J Cachexia Sarcopenia Muscle. 2025 Apr;16(2):e13661. doi: 10.1002/jcsm.13661.

ABSTRACT

BACKGROUND: Sarcopenia presents a pressing public health concern due to its association with age-related muscle mass decline, strength loss and reduced physical performance, particularly in the growing older population. Given the absence of approved pharmacological therapies for sarcopenia, the need to discover effective pharmacological interventions has become critical.

METHODS: To address this challenge and discover new therapies, we developed a novel Multi-Task Attention-aware method for Multi-Omics data (MTA-MO) to extract complex biological insights from various biomedical data sources, including transcriptome, methylome and genome data to identify drug targets and discover new therapies. Additionally, MTA-MO integrates human protein-protein interaction (PPI) networks and drug-target networks to improve target identification. The novel method is applied to a multi-omics dataset that included 1055 participants aged 20-50 (mean (± SD) age 36.88 (± 8.64)), comprising 37.82% African-American and 62.18% Caucasian/White individuals. Physical activity levels were self-reported and categorized into three groups: ≥ 3 times/week, < 3 times/week and no regular exercise. Mean (± SD) measures for grip strength, appendicular lean mass (ALM), exercise frequency and smoking status (no/yes, n (%)) were 38.72 (± 8.93) kg, 28.65 (± 4.63) kg, 4.31 (± 1.79) and 30.81%/69.19%, respectively. Significant differences (p < 0.05) were found between groups in age, ALM, smoking, and consumption of milk, alcohol, beer and wine.

RESULTS: Using the MTA-MO method, we identified 639 gene targets, and by analysing PPIs and querying public databases, we narrowed this list down to seven potential hub genes associated with sarcopenia (ESR1, ATM, CDC42, EP300, PIK3CA, EGF and PTK2B). These findings were further validated through diverse levels of pathobiological evidence associated with sarcopenia. Gene Ontology and KEGG pathways analysis highlighted five key functions and signalling pathways relevant to skeletal muscle. The interaction network analysis identified three transcriptional factors (GATA2, JUN and FOXC1) as the key transcriptional regulators of the seven potential genes. In silico analysis of 1940 drug candidates identified canagliflozin as a promising candidate for repurposing in sarcopenia, demonstrating the strongest binding affinity to the PTK2B protein (inhibition constant 6.97 μM). This binding is stabilized by hydrophobic bonds, Van der Waals forces, pi-alkyl interactions and pi-anion interactions around PTK2B's active residues, suggesting its potential as a therapeutic option.

CONCLUSIONS: Our novel approach effectively integrates multi-omics data to identify potential treatments for sarcopenia. The findings suggest that canagliflozin could be a promising therapeutic candidate for sarcopenia.

PMID:40045692 | DOI:10.1002/jcsm.13661

Drug Dev Ind Pharm. 2025 Mar 6:1-17. doi: 10.1080/03639045.2025.2476651. Online ahead of print.

ABSTRACT

OBJECTIVE: The propose of the present study was to apply the design of experiments (DoE) to develop an omeprazole enteric pellets suspension for use in the paediatric population.

METHODOLOGY: This experimental study employed a Full Factorial Design for drug development, encompassing three factors (Aerosil® R972, cetostearyl alcohol, and Span 80) at two levels (2% and 6% for factor A (Aerosil® R972) and 2% and 4% for factors B and C (cetostearyl alcohol and Span 80, respectively)).

RESULTS: Following the statistical optimization, the suspension F10 was formulated and subjected to a stability study for one month. The dissolution test results were suboptimal, achieving only an 22% release. Subsequently, eight additional suspensions were devised using hydrophilic oily vehicles (Labraphac Hydrophile WL 1219, Labrafil M2125 CS and Labrafil M 1944 CS) and excipients (Gelucire 44/14 and Aerosil® 200) to enhance the dissolution profile. Suspension F17 showed over 75% within 30 minutes, displaying superior sedimentation time when compared to all other formulations, along with effortless resuspension.

