Factors associated with engraftment success of patient-derived xenografts of breast cancer.

BACKGROUND: Patient-derived xenograft (PDX) models serve as a valuable tool for the preclinical evaluation of novel therapies. They closely replicate the genetic, phenotypic, and histopathological characteristics of primary breast tumors. Despite their promise, the rate of successful PDX engraftment is various in the literature. This study aimed to identify the key factors associated with successful PDX engraftment of primary breast cancer. METHODS: We integrated clinicopathological data with morphological attributes quantified using a trained artificial intelligence (AI) model to identify the principal factors affecting PDX engraftment. RESULTS: Multivariate logistic regression analyses demonstrated that several factors, including a high Ki-67 labeling index (Ki-67LI) (p < 0.001), younger age at diagnosis (p = 0.032), post neoadjuvant chemotherapy (NAC) (p = 0.006), higher histologic grade (p = 0.039), larger tumor size (p = 0.029), and AI-assessed higher intratumoral necrosis (p = 0.027) and intratumoral invasive carcinoma (p = 0.040) proportions, were significant factors for successful PDX engraftment (area under the curve [AUC] 0.905). In the NAC group, a higher Ki-67LI (p < 0.001), lower Miller-Payne grade (p < 0.001), and reduced proportion of intratumoral normal breast glands as assessed by AI (p = 0.06) collectively provided excellent prediction accuracy for successful PDX engraftment (AUC 0.89). CONCLUSIONS: We found that high Ki-67LI, younger age, post-NAC status, higher histologic grade, larger tumor size, and specific morphological attributes were significant factors for predicting successful PDX engraftment of primary breast cancer.

Proteomic analysis of breast cancer based on immune subtypes.

BACKGROUND: Immunotherapy is applied to breast cancer to resolve the limitations of survival gain in existing treatment modalities. With immunotherapy, a tumor can be classified into immune-inflamed, excluded and desert based on the distribution of immune cells. We assessed the clinicopathological features, each subtype's prognostic value and differentially expressed proteins between immune subtypes. METHODS: Immune subtyping and proteomic analysis were performed on 56 breast cancer cases with neoadjuvant chemotherapy. The immune subtyping was based on the level of tumor-infiltrating lymphocytes (TILs) and Klintrup criteria. If the level of TILs was ≥ 10%, it was classified as immune-inflamed type without consideration of the Klintrup criteria. In cases of 1-9% TIL, Klintrup criteria 1-3 were classified as the immune-excluded subtype and Klintrup criteria not available (NA) was classified as NA. Cases of 1% TILs and Klintrup 0 were classified as the immune-desert subtype. Mass spectrometry was used to identify differentially expressed proteins in formalin-fixed paraffin-embedded biopsy tissues. RESULTS: Of the 56 cases, 31 (55%) were immune-inflamed, 21 (38%) were immune-excluded, 2 (4%) were immune-desert and 2 (4%) were NA. Welch's t-test revealed two differentially expressed proteins between immune-inflamed and immune-excluded/desert subtypes. Coronin-1A was upregulated in immune-inflamed tumors (adjusted p = 0.008) and α-1-antitrypsin was upregulated in immune-excluded/desert tumors (adjusted p = 0.008). Titin was upregulated in pathologic complete response (pCR) than non-pCR among immune-inflamed tumors (adjusted p = 0.036). CONCLUSIONS: Coronin-1A and α-1-antitrypsin were upregulated in immune-inflamed and immune-excluded/desert subtypes, respectively. Titin's elevated expression in pCR within the immune-inflamed subtype may indicate a favorable prognosis. Further studies involving large representative cohorts are necessary to validate these findings.

CCR10-mediated Enhancement of T Cell Trafficking for Improved Tumor Immunotherapy.

