Title: New Multiple Sclerosis Treatment Shows Promise in Trial
Category: Health News
Created: 8/25/2022 12:00:00 AM
Last Editorial Review: 8/26/2022 12:00:00 AM

Sequence variation observed in populations of pathogens can be used for important public health and evolutionary genomic analyses, especially outbreak analysis and transmission reconstruction. Identifying this variation is typically achieved by aligning sequence reads to a reference genome, but this approach is susceptible to reference biases and requires careful filtering of called genotypes. There is a need for tools that can process this growing volume of bacterial genome data, providing rapid results, but that remain simple so they can be used without highly trained bioinformaticians, expensive data analysis, and long-term storage and processing of large files. Here we describe split k-mer analysis (SKA2), a method that supports both reference-free and reference-based mapping to quickly and accurately genotype populations of bacteria using sequencing reads or genome assemblies. SKA2 is highly accurate for closely related samples, and in outbreak simulations, we show superior variant recall compared with reference-based methods, with no false positives. SKA2 can also accurately map variants to a reference and be used with recombination detection methods to rapidly reconstruct vertical evolutionary history. SKA2 is many times faster than comparable methods and can be used to add new genomes to an existing call set, allowing sequential use without the need to reanalyze entire collections. With an inherent absence of reference bias, high accuracy, and a robust implementation, SKA2 has the potential to become the tool of choice for genotyping bacteria. SKA2 is implemented in Rust and is freely available as open-source software.

High-throughput sequencing (HTS) technologies have been instrumental in investigating biological questions at the bulk and single-cell levels. Comparative analysis of two HTS data sets often relies on testing the statistical significance for the difference of two negative binomial distributions (DOTNB). Although negative binomial distributions are well studied, the theoretical results for DOTNB remain largely unexplored. Here, we derive basic analytical results for DOTNB and examine its asymptotic properties. As a state-of-the-art application of DOTNB, we introduce DEGage, a computational method for detecting differentially expressed genes (DEGs) in scRNA-seq data. DEGage calculates the mean of the sample-wise differences of gene expression levels as the test statistic and determines significant differential expression by computing the P-value with DOTNB. Extensive validation using simulated and real scRNA-seq data sets demonstrates that DEGage outperforms five popular DEG analysis tools: DEGseq2, DEsingle, edgeR, Monocle3, and scDD. DEGage is robust against high dropout levels and exhibits superior sensitivity when applied to balanced and imbalanced data sets, even with small sample sizes. We utilize DEGage to analyze prostate cancer scRNA-seq data sets and identify marker genes for 17 cell types. Furthermore, we apply DEGage to scRNA-seq data sets of mouse neurons with and without fear memory and reveal eight potential memory-related genes overlooked in previous analyses. The theoretical results and supporting software for DOTNB can be widely applied to comparative analyses of dispersed count data in HTS and broad research questions.

The human major histocompatibility complex (MHC) is a ~4 Mb genomic segment on Chromosome 6 that plays a pivotal role in the immune response. Despite its importance in various traits and diseases, its complex nature makes it challenging to accurately characterize on a routine basis. We present a novel approach allowing targeted sequencing and de novo haplotypic assembly of the MHC region in heterozygous samples, using long-read sequencing technologies. Our approach is validated using two reference samples, two family trios, and an African-American sample. We achieved excellent coverage (96.6%–99.9% with at least 30x depth) and high accuracy (99.89%–99.99%) for the different haplotypes. This methodology offers a reliable and cost-effective method for sequencing and fully characterizing the MHC without the need for whole-genome sequencing, facilitating broader studies on this important genomic segment and having significant implications in immunology, genetics, and medicine.

