Dynamic mitochondrial transcription and translation in B cells control germinal center entry and lymphomagenesis.

Germinal center (GC) B cells undergo proliferation at very high rates in a hypoxic microenvironment but the cellular processes driving this are incompletely understood. Here we show that the mitochondria of GC B cells are highly dynamic, with significantly upregulated transcription and translation rates associated with the activity of transcription factor A, mitochondrial (TFAM). TFAM, while also necessary for normal B cell development, is required for entry of activated GC precursor B cells into the germinal center reaction; deletion of Tfam significantly impairs GC formation, function and output. Loss of TFAM in B cells compromises the actin cytoskeleton and impairs cellular motility of GC B cells in response to chemokine signaling, leading to their spatial disorganization. We show that B cell lymphoma substantially increases mitochondrial translation and that deletion of Tfam in B cells is protective against the development of lymphoma in a c-Myc transgenic mouse model. Finally, we show that pharmacological inhibition of mitochondrial transcription and translation inhibits growth of GC-derived human lymphoma cells and induces similar defects in the actin cytoskeleton.

Amplification of autoimmune organ damage by NKp46-activated ILC1.

In systemic lupus erythematosus (SLE) loss of immune tolerance, autoantibody production and immune complex deposition are required but not sufficient for organ damage1. How inflammatory signals are initiated and amplified in the setting of autoimmunity remains elusive. Here, we set out to dissect layers and hierarchies of autoimmune kidney inflammation in order to identify tissue-specific cellular hubs that amplify auto-inflammatory responses. Using high-resolution single-cell profiling of kidney immune and parenchymal cells, in combination with antibody blocking and genetic deficiency, we show that tissue-resident NKp46+ innate lymphoid cells (ILC) are crucial signal amplifiers of disease-associated macrophage expansion and epithelial cell injury in lupus nephritis, downstream of autoantibody production. NKp46 signaling in a distinct subset of ILC1 instructed an unconventional immune-regulatory transcriptional program, which included the expression of the myeloid cell growth factor CSF2. CSF2 production by NKp46+ ILC promoted the population expansion of monocyte-derived macrophages. Blockade of the NKp46 receptor (using the antibody mNCR1.152) or genetic deficiency of NKp46 abrogated epithelial cell injury. The same cellular and molecular patterns were operative in human lupus nephritis. Our data support that NKp46+ ILC1 promote parenchymal cell injury by granting monocyte-derived macrophages access to epithelial cell niches. NKp46 activation in ILC1 thus constitutes a previously unrecognized, critical tissue rheostat that amplifies organ damage in autoimmune hosts, with broad implications for inflammatory pathologies and therapies.

Mitochondrial control of lymphocyte homeostasis.

Mitochondria play a multitude of essential roles within mammalian cells, and understanding how they control immunity is an emerging area of study. Lymphocytes, as integral cellular components of the adaptive immune system, rely on mitochondria for their function, and mitochondria can dynamically instruct their differentiation and activation by undergoing rapid and profound remodelling. Energy homeostasis and ATP production are often considered the primary functions of mitochondria in immune cells; however, their importance extends across a spectrum of other molecular processes, including regulation of redox balance, signalling pathways, and biosynthesis. In this review, we explore the dynamic landscape of mitochondrial homeostasis in T and B cells, and discuss how mitochondrial disorders compromise adaptive immunity.

Autophagy-Dependent Generation of Free Fatty Acids Is Critical for Normal Neutrophil Differentiation.

Neutrophils are critical and short-lived mediators of innate immunity that require constant replenishment. Their differentiation in the bone marrow requires extensive cytoplasmic and nuclear remodeling, but the processes governing these energy-consuming changes are unknown. While previous studies show that autophagy is required for differentiation of other blood cell lineages, its function during granulopoiesis has remained elusive. Here, we have shown that metabolism and autophagy are developmentally programmed and essential for neutrophil differentiation in vivo. Atg7-deficient neutrophil precursors had increased glycolytic activity but impaired mitochondrial respiration, decreased ATP production, and accumulated lipid droplets. Inhibiting autophagy-mediated lipid degradation or fatty acid oxidation alone was sufficient to cause defective differentiation, while administration of fatty acids or pyruvate for mitochondrial respiration rescued differentiation in autophagy-deficient neutrophil precursors. Together, we show that autophagy-mediated lipolysis provides free fatty acids to support a mitochondrial respiration pathway essential to neutrophil differentiation.

