Single cell view of tumor microenvironment gradients in pleural mesothelioma.

Immunotherapies have shown great promise in pleural mesothelioma (PM), yet most patients still do not achieve significant clinical response, highlighting the importance of improving understanding of the tumor microenvironment (TME). Here, we utilized high-throughput, single-cell RNA-sequencing to de novo identify 54 expression programs and construct a comprehensive cellular catalogue of the PM TME. We found four cancer-intrinsic programs associated with poor disease outcome and a novel fetal-like, endothelial cell population that likely responds to VEGF signaling and promotes angiogenesis. Throughout cellular compartments, we observe substantial difference in the TME associated with a cancer-intrinsic sarcomatoid signature, including enrichment in fetal-like endothelial cells, CXCL9+ macrophages, cytotoxic, exhausted, and regulatory T cells, which we validated using imaging and bulk deconvolution analyses on independent cohorts. Finally, we show, both computationally and experimentally, that NKG2A-HLA-E interaction between NK and tumor cells represents an important new therapeutic axis in PM, especially for epithelioid cases.

The profile of blood microbiome in new-onset type 1 diabetes children.

Blood microbiome signatures in patients with type 1 diabetes (T1D) remain unclear. We profile blood microbiome using 16S rRNA gene sequencing in 77 controls and 64 children with new-onset T1D, and compared it with the gut and oral microbiomes. The blood microbiome of patients with T1D is characterized by increased diversity and perturbed microbial features, with a significant increase in potentially pathogenic bacteria compared with controls. Thirty-six representative genera of blood microbiome were identified by random forest analysis, providing strong discriminatory power for T1D with an AUC of 0.82. PICRUSt analysis suggested that bacteria capable of inducing inflammation were more likely to enter the bloodstream in T1D. The overlap of the gut and oral microbiome with the blood microbiome implied potential translocation of bacteria from the gut and oral cavity to the bloodstream. Our study raised the necessity of further mechanistic investigations into the roles of blood microbiome in T1D.

Merlin: A Vision Language Foundation Model for 3D Computed Tomography.

Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on the abdomen. Given the current shortage of both general and specialized radiologists, there is a large impetus to use artificial intelligence to alleviate the burden of interpreting these complex imaging studies while simultaneously using the images to extract novel physiological insights. Prior state-of-the-art approaches for automated medical image interpretation leverage vision language models (VLMs) that utilize both the image and the corresponding textual radiology reports. However, current medical VLMs are generally limited to 2D images and short reports. To overcome these shortcomings for abdominal CT interpretation, we introduce Merlin - a 3D VLM that leverages both structured electronic health records (EHR) and unstructured radiology reports for pretraining without requiring additional manual annotations. We train Merlin using a high-quality clinical dataset of paired CT scans (6+ million images from 15,331 CTs), EHR diagnosis codes (1.8+ million codes), and radiology reports (6+ million tokens) for training. We comprehensively evaluate Merlin on 6 task types and 752 individual tasks. The non-adapted (off-the-shelf) tasks include zero-shot findings classification (31 findings), phenotype classification (692 phenotypes), and zero-shot cross-modal retrieval (image to findings and image to impressions), while model adapted tasks include 5-year chronic disease prediction (6 diseases), radiology report generation, and 3D semantic segmentation (20 organs). We perform internal validation on a test set of 5,137 CTs, and external validation on 7,000 clinical CTs and on two public CT datasets (VerSe, TotalSegmentator). Beyond these clinically-relevant evaluations, we assess the efficacy of various network architectures and training strategies to depict that Merlin has favorable performance to existing task-specific baselines. We derive data scaling laws to empirically assess training data needs for requisite downstream task performance. Furthermore, unlike conventional VLMs that require hundreds of GPUs for training, we perform all training on a single GPU. This computationally efficient design can help democratize foundation model training, especially for health systems with compute constraints. We plan to release our trained models, code, and dataset, pending manual removal of all protected health information.

Serum Metabolomics Reveals Metabolomic Profile and Potential Biomarkers in Asthma.