CONCLUSION: The findings suggest that the optimal vehicle for the administration of omeprazole enteric pellets in suspension is the formulation comprising Labrafil M 1944 CS, Span 80, and Aerosil® 200. This study has paved the way for an oily suspension vehicle, opening new avenues of research for developing paediatric omeprazole formulations that fulfil gastro-resistance requirements.

PMID:40047104 | DOI:10.1080/03639045.2025.2476651

Mol Oncol. 2025 Mar 6. doi: 10.1002/1878-0261.70013. Online ahead of print.

ABSTRACT

While the incidence of endometrial cancer is increasing among all US women, Black women face higher mortality rates. The reasons for this remain unclear. In this study, whole genome differential methylation analysis, along with state-of-the-art computational methods such as the recursive feature elimination technique and supervised/unsupervised machine learning models, was used to identify 38 epigenetic signature genes (ESGs) and four core-ESGs (cg19933311: TRPC5; cg09651654: APOBEC1; cg27299712: PLEKHG5; cg03150409: WHSC1) in endometrial tumors from Black and White women, incorporating genetic ancestry estimation. Methylation at two Core-ESGs, namely APOBEC1 and PLEKHG5, showed statistically significant overall survival differences between the two ancestral groups (Likelihood ratio test; P value = 0.006). Moreover, our comprehensive ancestry-based analysis revealed that tumors from women with high African ancestry exhibited increased hypomethylation compared to those with low African ancestry. These hypomethylated genes were enriched in drug metabolism pathways, indicating a potential link between genetic ancestry, epigenetic modifications, and pharmacogenomic responses. Combining ancestry, race, and disease type may help identify which patient groups will benefit most from these biomarkers for targeted treatments.

PMID:40045917 | DOI:10.1002/1878-0261.70013

Am J Physiol Cell Physiol. 2025 Mar 6. doi: 10.1152/ajpcell.00026.2025. Online ahead of print.

ABSTRACT

The intestine, as a critical interface between the external environment and the internal body, plays a central role in nutrient absorption, immune regulation, and maintaining homeostasis. The intestinal epithelium, composed of specialized epithelial cells, hosts apical anion transporters that primarily mediate the transport of chloride and bicarbonate ions, essential for maintaining electrolyte balance, pH homeostasis, and fluid absorption/secretion. Additionally, the intestine hosts a diverse population of gut microbiota that plays a pivotal role in various physiological processes including nutrient metabolism, immune regulation and maintenance of intestinal barrier integrity, all of which are critical for host gut homeostasis and health. The anion transporters and gut microbiome are intricately interconnected, where alterations in one can trigger changes in the other leading to compromised barrier integrity and increasing susceptibility to pathophysiological states including gut inflammation. This review focuses on the interplay of key apical anion transporters including Down Regulated in Adenoma (DRA, SLC26A3), Putative Anion Transporter-1 (PAT1, SLC26A6) and Cystic Fibrosis Transmembrane Conductance Regulator (CFTR, ABCC7) with the gut microbiome, barrier integrity and their relationship to gut inflammation.

PMID:40047092 | DOI:10.1152/ajpcell.00026.2025

Cureus. 2025 Feb 3;17(2):e78441. doi: 10.7759/cureus.78441. eCollection 2025 Feb.

