PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications.

Protein language models (PLMs) play a dominant role in protein representation learning. Most existing PLMs regard proteins as sequences of 20 natural amino acids. The problem with this representation method is that it simply divides the protein sequence into sequences of individual amino acids, ignoring the fact that certain residues often occur together. Therefore, it is inappropriate to view amino acids as isolated tokens. Instead, the PLMs should recognize the frequently occurring combinations of amino acids as a single token. In this study, we use the byte-pair-encoding algorithm and unigram to construct advanced residue vocabularies for protein sequence tokenization, and we have shown that PLMs pre-trained using these advanced vocabularies exhibit superior performance on downstream tasks when compared to those trained with simple vocabularies. Furthermore, we introduce PETA, a comprehensive benchmark for systematically evaluating PLMs. We find that vocabularies comprising 50 and 200 elements achieve optimal performance. Our code, model weights, and datasets are available at https://github.com/ginnm/ProteinPretraining . SCIENTIFIC CONTRIBUTION: This study introduces advanced protein sequence tokenization analysis, leveraging the byte-pair-encoding algorithm and unigram. By recognizing frequently occurring combinations of amino acids as single tokens, our proposed method enhances the performance of PLMs on downstream tasks. Additionally, we present PETA, a new comprehensive benchmark for the systematic evaluation of PLMs, demonstrating that vocabularies of 50 and 200 elements offer optimal performance.

Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning.

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP's superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering.

LEAP: LLM instruction-example adaptive prompting framework for biomedical relation extraction.

OBJECTIVE: To investigate the demonstration in large language models (LLMs) for biomedical relation extraction. This study introduces a framework comprising three types of adaptive tuning methods to assess their impacts and effectiveness. MATERIALS AND METHODS: Our study was conducted in two phases. Initially, we analyzed a range of demonstration components vital for LLMs' biomedical data capabilities, including task descriptions and examples, experimenting with various combinations. Subsequently, we introduced the LLM instruction-example adaptive prompting (LEAP) framework, including instruction adaptive tuning, example adaptive tuning, and instruction-example adaptive tuning methods. This framework aims to systematically investigate both adaptive task descriptions and adaptive examples within the demonstration. We assessed the performance of the LEAP framework on the DDI, ChemProt, and BioRED datasets, employing LLMs such as Llama2-7b, Llama2-13b, and MedLLaMA_13B. RESULTS: Our findings indicated that Instruction + Options + Example and its expanded form substantially improved F1 scores over the standard Instruction + Options mode for zero-shot LLMs. The LEAP framework, particularly through its example adaptive prompting, demonstrated superior performance over conventional instruction tuning across all models. Notably, the MedLLAMA_13B model achieved an exceptional F1 score of 95.13 on the ChemProt dataset using this method. Significant improvements were also observed in the DDI 2013 and BioRED datasets, confirming the method's robustness in sophisticated data extraction scenarios. CONCLUSION: The LEAP framework offers a compelling strategy for enhancing LLM training strategies, steering away from extensive fine-tuning towards more dynamic and contextually enriched prompting methodologies, showcasing in biomedical relation extraction.

RT: a Retrieving and Chain-of-Thought framework for few-shot medical named entity recognition.

OBJECTIVES: This article aims to enhance the performance of larger language models (LLMs) on the few-shot biomedical named entity recognition (NER) task by developing a simple and effective method called Retrieving and Chain-of-Thought (RT) framework and to evaluate the improvement after applying RT framework. MATERIALS AND METHODS: Given the remarkable advancements in retrieval-based language model and Chain-of-Thought across various natural language processing tasks, we propose a pioneering RT framework designed to amalgamate both approaches. The RT approach encompasses dedicated modules for information retrieval and Chain-of-Thought processes. In the retrieval module, RT discerns pertinent examples from demonstrations during instructional tuning for each input sentence. Subsequently, the Chain-of-Thought module employs a systematic reasoning process to identify entities. We conducted a comprehensive comparative analysis of our RT framework against 16 other models for few-shot NER tasks on BC5CDR and NCBI corpora. Additionally, we explored the impacts of negative samples, output formats, and missing data on performance. RESULTS: Our proposed RT framework outperforms other LMs for few-shot NER tasks with micro-F1 scores of 93.50 and 91.76 on BC5CDR and NCBI corpora, respectively. We found that using both positive and negative samples, Chain-of-Thought (vs Tree-of-Thought) performed better. Additionally, utilization of a partially annotated dataset has a marginal effect of the model performance. DISCUSSION: This is the first investigation to combine a retrieval-based LLM and Chain-of-Thought methodology to enhance the performance in biomedical few-shot NER. The retrieval-based LLM aids in retrieving the most relevant examples of the input sentence, offering crucial knowledge to predict the entity in the sentence. We also conducted a meticulous examination of our methodology, incorporating an ablation study. CONCLUSION: The RT framework with LLM has demonstrated state-of-the-art performance on few-shot NER tasks.

