LIPS and PaO2/FiO2 Combined Plasma Biomarkers Predict Onset of Acute Respiratory Distress Syndrome in Patients of High Risks in SICU: A Prospective Exploratory Study.

Objective: To explore and validate the value of clinical parameters combined with plasma biomarkers for predicting acute respiratory distress syndrome (ARDS) in patients of high risks in the surgical intensive care unit (SICU). Materials and Methods: We conducted a prospective, observational study from January 2020 to December 2023, which enrolled 263 patients of high risks in the SICU of Peking University Third Hospital consecutively; they were classified as ARDS and non-ARDS according to whether ARDS occurred after enrollment. Collected clinical characteristics and blood samples within 24 hr of admission to SICU. Blood samples from the first day to the seventh day of SICU were collected from patients without ARDS, and patients with ARDS were collected until 1 day after ARDS onset, forming data based on time series. ELISA and CBA were used to measure plasma biomarkers. Endpoint of the study was the onset of ARDS. Cox proportional hazard regression analysis was used to find independent risk factors of the onset of ARDS, then constructed a nomogram and tested its goodness-of-fit. Results: About 84 of 263 patients ended with ARDS. Univariate analysis found 15 risk factors showed differences between ARDS and non-ARDS, namely, interleukin 6, interleukin 8 (IL-8), angiopoietin Ⅱ, LIPS, APACHEⅡ, SOFA, PaO2/FiO2, age, sex, shock, sepsis, acute abdomen, pulmonary contusion, pneumonia, hepatic dysfunction. We included factors with p < 0.2 in multivariate analysis and showed LIPS, PaO2/FiO2, IL-8, and receptor for advanced glycation end-products (RAGE) of the first day were independent risk factors for ARDS in SICU, a model combining them was good in predicting ARDS (C-index was 0.864 in total patients of high risks). The median of the C-index was 0.865, showed by fivefold cross-validation in the train cohort or validation cohort. The calibration curve shows an agreement between the probability of predicting ARDS and the actual probability of occurrence. Decision curve analysis indicated that the model had clinical use value. We constructed a nomogram that had the ability to predict ARDS in patients of high risks in SICU. Conclusions: LIPS, PaO2/FiO2, plasma IL-8, and RAGE of the first day were independent risk factors of the onset of ARDS. The predictive ability for ARDS can be greatly improved when combining clinical parameters and plasma biomarkers.

Novel Therapeutic Target for ALI/ARDS: Forkhead Box Transcription Factors.

ALI/ARDS can be a pulmonary manifestation of a systemic inflammatory response or a result of overexpression of the body's normal inflammatory response involving various effector cells, cytokines, and inflammatory mediators, which regulate the body's immune response through different signalling pathways. Forkhead box transcription factors are evolutionarily conserved transcription factors that play a crucial role in various cellular processes, such as cell cycle progression, proliferation, differentiation, migration, metabolism, and DNA damage response. Transcription factors control protein synthesis by regulating gene transcription levels, resulting in diverse biological outcomes. The Fox family plays a role in activating or inhibiting the expression of various molecules related to ALI/ARDS through phosphorylation, acetylation/deacetylation, and control of multiple signalling pathways. An in-depth analysis of the integrated Fox family's role in ALI/ARDS can aid in the development of potential diagnostic and therapeutic targets for the condition.

Genetic dissection of crayfish (Procambarus clarkii) high temperature tolerance and assessment of the potential application in breeding of the HSP genes.

Red swamp crayfish (Procambarus clarkii) is an important freshwater aquaculture species in China. In the process of crayfish aquaculture, high temperature stress is common, which seriously affects its yield and quality. It is urgently recommended to improve these traits in the breed. However, the application of high-temperature tolerance genes in molecular breeding of crayfish has not been reported. In this study, transcriptome analysis was used to explore the high-temperature tolerance genes of crayfish. The results showed that genes related to energy metabolism, antioxidant, immunity and body restoration were involved in high temperature adaptation of crayfish. Based on the selected high temperature tolerance genes Heat Stress Protein 70 and Heat Stress Protein 90 (HSP70 and HSP90), the genetic variation of their open reading frames was investigated. Totally, three and four SNPs of HSP70 and HSP90, were obtained respectively. In addition, three high-temperature stress experiments were conducted on crayfish to identify favoured haplotypes. HSP70-1 and HSP90-1 are the favoured haplotypes of HSP70 and HSP90, respectively. Furthermore, a series of molecular markers were developed to identify the favoured haplotype combinations of HSP70 and HSP90. Finally, we propose a molecular breeding strategy to improve crayfish tolerance to high temperature, thereby providing a potential to increase crayfish yield. Together, this study provides a theoretical basis and molecular markers for the breeding of high-temperature tolerant crayfish.

