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Background Nosema bombycis is a unicellular eukaryotic pathogen of the silkworm, Bombyx mori , and is an economic and occupational hazard in the silkworm industry. Because of its long incubation period and horizontal and vertical transmission, it is subject to quarantine measures in sericulture production. The microsporidian life-cycle includes a dormant extracellular phase and intracellular proliferation phase, with the proliferation period being the most active period. This latter period lacks spore wall protection and may be the most susceptible stage for control. Methods In order to find suitable target for the selective breeding of N. bombycis -resistant silkworm strains, we screen highly expressed membrane proteins from the transcriptome data of N. bombycis . The subcellular localization of the candidate protein was verified by Indirect immunofluorescence analysis (IFA) and immunoelectron microscopy (IEM), and its role in N. bombycis proliferation was verified by RNAi. Results The N. bombycis protein (NBO_76g0014) was identified as a transmembrane protein and named NbTMP1. It is homologous with hypothetical proteins NGRA_1734 from Nosema granulosis . NbTMP1 has a transmembrane region of 23 amino acids at the N-terminus. Indirect immunofluorescence analysis (IFA) results suggest that NbTMP1 is secreted on the plasma membrane as the spores develop. Western blot and qRT-PCR analysis showed that NbTMP1 was expressed in all developmental stages of N. bombycis in infected cells and in the silkworm midgut. Downregulation of NbTMP1 expression resulted in significant inhibition of N. bombycis proliferation. Conclusions We confirmed that NbTMP1 is a membrane protein of N. bombycis. Reduction of the transcription level of NbTMP1 significantly inhibited N. bombycis proliferation, and this protein may be a target for the selective breeding of N. bombycis -resistant silkworm strains. Graphical Abstract

Nosema bombycis is a destructive, obligate intracellular parasite of the Bombyx mori. In this study, a single-chain variable fragment (scFv) dependent technology is developed for the purpose of inhibiting parasite proliferation in insect cells. The scFv-G4, which we prepared from a mouse G4 monoclonal antibody, can target the N. bombycis spore wall protein 12 (NbSWP12). Indirect immunofluorescence assays showed that NbSWP12 located mainly on the outside of the N. bombycis cytoskeleton, although some of it co-localized with β-tubulin in the meront-stage of parasites. When meront division began, NbSWP12 became concentrated at both ends of each meront. Western blotting showed that scFv-G4 could express in Sf9-III cells and recognized native NbSWP12. The transgenic Sf9-III cell line showed better resistance than the controls when challenged with N. bombycis, indicating that NbSWP12 is a promising target in this parasite and this scFv dependent strategy could be a solution for construction of N. bombycis-resistant Bombyx mori.

Nosema bombycis is a destructive and specific intracellular parasite of silkworm, which is extremely harmful to the silkworm industry. N. bombycis is considered as a quarantine pathogen of sericulture because of its long incubation period and horizontal and vertical transmission. Herein, two single-chain antibodies targeting N. bombycis hexokinase (NbHK) were cloned and expressed in fusion with the N-terminal of Slmb (a Drosophila melanogaster FBP), which contains the F-box domain. Western blotting demonstrated that Sf9-III cells expressed NSlmb–scFv-7A and NSlmb–scFv-6H, which recognized native NbHK. Subsequently, the NbHK was degraded by host ubiquitination system. When challenged with N. bombycis, the transfected Sf9-III cells exhibited better resistance relative to the controls, demonstrating that NbHK is a prospective target for parasite controls and this approach represents a potential solution for constructing N. bombycis-resistant Bombyx mori.

Objectives Previous deep learning studies have not explored the synergistic effects of ROI dimensions (2D/2.5D/3D), peritumoral expansion levels (0–8 mm), and segmentation scenarios (ROI only vs. ROI original). Our study aims to evaluate the performance of multidimensional deep transfer learning models in distinguishing molecular subtypes of breast cancer (luminal vs. nonluminal) using DCE‐MRI. Under two segmentation scenarios, we systematically compare the effects of ROI dimensions and peritumoral expansion levels to optimize multidimensional deep learning models via transfer learning for distinguishing luminal from nonluminal breast cancers in DCE‐MRI‐based analysis. Materials and Methods From October 2020 to October 2023, data from 426 patients with primary invasive breast cancer were retrospectively collected. Patients were divided into three cohorts: (1) training cohort, n = 108, from SYSU Hospital (Zhuhai, China); (2) validation cohort 1, n = 165, from HZ Hospital (Huizhou, China); and (3) validation cohort 2, n = 153, from LY Hospital (Linyi, China). ROIs were delineated, and expansions of 2, 4, 6, and 8 mm beyond the lesion boundary were performed. We assessed the performance of various deep transfer learning models, considering precise segmentation (ROI only and ROI original) and varying peritumoral regions, using ROC curves and decision curve analysis. Results The 2.5D1‐based deep learning model (ROI original, 4 mm expansion) demonstrated optimal performance, achieving an AUC of 0.808 (95% CI 0.715–0.901) in the training cohort, 0.766 (95% CI 0.682–0.850) in validation cohort 1, and 0.799 (95% CI 0.725–0.874) in validation cohort 2. Conclusion The study highlights that the 2.5D1‐based deep learning model utilizing the three principal slices of the minimum bounding box (ROI original) with a 4 mm peritumoral region is effective in distinguishing between luminal and nonluminal breast cancer tumors, serving as a potential diagnostic tool.

