Effectiveness of immunoglobulin replacement therapy in preventing infections in patients with chronic obstructive pulmonary disease: a systematic review.

PURPOSE: Immunoglobulin replacement therapy is a standard treatment for patients with antibody production deficiencies, which is of interest in patients with chronic obstructive pulmonary disease (COPD). This systematic review, registered with PROSPERO (CRD42021281118), assessed the current literature regarding immunoglobulin replacement therapy on COPD clinical outcomes in patients with low immunoglobulin G (IgG) serum concentrations. METHODS: Literature searches conducted from inception to August 23, 2021, in databases including MEDLINE, EMBASE, and CINAHL. Population (sex, age, comorbidities), baseline clinical characteristics (pulmonary function testing results, IgG levels), and outcome (hospitalizations, emergency department visits) were extracted after title/abstract and full text screening. The Cochrane risk of bias assessment form was used for risk of bias assessment of randomized controlled trials and the National Heart, Lung, and Blood Institute (NHLBI) assessment was used for pre and post studies. RESULTS: A total of 1381 studies were identified in the preliminary search, and 874 records were screened after duplicates were removed. Screening 77 full texts yielded four studies that were included in the review. CONCLUSION: It is unclear whether immune globulin replacement therapy reduces acute exacerbation frequency and severity in COPD. Current evidence suggests that it is worth considering, but better developed protocols for administration of immune globulin supplementation is required for future randomized controlled trials.

High energy density in artificial heterostructures through relaxation time modulation.

Electrostatic capacitors are foundational components of advanced electronics and high-power electrical systems owing to their ultrafast charging-discharging capability. Ferroelectric materials offer high maximum polarization, but high remnant polarization has hindered their effective deployment in energy storage applications. Previous methodologies have encountered problems because of the deteriorated crystallinity of the ferroelectric materials. We introduce an approach to control the relaxation time using two-dimensional (2D) materials while minimizing energy loss by using 2D/3D/2D heterostructures and preserving the crystallinity of ferroelectric 3D materials. Using this approach, we were able to achieve an energy density of 191.7 joules per cubic centimeter with an efficiency greater than 90%. This precise control over relaxation time holds promise for a wide array of applications and has the potential to accelerate the development of highly efficient energy storage systems.

Functional protein dynamics in a crystal.

Proteins are molecular machines and to understand how they work, we need to understand how they move. New pump-probe time-resolved X-ray diffraction methods open up ways to initiate and observe protein motions with atomistic detail in crystals on biologically relevant timescales. However, practical limitations of these experiments demands parallel development of effective molecular dynamics approaches to accelerate progress and extract meaning. Here, we establish robust and accurate methods for simulating dynamics in protein crystals, a nontrivial process requiring careful attention to equilibration, environmental composition, and choice of force fields. With more than seven milliseconds of sampling of a single chain, we identify critical factors controlling agreement between simulation and experiments and show that simulated motions recapitulate ligand-induced conformational changes. This work enables a virtuous cycle between simulation and experiments for visualizing and understanding the basic functional motions of proteins.

Real time changes in the expression of eicosanoid synthesizing enzymes during inflammation.

Immune responses during inflammation involve complex, well-coordinated lipid signaling pathways. Eicosanoids are a class of lipid signaling molecules derived from polyunsaturated fatty acids such as arachidonic acid and constitute a major network that controls inflammation and its subsequent resolution. Arachidonic acid is metabolized by enzymes in three different pathways to form a variety of lipid metabolites that can be either pro- or anti-inflammatory. Therefore, an understanding of the time-dependent gene expression, lipid metabolite profiles and cytokine profiles during the initial inflammatory response is necessary, as it will allow for the design of time-dependent therapeutics. Herein, we investigate the multi-level regulation of this process. After stimulating RAW 264.7 cells, a mouse-derived macrophage cell line commonly used to examine inflammatory responses, we examine the gene expression of 44 relevant lipid metabolizing enzymes from the different eicosanoid synthesizing classes. We also measure the formation of lipid metabolites and production of cytokines at selected time points. Results reveal a dynamic relationship between the time-course of inflammation dependent gene expression of the three eicosanoid synthesizing enzymes.

