CNN-Informer: A hybrid deep learning model for seizure detection on long-term EEG.

Timely detecting epileptic seizures can significantly reduce accidental injuries of epilepsy patients and offer a novel intervention approach to improve their quality of life. Investigation on seizure detection based on deep learning models has achieved great success. However, there still remain challenging issues, such as the high computational complexity of the models and overfitting caused by the scarce availability of ictal electroencephalogram (EEG) signals for training. Therefore, we propose a novel end-to-end automatic seizure detection model named CNN-Informer, which leverages the capability of Convolutional Neural Network (CNN) to extract EEG local features of multi-channel EEGs, and the low computational complexity and memory usage ability of the Informer to capture the long-range dependencies. In view of the existence of various artifacts in long-term EEGs, we filter those raw EEGs using Discrete Wavelet Transform (DWT) before feeding them into the proposed CNN-Informer model for feature extraction and classification. Post-processing operations are further employed to achieve the final detection results. Our method is extensively evaluated on the CHB-MIT dataset and SH-SDU dataset with both segment-based and event-based criteria. The experimental outcomes demonstrate the superiority of the proposed CNN-Informer model and its strong generalization ability across two EEG datasets. In addition, the lightweight architecture of CNN-Informer makes it suitable for real-time implementation.

Uridine triphosphate hybrid catalyst for carbon­carbon bond formation reactions with enhanced enantioselectivity by mercury(II) ions.

DNA hybrid catalysts are constructed by embedding active metal species into the chiral scaffolds of DNA, which have been successfully applied to some important aqueous-phase enantioselective transformations. Owing to simple components and inherent chirality, nucleotide hybrid catalysts are emerging in response to soving the unclear locations of catalytic centers and the plausible catalytic mechanisms in DNA-based asymmetric catalysis. However, the tertiary structure of nucleotides lacks tunability, severely impeding further design of nucleotide hybrid catalysts for potential applications. To this end, a design strategy for tunable nucleotide hybrid catalysts is put forward by introducing metal-mediated base pairs. Herein, we found that the formation of uracil­mercury(II)-uracil (U-Hg2+-U) base pairs could enhance the enantioselectivity in uracil-containing nucleotide-based asymmetric reactions. Compared with uracil triphosphate (UTP) complexing with Cu2+ ions (UTP∙Cu2+), the presence of Hg2+ ions gave rise to an increased enantiomeric excess (ee) of 38 % in Diels-Alder reactions and 22 % ee in Michael reactions. The Hg2+-tuning behaviors of UTP hybrid catalyst have been demonstrated to largely depend on nucleotides, Hg2+ concentrations, metal cofactors, additives and reaction types. Based on ultraviolet-visible, circular dichroism and nuclear magnetic resonance spectroscopic techniques, the chiral enhancement of Hg2+-containing UTP hybrid catalyst is proved to largely depend on the formation of U-Hg2+-U base pairs and the plausible cross-linked structure of UTP-Hg2+-UTP/Cu2+ assembly. This work provides a tunable strategy based on the concept of metal-mediated base pairs, allowing further design of potent oligonucleotide-based catalysts for other enantioselective reactions.

A Lightweight Convolutional Neural Network-Reformer Model for Efficient Epileptic Seizure Detection.

