Comparison of the value of the GI-RADS and ADNEX models in the diagnosis of adnexal tumors by junior physicians.

Objective: To compare the diagnostic effectiveness of the Gynecologic Imaging Reporting and Data System (GI-RADS) and Neoplasias in the Adnexa (ADNEX) model for the diagnosis of benign and malignant ovarian tumors by junior physicians. Methods: The sonographic data of 634 patients with ovarian tumors confirmed by pathology in our hospital over 4 years were analyzed retrospectively by junior doctors. The diagnostic efficacy of the GI-RADS and ADNEX models was compared based on pathology. Results: (1) Regarding the diagnostic efficacy of the GI-RADS and ADNEX models, the sensitivity was 90.15% and 84.85%, the specificity was 87.65% and 85.86%, the accuracy rates were 88.17% and 85.65%, and the Youden Indices were 0.778 and 0.707, respectively. The areas under the receiver operating characteristic (ROC) curves were 0.924 (95% CI: 0.900-0.943) and 0.933 (95% CI: 0.911-0.951), respectively. The GI-RADS classification was equivalent to that of the ADNEX model in the diagnosis of adnexal tumors (P>0.05). These findings were highly consistent with the pathological results (Kappa values were 0.684 and 0.691, respectively). (2) When differentiating between different pathological types of adnexal tumors, the ADNEX model had the best diagnostic value for distinguishing between benign tumors and stage II-IV ovarian cancer (AUC=0.990, 95% CI: 0.978-0.997). Conclusions: (1) The diagnostic efficacy of the GI-RADS and ADNEX models in the diagnosis of benign and malignant ovarian tumors by junior physicians is excellent and comparable. (2) The ADNEX model shows good value for differentiating ovarian tumors of different pathological types by junior physicians.

A case of Bartonella vinsonii endocarditis.

The diagnosis of Bartonella is challenging due to its rarity and negative culture results. Once the diagnosis is delayed and proper treatment is not given, it can develop into infective endocarditis, which can be fatal. We reported a 60-year-old female patient who had recurrent fever for 5 months. After receiving ineffective treatment at the local hospital, she sought medical attention at our hospital. Laboratory blood indicators testing and imaging indicated infective endocarditis, and metagenomic Next Generation Sequencing (m-NGS) testing confirmed the diagnosis of Bartonella vinsonii infection. After surgical treatment and the combination of doxycycline and ceftriaxone sodium for anti-infective therapy, the patient recovered. Valuing the combination of multiple auxiliary diagnostic methods and improving the application of m-NGS in the detection of unknown pathogens can compensate for the current limitations in the diagnosis of Bartonella. Early diagnosis and treatment are extremely important for Bartonella endocarditis.

Bidirectional Inhibiting Interfacial Ion Migration in the Inorganic Hole Transport Layer for Perovskite Light-Emitting Diodes.

Cu2ZnSnS4 (CZTS) is strong candidate for hole transport in perovskite light emitting diodes (PeLEDs) due to their cost-effectiveness, deep highest occupied molecular orbital (HOMO), and high hole mobility. However, its inherent polymetallic ions usually deteriorate the quality of the perovskite emission layer (EML) affecting device performance. In this study, a bidirectional anchoring strategy is proposed by adding 15-crown-5 ether (15C5) into CZTS hole transport layer (HTL) to suppress the reaction between HTL and EML. The 15C5 molecule interacts with Cu+, Zn2+ and Sn2+ cations forming host-guest complexes to impede their migration, which is elucidated by density functional theory calculations. Additionally, 15C5 can neutralize lead (Pb) defects by the abundant oxygen (O) and high electronegative cavities to reduce the nonradiative recombination of FAPbBr3 film. This bidirectional anchoring strategy effectively improves hole charge transport efficiency and suppresses nonradiative recombination at the HTL/EML interface. As a result, the optimized PeLEDs present a 3.5 times peak external quantum efficiency (EQE) from 3.12% to 11.08% and the maximum luminance (Lmax) increased from 24495 to 50584 cd m-2. These findings offer innovative insights into addressing the metal ion migration issue commonly observed in inorganic HTLs.

