent for NIR-II fluorescence/photoacoustic imaging-guided tumor phototherapy. The molecular planarization strategy provides an approach for designing efficient NIR-II fluorophores with extending absorption/emission wavelength, high fluorescence brightness, and outstanding phototheranostic performance.

Bioinspired photoelectrochemical NADH regeneration based on a molecular catalyst-modified photocathode.

For photoelectrochemical NADH regeneration, an electrode-supported "lipid bilayer membrane" photocathode based on a p-Si semiconductor, an electron transport mediator (OBV2+), and a [Rh(Cp*)(bpy)Cl]+ catalyst was constructed by self-assembly. Mechanistic study shows that OBV2+ can enhance the charge transfer between the semiconductor and catalyst, leading to a significant improvement of the NADH photo-regeneration rate.

A Wireless Passive Pressure-Sensing Method for Cryogenic Applications Using Magnetoresistors.

In this study, we developed a novel wireless, passive pressure-sensing method functional at cryogenic temperatures (-196 °C). The currently used pressure sensors are inconvenient and complicated in cryogenic environments for their weak low-temperature tolerances and long wires for power supply and data transmission. We propose a novel pressure-sensing method for cryogenic applications by only using low-temperature-tolerant passive devices. By innovatively integrating a magnetoresistor (MR) on a backscattering antenna, the pressure inside a cryogenic environment is transferred to a wirelessly obtainable return loss. Wireless passive measurement is thus achieved using a backscattering method. In the measurement, the pressure causes a relative displacement between the MR and a magnet. The MR's resistance changes with the varied magnetic field, thus modulating the antenna's return loss. The experimental results indicate that our fabricated sensor successfully identified different pressures, with high sensitivities of 4.3 dB/MPa at room temperature (24 °C) and 1.3 dB/MPa at cryogenic temperature (-196 °C). Additionally, our method allows for simultaneous wireless readings of multi sensors via a single reading device by separating the frequency band of each sensor. Our method performs low-cost, simple, robust, passive, and wireless pressure measurement at -196 °C; thus, it is desirable for cryogenic applications.

Single-cell multi-omics analysis of lineage development and spatial organization in the human fetal cerebellum.

Human cerebellum encompasses numerous neurons, exhibiting a distinct developmental paradigm from cerebrum. Here we conducted scRNA-seq, scATAC-seq and spatial transcriptomic analyses of fetal samples from gestational week (GW) 13 to 18 to explore the emergence of cellular diversity and developmental programs in the developing human cerebellum. We identified transitory granule cell progenitors that are conserved across species. Special patterns in both granule cells and Purkinje cells were dissected multidimensionally. Species-specific gene expression patterns of cerebellar lobes were characterized and we found that PARM1 exhibited inconsistent distribution in human and mouse granule cells. A novel cluster of potential neuroepithelium at the rhombic lip was identified. We also resolved various subtypes of Purkinje cells and unipolar brush cells and revealed gene regulatory networks controlling their diversification. Therefore, our study offers a valuable multi-omics landscape of human fetal cerebellum and advances our understanding of development and spatial organization of human cerebellum.

An unsupervised water quality anomaly detection method based on a combination of time-frequency analysis and clustering.

Anomalies in water quality, which frequently arise due to pollution, constitute a substantial menace to human health. The preservation of public welfare critically entails the timely recognition of abnormal water quality. Conventional techniques for detecting water quality anomalies face obstacles such as the necessity of expert knowledge, limited accuracy in detection, and delays in identification. In this paper, we proposed an original unsupervised technique for identifying water quality anomalies combined with time-frequency analysis and clustering (TCAD). We chose time-frequency analysis because it effectively evaluates water quality changes, generating distinct multi-band signals that reflect different aspects of water quality dynamics. We also proposed a clustering technique which can identify water quality markers and amalgamate data from multi-band signals for accurate anomaly detection. We seek to clarify the reasoning behind our methodology by portraying how time-frequency analysis and clustering address the deficiencies of conventional methods. Our experiments evaluated various indicators of water quality, and the effectiveness of our proposed approach was supported by comparative analyses with commonly used models for detecting anomalies in water quality.

