Pub Date : 2025-01-01DOI: 10.1016/j.imu.2024.101607
Roman Klapaukh , Carol El-Hayek , Douglas IR Boyle
Free-text fields in clinical records contain information that may not show up in the structured health record. Automated anonymisation tools can lower the bar to using this data at scale. However, existing anonymisation tools do not always perform as well as expected when used outside of their country and domain of origin. We ran three US tertiary care targeting transformer models on 300 Australian general practice notes, and showed that they generalise better than purpose built tools.
{"title":"Do transformers generalise better than bespoke tools for anonymisation?","authors":"Roman Klapaukh , Carol El-Hayek , Douglas IR Boyle","doi":"10.1016/j.imu.2024.101607","DOIUrl":"10.1016/j.imu.2024.101607","url":null,"abstract":"<div><div>Free-text fields in clinical records contain information that may not show up in the structured health record. Automated anonymisation tools can lower the bar to using this data at scale. However, existing anonymisation tools do not always perform as well as expected when used outside of their country and domain of origin. We ran three US tertiary care targeting transformer models on 300 Australian general practice notes, and showed that they generalise better than purpose built tools.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101607"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.imu.2024.101604
Tariq Sha’ban, Ahmad M. Mustafa, Mostafa Z. Ali
Drug Target Interaction (DTI) prediction is one of the main challenges in the pharmaceutical and drug discovery domains due to its high costs, time-consuming nature, and complexity of manual experiments required to evaluate interactions between large numbers of drugs and targets. In addition, a single drug can bind to multiple targets. In contrast, a single target can also bind to a number of drugs; this complicates the DTI task. Existing silico models often struggle with these challenges, particularly in managing diverse datasets. To address these issues, we introduce the Weighted Average Ensemble Drug–Target Interaction (WAE-DTI) model. Our approach integrates several descriptors and fingerprint representations to enhance prediction accuracy and generalization, namely atom pair fingerprint, Avalon, MACCS, MH, Morgan, RDKit, SEC, topological torsion, and LDP for drug representation, and ESM-2 for target representation. WAE-DTI employs a weighted average ensemble technique to handle diverse datasets effectively. The model demonstrates significant improvements over state-of-the-art methods, achieving an average mean squared error of 0.190 ±0.001 on the Davis dataset, 0.127 ±0.001 on Kiba, 0.143 ±0.001 on DTC, 0.284 ±0.004 on Metz, 0.308 ±0.001 on ToxCast, and 0.934 ±0.004 on STITCH. As for the classification task, WAE-DTI outperforms existing models with an AUPRC of 0.943 ±0.001 on BioSNAP, 0.474 ±0.011 on Davis, and 0.707 ±0.005 on BindingDB. Our code is publicly available at 1.
{"title":"WAE-DTI: Ensemble-based architecture for drug–target interaction prediction using descriptors and embeddings","authors":"Tariq Sha’ban, Ahmad M. Mustafa, Mostafa Z. Ali","doi":"10.1016/j.imu.2024.101604","DOIUrl":"10.1016/j.imu.2024.101604","url":null,"abstract":"<div><div>Drug Target Interaction (DTI) prediction is one of the main challenges in the pharmaceutical and drug discovery domains due to its high costs, time-consuming nature, and complexity of manual experiments required to evaluate interactions between large numbers of drugs and targets. In addition, a single drug can bind to multiple targets. In contrast, a single target can also bind to a number of drugs; this complicates the DTI task. Existing silico models often struggle with these challenges, particularly in managing diverse datasets. To address these issues, we introduce the Weighted Average Ensemble Drug–Target Interaction (WAE-DTI) model. Our approach integrates several descriptors and fingerprint representations to enhance prediction accuracy and generalization, namely atom pair fingerprint, Avalon, MACCS, MH, Morgan, RDKit, SEC, topological torsion, and LDP for drug representation, and ESM-2 for target representation. WAE-DTI employs a weighted average ensemble technique to handle diverse datasets effectively. The model demonstrates significant improvements over state-of-the-art methods, achieving an average mean squared error of 0.190 ±0.001 on the Davis dataset, 0.127 ±0.001 on Kiba, 0.143 ±0.001 on DTC, 0.284 ±0.004 on Metz, 0.308 ±0.001 on ToxCast, and 0.934 ±0.004 on STITCH. As for the classification task, WAE-DTI outperforms existing models with an AUPRC of 0.943 ±0.001 on BioSNAP, 0.474 ±0.011 on Davis, and 0.707 ±0.005 on BindingDB. Our code is publicly available at <span><span><sup>1</sup></span></span>.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101604"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.imu.2025.101617
