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BACKGROUND: Diabetes is a chronic condition that can result in many long-term physiological, metabolic, and neurological complications. Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan. METHODS: In this study, we employed machine learning (ML) based case-control study on a diabetic cohort size of 1000 participants form Qatar Biobank to predict diabetes using clinical and bone health indicators from Dual Energy X-ray Absorptiometry (DXA) machines. ML models were utilized to distinguish diabetes groups from non-diabetes controls. Recursive feature elimination (RFE) was leveraged to identify a subset of features to improve the performance of model. SHAP based analysis was used for the importance of features and support the explainability of the proposed model. RESULTS: Ensemble based models XGboost and RF achieved over 84% accuracy for detecting diabetes. After applying RFE, we selected only 20 features which improved the model accuracy to 87.2%. From a clinical standpoint, higher HDL-Cholesterol and Neutrophil levels were observed in the diabetic group, along with lower vitamin B12 and testosterone levels. Lower sodium levels were found in diabetics, potentially stemming from clinical factors including specific medications, hormonal imbalances, unmanaged diabetes. We believe Dapagliflozin prescriptions in Qatar were associated with decreased Gamma Glutamyltransferase and Aspartate Aminotransferase enzyme levels, confirming prior research. We observed that bone area, bone mineral content, and bone mineral density were slightly lower in the Diabetes group across almost all body parts, but the difference against the control group was not statistically significant except in T12, troch and trunk area. No significant negative impact of diabetes progression on bone health was observed over a period of 5-15 yrs in the cohort. CONCLUSION: This study recommends the inclusion of ML model which combines both DXA and clinical data for the early diagnosis of diabetes.
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Absorciometria de Fóton , Diabetes Mellitus Tipo 2 , Aprendizado de Máquina , Humanos , Pessoa de Meia-Idade , Masculino , Estudos de Casos e Controles , Feminino , Catar , Adulto , Idoso , Densidade ÓsseaRESUMO
Tooth avulsion is a frequently encountered dental emergency. Children are commonly reported group due to frequent sports activities, trauma, accidents and falls. Prompt emergency management is vital for long term success and to avoid morbidity. The study was aimed to assess the understanding of intern dentists about the emergency handling of avulsed teeth cases as mostly they are first responders among health care personnel. In this study a fourteen-item questionnaire with predefined responses was shared as online Google survey form with intern dentists of 5 different dental teaching hospitals of Islamabad, Pakistan. The duration of the study was 6 months (01 March 2022 to 31 August 2022). The questions were intended to collect personal information and to check level of knowledge and awareness about the management of avulsed tooth among the dental interns. The data was analyzed by statistical methods and is presented through tables and descriptive methods. In total, 152 participants completed the shared questionnaire. The vast majority (71%) of them were aware of the initial management of avulsed teeth, 49% were aware of the ideal transport medium for an avulsed tooth, (43%) were aware of the critical time for successful replantation, while (62%) had knowledge of the multiple factors responsible for the outcome of the tooth replantation. For majority of the statements, female participants had better knowledge as compared to their male counterparts. Statistically significant difference was noted for the statement "If you found the knocked-out tooth and it is dirty what will be your initial approach?" with female participants having better knowledge as compared to the male (p value = 0.005). Based on our study results, generally dental interns are well-informed but still lack expected level of awareness regarding the proper management protocol for avulsed tooth. Hence, improvement is needed regarding the effective handling of avulsed teeth cases.
