Purification of inorganic nitrogen from the mariculture tail water by anaerobic/anoxic/oxic (A2O) process.

This study aims to address the suboptimal performance of conventional denitrifying strains in treating mariculture tail water (MTW) containing inorganic nitrogen (IN). The concentration of inorganic nitrogen in the mariculture tail water is about 5-20 mg·L-1. A biofilm treatment process was developed and evaluated using an anoxic-anoxic-aerobic biofilter composite system inoculated with the denitrifying strain Meyerozyma guilliermondii Y8. The removal effect of total nitrogen (TN), IN, and Chemical Oxygen Demand (CODMn) from MTW was investigated. The results indicate that the A2O composite biological filter has excellent pollutant removal efficiency within 25 days of operation, after the acclimation of the denitrifying microorganisms. The initial concentrations of TN, IN, and CODMn ranged between 10.24 and 12.89 mg·L-1, 7.84-10.49 mg·L-1, and 9.44-11.52 mg·L-1, respectively, and the removal rates of these indexes reached 38-68 %, 45-70 %, and 55-70 %, respectively. The experiments with different hydraulic retention times (HRT = 6 h, 8 h, 10 h) demonstrated that longer HRT was more conducive to the removal of inorganic nitrogen. Moreover, scanning electron microscopy observations revealed that the target strain successfully grew and attached to the filler in large quantities. The findings of this study provide practical guidance for the development of efficient biofilm processes for the treatment of MTW.

Advances in Engineered Nanoparticles for the Treatment of Ischemic Stroke by Enhancing Angiogenesis.

Angiogenesis, or the formation of new blood vessels, is a natural defensive mechanism that aids in the restoration of oxygen and nutrition delivery to injured brain tissue after an ischemic stroke. Angiogenesis, by increasing vessel development, may maintain brain perfusion, enabling neuronal survival, brain plasticity, and neurologic recovery. Induction of angiogenesis and the formation of new vessels aid in neurorepair processes such as neurogenesis and synaptogenesis. Advanced nano drug delivery systems hold promise for treatment stroke by facilitating efficient transportation across the the blood-brain barrier and maintaining optimal drug concentrations. Nanoparticle has recently been shown to greatly boost angiogenesis and decrease vascular permeability, as well as improve neuroplasticity and neurological recovery after ischemic stroke. We describe current breakthroughs in the development of nanoparticle-based treatments for better angiogenesis therapy for ischemic stroke employing polymeric nanoparticles, liposomes, inorganic nanoparticles, and biomimetic nanoparticles in this study. We outline new nanoparticles in detail, review the hurdles and strategies for conveying nanoparticle to lesions, and demonstrate the most recent advances in nanoparticle in angiogenesis for stroke treatment.

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance.

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

Darwin3: a large-scale neuromorphic chip with a novel ISA and on-chip learning.

Spiking neural networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture, which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind on the neuron scale. The experimental results showed that the code density was improved by up to 28.3× in Darwin3, and that the neuron core fan-in and fan-out were improved by up to 4096× and 3072× by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8× and 200.8× when mapping convolutional spiking neural networks onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.

Exploring the impact of insufficient thermal ablation on hepatocellular carcinoma: NDST2 overexpression mechanism and its role in facilitating growth and invasion of residual cancer cells.

OBJECTIVE: Incomplete thermal ablation (ITA) fosters the malignancy of residual cells in Hepatocellular carcinoma (HCC) with unclear mechanisms now. This study aims to investigate the expression changes of NDST2 following ITA of HCC and its impact on residual cancer cells. METHODS: An in vitro model of heat stress-induced liver cancer was constructed to measure the expression of NDST2 using Quantitative Real-Time PCR and Western blotting experiments. The sequencing data from nude mice were used for validation. The clinical significance of NDST2 in HCC was evaluated by integrating datasets. Gene ontology and pathway analysis were conducted to explore the potential signaling pathways regulated by NDST2. Additionally, NDST2 was knocked down in heat stress-induced HCC cells, and the effects of NDST2 on these cells were verified using Cell Counting Kit-8 assays, scratch assays, and Transwell assays. RESULTS: NDST2 expression levels are elevated in HCC, leading to a decrease in overall survival rates of HCC patients. Upregulation of immune checkpoint levels in high NDST2-expressing HCC may contribute to immune evasion by liver cancer cells. Additionally, the low mutation rate of NDST2 in HCC suggests a relatively stable expression of NDST2 in this disease. Importantly, animal and cell models treated with ITA demonstrate upregulated expression of NDST2. Knockdown of NDST2 in heat stress-induced liver cancer cells results in growth inhibition associated with gene downregulation. CONCLUSION: The upregulation of NDST2 can accelerate the progression of residual HCC after ITA, suggesting a potential role for NDST2 in the therapeutic efficacy and prognosis of residual HCC.