Md Omor Farque , Rahat Moinul Islam , Md Ferdous Rahman Joni , Mimona Akter , Shilpy Akter , Mohammad Didarul Islam , MD Jubaer Bin Salim , Ahamed Abdul Aziz , Emranul Kabir , Monir Uzzaman
Naproxen (Nap), a widely used nonsteroidal anti-inflammatory drug (NSAID), effectively reduces inflammation, pain, and fever by inhibiting cyclooxygenase enzymes (i.e., COX-1 and COX-2). However, its therapeutic use is often limited by significant adverse effects, including gastrointestinal hemorrhage, nephrotoxicity, hepatotoxicity, hematuria, and aphthous ulcers. In this study, we aimed to enhance both the efficacy and safety profile of Nap by making targeted structural modifications to the parent drug. Specifically, selected functional groups (i.e., CH3, OCH3, CF3, OCF3, NH2, CH2NH2, NHCONH2 and NHCOCH3) were introduced into the naphthalene nucleus. The geometry of the modified compounds was optimized via DFT with the B3LYP functional and 6-31+G (d, p) basis set, facilitating physicochemical and spectral analysis. Molecular docking studies were conducted against the human Prostaglandin G/H synthase 2 (5F19) and Mus musculus Prostaglandin-endoperoxide synthase 2 (3NT1), and these candidates were subjected to MD simulation. ADMET and PASS analyses were performed to evaluate the medicinal efficacy and toxicological profiles of the derivatives. Our findings identified several promising candidates with enhanced therapeutic benefits and reduced toxicity compared with the parent Nap. Docking analysis revealed that analogs exhibited stronger binding affinities compared to Nap and selectivity towards COX-2. These candidates demonstrated stability over a 100 ns MD simulation, exhibiting significant hydrogen bonding. Compared with the parent drug, most of these analogs displayed reduced hepatotoxicity, nephrotoxicity, carcinogenicity, and gastrointestinal hemorrhage activity, as supported by pharmacokinetic calculations. This study demonstrated that improved chemical and medicinal properties lead to a reduction in side effects.
{"title":"Structural modification of Naproxen; physicochemical, spectral, medicinal, and pharmacological evaluation","authors":"Md Omor Farque , Rahat Moinul Islam , Md Ferdous Rahman Joni , Mimona Akter , Shilpy Akter , Mohammad Didarul Islam , MD Jubaer Bin Salim , Ahamed Abdul Aziz , Emranul Kabir , Monir Uzzaman","doi":"10.1016/j.imu.2025.101617","DOIUrl":"10.1016/j.imu.2025.101617","url":null,"abstract":"<div><div>Naproxen (Nap), a widely used nonsteroidal anti-inflammatory drug (NSAID), effectively reduces inflammation, pain, and fever by inhibiting cyclooxygenase enzymes (i.e., COX-1 and COX-2). However, its therapeutic use is often limited by significant adverse effects, including gastrointestinal hemorrhage, nephrotoxicity, hepatotoxicity, hematuria, and aphthous ulcers. In this study, we aimed to enhance both the efficacy and safety profile of Nap by making targeted structural modifications to the parent drug. Specifically, selected functional groups (i.e., CH<sub>3,</sub> OCH<sub>3</sub>, CF<sub>3</sub>, OCF<sub>3</sub>, NH<sub>2</sub>, CH<sub>2</sub>NH<sub>2</sub>, NHCONH<sub>2</sub> and NHCOCH<sub>3</sub>) were introduced into the naphthalene nucleus. The geometry of the modified compounds was optimized via DFT with the B3LYP functional and 6-31+G (d, p) basis set, facilitating physicochemical and spectral analysis. Molecular docking studies were conducted against the human Prostaglandin G/H synthase 2 (5F19) and <em>Mus musculus</em> Prostaglandin-endoperoxide synthase 2 (3NT1), and these candidates were subjected to MD simulation. ADMET and PASS analyses were performed to evaluate the medicinal efficacy and toxicological profiles of the derivatives. Our findings identified several promising candidates with enhanced therapeutic benefits and reduced toxicity compared with the parent Nap. Docking analysis revealed that analogs exhibited stronger binding affinities compared to Nap and selectivity towards COX-2. These candidates demonstrated stability over a 100 ns MD simulation, exhibiting significant hydrogen bonding. Compared with the parent drug, most of these analogs displayed reduced hepatotoxicity, nephrotoxicity, carcinogenicity, and gastrointestinal hemorrhage activity, as supported by pharmacokinetic calculations. This study demonstrated that improved chemical and medicinal properties lead to a reduction in side effects.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101617"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.imu.2025.101620
Chetna Vaid Kwatra , Harpreet Kaur , Monika Mangla , Arun Singh , Swapnali N. Tambe , Saiprasad Potharaju
Background and objective
Improving patient outcomes and lowering death rates depend on the early identification of gynecological cancers. This work intends to improve the accuracy and dependability of early gynecological tumor diagnosis by means of a hybrid deep learning model combining ResNet50 and Inception v3 architectures.