NeuroMotion: Open-source platform with neuromechanical and deep network modules to generate surface EMG signals during voluntary movement.

Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, remain either limited in accuracy and conditions or are computationally heavy to apply. Here, we provide a computational platform to enable future work to overcome these limitations by presenting NeuroMotion, an open-source simulator that can modularly test a variety of approaches to the full-spectrum synthesis of EMG signals during voluntary movements. We demonstrate NeuroMotion using three sample modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, like the afore-estimated muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. We first show how MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regressing joint angles. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. In this way, NeuroMotion was able to generate full-spectrum EMG for the first use-case of human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this modular platform will enable validation of generative EMG models, complement experimental approaches and empower neuromechanical research.

LAT1 enables T cell activation under inflammatory conditions.

The aim of this study was to assess the L-type amino acid transporter-1 (LAT1) as a possible therapeutic target for rheumatoid arthritis (RA). Synovial LAT1 expression in RA was monitored by immunohistochemistry and transcriptomic datasets. The contribution of LAT1 to gene expression and immune synapse formation was assessed by RNA-sequencing and total internal reflection fluorescent (TIRF) microscopy, respectively. Mouse models of RA were used to assess the impact of therapeutic targeting of LAT1. LAT1 was strongly expressed by CD4+ T cells in the synovial membrane of people with active RA and the level of expression correlated with levels of ESR and CRP as well as DAS-28 scores. Deletion of LAT1 in murine CD4+ T cells inhibited the development of experimental arthritis and prevented the differentiation of CD4+ T cells expressing IFN-γ and TNF-α, without affecting regulatory T cells. LAT1 deficient CD4+ T cells demonstrated reduced transcription of genes associated with TCR/CD28 signalling, including Akt1, Akt2, Nfatc2, Nfkb1 and Nfkb2. Functional studies using TIRF microscopy revealed a significant impairment of immune synapse formation with reduced recruitment of CD3ζ and phospho-tyrosine signalling molecules in LAT1 deficient CD4+ T cells from the inflamed joints but not the draining lymph nodes of arthritic mice. Finally, it was shown that a small molecule LAT1 inhibitor, currently undergoing clinical trials in man, was highly effective in treating experimental arthritis in mice. It was concluded that LAT1 plays a critical role in activation of pathogenic T cell subsets under inflammatory conditions and represents a promising new therapeutic target for RA.

EPSIN1 Modulates the Plasma Membrane Abundance of FLAGELLIN SENSING2 for Effective Immune Responses.

The plasma membrane (PM) provides a critical interface between plant cells and their environment to control cellular responses. To perceive the bacterial flagellin peptide flg22 for effective defense signaling, the immune receptor FLAGELLIN SENSING2 (FLS2) needs to be at its site of function, the PM, in the correct abundance. However, the intracellular machinery that controls PM accumulation of FLS2 remains largely undefined. The Arabidopsis (Arabidopsis thaliana) clathrin adaptor EPSIN1 (EPS1) is implicated in clathrin-coated vesicle formation at the trans-Golgi network (TGN), likely aiding the transport of cargo proteins from the TGN for proper location; but EPS1's impact on physiological responses remains elusive. Here, we identify EPS1 as a positive regulator of flg22 signaling and pattern-triggered immunity against Pseudomonas syringae pv tomato DC3000. We provide evidence that EPS1 contributes to modulating the PM abundance of defense proteins for effective immune signaling because in eps1, impaired flg22 signaling correlated with reduced PM accumulation of FLS2 and its coreceptor BRASSINOSTEROID INSENSITIVE1-ASSOCIATED RECEPTOR KINASE1 (BAK1). The eps1 mutant also exhibited reduced responses to the pathogen/damage-associated molecular patterns elf26 and AtPep1, which are perceived by the coreceptor BAK1 and cognate PM receptors. Furthermore, quantitative proteomics of enriched PM fractions revealed that EPS1 was required for proper PM abundance of a discrete subset of proteins with different cellular functions. In conclusion, our study expands the limited understanding of the physiological roles of EPSIN family members in plants and provides novel insight into the TGN-associated clathrin-coated vesicle trafficking machinery that impacts plant PM-derived defense processes.

