Identification of MYC genes in four Cucurbitaceae species and their roles in the response to temperature stress.

BACKGROUND: Myelocytomatosis (MYC) transcription factors are crucial mediators of the response of plants to environmental stresses through via binding to DNA regulatory regions. However, few systematic characterizations of MYC genes are available in Cucurbitaceae species. RESULTS: In this study, we identified 10, 8, 12, and 10 MYC genes in Cucumis sativus, Cucumis melo, Citrullus lanatus, and Benincasa hispida, respectively. Characterization revealed that all of the MYC proteins contain a highly conserved H4-V5-E6-E8-R9-R11-R12 sequence, which is essential for the binding of DNA regulatory regions. Evolutionary analysis enabled us to categorize 40 predicted MYC proteins from seven species into five distinct groups and revealed that the expansion of the MYC genes occurred before the divergence of monocots and dicots. The upstream promoter regions of the MYC genes contain a variety of developmental, stress, and hormone-responsive regulatory elements. The expression of cucumber MYC genes varies significantly across organs, with particularly high expression of CsaV3_3G001710 observed across all organs. Transcriptomic analysis revealed that certain cucumber MYC genes undergo specific upregulation or downregulation in response to both biotic and abiotic stressors. In particular, under temperature stress, the cucumber genes CsaV3_3G007980 and CsaV3_3G001710 were significantly upregulated. Interestingly, the homologs of these two genes in C. lanatus presented a similar expression pattern to that in C. sativus, whereas in B. hispida, they presented the opposite pattern, i.e., significant downregulation. These findings indicated that these two genes indeed respond to temperature stress but with different expression patterns, highlighting the divergent functions of homologous genes across different species. CONCLUSIONS: This study analyzed the size and composition of the MYC gene family in four Cucurbitaceae species and investigated stress-responsive expression profiles, especially under temperature stress. All the results showed that MYC genes play important roles in development and stress responses, laying a theoretical foundation for further investigations of these response mechanisms.

A rare giant atrial septal cardiac blood cyst in an adult: a case report.

Background: Cardiac blood cysts are exceedingly rare cardiac tumours usually found on cardiac valves in infants. We report and discuss a rare unique case wherein a giant atrial septal cardiac blood cyst was found in an adult. Case summary: A 59-year-old Chinese lady with history of hypertension, hyperlipidemia and transient ischaemic attack presented with atypical chest pain. Her ECG suggested right ventricular strain. Echocardiography revealed a large right atrial intracardiac mass attached to the interatrial septum which prolapsed into the right ventricle during each cardiac cycle with associated dilatation of the right atrium and right ventricle. Computed tomography scans showed no evidence of malignancy or metastasis. Considering our patient's profile and the characteristics of the mass, it was opined to likely be a cardiac myxoma. After consultation with our cardiothoracic surgeons, she underwent surgical resection of the intracardiac mass. Histology revealed that the intracardiac mass was a cardiac blood cyst. Discussion: First described by Elsasser in 1844, cardiac blood cysts are extremely rare benign cardiac tumours very rarely seen in adults. Though benign, they have been associated with outflow tract obstruction, valvular regurgitation and embolization. Symptomatic patients do well with surgical resection, while long-term outcomes in asymptomatic patients followed up with serial echocardiograms are mixed. Due to its rarity, existing literature is limited to case reports and small case series, with a lack of guidelines on management till recently. Our case report serves to highlight this unique presentation of an exceedingly rare intracardiac tumour.

Therapeutic efficacy of ECs Foxp1 targeting Hif1α-Hk2 glycolysis signal to restrict angiogenesis.

Endothelial cells (ECs) rely on glycolysis for energy production to maintain vascular homeostasis and the normalization of hyperglycolysis in tumor vessels has recently gained attention as a therapeutic target. We analyzed the TCGA database and found reduced Foxp1 expression in lung carcinoma. Immunostaining demonstrated reduced expression more restricted at tumor vascular ECs. Therefore, we investigated the function and mechanisms of Foxp1 in EC metabolism for tumor angiogenesis required for tumor growth. EC-Foxp1 deletion mice exhibited a significant increase of tumor and retinal developmental angiogenesis and Hif1α was identified as Foxp1 target gene, and Hk2 as Hif1α target gene. The Foxp1-Hif1α-Hk2 pathway in ECs is important in the regulation of glycolytic metabolism to govern tumor angiogenesis. Finally, we used genetic deletion of EC-Hif1α and RGD-peptide nanoparticles EC target delivery of Hif1α/Hk2-siRNAs to knockdown gene expression which reduced the tumor EC hyperglycolysis state and restricted angiogenesis for tumor growth. This study advances our understanding of EC metabolism for tumor angiogenesis, and meanwhile provides evidence for future therapeutic intervention of hyperglycolysis in tumor ECs for suppression of tumor growth.

