| 1. |
- Berglund, Erik, et al.
(författare)
-
Clinical Significance of Alloantibodies in Hand Transplantation : A Multicenter Study
- 2019
-
Ingår i: Transplantation. - : LIPPINCOTT WILLIAMS & WILKINS. - 0041-1337 .- 1534-6080. ; 103:10, s. 2173-2182
-
Tidskriftsartikel (refereegranskat)abstract
- Background. Donor-specific antibodies (DSAs) have a strong negative correlation with long-term survival in solid organ transplantation. Although the clinical significance of DSA and antibody-mediated rejection (AMR) in upper extremity transplantation (UET) remains to be established, a growing number of single-center reports indicate their presence and potential clinical impact. Methods. We present a multicenter study assessing the occurrence and significance of alloantibodies in UET in reference to immunological parameters and functional outcome. Results. Our study revealed a high prevalence and early development of de novo DSA and non-DSA (43%, the majority detected within the first 3 postoperative y). HLA class II mismatch correlated with antibody development, which in turn significantly correlated with the incidence of acute cellular rejection. Cellular rejections preceded antibody development in almost all cases. A strong correlation between DSA and graft survival or function cannot be statistically established at this early stage but a correlation with a lesser outcome seems to emerge. Conclusions. While the phenotype and true clinical effect of AMR remain to be better defined, the high prevalence of DSA and the correlation with acute rejection highlight the need for optimizing immunosuppression, close monitoring, and the relevance of an HLA class II match in UET recipients.
|
|
| 2. |
- Demir, Ahmet U., et al.
(författare)
-
Prevalence of sleep disorders in the Turkish adult population epidemiology of sleep study
- 2015
-
Ingår i: Sleep and Biological Rhythms. - : Springer Science and Business Media LLC. - 1446-9235 .- 1479-8425. ; 13, s. 298-308
-
Tidskriftsartikel (refereegranskat)abstract
- © 2015 Japanese Society of Sleep Research. Sleep disorders constitute an important public health problem. Prevalence of sleep disorders in Turkish adult population was investigated in a nationwide representative sample of 5021 Turkish adults (2598 women and 2423 men, response rate: 91%) by an interviewer-administered questionnaire. Insomnia was defined by the DSM-IV criteria, habitual snoring and risk for sleep-related breathing disorders (SDB) by the Berlin questionnaire, excessive daytime sleepiness (EDS) by the Epworth sleepiness scale score, and restless legs syndrome (RLS) by the complaints according to the International Restless Legs Syndrome Study Group criteria. Mean age of the participants was 40.7 ± 15.1 (range 18 to 90) years. Prevalence rates (men/women) were insomnia 15.3% (10.5%/20.2%; P < 0.001), high probability of SDB 13.7% (11.1%/20.2%; P < 0.001), EDS 5.4% (5.0%/5.7%; P: 0.09), RLS 5.2% (3.0%/7.3%; P < 0.001). Aging and female gender were associated with higher prevalence of sleep disorders except for habitual snoring. Prevalence rates of the sleep disorders among Turkish adults based on the widely used questionnaires were close to the lower end of the previous estimates reported from different parts of the world. These findings would help for the assessment of the health burden of sleep disorders and addressing the risk groups for planning and implementation of health care. Sleep and Biological Rhythms
|
|
| 3. |
- Ulker, Pinar, et al.
(författare)
-
LDOES ischemic preconditioning increase flap survival by ADORA2B receptor activation?
