Preoperative hemoglobin thresholds for increased risk of "medical" complications in fast-track total hip and knee arthroplasty, a secondary analysis of a machine-learning algorithm.

BACKGROUND: There is increasing evidence that gender-specific hemoglobin thresholds may not be ideal in the surgical population. Thus, preoperative anemia defined as a hemoglobin of <13.0 g/dL is a well-established risk factor in elective surgery. However, few studies have investigated the specific influence of preoperative hemoglobin within a machine-learning model using data from an optimized fast-track surgical setup. STUDY DESIGN AND METHODS: A secondary analysis on the specific influence of preoperative hemoglobin level on a machine-learning model developed for identifying patients at increased risk of a length of stay (LOS) of >4 day or readmissions due to medical complications in fast-track total hip and knee arthroplasty within a well-defined fast-track protocol. To evaluate the effect of hemoglobin on the model we calculated SHaply Additive Explanation (SHAP) values for the 3913 patients from our previous test-dataset and stratified by gender and total hip and knee arthroplasty, respectively. RESULTS: The study period ran from January 2017 to August 2017. Median LOS was 1 day and mean preoperative Hb was 15.5 g/dL (SD:1.5), lower in women (14.9 vs. 16.2 g/dL) and with 30.5% of women versus 12.0% of men having a Hb of <13.0 g/dL. There was a steep increase in SHAP value with a preoperative Hb < 14.8 g/dL, and irrespective of gender age and procedure type. DISCUSSION: A machine-learning model found a hemoglobin threshold of <14.8 g/dL for increased risk of impaired recovery, regardless of gender or age, supporting reevaluation of preoperative anemia thresholds in the elective surgical setting.

Skin TARC/CCL17 increase precedes the development of childhood atopic dermatitis.

BACKGROUND: It is unknown whether skin biomarkers collected in infancy can predict the onset of atopic dermatitis (AD) and be used in future prevention trials to identify children at risk. OBJECTIVES: This study sought to examine whether skin biomarkers can predict AD during the first 2 years of life. METHODS: This study enrolled 300 term and 150 preterm children at birth and followed for AD until the age of 2 years. Skin tape strips were collected at 0 to 3 days and 2 months of age and analyzed for selected immune and barrier biomarkers. Hazard ratio (HR) with 95% confidence interval (CI) using Cox regression was calculated for the risk of AD. RESULTS: The 2-year prevalence of AD was 34.6% (99 of 286) and 21.2% (25 of 118) among term and preterm children, respectively. Skin biomarkers collected at birth did not predict AD. Elevated thymus- and activation-regulated chemokine/C-C motif chemokine ligand 17 -levels collected at 2 months of age increased the overall risk of AD (HR: 2.11; 95% CI: 1.36-3.26; P = .0008) and moderate-to-severe AD (HR: 4.97; 95% CI: 2.09-11.80; P = .0003). IL-8 and IL-18 predicted moderate-to-severe AD. Low filaggrin degradation product levels increased the risk of AD (HR: 2.04; 95% CI: 1.32-3.15; P = .001). Elevated biomarker levels at 2 months predicted AD at other skin sites and many months after collection. CONCLUSIONS: This study showed that noninvasively collected skin biomarkers of barrier and immune pathways can precede the onset of AD.

Skin biomarkers predict development of atopic dermatitis in infancy.

