Azido-mediated intermolecular interactions of transition metal complexes.

The coordinated azido ligand has a variety of ways to establish intermolecular contacts whose nature is computationally analysed in this work on dimers of the [N3-Hg(CF3)] complex with different interactions involving only N⋯N contacts, or with an additional Hg⋯N contact. The applied tools include the molecular electrostatic map of the monomer, an energy decomposition analysis (EDA), a topological AIM analysis of the electron density and the study of NCI (non-covalent interactions) isosurfaces. The interactions between two azido ligands are found to be weakly stabilizing (by 0.2 to 2.7 kcal mol-1), topology-dependent and require dispersion forces to complement orbital and electrostatic stabilization. Those interactions are supplemented by the formation of simultaneous Hg⋯N secondary interactions by about -1 kcal mol-1, and by the ability of the monomer to simultaneously interact with several neighbours in the crystal structure.

Structure and Bonding of Halonium Compounds.

The geometrical parameters and the bonding in [D···X···D]+ halonium compounds, where D is a Lewis base with N as the donor atom and X is Cl, Br, or I, have been investigated through a combined structural and computational study. Cambridge Structural Database (CSD) searches have revealed linear and symmetrical [D···X···D]+ frameworks with neutral donors. By means of density functional theory (DFT), molecular electrostatic potential (MEP), and energy decomposition analyses (EDA) calculations, we have studied the effect of various halogen atoms (X) on the [D···X···D]+ framework, the effect of different nitrogen-donor groups (D) attached to an iodonium cation (X = I), and the influence of the electron density alteration on the [D···I···D]+ halonium bond by variation of the R substituents at the N-donor upon the symmetry, strength, and nature of the interaction. The physical origin of the interaction arises from a subtle interplay between electrostatic and orbital contributions (σ-hole bond). Interaction energies as high as 45 kcal/mol suggest that halonium bonds can be exploited for the development of novel halonium transfer agents, in asymmetric halofunctionalization or as building blocks in supramolecular chemistry.

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades.

Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.

Detection of COVID-19 from CT scan images: A spiking neural network-based approach.

The outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world. Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to prevent the virus from spreading across the community. Developing deep learning models for analysing these kinds of radiological images is a well-known methodology in the domain of computer based medical image analysis. However, doing the same by mimicking the biological models and leveraging the newly developed neuromorphic computing chips might be more economical. These chips have been shown to be more powerful and are more efficient than conventional central and graphics processing units. Additionally, these chips facilitate the implementation of spiking neural networks (SNNs) in real-world scenarios. To this end, in this work, we have tried to simulate the SNNs using various deep learning libraries. We have applied them for the classification of chest CT scan images into COVID and non-COVID classes. Our approach has achieved very high F1 score of 0.99 for the potential-based model and outperforms many state-of-the-art models. The working code associated with our present work can be found here.

A software to prevent delirium in hospitalised older adults: development and feasibility assessment.

BACKGROUND: non-pharmacological interventions to prevent delirium are useful in hospitalised older adults. However, they are poorly implemented in clinical practice. We aimed to develop a software for bedside use by hospitalised older adults and to improve their access to these interventions. METHODS: a transdisciplinary team composed of healthcare professionals, designers, engineers and older adults participated in the development of the software. Scrum methodology was used to coordinate the work of the team, and the software was evaluated in a feasibility study. RESULTS: a software for touchscreen mobile devices that supports Android 5.0 or later was produced, including modules for time-spatial re-orientation, cognitive stimulation, early mobilisation, sensorial support use promotion, sleep hygiene and pain management optimisation. Horizontal disposition, use of colour contrast and large interaction areas were used to improve accessibility. The software's usability and accessibility were evaluated in 34 older adults (average age 73.2 ± 9.1 years) showing that 91.1% of them got access to all the software functions without previous instructions. The clinical feasibility assessment showed that 83.3% of the 30 enrolled hospitalised patients (76 ± 8 years) completed the 5-day protocol of software usage during hospitalisation. Software use was associated with a decreased trend in delirium incidence of 5 of 32 (15.6%) at baseline to 2 of 30 (6.6%) after its implementation. CONCLUSION: a highly accessible and implementable software, designed to improve access to non-pharmacological interventions to prevent delirium in hospitalised older adults, was developed. The effectiveness of the software will be evaluated in a randomised clinical trial.