Biological Experiments Research Articles

Predicting Lactobacillus delbrueckii subsp. bulgaricus-Streptococcus thermophilus interactions based on a highly accurate semi-supervised learning method.

Lactobacillus delbrueckii subsp. bulgaricus (L. bulgaricus) and Streptococcus thermophilus (S. thermophilus) are commonly used starters in milk fermentation. Fermentation experiments revealed that L. bulgaricus-S. thermophilus interactions (LbStI) substantially impact dairy product quality and production. Traditional biological humidity experiments are time-consuming and labor-intensive in screening interaction combinations, an artificial intelligence-based method for screening interactive starter combinations is necessary. However, in the current research on artificial intelligence based interaction prediction in the field of bioinformatics, most successful models adopt supervised learning methods, and there is a lack of research on interaction prediction with only a small number of labeled samples. Hence, this study aimed to develop a semi-supervised learning framework for predicting LbStI using genomic data from 362 isolates (181 per species). The framework consisted of a two-part model: a co-clustering prediction model (based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) dataset) and a Laplacian regularized least squares prediction model (based on K-mer analysis and gene composition of all isolates datasets). To enhance accuracy, we integrated the separate outcomes produced by each component of the two-part model to generate the ultimate LbStI prediction results, which were verified through milk fermentation experiments. Validation through milk fermentation experiments confirmed a high precision rate of 85% (17/20; validated with 20 randomly selected combinations of expected interacting isolates). Our data suggest that the biosynthetic pathways of cysteine, riboflavin, teichoic acid, and exopolysaccharides, as well as the ATP-binding cassette transport systems, contribute to the mutualistic relationship between these starter bacteria during milk fermentation. However, this finding requires further experimental verification. The presented model and data are valuable resources for academics and industry professionals interested in screening dairy starter cultures and understanding their interactions.

MisORFPred: A Novel Method to Mine Translatable sORFs in Plant Pri-miRNAs Using Enhanced Scalable k-mer and Dynamic Ensemble Voting Strategy.

The primary microRNAs (pri-miRNAs) have been observed to contain translatable small open reading frames (sORFs) that can encode peptides as an independent element. Relevant studies have proven that those of sORFs are of significance in regulating the expression of biological traits. The existing methods for predicting the coding potential of sORFs frequently overlook this data or categorize them as negative samples, impeding the identification of additional translatable sORFs in pri-miRNAs. In light of this, a novel method named misORFPred has been proposed. Specifically, an enhanced scalable k-mer (ESKmer) that simultaneously integrates the composition information within a sequence and distance information between sequences is designed to extract the nucleotide sequence features. After feature selection, the optimal features and several machine learning classifiers are combined to construct the ensemble model, where a newly devised dynamic ensemble voting strategy (DEVS) is proposed to dynamically adjust the weights of base classifiers and adaptively select the optimal base classifiers for each unlabeled sample. Cross-validation results suggest that ESKmer and DEVS are essential for this classification task and could boost model performance. Independent testing results indicate that misORFPred outperforms the state-of-the-art methods. Furthermore, we execute misORFPerd on the genomes of various plant species and perform a thorough analysis of the predicted outcomes. Taken together, misORFPred is a powerful tool for identifying the translatable sORFs in plant pri-miRNAs and can provide highly trusted candidates for subsequent biological experiments.

Scaled Brownian motion with random anomalous diffusion exponent

The scaled Brownian motion (SBM) is regarded as one of the paradigmatic random processes, characterized by anomalous diffusion through the diffusion exponent. It is a Gaussian, self-similar process with independent increments and has found applications across various fields, from turbulence and stochastic hydrology to biophysics. In our paper, inspired by recent single particle tracking biological experiments, we introduce a process called scaled Brownian motion with random exponent (SBMRE), which retains the key features of SBM at the level of individual trajectories, but with anomalous diffusion exponents that vary randomly across trajectories.We discuss the main probabilistic properties of SBMRE, including its probability density function (pdf) and the qth absolute moment. Additionally, we present the expected value of the time-averaged mean squared displacement (TAMSD) and the ergodicity breaking parameter. Furthermore, we analyze the pdf of the first hitting time in a semi-infinite domain, the martingale property of SBMRE, and its stochastic exponential. As special cases, we consider two distributions for the anomalous diffusion exponent, namely the mixture of two point distributions and beta distribution, and explore the asymptotics of the corresponding characteristics. Theoretical results for SBMRE are validated through numerical simulations and compared with the analogous characteristics of SBM.

