Popular Class Research Articles

Impact of DPP-4 Inhibitors in Patients with Diabetes Mellitus and Heart Failure: An In-Depth Review

The increasing prevalence of both type 2 diabetes mellitus and heart failure has underscored the urgent need for optimized therapeutic strategies that address the complex interplay between these conditions. Dipeptidyl peptidase-4 (DPP-4) inhibitors have emerged as a popular class of glucose-lowering agents due to their favorable glycemic effects, safety profile, and potential cardiovascular benefits. However, the impact of DPP-4 inhibitors on heart failure outcomes in patients with diabetes remains contentious, with conflicting evidence from clinical trials and observational studies. This review critically examines current evidence on the use of DPP-4 inhibitors in patients with coexisting diabetes and heart failure, focusing on pharmacodynamics, safety, and efficacy outcomes. We explore the physiological mechanisms by which DPP-4 inhibitors may influence heart failure risk, including modulation of inflammation, oxidative stress, and myocardial fibrosis. Clinical trials such as SAVOR-TIMI 53, EXAMINE, and TECOS are evaluated to provide a comprehensive analysis of DPP-4 inhibitors’ effects on hospitalization for heart failure, mortality, and cardiovascular events in diabetic patients. While some trials suggest an increased risk of HF hospitalizations with specific DPP-4 inhibitors (e.g., saxagliptin), others report neutral effects, raising questions about the class effects versus individual drug characteristics within this group. Additionally, we address discrepancies in outcomes related to patient demographics, HF phenotype, and comorbid conditions that may influence DPP-4 inhibitors’ risk–benefit profile. Comparative insights into alternative glucose-lowering therapies such as SGLT2 inhibitors and GLP-1 receptor agonists are also provided, highlighting potential implications for treatment selection in this high-risk population. In summary, this review synthesizes available evidence on DPP-4 inhibitors’ impact in diabetic patients with heart failure, aiming to guide clinicians in making informed therapeutic decisions. While DPP-4 inhibitors remain a viable option in diabetes management, caution is warranted in patients with advanced heart failure, and future research is essential to refine patient-specific guidelines.

Scalable marginalized particle filter to improve state estimation of one-way coupled PDE systems

Particle filtering is a popular class of methods to estimate the state of non-linear non-Gaussian state-space models in an online manner. However, in practice, their application to systems described by partial differential equations is limited due to issues of particle degeneracy in arbitrarily high dimension spaces and the prohibitively high computational cost of evaluating posteriors with direct numerical solvers. Here we use smooth transformation of prior particles into posterior, localization in which spurious correlations over large distances are suppressed, marginalize out conditionally linear-Gaussian state variables, employ a Physics-informed neural network to predict PDE solution over large time steps, and implement a cheap operator for recursive projection onto affine subspace of physical constraints. Through hardware-in-the-loop simulation testing on benchmark problem of a one-way coupled PDE system, it is validated that such an integrated framework maintains higher accuracy of the state estimates over existing methods when degree of non-linearity is increased. The efficiency and scalability of proposed framework paves way for state estimation of coupled PDE systems on embedded systems and mobile platforms.

Efficient Implementation of Interior-Point Methods for Quantum Relative Entropy

Quantum relative entropy (QRE) programming is a recently popular and challenging class of convex optimization problems with significant applications in quantum computing and quantum information theory. We are interested in modern interior-point (IP) methods based on optimal self-concordant barriers for the QRE cone. A range of theoretical and numerical challenges associated with such barrier functions and the QRE cones have hindered the scalability of IP methods. To address these challenges, we propose a series of numerical and linear algebraic techniques and heuristics aimed at enhancing the efficiency of gradient and Hessian computations for the self-concordant barrier function, solving linear systems, and performing matrix-vector products. We also introduce and deliberate about some interesting concepts related to QRE such as symmetric quantum relative entropy. We design a two-phase method for performing facial reduction that can significantly improve the performance of QRE programming. Our new techniques have been implemented in the latest version (DDS 2.2) of the software package Domain-Driven Solver (DDS). In addition to handling QRE constraints, DDS accepts any combination of several other conic and nonconic convex constraints. Our comprehensive numerical experiments encompass several parts, including (1) a comparison of DDS 2.2 with Hypatia for the nearest correlation matrix problem, (2) using DDS 2.2 for combining QRE constraints with various other constraint types, and (3) calculating the key rate for quantum key distribution (QKD) channels and presenting results for several QKD protocols. History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue. Funding: This work was supported by the National Science Foundation [Grant CMMI-2347120] and Discovery Grants from the Natural Sciences and Engineering Research Council of Canada. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0570 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0570 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Celecoxib in rheumatic diseases: possibilities and prospects. Brief descriptive survey

