Experimental Validation Studies Research Articles

Determination of the cervical cancer biomarker SCCA using CMK-3@UiO-66@AuNPs composite-modified glassy carbon electrodes

In order to detect the cervical cancer biomarker squamous cell carcinoma antigen (SCCA) in human serum, an electrochemical immunosensor was constructed. Zirconium 1,4-dicarboxybenzene MOF (UiO-66), with a high specific surface area, was coupled with gold nanoparticles (AuNPs), creating the UiO-66@AuNPs nanomaterial. This coupling increased the sensitivity of the electrochemical immunosensor by providing many biological binding sites for antibodies to bind to Au-S bonds. Then, mesoporous carbon (CMK-3) was added to the UiO-66@AuNPs nanomaterial to obtain a CMK-3@UiO-66@AuNPs nanocomposite. This further increased the electron transfer proficiency of the material and enhanced the electrochemical signal. Finally, the sensitive detection of squamous cell carcinoma antigen (SCCA) was made possible by unique immunological responses between antigens and antibodies. Transmission electron microscopy (TEM) analysis of CMK-3@UiO-66@AuNPs revealed embedded surfaces and voids; cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) confirmed the successful assembly of the electrochemical immunosensor. The developed electrochemical immunosensor demonstrated good linearity (R12 = 0.999) from 0.01 pg/mL to 10,000 pg/mL, which is the concentration range of SCCA in human serum. The limit of detection was 0.0034 pg/mL (at an S/N ratio of 3). The immunosensor exhibited excellent precision, stability and selectivity, with a high rate of recovery (98.26–101.8 %). The experimental validation studies demonstrated that the electrochemical immunosensor was effective for the detection of SCCA in human serum, offering a novel method for the early diagnosis and dynamic surveillance of cervical cancer.

The discovery of novel noncoding RNAs in 50 bacterial genomes.

Structured noncoding RNAs (ncRNAs) contribute to many important cellular processes involving chemical catalysis, molecular recognitionand gene regulation. Few ncRNA classes are broadly distributed among organisms from all three domains of life, but the list of rarer classes that exhibit surprisingly diverse functions is growing. We previously developed a computational pipeline that enables the near-comprehensive identification of structured ncRNAs expressed from individual bacterial genomes. The regions between protein coding genes are first sorted based on length and the fraction of guanosine and cytidine nucleotides. Long, GC-rich intergenic regions are then examined for sequence and structural similarity to other bacterial genomes. Herein, we describe the implementation of this pipeline on 50 bacterial genomes from varied phyla. More than 4700 candidate intergenic regions with the desired characteristics were identified, which yielded 44 novel riboswitch candidates and numerous other putative ncRNA motifs. Although experimental validation studies have yet to be conducted, this rate of riboswitch candidate discovery is consistent with predictions that many hundreds of novel riboswitch classes remain to be discovered among the bacterial species whose genomes have already been sequenced. Thus, many thousands of additional novel ncRNA classes likely remain to be discovered in the bacterial domain of life.

Progress in TOPCon solar cell technology: Investigating hafnium oxide through simulation

In the realm of solar energy technology, exploring hafnium oxide (HfO2) in Tunnel Oxide Passivated Contact (TOPCon) solar cells is pivotal. This study delineates HfO2's evolution from semiconductor applications, highlighting its crucial role in enhancing TOPCon solar cell performance. Utilizing ATLAS Silvaco software, the study anticipates a 21.3% increase in charge carrier lifetime through optimized HfO2 layers, addressing challenges in interface engineering and scalability. Innovative research integrates hafnium oxide (HfO2) into TOPCon solar cells, marking a leap in photovoltaic technology. Utilizing ATLAS Silvaco simulations, it shows that HfO2 layers can significantly enhance cell performance, increasing charge carrier lifetime by 21.3% and potentially boosting efficiency by 25%. This underscores HfO2's advantages, like a higher dielectric constant and thermal stability, in improving solar cell efficiency and durability. Future efforts target refining deposition processes, projecting a 25% boost in overall power conversion efficiency (PCE). Emphasizing HfO2's significance in solar cell technology, this research contributes to global sustainable energy initiatives. Integrating HfO2 in TOPCon solar cells signifies a key achievement in harnessing clean, renewable energy. Upcoming research focuses on experimental validation, interface engineering, optimization, stability assessments, scalability, and collaborative studies, aiming to leverage HfO2's potential for elevating solar energy conversion technologies.

