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Aim: To evaluate nurses' workload during the coronavirus disease 2019 (COVID‐19) pandemic and to identify their perception of medical serviced robots (MSRs) that may help with or replace the tasks of nurses in negative‐pressure isolated wards and general wards. Design: Cross‐sectional survey. Methods: A researcher‐made questionnaire was applied that assessed nurses' current workload and their attitudes towards and perceptions of MSRs. A visual analogue scale (VAS) from 1 to 10 was used to assess workload. Perceptions evaluated on a 5‐point Likert scale ranging from one point for 'strongly disagree' to five points for 'strongly agree'. A higher VAS score indicated a higher workload. On the other hands, a higher Likert score indicated a more positive perception and three points was neutral. The questionnaire was conducted on 150 nurses in negative‐pressure isolated wards for the management of COVID‐19 and 150 nurses in general wards. Quota sampling technique was used as sampling technique. Data analysis was performed through independent t‐tests, chi‐square tests and two‐tailed tests. The p‐value <0.05 was interpreted to statistically significant. Results: Two hundred eighty‐two participants responded and 142 belonged to the negative‐pressure isolated ward. The overall response rate was 94%, and 94.7% in the negative isolated ward. The mean score ± standard deviation for nursing‐related psychological stress at the current work site was 7.18 ± 1.58 points, and the mean score for physical workload was 7.65 ± 1.48. The need for MSRs was rated as 3.66 ± 0.86 out of 5. Overall, a positive attitude towards MSRs was confirmed, with no difference between ward groups. The overall ratings were 3.14 ± 1.15 for perceived availability and 3.26 ± 1.13 for perceived efficiency. Both nurse groups perceived that MSRs were most available and efficient for monitoring and measurements. Nurses in negative‐pressure isolated wards perceived MSRs more positively than did nurses in general wards regarding setting of alarms (p = 0.003) and delivery of medical devices/materials (p = 0.013). Based on these results, functional development of MSRs associated with monitoring, measurements, setting of alarms and delivery should be prioritized. No patient or public contribution. [ABSTRACT FROM AUTHOR]

Recent advances in robotics technology and artificial intelligence (AI) have sparked increased interest in humanoid robots that resemble humans and social robots capable of interacting socially. Alongside this trend, a new field of robot research called human-robot interaction (HRI) is gaining prominence. The aim of this review paper is to introduce the fundamental concepts of HRI and social robots, examine their current applications in the medical field, and discuss the current and future prospects of HRI and social robots in spinal care. HRI is an interdisciplinary field where robotics, AI, social sciences, design, and various disciplines collaborate organically to develop robots that successfully interact with humans as the ultimate goal. While social robots are not yet widely deployed in clinical environments, ongoing HRI research encompasses various areas such as nursing and caregiving support, social and emotional assistance, rehabilitation and cognitive enhancement for the elderly, medical information provision and education, as well as patient monitoring and data collection. Although still in its early stages, research related to spinal care includes studies on robotic support for rehabilitation exercises, assistance in gait training, and questionnaire-based assessments for spinal pain. Future applications of social robots in spinal care will require diverse HRI research efforts and active involvement from spinal specialists. [ABSTRACT FROM AUTHOR]

The glacial‐interglacial cycle of elements and the linkage with biological production is very important. Nonetheless, records on incorporation of elements into diatom frustules are very poor in the Southern Ocean where diatom productivity is very high. As a result, there is a big gap in our understanding of elemental cycles in association with biological production and the controlling mechanisms. Here, we documented concentrations of major, trace, and rare earth elements of diatom frustules in two cores collected from the Southern Ocean since the Last Glacial Maximum to understand changes of incorporation of these elements into diatom frustules and to investigate mechanisms for changing incorporation. We found that there are three types of glacial‐interglacial variation patterns. Most elements showed increased (decreased) concentrations during the glacial period (interglacial) with decreased (increased) diatom production in the surface water following the variation pattern of magnetic susceptibility, whereas P, Ge, and Sb showed the opposite trend following the variation pattern of biogenic opal. Li, Ni, Ag, W, U, and I showed no clear glacial‐interglacial variation pattern. These different variation patterns imply that elemental incorporation into diatom frustules are also different according to elements. Our results revealed that biologically mediated removal of most elements from surface water to deep‐sea is comparable between glacial (low diatom production with high elements assimilation) and interglacial (high diatom production with low elements assimilation) periods in the Southern Ocean. However, more removal of P, Sb, and Ge in the surface water by diatoms occurs during interglacial periods in the Southern Ocean. Key Points: Concentrations of multi‐elements within diatom frustules were documented from the Southern Ocean since the Last Glacial Maximum (LGM)Most elements were assimilated/incorporated into diatoms synchronously: high (low) during glacial (interglacial) periodsP, Ge, and Sb showed low incorporation into diatom frustules during the LGM but increased during the Holocene [ABSTRACT FROM AUTHOR]

Bone mineral density (BMD) is a very important factor in spinal fusion surgery using instrumentation. Our aim was to investigate the utility of Hounsfield units (HU) obtained from preoperative lumbar computed tomography (CT) to predict osteoporosis coupling with data of quantitative computed tomography (QCT) and dual X-ray absorptiometry (DEXA).