Transcriptome analysis provides new insight into the mechanism of Bombyx mori under zinc exposure.

Zinc is a significant source of heavy metal pollution that poses risks to both human health and biodiversity. Excessive concentrations of zinc can hinder the growth and development of insects and trigger cell death through oxidative damage. The midgut is the main organ affected by exposure to heavy metals. The silkworm, a prominent insect species belonging to the Lepidoptera class and widely used in China, serves as a model for studying the genetic response to heavy metal stress. In this study, high-throughput sequencing technology was employed to investigate detoxification-related genes in the midgut that are induced by zinc exposure. A total of 11,320 unigenes and 14,723 transcripts were identified, with 553 differentially expressed genes (DEGs) detected, among which 394 were up-regulated and 159 were down-regulated. The Gene Ontology (GO) analysis revealed that 452 DEGs were involved in 18 biological process subclasses, 14 cellular component subclasses and 8 molecular functional subclasses. Furthermore, the KEGG analysis demonstrated enrichment in pathways such as Protein digestion, absorption and Lysosome. Validation of the expression levels of 9 detoxification-related DEGs through qRT-PCR confirmed the accuracy of the RNA-seq results. This study not only contributes new insights into the detoxification mechanisms mechanism of silkworms against zinc contamination, but also serves as a foundation basis for understanding the molecular detoxification processes in lepidopteran insects.

R2R3 MYB Transcription Factor GhMYB201 Promotes Cotton Fiber Elongation via Cell Wall Loosening and Very-Long-Chain Fatty Acid Synthesis.

Cotton fiber is the leading natural textile material, and fiber elongation plays an essential role in the formation of cotton yield and quality. Although a number of components in the molecular network controlling cotton fiber elongation have been reported, a lot of players still need to be functionally dissected to understand the regulatory mechanism of fiber elongation comprehensively. In the present study, an R2R3-MYB transcription factor gene, GhMYB201, was characterized and functionally verified via CRISPR/Cas9-mediated gene editing. GhMYB201 was homologous to Arabidopsis AtMYB60, and both coding genes (GhMYB201At and GhMYB201Dt) were preferentially expressed in elongating cotton fibers. Knocking-out of GhMYB201 significantly reduced the rate and duration of fiber elongation, resulting in shorter and coarser mature fibers. It was found that GhMYB201 could bind and activate the transcription of cell wall loosening genes (GhRDLs) and also ß-ketoacyl-CoA synthase genes (GhKCSs) to enhance very-long-chain fatty acid (VLCFA) levels in elongating fibers. Taken together, our data demonstrated that the transcription factor GhMYB201s plays an essential role in promoting fiber elongation via activating genes related to cell wall loosening and VLCFA biosynthesis.

Identification of autophagy-related genes ATG18 subfamily genes in potato (Solanum tuberosum L.) and the role of StATG18a gene in heat stress.

