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Abstract Background Definitive concurrent chemoradiotherapy (dCCRT) is the gold standard for the treatment of locally advanced esophageal squamous cell carcinoma (ESCC). However, the potential benefits of consolidation chemotherapy after dCCRT in patients with esophageal cancer remain debatable. Prospective randomized controlled trials comparing the outcomes of dCCRT with or without consolidation chemotherapy in patients with ESCC are lacking. In this study, we aim to generate evidence regarding consolidation chemotherapy efficacy in patients with locally advanced, inoperable ESCC. Methods This is a multicenter, prospective, open-label, phase-III randomized controlled trial comparing non-inferiority of dCCRT alone to consolidation chemotherapy following dCCRT. In total, 600 patients will be enrolled and randomly assigned in a 1:1 ratio to receive either consolidation chemotherapy after dCCRT (Arm A) or dCCRT alone (Arm B). Overall survival will be the primary endpoint, whereas progression-free survival, locoregional progression-free survival, distant metastasis-free survival, and treatment-related toxicity will be the secondary endpoints. Discussion This study aid in further understanding the effects of consolidation chemotherapy after dCCRT in patients with locally advanced, inoperable ESCC. Trial registration ChiCTR1800017646.

Chemoimmunotherapy has emerged as a promising strategy for improving the efficacy of cancer treatment. Herein, we present PD-1 receptor-presenting membrane-coated paclitaxel dimers nanoparticles (PD-1@PTX2 NPs) for enhanced treatment efficacy. PD-1 cell membrane-cloaked PTX dimer exhibited effective cellular uptake and increased cytotoxicity against cancer cells. PD-1@PTX2 NPs could selectively bind with PD-L1 ligands expressed on breast cancer cells. Our nanoparticles exhibit a remarkable tumor growth inhibition rate of 71.3% in mice bearing 4T1 xenografts and significantly prolong survival in mouse models of breast cancer. Additionally, our nanoparticles promoted a significant 3.2-fold increase in CD8+ T cell infiltration and 73.7% regulatory T cell (Treg) depletion within tumors, boosting a robust antitumor immune response. These findings underscore the potential of utilizing immune checkpoint receptor-presented PTX nanoparticles to enhance the efficacy of chemoimmunotherapy, providing an alternative approach for improving cancer treatment.

Hyperspectral remote sensing technology plays a vital role in advancing modern precision agriculture due to its non-destructive and efficient nature. To achieve accurate monitoring of winter wheat chlorophyll content, this study utilized 68 sets of chlorophyll content data and hyperspectral measurements collected during the jointing stage of winter wheat over two consecutive years (2019–2020), under various fertilization types and nitrogen application levels. Continuous wavelet transform was applied to transform the original reflectance, ranging from 21 to 210, and the correlation matrix method was utilized to identify the spectral index at each scale, with the highest correlation to winter wheat chlorophyll content as the optimal spectral index combination input. Subsequently, winter wheat chlorophyll content prediction models were developed using three machine learning methods: random forest (RF), support vector machine (SVM), and a genetic algorithm-optimized backpropagation neural network (GA-BP). The results indicate that the spectral data processed through continuous wavelet transform at seven scales, from 21 to 27, show the highest correlation with winter wheat chlorophyll content at a scale of 26, with a correlation coefficient of 0.738, compared with the correlation of 0.611 of the original reflectance, and the accuracy is improved by 20.7%. The average highest correlation value between the spectral index at scale 26 and winter wheat chlorophyll content is 0.752. As the scale of wavelet transform increases, the correlation between the spectral index and winter wheat chlorophyll content and the accuracy of the predictive model show a trend of first increasing and then decreasing. The optimal input variables for predicting winter wheat chlorophyll content and the best machine learning method are the spectral data at a scale of 26 processing combined with the GA-BP model. The optimal predictive model has a validation set coefficient of determination (R2) of 0.859, root mean square error (RMSE) of 1.366, and mean relative error (MRE) of 2.920%. The results show that the prediction model can provide a technical basis for improving the hyperspectral inversion accuracy of winter wheat chlorophyll and modern precision agriculture.

Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021–2022), soybean (Glycine max L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients (p < 0.05) were selected as input variables for the model (combination 1: vegetation indices; combination 2: texture features; combination 3: randomly extracted texture indices in combination; combination 4: combination of vegetation indices, texture features, and randomly extracted texture indices). Subsequently, extreme learning machine (ELM), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN) were utilized to model the leaf moisture content. The results indicated that most vegetation indices exhibited higher correlation coefficients with soybean leaf moisture compared with texture features, while randomly extracted texture indices could enhance the correlation with soybean leaf moisture to some extent. RDTI, the random combination texture index, showed the highest correlation coefficient with leaf moisture at 0.683, with the texture combination being Variance1 and Correlation5. When combination 4 (combination of vegetation indices, texture features, and randomly extracted texture indices) was utilized as the input and the XGBoost model was employed for soybean leaf moisture monitoring, the highest level was achieved in this study. The coefficient of determination (R2) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth.

Nitrogen is a fundamental component for building amino acids and proteins, playing a crucial role in the growth and development of plants. Leaf nitrogen concentration (LNC) serves as a key indicator for assessing plant growth and development. Monitoring LNC provides insights into the absorption and utilization of nitrogen from the soil, offering valuable information for rational nutrient management. This, in turn, contributes to optimizing nutrient supply, enhancing crop yields, and minimizing adverse environmental impacts. Efficient and non-destructive estimation of crop LNC is of paramount importance for on-field crop management. Spectral technology, with its advantages of repeatability and high-throughput observations, provides a feasible method for obtaining LNC data. This study explores the responsiveness of spectral parameters to soybean LNC at different vertical scales, aiming to refine nitrogen management in soybeans. This research collected hyperspectral reflectance data and LNC data from different leaf layers of soybeans. Three types of spectral parameters, nitrogen-sensitive empirical spectral indices, randomly combined dual-band spectral indices, and “three-edge” parameters, were calculated. Four optimal spectral index selection strategies were constructed based on the correlation coefficients between the spectral parameters and LNC for each leaf layer. These strategies included empirical spectral index combinations (Combination 1), randomly combined dual-band spectral index combinations (Combination 2), “three-edge” parameter combinations (Combination 3), and a mixed combination (Combination 4). Subsequently, these four combinations were used as input variables to build LNC estimation models for soybeans at different vertical scales using partial least squares regression (PLSR), random forest (RF), and a backpropagation neural network (BPNN). The results demonstrated that the correlation coefficients between the LNC and spectral parameters reached the highest values in the upper soybean leaves, with most parameters showing significant correlations with the LNC (p < 0.05). Notably, the reciprocal difference index (VI6) exhibited the highest correlation with the upper-layer LNC at 0.732, with a wavelength combination of 841 nm and 842 nm. In constructing the LNC estimation models for soybeans at different leaf layers, the accuracy of the models gradually improved with the increasing height of the soybean plants. The upper layer exhibited the best estimation performance, with a validation set coefficient of determination (R2) that was higher by 9.9% to 16.0% compared to other layers. RF demonstrated the highest accuracy in estimating the upper-layer LNC, with a validation set R2 higher by 6.2% to 8.8% compared to other models. The RMSE was lower by 2.1% to 7.0%, and the MRE was lower by 4.7% to 5.6% compared to other models. Among different input combinations, Combination 4 achieved the highest accuracy, with a validation set R2 higher by 2.3% to 13.7%. In conclusion, by employing Combination 4 as the input, the RF model achieved the optimal estimation results for the upper-layer LNC, with a validation set R2 of 0.856, RMSE of 0.551, and MRE of 10.405%. The findings of this study provide technical support for remote sensing monitoring of soybean LNCs at different spatial scales.