2.5D transfer deep learning model for segmentation of contrast-enhancing lesions on brain magnetic resonance imaging of multiple sclerosis and neuromyelitis optica spectrum disorder.

Background: Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are the two mimic autoimmune diseases of the central nervous system, which are rare in East Asia. Quantitative detection of contrast-enhancing lesions (CELs) on contrast-enhancing T1-weighted magnetic resonance (MR) images is of great significance for assessing the disease activity of MS and NMOSD. However, it is challenging to develop automatic segmentation algorithms due to the lack of data. In this work, we present an automatic segmentation model of CELs based on Fully Convolutional with Attention DenseNet (FCA-DenseNet) and transfer learning strategy to address the challenge of CEL quantification in small-scale datasets. Methods: A transfer learning approach was employed in this study, whereby pretraining was conducted using 77 MS subjects from the open access datasets (MICCAI 2016, MICCAI 2017, ISBI 2015) for white matter hyperintensity segmentation, followed by fine-tuning using 24 MS and NMOSD subjects from the local dataset for CEL segmentation. The proposed FCA-DenseNet combined the Fully Convolutional DenseNet and Convolutional Block Attention Module in order to improve the learning capability. A 2.5D data slicing strategy was used to process complex 3D MR images. U-Net, ResUNet, TransUNet, and Attention-UNet are used as comparison models to FCA-DenseNet. Dice similarity coefficient (DSC), positive predictive value (PPV), true positive rate (TPR), and volume difference (VD) are used as evaluation metrics to evaluate the performances of different models. Results: FCA-DenseNet outperforms all other models in terms of all evaluation metrics, with a DSC of 0.661±0.187, PPV of 0.719±0.201, TPR of 0.680±0.254, and VD of 0.388±0.334. Transfer learning strategy has achieved success in building segmentation models on a small-scale local dataset where traditional deep learning approaches fail to train effectively. Conclusions: The improved FCA-DenseNet, combined with transfer learning strategy and 2.5D data slicing strategy, has successfully addressed the challenges in constructing deep learning models on small-scale datasets, making it conducive to clinical quantification of brain CELs and diagnosis of MS and NMOSD.

Fenton-like system of UV/Glucose-oxidase@Kaolin coupled with organic green rust: UV-enhanced enzyme activity and the mechanism of UV synergistic degradation of photosensitive pollutants.

This study introduces the UV/glucose-oxidase@Kaolin (GOD@Kaolin) coupled organic green rust (OGR) system (UV/OGR/GOD@Kaolin) to investigate the promotion of glucose oxidase activity by UV light and its synergistic degradation mechanism for photosensitive pollutants, specifically targeting the efficient degradation of 4-chlorophenol (4-CP). The enzyme system demonstrates its ability to overcome drawbacks associated with traditional Fenton systems, including a narrow pH range and high localized concentration of H2O2, by gradually releasing hydrogen peroxide in situ within a neutral environment. In the presence of UV radiation under specific conditions, enhanced enzyme activity is observed, resulting in increased efficiency in pollutant removal. The gradual release of hydrogen peroxide plays a crucial role in preventing unwanted reactions among active substances. These unique features facilitate the generation of highly reactive species, such as Fe(IV)O, •OH, and •O2-, tailored to efficiently target the organic components of interest. Additionally, the system establishes a positive iron cycle, ensuring a sustained reactive capability throughout the degradation process. The results highlight the UV/OGR/GOD@Kaolin system as an effective and environmentally friendly approach for the degradation of 4-CP, and the resilience of the enzyme extends the system's applicability to a broader range of scenarios.

Risk factor analysis and risk prediction study of obesity in steelworkers: model development based on an occupational health examination cohort dataset.