An ECG denoising technique based on AHIN block and gradient difference max loss.

The electrocardiogram (ECG) signal is susceptible to interference from unknown noises during the acquisition process due to their low frequency and amplitude, resulting in the loss of significant information in the signals. Recent advancements in deep learning models have shown promising results in signal processing. However, these models lack robustness against various types of noise and often overlook the gradient difference between denoised and original signals. In this study, we propose a novel deep learning denoising method based on an attention half instance normalization block (AHIN block) and a gradient difference max loss function (GDM Loss). Our approach consists of two stages: firstly, we input the noisy ECG signal to obtain a denoised version; secondly, we reconstruct the denoised signal by fusing preliminary results from the first stage while correcting waveform distortions caused by initial denoising to minimize information loss. Additionally, we introduce a new loss function that considers differences between slopes of the denoised ECG signal and clean ECG signal. To validate our proposed method's effectiveness, extensive experiments were conducted on both our model architecture and loss function compared with other state-of-the-art methods. Results demonstrate that our approach achieves excellent performance in terms of both signal-to-noise ratio (SNR) and root-mean-square error (RMSE). The proposed noise reduction method improves 8.86%, 12.05% and 10.50% respectively under BW, MA and EM noise.

Thiram exposure: Disruption of the blood-testis barrier and altered apoptosis-autophagy dynamics in testicular cells via the Bcl-2/Bax and mTOR/Atg5/p62 pathways in mice.

Thiram, a prevalent dithiocarbamate insecticide in agriculture, is widely employed as a crop insecticide and preservative. Chronic exposure to thiram has been linked to various irreversible damages, including tibial cartilage dysplasia, erythrocytotoxicity, renal issues, and immune system compromise. Limited research exists on its effects on reproductive organs. This study investigated the reproductive toxicology in mouse testes exposure to varying concentrations (0, 30, 60, and 120 mg/kg) of thiram. Our study uncovered a series of adverse effects in mice subjected to thiram exposure, including emaciation, stunted growth, decreased water intake, and postponed testicular maturation. Biochemical analysis in thiram-exposed mice showed elevated levels of LDH and AST, while ALP, TG, ALT, and urea were decreased. Histologically, thiram disrupted the testis' microarchitecture and compromised its barrier function by widening the gap between spermatogenic cells and promoting fibrosis. The expression of pro-apoptotic genes (Bax, APAF1, Cytc, and Caspase-3) was downregulated, whereas Bcl-2 expression increased in thiram-treated mice compared to controls. Conversely, the expression of Atg5 was upregulated, and mTOR and p62 expression decreased, with a trend towards lower LC3b levels. Thiram also disrupted the blood-testis barrier, significantly reducing the mRNA expression of zona occludens-1 (ZO-1) and occludin. In conclusion, chronic exposure to high thiram concentrations (120 mg/kg) caused testicular tissue damage, affecting the blood-testis barrier and modulating apoptosis and autophagy through the Bcl-2/Bax and mTOR/Atg5/p62 pathways. This study contributes to understanding the molecular basis of thiram-induced reproductive toxicity and underscores the need for further research and precautions for those chronically exposed to thiram and its environmental residuals.

Biochar of invasive plants alleviated impact of acid rain on soil microbial community structure and functionality better than liming.

