Improving interface quality for 1-cm2 all-perovskite tandem solar cells.

All-perovskite tandem solar cells provide high power conversion efficiency at a low cost1-4. Rapid efficiency improvement in small-area (<0.1 cm2) tandem solar cells has been primarily driven by advances in low-bandgap (approximately 1.25 eV) perovskite bottom subcells5-7. However, unsolved issues remain for wide-bandgap (> 1.75 eV) perovskite top subcells8, which at present have large voltage and fill factor losses, particularly for large-area (>1 cm2) tandem solar cells. Here we develop a self-assembled monolayer of (4-(7H-dibenzo[c,g]carbazol-7-yl)butyl)phosphonic acid as a hole-selective layer for wide-bandgap perovskite solar cells, which facilitates subsequent growth of high-quality wide-bandgap perovskite over a large area with suppressed interfacial non-radiative recombination, enabling efficient hole extraction. By integrating (4-(7H-dibenzo[c,g]carbazol-7-yl)butyl)phosphonic acid in devices, we demonstrate a high open-circuit voltage (VOC) of 1.31 V in a 1.77-eV perovskite solar cell, corresponding to a very low VOC deficit of 0.46 V (with respect to the bandgap). With these wide-bandgap perovskite subcells, we report 27.0% (26.4% certified stabilized) monolithic all-perovskite tandem solar cells with an aperture area of 1.044 cm2. The certified tandem cell shows an outstanding combination of a high VOC of 2.12 V and a fill factor of 82.6%. Our demonstration of the large-area tandem solar cells with high certified efficiency is a key step towards scaling up all-perovskite tandem photovoltaic technology.

Artificial intelligence-enabled quantitative phase imaging methods for life sciences.

Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.

Author Correction: The Apostasia genome and the evolution of orchids.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Spatio-temporal transcriptome dynamics coordinate rapid transition of core crop functions in 'lactating' pigeon.

Pigeons (Columba livia) are among a select few avian species that have developed a specialized reproductive mode wherein the parents produce a 'milk' in their crop to feed newborn squabs. Nonetheless, the transcriptomic dynamics and role in the rapid transition of core crop functions during 'lactation' remain largely unexplored. Here, we generated a de novo pigeon genome assembly to construct a high resolution spatio-temporal transcriptomic landscape of the crop epithelium across the entire breeding stage. This multi-omics analysis identified a set of 'lactation'-related genes involved in lipid and protein metabolism, which contribute to the rapid functional transitions in the crop. Analysis of in situ high-throughput chromatin conformation capture (Hi-C) sequencing revealed extensive reorganization of promoter-enhancer interactions linked to the dynamic expression of these 'lactation'-related genes between stages. Moreover, their expression is spatially localized in specific epithelial layers, and can be correlated with phenotypic changes in the crop. These results illustrate the preferential de novo synthesis of 'milk' lipids and proteins in the crop, and provides candidate enhancer loci for further investigation of the regulatory elements controlling pigeon 'lactation'.

Gut microbiota-derived butyrate promotes coronavirus TGEV infection through impairing RIG-I-triggered local type I interferon responses via class I HDAC inhibition.

Gut microbiota-derived metabolites are important for the replication and pathogenesis of many viruses. However, the roles of bacterial metabolites in swine enteric coronavirus (SECoV) infection remain poorly understood. Recent studies show that SECoVs infection in vivo significantly alters the composition of short-chain fatty acids (SCFAs)-producing gut microbiota. This prompted us to investigate whether and how SCFAs impact SECoV infection. Employing alphacoronavirus transmissible gastroenteritis virus (TGEV), a major cause of diarrhea in piglets, as a model, we found that SCFAs, particularly butyrate, enhanced TGEV infection both in porcine intestinal epithelial cells and swine testicular (ST) cells at the late stage of viral infection. This effect depended on the inhibited productions of virus-induced type I interferon (IFN) and downstream antiviral IFN-stimulated genes (ISGs) by butyrate. Mechanistically, butyrate suppressed the expression of retinoic acid-inducible gene I (RIG-I), a key viral RNA sensor, and downstream mitochondrial antiviral-signaling (MAVS) aggregation, thereby impairing type I IFN responses and increasing TGEV replication. Using pharmacological and genetic approaches, we showed that butyrate inhibited RIG-I-induced type I IFN signaling by suppressing class I histone deacetylase (HDAC). In summary, we identified a novel mechanism where butyrate enhances TGEV infection by suppressing RIG-I-mediated type I IFN responses. Our findings highlight that gut microbiota-derived metabolites like butyrate can be exploited by SECoV to dampen innate antiviral immunity and establish infection in the intestine.IMPORTANCESwine enteric coronaviruses (SECoVs) infection in vivo alters the composition of short-chain fatty acids (SCFAs)-producing gut microbiota, but whether microbiota-derived SCFAs impact coronavirus gastrointestinal infection is largely unknown. Here, we demonstrated that SCFAs, particularly butyrate, substantially increased alphacoronavirus TGEV infection at the late stage of infection, without affecting viral attachment or internalization. Furthermore, enhancement of TGEV by butyrate depended on impeding virus-induced type I interferon (IFN) responses. Mechanistically, butyrate suppressed the cytoplasmic viral RNA sensor RIG-I expression and downstream type I IFN signaling activation by inhibiting class I HDAC, thereby promoting TGEV infection. Our work reveals novel functions of gut microbiota-derived SCFAs in enhancing enteric coronavirus infection by impairing RIG-I-dependent type I IFN responses. This implies that bacterial metabolites could be therapeutic targets against SECoV infection by modulating antiviral immunity in the intestine.

