GSH exhaustion via inhibition of xCT-GSH-GPX4 pathway synergistically enhanced DSF/Cu-induced cuproptosis in myelodysplastic syndromes.

The clinical application of the therapeutic approach in myelodysplastic syndromes (MDS) remains an insurmountable challenge for the high propensity for progressing to acute myeloid leukemia and predominantly affecting elderly individuals. Thus, the discovery of molecular mechanisms underlying the regulatory network of different programmed cell death holds great promise for the identification of therapeutic targets and provides insights into new therapeutic avenues. Herein, we found that disulfiram/copper (DSF/Cu) significantly repressed the cell viability, increased reactive oxygen species (ROS) accumulation, destroyed mitochondrial morphology, and altered oxygen consumption rate. Further studies verified that DSF/Cu induces cuproptosis, as evidenced by the depletion of glutathione (GSH), aggregation of lipoylated DLAT, and induced loss of Fe-S cluster-containing proteins, which could be rescued by tetrathiomolybdate and knockdown of ferredoxin 1 (FDX1). Additionally, GSH contributed to the tolerance of DSF/Cu-mediated cuproptosis, while pharmacological chelation of GSH triggered ROS accumulation and sensitized cell death. The xCT-GSH-GPX4 axis is the ideal downstream component of ferroptosis that exerts a powerful protective mechanism. Notably, classical xCT inhibitors were capable of leading to the catastrophic accumulation of ROS and exerting synergistic cell death, while xCT overexpression restored these phenomena. Simvastatin, an inhibitor of HMG-CoA (3-hydroxy-3-methylglutaryl-coenzyme A) reductase, has beneficial effects in repurposing for inhibiting GPX4. Similarly, the combination treatment of DSF/Cu and simvastatin dramatically decreased the expression of GPX4 and Fe-S proteins, ultimately accelerating cell death. Moreover, we identified that the combination treatment of DSF/Cu and simvastatin also had a synergistic antitumor effect in the MDS mouse model, with the reduced GPX4, increased COX-2 and accumulated lipid peroxides. Overall, our study provided insight into developing a novel synergistic strategy to sensitize MDS therapy by targeting ferroptosis and cuproptosis.

Screening candidate genes for fruit size based on QTL-seq in Chinese jujube.

Introduction: Fruit size is an important economic trait affecting jujube fruit quality, which has always been the focus of marker-assisted breeding of jujube traits. However, despite a large number of studies have been carried out, the mechanism and key genes regulating jujube fruit size are mostly unknown. Methods: In this study, we used a new analysis method Quantitative Trait Loci sequencing (QTL-seq) (bulked segregant analysis) to screen the parents 'Yuhong' and 'Jiaocheng 5' with significant phenotypic differences and mixed offspring group with extreme traits of large fruit and small fruit, respectively, and, then, DNA mixed pool sequencing was carried out to further shortening the QTL candidate interval for fruit size trait and excavated candidate genes for controlling fruit size. Results: The candidate intervals related to jujube fruit size were mainly located on chromosomes 1, 5, and 10, and the frequency of chromosome 1 was the highest. Based on the QTL-seq results, the annotation results of ANNOVAR were extracted from 424 SNPs (single-nucleotide polymorphisms) and 164 InDels (insertion-deletion), from which 40 candidate genes were selected, and 37 annotated candidate genes were found in the jujube genome. Four genes (LOC107428904, LOC107415626, LOC125420708, and LOC107418290) that are associated with fruit size growth and development were identified by functional annotation of the genes in NCBI (National Center for Biotechnology Information). The genes can provide a basis for further exploration and identification on genes regulating jujube fruit size. Discussion: In summary, the data obtained in this study revealed that QTL intervals and candidate genes for fruit size at the genomic level provide valuable resources for future functional studies and jujube breeding.

ZjHXK5 and ZjHXK6 negatively regulate the sugar metabolism of Ziziphus jujuba Mill.