A real-time and reliable automatic detection system for epileptic seizures holds significant value in assisting physicians with rapid diagnosis and treatment of epilepsy. Aiming to address this issue, a novel lightweight model called Convolutional Neural Network-Reformer (CNN-Reformer) is proposed for seizure detection on long-term EEG. The CNN-Reformer consists of two main parts: the Data Reshaping (DR) module and the Efficient Attention and Concentration (EAC) module. This framework reduces network parameters while retaining effective feature extraction of multi-channel EEGs, thereby improving model computational efficiency and real-time performance. Initially, the raw EEG signals undergo Discrete Wavelet Transform (DWT) for signal filtering, and then fed into the DR module for data compression and reshaping while preserving local features. Subsequently, these local features are sent to the EAC module to extract global features and perform categorization. Post-processing involving sliding window averaging, thresholding, and collar techniques is further deployed to reduce the false detection rate (FDR) and improve detection performance. On the CHB-MIT scalp EEG dataset, our method achieves an average sensitivity of 97.57%, accuracy of 98.09%, and specificity of 98.11% at segment-based level, and a sensitivity of 96.81%, along with FDR of 0.27/h, and latency of 17.81 s at the event-based level. On the SH-SDU dataset we collected, our method yielded segment-based sensitivity of 94.51%, specificity of 92.83%, and accuracy of 92.81%, along with event-based sensitivity of 94.11%. The average testing time for 1[Formula: see text]h of multi-channel EEG signals is 1.92[Formula: see text]s. The excellent results and fast computational speed of the CNN-Reformer model demonstrate its potential for efficient seizure detection.

Solar-Driven Harvesting of Freshwater and Electricity Based on Three-Dimensional Hierarchical Cu2-xO@Cu Foam.

The integration of solar steam generation and the hydrovoltaic effect is a promising strategy for simultaneously solving water scarcity and energy crises. However, it is still a challenge to attain a high water evaporation rate and a strong output of electricity in a single device. Here, we report a three-dimensional (3D) hierarchical Cu2-xO@Cu foam for solar-driven harvesting of freshwater and electricity efficiently. The 3D Cu2-xO@Cu foam synthesized by chemical etching shows a rough surface and porous structure, making it have a hydrophilic surface, high light absorption performance, and excellent photothermal effect. For deionized water, the evaporation rate is as high as 3.03 kg m-2 h-1; meanwhile, the output voltage is 0.37 V under 1 solar irradiation. For real seawater, the evaporation rate decreases to about 2.48 kg m-2 h-1, the output voltage increases to 0.41 V, and the maximum output power density is 9.47 µW cm-2. Both the water evaporation and power generation performance are very competitive. Outdoor experiments demonstrate that the 3D hierarchical Cu2-xO@Cu foam can desalinate seawater, while generating electricity continuously.

Efficient Silver(I)-Containing I-Motif DNA Hybrid Catalyst for Enantioselective Diels-Alder Reactions.

The inherent chiral structures of DNA serve as attractive scaffolds to construct DNA hybrid catalysts for valuable enantioselective transformations. Duplex and G-quadruplex DNA-based enantioselective catalysis has made great progress, yet novel design strategies of DNA hybrid catalysts are highly demanding and atomistic analysis of active centers is still challenging. DNA i-motif structures could be finely tuned by different cytosine-cytosine base pairs, providing a new platform to design DNA catalysts. Herein, we found that a human telomeric i-motif DNA containing cytosine-silver(I)-cytosine (C-Ag+-C) base pairs interacting with Cu(II) ions (i-motif DNA(Ag+)/Cu2+) could catalyze Diels-Alder reactions with full conversions and up to 95 % enantiomeric excess. As characterized by various physicochemical techniques, the presence of Ag+ is proved to replace the protons in hemiprotonated cytosine-cytosine (C : C+) base pairs and stabilize the DNA i-motif to allow the acceptance of Cu(II) ions. The i-motif DNA(Ag+)/Cu2+ catalyst shows about 8-fold rate acceleration compared with DNA and Cu2+. Based on DNA mutation experiments, thermodynamic studies and density function theory calculations, the catalytic center of Cu(II) ion is proposed to be located in a specific loop region via binding to one nitrogen-7 atom of an unpaired adenine and two phosphate-oxygen atoms from nearby deoxythymidine monophosphate and deoxyadenosine monophosphate, respectively.

Epileptic Seizure Prediction Using Spatiotemporal Feature Fusion on EEG.