PURPOSE: Asthma is a highly heterogeneous disease. Metabolomics plays a pivotal role in the pathogenesis and development of asthma. The main aims of our study were to explore the underlying mechanism of asthma and to identify novel biomarkers through metabolomics approach. METHODS: Serum samples from 102 asthmatic patients and 18 healthy controls were collected and analyzed using liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) system. Multivariate analysis and weighted gene co-expression network analysis (WGCNA) were performed to explore asthma-associated metabolomics profile and metabolites. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for pathway enrichment analysis. Subsequently, 2 selected serum hub metabolites, myristoleic acid and dodecanoylcarnitine, were replicated in a validation cohort using ultra-high performance LC-MS/MS system (UHPLC-MS/MS). RESULTS: Distinct metabolomics profile of asthma was revealed by multivariate analysis. Then, 116 overlapped asthma-associated metabolites between multivariate analysis and WGCNA, including 12 hub metabolites, were identified. Clinical features-associated hub metabolites were also identified by WGCNA. Among 116 asthma-associated metabolites, Sphingolipid metabolism and valine, leucine and isoleucine biosynthesis were revealed by KEGG analysis. Furthermore, serum myristoleic acid and dodecanoylcarnitine were significantly higher in asthmatic patients than in healthy controls in validation cohort. Additionally, serum myristoleic acid and dodecanoylcarnitine demonstrated high sensitivities and specificities in predicting asthma. CONCLUSIONS: Collectively, asthmatic patients showed a unique serum metabolome. Sphingolipid metabolism and valine, leucine and isoleucine biosynthesis were involved in the pathogenesis of asthma. Furthermore, our results suggest the promising values of serum myristoleic acid and dodecanoylcarnitine for asthma diagnosis in adults.

A novel inhalable nanobody targeting IL-4Rα for the treatment of asthma.

BACKGROUND: Inhalable biologics represent a promising approach to improve the efficacy and safety of asthma treatment. Although several mAbs targeting IL-4 receptor α chain (IL-4Rα) have been approved or are undergoing clinical trials, the development of inhalable mAbs targeting IL-4Rα presents significant challenges. OBJECTIVE: Capitalizing on the distinctive advantages of nanobodies (Nbs) in maintaining efficacy during storage and administration, we sought to develop a novel inhalable IL-4Rα Nb for effectively treating asthma. METHODS: Three IL-4Rα immunized Nb libraries were used to generate specific and functional IL-4Rα Nbs. LQ036, a bivalent Nb comprising 2 HuNb103 units, was constructed with a high affinity and specificity for human IL-4Rα. The efficacy, pharmacokinetics, and safety of inhaled LQ036 were evaluated in B-hIL4/hIL4RA humanized mice. RESULTS: LQ036 inhibited secreted embryonic alkaline phosphatase reporter activity, inhibited TF-1 cell proliferation, and suppressed phosphorylated signal transducer and activator of transduction 6 in T cells from patients with asthma. Crystal structure analysis revealed a binding region similar to dupilumab but with higher affinity, leading to better efficacy in blocking the signaling pathway. HuNb103 competed with IL-4 and IL-13 for IL-4Rα binding. Additionally, LQ036 significantly inhibited ovalbumin-specific IgE levels in serum, CCL17 levels in bronchoalveolar lavage fluid, bronchial mucous cell hyperplasia, and airway goblet cell hyperplasia in B-hIL4/hIL4RA humanized mice. Inhaled LQ036 exhibited favorable pharmacokinetics, safety, and tissue distribution, with higher concentrations observed in the lungs and bronchi. CONCLUSIONS: These findings from preclinical studies establish the safety and efficacy of inhaled LQ036, underscoring its potential as a pioneering inhalable biologic therapy for asthma.

Flexible silk-fibroin-based microelectrode arrays for high-resolution neural recording.