Hexokinase (HXK) plays a crucial role in plants, catalyzing the phosphorylation of hexose substances, which is one of the key steps in sugar metabolism and energy production. While HXK genes have been well-studied in model plants, the evolutionary and functional characteristics of HXK gene family in jujube is unknow. In this study, the HXK gene family members were identified by bioinformatics methods, the key members regulating glucose metabolism were identified by transcriptome data, and finally the function of the key genes was verified by instantaneous and stable genetic transformation. Our results showed that seven HXK genes were identified in the jujube genome, all of which were predict located in the chloroplast and contain Hexokinase-1 (PF00349) and Hexokinase-2 (PF03727) conserved domains. Most of HXK proteins were transmembrane protein with stable, lipid-soluble, hydrophilic. The secondary structure of ZjHXK proteins main α-helix, and contains two distinct tertiary structure. All ZjHXK genes contain nine exons and eight introns. Predictions of cis-regulatory elements indicate that the promoter region of ZjHXK contains a large number of MeJA responsive elements. Finally, combined with the analysis of the relationship between the expression and glucose metabolism, found that ZjHXK5 and ZjHXK6 may the key genes regulating sugar metabolism. Transient overexpression of ZjHXK5 and ZjHXK6 on jujube, or allogeneic overexpression of ZjHXK5 and ZjHXK6 on tomato would significantly reduce the content of total sugar and various sugar components. Transient silencing of ZjHXK5 and ZjHXK6 genes results in a significant increase in sucrose and total sugar content. Interestingly, the expression of ZjHXK5 and ZjHXK6 were also affected by methyl jasmonate.

Study on the spatial specificity of phenolics in fruit of different jujube varieties.

Phenol, an important bioactive substance in jujube fruit, is one of the most important indexes of fruit quality. In order to clarify the phenolic substance composition, content and distribution characteristics of different varieties of jujube fruits, this study measured and compared the content of total phenols, total flavonoids and phenolic substance composition in different parts of fruits of six jujube varieties, and combined with the principal component analysis, the results showed that: Fourteen phenolic substances were identified in the fruits of different jujube varieties, and proanthocyanidins, epicatechin, catechin and rutin were the main components, accounting for 58.60%, 16.08%, 13.56% and 5.57% of the total phenolic substances, respectively. The total phenolic and total flavonoid contents of jujube fruits of each variety were between 18.11 and 21.45 mg GAE/g DW and 10.56-20.25 mg RE/g DW, respectively, and the differences in the contents between the varieties were significant, and the contents of 'Junzao' and 'Jinlingyuanzao' were higher than those of other varieties.The phenolic content of different parts of jujube fruits showed spatial distribution differences, manifested as the total phenols, total flavonoids and their fractions of the peel were significantly higher than that of the pulp, while the content of the pulp near the kernel was significantly higher than that of other parts of the pulp, This study provides a theoretical basis for analysing the differences in phenolic substances in jujube fruits.

Shift work and menstruation: A meta-analysis study.

Background: Shift work is a potential risk factor for women's reproductive health. Evidence suggests that shift work is associated with menstrual disorders, reproductive disturbances, and adverse pregnancy outcomes. However, previous studies did not systematically examine the results of menstrual irregularities, dysmenorrhea, and early menopause at the same time. Objective: To determine the relationship between shift work and women's menstrual characteristics (e.g., irregular menstruation, dysmenorrhea, and early menopause). Methods: Four databases (PubMed, Embase, Cochrane, and Web of Science) were searched up to December 2022. The study characteristics and risk assessment values of the literature were extracted from 21 studies that met the criteria. Odds ratios (ORs), relative risks (RRs), hazard ratios (HRs), and 95% confidence intervals (CIs) were calculated to assess the relationship between shift work exposure and menstruation. The included studies were evaluated for heterogeneity, publication bias, sensitivity analysis, and subgroup analysis. Results: A total of 21 studies with 195,538 female participants, including 16 cross-sectional studies and 5 cohort studies, were included in this meta-analysis. According to the quality evaluation, the included research had high methodological quality. The overall ORs of shift work for the likelihood of irregular menstruation and dysmenorrhea were 1.30 (95% CI, 1.23-1.36) (I2 = 41.9%, P < 0.05) and 1.35 (95% CI, 1.04-1.75) (I2 = 73.0%, P < 0.05), respectively. There was a significant positive association between shift work and the risk of early menopause (HR = 1.09, 95% CI, 1.04-1.14), without significant heterogeneity (I2 = 0.0%, P > 0.05). Conclusions: This meta-analysis indicated that shift workers have significantly higher odds of menstrual disorders, dysmenorrhea, and early menopause. This study focuses on female reproductive health and has broad implications for adjusting optimal working hours and shift schedules for female workers.

