Synthesis and biological evaluation of novel penindolone derivatives as potential antiproliferative agents against SCLC in vitro.

Small cell lung cancer (SCLC) keeps on the leading cause of cancer mortality world widely, while there is lack of efficient therapeutic drugs especially for the resistant ones. In this work, a compound named penindolone (PND) with new skeleton was found to show weak inhibitory effect (IC50 = 42.5 µM) on H69AR cells (SCLC, adriamycin-resistant) proliferation by screening our in-house compound library. With the aim of improving its low potency, a series of PND derivatives were synthesized and biologically evaluated by the Sulforhodamine B (SRB) assay. Among all tested derivatives, compound 5h possessed higher antiproliferation potency (IC50 = 1.6 µM). Furthermore, preliminary mechanism investigation revealed that 5h was able to induce apoptosis and arrest the cell cycle at G0/G1 phase. These findings suggest that this novel skeleton has expanded the anti-SCLC compound reservoir and provided a new drug lead.

Multimodal learning system integrating electronic medical records and hysteroscopic images for reproductive outcome prediction and risk stratification of endometrial injury: a multicenter diagnostic study.

OBJECTIVE: To develop a multimodal learning application system that integrates electronic medical records (EMR) and hysteroscopic images for reproductive outcome prediction and risk stratification of patients with intrauterine adhesions (IUAs) resulting from endometrial injuries. MATERIALS AND METHODS: EMR and 5014 revisited hysteroscopic images of 753 post hysteroscopic adhesiolysis patients from the multicenter IUA database we established were randomly allocated to training, validation, and test datasets. The respective datasets were used for model development, tuning, and testing of the multimodal learning application. MobilenetV3 was employed for image feature extraction, and XGBoost for EMR and image feature ensemble learning. The performance of the application was compared against the single-modal approaches (EMR or hysteroscopic images), DeepSurv and ElasticNet models, along with the clinical scoring systems. The primary outcome was the 1-year conception prediction accuracy, and the secondary outcome was the assisted reproductive technology (ART) benefit ratio after risk stratification. RESULTS: The multimodal learning system exhibited superior performance in predicting conception within 1-year, achieving areas under the curves of 0.967 (95% CI: 0.950-0.985), 0.936 (95% CI: 0.883-0.989), and 0.965 (95% CI: 0.935-0.994) in the training, validation, and test datasets, respectively, surpassing single-modal approaches, other models and clinical scoring systems (all P<0.05). The application of the model operated seamlessly on the hysteroscopic platform, with an average analysis time of 3.7±0.8 s per patient. By employing the application's conception probability-based risk stratification, mid-high-risk patients demonstrated a significant ART benefit (odds ratio=6, 95% CI: 1.27-27.8, P=0.02), while low-risk patients exhibited good natural conception potential, with no significant increase in conception rates from ART treatment (P=1). CONCLUSIONS: The multimodal learning system using hysteroscopic images and EMR demonstrates promise in accurately predicting the natural conception of patients with IUAs and providing effective postoperative stratification, potentially contributing to ART triage after IUA procedures.

Artemisinin Alleviates Myocardial Remodeling through TGF-ß1/Smad2/3 Pathway and NLRP3 Inflammasome.

