Preclinical evaluation of cyclophosphamide and fludarabine combined with CD19 CAR-T in the treatment of B-cell hematologic malignancies in vivo.

Background: Chimeric antigen receptor T (CAR-T) cell therapy has achieved marked therapeutic success in ameliorating hematological malignancies. However, there is an extant void in the clinical guidelines concerning the most effective chemotherapy regimen prior to chimeric antigen receptor T (CAR-T) cell therapy, as well as the optimal timing for CAR-T cell infusion post-chemotherapy. Materials and Methods: We employed cell-derived tumor xenograft (CDX) murine models to delineate the optimal pre-conditioning chemotherapy regimen and timing for CAR-T cell treatment. Furthermore, transcriptome sequencing was implemented to identify the therapeutic targets and elucidate the underlying mechanisms governing the treatment regimen. Results: Our preclinical in vivo evaluation determined that a combination of cyclophosphamide and fludarabine, followed by the infusion of CD19 CAR-T cells five days subsequent to the chemotherapy, exerts the most efficacious therapeutic effect in B-cell hematological malignancies. Concurrently, RNA-seq data indicated that the therapeutic efficacy predominantly perturbs tumor cell metabolism, primarily through the inhibition of key mitochondrial targets, such as C-Jun Kinase enzyme (C-JUN). Conclusion: In summary, the present study offers critical clinical guidance and serves as an authoritative reference for the deployment of CD19 CAR-T cell therapy in the treatment of B-cell hematological malignancies.

Analysis of the Distribution Pattern of Remaining Oil and Development Potential after Weak Gel Flooding in the Offshore LD Oilfield.

The LD oilfield is one of the representative offshore oilfields. After weak gel flooding, the recovery rate is significantly improved. However, the oilfield is then in a medium- to high-water content stage, presenting a complex distribution of the remaining oil. The measures for further enhanced oil recovery (EOR) are uncertain. As a result, it is necessary to clarify the distribution pattern and development potential of the remaining oil during the high-water content period after weak gel flooding. In this study, an online nuclear magnetic resonance (NMR) oil displacement experiment and microscopic oil displacement experiment were conducted, and the mechanisms of weak gel flooding and the distribution pattern of the remaining oil were clarified in the LD oilfield. Additionally, high-multiple water flooding and numerical simulation experiments were conducted to analyze the development potential after weak gel flooding. The results show that the effect of weak gel flooding was more significant in the core of 1500 mD, with an increase in oil recovery of 9% compared to 500 mD. At a permeability of 500 mD, the degree of crude oil mobilization in micropores and small pores caused by weak gel flooding was improved by 29.64% and 23.48%, respectively, compared with water flooding. At 1500 mD, the degree of crude oil mobilization in small pores caused by weak gel flooding was increased by 37.79% compared to water flooding. After weak gel flooding, the remaining oil was primarily distributed in medium and large pores. Microscopically, the remaining oil was dominated by cluster residual oil, accounting for 16.49%, followed by columnar, membranous, and blind-end residual oil. High multiple water flooding experiments demonstrated that weak gel flooding could significantly reduce development time. The ultimate oil recovery efficiency of 500 mD and 1500 mD reached 71.85% and 80.69%, respectively. Numerical simulation results show that the ultimate oil recovery efficiency increased from 62.04% to 71.3% after weak gel flooding. This indicated that the LD oilfield still had certain development potential after weak gel flooding. The subsequent direction for enhanced oil recovery focuses mainly on mobilizing oil in medium pores or clustered remaining oil. This will play a crucial role in further exploring methods for utilizing the remaining oil and increasing the recovery rate.

Analysis of the Influencing Factors on the Extraction of Residual Oil through the Gel Foam Flooding of Underground Reservoirs in the Tahe Oilfield.

Fractured-vuggy reservoirs are mainly composed of three types: underground rivers, vugs, and fractured-vuggy structures. Based on the similarity criterion, a 3D model can truly reflect the characteristics of the multi-scale space of a fractured-vuggy reservoir, and it can reflect fluid flow laws in the formation. Water flooding, gas flooding, and gel foam flooding were carried out in the model sequentially. Based on gas flooding, the enhanced recovery ratio of gel foam flooding in the underground river was approximately 12%. By changing the injection rate, the average recovery ratio of nitrogen flooding was 6.84% higher than that of other injection rates at 5 mL/min, and that of gel foam flooding was 1.88% higher than that of other injection rates at 5 mL/min. The experimental results showed that the gel foam induced four oil displacement mechanisms, which selectively plugged high-permeability channels, controlled the mobility ratio, reduced oil-water interfacial tension, and changed the wettability of rock surfaces. With different injection-production methods, gel foam flooding can spread across two underground river channels. Two cases of nitrogen flooding affected one underground river channel and two underground river channels. By adjusting the injection rate, it was found that after nitrogen flooding, there were mainly four types of residual oil, and gel foam flooding mainly yielded three types of remaining oil. This study verified the influencing factors of extracting residual oil from an underground river and provides theoretical support for the subsequent application of gel foam flooding in underground rivers.

