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Abstract The aim of this study was to establish and validate the precision of a novel radiomics approach that integrates 18Fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT) scan data with clinical information to improve the prognostication of survival rates in patients diagnosed with stage III Non-Small Cell Lung Cancer (NSCLC) who are not candidates for surgery. We evaluated pretreatment 18F-FDG PET-CT scans from 156 individuals diagnosed with stage III inoperable NSCLC at Shandong Cancer Hospital. These individuals were divided into two groups: a training set comprising 110 patients and an internal validation set consisting of 46 patients. By employing random forest classifier and cox proportional hazards model , we identified and utilized relevant features to create predictive models and a nomogram. The effectiveness of these models was assessed through the use of the receiver operating characteristics(ROC) curves, Kaplan–Meier (KM) curves, and the application of the nomogram. Our findings showed that the combined model, which integrates both clinical and radiomic data, outperformed those based solely on clinical or radiomic features in predicting 3-year overall survival(OS). Furthermore, calibration plots revealed a high level of agreement between predicted and actual survival times. The research successfully established a predictive radiomics model that integrates 18F-FDG PET/CT imaging with clinical indicators to enhance survival predictions for patients with stage III inoperable NSCLC.

[Introduction] In the nuclear power plant, the steam pipeline is generally installed with an insulation layer on the outer wall to improve heat transfer efficiency. Currently, the main detection means for ferromagnetic pipelines are conventional ultrasound and ultrasonic guided waves. Prior to the inspection, the insulation layer on the outer wall of the pipeline needs to be removed, leading to extended inspection time, increased labor costs, and an inability to meet the requirements for high-quality development in nuclear power plants. The application of the pulsed eddy current (PEC) technique for nuclear power plants can eliminate the need for insulation layer removal, enabling non-stop online screening. The defects testing by coil placement is an essential indicator of the PEC technique. [Method] In this paper, the modeling and simulation of the pipelines was conducted by applying ANSYS Maxwell, coaxial and vertical detection coils were designed respectively to simulate the detection capability of PEC on flat bottom defects with consistency in the lift-off distance, materials and other conditions. Sample pipes were selected from the nuclear power plant for coaxial and vertical PEC testing. The pulsed eddy current testing (PECT) results were cross-validated with ultrasonic thickness measurement, and the effects of two coil placement methods on PECT were compared. [Result] The results show that vertical coils are more effective in defect detection compared to coaxial coils. [Conclusion] The defects testing by coil placement has great significance for implementing PEC in the nuclear power sector.

Abstract The physical characteristics of classrooms can significantly impact the physical and mental health as well as the learning performance of college students. This study investigates the effects of classroom size and ceiling height on learning performance using virtual reality technology. Four classroom settings were created: two small classrooms (40.5 m2) with ceiling heights of 3.0 m and 3.9 m, and two large classrooms (62.1 m2) with ceiling heights of 3.9 m and 4.8 m. 34 students participated in task tests while their subjective evaluations and physiological data were recorded. Results showed higher subjective ratings in larger classrooms with the same ceiling height. Classroom size did not significantly affect task test scores. However, there is a significant difference in Task test scores for ceilings of different heights with the same size classroom. The task test improved by 17.3% in the Big and High Room (BHR) and by 20.1% in the Small and Low Room (SLR). Physiological data revealed significant effects of ceiling height, with HRV-nLF/nHF and EEG-β power increasing by 26.5% and 53.9% in BHR, and by 10.7% and 22.8% in SLR, respectively. This study concludes that classroom size and ceiling height plays a crucial role in learning performance and provides insights for classroom design. It also establishes a framework for future research on the interplay between heart rate variability, EEG, and learning performance.

Abstract Background and Aims In this study, a pediatric hospital was selected as the study setting, to investigate the effects of the environment of a pediatric operating room child‐friendly space on preschool children under anesthesia. Methods Interviews were conducted in pediatric hospitals through anesthesia of preschool children. This study randomizing children between the ages of 1 and 7 years (seven males, five females, mean age 3.8 years, standard deviation 0.7). Twelve participants took part in this interview, which was an in‐depth interview of preschoolers' perceptions of anesthesia using generalized data analysis to assess young children's perceived preferences and sensitivity to the environment. Preschoolers' preferences for feeling under anesthesia are reflected in four main areas. Results (1) Interesting and engaging environment. (2) Humanized design, free of fear and unfamiliarity. (3) Caring, patient, and compassionate care by healthcare professionals. (4) High cooperation of the child and good doctor–patient relationship. Conclusion The key findings of this study may help designers and clinicians to design a toddler‐friendly built environment where pediatric hospital child‐friendly spaces are designed to benefit their health and maximize their cooperation and psychological recovery.