DNAJC24 acts directly with PCNA and promotes malignant progression of LUAD by activating phosphorylation of AKT.

Heat shock proteins (HSPs) are a group of highly conserved proteins found in a wide range of organisms. In recent years, members of the HSP family were overexpressed in various tumors and widely involved in oncogenesis, tumor development, and therapeutic resistance. In our previous study, DNAJC24, a member of the DNAJ/HSP40 family of HSPs, was found to be closely associated with the malignant phenotype of hepatocellular carcinoma. However, its relationship with other malignancies needs to be further explored. Herein, we demonstrated that DNAJC24 exhibited upregulated expression in LUAD tissue samples and predicted poor survival in LUAD patients. The upregulation of DNAJC24 expression promoted proliferation and invasion of LUAD cells in A549 and NCI-H1299 cell lines. Further studies revealed that DNAJC24 could regulate the PI3K/AKT signaling pathway by affecting AKT phosphorylation. In addition, a series of experiments such as Co-IP and mass spectrometry confirmed that DNAJC24 could directly interact with PCNA and promoted the malignant phenotypic transformation of LUAD. In conclusion, our results suggested that DNAJC24 played an important role in the progression of LUAD and may serve as a specific prognostic biomarker for LUAD patients. The DNAJC24/PCNA/AKT axis may be a potential target for future individualized and precise treatment of LUAD patients.

DTKGIN: Predicting drug-target interactions based on knowledge graph and intent graph.

Knowledge graph intent graph attention mechanism Predicting drug-target interactions (DTIs) plays a crucial role in drug discovery and drug development. Considering the high cost and risk of biological experiments, developing computational approaches to explore the interactions between drugs and targets can effectively reduce the time and cost of drug development. Recently, many methods have made significant progress in predicting DTIs. However, existing approaches still suffer from the high sparsity of DTI datasets and the cold start problem. In this paper, we develop a new model to predict drug-target interactions via a knowledge graph and intent graph named DTKGIN. Our method can effectively capture biological environment information for targets and drugs by mining their associated relations in the knowledge graph and considering drug-target interactions at a fine-grained level in the intent graph. DTKGIN learns the representation of drugs and targets from the knowledge graph and the intent graph. Then the probabilities of interactions between drugs and targets are obtained through the inner product of the representation of drugs and targets. Experimental results show that our proposed method outperforms other state-of-the-art methods in 10-fold cross-validation, especially in cold-start experimental settings. Furthermore, the case studies demonstrate the effectiveness of DTKGIN in predicting potential drug-target interactions. The code is available on GitHub: https://github.com/Royluoyi123/DTKGIN.

A p21-ATD mouse model for monitoring and eliminating senescent cells and its application in liver regeneration post injury.

