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Timely diagnosis of meniscus injuries is key for preventing knee joint dysfunction and improving patient outcomes because it decreases morbidity and facilitates treatment planning.To train and evaluate a deep learning model for automated detection of meniscus tears on knee magnetic resonance imaging (MRI).Bicentric retrospective study.In total, 584 knee MRI studies, divided among training (n = 234), testing (n = 200), and external validation (n = 150) data sets, were used in this study. The public data set MRNet was used as a second external validation data set to evaluate the performance of the model.A 3 T, coronal, and sagittal images from T1-weighted proton density (PD) fast spin-echo (FSE) with fat saturation and T2-weighted FSE with fat saturation sequences.The detection system for meniscus tear was based on the improved YOLOv4 model with Darknet-53 as the backbone. The performance of the model was also compared with that of three radiologists of varying levels of experience. The determination of the presence of a meniscus tear from surgery reports was used as the ground truth for the images.Sensitivity, specificity, prevalence, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic curve were used to evaluate the performance of the detection model. Two-way analysis of variance, Wilcoxon signed-rank test, and Tukey's multiple tests were used to evaluate differences in performance between the model and radiologists.The overall accuracies for detecting meniscus tears using our model on the internal testing, internal validation, and external validation data sets were 95.4%, 95.8%, and 78.8%, respectively. One radiologist had significantly lower performance than our model in detecting meniscal tears (accuracy: 0.9025 ± 0.093 vs. 0.9580 ± 0.025).The proposed model had high sensitivity, specificity, and accuracy for detecting meniscus tears on knee MRIs.3 TECHNICAL EFFICACY: Stage 2.

Prosthetic joint infections (PJI) and especially tuberculosis (TB) PJI are rare diseases and hard to cure. The effectiveness of treatments for tuberculous PJI still remains a problem. The objective of this research was to indicate the success of two-stage revision replacement and also giving the associated criteria.From 2015 to 2020, five patients with tuberculous PJI were treated with two-stage revision at Cho Ray hospital, Vietnam. We collected the dataset which included demographic data, the interval from the time of joint replacement to reported infection, records of tuberculous PJI, administration of anti-TB medications (duration, months), history of operation(s), duration of follow-up, and specific type(s) of antibiotics loaded in bone cement. The approval for this study was made by the institutional review board from Cho Ray Hospital, Vietnam. We conducted a literature review based on the keywords "PJI" and "TB" on PubMed.Five patients [median age 66 years (range 35-84)] had found tuberculous PJI. The median time from arthroplasty to diagnosis was 19 months (range 4-48). The diagnosis was confirmed by joint aspirates or synovial tissue. Positive PCR was also reported in all cases. The average duration of anti-tuberculosis polytherapy administration was 14.4 months. The operative techniques on five patients included debridement and using spacer loaded with 2 g streptomycin (and 2 g vancomycin if they got a coinfection) for 1 pack of bone cement, and revision arthroplasty. In most cases, the outcome of treatment using two-stage revision replacement was 80%. Overall, the auxiliary bacterial infections were recognized in three patients with tuberculous PJI and Staphylococcus aureus. Streptomycin and vancomycin were loaded in a cement spacer to increase the success rate, and tuberculous PJI was controlled for all patients.Tuberculous PJI can be controlled with two-stage revision replacement with an antibiotic-loaded cement spacer that is molded intraoperatively with custom mold and prolonged anti-tuberculosis treatment in all cases.IV.

The tetraene 7,7-dimethoxy-2,3,5,6-tetramethylenebicyclo[2.2.1]heptane is a key compound for the preparation of a large variety of acenes protected by a carbonyl bridge. We report here a medium scale preparation in seven steps of this valuable starting material. Diels-Alder addition between 6,6-dimethyl fulvene and maleic anhydride, followed by carboxylation, ozonolysis of the double bond, reduction of the four ester group, then chlorination of the alcohol groups and dehydrochlorination give the target compound in 17% overall yield.

The skeleton is one of the most important organs in the human body in assisting our motion and activities; however, bone density attenuates gradually as we age. Among common bone diseases are osteoporosis and Paget's, two of the most frequently found diseases in the elderly. Nowadays, a combination of multiple drugs is the optimal therapy to decelerate osteoporosis and Paget's pathologic process, which comes with various underlying adverse effects due to drug-drug interactions (DDIs). Artificial intelligence (AI) has the potential to evaluate the interaction, pharmacodynamics, and possible side effects between drugs. In this research, we created an AI-based machine-learning model to predict the outcomes of interactions between drugs used for osteoporosis and Paget's treatment, which helps mitigate the cost and time to implement the best combination of medications in clinical practice. In this study, a DDI dataset was collected from the DrugBank database within the osteoporosis and Paget diseases. We then extracted a variety of chemical features from the simplified molecular-input line-entry system (SMILES) of defined drug pairs that interact with each other. Finally, machine-learning algorithms were implemented to learn the extracted features. Our stack ensemble model from Random Forest and XGBoost reached an average accuracy of 74 % in predicting DDIs. It was superior to individual models as well as previous methods in terms of most measurement metrics. This study showed the potential of AI models in predicting DDIs of Osteoporosis-Paget's disease in particular, and other diseases in general.