PurposeThis study was designed to determine the diagnostic performance of fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomics-based machine learning (ML) in the classification of cervical adenocarcinoma (AC) and squamous cell carcinoma (SCC).MethodsPretreatment 18F-FDG PET/CT data were retrospectively collected from patients who were diagnosed with locally advanced cervical cancer at two centers. Radiomics features were extracted and selected by the Pearson correlation coefficient and least absolute shrinkage and selection operator regression analysis. Six ML algorithms were then applied to establish models, and the best-performing classifier was selected based on accuracy, sensitivity, specificity, and area under the curve (AUC). The performance of different model was assessed and compared using the DeLong test.ResultsA total of 227 patients with locally advanced cervical cancer were enrolled in this study (N=136 for the training cohort, N=59 for the internal validation cohort, and N=32 for the external validation cohort). The PET radiomics model constructed based on the lightGBM algorithm had an accuracy of 0.915 and an AUC of 0.851 (95% confidence interval [CI], 0.715-0.986) in the internal validation cohort, which were higher than those of the CT radiomics model (accuracy: 0.661; AUC: 0.513 [95% CI, 0.339-0.688]). The DeLong test revealed no significant difference in AUC between the combined radiomics model and the PET radiomics model in either the training cohort (z=0.940, P=0.347) or the internal validation cohort (z=0.285, P=0.776). In the external validation cohort, the lightGBM-based PET radiomics model achieved good discrimination between SCC and AC (AUC = 0.730).ConclusionsThe lightGBM-based PET radiomics model had great potential to predict the fine histological subtypes of locally advanced cervical cancer and might serve as a promising noninvasive approach for the diagnosis and management of locally advanced cervical cancer.

Planthoppers are an interesting and contrasting model among insects for studying the subterranean environments. Their morphological and ethological adaptations to the underground conditions (complete darkness, lower temperatures, high hygrometry, stability of environmental constants, rarefied food sources, etc.), and their worldwide distribution in both temperate and tropical areas make them an interesting model among invertebrates. In this review, we highlight why cave planthoppers study matters, with particular emphasis on the Cixiidae. The two hypotheses proposed, the ‘climatic relict hypothesis’ and the ‘adaptive shift’, are not sufficient enough to clearly understand and explain the drivers to cavernicoly. Phylogenetic analyses approaches might help to better document and increase our knowledge on such peculiar environments. The singularity of the distribution pattern of the adaptation to cavernicoly in planthoppers raises also interesting questions to investigate and suggest contrasting scenarios to explore further, particularly should the Cixiidae be defined as a subtroglophile lineage?

IntroductionVerticillium dahliae, a soil-borne fungal pathogen, can cause cotton Verticillium wilt. In this study, VdP5CDH, the member of the ALDH_F4-17 family of carboxylate dehydrogenases, was identified in the genome of V. dahliae and investigated function in regulating virulence by generating gene deletion mutants and complementary mutants.MethodsHomologous recombination method was used to construct mutants, transcriptome sequencing revealed gene-related metabolic pathways, and disease degree of cotton was observed through pathogen infection experiments.ResultsThe conidial surface of VdP5CDH deletion strains was dented and shriveled, and the number of conidial spores increased. Compared with the wild-type (WT), the mycelial diameter of deletion mutants increased by 10.59%-11.16%, the mycelial growth showed irregular branching patterns, and misaligned arrangement. Although capable of penetrating cellophane, deletion mutants were unable to produce melanin. VdP5CDH was mainly associated with glucose metabolism, nitrogen metabolism, ABC transporter activity as well as various amino acid metabolic processes. After gene knockout, raffinose and pectin were used as the main carbon sources to promote the growth of strains and the growth rate of deletion strains in the medium containing raffinose was higher than that of WT. Consequently, the deletion mutant strains decreased utilization efficiency with which they utilized various nitrogen sources. The deletion mutants maintain responsiveness to osmotic stress and oxidative stress stimuli. Additionally, compared to WT strains, the deletion mutant strains exhibited differences in culture temperature tolerance, UV exposure response, and fungicide sensitivity. After cotton was infected with deletion strains conidial suspension, its disease index increased dramatically, while it gradually decreased after spraying with 2 mM glutamate in batches. With the increase of spraying times, the effect was more significant, and the disease index decreased by 18.95%-19.66% at 26 dpi.DiscussionThese results indicated that VdP5CDH regulates the pathogenicity of fungi and controls mycelia growth, melanin formation, conidia morphology, abiotic stress resistance, and the expression of infecting structure-related genes.