Magnetic hyperthermia in tissue-like media: Finite element simulation, experimental validation, parametric variations, and calibration studies

We report the experimental characterization and finite element modeling of magnetic fluid hyperthermia (MFH) in tissue-like media using tetramethyl ammonium hydroxide coated superparamagnetic iron oxide magnetic nanoparticles (MNPs) of size ∼19.6 ± 1.2 nm, prepared using a co-precipitation technique. MFH properties are probed for the MNPs in ∼1 wt. % agar, resembling the tumor and surrounding normal tissues. The field-induced temperature rise (ΔT) is experimentally measured in real-time utilizing an infrared camera. A finite element model (FEM) is utilized to simulate the spatiotemporal variations in the thermal profiles, which are found to be in good agreement with the experimental data. FEM-based parametric studies reveal that the thermal conductivity of the medium is the most significant parameter influencing the thermal profiles. The spatiotemporal variations in the thermal profiles are numerically studied for seven different tissues, and the obtained results indicate the highest ΔT for the breast tissue in the tumor and the surrounding regions, which is due to the lowest volumetric specific heat and the highest thermal conductivity of the breast tissue, respectively. Numerical studies on the thermal profiles for sub-surface tumors with parametrically varying depths indicate a strong exponential correlation between the surface and tumor temperature, where the regression coefficients are found to be correlated with the thermo-physical properties of the tissues. The obtained findings are beneficial for developing a simplistic and easily deployable framework for a priori generation of the thermal profiles for various tissues during MFH, which is useful for appropriate planning and parameter selection for MFH-based therapy.

Network pharmacology analysis and experimental validation to explore the effect and mechanism of tetramethylpyrazine for spinal cord injury

ObjectiveTo investigate the effect and mechanism of Tetramethylpyrazine (TMP) in treating Spinal Cord Injury (SCI) using network pharmacology analysis and animal experiments. MethodsThis study was based on public databases, including PharmMapper, BATMAN-TCM, and STRING, as well as KEGG pathway analysis and other methods of network pharmacology were used to preliminarily explore the molecular mechanism of TMP in the treatment of SCI. Using a mouse SCI compression injury model, the efficacy of TMP was evaluated, and the expression of predictive targets on the PI3K/AKT and MAPK signaling pathways was measured using Western blotting and q-PCR. ResultsNetwork pharmacology analysis showed that TMP may exert therapeutic effects through the MAPK and PI3K/AKT signaling pathways. In animal experimental validation studies, it was shown that after treatment with TMP, the hind limb motor function scores and ramp test scores of the TMP-treated mice improved significantly. HE staining showed that after treatment with TMP, cavities decreased, fewer glial cells proliferated, and fewer inflammatory cells infiltrated; Nielsen staining showed less neuronal loss. Western blot studies showed that compared with the model group, expression of RAS, ERK1/2, RAF1, PI3K, and p-AKT proteins in the spinal cord tissue of mice treated with high-dose TMP was significantly lower. Accordingly, q-PCR studies showed that compared with the model group, the expression levels of RAS, ERK1/2, RAF1, PI3K, and p-AKT genes in the spinal cords of mice in the high-dose TMP group were significantly lower. ConclusionTMP exhibits a good neuroprotective effect after SCI, which may be related to inhibition of the MAPK and PI3K/AKT signaling pathways.

Prediction of the mechanism for the combination of diallyl trisulfide and cisplatin against gastric cancer: a network pharmacology study and pharmacological evaluation.

Background: In this research, we aimed to explore the efficacy of diallyl trisulfide (DATS) combined with cisplatin (DDP) for gastric cancer treatment and its underlying mechanism based on network pharmacology. Methods: First, the pharmacological mechanism by which DATS combined with DDP acts against gastric cancer was predicted using network pharmacology. The TTD, GeneCards, and OMIM databases were used to extract drug and disease targets. The David Bioinformatics Resources 6.8 database was used to conduct GO and KEGG analyses. We investigated the efficacy of DATS combined with DDP against gastric cancer in SGC7901 cells and a xenograft model. Furthermore, the specific mechanism of DATS combined with DDP, inferred by network pharmacology, was identified by Western blotting and immunohistochemistry. Results: The combination of DDP and DATS significantly increased cytotoxicity and cell apoptosis compared to the DATS or DDP treatment group in vitro. In addition, continuous intraperitoneal injection of DATS markedly improved the tumor inhibitory effect of DDP in the SGC-7901 tumor-bearing mouse model. Furthermore, network pharmacology and experimental validation studies revealed that the combination of DATS and DDP synergistically enhanced antitumor activity by regulating endoplasmic reticulum stress and inhibiting STAT3/PKC-δ and MAPK signaling pathways. Conclusion: Our study showed that the combination of DATS and DDP could exert outstanding therapeutic effects in gastric cancer. Moreover, network pharmacology coupled with experimental validation revealed the molecular mechanisms of combination therapy for gastric cancer. This study offers a new adjuvant strategy based on DATS and DDP for the treatment of gastric cancer.

