Machine learning based method for analyzing vibration and noise in large cruise ships.

Cruise ships are distinguished as special passenger ships, transporting passengers to various ports and giving importance to comfort. High comfort can attract lots of passengers and generate substantial profits. Vibration and noise are the most important indicators for assessing the comfort of cruise ships. Existing methods for analyzing vibration and noise data have shown limitations in uncovering essential information and discerning critical disparities in vibration and noise levels across different ship districts. Conversely, the rapid development in machine learning present an opportunity to leverage sophisticated algorithms for a more insightful examination of vibration and noise aboard cruise ships. This study designed a machine learning-driven approach to analyze the vibration and noise data. Drawing data from China's first large-scale cruise ship, encompassing 127 noise samples, this study sets up a classification task, where decks were assigned as labels and frequencies served as features. Essential information was extracted by investigating this problem. Several machine learning algorithms, including feature ranking, selection, and classification algorithms, were adopted in this method. One or two essential noise frequencies related to each of the decks, except the 10th deck, were obtained, which were partly validated by the traditional statistical methods. Such findings were helpful in reducing and controlling the vibration and noise in cruise ships. Furthermore, the study develops a classifier to distinguish noise samples, which utilizes random forest as the classification algorithm with eight optimal frequency features identified by LightGBM. This classifier yielded a Matthews correlation coefficient of 0.3415. This study gives a new direction for investigating vibration and noise in ships.

VISTA antibody-loaded Fe3O4@TiO2 nanoparticles for sonodynamic therapy-synergistic immune checkpoint therapy of pancreatic cancer.

Breaking the poor permeability of immune checkpoint inhibitors (ICIs) caused by the stromal barrier and reversing the immunosuppressive microenvironment are significant challenges in pancreatic cancer immunotherapy. In this study, we synthesized core-shell Fe3O4@TiO2 nanoparticles to act as carriers for loading VISTA monoclonal antibodies to form Fe3O4@TiO2@VISTAmAb (FTV). The nanoparticles are designed to target the overexpressed ICIs VISTA in pancreatic cancer, aiming to improve magnetic resonance imaging-guided sonodynamic therapy (SDT)-facilitated immunotherapy. Laser confocal microscopy and flow cytometry results demonstrate that FTV nanoparticles are specifically recognized and phagocytosed by Panc-2 cells. In vivo experiments reveal that ultrasound-triggered TiO2 SDT can induce tumor immunogenic cell death (ICD) and recruit T-cell infiltration within the tumor microenvironment by releasing damage-associated molecular patterns (DAMPs). Furthermore, ultrasound loosens the dense fibrous stroma surrounding the pancreatic tumor and increases vascular density, facilitating immune therapeutic efficiency. In summary, our study demonstrates that FTV nanoparticles hold great promise for synergistic SDT and immunotherapy in pancreatic cancer.

Ciceribacter sichuanensis sp. nov., a plant growth promoting rhizobacterium isolated from root nodules of soybean in Sichuan, China.

The fast-growing rhizobia-like strains S101T and S153, isolated from root nodules of soybean (Glycine max) in Sichuan, People's Republic of China, underwent characterization using a polyphasic taxonomy approach. The strains exhibited growth at 20-40 °C (optimum, 28 °C), pH 4.0-10.0 (optimum, pH 7.0) and up to 2.0% (w/v) NaCl (optimum, 0.01%) on Yeast Mannitol Agar plates. The 16S rRNA gene of strain S101T showed 98.4% sequence similarity to the closest type strain, Ciceribacter daejeonense L61T. Major cellular fatty acids in strain S101T included summed feature 8 (C18:1ω7c and/or C18:1ω6c) and C19:0 cyclo ω8c. The predominant quinone was ubiquinone-10. The polar lipids of strain S101T included diphosphatidylglycerol, phosphatidylglycerol, phosphatidylmethyl ethanolamine, phosphatidyl ethanolamine, amino phospholipid, unidentified phosphoglycolipid and unidentified amino-containing lipids. The DNA G + C contents of S101T and S153 were 61.1 and 61.3 mol%, respectively. Digital DNA-DNA hybridization relatedness and average nucleotide identity values between S101T and C. daejeonense L61T were 46.2% and 91.4-92.2%, respectively. In addition, strain S101T promoted the growth of soybean and carried nitrogen fixation genes in its genome, hinting at potential applications in sustainable agriculture. We propose that strains S101T and S153 represent a novel species, named Ciceribacter sichuanensis sp. nov., with strain S101T as the type strain (= CGMCC 1.61309 T = JCM 35649 T).

Predictors and influence of postoperative moderate-to-severe pain of PACU in the patients with malignancy.

