Integration of photothermal water evaporation with photocatalytic microplastics upcycling via nanofluidic thermal management.

Photothermal heating and photocatalytic treatment are two solar-driven water processing approaches by harnessing NIR and UV-vis light, respectively, which can fully utilize solar energy if integrated. However, it remains a challenge to achieve high performance in both approaches when integrated in a material due to uncontrollable heat diffusion. Here, we report a demonstration of heat confinement on photothermal sites and fluid cooling on photocatalysis sites at the nanoscale, within a well-designed heat and fluid confinement nanofiber reactor. Photothermal and photocatalytic nanostructures were alternatively aligned in electrospun nanofibers for on-demand nanofluidic thermal management as well as easy folding into 3D structures with enhanced light utilization and mass transfer. Such a design showed simultaneously high photothermal evaporation rate (2.59 kg m-2 h-1, exceeding the limit rate) and efficient photocatalytic upcycling of microplastics pollutant into valued products. Enabled by controlled photothermal heating, the valued main product (i.e., methyl acetate) can be evaporated out with 100% selectivity by in situ separation.

Olink Proteomics for the Identification of Biomarkers for Early Diagnosis of Postmenopausal Osteoporosis.

This investigation aims to employ Olink proteomics in analyzing the distinct serum proteins associated with postmenopausal osteoporosis (PMOP) and identifying prognostic markers for early detection of PMOP via molecular mechanism research on postmenopausal osteoporosis. Postmenopausal women admitted to Beijing Jishuitan Hospital were randomly selected and categorized into three groups based on their dual-energy X-ray absorptiometry (DXA) T-scores: osteoporosis group (n = 24), osteopenia group (n = 20), and normal bone mass group (n = 16). Serum samples from all participants were collected for clinical and bone metabolism marker measurements. Olink proteomics was utilized to identify differentially expressed proteins (DEPs) that are highly associated with postmenopausal osteoporosis. The functional analysis of DEPs was performed using Gene Ontology and Kyto Encyclopedia Genes and Genomes (KEGG). The biological characteristics of these proteins and their correlation with PMOP were subsequently analyzed. ROC curve analysis was performed to identify potential biomarkers with the highest diagnostic accuracy for early stage PMOP. Through Olink proteomics, we identified five DEPs highly associated with PMOP, including two upregulated and three downregulated proteins. TWEAK and CDCP1 markers exhibited the highest area under the curve (0.8188 and 0.8031, respectively). TWEAK and CDCP1 have the potential to serve as biomarkers for early prediction of postmenopausal osteoporosis.

Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug-drug interactions prediction.

Multi-drug combinations for the treatment of complex diseases are gradually becoming an important treatment, and this type of treatment can take advantage of the synergistic effects among drugs. However, drug-drug interactions (DDIs) are not just all beneficial. Accurate and rapid identifications of the DDIs are essential to enhance the effectiveness of combination therapy and avoid unintended side effects. Traditional DDIs prediction methods use only drug sequence information or drug graph information, which ignores information about the position of atoms and edges in the spatial structure. In this paper, we propose Molormer, a method based on a lightweight attention mechanism for DDIs prediction. Molormer takes the two-dimension (2D) structures of drugs as input and encodes the molecular graph with spatial information. Besides, Molormer uses lightweight-based attention mechanism and self-attention distilling to process spatially the encoded molecular graph, which not only retains the multi-headed attention mechanism but also reduces the computational and storage costs. Finally, we use the Siamese network architecture to serve as the architecture of Molormer, which can make full use of the limited data to train the model for better performance and also limit the differences to some extent between networks dealing with drug features. Experiments show that our proposed method outperforms state-of-the-art methods in Accuracy, Precision, Recall and F1 on multi-label DDIs dataset. In the case study section, we used Molormer to make predictions of new interactions for the drugs Aliskiren, Selexipag and Vorapaxar and validated parts of the predictions. Code and models are available at https://github.com/IsXudongZhang/Molormer.

