A rat model of adenoid hypertrophy constructed by using ovalbumin and lipopolysaccharides to induce allergy, chronic inflammation, and chronic intermittent hypoxia.

BACKGROUND: Adenoid hypertrophy (AH) is a common pediatric disease that significantly impacts the growth and quality of life of children. However, there is no replicable and valid model for AH. METHODS: An AH rat model was developed via comprehensive allergic sensitization, chronic inflammation induction, and chronic intermittent hypoxia (CIH). The modeling process involved three steps: female Sprague-Dawley rats (aged 4-5 weeks) were used for modeling. Allergen sensitization was induced via intraperitoneal administration and intranasal provocation using ovalbumin (OVA); chronic nasal inflammation was induced through intranasal lipopolysaccharide (LPS) administration for sustained nasal irritation; CIH akin to obstructive sleep apnea/hypopnea syndrome was induced using an animal hypoxia chamber. Postmodel establishment, behaviors, and histological changes in nasopharynx-associated lymphoid tissue (NALT) and nasal mucosa were assessed. Arterial blood gas analysis and quantification of serum and tissue levels of (interleukin) IL-4 and IL-13, OVA-specific immunoglobulin E (sIgE), eosinophil cationic protein (ECP), tumor necrosis factor (TNF-α), IL-17, and transforming growth factor (TGF)-ß were conducted for assessment. The treatment group received a combination of mometasone furoate and montelukast sodium for a week and then was evaluated. RESULTS: Rats exhibited notable nasal symptoms and hypoxia after modeling. Histopathological analysis revealed NALT follicle hypertrophy and nasal mucosa inflammatory cell infiltration. Elevated IL-4, IL-13, IL-17, OVA-sIgE, ECP, and TNF-α levels and reduced TGF-ß levels were observed in the serum and tissue of model-group rats. After a week of treatment, the treatment group exhibited symptom and inflammatory factor improvement. CONCLUSION: The model effectively simulates AH symptoms and pathological changes. But it should be further validated for genetic, immunological, and hormonal backgrounds in the currently used and other strains and species.

Lactobacillus Reuteri Vesicles Regulate Mitochondrial Function of Macrophages to Promote Mucosal and Cutaneous Wound Healing.

The interplay between bacteria and their host influences the homeostasis of the human immune microenvironment, and this reciprocal interaction also affects the process of tissue damage repair. A variety of immunomodulatory commensal bacteria reside in the body, capable of delivering membrane vesicles (MVs) to host cells to regulate the local immune microenvironment. This research revealed, for the initial time, the significant enhancement of mucosal and cutaneous wound healing by MVs secreted by the human commensal Lactobacillus reuteri (RMVs) through modulation of the inflammatory environment in wound tissue. Local administration of RMVs reduces the proportion of pro-inflammatory macrophages in inflamed tissues and mitigates the level of local inflammation, thereby facilitating the healing of oral mucosa and cutaneous wounds. The elevated oxidative stress levels in activated pro-inflammatory macrophages can be modulated by RMVs, resulting in phenotypic transformation of macrophages. Furthermore, 3-hydroxypropionaldehyde present in RMVs can decrease the mitochondrial permeability of macrophages and stabilize the mitochondrial membrane potential, thereby promoting the conversion of macrophages to an anti-inflammatory phenotype. This study pioneers the significance of commensal bacterial MVs in tissue injury repair and presents a novel concept for the repair of tissue damage.

Types of Septic Cardiomyopathy: Prognosis and Influencing Factors - A Clinical Study.

