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.

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.

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.

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.

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.

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.

RefAI: a GPT-powered retrieval-augmented generative tool for biomedical literature recommendation and summarization.

OBJECTIVES: Precise literature recommendation and summarization are crucial for biomedical professionals. While the latest iteration of generative pretrained transformer (GPT) incorporates 2 distinct modes-real-time search and pretrained model utilization-it encounters challenges in dealing with these tasks. Specifically, the real-time search can pinpoint some relevant articles but occasionally provides fabricated papers, whereas the pretrained model excels in generating well-structured summaries but struggles to cite specific sources. In response, this study introduces RefAI, an innovative retrieval-augmented generative tool designed to synergize the strengths of large language models (LLMs) while overcoming their limitations. MATERIALS AND METHODS: RefAI utilized PubMed for systematic literature retrieval, employed a novel multivariable algorithm for article recommendation, and leveraged GPT-4 turbo for summarization. Ten queries under 2 prevalent topics ("cancer immunotherapy and target therapy" and "LLMs in medicine") were chosen as use cases and 3 established counterparts (ChatGPT-4, ScholarAI, and Gemini) as our baselines. The evaluation was conducted by 10 domain experts through standard statistical analyses for performance comparison. RESULTS: The overall performance of RefAI surpassed that of the baselines across 5 evaluated dimensions-relevance and quality for literature recommendation, accuracy, comprehensiveness, and reference integration for summarization, with the majority exhibiting statistically significant improvements (P-values <.05). DISCUSSION: RefAI demonstrated substantial improvements in literature recommendation and summarization over existing tools, addressing issues like fabricated papers, metadata inaccuracies, restricted recommendations, and poor reference integration. CONCLUSION: By augmenting LLM with external resources and a novel ranking algorithm, RefAI is uniquely capable of recommending high-quality literature and generating well-structured summaries, holding the potential to meet the critical needs of biomedical professionals in navigating and synthesizing vast amounts of scientific literature.

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.

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.

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/).

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.

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.

Negative family and interpersonal relationship are associated with centromedial amygdala functional connectivity alterations in adolescent depression.

The amygdala, known for its functional heterogeneity, plays a critical role in the neural mechanism of adolescent major depressive disorder (aMDD). However, changes in its subregional functional networks in relation to stressful factors remain unclear. We recruited 78 comorbidity-free, medication-naive aMDD patients and 40 matched healthy controls (HC) to explore changes in resting-state functional connectivity (FC) across four amygdala subregions: the centromedial nucleus (CM), the basolateral nucleus (LB), the superficial nucleus (SF), and the amygdalostriatal transition area (Astr). Then, we performed partial correlation analysis to investigate the relationship between amygdala subregional FC and stressful factors as measured by the Chinese Version of Family Environment Scale (FES-CV) and the Adolescent Self-Rated Life Events Scale (ASLEC). Compared to HC, aMDD patients demonstrated significantly decreased functional connectivity between the left CM and left precentral gyrus, as well as between left SF and left precentral gyrus, and between left LB and posterior cingulate gyrus (PCC)/precuneus. In aMDD group, left CM-precentral gyrus FC exhibited negative correlation with interpersonal relationship and punishment, and positive correlation with family cohesion and expressiveness. This study reveals distinct patterns of abnormal functional connectivity among amygdala subregions in aMDD. Our findings suggest that the CM network, in particular, may be involved in stress-related factors in aMDD, which provide a potential target for the prevention and treatment of adolescent depression.

Supramolecular 3 in 1: A Lubrication and Co-Delivery System for Synergistic Advanced Osteoarthritis Therapy.

The complexity, heterogeneity, and drug resistance of diseases necessitate a shift in therapeutic paradigms from monotherapy to combination therapy, which could augment treatment efficiency. Effective treatment of advanced osteoarthritis (OA) requires addressing three key factors contributing to its deterioration: chronic joint inflammation, lubrication dysfunction, and cartilage-tissue degradation. Herein, we present a supramolecular nanomedicine of multifunctionality via molecular recognition and self-assembly. The employed macrocyclic carrier, zwitterion-modified cavitand (CV-2), not only accurately loads various drugs but also functions as a therapeutic agent with lubricating properties for the treatment of OA. Kartogenin (KGN), a drug for articular cartilage regeneration and protection, and flurbiprofen (FP), an anti-inflammatory agent, were coloaded onto CV-2 assembly, forming a supramolecular nanomedicine KGN&FP@CV-2. The three-in-one combination therapy of KGN&FP@CV-2 addresses the three pathological features for treating OA collectively, and thus provides long-term therapeutic benefits for OA through sustained drug release and intrinsic lubrication in vivo. The multifunctional integration of macrocyclic delivery and therapeutics provides a simple, flexible, and universal platform for the synergistic treatment of diseases involving multiple drugs.

Low-Temperature Cross-Linked Hole Transport Layer for High-Performance Blue Quantum-Dot Light-Emitting Diodes.

