Miracle-dz/UniFAR-FTUs huggingface dataset:
Stem Cell Repor ts

Cerebral small vessel disease (SVD) affects the small vessels in the brain and is a leading cause of stroke and dementia. Emerging evidence supports a role of the extracellular matrix (ECM), at the interface between blood and brain, in the progression of SVD pathology, but this remains poorly characterized. To address ECM role in SVD, we developed a co-culture model of mural and endothelial cells using human induced pluripotent stem cells from patients with COL4A1 / A2 SVD-related mutations. This model revealed that these mutations induce apoptosis, migration defects, ECM remodeling, and transcriptome changes in mural cells. Importantly, these mural cell defects exert a detrimental effect on endothelial cell tight junctions through paracrine actions. COL4A1 / A2 models also express high levels of matrix metalloproteinases (MMPs), and inhibiting MMP activity partially rescues the ECM abnormalities and mural cell phenotypic changes. These data provide a basis for targeting MMP as a therapeutic opportunity in SVD.
Host E3 ubiquitin ligase ITCH mediates Toxoplasma gondii effector GRA35-triggered NLRP1 inflammasome activation and cell-autonomous immunity

ABSTRACT Toxoplasma gondii is an intracellular parasite that can activate the NLRP1 inflammasome leading to macrophage pyroptosis in Lewis rats, but the underlying mechanism is not well understood. In this study, we performed a genome-wide CRISPR screen and identified the dense granule proteins GRA35, GRA42, and GRA43 as the Toxoplasma effectors mediating cell death in Lewis rat macrophages. GRA35 localizes on the parasitophorous vacuole membrane, where it interacts with the host E3 ubiquitin ligase ITCH. Inhibition of proteasome activity or ITCH knockout prevented pyroptosis in Toxoplasma-infected Lewis rat macrophages, consistent with the “NLRP1 functional degradation model. ” However, there was no evidence that ITCH directly ubiquitinates or interacts with rat NLRP1. We also found that GRA35-ITCH interaction affected Toxoplasma fitness in IFNγ-activated human fibroblasts, likely due to ITCH’s role in recruiting ubiquitin and the parasite-restriction factor RNF213 to the parasitophorous vacuole membrane. These findings identify a new role of host E3 ubiquitin ligase ITCH in mediating effector-triggered immunity, a critical concept that involves recognizing intracellular pathogens and initiating host innate immune responses. IMPORTANCE Effector-triggered immunity represents an innate immune defense mechanism that plays a crucial role in sensing and controlling intracellular pathogen infection. The NLRP1 inflammasome in the Lewis rats can detect Toxoplasma infection, which triggers proptosis in infected macrophages and eliminates the parasite’s replication niche. The work reported here revealed that host E3 ubiquitin ligase ITCH is able to recognize and interact with Toxoplasma effector protein GRA35 localized on the parasite-host interface, leading to NLRP1 inflammasome activation in Lewis rat macrophages. Furthermore, ITCH-GRA35 interaction contributes to the restriction of Toxoplasma in human fibroblasts stimulated by IFNγ. Thus, this research provides valuable insights into understanding pathogen recognition and restriction mediated by host E3 ubiquitin ligase.
Molecular characterisation of lupus low disease activity state (LLDAS) and DORIS remission by whole-blood transcriptome-based pathways in a pan-European systemic lupus erythematosus cohort.

