Biomol Biomed. 2025 Mar 18. doi: 10.17305/bb.2025.12058. Online ahead of print.

ABSTRACT

Cervical cancer presents a significant healthcare challenge due to recurrent disease and drug resistance, highlighting the urgent need for novel therapeutic strategies. Network pharmacology facilitates drug repurposing by elucidating multi-target mechanisms of action. Adefovir, an acyclic nucleotide analog, has shown promising potential in cervical cancer treatment, particularly in HeLa cells. In vitro studies have demonstrated that adefovir inhibits HeLa cell proliferation by enhancing apoptosis while maintaining a low cytotoxicity profile at therapeutic concentrations, making it an attractive candidate for further exploration. A combined network pharmacology and in vitro study was conducted to investigate the molecular mechanism of adefovir against cervical cancer. Potential gene targets for adefovir and cervical cancer were predicted using database analysis. Hub targets were identified, and protein-protein interaction (PPI) networks were constructed. Molecular docking assessed adefovir's binding affinity to key targets. In vitro cytotoxic assays, including 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and crystal violet assays, were performed using 96-well plates to evaluate anti-proliferative effects in HeLa cells. Apoptosis was assessed via p53 immunocytochemistry Enzyme-Linked Immunosorbent Assay (ELISA), while Vascular Endothelial Growth Factor ELISA (VEGF ELISA) was used to measure cell proliferation. Venn analysis identified 144 common targets between adefovir and cervical cancer. Network analysis revealed key hub targets involved in oncogenic pathways. Molecular docking demonstrated strong binding between adefovir and Mitogen-Activated Protein Kinase 3 (MAPK3) and SRC proteins. In vitro, adefovir significantly suppressed HeLa cell viability, with an Inhibitory Concentration 50 (IC50) of 7.8 μM, outperforming 5-Fluorouracil (5-FU). Additionally, it induced apoptosis via p53 activation and inhibited cell proliferation through VEGF suppression. These integrated computational and experimental findings suggest that adefovir exerts multi-targeted effects against cervical cancer. Its promising preclinical efficacy warrants further investigation as a potential alternative therapy.

PMID:40105884 | DOI:10.17305/bb.2025.12058

Endocrinology. 2025 Mar 19:bqaf055. doi: 10.1210/endocr/bqaf055. Online ahead of print.

ABSTRACT

Diabetes is the most expensive chronic disease in the U.S. with over $400 billion in annual costs and it affects over 38 million Americans. While major advances in drug treatment have been made for type 2 diabetes (T2D) and the often-associated obesity, there are still no approved and effective medications targeting beta cell loss or islet dysfunction, which is one of the major underlying causes of both, type 1 diabetes (T1D) and T2D. In addition, there are no oral medications for T1D approved in the U.S. more than a hundred years after the discovery of insulin and attractive therapeutic targets are only starting to emerge. As we celebrate the 75th anniversary of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), progress is finally being made in this area with NIDDK support. This mini-review follows the discovery of thioredoxin-interacting protein inhibitors as an example of a methodical approach to identify and develop an oral beta cell treatment for T1D. It further discusses how the initial molecular findings were translated into novel clinical treatment approaches that promote the patient's own islet health and beta cell function using drug repurposing as well as new drug discovery.

PMID:40105688 | DOI:10.1210/endocr/bqaf055

Tokai J Exp Clin Med. 2025 Apr 20;50(1):34-36.

ABSTRACT

A 19-year-old woman with three seborrheic keratosis on her right abdomen and five seborrheic keratosis on her both buttocks is presented. That developed at the age of five and two months prior to the visit. At our initial dermatological examination, we noticed three oval, well defined, brown tumors on her right abdomen, and several round, well defined, brown nodules on her both buttocks. Dermoscopy findings showed comedo-like openings, fissures, and ridges. Histopathological examination showed hyperkeratosis and pseudohorn cysts, and basaloid keratinocytes proliferation with no dysplastic cells. These findings were consistent with SK. She was treated by cryotherapy using a liquid nitrogen spray, and her tumors and nodules dropped off entirely. Juvenile-onset of seborrheic keratosis is quite rare in East Asian countries and needs to be differentiated from keratinocytic epidermal nevus.