Autophagy is a highly conserved process in eukaryotes that is used to recycle the cellular components from the cytoplasm. It plays a crucial function in responding to both biotic and abiotic stress, as well as in the growth and development of plants. Autophagy-related genes (ATG) and their functions have been identified in numerous crop species. However, their specific tasks in potatoes (Solanum tuberosum L.), are still not well understood. This work is the first to identify and characterize the potato StATG18 subfamily gene at the whole-genome level, resulting in a total of 6 potential StATG18 subfamily genes. We analyzed the phylogenetic relationships, chromosome distribution and gene replication, conserved motifs and gene structure, interspecific collinearity relationship, and cis-regulatory elements of the ATG18 subfamily members using bioinformatics approaches. Furthermore, the quantitative real-time polymerase chain reaction (qRT-PCR) analysis suggested that StATG18 subfamily genes exhibit differential expression in various tissues and organs of potato plants. When exposed to heat stress, their expression pattern was observed in the root, stem, and leaf. Based on a higher expression profile, the StATG18a gene was further analyzed under heat stress in potatoes. The subcellular localization analysis of StATG18a revealed its presence in both the cytoplasm and nucleus. In addition, StATG18a altered the growth indicators, physiological characteristics, and photosynthesis of potato plants under heat stresses. In conclusion, this work offers a thorough assessment of StATG18 subfamily genes and provides essential recommendations for additional functional investigation of autophagy-associated genes in potato plants. Moreover, these results also contribute to our understanding of the potential mechanism and functional validation of the StATG18a gene's persistent tolerance to heat stress in potato plants.

Inorganic carbon utilization strategies of plateau aquatic plants in response to native habitats.

Aquatic plants are a crucial component of the aquatic ecosystem in the Tibetan Plateau region. Researching the adaptability of plateau aquatic plants in photosynthesis to the plateau environment can enhance understanding of the operational mechanisms of plateau ecosystems, thereby providing a scientific basis for the protection and management of plateau aquatic ecosystems. This study presents an investigation of photosynthetic inorganic carbon utilization strategies and photosynthetic efficiency of 17 aquatic plants under natural growing conditions in Niyang River basin on the Tibetan Plateau. In pH-drift experiments, 10 of 17 species were able to utilize HCO3-, and environmental factors like water pH were shown to have a significant effect on the ability of the tested species to utilize HCO3-. Titratable acidity in the leaves of Stuckenia filiformis, Zannichellia palustris, Batrachium bungei, and Myriophyllum spicatum showed significant diurnal fluctuations at certain sampling sites, indicating the presence of CAM. In B. bungei, water pH positively correlated with CAM activity, while CO2 concentration negatively correlated with CAM activity. The chlorophyll fluorescence analysis revealed that aquatic plants inhabiting the Tibetan Plateau exhibited photosynthetic adaptations. In conclusion, the aquatic plants on the Tibetan Plateau employ diverse strategies for utilizing inorganic carbon during photosynthesis, exhibiting their flexible adaptability to the native high-altitude habitats of the Tibetan Plateau.

Childhood maltreatment and its associations with trauma-related psychopathology: disentangling two classification approaches.

Childhood maltreatment (CM) is associated with various mental health disorders, including PTSD, depression, and anxiety. This study explores how specific classifications - dichotomous (abuse versus neglect) and dimensional (physical, emotional, sexual) - relate to distinct psychopathologies. We recruited 642 individuals, screening them for CM history and symptoms. ANOVA, regression, and SEM analyses compared CM approaches and symptom associations. The dichotomous approach showed significant effects of abuse and neglect on all symptoms. In the dimensional approach, sexual and physical CM were primary features for PTSD, while sexual and emotional CM were primary for depression and anxiety. Overall, the dimensional approach outperformed the dichotomous approach in capturing symptoms, suggesting its importance in understanding psychopathologies and guiding therapeutic interventions. Our findings highlight the differential associations of CM experiences with PTSD, depression, and anxiety symptoms. The findings suggest the importance of a dimensional CM approach for understanding psychopathologies and possibly informing targeted therapeutic interventions.

Graphene Oxide Covalently Functionalized with 5-Methyl-1,3,4-thiadiazol-2-amine for pH-Sensitive Ga3+ Recovery in Aqueous Solutions.