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Avulsão Dentária , Criança , Humanos , Masculino , Feminino , Avulsão Dentária/terapia , Conhecimentos, Atitudes e Prática em Saúde , Reimplante Dentário/métodos , Estudos Transversais , Inquéritos e Questionários , OdontólogosRESUMO
Understanding the complex and specific roles played by non-coding RNAs (ncRNAs), which comprise the bulk of the genome, is important for understanding virtually every hallmark of cancer. This large group of molecules plays pivotal roles in key regulatory mechanisms in various cellular processes. Regulatory mechanisms, mediated by long non-coding RNA (lncRNA) and RNA-binding protein (RBP) interactions, are well documented in several types of cancer. Their effects are enabled through networks affecting lncRNA and RBP stability, RNA metabolism including N6-methyladenosine (m6A) and alternative splicing, subcellular localization, and numerous other mechanisms involved in cancer. In this review, we discuss the reciprocal interplay between lncRNAs and RBPs and their involvement in epigenetic regulation via histone modifications, as well as their key role in resistance to cancer therapy. Other aspects of RBPs including their structural domains, provide a deeper knowledge on how lncRNAs and RBPs interact and exert their biological functions. In addition, current state-of-the-art knowledge, facilitated by machine and deep learning approaches, unravels such interactions in better details to further enhance our understanding of the field, and the potential to harness RNA-based therapeutics as an alternative treatment modality for cancer are discussed.
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Neoplasias , RNA Longo não Codificante , Humanos , RNA Longo não Codificante/genética , Epigênese Genética , Proteínas de Ligação a RNA/genética , Proteínas de Ligação a RNA/metabolismo , Neoplasias/genética , Aprendizado de MáquinaRESUMO
BACKGROUND: Subcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are laborious, time-consuming and expensive. Therefore, in silico approaches for this purpose are attaining great attention in the RNA community. METHODS: In this article, we propose MSLP, a machine learning-based method to predict the subcellular localization of mRNA. We propose a novel combination of four types of features representing k-mer, pseudo k-tuple nucleotide composition (PseKNC), physicochemical properties of nucleotides, and 3D representation of sequences based on Z-curve transformation to feed into machine learning algorithm to predict the subcellular localization of mRNAs. RESULTS: Considering the combination of the above-mentioned features, ennsemble-based models achieved state-of-the-art results in mRNA subcellular localization prediction tasks for multiple benchmark datasets. We evaluated the performance of our method in ten subcellular locations, covering cytoplasm, nucleus, endoplasmic reticulum (ER), extracellular region (ExR), mitochondria, cytosol, pseudopodium, posterior, exosome, and the ribosome. Ablation study highlighted k-mer and PseKNC to be more dominant than other features for predicting cytoplasm, nucleus, and ER localizations. On the other hand, physicochemical properties and Z-curve based features contributed the most to ExR and mitochondria detection. SHAP-based analysis revealed the relative importance of features to provide better insights into the proposed approach. AVAILABILITY: We have implemented a Docker container and API for end users to run their sequences on our model. Datasets, the code of API and the Docker are shared for the community in GitHub at: https://github.com/smusleh/MSLP .
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Algoritmos , Núcleo Celular , RNA Mensageiro/genética , Ribossomos , Aprendizado de Máquina , Biologia Computacional/métodosRESUMO
The primary goal of this study is to develop a deep neural network for action recognition that enhances accuracy and minimizes computational costs. In this regard, we propose a modified EMO-MoviNet-A2* architecture that integrates Evolving Normalization (EvoNorm), Mish activation, and optimal frame selection to improve the accuracy and efficiency of action recognition tasks in videos. The asterisk notation indicates that this model also incorporates the stream buffer concept. The Mobile Video Network (MoviNet) is a member of the memory-efficient architectures discovered through Neural Architecture Search (NAS), which balances accuracy and efficiency by integrating spatial, temporal, and spatio-temporal operations. Our research implements the MoviNet model on the UCF101 and HMDB51 datasets, pre-trained on the kinetics dataset. Upon implementation on the UCF101 dataset, a generalization gap was observed, with the model performing better on the training set than on the testing set. To address this issue, we replaced batch normalization with EvoNorm, which unifies normalization and activation functions. Another area that required improvement was key-frame selection. We also developed a novel technique called Optimal Frame Selection (OFS) to identify key-frames within videos more effectively than random or densely frame selection methods. Combining OFS with Mish nonlinearity resulted in a 0.8-1% improvement in accuracy in our UCF101 20-classes experiment. The EMO-MoviNet-A2* model consumes 86% fewer FLOPs and approximately 90% fewer parameters on the UCF101 dataset, with a trade-off of 1-2% accuracy. Additionally, it achieves 5-7% higher accuracy on the HMDB51 dataset while requiring seven times fewer FLOPs and ten times fewer parameters compared to the reference model, Motion-Augmented RGB Stream (MARS).