Biological Characteristics and Functional Analysis of the Linoleic Acid Synthase Gene ZjFAD2 in Jujube.

Jujube fruit is rich in linoleic acid and other bioactive components and has great potential to be used for the development of functional foods. However, the roles of FAD2 genes in linoleic acid biosynthesis in jujube fruit remain unclear. Here, we identified 15 major components in jujube and found that linoleic acid was the main unsaturated fatty acid; major differences in the content and distribution of linoleic acid in the pulp and seeds were observed, and levels of linoleic acid decreased during fruit maturation. Analysis of the fatty acid metabolome, genome, and gene expression patterns of cultivated and wild-type jujube revealed five ZjFAD2 family members highly related to linoleic acid biosynthesis. The heterologous expression of these five ZjFAD2 family members in tobacco revealed that all five of these genes increased the content of linoleic acid. Additionally, transient expression of these genes in jujube fruit and the virus-induced gene silencing (VIGS) test further confirmed the key roles of ZjFAD2-11 and ZjFAD2-1 in the biosynthesis of linoleic acid. The results of this research provide valuable insights into the molecular mechanism underlying linoleic acid synthesis in jujube and will aid the development of quality-oriented breeding strategies.

Five machine learning-based radiomics models for preoperative prediction of histological grade in hepatocellular carcinoma.

PURPOSE: To compare the efficacy of radiomics models via five machine learning algorithms in predicting the histological grade of hepatocellular carcinoma (HCC) before surgery and to develop the most stable model to classify high-risk HCC patients. METHODS: Contrast-enhanced computed tomography (CECT) images of 175 HCC patients before surgery were analysed, and radiomics features were extracted from CECT images (including arterial and portal phases). Five machine learning models, including Bayes, random forest (RF), k-nearest neighbors (KNN), logistic regression (LR), and support vector machine (SVM), were applied to establish the model. The stability of the five models was weighed by the relative standard deviation (RSD), and the lowest RSD value was chosen as the most stable model to predict the histological grade of HCC. The area under the curve (AUC) and Delong tests were devoted to assessing the predictive efficacy of the models. RESULTS: High-grade HCC accounted for 28.57% (50/175) of the 175 patients. The RSD value of AUC via the RF machine learning model was the lowest (2.3%), followed by Bayes (3.2%), KNN (6.4%), SVM (8.7%) and LR (31.3%). In addition, the RF model (AUC = 0.995) was better than the other four models in the training set (p < 0.05), as well as obtained good predictive performance in the test set (AUC = 0.837). CONCLUSION: Among the five machine learning models, the RF-based radiomics model was the most stable and performed excellently in identifying high histological grade of HCC.

Metabolome and transcriptome profiling revealed the enhanced synthesis of volatile esters in Korla pear.

BACKGROUND: Flavor contributes to the sensory quality of fruits, including taste and aroma aspects. The quality of foods is related to their flavor-associated compounds. Pear fruits have a fruity sense of smell, and esters are the main contributor of the aroma. Korla pear are well known due to its unique aroma, but the mechanism and genes related to volatile synthesis have not been fully investigated. RESULTS: Flavor-associated compounds, including 18 primary metabolites and 144 volatiles, were characterized in maturity fruits of ten pear cultivars from five species, respectively. Based on the varied metabolites profiles, the cultivars could be grouped into species, respectively, by using orthogonal partial least squares discrimination analysis (OPLS-DA). Simultaneously, 14 volatiles were selected as biomarkers to discriminate Korla pear (Pyrus sinkiangensis) from others. Correlation network analysis further revealed the biosynthetic pathways of the compounds in pear cultivars. Furthermore, the volatile profile in Korla pear throughout fruit development was investigated. Aldehydes were the most abundant volatiles, while numerous esters consistently accumulated especially at the maturity stages. Combined with transcriptomic and metabolic analysis, Ps5LOXL, PsADHL, and PsAATL were screened out as the key genes in ester synthesis. CONCLUSION: Pear species can be distinguished by their metabolic profiles. The most diversified volatiles as well as esters was found in Korla pear, in which the enhancement of lipoxygenase pathway may lead to the high level of volatile esters at maturity stages. The study will benefit the fully usage of pear germplasm resources to serve fruit flavor breeding goals.