BACKGROUND AND OBJECTIVES: Artemisinin and its derivatives, the well-known anti-malarial drugs extracted from traditional Chinese medicine, have been implicated in treating fibrotic diseases. However, whether artemisinin affects cardiac fibrosis in the pathogenesis of heart failure is still unknown. This study aimed to evaluate the possible effects of artemisinin on cardiac function and myocardial fibrosis in the heart failure model and to explore the underlying mechanisms. METHODS: Isoproterenol was injected subcutaneously for induction of the cardiac fibrosis model. Proteomic analysis was performed after 4 four weeks of artemisinin treatment. Echocardiography was used to evaluate cardiac function and structure. Hematoxylin and eosin (H&E) staining, as well as Masson staining, were performed for histopathology. The α-SMA, collagen I, and III expression in the myocardium was detected by Immunohistochemical staining. The ratio of heart weight (HW) to body weight (HW/BW, mg/kg) and the ratio of heart weight to tibia length (HW/TL, mg/mm) were calculated as indicators for cardiac remodeling. Brain natriuretic peptide (BNP) levels were quantified in rat plasma using enzyme-linked immunosorbent assay (ELISA). In contrast, the protein levels of TGF-ß1, p-Smad2/3, and Smad2/3 were assessed in myocardium and fibroblasts via western blot analysis. RT-qPCR analysis of Col-I, Col-III, α-SMA, NLRP3, Caspase-1, IL-1ß, and IL-18 was performed in the heart. RESULTS: Proteomic analysis identified 227 differentially expressed proteins (DEPs), including 119 upregulated and 108 downregulated proteins. These proteins were identified as the core proteins targeted by artemisinin for improving myocardial remodeling. GO annotation of the DEPs indicated that the DEPs were mainly associated with biological processes such as TGF-ß and NLRP3 inflammasome regulation. In the in vivo study of an isoproterenol-induced SD rat cardiac remodeling model, we found that artemisinin administration significantly ameliorated cardiac dysfunction and reduced collagen production by suppressing TGFß-1/Smads signaling and inhibiting NLRP3 inflammasome activation. As manifested by downregulating the expression of α-SMA, Col-I, and Col-III, NLRP3, IL-1ß, IL-18, Caspase-1 mRNA, and TGF-ß1, p-SMAD 2/3 protein in the myocardium. Similar beneficial effects of artemisinin were consistently observed in TGF-ß1 treated primary cardiac fibroblasts. CONCLUSIONS: Artemisinin relieves myocardial remodeling through TGF-ß1/Smad2/3 pathway and NLRP3 inflammasome.

Multi-Domain Based Dynamic Graph Representation Learning for EEG Emotion Recognition.

Graph neural networks (GNNs) have demonstrated efficient processing of graph-structured data, making them a promising method for electroencephalogram (EEG) emotion recognition. However, due to dynamic functional connectivity and nonlinear relationships between brain regions, representing EEG as graph data remains a great challenge. To solve this problem, we proposed a multi-domain based graph representation learning (MD 2 GRL) framework to model EEG signals as graph data. Specifically, MD 2 GRL leverages gated recurrent units (GRU) and power spectral density (PSD) to construct node features of two subgraphs. Subsequently, the self-attention mechanism is adopted to learn the similarity matrix between nodes and fuse it with the intrinsic spatial matrix of EEG to compute the corresponding adjacency matrix. In addition, we introduced a learnable soft thresholding operator to sparsify the adjacency matrix to reduce noise in the graph structure. In the downstream task, we designed a dual-branch GNN and incorporated spatial asymmetry for graph coarsening. We conducted experiments using the publicly available datasets SEED and DEAP, separately for subject-dependent and subject-independent, to evaluate the performance of our model in emotion classification. Experimental results demonstrated that our method achieved state-of-the-art (SOTA) classification performance in both subject-dependent and subject-independent experiments. Furthermore, the visualization analysis of the learned graph structure reveals EEG channel connections that are significantly related to emotion and suppress irrelevant noise. These findings are consistent with established neuroscience research and demonstrate the potential of our approach in comprehending the neural underpinnings of emotion.

Polyoxometalate Functionalized Cyclic Trinuclear Copper Compounds for Bifunctional Electrochemical Detection and Photocatalytic Reduction of Cr(VI).

The design and intentional construction of crystalline materials containing two clusters with redox properties in one framework still remains challenging. Linking oxidative polyoxometalate (POM) clusters and a reductive cyclic trinuclear copper complex (Cu-CTC) to prepare stable catalysts is rarely reported. Herein, we successfully obtained two new polyoxometalate-based metal-organic compounds (POMOCs) [CuII3(PyCA)3(µ3-OH)(ß-Mo8O26)0.5(H2O)2]·5H2O (1), [CuII3(PyCA)3(µ3-OH)]2(CuIIW12O40)[CuII(H2O)6] (2) (PyCA = 1H-pyrazole-4-carbaldehyde) by enabling precursors of Cu-CTC and POM cocrystallization in one pot via hydrothermal method. The [ß-Mo8O26]4- cluster in compound 1 combined with Cu-CTC units to form a 1D structure, and the [CuW12O40]6- unit in compound 2 linked two Cu-CTC units to form a sandwich-like 0D structure. Also, Cu-CTC CuI3(PyCA)3·H2O (Cu3) was synthesized for performance comparison. A series of characterizations indicate that compound 1 is more conducive to electron transfer than compound 2. In addition, compounds 1 and 2 can act as bifunctional catalysts for the electrochemical detection and photocatalytic reduction of Cr(VI). Particularly, the photoreduction rates of Cr(VI) by compounds 1 and 2 are 96.7% and 96.3% for only 10 and 14 min under visible light, respectively, and it is better than that of Cu3 and most other reported photocatalysts. Furthermore, the active sites and mechanisms for electrochemical detection and photocatalytic reduction of Cr(VI) were discussed.

