Heterogeneous Mechanism Research Articles

Systematic assessment of microRNA‐132 therapeutic targeting in Alzheimer’s disease using iPSC‐derived neurons and microglia

AbstractBackgroundMapping the mechanistic heterogeneity and multifactorial nature of Alzheimer’s Disease (AD) is a key challenge for therapeutic development and the main reason why network biology and multi‐modal therapies begin to attract attention also in AD research. MiRNA‐targeted therapeutics are particularly suited for such purposes, as they regulate multiple components of several molecular cascades converging on disease‐relevant patho‐phenotypes. MicroRNA‐132 (miR‐132), a potent neuroimmune regulator, has been identified as the most robustly and significantly downregulated microRNA in the brain of AD patients and its deficiency has been functionally linked to amyloid deposition, TAU hyperphosphorylation, neuronal cell death and memory decline, in both human and rodents. Additionally, miR‐132 was recently identified as a potent regulator of adult hippocampal neurogenesis (AHN), exerting multilayered proneurogenic effects in adult neural stem cells and their progeny. Together, these observations suggest that miR‐132‐dependent network‐based gene regulation in human AD brain converges onto biological pathways that can drive disease endophenotypes. Direct miR‐132 infusion in the AD mouse brain can counteract several aspects of pathology, providing a proof‐of‐concept of its therapeutic relevance. Despite the great benefit of the multi‐targeting nature of microRNAs for treating multifactorial diseases such as AD, such approaches also entail the risk of on‐ and off‐ target toxicity, highlighting the importance of systematic genome‐wide targetome profiling. The complex multicellular miR‐132 targetome involved in AHN and the inflammatory response in AD remains elusive, and its mapping is a crucial requirement for therapeutic targeting.MethodIn order to systematically profile the mechanisms underpinning the regulatory effects of miR‐132 in AD, we employed an RNA‐sequencing approach to assess transcriptomic responses to changes in the levels of miR‐132 in AD‐relevant study systems, such as human iPSC‐derived neurons and microglia‐like cells.ResultsWe identify novel miR‐132 targets with putative neuroimmune functions possibly involved in AD pathophysiology.ConclusionOur results shed light on the involvement of miR‐132 in AD and further add to the systematic profiling of miR‐132 targetome, a prerequisite for successful translation of miR‐132 supplementation in AD.

Recent Progress in Mass Spectrometry-Based Single-Cell Metabolic Analysis.

Single-cell analysis enables the measurement of biomolecules at the level of individual cells, facilitating in-depth investigations into cellular heterogeneity and precise interpretation of the related biological mechanisms. Among these biomolecules, cellular metabolites exhibit remarkable sensitivity to environmental and biochemical changes, unveiling a hidden world underlying cellular heterogeneity and allowing for the determination of cell physiological states. However, the metabolic analysis of single cells is challenging due to the extremely low concentrations, substantial content variations, and rapid turnover rates of cellular metabolites. Mass spectrometry (MS), characterized by its high sensitivity, wide dynamic range, and excellent selectivity, is employed in single-cell metabolic analysis. This review focuses on recent advances and applications of MS-based single-cell metabolic analysis, encompassing three key steps of single-cell isolation, detection, and application. It is anticipated that MS will bring profound implications in biomedical practices, serving as advanced tools to depict the single-cell metabolic landscape.

Export effects of non-tariff measures: The role of aid for trade

This study aims to examine whether aid for trade can help recipient countries improve their ability to meet the non-tariff measures imposed by trading partners and alleviate these measures’ negative impacts on their exports. For this purpose, we examine the impacts of technical barriers to trade that raise special trade concerns (TBT-STC), aid for trade and their interactions on the manufacturing exports of aid-recipient countries. The main findings are that the highly restrictive TBT measures have seriously hindered the manufacturing exports of recipient countries; however, aid for trade not only directly promotes exports of recipient countries but also helps alleviate the negative impact of TBT-STC on their exports. This result provides empirical evidence to support the proposal. We attempt a more-detailed analysis to assess the heterogeneity of the above-mentioned mechanisms on countries’ income levels and the types of aid for trade.