Machine Learning-Based Detection of Bladder Cancer by Urine cfDNA Fragmentation Hotspots that Capture Cancer-Associated Molecular Features.

BACKGROUND: cfDNA fragmentomics-based liquid biopsy is a potential option for noninvasive bladder cancer (BLCA) detection that remains an unmet clinical need. METHODS: We assessed the diagnostic performance of cfDNA hotspot-driven machine-learning models in a cohort of 55 BLCA patients, 51 subjects with benign conditions, and 11 healthy volunteers. We further performed functional bioinformatics analysis for biological understanding and interpretation of the tool's diagnostic capability. RESULTS: Urinary cfDNA hotspots-based machine-learning model enabled effective BLCA detection, achieving high performance (area under curve 0.96) and an 87% sensitivity at 100% specificity. It outperformed models using other cfDNA-derived features. In stage-stratified analysis, the sensitivity at 100% specificity of the urine hotspots-based model was 71% and 92% for early (low-grade Ta and T1) and advanced (high-grade T1 and muscle-invasive) disease, respectively. Biologically, cfDNA hotspots effectively retrieved regulatory elements and were correlated with the cell of origin. Urine cfDNA hotspots specifically captured BLCA-related molecular features, including key functional pathways, chromosome loci associated with BLCA risk as identified in genome-wide association studies, or presenting frequent somatic alterations in BLCA tumors, and the transcription factor regulatory landscape. CONCLUSIONS: Our findings support the applicability of urine cfDNA fragmentation hotspots for noninvasive BLCA diagnosis, as well as for future translational study regarding its molecular pathology and heterogeneity.

Automatic detection of fluorescent droplets for droplet digital PCR: a device capable of processing multiple microscope images.

Droplet digital PCR (ddPCR) is recognized as a high-precision method for nucleic acid quantification, extensively utilized in biomedical research and clinical diagnostics. This technique employs microfluidic technology to partition the nucleic acid-containing reaction mixture into discrete droplets for amplification, achieving absolute quantification by identifying and enumerating the number of fluorescent droplets. The accuracy of droplet quantification is pivotal to the success of the assay. However, current image-processing tools are operationally complex, and commercial instruments are costly. Moreover, the designed algorithms exhibit a need for enhanced accuracy and are often restricted to use by trained personnel with specific microscopy equipment. In response to these challenges, we introduce an automated device (A-MMD), capable of detecting fluorescent droplets in ddPCR images captured by multiple microscopes. The device integrates three distinct algorithms tailored for the image processing of Laser Scanning Confocal Microscopy (LSCM), inverted microscopy, and self-assembled microscopy. Experimental validation using λ DNA demonstrated a 100.00% identification rate for positive droplets across all three image types, and the average identification rates for total droplets being 99.27% for LSCM, 98.96% for inverted microscopy, and 99.08% for self-assembled microscopy. Furthermore, the A-MMD is equipped with a user-friendly interface (UI) that streamlines the operational process, enabling non-specialists to efficiently perform droplet detection tasks. Our device not only has good environmental adaptability and identification accuracy, but also significantly reduces costs and operational complexity. It offers an economical, efficient, and user-friendly solution for ddPCR image analysis, thereby further propelling the advancement and application of nucleic acid detection technology.

Hydroxyl-rich ferrofluid for efficient liquid phase microextraction of cinnamic acid derivatives in traditional Chinese medicine.