Methods
The proposed ensemble model combines multi-scale feature extraction of Inception v3 with the deep residual learning capability of ResNet50. A significant number of gynecological images were employed for training, testing, and assessment of the proposed model. By entailing accuracy, sensitivity, specificity, and F1 score, among other parameters the performance of the model was assessed.
Results
The first experiment depicted displays that the ensemble model performed better than single models with a training accuracy of 99.80 %, a validation accuracy of 99.80 %, and a test accuracy of 99.80 %. Comparing the two studies done in the current research, the model has shown to have a high sensitivity of 99 %, specificity of 99 %, and F1 score of 0.99, making it better in the identification of gynecological cancers and significantly reducing low true negatives and low true positives.
Conclusions
Ensembling of ResNet50 with Inception v3 for early diagnosis of gynecological cancers is promising and reproducible. Thus, according to the presented results, this method can contribute to the diagnoses of diseases by doctors quickly and accurately and, therefore, improve the treatment outcomes and the patient's health
{"title":"Early detection of gynecological malignancies using ensemble deep learning models: ResNet50 and inception V3","authors":"Chetna Vaid Kwatra , Harpreet Kaur , Monika Mangla , Arun Singh , Swapnali N. Tambe , Saiprasad Potharaju","doi":"10.1016/j.imu.2025.101620","DOIUrl":"10.1016/j.imu.2025.101620","url":null,"abstract":"<div><h3>Background and objective</h3><div>Improving patient outcomes and lowering death rates depend on the early identification of gynecological cancers. This work intends to improve the accuracy and dependability of early gynecological tumor diagnosis by means of a hybrid deep learning model combining ResNet50 and Inception v3 architectures.</div></div><div><h3>Methods</h3><div>The proposed ensemble model combines multi-scale feature extraction of Inception v3 with the deep residual learning capability of ResNet50. A significant number of gynecological images were employed for training, testing, and assessment of the proposed model. By entailing accuracy, sensitivity, specificity, and F1 score, among other parameters the performance of the model was assessed.</div></div><div><h3>Results</h3><div>The first experiment depicted displays that the ensemble model performed better than single models with a training accuracy of 99.80 %, a validation accuracy of 99.80 %, and a test accuracy of 99.80 %. Comparing the two studies done in the current research, the model has shown to have a high sensitivity of 99 %, specificity of 99 %, and F1 score of 0.99, making it better in the identification of gynecological cancers and significantly reducing low true negatives and low true positives.</div></div><div><h3>Conclusions</h3><div>Ensembling of ResNet50 with Inception v3 for early diagnosis of gynecological cancers is promising and reproducible. Thus, according to the presented results, this method can contribute to the diagnoses of diseases by doctors quickly and accurately and, therefore, improve the treatment outcomes and the patient's health</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101620"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study aimed to enhance point-of-care pulmonary function tests by developing a novel method for the spatiotemporal analysis of chest wall movements using a sequence of depth sensor images.
Methods
The proposed method employs singular value decomposition (SVD) to extract features from respiratory waveforms, which are then used to cluster pixels while preserving high resolution. The initial validation using simulated thoracic movement data confirmed the validity of the method. Further validation with clinical data capturing the chest wall movements of a patient undergoing interventional bronchology for a right bronchial tumor demonstrated the ability of this method to detect respiratory asynchrony.
