Search

Vaginal brachytherapy (VBT) is an essential component of curative intent treatment for many patients with endometrial cancer. The prevalence of trauma history in this population is unknown and important to understand considering VBT requires patients to have an instrument vaginally inserted while in the vulnerable lithotomy position. We aim to identify patients treated with intracavitary VBT and collect survey data to assess trauma endpoints. We retrospectively identified patients with endometrial cancer who underwent intracavitary VBT at our institution between 01/2017 and 08/2022. Patients were mailed and/or electronically mailed a survey that included demographics, psychosocial background, and validated trauma surveys to be filled out as they relate to their trauma experiences prior to VBT and again considering any trauma symptomatology related to VBT. Electronic medical record review was performed. Descriptive statistics as well as multivariate analysis were performed. 206 patients met inclusion criteria, 66 (32.1%) of whom returned the survey and were included for analysis. Thirty-two percent of patients self-reported a personal history of any prior mental health diagnosis. Eighty-eight percent of patients screened positive for a history of trauma exposure, 23% endorsed symptoms of PTSD related to their VBT experience, and 5% screened positive for a likely PTSD diagnosis from VBT. A majority of included patients had a history of trauma exposure prior to VBT. In a subset of patients, VBT re-induced trauma and was considered to be an independent traumatic event. This study highlights the importance of practicing trauma informed care, particularly in this patient population. • 88% of patients screened positive for trauma exposure on the BTQ, 20% indicated history of unwanted sexual contact. • 11% of patients indicated VBT triggered an increase in thoughts or flashbacks to traumatic event(s). • VBT was considered to independently induce trauma symptoms in 23% of patients. • 36% of patients would have accepted referral to psychiatric services. [ABSTRACT FROM AUTHOR]

We report an updated analysis of the outcomes and toxicities of MRI-based brachytherapy for locally advanced cervical cancer from a U.S. academic center. A retrospective review was performed on patients treated with MRI-based brachytherapy for cervical cancer. EBRT was standardly 45 Gy in 25 fractions with weekly cisplatin. MRI was performed with the brachytherapy applicator in situ. Dose specification was most commonly 7 Gy for 4 fractions with optimization aim of D90 HR-CTV EQD2 of 85–95 Gy α/β=10 Gy in 2 implants each delivering 2 fractions. Ninety-eight patients were included with median follow up of 24.5 months (IQR 11.9–39.8). Stage IIIA-IVB accounted for 31.6% of cases. Dosimetry results include median GTV D98 of 101.0 Gy (IQR 93.3–118.8) and HR-CTV D90 of 89 Gy (IQR 86.1–90.6). Median D2cc bladder, rectum, sigmoid, and bowel doses were 82.1 Gy (IQR 75.9–88.0), 65.9 Gy (IQR 59.6–71.2), 65.1 Gy (IQR 57.7–69.6), and 55 Gy (IQR 48.9–60.9). Chronic grade 3+ toxicities were seen in the bladder (8.2%), rectosigmoid (4.1%), and vagina (1.0%). Three-year LC, PFS, and OS were estimated to be 84%, 61.7%, and 76.1%, respectively. MRI-based brachytherapy demonstrates excellent local control and acceptable rates of high-grade morbidity. These results are possible in our population with relatively large volume primary tumors and extensive local disease. • Local control, overall survival, and progression-free survival at our institution are similar to larger studies. • Dose planning limits to organs-at-risk were met in a population with a higher proportion of stage IIIA-IVB patients. • Chronic grade 3+ toxicities were seen in 13.3% of patients, mostly in stage IIIB and IVA patients. [ABSTRACT FROM AUTHOR]

Lipids play essential roles in many biological processes and disease pathology, but unambiguous identification of lipids is complicated by the presence of multiple isomeric species differing by fatty acyl chain length, stereospecifically numbered (sn) position, and position/stereochemistry of double bonds. Conventional liquid chromatography-mass spectrometry (LC-MS/MS) analyses enable the determination of fatty acyl chain lengths (and in some cases sn position) and number of double bonds, but not carbon-carbon double bond positions. Ozone-induced dissociation (OzID) is a gas-phase oxidation reaction that produces characteristic fragments from lipids containing double bonds. OzID can be incorporated into ion mobility spectrometry (IMS)-MS instruments for the structural characterization of lipids, including additional isomer separation and confident assignment of double bond positions. The complexity and repetitive nature of OzID data analysis and lack of software tool support have limited the application of OzID for routine lipidomics studies. Here, we present an open-source Python tool, LipidOz, for the automated determination of lipid double bond positions from OzID-IMS-MS data, which employs a combination of traditional automation and deep learning approaches. Our results demonstrate the ability of LipidOz to robustly assign double bond positions for lipid standard mixtures and complex lipid extracts, enabling practical application of OzID for future lipidomics. Ozone-induced dissociation (OzID) coupled with ion mobility spectrometry-mass spectrometry (IMS-MS) provides the capacity for in-depth structural elucidation of lipids with isomer separation and confident assignment of double bond positions, however, OzID data analysis remains very challenging. Here, the authors develop a Python tool, LipidOz, for the automated determination of lipid double bond locations from complex LC-OzID-IMS-MS data, with a combination of traditional automation and deep learning approaches. [ABSTRACT FROM AUTHOR]