BACKGROUND/AIM: The effectiveness of adoptive T cell therapy for solid tumors remains suboptimal, partly attributed to insufficient T cell infiltration into the tumor site. A promising strategy involves directing T cells towards the tumor utilizing tumor-specific chemokine receptors. MATERIALS AND METHODS: We analyzed chemokine receptor expression in activated T cells and chemokine expression in breast and lung cancer using The Cancer Genome Atlas (TCGA) data. Subsequently, we generated 1G4 T cell receptor-engineered T (TCR-T) cells with CCR10 and performed in vitro and in vivo efficacy tests. RESULTS: CCR10 exhibited insufficient expression in various human T cells. Analysis of TCGA RNA sequencing data revealed elevated expression of the chemokine CCL28, the corresponding chemokine for CCR10, in breast and lung cancer. Consequently, we generated CCR10-1G4 TCR-T cells. CCR10-1G4 dual expressing TCR-T cells exhibited comparable cellular cytotoxicity but increased mobility compared to 1G4 TCR-T cells in vitro. Furthermore, injecting CCR10-1G4 dual expressing TCR-T cells into a xenograft tumor model demonstrated enhanced in vivo trafficking and a greater reduction of tumor burden. CONCLUSION: This study highlights the potential of CCR10 for developing efficient adoptive T-cell treatments targeting solid tumors.

Masked Kinematic Continuity-aware Hierarchical Attention Network for pose estimation in videos.

Existing methods for estimating human poses from video content exploit the temporal features of the video sequences and have shown impressive results. However, most methods address spatiotemporal issues separately. They compromise on accuracy to reduce jitter, or require high-resolution images to deal with occlusion, preventing full consideration of temporal features. Unfortunately, these two issues are interrelated. For example, occlusion causes uncertainty between successive frames, leading to unsmoothed results. To address these issues, we propose the Masked Kinematic Continuity-aware Hierarchical Attention Network (M-HANet) as a novel framework that exploits masked kinematic keypoint features by extending our framework HANet framework. First, we randomly select and mask a keypoint to treat the masked keypoint as it is occluded, which allows us to make the network resilient to occlusion. We also use the velocity and acceleration of each individual keypoint to effectively capture temporal features. Second, the proposed hierarchical transformer encoder refines a 2D or 3D input pose derived from existing estimators by aggregating the masked continuity of the spatiotemporal dependencies of human motion. Finally, to facilitate collaborative optimization, we perform an online cross-supervision between the final pose from our decoder and the refined input pose produced by our encoder. We validate the effectiveness of our model demonstrating that our proposed approach improves PCK@0.05 by 14.1% and MPJPE by 8.7 mm compared to the existing method on a variety of tasks, including 2D and 3D pose estimation, body mesh recovery, and sparsely annotated multi-human pose estimation.

Aberration Estimation for Synthetic Aperture Digital Holographic Microscope Using Deep Neural Network.

Digital holographic microscopy (DHM) is a valuable technique for investigating the optical properties of samples through the measurement of intensity and phase of diffracted beams. However, DHMs are constrained by Lagrange invariance, compromising the spatial bandwidth product (SBP) which relates resolution and field of view. Synthetic aperture DHM (SA-DHM) was introduced to overcome this limitation, but it faces significant challenges such as aberrations in synthesizing the optical information corresponding to the steering angle of incident wave. This paper proposes a novel approach utilizing deep neural networks (DNNs) for compensating aberrations in SA-DHM, extending the compensation scope beyond the numerical aperture (NA) of the objective lens. The method involves training a DNN from diffraction patterns and Zernike coefficients through a circular aperture, enabling effective aberration compensation in the illumination beam. This method makes it possible to estimate aberration coefficients from the only part of the diffracted beam cutoff by the circular aperture mask. With the proposed technique, the simulation results present improved resolution and quality of sample images. The integration of deep neural networks with SA-DHM holds promise for advancing microscopy capabilities and overcoming existing limitations.

Ultraviolet-C light at 222 nm has a high disinfecting spectrum in environments contaminated by infectious pathogens, including SARS-CoV-2.

Ultraviolet light (UV) acts as a powerful disinfectant and can prevent contamination of personal hygiene from various contaminated environments. The 222-nm wavelength of UV-C has a highly effective sterilization activity and is safer than 275-nm UV-C. We investigated the irradiation efficacy of 222-nm UV-C against contaminating bacteria and viruses in liquid and fabric environments. We conducted colony-forming unit assays to determine the number of viable cells and a 50% tissue culture infectious dose assay to evaluate the virus titration. A minimum dose of 27 mJ/cm2 of 222-nm UV-C was required for >95% germicidal activity for gram-negative and -positive bacteria. A 25.1 mJ/cm2 dose could ensure >95% virucidal activity against low-pathogenic avian influenza virus and severe acute respiratory syndrome coronavirus (SARS-CoV-2). In addition, this energy dose of 222-nm UV-C effectively inactivated SARS-CoV-2 variants, Delta and Omicron. These results provide valuable information on the disinfection efficiency of 222-nm UV-C in bacterial and virus-contaminated environments and can also develop into a powerful tool for individual hygiene.