MedChatZH: A tuning LLM for traditional Chinese medicine consultations.

Generative Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, including Question-Answering (QA) and dialogue systems. However, most models are trained on English data and lack strong generalization in providing answers in Chinese. This limitation is especially evident in specialized domains like traditional Chinese medical QA, where performance suffers due to the absence of fine-tuning and high-quality datasets. To address this, we introduce MedChatZH, a dialogue model optimized for Chinese medical QA based on transformer decoder with LLaMA architecture. Continued pre-training on a curated corpus of Chinese medical books is followed by fine-tuning with a carefully selected medical instruction dataset, resulting in MedChatZH outperforming several Chinese dialogue baselines on a real-world medical dialogue dataset. Our model, code, and dataset are publicly available on GitHub (https://github.com/tyang816/MedChatZH) to encourage further research in traditional Chinese medicine and LLMs.

Modeling effects of roadway lighting photometric criteria on nighttime pedestrian crashes on roadway segments.

INTRODUCTION: Nighttime crashes account for 74% of pedestrian fatalities in the United States, and reduced visibility is a significant cause of nighttime pedestrian crashes. Maintaining sufficient and uniform roadway lighting is an effective countermeasure to improve pedestrian visibility and prevent nighttime pedestrian crashes and injuries. Previous studies have not quantified the safety effects of roadway photometric patterns (i.e., average lighting level and uniformity) on nighttime pedestrian crashes on roadway segments. METHOD: This study investigated the association between two roadway photometric criteria (horizontal illuminance mean representing average lighting level and horizontal illuminance standard deviation representing lighting uniformity) and nighttime pedestrian crash occurrence in Florida roadway segments. The matched case-control method was used to decouple the confounding effects between the illuminance mean and standard deviation. Statistically-significant crash modification factors (CMFs) were developed to quantify the safety effects of the mean and standard deviation of horizontal illuminance on nighttime pedestrian crashes. RESULTS: The results show that if the average lighting level on a roadway segment is increased from a low illuminance mean (<0.2 foot-candle [fc]) to a medium illuminance mean [0.2 fc, 0.5 fc], a medium-high illuminance mean (0.5 fc, 1.0 fc], and a high illuminance mean (>1.0 fc), the relative likelihood of nighttime pedestrian crashes on midblock segments in Florida tends to be reduced by 77.5% (CMF = 0.225), 81.2% (CMF = 0.188), and 85.5% (CMF = 0.145), respectively. PRACTICAL APPLICATIONS: A poor uniformity (illuminance standard deviation ≥ 0.52 fc) is likely to increase the relative likelihood of nighttime pedestrian crashes on midblock segments in Florida by 80.3% (CMF = 1.803) compared to good uniformity (illuminance standard deviation < 0.52 fc).

Long-Term Aging Behavior of Plastic/Styrene Butadiene Rubber (SBR) Composite Modified Bitumen.