BACKGROUND: There is currently no insight into biomarkers that can predict the onset of pediatric atopic dermatitis (AD). METHODS: Nested in a prospective birth cohort study that examined the occurrence of physician-diagnosed AD in 300 children, 44 random children with onset of AD in the first year of life were matched on sex and season of birth with 44 children who did not develop AD. Natural moisturizing factor (NMF), corneocyte surface protrusions, cytokines, free sphingoid bases (SBs) of different chain lengths and their ceramides were analyzed from tape strips collected at 2 months of age before onset of AD using liquid chromatography, atomic force microscopy, multiplex immunoassay, and liquid chromatography mass spectrometry, respectively. RESULTS: Significant alterations were observed for four lipid markers, with phytosphingosine ([P]) levels being significantly lower in children who developed AD compared with children who did not (median 240 pmol/mg vs. 540 pmol/mg, p < 0.001). The two groups of children differed in the relative amounts of SB of different chain lengths (C17, C18 and C20). Thymus- and activation-regulated chemokine (TARC/CCL17) was slightly higher in children who developed AD, whereas NMF and corneocyte surface texture were similar. AD severity assessed by the eczema area and severity index (EASI) at disease onset was 4.2 (2.0;7.2). [P] had the highest prediction accuracy among the biomarkers (75.6%), whereas the combination of 5 lipid ratios gave an accuracy of 89.4%. CONCLUSION: This study showed that levels and SB chain length were altered in infants who later developed AD, and that TARC/CCL17 levels were higher.

Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study.

BACKGROUND: Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA). METHODS: Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS > 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values. RESULTS: Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication. CONCLUSION: A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care.

The Influence of Perioperative Fluid Therapy on N-terminal-pro-brain Natriuretic Peptide and the Association With Heart and Lung Complications in Patients Undergoing Colorectal Surgery: Secondary Results of a Clinical Randomized Assessor-blinded Multicenter Trial.

OBJECTIVE: To investigate the influence of intravenous (iv) fluid volumes on the secretion of N-terminal-pro-brain natriuretic peptide (NT-Pro-BNP) in colorectal surgical patients and its association with cardiopulmonary complications (CPC). In addition, to examine if preoperative NT-Pro-BNP can predict the risk for postoperative CPC. METHODS: Blood samples from patients enrolled in a previously published clinical randomized assessor-blinded multicenter trial were analyzed. Included were adult patients undergoing elective colorectal surgery with the American-Society-of-Anesthesiologists-scores of 1-3. Samples from 135 patients were available for analysis. Patients were allocated to either a restrictive (R-group) or a standard (S-group) iv-fluid regimen, commencing preoperatively and continuing until discharge. Blood was sampled every morning until the fourth postoperative day. The primary outcome for this study was NT-Pro-BNP changes and its association with fluid therapy and CPC. RESULTS: The S-group received more iv-fluid than the R-group on the day-of-surgery [milliliter, median (range) 6485 (4401-10750) vs 3730 (2250-8510); P < 0.001] and on the first postoperative day. NT-Pro-BNP was elevated in the S-group compared with the R-group on all postoperative days [area under the curve: median (interquartile range) pg/mL: 3285 (1697-6179) vs 1290 (758-3719); P < 0.001 and in patients developing CPC vs no-CPC (area under the curve), median (interquartile range): 5196 (1823-9061) vs 1934 (831-5301); P = 0.005]. NT-pro-BNP increased with increasing fluid volumes all days (P < 0.003). Preoperative NT-Pro-BNP predicted CPC [odds ratio (confidence interval): 1.573 (0.973-2.541), P = 0.032; positive predictive value = 0.257, negative predictive value = 0.929]. CONCLUSIONS: NT-pro-BNP increases with iv-fluid volumes given to colorectal surgical patients, and the level of NT-Pro-BNP is associated with CPC. Preoperative NT-Pro-BNP is predictive for CPC, but the diagnostic value is low.Clinicaltrials.gov NCT03537989.

Activated NKT Cells Can Condition Different Splenic Dendritic Cell Subsets To Respond More Effectively to TLR Engagement and Enhance Cross-Priming.