Effect of global stiffness on the force response of thin GFRP reinforced concrete slabs subjected to impact loading

The purpose of present study is to investigate the effect of global stiffness on the force response of thin GFRP reinforced concrete slabs simply supported by steel beams under drop-weight impact. Considering the strain rate effect of GFRP bars and concrete materials, a numerical model of GFRP-RC slabs was established and verified. The dynamic equilibrium relationship of the impact force, reaction force and inertial force was analyzed and intuitively explained from the velocity and displacement. Meanwhile, the effects of the global stiffness by varying the slab length, slab thickness and reinforcement ratio on the acceleration distribution, characteristic value of impact force and reaction force, failure pattern, displacement distribution and energy dissipation were evaluated in more detail. Eventually, the semi-empirical formulas considering key parameters were proposed to predict the reaction force time history of simply-supported RC/FRP-RC slabs under drop hammer impact. The results show that the effect of the slab stiffness by varying the slab length and thickness on the reaction force is significant, while the influence of reinforcement ratio is not obvious. The effect of global stiffness of the slab on the plateau value of the reaction force is similar to that on the plateau value of the impact force. The effect of the slab stiffness by varying the slab thickness and reinforcement ratio on the displacement is obvious, while varying the slab length hardly affects the deformation. The fitting equation considering key parameters can predict the simplified reaction force time history of simply-supported RC/FRP-RC slabs under impact loading. However, more experimental validation and extensive parametric studies are needed to improve their accuracy and applicability.

Unlocking therapeutic potential of trigonelline through molecular docking as a promising approach for treating diverse neurological disorders.

Neurological disorders pose significant challenges in terms of treatment options, necessitating the exploration of novel therapeutic approaches. Trigonelline, a naturally occurring alkaloid found in various plants, has emerged as a potential treatment option. It has also been reported that trigonelline is involved in several pathways like; Oxidative Stress and Antioxidant, Inflammatory, Neuroprotection and Neurotrophic, Mitochondrial Function and Energy Metabolism. This study aims to investigate the therapeutic potential of trigonelline for diverse neurological disorders using a molecular docking approach. Molecular docking simulations were performed to predict the binding affinity and interaction between trigonelline and target proteins implicated in neurological disorders. The structural requirements for effective binding were also explored. The molecular docking results revealed strong binding interactions and favorable binding affinities between trigonelline and the target proteins involved in diverse neurological disorders like Alzheimer's disease, Parkinson's disease, epilepsy, and depression etc. The predicted binding modes provided insights into the key molecular interactions governing the ligand-protein complexes. The findings suggest that trigonelline holds promise as a therapeutic approach for several neurological disorders. The molecular docking approach employed in this study provides a valuable tool for rational drug design and optimization of trigonelline-based compounds. Further experimental validation and preclinical studies are warranted to confirm the efficacy and safety of trigonelline as a potential treatment option, paving the way for the development of more effective and targeted therapies for neurological disorders.

Identification of SYT10 as a potential prognostic biomarker in esophageal cancer by comprehensive analysis of a mRNA-pseudogene/lncRNA-miRNA ceRNA network