CRISPR-Based Gene-Editing Screens Revealed Modulators of Dexamethasone Sensitivity in Multiple Myeloma

Background: Multiple myeloma (MM) is an incurable plasma cell malignancy that accounts for 10% of hematologic malignancies. Despite improved prognosis due to advances in treatment, most patients ultimately develop therapy resistance and relapse. The glucocorticoid dexamethasone is a potent anti-myeloma agent used in combination with proteasome inhibitors and Immunomodulatory drugs (IMIDs) as frontline treatment. Dexamethasone binds to the glucocorticoid receptor (GR), which induces apoptosis by driving an apoptotic transcriptional program. The mechanisms of Dexamethasone resistance in MM remain poorly understood. Aim: To define cellular processes that modulate dexamethasone sensitivity in MM. Methods: We performed loss-of-function CRISPR screens in the t(4;14), NSD2 overexpressing, MM cell lines KMS11 and LP1 using the genome-wide Brunello sgRNA library. Results: The screens identified genes whose loss altered MM cell sensitivity to dexamethasone, most being cell type-specific suggesting heterogeneity in mechanisms influencing dexamethasone cytotoxicity across MM patients. In KMS11 cells, disruption of the IL10/JAK/STAT signaling pathway and glycosaminoglycans/heparan sulfate synthesis pathway increases sensitivity to dexamethasone. By contrast, loss of chromatin remodeling factors including the polycomb group member EPC1 and the SWI/SNF complex components BRD7 and BRD9, altered dexamethasone response exclusively in LP1 cells. Perturbations resulting in dexamethasone resistance in both cell lines include the GR and its co-chaperone PTGES3 in addition to members of the NFKB inhibitor family (NFKBIA, NFKBIB). By contrast, disruption of the druggable GR co-chaperone FKBP5 or the histone deacetylase HDAC8 increased sensitivity to dexamethasone. In addition, the GID/CTLH E3 ubiquitin ligase complex was also identified as a general determinant of dexamethasone resistance in MM cells as disruption of several constituents of this complex resulted in increased dexamethasone sensitivity in both cell lines. The mechanism by which the GID/CTLH complex confers dexamethasone tolerance in MM cells is currently under investigation. Conclusions: Multiple cellular processes contribute to modulating the cytotoxic effect of dexamethasone in MM. Such processes often regulate expression, stability, or localization of the GR. NFKB signaling and the GID/CTLH E3 ubiquitin ligase activity are key determinants of dexamethasone resistance. Targeting these processes can increase the therapeutic effect of dexamethasone in MM.

Trend in Von Willebrand Factor Profile in Women With Von Willebrand Disease During Pregnancy and Postpartum

Introduction: Pregnancy is considered a hypercoagulable state, with levels of multiple coagulation factors including Factor VIII (FVIII) and von Willebrand Factor (VWF), progressively increasing throughout, reaching their peak levels during the third trimester. However, in women with von Willebrand disease (VWD), these changes are variable and could be blunted or absent due to the wide heterogeneity of phenotypes and pathophysiological mechanisms linked to this disorder, posing a significant clinical challenge. This study aims to describe the trend in the VWF profile in women with VWD during each trimester of pregnancy and in the postpartum period. Methods: This is a retrospective single-center study that included adult pregnant women diagnosed with VWD, who were followed at Henry Ford Health between 03/2012 and 11/2022. Patients with other bleeding disorders were excluded from the study. Data on baseline characteristics, including age, race, age at childbirth, and parity were collected. Additional data collected included VWF antigen (VWF:Ag) level, FVIII activity level, and VWF ristocetin cofactor (VWF:Rco) activity during the first, second, and third trimesters, as well as in the postpartum period. Results: A total of 33 cases were included in our study. At baseline, the mean VWF:Ag level in our patients was 50.5% (reference range 50%-150%). Compared to baseline, the VWF:Ag level increased by 48.71% in the first trimester, 80.59% in the second trimester, and 168.71% in the third trimester. Postpartum, the VWF:Ag level decreased by 51.29% compared to the third trimester; however, it remained 30.89% higher than baseline levels. While the FVIII level (reference range 50%-150%) was an average of 64.8% at baseline, it increased by 21.91% in the first trimester, 74.23% in the second trimester, and 143.67% in the third trimester, compared to baseline. Postpartum, the FVIII level decreased by 48.45% from the third trimester but remained 25.62% higher than baseline. Additionally, the VWF:Rco level was an average of 44.3% (reference range 51%-215%) at baseline. Compared to baseline, the VWF:Rco level increased by 61.85% in the first trimester, 90.52% in the second trimester, and 142.66% in the third trimester. It decreased by 49.12% postpartum compared to the third trimester but remained 23.48% higher than baseline. Conclusion: In women with VWD, VWF:Ag level, FVIII level, and VWF:Rco levels steadily increase during pregnancy, reaching their maximum levels during the third trimester. All three levels decreased in the postpartum period; however, they remain higher compared to baseline at a median follow up of 105 days after delivery.