The reuse of recycled waste plastics has long been attempted in pavement engineering as bitumen modifier. It was revealed that waste plastics can significantly enhance the high-temperature performance of bitumen and bitumen mixtures. Even so, the application of waste plastics as a bitumen modifier is still not widespread. This is attributable to the generally poor low-temperature performance of plastic-modified bitumen, which often fails to meet specification requirements. For this purpose, styrene butadiene rubber (SBR) was selected to improve the low-temperature performance of plastic-modified bitumen. However, due to the long-term aging process, the composite and structure of the modified bitumen will change, which negatively impacts its performance. The objective of this study is to investigate the long-term aging behavior of plastic/SBR composite-modified bitumen. For this purpose, waste polyethylene was used as a plastic modifier and was mixed with base bitumen and 3% SBR at ratios 4.5%, 6% and 7.5%. The rheological properties and molecular weight distribution of base bitumen, plastic and plastic/SBR-modified bitumen before and after long-term aging were measured. Results show that the incorporation of plastic can improve the complex modulus, rutting factor and percent recovery of bitumen and reduce the non-recoverable creep compliance of the bitumen, indicating the modification process enhances the high-temperature performance of bitumen. The enhancement effect is more pronounced with the increase of plastic content. For modified bitumen with 7.5% plastic modifier, the complex modulus of modified bitumen is increased by 1127.55% compared to base bitumen. The addition of 3% SBR modifier can further improve the high-temperature performance of the modified bitumen. In addition, the modification process also increases the large molecule size percentage (LMSP) and weight average molecular weight of bitumen. Compared with weight average molecular weight, the LMSP correlates well with the rheological properties of modified bitumen. In accordance with the complex modulus, using the LMSP and weight average molecular weight of bitumen before and after aging, the corresponding aging index was calculated. The quantitative results showed that the addition of plastic modifier can improve the aging resistance of bitumen, but the enhancement effect is not as obvious as that of SBR modifier.

Valley-dependent vortex emission from exciton-polariton in non-centrosymmetric transition metal dichalcogenide metasurfaces.

Transition metal dichalcogenides (TMDs) have attracted great attention in valleytronics. Owing to the giant valley coherence at room temperature, valley pseudospin of TMDs open a new degree of freedom to encode and process binary information. The valley pseudospin only exists in non-centrosymmetric TMDs (e.g., monolayer or 3R-stacked multilayer), which is prohibited in conventional centrosymmetric 2H-stacked crystals. Here, we propose a general recipe to generate valley-dependent vortex beams by using a mix-dimensional TMD metasurface composed of nanostructured 2H-stacked TMD crystals and monolayer TMDs. Such an ultrathin TMD metasurface involves a momentum-space polarization vortex around bound states in the continuum (BICs), which can simultaneously achieve strong coupling (i.e., form exciton polaritons) and valley-locked vortex emission. Moreover, we report that a full 3R-stacked TMD metasurface can also reveal the strong-coupling regime with an anti-crossing pattern and a Rabi splitting of 95 meV. The Rabi splitting can be precisely controlled by geometrically shaping the TMD metasurface. Our results provide an ultra-compact TMD platform for controlling and structuring valley exciton polariton, in which the valley information is linked with the topological charge of vortex emission, which may advance valleytronic, polaritonic, and optoelectronic applications.

Investigation on the Short-Term Aging Scheme for High Viscosity Modified Bitumen.

Due to the highly viscous characteristics of high viscosity modified bitumen (HVMB), the commonly used short-term aging schemes are not suitable for it. As such, the objective of this study is to introduce a suitable short-term aging scheme for HVMB by increasing the aging period and temperature. For this purpose, two kinds of commercial HVMB were aged via rolling thin-film oven test (RTFOT) and thin-film oven test (TFOT) at different aging periods and temperatures. At the same time, open-graded friction course (OGFC) mixtures prepared using HVMB were also aged via two aging schemes to simulate the short-term aging of bitumen at the mixing plant. With the aid of temperature sweep, frequency sweep, and multiple stress creep recovery tests, the rheological properties of short-term aged bitumen and the extracted bitumen were tested. By comparing the rheological properties of TFOT- and RTFOT-aged bitumen with those of extracted bitumen, suitable laboratory short-term aging schemes for HVMB were determined. Comparative results showed that aging the OGFC mixture in a 175 °C forced-draft oven for 2 h is suitable to simulate the short-term aging process of bitumen at the mixing plant. Compared with RTOFT, TFOT was more preferable for HVMB. Additionally, the recommended aging period and temperature for TFOT was 5 h and 178 °C, respectively.

Combating the COVID-19 infodemic using Prompt-Based curriculum learning.