The function of dendritic cells (DCs) can be modulated through multiple signals, including recognition of pathogen-associated molecular patterns, as well as signals provided by rapidly activated leukocytes in the local environment, such as innate-like T cells. In this article, we addressed the possibility that the roles of different murine DC subsets in cross-priming CD8(+) T cells can change with the nature and timing of activatory stimuli. We show that CD8α(+) DCs play a critical role in cross-priming CD8(+) T cell responses to circulating proteins that enter the spleen in close temporal association with ligands for TLRs and/or compounds that activate NKT cells. However, if NKT cells are activated first, then CD8α(-) DCs become conditioned to respond more vigorously to TLR ligation, and if triggered directly, these cells can also contribute to priming of CD8(+) T cell responses. In fact, the initial activation of NKT cells can condition multiple DC subsets to respond more effectively to TLR ligation, with plasmacytoid DCs making more IFN-α and both CD8α(+) and CD8α(-) DCs manufacturing more IL-12. These results suggest that different DC subsets can contribute to T cell priming if provided appropriately phased activatory stimuli, an observation that could be factored into the design of more effective vaccines.

An autologous leukemia cell vaccine prevents murine acute leukemia relapse after cytarabine treatment.

Acute leukemias with adverse prognostic features carry a high relapse rate without allogeneic stem cell transplantation (allo-SCT). Allo-SCT has a high morbidity and is precluded for many patients because of advanced age or comorbidities. Postremission therapies with reduced toxicities are urgently needed. The murine acute leukemia model C1498 was used to study the efficacy of an intravenously administered vaccine consisting of irradiated leukemia cells loaded with the natural killer T (NKT)-cell agonist α-galactosylceramide (α-GalCer). Prophylactically, the vaccine was highly effective at preventing leukemia development through the downstream activities of activated NKT cells, which were dependent on splenic langerin(+)CD8α(+) dendritic cells and which led to stimulation of antileukemia CD4(+) and CD8(+) T cells. However, hosts with established leukemia received no protective benefit from the vaccine, despite inducing NKT-cell activation. Established leukemia was associated with increases in regulatory T cells and myeloid-derived suppressor cells, and the leukemic cells themselves were highly suppressive in vitro. Although this suppressive environment impaired both effector arms of the immune response, CD4(+) T-cell responses were more severely affected. When cytarabine chemotherapy was administered prior to vaccination, all animals in remission posttherapy were protected against rechallenge with viable leukemia cells.

Myelin-specific regulatory T cells accumulate in the CNS but fail to control autoimmune inflammation.

Treatment with ex vivo-generated regulatory T cells (T-reg) has been regarded as a potentially attractive therapeutic approach for autoimmune diseases. However, the dynamics and function of T-reg in autoimmunity are not well understood. Thus, we developed Foxp3gfp knock-in (Foxp3gfp.KI) mice and myelin oligodendrocyte glycoprotein (MOG)(35-55)/IA(b) (MHC class II) tetramers to track autoantigen-specific effector T cells (T-eff) and T-reg in vivo during experimental autoimmune encephalomyelitis (EAE), an animal model for multiple sclerosis. MOG tetramer-reactive, Foxp3(+) T-reg expanded in the peripheral lymphoid compartment and readily accumulated in the central nervous system (CNS), but did not prevent the onset of disease. Foxp3(+) T cells isolated from the CNS were effective in suppressing naive MOG-specific T cells, but failed to control CNS-derived encephalitogenic T-eff that secreted interleukin (IL)-6 and tumor necrosis factor (TNF). Our data suggest that in order for CD4(+)Foxp3(+) T-reg to effectively control autoimmune reactions in the target organ, it may also be necessary to control tissue inflammation.

Vocal repertoire and individuality in the plains zebra (Equus quagga).

Acoustic signals are vital in animal communication, and quantifying them is fundamental for understanding animal behaviour and ecology. Vocalizations can be classified into acoustically and functionally or contextually distinct categories, but establishing these categories can be challenging. Newly developed methods, such as machine learning, can provide solutions for classification tasks. The plains zebra is known for its loud and specific vocalizations, yet limited knowledge exists on the structure and information content of its vocalzations. In this study, we employed both feature-based and spectrogram-based algorithms, incorporating supervised and unsupervised machine learning methods to enhance robustness in categorizing zebra vocalization types. Additionally, we implemented a permuted discriminant function analysis to examine the individual identity information contained in the identified vocalization types. The findings revealed at least four distinct vocalization types-the 'snort', the 'soft snort', the 'squeal' and the 'quagga quagga'-with individual differences observed mostly in snorts, and to a lesser extent in squeals. Analyses based on acoustic features outperformed those based on spectrograms, but each excelled in characterizing different vocalization types. We thus recommend the combined use of these two approaches. This study offers valuable insights into plains zebra vocalization, with implications for future comprehensive explorations in animal communication.