BackgroundEsophageal carcinoma (ESCA) is often diagnosed at the advanced stages, has a poor survival rate and overall is one of the deadliest cancers world-wide. Recent studies have elaborated the significance of non-coding RNAs such as pseudogenes, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in cancer progression. In this study, we constructed a four-component competing endogenous RNA (ceRNA) network in ECSA and suggested an RNA with prognostic potential. Materials and methodsExpression profiles of mRNAs, pseudogenes, lncRNAs and miRNAs were collected from The Cancer Genome Atlas (TCGA) database. A ceRNA network was then constructed based on the differentially-expressed RNAs (DERs). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) functional analysis and PPI (Protein-Protein Interaction) network analysis was carried out on differentially-expressed (DE) mRNAs of the ceRNA network. Survival analysis was carried out on a selection of RNAs with the highest degree centrality ranks to discover potential prognostic biomarkers. ResultsA four-component ceRNA network with 529 nodes and 729 edges was constructed. The most significant GO biological process terms included positive regulation of transcription from RNA polymerase II promoter, negative regulation of transcription from RNA polymerase II promoter and positive regulation of transcription, DNA-templated. The analysis of KEGG pathways showed that DE mRNAs were significantly enriched in pathways such as pathways in cancer, PI3K-Akt signaling pathways and MicroRNAs in cancer. Among the RNAs that were found to be associated with survival, Synaptotagmin 10 (SYT10) had the highest hazard ratio and thus, proved to be a potential prognostic biomarker for ESCA. ConclusionOur study presented a four-component ceRNA network for ESCA, and identified RNA candidates that were associated with survival of ECSA. Further experimental evaluations and precise validation studies are needed for their clinical significance and roles in the progression of ESCA.

Seminar: Extracellular Vesicles as Mediators of Environmental Stress in Human Disease.

Extracellular vesicles (EVs), membrane-bound particles containing a variety of RNA types, DNA, proteins, and other macromolecules, are now appreciated as an important means of communication between cells and tissues, both in normal cellular physiology and as a potential indicator of cellular stress, environmental exposures, and early disease pathogenesis. Extracellular signaling through EVs is a growing field of research for understanding fundamental mechanisms of health and disease and for the potential for biomarker discovery and therapy development. EVs are also known to play important roles in mediating the effects of exposure to environmental stress. This seminar addresses the application of new tools and approaches for EV research, developed in part through the National Institutes of Health (NIH) Extracellular RNA Communication Program, and reflects presentations and discussions from a workshop held 27-28 September 2021 by the National Institute of Environmental Health Sciences (NIEHS) and the National Center for Advancing Translational Sciences (NCATS) on "Extracellular Vesicles, Exosomes, and Cell-Cell Signaling in Response to Environmental Stress." The panel of experts discussed current research on EVs and environmental exposures, highlighted recent advances in EV isolation and characterization, and considered research gaps and opportunities toward identifying and characterizing the roles for EVs in environmentally related diseases, as well as the current challenges and opportunities in this field. The authors discuss the application of new experimental models, particularly organ-on-chip (OOC) systems and in vitro approaches and how these have the potential to extend findings in population-based studies of EVs in exposure-related diseases. Given the complex challenges of identifying cell-specific EVs related to environmental exposures, as well as the general heterogeneity and variability in EVs in blood and other accessible biological samples, there is a critical need for rigorous reporting of experimental methods and validation studies. The authors note that these efforts, combined with cross-disciplinary approaches, would ensure that future research efforts in environmental health studies on EV biomarkers are rigorous and reproducible. https://doi.org/10.1289/EHP12980.

Numerical analysis and experimental validation of the jet impingement cooling of a turbine-blade leading edge at different rotation speeds

A detailed experimental and numerical validation studies are conducted on internal channel jet impingement cooling where seven jets impinging inside a rotating semi-cylindrical channel. The study objective is to mimic the cooling flow structure in gas turbine leading edge by using the rotating the semi-circular channel. These studies are conducted by considering a jet Reynolds number of 7500 at five different rotation speeds (ranging from 0 to 200 rpm). Numerical analysis is performed using the shear stress transport (SST) k−ω turbulence model with a properly analyzed fine mesh containing around eight million nodes. A test setup with required instrumentation is developed inhouse for this study. Two temperature measurement techniques, namely thermochromic liquid crystals (TLCs) and thermocouples, are adopted. Further, the target surface temperature contours are precisely analyzed by comparing the TLC temperature measurements with the numerical temperature results. The captured temperature contours indicated points of minimum-temperature regions, which corresponded to the jet impingement regions. By examining the temperature distribution along the axial centerline, a good agreement has been established between the numerical results and the experimental measurements. For a jet Reynolds number of 7500, increasing channel rotation speed from 0 to 250 rpm has reduced the variation in temperature between different jets. The size of inlet port used to guide flow to the feeding duct has a strong impact on jet formation and flow structure, and it has led to different mass flow rate across jets. Furthermore, a small deviation between numerical and experimental data can be observed near the end side of the channel owing to the radial and lateral heat transfer losses and outlet flow restriction.