BACKGROUD:Lung volume measurements are important for monitoring functional aeration and recruitment and may help guide adjustments in ventilator settings. The expiratory phase of airway pressure release ventilation (APRV) may provide physiologic information about lung volume based on the expiratory flow-time slope, angle, and time to approach a no-flow state (expiratory time [TE]). We hypothesized that expiratory flow would correlate with estimated lung volume (ELV) as measured using a modified nitrogen washout/washin technique in a large-animal lung injury model.METHODS:Eight pigs (35.2 ± 1.0 kg) were mechanically ventilated using an Engström Carescape R860 on the APRV mode. All settings were held constant except the expiratory duration, which was adjusted based on the expiratory flow curve. Abdominal pressure was increased to 15 mm Hg in normal and injured lungs to replicate a combination of pulmonary and extrapulmonary lung injury. ELV was estimated using the Carescape FRC INview tool. The expiratory flow-time slope and TE were measured from the expiratory flow profile.RESULTS:Lung elastance increased with induced lung injury from 29.3 ± 7.3 cm H2O/L to 39.9 ± 15.1cm H2O/L, and chest wall elastance increased with increasing intra-abdominal pressures (IAPs) from 15.3 ± 4.1 cm H2O/L to 25.7 ± 10.0 cm H2O/L in the normal lung and 15.8 ± 6.0 cm H2O/L to 33.0 ± 6.2 cm H2O/L in the injured lung (P = .39). ELV decreased from 1.90 ± 0.83 L in the injured lung to 0.67 ± 0.10 L by increasing IAP to 15 mm Hg. This had a significant correlation with a TE decrease from 2.3 ± 0.8 s to 1.0 ± 0.1 s in the injured group with increasing insufflation pressures (ρ = 0.95) and with the expiratory flow-time slope, which increased from 0.29 ± 0.06 L/s2 to 0.63 ± 0.05 L/s2 (ρ = 0.78).CONCLUSIONS:Changes in ELV over time, and the TE and flow-time slope, could be used to demonstrate evolving lung injury during APRV. Using the slope to infer changes in functional lung volume represents a unique, reproducible, real-time, bedside technique that does not interrupt ventilation and may be used for clinical interpretation.

For clean-room technologies such as isolators and restricted access barrier systems (RABS), decontamination using hydrogen peroxide (H₂O₂) is increasingly attractive to fulfill regulatory requirements. Several approaches are currently used, ranging from manual wipe disinfection to vapor phase hydrogen peroxide (VPHP) or automated nebulization sanitization. Although the residual airborne H₂O₂ concentration can be easily monitored, detection of trace H₂O₂ residues in filled products is rather challenging. To simulate the filling process in a specific clean room, technical runs with water for injection (WfI) are popular. Thus, the ability to detect traces of H₂O₂ in water is an important prerequisite to ensure a safe and reliable use of H₂O₂ for isolator or clean room decontamination. The objective of this study was to provide a validated quantitative, fluorometric Amplex UltraRed assay, which satisfies the analytical target profile of quantifying H₂O₂ in WfI at low nanomolar to low micromolar concentrations (ppb range) with high accuracy and high precision. The Amplex UltraRed technology provides a solid basis for this purpose; however, no commercial assay kit that fulfills these requirements is available. Therefore, a customized Amplex UltraRed assay was developed, optimized, and validated. This approach resulted in an assay that is capable of quantifying H₂O₂ in WfI selectively, sensitively, accurately, precisely, and robustly. This assay is used in process development and qualification approaches using WfI in H₂O₂-decontaminated clean rooms and isolators.

Over the past 20 years, quantitative proteomics has contributed a wealth of protein expression data, which are currently used for a variety of systems pharmacology applications, as a complement or a surrogate for activity of the corresponding proteins. A symposium at the 25th North American International Society for the Study of Xenobiotics meeting, in Boston, in September 2023, was held to explore current and emerging applications of quantitative proteomics in translational pharmacology and strategies for improved integration into model-informed drug development based on practical experience of each of the presenters. A summary of the talks and discussions is presented in this perspective alongside future outlook that was outlined for future meetings.

The precision medicine initiative has driven a substantial change in the way scientists and health care practitioners think about diagnosing and treating disease. While it has long been recognized that drug response is determined by the intersection of genetic, environmental, and disease factors, improvements in technology have afforded precision medicine guided dosing of drugs to improve efficacy and reduce toxicity. Pharmacometabolomics aims to evaluate small molecule metabolites in plasma and/or urine to help evaluate mechanisms that predict and/or reflect drug efficacy and toxicity. In this mini review, we provide an overview of pharmacometabolomic approaches and methodologies. Relevant examples where metabolomic techniques have been used to better understand drug efficacy and toxicity in major depressive disorder and cancer chemotherapy are discussed. In addition, the utility of metabolomics in drug development and understanding drug metabolism, transport, and pharmacokinetics is reviewed. Pharmacometabolomic approaches can help describe factors mediating drug disposition, efficacy, and toxicity. While important advancements in this area have been made, there remain several challenges that must be overcome before this approach can be fully implemented into clinical drug therapy.