Quantum-dot light-emitting diodes (QLEDs), a kind of promising optoelectronic device, demonstrate potential superiority in next-generation display technology. Thermal cross-linked hole transport materials (HTMs) have been employed in solution-processed QLEDs due to their excellent thermal stability and solvent resistance, whereas the unbalanced charge injection and high cross-linking temperature of cross-linked HTMs can inhibit the efficiency of QLEDs and limit their application. Herein, a low-temperature cross-linked HTM of 4,4'-bis(3-(((4-vinylbenzyl)oxy)methyl)-9H-carbazol-9-yl)-1,1'-biphenyl (DV-CBP) with a flexible styrene side chain is introduced, which reduces the cross-linking temperature to 150 °C and enhances the hole mobility up to 1.01 × 10-3 cm2 V-1 s-1. More importantly, the maximum external quantum efficiency of 21.35% is successfully obtained on the basis of the DV-CBP as a cross-linked hole transport layer (HTL) for blue QLEDs. The low-temperature cross-linked high-mobility HTL using flexible side chains could be an excellent alternative for future HTL development.

Single Molecular Nanomedicines Based on Macrocyclic Carrier-Drug Conjugates for Concentration-Independent Encapsulation and Precise Activation of Drugs.

Nanomedicines often rely on noncovalent self-assembly and encapsulation for drug loading and delivery. However, challenges such as reproducibility issues due to the multicomponent nature, off-target activation caused by premature drug release, and complex pharmacokinetics arising from assembly dissociation have hindered their clinical translation. In this study, we introduce an innovative design concept termed single molecular nanomedicine (SMNM) based on macrocyclic carrier-drug conjugates. Through the covalent linkage of two chemotherapy drugs to a hypoxia-cleavable macrocyclic carrier, azocalix[4]arene, we obtained two self-included complexes to serve as SMNMs. The intramolecular inclusion feature of the SMNMs has not only demonstrated comprehensive shielding and protection for the drugs but also effectively prevented off-target drug leakage, thereby significantly reducing their side effects and enhancing their antitumor therapeutic efficacy. Additionally, the attributes of being a single component and molecularly dispersed confer advantages such as ease of preparation and good reproducibility for SMNMs, which is desirable for clinical applications.

Risk Factors Asscociated with Hypokalemia during Postanesthesia Recovery and Its Impact on Outcomes in Gynecological Patients: A Propensity Score Matching Study.

OBJECTIVE: This study aimed to explore the risk factors and outcomes of hypokalemia during the recovery period from anesthesia in the gynecological population. METHODS: This retrospective cohort study included 208 patients who underwent gynecological surgery at our institution between January 2021 and March 2022. Data were collected for each patient, including demographics, disease status, surgical data, and clinical information. Preoperative bowel preparation, postoperative gastrointestinal function, and electrolyte levels were compared between the two groups using propensity score matching (PSM). RESULTS: The incidence of hypokalemia (serum potassium level <3.5 mmol/L) during the recovery period from anesthesia was approximately 43.75%. After PSM, oral laxative use (96.4% vs. 82.4%, P=0.005), the number of general enemas (P=0.014), and the rate of ≥2 general enemas (92.9% vs. 77.8%, P=0.004) were identified as risk factors for hypokalemia, which was accompanied by decreased PaCO2 and hypocalcemia. There were no significant differences in postoperative gastrointestinal outcomes, such as the time to first flatus or feces, the I-FEED score (a scoring system was created to evaluate impaired postoperative gastrointestinal function), or postoperative recovery outcomes, between the hypokalemia group and the normal serum potassium group. CONCLUSION: Hypokalemia during postanesthesia recovery period occurred in 43.75% of gynecological patients, which resulted from preoperative mechanical bowel preparation; however, it did not directly affect clinical outcomes, including postoperative gastrointestinal function, postoperative complications, and length of hospital stay.

Involvement of ERK and Oxidative Stress in Airway Exposure to Cadmium Chloride Aggravates Airway Inflammation in Ovalbumin-Induced Asthmatic Mice.

Inhalation represents a significant route of cadmium (Cd) exposure, which is associated with an elevated risk of lung diseases. This research study aims to evaluate the impact of repeated low-dose cadmium inhalation on exacerbating airway inflammation induced by ovalbumin (OVA) in asthma-afflicted mice. Mice were grouped into four categories: control (Ctrl), OVA, cadmium chloride (CdCl2), and OVA + cadmium chloride (OVA + CdCl2). Mice in the OVA group displayed increased airway mucus secretion and peribronchial and airway inflammation characterized by eosinophil cell infiltration, along with elevated levels of Th2 cytokines (IL-4, IL-5, IL-13) in bronchoalveolar lavage fluids (BALFs). These parameters were further exacerbated in the OVA + CdCl2 group. Additionally, the OVA + CdCl2 group exhibited higher levels of the oxidative stress marker malondialdehyde (MDA), greater activity of glutathione peroxidase (GSH-Px), and higher phosphorylation of extracellular regulated kinase (ERK) in lung tissue. Treatment with U0126 (an ERK inhibitor) and α-tocopherol (an antioxidant) in the OVA + CdCl2 group resulted in reduced peribronchial and airway inflammation as well as decreased airway mucus secretion. These findings indicate that CdCl2 exacerbates airway inflammation in OVA-induced allergic asthma mice following airway exposure. ERK and oxidative stress are integral to this process, and the inhibition of these pathways significantly alleviates the adverse effects of CdCl2 on asthma exacerbation.