RSC Adv. 2024 Aug 27;14(37):26897-26910. doi: 10.1039/d4ra03965c. eCollection 2024 Aug 22.

ABSTRACT

While each massive pandemic has claimed the lives of millions of vulnerable populations over the centuries, one limitation exists: that the Edisonian approach (human-directed with trial errors) relies on repurposing pharmaceuticals, designing drugs, and herbal remedies with the violation of Lipinski's rule of five druglikeness. It may lead to adverse health effects with long-term health multimorbidity. Nevertheless, declining birth rates and aging populations will likely cause a shift in society due to a shortage of a scientific workforce to defend against the next pandemic incursion. The challenge of combating the ongoing post-COVID-19 pandemic has been exacerbated by the lack of gold standard drugs to deactivate multiple SARS-CoV-2 protein targets. Meanwhile, there are three FDA-approved antivirals, Remdesivir, Molnupiravir, and Paxlovid, with moderate clinical efficacy and drug resistance. There is a pressing need for additional antivirals and prepared omics technology to combat the current and future devastating coronavirus pandemics. While there is a limitation of existing contemporary inhibitors to deactivate viral RNA replication with minimal rotational bonds, one strategy is to create Lipinski inhibitors with less than 10 rotational bonds and precise halogen bond placement to destabilize multiple viral protomers. This work describes the efforts to design gold-standard oral inhibitors of bi- and tri-cyclic catalytic interceptors with electrophilic heads using double-shell deep learning. Here, KS1 with and KS2 compounds designed by lab-on-a-chip technology attain 5-fold novel filtered-Lipinski, GHOSE, VEBER, EGAN, and MUEGGE druglikeness. The graph neural network (GNN) relies on module-initiation, expansion, relabeling atom index, and termination (METORITE) iterations, while the deep neural network (DNN) engages pinning, extraction, convolution, pooling, and flattening (PROOF) operations. The cyclic compound's specific halogen atom location enhances the nitrile catalytic head, which deactivates several viral protein targets. Initiating this lab-on-a-chip that is not susceptible to the aging process for creating clinical compounds can leverage a new path to many valuable drugs with speedy oral drug discovery, especially to defend the loss of vulnerable population and prevent multimorbidity that is susceptible to hidden viral persistence in the continuing aging times.

PMID:39193274 | PMC:PMC11347926 | DOI:10.1039/d4ra03965c

IEEE Winter Conf Appl Comput Vis. 2024 Jan;2024:5911-5920. doi: 10.1109/wacv57701.2024.00582. Epub 2024 Apr 9.

ABSTRACT

A large portion of volumetric medical data, especially magnetic resonance imaging (MRI) data, is anisotropic, as the through-plane resolution is typically much lower than the in-plane resolution. Both 3D and purely 2D deep learning-based segmentation methods are deficient in dealing with such volumetric data since the performance of 3D methods suffers when confronting anisotropic data, and 2D methods disregard crucial volumetric information. Insufficient work has been done on 2.5D methods, in which 2D convolution is mainly used in concert with volumetric information. These models focus on learning the relationship across slices, but typically have many parameters to train. We offer a Cross-Slice Attention Module (CSAM) with minimal trainable parameters, which captures information across all the slices in the volume by applying semantic, positional, and slice attention on deep feature maps at different scales. Our extensive experiments using different network architectures and tasks demonstrate the usefulness and generalizability of CSAM. Associated code is available at https://github.com/aL3x-O-o-Hung/CSAM.

PMID:39193208 | PMC:PMC11349312 | DOI:10.1109/wacv57701.2024.00582

NAR Genom Bioinform. 2024 Aug 27;6(3):lqae112. doi: 10.1093/nargab/lqae112. eCollection 2024 Sep.

