Biological Experiments Research Articles

Unlocking the Potential of Disulfidptosis-Related LncRNAs in Lung Adenocarcinoma: A Promising Prognostic LncRNA Model for Survival and Immunotherapy Prediction.

Disulfidptosis was stimulated in high SLC7A11 expression cells starving to glucose. We attempted to identify disulfidptosis-related lncRNAs (DRLs), built a prognostic model to predict survival, and analyzed the tumor microenvironment. The TCGA database was utilized to procure the pertinent data. By utilizing both Cox regression and the least absolute shrinkage and selection operator (LASSO) method, a risk model based on DRLs was formulated for prognostic evaluation. The ability of survival prediction was validated by multiple approaches. The biological functions were screened through GO, KEGG, and GSEA. Various methods were employed to evaluate the tumor immune environment, which included ESTIMATE, tumor mutation burden (TMB) score, CIBERSORT algorithm, and tumor immune dysfunction and exclusion (TIDE) score. Ninety-one DRLs were recognized, and lncRNA AC092718.4, AL365181.2, AL606489.1, EMSLR, and ENTPD3-AS1 were involved in the risk model. The GEO database was used to verify the influence of these lncRNAs on survival. The following analyses showed that survival could be predicted excellently by the DRLs risk model. The results of enrichment analyses pointed toward the involvement of the cell cycle and IgA production pathways. In the low-risk patient group, there was a notable surge in stromal, immune, and ESTIMATE scores, while the TMB scores took a tumble. Conversely, the high-risk patient group displayed a converse trend. Notably, the group of patients with lower risk scores and higher TMB scores showed the most favorable survival outcomes, underscoring the importance of considering both risk score and TMB in predicting the response to immune checkpoint blockade therapy. Furthermore, patients classified as high-risk might display resistance to both chemotherapy and targeted therapy. Cellular biological experiments proved that lncRNA AC092718.4 promoted invasion, migration, and proliferation abilities invitro. These results provided valuable insights into the role of DRLs in LUAD and presented a possible effective treatment approach for LUAD. We developed a disulfidptosis-related risk model with 5 lncRNAs that enables survival prediciton for LUAD patients and aids cilinical decisions by forecasting the TME, TMB, and drug sensitivity, making it a valuable tool for outcomes prediction.

2-Aminobenzothiazole based adjuvant of polymyxin E against Gram-negative bacteria

Antibiotic resistance has emerged as a pressing global health issue. Polymyxin E, commonly regarded as a clinically important antibiotic, faces limitations due to its dose-dependent toxicity. A crucial strategy to combat this is the development of an antibiotic adjuvant to enhance efficacy while reducing dosage. Screening from our extensive in-house compound library, KJ1071 emerged as a promising adjuvant candidate for polymyxin E. Subsequent structure–activity relationship studies led to the discovery of compound A35, which demonstrated superior synergistic activity. However, its limited aqueous solubility posed challenges for biological experiments. To address this, we carried out a structural derivatization aimed at enhancing its solubility. The amino acid derivative A59 notably improved aqueous solubility to 3.237 mg/mL, a substantial 3237-fold increase compared to compound A35. Moreover, A59 amplified the efficacy of polymyxin E in eradicating a variety of Gram-negative bacteria, including certain clinical strains that are resistant to polymyxin E. The mechanism studies indicated that A59 might target the bacterial membrane. These findings suggest that the discovery of this innovative scaffold could provide critical insights for new adjuvant therapies.

Integrated transcriptomics and lipidomics reveals protective effect in vascular endothelial barrier of a polysaccharide from Typhae Pollen

Endothelial cell dysfunction caused by inflammation and even vascular leakage are important manifestations of blood stasis syndrome (BSS). Reversible regulation of vascular integrity to cure BSS has attracted considerable interest. Herein, a novel acidic polysaccharide (TPP-4) was purified and characterized from Typhae Pollen, a typical traditional Chinese medicine for treating BSS, especially for bleeding caused by blood stasis. A series of structural characterization methods, including spectroscopic methods (FT-IR and UV), chromatographic methods (HPGPC, HPAEC-PDA and GC–MS) and NMR, have been used to reveal the fine structure of TPP-4. TPP-4 was a homogeneous heteropolysaccharide comprised with RG-I backbone. TPP-4 showed fantastic activities in vascular integrity regulation both in vitro (HUVECs) and in vivo (zebrafish). Transcriptomics revealed that SOX7 and lipid metabolism were the potential targets. Lipidomics showed that TPP-4 could regulate lipid metabolism disorders caused by vascular inflammation, particularly affecting LPE levels. The above regulatory effects were furtherly demonstrated to be related with VEGFA/PI3K/mTOR signaling pathway through various molecular biological experiments.