High-precision neural recording plays a pivotal role in unraveling the intricate mechanisms that underlie information transmission of the nervous system, raising increasing interest in the development of implantable microelectrode arrays (MEAs). The challenge lies in providing a truly soft, highly conductive and low-impedance neural interface for precise recording of the electrophysiological signals of individual neurons or neural networks. Herein, by implementing a novel topological regulation strategy of silk fibroin (SF) crosslinking, we prepared a flexible, hydrophilic, and biocompatible MEA substrate, facilitating a biocompatible neural interface that minimizes mechanical mismatch with biological tissues. Additionally, we established a strategy involving screen-printing combined with post-coating to prepare MEAs with high conductivity, low impedance and high capacitance, by coating PEDOT:PSS on titanium carbide (Ti3C2) microarrays. The Ti3C2 nanosheets, as the conductive track of the MEAs, avoided the charge drifting associated with metals and facilitated the processing of the MEAs. Further coating PEDOT:PSS on the electrode points reduced the impedance 100-fold, from 105 to 103 Ω. Experimental validation confirmed the superior electrophysiological signal recording capabilities of the SF-based MEA (SMEA) in peripheral and cerebral nerves with a much higher signal-to-noise ratio (SNR) of 20. In particular, we achieved high-precision recording of the action potential (AP) induced by flash visual stimulation, demonstrating high performance in weak signal recording. In summary, the development of SMEA provides a robust foundation for future investigations into the mechanisms and principles of neural circuit information transmission in complex nervous systems.

Deep learning for colorectal cancer detection in contrast-enhanced CT without bowel preparation: a retrospective, multicentre study.

BACKGROUND: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. METHODS: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists' detection performance. FINDINGS: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p < 0.0001; 94.9% vs 85.3%, p < 0.0001), and significantly improved the accuracy of radiologists (93.4% vs 86.0%, p < 0.0001; 93.6% vs 85.3%, p < 0.0001). In the real-world test set, the DL model delivered sensitivity comparable to that of radiologists who had been informed about clinical indications for most cancer cases (94.3% vs 96.2%, p > 0.99), and it detected 2 cases that had been missed by radiologists. INTERPRETATION: The developed DL model can accurately detect colorectal cancer and improve radiologists' detection performance, showing its potential as an effective computer-aided detection tool. FUNDING: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).

CABP4 mutation in mice shows alteration in protein expression level and neuron discharge frequency.

Background: The calcium-binding protein 4 (CABP4) gene is a newly identified epilepsy-related gene that might be associated with a rare type of genetic focal epilepsy; that is, autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE). In vitro, mutant CABP4 causes an increased inward flow voltage of calcium ions and a significant increase in the electrical signal discharge in hippocampus neurons; however, the role of CABP4 in epilepsy has not yet been specifically described, and there is not yet a CABP4 mutant animal model recapitulating the epilepsy phenotype. Methods: We introduced a human CABP4 missense mutation into the C57BL/6J mouse genome and generated a knock-in strain carrying a glycine-to-aspartic acid mutation in the gene. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot were performed to evaluate the CABP4 expression level. Slice patch-clamp recording was carried out on pyramidal cells of prefrontal cortex layers II and III. Results: The CABP4G155D/+ mutant mice were viable and born at an expected Mendelian ratio. Surprisingly, the heterozygous (HE) mice did not display either an abnormal appearance or an overt seizure phenotype, and there was no statistically significant difference between the HE and wild-type (WT) mice in terms of overall messenger RNA (mRNA) and protein expression. However, the HE mutant mice showed an imbalance in the amount of protein expressed in the brain regions. Additionally, the patch-clamp recordings from the HE mouse layer II/III cortical pyramidal cells revealed an increase in the frequency of micro-excitatory post-synaptic currents (mEPSCs) but no change in the amplitude was observed. Conclusions: The findings of this study suggest that the CABP4 p.G155D mutation might be one of the mechanisms underlying seizure onset.

A single-cell atlas characterizes dysregulation of the bone marrow immune microenvironment associated with outcomes in multiple myeloma.

Multiple Myeloma (MM) remains incurable despite advances in treatment options. Although tumor subtypes and specific DNA abnormalities are linked to worse prognosis, the impact of immune dysfunction on disease emergence and/or treatment sensitivity remains unclear. We established a harmonized consortium to generate an Immune Atlas of MM aimed at informing disease etiology, risk stratification, and potential therapeutic strategies. We generated a transcriptome profile of 1,149,344 single cells from the bone marrow of 263 newly diagnosed patients enrolled in the CoMMpass study and characterized immune and hematopoietic cell populations. Associating cell abundances and gene expression with disease progression revealed the presence of a proinflammatory immune senescence-associated secretory phenotype in rapidly progressing patients. Furthermore, signaling analyses suggested active intercellular communication involving APRIL-BCMA, potentially promoting tumor growth and survival. Finally, we demonstrate that integrating immune cell levels with genetic information can significantly improve patient stratification.