ABSTRACT

Associating one or more Gene Ontology (GO) terms to a protein means making a statement about a particular functional characteristic of the protein. This association provides scientists with a snapshot of the biological context of the protein activity. This paper introduces PRONTO-TK, a Python-based software toolkit designed to democratize access to Neural-Network based complex protein function prediction workflows. PRONTO-TK is a user-friendly graphical interface (GUI) for empowering researchers, even those with minimal programming experience, to leverage state-of-the-art Deep Learning architectures for protein function annotation using GO terms. We demonstrate PRONTO-TK's effectiveness on a running example, by showing how its intuitive configuration allows it to easily generate complex analyses while avoiding the complexities of building such a pipeline from scratch.

PMID:39193069 | PMC:PMC11348006 | DOI:10.1093/nargab/lqae112

Cureus. 2024 Jul 28;16(7):e65557. doi: 10.7759/cureus.65557. eCollection 2024 Jul.

ABSTRACT

BACKGROUND: Hallux valgus (HV), also known as bunion deformity, is one of the most common forefoot deformities. Early diagnosis and proper evaluation of HV are important because timely management can improve symptoms and quality of life. Here, we propose a deep learning estimation for the radiographic measurement of HV based on a regression network where the input to the algorithm is digital photographs of the forefoot, and the radiographic measurement of HV is computed as output directly. The purpose of our study was to estimate the radiographic parameters of HV using deep learning, to classify the severity by grade, and to assess the agreement of the predicted measurement with the actual radiographic measurement.

METHODS: There were 131 patients enrolled in this study. A total of 248 radiographs and 337 photographs of the feet were acquired. Radiographic parameters, including the HV angle (HVA), M1-M2 angle, and M1-M5 angle, were measured. We constructed a convolutional neural network using Xception and made the classification model into the regression model. Then, we fine-tuned the model using images of the feet and the radiographic parameters. The coefficient of determination (R2) and root mean squared error (RMSE), as well as Cohen's kappa coefficient, were calculated to evaluate the performance of the model.

RESULTS: The radiographic parameters, including the HVA, M1-M2 angle, and M1-M5 angle, were predicted with a coefficient of determination (R2)=0.684, root mean squared error (RMSE)=7.91; R2=0.573, RMSE=3.29; R2=0.381, RMSE=5.80, respectively.

CONCLUSION: The present study demonstrated that our model could predict the radiographic parameters of HV from photography. Moreover, the agreement between the expected and actual grade of HV was substantial. This study shows a potential application of a convolutional neural network for the screening of HV.

PMID:39192936 | PMC:PMC11348822 | DOI:10.7759/cureus.65557

J Orthop Surg Res. 2024 Aug 27;19(1):516. doi: 10.1186/s13018-024-05013-2.

ABSTRACT

BACKGROUND: Accurate estimation of implant size before surgery is crucial in preparing for total knee arthroplasty. However, this task is time-consuming and labor-intensive. To alleviate this burden on surgeons, we developed a reliable artificial intelligence (AI) model to predict implant size.

METHODS: We enrolled 714 patients with knee osteoarthritis who underwent total knee arthroplasty from March 2010 to February 2014. All surgeries were performed by the same surgeon using implants from the same manufacturer. We collected 1412 knee anteroposterior (AP) and lateral view x-ray images and retrospectively investigated the implant size. We trained the AI model using both AP and lateral images without any clinical or demographic information and performed data augmentation to resolve issues of uneven distribution and insufficient data. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. We used ResNet-101 and optimized the model with the aim of minimizing the cross-entropy loss function using both the Stochastic Gradient Descent (SGD) and Adam optimizer.

RESULTS: The SGD optimizer achieved the best performance in internal validation. The model showed micro F1-score 0.91 for femur and 0.87 for tibia. For predicting within ± one size, micro F1-score was 0.99 for femur and 0.98 for tibia.

CONCLUSION: We developed a deep learning model with high predictive power for implant size using only simple x-ray images. This could help surgeons reduce the time and labor required for preoperative preparation in total knee arthroplasty. While similar studies have been conducted, our work is unique in its use of simple x-ray images without any other data, like demographic features, to achieve a model with strong predictive power.