Results
A phase lag of 867 ms was observed between the left and right sides of the rib cage preoperatively along with notable amplitude differences. These asynchronies resolved postoperatively. These results were consistent with the pulmonary pathophysiology, underscoring the clinical relevance of this method. The proposed system, integrated into an iOS app for an iPhone, is user-friendly and noninvasive and has the potential to become a valuable tool for the real-time assessment of interventional outcomes.
Conclusions
The novel method can be applied to various pulmonary diseases to detect the regional ventilation distribution. The method establishes a new generic framework for clinical studies of chest wall motion and pathophysiology.
{"title":"Spatiotemporal chest wall movement analysis using depth sensor imaging for detecting respiratory asynchrony","authors":"Masaru Mitsuya , Hiroki Nishine , Hiroshi Handa , Masamichi Mineshita , Masaki Kurosawa , Tetsuo Kirimoto , Shohei Sato , Takemi Matsui , Guanghao Sun","doi":"10.1016/j.imu.2025.101619","DOIUrl":"10.1016/j.imu.2025.101619","url":null,"abstract":"<div><h3>Background and objective</h3><div>This study aimed to enhance point-of-care pulmonary function tests by developing a novel method for the spatiotemporal analysis of chest wall movements using a sequence of depth sensor images.</div></div><div><h3>Methods</h3><div>The proposed method employs singular value decomposition (SVD) to extract features from respiratory waveforms, which are then used to cluster pixels while preserving high resolution. The initial validation using simulated thoracic movement data confirmed the validity of the method. Further validation with clinical data capturing the chest wall movements of a patient undergoing interventional bronchology for a right bronchial tumor demonstrated the ability of this method to detect respiratory asynchrony.</div></div><div><h3>Results</h3><div>A phase lag of 867 ms was observed between the left and right sides of the rib cage preoperatively along with notable amplitude differences. These asynchronies resolved postoperatively. These results were consistent with the pulmonary pathophysiology, underscoring the clinical relevance of this method. The proposed system, integrated into an iOS app for an iPhone, is user-friendly and noninvasive and has the potential to become a valuable tool for the real-time assessment of interventional outcomes.</div></div><div><h3>Conclusions</h3><div>The novel method can be applied to various pulmonary diseases to detect the regional ventilation distribution. The method establishes a new generic framework for clinical studies of chest wall motion and pathophysiology.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101619"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.imu.2024.101611
Wellington Kanyongo , Absalom E. Ezugwu , Tsitsi Moyo , Jean Vincent Fonou Dombeu
Non-adherence to medication among individuals with non-communicable diseases (NCDs) leads to increased morbidity, mortality, and healthcare costs. The integration of electronic drug prescription and dispensation systems enables comprehensive analysis of medication adherence (MA). Patient-level and medical claims data for 8141 diabetic and hypertensive patients in Harare, Zimbabwe, were analysed. Non-adherence was defined as medication refills falling below 75 % of the intended 12 monthly claims, while adherence required at least 75 % of the refills. Classification employed multiple machine learning algorithms, including SVM, KNN, DT, Naïve Bayes, DNN, LR, and RF in Python 3.11.3. Significant variables for MA were identified through the Random Forest (RF) feature importance mechanism and the information gain technique. These included the annual quantity of medical supplies, annual claim amount, patient age, wellness program subscription, medical aid cover, contribution towards medical aid cover, comorbidity, diagnosis, hospital cover type, complications development, gender, and medical aid scheme. The total units of medical supplies dispensed annually emerged as the most significant predictor of MA. Considering the 8-feature subset, which consistently produced the most robust machine learning models, the classification accuracy of the ML classifiers ranged from 84.9 % to 88.2 %, while the AUC values varied between 0.857 and 0.934. RF, an ensemble learning technique, was the most robust classifier, achieving 88.2 % accuracy, an AUC of 0.935, and superior precision, recall, and F1-score. This model shows promise as a prognostic tool for enhancing MA, aiding in identifying adherence levels among patients. These findings contribute to addressing disparities in medication refilling and adherence rates among patients with NCDs. The ML model holds potential for the development of intelligent MA and intervention applications to improve patient adherence to medication in the chronic disease domain.
