Unveiling Trends: Nanoscale Materials Shaping Emerging Biomedical Applications.

The realm of biomedical materials continues to evolve rapidly, driven by innovative research across interdisciplinary domains. Leveraging big data from the CAS Content Collection, this study employs quantitative analysis through natural language processing (NLP) to identify six emerging areas within nanoscale materials for biomedical applications. These areas encompass self-healing, bioelectronic, programmable, lipid-based, protein-based, and antibacterial materials. Our Nano Focus delves into the multifaceted utilization of nanoscale materials in these domains, spanning from augmenting physical and electronic properties for interfacing with human tissue to facilitating intricate functionalities like programmable drug delivery.

Fast Multichannel Inverse Design through Augmented Partial Factorization.

Computer-automated design and discovery have led to high-performance nanophotonic devices with diverse functionalities. However, massively multichannel systems such as metasurfaces controlling many incident angles and photonic-circuit components coupling many waveguide modes still present a challenge. Conventional methods require Min forward simulations and Min adjoint simulations-2Min simulations in total-to compute the objective function and its gradient for a design involving the response to Min input channels. Here, we develop a formalism that uses the recently proposed augmented partial factorization method to obtain both the objective function and its gradient for a massively multichannel system in a single or a few simulations, achieving over 2 orders of magnitude speedup and reduced memory usage. We use this method to inverse design a metasurface beam splitter that separates the incident light to the target diffraction orders for all incident angles of interest, a key component of the dot projector for 3D sensing. This formalism enables efficient inverse design for a wide range of multichannel optical systems.

SARS-CoV-2 spike-FLIPr fusion protein plus lipidated FLIPr protects against various SARS-CoV-2 variants in hamsters.

Vaccine-induced mucosal immunity and broad protective capacity against various severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants remain inadequate. Formyl peptide receptor-like 1 inhibitory protein (FLIPr), produced by Staphylococcus aureus, can bind to various Fcγ receptor subclasses. Recombinant lipidated FLIPr (rLF) was previously found to be an effective adjuvant. In this study, we developed a vaccine candidate, the recombinant Delta SARS-CoV-2 spike (rDS)-FLIPr fusion protein (rDS-F), which employs the property of FLIPr binding to various Fcγ receptors. Our study shows that rDS-F plus rLF promotes rDS capture by dendritic cells. Intranasal vaccination of mice with rDS-F plus rLF increases persistent systemic and mucosal antibody responses and CD4/CD8 T-cell responses. Importantly, antibodies induced by rDS-F plus rLF vaccination neutralize Delta, Wuhan, Alpha, Beta, and Omicron strains. Additionally, rDS-F plus rLF provides protective effects against various SARS-CoV-2 variants in hamsters by reducing inflammation and viral loads in the lung. Therefore, rDS-F plus rLF is a potential vaccine candidate to induce broad protective responses against various SARS-CoV-2 variants.IMPORTANCEMucosal immunity is vital for combating pathogens, especially in the context of respiratory diseases like COVID-19. Despite this, most approved vaccines are administered via injection, providing systemic but limited mucosal protection. Developing vaccines that stimulate both mucosal and systemic immunity to address future coronavirus mutations is a growing trend. However, eliciting strong mucosal immune responses without adjuvants remains a challenge. In our study, we have demonstrated that using a recombinant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike-formyl peptide receptor-like 1 inhibitory protein (FLIPr) fusion protein as an antigen, in combination with recombinant lipidated FLIPr as an effective adjuvant, induced simultaneous systemic and mucosal immune responses through intranasal immunization in mice and hamster models. This approach offered protection against various SARS-CoV-2 strains, making it a promising vaccine candidate for broad protection. This finding is pivotal for future broad-spectrum vaccine development.

Human mobility networks manifest dissimilar resilience characteristics at macroscopic, substructure, and microscopic scales.

