A three-dimensional liquid diode for soft, integrated permeable electronics.

Wearable electronics with great breathability enable a comfortable wearing experience and facilitate continuous biosignal monitoring over extended periods1-3. However, current research on permeable electronics is predominantly at the stage of electrode and substrate development, which is far behind practical applications with comprehensive integration with diverse electronic components (for example, circuitry, electronics, encapsulation)4-8. Achieving permeability and multifunctionality in a singular, integrated wearable electronic system remains a formidable challenge. Here we present a general strategy for integrated moisture-permeable wearable electronics based on three-dimensional liquid diode (3D LD) configurations. By constructing spatially heterogeneous wettability, the 3D LD unidirectionally self-pumps the sweat from the skin to the outlet at a maximum flow rate of 11.6 ml cm-2 min-1, 4,000 times greater than the physiological sweat rate during exercise, presenting exceptional skin-friendliness, user comfort and stable signal-reading behaviour even under sweating conditions. A detachable design incorporating a replaceable vapour/sweat-discharging substrate enables the reuse of soft circuitry/electronics, increasing its sustainability and cost-effectiveness. We demonstrated this fundamental technology in both advanced skin-integrated electronics and textile-integrated electronics, highlighting its potential for scalable, user-friendly wearable devices.

HCRNet: high-throughput circRNA-binding event identification from CLIP-seq data using deep temporal convolutional network.

Identifying genome-wide binding events between circular RNAs (circRNAs) and RNA-binding proteins (RBPs) can greatly facilitate our understanding of functional mechanisms within circRNAs. Thanks to the development of cross-linked immunoprecipitation sequencing technology, large amounts of genome-wide circRNA binding event data have accumulated, providing opportunities for designing high-performance computational models to discriminate RBP interaction sites and thus to interpret the biological significance of circRNAs. Unfortunately, there are still no computational models sufficiently flexible to accommodate circRNAs from different data scales and with various degrees of feature representation. Here, we present HCRNet, a novel end-to-end framework for identification of circRNA-RBP binding events. To capture the hierarchical relationships, the multi-source biological information is fused to represent circRNAs, including various natural language sequence features. Furthermore, a deep temporal convolutional network incorporating global expectation pooling was developed to exploit the latent nucleotide dependencies in an exhaustive manner. We benchmarked HCRNet on 37 circRNA datasets and 31 linear RNA datasets to demonstrate the effectiveness of our proposed method. To evaluate further the model's robustness, we performed HCRNet on a full-length dataset containing 740 circRNAs. Results indicate that HCRNet generally outperforms existing methods. In addition, motif analyses were conducted to exhibit the interpretability of HCRNet on circRNAs. All supporting source code and data can be downloaded from https://github.com/yangyn533/HCRNet and https://doi.org/10.6084/m9.figshare.16943722.v1. And the web server of HCRNet is publicly accessible at http://39.104.118.143:5001/.

THPLM: a sequence-based deep learning framework for protein stability changes prediction upon point variations using pretrained protein language model.

MOTIVATION: Quantitative determination of protein thermodynamic stability is a critical step in protein and drug design. Reliable prediction of protein stability changes caused by point variations contributes to developing-related fields. Over the past decades, dozens of structure-based and sequence-based methods have been proposed, showing good prediction performance. Despite the impressive progress, it is necessary to explore wild-type and variant protein representations to address the problem of how to represent the protein stability change in view of global sequence. With the development of structure prediction using learning-based methods, protein language models (PLMs) have shown accurate and high-quality predictions of protein structure. Because PLM captures the atomic-level structural information, it can help to understand how single-point variations cause functional changes. RESULTS: Here, we proposed THPLM, a sequence-based deep learning model for stability change prediction using Meta's ESM-2. With ESM-2 and a simple convolutional neural network, THPLM achieved comparable or even better performance than most methods, including sequence-based and structure-based methods. Furthermore, the experimental results indicate that the PLM's ability to generate representations of sequence can effectively improve the ability of protein function prediction. AVAILABILITY AND IMPLEMENTATION: The source code of THPLM and the testing data can be accessible through the following links: https://github.com/FPPGroup/THPLM.

Comparison Between Drug-Coated Balloon and Stents in Large De Novo Coronary Artery Disease: A Systematic Review and Meta-Analysis of RCT Data.

