Computational tools and algorithms for ion mobility spectrometry-mass spectrometry.

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.

Prevalence of trauma history and symptoms in patients who have received vaginal brachytherapy as part of their endometrial cancer treatment.

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Evaluating outcomes and toxicities for a newly implemented MRI-based brachytherapy program for cervical cancer.

OBJECTIVE: 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. METHODS: 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 Gyα/ß=10 Gy in 2 implants each delivering 2 fractions. RESULTS: 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. CONCLUSION: 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.

Development and Application of a Multidimensional Database for the Detection of Quaternary Ammonium Compounds and Their Phase I Hepatic Metabolites in Humans.

The COVID-19 pandemic has led to significantly increased human exposure to the widely used disinfectants quaternary ammonium compounds (QACs). Xenobiotic metabolism serves a critical role in the clearance of environmental molecules, yet limited data are available on the routes of QAC metabolism or metabolite levels in humans. To address this gap and to advance QAC biomonitoring capabilities, we analyzed 19 commonly used QACs and their phase I metabolites by liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS). In vitro generation of QAC metabolites by human liver microsomes produced a series of oxidized metabolites, with metabolism generally occurring on the alkyl chain group, as supported by MS/MS fragmentation. Discernible trends were observed in the gas-phase IM behavior of QAC metabolites, which, despite their increased mass, displayed smaller collision cross-section (CCS) values than those of their respective parent compounds. We then constructed a multidimensional reference SQLite database consisting of m/z, CCS, retention time (rt), and MS/MS spectra for 19 parent QACs and 81 QAC metabolites. Using this database, we confidently identified 13 parent QACs and 35 metabolites in de-identified human fecal samples. This is the first study to integrate in vitro metabolite biosynthesis with LC-IM-MS/MS for the simultaneous monitoring of parent QACs and their metabolites in humans.

MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry.

Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich spectra challenges existing data storage and access technologies, therefore precluding informatics advancements. We present MZA (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed of a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and cross-programming language access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable, example MS-data and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.

mzapy: An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format.

Analysis of ion mobility spectrometry (IMS) data has been challenging and limited the full utility of these measurements. Unlike liquid chromatography-mass spectrometry, where a plethora of tools with well-established algorithms exist, the incorporation of the additional IMS dimension requires upgrading existing computational pipelines and developing new algorithms to fully exploit the advantages of the technology. We have recently reported MZA, a new and simple mass spectrometry data structure based on the broadly supported HDF5 format and created to facilitate software development. While this format is inherently supportive of application development, the availability of core libraries in popular programming languages with standard mass spectrometry utilities will facilitate fast software development and broader adoption of the format. To this end, we present a Python package, mzapy, for efficient extraction and processing of mass spectrometry data in the MZA format, especially for complex data containing ion mobility spectrometry dimension. In addition to raw data extraction, mzapy contains supporting utilities enabling tasks including calibration, signal processing, peak finding, and generating plots. Being implemented in pure Python and having minimal and largely standardized dependencies makes mzapy uniquely suited to application development in the multiomics domain. The mzapy package is free and open-source, includes comprehensive documentation, and is structured to support future extension to meet the evolving needs of the MS community. The software source code is freely available at https://github.com/PNNL-m-q/mzapy.

High-throughput analysis of lipidomic phenotypes of methicillin-resistant Staphylococcus aureus by coupling in situ 96-well cultivation and HILIC-ion mobility-mass spectrometry.

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.

Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry-Mass Spectrometry Lipidomics Data.

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.

Temporal changes in the brain lipidome during neurodevelopment of Smith-Lemli-Opitz syndrome mice.

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 3ß-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.

Breaking Down Structural Diversity for Comprehensive Prediction of Ion-Neutral Collision Cross Sections.

Identification of unknowns is a bottleneck for large-scale untargeted analyses like metabolomics or drug metabolite identification. Ion mobility-mass spectrometry (IM-MS) provides rapid two-dimensional separation of ions based on their mobility through a neutral buffer gas. The mobility of an ion is related to its collision cross section (CCS) with the buffer gas, a physical property that is determined by the size and shape of the ion. This structural dependency makes CCS a promising characteristic for compound identification, but this utility is limited by the availability of high-quality reference CCS values. CCS prediction using machine learning (ML) has recently shown promise in the field, but accurate and broadly applicable models are still lacking. Here we present a novel ML approach that employs a comprehensive collection of CCS values covering a wide range of chemical space. Using this diverse database, we identified the structural characteristics, represented by molecular quantum numbers (MQNs), that contribute to variance in CCS and assessed the performance of a variety of ML algorithms in predicting CCS. We found that by breaking down the chemical structural diversity using unsupervised clustering based on the MQNs, specific and accurate prediction models for each cluster can be trained, which showed superior performance than a single model trained with all data. Using this approach, we have robustly trained and characterized a CCS prediction model with high accuracy on diverse chemical structures. An all-in-one web interface (https://CCSbase.net) was built for querying the CCS database and accessing the predictive model to support unknown compound identifications.

