Retrospective Analysis of the Impact of High- and Low-Quality Donor Livers for Patients with High-Acuity Illness

BACKGROUND Patients with high-acuity liver failure have increased access to marginal and split liver options, owing to historically high waitlist mortality rates. While most research states that donor liver quality has no impact on patients with high-acuity illness, there have been inconsistencies in recent research on how liver quality impacts post-transplant outcomes for these patients. We aimed to quantify donor liver quality with various post-transplantation patient outcomes for patients with high-acuity illness. MATERIAL AND METHODS Using the liver donor risk index (LDRI), model for end stage liver disease (MELD), and clinically relevant recipient factors, we used multivariate logistic regression to analyze how donor liver quality affects varying measures of patient outcomes for 9923 high-acuity patients from June 18, 2013, to June 18, 2022. RESULTS Using LDRI, high-quality livers had a significant protective impact on high-acuity patient mortality, compared with low-quality livers (OR=0.695 [0.549, 0.879], P=0.002). High-quality livers also had significant impact on graft survival (OR=0.706 [0.558, 0.894], P=0.004). Two sensitivity patient mortality analyses, excluding patients with status 1A and hepatocellular carcinoma, showed significant protective findings for high-quality livers. High-quality livers had insignificant outcomes on long-term survivor mortality, length of hospitalization, and primary non-function outcomes, compared with low-quality donor livers. CONCLUSIONS While our findings suggest donor quality has an impact on high-acuity patient outcomes, these findings indicate further research is needed in intent-to-treat analysis on clinical offer data to provide a clearer finding of how donor quality affects patients with high-acuity illness

Maximizing geographical efficiency : An analysis of the configuration of Colorado’s trauma system

ACKNOWLEDGEMENT The data used for this study were supplied by the Health Facilities and Emergency Medical Services Division of the Colorado Department of Public Health and Environment, which specifically disclaims responsibility for any analyses, interpretations, or conclusions it has not provided. The data used for this study were supplied by the Health Facilities and Emergency Medical Services Division of the Colorado Department of Public Health and Environment, which specifically disclaims responsibility for any analyses, interpretations, or conclusions it has not provided.Peer reviewedPostprin

Classification and examples of next generation machine elements

AbstractIn order to fully exploit the potential of the rapidly progressing digitalisation of technical systems, it is necessary to provide reliable and significant process and condition related data. In this context, solutions are especially aspired to allow a simple integration into the surrounding system and to influence it as little as possible. The main challenges regarding the measurement of process and condition data in the operation and control of technical systems as well as in test environments are identified and presented at the beginning of this article. A promising approach to meet the resulting requirements is the integration of sensory functions into simple standardised machine elements. In order to facilitate the discussion and interdisciplinary development of machine elements with sensory functions, an extension of the existing classification of mechatronic machine elements is introduced and illustrated with examples. The introduced classification takes into account the classification according to Stücheli and Meboldt and is based on a comparison of conventional and mechatronic machine elements on a functional level with regard to the function structure. As a result, the three classessensor carrying machine elements,sensor integrating machine elementsandsensory utilizable machine elementsare introduced and subsequently discussed in more detail on the basis of examples. Finally, an outlook is given on the main research areas with regard to the development of mechatronic machine elements. Key aspects include working principles and effects for application in mechatronic machine elements, system analysis with regard to load conditions, power supply of sensor and data processor in the environment of the machine element as well as data processing and signal transmission under typical environmental conditions of mechanical engineering.</jats:p

Examining the intersection of cognitive and physical function measures: Results from the brain networks and mobility (B-NET) study

Background and objectivesAlthough evidence exists that measures of mobility and cognition are correlated, it is not known to what extent they overlap, especially across various domains. This study aimed to investigate the intersection of 18 different objective cognitive and physical function measures from a sample of unimpaired adults aged 70 years and older.Research design and methodsCanonical correlation analysis was utilized to explore the joint cross-sectional relationship between 13 cognitive and 6 physical function measures in the baseline visit of the Brain Networks and Mobility Function (B-NET) Study (n = 192).ResultsMean age of participants was 76.4 years. Two synthetic functions were identified. Function 1 explained 26.3% of the shared variability between the cognition and physical function variables, whereas Function 2 explained 19.5%. Function 1 termed “cognitive and physical speed” related the expanded Short Physical Performance Battery (eSPPB), 400-m walk speed, and Dual Task gait speed measures of physical function to semantic fluency animals scores, Digit Symbol Coding (DSC), and Trail Making Test B. Function 2 termed “complex motor tasks and cognitive tasks” related the Force Plate Postural Sway Foam Task and Dual Task to the following cognitive variables: MoCA Adjusted Score, Verbal Fluency L words, Craft story immediate and delayed recall, and Trail Making Test B.Discussion and implicationsWe identified groups of cognitive and physical functional abilities that were linked in cross-sectional analyses, which may suggest shared underlying neural network pathway(s) related to speed (Function 1) or complexity (Function 2).Translational significanceWhether such neural processes decline before measurable functional losses or may be important targets for future interventions that aim to prevent disability also remains to be determined.</jats:sec

Molecular architecture of the nucleoprotein C-terminal domain from the Ebola and Marburg viruses

TheFiloviridaefamily of negative-sense, single-stranded RNA (ssRNA) viruses is comprised of two species ofMarburgvirus(MARV and RAVV) and five species ofEbolavirus,i.e.Zaire (EBOV), Reston (RESTV), Sudan (SUDV), Taï Forest (TAFV) and Bundibugyo (BDBV). In each of these viruses the ssRNA encodes seven distinct proteins. One of them, the nucleoprotein (NP), is the most abundant viral protein in the infected cell and within the viral nucleocapsid. It is tightly associated with the viral RNA in the nucleocapsid, and during the lifecycle of the virus is essential for transcription, RNA replication, genome packaging and nucleocapsid assembly prior to membrane encapsulation. The structure of the unique C-terminal globular domain of the NP from EBOV has recently been determined and shown to be structurally unrelated to any other known protein [Dziubańskaet al.(2014),Acta Cryst. D70, 2420–2429]. In this paper, a study of the C-terminal domains from the NP from the remaining four species ofEbolavirus, as well as from the MARV strain ofMarburgvirus, is reported. As expected, the crystal structures of the BDBV and TAFV proteins show high structural similarity to that from EBOV, while the MARV protein behaves like a molten globule with a core residual structure that is significantly different from that of the EBOV protein.</jats:p