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.

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.

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.

The Analysis of Phenolic Compounds in Walnut Husk and Pellicle by UPLC-Q-Orbitrap HRMS and HPLC.

Husk and pellicle as the agri-food waste in the walnut-product industry are in soaring demand because of their rich polyphenol content. This study investigated the differential compounds related to walnut polyphenol between husk and pellicle during fruit development stage. By using ultra-high performance liquid chromatography-quadrupole-orbitrap (UHPLC-Q-Orbitrap), a total of 110 bioactive components, including hydrolysable tannins, flavonoids, phenolic acids and quinones, were tentatively identified, 33 of which were different between husk and pellicle. The trend of dynamic content of 16 polyphenols was clarified during walnut development stage by high-performance liquid chromatography (HPLC). This is the first time to comprehensive identification of phenolic compounds in walnut husk and pellicle, and our results indicated that the pellicle is a rich resource of polyphenols. The dynamic trend of some polyphenols was consistent with total phenols. The comprehensive characterization of walnut polyphenol and quantification of main phenolic compounds will be beneficial for understanding the potential application value of walnut and for exploiting its metabolism pathway.

Sensory Quality Evaluation of Korla Pear from Different Orchards and Analysis of Their Primary and Volatile Metabolites.

Metabolites play vital roles in shaping the quality of fresh fruit. In this study, Korla pear fruit harvested from twelve orchards in South Xinjiang, China, were ranked in sensory quality by fuzzy logic sensory evaluation for two consecutive seasons. Then, gas chromatography-mass spectrometry (GC-MS) was applied to determine the primary metabolites and volatile compounds. Sensory evaluation results showed that the panelists were more concerned about 'mouth feel' and 'aroma' than about 'fruit size', 'fruit shape' and 'peel color'. In total, 20 primary metabolites and 100 volatiles were detected in the pear fruit. Hexanal, (E)-2-hexenal, nonanal, d-limonene, (Z)-3-hexen-1-yl acetate and hexyl acetate were identified as the major volatile compounds. Correlation analysis revealed that l-(+)-tartaric acid, hexanoic acid, trans-limonene oxide and 2,2,4-trimethyl-1,3-pentanediol diisobutyrate were negatively correlated with sensory scores. Furthermore, OPLS-DA results indicated that the fruit from three orchards with lower ranks in quality could be distinguished from other samples based on the contents of l-(+)-tartaric acid and other eight metabolites, which were all associated with 'mouth feel' and 'aroma'. This study reveals the metabolites that might be closely associated with the sensory quality attributes of Korla pear, which may provide some clues for promoting the fruit quality in actual production.

The status of proton pump inhibitor use: a prescription survey of 45 hospitals in China.

BACKGROUND: proton pump inhibitors (PPI) have been widely used in the clinic but inappropriate prescribing has also increased dramatically. OBJECTIVE: to describe the prescribing patterns and assess the appropriateness of the prescribed PPI use in 45 hospitals in China. MATERIALS AND METHODS: PPI prescriptions for non-hospitalized patients were collected from hospitals in Beijing, Chengdu, Guangzhou and Hangzhou of China over a 40-day period in 2016. These data were analyzed using the prescription number, proportion and economic indicators (defined daily dose system [DDD], defined daily cost [DDC] and drug utilization index [DUI]). The evaluation criteria of PPI use was based on Martindale: The Complete Drug Reference, New Materia Medica and drug instructions. RESULTS: in total, 357,687 prescriptions using oral PPI and 38,216 prescriptions using injectable PPI were assessed. The average age of PPI users was 53 years. The most commonly used oral PPI was rabeprazole, while the most common injectable PPI was pantoprazole. The DDD of oral rabeprazole and DDC of injectable rabeprazole were the highest. Meanwhile, only the DUI values of oral rabeprazole, lansoprazole and ilaprazole were less than 1.0. The clinical diagnosis of some users included well identified risky comorbidities such as kidney disease (2.9%). Furthermore, between 32.6% and 56.8% of the PPI prescriptions were used for inappropriate indications. CONCLUSION: this survey demonstrated that PPI use was accompanied by unapproved indications and excessive dosages. Comprehensive measures are urgently needed to improve PPI use and reduce unnecessary drug costs.