A Comprehensive Review of Diffusing Alpha-Emitters Radiation Therapy (DaRT): From Dosimetry to Its Biological Effectiveness

Diffusing alpha-emitters radiation therapy (DaRT) represents a groundbreaking development in cancer therapy, offering a solution to the limitations of conventional radiation therapy. By deploying <sup>224</sup>Ra embedded seeds, DaRT achieves targeted delivery of high-dose alpha particles directly to tumor sites, showing considerable efficacy in tumor control and minimal damage to adjacent healthy tissues. This comprehensive review analyzes the published literature regarding mechanisms, seed production, dose calculation, measurement, and biological experiments related to DaRT. It includes in-depth discussions on mathematical models, Monte Carlo simulations for dose distribution, real-time <i>in vivo</i> dosimetry developments, and biological experiments both in vitro and <i>in vivo</i>. Clinical trial outcomes are also examined to evaluate the therapy’s effectiveness in various cancer types. DaRT utilizes <sup>224</sup>Ra-labeled seeds, using the decay chain of <sup>224</sup>Ra to deliver alpha particles effectively within a tumor. Several asymptotic diffusion-leakage models were developed to calculate the alpha dose distribution of DaRT. <i>In vivo</i> dosimetry techniques have been developed for real-time monitoring. Biological experiments demonstrated the cytotoxic effects of DaRT across various cancer cells, with varying radiosensitivity. Additionally, the enhanced effects of combined therapy with chemotherapy and immunotherapy were suggested by many <i>in vivo</i> studies. Clinical trials have shown high complete response rate in squamous cell carcinoma, with minimal side effects, suggesting DaRT’s feasibility and safety. DaRT emerges as a highly localized cancer treatment method with minimal side effects compared to traditional radiation therapy. It directly ablates tumors and potentially enhances immune responses, indicating a significant advance in cancer therapy. Future research and ongoing clinical trials will further elucidate its efficacy across different cancer types and in combination with other treatments.

Construction of Biological Experiment Curriculum System in the Context of Innovation and Entrepreneurship Education

Innovation and entrepreneurship education is an important direction for the current reform of higher education and should be included in the professional talent training plan and the construction of the biological experiment curriculum system. Specific measures include in-depth excavation of the connotation of biological experiment teaching courses from the perspective of talent training, building an innovation and entrepreneurship biological experiment curriculum system guided by the needs of social development, establishing a comprehensive and diversified platform for practice and innovation, and constructing a teaching team that integrates innovation and entrepreneurship education and professional education.

Absorbance measurement for interdisciplinary educational experiment on cytotoxicity

Abstract We propose a multidisciplinary educational experiment, linking knowledge and skills in physics, biology and information technology through the training and application of the research method by students. From a physics point of view, the experiment consists in light absorption measurement, and in terms of biology, it demonstrates the process of cytotoxicity evaluation by means of the colorimetric method neutral red uptake in vitro test. The test allows to determine the concentration of a test drug at which 50% cytotoxicity (CC50 values) of the cells is observed. After initial exposure to the tested drug, neutral red (NR) is added to the cell culture medium, it is absorbed by the living cells and subsequently released in a desorb buffer. The cell culture medium becomes coloured, and the more intensive colour corresponds to higher cell viability, while more transparent solution corresponds to lower number of living cells. The NR concentration in the solution is estimated by the absorbance value. We measure the absorbance of NR by utilizing a very affordable educational-grade spectrometer and compare its output with that of a professional microplate reader. We show that the educational spectrometer gives a result on the CC50 value that is in agreement with the acceptable values of the official protocol. We include a comment on the results when performing this laboratory exercise in one class of 18 year old students (in their last year of secondary education). The experiment can be also successfully applied in the laboratory practicum of first year university study.

LncRNA-miRNA interactions prediction based on meta-path similarity and Gaussian kernel similarity.

