Recent Progress Research Articles

Physicochemical Stimulus-Responsive Systems Targeted with Antibody Derivatives.

The application of monoclonal antibodies and antibody fragments with the advent of recombinant antibody technology has made notable progress in clinical trials to provide a regulated drug release and extra targeting to the special conditions in the function site. Modification of antibodies has facilitated using mAbs and antibody fragments in numerous models of therapeutic and detection utilizations, such as stimuli-responsive systems. Antibodies and antibody derivatives conjugated with diverse stimuli-responsive materials have been constructed for drug delivery in response to a wide range of endogenous (electric, magnetic, light, radiation, ultrasound) and exogenous (temperature, pH, redox potential, enzymes) stimuli. In this report, we highlighted the recent progress on antibody-conjugated stimuli-responsive and dual/multi-responsive systems that affect modern medicine by improving a multitude of diagnostic and treatment strategies.

Targeting Histamine and Histamine Receptors for Memory Regulation: An Emotional Perspective.

Histamine has long been accepted as a pro-cognitive agent. However, lines of evidence have suggested that the roles of histamine in learning and memory processes are much more complex than previously thought. When explained by the spatial perspectives, there are many contradictory results. However, using emotional memory perspectives, we suspect that the histaminergic system may interplay with stress, reward inhibition, and attention to modulate emotional memory formation. The functional diversity of histamine makes it a viable target for clinical management of neuropsychiatric disorders. Here, we update the current knowledge about the functions of histamine in emotional memory and summarize the underlying molecular and neural circuit mechanisms. Finally, we review the main clinical studies about the impacts of histamine-related compounds on memory and discuss insights into future research on the roles of histamine in emotional memory. Despite the recent progress in histamine research, the histaminergic emotional memory circuits are poorly understood, and it is also worth verifying the functions of histamine receptors in a more spatiotemporally specific manner.

The Cutting-edge of CRISPR for Cancer Treatment and its Future Prospects.

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) is a versatile technology that allows precise modification of genes. One of its most promising applications is in cancer treatment. By targeting and editing specific genes involved in cancer development and progression, CRISPR has the potential to become a powerful tool in the fight against cancer. This review aims to assess the recent progress in CRISPR technology for cancer research and to examine the obstacles and potential strategies to address them. The two most commonly used CRISPR systems for gene editing are CRISPR/Cas9 and CRISPR/Cas12a. CRISPR/Cas9 employs different repairing systems, including homologous recombination (HR) and nonhomologous end joining (NHEJ), to introduce precise modifications to the target genes. However, off-target effects and low editing efficiency are some of the main challenges associated with this technology. To overcome these issues, researchers are exploring new delivery methods and developing CRISPR/Cas systems with improved specificity. Moreover, there are ethical concerns surrounding using CRISPR in gene editing, including the potential for unintended consequences and the creation of genetically modified organisms. It is important to address these issues through rigorous testing and strict regulations. Despite these challenges, the potential benefits of CRISPR in cancer therapy cannot be overlooked. By introducing precise modifications to cancer cells, CRISPR could offer a targeted and effective treatment option for patients with different types of cancer. Further investigation and development of CRISPR technology are necessary to overcome the existing challenges and harness its full potential in cancer therapy.

Towards Inductive and Efficient Explanations for Graph Neural Networks.

Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging and nascent problem. The leading method mainly considers the local explanations, i.e., important subgraph structure and node features, to interpret why a GNN model makes the prediction for a single instance, e.g. a node or a graph. As a result, the explanation generated is painstakingly customized at the instance level. The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to the lack of generalizability and hindering it from being used in the inductive setting. Besides, training the explanation model explaining for each instance is time-consuming for large-scale real-life datasets. In this study, we address these key challenges and propose PGExplainer, a parameterized explainer for GNNs. PGExplainer adopts a deep neural network to parameterize the generation process of explanations, which renders PGExplainer a natural approach to multi-instance explanations. Compared to the existing work, PGExplainer has better generalization ability and can be utilized in an inductive setting without training the model for new instances. Thus, PGExplainer is much more efficient than the leading method with significant speed-up. In addition, the explanation networks can also be utilized as a regularizer to improve the generalization power of existing GNNs when jointly trained with downstream tasks. Experiments on both synthetic and real-life datasets show highly competitive performance with up to 24.7% relative improvement in AUC on explaining graph classification over the leading baseline.