Electroencephalography (EEG) plays a crucial role in epilepsy analysis, and epileptic seizure prediction has significant value for clinical treatment of epilepsy. Currently, prediction methods using Convolutional Neural Network (CNN) primarily focus on local features of EEG, making it challenging to simultaneously capture the spatial and temporal features from multi-channel EEGs to identify the preictal state effectively. In order to extract inherent spatial relationships among multi-channel EEGs while obtaining their temporal correlations, this study proposed an end-to-end model for the prediction of epileptic seizures by incorporating Graph Attention Network (GAT) and Temporal Convolutional Network (TCN). Low-pass filtered EEG signals were fed into the GAT module for EEG spatial feature extraction, and followed by TCN to capture temporal features, allowing the end-to-end model to acquire the spatiotemporal correlations of multi-channel EEGs. The system was evaluated on the publicly available CHB-MIT database, yielding segment-based accuracy of 98.71%, specificity of 98.35%, sensitivity of 99.07%, and F1-score of 98.71%, respectively. Event-based sensitivity of 97.03% and False Positive Rate (FPR) of 0.03/h was also achieved. Experimental results demonstrated this system can achieve superior performance for seizure prediction by leveraging the fusion of EEG spatiotemporal features without the need of feature engineering.

Farnesoid X receptor mediates macrophage-intrinsic responses to suppress colitis-induced colon cancer progression.

Bile acids (BAs) affect the intestinal environment by ensuring barrier integrity, maintaining microbiota balance, regulating epithelium turnover, and modulating the immune system. As a master regulator of BA homeostasis, farnesoid X receptor (FXR) is severely compromised in patients with inflammatory bowel disease (IBD) and colitis-associated colorectal cancer (CAC). At the front line, gut macrophages react to the microbiota and metabolites that breach the epithelium. We aim to study the role of the BA/FXR axis in macrophages. This study demonstrates that inflammation-induced epithelial abnormalities compromised FXR signaling and altered BAs' profile in a mouse CAC model. Further, gut macrophage-intrinsic FXR sensed aberrant BAs, leading to pro-inflammatory cytokines' secretion, which promoted intestinal stem cell proliferation. Mechanistically, activation of FXR ameliorated intestinal inflammation and inhibited colitis-associated tumor growth, by regulating gut macrophages' recruitment, polarization, and crosstalk with Th17 cells. However, deletion of FXR in bone marrow or gut macrophages escalated the intestinal inflammation. In summary, our study reveals a distinctive regulatory role of FXR in gut macrophages, suggesting its potential as a therapeutic target for addressing IBD and CAC.

Epileptic Seizure Detection with an End-to-End Temporal Convolutional Network and Bidirectional Long Short-Term Memory Model.

Automatic seizure detection plays a key role in assisting clinicians for rapid diagnosis and treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and the capability of bidirectional long short-term memory (BiLSTM) in mining the long-range dependency of multi-channel time-series, we propose an automatic seizure detection method with a novel end-to-end TCN-BiLSTM model in this work. First, raw EEG is filtered with a 0.5-45 Hz band-pass filter, and the filtered data are input into the proposed TCN-BiLSTM network for feature extraction and classification. Post-processing process including moving average filtering, thresholding and collar technique is then employed to further improve the detection performance. The method was evaluated on two EEG database. On the CHB-MIT scalp EEG database, our method achieved a segment-based sensitivity of 94.31%, specificity of 97.13%, and accuracy of 97.09%. Meanwhile, an event-based sensitivity of 96.48% and an average false detection rate (FDR) of 0.38/h were obtained. On the SH-SDU database we collected, the segment-based sensitivity of 94.99%, specificity of 93.25%, and accuracy of 93.27% were achieved. In addition, an event-based sensitivity of 99.35% and a false detection rate of 0.54/h were yielded. The total detection time consumed for 1[Formula: see text]h EEG data was 5.65[Formula: see text]s. These results demonstrate the superiority and promising potential of the proposed method in real-time monitoring of epileptic seizures.

