Multi-omics analysis reveals the biosynthesis of flavonoids during the browning process of Malus sieversii explants

Malus sieversii is a precious apple germplasm resource. Browning of explants is one of the most important factors limiting the survival rate of plant tissue culture. In order to explore the molecular mechanism of the browning degree of different strains of Malus sieversii, we compared the dynamic changes of Malus sieversii and Malus robusta Rehd. during the whole browning process using a multi-group method. A total of 44 048 differentially expressed genes (DEGs) were identified by transcriptome analysis on the DNBSEQ-T7 sequencing platform. KEGG enrichment analysis showed that the DEGs were significantly enriched in the flavonoid biosynthesis pathway. In addition, metabonomic analysis showed that (-)-epicatechin, astragalin, chrysin, irigenin, isoquercitrin, naringenin, neobavaisoflavone and prunin exhibited different degrees of free radical scavenging ability in the tissue culture browning process, and their accumulation in different varieties led to differences in the browning degree among varieties. Comprehensive transcriptome and metabonomics analysis of the data related to flavonoid biosynthesis showed that PAL, 4CL, F3H, CYP73A, CHS, CHI, ANS, DFR and PGT1 were the key genes for flavonoid accumulation during browning. In addition, WGCNA analysis revealed a strong correlation between the known flavonoid structure genes and the selected transcriptional genes. Protein interaction predictions demonstrated that 19 transcription factors (7 MYBs and 12 bHLHs) and 8 flavonoid structural genes had targeted relationships. The results show that the interspecific differential expression of flavonoid genes is the key influencing factor of the difference in browning degree between Malus sieversii and Malus robusta Rehd., providing a theoretical basis for further study on the regulation of flavonoid biosynthesis.

Analysis of flavonoid metabolism of compounds in succulent fruits and leaves of three different colors of Rosaceae

Red flesh apple (Malus pumila var. medzwetzkyana Dieck), purple leaf plum (Prunus cerasifera Ehrhar f), and purple leaf peach (Prunus persica 'Atropurpurea') are significant ornamental plants within the Rosaceae family. The coloration of their fruits and leaves is crucial in their appearance and nutritional quality. However, qualitative and quantitative studies on flavonoids in the succulent fruits and leaves of multicolored Rosaceae plants are lacking. To unveil the diversity and variety-specificity of flavonoids in these three varieties, we conducted a comparative analysis of flavonoid metabolic components using ultra-high-performance liquid phase mass spectrometry (UPLC-MS/MS). The results revealed the detection of 311 metabolites, including 47 flavonoids, 105 flavonols, 16 chalcones, 37 dihydroflavonoids, 8 dihydroflavonols, 30 anthocyanins, 14 flavonoid carbon glycosides, 23 flavanols, 8 isoflavones, 11 tannins, and 12 proanthocyanidins. Notably, although the purple plum and peach leaves exhibited distinct anthocyanin compounds, paeoniflorin and corythrin glycosides were common but displayed varying glycosylation levels. While the green purple leaf peach fruit (PEF) and red flesh apple leaf (AL) possessed the lowest anthocyanin content, they exhibited the highest total flavonoid content. Conversely, the red flesh apple fruit (AF) displayed the highest anthocyanin content and a diverse range of anthocyanin glycosylation modifications, indicating that anthocyanins predominantly influenced the fruit's color. Purple PLF, PLL, and PEL showcased varying concentrations of anthocyanins, suggesting that their colors result from the co-color interaction between specific types of anthocyanins and secondary metabolites, such as flavonols, flavonoids, and dihydroflavonoids. This study provides novel insights into the variations in tissue metabolites among Rosaceae plants with distinct fruit and leaf colors.

A Comparative Study of Deep Learning Dose Prediction Models for Cervical Cancer Volumetric Modulated Arc Therapy