PMID:39192371 | DOI:10.1186/s13018-024-05013-2

BMC Med Inform Decis Mak. 2024 Aug 27;24(1):236. doi: 10.1186/s12911-024-02631-y.

ABSTRACT

Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutting-edge model called ProtICNN-BiLSTM, which combines attention-based Improved Convolutional Neural Networks (ICNN) and Bidirectional Long Short-Term Memory (BiLSTM) units seamlessly. Our main goal is to improve the accuracy of protein sequence classification by carefully optimizing performance through Bayesian Optimisation. ProtICNN-BiLSTM combines the power of CNN and BiLSTM architectures to effectively capture local and global protein sequence dependencies. In the proposed model, the ICNN component uses convolutional operations to identify local patterns. Captures long-range associations by analyzing sequence data forward and backwards. In advanced biological studies, Bayesian Optimisation optimizes model hyperparameters for efficiency and robustness. The model was extensively confirmed with PDB-14,189 and other protein data. We found that ProtICNN-BiLSTM outperforms traditional categorization models. Bayesian Optimization's fine-tuning and seamless integration of local and global sequence information make it effective. The precision of ProtICNN-BiLSTM improves comparative protein sequence categorization. The study improves computational bioinformatics for complex biological analysis. Good results from the ProtICNN-BiLSTM model improve protein sequence categorization. This powerful tool could improve medical and biological research. The breakthrough protein sequence classification model is ProtICNN-BiLSTM. Bayesian optimization, ICNN, and BiLSTM analyze biological data accurately.

PMID:39192227 | DOI:10.1186/s12911-024-02631-y

BMC Musculoskelet Disord. 2024 Aug 27;25(1):669. doi: 10.1186/s12891-024-07798-z.

ABSTRACT

BACKGROUND: If reduction images of fractures can be provided in advance with artificial-intelligence (AI)-based technology, it can assist with preoperative surgical planning. Recently, we developed the AI-based preoperative virtual reduction model for orthopedic trauma, which can provide an automatic segmentation and reduction of fractured fragments. The purpose of this study was to validate a quality of reduction model of Neer 3- or 4-part proximal humerus fractures established by AI-based technology.

METHODS: To develop the AI-based preoperative virtual reduction model, deep learning performed the segmentation of fracture fragments, and a Monte Carlo simulation completed the virtual reduction to determine the best model. A total of 20 pre/postoperative three-dimensional computed tomography (CT) scans of proximal humerus fracture were prepared. The preoperative CT scans were employed as the input of AI-based automated reduction (AI-R) to deduce the reduction models of fracture fragments, meanwhile, the manual reduction (MR) was conducted using the same CT images. Dice similarity coefficient (DSC) and intersection over union (IoU) between the reduction model from the AI-R/MR and postoperative CT scans were evaluated. Working times were compared between the two groups. Clinical validity agreement (CVA) and reduction quality score (RQS) were investigated for clinical validation outcomes by 20 orthopedic surgeons.

RESULTS: The mean DSC and IoU were better when using AI-R that when using MR (0.78 ± 0.13 vs. 0.69 ± 0.16, p < 0.001 and 0.65 ± 0.16 vs. 0.55 ± 0.18, p < 0.001, respectively). The working time of AI-R was, on average, 1.41% of that of MR. The mean CVA of all cases was 81%±14.7% (AI-R, 82.25%±14.27%; MR, 76.75%±14.17%, p = 0.06). The mean RQS was significantly higher when AI-R compared with MR was used (91.47 ± 1.12 vs. 89.30 ± 1.62, p = 0.045).

CONCLUSION: The AI-based preoperative virtual reduction model showed good performance in the reduction model in proximal humerus fractures with faster working times. Beyond diagnosis, classification, and outcome prediction, the AI-based technology can change the paradigm of preoperative surgical planning in orthopedic surgery.

LEVEL OF EVIDENCE: Level IV.