The COVID-19 pandemic has been accompanied by a proliferation of online misinformation and disinformation about the virus. Combating this 'infodemic' has been identified as one of the top priorities of the World Health Organization, because false and misleading information can lead to a range of negative consequences, including the spread of false remedies, conspiracy theories, and xenophobia. This paper aims to combat the COVID-19 infodemic on multiple fronts, including determining the credibility of information, identifying its potential harm to society, and the necessity of intervention by relevant organizations. We present a prompt-based curriculum learning method to achieve this goal. The proposed method could overcome the challenges of data sparsity and class imbalance issues. Using online social media texts as input, the proposed model can verify content from multiple perspectives by answering a series of questions concerning the text's reliability. Experiments revealed the effectiveness of prompt tuning and curriculum learning in assessing the reliability of COVID-19-related text. The proposed method outperforms typical text classification methods, including fastText and BERT. In addition, the proposed method is robust to the hyperparameter settings, making it more applicable with limited infrastructure resources.

Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach.

The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models.

The effect of Er3+ concentration on the kinetics of multiband upconversion in NaYF4:Yb/Er microcrystals.

In Yb-Er co-doped upconversion (UC) nanomaterials, upconversion luminescence (UCL) can be modulated to generate multiband UCL emissions by changing the concentration of activator Er3+. Nonetheless, the effect of the Er3+ concentrations on the kinetics of these emissions is still unknown. We here study the single ß-NaYF4:Yb3+/Er3+ microcrystal (MC) doped with different Er3+ concentrations by nanosecond time-resolved spectroscopy. Interestingly, different Er3+ doping concentrations exhibit different UCL emission bands and UCL response rates. At low Er3+ doping concentrations (1 mol%), multiband emission in ß-NaYF4:Yb3+/Er3+ (20/1 mol%) MCs could not be observed and the response rate of UCL was slow (5-10 µs) in ß-NaYF4:Yb3+/Er3+. Increasing the Er3+ doping concentration to 10 mol% can shorten the distance between Yb3+ ions and Er3+ ions, which promotes the energy transfer between them. ß-NaYF4:Yb3+/Er3+ (20/10 mol%) can achieve obvious multiband UCL and a quick response rate (0.3 µs). However, a further increase in the Er doping concentration (80 mol%) makes MCs limited by the CR process and cannot achieve the four-photon UC process (4F5/2 → 2K13/2 and 2H9/2 → 2D5/2). Therefore, the result shows that changing the Er3+ doping concentration could control the energy flow between the different energy levels in Er3+, which could affect the response time and UCL emission of the Yb/Er doped rare earth materials. Our work can facilitate the development of fast-response optoelectronics, optical-sensing, and display industries.

SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering.

Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (< 50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.

LLM Instruction-Example Adaptive Prompting (LEAP) Framework for Clinical Relation Extraction.

Objective: To investigate the demonstration in Large Language Models (LLMs) for clinical relation extraction. We focus on examining two types of adaptive demonstration: instruction adaptive prompting, and example adaptive prompting to understand their impacts and effectiveness. Materials and Methods: The study unfolds in two stages. Initially, we explored a range of demonstration components vital to LLMs' clinical data extraction, such as task descriptions and examples, and tested their combinations. Subsequently, we introduced the Instruction-Example Adaptive Prompting (LEAP) Framework, a system that integrates two types of adaptive prompts: one preceding instruction and another before examples. This framework is designed to systematically explore both adaptive task description and adaptive examples within the demonstration. We evaluated LEAP framework's performance on the DDI and BC5CDR chemical interaction datasets, applying it across LLMs such as Llama2-7b, Llama2-13b, and MedLLaMA_13B. Results: The study revealed that Instruction + Options + Examples and its expanded form substantially raised F1-scores over the standard Instruction + Options mode. LEAP framework excelled, especially with example adaptive prompting that outdid traditional instruction tuning across models. Notably, the MedLLAMA-13b model scored an impressive 95.13 F1 on the BC5CDR dataset with this method. Significant improvements were also seen in the DDI 2013 dataset, confirming the method's robustness in sophisticated data extraction. Conclusion: The LEAP framework presents a promising avenue for refining LLM training strategies, steering away from extensive finetuning towards more contextually rich and dynamic prompting methodologies.

One LLM is not Enough: Harnessing the Power of Ensemble Learning for Medical Question Answering.