A novel graphene-based composite, 5-methyl-1,3,4-thiadiazol-2-amine (MTA) covalently functionalized graphene oxide (GO-MTA), was rationally developed and used for the selective sorption of Ga3+ from aqueous solutions, showing a higher adsorption capacity (48.20 mg g-1) toward Ga3+ than In3+ (15.41 mg g-1) and Sc3+ (~0 mg g-1). The adsorption experiment's parameters, such as the contact time, temperature, initial Ga3+ concentration, solution pH, and desorption solvent, were investigated. Under optimized conditions, the GO-MTA composite displayed the highest adsorption capacity of 55.6 mg g-1 toward Ga3+. Moreover, a possible adsorption mechanism was proposed using various characterization methods, including scanning electron microscopy (SEM) equipped with X-ray energy-dispersive spectroscopy (EDS), elemental mapping analysis, Fourier transform infrared (FT-IR) spectroscopy, and X-ray photoelectron spectroscopy (XPS). Ga3+ adsorption with the GO-MTA composite could be better described by the linear pseudo-second-order kinetic model (R2 = 0.962), suggesting that the rate-limiting step may be chemical sorption or chemisorption through the sharing or exchange of electrons between the adsorbent and the adsorbate. Importantly, the calculated qe value (55.066 mg g-1) is closer to the experimental result (55.60 mg g-1). The well-fitted linear Langmuir isothermal model (R2 = 0.972~0.997) confirmed that an interfacial monolayer and cooperative adsorption occur on a heterogeneous surface. The results showed that the GO-MTA composite might be a potential adsorbent for the enrichment and/or separation of Ga3+ at low or ultra-low concentrations in aqueous solutions.

A novel syringe tube combined guide-wire technique for repositioning a nasobiliary catheter in endoscopic nasobiliary drainage.

BACKGROUND: In endoscopic nasobiliary drainage (ENBD), it is complicated to reposition the catheter from the mouth to nostril. We developed a new technique using an 1-mL syringe tube combined with guide-wire when repositioning an ENBD tube from mouth to nose. The aim of this study was to verify its utility. METHODS: A single-center, prospective, randomized, controlled study was conducted between January 2021 and December 2022. Compared to traditional guide-wire technique, the new technique added a 1-mL syringe tube readily available in clinical work. The primary outcome was the ENBD repositioning time.The secondary outcomes included number of ENBD repositioning operations and technical success rate. RESULTS: A total of 253 patients who underwent ENBD during the study period. Among them, 241 patients were enrolled in this study. The procedure time was significantly shorter in the new technique group than in the conventional group (60.7 vs. 98.7, p < 0.001). The median number of operations was 2 in both new technique and conventional technique groups(p = 0.36). Technical success was achieved in 95.0 %(113/119) of the new technique group and 98.4 % (120/122) of the conventional technique group(p = 0.14). Multiple linear regression analysis demonstrated that the new technique group (B = 36.9, 95%CI: 21.6 to 52.3, p < 0.001) was independent factor that reduce the ENBD repositioning time. CONCLUSIONS: The 1-mL syringe tube combined guide-wire technique for repositioning ENBD tube could improve the efficiency and shorten the procedure time than the guide-wire technique. Meanwhile, It is easy to obtain for popularization and application.

Human-Guided Metaverse Synthesis for Quantum Dots: Advancing Nanomaterial Research through Augmented Artificial Intelligence.

This study proposes an innovative paradigm for metaverse-based synthesis experiments, aiming to enhance experimental optimization efficiency through human-guided parameter tuning in the metaverse and augmented artificial intelligence (AI) with human expertise. By integration of the metaverse experimental system with automated synthesis techniques, our goal is to profoundly extend the efficiency and advancement of materials chemistry. Leveraging advanced software algorithms and simulation techniques within the metaverse, we dynamically adjust synthesis parameters in real time, thereby minimizing the conventional trial-and-error methods inherent in laboratory experiments. In comparison fully AI-driven adjustments, this human-intervened approach to metaverse parameter tuning achieves desired results more rapidly. Coupled with automated synthesis techniques, experiments in the metaverse system can be swiftly realized. We validate the high synthesis efficiency and precision of this system through NaYF4:Yb/Tm nanocrystal synthesis experiments, highlighting its immense potential in nanomaterial studies. This pioneering approach not only simplifies the process of nanocrystal preparation but also paves the way for novel methodologies, laying the foundation for future breakthroughs in materials science and nanotechnology.

A hot-emitter transistor based on stimulated emission of heated carriers.

