Stimuli-responsive nanodelivery systems for amplifying immunogenic cell death in cancer immunotherapy.

Immunogenic cell death (ICD) is a special pattern of tumor cell death, enabling to elicit tumor-specific immune response via the release of damage-associated molecular patterns and tumor-associated antigens in the tumor microenvironment. ICD-induced immunotherapy holds the promise for completely eliminating tumors and long-term protective antitumor immune response. Increasing ICD inducers have been discovered for boosting antitumor immunity via evoking ICD. Nonetheless, the utilization of ICD inducers remains insufficient owing to serious toxic reactions, low localization efficiency within the tumor microenvironmental niche, etc. For overcoming such limitations, stimuli-responsive multifunctional nanoparticles or nanocomposites with ICD inducers have been developed for improving immunotherapeutic efficiency via lowering toxicity, which represent a prospective scheme for fostering the utilization of ICD inducers in immunotherapy. This review outlines the advances in near-infrared (NIR)-, pH-, redox-, pH- and redox-, or NIR- and tumor microenvironment-responsive nanodelivery systems for ICD induction. Furthermore, we discuss their clinical translational potential. The progress of stimuli-responsive nanoparticles in clinical settings depends upon the development of biologically safer drugs tailored to patient needs. Moreover, an in-depth comprehending of ICD biomarkers, immunosuppressive microenvironment, and ICD inducers may accelerate the advance in smarter multifunctional nanodelivery systems to further amplify ICD.

Model Simulations and Predictions of Hydroxymethanesulfonate (HMS) in the Beijing-Tianjin-Hebei Region, China: Roles of Aqueous Aerosols and Atmospheric Acidity.

Hydroxymethanesulfonate (HMS) has been found to be an abundant organosulfur aerosol compound in the Beijing-Tianjin-Hebei (BTH) region with a measured maximum daily mean concentration of up to 10 µg per cubic meter in winter. However, the production medium of HMS in aerosols is controversial, and it is unknown whether chemical transport models are able to capture the variations of HMS during individual haze events. In this work, we modify the parametrization of HMS chemistry in the nested-grid GEOS-Chem chemical transport model, whose simulations provide a good account of the field measurements during winter haze episodes. We find the contribution of the aqueous aerosol pathway to total HMS is about 36% in winter in Beijing, due primarily to the enhancement effect of the ionic strength on the rate constants of the reaction between dissolved formaldehyde and sulfite. Our simulations suggest that the HMS-to-inorganic sulfate ratio will increase from the baseline of 7% to 13% in the near future, given the ambitious clean air and climate mitigation policies for the BTH region. The more rapid reductions in emissions of SO2 and NOx compared to NH3 alter the atmospheric acidity, which is a critical factor leading to the rising importance of HMS in particulate sulfur species.

Design, Synthesis, and Performance of Photo-Responsive Liquid Crystal Polymers with Stepwise Deformation Capability.

Photo-responsive liquid crystal polymers (LCPs) have potential application value in flexible robots, artificial muscles, and microfluidic control. In recent years, significant progress has been made in the development of LCPs. However, the preparation of LCPs with continuous and controllable stepwise deformation capabilities remains a challenge. In this study, visible photo-responsive cyanostilbene monomer, UV photo-responsive azobenzene monomer, and multiple hydrogen bond crosslinker are used to prepare photo-responsive LCPs capable of achieving continuously and controllable stepwise deformation. The comprehensive investigation of the multiple light response ability and photo-induced deformation properties of these copolymers is conducted. The results reveal that in the first stage of photo-induced deformation under 470 nm blue light irradiation, the deformation angle decreases with a reduction in cyanostilbene content in the copolymer component, ranging from 40° in AZ0-CS4 to 0° in AZ4-CS0. In the second stage of photo-induced deformation under 365 nm UV irradiation, the deformation angle increases with the increase of azobenzene content, ranging from 0° of AZ0-CS4 to 89.4° of AZ4-CS0. Importantly, the deformation between these two stages occurs as a continuous process, allowing for a direct transition from the first-stage to the second-stage deformation by switching the light source from 470 to 365 nm.

Natural Product Quercetin-3-methyl ether Promotes Colorectal Cancer Cell Apoptosis by Downregulating Intracellular Polyamine Signaling.