Nomogram Model for Cardiac Surgery-Associated Acute Kidney Injury Based on Clinical Characteristics Combined with Plasma suPAR.

Objective: Analyze risk factors for cardiac surgery-associated acute kidney injury (CSA-AKI) in adults and establish a nomogram model for CSA-AKI based on plasma soluble urokinase-type plasminogen activator receptor (suPAR) and clinical characteristics. Methods: In a study of 170 patients undergoing cardiac surgery with cardiopulmonary bypass, enzyme-linked immunosorbent assay (ELISA) measured plasma suPAR levels. Multivariable logistic regression analysis identified risk factors associated with CSA-AKI. Subsequently, the CSA-AKI nomogram model was developed using R software. Predictive performance was evaluated using a receiver operating characteristic (ROC) curve and the area under the curve (AUC). Internal validation was performed through the Bootstrap method with 1000 repeated samples. Additionally, decision curve analysis (DCA) assessed the clinical applicability of the model. Results: Multivariable logistic regression analysis revealed that being male, age ≥ 50 years, operation time ≥ 290 minutes, postoperative plasma suPAR at 2 hours, and preoperative left ventricular ejection fraction (LVEF) were independent risk factors for CSA-AKI. Employing these variables as predictive factors, a nomogram model was constructed, an ROC curve was generated, and the AUC was computed as 0.817 (95% CI 0.726-0.907). The calibration curve indicated the accuracy of the model, and the results of DCA demonstrated that the model could benefit the majority of patients. Conclusion: Being male, age ≥ 50 years, operation time ≥ 290 minutes, low preoperative LVEF, and elevated plasma suPAR at 2 hours are independent risk factors for CSA-AKI. The nomogram model established based on these risk factors has high accuracy and clinical value, serving as a predictive tool for assessing the risk of CSA-AKI.

Exploring nanodiamonds: leveraging their dual capacities for anticancer photothermal therapy and temperature sensing.

Cancer has become a primary global health concern, which has prompted increased attention towards targeted therapeutic approaches like photothermal therapy (PTT). The unique optical and magnetic properties of nanodiamonds (NDs) have made them versatile nanomaterials with promising applications in biomedicine. This comprehensive review focuses on the potential of NDs as a multifaceted platform for anticancer therapy, mainly focusing on their dual functionality in PTT and temperature sensing. The review highlighted NDs' ability to enhance PTT through hybridization or modification, underscoring their adaptability in delivering small molecule reagents effectively. Furthermore, NDs, particularly fluorescent nanodiamonds (FNDs) with negatively charged nitrogen-vacancy centers, enable precise temperature monitoring, enhancing PTT efficacy in anticancer treatment. Integrating FNDs into PTT holds promise for advancing therapeutic efficacy by providing valuable insights into localized temperature variations and cell death mechanisms. This review highlights new insights into cancer treatment strategies, showcasing the potential of NDs to revolutionize targeted therapeutics and improve patient outcomes.

Nanomaterials in the treatment and diagnosis of rheumatoid arthritis: Advanced approaches.

Rheumatoid arthritis (RA), a chronic inflammatory condition that affects persons between the ages of 20 and 40, causes synovium inflammation, cartilage loss, and joint discomfort as some of its symptoms. Diagnostic techniques for RA have traditionally been split into two main categories: imaging and serological tests. However, significant issues are associated with both of these methods. Imaging methods are costly and only helpful in people with obvious symptoms, while serological assays are time-consuming and require specialist knowledge. The drawbacks of these traditional techniques have led to the development of novel diagnostic approaches. The unique properties of nanomaterials make them well-suited as biosensors. Their compact dimensions are frequently cited for their outstanding performance, and their positive impact on the signal-to-noise ratio accounts for their capacity to detect biomarkers at low detection limits, with excellent repeatability and a robust dynamic range. In this review, we discuss the use of nanomaterials in RA theranostics. Scientists have recently synthesized, characterized, and modified nanomaterials and biomarkers commonly used to enhance RA diagnosis and therapy capabilities. We hope to provide scientists with the promising potential that nanomaterials hold for future theranostics and offer suggestions on further improving nanomaterials as biosensors, particularly for detecting autoimmune disorders.

