Feasibility of Multisolutions Optimization Technique for Real-Time HDR Brachytherapy of Prostate

The purpose of this study was to evaluate the efficacy of multisolutions optimization algorithm for High Dose Rate (HDR) brachytherapy of prostate. In this retrospective study, we included data from 20 prostate cancer patients who underwent ultrasound based real time HDR Brachytherapy at institution. The treatment plans of all 20 patients were optimized in Oncentra Prostate treatment planning system (TPS) using inverse dose volume histogram based optimization followed by graphical optimization (GRO) in real time. The data of all the patients were retrieved later, and the treatment plans were re-optimized using multisolutions dose volume histogram based optimization (MDVHO) and multisolutions variance based optimization (MVBO) algorithms with same set of dose constraints, same number of catheters, and same contour set as in GRO. Several Pareto optimal solutions were obtained by varying the weighting factors of composite objective function in finite steps of adequate resolutions.  These solutions were then stored in the database of TPS and same decision criteria was employed to pick the final solution using a decision engine. The average values for planning target volume receiving 100% of prescribed dose (V100) for MDVHO, MVBO, and GRO were 95.03%, 86.72% and 97.56%, respectively. The average V100 due to MDVHO was statistically significant (P = 0.002) in comparison to MVBO, whereas the average V100 due to MDVHO and GRO was not statistically significant (P = 0.066). In conclusion, the MDVHO can provide comparable solutions to typical clinical optimizations using GRO within clinically reasonable amount of time. In most of the cases, the plans created by MVBO were not clinically acceptable without users’ further manual intervention

pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model

Background: Protein S-nitrosylation (SNO) plays a key role in transferring nitric oxide-mediated signals in both animals and plants and has emerged as an important mechanism for regulating protein functions and cell signaling of all main classes of protein. It is involved in several biological processes including immune response, protein stability, transcription regulation, post translational regulation, DNA damage repair, redox regulation, and is an emerging paradigm of redox signaling for protection against oxidative stress. The development of robust computational tools to predict protein SNO sites would contribute to further interpretation of the pathological and physiological mechanisms of SNO. Results: Using an intermediate fusion-based stacked generalization approach, we integrated embeddings from supervised embedding layer and contextualized protein language model (ProtT5) and developed a tool called pLMSNOSite (protein language model-based SNO site predictor). On an independent test set of experimentally identified SNO sites, pLMSNOSite achieved values of 0.340, 0.735 and 0.773 for MCC, sensitivity and specificity respectively. These results show that pLMSNOSite performs better than the compared approaches for the prediction of S-nitrosylation sites. Conclusion: Together, the experimental results suggest that pLMSNOSite achieves significant improvement in the prediction performance of S-nitrosylation sites and represents a robust computational approach for predicting protein S-nitrosylation sites. pLMSNOSite could be a useful resource for further elucidation of SNO and is publicly available at https://github.com/KCLabMTU/pLMSNOSite

Dose-to-medium vs. dose-to-water: Dosimetric evaluation of dose reporting modes in Acuros XB for prostate, lung and breast cancer

Purpose: Acuros XB (AXB) dose calculation algorithm is available for external beam photon dose calculations in Eclipse treatment planning system (TPS). The AXB can report the absorbed dose in two modes: dose-to-water (Dw) and dose-to-medium (Dm). The main purpose of this study was to compare the dosimetric results of the AXB_Dm with that of AXB_Dw on real patient treatment plans. Methods: Four groups of patients (prostate cancer, stereotactic body radiation therapy (SBRT) lung cancer, left breast cancer, and right breast cancer) were selected for this study, and each group consisted of 5 cases. The treatment plans of all cases were generated in the Eclipse TPS. For each case, treatment plans were computed using AXB_Dw and AXB_Dm for identical beam arrangements. Dosimetric evaluation was done by comparing various dosimetric parameters in the AXB_Dw plans with that of AXB_Dm plans for the corresponding patient case. Results: For the prostate cancer, the mean planning target volume (PTV) dose in the AXB_Dw plans was higher by up to 1.0%, but the mean PTV dose was within ±0.3% for the SBRT lung cancer. The analysis of organs at risk (OAR) results in the prostate cancer showed that AXB_Dw plans consistently produced higher values for the bladder and femoral heads but not for the rectum. In the case of SBRT lung cancer, a clear trend was seen for the heart mean dose and spinal cord maximum dose, with AXB_Dw plans producing higher values than the AXB_Dm plans. However, the difference in the lung doses between the AXB_Dm and AXB_Dw plans did not always produce a clear trend, with difference ranged from -1.4% to 2.9%. For both the left and right breast cancer, the AXB_Dm plans produced higher maximum dose to the PTV for all cases. The evaluation of the maximum dose to the skin showed higher values in the AXB_Dm plans for all 5 left breast cancer cases, whereas only 2 cases had higher maximum dose to the skin in the AXB_Dm plans for the right breast cancer. Conclusion: The preliminary dosimetric results from our clinical study showed that the selection of either Dm or Dw in AXB is less likely to produce significant dosimetric differences in the clinical environment. However, the difference between the AXB_Dm and AXB_Dw calculations depends on the disease site, and even for the same type of disease (e.g., lung cancer), the results are patient specific