In this study, a hydroxyl-rich ferrofluid was prepared by dispersing silica-coated magnetic nanoparticles into a methyltrioctylammonium chloride-glycerol deep eutectic solvent and then employed in the preconcentration of trace-level of cinnamic acid derivatives (caffeic acid, p-hydroxycinnamic acid, ferulic acid, and cinnamic acid) in traditional Chinese medicine prior to high-performance liquid chromatography analysis. The structures of the synthesized materials were characterized by X-ray diffraction and infrared spectroscopy. The experimental parameters affecting the extraction performance, such as deep eutectic solvent composition, dosage of ferrofluid, pH of aqueous sample solution, salt concentration, extraction time, type, and volume of desorption solvent, were studied and optimized. Under the optimum conditions, the enrichment factors of four cinnamic acid derivatives were in the range of 107-114. Low detection limits (0.2-0.9 ng/mL), good precisions (relative standard deviations 1.2%-9.5%), and satisfactory recoveries (96.0%-104.7%) were achieved. Subsequently, the possible microextraction mechanism of the proposed method was explored and elucidated. It showed that the prepared ferrofluid is easily dispersed in the aqueous sample and achieved recovery after the extraction. The developed approach is a simple, convenient, and efficient method for preconcentration and determination of cinnamic acid derivatives in complex matrices.

A Novel Ultrasound Method of Evaluating Dynamic Extrusion of Lateral Meniscus in Healthy Population: Different Patterns of Dynamic Extrusion Revealed Between Lateral and Medial Meniscus.

OBJECTIVES: To establish a reliable ultrasound (US) method of evaluating dynamic extrusion of lateral meniscus in healthy population, and to investigate the pattern of dynamic meniscus extrusion (ME) in lateral meniscus under loading conditions. METHODS: The lateral ME was examined via US method in unloaded, double-leg standing, and single-leg standing positions. Two different US measurement methods were compared to the magnetic resonance imaging (MRI) results to determine the optimal measurement methods. The US results obtained by different researchers were tested for interobserver consistency and the results obtained by the same researcher on two separate days were tested for intraobserver consistency. The patterns of dynamic extrusion were compared between medial and lateral sides. RESULTS: A total of healthy 44 volunteers were included in the study, with 86 knees assessed by US, and 25 knees evaluated by MRI. The US evaluation of dynamic lateral ME demonstrated excellent interobserver and intraobserver reliability. The US measurements using method A were consistent with the MRI results with no significant difference (P = .861, intraclass correlation coefficient [ICC] = 0.868), while method B underestimated the lateral ME compared to MRI (P = .001, ICC = 0.649). Lateral ME decreased slightly from unloaded (1.0 ± 0.8 mm) to single-leg standing position (0.8 ± 0.8 mm), whereas medial ME increased significantly in both double-leg and single-leg standing positions (2.4 ± 0.7 mm, 2.6 ± 0.7 mm). CONCLUSION: A novel US evaluation method of lateral ME was established with reliable and accurate results compared to the MRI. Lateral ME in healthy populations decreased slightly as the loadings increased, which was different from the pattern of dynamic extrusion in medial meniscus.

Limited discectomy versus aggressive discectomy by spinal endoscopy with the transforaminal approach for lumbar disc herniation: a retrospective study.

OBJECTIVE: To compare the clinical and radiological outcomes of limited discectomy (LD) and aggressive discectomy (AD) performed via spinal endoscopy using the transforaminal approach in patients with lumbar disc herniation(LDH) METHODS: We conducted a retrospective review of patients who underwent percutaneous endoscopic transforaminal discectomy (PETD) at the L4-L5 lumbar spine segments in our department from January 2017 to December 2020. The follow-up period extended to 24 months postoperatively. Patients were categorized into the LD and AD groups based on the extent of intraoperative disc removal. We retrospectively collected and analyzed clinical and radiological data. RESULTS: The study followed 65 patients, with 36 in the LD group and 29 in the AD group. No statistically significant differences were noted in recurrence rates, the excellent and good Macnab rates, preoperative Disc Height Index (DHI), and preoperative Modic changes between the groups (P >0.05). However, significant differences were observed in operation duration, postoperative DHI and postoperative Modic change (P<0.05). No significant differences in Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) scores were detected between the groups preoperatively, or one and two years postoperatively (P>0.05). Nevertheless, notable differences in VAS and ODI scores were present one month postoperatively (P<0.05). CONCLUSION: As a conventional surgical method for treating LDH, PETD can achieve satisfactory clinical results in both LD and AD, with no significant variance in recurrence rates. However, AD is associated with longer operation times, and greater postoperative reductions in DHI and greater postoperative Modic changes compared to LD.