Dysregulation of cellular metabolism is a key marker of cancer, and it is suggested that metabolism should be considered as a targeted weakness of colorectal cancer. Increased polyamine metabolism is a common metabolic change in tumors. Thus, targeting polyamine metabolism for anticancer therapy, particularly polyamine blockade therapy, has gradually become a hot topic. Quercetin-3-methyl ether is a natural compound existed in various plants with diverse biological activities like antioxidant and antiaging. Here, we reported that Quercetin-3-methyl ether inhibits colorectal cancer cell viability, and promotes apoptosis in a dose-dependent and time-dependent manner. Intriguingly, the polyamine levels, including spermidine and spermine, in colorectal cancer cells were reduced upon treatment of Quercetin-3-methyl ether. This is likely resulted from the downregulation of SMOX, a key enzyme in polyamine metabolism that catalyzes the oxidation of spermine to spermidine. These findings suggest Quercetin-3-methyl ether decreases cellular polyamine level by suppressing SMOX expression, thereby inducing colorectal cancer cell apoptosis. Our results also reveal a correlation between the anti-tumor activity of Quercetin-3-methyl ether and the polyamine metabolism modulation, which may provide new insights into a better understanding of the pharmacological activity of Quercetin-3-methyl ether and how it reprograms cellular polyamine metabolism.

Single-cell RNA-seq analysis decodes the kidney microenvironment induced by polystyrene microplastics in mice receiving a high-fat diet.

In recent years, the environmental health issue of microplastics has aroused an increasingly significant concern. Some studies suggested that exposure to polystyrene microplastics (PS-MPs) may lead to renal inflammation and oxidative stress in animals. However, little is known about the essential effects of PS-MPs with high-fat diet (HFD) on renal development and microenvironment. In this study, we provided the single-cell transcriptomic landscape of the kidney microenvironment induced by PS-MPs and HFD in mouse models by unbiased single-cell RNA sequencing (scRNA-seq). The kidney injury cell atlases in mice were evaluated after continued PS-MPs exposure, or HFD treated for 35 days. Results showed that PS-MPs plus HFD treatment aggravated the kidney injury and profibrotic microenvironment, reshaping mouse kidney cellular components. First, we found that PS-MPs plus HFD treatment acted on extracellular matrix organization of renal epithelial cells, specifically the proximal and distal convoluted tubule cells, to inhibit renal development and induce ROS-driven carcinogenesis. Second, PS-MPs plus HFD treatment induced activated PI3K-Akt, MAPK, and IL-17 signaling pathways in endothelial cells. Besides, PS-MPs plus HFD treatment markedly increased the proportions of CD8+ effector T cells and proliferating T cells. Notably, mononuclear phagocytes exhibited substantial remodeling and enriched in oxidative phosphorylation and chemical carcinogenesis pathways after PS-MPs plus HFD treatment, typified by alterations tissue-resident M2-like PF4+ macrophages. Multispectral immunofluorescence and immunohistochemistry identified PF4+ macrophages in clear cell renal cell carcinoma (ccRCC) and adjacent normal tissues, indicating that activate PF4+ macrophages might regulate the profibrotic and pro-tumorigenic microenvironment after renal injury. In conclusion, this study first systematically revealed molecular variation of renal cells and immune cells in mice kidney microenvironment induced by PS-MPs and HFD with the scRNA-seq approach, which provided a molecular basis for decoding the effects of PS-MPs on genitourinary injury and understanding their potential profibrotic and carcinogenesis in mammals.

Investigating the relationship between fluctuations in daily milk yield as resilience indicators and health traits in Holstein cattle.

