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To investigate the water-binding mechanism of fresh sweet potato residue, the water distribution in starch processing residues from 10 varieties of sweet potato was determined by low-field nuclear magnetic resonance (LF-NMR), and the correlation between water states and major organic components was analyzed. Then, the causal relationship was verified through enzymatic hydrolysis experiments. The results showed that three or four states of water were found in the 10 varieties of fresh sweet potato residues, namely compound (T21, 0.658–2.310 ms), immobilized (T22, 7.055–21.550 ms), entrapped (T23, 377.410–705.480 ms) and free flowing water (T24, 705.480–3 764.936 ms). The percentage of entrapped water in sweet potato residues was the highest (93.59% on average), followed by immobilized water (5.15% on average). The percentages of compound water and free flowing water were only 0.85% and 0.41%, respectively. The percentage of entrapped water was significantly positively correlated with the contents of pectin, dietary fiber, and insoluble dietary fiber. The amount of water discharged from sweet potato residues significantly increase after being hydrolyzed by pectinase, but did not significantly change after being hydrolyzed by cellulase, hemicellulase or lignin peroxidase. Under scanning electron microscopy (SEM), it was observed that most of starch particles were encapsulated or bound to cell wall debris. This study can provide a theoretical basis for the development of an efficient dehydration technology for sweet potato residues.

Background and purpose: Long noncoding RNA (LncRNA) plays an important role in the development of distant metastasis and drug resistance of tumors, and it is highly expressed in breast cancer tissues and plasma of breast cancer patients. However, the dynamic changes of its expression level in neoadjuvant therapy (NAT) for breast cancer and its relationship with relevant clinicopathological features have not been reported. This study aimed to investigate the predictive value of dynamic changes of plasma long lncRNA H19 in NAT for different molecular types of breast cancer. Methods: Forty-five breast cancer patients undergoing NAT were prospectively selected from January 2021 to December 2021 in the Department of Gynecology, Xiaogan Central Hospital, Wuhan University of Science and Technology. The corresponding treatment plan and cycle were selected for all cases according to their pathological types and general conditions. Real-time fluorescence quantitative polymerase chain reaction (RTFQ-PCR) was used to dynamically detect the expression levels and changes of lncRNA H19 in peripheral blood before NAT, after treatment with NAT for 2, 4 and 6 cycles, and after surgery. The differences and dynamic changes of lncRNA H19 expression levels in different periods of NAT and different molecular types of breast cancer were compared and analyzed, and the efficacy of NAT was evaluated according to pathological features after surgery. Results: LncRNA H19 expression was detected in peripheral blood plasma of 45 breast cancer patients treated with NAT. Among them, 20 patients (44.4%) achieved pathological complete response (pCR), 25 patients (55.6%) did not achieve pCR, and the level of H19 before NAT in patients who achieved pCR was significantly higher compared with non-pCR patients (P0.05), while H19 expression level in TNBC patients was significantly lower compared with patients with hormone receptor-positive type (P0.05), and there were significant differences in tumor residual load and lncRNA H19 expression after NAT (P

Background: This study aimed to explore the accuracy of third-generation nanopore sequencing to diagnose extrapulmonary tuberculosis (EPTB). Methods: Samples were collected from the lesions of 67 patients with suspected EPTB admitted between April 2022 and August 2023. Nanopore sequencing, acid-fast bacilli (AFB) staining, DNA testing, and X-pert and mycobacterial cultures were performed. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the receiver operating characteristic curve (AUC) were calculated for different diagnostic methods, and their diagnostic accuracies were compared. Results: Nanopore sequencing demonstrated the highest correct diagnosis rate among 50 positive EPTB cases, independently diagnosing 19 positive cases missed by conventional methods. Its sensitivity (62.00%), specificity (94.10%), PPV (96.90%), NPV (45.70%) and AUC (0.781, 95% CI: 0.67–0.89) were superior to those of conventional methods, such as AFB staining, DNA testing, X-pert, and solid culture, indicating its significantly efficient advantage in EPTB detection. Conclusion: Nanopore sequencing technology significantly outperforms conventional methods such as AFB staining, DNA testing, X-pert, and mycobacterial culture to diagnose EPTB, promising to improve the diagnosis of EPTB. [ABSTRACT FROM AUTHOR]