Cellular senescence associates with pathological aging and tissue dysfunctions. Studies utilizing mouse models for cell lineage tracings have emphasized the importance of senescence heterogeneity in different organs and cell types. Here, we constructed a p21- (Akaluc - tdTomato - Diphtheria Toxin Receptor [DTR]) (ATD) mouse model to specifically study the undefined mechanism for p21-expressing senescent cells in the aged and liver injury animals. The successful expressions of these genes enabled in vitro flow cytometric sorting, in vivo tracing, and elimination of p21-expressing senescent cells. During the natural aging process, p21-expressing cells were found in various tissues of p21-ATD mice. Eliminating p21-expressing cells in the aged p21-ATD mice recovered their multiple biological functions. p21-ATD/Fah-/- mice, bred from p21-ATD mice and fumarylacetoacetate hydrolase (Fah)-/- mice of liver injury, showed that the majority of their senescent hepatocytes were the phenotype of p21+ rather than p16+. Furthermore, eliminating the p21-expressing hepatocytes significantly promoted the engraftment of grafted hepatocytes and facilitated liver repopulation, resulting in significant recovery from liver injury. Our p21-ATD mouse model serves as an optimal model for studying the pattern and function of p21-expressing senescent cells under the physical and pathological conditions during aging.

The nucleosome unwrapping free energy landscape defines distinct regions of transcription factor accessibility and kinetics.

Transcription factors (TF) require access to target sites within nucleosomes to initiate transcription. The target site position within the nucleosome significantly influences TF occupancy, but how is not quantitatively understood. Using ensemble and single-molecule fluorescence measurements, we investigated the targeting and occupancy of the transcription factor, Gal4, at different positions within the nucleosome. We observe a dramatic decrease in TF occupancy to sites extending past 30 base pairs (bp) into the nucleosome which cannot be explained by changes in the TF dissociation rate or binding site orientation. Instead, the nucleosome unwrapping free energy landscape is the primary determinant of Gal4 occupancy by reducing the Gal4 binding rate. The unwrapping free energy landscape defines two distinct regions of accessibility and kinetics with a boundary at 30 bp into the nucleosome where the inner region is over 100-fold less accessible. The Gal4 binding rate in the inner region no longer depends on its concentration because it is limited by the nucleosome unwrapping rate, while the frequency of nucleosome rewrapping decreases because Gal4 exchanges multiple times before the nucleosome rewraps. Our findings highlight the importance of the nucleosome unwrapping free energy landscape on TF occupancy and dynamics that ultimately influences transcription initiation.

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.

Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features. For any given compound as a target, a product CIMG is generated and exploited by a graph neural network (GNN) model to choose reaction template(s) leading to this product. A reactant CIMG is then inferred and used in two GNN models to select appropriate catalyst and solvent, respectively. Finally, a fourth GNN model compares the two CIMG descriptors to check the plausibility of the proposed reaction. A reaction vector is obtained for every molecule in training these models. The chemical wisdom of reaction propensity contained in the pretrained reaction vectors is exploited to autocategorize molecules/reactions and to accelerate Monte Carlo tree search (MCTS) for multistep retrosynthesis planning. Full synthetic routes with recommended catalysts/solvents are predicted efficiently using this CIMG-based approach.

Atomic-scale probing of heterointerface phonon bridges in nitride semiconductor.

Interface phonon modes that are generated by several atomic layers at the heterointerface play a major role in the interface thermal conductance for nanoscale high-power devices such as nitride-based high-electron-mobility transistors and light-emitting diodes. Here we measure the local phonon spectra across AlN/Si and AlN/Al interfaces using atomically resolved vibrational electron energy-loss spectroscopy in a scanning transmission electron microscope. At the AlN/Si interface, we observe various interface phonon modes, of which the extended and localized modes act as bridges to connect the bulk AlN modes and bulk Si modes and are expected to boost the phonon transport, thus substantially contributing to interface thermal conductance. In comparison, no such phonon bridge is observed at the AlN/Al interface, for which partially extended modes dominate the interface thermal conductivity. This work provides valuable insights into understanding the interfacial thermal transport in nitride semiconductors and useful guidance for thermal management via interface engineering.

Theoretical and computational methods for tip- and surface-enhanced Raman scattering.

Raman spectroscopy is a versatile tool for acquiring molecular structure information. The incorporation of plasmonic fields has significantly enhanced the sensitivity and resolution of surface-enhanced Raman scattering (SERS) and tip-enhanced Raman spectroscopy (TERS). The strong spatial confinement effect of plasmonic fields has challenged the conventional Raman theory, in which a plane wave approximation for the light has been adopted. In this review, we comprehensively survey the progress of a generalized theory for SERS and TERS in the framework of effective field Hamiltonian (EFH). With this approach, all characteristics of localized plasmonic fields can be well taken into account. By employing EFH, quantitative simulations at the first-principles level for state-of-the-art experimental observations have been achieved, revealing the underlying intrinsic physics in the measurements. The predictive power of EFH is demonstrated by several new phenomena generated from the intrinsic spatial, momentum, time, and energy structures of the localized plasmonic field. The corresponding experimental verifications are also carried out briefly. A comprehensive computational package for modeling of SERS and TERS at the first-principles level is introduced. Finally, we provide an outlook on the future developments of theory and experiments for SERS and TERS.