Identification of potential biomarkers of myopia based on machine learning algorithms

PurposeThis study aims to identify potential myopia biomarkers using machine learning algorithms, enhancing myopia diagnosis and prognosis prediction.MethodsGSE112155 and GSE15163 datasets from the GEO database were analyzed. We used “limma” for differential expression analysis and “GO plot” and “clusterProfiler” for functional and pathway enrichment analyses. The LASSO and SVM-RFE algorithms were employed to screen myopia-related biomarkers, followed by ROC curve analysis for diagnostic performance evaluation. Single-gene GSEA enrichment analysis was executed using GSEA 4.1.0.ResultsThe functional analysis of differentially expressed genes indicated their role in carbohydrate generation and polysaccharide synthesis. We identified 23 differentially expressed genes associated with myopia, four of which were highly effective diagnostic biomarkers. Single gene GSEA results showed these genes control the ubiquitin-mediated protein hydrolysis pathway.ConclusionOur study identifies four key myopia biomarkers, providing a foundation for future clinical and experimental validation studies.

UPLC-Q-TOF-MS based investigation into the bioactive compounds and molecular mechanisms of Lamiophlomis Herba against hepatic fibrosis

BackgroundLamiophlomis Herba (LH) is a valuable traditional medicinal plant found on the Qinghai-Tibetan Plateau that promotes blood circulation, removes blood stasis, and has antibacterial and anti-inflammatory properties. The main components of LH are iridoid glycosides, phenethyl alcohol glycosides, flavonoids, and polysaccharides. PurposeTo investigate the mechanism of the anti-liver fibrosis effects of LH and screen for its bioactive compounds. Study designScreening LH marker components and validating the LH anti-liver fibrosis mechanism. MethodsThe active ingredients of LH were identified using UPLC-Q-TOF-MS, and HotMap combined with principal components analysis (PCA) was used to screen for marker components. Network pharmacology and molecular docking techniques were used to predict the potential anti-fibrotic targets of LH. Immunofluorescence, enzyme-linked immunosorbent assay (ELISA), real-time PCR (RT-PCR), and western blotting were used for experimental validation and mechanistic studies. ResultsFifteen compounds that actively contributed to the cluster were identified as marker compounds. Acteoside, 8-O-acetyl shanzhiside methyl ester (8-O-ASME), Luteolin, Shanzhiside Methyl ester (SME), Loganin, Loganate were the main active components. Network pharmacology and molecular docking studies have shown that LH might improve liver fibrosis, inflammation, and oxidative stress, which might be related to key targets such as PTGS2, MAPK, EGFR, AKT1, SRC, Fn1, Col3a1, Col1a1, and PC-III. The results of ELISA, RT-PCR and western blot experiments showed that Acteoside, 8-O-ASME, Luteolin, SME, Loganin, Loganate, and the LH group could reduce the levels of fibronectin, Col1a1, Col3a1, α-SMA, Col-Ⅳ, LN, and PC-Ⅲ. ConclusionLH improves liver fibrosis induced by HSC-T6 cells and inhibits the deposition of extracellular matrix (ECM) in hepatocytes, resulting in a decrease in the degree of liver fibrosis and a good anti-liver fibrosis effect.

Structural analysis and molecular dynamics simulation studies of HIV-1 antisense protein predict its potential role in HIV replication and pathogenesis.

The functional significance of the HIV-1 Antisense Protein (ASP) has been a paradox since its discovery. The expression of this protein in HIV-1-infected cells and its involvement in autophagy, transcriptional regulation, and viral latency have sporadically been reported in various studies. Yet, the definite role of this protein in HIV-1 infection remains unclear. Deciphering the 3D structure of HIV-1 ASP would throw light on its potential role in HIV lifecycle and host-virus interaction. Hence, using extensive molecular modeling and dynamics simulation for 200 ns, we predicted the plausible 3D-structures of ASP from two reference strains of HIV-1 namely, Indie-C1 (subtype-C) and NL4-3 (subtype-B) so as to derive its functional implication through structural domain analysis. In spite of sequence and structural differences in subtype B and C ASP, both structures appear to share common domains like the Von Willebrand Factor Domain-A (VWFA), Integrin subunit alpha-X (ITGSX), and ETV6-Transcriptional repressor, thereby reiterating the potential role of HIV-1 ASP in transcriptional repression and autophagy, as reported in earlier studies. Gromos-based cluster analysis of the centroid structures also reassured the accuracy of the prediction. This is the first study to elucidate a highly plausible structure for HIV-1 ASP which could serve as a feeder for further experimental validation studies.