Tracking Epidemiological Characteristics and Risk Factors of Multi-Drug Resistant Bacteria in Intensive Care Units.

Objectives: Multi-drug resistance (MDR) emerged as a serious threat in intensive care unit (ICU) settings. Our study aimed to investigate the major pathogens in ICU and identify the risk factors for MDR infection. Methods: We performed a retrospective analysis of patients admitted to the ICU. Multivariate logistic regression was applied to identify the independent predictors, and then a nomogram to predict the probability of MDR infection. Results: A total of 278 patients with 483 positive cultures were included. 249 (51.55%) had at least one MDR pathogen, including extensively drug-resistant (XDR) 77 (30.92%) and pan drug-resistant (PDR) 39 (15.66%), respectively. Klebsiella pneumonia was the most frequently isolated pathogen. We identified the number of bacteria (OR 2.91, 95% CI 1.97-4.29, P < 0.001), multiple invasive procedures (OR 2.23, 95% CI 1.37-3.63, P = 0.001), length of stay (LOS) (OR 1.01, 95% CI 1.00-1.02, P = 0.007), Hemoglobin (Hb) (OR 0.99, 95% CI 0.98-1.00, P = 0.01) were independent risk factors for MDR infection. Our nomogram displayed good discrimination with curve AUC was 0.75 (95% CI: 0.70-0.81). The decision curves also indicate the clinical utility of our nomogram. Additionally, the in-hospital mortality with MDR pathogens was independently associated with XDR (HR, 2.60; 95% CI: 1.08-6.25; P = 0.03) and total protein (TP) (HR, 0.95; 95% CI: 0.91-0.99; P = 0.03). Conclusion: The number of bacteria, multiple invasive procedures, LOS, and Hb were the independent predictors associated with MDR pathogens. Our nomogram is potentially useful for predicting the occurrence of MDR infection. Besides, we also identify XDR and TP as the independent risk factors for in-hospital mortality with MDR infection. The current prevalence of MDR strains was also described. The results will contribute to the identification and preventive management of patients at increased risk of infection.

Lipidomics characterized TAG biosynthesis of developing kernels in three walnut cultivars in Xinjiang region.

Walnut oil with very high proportion of polyunsaturated fatty acids exhibits many health beneficial effects. We hypothesized that the oil composition is led by a special pattern/mechanism for triacylglycerol (TAG) biosynthesis as well as accumulation in walnut kernel during embryo development. To test this hypothesis, shotgun lipidomics was performed for class-targeted lipid analysis (including TAG, phosphatidylcholine, phosphatidylethanolamine, phosphatidic acid, phosphatidylglycerol, phosphatidylinositol, and lysophosphatidylcholine species) in walnut kernels from three cultivar collected at three critical stages of embryo development. The results indicated that TAG synthesis in the kernel happened before 84 days after flowering (DAF) and was significantly enhanced between 84 and 98 DAF. Moreover, TAG profile was changing along with DAFs due to the increased composition of 18:1 FA in TAG pool. Moreover, lipidomics also demonstrated that the enhanced acyl editing was responsible for the flux of FA through phosphatidylcholine for eventual TAG synthesis. Therefore, TAG biosynthesis in walnut kernel was characterized directly from lipid metabolism.

A case of intraocular infection caused by Nocardia brasiliensis in a patient who is immunocompetent.

