Cancer Epidemiol Biomarkers Prev. 2025 Feb 6;34(2):221-223. doi: 10.1158/1055-9965.EPI-24-1617.

ABSTRACT

The selective estrogen receptor modulator tamoxifen is a mainstay of endocrine breast cancer therapy. However, the clinical response rates of tamoxifen are inferior to those of aromatase inhibitors, which may be partially explained by variable drug exposure due to the pharmacogenetics of the drug-metabolizing enzyme cytochrome P450 (CYP) 2D6. Clinical trials investigating the association between CYP2D6 impairment and tamoxifen outcomes have yielded conflicting results. The results of a comprehensive meta-analysis of 33 single-center tamoxifen trials reported here address this inconsistency by adjusting for two biases that may affect the validity of previous association studies: allele coverage of CYP2D6 genotyping and loss of heterozygosity of the CYP2D6 locus in tumor-derived DNA. After adjustment for bias, meta-analyses show significantly reduced study heterogeneity and a higher risk of recurrence or death in patients with impaired CYP2D6 metabolism compared with those with normal activity. These data may support the use of pharmacogenetics-guided tamoxifen therapy to improve outcomes in patients with CYP2D6-compromised breast cancer. Prospective studies should be considered. See related article by MacLehose et al., p. 224.

PMID:39910985 | DOI:10.1158/1055-9965.EPI-24-1617

Clin Transl Sci. 2025 Feb;18(2):e70138. doi: 10.1111/cts.70138.

ABSTRACT

To plan future steps for the implementation and regulation of pharmacogenetic testing, any issue in the management of pharmacogenetic information by regulatory bodies must be identified. In this paper, an analysis of pharmacogenetic information in the summary of product characteristics (SmCPs) of drugs approved by Italian Drug Agency (AIFA) was conducted. Among 4214 SmCPs of 1063 active ingredients, 53.2% (n = 2240) included pharmacogenetic information in at least one section, most frequently for drugs in the Anatomical Therapeutic Chemical category "Antineoplastic and immunomodulatory agents". To contextualize these data in the international scenario, a pharmacogenetic level of actionability, based on AIFA SmCPs, was assigned to 608 drug/gene pairs included in FDA's "Table of Pharmacogenomic Biomarkers in Drug Labels", according to PharmGKB (The Pharmacogenomics Knowledge Base). Approximately 67% of drug/gene pairs were deemed classifiable: Based on SmCPs phrasing, for half of them the genetic testing was cataloged as "required" or "recommended" (mainly tumor somatic variants), whereas 40% as "actionable" (mostly PK/PD-related germline variants). The comparison with other regulatory agencies highlighted a discordance in the assigned pharmacogenetic levels of actionability ranging from 1% to 14%. This discrepancy may also point out the need to rethink the language used in AIFA-approved SmCPs to clarify whether a pharmacogenetic test is necessary or not and for which subjects it has been recommended. For the first time, a detailed evaluation and comparative analysis of the pharmacogenetic information on Italian SmCPs was presented, placing it in an international context and laying the groundwork for rethinking pharmacogenetic indications in AIFA-approved SmCPs.

PMID:39910906 | DOI:10.1111/cts.70138

J Clin Sleep Med. 2025 Feb 6. doi: 10.5664/jcsm.11600. Online ahead of print.

NO ABSTRACT

PMID:39912229 | DOI:10.5664/jcsm.11600

Pediatr Int. 2025 Jan-Dec;67(1):e15882. doi: 10.1111/ped.15882.

NO ABSTRACT

PMID:39911093 | DOI:10.1111/ped.15882

ACS Appl Mater Interfaces. 2025 Feb 6. doi: 10.1021/acsami.4c21563. Online ahead of print.

ABSTRACT

The way people interact with machines through flexible acoustic sensors is revolutionizing the way we live. However, developing a human-machine interaction acoustic sensor that simultaneously offers low cost, high stability, high fidelity, and high sensitivity remains a significant challenge. In this study, a sensor based on a sound-driven triboelectric nanogenerator was proposed. A poly(vinylidene fluoride) (PVDF)/graphene oxide (GO) composite nanofiber film was obtained as the dielectric layer through electrospinning, and copper-nickel alloy conductive fabric was used as the electrode. An imitation embroidery shed structure was designed in the shape of a ring to secure the upper and lower electrodes and the dielectric layer as a whole. Due to the porosity of the electrode, the large contact area of the dielectric layer, and the high stability of the imitation embroidery shed structure, the sensor achieves a sensitivity of 4.76 V·Pa-1 and a frequency response range of 20-2000 Hz. A multilayer attention convolutional network (MLACN) was designed for speech recognition. The designed speech recognition system achieved a 99.5% accuracy rate in recognizing common word pronunciations. The integration of sound-driven triboelectric nanogenerator-based flexible acoustic sensors with deep learning techniques holds great promise in the field of human-machine interaction.

