Evaluation of physical activity before and after respiratory rehabilitation in normal weight individuals with asthma: a feasibility study

BackgroundIndividuals with asthma spend less time engaging in physical activity compared to the general population. Increasing physical activity has become a patient-centered goal for the treatment of treatable traits of individuals with asthma. There are data showing the possible effects of a pulmonary rehabilitation program on physical activity in obese individuals with asthma but not in normal-weight asthmatics. The objective of this feasibility study is to estimate the number of daily steps and time spent on activity in normal-weight individuals with asthma, measured before and after a pulmonary rehabilitation program.MethodsNormal-weight individuals with moderate to severe asthma were evaluated. The individuals measured their daily steps with an accelerometer for 5 days before and after a pulmonary rehabilitation program. The study was registered on ClinicalTrials.gov: NCT05486689.ResultsIn total, 17 participants were enrolled; one dropout and data on the time in activity of two individuals are missing due to a software error during the download. Data from 16 patients were analyzed. The median number of steps/day at baseline was 5,578 (25th, 75th percentiles = 4,874, 9,685) while the median activity time was 214 min (25th, 75th percentiles = 165, 239). After the rehabilitation program, the number of daily steps increased by a median value of 472 (p-value = 0.561) and the time in activity reduced by 17 min (p-value = 0.357). We also found a significant difference in quality of life, muscle strength, and exercise capacity.ConclusionsThe results of this study make it possible to calculate the sample size of future studies whose main outcome is daily steps in normal-weight individuals with asthma. The difficulties encountered in downloading time in activity data do not allow the same for this outcome.Clinical Trial RegistrationClinicalTrials.gov, identifier NCT05486689

Roadmap on printable electronic materials for next-generation sensors

The dissemination of sensors is key to realizing a sustainable, ‘intelligent’ world, where everyday objects and environments are equipped with sensing capabilities to advance the sustainability and quality of our lives—e.g., via smart homes, smart cities, smart healthcare, smart logistics, Industry 4.0, and precision agriculture. The realization of the full potential of these applications critically depends on the availability of easy-to-make, low-cost sensor technologies. Sensors based on printable electronic materials offer the ideal platform: they can be fabricated through simple methods (e.g., printing and coating) and are compatible with high-throughput roll-to-roll processing. Moreover, printable electronic materials often allow the fabrication of sensors on flexible/stretchable/biodegradable substrates, thereby enabling the deployment of sensors in unconventional settings. Fulfilling the promise of printable electronic materials for sensing will require materials and device innovations to enhance their ability to transduce external stimuli—light, ionizing radiation, pressure, strain, force, temperature, gas, vapours, humidity, and other chemical and biological analytes. This Roadmap brings together the viewpoints of experts in various printable sensing materials—and devices thereof—to provide insights into the status and outlook of the field. Alongside recent materials and device innovations, the roadmap discusses the key outstanding challenges pertaining to each printable sensing technology. Finally, the Roadmap points to promising directions to overcome these challenges and thus enable ubiquitous sensing for a sustainable, ‘intelligent’ world

From unstructured data and word vectorization to meaning: text mining in insurance

By exploiting Natural Language Processing techniques we aim at grasping latent information useful for insurance to tune policy premiums. By using a large set of police reports, we classify medical and police reports based upon the profile of the people involved and according to the relevance of their content. At a second step, we match these risks with the customer profiles of a company in order to add new and relevant risk covariates to improve the precision and the determination of policy premiums

[Infection-related glomerulonephritis: the new face of an old disease]

In the last decades there have been important changes in the epidemiology and natural history of bacterial infection-related glomerulonephritides. Once defined as an infancy-onset acute nephritic syndrome following a streptococcal infection, and characterized by a relative benign course, infection-related glomerulonephritis nowadays also affects the adult population, particularly the elderly and the chronically ill. The infectious agents and infection sites have become more diversified, and the prognosis is burdened by a higher rate of mortality, chronic kidney disease, end-stage renal disease and acute overload complications. In this review we highlight the main clinical features of infection-related glomerulonephritis, offering an insight into its pathogenesis and the elements that allow an appropriate differential diagnosis. We also address the uncertainties around the role of immunosuppression in its therapeutic management

Text mining in insurance: from unstructured data to meaning

Every day insurance companies collect an enormous quantity of text data from multiple sources. By exploiting Natural Language Processing, we present a strategy to make beneficial use of the large information available in documents. After a brief review of the basics of text mining, we describe a case study where, by analyzing the accident narratives written by the researchers of the National Highway Traffic Safety Administration (NHTSA) of the U. S. Department of Transportation, we aim at grasping latent information useful to fine-tune policy premiums. The process is based on two steps. First, we classify the reports according to the relevance of their content to find the risk profile of the people involved. Next we use these profiles to add new latent risk covariates for the ratemaking process of the customers of a company

Text Mining in Insurance: From Unstructured Data to Meaning

Every day, insurance companies collect an enormous quantity of text data from multiple sources. We present a strategy to make beneficial use of the large amount of information available in documents by exploiting natural language processing. After a brief review of the basics of text mining, we describe a case study in which, by analyzing the accident narratives written by the researchers of the National Highway Traffic Safety Administration of the U.S. Department of Transportation, we aim to extract latent information that can be used to fine-tune policy premiums. The process involves two steps. First, we classify the reports according to the relevance of their content to determine the risk profiles of the people involved. Next, we use these profiles to create new latent risk covariates for a company’s ratemaking process

COVID-19 outcomes in patients with a history of immune-mediated glomerular diseases

Peer reviewed: TrueIntroductionPatients with immune-mediated glomerular diseases are considered at high risk for severe COVID-19 outcomes. However, conclusive evidence for this patient population is scarce.MethodsWe created a global registry and retrospectively collected clinical data of patients with COVID-19 and a previously diagnosed immune-mediated glomerular disease to characterize specific risk factors for severe COVID-19 outcomes.ResultsFifty-nine patients with a history of immune-mediated glomerular diseases were diagnosed with COVID-19 between 01.03.2020 and 31.08.2021. Over a mean follow-up period of 24.79 ± 18.89 days, ten patients (16.9%) developed acute kidney injury. Overall, 44.1% of patients were managed in an outpatient setting and therefore considered as having “non-severe” COVID-19, while 55.9% of patients had severe COVID-19 requiring hospitalization including worse outcomes. Comparing both groups, patients with severe COVID-19 were significantly older (53.55 ± 17.91 versus 39.77 ± 14.95 years, p = .003), had lower serum albumin levels at presentation (3.00 ± 0.80 g/dL versus 3.99 ± 0.68 g/dL, p = .016) and had a higher risk of developing acute kidney injury (27% versus 4%, p = .018). Male sex (p &amp;lt;.001) and ongoing intake of corticosteroids at presentation (p = .047) were also significantly associated with severe COVID-19 outcomes, while the overall use of ongoing immunosuppressive agents and glomerular disease remission status showed no significant association with the severity of COVID-19 (p = .430 and p = .326, respectively).ConclusionOlder age, male sex, ongoing intake of corticosteroids and lower serum albumin levels at presentation were identified as risk factors for severe COVID-19 outcomes in patients with a history of various immune-mediated glomerular diseases.</jats:sec