Catheter-associated bladder mucosal trauma during intermittent voiding: An experimental study in pigs.

Objective: The objective of this study is to characterize bladder mucosal trauma associated with intermittent catheterization with conventional eyelet catheters (CECs) and to assess if a microhole zone catheter (MHZC) design concept reduces this adverse effect. Materials and Methods: A porcine model was developed to reflect human catheterization and bladder drainage. Nine pigs were randomized for catheterization with CEC (n = 6) or MHZC (n = 3). The bladder was drained repeatedly 20 times through the catheter. Cystoscopy was performed before and after the procedure, and bladders were analysed by histopathology. Two additional pigs were used for cystoscopy visualization of suction events in vivo. Cystoscopy, gross pathology, histopathological score, leucocyte infiltration, and intracatheter pressure at flow stops during voiding were compared for each group. Results: A significant higher pressure gradient was measured inside the CECs compared with MHZCs during flow stop. Consequently, CECs resulted in suction events inflicting bladder trauma characterized by loss of epithelium, oedema, haemorrhage, and neutrophil tissue infiltration. No significant trauma was identified when using MHZC. Conclusions: Considerable mucosal bladder trauma is inflicted by CECs which may be an overlooked risk factor for urinary tract infection. Catheters can be designed to minimize mucosal suction and reduce associated trauma. This may be a solution to reduce infection frequency and increase user comfort. Furthermore, the study demonstrates the potential of pigs as an attractive animal model for investigating urinary catheter performances.

Predicting Psoriatic Arthritis in Psoriasis Patients - A Swiss Registry Study.

Background: Psoriatic arthritis (PsA) is a prevalent comorbidity among patients with psoriasis, heavily contributing to their burden of disease, usually diagnosed several years after the diagnosis of psoriasis. Objectives: To investigate the predictability of psoriatic arthritis in patients with psoriasis and to identify important predictors. Methods: Data from the Swiss Dermatology Network on Targeted Therapies (SDNTT) involving patients treated for psoriasis were utilized. A combination of gradient-boosted decision trees and mixed models was used to classify patients based on their diagnosis of PsA or its absence. The variables with the highest predictive power were identified. Time to PsA diagnosis was visualized with the Kaplan-Meier method and the relationship between severity of psoriasis and PsA was explored through quantile regression. Results: A diagnosis of psoriatic arthritis was registered at baseline of 407 (29.5%) treatment series. 516 patients had no registration of PsA, 257 patients had PsA at inclusion, and 91 patients were diagnosed with PsA after inclusion. The model's AUROCs was up to 73.7%, and variables with the highest discriminatory power were age, PASI, physical well-being, and severity of nail psoriasis. Among patients who developed PsA after inclusion, significantly more first treatment series were classified in the PsA-group, compared to those with no PsA registration. PASI was significantly correlated with the median burden/severity of PsA (P = .01). Conclusions: Distinguishing between patients with and without PsA based on clinical characteristics is feasible and even predicting future diagnoses of PsA is possible. Patients at higher risk can be identified using important predictors of PsA.

Constraints on new physics from baryogenesis and Large Hadron Collider data.

We demonstrate the power of constraining theories of new physics by insisting that they lead to electroweak baryogenesis, while agreeing with current data from the Large Hadron Collider. The general approach is illustrated with a singlet scalar extension of the standard model. Stringent bounds can already be obtained, which reduce the viable parameter space to a small island.

Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks: a case study using population data from Denmark.