A highly sensitive and selective colorimetric aptasensor for detecting sulfadiazine in river waters based on gold nanoparticles synthesized from discarded Longan seed extract.

Gold nanoparticles (AuNPs) were extensively employed for in-situ detection sulfadiazine (SDZ) residues, yet current synthesis methods suffer from complex procedures, reagent pollution of the environment, and low particle quality. This study presents a novel synthesis method using discarded longan seed extract as a reducing agent to synthesized high-quality AuNPs, and then can be used for in-situ SDZ detection. Response surface methodology (RSM) was employed to optimize synthesis parameters, which resulted in five optimal combinations that enhanced the flexibility of synthesis. These AuNPs, ranging in size from 18.26 nm to 33.8 nm with zeta potentials from - 29.5 mV to - 14.3 mV, were successfully loaded with functional groups from longan seed extract. In the detection of SDZ, the colorimetric aptasensor demonstrated excellent sensitivity and selectivity over other antibiotics with a limit of detection and quantification at 70.98 ng·mL-1 and 236.59 ng·mL-1 in the concentration range of 200-800 ng·mL-1. Recoveries of spiked SDZ samples ranged from 97.90% to 106.7%, with RSD values below 9.25%. Meanwhile, the aptasensor exhibited exceptional diagnostic efficacy (AUC: 0.976) compared to UV absorption methods in the ROC evaluation. In conclusion, this study highlights the potential of using AuNPs synthesized from longan seed extract coupled with aptamer technology as a straightforward detection method for SDZ in river water, offering promising applications in environmental monitoring.

End-to-end learning strategy based on a frequency domain feature decoupling network emulator with joint probabilistic shaping and equalization for a 300-Gbit/s OAM mode division multiplexing transmission.

Mode coupling and device nonlinear impairment appear to be a long-standing challenge in the orbital angular momentum (OAM) mode division multiplexing (MDM) of intensity modulation direct detection (IM/DD) transmission systems. In this paper, we propose an end-to-end (E2E) learning strategy based on a frequency domain feature decoupling network (FDFDnet) emulator with joint probabilistic shaping (PS) and equalization for an OAM-MDM IM/DD transmission with three modes. Our FDFDnet emulator can accurately build a complex nonlinear model of an OAM-MDM system by separating the signal into features from different frequency domains. Furthermore, a FDFDnet-based E2E strategy for joint PS and equalization is presented with the aim of compensating the signal impairment for the OAM-MDM IM/DD system. An experiment is carried out on a 300 Gbit/s carrierless amplitude phase-32 (CAP-32) signal with three OAM modes over a 10 km ring-core fiber transmission, and the results show that the proposed FDFDnet emulator outperforms the traditional CGAN emulator, with improvements in the modelling accuracy of 30.8%, 26.3% and 31% for the three OAM modes. Moreover, the receiver sensitivity of the proposed E2E learning strategy is higher than for the CGAN emulator by 3, 2.5, 2.2 dBm and the real channel by 5.5, 5.1, and 5.3 dBm for the three OAM modes, respectively. Our experimental results demonstrate that the proposed FDFDnet emulator-based E2E learning strategy is a promising contender for achieving ultra-high-capacity interconnectivity between data centers.

Characterization of prostatic cancer lesion and gleason grade using a continuous-time random-walk diffusion model at high b-values.

