Analysis Platform Research Articles

Low-Cost Approaches in Neuroscience to Teach Machine Learning Using a Cockroach Model.

In an effort to increase access to neuroscience education in underserved communities, we created an educational program that utilizes a simple task to measure place preference of the cockroach (Gromphadorhina portentosa) and the open-source free software, SLEAP Estimates Animal Pose (SLEAP) to quantify behavior. Cockroaches (n = 18) were trained to explore a linear track for 2 min while exposed to either air, vapor, or vapor with nicotine from a port on one side of the linear track over 14 d. The time the animal took to reach the port was measured, along with distance traveled, time spent in each zone, and velocity. As characterizing behavior is challenging and inaccessible for nonexperts new to behavioral research, we created an educational program using the machine learning algorithm, SLEAP, and cloud-based (i.e., Google Colab) low-cost platforms for data analysis. We found that SLEAP was within a 0.5% margin of error when compared with manually scoring the data. Cockroaches were found to have an increased aversive response to vapor alone compared with those that only received air. Using SLEAP, we demonstrate that the x-y coordinate data can be further classified into behavior using dimensionality-reducing clustering methods. This suggests that the linear track can be used to examine nicotine preference for the cockroach, and SLEAP can provide a fast, efficient way to analyze animal behavior. Moreover, this educational program is available for free for students to learn a complex machine learning algorithm without expensive hardware to study animal behavior.Significance Statement This method demonstrates a novel utilization of machine learning using free cloud-based programming to analyze cockroach behavior in a linear track. This educational program can be implemented in the classroom as a low-cost tool to teach neuroscience and machine learning. Further, implementing these computational tools allows students to explore important questions in behavioral neuroscience, such as learning and memory, drug seeking, and exploratory locomotor behavior.

A Graph-Based Machine-Learning Approach Combined with Optical Measurements to Understand Beating Dynamics of Cardiomyocytes.

The development of computational models for the prediction of cardiac cellular dynamics remains a challenge due to the lack of first-principled mathematical models. We develop a novel machine-learning approach hybridizing physics simulation and graph networks to deliver robust predictions of cardiomyocyte dynamics. Embedded with inductive physical priors, the proposed constraint-based interaction neural projection (CINP) algorithm can uncover hidden physical constraints from sparse image data on a small set of beating cardiac cells and provide robust predictions for heterogenous large-scale cell sets. We also implement an in vitro culture and imaging platform for cellular motion and calcium transient analysis to validate the model. We showcase our model's efficacy by predicting complex organoid cellular behaviors in both in silico and in vitro settings.

Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform.

The importance of maintaining data privacy and complying with regulatory requirements is highlighted especially when sharing omic data between different research centers. This challenge is even more pronounced in the scenario where a multi-center effort for collaborative omics studies is necessary. OmicSHIELD is introduced as an open-source tool aimed at overcoming these challenges by enabling privacy-protected federated analysis of sensitive omic data. In order to ensure this, multiple security mechanisms have been included in the software. This innovative tool is capable of managing a wide range of omic data analyses specifically tailored to biomedical research. These include genome and epigenome wide association studies and differential gene expression analyses. OmicSHIELD is designed to support both meta- and mega-analysis, so that it offers a wide range of capabilities for different analysis designs. We present a series of use cases illustrating some examples of how the software addresses real-world analyses of omic data.

Quantitative Evaluation of Multi-Channel Campaign Effectiveness Using Analytical Tools

As a result, more businesses are harnessing multi-channel marketing campaigns to both reach and engage customers spanning multiple channels in this competitive digital arena. Whether these campaigns succeed ultimately comes down to one thing: the ability to analyze and assess their impact in a holistic, data-driven way. This paper discusses how to measure multi-channel marketing campaigns quantitatively using various analytical tools. It focuses on the different methods businesses can use, including A/B testing, attribution models and advanced analytics platforms, to understand campaign performance across multiple touchpoints — from social media to email to search engines and websites. It also covers the confusion about collecting data from several sources, shines light on how to best avail and integrate analytical tools, talks about predictive analytics & machine learning and how can these technologies enhance campaign results. With these tools, companies can get actionable insights that allow them to refine their marketing campaigns, provide effective customer engagement or even make the most of return on investment (ROI) In addition, the paper explores future directions of multi-channel campaign measurement with a trend toward real- time analytics and AI in marketing decision-making. It touches upon how the grow in dependence on data point integration across a variety of marketing channels and the multi-touch for customer journey tracking always leads to value-added strategic decisions. As the technology grows, and businesses understand their own campaign efforts, they can not only fine-tune their ongoing campaigns but also obtain insights into the future trends and outcomes— thereby improving its long-term marketing results. Keywords Multi-Channel Marketing, Campaign Effectiveness, Analytical Tools, A/B Testing, Attribution Models, Predictive Analytics, Machine Learning, ROI, Real-Time Analytics, Artificial Intelligence.