Bias and variance reduction of high return levels for extreme hazard modelling

AbstractMany existing extremal data span only a few decades that result in large uncertainty in the estimated shape parameter of the extreme hazard model. This in turn leads to potentially large bias and variance of extrapolated extreme values at high return levels. This paper illustrates a statistical method that helps obtain hazard models that produce estimates at high return levels with reduced bias and variance. This method makes use of the maximum recorded values of extremal data that are independently recorded from a number of observational sites or generated by heterogeneous mechanisms. The logarithmically transformed average recurrence interval of the maximum recorded value at a site is shown to follow the Gumbel (Type I extreme value) distribution; therefore, multiple, say $$m$$ m , observational sites provide a sample of size $$m$$ m log-transformed average recurrence intervals, each from a distinct site. The sample can be treated as a sample from a Gumbel distribution, irrespective of the underlying hazard-generating mechanisms or the statistical hazard models. Application of the method is illustrated by analysis of the extreme wind gust data from automatic weather stations in South Australia and shown to lead to significant reduction of 2.9 m/s in potential bias of the mean and a reduction of 98.1% in variance of the 500-year gust speed at Adelaide Airport. The results are compared to the specifications in the Australian standard AS/NZS 1170.2:2021 and indicate that the standard may have overestimated the wind gust hazard; hence, the specified design wind speeds may fall on the conservative side for South Australia.

A novel block-chain based secure cross-domain interaction approach for intelligent transportation systems

In the Intelligent Transportation System (ITS), secure and efficient data communication among vehicles, road testing equipment, computing nodes, and transportation agencies is important for building a smart city integrated transportation system. However, the traditional centralized processing approach may face threats in terms of data leakage and trust. The use of distributed, tamper-proof blockchain technology can improve the decentralized storage and security of data in the ITS network. However, the cross-trust domain devices, terminals, and transportation agencies in the heterogeneous blockchain network of the ITS still face great challenges in trusted data communication and interoperability. In this paper, we propose a heterogeneous cross-chain interaction mechanism based on relay nodes and identity encryption to solve the problem of data cross-domain interaction between devices and agencies in the ITS. First, we propose the ITS cross-chain communication framework and improve the cross-chain interaction model. The relay nodes are interconnected through LibP2P to form a relay node chain, which is used for cross-chain information verification and transmission. Secondly, we propose a relay node secure access scheme based on identity-based encryption (IBE) to provide reliable identity authentication for relay nodes. Finally, we build a standard cross-chain communication protocol and cross-chain transaction lifecycle for this mechanism. We use Hyperledger Fabric and FISCO BCOS blockchain to design and implement this solution, and verify the feasibility of this cross-chain interaction mechanism. The experimental results show that the mechanism can achieve a stable data cross-chain read throughput of 2,000 transactions per second (TPS), which can meet the requirements of secure and efficient cross-chain communication and interaction among heterogeneous blockchains in the ITS, and has high application value.