Hot-carrier transistors are a class of devices that leverage the excess kinetic energy of carriers. Unlike regular transistors, which rely on steady-state carrier transport, hot-carrier transistors modulate carriers to high-energy states, resulting in enhanced device speed and functionality. These characteristics are essential for applications that demand rapid switching and high-frequency operations, such as advanced telecommunications and cutting-edge computing technologies1-5. However, the traditional mechanisms of hot-carrier generation are either carrier injection6-11 or acceleration12,13, which limit device performance in terms of power consumption and negative differential resistance14-17. Mixed-dimensional devices, which combine bulk and low-dimensional materials, can offer different mechanisms for hot-carrier generation by leveraging the diverse potential barriers formed by energy-band combinations18-21. Here we report a hot-emitter transistor based on double mixed-dimensional graphene/germanium Schottky junctions that uses stimulated emission of heated carriers to achieve a subthreshold swing lower than 1 millivolt per decade beyond the Boltzmann limit and a negative differential resistance with a peak-to-valley current ratio greater than 100 at room temperature. Multi-valued logic with a high inverter gain and reconfigurable logic states are further demonstrated. This work reports a multifunctional hot-emitter transistor with significant potential for low-power and negative-differential-resistance applications, marking a promising advancement for the post-Moore era.

Direct RNA sequencing in plants: Practical applications and future perspectives.

The transcriptome serves as a bridge that links genomic variation to phenotypic diversity. A vast number of studies using next-generation RNA sequencing (RNA-seq) over the last 2 decades have emphasized the essential roles of the plant transcriptome in response to developmental and environmental conditions, providing numerous insights into the dynamic changes, evolutionary traces, and elaborate regulation of the plant transcriptome. With substantial improvement in accuracy and throughput, direct RNA sequencing (DRS) has emerged as a new and powerful sequencing platform for precise detection of native and full-length transcripts, overcoming many limitations such as read length and PCR bias that are inherent to short-read RNA-seq. Here, we review recent advances in dissecting the complexity and diversity of plant transcriptomes using DRS as the main technological approach, covering many aspects of RNA metabolism, including novel isoforms, poly(A) tails, and RNA modification, and we propose a comprehensive workflow for processing of plant DRS data. Many challenges to the application of DRS in plants, such as the need for machine learning tools tailored to plant transcriptomes, remain to be overcome, and together we outline future biological questions that can be addressed by DRS, such as allele-specific RNA modification. This technology provides convenient support on which the connection of distinct RNA features is tightly built, sustainably refining our understanding of the biological functions of the plant transcriptome.

Enhanced machine learning approaches for OSA patient screening: model development and validation study.

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7-84.1%), 69.9% (95% CI 69.8-69.9%), 86.5% (95% CI 81.6-91.3%), 61.5% (95% CI 56.6-66.4%), 53.2% (95% CI 47.7-58.7%), 65.9% (95% CI 60.2-71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.

A Meta-analysis of Predicting Disorders of Consciousness After Traumatic Brain Injury by Machine Learning Models.

Objective: This study pursued a meta-analysis to evaluate the predictive accuracy of machine learning (ML) models in determining disorders of consciousness (DOC) among patients with traumatic brain injury (TBI). Methods: A comprehensive literature search was conducted to identify ML applications in the establishment of a predictive model of DOC after TBI as of August 6, 2023. Two independent reviewers assessed publication eligibility based on predefined criteria. The predictive accuracy was measured using areas under the receiver operating characteristic curves (AUCs). Subsequently, a random-effects model was employed to estimate the overall effect size, and statistical heterogeneity was determined based on I2 statistic. Additionally, funnel plot asymmetry was employed to examine publication bias. Finally, subgroup analyses were performed based on age, ML type, and relevant clinical outcomes. Results: Final analyses incorporated a total of 46 studies. Both the overall and subgroup analyses exhibited considerable statistical heterogeneity. Machine learning predictions for DOC in TBI yielded an overall pooled AUC of 0.83 (95% CI: 0.82-0.84). Subgroup analysis based on age revealed that the ML model in pediatric patients yielded an overall combined AUC of 0.88 (95% CI: 0.80-0.95); among the model subgroups, logistic regression was the most frequently employed, with an overall pooled AUC of 0.85 (95% CI: 0.83-0.87). In the clinical outcome subgroup analysis, the overall pooled AUC for distinguishing between consciousness recovery and consciousness disorders was 0.84 (95% CI: 0.82-0.85). Conclusion: The findings of this meta-analysis demonstrated outstanding accuracy of ML models in predicting DOC among patients with brain injuries, which presented substantial research value and potential of ML application in this domain.