Exploring the Intersection of Brands and Linguistics: A Comprehensive Bibliometric Study

Branding has emerged as a multidiscipline with complication, conveyed by integrated digital technologies, AI, and rich linguistic approaches. This paper depicts the research landscape at the juncture of branding and linguistics, supported by VOSviewer, Biblioshiny, and CiteSpace in pinpointing key trends, themes, and collaboration networks. Drawing on data collected from Scopus, this study investigates the shifts in traditional branding strategies caused by technologies of machine learning, sentiment analysis, and social media platforms. It highlights the contributions made by leading authors, major publication sources, and patterns of international collaboration in influential works. Finally, thematic and co-occurrence analysis shows significant movement into data-based and computational approaches. This also points out some research gaps in the field of digital branding’s long- term effects, ethical considerations of AI-photo strategies, and multilingual branding approaches that provide a useful basis for future research and practical application innovation in the field of branding.

Differences in second trimester risk estimates for trisomy 21 between Maglumi X3/Preaccu and Immulite/Prisca systems

Abstract Objectives Maternal serum alpha-fetoprotein (AFP), human chorionic gonadotropin (HCG) or free βHCG, and unconjugated estriol (uE3) concentrations are used to screen trisomy 21 in the second trimester. The performance of different analytical platforms has an impact on individual risk estimates. The aim of this study is to compare the multiple of median (MoM) values and risk estimates generated by Maglumi X3 analyzer/Preaccu software with the Immulite 2000 XPi device/Prisca software. Methods 164 pregnant women (including 20 pregnants with risk estimates above ≥1 in 250 for trisomy 21) analyzed with both platforms. Results Passing–Bablok indicated proportional bias (0.75 [95 % CI 0.70 to 0.82]) between AFP MoMs and both systematic (−0.20 [95 % CI –0.33 to −0.05]) and proportional (1.25 [95 % CI 1.06 to 1.44]) differences between the HCG/free βHCG MoMs, respectively. No significant differences (p=0.070) were present between calculated individual risks by both of the programmes (estimated median risk with Immulite/Prisca system was 1 in 1890 and 1 in 1220 with Maglumi X3/Preaccu system). The triple test result for three pregnant women was negative with the Prisca program, it was positive with the Preaccu. Conclusions Second trimester screening performance of Maglumi X3/Preaccu system achieves comparable performance. Determining regional median values before using will provide more accurate and reliable results.

Liang-Ge-San Decoction Ameliorates Acute Respiratory Distress Syndrome via Suppressing p38MAPK-NF-κ B Signaling Pathway.

To explore the potential effects and mechanisms of Liang-Ge-San (LGS) for the treatment of acute respiratory distress syndrome (ARDS) through network pharmacology analysis and to verify LGS activity through biological experiments. The key ingredients of LGS and related targets were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. ARDS-related targets were selected from GeneCards and DisGeNET databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using the Metascape Database. Molecular docking analysis was used to confirm the binding affinity of the core compounds with key therapeutic targets. Finally, the effects of LGS on key signaling pathways and biological processes were determined by in vitro and in vivo experiments. A total of LGS-related targets and 496 ARDS-related targets were obtained from the databases. Network pharmacological analysis suggested that LGS could treat ARDS based on the following information: LGS ingredients luteolin, wogonin, and baicalein may be potential candidate agents. Mitogen-activated protein kinase 14 (MAPK14), recombinant V-Rel reticuloendotheliosis viral oncogene homolog A (RELA), and tumor necrosis factor alpha (TNF-α) may be potential therapeutic targets. Reactive oxygen species metabolic process and the apoptotic signaling pathway were the main biological processes. The p38MAPK/NF-κ B signaling pathway might be the key signaling pathway activated by LGS against ARDS. Moreover, molecular docking demonstrated that luteolin, wogonin, and baicalein had a good binding affinity with MAPK14, RELA, and TNF α. In vitro experiments, LGS inhibited the expression and entry of p38 and p65 into the nucleation in human bronchial epithelial cells (HBE) cells induced by LPS, inhibited the inflammatory response and oxidative stress response, and inhibited HBE cell apoptosis (P<0.05 or P<0.01). In vivo experiments, LGS improved lung injury caused by ligation and puncture, reduced inflammatory responses, and inhibited the activation of p38MAPK and p65 (P<0.05 or P<0.01). LGS could reduce reactive oxygen species and inflammatory cytokine production by inhibiting p38MAPK/NF-κ B signaling pathway, thus reducing apoptosis and attenuating ARDS.