Bioorthogonally activated reactive species for target identification.

A target identification platform derived from the bioorthogonal activation of reactive species is described. We explore the reactivity of halogenated enamine N-oxides and report that the previously undisclosed α,γ-halogenated enamine N-oxides can be reduced biooorthogonally by diboron reagents to produce highly electrophilic α,ß-unsaturated haloiminium ions suitable for labeling a range of amino acid residues on proteins in a 1,2- or 1,4-fashion. Affinity labeling reagents bearing this motif enable ligand-directed protein modification and afford highly sensitive and selective target identification in unbiased chemoproteomics experiments. Target identification is supported in both cell lysate and live cells.

Nondestructive Single-Atom-Thick Crystallographic Scanner via Sticky-Note-Like van der Waals Assembling-Disassembling.

Crystallographic characteristics, including grain boundaries and crystallographic orientation of each grain, are crucial in defining the properties of two-dimensional materials (2DMs). To date, local microstructure analysis of 2DMs, which requires destructive and complex processes, is primarily used to identify unknown 2DM specimens, hindering the subsequent use of characterized samples. Here, a nondestructive large-area 2D crystallographic analytical method through sticky-note-like van der Waals (vdW) assembling-disassembling is presented. By the vdW assembling of veiled polycrystalline graphene (PCG) with a single-atom-thick single-crystalline graphene filter (SCG-filter), detailed crystallographic information of each grain in PCGs is visualized through a 2D Raman signal scan, which relies on the interlayer twist angle. The scanned PCGs are seamlessly separated from the SCG-filter using vdW disassembling, preserving their original condition. The remaining SCG-filter is then reused for additional crystallographic scans of other PCGs. It is believed that the methods can pave the way for advances in the crystallographic analysis of single-atom-thick materials, offering huge implications for the applications of 2DMs.

Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images.

Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.

AI prediction of cardiovascular events using opportunistic epicardial adipose tissue assessments from CT calcium score.

Background: Recent studies have used basic epicardial adipose tissue (EAT) assessments (e.g., volume and mean HU) to predict risk of atherosclerosis-related, major adverse cardiovascular events (MACE). Objectives: Create novel, hand-crafted EAT features, "fat-omics", to capture the pathophysiology of EAT and improve MACE prediction. Methods: We segmented EAT using a previously-validated deep learning method with optional manual correction. We extracted 148 radiomic features (morphological, spatial, and intensity) and used Cox elastic-net for feature reduction and prediction of MACE. Results: Traditional fat features gave marginal prediction (EAT-volume/EAT-mean-HU/BMI gave C-index 0.53/0.55/0.57, respectively). Significant improvement was obtained with 15 fat-omics features (C-index=0.69, test set). High-risk features included volume-of-voxels-having-elevated-HU-[-50, -30-HU] and HU-negative-skewness, both of which assess high HU, which as been implicated in fat inflammation. Other high-risk features include kurtosis-of-EAT-thickness, reflecting the heterogeneity of thicknesses, and EAT-volume-in-the-top-25%-of-the-heart, emphasizing adipose near the proximal coronary arteries. Kaplan-Meyer plots of Cox-identified, high- and low-risk patients were well separated with the median of the fat-omics risk, while high-risk group having HR 2.4 times that of the low-risk group (P<0.001). Conclusion: Preliminary findings indicate an opportunity to use more finely tuned, explainable assessments on EAT for improved cardiovascular risk prediction.

Novel BRD2::NUTM1 Fusion in NUT Carcinoma With Exceptional Response to Chemotherapy: A Case Report.