Objective: To explore the prognostic outcomes associated with different types of septic cardiomyopathy and analyze the factors that exert an influence on these outcomes. Methods: The data collected within 24 hours of ICU admission included cardiac troponin I (cTnI), N-terminal pro-Brain Natriuretic Peptide (NT-proBNP); SOFA (sequential organ failure assessment) scores, and the proportion of vasopressor use. Based on echocardiographic outcomes, septic cardiomyopathy was categorized into left ventricular (LV) systolic dysfunction, LV diastolic dysfunction, and right ventricular (RV) systolic dysfunction. Differences between the mortality and survival groups, as well as between each cardiomyopathy subgroup and the non-cardiomyopathy group were compared, to explore the influencing factors of cardiomyopathy. Results: A cohort of 184 patients were included in this study, with LV diastolic dysfunction having the highest incidence rate (43.5%). The mortality group had significantly higher SOFA scores, vasopressor use, and cTnI levels compared to the survival group; the survival group had better LV diastolic function than the mortality group (p < 0.05 for all). In contrast to the non-cardiomyopathy group, each subgroup within the cardiomyopathy category exhibited elevated levels of cTnI. The subgroup with left ventricular diastolic dysfunction demonstrated a higher prevalence of advanced age, hypertension, diabetes mellitus, coronary artery disease, and an increased mortality rate; the RV systolic dysfunction subgroup had higher SOFA scores and NT-proBNP levels, and a higher mortality rate (P < 0.05 for all); the LV systolic dysfunction subgroup had a similar mortality rate (P > 0.05). Conclusion: Patients with advanced age, hypertension, diabetes mellitus, or coronary artery disease are more prone to develop LV diastolic dysfunction type of cardiomyopathy; cardiomyopathy subgroups had higher levels of cTnI. The RV systolic dysfunction cardiomyopathy subgroup had higher SOFA scores and NT-proBNP levels. The occurrence of RV systolic dysfunction in patients with sepsis significantly increased the mortality rate.

The α subunit of AMP-activated protein kinase is critical for the metabolic success and tachyzoite proliferation of Toxoplasma gondii.

Toxoplasma gondii is a zoonotic parasite infecting humans and nearly all warm-blooded animals. Successful parasitism in diverse hosts at various developmental stages requires the parasites to fine tune their metabolism according to environmental cues and the parasite's needs. By manipulating the ß and γ subunits, we have previously shown that AMP-activated protein kinase (AMPK) has critical roles in regulating the metabolic and developmental programmes. However, the biological functions of the α catalytic subunit have not been established. T. gondii encodes a canonical AMPKα, as well as a KIN kinase whose kinase domain has high sequence similarities to those of classic AMPKα proteins. Here, we found that TgKIN is dispensable for tachyzoite growth, whereas TgAMPKα is essential. Depletion of TgAMPKα expression resulted in decreased ATP levels and reduced metabolic flux in glycolysis and the tricarboxylic acid cycle, confirming that TgAMPK is involved in metabolic regulation and energy homeostasis in the parasite. Sequential truncations at the C-terminus found an α-helix that is key for the function of TgAMPKα. The amino acid sequences of this α-helix are not conserved among various AMPKα proteins, likely because it is involved in interactions with TgAMPKß, which only have limited sequence similarities to AMPKß in other eukaryotes. The essential role of the less conserved C-terminus of TgAMPKα provides opportunities for parasite specific drug designs targeting TgAMPKα.

Influence of comorbidity of chronic diseases on basic activities of daily living among older adults in China: a propensity score-matched study.

Rationale: With the accelerating process of population aging, the comorbidity of chronic disease (CCD) has become a major public health problem that threatens the health of older adults. Objective: This study aimed to assess whether CCD is associated with basic activities of daily living (BADL) and explore the factors influencing BADL in older adults. Method: A cross-sectional community health survey with stratified random sampling among older residents (≥60 years old) was conducted in 2022. A questionnaire was used to collect information on BADL, chronic diseases, and other relevant aspects. Propensity score matching (PSM) was used to match the older adults with and without CCD. Univariate and multivariate logistic regression analyses were used to explore the factors influencing BADL. PSM was used to match participants with single-chronic disease (SCD) and CCD. Results: Among the 47,720 participants, those with CCD showed a higher prevalence of BADL disability (13.07%) than those with no CCD (6.33%) and SCD (7.39%). After adjusting for potential confounders with PSM, 6,513 pairs of cases with and without CCD were matched. The univariate analysis found that the older adults with CCD had a significantly higher prevalence of BADL disability (13.07%, 851 of 6,513) than those without CCD (9.83%, 640 of 6,513, P < 0.05). The multivariate logistic regression analysis revealed that CCD was a risk factor for BADL in older adults [OR = 1.496, 95% CI: 1.393-1.750, P < 0.001]. In addition, age, educational level, alcohol intake, social interaction, annual physical examination, retirement benefits, depression, weekly amount of exercise, and years of exercise were related to BADL disability (P < 0.05). PSM matching was performed on participants with CCD and SCD and showed that the older adults with CCD had a significantly higher prevalence of BADL disability (13.07%, 851 of 6,513) than those with SCD (11.39%, 742 of 6,513, P < 0.05). Conclusion: The older adults with CCD are at a higher risk of BADL disability than their counterparts with no CCD or SCD. Therefore, we advocate paying attention to and taking measures to improve the health and quality of life of these individuals.

