Concise synthesis of 3-C-glycosyl isocoumarins and 2-glycosyl-4H-chromen-4-ones.

A new Ru-catalyzed C-H activation/cyclization reaction for the synthesis of 3-C-glycosyl isocoumarins and 2-glycosyl-4H-chromen-4-ones with carbonyl sulfoxonium ylide glycogen are reported. In this catalytic system, benzoic acid and its derivatives react with carbonyl sulfoxonium ylide glycogen to yield isocoumarin C-glycosides, while 2-hydroxybenzaldehyde substrates react to produce chromone C-glycosides. These reactions were characterized by mild reaction conditions, broad substrate scope, high functional-group compatibility, and high stereoselectivity to yield several high-value isocoumarins and chromone skeleton-containing C-glycosides. The methods were successfully implemented in the context of large-scale reactions and the late-stage modification of complex natural products.

A New Benchmark: Clinical Uncertainty and Severity Aware Labeled Chest X-Ray Images with Multi-Relationship Graph Learning.

Chest radiography, commonly known as CXR, is frequently utilized in clinical settings to detect cardiopulmonary conditions. However, even seasoned radiologists might offer different evaluations regarding the seriousness and uncertainty associated with observed abnormalities. Previous research has attempted to utilize clinical notes to extract abnormal labels for training deep-learning models in CXR image diagnosis. However, these methods often neglected the varying degrees of severity and uncertainty linked to different labels. In our study, we initially assembled a comprehensive new dataset of CXR images based on clinical textual data, which incorporated radiologists' assessments of uncertainty and severity. Using this dataset, we introduced a multi-relationship graph learning framework that leverages spatial and semantic relationships while addressing expert uncertainty through a dedicated loss function. Our research showcases a notable enhancement in CXR image diagnosis and the interpretability of the diagnostic model, surpassing existing state-of-the-art methodologies. The dataset address of disease severity and uncertainty we extracted is: https://physionet.org/content/cad-chest/1.0/.

Preparation of bacterial cellulose/acrylic acid-based pH-responsive smart dressings by graft copolymerization method.

Conventional wound dressings used in trauma treatment have a single function and insufficient adaptability to the wound environment, making it difficult to meet the complex demands of the healing process. Stimuli-responsive hydrogels can respond specifically to the particular environment of the wound area and realize on-demand responsive release by loading active substances, which can effectively promote wound healing. In this paper, BC/PAA-pH responsive hydrogels (BPPRHs) were prepared by graft copolymerization of acrylic acid (AA) to the end of the molecular chain of bacterial cellulose (BC) network structure. Antibacterial pH-responsive 'smart' dressings were prepared by loading curcumin (Cur) onto the hydrogels. Surface morphology, chemical groups, crystallinity, rheological, and mechanical properties of BPPRHs were analyzed by different characterization methods. The drug release behavior under different physiological conditions and bacteriostatic properties of BPPRH-Cur dressings were also investigated. The results of structural characterization and performance studies show that the hydrogel has a three-dimensional mesh structure and can respond to wound pH in a 'smart' drug release capacity. The drug release behavior of the BPPRH-Cur dressings under different environmental conditions conformed to the logistic and Weibull kinetic models. BPPRH-Cur displayed good antimicrobial activity against common pathogens of wound infections such as E. coli, S. aureus, and P. aeruginosa by destroying the cell membrane and lysing the bacterial cells. This study lays the foundation for the development of new pharmaceutical dressings with positive health, economic and social benefits.

Exploring the role of a novel postbiotic bile acid: Interplay with gut microbiota, modulation of the farnesoid X receptor, and prospects for clinical translation.