We present the first known case of a patient with BRD2::NUTM1-driven NUT carcinoma. A 59-year-old woman presented with poorly differentiated squamous cell lung cancer metastatic to the pleura. Eventually, a positive NUT immunohistochemistry, NUT fluorescence in situ hybridization, and RNA next-generation sequencing with a BRD2::NUTM1 fusion led to the diagnosis of NUT carcinoma. She received multiple lines of chemotherapy with response and is still alive at 2 years postdiagnosis. This report expands on the known fusions in NUT carcinoma and highlights potential differences in patient prognosis on the basis of gene fusion partners.

A Constitutive EGFR Kinase Dimer to Study Inhibitor Pharmacology.

Lung cancer is commonly caused by activating mutations in the epidermal growth factor receptor (EGFR). Allosteric kinase inhibitors are unaffected by common ATP-site resistance mutations and represent a promising therapeutic strategy for targeting drug-resistant EGFR variants. However, allosteric inhibitors are antagonized by kinase dimerization, and understanding this phenomenon has been limited to cellular experiments. To facilitate the study of allosteric inhibitor pharmacology, we designed and purified a constitutive EGFR kinase dimer harboring the clinically relevant L858R/T790M mutations. Kinetic characterization revealed that the EGFR kinase dimer is more active than monomeric EGFR(L858R/T790M) kinase and has the same Km,ATP Biochemical profiling of a large panel of ATP-competitive and allosteric EGFR inhibitors showed that allosteric inhibitor potency decreased by >500-fold in the kinase dimer compared with monomer, yielding IC50 values that correlate well with Ba/F3 cellular potencies. Thus, this readily purifiable constitutive asymmetric EGFR kinase dimer represents an attractive tool for biochemical evaluation of EGFR inhibitor pharmacology, in particular for allosteric inhibitors. SIGNIFICANCE STATEMENT: Drugs targeting epidermal growth factor receptor (EGFR) kinase are commonly used to treat lung cancers but are affected by receptor dimerization. Here, we describe a locked kinase dimer that can be used to study EGFR inhibitor pharmacology.

Strain modulation in crumpled Si nanomembranes: Light detection beyond the Si absorption limit.

Although Si is extensively used in micro-nano electronics, its inherent optical absorption cutoff at 1100-nm limits its photonic and optoelectronic applications in visible to partly near infrared (NIR) spectral range. Recently, strain engineering has emerged as a promising approach for extending device functionality via tuning the material properties, including change in optical bandgap. In this study, the reduction in bandgap with applied strain was used for extending the absorption limit of crystalline Si up to 1310 nm beyond its intrinsic bandgap, which was achieved by creating the crumpled structures in Si nanomembranes (NMs). The concept was used to develop a prototype NIR image sensor by organizing metal-semiconductor-metal-configured crumpled Si NM photosensing pixels in 6 × 6 array. The geometry-controlled, self-sustained strain induction in Si NMs provided an exclusive photon management with shortening of optical bandgap and enhanced photoresponse beyond the conventional Si absorption limit.

Functional Protein Dynamics in a Crystal.

Proteins are molecular machines and to understand how they work, we need to understand how they move. New pump-probe time-resolved X-ray diffraction methods open up ways to initiate and observe protein motions with atomistic detail in crystals on biologically relevant timescales. However, practical limitations of these experiments demands parallel development of effective molecular dynamics approaches to accelerate progress and extract meaning. Here, we establish robust and accurate methods for simulating dynamics in protein crystals, a nontrivial process requiring careful attention to equilibration, environmental composition, and choice of force fields. With more than seven milliseconds of sampling of a single chain, we identify critical factors controlling agreement between simulation and experiments and show that simulated motions recapitulate ligand-induced conformational changes. This work enables a virtuous cycle between simulation and experiments for visualizing and understanding the basic functional motions of proteins.

Temperature Profile Measurement From Radiofrequency Nasal Airway Reshaping Device.