Time-Domain Structural Damage Identification Using Ensemble Bagged Trees and Evolutionary Optimization Algorithms

This paper presents a two-step vibration-based strategy for damage identification of framed structures using ensemble bagged trees known as a well-known supervised machine learning (ML) paradigm in conjunction with evolutionary optimization algorithms. The proposed model incorporates the actual response, wavelet coefficients, and wavelet energy to extract damage-sensitive features from the time-domain of the measured and simulated signals. Unlike available studies in this scope, the key objective of this research is to identify damage with a localization precision down to a single structural member, rather than limiting the evaluation to the group of elements. In order to increase the training performance in contributing to extremely large datasets with numerous class labels, the proposed strategy involves the artificial generation of features. Additionally, a modified genetic algorithm is proposed for fast damage localization. It is deduced that the damage locations are confidently detected within a fast computational time. Subsequently, damage identification is followed by the application of evolutionary optimization algorithms. For comparison purpose, the employment of the water cycle optimization algorithm (WCA) is comparatively investigated with the other three state-of-the-art optimizers, i.e., particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and differential evolution algorithm (DE). The numerical and experimental validation studies evidence satisfactorily reliable identification results with no false detection in dealing with multiple damage scenarios in large-scale and real-world applications. It is concluded that developing the most damage-sensitive features and using the proposed data fusion strategy lead to informative features with a reasonably small size and significantly improve the ML performance.

Network-Guided Multiomic Mapping of Aortic Valve Calcification.

Despite devastating clinical sequelae of calcific aortic valve disease that range from left ventricular remodeling to arrhythmias, heart failure, and early death, the molecular insights into disease initiation and progression are limited and pharmacotherapies remain unavailable. The pathobiology of calcific aortic valve disease is complex and comprehensive studies are challenging valvular calcification is heterogeneous and occurs preferentially on the aortic surface, along a fibrocalcific spectrum. Here, we review efforts to study (epi-)genomic, transcriptomic, proteomic, and metabolomic aspects of aortic valve calcification in combination with network medicine-/systems biology-based strategies to integrate multilayered omics datasets and prioritize druggable targets for experimental validation studies. Ultimately, such holistic approach efforts may open therapeutic avenues that go beyond invasive and costly valve replacement therapy.

Computational design of custom therapeutic cells to correct failing human cardiomyocytes.

Myocardial delivery of non-excitable cells-namely human mesenchymal stem cells (hMSCs) and c-kit+ cardiac interstitial cells (hCICs)-remains a promising approach for treating the failing heart. Recent empirical studies attempt to improve such therapies by genetically engineering cells to express specific ion channels, or by creating hybrid cells with combined channel expression. This study uses a computational modeling approach to test the hypothesis that custom hypothetical cells can be rationally designed to restore a healthy phenotype when coupled to human heart failure (HF) cardiomyocytes. Candidate custom cells were simulated with a combination of ion channels from non-excitable cells and healthy human cardiomyocytes (hCMs). Using a genetic algorithm-based optimization approach, candidate cells were accepted if a root mean square error (RMSE) of less than 50% relative to healthy hCM was achieved for both action potential and calcium transient waveforms for the cell-treated HF cardiomyocyte, normalized to the untreated HF cardiomyocyte. Custom cells expressing only non-excitable ion channels were inadequate to restore a healthy cardiac phenotype when coupled to either fibrotic or non-fibrotic HF cardiomyocytes. In contrast, custom cells also expressing cardiac ion channels led to acceptable restoration of a healthy cardiomyocyte phenotype when coupled to fibrotic, but not non-fibrotic, HF cardiomyocytes. Incorporating the cardiomyocyte inward rectifier K+ channel was critical to accomplishing this phenotypic rescue while also improving single-cell action potential metrics associated with arrhythmias, namely resting membrane potential and action potential duration. The computational approach also provided insight into the rescue mechanisms, whereby heterocellular coupling enhanced cardiomyocyte L-type calcium current and promoted calcium-induced calcium release. Finally, as a therapeutically translatable strategy, we simulated delivery of hMSCs and hCICs genetically engineered to express the cardiomyocyte inward rectifier K+ channel, which decreased action potential and calcium transient RMSEs by at least 24% relative to control hMSCs and hCICs, with more favorable single-cell arrhythmia metrics. Computational modeling facilitates exploration of customizable engineered cell therapies. Optimized cells expressing cardiac ion channels restored healthy action potential and calcium handling phenotypes in fibrotic HF cardiomyocytes and improved single-cell arrhythmia metrics, warranting further experimental validation studies of the proposed custom therapeutic cells.