Disease-related milk losses directly affect dairy herds' profitability and the production efficiency of the dairy industry. Therefore, this study aimed to quantify phenotypic variability in milk fluctuation periods related to diseases and to explore milk fluctuation traits as indicators of disease resilience. By combining high-frequency daily milk yield data with disease records of cows that were treated and recovered from the disease, we estimated milk variability trends within a fixed period around the treatment day of each record for 5 diseases: udder health, reproductive disorders, metabolic disorders, digestive disorders, and hoof health. The average milk yield decreased rapidly from 6 to 8 d before the treatment day for all diseases, with the largest milk reduction observed on the treatment day. Additionally, we assessed the significance of milk fluctuation periods highly related to diseases by defining milk fluctuations as a period of at least 10 consecutive days in which milk yield fell below 90% of the expected milk production values at least once. We defined the development and recovery phases of milk fluctuations using 3,847 milk fluctuation periods related to disease incidences, and estimated genetic parameters of milk fluctuation traits, including milk losses, duration of the fluctuation, variation rate in daily milk yield, and standard deviation of milk deviations for each phase and their genetic correlation with several important traits. In general, the disease-related milk fluctuation periods lasted 21.19 ± 10.36 d with a milk loss of 115.54 ± 92.49 kg per lactation. Compared with the development phase, the recovery phase lasted an average of 3.3 d longer, in which cows produced 11.04 kg less milk and exhibited a slower variation rate in daily milk yield of 0.35 kg/d. There were notable differences in milk fluctuation traits depending on the disease, and greater milk losses were observed when multiple diseases occurred simultaneously. All milk fluctuation traits evaluated were heritable with heritability estimates ranging from 0.01 to 0.10, and moderate to high genetic correlations with milk yield (0.34 to 0.64), milk loss throughout the lactation (0.22 to 0.97), and resilience indicator (0.39 to 0.95). These results indicate that cows with lower milk losses and higher resilience tend to have more stable milk fluctuations, which supports the potential for breeding for more disease-resilient cows based on milk fluctuation traits. Overall, this study confirms the high effect of diseases on milk yield variability and provides insightful information about their relationship with relevant traits in Holstein cattle. Furthermore, this study shows the potential of using high-frequency automatic monitoring of milk yield to assist on breeding practices and health management in dairy cows.

Genetic background of hematological parameters in Holstein cattle based on genome-wide association and RNA sequencing analyses.

Hematological parameters refer to the assessment of changes in the number and distribution of blood cells, including leukocytes (LES), erythrocytes (ERS), and platelets (PLS), which are essential for the early diagnosis of hematological system disorders and other systemic diseases in livestock. In this context, the primary objectives of this study were to investigate the genomic background of 19 hematological parameters in Holstein cattle, focusing on LES, ERS, and PLS blood components. Genetic and phenotypic (co)variances of hematological parameters were calculated based on the average information restricted maximum likelihood method and 1,610 genotyped individuals and 5,499 hematological parameter records from 4,543 cows. Furthermore, we assessed the genetic relationship between these hematological parameters and other economically important traits in dairy cattle breeding programs. We also carried out genome-wide association studies and candidate gene analyses. Blood samples from 21 primiparous cows were used to identify candidate genes further through RNA sequencing (RNA-seq) analyses. Hematological parameters generally exhibited low-to-moderate heritabilities ranging from 0.01 to 0.29, with genetic correlations between them ranging from -0.88 ± 0.09 (between mononuclear cell ratio and lymphocyte cell ratio) to 0.99 ± 0.01 (between white blood cell count and granulocyte cell count). Furthermore, low-to-moderate approximate genetic correlations between hematological parameters with one longevity, 4 fertility, and 5 health traits were observed. One hundred ninety-nine significant SNP located primarily on the Bos taurus autosomes (BTA) BTA4, BTA6, and BTA8 were associated with 16 hematological parameters. Based on the RNA-seq analyses, 6,687 genes were significantly downregulated and 4,119 genes were upregulated when comparing 2 groups of cows with high and low phenotypic values. By integrating genome-wide association studies (GWAS), RNA-seq, and previously published results, the main candidate genes associated with hematological parameters in Holstein cattle were ACRBP, ADAMTS3, CANT1, CCM2L, CNN3, CPLANE1, GPAT3, GRIP2, PLAGL2, RTL6, SOX4, WDFY3, and ZNF614. Hematological parameters are heritable and moderately to highly genetically correlated among themselves. The large number of candidate genes identified based on GWAS and RNA-seq indicate the polygenic nature and complex genetic determinism of hematological parameters in Holstein cattle.

Polar Decomposition of Jones Matrix and Mueller Matrix of Coherent Rayleigh Backscattering in Single-Mode Fibers.

