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Abstract As SARS-CoV-2 evolves, increasing in potential for greater transmissibility and immune escape, updated vaccines are needed to boost adaptive immunity to protect against COVID-19 caused by circulating strains. Here, we report features of the monovalent Omicron XBB.1.5-adapted BNT162b2 vaccine, which contains XBB.1.5-specific sequence changes, relative to the original BNT162b2 backbone, in the encoded prefusion-stabilized SARS-CoV-2 spike protein (S(P2)). Biophysical characterization of Omicron XBB.1.5 S(P2) demonstrated that it maintains a prefusion conformation and adopts a flexible, predominantly open, state, with high affinity for the human ACE-2 receptor. When administered as a 4th dose in BNT162b2-experienced mice, the monovalent Omicron XBB.1.5 vaccine elicited substantially higher serum neutralizing titers against pseudotyped viruses of Omicron XBB.1.5, XBB.1.16, XBB.1.16.1, XBB.2.3, EG.5.1 and HV.1 sublineages and phylogenetically distant BA.2.86 lineage than the bivalent Wild Type + Omicron BA.4/5 vaccine. Similar trends were observed against Omicron XBB sublineage pseudoviruses when the vaccine was administered as a 2-dose series in naive mice. Strong S-specific Th1 CD4+ and IFNγ+ CD8+ T cell responses were also observed. These findings, together with real world performance of the XBB.1.5-adapted vaccine, suggest that preclinical data for the monovalent Omicron XBB.1.5-adapted BNT162b2 was predictive of protective immunity against dominant SARS-CoV-2 strains.

Abstract The CC chemokine receptor 6 (CCR6) is a potential target for chronic inflammatory diseases. Previously, we reported an active CCR6 structure in complex with its cognate chemokine CCL20, revealing the molecular basis of CCR6 activation. Here, we present two inactive CCR6 structures in ternary complexes with different allosteric antagonists, CCR6/SQA1/OXM1 and CCR6/SQA1/OXM2. The oxomorpholine analogues, OXM1 and OXM2 are highly selective CCR6 antagonists which bind to an extracellular pocket and disrupt the receptor activation network. An energetically favoured U-shaped conformation in solution that resembles the bound form is observed for the active analogues. SQA1 is a squaramide derivative with close-in analogues reported as antagonists of chemokine receptors including CCR6. SQA1 binds to an intracellular pocket which overlaps with the G protein site, stabilizing a closed pocket that is a hallmark of inactive GPCRs. Minimal communication between the two allosteric pockets is observed, in contrast to the prevalent allosteric cooperativity model of GPCRs. This work highlights the versatility of GPCR antagonism by small molecules, complementing previous knowledge of CCR6 activation, and sheds light on drug discovery targeting CCR6.

Orthodontic extrusion of canines, although challenging, can yield excellent results without tooth extraction. Temporary anchorage devices (TADs), involving mini-implants in the alveolar bone, offer stable anchorage via biomechanics. This case report details orthodontic extrusion of bilaterally impacted maxillary canines using TAD mini-implants and elastics. The patient had bimaxillary protrusion, moderate crowding, deep bite, and vertical growth pattern, necessitating careful treatment planning. The combination of TAD mini-implants with Class-II elastics simplified biomechanics. The treatment achieved efficient results, maintained adjacent teeth positioning, and improved patient compliance.