The paradigm and recent progress in studying marine mercury isotopes

The paradigm and recent progress in studying marine mercury isotopes

Memory Reflections of the Microbiota-Gut and Oligodendrocyte Axis.

Memory is the persisting consequence of cognitive activities instigated by and engrossed on exterior information from the environment and commenced by an intensive on internal mental representations. Establishing a gut-brain axis (GBA) in health and disease has recently brought the gut, the main portal of communication with the external environment, to the forefront of this interaction. Dietary stimuli have long been linked to brain development, behavioral responses, and memory reflections. Vagus nerve, immune system, bacterial metabolites and products are just a few of the linkages that make up the GBA, a bidirectional arrangement of signaling pathways that connects the neurological system with the gastrointestinal tract. GBA involves two-way communication between central and enteric neural systems, connecting the brain's affective and cognitive regions to peripheral activities of the intestine. Recent scientific progress has highlighted the significance of gut microbiota in affecting these relationships. By controlling myelination at the prefrontal cortex, a crucial area for multifaceted cognitive behavior forecast and decision-making, this axis influences social behavior, including memory reflections. Humans may experience late myelination of the prefrontal cortex's axonal projections into the third decade of life, making it vulnerable to outside factors like microbial metabolites. It has been demonstrated that changes in the gut microbiome can change the microbial metabolome's composition, impacting highly permeable bioactive chemicals like p-cresol that may hinder oligodendrocyte differentiation. This review will discuss the memory reflections of the microbiota-gut and oligodendrocyte axis. Adopting this concept should encourage a new arena of thinking that recognizes the intricate central and periphery dynamics influencing behavior and uses that knowledge to develop novel therapies and interventions for maladjusted memory and learning systems.

Color Transfer for Images: A Survey

High-quality image generation is an important topic in digital visualization. As a sub-topic of the research, color transfer is to produce a high-quality image with ideal color scheme learned from the reference one. In this article, we investigate the mainstream methods of color transfer to provide a survey that introduces the related theories and frameworks. Such methods can be divided into three categories: statistical color transfer, semantic-based color transfer, and color transfer for special target. For these mainstream technical routes, we discuss the related research background, technical details, and representative methods. We also exhibit some new trends of the topic according to recent progress. Based on the comparisons, we discuss the unsolved issues of color transfer and potential solutions in future work.

Early Detection of Colorectal Cancer: Conventional Techniques and Current Biomarkers

Abstract: Colorectal cancer (CRC) is the third most common cancer worldwide, and the incidence of CRC seems to increase gradually. The survival of CRC varies in different countries, attributed to the screening program. Generally, diagnostic approaches for CRC can be divided into visual detection methods and laboratory methods. Colonoscopy, sigmoidoscopy, and computed tomography colonography are considered visual methods widely used in cancer detection. Although visual methods provide some benefits, some disadvantages such as late detection, are present, making them useless in rapidly progressing CRC patients. On the other hand, laboratory tests are developed to compensate for the disadvantages of visual methods. More recent progression in laboratory tests makes them able to superfine detection of CRC. For instance, molecular and genetic methods based on the components of cancer cells, like nucleic acid and proteins, can prognosticate further cancer development in susceptible patients. Alongside new therapeutic approaches developed within decades, the number of CRC detection methods has increased, which aims to reduce the duration between cancer initiation and detection. This review sought to survey the CRC detection methods, including conventional and recently-developed methods, to provide better insight into CRC screening.