ABSTRACT

Spirometry is the most common and straightforward examination following the mandatory initial steps of personal history and physical examination when assessing chronic and/or recurrent lung symptoms in children, especially cough or specific conditions that can impact lung function. When dealing with a chronic cough (lasting more than four weeks), it is not uncommon to find that no specific clues regarding the cause of the cough can be deduced from the patient's history alone. Moreover, clinical examinations can be quite normal without any abnormal lung sounds. In the next step (spirometry), surprisingly, as the child forcefully expels the air from the lungs, you can hear secretions moving along the bronchi as the rapidly moving air drifts excessive sputum to the upper airways. Less commonly, forceful exhalation during spirometry may uncover a brassy or honking sound, indicating potential collapsibility of the tracheal walls. Secretions in the bronchi can imply specific conditions, such as protracted bacterial bronchitis or suppurative bronchitis. Additionally, forced exhalation may uncover sputum in other chronic lung conditions, such as cystic fibrosis or primary cilia dyskinesia. The presence of tracheomalacia is an important parameter, as it can precipitate several respiratory symptoms, ranging from prolonged cough in acute bronchitis to recurrent and/or chronic wet cough. In conclusion, forceful exhalation during spirometry has the potential to uncover secretions in the bronchi or even tracheomalacia that might otherwise go unnoticed. Before watching the flow-volume loop or interpreting the results of the spirometry parameters, we should first "hear" the spirometry.

PMID:40046377 | PMC:PMC11882152 | DOI:10.7759/cureus.78441

Cureus. 2025 Feb 3;17(2):e78408. doi: 10.7759/cureus.78408. eCollection 2025 Feb.

ABSTRACT

Cystic fibrosis liver disease (CFLD) is a common complication of cystic fibrosis (CF), typically emerging within the first two decades of life. It significantly impacts both short- and long-term prognosis, being the third leading cause of mortality in this population. We present the case of a child with a rare CF mutation who was diagnosed with CFLD, portal hypertension, esophageal varices, and early-onset CF-related diabetes at the age of 6. This case provides valuable insights into the early onset and progression of CF liver disease, highlighting the importance of timely diagnosis and management.

PMID:40046358 | PMC:PMC11880950 | DOI:10.7759/cureus.78408

Phage (New Rochelle). 2024 Dec 18;5(4):203-222. doi: 10.1089/phage.2024.0011. eCollection 2024 Dec.

ABSTRACT

Rising antibiotic resistance among bacterial pathogens has become a substantial health issue for human civilization. The emergence of these pathogens in high-risk diseases such as cystic fibrosis (CF) has led to financial and nonfinancial losses, necessitating alternative therapies. This study presents an overview of such approaches, including phage therapy, antimicrobial peptides (AMPs), nanotechnology, monoclonal antibodies (mAbs), microbial therapies (probiotic therapy), clustered regularly interspaced short palindromic repeats technology (CRISPR), and aptamers focusing on their mechanisms of action and exploring the impact of combining phage and phage derivatives with the mentioned approaches. Although alternative approaches and their combinations with phages show promise, the phage-antibiotic combination has been the subject of most studies, and It has been proven to be highly effective in combating antibiotic-resistant infections. Other combinations also appear promising, but further studies are needed to determine their effectiveness. This emphasizes the need for more thorough research into different phage combination treatments beyond the well-established phage-antibiotic strategy.

PMID:40045937 | PMC:PMC11876824 | DOI:10.1089/phage.2024.0011

J Pediatr Orthop. 2025 Mar 6. doi: 10.1097/BPO.0000000000002947. Online ahead of print.

ABSTRACT

BACKGROUND: Although cystic fibrosis (CF) mainly affects the respiratory and gastrointestinal systems, it may frequently present with musculoskeletal manifestations including bone fractures, low bone mineral density, and spinal pathologies. Assessment of spinal pathologies in CF patients is of vital importance because the effects on lung capacity and spinal posture are clearly defined.

QUESTIONS/PURPOSES: The frequency of vertebral pathologies in CF patients has yet to be determined. The aim of this study was to investigate the frequency of scoliosis and hyperkyphosis and the relationship of coronal, sagittal, and spinopelvic parameters with disease severity in CF patients.

METHODS: Patients were tested with forced expiratory volume in 1 second (FEV1), dual-energy x-ray absorptiometry (DEXA), and full spine radiographs. Measurements were taken of the major coronal curve in the coronal plane, cervical and lumber lordosis, thoracic kyphosis, and C7 plumb line values. Patients were categorized into 3 groups based on the FEV1 values (severity) from respiratory function tests (severe: group 1 FEV 1≤40, moderate: group 2 FEV1 40 to 80, mild: group).