Nocardia is a rare opportunistic pathogen that primarily affects the skin, lungs, and central nervous system. Intraocular infection caused by Nocardia species is a rare event in individuals who are immunocompetent. We herein present a case of a female individual who is immunocompetent, with a left eye injury caused by a contaminated nail. Unfortunately, this exposure history was not recognized at the initial visit, which led to a delay in diagnosis and the eventual development of intraocular infections with multiple hospital admissions in a short period of patients. A definitive diagnosis of Nocardia brasiliensis by matrix-assisted laser desorption ionization-time of flight mass spectrometry. With the original intention of reporting the case, we hope that physicians should be aware of rare pathogen infections, especially when conventional antibiotic therapy is ineffective, to avoid untimely treatment and poor prognosis. Furthermore, matrix-assisted laser desorption ionization-time of flight mass spectrometry or next-generation sequencing should be considered as new techniques for identifying pathogens.

Methyl Jasmonate- and Salicylic Acid-Induced Transcription Factor ZjWRKY18 Regulates Triterpenoid Accumulation and Salt Stress Tolerance in Jujube.

Triterpenoids are important, pharmacologically active substances in jujube (Ziziphus jujuba Mill.), and play an important role in the plant's resistance to abiotic stress. However, regulation of their biosynthesis, and the underlying mechanism of their balance with stress resistance, remain poorly understood. In this study, we screened and functionally characterized the ZjWRKY18 transcription factor, which is associated with triterpenoid accumulation. The transcription factor is induced by methyl jasmonate and salicylic acid, and its activity was observed by gene overexpression and silencing experiments, combined with analyses of transcripts and metabolites. ZjWRKY18 gene silencing decreased the transcription of triterpenoid synthesis pathway genes and the corresponding triterpenoid content. Overexpression of the gene promoted the biosynthesis of jujube triterpenoids, as well as triterpenoids in tobacco and Arabidopsis thaliana. In addition, ZjWRKY18 binds to W-box sequences to activate promoters of 3-hydroxy-3-methyl glutaryl coenzyme A reductase and farnesyl pyrophosphate synthase, suggesting that ZjWRKY18 positively regulates the triterpenoid synthesis pathway. Overexpression of ZjWRKY18 also increased tolerance to salt stress in tobacco and Arabidopsis thaliana. These results highlight the potential use of ZjWRKY18 to improve triterpenoid biosynthesis and salt stress tolerance in plants, and provide a strong basis for metabolic engineering to improve the content of triterpenoids and breeding of jujube varieties that are resistant to stress.

Preoperative Prediction of Microsatellite Instability in Rectal Cancer Using Five Machine Learning Algorithms Based on Multiparametric MRI Radiomics.

Objectives: To establish and verify radiomics models based on multiparametric MRI for preoperatively identifying the microsatellite instability (MSI) status of rectal cancer (RC) by comparing different machine learning algorithms. Methods: This retrospective study enrolled 383 (training set, 268; test set, 115) RC patients between January 2017 and June 2022. A total of 4148 radiomics features were extracted from multiparametric MRI, including T2-weighted imaging, T1-weighted imaging, apparent diffusion coefficient, and contrast-enhanced T1-weighted imaging. The analysis of variance, correlation test, univariate logistic analysis, and a gradient-boosting decision tree were used for the dimension reduction. Logistic regression, Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and tree machine learning algorithms were used to build different radiomics models. The relative standard deviation (RSD) and bootstrap method were used to quantify the stability of these five algorithms. Then, predictive performances of different models were assessed using area under curves (AUCs). The performance of the best radiomics model was evaluated using calibration and discrimination. Results: Among these 383 patients, the prevalence of MSI was 14.62% (56/383). The RSD value of logistic regression algorithm was the lowest (4.64%), followed by Bayes (5.44%) and KNN (5.45%), which was significantly better than that of SVM (19.11%) and tree (11.94%) algorithms. The radiomics model based on logistic regression algorithm performed best, with AUCs of 0.827 and 0.739 in the training and test sets, respectively. Conclusions: We developed a radiomics model based on the logistic regression algorithm, which could potentially be used to facilitate the individualized prediction of MSI status in RC patients.