Diagnostic yield using electromagnetic navigation bronchoscopy for peripheral pulmonary nodules <2 cm.

BACKGROUND: Although electromagnetic navigation bronchoscopy (ENB) is highly sensitive in the diagnosis of peripheral pulmonary nodules (PPNs), its diagnostic yield for subgroups of smaller PPNs is under evaluation. OBJECTIVES: Diagnostic yield evaluation of biopsy using ENB for PPNs <2 cm. DESIGN: The diagnostic yield, sensitivity, specificity, positive predictive value, and negative predictive value of the ENB-mediated biopsy for PPNs were evaluated. METHODS: Patients who had PPNs with diameters <2 cm and underwent ENB-mediated biopsy between May 2015 and February 2020 were consecutively enrolled. The final diagnosis was made via pathological examination after surgery. RESULTS: A total of 82 lesions from 65 patients were analyzed. The median tumor size was 11 mm. All lesions were subjected to ENB-mediated biopsy, of which 29 and 53 were classified as malignant and benign, respectively. Subsequent segmentectomy, lobectomy, or wedge resection, following pathological examinations were performed on 64 nodules from 57 patients. The overall sensitivity, specificity, positive predictive value, and negative predictive value for nodules <2 cm were 53.3%, 91.7%, 92.3%, and 51.2%, respectively. The receiver operating curve showed an area under the curve of 0.721 (p < 0.001). Additionally, the sensitivity, specificity, positive predictive value, and negative predictive value were 62.5%, 100%, 100%, and 42.9%, respectively, for nodules with diameters equal to or larger than 1 cm; and 30.8%, 86.7%, 66.7%, and 59.1%, respectively, for nodules less than 1 cm. In the subgroup analysis, neither the lobar location nor the distance of the PPNs to the pleura affected the accuracy of the ENB diagnosis. However, the spiculated sign had a negative impact on the accuracy of the ENB biopsy (p = 0.010). CONCLUSION: ENB has good specificity and positive predictive value for diagnosing PPNs <2 cm; however, the spiculated sign may negatively affect ENB diagnostic accuracy. In addition, the diagnostic reliability may only be limited to PPNs equal to or larger than 1 cm.

A Preoperative Prediction Model for Lymph Node Metastasis in Patients with Gastric Cancer Using a Machine Learning-based Ultrasomics Approach.

OBJECTIVE: This study aimed to develop an ultrasomics model for predicting lymph node metastasis preoperative in patients with gastric cancer (GC). METHODS: This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was performed by an experienced radiologist to extract radiomics features using the Pyradiomics software. The Z-score algorithm was used for feature normalization, followed by the Wilcoxon test to identify the most informative features. Linear prediction models were constructed using the least absolute shrinkage and selection operator (LASSO). The performance of the ultrasomics model was evaluated using the area under curve (AUC), sensitivity, specificity, and the corresponding 95% confidence intervals (CIs). RESULTS: A total of 464 GC patients (mean age: 60.4 years ±11.3 [SD]; 328 men [70.7%]) were analyzed, of whom 291 had lymph node metastasis. The patients were randomly assigned to either the training (n=324) or test (n=140) sets, using a 7:3 ratio. An ultrasomics model that consisted of 19 radiomics features was developed using Wilcoxon and LASSO algorithms in the training set. Our ultrasomics model showed moderate performance for lymph node metastasis prediction in both the training (AUC: 0.802, 95%CI: 0.752-0.851, P<0.001) and test sets (AUC: 0.802, 95%CI: 0.724-0.879, P<0.001). The calibration curve analysis indicated good agreement between the predicted probabilities of ultrasomics and actual lymph node metastasis status. CONCLUSION: Our study highlights the potential of a machine learning-based ultrasomics model in predicting lymph node metastasis in GC patients, offering implications for personalized therapy approaches.