PMID:39192203 | DOI:10.1186/s12891-024-07798-z

Mem Cognit. 2024 Aug 27. doi: 10.3758/s13421-024-01618-4. Online ahead of print.

ABSTRACT

As psychological research embraces more naturalistic questions and large-scale analytic methods, drawing has emerged as an exciting tool for studying cognition. Drawing provides rich information about how we view the world, ranging from largely veridical perceptual representations to abstracted meta-cognitive representations. Drawing also requires the integration of multiple processes (e.g., vision, memory, motor learning), and experience with drawing can have an impact on such processes. As a result, drawing presents several interesting cognitive questions, while also providing a way to gain insight into a multitude of others. This Special Issue features 25 cutting-edge studies utilizing drawing to reveal discoveries transversing fields in psychology. These diverse studies investigate drawing across children, young adults, older adults, and special populations such as individuals with blindness, anterograde amnesia, apraxia, and semantic dementia. These studies detail new discoveries about the mechanisms underlying memory, attention, mathematical reasoning, and other cognitive processes. They employ a range of methods including psychophysical experiments, deep learning, and neuroimaging. Finally, many of these studies cover topics about the impact of drawing as a process on other cognitive processes, including how drawing expertise impacts other processes like visual memory or spatial abilities. Overall, this collection of studies paves the way for an exciting future of drawing as a commonplace tool used by psychologists to understand complex phenomena.

PMID:39192141 | DOI:10.3758/s13421-024-01618-4

J Pharmacokinet Pharmacodyn. 2024 Aug 27. doi: 10.1007/s10928-024-09935-6. Online ahead of print.

ABSTRACT

The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today's world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) increase available data (e.g., in low-density regions-i.e., for patients with under-represented characteristics). Generative methods employ a family of solutions for generating synthetic data. The objective of this research is to benchmark numerous state-of-the-art deep-learning generative methods across different scenarios and clinical datasets comprising patient covariates and several pharmacokinetic/pharmacodynamic endpoints. We did this by implementing various probabilistic models aimed at generating synthetic data, such as the Multi-layer Perceptron Conditioning Generative Adversarial Neural Network (MLP cGAN), Time-series Generative Adversarial Networks (TimeGAN), and a more traditional approach like Probabilistic Autoregressive (PAR). We evaluated their performance by calculating discriminative and predictive scores. Furthermore, we conducted comparisons between the distributions of real and synthetic data using Kolmogorov-Smirnov and Chi-square statistical tests, focusing respectively on covariate and output variables of the models. Lastly, we employed pharmacometrics-related metric to enhance interpretation of our results specific to our investigated scenarios. Results indicate that multi-layer perceptron-based conditional generative adversarial networks (MLP cGAN) exhibit the best overall performance for most of the considered metrics. This work highlights the opportunities to employ synthetic data generation in the field of clinical pharmacology for augmentation and sharing of proprietary data across institutions.

PMID:39192091 | DOI:10.1007/s10928-024-09935-6

Sci Rep. 2024 Aug 27;14(1):19846. doi: 10.1038/s41598-024-70801-z.

ABSTRACT

COVID-19 has resulted in a significant global impact on health, the economy, education, and daily life. The disease can range from mild to severe, with individuals over 65 or those with underlying medical conditions being more susceptible to severe illness. Early testing and isolation are vital due to the virus's variable incubation period. Chest radiographs (CXR) have gained importance as a diagnostic tool due to their efficiency and reduced radiation exposure compared to CT scans. However, the sensitivity of CXR in detecting COVID-19 may be lower. This paper introduces a deep learning framework for accurate COVID-19 classification and severity prediction using CXR images. U-Net is used for lung segmentation, achieving a precision of 0.9924. Classification is performed using a Convulation-capsule network, with high true positive rates of 86% for COVID-19, 93% for pneumonia, and 85% for normal cases. Severity assessment employs ResNet50, VGG-16, and DenseNet201, with DenseNet201 showing superior accuracy. Empirical results, validated with 95% confidence intervals, confirm the framework's reliability and robustness. This integration of advanced deep learning techniques with radiological imaging enhances early detection and severity assessment, improving patient management and resource allocation in clinical settings.

