ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

Improved GNNs for Log D7.4 Prediction by Transferring Knowledge from Low-Fidelity Data.

The n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4) is an indicator of lipophilicity, and it influences a wide variety of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and druggability of compounds. In log D7.4 prediction, graph neural networks (GNNs) can uncover subtle structure-property relationships (SPRs) by automatically extracting features from molecular graphs that facilitate the learning of SPRs, but their performances are often limited by the small size of available datasets. Herein, we present a transfer learning strategy called pretraining on computational data and then fine-tuning on experimental data (PCFE) to fully exploit the predictive potential of GNNs. PCFE works by pretraining a GNN model on 1.71 million computational log D data (low-fidelity data) and then fine-tuning it on 19,155 experimental log D7.4 data (high-fidelity data). The experiments for three GNN architectures (graph convolutional network (GCN), graph attention network (GAT), and Attentive FP) demonstrated the effectiveness of PCFE in improving GNNs for log D7.4 predictions. Moreover, the optimal PCFE-trained GNN model (cx-Attentive FP, Rtest2 = 0.909) outperformed four excellent descriptor-based models (random forest (RF), gradient boosting (GB), support vector machine (SVM), and extreme gradient boosting (XGBoost)). The robustness of the cx-Attentive FP model was also confirmed by evaluating the models with different training data sizes and dataset splitting strategies. Therefore, we developed a webserver and defined the applicability domain for this model. The webserver (http://tools.scbdd.com/chemlogd/) provides free log D7.4 prediction services. In addition, the important descriptors for log D7.4 were detected by the Shapley additive explanations (SHAP) method, and the most relevant substructures of log D7.4 were identified by the attention mechanism. Finally, the matched molecular pair analysis (MMPA) was performed to summarize the contributions of common chemical substituents to log D7.4, including a variety of hydrocarbon groups, halogen groups, heteroatoms, and polar groups. In conclusion, we believe that the cx-Attentive FP model can serve as a reliable tool to predict log D7.4 and hope that pretraining on low-fidelity data can help GNNs make accurate predictions of other endpoints in drug discovery.

[Interannual variation of soil organic nitrogen fractions and its response to straw returning.] / åœŸå£¤æœ‰æœºæ°®ç»„åˆ†çš„å¹´é™ å˜åŒ–åŠå ¶å¯¹ç§¸ç§†è¿˜ç”°çš„å“åº”.

Elucidating the interannual variation of soil organic nitrogen fractions and its response to straw returning is of great significance for rational regulation of soil organic nitrogen pool and sustainable soil utilization. We conducted a field microcosm experiment with typic hapludoll soil at the National Field Observation and Research Station of Shenyang Agroecosystems. Three treatments were set, including nitrogen fertilizer addition (200 kg N·hm-2, the same in other treatments), nitrogen fertilizer addition with 50% straw return, and nitrogen fertilizer addition with 100% straw return. We classified soil organic nitrogen fractions in the 1st, 3rd, 6th, and 9th years of the experiment by using the Bremner acid hydrolysis method. The results showed that the content of amino acid nitrogen increased with the tillage years, with an increase rate of 39.8% compared with 1st year. The content of hydrolyzable unknown nitrogen increased by 10.8% compared with 1st year, which reached the highest in the 3rd year. The content of total soil nitrogen and other organic nitrogen fractions showed limited variation with tillage years. The proportion of hydrolyzable total nitrogen that is relatively easy to mineralize in the total soil nitrogen gradually increased with the tillage years, and that of relatively stable acid insoluble nitrogen to total soil nitrogen gradually decreased, indicating that soil nitrogen availability increased with the tillage years, which would facilitate the soil nitrogen supply capacity. Compared with the treatment without straw returning, adding straw improved soil total nitrogen and each hydrolyzable nitrogen contents, with such positive effect be stronger under the treatment with heavier straw returning. The effect of straw returning on hydrolyzable nitrogen fractions mainly occurred in the 6th and 9th years. The components of soil total nitrogen that have been increased were mainly the amino acid nitrogen and hydrolyzed unknown nitrogen, resulting in increased proportion of hydrolyzable nitrogen. Straw returning could increase soil nitrogen pool and improve soil nitrogen conservation and supply capacity.