LiPydomics: A Python Package for Comprehensive Prediction of Lipid Collision Cross Sections and Retention Times and Analysis of Ion Mobility-Mass Spectrometry-Based Lipidomics Data.

Comprehensive profiling of lipid species in a biological sample, or lipidomics, is a valuable approach to elucidating disease pathogenesis and identifying biomarkers. Currently, a typical lipidomics experiment may track hundreds to thousands of individual lipid species. However, drawing biological conclusions requires multiple steps of data processing to enrich significantly altered features and confident identification of these features. Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification) involve performing various steps using different software applications, which imposes a practical limitation and potentially a negative impact on reproducibility. Hydrophilic interaction liquid chromatography-ion mobility-mass spectrometry (HILIC-IM-MS) has shown advantages in separating lipids through orthogonal dimensions. However, there are still gaps in the coverage of lipid classes in the literature. To enable reproducible and efficient analysis of HILIC-IM-MS lipidomics data, we developed an open-source Python package, LiPydomics, which enables performing statistical and multivariate analyses ("stats" module), generating informative plots ("plotting" module), identifying lipid species at different confidence levels ("identification" module), and carrying out all functions using a user-friendly text-based interface ("interactive" module). To support lipid identification, we assembled a comprehensive experimental database of m/z and CCS of 45 lipid classes with 23 classes containing HILIC retention times. Prediction models for CCS and HILIC retention time for 22 and 23 lipid classes, respectively, were trained using the large experimental data set, which enabled the generation of a large predicted lipid database with 145,388 entries. Finally, we demonstrated the utility of the Python package using Staphylococcus aureus strains that are resistant to various antimicrobials.

Log-periodic temporal apertures for grating lobe suppression in k-space tomography.

Millimeter-wave (mmW) imaging receivers have demonstrated the ability to sense radio-frequency (RF) waves using traditional phased antenna array techniques, and, through a coherent photonic up-conversion process, image these waves using free-space optical systems. Building upon the idea of coherent up-conversion, k-space tomography extends the functionality of the millimeter-wave imaging receiver as a two-dimensional spatial processing unit to three-dimensional sensing with the addition of frequency detection. In this configuration, an arrayed waveguide grating, or temporal aperture, is implemented following the photonic up-conversion of RF signals received by the phased array. These waveguides of varying length add a spectral beam-forming network to the existing spatial beam-forming of the mmW-imaging receiver. The introduction of three-dimensional phase information to the imaging system disrupts the ability to directly image the RF signal distribution on a photo-detector array, requiring the application of tomographic algorithms to reconstruct the power distribution of the received signals. In order to receive and properly recover the spatial-spectral distribution of RF sources, the antenna array and temporal array must be sampled adequately to avoid introduction of grating artifacts into the system response. Grating lobes, an artifact of regular spacing of elements within a grating, restrict the alias-free field of regard for antenna arrays, or the free spectral range for time-delay based arrays, thus limiting the spatial-spectral monitoring of RF sources via the k-space imaging modality. To alleviate this constraint, we present a non-uniform log-periodic array sampling for the k-space tomographic time-delay based aperture, greatly increasing the free spectral range of the system while maintaining the number of existing channels.

Characterization of the Impact of Drug Metabolism on the Gas-Phase Structures of Drugs Using Ion Mobility-Mass Spectrometry.

Conventional strategies for drug metabolite identification employ a combination of liquid chromatography-mass spectrometry (LC-MS), which offers higher throughput but provides limited structural information, and nuclear magnetic resonance spectroscopy, which can achieves the most definitive identification but lacks throughput. Ion mobility-mass spectrometry (IM-MS) is a rapid, two-dimensional analysis that separates ions on the basis of their gas-phase size and shape (reflected by collision cross section, CCS) and their mass-to-charge (m/z) ratios. The rapid nature of IM separation combined with the structural information provided by CCS make IM-MS a promising technique for obtaining more structural information on drug metabolites without sacrificing analytical throughput. Here, we present an in vitro biosynthesis coupled with IM-MS strategy for rapid generation and analysis of drug metabolites. Drug metabolites were generated in vitro using pooled subcellular fractions derived from human liver and analyzed using a rapid flow injection-IM-MS method. We measured CCS values for 19 parent drugs and their 37 metabolites generated in vitro (78 values in total), representing a wide variety of metabolic modifications. Post-IM fragmentation and computational modeling were used to support metabolite identifications and explore the structural characteristics driving behaviors observed in IM separation. Overall, we found the effects of metabolic modifications on the gas-phase structures of the metabolites to be highly dependent upon the structural characteristics of the parent compounds and the specific position of the modification. This in vitro biosynthesis coupled with rapid IM-MS analysis workflow represents a promising platform for rapid and high-confidence identification of drug metabolites, applicable at a large scale.