RESULTS: This cross-sectional study included 208 CF patients aged 5 to 21 years. The rates of scoliosis and thoracic hyperkyphosis were 31% (n=64) and 24% (n=50), respectively. The highest rates of scoliosis (63%) and thoracic hyperkyphosis (56%) were found in the severe CF group (P=0.016 and P=0.006, respectively). FEV1 and thoracic kyphosis were weakly and inversely but significantly correlated (rho: -0.200 and P=0.004). There was no difference in BMD between patients with and without scoliosis and between patients with and without hyperkyphosis. There was no significant difference in DEXA Z-score between patients with and without hyperkyphosis. The L1-L4 DEXA Z-score of patients without scoliosis was significantly higher (P=0.017).

CONCLUSIONS: Scoliosis and hyperkyphosis were more prevalent in the severe CF patients group, although the proportion of patients requiring treatment was relatively low. Understanding the relationship between disease severity and coronal and sagittal spinal balance, and spinopelvic parameters is crucial, as it guides the early detection and management of scoliosis in CF patients.

LEVEL OF EVIDENCE: Level II.

PMID:40045563 | DOI:10.1097/BPO.0000000000002947

J Insur Med. 2025 Jul 1;52(1):21-22. doi: 10.17849/insm-52-1-21-22.1.

ABSTRACT

In the past, the chest X-ray (CXR) was a traditional age and amount requirement used to assess potential mortality risk in life insurance applicants. It fell out of favor due to inconvenience to the applicant, cost, and lack of protective value. With the advent of deep learning techniques, can the results of the CXR, as a requirement, now add additional value to underwriting risk analysis?

PMID:40047110 | DOI:10.17849/insm-52-1-21-22.1

Imaging Neurosci (Camb). 2024 Jul 3;2:1-19. doi: 10.1162/imag_a_00215. eCollection 2024 Jul 1.

ABSTRACT

It remains largely unknown whether individualised longitudinal changes of brain imaging features can be predicted based only on the baseline brain images. This would be of great value, for example, for longitudinal data imputation, longitudinal brain-behaviour associations, and early prediction of brain-related diseases. We explore this possibility using longitudinal data of multiple modalities from UK Biobank brain imaging, with around 3,500 subjects. As baseline and follow-up images are generally similar in the case of short follow-up time intervals (e.g., 2 years), a simple copy of the baseline image may have a very good prediction performance. Therefore, for the first time, we propose a new mathematical framework for guiding the longitudinal prediction of brain images, providing answers to fundamental questions: (1) what is a suitable definition of longitudinal change; (2) how to detect the existence of changes; (3) what is the "null" prediction performance; and (4) can we distinguish longitudinal change prediction from simple data denoising. Building on these, we designed a deep U-Net based model for predicting longitudinal changes in multimodal brain images. Our results show that the proposed model can predict to a modest degree individualised longitudinal changes in almost all modalities, and outperforms other potential models. Furthermore, compared with the true longitudinal changes computed from real data, the predicted longitudinal changes have a similar or even improved accuracy in predicting subjects' non-imaging phenotypes, and have a high between-subject discriminability. Our study contributes a new theoretical framework for longitudinal brain imaging studies, and our results show the potential for longitudinal data imputation, along with highlighting several caveats when performing longitudinal data analysis.

PMID:40046980 | PMC:PMC11877422 | DOI:10.1162/imag_a_00215

Front Sports Act Living. 2025 Jan 30;6:1512010. doi: 10.3389/fspor.2024.1512010. eCollection 2024.