Alpha-fetoprotein-producing hepatoid adenocarcinoma of the lung responsive to sorafenib after multiline treatment: A case report.

BACKGROUND: Hepatoid adenocarcinoma of the lung (HAL) is an extremely rare malignant tumor, and many patients with HAL exhibit high levels of alpha-fetoprotein (AFP) expression. Currently, there is no standardized treatment strategy for advanced HAL and its prognosis is poor. CASE SUMMARY: We report a 55-year-old man with unresectable AFP-related HAL. The largest cross-sectional area of the mass in the upper lobe of the left lung at the beginning of treatment was 8.46 cm × 6.53 cm. The patient's serum AFP level was 9283 ng/mL. The mass increased in size to 8.86 cm × 8.21 cm after two courses of platinum-based combination chemotherapy and immunotherapy, and serum AFP reached its highest level (71232.2 ng/mL). The patient was treated with sorafenib (400 mg twice daily, per os). Forty days later, the mass was reduced to 5.63 cm × 5.29 cm and serum AFP level dropped to 786.8 ng/mL. The patient achieved partial remission for > 9 mo with sorafenib and an excellent biomarker response, as well as survival > 13 mo, which is among the longest reported for unresectable stage IV HAL. CONCLUSION: This is the first report to document successful treatment of unresectable AFP-related HAL with single-agent sorafenib after multiline therapy.

Sodium houttuyfonate protects against cardiac injury by regulating cardiac energy metabolism in diabetic rats.

Diabetic cardiomyopathy is a diabetic complication with complicated pathophysiological changes and pathogenesis and difficult treatment. Sodium houttuyfonate is the adduct of sodium bisulfite and houttuynin, the main volatile component in Houttuynia cordata Thunb, possesses a variety of activities including multiple interventions on inhibiting ventricular remodeling. The study aims to explore effect of sodium houttuyfonate on diabetic myocardial injury and its underlying mechanisms. The diabetes model was established by intraperitoneal injection of streptozotocin at a dose of 85 mg/kg. By intragastric administration for 26 days, sodium houttuyfonate (50 and 100 mg/kg/d) reversed the abnormal serum levels of triglyceride, total cholesterol, low-density lipoprotein cholesterol and low-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio, improved the abnormal levels of serum aspartate aminotransferase and brain natriuretic peptide, reduced electrocardiogram P-R and QRS interval extension, accelerated the heart rate, decreased serum malondialdehyde content, up-regulated the myocardial energy metabolism including elevated the contents of ATP, ADP, total adenine nucleotides and phosphocreatine in myocardium, decreased AMP/ATP ratio, elevated myocardial Ca2+-Mg2+-ATPase activity, and down-regulated the mRNA expressions of AMP protein activation kinase α2 (AMPK-α2) and peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α). In a conclusion, these results suggest that sodium houttuyfonate can improve cardiac energy metabolism disorder caused by diabetes by increasing cardiac Ca2+-Mg2+-ATPase activity and regulating AMPK signaling pathway, and then attenuates cardiac injury caused by hyperglycemia. In addition, sodium houttuyfonate also has the effects of anti-oxidation and improving abnormal levels of blood lipid.

Metastatic urothelial carcinoma harboring ERBB2/3 mutations dramatically respond to chemotherapy plus anti-PD-1 antibody: A case report.