The Jones matrix and the Mueller matrix of the coherent Rayleigh backscattering (RB) in single-mode fibers (SMFs) have been derived recently. It has been shown that both matrices depict two polarization effects-birefringence and polarization-dependent loss (PDL)-although the SMF under investigation is purely birefringent, having no PDL. In this paper, we aim to perform a theoretical analysis of both matrices using polar decomposition. The derived sub-Jones/Mueller matrices, representing birefringence and PDL, respectively, can be used to investigate the polarization properties of the coherent RB. As an application of the theoretical results, we use the derived formulas to investigate the polarization properties of the optical signals in phase-sensitive optical time-domain reflectometry (φ-OTDR). For the first time, to our knowledge, by using the derived birefringence-Jones matrix, the common optical phase of the optical signal in φ-OTDR is obtained as the function of the forward phase and birefringence distributions. By using the derived PDL-Mueller matrix, the optical intensity of the optical signal in φ-OTDR is obtained as the function of the forward phase and birefringence distributions as well as the input state of polarization (SOP). Further theoretical predictions show that, in φ-OTDR, the common optical phase depends on only the local birefringence in the first half of the fiber section, which is occupied by the sensing pulse, irrelevant of the input SOP. However, the intensity of the φ-OTDR signal is not a local parameter, which depends on the input SOP and the birefringence distribution along the entire fiber section before the optical pulse. Moreover, the PDL measured in φ-OTDR is theoretically proven to be a local parameter, which is determined by the local birefringence and local optical phase distributions.

The function and mechanisms of action of circular RNAs in Urologic Cancer.

Kidney, bladder, and prostate cancer are the three major tumor types of the urologic system that seriously threaten human health. Circular RNAs (CircRNAs), special non-coding RNAs with a stabile structure and a unique back-splicing loop-forming ability, have received recent scientific attention. CircRNAs are widely distributed within the body, with important biologic functions such as sponges for microRNAs, as RNA binding proteins, and as templates for regulation of transcription and protein translation. The abnormal expression of circRNAs in vivo is significantly associated with the development of urologic tumors. CircRNAs have now emerged as potential biomarkers for the diagnosis and prognosis of urologic tumors, as well as targets for the development of new therapies. Although we have gained a better understanding of circRNA, there are still many questions to be answered. In this review, we summarize the properties of circRNAs and detail their function, focusing on the effects of circRNA on proliferation, metastasis, apoptosis, metabolism, and drug resistance in kidney, bladder, and prostate cancers.

Special issue "The advance of solid tumor research in China": Multi-omics analysis based on 1311 clear cell renal cell carcinoma samples identifies a glycolysis signature associated with prognosis and treatment response.

In clear cell renal cell carcinoma (ccRCC), glycolysis is enhanced mainly because of the increased expression of key enzymes in glycolysis. Hence, the discovery of new molecular biomarkers for glycolysis may help guide and establish a precise system of diagnosis and treatment for ccRCC. Expression profiles of 1079 tumor samples of ccRCC patients (including 311 patients treated with everolimus or nivolumab) were downloaded from public databases. Proteomic profiles of 232 ccRCC samples were obtained from Fudan University Shanghai Cancer Center (FUSCC). Biological changes, tumor microenvironment and prognostic differences were explored between samples with various glycolysis characteristics. There were significant differences in CD8+ effector T cells, epithelial-to-mesenchymal transition and pan-fibroblast TGFb between the Low and High glyScore groups. The tumor mutation burden of the Low glyScore group was lower than that of the High glyScore group. And higher glyScore was significantly associated with worse overall survival (OS) in 768 ccRCC patients (P < .0001). External validation in FUSCC cohort also indicated that glyScore was of strong ability for predicting OS (P < .05). GlyScore may serve as a biomarker for predicting everolimus response in ccRCC patients due to its significant associations with progression-free survival (PFS). And glyScore may also predict overall survival in patients treated with nivolumab. We calculated the glyScore in ccRCC and the defined glyScore was of strong ability for predicting OS. In addition, glyScore may also serve as a biomarker for predicting PFS in patients treated with everolimus and could predict OS in patients treated with nivolumab.

A Universal and Accurate Method for Easily Identifying Components in Raman Spectroscopy Based on Deep Learning.