PMID:40105231

Urogynecology (Phila). 2025 Feb 14. doi: 10.1097/SPV.0000000000001668. Online ahead of print.

ABSTRACT

IMPORTANCE: Pharmacogenetics could address the challenge of predicting an individual's response to anticholinergic medications for urgency urinary incontinence (UUI).

OBJECTIVES: Our objectives were to evaluate whether the metabolizer status of cytochrome p450 2D6 (CYP2D6), the drug metabolizing enzyme for fesoterodine, is associated with effectiveness or moderate/severe adverse events (AEs) from fesoterodine fumarate in women with UUI.

STUDY DESIGN: In this pilot pharmacogenetics study, 58 women aged ≥50 with ≥3 UUI episodes on a 3-day bladder diary were treated with fesoterodine. Participants were categorized as normal metabolizers (NM), intermediate (IM), or poor metabolizers (PM) based on their genetic CYP2D6 sequence. Effectiveness was measured by Treatment Benefit Scale (responders were "improved" or "greatly improved" versus nonresponders were "not changed" or "worsened"). Moderate and severe AEs were defined by the National Cancer Institute Common Terminology Criteria for Adverse Events.

RESULTS: Among 58 women, 34 (58.6%) were NM, 22 (37.9%) were IM, and 2 (3.4%) were PM. Given the small proportion of PM, we compared the NM and IM groups. Regarding effectiveness for UUI, there was no significant difference between metabolizer cohorts at 4 weeks (82.8% vs 94.4%, P = 0.38 for NM vs IM, respectively). Metabolizer status was also not associated with moderate-severe AEs (14.7% vs 13.6% for NM vs IM, P = 1.0).

CONCLUSIONS: In this pilot study with limited sample size, CYP2D6 normal and IM metabolizer status was not associated with effectiveness or moderate-severe AEs to fesoterodine fumarate. The proportion of poor metabolizers was low; thus, further investigation in this population is warranted.

PMID:40105750 | DOI:10.1097/SPV.0000000000001668

Pediatr Pulmonol. 2025 Mar;60(3):e71048. doi: 10.1002/ppul.71048.

ABSTRACT

OBJECTIVE: To describe variants in the CFTR gene and demographic and clinical characteristics of individuals with Cystic Fibrosis (CF) from a Brazilian State pediatric reference center upon diagnosis.

METHODS: Cross-sectional retrospective cohort study of individuals with CF under 18 years old treated in a referral center between 2007 and 2023. Data was derived from medical records. A descriptive analysis of the variables at diagnosis, including pathogenic genetic variants in the CFTR gene, clinical findings, and demographic data.

RESULTS: The population of 110 patients was predominantly male (54.5%) and white (66.4%). Age at diagnosis ranged from 13 days to 17 years (median = 2 months), 78.2% were diagnosed at < 2 years, and 50.9% were diagnosed following neonatal screening. The most frequent clinical manifestations at diagnosis were steatorrhea (70.0%), persistent respiratory symptoms (58.2%), and malnutrition (36.4%). The most common CFTR variants were F508del (49.0%), G542X (7.3%), and 3120+1G>A (5.9%). Eighty-four (76.4%) were eligible to use at least 1 of the 4 CFTR modulator therapies available in Brazil, and 29 were eligible for 3 therapies because they are homozygous for F508del.

CONCLUSION: Identification of CFTR variants at diagnosis can provide many benefits to patients, such as early interventions and CFTR modulator therapy, and is feasible in Brazil. Because each country may have different distributions of CFTR variants, it is essential to evaluate these distributions as we advance methodologies for gene variant detection, particularly in the contexts of newborn screening and diagnostic testing.

PMID:40105401 | DOI:10.1002/ppul.71048

mSystems. 2025 Mar 19:e0135424. doi: 10.1128/msystems.01354-24. Online ahead of print.