ABSTRACT

The determination rock climbing route difficulty is notoriously subjective. While there is no official standard for determining the difficulty of a rock climbing route, various difficulty rating scales exist. But as the sport gains more popularity and prominence on the international stage at the Olympic Games, the need for standardized determination of route difficulty becomes more important. In commercial climbing gyms, consistency and accuracy in route production are crucial for success. Route setters often rely on personal judgment when determining route difficulty, but the success of commercial climbing gyms requires their objectivity in creating diverse, inclusive, and accurate routes. Machine and deep learning techniques have the potential to introduce a standardized form of route difficulty determination. This survey review categorizes machine and deep learning approaches taken, identifies the methods and algorithms used, reports their degree of success, and proposes areas of future work for determining route difficulty. The primary three approaches were from a route-centric, climber-centric, or path finding and path generation context. Of these, the most optimal methods used natural language processing or recurrent neural network algorithms. From these methods, it is argued that the objective difficulty of a rock climbing route has been best determined by route-centric, natural-language-like approaches.

PMID:40046938 | PMC:PMC11881084 | DOI:10.3389/fspor.2024.1512010

Exp Biol Med (Maywood). 2025 Feb 19;250:10444. doi: 10.3389/ebm.2025.10444. eCollection 2025.

ABSTRACT

Advancements in machine learning and deep learning have the potential to revolutionize the diagnosis of melanocytic choroidal tumors, including uveal melanoma, a potentially life-threatening eye cancer. Traditional machine learning methods rely heavily on manually selected image features, which can limit diagnostic accuracy and lead to variability in results. In contrast, deep learning models, particularly convolutional neural networks (CNNs), are capable of automatically analyzing medical images, identifying complex patterns, and enhancing diagnostic precision. This review evaluates recent studies that apply machine learning and deep learning approaches to classify uveal melanoma using imaging modalities such as fundus photography, optical coherence tomography (OCT), and ultrasound. The review critically examines each study's research design, methodology, and reported performance metrics, discussing strengths as well as limitations. While fundus photography is the predominant imaging modality being used in current research, integrating multiple imaging techniques, such as OCT and ultrasound, may enhance diagnostic accuracy by combining surface and structural information about the tumor. Key limitations across studies include small dataset sizes, limited external validation, and a reliance on single imaging modalities, all of which restrict model generalizability in clinical settings. Metrics such as accuracy, sensitivity, and area under the curve (AUC) indicate that deep learning models have the potential to outperform traditional methods, supporting their further development for integration into clinical workflows. Future research should aim to address current limitations by developing multimodal models that leverage larger, diverse datasets and rigorous validation, thereby paving the way for more comprehensive, reliable diagnostic tools in ocular oncology.

PMID:40046904 | PMC:PMC11879745 | DOI:10.3389/ebm.2025.10444

Med Image Comput Comput Assist Interv. 2024 Oct;15004:102-112. doi: 10.1007/978-3-031-72083-3_10. Epub 2024 Oct 14.

ABSTRACT

Whole slide image (WSI) classification plays a crucial role in digital pathology data analysis. However, the immense size of WSIs and the absence of fine-grained sub-region labels pose significant challenges for accurate WSI classification. Typical classification-driven deep learning methods often struggle to generate informative image representations, which can compromise the robustness of WSI classification. In this study, we address this challenge by incorporating both discriminative and contrastive learning techniques for WSI classification. Different from the existing contrastive learning methods for WSI classification that primarily rely on pseudo labels assigned to patches based on the WSI-level labels, our approach takes a different route to directly focus on constructing positive and negative samples at the WSI-level. Specifically, we select a subset of representative image patches to represent WSIs and create positive and negative samples at the WSI-level, facilitating effective learning of informative image features. Experimental results on two datasets and ablation studies have demonstrated that our method significantly improved the WSI classification performance compared to state-of-the-art deep learning methods and enabled learning of informative features that promoted robustness of the WSI classification.

PMID:40046787 | PMC:PMC11877581 | DOI:10.1007/978-3-031-72083-3_10

Front Big Data. 2025 Feb 19;8:1503883. doi: 10.3389/fdata.2025.1503883. eCollection 2025.

