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Cells communicate with each other through secreting and releasing proteins and vesicles. Many cells can migrate. In this study, we report the discovery of migracytosis, a cell migration-dependent mechanism for releasing cellular contents, and migrasomes, the vesicular structures that mediate migracytosis. As migrating cells move, they leave long tubular strands, called retraction fibers, behind them. Large vesicles, which contain numerous smaller vesicles, grow on the tips and intersections of retraction fibers. These fibers, which connect the vesicles with the main cell body, eventually break, and the vesieles are released into the extraeellular space or directly taken up by surrounding cells. Since the formation of these vesicles is migration-dependent, we named them ＂migrasomes＂. We also found that cytosolic contents can be transported into migrasomes and released from the cell through migrasomes. We named this migration-dependent release mechanism ＂migracytosis＂.

Dear Editor, Recently we reported the discovery of migrasome, a new organelle of migrating cells [1]. Migrasomes are large vesicles that grow on the tips or intersections of retraction fibers at the rear of migrating cells. Following cell migration, the retraction fibers eventually break and the migrasomes become detached. The migrasomes and their contents, including cytosolic components and vesi- cles of unknown origin, are released into the extracellu- lar space -- a process we named as migracytosis. We speculated that migracytosis may play important roles in cell-cell communication.

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features.

DNA base editors enable direct editing of adenine (A), cytosine (C), or guanine (G), but there is no base editor for direct thymine (T) editing currently. Here we develop two deaminase-free glycosylase-based base editors for direct T editing (gTBE) and C editing (gCBE) by fusing Cas9 nickase (nCas9) with engineered human uracil DNA glycosylase (UNG) variants. By several rounds of structure-informed rational mutagenesis on UNG in cultured human cells, we obtain gTBE and gCBE with high activity of T-to-S (i.e., T-to-C or T-to-G) and C-to-G conversions, respectively. Furthermore, we conduct parallel comparison of gTBE/gCBE with those recently developed using other protein engineering strategies, and find gTBE/gCBE show the outperformance. Thus, we provide several base editors, gTBEs and gCBEs, with corresponding engineered UNG variants, broadening the targeting scope of base editors. Efficient base editors for direct thymine (T) editing are highly desirable. Here, authors develop two deaminase-free glycosylase-based base editors for direct T editing (gTBE) and C editing (gCBE) by rounds of structure-informed mutagenesis on human DNA glycosylase UNG and further engineering.

Periodontitis is an inflammatory disease that affects the periodontal supporting tissues. Its cardinal clinical manifestations encompass gingival inflammation, periodontal pocket formation, and alveolar bone resorption. Urolithin A (UA), a gut microbiota-derived metabolite of ellagitannins, is known for its anti-inflammatory and osseous-protective properties. Nonetheless, the impact of UA on periodontitis remains unknown. To investigate the preventive effect of UA, we employed a lipopolysaccharide (LPS)-induced inflammation model in RAW 264.7 mouse macrophages, a receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclast differentiation model, and a ligature-induced periodontitis model in mice. The expression of inflammatory factors (tumor necrosis factor-α, TNF-α; interleukin-6, IL-6) was analyzed to assess anti-inflammatory efficacy. Bone loss in mice with periodontitis was assessed through histological and imaging techniques, including haematoxylin and eosin staining to evaluate alveolar bone morphology, Masson’s trichrome staining to visualize collagen fiber distribution, and micro-computed tomography scanning to quantify bone structural parameters. Additionally, we investigated the underlying mechanisms by examining osteoclast activity through tartrate-resistant acid phosphatase staining and the expression levels of proteins RANKL and osteoprotegerin (OPG). We found that UA reduced IL-6 and TNF-α levels in vitro and in vivo, inhibited osteoclast differentiation, and decreased the RANKL/OPG ratio in periodontitis mice.

Loss of AT‐interacting domain‐rich protein 1A (ARID1A) frequently occurs in human malignancies including lung cancer. The biological consequence of ARID1A mutation in lung cancer is not fully understood. This study was designed to determine the effect of ARID1A‐depleted lung cancer cells on fibroblast activation. Conditioned media was collected from ARID1A‐depleted lung cancer cells and employed to treat lung fibroblasts. The proliferation and migration of lung fibroblasts were investigated. The secretory genes were profiled in lung cancer cells upon ARID1A knockdown. Antibody‐based neutralization was utilized to confirm their role in mediating the cross‐talk between lung cancer cells and fibroblasts. NOD‐SCID‐IL2RgammaC‐null (NSG) mice received tumor tissues from patients with ARID1A‐mutated lung cancer to establish patient‐derived xenograft (PDX) models. Notably, ARID1A‐depleted lung cancer cells promoted the proliferation and migration of lung fibroblasts. Mechanistically, ARID1A depletion augmented the expression and secretion of prolyl 4‐hydroxylase beta (P4HB) in lung cancer cells, which induced the activation of lung fibroblasts through the β‐catenin signaling pathway. P4HB‐activated lung fibroblasts promoted the proliferation, invasion, and chemoresistance in lung cancer cells. Neutralizing P4HB hampered the tumor growth and increased cisplatin cytotoxic efficacy in two PDX models. Serum P4HB levels were higher in ARID1A‐mutated lung cancer patients than in healthy controls. Moreover, increased serum levels of P4HB were significantly associated with lung cancer metastasis. Together, our work indicates a pivotal role for P4HB in orchestrating the cross‐talk between ARID1A‐mutated cancer cells and cancer‐associated fibroblasts during lung cancer progression. P4HB may represent a promising target for improving lung cancer treatment. P4HB is upregulated in response to ARID1A loss. P4HB mediates the cross‐talk between ARID1A‐mutated lung cancer cells and cancer‐associated fibroblasts. Serum P4HB holds promise as a useful biomarker for ARID1A‐mutated lung cancer.

