Spatial Dissection of the Distinct Cellular Responses to Normal Aging and Alzheimer's Disease in Human Prefrontal Cortex at Single-Nucleus Resolution.

Aging significantly elevates the risk for Alzheimer's disease (AD), contributing to the accumulation of AD pathologies, such as amyloid-ß (Aß), inflammation, and oxidative stress. The human prefrontal cortex (PFC) is highly vulnerable to the impacts of both aging and AD. Unveiling and understanding the molecular alterations in PFC associated with normal aging (NA) and AD is essential for elucidating the mechanisms of AD progression and developing novel therapeutics for this devastating disease. In this study, for the first time, we employed a cutting-edge spatial transcriptome platform, STOmics® SpaTial Enhanced Resolution Omics-sequencing (Stereo-seq), to generate the first comprehensive, subcellular resolution spatial transcriptome atlas of the human PFC from six AD cases at various neuropathological stages and six age, sex, and ethnicity matched controls. Our analyses revealed distinct transcriptional alterations across six neocortex layers, highlighted the AD-associated disruptions in laminar architecture, and identified changes in layer-to-layer interactions as AD progresses. Further, throughout the progression from NA to various stages of AD, we discovered specific genes that were significantly upregulated in neurons experiencing high stress and in nearby non-neuronal cells, compared to cells distant from the source of stress. Notably, the cell-cell interactions between the neurons under the high stress and adjacent glial cells that promote Aß clearance and neuroprotection were diminished in AD in response to stressors compared to NA. Through cell-type specific gene co-expression analysis, we identified three modules in excitatory and inhibitory neurons associated with neuronal protection, protein dephosphorylation, and negative regulation of Aß plaque formation. These modules negatively correlated with AD progression, indicating a reduced capacity for toxic substance clearance in AD subject samples. Moreover, we have discovered a novel transcription factor, ZNF460, that regulates all three modules, establishing it as a potential new therapeutic target for AD. Overall, utilizing the latest spatial transcriptome platform, our study developed the first transcriptome-wide atlas with subcellular resolution for assessing the molecular alterations in the human PFC due to AD. This atlas sheds light on the potential mechanisms underlying the progression from NA to AD.

Investigating the angular distortion impact on vehicular optical camera communication (OCC) systems.

Optical camera communication (OCC) shows promise for optical wireless communication (OWC) in vehicular networks. However, vehicle mobility-induced angular distortions hinder system throughput by degrading non-isotropic vehicular OCC channel gain. Few of the prior works have ever made a comprehensive analysis of their impact, especially based on the pixel value which reflects the camera imaging features. To address this knowledge gap, a pixel value-described vehicular OCC system model accounting for transmitter imaging location and intensity from the geometry and radiometry aspects is presented in this paper with common types of the offset and rotation angles included. We integrate a MATLAB-based simulated vehicular OCC system with an experimentally designed testbed for validation and performance analysis. For a single-time snapshot, we investigate the impacts of common angular distortion types in vehicular OCC systems on maximum pixel value, imaging location, and communication-related metrics. Furthermore, we statistically analyze their influences by considering two driving scenarios with respective angular distributions. The angular distortion characterization from this work is expected to lay a stepping stone to addressing mobility in vehicular OCC systems.

Odontogenesis-Empowered Extracellular Vesicles Safeguard Donor-Recipient Stem Cell Interplay to Support Tooth Regeneration.

Harnessing the developmental events of mesenchymal condensation to direct postnatal dental stem cell aggregation represents a cutting-edge and promising approach to tooth regeneration. Tooth avulsion is among the most prevalent and serious dental injuries, and odontogenic aggregates assembled by stem cells from human exfoliated deciduous teeth (SHED) have proven effective in revitalizing avulsed teeth after replantation in the clinical trial. However, whether and how SHED aggregates (SA) communicate with recipient components and promote synergistic tissue regeneration to support replanted teeth remains elusive. Here, it is shown that SA-mediated avulsed tooth regeneration involves periodontal restoration and recovery of recipient Gli1+ stem cells, which are mobilized and necessarily contribute to the reestablishment of the tooth-periodontal ligament-bone interface. Mechanistically, the release of extracellular vesicles (EVs) is revealed indispensable for the implanted SA to mobilize recipient Gli1+ cells and regenerate avulsed teeth. Furthermore, SHED aggregates-released EVs (SA-EVs) are featured with odontogenic properties linked to tissue regeneration, which enhance migration, proliferation, and differentiation of Gli1+ cells. Importantly, local application of SA-EVs per se empowers recipient Gli1+ cells and safeguards regeneration of avulsed teeth. Collectively, the findings establish a paradigm in which odontogenesis-featured EVs govern donor-recipient stem cell interplay to achieve tooth regeneration, inspiring cell-free translational regenerative strategies.

Ultrasensitive Fluorescent Microsensors Based on Aptamers Modified with SYBR Green I for Visual Quantitative Detection of Organophosphate Pesticides.

Organophosphate pesticides (OPs) are widely utilized in agricultural production, and the residues threaten public health and environmental safety due to their toxicity. Herein, a novel and simple DNA aptamer-based sensor has been fabricated for the rapid, visual, and quantitative detection of profenofos and isocarbophos. The proposed DNA aptamers with a G-quadruplex spatial structure could be recognized by SYBR Green I (SG-I), resulting in strong green fluorescence emitted by SG-I. The DNA aptamers exhibit a higher specific binding ability to target OP molecules through aromatic ring stacking, disrupting the interaction between SG-I and DNA aptamers to induce green fluorescence quenching. Meanwhile, the fluorescence wavelength of G-quadruplex fluorescence emission peaks changes, accompanied by an obvious fluorescence variation from green to blue. SG-I-modified aptasensor without any additive reference fluorescence units for use in multicolor fluorescence assay for selective monitoring of OPs was first developed. The developed aptasensor provides a favorable linear range from 0 to 200 nM, with a low detection limit of 2.48 and 3.01 nM for profenofos and isocarbophos, respectively. Moreover, it offers high selectivity and stability in real sample detection with high recoveries. Then, a self-designed portable smartphone sensing platform was successfully used for quantitative result outputs, demonstrating experience in designing a neotype sensing strategy for point-of-care pesticide monitoring.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds.

Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.