BACKGROUND: Immune checkpoint inhibitors (ICIs) targeting the programmed death (PD)-1 pathway have substantially changed the clinical management of metastatic urothelial carcinoma (mUC); however, the response rate remains low. There are ongoing efforts to identify robust biomarkers that can effectively predict the treatment response to ICIs. Previous studies have suggested that ERBB2/3 mutations are associated with the efficacy of ICIs in gallbladder carcinoma. CASE SUMMARY: We present a 59-year-old man with mUC harboring ERBB2/3 mutations (in-frame insertion of ERBB2 and ERBB3 amplification), negative PD-ligand 1 expression, and low tumor mutation burden. He received anti-PD-1 antibodies and paclitaxel as second-line treatment. After two cycles of treatment, the lung metastases had significantly shrunk, achieving good partial remission. After six cycles of combination therapy, the patient received sindilimab 200 mg once every 3 wk as maintenance monotherapy. At the last follow-up, the patient continued to exhibit a partial response and progression-free survival for as long as 19 mo. CONCLUSION: ERBB2/3 mutations may represent a predictive biomarker for selecting a subgroup of mUC patients who will benefit from ICIs.

ABC-Net: a divide-and-conquer based deep learning architecture for SMILES recognition from molecular images.

Structural information for chemical compounds is often described by pictorial images in most scientific documents, which cannot be easily understood and manipulated by computers. This dilemma makes optical chemical structure recognition (OCSR) an essential tool for automatically mining knowledge from an enormous amount of literature. However, existing OCSR methods fall far short of our expectations for realistic requirements due to their poor recovery accuracy. In this paper, we developed a deep neural network model named ABC-Net (Atom and Bond Center Network) to predict graph structures directly. Based on the divide-and-conquer principle, we propose to model an atom or a bond as a single point in the center. In this way, we can leverage a fully convolutional neural network (CNN) to generate a series of heat-maps to identify these points and predict relevant properties, such as atom types, atom charges, bond types and other properties. Thus, the molecular structure can be recovered by assembling the detected atoms and bonds. Our approach integrates all the detection and property prediction tasks into a single fully CNN, which is scalable and capable of processing molecular images quite efficiently. Experimental results demonstrate that our method could achieve a significant improvement in recognition performance compared with publicly available tools. The proposed method could be considered as a promising solution to OCSR problems and a starting point for the acquisition of molecular information in the literature.

Pushing the Boundaries of Molecular Property Prediction for Drug Discovery with Multitask Learning BERT Enhanced by SMILES Enumeration.

Accurate prediction of pharmacological properties of small molecules is becoming increasingly important in drug discovery. Traditional feature-engineering approaches heavily rely on handcrafted descriptors and/or fingerprints, which need extensive human expert knowledge. With the rapid progress of artificial intelligence technology, data-driven deep learning methods have shown unparalleled advantages over feature-engineering-based methods. However, existing deep learning methods usually suffer from the scarcity of labeled data and the inability to share information between different tasks when applied to predicting molecular properties, thus resulting in poor generalization capability. Here, we proposed a novel multitask learning BERT (Bidirectional Encoder Representations from Transformer) framework, named MTL-BERT, which leverages large-scale pre-training, multitask learning, and SMILES (simplified molecular input line entry specification) enumeration to alleviate the data scarcity problem. MTL-BERT first exploits a large amount of unlabeled data through self-supervised pretraining to mine the rich contextual information in SMILES strings and then fine-tunes the pretrained model for multiple downstream tasks simultaneously by leveraging their shared information. Meanwhile, SMILES enumeration is used as a data enhancement strategy during the pretraining, fine-tuning, and test phases to substantially increase data diversity and help to learn the key relevant patterns from complex SMILES strings. The experimental results showed that the pretrained MTL-BERT model with few additional fine-tuning can achieve much better performance than the state-of-the-art methods on most of the 60 practical molecular datasets. Additionally, the MTL-BERT model leverages attention mechanisms to focus on SMILES character features essential to target properties for model interpretability.

Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules.