OBJECTIVES To unveil biological milieus underlying low disease activity (LDA) and remission versus active systemic lupus erythematosus (SLE). METHODS We determined differentially expressed pathways (DEPs) in SLE patients from the PRECISESADS project (NTC02890121) stratified into patients fulfilling and not fulfilling the criteria of (1) Lupus LDA State (LLDAS), (2) Definitions of Remission in SLE remission, and (3) LLDAS exclusive of remission. RESULTS We analysed data from 321 patients; 40.8% were in LLDAS, and 17.4% in DORIS remission. After exclusion of patients in remission, 28.3% were in LLDAS. Overall, 604 pathways differed significantly in LLDAS versus non-LLDAS patients with an false-discovery rate-corrected p (q)<0.05 and a robust effect size (dr)≥0.36. Accordingly, 288 pathways differed significantly between DORIS remitters and non-remitters (q<0.05 and dr≥0.36). DEPs yielded distinct molecular clusters characterised by differential serological, musculoskeletal, and renal activity. Analysis of partially overlapping samples showed no DEPs between LLDAS and DORIS remission. Drug repurposing potentiality for treating SLE was unveiled, as were important pathways underlying active SLE whose modulation could aid attainment of LLDAS/remission, including toll-like receptor (TLR) cascades, Bruton tyrosine kinase (BTK) activity, the cytotoxic T lymphocyte antigen 4 (CTLA-4)-related inhibitory signalling, and the nucleotide-binding oligomerization domain leucine-rich repeat-containing protein 3 (NLRP3) inflammasome pathway. CONCLUSIONS We demonstrated for the first time molecular signalling pathways distinguishing LLDAS/remission from active SLE. LLDAS/remission was associated with reversal of biological processes related to SLE pathogenesis and specific clinical manifestations. DEP clustering by remission better grouped patients compared with LLDAS, substantiating remission as the ultimate treatment goal in SLE; however, the lack of substantial pathway differentiation between the two states justifies LLDAS as an acceptable goal from a biological perspective.
A deep learning‐based framework (Co‐ReTr) for auto‐segmentation of non‐small cell‐lung cancer in computed tomography images

Abstract Purpose Deep learning‐based auto‐segmentation algorithms can improve clinical workflow by defining accurate regions of interest while reducing manual labor. Over the past decade, convolutional neural networks (CNNs) have become prominent in medical image segmentation applications. However, CNNs have limitations in learning long‐range spatial dependencies due to the locality of the convolutional layers. Transformers were introduced to address this challenge. In transformers with self‐attention mechanism, even the first layer of information processing makes connections between distant image locations. Our paper presents a novel framework that bridges these two unique techniques, CNNs and transformers, to segment the gross tumor volume (GTV) accurately and efficiently in computed tomography (CT) images of non‐small cell‐lung cancer (NSCLC) patients. Methods Under this framework, input of multiple resolution images was used with multi‐depth backbones to retain the benefits of high‐resolution and low‐resolution images in the deep learning architecture. Furthermore, a deformable transformer was utilized to learn the long‐range dependency on the extracted features. To reduce computational complexity and to efficiently process multi‐scale, multi‐depth, high‐resolution 3D images, this transformer pays attention to small key positions, which were identified by a self‐attention mechanism. We evaluated the performance of the proposed framework on a NSCLC dataset which contains 563 training images and 113 test images. Our novel deep learning algorithm was benchmarked against five other similar deep learning models. Results The experimental results indicate that our proposed framework outperforms other CNN‐based, transformer‐based, and hybrid methods in terms of Dice score (0.92) and Hausdorff Distance (1.33). Therefore, our proposed model could potentially improve the efficiency of auto‐segmentation of early‐stage NSCLC during the clinical workflow. This type of framework may potentially facilitate online adaptive radiotherapy, where an efficient auto‐segmentation workflow is required. Conclusions Our deep learning framework, based on CNN and transformer, performs auto‐segmentation efficiently and could potentially assist clinical radiotherapy workflow.
Propyl gallate induces human pulmonary fibroblast cell death through the regulation of Bax and caspase-3

