Selective Adsorption of Dyes and Fe3+ Sensing via Tb3+ Incorporation in an Anionic Cadmium-Organic Framework.

A three-dimensional (3D) anionic cadmium-organic framework, namely [(CH3)2NH2][Cd1.5(DMTDC)2] ⋅ 2DMA ⋅ 0.5H2O (Cd-MOF; DMA=N,N-dimethylacetamide), was successfully synthesized under solvothermal conditions by using a linear thienothiophene-containing dicarboxylate ligand, 3,4-dimethylthieno [2,3-b]-thiophene-2,5-dicar-boxylic acid (H2DMTDC). Single-crystal X-ray diffraction analysis reveals that Cd-MOF exhibits a 3D anionic framework with pcu α-Po topology, featuring rectangle and rhombus-shaped channels along b- and c- axis direction. Cd-MOF demonstrates selective adsorption of cationic dyes over anionic and neutral dyes. Additionally, Tb3+-loaded Cd-MOF serves as a fast-response fluorescence sensor for the sensitive detection of Fe3+ ions with a low limit of detection (8.90×10-7 M) through fluorescence quenching.

Terahertz flexible multiplexing chip enabled by synthetic topological phase transitions.

Flexible multiplexing chips that permit reconfigurable multidimensional channel utilization are indispensable for revolutionary 6G terahertz communications, but the insufficient manipulation capability of terahertz waves prevents their practical implementation. Herein, we propose the first experimental demonstration of a flexible multiplexing chip for terahertz communication by revealing the unique mechanism of topological phase (TP) transition and perseveration in a heterogeneously coupled bilayer valley Hall topological photonic system. The synthetic and individual TPs operated in the coupled and decoupled states enable controllable on-chip modular TP transitions and subchannel switching. Two time-frequency interleaved subchannels support 10- and 12-Gbit/s QAM-16 high-speed data streams along corresponding paths over carriers of 120 and 130 GHz with 2.5- and 3-GHz bandwidths, respectively. This work unlocks interlayer heterogeneous TPs for inspiring ingenious on-chip terahertz-wave regulation, allowing functionality-reconfigurable, compactly integrated and CMOS-compatible chips.

Dissecting the molecular basis of the ultra-large grain formation in rice.

The shape of rice grains not only determines the thousand-grain weight but also correlates closely with the grain quality. Here we identified an ultra-large grain accession (ULG) with a thousand-grain weight exceeding 60 g. The integrated analysis of QTL, BSA, de novo genome assembled, transcription sequencing, and gene editing was conducted to dissect the molecular basis of the ULG formation. The ULG pyramided advantageous alleles from at least four known grain-shaping genes, OsLG3, OsMADS1, GS3, GL3.1, and one novel locus, qULG2-b, which encoded a leucine-rich repeat receptor-like kinase. The collective impacts of OsLG3, OsMADS1, GS3, and GL3.1 on grain size were confirmed in transgenic plants and near-isogenic lines. The transcriptome analysis identified 112 genes cooperatively regulated by these four genes that were prominently involved in photosynthesis and carbon metabolism. By leveraging the pleiotropy of these genes, we enhanced the grain yield, appearance, and stress tolerance of rice var. SN265. Beyond showcasing the pyramiding of multiple grain size regulation genes that can produce ULG, our study provides a theoretical framework and valuable genomic resources for improving rice variety by leveraging the pleiotropy of grain size regulated genes.

Bioengineered human colon organoids with in vivo-like cellular complexity and function.

Organoids and organs-on-a-chip have emerged as powerful tools for modeling human gut physiology and disease in vitro. Although physiologically relevant, these systems often lack the environmental milieu, spatial organization, cell type diversity, and maturity necessary for mimicking human intestinal mucosa. To instead generate models closely resembling in vivo tissue, we herein integrated organoid and organ-on-a-chip technology to develop an advanced human organoid model, called "mini-colons." By employing an asymmetric stimulation with growth factors, we greatly enhanced tissue longevity and replicated in vivo-like diversity and patterning of proliferative and differentiated cell types. Mini-colons contain abundant mucus-producing goblet cells and, signifying mini-colon maturation, single-cell RNA sequencing reveals emerging mature and functional colonocytes. This methodology is expanded to generate microtissues from the small intestine and incorporate additional microenvironmental components. Finally, our bioengineered organoids provide a precise platform to systematically study human gut physiology and pathology, and a reliable preclinical model for drug safety assessment.

