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Abstract Defoliation is a primary agronomic traits, its variation depends on different plant species or cultivars. The present article assess the leaf morphological responses, oxidative metabolites and enzymatic activities at sheath base of sugarcane cultivars during defoliation stage of plant leaves. The mature leaf sheath of GT47 strongly wrapped to the stem, and no stem was exposed. The upper and lower edges of the immature fusing abscission zone were parallel, and slightly lower browning area (+ 3 to + 7 leaf position). The ROC22 cultivar was monitored highest leaf sheath-based cellulose and lignin content, followed by GT60 and GT47. Peroxidase activity was higher in leaf sheath base edge (ROC22) as compare to other cultivars. The malondialdehyde content was found highest in GT60, followed by ROC22, and GT47. The exo-β-1,4-glucanase/ cellobiohydrolase activity was found highest in the margin of GT47 than lateral and medial axis of ROC22 and GT60. The axis activity increased exponentially, and ROC22 gradually decreased from the periphery of the mid-axis and lower than GT47 and GT60 in the lateral and mid-axis of leaf. In conclusion, the mature leaves are easy to defoliate mainly loose leaf sheaths, large leaf sheath inclination angles, more deformation during the growth period of the abscission zone, early with large cracks, and slow browning process. Leaf sheaths with high fibre and lignin content showed significant hardness and thickness. The sugarcane cultivars showed positive correlation between peroxidase and malondialdehyde content with the browning process at the base of mature leaf sheaths.

Abstract Continuous renal replacement therapy (CRRT) is a life-saving procedure for sepsis but the benefit of CRRT varies and prediction of clinical outcomes is valuable in efficient treatment planning. This study aimed to use machine learning (ML) models trained using MIMIC III data for identifying sepsis patients who would benefit from CRRT. We first selected patients with sepsis and CRRT in the ICU setting and their gender, and an array of routine lab results were included as features to train machine learning models using 30-day mortality as the primary outcome. A total of 4161 patients were included for analysis, among whom there were 1342 deaths within 30 days. Without data augmentation, extreme gradient boosting (XGBoost) showed an accuracy of 64.2% with AUC-ROC of 0.61. Data augmentation using a conditional generative adversarial neural network (c-GAN) resulted in a significantly improved accuracy (82%) and ROC-AUC (0.78%). To enable prediction on pediatric patients, we adopted transfer learning approaches, where the weights of all but the last hidden layer were fixed, followed by fine-tuning of the weights of the last hidden layer using pediatric data of 200 patients as the inputs. A significant improvement was observed using the transfer learning approach (AUCROC = 0.76) compared to direct training on the pediatric cohort (AUCROC = 0.62). Through this transfer-learning-facilitated patient outcome prediction, our study showed that ML can aid in clinical decision-making by predicting patient responses to CRRT for managing pediatric sepsis.

In this study, the effect of recombinant porcine interferon alpha (rPoIFNα) on porcine transmissible gastroenteritis virus inactivated vaccine (TGEV IV) in terms of immunological augmentation was examined. Seven experimental piglet groups, including PBS group, rPoIFNα group, inactivated vaccine (IV) alone group, 4.0x104 U rPoIFNα+IV group, 2.0x105 U rPoIFNα+IV group,1.0x106 U rPoIFNα+IV group, 5.0x106 U rPoIFNα+IV group, were divided. The piglets in each group received a secondary vaccination at 28 days following the initial immunization. By using the ELISA assay, neutralization assay, MTT assay, and flow cytometry, we measured anti- TGEV-specific antibody expressions, neutralization antibodies, as well as lymphocyte proliferation index (Stimulation index, SI), specific IL-4 and IFN-γ production, and T cell subpopulations (CD3+, CD4+, and CD8+). Piglets injected with IV supplemented with rPoIFNα at 1.0x106 U or 5.0x106 U developed significantly higher anti-TGEVspecific and neutralizing antibodies compared to those treated with IV alone. IV therapy with rPoIFNα at 1.0x106 U or 5.0x106 U can boost cellular immunity against TGEV by increasing SI, IL-4, IFN-γ, and the ratio of CD3+, CD4+, and CD8+ cell subgroups. The IV+5.0x106 U rPoIFNα group showed a considerably larger immune increase than the IV+2.0x105 U rPoIFNα group, suggesting that it works in a dose-dependent manner. Therefore, rPoIFNα at 1.0x106 U or 5.0x106 U enhances the immune response against TGEV IV and may function as an immune stimulant.

Abstract Tertiary lymphoid structures are immune cell aggregates linked with cancer outcomes, but their interactions with tumour cell aggregates are unclear. Using nasopharyngeal carcinoma as a model, here we analyse single-cell transcriptomes of 343,829 cells from 77 biopsy and blood samples and spatially-resolved transcriptomes of 31,316 spots from 15 tumours to decipher their components and interactions with tumour cell aggregates. We identify essential cell populations in tertiary lymphoid structure, including CXCL13+ cancer-associated fibroblasts, stem-like CXCL13+CD8+ T cells, and B and T follicular helper cells. Our study shows that germinal centre reaction matures plasma cells. These plasma cells intersperse with tumour cell aggregates, promoting apoptosis of EBV-related malignant cells and enhancing immunotherapy response. CXCL13+ cancer-associated fibroblasts promote B cell adhesion and antibody production, activating CXCL13+CD8+ T cells that become exhausted in tumour cell aggregates. Tertiary lymphoid structure-related cell signatures correlate with prognosis and PD-1 blockade response, offering insights for therapeutic strategies in cancers.

