Arbuscular mycorrhizal (AM) symbiosis is a widespread, ancient mutualistic association between plants and fungi, and facilitates nutrient uptake into plants. Cell surface receptor-like kinases (RLKs) and receptor-like cytoplasmic kinases (RLCKs) play pivotal roles in transmembrane signaling, while few RLCKs are known to function in AM symbiosis. Here, we show that 27 out of 40 AM-induced kinases (AMKs) are transcriptionally upregulated by key AM transcription factors in Lotus japonicus. Nine AMKs are only conserved in AM-host lineages, among which the SPARK-RLK-encoding gene KINASE3 (KIN3) and the RLCK paralogues AMK8 and AMK24 are required for AM symbiosis. KIN3 expression is directly regulated by the AP2 transcription factor CTTC MOTIF-BINDING TRANSCRIPTION FACTOR1 (CBX1), which regulates the reciprocal exchange of nutrients in AM symbiosis, via the AW-box motif in the KIN3 promoter. Loss of function mutations in KIN3, AMK8, or AMK24 result in reduced mycorrhizal colonization in L. japonicus. AMK8 and AMK24 physically interact with KIN3. KIN3 and AMK24 are active kinases and AMK24 directly phosphorylates KIN3 in vitro. Moreover, CRISPR-Cas9-mediated mutagenesis of OsRLCK171, the sole homolog of AMK8 and AMK24 in rice (Oryza sativa), leads to diminished mycorrhization with stunted arbuscules. Overall, our results reveal a crucial role of the CBX1-driven RLK/RLCK complex in the evolutionarily conserved signaling pathway enabling arbuscule formation.

Background: The presence of circulating plasma cells (CPCs) is an important laboratory indicator for the diagnosis, staging, risk stratification, and progression monitoring of multiple myeloma (MM). Early detection of CPCs in the peripheral blood (PB) followed by timely interventions can significantly improve MM prognosis and delay its progression. Although the conventional cell morphology examination remains the predominant method for CPC detection because of accessibility, its sensitivity and reproducibility are limited by technician expertise and cell quantity constraints. This study aims to develop an artificial intelligence (AI)–based automated system for a more sensitive and efficient CPC morphology detection.Methods: A total of 137 bone marrow smears and 72 PB smears from patients with at Zhongshan Hospital, Fudan University, were retrospectively reviewed. Using an AI‐powered digital pathology platform, Morphogo, 305,019 cell images were collected for training. Morphogo’s efficacy in CPC detection was evaluated with additional 184 PB smears (94 from patients with MM and 90 from those with other hematological malignancies) and compared with manual microscopy.Results: Morphogo achieved 99.64% accuracy, 89.03% sensitivity, and 99.68% specificity in classifying CPCs. At a 0.60 threshold, Morphogo achieved a sensitivity of 96.15%, which was approximately twice that of manual microscopy, with a specificity of 78.03%. Patients with CPCs detected by AI scanning had a significantly shorter median progression‐free survival compared with those without CPC detection (18 months vs. 34 months, p< .01).Conclusions: Morphogo is a highly sensitive system for the automated detection of CPCs, with potential applications in initial screening, prognosis prediction, and posttreatment monitoring for MM patients.Plain Language Summary: Diagnosing and monitoring multiple myeloma (MM), a type of blood cancer, requires identifying and quantifying specific cells called circulating plasma cells (CPCs) in the blood. The conventional method for detecting CPCs is manual microscopic examination, which is time‐consuming and lacks sensitivity. This study introduces a highly sensitive CPC detection method using an artificial intelligence–based system, Morphogo. It demonstrated remarkable sensitivity and accuracy, surpassing conventional microscopy. This advanced approach suggests that early and accurate CPC detection is achievable by morphology examination, making efficient CPC screening more accessible for patients with MM. This innovative system has the potential to be used in the diagnosis and risk assessment of MM.