Experimental Study on Multi-Dimensional Visualization Simulation of Gas and Gel Foam Flooding in Fractured-Vuggy Reservoirs.

Gas flooding and foam flooding are potential technologies for tertiary oil recovery in fractured-vuggy reservoirs. The development and mechanism research of fractured-vuggy reservoirs is difficult due to the complex structures and the strong heterogeneity of fractured-vuggy reservoirs. Visualization simulation is one of the effective methods to study the flow behavior of fluid in fractured-vuggy reservoirs. In this study, an upscaling method of visualization simulation from one dimension (1D) to three dimensions (3D) was established, and the physical models of fractured-vuggy reservoirs were designed and fabricated. Water flooding, gas flooding, and gel foam flooding were carried out in the models. The experimental results showed that gas flooding has a single flow channel and water flooding has multiple flow channels in fractures and vugs. Gel foam with an excellent capability of mobility control and a high microscopic displacement efficiency swept in all directions at a uniform velocity. The EOR mechanisms of gel foam in fractured-vuggy reservoirs were mainly as follows: reducing interfacial tension, increasing mobility ratio, selectively plugging high permeability channels, and discontinuous flow. In the displacement process of fractured-vuggy reservoirs, water should be injected from the well at the bottom of the reservoir, and gas should be injected from the well located in the vug at the high part of the reservoir. Gel foam with strong stability and high viscosity should be selected and injected in most kinds of injection wells in fractured-vuggy reservoirs. This study provides a complete method of visualization simulation for the study of flow behavior in fractured-vuggy reservoirs and provides theoretical support for the application of gas flooding and gel foam flooding in fractured-vuggy reservoirs.

Enhancing piezocatalytic H2O2 production through morphology control of graphitic carbon nitride.

Piezocatalytic H2O2 production has attracted significant attention as a green alternative to traditional anthraquinone methods with heavy environmental pollution and high energy consumption. However, since the efficiency of piezocatalyst in producing H2O2 is poor, searching for a suitable method to improve the yield of H2O2 is of great interest. Herein, a series of graphitic carbon nitride (g-C3N4) with different morphologies (hollow nanotube, nanosheet and hollow nanosphere) are applied to enhance the piezocatalytic performance in yielding H2O2. The hollow nanotube g-C3N4 exhibited an outstanding H2O2 generation rate of 262 umol·g-1·h-1 without any co-catalyst, which is 1.5 and 6.2 times higher than nanosheets and hollow nanospheres, respectively. Piezoelectric response force microscopy, piezoelectrochemical tests, and Finite Element Simulation results revealed that the excellent piezocatalytic property of hollow nanotube g-C3N4 is mainly attributed to its larger piezoelectric coefficient, higher intrinsic carrier density, and stronger external stress absorption conversion. Furthermore, mechanism analysis indicated that piezocatalytic H2O2 production follows a two-step single-electro pathway, and the discovery of 1O2 furnishes a new insight into explore this mechanism. This study offers a new strategy for the eco-friendly manufacturing of H2O2 and a valuable guide for future research on morphological modulation in piezocatalysis.

Association between triglyceride glucose-body mass index and cardiovascular outcomes in patients undergoing percutaneous coronary intervention: a retrospective study.

BACKGROUND: The triglyceride glucose-body mass index (TyG-BMI index) has been considered a reliable surrogate measure of insulin resistance; however, its ability to predict the incidence of cardiovascular disease in individuals with coronary artery disease (CAD) remains uncertain. The aim of this study was to demonstrate the correlation between the TyG-BMI index and cardiovascular incidence. METHODS: A total of 2533 consecutive participants who underwent percutaneous coronary intervention (PCI) and drug-eluting stent (DES) implantation were included. Data from 1438 patients was analyzed in the study. The endpoint was defined as a composite of acute myocardial infarction, repeat revascularization, stroke, and all-cause mortality (major adverse cardiac and cerebrovascular events, MACCEs) at 34-month follow-up. The formula for calculating the TyG-BMI index is ln [fasting triglyceride (mg/dL) × fasting blood glucose (mg/dL)/2] × BMI. RESULTS: Among the 1438 participants, 195 incident patient cases of MACCEs were ascertained. The incidence of MACCEs showed no statistically significant differences in the TyG-BMI index tertiles in the overall population. Further exploratory subgroup analysis and multivariable logistic regression analysis revealed a linear relationship between the TyG-BMI index (per 1 SD increased) and MACCEs in the elderly patients (OR = 1.22, 95% CI 1.011-1.467, p = 0.038) and in the female patients (OR = 1.33, 95% CI 1.004-1.764, p = 0.047). The addition of the TyG-BMI index to traditional risk factor models in elderly and female patients did not improve risk prediction for MACCEs. CONCLUSION: A higher TyG-BMI index was proportionally related to an increased incidence of MACCEs in the elderly or female patients. However, the inclusion of the TyG-BMI index did not provide better predictive performance for MACCEs in the elderly, specifically in female patients.

Prioritizing risk genes as novel stratification biomarkers for acute monocytic leukemia by integrative analysis.