Multi-objective reinforcement learning for bi-objective time-dependent pickup and delivery problem with late penalties

This study addresses the bi-objective time-dependent pickup and delivery problem with late penalties (TDPDPLP). Incorporating time-dependent travel time into the problem formulation to model traffic congestion is critical, especially for problems with time-related costs, to decrease the difference in the projected quality of solutions when applying optimization methods in the real world. This study proposes a multi-objective reinforcement learning (MORL)-based method with hypernetwork and heterogeneous attention mechanism (HAM) with a two-stage training scheme to solve the bi-objective TDPDPLP. The proposed method can instantly generate an approximation of the Pareto optimal front (POF) after offline training. The conducted ablation study also showed that discarding coordinates from the features simplifies the model and saves several hours of training while improving the quality of the solutions. The performance of the trained model is evaluated on various instances, including real-world-based instances from Barcelona, Berlin, and Porto-Alegre. The performance of the proposed method is evaluated based on the hypervolume (HV) and additive epsilon (ϵ+) of the generated POF. We compare the performance of the proposed method to another MORL method, namely the preference-conditioned multi-objective combinatorial optimization (PMOCO) and several well-known multi-objective evolutionary algorithms (MOEAs). Experiments showed that the proposed method performs better than PMOCO and the employed MOEAs on various problem instances. The trained method only needs minutes to generate a POF approximation, while the MOEAs require hours. Furthermore, it also generalizes well on different characteristics of problem instances and performs well on instances from cities other than the city in the training instances.

New Energy Technology Innovation and Industry Carbon Emission Reduction Based on the Perspective of Unbalanced Regional Economic Development

Innovation in new energy technologies is a key driver in China’s efforts to achieve its environmental goals. However, the ability of different regions to develop and utilize new energy technologies may depend on their level of economic development. Based on a two-way fixed-effects panel data model, this paper empirically analyses the industry carbon emission reduction effect of new energy technology innovation and its heterogeneous performance at different stages of economic development, using data from 30 provinces and cities in China from 2000 to 2019. The results show that new energy technology innovation generally promotes CO2 emissions in China. The specific effects are closely related to the characteristics of the industry and the stage of economic development. At the same time, the implementation of environmental regulations will inhibit this positive effect, while the adjustment of the industrial structure may promote this positive effect. This paper discovers the mechanism of heterogeneity in new energy technology innovation among different provinces with different levels of economic development. This finding helps to fully assess the carbon emission reduction capacity and potential of different provinces and facilitates the rational disaggregation and formulation of climate policy goals among regions.

Insights into In Vivo Environmental Effects on Quantitative Biochemistry in Single Cells.

Biomacromolecules exist and function in a crowded and spatially confined intracellular milieu. Single-cell analysis has been an essential tool for deciphering the molecular mechanisms of cell biology and cellular heterogeneity. However, a sound understanding of in vivo environmental effects on single-cell quantification has not been well established. In this study, via cell mimicking with giant unilamellar vesicles and single-cell analysis by an approach called plasmonic immunosandwich assay (PISA) that we developed previously, we investigated the effects of two in vivo environmental factors, i.e., molecular crowding and spatial confinement, on quantitative biochemistry in the cytoplasm of single cells. We find that molecular crowding greatly affects the biomolecular interactions and immunorecognition-based detection while the effect of spatial confinement in cell-sized space is negligible. Without considering the effect of molecular crowding, the results by PISA were found to be apparently under-quantitated, being only 29.5-50.0% of those by the calibration curve considering the effect of molecular crowding. We further demonstrated that the use of a calibration curve established with standard solutions containing 20% (wt) polyethylene glycol 6000 can well offset the effect of intracellular crowding and thereby provide a simple but accurate calibration for the PISA measurement. Thus, this study not only sheds light on how intracellular environmental factors influence biomolecular interactions and immunorecognition-based single-cell quantification but also provides a simple but effective strategy to make the single-cell analysis more accurate.

Understanding heterogeneous mechanisms of heart failure with preserved ejection fraction through cardiorenal mathematical modeling.