Computational methods have become indispensable tools to accelerate the drug discovery process and alleviate the excessive dependence on time-consuming and labor-intensive experiments. Traditional feature-engineering approaches heavily rely on expert knowledge to devise useful features, which could be costly and sometimes biased. The emerging deep learning (DL) methods deliver a data-driven method to automatically learn expressive representations from complex raw data. Inspired by this, researchers have attempted to apply various deep neural network models to simplified molecular input line entry specification (SMILES) strings, which contain all the composition and structure information of molecules. However, current models usually suffer from the scarcity of labeled data. This results in a low generalization ability of SMILES-based DL models, which prevents them from competing with the state-of-the-art computational methods. In this study, we utilized the BiLSTM (bidirectional long short term merory) attention network (BAN) in which we employed a novel multi-step attention mechanism to facilitate the extracting of key features from the SMILES strings. Meanwhile, SMILES enumeration was utilized as a data augmentation method in the training phase to substantially increase the number of labeled data and enlarge the probability of mining more patterns from complex SMILES. We again took advantage of SMILES enumeration in the prediction phase to rectify model prediction bias and provide a more accurate prediction. Combined with the BAN model, our strategies can greatly improve the performance of latent features learned from SMILES strings. In 11 canonical absorption, distribution, metabolism, excretion and toxicity-related tasks, our method outperformed the state-of-the-art approaches.

MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction.

MOTIVATION: Accurate and efficient prediction of molecular properties is one of the fundamental issues in drug design and discovery pipelines. Traditional feature engineering-based approaches require extensive expertise in the feature design and selection process. With the development of artificial intelligence (AI) technologies, data-driven methods exhibit unparalleled advantages over the feature engineering-based methods in various domains. Nevertheless, when applied to molecular property prediction, AI models usually suffer from the scarcity of labeled data and show poor generalization ability. RESULTS: In this study, we proposed molecular graph BERT (MG-BERT), which integrates the local message passing mechanism of graph neural networks (GNNs) into the powerful BERT model to facilitate learning from molecular graphs. Furthermore, an effective self-supervised learning strategy named masked atoms prediction was proposed to pretrain the MG-BERT model on a large amount of unlabeled data to mine context information in molecules. We found the MG-BERT model can generate context-sensitive atomic representations after pretraining and transfer the learned knowledge to the prediction of a variety of molecular properties. The experimental results show that the pretrained MG-BERT model with a little extra fine-tuning can consistently outperform the state-of-the-art methods on all 11 ADMET datasets. Moreover, the MG-BERT model leverages attention mechanisms to focus on atomic features essential to the target property, providing excellent interpretability for the trained model. The MG-BERT model does not require any hand-crafted feature as input and is more reliable due to its excellent interpretability, providing a novel framework to develop state-of-the-art models for a wide range of drug discovery tasks.

Chrysin Improves Glucose and Lipid Metabolism Disorders by Regulating the AMPK/PI3K/AKT Signaling Pathway in Insulin-Resistant HepG2 Cells and HFD/STZ-Induced C57BL/6J Mice.

Natural products with minor side effects have been reported to be an effective adjuvant therapy for glucose and lipid metabolism disorders. Chrysin, a flavone, has a wide range of physiological effects, such as antioxidant, anti-inflammatory, anti-diabetes, anti-hyperlipidemia, and hepatoprotective. This study was designed to explore the effects and mechanism of chrysin on metabolic syndrome using insulin-resistant HepG2 cells and HFD/STZ-induced C57BL/6J mice. The results indicated that chrysin significantly decreased insulin resistance, oxidative stress, inflammation, and liver injury. In addition, chrysin improved glycogen synthesis and fatty acid oxidation and inhibited gluconeogenesis and fatty acid synthesis by regulating GSK3ß, G6Paes, PEPCK, SREBP1, FAS, and ACC1. Furthermore, the results of western blot and real-time PCR experiments demonstrated that chrysin modulated glucose and lipid metabolism through the AMPK/PI3K/AKT signaling pathway. Treatment with the AMPK inhibitor verified that AMPK activation is positively correlated with chrysin activity on glycolipid metabolism. This study confirms that chrysin is a potential treatment for glucose and lipid metabolism disorders.

[Suitable summer habitat of the khulan in the Mt. Kalamaili Ungulate Nature Reserve and estimation of its population]. / 新疆卡拉麦里山有蹄类野生动物自然保护区夏季蒙古野驴适宜生境与种群数量评估.