The gut microbiota, mainly resides in the colon, possesses a remarkable ability to metabolize different substrates to create bioactive substances, including short-chain fatty acids, indole-3-propionic acid, and secondary bile acids. In the liver, bile acids are synthesized from cholesterol and then undergo modification by the gut microbiota. Beyond those reclaimed by the enterohepatic circulation, small percentage of bile acids escaped reabsorption, entering the systemic circulation to bind to several receptors, such as farnesoid X receptor (FXR), thereby exert their biological effects. Gut microbiota interplays with bile acids by affecting their synthesis and determining the production of secondary bile acids. Reciprocally, bile acids shape out the structure of gut microbiota. The interplay of bile acids and FXR is involved in the development of multisystemic conditions, encompassing metabolic diseases, hepatobiliary diseases, immune associated disorders. In the review, we aim to provide a thorough review of the intricate crosstalk between the gut microbiota and bile acids, the physiological roles of bile acids and FXR in mammals' health and disease, and the clinical translational considerations of gut microbiota-bile acids-FXR in the treatment of the diseases.

Exploring the relationship between pyroptosis and inflammatory bone loss: Evidence from a cigarette smoke-induced osteoporosis mouse model.

Smoking is by far one of the greatest public health threats and is recognized as an important predisposing factor for osteoporosis. Exposure to cigarette smoke (CS) has been reported to be associated with inflammation-associated diseases through the induction of pyroptosis. Nevertheless, the correlation between pyroptosis and bone loss induced by CS remains uninvestigated. Here, a mouse model of mainstream smoke exposure-induced osteoporosis was established. µCT, biomechanical testing, and immunohistochemical staining of bone tissue were used to assess the deleterious effects of CS on bone metabolism. In vitro, the effects of cigarette smoke extracts (CSE) on mouse primary bone marrow-derived mesenchymal stem cells (BMSCs) were tested by cell viability assays, gene and protein expression assays, and alizarin red staining. The utilization of the pyroptosis inhibitor MCC950 served to confirm the critical role of BMSCs pyroptosis in CS-induced osteoporosis. Our results indicated that exposure to mainstream smoke led to a notable decrease in the quantity of osteoblasts and hindered the process of osteogenic differentiation in mice. Additionally, there was a significant increase in the expression of pyroptosis-related proteins in the bone marrow. The inhibitory effects of CSE on cell viability and osteogenic differentiation of BMSCs were found to be dose-dependent in vitro. However, the presence of the pyroptosis inhibitor MCC950 significantly improved the impaired osteogenic differentiation and bone mineralization caused by CSE. These results highlight the crucial involvement of BMSCs pyroptosis in the development of bone loss induced by CS. In summary, the findings of this study provide novel evidence that CS exerts a detrimental effect on the process of osteogenesis in BMSCs through the induction of pyroptosis, ultimately leading to bone loss. Inhibition of pyroptosis effectively attenuated the toxicological effects of CS on BMSCs, providing a new target for preventing inflammatory osteoporosis.

Intermittent Fasting Targets Osteocyte Neuropeptide Y to Relieve Osteoarthritis.

Osteoarthritis is a highly prevalent progressive joint disease that still requires an optimal therapeutic approach. Intermittent fasting is an attractive dieting strategy for improving health. Here this study shows that intermittent fasting potently relieves medial meniscus (DMM)- or natural aging-induced osteoarthritic phenotypes. Osteocytes, the most abundant bone cells, secrete excess neuropeptide Y (NPY) during osteoarthritis, and this alteration can be altered by intermittent fasting. Both NPY and the NPY-abundant culture medium of osteocytes (OCY-CM) from osteoarthritic mice possess pro-inflammatory, pro-osteoclastic, and pro-neurite outgrowth effects, while OCY-CM from the intermittent fasting-treated osteoarthritic mice fails to induce significant stimulatory effects on inflammation, osteoclast formation, and neurite outgrowth. Depletion of osteocyte NPY significantly attenuates DMM-induced osteoarthritis and abolishes the benefits of intermittent fasting on osteoarthritis. This study suggests that osteocyte NPY is a key contributing factor in the pathogenesis of osteoarthritis and intermittent fasting represents a promising nonpharmacological antiosteoarthritis method by targeting osteocyte NPY.

A simple and efficient gene functional analysis method for studying the growth and development of peach seedlings.