ABSTRACT

Burkholderia cenocepacia is an opportunistic pathogen that has been associated with nosocomial outbreaks in hospitals and can cause severe respiratory infections among immunocompromised patients and individuals suffering from cystic fibrosis. The transmissibility and intrinsic antibiotic resistance of B. cenocepacia pose a significant challenge in healthcare settings. In this study, with the aim to identify novel drug targets to fight B. cenocepacia infections, we employed a genome-wide transposon sequencing (Tn-seq) approach to unravel fitness determinants required for survival in Galleria mellonella (in vivo infection model) and pig lung tissue (ex vivo organ model). A total of 698 and 117 fitness genes were identified for each of the models, respectively, and 62 genes were found to be important for both. To confirm our results, we constructed individual mutants in selected genes and validated their fitness in the two models. Among the various determinants identified was a rare genomic island (I35_RS03700-I35_RS03770) involved in O-antigen and lipopolysaccharide synthesis. We demonstrate that this gene cluster is required for virulence in the G. mellonella infection model but, by contrast, counteracts efficient colonization of pig lung tissue. Our results highlight the power of the Tn-seq approach to unravel fitness determinants that could be used as therapeutic targets in the future and show that the choice of the infection model for mutant selection is paramount.

IMPORTANCE: The opportunistic pathogen Burkholderia cenocepacia has been associated with nosocomial infections in healthcare facilities, where it can cause outbreaks involving infections of the bloodstream, respiratory tract, and urinary tract as well as severe complications in immunocompromised patients. With the aim to identify novel targets to fight B. cenocepacia infections, we have used a genome-wide approach to unravel fitness genes required for host colonization in a clinical strain, B. cenocepacia H111. Among the various determinants that we identified is a rare genomic island that modifies the bacterial lipopolysaccharide. Our results highlight the power of the transposon sequencing approach to identify new targets for infection treatment and show the importance of using different infection models.

PMID:40105327 | DOI:10.1128/msystems.01354-24

JAMA Netw Open. 2025 Mar 3;8(3):e250991. doi: 10.1001/jamanetworkopen.2025.0991.

NO ABSTRACT

PMID:40105846 | DOI:10.1001/jamanetworkopen.2025.0991

JAMA Netw Open. 2025 Mar 3;8(3):e250984. doi: 10.1001/jamanetworkopen.2025.0984.

ABSTRACT

IMPORTANCE: While prospective studies have investigated the accuracy of artificial intelligence (AI) for detection of diabetic retinopathy (DR) and diabetic macular edema (DME), to date, little published data exist on the clinical performance of these algorithms.

OBJECTIVE: To evaluate the clinical performance of an automated retinal disease assessment (ARDA) algorithm in the postdeployment setting at Aravind Eye Hospital in India.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional analysis involved an approximate 1% sample of fundus photographs from patients screened using ARDA. Images were graded via adjudication by US ophthalmologists for DR and DME, and ARDA's output was compared against the adjudicated grades at 45 sites in Southern India. Patients were randomly selected between January 1, 2019, and July 31, 2023.

MAIN OUTCOMES AND MEASURES: Primary analyses were the sensitivity and specificity of ARDA for severe nonproliferative DR (NPDR) or proliferative DR (PDR). Secondary analyses focused on sensitivity and specificity for sight-threatening DR (STDR) (DME or severe NPDR or PDR).

RESULTS: Among the 4537 patients with 4537 images with adjudicated grades, mean (SD) age was 55.2 (11.9) years and 2272 (50.1%) were male. Among the 3941 patients with gradable photographs, 683 (17.3%) had any DR, 146 (3.7%) had severe NPDR or PDR, 109 (2.8%) had PDR, and 398 (10.1%) had STDR. ARDA's sensitivity and specificity for severe NPDR or PDR were 97.0% (95% CI, 92.6%-99.2%) and 96.4% (95% CI, 95.7%-97.0%), respectively. Positive predictive value (PPV) was 50.7% and negative predictive value (NPV) was 99.9%. The clinically important miss rate for severe NPDR or PDR was 0% (eg, some patients with severe NPDR or PDR were interpreted as having moderate DR and referred to clinic). ARDA's sensitivity for STDR was 95.9% (95% CI, 93.0%-97.4%) and specificity was 94.9% (95% CI, 94.1%-95.7%); PPV and NPV were 67.9% and 99.5%, respectively.