Autophagy is activated in systemic lupus erythematosus and required for plasmablast development.

BACKGROUND: Autophagy has emerged as a critical homeostatic mechanism in T lymphocytes, influencing proliferation and differentiation. Autophagy in B cells has been less studied, but genetic deficiency causes impairment of early and late developmental stages OBJECTIVES: To explore the role of autophagy in the pathogenesis of human and murine lupus, a disease in which B cells are critical effectors of pathology. METHODS: Autophagy was assessed using multiple techniques in NZB/W and control mice, and in patients with systemic lupus erythematosus (SLE) compared to healthy controls. We evaluated the phenotype of the B cell compartment in Vav-Atg7(-/-) mice in vivo, and examined human and murine plasmablast formation following inhibition of autophagy. RESULTS: We found activation of autophagy in early developmental and transitional stages of B cell development in a lupus mouse model even before disease onset, and which progressively increased with age. In human disease, again autophagy was activated compared with healthy controls, principally in naïve B cells. B cells isolated from Vav-Atg7(F/F) mice failed to effectively differentiate into plasma cells following stimulation in vitro. Similarly, human B cells stimulated in the presence of autophagy inhibition did not differentiate into plasmablasts. CONCLUSIONS: Our data suggest activation of autophagy is a mechanism for survival of autoreactive B cells, and also demonstrate that it is required for plasmablast differentiation, processes that induce significant cellular stress. The implication of autophagy in two major pathogenic pathways in SLE suggests the potential to use inhibition of autophagy as a novel treatment target in this frequently severe autoimmune disease.

Lifetime prevalence of chronic health conditions among persons with spinal cord injury.

OBJECTIVE: To assess lifetime prevalence of 7 chronic health conditions (CHCs) among a cohort of adults with chronic traumatic spinal cord injury (SCI). DESIGN: Cross-sectional. SETTING: Rehabilitation hospital. PARTICIPANTS: Adults with SCI who were ≥18 years of age, were ≥1 year postinjury, and had residual neurologic effects impeding full recovery (n=1678). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: CHCs were measured using questions from the Behavioral Risk Factor Surveillance System for diabetes (not including gestational), heart attack (also called a myocardial infarction), angina or coronary artery disease, stroke, hypertension (not including during pregnancy), high blood cholesterol, or cancer. RESULTS: Of participants, 49.5% reported having at least 1 CHC, with 23.2% reporting ≥2 CHCs. The most frequently reported CHC was high cholesterol (29.3%) followed by hypertension (28.7%) and diabetes (11.8%). Although the prevalence of CHCs significantly increased with increasing age, only hypertension and cancer were significantly associated with years postinjury. Four CHCs (diabetes, coronary artery disease, hypertension, high cholesterol) were significantly related to mobility status as measured by injury level and ambulatory status. However, after controlling for age, years postinjury, sex, and race, mobility status became nonsignificant in relation to coronary artery disease, but it remained significantly associated with diabetes, hypertension, and high cholesterol. CONCLUSIONS: Clinicians should be aware of the risk of CHCs in persons with SCI and should screen for these conditions and regular maintenance activities related to SCI.

Deep Metric Learning With Locality Sensitive Mining for Self-Correcting Source Separation of Neural Spiking Signals.

Automated source separation algorithms have become a central tool in neuroengineering and neuroscience, where they are used to decompose neurophysiological signal into its constituent spiking sources. However, in noisy or highly multivariate recordings these decomposition techniques often make a large number of errors. Such mistakes degrade online human-machine interfacing methods and require costly post-hoc manual cleaning in the offline setting. In this article we propose an automated error correction methodology using a deep metric learning (DML) framework, generating embedding spaces in which spiking events can be both identified and assigned to their respective sources. Furthermore, we investigate the relative ability of different DML techniques to preserve the intraclass semantic structure needed to identify incorrect class labels in neurophysiological time series. Motivated by this analysis, we propose locality sensitive mining, an easily implemented sampling-based augmentation to typical DML losses which substantially improves the local semantic structure of the embedding space. We demonstrate the utility of this method to generate embedding spaces which can be used to automatically identify incorrectly labeled spiking events with high accuracy.

Conditional Generative Models for Simulation of EMG During Naturalistic Movements.