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two typical types of non-coding RNAs that interact and play important regulatory roles in many animal organisms. Exploring the unknown interactions between lncRNAs and miRNAs contributes to a better understanding of their functional involvement. Currently, studying the interactions between lncRNAs and miRNAs heavily relies on laborious biological experiments. Therefore, it is necessary to design a computational method for predicting lncRNA-miRNA interactions. In this work, we propose a method called MPGK-LMI, which utilizes a graph attention network (GAT) to predict lncRNA-miRNA interactions in animals. First, we construct a meta-path similarity matrix based on known lncRNA-miRNA interaction information. Then, we use GAT to aggregate the constructed meta-path similarity matrix and the computed Gaussian kernel similarity matrix to update the feature matrix with neighbourhood information. Finally, a scoring module is used for prediction. By comparing with three state-of-the-art algorithms, MPGK-LMI achieves the best results in terms of performance, with AUC value of 0.9077, AUPR of 0.9327, ACC of 0.9080, F1-score of 0.9143 and precision of 0.8739. These results validate the effectiveness and reliability of MPGK-LMI. Additionally, we conduct detailed case studies to demonstrate the effectiveness and feasibility of our approach in practical applications. Through these empirical results, we gain deeper insights into the functional roles and mechanisms of lncRNA-miRNA interactions, providing significant breakthroughs and advancements in this field of research. In summary, our method not only outperforms others in terms of performance but also establishes its practicality and reliability in biological research through real-case analysis, offering strong support and guidance for future studies and applications.

Optimal resource allocation in micro-organisms under periodic nutrient fluctuations

Although microorganisms often live in dynamic environments, most studies, both experimental and theoretical, are carried out under static conditions. In this work, we investigate the issue of optimal resource allocation in bacteria growing in periodic environments. We consider a dynamic model describing the microbial metabolism under varying conditions, involving a control variable quantifying the protein precursors allocation. Our objective is to determine the optimal strategies maximizing the long-term growth of cells under a piecewise-constant periodic environment. Firstly, we perform a theoretical analysis of the resulting optimal control problem (OCP), based on the application the Pontryagin’s Maximum Principle (PMP). We determine that the structure of the optimal control must be bang–bang, with possibly some singular arcs corresponding to optimal equilibria of the system. If the control presents singular arcs, then these can only be reached and left through chattering arcs. We also use a direct optimization method, implemented in the BOCOP software, to solve the studied OCP. Our study reveals that the optimal solution over a large time horizon is related to the one over a single period of the varying environment with periodic constraints. Moreover, we observe that the maximal average growth rate attainable under periodic conditions can be higher than the one under a constant environment. We further extend our analysis to conduct a qualitative comparison between the predictions from our model and some recent biological experiments on E. coli. This analysis particularly highlights the mechanisms of action of the ppGpp signaling molecule, thus providing relevant explanations of the experimental observations. In conclusion, our study corroborates previous research indicating that this molecule plays a crucial role in the regulation of resource allocation of protein precursors in E. coli.

Feasibility study of YSO/SiPM based detectors for virtual monochromatic image synthesis.

The development of photon-counting CT systems has focused on semiconductor detectors like cadmium zinc telluride (CZT) and cadmium telluride (CdTe). However, these detectors face high costs and charge-sharing issues, distorting the energy spectrum. Indirect detection using Yttrium Orthosilicate (YSO) scintillators with silicon photomultiplier (SiPM) offers a cost-effective alternative with high detection efficiency, low dark count rate, and high sensor gain. This work aims to demonstrate the feasibility of the YSO/SiPM detector (DexScanner L103) based on the Multi-Voltage Threshold (MVT) sampling method as a photon-counting CT detector by evaluating the synthesis error of virtual monochromatic images. In this study, we developed a proof-of-concept benchtop photon-counting CT system, and employed a direct method for empirical virtual monochromatic image synthesis (EVMIS) by polynomial fitting under the principle of least square deviation without X-ray spectral information. The accuracy of the empirical energy calibration techniques was evaluated by comparing the reconstructed and actual attenuation coefficients of calibration and test materials using mean relative error (MRE) and mean square error (MSE). In dual-material imaging experiments, the overall average synthesis error for three monoenergetic images of distinct materials is 2.53% ±2.43%. Similarly, in K-edge imaging experiments encompassing four materials, the overall average synthesis error for three monoenergetic images is 4.04% ±2.63%. In rat biological soft-tissue imaging experiments, we further predicted the densities of various rat tissues as follows: bone density is 1.41±0.07 g/cm3, adipose tissue density is 0.91±0.06 g/cm3, heart tissue density is 1.09±0.04 g/cm3, and lung tissue density is 0.32±0.07 g/cm3. Those results showed that the reconstructed virtual monochromatic images had good conformance for each material. This study indicates the SiPM-based photon-counting detector could be used for monochromatic image synthesis and is a promising method for developing spectral computed tomography systems.