Chronic pain is the main manifestation of rheumatic diseases (RD), it determines the main complaints and worsens the quality of life of patients. The problem of effective control of chronic pain in rheumatology remains a current issue despite the successes in the development of new drugs for pathogenetic therapy, especially in immunoinflammatory RD. For example, 40-50% of patients with rheumatoid arthritis (RA), even those receiving biologic disease-modifying antirheumatic drugs and Janus kinase inhibitors, require analgesics. According to several population studies, about 50% of patients with the most common RD, osteoarthritis (OA) are forced to take various analgesics on a regular basis.The most popular class of analgesics with proven efficacy in RA, spondyloarthritis (SpA) and OA are non-steroidal anti-inflammatory drugs (NSAIDs). As has been shown in several meta-analyses, NSAIDs are superior to placebo and paracetamol in their therapeutic effect, are not inferior to opioids and are better tolerated overall. However, the use of NSAIDs can be associated with the development of dangerous adverse events (AEs), which requires careful monitoring of the patient's condition, considering comorbid diseases and risk factors. It is very important to choose a drug with a balanced ratio of efficacy and low risk of gastrointestinal and cardiovascular AEs. One such drug is celecoxib, whose therapeutic potential and relative safety have been confirmed in RA, SpA and OA. A differentiated approach to celecoxib prescription makes it possible to achieve a maximum therapeutic result with a minimum risk of AEs. For severe pain, treatment starts with a dose of 400 mg/day, followed by a switch to a maintenance dose of 200 mg/day.

PICE: Polyhedral Complex Informed Counterfactual Explanations

Polyhedral geometry can be used to shed light on the behaviour of piecewise linear neural networks, such as ReLU-based architectures. Counterfactual explanations are a popular class of methods for examining model behaviour by comparing a query to the closest point with a different label, subject to constraints. We present a new algorithm, Polyhedral-complex Informed Counterfactual Explanations (PICE), which leverages the decomposition of the piecewise linear neural network into a polyhedral complex to find counterfactuals that are provably minimal in the Euclidean norm and exactly on the decision boundary for any given query. Moreover, we develop variants of the algorithm that target popular counterfactual desiderata such as sparsity, robustness, speed, plausibility, and actionability. We empirically show on four publicly available real-world datasets that our method outperforms other popular techniques to find counterfactuals and adversarial attacks by distance to decision boundary and distance to query. Moreover, we successfully improve our baseline method in the dimensions of the desiderata we target, as supported by experimental evaluations.

On the existence of funneled orientations for classes of rooted phylogenetic networks

Recently, there has been a growing interest in the relationships between unrooted and rooted phylogenetic networks. In this context, a natural question to ask is if an unrooted phylogenetic network U can be oriented as a rooted phylogenetic network such that the latter satisfies certain structural properties. In a recent preprint, Bulteau et al. claim that it is NP-hard to decide if U has a funneled (resp. funneled tree-child) orientation, for when the internal vertices of U have degree at most 5. Unfortunately, the proof of their funneled tree-child result appears to be incorrect. In this paper, we show that, despite their incorrect proof, it is NP-hard to decide if U has a funneled tree-child orientation even if each internal vertex has degree 5 and that NP-hardness remains for other popular classes of rooted phylogenetic networks such as funneled normal and funneled reticulation-visible. Additionally, our results hold regardless of whether U is rooted at an existing vertex or by subdividing an edge with the root.

Coumarin-Derived Caging Groups in the Spotlight: Tailoring Physiochemical and Photophysical Properties.

The development of light-responsive molecular tools enables spatiotemporal control of biochemical processes with superior precision. Amongst these molecular tools, photolabile caging groups are employed to prevent critical binding interactions between a bioactive molecule and its corresponding target. Only upon irradiation with light, the bioactive is released in its 'active' form and is now readily available to bind to its target. Coumarin-derived caging groups constitute one of the most popular classes of photolabile protecting groups, due to their facile synthetic accessibility, ease of tuning photophysical properties via structural modification and rapid photolysis reactions. Herein, we highlight the recent progress made on the development of coumarin-derived caging groups, in which the red-shifting of absorption spectra, improving aqueous solubility and tailoring sub-cellular localisation has been of particular interest.

