China
Africa
Explore chapters and articles related to this topic
Role of Microfluidics-Based Point-of-Care Testing (POCT) for Clinical Applications
Published in Raju Khan, Chetna Dhand, S. K. Sanghi, Shabi Thankaraj Salammal, A. B. P. Mishra, Advanced Microfluidics-Based Point-of-Care Diagnostics, 2022
Arpana Parihar, Dipesh Singh Parihar, Pushpesh Ranjan, Raju Khan
Sepsis is a severe infection-related complication that can be fatal. It is characterized by a systemic inflammatory response syndrome (SIRS) that occurs as a result of chemicals secreted to fight infection [55]. The blood cleansing capacity of organs and the protective functions of immune cells can be overwhelmed by inflammation [56]. Several studies on sepsis, its causes, diagnosis, and prognosis have been published in the literature, and readers who are interested in learning more about sepsis, its causes, diagnosis, and prognosis should read these articles [56,57].
Health effects and the baby boomers — childhood
Published in J. Mangano Joseph, Low-Level Radiation and Immune System Damage, 2018
One disease depending strongly on the strength of the immune response worthy of inclusion in an analysis of radiation’s effects is septicemia. Commonly known as blood poisoning, septicemia (or sepsis) is a condition resulting from an invasion of bacteria and other microorganisms into the bloodstream, producing symptoms such as chills, fever, and exhaustion. Septicemia is often treatable but can be fatal when invading agents overpower the body’s immune defenses. Fatalities are most common among the very elderly but can occur at any age.
Biocomposites Based on Natural Fibers: Concept and Biomedical Applications
Published in Shakeel Ahmed, Saiqa Ikram, Suvardhan Kanchi, Krishna Bisetty, Biocomposites, 2018
Raoof Ahmad Najar, Aasim Majeed, Gagan Sharma, Villayat Ali, Pankaj Bhardwaj
Biocomposites based on natural fibers (cotton, jute, cellulose, silk, wool, hemp, and sisals) possess various biomedical properties such as antibacterial, drug delivery, and tissue engineering. Besides these biomedical activities, natural fibers have other promising applications in wound healing. Effective wound management is necessary to prevent sepsis. If left open, the ruptured tissues at the wound site get easily penetrated by the infectious bacteria, which leads to septic infections. Therefore, covering the wound effectively is the most straightforward approach in wound management. Consequently, numerous scaffolds have been prepared to cover the wound. The dressing material should be biocompatible and biodegradable. Further, it should rapidly absorb wound exudates to enhance quick drying for better healing besides possessing promising gaseous exchange properties plus barrier ability to bacterial penetration [93]. Different biomaterials have been used to prepare scaffolds and dressings for wound healing due to their excellent biodegradable, biocompatible, and bioresorbable properties such as chitosan, gelatin, collagen, and silk fibroin. They show promising advantage over synthetic polymers [94]. Among the natural fibers, silk is one of the important fibers used for wound sutures and biomedical scaffolds [95]. Silk fibroin possesses hemostatic and non-cytotoxic properties along with low antigenicity, so it can be used to produce scaffolds for biomedical use [96]. Further, it is also applicable in burn wound dressing, vascular prostheses, and structural implants [97]. Pineapple fiber has various desired characteristics for biomedical applications, such as high crosslinking, non-toxicity, durability, and biocompatibility [98], and due to these characteristic features, it is used as dressing in material for wounds.
Fabrication of a curcumin encapsulated bioengineered nano-cocktail formulation for stimuli-responsive targeted therapeutic delivery to enhance anti-inflammatory, anti-oxidant, and anti-bacterial properties in sepsis management
Published in Journal of Biomaterials Science, Polymer Edition, 2023
Li Teng, Yiliang Zhang, Li Chen, Ge Shi
Sepsis is a life-threatening condition that is caused by severe bacterial infection. In addition, bacterial infections can cause other severe and life-threatening diseases, including toxic shock syndrome, pneumonia, and endocarditis. Generally, the chronological existence of pathogenic bacteria and inflammatory responses of biological systems are precise targets to confiscate bacterial infections in microenvironments, eliminating the dispersion of bacterial cells and increased inflammatory responses that lead to sepsis [31,32,58]. Our facile design of drug delivery materials with pH-responsive characteristics to target IMEs could be effective in controlling bacterial infection in sepsis through multiple signaling pathways. As previously reported, the IMEs of well-studied cancers are known to be acute and temporary, contingent on their innate immune response. Notably, IMEs of sepsis have low pH conditions with bacterial enzymes [5,59]. Hence, pH-responsive nanovesicles can be designed and developed to be exploited in these environments for effective drug delivery on demand-based requirements.