Raman spectroscopy has been widely used to provide the structural fingerprint for molecular identification. Due to interference from coexisting components, noise, baseline, and systematic differences between spectrometers, component identification with Raman spectra is challenging, especially for mixtures. In this study, a method entitled DeepRaman has been proposed to solve those problems by combining the comparison ability of a pseudo-Siamese neural network (pSNN) and the input-shape flexibility of spatial pyramid pooling (SPP). DeepRaman was trained, validated, and tested with 41,564 augmented Raman spectra from two databases (pharmaceutical material and S.T. Japan). It can achieve 96.29% accuracy, 98.40% true positive rate (TPR), and 94.36% true negative rate (TNR) on the test set. Another six data sets measured on different instruments were used to evaluate the performance of the proposed method from different aspects. DeepRaman can provide accurate identification results and significantly outperform the hit quality index (HQI) method and other deep learning models. In addition, it performs well in cases of different spectral complexity and low-content components. Once the model is established, it can be used directly on different data sets without retraining or transfer learning. Furthermore, it also obtains promising results for the analysis of surface-enhanced Raman spectroscopy (SERS) data sets and Raman imaging data sets. In summary, it is an accurate, universal, and ready-to-use method for component identification in various application scenarios.

Fusion of Quality Evaluation Metrics and Convolutional Neural Network Representations for ROI Filtering in LC-MS.

Region of interest (ROI) extraction is a fundamental step in analyzing metabolomic datasets acquired by liquid chromatography-mass spectrometry (LC-MS). However, noises and backgrounds in LC-MS data often affect the quality of extracted ROIs. Therefore, developing effective ROI evaluation algorithms is necessary to eliminate false positives meanwhile keep the false-negative rate as low as possible. In this study, a deep fused filter of ROIs (dffROI) was proposed to improve the accuracy of ROI extraction by combining the handcrafted evaluation metrics with convolutional neural network (CNN)-learned representations. To evaluate the performance of dffROI, dffROI was compared with peakonly (CNN-learned representation) and five handcrafted metrics on three LC-MS datasets and a gas chromatography-mass spectrometry (GC-MS) dataset. Results show that dffROI can achieve higher accuracy, better true-positive rate, and lower false-positive rate. Its accuracy, true-positive rate, and false-positive rate are 0.9841, 0.9869, and 0.0186 on the test set, respectively. The classification error rate of dffROI (1.59%) is significantly reduced compared with peakonly (2.73%). The model-agnostic feature importance demonstrates the necessity of fusing handcrafted evaluation metrics with the convolutional neural network representations. dffROI is an automatic, robust, and universal method for ROI filtering by virtue of information fusion and end-to-end learning. It is implemented in Python programming language and open-sourced at https://github.com/zhanghailiangcsu/dffROI under BSD License. Furthermore, it has been integrated into the KPIC2 framework previously proposed by our group to facilitate real metabolomic LC-MS dataset analysis.

Prediction of drug-likeness using graph convolutional attention network.

MOTIVATION: The drug-likeness has been widely used as a criterion to distinguish drug-like molecules from non-drugs. Developing reliable computational methods to predict the drug-likeness of compounds is crucial to triage unpromising molecules and accelerate the drug discovery process. RESULTS: In this study, a deep learning method was developed to predict the drug-likeness based on the graph convolutional attention network (D-GCAN) directly from molecular structures. Results showed that the D-GCAN model outperformed other state-of-the-art models for drug-likeness prediction. The combination of graph convolution and attention mechanism made an important contribution to the performance of the model. Specifically, the application of the attention mechanism improved accuracy by 4.0%. The utilization of graph convolution improved the accuracy by 6.1%. Results on the dataset beyond Lipinski's rule of five space and the non-US dataset showed that the model had good versatility. Then, the billion-scale GDB-13 database was used as a case study to screen SARS-CoV-2 3C-like protease inhibitors. Sixty-five drug candidates were screened out, most substructures of which are similar to these of existing oral drugs. Candidates screened from S-GDB13 have higher similarity to existing drugs and better molecular docking performance than those from the rest of GDB-13. The screening speed on S-GDB13 is significantly faster than screening directly on GDB-13. In general, D-GCAN is a promising tool to predict the drug-likeness for selecting potential candidates and accelerating drug discovery by excluding unpromising candidates and avoiding unnecessary biological and clinical testing. AVAILABILITY AND IMPLEMENTATION: The source code, model and tutorials are available at https://github.com/JinYSun/D-GCAN. The S-GDB13 database is available at https://doi.org/10.5281/zenodo.7054367. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Suppressive stroma-immune prognostic signature impedes immunotherapy in ovarian cancer and can be reversed by PDGFRB inhibitors.

