Topically Applied therapies for the treatment of skin disease : Past Present and Future.

The purpose of this review is to summarize essential biological, pharmaceutical and clinical aspects in the field of topically applied medicines that may help scientists when trying to develop new topical medicines. After a brief history of topical drug delivery, a review of the structure and function of the skin, routes of drug absorption and their limitations is then provided. The most prevalent diseases and current topical treatment approaches are then detailed, the organization of which reflects the key disease categories of autoimmune and inflammatory, microbial infections, skin cancers and genetic skin diseases. The complexity of topical product development through to large scale manufacture along with recommended risk mitigation approaches is then highlighted. As such topical treatments are applied externally patient preferences along with the challenges they invoke are then described and finally the future of this field of drug delivery is discussed with the emphasis on areas that are more likely to yield significant improvements over the topical medicines in current use or would expand the range of medicines and diseases treatable by this route of administration. Significance Statement This review of the key aspects the skin, its associated diseases and current treatments along with the intricacies of topical formulation development should be helpful in making judicious decisions about the development of new or improved topical medicines. These aspects include the choices of the active ingredients, formulations, the target patient populations preferences and limitations and the future with regards to new skin diseases and topical medicine approaches.

Offline Two-Dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation.

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C. difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

Epitaxial Stabilization and Persistent Nucleation of the 3C Polymorph of Ba0.6Sr0.4MnO3.

Ba-rich compositions in the BaxSr1-xMnO3 (BSMO) cubic perovskite (3C) system are magnetic ferroelectrics and are of interest for their strong magnetoelectric coupling. Beyond x = 0.5, they only form in hexagonal polymorphs. Here, the 3C phase boundary is pushed to Ba0.6Sr0.4MnO3 for the thin films. Using regular pulsed laser deposition (rPLD), 3C Ba0.6Sr0.4MnO3 could be epitaxially stabilized on DyScO3 (101)o substrates by using a 0.1% O2/99.9% N2 gas mixture. However, the 3C phase was mixed with the 4H polymorph for films 24 nm thick and above, and the films were relatively rough. To improve flatness and phase purity, changes in growth kinetics were investigated and interval PLD (iPLD) was especially effective. In iPLD, deposition is interrupted after completion of approximately one monolayer, and the deposit is annealed for a specific period of time before repeating. Both film flatness and, more importantly, the volume of the 3C polymorph improved with iPLD, resulting in 40 nm single-phase films. The results imply that iPLD improves the persistent nucleation of highly metastable phases and offers a new approach to epitaxial stabilization of novel materials, including more Ba-rich BSMO compositions in the 3C structure.

Sex modulates the diet-induced changes to the plasma lipidome in a rat model of cardiorespiratory fitness.

OBJECTIVE: Individuals with higher intrinsic cardiorespiratory fitness (CRF) experience decreased rates of cardiometabolic disease and mortality, and high CRF is associated with increased utilization of fatty acids (FAs) for energy. Studies suggest a complex relationship between CRF, diet, and sex with health outcomes, but this interaction is understudied. We hypothesized that FA utilization differences by fitness and sex could be detected in the plasma metabolome when rats or humans were fed a high carbohydrate (HC) or high fat (HF) diet. METHODS: Male and female rats selectively bred for low (LCR) and high (HCR) CRF were fed a chow diet or a sucrose-free HF (45 % fat) or HC (10 % fat) diet. Plasma samples were collected at days 0, 3, and 14. Human plasma data was collected from male and female participants who were randomized into a HC or HF diet for 21 days. Samples were analyzed using liquid chromatography-mass spectrometry and regression statistics were used to quantify the effect of diet, CRF, and sex on the lipidome. RESULTS: In rats, the baseline lipidome is more significantly influenced by sex than by CRF, especially as elevated diglycerides, triglycerides, phosphatidylcholines, and lysophosphatidylcholines in males. A dynamic response to diet was observed 3 days after diet, but after 14 days of either diet, the lipidome was modulated by sex with a larger effect size than by diet. Data from the human study also suggests a sex-dependent response to diet with opposite directionality of affect compared to rats, highlighting species-dependent responses to dietary intervention.