Background: Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulfidptosis in the development of COPD could provide a opportunity for primary prediction, targeted prevention, and personalized treatment of the disease. Methods: We analyzed the expression profiles of DRGs and immune cell infiltration in COPD patients by using the GSE38974 dataset. According to the DRGs, molecular clusters and related immune cell infiltration levels were explored in individuals with COPD. Next, co-expression modules and cluster-specific differentially expressed genes were identified by the Weighted Gene Co-expression Network Analysis (WGCNA). Comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB), we constructed the ptimal machine learning model. Results: DE-DRGs, differential immune cells and two clusters were identified. Notable difference in DRGs, immune cell populations, biological processes, and pathway behaviors were noted among the two clusters. Besides, significant differences in DRGs, immune cells, biological functions, and pathway activities were observed between the two clusters.A nomogram was created to aid in the practical application of clinical procedures. The SVM model achieved the best results in differentiating COPD patients across various clusters. Following that, we identified the top five genes as predictor genes via SVM model. These five genes related to the model were strongly linked to traits of the individuals with COPD. Conclusion: Our study demonstrated the relationship between disulfidptosis and COPD and established an optimal machine-learning model to evaluate the subtypes and traits of COPD. DRGs serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in COPD, facilitating the transition from reactive medical services to PPPM in the management of the disease. [ABSTRACT FROM AUTHOR]

This paper uses the GJRSK model to estimate the high-order moments of energy (oil, natural gas, and coal), the carbon market, and tourism stocks. Then, it utilizes a novel TVP-VAR time-frequency connectedness approach to examine higher-order moments spillovers among them. The results show a strong connectedness among the three markets. The energy market is the emitter of volatility, skewness and kurtosis spillovers; tourism stock is the receiver; and the carbon market is the transmitter. From a time-domain perspective, the higher-order moments spillovers of the three markets are time-varying, especially during extreme periods, where the energy market's spillover effects on tourism stocks are significantly enhanced, indicating that tourism stocks bear a greater risk at leptokurtosis and fat-tail moment. From a frequency-domain perspective, the long-term asymmetric spillovers of oil, natural gas, and tourism markets on the carbon market are more pronounced than the short-term. Moreover, the COVID-19 pandemic exacerbated the higher-moment spillovers of energy and tourism stocks on the carbon market. Meanwhile, the Russia-Ukraine conflict led to extreme risk transmission within the energy market. These findings have significant implications for cross-industry investors and green finance risk management. [ABSTRACT FROM AUTHOR]

Systematic and scientific assessments on heavy metal pollution are greatly important to protecting the coastal eco-environment. In this paper, the spatial distribution, pollution degree, ecological toxicity and possible sources of eight heavy metal elements collected from the 126 marine and 715 terrestrial surface sediments surrounding Dingzi Bay were analyzed by obtaining concentration measurements. The results revealed that the concentrations of heavy metals followed a pattern: inner bay > terrestrial areas > outer bay. Cr, Ni, Cu, Zn, Cd, Pb, and Hg were found to accumulate in fine particles (<0.063 mm), while As showed an association with specific particles (>0.25 mm and <0.016 mm). Spatial pollution patterns varied from non-polluted to low pollution levels overall, with localized contamination by individual elements. In addition to natural sources, four types of anthropogenic pollution were identified in the marine and terrestrial settings. Agricultural pollution, characterized by As predominance, exerted profound effects on both terrestrial and marine environments. Industrial pollution, featuring Hg dominance, was widespread in land environment and predominantly linked to atmospheric deposition. Traffic pollution, marked by elevated Pb and Cd, was concentrated around factories and densely populated areas. Maritime pollution, comprising Hg, Cr, Cd, and Zn, primarily occurred in the nearshore areas outside the bay. The findings of this study provide scientific data to the authorities in charge of sustainable coastal zone management in the South Yellow Sea. [ABSTRACT FROM AUTHOR]