The Effectiveness of a Mechanical Ventilation System for Indoor PM2.5 in Residential Houses.

The mechanical ventilation systems used in houses are designed to reduce carbon dioxide emissions while minimizing the energy loss resulting from ventilation. However, the increase in indoor fine particulate (PM2.5) concentration because of external PM2.5 influx through the ventilation system poses a problem. Here, we analyzed the changes in indoor PM2.5 concentration, distinguishing between cases of high and low outdoor PM2.5 concentrations and considering the efficiency of the filters used in residential mechanical ventilation systems. When using filters with the minimum efficiency reporting value (MERV) of 10 in the ventilation system, the outdoor PM2.5 concentration was 5 µg/m³; compared to the initial concentration, the indoor PM2.5 concentration after 60 min decreased to 73%. When the outdoor PM2.5 concentration was 30-40 µg/m³, the indoor PM2.5 concentration reached 91%. However, when MERV 13 filters were used, the indoor PM2.5 concentration consistently dropped to 73-76%, regardless of the outdoor PM2.5 concentration. Furthermore, by comparing the established equation with the mass balance model, the error was confirmed to be within 5%, indicating a good fit. This allows for the prediction of indoor PM2.5 under various conditions when using mechanical ventilation systems, enabling the formulation of strategies for maintaining indoor PM2.5, as recommended by the World Health Organization.

Strategies for Effective Management of Indoor Air Quality in a Kindergarten: CO2 and Fine Particulate Matter Concentrations.

The educational and play-related activities of children proceed mainly indoors in a kindergarten. High concentrations of indoor PM2.5 and CO2 have been linked to various harmful effects on children, considerably impacting their educational outcomes in kindergarten. In this study, we explore different scenarios involving the operation of mechanical ventilation systems and air purifiers in kindergartens. Using numerical models to analyze indoor CO2 and PM2.5 concentration, we aim to optimize strategies that effectively reduce these harmful pollutants. We found that the amount of ventilation required to maintain good air quality, per child, was approximately 20.4 m3/h. However, we also found that as the amount of ventilation increased, so did the concentration of indoor PM2.5; we found that this issue can be resolved using a high-grade filter (i.e., a MERV 13 grade filter with a collection efficiency of 75%). This study provides a scientific basis for reducing PM2.5 concentrations in kindergartens, while keeping CO2 levels low.

Effect of activated carbon electrode material characteristics on hardness control performance of membrane capacitive deionization.

Capacitive deionization (CDI) is an electrochemical-based water treatment technology that has attracted attention as an effective hardness-control process. However, few systematic studies have reported the criteria for the selection of suitable electrode materials for membrane capacitive deionization (MCDI) to control hardness. In this study, the effect of electrode material characteristics on the MCDI performance for hardness control was quantitatively analyzed. The results showed that the deionization capacity and the deionization rate were affected by the specific capacitance and BET-specific surface area of the activated carbon electrode. In addition, the deionization rate also showed significant relationship with the BET specific surface area. Furthermore, it was observed that the deionization capacity and the deionization rate have a highly significant relationship with the BET specific surface area divided by the wettability performance expressed as the minimum wetting rate (MWR). These findings highlighted that the electrode material should have a large surface area and good wettability to increase the deionization capacity and the deionization rate of MCDI for hardness control. The results of this study are expected to provide effective criteria for selecting MCDI electrode materials aiming hardness control.

Conductance stable and mechanically durable bi-layer EGaIn composite-coated stretchable fiber for 1D bioelectronics.