US-based Sequential Algorithm Integrating an AI Model for Advanced Liver Fibrosis Screening.

Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis; however, the use of single tests may not be adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability to predict advanced liver fibrosis with that of other noninvasive tests. Materials and Methods This retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode US of the liver between January 2014 and September 2022 at three health care facilities. A US-based DL network (FIB-Net) was trained on US images to predict whether the shear-wave elastography (SWE) value was 8.7 kPa or higher, indicative of advanced fibrosis. In the internal and external test sets, a two-step algorithm (Two-step#1) using the Fibrosis-4 Index (FIB-4) followed by FIB-Net and a three-step algorithm (Three-step#1) using FIB-4 followed by FIB-Net and SWE were used to simulate screening scenarios where liver stiffness measurements were not or were available, respectively. Measures of diagnostic accuracy were calculated using liver biopsy as the reference standard and compared between FIB-4, SWE, FIB-Net, and European Association for the Study of the Liver guidelines (ie, FIB-4 followed by SWE), along with sequential algorithms. Results The training, validation, and test data sets included 3067 (median age, 42 years [IQR, 33-53 years]; 2083 male), 1599 (median age, 41 years [IQR, 33-51 years]; 1124 male), and 1228 (median age, 44 years [IQR, 33-55 years]; 741 male) patients, respectively. FIB-Net obtained a noninferior specificity with a margin of 5% (P < .001) compared with SWE (80% vs 82%). The Two-step#1 algorithm showed higher specificity and positive predictive value (PPV) than FIB-4 (specificity, 79% vs 57%; PPV, 44% vs 32%) while reducing unnecessary referrals by 42%. The Three-step#1 algorithm had higher specificity and PPV compared with European Association for the Study of the Liver guidelines (specificity, 94% vs 88%; PPV, 73% vs 64%) while reducing unnecessary referrals by 35%. Conclusion A sequential algorithm combining FIB-4 and a US DL model showed higher diagnostic accuracy and improved referral management for all-cause advanced liver fibrosis compared with FIB-4 or the DL model alone. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ghosh in this issue.

Oleanane and 30-noroleanane triterpenoids from the roots of Paeonia lactiflora.

An investigation of EtOAc extract from the roots of Paeonia lactiflora yielded three new 30-noroleanane triterpenoids paeonenoides L-N (1-3) and one new oleanane triterpenoid paeonenoide O (4) together with 7 known compounds (5-11). Extensive spectrographic experiments were applied to identify the structures of 1-4, and their absolute configurations were unambiguously determined by theoretical calculations of ECD spectra, as well as the single-crystal X-ray diffraction analysis. Compounds 8, 9 and 10 were isolated from the Paeonia genus for the first time. Moreover, compounds 8, 9 and 11 showed inhibitory activities against LPS-induced nitric oxide (NO) production in RAW264.7 macrophages with the IC50 values of 72. 17 ± 4.74, 30.02 ± 2.03 and 28.34 ± 1.85 µM, respectively.

Intra-individual comparison of Sonazoid contrast-enhanced ultrasound and SonoVue contrast-enhanced ultrasound in diagnosing hepatocellular carcinoma.

PURPOSE: To assess whether the diagnostic performance of Sonazoid contrast-enhanced ultrasound (SZUS) is non-inferior to that of SonoVue contrast-enhanced ultrasound (SVUS) in diagnosing hepatocellular carcinoma (HCC) in individuals with high risk. MATERIALS AND METHODS: This prospective study was conducted from October 2020 to May 2022 and included participants with a high risk of HCC who underwent SZUS and SVUS. All lesions were confirmed by clinical or pathological diagnosis. Each nodule was classified according to the Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System version 2017 (CEUS LI-RADS v2017) for SVUS and SZUS and the modified CEUS LI-RADS (using Kupffer phase defect instead of late and mild washout) for SZUS. The diagnostic performance of both two modalities for all observations was compared. Analysis of the vascular phase and Kupffer phase imaging characteristics of CEUS was performed. RESULTS: One hundred and fifteen focal liver lesions from 113 patients (94 HCCs, 12 non-HCC malignancies, and 9 benign lesions) were analysed. According to CEUS LI-RADS (v2017), SVUS and SZUS showed similar sensitivity (71.3% vs. 72.3%) and specificity (85.7% vs. 81.0%) in HCC diagnosis. However, the modified CEUS LI-RADS did not significantly improve the diagnostic efficacy of Sonazoid compared to CEUS LI-RADS v2017, having equivalent sensitivity (73.4% vs. 72.3%) and specificity (81.0% vs. 81.0%). The agreement between SVUS and SZUS for all observations was 0.610 (95% CI 0.475, 0.745), while for HCCs it was 0.452 (95% CI 0.257, 0.647). CONCLUSION: Using LI-RADS v2017, SZUS and SVUS showed non-inferior efficacy in evaluating HCC lesions. In addition, adding Kupffer phase defects to SZUS does not notably improve its diagnostic efficacy.