BACKGROUND: Obesity is increasingly recognized as a grave public health concern globally. It is associated with prevalent diseases including coronary heart disease, fatty liver, type 2 diabetes, and dyslipidemia. Prior research has identified demographic, socioeconomic, lifestyle, and genetic factors as contributors to obesity. Nevertheless, the influence of occupational risk factors on obesity among workers remains under-explored. Investigating risk factors specific to steelworkers is crucial for early detection, prediction, and effective intervention, thereby safeguarding their health. METHODS: This research utilized a cohort study examining health impacts on workers in an iron and steel company in Hebei Province, China. The study involved 5469 participants. By univariate analysis, multifactor analysis, and review of relevant literature, predictor variables were found. Three predictive models-XG Boost, Support Vector Machine (SVM), and Random Forest (RF)-were employed. RESULTS: Univariate analysis and cox proportional hazard regression modeling identified age, gender, smoking and drinking habits, dietary score, physical activity, shift work, exposure to high temperatures, occupational stress, and carbon monoxide exposure as key factors in the development of obesity in steelworkers. Test results indicated accuracies of 0.819, 0.868, and 0.872 for XG Boost, SVM, and RF respectively. Precision rates were 0.571, 0.696, and 0.765, while recall rates were 0.333, 0.592, and 0.481. The models achieved AUCs of 0.849, 0.908, and 0.912, with Brier scores of 0.128, 0.105, and 0.104, log losses of 0.409, 0.349, and 0.345, and calibration-in-the-large of 0.058, 0.054, and 0.051, respectively. Among these, the Random Forest model demonstrated superior performance. CONCLUSIONS: The research indicates that obesity in steelworkers results from a combination of occupational and lifestyle factors. Of the models tested, the Random Forest model exhibited superior predictive ability, highlighting its significant practical application.

Structure, Health Benefits, Mechanisms, and Gut Microbiota of Dendrobium officinale Polysaccharides: A Review.

Dendrobium officinale polysaccharides (DOPs) are important active polysaccharides found in Dendrobium officinale, which is commonly used as a conventional food or herbal medicine and is well known in China. DOPs can influence the composition of the gut microbiota and the degradation capacity of these symbiotic bacteria, which in turn may determine the efficacy of dietary interventions. However, the necessary analysis of the relationship between DOPs and the gut microbiota is lacking. In this review, we summarize the extraction, structure, health benefits, and related mechanisms of DOPs, construct the DOPs-host axis, and propose that DOPs are potential prebiotics, mainly composed of 1,4-ß-D-mannose, 1,4-ß-D-glucose, and O-acetate groups, which induce an increase in the abundance of gut microbiota such as Lactobacillus, Bifidobacterium, Akkermansia, Bacteroides, and Prevotella. In addition, we found that when exposed to DOPs with different structural properties, the gut microbiota may exhibit different diversity and composition and provide health benefits, such as metabolism regulations, inflammation modulation, immunity moderation, and cancer intervention. This may contribute to facilitating the development of functional foods and health products to improve human health.

Deferoxamine attenuates visual impairment in retinal ischemia‒reperfusion via inhibiting ferroptosis.

Retinal ischemia‒reperfusion (I/R) injury can cause significant damage to human retinal neurons, greatly compromising their functions. Existing interventions have been proven to have little effect. Ferroptosis is a newly discovered type of programmed cell death that has been found to be involved in the process of ischemia‒reperfusion in multiple organs throughout the body. Studies have shown that it is also present in retinal ischemia‒reperfusion injury. A rat model of retinal ischemia‒reperfusion injury was constructed and treated with deferoxamine. In this study, we found the accumulation of Fe2+, reactive oxygen species (ROS), malondialdehyde (MDA), and the consumption of glutathione (GSH) via ELISA testing; increased expression of transferrin; and decreased expression of ferritin, SLC7A11, and GPX4 via Western blotting (WB) and real-time PCR testing. Structural signs of ferroptosis (mitochondrial shrinkage) were observed across multiple cell types, including retinal ganglion cells (RGCs), photoreceptor cells, and pigment epithelial cells. Changes in visual function were detected by F-VEP and ERG. The results showed that iron and oxidative stress were increased in the retinal ischemia‒reperfusion injury model, resulting in ferroptosis and tissue damage. Deferoxamine protects the structural and functional soundness of the retina by inhibiting ferroptosis through the simultaneous inhibition of hemochromatosis, the initiation of transferrin, and the degradation of ferritin and activating the antioxidant capacity of the System Xc-GSH-GPX4 pathway.

