IGSF8 is an innate immune checkpoint and cancer immunotherapy target.

Antigen presentation defects in tumors are prevalent mechanisms of adaptive immune evasion and resistance to cancer immunotherapy, whereas how tumors evade innate immunity is less clear. Using CRISPR screens, we discovered that IGSF8 expressed on tumors suppresses NK cell function by interacting with human KIR3DL2 and mouse Klra9 receptors on NK cells. IGSF8 is normally expressed in neuronal tissues and is not required for cell survival in vitro or in vivo. It is overexpressed and associated with low antigen presentation, low immune infiltration, and worse clinical outcomes in many tumors. An antibody that blocks IGSF8-NK receptor interaction enhances NK cell killing of malignant cells in vitro and upregulates antigen presentation, NK cell-mediated cytotoxicity, and T cell signaling in vivo. In syngeneic tumor models, anti-IGSF8 alone, or in combination with anti-PD1, inhibits tumor growth. Our results indicate that IGSF8 is an innate immune checkpoint that could be exploited as a therapeutic target.

Epileptic Seizure Detection Based on Path Signature and Bi-LSTM Network With Attention Mechanism.

Automatic seizure detection using electroen-cephalogram (EEG) can significantly expedite the diagnosis of epilepsy, thereby facilitating prompt treatment and reducing the risk of future seizures and associated complications. While most existing EEG-based epilepsy detection studies employ deep learning models, they often ignore the chronological relationships between different EEG channels. To tackle this limitation, a novel automatic epilepsy detection method is proposed, which leverages path signature and Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with an attention mechanism. The path signature algorithm is used to extract discriminative features for capturing the dynamic dependencies between different channels of EEG, while Bi-LSTM with attention further analyzes the inherent temporal dependencies hidden in EEG signal features. Our method is evaluated on two public EEG databases with different sizes (CHB-MIT and TUEP) and a private database from a local hospital. Two experimental settings are used, i.e., five-fold cross-validation and leave-one-out cross-validation. Experimental results show that our method achieves 99.09%, 95.60%, and 99.87% average accuracies on CHB-MIT, TUEP with 250Hz, and TUEP with 256Hz, respectively. On the private dataset, our method also achieves 99.40% average accuracy, which outperforms other methods. Furthermore, our method exhibits robustness in patients, as demonstrated by the evaluation results of cross-patient experiments.

Advancements in Macrophage-Targeted Drug Delivery for Effective Disease Management.

Macrophages play a crucial role in tissue homeostasis and the innate immune system. They perform essential functions such as presenting antigens, regulating cytokines, and responding to inflammation. However, in diseases like cancer, cardiovascular disorders, and autoimmune conditions, macrophages undergo aberrant polarization, which disrupts tissue regulation and impairs their normal behavior. To address these challenges, there has been growing interest in developing customized targeted drug delivery systems specifically designed for macrophage-related functions in different anatomical locations. Nanomedicine, utilizing nanoscale drug systems, offers numerous advantages including improved stability, enhanced pharmacokinetics, controlled release kinetics, and precise temporal drug delivery. These advantages hold significant promise in achieving heightened therapeutic efficacy, specificity, and reduced side effects in drug delivery and treatment approaches. This review aims to explore the roles of macrophages in major diseases and present an overview of current strategies employed in targeted drug delivery to macrophages. Additionally, this article critically evaluates the design of macrophage-targeted delivery systems, highlighting limitations and discussing prospects in this rapidly evolving field. By assessing the strengths and weaknesses of existing approaches, we can identify areas for improvement and refinement in macrophage-targeted drug delivery.

Design, Synthesis and Biological Evaluation of Novel and Potent Protein Arginine Methyltransferases 5 Inhibitors for Cancer Therapy.