Evaluating the prognostic value of tumor deposits in non-metastatic lymph node-positive colon adenocarcinoma using Cox regression and machine learning.

BACKGROUND: The 8th AJCC TNM staging for non-metastatic lymph node-positive colon adenocarcinoma patients(NMLP-CA) stages solely by lymph node status, irrespective of the positivity of tumor deposits (TD). This study uses machine learning and Cox regression to predict the prognostic value of tumor deposits in NMLP-CA. METHODS: Patient data from the SEER registry (2010-2019) was used to develop CSS nomograms based on prognostic factors identified via multivariate Cox regression. Model performance was evaluated by c-index, dynamic calibration, and Schmid score. Shapley additive explanations (SHAP) were used to explain the selected models. RESULTS: The study included 16,548 NMLP-CA patients, randomized 7:3 into training (n = 11,584) and test (n = 4964) sets. Multivariate Cox analysis identified TD, age, marital status, primary site, grade, pT stage, and pN stage as prognostic for cancer-specific survival (CSS). In the test set, the gradient boosting machine (GBM) model achieved the best C-index (0.733) for CSS prediction, while the Cox model and GAMBoost model optimized dynamic calibration(6.473) and Schmid score (0.285), respectively. TD ranked among the top 3 most important features in the models, with increasing predictive significance over time. CONCLUSIONS: Positive tumor deposit status confers worse prognosis in NMLP-CA patients. Tumor deposits may confer higher TNM staging. Furthermore, TD could play a more significant role in the staging system.

Enhancing grain shape, thermotolerance, and alkaline tolerance via Gγ protein manipulation in rice.

KEY MESSAGE: Our findings highlight a valuable breeding resource, demonstrating the potential to concurrently enhance grain shape, thermotolerance, and alkaline tolerance by manipulating Gγ protein in rice. Temperate Geng/Japonica (GJ) rice yields have improved significantly, bolstering global food security. However, GJ rice breeding faces challenges, including enhancing grain quality, ensuring stable yields at warmer temperatures, and utilizing alkaline land. In this study, we employed CRISPR/Cas9 gene-editing technology to knock out the GS3 locus in seven elite GJ varieties with superior yield performance. Yield component measurements revealed that GS3 knockout mutants consistently enhanced grain length and reduced plant height in diverse genetic backgrounds. The impact of GS3 on the grain number per panicle and setting rate depended on the genetic background. GS3 knockout did not affect milling quality and minimally altered protein and amylose content but notably influenced chalkiness-related traits. GS3 knockout indiscriminately improved heat and alkali stress tolerance in the GJ varieties studied. Transcriptome analysis indicated differential gene expression between the GS3 mutants and their wild-type counterparts, enriched in biological processes related to photosynthesis, photosystem II stabilization, and pathways associated with photosynthesis and cutin, suberine, and wax biosynthesis. Our findings highlight GS3 as a breeding resource for concurrently improving grain shape, thermotolerance, and alkaline tolerance through Gγ protein manipulation in rice.

The DEP1 Mutation Improves Stem Lodging Resistance and Biomass Saccharification by Affecting Cell Wall Biosynthesis in Rice.