Circulating Extracellular Vesicles as Promising Biomarkers for Precession Diagnostics: A Perspective on Lung Cancer.

Extracellular vesicles (EVs) have emerged as promising biomarkers in liquid biopsy, owing to their ubiquitous presence in bodily fluids and their ability to carry disease-related cargo. Recognizing their significance in disease diagnosis and treatment, substantial efforts have been dedicated to developing efficient methods for EV isolation, detection, and analysis. EVs, heterogeneous membrane-encapsulated vesicles secreted by all cells, contain bioactive substances capable of modulating recipient cell biology upon internalization, including proteins, lipids, DNA, and various RNAs. Their prevalence across bodily fluids has positioned them as pivotal mediators in physiological and pathological processes, notably in cancer, where they hold potential as straightforward tumor biomarkers. This review offers a comprehensive examination of advanced nanotechnology-based techniques for detecting lung cancer through EV analysis. It begins by providing a brief overview of exosomes and their role in lung cancer progression. Furthermore, this review explores the evolving landscape of EV isolation and cargo analysis, highlighting the importance of characterizing specific biomolecular signatures within EVs for improved diagnostic accuracy in lung cancer patients. Innovative strategies for enhancing the sensitivity and specificity of EV isolation and detection, including the integration of microfluidic platforms and multiplexed biosensing technologies are summarized. The discussion then extends to key challenges associated with EV-based liquid biopsies, such as the standardization of isolation and detection protocols and the establishment of robust analytical platforms for clinical translation. This review highlights the transformative impact of EV-based liquid biopsy in lung cancer diagnosis, heralding a new era of personalized medicine and improved patient care.

Comparing numerical simulation methods for evaluating hysteretic response of self-centering shear walls reinforced with SMA-ECC under cyclic loading

Under the action of earthquake, the traditional shear wall structure has the problems of excessive residual deformation and macroscopic cracks. The SMA-ECC composite material formed by the combination of smart materials such as shape memory alloy (SMA) and high ductility engineered cementitious composite (ECC) not only has good energy dissipation capacity, but also can obtain superior self-centering ability and damage self-healing ability, thereby improving the seismic performance and durability of shear wall structures. In this paper, in order to explore the nonlinear behavior of the self-centering shear wall under earthquake action and accurately simulate it, ECC and SMA material tests are carried out to establish the uniaxial constitutive relationship of the material. Using OpenSees finite element analysis platform, considering the micro and macro perspectives, a variety of finite element models are established, namely layered shell model, fiber model and multi-vertical rod model. Combined with the low-cycle reciprocating loading test of shear walls, the numerical simulation results are compared with the experimental data. By considering the parameters such as yield load, peak load and simulation time, the feasibility, stability and calculation efficiency of these models are evaluated. The results show that among the three models, the model based on fiber element can capture well the hysteretic response of new materials, and simulate the hysteretic performance, pinch effect and stiffness degradation of various shear wall members. The simulated values and experimental values can be well matched. Compared with the microscopic model, the computational efficiency of the fiber model is greatly improved, and the model has good feasibility, accuracy and efficiency.This work provides a basis for further research on the optimization of design parameters of SMA-ECC shear wall.