Deformable semi-solid liquid metal particles (LMP) have emerged as a promising substitute for rigid conductive fillers due to their excellent electrical properties and stable conductance under strain. However, achieving a compact and robust coating of LMP on fibers remains a persistent challenge, mainly due to the incompatibility of conventional coating techniques with LMP. Additionally, the limited durability and absence of initial electrical conductivity of LMP restrict their widespread application. In this study, we propose a solution process that robustly and compactly assembles mechanically durable and initially conductive LMP on fibers. Specifically, we present a shearing-based deposition of polymer-attached LMP followed by additional coating with CNT-attached LMP to create bi-layer LMP composite with exceptional durability, electrical conductivity, stretchability, and biocompatibility on various fibers. The versatility and reliability of this manufacturing strategy for 1D electronics are demonstrated through the development of sewn electrical circuits, smart clothes, stretchable biointerfaced fiber, and multifunctional fiber probes.

Pseudoknot-targeting Cas13b combats SARS-CoV-2 infection by suppressing viral replication.

CRISPR-Cas13-mediated viral genome targeting is a novel strategy for defending against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Here, we generated mRNA-encoded Cas13b targeting the open reading frame 1b (ORF1b) region to effectively degrade the RNA-dependent RNA polymerase gene. Of the 12 designed CRISPR RNAs (crRNAs), those targeting the pseudoknot site upstream of ORF1b were found to be the most effective in suppressing SARS-CoV-2 propagation. Pseudoknot-targeting Cas13b reduced expression of the spike protein and attenuated viral replication by 99%. It also inhibited the replication of multiple SARS-CoV-2 variants, exhibiting broad potency. We validated the therapeutic efficacy of this system in SARS-CoV-2-infected hACE2 transgenic mice, demonstrating that crRNA treatment significantly reduced viral titers. Our findings suggest that the pseudoknot region is a strategic site for targeted genomic degradation of SARS-CoV-2. Hence, pseudoknot-targeting Cas13b could be a breakthrough therapy for overcoming infections by SARS-CoV-2 or other RNA viruses.

Amuvatinib Blocks SARS-CoV-2 Infection at the Entry Step of the Viral Life Cycle.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). SARS-CoV-2 propagation is mediated by the protein interaction between viral proteins and host cells. Tyrosine kinase has been implicated in viral replication, and hence, it has become a target for developing antiviral drugs. We have previously reported that receptor tyrosine kinase inhibitor blocks the replication of hepatitis C virus (HCV). In the present study, we investigated two receptor tyrosine kinase-specific inhibitors, amuvatinib and imatinib, for their potential antiviral efficacies against SARS-CoV-2. Treatment with either amuvatinib or imatinib displays an effective inhibitory activity against SARS-CoV-2 propagation without an obvious cytopathic effect in Vero E6 cells. Notably, amuvatinib exerts a stronger antiviral activity than imatinib against SARS-CoV-2 infection. Amuvatinib blocks SARS-CoV-2 infection with a 50% effective concentration (EC50) value ranging from ~0.36 to 0.45 µM in Vero E6 cells. We further demonstrate that amuvatinib inhibits SARS-CoV-2 propagation in human lung Calu-3 cells. Using pseudoparticle infection assay, we verify that amuvatinib blocks SARS-CoV-2 at the entry step of the viral life cycle. More specifically, amuvatinib inhibits SARS-CoV-2 infection at the binding-attachment step. Moreover, amuvatinib exhibits highly efficient antiviral activity against emerging SARS-CoV-2 variants. Importantly, we demonstrate that amuvatinib inhibits SARS-CoV-2 infection by blocking ACE2 cleavage. Taken together, our data suggest that amuvatinib may provide a potential therapeutic agent for the treatment of COVID-19. IMPORTANCE Tyrosine kinase has been implicated in viral replication and has become an antiviral drug target. Here, we chose two well-known receptor tyrosine kinase inhibitors, amuvatinib and imatinib, and evaluated their drug potencies against SARS-CoV-2. Surprisingly, amuvatinib displays a stronger antiviral activity than imatinib against SARS-CoV-2. Amuvatinib blocks SARS-CoV-2 infection by inhibiting ACE2 cleavage and the subsequent soluble ACE2 receptor. All these data suggest that amuvatinib may be a potential therapeutic agent in SARS-CoV-2 prevention for those experiencing vaccine breakthroughs.

Delta (B1.617.2) variant of SARS-CoV-2 induces severe neurotropic patterns in K18-hACE2 mice.

