PURPOSE OF REVIEWSarcoidosis is a systemic disease characterized by noncaseating granulomatous inflammation of multiple organ systems. Pulmonary, cardiac, and neurologic involvements have the worst ...prognosis. Current recommendations for the therapeutic management and follow-up of sarcoidosis involving these critical organs will be reviewed.
RECENT FINDINGSIn those sarcoidosis patients requiring immunosuppressive therapy, corticosteroids are used first at varying doses depending on the presenting manifestation. Patients with symptomatic pulmonary, cardiac, or neurologic involvement will be maintained on corticosteroids for at least a year. Many require a second immunosuppressive agent with methotrexate used most commonly. Anti-tumor necrosis factor agents, especially infliximab, are effective and recommendations for their use have been proposed.
SUMMARYEvidence-based treatment guidelines do not exist for most sarcoidosis clinical manifestations. Therefore, clinical care of these patients must rely on expert opinion. Patients are best served by a multidisciplinary approach to their care. Future research to identify environmental triggers, genetic associations, biomarkers for treatment response, and where to position new steroid-sparing immunosuppressive agents is warranted.
Many patients with an idiopathic interstitial pneumonia (IIP) have clinical features that suggest an underlying autoimmune process but do not meet established criteria for a connective tissue disease ...(CTD). Researchers have proposed differing criteria and terms to describe these patients, and lack of consensus over nomenclature and classification limits the ability to conduct prospective studies of a uniform cohort.The "European Respiratory Society/American Thoracic Society Task Force on Undifferentiated Forms of Connective Tissue Disease-associated Interstitial Lung Disease" was formed to create consensus regarding the nomenclature and classification criteria for patients with IIP and features of autoimmunity.The task force proposes the term "interstitial pneumonia with autoimmune features" (IPAF) and offers classification criteria organised around the presence of a combination of features from three domains: a clinical domain consisting of specific extra-thoracic features, a serologic domain consisting of specific autoantibodies, and a morphologic domain consisting of specific chest imaging, histopathologic or pulmonary physiologic features.A designation of IPAF should be used to identify individuals with IIP and features suggestive of, but not definitive for, a CTD. With IPAF, a sound platform has been provided from which to launch the requisite future research investigations of a more uniform cohort.
This commentary highlights the present dilemmas surrounding the classification of a patient with interstitial pneumonia who has clinical features suggesting an associated connective tissue disease ...but the features fall short of a clear diagnosis of connective tissue disease-associated interstitial lung disease under the current rheumatologic classification systems. This commentary illustrates what we perceive to be the limitations in the present approach to the classification of this group of patients and discusses problems with redefining the diagnosis of undifferentiated connective tissue disease to encompass patients with interstitial pneumonia. Finally, we advocate not only for a multidisciplinary approach to evaluation, but also disease classification and offer a proposal to define them as a distinct phenotype--lung-dominant CTD--for which prognostic, therapeutic, and pathobiologic implications can be tested in future, hopefully multiinstitutional, studies.
A large collection of element‐wise planar densities for compounds obtained from the Materials Project is calculated using brute force computational geometry methods, where the planar density is given ...by the total fractional area of atoms intersecting a supercell's crystallographic plane divided by the area of the supercell's crystallographic plane. It is demonstrated that the element‐wise maximum lattice plane densities can be useful as machine learning features. The methods described here are implemented in an open‐source Mathematica package hosted at https://github.com/sgbaird/LatticePlane.
A large database of CIFs is used in conjunction with computational geometry software to calculate element‐wise planar densities up to a maximum HKL index of 3.
Visceral fat is an independent predictor of the cardiovascular risk in subjects with type 2 diabetes (T2DM), but it is rarely assessed during an outpatient visit. Epicardial fat (EAT), the visceral ...fat of the heart, plays a role in coronary artery disease (CAD). EAT thickness can be clinically assessed with standard ultrasound. In this study we sought to evaluate the association of ambulatory ultrasound measured EAT thickness with CAD in asymptomatic well controlled T2DM subjects on metformin monotherapy during outpatient visits.
This was single center, pragmatic study in 142 T2DM patients. Each subject underwent baseline ultrasound EAT thickness measurement, anthropometric and biomarkers. The incidence of CAD was detected after 1 year. At baseline, HbA1c was 6.7 % and BMI 34.9 kg/m2, EAT thickness was 8.3 ± 2.3 in women and 9.4 ± 2.4 mm in men, higher than threshold values for high cardiovascular risk. In multivariate models, EAT was the only statistically significant correlate of CAD at 1-year f/u (p = 0.04).
Point of care ultrasound measured EAT thickness is a good correlate of CAD in well controlled and asymptomatic T2DM subjects on metformin monotherapy. EAT thickness predicted CAD better than traditional risk factors, such as BMI, HbA1c, age, blood pressure or duration of diabetes.
•Visceral fat plays a major role in diabetes-related cardiovascular risk. However visceral fat is not routinely assessed during outpatient visit. Cardiovascular risk is often based only on traditional parameters such as HbA1c or BMI.•Epicardial fat is strongly associated with diabetes related cardiovascular diseases.•Epicardial fat thickness can be easily and not invasively measured with ultrasound in an outpatient visit. Epicardial fat is marker of visceral fat.•In this real world study we found that ultrasound measured epicardial fat thickness was the best predictor of CAD in patients with well controlled type 2 diabetes and obesity on Metformin monotherapy.•Ultrasound assessment of visceral fat during outpatient visit can provide a better stratification of the cardiovascular risk than traditional markers in type 2 patients who would have been considered at low risk based only on A1c.