PMID:39912319 | DOI:10.1021/acsami.4c21563

Front Bioeng Biotechnol. 2025 Jan 22;13:1526478. doi: 10.3389/fbioe.2025.1526478. eCollection 2025.

ABSTRACT

BACKGROUND: The high prevalence of low back pain has led to an increasing demand for the analysis of lumbar magnetic resonance (MR) images. This study aimed to develop and evaluate a deep-learning-assisted automated system for diagnosing and grading lumbar intervertebral disc degeneration based on lumbar T2-weighted sagittal and axial MR images.

METHODS: This study included a total of 472 patients who underwent lumbar MR scans between January 2021 and November 2023, with 420 in the internal dataset and 52 in the external dataset. The MR images were evaluated and labeled by experts according to current guidelines, and the results were considered the ground truth. The annotations included the Pfirrmann grading of disc degeneration, disc herniation, and high-intensity zones (HIZ). The automated diagnostic model was based on the YOLOv5 network, modified by adding an attention module in the Cross Stage Partial part and a residual module in the Spatial Pyramid Pooling-Fast part. The model's diagnostic performance was evaluated by calculating the precision, recall, F1 score, and area under the receiver operating characteristic curve.

RESULTS: In the internal test set, the model achieved precisions of 0.78-0.91, 0.90-0.92, and 0.82 and recalls of 0.86-0.91, 0.90-0.93, and 0.81-0.88 for disc degeneration grading, disc herniation diagnosis, and HIZ detection, respectively. In the external test set, the precision values for disc degeneration grading, herniation diagnosis, and HIZ detection were 0.73-0.87, 0.86-0.92, and 0.74-0.84 and recalls were 0.79-0.87, 0.88-0.91, and 0.77-0.78, respectively.

CONCLUSION: The proposed model demonstrated a relatively high diagnostic and classification performance and exhibited considerable consistency with expert evaluation.

PMID:39912111 | PMC:PMC11794261 | DOI:10.3389/fbioe.2025.1526478

MethodsX. 2025 Jan 15;14:103172. doi: 10.1016/j.mex.2025.103172. eCollection 2025 Jun.

ABSTRACT

Plant Disease Detection in the early stage is paramount. Traditionally, it was done manually by the farmers, which is a laborious and time-intensive task. With the advent of AI, Machine Learning and Deep Learning methods are used to detect and categorize plant diseases. However, they rely on extensive datasets for accurate prediction, which is impracticable to acquire and annotate. Thus, Few Shot Learning is the state-of-the-art model in machine learning, which requires minimum examples to train the model for generalization. As humans need a few examples to recognize things, Few-shot Learning mimics the same human brain process. The proposed work uses a pre-trained convolution neural network, VGG16, as the backbone, fine-tuned on the corn disease dataset. An attention module is integrated with the backbone, and further, prototypical few-shot learning is used for corn disease prediction and classification with an accuracy of 98.25 %.•The proposed model intends to identify the diseases early, so the insights generated would be relevant for farmers, and probable losses can be reduced.•By applying Few-Shot Learning, the system avoids the significant challenges of requiring extensively annotated datasets, making it feasible for real-world agricultural applications.•Incorporating a fine-tuned VGG16 backbone along with an attention mechanism and prototypical Few-Shot Learning results in a robust and scalable solution with high accuracy for classifying corn diseases.

PMID:39911906 | PMC:PMC11795141 | DOI:10.1016/j.mex.2025.103172

R Soc Open Sci. 2025 Feb 5;12(2):241687. doi: 10.1098/rsos.241687. eCollection 2025 Feb.