Regioselective and Enantioselective Nickel-Catalyzed Intermolecular Reductive Coupling of Aliphatic Alkenes with Imines.

Unactivated aliphatic alkenes are particularly desirable as starting materials because they are readily accessible in large quantities, but the enantioselective intermolecular reductive coupling of unactivated alkenes with imines is challenging. In this paper, we report a method for nickel-catalyzed intermolecular reductive coupling reactions between aliphatic alkenes and imines to yield chiral amines with excellent enantioselectivities and good linear selectivities. The reaction conditions are compatible with a broad range of aliphatic alkenes, including those derived from bioactive molecules. The success of this method can be attributed to the use of newly developed monodentate chiral spiro phosphine ligands.

Enhanced Thermal Stability of Carbonyl Iron Nanocrystalline Microwave Absorbents by Pinning Grain Boundaries with SiBaFe Alloy Nanoparticles.

Nanocrystalline carbonyl iron (CI) particles are promising microwave absorbents at elevated temperature, whereas their excessive grain boundary energy leads to the growth of nanograins and a deterioration in permeability. In this work, we report a strategy to enhance the thermal stability of the grains and microwave absorption of CI particles by doping a SiBaFe alloy. Grain growth was effectively inhibited by the pinning effect of SiBaFe alloy nanoparticles at the grain boundaries. After heat treatment at 600 °C, the grain size of CI particles increased from ~10 nm to 85.1 nm, while that of CI/SiBaFe particles was only 32.0 nm; with the temperature rising to 700 °C, the grain size of CI particles sharply increased to 158.1 nm, while that of CI/SiBaFe particles was only 40.8 nm. Excellent stability in saturation magnetization and microwave absorption was also achieved in CI/SiBaFe particles. After heat treatment at 600 °C, the flaky CI/SiBaFe particles exhibited reflection loss below -10 dB over 7.01~10.11 GHz and a minimum of -14.92 dB when the thickness of their paraffin-based composite was 1.5 mm. We provided a low-cost and efficient kinetic strategy to stabilize the grain size in nanoscale and microwave absorption for nanocrystalline magnetic absorbents working at elevated temperature.

Tailoring amorphous boron nitride for high-performance two-dimensional electronics.

Two-dimensional (2D) materials have garnered significant attention in recent years due to their atomically thin structure and unique electronic and optoelectronic properties. To harness their full potential for applications in next-generation electronics and photonics, precise control over the dielectric environment surrounding the 2D material is critical. The lack of nucleation sites on 2D surfaces to form thin, uniform dielectric layers often leads to interfacial defects that degrade the device performance, posing a major roadblock in the realization of 2D-based devices. Here, we demonstrate a wafer-scale, low-temperature process (<250 °C) using atomic layer deposition (ALD) for the synthesis of uniform, conformal amorphous boron nitride (aBN) thin films. ALD deposition temperatures between 125 and 250 °C result in stoichiometric films with high oxidative stability, yielding a dielectric strength of 8.2 MV/cm. Utilizing a seed-free ALD approach, we form uniform aBN dielectric layers on 2D surfaces and fabricate multiple quantum well structures of aBN/MoS2 and aBN-encapsulated double-gated monolayer (ML) MoS2 field-effect transistors to evaluate the impact of aBN dielectric environment on MoS2 optoelectronic and electronic properties. Our work in scalable aBN dielectric integration paves a way towards realizing the theoretical performance of 2D materials for next-generation electronics.

Hybrid machine learning model based predictions for properties of poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose.