Large-Scale Structural Characterization of Drug and Drug-Like Compounds by High-Throughput Ion Mobility-Mass Spectrometry.

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.

Thoracic Reirradiation with Stereotactic Body Radiation Therapy (SBRT) for Recurrent Advanced Non-Small Cell Lung Cancer (NSCLC).

PURPOSE: Non-small cell lung cancer (NSCLC) local control remains suboptimal with rates around 75%. Stereotactic body radiation therapy (SBRT) is an option for isolated local recurrences of small-volume recurrences. This study investigates the safety and efficacy of 60 Gy in 8 fractions in large-volume local recurrences. METHODS AND MATERIALS: We conducted a retrospective chart review of patients treated with salvage SBRT for NSCLC lung parenchymal recurrence between July 2013 and February 2020. Reirradiation prescribed dose was 60 Gy in 8 fractions using the SBRT technique. The primary endpoint was local control at most recent follow-up or death. Secondary endpoints included overall survival, disease-free interval, cancer-specific survival, and treatment related toxicities. RESULTS: Seven patients met inclusion criteria. Median follow up time was 38 months (18.1-72.4). Median age was 67 years (63-80). Median time to reirradiation was 18.2 months (7.3-28.6). Retreatment median ITV was 57.9 cc (15.8-344.6), and PTV median was 113.6 cc (38.3-506.9). Local control was maintained in 4 of 7 patients (57.1%). Two of the 7 patients (28.6%) remained alive. Median disease-free interval was 22.5 months (11-65). Three of 7 patients (42.9%) had grade 2 toxicities. One patient (14.3%) had a grade 3 rib/chest wall toxicity with concurrent disease recurrence invading the chest wall. CONCLUSION: This study reports that SBRT of 60 Gy in 8 fractions was delivered safely and effectively to large volume recurrent NSCLC previously treated with radiation therapy. The disease-free interval of nearly 2 years is meaningful for patients' quality of life and duration of time off systemic therapy.

Standardized Management of Sickle Cell Disease Patients and the Effects on Care Utilization and Costs.

An individualized management program for patients with sickle cell disease (SCD) was created to reduce health care utilization and cost. The program was implemented to standardize the management of patients with SCD. SCD encounters from January 2010 to December 2020 were reviewed for analysis. Preintervention utilization of inpatient, emergency room, and outpatient settings was compared to postintervention. There were 7114 encounters analyzed. Outpatient encounters increased from 36.5% to 70.9%; inpatient encounters decreased from 38.6% to 20.3%; and emergency department visits decreased from 20.3% to 8.8%. The number of high inpatient utilizers decreased 8.4% and the number of individuals who received any emergency care decreased 11.9%. When comparing average charges per time period, the median charge per encounter decreased by $1838 postintervention compared to preintervention. This newly implemented SCD program demonstrated success through shifting the care of the SCD patient to the outpatient setting rather than the emergency department or inpatient hospitalizations.

Anti-aquaporin-4 immunoglobulin G/anti-myelin oligodendrocyte glycoprotein immunoglobulin G double-positive paraneoplastic neurological syndrome in a patient with triple-negative breast cancer.

We report a rare case of paraneoplastic neurological syndrome with dual seropositivity of anti-aquaporin-4 and myelin oligodendrocyte glycoprotein antibodies in a 40 year-old woman with metastatic triple-negative breast cancer. She received multiple lines of anti-neoplastic treatment, including immunotherapy with pembrolizumab, as well as cytotoxic chemotherapy. Paraneoplastic meningoencephalomyelitis developed 2 years after diagnosis of breast cancer and 1 year after discontinuation of immunotherapy with pembrolizumab. She first developed longitudinally extending transverse myelitis followed by left optic neuritis and meningoencephalitis with new enhancing lesions in the brain and spinal leptomeninges. Cerebrospinal fluid analysis during both episodes showed normal glucose and protein, and elevated white blood cell count. Cytology was negative for malignancy. Cerebrospinal fluid was positive for neuromyelitis optica immunoglobulin G antibody anti-aquaporin-4, and autoimmune myelopathy panel was positive for myelin oligodendrocyte glycoprotein antibody. The patient had significant clinical and radiographic improvement after completion of five cycles of plasmapheresis followed by intravenous immunoglobulin. She did not have recurrence of paraneoplastic syndrome with maintenance rituximab every 6 months and daily low-dose prednisone. She succumbed to progressive systemic metastatic disease 4.5 years after her breast cancer diagnosis. This case shows that these antibodies can occur concurrently and cause clinical features, such as both neuromyelitis optica spectrum disorder and myelin oligodendrocyte glycoprotein antibody disease, in a patient with a singular type of cancer. We highlight the importance of testing for paraneoplastic etiology in cancer patients with radiographic menigoencephalomyelitis or meningitis with atypical symptoms of meningeal carcinomatosis and/or cerebrospinal fluid profile negative for leptomeningeal carcinomatosis.