Offline Two-dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome.

Compound identification is an essential task in the workflow of untargeted metabolomics since the interpretation of the data in a biological context depends on the correct assignment of chemical identities to the features it contains. Current techniques fall short of identifying all or even most observable features in untargeted metabolomics data, even after rigorous data cleaning approaches to remove degenerate features are applied. Hence, new strategies are required to annotate the metabolome more deeply and accurately. The human fecal metabolome, which is the focus of substantial biomedical interest, is a more complex, more variable, yet lesser-investigated sample matrix compared to widely studied sample types like human plasma. This manuscript describes a novel experimental strategy using multidimensional chromatography to facilitate compound identification in untargeted metabolomics. Pooled fecal metabolite extract samples were fractionated using offline semi-preparative liquid chromatography. The resulting fractions were analyzed by an orthogonal LC-MS/MS method, and the data were searched against commercial, public, and local spectral libraries. Multidimensional chromatography yielded more than a 3-fold improvement in identified compounds compared to the typical single-dimensional LC-MS/MS approach and successfully identified several rare and novel compounds, including atypical conjugated bile acid species. Most features identified by the new approach could be matched to features that were detectable but not identifiable in the original single-dimension LC-MS data. Overall, our approach represents a powerful strategy for deeper annotation of the metabolome that can be implemented with commercially-available instrumentation, and should apply to any dataset requiring deeper annotation of the metabolome.

Architecture of androgen receptor pathways amplifying glucagon-like peptide-1 insulinotropic action in male pancreatic ß cells.

Male mice lacking the androgen receptor (AR) in pancreatic ß cells exhibit blunted glucose-stimulated insulin secretion (GSIS), leading to hyperglycemia. Testosterone activates an extranuclear AR in ß cells to amplify glucagon-like peptide-1 (GLP-1) insulinotropic action. Here, we examined the architecture of AR targets that regulate GLP-1 insulinotropic action in male ß cells. Testosterone cooperates with GLP-1 to enhance cAMP production at the plasma membrane and endosomes via: (1) increased mitochondrial production of CO2, activating the HCO3--sensitive soluble adenylate cyclase; and (2) increased Gαs recruitment to GLP-1 receptor and AR complexes, activating transmembrane adenylate cyclase. Additionally, testosterone enhances GSIS in human islets via a focal adhesion kinase/SRC/phosphatidylinositol 3-kinase/mammalian target of rapamycin complex 2 actin remodeling cascade. We describe the testosterone-stimulated AR interactome, transcriptome, proteome, and metabolome that contribute to these effects. This study identifies AR genomic and non-genomic actions that enhance GLP-1-stimulated insulin exocytosis in male ß cells.

Nutrient-dependent regulation of ß-cell proinsulin content.

Insulin is made from proinsulin, but the extent to which fasting/feeding controls the homeostatically regulated proinsulin pool in pancreatic ß-cells remains largely unknown. Here, we first examined ß-cell lines (INS1E and Min6, which proliferate slowly and are routinely fed fresh medium every 2-3 days) and found that the proinsulin pool size responds to each feeding within 1 to 2 h, affected both by the quantity of fresh nutrients and the frequency with which they are provided. We observed no effect of nutrient feeding on the overall rate of proinsulin turnover as quantified from cycloheximide-chase experiments. We show that nutrient feeding is primarily linked to rapid dephosphorylation of translation initiation factor eIF2α, presaging increased proinsulin levels (and thereafter, insulin levels), followed by its rephosphorylation during the ensuing hours that correspond to a fall in proinsulin levels. The decline of proinsulin levels is blunted by the integrated stress response inhibitor, ISRIB, or by inhibition of eIF2α rephosphorylation with a general control nonderepressible 2 (not PERK) kinase inhibitor. In addition, we demonstrate that amino acids contribute importantly to the proinsulin pool; mass spectrometry shows that ß-cells avidly consume extracellular glutamine, serine, and cysteine. Finally, we show that in both rodent and human pancreatic islets, fresh nutrient availability dynamically increases preproinsulin, which can be quantified without pulse-labeling. Thus, the proinsulin available for insulin biosynthesis is rhythmically controlled by fasting/feeding cycles.