Comparative Transcriptome Analysis of Genes Involved in Anthocyanin Biosynthesis in Red and Green Walnut (Juglans regia L.).

Fruit color is an important economic trait. The color of red walnut cultivars is mainly attributed to anthocyanins. The aim of this study was to explore the differences in the molecular mechanism of leaf and peel color change between red and green walnut. A reference transcriptome of walnut was sequenced and annotated to identify genes related to fruit color at the ripening stage. More than 290 million high-quality reads were assembled into 39,411 genes using a combined assembly strategy. Using Illumina digital gene expression profiling, we identified 4568 differentially expressed genes (DEGs) between red and green walnut leaf and 3038 DEGs between red and green walnut peel at the ripening stage. We also identified some transcription factor families (MYB, bHLH, and WD40) involved in the control of anthocyanin biosynthesis. The trends in the expression levels of several genes encoding anthocyanin biosynthetic enzymes and transcription factors in the leaf and peel of red and green walnut were verified by quantitative real-time PCR. Together, our results identified the genes involved in anthocyanin accumulation in red walnut. These data provide a valuable resource for understanding the coloration of red walnut.

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.

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.

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.

Simultaneous determination of 14 phenolic compounds in grape canes by HPLC-DAD-UV using wavelength switching detection.

The paper described a novel chromatographic method for the simultaneous determination of phenolic compounds such as gallic, protocatechuic, vanillic, caffeic, syringic, p-coumaric and salicylic acid, (+)-catechin, (‒)-epicatechin, rutin, morin, quercetin, coumarin and trans-resveratrol at their maximum absorbance wavelengths (MAW) employing reverse-phase high performance liquid chromatography combined with DAD and UV detection via detection wavelength switching. The method was based on MAW acquisition by DAD and quantification by UV. The separation process was performed on a Shim-Pack VP-ODS C18 column (250 mm × 4.6 mm, 5 µm) held at 30 °C, utilizing 3.0% acetic acid and acetonitrile as mobile phase at a flow rate of 0.8 mL/min in the gradient elution mode. The method was fully validated in terms of linearity (r2 > 0.9990, 10‒350 mg/L), precision (both intra-day and inter-day RSD < 4.22%), accuracy (97.31%‒104.66%), specificity, robustness (0.59% < RSD < 2.86%), limit of detection and quantification. The switching method significantly improved the sensitivities of most phenolics studied in comparison with the standard constant wavelength detection (280 nm). The proposed method has been successfully applied to the determination of 14 phenolic compounds in 89 varieties of one-year-old Chinese grape one-year-canes. Grape canes contain many phenolics, especially trans-resveratrol, (‒)-epicatechin, and (+)-catechin.

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.

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.

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.

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.

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.

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.

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.

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.

Prediction of Bronchopneumonia Inpatients' Total Hospitalization Expenses Based on BP Neural Network and Support Vector Machine Models.

Objective: BP neural network (BPNN) model and support vector machine (SVM) model were used to predict the total hospitalization expenses of patients with bronchopneumonia. Methods: A total of 355 patients with bronchopneumonia from January 2018 to December 2020 were collected and sorted out. The data set was randomly divided into a training set (n = 249) and a test set (n = 106) according to 7 : 3. The BPNN model and SVM model were constructed to analyze the predictors of total hospitalization expenses. The effectiveness was compared between these two prediction models. Results: The top three influencing factors and their importance for predicting total hospitalization cost by the BPNN model were hospitalization days (0.477), age (0.154), and discharge department (0.083). The top 3 factors predicted by the SVM model were hospitalization days (0.215), age (0.196), and marital status (0.172). The area under the curve of these two models is 0.838 (95% CI: 0.755~0.921) and 0.889 (95% CI: 0.819~0.959), respectively. Conclusion: Both the BPNN model and SVM model can predict the total hospitalization expenses of patients with bronchopneumonia, but the prediction effect of the SVM model is better than the BPNN model.