BRD4 Regulates Glycolysis-Dependent Nos2 Expression in Macrophages Upon H pylori Infection.

BACKGROUND & AIMS: Metabolic reprogramming is essential for the activation and functions of macrophages, including bacterial killing and cytokine production. Bromodomain-containing protein 4 (BRD4) has emerged as a critical regulator of innate immune response. However, the potential role of BRD4 in the metabolic reprogramming of macrophage activation upon Helicobacter pylori infection remains unclear. METHODS: Bone marrow-derived macrophages (BMDMs) from wild-type (WT) and Brd4-myeloid deletion conditional knockout (Brd4-CKO) mice were infected with H pylori. RNA sequencing was performed to evaluate the differential gene expression between WT and Brd4-deficient BMDMs upon infection. An in vivo model of H pylori infection using WT and Brd4-CKO mice was used to confirm the role of BRD4 in innate immune response to infection. RESULTS: Depletion of Brd4 in BMDMs showed impaired H pylori-induced glycolysis. In addition, H pylori-induced expression of glycolytic genes, including Slc2a1 and Hk2, was decreased in Brd4-deficient BMDMs. BRD4 was recruited to the promoters of Slc2a1 and Hk2 via hypoxia-inducible factor-1α, facilitating their expression. BRD4-mediated glycolysis stabilized H pylori-induced nitric oxide synthase (Nos2) messenger RNA to produce nitric oxide. The NO-mediated killing of H pylori decreased in Brd4-deficient BMDMs, which was rescued by pyruvate. Furthermore, Brd4-CKO mice infected with H pylori showed reduced gastric inflammation and increased H pylori colonization with reduced inducible NO synthase expression in gastric macrophages. CONCLUSIONS: Our study identified BRD4 as a key regulator of hypoxia-inducible factor-1α-dependent glycolysis and macrophage activation. Furthermore, we show a novel regulatory role of BRD4 in innate immunity through glycolysis to stabilize Nos2 messenger RNA for NO production to eliminate H pylori infection.

Insight into the High-Efficiency Benzo(a)pyrene Degradation Ability of Pseudomonas benzopyrenica BaP3 and Its Application in the Complete Bioremediation of Benzo(a)pyrene.

Polycyclic aromatic hydrocarbons (PAHs) are common carcinogens. Benzo(a)pyrene is one of the most difficult high-molecular-weight (HMW) PAHs to remove. Biodegradation has become an ideal method to eliminate PAH pollutants from the environment. The existing research is mostly limited to low-molecular-weight PAHs; there is little understanding of HMW PAHs, particularly benzo(a)pyrene. Research into the biodegradation of HMW PAHs contributes to the development of microbial metabolic mechanisms and also provides new systems for environmental treatments. Pseudomonas benzopyrenica BaP3 is a highly efficient benzo(a)pyrene-degrading strain that is isolated from soil samples, but its mechanism of degradation remains unknown. In this study, we aimed to clarify the high degradation efficiency mechanism of BaP3. The genes encoding Rhd1 and Rhd2 in strain BaP3 were characterized, and the results revealed that rhd1 was the critical factor for high degradation efficiency. Molecular docking and enzyme activity determinations confirmed this conclusion. A recombinant strain that could completely mineralize benzo(a)pyrene was also proposed for the first time. We explained the mechanism of the high-efficiency benzo(a)pyrene degradation ability of BaP3 to improve understanding of the degradation mechanism of highly toxic PAHs and to provide new solutions to practical applications via synthetic biology.

Epileptic Seizure Prediction Using Attention Augmented Convolutional Network.