Numerical models of electromyography (EMG) signals have provided a huge contribution to our fundamental understanding of human neurophysiology and remain a central pillar of motor neuroscience and the development of human-machine interfaces. However, while modern biophysical simulations based on finite element methods (FEMs) are highly accurate, they are extremely computationally expensive and thus are generally limited to modeling static systems such as isometrically contracting limbs. As a solution to this problem, we propose to use a conditional generative model to mimic the output of an advanced numerical model. To this end, we present BioMime, a conditional generative neural network trained adversarially to generate motor unit (MU) activation potential waveforms under a wide variety of volume conductor parameters. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy. Consequently, the computational load is dramatically reduced, which allows the rapid simulation of EMG signals during truly dynamic and naturalistic movements.

Absorption characteristics of epidural levobupivacaine with adrenaline and clonidine in children.

AIM: To determine if the addition of adrenaline, clonidine, or their combination altered the pharmacokinetic profile of levobupivacaine administered via the caudal epidural route in children. METHODS: Children aged <18 years old scheduled to undergo sub-umbilical surgery were administered caudal levobupivacaine plain 2.5 mg · ml(-1) or with adjuvants adrenaline 5 mcg · ml(-1) or clonidine 2 mcg · ml(-1) or their combination. Covariate analysis included weight and postnatal age (PNA). Time-concentration profile analysis was undertaken using nonlinear mixed effects models. A one-compartment linear disposition model with first-order input and first-order elimination was used to describe the data. The effect of either clonidine or adrenaline on absorption was investigated using a scaling parameter (Fabs(CLON), Fabs(ADR)) applied to the absorption half-life (Tabs). RESULTS: There were 240 children (median weight 11.0, range 1.9-56.1 kg; median postnatal age 16.7, range 0.6-167.6 months). Absorption of levobupivacaine was faster when mixed with clonidine (Fabs(CLON) 0.60; 95%CI 0.44, 0.83) but slower when mixed with adrenaline (Fabs(ADR) 2.12; 95%CI 1.45, 3.08). The addition of adrenaline to levobupivacaine resulted in a bifid absorption pattern. While initial absorption was unchanged (Tabs 0.15 h 95%CI 0.12, 0.18 h), there was a late absorption peak characterized by a Tabs(LATE) 2.34 h (95%CI 1.44, 4.97 h). The additional use of clonidine with adrenaline had minimal effect on the bifid absorption profile observed with adrenaline alone. Neither clonidine nor adrenaline had any effect on clearance. The population parameter estimate for volume of distribution was 157 l 70 kg(-1). Clearance was 6.5 l · h(-1) 70 kg(-1) at 1-month PNA and increased with a maturation half-time of 1.6 months to reach 90% of the mature value (18.5 l · h(-1) 70 kg(-1)) by 5 months PNA. CONCLUSIONS: The addition of adrenaline decreases the rate of levobupivacaine systemic absorption, reducing peak concentration by half. Levobupivacaine concentrations with adrenaline adjuvant were reduced compared to plain levobupivacaine for up to 3.5 hours. Clonidine as an adjuvant results in faster systemic absorption of levobupivacaine and similar concentration time profile to levobupivacaine alone. Adding adrenaline with clonidine does not alter the concentration profile observed with adrenaline alone.

A cellular overview of immunometabolism in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease, characterized by a breakdown of immune tolerance and the development of autoantibodies against nucleic self-antigens. Immunometabolism is a rapidly expanding scientific field investigating the metabolic programming of cells of the immune system. During the normal immune response, extensive reprogramming of cellular metabolism occurs, both to generate adenosine triphosphate and facilitate protein synthesis, and also to manage cellular stress. Major pathways upregulated include glycolysis, oxidative phosphorylation, the tricarboxylic acid cycle and the pentose phosphate pathway, among others. Metabolic reprogramming also occurs to aid resolution of inflammation. Immune cells of both patients with SLE and lupus-prone mice are characterized by metabolic abnormalities resulting in an altered functional and inflammatory state. Recent studies have described how metabolic reprogramming occurs in many cell populations in SLE, particularly CD4+ T cells, e.g. favouring a glycolytic profile by overactivation of the mechanistic target of rapamycin pathway. These advances have led to an increased understanding of the metabolic changes affecting the inflammatory profile of T and B cells, monocytes, dendritic cells and neutrophils, and how they contribute to autoimmunity and SLE pathogenesis. In the current review, we aim to summarize recent advances in the field of immunometabolism involved in SLE and how these could potentially lead to new therapeutic strategies in the future.