ABSTRACT

The swift development of artificial intelligence (AI) technology has triggered substantial changes, particularly evident in the emergence of chat-based services driven by large language models. With the increasing number of users utilizing these services, understanding and analysing user satisfaction becomes crucial for service improvement. While previous studies have explored leveraging online reviews as indicators of user satisfaction, efficiently collecting and analysing extensive datasets remain a challenge. This research aims to address this challenge by proposing a framework to handle extensive review datasets from the Google Play Store, employing natural language processing with machine learning techniques for sentiment analysis. Specifically, the authors collect review data of chat-based AI applications and perform filtering through majority voting of multiple unsupervised sentiment analyses. This framework is a proposed methodology for eliminating inconsistencies between ratings and contents. Subsequently, the authors conduct supervised sentiment analysis using various machine learning and deep learning algorithms. The experimental results confirm the effectiveness of the proposed approach showing improvement in prediction accuracy with cost efficiency. In summary, the findings of this study enhance the predictive performance of user satisfaction for improving service quality in chat-based AI applications and provide valuable insights for the advancement of next-generation chat-based AI services.

PMID:39911884 | PMC:PMC11793979 | DOI:10.1098/rsos.241687

Comput Struct Biotechnol J. 2025 Jan 22;27:467-477. doi: 10.1016/j.csbj.2025.01.016. eCollection 2025.

ABSTRACT

AlphaFold 2 (AF2) has revolutionised protein structure prediction but, like any new tool, its performance on specific classes of targets, especially those potentially under-represented in its training data, merits attention. Prompted by a highly confident prediction for a biologically meaningless, randomly permuted repeat sequence, we assessed AF2 performance on sequences composed of perfect repeats of random sequences of different lengths. AF2 frequently folds such sequences into β-solenoids which, while ascribed high confidence, contain unusual and implausible features such as internally stacked and uncompensated charged residues. A number of sequences confidently predicted as β-solenoids are predicted by other advanced methods as intrinsically disordered. The instability of some predictions is demonstrated by molecular dynamics. Importantly, other deep learning-based structure prediction tools predict different structures or β-solenoids with much lower confidence suggesting that AF2 alone has an unreasonable tendency to predict confident but unrealistic β-solenoids for perfect repeat sequences. The potential implications for structure prediction of natural (near-)perfect sequence repeat proteins are also explored.

PMID:39911842 | PMC:PMC11795689 | DOI:10.1016/j.csbj.2025.01.016

Front Microbiol. 2025 Jan 22;15:1516667. doi: 10.3389/fmicb.2024.1516667. eCollection 2024.

ABSTRACT

Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward.

PMID:39911715 | PMC:PMC11794229 | DOI:10.3389/fmicb.2024.1516667

Front Neurosci. 2025 Jan 22;19:1522227. doi: 10.3389/fnins.2025.1522227. eCollection 2025.

ABSTRACT

PURPOSE: To test the feasibility and consistency of a deep-learning (DL) accelerated QSM method for deep brain nuclei evaluation.

METHODS: Participants were scanned with both parallel imaging (PI)-QSM and DL-QSM methods. The PI- and DL-QSM scans had identical imaging parameters other than acceleration factors (AF). The DL-QSM employed Poisson disk style under-sampling scheme and a previously developed cascaded CNN based reconstruction model, with acquisition time of 4:35, 3:15, and 2:11 for AF of 3, 4, and 5, respectively. For PI-QSM acquisition, the AF was 2 and the acquisition time was 6:46. The overall image similarity was assessed between PI- and DL-QSM images using the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). QSM values from 7 deep brain nuclei were extracted and agreements between images with different Afs were assessed. Finally, the correlations between age and QSM values in the selected deep brain nuclei were evaluated.

RESULTS: 59 participants were recruited. Compared to PI-QSM images, the mean SSIM of DL images were 0.87, 0.86, and 0.85 for AF of 3, 4, and 5. The mean PSNR were 44.56, 44.53, and 44.23. Susceptibility values from DL-QSM were highly consistent with routine PI-QSM images, with differences of less than 5% at the group level. Furthermore, the associations between age and QSM values could be consistently revealed.

CONCLUSION: DL-QSM could be used for measuring susceptibility values of deep brain nucleus. An AF up to 5 did not significantly impact the correlation between age and susceptibility in deep brain nuclei.

PMID:39911700 | PMC:PMC11794186 | DOI:10.3389/fnins.2025.1522227

Front Plant Sci. 2025 Jan 22;15:1518294. doi: 10.3389/fpls.2024.1518294. eCollection 2024.

