A novel approach to evaluation of tumor response for advanced pulmonary adenocarcinoma using the intertumoral heterogeneity response score.

Treatment response and prognosis estimation in advanced pulmonary adenocarcinoma are challenged by the significant heterogeneity of the disease. The current Response Evaluation Criteria in Solid Tumors (RECIST) criteria, despite providing a basis for solid tumor response evaluation, do not fully encompass this heterogeneity. To better represent these nuances, we introduce the intertumoral heterogeneity response score (THRscore), a measure built upon and expanding the RECIST criteria. This retrospective study included patients with 3-10 measurable advanced lung adenocarcinoma lesions who underwent first-line chemotherapy or targeted therapy. The THRscore, derived from the coefficient of variation in size for each measurable tumor before and 4-6 weeks posttreatment, unveiled a correlation with patient outcomes. Specifically, a high THRscore was associated with shorter progression-free survival, lower tumor response rate, and a higher tumor mutation burden. These associations were further validated in an external cohort, confirming THRscore's effectiveness in stratifying patients based on progression risk and treatment response, and enhancing the utility of RECIST in capturing complex tumor behaviors in lung adenocarcinoma. These findings affirm the promise of THRscore as an enhanced tool for tumor response assessment in advanced lung adenocarcinoma, extending the RECIST criteria's utility.

Sustainable ultrasound-assisted extraction of Polygonatum sibiricum saponins using ionic strength-responsive natural deep eutectic solvents.

The sustainable extraction of saponins was investigated using natural deep eutectic solvents (NADESs) combined with ultrasound-assisted extraction. A novel NADES (butyric acid-urea) that was responsive to ionic strength was designed and used as the extractant. Ultrasound treatment and a catalyst ferric chloride with plant cell wall breaking function were applied to improve the extraction efficiency.Since the solubility of the NADES varied significantly with ionic strength, 95% of NADES was readily separated from the water phase after the addition of sodium chloride, while saponins remained in the water phase for easy collection. The reuse capacity of NADES, the eco-friendliness of the extraction method, and the antioxidant activity of the extract were further evaluated.NADES was continuously recovered and used to extract Polygonatum sibiricum powder: the yield of saponins did not decrease after five cycles of recovery and re-extraction. The penalty point on the "Eco-scale" suggested that the extraction method was "green" (i.e. eco-friendly).Compared with ethanol extracts, the NADES extracts showed a higher saponin concentration and antioxidant activity.The study can contribute to the sustainable and green extraction of hydrophilic active substances in the food and pharmaceutical industries.

Volatile Fingerprint and Differences in Volatile Compounds of Different Foxtail Millet (Setaria italica Beauv.) Varieties.

Aroma components in foxtail millet are one of the key factors in origin traceability and quality control, and they are associated with consumer acceptance and the corresponding processing suitability. However, the volatile differences based on the foxtail millet varieties have not been studied further. The present study was undertaken to develop the characteristic volatile fingerprint and analyze the differences in volatile compounds of 20 foxtail millet varieties by electronic nose (E-Nose), headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), and headspace solid-phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS). A total of 43 volatile compounds were tentatively identified in foxtail millet samples, 34 and 18 by GC-IMS and GC-MS, respectively. Aldehydes, alcohols, and ketones were the major volatile compounds, and the hexanal content was the highest. The characteristic volatile fingerprint of foxtail millet was successfully constructed. A total of 39 common volatile compounds were found in all varieties. The content of hexanal, heptanal, 1-pentanol, acetophenone, 2-heptanone, and nonanal were explored to explain the aroma characteristics among the different varieties, and different varieties can be separated based on these components. The results demonstrate that the combination of E-Nose, GC-IMS, and GC-MS can be a fast and accurate method to identify the general aroma peculiarities of different foxtail millet varieties.

Dual-stream pyramid registration network.

We propose a Dual-stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D brain image registration. Unlike recent CNN-based registration approaches, such as VoxelMorph, which computes a registration field from a pair of 3D volumes using a single-stream network, we design a two-stream architecture able to estimate multi-level registration fields sequentially from a pair of feature pyramids. Our main contributions are: (i) we design a two-stream 3D encoder-decoder network that computes two convolutional feature pyramids separately from two input volumes; (ii) we propose sequential pyramid registration where a sequence of pyramid registration (PR) modules is designed to predict multi-level registration fields directly from the decoding feature pyramids. The registration fields are refined gradually in a coarse-to-fine manner via sequential warping, which equips the model with a strong capability for handling large deformations; (iii) the PR modules can be further enhanced by computing local 3D correlations between the feature pyramids, resulting in the improved Dual-PRNet++ able to aggregate rich detailed anatomical structure of the brain; (iv) our Dual-PRNet++ can be integrated into a 3D segmentation framework for joint registration and segmentation, by precisely warping voxel-level annotations. Our methods are evaluated on two standard benchmarks for brain MRI registration, where Dual-PRNet++ outperforms the state-of-the-art approaches by a large margin, i.e., improving recent VoxelMorph from 0.511 to 0.748 (Dice score) on the Mindboggle101 dataset. In addition, we further demonstrate that our methods can greatly facilitate the segmentation task in a joint learning framework, by leveraging limited annotations.

