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The recently dominant SARS-CoV-2 Omicron JN.1 has evolved into multiple sublineages, with recurrent spike mutations R346T, F456L, and T572I, some of which exhibit growth advantages, such as KP.2 and KP.3. We investigated these mutations in JN.1, examining their individual and combined effects on immune evasion, ACE2 receptor affinity, and in vitro infectivity. F456L increased resistance to neutralization by human sera, including those after JN.1 breakthrough infections, and by RBD class-1 monoclonal antibodies, significantly altering JN.1 antigenicity. R346T enhanced ACE2-binding affinity and modestly boosted the infectivity of JN.1 pseudovirus, without a discernible effect on serum neutralization, while T572I slightly bolstered evasion of SD1-directed mAbs against JN.1's ancestor, BA.2, possibly by altering SD1 conformation. Importantly, expanding sublineages such as KP.2 containing R346T, F456L, and V1104L, showed similar neutralization resistance as JN.1 with R346T and F456L, suggesting V1104L does not appreciably affect antibody evasion. Furthermore, the hallmark mutation Q493E in KP.3 significantly reduced ACE2-binding affinity and viral infectivity, without noticeably impacting serum neutralization. Our findings illustrate how certain JN.1 mutations confer growth advantages in the population and could inform the design of the next COVID-19 vaccine booster.

Aim: This scoping review aims to identify the facilitators and barriers to the implementation of VR technology in the aged-care setting. Background: Virtual reality (VR) offers the potential to reduce social isolation and loneliness through increased social engagement in aged-care settings. Methods and Analysis: This scoping review followed the Joanna Briggs Institute scoping review methodology and took place between March and August 2022. The review included a three-step search strategy: (1) identifying keywords from CINHAL, Embase, Medline, PsycInfo, Scopus, and Web of Science (2) conducting a second search using all identified keywords and index terms across selected databases; and (3) searching the reference lists of all included articles and reports for additional studies. Results: The final review included 22 articles. The analysis identified factors affecting the VR technology implementation in aged care settings to reduce isolation and loneliness: (a) key facilitators are local champions and staff training. (b) barriers include technological adaptability, video quality, and organizational culture. Conclusion: Existing evidence points to VR as a promising intervention to decrease loneliness and feelings of isolation and improve social engagement in older people living in aged-care settings.

ObjectivePhysical activity (PA) decreases during childhood with PA tracking statistics showing moderate coefficients in early childhood moving to poor coefficients in late childhood. The age-related instability of PA tracking is attributed to variations in age-related PA behaviors when quantifying PA in different settings (in/out of school, sports camps, and habitual PA). This study has examined the stability of age-related PA for children (from 7 to 11 years) during self-paced guided active play (GAP) sessions separated by 12 months.MethodsChildren (n = 65) recruited from community camps in two consecutive years were assessed for growth and PA participation during GAP sessions (1 h.d−1 on 2 d.wk−1) using cooperative games. Accelerometer outputs were used to quantify PA and estimate energy expenditure (EE) and moderate-to-vigorous PA (MVPA). Tracking statistics were assessed by Spearman coefficients (r) and agreement scores for percentile rankings (Kappa (k)) after a 1-year interval.ResultsGrouped data for PA tracking (EE) over the 1-year interval showed strong coefficients (r = 0.88, p = 0.001) and moderate agreement scores (k = 0.54). Boys and girls showed similar results. During the 1-year interval, the MVPA tracking coefficient was r = 0.54 (p = 0.01) with a moderate k score of 0.47. Age-related PA (EE) tracking coefficients at 7 (r = 0.90), 8 (r = 0.88), 10 (r = 0.82), and 11 (r = 0.83) years showed strong coefficients except at 9 years (r = 0.51).ConclusionChildren's age-related PA tracking (EE and MVPA) is stable during self-paced GAP assessed over a 1-year interval. Since PA tracking is stable in self-paced GAP, GAP should be included in children's PA intervention strategies to improve health and fitness.