Alzheimer's disease (AD) is a neurodegenerative disease with clinical hallmarks of progressive cognitive impairment. Synergistic effects of the Aβ-Tau cascade reaction are tightly implicated in AD pathology, and microglial NLRP3 inflammasome activation drives neuronal tauopathy. However, the underlying mechanism of how Aβ mediates NLRP3 inflammasome remains unclear. Herein, we determined that oligomeric Aβ (o-Aβ) bound to microglial Kv2.1 and promoted Kv2.1-dependent potassium efflux to activate NLRP3 inflammasome resulting in neuronal tauopathy by using Kv2.1 inhibitor drofenine (Dfe) as a probe. The underlying mechanism has been intensively investigated by assays with Kv2.1 knockdown in vitro (si-Kv2.1) and in vivo (AAV-ePHP-si-Kv2.1). Dfe deprived o-Aβ of its capability to promote microglial NLRP3 inflammasome activation and neuronal Tau hyperphosphorylation by inhibiting the Kv2.1/JNK/NF-κB pathway while improving the cognitive impairment of 5×FAD-AD model mice. Our results have highly addressed that the Kv2.1 channel is required for o-Aβ-driven microglial NLRP3 inflammasome activation and neuronal tauopathy in AD model mice and highlighted that Dfe as a Kv2.1 inhibitor shows potential in the treatment of AD.

High-performance phototheranostics with combined photothermal therapy and photoacoustic imaging have been considered promising approaches for efficient cancer diagnosis and treatment. However, developing phototheranostic materials with efficient photothermal conversion efficiency (PCE), especially over the second near-infrared window (NIR-II, 1000-1700 nm), remains challenging. Herein, we report an ultraefficient NIR-II-activated nanomedicine with phototheranostic and vaccination capability for highly efficient in vivo tumor elimination and metastasis inhibition. The NIR-II nanomedicine of a semiconducting biradical oligomer with a motor-flexible design was demonstrated with a record-breaking PCE of 87% upon NIR-II excitation. This nanomedicine inherently features extraordinary photothermal stability, good biocompatibility, and excellent photoacoustic performance, contributing to high-contrast photoacoustic imaging in living mice and high-performance photothermal elimination of tumors. Moreover, a whole-cell vaccine based on a NIR-II nanomedicine with NIR-II-activated performance was further designed to remotely activate the antitumor immunologic memory and effectively inhibit tumor occurrence and metastasis in vivo, with good biosafety. Thus, this work paves a new avenue for designing NIR-II active semiconducting biradical materials as a promising theranostics platform and further promotes the development of NIR-II nanomedicine for personalized cancer treatment.

Obesity usually exacerbates the immunosuppressive tumor microenvironment (ITME), hindering CD8⁺ T cell infiltration and function, which further represents a significant barrier to the efficacy of immunotherapy. Herein, a multifunctional liposomal system (CR-Lip) for encapsulating celastrol (CEL) was utilized to remodel obesity-related ITME and improve cancer immunotherapy, wherein Ginsenoside Rg3 (Rg3) was detected interspersed in the phospholipid bilayer and its glycosyl exposed on the surface of the liposome. CR-Lip had a relatively uniform size (116.5 nm), facilitating favorable tumor tissue accumulation through the interaction between Rg3 and glucose transporter 1 overexpressed in obese tumor cells. Upon reaching the tumor region, CR-Lip was found to induce the immunogenic cell death (ICD) of HFD tumor cells. Notably, the level of PHD3 in HFD tumor cells was effectively boosted by CR-Lip to effectively block metabolic reprogramming and increase the availability of major free fatty acids fuel sources. In vivo, experiments studies revealed that the easy-obtained nano platform stimulated enhanced the production of various cytokines in tumor tissues, DC maturation, CD8⁺ T-cell infiltration, and synergistic anticancer therapeutic potency with aPD-1 (tumor inhibition rate = 82.1%) towards obesity-related melanoma. Consequently, this study presented an efficacious approach to tumor immunotherapy in obese mice by encompassing tumor eradication, inducing ICD, and reprogramming metabolism. Furthermore, it offered a unique insight into a valuable attempt at the immunotherapy of obesity-associated related tumors.