Supermacroporous composite cryogels with enhanced adjustable functionality have received extensive interest in bioseparation, tissue engineering, and drug delivery. However, the variations in their components significantly impactfinal properties. This study presents a two-step hybrid machine learning approach for predicting the properties of innovative poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose (pHEMA-PVA-BC) based on their compositions. By considering the ratios of HEMA (1.0-22.0 wt%), PVA (0.2-4.0 wt%), poly(ethylene glycol) diacrylate (1.0-4.5 wt%), BC (0.1-1.5 wt%), and water (68.0-96.0 wt%) as investigational variables, overlay sampling uniform design (OSUD) was employed to construct a high-quality dataset for model development. The random forest (RF) model was used to classify the preparation conditions. Then four models of artificial neural network, RF, gradient boosted regression trees (GBRT), and XGBoost were developed to predict the basic properties of the composite cryogels. The results showed that the RF model achieved an accurate three-class classification of preparation conditions. Among the four models, the GBRT model exhibited the best predictive performance of the basic properties, with the mean absolute percentage error of 16.04 %, 0.85 %, and 2.44 % for permeability, effective porosity, and height of theoretical plate (1.0 cm/min), respectively. Characterization results of the representative pHEMA-PVA-BC composite cryogel showed an effective porosity of 81.01 %, a permeability of 1.20 × 10-12 m2, and a range of height of theoretical plate between 0.40-0.49 cm at flow velocities of 0.5-3.0 cm/min. These indicate that the pHEMA-PVA-BC cryogel was an excellent material with supermacropores, low flow resistance and high mass transfer efficiency. Furthermore, the model output demonstrates that the alteration of the proportions of PVA (0.2-3.5 wt%) and BC (0.1-1.5 wt%) components in composite cryogels resulted in significant changes in the material basic properties. This work represents an attempt to efficiently design and prepare target composite cryogels using machine learning and providing valuable insights for the efficient development of polymers.

Experimental demonstration of an on-chip p-bit core based on stochastic magnetic tunnel junctions and 2D MoS2 transistors.

Probabilistic computing is a computing scheme that offers a more efficient approach than conventional complementary metal-oxide-semiconductor (CMOS)-based logic in a variety of applications ranging from optimization to Bayesian inference, and invertible Boolean logic. The probabilistic bit (or p-bit, the base unit of probabilistic computing) is a naturally fluctuating entity that requires tunable stochasticity; by coupling low-barrier stochastic magnetic tunnel junctions (MTJs) with a transistor circuit, a compact implementation is achieved. In this work, by combining stochastic MTJs with 2D-MoS2 field-effect transistors (FETs), we demonstrate an on-chip realization of a p-bit building block displaying voltage-controllable stochasticity. Supported by circuit simulations, we analyze the three transistor-one magnetic tunnel junction (3T-1MTJ) p-bit design, evaluating how the characteristics of each component influence the overall p-bit output. While the current approach has not reached the level of maturity required to compete with CMOS-compatible MTJ technology, the design rules presented in this work are valuable for future experimental implementations of scaled on-chip p-bit networks with reduced footprint.

Effect of Aggregation Structure on Capacitive Energy Storage in Conducting Polymer Films.

Conducting polymers (CPs), a significant class of electrochemical capacitor electrode materials, exhibit exceptional capacitive energy storage performance in aqueous electrolytes. Current research primarily concentrates on enhancing the electrical conductivity and capacitive performance of CPs via molecular design and structural control. However, the absence of a comprehensive understanding of the impact of molecular chain spatial order on ion/electron transport and capacitive performance impedes the development and optimization of advanced electrode materials. Here, a solvent treatment strategy is employed to modulate the molecular chain spatial order of PEDOT : PSS films. The results of electrochemical performance tests and Grazing Incidence Wide Angle X-ray Scattering (GIWAXS) show that Poly(3,4-ethylenedioxythiophene) : poly(styrenesulfonic acid) (PEDOT : PSS) films with both face-on and edge-on orientations exhibit exceptional electronic conductivity and ion diffusion efficiency, with capacitive performance 1.33 times higher than that of PEDOT : PSS films with only edge-on orientation. Consequently, molecular chain orientations conducive to charge transport not only enhance inter-chain coupling, but also effectively reduce ion transport resistance, enabling efficient capacitive energy storage. This research provides novel insights for the design and development of higher performance CPs-based electrode materials.

A Colorectal Coordinate-Driven Method for Colorectum and Colorectal Cancer Segmentation in Conventional CT Scans.