BackgroundLung squamous cell carcinoma (LUSC) ranks among the carcinomas with the highest incidence and dismal survival rates, suffering from a lack of effective therapeutic strategies. Consequently, biomarkers facilitating early diagnosis of LUSC could significantly enhance patient survival. This study aims to identify novel biomarkers for LUSC.MethodsUtilizing the TCGA, GTEx, and CGGA databases, we focused on the gene encoding Family with Sequence Similarity 20, Member A (FAM20A) across various cancers. We then corroborated these bioinformatic predictions with clinical samples. A range of analytical tools, including Kaplan-Meier, MethSurv database, Wilcoxon rank-sum, Kruskal-Wallis tests, Gene Set Enrichment Analysis, and TIMER database, were employed to assess the diagnostic and prognostic value of FAM20A in LUSC. These tools also helped evaluate immune cell infiltration, immune checkpoint genes, DNA repair-related genes, DNA methylation, and tumor-related pathways.ResultsFAM20A expression was found to be significantly reduced in LUSC, correlating with lower survival rates. It exhibited a negative correlation with key proteins in DNA repair signaling pathways, potentially contributing to LUSC’s radiotherapy resistance. Additionally, FAM20A showed a positive correlation with immune checkpoints like CTLA-4, indicating potential heightened sensitivity to immunotherapies targeting these checkpoints.ConclusionFAM20A emerges as a promising diagnostic and prognostic biomarker for LUSC, offering potential clinical applications.

Sirtuins are pivotal in orchestrating numerous cellular pathways, critically influencing cell metabolism, DNA repair, aging processes, and oxidative stress. In recent years, the involvement of sirtuins in tumor biology has garnered substantial attention, with a growing body of evidence underscoring their regulatory roles in various aberrant cellular processes within tumor environments. This article delves into the sirtuin family and its biological functions, shedding light on their dual roles—either as promoters or inhibitors—in various cancers including oral, breast, hepatocellular, lung, and gastric cancers. It further explores potential anti-tumor agents targeting sirtuins, unraveling the complex interplay between sirtuins, miRNAs, and chemotherapeutic drugs. The dual roles of sirtuins in cancer biology reflect the complexity of targeting these enzymes but also highlight the immense therapeutic potential. These advancements hold significant promise for enhancing clinical outcomes, marking a pivotal step forward in the ongoing battle against cancer.

Abstract Genomic dissection of genetic effects on desirable traits and the subsequent use of genomic selection hold great promise for accelerating the rate of genetic improvement of forest tree species. In this study, a total of 661 offspring trees from 66 open-pollinated families of Japanese larch (Larix kaempferi (Lam.) Carrière) were sampled at a test site. The contributions of additive and non-additive effects (dominance, imprinting and epistasis) were evaluated for nine valuable traits related to growth, wood physical and chemical properties, and competitive ability using three pedigree-based and four Genomics-based Best Linear Unbiased Predictions (GBLUP) models and used to determine the genetic model. The predictive ability (PA) of two genomic prediction methods, GBLUP and Reproducing Kernel Hilbert Spaces (RKHS), was compared. The traits could be classified into two types based on different quantitative genetic architectures: for type I, including wood chemical properties and Pilodyn penetration, additive effect is the main source of variation (38.20-67.46%); for type II, including growth, competitive ability and acoustic velocity, epistasis plays a significant role (50.76-91.26%). Dominance and imprinting showed low to moderate contributions (