A Simple and Efficient Magnesium Hydroxide Modification Strategy for Flame-Retardancy Epoxy Resin.

Magnesium hydroxide, as a green inorganic flame-retardancy additive, has been widely used in polymer flame retardancy. However, magnesium hydroxide is difficult to disperse with epoxy resin (EP), and its flame-retardancy performance is poor, so it is difficult to use in flame-retardant epoxy resin. In this study, an efficient magnesium hydroxide-based flame retardant (MH@PPAC) was prepared by surface modification of 2-(diphenyl phosphine) benzoic acid (PPAC) using a simple method. The effect of MH@PPAC on the flame-retardancy properties for epoxy resins was investigated, and the flame-retardancy mechanism was studied. The results show that 5 wt% MH@PPAC can increase the limiting oxygen index for EP from 24.1% to 38.9%, achieving a V-0 rating. At the same time, compared to EP, the peak heat release rate, peak smoke production rate, total smoke production rate, and peak CO generation rate for EP/5 wt% MH@PPAC composite material decreased by 53%, 45%, 51.85%, and 53.13% respectively. The cooperative effect for PPAC and MH promotes the formation of a continuous and dense char layer during the combustion process for the EP-blend material, significantly reducing the exchange for heat and combustible gases, and effectively hindering the combustion process. Additionally, the surface modification of PPAC enhances the dispersion of MH in the EP matrix, endowing EP with superior mechanical properties that meet practical application requirements, thereby expanding the application scope for flame-retardant EP-blend materials.

Oxygen Vacancy Mediation in SnO2 Electron Transport Layers Enables Efficient, Stable, and Scalable Perovskite Solar Cells.

Previous findings have suggested a close association between oxygen vacancies in SnO2 and charge carrier recombination as well as perovskite decomposition at the perovskite/SnO2 interface. Underlying the fundamental mechanism holds great significance in achieving a more favorable balance between the efficiency and stability. In this study, we prepared three SnO2 samples with different oxygen vacancy concentrations and observed that a low oxygen vacancy concentration is conducive to long-term device stability. Iodide ions were observed to easily diffuse into regions with high oxygen vacancies, thereby speeding up the deprotonation of FAI, as made evident by the detection of the decomposition product formamide. In contrast, a high oxygen vacancy concentration in SnO2 could prevent hole injection, leading to a decrease in interfacial recombination losses. To suppress this decomposition reaction and address the trade-off, we designed a bilayer SnO2 structure to ensure highly efficient carrier transport still while maintaining a chemically inert surface. As a result, an enhanced efficiency of 25.06% (certified at 24.55% with an active area of 0.09 cm2 under fast scan) was achieved, and the extended operational stability maintained 90% of their original efficiency (24.52%) after continuous operation for nearly 2000 h. Additionally, perovskite submodules with an active area of 14 cm2 were successfully assembled with a PCE of up to 22.96% (20.09% with an aperture area).

Acrolein-triggered atherosclerosis via AMPK/SIRT1-CLOCK/BMAL1 pathway and a protection from intermittent fasting.