CONCLUSIONS AND RELEVANCE: In this cross-sectional study investigating the clinical performance of ARDA, sensitivity and specificity for severe NPDR and PDR exceeded 96% and caught 100% of patients with severe NPDR and PDR for ophthalmology referral. This preliminary large-scale postmarketing report of the performance of ARDA after screening 600 000 patients in India underscores the importance of monitoring and publication an algorithm's clinical performance, consistent with recommendations by regulatory bodies.

PMID:40105843 | DOI:10.1001/jamanetworkopen.2025.0984

J Chem Theory Comput. 2025 Mar 19. doi: 10.1021/acs.jctc.4c01136. Online ahead of print.

ABSTRACT

A major challenge for many rare-event sampling strategies is the identification of progress coordinates that capture the slowest relevant motions. Machine-learning methods that can identify progress coordinates in an unsupervised manner have therefore been of great interest to the simulation community. Here, we developed a general method for identifying progress coordinates "on-the-fly" during weighted ensemble (WE) rare-event sampling via deep learning (DL) of outliers among sampled conformations. Our method identifies outliers in a latent space model of the system's sampled conformations that is periodically trained using a convolutional variational autoencoder. As a proof of principle, we applied our DL-enhanced WE method to simulate the NTL9 protein folding process. To enable rapid tests, our simulations propagated discrete-state synthetic molecular dynamics trajectories using a generative, fine-grained Markov state model. Results revealed that our on-the-fly DL of outliers enhanced the efficiency of WE by >3-fold in estimating the folding rate constant. Our efforts are a significant step forward in the unsupervised learning of slow coordinates during rare event sampling.

PMID:40105797 | DOI:10.1021/acs.jctc.4c01136

Technol Health Care. 2025 Mar 19:9287329241302736. doi: 10.1177/09287329241302736. Online ahead of print.

ABSTRACT

Ovarian cancer is a highly prevalent cancer among women; However, it remains difficult to find effective pharmacological solutions to treat this deadly disease. However, early detection can significantly increase life expectancy. To address this issue, a predictive model for early diagnosis of ovarian cancer was developed by applying statistical techniques and machine learning models to clinical data from 349 patients. A hybrid evolutionary deep learning model was proposed by integrating genetic and histopathological imaging modalities within a multimodal fusion framework. Machine learning pipelines have been built using feature selection and dilution approaches to identify the most relevant genes for disease classification. A comparison was performed between the UNeT and transformer models for semantic segmentation, leading to the development of an optimized fuzzy C-means clustering algorithm (FCM-NPOA-PM-UI) for the classification of gynecological abdominopelvic tumors. Performing better than individual classifiers and other machine learning methods, the suggested ensemble model achieved an average accuracy of 98.96%, precision of 97.44%, and F1 score of 98.7%. With average Dice scores of 0.98 and 0.97 for positive tumors and 0.99 and 0.98 for malignant tumors, the Transformer model performed better in segmentation than the UNeT model. Additionally, we observed a 92.8% increase in accuracy when combining five machine learning models with biomarker data: random forest, logistic regression, SVM, decision tree, and CNN. These results demonstrate that the hybrid model significantly improves the accuracy and efficiency of ovarian cancer detection and classification, offering superior performance compared to traditional methods and individual classifiers.

PMID:40105378 | DOI:10.1177/09287329241302736

J Chem Inf Model. 2025 Mar 19. doi: 10.1021/acs.jcim.5c00199. Online ahead of print.