Human mobility networks can reveal insights into resilience phenomena, such as population response to, impacts on, and recovery from crises. The majority of human mobility network resilience characterizations, however, focus mainly on macroscopic network properties; little is known about variation in measured resilience characteristics (i.e., the extent of impact and recovery duration) across macroscopic, substructure (motif), and microscopic mobility scales. To address this gap, in this study, we examine the human mobility network in eight parishes in Louisiana (USA) impacted by the 2021 Hurricane Ida. We constructed human mobility networks using location-based data and examined three sets of measures: (1) macroscopic measures, such as network density, giant component size, and modularity; (2) substructure measures, such as motif distribution; and (3) microscopic mobility measures, such as the radius of gyration and average travel distance. To determine the extent of impact and duration of recovery, for each measure, we established the baseline values and examined the fluctuation of measures during the perturbation caused by Hurricane Ida. The results reveal the variation of impact extent and recovery duration obtained from different sets of measures at different scales. Macroscopic measures, such as giant components, tend to recover more quickly than substructure and microscopic measures. In fact, microscopic measures tend to recover more slowly than measures in other scales. These findings suggest that resilience characteristics in human mobility networks are scale-variant, and thus, a single measure at a particular scale may not be representative of the perturbation impacts and recovery duration in the network as a whole. These results spotlight the need to use measures at different scales to properly characterize resilience in human mobility networks.

Temporal focusing multiphoton microscopy with cross-modality multi-stage 3D U-Net for fast and clear bioimaging.

Temporal focusing multiphoton excitation microscopy (TFMPEM) enables fast widefield biotissue imaging with optical sectioning. However, under widefield illumination, the imaging performance is severely degraded by scattering effects, which induce signal crosstalk and a low signal-to-noise ratio in the detection process, particularly when imaging deep layers. Accordingly, the present study proposes a cross-modality learning-based neural network method for performing image registration and restoration. In the proposed method, the point-scanning multiphoton excitation microscopy images are registered to the TFMPEM images by an unsupervised U-Net model based on a global linear affine transformation process and local VoxelMorph registration network. A multi-stage 3D U-Net model with a cross-stage feature fusion mechanism and self-supervised attention module is then used to infer in-vitro fixed TFMPEM volumetric images. The experimental results obtained for in-vitro drosophila mushroom body (MB) images show that the proposed method improves the structure similarity index measures (SSIMs) of the TFMPEM images acquired with a 10-ms exposure time from 0.38 to 0.93 and 0.80 for shallow- and deep-layer images, respectively. A 3D U-Net model, pretrained on in-vitro images, is further trained using a small in-vivo MB image dataset. The transfer learning network improves the SSIMs of in-vivo drosophila MB images captured with a 1-ms exposure time to 0.97 and 0.94 for shallow and deep layers, respectively.

Recombinant lipidated FLIPr effectively enhances mucosal and systemic immune responses for various vaccine types.

Formyl peptide receptor-like 1 inhibitor protein (FLIPr) is an immune evasion protein produced by Staphylococcus aureus, and FLIPr is a potential vaccine candidate for reducing Staphylococcus aureus virulence and biofilm formation. We produced recombinant lipidated FLIPr (rLF) to increase the immunogenicity of FLIPr and showed that rLF alone elicited potent anti-FLIPr antibody responses to overcome the FLIPr-mediated inhibition of phagocytosis. In addition, rLF has potent immunostimulatory properties. We demonstrated that rLF is an effective adjuvant. When an antigen is formulated with rLF, it can induce long-lasting antigen-specific immune responses and enhance mucosal and systemic antibody responses as well as broad-spectrum T-cell responses in mice. These findings support further exploration of rLF in the clinic as an adjuvant for various vaccine types with extra benefits to abolish FLIPr-mediated immunosuppressive effects.

Prediction of Bone Healing around Dental Implants in Various Boundary Conditions by Deep Learning Network.

Tissue differentiation varies based on patients' conditions, such as occlusal force and bone properties. Thus, the design of the implants needs to take these conditions into account to improve osseointegration. However, the efficiency of the design procedure is typically not satisfactory and needs to be significantly improved. Thus, a deep learning network (DLN) is proposed in this study. A data-driven DLN consisting of U-net, ANN, and random forest models was implemented. It serves as a surrogate for finite element analysis and the mechano-regulation algorithm. The datasets include the history of tissue differentiation throughout 35 days with various levels of occlusal force and bone properties. The accuracy of day-by-day tissue differentiation prediction in the testing dataset was 82%, and the AUC value of the five tissue phenotypes (fibrous tissue, cartilage, immature bone, mature bone, and resorption) was above 0.86, showing a high prediction accuracy. The proposed DLN model showed the robustness for surrogating the complex, time-dependent calculations. The results can serve as a design guideline for dental implants.