Protein abundance data of drug-metabolizing enzymes and transporters (DMETs) are useful for scaling in vitro and animal data to humans for accurate prediction and interpretation of drug clearance and toxicity. Targeted DMET proteomics that relies on synthetic stable isotope-labeled surrogate peptides as calibrators is routinely used for the quantification of selected proteins; however, the technique is limited to the quantification of a small number of proteins. Although the global proteomics-based total protein approach (TPA) is emerging as a better alternative for large-scale protein quantification, the conventional TPA does not consider differential sequence coverage by identifying unique peptides across proteins. Here, we optimized the TPA approach by correcting protein abundance data by the sequence coverage, which was applied to quantify 54 DMETs for characterization of 1) differential tissue DMET abundance in the human liver, kidney, and intestine, and 2) interindividual variability of DMET proteins in individual intestinal samples (n = 13). Uridine diphosphate-glucuronosyltransferase 2B7 (UGT2B7), microsomal glutathione S-transferases (MGST1, MGST2, and MGST3) carboxylesterase 2 (CES2), and multidrug resistance-associated protein 2 (MRP2) were expressed in all three tissues, whereas, as expected, four cytochrome P450s (CYP3A4, CYP3A5, CYP2C9, and CYP4F2), UGT1A1, UGT2B17, CES1, flavin-containing monooxygenase 5, MRP3, and P-glycoprotein were present in the liver and intestine. The top three DMET proteins in individual tissues were: CES1>CYP2E1>UGT2B7 (liver), CES2>UGT2B17>CYP3A4 (intestine), and MGST1>UGT1A6>MGST2 (kidney). CYP3A4, CYP3A5, UGT2B17, CES2, and MGST2 showed high interindividual variability in the intestine. These data are relevant for enhancing in vitro to in vivo extrapolation of drug absorption and disposition and can be used to enhance the accuracy of physiologically based pharmacokinetic prediction of systemic and tissue concentration of drugs.

Although acute inflammation serves essential functions in maintaining tissue homeostasis, chronic inflammation is causally linked to many diseases. Macrophages are a major cell type that orchestrates inflammatory processes. During inflammation, macrophages undergo polarization and activation, thereby mobilizing pro-inflammatory and anti-inflammatory transcriptional programs that regulate ensuing macrophage functions. Fatty acid binding protein 5 (FABP5) is a lipid chaperone highly expressed in macrophages. FABP5 deletion is implicated in driving macrophages toward an anti-inflammatory phenotype, yet signaling pathways regulated by macrophage-FABP5 have not been systematically profiled. We leveraged proteomic and phosphoproteomic approaches to characterize pathways modulated by FABP5 in M1 and M2 polarized bone marrow-derived macrophages (BMDMs). Stable isotope labeling by amino acids-based analysis of M1 and M2 polarized wild-type and FABP5 knockout BMDMs revealed numerous differentially regulated proteins and phosphoproteins. FABP5 deletion impacted downstream pathways associated with inflammation, cytokine production, oxidative stress, and kinase activity. Toll-like receptor 2 (TLR2) emerged as a novel target of FABP5 and pharmacological FABP5 inhibition blunted TLR2-mediated activation of downstream pathways, ascribing a novel role for FABP5 in TLR2 signaling. This study represents a comprehensive characterization of the impact of FABP5 deletion on the proteomic and phosphoproteomic landscape of M1 and M2 polarized BMDMs. Loss of FABP5 altered pathways implicated in inflammatory responses, macrophage function, and TLR2 signaling. This work provides a foundation for future studies seeking to investigate the therapeutic potential of FABP5 inhibition in pathophysiological states resulting from dysregulated inflammatory signaling.