A highly contagious virus, severe acute respiratory syndrome coronavirus 2, caused the coronavirus disease 19 (COVID-19) pandemic (SARS-CoV-2). SARS-CoV-2 genetic variants have been reported to circulate throughout the COVID-19 pandemic. COVID-19 symptoms include respiratory symptoms, fever, muscle pain, and breathing difficulty. In addition, up to 30% of COVID-19 patients experience neurological complications such as headaches, nausea, stroke, and anosmia. However, the neurotropism of SARS-CoV-2 infection remains largely unknown. This study investigated the neurotropic patterns between the B1.617.2 (Delta) and Hu-1 variants (Wuhan, early strain) in K18-hACE2 mice. Despite both the variants inducing similar pathogenic patterns in various organs, B1.617.2-infected K18-hACE2 mice demonstrated a higher range of disease phenotypes such as weight loss, lethality, and conjunctivitis when compared to those in Hu-1-infected mice. In addition, histopathological analysis revealed that B1.617.2 infects the brain of K18-hACE2 mice more rapidly and effectively than Hu-1. Finally, we discovered that, in B1.617.2-infected mice, the early activation of various signature genes involved innate cytokines and that the necrosis-related response was most pronounced than that in Hu-1-infected mice. The present findings indicate the neuroinvasive properties of SARS-CoV-2 variants in K18-hACE2 mice and link them to fatal neuro-dissemination during the disease onset.

Prediction of indoor PM2.5 concentrations and reduction strategies for cooking events through various IAQ management methods in an apartment of South Korea.

Indoor PM2.5 in apartments must be effectively managed to minimize adverse impacts on human health. Cooking is the one of the main PM2.5 sources in apartments, and indoor air quality (IAQ) management methods (natural ventilation, mechanical ventilations, range hoods, and air purifiers) are typically used to reduce PM2.5 generated during cooking. For effective control of indoor PM2.5 , prediction of PM2.5 reduction for various IAQ management methods is necessary. This study carefully predicted indoor PM2.5 concentrations in an apartment when IAQ management methods were applied separately and/or in combination during cooking. The infiltration and exfiltration were verified by comparing the experimental results of CO2 concentration with those predicted with or without mechanical ventilation. The deposition rate for PM2.5 generated by cooking was also derived by comparing the experimental PM2.5 changes with the predicted values for PM2.5 natural decay. Through this method, effective PM2.5 control ways during cooking in apartments can be proposed, such as natural ventilation with a range hood for 30 min and then the operation of an air purifier for 30 min. Additionally, if this prediction is combined with energy consumption, it will be possible to propose the most energy-efficient indoor PM2.5 control methods for various seasons and outdoor conditions.

Efficient Energy Saving Scenarios for Indoor PM2.5 Management in an Apartment of South Korea.

Indoor PM2.5 must be effectively controlled to minimize adverse impacts on public health. Cooking is one of the main sources of PM2.5 in residential areas, and indoor air quality (IAQ) management methods such as natural and mechanical ventilation, range hood, and air purifier are typically used to reduce cooking-generated PM2.5 concentrations. However, studies on the combined effects of various IAQ management methods on indoor PM2.5 reduction and energy consumption are limited. In this study, a theoretical model was established to estimate the performance of various IAQ management methods for controlling indoor PM2.5 concentrations and energy consumption. The model was verified by comparative experiments in which, various IAQ management methods were operated individually or combined. Seasonal energy consumption was calculated through the verified model, and energy consumption saving scenarios were derived for maintaining indoor PM2.5 concentrations less than 10 µg/m3, a World Health Organization annual guideline, under fair and poor outdoor PM2.5 concentrations of 15 and 50 µg/m3, respectively. Based on our results, we found that energy consumption could be reduced significantly by applying natural ventilation in spring, autumn, and summer and mechanical ventilation in winter. Our study identified efficient energy saving PM2.5 management scenarios using various IAQ management methods by predicting indoor PM2.5 concentration and energy consumption according to the annual life patterns of typical residents in South Korea.

The Actual Efficacy of an Air Purifier at Different Outdoor PM2.5 Concentrations in Residential Houses with Different Airtightness.