Circadian clock plays a vital role in the pathological progression of cardiovascular disease (CVD). Our previous studies showed that acrolein, an environmental pollutant, promoted atherosclerosis by reducing CLOCK/BMAL1 and disturbing circadian rhythm. Whereas, intermittent fasting (IF), a diet pattern, was able to ameliorate acrolein-induced atherosclerosis. In vivo, mice were fed acrolein 3 mg/kg/day via drinking water and IF for 18h (0:00-18:00). We observed that IF decreased acrolein-accelerated the formation of aortic lesion in ApoE -/- mice. Up-regulation of NF-κB, IL-1ß and TNF-α levels were found in liver and heart tissue upon acrolein exposure, while was down-regulated by IF. Interestingly, IF treatment exhibited higher AMPK, p-AMPK and SIRT1and lower MAPK expression which was caused by acrolein. Besides, circadian genes Clock/ Bmal1 expression were suppressed and disturbed treated with acrolein, while were reversed by IF. Furthermore, consistent with that in vivo, short-term starvation as a fasting cell model in vitro could improve the disorders of CLOCK/BMAL1 and raised SIRT1 via regulating AMPK, as well as ROS-MAPK induced by acrolein. In conclusion, we demonstrated that IF repressed ROS-MAPK while activated AMPK to elevate the expression of circadian clock genes to ameliorate acrolein-induced atherogenesis, which shed a novel light to prevent cardiovascular diseases.

Direct electron beam patterning of electro-optically active PEDOT:PSS.

The optical and electronic tunability of the conductive polymer poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) has enabled emerging applications as diverse as bioelectronics, flexible electronics, and micro- and nano-photonics. High-resolution spatial patterning of PEDOT:PSS opens up opportunities for novel active devices in a range of fields. However, typical lithographic processes require tedious indirect patterning and dry etch processes, while solution-processing methods such as ink-jet printing have limited spatial resolution. Here, we report a method for direct write nano-patterning of commercially available PEDOT:PSS through electron-beam induced solubility modulation. The written structures are water stable and maintain the conductivity as well as electrochemical and optical properties of PEDOT:PSS, highlighting the broad utility of our method. We demonstrate the potential of our strategy by preparing prototypical nano-wire structures with feature sizes down to 250 nm, an order of magnitude finer than previously reported direct write methods, opening the possibility of writing chip-scale microelectronic and optical devices. We finally use the high-resolution writing capabilities to fabricate electrically-switchable optical diffraction gratings. We show active switching in this archetypal system with >95 % contrast at CMOS-compatible voltages of +2 V and -3 V, offering a route towards highly-miniaturized dynamic optoelectronic devices.

Pediatric acute myeloid leukemia with t(8;21) and KIT mutation treatment with avapritinib post-stem cell transplantation: a report of four cases.

Acute myeloid leukemia (AML) with t(8;21) (q22;q22), which forms RUNX1::RUNX1T1 fusion gene, is classified as a favorable-risk group. However, the presence of mutations in KIT exon 17 results in an adverse prognosis in this group. Avapritinib, a novel tyrosine kinase inhibitor, was designed to target KIT mutation. We report a retrospective study of four pediatric patients with AML with t(8:21) and KIT exon 17 mutation who were treated with avapritinib, three of them failed to demethylate drugs and donor lymphocyte infusion targeting RUNX1::RUNX1T1-positivity after allogeneic hematopoietic stem cell transplantation (allo-HSCT). So far, all patients with RUNX1::RUNX1T1 positivity had turned negative after 1, 9, 7, 2 months of avapritinib treatment. The common adverse effect of avapritinib is neutropenia, which is well-tolerated. This case series indicates that avapritinib may be effective and safe for preemptive treatment of children with AML with t(8;21) and KIT mutation after allo-HSCT, providing a treatment option for preventing relapse after allo-HSCT.

Risk factors for hemorrhagic cystitis in children undergoing hematopoietic stem cell transplantation: a systematic review and meta-analysis.