Acid rain and invasive plants have quintessential adverse impacts on terrestrial ecosystems. As an environmentally safe method for disposal of invasive plants, we tested the effect of biochar produced from these plants in altering soil deterioration under acid rain as compared with lime. Given the impacts of the feedstock type and soil properties on the response of soil to the added biochar, we hypothesized that the microbial community and functions would respond differently to the charred invasive plants under acid rain. A pot experiment was conducted to examine the response of soil microbiomes and functions to the biochar produced from Blackjack (Biden Pilosa), Wedelia (Wedelia trilobata), and Bitter vine (Mikania micrantha Kunth), or quicklime (CaO) at a rate of 1 % (w/w) under acid rain. Like soil pH, the nutrient contents (nitrogen, phosphorus, and potassium), calcium, and cation exchange capacity (CEC) were important as dominant edaphic factors affecting soil microbial community and functionality. In this respect, lime decreased nutrients availability, driven by 11-fold, 44 %, and 2-fold increments in calcium content, pH, and C/N ratio. Meanwhile, biochar improved nutrients availability under acid rain owing to maintaining a neutral pH (∼6.5), increasing calcium (by only 2-fold), and improving CEC, water repellency, and aggregation while decreasing the C/N ratio and aluminum content. Unlike biochar, lime decreased the relative abundance of Nitrosomonadaceae (the dominant ammonia-oxidizing bacteria) while augmenting the relative abundance of some fungal pathogens such as Spizellomycetaceae and Sporormiaceae. Given the highest nitrogen and dissolved organic carbon content than other biochar types, Wedelia-biochar resulted in the greatest relative abundance of Nitrosomonadaceae; thus, the microbial carbon and nitrogen biomasses were maximized. This study outlined the responses of the soil biogeochemical properties and the related microbial community structure and functionality to the biochar produced from invasive plants under acid rain. This study suggests that biochar can replace lime to ameliorate the effects of acid rain on soil physical, chemical and biological properties.

Optical Amplification at 1.5 µm in ErIII Coordination Polymer-Doped Waveguides Based on Intramolecular Energy Transfer.

9,9-bis (diphenylphosphorylphenyl) fluorene (FDPO) and dibenzotetrathienoacene (DBTTA), are synthesized as the neutral and anionic ligands, respectively, to prepare the ErIII coordination polymer [Er(DBTTA)3(FDPO)]n. Based on the intramolecular energy transfer, optical gains at 1.5 µm are demonstrated in [Er(DBTTA)3(FDPO)]n-doped polymer waveguides under excitations of low-power light-emitting diodes (LEDs) instead of laser pumping. A ligand-sensitization scheme between organic ligands and Er3+ ions under an excitation of an ultraviolet (UV) LED is established. Relative gains of 10.5 and 8.5 dB cm-1 are achieved at 1.53 and 1.55 µm, respectively, on a 1-cm-long SU-8 channel waveguide with a cross-section of 2 × 3 µm2 and a 1.5-µm-thick [Er(DBTTA)3(FDPO)]n-doped polymethylmethacrylate (PMMA) as upper cladding. The ErIII coordination polymer [Er(DBTTA)3(FDPO)]n can be conveniently integrated with various low-loss inorganic waveguides to compensate for optical losses in the C-band window. Moreover, by relying on the intramolecular energy transfer and UV LED top-pumping technology, it is easy to achieve coupling packaging of erbium-doped waveguide amplifiers (EDWAs) with pump sources in planar photonic integrated chips, effectively reducing the commercial costs.

Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction.

Background: The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS). Methods: We retrospectively collected pathology imaging, molecular and clinical data from The Cancer Genome Atlas and one independent institution in China. We developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal (DeepClinMed-PGM) model for DFS prediction, integrating clinicopathological data with molecular insights. The patients included the training cohort (n = 741), internal validation cohort (n = 184), and external testing cohort (n = 95). Result: Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve (AUC) values. In the training cohort, AUC values for 1-, 3-, and 5-year DFS predictions increased to 0.979, 0.957, and 0.871, while in the external testing cohort, the values reached 0.851, 0.878, and 0.938 for 1-, 2-, and 3-year DFS predictions, respectively. The DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts, including the training cohort [hazard ratio (HR) 0.027, 95% confidence interval (CI) 0.0016-0.046, P < 0.0001], the internal validation cohort (HR 0.117, 95% CI 0.041-0.334, P < 0.0001), and the external cohort (HR 0.061, 95% CI 0.017-0.218, P < 0.0001). Additionally, the DeepClinMed-PGM model demonstrated C-index values of 0.925, 0.823, and 0.864 within the three cohorts, respectively. Conclusion: This study introduces an approach to breast cancer prognosis, integrating imaging and molecular and clinical data for enhanced predictive accuracy, offering promise for personalized treatment strategies.

Adaptive block imaging based on compressive sensing in AFM.