PMID:39191941 | DOI:10.1038/s41598-024-70801-z

Sci Rep. 2024 Aug 27;14(1):19888. doi: 10.1038/s41598-024-71063-5.

ABSTRACT

This study introduces a novel self-supervised learning method for single-frame subtraction and vessel segmentation in coronary angiography, addressing the scarcity of annotated medical samples in AI applications. We pretrain a U-Net model on a large dataset of unannotated coronary angiograms using an image-to-image translation framework, then fine-tune it on a limited set of manually annotated samples. The pretrained model excels at comprehensive single-frame subtraction, outperforming existing DSA methods. Fine-tuning with just 40 samples yields a Dice coefficient of 0.828 for vessel segmentation. On the public XCAD dataset, our model sets a new state-of-the-art benchmark with a Dice coefficient of 0.755, surpassing both unsupervised and supervised learning approaches. This method achieves robust single-frame subtraction and demonstrates that combining pretraining with minimal fine-tuning enables accurate coronary vessel segmentation with limited manual annotations. We successfully apply this approach to assist physicians in visualizing potential vascular stenosis sites during coronary angiography. Code, dataset, and a live demo will be available available at: https://github.com/newfyu/DeepSA .

PMID:39191858 | DOI:10.1038/s41598-024-71063-5

Sci Rep. 2024 Aug 27;14(1):19864. doi: 10.1038/s41598-024-70100-7.

ABSTRACT

Printed Circuit Boards (PCBs) are the foundational component of electronic devices, and the detection of PCB defects is essential for ensuring the quality control of electronic products. Aiming at the problem that the existing PCB plug-in solder defect detection algorithms cannot meet the requirements of high precision, low false alarm rate, and high speed at the same time, this paper proposes a method based on spatial convolution pooling and information fusion. Firstly, on the basis of YOLOv3, an attention-guided pyramid structure is used to fuse context information, and multiple convolutions of different size are used to explore richer high-level semantic information; Secondly, a coordinated attention network structure is introduced to calibrate the fused pyramidal feature information, highlighting the important feature channels, and reducing the adverse impact of redundant parameters generated by feature fusion; Finally, the ASPP (Atrous Spatial Pyramid Pooling) structure is implemented in the original Darknet53 backbone feature extraction network to acquire multi-scale feature information of the detection targets. With these improvements, the average detection accuracy of the enhanced network has been elevated from 94.45 to 96.43%. This experiments shows that the improved network is more suitable for PCB plug-in solder defect detection applications.

PMID:39191831 | DOI:10.1038/s41598-024-70100-7

Sci Rep. 2024 Aug 27;14(1):19810. doi: 10.1038/s41598-024-68084-5.

ABSTRACT

In this paper, we investigate the effectiveness of shape completion neural networks as clinical aids in maxillofacial surgery planning. We present a pipeline to apply shape completion networks to automatically reconstruct complete eumorphic 3D meshes starting from a partial input mesh, easily obtained from CT data routinely acquired for surgery planning. Most of the existing works introduced solutions to aid the design of implants for cranioplasty, i.e. all the defects are located in the neurocranium. In this work, we focus on reconstructing defects localized on both neurocranium and splanchnocranium. To this end, we introduce a new dataset, specifically designed for this task, derived from publicly available CT scans and subjected to a comprehensive pre-processing procedure. All the scans in the dataset have been manually cleaned and aligned to a common reference system. In addition, we devised a pre-processing stage to automatically extract point clouds from the scans and enrich them with virtual defects. We experimentally compare several state-of-the-art point cloud completion networks and identify the two most promising models. Finally, expert surgeons evaluated the best-performing network on a clinical case. Our results show how casting the creation of personalized implants as a problem of shape completion is a promising approach for automatizing this complex task.