BACKGROUND AND PURPOSE:

Overuse of CT-based cerebrovascular imaging in the emergency department and inpatient settings, notably CTA of the head and neck for minor and nonfocal neurologic presentations, stresses imaging services and exposes patients to radiation and contrast. Furthermore, such CT-based imaging is often insufficient for definitive diagnosis, necessitating additional MR imaging. Recent advances in fast MRI may allow timely assessment and a reduced need for head and neck CTA in select populations.

BACKGROUND AND PURPOSE:

Recently, artificial intelligence tools have been deployed with increasing speed in educational and clinical settings. However, the use of artificial intelligence by trainees across different levels of experience has not been well-studied. This study investigates the impact of artificial intelligence assistance on the diagnostic accuracy for intracranial hemorrhage and large-vessel occlusion by medical students and resident trainees.

Diffuse large B-cell lymphoma (DLBCL) is a heterogenous group of lymphoid malignancies. Based on gene expression profiling, it has been subdivided into germinal center (GC)-derived and activated B-cell (ABC) types. Advances in molecular methodologies have further refined the subclassification of DLBCL, based on recurrent genetic abnormalities. Here, we describe a distinct case of DLBCL that presented in leukemic form. DNA sequencing targeting 275 genes revealed pathogenically relevant mutations of CD79B, MyD88, TP53, TBL1XR1, and PIM1 genes, indicating that this lymphoma would be best classified as MCD/C5 DLBCL, an ABC subtype. Despite an initial good clinical response to BTK inhibitor ibrutinib, anti-CD20 antibody rituxan, alkylating agent bendamustine, and hematopoietic stem-cell transplant, the lymphoma relapsed, accompanied by morphologic and molecular evidence of disease progression. Specifically, the recurrent tumor developed loss of TP53 heterozygosity (LOH) and additional chromosomal changes central to ABC DLBCL pathogenesis, such as PRDM1 loss. Acquired resistance to ibrutinib and rituxan was indicated by the emergence of BTK and FOXO1 mutations, respectively, as well as apparent activation of alternative cell-activation pathways, through copy-number alterations (CNAs), detected by high-resolution chromosomal microarrays. In vitro, studies of relapsed lymphoma cells confirmed resistance to standard BTK inhibitors but sensitivity to vecabrutinib, a noncovalent inhibitor active against both wild-type as well as mutated BTK. In summary, we provide in-depth molecular characterization of a de novo leukemic DLBCL and discuss mechanisms that may have contributed to the lymphoma establishment, progression, and development of drug resistance.

PURPOSE

We undertook a trial to test the efficacy of a technology-assisted health coaching intervention for weight management, called Goals for Eating and Moving (GEM), within primary care.

PM Malcolm Turnbull says Australians should vote to keep a stable majority government in uncertain economic times, as the fallout from Brexit continues.

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Call Of Duty: Black Ops 6 has been out for a little while already, what with me giving its multiplayer largely a thumbs up. Still, it's an ever-evolving thing and Activision have announced the game's first seasonal drop. It's a hefty one with a lot of additions, so I'll try my best to break down the good stuff. TLDR: there's some new maps for multiplayer and zombies, new modes, and a few extra bits. I'm mildly excited for more. More in this case is good.

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Quantum computing is a devilishly complex technology, with many technical hurdles impacting its development. Of these challenges two critical issues stand out: miniaturization and qubit quality.

IBM has adopted the superconducting qubit road map of reaching a 1,121-qubit processor by 2023, leading to the expectation that 1,000 qubits with today’s qubit form factor is feasible. However, current approaches will require very large chips (50 millimeters on a side, or larger) at the scale of small wafers, or the use of chiplets on multichip modules. While this approach will work, the aim is to attain a better path toward scalability.

Now researchers at MIT have been able to both reduce the size of the qubits and done so in a way that reduces the interference that occurs between neighboring qubits. The MIT researchers have increased the number of superconducting qubits that can be added onto a device by a factor of 100.

“We are addressing both qubit miniaturization and quality,” said William Oliver, the director for the Center for Quantum Engineering at MIT. “Unlike conventional transistor scaling, where only the number really matters, for qubits, large numbers are not sufficient, they must also be high-performance. Sacrificing performance for qubit number is not a useful trade in quantum computing. They must go hand in hand.”