A rapid and sensitive aptamer-based biosensor for beta-lactoglobulin in milk.

Beta-lactoglobulin (ß-Lg), a prominent milk protein, is a major contributor to milk allergies. The quantitative assessment of ß-Lg is a valuable method for assessing the allergenic potential of dairy products. In this study, a specific aptamer, ß-Lg-01, with an affinity constant (KD) of 28.6 nM for ß-Lg was screened through seven rounds of magnetic bead SELEX (MB-SELEX). A novel bio-layer interferometry (BLI)-based aptasensor was developed, which had a limit of detection (LOD) of 0.3 ng mL-1, a linear range of 1.5 ng mL-1-15 µg mL-1, and a recovery rate of 102-116% among the milk samples. This aptasensor provides a potential tool for the detection and risk assessment of ß-Lg within 10 min.

Correlation between plasma lipoprotein-associated phospholipase A2 levels and risk of ischaemic stroke recurrence by gender in the Chinese population.

OBJECTIVE: To investigate the correlation between gender differences in plasma lipoprotein phospholipase A2 (Lp-PLA2) levels and the risk of recurrent stroke in patients with acute ischaemic stroke in China. METHODS: We conducted a prospective follow-up study that included baselineLp-PLA2 levels and NIH Stroke Scale (NIHSS) scores in patients with ischaemic stroke upon admission. The diagnostic efficacy of the baseline Lp-PLA2 level for stroke recurrence was evaluated. And Kaplan‒Meier method was used to analyse the difference in the risk of recurrent stroke between these two groups among males and females. A paired t test was used to analyse the difference in Lp-PLA2 levels in male and female patients after follow-up. RESULTS: Baseline plasma Lp-PLA2 was higher in men and women with recurrent stroke than in those without recurrent stroke. The correlation between baseline Lp-PLA2 and neurological impairment was higher in female than male stroke patients (R = 0.338 and 0.253, respectively). Although weakly correlated with neurological impairment, baseline Lp-PLA2 was more effective in predicting recurrent stroke (AUC = 0.705 in men, 0.788 in women). A Cox model was used to compare the risk of stroke between the high- and low-Lp-PLA2 groups (OR = 3.98 in men, 2.61 in women). According to the follow-up time of 6 months as the node, Lp-PLA2 will give different risk indicators. CONCLUSION: Elevated plasma Lp-PLA2 is an independent risk factor for recurrent ischaemic stroke but is not strongly associated with the degree of cerebral damage. The predictive value of baseline Lp-PLA2 for stroke recurrence risk was higher in females than in males.

Clinical knowledge-guided deep reinforcement learning for sepsis antibiotic dosing recommendations.

Sepsis is the third leading cause of death worldwide. Antibiotics are an important component in the treatment of sepsis. The use of antibiotics is currently facing the challenge of increasing antibiotic resistance (Evans et al., 2021). Sepsis medication prediction can be modeled as a Markov decision process, but existing methods fail to integrate with medical knowledge, making the decision process potentially deviate from medical common sense and leading to underperformance. (Wang et al., 2021). In this paper, we use Deep Q-Network (DQN) to construct a Sepsis Anti-infection DQN (SAI-DQN) model to address the challenge of determining the optimal combination and duration of antibiotics in sepsis treatment. By setting sepsis clinical knowledge as reward functions to guide DQN complying with medical guidelines, we formed personalized treatment recommendations for antibiotic combinations. The results showed that our model had a higher average value for decision-making than clinical decisions. For the test set of patients, our model predicts that 79.07% of patients will achieve a favorable prognosis with the recommended combination of antibiotics. By statistically analyzing decision trajectories and drug action selection, our model was able to provide reasonable medication recommendations that comply with clinical practices. Our model was able to improve patient outcomes by recommending appropriate antibiotic combinations in line with certain clinical knowledge.

Theoretical study on hydroformylation catalyzed by cationic cobalt(II) complexes.

