Mechanisms of response and tolerance to active RAS inhibition in KRAS-mutant NSCLC.

Resistance to inactive state-selective RASG12C inhibitors frequently entails accumulation of RASGTP, rendering effective inhibition of active RAS potentially desirable. Here, we evaluated the anti-tumor activity of the RAS(ON) multi-selective tri-complex inhibitor RMC-7977 and dissected mechanisms of response and tolerance in KRASG12C-mutant NSCLC. Broad-spectrum, reversible RASGTP inhibition with or without concurrent covalent targeting of active RASG12C yielded superior and differentiated antitumor activity across diverse co-mutational KRASG12C-mutant NSCLC mouse models of primary or acquired RASG12C(ON) or (OFF) inhibitor resistance. Interrogation of time-resolved single cell transcriptional responses established an in vivo atlas of multi-modal acute and chronic RAS pathway inhibition in the NSCLC ecosystem and uncovered a regenerative mucinous transcriptional program that supports long-term tumor cell persistence. In patients with advanced KRASG12C-mutant NSCLC, the presence of mucinous histological features portended poor response to sotorasib or adagrasib. Our results have potential implications for personalized medicine and the development of rational RAS inhibitor-anchored therapeutic strategies.

High-throughput microalgae sorting based on the deterministic lateral displacement technique.

Microalgae are a group of photosynthetic organisms that can grow autotrophically, performing photosynthesis to synthesize abundant organic compounds and release oxygen. They are rich in nutritional components and chemical precursors, presenting wide-ranging application prospects. However, potential contamination by foreign strains or bacteria can compromise their analytical applications. Therefore, the obtaining of pure algal strains is crucial for the subsequent analysis and application of microalgae. This study designed a deterministic lateral displacement (DLD) chip with dual input and dual outlet of equal width for the separation of Haematococcus pluvialis and Chlorella vulgaris. Optimal separation parameters were determined through a series of experiments, resulting in a purity of 99.80 % for Chlorella vulgaris and 94.58 % for Haematococcus pluvialis, with recovery rates maintained above 90 %, demonstrating high efficiency. This study provides a reliable foundation for future research and applications of microalgae, which holds considerable significance for the subsequent analysis and utilization of microalgae.

Bronchobiliary fistula after traumatic liver rupture: a case report.

INTRODUCTION: Bronchobiliary fistulas are rare and difficult to treat. Peacock first reported this entity in 1850 while treating a patient with hepatic encopresis. CASE PRESENTATION: A 67-year-old Chinese male patient presented to the outpatient clinic with a complaint of coughing up phlegm with chest tightness for 4 days with symptoms of intermittent bilirubin sputum with a sputum volume of about 500 ml per day but no symptoms of abdominal pain or jaundice and no yellow urine or steatorrhea. The examination revealed cyanosis of the lips and mouth, barrel chest, low breath sounds on the right side, and a large number of wet rales heard in both lungs. The imaging investigations were suggestive of bronchobiliary fistula. Therefore, the patient was operated on and discharged with no perioperative complications. CONCLUSION: Bronchobiliary fistula should be considered diagnostically in patients with known liver disease who also experience trauma or medical treatment and cough up bile-colored sputum, regardless of the presence of concurrent infections, and in conjunction with radiological expertise to identify it. Here, we report a case of bronchobiliary fistula and a brief review of the literature on it.

Orexin Neurons to Sublaterodorsal Tegmental Nucleus Pathway Prevents Sleep Onset REM Sleep-Like Behavior by Relieving the REM Sleep Pressure.