- 2020
-
Ingår i: Clinical hemorheology and microcirculation. - 1875-8622. ; 75:2, s. 151-162
-
Tidskriftsartikel (refereegranskat)abstract
- Ischemic preconditioning (IPC) is defined as raising tolerance to subsequent ischemic stress by exposing tissues to sub-lethal ischemia. Although many candidates have been suggested, recent studies have clearly demonstrated that adenosine-mediated ADORA2B receptor (ADORA2BR) activation is the main mechanism involved in IPC. While the tissue-protective role of this mechanism has been demonstrated in different ischemia/reperfusion (I/R) models, its role in flap surgery-derived I/R damage has not to date been investigated.To investigate the role of adenosine and ADORA2BR activation in IPC-mediated tissue protection in an epigastric flap model.Animals were divided into five main groups, all of which were then divided into two subgroups depending on whether or not they were exposed to IPC before the I/R procedure, which consisted of 6 hours of ischemia and 6 days of reperfusion. No drugs were administered in Group 1 (the control group). Animals in Group 2 were pretreated with CD73-inhibitor before IPC application or the ischemic period. Animals in Group 3 were pretreated with adenosine. Animals in Group 4 were pretreated with an ADORA2BR antagonist, and those in Group 5 with an ADORA2BR agonist. After 6 days of reperfusion, tissue survival was evaluated via histological and macroscopic analysis.IPC application significantly enhanced CD73 expressions and adenosine concentrations (p<0.01). Flap survivals were increased by IPC in Group 1 (p<0.05). However, CD73 inhibition blocked this increase (Group 2). In Group 3, adenosine improved flap survival even in the absence of IPC (p<0.01). While an ADORA2BR antagonist attenuated the tissue-protective effect of IPC (p<0.01), the ADORA2BR agonist improved flap survival by mimicking IPC in groups 4 and 5.These results provide pharmacological evidence for a contribution of CD73 enzyme-dependent adenosine generation and signaling through ADORA2BR to IPC-mediated tissue protection. They also suggest for the first time that ADORA2BR agonists may be used as a potential preventive therapy against I/R injury in flap surgeries.
|
|
| 4. |
|
|
| 5. |
- Ozkan-Okay, Merve, et al.
(författare)
-
A Comprehensive Survey : Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions
- 2024
-
Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 12, s. 12229-12256
-
Tidskriftsartikel (refereegranskat)abstract
- Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), has become essential in the realm of cybersecurity. These techniques have proven to be effective in detecting and mitigating cyberattacks, which can cause significant harm to individuals, organizations, and even countries. Machine learning algorithms use statistical methods to identify patterns and anomalies in large datasets, enabling security analysts to detect previously unknown threats. Deep learning, a subfield of ML, has shown great potential in improving the accuracy and efficiency of cybersecurity systems, particularly in image and speech recognition. On the other hand, RL is again a subfield of machine learning that trains algorithms to learn through trial and error, making it particularly effective in dynamic environments. We also evaluated the usage of ChatGPT-like AI tools in cyber-related problem domains on both sides, positive and negative. This article provides an overview of how ML, DL, and RL are applied in cybersecurity, including their usage in malware detection, intrusion detection, vulnerability assessment, and other areas. The paper also specifies several research questions to provide a more comprehensive framework to investigate the efficiency of AI and ML models in the cybersecurity domain. The state-of-the-art studies using ML, DL, and RL models are evaluated in each Section based on the main idea, techniques, and important findings. It also discusses these techniques' challenges and limitations, including data quality, interpretability, and adversarial attacks. Overall, the use of ML, DL, and RL in cybersecurity holds great promise for improving the effectiveness of security systems and enhancing our ability to protect against cyberattacks. Therefore, it is essential to continue developing and refining these techniques to address the ever-evolving nature of cyber threats. Besides, some promising solutions that rely on machine learning, deep learning, and reinforcement learning are susceptible to adversarial attacks, underscoring the importance of factoring in this vulnerability when devising countermeasures against sophisticated cyber threats. We also concluded that ChatGPT can be a valuable tool for cybersecurity, but it should be noted that ChatGPT-like tools can also be manipulated to threaten the integrity, confidentiality, and availability of data.
|
|
| 6. |
- Thomas, HS, et al.
(författare)
-
- 2019
-
swepub:Mat__t
|
|