Soil Microorganism Interactions under Biological Fumigations Compared with Chemical Fumigation.

Biological fumigation, a potential alternative to chemical fumigation, shows a wide range of prospective applications. In this study, we carried out biological fumigation experiments to evaluate its effect on alleviating consecutive cropping problems (CRPs) when compared with chemical fumigation. We designed five treatments, namely, CR (no treatment), LN (chemical fumigation with lime nitrogen), Ta (fumigation with marigold), Ra (fumigation with radish), and Br (fumigation with mustard), for soils for replanting eggplant and measured the crop's growth status, soil bacterial and fungal communities, and soil physicochemical properties. The results showed that the Br and Ra treatments formed similar microbial communities, while the Ta treatment formed unique microbial communities. The genera Olpidiomycota and Rozellomycota could be used as indicator species for the transformation process of soil microbial communities after the Br and Ta treatments, respectively. When compared with the CR and LN treatments, the soil's physicochemical properties were optimized under the Br treatment, and the soil organic matter content increased by 64.26% and 79.22%, respectively. Moreover, under the Br treatment, the soil's biological properties enhanced the bacterial and fungal alpha diversity, and the saprotrophic fungi increased with the depletion of pathotrophic fungi, while some specific probiotic microorganisms (such as Olpidiomycota, Microascales, Bacillus, etc.) were significantly enriched. In contrast, under the Ta treatment, soil nutrient levels decreased and the soil's biological indices deteriorated, whereas the bacterial diversity decreased and the pathogenic fungi increased. Among these three biological fumigation methods, the Br pre-treatment was the best way to alleviate the crop's CRPs and may be a good substitute for chemical fumigation in some situations. However, the Ta treatment also had some risks, such as the loss of land quality and reduced productivity.

Internal radiation dose estimates in organs of Wistar rats exposed to sprayed neutron-activated 31SiO2 microparticles: first results of international multicenter study.

Neutron-activated 31Si is an almost pure beta emitter and is one of the short-lived radionuclides, including beta-gamma emitter 56Mn, which were created in a form of residual radioactivity in the early period after the atomic bombing of Hiroshima and Nagasaki. The features of the biological effects of internal irradiation by these radionuclides are a subject of scientific discussions and research. The publication presents data on internal radiation doses in experimental Wistar rats that were exposed to sprayed neutron-activated microparticles of 31SiO2. Doses of internal radiation could be conditionally divided into three groups according to their values. It has been found that elevated values of internal radiation doses in rats' organs/tissues as a result of exposure to sprayed 31SiO2 microparticles with initial activity of 3.2 × 107Bq varied from 10 to 120mGy (eyes, lungs, skin, stomach, jejunum, large intestine). The moderate dose values were in the range from 1.9 to 3.7mGy (trachea, esophagus, ileum). The smallest doses were received by the kidney, testis, blood, cerebellum, heart, liver, cerebrum, bladder, spleen and thymus (from 0.11 to 0.94mGy). The obtained data are important for interpreting the results of ongoing and planned biological experiments with 31SiO2 microparticles-in comparison with the previously published data on features of biological effects caused by beta-gamma emitting 56MnO2 neutron-activated microparticles.

Molecular hydrogen as a biological marker and a drug

The aim of the review was to examine the main characteristics of hydrogen ion metabolism and its hemostatic function in the human body. For diagnostic purposes, molecular hydrogen (H2) is considered a biological marker. The results of the analysis of therapeutic programs for the clinical use of hydrogen are presented. The use of H2 as a drug in the treatment of more than 170 diseases is also discussed.Conclusion. The use of H2 in various fields of medicine, including oncological diseases, is actively studied based on experimental biological experiments and clinical trials.