The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature. [ABSTRACT FROM AUTHOR]

Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural confirmation. Ion mobility-mass spectrometry (IM-MS), on the other hand, separates analytes on a rapid (millisecond) time scale and enables the measurement of collision cross section (CCS), a unique physical property related to an ion's gas-phase size and shape, which can be used as an additional parameter for identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we assembled a large-scale database of drug and drug metabolite CCS values using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors, achieving high prediction accuracies (0.8–2.2% median relative error on test set data). The inclusion of 3D information in the prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers, which is not possible using conventional 2D descriptors. The prediction models, dmCCS, are available at https://CCSbase.net/dmccs_predictions. [ABSTRACT FROM AUTHOR]

Ion mobility-mass spectrometry (IM-MS) can provide orthogonal information, i.e., m/z and collision cross section (CCS), for the identification of drugs and drug metabolites. However, only a small number of CCS values are available for drugs, which limits the use of CCS as an identification parameter and the assessment of structure-function relationships of drugs using IM-MS. Here, we report the development of a rapid workflow for the measurement of CCS values of a large number of drug or drug-like molecules in nitrogen on the widely available traveling wave IM-MS (TWIM-MS) platform. Using a combination of small molecule and polypeptide CCS calibrants, we successfully determined the nitrogen CCS values of 1425 drug or drug-like molecules in the MicroSource Discovery Systems' Spectrum Collection using flow injection analysis of 384-well plates. Software was developed to streamline data extraction, processing, and calibration. We found that the overall drug collection covers a wide CCS range for the same mass, suggesting a large structural diversity of these drugs. However, individual drug classes appear to occupy a narrow and unique space in the CCS-mass 2D spectrum, suggesting a tight structure-function relationship for each class of drugs with a specific target. We observed bimodal distributions for several antibiotic species due to multiple protomers, including the known fluoroquinolone protomers and the new finding of cephalosporin protomers. Lastly, we demonstrated the utility of the high-throughput method and drug CCS database by quickly and confidently confirming the active component in a pharmaceutical product. [ABSTRACT FROM AUTHOR]

Ion mobility-mass spectrometry (IM-MS) has gained increased applications in the characterization and identification of drugs and drug metabolites, largely owning to the complementary separation of analyte ions based on their gas-phase size and shape in the IM dimension in addition to their mass-to-charge ratios. In this review, we discuss recent advances in such applications. We first introduce various types of IM techniques, focusing on those that allow the measurement of collision cross section (CCS), the physical property of an ion that reflects its gas-phase size and shape. Next, we discuss the IM-MS landscape of the large chemical space of drugs and multimodal distributions of certain drugs in IM separation due to the presence of protomers. We then review drug metabolism reactions and discuss the application of IM-MS in separation and identification of isomeric drug metabolites. Subsequently, we discuss various approaches to generate theoretical and predicted CCS data, including theory-based calculation methods and data-driven prediction models, and currently available resources on these approaches. Finally, current limitations and future directions of application of IM-MS are discussed. [Display omitted] • IM-MS landscape of the large chemical space of drugs. • Multimodal distribution of protomers of drugs in IM separation. • Characterization and identification of isomeric drug metabolites. • Theoretical approaches for the calculation and prediction of collision cross sections. [ABSTRACT FROM AUTHOR]

