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1.
J Pak Med Assoc ; 73(9): 1811-1815, 2023 Sep.
Article in English | MEDLINE | ID: mdl-37817689

ABSTRACT

Objectives: To investigate the prevalence of post-traumatic stress disorder in intensive care unit survivors, and disorder's correlation with analgesia use. METHODS: The single-centre retrospective cohort study was conducted at the First Affiliated Hospital of Jinan University, China, and comprised data from February 2021 to January 2022 related to patients of either gender aged =18 years who were admitted to the intensive care unit and were successfully transferred out to the general ward. Post- traumatic stress disorder Checklist-Civilian Version scale was used for follow-up within one month of getting transferred out of intensive care. Data was analysed using Empower Stats. RESULTS: Of the 121 patients with mean age 54.34±18.19 years, 52(43%) were positive for post-traumatic stress disorder; 32(61.5%) males and 20(38.5%) females with mean age 54.48±19.56 years.The remaining 69(57%) patients were negative; 40(58%) males and 29(42%) females with mean age 54.23±17.24 years (p>0.05). The positive rate of re- experiencing symptoms was noted in 68(56.20%) patients. Analgesia usage was positive in 61(50.4%) cases and negative in 60(49.6%) cases. Compared to the non-analgesic group, the risk of post-traumatic stress disorder occurrence in the analgesic group wassignificantly high (p=0.018). The duration of analgesia usage 24-48h was also significant (p=0.017). CONCLUSIONS: There was a high prevalence of post-traumatic stress disorder in intensive care unit survivors, which was affected by the use of analgesicsin intensive care settings.


Subject(s)
Stress Disorders, Post-Traumatic , Male , Female , Humans , Aged , Adult , Middle Aged , Stress Disorders, Post-Traumatic/epidemiology , Retrospective Studies , Prevalence , Intensive Care Units , Survivors , China/epidemiology , Analgesics/therapeutic use
2.
Inflammopharmacology ; 27(3): 453-464, 2019 Jun.
Article in English | MEDLINE | ID: mdl-30756223

ABSTRACT

Studies have demonstrated that susceptibility to type 2 diabetes (T2D) is influenced by common polymorphism in the zinc transporter 8 gene SLC30A8, providing novel insight into the role of zinc in diabetes. Intriguingly, zinc participates in every step of the process, including insulin synthesis, crystallization, storage, secretion and signaling. Zinc deficiency or overload is associated with various disorders, such as diabetes, cardiovascular disease and obesity. Zinc supplementation is considered as an effective means of treating or preventing T2D in people with certain SLC30A8 genotypes. Three important protein families-zinc transporters (ZnTs), zinc importers (ZiPs) and metallothionein (MT)-participate in maintaining zinc homeostasis. Here, we review research on the physiological characteristics of zinc and its role in the pancreas and homeostasis regulation mechanisms, along with the latest research on the structure and function of ZnT/ZiP and MT. In addition, we summarize the advancements in research on SLC30A8 gene polymorphism in search of a mechanism to explain the relationship between the R risk allele and zinc transporter activity.


Subject(s)
Pancreas/metabolism , Zinc/metabolism , Animals , Diabetes Mellitus, Type 2/metabolism , Diabetes Mellitus, Type 2/pathology , Homeostasis/physiology , Humans , Pancreas/pathology , Polymorphism, Genetic/physiology , Signal Transduction/physiology
3.
Comput Math Methods Med ; 2013: 106867, 2013.
Article in English | MEDLINE | ID: mdl-23853667

ABSTRACT

Feature is important for many applications in biomedical signal analysis and living system analysis. A fast discriminative stochastic neighbor embedding analysis (FDSNE) method for feature extraction is proposed in this paper by improving the existing DSNE method. The proposed algorithm adopts an alternative probability distribution model constructed based on its K-nearest neighbors from the interclass and intraclass samples. Furthermore, FDSNE is extended to nonlinear scenarios using the kernel trick and then kernel-based methods, that is, KFDSNE1 and KFDSNE2. FDSNE, KFDSNE1, and KFDSNE2 are evaluated in three aspects: visualization, recognition, and elapsed time. Experimental results on several datasets show that, compared with DSNE and MSNP, the proposed algorithm not only significantly enhances the computational efficiency but also obtains higher classification accuracy.


Subject(s)
Biostatistics , Computational Biology/methods , Stochastic Processes , Algorithms , Databases, Factual/statistics & numerical data , Linear Models , Mathematical Concepts , Nonlinear Dynamics
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