OBJECTIVE: Nasal airway obstruction (NAO) is caused by various disorders including nasal valve collapse (NVC). A bipolar radiofrequency (RF) device (VivAer®, Aerin Medical, Sunnyvale, CA) has been used to treat NAO through RF heat generation to the upper lateral cartilage (ULC). The purpose of this study is to measure temperature elevations in nasal tissue, using infrared (IR) radiometry to map the spatial and temporal evolution of temperature. STUDY DESIGN: Experimental and computational. METHODS: Composite porcine nasal septum was harvested and sectioned (1 mm and 2 mm). The device was used to heat the cartilage in composite porcine septum. An IR camera (FLIR® ExaminIR, Teledyne, Wilsonville, OR) was used to image temperature on the back surface of the specimen. These data were incorporated into a heat transfer finite element model that also calculated tissue damage using Arrhenius rate process. RESULTS: IR temperature imaging showed peak back surface temperatures of 49.57°C and 42.21°C in 1 and 2 mm thick septums respectively. Temperature maps were generated demonstrating the temporal and spatial evolution of temperature. A finite element model generated temperature profiles with respect to time and depth. Rate process models using Arrhenius coefficients showed 30% chondrocyte death at 1 mm depth after 18 s of RF treatment. CONCLUSION: The use of this device creates a thermal profile that may result in thermal injury to cartilage. Computational modeling suggests chondrocyte death extending as deep as 1.4 mm below the treatment surface. Further studies should be performed to improve dosimetry and optimize the heating process to reduce potential injury. Laryngoscope, 134:1063-1070, 2024.

Age-stratified comparison of active surveillance versus radiofrequency ablation for papillary thyroid microcarcinoma using decision analysis.

BACKGROUND: Papillary thyroid microcarcinomas may be treated with radiofrequency ablation, active surveillance, or surgery. The objective of this study was to use mathematical modeling to compare treatment alternatives for papillary thyroid microcarcinomas among those who decline surgery. We hypothesized that radiofrequency ablation would outperform active surveillance in avoiding progression and surgery but that the effect size would be small for older patients. METHODS: We engaged stakeholders to identify meaningful long-term endpoints for papillary thyroid microcarcinoma treatment-(1) cancer progression/surgery, (2) need for thyroid replacement therapy, and (3) permanent treatment complication. A Markov decision analysis model was created to compare the probability of these endpoints after radiofrequency ablation or active surveillance for papillary thyroid microcarcinomas and overall cost. Transition probabilities were extracted from published literature. Model outcomes were estimated to have a 10-year time horizon. RESULTS: The primary outcome yielded a number needed to treat of 18.1 for the avoidance of progression and 27.4 for the avoidance of lifelong thyroid replacement therapy for radiofrequency ablation compared to active surveillance. However, as patient age increased, the number needed to treat to avoid progression increased from 5.2 (age 20-29) to 39.1 (age 60+). The number needed to treat to avoid lifelong thyroid replacement therapy increased with age from 7.8 (age 20-29) to 59.3 (age 60+). The average 10-year cost/treatment for active surveillance and radiofrequency ablation were $6,400 and $11,700, respectively, translating to a cost per progression-avoided of $106,500. CONCLUSION: As an alternative to active surveillance, radiofrequency ablation may have a greater therapeutic impact in younger patients. However, routine implementation may be cost-prohibitive for most patients with papillary thyroid microcarcinomas.

Non-invasive hemoglobin concentration measurements with multi-wavelength reflectance mode PPG sensor and CNN data processing.