Early seizure prediction is crucial for epilepsy patients to reduce accidental injuries and improve their quality of life. Identifying pre-ictal EEG from the inter-ictal state is particularly challenging due to their nonictal nature and remarkable similarities. In this study, a novel epileptic seizure prediction method is proposed based on multi-head attention (MHA) augmented convolutional neural network (CNN) to address the issue of CNN's limit of capturing global information of input signals. First, data enhancement is performed on original EEG recordings to balance the pre-ictal and inter-ictal EEG data, and the EEG recordings are sliced into 6-second-long EEG segments. Subsequently, EEG time-frequency distribution is obtained using Stockwell transform (ST), and the attention augmented convolutional network is employed for feature extraction and classification. Finally, post-processing is utilized to reduce the false prediction rate (FPR). The CHB-MIT EEG database was used to evaluate the system. The validation results showed a segment-based sensitivity of 98.24% and an event-based sensitivity of 94.78% with a FPR of 0.05/h were yielded, respectively. The satisfying results of the proposed method demonstrate its possible potential for clinical applications.

Pseudomonas benzopyrenica sp. nov., isolated from soil, exhibiting high-efficiency degradation of benzo(a)pyrene.

A Gram-negative, yellow-pigmented, aerobic and rod-shaped bacterium, designated as strain BaP3T, was isolated from the soil. Strain BaP3T grew at 16-37℃ (optimum, 30 °C) and pH 6.0-8.0 (optimum, pH 7.0). Additionally, strain BaP3T could tolerate NaCl concentrations in the range 0-6 % (optimum, 1%). Moreover, strain BaP3T was motile by flagella. The phylogenetic analysis of 16S rRNA sequences showed that strain BaP3T belonged to the genus Pseudomonas, and the sequence was most closely related to Pseudomonas oryzihabitans CGMCC 1.3392T and Pseudomonas psychrotolerans DSM 15758T, with 99.66 % sequence similarity. Pseudomonas rhizoryzae RY24T was the next closely related species, exhibiting 99.38 % 16S rRNA gene sequence similarity. The DNA-DNA hybridization and average nucleotide identity values between strain BaP3T and its closely related types were below 50 and 92 %, respectively. Both results were below the cut-off for species distinction. The genomic DNA G+C content of strain BaP3T was 65.30 mol%. The predominant quinone in strain BaP3T was identified as ubiquinone Q-9. The major cellular fatty acids were summed feature 8 (C18 : 1 ω7c and/or C18 : 1 ω6c), summed feature 3 (C16 : 1 ω7c and/or C16 : 1 ω6c) and C16 : 0. These results indicated that strain BaP3T represents a novel species in the genus Pseudomonas. The type strain is BaP3T (CCTCC AB 2022379T=JCM 35914T), for which the name Pseudomonas benzopyrenica sp. nov. is proposed.

Construction of PdSe2/ZnIn2S4 heterojunctions with covalent interface for highly efficient photocatalytic hydrogen evolution.

Constructing semiconductor heterojunctions can enable novel schemes for highly efficient photocatalytic activity. However, introducing strong covalent bonding at the interface remains an open challenge. Herein, ZnIn2S4 (ZIS) with abundant sulfur vacancies (Sv) is synthesized with the presence of PdSe2 as an additional precursor. The sulfur vacancies of Sv-ZIS are filled by Se atoms of PdSe2, leading to the Zn-In-Se-Pd compound interface. Our density functional theory (DFT) calculations reveal the increased density of states at the interface, which will increase the local carrier concentration. Moreover, the length of the Se-H bond is longer than that of the SH bond, which is good for the evolution of H2 from the interface. In addition, the charge redistribution at the interface results in a built-in field, providing the driving force for efficient separation of photogenerated electron-hole. Therefore, the PdSe2/Sv-ZIS heterojunction with strong covalent interface exhibits an excellent photocatalytic hydrogen evolution performance (4423 µmol g-1h-1) with an apparent quantum efficiency (λ > 420 nm) of 9.1 %. This work will provide new inspirations to improve photocatalytic activity by engineering the interfaces of semiconductor heterojunctions.

Transgenic mice lacking FGF15/19-SHP phosphorylation display altered bile acids and gut bacteria, promoting nonalcoholic fatty liver disease.