Associations between Urinary Concentrations of Polycyclic Aromatic Hydrocarbons and Overactive Bladder in US Adults: Data from the National Health and Nutrition Examination Survey 2005-2016.

INTRODUCTION: Polycyclic aromatic hydrocarbons (PAHs) are a group of chemicals that can induce oxidative stress and related cytotoxicity. Whether urinary concentrations of PAHs have effects on overactive bladder (OAB) in the general population is still unclear. This study investigated the associations between urinary PAHs and OAB. METHODS: 7,146 adults aged over 20 who participated in the US National Health and Nutrition Examination Survey 2005-2016 were studied. The impact of the six PAHs on OAB was evaluated by multivariate logistic regression, and percent changes related to different quartiles of those six PAH levels were calculated. Confounders including age, logarithmic urinary creatinine, gender, race, body mass index, educational level, marriage, poverty income ratio, diabetes, hypertension, and metabolic syndrome were controlled. RESULTS: There is a significant positive correlation between urinary concentrations of the six PAHs we include in the study and the occurrence of OAB. Furthermore, individuals with higher PAH levels also reported a more severe OAB symptom score (OABSS). CONCLUSIONS: Our findings revealed that adult men in the USA with higher urinary PAHs had a higher risk of OAB incidence. These findings suggest the importance of strong environmental regulation of PAHs to protect population health. However, the underlying mechanisms still need further exploration.

Effect of alfalfa supplementary change dietary non-fibrous carbohydrate (NFC) to neutral detergent fiber (NDF) ratio on rumen fermentation and microbial function in Gansu alpine fine wool sheep (Ovis aries).

Bodyweight loss and rumen microbial dysfunction of grazing sheep was a challenge for the sheep production industry during cold season, which were considered to correlated with under-roughage-feeding. Alfalfa is a good roughage supplementary for ruminants, which can improve grazing sheep bodyweight-loss and rumen microbial dysfunction during grass-withering period. This study evaluated the effects of alfalfa hay supplementary change dietary non-fibrous carbohydrate/neutral detergent fiber (NFC/NDF) ratios on rumen fermentation and microbial function of Gansu alpine fine wool sheep during extreme cold season. 120 ewes (3-4 yrs) with an average body weight of 28.71 ± 1.22 kg were allocated randomly into three treatments, and fed NFC/NDF of 1.92 (H group), 1.11 (M group), and 0.68 (L group), respectively. This study was conducted for 107 d, including 7 d of adaption to the diets. The rumen fermentation parameters and microbial characteristics were measured after the end of feeding trials. The results showed that the concentrations of sheep body weight, nitrogen components (Total-N, Soluble protein-N and Ammonia-N), blood biochemical indices (LDH, BUN and CHO) and ruminal volatile fatty acids (TVFA and propionate) significantly increased with an increase in the proportion of NFC/NDF ratios (p < .05), and the acetate and acetate/propionat ratio presented a contrary decreasing trend (p < .05). A total of 1018 OTUs were obtained with 97% consistency. Ruminococcus, Ruminococcaceae and Prevotella were observed as the predominant phyla in ruminal fluid microbiota. Higher NFC/NDF ratios with Alfalfa supplementary increased the richness and diversity of ruminal fluid microbiota, and decreased ruminal fluid microbiota beta-diversity. Using clusters of orthologous groups (COG), the ruminal fluid microbiota of alfalfa supplementary feeding showed low immune pathway and high carbohydrate metabolism pathway. In summary, the study suggested that there was an increasing tendency in dietary NFC/NDF ratio of 1.92 in body weight, ruminal fermentation, microbial community composition and fermentation characteristics through developing alfalfa supplementary system.

SensorNet: An Adaptive Attention Convolutional Neural Network for Sensor Feature Learning.