Stable genetic transformation of peach [Prunus persica (L.) Batsch] still faces many technical challenges, and existing transient expression methods are limited by tissue type or developmental stage, making it difficult to conduct functional analysis of genes regulating shoot growth. To overcome this dilemma, we developed a three-step method for efficient analysis of gene functions during peach seedling growth and development. This method resulted in transformation frequencies ranging from 48 to 87%, depending on the gene. From transformation of germinating seeds to phenotyping of young saplings took just 1.5 months and can be carried out any time of year. To test the applicability of this method, the function of three tree architecture-related genes, namely PpPDS, PpMAX4, and PpWEEP, and two lateral root-related genes, PpIAA14-1 and -2, were confirmed. Since functional redundancy can challenge gene functional analyses, tests were undertaken with the growth-repressor DELLA, which has three homologous genes, PpDGYLA (DG), PpDELLA1 (D1), and -2 (D2), in peach that are functionally redundant. Silencing using a triple-target vector (TRV2-DG-D1-D2) resulted in transgenic plants taller than those carrying just TRV2-DG or TRV2. Simultaneously silencing the three DELLA genes also attenuated the stature of two dwarf genotypes, 'FHSXT' and 'HSX', which normally accumulate DELLA proteins. Our study provides a method for the functional analysis of genes in peach and can be used for the study of root, stem, and leaf development. We believe this method can be replicated in other woody plants.

Analysis of the Fungal Community Composition in Endemic Orchids with Terrestrial Habitat in Subtropical Regions.

Habenaria and Liparis are well-known orchid genera that grow in terrestrial habitats in the tropics, subtropics or temperate zones. Three species have been found in subtropical regions of China, inhabiting terrestrial to epiphytic habitats. This study focuses on three species, H. dentata (distributed in Asia), H. yachangensis, and L. gigantea. For H. yachangensis and L. gigantea, there is no information about the mycorrhizal community in these species. This study aims to conduct the fungal community screening of Chinese ground orchids from subtropical regions. We performed a comparative analysis of the fungal community among H. dentata, H. yachangensis, and L. gigantea, determining their ITS regions using NGS paired-end sequences. The results clarified the diversity and the predominance of fungal genera. Ascomycota was abundant compared to Basidiomycota or other fungi groups in all communities, with a high dominance in all populations, especially for L. gigantea. At different root spatial locations, the fungal community diversity and richness were higher in the soil than in the rhizosphere or inner root. However, the results suggest that L. gigantea has a different fungal community compared to Habenaria species. In this order, the subtropical terrestrial orchids have a different fungal network compared to the northern terrestrial orchids. Also, there is a high probability of co-existence and co-evolution of endophytic fungi in these terrestrial orchids, indicating the potential role of host plants in selecting an endophytic fungal community. Furthermore, our results highlight the need to elucidate the microbe interactions of these unique orchids for long-term purposes, such as isolating indigenous fungi for suitable inoculants for further orchid propagation, restoration, and conservation.

Emerging probing perspective of two-dimensional materials physics: terahertz emission spectroscopy.

Terahertz (THz) emission spectroscopy (TES) has emerged as a highly effective and versatile technique for investigating the photoelectric properties of diverse materials and nonlinear physical processes in the past few decades. Concurrently, research on two-dimensional (2D) materials has experienced substantial growth due to their atomically thin structures, exceptional mechanical and optoelectronic properties, and the potential for applications in flexible electronics, sensing, and nanoelectronics. Specifically, these materials offer advantages such as tunable bandgap, high carrier mobility, wideband optical absorption, and relatively short carrier lifetime. By applying TES to investigate the 2D materials, their interfaces and heterostructures, rich information about the interplay among photons, charges, phonons and spins can be unfolded, which provides fundamental understanding for future applications. Thus it is timely to review the nonlinear processes underlying THz emission in 2D materials including optical rectification, photon-drag, high-order harmonic generation and spin-to-charge conversion, showcasing the rich diversity of the TES employed to unravel the complex nature of these materials. Typical applications based on THz emissions, such as THz lasers, ultrafast imaging and biosensors, are also discussed. Step further, we analyzed the unique advantages of spintronic terahertz emitters and the future technological advancements in the development of new THz generation mechanisms leading to advanced THz sources characterized by wide bandwidth, high power and integration, suitable for industrial and commercial applications. The continuous advancement and integration of TES with the study of 2D materials and heterostructures promise to revolutionize research in different areas, including basic materials physics, novel optoelectronic devices, and chips for post-Moore's era.