Manually operated microtube automatic capper/decapper system for clinical laboratory and biological laboratory personnel

Polymerase chain reaction (PCR) is an effective method for diagnosing infectious diseases and has been the primary method throughout the novel coronavirus disease (COVID-19) pandemic. PCR tests (from specimen collection to result acquisition) involve sample pretreatment, nucleic acid extraction, and PCR procedure. Automating the pretreatment process is crucial to mitigate the risk of infection for workers and to reduce the likelihood of sample contamination-triggered misdiagnosis, particularly when handling centrifuge tubes, cryopreservation tubes, and microtubes. Robotic systems have been engineered to automate cell culture and PCR-based diagnosis, predominantly designed for use with screw-capped containers. However, this leaves a notable gap in automation solutions for microtubes equipped with press-type caps. To address this gap, we developed a versatile microtube capper/decapper system. On the other hand, many tasks of manual operation using microtubes, which are routinely conducted in clinical tests and biological experiments, are performed. Compared to screw-type caps for centrifuge and cryopreservation tubes, press-type caps for microtubes present a considerably higher risk of the worker's fingers contacting the inside of the cap and/or generating airborne droplets. Despite the risks of contamination and infection derived from the manual handling of microtube caps, which can compromise diagnosis/experiment accuracy and worker safety, devices for manually opening and closing microtube caps without direct contact remain lacking. Therefore, leveraging the technology from the developed versatile microtube capper/decapper system for laboratory automation, we created a manually operated microtube equipped with an automatic capper/decapper system tailored for personnel in clinical and biological laboratories.In this study, we first examined the required specifications and prerequisites for a manual microtube capper/decapper and clarified the operating methods, operating procedures, operation environment, device size, accompanying functions, etc. Based on the required specifications and preconditions, we proceeded with the mechanical and control design of the conceptual model, manufactured a prototype, and confirmed its basic functions and performance. The compliant to the required specifications and preconditions and the usefulness of the proposed manual microtube capper/decapper were validated through various experiments and demonstrations. Using the proposed microtube capper/decapper, even small-scale operations, which are challenging to streamline, can be performed nearly as efficiently as full manual operations. Although operation time was not reduced, the ability to open and close microtubes without manual contact is crucial for improving diagnostic and experimental accuracy and for reducing the burden on and enhancing the safety of laboratory personnel. Because microtubes are used in various clinical tests and biological experiments, we believe that the proposed system can markedly reduce the workload for personnel across numerous clinical and biological laboratories.

Atomic-scale strain engineering of atomically resolved Pt clusters transcending natural enzymes

Strain engineering plays an important role in tuning electronic structure and improving catalytic capability of biocatalyst, but it is still challenging to modify the atomic-scale strain for specific enzyme-like reactions. Here, we systematically design Pt single atom (Pt1), several Pt atoms (Ptn) and atomically-resolved Pt clusters (Ptc) on PdAu biocatalysts to investigate the correlation between atomic strain and enzyme-like catalytic activity by experimental technology and in-depth Density Functional Theory calculations. It is found that Ptc on PdAu (Ptc-PA) with reasonable atomic strain upshifts the d-band center and exposes high potential surface, indicating the sufficient active sites to achieve superior biocatalytic performances. Besides, the Pd shell and Au core serve as storage layers providing abundant energetic charge carriers. The Ptc-PA exhibits a prominent peroxidase (POD)-like activity with the catalytic efficiency (Kcat/Km) of 1.50 × 109 mM−1 min−1, about four orders of magnitude higher than natural horseradish peroxidase (HRP), while catalase (CAT)-like and superoxide dismutase (SOD)-like activities of Ptc-PA are also comparable to those of natural enzymes. Biological experiments demonstrate that the detection limit of the Ptc-PA-based catalytic detection system exceeds that of visual inspection by 132-fold in clinical cancer diagnosis. Besides, Ptc-PA can reduce multi-organ acute inflammatory damage and mitigate oxidative stress disorder.

