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"Pharmaceutical arithmetic."
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Numeracy in children's nursing
Numeracy in Children’s Nursing and Healthcare is a handy, practical book which highlights the importance of numbers, numeracy and calculations in children’s nursing practice, instilling nursing students and qualified nurses with confidence and competence when working with numbers and calculating drug doses. This accessible guide covers all aspects of numeracy from basic skills through to complex drug administration, and provides case studies throughout enabling the reader to apply the theory to practice. Each chapter adopts the same accessible and easy-to-follow format, featuring learning outcomes, a case scenario, key numeracy information, hints and tips, activities and exercises, and a glossary of terms.
eBook
Medical dosage calculations for dummies
Most medical dosage calculations are simple, and this guide provides helpful content in an approachable and easy-to-understand format. It give you the practice, confidence, and skills to get a grasp on dosing in the context of real medical conditions.
Book
Calculation skills for nurses
This book provides nursing students with words of wisdom and advice from real-life student nurses, Calculation Skills for Nurses enables you to calculate drug dosages with ease -- boosting your confidence and competence in this core area of nursing practice. The book takes away the fear of calculations, making it approachable, easy and fun and ties in with the NMC standards for pre-registration education and the Essential Skills Clusters. It is filled with examples and questions based on real life nursing and healthcare situations and includes removable quick reference guide to help on clinical placements.
eBook
Further essentials of pharmacology for nurses
\"The book is an easy to follow introductory text...It uses uncomplicated language and case studies and questions that provide students with concrete examples, relating to real life situations, upon which they can develop their pharmacological knowledge and understanding.
eBook
Calculation Skills for Nurses
Providing nursing students with words of wisdom and advice from real-life student nurses, Calculation Skills for Nurses enables you to calculate drug dosages with ease, boosting your confidence and competence in this core area of nursing practice. The book takes away the fear of calculations, making it approachable, easy and fun, and ties in with the NMC standards for pre-registration education and the Essential Skills Clusters. It is filled with examples and questions based on real life nursing and healthcare situations and includes key information displayed on the inside back cover for quick look-up on clinical placements.
eBook
Quantitative Evaluation of Safety in Drug Development
Written by a team of experienced leaders, this book brings the most advanced knowledge and statistical methods of drug safety to the statistical, clinical, and safety community. It explains design, monitoring, analysis, and reporting issues for both clinical trials and observational studies in biopharmaceutical product development. The book addresses key challenges across regulatory agencies, industry, and academia. It discusses quantitative approaches to safety evaluation and risk management in drug development, covering Bayesian methods, effective safety graphics, and risk-benefit evaluation.
eBook
Topological indices and QSPR modeling of gonalgia-associated drug molecules via M-polynomials
Topological indices, derived from graph-theoretical representations of molecular structure, have emerged as powerful tools for predicting the physicochemical properties of chemical compounds. In this study, we investigate a series of fifteen clinically significant drugs associated with the treatment of gonalgia (knee pain). The molecular graphs of these compounds are analyzed using the
M
-polynomial approach to compute seven key degree-based topological indices: the inverse sum index (ISI), harmonic arithmetic index (HA), inverse symmetric division deg index (ISDD), augmented Zagreb index (AZI), sum-connectivity index (SC), geometric arithmetic index (GA), and sum-Balaban index (SJ). A comprehensive quantitative structure–property relationship (QSPR) analysis is then performed to correlate these indices with critical physicochemical properties, including boiling point (BP), melting point (MP), critical temperature (CT), critical volume (CV), octanol–water partition coefficient (LogP), molar refractivity (MR), and calculated LogP (CLogP). Our results demonstrate strong predictive correlations, with the SC index showing exceptional performance for BP, MP, CT, CV, and MR, while the SJ index was the most effective for predicting LogP and CLogP. Among the regression models tested: linear, polynomial, and logarithmic the quadratic model consistently provided the highest accuracy, highlighting nonlinear relationships between molecular structure and properties. This study confirms that
M
-polynomial-derived topological indices, combined with polynomial regression, offer a reliable and efficient computational framework for predicting drug-like properties, providing valuable insights for pharmaceutical design and optimization.
