Background: Resilience is defined as the capacity to cope successfully with change or adversity. The aims of our study were to investigate levels of resilience in Italian healthcare professionals (HCPs) during the Coronavirus disease 2019 (COVID-19) pandemic and to identify potential predictors of resilience.
Methods: We performed a web-based survey of HCPs ( 1009) working in Italian hospitals during the COVID-19 pandemic. The survey contained a 14-item resilience scale (RS14) and questionnaires to evaluate depression and anxiety symptoms. Non-HCP individuals ( 375) from the general population were used for comparison.
Results: HCPs showed significantly lower resilience compared to the control group ( = 0.001). No significant differences were observed after stratification for geographical area, work setting, role, or suspected/confirmed diagnosis of COVID-19. In a linear regression analysis, RS14 was inversely correlated with depression (R = 0.227, < 0.001) and anxiety (R = 0.117, < 0.001) and directly correlated with age (R = 0.012, < 0.001) but not with body mass index (BMI, R = 0.002, = 0.213). In male HCPs, higher depression score (odds ratio (OR) 1.147, < 0.001) or BMI (OR 1.136, = 0.011) significantly predicted having low resilience. In female HCPs, higher depression score (OR 1.111, < 0.0001) and working in a COVID-19 free setting (OR 2.308, = 0.002) significantly predicted having low resilience. HCPs satisfied with personal protective equipment had higher levels of resilience ( < 0.010).
Conclusions: Our findings suggest that resilience was lower in Italian HCPs than in the general population after the first COVID-19 wave. Specific factors can be identified, and targeted interventions may have an important role to foster resilience of HCPs.
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http://dx.doi.org/10.3390/bs10120183 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples.
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UK Health Security Agency, London, United Kingdom.
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View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Psychiatry, Yongin Severance Hospital, Yongin, Republic of Korea.
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View Article and Find Full Text PDFACS Nano
January 2025
NOVA Medical School|Faculdade de Ciências Médicas, NMS|FCM, Universidade NOVA de Lisboa, Lisbon 1169-056, Portugal.
The "" under this Perspective underline the importance of interdisciplinary collaboration and partnerships across several disciplines, such as medical science and technology, medicine, bioengineering, and computational approaches, in bridging the gap between research, manufacturing, and clinical applications. Effective communication is key to bridging team gaps, enhancing trust, and resolving conflicts, thereby fostering teamwork and individual growth toward shared goals. Drawing from the success of the COVID-19 vaccine development, we advocate the application of similar collaborative models in other complex health areas such as nanomedicine and biomedical engineering.
View Article and Find Full Text PDFPLoS One
January 2025
School of Business, Anyang Normal University, Anyang, China.
The process of regional economic development is marked by a sustained exposure to external disturbances. In today's unpredictable and tumultuous global environment, such disturbances have become increasingly common, underlining the need to advance a region's economic resilience and foster adaptive mechanisms to handle environmental flux. Comparing the typical provinces in eastern, central, western and northeastern regions during the COVID-19 epidemic period, it found that the economic resilience performance of Henan Province, which is a representative of the central region, has the following characteristics.
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