Two modified conjugate gradient methods for unconstrained optimization

Conjugate gradient (CG) methods are a popular class of iterative methods for solving linear systems of equations and nonlinear optimization problems. Motivated by the construction of some modern CG methods, we propose two modified CG methods, named DHS* and DPRP*. Under the strong Wolfe line search (SWLS), the two presented methods are proven to be sufficient descent and globally convergent. Preliminary numerical results show that the DHS* and DPRP* methods are effective for the given test problems.

Fl-IRT-ing with Psychometrics to Improve NLP Bias Measurement

To prevent ordinary people from being harmed by natural language processing (NLP) technology, finding ways to measure the extent to which a language model is biased (e.g., regarding gender) has become an active area of research. One popular class of NLP bias measures are bias benchmark datasets—collections of test items that are meant to assess a language model’s preference for stereotypical versus non-stereotypical language. In this paper, we argue that such bias benchmarks should be assessed with models from the psychometric framework of item response theory (IRT). Specifically, we tie an introduction to basic IRT concepts and models with a discussion of how they could be relevant to the evaluation, interpretation and improvement of bias benchmark datasets. Regarding evaluation, IRT provides us with methodological tools for assessing the quality of both individual test items (e.g., the extent to which an item can differentiate highly biased from less biased language models) as well as benchmarks as a whole (e.g., the extent to which the benchmark allows us to assess not only severe but also subtle levels of model bias). Through such diagnostic tools, the quality of benchmark datasets could be improved, for example by deleting or reworking poorly performing items. Finally, in regards to interpretation, we argue that IRT models’ estimates for language model bias are conceptually superior to traditional accuracy-based evaluation metrics, as the former take into account more information than just whether or not a language model provided a biased response.

Addressing new aspects of surveillance in late mediaeval England, 1350-1550

The paper addresses new aspects of surveillance in late medieval England. New approaches to the study of the phenomenon are introduced, while a limited study puts some of the author’s suggestions to the test. The study addresses the modus operandi of political and labour surveillance focusing on late medieval English towns. Surveillance was intensified after the Black Death. Institutional and noninstitutional surveillance were part of everyday life, but they were also used by the ruling elites to consolidate their power. Surveillance was endured by the lower social ranks. Nevertheless, evidence suggests that sometimes popular classes resisted surveillance or even used it to their own advantage.

Class imbalanced data handling with cyberattack classification using Hybrid Salp Swarm Algorithm with deep learning approach

Cyberattack classification involves applying deep learning (DL) and machine learning (ML) models to categorize digital threats based on their features and behaviors. These models examine system logs, network traffic, or other associated data patterns to discriminate between standard activities and malicious actions. Efficient cyberattack classification is vital for on-time threat detection and response, permitting cybersecurity specialists to categorize and reduce potential risks to a system. Handling class-imbalanced data in cyberattack classification using DL is critical for achieving exact and robust models. In cybersecurity databases, instances of normal behavior frequently significantly outnumber instances of cyberattacks, foremost due to biased methods that may complete poorly on minority classes. To address this issue approaches such as oversampling the lesser class, undersampling the popular class, or using more advanced systems can be used. These plans defend that the DL technique is more complex when determining cyberattacks, so it increases complete performance and adapts the effect of the imbalance class on the classification results. This study presents a novel Hybrid Salp Swarm Algorithm with a DL Approach for Cyberattack Classification (HSSADL-CAC) technique. The HSSADL-CAC method intends to resolve class imbalance data handling with an optimum DL model for the recognition of cyberattacks. At first, the HSSADL-CAC method experiences data normalization as a pre-processing stage. The HSSADL-CAC technique uses the ADASYN approach to handle class imbalance problems. In addition, the HSSADL-CAC technique applies an HSSA-based feature selection approach. The HSSADL-CAC technique detects cyberattacks using a deep extreme learning machine (DELM) model. Finally, the hyperparameter tuning of the ELM model takes place by utilizing the beluga whale optimization (BWO) model. The performance analysis of the HSSADL-CAC technique employs a benchmark database. The comprehensive comparison research indicates the superior performance of the HSSADL-CAC technique in the cyberattack detection procedure.