Hydroformylation is one of the most important homogeneous reactions in industrial production. Herein, a density functional theory (DFT) method was employed to investigate two proposed reaction mechanisms of hydroformylation catalyzed by cationic cobalt(II) complexes, the carbonyl dissociative mechanism and the associative mechanism. The calculated results showed that the heterolytic H2 activation is the rate-determining step for both the dissociative mechanism and the associative mechanism, with energy barriers of 26.8 kcal mol-1 and 40.5 kcal mol-1, respectively. Meanwhile, the regioselectivity, the spin multiplicity of the catalyst and the substituent effects on the reaction were also investigated. The most stable cobalt(II) catalyst has a doublet state and the linear aldehyde is the dominant product. In addition, it was found that the energy barrier of the reaction decreased when the electron density of the Co center of the catalyst was increased by changing the ligand. The catalytic activity of the catalyst was proposed to be the best when the PEt2 group of the ligand is replaced by the P(tBu)2 group. This study might not only provide new insights for hydroformylation catalyzed by cobalt but also facilitate theory-guided design of novel transition metal catalysts for hydroformylation.

Comparison of pulmonary artery sarcoma and pulmonary thromboembolism according to clinical and computed tomography pulmonary angiography and magnetic resonance imaging characteristics: a single-center retrospective study.

Background: Pulmonary artery sarcoma (PAS) is a very rare malignancy with a poor prognosis; however, its clinical manifestations and imaging findings are often indistinguishable from pulmonary thromboembolism (PTE). We thus aimed to accurately diagnose PAS by comparing the clinical and computed tomography pulmonary angiography (CTPA) and magnetic resonance imaging (MRI) imaging characteristics of PAS and PTE. Methods: This case-control study retrospectively enrolled 20 patients with PAS (from March 2017 to September 2022), 40 patients with central acute PTE, and 40 patients with central chronic PTE (from January 2021 to December 2022) in the China-Japan Friendship Hospital. The following clinical and imaging findings were compared between the three groups: initial symptoms; D-dimer, C-reactive protein, and N-terminal pro B-type natriuretic peptide levels; wall-eclipsing sign (WES); scope of lesion involvement; and morphological characteristics. Signal intensity was also observed on different MRI sequences. Results: The D-dimer level in PAS was significantly lower than that in central acute PTE (P<0.001). The WES was present in 17 cases of PAS (85.0%), which was a greater proportion than that of the central acute PTE and chronic PTE groups (all P values <0.001). The involvement of the pulmonary valve or right ventricular outflow tract was observed in five PAS cases but none of the central acute PTE or chronic PTE cases (all P values =0.001). In 19 PAS cases (95.0%), the lesions grew expansively in the central pulmonary artery. The proximal margin of 18 patients with PAS (90.0%) was bulging or lobulated. Nine cases of PAS (45.0%) showed aneurysm-like dilatation (grape-like sign) of the distal pulmonary artery, representing significantly greater proportion than that of the central acute PTE and chronic PTE groups (all P values <0.001). In 37 patients with central acute PTE (92.5%), the clots were observed to be floating in the pulmonary artery lumen with saddle, tubular or polypoid shape. Eccentric filling defects attached to the pulmonary artery wall were observed in 32 cases of central chronic PTE (80.0%). On MRI, PAS lesions were hyperintense on fat-suppressed T2-weighted imaging and diffusion-weighted imaging, demonstrating heterogeneous enhancement. Conclusions: Comprehensive analysis of the clinical data and imaging features on CTPA and MRI can aid in the accurate differential diagnosis of PAS and PTE.

Antifreeze Polysaccharides from Wheat Bran: The Structural Characterization and Antifreeze Mechanism.

Exploring a novel natural cryoprotectant and understanding its antifreeze mechanism allows the rational design of future sustainable antifreeze analogues. In this study, various antifreeze polysaccharides were isolated from wheat bran, and the antifreeze activity was comparatively studied in relation to the molecular structure. The antifreeze mechanism was further revealed based on the interactions of polysaccharides and water molecules through dynamic simulation analysis. The antifreeze polysaccharides showed distinct ice recrystallization inhibition activity, and structural analysis suggested that the polysaccharides were arabinoxylan, featuring a xylan backbone with a majority of Araf and minor fractions of Manp, Galp, and Glcp involved in the side chain. The antifreeze arabinoxylan, characterized by lower molecular weight, less branching, and more flexible conformation, could weaken the hydrogen bonding of the surrounding water molecules more evidently, thus retarding the transformation of water molecules into the ordered ice structure.

