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Considering that the L455 is predominantly located at the epitope of receptor binding domain Class 1 antibodies, as indicated by earlier research, our study further examined the evasion capabilities of JN.1 in response to eight XBB.1·5-neutralising class 1 monoclonal antibodies.7 Pseudovirus neutralisation assays showed that the addition of the L455S mutation enhanced JN.1's ability to evade class 1 antibodies (figure D). In summary, JN.1, by inheriting BA.2.86's antigenic diversity and acquisition of L455S, rapidly achieved extensive resistance across receptor binding domain class 1, 2, and 3 antibodies,1 and showed higher immune evasion compared with BA.2.86 and other resistant strains like HV.1 and JD.1·1, at the expense of reduced human ACE2 binding. [...]strains could survive and transmit at low levels since their antigenic difference would allow them to target distinct populations compared with dominant strains and have the potential to quickly accumulate highly immune-evasive mutations at the cost of human ACE2 binding capabilities.

Continuous evolution of Omicron has led to a rapid and simultaneous emergence of numerous variants that display growth advantages over BA.5 (ref. 1 ). Despite their divergent evolutionary courses, mutations on their receptor-binding domain (RBD) converge on several hotspots. The driving force and destination of such sudden convergent evolution and its effect on humoral immunity remain unclear. Here we demonstrate that these convergent mutations can cause evasion of neutralizing antibody drugs and convalescent plasma, including those from BA.5 breakthrough infection, while maintaining sufficient ACE2-binding capability. BQ.1.1.10 (BQ.1.1 + Y144del), BA.4.6.3, XBB and CH.1.1 are the most antibody-evasive strains tested. To delineate the origin of the convergent evolution, we determined the escape mutation profiles and neutralization activity of monoclonal antibodies isolated from individuals who had BA.2 and BA.5 breakthrough infections 2 , 3 . Owing to humoral immune imprinting, BA.2 and especially BA.5 breakthrough infection reduced the diversity of the neutralizing antibody binding sites and increased proportions of non-neutralizing antibody clones, which, in turn, focused humoral immune pressure and promoted convergent evolution in the RBD. Moreover, we show that the convergent RBD mutations could be accurately inferred by deep mutational scanning profiles 4 , 5 , and the evolution trends of BA.2.75 and BA.5 subvariants could be well foreseen through constructed convergent pseudovirus mutants. These results suggest that current herd immunity and BA.5 vaccine boosters may not efficiently prevent the infection of Omicron convergent variants. Convergent mutations in hotspots of the SARS-CoV-2 Omicron receptor-binding domain can cause immune evasion and maintain sufficient ACE2-binding capability.

The continuing emergence of SARS-CoV-2 variants highlights the need to update COVID-19 vaccine compositions. However, immune imprinting induced by vaccination based on the ancestral (hereafter referred to as WT) strain would compromise the antibody response to Omicron-based boosters 1 – 5 . Vaccination strategies to counter immune imprinting are critically needed. Here we investigated the degree and dynamics of immune imprinting in mouse models and human cohorts, especially focusing on the role of repeated Omicron stimulation. In mice, the efficacy of single Omicron boosting is heavily limited when using variants that are antigenically distinct from WT—such as the XBB variant—and this concerning situation could be mitigated by a second Omicron booster. Similarly, in humans, repeated Omicron infections could alleviate WT vaccination-induced immune imprinting and generate broad neutralization responses in both plasma and nasal mucosa. Notably, deep mutational scanning-based epitope characterization of 781 receptor-binding domain (RBD)-targeting monoclonal antibodies isolated from repeated Omicron infection revealed that double Omicron exposure could induce a large proportion of matured Omicron-specific antibodies that have distinct RBD epitopes to WT-induced antibodies. Consequently, immune imprinting was largely mitigated, and the bias towards non-neutralizing epitopes observed in single Omicron exposures was restored. On the basis of the deep mutational scanning profiles, we identified evolution hotspots of XBB.1.5 RBD and demonstrated that these mutations could further boost the immune-evasion capability of XBB.1.5 while maintaining high ACE2-binding affinity. Our findings suggest that the WT component should be abandoned when updating COVID-19 vaccines, and individuals without prior Omicron exposure should receive two updated vaccine boosters. Exposure to early variants of SARS-CoV-2 results in immune imprinting in mouse models and in humans, reducing neutralizing antibody titres against Omicron variants, which could be mitigated with multiple updated boosters.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron sublineages BA.2.12.1, BA.4 and BA.5 exhibit higher transmissibility than the BA.2 lineage 1 . The receptor binding and immune-evasion capability of these recently emerged variants require immediate investigation. Here, coupled with structural comparisons of the spike proteins, we show that BA.2.12.1, BA.4 and BA.5 (BA.4 and BA.5 are hereafter referred collectively to as BA.4/BA.5) exhibit similar binding affinities to BA.2 for the angiotensin-converting enzyme 2 (ACE2) receptor. Of note, BA.2.12.1 and BA.4/BA.5 display increased evasion of neutralizing antibodies compared with BA.2 against plasma from triple-vaccinated individuals or from individuals who developed a BA.1 infection after vaccination. To delineate the underlying antibody-evasion mechanism, we determined the escape mutation profiles 2 , epitope distribution 3 and Omicron-neutralization efficiency of 1,640 neutralizing antibodies directed against the receptor-binding domain of the viral spike protein, including 614 antibodies isolated from people who had recovered from BA.1 infection. BA.1 infection after vaccination predominantly recalls humoral immune memory directed against ancestral (hereafter referred to as wild-type (WT)) SARS-CoV-2 spike protein. The resulting elicited antibodies could neutralize both WT SARS-CoV-2 and BA.1 and are enriched on epitopes on spike that do not bind ACE2. However, most of these cross-reactive neutralizing antibodies are evaded by spike mutants L452Q, L452R and F486V. BA.1 infection can also induce new clones of BA.1-specific antibodies that potently neutralize BA.1. Nevertheless, these neutralizing antibodies are largely evaded by BA.2 and BA.4/BA.5 owing to D405N and F486V mutations, and react weakly to pre-Omicron variants, exhibiting narrow neutralization breadths. The therapeutic neutralizing antibodies bebtelovimab 4 and cilgavimab 5 can effectively neutralize BA.2.12.1 and BA.4/BA.5, whereas the S371F, D405N and R408S mutations undermine most broadly sarbecovirus-neutralizing antibodies. Together, our results indicate that Omicron may evolve mutations to evade the humoral immunity elicited by BA.1 infection, suggesting that BA.1-derived vaccine boosters may not achieve broad-spectrum protection against new Omicron variants. Biochemical and structural studies of the interactions between antibodies and spike proteins from SARS-CoV-2 Omicron subvariants indicate how these variants have evolved to escape antibody-mediated neutralization.

