Precision arbovirus serology with a pan-arbovirus peptidome.

Arthropod-borne viruses represent a crucial public health threat. Current arboviral serology assays are either labor intensive or incapable of distinguishing closely related viruses, and many zoonotic arboviruses that may transition to humans lack any serologic assays. In this study, we present a programmable phage display platform, ArboScan, that evaluates antibody binding to overlapping peptides that represent the proteomes of 691 human and zoonotic arboviruses. We confirm that ArboScan provides detailed antibody binding information from animal sera, human sera, and an arthropod blood meal. ArboScan identifies distinguishing features of antibody responses based on exposure history in a Colombian cohort of Zika patients. Finally, ArboScan details epitope level information that rapidly identifies candidate epitopes with potential protective significance. ArboScan thus represents a resource for characterizing human and animal arbovirus antibody responses at cohort scale.

Age-induced changes in anti-tumor immunity alter the tumor immune infiltrate and impact response to immuno-oncology treatments.

Introduction: Immuno-oncology (IO) research relies heavily on murine syngeneic tumor models. However, whilst the average age for a cancer diagnosis is 60 years or older, for practical purposes the majority of preclinical studies are conducted in young mice, despite the fact that ageing has been shown to have a significant impact on the immune response. Methods: Using aged (60-72 weeks old) mice bearing CT26 tumors, we investigated the impact of ageing on tumor growth as well as the immune composition of the tumor and peripheral lymphoid organs. Results: We found many differences in the immune cell composition of both the tumor and tumor-draining lymph node between aged and young mice, such as a reduction in the naïve T cell population and a decreased intratumoral CD8/Treg ratio in aged animals. We hypothesized that these differences may contribute to impaired anti-cancer immune responses in aged mice and therefore assessed the anti-tumor efficacy of different IO therapies in aged mice, including both co-stimulation (using an anti-OX40 antibody) and immune checkpoint blockade (using anti-PD-L1 and anti-CTLA-4 antibodies). Whilst aged mice retained the capacity to generate anti-tumor immune responses, these were significantly attenuated when compared to the responses observed in young mice. Discussion: These differences highlight the importance of age-related immunological changes in assessing and refining the translational insights gained from preclinical mouse models.

Impact of biological sex and sex hormones on molecular signatures of skeletal muscle at rest and in response to distinct exercise training modes.

Substantial divergence in cardio-metabolic risk, muscle size, and performance exists between men and women. Considering the pivotal role of skeletal muscle in human physiology, we investigated and found, based on RNA sequencing (RNA-seq), that differences in the muscle transcriptome between men and women are largely related to testosterone and estradiol and much less related to genes located on the Y chromosome. We demonstrate inherent unique, sex-dependent differences in muscle transcriptional responses to aerobic, resistance, and combined exercise training in young and older cohorts. The hormonal changes with age likely explain age-related differential expression of transcripts. Furthermore, in primary human myotubes we demonstrate the profound but distinct effects of testosterone and estradiol on amino acid incorporation to multiple individual proteins with specific functions. These results clearly highlight the potential of designing exercise programs tailored specifically to men and women and have implications for people who change gender by altering their hormone profile.

Genetic insights into the age-specific biological mechanisms governing human ovarian aging.

There is currently little evidence that the genetic basis of human phenotype varies significantly across the lifespan. However, time-to-event phenotypes are understudied and can be thought of as reflecting an underlying hazard, which is unlikely to be constant through life when values take a broad range. Here, we find that 74% of 245 genome-wide significant genetic associations with age at natural menopause (ANM) in the UK Biobank show a form of age-specific effect. Nineteen of these replicated discoveries are identified only by our modeling framework, which determines the time dependency of DNA-variant age-at-onset associations without a significant multiple-testing burden. Across the range of early to late menopause, we find evidence for significantly different underlying biological pathways, changes in the signs of genetic correlations of ANM to health indicators and outcomes, and differences in inferred causal relationships. We find that DNA damage response processes only act to shape ovarian reserve and depletion for women of early ANM. Genetically mediated delays in ANM were associated with increased relative risk of breast cancer and leiomyoma at all ages and with high cholesterol and heart failure for late-ANM women. These findings suggest that a better understanding of the age dependency of genetic risk factor relationships among health indicators and outcomes is achievable through appropriate statistical modeling of large-scale biobank data.