PMID:39191797 | DOI:10.1038/s41598-024-68084-5

Nat Commun. 2024 Aug 27;15(1):7362. doi: 10.1038/s41467-024-50618-0.

ABSTRACT

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

PMID:39191725 | DOI:10.1038/s41467-024-50618-0

Sci Total Environ. 2024 Aug 25:175813. doi: 10.1016/j.scitotenv.2024.175813. Online ahead of print.

ABSTRACT

Investigating the interaction between influent particles and biomass is basic and important for the biological wastewater treatment. The micro-level methods allow for this, such as the microscope image analysis method with the conventional ImageJ processing software. However, these methods are cost and time-consuming, and require a large amount of work on manual parameter tuning. To deal with this problem, we proposed a deep learning (DL) method to automatically detect and quantify microparticles free from biomass and entrapped in biomass from microscope images. Firstly, we introduced a "TU Delft-Interaction between Particles and Biomass" dataset containing labeled microscope images. Then, we built DL models using this dataset with seven state-of-the-art model architectures for a instance segmentation task, such as Mask R-CNN, Cascade Mask R-CNN, Yolact and YOLOv8. The results show that the Cascade Mask R-CNN with ResNet50 backbone achieves promising detection accuracy, with a mAP50box and mAP50mask of 90.6 % on the test set. Then, we benchmarked our results against the conventional ImageJ processing method. The results show that the DL method significantly outperforms the ImageJ processing method in terms of detection accuracy and processing cost. The DL method shows a 13.8 % improvement in micro-average precision, and a 21.7 % improvement in micro-average recall, compared to the ImageJ method. Moreover, the DL method can process 70 images within 1 min, while the ImageJ method costs at least 6 h. The promising performance of our method allows it to offer a potential alternative to examine the interaction between microparticles and biomass in biological wastewater treatment process in an affordable manner. This approach offers more useful insights into the treatment process, enabling further reveal the microparticles transfer in biological treatment systems.

PMID:39191331 | DOI:10.1016/j.scitotenv.2024.175813

Phys Med Biol. 2024 Aug 27. doi: 10.1088/1361-6560/ad7454. Online ahead of print.

ABSTRACT

In this study, we propose a semi-supervised learning scheme using a patch-based deep learning framework to tackle the challenge of high-precision classification of seven lung tumor growth patterns, despite having a small amount of labeled data in whole slide images (WSIs). This scheme aims to enhance generalization ability with limited data and reduce dependence on large amounts of labeled data. It effectively addresses the common challenge of high demand for labeled data in medical image analysis.&#xD;&#xD;Approach. To address these challenges, the study employs a semi-supervised learning approach enhanced by a dynamic confidence threshold mechanism. This mechanism adjusts based on the quantity and quality of pseudo labels generated. This dynamic thresholding mechanism helps avoid the imbalance of pseudo-label categories and the low number of pseudo-labels that may result from a higher fixed threshold. Furthermore, the research introduces a multi-teacher knowledge distillation technique. This technique adaptively weights predictions from multiple teacher models to transfer reliable knowledge and safeguard student models from low-quality teacher predictions.&#xD;&#xD;Main results. The framework underwent rigorous training and evaluation using a dataset of 150 WSIs, each representing one of the seven growth patterns. The experimental results demonstrate that the framework is highly accurate in classifying lung tumor growth patterns in histopathology images. Notably, the performance of the framework is comparable to that of fully supervised models and human pathologists. In addition, the framework's evaluation metrics on a publicly available dataset are higher than those of previous studies, indicating good generalizability.&#xD;&#xD;Significance. This research demonstrates that a semi-supervised learning approach can achieve results comparable to fully supervised models and expert pathologists, thus opening new possibilities for efficient and cost-effective medical images analysis. The implementation of dynamic confidence thresholding and multi-teacher knowledge distillation techniques represents a significant advancement in applying deep learning to complex medical image analysis tasks. This advancement could lead to faster and more accurate diagnoses, ultimately improving patient outcomes and fostering the overall progress of healthcare technology.&#xD.