This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan–Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99, 95% CI, 2.27–3.93, p &lt, 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635–0.758), 0.705 (95% CI, 0.649–0.762), 0.657 (95% CI, 0.589–0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499–0.64), 0.552 (95% CI, 0.489–0.616), 0.621 (95% CI, 0.544–0.689) (AUC), gender 0.554, 0.546, 0.566 (AUC), stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan–Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (p &lt, 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction.

International audience; A large dissymmetric starphene molecule, the tetrabenzo[a,c,u,w]naphtho[2,3-l]nonaphene, can be obtained by first preparing a soluble precursor which is then sublimated on a Au(111) surface in ultra-high vacuum. In a second step, controlled annealings from 200°C to 275°C initiate two successive cyclodehydrogenation steps with the formation of 3 new carbon-carbon bonds. A second conformer is also stable enough during the annealing step to give another compound in similar yield, the benzodibenzo[7,8,9,10]naphthaceno[2,1-h]phenanthro[9,10p]hexaphene. The formation of this more hindered species stresses the importance of strong molecule-surface interactions during the cyclodehydrogenations steps of these large polyaromatic hydrocarbons.

Background: According to statistical data of many countries in the wold, the more proportion of patients in hip replacement have, the more prosthetic infection have been treated. In Vietnam, treatment of prosthetic infection is often difficult beacause of antibiotic resistance, high cost treatment and difficult rehabilitation in post-surgery. Nowadays, there are many methods of treatment for prosthetic infected patients, using antibiotic cement spacer for prosthetic infection have applied in common of a lot of countries all over the wold. We report five cases hip prosthetic infection treatment by using antibiotic impregnated cement spacer with custom mold. Aim of study: Inform 05 cases hip prosthetic infection treatment by using antibiotic impregnated cement spacer with custom mold. Methods: Serial cases report. Key words: Prosthetic infection, Antibiotic cement spacer

Possible food and drug interactivities could alter the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions is prone to the rise of drug-drug and drug-food/drug-nutrient interactions. The presence of these unfavorable interactions leads to other implications e.g., the decline in medicament’s effect, the withdrawals of various medications, and detrimental impacts on the patients’ health, etc. Yet the importance of drug-food/drug-nutrient interactions is remained underestimated, as the number of studies referring to these topics is constrained. Recently, state-of-the-art machine learning (ML) models have been created to unveil the hidden influences between drug and food compounds. However, there were still some limitations in terms of data mining, data input, and detailed annotations. In this study, we proposed a novel ML-based prediction model that could address most drawbacks of other models. A total of 4,384 food and 334 drug constituents were included in our training set, and two validation sets (containing 140,546 and 179 samples, respectively) were prepared for the model assessment. PyBioMed package has been used to obtain 3,780 chemical features, and then feature selection has been performed to select 25 most important features. The performance of our model was highly promising, as the accuracy score was 88.28% and 92.18% on the first and second validation set, respectively. More importantly, the DFIPred is capable of exhibiting the output recommendation about the interaction of any given pair of one drug and one food constituent based on their compound names. We believe that our model would contribute to the development of better and more powerful models used for the prediction of drug-food interactions in the future.

To shorten the clinical development process of a new drug or a combination of drugs in daily prescriptions, especially blockbuster drugs, predicting the effect of drug-drug interactions (DDIs) precisely is important for preventing adverse drug reactions and for more effective drug administration. Many computational algorithms have been developed to predict the effects of DDIs with the purpose of reducing hazards that can affect the health of patients in merging-medication therapies. In this research, we propose a comprehensive machine learning model to assist with the prediction of drug interactions in the histamine antagonist interaction-network. The data used in our research consisted of approved drugs of histamine antagonists that are connected to 67,317 DDI pairs in the DrugBank database. The drug-drug interaction features were extracted using the SMILE structure combined with the PyBioMed module. We then applied five algorithms in the histamine antagonist interaction-network inference (HAINI) framework: Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and Multilayer Perceptron (MP) with fivefold cross-validation to approach a large-scale DDI prediction. The predictive performance shows that our model outperformed previously published works on DDI prediction with the best precision of 0.898, a recall of 0.792, and an F1-score of 0.836 among 51 well-known DDIs. An important finding of the study is that our prediction is based solely on the SMILE and thus can be applied at early stage of drug development.