The key to this big increase in qubit density and reduction of interference comes down to the use of two-dimensional materials, in particular the 2D insulator hexagonal boron nitride (hBN). The MIT researchers demonstrated that a few atomic monolayers of hBN can be stacked to form the insulator in the capacitors of a superconducting qubit.

Just like other capacitors, the capacitors in these superconducting circuits take the form of a sandwich in which an insulator material is sandwiched between two metal plates. The big difference for these capacitors is that the superconducting circuits can operate only at extremely low temperatures—less than 0.02 degrees above absolute zero (-273.15 °C).

Superconducting qubits are measured at temperatures as low as 20 millikelvin in a dilution refrigerator.Nathan Fiske/MIT

In that environment, insulating materials that are available for the job, such as PE-CVD silicon oxide or silicon nitride, have quite a few defects that are too lossy for quantum computing applications. To get around these material shortcomings, most superconducting circuits use what are called coplanar capacitors. In these capacitors, the plates are positioned laterally to one another, rather than on top of one another.

As a result, the intrinsic silicon substrate below the plates and to a smaller degree the vacuum above the plates serve as the capacitor dielectric. Intrinsic silicon is chemically pure and therefore has few defects, and the large size dilutes the electric field at the plate interfaces, all of which leads to a low-loss capacitor. The lateral size of each plate in this open-face design ends up being quite large (typically 100 by 100 micrometers) in order to achieve the required capacitance.

In an effort to move away from the large lateral configuration, the MIT researchers embarked on a search for an insulator that has very few defects and is compatible with superconducting capacitor plates.

“We chose to study hBN because it is the most widely used insulator in 2D material research due to its cleanliness and chemical inertness,” said colead author Joel Wang, a research scientist in the Engineering Quantum Systems group of the MIT Research Laboratory for Electronics.

On either side of the hBN, the MIT researchers used the 2D superconducting material, niobium diselenide. One of the trickiest aspects of fabricating the capacitors was working with the niobium diselenide, which oxidizes in seconds when exposed to air, according to Wang. This necessitates that the assembly of the capacitor occur in a glove box filled with argon gas.

While this would seemingly complicate the scaling up of the production of these capacitors, Wang doesn’t regard this as a limiting factor.

“What determines the quality factor of the capacitor are the two interfaces between the two materials,” said Wang. “Once the sandwich is made, the two interfaces are “sealed” and we don’t see any noticeable degradation over time when exposed to the atmosphere.”

This lack of degradation is because around 90 percent of the electric field is contained within the sandwich structure, so the oxidation of the outer surface of the niobium diselenide does not play a significant role anymore. This ultimately makes the capacitor footprint much smaller, and it accounts for the reduction in cross talk between the neighboring qubits.

“The main challenge for scaling up the fabrication will be the wafer-scale growth of hBN and 2D superconductors like [niobium diselenide], and how one can do wafer-scale stacking of these films,” added Wang.

Wang believes that this research has shown 2D hBN to be a good insulator candidate for superconducting qubits. He says that the groundwork the MIT team has done will serve as a road map for using other hybrid 2D materials to build superconducting circuits.

In 6G telecom research today, a crucial portion of wireless spectrum has been neglected: the Frequency Range 3, or FR3, band. The shortcoming is partly due to a lack of viable software and hardware platforms for studying this region of spectrum, ranging from approximately 6 to 24 gigahertz. But a new, open-source wireless research kit is changing that equation. And research conducted using that kit, presented last week at a leading industry conference, offers proof of viability of this spectrum band for future 6G networks.

In fact, it’s also arguably signaling a moment of telecom industry re-evaluation. The high-bandwidth 6G future, according to these folks, may not be entirely centered around difficult millimeter wave-based technologies. Instead, 6G may leave plenty of room for higher-bandwidth microwave spectrum tech that is ultimately more familiar and accessible.

The FR3 band is a region of microwave spectrum just shy of millimeter-wave frequencies (30 to 300 GHz). FR3 is also already very popular today for satellite Internet and military communications. For future 5G and 6G networks to share the FR3 band with incumbent players would require telecom networks nimble enough to perform regular, rapid-response spectrum-hopping.