Proper timing of vigilance states serves fundamental brain functions. Although disturbance of sleep onset rapid eye movement (SOREM) sleep is frequently reported after orexin deficiency, their causal relationship still remains elusive. Here, we further study a specific subgroup of orexin neurons with convergent projection to the REM sleep promoting sublaterodorsal tegmental nucleus (OXSLD neurons). Intriguingly, although OXSLD and other projection-labeled orexin neurons exhibit similar activity dynamics during REM sleep, only the activation level of OXSLD neurons exhibits a significant positive correlation with the post-inter-REM sleep interval duration, revealing an essential role for the orexin-sublaterodorsal tegmental nucleus (SLD) neural pathway in relieving REM sleep pressure. Monosynaptic tracing reveals that multiple inputs may help shape this REM sleep-related dynamics of OXSLD neurons. Genetic ablation further shows that the homeostatic architecture of sleep/wakefulness cycles, especially avoidance of SOREM sleep-like transition, is dependent on this activity. A positive correlation between the SOREM sleep occurrence probability and depression states of narcoleptic patients further demonstrates the possible significance of the orexin-SLD pathway on REM sleep homeostasis.

Development of a Human B7-H3-Specific Antibody with Activity against Colorectal Cancer Cells through a Synthetic Nanobody Library.

Nanobodies have emerged as promising tools in biomedicine due to their single-chain structure and inherent stability. They generally have convex paratopes, which potentially prefer different epitope sites in an antigen compared to traditional antibodies. In this study, a synthetic phage display nanobody library was constructed and used to identify nanobodies targeting a tumor-associated antigen, the human B7-H3 protein. Combining next-generation sequencing and single-clone validation, two nanobodies were identified to specifically bind B7-H3 with medium nanomolar affinities. Further characterization revealed that these two clones targeted a different epitope compared to known B7-H3-specific antibodies, which have been explored in clinical trials. Furthermore, one of the clones, dubbed as A6, exhibited potent antibody-dependent cell-mediated cytotoxicity (ADCC) against a colorectal cancer cell line with an EC50 of 0.67 nM, upon conversion to an Fc-enhanced IgG format. These findings underscore a cost-effective strategy that bypasses the lengthy immunization process, offering potential rapid access to nanobodies targeting unexplored antigenic sites.

Heterogeneous CNF/MoO3 nanofluidic membranes with tunable surface plasmon resonances for solar-osmotic energy conversion.

Two-dimensional (2D) nanofluidic membranes are competitive candidates for osmotic energy harvesting and have been greatly developed. However, the use of diverse inherent characteristics of 2D nanosheets, such as electronic or optoelectronic properties, to achieve intelligent ion transport, still lacks sufficient exploration. Here, a cellulose nanofiber/molybdenum oxide (CNF/MoO3) heterogeneous nanofluidic membrane with high performance solar-osmotic energy conversion is reported, and how surface plasmon resonances (SPR) regulate selective cation transport is revealed. The SPR of amorphous MoO3 endows the heterogeneous nanofluidic membranes with tunable surface charge and good photothermal conversion. Through DFT calculations and finite element modeling, the regulation of electronic and optoelectronic properties on the surface of materials by SPR and the influence of surface charge density and temperature gradient on ion transport in nanofluidic membranes are demonstrated. By mixing 0.01/0.5 M NaCl solutions using SPR and photothermal effects, the power density can achieve a remarkable value of ≈13.24 W m-2, outperforming state-of-the-art 2D-based nanofluidic membranes. This work first reveals the regulation and mechanism of SPR on ion transport in nanofluidic membranes and systematically studies photon-electron-ion interactions in nanofluidic membranes, which could also provide a new viewpoint for promoting osmotic energy conversion.

A microfluidic microalgae detection system for cellular physiological response based on an object detection algorithm.

The composition of species and the physiological status of microalgal cells serve as significant indicators for monitoring marine environments. Symbiotic with corals, Symbiodiniaceae are more sensitive to the environmental response. However, current methods for evaluating microalgae tend to be population-based indicators that cannot be focused on single-cell level, ignoring potentially heterogeneous cells as well as cell state transitions. In this study, we proposed a microalgal cell detection method based on computer vision and microfluidics, which combined microscopic image processing, microfluidic chip and convolutional neural network to achieve label-free, sheathless, automated and high-throughput microalgae identification and cell state assessment. By optimizing the data import, training process and model architecture, we solved the problem of identifying tiny objects at the micron scale, and the optimized model was able to perform the tasks of cell multi-classification and physiological state assessment with more than 95% mean average precision. We discovered a novel transition state and explored the thermal sensitivity of three clades of Symbiodiniaceae, and discovered the phenomenon of cellular heat shock at high temperatures. The evolution of the physiological state of Symbiodiniaceae cells is very important for directional cell evolution and early warning of coral ecosystem health.