Background In the field of glioma, transcriptome subtypes have been considered as an important diagnostic and prognostic biomarker that may help improve the treatment efficacy. However, existing identification methods of transcriptome subtypes are limited due to the relatively long detection period, the unattainability of tumor specimens via biopsy or surgery, and the fleeting nature of intralesional heterogeneity. In search of a superior model over previous ones, this study evaluated the efficiency of eXtreme Gradient Boosting (XGBoost)-based radiomics model to classify transcriptome subtypes in glioblastoma patients. Methods This retrospective study retrieved patients from TCGA-GBM and IvyGAP cohorts with pathologically diagnosed glioblastoma, and separated them into different transcriptome subtypes groups. GBM patients were then segmented into three different regions of MRI: enhancement of the tumor core (ET), non-enhancing portion of the tumor core (NET), and peritumoral edema (ED). We subsequently used handcrafted radiomics features (n = 704) from multimodality MRI and two-level feature selection techniques (Spearman correlation and F-score tests) in order to find the features that could be relevant. Results After the feature selection approach, we identified 13 radiomics features that were the most meaningful ones that can be used to reach the optimal results. With these features, our XGBoost model reached the predictive accuracies of 70.9%, 73.3%, 88.4%, and 88.4% for classical, mesenchymal, neural, and proneural subtypes, respectively. Our model performance has been improved in comparison with the other models as well as previous works on the same dataset. Conclusion The use of XGBoost and two-level feature selection analysis (Spearman correlation and F-score) could be expected as a potential combination for classifying transcriptome subtypes with high performance and might raise public attention for further research on radiomics-based GBM models.

We study topological zeta functions of complex plane curve singularities using toric modifications and further developments. As applications of the research method, we prove that the topological zeta function is a topological invariant for complex plane curve singularities, we give a short and new proof of the monodromy conjecture for plane curves., 16 pages, final version

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5, TGM1, or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

— The research experimental results determiningrateconstant of the reaction between TCCA and CS according to Arrhenius equation was studied. Basing the set of graphs showing the relationship between (ln(k) - (1/T)) and (ln (k/T) - (1/T)), the activation energy (Ea) according to the Arrhenius equation and activation enthalpy variation (∆H#), activationentropy variation (∆S#), free activation energy Gibss (∆G#) according to the Eyring equation are determined. The products of TCCA reaction and CS at pH = 9 are2-chloro benzaldehyde oxirane-2,2-dicarbonitrile, 3-(2-chlorophenyl) so predicting the reaction mechanism consists of two reactions taking place in parallel, a hydrolysisand oxidation reactions.

Simple Summary Antioxidant compounds protect the human body from many kinds of diseases as well as the degeneration of age. Several micronutrients that were found in the last century such as vitamins A, C, and E have become popular in our life. Scientists are trying to find more and more antioxidant compounds not only from experimenting in the laboratory but also from assisting by the computer. Our research utilized a computational method for the swift and economic identification of antioxidant compounds. The research presents a predictor that got a high accuracy of 84.6% for the detection of antioxidants. Therefore, our predictor is promising to be a useful tool to discover a new antioxidant compound. Abstract Antioxidant proteins are involved importantly in many aspects of cellular life activities. They protect the cell and DNA from oxidative substances (such as peroxide, nitric oxide, oxygen-free radicals, etc.) which are known as reactive oxygen species (ROS). Free radical generation and antioxidant defenses are opposing factors in the human body and the balance between them is necessary to maintain a healthy body. An unhealthy routine or the degeneration of age can break the balance, leading to more ROS than antioxidants, causing damage to health. In general, the antioxidant mechanism is the combination of antioxidant molecules and ROS in a one-electron reaction. Creating computational models to promptly identify antioxidant candidates is essential in supporting antioxidant detection experiments in the laboratory. In this study, we proposed a machine learning-based model for this prediction purpose from a benchmark set of sequencing data. The experiments were conducted by using 10-fold cross-validation on the training process and validated by three different independent datasets. Different machine learning and deep learning algorithms have been evaluated on an optimal set of sequence features. Among them, Random Forest has been identified as the best model to identify antioxidant proteins with the highest performance. Our optimal model achieved high accuracy of 84.6%, as well as a balance in sensitivity (81.5%) and specificity (85.1%) for antioxidant protein identification on the training dataset. The performance results from different independent datasets also showed the significance in our model compared to previously published works on antioxidant protein identification.

Introduction: To treat hip prosthetic infection, 2-stage revision, including removal and reimplantation, remains the standard treatment for prosthetic infection. Articulating cement spacer has been shown to provide better functional results after reimplantation. However, its cost as a manufactured product is not cheap and the choice of antibiotics is not flexible either. We designed an aluminium mold to make an antibiotic impregnated cement spacer, and replaced it between the first and the second stage. The current study was conducted to test their clinical efficacy.Case report: We report 3 cases of hip prosthetic infection treatment by using antibiotic impregnated cement spacer made by an aluminium mold. All patients presented to us in first 2-years postoperation and all had a deep infection. Three patients with infected total hip arthroplasties were treated with 2-stage revision using articulating spacers made by an aluminium mold and had a good result.Conclusion: Treating hip prosthetic infection with these articulating spacers eradicates infection effectively, improves the life quality before reimplantation and provides good final results without significant complications.