Abstract Background The diagnosis of tuberculous pleurisy (TP) presents a significant challenge due to the low bacterial load in pleural effusion (PE) samples. Cell-free Mycobacterium tuberculosis DNA (cf-TB) in PE samples is considered an optimal biomarker for diagnosing TP. This study aimed to evaluate the applicability of cf-TB testing across diverse research sites with a relatively large sample size. Methods Patients suspected of TP and presenting with clinical symptoms and radiological evidence of PE were consecutively enrolled by treating physicians from 11 research sites across 6 provinces in China between April 2020 and August 2022. Following centrifugation, sediments obtained from PE were used for Xpert MTB/RIF (Xpert) and mycobacterial culture, while the supernatants were subjected to cf-TB testing. This study employed a composite reference standard to definite TP, which was characterized by any positive result for Mycobacterium tuberculosis (MTB) through either PE culture, PE Xpert, or pleural biopsy. Results A total of 1412 participants underwent screening, and 1344 (95.2%) were subsequently enrolled in this study. Data from 1241 (92.3%) participants were included, comprising 284 with definite TP, 677 with clinically diagnosed TP, and 280 without TP. The sensitivity of cf-TB testing in definite TP was 73.6% (95% CI 68.2–78.4), significantly higher than both Xpert (40.8%, 95% CI 35.3–46.7, P

Abstract Background Due to regional and cultural differences, the current status of extremely preterm infants(EPIs) treatment across different areas of mainland China remains unclear. This study investigated the survival rate and incidence of major diseases among EPIs in the southwest area of Fujian province. Method This retrospective and multicenter study collected perinatal data from EPIs with gestational ages between 22–27+ 6w and born in the southwest area of Fujian province. The study population was divided into 6 groups based on gestational age at delivery. The primary outcome was the survival status at ordered hospital discharge or correct gestational age of 40 weeks, and the secondary outcome was the incidence of major diseases. The study analyzed the actual survival status of EPIs in the area. Result A total of 2004 preterm infants with gestational ages of 22–27+ 6 weeks were enrolled in this study. Among them, 1535 cases (76.6%) were born in the delivery room but did not survive, 469 cases (23.4%) were transferred to the neonatal department for treatment, 101 cases (5.0%) received partial treatment, and 368 cases (18.4%) received complete treatment. The overall all-cause mortality rate was 84.4% (1691/2004). The survival rate and survival rate without major serious disease for EPIs who received complete treatment were 85.1% (313/368) and 31.5% (116/318), respectively. The survival rates for gestational ages 22–22+ 6w, 23–23+ 6w, 24–24+ 6w, 25–25+ 6w, 26–26+ 6w, and 27–27+ 6w were 0%, 0%, 59.1% (13/22), 83% (39/47), 88.8% (87/98), and 89.7% (174/198), respectively. The survival rates without major serious disease were 0%, 0%, 9.1% (2/22), 19.1% (9/47), 27.6% (27/98), and 40.2% (78/194), respectively. Conclusion The all-cause mortality of EPIs in the southwest area of Fujian Province remains high, with a significant number of infants were given up after birth in the delivery room being the main influencing factor. The survival rate of EPIs who received complete treatment at 25–27 weeks in the NICU was similar to that in developed countries. However, the survival rate without major serious disease was significantly lower compared to high-income countries.

Abstract The aim of this study was to develop a real-time risk prediction model for extrauterine growth retardation (EUGR). A total of 2514 very preterm infants were allocated into a training set and an external validation set. The most appropriate independent variables were screened using univariate analysis and Lasso regression with tenfold cross-validation, while the prediction model was designed using binary multivariate logistic regression. A visualization of the risk variables was created using a nomogram, while the calibration plot and receiver operating characteristic (ROC) curves were used to calibrate the prediction model. Clinical efficacy was assessed using the decision curve analysis (DCA) curves. Eight optimal predictors that namely birth weight, small for gestation age (SGA), hypertensive disease complicating pregnancy (HDCP), gestational diabetes mellitus (GDM), multiple births, cumulative duration of fasting, growth velocity and postnatal corticosteroids were introduced into the logistic regression equation to construct the EUGR prediction model. The area under the ROC curve of the training set and the external verification set was 83.1% and 84.6%, respectively. The calibration curve indicate that the model fits well. The DCA curve shows that the risk threshold for clinical application is 0–95% in both set. Introducing Birth weight, SGA, HDCP, GDM, Multiple births, Cumulative duration of fasting, Growth velocity and Postnatal corticosteroids into the nomogram increased its usefulness for predicting EUGR risk in very preterm infants.