The modelling of pandemics has become a critical aspect in modern society. Even though artificial intelligence can help the forecast, the implementation of ordinary differential equations which estimate the time development in the number of susceptible, (exposed), infected and recovered (SIR/SEIR) individuals is still important in order to understand the stage of the pandemic. These models are based on simplified assumptions which constitute approximations, but to what extent this are erroneous is not understood since many factors can affect the development. In this paper, we introduce an agent-based model including spatial clustering and heterogeneities in connectivity and infection strength. Based on Danish population data, we estimate how this impacts the early prediction of a pandemic and compare this to the long-term development. Our results show that early phase SEIR model predictions overestimate the peak number of infected and the equilibrium level by at least a factor of two. These results are robust to variations of parameters influencing connection distances and independent of the distribution of infection rates.

Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level.

Importance: Identifying the optimal long-term biologic therapy for patients with psoriasis is often done through trial and error. Objective: To identify the optimal biologic therapy for individual patients with psoriasis using predictive statistical and machine learning models. Design, Setting, and Participants: This population-based cohort study used data from Danish nationwide registries, primarily DERMBIO, and included adult patients treated for moderate-to-severe psoriasis with biologics. Data were processed and analyzed between spring 2021 and spring 2022. Main Outcomes and Measures: Patient clusters of clinical relevance were identified and their success rates estimated for each drug. Furthermore, predictive prognostic models to identify optimal biologic treatment at the individual level based on data from nationwide registries were evaluated. Results: Assuming a success criterion of 3 years of sustained treatment, this study included 2034 patients with a total of 3452 treatment series. Most treatment series involved male patients (2147 [62.2%]) originating from Denmark (3190 [92.4%]), and 2414 (69.9%) had finished an education longer than primary school. The average ages were 24.9 years at psoriasis diagnosis and 45.5 years at initiation of biologic therapy. Gradient-boosted decision trees and logistic regression were able to predict a specific cytokine target (eg, interleukin-17 inhibition) associated with a successful treatment with accuracies of 63.6% and 59.2%, and top 2 accuracies of 95.9% and 93.9%. When predicting specific drugs resulting in success, gradient boost and logistic regression had accuracies of 48.5% and 44.4%, top 2 accuracies of 77.6% and 75.9%, and top 3 accuracies of 89.9% and 89.0%. Conclusions and Relevance: Of the treatment prediction models used in this cohort study of patients with psoriasis, gradient-boosted decision trees performed significantly better than logistic regression when predicting specific biologic therapy (by drug as well as target) leading to a treatment duration of at least 3 years without discontinuation. Predicting the optimal biologic could benefit patients and clinicians by minimizing the number of failed treatment attempts.

Tumor antigen presentation by dendritic cells.

Tumor cells are generally regarded as poor stimulators of naive T cells. In contrast, dendritic cells (DCs) are highly specialized in this function, and are therefore likely to be important intermediaries in the process of stimulating T cell responses to tumors. While providing solid evidence that DCs participate in antitumor immunity has proved difficult, several lines of evidence point in this direction. First, animal models involving bone marrow chimeras have shown that cells of hematopoeitic origin are required to elicit T cell responses to whole-tumor vaccines. Second, compared with other cells of hematopoeitic origin, DCs are particularly well-equipped to cross-present exogenous antigens to CD8+ T cells, a critical function if intermediary cells are involved. Third, tumor-infiltrating DCs purified from tumor samples have the capacity to cross-present tumor antigens in vitro. Finally, priming of anti-tumor T cell responses can be abrogated in new in vivo models in which DCs can be specifically depleted. It is therefore significant that DCs in cancer patients are often kept in an immature or dysfunctional state, thereby preventing stimulation of tumor-specific T cells. This review describes the different steps required for DCs to elicit T cell responses to tumor-associated antigens, and highlights processes that are amenable to intervention as therapy. We conclude that effective anti-tumor activity may be dependent on the ability to re-program DCs resident in the host, perhaps even when transferred autologous DCs generated ex vivo are used as vaccines. In this context, recruiting the activity of cells of the innate immune system to condition host DCs may help elicit more effective T cell-mediated responses.