Neurodevelopment is an intricately orchestrated program of cellular events that occurs with tight temporal and spatial regulation. While it is known that the development and proper functioning of the brain, which is the second most lipid rich organ behind adipose tissue, greatly rely on lipid metabolism and signaling, the temporal lipidomic changes that occur throughout the course of neurodevelopment have not been investigated. Smith–Lemli–Opitz syndrome is a metabolic disorder caused by genetic mutations in the DHCR7 gene, leading to defective 3β-hydroxysterol-Δ7-reductase (DHCR7), the enzyme that catalyzes the last step of the Kandutsch-Russell pathway of cholesterol synthesis. Due to the close regulatory relationship between sterol and lipid homeostasis, we hypothesize that altered or dysregulated lipid metabolism beyond the primary defect of cholesterol biosynthesis is present in the pathophysiology of SLOS. Herein, we applied our HILIC-IM-MS method and LiPydomics Python package to streamline an untargeted lipidomics analysis of developing mouse brains in both wild-type and Dhcr7-KO mice, identifying lipids at Level 3 (lipid species level: lipid class/subclass and fatty acid sum composition). We compared relative lipid abundances throughout development, from embryonic day 12.5 to postnatal day 0 and determined differentially expressed brain lipids between wild-type and Dhcr7-KO mice at specific developmental time points, revealing lipid metabolic pathways that are affected in SLOS beyond the cholesterol biosynthesis pathway, such as glycerolipid, glycerophospholipid, and sphingolipid metabolism. Implications of the altered lipid metabolic pathways in SLOS pathophysiology are discussed. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance is a major threat to human health as resistant pathogens spread globally, and the development of new antimicrobials is slow. Since many antimicrobials function by targeting cell wall and membrane components, high-throughput lipidomics for bacterial phenotyping is of high interest for researchers to unveil lipid-mediated pathways when dealing with a large number of lab-selected or clinical strains. However, current practice for lipidomic analysis requires the cultivation of bacteria on a large scale, which does not replicate the growth conditions for high-throughput bioassays that are normally carried out in 96-well plates, such as susceptibility tests, growth curve measurements, and biofilm quantitation. Analysis of bacteria grown under the same condition as other bioassays would better inform the differences in susceptibility and other biological metrics. In this work, a high-throughput method for cultivation and lipidomic analysis of antimicrobial-resistant bacteria was developed for standard 96-well plates exemplified by methicillin-resistant Staphylococcus aureus (MRSA). By combining a 30-mm liquid chromatography (LC) column with ion mobility (IM) separation, elution time could be dramatically shortened to 3.6 min for a single LC run without losing major lipid features. Peak capacity was largely rescued by the addition of the IM dimension. Through multi-linear calibration, the deviation of retention time can be limited to within 5%, making database-based automatic lipid identification feasible. This high-throughput method was further validated by characterizing the lipidomic phenotypes of antimicrobial-resistant mutants derived from the MRSA strain, W308, grown in a 96-well plate. [ABSTRACT FROM AUTHOR]

Dalbavancin is a lipoglycopeptide active against methicillin-resistant Staphylococcus aureus (MRSA). Its long half-life (8.5–16 days) allows for once-weekly or single-dose treatments but could prolong the mutant selection window, promoting resistance and cross-resistance to related antimicrobials such as vancomycin. The objective of this study was to evaluate the capacity of post-distributional pharmacokinetic exposures of dalbavancin to select for resistance and cross-resistance in MRSA. We simulated average, post-distributional exposures of single-dose (1500 mg) dalbavancin (f Cmax 9.9 μg/mL, β-elimination t 1/2 204 h) in an in vitro pharmacokinetic/pharmacodynamic (PK/PD) model for 28 days (672 h) against five MRSA strains and one methicillin-susceptible strain (MSSA). Samples were collected at least daily, and surviving colonies were enumerated and screened for resistance on drug-free and dalbavancin-supplemented medium respectively. Isolates from resistance screening plates were subjected to whole-genome sequencing (WGS) and susceptibly testing against dalbavancin, vancomycin, daptomycin, and six β-lactams with varying penicillin-binding protein (PBP) affinities. Dalbavancin was bactericidal against most strains for days 1–4 before regrowth of less susceptible subpopulations occurred. Isolates with eight-fold increases in dalbavancin MIC were detected as early as day 4 but increased 64–128-fold in all models by day 28. Vancomycin and daptomycin MICs increased 4–16-fold, exceeding the susceptibly breakpoints for both antibiotics; β-lactam MICs generally decreased by two-to eight-fold, suggesting a dalbavancin–β-lactam seesaw effect, but increased by eight-fold or more in certain isolates. Resistant isolates carried mutations in a variety of genes, most commonly walKR , apt , stp1 , and atl. In our in vitro system, post-distributional dalbavancin exposures selected for stable mutants with reduced susceptibility to dalbavancin, vancomycin, and daptomycin, and generally increased susceptibility to β-lactams in all strains of MRSA tested. The clinical significance of these findings remains unclear, but created an opportunity to genotype a unique collection of dalbavancin-resistant strains for the first time. Mutations involved genes previously associated with vancomycin intermediate susceptibility and daptomycin non-susceptibility, most commonly walKR -associated genes. [ABSTRACT FROM AUTHOR]