Rehabilitation from musculoskeletal injuries focuses on reestablishing and monitoring muscle activation patterns to accurately produce force. The aim of this study is to explore the use of a novel low-powered wearable distributed Simultaneous Musculoskeletal Assessment with Real-Time Ultrasound (SMART-US) device to predict force during an isometric squat task. Participants (N = 5) performed maximum isometric squats under two medical imaging techniques; clinical musculoskeletal motion mode (m-mode) ultrasound on the dominant vastus lateralis and SMART-US sensors placed on the rectus femoris, vastus lateralis, medial hamstring, and vastus medialis. Ultrasound features were extracted, and a linear ridge regression model was used to predict ground reaction force. The performance of ultrasound features to predict measured force was tested using either the Clinical M-mode, SMART-US sensors on the vastus lateralis (SMART-US: VL), rectus femoris (SMART-US: RF), medial hamstring (SMART-US: MH), and vastus medialis (SMART-US: VMO) or utilized all four SMART-US sensors (Distributed SMART-US). Model training showed that the Clinical M-mode and the Distributed SMART-US model were both significantly different from the SMART-US: VL, SMART-US: MH, SMART-US: RF, and SMART-US: VMO models (p < 0.05). Model validation showed that the Distributed SMART-US model had an R2 of 0.80 ± 0.04 and was significantly different from SMART-US: VL but not from the Clinical M-mode model. In conclusion, a novel wearable distributed SMART-US system can predict ground reaction force using machine learning, demonstrating the feasibility of wearable ultrasound imaging for ground reaction force estimation.

Abstract Branched chain amino acid (BCAA) catabolic impairments have been implicated in several diseases. Branched chain ketoacid dehydrogenase (BCKDH) controls the rate limiting step in BCAA degradation, the activity of which is inhibited by BCKDH kinase (BDK)-mediated phosphorylation. Screening efforts to discover BDK inhibitors led to identification of thiophene PF-07208254, which improved cardiometabolic endpoints in mice. Structure-activity relationship studies led to identification of a thiazole series of BDK inhibitors; however, these inhibitors did not improve metabolism in mice upon chronic administration. While the thiophenes demonstrated sustained branched chain ketoacid (BCKA) lowering and reduced BDK protein levels, the thiazoles increased BCKAs and BDK protein levels. Thiazoles increased BDK proximity to BCKDH-E2, whereas thiophenes reduced BDK proximity to BCKDH-E2, which may promote BDK degradation. Thus, we describe two BDK inhibitor series that possess differing attributes regarding BDK degradation or stabilization and provide a mechanistic understanding of the desirable features of an effective BDK inhibitor.

IntroductionPatellar tendon adaptations occur in response to mechanical load. Appropriate loading is necessary to elicit positive adaptations with increased risk of injury and decreased performance likely if loading exceeds the capacity of the tendon. The aim of the current study was to examine intra-individual associations between workloads and patellar tendon properties and neuromuscular performance in collegiate volleyball athletes.MethodsNational Collegiate Athletics Association Division I men's volleyball athletes (n = 16, age: 20.33 ± 1.15 years, height: 193.50 ± 6.50 cm, body mass: 84.32 ± 7.99 kg, bodyfat%: 13.18 ± 4.72%) competing across 9 weeks of in-season competition participated. Daily measurements of external workloads (i.e., jump count) and internal workloads [i.e., session rating of perceived exertion (sRPE)] were recorded. Weekly measurements included neuromuscular performance assessments (i.e., countermovement jump, drop jump), and ultrasound images of the patellar tendon to evaluate structural adaptations. Repeated measures correlations (r-rm) assessed intra-individual associations among performance and patellar tendon metrics.ResultsWorkload measures exhibited significant negative small to moderate (r-rm =−0.26–0.31) associations with neuromuscular performance, negative (r-rm = −0.21–0.30), and positive (r-rm = 0.20–0.32) small to moderate associations with patellar tendon properties.DiscussionMonitoring change in tendon composition and performance adaptations alongside workloads may inform evidence-based frameworks toward managing and reducing the risk of the development of patellar tendinopathy in collegiate men's volleyball athletes.