PURPOSE: Although a number of studies involving small-vessel de novo coronary disease showed clinical benefits of drug-coated balloons (DCB), the role of DCB in large vessel lesions is still unclear. METHODS: We searched main electronic databases for randomized controlled trials (RCTs) comparing DCB with stents for large vessel de novo coronary artery disease. The primary endpoint was major cardiovascular adverse events (MACE), composite cardiovascular death (CD), myocardial infarction (MI), or target lesion revascularization (TLR). RESULTS: This study included 7 RCTs with 770 participants. DCB were associated with a marked risk reduction in MACE [Risk Ratio (RR): 0.48; 95% confidence interval [CI]: 0.24 to 0.97; P = 0.04], TLR (RR: 0.53; 95% CI: 0.25 to 1.14; P = 0.10), and late lumen loss [standard mean difference (SMD): -0.57; 95% CI: -1.09 to -0.05; P = 0.03] as compared with stents. There is no significant difference in MI (RR: 0.58; 95% CI: 0.21 to 1.54; P = 0.27), CD (RR: 0.33; 95% CI: 0.06 to 1.78; P = 0.19), and minimal lumen diameter (SMD: -0.34; 95% CI: -0.72 to 0.05; P = 0.08) between groups. In subgroup analyses, the risk reduction of MACE persisted in patients with chronic coronary syndrome (RR: 0.25; 95% CI: 0.07 to 0.89; P = 0.03), and patients receiving DCB vs. bare metal stent (RR: 0.19; 95% CI: 0.05 to 0.73; P = 0.01). In addition, there was no significant difference between the DCB group and the drug eluting stent group for MACE (RR: 0.69; 95% CI: 0.30 to 1.60; P = 0.38). CONCLUSION: DCB may be an effective therapeutic option in patients with large vessel de novo coronary artery disease.

Anti-Aging in Caenorhabditis elegans of Polysaccharides from Polygonatum cyrtonema Hua.

Polygonatum cyrtonema Hua, the dried rhizome of Polygonum multiflorum from the Liliaceae family, is a widely used medicinal herb with a long history of application. Its main active ingredients are polysaccharides, which have been demonstrated in contemporary studies to effectively delay the aging process. In the present study, homogeneous polysaccharide (PCP-1) was obtained after the purification and isolation of polysaccharides from Polygonatum cyrtonema Hua (PCP). The anti-aging activities of both were compared, and the possible mechanism of action for exerting anti-aging activity was explored using Caenorhabditis elegans (C. elegans). Research has indicated that PCP and PCP-1 exhibit potent anti-oxidant and anti-aging properties. Of particular note is that PCP-1 acts better than PCP. The two were able to prolong the lifespan of nematodes, improve the stress resistance of nematodes, reduce the accumulation of lipofuscin in the intestine, decrease the content of ROS and MDA in the body, increase the activity of the antioxidant enzymes SOD and CAT, promote the nuclear translocation of DAF-16, down-regulate the mRNA levels of the age-1 and daf-2 genes of the IIS pathway in nematodes, and up-regulate the expression of the daf-16, skn-1, sod-3, and hsp-16.2 genes. Based on the aforementioned findings, it is possible that the mechanism by which PCP and PCP-1 exert anti-aging effects may be through negative regulation of the IIS pathway, activation of the transcription factor DAF-16/FOXO, and enhancement of oxidative defenses and stress resistance in nematodes. Overall, the present study illustrated the great potential of polysaccharides from Polygonatum cyrtonema Hua in anti-aging and antioxidant activities. Specifically, PCP-1 demonstrated superior characteristics, which provides a reference for the future development of Polygonatum cyrtonema Hua polysaccharides.

Asymmetric Synthesis of Arboduridine.

The first asymmetric total synthesis of the monoterpenoid indole alkaloid arboduridine has been accomplished. The tricyclic A/B/D ring system was constructed by an enantioselective Michael reaction followed by intramolecular nucleophilic addition. Intramolecular α-amination of a ketone forged the piperidine ring, while a Horner-Wadsworth-Emmons (HWE) reaction was used to form the pyrrolidine ring. A reduction cyclization cascade led to formation of the tetrahydrofuran ring.

Asymmetric Total Synthesis of Alstrostine G Utilizing a Catalytic Asymmetric Desymmetrization Strategy.

A highly effective enantioselective monobenzoylation of 1,3-diols has been developed for the synthesis of 1,1-disubstituted tetrahydro-ß-carbolines. The chemistry has been successfully applied to the asymmetric total synthesis of (+)-alstrostine G, which also features a cascade Heck/hemiamination reaction enabling facile construction of the pivotal pentacyclic core.