Background Disability prior to death complicates end-of-life care. The present study aimed to explore the prior-to-death disability profiles of Chinese older adults, the profiles’ links to end-of-life care arrangements and place of death, and predictors of the profiles. Methods In total, data were extracted from the records of 10,529 deceased individuals from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Latent profile analyses, bivariate analysis, and multivariate logistic regression were applied to identify prior-to-death disability profiles, explore the profiles’ links to end-of-life care arrangements and place of death, and examine predictors in the profiles, respectively. Results Three prior-to-death disability profiles, namely, Disabled-Incontinent (37.6%), Disabled-Continent (34.6%), and Independent (27.8%), were identified. Those with the Independent profile were more likely to live alone or with a spouse and receive no care or care only from the spouse before death. Disabled-Continent older adults had a higher chance of dying at home. Being female, not “married and living with a spouse”, suffering from hypertension, diabetes, stroke or cerebrovascular disease (CVD), bronchitis/emphysema/pneumonia, cancer, or dementia, and dying in a later year were associated with more severe prior-to-death disability patterns. Not having public old-age insurance predicted lower chances of having a Disabled-Incontinent profile, and advanced age increased the chance of having a Disabled-Continent profile. Conclusions Three prior-to-death disability patterns were identified for Chinese adults aged 65 years and older. These profiles were significantly linked with the end-of-life caregiving arrangements and place of death among older adults. Both demographic information and health status predicted prior-to-death disability profiles.