Background: Distinguishing between prostatic cancer (PCa) and chronic prostatitis (CP) is sometimes challenging, and Gleason grading is strongly associated with prognosis in PCa. The continuous-time random-walk diffusion (CTRW) model has shown potential in distinguishing between PCa and CP as well as predicting Gleason grading. Purpose: This study aimed to quantify the CTRW parameters (α, ß & Dm) and apparent diffusion coefficient (ADC) of PCa and CP tissues; and then assess the diagnostic value of CTRW and ADC parameters in differentiating CP from PCa and low-grade PCa from high-grade PCa lesions. Study type: Retrospective (retrospective analysis using prospective designed data). Population: Thirty-one PCa patients undergoing prostatectomy (mean age 74 years, range 64-91 years), and thirty CP patients undergoing prostate needle biopsies (mean age 68 years, range 46-79 years). Field strength/Sequence: MRI scans on a 3.0T scanner (uMR790, United Imaging Healthcare, Shanghai, China). DWI were acquired with 12 b-values (0, 50, 100, 150, 200, 500, 800, 1200, 1500, 2000, 2500, 3000 s/mm2). Assessment: CTRW parameters and ADC were quantified in PCa and CP lesions. Statistical tests: The Mann-Whitney U test was used to evaluate the differences in CTRW parameters and ADC between PCa and CP, high-grade PCa, and low-grade PCa. Spearman's correlation of the pathologic grading group (GG) with CTRW parameters and ADC was evaluated. The usefulness of CTRW parameters, ADC, and their combinations (Dm, α and ß; Dm, α, ß, and ADC) to differentiate PCa from CP and high-grade PCa from low-grade PCa was determined by logistic regression and receiver operating characteristic curve (ROC) analysis. Delong test was used to compare the differences among AUCs. Results: Significant differences were found for the CTRW parameters (α, Dm) between CP and PCa (all P<0.001), high-grade PCa, and low-grade PCa (α:P=0.024, Dm:P=0.021). GG is correlated with certain CTRW parameters and ADC(α:P<0.001,r=-0.795; Dm:P<0.001,r=-0.762;ADC:P<0.001,r=-0.790). Moreover, CTRW parameters (α, ß, Dm) combined with ADC showed the best diagnostic efficacy for distinguishing between PCa and CP as well as predicting Gleason grading. The differences among AUCs of ADC, CTRW parameters and their combinations were not statistically significant (P=0.051-0.526). Conclusion: CTRW parameters α and Dm, as well as their combination were beneficial to distinguish between CA and PCa, low-grade PCa and high-grade PCa lesions, and CTRW parameters and ADC had comparable diagnostic performance.

Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases.

RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases. MATERIALS AND METHODS: In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 7:3 ratio. An additional 112 lesions from 61 patients at another clinical center served as an external test set. A DLR model based on contrast-enhanced CT of the liver was developed to distinguish the five pathological types of liver metastases. Stepwise classification was performed to improve the classification efficiency of the model. Lesions were first classified as digestive tract cancer (DTC) and non-digestive tract cancer (non-DTC). DTCs were divided into CRC, GC, and PC and non-DTCs were divided into LC and BC. To verify the feasibility of the DLR model, we trained classical machine learning (ML) models as comparison models. Model performance was evaluated using accuracy (ACC) and area under the receiver operating characteristic curve (AUC). RESULTS: The classification model constructed by the DLR algorithm showed excellent performance in the classification task compared to ML models. Among the five categories task, highest ACC and average AUC were achieved at 0.563 and 0.796 in the validation set, respectively. In the DTC and non-DTC and the LC and BC classification tasks, AUC was achieved at 0.907 and 0.809 and ACC was achieved at 0.843 and 0.772, respectively. In the CRC, GC, and PC classification task, ACC and average AUC were the highest, at 0.714 and 0.811, respectively. CONCLUSION: The DLR model is an effective method for identifying the primary source of liver metastases.

Carbonization of quercetin into nanogels: a leap in anticoagulant development.

Quercetin, a flavonoid abundantly found in onions, fruits, and vegetables, is recognized for its pharmacological potential, especially for its anticoagulant properties that work by inhibiting thrombin and coagulation factor Xa. However, its clinical application is limited due to poor water solubility and bioavailability. To address these limitations, we engineered carbonized nanogels derived from quercetin (CNGsQur) using controlled pyrolysis and polymerization techniques. This led to substantial improvements in its anticoagulation efficacy, water solubility, and biocompatibility. We generated a range of CNGsQur by subjecting quercetin to varying pyrolytic temperatures and then assessed their anticoagulation capacities both in vitro and in vivo. Coagulation metrics, including thrombin clotting time (TCT), activated partial thromboplastin time (aPTT), and prothrombin time (PT), along with a rat tail bleeding assay, were utilized to gauge the efficacy. CNGsQur showed a pronounced extension of coagulation time compared to uncarbonized quercetin. Specifically, CNGsQur synthesized at 270 °C (CNGsQur270) exhibited the most significant enhancement in TCT, with a binding affinity to thrombin exceeding 400 times that of quercetin. Moreover, variants synthesized at 310 °C (CNGsQur310) and 290 °C (CNGsQur290) showed the most substantial delays in PT and aPTT, respectively. Our findings indicate that the degree of carbonization significantly influences the transformation of quercetin into various CNGsQur forms, each affecting distinct coagulation pathways. Additionally, both intravenous and oral administrations of CNGsQur were found to extend rat tail bleeding times by up to fivefold. Our studies also demonstrate that CNGsQur270 effectively delays and even prevents FeCl3-induced vascular occlusion in a dose-dependent manner in mice. Thus, controlled pyrolysis offers an innovative approach for generating quercetin-derived CNGs with enhanced anticoagulation properties and water solubility, revealing the potential for synthesizing self-functional carbonized nanomaterials from other flavonoids for diverse biomedical applications.

