Machine intelligence accelerated design of conductive MXene aerogels with programmable properties.

Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative, time-consuming experiments across a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable properties. An automated pipetting robot is operated to prepare 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios/loadings. After freeze-drying, the aerogels' structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model conducts two-way design tasks: (1) predicting the aerogels' physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management.

Chronic infectious unilateral giant thyroid cyst related to diabetes mellitus: A case report.

BACKGROUND: Patients rarely develop complicated infections in thyroid cysts. Here, we describe a patient with chronic infected unilateral giant thyroid cyst related to diabetes mellitus (DM). CASE SUMMARY: A 66-year-old male was admitted due to an evident neck lump for 5 d after approximately 40 years of gradually progressive neck mass and 7 years of DM. Doppler ultrasound and computed tomography scan showed a giant lump in the left thyroid gland lobe. He was diagnosed with a large thyroid nodule complicated by tracheal dislocation and had surgical indications. Surgical exploration revealed evident inflammatory edema and exudation between the left anterior neck muscles, the nodule and glandular tissue. Fortunately, inflammatory lesions did not affect major neck vessels. Finally, a left partial thyroidectomy was performed. Macroscopic observation showed that the cystic thyroid mass consisted of extensive cystic wall calcification and was rich in massive rough sand-like calculi content and purulent matter. Postoperative pathology confirmed benign thyroid cyst with chronic infection. CONCLUSION: The progression of this chronic infectious unilateral giant thyroid cyst may have been related to DM, and identifying blood vessels involvement can prevent serious complications during operation.

Evaluating the adoption of irrigation technology in a well-irrigated winter wheat－summer maize cropping system.

Determining suitable irrigation technology is of paramount for promoting water-saving agriculture, particularly for winter wheat-summer maize rotation system in well-irrigated regions. To optimize and assess the efficacy of various irrigation technologies (specifically, semi-fixed sprinkler irrigation, walking sprinkler, semi-automatic buried telescopic sprinkler irrigation, thin-soft spray tape irrigation, drip irrigation, self-driven winch sprinkler and manually moving spray gun irrigation, marked as A, B, C, D, E, F and G) applied in south central North China Plain, we first conducted an economic analysis for the winter wheat-summer maize rotation. Subsequently, employing a comprehensive set of 20 indicators spanning economic, societal, technological, ecological, and resource aspects, we employed a TOPSIS model with integrative weighting approach using "AHP + Entropy". We also employed principal component analysis and the Sankey diagram method to explore characteristics of different irrigation techniques and indexes. Irrigation mode E, conserving energy by 63.19% compared to mode B and offering labor savings five times greater than the mode D. The highest economic benefit for the rotation system was observed with the mode C, resulting in a 25.26% increase compared to the mode G. The top three irrigation modes based on scores were D, G, and E, with scores of 0.532, 0.490, and 0.474, respectively. The Sankey diagram revealed distinct preferences among different agricultural entities for specific irrigation modes. For specific stakeholders, we recommend irrigation modes D, G, F, and B for small farmers, large and specialized family businesses, family farms, and farmer cooperatives, respectively. In conclusion, our findings provide valuable scientific support and recommendations for the practical application of irrigation technology in agricultural production.

Machine intelligence-accelerated discovery of all-natural plastic substitutes.

One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model's prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database.

Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.

Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.

Shaping immune landscape of colorectal cancer by cholesterol metabolites.

Cancer immunotherapies have achieved unprecedented success in clinic, but they remain largely ineffective in some major types of cancer, such as colorectal cancer with microsatellite stability (MSS CRC). It is therefore important to study tumor microenvironment of resistant cancers for developing new intervention strategies. In this study, we identify a metabolic cue that determines the unique immune landscape of MSS CRC. Through secretion of distal cholesterol precursors, which directly activate RORγt, MSS CRC cells can polarize T cells toward Th17 cells that have well-characterized pro-tumor functions in colorectal cancer. Analysis of large human cancer cohorts revealed an asynchronous pattern of the cholesterol biosynthesis in MSS CRC, which is responsible for the abnormal accumulation of distal cholesterol precursors. Inhibiting the cholesterol biosynthesis enzyme Cyp51, by pharmacological or genetic interventions, reduced the levels of intratumoral distal cholesterol precursors and suppressed tumor progression through a Th17-modulation mechanism in preclinical MSS CRC models. Our study therefore reveals a novel mechanism of cancer-immune interaction and an intervention strategy for the difficult-to-treat MSS CRC.

Methionine enkephalin (MENK) protected macrophages from ferroptosis by downregulating HMOX1 and ferritin.