Implementation and evaluation of digital twin framework for Internet of Things based healthcare systems

AbstractThe integration of digital twins (DTs) in healthcare is critical but remains limited in real‐time patient monitoring due to challenges in achieving low‐latency telemetry transmission and efficient resource management. This paper addresses these limitations by presenting a novel cloud‐based DT framework that optimises real‐time healthcare monitoring, providing a timely solution for critical healthcare needs. The framework incorporates a Pyomo‐based dynamic optimisation model, which reduces telemetry latency by 32% and improves response time by 52%, surpassing existing systems. Leveraging low‐cost, low‐latency multimodal sensors, the system continuously monitors critical physiological parameters, including SpO2, heart rate, and body temperature, enabling proactive health interventions. A DT definition language (Digital Twin Definition Language)‐based time series analysis and twin graph platform further enhance sensor connectivity and scalability. Additionally, the integration of machine learning (ML) strengthens predictive accuracy, achieving 98% real‐time accuracy and 99.58% under cross‐validation (cv = 20) using the XGBoost algorithm. Empirical results demonstrate substantial improvements in processing time, system stability, and learning capacity, with real‐time predictions completed in 17 ms. This framework represents a significant advancement in healthcare monitoring, offering a responsive and scalable solution to latency and resource constraints in real‐time applications. Future research could explore incorporating additional sensors and advanced ML models to further expand its impact in healthcare applications.

Network Pharmacology and In Vivo Experimental Verification of the Mechanism of the Qing'e Pill for Treating Intervertebral Disc Degeneration.

The Qing'e Pill (QEP) is widely used to alleviate low back pain and sciatica caused by Intervertebral Disc Degeneration (IDD). However, its active components, key targets, and molecular mechanisms are not fully understood. The aim of this study is to elucidate the molecular mechanisms through which the QEP improves IDD using database mining techniques. Active components and candidate targets of the QEP were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and the Bioinformatics Analysis Tool for Molecular Mechanisms of Traditional Chinese Medicine. IDD-related targets were obtained from the GeneCards database, and liver- and kidney-specific genes were retrieved from the BioGPS database. The intersection of these candidate targets was analyzed to identify potential targets for the QEP in IDD. A protein-protein interaction network analysis was performed using STRING and Cytoscape 3.7.2 software. Core targets were further analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking was used to assess the binding affinity of active components to candidate targets, and animal experiments were conducted for validation. We identified 65 potentially active components of the QEP that corresponded to 1,093 candidate targets, 2,108 IDD-related targets, and 1,113 liver- and kidney-specific genes. Key components included quercetin, berberine, isorhamnetin, and emodin. The primary candidate targets were Wnt5A, CTNNB1, IL-1β, MAPK14, MMP9, and MMP3. The GO and KEGG analyses revealed the involvement of these targets in Wnt signaling, TNF signaling, Wnt receptor activation, Frizzled binding, and Wnt-protein interactions. Molecular docking showed strong binding between these components and their targets. Animal experiments demonstrated that the QEP treatment significantly reduced the expression of Wnt5A, CTNNB1, IL-1β, MAPK14, MMP9, and MMP3 at high, medium, and low doses compared with the model group. The QEP alleviated IDD by modulating the Wnt/MAPK/MMP signaling pathways and reducing the release and activation of key factors.

Permeability-Engineered Compartmentalization System Promises Next-Generation Single-Cell Analysis.

Single-cell analysis, including sequencing, imaging, and biochemical assays, has become a fundamental strategy in biomedical research. Microplates, with their open system design, facilitate multistep reagent addition, subtraction, and buffer exchange, while their physically isolated wells prevent cross-contamination between biomolecules, establishing them as foundational compartmentalized platform for single-cell analysis. In contrast, water-in-oil droplets, produced by microfluidic systems, create nanoliter/picoliter-sized droplets that act as advanced compartmentalized platform. Although water-in-oil droplet systems offer significant advantages in single-cell analysis, their nearly complete isolation presents substantial limitations. This isolation impedes the development of ex vivo systems requiring material exchange, complicating complex multistep biochemical reactions and hindering the advancement of single-cell multiomics technologies and nonsequencing applications. Recent innovations in permeability-engineered compartmentalization systems, featuring unique materials and structures with controllable material exchange, promise to overcome these limitations. We discuss the latest advancements in permeability-engineered compartmentalization system, elucidates its underlying principles, and explores its potential applications in the field of single-cell analysis.