Possibility of non-invasive hemoglobin concentration measurements with wearable devices have been evaluated. The proposed solution is based on the assumption that PPG waveform shape measured at various wavelengths in the reflectance mode carries information about in-depth distribution of optical pathlength in the tissue. Decomposition of temporal and spectral features of PPG signal have been applied to correct estimation of hemoglobin concentration. The dataset including 840 PPG waveforms from 170 volunteers have been collected for the purpose of neural network training and validation. The achieved performance (MAE~13.6 g/l, R~0.62) is confirmed with the invasive blood test.Clinical Relevance - This paper establishes possibility of non-invasive real time hemoglobin concentration measurements by means of low-cost wearable sensor with accuracy comparable to non-invasive clinical instruments.

Short-Term Outcomes of Expedited Arthroscopic Tensionable Knotless Biologic Tuberoplasty for Massive Irreparable Rotator Cuff Tears.

Background: Massive irreparable rotator cuff tears in the nonarthritic patient are challenging because of the high failure rate and technical difficulty of intraoperative repair. We examined the outcomes of expedited arthroscopic tensionable knotless biologic tuberoplasty for massive irreparable rotator cuff tears. Methods: Eleven patients with an average follow-up of 8.2 months were included in the analysis. Patient-reported outcome measures were the visual analog scale (VAS) pain score, American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form (ASES) score, Single Assessment Numeric Evaluation (SANE) score, and Veterans RAND 12-Item Health Survey (VR-12) physical component score and mental component score. Results: In comparison to the preoperative mean, mean VAS pain scores were significantly reduced at 2 weeks, 6 weeks, 3 months, 6 months, and 1 year. The mean VAS pain scores decreased from 6.9 ± 1.3 preoperatively to 0.2 ± 0.4 at 1 year (P<0.001). Mean ASES scores and SANE scores were both significantly improved at 3 months, 6 months, and 1 year. Mean ASES scores increased from 40.3 ± 17 preoperatively to 93.0 ± 5.5 at 1 year (P=0.001), and mean SANE scores increased from 40.7 ± 23.7 preoperatively to 85.6 ± 8.9 at 1 year (P=0.007). The mean VR-12 physical component score was significantly improved at 6 months and 1 year postoperatively. The mean VR-12 mental component score was clinically improved at 6 months and 1 year postoperatively. Conclusion: Arthroscopic tensionable knotless biologic tuberoplasty is an effective treatment for massive irreparable rotator cuff tears and resulted in statistically significant improvements in VAS pain, ASES, SANE, and the VR-12 physical component scores and clinically significant improvements in the VR-12 mental component score in our patient cohort.

Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions.

Three-dimensional (3D) hetero-integration technology is poised to revolutionize the field of electronics by stacking functional layers vertically, thereby creating novel 3D circuity architectures with high integration density and unparalleled multifunctionality. However, the conventional 3D integration technique involves complex wafer processing and intricate interlayer wiring. Here we demonstrate monolithic 3D integration of two-dimensional, material-based artificial intelligence (AI)-processing hardware with ultimate integrability and multifunctionality. A total of six layers of transistor and memristor arrays were vertically integrated into a 3D nanosystem to perform AI tasks, by peeling and stacking of AI processing layers made from bottom-up synthesized two-dimensional materials. This fully monolithic-3D-integrated AI system substantially reduces processing time, voltage drops, latency and footprint due to its densely packed AI processing layers with dense interlayer connectivity. The successful demonstration of this monolithic-3D-integrated AI system will not only provide a material-level solution for hetero-integration of electronics, but also pave the way for unprecedented multifunctional computing hardware with ultimate parallelism.

Prediction of stent under-expansion in calcified coronary arteries using machine learning on intravascular optical coherence tomography images.