Antioxidative, cytoprotective and whitening activities of fragrant pear fruits at different growth stages.

Pear is one of the most popular fruits in the world. With the fruit ripening, a series of physiological changes have taken place in fragrant pear, but up to now, the research on the metabolism and biological activity of phenolic compounds in different growth stages of fragrant pear is still lacking. In this study, four kinds of Xinjiang pears were selected as research objects, and the changes of phenolic content, antioxidant capacity, cell protection and whitening activity during fruit development were analyzed. The results showed that the phenolic content and antioxidant capacity of four pear varieties presented a decreasing trend throughout the developmental stages. The phenolic content and antioxidant activity of the four pears in the young fruit stage were the highest, and the active ingredients of the Nanguo pear were higher than the other three pear fruits. Pear extract could protect cells by eliminating excessive ROS in cells, especially in young fruit stage. The western blot results showed that the extract of fragrant pear in the young fruit stage could inhibit the expression of TYR, TYR1 and MITF in B16 cells, and it was speculated that the extract of fragrant pear in the young fruit stage might have good whitening activity. Therefore, the findings suggest that young pear display a good antioxidant potential and could have a good application prospect in food preservation and health product industry.

Construction of a highly saturated genetic map and identification of quantitative trait loci for leaf traits in jujube.

Chinese jujube (Ziziphus jujuba Mill.), a member of the genus Ziziphus, which comes under the family Rhamnaceae, is the most important species in terms of its economic, ecological, and social benefits. To dissect the loci associated with important phenotypical traits and analyze their genetic and genomic information in jujube, a whole-genome resequencing (WGR) based highly saturated genetic map was constructed using an F1 hybrid population of 140 progeny individuals derived from the cross of 'JMS2' × 'Jiaocheng 5'. The average sequencing depth of the parents was 14.09× and that of the progeny was 2.62×, and the average comparison efficiency between the sample and the reference genome was 97.09%. Three sets of genetic maps were constructed for a female parent, a male parent, and integrated. A total of 8,684 markers, including 8,158 SNP and 526 InDel markers, were evenly distributed across all 12 linkage groups (LGs) in the integrated map, spanning 1,713.22 cM with an average marker interval of 0.2 cM. In terms of marker number and density, this is the most saturated genetic map of jujube to date, nearly doubling that of the best ones previously reported. Based on this genetic map and phenotype data from 2019 to 2021, 31 leaf trait QTLs were identified in the linkage groups (LG1, 15; LG3, 1; LG5, 8; LG7, 4; LG8, 1, and LG11, 2), including 17 major QTLs. There were 4, 8, 14, and 5 QTLs that contributed to leaf length, leaf width, leaf shape index, and leaf area, respectively. Six QTLs clusters were detected on LG1 (8.05 cM-9.52 cM; 13.12 cM-13.99 cM; 123.84 cM-126.09 cM), LG5 (50.58 cM-50.86 cM; 80.10 cM-81.76 cM) and LG11 (35.98 cM-48.62 cM). Eight candidate genes were identified within the QTLs cluster regions. Annotation information showed that 4 genes (LOC107418196, LOC107418241, LOC107417968, and LOC112492570) in these QTLs are related to cell division and cell wall integrity. This research will provide a valuable tool for further QTL analysis, candidate gene identification, map-based gene cloning, comparative mapping, and marker-assisted selection (MAS) in jujube.

Lead exposure dose-dependently affects oxidative stress, AsA-GSH, photosynthesis, and mineral content in pakchoi (Brassica chinensis L.).