The khulan (Equus hemionus hemionus) is a representative species in desert, semi-desert and desert steppe in Asia. The estimation of suitable habitat and population density of khulan can provide scientific decision-making basis for the conservation management. We investigated the distribution and population density of khulan in the Mt. Kalamaili Ungulate Nature Reserve in Xinjiang during May-August in 2018 and 2019, using the line transect method. The khulan population density, number and the encounter rate were estimated by the Distance 7.0 software, and through the MaxEnt model analysis and combining environmental variables, we assessed the most suitable habitats and main influencing factors. In total, we recorded 4782 individuals from 718 groups and 363 effective occurrences. Our results showed that the suitable summer habitat covered an area of 6737.5 km2, accounting for 45.4% of the total reserve area and located mainly in the central and eas-tern parts of the reserve. We estimated that the density of the summer khulan population was (0.5±0.1) individuals·km-2 and the total khulan population were (3246±575) individuals in the Mt. Kalamaili Ungulate Nature Reserve. The prediction accuracy of MaxEnt model was relatively high, with an average AUC of 0.890. The results of the Jackknife test showed that the distance to water sources, vegetation type, distance to human interference, distance to national highway G216, altitude, and precipitation of driest areas were the most important factors affecting the habitat suitability for khulan. For improving the protection of the khulan population in this reserve, we suggested strengthen the protection of water sources, adjust the core areas, monitor wildlife passages, and enhance traffic construction.

[Effects of different fertilization managements on microbial necromass and plant lignin accumulation in a Mollisol]. / 不同施肥管理措施对农田土壤中植物和微生物残留组分的影响.

Fertilization is an effective management to maintain and increase soil organic carbon (SOC) level in agroecosystems. Both microbial metabolism and plant component retention control SOC sequestration. Here, we used amino sugars and lignin as biomarkers to investigate the responses of distribution of microbial necromass and plant debris in a long-term cultivated soil (30 years) and SOC accumulation to different fertilization regime. The results showed that, compared with unfertilized treatment, inorganic fertilizer application (N fertilizer-only or the combination of organic or inorganic fertilizers) increased crop production and soil amino sugar accumulation, but did not affect the concentrations of lignin and SOC, indicating that inorganic fertilizer stimulated the assimilation of microbial substrate and accelerated the turnover of SOC and lignin in the plough layer. Compared with inorganic fertilizer treatment, long-term organic fertilizer application promoted SOC accumulation (38.3%), but did not affect amino sugar concentration in SOC, which indicated that soil could reach a 'saturation' state with respect to microbial residue accumulation. In contrast, the application of organic fertilizer increased the proportion of lignin in SOC,indicating that the contribution of plant residues to SOC persistence was enhanced. Compared with the manure-only treatment, organic-inorganic combined application mainly increased the contribution of amino sugar to SOC accumulation. Our findings indicated that long-term fertilization could affect SOC dynamics through modulating the accumulation processes of microbial necromass and plant debris.

Cryopreservation of shoot tips, evaluations of vegetative growth, and assessments of genetic and epigenetic changes in cryo-derived plants of Actinidia spp.

A droplet-vitrification protocol was described for cryopreservation of shoot tips of kiwifruit 'Yuxiang' (Actinidia chinensis var. deliciosa). No significant differences were found in root formation and shoot growth between the in vitro-derived shoots (the control) and cryo-derived ones when cultured in vitro. No significant differences were detected in survival and vegetative growth between the in vitro-derived plants (the control) and cryo-derived ones after re-establishment in greenhouse conditions. Inter-simple sequence repeat (ISSR) and amplified fragment length polymorphism (AFLP) did not detect any polymorphic bands in the cryo-derived shoots when cultured in vitro and the cryo-derived plants after re-establishment in greenhouse conditions. These data indicate rooting ability, vegetative growth and genetic stability are maintained in the cryo-derived kiwifruit plants recovered from the droplet-vitrification cryopreservation. Methylation sensitive amplification polymorphism (MSAP) detected 12.8% and 1.6% DNA methylation in the cryo-derived shoots when cultured in vitro and the cryo-derived plants after re-established in greenhouse conditions, respectively. This droplet-vitrification was applied to five cultivars and three rootstocks belonging to A. chinensis var. deliciosa, A. chinensis var. chinensis, A. macrosperma, A. polygama and A. valvata. The highest (68.3%) and lowest (22.5%) shoot regrowth were obtained in A. macrosperma and A. chinensis var. chinensis 'Jinmi', respectively, with an average of 46.4% shoot regrowth obtained across the eight genotypes. The droplet-vitrification protocol described here can be considered the most applicable cryopreservation method so far reported for the genus Actinidia. Results reported here provide theoretical and technical supports for setting up cryo-banks of genetic resources of Actinidia spp.