Age-related reduction in brain ACE-2 is not exacerbated by Alzheimer's disease pathology in mouse models of Alzheimer's disease.

An imbalance in the circulatory and organ-specific renin-angiotensin system (RAS) pathways is associated with age-related dysfunction and disease including cardiovascular burden and more recently Alzheimer's disease (AD). It is currently unclear whether an age-associated imbalance in components of the RAS within the brain precedes the onset of AD or whether a RAS imbalance is associated with the onset of disease pathology and cognitive decline. Angiotensin-converting enzyme-1 (ACE-1) and -2 (ACE-2) protein (ELISA) and enzyme activity (FRET assay), markers of the classical and counter-regulatory RAS axis respectively, and Ang-II and Ang-(1-7) peptide levels (ELISA), were measured in the left cortex across four transgenic AD mouse models of amyloid pathology (5xFAD - 2, 6, and 12 months of age; Apd9 - 3-4, 12, and 18 months of age; Tg2576 - 3-4 and 24 months of age; and PDAPP - 3-4, 7, 11, 15, and 18 months of age) and littermate wild-type (WT) controls. ACE-1 level, and enzyme activity, was unaltered in relation to age in WT mice and across all four models. In contrast, ACE-2 level and enzyme activity, was reduced and Ang-II increased with ageing in both WT animals and disease models. The changes in ACE-2 and Ang-II in AD models mirrored WT mice, except for the 5xFAD model, when the reduction in ACE-2 (and elevated Ang-II) was observed at a younger age. These data indicate an age-related dysregulation of brain RAS is likely to be driven by a reduction in ACE-2. The reduction in ACE-2 occurs at a young age, coinciding with early pathological changes and the initial deposition of Aß, and preceding neuronal loss and cognitive decline, in the transgenic AD models. However, the age-related loss was mirrored in WT mice suggesting that the change was independent of pathological Aß deposition.

Sustained Perturbation of Metabolism and Metabolic Subphenotypes Are Associated With Mortality and Protein Markers of the Host Response.

Perturbed host metabolism is increasingly recognized as a pillar of sepsis pathogenesis, yet the dynamic alterations in metabolism and its relationship to other components of the host response remain incompletely understood. We sought to identify the early host-metabolic response in patients with septic shock and to explore biophysiological phenotyping and differences in clinical outcomes among metabolic subgroups. DESIGN: We measured serum metabolites and proteins reflective of the host-immune and endothelial response in patients with septic shock. SETTING: We considered patients from the placebo arm of a completed phase II, randomized controlled trial conducted at 16 U.S. medical centers. Serum was collected at baseline (within 24 hr of the identification of septic shock), 24-hour, and 48-hour postenrollment. Linear mixed models were built to assess the early trajectory of protein analytes and metabolites stratified by 28-day mortality status. Unsupervised clustering of baseline metabolomics data was conducted to identify subgroups of patients. PATIENTS: Patients with vasopressor-dependent septic shock and moderate organ dysfunction that were enrolled in the placebo arm of a clinical trial. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Fifty-one metabolites and 10 protein analytes were measured longitudinally in 72 patients with septic shock. In the 30 patients (41.7%) who died prior to 28 days, systemic concentrations of acylcarnitines and interleukin (IL)-8 were elevated at baseline and persisted at T24 and T48 throughout early resuscitation. Concentrations of pyruvate, IL-6, tumor necrosis factor-α, and angiopoietin-2 decreased at a slower rate in patients who died. Two groups emerged from clustering of baseline metabolites. Group 1 was characterized by higher levels of acylcarnitines, greater organ dysfunction at baseline and postresuscitation (p < 0.05), and greater mortality over 1 year (p < 0.001). CONCLUSIONS: Among patients with septic shock, nonsurvivors exhibited a more profound and persistent dysregulation in protein analytes attributable to neutrophil activation and disruption of mitochondrial-related metabolism than survivors.