Iterative reconstruction has demonstrated superior performance in medical imaging under compressed, sparse, and limited-view sensing scenarios. However, iterative reconstruction algorithms are slow to converge and rely heavily on hand-crafted parameters to achieve good performance. Many iterations are usually required to reconstruct a high-quality image, which is computationally expensive due to repeated evaluations of the physical model. While learned iterative reconstruction approaches such as model-based learning (MBLr) can reduce the number of iterations through convolutional neural networks, it still requires repeated evaluations of the physical models at each iteration. Therefore, the goal of this study is to develop a Fast Iterative Reconstruction (FIRe) algorithm that incorporates a learned physical model into the learned iterative reconstruction scheme to further reduce the reconstruction time while maintaining robust reconstruction performance. We also propose an efficient training scheme for FIRe, which releases the enormous memory footprint required by learned iterative reconstruction methods through the concept of recursive training. The results of our proposed method demonstrate comparable reconstruction performance to learned iterative reconstruction methods with a 9x reduction in computation time and a 620x reduction in computation time compared to variational reconstruction.

Near-infrared photoacoustic imaging (NIR-PAI) combines the advantages of optical and ultrasound imaging to provide anatomical and functional information of tissues with high resolution. Although NIR-PAI is promising, its widespread use is hindered by the limited availability of NIR contrast agents. J-aggregates (JA) made of indocyanine green dye (ICG) represents an attractive class of biocompatible contrast agents for PAI. Here, we present a facile synthesis method that combines ICG and ICG-azide dyes for producing contrast agents with tunable size down to 230 nm and direct functionalization with targeting moieties. The ICG-JA platform has a detectable PA signal in vitro that is two times stronger than whole blood and high photostability. The targeting ability of ICG-JA was measured in vitro using HeLa cells. The ICG-JA platform was then injected into mice and in vivo NIR-PAI showed enhanced visualization of liver and spleen for 90 min post-injection with a contrast-to-noise ratio of 2.42.

Paraduodenal hernias (PDH) are the most frequently occurring internal hernia, caused due to non fusion between inferior mesentery and parietal peritoneum. The herniation of the bowel through the fossa of Landzert results in the left paraduodenal hernia which constitutes 75% of PDH. A case of a 34-year-old male with obstructed left PDH and a spontaneous reduction is demonstrated on Computed Tomography (CT). The patient had a cluster of jejunal loops forming a sac-like mass in the left anterior pararenal space, with surrounding mass effect with twisting and stretching of the mesenteric vessels at the hernia orifice, which showed a spontaneous reduction on subsequent contrast-enhanced and delayed CT studies. Despite the imaging evidence of spontaneous reduction of left PDH in the present case, surgery remains the mainstay in terms of management for left paraduodenal hernia.

Fluorescence and photoacoustic imaging techniques offer valuable insights into cell- and tissue-level processes. However, these optical imaging modalities are limited by scattering and absorption in tissue, resulting in the low-depth penetration of imaging. Contrast-enhanced imaging in the near-infrared window improves imaging penetration by taking advantage of reduced autofluorescence and scattering effects. Current contrast agents for fluorescence and photoacoustic imaging face several limitations from photostability and targeting specificity, highlighting the need for a novel imaging probe development. This review covers a broad range of near-infrared fluorescent and photoacoustic contrast agents, including organic dyes, polymers, and metallic nanostructures, focusing on their optical properties and applications in cellular and animal imaging. Similarly, we explore encapsulation and functionalization technologies toward building targeted, nanoscale imaging probes. Bioimaging applications such as angiography, tumor imaging, and the tracking of specific cell types are discussed. This review sheds light on recent advancements in fluorescent and photoacoustic nanoprobes in the near-infrared window. It serves as a valuable resource for researchers working in fields of biomedical imaging and nanotechnology, facilitating the development of innovative nanoprobes for improved diagnostic approaches in preclinical healthcare.