ABSTRACT

INTRODUCTION: Due to the limited computing power and fast flight speed of the picking of unmanned aerial vehicles (UAVs), it is important to design a quick and accurate detecting algorithm to obtain the fruit position.

METHODS: This paper proposes a lightweight deep learning algorithm, named YOLOv8s-Longan, to improve the detection accuracy and reduce the number of model parameters for fruitpicking UAVs. To make the network lightweight and improve its generalization performance, the Average and Max pooling attention (AMA) attention module is designed and integrated into the DenseAMA and C2f-Faster-AMA modules on the proposed backbone network. To improve the detection accuracy, a crossstage local network structure VOVGSCSPC module is designed, which can help the model better understand the information of the image through multiscale feature fusion and improve the perception and expression ability of the model. Meanwhile, the novel Inner-SIoU loss function is proposed as the loss function of the target bounding box.

RESULTS AND DISCUSSION: The experimental results show that the proposed algorithm has good detection ability for densely distributed and mutually occluded longan string fruit under complex backgrounds with a mAP@0.5 of 84.3%. Compared with other YOLOv8 models, the improved model of mAP@0.5 improves by 3.9% and reduces the number of parameters by 20.3%. It satisfies the high accuracy and fast detection requirements for fruit detection in fruit-picking UAV scenarios.

PMID:39911656 | PMC:PMC11794187 | DOI:10.3389/fpls.2024.1518294

Front Plant Sci. 2025 Jan 22;15:1501612. doi: 10.3389/fpls.2024.1501612. eCollection 2024.

ABSTRACT

The leaf area index (LAI) is a critical parameter for characterizing plant foliage abundance, canopy structure changes, and vegetation productivity in ecosystems. Traditional phenological measurements are often destructive, time-consuming, and labor-intensive. This paper proposes a high-throughput 3D point cloud data processing pipeline to segment field soybean plants and estimate their LAI. The 3D point cloud data is obtained from a UAV equipped with a LiDAR camera. First, The PointNet++ model was applied to simplify the segmentation process by isolating field soybean plants from their surroundings and eliminating environmental complexities. Subsequently, individual segmentation was achieved using the Watershed approach and k-means clustering algorithms, segmenting the field soybeans into individual plants. Finally, the LAI of soybean plant was estimated using a machine learning method and validated against measured values. The PointNet++ model improved segmentation accuracy by 6.73%, and the watershed algorithm achieved F1 scores of 0.89-0.90, outperforming k-means in complex adhesion cases. For LAI estimation, the SVM model showed the highest accuracy (R² = 0.79, RMSE = 0.47), with RF and XGBoost also performing well (R² > 0.69, RMSE< 0.65). This indicates that the individual segmentation algorithm, Watershed-based approach combined with PointNet++, can serve as a crucial foundation for extracting high-throughput plant phenotypic data. The experimental results confirm that the proposed method can rapidly calculate the morphological parameters of each soybean plant, making it suitable for high-throughput soybean phenotyping.

PMID:39911650 | PMC:PMC11794303 | DOI:10.3389/fpls.2024.1501612

Front Psychiatry. 2025 Jan 22;15:1512818. doi: 10.3389/fpsyt.2024.1512818. eCollection 2024.

ABSTRACT

Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, presenting challenges in social communication, repetitive behaviors, and sensory processing. Despite its prevalence, diagnosis can be lengthy, and access to appropriate treatment varies greatly. This project utilizes the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve Autism Spectrum Disorder diagnosis and treatment. A central data hub, the Master Data Plan (MDP), will aggregate and analyze information from diverse sources, feeding AI algorithms that can identify risk factors for ASD, personalize treatment plans based on individual needs, and even predict potential relapses. Furthermore, the project incorporates a patient-facing chatbot to provide information and support. By integrating patient data, empowering individuals with ASD, and supporting healthcare professionals, this platform aims to transform care accessibility, personalize treatment approaches, and optimize the entire care journey. Rigorous data governance measures will ensure ethical and secure data management. This project will improve access to care, personalize treatments for better outcomes, shorten wait times, boost patient involvement, and raise ASD awareness, leading to better resource allocation. This project marks a transformative shift toward data-driven, patient-centred ASD care in Italy. This platform enhances treatment outcomes for individuals with ASD and provides a scalable model for integrating AI into mental health, establishing a new benchmark for personalized patient care. Through AI integration and collaborative efforts, it aims to redefine mental healthcare standards, enhancing the well-being for individuals with ASD.