Human extracellular matrix (ECM)-like collagen and its bioactivity.

Collagen, the most abundant structural protein in the human extracellular matrix (ECM), provides essential support for tissues and guides tissue development. Despite its widespread use in tissue engineering, there remains uncertainty regarding the optimal selection of collagen sources. Animal-derived sources pose challenges such as immunogenicity, while the recombinant system is hindered by diminished bioactivity. In this study, we hypothesized that human ECM-like collagen (hCol) could offer an alternative for tissue engineering. In this study, a facile platform was provided for generating hCol derived from mesenchymal stem cells with a hierarchical structure and biochemical properties resembling native collagen. Our results further demonstrated that hCol could facilitate basal biological behaviors of human adipose-derived stem cells, including viability, proliferation, migration and adipocyte-like phenotype. Additionally, it could promote cutaneous wound closure. Due to its high similarity to native collagen and good bioactivity, hCol holds promise as a prospective candidate for in vitro and in vivo applications in tissue engineering.

Enhancement of methane production from anaerobic digestion of Erigeron canadensis via O2-nanobubble water supplementation.

Malignant invasive Erigeron canadensis, as a typical lignocellulosic biomass, is a formidable challenge for sustainable and efficient resource utilization, however nanobubble water (NBW) coupled with anaerobic digestion furnishes a prospective strategy with superior environmental and economic effectiveness. In this study, influence mechanism of various O2-NBW addition times on methanogenic performance of E. canadensis during anaerobic digestion were performed to achieve the optimal pollution-free energy conversion. Results showed that supplementation of O2-NBW in digestion system could significantly enhance the methane production by 10.70-16.17%, while the maximum cumulative methane production reached 343.18 mL g-1 VS in the case of one-time O2-NBW addition on day 0. Furthermore, addition of O2-NBW was conducive to an increase of 2-90% in the activities of dehydrogenase, α-glucosidase and coenzyme F420. Simultaneously, both facultative bacteria and methanogenic archaea were enriched as well, further indicating that O2-NBW might be responsible for facilitating hydrolytic acidification and methanogenesis. Based on Kyoto Encyclopedia of Genes and Genomes (KEGG) cluster analysis, provision of O2-NBW enhanced the metabolism of carbohydrate and amino acid, translation as well as membrane transport of bacteria and archaea. This study might offer the theoretical guidance and novel insights for efficient recovery of energy from lignocellulosic biomass on account of O2-NBW adhibition in anaerobic digestion system, progressing tenor of carbon-neutral vision.

NF-κB in biology and targeted therapy: new insights and translational implications.

NF-κB signaling has been discovered for nearly 40 years. Initially, NF-κB signaling was identified as a pivotal pathway in mediating inflammatory responses. However, with extensive and in-depth investigations, researchers have discovered that its role can be expanded to a variety of signaling mechanisms, biological processes, human diseases, and treatment options. In this review, we first scrutinize the research process of NF-κB signaling, and summarize the composition, activation, and regulatory mechanism of NF-κB signaling. We investigate the interaction of NF-κB signaling with other important pathways, including PI3K/AKT, MAPK, JAK-STAT, TGF-ß, Wnt, Notch, Hedgehog, and TLR signaling. The physiological and pathological states of NF-κB signaling, as well as its intricate involvement in inflammation, immune regulation, and tumor microenvironment, are also explicated. Additionally, we illustrate how NF-κB signaling is involved in a variety of human diseases, including cancers, inflammatory and autoimmune diseases, cardiovascular diseases, metabolic diseases, neurological diseases, and COVID-19. Further, we discuss the therapeutic approaches targeting NF-κB signaling, including IKK inhibitors, monoclonal antibodies, proteasome inhibitors, nuclear translocation inhibitors, DNA binding inhibitors, TKIs, non-coding RNAs, immunotherapy, and CAR-T. Finally, we provide an outlook for research in the field of NF-κB signaling. We hope to present a stereoscopic, comprehensive NF-κB signaling that will inform future research and clinical practice.