ABSTRACT

INTRODUCTION: Skin diseases significantly impact individuals' health and mental wellbeing. However, their classification remains challenging due to complex lesion characteristics, overlapping symptoms, and limited annotated datasets. Traditional convolutional neural networks (CNNs) often struggle with generalization, leading to suboptimal classification performance. To address these challenges, this study proposes a Hybrid Deep Transfer Learning Method (HDTLM) that integrates DenseNet121 and EfficientNetB0 for improved skin disease prediction.

METHODS: The proposed hybrid model leverages DenseNet121's dense connectivity for capturing intricate patterns and EfficientNetB0's computational efficiency and scalability. A dataset comprising 19 skin conditions with 19,171 images was used for training and validation. The model was evaluated using multiple performance metrics, including accuracy, precision, recall, and F1-score. Additionally, a comparative analysis was conducted against state-of-the-art models such as DenseNet121, EfficientNetB0, VGG19, MobileNetV2, and AlexNet.

RESULTS: The proposed HDTLM achieved a training accuracy of 98.18% and a validation accuracy of 97.57%. It consistently outperformed baseline models, achieving a precision of 0.95, recall of 0.96, F1-score of 0.95, and an overall accuracy of 98.18%. The results demonstrate the hybrid model's superior ability to generalize across diverse skin disease categories.

DISCUSSION: The findings underscore the effectiveness of the HDTLM in enhancing skin disease classification, particularly in scenarios with significant domain shifts and limited labeled data. By integrating complementary strengths of DenseNet121 and EfficientNetB0, the proposed model provides a robust and scalable solution for automated dermatological diagnostics.

PMID:40046767 | PMC:PMC11879938 | DOI:10.3389/fdata.2025.1503883

Front Oncol. 2025 Feb 19;15:1520972. doi: 10.3389/fonc.2025.1520972. eCollection 2025.

ABSTRACT

INTRODUCTION: The colony forming assay (CFA) stands as a cornerstone technique for evaluating the clonal expansion ability of single cancer cells and is crucial for assessing drug efficacy. However, traditional CFAs rely on labor-intensive, endpoint manual counting, offering limited insights into the dynamic effects of treatment. To overcome these limitations, we developed an Artificial Intelligence (AI)-assisted automated CFA combining time-lapse microscopy for real-time tracking of colony formation.

METHODS: Using B-acute lymphoblastic leukemia (B-ALL) cells from an E2A-PBX1 mouse model, we cultured them in a collagen-based 3D matrix with cytokines under static conditions in a low volume (60 µl) culture vessel and validated its comparability to methylcellulose-based media. No significant differences in final colony count or plating efficiency were observed. Our automated platform utilizes a deep learning and multi-object tracking approach for colony counting. Brightfield images were used to train a YOLOv8 object detection network, achieving a mAP50 score of 86% for identifying single cells, clusters, and colonies, and 97% accuracy for Z-stack colony identification with a multi-object tracking algorithm. The detection model accurately identified the majority of objects in the dataset.

RESULTS: This AI-assisted CFA was successfully applied for density optimization, enabling the determination of seeding densities that maximize plating efficiency (PE), and for IC50 determination, offering an efficient, less labor-intensive method for testing drug concentrations. In conclusion, our novel AI-assisted automated colony counting platform enables automated, high-throughput analysis of colony dynamics, significantly reducing labor and increasing accuracy. Furthermore, it allows detailed, long-term studies of cell-cell interactions and treatment responses using live-cell imaging and AI-assisted cell tracking.

DISCUSSION: Future integration with a perfusion-based drug screening system promises to enhance personalized cancer therapy by optimizing broad drug screening approaches and enabling real-time evaluation of therapeutic efficacy.

PMID:40046624 | PMC:PMC11879803 | DOI:10.3389/fonc.2025.1520972

Phys Imaging Radiat Oncol. 2025 Feb 1;33:100710. doi: 10.1016/j.phro.2025.100710. eCollection 2025 Jan.