The pathogenic germline ETV4 P433L mutation identified in multiple primary lung cancer affect tumor stem-like property by Wnt/β-catenin pathway

The occurrence of multiple primary lung cancer (MPLC) has witnessed a significant surge in recent years within the Chinese population. MPLC is distinguished by its potential genetic susceptibility and notable genetic heterogeneity. Investigating the etiology of MPLC holds substantial clinical importance.The whole genome sequencing (WGS) and genome-wide linkage analysis were performed in a family affected by a dominant form of lung abnormalities. Specifically, five family members were diagnosed with MPLC, while nine members had pulmonary nodules and one normal member. To confirm the potential pathogenic germline mutations sites, Sanger sequencing was performed in an additional 162 MPLC family patients. Furthermore, molecular biology experiments were conducted to investigate the function and the mechanism of the identified pathogenic mutation site in lung cancer A549 and H322, both in vitro and in vivo. Linkage analysis revealed the presence of shared genomic regions among affected family members. Subsequent exome sequencing identified a deleterious variant within these linkage intervals, specifically a heterozygous mutation in ETS-oncogene transcription factors 4 (ETV4). This particular variant was found in affected family members at a rate of 13 out of 15 individuals. Furthermore, ETV4 P433L mutation could be detected in an additional MPLC family patients and mutation frequency was 3.7% (6 out of 162). The ETV4 P433L mutations site was introduced into lung cancer cell lines, resulting in altered migration and stem-like properties of the cancer cells. Further investigation revealed that the activation of the Wnt/β-catenin signaling pathway, which is associated with stemness, could be attributed to the presence of the ETV4 P433L mutation, suggesting its involvement in tumor promotion. A novel pathogenic germline mutation, ETV4 P433L, was identified in a dominant MPLC family, with a mutation rate of 3.7% among MPLC family patients. The ETV4 P433L mutation was found to impact the stem-like properties and migration of tumors through Wnt/β-catenin signaling pathway.

Compression-based inference of network motif sets.

Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement fundamental logical and computational circuits and are referred to as "building blocks of complex networks". Their well-defined structures and small sizes also enable the testing of their functions in synthetic and natural biological experiments. Here, we develop a framework for motif mining based on lossless network compression using subgraph contractions. This provides an alternative definition of motif significance which allows us to compare different motifs and select the collectively most significant set of motifs as well as other prominent network features in terms of their combined compression of the network. Our approach inherently accounts for multiple testing and correlations between subgraphs and does not rely on a priori specification of an appropriate null model. It thus overcomes common problems in hypothesis testing-based motif analysis and guarantees robust statistical inference. We validate our methodology on numerical data and then apply it on synaptic-resolution biological neural networks, as a medium for comparative connectomics, by evaluating their respective compressibility and characterize their inferred circuit motifs.

Integrated Biological Experiments and Proteomic Analyses of Nicotiana tabacum Xylem Sap Revealed the Host Response to Tomato Spotted Wilt Orthotospovirus Infection.

The plant vascular system is not only a transportation system for delivering nutrients but also a highway transport network for spreading viruses. Tomato spotted wilt orthotospovirus (TSWV) is among the most destructive viruses that cause serious losses in economically important crops worldwide. However, there is minimal information about the long-distance movements of TSWV in the host plant vascular system. In this this study, we confirm that TSWV virions are present in the xylem as observed by transmission electron microscopy (TEM). Further, a quantitative proteomic analysis based on label-free methods was conducted to reveal the uniqueness of protein expression in xylem sap during TSWV infection. Thus, this study identified and quantified 3305 proteins in two groups. Furthermore, TSWV infection induced three viral structural proteins, N, Gn and Gc, and 315 host proteins differentially expressed in xylem (163 up-regulated and 152 down-regulated). GO enrichment analysis showed up-regulated proteins significantly enriched in homeostasis, wounding, defense response, and DNA integration terms, while down-regulated proteins significantly enriched in cell wall biogenesis/xyloglucan metabolic process-related terms. KEGG enrichment analysis showed that the differentially expressed proteins (DEPs) were most strongly associated with plant-pathogen interaction, MAPK signaling pathway, and plant hormone signal transduction. Cluster analysis of DEPs function showed the DEPs can be categorized into cell wall metabolism-related proteins, antioxidant proteins, PCD-related proteins, host defense proteins such as receptor-like kinases (RLKs), salicylic acid binding protein (SABP), pathogenesis related proteins (PR), DNA methylation, and proteinase inhibitor (PI). Finally, parallel reaction monitoring (PRM) validated 20 DEPs, demonstrating that the protein abundances were consistent between label-free and PRM data. Finally, 11 genes were selected for RT-qPCR validation of the DEPs and label-free-based proteomic analysis concordant results. Our results contribute to existing knowledge on the complexity of host plant xylem system response to virus infection and provide a basis for further study of the mechanism underlying TSWV long-distance movement in host plant vascular system.