Dysregulated bile acid (BA)/lipid metabolism and gut bacteria dysbiosis are tightly associated with the development of obesity and non-alcoholic fatty liver disease (NAFLD). The orphan nuclear receptor, Small Heterodimer Partner (SHP/NR0B2), is a key regulator of BA/lipid metabolism, and its gene-regulating function is markedly enhanced by phosphorylation at Thr-58 mediated by a gut hormone, fibroblast growth factor-15/19 (FGF15/19). To investigate the role of this phosphorylation in whole-body energy metabolism, we generated transgenic SHP-T58A knock-in mice. Compared with wild-type (WT) mice, the phosphorylation-defective SHP-T58A mice gained weight more rapidly with decreased energy expenditure and increased lipid/BA levels. This obesity-prone phenotype was associated with the upregulation of lipid/BA synthesis genes and downregulation of lipophagy/ß-oxidation genes. Mechanistically, defective SHP phosphorylation selectively impaired its interaction with LRH-1, resulting in de-repression of SHP/LRH-1 target BA/lipid synthesis genes. Remarkably, BA composition and selective gut bacteria which are known to impact obesity, were also altered in these mice. Upon feeding a high-fat diet, fatty liver developed more severely in SHP-T58A mice compared to WT mice. Treatment with antibiotics substantially improved the fatty liver phenotypes in both groups but had greater effects in the T58A mice so that the difference between the groups was largely eliminated. These results demonstrate that defective phosphorylation at a single nuclear receptor residue can impact whole-body energy metabolism by altering BA/lipid metabolism and gut bacteria, promoting complex metabolic disorders like NAFLD. Since posttranslational modifications generally act in gene- and context-specific manners, the FGF15/19-SHP phosphorylation axis may allow more targeted therapy for NAFLD.

Cyclic Dinucleotide-Based Enantioselective Fluorination in Water.

The diverse structures of DNA serve as potent chiral scaffolds for DNA-based asymmetric catalysis, yet in most cases tens to hundreds of nucleotides in DNA hybrid catalysts hinder the deep insight into their structure-activity relationship. Owing to the structural simplicity and design flexibility of nucleotides, nucleotide-based catalysts have been emerging as a promising way to obtain fine structural information and understand the catalytic mechanisms. Herein, we found that a cyclic dinucleotide of cyclic di-AMP (c-di-AMP) and 1,10-phenanthroline copper(II) nitrate (Cu(phen)(NO3)2) are assembled to a c-di-AMP-based catalyst (c-di-AMP/Cu(phen)(NO3)2), which could fast achieve enantioselective fluorination in water with 90-99% yields and up to 90% enantiomeric excess (ee). The host-guest interaction between c-di-AMP and Cu(phen)(NO3)2 has been proposed mainly in a supramolecular interaction mode as evidenced by spectroscopic techniques of ultraviolet-visible, fluorescence, circular dichroism, and nuclear magnetic resonance. Cu(phen)(NO3)2 tightly binds to c-di-AMP with a binding constant of 1.7 ± 0.3 × 105 M-1, and the assembly of c-di-AMP/Cu(phen)(NO3)2 shows a modest rate enhancement to carbon-fluorine bond formations as supported by kinetic studies.

Automatic Seizure Detection Based on Stockwell Transform and Transformer.

Epilepsy is a chronic neurological disease associated with abnormal neuronal activity in the brain. Seizure detection algorithms are essential in reducing the workload of medical staff reviewing electroencephalogram (EEG) records. In this work, we propose a novel automatic epileptic EEG detection method based on Stockwell transform and Transformer. First, the S-transform is applied to the original EEG segments, acquiring accurate time-frequency representations. Subsequently, the obtained time-frequency matrices are grouped into different EEG rhythm blocks and compressed as vectors in these EEG sub-bands. After that, these feature vectors are fed into the Transformer network for feature selection and classification. Moreover, a series of post-processing methods were introduced to enhance the efficiency of the system. When evaluating the public CHB-MIT database, the proposed algorithm achieved an accuracy of 96.15%, a sensitivity of 96.11%, a specificity of 96.38%, a precision of 96.33%, and an area under the curve (AUC) of 0.98 in segment-based experiments, along with a sensitivity of 96.57%, a false detection rate of 0.38/h, and a delay of 20.62 s in event-based experiments. These outstanding results demonstrate the feasibility of implementing this seizure detection method in future clinical applications.