BACKGROUND: Plant cell walls have evolved precise plasticity in response to environmental stimuli. The plant heterotrimeric G protein complexes could sense and transmit extracellular signals to intracellular signaling systems, and activate a series of downstream responses. dep1 (Dense and Erect Panicles 1), the gain-of-function mutation of DEP1 encoding a G protein γ subunit, confers rice multiple improved agronomic traits. However, the effects of DEP1 on cell wall biosynthesis and wall-related agronomic traits remain largely unknown. RESULTS: In this study, we showed that the DEP1 mutation affects cell wall biosynthesis, leading to improved lodging resistance and biomass saccharification. The DEP1 is ubiquitously expressed with a relatively higher expression level in tissues rich in cell walls. The CRISPR/Cas9 editing mutants of DEP1 (dep1-cs) displayed a significant enhancement in stem mechanical properties relative to the wild-type, leading to a substantial improvement in lodging resistance. Cell wall analyses showed that the DEP1 mutation increased the contents of cellulose, hemicelluloses, and pectin, and reduced lignin content and cellulose crystallinity (CrI). Additionally, the dep1-cs seedlings exhibited higher sensitivity to cellulose biosynthesis inhibitors, 2,6-Dichlorobenzonitrile (DCB) and isoxaben, compared with the wild-type, confirming the role of DEP1 in cellulose deposition. Moreover, the DEP1 mutation-mediated alterations of cell walls lead to increased enzymatic saccharification of biomass after the alkali pretreatment. Furthermore, the comparative transcriptome analysis revealed that the DEP1 mutation substantially altered expression of genes involved in carbohydrate metabolism, and cell wall biosynthesis. CONCLUSIONS: Our findings revealed the roles of DEP1 in cell wall biosynthesis, lodging resistance, and biomass saccharification in rice and suggested genetic modification of DEP1 as a potential strategy to develop energy rice varieties with high lodging resistance.

Dynamical effects of memristive electromagnetic induction on a 2D Wilson neuron model.

Electromagnetic induction plays a crucial impact on the firing activity of biological neurons, since it exists along with the mutual effect between membrane potential and ions transport. Flux-controlled memristor is an available candidate in characterizing the electromagnetic induction effect. Different from the previously reported literature, a non-ideal flux-controlled memristor with cosine mem-conductance function is employed to determine the periodic magnetization and leakage flux processes in neurons. Thereafter, a three-dimensional (3D) memristive Wilson (m-Wilson) neuron model is constructed under the consideration of this kind of electromagnetic induction. Numerical simulations are performed by multiple numerical tools, which demonstrate that the 3D m-Wilson neuron model can generate abundant firing activities. Interestingly, coexisting firing activities, antimonotonicity, and firing frequency regulation are discovered under special parameter settings. Furthermore, a PCB-based analog circuit is designed and hardware measurements are executed to verify the numerical simulations. These explorations in numerical and hardware surveys might provide insights to regulate the firing activities by appropriate electromagnetic induction.

Memristor-coupled dual-neuron mapping model: initials-induced coexisting firing patterns and synchronization activities.

Synaptic plasticity makes memristors particularly suitable for simulating the connection synapses between neurons that describe magnetic induction coupling. By applying a memristor to the synaptic coupling between two map-based neuron models, a memristor-coupled dual-neuron mapping (MCDN) model is proposed in this article. The MCDN model has a line fixed point set associated with the memristor initial state, which is always unstable for the model parameters and memristor initial state of interest. Complex spiking/bursting firing patterns and their transitions are disclosed using some dynamical analysis means. The numerical results show that these spiking/bursting firings are significantly relied on the memristor initial state, demonstrating the coexistence of firing patterns. Moreover, the initial effects of complete synchronization are explored for the homogeneous MCDN model, and it is clarified that in addition to being related to the coupling strength, the synchronization activities are extremely dependent on the initial states of the memristor and neurons. Finally, these numerical results are confirmed by the FPGA-based hardware experiments.

Cotton seed cultivar identification based on the fusion of spectral and textural features.