Lacking therapeutic targets highlights the crucial roles of chemotherapy and radiotherapy in the clinical management of triple-negative breast cancer (TNBC). To relieve the side effects of the chemoradiotherapy combination regimen, we design and develop a self-assembled micelle nanosystem consisting of perfluorocarbon chain-modified cisplatin prodrug. By incorporating perfluorodecalin, this nanosystem can effectively carry ozone and promote irradiation-derived reactive oxygen species (ROS) production. By leveraging the perfluorocarbon sidechain, the nanosystem exhibits efficient internalization by TNBC cells and effectively escapes from lysosomal entrapment. Under X-ray irradiation, ozone-generated ROS disrupts the intracellular redox balance, thereby facilitating the release of cisplatin in a reduction-responsive manner mediated by reduced glutathione. Moreover, oxygen derived from ozone decomposition enhances the efficacy of radiotherapy by alleviating tumor hypoxia. Notably, the combination of irradiation with ozone-loaded cisplatin prodrug nano system synergistically prompts antitumor efficacy and reduces cellular/systemic toxicity in vitro and in vivo. Furthermore, the combo regimen remodels the tumor microenvironment into an immune-favored state by triggering immunogenic cell death and relieving hypoxia, which provides a promising foundation for a combination regimen of immunotherapy. In conclusion, our nanosystem presents a novel strategy for integrating chemotherapy and radiotherapy to optimize the efficacy and safety of TNBC clinical treatment.

[This corrects the article DOI: 10.1016/j.jpha.2023.08.006.].

Liposomes serve as critical carriers for drugs and vaccines, with their biological effects influenced by their size. The microfluidic method, renowned for its precise control, reproducibility, and scalability, has been widely employed for liposome preparation. Although some studies have explored factors affecting liposomal size in microfluidic processes, most focus on small-sized liposomes, predominantly through experimental data analysis. However, the production of larger liposomes, which are equally significant, remains underexplored. In this work, we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning (ML) model capable of accurately predicting liposomal size. Experimental validation was conducted using a staggered herringbone micromixer (SHM) chip. Our findings reveal that most investigated variables significantly influence liposomal size, often interrelating in complex ways. We evaluated the predictive performance of several widely-used ML algorithms, including ensemble methods, through cross-validation (CV) for both liposome size and polydispersity index (PDI). A standalone dataset was experimentally validated to assess the accuracy of the ML predictions, with results indicating that ensemble algorithms provided the most reliable predictions. Specifically, gradient boosting was selected for size prediction, while random forest was employed for PDI prediction. We successfully produced uniform large (600 nm) and small (100 nm) liposomes using the optimised experimental conditions derived from the ML models. In conclusion, this study presents a robust methodology that enables precise control over liposome size distribution, offering valuable insights for medicinal research applications.

ObjectiveTo investigate the relationship between Jiaotai pill (JTP), its main component berberine (BBR), and the serotonin (5-HT) system in regulating islet hormone secretion and alleviating pancreatic β-cell dysfunction during type 2 diabetes mellitus (T2DM) progression.MethodsT2DM rat model was established using a high-fat diet and streptozotocin injection. JTP, BBR, and Metformin were intragastrically administered for 35 days. The analyzed indices included blood glucose, blood lipids, islet hormones, and proteins related to 5-HT synthesis, secretion, and transport. Additionally, an in vitro model of glucose injury in islet cells was established to study the effects of JTP and BBR on islet hormone secretion following tryptophan hydroxylase 1 (TPH1) inhibition.ResultsJTP and BBR significantly improved blood glucose and lipid levels and islet morphology in T2DM rats. Both models exhibited reduced islet 5-HT levels and impaired islet hormone secretion. However, the administration of JTP and BBR reversed these effects. Furthermore, JTP and BBR upregulated the expression of TPH1(P = .0194, P = .0413) transglutaminase 2 (TGase2; P = .0492, P = .0349), serotonin transporter (SERT, P = .0090), and 5- hydroxytryptamine 1F receptor (5-HT1FR) in the islet 5-HT pathway (P = .0194). In the cell model, the regulatory effects of JTP and BBR on islet hormone levels were significantly weakened after TPH1 inhibition (P = .001), suggesting that JTP and BBR influence islet hormone secretion through the pancreatic 5-HT system.ConclusionThe islet 5-HT system is correlated with islet hormone secretion dysfunction in T2DM. JTP and BBR can improve islet hormone secretion by activating the TPH1/TGase2/SERT/5-HT1FR pathway in the islet 5-HT system in T2DM rats.

