Simulating the Agostic Interaction in Electron-deficient (16-e) Group (VI) ML6 Complexes: [M(CO)5(C(Me)OMe)] (2+) (M = Cr, Mo, and W) as Models

A large number of theoretical studies have focused on understanding the molecular features of the agostic interaction in various kinds of molecular environments. However, there is a lack of electronic structure information about the agostic interaction in electron-deficient group (VI)ML6 organometallic complexes. In this simulation study, a unique case of an intramolecular stabilizing interaction has been discovered and evaluated. A geometric analysis revealed that beta-(C-H) and alpha-(C-C) can occupy the seventh and eighth coordination sites in the title Fischer carbene complexes as agostic interactions, which allows classifying the carbene as a η3 ligand in these cases. This theory was supported by the relative energies of the conformers and an NBO analysis. Both C2-C1 (σ) and C2-H1 (σ) were found to interact with the antibonding orbital of M-C6 (σ*), therefore these interactions are classified as σ→σ*. These two simultaneous interactions have significant impact on the carbene characteristics; the structure, the atomic charges, infrared stretching vibrations (C-H, C-C, and C-O), and the 1H and the 13C-NMR chemical shifts. From a fundamental organic-organometallic chemistry point of view, this is a new addition to the orbital interaction theory and to group (VI) chemistry.Keywords: DFT, agostic interaction, NBO analysis, NPA charges, IR, GIAO-NM

Design and implementation of a novel lightweight soft upper limb exoskeleton using pneumatic actuator muscles

Stroke is the leading cause of disability and weakness in the UK and around the world. Thus, stroke patients require an extensive rehabilitation therapy to regain some of the weaknesses. Many rehabilitation robotic devices have been designed and developed to assist the stroke patients to perform their activities of daily living and to perform repetitive movements. However, these devices remain unmanageable to use by the patients alone not only because they are cumbersome to use but also due to their weights, rigid, fix and non-portable characteristics. Thus there is a need to invent a novel exoskeleton soft arm that has a lightweight and a high power to rehab the elbow joint with lower cost and without the need to therapists. Here for elbow joint rehabilitation, we investigate and propose a novel exoskeleton soft robotic arm, which is wearable, lightweight and portable so that it would allow patients to perform repetitive motion therapy more often with a greater intensity in their homes and relevant to their Activities of Daily Living (ADL). The proposed arm consists of various bending pneumatic muscle actuators (pMA), where traditional pMA are not suitable. Testing on various pMA (traditional and bending) revealed its behaviour and the relationship between pressure, length, force, and bending angle in different setups such as isotonic and isometric. Experiments are done to analyse its non-linear behaviour, moreover, geometrical and numerical models are compared to the experimental results to validate the results. A developed control approach to control the soft arm is implemented to validate the design. Model reference adaptive control (MRAC) to control the arm using (Proportional, Integral, and Derivative) PID controller as an input for MRAC. Neural Network (NN) is also used in MRAC to improve the performance of MRAC

A cross-sectional study for assessment of menopausal symptoms and coping strategies among Jordanian women of 40-60 years age group

Background: Menopause typically occurs between the ages of 45 and 55 and can significantly impact a woman's quality of life. Understanding the prevalence of menopausal symptoms and the strategies of coping with them is crucial for developing targeted interventions that address their unique needs. Methods: This cross-sectional study targeted the Jordanian women aged 40-60 through online questionnaire during January 2023-June 2023. The menopause rating scale (MRS) was utilized to assess the severity of menopausal symptoms, including somatic, psychological, and urogenital domains. Results: The study included a total of 309 successfully completed participations by women from different socio-economic and educational backgrounds in Jordan. The analysis revealed a high prevalence of menopausal symptoms among the participants. The severity of symptoms varied; moderate to severe symptoms, were appreciable across the various domains. The coping strategies employed by the participants were multifaceted. Unfortunately, a proportion of participants admitted to using ineffective coping mechanisms, such as tobacco and excessive caffeine consumption. In general, the study revealed that there is an evident shortage in the overall awareness among women. Conclusions: The findings emphasize the necessity of comprehensive interventions to enhance the well-being of women and consequently the society. The qualitative and the quantitative aspects of the study shall contribute valuable perceptions to both of the academia and the authorized healthcare institutes.  