A Review on the Deep Learning-based Surface Reconstruction from the Point Clouds

Background: Point cloud has become one of the most important data formats for 3D presentation because of the increased availability of acquisition devices and its wide applications. Deep learning has the most powerful ability to capture features from data and has successfully solved various problems in the field of image, such as classification, segmentation, and generation. Deep learning is commonly used to process data with a structured grid, while point cloud is irregular and unstructured. The irregularity of point clouds makes it difficult to use deep learning to solve the problems represented by point clouds. Recently, numerous approaches have been proposed to process point clouds with deep learning to solve various problems. Objective: The objective of this study is to serve as a guide to new scholars in the field of deep learning on 3D surface reconstruction from point clouds as it presents the recent progress in deep learning-based surface reconstruction for point clouds. It helps scholars to grasp the current research situation better and further explore the search direction. Method: This study reviews the recent progress in deep learning-based methods used for surface reconstruction from point clouds and large-scale 3D point cloud benchmark datasets commonly used. Results: Several relevant articles on deep learning used for surface reconstruction from point clouds and some recent patents on deep learning applications are collected and reviewed in this paper. The difficulty of irregularity of point clouds can be overcome by deep learning methods, thus achieving remarkable progress in surface reconstruction. Conclusion: Deep learning for 3D surface reconstruction from point clouds is becoming a research hotspot due to its performance in terms of anti-interference and generalization. Although the advance is remarkable, there are still some challenges that need to be further studied.

Learning Robust Shape Regularization for Generalizable Medical Image Segmentation.

Generalizable medical image segmentation enables models to generalize to unseen target domains under domain shift issues. Recent progress demonstrates that the shape of the segmentation objective, with its high consistency and robustness across domains, can serve as a reliable regularization to aid the model for better cross-domain performance, where existing methods typically seek a shared framework to render segmentation maps and shape prior concurrently. However, due to the inherent texture and style preference of modern deep neural networks, the edge or silhouette of the extracted shape will inevitably be undermined by those domain-specific texture and style interferences of medical images under domain shifts. To address this limitation, we devise a novel framework with a separation between the shape regularization and the segmentation map. Specifically, we first customize a novel whitening transform-based probabilistic shape regularization extractor namely WT-PSE to suppress undesirable domain-specific texture and style interferences, leading to more robust and high-quality shape representations. Second, we deliver a Wasserstein distance-guided knowledge distillation scheme to help the WT-PSE to achieve more flexible shape extraction during the inference phase. Finally, by incorporating domain knowledge of medical images, we propose a novel instance-domain whitening transform method to facilitate a more stable training process with improved performance. Experiments demonstrate the performance of our proposed method on both multi-domain and single-domain generalization.

Recent progress in atomically precise Ag/Cu-based hydride clusters

Recent progress in atomically precise Ag/Cu-based hydride clusters

The Discovery and Development of Glucagon-Like Peptide-1 Receptor Agonists.

Diabetes mellitus has become a serious life-threatening disease. As one of the new drugs for the treatment of diabetes, GLP-1 receptor agonists have attracted a lot of attention. Compared with traditional hypoglycemic drugs, GLP-1 receptor agonists have good safety and tolerability. To a certain extent, they overcome the problem of the short half-life of natural GLP-1 in vivo and can exist stably in patients for a long time, achieving good results in the treatment of diabetes, as well as improving the symptoms of some complications. The GLP-1 receptor agonists in the market are all peptide drugs. Compared with peptide drugs, small molecule agonists have the advantages of low cost and oral administration. In this article, we review the recent research progress of GLP-1 receptor agonists.

A Review on the Development of Novel Heterocycles as α-Glucosidase Inhibitors for the Treatment of Type-2 Diabetes Mellitus.

One of the most effective therapeutic decencies in the treatment of Type 2 Diabetes Mellitus is the inhibition of α-glucosidase enzyme, which is present at the brush border of the intestine and plays an important role in carbohydrate digestion to form mono-, di-, and polysaccharides. Acarbose, Voglibose, Miglitol, and Erniglitate have been well-known α-glucosidase inhibitors in science since 1990. However, the long synthetic route and side effects of these inhibitors forced the researchers to move their focus to innovate simple and small heterocyclic scaffolds that work as excellent α-glucosidase inhibitors. Moreover, they are also effective against the postprandial hyperglycemic condition in Type 2 Diabetes Mellitus. In this aspect, this review summarizes recent progress in the discovery and development of heterocyclic molecules that have been appraised to show outstanding inhibition of α-glucosidase to yield positive effects against diabetes.