In this study, we investigated the performance of four deep learning frameworks of U-Net, U-NeXt, DeepLabV3+, and ConResNet in multi-class pixel-based segmentation of the extraocular muscles (EOMs) from coronal MRI. Performances of the four models were evaluated and compared with the standard F-measure-based metrics of intersection over union (IoU) and Dice, where the U-Net achieved the highest overall IoU and Dice scores of 0.77 and 0.85, respectively. Centroid distance offset between identified and ground truth EOM centroids was measured where U-Net and DeepLabV3+ achieved low offsets (p > 0.05) of 0.33 mm and 0.35 mm, respectively. Our results also demonstrated that segmentation accuracy varies in spatially different image planes. This study systematically compared factors that impact the variability of segmentation and morphometric accuracy of the deep learning models when applied to segmenting EOMs from MRI.

Electrical muscle stimulation (EMS) is widely used in rehabilitation and athletic training to generate involuntary muscle contractions. However, EMS leads to rapid muscle fatigue, limiting the force a muscle can produce during prolonged use. Currently available methods to monitor localized muscle fatigue and recovery are generally not compatible with EMS. The purpose of this study was to examine whether Doppler ultrasound imaging can assess changes in stimulated muscle twitches that are related to muscle fatigue from electrical stimulation. We stimulated five isometric muscle twitches in the medial and lateral gastrocnemius of 13 healthy subjects before and after a fatiguing EMS protocol. Tissue Doppler imaging of the medial gastrocnemius recorded muscle tissue velocities during each twitch. Features of the average muscle tissue velocity waveforms changed immediately after the fatiguing stimulation protocol (peak velocity: -38%, p = .022; time-to-zero velocity: +8%, p = .050). As the fatigued muscle recovered, the features of the average tissue velocity waveforms showed a return towards their baseline values similar to that of the normalized ankle torque. We also found that features of the average tissue velocity waveform could significantly predict the ankle twitch torque for each participant (R2 = 0.255–0.849, p < .001). Our results provide evidence that Doppler ultrasound imaging can detect changes in muscle tissue during isometric muscle twitch that are related to muscle fatigue, fatigue recovery, and the generated joint torque. Tissue Doppler imaging may be a feasible method to monitor localized muscle fatigue during EMS in a wearable device.

Simulation tools for photoacoustic wave propagation have played a key role in advancing photoacoustic imaging by providing quantitative and qualitative insights into parameters affecting image quality. Classical methods for numerically solving the photoacoustic wave equation rely on a fine discretization of space and can become computationally expensive for large computational grids. In this work, we applied Fourier Neural Operator (FNO) networks as a fast data-driven deep learning method for solving the 2D photoacoustic wave equation in a homogeneous medium. Comparisons between the FNO network and pseudo-spectral time domain approach were made for the forward and adjoint simulations. Results demonstrate that the FNO network generated comparable simulations with small errors and was orders of magnitude faster than the pseudo-spectral time domain methods (~26× faster on a 64 × 64 computational grid and ~15× faster on a 128 × 128 computational grid). Moreover, the FNO network was generalizable to the unseen out-of-domain test set with a root-mean-square error of 9.5 × 10−3 in Shepp–Logan, 1.5 × 10−2 in synthetic vasculature, 1.1 × 10−2 in tumor and 1.9 × 10−2 in Mason-M phantoms on a 64 × 64 computational grid and a root mean squared of 6.9 ± 5.5 × 10−3 in the AWA2 dataset on a 128 × 128 computational grid.

Conventional reconstruction methods for photoacoustic images are not suitable for the scenario of sparse sensing and geometrical limitation. To overcome these challenges and enhance the quality of reconstruction, several learning-based methods have recently been introduced for photoacoustic tomography reconstruction. The goal of this study is to compare and systematically evaluate the recently proposed learning-based methods and modified networks for photoacoustic image reconstruction. Specifically, learning-based post-processing methods and model-based learned iterative reconstruction methods are investigated. In addition to comparing the differences inherently brought by the models, we also study the impact of different inputs on the reconstruction effect. Our results demonstrate that the reconstruction performance mainly stems from the effective amount of information carried by the input. The inherent difference of the models based on the learning-based post-processing method does not provide a significant difference in photoacoustic image reconstruction. Furthermore, the results indicate that the model-based learned iterative reconstruction method outperforms all other learning-based post-processing methods in terms of generalizability and robustness.