BACKGROUND: The risk factors for hemorrhagic cystitis (HC) in children undergoing hematopoietic stem cell transplantation (HSCT) are unclear. Therefore, we conducted this systematic review and meta-analysis to investigate the risk factors for HC in children undergoing HSCT. METHODS: We performed this meta-analysis by retrieving studies from PubMed, EMBASE, and the Cochrane Library up to October 10, 2023, and analyzing those that met the inclusion criteria. I2 statistics were used to evaluate heterogeneity. RESULTS: Twelve studies, including 2,764 patients, were analyzed. Male sex (odds ratio [OR] = 1.52; 95% confidence interval [CI], 1.16-2.00; p = 0.003, I2 = 0%), allogeneic donor (OR = 5.28; 95% CI, 2.60-10.74; p < 0.00001, I2 = 0%), human leukocyte antigen (HLA) mismatched donor (OR = 1.86; 95% CI, 1.00-3.44; p = 0.05, I2 = 31%), unrelated donor (OR = 1.58; 95% CI, 1.10-2.28; p = 0.01, I2 = 1%), myeloablative conditioning (MAC) (OR = 3.17; 95% CI, 1.26-7.97; p = 0.01, I2 = 0%), busulfan (OR = 2.18; 95% CI, 1.33-3.58; p = 0.002, I2 = 0%) or anti-thymoglobulin (OR = 1.65; 95% CI, 1.07-2.54; p = 0.02, I2 = 16%) use, and cytomegalovirus (CMV) reactivation (OR = 2.64; 95% CI, 1.44-4.82; p = 0.002, I2 = 0%) were risk factors for HC in children undergoing HSCT. CONCLUSIONS: Male sex, allogeneic donor, HLA-mismatched, unrelated donor, MAC, use of busulfan or anti-thymoglobulin, and CMV reactivation are risk factors for HC in children undergoing HSCT.

tRNA-derived small RNA (tsr007330) regulates myocardial fibrosis after myocardial infarction through NAT10-mediated ac4C acetylation of EGR3 mRNA.

Small non-coding ribonucleic acids (sncRNAs) play an important role in cell regulation and are closely related to the pathogenesis of heart diseases. However, the role and molecular mechanism of transfer RNA-derived small RNAs (tsRNAs) in myocardial fibrosis after myocardial infarction (MI) remain unknown. In this study, we identified and validated sncRNAs (mainly miRNA and tsRNA) associated with myocardial fibrosis after MI through PANDORA sequencing of rat myocardial tissue. As a key enzyme of N4-acetylcytidine (ac4C) acetylation modification, N-acetyltransferase 10 (NAT10) plays an important role in regulating messenger RNA (mRNA) stability and translation efficiency. We found that NAT10 is highly expressed in infarcted myocardial tissue, and the results of acetylated RNA immunoprecipitation sequencing (acRIP-seq) analysis suggest that early growth response 3 (EGR3) may be an important molecule in the pathogenesis of NAT10-mediated myocardial fibrosis. Both in vivo and in vitro experiments have shown that inhibition of NAT10 can reduce the expression of EGR3 and alleviate myocardial fibrosis after MI. tsRNA can participate in gene regulation by inhibiting target genes. The expression of tsr007330 was decreased in myocardial infarction tissue. We found that overexpression of tsr007330 in rat myocardial tissue could antagonize NAT10, improve myocardial function in MI and alleviate myocardial fibrosis. In conclusion, tsRNAs (rno-tsr007330) may regulate the occurrence of myocardial fibrosis by regulating NAT10-mediated EGR3 mRNA acetylation. This study provides new insights into the improvement of myocardial fibrosis after MI by targeting tsRNA therapy.

Phosphocreatine promotes epigenetic reprogramming to facilitate glioblastoma growth through stabilizing BRD2.

Glioblastoma (GBM) exhibits profound metabolic plasticity for survival and therapeutic resistance, while the underlying mechanisms remain unclear. Here, we show that GBM stem cells (GSCs) reprogram the epigenetic landscape by producing substantial amounts of phosphocreatine (PCr). This production is attributed to the elevated transcription of brain-type creatine kinase (CKB), mediated by Zinc finger E-box binding homeobox 1 (ZEB1). PCr inhibits the poly-ubiquitination of the chromatin regulator bromodomain containing protein 2 (BRD2) by outcompeting the E3 ubiquitin ligase SPOP for BRD2 binding. Pharmacological disruption of PCr biosynthesis by cyclocreatine leads to BRD2 degradation and a decrease in its targets' transcription, which inhibits chromosome segregation and cell proliferation. Notably, cyclocreatine treatment significantly impedes tumor growth and sensitizes tumors to a BRD2 inhibitor in mouse GBM models without detectable side effects. These findings highlight that high production of PCr is a druggable metabolic feature of GBM and a promising therapeutic target for GBM treatment.

Exploiting biochemical data to improve osteosarcoma diagnosis with deep learning.