非传染性疾病（NCDs）患者不坚持用药会导致发病率、死亡率和医疗成本增加。通过整合电子处方和配药系统，可以对用药依从性（MA）进行全面分析。我们分析了津巴布韦哈拉雷 8141 名糖尿病和高血压患者的患者层面数据和医疗报销数据。不坚持用药的定义是药物补给量低于 12 个月预定补给量的 75%，而坚持用药至少需要 75% 的补给量。在 Python 3.11.3 中，分类采用了多种机器学习算法，包括 SVM、KNN、DT、Naïve Bayes、DNN、LR 和 RF。通过随机森林（RF）特征重要性机制和信息增益技术，确定了 MA 的重要变量。这些变量包括年度医疗用品数量、年度索赔金额、患者年龄、健康计划订阅情况、医疗补助覆盖范围、对医疗补助覆盖范围的贡献、合并症、诊断、医院覆盖类型、并发症发展情况、性别和医疗补助计划。每年配发的医疗用品总量是预测医疗保险的最重要因素。考虑到 8 个特征子集始终产生最稳健的机器学习模型，ML 分类器的分类准确率介于 84.9 % 到 88.2 % 之间，AUC 值介于 0.857 到 0.934 之间。RF是一种集合学习技术，是最稳健的分类器，准确率达到88.2%，AUC值为0.935，精确度、召回率和F1-分数都很出色。该模型有望成为增强 MA 的预后工具，帮助识别患者的依从性水平。这些发现有助于解决非传染性疾病患者在药物补充和依从率方面的差异。该 ML 模型具有开发智能 MA 和干预应用的潜力，可改善慢性病患者的用药依从性。
{"title":"Machine learning-based classification of medication adherence among patients with noncommunicable diseases","authors":"Wellington Kanyongo , Absalom E. Ezugwu , Tsitsi Moyo , Jean Vincent Fonou Dombeu","doi":"10.1016/j.imu.2024.101611","DOIUrl":"10.1016/j.imu.2024.101611","url":null,"abstract":"<div><div>Non-adherence to medication among individuals with non-communicable diseases (NCDs) leads to increased morbidity, mortality, and healthcare costs. The integration of electronic drug prescription and dispensation systems enables comprehensive analysis of medication adherence (MA). Patient-level and medical claims data for 8141 diabetic and hypertensive patients in Harare, Zimbabwe, were analysed. Non-adherence was defined as medication refills falling below 75 % of the intended 12 monthly claims, while adherence required at least 75 % of the refills. Classification employed multiple machine learning algorithms, including SVM, KNN, DT, Naïve Bayes, DNN, LR, and RF in Python 3.11.3. Significant variables for MA were identified through the Random Forest (RF) feature importance mechanism and the information gain technique. These included the annual quantity of medical supplies, annual claim amount, patient age, wellness program subscription, medical aid cover, contribution towards medical aid cover, comorbidity, diagnosis, hospital cover type, complications development, gender, and medical aid scheme. The total units of medical supplies dispensed annually emerged as the most significant predictor of MA. Considering the 8-feature subset, which consistently produced the most robust machine learning models, the classification accuracy of the ML classifiers ranged from 84.9 % to 88.2 %, while the AUC values varied between 0.857 and 0.934. RF, an ensemble learning technique, was the most robust classifier, achieving 88.2 % accuracy, an AUC of 0.935, and superior precision, recall, and F1-score. This model shows promise as a prognostic tool for enhancing MA, aiding in identifying adherence levels among patients. These findings contribute to addressing disparities in medication refilling and adherence rates among patients with NCDs. The ML model holds potential for the development of intelligent MA and intervention applications to improve patient adherence to medication in the chronic disease domain.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101611"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.imu.2024.101612
Mansour S. Alturki , Nada Tawfeeq , Amal Alissa , Zahra Ahbail , Mohamed S. Gomaa , Abdulaziz H. Al Khzem , Thankhoe A. Rants'o , Mohammad J. Akbar , Waleed S. Alharbi , Bayan Y. Alshehri , Amjad N. Alotaibi , Fahad A. Almughem , Abdullah A. Alshehri
The RAS protein is a notable target in cancer research, being the most often mutated oncogene in human malignancies. The RAS G12X mutation is predominantly found in non-small cell lung cancer, including G12C and G12S variants, which are associated with a poor prognosis. Despite the approval of two inhibitors for the KRAS G12C mutation (sotorasib and adagrasib), the necessity persists due to the emergence of resistance to these inhibitors, which has become a substantial concern. This work involved the repurposing of FDA-approved drugs through in silico methods to identify compounds capable of covalently binding to KRAS G12C (PDB entry: 6OIM) and G12S (PDB entry: 7TLG). The computational studies involved virtual screening, induced fit, and covalent docking, and molecular dynamics simulations, and identified five promising candidates, the antibiotics; capreomycin, cefadroxil, and Cefdinir, the antifungal; natamycin, and the anti-inflammatory, cortisone. The hits exhibited binding affinities between −9.98 and −11.35 kcal/mol compared to −9.81 for sotorasib and were found to be covalent binders targeting KRAS G12C and G12S. The computational results were