Patients Undergoing Systemic Anti-Cancer Therapy Who Require Surgical Intervention: What Surgeons Need to Know.

As part of routine cancer care, patients may undergo elective surgery with the aim of long-term cure. Some of these patients will receive systemic anti-cancer therapy (SACT) in the neoadjuvant and adjuvant settings. The majority of patients, usually with locally advanced or metastatic disease, will receive SACT with palliative intent. These treatment options are expanding beyond traditional chemotherapy to include targeted therapies, immunotherapy, hormone therapy, radionuclide therapy and gene therapy. During treatment, some patients will require surgical intervention on an urgent or emergency basis. This narrative review examined the evidence base for SACT-associated surgical risk and the precautions that a surgical team should consider in patients undergoing SACT.

IL-38 blockade induces anti-tumor immunity by abrogating tumor-mediated suppression of early immune activation.

Immune checkpoint inhibitors that overcome T cell suppressive mechanisms in tumors have revolutionized the treatment of cancer but are only efficacious in a small subset of patients. Targeting suppressive mechanisms acting on innate immune cells could significantly improve the incidence of clinical response by facilitating a multi-lineage response against the tumor involving both adaptive and innate immune systems. Here, we show that intra-tumoral interleukin (IL)-38 expression is a feature of a large frequency of head and neck, lung and cervical squamous cancers and correlates with reduced immune cell numbers. We generated IMM20324, an antibody that binds human and mouse IL-38 proteins and inhibits the binding of IL-38 to its putative receptors, interleukin 1 receptor accessory protein-like 1 (IL1RAPL) and IL-36R. In vivo, IMM20324 demonstrated a good safety profile, delayed tumor growth in a subset of mice in an EMT6 syngeneic model of breast cancer, and significantly inhibited tumor expansion in a B16.F10 melanoma model. Notably, IMM20324 treatment resulted in the prevention of tumor growth following re-implantation of tumor cells, indicating the induction of immunological memory. Furthermore, exposure of IMM20324 correlated with decreased tumor volume and increased levels of intra-tumoral chemokines. Together, our data suggest that IL-38 is expressed in a high frequency of cancer patients and allows tumor cells to suppress anti-tumor immunity. Blockade of IL-38 activity using IMM20324 can re-activate immunostimulatory mechanisms in the tumor microenvironment leading to immune infiltration, the generation of tumor-specific memory and abrogation of tumor growth.

Future therapeutic strategies in the treatment of extrapulmonary neuroendocrine carcinoma: a review.

Neuroendocrine neoplasms (NENs) are rare malignancies arising most commonly in the gastrointestinal and bronchopulmonary systems. Neuroendocrine carcinomas (NECs) are a subgroup of NENs characterised by aggressive tumour biology, poor differentiation and dismal prognosis. Most NEC primary lesions arise in the pulmonary system. However, a small proportion arise outside of the lung and are termed extrapulmonary (EP)-, poorly differentiated (PD)-NECs. Patients with local or locoregional disease may benefit from surgical excision; however, this is often not an option, due to late presentation. To date, treatment has mirrored that of small-cell lung cancer, with platinum-etoposide forming the basis of first-line treatment. There is a lack of consensus in relation to the most effective second-line treatment option. Low incidence, an absence of representative preclinical models and a lack of understanding of the tumour microenvironment all present challenges to drug development in this disease group. However, progress made in elucidating the mutational landscape of EP-PD-NEC and the observations made in several clinical trials are paving the way towards improving outcomes for these patients. The optimisation and strategic delivery of chemotherapeutic interventions according to tumour characteristics and the utilisation of targeted and immune therapies in clinical studies have yielded mixed results. Targeted therapies that complement specific genetic aberrations are under investigation, including AURKA inhibitors in those with MYCN amplifications, BRAF inhibitors in those with BRAFV600E mutations and EGFR suppression, and Ataxia Telangiectasia and Rad3-related inhibitors in patients with ATM mutations. Immune checkpoint inhibitors (ICIs) have conferred promising results in several clinical trials, particularly with dual ICIs and in combination with targeted therapy or chemotherapy. However, further prospective investigations are required to elucidate the impact of programmed cell death ligand 1 expression, tumour mutational burden and microsatellite instability on response. This review aims to explore the most recent developments in the treatment of EP-PD-NEC and contribute towards the requirement for clinical guidance founded on prospective evidence.