High-Performance Perovskite Solar Cells Based on NaCsWO3@ NaYF4@NaYF4:Yb,Er Upconversion Nanoparticles.

Extending photoelectric response to the near-infrared (NIR) region using upconversion luminescent (UCL) materials is one promising approach to obtain high-efficiency perovskite solar cells (PSCs). However, challenges remain due to the shortage of highly efficient UCL materials and device structure. NaCsWO3 nanocrystals exhibit near-infrared absorption arising from the local surface plasmon resonance (LSPR) effect, which can be used to boost the UCL of rare-earth-doped upconversion nanoparticles (UCNPs). In this study, using NaCsWO3 as the LSPR center, NaCsWO3@NaYF4@NaYF4:Yb,Er nanoparticles were synthesized and the UCL intensity could be enhanced by more than 124 times when the amount of NaCsWO3 was 2.8 mmol %. Then, such efficient UCNPs were not only doped into the hole transport layer but also used to modify the perovskite film in PSCs, resulting in the highest power conversion efficiency (PCE) reaching 18.89% (that of the control device was 16.01% and the PCE improvement was 17.99%). Possible factors for the improvement of PSCs were studied and analyzed. It is found that UCNPs can broaden the response range of PSCs to the NIR region due to the LSPR-enhanced UCL and increase the visible light reabsorption of PSCs due to the scattering and reflection effect, which generate more photocurrent in PSCs. In addition, UCNPs modify the perovskite film by effectively filling the holes and gaps at the grain boundary and eliminating the perovskite surface defects, which lead to less carrier recombination and then effectively improve the performance of PSC devices.

Stem cell therapy for chronic obstructive pulmonary disease / 中华医学杂志(英文版)

Chronic obstructive pulmonary disease (COPD), characterized by persistent and not fully reversible airflow restrictions, is currently one of the most widespread chronic lung diseases in the world. The most common symptoms of COPD are cough, expectoration, and exertional dyspnea. Although various strategies have been developed during the last few decades, current medical treatment for COPD only focuses on the relief of symptoms, and the reversal of lung function deterioration and improvement in patient's quality of life are very limited. Consequently, development of novel effective therapeutic strategies for COPD is urgently needed. Stem cells were known to differentiate into a variety of cell types and used to regenerate lung parenchyma and airway structures. Stem cell therapy is a promising therapeutic strategy that has the potential to restore the lung function and improve the quality of life in patients with COPD. This review summarizes the current state of knowledge regarding the clinical research on the treatment of COPD with mesenchymal stem cells (MSCs) and aims to update the understanding of the role of MSCs in COPD treatment, which may be helpful for developing effective therapeutic strategies in clinical settings.

Strong upconverting and downshifting emission of Mn2+ ions in a Yb,Tm:NaYF4@NaLuF4/Mn:CsPbCl3 core/shell heterostructure towards dual-model anti-counterfeiting.

Herein, a Yb,Tm:NaYF4@NaLuF4/Mn:CsPbCl3 quasi-core/shell heterostructure is synthesized with the assistance of silica. The strong upconverting and downshifting emission of Mn2+ ions was observed in the nanocomposite with a quasi-core/shell structure. The FRET process further improves the energy utilization efficiency of PQDs for UCNPs, which depends on the quasi-core/shell heterostructure. Considering the dual-model fluorescence emission behavior of Mn2+ ions, the stable Yb,Tm:NaYF4@NaLuF4/Mn:CsPbCl3 nanocomposite is used in anti-counterfeiting applications.

Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder.

Feature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE). The detailed procedure presented in this paper can be summarized as follows: firstly, 23 baseline features and Three-Patch Local Binary Pattern (TPLBP) features are extracted. These features can describe the global and local aspects of the image with less redundancy and more complementarity, providing richer information for feature fusion. Secondly, an effective feature fusion network is designed. Baseline and TPLBP features are cascaded and fed into a SAE. Then, with an unsupervised learning algorithm, the SAE is pre-trained by greedy layer-wise training method. Capable of feature expression, SAE makes the fused features more distinguishable. Finally, the model is fine-tuned by a softmax classifier and applied to the classification of targets. 10-class SAR targets based on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset got a classification accuracy up to 95.43%, which verifies the effectiveness of the presented algorithm.