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The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.
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Biomarcadores Tumorais , Neoplasias , Humanos , Prognóstico , Estudos Prospectivos , Biomarcadores/análise , Medicina de Precisão , Aprendizado de Máquina , Neoplasias/diagnósticoRESUMO
Since December 2019, COVID-19 has posed the most serious threat to living beings. With the advancement of vaccination programs around the globe, the need to quickly diagnose COVID-19 in general with little logistics is fore important. As a consequence, the fastest diagnostic option to stop COVID-19 from spreading, especially among senior patients, should be the development of an automated detection system. This study aims to provide a lightweight deep learning method that incorporates a convolutional neural network (CNN), discrete wavelet transform (DWT), and a long short-term memory (LSTM), called CORONA-NET for diagnosing COVID-19 from chest X-ray images. In this system, deep feature extraction is performed by CNN, the feature vector is reduced yet strengthened by DWT, and the extracted feature is detected by LSTM for prediction. The dataset included 3000 X-rays, 1000 of which were COVID-19 obtained locally. Within minutes of the test, the proposed test platform's prototype can accurately detect COVID-19 patients. The proposed method achieves state-of-the-art performance in comparison with the existing deep learning methods. We hope that the suggested method will hasten clinical diagnosis and may be used for patients in remote areas where clinical labs are not easily accessible due to a lack of resources, location, or other factors.
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It is imperative to manage children with empathy and concern for their well-being in order to carry out any dental procedure smoothly. Owing to the inherent fear of dental operatory, behaviour management of children is an important aspect of pediatric dental care. Many techniques are available to help manage the behaviour of children. It is, however important to educate parents about these techniques and to get their cooperation for these techniques to be used on their children.This study aimed to familiarize the parents with non-pharmacological behavior management techniques and to determine the parental acceptance of such techniques in children seeking dental treatment in specialty care dental units. A total of 303 parents were evaluated through online questionnaires in this research. They were shown videos of randomly selected non-pharmacologic behaviour management techniques including tell-show-do, positive reinforcement, modelling and voice control. Parents were asked to watch the videos and give their response on seven-items inquiring about their acceptance levels regarding the respective techniques. The responses were recorded on a Likert scales ranging from strongly disagree to strongly agree. According to parental acceptance score (PAS), positive reinforcement was the most accepted technique whereas voice control was the least acceptable technique. Majority of the parents were more receptive towards those techniques that involved a healthy and friendly communication between a dentist and the pediatric patient such as, positive reinforcement, tell show do and modelling. Most significantly the people having low socio-economic status (SES) in Pakistan were more acceptable of voice control than people with high SES.