Originally used as an antimalarial drug,hydroxychloroquine is now widely used in the treatment of rheumatic immune diseases due to its cost-effectiveness,safety,and efficacy.In addition to its immunomodulatory effects,hydroxychloroquine also exhibits anti thrombotic,anti-hypolipidemic,and anti-hypoglycemic properties.Hydroxychloroquine blood levels are correlated with clinical outcomes and adverse reactions,and can reflect patient compliance.However,due to the complex pharmacokinetic profile of hydroxychloroquine,significant inter-individual differences in blood concentration exist even with the administration of the same dosage.This study investigates the factors affecting the blood concentration of hydroxychloroquine in terms of physiological factors,pathological factors,metabolic enzyme gene polymorphisms,and drug-related factors.The aim is to provide a reference for rational clinical use and the development of individualized dosing.

Objective To investigate the association between microvariants at locus DYS570 and Y-SNPs haplogroup.Methods 89 Y-SNPs and 34 Y-STRs in AIYSNP42,AIYSNP47 and YfilerTM Platinum kits were used to detect the genotype of 116 microvariants at locus DYS570 in Kunming,and the Set-B kit was used to detect the core repeat sequences of the DYS570 locus.The data were statistically analyzed by direct counting method.Then,a network map was drawn by Network 10.2,in order to visualize the genetic information of the sample.Results The results demonstrated that 111 DYS570/18.3-21.3 samples had a core repeat sequence of TTT[TITC]18-21,belonging to subgroup O2a2b1a1a1a4-F14494.A DYS570/20.3 sample had a core repeat sequence of[TTTC]15TTC[TTTC]5,belonging to O2a1b1a1a1a1e-F1365 subgroup.A DYS570/17.1 sample had a core repeat sequence of[TTTC]17 T,belonging to the O2a1b1a1a1a-F11 subgroup.Three DYS570(19.2)samples had[TTTC]3 TT[TTTC]16,belonging to the D1a1a-M15 haplogroup.Conclusion The results indicated that the microvariant with the same core repeat structure at locus DYS570 was associated with haplogroups,and the ancestry origin of samples can be inferenced from microvariant characteristics during the practice of forensic medicine.

Objective To construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators.Methods A case-control study was conducted on 315 patients with liver injury after non-liver surgery selected from the databases developed by 3 large general hospitals from January 2014 to September 2022.With the positive/negative ratio of 1 ∶3,928 cases in corresponding period with non-liver surgery and without liver injury were randomly matched as negative control cases.These 1243 patients were randomly divided into the modeling group(n=869)and the validation group(n=374)in a ratio of 7∶3 using the R language setting code.Preoperative clinical indicators(basic information,medical history,relevant scale score,surgical information and results of laboratory tests)and intraoperative medication were used to construct the prediction model for liver injury after non-liver surgery based on 4 machine learning algorithms,k-nearest neighbor(KNN),support vector machine linear(SVM),logic regression(LR)and extreme gradient boosting(XGBoost).In the validation group,receiver operating characteristic(ROC)curve,precision-recall curve(P-R),decision curve analysis(DCA)curve,Kappa value,sensitivity,specificity,Brier score,and F1 score were applied to evaluate the efficacy of model.Results The model established by 4 machine learning algorithms to predict postoperative liver injury after non-liver surgery was optimal using the XGBoost algorithm.The area under the receiver operating characteristic curve(AUROC)was 0.916(95%CI:0.883～0.949),area under the precision-recall curve(AUPRC)was 0.841,Brier score was 0.097,and sensitivity and specificity was 78.95%and 87.10%,respectively.Conclusion The postoperative liver injury prediction model for non-liver surgery based on the XGBoost algorithm has effective prediction for the occurrence of postoperative liver injury.