In contrast to heart failure (HF) with reduced ejection fraction (HFrEF), effective interventions for HF with preserved ejection fraction (HFpEF) have proven elusive, in part because it is a heterogeneous syndrome with incompletely understood pathophysiology. This study utilized mathematical modeling to evaluate mechanisms distinguishing HFpEF and HFrEF. HF was defined as a state of chronically elevated left ventricle end diastolic pressure (LVEDP > 20mmHg). First, using a previously developed cardiorenal model, sensitivities of LVEDP to potential contributing mechanisms of HFpEF, including increased myocardial, arterial, or venous stiffness, slowed ventricular relaxation, reduced LV contractility, hypertension, or reduced venous capacitance, were evaluated. Elevated LV stiffness was identified as the most sensitive factor. Large LV stiffness increases alone, or milder increases combined with either decreased LV contractility, increased arterial stiffness, or hypertension, could increase LVEDP into the HF range without reducing EF. We then evaluated effects of these mechanisms on mechanical signals of cardiac outward remodeling, and tested the ability to maintain stable EF (as opposed to progressive EF decline) under two remodeling assumptions: LV passive stress-driven vs. strain-driven remodeling. While elevated LV stiffness increased LVEDP and LV wall stress, it mitigated wall strain rise for a given LVEDP. This suggests that if LV strain drives outward remodeling, a stiffer myocardium will experience less strain and less outward dilatation when additional factors such as impaired contractility, hypertension, or arterial stiffening exacerbate LVEDP, allowing EF to remain normal even at high filling pressures. Thus, HFpEF heterogeneity may result from a range of different pathologic mechanisms occurring in an already stiffened myocardium. Together, these simulations further support LV stiffening as a critical mechanism contributing to elevated cardiac filling pressures; support LV passive strain as the outward dilatation signal; offer an explanation for HFpEF heterogeneity; and provide a mechanistic explanation distinguishing between HFpEF and HFrEF.

EXTH-51. IDENTIFYING REGULATORS OF HETEROGENEITY IN DMGS USING SCREGCLUST

Abstract Diffuse Midline Gliomas (DMGs) are pediatric brain tumors that arise in the pons, thalamus or the brainstem. DMGs present a pressing clinical problem since they cannot be surgically removed and respond poorly to radio- and chemotherapies. Like adult gliomas, DMGs present a high degree of intra-tumoral heterogeneity, with cells adopting different levels of differentiation similar to oligodendrocytes, astrocytes, precursor and mesenchymal cells. These different phenotypes possibly play distinct roles in tumor progression. Yet, we lack a good understanding of the underlying mechanisms of DMG heterogeneity. Here, we present a novel strategy to uncover regulator genes of DMG heterogeneity in a comprehensive and unbiased fashion, using an integration of machine learning with Cas9-based Perturb-seq. We first use a new data-driven modeling technique, termed single cell regulatory-driven clustering (scRegClust; Larsson et al., BioRxiv 2023) to uncover transcriptional modules, and their upstream transcriptional and kinase regulators. The goal of this analysis is to identify regulator genes that control cellular processes relevant to the disease, thereby enhancing our understanding of DMGs and uncover potential therapeutic targets. Applied to recently available single-cell data from 90 DMG patients, scRegClust revealed 160 regulators of meta-modules including oligodendrocyte-like, oligodendrocyte-progenitor-like, astrocyte-like and mesenchymal like cells. To validate the importance of these regulators, we are establishing a Perturb-seq experimental pipeline by combining pooled CRISPR knock-out and single cell RNA sequencing in patient-derived DMG cell lines. By perturbing the candidate regulators both in vitro and in vivo we aim to evaluate their impact on DMG tumorigenicity. This comprehensive strategy will ultimately increase our understanding of the molecular processes in DMGs and present the functional significance of these regulators and their involvement in disease progression.

Sonoelectrochemical system mechanisms, design, and machine learning for predicting degradation kinetic constants of pharmaceutical pollutants