BACKGROUND: This study was identified the risk factors for and designed to investigate influence of postoperative moderate-to-severe pain of post anaesthesia care unit (PACU) in patients with malignancy. METHODS: A retrospective study was performed on 22,600 cancer patients with malignancy who underwent elective radical surgery in the new hospital of First Affiliated Hospital of Wenzhou Medical University, between January 2016 and June 2021. All patients were transferred to the PACU after tracheal extubation. Patients were divided into two groups according to a visual analogue scale (VAS) score of > 3: the no-moderate-severe-pain group and moderate-to-severe-pain group. Data pertaining to demographic, surgical, anaesthetic, and other factors were recorded. Lasso and logistic regression analysis was performed to explore the risk factors, then a nomogram was constructed to predict the moderate-severe-pain in the PACU. Validation was performed by using another 662 cancer patients in old hospital. The ROC curves and calibration curve were used to evaluate the accuracy and predictive ability of the nomogram. RESULTS: The incidence of postoperative moderate-to-severe pain of PACU in patients with malignancy was 1.42%. Gender, type of surgery, postoperative use of PCA, intraoperative adjuvant opioid agonists, NSAIDS, epidural analgesia, duration of anaesthesia, intraoperative massive haemorrhage, PACU vomiting were independent predictors for postoperative moderate-to-severe pain of PACU in the patients with malignancy. The area under the ROC curve of the predictive models in the primary and validation groups were 0.817 and 0.786, respectively. Moderate-to-severe pain in the PACU correlated with hypertension, hyperglycaemia, agitation, and hypoxemia (P < 0.05). CONCLUSIONS: The prediction model for postoperative moderate-to-severe pain of PACU in patients with malignancy has good predictive ability and high accuracy, which is helpful for PACU medical staff to identify and prevent postoperative moderate-to-severe pain in advance. TRIAL REGISTRATION: The study was approved by the Clinical Research Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University (No.KY2021-097) and registered in the Chictr.org.cn registration system on 06/12/2021 (ChiCTR2100054013).

Cancer-associated fibroblasts reprogram cysteine metabolism to increase tumor resistance to ferroptosis in pancreatic cancer.

Background: Pancreatic ductal adenocarcinoma (PDAC) is an insidious, rapidly progressing malignancy of the gastrointestinal tract. Due to its dense fibrous stroma and complex tumor microenvironment, neither of which is sensitive to radiotherapy, pancreatic adenocarcinoma is one of the malignancies with the poorest prognosis. Therefore, detailed elucidation of the inhibitory microenvironment of PDAC is essential for the development of novel therapeutic strategies. Methods: We analyzed the association between cancer-associated fibroblasts (CAFs) and resistance to ferroptosis in PDAC using conditioned CAF medium and co-culture of pancreatic cancer cells. Abnormal cysteine metabolism was observed in CAFs using non-targeted metabolomics analysis with liquid chromatography-tandem mass spectrometry (LC-MS/MS). The regulatory effects of cysteine were investigated in PDAC cells through measurement of cell cloning, cell death, cell function, and EdU assays. The effects of exogenous cysteine intake were examined in a mouse xenograft model and the effects of the cysteine pathway on ferroptosis in PDAC were investigated by western blotting, measurement of glutathione and reactive oxygen species levels, among others. Results: It was found that CAFs played a critical role in PDAC metabolism by secreting cysteine, which could increase tumor resistance to ferroptosis. A previously unrecognized function of the sulfur transfer pathway in CAFs was identified, which increased the extracellular supply of cysteine to support glutathione synthesis and thus inducing ferroptosis resistance. Cysteine secretion by CAFs was found to be mediated by the TGF-ß/SMAD3/ATF4 signaling axis. Conclusion: Taken together, the findings demonstrate a novel metabolic relationship between CAFs and cancer cells, in which cysteine generated by CAFs acts as a substrate in the prevention of oxidative damage in PDAC and thus suggests new therapeutic targets for PDAC.

Clinical Application of Automatic Assessment of Scoliosis Cobb Angle Based on Deep Learning.

INTRODUCTION: A recently developed deep-learning-based automatic evaluation model provides reliable and efficient Cobb angle measurements for scoliosis diagnosis. However, few studies have explored its clinical application, and external validation is lacking. Therefore, this study aimed to explore the value of automated assessment models in clinical practice by comparing deep-learning models with manual measurement methods. METHODS: The 481 spine radiographs from an open-source dataset were divided into training and validation sets, and 119 spine radiographs from a private dataset were used as the test set. The mean Cobb angle values assessed by three physicians in the hospital's PACS system served as the reference standard. The results of Seg4Reg, VFLDN, and manual measurement were statistically analyzed. The intra-class correlation coefficients (ICC) and the Pearson correlation coefficient (PCC) were used to compare their reliability and correlation. The Bland-Altman method was used to compare their agreement. The Kappa statistic was used to compare the consistency of Cobb angles at different severity levels. RESULTS: The mean Cobb angle values measured were 35.89° ± 9.33° with Seg4Reg, 31.54° ± 9.78° with VFLDN, and 32.23° ± 9.28° with manual measurement. The ICCs for the reliability of Seg4Reg and VFLDN were 0.809 and 0.974, respectively. The PCC and MAD between Seg4Reg and manual measurements were 0.731 (p<0.001) and 6.51°, while those between VFLDN and manual measurements were 0.952 (p<0.001) and 2.36°. The Kappa statistic indicated VFLDN (k= 0.686, p< 0.001) was superior to Seg4Reg and manual measurements for Cobb angle severity classification. CONCLUSION: The deep-learning-based automatic scoliosis Cobb angle assessment model is feasible in clinical practice. Specifically, the keypoint-based VFLDN is more valuable in actual clinical work with higher accuracy, transparency, and interpretability.