Abstract Propyl gallate (PG) has been found to exert an inhibitory effect on the growth of different cell types, including lung cancer cells. However, little is known about the cytotoxicological effects of PG specifically on normal primary lung cells. The current study examined the cellular effects and cell death resulting from PG treatment in human pulmonary fibroblast (HPF) cells. DNA flow cytometry results demonstrated that PG (100–1,600 μM) had a significant impact on the cell cycle, leading to G1 phase arrest. Notably, 1,600 μM PG slightly increased the number of sub-G1 cells. Additionally, PG (400–1,600 μM) resulted in the initiation of cell death, a process that coincided with a loss of mitochondrial membrane potential (MMP; ΔΨm). This loss of MMP (ΔΨm) was evaluated using a FACS cytometer. In PG-treated HPF cells, inhibitors targeting pan-caspase, caspase-3, caspase-8, and caspase-9 showed no significant impact on the quantity of annexin V-positive and MMP (ΔΨm) loss cells. The administration of siRNA targeting Bax or caspase-3 demonstrated a significant attenuation of PG-induced cell death in HPF cells. However, the use of siRNAs targeting p53, Bcl-2, or caspase-8 did not exhibit any notable effect on cell death. Furthermore, none of the tested MAPK inhibitors, including MEK, c-Jun N-terminal kinase (JNK), and p38, showed any impact on PG-induced cell death or the loss of MMP (ΔΨm) in HPF cells. In conclusion, PG induces G1 phase arrest of the cell cycle and cell death in HPF cells through apoptosis and/or necrosis. The observed HPF cell death is mediated by the modulation of Bax and caspase-3. These findings offer insights into the cytotoxic and molecular effects of PG on normal HPF cells.
Deciphering spatial domains from spatially resolved transcriptomics with Siamese graph autoencoder

Abstract Background Cell clustering is a pivotal aspect of spatial transcriptomics (ST) data analysis as it forms the foundation for subsequent data mining. Recent advances in spatial domain identification have leveraged graph neural network (GNN) approaches in conjunction with spatial transcriptomics data. However, such GNN-based methods suffer from representation collapse, wherein all spatial spots are projected onto a singular representation. Consequently, the discriminative capability of individual representation feature is limited, leading to suboptimal clustering performance. Results To address this issue, we proposed SGAE, a novel framework for spatial domain identification, incorporating the power of the Siamese graph autoencoder. SGAE mitigates the information correlation at both sample and feature levels, thus improving the representation discrimination. We adapted this framework to ST analysis by constructing a graph based on both gene expression and spatial information. SGAE outperformed alternative methods by its effectiveness in capturing spatial patterns and generating high-quality clusters, as evaluated by the Adjusted Rand Index, Normalized Mutual Information, and Fowlkes–Mallows Index. Moreover, the clustering results derived from SGAE can be further utilized in the identification of 3-dimensional (3D) Drosophila embryonic structure with enhanced accuracy. Conclusions Benchmarking results from various ST datasets generated by diverse platforms demonstrate compelling evidence for the effectiveness of SGAE against other ST clustering methods. Specifically, SGAE exhibits potential for extension and application on multislice 3D reconstruction and tissue structure investigation. The source code and a collection of spatial clustering results can be accessed at https://github.com/STOmics/SGAE/.
Dietary resistant starch supplementation increases gut luminal deoxycholic acid abundance in mice

ABSTRACT Bile acids (BA) are among the most abundant metabolites produced by the gut microbiome. Primary BAs produced in the liver are converted by gut bacterial 7-α-dehydroxylation into secondary BAs, which can differentially regulate host health via signaling based on their varying affinity for BA receptors. Despite the importance of secondary BAs in host health, the regulation of 7-α-dehydroxylation and the role of diet in modulating this process is incompletely defined. Understanding this process could lead to dietary guidelines that beneficially shift BA metabolism. Dietary fiber regulates gut microbial composition and metabolite production. We tested the hypothesis that feeding mice a diet rich in a fermentable dietary fiber, resistant starch (RS), would alter gut bacterial BA metabolism. Male and female wild-type mice were fed a diet supplemented with RS or an isocaloric control diet (IC). Metabolic parameters were similar between groups. RS supplementation increased gut luminal deoxycholic acid (DCA) abundance. However, gut luminal cholic acid (CA) abundance, the substrate for 7-α-dehydroxylation in DCA production, was unaltered by RS. Further, RS supplementation did not change the mRNA expression of hepatic BA producing enzymes or ileal BA transporters. Metagenomic assessment of gut bacterial composition revealed no change in the relative abundance of bacteria known to perform 7-α-dehydroxylation. P. ginsenosidimutans and P. multiformis were positively correlated with gut luminal DCA abundance and increased in response to RS supplementation. These data demonstrate that RS supplementation enriches gut luminal DCA abundance without increasing the relative abundance of bacteria known to perform 7-α-dehydroxylation.
Deep learning‐based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention

Abstract Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical measurements following lifestyle interventions in overweight population could be reflected in brain morphology. In the DIRECT‐PLUS clinical trial, participants with criterion for metabolic syndrome underwent an 18‐month lifestyle intervention. Structural brain MRIs were acquired before and after the intervention. We utilized an ensemble learning framework to predict Body‐Mass Index (BMI) scores, which correspond to adiposity‐related clinical measurements from brain MRIs. We revealed that patient‐specific reduction in BMI predictions was associated with actual weight loss and was significantly higher in active diet groups compared to a control group. Moreover, explainable AI (XAI) maps highlighted brain regions contributing to BMI predictions that were distinct from regions associated with age prediction. Our DIRECT‐PLUS analysis results imply that predicted BMI and its reduction are unique neural biomarkers for obesity‐related brain modifications and weight loss.
Effects of a mobile health intervention on health-related outcomes in Japanese office workers: a pilot study.

OBJECTIVES The purpose of the current study was to explore the effects of a mobile health (mHealth) intervention based on the Persuasive System Design (PSD) model on health-related outcomes among office workers. MATERIAL AND METHODS The authors conducted a trial that consisted of a 4-week baseline and an 8-week intervention period by reference to 23 office workers in a private research company. The mHealth application was developed to improve these workers' daily step count, decrease their sedentary time, and increase their sleep duration in accordance with the PSD model. The app features included at least 1 principal factor from each of the 4 main categories of the PSD model (primary task support, dialogue support, system credibility support, and social support). The objective health-related variables were measured using a smartwatch (Fitbit Luxe) that was synchronized with the application using the Fitbit Web Application Programming Interface. Subjects used the app, which included self-monitoring, personalized messages, education, and a competition system for users, during the intervention period. RESULTS Sedentary time exhibited a significant decrease (a median reduction of 14 min/day, p < 0.05) during the intervention period. No significant differences in daily step count and sleep duration were observed between the baseline and intervention periods. CONCLUSIONS This study suggests that the mHealth intervention based on the PSD model was useful for reducing sedentary time among office workers. Given that many previous studies on this topic have not been based on any theories, future studies should investigate the impact of structured selection behavior change theories on health-related outcomes among office workers. Int J Occup Med Environ Health. 2024;37(2).
On the Last Occurrence of Marginella Lamarck, 1799 (Gastropoda, Marginellidae) in the Mediterranean: Description of a New Species from the Early Pleistocene and Paleoceanographic Implications

ABSTRACT A new species of Marginella Lamarck, 1799 is described from the Early Pleistocene of the Messina Strait area, M. seguenzai n. sp. The remarkable variability in shell size and shape required a preliminary morphometric analysis, which provided evidence of a single species. This is the last known representative of the genus Marginella in the Mediterranean, today highly diverse in the tropical and subtropical shallow waters of West Africa, up to about 28°N. The genus Marginella was thought to have disappeared from the Mediterranean and the adjacent Atlantic after the Early Pliocene, due to early cooling phases. Anyway, Marginella seguenzai n. sp. was a deep water species, and its extinction was most probably due to the loss of psychrosphere, in the Early Pleistocene, rather than to climatic deterioration. The remarkable closeness between the new species and a deep water living species, endemic to three seamounts in the NE Atlantic, suggests a common origin for both species. High productivity seems to have been a key factor in the Mediterranean distribution of Marginella, either in shallow and deep waters.
A widely targeted metabolite modificomics strategy for modified metabolites identification in tomato.