PMID:39911557 | PMC:PMC11795314 | DOI:10.3389/fpsyt.2024.1512818

Stat Med. 2025 Feb 28;44(5):e10334. doi: 10.1002/sim.10334.

ABSTRACT

High-throughput single-cell RNA-seq (scRNA-seq) data contains an excess of zero values, which can be contributed by unexpressed genes and detection signal dropouts. Existing imputation methods fail to distinguish between these two types of zeros. In this study, we introduce a statistical framework that effectively differentiates true zeros (lack of expression) from false zeros (dropouts). By focusing only on imputing the dropout zeros, we developed a new imputation tool, scRecover. Our approach utilizes a zero-inflated negative binomial framework to model the gene expression of each gene in each cell, enabling the estimation of zero-dropout probability. Additionally, we employ a modified version of the Good and Toulmin model to identify true zeros for each gene. To achieve imputation, scRecover is combined with other imputation methods such as scImpute, SAVER and MAGIC. Down-sampling experiments show that it recovers dropout zeros with higher accuracy and avoids over-imputing true zero values. Experiments conducted on real world data highlight the ability of scRecover to enhance downstream analysis and visualization.

PMID:39912305 | DOI:10.1002/sim.10334

Chem Commun (Camb). 2025 Feb 6. doi: 10.1039/d4cc03807j. Online ahead of print.

ABSTRACT

Bright near-infrared fluorophores are in demand for microscopy. We showcase a deuterated oxazine being 23% brighter vs. ATTO700. With a longer lifetime of 1.85 nanoseconds, we find the best-in-class SulfoOxazine700-d10 to stain mitochondria for confocal microscopy, and demonstrate unaffected diffusion properties in single molecule fluorescence correlation spectroscopy.

PMID:39912228 | DOI:10.1039/d4cc03807j

Biotechnol Notes. 2025 Jan 13;6:67-78. doi: 10.1016/j.biotno.2025.01.001. eCollection 2025.

ABSTRACT

Microbiome engineering has emerged as a transformative approach to enhancing food safety and quality by strategically modulating microbial communities. This review critically examines state-of-the-art techniques, including synthetic biology, artificial intelligence (AI), and systems biology, that are revolutionizing our ability to improve nutritional profiles, extend shelf life, and optimize food production processes. The review further explores complex social, ethical, and regulatory considerations, emphasizing the importance of robust public engagement and the establishment of standardized frameworks to ensure safe and effective implementation. While microbiome engineering holds significant promise for revolutionizing food safety and quality control, further research is needed to address critical challenges, including understanding microbial dynamics in complex food systems and developing harmonized regulatory frameworks. By bridging interdisciplinary gaps, this paper underscores the necessity of collaborative efforts to unlock the full potential of microbiome-driven innovations for a more resilient and sustainable food industry.

PMID:39912062 | PMC:PMC11795101 | DOI:10.1016/j.biotno.2025.01.001

Front Pharmacol. 2025 Jan 22;15:1470931. doi: 10.3389/fphar.2024.1470931. eCollection 2024.

ABSTRACT

INTRODUCTION: Most drugs fail during development and there is a clear and unmet need for approaches to better understand mechanistically how drugs exert both their intended and adverse effects. Gaining traction in this field is the use of disease data linking genes with pathological phenotypes and combining this with drugtarget interaction data.

METHODS: We introduce methodology to associate drugs with effects, both intended and adverse, using a tripartite network approach that combines drug-target and target-phenotype data, in which targets can be represented as proteins and protein domains.

RESULTS: We were able to detect associations for over 140,000 ChEMBL drugs and 3,800 phenotypes, represented as Human Phenotype Ontology (HPO) terms. The overlap of these results with the SIDER databases of known drug side effects was up to 10 times higher than random, depending on the target type, disease database and score threshold used. In terms of overlap with drug-phenotype pairs extracted from the literature, the performance of our methodology was up to 17.47 times greater than random. The top results include phenotype-drug associations that represent intended effects, particularly for cancers such as chronic myelogenous leukemia, which was linked with nilotinib. They also include adverse side effects, such as blurred vision being linked with tetracaine.

DISCUSSION: This work represents an important advance in our understanding of how drugs cause intended and adverse side effects through their action on disease causing genes and has potential applications for drug development and repositioning.