Application of quantum dot light-emitting diode harvest by quantum dot solar cells

Recent progress in photovoltaic (PV) cell usage has been hindered due to a shortage of suitable materials that emit adequate wavelengths of light energy and convert it to electricity. Quantum dot light-emitting diodes (QLEDs) and quantum dot solar cells (QDSCs) have been identified as promising artificial light sources and PV cell types to nicely fit into this solution, not only complying with that but also being controllable, flexible, portable, and lightweight. The purpose of this study is to provide a review of the application of how QLEDs can be collected for QDSCs. First, we introduce QLEDs as artificial light sources, briefly outline their mechanics and benefits, and compare them to other light sources for QDSC harvesting. Then, we summarize the mechanisms that occur when a QDSC absorbs a photon from a QLED, as well as how these energy flow pathways influence artificial light energy harvest. Finally, we explore the optoelectronic properties of lead sulphide (PbS) and lead selenide (PbSe) as appropriate quantum dots (QDs) materials for current applications in QDSC and QLED technologies. We anticipate that the literature will be comprehensive and relevant to the scientific community interested in creating artificial light energy harvesting and thus researching the most recent alternative method of getting healthy energy.

Research Progress in the Detection of Aflatoxin B1 Based on Aptamers

Abstract: Aflatoxin B1 is highly toxic, mutagenic, teratogenic, and carcinogenic and is a class I carcinogen. Peanuts, cotton, and corn may be affected by AFB1 during cultivation, which can seriously jeopardize human health. Developing a simple, sensitive, and selective method for detecting AFB1 is imminent. Aptamers are obtained through in vitro screening of ligands by single-stranded oligonucleotides (DNA or RNA) through exponential enrichment (SELEX) technology. As emerging highly selective recognition molecules, they have the advantages of strong affinity, good stability, and strong specificity. Because it does not have the function of signal conversion, it cannot produce physicochemical signals that can be detected in the process of specific binding with target molecules, so it is necessary to convert the process of specific binding of aptamers to target molecules into a process of easily detectable physicochemical signal changes. According to different conversion methods, aptamer biosensors are divided into electrochemical aptamer sensors, fluorescent aptamer sensors, colorimetric aptamer sensors, surface Raman-enhanced aptamer sensors, and so on. Herein, the recent progress and application of aflatoxin B1 detection by nucleic acid aptamer biosensors based on the above signals are reviewed, and the future development prospects and challenges of this kind of biosensor are summarized.

Current Advances in Production and Drying Technologies of Lactoferrin

Lactoferrin, a multifunctional iron-binding glycoprotein present in milk, exhibits a remarkable array of beneficial properties, including antibacterial, anticancer, antiviral, antioxidant, and anti-inflammatory effects. Its potential as a novel food and nutritional supplement has sparked substantial interest among researchers and the industry. This review summarizes the recent research progress on the physiological functions of lactoferrin, and summarizes various technological advances in the separation and purification of lactoferrin. Furthermore, it conducts a detailed comparative analysis of various production and drying methods, providing fresh insights and a robust theoretical framework for advancing lactoferrin-related applications. The development of cost-effective and efficient lactoferrin production techniques holds paramount importance, as it plays a pivotal role in accelerating industrial lactoferrin production and driving growth in the dairy industry.

Structured Pruning for Deep Convolutional Neural Networks: A Survey.

The remarkable performance of deep Convolutional neural networks (CNNs) is generally attributed to their deeper and wider architectures, which can come with significant computational costs. Pruning neural networks has thus gained interest since it effectively lowers storage and computational costs. In contrast to weight pruning, which results in unstructured models, structured pruning provides the benefit of realistic acceleration by producing models that are friendly to hardware implementation. The special requirements of structured pruning have led to the discovery of numerous new challenges and the development of innovative solutions. This article surveys the recent progress towards structured pruning of deep CNNs. We summarize and compare the state-of-the-art structured pruning techniques with respect to filter ranking methods, regularization methods, dynamic execution, neural architecture search, the lottery ticket hypothesis, and the applications of pruning. While discussing structured pruning algorithms, we briefly introduce the unstructured pruning counterpart to emphasize their differences. Furthermore, we provide insights into potential research opportunities in the field of structured pruning. A curated list of neural network pruning papers can be found at: https://github.com/he-y/Awesome-Pruning. A dedicated website offering a more interactive comparison of structured pruning methods can be found at: https://huggingface.co/spaces/he-yang/Structured-Pruning-Survey.

