Dysfunctions caused by missense mutations in the tumour suppressor p53 have been extensively shown to be a leading driver of many cancers. Unfortunately, it is time-consuming and labour-intensive to ...experimentally elucidate the effects of all possible missense variants. Recent works presented a comprehensive dataset and machine learning model to predict the functional outcome of mutations in p53. Despite the well-established dataset and precise predictions, this tool was trained on a complicated model with limited predictions on p53 mutations. In this work, we first used computational biophysical tools to investigate the functional consequences of missense mutations in p53, informing a bias of deleterious mutations with destabilizing effects. Combining these insights with experimental assays, we present two interpretable machine learning models leveraging both experimental assays and in silico biophysical measurements to accurately predict the functional consequences on p53 and validate their robustness on clinical data. Our final model based on nine features obtained comparable predictive performance with the state-of-the-art p53 specific method and outperformed other generalized, widely used predictors. Interpreting our models revealed that information on residue p53 activity, polar atom distances and changes in p53 stability were instrumental in the decisions, consistent with a bias of the properties of deleterious mutations. Our predictions have been computed for all possible missense mutations in p53, offering clinical diagnostic utility, which is crucial for patient monitoring and the development of personalized cancer treatment.
The liquorice tribe Glycyrrhizeae is a leguminous herbaceous group of plants comprised of the genera Glycyrrhiza and Glycyrrhizopsis. Some Glycyrrhiza taxa contain glycyrrhizin, a pharmacologically ...significant sweet substance that also has applications in crafting industrial materials. Here, we utilized an expanded taxon sampling of Glycyrrhizeae to reconstruct the phylogenetic relationships in the tribe based on genome skimming data, including whole chloroplast genomes, nuclear ribosomal DNA, and low‐copy nuclear DNA. We also launched machine learning analysis (MLA) for one species pair with controversial taxonomic boundary. The integrated results indicated Glycyrrhizopsis should be split from Glycyrrhiza, while the former genus Meristotropis should be treated as part of Glycyrrhiza. Glycyrrhizopsis includes two species, Glycyrrhizopsis asymmetrica and Glycyrrhizopsis flavescens, and we recognize 13 species in Glycyrrhiza: Glycyrrhiza acanthocarpa, Glycyrrhiza astragalina, Glycyrrhiza bucharica, Glycyrrhiza echinata, Glycyrrhiza foetida, Glycyrrhiza glabra, Glycyrrhiza gontscharovii, Glycyrrhiza lepidota, Glycyrrhiza macedonica, Glycyrrhiza pallidiflora, Glycyrrhiza squamulosa, Glycyrrhiza triphylla, and Glycyrrhiza yunnanensis. We propose a broader G. glabra that includes former Glycyrrhiza aspera, G. glabra s.s., Glycyrrhiza inflata, and Glycyrrhiza uralensis, and represents the glycyrrhizin‐contained medicinal group. Our ancestral state inferences show the ancestor of Glycyrrhiza lacked glycyrrhizin, and the presence of glycyrrhizin evolved twice within Glycyrrhiza during the last one million years. Our integrative phylogenomics‐MLA study not only provides new insights into long‐standing taxonomic controversies of Glycyrrhizeae, but also represents a useful approach for future taxonomic studies on other plant taxa.
With an expanded taxon sampling of the liquorice tribe, Glycyrrhizeae, we launched phylogenetic analyses based on chloroplast coding sequences (cp CDSs), nuclear ribosomal DNA (nrDNA), and low‐copy nuclear (LCN) loci, as well as machine learning analyses (MLAs), to recognize two and 13 species within genera Glycyrrhizopsis and Glycyrrhiza, respectively. Our ancestral state inferences show the ancestor of Glycyrrhiza lacked glycyrrhizin, and that the presence of glycyrrhizin evolved twice within Glycyrrhiza during the last one million years. Our integrative phylogenomics‐MLA study not only provides new insights into long‐standing taxonomic controversies of Glycyrrhizeae, but also represents a useful approach for future taxonomic studies on other plant taxa.
Background
Recently, intestinal bacteria have attracted attention as factors affecting the prognosis of patients with cancer. However, the intestinal microbiome is composed of several hundred types ...of bacteria, necessitating the development of an analytical method that can allow the use of this information as a highly accurate biomarker. In this study, we investigated whether the preoperative intestinal bacterial profile in patients with esophageal cancer who underwent surgery after preoperative chemotherapy could be used as a biomarker of postoperative recurrence of esophageal cancer.
Methods
We determined the gut microbiome of the patients using 16S rRNA metagenome sequencing, followed by statistical analysis. Simultaneously, we performed a machine learning analysis using a random forest model with hyperparameter tuning and compared the data obtained.
Results
Statistical and machine learning analyses revealed two common bacterial genera,
Butyricimonas
and
Actinomyces
, which were abundant in cases with recurrent esophageal cancer.