PMID:39911831 | PMC:PMC11794328 | DOI:10.3389/fphar.2024.1470931

Curr Genomics. 2025;26(1):48-80. doi: 10.2174/0113892029308243240709073945. Epub 2024 Jul 18.

ABSTRACT

BACKGROUND: The damage in the liver and hepatocytes is where the primary liver cancer begins, and this is referred to as Hepatocellular Carcinoma (HCC). One of the best methods for detecting changes in gene expression of hepatocellular carcinoma is through bioinformatics approaches.

OBJECTIVE: This study aimed to identify potential drug target(s) hubs mediating HCC progression using computational approaches through gene expression and protein-protein interaction datasets.

METHODOLOGY: Four datasets related to HCC were acquired from the GEO database, and Differentially Expressed Genes (DEGs) were identified. Using Evenn, the common genes were chosen. Using the Fun Rich tool, functional associations among the genes were identified. Further, protein-protein interaction networks were predicted using STRING, and hub genes were identified using Cytoscape. The selected hub genes were subjected to GEPIA and Shiny GO analysis for survival analysis and functional enrichment studies for the identified hub genes. The up-regulating genes were further studied for immunohistopathological studies using HPA to identify gene/protein expression in normal vs HCC conditions. Drug Bank and Drug Gene Interaction Database were employed to find the reported drug status and targets. Finally, STITCH was performed to identify the functional association between the drugs and the identified hub genes.

RESULTS: The GEO2R analysis for the considered datasets identified 735 upregulating and 284 downregulating DEGs. Functional gene associations were identified through the Fun Rich tool. Further, PPIN network analysis was performed using STRING. A comparative study was carried out between the experimental evidence and the other seven data evidence in STRING, revealing that most proteins in the network were involved in protein-protein interactions. Further, through Cytoscape plugins, the ranking of the genes was analyzed, and densely connected regions were identified, resulting in the selection of the top 20 hub genes involved in HCC pathogenesis. The identified hub genes were: KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Further, GEPIA and Shiny GO analyses provided insights into survival ratios and functional enrichment studied for the hub genes. The HPA database studies further found that upregulating genes were involved in changes in protein expression in Normal vs HCC tissues. These findings indicated that hub genes were certainly involved in the progression of HCC. STITCH database studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, could be used as leads to identify novel drugs, and identified hub genes could also be considered as potential and promising drug targets as they are involved in the gene-chemical interaction networks.

CONCLUSION: The present study involved various integrated bioinformatics approaches, analyzing gene expression and protein-protein interaction datasets, resulting in the identification of 20 top-ranked hubs involved in the progression of HCC. They are KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Gene-chemical interaction network studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, can be used as leads to identify novel drugs, and the identified hub genes can be promising drug targets. The current study underscores the significance of targeting these hub genes and utilizing existing molecules to generate new molecules to combat liver cancer effectively and can be further explored in terms of drug discovery research to develop treatments for HCC.

PMID:39911278 | PMC:PMC11793067 | DOI:10.2174/0113892029308243240709073945

Pathog Immun. 2025 Jan 29;10(1):120-139. doi: 10.20411/pai.v10i1.770. eCollection 2024.

ABSTRACT

RATIONALE: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of Mycobacterium tuberculosis. Recently, host RNA signatures have been used to monitor the response to tuberculosis treatment.

OBJECTIVE: Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy.

METHODS: Using a multi-step machine learning algorithm to identify an RNA-based algorithm to predict the remaining time to culture conversion at flexible time points during anti-tuberculosis therapy.

RESULTS: The identification cohort included 149 patients split into a training and a test cohort, to develop a multistep algorithm consisting of 27 genes (TB27) for predicting the remaining time to culture conversion (TCC) at any given time. In the test dataset, predicted TCC and observed TCC achieved a correlation coefficient of r=0.98. An external validation cohort of 34 patients shows a correlation between predicted and observed days to TCC also of r=0.98.

CONCLUSION: We identified and validated a whole blood-based RNA signature (TB27) that demonstrates an excellent agreement between predicted and observed times to M. tuberculosis culture conversion during tuberculosis therapy. TB27 is a potential useful biomarker for anti-tuberculosis drug development and for prediction of treatment responses in clinical practice.

PMID:39911144 | PMC:PMC11792529 | DOI:10.20411/pai.v10i1.770