Investigation into the optoelectrowetting droplet transport mechanism.

To explore the optoelectronic wetting droplet transport mechanism, a transient numerical model of optoelectrowetting (OEW) under the coupling of flow and electric fields is established. The study investigates the impact of externally applied voltage, dielectric constant of the dielectric layer, and interfacial tension between the two phases on the dynamic behavior of droplets during transport. The proposed model employs an improved Young's equation to calculate the instantaneous voltage and contact angle of the droplet on the dielectric layer. Results indicate that, under the influence of OEW, significant variations in the interface contact angle of droplets occur in bright and dark regions, inducing droplet movement. Moreover, the dynamic behavior of droplet transport is closely associated with various parameters, including externally applied voltage, dielectric layer material, and interfacial tension between the two phases, all of which impact the contact angle and, consequently, the transport process. By summarizing the influence patterns of the three key parameters studied, the optimization of droplet transport performance is achieved. The study employs two-dimensional simulation models to emulate the droplet motion under the influence of the electric field, investigating the OEW droplet transport mechanism. The continuous movement of droplets involves three stages: initial wetting, continuous transport, and reaching a steady position. The findings contribute theoretical support for the efficient design of digital microfluidic devices for OEW droplet movement and the selection of key parameters for droplet manipulation.

Overlapping cytoplasms segmentation via constrained multi-shape evolution for cervical cancer screening.

Segmenting overlapping cytoplasms in cervical smear images is a clinically essential task for quantitatively measuring cell-level features to screen cervical cancer This task, however, remains rather challenging, mainly due to the deficiency of intensity (or color) information in the overlapping region Although shape prior-based models that compensate intensity deficiency by introducing prior shape information about cytoplasm are firmly established, they often yield visually implausible results, as they model shape priors only by limited shape hypotheses about cytoplasm, exploit cytoplasm-level shape priors alone, and impose no shape constraint on the resulting shape of the cytoplasm In this paper, we present an effective shape prior-based approach, called constrained multi-shape evolution, that segments all overlapping cytoplasms in the clump simultaneously by jointly evolving each cytoplasm's shape guided by the modeled shape priors We model local shape priors (cytoplasm-level) by an infinitely large shape hypothesis set which contains all possible shapes of the cytoplasm In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump-level) modeled by considering mutual shape constraints of cytoplasms in the clump We also constrain the resulting shape in each evolution to be in the built shape hypothesis set for further reducing implausible segmentation results We evaluated the proposed method in two typical cervical smear datasets, and the extensive experimental results confirm its effectiveness.

Convolutional neural network in rice disease recognition: accuracy, speed and lightweight.

There are many rice diseases, which have very serious negative effects on rice growth and final yield. It is very important to identify the categories of rice diseases and control them. In the past, the identification of rice disease types was completely dependent on manual work, which required a high level of human experience. But the method often could not achieve the desired effect, and was difficult to popularize on a large scale. Convolutional neural networks are good at extracting localized features from input data, converting low-level shape and texture features into high-level semantic features. Models trained by convolutional neural network technology based on existing data can extract common features of data and make the framework have generalization ability. Applying ensemble learning or transfer learning techniques to convolutional neural network can further improve the performance of the model. In recent years, convolutional neural network technology has been applied to the automatic recognition of rice diseases, which reduces the manpower burden and ensures the accuracy of recognition. In this paper, the applications of convolutional neural network technology in rice disease recognition are summarized, and the fruitful achievements in rice disease recognition accuracy, speed, and mobile device deployment are described. This paper also elaborates on the lightweighting of convolutional neural networks for real-time applications as well as mobile deployments, and the various improvements in the dataset and model structure to enhance the model recognition performance.