BACKGROUND: As the most lethal gynecologic cancer, ovarian cancer (OV) holds the potential of being immunotherapy-responsive. However, only modest therapeutic effects have been achieved by immunotherapies such as immune checkpoint blockade. This study aims to propose a generalized stroma-immune prognostic signature (SIPS) to identify OV patients who may benefit from immunotherapy. METHODS: The 2097 OV patients included in the study were significant with high-grade serous ovarian cancer in the III/IV stage. The 470 immune-related signatures were collected and analyzed by the Cox regression and Lasso algorithm to generalize a credible SIPS. Correlations between the SIPS signature and tumor microenvironment were further analyzed. The critical immunosuppressive role of stroma indicated by the SIPS was further validated by targeting the major suppressive stroma component (CAFs, Cancer-associated fibroblasts) in vitro and in vivo. With four machine-learning methods predicting tumor immune subtypes, the stroma-immune signature was upgraded to a 23-gene signature. RESULTS: The SIPS effectively discriminated the high-risk individuals in the training and validating cohorts, where the high SIPS succeeded in predicting worse survival in several immunotherapy cohorts. The SIPS signature was positively correlated with stroma components, especially CAFs and immunosuppressive cells in the tumor microenvironment, indicating the critical suppressive stroma-immune network. The combination of CAFs' marker PDGFRB inhibitors and frontline PARP inhibitors substantially inhibited tumor growth and promoted the survival of OV-bearing mice. The stroma-immune signature was upgraded to a 23-gene signature to improve clinical utility. Several drug types that suppress stroma-immune signatures, such as EGFR inhibitors, could be candidates for potential immunotherapeutic combinations in ovarian cancer. CONCLUSIONS: The stroma-immune signature could efficiently predict the immunotherapeutic sensitivity of OV patients. Immunotherapy and auxiliary drugs targeting stroma could enhance immunotherapeutic efficacy in ovarian cancer.

Resolution enhancement for interrogating fiber Bragg grating sensor network using dilated U-Net.

In the fiber Bragg grating (FBG) sensor network, the signal resolution of the reflected spectrum is correlated with the network's sensing accuracy. The interrogator determines the signal resolution limits, and a coarser resolution results in an enormous uncertainty in sensing measurement. In addition, the multi-peak signals from the FBG sensor network are often overlapped; this increases the complexity of the resolution enhancement task, especially when the signals have a low signal-to-noise ratio (SNR). Here, we show that deep learning with U-Net architecture can enhance the signal resolution for interrogating the FBG sensor network without hardware modifications. The signal resolution is effectively enhanced by 100 times with an average root mean square error (RMSE) < 2.25 pm. The proposed model, therefore, allows the existing low-resolution interrogator in the FBG setup to function as though it contains a much higher-resolution interrogator.

Association of computed tomography-based body composition with survival in metastatic renal cancer patient received immunotherapy: a multicenter, retrospective study.

OBJECTIVES: To investigate the association of computed tomography-assessed body composition with survival outcomes of metastatic renal cell carcinoma (mRCC) received immunotherapy. METHODS: In this multicenter, retrospective study, we reviewed 251 mRCC patients who received anti-PD1 from five centers. We analyzed the relationship between BMI, skeletal muscle area (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and subcutaneous adipose percentage (SAT%) with progression-free survival (PFS) and overall survival (OS). The spatial localization T cells was investigated by multiplex immunofluorescence. RESULTS: Among 224 evaluable patients, 23 (10.3%) patients were underweight, 118 (52.7%) had normal weight, 65 (29%) were overweight, and 18 patients (8%) were obese. The median age was 55 years and most patients were male (71%). No significant improvement in PFS (HR, 0.61; 95% CI, 0.27-1.42) or OS (HR, 1.09; 95% CI, 0.38-3.13) was observed for the obese patients. Besides, SM, VAT, and SAT were not associated with survival outcomes (all p > 0.05). Interestingly, SAT% independently predicted PFS (as continuous variable, HR: 0.02; 95% CI, 0.01-0.11) and OS (HR:0.05; 95% CI, 0.01-0.39), which remained significant in multivariate modeling (as continuous variable, adjusted HR for PFS, 0.01; 95% CI, 0.00-0.04; adjusted HR for OS, 0.08; 95% CI, 0.01-0.72). These associations were consistent in subgroup analysis of different gender, BMI, PD-L1 positive, and sarcopenia group. Tumor of high SAT% patients had a higher intratumoral PD1+ CD8+ T cell density and ratio. CONCLUSION: High SAT% predicts better outcomes in mRCC patients treated with anti-PD1 and T cell location may account for the better response. KEY POINTS: • CT-based subcutaneous adipose percentage independently predicted progression-free survival and overall survival. • Patients with a higher subcutaneous adipose percentage had a higher intratumoral PD1+ CD8+ T cell density and ratio.