Early and accurate diagnosis of osteosarcomas (OS) is of great clinical significance, and machine learning (ML) based methods are increasingly adopted. However, current ML-based methods for osteosarcoma diagnosis consider only X-ray images, usually fail to generalize to new cases, and lack explainability. In this paper, we seek to explore the capability of deep learning models in diagnosing primary OS, with higher accuracy, explainability, and generality. Concretely, we analyze the added value of integrating the biochemical data, i.e., alkaline phosphatase (ALP) and lactate dehydrogenase (LDH), and design a model that incorporates the numerical features of ALP and LDH and the visual features of X-ray imaging through a late fusion approach in the feature space. We evaluate this model on real-world clinic data with 848 patients aged from 4 to 81. The experimental results reveal the effectiveness of incorporating ALP and LDH simultaneously in a late fusion approach, with the accuracy of the considered 2608 cases increased to 97.17%, compared to 94.35% in the baseline. Grad-CAM visualizations consistent with orthopedic specialists further justified the model's explainability.

Thermal conductivity of wrinkled graphene ring with defects.

Graphene rings have great prospects in the fields of biological modulators, electrochemical biosensors, and resonators, but are prone to wrinkling which can affect their physical properties. This work establishes a theoretical model predicting the torsional wrinkling behavior of defective monolayer graphene rings, which provides direct understanding and reliable accuracy of the wrinkle levels. Then the thermal conductivity of wrinkled graphene rings is studied considering different wrinkle levels, defect concentrations and radii. It is found that with increased radius, defect concentration and torsional angle, the ratio of wrinkle amplitude to wavelength increases gradually. Vacancy defects and radii have more significant influences on the thermal conductivity than torsional wrinkles. The main influence mechanism of wrinkles and defects on thermal conductivity is revealed by phonon density of state. This work provides theoretical guidance for thermal manipulation based on the wrinkle-tuning approach.

Artificial intelligence-driven prognostic system for conception prediction and management in intrauterine adhesions following hysteroscopic adhesiolysis: a diagnostic study using hysteroscopic images.

Introduction: Intrauterine adhesions (IUAs) caused by endometrial injury, commonly occurring in developing countries, can lead to subfertility. This study aimed to develop and evaluate a DeepSurv architecture-based artificial intelligence (AI) system for predicting fertility outcomes after hysteroscopic adhesiolysis. Methods: This diagnostic study included 555 intrauterine adhesions (IUAs) treated with hysteroscopic adhesiolysis with 4,922 second-look hysteroscopic images from a prospective clinical database (IUADB, NCT05381376) with a minimum of 2 years of follow-up. These patients were randomly divided into training, validation, and test groups for model development, tuning, and external validation. Four transfer learning models were built using the DeepSurv architecture and a code-free AI application for pregnancy prediction was also developed. The primary outcome was the model's ability to predict pregnancy within a year after adhesiolysis. Secondary outcomes were model performance which evaluated using time-dependent area under the curves (AUCs) and C-index, and ART benefits evaluated by hazard ratio (HR) among different risk groups. Results: External validation revealed that using the DeepSurv architecture, InceptionV3+ DeepSurv, InceptionResNetV2+ DeepSurv, and ResNet50+ DeepSurv achieved AUCs of 0.94, 0.95, and 0.93, respectively, for one-year pregnancy prediction, outperforming other models and clinical score systems. A code-free AI application was developed to identify candidates for ART. Patients with lower natural conception probability indicated by the application had a higher ART benefit hazard ratio (HR) of 3.13 (95% CI: 1.22-8.02, p = 0.017). Conclusion: InceptionV3+ DeepSurv, InceptionResNetV2+ DeepSurv, and ResNet50+ DeepSurv show potential in predicting the fertility outcomes of IUAs after hysteroscopic adhesiolysis. The code-free AI application based on the DeepSurv architecture facilitates personalized therapy following hysteroscopic adhesiolysis.

Microglial P2Y6 calcium signaling promotes phagocytosis and shapes neuroimmune responses in epileptogenesis.