OBJECTIVE: The aim of this work was to investigate the immunological effect of MENK by analyzing the protein spectrum and bioinformatics of macrophage RAW264.7, and to explore the relationship between macrophage and ferroptosis. RESULT: We employed proteomic analysis to identify differentially expressed proteins (DEPs) between macrophages and macrophages intervened by MENK. A total of 208 DEPs were identified. Among these, 96 proteins had upregulated expression and 112 proteins had downregulated expression. Proteomic analysis revealed a significant enrichment of DEPs associated with iron metabolism. The identification of hub genes was conducted using KEGG pathway diagrams and protein-protein interaction (PPI) analysis. The hub genes identified in this study include HMOX1 and Ferritin (FTH and FTL). A correlation was established between HMOX1, FTH, and FTL in the GO and KEGG databases. The results of PCR, WB and immunofluorescence showed that MENK downregulated the level of HMOX1 and FTH. CONCLUSION: MENK had the potential to become an adjuvant chemotherapy drug by regulating iron metabolism in macrophages, reducing levels of HMOX1 and ferritin. We proposed an innovative research direction on the therapeutic potential of MENK, focusing on the relationship between ferroptosis and macrophage activity.

Machine Learning Integrated Workflow for Predicting Schwann Cell Viability on Conductive MXene Biointerfaces.

Severe injuries to the peripheral nervous system (PNS) require Schwann cells to aid in neuronal regeneration. Low-frequency electrical stimulation is known to induce the cogrowth of neurons and Schwann cells in an injured PNS. However, the correlations between electrical stimulation and Schwann cell viability are complex and not well understood. In this work, we develop a machine learning (ML)-integrated workflow that uses conductive hydrogel biointerfaces to evaluate the impacts of fabrication parameters and electrical stimulation on the Schwann cell viability. First, a hydrogel array with varying MXene and peptide loadings is fabricated, which serves as conductive biointerfaces to incubate Schwann cells and introduce various electrical stimulation (at different voltages and frequencies). Upon specific fabrication parameters and stimulation, the cell viability is evaluated and input into an artificial neural network model to train the model. Additionally, a data augmentation method is applied to synthesize 1000-fold virtual data points, enabling the construction of a high-accuracy prediction model (with a testing mean absolute error ≤11%). By harnessing the model's predictive power, we can accurately predict Schwann cell viability based on a given set of fabrication/stimulation parameters. Finally, the SHapley Additive exPlanations model interpretation provides several data-scientific insights that are validated by microscopic cellular observations. Our hybrid approach, involving conductive biointerface fabrication, ML algorithms, and data analysis, offers an unconventional platform to construct a preclinical prediction model at the cellular level.

Exhaustion-associated cholesterol deficiency dampens the cytotoxic arm of antitumor immunity.

The concept of targeting cholesterol metabolism to treat cancer has been widely tested in clinics, but the benefits are modest, calling for a complete understanding of cholesterol metabolism in intratumoral cells. We analyze the cholesterol atlas in the tumor microenvironment and find that intratumoral T cells have cholesterol deficiency, while immunosuppressive myeloid cells and tumor cells display cholesterol abundance. Low cholesterol levels inhibit T cell proliferation and cause autophagy-mediated apoptosis, particularly for cytotoxic T cells. In the tumor microenvironment, oxysterols mediate reciprocal alterations in the LXR and SREBP2 pathways to cause cholesterol deficiency of T cells, subsequently leading to aberrant metabolic and signaling pathways that drive T cell exhaustion/dysfunction. LXRß depletion in chimeric antigen receptor T (CAR-T) cells leads to improved antitumor function against solid tumors. Since T cell cholesterol metabolism and oxysterols are generally linked to other diseases, the new mechanism and cholesterol-normalization strategy might have potential applications elsewhere.

Adversarial interspecies relationships facilitate population suppression by gene drive in spatially explicit models.

Suppression gene drives bias their inheritance to spread through a population, potentially eliminating it when they reach high frequency. CRISPR homing suppression drives have already seen success in the laboratory, but several models predict that success may be elusive in population with realistic spatial structure due to extinction-recolonization cycles. Here, we extend our continuous space framework to include two competing species or predator-prey pairs. We find that in both general and mosquito-specific models, competing species or predators can facilitate drive-based suppression, albeit at the cost of an increased rate of drive loss outcomes. These results are robust in mosquito models with seasonal fluctuations. Our study illustrates the difficulty of predicting outcomes in complex ecosystems. However, our results are promising for the prospects of less powerful suppression gene drives to successfully eliminate target mosquito and other pest populations.

A submillimeter bundled microtubular flow battery cell with ultrahigh volumetric power density.

