| 1. |
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
- Vujasinovic, M, et al.
(author)
-
Risk of Developing Pancreatic Cancer in Patients with Chronic Pancreatitis
- 2020
-
In: Journal of clinical medicine. - : MDPI AG. - 2077-0383. ; 9:11
-
Journal article (peer-reviewed)abstract
- Background: Patients with chronic pancreatitis (CP) have an increased risk of developing pancreatic ductal adenocarcinoma (PDAC). We present data on PDAC in one of the most extensive European single-centre cohort studies of patients with CP. Methods: Retrospective analysis of prospectively collected data of patients with CP was performed. Aetiology of CP was determined according to the M-ANNHEIM classification system and only patients with definite CP > 18 years at data analysis were included. The final dataset included 581 patients with definite CP diagnosed between 2003 and 2018. Results: At CP diagnosis, there were 371 (63.9%) males and 210 (36.1%) females (median age 57 years, range 2–86). During 3423 person-years of observation, six pancreatic cancers were diagnosed (0.2% year). The mean time between diagnosis of CP and the occurrence of PDAC was 5.0 years (range 2.7–8.6). None of the cancer patients had a family history of PDAC. Diabetes mellitus (DM) was present in five of six (83.3%) patients with PDAC: in three patients before and in two after CP diagnosis. Clinical/laboratory signs of pancreatic exocrine insufficiency (PEI) were present in five of six (83.3%) patients with PDAC: in two at diagnosis of CP and in three after diagnosis. The mean survival time was 4 months after the diagnosis of PDAC (range 0.5–13). PDAC occurred significantly more often (p < 0.001) in two groups of patients without previous acute pancreatitis (AP): 2 of 20 patients (10%) with low body mass index (BMI) and PEI and in 3 of 10 (30%) patients with high BMI and DM at diagnosis of CP. Conclusions: Patients with CP have a high risk of developing PDAC, although risk is low in absolute terms. Our data suggest the possibility of defining subgroups of patients with a particularly elevated risk of PDAC. Such a possibility would open a path to personalised decision making on initiation of PDAC surveillance of patients with no previous episode of AP, (i) with low BMI and PEI, or (ii) elevated BMI and DM.
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
- Luo, Yifei, et al.
(author)
-
Technology Roadmap for Flexible Sensors
- 2023
-
In: ACS Nano. - : American Chemical Society. - 1936-0851 .- 1936-086X. ; 17:6, s. 5211-5295
-
Research review (peer-reviewed)abstract
- Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.
|
|
| 9. |
- Mir-Coll, J, et al.
(author)
-
Human Islet Microtissues as an In Vitro and an In Vivo Model System for Diabetes
- 2021
-
In: International journal of molecular sciences. - : MDPI AG. - 1422-0067. ; 22:4
-
Journal article (peer-reviewed)abstract
- Loss of pancreatic β-cell function is a critical event in the pathophysiology of type 2 diabetes. However, studies of its underlying mechanisms as well as the discovery of novel targets and therapies have been hindered due to limitations in available experimental models. In this study we exploited the stable viability and function of standardized human islet microtissues to develop a disease-relevant, scalable, and reproducible model of β-cell dysfunction by exposing them to long-term glucotoxicity and glucolipotoxicity. Moreover, by establishing a method for highly-efficient and homogeneous viral transduction, we were able to monitor the loss of functional β-cell mass in vivo by transplanting reporter human islet microtissues into the anterior chamber of the eye of immune-deficient mice exposed to a diabetogenic diet for 12 weeks. This newly developed in vitro model as well as the described in vivo methodology represent a new set of tools that will facilitate the study of β-cell failure in type 2 diabetes and would accelerate the discovery of novel therapeutic agents.
|
|
| 10. |
- Rao, Akhila, et al.
(author)
-
Prediction and exposure of delays from a base station perspective in 5G and beyond networks
- 2022
-
In: 5G-MeMU 2022 - Proceedings of the ACM SIGCOMM 2022 Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases - Part of SIGCOMM 2022. - New York, NY, USA : Association for Computing Machinery, Inc. - 9781450393935 ; , s. 8-14
-
Conference paper (peer-reviewed)abstract
- The inherent flexibility of 5G networks come with a high degree of configuration and management complexity. This makes the performance outcome for UEs, more than ever, dependent on intricate configurations and interplay between algorithms at various network components. In this paper, we take initial steps towards a performance exposure system at the base station using a data-driven approach for predicting performance violations in terms of RTT, as observed by the UE, in a 5G mmWave network. We present ML models to predict RTT using low-level and high-frequency base station metrics from a 5G mmWave testbed based on commercially available equipment. Predicting UE performance from a base station perspective, and exposing this knowledge, is valuable for applications to proactively address performance violations. We also compare several methods for feature reduction, which have a significant impact on monitoring load. We demonstrate our model's ability to identify RTT violations, paving the way for network providers towards an intelligent performance exposure system.
|
|