Limited data exist on the association between breast cancer treatments and coronary artery disease anatomy, particularly in males. We describe an unusual case of diffuse coronary ectasia in a man with breast cancer presenting with acute coronary syndrome (ACS). A 66-year-old man with breast cancer on paclitaxel, tamoxifen, and carboplatin chemotherapy regimen, presents with new onset chest pain. Electrocardiogram reveals anterolateral ST-segment depressions and elevated troponin I level. Emergent angiography revealed grossly ectatic coronary arteries with a total thrombotic occlusion of the mid right coronary artery. Serial intracoronary aspiration thrombectomy revealed fragments of red thrombus. Intracoronary tenectaplase was ultimately administered to restore perfusion. The patient clinically improved and warfarin was added to his cardiac regimen. Laboratory work up for connective tissue disease was negative. Although paclitaxel has been implicated in coronary artery neointima and media proliferation in the setting of drug-eluting stents, we believe our case is the first to describe massive coronary ectasia with significant thrombus burden requiring complex coronary intervention and thrombolysis in the setting of breast cancer therapy in a man.

Rehabilitation from musculoskeletal injuries focuses on reestablishing and monitoring muscle activation patterns to accurately produce force. The aim of this study is to explore the use of a novel low-powered wearable distributed Simultaneous Musculoskeletal Assessment with Real-Time Ultrasound (SMART-US) device to predict force during an isometric squat task. Participants (N = 5) performed maximum isometric squats under two medical imaging techniques; clinical musculoskeletal motion mode (m-mode) ultrasound on the dominant vastus lateralis and SMART-US sensors placed on the rectus femoris, vastus lateralis, medial hamstring, and vastus medialis. Ultrasound features were extracted, and a linear ridge regression model was used to predict ground reaction force. The performance of ultrasound features to predict measured force was tested using either the Clinical M-mode, SMART-US sensors on the vastus lateralis (SMART-US: VL), rectus femoris (SMART-US: RF), medial hamstring (SMART-US: MH), and vastus medialis (SMART-US: VMO) or utilized all four SMART-US sensors (Distributed SMART-US). Model training showed that the Clinical M-mode and the Distributed SMART-US model were both significantly different from the SMART-US: VL, SMART-US: MH, SMART-US: RF, and SMART-US: VMO models (p < 0.05). Model validation showed that the Distributed SMART-US model had an R2 of 0.80 ± 0.04 and was significantly different from SMART-US: VL but not from the Clinical M-mode model. In conclusion, a novel wearable distributed SMART-US system can predict ground reaction force using machine learning, demonstrating the feasibility of wearable ultrasound imaging for ground reaction force estimation. [ABSTRACT FROM AUTHOR]

Nanourchin VO2 nanostructures have been effectively synthesized by hydrothermal method. The synthesized hierarchical nanostructure has been characterized by various characterization techniques and electrochemical behaviour has been studied. X-ray diffraction analysis revealed the monoclinic phase and morphological analysis (FESEM) of VO2 showed nanourchin-like structure with arrangement of various nanorods in radial fashion. Over these nanorods, formation of secondary growth of asterisk-shape structure was observed. The width and length of nanorods were ~ 50 and ~ 75 nm, respectively. For the electrochemical measurements, 2032 type coin cells were fabricated using VO2 as electrode material. The electrochemical study of VO2 exhibited the pseudocapacitive behaviour with specific capacitance of 44 F g−1 in organic electrolyte with symmetric capacitor of two electrode system. The maximum energy density and power density were 44 and 8635 Wh kg−1, respectively. Cycling performance exhibited symmetric charge–discharge curves, and also specific capacitance was maintained up to 82% of initial value. [ABSTRACT FROM AUTHOR]