Irradiation system for biological experiments on BL14B1 at SPring-8

We present an irradiation system for biological experiments on BL14B1 beam line of SPring-8, where enables to use white and monochromatic X-rays (from 5 to 90 keV). An effective energy of white X-rays is assessed as 120 keV and their average dose rate is determined as approximately 170 Gy/s using a l-alanine dosimeter. Also, we speak about dosimetry of monochromatic X-rays using an optical stimulated luminescence detector. Average dose rates of monochromatic X-rays with 33.0, 33.2, 33.4 and 34.0 keV are estimated as 0.099 ± 0.006 Gy/s. The irradiation dose of white and monochromatic X-rays, of course, can be monitored using ionization chamber. When we use monochromatic X-rays with 33.2 keV, where is the K-shell absorption edge of iodine, Auger electrons are emitted, leading significant improvement of biological effectiveness. Thus, we have estimated the absorbed dose low-energy secondaries, such as Auger electrons and characteristic X-rays, emitted from iodine as 1.3 × 10−2 Gy/mM/min at BL14B1, depending on iodine concentration.

Research on Drug Efficacy using a Terahertz Metasurface Microfluidic Biosensor Based on Fano Resonance Effect.

Advanced biosensors must exhibit high sensitivity, reliability, and convenience, making them suitable for detecting trace samples in biological or medical applications. Currently, biometric identification is the predominant method in clinical practice, but it is complex and time-consuming. In this study, we propose an optical metasurface utilizing the Fano resonance effect, which exhibits a sharp resonance with a transmittance of 32% at 0.65 THz. The resonance dip has a narrow bandwidth of 0.07 THz and a high Q-factor of 42. This resonance arises from the coupling of bright and dark modes, underpinned by the electromagnetic mechanism of Fano resonance. We integrated the metasurface into a microfluidic platform and fabricated low-temperature gallium arsenide photoconductive antennas (LT-GaAs-PCAs) on both sides of the microfluidics to efficiently generate and detect THz waves, significantly reducing the system's volume. The biosensor's detection limits for Escherichia coli (E. coli) and cefamandole nafate are 5 × 103 cells/mL and 5 μg/mL, respectively. Experimentally, when E. coli and cefamandole nafate solutions were sequentially injected into the microfluidic chip, a blue shift in the spectrum was observed. The sensor measured a 95.2% killing rate of E. coli by 40 μg/mL cefamandole nafate solution, with only a 3% deviation from biological experiments. Additionally, a timed killing experiment using 40 μg/mL cefamandole nafate on E. coli revealed a 93.7% killing rate within 3 min. This research presents a THz microfluidic biosensor with rapid detection, high sensitivity, and enhanced portability and integration, offering a promising approach for biomedical research, including antibiotic efficacy assessment and bacterial concentration monitoring.

Monomethyl Branched-Chain Fatty Acids Suppress M1 Macrophage Polarization via FABP4/PPAR-γ Signaling Pathway.

Monomethyl-branched chain fatty acids (mmBCFAs) are found in a variety of food sources and are of great interest due to their potent antiinflammatory properties. However, most of the current researches have concentrated on the relationship between mmBCFAs and intestinal inflammation, and there is a large gap in the biological mechanisms involved behind their antiinflammatory effects. The present study examines the role of mmBCFAs in modulating macrophage polarization. The results demonstrate that iso-C16:0 significantly inhibits macrophages M1 proinflammatory polarization through regulating FABP4/PPAR-γ pathway. Proteomics and molecular biology experiments verify that metabolic reprogramming is involved in the inhibition of M1 macrophage, referring to the upregulation of fatty acid oxidation, TCA cycle, and oxidative phosphorylation, as well as downregulation of glycolytic flux. In summary, this study offers a novel perspective on the antiinflammatory effects mediated by mmBCFAs.

Development and experimental validation of computational methods for human antibody affinity enhancement.