Langerin+ CD8alpha+ dendritic cells are critical for cross-priming and IL-12 production in response to systemic antigens.

Distinct dendritic cell (DC) subsets differ with respect to pathways of Ag uptake and intracellular routing to MHC class I or MHC class II molecules. Murine studies suggest a specialized role for CD8alpha(+) DC in cross-presentation, where exogenous Ags are presented on MHC class I molecules to CD8(+) T cells, while CD8alpha(-) DC are more likely to present extracellular Ags on MHC class II molecules to CD4(+) T cells. As a proportion of CD8alpha(+) DC have been shown to express langerin (CD207), we investigated the role of langerin(+)CD8alpha(+) DC in presenting Ag and priming T cell responses to soluble Ags. When splenic DC populations were sorted from animals administered protein i.v., the ability to cross-present Ag was restricted to the langerin(+) compartment of the CD8alpha(+) DC population. The langerin(+)CD8alpha(+) DC population was also susceptible to depletion following administration of cytochrome c, which is known to trigger apoptosis if diverted to the cytosol. Cross-priming of CTL in the presence of the adjuvant activity of the TLR2 ligand N-palmitoyl-S-[2,3-bis(palmitoyloxy)-(2RS)-propyl]-[R]-Cys-[S]-Serl-[S]-Lys4-trihydrochloride or the invariant NKT cell ligand alpha-galactosylceramide was severely impaired in animals selectively depleted of langerin(+) cells in vivo. The production of IL-12p40 in response to these systemic activation stimuli was restricted to langerin(+)CD8alpha(+) DC, and the release of IL-12p70 into the serum following invariant NKT cell activation was ablated in the absence of langerin(+) cells. These data suggest a critical role for the langerin(+) compartment of the CD8alpha(+) DC population in cross-priming and IL-12 production.

Potent anti-tumor responses to immunization with dendritic cells loaded with tumor tissue and an NKT cell ligand.

Cancer immunotherapy is well tolerated and specific, but its efficacy remains variable. To enhance anti-tumor CD8(+) T-cell responses induced by immunization with antigen-loaded dendritic cells (DCs), we explored the impact of eliciting a potent source of T-cell help from activated invariant natural killer (NK)-like T cells (iNKT cells) using the specific glycolipid ligand alpha-galactosylceramide (alpha-GalCer). As cytokines released by iNKT cells may drive proliferation of CD4(+)CD25(+) regulatory T cells (Tregs), we assessed this immunization strategy in animals treated with anti-CD25 antibody to inactivate Treg function. Combining DC immunization with iNKT cell activation was found to significantly enhance anti-tumor activity, which was improved further by the prior inactivation of Tregs. The improved anti-tumor activity with Treg inactivation was associated with a prolonged proliferative burst of responding CD8(+) T cells. We could find no evidence that inclusion of alpha-GalCer in the vaccine enhanced Treg numbers, or that the 'helper' function of iNKT cells was improved in the absence of Treg activity. Rather, the two activities appeared to act independently to improve the tumor-specific T-cell response. Inactivating regulatory T cells and eliciting iNKT cell activation are therefore two useful strategies that can be used in combination to improve anti-tumor immunization with antigen-loaded DCs.

DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning.

Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated, open-source standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histograms of biomolecule behavior, is a user-adjustable quality threshold. Integrating standard features of smFRET analysis, DeepFRET consequently outputs the common kinetic information metrics. Its classification accuracy on ground truth data reached >95% outperforming human operators and commonly used threshold, only requiring ~1% of the time. Its precise and rapid operation on real data demonstrates DeepFRET's capacity to objectively quantify biomolecular dynamics and the potential to contribute to benchmarking smFRET for dynamic structural biology.