The mixing of cotton seeds of different cultivars and qualities can lead to differences in growth conditions and make field management difficult. In particular, except for yield loss, it can also lead to inconsistent cotton quality and poor textile product quality, causing huge economic losses to farmers and the cotton processing industry. However, traditional cultivar identification methods for cotton seeds are time-consuming, labor-intensive, and cumbersome, which cannot meet the needs of modern agriculture and modern cotton processing industry. Therefore, there is an urgent need for a fast, accurate, and non-destructive method for identifying cotton seed cultivars. In this study, hyperspectral images (397.32 nm-1003.58 nm) of five cotton cultivars, namely Jinke 20, Jinke 21, Xinluzao 64, Xinluzao 74, and Zhongmiansuo 5, were captured using a Specim IQ camera, and then the average spectral information of seeds of each cultivar was used for spectral analysis, aiming to estab-lish a cotton seed cultivar identification model. Due to the presence of many obvious noises in the < 400 nm and > 1000 nm regions of the collected spectral data, spectra from 400 nm to 1000 nm were selected as the representative spectra of the seed samples. Then, various denoising techniques, including Savitzky-Golay (SG), Standard Normal Variate (SNV), and First Derivative (FD), were applied individually and in combination to improve the quality of the spectra. Additionally, a successive projections algorithm (SPA) was employed for spectral feature selection. Based on the full-band spectra, a Partial Least Squares-Discriminant Analysis (PLS-DA) model was established. Furthermore, spectral features and textural features were fused to create Random Forest (RF), Convolutional Neural Network (CNN), and Extreme Learning Machine (ELM) identification models. The results showed that: (1) The SNV-FD preprocessing method showed the optimal denoising performance. (2) SPA highlighted the near-infrared region (800-1000 nm), red region (620-700 nm), and blue-green region (420-570 nm) for identifying cotton cultivar. (3) The fusion of spectral features and textural features did not consistently improve the accuracy of all modeling strategies, suggesting the need for further research on appropriate modeling strategies. (4) The ELM model had the highest cotton cultivar identification accuracy, with an accuracy of 100% for the training set and 98.89% for the test set. In conclusion, this study successfully developed a highly accurate cotton seed cultivar identification model (ELM model). This study provides a new method for the rapid and non-destructive identification of cotton seed cultivars, which will help ensure the cultivar consistency of seeds used in cotton planting, and improve the overall quality and yield of cotton.

ULK1-dependent phosphorylation of PKM2 antagonizes O-GlcNAcylation and regulates the Warburg effect in breast cancer.

Pyruvate kinase M2 (PKM2) is a central metabolic enzyme driving the Warburg effect in tumor growth. Previous investigations have demonstrated that PKM2 is subject to O-linked ß-N-acetylglucosamine (O-GlcNAc) modification, which is a nutrient-sensitive post-translational modification. Here we found that unc-51 like autophagy activating kinase 1 (ULK1), a glucose-sensitive kinase, interacts with PKM2 and phosphorylates PKM2 at Ser333. Ser333 phosphorylation antagonizes PKM2 O-GlcNAcylation, promotes its tetramer formation and enzymatic activity, and decreases its nuclear localization. As PKM2 is known to have a nuclear role in regulating c-Myc, we also show that PKM2-S333 phosphorylation inhibits c-Myc expression. By downregulating glucose consumption and lactate production, PKM2 pS333 attenuates the Warburg effect. Through mouse xenograft assays, we demonstrate that the phospho-deficient PKM2-S333A mutant promotes tumor growth in vivo. In conclusion, we identified a ULK1-PKM2-c-Myc axis in inhibiting breast cancer, and a glucose-sensitive phosphorylation of PKM2 in modulating the Warburg effect.

Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.

Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.

H2O2 self-supplying and GSH-depleting nanosystem for amplified NIR mediated-chemodynamic therapy of MRSA biofilm-associated infections.

Reactive oxygen species (ROS) has emerged as potent therapeutic agents for biofilm-associated bacterial infections. Chemodynamic therapy (CDT), involving the generation of high-energy ROS, displays great potential in the therapy of bacterial infections. However, challenges such as insufficient hydrogen peroxide (H2O2) and over-expressed glutathione (GSH) levels within the microenvironment of bacterial biofilms severely limit the antibacterial efficacy of CDT. Herein, we have developed a multifunctional nanoplatform (CuS@CaO2@Dex) by integrating copper sulfide (CuS) and calcium peroxide (CaO2) into dextran (Dex)-coated nanoparticles. This innovative platform enhanced ROS generation for highly efficient biofilm elimination by simultaneously supplying H2O2 and depleting GSH. The Dex-coating facilitated the penetrability of CuS@CaO2@Dex into biofilms, while CaO2 generated a substantial amount of H2O2 in the acidic biofilm microenvironment. CuS, through a Fenton-like reaction, catalyzed the conversion of self-supplied H2O2 into hydroxyl radicals (•OH) and consumed the overexpressed GSH. Additionally, the incorporation of near-infrared II (NIR II) laser irradiation enhanced the photothermal properties of CuS, improving the catalytic efficiency of the Fenton-like reaction for enhanced antibacterial effects. In vivo experiments have demonstrated that CuS@CaO2@Dex exhibited remarkable antibacterial and antibiofilm efficacy, exceptional wound healing capabilities, and notable biosafety. In summary, the Dex-coated nanoplatform proposed in this study, with its self-sterilization capability through ROS, holds significant potential for future biomedical applications.