Automated colorectal cancer (CRC) segmentation in medical imaging is the key to achieving automation of CRC detection, staging, and treatment response monitoring. Compared with magnetic resonance imaging (MRI) and computed tomography colonography (CTC), conventional computed tomography (CT) has enormous potential because of its broad implementation, superiority for the hollow viscera (colon), and convenience without needing bowel preparation. However, the segmentation of CRC in conventional CT is more challenging due to the difficulties presenting with the unprepared bowel, such as distinguishing the colorectum from other structures with similar appearance and distinguishing the CRC from the contents of the colorectum. To tackle these challenges, we introduce DeepCRC-SL, the first automated segmentation algorithm for CRC and colorectum in conventional contrast-enhanced CT scans. We propose a topology-aware deep learning-based approach, which builds a novel 1-D colorectal coordinate system and encodes each voxel of the colorectum with a relative position along the coordinate system. We then induce an auxiliary regression task to predict the colorectal coordinate value of each voxel, aiming to integrate global topology into the segmentation network and thus improve the colorectum's continuity. Self-attention layers are utilized to capture global contexts for the coordinate regression task and enhance the ability to differentiate CRC and colorectum tissues. Moreover, a coordinate-driven self-learning (SL) strategy is introduced to leverage a large amount of unlabeled data to improve segmentation performance. We validate the proposed approach on a dataset including 227 labeled and 585 unlabeled CRC cases by fivefold cross-validation. Experimental results demonstrate that our method outperforms some recent related segmentation methods and achieves the segmentation accuracy in DSC for CRC of 0.669 and colorectum of 0.892, reaching to the performance (at 0.639 and 0.890, respectively) of a medical resident with two years of specialized CRC imaging fellowship.

Extracellular vesicles as carriers for noncoding RNA-based regulation of macrophage/microglia polarization: an emerging candidate regulator for lung and traumatic brain injuries.

Macrophage/microglia function as immune defense and homeostatic cells that originate from bone marrow progenitor cells. Macrophage/microglia activation is historically divided into proinflammatory M1 or anti-inflammatory M2 states based on intracellular dynamics and protein production. The polarization of macrophages/microglia involves a pivotal impact in modulating the development of inflammatory disorders, namely lung and traumatic brain injuries. Recent evidence indicates shared signaling pathways in lung and traumatic brain injuries, regulated through non-coding RNAs (ncRNAs) loaded into extracellular vesicles (EVs). This packaging protects ncRNAs from degradation. These vesicles are subcellular components released through a paracellular mechanism, constituting a group of nanoparticles that involve exosomes, microvesicles, and apoptotic bodies. EVs are characterized by a double-layered membrane and are abound with proteins, nucleic acids, and other bioactive compounds. ncRNAs are RNA molecules with functional roles, despite their absence of coding capacity. They actively participate in the regulation of mRNA expression and function through various mechanisms. Recent studies pointed out that selective packaging of ncRNAs into EVs plays a role in modulating distinct facets of macrophage/microglia polarization, under conditions of lung and traumatic brain injuries. This study will explore the latest findings regarding the role of EVs in the progression of lung and traumatic brain injuries, with a specific focus on the involvement of ncRNAs within these vesicles. The conclusion of this review will emphasize the clinical opportunities presented by EV-ncRNAs, underscoring their potential functions as both biomarkers and targets for therapeutic interventions.

Redoxhigh phenotype mediated by KEAP1/STK11/SMARCA4/NRF2 mutations diminishes tissue-resident memory CD8+ T cells and attenuates the efficacy of immunotherapy in lung adenocarcinoma.