Interpretable medical image Visual Question Answering via multi-modal relationship graph learning.

Medical Visual Question Answering (VQA) is an important task in medical multi-modal Large Language Models (LLMs), aiming to answer clinically relevant questions regarding input medical images. This technique has the potential to improve the efficiency of medical professionals while relieving the burden on the public health system, particularly in resource-poor countries. However, existing medical VQA datasets are small and only contain simple questions (equivalent to classification tasks), which lack semantic reasoning and clinical knowledge. Our previous work proposed a clinical knowledge-driven image difference VQA benchmark using a rule-based approach (Hu et al., 2023). However, given the same breadth of information coverage, the rule-based approach shows an 85% error rate on extracted labels. We trained an LLM method to extract labels with 62% increased accuracy. We also comprehensively evaluated our labels with 2 clinical experts on 100 samples to help us fine-tune the LLM. Based on the trained LLM model, we proposed a large-scale medical VQA dataset, Medical-CXR-VQA, using LLMs focused on chest X-ray images. The questions involved detailed information, such as abnormalities, locations, levels, and types. Based on this dataset, we proposed a novel VQA method by constructing three different relationship graphs: spatial relationships, semantic relationships, and implicit relationship graphs on the image regions, questions, and semantic labels. We leveraged graph attention to learn the logical reasoning paths for different questions. These learned graph VQA reasoning paths can be further used for LLM prompt engineering and chain-of-thought, which are crucial for further fine-tuning and training multi-modal large language models. Moreover, we demonstrate that our approach has the qualities of evidence and faithfulness, which are crucial in the clinical field. The code and the dataset is available at https://github.com/Holipori/Medical-CXR-VQA.

Interactions between rice starch and flavor components and their impact on flavor.

Flavor is considered one of the most significant factors affecting food quality. However, it is often susceptible to environmental factors, so encapsulation is highly necessary to facilitate proper handling and processing. In this study, the structural changes in starch encapsulation and their effects on flavor retention were investigated using indica starch (RS) as a matrix to encapsulate three flavoring compounds, namely nonanoic acid, 1-octanol, and 2-pentylfuran. The rheological and textural results suggested that the inclusion of flavor compounds improved the intermolecular interactions between starch molecules, resulting in a significant increase in the physicochemical properties of starch gels in the order: nonanoic acid > 1-octanol > 2-pentylfuran. The XRD results confirmed the successful preparation of v-starch. Additionally, the inclusion complexes (ICs) were characterized using FT-IR, SEM, and DSC techniques. The results showed that v-starch formed complexes with Flavor molecules. The higher enthalpy of the complexes suggested that the addition of alcohols and acids could improve the intermolecular complexation between starch molecules. The retention rates of three flavor compounds in starch were determined using HS-GC, with the values of 51.7 %, 32.37 %, and 35.62 %. Overall, this study provides insights into novel approaches to enhance the quality and flavor retention, improve the storability and stability, reduce losses during processing and storage, and extend the shelf life of starchy products.

Age-related changes in the bacterial composition of healthy female facial skin in Beijing area.

OBJECTIVE: Exploring the effects of age on microbial community structure and understanding the effects of chronological ageing as well as sun exposure on microbial community diversity. METHOD: The microbial characteristics of the facial skin of 98 adult women aged 18-70 years were studied using 16S rRNA gene sequencing, and differences based on age and reported sun exposure were assessed. RESULTS: The cheek skin's bacterial diversity and richness increased with age. The relative abundance of Cutibacterium decreased with age, while the relative abundance of Corynebacterium, Anaerococcus, Paracoccus, Micrococcus, Kocuria, Kytococcus, and Chryseobacterium increased. In addition, an increase in Micrococcus and a decrease in Cutibacterium were observed in volunteers with more than 2 h of daily sun exposure compared to volunteers with <2 h of daily sun exposure. Under low-sunlight conditions, Cutibacterium was more prevalent in the youth group, and Corynebacterium, Anaerococcus, and Kytococcus were more prevalent in the older group. CONCLUSION: The diversity and composition of the bacterial community on the cheeks are affected by age and extrinsic factors (sun exposure) may also play a role in this.