A nested case-control study of the effects of dust exposure, smoking on COPD in coal workers.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) represents a prevalent ailment, progressively surging within the ranks of coal mine laborers. The current study endeavors to elucidate the effects of dust exposure and smoking on COPD incidence amongst coal mine workers, while concurrently devising preventive strategies for this affliction. METHOD: A nested case-control study was conducted encompassing 1,416 participants aged ≥ 18 years, spanning the duration from (2017-2018) until 2020. A meticulous matching process yielded a cohort of 708 COPD patients, each paired with a control subject, forming a harmonious 1:1 ratio. Multiple logistic regression analysis was employed to scrutinize the associations between smoking, dust exposure with COPD among coal workers. RESULTS: The COPD prevalence within the cohort of coal workers under investigation amounted to 22.66%, with an accompanying incidence density of 0.09/person-year. Following meticulous adjustment for confounding variables, it was discerned that cumulative dust exposure within the range of 47.19 ~ (OR: 1.90, 95% CI: 1.05, 3.44), 101.27 ~ (OR: 1.99, 95% CI: 1.17, 3.39), as well as smoking indices of 72 ~ (OR: 1.85, 95% CI: 1.19, 2.88), 145 ~ (OR: 1.74, 95% CI: 1.17, 2.61), 310 ~ (OR: 1.85, 95% CI: 1.23, 2.77) engender an escalated vulnerability to COPD among coal workers. Furthermore, interaction analysis discerned an absence of both multiplicative and additive interactions between dust exposure, smoking, and COPD occurrence amidst coal workers. CONCLUSION: Dust exposure and smoking were unequivocally identified as precipitating risk factors for COPD incidence within the population of coal workers, albeit devoid of any discernible interaction between these two causal agents.

Modeling Impacts of Urbanization on Winter Boundary Layer Meteorology and Aerosol Pollution in the Central Liaoning City Cluster, China.

The influence of urbanization on the frequent winter aerosol pollution events in Northeast China is not fully understood. The Weather Research and Forecasting Model with Chemistry (WRF-Chem) coupled with urban canopy (UC) models was used to simulate the impact of urbanization on an aerosol pollution process in the Central Liaoning city cluster (CLCC), China. To investigate the main mechanisms of urban expansion and UC on the winter atmospheric environment and the atmospheric diffusion capacity (ADC) in the CLCC, three simulation cases were designed using land-use datasets from different periods and different UC schemes. A comparative analysis of the simulation results showed that the land-use change (LU) and both LU and UC (LUUC) effects lead to higher surface temperature and lower relative humidity and wind speed in the CLCC by decreasing surface albedo, increasing sensible heat flux, and increasing surface roughness, with a spatial distribution similar to the distribution of LU. The thermal effect leads to an increase in atmospheric instability, an increase in boundary layer height and diffusion coefficient, and an increase in the ADC. The LU and LUUC effects lead to a significant decrease in near-surface PM2.5 concentrations in the CLCC due to changes in meteorological conditions and ADC within the boundary layer. The reduction in surface PM2.5 concentrations due to the LU effect is stronger at night than during daytime, while the LUUC effect leads to a greater reduction in surface PM2.5 concentrations during the day, mainly due to stronger diffusion and dilution caused by the effect of urban turbulence within different levels caused by the more complex UC scheme. In this study, the LU and LUUC effects result in greater thermal than dynamic effects, and both have a negative impact on surface PM2.5 concentrations, but redistribute pollutants from the lower urban troposphere to higher altitudes.

MFPred: prediction of ncRNA families based on multi-feature fusion.