This study explores the mechanism, design, and application of machine learning in the sonoelectrochemical (US-EC) systems and select ibuprofen (IBP) as the target pollutant. Mechanism investigation shows that OH and S O4·- are the primary chemical oxidation species through scavenger experiments. Based on the heterogeneous nucleation mechanism, the electrodes in the ultrasound (US) system play the role of electrode-sonocataliytic and can promote the degradation kinetic constant of pharmaceutical pollutants. The effects of design parameters, including US frequency, voltage, electrolyte, electrode area, gap, and position on IBP degradation were investigated. The results demonstrated that under optimized parameters: US frequency of 35 kHz, voltage of 5 V, 0.1 M Na2SO4 electrolyte, 1.5 cm gap, and placement at P8, the US-EC system achieved a kinetic constant of 0.016 min−1. Chemiluminescence was used to visualize the spatial distribution of the oxidant, and provided theoretical support for mechanism and design parameter optimization. The eXtreme Gradient Boosting model was used to predict the kinetic constant of pharmaceutical contaminants including IBP, indicating excellent model performance with results of R2 and RMSE reaching 0.98 and 0.0005, respectively. SHapley Additive exPlanations was employed to assess the impact of design parameters on pharmaceutical pollutants degradation. The results showed that US frequency, US power, and the distance 'r' from the US transmitter to the anode have the most significant impact on the prediction performance of the model. Two sets of new experiments were verified using this model, and the prediction accuracy reached 76% and 82% respectively, demonstrating that machine learning can effectively predict the kinetic constants of pharmaceutical contaminants under complex factors affecting the US-EC system, assisting researchers in swiftly evaluating the system's pollutant treatment performance and simplifying the experimental workload.

A theoretical model for the prediction of fracture process zone in concrete under fatigue loading: Energy based approach

Characterizing the fracture behaviour of concrete and accurately predicting its service life under fatigue loading poses a significant challenge due to its heterogeneous nature and complex fracture mechanisms. The proposed study focuses on developing an energy based theoretical formulation for predicting the evolution of the fracture process zone throughout repetitive loading cycles. A stiffness degradation approach has been adopted for developing the formulations for the critical energy dissipation and fully developed fracture process zone. Subsequently, the proposed analytical expressions have been calibrated and validated by performing experiments under centre point bending and using available experimental results in the literature. Experiments have been conducted on a centre-point bend beam with varying aggregate size in conjunction with the digital image correlation (DIC) technique to estimate the fracture characteristics. The influence of specimen size and heterogeneity has been discussed in the context of predicting the fracture behaviour, critical dissipated energy, and process zone length of plain concrete beam specimens under fatigue loading. The results indicate that the process zone length and critical energy release rate increase with an increase in specimen size, while the same decreases with an increase in aggregate size.

Biomarkers of immediate drug hypersensitivity.

Immediate drug hypersensitivity reactions (IDHRs) are a burden for patients and the health systems. This problem increases when taking into account that only a small proportion of patients initially labelled as allergic are finally confirmed after an allergological workup. The diverse nature of drugs involved will imply different interactions with the immunological system. Therefore, IDHRs can be produced by a wide array of mechanisms mediated by the drug interaction with specific antibodies or directly on effector target cells. These heterogeneous mechanisms imply an enhanced complexity for an accurate diagnosis and the identification of the phenotype and endotype at early stages of the reaction is of vital importance. Currently, several endophenotypic categories (type I IgE/non-IgE, cytokine release, Mast-related G-protein coupled receptor X2 (MRGPRX2) or Cyclooxygenase-1 (COX-1) inhibition and their associated biomarkers have been proposed. A precise knowledge of endotypes will permit to discriminate patients within the same phenotype, which is crucial in order to personalise diagnosis, future treatment and prevention to improve the patient's quality of life.

Unraveling intratumoral complexity in metastatic dermatofibrosarcoma protuberans through single-cell RNA sequencing analysis.

Dermatofibrosarcoma protuberans (DFSP) stands as a rare and locally aggressive soft tissue tumor, characterized by intricated molecular alterations. The imperative to unravel the complexities of intratumor heterogeneity underscores effective clinical management. Herein, we harnessed single-cell RNA sequencing (scRNA-seq) to conduct a comprehensive analysis encompassing samples from primary sites, satellite foci, and lymph node metastases. Rigorous preprocessing of raw scRNA-seq data ensued, and employing t-distributed stochastic neighbor embedding (tSNE) analysis, we unveiled seven major cell populations and fifteen distinct subpopulations. Malignant cell subpopulations were delineated using infercnv for copy number variation calculations. Functional and metabolic variations of diverse malignant cell populations across samples were deciphered utilizing GSVA and the scMetabolism R packages. Additionally, the exploration of differentiation trajectories within diverse fibroblast subpopulations was orchestrated through pseudotime trajectory analyses employing CytoTRACE and Monocle2, and further bolstered by GO analyses to elucidate the functional disparities across distinct differentiation states. In parallel, we segmented the cellular components of the immune microenvironment and verified the presence of SPP1+ macrophage, which constituted the major constituent in lymph node metastases. Remarkably, the CellChat facilitated a comprehensive intercellular communication analysis. This study culminates in an all-encompassing single-cell transcriptome atlas, propounding novel insights into the multifaceted nature of intratumor heterogeneity and fundamental molecular mechanisms propelling metastatic DFSP.