Metabolism reprogramming within the tumor microenvironment (TME) can have a profound impact on immune cells. Identifying the association between metabolic phenotypes and immune cells in lung adenocarcinoma (LUAD) may reveal mechanisms of resistance to immune checkpoint inhibitors (ICIs). Metabolic phenotypes were classified by expression of metabolic genes. Somatic mutations and transcriptomic features were compared across the different metabolic phenotypes. The metabolic phenotype of LUAD is predominantly determined by reductase-oxidative activity and is divided into two categories: redoxhigh LUAD and redoxlow LUAD. Genetically, redoxhigh LUAD is mainly driven by mutations in KEAP1, STK11, NRF2, or SMARCA4. These mutations are more prevalent in redoxhigh LUAD (72.5%) compared to redoxlow LUAD (17.4%), whereas EGFR mutations are more common in redoxlow LUAD (19.0% vs. 0.7%). Single-cell RNA profiling of pre-treatment and post-treatment samples from patients receiving neoadjuvant chemoimmunotherapy revealed that tissue-resident memory CD8+ T cells are responders to ICIs. However, these cells are significantly reduced in redoxhigh LUAD. The redoxhigh phenotype is primarily attributed to tumor cells and is positively associated with mTORC1 signaling. LUAD with the redoxhigh phenotype demonstrates a lower response rate (39.1% vs. 70.8%, p = 0.001), shorter progression-free survival (3.3 vs. 14.6 months, p = 0.004), and overall survival (12.1 vs. 31.2 months, p = 0.022) when treated with ICIs. The redoxhigh phenotype in LUAD is predominantly driven by mutations in KEAP1, STK11, NRF2, and SMARCA4. This phenotype diminishes the number of tissue-resident memory CD8+ T cells and attenuates the efficacy of ICIs.

MSC-derived exosomes mitigate cadmium-induced male reproductive injury by ameliorating DNA damage and autophagic flux.

Cadmium, an environmental toxicant, severely impairs male reproductive functions and currently lacks effective clinical treatments. Mesenchymal stem cell-derived exosomes (MSC-Exos) are increasingly recognized as a potential alternative to whole-cell therapy for tissue injury and regeneration. This study aims to investigate the protective effects of MSC-Exos against cadmium toxicity on male reproduction. Our findings reveal that MSC-Exos treatment significantly promotes spermatogenesis, improves sperm quality, and reduces germ cell apoptosis in cadmium-exposed mice. Mechanistically, MSC-Exos dramatically mitigate cadmium-induced cell apoptosis in a spermatogonia cell line (GC-1 spg) in vitro by reducing DNA damage and promoting autophagic flux. These results suggest that MSC-Exos have a protective effect on cadmium-induced germ cell apoptosis by ameliorating DNA damage and autophagy flux, demonstrating the therapeutic potential of MSC-Exos for cadmium toxicity on male reproduction.

Correlation of distribution characteristics and dynamic changes of gut microbiota with the efficacy of immunotherapy in EGFR-mutated non-small cell lung cancer.

BACKGROUND: The effects of gut microbiota and metabolites on the responses to immune checkpoint inhibitors (ICIs) in advanced epidermal growth factor receptor (EGFR) wild-type non-small cell lung cancer (NSCLC) have been studied. However, their effects on EGFR-mutated (EGFR +) NSCLC remain unknown. METHODS: We prospectively recorded the clinicopathological characteristics of patients with advanced EGFR + NSCLC and assessed potential associations between the use of antibiotics or probiotics and immunotherapy efficacy. Fecal samples were collected at baseline, early on-treatment, response and progression status and were subjected to metagenomic next-generation sequencing and ultra-high-performance liquid chromatography-mass spectrometry analyses to assess the effects of gut microbiota and metabolites on immunotherapy efficacy. RESULTS: The clinical data of 74 advanced EGFR + NSCLC patients were complete and 18 patients' fecal samples were dynamically collected. Patients that used antibiotics had shorter progression-free survival (PFS) (mPFS, 4.8 vs. 6.7 months; P = 0.037); probiotics had no impact on PFS. Two dynamic types of gut microbiota during immunotherapy were identified: one type showed the lowest relative abundance at the response time point, whereas the other type showed the highest abundance at the response time point. Metabolomics revealed significant differences in metabolites distribution between responders and non-responders. Deoxycholic acid, glycerol, and quinolinic acid were enriched in responders, whereas L-citrulline was enriched in non-responders. There was a significant correlation between gut microbiota and metabolites. CONCLUSIONS: The use of antibiotics weakens immunotherapy efficacy in patients with advanced EGFR + NSCLC. The distribution characteristics and dynamic changes of gut microbiota and metabolites may indicate the efficacy of immunotherapy in advanced EGFR + NSCLC.