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Comportamento Infantil , Restrição Física , Criança , Humanos , Estudos Transversais , Pais , Assistência OdontológicaRESUMO
PURPOSE: As treatment options for children with sickle cell anemia (SCA) continue to expand survival, evaluation of factors associated with health-related quality of life (HRQoL) is becoming an important aspect for further improving clinical management. Although the general features of SCA are similar, factors influencing HRQoL within a country may differ from those of other countries, therefore this study aimed to explore factors affecting HRQoL in children with SCA living in the Sultanate of Oman. METHODS: This was a cross-sectional study in which the PedsQL™ Sickle Cell Disease Module was used to evaluate the overall HRQoL in children with SCA. The socio-demographic data, clinical, and treatment outcomes were collected. Univariate and multivariate linear regression analyses were used to identify predictors of HRQoL. RESULTS: A total of 123 children with SCA, aged from 2 to 16 years were enrolled. The mean total HRQoL score was 52 ± 15% (9-94), where Worry II scale recorded the highest score. The multiple regression analysis revealed that the only predictors of total HRQoL score were hemoglobin F (B = 0.64, 95% confidence interval [CI] 0.149-1.118, P = 0.009) and to a lesser degree white blood cell count (B = - 0.99, 95% CI - 1.761 to - 0.198, P = 0.01), independently of other study parameters such as age, gender, spleen status, and hydroxyurea therapy. CONCLUSIONS: Collectively, these findings indicated that hemoglobin F out-weighted white blood cell count in predicting HRQoL in Omani children with SCA. Recognition of these factors could help health professionals to develop effective strategies to improve the overall HRQoL in these young patients.
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Anemia Falciforme/diagnóstico , Hemoglobina Fetal/metabolismo , Qualidade de Vida/psicologia , Adolescente , Anemia Falciforme/patologia , Criança , Pré-Escolar , Estudos Transversais , Feminino , Humanos , Masculino , Resultado do TratamentoRESUMO
Recently attention-based networks have been successful for image restoration tasks. However, existing methods are either computationally expensive or have limited receptive fields, adding constraints to the model. They are also less resilient in spatial and contextual aspects and lack pixel-to-pixel correspondence, which may degrade feature representations. In this paper, we propose a novel and computationally efficient architecture Single Stage Adaptive Multi-Attention Network (SSAMAN) for image restoration tasks, particularly for image denoising and image deblurring. SSAMAN efficiently addresses computational challenges and expands receptive fields, enhancing robustness in spatial and contextual feature representation. Its Adaptive Multi-Attention Module (AMAM), which consists of Adaptive Pixel Attention Branch (APAB) and an Adaptive Channel Attention Branch (ACAB), uniquely integrates channel and pixel-wise dimensions, significantly improving sensitivity to edges, shapes, and textures. We perform extensive experiments and ablation studies to validate the performance of SSAMAN. Our model shows state-of-the-art results on various benchmarks, for example, on image denoising tasks, SSAMAN achieves a notable 40.08 dB PSNR on SIDD dataset, outperforming Restormer by 0.06 dB PSNR, with 41.02% less computational cost, and achieves a 40.05 dB PSNR on the DND dataset. For image deblurring, SSAMAN achieves 33.53 dB PSNR on GoPro dataset. Code and models are available at Github.
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Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson's patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson's dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson's disease analysis.
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Algoritmos , Doença de Parkinson , Humanos , Mineração de Dados/métodos , IncertezaRESUMO
INTRODUCTION: Circulating microparticles (MP) are being described as potential biomarkers for disease activity in a variety of conditions including sickle cell anemia (SCA). However, relatively little is known about the influence of spleen status on MP levels in patients with SCA. METHODS: Using a prospective study design we characterize circulating MP in 144 patients with SCA in steady state by assessing their cellular origin and their relationships to spleen status defined by clinical and imaging findings. In addition, MP levels were studied according to demographic characteristics, clinical status, treatment modalities, and other hematological and biochemical parameters. Absolute plasma concentrations of MP were determined by flow cytometry. RESULTS: Patients with SCA displayed a 10-fold increase in levels of MP derived from red blood cell (RBC) and platelets (PLT) when compared to their healthy counterparts (p < 0.0001). Splenectomized patients with SCA have more pronounced levels of MPRBC and MPPLT, and remained elevated after several weeks of follow-up. Levels of MP were not significantly associated with spleen removal procedures, age, gender, clinical severity score, hydroxyurea therapy, hemoglobin F, and co-existence of glucose-6-phosphate dehydrogenase deficiency. CONCLUSION: Collectively, these results suggest that splenectomy affects circulating levels of MP regardless of the known SCA modifiers and correlates.