Analytical Model of Optical-Field-Driven Subcycle Electron Tunneling Pulses from Two-Dimensional Materials.

We develop analytical models of optical-field-driven electron tunneling from the edge and surface of free-standing two-dimensional (2D) materials. We discover a universal scaling between the tunneling current density (J) and the electric field near the barrier (F): In(J/|F|ß) ∝ 1/|F| with ß values of 3/2 and 1 for edge emission and vertical surface emission, respectively. At ultrahigh values of F, the current density exhibits an unexpected high-field saturation effect due to the reduced dimensionality of the 2D material, which is absent in the traditional bulk material. Our calculation reveals the dc bias as an efficient method for modulating the optical-field tunneling subcycle emission characteristics. Importantly, our model is in excellent agreement with a recent experiment on graphene. Our results offer a useful framework for understanding optical-field tunneling emission from 2D materials, which are helpful for the development of optoelectronics and emerging petahertz vacuum nanoelectronics.

Roles of S100A8, S100A9 and S100A12 in infection, inflammation and immunity.

S100 proteins are small proteins that are only expressed in vertebrates. They are widely expressed in many different cell types and are involved in the regulation of calcium homeostasis, glucose metabolism, cell proliferation, apoptosis, inflammation and tumorigenesis. As members of the S100 protein subfamily of myeloid-related proteins, S100A8, S100A9 and S100A12 play a crucial role in resisting microbial infection and maintaining immune homeostasis. These proteins chelate the necessary metal nutrients of pathogens invading the host by means of 'nutritional immunity' and directly inhibit the growth of pathogens in the host. They interact with receptors on the cell surface to initiate inflammatory signal transduction, induce cytokine expression and participate in the inflammatory response and immune regulation. Furthermore, the increased content of these proteins during the pathological process makes them useful as disease markers for screening and detecting related diseases. This article summarizes the structure and function of the proteins S100A8, S100A9 and S100A12 and lays the foundation for further understanding their roles in infection, immunity and inflammation, as well as their potential applications in the prevention and treatment of infectious diseases.

Dinitrogen Cleavage and Multicoupling with Isocyanides in a Dititanium Dihydride Framework.

Dinitrogen (N2) activation and functionalization through N-N bond cleavage and N-C bond formation are of great interest and importance but remain highly challenging. We report here for the first time N2 cleavage and selective multicoupling with isocyanides in a dititanium dihydride framework. The reaction of a dinitrogen dititanium dihydride complex [{(acriPNP)Ti}2(µ-η1:η2-N2)(µ-H)2] (1) with an excess (four or more equivalents) of p-methoxyphenyl isocyanide at room temperature gave a novel amidoamidinatoguanidinate complex [(acriPNP)Ti{NC(═NR)NC(═NR)CH2NR}Ti(acriPNP)(CNR)] (2, acriPNP = 4,5-bis(diisopropylphosphino)-2,7,9,9-tetramethyl-9H-acridin-10-ide; R = p-MeOC6H4) through N2 splitting and coupling with three isocyanide molecules. When 1 equiv of p-methoxyphenyl isocyanide was used to react with 1 at -30 °C, the hydrogenation of the isocyanide unit by the two hydride ligands in 1 took place, affording an amidomethylene-bridged dititanium dinitrogen complex [{(acriPNP)Ti}2(µ-η1:η2-N2){µ-η1:η2-CH2N(p-MeOC6H4)}] (3), which upon reaction with another equivalent of p-methoxyphenyl isocyanide at room temperature gave an amidomethylene/nitrido/carbodiimido complex [(acriPNP)Ti(N═C═NR)(µ-N)(µ-η1:η2-CH2NR)Ti(acriPNP)] (4) through N2 cleavage and N═C bond formation. Further reaction of 4 with 1 equiv of p-methoxyphenyl isocyanide led to an unprecedented four-component (carbodiimido, nitrido, isocyanide, and amidomethylene) coupling, yielding an amidoamidinatoguanidinate complex [{(acriPNP)Ti}2{NC(═NR)NC(═NR)CH2NR}] (5), which on reaction with another equivalent of p-methoxyphenyl isocyanide afforded the isocyanide-coordinated analogue 2. The reaction of 1 with 2-naphthyl isocyanide also took place in a similar multicoupling fashion. Moreover, the cross-coupling reactions of the p-methoxyphenyl isocyanide-derived amidomethylene/nitrido/carbodiimido complex 4 with 2-naphthyl isocyanide, cyclohexyl isocyanide, and tert-butyl isocyanide were also achieved, which afforded the corresponding amidoamidinatoguanidinate products consisting of two different isocyanides. Density functional theory (DFT) calculations further elucidated the mechanistic details.