It can be difficult/impossible to fully expand a coronary artery stent in a heavily calcified coronary artery lesion. Under-expanded stents are linked to later complications. Here we used machine/deep learning to analyze calcifications in pre-stent intravascular optical coherence tomography (IVOCT) images and predicted the success of vessel expansion. Pre- and post-stent IVOCT image data were obtained from 110 coronary lesions. Lumen and calcifications in pre-stent images were segmented using deep learning, and lesion features were extracted. We analyzed stent expansion along the lesion, enabling frame, segmental, and whole-lesion analyses. We trained regression models to predict the post-stent lumen area and then computed the stent expansion index (SEI). Best performance (root-mean-square-error = 0.04 ± 0.02 mm2, r = 0.94 ± 0.04, p < 0.0001) was achieved when we used features from both lumen and calcification to train a Gaussian regression model for segmental analysis of 31 frames in length. Stents with minimum SEI > 80% were classified as "well-expanded;" others were "under-expanded." Under-expansion classification results (e.g., AUC = 0.85 ± 0.02) were significantly improved over a previous, simple calculation, as well as other machine learning solutions. Promising results suggest that such methods can identify lesions at risk of under-expansion that would be candidates for intervention lesion preparation (e.g., atherectomy).

Conversion of the bronchial tree into a conforming electrode to ablate the lung nodule in a porcine model.

BACKGROUND: Radiofrequency ablation (RFA) is one of the treatment options for lung nodules. However, the need for exact delivery of the rigid metal electrode into the center of the target mass often leads to complications or suboptimal results. To overcome these limitations, a concept of conforming electrodes using a flexible material has been tested in this study. METHODS: A bronchoscopy-guided RFA (CAROL) under a temperature-controlled mode was tested in in-vivo and ex-vivo porcine lungs. Gallium-based liquid metal was used for turning the bronchial tree into temporary RF electrodes. A customized bronchoscopy-guided balloon-tipped guiding catheter (CAROL catheter) was used to make the procedure feasible under fluoroscopy imaging guidance. The computer simulation was also performed to gain further insight into the ablation results. Safety was also assessed including the liquid metal remaining in the body. RESULTS: The bronchial electrode injected from the CAROL catheter was able to turn the target site bronchial air pipe into a temporally multi-tined RF electrode. The mean volume of Gallium for each effective CAROL was 0.46 ± 0.47 ml. The ablation results showed highly efficacious and consistent results, especially in the peripheral lung. Most bronchial electrodes were also retrieved by either bronchoscopic suction immediately after the procedure or by natural expectoration thereafter. The liquid metal used in these experiments did not have any significant safety issues. Computer simulation also supports these results. CONCLUSION: The CAROL ablation was very effective and safe in porcine lungs showing encouraging potential to overcome the conventional approaches.

Lung cancer can be treated by inserting a metal device into the lung via the throat and using this to send radio waves into the cancer. However, using a rigid metal device can cause damage to other areas of the lung and can only treat small cancers. Here, we describe an alternative method to treat lung cancers in which liquid metal is used to fill the spaces within the lung closest to the cancer. We demonstrate that this method can be used to treat cancer in a swine model of lung cancer. Given the positive results we obtained, we think this approach should be tested in a clinical trial in human patients with lung cancer, as it might improve cancer treatment.

Functionalizing nanophotonic structures with 2D van der Waals materials.

The integration of two-dimensional (2D) van der Waals materials with nanostructures has triggered a wide spectrum of optical and optoelectronic applications. Photonic structures of conventional materials typically lack efficient reconfigurability or multifunctionality. Atomically thin 2D materials can thus generate new functionality and reconfigurability for a well-established library of photonic structures such as integrated waveguides, optical fibers, photonic crystals, and metasurfaces, to name a few. Meanwhile, the interaction between light and van der Waals materials can be drastically enhanced as well by leveraging micro-cavities or resonators with high optical confinement. The unique van der Waals surfaces of the 2D materials enable handiness in transfer and mixing with various prefabricated photonic templates with high degrees of freedom, functionalizing as the optical gain, modulation, sensing, or plasmonic media for diverse applications. Here, we review recent advances in synergizing 2D materials to nanophotonic structures for prototyping novel functionality or performance enhancements. Challenges in scalable 2D materials preparations and transfer, as well as emerging opportunities in integrating van der Waals building blocks beyond 2D materials are also discussed.