Dual-resonant scanning multiphoton microscope with ultrasound lens and resonant mirror for rapid volumetric imaging.

A dual-resonant scanning multiphoton (DRSM) microscope incorporating a tunable acoustic gradient index of refraction lens with a resonant mirror is developed for high-speed volumetric imaging. In the proposed microscope, the pulse train signal of a femtosecond laser is used to trigger an embedded field programmable gate array to sample the multiphoton excited fluorescence signal at the rate of one pixel per laser pulse. It is shown that a frame rate of around 8000 Hz can be obtained in the x-z plane for an image region with a size of 256 × 80 pixels. Moreover, a volumetric imaging rate of over 30 Hz can be obtained for a large image volume of 343 × 343 × 120 µm3 with an image size of 256 × 256 × 80 voxels. Moreover, a volumetric imaging rate of over 30 Hz can be obtained for a large image volume of 256 × 256 × 80 voxels, which represents 343 × 343 × 120 µm3 in field-of-view. The rapid volumetric imaging rate eliminates the aliasing effect for observed temporal frequencies lower than 15 Hz. The practical feasibility of the DRSM microscope is demonstrated by observing the mushroom bodies of a drosophila brain and performing 3D dynamic observations of moving 10-µm fluorescent beads.

Mass spectrometry in the discovery of peptides involved in intercellular communication: From targeted to untargeted peptidomics approaches.

Endogenous peptide hormones represent an essential class of biomolecules, which regulate cell-cell communications in diverse physiological processes of organisms. Mass spectrometry (MS) has been developed to be a powerful technology for identifying and quantifying peptides in a highly efficient manner. However, it is difficult to directly identify these peptide hormones due to their diverse characteristics, dynamic regulations, low abundance, and existence in a complicated biological matrix. Here, we summarize and discuss the roles of targeted and untargeted MS in discovering peptide hormones using bioassay-guided purification, bioinformatics screening, or the peptidomics-based approach. Although the peptidomics approach is expected to discover novel peptide hormones unbiasedly, only a limited number of successful cases have been reported. The critical challenges and corresponding measures for peptidomics from the steps of sample preparation, peptide extraction, and separation to the MS data acquisition and analysis are also discussed. We also identify emerging technologies and methods that can be integrated into the discovery platform toward the comprehensive study of endogenous peptide hormones.

Omicron-specific mRNA vaccine induced cross-protective immunity against ancestral SARS-CoV-2 infection with low neutralizing antibodies.

The major challenge in COVID-19 vaccine effectiveness is immune escape by SARS-CoV-2 variants. To overcome this, an Omicron-specific messenger RNA (mRNA) vaccine was designed. The extracellular domain of the spike of the Omicron variant was fused with a modified GCN4 trimerization domain with low immunogenicity (TSomi). After immunization with TSomi mRNA in hamsters, animals were challenged with SARS-CoV-2 virus. The raised nonneutralizing antibodies or cytokine secretion responses can recognize both Wuhan S and Omicron S. However, the raised antibodies neutralized SARS-CoV-2 Omicron virus infection but failed to generate Wuhan virus neutralizing antibodies. Surprisingly, TSomi mRNA immunization protected animals from Wuhan virus challenge. These data indicated that non-neutralizing antibodies or cellular immunity may play a more important role in vaccine-induced protection than previously believed. Next-generation COVID-19 vaccines using the Omicron S antigen may provide sufficient protection against ancestral or current SARS-CoV-2 variants.

YMLA: A comparative platform to carry out functional enrichment analysis for multiple gene lists in yeast.