Abstract Background The existing staging system cannot meet the needs of accurate survival prediction. Accurate survival prediction for locally advanced cervical cancer (LACC) patients who have undergone concurrent radiochemotherapy (CCRT) can improve their treatment management. Thus, this present study aimed to develop and validate radiomics models based on pretreatment 18Fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT) images to accurately predict the prognosis in patients. Methods The data from 190 consecutive patients with LACC who underwent pretreatment 18F-FDG PET-CT and CCRT at two cancer hospitals were retrospectively analyzed; 176 patients from the same hospital were randomly divided into training (n = 117) and internal validation (n = 50) cohorts. Clinical features were selected from the training cohort using univariate and multivariate Cox proportional hazards models; radiomic features were extracted from PET and CT images and filtered using least absolute shrinkage and selection operator and Cox proportional hazard regression. Three prediction models and a nomogram were then constructed using the previously selected clinical, CT and PET radiomics features. The external validation cohort that was used to validate the models included 23 patients with LACC from another cancer hospital. The predictive performance of the constructed models was evaluated using receiver operator characteristic curves, Kaplan Meier curves, and a nomogram. Results In total, one clinical, one PET radiomics, and three CT radiomics features were significantly associated with progression-free survival in the training cohort. Across all three cohorts, the combined model displayed better efficacy and clinical utility than any of these parameters alone in predicting 3-year progression-free survival (area under curve: 0.661, 0.718, and 0.775; C-index: 0.698, 0.724, and 0.705, respectively) and 5-year progression-free survival (area under curve: 0.661, 0.711, and 0.767; C-index, 0.698, 0.722, and 0.676, respectively). On subsequent construction of a nomogram, the calibration curve demonstrated good agreement between actually observed and nomogram-predicted values. Conclusions In this study, a clinico-radiomics prediction model was developed and successfully validated using an independent external validation cohort. The nomogram incorporating radiomics and clinical features could be a useful clinical tool for the early and accurate assessment of long-term prognosis in patients with LACC patients who undergo concurrent chemoradiotherapy.

Sti1/Hop, a stress-induced co-chaperone protein, serves as a crucial link between Hsp70 and Hsp90 during cellular stress responses. Despite its importance in stress defense mechanisms, the biological role of Sti1 in Verticillium dahliae, a destructive fungal pathogen, remains largely unexplored. This study focused on identifying and characterizing Sti1 homologues in V. dahliae by comparing them to those found in Saccharomyces cerevisiae. The results indicated that the VdSti1-deficient mutant displayed increased sensitivity to drugs targeting the ergosterol synthesis pathway, leading to a notable inhibition of ergosterol biosynthesis. Moreover, the mutant exhibited reduced production of microsclerotia and melanin, accompanied by decreased expression of microsclerotia and melanin-related genes VDH1, Vayg1, and VaflM. Additionally, the mutant’s conidia showed more severe damage under heat shock conditions and displayed growth defects under various stressors such as temperature, SDS, and CR stress, as well as increased sensitivity to H2O2, while osmotic stress did not impact its growth. Importantly, the VdSti1-deficient mutant demonstrated significantly diminished pathogenicity compared to the wild-type strain. This study sheds light on the functional conservation and divergence of Sti1 homologues in fungal biology and underscores the critical role of VdSti1 in microsclerotia development, stress response, and pathogenicity of V. dahliae.

The impact of short-term exposure to air pollutants on the morbidity of schizophrenia, particularly in low- and middle-income countries, remains inadequately explored. The objective of this research was to investigate the relationship between short-term exposure to ambient air pollutants and the risk of schizophrenia hospitalization in Jingmen, China. We performed a time-stratified case-crossover study using daily records of hospital admissions due to schizophrenia in Jingmen Mental Health Center from 2015 to 2017. Environmental exposures to air pollutants and meteorological conditions on case and control days were estimated on the basis of measurements from ground monitoring stations. To investigate the relationship between short-term exposure to ambient air pollutants and the risk of hospitalization for schizophrenia, a conditional logistic regression model was employed. We performed subgroup analyses stratified according to sex, age groups, and season. In total, 4079 schizophrenia hospitalizations were recorded during the designated period. Increased risk of schizophrenia was merely associated with short-term exposure to SO2 and NO2. The estimated odds per interquartile range (IQR) increase in exposure was 1.112 (95% confidence interval (CI): 1.033, 1.196) for SO2 (IQR = 12 µg/m3) and 1.112 (95% CI: 1.033, 1.197) for NO2 (IQR = 18 µg/m3) on lag-0 day. Greater air pollution-schizophrenia associations were observed among middle-aged and older adults (over 45 years of age) and during the cold season. This study added case-crossover evidence indicating that short-term exposure to ambient air pollution, specifically SO2 and NO2, is linked to a higher risk of hospital admissions for schizophrenia. These findings contribute to a better understanding of the detrimental effects of air pollution on neuropsychiatric health conditions.