Atomic force microscopy (AFM) is a kind of high-precision instrument to measure the surface morphology of various conductive or nonconductive samples. However, obtaining a high-resolution image with standard AFM scanning requires more time. Using block compressive sensing (BCS) is an effective approach to achieve rapid AFM imaging. But, the routine BCS-AFM imaging is difficult to balance the image quality of each local area. It is easy to lead to excessive sampling in some flat areas, resulting in time-consuming. At the same time, there is a lack of sampling in some areas with significant details, resulting in poor imaging quality. Thus, an innovative adaptive BCS-AFM imaging method is proposed. The overlapped block is used to eliminate blocking artifacts. Characteristic parameters (GTV, Lu, and SD) are used to predict the local morphological characteristics of the samples. Back propagation neural network is employed to acquire the appropriate sampling rate of each sub-block. Sampling points are obtained by pre-scanning and adaptive supplementary scanning. Afterward, all sub-block images are reconstructed using the TVAL3 algorithm. Each sample is capable of achieving uniform, excellent image quality. Image visual effects and evaluation indicators (PSNR and SSIM) are employed for the purpose of evaluating and analyzing the imaging effects of samples. Compared with two nonadaptive and two other adaptive imaging schemes, our proposed scheme has the characteristics of a high degree of automation, uniformly high-quality imaging, and rapid imaging speed. HIGHLIGHTS: The proposed adaptive BCS method can address the issues of uneven image quality and slow imaging speed in AFM. The appropriate sampling rate of each sub-block of the sample can be obtained by BP neural network. The introduction of GTV, Lu, and SD can effectively reveal the morphological features of AFM images. Seven samples with different morphology are used to test the performance of the proposed adaptive algorithm. Practical experiments are carried out with two samples to verify the feasibility of the proposed adaptive algorithm.

Engineering dynamical photon blockade with Liouville exceptional points.

We investigate the dynamical blockade in a nonlinear cavity and demonstrate the connection between the correlation function g(2)(t) and system parameters in the entire nonlinear region. Utilizing the Liouville exceptional points (LEPs) and quantum dynamics, a near-perfect single-photon blockade (1PB) can be achieved. By fine-tuning system parameters to approach the second-order LEP (LEP2), we improved single-photon statistics in both weak and strong nonlinearity regimes, including a significant reduction of g(2)(t) and a pronounced increase in the single-photon occupation number. In the strong nonlinearity region, the maximum photon population may correspond to stronger antibunching effect. Simultaneously, the time window and period of blockade can be controlled by selecting detuning based on the LEP2. Furthermore, the 1PB exhibits robustness against parameter fluctuations, and this feature can be generalized to systems for implementing single-photon sources with nonharmonic energy levels.

Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer.

Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AUC, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.

Multi-omics analysis for ferroptosis-related genes as prognostic factors in cutaneous melanoma. / é“æ­»äº¡ç›¸å ³åŸºå› ä½œä¸ºçš®è‚¤é»‘è‰²ç´ ç˜¤é¢„åŽå› ç´ çš„å¤šç»„å­¦åˆ†æžï¼ˆè‹±æ–‡ï¼‰.

OBJECTIVES: Melanoma is highly malignant and heterogeneous. It is essential to develop a specific prognostic model for improving the patients' survival and treatment strategies. Recent studies have shown that ferroptosis results from the overproduction of lipid peroxidation and is an iron-dependent form of programmed cell death. Despite this, ferroptosis-related genes (FRGs) and their clinical significances remain unknown in malignant melanoma. This study aims to assess the role of FRGs in melanoma, with the goal of developing a novel prognostic model that provides new insights into personalized treatment and improvement of therapeutic outcomes for melanoma. METHODS: We systematically characterized the genetic alterations and mRNA expression of 73 FRGs in The Cancer Genome Atlas (TCGA)-skin cutaneous melanoma (SKCM) dataset in this study. The results were validated with real-time RT-PCR and Western blotting. Subsequently, a multi-gene feature model was constructed using the TCGA-SKCM cohort. Melanoma patients were classified into a high-risk group and a low-risk group based on the feature model. As a final step, correlations between ferroptosis-related signatures and immune features, immunotherapy efficacy, or drug response were analyzed. RESULTS: By analyzing melanoma samples from TCGA-SKCM dataset, FRGs exhibited a high frequency of genetic mutations and copy number variations (CNVs), significantly impacting gene expression. Additionally, compared with normal skin tissue, 30 genes with significantly differential expression were identified in melanoma tissues. A prognostic model related to FRGs, constructed using the LASSO Cox regression method, identified 13 FRGs associated with overall survival prognosis in patients and was validated with external datasets. Finally, functional enrichment and immune response analysis further indicated significant differences in immune cell infiltration, mutation burden, and hypoxia status between the high-risk group and the low-risk group, and the model was effective in predicting responses to immunotherapy and drug sensitivity. CONCLUSIONS: This study develops a strong ferroptosis-related prognostic signature model which could put forward new insights into target therapy and immunotherapy for patients with melanoma.

Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma.

BACKGROUND: Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine learning (ML)-based prognostic models to predict overall survival of patients with AFP-positive HCC. METHODS: Data on patients with AFP-positive HCC were extracted from the Surveillance, Epidemiology, and End Results database. Six ML algorithms (extreme gradient boosting [XGBoost], logistic regression [LR], support vector machine [SVM], random forest [RF], K-nearest neighbor [KNN], and decision tree [ID3]) were used to develop the prognostic models of patients with AFP-positive HCC at one year, three years, and five years. Area under the receiver operating characteristic curve (AUC), confusion matrix, calibration curves, and decision curve analysis (DCA) were used to evaluate the model. RESULTS: A total of 2,038 patients with AFP-positive HCC were included for analysis. The 1-, 3-, and 5-year overall survival rates were 60.7%, 28.9%, and 14.3%, respectively. Seventeen features regarding demographics and clinicopathology were included in six ML algorithms to generate a prognostic model. The XGBoost model showed the best performance in predicting survival at 1-year (train set: AUC = 0.771; test set: AUC = 0.782), 3-year (train set: AUC = 0.763; test set: AUC = 0.749) and 5-year (train set: AUC = 0.807; test set: AUC = 0.740). Furthermore, for 1-, 3-, and 5-year survival prediction, the accuracy in the training and test sets was 0.709 and 0.726, 0.721 and 0.726, and 0.778 and 0.784 for the XGBoost model, respectively. Calibration curves and DCA exhibited good predictive performance as well. CONCLUSIONS: The XGBoost model exhibited good predictive performance, which may provide physicians with an effective tool for early medical intervention and improve the survival of patients.

Axl as a potential therapeutic target for adamantinomatous craniopharyngiomas: Based on single nucleus RNA-seq and spatial transcriptome profiling.

Craniopharyngiomas (CPs), particularly Adamantinomatous Craniopharyngiomas (ACPs), often exhibit a heightened risk of postoperative recurrence and severe complications of the endocrine and hypothalamic function. The primary objective of this study is to investigate potential novel targeted therapies within the microenvironment of ACP tumors. Cancer-Associated Fibroblasts (CAFs) were identified in the craniopharyngioma microenvironment, notably in regions characterized by cholesterol clefts, wet keratin, ghost cells, and fibrous stroma in ACPs. CAFs, alongside ghost cells, basaloid-like epithelium cells and calcifications, were found to secrete PROS1 and GAS6, which can activate AXL receptors on the surface of tumor epithelium cells, promoting immune suppression and tumor progression in ACPs. Additionally, the AXL inhibitor Bemcentinib effectively inhibited the proliferation organoids and enhanced the immunotherapeutic efficacy of Atezolizumab. Furthermore, neural crest-like cells were observed in the glial reactive tissue surrounding finger-like protrusions. Overall, our results revealed that the AXL might be a potentially effective therapeutic target for ACPs.

First Potassium Fluoroaluminate Ionic Exchanger for Rapid and Selective Removal of Sr2+ with High Capacity.