The pathogens responsible for porcine viral diarrhea are diverse, causing significant economic losses to the pig industry. PEDV and TGEV are well-known pathogens causing diarrheal diseases in pigs, leading to significant economic losses in the breeding industry. In contrast, the newly identified diarrhea virus, PKV, has not garnered as much attention. However, co-infection of PKV with PEDV results in more severe symptoms in piglets, such as acute gastroenteritis, and promotes increased replication of PEDV. Rapid and accurate diagnosis of viral diarrhea is essential for farms to identify pathogens early and mitigate economic losses. This study describes the development of a triplex real-time fluorescent quantitative RT-qPCR technique that can simultaneously detect three RNA viruses associated with porcine viral diarrhea: PEDV, TGEV, and PKV. To establish the triplex RT-qPCR method for the simultaneous detection and identification of the above three diarrhea viruses, conserved regions of the M gene of TGEV, the N gene of PEDV, and the 3D gene of PKV were selected to design specific primers and probes. After optimizing the reaction conditions, the method’s specificity, sensitivity, and reproducibility were evaluated. The triplex RT-qPCR method did not show a significant difference in PCR efficiency compared to the single RT-qPCR method. The method is specific to TGEV, PKV, and PEDV, exhibits no cross-reactivity with other pathogens, and demonstrates satisfactory sensitivity and reproducibility; the limit of detection (LOD) of PEDV, TGEV, and PKV is 11.42 copies/μL. Furthermore, the performance of the triplex RT-qPCR assay was compared with the Chinese standard single-assay method for detecting TGEV, PKV, and PEDV, showing complete consistency between the two methods (100% compliant). Subsequently, 1502 clinical diarrhea samples were collected from the Guangxi Zhuang Autonomous Region to investigate the local prevalence of TGEV, PKV, and PEDV and the positive rates were 16.38% (246/1502), 1.46% (22/1502), and 45.14% (678/1502), respectively. Co-infection of PEDV and PKV were most common, with a rate of 12.12% (182/1502). This study presents a valuable method for the rapid and simultaneous identification of PEDV, TGEV, and PKV in clinical animal farming practices, and provides a reassessment of the epidemiology of these diarrhea-causing viral pathogens in the Guangxi Zhuang Autonomous Region.

Ulcerative colitis (UC) is one of the primary inflammatory bowel diseases (IBDs) and causes a serious threat to human public health around the world. Currently, there are no proven safe and effective treatment options to treat UC. Fraxetin (Fxt) is a widely recognized antioxidant and anti-inflammatory legume derived from ash bark. In the present study, we investigated the protective effect and mechanism of Fxt on UC. Our results showed that Fxt significantly attenuated the body weight, colon length reduction, tissue damage, and disease activity index induced by dextran sodium sulphate (DSS). Moreover, the DSS-induced activation of the NF-κB pathway and NLRP3 inflammasomes was inhibited, and the inflammatory response was reduced. Fxt restored gut barrier function by increasing the number of goblet cells and the levels of tight junction proteins (ZO-1 and occludin). In addition, Fxt can alter the intestinal microbiota by enhancing the diversity of the microbiota, increasing the relative abundance of beneficial bacteria and inhibiting the growth of harmful bacteria. These results revealed that Fxt alleviates DSS-induced colitis by modulating the inflammatory response, enhancing epithelial barrier integrity and regulating the gut microbiota. This study may provide a scientific basis for the potential therapeutic effect of Fxt in the prevention of colitis and other related diseases.