Clustering-Triggered Emission Liquid Crystalline Polymer Bearing Cholesterol: Tunable Circularly Polarized Luminescence and Room-Temperature Phosphorescence.

Circularly polarized luminescence (CPL) materials with clustering-triggered emission (CTE) characteristic have gradually attracted attention for their unique photophysical properties. However, the majority of reported clusteroluminogens lack chirality and exhibit heterogeneity, making it challenging to achieve a well-defined helical structure necessary for efficient CPL with high dissymmetry factor (glum ). In this paper, chiral liquid crystals are constructed to obtain CTE-based CPL materials with high glum values. Side chain liquid crystal polymer PM6Chol bearing cholesterol clusteroluminogens are designed and synthesized. PM6Chol-coated film and PM6Chol thermal-treated film are also successfully prepared by different film-forming methods. Both the films inherit the CTE characteristic of cholesterol and show excitation wavelength-dependent luminescent behavior. However, the two polymer films exhibit different liquid crystal phase structures, with PM6Chol-coated film being a chiral bilayer smectic C phase and PM6Chol thermal-treated film being an achiral bilayer smectic A phase. Attributed to helical arrangement of cholesterol, PM6Chol-coated film emits efficient CPL with glum values up to 1.0 × 10-1 . For PM6Chol thermal-treated film, no CPL signal is detected due to the absence of helical structure. However, it shows obvious room-temperature phosphorescence with 2.0 s afterglow and 23.9 ms lifetime.

Robust method for BOTDA sensing information extraction in the Fourier transform domain.

Most of the existing schemes for extracting the Brillouin frequency shift (BFS) are based on the line shape of the Brillouin gain spectrum (BGS) curve. However, in some circumstances, such as in this paper, there is a cyclic shift in the BGS curve, causing difficulty in obtaining the BFS accurately with traditional methods. To solve this problem, we propose a method for extracting Brillouin optical time domain analyzer sensing information in the transform domain-the fast Fourier Lorentz curve fitting method. It shows better performance especially when the cyclic start frequency is near the BGS central frequency position or when the full width at half maximum is large. The results show that our method can obtain BGS parameters more accurately in most cases than the Lorenz curve fitting method.

Compound lidocaine/prilocaine cream combined with tetracaine prevents cough caused by extubation after general anaesthesia: a randomised controlled trial.