High affinity is crucial for the efficacy and specificity of antibody. Due to involving high-throughput screens, biological experiments for antibody affinity maturation are time-consuming and have a low success rate. Precise computational-assisted antibody design promises to accelerate this process, but there is still a lack of effective computational methods capable of pinpointing beneficial mutations within the complementarity-determining region (CDR) of antibodies. Moreover, random mutations often lead to challenges in antibody expression and immunogenicity. In this study, to enhance the affinity of a human antibody against avian influenza virus, a CDR library was constructed and evolutionary information was acquired through sequence alignment to restrict the mutation positions and types. Concurrently, a statistical potential methodology was developed based on amino acid interactions between antibodies and antigens to calculate potential affinity-enhanced antibodies, which were further subjected to molecular dynamics simulations. Subsequently, experimental validation confirmed that a point mutation enhancing 2.5-fold affinity was obtained from 10 designs, resulting in the antibody affinity of 2nM. A predictive model for antibody-antigen interactions based on the binding interface was also developed, achieving an Area Under the Curve (AUC) of 0.83 and a precision of 0.89 on the test set. Lastly, a novel approach involving combinations of affinity-enhancing mutations and an iterative mutation optimization scheme similar to the Monte Carlo method were proposed. This study presents computational methods that rapidly and accurately enhance antibody affinity, addressing issues related to antibody expression and immunogenicity.

Adversarial regularized autoencoder graph neural network for microbe-disease associations prediction.

Microorganisms inhabit various regions of the human body and significantly contribute to numerous diseases. Predicting the associations between microbes and diseases is crucial for understanding pathogenic mechanisms and informing prevention and treatment strategies. Biological experiments to determine these associations are time-consuming and costly. Therefore, integrating deep learning with biological networks can efficiently identify potential microbe-disease associations on a large scale. We propose an adversarial regularized autoencoder graph neural network algorithm, named Stacked Adversarial Regularization for Microbe-Disease Associations Prediction (SARMDA), for predicting associations between microbes and diseases. First, we integrate topological structural similarity and functional similarity metrics of microbes and diseases to construct a heterogeneous network. Then, utilizing an autoencoder based on GraphSAGE, we learn both the topological and attribute representations of nodes within the constructed network. Finally, we introduce an adversarial regularized autoencoder graph neural network embedding model to address the inherent limitations of traditional GraphSAGE autoencoders in capturing global information. Under the five-fold cross-validation on microbe-disease pairs, SARMDA was compared with eight advanced methods using the Human Microbe-Disease Association Database (HMDAD) and Disbiome databases. The best area under the ROC curve (AUC) achieved by SARMDA on HMDAD was 0.9891$\pm$0.0057, and the best area under the precision-recall curve (AUPR) was 0.9902$\pm$0.0128. On the Disbiome dataset, the AUC was 0.9328$\pm$0.0072, and the best AUPR was 0.9233$\pm$0.0089, outperforming the other eight MDAs prediction methods. Furthermore, the effectiveness of our model was demonstrated through a detailed analysis of asthma and inflammatory bowel disease cases.

Forecasting and Predicting Stochastic Agent-Based Model Data with Biologically-Informed Neural Networks.

Collective migration is an important component of many biological processes, including wound healing, tumorigenesis, and embryo development. Spatial agent-based models (ABMs) are often used to model collective migration, but it is challenging to thoroughly predict these models' behavior throughout parameter space due to their random and computationally intensive nature. Modelers often coarse-grain ABM rules into mean-field differential equation (DE) models. While these DE models are fast to simulate, they suffer from poor (or even ill-posed) ABM predictions in some regions of parameter space. In this work, we describe how biologically-informed neural networks (BINNs) can be trained to learn interpretable BINN-guided DE models capable of accurately predicting ABM behavior. In particular, we show that BINN-guided partial DE (PDE) simulations can (1) forecast future spatial ABM data not seen during model training, and (2) predict ABM data at previously-unexplored parameter values. This latter task is achieved by combining BINN-guided PDE simulations with multivariate interpolation. We demonstrate our approach using three case study ABMs of collective migration that imitate cell biology experiments and find that BINN-guided PDEs accurately forecast and predict ABM data with a one-compartment PDE when the mean-field PDE is ill-posed or requires two compartments. This work suggests that BINN-guided PDEs allow modelers to efficiently explore parameter space, which may enable data-driven tasks for ABMs, such as estimating parameters from experimental data. All code and data from our study is available at https://github.com/johnnardini/Forecasting_predicting_ABMs .