Respiratory illnesses present a significant threat to porcine health, with co-infections involving Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), Streptococcus suis (SS), Porcine Circovirus Type 2 (PCV2), and Porcine Circovirus Type 3 (PCV3) acting as the primary causative agents. As a result, the precise diagnosis of PRRSV, PCV2, PCV3 and SS is of paramount importance in the prevention and control of respiratory diseases in swine. Therefore, we conducted a molecular bioinformatical analysis to concurrently detect and differentiate PRRSV, PCV2, PCV3 and SS. We selected the ORF6 gene of PRRSV, the ORF2 gene of PCV2 and PCV3, and the glutamate dehydrogenase (GDH) gene of SS as targets. Specific primers and probes were designed for each pathogen, and following meticulous optimization of reaction conditions, we established a multiple TaqMan fluorescence quantitative PCR detection method. Subsequently, we subjected this method to a comprehensive assessment, evaluating its specificity, sensitivity, and repeatability. The research results demonstrated that the established multiple TaqMan fluorescence quantitative PCR detection method displays displayed exemplary specificity, with no instances of cross-reactivity with other pathogens. The method’s minimum detection concentrations for PRRSV, PCV2, PCV3, and SS were 2.80 × 101 copies/µL, 1.96 × 102 copies/µL, 2.30 × 102 copies/µL, and 1.75 × 103 copies/µL, respectively. When applied to the analysis of 30 clinical samples, the results closely mirrored those obtained through Chinese standard uniplex real-time qPCR detection method for PRRSV, as well as the general PCR methods for SS, PCV2, and PCV3. This study underscores the robust specificity, high sensitivity, and consistent stability of the multiple TaqMan fluorescence quantitative PCR detection method that we have developed. It is ideally suited to the clinical monitoring of PRRSV, PCV2, PCV3, and SS, and it carries significant importance in ongoing efforts to prevent and manage respiratory diseases in porcine populations.

Vibrio species are prevalent in ocean ecosystems, particularly Vibrio coralliilyticus, and pose a threat to corals and other marine organisms under global warming conditions. While microbiota manipulation is considered for coral disease management, understanding the role of commensal bacteria in stress resilience remains limited. Here, a single bacterial species (Ruegeria profundi) rather than a consortium of native was used to combat pathogenic V. coralliilyticus and protect corals from bleaching. R. profundi showed therapeutic activity in vivo, preventing a significant reduction in bacterial diversity in bleached corals. Notably, the structure of the bacterial community differed significantly among all the groups. In addition, compared with the bleached corals caused by V. coralliilyticus, the network analysis revealed that complex interactions and positive correlations in the bacterial community of the R. profundi protected non-bleached corals, indicating R. profundi's role in fostering synergistic associations. Many genera of bacteria significantly increased in abundance during V. coralliilyticus infection, including Vibrio, Alteromonas, Amphritea, and Nautella, contributing to the pathogenicity of the bacterial community. However, R. profundi effectively countered the proliferation of these genera, promoting potential probiotic Endozoicomonas and other taxa, while reducing the abundance of betaine lipids and the type VI section system of the bacterial community. These changes ultimately influenced the interactive relationships among symbionts and demonstrated that probiotic R. profundi intervention can modulate coral-associated bacterial community, alleviate pathogenic-induced dysbiosis, and preserve coral health. These findings elucidated the relationship between the behavior of the coral-associated bacterial community and the occurrence of pathological coral bleaching. IMPORTANCE Changes in the global climate and marine environment can influence coral host and pathogen repartition which refers to an increased likelihood of pathogen infection in hosts. The risk of Vibrio coralliilyticus-induced coral disease is significantly heightened, primarily due to its thermos-dependent expression of virulent and populations. This study investigates how coral-associated bacterial communities respond to bleaching induced by V. coralliilyticus. Our findings demonstrate that Ruegeria profundi exhibits clear evidence of defense against pathogenic bacterial infection, contributing to the maintenance of host health and symbiont homeostasis. This observation suggests that bacterial pathogens could cause dysbiosis in coral holobionts. Probiotic bacteria display an essential capability in restructuring and manipulating coral-associated bacterial communities. This restructuring effectively reduces bacterial community virulence and enhances the pathogenic resistance of holobionts. The study provides valuable insights into the correlation between the health status of corals and how coral-associated bacterial communities may respond to both pathogens and probiotics. [ABSTRACT FROM AUTHOR]