Long-range infrared absorption spectroscopy and fast mass spectrometry for rapid online measurements of volatile organic compounds from black tea fermentation.

Fermentation is the key process to determine the quality of black tea. Traditional physical and chemical analyses are time consuming, it cannot meet the needs of online monitoring. The existing rapid testing techniques cannot determine the specific volatile organic compounds (VOCs) produced at different stages of fermentation, resulting in poor model transferability; therefore, the current degree of black tea fermentation mainly relies on the sensory judgment of tea makers. This study used proton transfer reaction mass spectrometry (PTR-MS) and fourier transform infrared spectroscopy (FTIR) combined with different injection methods to collect VOCs of the samples, the rule of change of specific VOCs was clarified, and the extreme learning machine (ELM) model was established after principal component analysis (PCA), the prediction accuracy reached 95% and 100%, respectively. Finally, different application scenarios of the two technologies in the actual production of black tea are discussed based on their respective advantages.

Toll-interacting protein is activated by the transcription factor GATA1 and Sp1 to negatively regulate NF-κB and MAPK pathways in the Japanese eel (Anguilla japonica).

Toll-interacting protein (Tollip) serves as a crucial inhibitory factor in the modulation of Toll-like receptor (TLR)-mediated innate immunological responses. The structure and function of Tollip have been well documented in mammals, yet the information in teleost remained limited. This work employed in vitro overexpression and RNA interference in vivo and in vitro to comprehensively examine the regulatory effects of AjTollip on NF-κB and MAPK signaling pathways. The levels of p65, c-Fos, c-Jun, IL-1, IL-6, and TNF-α were dramatically reduced following overexpression of AjTollip, whereas knocking down AjTollip in vivo and in vitro enhanced those genes' expression. Protein molecular docking simulations showed AjTollip interacts with AjTLR2, AjIRAK4a, and AjIRAK4b. A better understanding of the transcriptional regulation of AjTollip is crucial to elucidating the role of Tollip in fish antibacterial response. Herein, we cloned and characterized a 2.2 kb AjTollip gene promoter sequence. The transcription factors GATA1 and Sp1 were determined to be associated with the activation of AjTollip expression by using promoter truncation and targeted mutagenesis techniques. Collectively, our results indicate that AjTollip suppresses the NF-κB and MAPK signaling pathways, leading to the decreased expression of the downstream inflammatory factors, and GATA1 and Sp1 play a vital role in regulating AjTollip expression.

The effects of Mediterranean diet on cardiovascular risk factors, glycemic control and weight loss in patients with type 2 diabetes: a meta-analysis.