BACKGROUND: Coughing caused by tracheal extubation is common following general anaesthesia. Heavy aerosol production by coughing during recovery from general anaesthesia in patients with respiratory infections (especially COVID-19) may be one of the highest risk factors for infection in healthcare workers. The application of local anaesthetics to the endotracheal tube is an effective method to reduce coughing. The most commonly used anaesthetics are compound lidocaine/prilocaine cream and tetracaine spray. However, coughing still occurs when the two anaesthetics are used alone. We speculated that the application of compound lidocaine/prilocaine combined with tetracaine spray would better prevent coughing caused by tracheal extubation. METHODS: Patients scheduled for laparoscopic cholecystectomy or cholecystectomy combined with common bile duct exploration under general anaesthesia were randomly assigned to Group C (saline spray), Group L (2 g compound lidocaine/prilocaine cream contains 5 mg of lidocaine and 5 mg prilocaine)), Group T (tetracaine) and Group F (compound lidocaine/prilocaine cream combined with tetracaine). The incidence of coughing, the endotracheal tube tolerance assessment, the incidence of agitation, the active extubation rate, the incidence of postoperative pharyngeal pain and the incidence of postoperative cough were recorded and analysed. Systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), and the plasma concentrations of epinephrine and norepinephrine were measured immediately before extubation and 1 min after extubation. RESULTS: A total of 211 patients were randomly assigned to Group C (53 cases), Group L (52 cases), Group T (52 cases) and Group F (54 cases). The primary result is assessment of the incidence of cough. The patients emerged from general anaesthesia, 96% of Group C had cough, which was significantly reduced in Group L (61.5%, P < 0.001), Group T (75%, P < 0.05) and Group F (22.2%, P < 0.001). Group F had a significantly reduced incidence of cough compared to Group L and Group T (P < 0.05 or P < 0.01, respectively). The secondary results were assessed. The endotracheal tube tolerance score in Group C ((1, 3) 4, P < 0.001) was higher than Group L ((0, 1) 2), Group T ((0, 1.25) 3) and Group F ((0, 0) 1). Group F had a significantly lower score than Group L and Group T (P < 0.05, P < 0.01, respectively). The incidence of agitation and the active extubation rate were also higher in Group C (96.2% and 71.7%, respectively, P < 0.001) than Group L (48.1% and 15.4%, respectively), Group T (61.5% and 26.9%, respectively) and Group F (17.3% and 7.7%, respectively). Blood pressure, HR and plasma concentrations of epinephrine and norepinephrine were significantly higher in Group C than in all other groups at the time of extubation and 1 min after extubation (P < 0.001). Group F exhibited significantly reduced blood pressure, heart rate and plasma concentrations of epinephrine and norepinephrine compared to Group L and Group T (P < 0.05, P < 0.01 or P < 0.001, respectively). The incidence of postoperative pharyngeal pain and the incidence of postoperative cough were not significantly different among the groups. CONCLUSIONS: Compound lidocaine/prilocaine cream combined with tetracaine may be a more effective approach for preventing coughing and stabilising circulation during extubation following general anaesthesia. This may play an important role in preventing medical staff from contracting respiratory infectious diseases. TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR2200058429 (registration date: 09-04-2022) "retrospectively registered".

Identification of prognostic and therapeutic biomarkers in type 2 papillary renal cell carcinoma.

BACKGROUND: Papillary renal cell carcinoma (PRCC) can be divided into type 1 (PRCC1) and type 2 (PRCC2) and PRCC2 share a more invasive phenotype and worse prognosis. This study aims to identify potential prognostic and therapeutic biomarkers in PRCC2. METHODS: A cohort from The Cancer Genome Atlas and two datasets from Gene Expression Omnibus were examined. Common differentially expressed genes (DEGs) were screened and potential biomarkers were explored by using Kaplan-Meier method and cox regression analysis. Functional enrichment analysis was utilized to evaluate the potential biological functions. Tumor infiltrating immune cells were estimated by CIBERSORT algorithm. Ninety-two PRCC2 samples from Fudan University Shanghai Cancer Center were obtained, and immunostaining was performed to validate prognostic and therapeutic significance of the potential biomarker. RESULTS: PRCC2 has worse overall survival and shares distinct molecular characteristics from PRCC1. There was significant higher expression level of Targeting protein for Xklp2 (TPX2) in PRCC2 compared with normal tissues. Higher expression level of TPX2 was significantly associated with worse overall survival in PRCC2 and kinesin family genes expression were found significantly elevated in high risk PRCC2. Abundance of tumor infiltrating M1 macrophage was significantly higher in PRCC2 and it was also associated with worse overall survival. In the FUSCC cohort, higher TPX2 expression was significantly correlated with worse overall and progression-free survival. Retrospective analysis indicated that mTOR inhibitor (everolimus) had greater efficacy in the high-risk group than in the low-risk group (overall response rate: 28.6% vs. 16.7%) and that everolimus had greater efficacy than sunitinib in the high-risk group (overall response rate: 28.6% vs. 20%). CONCLUSIONS: TPX2 was a prognostic and therapeutic biomarker in PRCC2. Higher abundance of tumor infiltrating M1 macrophage was significantly associated with worse overall survival in PRCC2. mTOR inhibitors may have good efficacy in patients with high-risk PRCC2.