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Anemia Falciforme , Esplenectomia , Humanos , Estudos Prospectivos , Eritrócitos , Hemoglobina FetalRESUMO
OBJECTIVES: To determine the levels of 25-hydroxyvitamin [25(OH)D3] in patients with acute leukemia and the effect of remission-induction chemotherapy. METHODOLOGY: This study was case control, all newly diagnosed patients of acute leukemia between the age of one to sixty years and residents of Pakistan were enrolled and evaluated. Those who were unwilling or unable to provide written informed consent were excluded. All selected patients (n=86) were grouped in to acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). AML was further categorized as A1 before remission-induction (n=17) and B1 after remission induction (n=13), ALL was further categorized as A2 before remission-induction (n=31) and B2 after remission induction (n=25). The 25-hydroxyvitamin [25(OH)D3] levels were measured in the sera of all patients (before and after remission-induction) by one step delayed chemiluminescent micro particle immunoassay (CMIA).We compared 25(OH)D3 levels in all patients before and after the remission-induction chemotherapy. RESULTS: A total of 86 patients were analyzed, in which 60 patients were male. Mean age was 24.39 years (range, 1 to 60 years); the mean levels of 25(OH)D in group A1 (n=17) was 17.70±3.2 ng/ml, in group B1 (n=13) 14.06±2.4 ng/ml, 19.07±7.08 ng/ml in group A2 (n=31), while 10.59±3.9 ng/ml found in group B2 (n=25). CONCLUSION: 25(OH)D3 insufficiency was evident subnormal in majority of patients with acute leukemia and 25(OH)D3 were further reduced after remission-induction as compared to untreated group, difference was statistically significant when compared with each group.
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BACKGROUND: Transformer-based models are gaining popularity in medical imaging and cancer imaging applications. Many recent studies have demonstrated the use of transformer-based models for brain cancer imaging applications such as diagnosis and tumor segmentation. OBJECTIVE: This study aims to review how different vision transformers (ViTs) contributed to advancing brain cancer diagnosis and tumor segmentation using brain image data. This study examines the different architectures developed for enhancing the task of brain tumor segmentation. Furthermore, it explores how the ViT-based models augmented the performance of convolutional neural networks for brain cancer imaging. METHODS: This review performed the study search and study selection following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search comprised 4 popular scientific databases: PubMed, Scopus, IEEE Xplore, and Google Scholar. The search terms were formulated to cover the interventions (ie, ViTs) and the target application (ie, brain cancer imaging). The title and abstract for study selection were performed by 2 reviewers independently and validated by a third reviewer. Data extraction was performed by 2 reviewers and validated by a third reviewer. Finally, the data were synthesized using a narrative approach. RESULTS: Of the 736 retrieved studies, 22 (3%) were included in this review. These studies were published in 2021 and 2022. The most commonly addressed task in these studies was tumor segmentation using ViTs. No study reported early detection of brain cancer. Among the different ViT architectures, Shifted Window transformer-based architectures have recently become the most popular choice of the research community. Among the included architectures, UNet transformer and TransUNet had the highest number of parameters and thus needed a cluster of as many as 8 graphics processing units for model training. The brain tumor segmentation challenge data set was the most popular data set used in the included studies. ViT was used in different combinations with convolutional neural networks to capture both the global and local context of the input brain imaging data. CONCLUSIONS: It can be argued that the computational complexity of transformer architectures is a bottleneck in advancing the field and enabling clinical transformations. This review provides the current state of knowledge on the topic, and the findings of this review will be helpful for researchers in the field of medical artificial intelligence and its applications in brain cancer.