Identification of haploinsufficient genes from epigenomic data using deep forest.

Haploinsufficiency, wherein a single allele is not enough to maintain normal functions, can lead to many diseases including cancers and neurodevelopmental disorders. Recently, computational methods for identifying haploinsufficiency have been developed. However, most of those computational methods suffer from study bias, experimental noise and instability, resulting in unsatisfactory identification of haploinsufficient genes. To address those challenges, we propose a deep forest model, called HaForest, to identify haploinsufficient genes. The multiscale scanning is proposed to extract local contextual representations from input features under Linear Discriminant Analysis. After that, the cascade forest structure is applied to obtain the concatenated features directly by integrating decision-tree-based forests. Meanwhile, to exploit the complex dependency structure among haploinsufficient genes, the LightGBM library is embedded into HaForest to reveal the highly expressive features. To validate the effectiveness of our method, we compared it to several computational methods and four deep learning algorithms on five epigenomic data sets. The results reveal that HaForest achieves superior performance over the other algorithms, demonstrating its unique and complementary performance in identifying haploinsufficient genes. The standalone tool is available at https://github.com/yangyn533/HaForest.

iCircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network.

Circular RNAs (circRNAs) are widely expressed in eukaryotes. The genome-wide interactions between circRNAs and RNA-binding proteins (RBPs) can be probed from cross-linking immunoprecipitation with sequencing data. Therefore, computational methods have been developed for identifying RBP binding sites on circRNAs. Unfortunately, those computational methods often suffer from the low discriminative power of feature representations, numerical instability and poor scalability. To address those limitations, we propose a novel computational method called iCircRBP-DHN using deep hierarchical network for discriminating circRNA-RBP binding sites. The network architecture can be regarded as a deep multi-scale residual network followed by bidirectional gated recurrent units (BiGRUs) with the self-attention mechanism, which can simultaneously extract local and global contextual information. Meanwhile, we propose novel encoding schemes by integrating CircRNA2Vec and the K-tuple nucleotide frequency pattern to represent different degrees of nucleotide dependencies. To validate the effectiveness of our proposed iCircRBP-DHN, we compared its performance with other computational methods on 37 circRNAs datasets and 31 linear RNAs datasets, respectively. The experimental results reveal that iCircRBP-DHN can achieve superior performance over those state-of-the-art algorithms. Moreover, we perform motif analysis on circRNAs bound by those different RBPs, demonstrating that our proposed CircRNA2Vec encoding scheme can be promising. The iCircRBP-DHN method is made available at https://github.com/houzl3416/iCircRBP-DHN.

GMHCC: high-throughput analysis of biomolecular data using graph-based multiple hierarchical consensus clustering.

MOTIVATION: Thanks to the development of high-throughput sequencing technologies, massive amounts of various biomolecular data have been accumulated to revolutionize the study of genomics and molecular biology. One of the main challenges in analyzing this biomolecular data is to cluster their subtypes into subpopulations to facilitate subsequent downstream analysis. Recently, many clustering methods have been developed to address the biomolecular data. However, the computational methods often suffer from many limitations such as high dimensionality, data heterogeneity and noise. RESULTS: In our study, we develop a novel Graph-based Multiple Hierarchical Consensus Clustering (GMHCC) method with an unsupervised graph-based feature ranking (FR) and a graph-based linking method to explore the multiple hierarchical information of the underlying partitions of the consensus clustering for multiple types of biomolecular data. Indeed, we first propose to use a graph-based unsupervised FR model to measure each feature by building a graph over pairwise features and then providing each feature with a rank. Subsequently, to maintain the diversity and robustness of basic partitions (BPs), we propose multiple diverse feature subsets to generate several BPs and then explore the hierarchical structures of the multiple BPs by refining the global consensus function. Finally, we develop a new graph-based linking method, which explicitly considers the relationships between clusters to generate the final partition. Experiments on multiple types of biomolecular data including 35 cancer gene expression datasets and eight single-cell RNA-seq datasets validate the effectiveness of our method over several state-of-the-art consensus clustering approaches. Furthermore, differential gene analysis, gene ontology enrichment analysis and KEGG pathway analysis are conducted, providing novel insights into cell developmental lineages and characterization mechanisms. AVAILABILITY AND IMPLEMENTATION: The source code is available at GitHub: https://github.com/yifuLu/GMHCC. The software and the supporting data can be downloaded from: https://figshare.com/articles/software/GMHCC/17111291. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

EDCNN: identification of genome-wide RNA-binding proteins using evolutionary deep convolutional neural network.