Energy Applications of Ionic Liquids: Recent Developments and Future Prospects.

Ionic liquids (ILs) consisting entirely of ions exhibit many fascinating and tunable properties, making them promising functional materials for a large number of energy-related applications. For example, ILs have been employed as electrolytes for electrochemical energy storage and conversion, as heat transfer fluids and phase-change materials for thermal energy transfer and storage, as solvents and/or catalysts for CO2 capture, CO2 conversion, biomass treatment and biofuel extraction, and as high-energy propellants for aerospace applications. This paper provides an extensive overview on the various energy applications of ILs and offers some thinking and viewpoints on the current challenges and emerging opportunities in each area. The basic fundamentals (structures and properties) of ILs are first introduced. Then, motivations and successful applications of ILs in the energy field are concisely outlined. Later, a detailed review of recent representative works in each area is provided. For each application, the role of ILs and their associated benefits are elaborated. Research trends and insights into the selection of ILs to achieve improved performance are analyzed as well. Challenges and future opportunities are pointed out before the paper is concluded.

A portable automated chip for simultaneous rapid point-of-care testing of multiple ß-agonists.

Abusive use of ß-agonists as feed additives for animals and medication is detrimental to human health and food safety. Conventional assays are restricted to a single type of ß-agonists detection and cannot match the multiplexing features to perform automated, high throughput, and rapid quantitative analysis in real samples. In this research, we develop a portable automated chip system (PACS) with highly integrated automated devices in conjunction with portable microfluidic chips to provide simultaneous point-of-care testing of multiple ß-agonists in the field, simplifying complex manual methods, shortening assay times, and improving sensitivity. Specifically, silicon film is used as reaction substrates for immobilizing the conjugates of ß-agonists to increase the sensitivity of the assay result. Then, the PACS with a chemiluminescence imaging detector is established for automatic high-throughput and sensitive detection of Clenbuterol, Ractopamine, and Salbutamol based on the indirect immunoassay. Newly developed chip with high mixing performance can improve the sensitivity of target determination. Multiplex assays were carried out using the developed system for Clenbuterol, Ractopamine, and Salbutamol with a limit of detection of 54 pg mL-1,59 pg mL-1, and 93 pg mL-1, respectively. Except for sample preparation and coating, the detection in the PACS takes less than 47 min. A satisfactory sample recovery (86.33%-108.12%) was obtained, validating the reliability and practical applicability of this PACS. Meanwhile, the PACS enables sensitive and rapid detection of multiple ß-agonists in farms or markets where lacking advanced laboratory facilities.

Ion transport properties in the pH-dependent bipolar nanochannels.

In recent years, researchers have made significant strides in understanding the ion transport characteristics of nanochannels, resulting in the development of various materials, modifications, and shapes of nano ion channel membranes. The aim is to create a nanochannel membrane with optimal ion transport properties and high stability by adjusting factors, such as channel size, surface charge, and wettability. However, during the nanochannel film fabrication process, controlling the geometric structures of nanochannels can be challenging. Therefore, exploring the stability of nanochannel performance under different geometric structures has become an essential aspect of nanochannel design. This article focuses on the study of cylindrical nanochannel structures, which are categorized based on the different methods for generating bipolar surface charges on the channel's inner surface, either through pH gradient effects or different material types. Through these two approaches, the study designed and analyzed the stability of ion transport characteristics in two nanochannel models under varying geometric structures. Our findings indicate that nanochannels with bipolar properties generated through pH gradients demonstrate more stable ion selection, whereas nanochannels with bipolar properties generated through different materials show stronger stability in ion rectification. This conclusion provides a theoretical foundation for future nanochannel designs.

Machine learning boosts three-dimensional bioprinting.