Measuring and decomposing natural capital use in Xinjiang from a regional-industry perspective.

Accurately portraying the mechanism of the flow of natural resource consumption between regions and its impact on ecology is of crucial value in deepening the understanding of the coordinated relationship between population, resources, environment and development. Consequently, this promotes the sustainable development of the natural economy and society. Based on a regional-industrial perspective, this study used a localized three-dimensional ecological footprint model to measure and decompose natural resources in Xinjiang from 2005 to 2020. In doing so, the study clarified the supply, demand, and flow utilization of natural capital in Xinjiang, the balance of spatial and temporal allocation of resources, the coupling between economic growth and resource consumption, and the coordination between industrial structure and ecological environment. The results showed that (1) Xinjiang's per capita ecological deficit grew from 2.096 to 11.667 in 2005-2020. Moreover, the energy footprint was a decisive part of the ecological deficit throughout the study period. Furthermore, the trend of increased ecological pressure was higher in northern and eastern Xinjiang than in southern Xinjiang. (2) The overall Gini coefficient of Xinjiang's ecological carrying capacity was at the critical value of spatial equilibrium (0.4), with differences between the groups: Northern & Southern Xinjiang > Northern & Eastern Xinjiang > Eastern & Southern Xinjiang. The reasons for this inter-regional economic disparity are related to fiscal expenditure/GDP, level of urbanization, and regional industrial output. Overall, the decoupling relationship between environmental pressures and economic growth was optimistic. (3) From an industrial perspective, the levels of industrial structural efficiency and the industrial ecological harmony index were still relatively low, but the overall trend was on the rise. (4) Resource endowment, economic development, consumption structure, and population had significant driving effects on the ecological footprint, whereas environmental protection, science, and technology could inhibit its growth to a certain extent. This study aimed to provide an in-depth analysis of the current situation and problems of natural resource use in Xinjiang and provide theoretical and practical references for sustainable development in the region.

[Clinical Efficacy of Hypomethylating Agent Therapy in Patients with Chronic Myelomonocytic Leukemia].

OBJECTIVE: To observe the clinical efficacy and safety of hypomethylating agent therapy in chronic myelomonocytic leukemia (CMML). METHODS: From February 2014 to June 2021, the clinical data, efficacy, survival time and safety of CMML patients diagnosed in the Second Hospital of Hebei Medical University and treated with hypomethylating agent therapy were retrospectively analyzed. RESULTS: A total of 25 CMML patients received hypomethylating agent therapy, including 18 cases treated with decitabine (DEC) and 7 cases treated with azacytidine (AZA) as the basic treatment. Among them, 20 patients responded, and 7 patients got complete remission (CR). All patients with CR were treated with DEC as the basic treatment. Five cases of CR occurred in the first 4 courses of treatment. After a median follow-up of 16.4 (9.4-20.5) months, 4 patients with CR progressed to acute myeloid leukemia (AML). The median overall survival (OS) time of 25 CMML patients was 17.4 months (95%CI: 12.437-22.363). According to MD Anderson prognostic scoring system (MDAPS), CMML-specific prognostic scoring system (CPSS), CPSS molecular (CPSS-mol), Mayo molecular model (MMM), risk stratification of patients was performed, and the difference only between different risk stratification of MDAPS and survival time was statistically significant. Common adverse reactions of hypomethylating agent therapy in CMML patients included infection, gastrointestinal reaction, hematological toxicity, skin allergy and liver function damage. All patients' symptoms were improved after corresponding treatment. CONCLUSION: Hypomethylating agent therapy is effective and safe for CMML patients. CR mostly occurs in the first 4 courses of treatment, and hypomethylating agent therapy combined with low-dose chemotherapy can be used for patients who do not respond. Hypomethylating agent therapy can delay the disease, but can't prevent progression.

