[Application of artificial intelligence methods in the diagnosis and treatment of primary angle-closure disease].

This article reviews literature on the use of artificial intelligence (AI) methods for the diagnosis and treatment of primary angle-closure disease (PACD). The review describes how AI techniques enhance the efficiency of population screening for anterior chamber angle closure, presents technologies utilizing deep learning, including neural networks, for the analysis of large datasets obtained through anterior segment imaging methods, such as anterior segment optical coherence tomography (AS-OCT), digital gonioscopy, and ultrasound biomicroscopy, and discusses methods for treating PACD with the help of AI. Integration of deep learning and imaging techniques represents a crucial step in optimizing the diagnosis and treatment of PACD, reducing the burden on the healthcare system.

[Application of artificial intelligence in glaucoma. Part 2. Neural networks and machine learning in the monitoring and treatment of glaucoma].

The second part of the literature review on the application of artificial intelligence (AI) methods for screening, diagnosing, monitoring, and treating glaucoma provides information on how AI methods enhance the effectiveness of glaucoma monitoring and treatment, presents technologies that use machine learning, including neural networks, to predict disease progression and determine the need for anti-glaucoma surgery. The article also discusses the methods of personalized treatment based on projection machine learning methods and outlines the problems and prospects of using AI in solving tasks related to screening, diagnosing, and treating glaucoma.

Authentication of Argentinean extra-virgin olive oils using three-way fluorescence and two-way near-infrared data fused with multi-block DD-SIMCA.

A trending problem of Extra Virgin Olive Oil (EVOO) adulteration is investigated using two analytical platforms, involving: (1) Near Infrared (NIR) spectroscopy, resulting in a two-way data set, and (2) Fluorescence Excitation-Emission Matrix (EEFM) spectroscopy, producing three-way data. The related instruments were employed to study genuine and adulterated samples. Each data set was first separately analyzed using the Data Driven-Soft Independent Modeling of Class Analogies (DD-SIMCA) method, based on Principal Component Analysis (for the two-way NIR data) and PARallel FACtor analysis (for the three-way EEFM data).

[Application of artificial intelligence in glaucoma. Part 1. Neural networks and deep learning in glaucoma screening and diagnosis].

This article reviews literature on the use of artificial intelligence (AI) for screening, diagnosis, monitoring and treatment of glaucoma. The first part of the review provides information how AI methods improve the effectiveness of glaucoma screening, presents the technologies using deep learning, including neural networks, for the analysis of big data obtained by methods of ocular imaging (fundus imaging, optical coherence tomography of the anterior and posterior eye segments, digital gonioscopy, ultrasound biomicroscopy, etc.), including a multimodal approach.

Data-Driven Version of Multiway Soft Independent Modeling of Class Analogy (N-Way DD-SIMCA): Theory and Application.

One-class classification (OCC) is discussed in the framework of the measurement and processing of multiway data. Data-driven soft independent modeling of class analogy (DD-SIMCA) is applied in the following formats: (1) multiblock and (2) Tucker 3 N-way SIMCA, which are shown to be useful tools for solving classification tasks. A new decision rule for N-way DD-SIMCA is adopted based on the conventional two-way DD-SIMCA model.

Machine learning-couched treatment algorithms tailored to individualized profile of patients with primary anterior chamber angle closure predisposed to the glaucomatous optic neuropathy.

Background: Primary angle closure glaucoma (PACG) is still one of the leading causes of irreversible blindness, with a trend towards an increase in the number of patients to 32.04 million by 2040, an increase of 58.4% compared with 2013.

Multi-block DD-SIMCA as a high-level data fusion tool.

Multi-block classification method based on the Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) is presented. A high-level data fusion approach is used for the joint analysis of data collected with the help of different analytical instruments. The proposed fusion technique is very simple and straightforward.

Expanding the DD-SIMCA concept: A case study of precision medicine.

It is proposed to use DD-SIMCA method, and, particularly, Full Distancse (FD) as an analytical signal that characterizes each sample in the frame of a classification task. The approach is demonstrated using medical data. FD values help to assess the proximity of each patient to the target class of the control (healthy) subjects.

Comparison of Lens Extraction Versus Laser Iridotomy on Anterior Segment, Choroid, and Intraocular Pressure in Primary Angle Closure Using Machine Learning.

Prcis: Treatment strategy of primary angle closure (PAC) is not clear due to the large number of clinical and anatomic-topographic parameters in PAC, influencing the treatment algorithm. Using the machine learning method DD-SIMCA, we justify the expediency of early lens extraction (LE) in PAC.

Purpose: To compare the anatomic and functional efficacy of LE and laser peripheral iridotomy (LPI) in patients with PAC using Machine Learning.

Kinetic Model of Diclofenac Degradation Developed Using Multivariate Curve Resolution Method.

This study presents the kinetic modeling of the natural long-term aging of the pharmaceutical substance as well as the intact tablets of Diclofenac. Datasets are collections of near-infrared spectra acquired from the intact tablets packed in plastic blisters and the spectra of the pure substance. Fresh samples and samples at different stages of degradation are analyzed.

Monitoring of the natural aging of Diclofenac tablets, NIR and MIR-ATR spectroscopy coupled with chemometrics data analysis.