MOTIVATION: RNA-binding proteins (RBPs) are a group of proteins associated with RNA regulation and metabolism, and play an essential role in mediating the maturation, transport, localization and translation of RNA. Recently, Genome-wide RNA-binding event detection methods have been developed to predict RBPs. Unfortunately, the existing computational methods usually suffer some limitations, such as high-dimensionality, data sparsity and low model performance. RESULTS: Deep convolution neural network has a useful advantage for solving high-dimensional and sparse data. To improve further the performance of deep convolution neural network, we propose evolutionary deep convolutional neural network (EDCNN) to identify protein-RNA interactions by synergizing evolutionary optimization with gradient descent to enhance deep conventional neural network. In particular, EDCNN combines evolutionary algorithms and different gradient descent models in a complementary algorithm, where the gradient descent and evolution steps can alternately optimize the RNA-binding event search. To validate the performance of EDCNN, an experiment is conducted on two large-scale CLIP-seq datasets, and results reveal that EDCNN provides superior performance to other state-of-the-art methods. Furthermore, time complexity analysis, parameter analysis and motif analysis are conducted to demonstrate the effectiveness of our proposed algorithm from several perspectives. AVAILABILITY AND IMPLEMENTATION: The EDCNN algorithm is available at GitHub: https://github.com/yaweiwang1232/EDCNN. Both the software and the supporting data can be downloaded from: https://figshare.com/articles/software/EDCNN/16803217. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Association between Plasma FGF21 Levels and Body Composition in Patients with Gastric Cancer.

BACKGROUND: Accumulating evidence has suggested that Fibroblast growth factor 21 (FGF21) plays an important role in metabolic diseases. This study aimed to investigate the relationship between plasma FGF21 levels and body composition parameters in gastric cancer (GC) patients. METHODS: This study was cross-sectional based on a prospective cohort of GC patients in a single center. Computer tomography (CT) and bioelectrical impedance analysis (BIA) were used to estimate skeletal muscle and adipose tissue mass. Blood samples were collected and plasma concentrations of FGF21 were measured by ELISA. Spearman's rank correlation test and logistic regression analysis were performed to assess associations between plasma FGF21 levels and these body composition parameters. RESULTS: A total of 66 GC patients were enrolled in this study. Plasma FGF21 levels were significantly higher in women compared with men. The plasma FGF21 levels were positively correlated with fat mass index (FMI), fat mass percentage (FM%), and subcutaneous adipose tissue index (SATI). Furthermore, after adjustment for confounders, the lower plasma FGF21 levels were remain associated with increased odds for low SATI. CONCLUSIONS: Plasma FGF21 levels were positively associated with FMI, FM%, and SATI in GC patients, suggesting a potential mechanistic link between FGF21 and subcutaneous adipose tissue in GC.

Summertime Urban Ammonia Emissions May Be Substantially Underestimated in Beijing, China.

Ammonia (NH3) is critical to the nitrogen cycle and PM2.5 formation, yet a great deal of uncertainty exists in its urban emission quantifications. Model-underestimated NH3 concentrations have been reported for cities, yet few studies have provided an explanation. Here, we explore reasons for severe WRF-Chem model underestimations of NH3 concentrations in Beijing in August 2018, including simulated gas-particle partitioning, meteorology, regional transport, and emissions, using spatially refined (3 km resolution) NH3 emission estimates in the agricultural sector for Beijing-Tianjin-Hebei and in the traffic sector for Beijing. We find that simulated NH3 concentrations are significantly lower than ground-based and satellite observations during August in Beijing, while wintertime underestimations are much more moderate. Further analyses and sensitivity experiments show that such discrepancies cannot be attributed to factors other than biases in NH3 emissions. Using site measurements as constraints, we estimate that both agricultural and non-agricultural NH3 emission totals in Beijing shall increase by ∼5 times to match the observations. Future research should be performed to allocate underestimations to urban fertilizer, power, traffic, or residential sources. Dense and regular urban NH3 observations are necessary to constrain and validate bottom-up inventories and NHx simulation.

In Situ Observations of Halogenated Gases at the Shangdianzi Background Station and Emission Estimates for Northern China.