ABSTRACT

Cell-penetrating peptides (CPPs) are usually short oligopeptides with 5-30 amino acid residues. CPPs have been proven as important drug delivery vehicles into cells through different mechanisms, demonstrating their potential as therapeutic candidates. However, experimental screening and synthesis of CPPs could be time-consuming and expensive. Recently, numerous attempts have been made to develop computational methods as a cost-effective way for screening a number of potential CPP candidates. Despite significant advancements, current methods exhibit limited feature representation capabilities, thereby constraining the potential for further performance enhancements. In this study, we developed a deep learning framework called CPPCGM, which uses protein language models (PLMs) to identify and generate novel CPPs. There are two separate blocks in this framework: CPPClassifier and CPPGenerator. The former utilizes three pretrained models for simple voting, thereby accurately categorizing CPPs and non-CPPs. The latter, similar to a generative adversarial network, including a discriminator and a generator, generates peptides that are not present in the training data set. Our proposed CPPCGM has achieved remarkably high Matthews correlation coefficient scores of 0.876, 0.923, and 0.664 on three data sets based on the classification results. Compared with the state-of-the-art methods, the performance of our method is significantly improved. The results also demonstrated the generating potential of CPPCGM through qualitative and quantitative evaluation of the generated samples. Significantly, using PLM-based methods can optimize peptides for biochemical functions, benefiting drug delivery and biomedical applications. Materials related are publicly available at https://github.com/QiufenChen/CPPCGM.

PMID:40105337 | DOI:10.1021/acs.jcim.5c00199

Eur J Prosthodont Restor Dent. 2025 Mar 13. doi: 10.1922/EJPRD_2801Hassan09. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to develop an artificial intelligence system for automated classification of partially edentulous arches from panoramic radiographs using the Kennedy classification system and Applegate's rules, alongside identifying existing teeth for automated reporting.

METHODS: From 5261 anonymized digital panoramic radiographs collected from publicly available datasets, 1875 high-quality images were selected and divided into training (80%), validation (10%), and testing (10%) sets. Teeth were manually annotated on the Roboflow platform following the Universal Numbering System. To enhance model robustness, data augmentation techniques were applied, expanding the dataset to 2398 images. For tooth detection, a YOLOv8s deep learning model was trained for 80 epochs (batch size: 16, learning rate: 0.01). Performance was evaluated using precision, recall, F1 score, and mean average precision. Detected teeth were used to classify partially edentulous areas based on the Kennedy system. Modification areas were identified by analyzing detected and missing teeth, measuring bounded distances in millimetres, and classifying free-end saddle gaps.

RESULTS: The YOLOv8s model achieved a mean average precision (mAP50) of 98.1% for tooth identification, with precision and recall of 95.7% and 95.8%, respectively. For Kennedy classification, the model demonstrated precision of 0.962, recall of 0.931, and an F1-score of 0.939 across maxillary and mandibular arches.

CONCLUSIONS: The high accuracy and efficiency of this AI-driven approach can standardize classification, reduce diagnostic variability, and alleviate the workload for dental professionals, enabling seamless integration into clinical practice.

CLINICAL RELEVANCE: This AI system provides a consistent, accurate, and reliable method for classifying partially edentulous arches from panoramic radiographs, reducing manual assessment variability, easing practitioner workload, and enabling large-scale analysis of partial edentulism prevalence.

PMID:40105321 | DOI:10.1922/EJPRD_2801Hassan09

mSystems. 2025 Mar 19:e0018325. doi: 10.1128/msystems.00183-25. Online ahead of print.