Developing the Lung Graph-Based Machine Learning Model for Identification of Fibrotic Interstitial Lung Diseases.

Accurate detection of fibrotic interstitial lung disease (f-ILD) is conducive to early intervention. Our aim was to develop a lung graph-based machine learning model to identify f-ILD. A total of 417 HRCTs from 279 patients with confirmed ILD (156 f-ILD and 123 non-f-ILD) were included in this study. A lung graph-based machine learning model based on HRCT was developed for aiding clinician to diagnose f-ILD. In this approach, local radiomics features were extracted from an automatically generated geometric atlas of the lung and used to build a series of specific lung graph models. Encoding these lung graphs, a lung descriptor was gained and became as a characterization of global radiomics feature distribution to diagnose f-ILD. The Weighted Ensemble model showed the best predictive performance in cross-validation. The classification accuracy of the model was significantly higher than that of the three radiologists at both the CT sequence level and the patient level. At the patient level, the diagnostic accuracy of the model versus radiologists A, B, and C was 0.986 (95% CI 0.959 to 1.000), 0.918 (95% CI 0.849 to 0.973), 0.822 (95% CI 0.726 to 0.904), and 0.904 (95% CI 0.836 to 0.973), respectively. There was a statistically significant difference in AUC values between the model and 3 physicians (p < 0.05). The lung graph-based machine learning model could identify f-ILD, and the diagnostic performance exceeded radiologists which could aid clinicians to assess ILD objectively.

Effect of biochars on the immobilization and form of Cadmium (Cd) in simulated Cd deposition of iron rich soils.

Atmospheric deposition of Cd poses a serious threat to ecosystem security. Biochar is widely used for polluted soil remediation, however, whether biochar already applied to the soil can reduce the hazards of newly deposited Cd remains to be studied. Thus, an indoor cultural experiment and static adsorption method were conducted to study the isothermal and kinetic adsorption processes of three types of biochar (rice husk, rubber wood, and tobacco stem biochars) on Cd in iron rich soils and the effect of biochar on the morphological distribution of Cd in the soil and the soil pH. The results showed that the soil with biochar in our study could quickly fix "the new deposited Cd" in the soil in 3 h with the maximum adsorption capacity in rubber wood biochar-treated sample (3227.34 mg/kg）. The addition of all three biochar treatments significantly increased the soil pH and reduced the soil exchange state Cd content, with a 13.69-17.32% increase in the pH and a 13.22-54.39% reduction in the exchange state Cd content when contrasted with the control, which could promote those Cd converting into unavailable Cd (carbonate-bound form Cd, Fe-Mn oxide-bound form Cd, or residual form Cd) for crops. In summary, the addition of three kinds of biochar treatments could effectively reduce the ecological and environmental risk of soil that was contaminated by Cd and could provide a reliable theoretical basis for the effect of biochar on the improvement of the quality of soil that is contaminated by heavy metals.

Scientific mapping of hotspots and trends of post and core research based on the Web of Science: A bibliometric analysis.

Statement of problem: Post and core (PC) restoration has been widely used to restore endodontically treated teeth. Nevertheless, bibliometric studies focusing on PC research are lacking. Purpose: The purpose of this bibliometric analysis was to review the general situation and determine the research direction of PC restoration by means of visualization, including the time development, countries, institutions, authors, journals, research categories, and the information of references with the strongest citation burst. Material and methods: The search was carried out within the topic field of the Web of Science (WoS) database. CiteSpace, VOSviewer, and R language were used to analyze the literature. Bibliometric indicators in terms of title, keyword, reference, publication time, institution, country and citation information were analyzed. Results: Between 1966 and 2022, dental research on PC trended upward, especially in 2011 and up to 2022. Scholars in Brazil, the United States, and Italy actively participated in PC research. The country that published the most was Brazil. Italy both ranked first in the H-index and average citations per item and had cooperative relations with several countries. The United States ranked first in the world in total citation count. The University of Siena published the most articles. The Journal of Prosthetic Dentistry (JPD) was the major contributing journal. The top three authors in this field were Ferrari M, Goracci C and Naumann M. The largest cooperative network of authors consisted of four Italian scholars. There were six main clusters of research topics on PC through CiteSpace co-citations. In addition, CiteSpace co-citations, CiteSpace Burst detection and VOSviewer keyword analysis showed the most important research interests, such as bond strength, fiber post, fracture resistance, and finite element analysis. Photodynamic therapy is the latest research hotspot. Finally, there were the top 25 references with the strongest citation bursts, which mainly focused on four aspects, namely, studies related to fiber posts, PC bonding strength, finite element analysis, and residual teeth and the ferrule effect. Conclusions: This bibliometric analysis provides a comprehensive overview of PC research, including the research time, country, institution, author, journal, article clustering, article keywords, and important literature analysis, so that researchers can have a clear understanding of the research situation in this field.