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BACKGROUND: The objective of this study was to determine the prevalence of the second mesiobuccal canal in permanent maxillary second molar in patients presenting to Peshawar Dental College and Hospital. METHODS: One hundred and twenty patients advised for root canal treatment in the maxillary second molars participated in the study. Two detection procedures, clinical and radio graphical examination were used. Two pre-operative radiographs with different angulations and one post-operative radiograph were taken to examine roots and root canals. Access cavities were prepared and the second mesiobuccal canal was explored using magnifying dental loupes (3.5 x), endodontic explorer (DG16) and size 10 K-file. Descriptive statistics were recorded as percentages, frequencies and mean. The chi-square test was used for gender, age-wise comparison and right and left side of the maxillary jaw. RESULTS: One hundred and twenty patients were recruited in the study. There were 65 (54.2%) males and 55 (45.8%) females. The second mesiobuccal canal was more common in males compared to females (p-value=0.434). The second mesiobuccal canal was most commonly found in 3rd decade with mean age, of 40.5±12.31, (p-value =0.51). The frequencies and percentages of the second mesiobuccal canal in maxillary second molars on the right and left side of the jaw were 70 (58.3%) and 50 (41.7%) respectively (p-value =0.310). CONCLUSIONS: The second mesiobuccal canal was found in less than half of the second molars. The most successful method of detection in this study was both clinical and radiographic.
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Cavidade Pulpar , Raiz Dentária , Masculino , Feminino , Humanos , Cavidade Pulpar/diagnóstico por imagem , Prevalência , Tomografia Computadorizada de Feixe Cônico/métodos , Dente Molar/diagnóstico por imagem , Maxila/diagnóstico por imagemRESUMO
Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.
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Antineoplásicos , Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Humanos , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/metabolismo , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/metabolismo , Antineoplásicos/farmacologia , Antineoplásicos/uso terapêutico , Inibidores de Proteínas Quinases/farmacologia , Inibidores de Proteínas Quinases/uso terapêutico , Resistencia a Medicamentos Antineoplásicos/genética , Receptores ErbB/metabolismo , Desenho de Fármacos , Mutação/genéticaRESUMO
The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.
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COVID-19 has taken a huge toll on our lives over the last 3 years. Global initiatives put forward by all stakeholders are still in place to combat this pandemic and help us learn lessons for future ones. While the vaccine rollout was not able to curb the spread of the disease for all strains, the research community is still trying to develop effective therapeutics for COVID-19. Although Paxlovid and remdesivir have been approved by the FDA against COVID-19, they are not free of side effects. Therefore, the search for a therapeutic solution with high efficacy continues in the research community. To support this effort, in this latest version (v3) of COVID-19Base, we have summarized the biomedical entities linked to COVID-19 that have been highlighted in the scientific literature after the vaccine rollout. Eight different topic-specific dictionaries, i.e., gene, miRNA, lncRNA, PDB entries, disease, alternative medicines registered under clinical trials, drugs, and the side effects of drugs, were used to build this knowledgebase. We have introduced a BLSTM-based deep-learning model to predict the drug-disease associations that outperforms the existing model for the same purpose proposed in the earlier version of COVID-19Base. For the very first time, we have incorporated disease-gene, disease-miRNA, disease-lncRNA, and drug-PDB associations covering the largest number of biomedical entities related to COVID-19. We have provided examples of and insights into different biomedical entities covered in COVID-19Base to support the research community by incorporating all of these entities under a single platform to provide evidence-based support from the literature. COVID-19Base v3 can be accessed from: https://covidbase-v3.vercel.app/. The GitHub repository for the source code and data dictionaries is available to the community from: https://github.com/91Abdullah/covidbasev3.0.
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COVID-19 , MicroRNAs , RNA Longo não Codificante , Humanos , SARS-CoV-2 , Bases de ConhecimentoRESUMO
BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context. METHODS: In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics. FINDINGS: Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features. INTERPRETATION: This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer. FUNDING: National Institutes of Health, Mark Foundation Damon Runyon Foundation Physician Scientist Award, MD Anderson Strategic Initiative Development Program, MD Anderson Lung Moon Shot Program, Andrea Mugnaini, and Edward L C Smith.