To explore the impact of the Mediterranean diet on cardiovascular risk factors, glycemic control and weight loss in patients with type 2 diabetes(T2D) by a meta-analysis of randomized controlled trials (RCTs). We systematically searched PubMed, Cochrance Library, EMBASE and four Chinese databases to identify RCTs that compared the Mediterranean diet with control diets in patients with T2D up to December 2021. The Risk of Bias of the included studies was assessed using the version 2 of the Cochrane risk-of-bias tools for randomized trials (ROB 2). Seven RCTs with 1371 patients met the eligibility criteria and entered into the meta-analysis. Compared to control diets, the beneficial effects of Mediterranean diet were not statistically significant in high-density lipoprotein (MD = 2.33; 95% CI: -0.27 to 4.92), low-density lipoprotein (MD = -2.34; 95% CI -5.67 to 0.99) and total cholesterol (MD = 2.60; 95% CI: -0.95 to 6.15). But Mediterranean diet led to reduce the level of diastolic blood pressure (MD = -1.20; 95% CI: -2.21 to -0.19) and systolic blood pressure (MD = -4.17; 95% CI: -7.12 to -1.22). Meanwhile, Mediterranean diet showed beneficial effects in glycemic control (HbA1[%]: MD = -0.39, 95% CI: -0.58 to -0.20; fasting plasma glucose: MD = -15.12, 95% CI: -24.69 to -5.55) and weight loss (BMI: MD = -0.71, 95% CI: -1.30 to -0.78; WC: MD = -1.69; 95% CI: -3.35 to -0.02) compared to the control diets. The meta-analysis presented evidence supporting the beneficial effects of the Mediterranean diet on blood pressure, glycemic control, and weight loss. However, the impact of the Mediterranean diet on the lipid profile was not found to be significant, warranting further verification. This Meta-analysis was registered on the INPLASY website (Registration number: INPLASY 202160096).

Pulse Accumulation Approach Based on Signal Phase Estimation for Doppler Wind Lidar.

Coherent Doppler wind lidar (CDWL) uses transmitted laser pulses to measure wind velocity distribution. However, the echo signal of CDWL is easily affected by atmospheric turbulence, which can decrease the signal-to-noise ratio (SNR) of lidar. To improve the SNR, this paper proposes a pulse accumulation method based on the cross-correlation function to estimate the phase of the signal. Compared with incoherent pulse accumulation, the proposed method significantly enhances the correlation between signals from different periods to obtain high SNR gains that arise from pulse accumulation. Using simulation, the study evaluates the effectiveness of this phase estimation method and its robustness against noise in algorithms which analyze Doppler frequency shifts. Furthermore, a CDWL is developed for measuring the speed of an indoor motor turntable and the outdoor atmospheric wind field. The phase estimation method yielded SNR gains of 28.18 dB and 32.03 dB for accumulation numbers of 500 and 1500, respectively. The implementation of this method in motor turntable speed measurements demonstrated a significant reduction in speed error-averaging 9.18% lower than that of incoherent accumulation lidar systems. In experiments that measure atmospheric wind fields, the linear fit curve slope between the measured wind speed and the wind speed measured via a commercial wind-measuring lidar can be reduced from 1.146 to 1.093.

Germline MLH1 and MSH6 mutations from two Lynch syndrome families identified in a patient with early-onset of endometrial cancer: A case report.

Introduction: Lynch syndrome is caused by a germline mutation in mismatch repair (MMR) genes, leading to the loss of expression of MMR heterodimers, either MLH1/PMS2 or MSH2/MSH6, or isolated loss of PMS2 or MSH6. Concurrent loss of both heterodimers is uncommon, and patients carrying pathogenic variants affecting different MMR genes are rare, leading to the lack of cancer screening recommendation for these patients.Case presentation:Here, we reported a female with a family history of Lynch syndrome with MLH1 c.676C > T mutation. She developed endometrial cancer at 37 years old, with loss of MLH1/PMS2 expression. Immunohistochemical staining on tumor samples incidentally detected the additional loss of MSH6 expression. Whole exome sequencing on genomic DNA from peripheral blood revealed MSH6 c.2731C > T mutation, which was confirmed to be inherited from her mother, who had an early-onset ascending colon cancer without cancer family history. Conclusion: This is a rare case of the Lynch syndrome harboring germline mutations simultaneously in two different MMR genes inherited from two families with Lynch syndrome. The diagnosis of endometrial cancer at the age less than 40 years is uncommon for Lynch syndrome-related endometrial cancer. This suggests an earlier cancer screening for patients carrying two MMR mutations.

Oscillatory Neural Network-Based Ising Machine Using 2D Memristors.