New insight on the nature of cosmic reionizers from the CEERS survey

The Epoch of Reionization (EoR) began when galaxies grew in abundance and luminosity, so their escaping Lyman continuum (LyC) radiation started ionizing the surrounding neutral intergalactic medium (IGM). Despite significant recent progress, the nature and role of cosmic reionizers are still unclear: in order to define them, it would be necessary to directly measure their LyC escape fraction (fesc). However, this is impossible during the EoR due to the opacity of the IGM. Consequently, many efforts at low and intermediate redshift have been made to determine measurable indirect indicators in high-redshift galaxies so that their fesc can be predicted. This work presents the analysis of the indirect indicators of 62 spectroscopically confirmed star-forming galaxies at 6 ≤ z ≤ 9 from the Cosmic Evolution Early Release Science (CEERS) survey, combined with 12 sources with public data from other JWST-ERS campaigns. From the NIRCam and NIRSpec observations, we measured their physical and spectroscopic properties. We discovered that on average 6 < z < 9 star-forming galaxies are compact in the rest-frame UV (re ∼ 0.4 kpc), are blue sources (UV-β slope ∼ − 2.17), and have a predicted fesc of about 0.13. A comparison of our results to models and predictions as well as an estimation of the ionizing budget suggests that low-mass galaxies with UV magnitudes fainter than M1500 = −18 that we currently do not characterize with JWST observations probably played a key role in the process of reionization.

Optimized Transition Metal Phosphides for Direct Seawater Electrolysis: Current Trends.

Seawater electrolysis presents a viable route for sustainable large-scale hydrogen production, yet its practical application is hindered by several technical challenges. These include the sluggish kinetics of hydrogen evolution, poor stability, cation deposition at the cathode, electrode corrosion, and competing chloride oxidation at the anode. To overcome these obstacles, the development of innovative electrocatalysts is crucial. Transition metal phosphides (TMPs) have emerged as promising candidates owing to their superior catalytic performance and tunable structural properties. This review provides a comprehensive analysis of recent progress in the structural engineering of TMPs tailored for efficient seawater electrolysis. We delve into the catalytic mechanisms underpinning hydrogen and oxygen evolution reactions in different pH conditions, along with the detrimental side reactions that impede hydrogen production efficiency. Several methods to prepare TMPs are then introduced. Additionally, detailed discussions on structural modifications and interface engineering tactics are presented, showcasing strategies to enhance the activity and durability of TMP electrocatalysts. By analyzing current research findings, our review aims to inform ongoing research endeavors and foster advancements in seawater electrolysis for practical and ecologically sound hydrogen generation.

Cubosomes: An Emerging and Promising Drug Delivery System for Enhancing Cancer Therapy.

Cancer and other diseases can be treated with cubosomes, which are lyotropic nonlamellar liquid crystalline nanoparticles (LCNs). These cubosomes can potentially be a highly versatile carrier with theranostic efficacy, as they can be ingested, applied topically, or injected intravenously. Recent years have seen substantial progress in the synthesis, characterization, regulation of drug release patterns, and target selectivity of loaded anticancer bioactive compounds. However, its use in clinical settings has been slow and necessitates additional proof. Recent progress and roadblocks in using cubosomes as a nanotechnological intervention against various cancers are highlighted. In the last few decades, advances in biomedical nanotechnology have allowed for the development of "smart" drug delivery devices that can adapt to external stimuli. By improving therapeutic targeting efficacy and lowering the negative effects of payloads, these well-defined nanoplatforms can potentially promote patient compliance in response to specific stimuli. Liposomes and niosomes, two other well-known vesicular systems, share a lipid basis with cubosomes. Possible applications include a novel medication delivery system for hydrophilic, lipophilic, and amphiphilic drugs. We evaluate the literature on cubosomes, emphasizing their potential use in tumor-targeted drug delivery applications and critiquing existing explanations for cubosome self-assembly, composition, and production. As cubosome dispersion has bioadhesive and compatible features, numerous drug delivery applications, including oral, ocular, and transdermal, are also discussed in this review.