FMO rewires metabolism to promote longevity through tryptophan and one carbon metabolism in C. elegans.

Flavin containing monooxygenases (FMOs) are promiscuous enzymes known for metabolizing a wide range of exogenous compounds. In C. elegans, fmo-2 expression increases lifespan and healthspan downstream of multiple longevity-promoting pathways through an unknown mechanism. Here, we report that, beyond its classification as a xenobiotic enzyme, fmo-2 expression leads to rewiring of endogenous metabolism principally through changes in one carbon metabolism (OCM). These changes are likely relevant, as we find that genetically modifying OCM enzyme expression leads to alterations in longevity that interact with fmo-2 expression. Using computer modeling, we identify decreased methylation as the major OCM flux modified by FMO-2 that is sufficient to recapitulate its longevity benefits. We further find that tryptophan is decreased in multiple mammalian FMO overexpression models and is a validated substrate for FMO-2. Our resulting model connects a single enzyme to two previously unconnected key metabolic pathways and provides a framework for the metabolic interconnectivity of longevity-promoting pathways such as dietary restriction. FMOs are well-conserved enzymes that are also induced by lifespan-extending interventions in mice, supporting a conserved and important role in promoting health and longevity through metabolic remodeling.

The unknown lipids project: harmonized methods improve compound identification and data reproducibility in an inter-laboratory untargeted lipidomics study.

Untargeted lipidomics allows analysis of a broader range of lipids than targeted methods and permits discovery of unknown compounds. Previous ring trials have evaluated the reproducibility of targeted lipidomics methods, but inter-laboratory comparison of compound identification and unknown feature detection in untargeted lipidomics has not been attempted. To address this gap, five laboratories analyzed a set of mammalian tissue and biofluid reference samples using both their own untargeted lipidomics procedures and a common chromatographic and data analysis method. While both methods yielded informative data, the common method improved chromatographic reproducibility and resulted in detection of more shared features between labs. Spectral search against the LipidBlast in silico library enabled identification of over 2,000 unique lipids. Further examination of LC-MS/MS and ion mobility data, aided by hybrid search and spectral networking analysis, revealed spectral and chromatographic patterns useful for classification of unknown features, a subset of which were highly reproducible between labs. Overall, our method offers enhanced compound identification performance compared to targeted lipidomics, demonstrates the potential of harmonized methods to improve inter-site reproducibility for quantitation and feature alignment, and can serve as a reference to aid future annotation of untargeted lipidomics data.

Functional Engagement of the PD-1/PD-L1 Complex But Not PD-L1 Expression Is Highly Predictive of Patient Response to Immunotherapy in Non-Small-Cell Lung Cancer.

PURPOSE: In many cancers, the expression of immunomodulatory ligands leads to immunoevasion, as exemplified by the interaction of PD-L1 with PD-1 on tumor-infiltrating lymphocytes. Profound advances in cancer treatments have come with the advent of immunotherapies directed at blocking these immuno-suppressive ligand-receptor interactions. However, although there has been success in the use of these immune checkpoint interventions, correct patient stratification for these therapies has been challenging. MATERIALS AND METHODS: To address this issue of patient stratification, we have quantified the intercellular PD-1/PD-L1 interaction in formalin-fixed paraffin-embedded tumor samples from patients with non-small cell lung carcinoma, using a high-throughput automated quantitative imaging platform (quantitative functional proteomics [QF-Pro]). RESULTS: The multisite blinded analysis across a cohort of 188 immune checkpoint inhibitor-treated patients demonstrated the intra- and intertumoral heterogeneity of PD-1/PD-L1 immune checkpoint engagement and notably showed no correlation between the extent of PD-1/PD-L1 interaction and PD-L1 expression. Importantly, PD-L1 expression scores used clinically to stratify patients correlated poorly with overall survival; by contrast, patients showing a high PD-1/PD-L1 interaction had significantly better responses to anti-PD-1/PD-L1 treatments, as evidenced by increased overall survival. This relationship was particularly strong in the setting of first-line treatments. CONCLUSION: The functional readout of PD-1/PD-L1 interaction as a predictive biomarker for the stratification of patients with non-small-cell lung carcinoma, combined with PD-L1 expression, should significantly improve the response rates to immunotherapy. This would both capture patients excluded from checkpoint immunotherapy (high PD-1/PD-L1 interaction but low PD-L1 expression, 24% of patients) and additionally avoid treating patients who despite their high PD-L1 expression do not respond and suffer from side effects.