Background: Acute myeloid leukemia (AML) with monocytic differentiation poses significant clinical challenges, including high relapse rates and chemotherapy resistance. Current morphological assessment is limited by inter-observer variability, low sensitivity, and inefficiency, especially for detecting low-level residual disease. This creates an urgent need for automated, objective tools to improve diagnostic consistency and monitoring. Artificial intelligence, particularly deep learning, offers potential for extracting high-dimensional cytomorphological features to address these gaps. Methods: A retrospective cohort of 184 bone marrow smear slides from patients with monocytic leukemia was used. The core biomarker was the immature monocyte percentage (IMMP), defined as monoblasts plus promonocytes among nucleated cells, with a 2.0% clinical cutoff. An EfficientNet-based convolutional neural network was developed via transfer learning and trained to classify four cell types: monoblasts, promonocytes, monocytes, and other cells. Results: The model achieved robust cell-level classification, with F1 scores of 0.82 for monoblasts and 0.34 for promonocytes. At the slide level, using an optimized IMMP threshold of 0.045, it accurately assessed persistent leukemic cell burden with 78.9% Accuracy, 81.1% Recall, and 76.9% Specificity. Model-predicted IMMP values showed strong correlation with expert-derived values (Pearson r = 0.827), demonstrating reliable quantitative agreement. Conclusions: This deep learning model provides an automated, objective tool for quantifying immature monocytes, addressing key limitations in morphological assessment of monocytic AML. The IMMP metric shows promise for monitoring treatment response, predicting relapse, and potentially identifying patients at risk of venetoclax-based therapy resistance. While promising, prospective multicenter validation is needed to translate these findings into routine clinical practice.

Gel-grown method has been adopted to grow single crystals for over a century, and has triggered increasing interest with a large number of single crystals obtained. Some organisms construct their hard tissues where foreign macromolecules have been incorporated into the brittle crystals to enhance their mechanical properties. In 1969, the researchers found out that gel-network could be incorporated into the single-crystalline matrix in the similar way. The unique property of the gel-incorporated single-crystals is the combination of long-range order (single-crystallinity) and composite structures, which has enlightened the research on not only understanding biomineralization, but also exploring possible applications of these unique composites. This review will briefly introduce recent progress in gel-network/single-crystal composites formed in gel media in five aspects. Firstly, we will discuss the growth process of the composites. The growth procedures fall into four categories: (1) The reaction method, (2) the chemical reduction method, (3) the solubility reduction method, (4) the decomplexing method, and various types of crystals have been testified the successful inclusion of gel media including soluble and insoluble compounds, metal, etc. Secondly, we will briefly introduce several characterizations to demonstrate the distinctive structure of the composites. Incorporated gel fibers can be seen clearly inside the etched pits of the composite under scanning electron microscope. The fibrous structure can be further demonstrated using scanning transmission electron microscope and electron tomography by imaging a thin section cut from a gel-grown crystal. For example, tomographic reconstruction of an agarose network inside of a section of the crystal proves the three-dimensional random-interpenetrating structure between the gel guest and the crystal host while selected-area (area contains both crystal and fibers) electron diffraction pattern shows that the crystal remains its single-crystallinity. Thirdly, we will discuss several factors, which influence the effective absorption of gel inside the crystals. By measuring the amount of incorporated gel-network using thermal gravimetric analyzer, researchers have concluded that gel-network with higher strength tend to be embedded inside the crystal while weaker gel is more likely to be pushed away. Besides, faster growth rate as well as stronger interaction between the crystal host and the gel guest favors the gel incorporation. Fourthly, we will discuss a new approach to functionalize single crystals. The reinforced mechanical properties of gel-incorporated crystals inspire scientists to introduce other functional components inside the crystals to achieve multiple nonintrinsic properties. The gel-mediating strategy offers a facile way to realize this idea by trapping functional nanoparticles within gel media and inducing inclusion of nanomaterials into crystals along with gel-networks. Finally, we will put forward some promising research directions in this area including intensive study of the mechanism involved, exploring more crystal-gel combinations, putting heroic efforts on the functionalization of the composites and most importantly, digging up potential application prospects.