The Use of Artificial Neural Networks for the Prediction of Surgical Site Infection Following TKA.

This is a retrospective study. Surgical site infection (SSI) is associated with adverse postoperative outcomes following total knee arthroplasty (TKA). However, accurately predicting SSI remains a clinical challenge due to the multitude of patient and surgical factors associated with SSI. This study aimed to develop and validate machine learning models for the prediction of SSI following primary TKA. This is a retrospective study for patients who underwent primary TKA. Chart review was performed to identify patients with superficial or deep SSIs, defined in concordance with the criteria of the Musculoskeletal Infection Society. All patients had a minimum follow-up of 2 years (range: 2.1-4.7 years). Five machine learning algorithms were developed to predict this outcome, and model assessment was performed by discrimination, calibration, and decision curve analysis. A total of 10,021 consecutive primary TKA patients was included in this study. At an average follow-up of 2.8 ± 1.1 years, SSIs were reported in 404 (4.0%) TKA patients, including 223 superficial SSIs and 181 deep SSIs. The neural network model achieved the best performance across discrimination (area under the receiver operating characteristic curve = 0.84), calibration, and decision curve analysis. The strongest predictors of the occurrence of SSI following primary TKA, in order, were Charlson comorbidity index, obesity (BMI >30 kg/m2), and smoking. The neural network model presented in this study represents an accurate method to predict patient-specific superficial and deep SSIs following primary TKA, which may be employed to assist in clinical decision-making to optimize outcomes in at-risk patients.

Can machine learning models predict failure of revision total hip arthroplasty?

INTRODUCTION: Revision total hip arthroplasty (THA) represents a technically demanding surgical procedure which is associated with significant morbidity and mortality. Understanding risk factors for failure of revision THA is of clinical importance to identify at-risk patients. This study aimed to develop and validate novel machine learning algorithms for the prediction of re-revision surgery for patients following revision total hip arthroplasty. METHODS: A total of 2588 consecutive patients that underwent revision THA was evaluated, including 408 patients (15.7%) with confirmed re-revision THA. Electronic patient records were manually reviewed to identify patient demographics, implant characteristics and surgical variables that may be associated with re-revision THA. Machine learning algorithms were developed to predict re-revision THA and these models were assessed by discrimination, calibration and decision curve analysis. RESULTS: The strongest predictors for re-revision THA as predicted by the four validated machine learning models were the American Society of Anaesthesiology score, obesity (> 35 kg/m2) and indication for revision THA. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.80), calibration and decision curve analysis. Higher net benefits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients. CONCLUSION: This study developed four machine learning models for the prediction of re-revision surgery for patients following revision total hip arthroplasty. The study findings show excellent model performance, highlighting the potential of these computational models to assist in preoperative patient optimization and counselling to improve revision THA patient outcomes. LEVEL OF EVIDENCE: Level III, case-control retrospective analysis.

An evaluation of the inlet obturator oblique view for sacroiliac and transsacral screw placement.