Mechanisms of neural infiltration-mediated tumor metabolic reprogramming impacting immunotherapy efficacy in non-small cell lung cancer

BackgroundCurrent evidence underlines the active role of neural infiltration and axonogenesis within the tumor microenvironment (TME), with implications for tumor progression. Infiltrating nerves stimulate tumor growth and dissemination by secreting neurotransmitters, whereas tumor cells influence nerve growth and differentiation through complex interactions, promoting tumor progression. However, the role of neural infiltration in the progression of non-small cell lung cancer (NSCLC) remains unclear.MethodsThis study employs the techniques of immunohistochemistry, immunofluorescence, RNA sequencing, molecular biology experiments, and a murine orthotopic lung cancer model to deeply analyze the specific mechanisms behind the differential efficacy of NSCLC immunotherapy from the perspectives of neuro-tumor signal transduction, tumor metabolism, and tumor immunity.ResultsThis study demonstrates that nerve growth factor (NGF) drives neural infiltration in NSCLC, and 5-hydroxytryptamine (5-HT), which is secreted by nerves, is significantly elevated in tumors with extensive neural infiltration. Transcriptome sequencing revealed that 5-HT enhanced glycolysis in NSCLC cells. Pathway analysis indicated that 5-HT activated the PI3K/Akt/mTOR pathway, promoting tumor metabolic reprogramming. This reprogramming exacerbated immunosuppression in the TME. Neutralizing 5-HT-mediated metabolic reprogramming in tumor immunity enhanced the efficacy of PD-1 monoclonal antibody treatment in mice.ConclusionsThe findings of this study provide a novel perspective on the crosstalk between nerves and lung cancer cells and provide insights into further investigations into the role of nerve infiltration in NSCLC progression.

Identification of a senescence-related transcriptional signature to uncover molecular subtypes and key genes in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a cancer caused by abnormal cell growth due to faulty signal transduction. Cells secrete tumor suppressor factors in response to potential carcinogenic signals, inducing cellular senescence (CS) as a countermeasure. However, accurately measuring CS levels in different types of tumors is challenging due to tumor heterogeneity and the lack of universal and specific CS markers. Machine learning has revealed unique molecular traits in HCC patients, leading to clinical advantages. More research is needed to understand senescence-related molecular features in these patients. In this study, the gene expression profile features of patients with HCC were analyzed by integrating single-cell RNA sequencing and bulk RNA-seq datasets from HCC samples. The analysis identified the senescence-related pathways exhibiting HCC specificity. Subsequently, genes from these pathways were used to identify senescence-related molecular subtypes in HCC, showing significant variations in biological and clinical attributes. An HCC-specific CS risk model developed in this study revealed substantial associations between the patients' CS scores and prognosis grouping, clinical staging, immune infiltration levels, immunotherapy response, and drug sensitivity levels. Within the constructed model, G6PD was identified as a key gene, potentially serving as a senescence-related target in liver cancer. Molecular biology experiments demonstrated that overexpression of G6PD effectively promotes the proliferative, invasive, and migration capacities of HepG2 and SK-HEP-1 cells. In conclusion, this analysis offers a valuable framework for understanding senescence in HCC and introduces a new biomarker. These findings improve our understanding of senescence in HCC and have potential for future research.

Leonurine Inhibits Hepatic Lipid Synthesis to Ameliorate NAFLD via the ADRA1a/AMPK/SCD1 Axis.