Highly Efficient Photocatalytic Hydrogen Evolution over Mo-Doped ZnIn2S4 with Sulfur Vacancies.

The introduction of impure atoms or crystal defects is a promising strategy for enhancing the photocatalytic activity of semiconductors. However, the synergy of these two effects in 2D atomic layers remains unexplored. In this case, the preparation of molybdenum-doped thin ZnIn2S4-containing S vacancies (Mo-doped Sv-ZnIn2S4) is conducted using a one-pot solvothermal method. The coordination of Mo doping and S vacancies not only enhances visible light absorption and facilitates the separation of photogenerated carriers but also provides many active sites for photocatalytic reactions. Meanwhile, the Mo-S bonds play function as high-speed channels to rapidly transfer carriers to the active sites, which can directly promote hydrogen evolution. Consequently, Sv-ZnIn2S4 with an optimized amount of Mo doping exhibits a high hydrogen evolution rate of 5739 µmol g-1 h-1 with a corresponding apparent quantum yield (AQY) of 21.24% at 420 nm, which is approximately 5.4 times higher than the original ZnIn2S4. This work provides a new strategy for the development of highly efficient and sustainable 2D atomic photocatalysts for hydrogen evolution.

Development of a variant of dinutuximab with enhanced antitumor efficacy and reduced induction of neuropathic pain.

Dinutuximab (ch14.18) was the first approved monoclonal antibody against the tumor-associated antigen disialoganglioside GD2. Despite its success in treating neuroblastoma (NB), it triggers a significant amount of neuropathic pain in patients, possibly through complement-dependent cytotoxicity (CDC). We hypothesized that modifying ch14.18 using antibody engineering techniques, such as humanization, affinity maturation, and Fc engineering, may enable the development of next-generation GD2-specific antibodies with reduced neuropathic pain and enhanced antitumor activity. In this study we developed the H3-16 IgG1m4 antibody from ch14.18 IgG1. H3-16 IgG1m4 exhibited enhanced binding activity to GD2 molecules and GD2-positive cell lines as revealed by ELISA, and its cross-binding activity to other gangliosides was not altered. The CDC activity of H3-16 IgG1m4 was decreased, and the antibody-dependent cellular cytotoxicity (ADCC) activity was enhanced. The pain response after H3-16 IgG1m4 antibody administration was also reduced, as demonstrated using the von Frey test in Sprague-Dawley (SD) rats. In summary, H3-16 IgG1m4 may have potential as a monoclonal antibody with reduced side effects.

Analgesic effects of nerve growth factor-directed monoclonal antibody on diabetic neuralgia in an animal model.

Current treatment options for diabetic neuralgia are limited and unsatisfactory. Tanezumab, a monoclonal antibody that blocks nerve growth factor (NGF) signaling, has been shown to be effective in relieving the clinical symptoms of osteoarthritis pain, chronic low back pain, cancer pain induced by bone metastasis, and diabetic neuralgia. However, the clinical development of tanezumab has been terminated due to the risk of induction of rapidly progressive osteoarthritis (RPOA), and no other NGF antibodies have been examined for their ability to treat diabetic neuralgia in either animal models or clinical trials. In this study, a humanized high-affinity NGF monoclonal antibody (mAb), huAb45 that could neutralize the interaction between NGF and its high-affinity receptor TrkA. In a mouse diabetic neuralgia model, it effectively relieved neuropathic pain. This study may serve as the necessary foundation for future studies of huAb45 to potentially treat diabetic neuralgia.