Neural networks are increasingly used to solve optimization problems in various fields, including operations research, design automation, and gene sequencing. However, these networks face challenges due to the nondeterministic polynomial time (NP)-hard issue, which results in exponentially increasing computational complexity as the problem size grows. Conventional digital hardware struggles with the von Neumann bottleneck, the slowdown of Moore's law, and the complexity arising from heterogeneous system design. Two-dimensional (2D) memristors offer a potential solution to these hardware challenges, with their in-memory computing, decent scalability, and rich dynamic behaviors. In this study, we explore the use of nonvolatile 2D memristors to emulate synapses in a discrete-time Hopfield neural network, enabling the network to solve continuous optimization problems, like finding the minimum value of a quadratic polynomial, and tackle combinatorial optimization problems like Max-Cut. Additionally, we coupled volatile memristor-based oscillators with nonvolatile memristor synapses to create an oscillatory neural network-based Ising machine, a continuous-time analog dynamic system capable of solving combinatorial optimization problems including Max-Cut and map coloring through phase synchronization. Our findings demonstrate that 2D memristors have the potential to significantly enhance the efficiency, compactness, and homogeneity of integrated Ising machines, which is useful for future advances in neural networks for optimization problems.

Dynamical Janus-Like Behavior Excited by Passive Cold-Heat Modulation in the Earth-Sun/Universe System: Opportunities and Challenges.

The utilization of solar-thermal energy and universal cold energy has led to many innovative designs that achieve effective temperature regulation in different application scenarios. Numerous studies on passive solar heating and radiation cooling often operate independently (or actively control the conversion) and lack a cohesive framework for deep connections. This work provides a concise overview of the recent breakthroughs in solar heating and radiation cooling by employing a mechanism material in the application model. Furthermore, the utilization of dynamic Janus-like behavior serves as a novel nexus to elucidate the relationship between solar heating and radiation cooling, allowing for the analysis of dynamic conversion strategies across various applications. Additionally, special discussions are provided to address specific requirements in diverse applications, such as optimizing light transmission for clothing or window glass. Finally, the challenges and opportunities associated with the development of solar heating and radiation cooling applications are underscored, which hold immense potential for substantial carbon emission reduction and environmental preservation. This work aims to ignite interest and lay a solid foundation for researchers to conduct in-depth studies on effective and self-adaptive regulation of cooling and heating.

A new resorcylic acid lactone from the endophytic fungus Chaetosphaeronema sp.

A new 14-membered resorcylic acid lactone (RAL14), chaetolactone A (1), along with three known ones (2-4), was obtained from the fermentation of the soil-derived fungus Chaetosphaeronema sp. SSJZ001. Their structures were established based on extensive spectroscopic data analyses (UV, IR, HRESIMS, 1D, and 2D NMR),13C NMR chemical shifts calculations coupled with the DP4+ probability method, theoretical calculations of ECD spectra, as well as X-ray diffraction analysis. All compounds were evaluated for their cytotoxic effects against A549, HO-8910, and MCF-7 cell lines.

Development and validation of a multi-modal ultrasomics model to predict response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

OBJECTIVES: To assess the performance of multi-modal ultrasomics model to predict efficacy to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) and compare with the clinical model. MATERIALS AND METHODS: This study retrospectively included 106 patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 at our hospital, randomly divided into a training set of 74 and a validation set of 32 in a 7: 3 ratios. Ultrasomics features were extracted from the tumors' region of interest of B-mode ultrasound (BUS) and contrast-enhanced ultrasound (CEUS) images based on PyRadiomics. Mann-Whitney U test, spearman, and least absolute shrinkage and selection operator algorithms were utilized to reduce features dimension. Five models were built with ultrasomics and clinical analysis using multilayer perceptron neural network classifier based on python. Including BUS, CEUS, Combined_1, Combined_2 and Clinical models. The diagnostic performance of models was assessed with the area under the curve (AUC) of the receiver operating characteristic. The DeLong testing algorithm was utilized to compare the models' overall performance. RESULTS: The AUC (95% confidence interval [CI]) of the five models in the validation cohort were as follows: BUS 0.675 (95%CI: 0.481-0.868), CEUS 0.821 (95%CI: 0.660-0.983), Combined_1 0.829 (95%CI: 0.673-0.985), Combined_2 0.893 (95%CI: 0.780-1.000), and Clinical 0.690 (95%CI: 0.509-0.872). The Combined_2 model was the best in the overall prediction performance, showed significantly better compared to the Clinical model after DeLong testing (P < 0.01). Both univariate and multivariate logistic regression analyses showed that age (P < 0.01) and clinical stage (P < 0.01) could be an independent predictor of efficacy after nCRT in patients with LARC. CONCLUSION: The ultrasomics model had better diagnostic performance to predict efficacy to nCRT in patients with LARC than the Clinical model.