ABSTRACT

BACKGROUND AND PURPOSE: A comprehensive understanding of radiotherapy toxicity requires analysis of multimodal data. However, it is challenging to develop a model that can analyse both 3D imaging and clinical data simultaneously. In this study, a deep learning model is proposed for simultaneously analysing computed tomography scans, dose distributions, and clinical metadata to predict toxicity, and identify the impact of clinical risk factors and anatomical regions.

MATERIALS AND METHODS: : A deep model based on multiple instance learning with feature-level fusion and attention was developed. The study used a dataset of 313 patients treated with 3D conformal radiation therapy and volumetric modulated arc therapy, with heterogeneous cohorts varying in dose, volume, fractionation, concomitant therapies, and follow-up periods. The dataset included 3D computed tomography scans, planned dose distributions to the bowel cavity, and patient clinical data. The model was trained on patient-reported data on late bowel toxicity.

RESULTS: Results showed that the network can identify potential risk factors and critical anatomical regions. Analysis of clinical data jointly with imaging and dose for bowel urgency and faecal incontinence improved performance (area under receiver operating characteristic curve [AUC] of 88% and 78%, respectively) while best performance for diarrhoea was when analysing clinical features alone (68% AUC).

CONCLUSIONS: Results demonstrated that feature-level fusion along with attention enables the network to analyse multimodal data. This method also provides explanations for each input's contribution to the final result and detects spatial associations of toxicity.

PMID:40046574 | PMC:PMC11880715 | DOI:10.1016/j.phro.2025.100710

Phys Imaging Radiat Oncol. 2025 Feb 14;33:100733. doi: 10.1016/j.phro.2025.100733. eCollection 2025 Jan.

ABSTRACT

BACKGROUND AND PURPOSE: Deep learning (DL) models can extract prognostic image features from pre-treatment PET/CT scans. The study objective was to explore the potential benefits of incorporating pathologic lymph node (PL) spatial information in addition to that of the primary tumor (PT) in DL-based models for predicting local control (LC), regional control (RC), distant-metastasis-free survival (DMFS), and overall survival (OS) in oropharyngeal cancer (OPC) patients.

MATERIALS AND METHODS: The study included 409 OPC patients treated with definitive (chemo)radiotherapy between 2010 and 2022. Patient data, including PET/CT scans, manually contoured PT (GTVp) and PL (GTVln) structures, clinical variables, and endpoints, were collected. Firstly, a DL-based method was employed to segment tumours in PET/CT, resulting in predicted probability maps for PT (TPMp) and PL (TPMln). Secondly, different combinations of CT, PET, manual contours and probability maps from 300 patients were used to train DL-based outcome prediction models for each endpoint through 5-fold cross validation. Model performance, assessed by concordance index (C-index), was evaluated using a test set of 100 patients.

RESULTS: Including PL improved the C-index results for all endpoints except LC. For LC, comparable C-indices (around 0.66) were observed between models trained using only PT and those incorporating PL as additional structure. Models trained using PT and PL combined into a single structure achieved the highest C-index of 0.65 and 0.80 for RC and DMFS prediction, respectively. Models trained using these target structures as separate entities achieved the highest C-index of 0.70 for OS.

CONCLUSION: Incorporating lymph node spatial information improved the prediction performance for RC, DMFS, and OS.

PMID:40046573 | PMC:PMC11880716 | DOI:10.1016/j.phro.2025.100733

Biophys Physicobiol. 2025 Jan 30;22(1):e220004. doi: 10.2142/biophysico.bppb-v22.0004. eCollection 2025.