This work develops a generalizable neural network, SENSORNET, for sensor feature learning across various applications. The primary challenge addressed is the poor portability of pretrained neural networks to new applications with limited sensor data. To solve this challenge, we design SensorNet, which integrates the flexibility of self-attention with multi-scale feature locality of convolution. Moreover, we invent patch-wise self-attention with stacked multi-heads to enrich the sensor feature representation. SensorNet is generalizable to pervasive applications with any number of sensor inputs, and is much smaller than the state-of-the-art self-attention and convolution hybrid baseline (0.83 M vs. 3.87 M parameters) with similar performance. The experimental results show that SensorNet is able to achieve state-of-the-art performance compared with the top five models on a competition activity recognition dataset (SHL'18). Moreover, pretrained SensorNet in a large inertial measurement unit (IMU) dataset can be fine-tuned to achieve the best accuracy on a much smaller IMU dataset (up to 5% improvement in WISDM) and to achieve the state-of-the-art performance on an EEG dataset (SLEEP-EDF-20), showing the strong generalizability of our approach.

The impact of inflammatory biomarkers on amputation rates in patients with diabetic foot ulcers.

Diabetic Foot Ulcers (DFUs) are a major complication of diabetes, often leading to amputation. Understanding the relationship between haematological inflammatory markers and the incidence of amputation in DFU patients with infectious complications is crucial for improving management and outcomes. This retrospective study, conducted from May 2020 to October 2022, involved 109 patients with DFUs, categorised into amputation (AM) and non-amputation (NAM) groups. Patients were evaluated for various factors, including demographic data, DFU duration, and blood parameters such as haemoglobin A1c (HbA1c), haemoglobin (Hb), albumin (ALB), white blood cell count (WBC), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR). Statistical analyses were performed using independent sample t-tests, Mann-Whitney U test and logistic regression. The univariate analysis showed no significant difference in BMI, DM duration or DFU duration between groups. However, significant differences were noted in PCT, Hb, ESR, ALB, HbA1c and WBC levels, and in inflammatory ratios (NLR, PLR and LMR). Multivariate logistic regression identified CRP, NLR and PLR as independent risk factors for amputation. The study highlights CRP, PLR and NLR as key independent risk factors for amputation in patients with DFUs. These easily obtainable markers from routine blood tests can effectively aid in predicting the risk of osteomyelitis and amputation, enhancing clinical decision making and patient care strategies.

SERS Detection of Trace Carcinogenic Aromatic Amines Based on Amorphous MoO3 Monolayers.

Aromatic amines are important commercial chemicals, but their carcinogenicity poses a threat to humans and other organisms, making their rapid quantitative detection increasingly urgent. Here, amorphous MoO3 (a-MoO3) monolayers with localized surface plasmon resonance (LSPR) effect in the visible region are designed for the trace detection of carcinogenic aromatic amine molecules. The hot-electron fast decay component of a-MoO3 decreases from 301 fs to 150 fs after absorption with methyl orange (MO) molecules, indicating the plasmon-induced hot-electron transfer (PIHET) process from a-MoO3 to MO. Therefore, a-MoO3 monolayers present high SERS performance due to the synergistic effect of electromagnetic enhancement (EM) and PIHET, proposing the EM-PIHET synergistic mechanism in a-MoO3. In addition, a-MoO3 possesses higher electron delocalization and electronic state density than crystal MoO3 (c-MoO3), which is conducive to the PIHET. The limit of detection (LOD) for o-aminoazotoluene (o-AAT) is 10-9 M with good uniformity, acid resistance, and thermal stability. In this work, trace detection and identification of various carcinogenic aromatic amines based on a-MoO3 monolayers is realized, which is of great significance for reducing cancer infection rates.

Newly Discovered Antimicrobial Peptide Scyampcin44-63 from Scylla paramamosain Exhibits a Multitargeted Candidacidal Mechanism In Vitro and Is Effective in a Murine Model of Vaginal Candidiasis.