Machine learning models to detect anxiety and depression through social media : a scoping review

Despite improvement in detection rates, the prevalence of mental health disorders such as anxiety and depression are on the rise especially since the outbreak of the COVID-19 pandemic. Symptoms of mental health disorders have been noted and observed on social media forums such Facebook. We explored machine learning models used to detect anxiety and depression through social media. Six bibliographic databases were searched for conducting the review following PRISMA-ScR protocol. We included 54 of 2219 retrieved studies. Users suffering from anxiety or depression were identified in the reviewed studies by screening their online presence and their sharing of diagnosis by patterns in their language and online activity. Majority of the studies (70%, 38/54) were conducted at the peak of the COVID-19 pandemic (2019–2020). The studies made use of social media data from a variety of different platforms to develop predictive models for the detection of depression or anxiety. These included Twitter, Facebook, Instagram, Reddit, Sina Weibo, and a combination of different social sites posts. We report the most common Machine Learning models identified. Identification of those suffering from anxiety and depression disorders may be achieved using prediction models to detect user's language on social media and has the potential to complimenting traditional screening. Such analysis could also provide insights into the mental health of the public especially so when access to health professionals can be restricted due to lockdowns and temporary closure of services such as we saw during the peak of the COVID-19 pandemic

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Modulating the electronic structure of amino acids: interaction of model lewis acids with anthranilic acid

On the basis of theoretical B3LYP calculations, Yáñez and co-workers (J. Chem. Theory Comput. 2012, 8, 2293) illustrated that beryllium ions are capable of significantly modulating (changing) the electronic structures of imidazole. In this computational organic chemistry study, the interaction of this β-amino acid and five model Lewis acids (BeF1+, Be2+, AlF2(1+), AlF2+, and Al3+) were investigated. Several aspects were addressed: natural bond orbitals, including second order perturbation analysis of intra-molecular charge delocalization and the natural population analysis atomic charges; molecular geometries; selected infrared stretching frequencies (C-N, C-O, and N-H), and selected ¹H-NMR chemical shifts. The data illustrate that this interaction can weaken the H-O bond and goes beyond strengthening the intra-molecular hydrogen bond (N...H-O) to cause a spontaneous transfer of the proton to the nitrogen atom in five cases generating zwitterion structures. Many new features are observed. Most importantly, the zwitterion structures include a stabilizing hydrogen bond (N-H...O) that varies in relative strength according to the Lewis acid. These findings explain the experimental observations of α-amino acids (for example: J. Am. Chem. Soc. 2001, 123, 3577) and are the first reported fundamental electronic structure characterization of β-amino acids in zwitterion form

Toward Exploring Novel Organic Materials: MP4-DFT Properties of 4-Amino-3-Iminoindene

Tautomerism links with many applications and remains an attracting feature in exploring novel systems. In this regard, properties of indene-based HNCCCN segments have not received any considerable attention. In this computational organic chemistry study, first, to calculate the proton transfer energy barrier at a reasonable cost, the study identified an accurate forth order Møller–Plesset perturbation theory-density functional theory (MP4-DFT) protocol equivalent to the outstanding pioneering benchmark calculations. The calculations illustrate that the two tautomers of the 4-amino-3-iminoindene nucleus are separated by a considerable energy barrier while featuring different molecular orbital characteristics; frontier orbital distribution, λmax, and energies, which are known basic requirements in molecular switching and logic circuit applications. The N-H/BH2 substitution was found to have significant influence on the electronic structure of the skeleton. Similarities in the two tautomers and the boron derivative to properties of known molecular materials have been found