DFT-aided machine learning-based discovery of magnetism in Fe-based bimetallic chalcogenides

With the technological advancement in recent years and the widespread use of magnetism in every sector of the current technology, a search for a low-cost magnetic material has been more important than ever. The discovery of magnetism in alternate materials such as metal chalcogenides with abundant atomic constituents would be a milestone in such a scenario. However, considering the multitude of possible chalcogenide configurations, predictive computational modeling or experimental synthesis is an open challenge. Here, we recourse to a stacked generalization machine learning model to predict magnetic moment (µB) in hexagonal Fe-based bimetallic chalcogenides, FexAyB; A represents Ni, Co, Cr, or Mn, and B represents S, Se, or Te, and x and y represent the concentration of respective atoms. The stacked generalization model is trained on the dataset obtained using first-principles density functional theory. The model achieves MSE, MAE, and R2 values of 1.655 (µB)2, 0.546 (µB), and 0.922 respectively on an independent test set, indicating that our model predicts the compositional dependent magnetism in bimetallic chalcogenides with a high degree of accuracy. A generalized algorithm is also developed to test the universality of our proposed model for any concentration of Ni, Co, Cr, or Mn up to 62.5% in bimetallic chalcogenides

Similarity Computing on Electronic Health Records

Similarity computing on real world applications like Electronic Health Records (EHRs) can reveal numerous interesting knowledge. Similarity measures the closeness between comparable things such as patients. Like similarity computing amongst Intensive Care Unit (ICU) patients can create various benefits, such as case based patient retrieval, unearthing of similar patient groups. However, many classical methods such as euclidean distance, cosine similarity can’t be directly applicable as similarity computing in EHRs is subjective and in many cases conditional. Also, many intrinsic relationships between the data are lost due to poor data representation and conversion. To address these challenges, firstly, we propose structural network representation for EHRs to preserve inherent relationship. And, to make them more comparable, we do data enrichment e.g. adding abstract information. Then, we extract different similarity feature sets to generate different similarity metrics and retrieve top similar patients. Finally, we perform experiment which shows promising results over classical methods

Integrating Embeddings from Multiple Protein Language Models to Improve Protein O-GlcNAc Site Prediction

O-linked β-N-acetylglucosamine (O-GlcNAc) is a distinct monosaccharide modification of serine (S) or threonine (T) residues of nucleocytoplasmic and mitochondrial proteins. O-GlcNAc modification (i.e., O-GlcNAcylation) is involved in the regulation of diverse cellular processes, including transcription, epigenetic modifications, and cell signaling. Despite the great progress in experimentally mapping O-GlcNAc sites, there is an unmet need to develop robust prediction tools that can effectively locate the presence of O-GlcNAc sites in protein sequences of interest. In this work, we performed a comprehensive evaluation of a framework for prediction of protein O-GlcNAc sites using embeddings from pre-trained protein language models. In particular, we compared the performance of three protein sequence-based large protein language models (pLMs), Ankh, ESM-2, and ProtT5, for prediction of O-GlcNAc sites and also evaluated various ensemble strategies to integrate embeddings from these protein language models. Upon investigation, the decision-level fusion approach that integrates the decisions of the three embedding models, which we call LM-OGlcNAc-Site, outperformed the models trained on these individual language models as well as other fusion approaches and other existing predictors in almost all of the parameters evaluated. The precise prediction of O-GlcNAc sites will facilitate the probing of O-GlcNAc site-specific functions of proteins in physiology and diseases. Moreover, these findings also indicate the effectiveness of combined uses of multiple protein language models in post-translational modification prediction and open exciting avenues for further research and exploration in other protein downstream tasks. LM-OGlcNAc-Site’s web server and source code are publicly available to the community

Improving protein succinylation sites prediction using embeddings from protein language model

Protein succinylation is an important post-translational modification (PTM) responsible for many vital metabolic activities in cells, including cellular respiration, regulation, and repair. Here, we present a novel approach that combines features from supervised word embedding with embedding from a protein language model called ProtT5-XL-UniRef50 (hereafter termed, ProtT5) in a deep learning framework to predict protein succinylation sites. To our knowledge, this is one of the first attempts to employ embedding from a pre-trained protein language model to predict protein succinylation sites. The proposed model, dubbed LMSuccSite, achieves state-of-the-art results compared to existing methods, with performance scores of 0.36, 0.79, 0.79 for MCC, sensitivity, and specificity, respectively. LMSuccSite is likely to serve as a valuable resource for exploration of succinylation and its role in cellular physiology and disease

Health system’s readiness to provide cardiovascular, diabetes and chronic respiratory disease related services in Nepal: analysis using 2015 health facility survey