Flow batteries are a promising energy storage solution. However, the footprint and capital cost need further reduction for flow batteries to be commercially viable. The flow cell, where electron exchange takes place, is a central component of flow batteries. Improving the volumetric power density of the flow cell (W/Lcell) can reduce the size and cost of flow batteries. While significant progress has been made on flow battery redox, electrode, and membrane materials to improve energy density and durability, conventional flow batteries based on the planar cell configuration exhibit a large cell size with multiple bulky accessories such as flow distributors, resulting in low volumetric power density. Here, we introduce a submillimeter bundled microtubular (SBMT) flow battery cell configuration that significantly improves volumetric power density by reducing the membrane-to-membrane distance by almost 100 times and eliminating the bulky flow distributors completely. Using zinc-iodide chemistry as a demonstration, our SBMT cell shows peak charge and discharge power densities of 1,322 W/Lcell and 306.1 W/Lcell, respectively, compared with average charge and discharge power densities of <60 W/Lcell and 45 W/Lcell, respectively, of conventional planar flow battery cells. The battery cycled for more than 220 h corresponding to >2,500 cycles at off-peak conditions. Furthermore, the SBMT cell has been demonstrated to be compatible with zinc-bromide, quinone-bromide, and all-vanadium chemistries. The SBMT flow cell represents a device-level innovation to enhance the volumetric power of flow batteries and potentially reduce the size and cost of the cells and the entire flow battery.

Cellular basis of enhanced humoral immunity to SARS-CoV-2 upon homologous or heterologous booster vaccination analyzed by single-cell immune profiling.

SARS-CoV-2 vaccine booster dose can induce a robust humoral immune response, however, its cellular mechanisms remain elusive. Here, we investigated the durability of antibody responses and single-cell immune profiles following booster dose immunization, longitudinally over 6 months, in recipients of a homologous BBIBP-CorV/BBIBP-CorV or a heterologous BBIBP-CorV/ZF2001 regimen. The production of neutralizing antibodies was dramatically enhanced by both booster regimens, and the antibodies could last at least six months. The heterologous booster induced a faster and more robust plasmablast response, characterized by activation of plasma cells than the homologous booster. The response was attributed to recall of memory B cells and the de novo activation of B cells. Expanded B cell clones upon booster dose vaccination could persist for months, and their B cell receptors displayed accumulated mutations. The production of antibody was positively correlated with antigen presentation by conventional dendritic cells (cDCs), which provides support for B cell maturation through activation and development of follicular helper T (Tfh) cells. The proper activation of cDC/Tfh/B cells was likely fueled by active energy metabolism, and glutaminolysis might also play a general role in promoting humoral immunity. Our study unveils the cellular mechanisms of booster-induced memory/adaptive humoral immunity and suggests potential strategies to optimize vaccine efficacy and durability in future iterations.

Thermal Percolation of Antiperovskite Superionic Conductor into Porous MXene Scaffold for High-Capacity and Stable Lithium Metal Battery.

Lithium metal battery is considered an emerging energy storage technology due to its high theoretical capacity and low electrochemical potential. However, the practical exploitations of lithium metal batteries are not realized because of uncontrollable lithium deposition and severe dendrite formation. Herein, a thermal percolation strategy is developed to fabricate a dual-conductive framework using electronically conductive Ti3 C2 Tx MXene aerogels (MXAs) and Li2 OHCl antiperovskite superionic conductor. By melting Li2 OHCl at a low temperature, the molten antiperovskite phase can penetrate the MXA scaffold, resulting in percolative electron/ion pathways. Through density functional theory calculations and electrochemical characterizations, the hybridized lithiophilic (MXA)-lithiophobic (antiperovskite) interfaces can spatially guide the deposition of lithium metals and suppress the growth of lithium dendrites. The symmetric cell with MXA-antiperovskite electrodes exhibits superior cycling stability at high areal capacities of 4 mAh cm-2 over 1000 h. Moreover, the full cell with MXA-antiperovskite anode and high-loading LiFePO4 cathode demonstrates high energy and power densities (415.7 Wh kgcell -1 and 231.0 W kgcell -1 ) with ultralong lifespans. The thermal percolation of lithium superionic conductor into electronically conductive scaffolds promises an efficient strategy to fabricate dual-conductive electrodes, which benefits the development of dendrite-free lithium metal anodes with high energy/power densities.

Topographic design in wearable MXene sensors with in-sensor machine learning for full-body avatar reconstruction.

Wearable strain sensors that detect joint/muscle strain changes become prevalent at human-machine interfaces for full-body motion monitoring. However, most wearable devices cannot offer customizable opportunities to match the sensor characteristics with specific deformation ranges of joints/muscles, resulting in suboptimal performance. Adequate wearable strain sensor design is highly required to achieve user-designated working windows without sacrificing high sensitivity, accompanied with real-time data processing. Herein, wearable Ti3C2Tx MXene sensor modules are fabricated with in-sensor machine learning (ML) models, either functioning via wireless streaming or edge computing, for full-body motion classifications and avatar reconstruction. Through topographic design on piezoresistive nanolayers, the wearable strain sensor modules exhibited ultrahigh sensitivities within the working windows that meet all joint deformation ranges. By integrating the wearable sensors with a ML chip, an edge sensor module is fabricated, enabling in-sensor reconstruction of high-precision avatar animations that mimic continuous full-body motions with an average avatar determination error of 3.5 cm, without additional computing devices.