Complete response to trastuzumab and chemotherapy in recurrent urothelial bladder carcinoma with HER2 gene amplification: A case report.

BACKGROUND: Targeted treatments may greatly affect the natural history of urothelial carcinoma based on their pharmacokinetics. A phase II trial has explored the combination of cytotoxic chemotherapy with the anti-HER-2 monoclonal antibody trastuzumab in selected patients with metastatic bladder cancer, but it failed. CASE SUMMARY: Here, we report a case of recurrent urothelial bladder carcinoma (UBC) in a patient who has undergone three operations, and further illuminate its diagnosis and treatment. The diagnosis of UBC was rendered according to the pathological indices. Next-generation sequencing on formalin fixed paraffin-embedded (FFPE) tissue was also performed and suggested HER2 gene amplification in the FFPE tissue. Based on HER2 gene amplification in FFPE, the patient was treated with chemotherapy in combination with trastuzumab after his third surgery. Fortunately, the patient got a clinically complete remission to trastuzumab for 34 mo. CONCLUSION: There is not enough clinical evidence for incorporating trastuzumab in routine treatment of UBC. This case hinted that recurrent UBC patients with HER2 gene amplification may benefit from targeted trastuzumab. Further studies are needed to further investigate the status of HER2 gene and better determine trastuzumab in the management of UBC.

Chalcomoracin inhibits cell proliferation and increases sensitivity to radiotherapy in human non-small cell lung cancer cells via inducing endoplasmic reticulum stress-mediated paraptosis.

Chalcomoracin (CMR) is a kind of Diels-Alder adduct extracted from the mulberry leaves. Recent studies showed that CMR has a broad spectrum of anticancer activities and induces paraptosis in breast cancer and prostate cancer cells. In this study, we investigated the effects of CMR against human non-small cell lung cancer cells and the underlying mechanisms. We found that CMR dose-dependently inhibited the proliferation of human lung cancer H460, A549 and PC-9 cells. Furthermore, exposure to low and median doses of CMR induced paraptosis but not apoptosis, which was presented as the formation of extensive cytoplasmic vacuolation with increased expression of endoplasmic reticulum stress markers, Bip and Chop, as well as activation of MAPK pathway in the lung cancer cells. Knockdown of Bip with siRNA not only reduced the cell-killing effect of CMR, but also decreased the percentage of cytoplasmic vacuoles in H460 cells. Moreover, CMR also increased the sensitivity of lung cancer cells to radiotherapy through enhanced endoplasmic reticulum stress. In lung cancer H460 cell xenograft nude mice, combined treatment of CMR and radiation caused greatly enhanced tumor growth inhibition with upregulation of endoplasmic reticulum stress proteins and activation of pErk in xenograft tumor tissue. These data demonstrate that the anticancer activity and radiosensitization effect of CMR result from inducing paraptosis, suggesting that CMR could be considered as a potential anticancer agent and radiation sensitizer in the future cancer therapeutics.

Photo-Induced Charge Transfer on Pt/Bi2WO6 Composite Photocatalysts.