Halogenated gases include ozone-depleting substances and greenhouse gases, such as chlorofluorocarbons, halons, hydrochlorofluorocarbons, hydrofluorocarbons, and perfluorinated gases. In situ atmospheric observations of major halogenated gases were conducted at the Shangdianzi (SDZ) background station, China, from October 2020 to September 2021 using ODS5-pro, a newly developed measurement system. The measurement time series of 36 halogenated gases showed occasional pollution events, where background conditions represented 25% (CH2Cl2) to 81% (CF3Cl, CFC-13) of the measurements. The annual mean background mole fractions of most species at SDZ were consistent with those obtained at the Mace Head station in Ireland. The background conditions were distinguished from pollution events, and the enhanced mole fractions were used to estimate the emissions of four categories of fluorinated gases (F-gases) from northern China using a tracer ratio method. The CO2-equivalent (CO2-equiv) emission of F-gases from northern China reached 181 ± 18 Tg year-1 during 2020-2021. Among the four categories of F-gases estimated, SF6 accounted for the highest proportion of CO2-equiv emissions (24%), followed by HFC-23 (22%), HFC-125 (17%), HFC-134a (13%), NF3 (10%), CF4 (5.9%), HFC-143a (3.9%), HFC-32 (3.4%), and HFC-152a (0.2%).

Validating HONO as an Intermediate Tracer of the External Cycling of Reactive Nitrogen in the Background Atmosphere.

In the urban atmosphere, nitrogen oxide (NOx═NO + NO2)-related reactions dominate the formation of nitrous acid (HONO). Here, we validated an external cycling route of HONO and NOx, i.e., formation of HONO resulting from precursors other than NOx, in the background atmosphere. A chemical budget closure experiment of HONO and NOx was conducted at a background site on the Tibetan Plateau and provided direct evidence of the external cycling. An external daytime HONO source of 100 pptv h-1 was determined. Both soil emissions and photolysis of nitrate on ambient surfaces constituted likely candidate mechanisms characterizing this external source. The external source dominated the chemical production of NOx with HONO as an intermediate tracer. The OH production was doubled as a result of the external cycling. A high HONO/NOx ratio (0.31 ± 0.06) during the daytime was deduced as a sufficient condition for the external cycling. Literature review suggested the prevalence of high HONO/NOx ratios in various background environments, e.g., polar regions, pristine mountains, and forests. Our analysis validates the prevalence of external cycling in general background atmosphere and highlights the promotional role of external cycling regarding the atmospheric oxidative capacity.

Impact of COVID-19 pandemic on health care workers (HCWs) in Sindh Province of Pakistan.

BACKGROUND: In Pakistan, the COVID-19 outbreak posed a significant challenge for healthcare workers in the country's public hospitals. The HCWs faced several problems in terms of the COVID-19 pandemic. Therefore this study investigated how the COVID-19 pandemic has affected the medical staff at the public hospital in Sindh Province, Pakistan. METHODS: In this study, a qualitative exploratory design was used. Semi-Structure interviews (SSI) were conducted by using an open-indeed questionnaire (OIQ) for data collection. An inductive approach was used for theoretical data analysis. A total of 320 HCWs participated to complete the criteria of the study from 10 different public hospitals. RESULTS: The study result showed the Sindh public hospital's insufficient infrastructure, lack of health protective equipment, shortages of isolation rooms and beds, and emergencies during the COVID-19 pandemic caused HCWs to experience physical and psychological weariness, sleep disturbance, mental stress, and fear of infection. CONCLUSION: The study concluded that public hospitals' insufficient infrastructure, furniture, emergency wards, and safety equipment during the COVID-19 pandemic significantly damaged HCWs' physical and psychological health, generating fear of infection and sleep disturbance. Additionally, Sindh healthcare workers' fear of illness and isolation may impair family connections.

Urolithin A suppresses tumor progression and induces autophagy in gastric cancer via the PI3K/Akt/mTOR pathway.

Urolithin A (UA) is a microbial metabolite of natural polyphenols ellagitannins and ellagic acid with well-established antitumor properties against various malignancies. However, the exact role of UA in gastric cancer (GC) progression remains largely unclear. In the present study, we investigated the effects and potential mechanisms of UA in GC in vitro and in vivo. Our results revealed that UA could suppress GC cell proliferation, inhibit migration and invasion, promote apoptosis, and induce autophagy via the phosphatidylinositol-3-kinase/protein kinase B/mammalian target of rapamycin pathway in vitro. The autophagy inhibitors 3-methyladenine and chloroquine augmented the inhibitory effect of UA on proliferation and promoted apoptosis, implying that UA mediated the cytoprotective role of autophagy. Meanwhile, the in vivo experiments showed that UA effectively suppressed tumor growth, enhanced the therapeutic effects, and alleviated chemotherapy toxicity in xenograft models. Overall, these findings offer novel insights into the role of UA in tumor therapy and suggest that UA may possess potential therapeutic applications for GC.