INTRODUCTION: Between 2005 and 2017, the number of closed reduction and internal fixation of pelvic ring injuries increased by 1116%. Percutaneous fixation is currently the only minimally invasive technique that can stabilize the posterior elements of the pelvis. The purpose of this study was to investigate the utility of the inlet obturator oblique view (IOO) with the hypothesis that the IOO view will improve the accuracy of sacroiliac and transsacral screw placement in the S1 or S2 body and improve the accuracy of assessing whether the implant is fully seated against the outer cortex of the ilium. MATERIALS AND METHODS: Ten male pelvic training models were used. Thirty-six screw configurations were inserted by a fellowship trained orthopedic trauma surgeon in appropriately and inappropriately placed sacroiliac and transsacral screw configurations. These configurations were imaged using fluoroscopy in different planes and saved for survey. RESULTS: Fourteen orthopedic professionals reviewed 313 fluoroscopic images. Interrater reliability demonstrated marked improvement in assessment of whether the screw head was seated against the outer cortex of the ilium with the IOO view (kappa = 0.841, without IOO kappa = 0.027). There was a statistically significant difference in overall accuracy (p value < 0.001, OR = 1.57, 95% CI = 1.35-1.84) and whether the screw head was seated (p value < 0.001, OR = 8.14, 95% CI = 5.52-11.99) when compared with and without the IOO view (accuracy with IOO view: 85%, accuracy without IOO view: 78.26%; screw seated with IOO view: 93.93%, screw seated without IOO view: 65.54%). There was no significant difference (p value 0.465, OR = 1.13, 95% CI = 0.82-1.55) determining if the screw was in a safe position (safe with IOO view: 84.64%, safe without IOO view: 83.04%). CONCLUSIONS: Our findings demonstrate that misinterpretation of sacroiliac and transsacral screw placement can occur with the standard fluoroscopic imaging. We suggest the addition of the IOO view increases the overall accuracy of screw placement and whether the screw head is fully seated against the outer table of the ilium. This in turn can improve fixation and potentially improve patient outcomes and decrease adverse events.

The heightened importance of the microbiome in cancer immunotherapy.

The human microbiome is recognized as a key factor in health and disease. This has been further corroborated by identifying changes in microbiome composition and function as a novel hallmark in cancer. These effects are exerted through microbiome interactions with host cells, impacting a wide variety of developmental and physiological processes. In this review, we discuss some of the latest findings on how the bacterial component of the microbiome can influence outcomes for different cancer immunotherapy modalities, highlighting identified mechanisms of action. We also address the clinical efforts to utilize this knowledge to achieve better responses to immunotherapy. A refined understanding of microbiome variations in patients and microbiome-host interactions with cancer therapies is essential to realize optimal clinical responses.

Comparison of DNA methylation patterns across tissue types in infants with tetralogy of Fallot.

BACKGROUND: Environmental factors may influence the development of tetralogy of Fallot (TOF), and DNA methylation patterns may reveal specific chemical signatures of perturbations during cardiac development. We investigated whether blood and buccal cells could be viable surrogates for myocardium. METHODS: We measured epigenome-wide DNA methylation at 866,895 5'-cytosine-phosphate-guanine-3' (CpG) sites in blood (n=3), buccal cells (n=3), and right ventricular myocardium (n=4) collected from infants with TOF and compared the percent of differentially methylated CpG sites across tissue types. Gene-specific DNA methylation profiles were also analyzed for ten representative genes associated with heart development. Welch's ANOVAs compared general methylation between tissue types. RESULTS: Comparison of DNA methylation profiles across blood, buccal, and myocardium suggested myocardium and buccal samples were most similar, differing in DNA methylation at only 1.3% (11,386) of CpG sites whereas myocardium and blood were most dissimilar, having 146,857 statistically dissimilar methylated CpG sites (~17% dissimilarity; adjusted p < 0.01 for each site). Buccal swabs were significantly more variable (p < .001) than either blood or myocardial samples. In gene-specific analyses, SCO2, GATA4, NOTCH4, WNT7A, and DKK2 showed conserved DNA methylation profiles across tissue types, while HAND1, JAG1, NKX2-5, TBX5 and TBX20 showed more distinctive tissue-specific patterns of DNA methylation. CONCLUSIONS: Compared with blood, buccal tissue more closely mirrors the myocardial methylome, with >10-fold similarity. Nevertheless, both buccal and blood tissue capture highly conserved DNA methylation patterns at specific genetic loci related to cardiac development. Buccal cheek swabs may be a useful surrogate tissue type for future investigations of TOF-specific epigenetic profiles.

Characterization of cellular senescence in aging skeletal muscle.

Senescence is a cell fate that contributes to multiple aging-related pathologies. Despite profound age-associated changes in skeletal muscle (SkM), whether its constituent cells are prone to senesce has not been methodically examined. Herein, using single cell and bulk RNA-sequencing and complementary imaging methods on SkM of young and old mice, we demonstrate that a subpopulation of old fibroadipogenic progenitors highly expresses p16 Ink4a together with multiple senescence-related genes and, concomitantly, exhibits DNA damage and chromatin reorganization. Through analysis of isolated myofibers, we also detail a senescence phenotype within a subset of old cells, governed instead by p2 Cip1 . Administration of a senotherapeutic intervention to old mice countered age-related molecular and morphological changes and improved SkM strength. Finally, we found that the senescence phenotype is conserved in SkM from older humans. Collectively, our data provide compelling evidence for cellular senescence as a hallmark and potentially tractable mediator of SkM aging.