Equalization system of low differential mode delay few-mode fibers based on the neural network MIMO algorithm.

In recent years, with the development of information networks, higher requirements for transmission capacity have been recommended. Yet, at the same time, the capacity of single-mode fiber is rapidly approaching the theoretical limit. The multidimensional multiplexing technique is an effective way to solve this problem. Since the high differential mode delay (DMD) of transmission fiber increases the complexity of demultiplexing in equalization algorithms, we use an intelligent design method to optimize the trench-assisted gradient refractive index structure in this paper. The maximum DMD of the optimized optical fiber structure is 19.6 ps/km. A least mean squares-feedforward neural network constant modulus algorithm (LMS-FNNCMA) is also designed by using the theory of the least mean squares (LMS), constant modulus algorithm (CMA), and the multiple input multiple output (MIMO) neural networks. In order to verify the accuracy of the algorithm, a polarization division multiplexing-wavelength division multiplexing-mode division multiplexing (PDM-WDM-MDM) optical transmission system is constructed through simulation. The algorithm successfully realizes the de-crosstalk over a transmission distance of 1200 km at a rate of 1.2 Tbps under simulation conditions.

Real-Time Monitoring of the Evaporation and Fission of Electrospray-Ionized Polystyrene Beads and Bacterial Pellets at Elevated Temperatures.

This study uses real-time monitoring, at microsecond time scales, with a charge-sensing particle detector to investigate the evaporation and fission processes of methanol/micrometer-sized polystyrene beads (PS beads) droplets and bacterial particles droplets generated via electrospray ionization (ESI) under elevated temperatures. By incrementally raising capillary temperatures, the solvent, such as methanol on 0.75 µm PS beads, experiences partial evaporation. Further temperature increase induces fission, and methanol molecules continue to evaporate until PS ions are detected after this range. Similar partial evaporation is observed on 3 µm PS beads. However, the shorter period of the fission temperature range is necessary compared to 0.75 µm PS beads. For the spherical-shaped bacterium, Staphylococcus aureus, the desolvation process shows a similar fission period as compared to 0.75 µm PS beads. Comparably, the rod-shaped bacteria, Escherichia coli EC11303, and E. coli strain W have shorter fission periods than S. aureus. This research provides insights into the evaporation and fission mechanisms of ESI droplets containing different sizes and shapes of micrometer-sized particles, contributing to a better understanding of gaseous macroion formation.

DACNN-aided nonlinear equalizer for a probabilistic shaping coherent optical communication system.

The probabilistic shaping (PS) technique is a key technology for fiber optic communication systems to further approach the Shannon limit. To solve the problem that nonlinear equalizers are ineffective for probabilistic shaping optical communication systems with non-uniform distribution, a distribution alignment convolutional neural network (DACNN)-aided nonlinear equalizer is proposed. The approach calibrates the equalizer using the probabilistic shaping prior distribution, which reduces the training complexity and improves the performance of the equalizer simultaneously. Experimental results show nonlinear equalization of 120 Gb/s PS 64QAM signals in a 375 km transmission scenario. The proposed DACNN equalizer improves the receiver sensitivity by 2.6 dB and 1.1 dB over the Volterra equalizer and convolutional neural network (CNN) equalizer, respectively. Meanwhile, DACNN converges with fewer training epochs than CNN, which provides great potential for mitigating the nonlinear distortion of PS signals in fiber optic communication systems.

Fluorescent nanodiamond immunosensors for clinical diagnostics of tuberculosis.