Regulation of sleep amount by CRTC1 via transcription ofCrhin mice

The cAMP-response element binding protein (CREB) is required for regulation of daily sleep amount, whereas gain-of-function of CREB-regulated transcription coactivator 1 (CRTC1) causes severe insomnia in mice. However, the physiological functions of CRTCs and their downstream target genes in the regulation of sleep amount remain unclear. Here, we use adult brain chimeric (ABC)-expression/knockout platform for somatic genetics analysis of sleep in adult male mice. ABC-expression of constitutively active mutant CRTC1/2CAin the mouse brain neurons significantly reduces the amount of non-rapid eye movement sleep (NREMS) and/or REMS. Consistent with that SIK3 phosphorylates and inhibits CRTCs, ABC-expression of CRTC1/2/3CArescues the hypersomnia phenotype ofSleepy(Sik3Slp) mice. While ABC-Crtc2KOorCrtc3KOcauses no sleep phenotype, ABC-Crtc1KOor ABC-expression of dominant-negative CRTC (dnCRTC) results in modest reduction of NREMS amount accompanied with elevated NREMS delta power. Moreover, ABC-expression of CRTC1CAor dnCRTC in the excitatory neurons causes bidirectional changes of NREMS/REMS amount and/or NREMS delta power, whereas expression of CRTC1CAin the inhibitory neurons decreases REMS amount and increases NREMS delta power. The ability of CRTC1CAto regulate sleep requires its transactivation domain and CREB-binding domain and is dependent on CREB. Furthermore, we showed that inducible ABC-expression of corticotropin releasing hormone (Crh) and brain-derived neurotrophic factor (Bdnf)–two target genes of CRTCs–significantly reduces daily sleep amount. Notably, ABC-CrhKO, but notBdnfKO, rescues the insomnia phenotype of ABC-CRTC1CAmice. Taken together, these results indicate that CREB-CRTC1 complex regulates daily sleep amount by modulating the transcription ofCrhin the mouse brain neurons.Significance StatementCRTCs function as coactivators for CREB, a transcription factor required for the regulation of daily sleep amount in mice. Here, we found that CRTC1/2/3 function similarly to suppress NREMS and REMS in a CREB-dependent manner. Consistent with that CRTCs are substrates of SIK3 kinase, expression of constitutive active mutant CRTCsCArescue the hypersomnia phenotype ofSleepy(Sik3Slp) mice. Furthermore, we showed that CRTC1 reduces NREMS amount by upregulating the expression of neuropeptide CRH. These results elucidate the mechanism by which CRTCs regulates sleep amount and suggest a potential transcriptional mechanism in the stress-induced sleep/wake regulation.

Antibody Avidity Maturation Following Booster Vaccination with an Intranasal Adenovirus Salnavac Vaccine

Background: The COVID-19 pandemic has led to the rapid development of new vaccines and methods of testing vaccine-induced immunity. Despite the extensive research that has been conducted on the level of specific antibodies, less attention has been paid to studying the avidity of these antibodies. The avidity of serum antibodies is associated with a vaccine showing high effectiveness and reflects the process of affinity maturation. In the context of vaccines against SARS-CoV-2, only a limited number of studies have investigated the avidity of antibodies, often solely focusing on the wild-type virus following vaccination. This study provides new insights into the avidity of serum antibodies following adenovirus-based boosters. We focused on the effects of an intranasal Salnavac booster, which is compared, using a single analytical platform, to an intramuscular Sputnik V. Methods: The avidity of RBD-specific IgGs and IgAs was investigated through ELISA using urea and biolayer interferometry. Results: The results demonstrated the similar avidities of serum antibodies, which were induced by both vaccines for six months post-booster. However, an increase in antibody avidity was observed for the wild-type and Delta variants, but not for the BA.4/5 variant. Conclusions: Collectively, our data provide the insights into antibody avidity maturation after the adenovirus-based vaccines against SARS-CoV-2.

Gut-derived PPAR-γ signaling and risk of bacterial enteric infection: insight from thiazolidinedione users in a US population-based study.

Background and Aims The ongoing antimicrobial resistant crisis heralds the need for new therapeutics against enteric infection. In mouse models, colon epithelial peroxisome proliferator-activated receptor-γ (PPAR-γ) signaling limits oxygen and nitrate luminal bioavailability, thereby preventing bacterial pathogen colonization. However, whether this mechanism operates similarly in humans remains uncertain. Methods To investigate, we used the cloud-based TriNetX Analytics Platform which aggregates health records from 117 million patients across 66 US healthcare organizations, to assess the risk of bacterial enteric infection among diabetic patients prescribed thiazolidinediones, a class of PPAR-γ agonists, to other anti0diabetes medications/ . Results Among 85,117 thiazolidinedione users, we observed a 22-49% lower risk of bacterial enteric infections compared to users of other anti-diabetes medications. This reduction in risk was consistent across high-risk individuals, regardless of sex or age. Similar results were replicated in high-risk patients when thiazolidinedione users were directly compared to those on DPP-4 inhibitors. Conclusion These findings support the potential protective role of PPAR-γ signaling against bacterial enteric infection and call for further clinical investigation.