Graphical abstract
Journal Article
Polyphasic Characterisation of Non-Starter Lactic Acid Bacteria from Algerian Raw Camel’s Milk and Their Technological Aptitudes
Research background. Consumption of spontaneously fermented camel´s milk is usual in Algeria, making it a feasible source of diverse lactic acid bacteria (LAB) with the potential to be used as adjunct cultures to improve quality and safety of dairy fermented products. Experimental approach. Twelve raw camel´s milk samples were used as source of indigenous LAB, which were further characterised by examining 39 phenotypic traits with technological relevance. Results and conclusions. Thirty-five non-starter LAB (NSLAB) were isolated from 12 Algerian raw camel's milk samples and they were microbiologically, biochemically and genetically characterised. Some isolates showed proteolytic activity, acidifying capacity, the ability to use citrate, and to produce dextran and acetoin. Ethanol, acetaldehyde, methyl acetate, acetoin and acetic acid were the major volatile compounds detected. Cluster analysis performed using the UPGMA method, and based on the thirty-nine phenotypic characteristics investigated, reflected the microbial diversity that can be found in raw camel´s milk. Novelty and scientific contribution. The isolated strains, from a non-typical source, showed interesting technological traits to be considered as potential adjunct cultures. Cluster analysis based on the phenotypic characteristics examined turned out to be a useful tool for the typification of isolates when no genetic information is available. These findings may be of use towards an industrialised production of camel's milk dairy products.
Journal Article
Predicting the critical micelle concentration of binary surfactant mixtures using machine learning
Surfactant mixtures play a critical role in industries such as drug delivery, cosmetics, firefighting foams, and lubrication, serving as foundational components of the global economy. Their performance hinges on micelle formation, a self-assembly process governed by the critical micelle concentration (CMC), which enables key functions like solubilization, emulsification, and targeted molecular delivery. However, rapidly and accurately predicting the CMC of mixtures remains a significant challenge due to the chemical diversity and nonlinear interactions between surfactants. Here, we introduce an artificial neural network (ANN)-based machine learning framework to predict the CMC of binary surfactant mixtures. Our workflow leverages cheminformatics-derived molecular descriptors for each surfactant component, which are then aggregated using strategies such as concatenation, arithmetic mean, and harmonic mean. We find that pairing the arithmetic mean strategy with ANN yields the best performance, effectively capturing complex molecular interactions and enabling dual predictive capabilities: (1) precise interpolation of CMC values at untested mole fractions within known mixtures, and (2) accurate prediction of complete CMC–composition profiles for entirely novel surfactant combinations. SHAP-based interpretability analysis highlights that features such as hydrophobic surface area, electronic topological descriptors, and headgroup basicity drive model predictions, aligning with core principles of surfactant chemistry and reinforcing the mechanistic validity of our model. Overall, this framework accelerates data-driven surfactant design by reducing experimental burden and enabling rapid, rational optimization of formulations across pharmaceuticals, personal care, environmental remediation, and enhanced oil recovery.
Scientific contribution
This study presents a novel machine learning framework that, for the first time, predicts full critical micelle concentration (CMC)–composition profiles for binary surfactant mixtures, including untrained systems. By strategically combining the features of individual components of mixtures using arithmetic mean, our artificial neural network model deciphers nonlinear interactions between chemically distinct surfactants, enabling accurate and generalizable CMC predictions. Beyond performance gains, this framework facilitates rapid and systematic exploration of formulation space via inverse design and high-throughput screening, establishing a powerful foundation for the rational development of next-generation surfactants with applications in energy, environmental remediation, pharmaceuticals, and biomedical science.
Graphical Abstract
Journal Article