supported with in vitro evaluation on A549 malignant cells and HFF-1 non-cancerous cells. The antiproliferative efficacy of these drugs was evaluated by MTS tests, and their IC50 values were determined in which natamycin, although non-selective, and cortisone showed the highest activity with IC50 of 53.42 and 53.51 μg/mL, respectively, followed by cefadroxil (84.63 μg/mL). This study promisingly repurposed five drugs for KRAS mutant lung cancer, of which cefadroxil, and cortisone are particularly warranting further assessment either as a standalone or combination therapy while capreomycin is still an effective inhibitor for KRAS G12C mutant as evident from in silico and in vitro studies.
{"title":"Targeting KRAS G12C and G12S mutations in lung cancer: In silico drug repurposing and antiproliferative assessment on A549 cells","authors":"Mansour S. Alturki , Nada Tawfeeq , Amal Alissa , Zahra Ahbail , Mohamed S. Gomaa , Abdulaziz H. Al Khzem , Thankhoe A. Rants'o , Mohammad J. Akbar , Waleed S. Alharbi , Bayan Y. Alshehri , Amjad N. Alotaibi , Fahad A. Almughem , Abdullah A. Alshehri","doi":"10.1016/j.imu.2024.101612","DOIUrl":"10.1016/j.imu.2024.101612","url":null,"abstract":"<div><div>The RAS protein is a notable target in cancer research, being the most often mutated oncogene in human malignancies. The RAS G12X mutation is predominantly found in non-small cell lung cancer, including G12C and G12S variants, which are associated with a poor prognosis. Despite the approval of two inhibitors for the KRAS G12C mutation (sotorasib and adagrasib), the necessity persists due to the emergence of resistance to these inhibitors, which has become a substantial concern. This work involved the repurposing of FDA-approved drugs through <em>in silico</em> methods to identify compounds capable of covalently binding to KRAS G12C (PDB entry: 6OIM) and G12S (PDB entry: 7TLG). The computational studies involved virtual screening, induced fit, and covalent docking, and molecular dynamics simulations, and identified five promising candidates, the antibiotics; capreomycin, cefadroxil, and Cefdinir, the antifungal; natamycin, and the anti-inflammatory, cortisone. The hits exhibited binding affinities between −9.98 and −11.35 kcal/mol compared to −9.81 for sotorasib and were found to be covalent binders targeting KRAS G12C and G12S. The computational results were supported with <em>in vitro</em> evaluation on A549 malignant cells and HFF-1 non-cancerous cells. The antiproliferative efficacy of these drugs was evaluated by MTS tests, and their IC<sub>50</sub> values were determined in which natamycin, although non-selective, and cortisone showed the highest activity with IC<sub>50</sub> of 53.42 and 53.51 μg/mL, respectively, followed by cefadroxil (84.63 μg/mL). This study promisingly repurposed five drugs for KRAS mutant lung cancer, of which cefadroxil, and cortisone are particularly warranting further assessment either as a standalone or combination therapy while capreomycin is still an effective inhibitor for KRAS G12C mutant as evident from <em>in silico</em> and <em>in vitro</em> studies.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101612"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1016/j.imu.2025.101621
Tayyaba Ali, Sidra Iqbal
Extensive parental use of electronic devices correlates with poorer parent-adolescent interactions, though research has not investigated any potential effects on adolescent behavior. This research investigated whether increased technoference is associated with higher levels of adolescents' internalizing and externalizing behaviors, along with diminished prosocial behaviors. 450 pakistani adolescents from public and private schools aged 11–17 completed the self-reported versions of The Technoference Scale and the Strengths and Difficulties Questionnaire. Results indicated that parental and adolescent technoference was positively correlated with internalizing and externalizing behavior problems, while negatively correlated with prosocial behavior. Strong association between parental and adolescent technoference was observed. Findings from this study highlight the significant influence of technoference on adolescent behavior, suggesting that managing technology within families is essential for promoting healthier behavioral patterns. The significant correlations between technoference and both internalizing and externalizing behaviors underscore the potential risks associated with excessive media use and disrupted family interactions.