Endothelial heterogeneity and their relevance in cardiac development and coronary artery disease

Micro- and macrovascular endothelial cells (ECs) are characterized by structural and functional heterogeneity, which is also reflected in their secretory activity. The root of this heterogeneity and related regulatory mechanisms are still poorly understood. During embryogenesis, microvascular ECs participate in organogenesis prior to the development of the fetal circulation, suggesting that ECs are capable of releasing paracrine trophogens, termed angiocrine factors (AFs). These are angiocrine growth factors, adhesion molecules, and chemokines, which are intended to promote morphogenesis and repair of the adjacent parenchyma/stroma where the vessels are located. There is a tissue and organ-specificity of AFs that traces the heterogeneity of ECs. This AF heterogeneity also traces how ECs respond to pathological conditions or exposure to cardiovascular risk factors. The study of the mechanisms that regulate endothelial and paracrine heterogeneity and that contribute to endotheliopathy represents a broad and as yet understudied area of research. A better understanding of the cellular and molecular mechanisms that regulate this heterogeneity, leading to endotheliopathy is an exciting challenge. In this brief review we will discuss experimental advances in the heterogeneity of ECs and their AF, with a focus on their involvement in the pathogenesis of coronary artery disease.

Real-time crash risk prediction in freeway tunnels considering features interaction and unobserved heterogeneity: A two-stage deep learning modeling framework

Real-time prediction of crash risk is an effective method for enhancing traffic safety, but it is not fully explored in freeway tunnels. A two-stage deep learning modeling framework comprising a preliminary exploration stage and a prediction and analysis stage is proposed for real-time crash risk prediction in freeway tunnels. A random parameters logit model with heterogeneity in means and variances is used in the preliminary exploration stage to investigate the unobserved heterogeneity and influence mechanism of precursors on real-time crash risk. In the prediction and analysis stage, a random deep and cross network model considering feature interactions and unobserved heterogeneities is developed to predict and analyze real-time crash risk, which is interpreted by the shapley additive explanations approach. The multi-source fusion dataset, collected from the Caltrans performance measurement system and the weather information website, is used to validate the proposed framework for exploring real-time crash risk in freeway tunnels. Results reveal that: (1) the random parameters logit model with heterogeneity in means and variances outperforms the traditional logit model in terms of the model fitting, providing a reference for deep learning modeling that may be able to improve model performance by addressing heterogeneity; (2) the important crash precursors such as the average difference in speed between detectors of tunnel entrance and exit are discovered based on the marginal effect analysis of the random parameters logit model with heterogeneity in means and variances; (3) the random deep and cross network model yields the best prediction performance compared to its counterparts (some other data-driven models), demonstrating the superior performance of deep learning models for real-time risk prediction tasks. It also indicates that considering feature interaction and heterogeneity in deep learning modeling can improve prediction performance; and (4) the important precursors found in the random deep and cross network model using the shapley additive explanations approach are close to those discovered in the statistical model, indicating that the proposed deep learning model can capture the similar effects of precursors as the statistical models, and the precursor interactions and heterogeneities also can be observed by the shapley additive explanations approach.

Use of Cilta-Cel CAR T Cells Following Previous Use of a BCMA-Directed CAR T-Cell Product in Heavily Treated Patients with Relapsed/Refractory Multiple Myeloma: A Single Institution Case Series