Protein arginine methyltransferases 5 (PRMT5) is a clinically promising epigenetic target that is upregulated in a variety of tumors. Currently, there are several PRMT5 inhibitors under preclinical or clinical development, however the established clinical inhibitors show favorable toxicity. Thus, it remains an unmet need to discover novel and structurally diverse PRMT5 inhibitors with characterized therapeutic utility. Herein, a series of tetrahydroisoquinoline (THIQ) derivatives were designed and synthesized as PRMT5 inhibitors using GSK-3326595 as the lead compound. Among them, compound 20 (IC50: 4.2 nM) exhibits more potent PRMT5 inhibitory activity than GSK-3326595 (IC50: 9.2 nM). In addition, compound 20 shows high anti-proliferative effects on MV-4-11 and MDA-MB-468 tumor cells and low cytotoxicity on AML-12 hepatocytes. Furthermore, compound 20 possesses acceptable pharmacokinetic profiles and displays considerable in vivo antitumor efficacy in a MV-4-11 xenograft model. Taken together, compound 20 is an antitumor compound worthy of further study.

Combining Machine Learning with a Rule-Based Algorithm to Detect and Identify Related Entities of Documented Adverse Drug Reactions on Hospital Discharge Summaries.

INTRODUCTION: Discharge summaries contain valuable information about adverse drug reactions, but their unstructured nature makes them challenging to analyse and use as a signal source for pharmacovigilance. Machine learning has shown promise in identifying discharge summaries that contain related drug-adverse event pairs but has fared relatively poorer in entity extraction. METHODS: A hybrid model is developed combining rule-based and machine learning algorithms using discharge summaries with the aim of maximising capture of related drug-adverse event pairs. The rule first identifies segments containing adverse event entities within a 100-character distance from a drug term; machine learning subsequently estimates the relatedness of the drug and adverse event entities contained. The approach is validated on four independent datasets that are temporally and geographically separated from model development data. The impact of restricted drug-adverse event pair detection on recall is evaluated by using two of the four validation datasets that do not impose rule-based restrictions to annotations. RESULTS: The hybrid model achieves a recall of 0.80 (fivefold cross validation), 0.80 (temporal) and 0.76 (geographical) on validation using datasets containing only pre-identified target text segments that fulfil the rule-based algorithm criteria. When tested on datasets that additionally contained drug-adverse event pairs not restricted by the rule-based criteria, recall of the model declines to 0.68 and 0.62 on temporally and geographically separated datasets, respectively. CONCLUSIONS: The proposed hybrid model demonstrates reasonable generalisability on external validation. Rule-based restriction of the detection space results in an approximately 12-14% reduction in recall but improves identification of the related drug and adverse event terms.

Electrospun Nanofiber-Based Membranes for Water Treatment.

Water purification and water desalination via membrane technology are generally deemed as reliable supplementaries for abundant potable water. Electrospun nanofiber-based membranes (ENMs), benefitting from characteristics such as a higher specific surface area, higher porosity, lower thickness, and possession of attracted broad attention, has allowed it to evolve into a promising candidate rapidly. Here, great attention is placed on the current status of ENMs with two categories according to the roles of electrospun nanofiber layers: (i) nanofiber layer serving as a selective layer, (ii) nanofiber layer serving as supporting substrate. For the nanofiber layer's role as a selective layer, this work presents the structures and properties of conventional ENMs and mixed matrix ENMs. Fabricating parameters and adjusting approaches such as polymer and cosolvent, inorganic and organic incorporation and surface modification are demonstrated in detail. It is crucial to have a matched selective layer for nanofiber layers acting as a supporting layer. The various selective layers fabricated on the nanofiber layer are put forward in this paper. The fabrication approaches include inorganic deposition, polymer coating, and interfacial polymerization. Lastly, future perspectives and the main challenges in the field concerning the use of ENMs for water treatment are discussed. It is expected that the progress of ENMs will promote the prosperity and utilization of various industries such as water treatment, environmental protection, healthcare, and energy storage.

FDI-6 and olaparib synergistically inhibit the growth of pancreatic cancer by repressing BUB1, BRCA1 and CDC25A signaling pathways.