Microglial calcium signaling is rare in a baseline state but strongly engaged during early epilepsy development. The mechanism(s) governing microglial calcium signaling are not known. By developing an in vivo uridine diphosphate (UDP) fluorescent sensor, GRABUDP1.0, we discovered that UDP release is a conserved response to seizures and excitotoxicity across brain regions. UDP can signal through the microglial-enriched P2Y6 receptor to increase calcium activity during epileptogenesis. P2Y6 calcium activity is associated with lysosome biogenesis and enhanced production of NF-κB-related cytokines. In the hippocampus, knockout of the P2Y6 receptor prevents microglia from fully engulfing neurons. Attenuating microglial calcium signaling through calcium extruder ("CalEx") expression recapitulates multiple features of P2Y6 knockout, including reduced lysosome biogenesis and phagocytic interactions. Ultimately, P2Y6 knockout mice retain more CA3 neurons and better cognitive task performance during epileptogenesis. Our results demonstrate that P2Y6 signaling impacts multiple aspects of myeloid cell immune function during epileptogenesis.

Genetically Encoded Sensors for the In Vivo Detection of Neurochemical Dynamics.

The ability to measure dynamic changes in neurochemicals with high spatiotemporal resolution is essential for understanding the diverse range of functions mediated by the brain. We review recent advances in genetically encoded sensors for detecting neurochemicals and discuss their in vivo applications. For example, notable progress has been made with respect to sensors for second messengers such as cyclic adenosine monophosphate, enabling in vivo real-time monitoring of these messengers at single-cell and even subcellular resolution. Moreover, the emergence of highly sensitive sensors for neurotransmitters and neuromodulators has greatly accelerated the study of these signaling molecules in a wide variety of behavioral models using an array of powerful imaging techniques. Finally, we discuss the future direction of neurochemical sensors, including their ability to measure neurochemical concentrations and the potential for multiplex imaging.

The causal relationship between sleep disturbances and the risk of frailty: a two-sample Mendelian randomization study.

OBJECTIVE: Adequate sleep is closely related to people's health. However, with increasing age, the quality of sleep worsens. At the same time, among elderly individuals, frailty is also a disturbing factor, which makes elderly individuals more vulnerable to negative factors. To explore the relationship between the two, we conducted this study. METHODS: In this paper, independent genetic variations related to insomnia, sleep duration and daytime sleepiness were selected as IVs, and related genetic tools were used to search published genome-wide association studies for a two-sample Mendelian randomization (TSMR) analysis. The inverse-variance weighted (IVW) method was used as the main Mendelian randomization analysis method. Cochran's Q test was used to test heterogeneity, MR‒Egger was used to test horizontal pleiotropy, and the MR-PRESSO test was used to remove outliers. RESULTS: According to our research, insomnia (OR = 1.10, 95% CI 1.03-1.17, P = 2.59e-97), long sleep duration (OR = 0.66, 95% CI 0.37-1.17, P = 0.02), short sleep duration (OR = 1.30, 95% CI 1.22-1.38, P = 2.23e-17) and daytime sleepiness (OR = 1.49, 95% CI 1.25-1.77, P = 0.96e-4) had a bidirectional causal relationship with frailty. CONCLUSIONS: Our research showed that there is a causal relationship between sleep disturbances and frailty. This result was obtained by a TSMR analysis, which involves the use of genetic variation as an IV to determine causal relationships between exposure and outcome. Future TSMR studies should include a larger sample for analysis.

Explainable Federated Medical Image Analysis Through Causal Learning and Blockchain.

Federated learning (FL) enables collaborative training of machine learning models across distributed medical data sources without compromising privacy. However, applying FL to medical image analysis presents challenges like high communication overhead and data heterogeneity. This paper proposes novel FL techniques using explainable artificial intelligence (XAI) for efficient, accurate, and trustworthy analysis. A heterogeneity-aware causal learning approach selectively sparsifies model weights based on their causal contributions, significantly reducing communication requirements while retaining performance and improving interpretability. Furthermore, blockchain provides decentralized quality assessment of client datasets. The assessment scores adjust aggregation weights so higher-quality data has more influence during training, improving model generalization. Comprehensive experiments show our XAI-integrated FL framework enhances efficiency, accuracy and interpretability. The causal learning method decreases communication overhead while maintaining segmentation accuracy. The blockchain-based data valuation mitigates issues from low-quality local datasets. Our framework provides essential model explanations and trust mechanisms, making FL viable for clinical adoption in medical image analysis.