Transverse oscillating bubble enhanced laser-driven betatron X-ray radiation generation.

Ultrafast high-brightness X-ray pulses have proven invaluable for a broad range of research. Such pulses are typically generated via synchrotron emission from relativistic electron bunches using large-scale facilities. Recently, significantly more compact X-ray sources based on laser-wakefield accelerated (LWFA) electron beams have been demonstrated. In particular, laser-driven sources, where the radiation is generated by transverse oscillations of electrons within the plasma accelerator structure (so-called betatron oscillations) can generate highly-brilliant ultrashort X-ray pulses using a comparably simple setup. Here, we experimentally demonstrate a method to markedly enhance the parameters of LWFA-driven betatron X-ray emission in a proof-of-principle experiment. We show a significant increase in the number of generated photons by specifically manipulating the amplitude of the betatron oscillations by using our novel Transverse Oscillating Bubble Enhanced Betatron Radiation scheme. We realize this through an orchestrated evolution of the temporal laser pulse shape and the accelerating plasma structure. This leads to controlled off-axis injection of electrons that perform large-amplitude collective transverse betatron oscillations, resulting in increased radiation emission. Our concept holds the promise for a method to optimize the X-ray parameters for specific applications, such as time-resolved investigations with spatial and temporal atomic resolution or advanced high-resolution imaging modalities, and the generation of X-ray beams with even higher peak and average brightness.

Insurance Payer Type Affects Outcomes after Revision Total Joint Arthroplasty: A Matched Cohort Analysis.

Background: The aim of this study is to evaluate the potential effects of insurance payer type on the postoperative outcomes following revision TJA. Methods: A single-institution database was utilized to identify 4,302 consecutive revision THA and TKA. Patient demographics and indications for revision were collected and compared based on patient insurance payer type: (1) Medicaid, (2) Medicare, and (3) private. Propensity score matching and, subsequent, multivariate regression analyses were applied to control for baseline differences between payer groups. Outcomes of interest were rates of complications occurring perioperatively and 90 days post-discharge. Results: After propensity-score-based matching, a total of 2,328 patients remained for further multivariate regression analyses (300 [12.9%] Medicaid, 1022 [43.9%] Medicare, 1006 [43.2%] private). Compared to privately insured patients, Medicaid and Medicare patients had 71% (P<0.01) and 53% (P=0.03) increased odds, respectively, for developing an in-hospital complication. At 90 days post-discharge, compared to privately insured patients, Medicaid and Medicare patients had 88% and 43% odds, respectively, for developing overall major complications. Conclusion: Our propensity-score-matched cohort study found that, compared to privately insured patients, patients with government-sponsored insurance were at an increased risk for developing both major or minor complications perioperatively and at 90-days post-discharge for revision TJA. This suggests that insurance payer type is an independent risk factor for poor outcomes following revision TJA.

The MITRE trial protocol: a study to evaluate the microbiome as a biomarker of efficacy and toxicity in cancer patients receiving immune checkpoint inhibitor therapy.