Lead (Pb) is a heavy metal pollutant and negatively affects agriculture and ecosystems. Pb can cause oxidative stress and abnormal plant growth. The ascorbic acid-glutathione (AsA-GSH) cycle mainly exists in chloroplasts and resists oxidative stress, scavenges reactive oxygen radicals, and maintains normal photosynthesis. However, the dosage related effects of Pb on pakchoi photosynthesis, via oxidative stress and the AsA-GSH system, remains unclear. In this study, various Pb dosage stress models were tested (low: 300 mg/kg; medium: 600 mg/kg; high: 900 mg/kg). Pb stress induced a dose-dependent increase in Pb content in pakchoi leaves (P < 0.05). Principal component analysis showed that Se, B, and Pb were significantly and negatively correlated. Pb stress also increased MDA content and decreased antioxidant enzymes SOD, GSH-Px, and T-AOC activities (P < 0.05). We also found that Vc content, as well as the GSH/GSSG ratio, decreased. Additionally, Pb stress destroyed chloroplast structure, decreased photosynthesis indicators Pn, Tr, Gs, Ci and VPD, and attenuated Fv/Fm and Fv/Fo (P < 0.05). In the high-dose group, the contents of chlorophyll a, chlorophyll b, and carotenoids decreased significantly, while the expression of chloroplast development genes (GLK, GLN2) decreased (P < 0.05). Our data suggest that Pb stress leads to dosage-dependent, aberrant photosynthesis by inhibiting the AsA-GSH system in pakchoi. This study expands the Pb toxicology research field and provides indications for screening antagonists.

Continuous Psychological Nursing Based on Grey Clustering Algorithm in Patients after Transurethral Resection of Prostate.

Objective: To explore the effect of continuous psychological nursing based on the grey clustering algorithm on erectile function, bad psychological emotion, and complications in patients after transurethral resection of prostate (TURP). Methods: 98 patients who underwent TURP were randomly divided into observation and control groups (routine nursing). The observation group first used the grey clustering algorithm to evaluate the psychological intelligence, found patients with abnormal psychological behavior, and then implemented continuous psychological nursing combined with pelvic floor muscle exercise. The patients were followed up for 4 months. The International Index of Erectile Function-5 (IIEF-5), the incidence of complications, the Hamilton Depression Scale (HAMD), the Hamilton Anxiety Scale (HAMA) scores, and the nursing satisfaction were analyzed and compared between these two groups. Results: The grey clustering algorithm can accurately reflect the characteristics of patients' psychological changes. After targeted nursing, compared with the control group, the IIEF-5 in the observation group was higher [(24.87 ± 1.85) vs. (22.24 ± 1.47), P < 0.05], the incidence of total complications was lower (10.20% vs. 26.53%, P < 0.05), the score of HAMA was lower [(6.11 ± 2.57) vs. (10.98 ± 2.29), P < 0.05], the score of HAMD was lower [(6.97 ± 2.85) vs. (11.35 ± 2.19), P < 0.05], and the nursing satisfaction was higher (100% vs. 85.71%, P < 0.05). Conclusion: Mental intelligence evaluation based on the grey clustering algorithm combined with pelvic floor muscle exercise can significantly improve the rehabilitation effect of erectile function in patients after TURP, reduce the incidence of postoperative complications, and alleviate patients' anxiety and depression.

Preoperative Prediction of Lymph Node Metastasis of Pancreatic Ductal Adenocarcinoma Based on a Radiomics Nomogram of Dual-Parametric MRI Imaging.