Abstract MXenes are a family of two‐dimensional (2D) layered transition metal carbides/nitrides that show promising potential for energy storage applications due to their high‐specific surface areas, excellent electron conductivity, good hydrophilicity, and tunable terminations. Among various types of MXenes, Ti3C2Tx is the most widely studied for use in capacitive energy storage applications, especially in supercapacitors (SCs). However, the stacking and oxidation of MXene sheets inevitably lead to a significant loss of electrochemically active sites. To overcome such challenges, carbon materials are frequently incorporated into MXenes to enhance their electrochemical properties. This review introduces the common strategies used for synthesizing Ti3C2Tx, followed by a comprehensive overview of recent developments in Ti3C2Tx/carbon composites as electrode materials for SCs. Ti3C2Tx/carbon composites are categorized based on the dimensions of carbons, including 0D carbon dots, 1D carbon nanotubes and fibers, 2D graphene, and 3D carbon materials (activated carbon, polymer‐derived carbon, etc.). Finally, this review also provides a perspective on developing novel MXenes/carbon composites as electrodes for application in SCs.

Prior investigations have primarily focused on the impact of L-arabinose on gut microbiota, with limited exploration into the dual mechanisms of L-arabinose on microbiota and metabolism both in vivo and in vitro. Thus, this study aimed to elucidate the anti-inflammatory effects of 5 % L-arabinose under different diet in mice. Additionally, a colon simulation system (CDMN) with utilizing 1.72 % (w/v) L-arabinose in the lumen and mucus layers, to provide further insights into the underlying mechanisms. The results revealed that L-arabinose significantly altered the gut microbiome structure, particularly elevating Bifidobacterium and short-chain fatty acids (SCFAs) both in vivo and in vitro. Subsequent in vitro experiments confirmed that L-arabinose increased the relative abundance of SCFAs-producing bacteria, facilitated linoleic acid metabolism, and enhanced downstream metabolites (12,13-DHOME, 9,10-DHOME), potentially mediated by Bifidobacterium in the descending colon. These findings underscore the role of L-arabinose in modulating intestinal flora and metabolism, thereby promoting intestinal and body health.

Noninvasive differentiation between the squamous cell carcinoma (SCC) and adenocarcinoma (ADC) subtypes of non-small cell lung cancer (NSCLC) could benefit patients who are unsuitable for invasive diagnostic procedures. Therefore, this study evaluates the predictive performance of a PET/CT-based radiomics model. It aims to distinguish between the histological subtypes of lung adenocarcinoma and squamous cell carcinoma, employing four different machine learning techniques. A total of 255 Non-Small Cell Lung Cancer (NSCLC) patients were retrospectively analyzed and randomly divided into the training (n = 177) and validation (n = 78) sets, respectively. Radiomics features were extracted, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was employed for feature selection. Subsequently, models were constructed using four distinct machine learning techniques, with the top-performing algorithm determined by evaluating metrics such as accuracy, sensitivity, specificity, and the area under the curve (AUC). The efficacy of the various models was appraised and compared using the DeLong test. A nomogram was developed based on the model with the best predictive efficiency and clinical utility, and it was validated using calibration curves. Results indicated that the logistic regression classifier had better predictive power in the validation cohort of the radiomic model. The combined model (AUC 0.870) exhibited superior predictive power compared to the clinical model (AUC 0.848) and the radiomics model (AUC 0.774). In this study, we discovered that the combined model, refined by the logistic regression classifier, exhibited the most effective performance in classifying the histological subtypes of NSCLC.