Assessment of lung deformation in patients with idiopathic pulmonary fibrosis with elastic registration technique on pulmonary three-dimensional ultrashort echo time MRI.

OBJECTIVE: To assess lung deformation in patients with idiopathic pulmonary fibrosis (IPF) using with elastic registration algorithm applied to three-dimensional ultrashort echo time (3D-UTE) MRI and analyze relationship of lung deformation with the severity of IPF. METHODS: Seventy-six patients with IPF (mean age: 62 ± 6 years) and 62 age- and gender-matched healthy controls (mean age: 58 ± 4 years) were prospectively enrolled. End-inspiration and end-expiration images acquired with a single breath-hold 3D-UTE sequence were registered using elastic registration algorithm. Jacobian determinants were calculated from deformation fields and represented on color maps. Jac-mean (absolute value of the log means of Jacobian determinants) and the Dice similarity coefficient (Dice) were compared between different groups. RESULTS: Compared with healthy controls, the Jac-mean of IPF patients significantly decreased (0.21 ± 0.08 vs. 0.27 ± 0. 07, p < 0.001). Furthermore, the Jac-mean and Dice correlated with the metrics of pulmonary function tests and the composite physiological index. The lung deformation in IPF patients with dyspnea Medical Research Council (MRC) ≥ 3 (Jac-mean: 0.16 ± 0.03; Dice: 0.06 ± 0.02) was significantly lower than MRC1 (Jac-mean: 0. 25 ± 0.03, p < 0.001; Dice: 0.10 ± 0.01, p < 0.001) and MRC 2 (Jac-mean: 0.22 ± 0.11, p = 0.001; Dice: 0.08 ± 0.03, p = 0.006). Meanwhile, Jac-mean and Dice correlated with health-related quality of life, 6 min-walk distance, and the extent of pulmonary fibrosis. Jac-mean correlated with pulmonary vascular-related indexes on high-resolution CT. CONCLUSION: The decreased lung deformation in IPF patients correlated with the clinical severity of IPF patients. Elastic registration of inspiratory-to-expiratory 3D UTE MRI may be a new morphological and functional marker for non-radiation and noninvasive evaluation of IPF. CRITICAL RELEVANCE STATEMENT: This prospective study demonstrated that lung deformation decreased in idiopathic pulmonary fibrosis (IPF) patients and correlated with the severity of IPF. Elastic registration of inspiratory-to-expiratory three-dimensional ultrashort echo time (3D UTE) MRI may be a new morphological and functional marker for non-radiation and noninvasive evaluation of IPF. KEY POINTS: • Elastic registration of inspiratory-to-expiratory three-dimensional ultrashort echo time (3D UTE) MRI could evaluate lung deformation. • Lung deformation significantly decreased in idiopathic pulmonary fibrosis (IPF) patients, compared with the healthy controls. • Reduced lung deformation of IPF patients correlated with worsened pulmonary function and the composite physiological index (CPI).

Multi-Species Surface Reconstruction for High-Efficiency Perovskite Nanocrystal Light-Emitting Diodes.