It is important to control airborne particles in residential houses for protecting human health. Indoor particulate matter of <2.5 µm (PM2.5) can be effectively monitored and managed using an air purifier. In this study, the actual clean air delivery rates in residential houses (CADRActual) were acquired by comparing decay rates of fine particles with and without operations of the air purifier under actual conditions, following the standard CADR of an air purifier obtained in a closed test chamber. The measurements of CADRActual at different outdoor PM2.5 concentrations over a month in two residential houses revealed different airtightness levels, compared to the standardized clean air delivery rate of the air purifier (CADRAP). Air changes per hour at 50 Pa (ACH50) was 4.8 h−1 for "house A" (built in 2007) and 2.1 h−1 for "house B" (built in 2018). The CADR of the air purifier used in this study was 10.6 m3/min, while the averaged CADRActual at the "house A" was 7.2 m3/min (approximately 66% of the CADR of the air purifier) and 9.5 m3/min at "house B" (approximately 90% of the CADR of the air purifier). Under the outdoor PM2.5 concentrations of <35 µg/m3, the averaged CADRActual of house A and house B were 7.8 ± 0.3 and 9.7 ± 0.4 m3/min, respectively. However, under the outdoor PM2.5 concentrations of >35 µg/m3, the analogous averaged concentrations were 6.8 ± 0.6 and 9.6 ± 0.3 m3/min for houses A and B, respectively. The measured CADRActual agreed well with the theoretical estimates of CADRActual acquired by the mass balance equation using the infiltration rate of ACH50/20. We also estimated CADRActual/CADRAP for house C built in 2017, where the ACH50 was 1.8 h−1. Overall, this study demonstrated how CADRActual/CADRAP of an air purifier at residential houses can be predicted according to outdoor PM2.5 concentration and airtightness of the house. As shown, it can be closer to 1 at lower ACH50 houses and at lower outdoor PM2.5 concentrations.

Reversible Wrinkling Surfaces for Enhanced Grip on Wet/Dry Conditions.

Friction is important in material design for robotic systems that need to perform tasks regardless of environmental changes. Generally, robotic systems lose their friction in wet environments and fail to accomplish their tasks. Despite the significance of maintaining friction in dry and wet environments, it is still challenging. Here, we report a smart switching surface, which helps to complete missions in both wet and dry environments. Inspired by the reversible wrinkling mechanism of a human finger, the surface reversibly generates and removes wrinkles to adapt to both environments using volume-changing characteristics of the Nafion film. The switchable surfaces with manipulated wrinkle morphologies via patterns of diverse densities, sizes, and shapes induce a relationship between the wrinkle morphologies and friction: wrinkles on denser and smaller hexagonal patterns generate six times more friction than non-switching flat surfaces in wet environments and a similar amount of friction to the flat surfaces in dry environments. In addition, the wrinkle morphologies according to the patterns are predicted through numerical simulation, which is in good agreement with experimental results. This work presents potential applications in robotic systems that are required to perform in and out of water and paves the way for further understanding of wrinkling dynamics, manipulation, and evolutionary function in skin.

Attentional feature pyramid network for small object detection.

Recent state-of-the-art detectors generally exploit the Feature Pyramid Networks (FPN) due to its advantage of detecting objects at different scales. Despite significant advances in object detection owing to the design of feature pyramids, it is still challenging to detect small objects with low resolution and dense distribution in complex scenes. To address these problems, we propose Attentional Feature Pyramid Network, a new feature pyramid architecture named AFPN which consists of three components to enhance the small object detection ability, specifically: Dynamic Texture Attention, Foreground-Aware Co-Attention, and Detail Context Attention. First, Dynamic Texture Attention augments the texture features dynamically by filtering out redundant semantics to highlight small objects in lower layers and amplifying credible details to emphasize large objects in higher layers. Then, Foreground-Aware Co-Attention is explored to detect densely arranged small objects by enhancing the objects feature via foreground-correlated contexts and suppressing the background noise. Finally, to better capture the features of small objects, Detail Context Attention adaptively aggregates detail cues of RoI features with different scales for a more accurate feature representation. By substituting FPN with AFPN in Faster R-CNN, our method performs on par with the state-of-the-art performance on Tsinghua-Tencent 100K. Furthermore, we achieve highly competitive results on small category of both PASCAL VOC and MS COCO.