Leonurine is a natural product unique to the Lamiaceae plant Leonurus japonicus Houtt., and it has attracted attention due to its anti-oxidative stress, anti-apoptosis, anti-fibrosis, and metabolic regulation properties. Also, it plays an important role in the prevention and treatment of nonalcoholic fatty liver disease (NAFLD) through a variety of biological mechanisms, but its mechanism of action remains to be elucidated. Therefore, this study aims to preliminarily explore the mechanisms of action of leonurine in NAFLD. Mice were randomly divided into four groups: the normal control (NC) group, the Model (M) group, the leonurine treatment (LH) group, and the fenofibrate treatment (FB) group. The NAFLD model was induced by a high-fat high-sugar diet (HFHSD) for 12 weeks, and liver pathological changes and biochemical indices were observed after 12 weeks. Transcriptomic analysis results indicated that leonurine intervention reversed the high-fat high-sugar diet-induced changes in lipid metabolism-related genes such as stearoyl-CoA desaturase 1 (Scd1), Spermine Synthase (Sms), AP-1 Transcription Factor Subunit (Fos), Oxysterol Binding Protein Like 5 (Osbpl5), and FK506 binding protein 5 (Fkbp5) in liver tissues. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis results suggest that leonurine may exert its lipid-lowering effects through the AMP-activated protein kinase (AMPK) signaling pathway. Liver lipidomic analysis showed that leonurine could alter the abundance of lipid molecules related to fatty acyl (FAs) and glycerophospholipids (GPs) such as TxB3, carnitine C12-OH, carnitine C18:1-OH, and LPC (20:3/0:0). Molecular biology experiments and molecular docking techniques verified that leonurine might improve hepatic lipid metabolism through the alpha-1A adrenergic receptor (ADRA1a)/AMPK/SCD1 axis. In summary, the present study explored the mechanism by which leonurine ameliorated NAFLD by inhibiting hepatic lipid synthesis via the ADRA1a/AMPK/SCD1 axis.

Plant lncRNA-miRNA Interaction Prediction Based on Counterfactual Heterogeneous Graph Attention Network.

Identifying interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) provides a new perspective for understanding regulatory relationships in plant life processes. Recently, computational methods based on graph neural networks (GNNs) have been widely employed to predict lncRNA-miRNA interactions (LMIs), which compensate for the inadequacy of biological experiments. However, the low-semantic and noise of graph limit the performance of existing GNN-based methods. In this paper, we develop a novel Counterfactual Heterogeneous Graph Attention Network (CFHAN) to improve the robustness to against the noise and the prediction of plant LMIs. Firstly, we construct a real-world based lncRNA-miRNA (L-M) heterogeneous network. Secondly, CFHAN utilizes the node-level attention, the semantic-level attention, and the counterfactual links to enhance the node embeddings learning. Finally, these embeddings are used as inputs for Multilayer Perceptron (MLP) to predict the interactions between lncRNAs and miRNAs. Evaluating our method on a benchmark dataset of plant LMIs, CFHAN outperforms five state-of-the-art methods, and achieves an average AUC and average ACC of 0.9953 and 0.9733, respectively. This demonstrates CFHAN's ability to predict plant LMIs and exhibits promising cross-species prediction ability, offering valuable insights for experimental LMI researches.

An adaptive null proportion estimator for false discovery rate control

SUMMARY False discovery rate (FDR) is a commonly used criterion in multiple testing and the BenjaminiHochberg (BH) procedure is a standard approach for FDR control. To boost power, the adaptive BH procedure has been proposed by incorporating null proportion estimators, among which Storey’s estimator has gained substantial popularity. The performance of Storey’s estimator hinges on a critical hyper-parameter, where a pre-fixed configuration may lack power and existing data-driven hyper-parameters compromise the FDR control. In this work, we propose a novel class of adaptive hyper-parameters and establish the FDR control of the associated adaptive BH procedure using a martingale argument. Within this class of data-driven hyper-parameters, we further present a specific configuration designed to maximize the number of rejections and characterize its convergence to the optimal hyper-parameter under a mixture model. Our proposal exhibits significant power gains particularly in cases with a conservative null distribution common in composite null testing or a moderate proportion of weak non-nulls typically observed in biological experiments with enrichment processes.

Artificial Intelligence and Machine Learning with Moment Generating Functions to Enhance Biological Count Data Analysis

This research presents artificial intelligence with supervised machine learning to determine the median count given training data from biological laboratory experimentation. After determining the central moment of the median, machine learning is then used to predict the population median value. When computer programs learn they access available data, then autonomously analyze the information to make informed, data-driven decisions. Moment generating function restrictions identify a parameter of interest by restricting the expected value of the moment generating function. This research proposes a theoretical foundation for achieving such predictions. A summary of the key elements underlying the statistical power of the Mann– Whitney test, a nonparametric hypothesis test for the median of count data, is also presented. A nonparametric approach allows for accurate predictions while relaxing distributional assumptions such as normality.

Systematic Comparison of CRISPR and shRNA Screens to Identify Essential Genes Using a Graph-Based Unsupervised Learning Model.