Background: BCMA-directed CAR T-cell (CAR T) therapy has demonstrated remarkable efficacy in patients with relapsed/refractory multiple myeloma (rrMM). Pivotal trials such as the KarMMa and CARTITUDE studies using ide-cel and cilta-cel respectively, have shown ORR of 73% and 98%, leading to FDA approval of these products. Notably, ide-cel conferred a median overall survival (OS) of 19.4 months with a median duration of response (DOR) of 10.7 months. Similarly, cilta-cel conferred a median progression-free survival (PFS) of 34.9 months and a 27-month OS rate of 70%. Although these and real-world outcomes are promising, relapse after BCMA CAR T therapy occurs in 27-64% of responders and remains a challenge. Various salvage treatments are being explored including repeat BCMA CAR T therapy with a different product. Method: This is a case series of 5 patients with rrMM from a single institution treated with BCMA-directed CAR T therapy between 2019-2022, followed by a second BCMA-directed CAR T therapy using cilta-cel at the time of relapse. Here we report the response and toxicity profile following the second CAR T therapy. Results: Patients had a median age of 64 (40-76). 2 out of 5 patients had high risk disease based on cytogenetics. Prior to the second CAR T therapy with cilta-cel, patients had received a median of 7 (5-8) lines of treatment including the first CAR T therapy. For the first CAR T therapy, 3 patients received an investigational BCMA-directed CAR T therapy at the recommended doses of the product and 2 patients received ide-cel. At the time of subsequent relapse all patients eventually received a second CAR T therapy with cilta-cel. Overall response rate to the first CAR T-cell therapy was 80% with 4 patients achieving ≥ VGPR (n=2 VGPR; n= 2 sCR), and 1 patient with refractory disease. The median DOR was 16 months (7-22 months) for responders and the median PFS was 10 months after the first CAR T therapy. The time between two CAR T therapies ranged from 8-38 months, with all patients receiving at least 1 and up to 2 lines of treatment including bridging therapy before cilta-cel. At the time of this report, with follow-up ranging from 3.1-11 months after the second CAR T therapy, 4 out of 5 patients had achieved a response (n=3 CR; n=1 PR) with a 6-month PFS of 75%. One patient had refractory disease and required subsequent treatment. Notably, this was the same patient who had been refractory to the previous CAR T cell therapy. Grade 2 cytokine-release syndrome after cilta-cel was seen in 1 out of 5 patients. Hematologic toxicities were seen in all patients with grade 3-4 neutropenia in 5, grade 3 anemia in 3 and grade 4 thrombocytopenia in 2 patients. Prolonged grade 3-4 thrombocytopenia was seen in 1 responder requiring transfusions and thrombopoietin analogs. Conclusion: This case series demonstrates the early efficacy and safety of a second BCMA-directed CAR T therapy, specifically in patients previously responsive to similar BCMA-directed therapy. This highlights the need to better understand the clinical utility of this approach, especially with ide-cel and cilta-cel being explored in earlier stages of disease, and to delineate the heterogenous disease course and mechanism of resistance post BCMA-directed CAR T therapy.

Potential mechanisms of soil nitrogen content heterogeneity associated with biocrust development in drylands

AbstractBiological soil crusts (biocrusts) are complex communities of micro‐ and macro‐organisms dwelling at the soil surface in dryland regions worldwide, which contribute to important ecological functions in these areas. Biocrusts may reach different developmental stages, associated to autogenic succession of specific phototrophic organisms. However, notwithstanding the large amount of existing literature, little is known regarding the relationship between biocrust stages of development and soil nutrient dynamics. In this opinion paper, we specifically focused on soil nitrogen, and compared the total nitrogen content and potential nitrogenase activity of different developmental types of biocrust (‐covered) soils. Based on published reports, we looked at a possible relationship between nitrogen content at the soil surface and biocrust development, discussing the potential mechanisms leading to the observed soil nitrogen content heterogeneity. The results showed that a higher nitrogen content remained associated to the presence of moss‐dominated biocrusts, and this biocrust morphotype was characterized by a lower potential nitrogenase activity compared to the cyanobacteria‐ and/or lichen‐dominated biocrusts. We hypothesized that these seemingly contradictory findings might be attributed to three potential mechanisms: nitrogen inheritance, nutrient retention and nutrient transfer, which we hereby examined one by one. Altogether, our opinion supports the theory of biocrust succession from incipient cyanobacteria‐dominated stages to more ‘mature’ stages dominated by mosses. We stress how the heterogeneous distribution of soil nitrogen, which is closely related with biocrust development and community types, eventually affects regional and even global nitrogen dynamic and storage.