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Fluorinated organic compounds (FOCs) represent a class of synthetic chemicals distinguished by their resilient carbon-fluorine bonds, which demonstrate an ability to withstand environmental degradation over an extended period. The integration of FOCs into cutting-edge applications, including lithium-ion batteries (LiBs), presents considerable potential for environmental harm that has not yet been sufficiently addressed. This study focuses on the environmental fate of two fluorinated aromatics, tris(pentafluorophenyl)borane (TPFPB) and tris(pentafluorophenyl)phosphine (TPFPP), given their important role in improving the performance of LiBs. To achieve this, laboratory simulation methods including total oxidizable precursor assay, electrochemistry (EC), Fenton reaction, UV-C irradiation, and hydrolysis were employed. Liquid chromatography and gas chromatography coupled with high-resolution mass spectrometry were used for identification of transformation products (TPs) and prediction of their molecular formulae. Despite the structural similarity between TPFPB and TPFPP, distinct differences in electrochemical behavior and degradation pathways were observed. TPFPB readily underwent hydroxylation and hydrolysis, resulting in a wide range of 49 TPs. A total of 28 TPs were newly identified, including oligomers and highly toxic dioxins. In contrast, TPFPP degraded exclusively under harsh conditions, requiring the development of innovative conditioning protocols for EC. In total, the simulation experiments yielded nine structurally different compounds, including seven previously undescribed, partially defluorinated TPs. This study highlights the potential risks associated with the use of FOCs in LiBs and provides insight into the complex environmental behavior of FOCs., (© 2024. The Author(s).)

The PII signaling proteins are ubiquitous in prokaryotes serving as crucial metabolic hubs in different metabolic pathways because of their ability to sense and integrate signals of the cellular nitrogen, carbon, and energy levels. In this study, we used ligand fishing assays to identify the ribonucleotide monophosphatase UmpH enzyme as a novel target of the PII signaling protein GlnK in Escherichia coli. In vitro analyses showed that UmpH interacts specifically with the PII protein GlnK but not with its paralog protein GlnB. The UmpH-GlnK complex is modulated by the GlnK uridylylation status and by the levels of the GlnK allosteric effectors ATP, ADP, and 2-oxoglutarate. Upon engaging interaction with GlnK, UmpH becomes less active toward its substrate uridine 5'-monophosphate. We suggest a model where GlnK will physically interact to reduce the UmpH activity during the transition from N-starvation to N-sufficient conditions. Such a mechanism may help the cells to reprogram the fate of uridine 5'-monophosphate from catabolism to anabolism avoiding futile cycling of key nutrients., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background and Objective: The overall benefits of using clinical decision support systems (CDSSs) can be restrained if physicians inadvertently ignore clinically useful alerts due to "alert fatigue" caused by an excessive number of clinically irrelevant warnings. Moreover, inappropriate drug errors, look-alike/sound-alike (LASA) drug errors, and problem list documentation are common, costly, and potentially harmful. This study sought to evaluate the overall performance of a machine learning-based CDSS (MedGuard) for triggering clinically relevant alerts, acceptance rate, and to intercept inappropriate drug errors as well as LASA drug errors., Methods: We conducted a retrospective study that evaluated MedGuard alerts, the alert acceptance rate, and the rate of LASA alerts between July 1, 2019, and June 31, 2021, from outpatient settings at an academic hospital. An expert pharmacist checked the suitability of the alerts, rate of acceptance, wrong-drug errors, and confusing drug pairs., Results: Over the two-year study period, 1,206,895 prescriptions were ordered and a total of 28,536 alerts were triggered (alert rate: 2.36 %). Of the 28,536 alerts presented to physicians, 13,947 (48.88 %) were accepted. A total of 8,014 prescriptions were changed/modified (28.08 %, 8,014/28,534) with the most common reasons being adding and/or deleting diseases (52.04 %, 4,171/8,014), adding and/or deleting drugs (21.89 %, 1,755/8,014) and others (35.48 %, 2,844/ 8,014). However, the rate of drug error interception was 1.64 % (470 intercepted errors out of 28,536 alerts), which equates to 16.4 intercepted errors per 1000 alerted orders., Conclusion: This study shows that machine learning based CDSS, MedGuard, has an ability to improve patients' safety by triggering clinically valid alerts. This system can also help improve problem list documentation and intercept inappropriate drug errors and LASA drug errors, which can improve medication safety. Moreover, high acceptance of alert rates can help reduce clinician burnout and adverse events., Competing Interests: Declaration of Competing Interest Authors Jeremiah Scholl and Yu-Chuan (Jack) Li are significant shareholders in, and continue to work with, AESOP Technology, the company that commercialized the Medguard decision support system., (Copyright © 2023. Published by Elsevier B.V.)