Inducing homologous recombination (HR) deficiency is a promising strategy to broaden the indication of PARP1/2 inhibitors in pancreatic cancer treatment. In addition to inhibition kinases, repression of the transcriptional function of FOXM1 has been reported to inhibit HR-mediated DNA repair. We found that FOXM1 inhibitor FDI-6 and PARP1/2 inhibitor Olaparib synergistically inhibited the malignant growth of pancreatic cancer cells in vitro and in vivo. The results of bioinformatic analysis and mechanistic study showed that FOXM1 directly interacted with PARP1. Olaparib induced the feedback overexpression of PARP1/2, FOXM1, CDC25A, CCND1, CDK1, CCNA2, CCNB1, CDC25B, BRCA1/2 and Rad51 to promote the acceleration of cell mitosis and recovery of DNA repair, which caused the generation of adaptive resistance. FDI-6 reversed Olaparib-induced adaptive resistance and inhibited cell cycle progression and DNA damage repair by repressing the expression of FOXM1, PARP1/2, BUB1, CDC25A, BRCA1 and other genes-involved in cell cycle control and DNA damage repair. We believe that targeting FOXM1 and PARP1/2 is a promising combination therapy for pancreatic cancer without HR deficiency.

FDI-6 inhibits the expression and function of FOXM1 to sensitize BRCA-proficient triple-negative breast cancer cells to Olaparib by regulating cell cycle progression and DNA damage repair.

Inducing homologous-recombination (HR) deficiency is an effective strategy to broaden the indications of PARP inhibitors in the treatment of triple-negative breast cancer (TNBC). Herein, we find that repression of the oncogenic transcription factor FOXM1 using FOXM1 shRNA or FOXM1 inhibitor FDI-6 can sensitize BRCA-proficient TNBC to PARP inhibitor Olaparib in vitro and in vivo. Mechanistic studies show that Olaparib causes adaptive resistance by arresting the cell cycle at S and G2/M phases for HR repair, increasing the expression of CDK6, CCND1, CDK1, CCNA1, CCNB1, and CDC25B to promote cell cycle progression, and inducing the overexpression of FOXM1, PARP1/2, BRCA1/2, and Rad51 to activate precise repair of damaged DNA. FDI-6 inhibits the expression of FOXM1, PARP1/2, and genes involved in cell cycle control and DNA damage repair to sensitize TNBC cells to Olaparib by blocking cell cycle progression and DNA damage repair. Simultaneously targeting FOXM1 and PARP1/2 is an innovative therapy for more patients with TNBC.

Precise Delivery of Nanomedicines to M2 Macrophages by Combining "Eat Me/Don't Eat Me" Signals and Its Anticancer Application.

Targeted delivery of nanomedicines to M2 tumor-associated macrophages (TAMs) has been proposed to reduce tumor promotion and enhance the efficacy of anticancer therapy. However, upregulated receptors on M2 TAMs are also expressed on M1 TAMs and other macrophages in normal tissues. Therefore, improving targeting specificity remains a key challenge. Here, we developed a precise M2 TAM-targeted delivery system using "eat-me" and "don't-eat-me" signals. A CD47-derived self-peptide ligand (don't-eat-me signal) and galactose ligand (eat-me signal) were introduced on liposomes. Cleavable phospholipid-polyethylene glycol was covered on the surface and could combine with the self-peptide to inhibit macrophage recognition even after immunoglobulin M adsorption and protect galactose from hepatic clearance to prolong the circulation time and promote the accumulation of liposomes in tumors. This detachable polymer can be removed by the redox microenvironment upon transcytosis through the tumor endothelium and re-expose the self-peptide and galactose. The self-peptide highly reduced M1 macrophage phagocytosis, and the galactose ligand enhanced the interaction between the liposomes and M2 macrophages. Thus, the modified liposomes enabled specific recognition of M1/M2 TAMs. In vitro evidence revealed reduced endocytosis of the liposomes by M1 macrophages. Moreover, in vivo studies demonstrated that doxorubicin-loaded liposomes efficiently eliminated M2 TAMs but did not affect M1 TAMs, enhancing the potency of the antitumor therapy. Collectively, our results demonstrate the potential of combining active escape and active targeting for precisely delivering a drug of interest to M2 macrophages and suggest its application in anticancer therapy.