ABSTRACT

In the field of synthetic biology, the engineering of synthetic promoters that outperform their natural counterparts is of paramount importance, which can optimize the expression of exogenous genes, enhance the efficiency of metabolic pathways, and possess substantial commercial value. Research indicates that some synthetic promoters have higher transcriptional activity compared to strong natural promoters. However, with the exponential increase in complexity due to the 4n potential combinations in a promoter sequence of length n, identifying effective synthetic promoters remains a formidable challenge. Deep learning models, by adaptively learning from extensive data sets, have become instrumental in analyzing biological data. This study introduces a diffusion model-based approach for designing promoters viable in model bacteria such as Escherichia coli and cyanobacteria. This model proficiently assimilates and utilizes inherent biological features from natural promoter sequences to engineer synthetic variants. Additionally, we employed a transformer model to evaluate the efficacy of these synthetic promoters, aiming at screening those with high performance. The experimental findings suggest that the synthetic promoters by the diffusion model not only share key biological features with their natural counterparts but also demonstrate greater similarity to natural promoters than those generated by a variational autoencoder. In predicting promoter strength, the transformer model demonstrated improved performance over the convolutional neural network. Finally, we developed an integrated platform for generating promoters and predicting their strength.

IMPORTANCE: We demonstrated that diffusion models are superior in accomplishing the promoter synthesis task compared to other state-of-the-art deep learning models. The effectiveness of our method was validated using data sets of Escherichia coli and cyanobacteria promoters, showing more stable and prompt convergence and more natural-like promoters than the variational autoencoder model. We extracted sequence information, dimer information, and position information from promoters and combined them with a transformer model to predict promoter strength. Our prediction results were more accurate than those obtained with a convolutional neural network model. Our in silico experiments systematically introduced mutations in promoter sequences and explored their contribution to promoter strength, highlighting the depth of learning in our model.

PMID:40105319 | DOI:10.1128/msystems.00183-25

Nanoscale. 2025 Mar 19. doi: 10.1039/d4nr05031b. Online ahead of print.

ABSTRACT

Bismuth selenide (Bi2Se3) has emerged as a promising material for high-performance photodetectors due to its wideband spectral response, strong in-plane anisotropy, narrow bandgap, high absorption coefficient, and carrier mobility. However, inherent defects and states in Bi2Se3-based devices reduce optical conversion efficiency and stability. To address these challenges, we report the design and preparation of Bi2Se2.33S0.67 nanowires by a facile chemical vapor transport method. The individual Bi2Se2.33S0.67 nanowire photodetectors exhibit remarkable photoresponse over a broadband wavelength region ranging from ultraviolet C (254 nm) to near-infrared (1064 nm) with a low dark current of 0.015 nA and the measured maximum photoresponsivity of 2.52 A W-1 at 532 nm, together with a detectivity of around 5.2 × 1011 Jones. Furthermore, the photoresponse of photodetectors exhibits polarization angle sensitivity within a broadband range of 355 to 808 nm. The structural anisotropy of the Bi2Se2.33S0.67 crystal leads to a maximum dichroic ratio of about 1.8 at 355 nm. Additionally, cat images produced by this device further demonstrate the potential of the high-performance devices, and the effectiveness of photodetectors in deep learning image recognition validates their wide-spectrum, high-responsivity, and superior polarization-sensitive detection capabilities.

PMID:40105281 | DOI:10.1039/d4nr05031b

Technol Health Care. 2025 Mar;33(2):1099-1120. doi: 10.1177/09287329241296438. Epub 2024 Dec 4.

ABSTRACT

BackgroundBreast cancer results from an uncontrolled growth of breast tissue. Many methods of diagnosis are using multi-omics data to better understand the complexity of breast cancer.ObjectiveThe new strategy laid out in this work, called "Hybrid-OmniSeq," is a deep learning-based multi-omics data analysis technology that uses molecular subtypes of breast cancer gene to increase the precision and effectiveness of breast cancer diagnosis.MethodFor preprocessing, the BC-VM procedure is utilized, and for molecular subtype analysis, the BC-MSA procedure is utilized. The implementation of Deep Neural Network (DNN) technology in conjunction with Sequential Forward Floating Selection (SFFS) and Truncated Singular Value Decomposition (TSVD) entropy enable adaptive learning from multi-omics gene data. Five machine learning classifiers are used for classification purpose. Hybrid-OmniSeq uses a variety of machine learning classifiers in a thorough analytical process to achieve remarkable diagnostic accuracy. Deep Learning-based multi-omics sequential approach was evaluated using METABRIC RNA-seq data sets of intrinsic subtypes of breast cancer.ResultsAccording to test results, Logistic Regression (LR) had ER (Estrogen Receptor) status values of 94.51%, ER status values of 96.33%, and HER2 (Human Epidermal growth factor Receptor) status values of 92.3%; Random Forest (RF) had ER status values of 93.77%, ER status values of 95.23%, and HER2 status values of 93.4%.ConclusionLR and RF increase the cancer detection accuracy for all subtypes when compared to alternative machine learning classifiers or the majority voting method, providing a comprehensive understanding of the underlying causes of breast cancer.