Bone tissue engineering (BTE) is a promising approach for repairing and regenerating damaged bone tissue, using stem cells and scaffold structures. Among various stem cell sources, dental pulp stem cells (DPSCs) have emerged as a potential candidate due to their multipotential capabilities, ability to undergo osteogenic differentiation, low immunogenicity, and ease of isolation. This article reviews the biological characteristics of DPSCs, their potential for BTE, and the underlying transcription factors and signaling pathways involved in osteogenic differentiation; it also highlights the application of DPSCs in inducing scaffold tissues for bone regeneration and summarizes animal and clinical studies conducted in this field. This review demonstrates the potential of DPSC-based BTE for effective bone repair and regeneration, with implications for clinical translation.

Background Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs. Methods To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the “borrowing of information” across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model. Results We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs. Conclusion The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

Objective To develop and validate a machine learning-based prognostic model that provides enhanced risk stratification for AL cardiac amyloidosis patients beyond existing staging system. Methods We conducted a retrospective cohort study of newly diagnosed AL cardiac amyloidosis patients (2006–2024), randomly allocating participants into training and test sets (8:2 ratio). Cardiac involvement required elevated cardiac biomarkers (NT-proBNP > 332 ng/L) or increased wall thickness (mean wall thickness > 12 mm). Using all-cause mortality as the primary endpoint, we compared five machine learning algorithms (support vector machine, CoxBoost, random survival forest, multi-layer perceptron, and k-neighbors classifier) and a traditional Cox model against the 2015 European-modified Mayo staging system. Results Among 132 enrolled patients (median age 60 years; 56.8% male), 83 deaths (62.8%) occurred during median 14.5-month follow-up. Feature selection identified six key predictors: cardiac response (36% vs. 15%, P  < 0.001), complete hematological response (35% vs. 20%, P  < 0.001), E/e’ ratio (15.0 [12.0, 20.0] vs. 19.0 [15.0, 25.0], P  = 0.009), left ventricular global longitudinal strain (-14.1 [-11.2 – -16.1] vs. -10.7 [-8.8 – -13.6], P  = 0.003), serum uric acid (341.0 [292.0–439.0] vs. 429.0 [345.5–518.0], P  = 0.002), and weight loss (30.6% vs. 65.1%, P  < 0.001). The CoxBoost model demonstrated superior discrimination (AUC 92%) and calibration (brier score 0.11). Conversely, the predictive value of the revised Mayo 2004 staging system was unsatisfactory, with an AUC of 74% and a brier score of 0.19. Conclusions Machine learning incorporating multi-dimensional parameters (including myocardial strain and dynamic clinical variables) provides significantly more accurate prognostication than NT-proBNP/troponin-dependent staging, enabling a new era of personalized risk stratification in AL cardiac amyloidosis.

Background Numerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy assessment model that accounts for these uncertainties while allowing for continuous monitoring throughout the study using Bayesian monitoring techniques. Methods We provide a detailed look at the challenges and solutions for model development, describing the process that used extensive simulations to enable us to finalize the analysis plan. This includes establishing prior distribution assumptions, assessing and improving model convergence under different study composition scenarios, and assessing whether we can extend the model to accommodate multi-site RCTs and evaluate heterogeneous treatment effects. In addition, we recognized that we would need to assess our model for goodness-of-fit, so we explored an approach that used posterior predictive checking. Lastly, given the urgency of the research in the context of evolving pandemic, we were committed to frequent monitoring of the data to assess efficacy, and we set Bayesian monitoring rules calibrated for type 1 error rate and power. Results The primary outcome is an 11-point ordinal scale. We present the operating characteristics of the proposed cumulative proportional odds model for estimating treatment effectiveness. The model can estimate the treatment’s effect under enormous uncertainties in study design. We investigate to what degree the proportional odds assumption has to be violated to render the model inaccurate. We demonstrate the flexibility of a Bayesian monitoring approach by performing frequent interim analyses without increasing the probability of erroneous conclusions. Conclusion This paper describes a translatable framework using simulation to support the design of prospective IPD meta-analyses.