The current status of robotic colorectal surgery training programmes.

Robotic-assisted colorectal surgery (RACS) is steadily increasing in popularity with an annual growth in the number of colorectal procedures undertaken robotically. Further upscaling of RACS requires structured and standardised robotic training to safeguard high-quality clinical outcomes. The aims of this systematic review were to assess the structure and assessment metrics of currently established RACS training programmes. A systematic review following Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines was performed. Searches were performed of the Ovid Medline, Embase and Web of Science databases between 2000 and 27th November 2021 to identify studies reporting on training curricula in RACS. Core components of training programmes and their relevant outcome assessment metrics were extracted. Thirteen studies were identified, with all training programmes designed for the da Vinci platform (Intuitive Surgical, Inc., Sunnyvale, CA, USA). Common elements of multimodal programmes included theoretical knowledge (76.9%), case observation (53.8%), simulation (100%) and proctored training (76.9%). Robotic skills acquisition was assessed primarily during the simulation phase (n = 4, 30.1%) and proctoring phase (n = 10, 76.9%). Performance metrics, consisting of time or assessment scores for VR simulation were only mandated in four (30.1%) studies. Objective assessment following proctored training was variably reported and employed a range of assessment metrics, including direct feedback (n = 3, 23.1%) or video feedback (n = 8, 61.5%). Five (38.4%) training programmes used the Global Assessment Score (GAS) forms. There is a broad consensus on the core multimodal components across current RACS training programmes; however, validated objective assessment is limited and needs to be appropriately standardised to ensure reproducible progression criteria and competency-based metrics are produced to robustly assess progression and competence.

The robotic learning curve for a newly appointed colorectal surgeon.

Robotic colorectal surgery allows for better ergonomics, superior retraction, and fine movements in the narrow anatomy of the pelvis. Recent years have seen the uptake of robotic surgery in all pelvic surgeries specifically in low rectal malignancies. However, the learning curve of robotic surgery in this cohort is unclear as established training pathways are not formalized. This study looks at the experience and learning curve of a single laparoscopic trained surgeon in performing safe and effective resections, mainly for low rectal and anal malignancies using the da Vinci robotic system by evaluating metrics related to surgical process and patient outcome. A serial retrospective review of the robotic colorectal surgery database, in the University Hospital Coventry and Warwickshire (UHCW), was undertaken. All 48 consecutive cases, performed by a recently qualified colorectal surgeon, were included in our study. The surgical process was evaluated using both console and total operative time recorded in each case along with the adequacy of resections performed; in addition, patient-related outcomes including intraoperative and postoperative complications were analyzed to assess differences in the learning curve. Forty eight sequential recto-sigmoid resections were included in the study performed by a single surgeon. The cases were divided into four cohorts in chronological order with comparable demographics, tumour stage, location, and complexity of the operation (mean age 65, male 79%, and female 29%). The results showed that the mean console time dropped from 3 to 2.5 h, while total operative time dropped from 6 h to 5.5 h as the surgeon became more experienced; however, this was not found to be statistically significant. In addition, no significant difference in pathological staging was seen over the study period. No major intra-op and post-op complications were observed and no 30-day mortality was recorded. Moreover, after 30 cases, the learning curve developed the plateau phase, suggesting the gain of maximum proficiency of skills required for robotic colorectal resections. The learning curve in robotic rectal surgery is short and flattens early; complication rates are low during the learning curve and continue to decrease with time. This shows that with proper training and proctoring, new colorectal surgeons can be trained in a short time to perform elective colorectal pelvic resections.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics.