OBJECTIF: Étudier les effets de l'âge sur la structure de la communauté microbienne et comprendre les effets du vieillissement chronologique ainsi que de l'exposition au soleil sur la diversité de la communauté microbienne. MÉTHODE: Les caractéristiques microbiennes de la peau du visage de 98 femmes adultes âgées de 18 à 70 ans ont été étudiées à l'aide du séquençage génétique de l'ARNr 16S, et les différences basées sur l'âge et l'exposition au soleil rapportée ont été évaluées. RÉSULTAT: La diversité et la richesse bactériennes de la peau des joues ont augmenté avec l'âge. L'abondance relative de Cutibacterium a diminué avec l'âge, tandis que l'abondance relative de Corynebacterium, Anaerococcus, Paracoccus, Micrococcus, Kocuria, Kytococcus et Chryseobacterium a augmenté. De plus, une augmentation de Micrococcus et une diminution de Cutibacterium ont été observées chez des volontaires ayant été exposés au soleil pendant plus de 2 heures par jour par rapport à des volontaires ayant été exposés au soleil pendant moins de 2 heures par jour. Dans des conditions de faible luminosité, Cutibacterium était plus prévalent dans le bras des personnes jeunes, et Corynebacterium, Anaerococcus et Kytococcus étaient plus prévalents dans le bras des personnes plus âgées. CONCLUSION: La diversité et la composition de la communauté bactérienne sur les joues sont affectées par l'âge et des facteurs extrinsèques (exposition au soleil) peuvent également y jouer un rôle.

Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study.

BACKGROUND: Alzheimer disease and related dementias (ADRD) rank as the sixth leading cause of death in the United States, underlining the importance of accurate ADRD risk prediction. While recent advancements in ADRD risk prediction have primarily relied on imaging analysis, not all patients undergo medical imaging before an ADRD diagnosis. Merging machine learning with claims data can reveal additional risk factors and uncover interconnections among diverse medical codes. OBJECTIVE: The study aims to use graph neural networks (GNNs) with claim data for ADRD risk prediction. Addressing the lack of human-interpretable reasons behind these predictions, we introduce an innovative, self-explainable method to evaluate relationship importance and its influence on ADRD risk prediction. METHODS: We used a variationally regularized encoder-decoder GNN (variational GNN [VGNN]) integrated with our proposed relation importance method for estimating ADRD likelihood. This self-explainable method can provide a feature-important explanation in the context of ADRD risk prediction, leveraging relational information within a graph. Three scenarios with 1-year, 2-year, and 3-year prediction windows were created to assess the model's efficiency, respectively. Random forest (RF) and light gradient boost machine (LGBM) were used as baselines. By using this method, we further clarify the key relationships for ADRD risk prediction. RESULTS: In scenario 1, the VGNN model showed area under the receiver operating characteristic (AUROC) scores of 0.7272 and 0.7480 for the small subset and the matched cohort data set. It outperforms RF and LGBM by 10.6% and 9.1%, respectively, on average. In scenario 2, it achieved AUROC scores of 0.7125 and 0.7281, surpassing the other models by 10.5% and 8.9%, respectively. Similarly, in scenario 3, AUROC scores of 0.7001 and 0.7187 were obtained, exceeding 10.1% and 8.5% than the baseline models, respectively. These results clearly demonstrate the significant superiority of the graph-based approach over the tree-based models (RF and LGBM) in predicting ADRD. Furthermore, the integration of the VGNN model and our relation importance interpretation could provide valuable insight into paired factors that may contribute to or delay ADRD progression. CONCLUSIONS: Using our innovative self-explainable method with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships. This methodology not only enables ADRD risk modeling but also shows potential for other image analysis predictions using claims data.