Nurses' knowledge, attitude and practice in preventing medical device-related pressure injuries and its influencing factors: A cross-sectional study.

AIM: The purpose of this study was to explore the knowledge, attitude, and practice about preventing medical device-related pressure injuries among Chinese nurses. METHOD: A cross-sectional survey was used to gather self-reported data from 2236 nursing staff, from 164 nursing units in a tertiary hospital of China by using MDRPI knowledge, attitude, and practice questionnaire. RESULTS: The median total score for nursing staff in preventing MDRPI is 151 (with a quartile range of 138-165) points, and the score rate is 79.40 %，the median total score for the knowledge dimension is 54 (with a quartile range of 45-60), with a score rate of 70.67 %, the median total score for the attitude dimension is 37 (with a quartile range of 36-44), with a score rate of 85.06 %，the median total score for the practice dimension is 59 (with a quartile range of 55-68), with a score rate of 85.48 %. Multiple linear regression analysis indicated that position and whether training or not are key factors influencing the total score of nursing staff in MDRPI prevention. CONCLUSIONS: The study found that while nurses' attitude and practice score in preventing MDRPI are high, there is room for improvement in their knowledge score. Factors such as nurse position and training were identified as promoting MDRPI prevention. To enhance patient safety and quality of care, it is recommended that medical institutions focus on training programs to improve nurses' knowledge and attitude towards preventing MDRPI.

Reactive oxygen species responsive chitooligosaccharides based nanoplatform for sonodynamic therapy in mammary cancer.

Sonodynamic therapy (SDT) has been extensively studied as a new type of non-invasive treatment for mammary cancer. However, the poor water solubility and defective biocompatibility of sonosensitizers during SDT hinder the sonodynamic efficacy. Herein, a nanoplatform has been developed to achieve high efficient SDT against mammary cancer through the host-guest interaction of ß-cyclodextrin/5-(4-hydroxyphenyl)-10,15,20-triphenylporphyrin (ß-CD-TPP) and ferrocenecarboxylic acid/chitooligosaccharides (FC-COS). Moreover, the glucose oxidase (GOx) was loaded through electrostatic adsorption, which efficiently restricts the energy supply in tumor tissues, thus enhancing the therapeutic efficacy of SDT for tumors. Under optimal conditions, the entire system exhibited favorable water solubility, suitable particle size and viable biocompatibility. This facilitated the integration of the characteristics of starvation therapy and sonodynamic therapy, resulting in efficient inhibition of tumor growth with minimal side effects in vivo. This work may provide new insights into the application of natural oligosaccharides for construct multifunctional nanocarrier systems, which could optimize the design and development of sonodynamic therapy strategies and even combination therapy strategies.

Model Selection for Exposure-Mediator Interaction.

In mediation analysis, the exposure often influences the mediating effect, i.e., there is an interaction between exposure and mediator on the dependent variable. When the mediator is high-dimensional, it is necessary to identify non-zero mediators M and exposure-by-mediator ( X -by- M ) interactions. Although several high-dimensional mediation methods can naturally handle X -by- M interactions, research is scarce in preserving the underlying hierarchical structure between the main effects and the interactions. To fill the knowledge gap, we develop the XMInt procedure to select M and X -by- M interactions in the high-dimensional mediators setting while preserving the hierarchical structure. Our proposed method employs a sequential regularization-based forward-selection approach to identify the mediators and their hierarchically preserved interaction with exposure. Our numerical experiments showed promising selection results. Further, we applied our method to ADNI morphological data and examined the role of cortical thickness and subcortical volumes on the effect of amyloid-beta accumulation on cognitive performance, which could be helpful in understanding the brain compensation mechanism.

Prognostic impact of the high-sensitivity modified glasgow prognostic score on patients undergoing radical surgery for hepatocellular carcinoma : Authorship.