Proteins are folded into particular shapes in order to carry out their roles in the cell. However, their structures are not rigid: proteins bend and rotate in response to their environment. Identifying these movements is an important part of understanding how proteins work and interact with each other. Unfortunately, when researchers study the structures of proteins, they often look at the 'average' shape a protein takes, missing out on other conformations the protein might only be in temporarily. An important technique for studying protein flexibility is known as single molecule Förster resonance energy transfer (FRET). In this technique, two light-sensitive tags are attached to the same protein molecule and give off a signal when they come into close contact. This nano-scale sensor allows structural biologists to get information from individual protein movements that can be lost when looking at the average conformations of proteins. Advances in the instruments used to perform FRET have made observing the motion of individual proteins more widely accessible to non-specialists, but the analysis of the data that these instruments produce still requires a high level of expertise. To lower the barrier for non-specialists to use the technology, and to ensure that experiments can be reproduced on different instruments and by different researchers, Thomsen et al. have developed a new way to automate the data analysis. They used machine learning technology to recognize, filter and characterize data so as to produce reliable results, with the user only needing to perform a couple of steps. This new analysis approach could help expand the use of single-molecule FRET to different fields , allowing researchers to investigate the importance of protein flexibility for certain diseases, or to better understand the roles that proteins have in a cell.

A chimeric TCR-beta chain confers increased susceptibility to EAE.

Autoreactive myelin-specific CD4(+) T cells play an important role in CNS demyelination observed in MS and EAE. Consequently, it is important to understand the mechanisms of T cell receptor signalling leading to the activation of autoreactive T cells. We have previously generated a chimeric T cell receptor beta-chain (betaIII) displaying increased antigen sensitivity by exchanging most of the transmembrane and the intracellular domain of the TCR-beta chain with the corresponding TCR-gamma sequence. To investigate the effect of this "super-signalling" TCR in an autoimmune setting, we generated MOG(35-55) specific TCR transgenic mice expressing either the wild-type or the chimeric betaIII TCR-beta chain. We found that naïve transgenic T cells expressing the chimeric betaIII chain proliferated more extensively than wild-type cells in response to MOG(35-55)in vitro. Likewise, betaIII T cells skewed into a TH1 phenotype maintained the proliferative advantage over wild-type TH1 T cells at low antigen concentration. However, when skewed into a TH2 phenotype, there was no difference in proliferation between wild-type and betaIII T cells. Blocking of Fas-mediated cell death evenly affected wild-type and betaIII TH1 T cells and resulted in increased proliferation of both subsets, suggesting that betaIII T cells did not show defective Fas-FasL signalling. Finally, we found that betaIII TCR transgenic mice are more susceptible to EAE than wild-type TCR transgenic mice. We conclude that the change in the transmembrane domain of the TCR-beta chain affects TH1 T cells and the susceptibility to EAE, but does not affect TH2 cells. Investigating the molecular interaction within the TCR complex will help us to identify signalling pathways that can be manipulated to stop the progression of MS.

Langerin+ CD8α+ Dendritic Cells Drive Early CD8+ T Cell Activation and IL-12 Production During Systemic Bacterial Infection.

Bloodstream infections induce considerable morbidity, high mortality, and represent a significant burden of cost in health care; however, our understanding of the immune response to bacteremia is incomplete. Langerin+ CD8α+ dendritic cells (DCs), residing in the marginal zone of the murine spleen, have the capacity to cross-prime CD8+ T cells and produce IL-12, both of which are important components of antimicrobial immunity. Accordingly, we hypothesized that this DC subset may be a key promoter of adaptive immune responses to blood-borne bacterial infections. Utilizing mice that express the diphtheria toxin receptor under control of the langerin promoter, we investigated the impact of depleting langerin+ CD8α+ DCs in a murine model of intravenous infection with Mycobacterium bovis bacille Calmette-Guerin (BCG). In the absence of langerin+ CD8α+ DCs, the immune response to blood-borne BCG infection was diminished: bacterial numbers in the spleen increased, serum IL-12p40 decreased, and delayed CD8+ T cell activation, proliferation, and IFN-γ production was evident. Our data revealed that langerin+ CD8α+ DCs play a pivotal role in initiating CD8+ T cell responses and IL-12 production in response to bacteremia and may influence the early control of systemic bacterial infections.