Design of a Student Recommendation Platform Based on Learning Behavior and Habit Training

This study innovatively built an intelligent analysis platform for learning behavior, which deeply integrated the cutting-edge technology of big data and Artificial Intelligence (AI), \mined and analyzed students’ learning data, and realized the personalized customization of learning resources and the accurate matching of intelligent learning partners. With the help of advanced algorithms and multi-dimensional data fusion strategies, the platform not only promotes positive interaction and collaboration in the learning environment but also provides teachers with comprehensive and in-depth students’ learning portraits, which provides solid support for the implementation of precision education and the personalized adjustment of teaching strategies. In this study, a recommender system based on user similarity evaluation and a collaborative filtering mechanism is carefully designed, and its technical architecture and implementation process are described in detail.

Role of phosphorylated Y1252, Y1336 and Y1472 on NR2B subunits in hypoxia tolerance of neuronal cell in vitro.

The N-methyl-D-aspartate (NMDA) receptors are related to the various functioning of the nervous system. It has been shown that the NR2B subunit plays an important role in neurological hypoxic/ischemic diseases by regulating NMDA receptor function. NR2B tyrosine phosphorylation is also an important regulatory mechanism for NMDA receptor function. However, the mechanism of NR2B tyrosine phosphorylation in hypoxic/ischemic injury is still unclear. Therefore, in the present study, we aimed to further clarify the changes in NR2B tyrosine phosphorylation in hypoxic/ischemic damage in the brain and its relationship with neuronal survival under hypoxic/ischemic conditions. Four types of NR2B tyrosine site mutants (Tyr → Phe at 1252, 1336, and 1472, and all three mutations together, named Y1252F, Y1336F, Y1472F, and Triple) and wild-type plasmids were transfected into HT22 cells. The cells were then exposed to oxygen-glucose deprivation and reoxygenation (OGD/R). NR2B, cell apoptosis-related molecules, and neuronal survival factor CREB-related signaling proteins (CaMKII, ERK, Akt) were measured. Cell viability was assessed using the CCK-8 assay. Cell apoptosis and cell cycle were evaluated using flow cytometry. The death ratio of HT22 cells under OGD conditions was further tested using a live cell analysis platform. The viability of HT22 cells in the Y1252F, Y1336F, Y1472F, Triple mutants, and wild-type groups was elevated. Compared to the wild-type, western blotting and real-time PCR showed that Y1252F, Y1336F, Y1472F, and Triple mutants downregulated the expression of apoptosis factors and upregulated anti-apoptosis factors in the OGD/R model. Flow cytometry and cell cycle analysis demonstrated that Y1252F, Y1336F, Y1472F, and Triple mutants reduced the apoptosis rate. The percentage of cells in the S phase decreased significantly. Live cell analysis illustrated that the Y1252F, Y1336F, Y1472F, and Triple mutants contributed to HT22 cell survival under OGD conditions. Additionally, the Y1252F, Y1336F, Y1472F, and Triple mutants activated the survival signaling pathway. Furthermore, compared to the control group (without plasmid), only the Y1336F, Y1472F, and Triple mutants groups showed significant differences in the above tests. The tyrosine phosphorylation of NR2B at Y1336 and Y1472 plays key roles in hypoxic/ischemic injury. These phosphorylation sites may be potential targets for hypoxic/ischemic neural protection.