Comparative analysis among multiple gene lists on their functional features is now a routine task due to the advancement of high-throughput experiments. Several enrichment analysis tools were developed in the past. However, these tools mainly focus on one gene list and contain only gene ontology or interaction features. What makes it worse, comparative investigation and customized feature set reanalysis are still unavailable. Therefore, we constructed the YMLA (Yeast Multiple List Analyzer) platform in this research. YMLA includes 39 yeast features and facilitates comparative analysis among multiple gene lists via tabular views, heatmaps, and network plots. Moreover, the customized feature set reanalysis function was implemented in YMLA to help form mechanism hypotheses based on a selected enriched feature subset. We demonstrated the biological applicability of YMLA via example lists consisting of genes with top/bottom translation efficiency values. The analysis results provided by YMLA reveal novel facts consistent with previous experiments. YMLA is available at https://cosbi7.ee.ncku.edu.tw/YMLA/.

Thickness bound for nonlocal wide-field-of-view metalenses.

Metalenses-flat lenses made with optical metasurfaces-promise to enable thinner, cheaper, and better imaging systems. Achieving a sufficient angular field of view (FOV) is crucial toward that goal and requires a tailored incident-angle-dependent response. Here, we show that there is an intrinsic trade-off between achieving a desired broad-angle response and reducing the thickness of the device. Like the memory effect in disordered media, this thickness bound originates from the Fourier transform duality between space and angle. One can write down the transmission matrix describing the desired angle-dependent response, convert it to the spatial basis where its degree of nonlocality can be quantified through a lateral spreading, and determine the minimal device thickness based on such a required lateral spreading. This approach is general. When applied to wide-FOV lenses, it predicts the minimal thickness as a function of the FOV, lens diameter, and numerical aperture. The bound is tight, as some inverse-designed multi-layer metasurfaces can approach the minimal thickness we found. This work offers guidance for the design of nonlocal metasurfaces, proposes a new framework for establishing bounds, and reveals the relation between angular diversity and spatial footprint in multi-channel systems.

Coherent enhancement of optical remission in diffusive media.

Remitted waves are used for sensing and imaging in diverse diffusive media from the Earth's crust to the human brain. Separating the source and detector increases the penetration depth of light, but the signal strength decreases rapidly, leading to a poor signal-to-noise ratio. Here, we show, experimentally and numerically, that wavefront shaping a laser beam incident on a diffusive sample enables an enhancement of remission by an order of magnitude at depths of up to 10 transport mean free paths. We develop a theoretical model which predicts the maximal remission enhancement. Our analysis reveals a significant improvement in the sensitivity of remitted waves to local changes of absorption deep inside diffusive media. This work illustrates the potential of coherent wavefront control for noninvasive diffuse wave imaging applications, such as diffuse optical tomography and functional near-infrared spectroscopy.

Trace Determination of Grouper Nervous Necrosis Virus in Contaminated Larvae and Pond Water Samples Using Label-Free Fiber Optic Nanoplasmonic Biosensor.

We developed a fast (<20 min), label-free fiber optic particle plasmon resonance (FOPPR) immunosensing method to detect nervous necrosis virus (NNV), which often infects high-value economic aquatic species, such as grouper. Using spiked NNV particles in a phosphate buffer as samples, the standard calibration curve obtained was linear (R2 = 0.99) and the limit of detection (LOD) achieved was 2.75 × 104 TCID50/mL, which is superior to that obtained using enzyme-linked immunosorbent assay (ELISA). By using an enhancement method called fiber optic nanogold-linked immunosorbent assay (FONLISA), the LOD can be further improved to <1 TCID50/mL, which is comparable to that found by the conventional qPCR method. Employing the larvae homogenate samples of NNV-infected grouper, the results obtained by the FOPPR biosensor agree with those obtained by the quantitative polymerase chain reaction (qPCR) method. We also examined pond water samples from an infected container in an indoor aquaculture facility. The lowest detectable level of NNV coat protein was found to be 0.17 µg/mL, which is one order lower than the LOD reported by ELISA. Therefore, we demonstrated the potential of the FOPPR biosensor as an outbreak surveillance tool, which is able to give warning indication even when the trend of larvae death toll increment is still not clear.

Chemical and enzymatic modifications of 5-methylcytosine at the intersection of DNA damage, repair, and epigenetic reprogramming.