The emergence of azole-resistant and biofilm-forming Candida spp. contributes to the constantly increasing incidence of vulvovaginal candidiasis. It is imperative to explore new antifungal drugs or potential substituents, such as antimicrobial peptides, to alleviate the serious crisis caused by resistant fungi. In this study, a novel antimicrobial peptide named Scyampcin44-63 was identified in the mud crab Scylla paramamosain. Scyampcin44-63 exhibited broad-spectrum antimicrobial activity against bacteria and fungi, was particularly effective against planktonic and biofilm cells of Candida albicans, and exhibited no cytotoxicity to mammalian cells (HaCaT and RAW264.7) or mouse erythrocytes. Transcriptomic analysis revealed four potential candidacidal modes of Scyampcin44-63, including promotion of apoptosis and autophagy and inhibition of ergosterol biosynthesis and the cell cycle. Further study showed that Scyampcin44-63 caused damage to the plasma membrane and induced apoptosis and cell cycle arrest at G2/M in C. albicans. Scanning and transmission electron microscopy demonstrated that Scyampcin44-63-treated C. albicans cells were deformed with vacuolar expansion and destruction of organelles. In addition, C. albicans cells pretreated with the autophagy inhibitor 3-methyladenine significantly delayed the candidacidal effect of Scyampcin44-63, suggesting that Scyampcin44-63 might contribute to autophagic cell death. In a murine model of vulvovaginal candidiasis, the fungal burden of vaginal lavage was significantly decreased after treatment with Scyampcin44-63.

Partially coherent light propagation through a kinoform lens.

Combining wave optics propagation and geometric ray tracing, the mutual optical intensity (MOI) model is extended to quantitatively simulate the propagation of partially coherent light through a kinoform lens at high speed. The MOI model can provide both a high accuracy and a high efficiency simulation. The intensity and coherence degree distributions at the focal plane are calculated using the MOI model. It is beneficial to improve the focusing capability of the kinoform lens by reducing the coherence or increasing the number of lens steps. In addition, increasing the number of steps is also beneficial to increase the photon flux and reduce the depth of focus.

Mutual optical intensity propagation through non-ideal two-dimensional mirrors.

The mutual optical intensity (MOI) model is a partially coherent radiation propagation tool that can sequentially simulate beamline optics and provide beam intensity, local degree of coherence and phase distribution at any location along a beamline. This paper extends the MOI model to non-ideal two-dimensional (2D) optical systems, such as ellipsoidal and toroidal mirrors with 2D figure errors. Simulation results show that one can tune the trade-off between calculation efficiency and accuracy by varying the number of wavefront elements. The focal spot size of an ellipsoidal mirror calculated with 100 × 100 elements gives less than 0.4% deviation from that with 250 × 250 elements, and the computation speed is nearly two orders of magnitude faster. Effects of figure errors on 2D focusing are also demonstrated for a non-ideal ellipsoidal mirror and by comparing the toroidal and ellipsoidal mirrors. Finally, the MOI model is benchmarked against the multi-electron Synchrotron Radiation Workshop (SRW) code showing the model's high accuracy.

Bridging heterogeneous mutation data to enhance disease gene discovery.

Bridging heterogeneous mutation data fills in the gap between various data categories and propels discovery of disease-related genes. It is known that genome-wide association study (GWAS) infers significant mutation associations that link genotype and phenotype. However, due to the differences of size and quality between GWAS studies, not all de facto vital variations are able to pass the multiple testing. In the meantime, mutation events widely reported in literature unveil typical functional biological process, including mutation types like gain of function and loss of function. To bring together the heterogeneous mutation data, we propose a 'Gene-Disease Association prediction by Mutation Data Bridging (GDAMDB)' pipeline with a statistic generative model. The model learns the distribution parameters of mutation associations and mutation types and recovers false-negative GWAS mutations that fail to pass significant test but represent supportive evidences of functional biological process in literature. Eventually, we applied GDAMDB in Alzheimer's disease (AD) and predicted 79 AD-associated genes. Besides, 12 of them from the original GWAS, 60 of them are supported to be AD-related by other GWAS or literature report, and rest of them are newly predicted genes. Our model is capable of enhancing the GWAS-based gene association discovery by well combining text mining results. The positive result indicates that bridging the heterogeneous mutation data is contributory for the novel disease-related gene discovery.