Butyricimonas
primarily produces butyrate, whereas
Actinomyces
are oral bacteria whose function in the gut is unknown.
Conclusion
Our results indicate that
Butyricimonas
spp. may be a biomarker of postoperative recurrence of esophageal cancer. Although the extent of the involvement of these bacteria in immune regulation remains unknown, future research should investigate their presence in other pathological conditions. Such research could potentially lead to a better understanding of the immunological impact of these bacteria on patients with cancer and their application as biomarkers.
The clinical benefit of neoadjuvant chemotherapy (NACT) before concurrent chemoradiotherapy (CCRT) vs. adjuvant chemotherapy after CCRT is debated. Non-response to platinum-based NACT is a major ...contributor to poor prognosis, but there is currently no reliable method for predicting the response to NACT (rNACT) in patients with locally advanced cervical cancer (LACC). In this study we developed a machine learning (ML)-assisted model to accurately predict rNACT. We retrospectively analyzed data on 636 patients diagnosed with stage IB2 to IIA2 cervical cancer at our hospital between January 1, 2010 and December 1, 2020. Five ML-assisted models were developed from candidate clinical features using 2-step estimation methods. Receiver operating characteristic curve (ROC), clinical impact curve, and decision curve analyses were performed to evaluate the robustness and clinical applicability of each model. A total of 30 candidate variables were ultimately included in the rNACT prediction model. The areas under the ROC curve of models constructed using the random forest classifier (RFC), support vector machine, eXtreme gradient boosting, artificial neural network, and decision tree ranged from 0.682 to 0.847. The RFC model had the highest predictive accuracy, which was achieved by incorporating inflammatory factors such as platelet-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio, neutrophil-to-albumin ratio, and lymphocyte-to-monocyte ratio. These results demonstrate that the ML-based prediction model developed using the RFC can be used to identify LACC patients who are likely to respond to rNACT, which can guide treatment selection and improve clinical outcomes.
Highlights • A system to acquire and analyse gait data to classify fall risk is presented. • A Fall Risk Index based on gait parameters and pattern recognition is presented. • An initial validation ...of the index is carried on with older volunteers. • The performance of the Fall Risk Index is compared with that of functional tests. • The index captures the risk of falls with a similar accuracy of the functional tests.
The capacity to diagnose obstructive sleep apnoea (OSA) must be expanded to meet an estimated disease burden of nearly one billion people worldwide. Validated alternatives to the gold standard ...polysomnography (PSG) will improve access to testing and treatment. This study aimed to evaluate the diagnosis of OSA, using measurements of mandibular movement (MM) combined with automated machine learning analysis, compared to in-home PSG.
40 suspected OSA patients underwent single overnight in-home sleep testing with PSG (Nox A1, ResMed, Australia) and simultaneous MM monitoring (Sunrise, Sunrise SA, Belgium). PSG recordings were manually analysed by two expert sleep centres (Grenoble and London); MM analysis was automated. The Obstructive Respiratory Disturbance Index calculated from the MM monitoring (MM-ORDI) was compared to the PSG (PSG-ORDI) using intraclass correlation coefficient and Bland-Altman analysis. Receiver operating characteristic curves (ROC) were constructed to optimise the diagnostic performance of the MM monitor at different PSG-ORDI thresholds (5, 15, and 30 events/hour).
31 patients were included in the analysis (58% men; mean (SD) age: 48 (15) years; BMI: 30.4 (7.6) kg/m
). Good agreement was observed between MM-ORDI and PSG-ORDI (median bias 0.00; 95% CI -23.25 to + 9.73 events/hour). However, for 15 patients with no or mild OSA, MM monitoring overestimated disease severity (PSG-ORDI < 5: MM-ORDI mean overestimation + 5.58 (95% CI + 2.03 to + 7.46) events/hour; PSG-ORDI > 5-15: MM-ORDI overestimation + 3.70 (95% CI -0.53 to + 18.32) events/hour). In 16 patients with moderate-severe OSA (
= 9 with PSG-ORDI 15-30 events/h and
= 7 with a PSG-ORD > 30 events/h), there was an underestimation (PSG-ORDI > 15: MM-ORDI underestimation -8.70 (95% CI -28.46 to + 4.01) events/hour). ROC optimal cut-off values for PSG-ORDI thresholds of 5, 15, 30 events/hour were: 9.53, 12.65 and 24.81 events/hour, respectively. These cut-off values yielded a sensitivity of 88, 100 and 79%, and a specificity of 100, 75, 96%. The positive predictive values were: 100, 80, 95% and the negative predictive values 89, 100, 82%, respectively.
The diagnosis of OSA, using MM with machine learning analysis, is comparable to manually scored in-home PSG. Therefore, this novel monitor could be a convenient diagnostic tool that can easily be used in the patients' own home.
https://clinicaltrials.gov, identifier NCT04262557.