Clicked into Place: Biomimetic Copolymer Adhesive for Covalent Conjugation of Functionalities.

Polydopamines (PDA) are a popular class of materials and promising candidates as adhesives for new fastening techniques. PDA layers can be formed on a wide range of substrates in various environments. Here, we present a novel method for functionalizing PDA-based copolymer films by using click chemistry. These copolymers adhere strongly to various surfaces and simultaneously have active groups that allow the attachment of functional groups. We discuss the coupling of two types of chitosan and a rhodamine B dye molecule to the alkyne groups of the copolymers by employing click reactions. Azidopropyl methacrylate (AzMA), methyl methacrylate (MMA), and dopamine methacrylamide (DOMA) are copolymerized and codeposited with (3-aminopropyl)triethoxysilane on silicon wafers, polyethylene (PE), and polytetrafluoroethylene (PTFE). AzMA provides the surfaces with azides for use in click reactions, MMA functions to control the polymer as a nonfunctional diluent, whereas DOMA provides adhesion, as well as cross-linking groups. After codeposition, the dyes are grafted to the copolymer to illustrate the ability of the films to link functional groups covalently. Fourier transform infrared spectroscopy confirms the successful click reaction in solution, and atomic force microscopy shows the surface morphologies following grafting. Fluorescence microscopy provides evidence of successful grafting. As an example of a possible application, layers exhibiting antifouling properties are prepared. Chitosan grafted to PE is tested for antifouling performance. These functionalized layers show nonspecific inhibition of protein adsorption. We find that chitosan can lower the adsorption of fluorescein-labeled bovine serum albumin (BSA) protein by more than 90% for the best performing fluorescein-labeled BSA protein and by more than 90% for the best-performing layer. These results demonstrate the viability of our PDA-based copolymers for surface functionalization through click chemistry grafting at challenging adhesion to surfaces.

MINI REVIEW ON PIROXICAM DERIVATIVES SYNTHESIS IN THE LAST 10 YEARS

Today the focus is on reducing the likelihood of creating a potentially hazardous substance by concentrating on the synthesis of derivatives from approved medications. Non-steroidal anti-inflammatory medicines (NSAIDs) are a popular class of medications used to treat painful arthritis. The medication piroxicam is a non-steroidal anti-inflammatory. Piroxicam has been the basis for the synthesis of several derivatives with various biological activity in recent years. This mini review aims to describe the biological activities of these derivatives that have emerged in the previous ten years. KEY WORDS: piroxicam , non-steroidal anti-inflammatory, anti-glycation , anti-nociceptive , anti-HIV.

Evaluating the robustness of parameter estimates in cognitive models: A meta-analytic review of multinomial processing tree models across the multiverse of estimation methods.

Researchers have become increasingly aware that data-analysis decisions affect results. Here, we examine this issue systematically for multinomial processing tree (MPT) models, a popular class of cognitive models for categorical data. Specifically, we examine the robustness of MPT model parameter estimates that arise from two important decisions: the level of data aggregation (complete-pooling, no-pooling, or partial-pooling) and the statistical framework (frequentist or Bayesian). These decisions span a multiverse of estimation methods. We synthesized the data from 13,956 participants (164 published data sets) with a meta-analytic strategy and analyzed the magnitude of divergence between estimation methods for the parameters of nine popular MPT models in psychology (e.g., process-dissociation, source monitoring). We further examined moderators as potential sources of divergence. We found that the absolute divergence between estimation methods was small on average (<.04; with MPT parameters ranging between 0 and 1); in some cases, however, divergence amounted to nearly the maximum possible range (.97). Divergence was partly explained by few moderators (e.g., the specific MPT model parameter, uncertainty in parameter estimation), but not by other plausible candidate moderators (e.g., parameter trade-offs, parameter correlations) or their interactions. Partial-pooling methods showed the smallest divergence within and across levels of pooling and thus seem to be an appropriate default method. Using MPT models as an example, we show how transparency and robustness can be increased in the field of cognitive modeling. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

To the Brain and Beyond: Neurological Implications of Glucagon-Like Peptide-1 Receptor Agonists.