Although colloidal perovskite nanocrystal (PNC) solution has exhibited near-unity photoluminescence quantum yield (PLQY), the luminance would be severely quenched when the PNC solution is assembled into thin films due to the agglomeration and fusion of NCs caused by the exfoliation of surface ligands and non-radiative Förster resonance energy transfer (FRET) from small to large particle sizes, which seriously affected the performances of light-emitting diodes (LEDs). Here, we used Guanidine thiocyanate (GASCN) and Sodium thiocyanate (NaSCN) to achieve effective CsPbI3 PNC surface reconstruction. Due to the strong coordination ability of these small molecules with the anions and cations on the surface of the PNCs, they can provide strong surface protection against PNC fusion during centrifugal purification process and repair the surface defects of PNCs, so that the original uniform size distribution of PNCs can be maintained and FRET between close-packed PNC films is effectively suppressed, which allows the emission characteristics of the films to be preserved. As a result, highly oriented, smooth and nearly defect-free high-quality PNC thin films are obtained, with PLQY as high as 95.1 %, far exceeding that of the original film, and corresponding LEDs exhibit a maximum external quantum efficiency of 24.5 %.

CLCLSA: Cross-omics linked embedding with contrastive learning and self attention for integration with incomplete multi-omics data.

Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding etiology of complex genetic diseases. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. Studies may fail if certain aspects of the subjects are missing or incomplete. In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model employs cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-omics contrastive learning is employed, which maximizes the mutual information between different types of omics. In addition, the feature-level self-attention and omics-level self-attention are employed to dynamically identify the most informative features for multi-omics data integration. Finally, a Softmax classifier is employed to perform multi-omics data classification. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicate that our proposed CLCLSA produces promising results in multi-omics data classification using both complete and incomplete multi-omics data.

Autosurv: interpretable deep learning framework for cancer survival analysis incorporating clinical and multi-omics data.

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can also reveal the underlying disease mechanisms at the molecular level. In this study, we developed and validated a deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian-cancer patients using multiple independent multi-omics datasets. Our model achieved significantly better prognosis prediction than the current machine learning and deep learning approaches in various settings. Moreover, an interpretation method was applied to tackle the "black-box" nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that were important to distinguish predicted high- and low-risk patients. The significance of the identified features was partially supported by previous studies.

Near-Infrared Off-Axis Cavity-Enhanced Optical Frequency Comb Spectroscopy for CO2/CO Dual-Gas Detection Assisted by Machine Learning.

Cavity-enhanced direct frequency comb spectroscopy (CE-DFCS) is widely used as a highly sensitive gas sensing technology in various gas detection fields. For the on-axis coupling incidence scheme, the detection accuracy and stability are seriously affected by the cavity-mode noise, and therefore, stable operation inevitably requires external electronic mode-locking and sweeping devices, substantially increasing system complexity. To address this issue, we propose off-axis cavity-enhanced optical frequency comb spectroscopy from both theoretical and experimental aspects, which is applied to the detection of single- and dual-gas of carbon monoxide (CO) and carbon dioxide (CO2) in the near-infrared. An erbium-doped fiber frequency comb with a repetition frequency of ∼41.709 MHz is coupled into a resonant cavity with a length of ∼360 mm in an off-axis manner, exciting numerous high-order modes to effectively suppress cavity-mode noise. The performance of multiple machine learning models is compared for the inversion of a single/dual gas concentration. A few absorbance spectra are collected to build a sample data set, which is then utilized for model training and learning. The results demonstrate that the Particle Swarm Optimization Support Vector Machine (PSO-SVM) model achieves the highest predictive accuracy for gas concentration and is ultimately applied to the detection system. Based on Allan deviation, the detection limit for CO in single-gas detection can reach 8.247 parts per million by volume (ppmv) by averaging 87 spectra. Meanwhile, for simultaneous CO2/CO measurement with highly overlapping absorbance spectra, the LoD can be reduced to 13.196 and 4.658 ppmv, respectively. The proposed optical gas sensing technique indicates the potential for the development of a field-deployable and intelligent sensor system capable of simultaneous detection of multiple gases.

A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure.

Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur. We developed a global FEA-computed fracture risk index to increase the prediction accuracy of hip fracture incidence. PURPOSE: Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur. METHODS: We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling. RESULTS: We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value < 0.001). The area under the receiver operating characteristic curve (AUC) using PC1 (0.776) was higher than that using all FE parameters combined (0.737) in the males (p-value < 0.001). CONCLUSIONS: The global FEA-computed fracture risk index increased hip fracture risk prediction accuracy in males.