PMID:40105178 | DOI:10.1177/09287329241296438

Technol Health Care. 2025 Mar;33(2):989-999. doi: 10.1177/09287329241297900. Epub 2024 Nov 20.

ABSTRACT

BackgroundDeep learning excels at processing raw data because it automatically extracts and classifies high-level features. Despite biology's low popularity in data analysis, incorporating computer technology can improve biological research.ObjectiveTo create a deep learning model that can identify nucleosomes from nucleotide sequences and to show that simpler models outperform more complicated ones in solving biological challenges.MethodsA classifier was created utilising deep learning and machine learning approaches. The final model consists of two convolutional layers, one max pooling layer, two fully connected layers, and a dropout regularisation layer. This structure was chosen on the basis of the 'less is frequently more' approach, which emphasises simple design without large hidden layers.ResultsExperimental results show that deep learning methods, specifically deep neural networks, outperform typical machine learning algorithms for recognising nucleosomes. The simplified network architecture proved suitable without the requirement for numerous hidden neurons, resulting in effective network performance.ConclusionThis study demonstrates that machine learning and other computational techniques may streamline and expedite the resolution of biological issues. The model helps identify nucleosomes and can be used in future research or labs. This study discusses the challenges of understanding and addressing simple biological problems with sophisticated computer technology and offers practical solutions for academic and economic sectors.

PMID:40105177 | DOI:10.1177/09287329241297900

Dis Model Mech. 2025 Mar 1;18(3):dmm052113. doi: 10.1242/dmm.052113. Epub 2025 Mar 19.

ABSTRACT

Cancer screening relies upon a linear model of neoplastic growth and progression. Yet, historical observations suggest that malignant progression is uncoupled from growth, which may explain the paradoxical increase in early-stage breast cancer detection without a dramatic reduction in metastasis. Here, we lineage trace millions of transformed cells and thousands of tumors using a cancer rainbow mouse model of HER2 (also known as ERBB2)-positive breast cancer. Transition rates from field cell to screen-detectable tumor to symptomatic tumor were estimated from a dynamical model of tumor development. Field cells were orders of magnitude less likely to transition to a screen-detectable tumor than the subsequent transition from screen-detectable tumor to symptomatic tumor. Our model supports a critical 'occult' transition in tumor development during which a transformed cell becomes a bona fide neoplasm. Lineage tracing and test by transplantation revealed that nonlinear progression during the occult transition gives rise to nascent lethal cancers at screen detection. Simulations illustrated how occult transition rates are a critical determinant of tumor growth and malignancy. Our data provide direct experimental evidence that cancers can deviate from the predictable linear progression model that is foundational to current screening paradigms.

PMID:40105775 | DOI:10.1242/dmm.052113

Front Digit Health. 2025 Mar 4;7:1550407. doi: 10.3389/fdgth.2025.1550407. eCollection 2025.