Objective: To enhance the accuracy and reliability of diverse medical question-answering (QA) tasks and investigate efficient approaches deploying the Large Language Models (LLM) technologies, We developed a novel ensemble learning pipeline by utilizing state-of-the-art LLMs, focusing on improving performance on diverse medical QA datasets. Materials and Methods: Our study employs three medical QA datasets: PubMedQA, MedQA-USMLE, and MedMCQA, each presenting unique challenges in biomedical question-answering. The proposed LLM-Synergy framework, focusing exclusively on zero-shot cases using LLMs, incorporates two primary ensemble methods. The first is a Boosting-based weighted majority vote ensemble, where decision-making is expedited and refined by assigning variable weights to different LLMs through a boosting algorithm. The second method is Cluster-based Dynamic Model Selection, which dynamically selects the most suitable LLM votes for each query, based on the characteristics of question contexts, using a clustering approach. Results: The Majority Weighted Vote and Dynamic Model Selection methods demonstrate superior performance compared to individual LLMs across three medical QA datasets. Specifically, the accuracies are 35.84%, 96.21%, and 37.26% for MedMCQA, PubMedQA, and MedQA-USMLE, respectively, with the Majority Weighted Vote. Correspondingly, the Dynamic Model Selection yields slightly higher accuracies of 38.01%, 96.36%, and 38.13%. Conclusion: The LLM-Synergy framework with two ensemble methods, represents a significant advancement in leveraging LLMs for medical QA tasks and provides an innovative way of efficiently utilizing the development with LLM Technologies, customing for both existing and potentially future challenge tasks in biomedical and health informatics research.

Research Progress of p-Type Oxide Thin-Film Transistors.

The development of transparent electronics has advanced metal-oxide-semiconductor Thin-Film transistor (TFT) technology. In the field of flat-panel displays, as basic units, TFTs play an important role in achieving high speed, brightness, and screen contrast ratio to display information by controlling liquid crystal pixel dots. Oxide TFTs have gradually replaced silicon-based TFTs owing to their field-effect mobility, stability, and responsiveness. In the market, n-type oxide TFTs have been widely used, and their preparation methods have been gradually refined; however, p-Type oxide TFTs with the same properties are difficult to obtain. Fabricating p-Type oxide TFTs with the same performance as n-type oxide TFTs can ensure more energy-efficient complementary electronics and better transparent display applications. This paper summarizes the basic understanding of the structure and performance of the p-Type oxide TFTs, expounding the research progress and challenges of oxide transistors. The microstructures of the three types of p-Type oxides and significant efforts to improve the performance of oxide TFTs are highlighted. Finally, the latest progress and prospects of oxide TFTs based on p-Type oxide semiconductors and other p-Type semiconductor electronic devices are discussed.

Spectrum-Effect Relationship Between Antioxidant and Anti-inflammatory Effects of Banxia Baizhu Tianma Decoction: An Identification Method of Active Substances With Endothelial Cell Protective Effect.

Banxia Baizhu Tianma decoction (BBTD), a six-herb Chinese medicine formula first described approximately 1732 AD, is commonly prescribed for Hypertension with Phlegm-dampness Stagnation (HPDS) as an adjuvant therapy in China. Obesity is an important risk factor for the increasing prevalence of hypertension year by year in China. In Traditional Chinese medicine, obesity is often differentiated as the syndrome of excessive phlegm-dampness.Vascular endothelial cell injury plays an important role in the development and occurrence of HPDS. In this study, the protective effects of 18 batches of BBTD samples from different origins on HUVEC cells were evaluated, including antioxidant and anti-inflammatory activities. Ultrahigh performance liquid chromatography (UPLC) was used to establish fingerprints, and combined with pharmacodynamic indexes, the protective components of BBTD on endothelial cells were analyzed. Antioxidant and anti-inflammatory activities were evaluated by ROS and Hs-CRP models, respectively. Hierarchical cluster analysis (HCA) and Bivariate correlation analysis (BCA) were used to investigate the potential correlation between chemical components and endothelial cell protection. The results indicated that BBTD could reduce ROS and hs-CRP levels in HUVEC cells, and the pharmacological activities in 18 batches of BBTD samples were significantly different. The results of BCA indicated that Gastrodin, Liquiritin, Hesperidin, Isoliquiritin, Hesperetin, and Isoliquiritigenin might be the active constituents to activate ROS and suppress hs-CRP as determined by spectrum-effect relationships. The antioxidant and anti-inflammatory activities of the 6 components at different concentration were verified, and the results showed that all of them had good antioxidant and anti-inflammatory activities in a concentration-dependent manner. This study showed that activity determination and spectral correlation can be used to search for active substances in Chinese medicine formula and provide data support for quality control of Traditional Chinese medicine (TCM).