Recent advances in next-generation sequencing (NGS) technologies have opened the door to a wellspring of information regarding the composition of the gut microbiota. Leveraging NGS technology, early ...metagenomic studies revealed that several diseases, such as Alzheimer's disease, Parkinson's disease, autism, and myalgic encephalomyelitis, are characterized by alterations in the diversity of gut-associated microbes. More recently, interest has shifted toward understanding how these microbes impact their host, with a special emphasis on their interactions with the brain. Such interactions typically occur either systemically, through the production of small molecules in the gut that are released into circulation, or through signaling via the vagus nerves which directly connect the enteric nervous system to the central nervous system. Collectively, this system of communication is now commonly referred to as the gut-microbiota-brain axis. While equally important, little attention has focused on the causes of the alterations in the composition of gut microbiota. Although several factors can contribute, mucosal immunity plays a significant role in shaping the microbiota in both healthy individuals and in association with several diseases. The purpose of this review is to provide a brief overview of the components of mucosal immunity that impact the gut microbiota and then discuss how altered immunological conditions may shape the gut microbiota and consequently affect neuroimmune diseases, using a select group of common neuroimmune diseases as examples.
Learn how to build a Closed-loop Spectroscopy Lab: Light-mixing demo (CLSLab:Light) to perform color matching via RGB LEDs and a light sensor for under 100 USD and less than an hour of setup. Our ...tutorial covers ordering parts, verifying prerequisites, software setup, sensor mounting, testing, and an optimization algorithm comparison tutorial. We use secure IoT-style communication via MQTT, MicroPython firmware on a pre-soldered Pico W microcontroller, and the self-driving-lab-demo Python package. A video tutorial is available at https://youtu.be/D54yfxRSY6s.
For complete details on the use and execution of this protocol, please refer to Baird et al.1
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•Learn self-driving laboratories and lab automation with a simple demo•Use in hands-on classroom settings and as an example for public outreach•Use as a prototyping tool for research applications and self-driving lab concepts•Accompanied by a video build tutorial on YouTube
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
Learn how to build a Closed-loop Spectroscopy Lab: Light-mixing Demo (CLSLab:Light) to perform color matching via RGB LEDs and a light sensor for under 100 USD and less than an hour of setup. Our tutorial covers ordering parts, verifying prerequisites, software setup, sensor mounting, testing, and an optimization algorithm comparison tutorial. We use secure IoT-style communication via MQTT, MicroPython firmware on a pre-soldered Pico W microcontroller, and the self-driving-lab-demo Python package. A video tutorial is available at https://youtu.be/D54yfxRSY6s.
In an unbiased approach to biomarker discovery, we applied a highly multiplexed proteomic technology (SOMAscan, SomaLogic, Inc, Boulder, CO) to understand changes in proteins from paired serum ...samples at enrollment and after 8 weeks of TB treatment from 39 patients with pulmonary TB from Kampala, Uganda enrolled in the Center for Disease Control and Prevention's Tuberculosis Trials Consortium (TBTC) Study 29. This work represents the first large-scale proteomic analysis employing modified DNA aptamers in a study of active tuberculosis (TB). We identified multiple proteins that exhibit significant expression differences during the intensive phase of TB therapy. There was enrichment for proteins in conserved networks of biological processes and function including antimicrobial defense, tissue healing and remodeling, acute phase response, pattern recognition, protease/anti-proteases, complement and coagulation cascade, apoptosis, immunity and inflammation pathways. Members of cytokine pathways such as interferon-gamma, while present, were not as highly represented as might have been predicted. The top proteins that changed between baseline and 8 weeks of therapy were TSP4, TIMP-2, SEPR, MRC-2, Antithrombin III, SAA, CRP, NPS-PLA2, LEAP-1, and LBP. The novel proteins elucidated in this work may provide new insights for understanding TB disease, its treatment and subsequent healing processes that occur in response to effective therapy.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In scientific disciplines, benchmarks play a vital role in driving progress forward. For a benchmark to be effective, it must closely resemble real-world tasks. If the level of difficulty or ...relevance is inadequate, it can impede progress in the field. Moreover, benchmarks should have low computational overhead to ensure accessibility and repeatability. The objective is to achieve a kind of ``Turing test'' by creating a surrogate model that is practically indistinguishable from the ground truth observation, at least within the dataset's explored boundaries. This objective necessitates a large quantity of data. This data encompasses numerous features that are characteristic of chemistry and materials science optimization tasks that are relevant to industry. These features include high levels of noise, multiple fidelities, multiple objectives, linear constraints, non-linear correlations, and failure regions. We performed 494498 random hard-sphere packing simulations representing 206 CPU days’ worth of computational overhead. Simulations required nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters. The data was logged in a free-tier shared MongoDB Atlas database, producing two core tabular datasets: a failure probability dataset and a regression dataset. The failure probability dataset maps unique input parameter sets to the estimated probabilities that the simulation will fail. The regression dataset maps input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets were used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. In the regression dataset, percentile ranks were calculated for each group of identical parameter sets to account for heteroskedastic noise, thereby ensuring reliable and accurate data. This differs from the conventional approach that imposes a-priori assumptions, such as Gaussian noise, by specifying mean and standard deviation. This technique can be extended to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and the complex optimization scenarios encountered in the real world.
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