Evaluation of a Reference-Free Collision Cross Section Calibration Strategy for Proteomics Using SLIM-Based High-Resolution Ion Mobility Spectrometry-Mass Spectrometry.

Ion mobility spectrometry (IMS) is a gas-phase analytical technique that separates ions with different sizes and shapes and is compatible with mass spectrometry (MS) to provide an additional separation dimension. The rapid nature of the IMS separation combined with the high sensitivity of MS-based detection and the ability to derive structural information on analytes in the form of the property collision cross section (CCS) makes IMS particularly well-suited for characterizing complex samples in -omics applications. In such applications, the quality of CCS from IMS measurements is critical to confident annotation of the detected components in the complex -omics samples. However, most IMS instrumentation in mainstream use requires calibration to calculate CCS from measured arrival times, with the most notable exception being drift tube IMS measurements using multifield methods. The strategy for calibrating CCS values, particularly selection of appropriate calibrants, has important implications for CCS accuracy, reproducibility, and transferability between laboratories. The conventional approach to CCS calibration involves explicitly defining calibrants ahead of data acquisition and crucially relies upon availability of reference CCS values. In this work, we present a novel reference-free approach to CCS calibration which leverages trends among putatively identified features and computational CCS prediction to conduct calibrations post-data acquisition and without relying on explicitly defined calibrants. We demonstrated the utility of this reference-free CCS calibration strategy for proteomics application using high-resolution structures for lossless ion manipulations (SLIM)-based IMS-MS. We first validated the accuracy of CCS values using a set of synthetic peptides and then demonstrated using a complex peptide sample from cell lysate.

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis.

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to understand their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work, we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transformed with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produced. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of detailed DB positions and enzymatic specificity.

A Systematic Review of Clinical Trials Comparing Radiation Therapy Versus Radical Prostatectomy in Prostate Cancer.

The treatment landscape for localized and regional prostate cancer includes active surveillance, radiation therapy (RT), and radical prostatectomy (RP). Population-based studies comparing RP to radiation reveal conflicting results due to methodological flaws. This systematic review and pooled analysis of studies aim to compare cause-specific survival (CSS), overall survival (OS), disease-free survival (DFS) and toxicity outcomes, comparing RP to RT in the management of prostate cancer. This systematic review search included the PubMed, Embase, and Cochrane libraries according to the PRISMA statement with the inception of each database up to June 24, 2023. Randomized phase 2 or 3 clinical trials that compared RP to RT in prostate cancer were included. The forest plot for the Odds ratio (OR) was plotted using the Mantel-Haenszel method, and the Z test was used to assess significance. A fixed effects model was used for meta-analysis. The search yielded seven completed randomized clinical trials and four ongoing trials. The majority of complete trials had low to intermediate-risk patient populations. OR for OS was 1.00 with 95% CI, 0.71-1.41 (P-value: 0.98), CSS OR was 0.99 with 95% CI, 0.45-2.18 (P-value 0.11), OR for DFS was 1.26 with 95% CI, 0.89-1.78 (P-value 0.19) when comparing RP to RT. The rate of distant metastatic disease was 2.3% in the RP versus 2.9% in the RT at 10 years. The rate of second malignant neoplasms was 4.5% in the RP compared to 4.2% in the RT arm at 10 years. RP caused more urinary symptoms, with a predominance of the need for urinary pads and a higher incidence of sexual dysfunction, and RT caused a higher incidence of bowel symptoms, such as blood in stools and fecal incontinence. This study provides evidence that the treatment-related outcomes are similar in patients with low to intermediate-risk prostate cancer when comparing RP to RT. Multidisciplinary treatment approaches and factoring patients' values and preferences should form the cornerstone of the ideal treatment option for each patient with localized prostate cancer. Patients with prostate cancer have an equal chance of being cancer-free and alive at 10 years with either RP or RT. In terms of side effects, RP causes more urine leakage and loss of erections, whereas RT tends to cause more bowel side effects, such as blood in stools and fecal leakage.