Zinc-Embedded Polyamide Fabrics Inactivate SARS-CoV-2 and Influenza A Virus.

Influenza A viruses (IAV) and SARS-CoV-2 can spread via liquid droplets and aerosols. Face masks and other personal protective equipment (PPE) can act as barriers that prevent the spread of these viruses. However, IAV and SARS-CoV-2 are stable for hours on various materials, which makes frequent and correct disposal of these PPE important. Metal ions embedded into PPE may inactivate respiratory viruses, but confounding factors such as adsorption of viruses make measuring and optimizing the inactivation characteristics difficult. Here, we used polyamide 6.6 (PA66) fibers containing embedded zinc ions and systematically investigated if these fibers can adsorb and inactivate SARS-CoV-2 and IAV H1N1 when woven into a fabric. We found that our PA66-based fabric decreased the IAV H1N1 and SARS-CoV-2 titer by approximately 100-fold. Moreover, we found that the zinc content and the virus inactivating property of the fabric remained stable over 50 standardized washes. Overall, these results provide insights into the development of reusable PPE that offer protection against RNA virus spread

The FMR1 CGG repeat mouse displays ubiquitin-positive intranuclear neuronal inclusions; implications for the cerebellar tremor/ataxia syndrome

Recent studies have reported that alleles in the premutation range in the FMR1 gene in males result in increased FMR1 mRNA levels and at the same time mildly reduced FMR1 protein levels. Some elderly males with premutations exhibit an unique neurodegenerative syndrome characterized by progressive intention tremor and ataxia. We describe neurohistological, biochemical and molecular studies of the brains of mice with an expanded CGG repeat and report elevated Fmr1 mRNA levels and intranuclear inclusions with ubiquitin, Hsp40 and the 20S catalytic core complex of the proteasome as constituents. An increase was observed of both the number and the size of the inclusions during the course of life, which correlates with the progressive character of the cerebellar tremor/ataxia syndrome in humans. The observations in expanded-repeat mice support a direct role of the Fmr1 gene, by either CGG expansion per se or by mRNA level, in the formation of the inclusions and suggest a correlation between the presence of intranuclear inclusions in distinct regions of the brain and the clinical features in symptomatic premutation carriers. This mouse model will facilitate the possibilities to perform studies at the molecular level from onset of symptoms until the final stage of the disease

Predicting need for hospital-specific interventional care after surgery using electronic health record data

BACKGROUND: A significant proportion of surgical inpatients is often admitted longer than necessary. Early identification of patients who do not need care that is strictly provided within hospitals would allow timely discharge of patients to a postoperative nursing home for further recovery. We aimed to develop a model to predict whether a patient needs hospital-specific interventional care beyond the second postoperative day. METHODS: This study included all adult patients discharged from surgical care in the surgical oncology department from June 2017 to February 2020. The primary outcome was to predict whether a patient still needs hospital-specific interventional care beyond the second postoperative day. Hospital-specific care was defined as unplanned reoperations, radiological interventions, and intravenous antibiotics administration. Different analytical methods were compared with respect to the area under the receiver-operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS: Each model was trained on 1,174 episodes. In total, 847 (50.5%) patients required an intervention during postoperative admission. A random forest model performed best with an area under the receiver-operating characteristics curve of 0.88 (95% confidence interval 0.83-0.93), sensitivity of 79.1% (95% confidence interval 0.67-0.92), specificity of 80.0% (0.73-0.87), positive predictive value of 57.6% (0.45-0.70) and negative predictive value of 91.7% (0.87-0.97). CONCLUSION: This proof-of-concept study found that a random forest model could successfully predict whether a patient could be safely discharged to a nursing home and does not need hospital care anymore. Such a model could aid hospitals in addressing capacity challenges and improve patient flow, allowing for timely surgical care

Optimizing discharge after major surgery using an artificial intelligence–based decision support tool (DESIRE): An external validation study

Background: In the DESIRE study (Discharge aftEr Surgery usIng aRtificial intElligence), we have previously developed and validated a machine learning concept in 1,677 gastrointestinal and oncology surgery patients that can predict safe hospital discharge after the second postoperative day. Despite strong model performance (area under the receiver operating characteristics curve of 0.88) in an academic surgical population, it remains unknown whether these findings can be translated to other hospitals and surgical populations. We therefore aimed to determine the generalizability of the previously developed machine learning concept. Methods: We externally validated the machine learning concept in gastrointestinal and oncology surgery patients admitted to 3 nonacademic hospitals in The Netherlands between January 2017 and June 2021, who remained admitted 2 days after surgery. Primary outcome was the ability to predict hospital interventions after the second postoperative day, which were defined as unplanned reoperations, radiological interventions, and/or intravenous antibiotics administration. Four forest models were locally trained and evaluated with respect to area under the receiver operating characteristics curve, sensitivity, specificity, positive predictive value, and negative predictive value. Results: All models were trained on 1,693 epsiodes, of which 731 (29.9%) required a hospital intervention and demonstrated strong performance (area under the receiver operating characteristics curve only varied 4%). The best model achieved an area under the receiver operating characteristics curve of 0.83 (95% confidence interval [0.81–0.85]), sensitivity of 77.9% (0.67–0.87), specificity of 79.2% (0.72–0.85), positive predictive value of 61.6% (0.54–0.69), and negative predictive value of 89.3% (0.85–0.93). Conclusion: This study showed that a previously developed machine learning concept can predict safe discharge in different surgical populations and hospital settings (academic versus nonacademic) by training a model on local patient data. Given its high accuracy, integration of the machine learning concept into the clinical workflow could expedite surgical discharge and aid hospitals in addressing capacity challenges by reducing avoidable bed-days

Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter

Objective Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians' understanding and to promote quality of medical AI research. Methods We summarised key elements (such as current guidelines, challenges, regulatory documents and good practices) that are needed to develop and safely implement AI in medicine. Conclusion This overview complements other frameworks in a way that it is accessible to stakeholders without prior AI knowledge and as such provides a step-by-step approach incorporating all the key elements and current guidelines that are essential for implementation, and can thereby help to move AI from bytes to bedside