Machine learning-enabled performance prediction and optimization for iron-chromium redox flow batteries.

Iron-chromium flow batteries (ICRFBs) are regarded as one of the most promising large-scale energy storage devices with broad application prospects in recent years. However, transitioning from laboratory-scale development to industrial-scale deployment can be a time-consuming process due to the multitude of complex factors that impact ICRFB stack performance. Herein, a data-driven optimization methodology applying active learning, informed by an extensive survey of the literature encompassing diverse experimental conditions, is proposed to enable exceptional precision in predicting ICRFB system performance considering both operation conditions and key materials selection. Specifically, multitask ML models are trained on experimental data with a high prediction accuracy (R2 > 0.92) to link ICRFB properties to energy efficiency, coulombic efficiency, and capacity. We also interpret the ML models based on Shapley additive explanations and extract valuable insights into the importance of descriptors. It is noted that the operation conditions (current density and cycle number) and the electrode type are the most critical descriptors affecting the voltage efficiency and coulombic efficiency while the electrode size strongly affects the capacity. Moreover, active learning is used to explore the most optimized cases considering the highest energy efficiency and capacity. The versatility and robustness of the approach are demonstrated by the successful validation between ML prediction and our experiments of energy efficiency (±0.15%) and capacity (±0.8%). This work not only affords fruitful data-driven insight into the property-performance relationship, but also unveils the explainability of critical properties on the performance of ICRFBs, which accelerates the rational design of next-generation ICRFBs.

Irisin attenuates vascular remodeling in hypertensive mice induced by Ang II by suppressing Ca2+-dependent endoplasmic reticulum stress in VSMCs.

Vascular remodeling plays a vital role in hypertensive diseases and is an important target for hypertension treatment. Irisin, a newly discovered myokine and adipokine, has been found to have beneficial effects on various cardiovascular diseases. However, the pharmacological effect of irisin in antagonizing hypertension-induced vascular remodeling is not well understood. In the present study, we investigated the protection and mechanisms of irisin against hypertension and vascular remodeling induced by angiotensin II (Ang II). Adult male mice of wild-type, FNDC5 (irisin-precursor) knockout, and FNDC5 overexpression were used to develop hypertension by challenging them with Ang II subcutaneously in the back using a microosmotic pump for 4 weeks. Similar to the attenuation of irisin on Ang II-induced VSMCs remodeling, endogenous FNDC5 ablation exacerbated, and exogenous FNDC5 overexpression alleviated Ang II-induced hypertension and vascular remodeling. Aortic RNA sequencing showed that irisin deficiency exacerbated intracellular calcium imbalance and increased vasoconstriction, which was parallel to the deterioration in both ER calcium dysmetabolism and ER stress. FNDC5 overexpression/exogenous irisin supplementation protected VSMCs from Ang II-induced remodeling by improving endoplasmic reticulum (ER) homeostasis. This improvement includes inhibiting Ca2+ release from the ER and promoting the re-absorption of Ca2+ into the ER, thus relieving Ca2+-dependent ER stress. Furthermore, irisin was confirmed to bind to its receptors, αV/ß5 integrins, to further activate the AMPK pathway and inhibit the p38 pathway, leading to vasoprotection in Ang II-insulted VSMCs. These results indicate that irisin protects against hypertension and vascular remodeling in Ang II-challenged mice by restoring calcium homeostasis and attenuating ER stress in VSMCs via activating AMPK and suppressing p38 signaling.

Gustation-Inspired Dual-Responsive Hydrogels for Taste Sensing Enabled by Machine Learning.