PMID:39191290 | DOI:10.1088/1361-6560/ad7454

Phys Med. 2024 Aug 26;125:104500. doi: 10.1016/j.ejmp.2024.104500. Online ahead of print.

ABSTRACT

PURPOSE: To evaluate a deep learning model's performance in predicting and classifying patient-specific quality assurance (PSQA) results for volumetric modulated arc therapy (VMAT), aiming to streamline PSQA workflows and reduce the onsite measurement workload.

METHODS: A total of 761 VMAT plans were analyzed using 3D-MResNet to process multileaf collimator images and monitor unit data, with the gamma passing rate (GPR) as the output. Thresholds for the predicted GPR (Th-p) and measured GPR (Th-m) were established to aid in PSQA decision-making, using cost curves and error rates to assess classification performance.

RESULTS: The mean absolute errors of the model for the test set were 1.63 % and 2.38 % at 3 %/2 mm and 2 %/2 mm, respectively. For the classification of the PSQA results, Th-m was 88.3 % at 2 %/2 mm and 93.3 % at 3 %/2 mm. The lowest cost-sensitive error rates of 0.0127 and 0.0925 were obtained when Th-p was set as 91.2 % at 2 %/2 mm and 96.4 % at 3 %/2 mm, respectively. Additionally, the 2 %/2 mm classifier also achieved a lower total expected cost of 0.069 compared with 0.110 for the 3 %/2 mm classifier. The deep learning classifier under the 2 %/2 mm gamma criterion had a sensitivity and specificity of 100 % (10/10) and 83.5 % (167/200), respectively, for the test set.

CONCLUSIONS: The developed 3D-MResNet model can accurately predict and classify PSQA results based on VMAT plans. The introduction of a deep learning model into the PSQA workflow has considerable potential for improving the VMAT PSQA process and reducing workloads.

PMID:39191190 | DOI:10.1016/j.ejmp.2024.104500

EBioMedicine. 2024 Aug 26;107:105311. doi: 10.1016/j.ebiom.2024.105311. Online ahead of print.

ABSTRACT

BACKGROUND: The accurate evaluation of axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer holds great value. This study aimed to develop an artificial intelligence system utilising multiregional dynamic contrast-enhanced MRI (DCE-MRI) and clinicopathological characteristics to predict axillary pathological complete response (pCR) after NAC in breast cancer.

METHODS: This study included retrospective and prospective datasets from six medical centres in China between May 2018 and December 2023. A fully automated integrated system based on deep learning (FAIS-DL) was built to perform tumour and ALN segmentation and axillary pCR prediction sequentially. The predictive performance of FAIS-DL was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RNA sequencing analysis were conducted on 45 patients to explore the biological basis of FAIS-DL.

FINDINGS: 1145 patients (mean age, 50 years ±10 [SD]) were evaluated. Among these patients, 506 were in the training and validation sets (axillary pCR rate of 40.3%), 127 in the internal test set (axillary pCR rate of 37.8%), 414 in the pooled external test set (axillary pCR rate of 48.8%), and 98 in the prospective test set (axillary pCR rate of 43.9%). For predicting axillary pCR, FAIS-DL achieved AUCs of 0.95, 0.93, and 0.94 in the internal test set, pooled external test set, and prospective test set, respectively, which were also significantly higher than those of the clinical model and deep learning models based on single-regional DCE-MRI (all P < 0.05, DeLong test). In the pooled external and prospective test sets, the FAIS-DL decreased the unnecessary axillary lymph node dissection rate from 47.9% to 6.8%, and increased the benefit rate from 52.2% to 86.5%. RNA sequencing analysis revealed that high FAIS-DL scores were associated with the upregulation of immune-mediated genes and pathways.

INTERPRETATION: FAIS-DL has demonstrated satisfactory performance in predicting axillary pCR, which may guide the formulation of personalised treatment regimens for patients with breast cancer in clinical practice.