Microsporidia are eukaryotic, unicellular parasites that have been studied for more than 150 years. These organisms are extraordinary in their ability to invade a wide range of hosts including vertebrates and invertebrates, such as human and commercially important animals. A lack of appropriate labeling methods has limited the research of the cell cycle and protein locations in intracellular stages. In this report, an easy fluorescent labeling method has been developed to mark the proliferative and sporogonic phases of microsporidia Nosema bombycis in host cells. Based on the presence of chitin, Calcofluor White M2R was used to label the sporogonic phase, while β-tubulin antibody coupled with fluorescence secondary antibody were used to label the proliferative phase by immunofluorescence. This method is simple, efficient and can be used on both infected cells and tissue slices, providing a great potential application in microsporidia research.

Mesenchymal stem cells (MSCs) have attracted considerable attention for clinical translation in regenerative medicine, primarily due to their validated paracrine effects, prominent immunomodulatory properties, and superior multipotent differentiation capabilities. However, the limited homing efficiency and poor post-transplant survival of MSCs severely compromise therapeutic efficacy, thereby giving rise to suboptimal and inconsistent treatment outcomes. To circumvent these critical drawbacks and fully harness the therapeutic potential of MSCs, researchers have incorporated a diverse array of effective strategies, including genetic engineering, preconditioning with cytokines, small molecular compounds or hypoxic stimuli, and scaffold-based culture systems. Given these promising research advances, this review systematically summarizes recent advances in MSC-enhanced therapeutic strategies, and elaborates on their core molecular mechanisms as well as how these mechanisms modulate MSC survival, homing capacity and immunomodulatory efficacy. On this basis, we further analyze the practical applicability of these enhanced MSCs in clinical trials and seek to provide critical insights for the clinical selection of MSC-based enhanced therapies.

The microsporidian Nosema bombycis is an obligate intracellular parasite of Bombyx mori , that lost its intact tricarboxylic acid cycle and mitochondria during evolution but retained its intact glycolysis pathway. N. bombycis hexokinase (NbHK) is not only a rate-limiting enzyme of glycolysis but also a secretory protein. Indirect immunofluorescence assays and recombinant HK overexpressed in BmN cells showed that NbHK localized in the nucleus and cytoplasm of host cell during the meront stage. When N. bombycis matured, NbHK tended to concentrate at the nuclei of host cells. Furthermore, the transcriptional profile of NbHK implied it functioned during N. bombycis’ proliferation stages. A knock-down of NbHK effectively suppressed the proliferation of N. bombycis indicating that NbHK is an important protein for parasite to control its host.

Combining immunotherapy with surgical intervention is a prevailing and radical therapeutic strategy for individuals afflicted with gastric carcinoma; nonetheless, certain patients exhibit unfavorable prognoses even subsequent to this treatment regimen. This research endeavors to devise a machine learning algorithm to recognize risk factors with a high probability of inducing mortality among patients diagnosed with gastric cancer, both prior to and during their course of treatment. Within the purview of this investigation, a cohort of 1015 individuals with gastric cancer were incorporated, and 39 variables encompassing diverse features were recorded. To construct the models, we employed three distinct machine learning algorithms, specifically extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor algorithm (KNN). The models were subjected to internal validation through employment of the k-fold cross-validation technique, and subsequently, an external dataset was utilized to externally validate the models. In comparison to other machine learning algorithms employed, the XGBoost algorithm demonstrated superior predictive capacity regarding the risk factors that affect mortality after combination therapy in gastric cancer patients for a duration of one year, three years, and five years posttreatment. The common risk factors that significantly impacted patient survival during the aforementioned time intervals were identified as advanced age, tumor invasion, tumor lymph node metastasis, tumor peripheral nerve invasion (PNI), multiple tumors, tumor size, carcinoembryonic antigen (CEA) level, carbohydrate antigen 125 (CA125) level, carbohydrate antigen 72-4 (CA72-4) level, and infection. The XGBoost algorithm can assist clinicians in identifying pivotal prognostic factors that are of clinical significance and can contribute toward individualized patient monitoring and management.

Microsporidia are a big group of single-celled obligate intracellular organisms infecting most animals and some protozoans. These minimalist eukaryotes lack numerous genes in metabolism and vesicle trafficking. Here, we demonstrated that the spore wall protein NbSWP12 of microsporidium Nosema bombycis belongs to Bin/Amphiphysin/Rvs (BAR) protein family and can specifically bind with phosphatidylinositol 3-phosphate [Ptdlns(3)P]. Since Ptdlns(3)P is involved in endosomal vesicle biogenesis and trafficking, we heterologous expressed NbSWP12 in yeast Saccharomyces cerevisiae and proved that NbSWP12 can target the cell membrane and endocytic vesicles. Nbswp12 transformed into Gvp36 (a BAR protein of S. cerevisiae) deletion mutant rescued the defect phenotype of vesicular traffic. This study identified a BAR protein function in vesicle genesis and sorting and provided clues for further understanding of how microsporidia internalize nutrients and metabolites during proliferation.

Partial dental point clouds often suffer from large missing regions caused by occlusion and limited scanning views, which bias encoder-only global features and force decoders to hallucinate structures. We propose a retrieval-augmented framework for tooth completion that integrates a prototype memory into standard encoder--decoder pipelines. After encoding a partial input into a global descriptor, the model retrieves the nearest manifold prototype from a learnable memory and fuses it with the query feature through confidence-gated weighting before decoding. The memory is optimized end-to-end and self-organizes into reusable tooth-shape prototypes without requiring tooth-position labels, thereby providing structural priors that stabilize missing-region inference and free decoder capacity for detail recovery. The module is plug-and-play and compatible with common completion backbones, while keeping the same training losses. Experiments on a self-processed Teeth3DS benchmark demonstrate consistent improvements in Chamfer Distance, with visualizations showing sharper cusps, ridges, and interproximal transitions. Our approach provides a simple yet effective way to exploit cross-sample regularities for more accurate and faithful dental point-cloud completion.