The DNA of all living organisms is persistently damaged by endogenous reactions including deamination and oxidation. Such damage, if not repaired correctly, can result in mutations that drive tumor development. In addition to chemical damage, recent studies have established that DNA bases can be enzymatically modified, generating many of the same modified bases. Irrespective of the mechanism of formation, modified bases can alter DNA-protein interactions and therefore modulate epigenetic control of gene transcription. The simultaneous presence of both chemically and enzymatically modified bases in DNA suggests a potential intersection, or collision, between DNA repair and epigenetic reprogramming. In this paper, we have prepared defined sequence oligonucleotides containing the complete set of oxidized and deaminated bases that could arise from 5-methylcytosine. We have probed these substrates with human glycosylases implicated in DNA repair and epigenetic reprogramming. New observations reported here include: SMUG1 excises 5-carboxyuracil (5caU) when paired with A or G. Both TDG and MBD4 cleave 5-formyluracil and 5caU when mispaired with G. Further, TDG not only removes 5-formylcytosine and 5-carboxycytosine when paired with G, but also when mispaired with A. Surprisingly, 5caU is one of the best substrates for human TDG, SMUG1 and MBD4, and a much better substrate than T. The data presented here introduces some unexpected findings that pose new questions on the interactions between endogenous DNA damage, repair, and epigenetic reprogramming pathways.

Ocean Surface Flux Algorithm Effects on Tropical Indo-Pacific Intraseasonal Precipitation.

Surface latent heat fluxes help maintain tropical intraseasonal precipitation. We develop a latent heat flux diagnostic that depicts how latent heat fluxes vary with the near-surface specific humidity vertical gradient (Δq) and surface wind speed (|V|). Compared to fluxes estimated from |V| and Δq measured at tropical moorings and the Coupled Ocean Atmosphere Response Experiment 3.0 (COARE3.0) algorithm, tropical latent heat fluxes in the National Center for Atmospheric Research CEMS2 and Department of Energy E3SMv1 models are significantly overestimated at |V| and Δq extrema. Madden-Julian oscillation (MJO) sensitivity to surface flux algorithm is tested with offline and inline flux corrections. The offline correction adjusts model output fluxes toward mooring-estimated fluxes; the inline correction replaces the original bulk flux algorithm with the COARE3.0 algorithm in atmosphere-only simulations of each model. Both corrections indicate reduced latent heat flux feedback to intraseasonal precipitation, in better agreement with observations, suggesting that model-simulated fluxes are overly supportive for maintaining MJO convection.

A combinatorial system to examine the enzymatic repair of multiply damaged DNA substrates.

DNA damage drives genetic mutations that underlie the development of cancer in humans. Multiple pathways have been described in mammalian cells which can repair this damage. However, most work to date has focused upon single lesions in DNA. We present here a combinatorial system which allows assembly of duplexes containing single or multiple types of damage by ligating together six oligonucleotides containing damaged or modified bases. The combinatorial system has dual fluorescent labels allowing examination of both strands simultaneously, in order to study interactions or competition between different DNA repair pathways. Using this system, we demonstrate how repair of oxidative damage in one DNA strand can convert a mispaired T:G deamination intermediate into a T:A mutation. We also demonstrate that slow repair of a T:G mispair, relative to a U:G mispair, by the human methyl-binding domain 4 DNA glycosylase provides a competitive advantage to competing repair pathways, and could explain why CpG dinucleotides are hotspots for C to T mutations in human tumors. Data is also presented that suggests repair of closely spaced lesions in opposing strands can be repaired by a combination of short and long-patch base excision repair and simultaneous repair of multiply damage sites can potentially lead to lethal double strand breaks.

Induction of high affinity monoclonal antibodies against SARS-CoV-2 variant infection using a DNA prime-protein boost strategy.