90Sr, as a typical artificial radionuclide, poses a serious threat to human health and the ecological environment. The selective removal of this radionuclide from industrial nuclear waste is crucial for our environment. Here we report a novel potassium fluoroaluminate, K2[(AlF5)H2O], which was synthesized by a simple low-temperature one-step method. It adopts a 1D AlF6-chain structure, which consists of exchangeable potassium ions in between the infinite chains of octahedral Al centers. As a remarkable inorganic ionic exchanger, K2[(AlF5)H2O] has a high chemical stability (resistance of pH=~3-12) and thermal stability (≥~300 °C). It possesses an excellent adsorption selectivity (Kd=~6.1×104 mL ⋅ g-1) and a maximum adsorption capacity of qm=~120.32 mg ⋅ g-1 for Sr2+. Importantly, it still keep a very good selectivity for Sr2+ ions even in the presence of competing Na+, Mg2+ and Ca2+ aqueous solutions. K2[(AlF5)H2O] is the first example of fluoroaluminate ionic exchange materials that can capture Sr2+. This result opens up a new way to design and synthesize inorganic ionic exchangers for the selective removal of Sr2+ ions from radioactive waste water.

Benchmarking Supervised and Self-Supervised Learning Methods in A Large Ultrasound Muti-task Images Dataset.

Deep learning in ultrasound(US) imaging aims to construct foundational models that accurately reflect the modality's unique characteristics. Nevertheless, the limited datasets and narrow task types have restricted this field in recent years. To address these challenges, we introduce US-MTD120K, a multi-task ultrasound dataset with 120,354 real-world two-dimensional images. This dataset covers three standard plane recognition and two diagnostic tasks in ultrasound imaging, providing a rich basis for model training and evaluation. We detail the data collection, distribution, and labelling processes, ensuring a thorough understanding of the dataset's structure. Furthermore, we conduct extensive benchmark tests on 27 state-of-the-art methods from both supervised and self-supervised learning(SSL) perspectives. In the realm of supervised learning, we analyze the sensitivity of two main feature computation methods to ultrasound images at the representational level, highlighting that models which judiciously constrain global feature computation could potentially serve as a viable analytical approach for US image analysis. In the context of self-supervised learning, we delved into the modelling process of self-supervised learning models for medical images and proposed an improvement strategy, named MoCo-US, a solution that addresses the excessive reliance on pretext task design from the input side. It achieves competitive performance with minimal pretext task design and enhances other SSL methods simply. The dataset and the code will be available at https://github.com/JsongZhang/CDOA-for-UMTD.

Lung Ultrasound Assessment of Lung Hyperinflation in Patients with Stable COPD: An Effective Diagnostic Tool.

Purpose: To evaluate the degree of lung hyperinflation (LH) in patients with stable chronic obstructive pulmonary disease (COPD) by lung ultrasound score (LUS) and assess its value. Patients and Methods: We conducted a study of 149 patients with stable COPD and 100 healthy controls recruited by the Second Affiliated Hospital of Fujian Medical University. The pleural sliding displacement (PSD) was measured, the sliding of the pleura in different areas was observed, and LUS was calculated from both of them. The diaphragm excursion (DE), residual capacity (RV), total lung capacity (TLC), inspiratory capacity (IC) and functional residual capacity (FRC) were measured. We described the correlation between ultrasound indicators and pulmonary function indicators reflecting LH. Multiple linear regression analysis was used. The ROC curves of LUS and DE were drawn to evaluate their diagnostic efficacy, and De Long method was used for comparison. Results: (1) The LUS of patients with stable COPD were positively correlated with RV, TLC, RV/TLC and FRC and negatively correlated with IC and IC/TLC (r1=0.72, r2=0.41, r3=0.72, r4=0.70, r5=-0.56, r6=-0.65, P < 0.001). The correlation was stronger than that between DE at maximal deep inspiration and the corresponding pulmonary function indices (r1=-0.41, r2=-0.26, r3=-0.40, r4=-0.43, r5=0.30, r6=0.37, P < 0.001). (2) Multiple linear regression analysis showed that LUS were significantly correlated with IC/TLC and RV/TLC. (3) With IC/TLC<25% and RV/TLC>60% as the diagnostic criterion of severe LH, the areas under the ROC curves of LUS and DE at maximal deep inspiration for diagnosing severe LH were 0.914 and 0.385, 0.845 and 0.543, respectively (P < 0.001). Conclusion: The lung ultrasound score is an important parameter for evaluating LH. LUS is better than DE at maximal deep inspiration for diagnosing severe LH and is expected to become an effective auxiliary tool for evaluating LH.