Deep Learning-Based Prediction Modeling of Major Adverse Cardiovascular Events After Liver Transplantation.

Objective: To validate deep learning models' ability to predict post-transplantation major adverse cardiovascular events (MACE) in patients undergoing liver transplantation (LT). Patients and Methods: We used data from Optum's de-identified Clinformatics Data Mart Database to identify liver transplant recipients between January 2007 and March 2020. To predict post-transplantation MACE risk, we considered patients' demographics characteristics, diagnoses, medications, and procedural data recorded back to 3 years before the LT procedure date (index date). MACE is predicted using the bidirectional gated recurrent units (BiGRU) deep learning model in different prediction interval lengths up to 5 years after the index date. In total, 18,304 liver transplant recipients (mean age, 57.4 years [SD, 12.76]; 7158 [39.1%] women) were used to develop and test the deep learning model's performance against other baseline machine learning models. Models were optimized using 5-fold cross-validation on 80% of the cohort, and model performance was evaluated on the remaining 20% using the area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR). Results: Using different prediction intervals after the index date, the top-performing model was the deep learning model, BiGRU, and achieved an AUC-ROC of 0.841 (95% CI, 0.822-0.862) and AUC-PR of 0.578 (95% CI, 0.537-0.621) for a 30-day prediction interval after LT. Conclusion: Using longitudinal claims data, deep learning models can efficiently predict MACE after LT, assisting clinicians in identifying high-risk candidates for further risk stratification or other management strategies to improve transplant outcomes based on important features identified by the model.

Perioperative liberal drinking management promotes postoperative gastrointestinal function recovery after gynecological laparoscopic surgery: A randomized controlled trial.

STUDY OBJECTIVE: This study aims to evaluate the effect of perioperative liberal drinking management, including preoperative carbohydrate loading (PCL) given 2 h before surgery and early oral feeding (EOF) at 6 h postoperatively, in enhancing postoperative gastrointestinal function and improving outcomes in gynecologic patients. The hypotheses are that the perioperative liberal drinking management accelerates the recovery of gastrointestinal function, enhances dietary tolerance throughout hospitalization, and ultimately reduces the length of hospitalization. DESIGN: A prospective randomized controlled trial. SETTING: Operating room and gynecological ward in Wuhan Union Hospital. PATIENTS: We enrolled 210 patients undergoing elective gynecological laparoscopic surgery, and 157 patients were included in the final analysis. INTERVENTIONS: Patients were randomly allocated in a 1:1:1 ratio into three groups, including the control, PCL, and PCL-EOF groups. The anesthetists and follow-up staff were blinded to group assignment. MEASUREMENTS: The primary outcome was the postoperative Intake, Feeling nauseated, Emesis, Examination, and Duration of symptoms (I-FEED) score (range 0 to 14, higher scores worse). Secondary outcomes included the incidence of I-FEED scores >2, and other additional indicators to monitor postoperative gastrointestinal function, including time to first flatus, time to first defecation, time to feces Bristol grade 3-4, and time to tolerate diet. Additionally, we collected other ERAS recovery indicators, including the incidence of PONV, complications, postoperative pain score, satisfaction score, and the quality of postoperative functional recovery at discharge. MAIN RESULTS: The PCL-EOF exhibited significantly enhanced gastrointestinal function recovery compared to control group and PCL group (p < 0.05), with the lower I-FEED score (PCL: 0[0,1] vs. PCL-EOF: 0[0,0] vs. control: 1[0,2]) and the reduced incidence of I-FEED >2 (PCL:8% vs. PCL-EOF: 2% vs. control:21%). Compared to the control, the intervention of PCL-EOF protected patients from the incidence of I-FEED score > 2 [HR:0.09, 95%CI (0.01-0.72), p = 0.023], and was beneficial in promoting the patient's postoperative first flatus [PCL-EOF: HR:3.33, 95%CI (2.14-5.19),p < 0.001], first defecation [PCL-EOF: HR:2.76, 95%CI (1.83-4.16), p < 0.001], Bristol feces grade 3-4 [PCL-EOF: HR:3.65, 95%CI (2.36-5.63), p < 0.001], first fluid diet[PCL-EOF: HR:2.76, 95%CI (1.83-4.16), p < 0.001], and first normal diet[PCL-EOF: HR:6.63, 95%CI (4.18-10.50), p < 0.001]. Also, the length of postoperative hospital stay (PCL-EOF: 5d vs. PCL: 6d and control: 6d, p < 0.001), the total cost (PCL-EOF: 25052 ± 3650y vs. PCL: 27914 ± 4684y and control: 26799 ± 4775y, p = 0.005), and postoperative VAS pain score values [POD0 (PCL-EOF: 2 vs. control: 4 vs. PCL: 4, p < 0.001), POD1 (PCL-EOF: 1 vs. control: 3 vs. PCL: 2, p < 0.001), POD2 (PCL-EOF: 1 vs. control:2 vs. PCL: 1, p < 0.001), POD3 (PCL-EOF: 0 vs. control: 1 vs. PCL: 1, p < 0.001)] were significantly reduced in PCL-EOF group. CONCLUSIONS: Our primary endpoint, I-FEED score demonstrated significant reduction with perioperative liberal drinking, serving as a protective intervention against I-FEED>2. Gastrointestinal recovery metrics, such as time to first flatus and defecation, also showed substantial improvements. Furthermore, the intervention enhanced postoperative dietary tolerance and expedited early recovery. TRIAL REGISTRATION: ChiCTR2300071047(https://www.chictr.org.cn/).