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have emerged as an increasingly popular class of medications commonly used for glycemic control in patients with type 2 diabetes mellitus and for weight loss. GLP-1 RAs also have potential benefits in patients with various neurological diseases independent of their glucose-modulating effect. In this focused review, we explore animal and clinical evidence evaluating the impact of GLP-1 RAs in common neurological diseases. Our aim is to provide a basis for hypothesis generation for future studies to assess the role that GLP-1 RAs may have on the nervous system, including implications for the perioperative period.

L2XGNN: learning to explain graph neural networks

Graph Neural Networks (GNNs) are a popular class of machine learning models. Inspired by the learning to explain (L2X) paradigm, we propose L2xGnn, a framework for explainable GNNs which provides faithful explanations by design. L2xGnn learns a mechanism for selecting explanatory subgraphs (motifs) which are exclusively used in the GNNs message-passing operations. L2xGnn is able to select, for each input graph, a subgraph with specific properties such as being sparse and connected. Imposing such constraints on the motifs often leads to more interpretable and effective explanations. Experiments on several datasets suggest that L2xGnn achieves the same classification accuracy as baseline methods using the entire input graph while ensuring that only the provided explanations are used to make predictions. Moreover, we show that L2xGnn is able to identify motifs responsible for the graph’s properties it is intended to predict.

A Comprehensive on Synthesis and Antimicrobial Evaluation of Substituted-Arylideneamino-5-(5-Chlorobenzofuran-2-yl)-1, 2, 4-Triazole-3-Thiol Derivatives/ Schiff Bases.

Schiff bases are well known and popular classes of organic compounds containing imine (R2C = NH) group that are widely used as catalysts and intermediates in numerous organic transformations. Schiff bases are medicinally very important because they exhibit antimicrobial like antibacterial, antiviral and anticancer activities. Benzofuran based Schiff bases have been found as interesting scaffolds for the synthesis and design of biologically active agents. Moreover, they possess a wide range of biological activities against fungal, bacterial, malarial, inflammatory and viral diseases. In this reviw, substituted-arylideneamino-5-(5-chlorobenzofuran-2-yl)-1,2,4-triazole-3-thiols have been synthesized by using efficient synthetic protocols. The synthesized derivatives are also evaluated against different bacterial strains.

Another hybrid conjugate gradient method as a convex combination of WYL and CD methods

Abstract Conjugate gradient (CG) methods are a popular class of iterative methods for solving linear systems of equations and nonlinear optimization problems. In this paper, a new hybrid conjugate gradient (CG) method is presented and analyzed for solving unconstrained optimization problems, where the parameter β k \beta_{k} is a convex combination of β k WYL \beta_{k}^{\mathrm{WYL}} and β k CD \beta_{k}^{\mathrm{CD}} . Under the strong Wolfe line search, the new method possesses the sufficient descent condition and the global convergence properties. The preliminary numerical results show the efficiency of our method in comparison with other CG methods. Furthermore, the proposed algorithm HWYLCD was extended to solve the problem of a mode function.

Utilization of Fermentation Process for Production of Two Popular Classes of Antibiotics

The gradual resistance to existing antibiotics can be overcome with the use or clinical testing of novel natural products as continuing source of chemical diversity. Often natural products are difficult to be synthesized commercially by chemical methods since their structures are very complex with a number of asymmetric centers. Microbial fermentation remains the only way to isolate wide range of antibiotics. In this article, authors discuss about the production process of two popular classes of antibiotics, cephalosporins and glycopeptides, utilizing fermentation techniques. Yield and quality of the active pharmaceutical ingredient can be improved by using genetically engineered strains and supplying proper nutrition to the fermentation media.

Contrastive Learning Framework for Bitcoin Crash Prediction

Due to spectacular gains during periods of rapid price increase and unpredictably large drops, Bitcoin has become a popular emergent asset class over the past few years. In this paper, we are interested in predicting the crashes of Bitcoin market. To tackle this task, we propose a framework for deep learning time series classification based on contrastive learning. The proposed framework is evaluated against six machine learning (ML) and deep learning (DL) baseline models, and outperforms them by 15.8% in balanced accuracy. Thus, we conclude that the contrastive learning strategy significantly enhance the model’s ability of extracting informative representations, and our proposed framework performs well in predicting Bitcoin crashes.