Digital-twin-based Online Parameter Personalization for Implantable Cardiac Defibrillators.

Implantable cardioverter defibrillators (ICDs) are developed to provide timely therapies when adverse patient conditions are detected. Device therapies need to be adjusted for individual patients and evolving patient conditions, which can be achieved by adjusting device parameter settings. However, there are no validated clinical guidelines for parameter personalization, especially for patients with complex and rare conditions. In this paper, we propose a reinforcement learning framework for online parameter personalization of ICDs. Heart states can be inferred from ECG signals from ECG patches, which can be used to create a digital twin of the patient. Reinforcement learning then use the digital twin as environment to explore parameter settings with less misdiagnosis. Experiments were performed on three virtual patients with specific and evolving heart conditions, and the result shows that our proposed approach can identify ICD parameter settings that can achieve better performance compared to default parameter settings. Clinical relevance-Patients with ICD and ECG patch can receive periodic ICD parameter adjustments that are appropriate for their current heart conditions.

Predicting Synthetic Lethality in Human Cancers via Multi-Graph Ensemble Neural Network.

Synthetic lethality (SL) is currently one of the most effective methods to identify new drugs for cancer treatment. It means that simultaneous inactivation target of two non-lethal genes will cause cell death, but loss of either will not. However, detecting SL pair is challenging due to the experimental costs. Artificial intelligence (AI) is a low-cost way to predict the potential SL relation between two genes. In this paper, a new Multi-Graph Ensemble (MGE) network structure combining graph neural network and existing knowledge about genes is proposed to predict SL pairs, which integrates the embedding of each feature with different neural networks to predict if a pair of genes have SL relation. It has a higher prediction performance compared with existing SL prediction methods. Also, with the integration of other biological knowledge, it has the potential of interpretability.

Polypropylene Carbonate-Based Adaptive Buffer Layer for Stable Interfaces of Solid Polymer Lithium Metal Batteries.

Solid polymer electrolytes (SPEs) have the potential to enhance the safety and energy density of lithium batteries. However, poor interfacial contact between the lithium metal anode and SPE leads to high interfacial resistance and low specific capacity of the battery. In this work, we present a novel strategy to improve this solid-solid interface problem and maintain good interfacial contact during battery cycling by introducing an adaptive buffer layer (ABL) between the Li metal anode and SPE. The ABL consists of low molecular-weight polypropylene carbonate , poly(ethylene oxide) (PEO), and lithium salt. Rheological experiments indicate that ABL is viscoelastic and that it flows with a higher viscosity compared to PEO-only SPE. ABL also has higher ionic conductivity than PEO-only SPE. In the presence of ABL, the interface resistance of the Li/ABL/SPE/LiFePO4 battery only increased 20% after 150 cycles, whereas that of the battery without ABL increased by 117%. In addition, because ABL makes a good solid-solid interface contact between the Li metal anode and SPE, the battery with ABL delivered an initial discharge specific capacity of >110 mA·h/g, which is nearly twice that of the battery without ABL, which is 60 mA·h/g. Moreover, ABL is able to maintain electrode-electrolyte interfacial contact during battery cycling, which stabilizes the battery Coulombic efficiency.

Effect of the A-Type Linkage on the Pharmacokinetics and Intestinal Metabolism of Litchi Pericarp Oligomeric Procyanidins.

The bioavailability of A-type procyanidins in vivo has been rarely investigated; as such, this study discusses the effect of A-type linkage and degree of polymerization on the metabolism of procyanidins extracted from litchi pericarp (LPOPC). Sprague-Dawley rats were gavaged with (-)-epicatechin (EC) and LPOPC and sacrificed at different time points after ingestion. A-type linkage procyanidin oligomers inhibited the absorption of EC. Analysis of urinary contents from rats administered with EC, A-type procyanidin dimer (A-2), and A-type procyanidin trimer (A-3) showed distinct native and metabolite profiles for each rat. Rats fed with A-2 and A-3 presented significantly higher levels of shikimic acid and less amount of m(p)-coumaric acid metabolites in vivo and provide insight into the quantitative structure-activity relationship of procyanidin oligomers during metabolism, indicating that procyanidins with A-type linkage could induce an altered metabolic pathway of oligomers in the gastrointestinal system.