Accurate Detection of Multiple Tumor Mutations in Formalin-Fixed Paraffin-Embedded Tissues by Coupling Sequence Artifacts Elimination and Mutation Enrichment With MeltArray.

Formalin-fixed paraffin-embedded (FFPE) tissues are the primary source of DNA for companion diagnostics (CDx) of cancers. Degradation of FFPE tissue DNA and inherent tumor heterogeneity constitute serious challenges in current CDx assays. To address these limitations, we introduced sequence artifact elimination and mutation enrichment to MeltArray, a highly multiplexed PCR approach, to establish an integrated protocol that provides accuracy, ease of use, and rapidness. Using PIK3CA mutations as a model, we established a MeltArray protocol that could eliminate sequence artifacts completely and enrich mutations from 23.5- to 59.4-fold via a single-reaction pretreatment step comprising uracil-DNA-glycosylase excision and PCR clamping. The entire protocol could identify 13 PIK3CA hotspot mutations of 0.05% to 0.5% mutant allele fractions within 5 hours. Evaluation of 106 breast cancer and 40 matched normal FFPE tissue samples showed that all 47 PIK3CA mutant samples were from the cancer tissue, and no false-positive results were detected in the normal samples. Further evaluation of 105 colorectal and 40 matched normal FFPE tissue samples revealed that 11 PIK3CA mutants were solely from the cancer sample. The detection results of our protocol were consistent with those of the droplet digital PCR assays that underwent sequence artifact elimination. Of the 60 colorectal samples with next-generation sequencing results, the MeltArray protocol detected 2 additional mutant samples with low mutant allele fractions. We conclude that the new protocol provides an improved alternative to current CDx assays for detecting tumor mutations in FFPE tissue DNA.

Comparison of long-term outcomes between imatinib and dasatinib prophylaxis after allogeneic stem cell transplantation in patients with Philadelphia-positive acute lymphoblastic leukemia: A multicenter retrospective study.

BACKGROUND: Although the prognosis of Philadelphia-positive acute lymphoblastic leukemia (Ph+ ALL) has improved with the introduction of tyrosine kinase inhibitors (TKIs) and stem cell transplantation, prevention of relapse after transplantation remains a concern. The aim of this study was to compare the impact of TKI prophylaxis with imatinib and dasatinib on long-term outcomes after transplantation. METHODS: Patients with Ph+ ALL who underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT) at first complete remission (CR1) and received TKI prophylaxis after allo-HSCT were included in this retrospective analysis. Two cohorts were established based on the choice of TKI prophylaxis: the imatinib (Ima) and dasatinib (Das) cohorts. The survival and safety outcomes of these cohorts were compared. RESULTS: Ninety-one patients in the Ima cohort and 50 in the Das cohort were included. After a median follow-up of 50.6 months, the 5-year cumulative incidence of relapse, nonrelapse mortality rate, and overall survival in the Ima and Das cohorts were 16.1% and 12.5%, 5.2% and 9.8%, and 86.5% and 77.6%, respectively, with no statistical differences. The cumulative incidence of mild chronic graft-versus-host disease was higher in the Das cohort. The most common adverse event was neutropenia (64.7% vs. 69.5%). The Das cohort had a higher incidence of gastrointestinal bleeding (25.5% vs. 2.3%) and gastrointestinal reaction (48.9% vs. 31.4%) than the Ima cohort. The proportion of patients treated on schedule was significantly lower in the Das cohort than in the Ima cohort, and drug intolerance was the main reason for protocol violation. CONCLUSIONS: For patients with Ph+ ALL undergoing allo-HSCT in CR1, imatinib prophylaxis achieved long-term outcomes similar to those of dasatinib.