Three-dimensional (3D) bioprinting is a computer-controlled technology that combines biological factors and bioinks to print an accurate 3D structure in a layer- by-layer fashion. 3D bioprinting is a new tissue engineering technology based on rapid prototyping and additive manufacturing technology, combined with various disciplines. In addition to the problems in in vitro culture process, the bioprinting procedure is also afflicted with a few issues: (1) difficulty in looking for the appropriate bioink to match the printing parameters to reduce cell damage and mortality; and (2) difficulty in improving the printing accuracy in the printing process. Data- driven machine learning algorithms with powerful predictive capabilities have natural advantages in behavior prediction and new model exploration. Combining machine learning algorithms with 3D bioprinting helps to find more efficient bioinks, determine printing parameters, and detect defects in the printing process. This paper introduces several machine learning algorithms in detail, summarizes the role of machine learning in additive manufacturing applications, and reviews the research progress of the combination of 3D bioprinting and machine learning in recent years, especially the improvement of bioink generation, the optimization of printing parameter, and the detection of printing defect.

Numerical investigation of the blocking effect of a short duct on propeller noise.

Electric vertical and takeoff/landing vehicles for urban aerial mobility have attracted considerable attention in recent years. Some of these vehicles are equipped with ducted propellers to improve power efficiency, but the duct may also affect propeller noise generation and radiation. This work presents thorough numerical investigations to assess the importance of a short duct on propeller noise radiation. An analytical model is employed to predict noise emission from an isolated propeller, and the boundary element method is adopted to account for acoustic scattering effects. Additionally, an efficient data-clustering method is proposed to accelerate the overall noise prediction process. Parametric studies concerning geometries and the propeller's installation location are performed to exploit the duct's feasibility for low-noise vehicle development. Results suggest that the blocking effect can significantly benefit noise control for different rotating speeds, and installing the propeller at the symmetric plane of the duct can achieve the most noise reduction.

Comutations and KRASG12C Inhibitor Efficacy in Advanced NSCLC.

Molecular modifiers of KRASG12C inhibitor (KRASG12Ci) efficacy in advanced KRASG12C-mutant NSCLC are poorly defined. In a large unbiased clinicogenomic analysis of 424 patients with non-small cell lung cancer (NSCLC), we identified and validated coalterations in KEAP1, SMARCA4, and CDKN2A as major independent determinants of inferior clinical outcomes with KRASG12Ci monotherapy. Collectively, comutations in these three tumor suppressor genes segregated patients into distinct prognostic subgroups and captured ∼50% of those with early disease progression (progression-free survival ≤3 months) with KRASG12Ci. Pathway-level integration of less prevalent coalterations in functionally related genes nominated PI3K/AKT/MTOR pathway and additional baseline RAS gene alterations, including amplifications, as candidate drivers of inferior outcomes with KRASG12Ci, and revealed a possible association between defective DNA damage response/repair and improved KRASG12Ci efficacy. Our findings propose a framework for patient stratification and clinical outcome prediction in KRASG12C-mutant NSCLC that can inform rational selection and appropriate tailoring of emerging combination therapies. SIGNIFICANCE: In this work, we identify co-occurring genomic alterations in KEAP1, SMARCA4, and CDKN2A as independent determinants of poor clinical outcomes with KRASG12Ci monotherapy in advanced NSCLC, and we propose a framework for patient stratification and treatment personalization based on the comutational status of individual tumors. See related commentary by Heng et al., p. 1513. This article is highlighted in the In This Issue feature, p. 1501.

Phase Separation Microparticles as a Three-Dimensional Cell Culture System To Promote Stem Cell Expansion.

Stem cell therapy is developing rapidly, but existing expansion techniques are insufficient for the use of a large number of cells. The surface chemistry and surface morphology characteristics of materials play a critical role in cellular behaviors and functions and have guiding significance for the design of biomaterials. Many studies have proven that these factors are essential to affect cell adhesion and growth. How to design a suitable biomaterial interface is the focus of recent studies. Here, the mechanosensing of human adipose-derived stem cells (hASC) on a set of materials and materials with various porosity is systematically studied. Guided by the mechanism discoveries, three-dimensional (3D) microparticles with optimized hydrophilicity and morphology are designed via liquid-liquid phase separation technology. The microparticles support scalable stem cell culture and extracellular matrix (ECM) collection, exhibiting great potential for stem cell applications.