This study presents the analysis of the natural long-term aging of both the intact tablets and the active pharmaceutical ingredient. No forced aging conditions were applied to the samples. It is shown that the near infrared spectroscopy of the intact tablets packed in plastic blisters, supported by chemometrics, is a reliable method for detection of even slight deviations of the medicine from its regular state.

Aerosol Dry Printing for SERS and Photoluminescence-Active Gold Nanostructures Preparation for Detection of Traces in Dye Mixtures.

We proposed a novel method of nanostructure preparation for observation of surface-enhanced Raman spectroscopy (SERS) and metal-enhanced fluorescence (MEF) based on the deposition of gold nanoparticles (GNPs) above the thin dye film by dry aerosol printing. We detected various enhanced SERS and MEF signals of films of malachite green (MG) and rhodamine B (RhB) mixtures, depending on the surface packing density of Au NPs on the strip, and found the optimum one to achieve the 3.5 × 10 SERS enhancement.

Uridine treatment prevents myocardial injury in rat models of acute ischemia and ischemia/reperfusion by activating the mitochondrial ATP-dependent potassium channel.

The effect of uridine on the myocardial ischemic and reperfusion injury was investigated. A possible mechanism of its cardioprotective action was established. Two rat models were used: (1) acute myocardial ischemia induced by occlusion of the left coronary artery for 60 min; and (2) myocardial ischemia/reperfusion with 30-min ischemia and 120-min reperfusion.

Influence of the quality of capsule shell on the non-invasive monitoring of medicines using Terizidone as an example.

The aim of this study is to investigate the influence of hard capsule shells on the possibility of non-invasive monitoring and authentication of medicines presented in capsules dosage form. It is shown that the NIR measurements followed by the chemometric analysis, reflects all macro-components of the analyzed samples, which are the PVC blister, the capsule shell, and the capsule contents. The special variable selection procedure, based on the pure spectra of all components, makes it possible to develop a model that is insensitive to small variations of the capsule shell.

Procrustes Cross-Validation of short datasets in PCA context.

We suggest using a new tool, Procrustes cross-validation, as an alternative to a regular cross-validation for short datasets where each sample is important and, therefore, cannot be removed in line with the conventional leave-one-out cross-validation procedure. The advantages of the new approach are demonstrated using two real-world examples: the first one contains discrete variables (chemical profiles). The second one is based on continuous data (spectra).

[Predictors of external pancreatic fistulae after pancreatic resection].

Despite a significant decrease in postoperative mortality after pancreatic resections in recent years (5.2-15% after pancreatoduodenectomy and about 5% after distal pancreatectomy), incidence of postoperative complications remains high (30-50% and 22-50%, respectively). Postoperative pancreatic fistula is one of the most common and formidable complications.

Procrustes Cross-Validation-A Bridge between Cross-Validation and Independent Validation Sets.

In this paper, we propose a new approach for validation of chemometric models. It is based on -fold cross-validation algorithm, but in contrast to conventional cross-validation, our approach makes it possible to create a new dataset, which carries sampling uncertainty estimated by the cross-validation procedure. This dataset, called a pseudo-validation set, can be used similar to an independent test set, giving a possibility to compute residual distances, explained variance, scores, and other results, which cannot be obtained in the conventional cross-validation.

Chemometric tools for food fraud detection: The role of target class in non-targeted analysis.

The chemometric issues related to the application of non-targeted analysis for the detection of food frauds were analyzed employing discriminant analysis and a one-class classifier. The similarities and differences between the two methods were investigated. The results of classification are characterized by a set of indices called figures of merit.

Detection of Outliers in Projection-Based Modeling.

Previously, we have introduced an approach for calculation of the full object distance in the frame of Principal Component Analysis that can be applied to data exploration and classification. Now, a similar approach has been developed for regression problems in which a total distance can be calculated for every sample in projection modeling. Based on the total distance, a threshold for outlier detection has been developed by means of a data-driven estimation of the degrees of freedom and scaling parameters for the partial distances in the projection models.

Chemometric Authentication of Brazilian Coffees Based on Chemical Profiling.

In this work, different chemometric tools were compared to classify n = 26 conventional (CONV) and n = 19 organic (ORG) coffees from the main Brazilian producing regions based on the chemical composition, physicochemical properties, and antioxidant activity. Principal component analysis separated ORG and CONV coffees but the distinction among the producing regions of Brazilian coffee was not possible. Partial least squares discriminant analysis classified all ORG and CONV coffees in the external validation.

Detection of counterfeit and substandard tablets using non-invasive NIR and chemometrics - A conceptual framework for a big screening system.

A detailed step-by-step procedure for revealing counterfeit and substandard tablets is presented. Non-invasive NIR measurements are used for data collection. The entire complex multi-layer object as the "packaging -coating-core" system requires special treatment at all stages of model development and validation.

[The Role of Ecological Factors in Acute Enteric Infection Morbidity of Population of the People's Republic of Bangladesh].

The studying of influence of natural factors on distribution of acute intestinal infections in the territory of the People's Republic of Bangladesh was organized and carried out for the first time. Bangladesh is one of the most densely populated countries in the world. The country occupies the 8th place of population density making up to 1237,51 people per km2.