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Pub Date : 2025-01-01DOI: 10.1016/j.imu.2024.101608
Gianni S.S. Liveraro , Maria E.S. Takahashi , Fabiana Lascala , Luiz R. Lopes , Nelson A. Andreollo , Maria C.S. Mendes , Jun Takahashi , José B.C. Carvalheira
We evaluate the significance of body composition radiomics in predicting outcomes for resectable gastric cancer (GC) patients, as these parameters can enhance optimal surveillance strategies and risk-stratification models. Automated segmentation using deep learning algorithms was employed on CT images to analyze body composition in 276 GC patients, retrospectively recruited from the Clinical Hospital of the University of Campinas. Radiomics features of skeletal muscle (SM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were calculated. Body composition radiomics were integrated with clinicopathological factors using machine learning (ML) algorithms trained for patient outcome prediction. We compared results using Random Forest, Logistic Regression and Boosted Decision Tree algorithms. To identify the relevant features for the prognosis, recursive feature inclusion (RFI) was performed using SHAP Importance ranking. Our study uncovered novel body composition radiomic features that enhance patient prognosis, particularly the 90th percentile radiodensity value (HU) for SM and VAT. The ML model output also refined pathological staging: Stage II patients with a higher predicted mortality risk by the model had overall survival (OS) similar to Stage III patients, while Stage III patients with lower predicted risk showed OS comparable to Stage II. This approach demonstrates that the integration of clinical and radiomic features enhances the accuracy of pathological staging and offers more detailed information to refine treatment strategies for gastric cancer patients. Skeletal muscle and visceral adipose tissue radiodensity percentiles emerged as crucial determinants of patient outcomes.
我们评估了身体成分放射组学在预测可切除胃癌（GC）患者预后方面的意义，因为这些参数可以增强最佳监控策略和风险分层模型。利用深度学习算法对 CT 图像进行了自动分割，以分析从坎皮纳斯大学临床医院回顾性招募的 276 名 GC 患者的身体成分。计算了骨骼肌（SM）、内脏脂肪组织（VAT）和皮下脂肪组织（SAT）的放射组学特征。利用机器学习（ML）算法将身体成分放射组学与临床病理因素整合在一起，对患者的预后进行预测。我们比较了随机森林算法、逻辑回归算法和提升决策树算法的结果。为了识别与预后相关的特征，我们使用 SHAP 重要性排序法进行了递归特征包含（RFI）。我们的研究发现了可增强患者预后的新型身体成分放射学特征，尤其是 SM 和 VAT 的第 90 百分位放射密度值（HU）。ML 模型输出还完善了病理分期：模型预测死亡风险较高的 II 期患者的总生存期（OS）与 III 期患者相似，而预测风险较低的 III 期患者的 OS 与 II 期患者相当。这种方法表明，整合临床和放射学特征可提高病理分期的准确性，并为完善胃癌患者的治疗策略提供更详细的信息。骨骼肌和内脏脂肪组织放射密度百分位数是决定患者预后的关键因素。
{"title":"Improving resectable gastric cancer prognosis prediction: A machine learning analysis combining clinical features and body composition radiomics","authors":"Gianni S.S. Liveraro , Maria E.S. Takahashi , Fabiana Lascala , Luiz R. Lopes , Nelson A. Andreollo , Maria C.S. Mendes , Jun Takahashi , José B.C. Carvalheira","doi":"10.1016/j.imu.2024.101608","DOIUrl":"10.1016/j.imu.2024.101608","url":null,"abstract":"<div><div>We evaluate the significance of body composition radiomics in predicting outcomes for resectable gastric cancer (GC) patients, as these parameters can enhance optimal surveillance strategies and risk-stratification models. Automated segmentation using deep learning algorithms was employed on CT images to analyze body composition in 276 GC patients, retrospectively recruited from the Clinical Hospital of the University of Campinas. Radiomics features of skeletal muscle (SM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were calculated. Body composition radiomics were integrated with clinicopathological factors using machine learning (ML) algorithms trained for patient outcome prediction. We compared results using Random Forest, Logistic Regression and Boosted Decision Tree algorithms. To identify the relevant features for the prognosis, recursive feature inclusion (RFI) was performed using SHAP Importance ranking. Our study uncovered novel body composition radiomic features that enhance patient prognosis, particularly the 90th percentile radiodensity value (HU) for SM and VAT. The ML model output also refined pathological staging: Stage II patients with a higher predicted mortality risk by the model had overall survival (OS) similar to Stage III patients, while Stage III patients with lower predicted risk showed OS comparable to Stage II. This approach demonstrates that the integration of clinical and radiomic features enhances the accuracy of pathological staging and offers more detailed information to refine treatment strategies for gastric cancer patients. Skeletal muscle and visceral adipose tissue radiodensity percentiles emerged as crucial determinants of patient outcomes.