PI3K inhibitor promotes tumor vessel normalization and improves treatment outcomes of breast cancer with doxorubicin

HS-173 is a specific inhibitor of the Phosphoinositide 3-Kinase α (PI3Kα) subtype. Although it was shown to potentially inhibit tumor angiogenesis, experimental validation studies are still needed. This study provides an experimental basis for the role of HS-173 in breast cancer. A mouse model of subcutaneous transplantation breast cancer was constructed. The mice were treated with different concentrations of HS-173. Immunohistochemical staining was used to detect tumor microvessel density, and the appropriate concentration was determined. Immunofluorescence was used to detect the morphology integrity of tumor vessels’ lumen, transmission electron microscopy to detect tight junctions between endothelial cells and the integrity of the basement membrane, Doppler ultrasound to detect tumor blood perfusion, and small animal live imaging to detect the penetration of doxorubicin in the tumor tissues. After HS-173 treatment, the number of tumor interstitial microvessels decreased, the lack of tumor vascular lumen was reduced, and the continuity and integrity of the vascular lumen were increased. Vascular endothelial cells showed complete morphology with good tight junctions. The extracellular matrix was rich in components and tended to form basement membranes. HS-173 also increased the blood perfusion in the tumor tissue compared with the doxorubicin treatment alone. Further, the fluorescence signal intensity of the tumor tissue doxorubicin was significantly enhanced after HS-173 treatment. The PI3K inhibitor HS-173 showed promising potential in inhibiting tumor angiogenesis and improving the structure and function of blood vessels in the tumor microenvironment.

Deep Neural Network Denoising Model Based on Sparse Representation Algorithm for ECG Signal

Electrocardiogram (ECG) denoising is very important for heart diseases diagnosis. The traditional ECG denoising models have problems such as single noise type and poor interpretability of deep neural networks. The innovation of the proposed method is to incorporate the precious achievements of traditional methods into the design of neural networks and to build a bridge between them. Therefore, a novel interpretable deep denoising framework based on sparse representation is proposed in this study, and the half quadratic splitting (HQS) algorithm is applied to decompose the denoising method into sparse representations as an iterative solution process. In addition, a new weight distribution module is designed to extract adpative hyperparameters based on ECG correlation instead of empirical values and greatly improves the efficiency of hyperparameter selection. To demonstrate the fairness and the effectiveness of the proposed method, four different denoising models with different data preprocessing technique are used for comparison. The extensive experimental validation and simulation studies demonstrated that the proposed framework has excellent performance in quantitative and visual evaluation.

Quantum Power Flow: Revolutionizing Power Systems Analysis

This research investigates the transformative potential of Quantum Power Flow (QPF) in power systems analysis. Leveraging quantum computing principles, the study explores quantum algorithms for power flow simulations to enhance computational efficiency andsolution accuracy. The Quantum Power Flow method is introduced, addressing challenges arising from the growing complexity of power systems, especially with increased renewable energy integration. The mathematical foundation of the Quantum Power Flow method is detailed, emphasizing quantum parallelism and computational advantages. Experimental validation, simulations, and research case studies collectively establish the practical viability and versatility of the proposed quantum approach. A 5 and 3-bus power system case study illustrates its applicability.Acknowledging current quantum computing challenges, including error susceptibility, the study highlights ongoing advancements and error correction mechanisms' potential to overcome limitations. The research concludes by discussing broader implications of quantum power system analysis and outlining future research avenues. In summary, this research comprehensively explores Quantum Power Flow, offering theoretical insights and practical demonstrations. As quantum computing evolves, this study contributes to the paradigm shift in power system analysis, opening new horizons for research and application..