Acute myeloid leukemia (AML) is a blood cancer with high heterogeneity and stratified as M0-M7 subtypes in the French-American-British (FAB) diagnosis system. Improved diagnosis with leverage of key molecular inputs will assist precisive medicine. Through deep-analyzing the transcriptomic data and mutations of AML, we report that a modern clustering algorithm, t-distributed Stochastic Neighbor Embedding (t-SNE), successfully demarcates M2, M3 and M5 territories while M4 bias to M5 and M0 & M1 bias to M2, consistent with the traditional FAB classification. Combining with mutation profiles, the results show that top recurrent AML mutations were unbiasedly allocated into M2 and M5 territories, indicating the t-SNE instructed transcriptomic stratification profoundly outperforms mutation profiling in the FAB system. Further functional data mining prioritizes several myeloid-specific genes as potential regulators of AML progression and treatment by Venetoclax, a BCL2 inhibitor. Among them two encode membrane proteins, LILRB4 and LRRC25, which could be utilized as cell surface biomarkers for monocytic AML or for innovative immuno-therapy candidates in future. In summary, our deep functional data-mining analysis warrants several unappreciated immune signaling-encoding genes as novel diagnostic biomarkers and potential therapeutic targets.

CircRNA_0001449 disturbs phosphatidylinositol homeostasis and AKT activity by enhancing Osbpl5 translation in transient cerebral ischemia.

Phosphatidylinositol-3,4,5-trisphosphate [PI(3,4,5)P3] is a phosphorylated derivative of phosphatidylinositol 4-phosphate [PI(4)P] and phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2], which recruit and activate AKT in the plasma membrane (PM) to promote cellular survival. ORP5 anchors at the endoplasmic reticulum (ER)-PM contact sites and acts as a PI(4)P and PI(4,5)P2/phosphatidylserine (PS) exchanger. Here, a lipidomics analysis of the sensorimotor cortex revealed that transient middle cerebral artery occlusion (tMCAO) disturbs the homeostasis of phosphatidylinositols (PIs) and PS between the PM and ER. Conditional knockout mice showed that ORP5 contributes to this abnormal distribution. Abolishing the ORP5 gene significantly inhibited apoptosis and autophagy. RNA sequencing and RNA pull down analyses confirmed a competing endogenous RNA pathway in which circ_0001449 sponges miR-124-3p and miR-32-5p to promote Osbpl5 translation. Our data showed that circRNA_0001449 regulates membrane homeostasis via ORP5 and is involved in the AKT survival pathway.

Application of Simplicillium chinense for Cd and Pb biosorption and enhancing heavy metal phytoremediation of soils.

Phytoremediation is an effective approach to control soil heavy metal pollution. This study isolated a fungus strain from soils contaminated by cadmium (Cd) and lead (Pb) in Zhalong Wetland (China), which was identified as Simplicillium chinense QD10 via both genotypic and phenotypic analysis. The performance and mechanism of S. chinense QD10 in Cd and Pb adsorption was unraveled by morphological analysis and biosorption test, and its roles in ameliorating phytoremediation by Phragmites communis were tested in pot-experiments. Cd biosorption was attributed to the formation of Cd-chelate, whereas Pb was predominantly adsorbed by extracellular polymeric substances. Metal biosorption followed Langmuir isotherm, and the maximum biosorption capacity was 88.5 and 57.8 g/kg for Cd and Pb, respectively. Colonized in soils, such biosorption behavior of S. chinense QD10 can generate gradients of available Cr or Pb and drive their enrichment. Accordingly, S. chinense QD10 amendment significantly enhanced the phytoextraction of Cd and Pb by P. communis, possibly attributing to rhizospheric enrichment of Cd or Pb and defending effects on plants, explained by the significant removal of acid-extractable and reducible metals in soils and the increase of Cd and Pb content in P. communis tissues. The present study explored the mechanisms of S. chinense QD10 in Cd and Pb biosorption and proved its potential in ameliorating the phytoremediation performance at metal contaminated sites.

Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data.

Integrating large-scale functional genomic data has significantly accelerated our understanding of gene functions. However, no algorithm has been developed to differentiate functions for isoforms of the same gene using high-throughput genomic data. This is because standard supervised learning requires 'ground-truth' functional annotations, which are lacking at the isoform level. To address this challenge, we developed a generic framework that interrogates public RNA-seq data at the transcript level to differentiate functions for alternatively spliced isoforms. For a specific function, our algorithm identifies the 'responsible' isoform(s) of a gene and generates classifying models at the isoform level instead of at the gene level. Through cross-validation, we demonstrated that our algorithm is effective in assigning functions to genes, especially the ones with multiple isoforms, and robust to gene expression levels and removal of homologous gene pairs. We identified genes in the mouse whose isoforms are predicted to have disparate functionalities and experimentally validated the 'responsible' isoforms using data from mammary tissue. With protein structure modeling and experimental evidence, we further validated the predicted isoform functional differences for the genes Cdkn2a and Anxa6. Our generic framework is the first to predict and differentiate functions for alternatively spliced isoforms, instead of genes, using genomic data. It is extendable to any base machine learner and other species with alternatively spliced isoforms, and shifts the current gene-centered function prediction to isoform-level predictions.