BACKGROUND: The burgeoning rise of non-communicable diseases (NCDs) is posing serious challenges in resource constrained health facilities of Nepal. The main objective of this study was to assess the readiness of health facilities for cardiovascular diseases (CVDs), diabetes and chronic respiratory diseases (CRDs) services in Nepal. METHODS: This study utilized data from the Nepal Health Facility Survey 2015. General readiness of 940 health facilities along with disease specific readiness for CVDs, diabetes, and CRDs were assessed using the Service Availability and Readiness Assessment manual of the World Health Organization. Health facilities were categorized into public and private facilities. RESULTS: Out of a total of 940 health facilities assessed, private facilities showed higher availability of items of general service readiness except for standard precautions for infection prevention, compared to public facilities. The multivariable adjusted regression coefficients for CVDs (β = 2.87, 95%CI: 2.42-3.39), diabetes (β =3.02, 95%CI: 2.03-4.49), and CRDs (β = 15.95, 95%CI: 4.61-55.13) at private facilities were higher than the public facilities. Health facilities located in the hills had a higher readiness index for CVDs (β = 1.99, 95%CI: 1.02-1.39). Service readiness for CVDs (β = 1.13, 95%CI: 1.04-1.23) and diabetes (β = 1.78, 95%CI: 1.23-2.59) were higher in the urban municipalities than in rural municipalities. Finally, disease-related services readiness index was sub-optimal with some degree of variation at the province level in Nepal. Compared to province 1, province 2 (β = 0.83, 95%CI: 0.73-0.95) had lower, and province 4 (β =1.24, 95%CI: 1.07-1.43) and province 5 (β =1.17, 95%CI: 1.02-1.34) had higher readiness index for CVDs. CONCLUSION: This study found sub-optimal readiness of services related to three NCDs at the public facilities in Nepal. Compared to public facilities, private facilities showed higher readiness scores for CVDs, diabetes, and CRDs. There is an urgent need for policy reform to improve the health services for NCDs, particularly in public facilities

Performance Evaluation of Convolutional Neural Network Using Synthetic Medical Data Augmentation Generated by GAN

Data augmentation is widely used in image processing and pattern recognition problems in order to increase the richness in diversity of available data. It is commonly used to improve the classification accuracy of images when the available datasets are limited. Deep learning approaches have demonstrated an immense breakthrough in medical diagnostics over the last decade. A significant amount of datasets are needed for the effective training of deep neural networks. The appropriate use of data augmentation techniques prevents the model from over-fitting and thus increases the generalization capability of the network while testing afterward on unseen data. However, it remains a huge challenge to obtain such a large dataset from rare diseases in the medical field. This study presents the synthetic data augmentation technique using Generative Adversarial Networks to evaluate the generalization capability of neural networks using existing data more effectively. In this research, the convolutional neural network (CNN) model is used to classify the X-ray images of the human chest in both normal and pneumonia conditions; then, the synthetic images of the X-ray from the available dataset are generated by using the deep convolutional generative adversarial network (DCGAN) model. Finally, the CNN model is trained again with the original dataset and augmented data generated using the DCGAN model. The classification performance of the CNN model is improved by 3.2% when the augmented data were used along with the originally available dataset

Dose-to-medium vs. dose-to-water: Dosimetric evaluation of dose reporting modes in Acuros XB for prostate, lung and breast cancer

Purpose: Acuros XB (AXB) dose calculation algorithm is available for external beam photon dose calculations in Eclipse treatment planning system (TPS). The AXB can report the absorbed dose in two modes: dose-to-water (Dw) and dose-to-medium (Dm). The main purpose of this study was to compare the dosimetric results of the AXB_Dm with that of AXB_Dw on real patient treatment plans. Methods: Four groups of patients (prostate cancer, stereotactic body radiation therapy (SBRT) lung cancer, left breast cancer, and right breast cancer) were selected for this study, and each group consisted of 5 cases. The treatment plans of all cases were generated in the Eclipse TPS. For each case, treatment plans were computed using AXB_Dw and AXB_Dm for identical beam arrangements. Dosimetric evaluation was done by comparing various dosimetric parameters in the AXB_Dw plans with that of AXB_Dm plans for the corresponding patient case. Results: For the prostate cancer, the mean planning target volume (PTV) dose in the AXB_Dw plans was higher by up to 1.0%, but the mean PTV dose was within ±0.3% for the SBRT lung cancer. The analysis of organs at risk (OAR) results in the prostate cancer showed that AXB_Dw plans consistently produced higher values for the bladder and femoral heads but not for the rectum. In the case of SBRT lung cancer, a clear trend was seen for the heart mean dose and spinal cord maximum dose, with AXB_Dw plans producing higher values than the AXB_Dm plans. However, the difference in the lung doses between the AXB_Dm and AXB_Dw plans did not always produce a clear trend, with difference ranged from -1.4% to 2.9%. For both the left and right breast cancer, the AXB_Dm plans produced higher maximum dose to the PTV for all cases. The evaluation of the maximum dose to the skin showed higher values in the AXB_Dm plans for all 5 left breast cancer cases, whereas only 2 cases had higher maximum dose to the skin in the AXB_Dm plans for the right breast cancer. Conclusion: The preliminary dosimetric results from our clinical study showed that the selection of either Dm or Dw in AXB is less likely to produce significant dosimetric differences in the clinical environment. However, the difference between the AXB_Dm and AXB_Dw calculations depends on the disease site, and even for the same type of disease (e.g., lung cancer), the results are patient specific.</p