Doctors are not pilots and patients are not airplanes: Quality improvement in medicine
Published in Quality Engineering, 2019
The American College of Surgeons (ACS) is the umbrella organization of surgical societies in this country. The vast majority of surgeons belong to the ACS, and it is the source of much of the information we have about overall changes in surgery. In 2001, the ACS bought the rights to the VA quality improvement process called the National Surgical Quality Improvement Program (NSQIP). A series of alpha and then beta hospital sites tested the program and proved that it worked as well outside the VA (Hall et al. 2009). By 2005, individual hospitals were encouraged to join the program and have designated nurse reviewers trained. This was fairly expensive, and the process was time-consuming making initial adoption of this program relatively slow. But over time more and more hospitals began to join the program making the database larger and more robust. There are currently over 750 hospital enrolled covering the vast majority of all surgery in the country. It is now one of just a few validated databases in medicine. Stepwise logistic regression is used to create risk models, with results initially presented as observed to expected ratios and later as odds ratios. This is a valid and robust method of risk adjustment that allows hospitals to be compared and to work toward improvements. Although it is the best we have it is still far from perfect. Stepwise logistic regression will underestimate the interaction of variables, an important consideration in a system as complex as a human being (Livingston 2010). All 62 preoperative variables and 30 intraoperative variables are considered independent in the stepwise logistic regression, but they are not. The patient with severe infection (sepsis) is also likely to have kidney failure, respiratory failure, and other associated symptoms or conditions. Many of the variables are linked. Livingston also stated that the stepwise logistic regression process will overestimate the significance of sporadic associations that will occur because of the large number of variables.
Deep learning approach on tabular data to predict early-onset neonatal sepsis
Published in Journal of Information and Telecommunication, 2021
Redwan Hasif Alvi, Md. Habibur Rahman, Adib Al Shaeed Khan, Rashedur M. Rahman
In recent years a good number of research papers reported the use of machine learning algorithms on neonatal sepsis. Mani et al. (2014) implemented many traditional predicting models to test 299 infants for late-onset neonatal sepsis. Highly predictive features are selected using feature selection algorithms. The study achieved an AUC of 78%. Some of the models used include Lazy Bayesian Rules, regression trees, and support vector machines Griffin et al. (2005) used multivariable logistic regression to detect sepsis in neonates, and reported an AUC of 82%. Calvert et al. (2016) developed a high-performance model to detect early-onset sepsis. The model took nine vital symptomatic signs which include white blood cell count, pH, blood oxygen saturation, systolic blood pressure, pulse pressure, heart rate, temperature, respiration rate, and age of subjects as predictive features. A mean AUC of 92% was achieved in this study. Desautels et al. (2016) used a combination of patient data such as vitals, age and peripheral capillary oxygen saturation, and applied them to the classification model developed by Calvert (Calvert et al., 2016) to report a mean AUC of 88% Horng et al. (2017) presents a model that identifies patients with sepsis in the emergency room. The study achieved an AUC of 85%. Kam and Kim (2017) implemented deep learning methodologies to develop a detection classifier and compared the model to regression techniques. The classifier scored 92.9% for AUC. López-Martínez et al. (2019) presents an artificial neural network classifier to predict early-onset neonatal sepsis. The dataset used in this study contains 555 samples, and it has an imbalanced class distribution. The model achieved an AUC 92.5%. We used this work as the baseline for our initial study since we used the same dataset as our main source of data. Masino et al. (2019) developed and evaluated machine learning models to detect sepsis in newborns hospitalized at least four hours before they are suspected clinically. We also use a second dataset from this study in order to have a versatile selection of data for our classifiers.