HomoeOmicsDB: A global reference platform for accelerating basic and application research in homeopathy

Homeopathy, a globally practiced alternative medicine, faces criticism due to the lack of molecular mechanisms elucidating its drug actions. To address this, researchers are examining the mechanistic action of homeopathic drugs by assessing the molecular markers of various disorders in real-time patients and/or disease models. However, there is a gap: the lack of an assembled view of publicly available molecular-level studies hampers the research on homeopathic drugs. For example, the information on the mechanistic action of Arnica montana tinctures/dilutions - anti-inflammatory, apoptotic, anti-migratory, and healing properties through gene expression and cellular assays – are scattered over published scientific literature. To bridge this gap and accelerate research, we developed the HomoeOmicsDB database (https://ciods.in/homoeomicsdb/), the first reference and molecular analysis platform for multi-omic research in homeopathy. Currently, HomoeOmicsDB hosts information on 65 homeopathic drugs' effects on the expression of around 600 genes. This information is curated from over 100 peer-reviewed published research articles involving more than 50 human and animal cell lines. For each drug, the database integrates the studied host system (body fluid, tissue, host cell/line, cell type), and its regulated expression of genes at RNA, protein, and post-translational modification levels. The information assembled in HomoeOmicsDB, might galvanize research on molecular mechanisms of other homeopathic drugs with similar clinico-physiological outcomes. Integrated with the DisGeNet database, HomoeOmicsDB entails this comparative analysis of homeopathic drug efficacy for multiple disorders in practice. This would subsequently embrace translational/re-purposing options for diseases with limited or no therapeutic alternatives. Together, HomoeOmicsDB aims to serve as a platform for reference and integration of multi-omic data relevant to homeopathic research. The evaluation of the database by the practitioners and researchers would foster greater acceptance and integration of homeopathic medicine.

Dual signal Amplification-Mediated Aptamer-Based electrochemical biosensor for sensitive detection of exosomes

Exosomes, a class of extracellular vesicles carrying the biological information of their parent cells, have emerged as one of the most promising biomarkers for the early screening and prognostic monitoring of cancer. However, the effective capture and sensitive detection of cancer cell-derived exosomes pose significant challenges due to the inevitable interference from internal environment and the limited exosome abundance in clinical samples. Herein, we proposed an aptamer-based electrochemical biosensor combining with Au nanoprobe-mediated dual signal amplification for sensitive detection of tumor-related exosomes. Benefiting from the specific recognition of CD63 aptamer (CD63 Apt), the target exosomes were efficiently captured on the gold electrode, followed by trapping the iDNA-modified Au nanoprobe, which could induce the generation of hybridization chain reaction (HCR). The long double-stranded DNA produced by HCR procedure provided numerous insertion sites to RuHex, achieving the high sensitivity for exosome detection. Under the optimal experimental conditions, this aptamer-based electrochemical biosensor for MCF-7 cancer cell-derived exosome detection exhibited a good linear range within 5 orders of magnitude (from 1.29 × 104 to 1.29 × 108 particle/mL) with a low limit of detection (LOD) to 1.65 × 103 particle/mL. Furthermore, this biosensor possessed good stability and anti-interference ability. Therefore, this analysis platform is considered as a promising candidate for the exosome-based diagnosis and prognosis of cancer in clinical applications.

Mechanism of Polygala-Acorus in Treating Autism Spectrum Disorder Based on Network Pharmacology and Molecular Docking.

Recent epidemic survey data have revealed a globally increasing prevalence of autism spectrum disorders (ASDs). Currently, while Western medicine mostly uses a combination of comprehensive intervention and rehabilitative treatment, patient outcomes remain unsatisfactory. Polygala-Acorus, used as a pair drug, positively affects the brain and kidneys, and can improve intelligence, wisdom, and awareness; however, the underlying mechanism of action is unclear. We performed network pharmacology analysis of the mechanism of Polygala-Acorus in treating ASD and its potential therapeutic effects to provide a scientific basis for the pharmaceutical's clinical application. The chemical compositions and targets corresponding to Polygala-Acorus were obtained using the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform, Chemical Source Website, and PharmMapper database. Disease targets in ASD were screened using the DisGeNET, DrugBank, and GeneCards databases. Gene Ontology functional analysis and metabolic pathway analysis (Kyoto Encyclopedia of Genes and Genomes) were performed using the Metascape database and validated via molecular docking using AutoDock Vina and PyMOL software. Molecular docking analysis showed that the key active components of Polygala- Acorus interacted with the following key targets: EGFR, SRC, MAPK1, and ALB. Thus, the key active components of Polygala-Acorus (sibiricaxanthone A, sibiricaxanthone B tenuifolin, polygalic acid, cycloartenol, and 8-isopentenyl-kaempferol) have been found to bind to EGFR, SRC, MAPK1, and ALB. This study has preliminarily revealed the active ingredients and underlying mechanism of Polygala-Acorus in the treatment of ASD, and our predictions need to be proven by further experimentation.