OBJECTIVE: Our study aimed to assess the ability of high-sensitivity modified Glasgow prognostic Score (HS-mGPS) predicting survival in patients undergoing radical surgery for hepatocellular carcinoma (HCC) and to compare the impact with other Inflammation-Based prognostic scoring systems including Glasgow prognostic Score (GPS) and modified GPS (mGPS). METHODS: Our study evaluated 293 patients with HCC who had undergone hepatectomy at the Third Affiliated Hospital of Soochow University between 2010 and 2018. The HS-mGPS, mGPS, and GPS were calculated based on particular cut-off values of preoperative C-reactive protein and albumin, and the correlations between HS-mGPS and clinicopathological parameters were evaluated. Univariate and multivariate survival analyses were conducted by Kaplan-Meier method and Cox proportional hazards model. To evaluate the discrimination ability of each prognostic score, the receiver operating characteristic (ROC) curve were generated and the areas under the curve (AUC) were measured and compared. RESULT: The study results indicated a correlation between elevated HS-mGPS scores and adverse clinical factors, including higher BCLC stage, C-P grade, multiple tumors, and larger tumor diameter. Kaplan-Meier and univariate survival analyses revealed that higher scores of HS-mGPS, GPS, and mGPS were all associated with significantly reduced overall survival (OS) (all p < 0.001). In multivariate survival analysis, HS-mGPS emerged as an independent risk factor for poor OS in patients undergoing hepatectomy for HCC (p = 0.010), along with factors including maximal tumor diameter (p < 0.001), microvascular invasion (MVI) (p = 0.008), and BCLC stage (p = 0.001). The analysis of ROC curves and the AUC values indicated that HS-mGPS outperforms GPS and mGPS in predicting the long-term prognosis of patients with resectable HCC. CONCLUSION: Preoperative HS-mGPS proves superior in predicting adverse long-term outcomes in HCC patients undergoing radical surgery.

Knowledge, Attitude and Practice of Radiologists Regarding Artificial Intelligence in Medical Imaging.

Purpose: This study aimed to investigate the knowledge, attitudes, and practice (KAP) of radiologists regarding artificial intelligence (AI) in medical imaging in the southeast of China. Methods: This cross-sectional study was conducted among radiologists in the Jiangsu, Zhejiang, and Fujian regions from October to December 2022. A self-administered questionnaire was used to collect demographic data and assess the KAP of participants towards AI in medical imaging. A structural equation model (SEM) was used to analyze the relationships between KAP. Results: The study included 452 valid questionnaires. The mean knowledge score was 9.01±4.87, the attitude score was 48.96±4.90, and 75.22% of participants actively engaged in AI-related practices. Having a master's degree or above (OR=1.877, P=0.024), 5-10 years of radiology experience (OR=3.481, P=0.010), AI diagnosis-related training (OR=2.915, P<0.001), and engaging in AI diagnosis-related research (OR=3.178, P<0.001) were associated with sufficient knowledge. Participants with a junior college degree (OR=2.139, P=0.028), 5-10 years of radiology experience (OR=2.462, P=0.047), and AI diagnosis-related training (OR=2.264, P<0.001) were associated with a positive attitude. Higher knowledge scores (OR=5.240, P<0.001), an associate senior professional title (OR=4.267, P=0.026), 5-10 years of radiology experience (OR=0.344, P=0.044), utilizing AI diagnosis (OR=3.643, P=0.001), and engaging in AI diagnosis-related research (OR=6.382, P<0.001) were associated with proactive practice. The SEM showed that knowledge had a direct effect on attitude (ß=0.481, P<0.001) and practice (ß=0.412, P<0.001), and attitude had a direct effect on practice (ß=0.135, P<0.001). Conclusion: Radiologists in southeastern China hold a favorable outlook on AI-assisted medical imaging, showing solid understanding and enthusiasm for its adoption, despite half lacking relevant training. There is a need for more AI diagnosis-related training, an efficient standardized AI database for medical imaging, and active promotion of AI-assisted imaging in clinical practice. Further research with larger sample sizes and more regions is necessary.