BACKGROUND: The gut microbiome is implicated as a marker of response to immune checkpoint inhibitors (ICI) based on preclinical mouse models and preliminary observations in limited patient series. Furthermore, early studies suggest faecal microbial transfer may have therapeutic potential, converting ICI non-responders into responders. So far, identification of specific responsible bacterial taxa has been inconsistent, which limits future application. The MITRE study will explore and validate a microbiome signature in a larger scale prospective study across several different cancer types. METHODS: Melanoma, renal cancer and non-small cell lung cancer patients who are planned to receive standard immune checkpoint inhibitors are being recruited to the MITRE study. Longitudinal stool samples are collected prior to treatment, then at 6 weeks, 3, 6 and 12 months during treatment, or at disease progression/recurrence (whichever is sooner), as well as after a severe (≥grade 3 CTCAE v5.0) immune-related adverse event. Additionally, whole blood, plasma, buffy coat, RNA and peripheral blood mononuclear cells (PBMCs) is collected at similar time points and will be used for exploratory analyses. Archival tumour tissue, tumour biopsies at progression/relapse, as well as any biopsies from body organs collected after a severe toxicity are collected. The primary outcome measure is the ability of the microbiome signature to predict 1 year progression-free survival (PFS) in patients with advanced disease. Secondary outcomes include microbiome correlations with toxicity and other efficacy end-points. Biosamples will be used to explore immunological and genomic correlates. A sub-study will evaluate both COVID-19 antigen and antibody associations with the microbiome. DISCUSSION: There is an urgent need to identify biomarkers that are predictive of treatment response, resistance and toxicity to immunotherapy. The data generated from this study will both help inform patient selection for these drugs and provide information that may allow therapeutic manipulation of the microbiome to improve future patient outcomes. TRIAL REGISTRATION: NCT04107168 , ClinicalTrials.gov, registered 09/27/2019. Protocol V3.2 (16/04/2021).

Artificial intelligence algorithms accurately predict prolonged length of stay following revision total knee arthroplasty.

PURPOSE: Although the average length of hospital stay following revision total knee arthroplasty (TKA) has decreased over recent years due to improved perioperative and intraoperative techniques and planning, prolonged length of stay (LOS) continues to be a substantial driver of hospital costs. The purpose of this study was to develop and validate artificial intelligence algorithms for the prediction of prolonged length of stay for patients following revision TKA. METHODS: A total of 2512 consecutive patients who underwent revision TKA were evaluated. Those patients with a length of stay greater than 75th percentile for all length of stays were defined as patients with prolonged LOS. Three artificial intelligence algorithms were developed to predict prolonged LOS following revision TKA and these models were assessed by discrimination, calibration and decision curve analysis. RESULTS: The strongest predictors for prolonged length of stay following revision TKA were age (> 75 years; p < 0.001), Charlson Comorbidity Index (> 6; p < 0.001) and body mass index (> 35 kg/m2; p < 0.001). The three artificial intelligence algorithms all achieved excellent performance across discrimination (AUC > 0.84) and decision curve analysis (p < 0.01). CONCLUSION: The study findings demonstrate excellent performance on discrimination, calibration and decision curve analysis for all three candidate algorithms. This highlights the potential of these artificial intelligence algorithms to assist in the preoperative identification of patients with an increased risk of prolonged LOS following revision TKA, which may aid in strategic discharge planning. LEVEL OF EVIDENCE: IV.

Machine learning algorithms predict extended postoperative opioid use in primary total knee arthroplasty.

PURPOSE: Adequate postoperative pain control following total knee arthroplasty (TKA) is required to achieve optimal patient recovery. However, the postoperative recovery may lead to an unnaturally extended opioid use, which has been associated with adverse outcomes. This study hypothesizes that machine learning models can accurately predict extended opioid use following primary TKA. METHODS: A total of 8873 consecutive patients that underwent primary TKA were evaluated, including 643 patients (7.2%) with extended postoperative opioid use (> 90 days). Electronic patient records were manually reviewed to identify patient demographics and surgical variables associated with prolonged postoperative opioid use. Five machine learning algorithms were developed, encompassing the breadth of state-of-the-art machine learning algorithms available in the literature, to predict extended opioid use following primary TKA, and these models were assessed by discrimination, calibration, and decision curve analysis. RESULTS: The strongest predictors for prolonged opioid prescription following primary TKA were preoperative opioid duration (100% importance; p < 0.01), drug abuse (54% importance; p < 0.01), and depression (47% importance; p < 0.01). The five machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration, and decision curve analysis. Higher net benefits for all machine learning models were demonstrated, when compared to the default strategies of changing management for all patients or no patients. CONCLUSION: The study findings show excellent model performance for the prediction of extended postoperative opioid use following primary total knee arthroplasty, highlighting the potential of these models to assist in preoperatively identifying at risk patients, and allowing the implementation of individualized peri-operative counselling and pain management strategies to mitigate complications associated with prolonged opioid use. LEVEL OF EVIDENCE: IV.