Sex differences in aberrant functional connectivity of three core networks and subcortical networks in medication-free adolescent-onset major depressive disorder.

Major depressive disorder demonstrated sex differences in prevalence and symptoms, which were more pronounced during adolescence. Yet, research on sex-specific brain network characteristics in adolescent-onset major depressive disorder remains limited. This study investigated sex-specific and nonspecific alterations in resting-state functional connectivity of three core networks (frontoparietal network, salience network, and default mode network) and subcortical networks in adolescent-onset major depressive disorder, using seed-based resting-state functional connectivity in 50 medication-free patients with adolescent-onset major depressive disorder and 56 healthy controls. Irrespective of sex, compared with healthy controls, adolescent-onset major depressive disorder patients showed hypoconnectivity between bilateral hippocampus and right superior temporal gyrus (default mode network). More importantly, we further found that females with adolescent-onset major depressive disorder exhibited hypoconnectivity within the default mode network (medial prefrontal cortex), and between the subcortical regions (i.e. amygdala, striatum, and thalamus) with the default mode network (angular gyrus and posterior cingulate cortex) and the frontoparietal network (dorsal prefrontal cortex), while the opposite patterns of resting-state functional connectivity alterations were observed in males with adolescent-onset major depressive disorder, relative to their sex-matched healthy controls. Moreover, several sex-specific resting-state functional connectivity changes were correlated with age of onset, sleep disturbance, and anxiety in adolescent-onset major depressive disorder with different sex. These findings suggested that these sex-specific resting-state functional connectivity alterations may reflect the differences in brain development or processes related to early illness onset, underscoring the necessity for sex-tailored diagnostic and therapeutic approaches in adolescent-onset major depressive disorder.

Assembly of Heterocyclic C-Glycosides by Ru-Catalyzed C-H Activation/Cyclization with Carbonyl Sulfoxonium Ylide Glyco-Reagents.

New carbonyl sulfoxonium ylide glyco-reagents have been developed, enabling the synthesis of versatile heteroarene C-glycosides through a Ru-catalyzed C-H activation/annulation strategy. These reactions tolerate various saccharide donors and represent a significant advance in the stereoselective synthesis of heterocyclic C-glycosides. Furthermore, the strategy and methods could be applied to large-scale reactions and late-stage modifications of some structurally complex natural products or drugs.

Large Language Models for Social Determinants of Health Information Extraction from Clinical Notes - A Generalizable Approach across Institutions.