A myoelectric digital twin for fast and realistic modelling in deep learning.

Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces.

A computational fluid dynamics assessment of 3D printed ventilator splitters and restrictors for differential multi-patient ventilation.

BACKGROUND: The global pandemic of novel coronavirus (SARS-CoV-2) has led to global shortages of ventilators and accessories. One solution to this problem is to split ventilators between multiple patients, which poses the difficulty of treating two patients with dissimilar ventilation needs. A proposed solution to this problem is the use of 3D-printed flow splitters and restrictors. There is little data available on the reliability of such devices and how the use of different 3D printing methods might affect their performance. METHODS: We performed flow resistance measurements on 30 different 3D-printed restrictor designs produced using a range of fused deposition modelling and stereolithography printers and materials, from consumer grade printers using polylactic acid filament to professional printers using surgical resin. We compared their performance to novel computational fluid dynamics models driven by empirical ventilator flow rate data. This indicates the ideal performance of a part that matches the computer model. RESULTS: The 3D-printed restrictors varied considerably between printers and materials to a sufficient degree that would make them unsafe for clinical use without individual testing. This occurs because the interior surface of the restrictor is rough and has a reduced nominal average diameter when compared to the computer model. However, we have also shown that with careful calibration it is possible to tune the end-inspiratory (tidal) volume by titrating the inspiratory time on the ventilator. CONCLUSIONS: Computer simulations of differential multi patient ventilation indicate that the use of 3D-printed flow splitters is viable. However, in situ testing indicates that using 3D printers to produce flow restricting orifices is not recommended, as the flow resistance can deviate significantly from expected values depending on the type of printer used. TRIAL REGISTRATION: Not applicable.

A strength inversion origin for non-volcanic tremor.

Non-volcanic tremor is a particularly enigmatic form of seismic activity. In its most studied subduction zone setting, tremor typically occurs within the plate interface at or near the shallow and deep edges of the interseismically locked zone. Detailed seismic observations have shown that tremor is composed of repeating small low-frequency earthquakes, often accompanied by very-low-frequency earthquakes, all involving shear failure and slip. However, low-frequency earthquakes and very-low-frequency earthquakes within each cluster show nearly constant source durations for all observed magnitudes, which implies characteristic tremor sub-event sources of near-constant size. Here we integrate geological observations and geomechanical lab measurements on heterogeneous rock assemblages representative of the shallow tremor region offshore the Middle America Trench with numerical simulations to demonstrate that these tremor events are consistent with the seismic failure of relatively weaker blocks within a stronger matrix. In these subducting rocks, hydrothermalism has led to a strength-inversion from a weak matrix with relatively stronger blocks to a stronger matrix with embedded relatively weaker blocks. Tremor naturally occurs as the now-weaker blocks fail seismically while their surrounding matrix has not yet reached a state of general seismic failure.

Online Tracking of the Phase Difference Between Neural Drives to Antagonist Muscle Pairs in Essential Tremor Patients.

Transcutaneous electrical stimulation has been applied in tremor suppression applications. Out-of-phase stimulation strategies applied above or below motor threshold result in a significant attenuation of pathological tremor. For stimulation to be properly timed, the varying phase relationship between agonist-antagonist muscle activity during tremor needs to be accurately estimated in real-time. Here we propose an online tremor phase and frequency tracking technique for the customized control of electrical stimulation, based on a phase-locked loop (PLL) system applied to the estimated neural drive to muscles. Surface electromyography signals were recorded from the wrist extensor and flexor muscle groups of 13 essential tremor patients during postural tremor. The EMG signals were pre-processed and decomposed online and offline via the convolution kernel compensation algorithm to discriminate motor unit spike trains. The summation of motor unit spike trains detected for each muscle was bandpass filtered between 3 to 10 Hz to isolate the tremor related components of the neural drive to muscles. The estimated tremorogenic neural drive was used as input to a PLL that tracked the phase differences between the two muscle groups. The online estimated phase difference was compared with the phase calculated offline using a Hilbert Transform as a ground truth. The results showed a rate of agreement of 0.88 ± 0.22 between offline and online EMG decomposition. The PLL tracked the phase difference of tremor signals in real-time with an average correlation of 0.86 ± 0.16 with the ground truth (average error of 6.40° ± 3.49°). Finally, the online decomposition and phase estimation components were integrated with an electrical stimulator and applied in closed-loop on one patient, to representatively demonstrate the working principle of the full tremor suppression system. The results of this study support the feasibility of real-time estimation of the phase of tremorogenic neural drive to muscles, providing a methodology for future tremor-suppression neuroprostheses.

