A qualitative study on the impacts of COVID-19 pandemic on the Austrian agriculture

Summary: Measures taken to contain the COVID-19 pandemic in addition to changes in consumer attitudes and behavior have impacted food supply chains globally. In order to examine the effects of COVID-19 on Austrian agriculture, interviews with 34 farmers were conducted in early 2021. The results of a qualitative content analysis show that the diverse and heterogeneous impacts of COVID-19 on farms create opportunities as well as challenges. Negative impacts include sales difficulties due to the closure of restaurants and markets, the ban on non-agricultural activities, the disrupted availability of workers and agricultural inputs, and burdens on farmers. Mitigation of negative effects through political measures and interest groups showed limited effectiveness. Nevertheless, the functionality of agricultural production was maintained as direct impacts were minimal and adjustments adequate. Positive effects result from the increased demand in direct marketing and food retailing, the improved perception of agriculture in society and advantages in the farmers' private lives. Long-term changes of farms are mainly driven by digitization, investments and adjustments concerning marketing. © 2022 Henriette Elisabeth Quehl et al., published by Sciendo.

Being a Farmer in Austria during COVID-19-A Qualitative Study on Challenges and Opportunities

To assess the effects of COVID-19 on Austrian farmers, a qualitative study design including computer-aided, qualitative content analysis was applied. Interviews with 34 Austrian farmers covering a broad spectrum of the sector were conducted to identify the impacts, obstacles, and opportunities during the first year of the COVID-19 pandemic. The results show that the diverse and heterogeneous impacts of COVID-19 on farms created significant challenges but also opportunities. Negative impacts included sales difficulties due to the closure of the hospitality industry and farmers markets, the ban on non-agricultural activities, the disrupted availability of workers and agricultural inputs, and the related additional workload. Mitigation of negative effects through political measures and interest groups showed limited effectiveness. Nevertheless, the functionality of the agricultural sector was maintained. During the initial phase of the pandemic, direct impacts on agricultural production were minimal and adjustments were adequate. There were also positive effects on parts of the agricultural sector, such as a boost in direct sales, increased demand in the food retail sector, and improved public perception of agriculture and farmers. Long-term changes in farms have been triggered and/or reinforced by the COVID-19 pandemic, mainly driven by digitization, investments in product and/or process innovations, and adjustments concerning marketing.

Protocol for a randomized controlled multicenter trial assessing the efficacy of leuprorelin for severe polycystic liver disease: the AGAINST-PLD study.

BACKGROUND: In patients with severe polycystic liver disease (PLD), there is a need for new treatments. Estrogens and possibly other female sex hormones stimulate growth in PLD. In some patients, liver volume decreases after menopause. Female sex hormones could therefore be a target for therapy. The AGAINST-PLD study will examine the efficacy of the GnRH agonist leuprorelin, which blocks the production of estrogen and other sex hormones, to reduce liver growth in PLD. METHODS: The AGAINST-PLD study is an investigator-driven, multicenter, randomized controlled trial. Institutional review board (IRB) approval was received at the University Medical Center of Groningen and will be collected in other sites before opening these sites. Thirty-six female, pre-menopausal patients, with a very large liver volume for age (upper 10% of the PLD population) and ongoing liver growth despite current treatment options will be randomized to direct start of leuprorelin or to 18 months standard of care and delayed start of leuprorelin. Leuprorelin is given as 3.75 mg subcutaneously (s.c.) monthly for the first 3 months followed by 3-monthly depots of 11.25 mg s.c. The trial duration is 36 months. MRI scans to measure liver volume will be performed at screening, 6 months, 18 months, 24 months and 36 months. In addition, blood will be drawn, DEXA-scans will be performed and questionnaires will be collected. This design enables comparison between patients on study treatment and standard of care (first 18 months) and within patients before and during treatment (whole trial). Main outcome is annualized liver growth rate compared between standard of care and study treatment. Secondary outcomes are PLD disease severity, change in liver growth within individuals and (serious) adverse events. The study is designed as a prospective open-label study with blinded endpoint assessment (PROBE). DISCUSSION: In this trial, we combined the expertise of hepatologist, nephrologists and gynecologists to study the effect of leuprorelin on liver growth in PLD. In this way, we hope to stop liver growth, reduce symptoms and reduce the need for liver transplantation in severe PLD. Trial registration Eudra CT number 2020-005949-16, registered at 15 Dec 2020. https://www.clinicaltrialsregister.eu/ctr-search/search?query=2020-005949-16 .

On the Mathematics of Social Distancing

Early in the COVID-19 pandemic, when spring began to make itself felt, photos showed New Yorkers enjoying the outdoors, while properly socially distanced, by sitting on the grass in a square lattice of circles. But the planners should have consulted a mathematician for the design, because significantly more people (over 15% more) could enjoy the same area safely if the circles were closer packed into a hexagonal lattice.

2AI&7D Model of Resistomics to Counter the Accelerating Antibiotic Resistance and the Medical Climate Crisis

The antimicrobial resistance (AMR) crisis is referred to as ‘Medical Climate Crisis’. Inappropriate use of antimicrobial drugs is driving the resistance evolution in pathogenic microorganisms. In 2014 it was estimated that by 2050 more people will die due to antimicrobial resistance compared to cancer. It will cause a reduction of 2% to 3.5% in Gross Domestic Product (GDP) and cost the world up to 100 trillion USD. The indiscriminate use of antibiotics for COVID-19 patients has accelerated the resistance rate. COVID-19 reduced the window of opportunity for the fight against AMR. This man-made crisis can only be averted through accurate actionable antibiotic knowledge, usage, and a knowledge driven Resistomics. In this paper, we present the 2AI (Artificial Intelligence and Augmented Intelligence) and 7D (right Diagnosis, right Disease-causing-agent, right Drug, right Dose, right Duration, right Documentation, and De-escalation) model of antibiotic stewardship. The resistance related integrated knowledge of resistomics is stored as a knowledge graph in a Neo4j properties graph database for 24 × 7 access. This actionable knowledge is made available through smartphones and the Web as a Progressive Web Applications (PWA). The 2AI&7D Model delivers the right knowledge at the right time to the specialists and non-specialist alike at the point-of-action (Stewardship committee, Smart Clinic, and Smart Hospital) and then delivers the actionable accurate knowledge to the healthcare provider at the point-of-care in realtime. © 2021, Springer Nature Switzerland AG.

AIoT: AI meets IoT and Web in Smart Healthcare

Extending quality healthcare to all communities is an important goal for sustainable societies and economies. Even in the most sophisticated healthcare systems the doctors tend to spend less time and are under increasing workload and time pressure. Telemedicine, AI, Web technologies, and Big Data can play an important role to increase efficiency in diagnosis and improve the quality of care. Telemedicine over Web is becoming increasingly popular and the COVID-19 pandemic has accelerated this trend. In this paper we present a novel smartphone-based care solution that uses progressive web apps (PWA) to capture patient data, integrates this data with a diverse set of medical knowledge sources and deploys AI to support differential diagnosis and patient stratification. The system can suggest action and treatment plans and has been designed with special consideration to cyber security. The smart care system is based on technologies of the next generation web like PWA, WebBluetooth, Web Speech API, WebUSB, and WebRTC and integrates well into the concept of a smart hospital. © 2021 ACM.