Does Economic Policy Uncertainty Matter for Healthcare Expenditure in China? A Spatial Econometric Analysis.

A growing body of research has documented the determinants of healthcare expenditure, but no known empirical research has focused on investigating the spatial effects between economic policy uncertainty (EPU) and healthcare expenditure. This study aims to explore the spatial effects of EPU on healthcare expenditure using the panel data of 29 regions in China from 2007 to 2017. Our findings show that healthcare expenditure in China has the characteristics of spatial clustering and spatial spillover effects. Our study also shows that EPU has positive spatial spillover effects on healthcare expenditure in China; that is, EPU affects not only local healthcare expenditure but also that in other geographically close or economically connected regions. Our study further indicates that the spatial spillover effects of EPU on healthcare expenditure only exist in the eastern area. The findings of this research provide some key implications for policymakers in emerging markets.

Engineered liposomes targeting the gut-CNS Axis for comprehensive therapy of spinal cord injury.

Effective curative therapies for spinal cord injury (SCI), which is often accompanied by intestinal complications, are lacking. Potential therapeutic targets include astrocytes and their enteric nervous system counterpart, enteric glial cells (EGCs). Based on shared biomarkers and similar functions of both cell types, we designed an orally administered targeted delivery system in which the neuropeptide apamin, stabilized by sulfur replacement with selenium, was adopted as a targeting moiety, and the liposome surface was protected with a non-covalent cross-linked chitosan oligosaccharide lactate layer. The system effectively permeated through oral absorption barriers, targeted local EGCs and astrocytes after systemic circulation, allowing for comprehensive SCI therapy. Given the involvement of the gut-organ axis in a growing number of diseases, our research may shed light on new aspects of the oral administration route as a bypass for multiple interventions and targeted therapy.

Overcoming the Reticuloendothelial System Barrier to Drug Delivery with a "Don't-Eat-Us" Strategy.

Overcoming the reticuloendothelial system (RES) has long been a vital challenge to nanoparticles as drug carriers. Modification of nanoparticles with polyethylene glycol helps them avoid clearance by macrophages but also suppresses their internalization by target cells. To overcome this paradox, we developed an RES-specific blocking system utilizing a "don't-eat-us" strategy. First, a CD47-derived, enzyme-resistant peptide ligand was designed and placed on liposomes (d-self-peptide-labeled liposome, DSL). After mainline administration, DSL was quickly adsorbed onto hepatic phagocyte membranes (including those of Kupffer cells and liver sinusoidal endothelial cells), forming a long-lasting mask that enclosed the cell membranes and thus reducing interactions between phagocytes and subsequently injected nanoparticles. Compared with blank conventional liposomes (CL), DSL blocked the RES at a much lower dose, and the effect was sustained for a much longer time, highly prolonging the elimination half-life of the subsequently injected nanoparticles. This "don't-eat-us" strategy by DSL was further verified on the brain-targeted delivery against a cryptococcal meningitis model, providing dramatically enhanced brain accumulation of the targeted delivery system and superior therapeutic outcome of model drug Amphotericin B compared with CL. Our study demonstrates a strategy that blocks the RES by masking phagocyte surfaces to prolong nanoparticle circulation time without excess modification and illustrates its utility in enhancing nanoparticle delivery.

Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer.