Plasma iron controls neutrophil production and function.

Low plasma iron (hypoferremia) induced by hepcidin is a conserved inflammatory response that protects against infections but inhibits erythropoiesis. How hypoferremia influences leukocytogenesis is unclear. Using proteomic data, we predicted that neutrophil production would be profoundly more iron-demanding than generation of other white blood cell types. Accordingly in mice, hepcidin-mediated hypoferremia substantially reduced numbers of granulocytes but not monocytes, lymphocytes, or dendritic cells. Neutrophil rebound after anti-Gr-1-induced neutropenia was blunted during hypoferremia but was rescued by supplemental iron. Similarly, hypoferremia markedly inhibited pharmacologically stimulated granulopoiesis mediated by granulocyte colony-stimulating factor and inflammation-induced accumulation of neutrophils in the spleen and peritoneal cavity. Furthermore, hypoferremia specifically altered neutrophil effector functions, suppressing antibacterial mechanisms but enhancing mitochondrial reactive oxygen species-dependent NETosis associated with chronic inflammation. Notably, antagonizing endogenous hepcidin during acute inflammation enhanced production of neutrophils. We propose plasma iron modulates the profile of innate immunity by controlling monocyte-to-neutrophil ratio and neutrophil activity in a therapeutically targetable system.

Contextual factors influencing success or failure of emergency department interventions for cognitively impaired older people: a scoping and integrative review.

AIM: This paper is a report of a scoping review of research on cognitive impairment in older adults who visit Emergency Departments of acute care hospitals, followed by an integrative review that included a quality assessment to determine the effectiveness of interventions for this population. BACKGROUND: Being old and cognitively impaired in the Emergency Department--a fast-paced intervention system--is a complex phenomenon that challenges many healthcare professionals. The rise in the incidence and prevalence of dementia will have a large impact on healthcare systems. DATA SOURCES: MEDLINE, EMBASE, CINAHL, PsycInfo, AgeLine, Abstracts in Social Gerontology, the Cochrane Database of Systematic Reviews, the Cochrane Central Register of Controlled Trials and Google Scholar between 1990 and 2008, for qualitative or quantitative studies reporting extractable data on delirium or dementia in non-institutionalized older people (65+ years) in the Emergency Department. REVIEW METHODS: Titles screened by a project researcher and checked against inclusion criteria by another researcher. Two reviewers completed independent data extraction and synthesis of included studies. Quality assessment occurred using the Critical Appraisal Skills Programme Tools. RESULTS: Fifteen studies met the inclusion criteria for integrative review. Analysis of these studies indicates that the prevalence of cognitive impairment is high and improvements are needed. Contextual details and relevant features of an appropriate intervention are poorly explained. CONCLUSION: Although the prevalence and incidence of cognitive impairment is recognized, appropriate interventions and programmatic responses remain elusive. Quality improvements require more thorough examination of emergency department context to identify modifiable influencing factors that are transferable across settings.

B Cell Metabolism and Autophagy in Autoimmunity.

B cells are central to the pathogenesis of multiple autoimmune diseases, through antigen presentation, cytokine secretion, and the production of autoantibodies. During development and differentiation, B cells undergo drastic changes in their physiology. It is emerging that these are accompanied by equally significant shifts in metabolic phenotype, which may themselves also drive and enforce the functional properties of the cell. The dysfunction of B cells during autoimmunity is characterised by the breaching of tolerogenic checkpoints, and there is developing evidence that the metabolic state of B cells may contribute to this. Determining the metabolic phenotype of B cells in autoimmunity is an area of active study, and is important because intervention by metabolism-altering therapeutic approaches may represent an attractive treatment target.