The potential crosstalk between tumor and plasma cells and its association with clinical outcome and immunotherapy response in bladder cancer.

BACKGROUND: Although immunotherapy is effective in improving the clinical outcomes of patients with bladder cancer (BC), it is only effective in a small percentage of patients. Intercellular crosstalk in the tumor microenvironment strongly influences patient response to immunotherapy, while the crosstalk patterns of plasma cells (PCs) as endogenous antibody-producing cells remain unknown. Here, we aimed to explore the heterogeneity of PCs and their potential crosstalk patterns with BC tumor cells. METHODS: Crosstalk patterns between PCs and tumor cells were revealed by performing integrated bulk and single-cell RNA sequencing (RNA-seq) and spatial transcriptome data analysis. A risk model was constructed based on ligand/receptor to quantify crosstalk patterns by stepwise regression Cox analysis. RESULTS: Based on cell infiltration scores inferred from bulk RNA-seq data (n = 728), we found that high infiltration of PCs was associated with better overall survival (OS) and response to immunotherapy in BC. Further single-cell transcriptome analysis (n = 8; 41,894 filtered cells) identified two dominant types of PCs, IgG1 and IgA1 PCs. Signal transduction from tumor cells of specific states (stress-like and hypoxia-like tumor cells) to PCs, for example, via the LAMB3/CD44 and ANGPTL4/SDC1 ligand/receptor pairs, was validated by spatial transcriptome analysis and associated with poorer OS as well as nonresponse to immunotherapy. More importantly, a ligand/receptor pair-based risk model was constructed and showed excellent performance in predicting patient survival and immunotherapy response. CONCLUSIONS: PCs are an important component of the tumor microenvironment, and their crosstalk with tumor cells influences clinical outcomes and response to immunotherapies in BC patients.

Chaos with Gaussian invariant distribution by quantum-noise random phase feedback.

We experimentally present a random phase feedback based on quantum noise to generate a chaotic laser with Gaussian invariant distribution. The quantum noise from vacuum fluctuations is acquired by balanced homodyne detection and injected into a phase modulator to form a random phase feedback. An optical switch using high-speed intensity modulator is employed to reset the chaotic states repeatedly and the time evolutions of intensity statistical distributions of the chaotic states stemming from the initial noise are measured. By the quantum-noise random phase feedback, the transient intensity distributions of the chaotic outputs are improved from asymmetric invariant distributions to Gaussian invariant distributions, and the Gaussian invariant distribution indicates a randomly perturbed dynamical transition from microscopic initial noise to macroscopic stochastic fluctuation. The effects of phase feedback bandwidth and modulation depth on the invariant distributions are investigated experimentally. The chaotic time-delay signature and mean permutation entropy are suppressed to 0.036 and enhanced to 0.999 using the random phase feedback, respectively. The high-quality chaotic laser with Gaussian invariant distribution can be a desired random source for ultrafast random number generation and secure communication.

Regulatory mechanisms and clinical applications of tumor-driven exosomal circRNAs in cancers.

Malignant tumors seriously affect people's survival and prognosis. Exosomes, as vesicle structures widely existing in human tissues and body fluids, are involved in cell-to-cell transmission. Tumor-derived exosomes were secreted from tumors and involved in the development of carcinogenesis. Circular RNA (circRNA), a novel member of endogenous noncoding RNAs, is widespread in human and play a vital role in many physiological or pathological processes. Tumor-driven exosomal circRNAs are often involved in tumorigenesis and development including the proliferation, invasion, migration and chemo-or-radiotherapy sensitivity of tumor cell by multiple regulatory mechanisms. In this review, we will elaborate the roles and functions of tumor-driven exosomal circRNAs in cancers which may be used as potential cancer biomarkers and novel therapeutic targets.