Purpose: This study aims to uncover and validate an MRI-based radiomics nomogram for detecting lymph node metastasis (LNM) in pancreatic ductal adenocarcinoma (PDAC) patients prior to surgery. Materials and Methods: We retrospectively collected 141 patients with pathologically confirmed PDAC who underwent preoperative T2-weighted imaging (T2WI) and portal venous phase (PVP) contrast-enhanced T1-weighted imaging (T1WI) scans between January 2017 and December 2021. The patients were randomly divided into training (n = 98) and validation (n = 43) cohorts at a ratio of 7:3. For each sequence, 1037 radiomics features were extracted and analyzed. After applying the gradient-boosting decision tree (GBDT), the key MRI radiomics features were selected. Three radiomics scores (rad-score 1 for PVP, rad-score 2 for T2WI, and rad-score 3 for T2WI combined with PVP) were calculated. Rad-score 3 and clinical independent risk factors were combined to construct a nomogram for the prediction of LNM of PDAC by multivariable logistic regression analysis. The predictive performances of the rad-scores and the nomogram were assessed by the area under the operating characteristic curve (AUC), and the clinical utility of the radiomics nomogram was assessed by decision curve analysis (DCA). Results: Six radiomics features of T2WI, eight radiomics features of PVP and ten radiomics features of T2WI combined with PVP were found to be associated with LNM. Multivariate logistic regression analysis showed that rad-score 3 and MRI-reported LN status were independent predictors. In the training and validation cohorts, the AUCs of rad-score 1, rad-score 2 and rad-score 3 were 0.769 and 0.751, 0.807 and 0.784, and 0.834 and 0.807, respectively. The predictive value of rad-score 3 was similar to that of rad-score 1 and rad-score 2 in both the training and validation cohorts (P > 0.05). The radiomics nomogram constructed by rad-score 3 and MRI-reported LN status showed encouraging clinical benefit, with an AUC of 0.845 for the training cohort and 0.816 for the validation cohort. Conclusions: The radiomics nomogram derived from the rad-score based on MRI features and MRI-reported lymph status showed outstanding performance for the preoperative prediction of LNM of PDAC.

CT-Based Radiomics Nomogram Improves Risk Stratification and Prediction of Early Recurrence in Hepatocellular Carcinoma After Partial Hepatectomy.

Objectives: To develop and validate an intuitive computed tomography (CT)-based radiomics nomogram for the prediction and risk stratification of early recurrence (ER) in hepatocellular carcinoma (HCC) patients after partial hepatectomy. Methods: A total of 132 HCC patients treated with partial hepatectomy were retrospectively enrolled and assigned to training and test sets. Least absolute shrinkage and selection operator and gradient boosting decision tree were used to extract quantitative radiomics features from preoperative contrast-enhanced CT images of the HCC patients. The radiomics features with predictive value for ER were used, either alone or in combination with other predictive features, to construct predictive models. The best performing model was then selected to develop an intuitive, simple-to-use nomogram, and its performance in the prediction and risk stratification of ER was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results: The radiomics model based on the radiomics score (Rad-score) achieved AUCs of 0.870 and 0.890 in the training and test sets, respectively. Among the six predictive models, the combined model based on the Rad-score, Edmondson grade, and tumor size had the highest AUCs of 0.907 in the training set and 0.948 in the test set and was used to develop an intuitive nomogram. Notably, the calibration curve and DCA for the nomogram showed good calibration and clinical application. Moreover, the risk of ER was significantly different between the high- and low-risk groups stratified by the nomogram (p <0.001). Conclusions: The CT-based radiomics nomogram developed in this study exhibits outstanding performance for ER prediction and risk stratification. As such, this intuitive nomogram holds promise as a more effective and user-friendly tool in predicting ER for HCC patients after partial hepatectomy.

Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma.

Objectives: The study developed and validated a radiomics nomogram based on a combination of computed tomography (CT) radiomics signature and clinical factors and explored the ability of radiomics for individualized prediction of Ki-67 expression in hepatocellular carcinoma (HCC). Methods: First-order, second-order, and high-order radiomics features were extracted from preoperative enhanced CT images of 172 HCC patients, and the radiomics features with predictive value for high Ki-67 expression were extracted to construct the radiomic signature prediction model. Based on the training group, the radiomics nomogram was constructed based on a combination of radiomic signature and clinical factors that showed an independent association with Ki-67 expression. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram. Results: Sixteen higher-order radiomic features that were associated with Ki-67 expression were used to construct the radiomics signature (AUC: training group, 0.854; validation group, 0.744). In multivariate logistic regression, alfa-fetoprotein (AFP) and Edmondson grades were identified as independent predictors of Ki-67 expression. Thus, the radiomics signature was combined with AFP and Edmondson grades to construct the radiomics nomogram (AUC: training group, 0.884; validation group, 0.819). The calibration curve and DCA showed good clinical application of the nomogram. Conclusion: The radiomics nomogram developed in this study based on the high-order features of CT images can accurately predict high Ki-67 expression and provide individualized guidance for the treatment and clinical monitoring of HCC patients.