Evaluation of extravascular lung water and cardiac function in normal vaginal delivery by intrapartum bedside ultrasound.

BACKGROUND: Healthy parturients may experience pulmonary edema and disturbed cardiac function during labor. We aimed to evaluate the extravascular lung water (EVLW), intravascular volume, and cardiac function of normal parturients during spontaneous vaginal delivery by bedside ultrasound. And to explore the correlation between EVLW and intravascular volume, cardiac function. METHODS: This was a prospective observational study including 30 singleton-term pregnant women undergoing spontaneous vaginal delivery. Bedside ultrasound was performed at the early labor, the end of the second stage of labor, 2 and 24 h postpartum, and 120 scanning results were recorded. EVLW was evaluated by the echo comet score (ECS) obtained by the 28-rib interspaces technique. Inferior vena cava collapsibility index (IVC-CI), left ventricle ejection fraction, right ventricle fractional area change, left and right ventricular E/A ratio, and left and right ventricular index of myocardial performance (LIMP and RIMP) were measured. Measurements among different time points were compared, and the correlations between ECS and other measurements were analyzed. RESULTS: During the spontaneous vaginal delivery of healthy pregnant women, 2 had a mild EVLW increase at the early labor, 8 at the end of the second stage of labor, 13 at 2 h postpartum, and 4 at 24 h postpartum (P < 0.001). From the early labor to 24 h postpartum, ECS first increased and then decreased, reaching its peak at 2 h postpartum (P < 0.001). IVC-CI first decreased and then increased, reaching its minimum at the end of the second stage of labor (P < 0.001). RIMP exceeded the cut-off value of 0.43 at the end of the second stage of labor. ECS was weakly correlated with IVC-CI (r=-0.373, P < 0.001), LIMP (r = 0.298, P = 0.022) and RIMP (r = 0.211, P = 0.021). CONCLUSIONS: During spontaneous vaginal delivery, the most vital period of perinatal care is between the end of the second stage of labor and 2 h postpartum, because the risk of pulmonary edema is higher and the right ventricle function may decline. IVC-CI can be used to evaluate maternal intravascular volume. The increase in EVLW may be related to the increase in intravascular volume and the decrease in ventricular function.

[Degradation of Ciprofloxacin by Activating Peroxymonosulfate with Sludge Biochar].

Sludge biochar(BC), which was prepared by the pyrolysis of waste-activated sludge at 450℃, was applied for peroxymonosulfate(PMS) activation to construct a BC/PMS system for ciprofloxacin(CIP) degradation. The physical and chemical properties of BC were studied using scanning electron microscopy(SEM), an energy dispersive spectrometer(EDS), a Fourier transform infrared spectrometer(FTIR), X-ray diffraction(XRD), a Zeta potential analyzer, and electron paramagnetic resonance spectroscopy(EPR). The effects of BC dosage, PMS dosage, initial pH value, and inorganic anions on CIP removal in the BC/PMS system were investigated. Further, the degradation mechanism of the BC/PMS system was speculated through the free radical quenching experiment and X-ray photoelectron spectroscopy(XPS) analysis. The results showed that the CIP degradation rate was 49.09% at a BC dosage of 1.0 g·L-1, PMS of 3.0 mmol·L-1, CIP of 20 mg·L-1, and pH of 6.0 in 120 min. SO42- and NO3- had no obvious effect on the removal of CIP in the BC/PMS system, whereas HCO3- and Cl-could inhibit CIP degradation significantly. The CIP removal in the BC/PMS system was attributed to the common function of the radical pathway dominated by ·OH and SO4-· and the non-radical pathway dominated by 1O2. The CIP degradation pathway mainly included piperazine ring opening and hydroxylation reaction.

Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study.

BACKGROUND: Several studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms. METHODS: In this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics. RESULTS: The RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02-0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort. CONCLUSIONS: This study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.