ABSTRACT

Early diagnosis and accurate prognosis play a pivotal role in the clinical management of cancer and in preventing cancer-related mortalities. The burgeoning population of Asia in general and South Asian countries like India in particular pose significant challenges to the healthcare system. Regrettably, the demand for healthcare services in India far exceeds the available resources, resulting in overcrowded hospitals, prolonged wait times, and inadequate facilities. The scarcity of trained manpower in rural settings, lack of awareness and low penetrance of screening programs further compounded the problem. Artificial Intelligence (AI), driven by advancements in machine learning, deep learning, and natural language processing, can profoundly transform the underlying shortcomings in the healthcare industry, more for populous nations like India. With about 1.4 million cancer cases reported annually and 0.9 million deaths, India has a significant cancer burden that surpassed several nations. Further, India's diverse and large ethnic population is a data goldmine for healthcare research. Under these circumstances, AI-assisted technology, coupled with digital health solutions, could support effective oncology care and reduce the economic burden of GDP loss in terms of years of potential productive life lost (YPPLL) due to India's stupendous cancer burden. This review explores different aspects of cancer management, such as prevention, diagnosis, precision treatment, prognosis, and drug discovery, where AI has demonstrated promising clinical results. By harnessing the capabilities of AI in oncology research, healthcare professionals can enhance their ability to diagnose cancers at earlier stages, leading to more effective treatments and improved patient outcomes. With continued research and development, AI and digital health can play a transformative role in mitigating the challenges posed by the growing population and advancing the fight against cancer in India. Moreover, AI-driven technologies can assist in tailoring personalized treatment plans, optimizing therapeutic strategies, and supporting oncologists in making well-informed decisions. However, it is essential to ensure responsible implementation and address potential ethical and privacy concerns associated with using AI in healthcare.

PMID:40103737 | PMC:PMC11913822 | DOI:10.3389/fdgth.2025.1550407

Hum Genomics. 2025 Mar 19;19(1):29. doi: 10.1186/s40246-025-00733-w.

ABSTRACT

BACKGROUND: There is a vast prevalence of mental disorders, but patient responses to psychiatric medication fluctuate. As food choices and daily habits play a fundamental role in this fluctuation, integrating machine learning with network medicine can provide valuable insights into disease systems and the regulatory leverage of lifestyle in mental health.

METHODS: This study analyzed coexpression network modules of MDD and PTSD blood transcriptomic profile using modularity optimization method, the first runner-up of Disease Module Identification DREAM challenge. The top disease genes of both MDD and PTSD modules were detected using random forest model. Afterward, the regulatory signature of two predominant habitual phenotypes, diet-induced obesity and smoking, were identified. These transcription/translation regulating factors (TRFs) signals were transduced toward the two disorders' disease genes. A bipartite network of drugs that target the TRFS together with PTSD or MDD hubs was constructed.

RESULTS: The research revealed one MDD hub, the CENPJ, which is known to influence intellectual ability. This observation paves the way for additional investigations into the potential of CENPJ as a novel target for MDD therapeutic agents development. Additionally, most of the predicted PTSD hubs were associated with multiple carcinomas, of which the most notable was SHCBP1. SHCBP1 is a known risk factor for glioma, suggesting the importance of continuous monitoring of patients with PTSD to mitigate potential cancer comorbidities. The signaling network illustrated that two PTSD and three MDD biomarkers were co-regulated by habitual phenotype TRFs. 6-Prenylnaringenin and Aflibercept were identified as potential candidates for targeting the MDD and PTSD hubs: ATP6V0A1 and PIGF. However, habitual phenotype TRFs have no leverage over ATP6V0A1 and PIGF.

CONCLUSION: Combining machine learning and network biology succeeded in revealing biomarkers for two notoriously spreading disorders, MDD and PTSD. This approach offers a non-invasive diagnostic pipeline and identifies potential drug targets that could be repurposed under further investigation. These findings contribute to our understanding of the complex interplay between mental disorders, daily habits, and psychiatric interventions, thereby facilitating more targeted and personalized treatment strategies.

PMID:40102990 | DOI:10.1186/s40246-025-00733-w

Nat Rev Drug Discov. 2025 Mar 18. doi: 10.1038/s41573-025-01164-x. Online ahead of print.

ABSTRACT

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

PMID:40102635 | DOI:10.1038/s41573-025-01164-x