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101608"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information on the location of the fossa ovalis (FO) is necessary for planning interventional procedures that require an inter-atrial septal (IAS) puncture. At present, this information is obtained manually from pre-procedural medical images, which is time consuming with limited reproducibility. In this paper, we propose a method to automatically segment the FO region from computed tomography (CT) images.
Methods
Our proposed method roughly crops CT images based on atlas information of the FO and heart chambers, and inputs the cropped CT images to a U-Net-based deep neural network (DNN) to segment the FO region. This method was evaluated by five-fold cross validation using 215 CT images with manually annotated FO regions, and its segmentation accuracy was compared to two previously reported methods based on thinness of the IAS wall and on simple DNN.
Results
The segmentation process was successful in all cases for the IAS-based method, but failed in 4 cases for the proposed method and in 30 cases for the DNN method due to irregular heart structure. For the segmentation accuracies of our proposed method, the IAS wall thinness-based method and DNN based-method, mean chamfer distances were 2.16 ± 1.43, 3.30 ± 1.37 and 2.66 ± 1.32 respectively, with the difference being statistically significant.
Conclusions
These results suggest that our proposed method can automatically segment the FO region more accurately with fewer failures.
{"title":"Fully automatic fossa ovalis segmentation from computed tomography images using deep neural network with atlas-based localization","authors":"Gakuto Aoyama , Toru Tanaka , Yukiteru Masuda , Naoki Matsuki , Ryo Ishikawa , Masahiko Asami , Kiyohide Satoh , Takuya Sakaguchi","doi":"10.1016/j.imu.2025.101613","DOIUrl":"10.1016/j.imu.2025.101613","url":null,"abstract":"<div><h3>Background and objective</h3><div>Information on the location of the fossa ovalis (FO) is necessary for planning interventional procedures that require an inter-atrial septal (IAS) puncture. At present, this information is obtained manually from pre-procedural medical images, which is time consuming with limited reproducibility. In this paper, we propose a method to automatically segment the FO region from computed tomography (CT) images.</div></div><div><h3>Methods</h3><div>Our proposed method roughly crops CT images based on atlas information of the FO and heart chambers, and inputs the cropped CT images to a U-Net-based deep neural network (DNN) to segment the FO region. This method was evaluated by five-fold cross validation using 215 CT images with manually annotated FO regions, and its segmentation accuracy was compared to two previously reported methods based on thinness of the IAS wall and on simple DNN.</div></div><div><h3>Results</h3><div>The segmentation process was successful in all cases for the IAS-based method, but failed in 4 cases for the proposed method and in 30 cases for the DNN method due to irregular heart structure. For the segmentation accuracies of our proposed method, the IAS wall thinness-based method and DNN based-method, mean chamfer distances were 2.16 ± 1.43, 3.30 ± 1.37 and 2.66 ± 1.32 respectively, with the difference being statistically significant.</div></div><div><h3>Conclusions</h3><div>These results suggest that our proposed method can automatically segment the FO region more accurately with fewer failures.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101613"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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