BACKGROUND: Calls for the coronavirus to be treated as an endemic illness, such as the flu, are increasing. After achieving high coverage of COVID-19 vaccination, therapeutic drugs have become important for future SARS-CoV-2 variant outbreaks. Although many monoclonal antibodies have been approved for emergency use as treatments for SARS-CoV-2 infection, some monoclonal antibodies are not authorized for variant treatment. Broad-spectrum monoclonal antibodies are unmet medical needs. METHODS: We used a DNA prime-protein boost approach to generate high-quality monoclonal antibodies. A standard ELISA was employed for the primary screen, and spike protein-human angiotensin-converting enzyme 2 blocking assays were used for the secondary screen. The top 5 blocking clones were selected for further characterization, including binding ability, neutralization potency, and epitope mapping. The therapeutic effects of the best monoclonal antibody against SARS-CoV-2 infection were evaluated in a hamster infection model. RESULTS: Several monoclonal antibodies were selected that neutralize different SARS-CoV-2 variants of concern (VOCs). These VOCs include Alpha, Beta, Gamma, Delta, Kappa and Lambda variants. The high neutralizing antibody titers against the Beta variant would be important to treat Beta-like variants. Among these monoclonal antibodies, mAb-S5 displays the best potency in terms of binding affinity and neutralizing capacity. Importantly, mAb-S5 protects animals from SARS-CoV-2 challenge, including the Wuhan strain, D614G, Alpha and Delta variants, although mAb-S5 exhibits decreased neutralization potency against the Delta variant. Furthermore, the identified neutralizing epitopes of monoclonal antibodies are all located in the receptor-binding domain (RBD) of the spike protein but in different regions. CONCLUSIONS: Our approach generates high-potency monoclonal antibodies against a broad spectrum of VOCs. Multiple monoclonal antibody combinations may be the best strategy to treat future SARS-CoV-2 variant outbreaks.

Two-stage screened selection designs for randomized phase II trials with time-to-event endpoints.

Phase II exploratory multiarm studies that randomize among new treatments are found to be broadly useful and appear to be of value both scientifically and logistically, especially in the areas of unmet needs, for example, pediatric cancer. This multiarm design also has a faster recruitment rate because it provides patients with more treatment choices than traditional two-arm randomized controlled trials do. In contrast to direct formal comparisons in multiarm multistage designs, for example, umbrella or platform designs, the screened selection design (SSD) recommends using a promising treatment arm by ranking according to the effect size, which often needs lesser sample sizes than the former. In this paper, the usefulness of the phase II SSD design is exemplified by three real trials. However, the existing SSD methods can only deal with binary endpoints. Motivated by the real trials in the authors' respective institutions, we propose using the two-stage SSD and its variant for randomized phase II trials with the time-to-event endpoint. The proposed methods not only provide a high probability of selecting a superior treatment arm but also control the type I error rate for testing the efficacy of each treatment arm versus a common external control. Sample size calculations have been derived and simulation studies demonstrate desirable operating characteristics. The proposed design has been used for designing three real trials. An R package frequentistSSD has been developed and is freely accessible for practitioners.

A semi-mechanistic dose-finding design in oncology using pharmacokinetic/pharmacodynamic modeling.

While a number of phase I dose-finding designs in oncology exist, the commonly used ones are either algorithmic or empirical model-based. We propose a new framework for modeling the dose-response relationship, by systematically incorporating the pharmacokinetic (PK) data collected in the trial and the hypothesized mechanisms of the drug effects, via dynamic PK/PD modeling, as well as modeling of the relationship between a latent cumulative pharmacologic effect and a binary toxicity outcome. This modeling framework naturally incorporates the information on the impact of dose, schedule and method of administration (e.g., drug formulation and route of administration) on toxicity. The resulting design is an extension of existing designs that make use of pre-specified summary PK information (such as the area under the concentration-time curve [AUC] or maximum serum concentration [Cmax ]). Our simulation studies show, with moderate departure from the hypothesized mechanisms of the drug action, that the performance of the proposed design on average improves upon those of the common designs, including the continual reassessment method (CRM), Bayesian optimal interval (BOIN) design, modified toxicity probability interval (mTPI) method, and a design called PKLOGIT that models the effect of the AUC on toxicity. In case of considerable departure from the underlying drug effect mechanism, the performance of the design is shown to be comparable with that of the other designs. We illustrate the proposed design by applying it to the setting of a phase I trial of a γ-secretase inhibitor in metastatic or locally advanced solid tumors. We also provide R code to implement the proposed design.