Human gustatory system recognizes salty/sour or sweet tastants based on their different ionic or nonionic natures using two different signaling pathways. This suggests that evolution has selected this detection dualism favorably. Analogically, this work constructs herein bioinspired stimulus-responsive hydrogels to recognize model salty/sour or sweet tastes based on two different responses, that is, electrical and volumetric responsivities. Different compositions of zwitter-ionic sulfobetainic N-(3-sulfopropyl)-N-(methacryloxyethyl)-N,N-dimethylammonium betaine (DMAPS) and nonionic 2-hydroxyethyl methacrylate (HEMA) are co-polymerized to explore conditions for gelation. The hydrogel responses upon adding model tastant molecules are explored using electrical and visual de-swelling observations. Beyond challenging electrochemical impedance spectroscopy measurements, naive multimeter electrical characterizations are performed, toward facile applicability. Ionic model molecules, for example, sodium chloride and acetic acid, interact electrostatically with DMAPS groups, whereas nonionic molecules, for example, D(-)fructose, interact by hydrogen bonding with HEMA. The model tastants induce complex combinations of electrical and volumetric responses, which are then introduced as inputs for machine learning algorithms. The fidelity of such a trained dual response approach is tested for a more general taste identification. This work envisages that the facile dual electric/volumetric hydrogel responses combined with machine learning proposes a generic bioinspired avenue for future bionic designs of artificial taste recognition, amply needed in applications.

Natural bioactive lysosomes extracted from multiple cells for tumor therapy.

Nanoparticles of biological origin exhibit many unique properties in biological applications due to their exquisite structure, specific composition, and natural biological functionality. In this study, we obtained lysosomes from three distinct cell types (one normal cell and two activated immune cells) and demonstrated their potential as natural therapeutic nanoparticles for tumor therapy. In vitro experiments revealed that these lysosomes maintained their structural integrity, were well-distributed, and exhibited significant biological activity, which effectively induced cancer cell death by generating ROS and disrupting biological substrates. Additionally, in vivo investigations showed that these lysosomes could accumulate in tumor tissues after intravenous administration and exhibited exceptional therapeutic effects through the destruction of tumor blood vessels and the degradation of immunosuppressive proteins, with complete tumor disappearance in a single treatment. This research on the utilization of bioactive lysosomes for tumor treatment provides valuable insights into drug development and tumor treatment, particularly when conventional approaches have proven ineffective.

Characterization of a Novel Polysaccharide Derived from Rhizospheric Paecilomyces vaniformisi and Its Mechanism for Enhancing Salinity Resistance in Rice Seedlings.

Soil salinity is an important limiting factor in agricultural production. Rhizospheric fungi can potentially enhance crop salinity tolerance, but the precise role of signaling substances is still to be systematically elucidated. A rhizospheric fungus identified as Paecilomyces vaniformisi was found to enhance the salinity tolerance of rice seedlings. In this study, a novel polysaccharide (PPL2b) was isolated from P. vaniformisi and identified as consisting of Manp, Glcp, GalpA, and Galp. In a further study, PPL2b showed significant activity in alleviating salinity stress-induced growth inhibition in rice seedlings. The results indicated that under salinity stress, PPL2b enhances seed germination, plant growth (height and biomass), and biochemical parameters (soluble sugar and protein contents). Additionally, PPL2b regulates genes such as SOS1 and SKOR to decrease K+ efflux and increase Na+ efflux. PPL2b increased the expression and activity of genes related to antioxidant enzymes and nonenzyme substances in salinity-induced oxidative stress. Further study indicated that PPL2b plays a crucial role in regulating osmotic substances, such as proline and betaine, in maintaining the osmotic balance. It also modulates plant hormones to promote rice seedling growth and enhance their tolerance to soil salinity. The variables interacted and were divided into two groups (PC1 77.39% and PC2 18.77%) based on their relative values. Therefore, these findings indicate that PPL2b from P. vaniformisi can alleviate the inhibitory effects of salinity stress on root development, osmotic adjustment, ion balance, oxidative stress balance, and growth of rice seedlings. Furthermore, it suggests that polysaccharides produced by rhizospheric fungi could be utilized to enhance crop tolerance to salinity.