Purpose: Deep learning (DL) is widely used in dose prediction for radiation oncology, multiple DL techniques comparison is often lacking in the literature. To compare the performance of 4 state-of-the-art DL models in predicting the voxel-level dose distribution for cervical cancer volumetric modulated arc therapy (VMAT). Methods and Materials: A total of 261 patients' plans for cervical cancer were retrieved in this retrospective study. A three-channel feature map, consisting of a planning target volume (PTV) mask, organs at risk (OARs) mask, and CT image was fed into the three-dimensional (3D) U-Net and its 3 variants models. The data set was randomly divided into 80% as training-validation and 20% as testing set, respectively. The model performance was evaluated on the 52 testing patients by comparing the generated dose distributions against the clinical approved ground truth (GT) using mean absolute error (MAE), dose map difference (GT-predicted), clinical dosimetric indices, and dice similarity coefficients (DSC). Results: The 3D U-Net and its 3 variants DL models exhibited promising performance with a maximum MAE within the PTV 0.83% ± 0.67% in the UNETR model. The maximum MAE among the OARs is the left femoral head, which reached 6.95% ± 6.55%. For the body, the maximum MAE was observed in UNETR, which is 1.19 ± 0.86%, and the minimum MAE was 0.94 ± 0.85% for 3D U-Net. The average error of the Dmean difference for different OARs is within 2.5 Gy. The average error of V40 difference for the bladder and rectum is about 5%. The mean DSC under different isodose volumes was above 90%. Conclusions: DL models can predict the voxel-level dose distribution accurately for cervical cancer VMAT treatment plans. All models demonstrated almost analogous performance for voxel-wise dose prediction maps. Considering all voxels within the body, 3D U-Net showed the best performance. The state-of-the-art DL models are of great significance for further clinical applications of cervical cancer VMAT.

Experimental study on the gas desorption law in coal affected by dynamic water injection

Water from hydraulic technology affects the desorption of gas from coal seams. Gas desorption behavior is critical information for gas control in coal mines. In this study, a designed coal seam water injection simulation experimental device was utilized to conduct dynamic water injection experiments on coal samples at different adsorption equilibrium pressures, analyzing the gas desorption law under dynamic water injection, as well as the role of water replacement, water imbibition and water blockage in gas desorption. The results showed that water altered the gas desorption rate in coal, causing fluctuating attenuation of the desorption rate of a water-injected coal sample (WCS). Under the same adsorption equilibrium pressure, the relationship between the desorption rate of the WCS and the non-water-injected coal samples (NCS) underwent a transition in desorption time. In contrast to the NCS desorption curves, the WCS desorption curves lacked a rapid growth phase and exhibited only a slow growth phase and a stopping phase. Water imbibition and water replacement promoted the desorption of gas in the non-wet area during the water injection process, while it inhibited the desorption of gas in the wet area. Under the effects of water imbibition, water blockage, and water replacement, the discharge rate of WCS is greater than the desorption rate of NCS, indicating that water injection increases the total amount of gas desorption. The study results have significant implications for gas extraction and the prevention and control of coal and gas outbursts.

[Research advances of natural biomaterials in promoting wound repair]

Acute and chronic wounds seriously threaten patients' life health and quality of life, therefore, wound repair has become a hot topic of research for scholars at home and abroad in recent years. With the development of material science and tissue engineering, more and more biomaterials prepared from natural ingredients were used in basic research and clinical treatment of wound repair. Such biomaterials can be used as templates for wound tissue regeneration to induce autologous cell adhesion and migration, and promote the deposition of extracellular matrix, which have broad clinical application prospects. This paper reviews the characteristics and application advance of natural biomaterials which are popular in the field of wound repair, aiming to provide ideas for the research and development of new wound dressing and tissue engineering skin.

Impaired coronary flow reserve in patients with supra-normal left ventricular ejection fraction at rest

PURPOSE

Recently, a "U" hazard ratio curve between resting left ventricular ejection fraction (LVEF) and prognosis has been observed in patients referred for routine clinical echocardiograms. The present study sought to explore whether a similar "U" curve existed between resting LVEF and coronary flow reserve (CFR) in patients without severe cardiovascular disease (CVD) and whether impaired CFR played a role in the adverse outcome of patients with supra-normal LVEF (snLVEF, LVEF ≥ 65%).

METHODS

Two hundred ten consecutive patients (mean age 52.3 ± 9.3 years, 104 women) without severe CVD underwent clinically indicated rest/dipyridamole stress electrocardiography (ECG)-gated 13 N-ammonia positron emission tomography/computed tomography (PET/CT). Major adverse cardiac events (MACE) were followed up for 27.3 ± 9.5 months, including heart failure, late revascularization, re-hospitalization, and re-coronary angiography for any cardiac reason. Clinical characteristics, corrected CFR (cCFR), and MACE were compared among the three groups categorized by resting LVEF detected by PET/CT. Dose-response analyses using restricted cubic spline (RCS) functions, multivariate logistic regression, and Kaplan-Meier survival analysis were conducted to evaluate the relationship between resting LVEF and CFR/outcome.