BACKGROUND: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to find cases of drug-adverse event (AE) relations. PURPOSE: The objective of this paper is to develop a natural language processing (NLP) framework to detect drug-AE relations from unstructured hospital discharge summaries. BASIC PROCEDURES: An NLP algorithm was designed using customized dictionaries of drugs, adverse event (AE) terms, and rules based on trigger phrases, negations, fuzzy logic and word distances to recognize drug, AE terms and to detect drug-AE relations. Furthermore, a customized annotation tool was developed to facilitate expert review of discharge summaries from a tertiary hospital in Singapore in 2011. MAIN FINDINGS: A total of 33 trial sets with 50 to 100 records per set were evaluated (1620 discharge summaries) by our algorithm and reviewed by pharmacovigilance experts. After every 6 trial sets, drug and AE dictionaries were updated, and rules were modified to improve the system. Excellent performance was achieved for drug and AE entity recognition with over 92% precision and recall. On the final 6 sets of discharge summaries (600 records), our algorithm achieved 75% precision and 59% recall for identification of valid drug-AE relations. PRINCIPAL CONCLUSIONS: Adverse drug reactions are a significant contributor to health care costs and utilization. Our algorithm is not restricted to particular drugs, drug classes or specific medical specialties, which is an important attribute for a national regulatory authority to carry out comprehensive safety monitoring of drug products. Drug and AE dictionaries may be updated periodically to ensure that the tool remains relevant for performing surveillance activities. The development of the algorithm, and the ease of reviewing and correcting the results of the algorithm as part of an iterative machine learning process, is an important step towards use of hospital discharge summaries for an active pharmacovigilance program.

Nanoparticles Targeted against Cryptococcal Pneumonia by Interactions between Chitosan and Its Peptide Ligand.

Inspired by the fact that chitosan is a representative constituent of the ectocellular structure of Cryptococcus neoformans and a typical biomaterial for improving drug oral absorption, we designed an elegant and efficient C. neoformans-targeted drug delivery system via oral administration. A chitosan-binding peptide screened by phage display was used as the targeting moiety, followed by conjugation to the surface of poly(lactic- co-glycolic acid) nanoparticles as the drug carrier, which was then incubated with free chitosan. The noncovalently bound chitosan adheres to mucus layers and significantly enhances penetration of nanoparticles through the oral absorption barrier into circulation and then re-exposed the targeting ligand for later recognition of the fungal pathogen at the site of infection. After loading itraconazole as a model drug, our drug delivery system remarkably cleared lung infections of C. neoformans and increased survival of model mice. Currently, targeted drug delivery is mainly performed intravenously; however, the system described in our study may provide a universal means to facilitate drug targeting to specific tissues and disease sites by oral administration and may be especially powerful in the fight against increasingly severe fungal infections.

Author Correction: Targeted inhibition of STAT/TET1 axis as a therapeutic strategy for acute myeloid leukemia.

The original version of this Article contained an error in the spelling of the author James C. Mulloy, which was incorrectly given as James Mulloy. This has now been corrected in both the PDF and HTML versions of the Article.

Targeted inhibition of STAT/TET1 axis as a therapeutic strategy for acute myeloid leukemia.

Effective therapy of acute myeloid leukemia (AML) remains an unmet need. DNA methylcytosine dioxygenase Ten-eleven translocation 1 (TET1) is a critical oncoprotein in AML. Through a series of data analysis and drug screening, we identified two compounds (i.e., NSC-311068 and NSC-370284) that selectively suppress TET1 transcription and 5-hydroxymethylcytosine (5hmC) modification, and effectively inhibit cell viability in AML with high expression of TET1 (i.e., TET1-high AML), including AML carrying t(11q23)/MLL-rearrangements and t(8;21) AML. NSC-311068 and especially NSC-370284 significantly repressed TET1-high AML progression in vivo. UC-514321, a structural analog of NSC-370284, exhibited a more potent therapeutic effect and prolonged the median survival of TET1-high AML mice over three fold. NSC-370284 and UC-514321 both directly target STAT3/5, transcriptional activators of TET1, and thus repress TET1 expression. They also exhibit strong synergistic effects with standard chemotherapy. Our results highlight the therapeutic potential of targeting the STAT/TET1 axis by selective inhibitors in AML treatment.