Fluorescent nanodiamonds (FNDs) are carbon nanoparticles containing a dense ensemble of nitrogen-vacancy defects as color centers. These centers have exceptional photostability and unique quantum properties, making them useful for ultrasensitive biosensing applications. This work employed FNDs conjugated with antibodies as magneto-optical immunosensors for tuberculosis (TB) diagnostics using competitive spin-enhanced lateral flow immunoassay (SELFIA). ESAT6 (6-kDa early secretory antigenic target) of Mycobacterium tuberculosis is a clinical marker of TB. We evaluated the assay's performance using the recombinant ESAT6 antigen and its antibodies noncovalently coated on FNDs. A detection limit of ∼0.02 ng mL-1 was achieved with the lateral flow membrane strip pre-structured with a narrow channel of 1 mm width. Adopting a cut-off value of 24.0 ng mm-1 for 100-nm FNDs on the strips, the method detected 49 out of 50 clinical samples with Mycobacterium tuberculosis complexes. In contrast, none of the assays for 10 clinical samples with non-tuberculous mycobacteria (NTM) isolates exhibited the presence of ESAT6. These results suggest that the SELFIA platform is applicable for TB detection and can differentiate TB from NTM infections, which also affect the human respiratory system. The FND-enabled immunosensing techniques are versatile and promising for early detection of TB and other diseases, opening a new avenue for biomedical applications of carbon-based nanomaterials.

A Deep Learning Based Platform for Remote Sensing Images Change Detection Integrating Crowdsourcing and Active Learning.

Remote sensing images change detection technology has become a popular tool for monitoring the change type, area, and distribution of land cover, including cultivated land, forest land, photovoltaic, roads, and buildings. However, traditional methods which rely on pre-annotation and on-site verification are time-consuming and challenging to meet timeliness requirements. With the emergence of artificial intelligence, this paper proposes an automatic change detection model and a crowdsourcing collaborative framework. The framework uses human-in-the-loop technology and an active learning approach to transform the manual interpretation method into a human-machine collaborative intelligent interpretation method. This low-cost and high-efficiency framework aims to solve the problem of weak model generalization caused by the lack of annotated data in change detection. The proposed framework can effectively incorporate expert domain knowledge and reduce the cost of data annotation while improving model performance. To ensure data quality, a crowdsourcing quality control model is constructed to evaluate the annotation qualification of the annotators and check their annotation results. Furthermore, a prototype of automatic detection and crowdsourcing collaborative annotation management platform is developed, which integrates annotation, crowdsourcing quality control, and change detection applications. The proposed framework and platform can help natural resource departments monitor land cover changes efficiently and effectively.

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study.

The aim of this study was to investigate the feasibility of deep learning (DL) based on multiparametric MRI to differentiate the pathological subtypes of brain metastasis (BM) in lung cancer patients. This retrospective analysis collected 246 patients (456 BMs) from five medical centers from July 2016 to June 2022. The BMs were from small-cell lung cancer (SCLC, n = 230) and non-small-cell lung cancer (NSCLC, n = 226; 119 adenocarcinoma and 107 squamous cell carcinoma). Patients from four medical centers were assigned to training set and internal validation set with a ratio of 4:1, and we selected another medical center as an external test set. An attention-guided residual fusion network (ARFN) model for T1WI, T2WI, T2-FLAIR, DWI, and contrast-enhanced T1WI based on the ResNet-18 basic network was developed. The area under the receiver operating characteristic curve (AUC) was used to assess the classification performance. Compared with models based on five single-sequence and other combinations, a multiparametric MRI model based on five sequences had higher specificity in distinguishing BMs from different types of lung cancer. In the internal validation and external test sets, AUCs of the model for the classification of SCLC and NSCLC brain metastasis were 0.796 and 0.751, respectively; in terms of differentiating adenocarcinoma from squamous cell carcinoma BMs, the AUC values of the prediction models combining the five sequences were 0.771 and 0.738, respectively. DL together with multiparametric MRI has discriminatory feasibility in identifying pathology type of BM from lung cancer.

ABCC4 suppresses glioblastoma progression and recurrence by restraining cGMP-PKG signalling.