Non-coding RNA (ncRNA) plays a critical role in biology. ncRNAs from the same family usually have similar functions, as a result, it is essential to predict ncRNA families before identifying their functions. There are two primary methods for predicting ncRNA families, namely, traditional biological methods and computational methods. In traditional biological methods, a lot of manpower and resources are required to predict ncRNA families. Therefore, this paper proposed a new ncRNA family prediction method called MFPred based on computational methods. MFPred identified ncRNA families by extracting sequence features of ncRNAs, and it possessed three primary modules, including (1) four ncRNA sequences encoding and feature extraction module, which encoded ncRNA sequences and extracted four different features of ncRNA sequences, (2) dynamic Bi_GRU and feature fusion module, which extracted contextual information features of the ncRNA sequence and (3) ResNet_SE module that extracted local information features of the ncRNA sequence. In this study, MFPred was compared with the previously proposed ncRNA family prediction methods using two frequently used public ncRNA datasets, NCY and nRC. The results showed that MFPred outperformed other prediction methods in the two datasets.

Revisiting molecularly conformation-planarized organic dyes for NIR-II fluorescence imaging.

Fluorescence imaging in the second window (NIR-II, 1000-1700 nm) provides deeper penetration depth and higher resolution, but there is still a dilemma for designing NIR-II dyes for simultaneously enhancing fluorescence efficiency and prolonging excitation wavelength. Herein, a molecular conformation planarization strategy has been revisited to guide the synthesis of two donor-acceptor-donor dyes (named T-BBT and BT-BBT). On the one hand, conformational planarization can extend the absorption peaks of T-BBT and BT-BBT to the NIR region with high molar extinction coefficients of 30.5 × 103 and 16.4 × 103 L (mol-1 cm-1) at 1064 nm, respectively. On the other hand, structural rigidity can weaken electronic vibration coupling-related non-radiative decay pathways, whereby both T-BBT and BT-BBT display rather high fluorescence efficiencies of 3.6% and 13.5% in solution. Furthermore, a molecular doping strategy is adopted to alleviate fluorescence quenching in the aggregated state by suppressing long-distance energy migration, and 2.5 wt% doped BT-BBT nanoparticles show a high fluorescence efficiency of 2.0%, which enables the application of in vivo deep NIR-II fluorescence imaging for vessels and tumors with high resolution under 980 nm excitation. This work demonstrates that organic dyes with structural planarization can bridge the gap between NIR-II absorption and fluorescence efficiency.

Discovery of 2,4-diphenyl-substituted thiazole derivatives as PRMT1 inhibitors and investigation of their anti-cervical cancer effects.

Cervical cancer is one of the most common cancers that affects middle-aged women and the discovery of new drugs to aid clinical management is needed. As an important member of the protein arginine methyltransferases (PRMTs) family, PRMT1 catalyzes the methylation of protein arginine, which can influence multiple biological processes of cancer cells, such as activating epithelial-mesenchymal transformation (EMT) and acquiring resistance to apoptosis. Therefore, PRMT1 can be considered as a potential drug target for cervical cancer. In the current study, a new sub-binding pocket was discovered by molecular modeling, and by introducing a third substitute on the thiazole group to occupy this pocket, a series of compounds were designed and synthesized as potential PRMT1 inhibitors. Of these, two compounds (ZJG51 and ZJG58) exhibited significant inhibitory activities against PRMT1 without significantly inhibiting PRMT5. Both ZJG51 and ZJG58 displayed potent inhibitory effects on the proliferation of four cancer-derived cell lines and ZJG51 exerted relative selectivity against the cervical cancer cell line, HeLa. Further studies showed that ZJG51 inhibited migration and induce the apoptosis of HeLa cells. Mechanistically, ZJG51 significantly regulated PRMT1 related proteins, and indicated that the induction of apoptosis and inhibition of migration by ZJG51 may involve the activation of Caspase 9 and the inhibition of EMT, respectively. Molecular dynamic simulation and free energy calculation showed that ZJG51 can bind to PRMT1 stably and the binding mode was predicted. These data indicated that introducing the third substitute on the five-membered ring could be a future direction for structure-based optimization of PRMT1 inhibitors, and ZJG51 could be an important lead compound to inform the design of more potent inhibitors.