Single-domain antibody screening by isPLA-seq.

Single-domain antibody (sdAb) holds the promising strategies for diverse research and translational applications. Here, we describe a method for the adaptation of the in situ proximity ligation assay (isPLA) followed by sequencing (isPLA-seq) to facilitate screening of a high-sensitive, high-throughput sdAb library for a given protein at subcellular and single-cell resolution. Based on the sequence of complementarity-determining region 3 (CDR3), the recombinant sdAb can be produced for in vitro and in vivo utilities. This method provides a general means to identify the functional measure of sdAb and its complementary epitopes and its potential applications to investigate cellular processes.

Autophagic flux in cancer cells at the invasive front in the tumor-stroma border.

Cancer cells at the invasive front directly interact with stromal tissue that provides a microenvironment with mechanical, nutrient, and oxygen supply characteristics distinct from those of intratumoral tissues. It has long been known that cancer cells at the invasive front and cancer cells inside the tumor body exhibit highly differentiated functions and behaviors. However, it is unknown whether cancer cells at different locations exhibit a variety of autophagic flux, an important catabolic process to maintain cellular homeostasis in response to environmental changes. Here, using transmission electron microscopy (TEM), we found that invading cancer cells at the invasive front, which show mesenchymal transcriptomic traits, exhibit higher autophagic flux than cancer cells inside the tumor body in human primary non-small cell lung cancer (NSCLC) tissues. This autophagic feature was further confirmed by a live cell autophagic flux monitoring system combined with a 3D organotypic invasion coculture system. Additionally, the increased autophagic flux endows cancer cells with invasive behavior and positively correlates with the advanced tumor stages and the reduced survival period of lung cancer patients. These findings expand the understanding of autophagic dynamics during cancer invasion.

Nesfatin-1 protects H9c2 cardiomyocytes against cobalt chloride-induced hypoxic injury by modulating the MAPK and Notch1 signaling pathways.

BACKGROUND: This study aimed to explore the effect of nesfatin-1 on cobalt chloride (CoCl2)-induced hypoxic injury in cardiomyocyte H9c2 cells. METHODS: H9c2 cardiomyocytes were induced by different concentrations of CoCl2 to mimic the hypoxia condition. Cell viability was detected by MTT assay. Cell apoptosis was detected by TUNEL staining and flow cytometry. ROS production was detected using the fluorescence probe DCFH-DA. The mitochondrial membrane potential (MMP) was detected using the TMRE method. The levels of released lactate dehydrogenase (LDH), malondialdehyde (MDA), superoxide dismutase (SOD), glutathione (GSH), and catalase (CAT) were detected using the commercial kits. The protein levels of MAPK signaling members (p-JNK1/2, p-ERK1/2, and p-p38) and Notch1 signaling members (Notch1, Hes 1, and Jagged 1) were detected by Western blot. RESULTS: CoCl2 significantly promoted cell apoptosis, increased LDH leakage, MDA concentration, and decreased cell viability, SOD activity, GSH production, and CAT activity. CoCl2-induced hypoxic injury in H9c2 cells was partially restored by nesfatin-1 treatment. Moreover, nesfatin-1 treatment attenuated CoCl2-induced increase in ROS production and mitochondrial dysfunction, decreased mitochondrial membrane potential, Bax/Bcl-2 imbalance, as well as c-caspase-9 and c-caspase-3 levels. Moreover, nesfatin-1 treatment inhibited the activation of MAPK and Notch1 signaling pathways. CONCLUSIONS: Nesfatin-1 could effectively protect H9c2 cells against CoCl2-induced hypoxic injury by blocking MAPK and Notch1 signaling pathways, suggesting that nesfatin-1 might be a promising therapeutic agent for hypoxic cardiac injury.