Yet spectrum-hopping might still be an easier problem to solve than those posed by the inherent physical shortcomings of some portions of millimeter-wave spectrum—shortcomings that include limited range, poor penetration, line-of-sight operations, higher power requirements, and susceptibility to weather.

Pi-Radio’s New Face

Earlier this year, the Brooklyn, N.Y.-based startup Pi-Radio—a spinoff from New York University’s Tandon School of Engineering—released a wireless spectrum hardware and software kit for telecom research and development. Pi-Radio’s FR-3 is a software-defined radio system developed for the FR3 band specifically, says company co-founder Sundeep Rangan.

“Software-defined radio is basically a programmable platform to experiment and build any type of wireless technology,” says Rangan, who is also the associate director of NYU Wireless. “In the early stages when developing systems, all researchers need these.”

For instance, the Pi-Radio team presented one new research finding that infers direction to an FR3 antenna from measurements taken by a mobile Pi-Radio receiver—presented at the IEEE Signal Processing Society‘s Asilomar Conference on Signals, Systems and Computers in Pacific Grove, Calif. on 30 October.

According to Pi-Radio co-founder Marco Mezzavilla, who’s also an associate professor at the Polytechnic University of Milan, the early-stage FR3 research that the team presented at Asilomar will enable researchers “to capture [signal] propagation in these frequencies and will allow us to characterize it, understand it, and model it... And this is the first stepping stone towards designing future wireless systems at these frequencies.”

There’s a good reason researchers have recently rediscovered FR3, says Paolo Testolina, postdoctoral research fellow at Northeastern University’s Institute for the Wireless Internet of Things unaffiliated with the current research effort. “The current scarcity of spectrum for communications is driving operators and researchers to look in this band, where they believe it is possible to coexist with the current incumbents,” he says. “Spectrum sharing will be key in this band.”

Rangan notes that the work on which Pi-Radio was built has been published earlier this year both on the more foundational aspects of building networks in the FR3 band as well as the specific implementation of Pi-Radio’s unique, frequency-hopping research platform for future wireless networks. (Both papers were published in IEEE journals.)

“If you have frequency hopping, that means you can get systems that are resilient to blockage,” Rangan says. “But even, potentially, if it was attacked or compromised in any other way, this could actually open up a new type of dimension that we typically haven’t had in the cellular infrastructure.” The frequency-hopping that FR3 requires for wireless communications, in other words, could introduce a layer of hack-proofing that might potentially strengthen the overall network.

Complement, Not Replacement

The Pi-Radio team stresses, however, that FR3 would not supplant or supersede other new segments of wireless spectrum. There are, for instance, millimeter wave 5G deployments already underway today that will no doubt expand in scope and performance into the 6G future. That said, the ways that FR3 expand future 5G and 6G spectrum usage is an entirely unwritten chapter: Whether FR3 as a wireless spectrum band fizzles, or takes off, or finds a comfortable place somewhere in between depends in part on how it’s researched and developed now, the Pi-Radio team says.

“We’re at this tipping point where researchers and academics actually are empowered by the combination of this cutting-edge hardware with open-source software,” Mezzavilla says. “And that will enable the testing of new features for communications in these new frequency bands.” (Mezzavilla credits the National Telecommunications and Information Administration for recognizing the potential of FR3, and for funding the group’s research.)

By contrast, millimeter-wave 5G and 6G research has to date been bolstered, the team says, by the presence of a wide range of millimeter-wave software-defined radio (SDR) systems and other research platforms.

“Companies like Qualcomm, Samsung, Nokia, they actually had excellent millimeter wave development platforms,” Rangan says. “But they were in-house. And the effort it took to build one—an SDR at a university lab—was sort of insurmountable.”

So releasing an inexpensive open-source SDR in the FR3 band, Mezzavilla says, could jump start a whole new wave of 6G research.

“This is just the starting point,” Mezzavilla says. “From now on we’re going to build new features—new reference signals, new radio resource control signals, near-field operations... We’re ready to ship these yellow boxes to other academics around the world to test new features and test them quickly, before 6G is even remotely near us.”

This story was updated on 7 November 2024 to include detail about funding from the National Telecommunications and Information Administration.

Harris has made history multiple times over.

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