FUNDING: This study was supported by the National Natural Science Foundation of China (82371933), National Natural Science Foundation of Shandong Province of China (ZR2021MH120), Mount Taishan Scholars and Young Experts Program (tsqn202211378), Key Projects of China Medicine Education Association (2022KTM030), China Postdoctoral Science Foundation (314730), and Beijing Postdoctoral Research Foundation (2023-zz-012).

PMID:39191174 | DOI:10.1016/j.ebiom.2024.105311

Comput Biol Chem. 2024 Aug 21;112:108175. doi: 10.1016/j.compbiolchem.2024.108175. Online ahead of print.

ABSTRACT

Cancer drug response (CDR) prediction is an important area of research that aims to personalize cancer therapy, optimizing treatment plans for maximum effectiveness while minimizing potential negative effects. Despite the advancements in Deep learning techniques, the effective integration of multi-omics data for drug response prediction remains challenging. In this paper, a regression method using Deep ResNet for CDR (DRN-CDR) prediction is proposed. We aim to explore the potential of considering sole cancer genes in drug response prediction. Here the multi-omics data such as gene expressions, mutation data, and methylation data along with the molecular structural information of drugs were integrated to predict the IC50 values of drugs. Drug features are extracted by employing a Uniform Graph Convolution Network, while Cell line features are extracted using a combination of Convolutional Neural Network and Fully Connected Networks. These features are then concatenated and fed into a deep ResNet for the prediction of IC50 values between Drug - Cell line pairs. The proposed method yielded higher Pearson's correlation coefficient (rp) of 0.7938 with lowest Root Mean Squared Error (RMSE) value of 0.92 when compared with similar methods of tCNNS, MOLI, DeepCDR, TGSA, NIHGCN, DeepTTA, GraTransDRP and TSGCNN. Further, when the model is extended to a classification problem to categorize drugs as sensitive or resistant, we achieved AUC and AUPR measures of 0.7623 and 0.7691, respectively. The drugs such as Tivozanib, SNX-2112, CGP-60474, PHA-665752, Foretinib etc., exhibited low median IC50 values and were found to be effective anti-cancer drugs. The case studies with different TCGA cancer types also revealed the effectiveness of SNX-2112, CGP-60474, Foretinib, Cisplatin, Vinblastine etc. This consistent pattern strongly suggests the effectiveness of the model in predicting CDR.

PMID:39191166 | DOI:10.1016/j.compbiolchem.2024.108175

Comput Biol Chem. 2024 Aug 13;112:108172. doi: 10.1016/j.compbiolchem.2024.108172. Online ahead of print.

ABSTRACT

Cryptosporidiosis, a prevalent gastrointestinal illness worldwide, is caused by the protozoan parasite Cryptosporidium parvum. Calcium-dependent protein kinase 1 (CpCDPK1), crucial for the parasite's life cycle, serves as a promising drug target due to its role in regulating invasion and egress from host cells. While potent Pyrazolopyrimidine analogs have been identified as candidate hit molecules, they exhibit limitations in inhibiting Cryptosporidium growth in cell culture, prompting exploration of alternative scaffolds. Leveraging the most potent compound, RM-1-95, co-crystallized with CpCDPK1, an E-pharmacophore model was generated and validated alongside a deep learning model trained on known CpCDPK1 compounds. These models facilitated screening Enamine's 2 million HTS compound library for novel CpCDPK1 inhibitors. Subsequent hierarchical docking prioritized hits, with final selections subjected to Quantum polarized docking for accurate ranking. Results from docking studies and MD simulations highlighted similarities in interactions between the cocrystallized ligand RM-1-95 and identified hit molecules, indicating comparable inhibitory potential against CpCDPK1. Furthermore, assessing metabolic stability through Cytochrome 450 site of metabolism prediction offered crucial insights for drug design, optimization, and regulatory approval processes.

PMID:39191165 | DOI:10.1016/j.compbiolchem.2024.108172