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

Efficacy and Safety of a Colic Relief Remedy in Infantile Colic.

Infantile colic is the most frequent reason of infant and parental distress. An open-label single-group clinical study was conducted primarily to evaluate the effect of 14-day colic relief remedy administration (1.25 ml orally during colic episode) on average daily crying time in infants compared to baseline. In addition, the percentage of responders, sleep pattern, frequency and severity of gastrointestinal symptoms, tolerability of the product and percentage population with relapse of symptoms post product discontinuation were evaluated. Thirty infants aged 3 to 16 weeks with no significant clinical illness were enrolled in the study with their caregivers. Daily inconsolable crying time and flatulence were significantly reduced (P < .05) within a week of intervention and a sustained effect was observed after discontinuation of the product. The findings of the present study indicate that this product effectively reduced abdominal distension and pain, resulting in a significant decrease in the daily crying of the infants. Trial registration: The study was registered with the Clinical Trials Registry India (CTRI) (http://ctri.nic.in/Clinicaltrials/login.php) bearing Reg. No: CTRI/2021/03/031762, Date: March 8, 2021.

London parochial burial records from 1563 to 1665 indicate higher plague death rates for males than females: Some possible demographic and social explanations.

The burial rates of males and females in early modern central London were compared to investigate a possible bias towards male mortality in the plague years of 1563, 1593, 1603, 1625 and 1665. The burial records of sixteen parishes were examined and compared with the five-year periods immediately preceding each plague year when recorded burials were substantially less. A markedly higher burial rate for males was detected in each plague year but this can be partly attributed to a general preponderance of males in the central London population since there was a similar but lesser bias in non-plague years. In the plague years the difference between the frequency of male and female adult burials appears to have been enhanced by the preferential migration of women of childbearing age out of the city since fewer births were recorded in months when plague was rife. Furthermore, when a sample of households was investigated, husbands were significantly more likely to have been buried than their wives. These findings were largely applicable to the plague years of 1603, 1625 and 1665 but were far less apparent in 1563 and 1593. In general, there were more burials of boys than girls in non-plague years which is the expected consequence of their greater vulnerability to childhood diseases. This difference diminished in plague years so that the burials of girls and boys approached parity at a time when burials of children of both sexes were significantly increased. Possibly, plague did not discriminate between the sexes and this characteristic tended to mask the usual vulnerability of boys.

Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics.

Untargeted metabolomics is an essential component of systems biology research, but it is plagued by a high proportion of detectable features not identified with a chemical structure. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments produce spectra that can be searched against databases to help identify or classify these unknowns, but many features do not generate spectra of sufficient quality to enable successful annotation. Here, we explore alterations to gradient length, mass loading, and rolling precursor ion exclusion parameters for reversed phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) that improve compound identification performance for human plasma samples. A manual review of spectral matches from the HILIC data set was used to determine reasonable thresholds for search score and other metrics to enable semi-automated MS/MS data analysis. Compared to typical LC-MS/MS conditions, methods adapted for compound identification increased the total number of unique metabolites that could be matched to a spectral database from 214 to 2052. Following data alignment, 68.0% of newly identified features from the modified conditions could be detected and quantitated using a routine 20-min LC-MS run. Finally, a localized machine learning model was developed to classify the remaining unknowns and select a subset that shared spectral characteristics with successfully identified features. A total of 576 and 749 unidentified features in the HILIC and RPLC data sets were classified by the model as high-priority unknowns or higher-importance targets for follow-up analysis. Overall, our study presents a simple strategy to more deeply annotate untargeted metabolomics data for a modest additional investment of time and sample.