BACKGROUND: Cyclic nucleotides are critical mediators of cellular signalling in glioblastoma. However, the clinical relevance and mechanisms of regulating cyclic nucleotides in glioblastoma progression and recurrence have yet to be thoroughly explored. METHODS: In silico, mRNA, and protein level analyses identified the primary regulator of cyclic nucleotides in recurrent human glioblastoma. Lentiviral and pharmacological manipulations examined the functional impact of cyclic nucleotide signalling in human glioma cell lines and primary glioblastoma cells. An orthotopic xenograft mice model coupled with aspirin hydrogels verified the in vivo outcome of targeting cyclic nucleotide signalling. RESULTS: Elevated intracellular levels of cGMP, instead of cAMP, due to a lower substrate efflux from ATP-binding cassette sub-family C member 4 (ABCC4) is engaged in the recurrence of glioblastoma. ABCC4 gene expression is negatively associated with recurrence and overall survival outcomes in glioblastoma specimens. ABCC4 loss-of-function activates cGMP-PKG signalling, promoting malignancy in glioblastoma cells and xenografts. Hydrogels loaded with aspirin, inhibiting glioblastoma progression partly by upregulating ABCC4 expressions, augment the efficacy of standard-of-care therapies in orthotopic glioblastoma xenografts. CONCLUSION: ABCC4, repressing the cGMP-PKG signalling pathway, is a tumour suppressor in glioblastoma progression and recurrence. Aspirin hydrogels impede glioblastoma progression through ABCC4 restoration and constitute a viable translational approach.

OAM mode-division multiplexing IM/DD transmission at 4.32†Tbit/s with a low-complexity adaptive-network-based fuzzy inference system nonlinear equalizer.

Stochastic nonlinear impairment is the primary factor that limits the transmission performance of high-speed orbital angular momentum (OAM) mode-division multiplexing (MDM) optical fiber communication systems. This Letter presents a low-complexity adaptive-network-based fuzzy inference system (LANFIS) nonlinear equalizer for OAM-MDM intensity-modulation direct-detection (IM/DD) transmission with three OAM modes and 15 wavelength division multiplex (WDM) channels. The LANFIS equalizer could adjust the probability distribution functions (PDFs) of the distorted pulse amplitude modulation (PAM) symbols to fit the statistical characteristics of the WDM-OAM-MDM transmission channel. Therefore, although the transmission symbols in the WDM-OAM-MDM system are subjected to a stochastic nonlinear impairment, the proposed LANFIS equalizer can effectively compensate the distorted signals. The proposed equalizer outperforms the Volterra equalizer with improvements in receiver sensitivity of 2, 1.5, and 1.3 dB for three OAM modes at a wavelength of 1550.12 nm, respectively. It also outperforms a CNN equalizer, with improvements in receiver sensitivity of 1, 0.5, and 0.3 dB, respectively. Moreover, complexity reductions of 67%, 74%, and 99.9% are achieved for the LANFIS equalizer compared with the Volterra, CNN, and ANFIS equalizers, respectively. The proposed equalizer has high performance and low complexity, making it a promising candidate for a high-speed WDM-OAM-MDM system.

Augmenting Neutrophil Extracellular Traps with Carbonized Polymer Dots: A Potential Treatment for Bacterial Sepsis.

Sepsis is a life-threatening condition that can progress to septic shock as the body's extreme response to pathogenesis damages its own vital organs. Staphylococcus aureus (S. aureus) accounts for 50% of nosocomial infections, which are clinically treated with antibiotics. However, methicillin-resistant strains (MRSA) have emerged and can withstand harsh antibiotic treatment. To address this problem, curcumin (CCM) is employed to prepare carbonized polymer dots (CPDs) through mild pyrolysis. Contrary to curcumin, the as-formed CCM-CPDs are highly biocompatible and soluble in aqueous solution. Most importantly, the CCM-CPDs induce the release of neutrophil extracellular traps (NETs) from the neutrophils, which entrap and eliminate microbes. In an MRSA-induced septic mouse model, it is observed that CCM-CPDs efficiently suppress bacterial colonization. Moreover, the intrinsic antioxidative, anti-inflammatory, and anticoagulation activities resulting from the preserved functional groups of the precursor molecule on the CCM-CPDs prevent progression to severe sepsis. As a result, infected mice treated with CCM-CPDs show a significant decrease in mortality even through oral administration. Histological staining indicates negligible organ damage in the MRSA-infected mice treated with CCM-CPDs. It is believed that the in vivo studies presented herein demonstrate that multifunctional therapeutic CPDs hold great potential against life-threatening infectious diseases.