Purpose The links between obesity and dementia remain equivocal. Therefore, this study aimed to explore the association between weight-adjusted waist index (WWI), a new anthropometric indicator reflecting obesity, and dementia in the Chinese population with hypertension. Methods A total of 10,289 participants with hypertension were enrolled in this cross-sectional study, a subset of the China H-type hypertension registry study. WWI was calculated as waist circumference (WC) divided by the square root of bodyweight. Chinese adapted MMSE (CAMSE) scale was performed to evaluate the cognitive function. According to educational background, different MMSE cut-off values were applied to define dementia: < 24 for participants with ≥ 7 years of education, < 20 for those with 1–6 years of education, and < 17 for illiterate participants. Multivariable linear regression and multivariable binary logistic regression analyses were conducted to assess the associations between WWI and MMSE and dementia, respectively. Results Overall, the mean age was 63.7 ± 9.7 years, and 49.0% were males. Multivariate linear regression analyses showed that WWI was negatively associated with MMSE ( β , -1.09; 95% confidence interval [CI]: -1.24, -0.94). Consistently, multivariable binary logistic regression analyses found a positive association between WWI and the risk of dementia (odds ratio [OR], 1.45; 95% CI: 1.35, 1.56). Compared with individuals in quartile 1 of WWI, the adjusted β and OR values of WWI for MMSE and dementia were -2.28 (95% CI: -2.62, -1.94) and 2.12 (95% CI: 1.81, 2.48), respectively. Results of smoothing curve fitting confirmed the linear association between WWI and MMSE and dementia. Subgroup analysis showed a stronger association between WWI and dementia in participants with hypertension with midday napping. Conclusion WWI was independently and positively associated with dementia among the population with hypertension, especially in those with midday napping. The data suggests that WWI may serve as a simple and effective tool for the assessment of the risk of dementia in clinical practice.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) XBB lineages have achieved dominance worldwide and keep on evolving. Convergent evolution of XBB lineages on the receptor-binding domain (RBD) L455F and F456L is observed, resulting in variants with substantial growth advantages, such as EG.5, FL.1.5.1, XBB.1.5.70, and HK.3. Here, we show that neutralizing antibody (NAb) evasion drives the convergent evolution of F456L, while the epistatic shift caused by F456L enables the subsequent convergence of L455F through ACE2 binding enhancement and further immune evasion. L455F and F456L evade RBD-targeting Class 1 public NAbs, reducing the neutralization efficacy of XBB breakthrough infection (BTI) and reinfection convalescent plasma. Importantly, L455F single substitution significantly dampens receptor binding; however, the combination of L455F and F456L forms an adjacent residue flipping, which leads to enhanced NAbs resistance and ACE2 binding affinity. The perturbed receptor-binding mode leads to the exceptional ACE2 binding and NAb evasion, as revealed by structural analyses. Our results indicate the evolution flexibility contributed by epistasis cannot be underestimated, and the evolution potential of SARS-CoV-2 RBD remains high.

This study explored the relationship between self-efficacy, professional identity and competence among nursing students and analyzed the mediating role of self-efficacy in the relationship between professional identity and competence. Background: Increasing attention has been paid to the cultivation of competence among nursing students; however, few studies to date have analyzed its related factors and examined their relationship. A quantitative study with a descriptive design was performed in this study, adopting an online survey with convenience and snowball sampling. A cross-sectional sample of 887 nursing students in the internship period of their education program in mainland China was recruited from November to December 2020. The Nursing Students Competence Instrument, Professional Identity Questionnaire for Nurse Students and General Self-efficacy Scale were distributed online. Descriptive statistics, Pearson’s correlation, structural equation modeling (SEM) and the bootstrap method were employed in data analysis. Competence was significantly and positively correlated with professional identity (r = 0.598; P < 0.01) and self-efficacy (r = 0.692; P < 0.01). SEM analysis revealed that professional identity (β = 0.31; P < 0.01) or self-efficacy (β = 0.31; P < 0.01) could have a positive impact on competence. Meanwhile, self-efficacy played a mediating role in the relationship between professional identity and competence, with an indirect effect of professional identity creation through self-efficacy accounting for 52% of the total effect. Self-efficacy mediates the relationship between professional identity and competence to some extent. School educators and clinical tutors should pay greater attention to students’ professional identity and self-efficacy to improve students’ competence.

Closed-loop (CL) transcutaneous auricular vagal nerve stimulation (taVNS) was officially proposed in 2020. This work firstly reviewed two existing CL-taVNS forms: motor-activated auricular vagus nerve stimulation (MAAVNS) and respiratory-gated auricular vagal afferent nerve stimulation (RAVANS), and then proposed three future CL-taVNS systems: electroencephalography (EEG)-gated CL-taVNS, electrocardiography (ECG)-gated CL-taVNS, and subcutaneous humoral signals (SHS)-gated CL-taVNS. We also highlighted the mechanisms, targets, technical issues, and patterns of CL-taVNS. By reviewing, proposing, and highlighting, this work might draw a preliminary blueprint for the development of CL-taVNS.

Deep vein thrombosis (DVT) is a common complication in patients with lower extremity fractures. Once it occurs, it will seriously affect the quality of life and postoperative recovery of patients. Therefore, early prediction and prevention of DVT can effectively improve the prognosis of patients. This study constructed different machine learning models to explore their effectiveness in predicting DVT. Five prediction models were applied to the study, including Extreme Gradient Boosting (XGBoost) model, Logistic Regression (LR) model, RandomForest (RF) model, Multilayer Perceptron (MLP) model, and Support Vector Machine (SVM) model. Afterwards, the performance of the obtained prediction models was evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, F1 score, and Kappa. The prediction performances of the models based on machine learning are as follows: XGBoost model (AUC = 0.979, accuracy = 0.931), LR model (AUC = 0.821, accuracy = 0.758), RF model (AUC = 0.970, accuracy = 0.921), MLP model (AUC = 0.830, accuracy = 0.756), SVM model (AUC = 0.713, accuracy = 0.661). On our data set, the XGBoost model has the best performance. However, the model still needs external verification research before clinical application.