TiN as Radical Scavenger in Fe─N─C Aerogel Oxygen Reduction Catalyst for Durable Fuel Cell.

Fe─N─C is the most promising alternative to platinum-based catalysts to lower the cost of proton-exchange-membrane fuel cell (PEMFC). However, the deficient durability of Fe─N─C has hindered their application. Herein, a TiN-doped Fe─N─C (Fe─N─C/TiN) is elaborately synthesized via the sol-gel method for the oxygen-reduction reaction (ORR) in PEMFC. The interpenetrating network composed by Fe─N─C and TiN can simultaneously eliminate the free radical intermediates while maintaining the high ORR activity. As a result, the H2 O2 yields of Fe─N─C/TiN are suppressed below 4%, ≈4 times lower than the Fe─N─C, and the half-wave potential only lost 15 mV after 30 kilo-cycle accelerated durability test (ADT). In a H2 ─O2 fuel cell assembled with Fe─N─C/TiN, it presents 980 mA cm-2 current density at 0.6 V, 880 mW cm-2 peak power density, and only 17 mV voltage loss at 0.80 A cm-2 after 10 kilo-cycle ADT. The experiment and calculation results prove that the TiN has a strong adsorption interaction for the free radical intermediates (such as *OH, *OOH, etc.), and the radicals are scavenged subsequently. The rational integration of Fe single-atom, TiN radical scavenger, and highly porous network adequately utilize the intrinsic advantages of composite structure, enabling a durable and active Pt-metal-free catalyst for PEMFC.

pH/H2 O2 Cascade-Responsive Nanoparticles of Lipid-Like Prodrugs through Dynamic-Covalent and Coordination Interactions for Chemotherapy.

Traditional lipid nanoparticles (LNPs) suffer from low drug loading capacity (DLC), weak stability, and lack of responsiveness. Conventional approaches to address these issues involve the synthesis of lipid-prodrug by incorporating responsive covalent linkers. However, such approaches often result in suboptimal sensitivity for drug release and undermine therapeutic effectiveness. Herein, the study reports a fundamentally different concept for designing lipid-like prodrugs through boron-nitrogen (B-N) coordination and dynamic covalent interaction. The 5-fluorouracil-based lipid-like prodrugs, featuring a borate ester consisting of a glycerophosphoryl choline head and a boronic acid-modified 5Fu/dodecanamine complex tail, are used to prepare pH/H2 O2 cascade-responsive LNPs (5Fu-LNPs). The 5Fu-LNPs exhibit enhanced DLC and stability in a neutral physiological environment due to the B-N coordination and enhanced hydrophobicity. In tumors, acidic pH triggers the dissociation of B-N coordination to release prodrugs, which further responds to low H2 O2 concentrations to release drugs, showcasing a potent pH/H2 O2 -cascade-responsive property. Importantly, 5Fu-LNPs demonstrate greater antitumor efficiency and lower toxicity compared to the commercial 5Fu. These results highlight 5Fu-LNPs as a safer and more effective alternative to chemotherapy. This work presents a unique LNP fabrication strategy that can overcome the limitations of conventional LNPs and broaden the range of intelligent nanomaterial preparation techniques.

SSK1-Loaded Neurotransmitter-Derived Nanoparticles for Alzheimer's Disease Therapy via Clearance of Senescent Cells.

Age is a significant contributor to the onset of AD. Senolysis has been recently demonstrated to ameliorate aging-associated diseases that showing a great potential in AD therapy. However, due to the presence of BBB, the anti-AD activity of senolytics are significantly diminished. SSK1 is a prodrug that can be activated by ß-gal, a lysosomal enzyme commonly upregulated in senescent cells, and thus selectively eliminates senescent cells. Furthermore, the level of ß-gal is significantly correlated with conventional AD genes from clinical sequencing data. SSK1-loaded neurotransmitter -derived lipid nanoparticles are herein developed (SSK1-NPs) that revealing good BBB penetration and bioavailability of in the body. At the brain lesion, SSK1-NP treatment significantly reduces the expression of genes associated with senescence, induced senescent cells elimination, decreased amyloid-beta accumulation, and eventually improve cognitive function of aged AD mice. SSK1-NPs, a novel nanomedicine displaying potent anti-AD activity and excellent safety profile, provides a promising strategy for AD therapy.