Raman spectroscopy has emerged as a powerful tool in medical, biochemical, and biological research with high specificity, sensitivity, and spatial and temporal resolution. Recent advanced Raman ...systems, such as portable Raman systems and fiber-optic probes, provide the potential for accurate in vivo discrimination between healthy and cancerous tissues. In our study, a portable Raman probe spectrometer was tested in immunosuppressed mice for the in vivo localization of colorectal cancer malignancies from normal tissue margins. The acquired Raman spectra were preprocessed, and principal component analysis (PCA) was performed to facilitate discrimination between malignant and normal tissues and to highlight their biochemical differences using loading plots. A transfer learning model based on a one-dimensional convolutional neural network (1D-CNN) was employed for the Raman spectra data to assess the classification accuracy of Raman spectra in live animals. The 1D-CNN model yielded an 89.9% accuracy and 91.4% precision in tissue classification. Our results contribute to the field of Raman spectroscopy in cancer diagnosis, highlighting its promising role within clinical applications.
This article examines perceptions of social media use (e.g. the WhatsApp application), in particular looking at how Bahraini women use such technology and how mobile communication is used by such a ...segment of the population. Mobile devices are very accessible to Bahraini women and this needs even further study to learn ways of using applications for information and other things. This analysis is based on an online survey, conducted among 1137 Bahraini women, using a nonrepresentative sample (snowball). Such data were analyzed using a deep learning approach which utilizes, in particular, the Fuzzy Proximity Knowledge Mining technique to examine the provided answers. The study found that WhatsApp has enabled Bahraini women to communicate and share information with others. They spent 2–3 h daily sending and enjoying comics and entertainment clips and important and rare news stories. Social interaction, communication, and escapism featured strongly as the most popular reasons for using WhatsApp. Overall, WhatsApp served as a platform used to participate in social and communication activities.
Advancements in mobile health technologies and machine learning approaches have expanded the framework of behavioral phenotypes in obesity treatment to explore the dynamics of temporal changes.
This ...study aimed to investigate the dynamics of behavioral changes during obesity intervention and identify behavioral phenotypes associated with weight change using a hybrid machine learning approach.
In total, 88 children and adolescents (ages 8-16 years; 62/88, 71% male) with age- and sex-specific BMI ≥85th percentile participated in the study. Behavioral phenotypes were identified using a hybrid 2-stage procedure based on the temporal dynamics of adherence to the 5 behavioral goals during the intervention. Functional principal component analysis was used to determine behavioral phenotypes by extracting principal component factors from the functional data of each participant. Elastic net regression was used to investigate the association between behavioral phenotypes and weight change.
Functional principal component analysis identified 2 distinctive behavioral phenotypes, which were named the high or low adherence level and late or early behavior change. The first phenotype explained 47% to 69% of each factor, whereas the second phenotype explained 11% to 17% of the total behavioral dynamics. High or low adherence level was associated with weight change for adherence to screen time (β=-.0766, 95% CI -.1245 to -.0312), fruit and vegetable intake (β=.1770, 95% CI .0642-.2561), exercise (β=-.0711, 95% CI -.0892 to -.0363), drinking water (β=-.0203, 95% CI -.0218 to -.0123), and sleep duration. Late or early behavioral changes were significantly associated with weight loss for changes in screen time (β=.0440, 95% CI .0186-.0550), fruit and vegetable intake (β=-.1177, 95% CI -.1441 to -.0680), and sleep duration (β=-.0991, 95% CI -.1254 to -.0597).
Overall level of adherence, or the high or low adherence level, and a gradual improvement or deterioration in health-related behaviors, or the late or early behavior change, were differently associated with weight loss for distinctive obesity-related lifestyle behaviors. A large proportion of health-related behaviors remained stable throughout the intervention, which indicates that health care professionals should closely monitor changes made during the early stages of the intervention.
Clinical Research Information Science KCT0004137; https://tinyurl.com/ytxr83ay.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Educational data mining and learning analytics have become a very important topic in the field of education technology. Many frameworks have been proposed for learning analytics which make it ...possible to identify learning behavior patterns or strategies. However, it is difficult to understand the reason why behavior patterns occur and why certain strategies are used. In other words, all of the existing frameworks lack an important step, that is, result confirmation. In this paper, we propose a Result Confirmation-based Learning Behavior Analysis (ReCoLBA) framework, which adds a result confirmation step for exploring the hidden reasons underlying the learning patterns and strategies. Using this ReCoLBA framework, a case study was conducted which analyzed e-book reading data. In the case study, we found that the students had a tendency to delete markers after adding them. Through an investigation, we found that the students did this because they could not grasp the learning emphasis. To apply this finding, we proposed a learning strategy whereby the teacher highlights the learning emphasis before students read the learning materials. An experiment was conducted to examine the effectiveness of this strategy, and we found that it could indeed help students achieve better results, reduce repetitive behaviors and save time. The framework was therefore shown to be effective.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
