ALBERT

Description: Objectives: To evaluate the therapeutic response to treatment with transcriptional therapy that combines hydralazine / valproate(Transkrip) in cancer patients with cutaneous T-cell lymphoma Determine toxicity, duration of response and time to progression of epigenetic therapy with hydralazine / valproate in patients with T-cell lymphomas Material and methods an open-label, phase II, prospective and longitudinal in the National Cancer Institute of Mexico was carried out, recruiting patients diagnosed with Cutaneous T based on WHO classification 2008, newly diagnosed untreated, demographic data were analyzed and vital and somatometry signs, assessment of efficacy was established according to the therapeutic response in lymphomas measured at each visit at the affected sites for each patient, using the m-SWAT system sorted in complete response (CR), partial response (PR ), stable disease (SD) and progression (PE) and is considered response to patients with complete or partial response, these data were collected in the form of case report at baseline in the first consultation, during treatment and at the end the study at each visit to the doctor. Statistical analysis was performed with SPSS version 13.0 software. results 16 patients were selected with diagnosis of cutaneous T-cell lymphoma, one patients discontinued the study at baseline and 4 patients had disease progression during the first months of the study, a total of 10 patients who completed 18 months of study continuing the compassionate use treatment. The median age of patients was 45.8 years analyzed (18-83 years) of which 8 are women (53%) and 7 men (47%) with an ECOG 1 in 93.3% of patients, with an average weight of 72.5 ± 15.99 kg, height 1.6 ± 0.1 m, body mass index 28.3 ± 5.7 kg / m2, 46.7% of overweight patients, systolic blood pressure an average of 116.2 ± 14.3 mmHg and diastolic 75.1 ± 11.04 mmHg with respiratory rate 19.43 ± 1.74 resp / min and 77.8 ± 12.3 cardiac beats / min. Of the 10 patients who completed the study, 100% had complete or partial response itching to 6 month continuing unchanged at month 18, the dream as an adverse event occurred in 33% of patients, this being more frequent, adverse events attributed to the drug were known, expected and mild. From 6 months of treatment, the percentage of partial response and complete response of m-SWAT and pruritus were greater than 90% of patients. conclusions: Hydralazine/Valproate (Transkrip) is a medication that offers patients with cutaneous T-cell lymphoma, both first-line and refractory, a therapeutic alternative with high efficiency and good safety profile. Disclosures No relevant conflicts of interest to declare.

Description: A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (DNT) of concentrations from 0% to 20% w/w with soil samples were investigated. Three types of soils, bentonite, synthetic soil, and natural soil, were used. A partial least squares (PLS) regression model was generated for predicting DNT concentrations. To increase the selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as pentaerythritol tetranitrate (PETN), trinitrotoluene (TNT), cyclotrimethylenetrinitramine (RDX), and non-explosives such as benzoic acid and ibuprofen. For the detection experiments, mixes of different explosives with soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with spectra of soils stored in a database to identify the most similar soils based on QCL spectroscopy. Next, a preprocessing based on classical least squares (Pre-CLS) was applied to the spectra of soils selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A random forest model worked best with 0.996 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils.

Description: A simple, remote-sensed method of detection of traces of petroleum in soil combining artificial intelligence (AI) with mid-infrared (MIR) laser spectroscopy is presented. A portable MIR quantum cascade laser (QCL) was used as an excitation source, making the technique amenable to field applications. The MIR spectral region is more informative and useful than the near IR region for the detection of pollutants in soil. Remote sensing, coupled with a support vector machine (SVM) algorithm, was used to accurately identify the presence/absence of traces of petroleum in soil mixtures. Chemometrics tools such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and SVM demonstrated the effectiveness of rapidly differentiating between different soil types and detecting the presence of petroleum traces in different soil matrices such as sea sand, red soil, and brown soil. Comparisons between results of PLS-DA and SVM were based on sensitivity, selectivity, and areas under receiver-operator curves (ROC). An innovative statistical analysis method of calculating limits of detection (LOD) and limits of decision (LD) from fits of the probability of detection was developed. Results for QCL/PLS-DA models achieved LOD and LD of 0.2% and 0.01% for petroleum/soil, respectively. The superior performance of QCL/SVM models improved these values to 0.04% and 0.003%, respectively, providing better identification probability of soils contaminated with petroleum.

Description: Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.