Pt/Bi2WO6 composite photocatalysts were prepared by a facile photoreduction method. Pt nanoparticles with an average size of 5-8 nm were successfully deposited on the surface of Bi2WO6 microspheres and the photocatalytic activity of Bi2WO6 was greatly improved by Pt nanoparticles. The photo-induced charge transfer properties of samples were studied by means of surface photovoltage (SPV) and transient photovoltage (TPV) techniques, giving an insight into the intrinsic reasons of the improvement in photocatalytic activity. The SPV and TPV results revealed that the deposited Pt nanoparticles could trap photo-induced electrons and then largely enhance the separation efficiency of photo-induced charge carriers.

The efficacy and toxicity of chemotherapy in the elderly with advanced pancreatic cancer.

OBJECTIVES: FOLFIRINOX (FFX) or abraxane plus gemcitabine (AG)-based chemotherapy is used widely as firstline treatment for patients with pancreatic cancer. However, their use in the elderly is discouraged because of adverse events. More clinical data about the therapeutic response and tolerability to FFX or AG in elderly patents (over 70 years old) are required. METHODS: Patients with advanced pancreatic cancer (n = 203; 131 metastatic pancreatic cancer patients (MPC) and 72 locally advanced pancreatic cancer patients (LAPC)) were treated using modified-FFX (mFFX) or AG and mFFX sequentially. The patients were grouped according to their age, patients below 70 years old and patients above 70 years old. The objective response rate (ORR), disease control rate (DCR), progression free survival (PFS), overall survival (OS) and adverse events were compared between the groups. RESULTS: The ORRs in the elderly and in patients below 70 were similar (30.0% versus 32.3%). The median OS and PFS were also similar between the groups (mOS 13.3 m vs 12.7 m, p = 0.729, HR 0.874 (95% CI 0.5310 to 1.438); mPFS mPFS 10.6 m vs 10.3 m, p = 0.363, HR 0.800 (95% CI 0.4954 to 1.293)). However, the elderly patients suffered a higher incidence of severe adverse events (50% vs. 28.3%). CONCLUSIONS: These data could provide guidance for chemotherapy use in elderly patients with advanced pancreatic cancer. Age did not affect treatment outcome; however, supportive treatment is very important for elderly patients receiving chemotherapy.

Idiopathic adulthood ductopenia with elevated transaminase only: A case report.

BACKGROUND: Idiopathic adulthood ductopenia (IAD) is a chronic cholestatic liver disease of unknown etiology that usually presents as unexplained jaundice. It is characterized by adult onset, lack of autoantibodies, inflammatory bowel disease and loss of interlobular bile ducts. CASE SUMMARY: This case presents a 27-year-old woman with elevation of transaminases and alkaline phosphatase without clinical symptoms. Five years ago, the patient had abnormal transaminases but no cholestasis. Three months before admission, physical examination revealed an increase in transaminases. Oral hepatoprotective drugs did not show any significant improvement, and she was admitted to hospital for further diagnosis and treatment. Liver biopsy confirmed IAD. After about 2 wk of ursodeoxycholic acid treatment, serological and histological examination showed a significant response. CONCLUSION: IAD is a manifestation of cholestasis, but also may be an abnormal increase in transaminase in the early stage.

Development, progress and future prospects in cryobiotechnology of Lilium spp.

Lilium is one of the most popular flower crops worldwide, and some species are also used as vegetables and medicines. The availability of and easy access to diverse Lilium genetic resources are essential for plant genetic improvements. Cryopreservation is currently considered as an ideal means for the long-term preservation of plant germplasm. Over the last two decades, great efforts have been exerted in studies of Lilium cryopreservation and progress has been made in the successful cryopreservation of pollen, seeds and shoot tips in Lilium. Genes that exist in Lilium, including those that regulate flower shape, color and size, and that are resistant to cold stress and diseases caused by fungi and viruses, provide a rich source of valuable genetic resources for breeding programs to create novel cultivars required by the global floriculture and ornamental markets. Successful cryopreservation of Lilium spp. is a way to preserve these valuable genes. The present study provides updated and comprehensive information about the development of techniques that have advanced Lilium cryopreservation. Further ideas are proposed to better direct future studies on Lilium cryobiotechnology.