Structural Insights into the Binding of Red Fluorescent Protein mCherry-Specific Nanobodies.

Red fluorescent proteins (RFPs) have broad applications in life science research, and the manipulation of RFPs using nanobodies can expand their potential uses. However, the structural information available for nanobodies that bind with RFPs is still insufficient. In this study, we cloned, expressed, purified, and crystallized complexes formed by mCherry with LaM1, LaM3, and LaM8. Then, we analyzed the biochemical properties of the complexes using mass spectrometry (MS), fluorescence-detected size exclusion chromatography (FSEC), isothermal titration calorimetry (ITC), and bio-layer interferometry (BLI) technology. We determined the crystal structure of mCherry-LaM1, mCherry-LaM3, and mCherry-LaM8, with resolutions of 2.05 Å, 3.29 Å, and 1.31 Å, respectively. In this study, we systematically compared various parameters of several LaM series nanobodies, including LaM1, LaM3, and LaM8, with previously reported data on LaM2, LaM4, and LaM6, specifically examining their structural information. After designing multivalent tandem LaM1-LaM8 and LaM8-LaM4 nanobodies based on structural information, we characterized their properties, revealing their higher affinity and specificity to mCherry. Our research provides novel structural insights that could aid in understanding nanobodies targeting a specific target protein. This could provide a starting point for developing enhanced mCherry manipulation tools.

Influence of circulation types on temporal and spatial variations of ozone in Beijing.

This study analyzes the impact of circulation types (CTs) on ozone (O3) pollution in Beijing. The easterly high-pressure (SWW) circulation occurred most frequently (30%; 276 day), followed by northwesterly high-pressure (AN) circulation (24.3%; 224 day). The SWW type had the highest O3 anomaly of +17.28 µg/m3, which was caused by excellent photochemical reactions, poor diffusion ability and regional transport. Due to the higher humidity and precipitation in the low-pressure type (C), the O3 increase (+8.02 µg/m3) was less than that in the SWW type. Good diffusion/wet deposition and weak formation ability contributed to O3 decrease in AN (-12.54 µg/m3) and northerly high-pressure (ESN) CTs (-12.26 µg/m3). The intra-area transport of O3 was significant in polluted circulations (SWW- and C-CTs). In addition, higher temperature, radiation and less rainfall also contributed to higher O3 in northern Beijing under the SWW type. For the clean CTs (AN and ESN CTs), precursor amount and intra-area transport played a dominant role in O3 distribution. Under the northeasterly low-pressure CT, better formation conditions and higher precursor amount combined with the intra-area southerly transport to cause higher O3 values in the south than in the north. The higher O3 in the northwestern area under the northeasterly high-pressure type was influenced by weaker titration loss and high O3 concentration in previous day. Annual variation in the CTs contributed up to 86.1% of the annual variation in O3. About 78%-83% of the diurnal variation in O3 resulted from local meteorological factors.

Coupling Chlorin-Based Photosensitizers and Histone Deacetylase Inhibitors for Photodynamic Chemotherapy.

Photodynamic therapy combined with chemotherapy is a promising strategy to improve the antitumor efficacy. On the basis of coupling the chlorin-based photosensitizer pyropheophorbide a (Pyro) and histone deacetylase inhibitors (HDACis) to fabricate dual-mode antitumor molecules, a series of dual-mode antitumor prodrug molecules were synthesized and assessed for antitumor activity in vitro and in vivo. The data demonstrated that compound 4, with the most favorable phototoxicity and dark toxicity, could significantly inhibit the cell migration and upregulate the expression of acetyl-H3 protein, functioning as a photosensitizer and HDACi, respectively. Furthermore, compared with talaporfin, Pyro, and SAHA, compound 4 demonstrated the best inhibitory effect on tumor growth and metastasis in tumor-bearing mice; therefore, represented by compound 4, this pharmacophore coupling strategy is much more promising and effective than the pharmacophore fusion strategy for fabricating photodynamic and chemotherapeutical dual-mode molecules.