ABSTRACT

Visualization of hydration structures over the entire protein surface is necessary to understand why the aqueous environment is essential for protein folding and functions. However, it is still difficult for experiments. Recently, we developed a convolutional neural network (CNN) to predict the probability distribution of hydration water molecules over protein surfaces and in protein cavities. The deep network was optimized using solely the distribution patterns of protein atoms surrounding each hydration water molecule in high-resolution X-ray crystal structures and successfully provided probability distributions of hydration water molecules. Despite the effectiveness of the probability distribution, the positional differences of the predicted positions obtained from the local maxima as predicted sites remained inadequate in reproducing the hydration sites in the crystal structure models. In this work, we modified the deep network by subdividing atomic classes based on the electronic properties of atoms composing amino acids. In addition, the exclusion volumes of each protein atom and hydration water molecule were taken to predict the hydration sites from the probability distribution. These information on chemical properties of atoms leads to an improvement in positional prediction accuracy. We selected the best CNN from 47 CNNs constructed by systematically varying the number of channels and layers of neural networks. Here, we report the improvements in prediction accuracy by the reorganized CNN together with the details in the architecture, training data, and peak search algorithm.

PMID:40046557 | PMC:PMC11876803 | DOI:10.2142/biophysico.bppb-v22.0004

F1000Res. 2025 Jan 15;14:88. doi: 10.12688/f1000research.157325.1. eCollection 2025.

ABSTRACT

BACKGROUND: Structuring data analysis projects, that is, defining the layout of files and folders needed to analyze data using existing tools and novel code, largely follows personal preferences. Open Science calls for more accessible, transparent and understandable research. We believe that Open Science principles can be applied to the way data analysis projects are structured.

METHODS: We examine the structure of several data analysis project templates by analyzing project template repositories present in GitHub. Through visualization of the resulting consensus structure, we draw observations regarding how the ecosystem of project structures is shaped, and what salient characteristics it has.

RESULTS: Project templates show little overlap, but many distinct practices can be highlighted. We take them into account with the wider Open Science philosophy to draw a few fundamental Design Principles to guide researchers when designing a project space. We present Kerblam!, a project management tool that can work with such a project structure to expedite data handling, execute workflow managers, and share the resulting workflow and analysis outputs with others.

CONCLUSIONS: We hope that, by following these principles and using Kerblam!, the landscape of data analysis projects can become more transparent, understandable, and ultimately useful to the wider community.

PMID:40047014 | PMC:PMC11880754 | DOI:10.12688/f1000research.157325.1

Biol Methods Protoc. 2025 Feb 22;10(1):bpaf014. doi: 10.1093/biomethods/bpaf014. eCollection 2025.

ABSTRACT

Polysaccharide quantification plays a vital role in understanding ecological and nutritional processes in microbes, plants, and animals. Traditional methods typically hydrolyze these large molecules into monomers using chemical methods, but such approaches do not work for all polysaccharides. Enzymatic degradation is a promising alternative but typically requires the use of characterized recombinant enzymes or characterized microbial isolates that secrete enzymes. In this study, we introduce a versatile method that employs undefined enzyme cocktails secreted by individual microbes or complex environmental microbial communities for the hydrolysis of polysaccharides. We focus on colloidal chitin and laminarin as representative polysaccharides of ecological relevance. Our results demonstrate that colloidal chitin can be effectively digested with an enzyme cocktail derived from a chitin-degrading Psychromonas sp. isolate. Utilizing a 3,5-dinitrosalicylic acid reducing sugar assay or liquid chromatography-mass spectrometry for monomer and oligomer detection, we successfully determined chitin concentrations as low as 62 and 15 mg/l, respectively. This allows for effective monitoring of microbial chitin degradation. To extend the applicability of our method, we also leveraged complex, undefined microbial communities as sources of enzyme cocktails capable of degrading laminarin. With this approach, we achieved a detection limit of 30 mg/l laminarin through the reducing sugar assay. Our findings highlight the potential of utilizing enzyme cocktails from both individual microbes and, notably, from undefined microbial communities for polysaccharide quantification. This advancement addresses limitations associated with traditional chemical hydrolysis methods.

PMID:40046731 | PMC:PMC11882305 | DOI:10.1093/biomethods/bpaf014