RESULTS

An inverted "U" curve existed between resting LVEF and cCFR (p = 0.06). Both patients with snLVEF (n = 38) and with reduced LVEF (rLVEF, LVEF < 55%) (n = 66) displayed a higher incidence of reduced cCFR than those with normal LVEF (nLVEF, 55% ≤ LVEF < 65%) (n = 106) (57.9% vs 54.5% vs 34.3%, p < 0.01, respectively). Both snLVEF (p < 0.01) and rLVEF (p < 0.05) remained independent predictors for reduced cCFR after multivariable adjustment. Patients with snLVEF encountered more MACE than those with nLVEF (10.5% vs 0.9%, log-rank p = 0.01).

CONCLUSIONS

Patients with snLVEF are prone to impaired cCFR, which may be related to the adverse prognosis. Further investigations are warranted to explore its underlying pathological mechanism and clinical significance.

Differential early diagnosis of benign versus malignant lung cancer using systematic pathway flux analysis of peripheral blood leukocytes

Early diagnosis of lung cancer is critically important to reduce disease severity and improve overall survival. Newer, minimally invasive biopsy procedures often fail to provide adequate specimens for accurate tumor subtyping or staging which is necessary to inform appropriate use of molecular targeted therapies and immune checkpoint inhibitors. Thus newer approaches to diagnosis and staging in early lung cancer are needed. This exploratory pilot study obtained peripheral blood samples from 139 individuals with clinically evident pulmonary nodules (benign and malignant), as well as ten healthy persons. They were divided into three cohorts: original cohort (n = 99), control cohort (n = 10), and validation cohort (n = 40). Average RNAseq sequencing of leukocytes in these samples were conducted. Subsequently, data was integrated into artificial intelligence (AI)-based computational approach with system-wide gene expression technology to develop a rapid, effective, non-invasive immune index for early diagnosis of lung cancer. An immune-related index system, IM-Index, was defined and validated for the diagnostic application. IM-Index was applied to assess the malignancies of pulmonary nodules of 109 participants (original + control cohorts) with high accuracy (AUC: 0.822 [95% CI: 0.75-0.91, p < 0.001]), and to differentiate between phases of cancer immunoediting concept (odds ratio: 1.17 [95% CI: 1.1-1.25, p < 0.001]). The predictive ability of IM-Index was validated in a validation cohort with a AUC: 0.883 (95% CI: 0.73-1.00, p < 0.001). The difference between molecular mechanisms of adenocarcinoma and squamous carcinoma histology was also determined via the IM-Index (OR: 1.2 [95% CI 1.14-1.35, p = 0.019]). In addition, a structural metabolic behavior pattern and signaling property in host immunity were found (bonferroni correction, p = 1.32e - 16). Taken together our findings indicate that this AI-based approach may be used for "Super Early" cancer diagnosis and amend the current immunotherpay for lung cancer.

Development and validation of prognostic model for predicting mortality of COVID-19 patients in Wuhan, China

Novel coronavirus 2019 (COVID-19) infection is a global public health issue, that has now affected more than 200 countries worldwide and caused a second wave of pandemic. Severe adult respiratory syndrome-CoV-2 (SARS-CoV-2) pneumonia is associated with a high risk of mortality. However, prognostic factors predicting poor clinical outcomes of individual patients with SARS-CoV-2 pneumonia remain under intensive investigation. We conducted a retrospective, multicenter study of patients with SARS-CoV-2 who were admitted to four hospitals in Wuhan, China from December 2019 to February 2020. Mortality at the end of the follow up period was the primary outcome. Factors predicting mortality were also assessed and a prognostic model was developed, calibrated and validated. The study included 492 patients with SARS-CoV-2 who were divided into three cohorts: the training cohort (n = 237), the validation cohort 1 (n = 120), and the validation cohort 2 (n = 135). Multivariate analysis showed that five clinical parameters were predictive of mortality at the end of follow up period, including advanced age [odds ratio (OR), 1.1/years increase (p < 0.001)], increased neutrophil-to-lymphocyte ratio [(NLR) OR, 1.14/increase (p < 0.001)], elevated body temperature on admission [OR, 1.53/°C increase (p = 0.005)], increased aspartate transaminase [OR, 2.47 (p = 0.019)], and decreased total protein [OR, 1.69 (p = 0.018)]. Furthermore, the prognostic model drawn from the training cohort was validated with validation cohorts 1 and 2 with comparable area under curves (AUC) at 0.912, 0.928, and 0.883, respectively. While individual survival probabilities were assessed, the model yielded a Harrell's C index of 0.758 for the training cohort, 0.762 for the validation cohort 1, and 0.711 for the validation cohort 2, which were comparable among each other. A validated prognostic model was developed to assist in determining the clinical prognosis for SARS-CoV-2 pneumonia. Using this established model, individual patients categorized in the high risk group were associated with an increased risk of mortality, whereas patients predicted to be in the low risk group had a higher probability of survival.