Generally, essential genes identified using shRNA and CRISPR are not always the same, raising questions about the choice between these two screening platforms. To address this, we systematically compared the performance of CRISPR and shRNA to identify essential genes across different gene expression levels in 254 cell lines. As both platforms have a notable false positive rate, to correct this confounding factor, we first developed a graph-based unsupervised machine learning model to predict common essential genes. Furthermore, to maintain the unique characteristics of individual cell lines, we intersect essential genes derived from the biological experiment with the predicted common essential genes. Finally, we employed statistical methods to compare the ability of these two screening platforms to identify essential genes that exhibit differential expression across various cell lines. Our analysis yielded several noteworthy findings: (1) shRNA outperforms CRISPR in the identification of lowly expressed essential genes; (2) both screening methodologies demonstrate strong performance in identifying highly expressed essential genes but with limited overlap, so we suggest using a combination of these two platforms for highly expressed essential genes; (3) notably, we did not observe a single gene that becomes universally essential across all cancer cell lines.

Transmutation Based on Cold Fusion Affects the Electrochemical and Biological Experiments

Transmutation Based on Cold Fusion Affects the Electrochemical and Biological Experiments

Optical tweezing of microparticles and cells using silicon-photonics-based optical phased arrays

Integrated optical tweezers have the potential to enable highly-compact, low-cost, mass-manufactured, and broadly-accessible optical manipulation when compared to standard bulk-optical tweezers. However, integrated demonstrations to date have been fundamentally limited to micron-scale standoff distances and, often, passive trapping functionality, making them incompatible with many existing applications and significantly limiting their utility, especially for biological studies. In this work, we demonstrate optical trapping and tweezing using an integrated OPA for the first time, increasing the standoff distance of integrated optical tweezers by over two orders of magnitude compared to prior demonstrations. First, we demonstrate trapping of polystyrene microspheres 5 mm above the surface of the chip and calibrate the trap force. Next, we show tweezing of polystyrene microspheres in one dimension by non-mechanically steering the trap by varying the input laser wavelength. Finally, we use the OPA tweezers to demonstrate, to the best of our knowledge, the first cell experiments using single-beam integrated optical tweezers, showing controlled deformation of mouse lymphoblast cells. This work introduces a new modality for integrated optical tweezers, significantly expanding their utility and compatibility with existing applications, especially for biological experiments.

Exploring the anti-NSCLC mechanism of phillyrin targeting inhibition of theHSP90-AKT pathway.

Phillyrin (PHN), derived from the dried fruit of Forsythia suspensa (Thunb.) Vahl, is a kind of Chinese herbal medicine with the effect of clearing heat, and has been used in China for thousands of years in treating various tumors. However, the mechanism of its main components on non-small cell lung cancer (NSCLC) remains unclear. PHN is a distinct component extracted from Forsythia suspensa with promising anti-cancer activity against various tumor types. This study sought to elucidate the promising effects of PHN on NSCLC. Based on network pharmacology results, we identified potential PHN targets and pathways for NSCLC treatment. CCK-8 assay, wound healing assay, apoptosis assay, western blot, and in vivo experiments verified the inhibitory effect of PHN on NSCLC. Network pharmacology identified 160 potential PHN targets, 955 NSCLC-related targets, and 54 common targets, along with 132 pathways and 2 core genes. Biological experiments demonstrated that PHN significantly inhibited the growth and migration of A549 and LLC cells while promoting their apoptosis. Western blot analysis revealed down-regulation of AKT, HSP90AA1, and CDC37 expression, suggesting that PHN inhibits A549 and LLC cell proliferation by down-regulating the HSP90-AKT pathway. In vivo experiments confirmed that PHN significantly inhibited NSCLC growth with low toxicity. This study, using network pharmacology and biological experiments, verified the effectiveness of PHN against NSCLC through the HSP90-AKT pathway. These findings provide a foundation for further research and analysis.

In silico modeling of reservoir-based predictive coding in biological neuronal networks on microelectrode arrays

Reservoir computing and predictive coding together yield a computational model for exploring how neuronal dynamics in the mammalian cortex underpin temporal signal processing. Here, we construct an in-silico model of biological neuronal networks grown on microelectrode arrays and explore their computing capabilities through a sine wave prediction task in a reservoir-based predictive coding framework. Our results show that the time interval between stimulation pulses is a critical determinant of task performance. Additionally, under a fixed feedback latency, pulse amplitude modulation is a favorable encoding scheme for input signals. These findings provide practical guidelines for future implementation of the model in biological experiments.