Long non-coding RNA PVT1 regulates TGF-β and promotes the proliferation, migration and invasion of hypopharyngeal carcinoma FaDu cells

Hypopharyngeal carcinoma is one of the malignant tumors of the head and neck with a particularly poor prognosis. Recurrence and metastasis are important reasons for poor prognosis of hypopharyngeal cancer patients, and malignant proliferation, migration, and invasion of tumor cells are important factors for recurrence and metastasis of hypopharyngeal cancer. Therefore, elucidating hypopharyngeal cancer cells’ proliferation, migration, and invasion mechanism is essential for improving diagnosis, treatment, and prognosis. Plasmacytoma Variant Translocation 1 (PVT1) is considered a potential diagnostic marker and therapeutic target for tumors. However, it remains unclear whether PVT1 is related to the occurrence and development of hypopharyngeal cancer and its specific mechanism. In this study, the promoting effect of PVT1 on the proliferation, migration, and invasion of hypopharyngeal carcinoma FaDu cells was verified by cell biology experiments and animal studies, and it was found that PVT1 inhibited the expression of TGF-β, suggesting that PVT1 may regulate the occurrence and development of hypopharyngeal carcinoma FaDu cells through TGF-β.

Primary Sjogren's Syndrome Associated with Lung Adenocarcinoma: Probing the Potential Common Pathogenic Mechanisms and Experimental Verification.

This study aimed to probe the potential common pathogenic mechanisms linking primary Sjogren's syndrome (pSS) and lung adenocarcinoma (LUAD) through bioinformatics analysis and experimental verification. The relevant genes associated with pSS and LUAD were retrieved from the Gene Expression Omnibus (GEO) database and Genecard database. Subsequently, differentially expressed genes (DEGs) associated with pSS and LUAD were screened as pSS-LUAD-DEGs. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed to elucidate the significant biological functions of pSS-LUAD-DEGs. Core targets were identified by constructing the protein-protein interaction (PPI) network, further assessing hub gene diagnostic accuracy through Receiver Operating Characteristic (ROC) curve analyses. In this study, NOD/Ltj mice served as pSS animal models and were stimulated with particulate matter 2.5 (PM2.5) to generate an inflammatory reaction. Quantitative real-time polymerase chain reaction (qPCR), enzyme-linked immunosorbent assay (ELISA), and western blotting were employed for relevant molecular biology experiment verification. The results revealed through KEGG and GO enrichment analyses indicate that inflammation plays a critical role in linking pSS and LUAD. IL6, CCNA2, JAK2, IL1B, ASPM, CCNB2, NUSAP1, and CEP55 were determined as key targets of pSS-LUAD. BALB/c mice and NOD/Ltj mice exhibited enhanced expression of inflammatory cytokines IL-6 and IL-1β in lung tissues following 21 days of stimulation with PM2.5, activating the JAK2/STAT3 signaling pathway and up-regulating the expression of tumor-associated genes CCNA2, CCNB2, and CEP55, with NOD/Ltj mice exhibiting more pronounced changes than BALB/c mice. This protocol demonstrates that carcinogenesis induced by the pulmonary inflammatory microenvironment may be a key reason for the high incidence of LUAD in pSS patients. Additionally, blocking-related mechanisms may help prevent the occurrence of LUAD in pSS patients.

SPLHRNMTF: robust orthogonal non-negative matrix tri-factorization with self-paced learning and dual hypergraph regularization for predicting miRNA-disease associations

MicroRNAs (miRNAs) have been demonstrated to be closely related to human diseases. Studying the potential associations between miRNAs and diseases contributes to our understanding of disease pathogenic mechanisms. As traditional biological experiments are costly and time-consuming, computational models can be considered as effective complementary tools. In this study, we propose a novel model of robust orthogonal non-negative matrix tri-factorization (NMTF) with self-paced learning and dual hypergraph regularization, named SPLHRNMTF, to predict miRNA-disease associations. More specifically, SPLHRNMTF first uses a non-linear fusion method to obtain miRNA and disease comprehensive similarity. Subsequently, the improved miRNA-disease association matrix is reformulated based on weighted k-nearest neighbor profiles to correct false-negative associations. In addition, we utilize L2,1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L_{2,1}$$\\end{document} norm to replace Frobenius norm to calculate residual error, alleviating the impact of noise and outliers on prediction performance. Then, we integrate self-paced learning into NMTF to alleviate the model from falling into bad local optimal solutions by gradually including samples from easy to complex. Finally, hypergraph regularization is introduced to capture high-order complex relations from hypergraphs related to miRNAs and diseases. In 5-fold cross-validation five times experiments, SPLHRNMTF obtains higher average AUC values than other baseline models. Moreover, the case studies on breast neoplasms and lung neoplasms further demonstrate the accuracy of SPLHRNMTF. Meanwhile, the potential associations discovered are of biological significance.