The consistent and persuasive evidence illustrating the influence of social determinants on health has prompted a growing realization throughout the health care sector that enhancing health and health equity will likely depend, at least to some extent, on addressing detrimental social determinants. However, detailed social determinants of health (SDoH) information is often buried within clinical narrative text in electronic health records (EHRs), necessitating natural language processing (NLP) methods to automatically extract these details. Most current NLP efforts for SDoH extraction have been limited, investigating on limited types of SDoH elements, deriving data from a single institution, focusing on specific patient cohorts or note types, with reduced focus on generalizability. This study aims to address these issues by creating cross-institutional corpora spanning different note types and healthcare systems, and developing and evaluating the generalizability of classification models, including novel large language models (LLMs), for detecting SDoH factors from diverse types of notes from four institutions: Harris County Psychiatric Center, University of Texas Physician Practice, Beth Israel Deaconess Medical Center, and Mayo Clinic. Four corpora of deidentified clinical notes were annotated with 21 SDoH factors at two levels: level 1 with SDoH factor types only and level 2 with SDoH factors along with associated values. Three traditional classification algorithms (XGBoost, TextCNN, Sentence BERT) and an instruction tuned LLM-based approach (LLaMA) were developed to identify multiple SDoH factors. Substantial variation was noted in SDoH documentation practices and label distributions based on patient cohorts, note types, and hospitals. The LLM achieved top performance with micro-averaged F1 scores over 0.9 on level 1 annotated corpora and an F1 over 0.84 on level 2 annotated corpora. While models performed well when trained and tested on individual datasets, cross-dataset generalization highlighted remaining obstacles. To foster collaboration, access to partial annotated corpora and models trained by merging all annotated datasets will be made available on the PhysioNet repository.

Calixarene-Based Supramolecular Sensor Array for Pesticide Discrimination.

The identification and detection of pesticides is crucial to protecting both the environment and human health. However, it can be challenging to conveniently and rapidly differentiate between different types of pesticides. We developed a supramolecular fluorescent sensor array, in which calixarenes with broad-spectrum encapsulation capacity served as recognition receptors. The sensor array exhibits distinct fluorescence change patterns for seven tested pesticides, encompassing herbicides, insecticides, and fungicides. With a reaction time of just three minutes, the sensor array proves to be a rapid and efficient tool for the discrimination of pesticides. Furthermore, this supramolecular sensing approach can be easily extended to enable real-time and on-site visual detection of varying concentrations of imazalil using a smartphone with a color scanning application. This work not only provides a simple and effective method for pesticide identification and quantification, but also offers a versatile and advantageous platform for the recognition of other analytes in relevant fields.

Functional connectivity of the default mode network in first-episode drug-naïve patients with major depressive disorder.

BACKGROUND: Alterations in the default mode network (DMN) have been reported in major depressive disorder (MDD), well-replicated robust alterations of functional connectivity (FC) of DMN remain to be established. Investigating the functional connections of DMN at the overall and subsystem level in early MDD patients has the potential to advance our understanding of the physiopathology of this disorder. METHODS: We recruited 115 first-episode drug-naïve patients with MDD and 137 demographic-matched healthy controls (HCs). We first compared FC within the DMN, within/between the DMN subsystems, and from DMN subsystems to the whole brain between groups. Subsequently, we explored correlations between clinical features and identified alterations in FC. RESULTS: First-episode drug-naïve patients with MDD showed significantly increased FC within the DMN, dorsal DMN and medial DMN. Each subsystem showed a distinct FC pattern with other brain networks. Increased FC between the subsystems (core DMN, dorsal DMN) and other networks was associated with more severe depressive symptoms, while medial DMN-related connectivity correlated with memory performance. LIMITATIONS: The relatively large "pure" MDD sample could only be generalized to a limited population. And, atypical asymmetric FCs in the DMN related to MDD might be missed for only left-lateralized ROIs were used to avoid strong correlations between mirrored (right/left) seed regions. CONCLUSION: These findings suggest patients with early MDD showed distinct patterns of FC alterations throughout DMN and its subsystems, which were related to illness severity and illness-associated cognitive impairment, highlighting their clinical significance.