The structural and functional diversity of plant metabolites is largely created via chemical modification of a basic backbone. However, metabolite modifications in plants have still not been thoroughly investigated by metabolomics approaches. In this study, a widely targeted metabolite modificomics (WTMM) strategy was developed based on ultra-high performance liquid chromatography-quadrupole-linear ion trap (UHPLC-Q-Trap) and UHPLC-Q-Exactive-Orbitrap (UHPLC-QE-Orbitrap), which greatly improved the detection sensitivity and the efficiency of identification of modified metabolites. A metabolite modificomics study was carried out using tomato as a model, and over 34,000 signals with MS2 information were obtained from approximately 232 neutral loss transitions. Unbiased metabolite profiling was also performed by utilizing high-resolution mass spectrometry data to annotate a total of 2,118 metabolites with 125 modification types; of these, 165 modified metabolites were identified in this study. Next, the WTMM database was used to assess diseased tomato tissues and 29 biomarkers were analyzed. In summary, the WTMM strategy is not only capable of large-scale detection and quantitative analysis of plant-modified metabolites in plants, but also can be used for plant biomarker development.
Diffraction of electromagnetic waves due to a point source by a three part boundary satisfying perfect electromagnetic conductor conditions

Approximate analytical solution of point source diffraction from a strip satisfying a recently introduced boundary condition is presented in this paper. Both separated and interacted fields are computed separately and then related transformations are applied to obtain the field diffracted from a strip satisfying non-reciprocal boundary condition. The correctness of mathematical results is also verified graphically and a good agreement with the existing literature is observed.
Estimating biofuel feedstock water footprints using system dynamics

Increased biofuel production has prompted concerns about the environmental tradeoffs of biofuels compared to petroleum-based fuels. Biofuel production in general, and feedstock production in particular, is under increased scrutiny. Water footprinting (measuring direct and indirect water use) has been proposed as one measure to evaluate water use in the context of concerns about depleting rural water supplies through activities such as irrigation for large-scale agriculture. Water footprinting literature has often been limited in one or more key aspects: complete assessment across multiple water stocks (e.g., vadose zone, surface, and ground water stocks), geographical resolution of data, consistent representation of many feedstocks, and flexibility to perform scenario analysis. We developed a model called BioSpatial H2O using a system dynamics modeling and database framework. BioSpatial H2O could be used to consistently evaluate the complete water footprints of multiple biomass feedstocks at high geospatial resolutions. BioSpatial H2O has the flexibility to perform simultaneous scenario analysis of current and potential future crops under alternative yield and climate conditions. In this proof-of-concept paper, we modeled corn grain (Zea mays L.) and soybeans (Glycine max) under current conditions as illustrative results. BioSpatial H2O links to a unique database that houses annual spatially explicit climate, soil, and plant physiological data. Parameters from the database are used as inputs to our system dynamics model for estimating annual crop water requirements using daily time steps. Based on our review of the literature, estimated green water footprints are comparable to other modeled results, suggesting that BioSpatial H2O is computationally sound for future scenario analysis. Our modeling framework builds on previous water use analyses to provide a platform for scenario-based assessment. BioSpatial H2O's system dynamics is a flexible and user-friendly interface for on-demand, spatially explicit, water use scenario analysis for many US agricultural crops. Built-in controls permit users to quickly make modifications to the model assumptions, such as those affecting yield, and to see the implications of those results in real time. BioSpatial H2O's dynamic capabilities and adjustable climate data allow for analyses of water use and management scenarios to inform current and potential future bioenergy policies. The model could also be adapted for scenario analysis of alternative climatic conditions and comparison of multiple crops. The results of such an analysis would help identify risks associated with water use competition among feedstocks in certain regions. Results could also inform research and development efforts that seek to reduce water-related risks of biofuel pathways.
ESCAP: Economic and Social Commission for Asia and the Pacific.

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